From 96c7900d6828c77258ba701f43cb7ab41cba9272 Mon Sep 17 00:00:00 2001 From: Fabricio Olivetti de Franca Date: Sat, 12 Apr 2025 08:53:54 -0300 Subject: [PATCH 001/166] Initial commit --- .gitignore | 23 ++ LICENSE | 674 +++++++++++++++++++++++++++++++++++++++++++++++++++++ README.md | 2 + 3 files changed, 699 insertions(+) create mode 100644 .gitignore create mode 100644 LICENSE create mode 100644 README.md diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..4c9e245 --- /dev/null +++ b/.gitignore @@ -0,0 +1,23 @@ +dist +dist-* +cabal-dev +*.o +*.hi +*.hie +*.chi +*.chs.h +*.dyn_o +*.dyn_hi +.hpc +.hsenv +.cabal-sandbox/ +cabal.sandbox.config +*.prof +*.aux +*.hp +*.eventlog +.stack-work/ +cabal.project.local +cabal.project.local~ +.HTF/ +.ghc.environment.* diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..f288702 --- /dev/null +++ b/LICENSE @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode: + + Copyright (C) + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. diff --git a/README.md b/README.md new file mode 100644 index 0000000..7f3b30d --- /dev/null +++ b/README.md @@ -0,0 +1,2 @@ +# pyeggp +Python wraper for eggp From da5bac5e71aa52d22d5e38d33c098c8cbcdd5b41 Mon Sep 17 00:00:00 2001 From: folivetti Date: Sat, 12 Apr 2025 14:17:44 -0300 Subject: [PATCH 002/166] testing wheel building machine --- .github/dependabot.yml | 14 + .github/workflows/release.yml | 199 + .github/workflows/release.yml.bkp | 130 + .github/workflows/release.yml.working | 155 + .github/workflows/release.yml_winbkp | 124 + .pre-commit-config.yaml | 16 + .python-version | 6 + Setup.hs | 64 + .../pyeggp/__init__.py | 51 + .../pyeggp/__main__.py | 11 + .../pyeggp/_binding.pyi | 7 + .../pyeggp/binding.i | 64 + .../pyeggp/binding.py | 76 + .../pyeggp/typing.py | 14 + cabal.project | 7 + pyeggp.cabal | 75 + pyproject.toml | 138 + requirements-ci.txt | 1 + scripts/build-wheel.sh | 80 + setup.py | 138 + src/Pyeggp.hs | 409 ++ src/README.md | 137 + src/Random.hs | 54 + src/Util.hs | 266 ++ src/pyeggp/__init__.py | 51 + src/pyeggp/__main__.py | 11 + src/pyeggp/_binding.pyi | 7 + src/pyeggp/binding.def | 2 + src/pyeggp/binding.i | 64 + src/pyeggp/binding.py | 76 + src/pyeggp/binding_wrap.c | 4083 +++++++++++++++++ src/pyeggp/typing.py | 14 + src/srtree.egg-info/PKG-INFO | 699 +++ src/srtree.egg-info/SOURCES.txt | 15 + src/srtree.egg-info/dependency_links.txt | 1 + src/srtree.egg-info/requires.txt | 6 + src/srtree.egg-info/top_level.txt | 2 + 37 files changed, 7267 insertions(+) create mode 100644 .github/dependabot.yml create mode 100644 .github/workflows/release.yml create mode 100644 .github/workflows/release.yml.bkp create mode 100644 .github/workflows/release.yml.working create mode 100644 .github/workflows/release.yml_winbkp create mode 100644 .pre-commit-config.yaml create mode 100644 .python-version create mode 100755 Setup.hs create mode 100644 build/lib.linux-x86_64-cpython-310/pyeggp/__init__.py create mode 100644 build/lib.linux-x86_64-cpython-310/pyeggp/__main__.py create mode 100644 build/lib.linux-x86_64-cpython-310/pyeggp/_binding.pyi create mode 100644 build/lib.linux-x86_64-cpython-310/pyeggp/binding.i create mode 100644 build/lib.linux-x86_64-cpython-310/pyeggp/binding.py create mode 100644 build/lib.linux-x86_64-cpython-310/pyeggp/typing.py create mode 100644 cabal.project create mode 100755 pyeggp.cabal create mode 100644 pyproject.toml create mode 100644 requirements-ci.txt create mode 100644 scripts/build-wheel.sh create mode 100644 setup.py create mode 100644 src/Pyeggp.hs create mode 100644 src/README.md create mode 100644 src/Random.hs create mode 100644 src/Util.hs create mode 100644 src/pyeggp/__init__.py create mode 100644 src/pyeggp/__main__.py create mode 100644 src/pyeggp/_binding.pyi create mode 100644 src/pyeggp/binding.def create mode 100644 src/pyeggp/binding.i create mode 100644 src/pyeggp/binding.py create mode 100644 src/pyeggp/binding_wrap.c create mode 100644 src/pyeggp/typing.py create mode 100644 src/srtree.egg-info/PKG-INFO create mode 100644 src/srtree.egg-info/SOURCES.txt create mode 100644 src/srtree.egg-info/dependency_links.txt create mode 100644 src/srtree.egg-info/requires.txt create mode 100644 src/srtree.egg-info/top_level.txt diff --git a/.github/dependabot.yml b/.github/dependabot.yml new file mode 100644 index 0000000..c9c3f21 --- /dev/null +++ b/.github/dependabot.yml @@ -0,0 +1,14 @@ +version: 2 + +updates: + # Maintain dependencies for GitHub Actions + - package-ecosystem: "github-actions" + directory: "/" + schedule: + interval: "weekly" + + # Maintain dependencies for Python + - package-ecosystem: "pip" + directory: "/" + schedule: + interval: "weekly" diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml new file mode 100644 index 0000000..6931333 --- /dev/null +++ b/.github/workflows/release.yml @@ -0,0 +1,199 @@ +name: CI + +on: + push: + branches: + - main + tags: + - "v*" + pull_request: + types: + - opened + - synchronize + - reopened + merge_group: + workflow_dispatch: + +defaults: + run: + shell: sh + +env: + # 04-08-2023: + # cibuildwheel builds linux wheels in a manylinux_ghc container, which + # hardcodes the GHC and Cabal version. To change the GHC and Cabal versions, + # you must update the containers in pyproject.toml under tool.cibuildwheel. + # + # See: https://github.com/wenkokke/manylinux_ghc + DEFAULT_GHC_VERSION: "9.4.8" + DEFAULT_CABAL_VERSION: "3.10.2.1" + DEFAULT_PYTHON_VERSION: "3.11" + +jobs: + ################################################################################ + # Build package with cibuildwheel + ################################################################################ + + build: + name: "Build / ${{ matrix.job.os.name }} / ${{ matrix.job.os.plat}}-${{ matrix.job.os.arch }}" + runs-on: ${{ matrix.job.os.runner }} + + strategy: + matrix: + job: + - os: + name: "Linux" + runner: "ubuntu-latest" + arch: "aarch64" + plat: "manylinux" + - os: + name: "Linux" + runner: "ubuntu-latest" + arch: "i686" + plat: "manylinux" + - os: + name: "Linux" + runner: "ubuntu-latest" + arch: "x86_64" + plat: "manylinux" + - os: + name: "Linux" + runner: "ubuntu-latest" + arch: "x86_64" + plat: "musllinux" + - os: + name: "macOS" + runner: "macos-12" + arch: "x86_64" + plat: "macosx" + - os: + name: "macOS" + runner: "macos-14" + arch: "arm64" + plat: "macosx" + - os: + name: "Windows" + runner: "windows-latest" + arch: "AMD64" + plat: "win" + + steps: + - uses: actions/checkout@v4 + + - # 12-02-2024: + # Print the macOS architecture. + if: matrix.job.os.name == 'macOS' + run: echo "::debug::$(uname -a)" + + - # Required to run job via act: https://github.com/nektos/act + uses: actions/setup-python@v5 + with: + cache: "pip" + cache-dependency-path: "./requirements-ci.txt" + python-version: ${{ env.DEFAULT_PYTHON_VERSION }} + + - # Required to run job via act: https://github.com/nektos/act + run: pip install -r "./requirements-ci.txt" + + - # 19-05-2023: + # Required to build the wheels on macOS and Windows, but not on Linux, + # as Linux builds are run in containers, where GHC and Cabal are built + # by the `./scripts/before-all.sh` scripts. + if: matrix.job.os.name == 'macOS' || matrix.job.os.name == 'Windows' + uses: haskell-actions/setup@v2 + with: + ghc-version: ${{ env.DEFAULT_GHC_VERSION }} + cabal-version: ${{ env.DEFAULT_CABAL_VERSION }} + + - # 12-02-2024: + # Print the GHC architecture. + if: matrix.job.os.name == 'macOS' || matrix.job.os.name == 'Windows' + run: echo "::debug::$(ghc --info)" + + - # 19-06-2023: + # Required to build aarch64 wheels. + if: matrix.job.os.name == 'Linux' && matrix.job.os.arch == 'aarch64' + name: Set up QEMU (linux/aarch64) + uses: docker/setup-qemu-action@v3 + with: + platforms: "arm64" + + - # 19-06-2023: + # Required to build i686 wheels. + if: matrix.job.os.name == 'Linux' && matrix.job.os.arch == 'i686' + name: Set up QEMU (linux/i686) + uses: docker/setup-qemu-action@v3 + with: + platforms: "386" + + - uses: pypa/cibuildwheel@v2.21.3 + with: + output-dir: "./wheelhouse" + config-file: "{package}/pyproject.toml" + env: + CIBW_ARCHS: "${{ matrix.job.os.arch }}" + CIBW_BUILD: "*${{ matrix.job.os.plat }}*" + MACOSX_DEPLOYMENT_TARGET: "10.10" + + - name: Upload wheels + uses: actions/upload-artifact@v4 + with: + name: "wheelhouse-${{ matrix.job.os.plat }}-${{ matrix.job.os.arch }}" + path: ./wheelhouse/*.whl + + ################################################################################ + # Publish package to GitHub Releases + ################################################################################ + + publish-to-github-releases: + name: Publish to GitHub Releases + runs-on: ubuntu-latest + if: startsWith(github.ref, 'refs/tags/') + needs: [build] + + permissions: + contents: write + + steps: + - name: Download artifacts + uses: actions/download-artifact@v4 + with: + pattern: "wheelhouse-*-*" + path: "./wheelhouse" + merge-multiple: true + + - name: Publish to GitHub Releases + uses: softprops/action-gh-release@v2 + with: + files: "./wheelhouse/*.whl" + fail_on_unmatched_files: true + + ################################################################################ + # Publish package to PyPI + ################################################################################ + + publish-to-pypi: + name: Publish to PyPI + runs-on: ubuntu-latest + if: startsWith(github.ref, 'refs/tags/') + needs: [build] + + environment: + name: pypi + url: https://pypi.org/p/eggp + + permissions: + id-token: write # IMPORTANT: this permission is mandatory for trusted publishing + + steps: + - name: Download artifacts + uses: actions/download-artifact@v4 + with: + pattern: "wheelhouse-*-*" + path: "./wheelhouse" + merge-multiple: true + + - name: Publish to PyPI + uses: pypa/gh-action-pypi-publish@release/v1 + with: + packages-dir: "./wheelhouse" diff --git a/.github/workflows/release.yml.bkp b/.github/workflows/release.yml.bkp new file mode 100644 index 0000000..68f4d9f --- /dev/null +++ b/.github/workflows/release.yml.bkp @@ -0,0 +1,130 @@ +name: srtols Release + +on: + # Trigger the workflow on the new 'v*' tag created + push: + tags: + - "v*" + +jobs: + create_release: + name: Create Github Release + runs-on: ubuntu-latest + outputs: + upload_url: ${{ steps.create_release.outputs.upload_url }} + steps: + - name: Create Release + id: create_release + uses: actions/create-release@v1.1.4 + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + with: + tag_name: ${{ github.ref }} + release_name: Release ${{ github.ref }} + draft: true + prerelease: false + + build_artifact: + needs: [create_release] + name: ${{ matrix.os }}/GHC ${{ matrix.ghc }}/${{ github.ref }} + runs-on: ${{ matrix.os }} + strategy: + matrix: + os: [ubuntu-latest, windows-latest] + ghc: + - 9.10.1 + cabal: ["3.14.1.1"] + defaults: + run: + shell: bash + + steps: + - name: Check out code + uses: actions/checkout@v3 + + - name: Set tag name + uses: olegtarasov/get-tag@v2.1 + id: tag + with: + tagRegex: "v(.*)" + tagRegexGroup: 1 + + - name: Setup Haskell + uses: haskell-actions/setup@v2 + id: setup-haskell-cabal + with: + ghc-version: ${{ matrix.ghc }} + cabal-version: ${{ matrix.cabal }} + + - name: Cache ~/.cabal/store + uses: actions/cache@v2 + with: + key: ${{ runner.os }}-ghc-${{ matrix.ghc }}-${{ github.sha }} + path: ~/.cabal/store + + - if: matrix.os == 'ubuntu-latest' + name: Install dependencies + run: | + sudo apt-get update + sudo apt-get install -y libnlopt-dev libnlopt0 + + - if: matrix.os == 'macOS-latest' + name: Install dependencies + run: | + brew install nlopt + + - if: matrix.os == 'windows-latest' + name: Install dependencies + uses: msys2/setup-msys2@v2 + with: + install: pacman-mirrors pkg-config base-devel mingw-w64-x86_64-toolchain upx mingw-w64-x86_64-dlfcn git + update: false + - if: matrix.os == 'windows-latest' + name: Install nlopt + shell: msys2 {0} + run: | + pacman -Sy --noconfirm mingw-w64-x86_64-nlopt + + - if: matrix.os == 'windows-latest' + name: Build windows binary + run: | + mkdir dist + cabal install exe:egraphGP --install-method=copy --overwrite-policy=always --installdir=dist --extra-lib-dirs=/mingw64/lib --extra-lib-dirs=/mingw64/bin --extra-include-dirs=/mingw64/include + cabal install exe:egraphSearch --install-method=copy --overwrite-policy=always --installdir=dist --extra-lib-dirs=/mingw64/lib --extra-lib-dirs=/mingw64/bin --extra-include-dirs=/mingw64/include + cabal install exe:srtools --install-method=copy --overwrite-policy=always --installdir=dist --extra-lib-dirs=/mingw64/lib --extra-lib-dirs=/mingw64/bin --extra-include-dirs=/mingw64/include + cabal install exe:rEGGression --install-method=copy --overwrite-policy=always --installdir=dist --extra-lib-dirs=/mingw64/lib --extra-lib-dirs=/mingw64/bin --extra-include-dirs=/mingw64/include + cabal install exe:srsimplify --install-method=copy --overwrite-policy=always --installdir=dist --extra-lib-dirs=/mingw64/lib --extra-lib-dirs=/mingw64/bin --extra-include-dirs=/mingw64/include + - if: matrix.os != 'windows-latest' + name: Build binary + run: | + mkdir dist + cabal install exe:egraphGP --install-method=copy --overwrite-policy=always --installdir=dist + cabal install exe:egraphSearch --install-method=copy --overwrite-policy=always --installdir=dist + cabal install exe:srtools --install-method=copy --overwrite-policy=always --installdir=dist + cabal install exe:rEGGression --install-method=copy --overwrite-policy=always --installdir=dist + cabal install exe:srsimplify --install-method=copy --overwrite-policy=always --installdir=dist + + - if: matrix.os == 'windows-latest' + name: Set extension to .exe on Windows + run: echo "EXT=.exe" >> $GITHUB_ENV + + - name: Set binary path name + run: | + echo "BINARY_PATH1=./dist/srsimplify${{ env.EXT }}" >> $GITHUB_ENV + echo "BINARY_PATH2=./dist/srtools${{ env.EXT }}" >> $GITHUB_ENV + echo "BINARY_PATH2=./dist/egraphGP${{ env.EXT }}" >> $GITHUB_ENV + echo "BINARY_PATH2=./dist/egraphSearch${{ env.EXT }}" >> $GITHUB_ENV + echo "BINARY_PATH2=./dist/rEGGression${{ env.EXT }}" >> $GITHUB_ENV + + - name: Upload Release Asset + id: upload-release-asset + uses: softprops/action-gh-release@v2 + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + with: + files: | + ${{ env.BINARY_PATH1 }}/srsimplify-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }}${{ env.EXT }} + ${{ env.BINARY_PATH2 }}/srtools-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }}${{ env.EXT }} + ${{ env.BINARY_PATH2 }}/egraphSearch-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }}${{ env.EXT }} + ${{ env.BINARY_PATH2 }}/egraphGP-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }}${{ env.EXT }} + ${{ env.BINARY_PATH2 }}/rEGGression-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }}${{ env.EXT }} diff --git a/.github/workflows/release.yml.working b/.github/workflows/release.yml.working new file mode 100644 index 0000000..aa2bfd3 --- /dev/null +++ b/.github/workflows/release.yml.working @@ -0,0 +1,155 @@ +name: srtols Release + +on: + # Trigger the workflow on the new 'v*' tag created + push: + tags: + - "v*" + +jobs: + create_release: + name: Create Github Release + runs-on: ubuntu-latest + outputs: + upload_url: ${{ steps.create_release.outputs.upload_url }} + steps: + - name: Create Release + id: create_release + uses: actions/create-release@v1.1.4 + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + with: + tag_name: ${{ github.ref }} + release_name: Release ${{ github.ref }} + draft: true + prerelease: false + + build_artifact: + needs: [create_release] + name: ${{ matrix.os }}/GHC ${{ matrix.ghc }}/${{ github.ref }} + runs-on: ${{ matrix.os }} + strategy: + matrix: + os: [ubuntu-latest, windows-latest, macOS-latest] + ghc: + - 9.10.1 + cabal: ["3.14.1.1"] + defaults: + run: + shell: bash + + steps: + - name: Check out code + uses: actions/checkout@v3 + + - name: Set tag name + uses: olegtarasov/get-tag@v2.1 + id: tag + with: + tagRegex: "v(.*)" + tagRegexGroup: 1 + + - name: Setup Haskell + uses: haskell-actions/setup@v2 + id: setup-haskell-cabal + with: + ghc-version: ${{ matrix.ghc }} + cabal-version: ${{ matrix.cabal }} + + - name: Cache ~/.cabal/store + uses: actions/cache@v2 + with: + key: ${{ runner.os }}-ghc-${{ matrix.ghc }}-${{ github.sha }} + path: ~/.cabal/store + + - if: matrix.os == 'ubuntu-latest' + name: Install ubuntu dependencies + run: | + sudo apt-get update + sudo apt-get install -y libnlopt-dev libnlopt0 + + - if: matrix.os == 'macOS-latest' + name: Install macOS dependencies + run: | + brew install nlopt + ls $(brew --prefix)/Cellar/nlopt/2.9.1/lib/ + echo "PKG_CONFIG_PATH=${PKG_CONFIG_PATH}" + echo "PKG_CONFIG_PATH=$(brew --prefix)/Cellar/nlopt/2.9.1/lib/pkgconfig" >> "${GITHUB_ENV}" + echo "LD_LIBRARY_PATH=${LD_LIBRARY_PATH}" + echo "LD_LIBRARY_PATH=$(brew --prefix)/Cellar/nlopt/2.9.1/lib" >> "${GITHUB_ENV}" + echo "DYLD_LIBRARY_PATH=${DYLD_LIBRARY_PATH}" + echo "DYLD_LIBRARY_PATH=$(brew --prefix)/Cellar/nlopt/2.9.1/lib" >> "${GITHUB_ENV}" + + - if: matrix.os == 'windows-latest' + name: Build windows binary + run: | + stack exec -- pacman --noconfirm -Sy msys2-keyring + stack exec -- pacman --noconfirm -S mingw-w64-x86_64-pkgconf + stack exec -- pacman --noconfirm -S mingw-w64-x86_64-nlopt + mkdir dist + stack build srtree:eggp --copy-bins --local-bin-path=dist + stack build srtree:symregg --copy-bins --local-bin-path=dist + stack build srtree:srtools --copy-bins --local-bin-path=dist + stack build srtree:reggression --copy-bins --local-bin-path=dist + stack build srtree:srsimplify --copy-bins --local-bin-path=dist + mv dist/eggp.exe dist/eggp-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }}.exe + mv dist/symregg.exe dist/symregg-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }}.exe + mv dist/srtools.exe dist/srtools-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }}.exe + mv dist/reggression.exe dist/reggression-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }}.exe + mv dist/srsimplify.exe dist/srsimplify-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }}.exe + + - if: matrix.os == 'macOS-latest' + name: Build binary on maxOS + run: | + mkdir dist + cabal install srtree:eggp --install-method=copy --overwrite-policy=always --installdir=dist --extra-lib-dirs=/opt/homebrew/Cellar/nlopt/2.9.1/lib --extra-include-dirs=/opt/homebrew/Cellar/nlopt/2.9.1/include + cabal install srtree:symregg --install-method=copy --overwrite-policy=always --installdir=dist --extra-lib-dirs=/opt/homebrew/Cellar/nlopt/2.9.1/lib --extra-include-dirs=/opt/homebrew/Cellar/nlopt/2.9.1/include + cabal install srtree:srtools --install-method=copy --overwrite-policy=always --installdir=dist --extra-lib-dirs=/opt/homebrew/Cellar/nlopt/2.9.1/lib --extra-include-dirs=/opt/homebrew/Cellar/nlopt/2.9.1/include + cabal install srtree:rEGGression --install-method=copy --overwrite-policy=always --installdir=dist --extra-lib-dirs=/opt/homebrew/Cellar/nlopt/2.9.1/lib --extra-include-dirs=/opt/homebrew/Cellar/nlopt/2.9.1/include + cabal install srtree:srsimplify --install-method=copy --overwrite-policy=always --installdir=dist --extra-lib-dirs=/opt/homebrew/Cellar/nlopt/2.9.1/lib --extra-include-dirs=/opt/homebrew/Cellar/nlopt/2.9.1/include + mv dist/eggp dist/eggp-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }} + mv dist/symregg dist/symregg-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }} + mv dist/srtools dist/srtools-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }} + mv dist/reggression dist/reggression-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }} + mv dist/srsimplify dist/srsimplify-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }} + + - if: matrix.os == 'ubuntu-latest' + name: Build binary on Ubuntu + run: | + mkdir dist + cabal install srtree:eggp --install-method=copy --overwrite-policy=always --installdir=dist + cabal install srtree:symregg --install-method=copy --overwrite-policy=always --installdir=dist + cabal install srtree:srtools --install-method=copy --overwrite-policy=always --installdir=dist + cabal install srtree:reggression --install-method=copy --overwrite-policy=always --installdir=dist + cabal install srtree:srsimplify --install-method=copy --overwrite-policy=always --installdir=dist + mv dist/eggp dist/eggp-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }} + mv dist/symregg dist/symregg-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }} + mv dist/srtools dist/srtools-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }} + mv dist/reggression dist/reggression-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }} + mv dist/srsimplify dist/srsimplify-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }} + ls dist + + - if: matrix.os == 'windows-latest' + name: Set extension to .exe on Windows + run: echo "EXT=.exe" >> $GITHUB_ENV + + - name: Set binary path name + run: | + echo "BINARY_PATH1=./dist" >> $GITHUB_ENV + echo "BINARY_PATH2=./dist" >> $GITHUB_ENV + echo "BINARY_PATH2=./dist" >> $GITHUB_ENV + echo "BINARY_PATH2=./dist" >> $GITHUB_ENV + echo "BINARY_PATH2=./dist" >> $GITHUB_ENV + + - name: Upload Release Asset + id: upload-release-asset + uses: softprops/action-gh-release@v2 + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + with: + files: | + ${{ env.BINARY_PATH1 }}/srsimplify-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }}${{ env.EXT }} + ${{ env.BINARY_PATH2 }}/srtools-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }}${{ env.EXT }} + ${{ env.BINARY_PATH2 }}/symregg-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }}${{ env.EXT }} + ${{ env.BINARY_PATH2 }}/eggp-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }}${{ env.EXT }} + ${{ env.BINARY_PATH2 }}/reggression-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }}${{ env.EXT }} diff --git a/.github/workflows/release.yml_winbkp b/.github/workflows/release.yml_winbkp new file mode 100644 index 0000000..990d16d --- /dev/null +++ b/.github/workflows/release.yml_winbkp @@ -0,0 +1,124 @@ +name: srtols Release + +on: + # Trigger the workflow on the new 'v*' tag created + push: + tags: + - "v*" + +jobs: + create_release: + name: Create Github Release + runs-on: ubuntu-latest + outputs: + upload_url: ${{ steps.create_release.outputs.upload_url }} + steps: + - name: Create Release + id: create_release + uses: actions/create-release@v1.1.4 + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + with: + tag_name: ${{ github.ref }} + release_name: Release ${{ github.ref }} + draft: true + prerelease: false + + build_artifact: + needs: [create_release] + name: ${{ matrix.os }}/GHC ${{ matrix.ghc }}/${{ github.ref }} + runs-on: ${{ matrix.os }} + strategy: + matrix: + os: [windows-latest] + ghc: + - 9.10.1 + cabal: ["3.14.1.1"] + defaults: + run: + shell: bash + + steps: + - name: Check out code + uses: actions/checkout@v3 + + - name: Set tag name + uses: olegtarasov/get-tag@v2.1 + id: tag + with: + tagRegex: "v(.*)" + tagRegexGroup: 1 + + - name: Setup Haskell + uses: haskell-actions/setup@v2 + id: setup-haskell-cabal + with: + ghc-version: ${{ matrix.ghc }} + cabal-version: ${{ matrix.cabal }} + + - name: Cache ~/.cabal/store + uses: actions/cache@v2 + with: + key: ${{ runner.os }}-ghc-${{ matrix.ghc }}-${{ github.sha }} + path: ~/.cabal/store + + - if: matrix.os == 'ubuntu-latest' + name: Install dependencies + run: | + sudo apt-get update + sudo apt-get install -y libnlopt-dev libnlopt0 + + - if: matrix.os == 'macOS-latest' + name: Install dependencies + run: | + brew install nlopt + + - if: matrix.os == 'windows-latest' + name: Build windows binary + run: | + pwd + mkdir /d/a/nlopt + curl -L https://github.com/stevengj/NLoptBuilder/releases/download/v2.6.1/NLopt.v2.6.1.i686-w64-mingw32.tar.gz -o nlopt.tar.gz + tar zxvf nlopt.tar.gz -C /d/a/nlopt + dir /d/a/nlopt + cp /d/a/nlopt/bin/* /usr/lib + mkdir dist + cabal install exe:egraphGP --install-method=copy --overwrite-policy=always --installdir=dist --extra-lib-dirs=/d/a/nlopt/lib --extra-lib-dirs=/d/a/nlopt/bin --extra-include-dirs=/d/a/nlopt/include + # cabal install exe:egraphSearch --install-method=copy --overwrite-policy=always --installdir=dist --extra-lib-dirs=nlopt/lib --extra-lib-dirs=nlopt/bin --extra-include-dirs=nlopt/include + # cabal install exe:srtools --install-method=copy --overwrite-policy=always --installdir=dist --extra-lib-dirs=nlopt/lib --extra-lib-dirs=nlopt/bin --extra-include-dirs=nlopt/include + # cabal install exe:rEGGression --install-method=copy --overwrite-policy=always --installdir=dist --extra-lib-dirs=nlopt/lib --extra-lib-dirs=nlopt/bin --extra-include-dirs=nlopt/include + # cabal install exe:srsimplify --install-method=copy --overwrite-policy=always --installdir=dist --extra-lib-dirs=nlopt/lib --extra-lib-dirs=nlopt/bin --extra-include-dirs=nlopt/include + - if: matrix.os != 'windows-latest' + name: Build binary + run: | + mkdir dist + cabal install exe:egraphGP --install-method=copy --overwrite-policy=always --installdir=dist + cabal install exe:egraphSearch --install-method=copy --overwrite-policy=always --installdir=dist + cabal install exe:srtools --install-method=copy --overwrite-policy=always --installdir=dist + cabal install exe:rEGGression --install-method=copy --overwrite-policy=always --installdir=dist + cabal install exe:srsimplify --install-method=copy --overwrite-policy=always --installdir=dist + + - if: matrix.os == 'windows-latest' + name: Set extension to .exe on Windows + run: echo "EXT=.exe" >> $GITHUB_ENV + + - name: Set binary path name + run: | + echo "BINARY_PATH1=./dist/srsimplify${{ env.EXT }}" >> $GITHUB_ENV + echo "BINARY_PATH2=./dist/srtools${{ env.EXT }}" >> $GITHUB_ENV + echo "BINARY_PATH2=./dist/egraphGP${{ env.EXT }}" >> $GITHUB_ENV + echo "BINARY_PATH2=./dist/egraphSearch${{ env.EXT }}" >> $GITHUB_ENV + echo "BINARY_PATH2=./dist/rEGGression${{ env.EXT }}" >> $GITHUB_ENV + + - name: Upload Release Asset + id: upload-release-asset + uses: softprops/action-gh-release@v2 + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + with: + files: | + ${{ env.BINARY_PATH1 }}/srsimplify-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }}${{ env.EXT }} + ${{ env.BINARY_PATH2 }}/srtools-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }}${{ env.EXT }} + ${{ env.BINARY_PATH2 }}/egraphSearch-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }}${{ env.EXT }} + ${{ env.BINARY_PATH2 }}/egraphGP-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }}${{ env.EXT }} + ${{ env.BINARY_PATH2 }}/rEGGression-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }}${{ env.EXT }} diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..8fd74fe --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,16 @@ +# See https://pre-commit.com for more information +# See https://pre-commit.com/hooks.html for more hooks +repos: + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v3.2.0 + hooks: + - id: trailing-whitespace + - id: end-of-file-fixer + - id: check-yaml + - id: check-added-large-files + - repo: https://github.com/pre-commit/mirrors-mypy + rev: v1.3.0 + hooks: + - id: mypy + additional_dependencies: + - types_setuptools diff --git a/.python-version b/.python-version new file mode 100644 index 0000000..88360e1 --- /dev/null +++ b/.python-version @@ -0,0 +1,6 @@ +3.12 +3.11 +3.10 +3.9 +3.8 +3.7 diff --git a/Setup.hs b/Setup.hs new file mode 100755 index 0000000..d48695a --- /dev/null +++ b/Setup.hs @@ -0,0 +1,64 @@ +{-# LANGUAGE NamedFieldPuns #-} + +import Control.Exception (catch, handle, throwIO) +import Control.Monad (unless) +import Data.Foldable (for_) +import Data.Traversable (for) +import Distribution.PackageDescription (ComponentName (..), ForeignLib (..), PackageDescription (..)) +import Distribution.Simple (UserHooks (..), defaultMainWithHooks, simpleUserHooks) +import Distribution.Simple.LocalBuildInfo (componentBuildDir) +import Distribution.Simple.Setup (BuildFlags (..), fromFlagOrDefault) +import Distribution.Simple.Utils (die', info) +import Distribution.Types.LocalBuildInfo (componentNameCLBIs) +import Distribution.Types.UnqualComponentName (UnqualComponentName, unUnqualComponentName) +import Distribution.Verbosity (Verbosity, normal) +import System.Directory (doesDirectoryExist, copyFile) +import System.Environment (getEnv) +import System.FilePath ((<.>)) +import System.FilePath.Posix (()) +import System.IO.Error (isDoesNotExistError) +import System.Info (os) + +main :: IO () +main = + defaultMainWithHooks + simpleUserHooks + { postBuild = \args buildFlags packageDescription localBuildInfo -> do + let verbosity = fromFlagOrDefault normal (buildVerbosity buildFlags) + maybeInstallDir <- getInstallDir verbosity + for_ maybeInstallDir $ \installDir -> do + let PackageDescription {foreignLibs} = packageDescription + for_ foreignLibs $ \foreignLib -> do + let ForeignLib {foreignLibName} = foreignLib + let foreignLibCLBIs = componentNameCLBIs localBuildInfo (CFLibName foreignLibName) + for_ foreignLibCLBIs $ \foreignLibCLBI -> do + let foreignLibBuildDir = componentBuildDir localBuildInfo foreignLibCLBI + let foreignLibFileName = getForeignLibFileName foreignLibName + let foreignLibBuildPath = foreignLibBuildDir foreignLibFileName + let foreignLibInstallPath = installDir foreignLibFileName + info verbosity $ "Installing " <> foreignLibFileName <> " to " <> installDir + copyFile foreignLibBuildPath foreignLibInstallPath + } + +getForeignLibFileName :: UnqualComponentName -> FilePath +getForeignLibFileName foreignLibName + | System.Info.os == "mingw32" = libName <.> "dll" + | System.Info.os == "darwin" = "lib" <> libName <.> "dylib" + | otherwise = "lib" <> libName <.> "so" + where + libName = unUnqualComponentName foreignLibName + +getInstallDir :: Verbosity -> IO (Maybe FilePath) +getInstallDir verbosity = + handle doesNotExistErrorHandler unsafeGetInstallDir + where + unsafeGetInstallDir = do + installDir <- getEnv "INSTALLDIR" + installDirExists <- doesDirectoryExist installDir + unless installDirExists $ + die' verbosity $ + "INSTALLDIR '" <> installDir <> "' does not exist" + return $ Just installDir + + doesNotExistErrorHandler e = + if isDoesNotExistError e then return Nothing else throwIO e diff --git a/build/lib.linux-x86_64-cpython-310/pyeggp/__init__.py b/build/lib.linux-x86_64-cpython-310/pyeggp/__init__.py new file mode 100644 index 0000000..db02eed --- /dev/null +++ b/build/lib.linux-x86_64-cpython-310/pyeggp/__init__.py @@ -0,0 +1,51 @@ +import atexit +from contextlib import contextmanager +from threading import Lock +from typing import Iterator, List + +from ._binding import ( + unsafe_hs_pyeggp_version, + unsafe_hs_pyeggp_main, + unsafe_hs_pyeggp_fib, + unsafe_hs_pyeggp_init, + unsafe_hs_pyeggp_exit, +) + +VERSION: str = "1.3.0" + + +_hs_rts_init: bool = False +_hs_rts_lock: Lock = Lock() + + +def hs_rts_exit() -> None: + global _hs_rts_lock + with _hs_rts_lock: + unsafe_hs_pyeggp_exit() + + +@contextmanager +def hs_rts_init(args: List[str] = []) -> Iterator[None]: + global _hs_rts_init + global _hs_rts_lock + with _hs_rts_lock: + if not _hs_rts_init: + _hs_rts_init = True + unsafe_hs_pyeggp_init(args) + atexit.register(hs_rts_exit) + yield None + + +def version() -> str: + with hs_rts_init(): + return unsafe_hs_pyeggp_version() + + +def main(args: List[str] = []) -> int: + with hs_rts_init(args): + return unsafe_hs_pyeggp_main() + + +def pyegpp(n: List[str]) -> str: + with hs_rts_init(): + return unsafe_hs_pyeggp(n) diff --git a/build/lib.linux-x86_64-cpython-310/pyeggp/__main__.py b/build/lib.linux-x86_64-cpython-310/pyeggp/__main__.py new file mode 100644 index 0000000..3fa9edf --- /dev/null +++ b/build/lib.linux-x86_64-cpython-310/pyeggp/__main__.py @@ -0,0 +1,11 @@ +import sys +from typing import NoReturn +import pyeggp + + +def main() -> NoReturn: + sys.exit(pyeggp.main(sys.argv)) + + +if __name__ == "__main__": + main() diff --git a/build/lib.linux-x86_64-cpython-310/pyeggp/_binding.pyi b/build/lib.linux-x86_64-cpython-310/pyeggp/_binding.pyi new file mode 100644 index 0000000..22251f5 --- /dev/null +++ b/build/lib.linux-x86_64-cpython-310/pyeggp/_binding.pyi @@ -0,0 +1,7 @@ +from typing import List + +def unsafe_hs_pyeggp_version() -> str: ... +def unsafe_hs_pyeggp_main() -> int: ... +def unsafe_hs_pyeggp(n: List[str]) -> str: ... +def unsafe_hs_pyeggp_init(args: List[str]) -> None: ... +def unsafe_hs_pyeggp_exit() -> None: ... diff --git a/build/lib.linux-x86_64-cpython-310/pyeggp/binding.i b/build/lib.linux-x86_64-cpython-310/pyeggp/binding.i new file mode 100644 index 0000000..6155386 --- /dev/null +++ b/build/lib.linux-x86_64-cpython-310/pyeggp/binding.i @@ -0,0 +1,64 @@ +%module binding +%{ +#include "ExampleHaskellWheelBinding_stub.h" + +char * unsafe_hs_pyeggp_version() { + return hs_pyeggp_version(); +} + +int unsafe_hs_pyeggp_main() { + return hs_pyeggp_main(); +} + +char * unsafe_hs_pyeggp(char **n) { + return hs_pyeggp(&n); +} + +void unsafe_hs_pyeggp_init(int argc, char **argv) { + hs_init(&argc, &argv); +} + +void unsafe_hs_pyeggp_exit() { + hs_exit(); +} + +void unsafe_py_write_stdout(const char * str) { + PySys_FormatStdout("%s", str); +} + +void unsafe_py_write_stderr(const char * str) { + PySys_FormatStderr("%s", str); +} +%} + +%typemap(in) (int argc, char **argv) { + /* Check if is a list */ + if (PyList_Check($input)) { + int i; + $1 = PyList_Size($input); + $2 = (char **) malloc(($1+1)*sizeof(char *)); + for (i = 0; i < $1; i++) { + PyObject *o = PyList_GetItem($input, i); + if (PyUnicode_Check(o)) { + $2[i] = (char *) PyUnicode_AsUTF8AndSize(o, 0); + } else { + PyErr_SetString(PyExc_TypeError, "list must contain strings"); + SWIG_fail; + } + } + $2[i] = 0; + } else { + PyErr_SetString(PyExc_TypeError, "not a list"); + SWIG_fail; + } +} + +%typemap(freearg) (int argc, char **argv) { + free((char *) $2); +} + +char * unsafe_hs_pyeggp_version(); +int unsafe_hs_pyeggp_main(); +char * unsafe_hs_pyeggp(char **n); +void unsafe_hs_pyeggp_init(int argc, char **argv); +void unsafe_hs_pyeggp_exit(); diff --git a/build/lib.linux-x86_64-cpython-310/pyeggp/binding.py b/build/lib.linux-x86_64-cpython-310/pyeggp/binding.py new file mode 100644 index 0000000..4b2df33 --- /dev/null +++ b/build/lib.linux-x86_64-cpython-310/pyeggp/binding.py @@ -0,0 +1,76 @@ +# This file was automatically generated by SWIG (https://www.swig.org). +# Version 4.3.0 +# +# Do not make changes to this file unless you know what you are doing - modify +# the SWIG interface file instead. + +from sys import version_info as _swig_python_version_info +# Import the low-level C/C++ module +if __package__ or "." in __name__: + from . import _binding +else: + import _binding + +try: + import builtins as __builtin__ +except ImportError: + import __builtin__ + +def _swig_repr(self): + try: + strthis = "proxy of " + self.this.__repr__() + except __builtin__.Exception: + strthis = "" + return "<%s.%s; %s >" % (self.__class__.__module__, self.__class__.__name__, strthis,) + + +def _swig_setattr_nondynamic_instance_variable(set): + def set_instance_attr(self, name, value): + if name == "this": + set(self, name, value) + elif name == "thisown": + self.this.own(value) + elif hasattr(self, name) and isinstance(getattr(type(self), name), property): + set(self, name, value) + else: + raise AttributeError("You cannot add instance attributes to %s" % self) + return set_instance_attr + + +def _swig_setattr_nondynamic_class_variable(set): + def set_class_attr(cls, name, value): + if hasattr(cls, name) and not isinstance(getattr(cls, name), property): + set(cls, name, value) + else: + raise AttributeError("You cannot add class attributes to %s" % cls) + return set_class_attr + + +def _swig_add_metaclass(metaclass): + """Class decorator for adding a metaclass to a SWIG wrapped class - a slimmed down version of six.add_metaclass""" + def wrapper(cls): + return metaclass(cls.__name__, cls.__bases__, cls.__dict__.copy()) + return wrapper + + +class _SwigNonDynamicMeta(type): + """Meta class to enforce nondynamic attributes (no new attributes) for a class""" + __setattr__ = _swig_setattr_nondynamic_class_variable(type.__setattr__) + + + +def unsafe_hs_pyeggp_version(): + return _binding.unsafe_hs_pyeggp_version() + +def unsafe_hs_pyeggp_main(): + return _binding.unsafe_hs_pyeggp_main() + +def unsafe_hs_pyeggp(n): + return _binding.unsafe_hs_pyeggp(n) + +def unsafe_hs_pyeggp_init(argc): + return _binding.unsafe_hs_pyeggp_init(argc) + +def unsafe_hs_pyeggp_exit(): + return _binding.unsafe_hs_pyeggp_exit() + diff --git a/build/lib.linux-x86_64-cpython-310/pyeggp/typing.py b/build/lib.linux-x86_64-cpython-310/pyeggp/typing.py new file mode 100644 index 0000000..88f7e4a --- /dev/null +++ b/build/lib.linux-x86_64-cpython-310/pyeggp/typing.py @@ -0,0 +1,14 @@ +import sys + +from typing import List + +if sys.version_info < (3, 8): + from typing_extensions import Protocol +else: + from typing import Protocol + + +class Session(Protocol): + def version(self) -> str: ... + def main(self, args: List[str] = []) -> int: ... + def pyeggp(self, n: List[str]) -> str: ... diff --git a/cabal.project b/cabal.project new file mode 100644 index 0000000..c466d2a --- /dev/null +++ b/cabal.project @@ -0,0 +1,7 @@ +packages: . +allow-newer: table-layout:*, haskeline:*, aeson:* + +source-repository-package + type: git + location: https://github.com/haskell/mwc-random + tag: 2cce257158befe52417433d1e7717f11ce718aec diff --git a/pyeggp.cabal b/pyeggp.cabal new file mode 100755 index 0000000..69634db --- /dev/null +++ b/pyeggp.cabal @@ -0,0 +1,75 @@ +cabal-version: 1.12 + +-- This file has been generated from package.yaml by hpack version 0.37.0. +-- +-- see: https://github.com/sol/hpack + +name: pyeggp +version: 1.0.0 +synopsis: A general library to work with Symbolic Regression expression trees. +description: A Symbolic Regression Tree data structure to work with mathematical expressions with support to first order derivative and simplification; +category: Math, Data, Data Structures +homepage: https://github.com/folivetti/pyeggp#readme +bug-reports: https://github.com/folivetti/pyeggp/issues +author: Fabricio Olivetti de França +maintainer: fabricio.olivetti@gmail.com +copyright: 2023 Fabricio Olivetti de França +license: BSD3 +license-file: LICENSE +build-type: Simple +extra-source-files: + README.md + ChangeLog.md + +source-repository head + type: git + location: https://github.com/folivetti/pyeggp + +foreign-library _binding + import: warnings + type: native-shared + + -- 01-04-2023: + -- Standalone builds appear to work with macOS builds of GHC + if os(osx) + options: standalone + + if os(windows) + options: standalone + mod-def-file: src/pyeggp/binding.def + + --c-sources: src/pyeggp/binding_wrap.c + hs-source-dirs: src + other-modules: + Paths_pyeggp + extra-libraries: + nlopt + build-depends: + attoparsec >=0.14.4 && <0.15 + , attoparsec-expr >=0.1.1.2 && <0.2 + , base >=4.19 && <5 + , binary >=0.8.9.1 && <0.9 + , bytestring >=0.11 && <0.13 + , containers >=0.6.7 && <0.8 + , dlist ==1.0.* + , exceptions >=0.10.7 && <0.11 + , filepath >=1.4.0.0 && <1.6 + , hashable >=1.4.4.0 && <1.6 + , ieee754 >=0.8.0 && <0.9 + , lens >=5.2.3 && <5.4 + , list-shuffle >=1.0.0.1 && <1.1 + , massiv >=1.0.4.1 && <1.1 + , mtl >=2.2 && <2.4 + , optparse-applicative >=0.17 && <0.19 + , random >=1.2 && <1.4 + , scheduler >=2.0.0.1 && <3 + , split >=0.2.5 && <0.3 + , srtree + , statistics >=0.16.2.1 && <0.17 + , transformers >=0.6.1.0 && <0.7 + , unliftio >=0.2.10 && <1 + , unliftio-core >=0.2.1 && <1 + , unordered-containers ==0.2.* + , vector >=0.12 && <0.14 + , zlib >=0.6.3 && <0.8 + default-language: Haskell2010 diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..534ad82 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,138 @@ +[build-system] +requires = [ + "find_libpython >=0.3,<0.5; sys_platform == 'win32'", + "setuptools >=45", + "swig >=4,<5", + "nlopt >= 2.9.1" +] +build-backend = "setuptools.build_meta" + +[project] +name = "srtree" +version = "1.0.0" +authors = [{ name = "Fabricio Olivetti", email = "folivetti@users.noreply.github.com" }] +description = "Python Wheels for eggp algorithm." +readme = "README.md" +license = { file = "LICENSE" } +requires-python = ">=3.9,<3.13" +classifiers = [ + "License :: OSI Approved :: MIT License", + "Operating System :: MacOS", + "Operating System :: Microsoft :: Windows", + "Operating System :: POSIX :: Linux", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: Implementation :: CPython", +] +dependencies = ["typing_extensions >=4.6,<5; python_version<'3.8'"] + +[project.optional-dependencies] +mypy = ["types_setuptools >=45"] + +#[project.scripts] +#"example-haskell-wheel" = "example_haskell_wheel.__main__:main" + +[tool.bumpver] +current_version = "v1.0.0" +version_pattern = "vMAJOR.MINOR.PATCH[-TAG]" +commit_message = "Bump version {old_version} -> {new_version}" +commit = true +tag = true +push = true + +[tool.bumpver.file_patterns] +"pyproject.toml" = [ + '^version = "{pep440_version}"$', + '^current_version = "{version}"$', +] +"src/example_haskell_wheel/__init__.py" = [ + '^VERSION: str = "{pep440_version}"$', +] +"srtree.cabal" = ['^version: {pep440_version}$'] + +[tool.cibuildwheel] +# 18-04-2023: +# Cross-compilation does not work with GHC and Cabal. +archs = ["native"] +build-frontend = "build" +build = [ + # GHC provides x86_64 and arm64 builds for macOS. + "*-macosx_x86_64", + "*-macosx_arm64", + # GHC provides an AMD64 build for Windows. + "*-win_amd64", + # GHC provides an x86_64 build for CentOS 7 (manylinux2014). + "*-manylinux_x86_64", + # GHC provides an i686 build for Debian 9 (manylinux_2014 compatible). + "*-manylinux_i686", + # GHC provides an aarch64 build for Debian 10 (manylinux_2_28 compatible). + "*-manylinux_aarch64", + # GHC provides an x86_64 build for Alpine 3.12 (manylinux_2_28 compatible). + "*-musllinux_x86_64", +] +skip = [ + # 18-04-2023: + # PyPy builds do not work. + "pp*", +] + +[tool.mypy] +mypy_path = "src" +python_version = "3.9" +strict = true +implicit_reexport = false + +[tool.setuptools.packages.find] +where = ["src"] + +[tool.tox] +legacy_tox_ini = """ +[tox] +min_version = 4 +env_list = py{39,310,311,312}-{lin,mac,win} + +[testenv:py{39,310,311,312}-{lin,mac,win}] +package = external +package_env = build-{env_name} +platform = + lin: linux + mac: darwin + win: win32 +allowlist_externals = + sh +extras = + test +commands = + {env_python} -m pytest {posargs} + +[testenv:build-py{39,310,311,312}-{lin,mac,win}] +deps = + build + auditwheel; sys_platform == 'linux' + delocate; sys_platform == 'darwin' +package_glob = + py37-lin: {package_root}{/}dist{/}*cp37*manylinux*.whl + py38-lin: {package_root}{/}dist{/}*cp38*manylinux*.whl + py39-lin: {package_root}{/}dist{/}*cp39*manylinux*.whl + py310-lin: {package_root}{/}dist{/}*cp310*manylinux*.whl + py311-lin: {package_root}{/}dist{/}*cp311*manylinux*.whl + py312-lin: {package_root}{/}dist{/}*cp312*manylinux*.whl + py37-mac: {package_root}{/}dist{/}*cp37*macosx*.whl + py38-mac: {package_root}{/}dist{/}*cp38*macosx*.whl + py39-mac: {package_root}{/}dist{/}*cp39*macosx*.whl + py310-mac: {package_root}{/}dist{/}*cp310*macosx*.whl + py311-mac: {package_root}{/}dist{/}*cp311*macosx*.whl + py312-mac: {package_root}{/}dist{/}*cp312*macosx*.whl + py37-win: {package_root}{/}dist{/}*cp37*win*.whl + py38-win: {package_root}{/}dist{/}*cp38*win*.whl + py39-win: {package_root}{/}dist{/}*cp39*win*.whl + py310-win: {package_root}{/}dist{/}*cp310*win*.whl + py311-win: {package_root}{/}dist{/}*cp311*win*.whl + py312-win: {package_root}{/}dist{/}*cp312*win*.whl +allowlist_externals = + sh +commands = + sh {package_root}/scripts/build-wheel.sh +""" diff --git a/requirements-ci.txt b/requirements-ci.txt new file mode 100644 index 0000000..4fcdc16 --- /dev/null +++ b/requirements-ci.txt @@ -0,0 +1 @@ +pipx >=1.2.0, <2 diff --git a/scripts/build-wheel.sh b/scripts/build-wheel.sh new file mode 100644 index 0000000..8eaba75 --- /dev/null +++ b/scripts/build-wheel.sh @@ -0,0 +1,80 @@ +#!/bin/sh + +# Get configuration from Tox +# 2024-04-16: The substitution for '{env_python}' and other environment variables no longer appears to work. +python="python" +package_root="${package_root:-"$(dirname "$( cd -- "$(dirname "$0")" >/dev/null 2>&1 || exit ; pwd -P )" )"}" +dist_dir="${package_root}/dist" +dist_tmp_dir="${package_root}/dist/_tmp" + +# Get target platform; +platform=$("${python}" -c 'import sys; print(sys.platform)') + +# Check if dependencies are available: + +# Check if cabal is available: +env_cabal="$(which cabal)" +if [ "${env_cabal}" = '' ] +then + echo "${0} requires 'cabal'" + echo "See: https://cabal.readthedocs.io/en/stable/" + exit 1 +fi + +# Check if build is available: +has_build=$("${python}" -c 'import importlib.util; print(importlib.util.find_spec("build") is not None)') +if [ "${has_build}" = 'False' ] +then + echo "${0} requires 'build'" + echo "See: https://pypi.org/project/build/" + exit 1 +fi + +# Check if platform-specific dependencies are available: +case "${platform}" in + 'linux') + # Check if auditwheel is available: + has_auditwheel=$("${python}" -c 'import importlib.util; print(importlib.util.find_spec("auditwheel") is not None)') + if [ "${has_auditwheel}" = 'False' ] + then + echo "${0} requires 'auditwheel'" + echo "See: https://pypi.org/project/auditwheel/" + exit 1 + fi + ;; + 'darwin') + # Check if delocate is available: + has_delocate=$("${python}" -c 'import importlib.util; print(importlib.util.find_spec("delocate") is not None)') + if [ "${has_delocate}" = 'False' ] + then + echo "${0} requires 'delocate'" + echo "See: https://pypi.org/project/delocate/" + exit 1 + fi + ;; +esac + +# Update the hackage package list +"${env_cabal}" v2-update + +# Build the wheel +"${python}" -m build --wheel --outdir "${dist_tmp_dir}" "${package_root}" + +# Delocate the wheel +case "${platform}" in + 'linux') + # Repair wheel with auditwheel + libc_xy="$("${python}" -c 'import platform; print(platform.libc_ver()[1].replace(".","_"))')" + machine="$("${python}" -c 'import platform; print(platform.machine())')" + auditwheel repair --wheel-dir "${dist_dir}" --plat "manylinux_${libc_xy}_${machine}" "${dist_tmp_dir}"/*.whl + ;; + 'darwin') + # Repair wheel with delocate-wheel + delocate-wheel --wheel-dir "${dist_dir}" "${dist_tmp_dir}"/*.whl + ;; + *) + mkdir -p "${dist_dir}" + cp "${dist_tmp_dir}"/*.whl "${dist_dir}"/ + ;; +esac +echo "The built wheel is located in ${dist_dir}" diff --git a/setup.py b/setup.py new file mode 100644 index 0000000..9166ba0 --- /dev/null +++ b/setup.py @@ -0,0 +1,138 @@ +""" +adapted from https://github.com/wenkokke/example-haskell-wheel +""" +import os.path +import sys +import subprocess +import shutil +from typing import Dict, List, Optional + +from setuptools import Extension, setup +from setuptools.command.build_ext import build_ext + +ext_module = Extension( + name="eggp._binding", + sources=["src/pyeggp/binding.i"], +) + + +class cabal_build_ext(build_ext): + def finalize_options(self) -> None: + super().finalize_options() + + if sys.platform in ["win32", "cygwin"]: + import find_libpython + + library_dir, library = os.path.split(find_libpython.find_libpython()) + libname, _libext = os.path.splitext(library) + libname = libname[3:] if libname.startswith("lib") else libname + self.libraries.append(libname) + self.library_dirs.append(library_dir) + + def build_extension(self, ext: Extension) -> None: + # Taken from setuptools: + # https://github.com/pypa/setuptools/blob/245d72a8aa4d47e1811425213aba2a06a0bb64fa/setuptools/command/build_ext.py#L240-L241 + if hasattr(ext, "_convert_pyx_sources_to_lang"): + ext._convert_pyx_sources_to_lang() + + # Taken from distutils: + # https://github.com/pypa/distutils/blob/4435cec31b8eb5712aa8bf993bea3f07051c24d8/distutils/command/build_ext.py#L504-L513 + sources = ext.sources + if sources is None or not isinstance(sources, (list, tuple)): + raise ValueError( + "in 'ext_modules' option (extension '%s'), " + "'sources' must be present and must be " + "a list of source filenames" % ext.name + ) + # sort to make the resulting .so file build reproducible + sources = sorted(sources) + + # First, scan the sources for SWIG definition files (.i), run + # SWIG on 'em to create .c files, and modify the sources list + # accordingly. + sources = self.swig_sources(sources, ext) # type: ignore[no-untyped-call] + + # Next, build the sources with Cabal. + # NOTE: This requires a valid .cabal file that defines a foreign library called _binding. + self.mkpath(self.build_temp) + self.cabal_configure_ext(ext) + self.cabal_build_ext(ext) + + # Taken from setuptools: + # https://github.com/pypa/setuptools/blob/245d72a8aa4d47e1811425213aba2a06a0bb64fa/setuptools/command/build_ext.py#L247-L249 + if hasattr(ext, "_needs_stub"): + command = self.get_finalized_command("build_py") + if hasattr(command, "build_lib"): + build_lib = command.build_lib + self.write_stub(build_lib, ext) + + def cabal_configure_ext(self, ext: Extension) -> None: + library_dirs = [*(self.library_dirs or []), *(ext.library_dirs or [])] + include_dirs = [*(self.include_dirs or []), *(ext.include_dirs or [])] + libraries = [*(self.libraries or []), *(ext.libraries or [])] + define = [*(self.define or []), *(ext.define_macros or [])] + undef = [*(self.undef or []), *(ext.undef_macros or [])] + self.cabal( + [ + "configure", + *(f"--extra-lib-dirs={dir}" for dir in library_dirs), + *(f"--extra-include-dirs={dir}" for dir in include_dirs), + *(f"--ghc-options=-optl-l{library}" for library in libraries), + *(f"--ghc-options=-D{symbol}={value}" for symbol, value in define), + *(f"--ghc-options=-U{symbol}" for symbol in undef), + ] + ) + + def cabal_build_ext(self, ext: Extension) -> None: + self.mkpath(self.build_temp) + self.cabal(["build"], env={"INSTALLDIR": self.build_temp, **os.environ}) + lib_filename = self.get_cabal_foreign_library_filename(ext) + ext_fullpath = self.get_ext_fullpath(ext.name) + self.mkpath(os.path.dirname(ext_fullpath)) + self.copy_file(os.path.join(self.build_temp, lib_filename), ext_fullpath) + + def get_cabal_foreign_library_filename(self, ext: Extension) -> str: + if sys.platform not in ["darwin", "linux", "win32", "cygwin"]: + raise NotImplementedError(f"unsupported platform {self.plat_name}") + library_prefix = "" if sys.platform in ["win32", "cygwin"] else "lib" + component_name = ext.name.split(".")[-1] + dynlib_extension = { + "darwin": "dylib", + "linux": "so", + "win32": "dll", + "cygwin": "dll", + }[sys.platform] + return f"{library_prefix}{component_name}{os.path.extsep}{dynlib_extension}" + + def cabal( + self, + args: List[str], + *, + env: Optional[Dict[str, str]] = None, + ) -> None: + args = [self.find_cabal(), *args] + cmd = " ".join(args) + print(cmd) + exitCode = subprocess.call(args, env=env) + if exitCode != 0: + raise OSError(f"error occurred when running '{cmd}'") + + _cabal: Optional[str] = None + + def find_cabal(self) -> str: + if self._cabal is None: + self._cabal = shutil.which("cabal") + if self._cabal is None: + raise OSError("could not find executable 'cabal'") + return self._cabal + + +def main() -> None: + setup( + ext_modules=[ext_module], + cmdclass={"build_ext": cabal_build_ext}, + ) + + +if __name__ == "__main__": + main() diff --git a/src/Pyeggp.hs b/src/Pyeggp.hs new file mode 100644 index 0000000..616537e --- /dev/null +++ b/src/Pyeggp.hs @@ -0,0 +1,409 @@ +{-# LANGUAGE BlockArguments #-} +{-# LANGUAGE TupleSections #-} +{-# LANGUAGE MultiWayIf #-} +{-# LANGUAGE OverloadedStrings #-} +{-# LANGUAGE BangPatterns #-} +{-# LANGUAGE TypeSynonymInstances, FlexibleInstances #-} + +module Pyeggp where + +import Algorithm.EqSat.Egraph +import Algorithm.EqSat.Simplify +import Algorithm.EqSat.Build +import Algorithm.EqSat.Queries +import Algorithm.EqSat.Info +import Algorithm.EqSat.DB +import Algorithm.SRTree.Likelihoods +import Algorithm.SRTree.ModelSelection +import Algorithm.SRTree.Opt +import Control.Lens (element, makeLenses, over, (&), (+~), (-~), (.~), (^.)) +import Control.Monad (foldM, forM_, forM, when, unless, filterM, (>=>), replicateM, replicateM_) +import Control.Monad.State.Strict +import qualified Data.IntMap.Strict as IM +import qualified Data.Map.Strict as Map +import Data.Massiv.Array as MA hiding (forM_, forM) +import Data.Maybe (fromJust, isNothing, isJust) +import Data.SRTree +import Data.SRTree.Datasets +import Data.SRTree.Eval +import Data.SRTree.Random (randomTree) +import Data.SRTree.Print +import Random +import System.Random +import qualified Data.HashSet as Set +import Data.List ( sort, maximumBy, intercalate, sortOn, intersperse, nub ) +import Data.IntSet (IntSet) +import qualified Data.IntSet as IntSet +import qualified Data.Sequence as FingerTree +import Data.Function ( on ) +import qualified Data.Foldable as Foldable +import qualified Data.IntMap as IntMap +import List.Shuffle ( shuffle ) +import Algorithm.SRTree.NonlinearOpt +import Data.Binary ( encode, decode ) +import qualified Data.ByteString.Lazy as BS + +import Algorithm.EqSat (runEqSat,applySingleMergeOnlyEqSat) + +import GHC.IO (unsafePerformIO) +import Control.Scheduler +import Control.Monad.IO.Unlift +import Data.SRTree (convertProtectedOps) + +import Util + +import Foreign.C (CInt (..)) +import Foreign.C.String (CString, newCString, withCString) +import Paths_pyeggp_wheel (version) +import System.Environment (getArgs) +import System.Exit (ExitCode (..)) +import Text.Read (readMaybe) + +foreign import ccall unsafe_py_write_stdout :: CString -> IO () + +py_write_stdout :: String -> IO () +py_write_stdout str = withCString str unsafe_py_write_stdout + +foreign import ccall unsafe_py_write_stderr :: CString -> IO () + +py_write_stderr :: String -> IO () +py_write_stderr str = withCString str unsafe_py_write_stderr + +foreign export ccall hs_pyeggp_version :: IO CInt + +hs_pyeggp_version :: IO CString +hs_pyeggp_version = + newCString (showVersion version) + +foreign export ccall hs_pyeggp_main :: IO CString + +exitHandler :: ExitCode -> IO CInt +exitHandler ExitSuccess = return 0 +exitHandler (ExitFailure n) = return (fromIntegral n) + +uncaughtExceptionHandler :: SomeException -> IO CInt +uncaughtExceptionHandler (SomeException e) = + py_write_stderr (displayException e) >> return 1 + +hs_pyeggp_main :: IO CInt +hs_pyeggp_main = + handle uncaughtExceptionHandler $ + handle exitHandler $ do + getArgs >>= \args -> + case eggp args of + "" -> py_write_stdout $ "wrong arguments" + css -> py_write_stdout css + return 0 + +foreign export ccall hs_pyeggp :: [CString] -> IO CString + +hs_pyeggp :: [CString] -> IO CString +hs_pyeggp cargs = do + args <- traverse peekCString cargs + newCString $ pyeggp args + +egraphGP :: [(DataSet, DataSet)] -> Args -> StateT EGraph (StateT StdGen IO) () +egraphGP dataTrainVals args = do + when ((not.null) (_loadFrom args)) $ (io $ BS.readFile (_loadFrom args)) >>= \eg -> put (decode eg) + + pop <- replicateM (_nPop args) $ do ec <- insertRndExpr (_maxSize args) rndTerm rndNonTerm >>= canonical + updateIfNothing fitFun ec + pure ec + insertTerms + evaluateUnevaluated fitFun + pop' <- Prelude.mapM canonical pop + let m = (_nPop args) `div` (_maxSize args) + + finalPop <- iterateFor (_gens args) pop' $ \it ps' -> do + newPop' <- replicateM (_nPop args) (evolve ps') + + totSz <- gets (Map.size . _eNodeToEClass) -- (IntMap.size . _eClass) + let full = totSz > max maxMem (_nPop args) + when full (cleanEGraph >> cleanDB) + + newPop <- let n_paretos = (_nPop args) `div` (_maxSize args) + pareto <- concat <$> (forM [1 .. _maxSize args] $ \n -> getTopFitEClassWithSize n 2) + let remainder = _nPop args - length pareto + lft <- if full + then getTopFitEClassThat remainder (const True) + else pure $ Prelude.take remainder newPop' + Prelude.mapM canonical (pareto <> lft) + pure newPop + + io $ putStrLn "id,Expression,Numpy,theta,size" + paretoFront fitFun (_maxSize args) printExpr + when ((not.null) (_dumpTo args)) $ get >>= (io . BS.writeFile (_dumpTo args) . encode ) + where + maxSize = (_maxSize args) + maxMem = 2000000 -- running 1 iter of eqsat for each new individual will consume ~3GB + fitFun = fitnessMV shouldReparam (_optRepeat args) (_optIter args) (_distribution args) dataTrainVals + nonTerms = parseNonTerms (_nonterminals args) + (Sz2 _ nFeats) = MA.size (getX .fst . head $ dataTrainVals) + params = if _nParams args == -1 then [param 0] else Prelude.map param [0 .. _nParams args - 1] + shouldReparam = _nParams args == -1 + relabel = if shouldReparam then relabelParams else relabelParamsOrder + terms = if _distribution args == ROXY + then (var 0 : params) + else [var ix | ix <- [0 .. nFeats-1]] -- <> params + uniNonTerms = [t | t <- nonTerms, isUni t] + binNonTerms = [t | t <- nonTerms, isBin t] + isUni (Uni _ _) = True + isUni _ = False + isBin (Bin _ _ _) = True + isBin _ = False + + -- TODO: merge two or more egraphs + cleanEGraph = do let nParetos = 10 -- (maxMem `div` 5) `div` _maxSize args + io . putStrLn $ "cleaning" + pareto <- (concat <$> (forM [1 .. _maxSize args] $ \n -> getTopFitEClassWithSize n nParetos)) + >>= Prelude.mapM canonical + infos <- forM pareto (\c -> gets (_info . (IntMap.! c) . _eClass)) + exprs <- forM pareto getBestExpr + put emptyGraph + newIds <- fromTrees myCost $ Prelude.map relabel exprs + forM_ (Prelude.zip newIds (Prelude.reverse infos)) $ \(eId, info) -> + insertFitness eId (fromJust $ _fitness info) (_theta info) + + rndTerm = do coin <- toss + if coin then Random.randomFrom terms else Random.randomFrom params + rndNonTerm = Random.randomFrom nonTerms + + refitChanged = do ids <- gets (_refits . _eDB) >>= Prelude.mapM canonical . Set.toList >>= pure . nub + modify' $ over (eDB . refits) (const Set.empty) + forM_ ids $ \ec -> do t <- getBestExpr ec + (f, p) <- fitFun t + insertFitness ec f p + + iterateFor 0 xs f = pure xs + iterateFor n xs f = do xs' <- f n xs + iterateFor (n-1) xs' f + + evolve xs' = do xs <- Prelude.mapM canonical xs' + parents <- tournament xs + offspring <- combine parents + --applySingleMergeOnlyEqSat myCost rewritesParams >> cleanDB + if _nParams args == 0 + then runEqSat myCost rewritesWithConstant 1 >> cleanDB >> refitChanged + else runEqSat myCost rewritesParams 1 >> cleanDB >> refitChanged + canonical offspring >>= updateIfNothing fitFun + canonical offspring + --pure offspring + + tournament xs = do p1 <- applyTournament xs >>= canonical + p2 <- applyTournament xs >>= canonical + pure (p1, p2) + + applyTournament :: [EClassId] -> RndEGraph EClassId + applyTournament xs = do challengers <- replicateM (_nTournament args) (rnd $ randomFrom xs) >>= traverse canonical + fits <- Prelude.map fromJust <$> Prelude.mapM getFitness challengers + pure . snd . maximumBy (compare `on` fst) $ Prelude.zip fits challengers + + combine (p1, p2) = (crossover p1 p2 >>= mutate) >>= canonical + + crossover p1 p2 = do sz <- getSize p1 + coin <- rnd $ tossBiased (_pc args) + if sz == 1 || not coin + then rnd (randomFrom [p1, p2]) + else do pos <- rnd $ randomRange (1, sz-1) + cands <- getAllSubClasses p2 + tree <- getSubtree pos 0 Nothing [] cands p1 + fromTree myCost (relabel tree) >>= canonical + + getSubtree :: Int -> Int -> Maybe (EClassId -> ENode) -> [Maybe (EClassId -> ENode)] -> [EClassId] -> EClassId -> RndEGraph (Fix SRTree) + getSubtree 0 sz (Just parent) mGrandParents cands p' = do + p <- canonical p' + candidates' <- filterM (\c -> ( getSize c) cands + candidates <- filterM (\c -> doesNotExistGens mGrandParents (parent c)) candidates' + >>= traverse canonical + if null candidates + then getBestExpr p + else do subtree <- rnd (randomFrom candidates) + getBestExpr subtree + getSubtree pos sz parent mGrandParents cands p' = do + p <- canonical p' + root <- getBestENode p >>= canonize + case root of + Param ix -> pure . Fix $ Param ix + Const x -> pure . Fix $ Const x + Var ix -> pure . Fix $ Var ix + Uni f t' -> do t <- canonical t' + (Fix . Uni f) <$> getSubtree (pos-1) (sz+1) (Just $ Uni f) (parent:mGrandParents) cands t + Bin op l'' r'' -> + do l <- canonical l'' + r <- canonical r'' + szLft <- getSize l + szRgt <- getSize r + if szLft < pos + then do l' <- getBestExpr l + r' <- getSubtree (pos-szLft-1) (sz+szLft+1) (Just $ Bin op l) (parent:mGrandParents) cands r + pure . Fix $ Bin op l' r' + else do l' <- getSubtree (pos-1) (sz+szRgt+1) (Just (\t -> Bin op t r)) (parent:mGrandParents) cands l + r' <- getBestExpr r + pure . Fix $ Bin op l' r' + + getAllSubClasses p' = do + p <- canonical p' + en <- getBestENode p + case en of + Bin _ l r -> do ls <- getAllSubClasses l + rs <- getAllSubClasses r + pure (p : (ls <> rs)) + Uni _ t -> (p:) <$> getAllSubClasses t + _ -> pure [p] + + mutate p = do sz <- getSize p + coin <- rnd $ tossBiased (_pm args) + if coin + then do pos <- rnd $ randomRange (0, sz-1) + tree <- mutAt pos maxSize Nothing p + fromTree myCost (relabel tree) >>= canonical + else pure p + + peel :: Fix SRTree -> SRTree () + peel (Fix (Bin op l r)) = Bin op () () + peel (Fix (Uni f t)) = Uni f () + peel (Fix (Param ix)) = Param ix + peel (Fix (Var ix)) = Var ix + peel (Fix (Const x)) = Const x + + mutAt :: Int -> Int -> Maybe (EClassId -> ENode) -> EClassId -> RndEGraph (Fix SRTree) + mutAt 0 sizeLeft Nothing _ = (insertRndExpr sizeLeft rndTerm rndNonTerm >>= canonical) >>= getBestExpr -- we chose to mutate the root + mutAt 0 1 _ _ = rnd $ randomFrom terms -- we don't have size left + mutAt 0 sizeLeft (Just parent) _ = do -- we reached the mutation place + ec <- insertRndExpr sizeLeft rndTerm rndNonTerm >>= canonical -- create a random expression with the size limit + (Fix tree) <- getBestExpr ec -- + root <- getBestENode ec + exist <- canonize (parent ec) >>= doesExist + if exist + -- the expression `parent ec` already exists, try to fix + then do let children = childrenOf root + candidates <- case length children of + 0 -> filterM (checkToken parent . (replaceChildren children)) (Prelude.map peel terms) + 1 -> filterM (checkToken parent . (replaceChildren children)) uniNonTerms + 2 -> filterM (checkToken parent . (replaceChildren children)) binNonTerms + if null candidates + then pure $ Fix tree -- there's no candidate, so we failed and admit defeat + else do newToken <- rnd (randomFrom candidates) + pure . Fix $ replaceChildren (childrenOf tree) newToken + + else pure . Fix $ tree + + mutAt pos sizeLeft parent p' = do + p <- canonical p' + root <- getBestENode p >>= canonize + case root of + Param ix -> pure . Fix $ Param ix + Const x -> pure . Fix $ Const x + Var ix -> pure . Fix $ Var ix + Uni f t' -> canonical t' >>= \t -> (Fix . Uni f) <$> mutAt (pos-1) (sizeLeft-1) (Just $ Uni f) t + Bin op ln rn -> do l <- canonical ln + r <- canonical rn + szLft <- getSize l + szRgt <- getSize r + if szLft < pos + then do l' <- getBestExpr l + r' <- mutAt (pos-szLft-1) (sizeLeft-szLft-1) (Just $ Bin op l) r + pure . Fix $ Bin op l' r' + else do l' <- mutAt (pos-1) (sizeLeft-szRgt-1) (Just (\t -> Bin op t r)) l + r' <- getBestExpr r + pure . Fix $ Bin op l' r' + + checkToken parent en' = do en <- canonize en' + mEc <- gets ((Map.!? en) . _eNodeToEClass) + case mEc of + Nothing -> pure True + Just ec -> do ec' <- canonical ec + ec'' <- canonize (parent ec') + not <$> doesExist ec'' + doesExist, doesNotExist :: ENode -> RndEGraph Bool + doesExist en = gets ((Map.member en) . _eNodeToEClass) + doesNotExist en = gets ((Map.notMember en) . _eNodeToEClass) + + doesNotExistGens :: [Maybe (EClassId -> ENode)] -> ENode -> RndEGraph Bool + doesNotExistGens [] en = gets ((Map.notMember en) . _eNodeToEClass) + doesNotExistGens (mGrand:grands) en = do b <- gets ((Map.notMember en) . _eNodeToEClass) + if b + then pure True + else case mGrand of + Nothing -> pure False + Just gf -> do ec <- gets ((Map.! en) . _eNodeToEClass) + en' <- canonize (gf ec) + doesNotExistGens grands en' + + printExpr :: Int -> EClassId -> RndEGraph () + printExpr ix ec = do + thetas' <- gets (_theta . _info . (IM.! ec) . _eClass) + bestExpr <- (if _simplify args then simplifyEqSatDefault else id) <$> getBestExpr ec + let nParams = countParamsUniq bestExpr + fromSz (MA.Sz x) = x + nThetas = Prelude.map (fromSz . MA.size) thetas' + (_, thetas) <- if Prelude.any (/=nParams) nThetas || _simplify args + then fitFun bestExpr + else pure (1.0, thetas') + + forM_ (Prelude.zip dataTrainVals thetas) $ \((dataTrain, dataVal), theta) -> do + let (x, y, mYErr) = dataTrain + (x_val, y_val, mYErr_val) = dataVal + distribution = _distribution args + best' = if shouldReparam then relabelParams bestExpr else relabelParamsOrder bestExpr + expr = paramsToConst (MA.toList theta) best' + thetaStr = intercalate ";" $ Prelude.map show (MA.toList theta) + showFun = showExpr expr + showFunNp = "\"" <> showPython best' <> "\"" + io . putStrLn $ show ix <> "," <> showFun <> "," <> showFunNp <> "," + <> thetaStr <> "," <> show (countNodes $ convertProtectedOps expr) + insertTerms = + forM terms $ \t -> do fromTree myCost t >>= canonical + +data Args = Args + { _dataset :: String, + _gens :: Int, + _maxSize :: Int, + _split :: Int, + _distribution :: Distribution, + _optIter :: Int, + _optRepeat :: Int, + _nParams :: Int, + _nPop :: Int, + _nTournament :: Int, + _pc :: Double, + _pm :: Double, + _nonterminals :: String, + _dumpTo :: String, + _loadFrom :: String, + _simplify :: Bool + } + deriving (Show) + + +pyeggp :: String -> String +pyeggp args = + case args of + [dataset', gens', maxSize', distribution', optIter', optRepeat', nParams, nTournament, pc, pm, nonterminals, dumpTo, loadFrom, simplify] -> + let marg = Args <$> Just dataset + <*> readMaybe gens + <*> readMaybe maxSize + <*> readMaybe distribution + <*> readMaybe optIter + <*> readMaybe optRepeat + <*> readMaybe nParams + <*> readMaybe nTournament + <*> readMaybe pc + <*> readMaybe pm + <*> Just nonterminals + <*> Just dumpTo + <*> Just loadFrom + in case marg of + Just arg -> eggp dataset arg + Nothing -> "" + _ -> "" + +eggp :: String -> Args -> String +eggp dataset args = do + g <- getStdGen + let datasets = words (_dataset args) + dataTrains' <- Prelude.mapM (flip loadTrainingOnly True) datasets -- load all datasets + + let (dataTrainVals, g') = runState (Prelude.mapM (`splitData` (_split args)) dataTrains') g + alg = evalStateT (egraphGP dataTrainVals args) emptyGraph + evalStateT alg g' diff --git a/src/README.md b/src/README.md new file mode 100644 index 0000000..ec5894f --- /dev/null +++ b/src/README.md @@ -0,0 +1,137 @@ +# eggp - E-graph GP + +*eggp* (**e**-**g**raph **g**enetic **p**rogramming), follows the same structure as the traditional GP. The initial population is created using ramped half-and-half respecting a maximum size and maximum depth parameter and, for a number of generations, it will choose two parents using tournament selection, apply the subtree crossover with probability $pc$ followed by the subtree mutation with probability $pm$, when the offsprings replace the current population following a dominance criteria. + +The key differences of *eggp* are: + + - new solutions are inserted into the e-graph followed by one step of equality saturation to find and store some of the equivalent expressions of the new offspring. + - the current population is replaced by the set of individuals formed by: the Pareto front, the next front after excluding the first Pareto-front, and a selection of the last offspring at random until it reaches the desired population size. + - the subtree crossover and mutation are modified to try to generate an unvisited exp + +## How to use + +```bash +eggp - E-graph Genetic Programming for Symbolic Regression. + +Usage: eggp (-d|--dataset INPUT-FILE) [-t|--test ARG] [-g|--generations GENS] + (-s|--maxSize ARG) [-k|--split ARG] [--print-pareto] [--trace] + [--loss ARG] [--opt-iter ARG] [--opt-retries ARG] + [--number-params ARG] [--nPop ARG] [--tournament-size ARG] + [--pc ARG] [--pm ARG] [--non-terminals ARG] [--dump-to ARG] + [--load-from ARG] [--moo] + + An implementation of GP with modified crossover and mutation operators + designed to exploit equality saturation and e-graphs. + https://arxiv.org/abs/2501.17848 + +Available options: + -d,--dataset INPUT-FILE CSV dataset. + -t,--test ARG test data (default: "") + -g,--generations GENS Number of generations. (default: 100) + -s,--maxSize ARG max-size. + -k,--split ARG k-split ratio training-validation (default: 1) + --print-pareto print Pareto front instead of best found expression + --trace print all evaluated expressions. + --loss ARG loss function: MSE, Gaussian, Poisson, Bernoulli. + (default: MSE) + --opt-iter ARG number of iterations in parameter optimization. + (default: 30) + --opt-retries ARG number of retries of parameter fitting. (default: 1) + --number-params ARG maximum number of parameters in the model. If this + argument is absent, the number is bounded by the + maximum size of the expression and there will be no + repeated parameter. (default: -1) + --nPop ARG population size (Default: 100). (default: 100) + --tournament-size ARG tournament size. (default: 2) + --pc ARG probability of crossover. (default: 1.0) + --pm ARG probability of mutation. (default: 0.3) + --non-terminals ARG set of non-terminals to use in the search. + (default: "Add,Sub,Mul,Div,PowerAbs,Recip") + --dump-to ARG dump final e-graph to a file. (default: "") + --load-from ARG load initial e-graph from a file. (default: "") + --moo replace the current population with the pareto front + instead of replacing it with the generated children. + -h,--help Show this help text +``` + +The dataset file must contain a header with each features name, and the `--dataset` and `--test` arguments can be accompanied by arguments separated by ':' following the format: + +`filename.ext:start_row:end_row:target:features` + +where each ':' field is optional. The fields are: + +- **start_row:end_row** is the range of the training rows (default 0:nrows-1). + every other row not included in this range will be used as validation +- **target** is either the name of the (if the datafile has headers) or the index + of the target variable +- **features** is a comma separated list of names or indices to be used as + input variables of the regression model. + +Example of valid names: `dataset.csv`, `mydata.tsv`, `dataset.csv:20:100`, `dataset.tsv:20:100:price:m2,rooms,neighborhood`, `dataset.csv:::5:0,1,2`. + +The format of the file will be determined by the extension (e.g., csv, tsv,...). To use multi-view, simply pass multiple filenames in double-quotes: + +```bash +eggp --dataset "dataset1.csv dataset2.csv dataset3.csv" ... +``` + +## Installation + +To install eggp you'll need: + +- `libz` +- `libnlopt` +- `libgmp` +- `ghc-9.6.6` +- `cabal` or `stack` + +### Method 1: Pre-compile binaries + +Go to the [releases](https://github.com/folivetti/srtree/tags) page, download the eggp binary corresponding to your OS and rename it to `eggp`. + +Note: Windows and OSX releases are untested, for Windows Mingw with libnlopt are required. + +### Recommended step-by-step + +After installing the dependencies (e.g., `apt install libz libnlopt libgmp`), install [`ghcup`](https://www.haskell.org/ghcup/#) + +For Linux, macOS, FreeBSD or WSL2: + +```bash +curl --proto '=https' --tlsv1.2 -sSf https://get-ghcup.haskell.org | sh +``` + +For Windows, run the following in a PowerShell: + +```bash +Set-ExecutionPolicy Bypass -Scope Process -Force;[System.Net.ServicePointManager]::SecurityProtocol = [System.Net.ServicePointManager]::SecurityProtocol -bor 3072; try { & ([ScriptBlock]::Create((Invoke-WebRequest https://www.haskell.org/ghcup/sh/bootstrap-haskell.ps1 -UseBasicParsing))) -Interactive -DisableCurl } catch { Write-Error $_ } +``` + +After the installation, run `ghcup tui` and install the latest `stack` or `cabal` together with `ghc-9.6.6` (select the items and press `i`). +To install `srsimplify` simply run: + +```bash +cabal build srtree:eggp +``` + +or + +```bash +stack build srtree:eggp +``` + +## Citing this work + +If you use eggp for your research, please cite: + +``` +@misc{defranca2025improvinggeneticprogrammingsymbolic, + title={Improving Genetic Programming for Symbolic Regression with Equality Graphs}, + author={Fabricio Olivetti de Franca and Gabriel Kronberger}, + year={2025}, + eprint={2501.17848}, + archivePrefix={arXiv}, + primaryClass={cs.LG}, + url={https://arxiv.org/abs/2501.17848}, +} +``` diff --git a/src/Random.hs b/src/Random.hs new file mode 100644 index 0000000..95d239e --- /dev/null +++ b/src/Random.hs @@ -0,0 +1,54 @@ +module Random where + +import System.Random +import Control.Monad.State.Strict +import Control.Monad +import Data.SRTree +import Data.SRTree.Eval +import Data.Massiv.Array as MA + +type Rng a = StateT StdGen IO a + +toss :: Rng Bool +toss = state random +{-# INLINE toss #-} + +tossBiased :: Double -> Rng Bool +tossBiased p = do r <- state random + pure (r < p) + +randomVal :: Rng Double +randomVal = state random + +randomRange :: (Ord val, Random val) => (val, val) -> Rng val +randomRange rng = state (randomR rng) +{-# INLINE randomRange #-} + +randomFrom :: [a] -> Rng a +randomFrom funs = do n <- randomRange (0, length funs - 1) + pure $ funs !! n +{-# INLINE randomFrom #-} + +randomVec :: Int -> Rng PVector +randomVec n = MA.fromList compMode <$> replicateM n (randomRange (-1, 1)) + +randomTree :: Int -> Int -> Int -> Rng (Fix SRTree) -> Rng (SRTree ()) -> Bool -> Rng (Fix SRTree) +randomTree minDepth maxDepth maxSize genTerm genNonTerm grow + | noSpaceLeft = genTerm + | needNonTerm = genRecursion + | otherwise = do r <- toss + if r + then genTerm + else genRecursion + where + noSpaceLeft = maxDepth <= 1 || maxSize <= 2 + needNonTerm = (minDepth >= 0 || (maxDepth > 2 && not grow)) -- && maxSize > 2 + + genRecursion = do + node <- genNonTerm + case node of + Uni f _ -> Fix . Uni f <$> randomTree (minDepth - 1) (maxDepth - 1) (maxSize - 1) genTerm genNonTerm grow + Bin op _ _ -> do l <- randomTree (minDepth - 1) (maxDepth - 1) (if grow then maxSize - 2 else maxSize `div` 2) genTerm genNonTerm grow + r <- randomTree (minDepth - 1) (maxDepth - 1) (maxSize - 1 - countNodes l) genTerm genNonTerm grow + pure . Fix $ Bin op l r +{-# INLINE randomTree #-} diff --git a/src/Util.hs b/src/Util.hs new file mode 100644 index 0000000..7cd2ac9 --- /dev/null +++ b/src/Util.hs @@ -0,0 +1,266 @@ +{-# LANGUAGE BlockArguments #-} +{-# LANGUAGE TupleSections #-} + +module Util where + +import qualified Data.Map.Strict as Map +import Data.Massiv.Array as MA hiding (forM_, forM) +import Data.SRTree +import Data.SRTree.Eval +import Algorithm.SRTree.Opt +import Algorithm.EqSat.Egraph +import Algorithm.EqSat.Build +import Algorithm.EqSat.Info + +import Algorithm.SRTree.NonlinearOpt +import System.Random +import Random +import Algorithm.SRTree.Likelihoods +import Data.SRTree.Print +--import Algorithm.SRTree.ModelSelection +--import Algorithm.SRTree.Opt +import qualified Data.IntMap.Strict as IM +import Control.Monad.State.Strict +import Control.Monad ( when, replicateM, forM, forM_ ) +import Data.Maybe ( fromJust ) +import Data.List ( maximumBy ) +import Data.Function ( on ) +import List.Shuffle ( shuffle ) +import Data.List.Split ( splitOn ) +import Data.Char ( toLower ) +import qualified Data.IntSet as IntSet +import Data.SRTree.Datasets +import Algorithm.EqSat.Queries + +import Debug.Trace + +type RndEGraph a = EGraphST (StateT StdGen IO) a +type DataSet = (SRMatrix, PVector, Maybe PVector) + +csvHeader :: String +csvHeader = "id,Expression,theta,size,MSE_train,MSE_val,MSE_test,R2_train,R2_val,R2_test,nll_train,nll_val,nll_test,mdl_train,mdl_val,mdl_test" + +io :: IO a -> RndEGraph a +io = lift . lift +{-# INLINE io #-} +rnd :: StateT StdGen IO a -> RndEGraph a +rnd = lift +{-# INLINE rnd #-} + +myCost :: SRTree Int -> Int +myCost (Var _) = 1 +myCost (Const _) = 1 +myCost (Param _) = 1 +myCost (Bin _ l r) = 2 + l + r +myCost (Uni _ t) = 3 + t + +while :: Monad f => (t -> Bool) -> t -> (t -> f t) -> f () +while p arg prog = do when (p arg) do arg' <- prog arg + while p arg' prog + +fitnessFun :: Int -> Distribution -> DataSet -> DataSet -> Fix SRTree -> PVector -> (Double, PVector) +fitnessFun nIter distribution (x, y, mYErr) (x_val, y_val, mYErr_val) tree thetaOrig = + if isNaN val -- || isNaN tr + then (-(1/0), theta) -- infinity + else (val, theta) + where + --tree = relabelParams _tree + nParams = countParamsUniq tree + if distribution == ROXY then 3 else if distribution == Gaussian then 1 else 0 + (theta, _, _) = minimizeNLL' VAR1 distribution mYErr nIter x y tree thetaOrig + evalF a b c = negate $ nll distribution c a b tree $ if nParams == 0 then thetaOrig else theta + --tr = evalF x y mYErr + val = evalF x_val y_val mYErr_val + +--{-# INLINE fitnessFun #-} + +fitnessFunRep :: Int -> Int -> Distribution -> DataSet -> DataSet -> Fix SRTree -> RndEGraph (Double, PVector) +fitnessFunRep nRep nIter distribution dataTrain dataVal tree = do + let nParams = countParamsUniq tree + if distribution == ROXY then 3 else if distribution == Gaussian then 1 else 0 + thetaOrigs <- replicateM nRep (rnd $ randomVec nParams) + let fits = maximumBy (compare `on` fst) $ Prelude.map (fitnessFun nIter distribution dataTrain dataVal tree) thetaOrigs + pure fits +--{-# INLINE fitnessFunRep #-} + + +fitnessMV :: Bool -> Int -> Int -> Distribution -> [(DataSet, DataSet)] -> Fix SRTree -> RndEGraph (Double, [PVector]) +fitnessMV shouldReparam nRep nIter distribution dataTrainsVals _tree = do + let tree = if shouldReparam then relabelParams _tree else relabelParamsOrder _tree + response <- forM dataTrainsVals $ \(dt, dv) -> fitnessFunRep nRep nIter distribution dt dv tree + pure (minimum (Prelude.map fst response), Prelude.map snd response) + +-- helper query functions +-- TODO: move to egraph lib +getFitness :: EClassId -> RndEGraph (Maybe Double) +getFitness c = gets (_fitness . _info . (IM.! c) . _eClass) +{-# INLINE getFitness #-} +getTheta :: EClassId -> RndEGraph ([PVector]) +getTheta c = gets (_theta . _info . (IM.! c) . _eClass) +{-# INLINE getTheta #-} +getSize :: EClassId -> RndEGraph Int +getSize c = gets (_size . _info . (IM.! c) . _eClass) +{-# INLINE getSize #-} +isSizeOf :: (Int -> Bool) -> EClass -> Bool +isSizeOf p = p . _size . _info +{-# INLINE isSizeOf #-} + +getBestFitness :: RndEGraph (Maybe Double) +getBestFitness = do + bec <- (gets (snd . getGreatest . _fitRangeDB . _eDB) >>= canonical) + gets (_fitness . _info . (IM.! bec) . _eClass) + +-- TODO: move to dataset lib +chunksOf :: Int -> [e] -> [[e]] +chunksOf i ls = Prelude.map (Prelude.take i) (build (splitter ls)) + where + splitter :: [e] -> ([e] -> a -> a) -> a -> a + splitter [] _ n = n + splitter l c n = l `c` splitter (Prelude.drop i l) c n + build :: ((a -> [a] -> [a]) -> [a] -> [a]) -> [a] + build g = g (:) [] + +splitData :: DataSet ->Int -> State StdGen (DataSet, DataSet) +splitData (x, y, mYErr) k = do + if k == 1 + then pure ((x, y, mYErr), (x, y, mYErr)) + else do + ixs' <- (state . shuffle) [0 .. sz-1] + let ixs = chunksOf k ixs' + + let (x_tr, x_te) = getX ixs x + (y_tr, y_te) = getY ixs y + mY = fmap (getY ixs) mYErr + (y_err_tr, y_err_te) = (fmap fst mY, fmap snd mY) + pure ((x_tr, y_tr, y_err_tr), (x_te, y_te, y_err_te)) + where + (MA.Sz sz) = MA.size y + comp_x = MA.getComp x + comp_y = MA.getComp y + + getX :: [[Int]] -> SRMatrix -> (SRMatrix, SRMatrix) + getX ixs xs' = let xs = MA.toLists xs' :: [MA.ListItem MA.Ix2 Double] + in ( MA.fromLists' comp_x [xs !! ix | ixs_i <- ixs, ix <- Prelude.tail ixs_i] + , MA.fromLists' comp_x [xs !! ix | ixs_i <- ixs, let ix = Prelude.head ixs_i] + ) + getY :: [[Int]] -> PVector -> (PVector, PVector) + getY ixs ys = ( MA.fromList comp_y [ys MA.! ix | ixs_i <- ixs, ix <- Prelude.tail ixs_i] + , MA.fromList comp_y [ys MA.! ix | ixs_i <- ixs, let ix = Prelude.head ixs_i] + ) + +getTrain :: ((a, b1, c1, d1), (c2, b2), c3, d2) -> (a, b1, c2) +getTrain ((a, b, _, _), (c, _), _, _) = (a,b,c) + +getX :: DataSet -> SRMatrix +getX (a, _, _) = a + +getTarget :: DataSet -> PVector +getTarget (_, b, _) = b + +getError :: DataSet -> Maybe PVector +getError (_, _, c) = c + +loadTrainingOnly fname b = getTrain <$> loadDataset fname b + +parseNonTerms :: String -> [SRTree ()] +parseNonTerms = Prelude.map toNonTerm . splitOn "," + where + binTerms = Map.fromList [ (Prelude.map toLower (show op), op) | op <- [Add .. AQ]] + uniTerms = Map.fromList [ (Prelude.map toLower (show f), f) | f <- [Abs .. Cube]] + toNonTerm xs' = let xs = Prelude.map toLower xs' + in case binTerms Map.!? xs of + Just op -> Bin op () () + Nothing -> case uniTerms Map.!? xs of + Just f -> Uni f () + Nothing -> error $ "invalid non-terminal " <> show xs + +-- RndEGraph utils +-- fitFun fitnessFunRep rep iter distribution x y mYErr x_val y_val mYErr_val +insertExpr :: Fix SRTree -> (Fix SRTree -> RndEGraph (Double, [PVector])) -> RndEGraph EClassId +insertExpr t fitFun = do + ecId <- fromTree myCost t >>= canonical + (f, p) <- fitFun t + insertFitness ecId f p + io . putStrLn $ "Best fit global: " <> show f + pure ecId + where powabs l r = Fix (Bin PowerAbs l r) + +updateIfNothing fitFun ec = do + mf <- getFitness ec + case mf of + Nothing -> do + t <- getBestExpr ec + (f, p) <- fitFun t + insertFitness ec f p + pure True + Just _ -> pure False + +pickRndSubTree :: RndEGraph (Maybe EClassId) +pickRndSubTree = do ecIds <- gets (IntSet.toList . _unevaluated . _eDB) + if not (null ecIds) + then do rndId' <- rnd $ randomFrom ecIds + rndId <- canonical rndId' + constType <- gets (_consts . _info . (IM.! rndId) . _eClass) + case constType of + NotConst -> pure $ Just rndId + _ -> pure Nothing + else pure Nothing + +getParetoEcsUpTo n maxSize = concat <$> forM [1..maxSize] (\i -> getTopFitEClassWithSize i n) + +getBestExprWithSize n = + do ec <- getTopFitEClassWithSize n 1 >>= traverse canonical + if (not (null ec)) + then do + bestFit <- getFitness $ head ec + bestP <- gets (_theta . _info . (IM.! (head ec)) . _eClass) + pure [(head ec, bestFit)] + else pure [] + +insertRndExpr maxSize rndTerm rndNonTerm = + do grow <- rnd toss + n <- rnd (randomFrom [if maxSize > 4 then 4 else 1 .. maxSize]) + t <- rnd $ Random.randomTree 3 8 n rndTerm rndNonTerm grow + fromTree myCost t >>= canonical + +refit fitFun ec = do + t <- getBestExpr ec + (f, p) <- fitFun t + insertFitness ec f p + +--printBest :: (Int -> EClassId -> RndEGraph ()) -> RndEGraph () +printBest fitFun printExprFun = do + bec <- gets (snd . getGreatest . _fitRangeDB . _eDB) >>= canonical + bestFit <- gets (_fitness. _info . (IM.! bec) . _eClass) + --refit fitFun bec + --io.print $ "should be " <> show bestFit + printExprFun 0 bec + +--paretoFront :: Int -> (Int -> EClassId -> RndEGraph ()) -> RndEGraph () +paretoFront fitFun maxSize printExprFun = go 1 0 (-(1.0/0.0)) + where + go :: Int -> Int -> Double -> RndEGraph () + go n ix f + | n > maxSize = pure () + | otherwise = do + ecList <- getBestExprWithSize n + if not (null ecList) + then do let (ec, mf) = head ecList + improved = fromJust mf > f + ec' <- canonical ec + when improved $ refit fitFun ec' >> printExprFun ix ec' + go (n+1) (ix + if improved then 1 else 0) (max f (fromJust mf)) + else go (n+1) ix f + +evaluateUnevaluated fitFun = do + ec <- gets (IntSet.toList . _unevaluated . _eDB) + forM_ ec $ \c -> do + t <- getBestExpr c + (f, p) <- fitFun t + insertFitness c f p + +evaluateRndUnevaluated fitFun = do + ec <- gets (IntSet.toList . _unevaluated . _eDB) + c <- rnd . randomFrom $ ec + t <- getBestExpr c + (f, p) <- fitFun t + insertFitness c f p + pure c diff --git a/src/pyeggp/__init__.py b/src/pyeggp/__init__.py new file mode 100644 index 0000000..db02eed --- /dev/null +++ b/src/pyeggp/__init__.py @@ -0,0 +1,51 @@ +import atexit +from contextlib import contextmanager +from threading import Lock +from typing import Iterator, List + +from ._binding import ( + unsafe_hs_pyeggp_version, + unsafe_hs_pyeggp_main, + unsafe_hs_pyeggp_fib, + unsafe_hs_pyeggp_init, + unsafe_hs_pyeggp_exit, +) + +VERSION: str = "1.3.0" + + +_hs_rts_init: bool = False +_hs_rts_lock: Lock = Lock() + + +def hs_rts_exit() -> None: + global _hs_rts_lock + with _hs_rts_lock: + unsafe_hs_pyeggp_exit() + + +@contextmanager +def hs_rts_init(args: List[str] = []) -> Iterator[None]: + global _hs_rts_init + global _hs_rts_lock + with _hs_rts_lock: + if not _hs_rts_init: + _hs_rts_init = True + unsafe_hs_pyeggp_init(args) + atexit.register(hs_rts_exit) + yield None + + +def version() -> str: + with hs_rts_init(): + return unsafe_hs_pyeggp_version() + + +def main(args: List[str] = []) -> int: + with hs_rts_init(args): + return unsafe_hs_pyeggp_main() + + +def pyegpp(n: List[str]) -> str: + with hs_rts_init(): + return unsafe_hs_pyeggp(n) diff --git a/src/pyeggp/__main__.py b/src/pyeggp/__main__.py new file mode 100644 index 0000000..3fa9edf --- /dev/null +++ b/src/pyeggp/__main__.py @@ -0,0 +1,11 @@ +import sys +from typing import NoReturn +import pyeggp + + +def main() -> NoReturn: + sys.exit(pyeggp.main(sys.argv)) + + +if __name__ == "__main__": + main() diff --git a/src/pyeggp/_binding.pyi b/src/pyeggp/_binding.pyi new file mode 100644 index 0000000..22251f5 --- /dev/null +++ b/src/pyeggp/_binding.pyi @@ -0,0 +1,7 @@ +from typing import List + +def unsafe_hs_pyeggp_version() -> str: ... +def unsafe_hs_pyeggp_main() -> int: ... +def unsafe_hs_pyeggp(n: List[str]) -> str: ... +def unsafe_hs_pyeggp_init(args: List[str]) -> None: ... +def unsafe_hs_pyeggp_exit() -> None: ... diff --git a/src/pyeggp/binding.def b/src/pyeggp/binding.def new file mode 100644 index 0000000..a9ff719 --- /dev/null +++ b/src/pyeggp/binding.def @@ -0,0 +1,2 @@ +EXPORTS + PyInit__binding diff --git a/src/pyeggp/binding.i b/src/pyeggp/binding.i new file mode 100644 index 0000000..6155386 --- /dev/null +++ b/src/pyeggp/binding.i @@ -0,0 +1,64 @@ +%module binding +%{ +#include "ExampleHaskellWheelBinding_stub.h" + +char * unsafe_hs_pyeggp_version() { + return hs_pyeggp_version(); +} + +int unsafe_hs_pyeggp_main() { + return hs_pyeggp_main(); +} + +char * unsafe_hs_pyeggp(char **n) { + return hs_pyeggp(&n); +} + +void unsafe_hs_pyeggp_init(int argc, char **argv) { + hs_init(&argc, &argv); +} + +void unsafe_hs_pyeggp_exit() { + hs_exit(); +} + +void unsafe_py_write_stdout(const char * str) { + PySys_FormatStdout("%s", str); +} + +void unsafe_py_write_stderr(const char * str) { + PySys_FormatStderr("%s", str); +} +%} + +%typemap(in) (int argc, char **argv) { + /* Check if is a list */ + if (PyList_Check($input)) { + int i; + $1 = PyList_Size($input); + $2 = (char **) malloc(($1+1)*sizeof(char *)); + for (i = 0; i < $1; i++) { + PyObject *o = PyList_GetItem($input, i); + if (PyUnicode_Check(o)) { + $2[i] = (char *) PyUnicode_AsUTF8AndSize(o, 0); + } else { + PyErr_SetString(PyExc_TypeError, "list must contain strings"); + SWIG_fail; + } + } + $2[i] = 0; + } else { + PyErr_SetString(PyExc_TypeError, "not a list"); + SWIG_fail; + } +} + +%typemap(freearg) (int argc, char **argv) { + free((char *) $2); +} + +char * unsafe_hs_pyeggp_version(); +int unsafe_hs_pyeggp_main(); +char * unsafe_hs_pyeggp(char **n); +void unsafe_hs_pyeggp_init(int argc, char **argv); +void unsafe_hs_pyeggp_exit(); diff --git a/src/pyeggp/binding.py b/src/pyeggp/binding.py new file mode 100644 index 0000000..4b2df33 --- /dev/null +++ b/src/pyeggp/binding.py @@ -0,0 +1,76 @@ +# This file was automatically generated by SWIG (https://www.swig.org). +# Version 4.3.0 +# +# Do not make changes to this file unless you know what you are doing - modify +# the SWIG interface file instead. + +from sys import version_info as _swig_python_version_info +# Import the low-level C/C++ module +if __package__ or "." in __name__: + from . import _binding +else: + import _binding + +try: + import builtins as __builtin__ +except ImportError: + import __builtin__ + +def _swig_repr(self): + try: + strthis = "proxy of " + self.this.__repr__() + except __builtin__.Exception: + strthis = "" + return "<%s.%s; %s >" % (self.__class__.__module__, self.__class__.__name__, strthis,) + + +def _swig_setattr_nondynamic_instance_variable(set): + def set_instance_attr(self, name, value): + if name == "this": + set(self, name, value) + elif name == "thisown": + self.this.own(value) + elif hasattr(self, name) and isinstance(getattr(type(self), name), property): + set(self, name, value) + else: + raise AttributeError("You cannot add instance attributes to %s" % self) + return set_instance_attr + + +def _swig_setattr_nondynamic_class_variable(set): + def set_class_attr(cls, name, value): + if hasattr(cls, name) and not isinstance(getattr(cls, name), property): + set(cls, name, value) + else: + raise AttributeError("You cannot add class attributes to %s" % cls) + return set_class_attr + + +def _swig_add_metaclass(metaclass): + """Class decorator for adding a metaclass to a SWIG wrapped class - a slimmed down version of six.add_metaclass""" + def wrapper(cls): + return metaclass(cls.__name__, cls.__bases__, cls.__dict__.copy()) + return wrapper + + +class _SwigNonDynamicMeta(type): + """Meta class to enforce nondynamic attributes (no new attributes) for a class""" + __setattr__ = _swig_setattr_nondynamic_class_variable(type.__setattr__) + + + +def unsafe_hs_pyeggp_version(): + return _binding.unsafe_hs_pyeggp_version() + +def unsafe_hs_pyeggp_main(): + return _binding.unsafe_hs_pyeggp_main() + +def unsafe_hs_pyeggp(n): + return _binding.unsafe_hs_pyeggp(n) + +def unsafe_hs_pyeggp_init(argc): + return _binding.unsafe_hs_pyeggp_init(argc) + +def unsafe_hs_pyeggp_exit(): + return _binding.unsafe_hs_pyeggp_exit() + diff --git a/src/pyeggp/binding_wrap.c b/src/pyeggp/binding_wrap.c new file mode 100644 index 0000000..a2f6e3f --- /dev/null +++ b/src/pyeggp/binding_wrap.c @@ -0,0 +1,4083 @@ +/* ---------------------------------------------------------------------------- + * This file was automatically generated by SWIG (https://www.swig.org). + * Version 4.3.0 + * + * Do not make changes to this file unless you know what you are doing - modify + * the SWIG interface file instead. + * ----------------------------------------------------------------------------- */ + + +#define SWIG_VERSION 0x040300 +#define SWIGPYTHON +#define SWIG_PYTHON_DIRECTOR_NO_VTABLE + +#define SWIG_name "_binding" +/* ----------------------------------------------------------------------------- + * This section contains generic SWIG labels for method/variable + * declarations/attributes, and other compiler dependent labels. + * ----------------------------------------------------------------------------- */ + +/* template workaround for compilers that cannot correctly implement the C++ standard */ +#ifndef SWIGTEMPLATEDISAMBIGUATOR +# if defined(__SUNPRO_CC) && (__SUNPRO_CC <= 0x560) +# define SWIGTEMPLATEDISAMBIGUATOR template +# elif defined(__HP_aCC) +/* Needed even with `aCC -AA' when `aCC -V' reports HP ANSI C++ B3910B A.03.55 */ +/* If we find a maximum version that requires this, the test would be __HP_aCC <= 35500 for A.03.55 */ +# define SWIGTEMPLATEDISAMBIGUATOR template +# else +# define SWIGTEMPLATEDISAMBIGUATOR +# endif +#endif + +/* inline attribute */ +#ifndef SWIGINLINE +# if defined(__cplusplus) || (defined(__GNUC__) && !defined(__STRICT_ANSI__)) +# define SWIGINLINE inline +# else +# define SWIGINLINE +# endif +#endif + +/* attribute recognised by some compilers to avoid 'unused' warnings */ +#ifndef SWIGUNUSED +# if defined(__GNUC__) +# if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4)) +# define SWIGUNUSED __attribute__ ((__unused__)) +# else +# define SWIGUNUSED +# endif +# elif defined(__ICC) +# define SWIGUNUSED __attribute__ ((__unused__)) +# else +# define SWIGUNUSED +# endif +#endif + +#ifndef SWIG_MSC_UNSUPPRESS_4505 +# if defined(_MSC_VER) +# pragma warning(disable : 4505) /* unreferenced local function has been removed */ +# endif +#endif + +#ifndef SWIGUNUSEDPARM +# ifdef __cplusplus +# define SWIGUNUSEDPARM(p) +# else +# define SWIGUNUSEDPARM(p) p SWIGUNUSED +# endif +#endif + +/* internal SWIG method */ +#ifndef SWIGINTERN +# define SWIGINTERN static SWIGUNUSED +#endif + +/* internal inline SWIG method */ +#ifndef SWIGINTERNINLINE +# define SWIGINTERNINLINE SWIGINTERN SWIGINLINE +#endif + +/* exporting methods */ +#if defined(__GNUC__) +# if (__GNUC__ >= 4) || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4) +# ifndef GCC_HASCLASSVISIBILITY +# define GCC_HASCLASSVISIBILITY +# endif +# endif +#endif + +#ifndef SWIGEXPORT +# if defined(_WIN32) || defined(__WIN32__) || defined(__CYGWIN__) +# if defined(STATIC_LINKED) +# define SWIGEXPORT +# else +# define SWIGEXPORT __declspec(dllexport) +# endif +# else +# if defined(__GNUC__) && defined(GCC_HASCLASSVISIBILITY) +# define SWIGEXPORT __attribute__ ((visibility("default"))) +# else +# define SWIGEXPORT +# endif +# endif +#endif + +/* calling conventions for Windows */ +#ifndef SWIGSTDCALL +# if defined(_WIN32) || defined(__WIN32__) || defined(__CYGWIN__) +# define SWIGSTDCALL __stdcall +# else +# define SWIGSTDCALL +# endif +#endif + +/* Deal with Microsoft's attempt at deprecating C standard runtime functions */ +#if !defined(SWIG_NO_CRT_SECURE_NO_DEPRECATE) && defined(_MSC_VER) && !defined(_CRT_SECURE_NO_DEPRECATE) +# define _CRT_SECURE_NO_DEPRECATE +#endif + +/* Deal with Microsoft's attempt at deprecating methods in the standard C++ library */ +#if !defined(SWIG_NO_SCL_SECURE_NO_DEPRECATE) && defined(_MSC_VER) && !defined(_SCL_SECURE_NO_DEPRECATE) +# define _SCL_SECURE_NO_DEPRECATE +#endif + +/* Deal with Apple's deprecated 'AssertMacros.h' from Carbon-framework */ +#if defined(__APPLE__) && !defined(__ASSERT_MACROS_DEFINE_VERSIONS_WITHOUT_UNDERSCORES) +# define __ASSERT_MACROS_DEFINE_VERSIONS_WITHOUT_UNDERSCORES 0 +#endif + +/* Intel's compiler complains if a variable which was never initialised is + * cast to void, which is a common idiom which we use to indicate that we + * are aware a variable isn't used. So we just silence that warning. + * See: https://github.com/swig/swig/issues/192 for more discussion. + */ +#ifdef __INTEL_COMPILER +# pragma warning disable 592 +#endif + +#if defined(__cplusplus) && __cplusplus >=201103L +# define SWIG_NULLPTR nullptr +#else +# define SWIG_NULLPTR NULL +#endif + +/* ----------------------------------------------------------------------------- + * swigcompat.swg + * + * Macros to provide support compatibility with older C and C++ standards. + * + * Note that SWIG expects __cplusplus to be defined to the appropriate C++ standard. + * MSVC users are urged to check and examine the /Zc:__cplusplus compiler option. + * See https://learn.microsoft.com/en-us/cpp/build/reference/zc-cplusplus. + * ----------------------------------------------------------------------------- */ + +/* C99 and C++11 should provide snprintf, but define SWIG_NO_SNPRINTF + * if you're missing it. + */ +#if ((defined __STDC_VERSION__ && __STDC_VERSION__ >= 199901L) || \ + (defined __cplusplus && __cplusplus >= 201103L) || \ + defined SWIG_HAVE_SNPRINTF) && \ + !defined SWIG_NO_SNPRINTF +# define SWIG_snprintf(O,S,F,A) snprintf(O,S,F,A) +# define SWIG_snprintf2(O,S,F,A,B) snprintf(O,S,F,A,B) +#else +/* Fallback versions ignore the buffer size, but most of our uses either have a + * fixed maximum possible size or dynamically allocate a buffer that's large + * enough. + */ +# define SWIG_snprintf(O,S,F,A) sprintf(O,F,A) +# define SWIG_snprintf2(O,S,F,A,B) sprintf(O,F,A,B) +#endif + + +#if defined(__GNUC__) && defined(_WIN32) && !defined(SWIG_PYTHON_NO_HYPOT_WORKAROUND) +/* Workaround for '::hypot' has not been declared', see https://bugs.python.org/issue11566 */ +# include +#endif + +#if !defined(PY_SSIZE_T_CLEAN) && !defined(SWIG_NO_PY_SSIZE_T_CLEAN) +#define PY_SSIZE_T_CLEAN +#endif + +#if __GNUC__ >= 7 +#pragma GCC diagnostic push +#if defined(__cplusplus) && __cplusplus >=201703L +#pragma GCC diagnostic ignored "-Wregister" /* For python-2.7 headers that use register */ +#endif +#endif + +#if defined(_DEBUG) && defined(SWIG_PYTHON_INTERPRETER_NO_DEBUG) +/* Use debug wrappers with the Python release dll */ + +#if defined(_MSC_VER) && _MSC_VER >= 1929 +/* Workaround compilation errors when redefining _DEBUG in MSVC 2019 version 16.10 and later + * See https://github.com/swig/swig/issues/2090 */ +# include +#endif + +# undef _DEBUG +# include +# define _DEBUG 1 +#else +# include +#endif + +#if !defined(SWIGPYTHON_BUILTIN) && PY_VERSION_HEX >= 0x03030000 +# define SWIG_HEAPTYPES + +/* Note: Currently this won't activate - it is in place ready for when the + * SWIGPYTHON_BUILTIN condition above gets removed. */ +# if PY_VERSION_HEX < 0x030c0000 && defined(SWIGPYTHON_BUILTIN) +# include +# define Py_READONLY READONLY +# define Py_T_PYSSIZET T_PYSSIZET +# endif +#endif + +#if __GNUC__ >= 7 +#pragma GCC diagnostic pop +#endif + +#include +#include + +/* ----------------------------------------------------------------------------- + * swigrun.swg + * + * This file contains generic C API SWIG runtime support for pointer + * type checking. + * ----------------------------------------------------------------------------- */ + +/* This should only be incremented when either the layout of swig_type_info changes, + or for whatever reason, the runtime changes incompatibly */ +#define SWIG_RUNTIME_VERSION "4" + +/* define SWIG_TYPE_TABLE_NAME as "SWIG_TYPE_TABLE" */ +#ifdef SWIG_TYPE_TABLE +# define SWIG_QUOTE_STRING(x) #x +# define SWIG_EXPAND_AND_QUOTE_STRING(x) SWIG_QUOTE_STRING(x) +# define SWIG_TYPE_TABLE_NAME SWIG_EXPAND_AND_QUOTE_STRING(SWIG_TYPE_TABLE) +#else +# define SWIG_TYPE_TABLE_NAME +#endif + +/* + You can use the SWIGRUNTIME and SWIGRUNTIMEINLINE macros for + creating a static or dynamic library from the SWIG runtime code. + In 99.9% of the cases, SWIG just needs to declare them as 'static'. + + But only do this if strictly necessary, ie, if you have problems + with your compiler or suchlike. +*/ + +#ifndef SWIGRUNTIME +# define SWIGRUNTIME SWIGINTERN +#endif + +#ifndef SWIGRUNTIMEINLINE +# define SWIGRUNTIMEINLINE SWIGRUNTIME SWIGINLINE +#endif + +/* Generic buffer size */ +#ifndef SWIG_BUFFER_SIZE +# define SWIG_BUFFER_SIZE 1024 +#endif + +/* Flags for pointer conversions */ +#define SWIG_POINTER_DISOWN 0x1 +#define SWIG_CAST_NEW_MEMORY 0x2 +#define SWIG_POINTER_NO_NULL 0x4 +#define SWIG_POINTER_CLEAR 0x8 +#define SWIG_POINTER_RELEASE (SWIG_POINTER_CLEAR | SWIG_POINTER_DISOWN) + +/* Flags for new pointer objects */ +#define SWIG_POINTER_OWN 0x1 + + +/* + Flags/methods for returning states. + + The SWIG conversion methods, as ConvertPtr, return an integer + that tells if the conversion was successful or not. And if not, + an error code can be returned (see swigerrors.swg for the codes). + + Use the following macros/flags to set or process the returning + states. + + In old versions of SWIG, code such as the following was usually written: + + if (SWIG_ConvertPtr(obj,vptr,ty.flags) != -1) { + // success code + } else { + //fail code + } + + Now you can be more explicit: + + int res = SWIG_ConvertPtr(obj,vptr,ty.flags); + if (SWIG_IsOK(res)) { + // success code + } else { + // fail code + } + + which is the same really, but now you can also do + + Type *ptr; + int res = SWIG_ConvertPtr(obj,(void **)(&ptr),ty.flags); + if (SWIG_IsOK(res)) { + // success code + if (SWIG_IsNewObj(res) { + ... + delete *ptr; + } else { + ... + } + } else { + // fail code + } + + I.e., now SWIG_ConvertPtr can return new objects and you can + identify the case and take care of the deallocation. Of course that + also requires SWIG_ConvertPtr to return new result values, such as + + int SWIG_ConvertPtr(obj, ptr,...) { + if () { + if () { + *ptr = ; + return SWIG_NEWOBJ; + } else { + *ptr = ; + return SWIG_OLDOBJ; + } + } else { + return SWIG_BADOBJ; + } + } + + Of course, returning the plain '0(success)/-1(fail)' still works, but you can be + more explicit by returning SWIG_BADOBJ, SWIG_ERROR or any of the + SWIG errors code. + + Finally, if the SWIG_CASTRANK_MODE is enabled, the result code + allows returning the 'cast rank', for example, if you have this + + int food(double) + int fooi(int); + + and you call + + food(1) // cast rank '1' (1 -> 1.0) + fooi(1) // cast rank '0' + + just use the SWIG_AddCast()/SWIG_CheckState() +*/ + +#define SWIG_OK (0) +/* Runtime errors are < 0 */ +#define SWIG_ERROR (-1) +/* Errors in range -1 to -99 are in swigerrors.swg (errors for all languages including those not using the runtime) */ +/* Errors in range -100 to -199 are language specific errors defined in *errors.swg */ +/* Errors < -200 are generic runtime specific errors */ +#define SWIG_ERROR_RELEASE_NOT_OWNED (-200) + +#define SWIG_IsOK(r) (r >= 0) +#define SWIG_ArgError(r) ((r != SWIG_ERROR) ? r : SWIG_TypeError) + +/* The CastRankLimit says how many bits are used for the cast rank */ +#define SWIG_CASTRANKLIMIT (1 << 8) +/* The NewMask denotes the object was created (using new/malloc) */ +#define SWIG_NEWOBJMASK (SWIG_CASTRANKLIMIT << 1) +/* The TmpMask is for in/out typemaps that use temporary objects */ +#define SWIG_TMPOBJMASK (SWIG_NEWOBJMASK << 1) +/* Simple returning values */ +#define SWIG_BADOBJ (SWIG_ERROR) +#define SWIG_OLDOBJ (SWIG_OK) +#define SWIG_NEWOBJ (SWIG_OK | SWIG_NEWOBJMASK) +#define SWIG_TMPOBJ (SWIG_OK | SWIG_TMPOBJMASK) +/* Check, add and del object mask methods */ +#define SWIG_AddNewMask(r) (SWIG_IsOK(r) ? (r | SWIG_NEWOBJMASK) : r) +#define SWIG_DelNewMask(r) (SWIG_IsOK(r) ? (r & ~SWIG_NEWOBJMASK) : r) +#define SWIG_IsNewObj(r) (SWIG_IsOK(r) && (r & SWIG_NEWOBJMASK)) +#define SWIG_AddTmpMask(r) (SWIG_IsOK(r) ? (r | SWIG_TMPOBJMASK) : r) +#define SWIG_DelTmpMask(r) (SWIG_IsOK(r) ? (r & ~SWIG_TMPOBJMASK) : r) +#define SWIG_IsTmpObj(r) (SWIG_IsOK(r) && (r & SWIG_TMPOBJMASK)) + +/* Cast-Rank Mode */ +#if defined(SWIG_CASTRANK_MODE) +# ifndef SWIG_TypeRank +# define SWIG_TypeRank unsigned long +# endif +# ifndef SWIG_MAXCASTRANK /* Default cast allowed */ +# define SWIG_MAXCASTRANK (2) +# endif +# define SWIG_CASTRANKMASK ((SWIG_CASTRANKLIMIT) -1) +# define SWIG_CastRank(r) (r & SWIG_CASTRANKMASK) +SWIGINTERNINLINE int SWIG_AddCast(int r) { + return SWIG_IsOK(r) ? ((SWIG_CastRank(r) < SWIG_MAXCASTRANK) ? (r + 1) : SWIG_ERROR) : r; +} +SWIGINTERNINLINE int SWIG_CheckState(int r) { + return SWIG_IsOK(r) ? SWIG_CastRank(r) + 1 : 0; +} +#else /* no cast-rank mode */ +# define SWIG_AddCast(r) (r) +# define SWIG_CheckState(r) (SWIG_IsOK(r) ? 1 : 0) +#endif + + +#include + +#ifdef __cplusplus +extern "C" { +#endif + +typedef void *(*swig_converter_func)(void *, int *); +typedef struct swig_type_info *(*swig_dycast_func)(void **); + +/* Structure to store information on one type */ +typedef struct swig_type_info { + const char *name; /* mangled name of this type */ + const char *str; /* human readable name of this type */ + swig_dycast_func dcast; /* dynamic cast function down a hierarchy */ + struct swig_cast_info *cast; /* linked list of types that can cast into this type */ + void *clientdata; /* language specific type data */ + int owndata; /* flag if the structure owns the clientdata */ +} swig_type_info; + +/* Structure to store a type and conversion function used for casting */ +typedef struct swig_cast_info { + swig_type_info *type; /* pointer to type that is equivalent to this type */ + swig_converter_func converter; /* function to cast the void pointers */ + struct swig_cast_info *next; /* pointer to next cast in linked list */ + struct swig_cast_info *prev; /* pointer to the previous cast */ +} swig_cast_info; + +/* Structure used to store module information + * Each module generates one structure like this, and the runtime collects + * all of these structures and stores them in a circularly linked list.*/ +typedef struct swig_module_info { + swig_type_info **types; /* Array of pointers to swig_type_info structures that are in this module */ + size_t size; /* Number of types in this module */ + struct swig_module_info *next; /* Pointer to next element in circularly linked list */ + swig_type_info **type_initial; /* Array of initially generated type structures */ + swig_cast_info **cast_initial; /* Array of initially generated casting structures */ + void *clientdata; /* Language specific module data */ +} swig_module_info; + +/* + Compare two type names skipping the space characters, therefore + "char*" == "char *" and "Class" == "Class", etc. + + Return 0 when the two name types are equivalent, as in + strncmp, but skipping ' '. +*/ +SWIGRUNTIME int +SWIG_TypeNameComp(const char *f1, const char *l1, + const char *f2, const char *l2) { + for (;(f1 != l1) && (f2 != l2); ++f1, ++f2) { + while ((*f1 == ' ') && (f1 != l1)) ++f1; + while ((*f2 == ' ') && (f2 != l2)) ++f2; + if (*f1 != *f2) return (*f1 > *f2) ? 1 : -1; + } + return (int)((l1 - f1) - (l2 - f2)); +} + +/* + Check type equivalence in a name list like ||... + Return 0 if equal, -1 if nb < tb, 1 if nb > tb +*/ +SWIGRUNTIME int +SWIG_TypeCmp(const char *nb, const char *tb) { + int equiv = 1; + const char* te = tb + strlen(tb); + const char* ne = nb; + while (equiv != 0 && *ne) { + for (nb = ne; *ne; ++ne) { + if (*ne == '|') break; + } + equiv = SWIG_TypeNameComp(nb, ne, tb, te); + if (*ne) ++ne; + } + return equiv; +} + +/* + Check type equivalence in a name list like ||... + Return 0 if not equal, 1 if equal +*/ +SWIGRUNTIME int +SWIG_TypeEquiv(const char *nb, const char *tb) { + return SWIG_TypeCmp(nb, tb) == 0 ? 1 : 0; +} + +/* + Check the typename +*/ +SWIGRUNTIME swig_cast_info * +SWIG_TypeCheck(const char *c, swig_type_info *ty) { + if (ty) { + swig_cast_info *iter = ty->cast; + while (iter) { + if (strcmp(iter->type->name, c) == 0) { + if (iter == ty->cast) + return iter; + /* Move iter to the top of the linked list */ + iter->prev->next = iter->next; + if (iter->next) + iter->next->prev = iter->prev; + iter->next = ty->cast; + iter->prev = 0; + if (ty->cast) ty->cast->prev = iter; + ty->cast = iter; + return iter; + } + iter = iter->next; + } + } + return 0; +} + +/* + Identical to SWIG_TypeCheck, except strcmp is replaced with a pointer comparison +*/ +SWIGRUNTIME swig_cast_info * +SWIG_TypeCheckStruct(const swig_type_info *from, swig_type_info *ty) { + if (ty) { + swig_cast_info *iter = ty->cast; + while (iter) { + if (iter->type == from) { + if (iter == ty->cast) + return iter; + /* Move iter to the top of the linked list */ + iter->prev->next = iter->next; + if (iter->next) + iter->next->prev = iter->prev; + iter->next = ty->cast; + iter->prev = 0; + if (ty->cast) ty->cast->prev = iter; + ty->cast = iter; + return iter; + } + iter = iter->next; + } + } + return 0; +} + +/* + Cast a pointer up an inheritance hierarchy +*/ +SWIGRUNTIMEINLINE void * +SWIG_TypeCast(swig_cast_info *ty, void *ptr, int *newmemory) { + return ((!ty) || (!ty->converter)) ? ptr : (*ty->converter)(ptr, newmemory); +} + +/* + Dynamic pointer casting. Down an inheritance hierarchy +*/ +SWIGRUNTIME swig_type_info * +SWIG_TypeDynamicCast(swig_type_info *ty, void **ptr) { + swig_type_info *lastty = ty; + if (!ty || !ty->dcast) return ty; + while (ty && (ty->dcast)) { + ty = (*ty->dcast)(ptr); + if (ty) lastty = ty; + } + return lastty; +} + +/* + Return the name associated with this type +*/ +SWIGRUNTIMEINLINE const char * +SWIG_TypeName(const swig_type_info *ty) { + return ty->name; +} + +/* + Return the pretty name associated with this type, + that is an unmangled type name in a form presentable to the user. +*/ +SWIGRUNTIME const char * +SWIG_TypePrettyName(const swig_type_info *type) { + /* The "str" field contains the equivalent pretty names of the + type, separated by vertical-bar characters. Choose the last + name. It should be the most specific; a fully resolved name + but not necessarily with default template parameters expanded. */ + if (!type) return NULL; + if (type->str != NULL) { + const char *last_name = type->str; + const char *s; + for (s = type->str; *s; s++) + if (*s == '|') last_name = s+1; + return last_name; + } + else + return type->name; +} + +/* + Set the clientdata field for a type +*/ +SWIGRUNTIME void +SWIG_TypeClientData(swig_type_info *ti, void *clientdata) { + swig_cast_info *cast = ti->cast; + /* if (ti->clientdata == clientdata) return; */ + ti->clientdata = clientdata; + + while (cast) { + if (!cast->converter) { + swig_type_info *tc = cast->type; + if (!tc->clientdata) { + SWIG_TypeClientData(tc, clientdata); + } + } + cast = cast->next; + } +} +SWIGRUNTIME void +SWIG_TypeNewClientData(swig_type_info *ti, void *clientdata) { + SWIG_TypeClientData(ti, clientdata); + ti->owndata = 1; +} + +/* + Search for a swig_type_info structure only by mangled name + Search is a O(log #types) + + We start searching at module start, and finish searching when start == end. + Note: if start == end at the beginning of the function, we go all the way around + the circular list. +*/ +SWIGRUNTIME swig_type_info * +SWIG_MangledTypeQueryModule(swig_module_info *start, + swig_module_info *end, + const char *name) { + swig_module_info *iter = start; + do { + if (iter->size) { + size_t l = 0; + size_t r = iter->size - 1; + do { + /* since l+r >= 0, we can (>> 1) instead (/ 2) */ + size_t i = (l + r) >> 1; + const char *iname = iter->types[i]->name; + if (iname) { + int compare = strcmp(name, iname); + if (compare == 0) { + return iter->types[i]; + } else if (compare < 0) { + if (i) { + r = i - 1; + } else { + break; + } + } else if (compare > 0) { + l = i + 1; + } + } else { + break; /* should never happen */ + } + } while (l <= r); + } + iter = iter->next; + } while (iter != end); + return 0; +} + +/* + Search for a swig_type_info structure for either a mangled name or a human readable name. + It first searches the mangled names of the types, which is a O(log #types) + If a type is not found it then searches the human readable names, which is O(#types). + + We start searching at module start, and finish searching when start == end. + Note: if start == end at the beginning of the function, we go all the way around + the circular list. +*/ +SWIGRUNTIME swig_type_info * +SWIG_TypeQueryModule(swig_module_info *start, + swig_module_info *end, + const char *name) { + /* STEP 1: Search the name field using binary search */ + swig_type_info *ret = SWIG_MangledTypeQueryModule(start, end, name); + if (ret) { + return ret; + } else { + /* STEP 2: If the type hasn't been found, do a complete search + of the str field (the human readable name) */ + swig_module_info *iter = start; + do { + size_t i = 0; + for (; i < iter->size; ++i) { + if (iter->types[i]->str && (SWIG_TypeEquiv(iter->types[i]->str, name))) + return iter->types[i]; + } + iter = iter->next; + } while (iter != end); + } + + /* neither found a match */ + return 0; +} + +/* + Pack binary data into a string +*/ +SWIGRUNTIME char * +SWIG_PackData(char *c, void *ptr, size_t sz) { + static const char hex[17] = "0123456789abcdef"; + const unsigned char *u = (unsigned char *) ptr; + const unsigned char *eu = u + sz; + for (; u != eu; ++u) { + unsigned char uu = *u; + *(c++) = hex[(uu & 0xf0) >> 4]; + *(c++) = hex[uu & 0xf]; + } + return c; +} + +/* + Unpack binary data from a string +*/ +SWIGRUNTIME const char * +SWIG_UnpackData(const char *c, void *ptr, size_t sz) { + unsigned char *u = (unsigned char *) ptr; + const unsigned char *eu = u + sz; + for (; u != eu; ++u) { + char d = *(c++); + unsigned char uu; + if ((d >= '0') && (d <= '9')) + uu = (unsigned char)((d - '0') << 4); + else if ((d >= 'a') && (d <= 'f')) + uu = (unsigned char)((d - ('a'-10)) << 4); + else + return (char *) 0; + d = *(c++); + if ((d >= '0') && (d <= '9')) + uu |= (unsigned char)(d - '0'); + else if ((d >= 'a') && (d <= 'f')) + uu |= (unsigned char)(d - ('a'-10)); + else + return (char *) 0; + *u = uu; + } + return c; +} + +/* + Pack 'void *' into a string buffer. +*/ +SWIGRUNTIME char * +SWIG_PackVoidPtr(char *buff, void *ptr, const char *name, size_t bsz) { + char *r = buff; + if ((2*sizeof(void *) + 2) > bsz) return 0; + *(r++) = '_'; + r = SWIG_PackData(r,&ptr,sizeof(void *)); + if (strlen(name) + 1 > (bsz - (r - buff))) return 0; + strcpy(r,name); + return buff; +} + +SWIGRUNTIME const char * +SWIG_UnpackVoidPtr(const char *c, void **ptr, const char *name) { + if (*c != '_') { + if (strcmp(c,"NULL") == 0) { + *ptr = (void *) 0; + return name; + } else { + return 0; + } + } + return SWIG_UnpackData(++c,ptr,sizeof(void *)); +} + +SWIGRUNTIME char * +SWIG_PackDataName(char *buff, void *ptr, size_t sz, const char *name, size_t bsz) { + char *r = buff; + size_t lname = (name ? strlen(name) : 0); + if ((2*sz + 2 + lname) > bsz) return 0; + *(r++) = '_'; + r = SWIG_PackData(r,ptr,sz); + if (lname) { + strncpy(r,name,lname+1); + } else { + *r = 0; + } + return buff; +} + +SWIGRUNTIME const char * +SWIG_UnpackDataName(const char *c, void *ptr, size_t sz, const char *name) { + if (*c != '_') { + if (strcmp(c,"NULL") == 0) { + memset(ptr,0,sz); + return name; + } else { + return 0; + } + } + return SWIG_UnpackData(++c,ptr,sz); +} + +#ifdef __cplusplus +} +#endif + +/* SWIG Errors applicable to all language modules, values are reserved from -1 to -99 */ +#define SWIG_UnknownError -1 +#define SWIG_IOError -2 +#define SWIG_RuntimeError -3 +#define SWIG_IndexError -4 +#define SWIG_TypeError -5 +#define SWIG_DivisionByZero -6 +#define SWIG_OverflowError -7 +#define SWIG_SyntaxError -8 +#define SWIG_ValueError -9 +#define SWIG_SystemError -10 +#define SWIG_AttributeError -11 +#define SWIG_MemoryError -12 +#define SWIG_NullReferenceError -13 + + +/* Compatibility macros for Python 3 */ +#if PY_VERSION_HEX >= 0x03000000 + +#define PyClass_Check(obj) PyObject_IsInstance(obj, (PyObject *)&PyType_Type) +#define PyInt_Check(x) PyLong_Check(x) +#define PyInt_AsLong(x) PyLong_AsLong(x) +#define PyInt_FromLong(x) PyLong_FromLong(x) +#define PyInt_FromSize_t(x) PyLong_FromSize_t(x) +#define PyString_Check(name) PyBytes_Check(name) +#define PyString_FromString(x) PyUnicode_FromString(x) +#define PyString_Format(fmt, args) PyUnicode_Format(fmt, args) +#define PyString_AsString(str) PyBytes_AsString(str) +#define PyString_Size(str) PyBytes_Size(str) +#define PyString_InternFromString(key) PyUnicode_InternFromString(key) +#define Py_TPFLAGS_HAVE_CLASS Py_TPFLAGS_BASETYPE +#define _PyLong_FromSsize_t(x) PyLong_FromSsize_t(x) + +#endif + +/* SWIG APIs for compatibility of both Python 2 & 3 */ + +#if PY_VERSION_HEX >= 0x03000000 +# define SWIG_Python_str_FromFormat PyUnicode_FromFormat +#else +# define SWIG_Python_str_FromFormat PyString_FromFormat +#endif + + +/* Wrapper around PyUnicode_AsUTF8AndSize - call Py_XDECREF on the returned pbytes when finished with the returned string */ +SWIGINTERN const char * +SWIG_PyUnicode_AsUTF8AndSize(PyObject *str, Py_ssize_t *psize, PyObject **pbytes) +{ +#if PY_VERSION_HEX >= 0x03030000 +# if !defined(Py_LIMITED_API) || Py_LIMITED_API+0 >= 0x030A0000 + *pbytes = NULL; + return PyUnicode_AsUTF8AndSize(str, psize); +# else + const char *chars; + *pbytes = PyUnicode_AsUTF8String(str); + chars = *pbytes ? PyBytes_AsString(*pbytes) : NULL; + if (chars && psize) + *psize = PyBytes_Size(*pbytes); + return chars; +# endif +#else + char *chars = NULL; + *pbytes = NULL; + PyString_AsStringAndSize(str, &chars, psize); + return chars; +#endif +} + +SWIGINTERN PyObject* +SWIG_Python_str_FromChar(const char *c) +{ +#if PY_VERSION_HEX >= 0x03000000 + return PyUnicode_FromString(c); +#else + return PyString_FromString(c); +#endif +} + +/* SWIGPY_USE_CAPSULE is no longer used within SWIG itself, but some user interface files check for it. */ +# define SWIGPY_USE_CAPSULE +#ifdef SWIGPYTHON_BUILTIN +# define SWIGPY_CAPSULE_ATTR_NAME "type_pointer_capsule_builtin" SWIG_TYPE_TABLE_NAME +#else +# define SWIGPY_CAPSULE_ATTR_NAME "type_pointer_capsule" SWIG_TYPE_TABLE_NAME +#endif +# define SWIGPY_CAPSULE_NAME ("swig_runtime_data" SWIG_RUNTIME_VERSION "." SWIGPY_CAPSULE_ATTR_NAME) + +#if PY_VERSION_HEX < 0x03020000 +#define PyDescr_TYPE(x) (((PyDescrObject *)(x))->d_type) +#define PyDescr_NAME(x) (((PyDescrObject *)(x))->d_name) +#define Py_hash_t long +#endif + +#ifdef Py_LIMITED_API +# define PyTuple_GET_ITEM PyTuple_GetItem +/* Note that PyTuple_SetItem() has different semantics from PyTuple_SET_ITEM as it decref's the original tuple item, so in general they cannot be used + interchangeably. However in SWIG-generated code PyTuple_SET_ITEM is only used with newly initialized tuples without any items and for them this does work. */ +# define PyTuple_SET_ITEM PyTuple_SetItem +# define PyTuple_GET_SIZE PyTuple_Size +# define PyCFunction_GET_FLAGS PyCFunction_GetFlags +# define PyCFunction_GET_FUNCTION PyCFunction_GetFunction +# define PyCFunction_GET_SELF PyCFunction_GetSelf +# define PyList_GET_ITEM PyList_GetItem +# define PyList_SET_ITEM PyList_SetItem +# define PySliceObject PyObject +#endif + +/* Increment and Decrement wrappers - for portability when using the stable abi and for performance otherwise */ +#ifdef Py_LIMITED_API +# define SWIG_Py_INCREF Py_IncRef +# define SWIG_Py_XINCREF Py_IncRef +# define SWIG_Py_DECREF Py_DecRef +# define SWIG_Py_XDECREF Py_DecRef +#else +# define SWIG_Py_INCREF Py_INCREF +# define SWIG_Py_XINCREF Py_XINCREF +# define SWIG_Py_DECREF Py_DECREF +# define SWIG_Py_XDECREF Py_XDECREF +#endif + +/* ----------------------------------------------------------------------------- + * error manipulation + * ----------------------------------------------------------------------------- */ + +SWIGRUNTIME PyObject* +SWIG_Python_ErrorType(int code) { + PyObject* type = 0; + switch(code) { + case SWIG_MemoryError: + type = PyExc_MemoryError; + break; + case SWIG_IOError: + type = PyExc_IOError; + break; + case SWIG_RuntimeError: + type = PyExc_RuntimeError; + break; + case SWIG_IndexError: + type = PyExc_IndexError; + break; + case SWIG_TypeError: + type = PyExc_TypeError; + break; + case SWIG_DivisionByZero: + type = PyExc_ZeroDivisionError; + break; + case SWIG_OverflowError: + type = PyExc_OverflowError; + break; + case SWIG_SyntaxError: + type = PyExc_SyntaxError; + break; + case SWIG_ValueError: + type = PyExc_ValueError; + break; + case SWIG_SystemError: + type = PyExc_SystemError; + break; + case SWIG_AttributeError: + type = PyExc_AttributeError; + break; + case SWIG_NullReferenceError: + type = PyExc_TypeError; + break; + default: + type = PyExc_RuntimeError; + } + return type; +} + + +SWIGRUNTIME void +SWIG_Python_AddErrorMsg(const char* mesg) +{ + PyObject *type = 0; + PyObject *value = 0; + PyObject *traceback = 0; + + if (PyErr_Occurred()) + PyErr_Fetch(&type, &value, &traceback); + if (value) { + PyObject *old_str = PyObject_Str(value); + PyObject *bytes = NULL; + const char *tmp = SWIG_PyUnicode_AsUTF8AndSize(old_str, NULL, &bytes); + PyErr_Clear(); + SWIG_Py_XINCREF(type); + if (tmp) + PyErr_Format(type, "%s %s", tmp, mesg); + else + PyErr_Format(type, "%s", mesg); + SWIG_Py_XDECREF(bytes); + SWIG_Py_DECREF(old_str); + SWIG_Py_DECREF(value); + } else { + PyErr_SetString(PyExc_RuntimeError, mesg); + } +} + +SWIGRUNTIME int +SWIG_Python_TypeErrorOccurred(PyObject *obj) +{ + PyObject *error; + if (obj) + return 0; + error = PyErr_Occurred(); + return error && PyErr_GivenExceptionMatches(error, PyExc_TypeError); +} + +SWIGRUNTIME void +SWIG_Python_RaiseOrModifyTypeError(const char *message) +{ + if (SWIG_Python_TypeErrorOccurred(NULL)) { + /* Use existing TypeError to preserve stacktrace and enhance with given message */ + PyObject *newvalue; + PyObject *type = NULL, *value = NULL, *traceback = NULL; + PyErr_Fetch(&type, &value, &traceback); +#if PY_VERSION_HEX >= 0x03000000 + newvalue = PyUnicode_FromFormat("%S\nAdditional information:\n%s", value, message); +#else + newvalue = PyString_FromFormat("%s\nAdditional information:\n%s", PyString_AsString(value), message); +#endif + if (newvalue) { + SWIG_Py_XDECREF(value); + PyErr_Restore(type, newvalue, traceback); + } else { + PyErr_Restore(type, value, traceback); + } + } else { + /* Raise TypeError using given message */ + PyErr_SetString(PyExc_TypeError, message); + } +} + +#if defined(SWIG_PYTHON_NO_THREADS) +# if defined(SWIG_PYTHON_THREADS) +# undef SWIG_PYTHON_THREADS +# endif +#endif +#if defined(SWIG_PYTHON_THREADS) /* Threading support is enabled */ +# if !defined(SWIG_PYTHON_USE_GIL) && !defined(SWIG_PYTHON_NO_USE_GIL) +# define SWIG_PYTHON_USE_GIL +# endif +# if defined(SWIG_PYTHON_USE_GIL) /* Use PyGILState threads calls */ +# if !defined(SWIG_PYTHON_INITIALIZE_THREADS) +# if PY_VERSION_HEX < 0x03070000 +# define SWIG_PYTHON_INITIALIZE_THREADS PyEval_InitThreads() +# else +# define SWIG_PYTHON_INITIALIZE_THREADS +# endif +# endif +# ifdef __cplusplus /* C++ code */ + class SWIG_Python_Thread_Block { + bool status; + PyGILState_STATE state; + public: + void end() { if (status) { PyGILState_Release(state); status = false;} } + SWIG_Python_Thread_Block() : status(true), state(PyGILState_Ensure()) {} + ~SWIG_Python_Thread_Block() { end(); } + }; + class SWIG_Python_Thread_Allow { + bool status; + PyThreadState *save; + public: + void end() { if (status) { status = false; PyEval_RestoreThread(save); }} + SWIG_Python_Thread_Allow() : status(true), save(PyEval_SaveThread()) {} + ~SWIG_Python_Thread_Allow() { end(); } + }; +# define SWIG_PYTHON_THREAD_BEGIN_BLOCK SWIG_Python_Thread_Block _swig_thread_block +# define SWIG_PYTHON_THREAD_END_BLOCK _swig_thread_block.end() +# define SWIG_PYTHON_THREAD_BEGIN_ALLOW SWIG_Python_Thread_Allow _swig_thread_allow +# define SWIG_PYTHON_THREAD_END_ALLOW _swig_thread_allow.end() +# else /* C code */ +# define SWIG_PYTHON_THREAD_BEGIN_BLOCK PyGILState_STATE _swig_thread_block = PyGILState_Ensure() +# define SWIG_PYTHON_THREAD_END_BLOCK PyGILState_Release(_swig_thread_block) +# define SWIG_PYTHON_THREAD_BEGIN_ALLOW PyThreadState *_swig_thread_allow = PyEval_SaveThread() +# define SWIG_PYTHON_THREAD_END_ALLOW PyEval_RestoreThread(_swig_thread_allow) +# endif +# else /* Old thread way, not implemented, user must provide it */ +# if !defined(SWIG_PYTHON_INITIALIZE_THREADS) +# define SWIG_PYTHON_INITIALIZE_THREADS +# endif +# if !defined(SWIG_PYTHON_THREAD_BEGIN_BLOCK) +# define SWIG_PYTHON_THREAD_BEGIN_BLOCK +# endif +# if !defined(SWIG_PYTHON_THREAD_END_BLOCK) +# define SWIG_PYTHON_THREAD_END_BLOCK +# endif +# if !defined(SWIG_PYTHON_THREAD_BEGIN_ALLOW) +# define SWIG_PYTHON_THREAD_BEGIN_ALLOW +# endif +# if !defined(SWIG_PYTHON_THREAD_END_ALLOW) +# define SWIG_PYTHON_THREAD_END_ALLOW +# endif +# endif +#else /* No thread support */ +# define SWIG_PYTHON_INITIALIZE_THREADS +# define SWIG_PYTHON_THREAD_BEGIN_BLOCK +# define SWIG_PYTHON_THREAD_END_BLOCK +# define SWIG_PYTHON_THREAD_BEGIN_ALLOW +# define SWIG_PYTHON_THREAD_END_ALLOW +#endif + +/* ----------------------------------------------------------------------------- + * Python API portion that goes into the runtime + * ----------------------------------------------------------------------------- */ + +#ifdef __cplusplus +extern "C" { +#endif + +/* ----------------------------------------------------------------------------- + * Constant declarations + * ----------------------------------------------------------------------------- */ + +/* Constant Types */ +#define SWIG_PY_POINTER 4 +#define SWIG_PY_BINARY 5 + +/* Constant information structure */ +typedef struct swig_const_info { + int type; + const char *name; + long lvalue; + double dvalue; + void *pvalue; + swig_type_info **ptype; +} swig_const_info; + +#ifdef __cplusplus +} +#endif + + +/* ----------------------------------------------------------------------------- + * pyrun.swg + * + * This file contains the runtime support for Python modules + * and includes code for managing global variables and pointer + * type checking. + * + * ----------------------------------------------------------------------------- */ + +#if PY_VERSION_HEX < 0x02070000 /* 2.7.0 */ +# error "This version of SWIG only supports Python >= 2.7" +#endif + +#if PY_VERSION_HEX >= 0x03000000 && PY_VERSION_HEX < 0x03030000 +# error "This version of SWIG only supports Python 3 >= 3.3" +#endif + +/* Common SWIG API */ + +/* for raw pointers */ +#define SWIG_Python_ConvertPtr(obj, pptr, type, flags) SWIG_Python_ConvertPtrAndOwn(obj, pptr, type, flags, 0) +#define SWIG_ConvertPtr(obj, pptr, type, flags) SWIG_Python_ConvertPtr(obj, pptr, type, flags) +#define SWIG_ConvertPtrAndOwn(obj,pptr,type,flags,own) SWIG_Python_ConvertPtrAndOwn(obj, pptr, type, flags, own) + +#ifdef SWIGPYTHON_BUILTIN +#define SWIG_NewPointerObj(ptr, type, flags) SWIG_Python_NewPointerObj(self, ptr, type, flags) +#else +#define SWIG_NewPointerObj(ptr, type, flags) SWIG_Python_NewPointerObj(NULL, ptr, type, flags) +#endif + +#define SWIG_InternalNewPointerObj(ptr, type, flags) SWIG_Python_NewPointerObj(NULL, ptr, type, flags) + +#define SWIG_CheckImplicit(ty) SWIG_Python_CheckImplicit(ty) +#define SWIG_AcquirePtr(ptr, src) SWIG_Python_AcquirePtr(ptr, src) +#define swig_owntype int + +/* for raw packed data */ +#define SWIG_ConvertPacked(obj, ptr, sz, ty) SWIG_Python_ConvertPacked(obj, ptr, sz, ty) +#define SWIG_NewPackedObj(ptr, sz, type) SWIG_Python_NewPackedObj(ptr, sz, type) + +/* for class or struct pointers */ +#define SWIG_ConvertInstance(obj, pptr, type, flags) SWIG_ConvertPtr(obj, pptr, type, flags) +#define SWIG_NewInstanceObj(ptr, type, flags) SWIG_NewPointerObj(ptr, type, flags) + +/* for C or C++ function pointers */ +#define SWIG_ConvertFunctionPtr(obj, pptr, type) SWIG_Python_ConvertFunctionPtr(obj, pptr, type) +#define SWIG_NewFunctionPtrObj(ptr, type) SWIG_Python_NewPointerObj(NULL, ptr, type, 0) + +/* for C++ member pointers, ie, member methods */ +#define SWIG_ConvertMember(obj, ptr, sz, ty) SWIG_Python_ConvertPacked(obj, ptr, sz, ty) +#define SWIG_NewMemberObj(ptr, sz, type) SWIG_Python_NewPackedObj(ptr, sz, type) + + +/* Runtime API */ + +#define SWIG_GetModule(clientdata) SWIG_Python_GetModule(clientdata) +#define SWIG_SetModule(clientdata, pointer) SWIG_Python_SetModule(pointer) +#define SWIG_NewClientData(obj) SwigPyClientData_New(obj) + +#define SWIG_SetErrorObj SWIG_Python_SetErrorObj +#define SWIG_SetErrorMsg SWIG_Python_SetErrorMsg +#define SWIG_ErrorType(code) SWIG_Python_ErrorType(code) +#define SWIG_Error(code, msg) SWIG_Python_SetErrorMsg(SWIG_ErrorType(code), msg) +#define SWIG_fail goto fail + + +/* Runtime API implementation */ + +/* Error manipulation */ + +SWIGINTERN void +SWIG_Python_SetErrorObj(PyObject *errtype, PyObject *obj) { + SWIG_PYTHON_THREAD_BEGIN_BLOCK; + PyErr_SetObject(errtype, obj); + SWIG_Py_DECREF(obj); + SWIG_PYTHON_THREAD_END_BLOCK; +} + +SWIGINTERN void +SWIG_Python_SetErrorMsg(PyObject *errtype, const char *msg) { + SWIG_PYTHON_THREAD_BEGIN_BLOCK; + PyErr_SetString(errtype, msg); + SWIG_PYTHON_THREAD_END_BLOCK; +} + +#define SWIG_Python_Raise(obj, type, desc) SWIG_Python_SetErrorObj(SWIG_Python_ExceptionType(desc), obj) + +/* Set a constant value */ + +#if defined(SWIGPYTHON_BUILTIN) + +SWIGINTERN void +SwigPyBuiltin_AddPublicSymbol(PyObject *seq, const char *key) { + PyObject *s = PyString_InternFromString(key); + PyList_Append(seq, s); + SWIG_Py_DECREF(s); +} + +SWIGINTERN void +SWIG_Python_SetConstant(PyObject *d, PyObject *public_interface, const char *name, PyObject *obj) { + PyDict_SetItemString(d, name, obj); + SWIG_Py_DECREF(obj); + if (public_interface) + SwigPyBuiltin_AddPublicSymbol(public_interface, name); +} + +#else + +SWIGINTERN void +SWIG_Python_SetConstant(PyObject *d, const char *name, PyObject *obj) { + PyDict_SetItemString(d, name, obj); + SWIG_Py_DECREF(obj); +} + +#endif + +/* Append a value to the result obj */ + +SWIGINTERN PyObject* +SWIG_Python_AppendOutput(PyObject* result, PyObject* obj, int is_void) { + if (!result) { + result = obj; + } else if (result == Py_None && is_void) { + SWIG_Py_DECREF(result); + result = obj; + } else { + if (!PyList_Check(result)) { + PyObject *o2 = result; + result = PyList_New(1); + if (result) { + PyList_SET_ITEM(result, 0, o2); + } else { + SWIG_Py_DECREF(obj); + return o2; + } + } + PyList_Append(result,obj); + SWIG_Py_DECREF(obj); + } + return result; +} + +/* Unpack the argument tuple */ + +SWIGINTERN Py_ssize_t +SWIG_Python_UnpackTuple(PyObject *args, const char *name, Py_ssize_t min, Py_ssize_t max, PyObject **objs) +{ + if (!args) { + if (!min && !max) { + return 1; + } else { + PyErr_Format(PyExc_TypeError, "%s expected %s%d arguments, got none", + name, (min == max ? "" : "at least "), (int)min); + return 0; + } + } + if (!PyTuple_Check(args)) { + if (min <= 1 && max >= 1) { + Py_ssize_t i; + objs[0] = args; + for (i = 1; i < max; ++i) { + objs[i] = 0; + } + return 2; + } + PyErr_SetString(PyExc_SystemError, "UnpackTuple() argument list is not a tuple"); + return 0; + } else { + Py_ssize_t l = PyTuple_GET_SIZE(args); + if (l < min) { + PyErr_Format(PyExc_TypeError, "%s expected %s%d arguments, got %d", + name, (min == max ? "" : "at least "), (int)min, (int)l); + return 0; + } else if (l > max) { + PyErr_Format(PyExc_TypeError, "%s expected %s%d arguments, got %d", + name, (min == max ? "" : "at most "), (int)max, (int)l); + return 0; + } else { + Py_ssize_t i; + for (i = 0; i < l; ++i) { + objs[i] = PyTuple_GET_ITEM(args, i); + } + for (; l < max; ++l) { + objs[l] = 0; + } + return i + 1; + } + } +} + +SWIGINTERN int +SWIG_Python_CheckNoKeywords(PyObject *kwargs, const char *name) { + int no_kwargs = 1; + if (kwargs) { + assert(PyDict_Check(kwargs)); + if (PyDict_Size(kwargs) > 0) { + PyErr_Format(PyExc_TypeError, "%s() does not take keyword arguments", name); + no_kwargs = 0; + } + } + return no_kwargs; +} + +/* A functor is a function object with one single object argument */ +#define SWIG_Python_CallFunctor(functor, obj) PyObject_CallFunctionObjArgs(functor, obj, NULL); + +/* + Helper for static pointer initialization for both C and C++ code, for example + static PyObject *SWIG_STATIC_POINTER(MyVar) = NewSomething(...); +*/ +#ifdef __cplusplus +#define SWIG_STATIC_POINTER(var) var +#else +#define SWIG_STATIC_POINTER(var) var = 0; if (!var) var +#endif + +#ifdef __cplusplus +extern "C" { +#endif + +/* Python-specific SWIG API */ +#define SWIG_newvarlink() SWIG_Python_newvarlink() +#define SWIG_addvarlink(p, name, get_attr, set_attr) SWIG_Python_addvarlink(p, name, get_attr, set_attr) +#define SWIG_InstallConstants(d, constants) SWIG_Python_InstallConstants(d, constants) + +/* ----------------------------------------------------------------------------- + * global variable support code. + * ----------------------------------------------------------------------------- */ + +typedef struct swig_globalvar { + char *name; /* Name of global variable */ + PyObject *(*get_attr)(void); /* Return the current value */ + int (*set_attr)(PyObject *); /* Set the value */ + struct swig_globalvar *next; +} swig_globalvar; + +typedef struct swig_varlinkobject { + PyObject_HEAD + swig_globalvar *vars; +} swig_varlinkobject; + +SWIGINTERN PyObject * +swig_varlink_repr(PyObject *SWIGUNUSEDPARM(v)) { +#if PY_VERSION_HEX >= 0x03000000 + return PyUnicode_InternFromString(""); +#else + return PyString_FromString(""); +#endif +} + +SWIGINTERN PyObject * +swig_varlink_str(PyObject *o) { + swig_varlinkobject *v = (swig_varlinkobject *) o; +#if PY_VERSION_HEX >= 0x03000000 + PyObject *str = PyUnicode_InternFromString("("); + PyObject *tail; + PyObject *joined; + swig_globalvar *var; + for (var = v->vars; var; var=var->next) { + tail = PyUnicode_FromString(var->name); + joined = PyUnicode_Concat(str, tail); + SWIG_Py_DECREF(str); + SWIG_Py_DECREF(tail); + str = joined; + if (var->next) { + tail = PyUnicode_InternFromString(", "); + joined = PyUnicode_Concat(str, tail); + SWIG_Py_DECREF(str); + SWIG_Py_DECREF(tail); + str = joined; + } + } + tail = PyUnicode_InternFromString(")"); + joined = PyUnicode_Concat(str, tail); + SWIG_Py_DECREF(str); + SWIG_Py_DECREF(tail); + str = joined; +#else + PyObject *str = PyString_FromString("("); + swig_globalvar *var; + for (var = v->vars; var; var=var->next) { + PyString_ConcatAndDel(&str,PyString_FromString(var->name)); + if (var->next) PyString_ConcatAndDel(&str,PyString_FromString(", ")); + } + PyString_ConcatAndDel(&str,PyString_FromString(")")); +#endif + return str; +} + +SWIGINTERN void +swig_varlink_dealloc(PyObject *o) { + swig_varlinkobject *v = (swig_varlinkobject *) o; + swig_globalvar *var = v->vars; + while (var) { + swig_globalvar *n = var->next; + free(var->name); + free(var); + var = n; + } +} + +SWIGINTERN PyObject * +swig_varlink_getattr(PyObject *o, char *n) { + swig_varlinkobject *v = (swig_varlinkobject *) o; + PyObject *res = NULL; + swig_globalvar *var = v->vars; + while (var) { + if (strcmp(var->name,n) == 0) { + res = (*var->get_attr)(); + break; + } + var = var->next; + } + if (res == NULL && !PyErr_Occurred()) { + PyErr_Format(PyExc_AttributeError, "Unknown C global variable '%s'", n); + } + return res; +} + +SWIGINTERN int +swig_varlink_setattr(PyObject *o, char *n, PyObject *p) { + swig_varlinkobject *v = (swig_varlinkobject *) o; + int res = 1; + swig_globalvar *var = v->vars; + while (var) { + if (strcmp(var->name,n) == 0) { + res = (*var->set_attr)(p); + break; + } + var = var->next; + } + if (res == 1 && !PyErr_Occurred()) { + PyErr_Format(PyExc_AttributeError, "Unknown C global variable '%s'", n); + } + return res; +} + +SWIGINTERN PyTypeObject* +swig_varlink_type(void) { + static char varlink__doc__[] = "Swig var link object"; +#ifndef SWIG_HEAPTYPES + static PyTypeObject varlink_type; + static int type_init = 0; + if (!type_init) { + const PyTypeObject tmp = { +#if PY_VERSION_HEX >= 0x03000000 + PyVarObject_HEAD_INIT(NULL, 0) +#else + PyObject_HEAD_INIT(NULL) + 0, /* ob_size */ +#endif + "swigvarlink", /* tp_name */ + sizeof(swig_varlinkobject), /* tp_basicsize */ + 0, /* tp_itemsize */ + (destructor) swig_varlink_dealloc, /* tp_dealloc */ +#if PY_VERSION_HEX < 0x030800b4 + (printfunc)0, /* tp_print */ +#else + (Py_ssize_t)0, /* tp_vectorcall_offset */ +#endif + (getattrfunc) swig_varlink_getattr, /* tp_getattr */ + (setattrfunc) swig_varlink_setattr, /* tp_setattr */ + 0, /* tp_compare */ + (reprfunc) swig_varlink_repr, /* tp_repr */ + 0, /* tp_as_number */ + 0, /* tp_as_sequence */ + 0, /* tp_as_mapping */ + 0, /* tp_hash */ + 0, /* tp_call */ + (reprfunc) swig_varlink_str, /* tp_str */ + 0, /* tp_getattro */ + 0, /* tp_setattro */ + 0, /* tp_as_buffer */ + 0, /* tp_flags */ + varlink__doc__, /* tp_doc */ + 0, /* tp_traverse */ + 0, /* tp_clear */ + 0, /* tp_richcompare */ + 0, /* tp_weaklistoffset */ + 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, /* tp_iter -> tp_weaklist */ + 0, /* tp_del */ + 0, /* tp_version_tag */ +#if PY_VERSION_HEX >= 0x03040000 + 0, /* tp_finalize */ +#endif +#if PY_VERSION_HEX >= 0x03080000 + 0, /* tp_vectorcall */ +#endif +#if (PY_VERSION_HEX >= 0x03080000) && (PY_VERSION_HEX < 0x03090000) + 0, /* tp_print */ +#endif +#if PY_VERSION_HEX >= 0x030c0000 + 0, /* tp_watched */ +#endif +#if PY_VERSION_HEX >= 0x030d00a4 + 0, /* tp_versions_used */ +#endif +#ifdef COUNT_ALLOCS + 0, /* tp_allocs */ + 0, /* tp_frees */ + 0, /* tp_maxalloc */ + 0, /* tp_prev */ + 0 /* tp_next */ +#endif + }; + varlink_type = tmp; + type_init = 1; + if (PyType_Ready(&varlink_type) < 0) + return NULL; + } + return &varlink_type; +#else + PyType_Slot slots[] = { + { Py_tp_dealloc, (void *)swig_varlink_dealloc }, + { Py_tp_repr, (void *)swig_varlink_repr }, + { Py_tp_getattr, (void *)swig_varlink_getattr }, + { Py_tp_setattr, (void *)swig_varlink_setattr }, + { Py_tp_str, (void *)swig_varlink_str }, + { Py_tp_doc, (void *)varlink__doc__ }, + { 0, NULL } + }; + PyType_Spec spec = { + "swigvarlink", + sizeof(swig_varlinkobject), + 0, + Py_TPFLAGS_DEFAULT, + slots + }; + return (PyTypeObject *)PyType_FromSpec(&spec); +#endif +} + +/* Create a variable linking object for use later */ +SWIGINTERN PyObject * +SWIG_Python_newvarlink(void) { + swig_varlinkobject *result = PyObject_New(swig_varlinkobject, swig_varlink_type()); + if (result) { + result->vars = 0; + } + return ((PyObject*) result); +} + +SWIGINTERN void +SWIG_Python_addvarlink(PyObject *p, const char *name, PyObject *(*get_attr)(void), int (*set_attr)(PyObject *p)) { + swig_varlinkobject *v = (swig_varlinkobject *) p; + swig_globalvar *gv = (swig_globalvar *) malloc(sizeof(swig_globalvar)); + if (gv) { + size_t size = strlen(name)+1; + gv->name = (char *)malloc(size); + if (gv->name) { + memcpy(gv->name, name, size); + gv->get_attr = get_attr; + gv->set_attr = set_attr; + gv->next = v->vars; + } + } + v->vars = gv; +} + + +static PyObject *Swig_Globals_global = NULL; + +SWIGINTERN PyObject * +SWIG_globals(void) { + if (Swig_Globals_global == NULL) { + Swig_Globals_global = SWIG_newvarlink(); + } + return Swig_Globals_global; +} + +#ifdef __cplusplus +} +#endif + +/* ----------------------------------------------------------------------------- + * Pointer declarations + * ----------------------------------------------------------------------------- */ + +/* Flags for new pointer objects */ +#define SWIG_POINTER_NOSHADOW (SWIG_POINTER_OWN << 1) +#define SWIG_POINTER_NEW (SWIG_POINTER_NOSHADOW | SWIG_POINTER_OWN) + +#define SWIG_POINTER_IMPLICIT_CONV (SWIG_POINTER_DISOWN << 1) + +#define SWIG_BUILTIN_TP_INIT (SWIG_POINTER_OWN << 2) +#define SWIG_BUILTIN_INIT (SWIG_BUILTIN_TP_INIT | SWIG_POINTER_OWN) + +#ifdef __cplusplus +extern "C" { +#endif + +/* The python void return value */ + +SWIGRUNTIMEINLINE PyObject * +SWIG_Py_Void(void) +{ + PyObject *none = Py_None; + SWIG_Py_INCREF(none); + return none; +} + +/* SwigPyClientData */ + +typedef struct { + PyObject *klass; + PyObject *newraw; + PyObject *newargs; + PyObject *destroy; + int delargs; + int implicitconv; + PyTypeObject *pytype; +} SwigPyClientData; + +SWIGRUNTIMEINLINE int +SWIG_Python_CheckImplicit(swig_type_info *ty) +{ + SwigPyClientData *data = (SwigPyClientData *)ty->clientdata; + int fail = data ? data->implicitconv : 0; + if (fail) + PyErr_SetString(PyExc_TypeError, "Implicit conversion is prohibited for explicit constructors."); + return fail; +} + +SWIGRUNTIMEINLINE PyObject * +SWIG_Python_ExceptionType(swig_type_info *desc) { + SwigPyClientData *data = desc ? (SwigPyClientData *) desc->clientdata : 0; + PyObject *klass = data ? data->klass : 0; + return (klass ? klass : PyExc_RuntimeError); +} + + +SWIGRUNTIME SwigPyClientData * +SwigPyClientData_New(PyObject* obj) +{ + if (!obj) { + return 0; + } else { + SwigPyClientData *data = (SwigPyClientData *)malloc(sizeof(SwigPyClientData)); + /* the klass element */ + data->klass = obj; + SWIG_Py_INCREF(data->klass); + /* the newraw method and newargs arguments used to create a new raw instance */ + if (PyClass_Check(obj)) { + data->newraw = 0; + SWIG_Py_INCREF(obj); + data->newargs = obj; + } else { + data->newraw = PyObject_GetAttrString(data->klass, "__new__"); + if (data->newraw) { + data->newargs = PyTuple_New(1); + if (data->newargs) { + SWIG_Py_INCREF(obj); + PyTuple_SET_ITEM(data->newargs, 0, obj); + } else { + SWIG_Py_DECREF(data->newraw); + SWIG_Py_DECREF(data->klass); + free(data); + return 0; + } + } else { + SWIG_Py_INCREF(obj); + data->newargs = obj; + } + } + /* the destroy method, aka as the C++ delete method */ + data->destroy = PyObject_GetAttrString(data->klass, "__swig_destroy__"); + if (PyErr_Occurred()) { + PyErr_Clear(); + data->destroy = 0; + } + if (data->destroy) { + data->delargs = !(PyCFunction_GET_FLAGS(data->destroy) & METH_O); + } else { + data->delargs = 0; + } + data->implicitconv = 0; + data->pytype = 0; + return data; + } +} + +SWIGRUNTIME void +SwigPyClientData_Del(SwigPyClientData *data) +{ + SWIG_Py_XDECREF(data->klass); + SWIG_Py_XDECREF(data->newraw); + SWIG_Py_XDECREF(data->newargs); + SWIG_Py_XDECREF(data->destroy); + free(data); +} + +/* =============== SwigPyObject =====================*/ + +typedef struct { + PyObject_HEAD + void *ptr; + swig_type_info *ty; + int own; + PyObject *next; +#ifdef SWIGPYTHON_BUILTIN + PyObject *dict; +#endif +} SwigPyObject; + + +#ifdef SWIGPYTHON_BUILTIN + +SWIGRUNTIME PyObject * +SwigPyObject_get___dict__(PyObject *v, PyObject *SWIGUNUSEDPARM(args)) +{ + SwigPyObject *sobj = (SwigPyObject *)v; + + if (!sobj->dict) + sobj->dict = PyDict_New(); + + SWIG_Py_XINCREF(sobj->dict); + return sobj->dict; +} + +#endif + +SWIGRUNTIME PyObject * +SwigPyObject_long(SwigPyObject *v) +{ + return PyLong_FromVoidPtr(v->ptr); +} + +SWIGRUNTIME PyObject * +SwigPyObject_format(const char* fmt, SwigPyObject *v) +{ + PyObject *res = NULL; + PyObject *args = PyTuple_New(1); + if (args) { + PyObject *val = SwigPyObject_long(v); + if (val) { + PyObject *ofmt; + PyTuple_SET_ITEM(args, 0, val); + ofmt = SWIG_Python_str_FromChar(fmt); + if (ofmt) { +#if PY_VERSION_HEX >= 0x03000000 + res = PyUnicode_Format(ofmt,args); +#else + res = PyString_Format(ofmt,args); +#endif + SWIG_Py_DECREF(ofmt); + } + } + SWIG_Py_DECREF(args); + } + return res; +} + +SWIGRUNTIME PyObject * +SwigPyObject_oct(SwigPyObject *v) +{ + return SwigPyObject_format("%o",v); +} + +SWIGRUNTIME PyObject * +SwigPyObject_hex(SwigPyObject *v) +{ + return SwigPyObject_format("%x",v); +} + +SWIGRUNTIME PyObject * +SwigPyObject_repr(SwigPyObject *v) +{ + const char *name = SWIG_TypePrettyName(v->ty); + PyObject *repr = SWIG_Python_str_FromFormat("", (name ? name : "unknown"), (void *)v); + if (repr && v->next) { + PyObject *nrep = SwigPyObject_repr((SwigPyObject *)v->next); + if (nrep) { +# if PY_VERSION_HEX >= 0x03000000 + PyObject *joined = PyUnicode_Concat(repr, nrep); + SWIG_Py_DECREF(repr); + SWIG_Py_DECREF(nrep); + repr = joined; +# else + PyString_ConcatAndDel(&repr,nrep); +# endif + } else { + SWIG_Py_DECREF(repr); + repr = NULL; + } + } + return repr; +} + +/* We need a version taking two PyObject* parameters so it's a valid + * PyCFunction to use in swigobject_methods[]. */ +SWIGRUNTIME PyObject * +SwigPyObject_repr2(PyObject *v, PyObject *SWIGUNUSEDPARM(args)) +{ + return SwigPyObject_repr((SwigPyObject*)v); +} + +SWIGRUNTIME int +SwigPyObject_compare(SwigPyObject *v, SwigPyObject *w) +{ + void *i = v->ptr; + void *j = w->ptr; + return (i < j) ? -1 : ((i > j) ? 1 : 0); +} + +/* Added for Python 3.x, would it also be useful for Python 2.x? */ +SWIGRUNTIME PyObject* +SwigPyObject_richcompare(SwigPyObject *v, SwigPyObject *w, int op) +{ + PyObject* res = NULL; + if (!PyErr_Occurred()) { + if (op != Py_EQ && op != Py_NE) { + SWIG_Py_INCREF(Py_NotImplemented); + return Py_NotImplemented; + } + res = PyBool_FromLong( (SwigPyObject_compare(v, w)==0) == (op == Py_EQ) ? 1 : 0); + } + return res; +} + + +SWIGRUNTIME PyTypeObject* SwigPyObject_TypeOnce(void); + +#ifdef SWIGPYTHON_BUILTIN +static swig_type_info *SwigPyObject_stype = 0; +SWIGRUNTIME PyTypeObject* +SwigPyObject_type(void) { + SwigPyClientData *cd; + assert(SwigPyObject_stype); + cd = (SwigPyClientData*) SwigPyObject_stype->clientdata; + assert(cd); + assert(cd->pytype); + return cd->pytype; +} +#else +SWIGRUNTIME PyTypeObject* +SwigPyObject_type(void) { + static PyTypeObject *SWIG_STATIC_POINTER(type) = SwigPyObject_TypeOnce(); + return type; +} +#endif + +SWIGRUNTIMEINLINE int +SwigPyObject_Check(PyObject *op) { + PyTypeObject *target_tp = SwigPyObject_type(); + PyTypeObject *op_type = Py_TYPE(op); +#ifdef SWIGPYTHON_BUILTIN + if (PyType_IsSubtype(op_type, target_tp)) + return 1; + return (strcmp(op_type->tp_name, "SwigPyObject") == 0); +#else +# ifdef Py_LIMITED_API + int cmp; + PyObject *tp_name; +#endif + if (op_type == target_tp) + return 1; +# ifdef Py_LIMITED_API + tp_name = PyObject_GetAttrString((PyObject *)op_type, "__name__"); + if (!tp_name) + return 0; + cmp = PyUnicode_CompareWithASCIIString(tp_name, "SwigPyObject"); + SWIG_Py_DECREF(tp_name); + return cmp == 0; +# else + return (strcmp(op_type->tp_name, "SwigPyObject") == 0); +# endif +#endif +} + +SWIGRUNTIME PyObject * +SwigPyObject_New(void *ptr, swig_type_info *ty, int own); + +static PyObject* Swig_Capsule_global = NULL; + +SWIGRUNTIME void +SwigPyObject_dealloc(PyObject *v) +{ + SwigPyObject *sobj = (SwigPyObject *) v; + PyObject *next = sobj->next; + if (sobj->own == SWIG_POINTER_OWN) { + swig_type_info *ty = sobj->ty; + SwigPyClientData *data = ty ? (SwigPyClientData *) ty->clientdata : 0; + PyObject *destroy = data ? data->destroy : 0; + if (destroy) { + /* destroy is always a VARARGS method */ + PyObject *res; + + /* PyObject_CallFunction() has the potential to silently drop + the active exception. In cases of unnamed temporary + variable or where we just finished iterating over a generator + StopIteration will be active right now, and this needs to + remain true upon return from SwigPyObject_dealloc. So save + and restore. */ + + PyObject *type = NULL, *value = NULL, *traceback = NULL; + PyErr_Fetch(&type, &value, &traceback); + + if (data->delargs) { + /* we need to create a temporary object to carry the destroy operation */ + PyObject *tmp = SwigPyObject_New(sobj->ptr, ty, 0); + if (tmp) { + res = SWIG_Python_CallFunctor(destroy, tmp); + } else { + res = 0; + } + SWIG_Py_XDECREF(tmp); + } else { + PyCFunction meth = PyCFunction_GET_FUNCTION(destroy); + PyObject *mself = PyCFunction_GET_SELF(destroy); + res = ((*meth)(mself, v)); + } + if (!res) + PyErr_WriteUnraisable(destroy); + + PyErr_Restore(type, value, traceback); + + SWIG_Py_XDECREF(res); + } +#if !defined(SWIG_PYTHON_SILENT_MEMLEAK) + else { + const char *name = SWIG_TypePrettyName(ty); + printf("swig/python detected a memory leak of type '%s', no destructor found.\n", (name ? name : "unknown")); + } +#endif + SWIG_Py_XDECREF(Swig_Capsule_global); + } + SWIG_Py_XDECREF(next); +#ifdef SWIGPYTHON_BUILTIN + SWIG_Py_XDECREF(sobj->dict); +#endif + PyObject_Free(v); +} + +SWIGRUNTIME PyObject* +SwigPyObject_append(PyObject* v, PyObject* next) +{ + SwigPyObject *sobj = (SwigPyObject *) v; + if (!SwigPyObject_Check(next)) { + PyErr_SetString(PyExc_TypeError, "Attempt to append a non SwigPyObject"); + return NULL; + } + ((SwigPyObject *)next)->next = sobj->next; + sobj->next = next; + SWIG_Py_INCREF(next); + return SWIG_Py_Void(); +} + +SWIGRUNTIME PyObject* +SwigPyObject_next(PyObject* v, PyObject *SWIGUNUSEDPARM(args)) +{ + SwigPyObject *sobj = (SwigPyObject *) v; + if (sobj->next) { + SWIG_Py_INCREF(sobj->next); + return sobj->next; + } else { + return SWIG_Py_Void(); + } +} + +SWIGINTERN PyObject* +SwigPyObject_disown(PyObject* v, PyObject *SWIGUNUSEDPARM(args)) +{ + SwigPyObject *sobj = (SwigPyObject *)v; + sobj->own = 0; + return SWIG_Py_Void(); +} + +SWIGINTERN PyObject* +SwigPyObject_acquire(PyObject* v, PyObject *SWIGUNUSEDPARM(args)) +{ + SwigPyObject *sobj = (SwigPyObject *)v; + sobj->own = SWIG_POINTER_OWN; + return SWIG_Py_Void(); +} + +SWIGINTERN PyObject* +SwigPyObject_own(PyObject *v, PyObject *args) +{ + PyObject *val = 0; + if (!PyArg_UnpackTuple(args, "own", 0, 1, &val)) { + return NULL; + } else { + SwigPyObject *sobj = (SwigPyObject *)v; + PyObject *obj = PyBool_FromLong(sobj->own); + if (val) { + if (PyObject_IsTrue(val)) { + SWIG_Py_DECREF(SwigPyObject_acquire(v,args)); + } else { + SWIG_Py_DECREF(SwigPyObject_disown(v,args)); + } + } + return obj; + } +} + +static PyMethodDef +swigobject_methods[] = { + {"disown", SwigPyObject_disown, METH_NOARGS, "releases ownership of the pointer"}, + {"acquire", SwigPyObject_acquire, METH_NOARGS, "acquires ownership of the pointer"}, + {"own", SwigPyObject_own, METH_VARARGS, "returns/sets ownership of the pointer"}, + {"append", SwigPyObject_append, METH_O, "appends another 'this' object"}, + {"next", SwigPyObject_next, METH_NOARGS, "returns the next 'this' object"}, + {"__repr__",SwigPyObject_repr2, METH_NOARGS, "returns object representation"}, + {0, 0, 0, 0} +}; + +SWIGRUNTIME PyTypeObject* +SwigPyObject_TypeOnce(void) { + static char swigobject_doc[] = "Swig object carries a C/C++ instance pointer"; +#ifndef SWIG_HEAPTYPES + static PyNumberMethods SwigPyObject_as_number = { + (binaryfunc)0, /*nb_add*/ + (binaryfunc)0, /*nb_subtract*/ + (binaryfunc)0, /*nb_multiply*/ + /* nb_divide removed in Python 3 */ +#if PY_VERSION_HEX < 0x03000000 + (binaryfunc)0, /*nb_divide*/ +#endif + (binaryfunc)0, /*nb_remainder*/ + (binaryfunc)0, /*nb_divmod*/ + (ternaryfunc)0,/*nb_power*/ + (unaryfunc)0, /*nb_negative*/ + (unaryfunc)0, /*nb_positive*/ + (unaryfunc)0, /*nb_absolute*/ + (inquiry)0, /*nb_nonzero*/ + 0, /*nb_invert*/ + 0, /*nb_lshift*/ + 0, /*nb_rshift*/ + 0, /*nb_and*/ + 0, /*nb_xor*/ + 0, /*nb_or*/ +#if PY_VERSION_HEX < 0x03000000 + 0, /*nb_coerce*/ +#endif + (unaryfunc)SwigPyObject_long, /*nb_int*/ +#if PY_VERSION_HEX < 0x03000000 + (unaryfunc)SwigPyObject_long, /*nb_long*/ +#else + 0, /*nb_reserved*/ +#endif + (unaryfunc)0, /*nb_float*/ +#if PY_VERSION_HEX < 0x03000000 + (unaryfunc)SwigPyObject_oct, /*nb_oct*/ + (unaryfunc)SwigPyObject_hex, /*nb_hex*/ +#endif +#if PY_VERSION_HEX >= 0x03050000 /* 3.5 */ + 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 /* nb_inplace_add -> nb_inplace_matrix_multiply */ +#elif PY_VERSION_HEX >= 0x03000000 /* 3.0 */ + 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 /* nb_inplace_add -> nb_index, nb_inplace_divide removed */ +#else + 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 /* nb_inplace_add -> nb_index */ +#endif + }; + + static PyTypeObject swigpyobject_type; + static int type_init = 0; + if (!type_init) { + const PyTypeObject tmp = { +#if PY_VERSION_HEX >= 0x03000000 + PyVarObject_HEAD_INIT(NULL, 0) +#else + PyObject_HEAD_INIT(NULL) + 0, /* ob_size */ +#endif + "SwigPyObject", /* tp_name */ + sizeof(SwigPyObject), /* tp_basicsize */ + 0, /* tp_itemsize */ + (destructor)SwigPyObject_dealloc, /* tp_dealloc */ +#if PY_VERSION_HEX < 0x030800b4 + (printfunc)0, /* tp_print */ +#else + (Py_ssize_t)0, /* tp_vectorcall_offset */ +#endif + (getattrfunc)0, /* tp_getattr */ + (setattrfunc)0, /* tp_setattr */ +#if PY_VERSION_HEX >= 0x03000000 + 0, /* tp_reserved in 3.0.1, tp_compare in 3.0.0 but not used */ +#else + (cmpfunc)SwigPyObject_compare, /* tp_compare */ +#endif + (reprfunc)SwigPyObject_repr, /* tp_repr */ + &SwigPyObject_as_number, /* tp_as_number */ + 0, /* tp_as_sequence */ + 0, /* tp_as_mapping */ + (hashfunc)0, /* tp_hash */ + (ternaryfunc)0, /* tp_call */ + 0, /* tp_str */ + PyObject_GenericGetAttr, /* tp_getattro */ + 0, /* tp_setattro */ + 0, /* tp_as_buffer */ + Py_TPFLAGS_DEFAULT, /* tp_flags */ + swigobject_doc, /* tp_doc */ + 0, /* tp_traverse */ + 0, /* tp_clear */ + (richcmpfunc)SwigPyObject_richcompare,/* tp_richcompare */ + 0, /* tp_weaklistoffset */ + 0, /* tp_iter */ + 0, /* tp_iternext */ + swigobject_methods, /* tp_methods */ + 0, /* tp_members */ + 0, /* tp_getset */ + 0, /* tp_base */ + 0, /* tp_dict */ + 0, /* tp_descr_get */ + 0, /* tp_descr_set */ + 0, /* tp_dictoffset */ + 0, /* tp_init */ + 0, /* tp_alloc */ + 0, /* tp_new */ + 0, /* tp_free */ + 0, /* tp_is_gc */ + 0, /* tp_bases */ + 0, /* tp_mro */ + 0, /* tp_cache */ + 0, /* tp_subclasses */ + 0, /* tp_weaklist */ + 0, /* tp_del */ + 0, /* tp_version_tag */ +#if PY_VERSION_HEX >= 0x03040000 + 0, /* tp_finalize */ +#endif +#if PY_VERSION_HEX >= 0x03080000 + 0, /* tp_vectorcall */ +#endif +#if (PY_VERSION_HEX >= 0x03080000) && (PY_VERSION_HEX < 0x03090000) + 0, /* tp_print */ +#endif +#if PY_VERSION_HEX >= 0x030c0000 + 0, /* tp_watched */ +#endif +#if PY_VERSION_HEX >= 0x030d00a4 + 0, /* tp_versions_used */ +#endif +#ifdef COUNT_ALLOCS + 0, /* tp_allocs */ + 0, /* tp_frees */ + 0, /* tp_maxalloc */ + 0, /* tp_prev */ + 0 /* tp_next */ +#endif + }; + swigpyobject_type = tmp; + type_init = 1; + if (PyType_Ready(&swigpyobject_type) != 0) + return NULL; + } + return &swigpyobject_type; +#else + PyType_Slot slots[] = { + { Py_tp_dealloc, (void *)SwigPyObject_dealloc }, + { Py_tp_repr, (void *)SwigPyObject_repr }, + { Py_tp_getattro, (void *)PyObject_GenericGetAttr }, + { Py_tp_doc, (void *)swigobject_doc }, + { Py_tp_richcompare, (void *)SwigPyObject_richcompare }, + { Py_tp_methods, (void *)swigobject_methods }, + { Py_nb_int, (void *)SwigPyObject_long }, + { 0, NULL } + }; + PyType_Spec spec = { + "SwigPyObject", + sizeof(SwigPyObject), + 0, + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_BASETYPE, + slots + }; + return (PyTypeObject *)PyType_FromSpec(&spec); +#endif +} + +SWIGRUNTIME PyObject * +SwigPyObject_New(void *ptr, swig_type_info *ty, int own) +{ + SwigPyObject *sobj = PyObject_New(SwigPyObject, SwigPyObject_type()); + if (sobj) { + sobj->ptr = ptr; + sobj->ty = ty; + sobj->own = own; + sobj->next = 0; +#ifdef SWIGPYTHON_BUILTIN + sobj->dict = 0; +#endif + if (own == SWIG_POINTER_OWN) { + /* Obtain a reference to the Python capsule wrapping the module information, so that the + * module information is correctly destroyed after all SWIG python objects have been freed + * by the GC (and corresponding destructors invoked) */ + SWIG_Py_XINCREF(Swig_Capsule_global); + } + } + return (PyObject *)sobj; +} + +/* ----------------------------------------------------------------------------- + * Implements a simple Swig Packed type, and use it instead of string + * ----------------------------------------------------------------------------- */ + +typedef struct { + PyObject_HEAD + void *pack; + swig_type_info *ty; + size_t size; +} SwigPyPacked; + +SWIGRUNTIME PyObject * +SwigPyPacked_repr(SwigPyPacked *v) +{ + char result[SWIG_BUFFER_SIZE]; + if (SWIG_PackDataName(result, v->pack, v->size, 0, sizeof(result))) { + return SWIG_Python_str_FromFormat("", result, v->ty->name); + } else { + return SWIG_Python_str_FromFormat("", v->ty->name); + } +} + +SWIGRUNTIME PyObject * +SwigPyPacked_str(SwigPyPacked *v) +{ + char result[SWIG_BUFFER_SIZE]; + if (SWIG_PackDataName(result, v->pack, v->size, 0, sizeof(result))){ + return SWIG_Python_str_FromFormat("%s%s", result, v->ty->name); + } else { + return SWIG_Python_str_FromChar(v->ty->name); + } +} + +SWIGRUNTIME int +SwigPyPacked_compare(SwigPyPacked *v, SwigPyPacked *w) +{ + size_t i = v->size; + size_t j = w->size; + int s = (i < j) ? -1 : ((i > j) ? 1 : 0); + return s ? s : strncmp((const char *)v->pack, (const char *)w->pack, 2*v->size); +} + +SWIGRUNTIME PyTypeObject* SwigPyPacked_TypeOnce(void); + +SWIGRUNTIME PyTypeObject* +SwigPyPacked_type(void) { + static PyTypeObject *SWIG_STATIC_POINTER(type) = SwigPyPacked_TypeOnce(); + return type; +} + +SWIGRUNTIMEINLINE int +SwigPyPacked_Check(PyObject *op) { +#ifdef Py_LIMITED_API + int cmp; + PyObject *tp_name; +#endif + PyTypeObject* op_type = Py_TYPE(op); + if (op_type == SwigPyPacked_TypeOnce()) + return 1; +#ifdef Py_LIMITED_API + tp_name = PyObject_GetAttrString((PyObject *)op_type, "__name__"); + if (!tp_name) + return 0; + cmp = PyUnicode_CompareWithASCIIString(tp_name, "SwigPyPacked"); + SWIG_Py_DECREF(tp_name); + return cmp == 0; +#else + return (strcmp(op_type->tp_name, "SwigPyPacked") == 0); +#endif +} + +SWIGRUNTIME void +SwigPyPacked_dealloc(PyObject *v) +{ + if (SwigPyPacked_Check(v)) { + SwigPyPacked *sobj = (SwigPyPacked *) v; + free(sobj->pack); + } + PyObject_Free(v); +} + +SWIGRUNTIME PyTypeObject* +SwigPyPacked_TypeOnce(void) { + static char swigpacked_doc[] = "Swig object carries a C/C++ instance pointer"; +#ifndef SWIG_HEAPTYPES + static PyTypeObject swigpypacked_type; + static int type_init = 0; + if (!type_init) { + const PyTypeObject tmp = { +#if PY_VERSION_HEX>=0x03000000 + PyVarObject_HEAD_INIT(NULL, 0) +#else + PyObject_HEAD_INIT(NULL) + 0, /* ob_size */ +#endif + "SwigPyPacked", /* tp_name */ + sizeof(SwigPyPacked), /* tp_basicsize */ + 0, /* tp_itemsize */ + (destructor)SwigPyPacked_dealloc, /* tp_dealloc */ +#if PY_VERSION_HEX < 0x030800b4 + (printfunc)0, /* tp_print */ +#else + (Py_ssize_t)0, /* tp_vectorcall_offset */ +#endif + (getattrfunc)0, /* tp_getattr */ + (setattrfunc)0, /* tp_setattr */ +#if PY_VERSION_HEX>=0x03000000 + 0, /* tp_reserved in 3.0.1 */ +#else + (cmpfunc)SwigPyPacked_compare, /* tp_compare */ +#endif + (reprfunc)SwigPyPacked_repr, /* tp_repr */ + 0, /* tp_as_number */ + 0, /* tp_as_sequence */ + 0, /* tp_as_mapping */ + (hashfunc)0, /* tp_hash */ + (ternaryfunc)0, /* tp_call */ + (reprfunc)SwigPyPacked_str, /* tp_str */ + PyObject_GenericGetAttr, /* tp_getattro */ + 0, /* tp_setattro */ + 0, /* tp_as_buffer */ + Py_TPFLAGS_DEFAULT, /* tp_flags */ + swigpacked_doc, /* tp_doc */ + 0, /* tp_traverse */ + 0, /* tp_clear */ + 0, /* tp_richcompare */ + 0, /* tp_weaklistoffset */ + 0, /* tp_iter */ + 0, /* tp_iternext */ + 0, /* tp_methods */ + 0, /* tp_members */ + 0, /* tp_getset */ + 0, /* tp_base */ + 0, /* tp_dict */ + 0, /* tp_descr_get */ + 0, /* tp_descr_set */ + 0, /* tp_dictoffset */ + 0, /* tp_init */ + 0, /* tp_alloc */ + 0, /* tp_new */ + 0, /* tp_free */ + 0, /* tp_is_gc */ + 0, /* tp_bases */ + 0, /* tp_mro */ + 0, /* tp_cache */ + 0, /* tp_subclasses */ + 0, /* tp_weaklist */ + 0, /* tp_del */ + 0, /* tp_version_tag */ +#if PY_VERSION_HEX >= 0x03040000 + 0, /* tp_finalize */ +#endif +#if PY_VERSION_HEX >= 0x03080000 + 0, /* tp_vectorcall */ +#endif +#if (PY_VERSION_HEX >= 0x03080000) && (PY_VERSION_HEX < 0x03090000) + 0, /* tp_print */ +#endif +#if PY_VERSION_HEX >= 0x030c0000 + 0, /* tp_watched */ +#endif +#if PY_VERSION_HEX >= 0x030d00a4 + 0, /* tp_versions_used */ +#endif +#ifdef COUNT_ALLOCS + 0, /* tp_allocs */ + 0, /* tp_frees */ + 0, /* tp_maxalloc */ + 0, /* tp_prev */ + 0 /* tp_next */ +#endif + }; + swigpypacked_type = tmp; + type_init = 1; + if (PyType_Ready(&swigpypacked_type) != 0) + return NULL; + } + return &swigpypacked_type; +#else + PyType_Slot slots[] = { + { Py_tp_dealloc, (void *)SwigPyPacked_dealloc }, + { Py_tp_repr, (void *)SwigPyPacked_repr }, + { Py_tp_str, (void *)SwigPyPacked_str }, + { Py_tp_getattro, (void *)PyObject_GenericGetAttr }, + { Py_tp_doc, (void *)swigpacked_doc }, + { 0, NULL } + }; + PyType_Spec spec = { + "SwigPyPacked", + sizeof(SwigPyPacked), + 0, + Py_TPFLAGS_DEFAULT, + slots + }; + return (PyTypeObject *)PyType_FromSpec(&spec); +#endif +} + +SWIGRUNTIME PyObject * +SwigPyPacked_New(void *ptr, size_t size, swig_type_info *ty) +{ + SwigPyPacked *sobj = PyObject_New(SwigPyPacked, SwigPyPacked_type()); + if (sobj) { + void *pack = malloc(size); + if (pack) { + memcpy(pack, ptr, size); + sobj->pack = pack; + sobj->ty = ty; + sobj->size = size; + } else { + PyObject_Free((PyObject *)sobj); + sobj = 0; + } + } + return (PyObject *) sobj; +} + +SWIGRUNTIME swig_type_info * +SwigPyPacked_UnpackData(PyObject *obj, void *ptr, size_t size) +{ + if (SwigPyPacked_Check(obj)) { + SwigPyPacked *sobj = (SwigPyPacked *)obj; + if (sobj->size != size) return 0; + memcpy(ptr, sobj->pack, size); + return sobj->ty; + } else { + return 0; + } +} + +/* ----------------------------------------------------------------------------- + * pointers/data manipulation + * ----------------------------------------------------------------------------- */ + +static PyObject *Swig_This_global = NULL; + +SWIGRUNTIME PyObject * +SWIG_This(void) +{ + if (Swig_This_global == NULL) + Swig_This_global = SWIG_Python_str_FromChar("this"); + return Swig_This_global; +} + +/* #define SWIG_PYTHON_SLOW_GETSET_THIS */ + +/* TODO: I don't know how to implement the fast getset in Python 3 right now */ +#if PY_VERSION_HEX>=0x03000000 +#define SWIG_PYTHON_SLOW_GETSET_THIS +#endif + +SWIGRUNTIME SwigPyObject * +SWIG_Python_GetSwigThis(PyObject *pyobj) +{ + PyObject *obj; + + if (SwigPyObject_Check(pyobj)) + return (SwigPyObject *) pyobj; + +#ifdef SWIGPYTHON_BUILTIN + (void)obj; +# ifdef PyWeakref_CheckProxy + if (PyWeakref_CheckProxy(pyobj)) { +#if PY_VERSION_HEX >= 0x030d0000 + PyWeakref_GetRef(pyobj, &pyobj); + Py_DECREF(pyobj); +#else + pyobj = PyWeakref_GET_OBJECT(pyobj); +#endif + if (pyobj && SwigPyObject_Check(pyobj)) + return (SwigPyObject*) pyobj; + } +# endif + return NULL; +#else + + obj = 0; + +#if !defined(SWIG_PYTHON_SLOW_GETSET_THIS) + if (PyInstance_Check(pyobj)) { + obj = _PyInstance_Lookup(pyobj, SWIG_This()); + } else { + PyObject **dictptr = _PyObject_GetDictPtr(pyobj); + if (dictptr != NULL) { + PyObject *dict = *dictptr; + obj = dict ? PyDict_GetItem(dict, SWIG_This()) : 0; + } else { +#ifdef PyWeakref_CheckProxy + if (PyWeakref_CheckProxy(pyobj)) { + PyObject *wobj = PyWeakref_GET_OBJECT(pyobj); + return wobj ? SWIG_Python_GetSwigThis(wobj) : 0; + } +#endif + obj = PyObject_GetAttr(pyobj,SWIG_This()); + if (obj) { + SWIG_Py_DECREF(obj); + } else { + if (PyErr_Occurred()) PyErr_Clear(); + return 0; + } + } + } +#else + obj = PyObject_GetAttr(pyobj,SWIG_This()); + if (obj) { + SWIG_Py_DECREF(obj); + } else { + if (PyErr_Occurred()) PyErr_Clear(); + return 0; + } +#endif + if (obj && !SwigPyObject_Check(obj)) { + /* a PyObject is called 'this', try to get the 'real this' + SwigPyObject from it */ + return SWIG_Python_GetSwigThis(obj); + } + return (SwigPyObject *)obj; +#endif +} + +/* Acquire a pointer value */ + +SWIGRUNTIME int +SWIG_Python_AcquirePtr(PyObject *obj, int own) { + if (own == SWIG_POINTER_OWN) { + SwigPyObject *sobj = SWIG_Python_GetSwigThis(obj); + if (sobj) { + int oldown = sobj->own; + sobj->own = own; + return oldown; + } + } + return 0; +} + +/* Convert a pointer value */ + +SWIGRUNTIME int +SWIG_Python_ConvertPtrAndOwn(PyObject *obj, void **ptr, swig_type_info *ty, int flags, int *own) { + int res; + SwigPyObject *sobj; + int implicit_conv = (flags & SWIG_POINTER_IMPLICIT_CONV) != 0; + + if (!obj) + return SWIG_ERROR; + if (obj == Py_None && !implicit_conv) { + if (ptr) + *ptr = 0; + return (flags & SWIG_POINTER_NO_NULL) ? SWIG_NullReferenceError : SWIG_OK; + } + + res = SWIG_ERROR; + + sobj = SWIG_Python_GetSwigThis(obj); + if (own) + *own = 0; + while (sobj) { + void *vptr = sobj->ptr; + if (ty) { + swig_type_info *to = sobj->ty; + if (to == ty) { + /* no type cast needed */ + if (ptr) *ptr = vptr; + break; + } else { + swig_cast_info *tc = SWIG_TypeCheck(to->name,ty); + if (!tc) { + sobj = (SwigPyObject *)sobj->next; + } else { + if (ptr) { + int newmemory = 0; + *ptr = SWIG_TypeCast(tc,vptr,&newmemory); + if (newmemory == SWIG_CAST_NEW_MEMORY) { + assert(own); /* badly formed typemap which will lead to a memory leak - it must set and use own to delete *ptr */ + if (own) + *own = *own | SWIG_CAST_NEW_MEMORY; + } + } + break; + } + } + } else { + if (ptr) *ptr = vptr; + break; + } + } + if (sobj) { + if (((flags & SWIG_POINTER_RELEASE) == SWIG_POINTER_RELEASE) && !sobj->own) { + res = SWIG_ERROR_RELEASE_NOT_OWNED; + } else { + if (own) + *own = *own | sobj->own; + if (flags & SWIG_POINTER_DISOWN) { + sobj->own = 0; + } + if (flags & SWIG_POINTER_CLEAR) { + sobj->ptr = 0; + } + res = SWIG_OK; + } + } else { + if (implicit_conv) { + SwigPyClientData *data = ty ? (SwigPyClientData *) ty->clientdata : 0; + if (data && !data->implicitconv) { + PyObject *klass = data->klass; + if (klass) { + PyObject *impconv; + data->implicitconv = 1; /* avoid recursion and call 'explicit' constructors*/ + impconv = SWIG_Python_CallFunctor(klass, obj); + data->implicitconv = 0; + if (PyErr_Occurred()) { + PyErr_Clear(); + impconv = 0; + } + if (impconv) { + SwigPyObject *iobj = SWIG_Python_GetSwigThis(impconv); + if (iobj) { + void *vptr; + res = SWIG_Python_ConvertPtrAndOwn((PyObject*)iobj, &vptr, ty, 0, 0); + if (SWIG_IsOK(res)) { + if (ptr) { + *ptr = vptr; + /* transfer the ownership to 'ptr' */ + iobj->own = 0; + res = SWIG_AddCast(res); + res = SWIG_AddNewMask(res); + } else { + res = SWIG_AddCast(res); + } + } + } + SWIG_Py_DECREF(impconv); + } + } + } + if (!SWIG_IsOK(res) && obj == Py_None) { + if (ptr) + *ptr = 0; + if (PyErr_Occurred()) + PyErr_Clear(); + res = SWIG_OK; + } + } + } + return res; +} + +/* Convert a function ptr value */ + +SWIGRUNTIME int +SWIG_Python_ConvertFunctionPtr(PyObject *obj, void **ptr, swig_type_info *ty) { + if (!PyCFunction_Check(obj)) { + return SWIG_ConvertPtr(obj, ptr, ty, 0); + } else { + void *vptr = 0; + swig_cast_info *tc; + + /* here we get the method pointer for callbacks */ +#ifndef Py_LIMITED_API + const char *doc = (((PyCFunctionObject *)obj) -> m_ml -> ml_doc); +#else + PyObject* pystr_doc = PyObject_GetAttrString(obj, "__doc__"); + PyObject *bytes = NULL; + const char *doc = pystr_doc ? SWIG_PyUnicode_AsUTF8AndSize(pystr_doc, NULL, &bytes) : 0; +#endif + const char *desc = doc ? strstr(doc, "swig_ptr: ") : 0; + if (desc) + desc = ty ? SWIG_UnpackVoidPtr(desc + 10, &vptr, ty->name) : 0; +#ifdef Py_LIMITED_API + SWIG_Py_XDECREF(bytes); + SWIG_Py_XDECREF(pystr_doc); +#endif + if (!desc) + return SWIG_ERROR; + tc = SWIG_TypeCheck(desc,ty); + if (tc) { + int newmemory = 0; + *ptr = SWIG_TypeCast(tc,vptr,&newmemory); + assert(!newmemory); /* newmemory handling not yet implemented */ + } else { + return SWIG_ERROR; + } + return SWIG_OK; + } +} + +/* Convert a packed pointer value */ + +SWIGRUNTIME int +SWIG_Python_ConvertPacked(PyObject *obj, void *ptr, size_t sz, swig_type_info *ty) { + swig_type_info *to = SwigPyPacked_UnpackData(obj, ptr, sz); + if (!to) return SWIG_ERROR; + if (ty) { + if (to != ty) { + /* check type cast? */ + swig_cast_info *tc = SWIG_TypeCheck(to->name,ty); + if (!tc) return SWIG_ERROR; + } + } + return SWIG_OK; +} + +/* ----------------------------------------------------------------------------- + * Create a new pointer object + * ----------------------------------------------------------------------------- */ + +/* + Create a new instance object, without calling __init__, and set the + 'this' attribute. +*/ + +SWIGRUNTIME PyObject* +SWIG_Python_NewShadowInstance(SwigPyClientData *data, PyObject *swig_this) +{ + PyObject *inst = 0; + PyObject *newraw = data->newraw; + if (newraw) { + inst = PyObject_Call(newraw, data->newargs, NULL); + if (inst) { +#if !defined(SWIG_PYTHON_SLOW_GETSET_THIS) + PyObject **dictptr = _PyObject_GetDictPtr(inst); + if (dictptr != NULL) { + PyObject *dict = *dictptr; + if (dict == NULL) { + dict = PyDict_New(); + *dictptr = dict; + } + if (dict) { + PyDict_SetItem(dict, SWIG_This(), swig_this); + } else{ + SWIG_Py_DECREF(inst); + inst = 0; + } + } +#else + if (PyObject_SetAttr(inst, SWIG_This(), swig_this) == -1) { + SWIG_Py_DECREF(inst); + inst = 0; + } +#endif + } + } else { +#if PY_VERSION_HEX >= 0x03000000 + PyObject *empty_args = PyTuple_New(0); + if (empty_args) { + PyObject *empty_kwargs = PyDict_New(); + if (empty_kwargs) { +#ifndef Py_LIMITED_API + newfunc newfn = ((PyTypeObject *)data->newargs)->tp_new; +#else + newfunc newfn = (newfunc)PyType_GetSlot((PyTypeObject *)data->newargs, Py_tp_new); +#endif + inst = newfn((PyTypeObject *)data->newargs, empty_args, empty_kwargs); + SWIG_Py_DECREF(empty_kwargs); + if (inst) { + if (PyObject_SetAttr(inst, SWIG_This(), swig_this) == -1) { + SWIG_Py_DECREF(inst); + inst = 0; + } else { + PyType_Modified(Py_TYPE(inst)); + } + } + } + SWIG_Py_DECREF(empty_args); + } +#else + PyObject *dict = PyDict_New(); + if (dict) { + PyDict_SetItem(dict, SWIG_This(), swig_this); + inst = PyInstance_NewRaw(data->newargs, dict); + SWIG_Py_DECREF(dict); + } +#endif + } + return inst; +} + +SWIGRUNTIME int +SWIG_Python_SetSwigThis(PyObject *inst, PyObject *swig_this) +{ +#if !defined(SWIG_PYTHON_SLOW_GETSET_THIS) + PyObject **dictptr = _PyObject_GetDictPtr(inst); + if (dictptr != NULL) { + PyObject *dict = *dictptr; + if (dict == NULL) { + dict = PyDict_New(); + *dictptr = dict; + } + if (dict) { + return PyDict_SetItem(dict, SWIG_This(), swig_this); + } else{ + return -1; + } + } +#endif + return PyObject_SetAttr(inst, SWIG_This(), swig_this); +} + + +SWIGINTERN PyObject * +SWIG_Python_InitShadowInstance(PyObject *args) { + PyObject *obj[2]; + if (!SWIG_Python_UnpackTuple(args, "swiginit", 2, 2, obj)) { + return NULL; + } else { + SwigPyObject *sthis = SWIG_Python_GetSwigThis(obj[0]); + if (sthis) { + SWIG_Py_DECREF(SwigPyObject_append((PyObject*) sthis, obj[1])); + } else { + if (SWIG_Python_SetSwigThis(obj[0], obj[1]) != 0) + return NULL; + } + return SWIG_Py_Void(); + } +} + +/* Create a new pointer object */ + +SWIGRUNTIME PyObject * +SWIG_Python_NewPointerObj(PyObject *self, void *ptr, swig_type_info *type, int flags) { + SwigPyClientData *clientdata; + PyObject * robj; + int own; + + if (!ptr) + return SWIG_Py_Void(); + + clientdata = type ? (SwigPyClientData *)(type->clientdata) : 0; + own = (flags & SWIG_POINTER_OWN) ? SWIG_POINTER_OWN : 0; + if (clientdata && clientdata->pytype) { + SwigPyObject *newobj; + if (flags & SWIG_BUILTIN_TP_INIT) { + newobj = (SwigPyObject*) self; + if (newobj->ptr) { +#ifndef Py_LIMITED_API + allocfunc alloc = clientdata->pytype->tp_alloc; +#else + allocfunc alloc = (allocfunc)PyType_GetSlot(clientdata->pytype, Py_tp_alloc); +#endif + PyObject *next_self = alloc(clientdata->pytype, 0); + while (newobj->next) + newobj = (SwigPyObject *) newobj->next; + newobj->next = next_self; + newobj = (SwigPyObject *)next_self; +#ifdef SWIGPYTHON_BUILTIN + newobj->dict = 0; +#endif + } + } else { + newobj = PyObject_New(SwigPyObject, clientdata->pytype); +#ifdef SWIGPYTHON_BUILTIN + if (newobj) { + newobj->dict = 0; + } +#endif + } + if (newobj) { + newobj->ptr = ptr; + newobj->ty = type; + newobj->own = own; + newobj->next = 0; + return (PyObject*) newobj; + } + return SWIG_Py_Void(); + } + + assert(!(flags & SWIG_BUILTIN_TP_INIT)); + + robj = SwigPyObject_New(ptr, type, own); + if (robj && clientdata && !(flags & SWIG_POINTER_NOSHADOW)) { + PyObject *inst = SWIG_Python_NewShadowInstance(clientdata, robj); + SWIG_Py_DECREF(robj); + robj = inst; + } + return robj; +} + +/* Create a new packed object */ + +SWIGRUNTIMEINLINE PyObject * +SWIG_Python_NewPackedObj(void *ptr, size_t sz, swig_type_info *type) { + return ptr ? SwigPyPacked_New((void *) ptr, sz, type) : SWIG_Py_Void(); +} + +/* -----------------------------------------------------------------------------* + * Get type list + * -----------------------------------------------------------------------------*/ + +#ifdef SWIG_LINK_RUNTIME +void *SWIG_ReturnGlobalTypeList(void *); +#endif + +static PyObject *Swig_TypeCache_global = NULL; + +/* The python cached type query */ +SWIGRUNTIME PyObject * +SWIG_Python_TypeCache(void) { + if (Swig_TypeCache_global == NULL) { + Swig_TypeCache_global = PyDict_New(); + } + return Swig_TypeCache_global; +} + +SWIGRUNTIME swig_module_info * +SWIG_Python_GetModule(void *SWIGUNUSEDPARM(clientdata)) { +#ifdef SWIG_LINK_RUNTIME + static void *type_pointer = (void *)0; + /* first check if module already created */ + if (!type_pointer) { + type_pointer = SWIG_ReturnGlobalTypeList((void *)0); + } +#else + void *type_pointer = PyCapsule_Import(SWIGPY_CAPSULE_NAME, 0); + if (PyErr_Occurred()) { + PyErr_Clear(); + type_pointer = (void *)0; + } +#endif + return (swig_module_info *) type_pointer; +} + + +static int interpreter_counter = 0; /* how many (sub-)interpreters are using swig_module's types */ + +SWIGRUNTIME void +SWIG_Python_DestroyModule(PyObject *obj) +{ + swig_module_info *swig_module = (swig_module_info *) PyCapsule_GetPointer(obj, SWIGPY_CAPSULE_NAME); + swig_type_info **types = swig_module->types; + size_t i; + if (--interpreter_counter != 0) /* another sub-interpreter may still be using the swig_module's types */ + return; + for (i =0; i < swig_module->size; ++i) { + swig_type_info *ty = types[i]; + if (ty->owndata) { + SwigPyClientData *data = (SwigPyClientData *) ty->clientdata; + ty->clientdata = 0; + if (data) SwigPyClientData_Del(data); + } + } + SWIG_Py_DECREF(SWIG_This()); + Swig_This_global = NULL; + SWIG_Py_DECREF(SWIG_globals()); + Swig_Globals_global = NULL; + SWIG_Py_DECREF(SWIG_Python_TypeCache()); + Swig_TypeCache_global = NULL; + Swig_Capsule_global = NULL; +} + +SWIGRUNTIME void +SWIG_Python_SetModule(swig_module_info *swig_module) { +#if PY_VERSION_HEX >= 0x03000000 + /* Add a dummy module object into sys.modules */ + PyObject *module = PyImport_AddModule("swig_runtime_data" SWIG_RUNTIME_VERSION); +#else + static PyMethodDef swig_empty_runtime_method_table[] = { {NULL, NULL, 0, NULL} }; /* Sentinel */ + PyObject *module = Py_InitModule("swig_runtime_data" SWIG_RUNTIME_VERSION, swig_empty_runtime_method_table); +#endif + PyObject *pointer = PyCapsule_New((void *) swig_module, SWIGPY_CAPSULE_NAME, SWIG_Python_DestroyModule); + if (pointer && module) { + if (PyModule_AddObject(module, SWIGPY_CAPSULE_ATTR_NAME, pointer) == 0) { + ++interpreter_counter; + Swig_Capsule_global = pointer; + } else { + SWIG_Py_DECREF(pointer); + } + } else { + SWIG_Py_XDECREF(pointer); + } +} + +SWIGRUNTIME swig_type_info * +SWIG_Python_TypeQuery(const char *type) +{ + PyObject *cache = SWIG_Python_TypeCache(); + PyObject *key = SWIG_Python_str_FromChar(type); + PyObject *obj = PyDict_GetItem(cache, key); + swig_type_info *descriptor; + if (obj) { + descriptor = (swig_type_info *) PyCapsule_GetPointer(obj, NULL); + } else { + swig_module_info *swig_module = SWIG_GetModule(0); + descriptor = SWIG_TypeQueryModule(swig_module, swig_module, type); + if (descriptor) { + obj = PyCapsule_New((void*) descriptor, NULL, NULL); + if (obj) { + PyDict_SetItem(cache, key, obj); + SWIG_Py_DECREF(obj); + } + } + } + SWIG_Py_DECREF(key); + return descriptor; +} + +/* + For backward compatibility only +*/ +#define SWIG_POINTER_EXCEPTION 0 +#define SWIG_arg_fail(arg) SWIG_Python_ArgFail(arg) +#define SWIG_MustGetPtr(p, type, argnum, flags) SWIG_Python_MustGetPtr(p, type, argnum, flags) + +SWIGRUNTIME int +SWIG_Python_AddErrMesg(const char* mesg, int infront) +{ + if (PyErr_Occurred()) { + PyObject *type = 0; + PyObject *value = 0; + PyObject *traceback = 0; + PyErr_Fetch(&type, &value, &traceback); + if (value) { + PyObject *old_str = PyObject_Str(value); + PyObject *bytes = NULL; + const char *tmp = SWIG_PyUnicode_AsUTF8AndSize(old_str, NULL, &bytes); + const char *errmesg = tmp ? tmp : "Invalid error message"; + SWIG_Py_XINCREF(type); + PyErr_Clear(); + if (infront) { + PyErr_Format(type, "%s %s", mesg, errmesg); + } else { + PyErr_Format(type, "%s %s", errmesg, mesg); + } + SWIG_Py_XDECREF(bytes); + SWIG_Py_DECREF(old_str); + } + return 1; + } else { + return 0; + } +} + +SWIGRUNTIME int +SWIG_Python_ArgFail(int argnum) +{ + if (PyErr_Occurred()) { + /* add information about failing argument */ + char mesg[256]; + PyOS_snprintf(mesg, sizeof(mesg), "argument number %d:", argnum); + return SWIG_Python_AddErrMesg(mesg, 1); + } else { + return 0; + } +} + +SWIGRUNTIMEINLINE const char * +SwigPyObject_GetDesc(PyObject *self) +{ + SwigPyObject *v = (SwigPyObject *)self; + swig_type_info *ty = v ? v->ty : 0; + return ty ? ty->str : ""; +} + +SWIGRUNTIME void +SWIG_Python_TypeError(const char *type, PyObject *obj) +{ + (void) obj; + if (type) { +#if defined(SWIG_COBJECT_TYPES) + if (obj && SwigPyObject_Check(obj)) { + const char *otype = (const char *) SwigPyObject_GetDesc(obj); + if (otype) { + PyErr_Format(PyExc_TypeError, "a '%s' is expected, 'SwigPyObject(%s)' is received", + type, otype); + return; + } + } else +#endif + { +#ifndef Py_LIMITED_API + /* tp_name is not accessible */ + const char *otype = (obj ? obj->ob_type->tp_name : 0); + if (otype) { + PyObject *str = PyObject_Str(obj); + PyObject *bytes = NULL; + const char *cstr = str ? SWIG_PyUnicode_AsUTF8AndSize(str, NULL, &bytes) : 0; + if (cstr) { + PyErr_Format(PyExc_TypeError, "a '%s' is expected, '%s(%s)' is received", + type, otype, cstr); + } else { + PyErr_Format(PyExc_TypeError, "a '%s' is expected, '%s' is received", + type, otype); + } + SWIG_Py_XDECREF(bytes); + SWIG_Py_XDECREF(str); + return; + } +#endif + } + PyErr_Format(PyExc_TypeError, "a '%s' is expected", type); + } else { + PyErr_Format(PyExc_TypeError, "unexpected type is received"); + } +} + + +/* Convert a pointer value, signal an exception on a type mismatch */ +SWIGRUNTIME void * +SWIG_Python_MustGetPtr(PyObject *obj, swig_type_info *ty, int SWIGUNUSEDPARM(argnum), int flags) { + void *result; + if (SWIG_Python_ConvertPtr(obj, &result, ty, flags) == -1) { + PyErr_Clear(); + } + return result; +} + +#ifdef SWIGPYTHON_BUILTIN +SWIGRUNTIME int +SWIG_Python_NonDynamicSetAttr(PyObject *obj, PyObject *name, PyObject *value) { + PyTypeObject *tp = obj->ob_type; + PyObject *descr; + PyObject *encoded_name; + descrsetfunc f; + int res = -1; + +# ifdef Py_USING_UNICODE + if (PyString_Check(name)) { + name = PyUnicode_Decode(PyString_AsString(name), PyString_Size(name), NULL, NULL); + if (!name) + return -1; + } else if (!PyUnicode_Check(name)) +# else + if (!PyString_Check(name)) +# endif + { + PyErr_Format(PyExc_TypeError, "attribute name must be string, not '%.200s'", name->ob_type->tp_name); + return -1; + } else { + SWIG_Py_INCREF(name); + } + + if (!tp->tp_dict) { + if (PyType_Ready(tp) != 0) + goto done; + } + + descr = _PyType_Lookup(tp, name); + f = NULL; + if (descr != NULL) + f = descr->ob_type->tp_descr_set; + if (!f) { + if (PyString_Check(name)) { + encoded_name = name; + SWIG_Py_INCREF(name); + } else { + encoded_name = PyUnicode_AsUTF8String(name); + if (!encoded_name) + goto done; + } + PyErr_Format(PyExc_AttributeError, "'%.100s' object has no attribute '%.200s'", tp->tp_name, PyString_AsString(encoded_name)); + SWIG_Py_DECREF(encoded_name); + } else { + res = f(descr, obj, value); + } + + done: + SWIG_Py_DECREF(name); + return res; +} +#endif + + +#ifdef __cplusplus +} +#endif + + + +#define SWIG_exception_fail(code, msg) do { SWIG_Error(code, msg); SWIG_fail; } while(0) + +#define SWIG_contract_assert(expr, msg) do { if (!(expr)) { SWIG_Error(SWIG_RuntimeError, msg); SWIG_fail; } } while (0) + + + +/* -------- TYPES TABLE (BEGIN) -------- */ + +#define SWIGTYPE_p_char swig_types[0] +#define SWIGTYPE_p_p_char swig_types[1] +static swig_type_info *swig_types[3]; +static swig_module_info swig_module = {swig_types, 2, 0, 0, 0, 0}; +#define SWIG_TypeQuery(name) SWIG_TypeQueryModule(&swig_module, &swig_module, name) +#define SWIG_MangledTypeQuery(name) SWIG_MangledTypeQueryModule(&swig_module, &swig_module, name) + +/* -------- TYPES TABLE (END) -------- */ + +#ifdef SWIG_TypeQuery +# undef SWIG_TypeQuery +#endif +#define SWIG_TypeQuery SWIG_Python_TypeQuery + +/*----------------------------------------------- + @(target):= _binding.so + ------------------------------------------------*/ +#if PY_VERSION_HEX >= 0x03000000 +# define SWIG_init PyInit__binding + +#else +# define SWIG_init init_binding + +#endif + +#define SWIG_as_voidptr(a) (void *)((const void *)(a)) +#define SWIG_as_voidptrptr(a) ((void)SWIG_as_voidptr(*a),(void**)(a)) + + +#include "ExampleHaskellWheelBinding_stub.h" + +char * unsafe_hs_pyeggp_version() { + return hs_pyeggp_version(); +} + +int unsafe_hs_pyeggp_main() { + return hs_pyeggp_main(); +} + +char * unsafe_hs_pyeggp(char **n) { + return hs_pyeggp(&n); +} + +void unsafe_hs_pyeggp_init(int argc, char **argv) { + hs_init(&argc, &argv); +} + +void unsafe_hs_pyeggp_exit() { + hs_exit(); +} + +void unsafe_py_write_stdout(const char * str) { + PySys_FormatStdout("%s", str); +} + +void unsafe_py_write_stderr(const char * str) { + PySys_FormatStderr("%s", str); +} + + +SWIGINTERN swig_type_info* +SWIG_pchar_descriptor(void) +{ + static swig_type_info* info = 0; + if (!info) { + info = SWIG_TypeQuery("_p_char"); + } + return info; +} + + +SWIGINTERNINLINE PyObject * +SWIG_FromCharPtrAndSize(const char* carray, size_t size) +{ + if (carray) { + if (size > INT_MAX) { + swig_type_info* pchar_descriptor = SWIG_pchar_descriptor(); + return pchar_descriptor ? + SWIG_InternalNewPointerObj((char *)(carray), pchar_descriptor, 0) : SWIG_Py_Void(); + } else { +#if PY_VERSION_HEX >= 0x03000000 +#if defined(SWIG_PYTHON_STRICT_BYTE_CHAR) + return PyBytes_FromStringAndSize(carray, (Py_ssize_t)(size)); +#else + return PyUnicode_DecodeUTF8(carray, (Py_ssize_t)(size), "surrogateescape"); +#endif +#else + return PyString_FromStringAndSize(carray, (Py_ssize_t)(size)); +#endif + } + } else { + return SWIG_Py_Void(); + } +} + + +SWIGINTERNINLINE PyObject * +SWIG_FromCharPtr(const char *cptr) +{ + return SWIG_FromCharPtrAndSize(cptr, (cptr ? strlen(cptr) : 0)); +} + + +SWIGINTERNINLINE PyObject* + SWIG_From_int (int value) +{ + return PyInt_FromLong((long) value); +} + + +#include +#if !defined(SWIG_NO_LLONG_MAX) +# if !defined(LLONG_MAX) && defined(__GNUC__) && defined (__LONG_LONG_MAX__) +# define LLONG_MAX __LONG_LONG_MAX__ +# define LLONG_MIN (-LLONG_MAX - 1LL) +# define ULLONG_MAX (LLONG_MAX * 2ULL + 1ULL) +# endif +#endif + + +SWIGINTERN int +SWIG_AsVal_double (PyObject *obj, double *val) +{ + int res = SWIG_TypeError; + if (PyFloat_Check(obj)) { + if (val) *val = PyFloat_AsDouble(obj); + return SWIG_OK; +#if PY_VERSION_HEX < 0x03000000 + } else if (PyInt_Check(obj)) { + if (val) *val = (double) PyInt_AsLong(obj); + return SWIG_OK; +#endif + } else if (PyLong_Check(obj)) { + double v = PyLong_AsDouble(obj); + if (!PyErr_Occurred()) { + if (val) *val = v; + return SWIG_OK; + } else { + PyErr_Clear(); + } + } +#ifdef SWIG_PYTHON_CAST_MODE + { + int dispatch = 0; + double d = PyFloat_AsDouble(obj); + if (!PyErr_Occurred()) { + if (val) *val = d; + return SWIG_AddCast(SWIG_OK); + } else { + PyErr_Clear(); + } + if (!dispatch) { + long v = PyLong_AsLong(obj); + if (!PyErr_Occurred()) { + if (val) *val = v; + return SWIG_AddCast(SWIG_AddCast(SWIG_OK)); + } else { + PyErr_Clear(); + } + } + } +#endif + return res; +} + + +#include + + +#include + + +SWIGINTERNINLINE int +SWIG_CanCastAsInteger(double *d, double min, double max) { + double x = *d; + if ((min <= x && x <= max)) { + double fx, cx, rd; + errno = 0; + fx = floor(x); + cx = ceil(x); + rd = ((x - fx) < 0.5) ? fx : cx; /* simple rint */ + if ((errno == EDOM) || (errno == ERANGE)) { + errno = 0; + } else { + double summ, reps, diff; + if (rd < x) { + diff = x - rd; + } else if (rd > x) { + diff = rd - x; + } else { + return 1; + } + summ = rd + x; + reps = diff/summ; + if (reps < 8*DBL_EPSILON) { + *d = rd; + return 1; + } + } + } + return 0; +} + + +SWIGINTERN int +SWIG_AsVal_long (PyObject *obj, long* val) +{ +#if PY_VERSION_HEX < 0x03000000 + if (PyInt_Check(obj)) { + if (val) *val = PyInt_AsLong(obj); + return SWIG_OK; + } else +#endif + if (PyLong_Check(obj)) { + long v = PyLong_AsLong(obj); + if (!PyErr_Occurred()) { + if (val) *val = v; + return SWIG_OK; + } else { + PyErr_Clear(); + return SWIG_OverflowError; + } + } +#ifdef SWIG_PYTHON_CAST_MODE + { + int dispatch = 0; + long v = PyInt_AsLong(obj); + if (!PyErr_Occurred()) { + if (val) *val = v; + return SWIG_AddCast(SWIG_OK); + } else { + PyErr_Clear(); + } + if (!dispatch) { + double d; + int res = SWIG_AddCast(SWIG_AsVal_double (obj,&d)); + // Largest double not larger than LONG_MAX (not portably calculated easily) + // Note that double(LONG_MAX) is stored in a double rounded up by one (for 64-bit long) + // 0x7ffffffffffffc00LL == (int64_t)std::nextafter(double(__uint128_t(LONG_MAX)+1), double(0)) + const double long_max = sizeof(long) == 8 ? 0x7ffffffffffffc00LL : LONG_MAX; + // No equivalent needed for 64-bit double(LONG_MIN) is exactly LONG_MIN + if (SWIG_IsOK(res) && SWIG_CanCastAsInteger(&d, LONG_MIN, long_max)) { + if (val) *val = (long)(d); + return res; + } + } + } +#endif + return SWIG_TypeError; +} + + +SWIGINTERN int +SWIG_AsVal_int (PyObject * obj, int *val) +{ + long v; + int res = SWIG_AsVal_long (obj, &v); + if (SWIG_IsOK(res)) { + if ((v < INT_MIN || v > INT_MAX)) { + return SWIG_OverflowError; + } else { + if (val) *val = (int)(v); + } + } + return res; +} + +#ifdef __cplusplus +extern "C" { +#endif +SWIGINTERN PyObject *_wrap_unsafe_hs_pyeggp_version(PyObject *self, PyObject *args) { + PyObject *resultobj = 0; + char *result = 0 ; + + (void)self; + if (!SWIG_Python_UnpackTuple(args, "unsafe_hs_pyeggp_version", 0, 0, 0)) SWIG_fail; + result = (char *)unsafe_hs_pyeggp_version(); + resultobj = SWIG_FromCharPtr((const char *)result); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_unsafe_hs_pyeggp_main(PyObject *self, PyObject *args) { + PyObject *resultobj = 0; + int result; + + (void)self; + if (!SWIG_Python_UnpackTuple(args, "unsafe_hs_pyeggp_main", 0, 0, 0)) SWIG_fail; + result = (int)unsafe_hs_pyeggp_main(); + resultobj = SWIG_From_int((int)(result)); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_unsafe_hs_pyeggp(PyObject *self, PyObject *args) { + PyObject *resultobj = 0; + char **arg1 = (char **) 0 ; + void *argp1 = 0 ; + int res1 = 0 ; + PyObject *swig_obj[1] ; + char *result = 0 ; + + (void)self; + if (!args) SWIG_fail; + swig_obj[0] = args; + res1 = SWIG_ConvertPtr(swig_obj[0], &argp1,SWIGTYPE_p_p_char, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "unsafe_hs_pyeggp" "', argument " "1"" of type '" "char **""'"); + } + arg1 = (char **)(argp1); + result = (char *)unsafe_hs_pyeggp(arg1); + resultobj = SWIG_FromCharPtr((const char *)result); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_unsafe_hs_pyeggp_init(PyObject *self, PyObject *args) { + PyObject *resultobj = 0; + int arg1 ; + char **arg2 = (char **) 0 ; + PyObject *swig_obj[1] ; + + (void)self; + if (!args) SWIG_fail; + swig_obj[0] = args; + { + /* Check if is a list */ + if (PyList_Check(swig_obj[0])) { + int i; + arg1 = PyList_Size(swig_obj[0]); + arg2 = (char **) malloc((arg1+1)*sizeof(char *)); + for (i = 0; i < arg1; i++) { + PyObject *o = PyList_GetItem(swig_obj[0], i); + if (PyUnicode_Check(o)) { + arg2[i] = (char *) PyUnicode_AsUTF8AndSize(o, 0); + } else { + PyErr_SetString(PyExc_TypeError, "list must contain strings"); + SWIG_fail; + } + } + arg2[i] = 0; + } else { + PyErr_SetString(PyExc_TypeError, "not a list"); + SWIG_fail; + } + } + unsafe_hs_pyeggp_init(arg1,arg2); + resultobj = SWIG_Py_Void(); + { + free((char *) arg2); + } + return resultobj; +fail: + { + free((char *) arg2); + } + return NULL; +} + + +SWIGINTERN PyObject *_wrap_unsafe_hs_pyeggp_exit(PyObject *self, PyObject *args) { + PyObject *resultobj = 0; + + (void)self; + if (!SWIG_Python_UnpackTuple(args, "unsafe_hs_pyeggp_exit", 0, 0, 0)) SWIG_fail; + unsafe_hs_pyeggp_exit(); + resultobj = SWIG_Py_Void(); + return resultobj; +fail: + return NULL; +} + + +static PyMethodDef SwigMethods[] = { + { "unsafe_hs_pyeggp_version", _wrap_unsafe_hs_pyeggp_version, METH_NOARGS, NULL}, + { "unsafe_hs_pyeggp_main", _wrap_unsafe_hs_pyeggp_main, METH_NOARGS, NULL}, + { "unsafe_hs_pyeggp", _wrap_unsafe_hs_pyeggp, METH_O, NULL}, + { "unsafe_hs_pyeggp_init", _wrap_unsafe_hs_pyeggp_init, METH_O, NULL}, + { "unsafe_hs_pyeggp_exit", _wrap_unsafe_hs_pyeggp_exit, METH_NOARGS, NULL}, + { NULL, NULL, 0, NULL } +}; + + +/* -------- TYPE CONVERSION AND EQUIVALENCE RULES (BEGIN) -------- */ + +static swig_type_info _swigt__p_char = {"_p_char", "char *", 0, 0, (void*)0, 0}; +static swig_type_info _swigt__p_p_char = {"_p_p_char", "char **", 0, 0, (void*)0, 0}; + +static swig_type_info *swig_type_initial[] = { + &_swigt__p_char, + &_swigt__p_p_char, +}; + +static swig_cast_info _swigc__p_char[] = { {&_swigt__p_char, 0, 0, 0},{0, 0, 0, 0}}; +static swig_cast_info _swigc__p_p_char[] = { {&_swigt__p_p_char, 0, 0, 0},{0, 0, 0, 0}}; + +static swig_cast_info *swig_cast_initial[] = { + _swigc__p_char, + _swigc__p_p_char, +}; + + +/* -------- TYPE CONVERSION AND EQUIVALENCE RULES (END) -------- */ + +static swig_const_info swig_const_table[] = { +{0, 0, 0, 0.0, 0, 0}}; + +#ifdef __cplusplus +} +#endif +/* ----------------------------------------------------------------------------- + * Type initialization: + * This problem is tough by the requirement that no dynamic + * memory is used. Also, since swig_type_info structures store pointers to + * swig_cast_info structures and swig_cast_info structures store pointers back + * to swig_type_info structures, we need some lookup code at initialization. + * The idea is that swig generates all the structures that are needed. + * The runtime then collects these partially filled structures. + * The SWIG_InitializeModule function takes these initial arrays out of + * swig_module, and does all the lookup, filling in the swig_module.types + * array with the correct data and linking the correct swig_cast_info + * structures together. + * + * The generated swig_type_info structures are assigned statically to an initial + * array. We just loop through that array, and handle each type individually. + * First we lookup if this type has been already loaded, and if so, use the + * loaded structure instead of the generated one. Then we have to fill in the + * cast linked list. The cast data is initially stored in something like a + * two-dimensional array. Each row corresponds to a type (there are the same + * number of rows as there are in the swig_type_initial array). Each entry in + * a column is one of the swig_cast_info structures for that type. + * The cast_initial array is actually an array of arrays, because each row has + * a variable number of columns. So to actually build the cast linked list, + * we find the array of casts associated with the type, and loop through it + * adding the casts to the list. The one last trick we need to do is making + * sure the type pointer in the swig_cast_info struct is correct. + * + * First off, we lookup the cast->type name to see if it is already loaded. + * There are three cases to handle: + * 1) If the cast->type has already been loaded AND the type we are adding + * casting info to has not been loaded (it is in this module), THEN we + * replace the cast->type pointer with the type pointer that has already + * been loaded. + * 2) If BOTH types (the one we are adding casting info to, and the + * cast->type) are loaded, THEN the cast info has already been loaded by + * the previous module so we just ignore it. + * 3) Finally, if cast->type has not already been loaded, then we add that + * swig_cast_info to the linked list (because the cast->type) pointer will + * be correct. + * ----------------------------------------------------------------------------- */ + +#ifdef __cplusplus +extern "C" { +#if 0 +} /* c-mode */ +#endif +#endif + +#if 0 +#define SWIGRUNTIME_DEBUG +#endif + +#ifndef SWIG_INIT_CLIENT_DATA_TYPE +#define SWIG_INIT_CLIENT_DATA_TYPE void * +#endif + +SWIGRUNTIME void +SWIG_InitializeModule(SWIG_INIT_CLIENT_DATA_TYPE clientdata) { + size_t i; + swig_module_info *module_head, *iter; + int init; + + /* check to see if the circular list has been setup, if not, set it up */ + if (swig_module.next==0) { + /* Initialize the swig_module */ + swig_module.type_initial = swig_type_initial; + swig_module.cast_initial = swig_cast_initial; + swig_module.next = &swig_module; + init = 1; + } else { + init = 0; + } + + /* Try and load any already created modules */ + module_head = SWIG_GetModule(clientdata); + if (!module_head) { + /* This is the first module loaded for this interpreter */ + /* so set the swig module into the interpreter */ + SWIG_SetModule(clientdata, &swig_module); + } else { + /* the interpreter has loaded a SWIG module, but has it loaded this one? */ + iter=module_head; + do { + if (iter==&swig_module) { + /* Our module is already in the list, so there's nothing more to do. */ + return; + } + iter=iter->next; + } while (iter!= module_head); + + /* otherwise we must add our module into the list */ + swig_module.next = module_head->next; + module_head->next = &swig_module; + } + + /* When multiple interpreters are used, a module could have already been initialized in + a different interpreter, but not yet have a pointer in this interpreter. + In this case, we do not want to continue adding types... everything should be + set up already */ + if (init == 0) return; + + /* Now work on filling in swig_module.types */ +#ifdef SWIGRUNTIME_DEBUG + printf("SWIG_InitializeModule: size %lu\n", (unsigned long)swig_module.size); +#endif + for (i = 0; i < swig_module.size; ++i) { + swig_type_info *type = 0; + swig_type_info *ret; + swig_cast_info *cast; + +#ifdef SWIGRUNTIME_DEBUG + printf("SWIG_InitializeModule: type %lu %s\n", (unsigned long)i, swig_module.type_initial[i]->name); +#endif + + /* if there is another module already loaded */ + if (swig_module.next != &swig_module) { + type = SWIG_MangledTypeQueryModule(swig_module.next, &swig_module, swig_module.type_initial[i]->name); + } + if (type) { + /* Overwrite clientdata field */ +#ifdef SWIGRUNTIME_DEBUG + printf("SWIG_InitializeModule: found type %s\n", type->name); +#endif + if (swig_module.type_initial[i]->clientdata) { + type->clientdata = swig_module.type_initial[i]->clientdata; +#ifdef SWIGRUNTIME_DEBUG + printf("SWIG_InitializeModule: found and overwrite type %s \n", type->name); +#endif + } + } else { + type = swig_module.type_initial[i]; + } + + /* Insert casting types */ + cast = swig_module.cast_initial[i]; + while (cast->type) { + /* Don't need to add information already in the list */ + ret = 0; +#ifdef SWIGRUNTIME_DEBUG + printf("SWIG_InitializeModule: look cast %s\n", cast->type->name); +#endif + if (swig_module.next != &swig_module) { + ret = SWIG_MangledTypeQueryModule(swig_module.next, &swig_module, cast->type->name); +#ifdef SWIGRUNTIME_DEBUG + if (ret) printf("SWIG_InitializeModule: found cast %s\n", ret->name); +#endif + } + if (ret) { + if (type == swig_module.type_initial[i]) { +#ifdef SWIGRUNTIME_DEBUG + printf("SWIG_InitializeModule: skip old type %s\n", ret->name); +#endif + cast->type = ret; + ret = 0; + } else { + /* Check for casting already in the list */ + swig_cast_info *ocast = SWIG_TypeCheck(ret->name, type); +#ifdef SWIGRUNTIME_DEBUG + if (ocast) printf("SWIG_InitializeModule: skip old cast %s\n", ret->name); +#endif + if (!ocast) ret = 0; + } + } + + if (!ret) { +#ifdef SWIGRUNTIME_DEBUG + printf("SWIG_InitializeModule: adding cast %s\n", cast->type->name); +#endif + if (type->cast) { + type->cast->prev = cast; + cast->next = type->cast; + } + type->cast = cast; + } + cast++; + } + /* Set entry in modules->types array equal to the type */ + swig_module.types[i] = type; + } + swig_module.types[i] = 0; + +#ifdef SWIGRUNTIME_DEBUG + printf("**** SWIG_InitializeModule: Cast List ******\n"); + for (i = 0; i < swig_module.size; ++i) { + int j = 0; + swig_cast_info *cast = swig_module.cast_initial[i]; + printf("SWIG_InitializeModule: type %lu %s\n", (unsigned long)i, swig_module.type_initial[i]->name); + while (cast->type) { + printf("SWIG_InitializeModule: cast type %s\n", cast->type->name); + cast++; + ++j; + } + printf("---- Total casts: %d\n",j); + } + printf("**** SWIG_InitializeModule: Cast List ******\n"); +#endif +} + +/* This function will propagate the clientdata field of type to +* any new swig_type_info structures that have been added into the list +* of equivalent types. It is like calling +* SWIG_TypeClientData(type, clientdata) a second time. +*/ +SWIGRUNTIME void +SWIG_PropagateClientData(void) { + size_t i; + swig_cast_info *equiv; + static int init_run = 0; + + if (init_run) return; + init_run = 1; + + for (i = 0; i < swig_module.size; i++) { + if (swig_module.types[i]->clientdata) { + equiv = swig_module.types[i]->cast; + while (equiv) { + if (!equiv->converter) { + if (equiv->type && !equiv->type->clientdata) + SWIG_TypeClientData(equiv->type, swig_module.types[i]->clientdata); + } + equiv = equiv->next; + } + } + } +} + +#ifdef __cplusplus +#if 0 +{ + /* c-mode */ +#endif +} +#endif + + + +#ifdef __cplusplus +extern "C" { +#endif + + /* ----------------------------------------------------------------------------- + * constants/methods manipulation + * ----------------------------------------------------------------------------- */ + + /* Install Constants */ + SWIGINTERN void + SWIG_Python_InstallConstants(PyObject *d, swig_const_info constants[]) { + PyObject *obj = 0; + size_t i; + for (i = 0; constants[i].type; ++i) { + switch(constants[i].type) { + case SWIG_PY_POINTER: + obj = SWIG_InternalNewPointerObj(constants[i].pvalue, *(constants[i]).ptype,0); + break; + case SWIG_PY_BINARY: + obj = SWIG_NewPackedObj(constants[i].pvalue, constants[i].lvalue, *(constants[i].ptype)); + break; + default: + obj = 0; + break; + } + if (obj) { + PyDict_SetItemString(d, constants[i].name, obj); + SWIG_Py_DECREF(obj); + } + } + } + + /* ----------------------------------------------------------------------------- + * Patch %callback methods' docstrings to hold the callback ptrs + * -----------------------------------------------------------------------------*/ + + SWIGINTERN void + SWIG_Python_FixMethods(PyMethodDef *methods, const swig_const_info *const_table, swig_type_info **types, swig_type_info **types_initial) { + size_t i; + for (i = 0; methods[i].ml_name; ++i) { + const char *c = methods[i].ml_doc; + if (!c) continue; + c = strstr(c, "swig_ptr: "); + if (c) { + int j; + const swig_const_info *ci = 0; + const char *name = c + 10; + for (j = 0; const_table[j].type; ++j) { + if (strncmp(const_table[j].name, name, + strlen(const_table[j].name)) == 0) { + ci = &(const_table[j]); + break; + } + } + if (ci) { + void *ptr = (ci->type == SWIG_PY_POINTER) ? ci->pvalue : 0; + if (ptr) { + size_t shift = (ci->ptype) - types; + swig_type_info *ty = types_initial[shift]; + size_t ldoc = (c - methods[i].ml_doc); + size_t lptr = strlen(ty->name)+2*sizeof(void*)+2; + char *ndoc = (char*)malloc(ldoc + lptr + 10); + if (ndoc) { + char *buff = ndoc; + memcpy(buff, methods[i].ml_doc, ldoc); + buff += ldoc; + memcpy(buff, "swig_ptr: ", 10); + buff += 10; + SWIG_PackVoidPtr(buff, ptr, ty->name, lptr); + methods[i].ml_doc = ndoc; + } + } + } + } + } + } + +#ifdef __cplusplus +} +#endif + + + + +/* -----------------------------------------------------------------------------* + * Partial Init method + * -----------------------------------------------------------------------------*/ + +#ifdef __cplusplus +extern "C" +#endif + +SWIGEXPORT +#if PY_VERSION_HEX >= 0x03000000 +PyObject* +#else +void +#endif +SWIG_init(void) { + PyObject *m, *d, *md, *globals; + +#if PY_VERSION_HEX >= 0x03000000 + static struct PyModuleDef SWIG_module = { + PyModuleDef_HEAD_INIT, + SWIG_name, + NULL, + -1, + SwigMethods, + NULL, + NULL, + NULL, + NULL + }; +#endif + +#if defined(SWIGPYTHON_BUILTIN) + static SwigPyClientData SwigPyObject_clientdata = { + 0, 0, 0, 0, 0, 0, 0 + }; + static PyGetSetDef this_getset_def = { + (char *)"this", &SwigPyBuiltin_ThisClosure, NULL, NULL, NULL + }; + static SwigPyGetSet thisown_getset_closure = { + SwigPyObject_own, + SwigPyObject_own + }; + static PyGetSetDef thisown_getset_def = { + (char *)"thisown", SwigPyBuiltin_GetterClosure, SwigPyBuiltin_SetterClosure, NULL, &thisown_getset_closure + }; + PyTypeObject *builtin_pytype; + int builtin_base_count; + swig_type_info *builtin_basetype; + PyObject *tuple; + PyGetSetDescrObject *static_getset; + PyTypeObject *metatype; + PyTypeObject *swigpyobject; + SwigPyClientData *cd; + PyObject *public_interface, *public_symbol; + PyObject *this_descr; + PyObject *thisown_descr; + PyObject *self = 0; + int i; + + (void)builtin_pytype; + (void)builtin_base_count; + (void)builtin_basetype; + (void)tuple; + (void)static_getset; + (void)self; + + /* Metaclass is used to implement static member variables */ + metatype = SwigPyObjectType(); + assert(metatype); +#endif + + (void)globals; + + /* Create singletons now to avoid potential deadlocks with multi-threaded usage after module initialization */ + SWIG_This(); + SWIG_Python_TypeCache(); + SwigPyPacked_type(); +#ifndef SWIGPYTHON_BUILTIN + SwigPyObject_type(); +#endif + + /* Fix SwigMethods to carry the callback ptrs when needed */ + SWIG_Python_FixMethods(SwigMethods, swig_const_table, swig_types, swig_type_initial); + +#if PY_VERSION_HEX >= 0x03000000 + m = PyModule_Create(&SWIG_module); +#else + m = Py_InitModule(SWIG_name, SwigMethods); +#endif + + md = d = PyModule_GetDict(m); + (void)md; + + SWIG_InitializeModule(0); + +#ifdef SWIGPYTHON_BUILTIN + swigpyobject = SwigPyObject_TypeOnce(); + + SwigPyObject_stype = SWIG_MangledTypeQuery("_p_SwigPyObject"); + assert(SwigPyObject_stype); + cd = (SwigPyClientData*) SwigPyObject_stype->clientdata; + if (!cd) { + SwigPyObject_stype->clientdata = &SwigPyObject_clientdata; + SwigPyObject_clientdata.pytype = swigpyobject; + } else if (swigpyobject->tp_basicsize != cd->pytype->tp_basicsize) { + PyErr_SetString(PyExc_RuntimeError, "Import error: attempted to load two incompatible swig-generated modules."); +# if PY_VERSION_HEX >= 0x03000000 + return NULL; +# else + return; +# endif + } + + /* All objects have a 'this' attribute */ + this_descr = PyDescr_NewGetSet(SwigPyObject_type(), &this_getset_def); + (void)this_descr; + + /* All objects have a 'thisown' attribute */ + thisown_descr = PyDescr_NewGetSet(SwigPyObject_type(), &thisown_getset_def); + (void)thisown_descr; + + public_interface = PyList_New(0); + public_symbol = 0; + (void)public_symbol; + + PyDict_SetItemString(md, "__all__", public_interface); + SWIG_Py_DECREF(public_interface); + for (i = 0; SwigMethods[i].ml_name != NULL; ++i) + SwigPyBuiltin_AddPublicSymbol(public_interface, SwigMethods[i].ml_name); + for (i = 0; swig_const_table[i].name != 0; ++i) + SwigPyBuiltin_AddPublicSymbol(public_interface, swig_const_table[i].name); +#endif + + SWIG_InstallConstants(d,swig_const_table); + +#if PY_VERSION_HEX >= 0x03000000 + return m; +#else + return; +#endif +} + diff --git a/src/pyeggp/typing.py b/src/pyeggp/typing.py new file mode 100644 index 0000000..88f7e4a --- /dev/null +++ b/src/pyeggp/typing.py @@ -0,0 +1,14 @@ +import sys + +from typing import List + +if sys.version_info < (3, 8): + from typing_extensions import Protocol +else: + from typing import Protocol + + +class Session(Protocol): + def version(self) -> str: ... + def main(self, args: List[str] = []) -> int: ... + def pyeggp(self, n: List[str]) -> str: ... diff --git a/src/srtree.egg-info/PKG-INFO b/src/srtree.egg-info/PKG-INFO new file mode 100644 index 0000000..aa1763a --- /dev/null +++ b/src/srtree.egg-info/PKG-INFO @@ -0,0 +1,699 @@ +Metadata-Version: 2.4 +Name: srtree +Version: 1.0.0 +Summary: Python Wheels for eggp algorithm. +Author-email: Fabricio Olivetti +License: GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for + software and other kinds of works. + + The licenses for most software and other practical works are designed + to take away your freedom to share and change the works. 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If not, see . + + Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short + notice like this when it starts in an interactive mode: + + Copyright (C) + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + + The hypothetical commands `show w' and `show c' should show the appropriate + parts of the General Public License. Of course, your program's commands + might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, + if any, to sign a "copyright disclaimer" for the program, if necessary. + For more information on this, and how to apply and follow the GNU GPL, see + . + + The GNU General Public License does not permit incorporating your program + into proprietary programs. If your program is a subroutine library, you + may consider it more useful to permit linking proprietary applications with + the library. If this is what you want to do, use the GNU Lesser General + Public License instead of this License. But first, please read + . + +Classifier: License :: OSI Approved :: MIT License +Classifier: Operating System :: MacOS +Classifier: Operating System :: Microsoft :: Windows +Classifier: Operating System :: POSIX :: Linux +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3.12 +Classifier: Programming Language :: Python :: Implementation :: CPython +Requires-Python: <3.13,>=3.9 +Description-Content-Type: text/markdown +License-File: LICENSE +Requires-Dist: typing_extensions<5,>=4.6; python_version < "3.8" +Provides-Extra: mypy +Requires-Dist: types_setuptools>=45; extra == "mypy" +Dynamic: license-file + +# pyeggp +Python wraper for eggp diff --git a/src/srtree.egg-info/SOURCES.txt b/src/srtree.egg-info/SOURCES.txt new file mode 100644 index 0000000..d25c21d --- /dev/null +++ b/src/srtree.egg-info/SOURCES.txt @@ -0,0 +1,15 @@ +LICENSE +README.md +pyproject.toml +setup.py +src/pyeggp/__init__.py +src/pyeggp/__main__.py +src/pyeggp/_binding.pyi +src/pyeggp/binding.i +src/pyeggp/binding.py +src/pyeggp/typing.py +src/srtree.egg-info/PKG-INFO +src/srtree.egg-info/SOURCES.txt +src/srtree.egg-info/dependency_links.txt +src/srtree.egg-info/requires.txt +src/srtree.egg-info/top_level.txt \ No newline at end of file diff --git a/src/srtree.egg-info/dependency_links.txt b/src/srtree.egg-info/dependency_links.txt new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/src/srtree.egg-info/dependency_links.txt @@ -0,0 +1 @@ + diff --git a/src/srtree.egg-info/requires.txt b/src/srtree.egg-info/requires.txt new file mode 100644 index 0000000..c976189 --- /dev/null +++ b/src/srtree.egg-info/requires.txt @@ -0,0 +1,6 @@ + +[:python_version < "3.8"] +typing_extensions<5,>=4.6 + +[mypy] +types_setuptools>=45 diff --git a/src/srtree.egg-info/top_level.txt b/src/srtree.egg-info/top_level.txt new file mode 100644 index 0000000..4790dfe --- /dev/null +++ b/src/srtree.egg-info/top_level.txt @@ -0,0 +1,2 @@ +eggp +pyeggp From 5ef8641e1e6eae77921fd647b03891cb056831f9 Mon Sep 17 00:00:00 2001 From: folivetti Date: Sat, 12 Apr 2025 14:37:57 -0300 Subject: [PATCH 003/166] testing wheel building machine --- .github/workflows/release.yml | 2 +- .../pyeggp/__init__.py | 51 ------------- .../pyeggp/__main__.py | 11 --- .../pyeggp/_binding.pyi | 7 -- .../pyeggp/binding.i | 64 ---------------- .../pyeggp/binding.py | 76 ------------------- .../pyeggp/typing.py | 14 ---- pyproject.toml | 14 ++-- 8 files changed, 10 insertions(+), 229 deletions(-) delete mode 100644 build/lib.linux-x86_64-cpython-310/pyeggp/__init__.py delete mode 100644 build/lib.linux-x86_64-cpython-310/pyeggp/__main__.py delete mode 100644 build/lib.linux-x86_64-cpython-310/pyeggp/_binding.pyi delete mode 100644 build/lib.linux-x86_64-cpython-310/pyeggp/binding.i delete mode 100644 build/lib.linux-x86_64-cpython-310/pyeggp/binding.py delete mode 100644 build/lib.linux-x86_64-cpython-310/pyeggp/typing.py diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 6931333..0f1398a 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -27,7 +27,7 @@ env: # See: https://github.com/wenkokke/manylinux_ghc DEFAULT_GHC_VERSION: "9.4.8" DEFAULT_CABAL_VERSION: "3.10.2.1" - DEFAULT_PYTHON_VERSION: "3.11" + DEFAULT_PYTHON_VERSION: "3.13.3" jobs: ################################################################################ diff --git a/build/lib.linux-x86_64-cpython-310/pyeggp/__init__.py b/build/lib.linux-x86_64-cpython-310/pyeggp/__init__.py deleted file mode 100644 index db02eed..0000000 --- a/build/lib.linux-x86_64-cpython-310/pyeggp/__init__.py +++ /dev/null @@ -1,51 +0,0 @@ -import atexit -from contextlib import contextmanager -from threading import Lock -from typing import Iterator, List - -from ._binding import ( - unsafe_hs_pyeggp_version, - unsafe_hs_pyeggp_main, - unsafe_hs_pyeggp_fib, - unsafe_hs_pyeggp_init, - unsafe_hs_pyeggp_exit, -) - -VERSION: str = "1.3.0" - - -_hs_rts_init: bool = False -_hs_rts_lock: Lock = Lock() - - -def hs_rts_exit() -> None: - global _hs_rts_lock - with _hs_rts_lock: - unsafe_hs_pyeggp_exit() - - -@contextmanager -def hs_rts_init(args: List[str] = []) -> Iterator[None]: - global _hs_rts_init - global _hs_rts_lock - with _hs_rts_lock: - if not _hs_rts_init: - _hs_rts_init = True - unsafe_hs_pyeggp_init(args) - atexit.register(hs_rts_exit) - yield None - - -def version() -> str: - with hs_rts_init(): - return unsafe_hs_pyeggp_version() - - -def main(args: List[str] = []) -> int: - with hs_rts_init(args): - return unsafe_hs_pyeggp_main() - - -def pyegpp(n: List[str]) -> str: - with hs_rts_init(): - return unsafe_hs_pyeggp(n) diff --git a/build/lib.linux-x86_64-cpython-310/pyeggp/__main__.py b/build/lib.linux-x86_64-cpython-310/pyeggp/__main__.py deleted file mode 100644 index 3fa9edf..0000000 --- a/build/lib.linux-x86_64-cpython-310/pyeggp/__main__.py +++ /dev/null @@ -1,11 +0,0 @@ -import sys -from typing import NoReturn -import pyeggp - - -def main() -> NoReturn: - sys.exit(pyeggp.main(sys.argv)) - - -if __name__ == "__main__": - main() diff --git a/build/lib.linux-x86_64-cpython-310/pyeggp/_binding.pyi b/build/lib.linux-x86_64-cpython-310/pyeggp/_binding.pyi deleted file mode 100644 index 22251f5..0000000 --- a/build/lib.linux-x86_64-cpython-310/pyeggp/_binding.pyi +++ /dev/null @@ -1,7 +0,0 @@ -from typing import List - -def unsafe_hs_pyeggp_version() -> str: ... -def unsafe_hs_pyeggp_main() -> int: ... -def unsafe_hs_pyeggp(n: List[str]) -> str: ... -def unsafe_hs_pyeggp_init(args: List[str]) -> None: ... -def unsafe_hs_pyeggp_exit() -> None: ... diff --git a/build/lib.linux-x86_64-cpython-310/pyeggp/binding.i b/build/lib.linux-x86_64-cpython-310/pyeggp/binding.i deleted file mode 100644 index 6155386..0000000 --- a/build/lib.linux-x86_64-cpython-310/pyeggp/binding.i +++ /dev/null @@ -1,64 +0,0 @@ -%module binding -%{ -#include "ExampleHaskellWheelBinding_stub.h" - -char * unsafe_hs_pyeggp_version() { - return hs_pyeggp_version(); -} - -int unsafe_hs_pyeggp_main() { - return hs_pyeggp_main(); -} - -char * unsafe_hs_pyeggp(char **n) { - return hs_pyeggp(&n); -} - -void unsafe_hs_pyeggp_init(int argc, char **argv) { - hs_init(&argc, &argv); -} - -void unsafe_hs_pyeggp_exit() { - hs_exit(); -} - -void unsafe_py_write_stdout(const char * str) { - PySys_FormatStdout("%s", str); -} - -void unsafe_py_write_stderr(const char * str) { - PySys_FormatStderr("%s", str); -} -%} - -%typemap(in) (int argc, char **argv) { - /* Check if is a list */ - if (PyList_Check($input)) { - int i; - $1 = PyList_Size($input); - $2 = (char **) malloc(($1+1)*sizeof(char *)); - for (i = 0; i < $1; i++) { - PyObject *o = PyList_GetItem($input, i); - if (PyUnicode_Check(o)) { - $2[i] = (char *) PyUnicode_AsUTF8AndSize(o, 0); - } else { - PyErr_SetString(PyExc_TypeError, "list must contain strings"); - SWIG_fail; - } - } - $2[i] = 0; - } else { - PyErr_SetString(PyExc_TypeError, "not a list"); - SWIG_fail; - } -} - -%typemap(freearg) (int argc, char **argv) { - free((char *) $2); -} - -char * unsafe_hs_pyeggp_version(); -int unsafe_hs_pyeggp_main(); -char * unsafe_hs_pyeggp(char **n); -void unsafe_hs_pyeggp_init(int argc, char **argv); -void unsafe_hs_pyeggp_exit(); diff --git a/build/lib.linux-x86_64-cpython-310/pyeggp/binding.py b/build/lib.linux-x86_64-cpython-310/pyeggp/binding.py deleted file mode 100644 index 4b2df33..0000000 --- a/build/lib.linux-x86_64-cpython-310/pyeggp/binding.py +++ /dev/null @@ -1,76 +0,0 @@ -# This file was automatically generated by SWIG (https://www.swig.org). -# Version 4.3.0 -# -# Do not make changes to this file unless you know what you are doing - modify -# the SWIG interface file instead. - -from sys import version_info as _swig_python_version_info -# Import the low-level C/C++ module -if __package__ or "." in __name__: - from . import _binding -else: - import _binding - -try: - import builtins as __builtin__ -except ImportError: - import __builtin__ - -def _swig_repr(self): - try: - strthis = "proxy of " + self.this.__repr__() - except __builtin__.Exception: - strthis = "" - return "<%s.%s; %s >" % (self.__class__.__module__, self.__class__.__name__, strthis,) - - -def _swig_setattr_nondynamic_instance_variable(set): - def set_instance_attr(self, name, value): - if name == "this": - set(self, name, value) - elif name == "thisown": - self.this.own(value) - elif hasattr(self, name) and isinstance(getattr(type(self), name), property): - set(self, name, value) - else: - raise AttributeError("You cannot add instance attributes to %s" % self) - return set_instance_attr - - -def _swig_setattr_nondynamic_class_variable(set): - def set_class_attr(cls, name, value): - if hasattr(cls, name) and not isinstance(getattr(cls, name), property): - set(cls, name, value) - else: - raise AttributeError("You cannot add class attributes to %s" % cls) - return set_class_attr - - -def _swig_add_metaclass(metaclass): - """Class decorator for adding a metaclass to a SWIG wrapped class - a slimmed down version of six.add_metaclass""" - def wrapper(cls): - return metaclass(cls.__name__, cls.__bases__, cls.__dict__.copy()) - return wrapper - - -class _SwigNonDynamicMeta(type): - """Meta class to enforce nondynamic attributes (no new attributes) for a class""" - __setattr__ = _swig_setattr_nondynamic_class_variable(type.__setattr__) - - - -def unsafe_hs_pyeggp_version(): - return _binding.unsafe_hs_pyeggp_version() - -def unsafe_hs_pyeggp_main(): - return _binding.unsafe_hs_pyeggp_main() - -def unsafe_hs_pyeggp(n): - return _binding.unsafe_hs_pyeggp(n) - -def unsafe_hs_pyeggp_init(argc): - return _binding.unsafe_hs_pyeggp_init(argc) - -def unsafe_hs_pyeggp_exit(): - return _binding.unsafe_hs_pyeggp_exit() - diff --git a/build/lib.linux-x86_64-cpython-310/pyeggp/typing.py b/build/lib.linux-x86_64-cpython-310/pyeggp/typing.py deleted file mode 100644 index 88f7e4a..0000000 --- a/build/lib.linux-x86_64-cpython-310/pyeggp/typing.py +++ /dev/null @@ -1,14 +0,0 @@ -import sys - -from typing import List - -if sys.version_info < (3, 8): - from typing_extensions import Protocol -else: - from typing import Protocol - - -class Session(Protocol): - def version(self) -> str: ... - def main(self, args: List[str] = []) -> int: ... - def pyeggp(self, n: List[str]) -> str: ... diff --git a/pyproject.toml b/pyproject.toml index 534ad82..c318ece 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -8,7 +8,7 @@ requires = [ build-backend = "setuptools.build_meta" [project] -name = "srtree" +name = "pyeggp" version = "1.0.0" authors = [{ name = "Fabricio Olivetti", email = "folivetti@users.noreply.github.com" }] description = "Python Wheels for eggp algorithm." @@ -16,7 +16,6 @@ readme = "README.md" license = { file = "LICENSE" } requires-python = ">=3.9,<3.13" classifiers = [ - "License :: OSI Approved :: MIT License", "Operating System :: MacOS", "Operating System :: Microsoft :: Windows", "Operating System :: POSIX :: Linux", @@ -31,8 +30,8 @@ dependencies = ["typing_extensions >=4.6,<5; python_version<'3.8'"] [project.optional-dependencies] mypy = ["types_setuptools >=45"] -#[project.scripts] -#"example-haskell-wheel" = "example_haskell_wheel.__main__:main" +[project.scripts] +"pyeggp" = "pyeggp.__main__:main" [tool.bumpver] current_version = "v1.0.0" @@ -50,7 +49,7 @@ push = true "src/example_haskell_wheel/__init__.py" = [ '^VERSION: str = "{pep440_version}"$', ] -"srtree.cabal" = ['^version: {pep440_version}$'] +"pyeggp.cabal" = ['^version: {pep440_version}$'] [tool.cibuildwheel] # 18-04-2023: @@ -77,6 +76,11 @@ skip = [ # PyPy builds do not work. "pp*", ] +manylinux-aarch64-image = "wenkokke/manylinux_2_28_ghc948_aarch64" +manylinux-i686-image = "wenkokke/manylinux2014_ghc948_i686" +manylinux-x86_64-image = "wenkokke/manylinux2014_ghc948_x86_64" +musllinux-x86_64-image = "wenkokke/musllinux_1_1_ghc948_x86_64" + [tool.mypy] mypy_path = "src" From c062965107ab46af3aeef8abc6cfb01b7119cd5c Mon Sep 17 00:00:00 2001 From: folivetti Date: Sat, 12 Apr 2025 14:41:51 -0300 Subject: [PATCH 004/166] testing wheel building machine --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index c318ece..96232e9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,7 +3,7 @@ requires = [ "find_libpython >=0.3,<0.5; sys_platform == 'win32'", "setuptools >=45", "swig >=4,<5", - "nlopt >= 2.9.1" + "nlopt >= 2.6.2" ] build-backend = "setuptools.build_meta" From 20665391a101bbcd7384e63d37aeeb935062c20f Mon Sep 17 00:00:00 2001 From: folivetti Date: Sat, 12 Apr 2025 14:47:37 -0300 Subject: [PATCH 005/166] testing wheel building machine --- .github/workflows/release.yml | 6 +++--- pyproject.toml | 8 ++++---- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 0f1398a..ea331ba 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -25,8 +25,8 @@ env: # you must update the containers in pyproject.toml under tool.cibuildwheel. # # See: https://github.com/wenkokke/manylinux_ghc - DEFAULT_GHC_VERSION: "9.4.8" - DEFAULT_CABAL_VERSION: "3.10.2.1" + DEFAULT_GHC_VERSION: "9.12.1" + DEFAULT_CABAL_VERSION: "3.14.1.1" DEFAULT_PYTHON_VERSION: "3.13.3" jobs: @@ -99,7 +99,7 @@ jobs: # Required to build the wheels on macOS and Windows, but not on Linux, # as Linux builds are run in containers, where GHC and Cabal are built # by the `./scripts/before-all.sh` scripts. - if: matrix.job.os.name == 'macOS' || matrix.job.os.name == 'Windows' + #if: matrix.job.os.name == 'macOS' || matrix.job.os.name == 'Windows' uses: haskell-actions/setup@v2 with: ghc-version: ${{ env.DEFAULT_GHC_VERSION }} diff --git a/pyproject.toml b/pyproject.toml index 96232e9..5e98e96 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -76,10 +76,10 @@ skip = [ # PyPy builds do not work. "pp*", ] -manylinux-aarch64-image = "wenkokke/manylinux_2_28_ghc948_aarch64" -manylinux-i686-image = "wenkokke/manylinux2014_ghc948_i686" -manylinux-x86_64-image = "wenkokke/manylinux2014_ghc948_x86_64" -musllinux-x86_64-image = "wenkokke/musllinux_1_1_ghc948_x86_64" +#manylinux-aarch64-image = "wenkokke/manylinux_2_28_ghc948_aarch64" +#manylinux-i686-image = "wenkokke/manylinux2014_ghc948_i686" +#manylinux-x86_64-image = "wenkokke/manylinux2014_ghc948_x86_64" +#musllinux-x86_64-image = "wenkokke/musllinux_1_1_ghc948_x86_64" [tool.mypy] From bd8283d948586758527426652f3ca0caeb01abaa Mon Sep 17 00:00:00 2001 From: folivetti Date: Sat, 12 Apr 2025 14:55:07 -0300 Subject: [PATCH 006/166] testing wheel building machine --- .github/workflows/release.yml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index ea331ba..2c6f5c1 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -25,8 +25,8 @@ env: # you must update the containers in pyproject.toml under tool.cibuildwheel. # # See: https://github.com/wenkokke/manylinux_ghc - DEFAULT_GHC_VERSION: "9.12.1" - DEFAULT_CABAL_VERSION: "3.14.1.1" + DEFAULT_GHC_VERSION: "9.12" + DEFAULT_CABAL_VERSION: "3.14" DEFAULT_PYTHON_VERSION: "3.13.3" jobs: @@ -107,7 +107,7 @@ jobs: - # 12-02-2024: # Print the GHC architecture. - if: matrix.job.os.name == 'macOS' || matrix.job.os.name == 'Windows' + #if: matrix.job.os.name == 'macOS' || matrix.job.os.name == 'Windows' run: echo "::debug::$(ghc --info)" - # 19-06-2023: From a6b95131c373ff885f3f50c54b1eda9026adbdb5 Mon Sep 17 00:00:00 2001 From: folivetti Date: Sat, 12 Apr 2025 15:17:59 -0300 Subject: [PATCH 007/166] testing wheel building machine --- .github/workflows/release.yml | 4 ++-- pyeggp.cabal | 2 +- pyproject.toml | 8 ++++---- 3 files changed, 7 insertions(+), 7 deletions(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 2c6f5c1..24d72cb 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -99,7 +99,7 @@ jobs: # Required to build the wheels on macOS and Windows, but not on Linux, # as Linux builds are run in containers, where GHC and Cabal are built # by the `./scripts/before-all.sh` scripts. - #if: matrix.job.os.name == 'macOS' || matrix.job.os.name == 'Windows' + if: matrix.job.os.name == 'macOS' || matrix.job.os.name == 'Windows' uses: haskell-actions/setup@v2 with: ghc-version: ${{ env.DEFAULT_GHC_VERSION }} @@ -107,7 +107,7 @@ jobs: - # 12-02-2024: # Print the GHC architecture. - #if: matrix.job.os.name == 'macOS' || matrix.job.os.name == 'Windows' + if: matrix.job.os.name == 'macOS' || matrix.job.os.name == 'Windows' run: echo "::debug::$(ghc --info)" - # 19-06-2023: diff --git a/pyeggp.cabal b/pyeggp.cabal index 69634db..c12e820 100755 --- a/pyeggp.cabal +++ b/pyeggp.cabal @@ -47,7 +47,7 @@ foreign-library _binding build-depends: attoparsec >=0.14.4 && <0.15 , attoparsec-expr >=0.1.1.2 && <0.2 - , base >=4.19 && <5 + , base >=4.18 && <5 , binary >=0.8.9.1 && <0.9 , bytestring >=0.11 && <0.13 , containers >=0.6.7 && <0.8 diff --git a/pyproject.toml b/pyproject.toml index 5e98e96..8d289b8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -76,10 +76,10 @@ skip = [ # PyPy builds do not work. "pp*", ] -#manylinux-aarch64-image = "wenkokke/manylinux_2_28_ghc948_aarch64" -#manylinux-i686-image = "wenkokke/manylinux2014_ghc948_i686" -#manylinux-x86_64-image = "wenkokke/manylinux2014_ghc948_x86_64" -#musllinux-x86_64-image = "wenkokke/musllinux_1_1_ghc948_x86_64" +manylinux-aarch64-image = "wenkokke/manylinux_2_28_ghc965_aarch64" +manylinux-i686-image = "wenkokke/manylinux2014_ghc965_i686" +manylinux-x86_64-image = "wenkokke/manylinux2014_ghc965_x86_64" +musllinux-x86_64-image = "wenkokke/musllinux_1_1_ghc965_x86_64" [tool.mypy] From 54dae341916c1b1353db9ca7868594d9dc57b1d7 Mon Sep 17 00:00:00 2001 From: folivetti Date: Sat, 12 Apr 2025 15:29:07 -0300 Subject: [PATCH 008/166] testing wheel building machine --- pyeggp.cabal | 2 +- pyproject.toml | 8 ++++---- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/pyeggp.cabal b/pyeggp.cabal index c12e820..f3c5e02 100755 --- a/pyeggp.cabal +++ b/pyeggp.cabal @@ -47,7 +47,7 @@ foreign-library _binding build-depends: attoparsec >=0.14.4 && <0.15 , attoparsec-expr >=0.1.1.2 && <0.2 - , base >=4.18 && <5 + , base >=4.17 && <5 , binary >=0.8.9.1 && <0.9 , bytestring >=0.11 && <0.13 , containers >=0.6.7 && <0.8 diff --git a/pyproject.toml b/pyproject.toml index 8d289b8..96232e9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -76,10 +76,10 @@ skip = [ # PyPy builds do not work. "pp*", ] -manylinux-aarch64-image = "wenkokke/manylinux_2_28_ghc965_aarch64" -manylinux-i686-image = "wenkokke/manylinux2014_ghc965_i686" -manylinux-x86_64-image = "wenkokke/manylinux2014_ghc965_x86_64" -musllinux-x86_64-image = "wenkokke/musllinux_1_1_ghc965_x86_64" +manylinux-aarch64-image = "wenkokke/manylinux_2_28_ghc948_aarch64" +manylinux-i686-image = "wenkokke/manylinux2014_ghc948_i686" +manylinux-x86_64-image = "wenkokke/manylinux2014_ghc948_x86_64" +musllinux-x86_64-image = "wenkokke/musllinux_1_1_ghc948_x86_64" [tool.mypy] From 371d97780a0cfe093d4eabb6d1151b62d7951cfb Mon Sep 17 00:00:00 2001 From: folivetti Date: Sat, 12 Apr 2025 15:38:17 -0300 Subject: [PATCH 009/166] testing wheel building machine --- pyeggp.cabal | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyeggp.cabal b/pyeggp.cabal index f3c5e02..cc10dd1 100755 --- a/pyeggp.cabal +++ b/pyeggp.cabal @@ -58,7 +58,7 @@ foreign-library _binding , ieee754 >=0.8.0 && <0.9 , lens >=5.2.3 && <5.4 , list-shuffle >=1.0.0.1 && <1.1 - , massiv >=1.0.4.1 && <1.1 + , massiv >=1.0.3 && <1.1 , mtl >=2.2 && <2.4 , optparse-applicative >=0.17 && <0.19 , random >=1.2 && <1.4 From f4d34d32fe43fc9e9a6d1889fd773e32fe0dd685 Mon Sep 17 00:00:00 2001 From: folivetti Date: Sat, 12 Apr 2025 16:54:39 -0300 Subject: [PATCH 010/166] testing wheel building machine --- pyeggp.cabal | 6 +++--- pyproject.toml | 8 ++++---- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/pyeggp.cabal b/pyeggp.cabal index cc10dd1..76bc2dc 100755 --- a/pyeggp.cabal +++ b/pyeggp.cabal @@ -38,7 +38,7 @@ foreign-library _binding options: standalone mod-def-file: src/pyeggp/binding.def - --c-sources: src/pyeggp/binding_wrap.c + -- c-sources: src/pyeggp/binding_wrap.c hs-source-dirs: src other-modules: Paths_pyeggp @@ -47,7 +47,7 @@ foreign-library _binding build-depends: attoparsec >=0.14.4 && <0.15 , attoparsec-expr >=0.1.1.2 && <0.2 - , base >=4.17 && <5 + , base >=4.19 && <5 , binary >=0.8.9.1 && <0.9 , bytestring >=0.11 && <0.13 , containers >=0.6.7 && <0.8 @@ -58,7 +58,7 @@ foreign-library _binding , ieee754 >=0.8.0 && <0.9 , lens >=5.2.3 && <5.4 , list-shuffle >=1.0.0.1 && <1.1 - , massiv >=1.0.3 && <1.1 + , massiv >=1.0.4.1 && <1.1 , mtl >=2.2 && <2.4 , optparse-applicative >=0.17 && <0.19 , random >=1.2 && <1.4 diff --git a/pyproject.toml b/pyproject.toml index 96232e9..227b823 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -76,10 +76,10 @@ skip = [ # PyPy builds do not work. "pp*", ] -manylinux-aarch64-image = "wenkokke/manylinux_2_28_ghc948_aarch64" -manylinux-i686-image = "wenkokke/manylinux2014_ghc948_i686" -manylinux-x86_64-image = "wenkokke/manylinux2014_ghc948_x86_64" -musllinux-x86_64-image = "wenkokke/musllinux_1_1_ghc948_x86_64" +manylinux-aarch64-image = "folivetti/manylinux_2_28_ghc9121_aarch64" +manylinux-i686-image = "folivetti/manylinux2014_ghc9121_i686" +manylinux-x86_64-image = "folivetti/manylinux2014_ghc9121_x86_64" +musllinux-x86_64-image = "folivetti/musllinux_1_1_ghc9121_x86_64" [tool.mypy] From a7a3b86b4ad3fb1222fe21b89572c6203ca4886a Mon Sep 17 00:00:00 2001 From: folivetti Date: Mon, 14 Apr 2025 14:40:26 -0300 Subject: [PATCH 011/166] testing wheel building machine --- README.md | 15 +- Setup.hs | 3 +- pyeggp.cabal | 27 +- pyproject.toml | 14 +- setup.py | 6 +- src/Pyeggp.hs | 96 +- src/Util.hs | 14 +- src/pyeggp/__init__.py | 28 +- src/pyeggp/_binding.pyi | 2 +- src/pyeggp/binding.i | 8 +- src/pyeggp/binding.py | 76 - src/pyeggp/binding_wrap.c | 4083 ---------------------- src/pyeggp/typing.py | 2 +- src/srtree.egg-info/PKG-INFO | 699 ---- src/srtree.egg-info/SOURCES.txt | 15 - src/srtree.egg-info/dependency_links.txt | 1 - src/srtree.egg-info/requires.txt | 6 - src/srtree.egg-info/top_level.txt | 2 - test/data.csv | 36 + test/test_pyeggp.py | 6 + 20 files changed, 174 insertions(+), 4965 deletions(-) delete mode 100644 src/pyeggp/binding.py delete mode 100644 src/pyeggp/binding_wrap.c delete mode 100644 src/srtree.egg-info/PKG-INFO delete mode 100644 src/srtree.egg-info/SOURCES.txt delete mode 100644 src/srtree.egg-info/dependency_links.txt delete mode 100644 src/srtree.egg-info/requires.txt delete mode 100644 src/srtree.egg-info/top_level.txt create mode 100644 test/data.csv create mode 100644 test/test_pyeggp.py diff --git a/README.md b/README.md index 7f3b30d..8bcf857 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,13 @@ -# pyeggp -Python wraper for eggp +# pyeggp - Python e-graph GP + +Python bindings for [eggp](https://github.com/folivetti/srtree/blob/main/apps/eggp/README.md). + +ggp (e-graph genetic programming), follows the same structure as the traditional GP. The initial population is created using ramped half-and-half respecting a maximum size and maximum depth parameter and, for a number of generations, it will choose two parents using tournament selection, apply the subtree crossover with probability $pc$ followed by the subtree mutation with probability $pm$, when the offsprings replace the current population following a dominance criteria. + +How to install the package: + +```bash +pip install pyeggp +``` + +The bindings were created following the amazing example written by [wenkokke](https://github.com/wenkokke/example-haskell-wheel) diff --git a/Setup.hs b/Setup.hs index d48695a..6fe0171 100755 --- a/Setup.hs +++ b/Setup.hs @@ -11,6 +11,7 @@ import Distribution.Simple.Setup (BuildFlags (..), fromFlagOrDefault) import Distribution.Simple.Utils (die', info) import Distribution.Types.LocalBuildInfo (componentNameCLBIs) import Distribution.Types.UnqualComponentName (UnqualComponentName, unUnqualComponentName) +import Distribution.Utils.Path (interpretSymbolicPathCWD) import Distribution.Verbosity (Verbosity, normal) import System.Directory (doesDirectoryExist, copyFile) import System.Environment (getEnv) @@ -34,7 +35,7 @@ main = for_ foreignLibCLBIs $ \foreignLibCLBI -> do let foreignLibBuildDir = componentBuildDir localBuildInfo foreignLibCLBI let foreignLibFileName = getForeignLibFileName foreignLibName - let foreignLibBuildPath = foreignLibBuildDir foreignLibFileName + let foreignLibBuildPath = interpretSymbolicPathCWD foreignLibBuildDir foreignLibFileName let foreignLibInstallPath = installDir foreignLibFileName info verbosity $ "Installing " <> foreignLibFileName <> " to " <> installDir copyFile foreignLibBuildPath foreignLibInstallPath diff --git a/pyeggp.cabal b/pyeggp.cabal index 76bc2dc..8bc04f1 100755 --- a/pyeggp.cabal +++ b/pyeggp.cabal @@ -1,10 +1,10 @@ -cabal-version: 1.12 +cabal-version: 2.2 -- This file has been generated from package.yaml by hpack version 0.37.0. -- -- see: https://github.com/sol/hpack -name: pyeggp +name: Pyeggp version: 1.0.0 synopsis: A general library to work with Symbolic Regression expression trees. description: A Symbolic Regression Tree data structure to work with mathematical expressions with support to first order derivative and simplification; @@ -14,9 +14,8 @@ bug-reports: https://github.com/folivetti/pyeggp/issues author: Fabricio Olivetti de França maintainer: fabricio.olivetti@gmail.com copyright: 2023 Fabricio Olivetti de França -license: BSD3 license-file: LICENSE -build-type: Simple +build-type: Custom extra-source-files: README.md ChangeLog.md @@ -25,6 +24,16 @@ source-repository head type: git location: https://github.com/folivetti/pyeggp +custom-setup + setup-depends: + base >=4.19 + , Cabal >=3.14 + , directory >=1 + , filepath >=1.4 + +common warnings + ghc-options: -Wall + foreign-library _binding import: warnings type: native-shared @@ -38,10 +47,13 @@ foreign-library _binding options: standalone mod-def-file: src/pyeggp/binding.def - -- c-sources: src/pyeggp/binding_wrap.c + c-sources: src/pyeggp/binding_wrap.c hs-source-dirs: src other-modules: - Paths_pyeggp + Pyeggp + Util + Random + Paths_Pyeggp extra-libraries: nlopt build-depends: @@ -64,7 +76,7 @@ foreign-library _binding , random >=1.2 && <1.4 , scheduler >=2.0.0.1 && <3 , split >=0.2.5 && <0.3 - , srtree + , srtree >= 2.0.1.2 , statistics >=0.16.2.1 && <0.17 , transformers >=0.6.1.0 && <0.7 , unliftio >=0.2.10 && <1 @@ -72,4 +84,5 @@ foreign-library _binding , unordered-containers ==0.2.* , vector >=0.12 && <0.14 , zlib >=0.6.3 && <0.8 + , directory default-language: Haskell2010 diff --git a/pyproject.toml b/pyproject.toml index 227b823..230f2a0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -14,7 +14,7 @@ authors = [{ name = "Fabricio Olivetti", email = "folivetti@users.noreply.github description = "Python Wheels for eggp algorithm." readme = "README.md" license = { file = "LICENSE" } -requires-python = ">=3.9,<3.13" +requires-python = ">=3.9,<3.14" classifiers = [ "Operating System :: MacOS", "Operating System :: Microsoft :: Windows", @@ -23,6 +23,7 @@ classifiers = [ "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", "Programming Language :: Python :: Implementation :: CPython", ] dependencies = ["typing_extensions >=4.6,<5; python_version<'3.8'"] @@ -46,7 +47,7 @@ push = true '^version = "{pep440_version}"$', '^current_version = "{version}"$', ] -"src/example_haskell_wheel/__init__.py" = [ +"src/pyeggp/__init__.py" = [ '^VERSION: str = "{pep440_version}"$', ] "pyeggp.cabal" = ['^version: {pep440_version}$'] @@ -95,9 +96,9 @@ where = ["src"] legacy_tox_ini = """ [tox] min_version = 4 -env_list = py{39,310,311,312}-{lin,mac,win} +env_list = py{39,310,311,312,313}-{lin,mac,win} -[testenv:py{39,310,311,312}-{lin,mac,win}] +[testenv:py{39,310,311,312,313}-{lin,mac,win}] package = external package_env = build-{env_name} platform = @@ -111,7 +112,7 @@ extras = commands = {env_python} -m pytest {posargs} -[testenv:build-py{39,310,311,312}-{lin,mac,win}] +[testenv:build-py{39,310,311,312,313}-{lin,mac,win}] deps = build auditwheel; sys_platform == 'linux' @@ -123,18 +124,21 @@ package_glob = py310-lin: {package_root}{/}dist{/}*cp310*manylinux*.whl py311-lin: {package_root}{/}dist{/}*cp311*manylinux*.whl py312-lin: {package_root}{/}dist{/}*cp312*manylinux*.whl + py313-lin: {package_root}{/}dist{/}*cp313*manylinux*.whl py37-mac: {package_root}{/}dist{/}*cp37*macosx*.whl py38-mac: {package_root}{/}dist{/}*cp38*macosx*.whl py39-mac: {package_root}{/}dist{/}*cp39*macosx*.whl py310-mac: {package_root}{/}dist{/}*cp310*macosx*.whl py311-mac: {package_root}{/}dist{/}*cp311*macosx*.whl py312-mac: {package_root}{/}dist{/}*cp312*macosx*.whl + py313-mac: {package_root}{/}dist{/}*cp313*macosx*.whl py37-win: {package_root}{/}dist{/}*cp37*win*.whl py38-win: {package_root}{/}dist{/}*cp38*win*.whl py39-win: {package_root}{/}dist{/}*cp39*win*.whl py310-win: {package_root}{/}dist{/}*cp310*win*.whl py311-win: {package_root}{/}dist{/}*cp311*win*.whl py312-win: {package_root}{/}dist{/}*cp312*win*.whl + py313-win: {package_root}{/}dist{/}*cp313*win*.whl allowlist_externals = sh commands = diff --git a/setup.py b/setup.py index 9166ba0..7d5b282 100644 --- a/setup.py +++ b/setup.py @@ -11,7 +11,7 @@ from setuptools.command.build_ext import build_ext ext_module = Extension( - name="eggp._binding", + name="pyeggp._binding", sources=["src/pyeggp/binding.i"], ) @@ -84,8 +84,10 @@ def cabal_configure_ext(self, ext: Extension) -> None: ) def cabal_build_ext(self, ext: Extension) -> None: + #self.build_temp = self.build_temp.replace("temp","lib") self.mkpath(self.build_temp) - self.cabal(["build"], env={"INSTALLDIR": self.build_temp, **os.environ}) + print(self.build_temp, "<<<<<<<<<<<<<<<<<<<<<<<<<<<<") + self.cabal(["build"], env={"CABAL_BUILDDIR": self.build_temp, **os.environ}) lib_filename = self.get_cabal_foreign_library_filename(ext) ext_fullpath = self.get_ext_fullpath(ext.name) self.mkpath(os.path.dirname(ext_fullpath)) diff --git a/src/Pyeggp.hs b/src/Pyeggp.hs index 616537e..b5cc2fd 100644 --- a/src/Pyeggp.hs +++ b/src/Pyeggp.hs @@ -52,12 +52,14 @@ import Data.SRTree (convertProtectedOps) import Util -import Foreign.C (CInt (..)) -import Foreign.C.String (CString, newCString, withCString) -import Paths_pyeggp_wheel (version) +import Foreign.C (CInt (..), CDouble (..)) +import Foreign.C.String (CString, newCString, withCString, peekCString, peekCAString, newCAString) +import Paths_Pyeggp (version) import System.Environment (getArgs) import System.Exit (ExitCode (..)) import Text.Read (readMaybe) +import Data.Version (showVersion) +import Control.Exception (Exception (..), SomeException (..), handle) foreign import ccall unsafe_py_write_stdout :: CString -> IO () @@ -69,13 +71,13 @@ foreign import ccall unsafe_py_write_stderr :: CString -> IO () py_write_stderr :: String -> IO () py_write_stderr str = withCString str unsafe_py_write_stderr -foreign export ccall hs_pyeggp_version :: IO CInt +foreign export ccall hs_pyeggp_version :: IO CString hs_pyeggp_version :: IO CString hs_pyeggp_version = newCString (showVersion version) -foreign export ccall hs_pyeggp_main :: IO CString +foreign export ccall hs_pyeggp_main :: IO CInt exitHandler :: ExitCode -> IO CInt exitHandler ExitSuccess = return 0 @@ -89,20 +91,26 @@ hs_pyeggp_main :: IO CInt hs_pyeggp_main = handle uncaughtExceptionHandler $ handle exitHandler $ do - getArgs >>= \args -> - case eggp args of + getArgs >>= \args -> do + --out <- pyeggp args + case "" of "" -> py_write_stdout $ "wrong arguments" css -> py_write_stdout css return 0 -foreign export ccall hs_pyeggp :: [CString] -> IO CString +foreign export ccall hs_pyeggp_run :: CString -> CInt -> CInt -> CInt -> CInt -> CDouble -> CDouble -> CString -> CString -> CInt -> CInt -> CInt -> CInt -> CInt -> CString -> CString -> IO CString -hs_pyeggp :: [CString] -> IO CString -hs_pyeggp cargs = do - args <- traverse peekCString cargs - newCString $ pyeggp args +hs_pyeggp_run :: CString -> CInt -> CInt -> CInt -> CInt -> CDouble -> CDouble -> CString -> CString -> CInt -> CInt -> CInt -> CInt -> CInt -> CString -> CString -> IO CString +hs_pyeggp_run dataset gens nPop maxSize nTournament pc pm nonterminals loss optIter optRepeat nParams split simplify dumpTo loadFrom = do + dataset' <- peekCString dataset + nonterminals' <- peekCString nonterminals + loss' <- peekCString loss + dumpTo' <- peekCString dumpTo + loadFrom' <- peekCString loadFrom + out <- pyeggp dataset' (fromIntegral gens) (fromIntegral nPop) (fromIntegral maxSize) (fromIntegral nTournament) (realToFrac pc) (realToFrac pm) nonterminals' loss' (fromIntegral optIter) (fromIntegral optRepeat) (fromIntegral nParams) (fromIntegral split) (simplify /= 0) dumpTo' loadFrom' + newCString out -egraphGP :: [(DataSet, DataSet)] -> Args -> StateT EGraph (StateT StdGen IO) () +egraphGP :: [(DataSet, DataSet)] -> Args -> StateT EGraph (StateT StdGen IO) String egraphGP dataTrainVals args = do when ((not.null) (_loadFrom args)) $ (io $ BS.readFile (_loadFrom args)) >>= \eg -> put (decode eg) @@ -121,18 +129,18 @@ egraphGP dataTrainVals args = do let full = totSz > max maxMem (_nPop args) when full (cleanEGraph >> cleanDB) - newPop <- let n_paretos = (_nPop args) `div` (_maxSize args) - pareto <- concat <$> (forM [1 .. _maxSize args] $ \n -> getTopFitEClassWithSize n 2) - let remainder = _nPop args - length pareto - lft <- if full - then getTopFitEClassThat remainder (const True) - else pure $ Prelude.take remainder newPop' - Prelude.mapM canonical (pareto <> lft) + newPop <- do let n_paretos = (_nPop args) `div` (_maxSize args) + pareto <- concat <$> (forM [1 .. _maxSize args] $ \n -> getTopFitEClassWithSize n 2) + let remainder = _nPop args - length pareto + lft <- if full + then getTopFitEClassThat remainder (const True) + else pure $ Prelude.take remainder newPop' + Prelude.mapM canonical (pareto <> lft) pure newPop - io $ putStrLn "id,Expression,Numpy,theta,size" - paretoFront fitFun (_maxSize args) printExpr when ((not.null) (_dumpTo args)) $ get >>= (io . BS.writeFile (_dumpTo args) . encode ) + pf <- paretoFront fitFun (_maxSize args) printExpr + pure $ "id,Expression,Numpy,theta,size\n" <> pf where maxSize = (_maxSize args) maxMem = 2000000 -- running 1 iter of eqsat for each new individual will consume ~3GB @@ -330,7 +338,7 @@ egraphGP dataTrainVals args = do en' <- canonize (gf ec) doesNotExistGens grands en' - printExpr :: Int -> EClassId -> RndEGraph () + printExpr :: Int -> EClassId -> RndEGraph String printExpr ix ec = do thetas' <- gets (_theta . _info . (IM.! ec) . _eClass) bestExpr <- (if _simplify args then simplifyEqSatDefault else id) <$> getBestExpr ec @@ -341,7 +349,7 @@ egraphGP dataTrainVals args = do then fitFun bestExpr else pure (1.0, thetas') - forM_ (Prelude.zip dataTrainVals thetas) $ \((dataTrain, dataVal), theta) -> do + ts <- forM (Prelude.zip dataTrainVals thetas) $ \((dataTrain, dataVal), theta) -> do let (x, y, mYErr) = dataTrain (x_val, y_val, mYErr_val) = dataVal distribution = _distribution args @@ -349,9 +357,10 @@ egraphGP dataTrainVals args = do expr = paramsToConst (MA.toList theta) best' thetaStr = intercalate ";" $ Prelude.map show (MA.toList theta) showFun = showExpr expr - showFunNp = "\"" <> showPython best' <> "\"" - io . putStrLn $ show ix <> "," <> showFun <> "," <> showFunNp <> "," - <> thetaStr <> "," <> show (countNodes $ convertProtectedOps expr) + showFunNp = showPython best' + pure $ show ix <> "," <> showFun <> "," <> showFunNp <> "," + <> thetaStr <> "," <> show (countNodes $ convertProtectedOps expr) <> "\n" + pure (concat ts) insertTerms = forM terms $ \t -> do fromTree myCost t >>= canonical @@ -376,30 +385,15 @@ data Args = Args deriving (Show) -pyeggp :: String -> String -pyeggp args = - case args of - [dataset', gens', maxSize', distribution', optIter', optRepeat', nParams, nTournament, pc, pm, nonterminals, dumpTo, loadFrom, simplify] -> - let marg = Args <$> Just dataset - <*> readMaybe gens - <*> readMaybe maxSize - <*> readMaybe distribution - <*> readMaybe optIter - <*> readMaybe optRepeat - <*> readMaybe nParams - <*> readMaybe nTournament - <*> readMaybe pc - <*> readMaybe pm - <*> Just nonterminals - <*> Just dumpTo - <*> Just loadFrom - in case marg of - Just arg -> eggp dataset arg - Nothing -> "" - _ -> "" - -eggp :: String -> Args -> String -eggp dataset args = do +pyeggp :: String -> Int -> Int -> Int -> Int -> Double -> Double -> String -> String -> Int -> Int -> Int -> Int -> Bool -> String -> String -> IO String +pyeggp dataset gens nPop maxSize nTournament pc pm nonterminals loss optIter optRepeat nParams split simplify dumpTo loadFrom = + case readMaybe loss of + Nothing -> pure $ "Invalid loss function " <> loss + Just l -> let arg = Args dataset gens maxSize split l optIter optRepeat nParams nPop nTournament pc pm nonterminals dumpTo loadFrom simplify + in eggp arg + +eggp :: Args -> IO String +eggp args = do g <- getStdGen let datasets = words (_dataset args) dataTrains' <- Prelude.mapM (flip loadTrainingOnly True) datasets -- load all datasets diff --git a/src/Util.hs b/src/Util.hs index 7cd2ac9..c0922bc 100644 --- a/src/Util.hs +++ b/src/Util.hs @@ -235,19 +235,23 @@ printBest fitFun printExprFun = do printExprFun 0 bec --paretoFront :: Int -> (Int -> EClassId -> RndEGraph ()) -> RndEGraph () -paretoFront fitFun maxSize printExprFun = go 1 0 (-(1.0/0.0)) +paretoFront fitFun maxSize printExprFun = concat <$> go 1 0 (-(1.0/0.0)) where - go :: Int -> Int -> Double -> RndEGraph () + go :: Int -> Int -> Double -> RndEGraph [String] go n ix f - | n > maxSize = pure () + | n > maxSize = pure [] | otherwise = do ecList <- getBestExprWithSize n if not (null ecList) then do let (ec, mf) = head ecList improved = fromJust mf > f ec' <- canonical ec - when improved $ refit fitFun ec' >> printExprFun ix ec' - go (n+1) (ix + if improved then 1 else 0) (max f (fromJust mf)) + if improved + then do refit fitFun ec' + t <- printExprFun ix ec' + ts <- go (n+1) (ix + if improved then 1 else 0) (max f (fromJust mf)) + pure (t:ts) + else go (n+1) (ix + if improved then 1 else 0) (max f (fromJust mf)) else go (n+1) ix f evaluateUnevaluated fitFun = do diff --git a/src/pyeggp/__init__.py b/src/pyeggp/__init__.py index db02eed..37aa251 100644 --- a/src/pyeggp/__init__.py +++ b/src/pyeggp/__init__.py @@ -6,7 +6,7 @@ from ._binding import ( unsafe_hs_pyeggp_version, unsafe_hs_pyeggp_main, - unsafe_hs_pyeggp_fib, + unsafe_hs_pyeggp_run, unsafe_hs_pyeggp_init, unsafe_hs_pyeggp_exit, ) @@ -45,7 +45,27 @@ def main(args: List[str] = []) -> int: with hs_rts_init(args): return unsafe_hs_pyeggp_main() - -def pyegpp(n: List[str]) -> str: +def pyeggp_run(dataset: str, gen: int, nPop: int, maxSize: int, nTournament: int, pc: float, pm: float, nonterminals: str, loss: str, optIter: int, optRepeat: int, nParams: int, split: int, simplify: int, dumpTo: str, loadFrom: str) -> str: with hs_rts_init(): - return unsafe_hs_pyeggp(n) + return unsafe_hs_pyeggp_run(dataset, gen, nPop, maxSize, nTournament, pc, pm, nonterminals, loss, optIter, optRepeat, nParams, split, simplify, dumpTo, loadFrom) + +class PyEGGP(): + def __init__(self, gen = 100, nPop = 100, maxSize = 15, nTournament = 3, pc = 0.9, pm = 0.3, nonterminals = "add,sub,mul,div", loss = "MSE", optIter = 50, optRepeat = 2, nParams = -1, split = 1, simplify = False, dumpTo = "", loadFrom = ""): + self.gen = gen + self.nPop = nPop + self.maxSize = maxSize + self.nTournament = nTournament + self.pc = pc + self.pm = pm + self.nonterminals = nonterminals + self.loss = loss + self.optIter = optIter + self.optRepeat = optRepeat + self.nParams = nParams + self.split = split + self.simplify = int(simplify) + self.dumpTo = dumpTo + self.loadFrom = loadFrom + def fit(self, dataset): + output = pyeggp_run(dataset, self.gen, self.nPop, self.maxSize, self.nTournament, self.pc, self.pm, self.nonterminals, self.loss, self.optIter, self.optRepeat, self.nParams, self.split, self.simplify, self.dumpTo, self.loadFrom) + return output diff --git a/src/pyeggp/_binding.pyi b/src/pyeggp/_binding.pyi index 22251f5..ab59318 100644 --- a/src/pyeggp/_binding.pyi +++ b/src/pyeggp/_binding.pyi @@ -2,6 +2,6 @@ from typing import List def unsafe_hs_pyeggp_version() -> str: ... def unsafe_hs_pyeggp_main() -> int: ... -def unsafe_hs_pyeggp(n: List[str]) -> str: ... +def unsafe_hs_pyeggp_run(dataset: str, gen: int, nPop: int, maxSize: int, nTournament: int, pc: float, pm: float, nonterminals: str, loss: str, optIter: int, optRepeat: int, nParams: int, split: int, simplify: int, dumpTo: str, loadFrom: str) -> str: ... def unsafe_hs_pyeggp_init(args: List[str]) -> None: ... def unsafe_hs_pyeggp_exit() -> None: ... diff --git a/src/pyeggp/binding.i b/src/pyeggp/binding.i index 6155386..f6eeda6 100644 --- a/src/pyeggp/binding.i +++ b/src/pyeggp/binding.i @@ -1,6 +1,6 @@ %module binding %{ -#include "ExampleHaskellWheelBinding_stub.h" +#include "Pyeggp_stub.h" char * unsafe_hs_pyeggp_version() { return hs_pyeggp_version(); @@ -10,8 +10,8 @@ int unsafe_hs_pyeggp_main() { return hs_pyeggp_main(); } -char * unsafe_hs_pyeggp(char **n) { - return hs_pyeggp(&n); +char * unsafe_hs_pyeggp_run(const char *dataset, int gens, int nPop, int maxSize, int nTournament, double pc, double pm, const char *nonterminals, const char *loss, int optIter, int optRepeat, int nParams, int split, int simplify, const char *dumpTo, const char *loadFrom ) { + return hs_pyeggp_run(dataset, gens, nPop, maxSize, nTournament, pc, pm, nonterminals, loss, optIter, optRepeat, nParams, split, simplify, dumpTo, loadFrom); } void unsafe_hs_pyeggp_init(int argc, char **argv) { @@ -59,6 +59,6 @@ void unsafe_py_write_stderr(const char * str) { char * unsafe_hs_pyeggp_version(); int unsafe_hs_pyeggp_main(); -char * unsafe_hs_pyeggp(char **n); +char * unsafe_hs_pyeggp_run(const char *dataset, int gens, int nPop, int maxSize, int nTournament, double p, double pm, const char *nonterminals, const char *loss, int optIter, int optRepeat, int nParams, int split, int simplify, const char *dumpTo, const char *loadFrom); void unsafe_hs_pyeggp_init(int argc, char **argv); void unsafe_hs_pyeggp_exit(); diff --git a/src/pyeggp/binding.py b/src/pyeggp/binding.py deleted file mode 100644 index 4b2df33..0000000 --- a/src/pyeggp/binding.py +++ /dev/null @@ -1,76 +0,0 @@ -# This file was automatically generated by SWIG (https://www.swig.org). -# Version 4.3.0 -# -# Do not make changes to this file unless you know what you are doing - modify -# the SWIG interface file instead. - -from sys import version_info as _swig_python_version_info -# Import the low-level C/C++ module -if __package__ or "." in __name__: - from . import _binding -else: - import _binding - -try: - import builtins as __builtin__ -except ImportError: - import __builtin__ - -def _swig_repr(self): - try: - strthis = "proxy of " + self.this.__repr__() - except __builtin__.Exception: - strthis = "" - return "<%s.%s; %s >" % (self.__class__.__module__, self.__class__.__name__, strthis,) - - -def _swig_setattr_nondynamic_instance_variable(set): - def set_instance_attr(self, name, value): - if name == "this": - set(self, name, value) - elif name == "thisown": - self.this.own(value) - elif hasattr(self, name) and isinstance(getattr(type(self), name), property): - set(self, name, value) - else: - raise AttributeError("You cannot add instance attributes to %s" % self) - return set_instance_attr - - -def _swig_setattr_nondynamic_class_variable(set): - def set_class_attr(cls, name, value): - if hasattr(cls, name) and not isinstance(getattr(cls, name), property): - set(cls, name, value) - else: - raise AttributeError("You cannot add class attributes to %s" % cls) - return set_class_attr - - -def _swig_add_metaclass(metaclass): - """Class decorator for adding a metaclass to a SWIG wrapped class - a slimmed down version of six.add_metaclass""" - def wrapper(cls): - return metaclass(cls.__name__, cls.__bases__, cls.__dict__.copy()) - return wrapper - - -class _SwigNonDynamicMeta(type): - """Meta class to enforce nondynamic attributes (no new attributes) for a class""" - __setattr__ = _swig_setattr_nondynamic_class_variable(type.__setattr__) - - - -def unsafe_hs_pyeggp_version(): - return _binding.unsafe_hs_pyeggp_version() - -def unsafe_hs_pyeggp_main(): - return _binding.unsafe_hs_pyeggp_main() - -def unsafe_hs_pyeggp(n): - return _binding.unsafe_hs_pyeggp(n) - -def unsafe_hs_pyeggp_init(argc): - return _binding.unsafe_hs_pyeggp_init(argc) - -def unsafe_hs_pyeggp_exit(): - return _binding.unsafe_hs_pyeggp_exit() - diff --git a/src/pyeggp/binding_wrap.c b/src/pyeggp/binding_wrap.c deleted file mode 100644 index a2f6e3f..0000000 --- a/src/pyeggp/binding_wrap.c +++ /dev/null @@ -1,4083 +0,0 @@ -/* ---------------------------------------------------------------------------- - * This file was automatically generated by SWIG (https://www.swig.org). - * Version 4.3.0 - * - * Do not make changes to this file unless you know what you are doing - modify - * the SWIG interface file instead. - * ----------------------------------------------------------------------------- */ - - -#define SWIG_VERSION 0x040300 -#define SWIGPYTHON -#define SWIG_PYTHON_DIRECTOR_NO_VTABLE - -#define SWIG_name "_binding" -/* ----------------------------------------------------------------------------- - * This section contains generic SWIG labels for method/variable - * declarations/attributes, and other compiler dependent labels. - * ----------------------------------------------------------------------------- */ - -/* template workaround for compilers that cannot correctly implement the C++ standard */ -#ifndef SWIGTEMPLATEDISAMBIGUATOR -# if defined(__SUNPRO_CC) && (__SUNPRO_CC <= 0x560) -# define SWIGTEMPLATEDISAMBIGUATOR template -# elif defined(__HP_aCC) -/* Needed even with `aCC -AA' when `aCC -V' reports HP ANSI C++ B3910B A.03.55 */ -/* If we find a maximum version that requires this, the test would be __HP_aCC <= 35500 for A.03.55 */ -# define SWIGTEMPLATEDISAMBIGUATOR template -# else -# define SWIGTEMPLATEDISAMBIGUATOR -# endif -#endif - -/* inline attribute */ -#ifndef SWIGINLINE -# if defined(__cplusplus) || (defined(__GNUC__) && !defined(__STRICT_ANSI__)) -# define SWIGINLINE inline -# else -# define SWIGINLINE -# endif -#endif - -/* attribute recognised by some compilers to avoid 'unused' warnings */ -#ifndef SWIGUNUSED -# if defined(__GNUC__) -# if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4)) -# define SWIGUNUSED __attribute__ ((__unused__)) -# else -# define SWIGUNUSED -# endif -# elif defined(__ICC) -# define SWIGUNUSED __attribute__ ((__unused__)) -# else -# define SWIGUNUSED -# endif -#endif - -#ifndef SWIG_MSC_UNSUPPRESS_4505 -# if defined(_MSC_VER) -# pragma warning(disable : 4505) /* unreferenced local function has been removed */ -# endif -#endif - -#ifndef SWIGUNUSEDPARM -# ifdef __cplusplus -# define SWIGUNUSEDPARM(p) -# else -# define SWIGUNUSEDPARM(p) p SWIGUNUSED -# endif -#endif - -/* internal SWIG method */ -#ifndef SWIGINTERN -# define SWIGINTERN static SWIGUNUSED -#endif - -/* internal inline SWIG method */ -#ifndef SWIGINTERNINLINE -# define SWIGINTERNINLINE SWIGINTERN SWIGINLINE -#endif - -/* exporting methods */ -#if defined(__GNUC__) -# if (__GNUC__ >= 4) || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4) -# ifndef GCC_HASCLASSVISIBILITY -# define GCC_HASCLASSVISIBILITY -# endif -# endif -#endif - -#ifndef SWIGEXPORT -# if defined(_WIN32) || defined(__WIN32__) || defined(__CYGWIN__) -# if defined(STATIC_LINKED) -# define SWIGEXPORT -# else -# define SWIGEXPORT __declspec(dllexport) -# endif -# else -# if defined(__GNUC__) && defined(GCC_HASCLASSVISIBILITY) -# define SWIGEXPORT __attribute__ ((visibility("default"))) -# else -# define SWIGEXPORT -# endif -# endif -#endif - -/* calling conventions for Windows */ -#ifndef SWIGSTDCALL -# if defined(_WIN32) || defined(__WIN32__) || defined(__CYGWIN__) -# define SWIGSTDCALL __stdcall -# else -# define SWIGSTDCALL -# endif -#endif - -/* Deal with Microsoft's attempt at deprecating C standard runtime functions */ -#if !defined(SWIG_NO_CRT_SECURE_NO_DEPRECATE) && defined(_MSC_VER) && !defined(_CRT_SECURE_NO_DEPRECATE) -# define _CRT_SECURE_NO_DEPRECATE -#endif - -/* Deal with Microsoft's attempt at deprecating methods in the standard C++ library */ -#if !defined(SWIG_NO_SCL_SECURE_NO_DEPRECATE) && defined(_MSC_VER) && !defined(_SCL_SECURE_NO_DEPRECATE) -# define _SCL_SECURE_NO_DEPRECATE -#endif - -/* Deal with Apple's deprecated 'AssertMacros.h' from Carbon-framework */ -#if defined(__APPLE__) && !defined(__ASSERT_MACROS_DEFINE_VERSIONS_WITHOUT_UNDERSCORES) -# define __ASSERT_MACROS_DEFINE_VERSIONS_WITHOUT_UNDERSCORES 0 -#endif - -/* Intel's compiler complains if a variable which was never initialised is - * cast to void, which is a common idiom which we use to indicate that we - * are aware a variable isn't used. 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Of course that - also requires SWIG_ConvertPtr to return new result values, such as - - int SWIG_ConvertPtr(obj, ptr,...) { - if () { - if () { - *ptr = ; - return SWIG_NEWOBJ; - } else { - *ptr = ; - return SWIG_OLDOBJ; - } - } else { - return SWIG_BADOBJ; - } - } - - Of course, returning the plain '0(success)/-1(fail)' still works, but you can be - more explicit by returning SWIG_BADOBJ, SWIG_ERROR or any of the - SWIG errors code. - - Finally, if the SWIG_CASTRANK_MODE is enabled, the result code - allows returning the 'cast rank', for example, if you have this - - int food(double) - int fooi(int); - - and you call - - food(1) // cast rank '1' (1 -> 1.0) - fooi(1) // cast rank '0' - - just use the SWIG_AddCast()/SWIG_CheckState() -*/ - -#define SWIG_OK (0) -/* Runtime errors are < 0 */ -#define SWIG_ERROR (-1) -/* Errors in range -1 to -99 are in swigerrors.swg (errors for all languages including those not using the runtime) */ -/* Errors in range -100 to -199 are language specific errors defined in *errors.swg */ -/* Errors < -200 are generic runtime specific errors */ -#define SWIG_ERROR_RELEASE_NOT_OWNED (-200) - -#define SWIG_IsOK(r) (r >= 0) -#define SWIG_ArgError(r) ((r != SWIG_ERROR) ? r : SWIG_TypeError) - -/* The CastRankLimit says how many bits are used for the cast rank */ -#define SWIG_CASTRANKLIMIT (1 << 8) -/* The NewMask denotes the object was created (using new/malloc) */ -#define SWIG_NEWOBJMASK (SWIG_CASTRANKLIMIT << 1) -/* The TmpMask is for in/out typemaps that use temporary objects */ -#define SWIG_TMPOBJMASK (SWIG_NEWOBJMASK << 1) -/* Simple returning values */ -#define SWIG_BADOBJ (SWIG_ERROR) -#define SWIG_OLDOBJ (SWIG_OK) -#define SWIG_NEWOBJ (SWIG_OK | SWIG_NEWOBJMASK) -#define SWIG_TMPOBJ (SWIG_OK | SWIG_TMPOBJMASK) -/* Check, add and del object mask methods */ -#define SWIG_AddNewMask(r) (SWIG_IsOK(r) ? (r | SWIG_NEWOBJMASK) : r) -#define SWIG_DelNewMask(r) (SWIG_IsOK(r) ? (r & ~SWIG_NEWOBJMASK) : r) -#define SWIG_IsNewObj(r) (SWIG_IsOK(r) && (r & SWIG_NEWOBJMASK)) -#define SWIG_AddTmpMask(r) (SWIG_IsOK(r) ? (r | SWIG_TMPOBJMASK) : r) -#define SWIG_DelTmpMask(r) (SWIG_IsOK(r) ? (r & ~SWIG_TMPOBJMASK) : r) -#define SWIG_IsTmpObj(r) (SWIG_IsOK(r) && (r & SWIG_TMPOBJMASK)) - -/* Cast-Rank Mode */ -#if defined(SWIG_CASTRANK_MODE) -# ifndef SWIG_TypeRank -# define SWIG_TypeRank unsigned long -# endif -# ifndef SWIG_MAXCASTRANK /* Default cast allowed */ -# define SWIG_MAXCASTRANK (2) -# endif -# define SWIG_CASTRANKMASK ((SWIG_CASTRANKLIMIT) -1) -# define SWIG_CastRank(r) (r & SWIG_CASTRANKMASK) -SWIGINTERNINLINE int SWIG_AddCast(int r) { - return SWIG_IsOK(r) ? ((SWIG_CastRank(r) < SWIG_MAXCASTRANK) ? (r + 1) : SWIG_ERROR) : r; -} -SWIGINTERNINLINE int SWIG_CheckState(int r) { - return SWIG_IsOK(r) ? SWIG_CastRank(r) + 1 : 0; -} -#else /* no cast-rank mode */ -# define SWIG_AddCast(r) (r) -# define SWIG_CheckState(r) (SWIG_IsOK(r) ? 1 : 0) -#endif - - -#include - -#ifdef __cplusplus -extern "C" { -#endif - -typedef void *(*swig_converter_func)(void *, int *); -typedef struct swig_type_info *(*swig_dycast_func)(void **); - -/* Structure to store information on one type */ -typedef struct swig_type_info { - const char *name; /* mangled name of this type */ - const char *str; /* human readable name of this type */ - swig_dycast_func dcast; /* dynamic cast function down a hierarchy */ - struct swig_cast_info *cast; /* linked list of types that can cast into this type */ - void *clientdata; /* language specific type data */ - int owndata; /* flag if the structure owns the clientdata */ -} swig_type_info; - -/* Structure to store a type and conversion function used for casting */ -typedef struct swig_cast_info { - swig_type_info *type; /* pointer to type that is equivalent to this type */ - swig_converter_func converter; /* function to cast the void pointers */ - struct swig_cast_info *next; /* pointer to next cast in linked list */ - struct swig_cast_info *prev; /* pointer to the previous cast */ -} swig_cast_info; - -/* Structure used to store module information - * Each module generates one structure like this, and the runtime collects - * all of these structures and stores them in a circularly linked list.*/ -typedef struct swig_module_info { - swig_type_info **types; /* Array of pointers to swig_type_info structures that are in this module */ - size_t size; /* Number of types in this module */ - struct swig_module_info *next; /* Pointer to next element in circularly linked list */ - swig_type_info **type_initial; /* Array of initially generated type structures */ - swig_cast_info **cast_initial; /* Array of initially generated casting structures */ - void *clientdata; /* Language specific module data */ -} swig_module_info; - -/* - Compare two type names skipping the space characters, therefore - "char*" == "char *" and "Class" == "Class", etc. - - Return 0 when the two name types are equivalent, as in - strncmp, but skipping ' '. -*/ -SWIGRUNTIME int -SWIG_TypeNameComp(const char *f1, const char *l1, - const char *f2, const char *l2) { - for (;(f1 != l1) && (f2 != l2); ++f1, ++f2) { - while ((*f1 == ' ') && (f1 != l1)) ++f1; - while ((*f2 == ' ') && (f2 != l2)) ++f2; - if (*f1 != *f2) return (*f1 > *f2) ? 1 : -1; - } - return (int)((l1 - f1) - (l2 - f2)); -} - -/* - Check type equivalence in a name list like ||... - Return 0 if equal, -1 if nb < tb, 1 if nb > tb -*/ -SWIGRUNTIME int -SWIG_TypeCmp(const char *nb, const char *tb) { - int equiv = 1; - const char* te = tb + strlen(tb); - const char* ne = nb; - while (equiv != 0 && *ne) { - for (nb = ne; *ne; ++ne) { - if (*ne == '|') break; - } - equiv = SWIG_TypeNameComp(nb, ne, tb, te); - if (*ne) ++ne; - } - return equiv; -} - -/* - Check type equivalence in a name list like ||... - Return 0 if not equal, 1 if equal -*/ -SWIGRUNTIME int -SWIG_TypeEquiv(const char *nb, const char *tb) { - return SWIG_TypeCmp(nb, tb) == 0 ? 1 : 0; -} - -/* - Check the typename -*/ -SWIGRUNTIME swig_cast_info * -SWIG_TypeCheck(const char *c, swig_type_info *ty) { - if (ty) { - swig_cast_info *iter = ty->cast; - while (iter) { - if (strcmp(iter->type->name, c) == 0) { - if (iter == ty->cast) - return iter; - /* Move iter to the top of the linked list */ - iter->prev->next = iter->next; - if (iter->next) - iter->next->prev = iter->prev; - iter->next = ty->cast; - iter->prev = 0; - if (ty->cast) ty->cast->prev = iter; - ty->cast = iter; - return iter; - } - iter = iter->next; - } - } - return 0; -} - -/* - Identical to SWIG_TypeCheck, except strcmp is replaced with a pointer comparison -*/ -SWIGRUNTIME swig_cast_info * -SWIG_TypeCheckStruct(const swig_type_info *from, swig_type_info *ty) { - if (ty) { - swig_cast_info *iter = ty->cast; - while (iter) { - if (iter->type == from) { - if (iter == ty->cast) - return iter; - /* Move iter to the top of the linked list */ - iter->prev->next = iter->next; - if (iter->next) - iter->next->prev = iter->prev; - iter->next = ty->cast; - iter->prev = 0; - if (ty->cast) ty->cast->prev = iter; - ty->cast = iter; - return iter; - } - iter = iter->next; - } - } - return 0; -} - -/* - Cast a pointer up an inheritance hierarchy -*/ -SWIGRUNTIMEINLINE void * -SWIG_TypeCast(swig_cast_info *ty, void *ptr, int *newmemory) { - return ((!ty) || (!ty->converter)) ? ptr : (*ty->converter)(ptr, newmemory); -} - -/* - Dynamic pointer casting. Down an inheritance hierarchy -*/ -SWIGRUNTIME swig_type_info * -SWIG_TypeDynamicCast(swig_type_info *ty, void **ptr) { - swig_type_info *lastty = ty; - if (!ty || !ty->dcast) return ty; - while (ty && (ty->dcast)) { - ty = (*ty->dcast)(ptr); - if (ty) lastty = ty; - } - return lastty; -} - -/* - Return the name associated with this type -*/ -SWIGRUNTIMEINLINE const char * -SWIG_TypeName(const swig_type_info *ty) { - return ty->name; -} - -/* - Return the pretty name associated with this type, - that is an unmangled type name in a form presentable to the user. -*/ -SWIGRUNTIME const char * -SWIG_TypePrettyName(const swig_type_info *type) { - /* The "str" field contains the equivalent pretty names of the - type, separated by vertical-bar characters. Choose the last - name. It should be the most specific; a fully resolved name - but not necessarily with default template parameters expanded. */ - if (!type) return NULL; - if (type->str != NULL) { - const char *last_name = type->str; - const char *s; - for (s = type->str; *s; s++) - if (*s == '|') last_name = s+1; - return last_name; - } - else - return type->name; -} - -/* - Set the clientdata field for a type -*/ -SWIGRUNTIME void -SWIG_TypeClientData(swig_type_info *ti, void *clientdata) { - swig_cast_info *cast = ti->cast; - /* if (ti->clientdata == clientdata) return; */ - ti->clientdata = clientdata; - - while (cast) { - if (!cast->converter) { - swig_type_info *tc = cast->type; - if (!tc->clientdata) { - SWIG_TypeClientData(tc, clientdata); - } - } - cast = cast->next; - } -} -SWIGRUNTIME void -SWIG_TypeNewClientData(swig_type_info *ti, void *clientdata) { - SWIG_TypeClientData(ti, clientdata); - ti->owndata = 1; -} - -/* - Search for a swig_type_info structure only by mangled name - Search is a O(log #types) - - We start searching at module start, and finish searching when start == end. - Note: if start == end at the beginning of the function, we go all the way around - the circular list. -*/ -SWIGRUNTIME swig_type_info * -SWIG_MangledTypeQueryModule(swig_module_info *start, - swig_module_info *end, - const char *name) { - swig_module_info *iter = start; - do { - if (iter->size) { - size_t l = 0; - size_t r = iter->size - 1; - do { - /* since l+r >= 0, we can (>> 1) instead (/ 2) */ - size_t i = (l + r) >> 1; - const char *iname = iter->types[i]->name; - if (iname) { - int compare = strcmp(name, iname); - if (compare == 0) { - return iter->types[i]; - } else if (compare < 0) { - if (i) { - r = i - 1; - } else { - break; - } - } else if (compare > 0) { - l = i + 1; - } - } else { - break; /* should never happen */ - } - } while (l <= r); - } - iter = iter->next; - } while (iter != end); - return 0; -} - -/* - Search for a swig_type_info structure for either a mangled name or a human readable name. - It first searches the mangled names of the types, which is a O(log #types) - If a type is not found it then searches the human readable names, which is O(#types). - - We start searching at module start, and finish searching when start == end. - Note: if start == end at the beginning of the function, we go all the way around - the circular list. -*/ -SWIGRUNTIME swig_type_info * -SWIG_TypeQueryModule(swig_module_info *start, - swig_module_info *end, - const char *name) { - /* STEP 1: Search the name field using binary search */ - swig_type_info *ret = SWIG_MangledTypeQueryModule(start, end, name); - if (ret) { - return ret; - } else { - /* STEP 2: If the type hasn't been found, do a complete search - of the str field (the human readable name) */ - swig_module_info *iter = start; - do { - size_t i = 0; - for (; i < iter->size; ++i) { - if (iter->types[i]->str && (SWIG_TypeEquiv(iter->types[i]->str, name))) - return iter->types[i]; - } - iter = iter->next; - } while (iter != end); - } - - /* neither found a match */ - return 0; -} - -/* - Pack binary data into a string -*/ -SWIGRUNTIME char * -SWIG_PackData(char *c, void *ptr, size_t sz) { - static const char hex[17] = "0123456789abcdef"; - const unsigned char *u = (unsigned char *) ptr; - const unsigned char *eu = u + sz; - for (; u != eu; ++u) { - unsigned char uu = *u; - *(c++) = hex[(uu & 0xf0) >> 4]; - *(c++) = hex[uu & 0xf]; - } - return c; -} - -/* - Unpack binary data from a string -*/ -SWIGRUNTIME const char * -SWIG_UnpackData(const char *c, void *ptr, size_t sz) { - unsigned char *u = (unsigned char *) ptr; - const unsigned char *eu = u + sz; - for (; u != eu; ++u) { - char d = *(c++); - unsigned char uu; - if ((d >= '0') && (d <= '9')) - uu = (unsigned char)((d - '0') << 4); - else if ((d >= 'a') && (d <= 'f')) - uu = (unsigned char)((d - ('a'-10)) << 4); - else - return (char *) 0; - d = *(c++); - if ((d >= '0') && (d <= '9')) - uu |= (unsigned char)(d - '0'); - else if ((d >= 'a') && (d <= 'f')) - uu |= (unsigned char)(d - ('a'-10)); - else - return (char *) 0; - *u = uu; - } - return c; -} - -/* - Pack 'void *' into a string buffer. -*/ -SWIGRUNTIME char * -SWIG_PackVoidPtr(char *buff, void *ptr, const char *name, size_t bsz) { - char *r = buff; - if ((2*sizeof(void *) + 2) > bsz) return 0; - *(r++) = '_'; - r = SWIG_PackData(r,&ptr,sizeof(void *)); - if (strlen(name) + 1 > (bsz - (r - buff))) return 0; - strcpy(r,name); - return buff; -} - -SWIGRUNTIME const char * -SWIG_UnpackVoidPtr(const char *c, void **ptr, const char *name) { - if (*c != '_') { - if (strcmp(c,"NULL") == 0) { - *ptr = (void *) 0; - return name; - } else { - return 0; - } - } - return SWIG_UnpackData(++c,ptr,sizeof(void *)); -} - -SWIGRUNTIME char * -SWIG_PackDataName(char *buff, void *ptr, size_t sz, const char *name, size_t bsz) { - char *r = buff; - size_t lname = (name ? strlen(name) : 0); - if ((2*sz + 2 + lname) > bsz) return 0; - *(r++) = '_'; - r = SWIG_PackData(r,ptr,sz); - if (lname) { - strncpy(r,name,lname+1); - } else { - *r = 0; - } - return buff; -} - -SWIGRUNTIME const char * -SWIG_UnpackDataName(const char *c, void *ptr, size_t sz, const char *name) { - if (*c != '_') { - if (strcmp(c,"NULL") == 0) { - memset(ptr,0,sz); - return name; - } else { - return 0; - } - } - return SWIG_UnpackData(++c,ptr,sz); -} - -#ifdef __cplusplus -} -#endif - -/* SWIG Errors applicable to all language modules, values are reserved from -1 to -99 */ -#define SWIG_UnknownError -1 -#define SWIG_IOError -2 -#define SWIG_RuntimeError -3 -#define SWIG_IndexError -4 -#define SWIG_TypeError -5 -#define SWIG_DivisionByZero -6 -#define SWIG_OverflowError -7 -#define SWIG_SyntaxError -8 -#define SWIG_ValueError -9 -#define SWIG_SystemError -10 -#define SWIG_AttributeError -11 -#define SWIG_MemoryError -12 -#define SWIG_NullReferenceError -13 - - -/* Compatibility macros for Python 3 */ -#if PY_VERSION_HEX >= 0x03000000 - -#define PyClass_Check(obj) PyObject_IsInstance(obj, (PyObject *)&PyType_Type) -#define PyInt_Check(x) PyLong_Check(x) -#define PyInt_AsLong(x) PyLong_AsLong(x) -#define PyInt_FromLong(x) PyLong_FromLong(x) -#define PyInt_FromSize_t(x) PyLong_FromSize_t(x) -#define PyString_Check(name) PyBytes_Check(name) -#define PyString_FromString(x) PyUnicode_FromString(x) -#define PyString_Format(fmt, args) PyUnicode_Format(fmt, args) -#define PyString_AsString(str) PyBytes_AsString(str) -#define PyString_Size(str) PyBytes_Size(str) -#define PyString_InternFromString(key) PyUnicode_InternFromString(key) -#define Py_TPFLAGS_HAVE_CLASS Py_TPFLAGS_BASETYPE -#define _PyLong_FromSsize_t(x) PyLong_FromSsize_t(x) - -#endif - -/* SWIG APIs for compatibility of both Python 2 & 3 */ - -#if PY_VERSION_HEX >= 0x03000000 -# define SWIG_Python_str_FromFormat PyUnicode_FromFormat -#else -# define SWIG_Python_str_FromFormat PyString_FromFormat -#endif - - -/* Wrapper around PyUnicode_AsUTF8AndSize - call Py_XDECREF on the returned pbytes when finished with the returned string */ -SWIGINTERN const char * -SWIG_PyUnicode_AsUTF8AndSize(PyObject *str, Py_ssize_t *psize, PyObject **pbytes) -{ -#if PY_VERSION_HEX >= 0x03030000 -# if !defined(Py_LIMITED_API) || Py_LIMITED_API+0 >= 0x030A0000 - *pbytes = NULL; - return PyUnicode_AsUTF8AndSize(str, psize); -# else - const char *chars; - *pbytes = PyUnicode_AsUTF8String(str); - chars = *pbytes ? PyBytes_AsString(*pbytes) : NULL; - if (chars && psize) - *psize = PyBytes_Size(*pbytes); - return chars; -# endif -#else - char *chars = NULL; - *pbytes = NULL; - PyString_AsStringAndSize(str, &chars, psize); - return chars; -#endif -} - -SWIGINTERN PyObject* -SWIG_Python_str_FromChar(const char *c) -{ -#if PY_VERSION_HEX >= 0x03000000 - return PyUnicode_FromString(c); -#else - return PyString_FromString(c); -#endif -} - -/* SWIGPY_USE_CAPSULE is no longer used within SWIG itself, but some user interface files check for it. */ -# define SWIGPY_USE_CAPSULE -#ifdef SWIGPYTHON_BUILTIN -# define SWIGPY_CAPSULE_ATTR_NAME "type_pointer_capsule_builtin" SWIG_TYPE_TABLE_NAME -#else -# define SWIGPY_CAPSULE_ATTR_NAME "type_pointer_capsule" SWIG_TYPE_TABLE_NAME -#endif -# define SWIGPY_CAPSULE_NAME ("swig_runtime_data" SWIG_RUNTIME_VERSION "." SWIGPY_CAPSULE_ATTR_NAME) - -#if PY_VERSION_HEX < 0x03020000 -#define PyDescr_TYPE(x) (((PyDescrObject *)(x))->d_type) -#define PyDescr_NAME(x) (((PyDescrObject *)(x))->d_name) -#define Py_hash_t long -#endif - -#ifdef Py_LIMITED_API -# define PyTuple_GET_ITEM PyTuple_GetItem -/* Note that PyTuple_SetItem() has different semantics from PyTuple_SET_ITEM as it decref's the original tuple item, so in general they cannot be used - interchangeably. However in SWIG-generated code PyTuple_SET_ITEM is only used with newly initialized tuples without any items and for them this does work. */ -# define PyTuple_SET_ITEM PyTuple_SetItem -# define PyTuple_GET_SIZE PyTuple_Size -# define PyCFunction_GET_FLAGS PyCFunction_GetFlags -# define PyCFunction_GET_FUNCTION PyCFunction_GetFunction -# define PyCFunction_GET_SELF PyCFunction_GetSelf -# define PyList_GET_ITEM PyList_GetItem -# define PyList_SET_ITEM PyList_SetItem -# define PySliceObject PyObject -#endif - -/* Increment and Decrement wrappers - for portability when using the stable abi and for performance otherwise */ -#ifdef Py_LIMITED_API -# define SWIG_Py_INCREF Py_IncRef -# define SWIG_Py_XINCREF Py_IncRef -# define SWIG_Py_DECREF Py_DecRef -# define SWIG_Py_XDECREF Py_DecRef -#else -# define SWIG_Py_INCREF Py_INCREF -# define SWIG_Py_XINCREF Py_XINCREF -# define SWIG_Py_DECREF Py_DECREF -# define SWIG_Py_XDECREF Py_XDECREF -#endif - -/* ----------------------------------------------------------------------------- - * error manipulation - * ----------------------------------------------------------------------------- */ - -SWIGRUNTIME PyObject* -SWIG_Python_ErrorType(int code) { - PyObject* type = 0; - switch(code) { - case SWIG_MemoryError: - type = PyExc_MemoryError; - break; - case SWIG_IOError: - type = PyExc_IOError; - break; - case SWIG_RuntimeError: - type = PyExc_RuntimeError; - break; - case SWIG_IndexError: - type = PyExc_IndexError; - break; - case SWIG_TypeError: - type = PyExc_TypeError; - break; - case SWIG_DivisionByZero: - type = PyExc_ZeroDivisionError; - break; - case SWIG_OverflowError: - type = PyExc_OverflowError; - break; - case SWIG_SyntaxError: - type = PyExc_SyntaxError; - break; - case SWIG_ValueError: - type = PyExc_ValueError; - break; - case SWIG_SystemError: - type = PyExc_SystemError; - break; - case SWIG_AttributeError: - type = PyExc_AttributeError; - break; - case SWIG_NullReferenceError: - type = PyExc_TypeError; - break; - default: - type = PyExc_RuntimeError; - } - return type; -} - - -SWIGRUNTIME void -SWIG_Python_AddErrorMsg(const char* mesg) -{ - PyObject *type = 0; - PyObject *value = 0; - PyObject *traceback = 0; - - if (PyErr_Occurred()) - PyErr_Fetch(&type, &value, &traceback); - if (value) { - PyObject *old_str = PyObject_Str(value); - PyObject *bytes = NULL; - const char *tmp = SWIG_PyUnicode_AsUTF8AndSize(old_str, NULL, &bytes); - PyErr_Clear(); - SWIG_Py_XINCREF(type); - if (tmp) - PyErr_Format(type, "%s %s", tmp, mesg); - else - PyErr_Format(type, "%s", mesg); - SWIG_Py_XDECREF(bytes); - SWIG_Py_DECREF(old_str); - SWIG_Py_DECREF(value); - } else { - PyErr_SetString(PyExc_RuntimeError, mesg); - } -} - -SWIGRUNTIME int -SWIG_Python_TypeErrorOccurred(PyObject *obj) -{ - PyObject *error; - if (obj) - return 0; - error = PyErr_Occurred(); - return error && PyErr_GivenExceptionMatches(error, PyExc_TypeError); -} - -SWIGRUNTIME void -SWIG_Python_RaiseOrModifyTypeError(const char *message) -{ - if (SWIG_Python_TypeErrorOccurred(NULL)) { - /* Use existing TypeError to preserve stacktrace and enhance with given message */ - PyObject *newvalue; - PyObject *type = NULL, *value = NULL, *traceback = NULL; - PyErr_Fetch(&type, &value, &traceback); -#if PY_VERSION_HEX >= 0x03000000 - newvalue = PyUnicode_FromFormat("%S\nAdditional information:\n%s", value, message); -#else - newvalue = PyString_FromFormat("%s\nAdditional information:\n%s", PyString_AsString(value), message); -#endif - if (newvalue) { - SWIG_Py_XDECREF(value); - PyErr_Restore(type, newvalue, traceback); - } else { - PyErr_Restore(type, value, traceback); - } - } else { - /* Raise TypeError using given message */ - PyErr_SetString(PyExc_TypeError, message); - } -} - -#if defined(SWIG_PYTHON_NO_THREADS) -# if defined(SWIG_PYTHON_THREADS) -# undef SWIG_PYTHON_THREADS -# endif -#endif -#if defined(SWIG_PYTHON_THREADS) /* Threading support is enabled */ -# if !defined(SWIG_PYTHON_USE_GIL) && !defined(SWIG_PYTHON_NO_USE_GIL) -# define SWIG_PYTHON_USE_GIL -# endif -# if defined(SWIG_PYTHON_USE_GIL) /* Use PyGILState threads calls */ -# if !defined(SWIG_PYTHON_INITIALIZE_THREADS) -# if PY_VERSION_HEX < 0x03070000 -# define SWIG_PYTHON_INITIALIZE_THREADS PyEval_InitThreads() -# else -# define SWIG_PYTHON_INITIALIZE_THREADS -# endif -# endif -# ifdef __cplusplus /* C++ code */ - class SWIG_Python_Thread_Block { - bool status; - PyGILState_STATE state; - public: - void end() { if (status) { PyGILState_Release(state); status = false;} } - SWIG_Python_Thread_Block() : status(true), state(PyGILState_Ensure()) {} - ~SWIG_Python_Thread_Block() { end(); } - }; - class SWIG_Python_Thread_Allow { - bool status; - PyThreadState *save; - public: - void end() { if (status) { status = false; PyEval_RestoreThread(save); }} - SWIG_Python_Thread_Allow() : status(true), save(PyEval_SaveThread()) {} - ~SWIG_Python_Thread_Allow() { end(); } - }; -# define SWIG_PYTHON_THREAD_BEGIN_BLOCK SWIG_Python_Thread_Block _swig_thread_block -# define SWIG_PYTHON_THREAD_END_BLOCK _swig_thread_block.end() -# define SWIG_PYTHON_THREAD_BEGIN_ALLOW SWIG_Python_Thread_Allow _swig_thread_allow -# define SWIG_PYTHON_THREAD_END_ALLOW _swig_thread_allow.end() -# else /* C code */ -# define SWIG_PYTHON_THREAD_BEGIN_BLOCK PyGILState_STATE _swig_thread_block = PyGILState_Ensure() -# define SWIG_PYTHON_THREAD_END_BLOCK PyGILState_Release(_swig_thread_block) -# define SWIG_PYTHON_THREAD_BEGIN_ALLOW PyThreadState *_swig_thread_allow = PyEval_SaveThread() -# define SWIG_PYTHON_THREAD_END_ALLOW PyEval_RestoreThread(_swig_thread_allow) -# endif -# else /* Old thread way, not implemented, user must provide it */ -# if !defined(SWIG_PYTHON_INITIALIZE_THREADS) -# define SWIG_PYTHON_INITIALIZE_THREADS -# endif -# if !defined(SWIG_PYTHON_THREAD_BEGIN_BLOCK) -# define SWIG_PYTHON_THREAD_BEGIN_BLOCK -# endif -# if !defined(SWIG_PYTHON_THREAD_END_BLOCK) -# define SWIG_PYTHON_THREAD_END_BLOCK -# endif -# if !defined(SWIG_PYTHON_THREAD_BEGIN_ALLOW) -# define SWIG_PYTHON_THREAD_BEGIN_ALLOW -# endif -# if !defined(SWIG_PYTHON_THREAD_END_ALLOW) -# define SWIG_PYTHON_THREAD_END_ALLOW -# endif -# endif -#else /* No thread support */ -# define SWIG_PYTHON_INITIALIZE_THREADS -# define SWIG_PYTHON_THREAD_BEGIN_BLOCK -# define SWIG_PYTHON_THREAD_END_BLOCK -# define SWIG_PYTHON_THREAD_BEGIN_ALLOW -# define SWIG_PYTHON_THREAD_END_ALLOW -#endif - -/* ----------------------------------------------------------------------------- - * Python API portion that goes into the runtime - * ----------------------------------------------------------------------------- */ - -#ifdef __cplusplus -extern "C" { -#endif - -/* ----------------------------------------------------------------------------- - * Constant declarations - * ----------------------------------------------------------------------------- */ - -/* Constant Types */ -#define SWIG_PY_POINTER 4 -#define SWIG_PY_BINARY 5 - -/* Constant information structure */ -typedef struct swig_const_info { - int type; - const char *name; - long lvalue; - double dvalue; - void *pvalue; - swig_type_info **ptype; -} swig_const_info; - -#ifdef __cplusplus -} -#endif - - -/* ----------------------------------------------------------------------------- - * pyrun.swg - * - * This file contains the runtime support for Python modules - * and includes code for managing global variables and pointer - * type checking. - * - * ----------------------------------------------------------------------------- */ - -#if PY_VERSION_HEX < 0x02070000 /* 2.7.0 */ -# error "This version of SWIG only supports Python >= 2.7" -#endif - -#if PY_VERSION_HEX >= 0x03000000 && PY_VERSION_HEX < 0x03030000 -# error "This version of SWIG only supports Python 3 >= 3.3" -#endif - -/* Common SWIG API */ - -/* for raw pointers */ -#define SWIG_Python_ConvertPtr(obj, pptr, type, flags) SWIG_Python_ConvertPtrAndOwn(obj, pptr, type, flags, 0) -#define SWIG_ConvertPtr(obj, pptr, type, flags) SWIG_Python_ConvertPtr(obj, pptr, type, flags) -#define SWIG_ConvertPtrAndOwn(obj,pptr,type,flags,own) SWIG_Python_ConvertPtrAndOwn(obj, pptr, type, flags, own) - -#ifdef SWIGPYTHON_BUILTIN -#define SWIG_NewPointerObj(ptr, type, flags) SWIG_Python_NewPointerObj(self, ptr, type, flags) -#else -#define SWIG_NewPointerObj(ptr, type, flags) SWIG_Python_NewPointerObj(NULL, ptr, type, flags) -#endif - -#define SWIG_InternalNewPointerObj(ptr, type, flags) SWIG_Python_NewPointerObj(NULL, ptr, type, flags) - -#define SWIG_CheckImplicit(ty) SWIG_Python_CheckImplicit(ty) -#define SWIG_AcquirePtr(ptr, src) SWIG_Python_AcquirePtr(ptr, src) -#define swig_owntype int - -/* for raw packed data */ -#define SWIG_ConvertPacked(obj, ptr, sz, ty) SWIG_Python_ConvertPacked(obj, ptr, sz, ty) -#define SWIG_NewPackedObj(ptr, sz, type) SWIG_Python_NewPackedObj(ptr, sz, type) - -/* for class or struct pointers */ -#define SWIG_ConvertInstance(obj, pptr, type, flags) SWIG_ConvertPtr(obj, pptr, type, flags) -#define SWIG_NewInstanceObj(ptr, type, flags) SWIG_NewPointerObj(ptr, type, flags) - -/* for C or C++ function pointers */ -#define SWIG_ConvertFunctionPtr(obj, pptr, type) SWIG_Python_ConvertFunctionPtr(obj, pptr, type) -#define SWIG_NewFunctionPtrObj(ptr, type) SWIG_Python_NewPointerObj(NULL, ptr, type, 0) - -/* for C++ member pointers, ie, member methods */ -#define SWIG_ConvertMember(obj, ptr, sz, ty) SWIG_Python_ConvertPacked(obj, ptr, sz, ty) -#define SWIG_NewMemberObj(ptr, sz, type) SWIG_Python_NewPackedObj(ptr, sz, type) - - -/* Runtime API */ - -#define SWIG_GetModule(clientdata) SWIG_Python_GetModule(clientdata) -#define SWIG_SetModule(clientdata, pointer) SWIG_Python_SetModule(pointer) -#define SWIG_NewClientData(obj) SwigPyClientData_New(obj) - -#define SWIG_SetErrorObj SWIG_Python_SetErrorObj -#define SWIG_SetErrorMsg SWIG_Python_SetErrorMsg -#define SWIG_ErrorType(code) SWIG_Python_ErrorType(code) -#define SWIG_Error(code, msg) SWIG_Python_SetErrorMsg(SWIG_ErrorType(code), msg) -#define SWIG_fail goto fail - - -/* Runtime API implementation */ - -/* Error manipulation */ - -SWIGINTERN void -SWIG_Python_SetErrorObj(PyObject *errtype, PyObject *obj) { - SWIG_PYTHON_THREAD_BEGIN_BLOCK; - PyErr_SetObject(errtype, obj); - SWIG_Py_DECREF(obj); - SWIG_PYTHON_THREAD_END_BLOCK; -} - -SWIGINTERN void -SWIG_Python_SetErrorMsg(PyObject *errtype, const char *msg) { - SWIG_PYTHON_THREAD_BEGIN_BLOCK; - PyErr_SetString(errtype, msg); - SWIG_PYTHON_THREAD_END_BLOCK; -} - -#define SWIG_Python_Raise(obj, type, desc) SWIG_Python_SetErrorObj(SWIG_Python_ExceptionType(desc), obj) - -/* Set a constant value */ - -#if defined(SWIGPYTHON_BUILTIN) - -SWIGINTERN void -SwigPyBuiltin_AddPublicSymbol(PyObject *seq, const char *key) { - PyObject *s = PyString_InternFromString(key); - PyList_Append(seq, s); - SWIG_Py_DECREF(s); -} - -SWIGINTERN void -SWIG_Python_SetConstant(PyObject *d, PyObject *public_interface, const char *name, PyObject *obj) { - PyDict_SetItemString(d, name, obj); - SWIG_Py_DECREF(obj); - if (public_interface) - SwigPyBuiltin_AddPublicSymbol(public_interface, name); -} - -#else - -SWIGINTERN void -SWIG_Python_SetConstant(PyObject *d, const char *name, PyObject *obj) { - PyDict_SetItemString(d, name, obj); - SWIG_Py_DECREF(obj); -} - -#endif - -/* Append a value to the result obj */ - -SWIGINTERN PyObject* -SWIG_Python_AppendOutput(PyObject* result, PyObject* obj, int is_void) { - if (!result) { - result = obj; - } else if (result == Py_None && is_void) { - SWIG_Py_DECREF(result); - result = obj; - } else { - if (!PyList_Check(result)) { - PyObject *o2 = result; - result = PyList_New(1); - if (result) { - PyList_SET_ITEM(result, 0, o2); - } else { - SWIG_Py_DECREF(obj); - return o2; - } - } - PyList_Append(result,obj); - SWIG_Py_DECREF(obj); - } - return result; -} - -/* Unpack the argument tuple */ - -SWIGINTERN Py_ssize_t -SWIG_Python_UnpackTuple(PyObject *args, const char *name, Py_ssize_t min, Py_ssize_t max, PyObject **objs) -{ - if (!args) { - if (!min && !max) { - return 1; - } else { - PyErr_Format(PyExc_TypeError, "%s expected %s%d arguments, got none", - name, (min == max ? "" : "at least "), (int)min); - return 0; - } - } - if (!PyTuple_Check(args)) { - if (min <= 1 && max >= 1) { - Py_ssize_t i; - objs[0] = args; - for (i = 1; i < max; ++i) { - objs[i] = 0; - } - return 2; - } - PyErr_SetString(PyExc_SystemError, "UnpackTuple() argument list is not a tuple"); - return 0; - } else { - Py_ssize_t l = PyTuple_GET_SIZE(args); - if (l < min) { - PyErr_Format(PyExc_TypeError, "%s expected %s%d arguments, got %d", - name, (min == max ? "" : "at least "), (int)min, (int)l); - return 0; - } else if (l > max) { - PyErr_Format(PyExc_TypeError, "%s expected %s%d arguments, got %d", - name, (min == max ? "" : "at most "), (int)max, (int)l); - return 0; - } else { - Py_ssize_t i; - for (i = 0; i < l; ++i) { - objs[i] = PyTuple_GET_ITEM(args, i); - } - for (; l < max; ++l) { - objs[l] = 0; - } - return i + 1; - } - } -} - -SWIGINTERN int -SWIG_Python_CheckNoKeywords(PyObject *kwargs, const char *name) { - int no_kwargs = 1; - if (kwargs) { - assert(PyDict_Check(kwargs)); - if (PyDict_Size(kwargs) > 0) { - PyErr_Format(PyExc_TypeError, "%s() does not take keyword arguments", name); - no_kwargs = 0; - } - } - return no_kwargs; -} - -/* A functor is a function object with one single object argument */ -#define SWIG_Python_CallFunctor(functor, obj) PyObject_CallFunctionObjArgs(functor, obj, NULL); - -/* - Helper for static pointer initialization for both C and C++ code, for example - static PyObject *SWIG_STATIC_POINTER(MyVar) = NewSomething(...); -*/ -#ifdef __cplusplus -#define SWIG_STATIC_POINTER(var) var -#else -#define SWIG_STATIC_POINTER(var) var = 0; if (!var) var -#endif - -#ifdef __cplusplus -extern "C" { -#endif - -/* Python-specific SWIG API */ -#define SWIG_newvarlink() SWIG_Python_newvarlink() -#define SWIG_addvarlink(p, name, get_attr, set_attr) SWIG_Python_addvarlink(p, name, get_attr, set_attr) -#define SWIG_InstallConstants(d, constants) SWIG_Python_InstallConstants(d, constants) - -/* ----------------------------------------------------------------------------- - * global variable support code. - * ----------------------------------------------------------------------------- */ - -typedef struct swig_globalvar { - char *name; /* Name of global variable */ - PyObject *(*get_attr)(void); /* Return the current value */ - int (*set_attr)(PyObject *); /* Set the value */ - struct swig_globalvar *next; -} swig_globalvar; - -typedef struct swig_varlinkobject { - PyObject_HEAD - swig_globalvar *vars; -} swig_varlinkobject; - -SWIGINTERN PyObject * -swig_varlink_repr(PyObject *SWIGUNUSEDPARM(v)) { -#if PY_VERSION_HEX >= 0x03000000 - return PyUnicode_InternFromString(""); -#else - return PyString_FromString(""); -#endif -} - -SWIGINTERN PyObject * -swig_varlink_str(PyObject *o) { - swig_varlinkobject *v = (swig_varlinkobject *) o; -#if PY_VERSION_HEX >= 0x03000000 - PyObject *str = PyUnicode_InternFromString("("); - PyObject *tail; - PyObject *joined; - swig_globalvar *var; - for (var = v->vars; var; var=var->next) { - tail = PyUnicode_FromString(var->name); - joined = PyUnicode_Concat(str, tail); - SWIG_Py_DECREF(str); - SWIG_Py_DECREF(tail); - str = joined; - if (var->next) { - tail = PyUnicode_InternFromString(", "); - joined = PyUnicode_Concat(str, tail); - SWIG_Py_DECREF(str); - SWIG_Py_DECREF(tail); - str = joined; - } - } - tail = PyUnicode_InternFromString(")"); - joined = PyUnicode_Concat(str, tail); - SWIG_Py_DECREF(str); - SWIG_Py_DECREF(tail); - str = joined; -#else - PyObject *str = PyString_FromString("("); - swig_globalvar *var; - for (var = v->vars; var; var=var->next) { - PyString_ConcatAndDel(&str,PyString_FromString(var->name)); - if (var->next) PyString_ConcatAndDel(&str,PyString_FromString(", ")); - } - PyString_ConcatAndDel(&str,PyString_FromString(")")); -#endif - return str; -} - -SWIGINTERN void -swig_varlink_dealloc(PyObject *o) { - swig_varlinkobject *v = (swig_varlinkobject *) o; - swig_globalvar *var = v->vars; - while (var) { - swig_globalvar *n = var->next; - free(var->name); - free(var); - var = n; - } -} - -SWIGINTERN PyObject * -swig_varlink_getattr(PyObject *o, char *n) { - swig_varlinkobject *v = (swig_varlinkobject *) o; - PyObject *res = NULL; - swig_globalvar *var = v->vars; - while (var) { - if (strcmp(var->name,n) == 0) { - res = (*var->get_attr)(); - break; - } - var = var->next; - } - if (res == NULL && !PyErr_Occurred()) { - PyErr_Format(PyExc_AttributeError, "Unknown C global variable '%s'", n); - } - return res; -} - -SWIGINTERN int -swig_varlink_setattr(PyObject *o, char *n, PyObject *p) { - swig_varlinkobject *v = (swig_varlinkobject *) o; - int res = 1; - swig_globalvar *var = v->vars; - while (var) { - if (strcmp(var->name,n) == 0) { - res = (*var->set_attr)(p); - break; - } - var = var->next; - } - if (res == 1 && !PyErr_Occurred()) { - PyErr_Format(PyExc_AttributeError, "Unknown C global variable '%s'", n); - } - return res; -} - -SWIGINTERN PyTypeObject* -swig_varlink_type(void) { - static char varlink__doc__[] = "Swig var link object"; -#ifndef SWIG_HEAPTYPES - static PyTypeObject varlink_type; - static int type_init = 0; - if (!type_init) { - const PyTypeObject tmp = { -#if PY_VERSION_HEX >= 0x03000000 - PyVarObject_HEAD_INIT(NULL, 0) -#else - PyObject_HEAD_INIT(NULL) - 0, /* ob_size */ -#endif - "swigvarlink", /* tp_name */ - sizeof(swig_varlinkobject), /* tp_basicsize */ - 0, /* tp_itemsize */ - (destructor) swig_varlink_dealloc, /* tp_dealloc */ -#if PY_VERSION_HEX < 0x030800b4 - (printfunc)0, /* tp_print */ -#else - (Py_ssize_t)0, /* tp_vectorcall_offset */ -#endif - (getattrfunc) swig_varlink_getattr, /* tp_getattr */ - (setattrfunc) swig_varlink_setattr, /* tp_setattr */ - 0, /* tp_compare */ - (reprfunc) swig_varlink_repr, /* tp_repr */ - 0, /* tp_as_number */ - 0, /* tp_as_sequence */ - 0, /* tp_as_mapping */ - 0, /* tp_hash */ - 0, /* tp_call */ - (reprfunc) swig_varlink_str, /* tp_str */ - 0, /* tp_getattro */ - 0, /* tp_setattro */ - 0, /* tp_as_buffer */ - 0, /* tp_flags */ - varlink__doc__, /* tp_doc */ - 0, /* tp_traverse */ - 0, /* tp_clear */ - 0, /* tp_richcompare */ - 0, /* tp_weaklistoffset */ - 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, /* tp_iter -> tp_weaklist */ - 0, /* tp_del */ - 0, /* tp_version_tag */ -#if PY_VERSION_HEX >= 0x03040000 - 0, /* tp_finalize */ -#endif -#if PY_VERSION_HEX >= 0x03080000 - 0, /* tp_vectorcall */ -#endif -#if (PY_VERSION_HEX >= 0x03080000) && (PY_VERSION_HEX < 0x03090000) - 0, /* tp_print */ -#endif -#if PY_VERSION_HEX >= 0x030c0000 - 0, /* tp_watched */ -#endif -#if PY_VERSION_HEX >= 0x030d00a4 - 0, /* tp_versions_used */ -#endif -#ifdef COUNT_ALLOCS - 0, /* tp_allocs */ - 0, /* tp_frees */ - 0, /* tp_maxalloc */ - 0, /* tp_prev */ - 0 /* tp_next */ -#endif - }; - varlink_type = tmp; - type_init = 1; - if (PyType_Ready(&varlink_type) < 0) - return NULL; - } - return &varlink_type; -#else - PyType_Slot slots[] = { - { Py_tp_dealloc, (void *)swig_varlink_dealloc }, - { Py_tp_repr, (void *)swig_varlink_repr }, - { Py_tp_getattr, (void *)swig_varlink_getattr }, - { Py_tp_setattr, (void *)swig_varlink_setattr }, - { Py_tp_str, (void *)swig_varlink_str }, - { Py_tp_doc, (void *)varlink__doc__ }, - { 0, NULL } - }; - PyType_Spec spec = { - "swigvarlink", - sizeof(swig_varlinkobject), - 0, - Py_TPFLAGS_DEFAULT, - slots - }; - return (PyTypeObject *)PyType_FromSpec(&spec); -#endif -} - -/* Create a variable linking object for use later */ -SWIGINTERN PyObject * -SWIG_Python_newvarlink(void) { - swig_varlinkobject *result = PyObject_New(swig_varlinkobject, swig_varlink_type()); - if (result) { - result->vars = 0; - } - return ((PyObject*) result); -} - -SWIGINTERN void -SWIG_Python_addvarlink(PyObject *p, const char *name, PyObject *(*get_attr)(void), int (*set_attr)(PyObject *p)) { - swig_varlinkobject *v = (swig_varlinkobject *) p; - swig_globalvar *gv = (swig_globalvar *) malloc(sizeof(swig_globalvar)); - if (gv) { - size_t size = strlen(name)+1; - gv->name = (char *)malloc(size); - if (gv->name) { - memcpy(gv->name, name, size); - gv->get_attr = get_attr; - gv->set_attr = set_attr; - gv->next = v->vars; - } - } - v->vars = gv; -} - - -static PyObject *Swig_Globals_global = NULL; - -SWIGINTERN PyObject * -SWIG_globals(void) { - if (Swig_Globals_global == NULL) { - Swig_Globals_global = SWIG_newvarlink(); - } - return Swig_Globals_global; -} - -#ifdef __cplusplus -} -#endif - -/* ----------------------------------------------------------------------------- - * Pointer declarations - * ----------------------------------------------------------------------------- */ - -/* Flags for new pointer objects */ -#define SWIG_POINTER_NOSHADOW (SWIG_POINTER_OWN << 1) -#define SWIG_POINTER_NEW (SWIG_POINTER_NOSHADOW | SWIG_POINTER_OWN) - -#define SWIG_POINTER_IMPLICIT_CONV (SWIG_POINTER_DISOWN << 1) - -#define SWIG_BUILTIN_TP_INIT (SWIG_POINTER_OWN << 2) -#define SWIG_BUILTIN_INIT (SWIG_BUILTIN_TP_INIT | SWIG_POINTER_OWN) - -#ifdef __cplusplus -extern "C" { -#endif - -/* The python void return value */ - -SWIGRUNTIMEINLINE PyObject * -SWIG_Py_Void(void) -{ - PyObject *none = Py_None; - SWIG_Py_INCREF(none); - return none; -} - -/* SwigPyClientData */ - -typedef struct { - PyObject *klass; - PyObject *newraw; - PyObject *newargs; - PyObject *destroy; - int delargs; - int implicitconv; - PyTypeObject *pytype; -} SwigPyClientData; - -SWIGRUNTIMEINLINE int -SWIG_Python_CheckImplicit(swig_type_info *ty) -{ - SwigPyClientData *data = (SwigPyClientData *)ty->clientdata; - int fail = data ? data->implicitconv : 0; - if (fail) - PyErr_SetString(PyExc_TypeError, "Implicit conversion is prohibited for explicit constructors."); - return fail; -} - -SWIGRUNTIMEINLINE PyObject * -SWIG_Python_ExceptionType(swig_type_info *desc) { - SwigPyClientData *data = desc ? (SwigPyClientData *) desc->clientdata : 0; - PyObject *klass = data ? data->klass : 0; - return (klass ? klass : PyExc_RuntimeError); -} - - -SWIGRUNTIME SwigPyClientData * -SwigPyClientData_New(PyObject* obj) -{ - if (!obj) { - return 0; - } else { - SwigPyClientData *data = (SwigPyClientData *)malloc(sizeof(SwigPyClientData)); - /* the klass element */ - data->klass = obj; - SWIG_Py_INCREF(data->klass); - /* the newraw method and newargs arguments used to create a new raw instance */ - if (PyClass_Check(obj)) { - data->newraw = 0; - SWIG_Py_INCREF(obj); - data->newargs = obj; - } else { - data->newraw = PyObject_GetAttrString(data->klass, "__new__"); - if (data->newraw) { - data->newargs = PyTuple_New(1); - if (data->newargs) { - SWIG_Py_INCREF(obj); - PyTuple_SET_ITEM(data->newargs, 0, obj); - } else { - SWIG_Py_DECREF(data->newraw); - SWIG_Py_DECREF(data->klass); - free(data); - return 0; - } - } else { - SWIG_Py_INCREF(obj); - data->newargs = obj; - } - } - /* the destroy method, aka as the C++ delete method */ - data->destroy = PyObject_GetAttrString(data->klass, "__swig_destroy__"); - if (PyErr_Occurred()) { - PyErr_Clear(); - data->destroy = 0; - } - if (data->destroy) { - data->delargs = !(PyCFunction_GET_FLAGS(data->destroy) & METH_O); - } else { - data->delargs = 0; - } - data->implicitconv = 0; - data->pytype = 0; - return data; - } -} - -SWIGRUNTIME void -SwigPyClientData_Del(SwigPyClientData *data) -{ - SWIG_Py_XDECREF(data->klass); - SWIG_Py_XDECREF(data->newraw); - SWIG_Py_XDECREF(data->newargs); - SWIG_Py_XDECREF(data->destroy); - free(data); -} - -/* =============== SwigPyObject =====================*/ - -typedef struct { - PyObject_HEAD - void *ptr; - swig_type_info *ty; - int own; - PyObject *next; -#ifdef SWIGPYTHON_BUILTIN - PyObject *dict; -#endif -} SwigPyObject; - - -#ifdef SWIGPYTHON_BUILTIN - -SWIGRUNTIME PyObject * -SwigPyObject_get___dict__(PyObject *v, PyObject *SWIGUNUSEDPARM(args)) -{ - SwigPyObject *sobj = (SwigPyObject *)v; - - if (!sobj->dict) - sobj->dict = PyDict_New(); - - SWIG_Py_XINCREF(sobj->dict); - return sobj->dict; -} - -#endif - -SWIGRUNTIME PyObject * -SwigPyObject_long(SwigPyObject *v) -{ - return PyLong_FromVoidPtr(v->ptr); -} - -SWIGRUNTIME PyObject * -SwigPyObject_format(const char* fmt, SwigPyObject *v) -{ - PyObject *res = NULL; - PyObject *args = PyTuple_New(1); - if (args) { - PyObject *val = SwigPyObject_long(v); - if (val) { - PyObject *ofmt; - PyTuple_SET_ITEM(args, 0, val); - ofmt = SWIG_Python_str_FromChar(fmt); - if (ofmt) { -#if PY_VERSION_HEX >= 0x03000000 - res = PyUnicode_Format(ofmt,args); -#else - res = PyString_Format(ofmt,args); -#endif - SWIG_Py_DECREF(ofmt); - } - } - SWIG_Py_DECREF(args); - } - return res; -} - -SWIGRUNTIME PyObject * -SwigPyObject_oct(SwigPyObject *v) -{ - return SwigPyObject_format("%o",v); -} - -SWIGRUNTIME PyObject * -SwigPyObject_hex(SwigPyObject *v) -{ - return SwigPyObject_format("%x",v); -} - -SWIGRUNTIME PyObject * -SwigPyObject_repr(SwigPyObject *v) -{ - const char *name = SWIG_TypePrettyName(v->ty); - PyObject *repr = SWIG_Python_str_FromFormat("", (name ? name : "unknown"), (void *)v); - if (repr && v->next) { - PyObject *nrep = SwigPyObject_repr((SwigPyObject *)v->next); - if (nrep) { -# if PY_VERSION_HEX >= 0x03000000 - PyObject *joined = PyUnicode_Concat(repr, nrep); - SWIG_Py_DECREF(repr); - SWIG_Py_DECREF(nrep); - repr = joined; -# else - PyString_ConcatAndDel(&repr,nrep); -# endif - } else { - SWIG_Py_DECREF(repr); - repr = NULL; - } - } - return repr; -} - -/* We need a version taking two PyObject* parameters so it's a valid - * PyCFunction to use in swigobject_methods[]. */ -SWIGRUNTIME PyObject * -SwigPyObject_repr2(PyObject *v, PyObject *SWIGUNUSEDPARM(args)) -{ - return SwigPyObject_repr((SwigPyObject*)v); -} - -SWIGRUNTIME int -SwigPyObject_compare(SwigPyObject *v, SwigPyObject *w) -{ - void *i = v->ptr; - void *j = w->ptr; - return (i < j) ? -1 : ((i > j) ? 1 : 0); -} - -/* Added for Python 3.x, would it also be useful for Python 2.x? */ -SWIGRUNTIME PyObject* -SwigPyObject_richcompare(SwigPyObject *v, SwigPyObject *w, int op) -{ - PyObject* res = NULL; - if (!PyErr_Occurred()) { - if (op != Py_EQ && op != Py_NE) { - SWIG_Py_INCREF(Py_NotImplemented); - return Py_NotImplemented; - } - res = PyBool_FromLong( (SwigPyObject_compare(v, w)==0) == (op == Py_EQ) ? 1 : 0); - } - return res; -} - - -SWIGRUNTIME PyTypeObject* SwigPyObject_TypeOnce(void); - -#ifdef SWIGPYTHON_BUILTIN -static swig_type_info *SwigPyObject_stype = 0; -SWIGRUNTIME PyTypeObject* -SwigPyObject_type(void) { - SwigPyClientData *cd; - assert(SwigPyObject_stype); - cd = (SwigPyClientData*) SwigPyObject_stype->clientdata; - assert(cd); - assert(cd->pytype); - return cd->pytype; -} -#else -SWIGRUNTIME PyTypeObject* -SwigPyObject_type(void) { - static PyTypeObject *SWIG_STATIC_POINTER(type) = SwigPyObject_TypeOnce(); - return type; -} -#endif - -SWIGRUNTIMEINLINE int -SwigPyObject_Check(PyObject *op) { - PyTypeObject *target_tp = SwigPyObject_type(); - PyTypeObject *op_type = Py_TYPE(op); -#ifdef SWIGPYTHON_BUILTIN - if (PyType_IsSubtype(op_type, target_tp)) - return 1; - return (strcmp(op_type->tp_name, "SwigPyObject") == 0); -#else -# ifdef Py_LIMITED_API - int cmp; - PyObject *tp_name; -#endif - if (op_type == target_tp) - return 1; -# ifdef Py_LIMITED_API - tp_name = PyObject_GetAttrString((PyObject *)op_type, "__name__"); - if (!tp_name) - return 0; - cmp = PyUnicode_CompareWithASCIIString(tp_name, "SwigPyObject"); - SWIG_Py_DECREF(tp_name); - return cmp == 0; -# else - return (strcmp(op_type->tp_name, "SwigPyObject") == 0); -# endif -#endif -} - -SWIGRUNTIME PyObject * -SwigPyObject_New(void *ptr, swig_type_info *ty, int own); - -static PyObject* Swig_Capsule_global = NULL; - -SWIGRUNTIME void -SwigPyObject_dealloc(PyObject *v) -{ - SwigPyObject *sobj = (SwigPyObject *) v; - PyObject *next = sobj->next; - if (sobj->own == SWIG_POINTER_OWN) { - swig_type_info *ty = sobj->ty; - SwigPyClientData *data = ty ? (SwigPyClientData *) ty->clientdata : 0; - PyObject *destroy = data ? data->destroy : 0; - if (destroy) { - /* destroy is always a VARARGS method */ - PyObject *res; - - /* PyObject_CallFunction() has the potential to silently drop - the active exception. In cases of unnamed temporary - variable or where we just finished iterating over a generator - StopIteration will be active right now, and this needs to - remain true upon return from SwigPyObject_dealloc. So save - and restore. */ - - PyObject *type = NULL, *value = NULL, *traceback = NULL; - PyErr_Fetch(&type, &value, &traceback); - - if (data->delargs) { - /* we need to create a temporary object to carry the destroy operation */ - PyObject *tmp = SwigPyObject_New(sobj->ptr, ty, 0); - if (tmp) { - res = SWIG_Python_CallFunctor(destroy, tmp); - } else { - res = 0; - } - SWIG_Py_XDECREF(tmp); - } else { - PyCFunction meth = PyCFunction_GET_FUNCTION(destroy); - PyObject *mself = PyCFunction_GET_SELF(destroy); - res = ((*meth)(mself, v)); - } - if (!res) - PyErr_WriteUnraisable(destroy); - - PyErr_Restore(type, value, traceback); - - SWIG_Py_XDECREF(res); - } -#if !defined(SWIG_PYTHON_SILENT_MEMLEAK) - else { - const char *name = SWIG_TypePrettyName(ty); - printf("swig/python detected a memory leak of type '%s', no destructor found.\n", (name ? name : "unknown")); - } -#endif - SWIG_Py_XDECREF(Swig_Capsule_global); - } - SWIG_Py_XDECREF(next); -#ifdef SWIGPYTHON_BUILTIN - SWIG_Py_XDECREF(sobj->dict); -#endif - PyObject_Free(v); -} - -SWIGRUNTIME PyObject* -SwigPyObject_append(PyObject* v, PyObject* next) -{ - SwigPyObject *sobj = (SwigPyObject *) v; - if (!SwigPyObject_Check(next)) { - PyErr_SetString(PyExc_TypeError, "Attempt to append a non SwigPyObject"); - return NULL; - } - ((SwigPyObject *)next)->next = sobj->next; - sobj->next = next; - SWIG_Py_INCREF(next); - return SWIG_Py_Void(); -} - -SWIGRUNTIME PyObject* -SwigPyObject_next(PyObject* v, PyObject *SWIGUNUSEDPARM(args)) -{ - SwigPyObject *sobj = (SwigPyObject *) v; - if (sobj->next) { - SWIG_Py_INCREF(sobj->next); - return sobj->next; - } else { - return SWIG_Py_Void(); - } -} - -SWIGINTERN PyObject* -SwigPyObject_disown(PyObject* v, PyObject *SWIGUNUSEDPARM(args)) -{ - SwigPyObject *sobj = (SwigPyObject *)v; - sobj->own = 0; - return SWIG_Py_Void(); -} - -SWIGINTERN PyObject* -SwigPyObject_acquire(PyObject* v, PyObject *SWIGUNUSEDPARM(args)) -{ - SwigPyObject *sobj = (SwigPyObject *)v; - sobj->own = SWIG_POINTER_OWN; - return SWIG_Py_Void(); -} - -SWIGINTERN PyObject* -SwigPyObject_own(PyObject *v, PyObject *args) -{ - PyObject *val = 0; - if (!PyArg_UnpackTuple(args, "own", 0, 1, &val)) { - return NULL; - } else { - SwigPyObject *sobj = (SwigPyObject *)v; - PyObject *obj = PyBool_FromLong(sobj->own); - if (val) { - if (PyObject_IsTrue(val)) { - SWIG_Py_DECREF(SwigPyObject_acquire(v,args)); - } else { - SWIG_Py_DECREF(SwigPyObject_disown(v,args)); - } - } - return obj; - } -} - -static PyMethodDef -swigobject_methods[] = { - {"disown", SwigPyObject_disown, METH_NOARGS, "releases ownership of the pointer"}, - {"acquire", SwigPyObject_acquire, METH_NOARGS, "acquires ownership of the pointer"}, - {"own", SwigPyObject_own, METH_VARARGS, "returns/sets ownership of the pointer"}, - {"append", SwigPyObject_append, METH_O, "appends another 'this' object"}, - {"next", SwigPyObject_next, METH_NOARGS, "returns the next 'this' object"}, - {"__repr__",SwigPyObject_repr2, METH_NOARGS, "returns object representation"}, - {0, 0, 0, 0} -}; - -SWIGRUNTIME PyTypeObject* -SwigPyObject_TypeOnce(void) { - static char swigobject_doc[] = "Swig object carries a C/C++ instance pointer"; -#ifndef SWIG_HEAPTYPES - static PyNumberMethods SwigPyObject_as_number = { - (binaryfunc)0, /*nb_add*/ - (binaryfunc)0, /*nb_subtract*/ - (binaryfunc)0, /*nb_multiply*/ - /* nb_divide removed in Python 3 */ -#if PY_VERSION_HEX < 0x03000000 - (binaryfunc)0, /*nb_divide*/ -#endif - (binaryfunc)0, /*nb_remainder*/ - (binaryfunc)0, /*nb_divmod*/ - (ternaryfunc)0,/*nb_power*/ - (unaryfunc)0, /*nb_negative*/ - (unaryfunc)0, /*nb_positive*/ - (unaryfunc)0, /*nb_absolute*/ - (inquiry)0, /*nb_nonzero*/ - 0, /*nb_invert*/ - 0, /*nb_lshift*/ - 0, /*nb_rshift*/ - 0, /*nb_and*/ - 0, /*nb_xor*/ - 0, /*nb_or*/ -#if PY_VERSION_HEX < 0x03000000 - 0, /*nb_coerce*/ -#endif - (unaryfunc)SwigPyObject_long, /*nb_int*/ -#if PY_VERSION_HEX < 0x03000000 - (unaryfunc)SwigPyObject_long, /*nb_long*/ -#else - 0, /*nb_reserved*/ -#endif - (unaryfunc)0, /*nb_float*/ -#if PY_VERSION_HEX < 0x03000000 - (unaryfunc)SwigPyObject_oct, /*nb_oct*/ - (unaryfunc)SwigPyObject_hex, /*nb_hex*/ -#endif -#if PY_VERSION_HEX >= 0x03050000 /* 3.5 */ - 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 /* nb_inplace_add -> nb_inplace_matrix_multiply */ -#elif PY_VERSION_HEX >= 0x03000000 /* 3.0 */ - 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 /* nb_inplace_add -> nb_index, nb_inplace_divide removed */ -#else - 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 /* nb_inplace_add -> nb_index */ -#endif - }; - - static PyTypeObject swigpyobject_type; - static int type_init = 0; - if (!type_init) { - const PyTypeObject tmp = { -#if PY_VERSION_HEX >= 0x03000000 - PyVarObject_HEAD_INIT(NULL, 0) -#else - PyObject_HEAD_INIT(NULL) - 0, /* ob_size */ -#endif - "SwigPyObject", /* tp_name */ - sizeof(SwigPyObject), /* tp_basicsize */ - 0, /* tp_itemsize */ - (destructor)SwigPyObject_dealloc, /* tp_dealloc */ -#if PY_VERSION_HEX < 0x030800b4 - (printfunc)0, /* tp_print */ -#else - (Py_ssize_t)0, /* tp_vectorcall_offset */ -#endif - (getattrfunc)0, /* tp_getattr */ - (setattrfunc)0, /* tp_setattr */ -#if PY_VERSION_HEX >= 0x03000000 - 0, /* tp_reserved in 3.0.1, tp_compare in 3.0.0 but not used */ -#else - (cmpfunc)SwigPyObject_compare, /* tp_compare */ -#endif - (reprfunc)SwigPyObject_repr, /* tp_repr */ - &SwigPyObject_as_number, /* tp_as_number */ - 0, /* tp_as_sequence */ - 0, /* tp_as_mapping */ - (hashfunc)0, /* tp_hash */ - (ternaryfunc)0, /* tp_call */ - 0, /* tp_str */ - PyObject_GenericGetAttr, /* tp_getattro */ - 0, /* tp_setattro */ - 0, /* tp_as_buffer */ - Py_TPFLAGS_DEFAULT, /* tp_flags */ - swigobject_doc, /* tp_doc */ - 0, /* tp_traverse */ - 0, /* tp_clear */ - (richcmpfunc)SwigPyObject_richcompare,/* tp_richcompare */ - 0, /* tp_weaklistoffset */ - 0, /* tp_iter */ - 0, /* tp_iternext */ - swigobject_methods, /* tp_methods */ - 0, /* tp_members */ - 0, /* tp_getset */ - 0, /* tp_base */ - 0, /* tp_dict */ - 0, /* tp_descr_get */ - 0, /* tp_descr_set */ - 0, /* tp_dictoffset */ - 0, /* tp_init */ - 0, /* tp_alloc */ - 0, /* tp_new */ - 0, /* tp_free */ - 0, /* tp_is_gc */ - 0, /* tp_bases */ - 0, /* tp_mro */ - 0, /* tp_cache */ - 0, /* tp_subclasses */ - 0, /* tp_weaklist */ - 0, /* tp_del */ - 0, /* tp_version_tag */ -#if PY_VERSION_HEX >= 0x03040000 - 0, /* tp_finalize */ -#endif -#if PY_VERSION_HEX >= 0x03080000 - 0, /* tp_vectorcall */ -#endif -#if (PY_VERSION_HEX >= 0x03080000) && (PY_VERSION_HEX < 0x03090000) - 0, /* tp_print */ -#endif -#if PY_VERSION_HEX >= 0x030c0000 - 0, /* tp_watched */ -#endif -#if PY_VERSION_HEX >= 0x030d00a4 - 0, /* tp_versions_used */ -#endif -#ifdef COUNT_ALLOCS - 0, /* tp_allocs */ - 0, /* tp_frees */ - 0, /* tp_maxalloc */ - 0, /* tp_prev */ - 0 /* tp_next */ -#endif - }; - swigpyobject_type = tmp; - type_init = 1; - if (PyType_Ready(&swigpyobject_type) != 0) - return NULL; - } - return &swigpyobject_type; -#else - PyType_Slot slots[] = { - { Py_tp_dealloc, (void *)SwigPyObject_dealloc }, - { Py_tp_repr, (void *)SwigPyObject_repr }, - { Py_tp_getattro, (void *)PyObject_GenericGetAttr }, - { Py_tp_doc, (void *)swigobject_doc }, - { Py_tp_richcompare, (void *)SwigPyObject_richcompare }, - { Py_tp_methods, (void *)swigobject_methods }, - { Py_nb_int, (void *)SwigPyObject_long }, - { 0, NULL } - }; - PyType_Spec spec = { - "SwigPyObject", - sizeof(SwigPyObject), - 0, - Py_TPFLAGS_DEFAULT|Py_TPFLAGS_BASETYPE, - slots - }; - return (PyTypeObject *)PyType_FromSpec(&spec); -#endif -} - -SWIGRUNTIME PyObject * -SwigPyObject_New(void *ptr, swig_type_info *ty, int own) -{ - SwigPyObject *sobj = PyObject_New(SwigPyObject, SwigPyObject_type()); - if (sobj) { - sobj->ptr = ptr; - sobj->ty = ty; - sobj->own = own; - sobj->next = 0; -#ifdef SWIGPYTHON_BUILTIN - sobj->dict = 0; -#endif - if (own == SWIG_POINTER_OWN) { - /* Obtain a reference to the Python capsule wrapping the module information, so that the - * module information is correctly destroyed after all SWIG python objects have been freed - * by the GC (and corresponding destructors invoked) */ - SWIG_Py_XINCREF(Swig_Capsule_global); - } - } - return (PyObject *)sobj; -} - -/* ----------------------------------------------------------------------------- - * Implements a simple Swig Packed type, and use it instead of string - * ----------------------------------------------------------------------------- */ - -typedef struct { - PyObject_HEAD - void *pack; - swig_type_info *ty; - size_t size; -} SwigPyPacked; - -SWIGRUNTIME PyObject * -SwigPyPacked_repr(SwigPyPacked *v) -{ - char result[SWIG_BUFFER_SIZE]; - if (SWIG_PackDataName(result, v->pack, v->size, 0, sizeof(result))) { - return SWIG_Python_str_FromFormat("", result, v->ty->name); - } else { - return SWIG_Python_str_FromFormat("", v->ty->name); - } -} - -SWIGRUNTIME PyObject * -SwigPyPacked_str(SwigPyPacked *v) -{ - char result[SWIG_BUFFER_SIZE]; - if (SWIG_PackDataName(result, v->pack, v->size, 0, sizeof(result))){ - return SWIG_Python_str_FromFormat("%s%s", result, v->ty->name); - } else { - return SWIG_Python_str_FromChar(v->ty->name); - } -} - -SWIGRUNTIME int -SwigPyPacked_compare(SwigPyPacked *v, SwigPyPacked *w) -{ - size_t i = v->size; - size_t j = w->size; - int s = (i < j) ? -1 : ((i > j) ? 1 : 0); - return s ? s : strncmp((const char *)v->pack, (const char *)w->pack, 2*v->size); -} - -SWIGRUNTIME PyTypeObject* SwigPyPacked_TypeOnce(void); - -SWIGRUNTIME PyTypeObject* -SwigPyPacked_type(void) { - static PyTypeObject *SWIG_STATIC_POINTER(type) = SwigPyPacked_TypeOnce(); - return type; -} - -SWIGRUNTIMEINLINE int -SwigPyPacked_Check(PyObject *op) { -#ifdef Py_LIMITED_API - int cmp; - PyObject *tp_name; -#endif - PyTypeObject* op_type = Py_TYPE(op); - if (op_type == SwigPyPacked_TypeOnce()) - return 1; -#ifdef Py_LIMITED_API - tp_name = PyObject_GetAttrString((PyObject *)op_type, "__name__"); - if (!tp_name) - return 0; - cmp = PyUnicode_CompareWithASCIIString(tp_name, "SwigPyPacked"); - SWIG_Py_DECREF(tp_name); - return cmp == 0; -#else - return (strcmp(op_type->tp_name, "SwigPyPacked") == 0); -#endif -} - -SWIGRUNTIME void -SwigPyPacked_dealloc(PyObject *v) -{ - if (SwigPyPacked_Check(v)) { - SwigPyPacked *sobj = (SwigPyPacked *) v; - free(sobj->pack); - } - PyObject_Free(v); -} - -SWIGRUNTIME PyTypeObject* -SwigPyPacked_TypeOnce(void) { - static char swigpacked_doc[] = "Swig object carries a C/C++ instance pointer"; -#ifndef SWIG_HEAPTYPES - static PyTypeObject swigpypacked_type; - static int type_init = 0; - if (!type_init) { - const PyTypeObject tmp = { -#if PY_VERSION_HEX>=0x03000000 - PyVarObject_HEAD_INIT(NULL, 0) -#else - PyObject_HEAD_INIT(NULL) - 0, /* ob_size */ -#endif - "SwigPyPacked", /* tp_name */ - sizeof(SwigPyPacked), /* tp_basicsize */ - 0, /* tp_itemsize */ - (destructor)SwigPyPacked_dealloc, /* tp_dealloc */ -#if PY_VERSION_HEX < 0x030800b4 - (printfunc)0, /* tp_print */ -#else - (Py_ssize_t)0, /* tp_vectorcall_offset */ -#endif - (getattrfunc)0, /* tp_getattr */ - (setattrfunc)0, /* tp_setattr */ -#if PY_VERSION_HEX>=0x03000000 - 0, /* tp_reserved in 3.0.1 */ -#else - (cmpfunc)SwigPyPacked_compare, /* tp_compare */ -#endif - (reprfunc)SwigPyPacked_repr, /* tp_repr */ - 0, /* tp_as_number */ - 0, /* tp_as_sequence */ - 0, /* tp_as_mapping */ - (hashfunc)0, /* tp_hash */ - (ternaryfunc)0, /* tp_call */ - (reprfunc)SwigPyPacked_str, /* tp_str */ - PyObject_GenericGetAttr, /* tp_getattro */ - 0, /* tp_setattro */ - 0, /* tp_as_buffer */ - Py_TPFLAGS_DEFAULT, /* tp_flags */ - swigpacked_doc, /* tp_doc */ - 0, /* tp_traverse */ - 0, /* tp_clear */ - 0, /* tp_richcompare */ - 0, /* tp_weaklistoffset */ - 0, /* tp_iter */ - 0, /* tp_iternext */ - 0, /* tp_methods */ - 0, /* tp_members */ - 0, /* tp_getset */ - 0, /* tp_base */ - 0, /* tp_dict */ - 0, /* tp_descr_get */ - 0, /* tp_descr_set */ - 0, /* tp_dictoffset */ - 0, /* tp_init */ - 0, /* tp_alloc */ - 0, /* tp_new */ - 0, /* tp_free */ - 0, /* tp_is_gc */ - 0, /* tp_bases */ - 0, /* tp_mro */ - 0, /* tp_cache */ - 0, /* tp_subclasses */ - 0, /* tp_weaklist */ - 0, /* tp_del */ - 0, /* tp_version_tag */ -#if PY_VERSION_HEX >= 0x03040000 - 0, /* tp_finalize */ -#endif -#if PY_VERSION_HEX >= 0x03080000 - 0, /* tp_vectorcall */ -#endif -#if (PY_VERSION_HEX >= 0x03080000) && (PY_VERSION_HEX < 0x03090000) - 0, /* tp_print */ -#endif -#if PY_VERSION_HEX >= 0x030c0000 - 0, /* tp_watched */ -#endif -#if PY_VERSION_HEX >= 0x030d00a4 - 0, /* tp_versions_used */ -#endif -#ifdef COUNT_ALLOCS - 0, /* tp_allocs */ - 0, /* tp_frees */ - 0, /* tp_maxalloc */ - 0, /* tp_prev */ - 0 /* tp_next */ -#endif - }; - swigpypacked_type = tmp; - type_init = 1; - if (PyType_Ready(&swigpypacked_type) != 0) - return NULL; - } - return &swigpypacked_type; -#else - PyType_Slot slots[] = { - { Py_tp_dealloc, (void *)SwigPyPacked_dealloc }, - { Py_tp_repr, (void *)SwigPyPacked_repr }, - { Py_tp_str, (void *)SwigPyPacked_str }, - { Py_tp_getattro, (void *)PyObject_GenericGetAttr }, - { Py_tp_doc, (void *)swigpacked_doc }, - { 0, NULL } - }; - PyType_Spec spec = { - "SwigPyPacked", - sizeof(SwigPyPacked), - 0, - Py_TPFLAGS_DEFAULT, - slots - }; - return (PyTypeObject *)PyType_FromSpec(&spec); -#endif -} - -SWIGRUNTIME PyObject * -SwigPyPacked_New(void *ptr, size_t size, swig_type_info *ty) -{ - SwigPyPacked *sobj = PyObject_New(SwigPyPacked, SwigPyPacked_type()); - if (sobj) { - void *pack = malloc(size); - if (pack) { - memcpy(pack, ptr, size); - sobj->pack = pack; - sobj->ty = ty; - sobj->size = size; - } else { - PyObject_Free((PyObject *)sobj); - sobj = 0; - } - } - return (PyObject *) sobj; -} - -SWIGRUNTIME swig_type_info * -SwigPyPacked_UnpackData(PyObject *obj, void *ptr, size_t size) -{ - if (SwigPyPacked_Check(obj)) { - SwigPyPacked *sobj = (SwigPyPacked *)obj; - if (sobj->size != size) return 0; - memcpy(ptr, sobj->pack, size); - return sobj->ty; - } else { - return 0; - } -} - -/* ----------------------------------------------------------------------------- - * pointers/data manipulation - * ----------------------------------------------------------------------------- */ - -static PyObject *Swig_This_global = NULL; - -SWIGRUNTIME PyObject * -SWIG_This(void) -{ - if (Swig_This_global == NULL) - Swig_This_global = SWIG_Python_str_FromChar("this"); - return Swig_This_global; -} - -/* #define SWIG_PYTHON_SLOW_GETSET_THIS */ - -/* TODO: I don't know how to implement the fast getset in Python 3 right now */ -#if PY_VERSION_HEX>=0x03000000 -#define SWIG_PYTHON_SLOW_GETSET_THIS -#endif - -SWIGRUNTIME SwigPyObject * -SWIG_Python_GetSwigThis(PyObject *pyobj) -{ - PyObject *obj; - - if (SwigPyObject_Check(pyobj)) - return (SwigPyObject *) pyobj; - -#ifdef SWIGPYTHON_BUILTIN - (void)obj; -# ifdef PyWeakref_CheckProxy - if (PyWeakref_CheckProxy(pyobj)) { -#if PY_VERSION_HEX >= 0x030d0000 - PyWeakref_GetRef(pyobj, &pyobj); 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PyDict_GetItem(dict, SWIG_This()) : 0; - } else { -#ifdef PyWeakref_CheckProxy - if (PyWeakref_CheckProxy(pyobj)) { - PyObject *wobj = PyWeakref_GET_OBJECT(pyobj); - return wobj ? SWIG_Python_GetSwigThis(wobj) : 0; - } -#endif - obj = PyObject_GetAttr(pyobj,SWIG_This()); - if (obj) { - SWIG_Py_DECREF(obj); - } else { - if (PyErr_Occurred()) PyErr_Clear(); - return 0; - } - } - } -#else - obj = PyObject_GetAttr(pyobj,SWIG_This()); - if (obj) { - SWIG_Py_DECREF(obj); - } else { - if (PyErr_Occurred()) PyErr_Clear(); - return 0; - } -#endif - if (obj && !SwigPyObject_Check(obj)) { - /* a PyObject is called 'this', try to get the 'real this' - SwigPyObject from it */ - return SWIG_Python_GetSwigThis(obj); - } - return (SwigPyObject *)obj; -#endif -} - -/* Acquire a pointer value */ - -SWIGRUNTIME int -SWIG_Python_AcquirePtr(PyObject *obj, int own) { - if (own == SWIG_POINTER_OWN) { - SwigPyObject *sobj = SWIG_Python_GetSwigThis(obj); - if (sobj) { - int oldown = sobj->own; - sobj->own = own; - return oldown; - } - } - return 0; -} - -/* Convert a pointer value */ - -SWIGRUNTIME int -SWIG_Python_ConvertPtrAndOwn(PyObject *obj, void **ptr, swig_type_info *ty, int flags, int *own) { - int res; - SwigPyObject *sobj; - int implicit_conv = (flags & SWIG_POINTER_IMPLICIT_CONV) != 0; - - if (!obj) - return SWIG_ERROR; - if (obj == Py_None && !implicit_conv) { - if (ptr) - *ptr = 0; - return (flags & SWIG_POINTER_NO_NULL) ? 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(SwigPyClientData *) ty->clientdata : 0; - if (data && !data->implicitconv) { - PyObject *klass = data->klass; - if (klass) { - PyObject *impconv; - data->implicitconv = 1; /* avoid recursion and call 'explicit' constructors*/ - impconv = SWIG_Python_CallFunctor(klass, obj); - data->implicitconv = 0; - if (PyErr_Occurred()) { - PyErr_Clear(); - impconv = 0; - } - if (impconv) { - SwigPyObject *iobj = SWIG_Python_GetSwigThis(impconv); - if (iobj) { - void *vptr; - res = SWIG_Python_ConvertPtrAndOwn((PyObject*)iobj, &vptr, ty, 0, 0); - if (SWIG_IsOK(res)) { - if (ptr) { - *ptr = vptr; - /* transfer the ownership to 'ptr' */ - iobj->own = 0; - res = SWIG_AddCast(res); - res = SWIG_AddNewMask(res); - } else { - res = SWIG_AddCast(res); - } - } - } - SWIG_Py_DECREF(impconv); - } - } - } - if (!SWIG_IsOK(res) && obj == Py_None) { - if (ptr) - *ptr = 0; - if (PyErr_Occurred()) - PyErr_Clear(); - res = SWIG_OK; - } - } - } - return res; -} - -/* Convert a function ptr value */ - -SWIGRUNTIME int -SWIG_Python_ConvertFunctionPtr(PyObject *obj, void **ptr, swig_type_info *ty) { - if (!PyCFunction_Check(obj)) { - return SWIG_ConvertPtr(obj, ptr, ty, 0); - } else { - void *vptr = 0; - swig_cast_info *tc; - - /* here we get the method pointer for callbacks */ -#ifndef Py_LIMITED_API - const char *doc = (((PyCFunctionObject *)obj) -> m_ml -> ml_doc); -#else - PyObject* pystr_doc = PyObject_GetAttrString(obj, "__doc__"); - PyObject *bytes = NULL; - const char *doc = pystr_doc ? SWIG_PyUnicode_AsUTF8AndSize(pystr_doc, NULL, &bytes) : 0; -#endif - const char *desc = doc ? strstr(doc, "swig_ptr: ") : 0; - if (desc) - desc = ty ? SWIG_UnpackVoidPtr(desc + 10, &vptr, ty->name) : 0; -#ifdef Py_LIMITED_API - SWIG_Py_XDECREF(bytes); - SWIG_Py_XDECREF(pystr_doc); -#endif - if (!desc) - return SWIG_ERROR; - tc = SWIG_TypeCheck(desc,ty); - if (tc) { - int newmemory = 0; - *ptr = SWIG_TypeCast(tc,vptr,&newmemory); - assert(!newmemory); /* newmemory handling not yet implemented */ - } else { - return SWIG_ERROR; - } - return SWIG_OK; - } -} - -/* Convert a packed pointer value */ - -SWIGRUNTIME int -SWIG_Python_ConvertPacked(PyObject *obj, void *ptr, size_t sz, swig_type_info *ty) { - swig_type_info *to = SwigPyPacked_UnpackData(obj, ptr, sz); - if (!to) return SWIG_ERROR; - if (ty) { - if (to != ty) { - /* check type cast? */ - swig_cast_info *tc = SWIG_TypeCheck(to->name,ty); - if (!tc) return SWIG_ERROR; - } - } - return SWIG_OK; -} - -/* ----------------------------------------------------------------------------- - * Create a new pointer object - * ----------------------------------------------------------------------------- */ - -/* - Create a new instance object, without calling __init__, and set the - 'this' attribute. -*/ - -SWIGRUNTIME PyObject* -SWIG_Python_NewShadowInstance(SwigPyClientData *data, PyObject *swig_this) -{ - PyObject *inst = 0; - PyObject *newraw = data->newraw; - if (newraw) { - inst = PyObject_Call(newraw, data->newargs, NULL); - if (inst) { -#if !defined(SWIG_PYTHON_SLOW_GETSET_THIS) - PyObject **dictptr = _PyObject_GetDictPtr(inst); - if (dictptr != NULL) { - PyObject *dict = *dictptr; - if (dict == NULL) { - dict = PyDict_New(); - *dictptr = dict; - } - if (dict) { - PyDict_SetItem(dict, SWIG_This(), swig_this); - } else{ - SWIG_Py_DECREF(inst); - inst = 0; - } - } -#else - if (PyObject_SetAttr(inst, SWIG_This(), swig_this) == -1) { - SWIG_Py_DECREF(inst); - inst = 0; - } -#endif - } - } else { -#if PY_VERSION_HEX >= 0x03000000 - PyObject *empty_args = PyTuple_New(0); - if (empty_args) { - PyObject *empty_kwargs = PyDict_New(); - if (empty_kwargs) { -#ifndef Py_LIMITED_API - newfunc newfn = ((PyTypeObject *)data->newargs)->tp_new; -#else - newfunc newfn = (newfunc)PyType_GetSlot((PyTypeObject *)data->newargs, Py_tp_new); -#endif - inst = newfn((PyTypeObject *)data->newargs, empty_args, empty_kwargs); - SWIG_Py_DECREF(empty_kwargs); - if (inst) { - if (PyObject_SetAttr(inst, SWIG_This(), swig_this) == -1) { - SWIG_Py_DECREF(inst); - inst = 0; - } else { - PyType_Modified(Py_TYPE(inst)); - } - } - } - SWIG_Py_DECREF(empty_args); - } -#else - PyObject *dict = PyDict_New(); - if (dict) { - PyDict_SetItem(dict, SWIG_This(), swig_this); - inst = PyInstance_NewRaw(data->newargs, dict); - SWIG_Py_DECREF(dict); - } -#endif - } - return inst; -} - -SWIGRUNTIME int -SWIG_Python_SetSwigThis(PyObject *inst, PyObject *swig_this) -{ -#if !defined(SWIG_PYTHON_SLOW_GETSET_THIS) - PyObject **dictptr = _PyObject_GetDictPtr(inst); - if (dictptr != NULL) { - PyObject *dict = *dictptr; - if (dict == NULL) { - dict = PyDict_New(); - *dictptr = dict; - } - if (dict) { - return PyDict_SetItem(dict, SWIG_This(), swig_this); - } else{ - return -1; - } - } -#endif - return PyObject_SetAttr(inst, SWIG_This(), swig_this); -} - - -SWIGINTERN PyObject * -SWIG_Python_InitShadowInstance(PyObject *args) { - PyObject *obj[2]; - if (!SWIG_Python_UnpackTuple(args, "swiginit", 2, 2, obj)) { - return NULL; - } else { - SwigPyObject *sthis = SWIG_Python_GetSwigThis(obj[0]); - if (sthis) { - SWIG_Py_DECREF(SwigPyObject_append((PyObject*) sthis, obj[1])); - } else { - if (SWIG_Python_SetSwigThis(obj[0], obj[1]) != 0) - return NULL; - } - return SWIG_Py_Void(); - } -} - -/* Create a new pointer object */ - -SWIGRUNTIME PyObject * -SWIG_Python_NewPointerObj(PyObject *self, void *ptr, swig_type_info *type, int flags) { - SwigPyClientData *clientdata; - PyObject * robj; - int own; - - if (!ptr) - return SWIG_Py_Void(); - - clientdata = type ? (SwigPyClientData *)(type->clientdata) : 0; - own = (flags & SWIG_POINTER_OWN) ? SWIG_POINTER_OWN : 0; - if (clientdata && clientdata->pytype) { - SwigPyObject *newobj; - if (flags & SWIG_BUILTIN_TP_INIT) { - newobj = (SwigPyObject*) self; - if (newobj->ptr) { -#ifndef Py_LIMITED_API - allocfunc alloc = clientdata->pytype->tp_alloc; -#else - allocfunc alloc = (allocfunc)PyType_GetSlot(clientdata->pytype, Py_tp_alloc); -#endif - PyObject *next_self = alloc(clientdata->pytype, 0); - while (newobj->next) - newobj = (SwigPyObject *) newobj->next; - newobj->next = next_self; - newobj = (SwigPyObject *)next_self; -#ifdef SWIGPYTHON_BUILTIN - newobj->dict = 0; -#endif - } - } else { - newobj = PyObject_New(SwigPyObject, clientdata->pytype); -#ifdef SWIGPYTHON_BUILTIN - if (newobj) { - newobj->dict = 0; - } -#endif - } - if (newobj) { - newobj->ptr = ptr; - newobj->ty = type; - newobj->own = own; - newobj->next = 0; - return (PyObject*) newobj; - } - return SWIG_Py_Void(); - } - - assert(!(flags & SWIG_BUILTIN_TP_INIT)); - - robj = SwigPyObject_New(ptr, type, own); - if (robj && clientdata && !(flags & SWIG_POINTER_NOSHADOW)) { - PyObject *inst = SWIG_Python_NewShadowInstance(clientdata, robj); - SWIG_Py_DECREF(robj); - robj = inst; - } - return robj; -} - -/* Create a new packed object */ - -SWIGRUNTIMEINLINE PyObject * -SWIG_Python_NewPackedObj(void *ptr, size_t sz, swig_type_info *type) { - return ptr ? SwigPyPacked_New((void *) ptr, sz, type) : SWIG_Py_Void(); -} - -/* -----------------------------------------------------------------------------* - * Get type list - * -----------------------------------------------------------------------------*/ - -#ifdef SWIG_LINK_RUNTIME -void *SWIG_ReturnGlobalTypeList(void *); -#endif - -static PyObject *Swig_TypeCache_global = NULL; - -/* The python cached type query */ -SWIGRUNTIME PyObject * -SWIG_Python_TypeCache(void) { - if (Swig_TypeCache_global == NULL) { - Swig_TypeCache_global = PyDict_New(); - } - return Swig_TypeCache_global; -} - -SWIGRUNTIME swig_module_info * -SWIG_Python_GetModule(void *SWIGUNUSEDPARM(clientdata)) { -#ifdef SWIG_LINK_RUNTIME - static void *type_pointer = (void *)0; - /* first check if module already created */ - if (!type_pointer) { - type_pointer = SWIG_ReturnGlobalTypeList((void *)0); - } -#else - void *type_pointer = PyCapsule_Import(SWIGPY_CAPSULE_NAME, 0); - if (PyErr_Occurred()) { - PyErr_Clear(); - type_pointer = (void *)0; - } -#endif - return (swig_module_info *) type_pointer; -} - - -static int interpreter_counter = 0; /* how many (sub-)interpreters are using swig_module's types */ - -SWIGRUNTIME void -SWIG_Python_DestroyModule(PyObject *obj) -{ - swig_module_info *swig_module = (swig_module_info *) PyCapsule_GetPointer(obj, SWIGPY_CAPSULE_NAME); - swig_type_info **types = swig_module->types; - size_t i; - if (--interpreter_counter != 0) /* another sub-interpreter may still be using the swig_module's types */ - return; - for (i =0; i < swig_module->size; ++i) { - swig_type_info *ty = types[i]; - if (ty->owndata) { - SwigPyClientData *data = (SwigPyClientData *) ty->clientdata; - ty->clientdata = 0; - if (data) SwigPyClientData_Del(data); - } - } - SWIG_Py_DECREF(SWIG_This()); - Swig_This_global = NULL; - SWIG_Py_DECREF(SWIG_globals()); - Swig_Globals_global = NULL; - SWIG_Py_DECREF(SWIG_Python_TypeCache()); - Swig_TypeCache_global = NULL; - Swig_Capsule_global = NULL; -} - -SWIGRUNTIME void -SWIG_Python_SetModule(swig_module_info *swig_module) { -#if PY_VERSION_HEX >= 0x03000000 - /* Add a dummy module object into sys.modules */ - PyObject *module = PyImport_AddModule("swig_runtime_data" SWIG_RUNTIME_VERSION); -#else - static PyMethodDef swig_empty_runtime_method_table[] = { {NULL, NULL, 0, NULL} }; /* Sentinel */ - PyObject *module = Py_InitModule("swig_runtime_data" SWIG_RUNTIME_VERSION, swig_empty_runtime_method_table); -#endif - PyObject *pointer = PyCapsule_New((void *) swig_module, SWIGPY_CAPSULE_NAME, SWIG_Python_DestroyModule); - if (pointer && module) { - if (PyModule_AddObject(module, SWIGPY_CAPSULE_ATTR_NAME, pointer) == 0) { - ++interpreter_counter; - Swig_Capsule_global = pointer; - } else { - SWIG_Py_DECREF(pointer); - } - } else { - SWIG_Py_XDECREF(pointer); - } -} - -SWIGRUNTIME swig_type_info * -SWIG_Python_TypeQuery(const char *type) -{ - PyObject *cache = SWIG_Python_TypeCache(); - PyObject *key = SWIG_Python_str_FromChar(type); - PyObject *obj = PyDict_GetItem(cache, key); - swig_type_info *descriptor; - if (obj) { - descriptor = (swig_type_info *) PyCapsule_GetPointer(obj, NULL); - } else { - swig_module_info *swig_module = SWIG_GetModule(0); - descriptor = SWIG_TypeQueryModule(swig_module, swig_module, type); - if (descriptor) { - obj = PyCapsule_New((void*) descriptor, NULL, NULL); - if (obj) { - PyDict_SetItem(cache, key, obj); - SWIG_Py_DECREF(obj); - } - } - } - SWIG_Py_DECREF(key); - return descriptor; -} - -/* - For backward compatibility only -*/ -#define SWIG_POINTER_EXCEPTION 0 -#define SWIG_arg_fail(arg) SWIG_Python_ArgFail(arg) -#define SWIG_MustGetPtr(p, type, argnum, flags) SWIG_Python_MustGetPtr(p, type, argnum, flags) - -SWIGRUNTIME int -SWIG_Python_AddErrMesg(const char* mesg, int infront) -{ - if (PyErr_Occurred()) { - PyObject *type = 0; - PyObject *value = 0; - PyObject *traceback = 0; - PyErr_Fetch(&type, &value, &traceback); - if (value) { - PyObject *old_str = PyObject_Str(value); - PyObject *bytes = NULL; - const char *tmp = SWIG_PyUnicode_AsUTF8AndSize(old_str, NULL, &bytes); - const char *errmesg = tmp ? tmp : "Invalid error message"; - SWIG_Py_XINCREF(type); - PyErr_Clear(); - if (infront) { - PyErr_Format(type, "%s %s", mesg, errmesg); - } else { - PyErr_Format(type, "%s %s", errmesg, mesg); - } - SWIG_Py_XDECREF(bytes); - SWIG_Py_DECREF(old_str); - } - return 1; - } else { - return 0; - } -} - -SWIGRUNTIME int -SWIG_Python_ArgFail(int argnum) -{ - if (PyErr_Occurred()) { - /* add information about failing argument */ - char mesg[256]; - PyOS_snprintf(mesg, sizeof(mesg), "argument number %d:", argnum); - return SWIG_Python_AddErrMesg(mesg, 1); - } else { - return 0; - } -} - -SWIGRUNTIMEINLINE const char * -SwigPyObject_GetDesc(PyObject *self) -{ - SwigPyObject *v = (SwigPyObject *)self; - swig_type_info *ty = v ? v->ty : 0; - return ty ? ty->str : ""; -} - -SWIGRUNTIME void -SWIG_Python_TypeError(const char *type, PyObject *obj) -{ - (void) obj; - if (type) { -#if defined(SWIG_COBJECT_TYPES) - if (obj && SwigPyObject_Check(obj)) { - const char *otype = (const char *) SwigPyObject_GetDesc(obj); - if (otype) { - PyErr_Format(PyExc_TypeError, "a '%s' is expected, 'SwigPyObject(%s)' is received", - type, otype); - return; - } - } else -#endif - { -#ifndef Py_LIMITED_API - /* tp_name is not accessible */ - const char *otype = (obj ? obj->ob_type->tp_name : 0); - if (otype) { - PyObject *str = PyObject_Str(obj); - PyObject *bytes = NULL; - const char *cstr = str ? SWIG_PyUnicode_AsUTF8AndSize(str, NULL, &bytes) : 0; - if (cstr) { - PyErr_Format(PyExc_TypeError, "a '%s' is expected, '%s(%s)' is received", - type, otype, cstr); - } else { - PyErr_Format(PyExc_TypeError, "a '%s' is expected, '%s' is received", - type, otype); - } - SWIG_Py_XDECREF(bytes); - SWIG_Py_XDECREF(str); - return; - } -#endif - } - PyErr_Format(PyExc_TypeError, "a '%s' is expected", type); - } else { - PyErr_Format(PyExc_TypeError, "unexpected type is received"); - } -} - - -/* Convert a pointer value, signal an exception on a type mismatch */ -SWIGRUNTIME void * -SWIG_Python_MustGetPtr(PyObject *obj, swig_type_info *ty, int SWIGUNUSEDPARM(argnum), int flags) { - void *result; - if (SWIG_Python_ConvertPtr(obj, &result, ty, flags) == -1) { - PyErr_Clear(); - } - return result; -} - -#ifdef SWIGPYTHON_BUILTIN -SWIGRUNTIME int -SWIG_Python_NonDynamicSetAttr(PyObject *obj, PyObject *name, PyObject *value) { - PyTypeObject *tp = obj->ob_type; - PyObject *descr; - PyObject *encoded_name; - descrsetfunc f; - int res = -1; - -# ifdef Py_USING_UNICODE - if (PyString_Check(name)) { - name = PyUnicode_Decode(PyString_AsString(name), PyString_Size(name), NULL, NULL); - if (!name) - return -1; - } else if (!PyUnicode_Check(name)) -# else - if (!PyString_Check(name)) -# endif - { - PyErr_Format(PyExc_TypeError, "attribute name must be string, not '%.200s'", name->ob_type->tp_name); - return -1; - } else { - SWIG_Py_INCREF(name); - } - - if (!tp->tp_dict) { - if (PyType_Ready(tp) != 0) - goto done; - } - - descr = _PyType_Lookup(tp, name); - f = NULL; - if (descr != NULL) - f = descr->ob_type->tp_descr_set; - if (!f) { - if (PyString_Check(name)) { - encoded_name = name; - SWIG_Py_INCREF(name); - } else { - encoded_name = PyUnicode_AsUTF8String(name); - if (!encoded_name) - goto done; - } - PyErr_Format(PyExc_AttributeError, "'%.100s' object has no attribute '%.200s'", tp->tp_name, PyString_AsString(encoded_name)); - SWIG_Py_DECREF(encoded_name); - } else { - res = f(descr, obj, value); - } - - done: - SWIG_Py_DECREF(name); - return res; -} -#endif - - -#ifdef __cplusplus -} -#endif - - - -#define SWIG_exception_fail(code, msg) do { SWIG_Error(code, msg); SWIG_fail; } while(0) - -#define SWIG_contract_assert(expr, msg) do { if (!(expr)) { SWIG_Error(SWIG_RuntimeError, msg); SWIG_fail; } } while (0) - - - -/* -------- TYPES TABLE (BEGIN) -------- */ - -#define SWIGTYPE_p_char swig_types[0] -#define SWIGTYPE_p_p_char swig_types[1] -static swig_type_info *swig_types[3]; -static swig_module_info swig_module = {swig_types, 2, 0, 0, 0, 0}; -#define SWIG_TypeQuery(name) SWIG_TypeQueryModule(&swig_module, &swig_module, name) -#define SWIG_MangledTypeQuery(name) SWIG_MangledTypeQueryModule(&swig_module, &swig_module, name) - -/* -------- TYPES TABLE (END) -------- */ - -#ifdef SWIG_TypeQuery -# undef SWIG_TypeQuery -#endif -#define SWIG_TypeQuery SWIG_Python_TypeQuery - -/*----------------------------------------------- - @(target):= _binding.so - ------------------------------------------------*/ -#if PY_VERSION_HEX >= 0x03000000 -# define SWIG_init PyInit__binding - -#else -# define SWIG_init init_binding - -#endif - -#define SWIG_as_voidptr(a) (void *)((const void *)(a)) -#define SWIG_as_voidptrptr(a) ((void)SWIG_as_voidptr(*a),(void**)(a)) - - -#include "ExampleHaskellWheelBinding_stub.h" - -char * unsafe_hs_pyeggp_version() { - return hs_pyeggp_version(); -} - -int unsafe_hs_pyeggp_main() { - return hs_pyeggp_main(); -} - -char * unsafe_hs_pyeggp(char **n) { - return hs_pyeggp(&n); -} - -void unsafe_hs_pyeggp_init(int argc, char **argv) { - hs_init(&argc, &argv); -} - -void unsafe_hs_pyeggp_exit() { - hs_exit(); -} - -void unsafe_py_write_stdout(const char * str) { - PySys_FormatStdout("%s", str); -} - -void unsafe_py_write_stderr(const char * str) { - PySys_FormatStderr("%s", str); -} - - -SWIGINTERN swig_type_info* -SWIG_pchar_descriptor(void) -{ - static swig_type_info* info = 0; - if (!info) { - info = SWIG_TypeQuery("_p_char"); - } - return info; -} - - -SWIGINTERNINLINE PyObject * -SWIG_FromCharPtrAndSize(const char* carray, size_t size) -{ - if (carray) { - if (size > INT_MAX) { - swig_type_info* pchar_descriptor = SWIG_pchar_descriptor(); - return pchar_descriptor ? - SWIG_InternalNewPointerObj((char *)(carray), pchar_descriptor, 0) : SWIG_Py_Void(); - } else { -#if PY_VERSION_HEX >= 0x03000000 -#if defined(SWIG_PYTHON_STRICT_BYTE_CHAR) - return PyBytes_FromStringAndSize(carray, (Py_ssize_t)(size)); -#else - return PyUnicode_DecodeUTF8(carray, (Py_ssize_t)(size), "surrogateescape"); -#endif -#else - return PyString_FromStringAndSize(carray, (Py_ssize_t)(size)); -#endif - } - } else { - return SWIG_Py_Void(); - } -} - - -SWIGINTERNINLINE PyObject * -SWIG_FromCharPtr(const char *cptr) -{ - return SWIG_FromCharPtrAndSize(cptr, (cptr ? strlen(cptr) : 0)); -} - - -SWIGINTERNINLINE PyObject* - SWIG_From_int (int value) -{ - return PyInt_FromLong((long) value); -} - - -#include -#if !defined(SWIG_NO_LLONG_MAX) -# if !defined(LLONG_MAX) && defined(__GNUC__) && defined (__LONG_LONG_MAX__) -# define LLONG_MAX __LONG_LONG_MAX__ -# define LLONG_MIN (-LLONG_MAX - 1LL) -# define ULLONG_MAX (LLONG_MAX * 2ULL + 1ULL) -# endif -#endif - - -SWIGINTERN int -SWIG_AsVal_double (PyObject *obj, double *val) -{ - int res = SWIG_TypeError; - if (PyFloat_Check(obj)) { - if (val) *val = PyFloat_AsDouble(obj); - return SWIG_OK; -#if PY_VERSION_HEX < 0x03000000 - } else if (PyInt_Check(obj)) { - if (val) *val = (double) PyInt_AsLong(obj); - return SWIG_OK; -#endif - } else if (PyLong_Check(obj)) { - double v = PyLong_AsDouble(obj); - if (!PyErr_Occurred()) { - if (val) *val = v; - return SWIG_OK; - } else { - PyErr_Clear(); - } - } -#ifdef SWIG_PYTHON_CAST_MODE - { - int dispatch = 0; - double d = PyFloat_AsDouble(obj); - if (!PyErr_Occurred()) { - if (val) *val = d; - return SWIG_AddCast(SWIG_OK); - } else { - PyErr_Clear(); - } - if (!dispatch) { - long v = PyLong_AsLong(obj); - if (!PyErr_Occurred()) { - if (val) *val = v; - return SWIG_AddCast(SWIG_AddCast(SWIG_OK)); - } else { - PyErr_Clear(); - } - } - } -#endif - return res; -} - - -#include - - -#include - - -SWIGINTERNINLINE int -SWIG_CanCastAsInteger(double *d, double min, double max) { - double x = *d; - if ((min <= x && x <= max)) { - double fx, cx, rd; - errno = 0; - fx = floor(x); - cx = ceil(x); - rd = ((x - fx) < 0.5) ? fx : cx; /* simple rint */ - if ((errno == EDOM) || (errno == ERANGE)) { - errno = 0; - } else { - double summ, reps, diff; - if (rd < x) { - diff = x - rd; - } else if (rd > x) { - diff = rd - x; - } else { - return 1; - } - summ = rd + x; - reps = diff/summ; - if (reps < 8*DBL_EPSILON) { - *d = rd; - return 1; - } - } - } - return 0; -} - - -SWIGINTERN int -SWIG_AsVal_long (PyObject *obj, long* val) -{ -#if PY_VERSION_HEX < 0x03000000 - if (PyInt_Check(obj)) { - if (val) *val = PyInt_AsLong(obj); - return SWIG_OK; - } else -#endif - if (PyLong_Check(obj)) { - long v = PyLong_AsLong(obj); - if (!PyErr_Occurred()) { - if (val) *val = v; - return SWIG_OK; - } else { - PyErr_Clear(); - return SWIG_OverflowError; - } - } -#ifdef SWIG_PYTHON_CAST_MODE - { - int dispatch = 0; - long v = PyInt_AsLong(obj); - if (!PyErr_Occurred()) { - if (val) *val = v; - return SWIG_AddCast(SWIG_OK); - } else { - PyErr_Clear(); - } - if (!dispatch) { - double d; - int res = SWIG_AddCast(SWIG_AsVal_double (obj,&d)); - // Largest double not larger than LONG_MAX (not portably calculated easily) - // Note that double(LONG_MAX) is stored in a double rounded up by one (for 64-bit long) - // 0x7ffffffffffffc00LL == (int64_t)std::nextafter(double(__uint128_t(LONG_MAX)+1), double(0)) - const double long_max = sizeof(long) == 8 ? 0x7ffffffffffffc00LL : LONG_MAX; - // No equivalent needed for 64-bit double(LONG_MIN) is exactly LONG_MIN - if (SWIG_IsOK(res) && SWIG_CanCastAsInteger(&d, LONG_MIN, long_max)) { - if (val) *val = (long)(d); - return res; - } - } - } -#endif - return SWIG_TypeError; -} - - -SWIGINTERN int -SWIG_AsVal_int (PyObject * obj, int *val) -{ - long v; - int res = SWIG_AsVal_long (obj, &v); - if (SWIG_IsOK(res)) { - if ((v < INT_MIN || v > INT_MAX)) { - return SWIG_OverflowError; - } else { - if (val) *val = (int)(v); - } - } - return res; -} - -#ifdef __cplusplus -extern "C" { -#endif -SWIGINTERN PyObject *_wrap_unsafe_hs_pyeggp_version(PyObject *self, PyObject *args) { - PyObject *resultobj = 0; - char *result = 0 ; - - (void)self; - if (!SWIG_Python_UnpackTuple(args, "unsafe_hs_pyeggp_version", 0, 0, 0)) SWIG_fail; - result = (char *)unsafe_hs_pyeggp_version(); - resultobj = SWIG_FromCharPtr((const char *)result); - return resultobj; -fail: - return NULL; -} - - -SWIGINTERN PyObject *_wrap_unsafe_hs_pyeggp_main(PyObject *self, PyObject *args) { - PyObject *resultobj = 0; - int result; - - (void)self; - if (!SWIG_Python_UnpackTuple(args, "unsafe_hs_pyeggp_main", 0, 0, 0)) SWIG_fail; - result = (int)unsafe_hs_pyeggp_main(); - resultobj = SWIG_From_int((int)(result)); - return resultobj; -fail: - return NULL; -} - - -SWIGINTERN PyObject *_wrap_unsafe_hs_pyeggp(PyObject *self, PyObject *args) { - PyObject *resultobj = 0; - char **arg1 = (char **) 0 ; - void *argp1 = 0 ; - int res1 = 0 ; - PyObject *swig_obj[1] ; - char *result = 0 ; - - (void)self; - if (!args) SWIG_fail; - swig_obj[0] = args; - res1 = SWIG_ConvertPtr(swig_obj[0], &argp1,SWIGTYPE_p_p_char, 0 | 0 ); - if (!SWIG_IsOK(res1)) { - SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "unsafe_hs_pyeggp" "', argument " "1"" of type '" "char **""'"); - } - arg1 = (char **)(argp1); - result = (char *)unsafe_hs_pyeggp(arg1); - resultobj = SWIG_FromCharPtr((const char *)result); - return resultobj; -fail: - return NULL; -} - - -SWIGINTERN PyObject *_wrap_unsafe_hs_pyeggp_init(PyObject *self, PyObject *args) { - PyObject *resultobj = 0; - int arg1 ; - char **arg2 = (char **) 0 ; - PyObject *swig_obj[1] ; - - (void)self; - if (!args) SWIG_fail; - swig_obj[0] = args; - { - /* Check if is a list */ - if (PyList_Check(swig_obj[0])) { - int i; - arg1 = PyList_Size(swig_obj[0]); - arg2 = (char **) malloc((arg1+1)*sizeof(char *)); - for (i = 0; i < arg1; i++) { - PyObject *o = PyList_GetItem(swig_obj[0], i); - if (PyUnicode_Check(o)) { - arg2[i] = (char *) PyUnicode_AsUTF8AndSize(o, 0); - } else { - PyErr_SetString(PyExc_TypeError, "list must contain strings"); - SWIG_fail; - } - } - arg2[i] = 0; - } else { - PyErr_SetString(PyExc_TypeError, "not a list"); - SWIG_fail; - } - } - unsafe_hs_pyeggp_init(arg1,arg2); - resultobj = SWIG_Py_Void(); - { - free((char *) arg2); - } - return resultobj; -fail: - { - free((char *) arg2); - } - return NULL; -} - - -SWIGINTERN PyObject *_wrap_unsafe_hs_pyeggp_exit(PyObject *self, PyObject *args) { - PyObject *resultobj = 0; - - (void)self; - if (!SWIG_Python_UnpackTuple(args, "unsafe_hs_pyeggp_exit", 0, 0, 0)) SWIG_fail; - unsafe_hs_pyeggp_exit(); - resultobj = SWIG_Py_Void(); - return resultobj; -fail: - return NULL; -} - - -static PyMethodDef SwigMethods[] = { - { "unsafe_hs_pyeggp_version", _wrap_unsafe_hs_pyeggp_version, METH_NOARGS, NULL}, - { "unsafe_hs_pyeggp_main", _wrap_unsafe_hs_pyeggp_main, METH_NOARGS, NULL}, - { "unsafe_hs_pyeggp", _wrap_unsafe_hs_pyeggp, METH_O, NULL}, - { "unsafe_hs_pyeggp_init", _wrap_unsafe_hs_pyeggp_init, METH_O, NULL}, - { "unsafe_hs_pyeggp_exit", _wrap_unsafe_hs_pyeggp_exit, METH_NOARGS, NULL}, - { NULL, NULL, 0, NULL } -}; - - -/* -------- TYPE CONVERSION AND EQUIVALENCE RULES (BEGIN) -------- */ - -static swig_type_info _swigt__p_char = {"_p_char", "char *", 0, 0, (void*)0, 0}; -static swig_type_info _swigt__p_p_char = {"_p_p_char", "char **", 0, 0, (void*)0, 0}; - -static swig_type_info *swig_type_initial[] = { - &_swigt__p_char, - &_swigt__p_p_char, -}; - -static swig_cast_info _swigc__p_char[] = { {&_swigt__p_char, 0, 0, 0},{0, 0, 0, 0}}; -static swig_cast_info _swigc__p_p_char[] = { {&_swigt__p_p_char, 0, 0, 0},{0, 0, 0, 0}}; - -static swig_cast_info *swig_cast_initial[] = { - _swigc__p_char, - _swigc__p_p_char, -}; - - -/* -------- TYPE CONVERSION AND EQUIVALENCE RULES (END) -------- */ - -static swig_const_info swig_const_table[] = { -{0, 0, 0, 0.0, 0, 0}}; - -#ifdef __cplusplus -} -#endif -/* ----------------------------------------------------------------------------- - * Type initialization: - * This problem is tough by the requirement that no dynamic - * memory is used. Also, since swig_type_info structures store pointers to - * swig_cast_info structures and swig_cast_info structures store pointers back - * to swig_type_info structures, we need some lookup code at initialization. - * The idea is that swig generates all the structures that are needed. - * The runtime then collects these partially filled structures. - * The SWIG_InitializeModule function takes these initial arrays out of - * swig_module, and does all the lookup, filling in the swig_module.types - * array with the correct data and linking the correct swig_cast_info - * structures together. - * - * The generated swig_type_info structures are assigned statically to an initial - * array. We just loop through that array, and handle each type individually. - * First we lookup if this type has been already loaded, and if so, use the - * loaded structure instead of the generated one. Then we have to fill in the - * cast linked list. The cast data is initially stored in something like a - * two-dimensional array. Each row corresponds to a type (there are the same - * number of rows as there are in the swig_type_initial array). Each entry in - * a column is one of the swig_cast_info structures for that type. - * The cast_initial array is actually an array of arrays, because each row has - * a variable number of columns. So to actually build the cast linked list, - * we find the array of casts associated with the type, and loop through it - * adding the casts to the list. The one last trick we need to do is making - * sure the type pointer in the swig_cast_info struct is correct. - * - * First off, we lookup the cast->type name to see if it is already loaded. - * There are three cases to handle: - * 1) If the cast->type has already been loaded AND the type we are adding - * casting info to has not been loaded (it is in this module), THEN we - * replace the cast->type pointer with the type pointer that has already - * been loaded. - * 2) If BOTH types (the one we are adding casting info to, and the - * cast->type) are loaded, THEN the cast info has already been loaded by - * the previous module so we just ignore it. - * 3) Finally, if cast->type has not already been loaded, then we add that - * swig_cast_info to the linked list (because the cast->type) pointer will - * be correct. - * ----------------------------------------------------------------------------- */ - -#ifdef __cplusplus -extern "C" { -#if 0 -} /* c-mode */ -#endif -#endif - -#if 0 -#define SWIGRUNTIME_DEBUG -#endif - -#ifndef SWIG_INIT_CLIENT_DATA_TYPE -#define SWIG_INIT_CLIENT_DATA_TYPE void * -#endif - -SWIGRUNTIME void -SWIG_InitializeModule(SWIG_INIT_CLIENT_DATA_TYPE clientdata) { - size_t i; - swig_module_info *module_head, *iter; - int init; - - /* check to see if the circular list has been setup, if not, set it up */ - if (swig_module.next==0) { - /* Initialize the swig_module */ - swig_module.type_initial = swig_type_initial; - swig_module.cast_initial = swig_cast_initial; - swig_module.next = &swig_module; - init = 1; - } else { - init = 0; - } - - /* Try and load any already created modules */ - module_head = SWIG_GetModule(clientdata); - if (!module_head) { - /* This is the first module loaded for this interpreter */ - /* so set the swig module into the interpreter */ - SWIG_SetModule(clientdata, &swig_module); - } else { - /* the interpreter has loaded a SWIG module, but has it loaded this one? */ - iter=module_head; - do { - if (iter==&swig_module) { - /* Our module is already in the list, so there's nothing more to do. */ - return; - } - iter=iter->next; - } while (iter!= module_head); - - /* otherwise we must add our module into the list */ - swig_module.next = module_head->next; - module_head->next = &swig_module; - } - - /* When multiple interpreters are used, a module could have already been initialized in - a different interpreter, but not yet have a pointer in this interpreter. - In this case, we do not want to continue adding types... everything should be - set up already */ - if (init == 0) return; - - /* Now work on filling in swig_module.types */ -#ifdef SWIGRUNTIME_DEBUG - printf("SWIG_InitializeModule: size %lu\n", (unsigned long)swig_module.size); -#endif - for (i = 0; i < swig_module.size; ++i) { - swig_type_info *type = 0; - swig_type_info *ret; - swig_cast_info *cast; - -#ifdef SWIGRUNTIME_DEBUG - printf("SWIG_InitializeModule: type %lu %s\n", (unsigned long)i, swig_module.type_initial[i]->name); -#endif - - /* if there is another module already loaded */ - if (swig_module.next != &swig_module) { - type = SWIG_MangledTypeQueryModule(swig_module.next, &swig_module, swig_module.type_initial[i]->name); - } - if (type) { - /* Overwrite clientdata field */ -#ifdef SWIGRUNTIME_DEBUG - printf("SWIG_InitializeModule: found type %s\n", type->name); -#endif - if (swig_module.type_initial[i]->clientdata) { - type->clientdata = swig_module.type_initial[i]->clientdata; -#ifdef SWIGRUNTIME_DEBUG - printf("SWIG_InitializeModule: found and overwrite type %s \n", type->name); -#endif - } - } else { - type = swig_module.type_initial[i]; - } - - /* Insert casting types */ - cast = swig_module.cast_initial[i]; - while (cast->type) { - /* Don't need to add information already in the list */ - ret = 0; -#ifdef SWIGRUNTIME_DEBUG - printf("SWIG_InitializeModule: look cast %s\n", cast->type->name); -#endif - if (swig_module.next != &swig_module) { - ret = SWIG_MangledTypeQueryModule(swig_module.next, &swig_module, cast->type->name); -#ifdef SWIGRUNTIME_DEBUG - if (ret) printf("SWIG_InitializeModule: found cast %s\n", ret->name); -#endif - } - if (ret) { - if (type == swig_module.type_initial[i]) { -#ifdef SWIGRUNTIME_DEBUG - printf("SWIG_InitializeModule: skip old type %s\n", ret->name); -#endif - cast->type = ret; - ret = 0; - } else { - /* Check for casting already in the list */ - swig_cast_info *ocast = SWIG_TypeCheck(ret->name, type); -#ifdef SWIGRUNTIME_DEBUG - if (ocast) printf("SWIG_InitializeModule: skip old cast %s\n", ret->name); -#endif - if (!ocast) ret = 0; - } - } - - if (!ret) { -#ifdef SWIGRUNTIME_DEBUG - printf("SWIG_InitializeModule: adding cast %s\n", cast->type->name); -#endif - if (type->cast) { - type->cast->prev = cast; - cast->next = type->cast; - } - type->cast = cast; - } - cast++; - } - /* Set entry in modules->types array equal to the type */ - swig_module.types[i] = type; - } - swig_module.types[i] = 0; - -#ifdef SWIGRUNTIME_DEBUG - printf("**** SWIG_InitializeModule: Cast List ******\n"); - for (i = 0; i < swig_module.size; ++i) { - int j = 0; - swig_cast_info *cast = swig_module.cast_initial[i]; - printf("SWIG_InitializeModule: type %lu %s\n", (unsigned long)i, swig_module.type_initial[i]->name); - while (cast->type) { - printf("SWIG_InitializeModule: cast type %s\n", cast->type->name); - cast++; - ++j; - } - printf("---- Total casts: %d\n",j); - } - printf("**** SWIG_InitializeModule: Cast List ******\n"); -#endif -} - -/* This function will propagate the clientdata field of type to -* any new swig_type_info structures that have been added into the list -* of equivalent types. It is like calling -* SWIG_TypeClientData(type, clientdata) a second time. -*/ -SWIGRUNTIME void -SWIG_PropagateClientData(void) { - size_t i; - swig_cast_info *equiv; - static int init_run = 0; - - if (init_run) return; - init_run = 1; - - for (i = 0; i < swig_module.size; i++) { - if (swig_module.types[i]->clientdata) { - equiv = swig_module.types[i]->cast; - while (equiv) { - if (!equiv->converter) { - if (equiv->type && !equiv->type->clientdata) - SWIG_TypeClientData(equiv->type, swig_module.types[i]->clientdata); - } - equiv = equiv->next; - } - } - } -} - -#ifdef __cplusplus -#if 0 -{ - /* c-mode */ -#endif -} -#endif - - - -#ifdef __cplusplus -extern "C" { -#endif - - /* ----------------------------------------------------------------------------- - * constants/methods manipulation - * ----------------------------------------------------------------------------- */ - - /* Install Constants */ - SWIGINTERN void - SWIG_Python_InstallConstants(PyObject *d, swig_const_info constants[]) { - PyObject *obj = 0; - size_t i; - for (i = 0; constants[i].type; ++i) { - switch(constants[i].type) { - case SWIG_PY_POINTER: - obj = SWIG_InternalNewPointerObj(constants[i].pvalue, *(constants[i]).ptype,0); - break; - case SWIG_PY_BINARY: - obj = SWIG_NewPackedObj(constants[i].pvalue, constants[i].lvalue, *(constants[i].ptype)); - break; - default: - obj = 0; - break; - } - if (obj) { - PyDict_SetItemString(d, constants[i].name, obj); - SWIG_Py_DECREF(obj); - } - } - } - - /* ----------------------------------------------------------------------------- - * Patch %callback methods' docstrings to hold the callback ptrs - * -----------------------------------------------------------------------------*/ - - SWIGINTERN void - SWIG_Python_FixMethods(PyMethodDef *methods, const swig_const_info *const_table, swig_type_info **types, swig_type_info **types_initial) { - size_t i; - for (i = 0; methods[i].ml_name; ++i) { - const char *c = methods[i].ml_doc; - if (!c) continue; - c = strstr(c, "swig_ptr: "); - if (c) { - int j; - const swig_const_info *ci = 0; - const char *name = c + 10; - for (j = 0; const_table[j].type; ++j) { - if (strncmp(const_table[j].name, name, - strlen(const_table[j].name)) == 0) { - ci = &(const_table[j]); - break; - } - } - if (ci) { - void *ptr = (ci->type == SWIG_PY_POINTER) ? ci->pvalue : 0; - if (ptr) { - size_t shift = (ci->ptype) - types; - swig_type_info *ty = types_initial[shift]; - size_t ldoc = (c - methods[i].ml_doc); - size_t lptr = strlen(ty->name)+2*sizeof(void*)+2; - char *ndoc = (char*)malloc(ldoc + lptr + 10); - if (ndoc) { - char *buff = ndoc; - memcpy(buff, methods[i].ml_doc, ldoc); - buff += ldoc; - memcpy(buff, "swig_ptr: ", 10); - buff += 10; - SWIG_PackVoidPtr(buff, ptr, ty->name, lptr); - methods[i].ml_doc = ndoc; - } - } - } - } - } - } - -#ifdef __cplusplus -} -#endif - - - - -/* -----------------------------------------------------------------------------* - * Partial Init method - * -----------------------------------------------------------------------------*/ - -#ifdef __cplusplus -extern "C" -#endif - -SWIGEXPORT -#if PY_VERSION_HEX >= 0x03000000 -PyObject* -#else -void -#endif -SWIG_init(void) { - PyObject *m, *d, *md, *globals; - -#if PY_VERSION_HEX >= 0x03000000 - static struct PyModuleDef SWIG_module = { - PyModuleDef_HEAD_INIT, - SWIG_name, - NULL, - -1, - SwigMethods, - NULL, - NULL, - NULL, - NULL - }; -#endif - -#if defined(SWIGPYTHON_BUILTIN) - static SwigPyClientData SwigPyObject_clientdata = { - 0, 0, 0, 0, 0, 0, 0 - }; - static PyGetSetDef this_getset_def = { - (char *)"this", &SwigPyBuiltin_ThisClosure, NULL, NULL, NULL - }; - static SwigPyGetSet thisown_getset_closure = { - SwigPyObject_own, - SwigPyObject_own - }; - static PyGetSetDef thisown_getset_def = { - (char *)"thisown", SwigPyBuiltin_GetterClosure, SwigPyBuiltin_SetterClosure, NULL, &thisown_getset_closure - }; - PyTypeObject *builtin_pytype; - int builtin_base_count; - swig_type_info *builtin_basetype; - PyObject *tuple; - PyGetSetDescrObject *static_getset; - PyTypeObject *metatype; - PyTypeObject *swigpyobject; - SwigPyClientData *cd; - PyObject *public_interface, *public_symbol; - PyObject *this_descr; - PyObject *thisown_descr; - PyObject *self = 0; - int i; - - (void)builtin_pytype; - (void)builtin_base_count; - (void)builtin_basetype; - (void)tuple; - (void)static_getset; - (void)self; - - /* Metaclass is used to implement static member variables */ - metatype = SwigPyObjectType(); - assert(metatype); -#endif - - (void)globals; - - /* Create singletons now to avoid potential deadlocks with multi-threaded usage after module initialization */ - SWIG_This(); - SWIG_Python_TypeCache(); - SwigPyPacked_type(); -#ifndef SWIGPYTHON_BUILTIN - SwigPyObject_type(); -#endif - - /* Fix SwigMethods to carry the callback ptrs when needed */ - SWIG_Python_FixMethods(SwigMethods, swig_const_table, swig_types, swig_type_initial); - -#if PY_VERSION_HEX >= 0x03000000 - m = PyModule_Create(&SWIG_module); -#else - m = Py_InitModule(SWIG_name, SwigMethods); -#endif - - md = d = PyModule_GetDict(m); - (void)md; - - SWIG_InitializeModule(0); - -#ifdef SWIGPYTHON_BUILTIN - swigpyobject = SwigPyObject_TypeOnce(); - - SwigPyObject_stype = SWIG_MangledTypeQuery("_p_SwigPyObject"); - assert(SwigPyObject_stype); - cd = (SwigPyClientData*) SwigPyObject_stype->clientdata; - if (!cd) { - SwigPyObject_stype->clientdata = &SwigPyObject_clientdata; - SwigPyObject_clientdata.pytype = swigpyobject; - } else if (swigpyobject->tp_basicsize != cd->pytype->tp_basicsize) { - PyErr_SetString(PyExc_RuntimeError, "Import error: attempted to load two incompatible swig-generated modules."); -# if PY_VERSION_HEX >= 0x03000000 - return NULL; -# else - return; -# endif - } - - /* All objects have a 'this' attribute */ - this_descr = PyDescr_NewGetSet(SwigPyObject_type(), &this_getset_def); - (void)this_descr; - - /* All objects have a 'thisown' attribute */ - thisown_descr = PyDescr_NewGetSet(SwigPyObject_type(), &thisown_getset_def); - (void)thisown_descr; - - public_interface = PyList_New(0); - public_symbol = 0; - (void)public_symbol; - - PyDict_SetItemString(md, "__all__", public_interface); - SWIG_Py_DECREF(public_interface); - for (i = 0; SwigMethods[i].ml_name != NULL; ++i) - SwigPyBuiltin_AddPublicSymbol(public_interface, SwigMethods[i].ml_name); - for (i = 0; swig_const_table[i].name != 0; ++i) - SwigPyBuiltin_AddPublicSymbol(public_interface, swig_const_table[i].name); -#endif - - SWIG_InstallConstants(d,swig_const_table); - -#if PY_VERSION_HEX >= 0x03000000 - return m; -#else - return; -#endif -} - diff --git a/src/pyeggp/typing.py b/src/pyeggp/typing.py index 88f7e4a..5a2dc19 100644 --- a/src/pyeggp/typing.py +++ b/src/pyeggp/typing.py @@ -11,4 +11,4 @@ class Session(Protocol): def version(self) -> str: ... def main(self, args: List[str] = []) -> int: ... - def pyeggp(self, n: List[str]) -> str: ... + def pyeggp_run(self, dataset: str, gen: int, nPop: int, maxSize: int, nTournament: int, pc: float, pm: float, nonterminals: str, loss: str, optIter: int, optRepeat: int, nParams: int, split: int, simplify: int, dumpTo: str, loadFrom: str) -> str: ... diff --git a/src/srtree.egg-info/PKG-INFO b/src/srtree.egg-info/PKG-INFO deleted file mode 100644 index aa1763a..0000000 --- a/src/srtree.egg-info/PKG-INFO +++ /dev/null @@ -1,699 +0,0 @@ -Metadata-Version: 2.4 -Name: srtree -Version: 1.0.0 -Summary: Python Wheels for eggp algorithm. -Author-email: Fabricio Olivetti -License: GNU GENERAL PUBLIC LICENSE - Version 3, 29 June 2007 - - Copyright (C) 2007 Free Software Foundation, Inc. - Everyone is permitted to copy and distribute verbatim copies - of this license document, but changing it is not allowed. - - Preamble - - The GNU General Public License is a free, copyleft license for - software and other kinds of works. - - The licenses for most software and other practical works are designed - to take away your freedom to share and change the works. By contrast, - the GNU General Public License is intended to guarantee your freedom to - share and change all versions of a program--to make sure it remains free - software for all its users. We, the Free Software Foundation, use the - GNU General Public License for most of our software; it applies also to - any other work released this way by its authors. You can apply it to - your programs, too. - - When we speak of free software, we are referring to freedom, not - price. Our General Public Licenses are designed to make sure that you - have the freedom to distribute copies of free software (and charge for - them if you wish), that you receive source code or can get it if you - want it, that you can change the software or use pieces of it in new - free programs, and that you know you can do these things. - - To protect your rights, we need to prevent others from denying you - these rights or asking you to surrender the rights. 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If not, see . - - Also add information on how to contact you by electronic and paper mail. - - If the program does terminal interaction, make it output a short - notice like this when it starts in an interactive mode: - - Copyright (C) - This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. - This is free software, and you are welcome to redistribute it - under certain conditions; type `show c' for details. - - The hypothetical commands `show w' and `show c' should show the appropriate - parts of the General Public License. Of course, your program's commands - might be different; for a GUI interface, you would use an "about box". - - You should also get your employer (if you work as a programmer) or school, - if any, to sign a "copyright disclaimer" for the program, if necessary. - For more information on this, and how to apply and follow the GNU GPL, see - . - - The GNU General Public License does not permit incorporating your program - into proprietary programs. If your program is a subroutine library, you - may consider it more useful to permit linking proprietary applications with - the library. If this is what you want to do, use the GNU Lesser General - Public License instead of this License. But first, please read - . - -Classifier: License :: OSI Approved :: MIT License -Classifier: Operating System :: MacOS -Classifier: Operating System :: Microsoft :: Windows -Classifier: Operating System :: POSIX :: Linux -Classifier: Programming Language :: Python :: 3.9 -Classifier: Programming Language :: Python :: 3.10 -Classifier: Programming Language :: Python :: 3.11 -Classifier: Programming Language :: Python :: 3.12 -Classifier: Programming Language :: Python :: Implementation :: CPython -Requires-Python: <3.13,>=3.9 -Description-Content-Type: text/markdown -License-File: LICENSE -Requires-Dist: typing_extensions<5,>=4.6; python_version < "3.8" -Provides-Extra: mypy -Requires-Dist: types_setuptools>=45; extra == "mypy" -Dynamic: license-file - -# pyeggp -Python wraper for eggp diff --git a/src/srtree.egg-info/SOURCES.txt b/src/srtree.egg-info/SOURCES.txt deleted file mode 100644 index d25c21d..0000000 --- a/src/srtree.egg-info/SOURCES.txt +++ /dev/null @@ -1,15 +0,0 @@ -LICENSE -README.md -pyproject.toml -setup.py -src/pyeggp/__init__.py -src/pyeggp/__main__.py -src/pyeggp/_binding.pyi -src/pyeggp/binding.i -src/pyeggp/binding.py -src/pyeggp/typing.py -src/srtree.egg-info/PKG-INFO -src/srtree.egg-info/SOURCES.txt -src/srtree.egg-info/dependency_links.txt -src/srtree.egg-info/requires.txt -src/srtree.egg-info/top_level.txt \ No newline at end of file diff --git a/src/srtree.egg-info/dependency_links.txt b/src/srtree.egg-info/dependency_links.txt deleted file mode 100644 index 8b13789..0000000 --- a/src/srtree.egg-info/dependency_links.txt +++ /dev/null @@ -1 +0,0 @@ - diff --git a/src/srtree.egg-info/requires.txt b/src/srtree.egg-info/requires.txt deleted file mode 100644 index c976189..0000000 --- a/src/srtree.egg-info/requires.txt +++ /dev/null @@ -1,6 +0,0 @@ - -[:python_version < "3.8"] -typing_extensions<5,>=4.6 - -[mypy] -types_setuptools>=45 diff --git a/src/srtree.egg-info/top_level.txt b/src/srtree.egg-info/top_level.txt deleted file mode 100644 index 4790dfe..0000000 --- a/src/srtree.egg-info/top_level.txt +++ /dev/null @@ -1,2 +0,0 @@ -eggp -pyeggp diff --git a/test/data.csv b/test/data.csv new file mode 100644 index 0000000..74940e6 --- /dev/null +++ b/test/data.csv @@ -0,0 +1,36 @@ +lugs_1989,lugs_1990,target +1.0,5.0,9.5 +3.0,8.0,17.5 +3.0,11.0,18.0 +3.0,9.0,20.0 +5.0,9.5,20.5 +3.5,10.0,20.0 +3.0,12.0,19.0 +4.5,10.0,21.0 +5.0,11.5,19.0 +5.0,10.0,26.0 +6.0,11.0,20.0 +5.0,10.0,23.0 +4.0,10.5,22.0 +6.0,10.0,20.0 +5.0,11.0,20.0 +6.5,11.0,24.0 +4.0,11.5,23.0 +6.0,12.0,23.0 +3.0,10.0,17.0 +2.0,7.0,21.0 +2.0,10.0,17.0 +2.0,8.0,20.0 +2.0,10.0,16.0 +2.0,9.0,20.0 +1.5,10.0,15.0 +1.5,8.0,17.0 +1.5,11.0,17.0 +1.0,10.0,20.0 +1.0,7.0,15.0 +1.0,5.0,14.0 +1.0,7.0,12.0 +0.5,8.0,11.0 +0.5,6.0,11.0 +0.0,7.5,8.5 +0.0,2.5,2.5 diff --git a/test/test_pyeggp.py b/test/test_pyeggp.py new file mode 100644 index 0000000..54a545b --- /dev/null +++ b/test/test_pyeggp.py @@ -0,0 +1,6 @@ +from pyeggp import pyeggp_run + +args = ["test/data.csv", "100", "10", "1", "MSE", "50", "2", "-1", "100", "3", "0.9", "0.3", "add,sub,mul,div", "\"\"", "\"\"", "True"] +output = pyeggp_run("test/data.csv", 100, 100, 10, 3, 0.9, 0.3, "add,sub,mul,div", "MSE", 50, 2, -1, 3, 0, "", "") + +print(output) From 06437c73e3d4c3a99f607265a2a1e656c02469fa Mon Sep 17 00:00:00 2001 From: folivetti Date: Mon, 14 Apr 2025 14:46:05 -0300 Subject: [PATCH 012/166] testing wheel building machine --- .github/workflows/release.yml | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 24d72cb..8db2edd 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -27,7 +27,7 @@ env: # See: https://github.com/wenkokke/manylinux_ghc DEFAULT_GHC_VERSION: "9.12" DEFAULT_CABAL_VERSION: "3.14" - DEFAULT_PYTHON_VERSION: "3.13.3" + DEFAULT_PYTHON_VERSION: "3.12" jobs: ################################################################################ @@ -56,11 +56,11 @@ jobs: runner: "ubuntu-latest" arch: "x86_64" plat: "manylinux" - - os: - name: "Linux" - runner: "ubuntu-latest" - arch: "x86_64" - plat: "musllinux" + #- os: + # name: "Linux" + # runner: "ubuntu-latest" + # arch: "x86_64" + # plat: "musllinux" - os: name: "macOS" runner: "macos-12" From 338f5a58706a1a604ea0e63e079b9f199536b1c3 Mon Sep 17 00:00:00 2001 From: folivetti Date: Mon, 14 Apr 2025 14:49:24 -0300 Subject: [PATCH 013/166] testing wheel building machine --- .github/workflows/release.yml | 50 +++++++++++++++++------------------ 1 file changed, 25 insertions(+), 25 deletions(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 8db2edd..463504a 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -41,16 +41,16 @@ jobs: strategy: matrix: job: - - os: - name: "Linux" - runner: "ubuntu-latest" - arch: "aarch64" - plat: "manylinux" - - os: - name: "Linux" - runner: "ubuntu-latest" - arch: "i686" - plat: "manylinux" + #- os: + # name: "Linux" + # runner: "ubuntu-latest" + # arch: "aarch64" + # plat: "manylinux" + #- os: + # name: "Linux" + # runner: "ubuntu-latest" + # arch: "i686" + # plat: "manylinux" - os: name: "Linux" runner: "ubuntu-latest" @@ -61,21 +61,21 @@ jobs: # runner: "ubuntu-latest" # arch: "x86_64" # plat: "musllinux" - - os: - name: "macOS" - runner: "macos-12" - arch: "x86_64" - plat: "macosx" - - os: - name: "macOS" - runner: "macos-14" - arch: "arm64" - plat: "macosx" - - os: - name: "Windows" - runner: "windows-latest" - arch: "AMD64" - plat: "win" + #- os: + # name: "macOS" + # runner: "macos-12" + # arch: "x86_64" + # plat: "macosx" + #- os: + # name: "macOS" + # runner: "macos-14" + # arch: "arm64" + # plat: "macosx" + #- os: + # name: "Windows" + # runner: "windows-latest" + # arch: "AMD64" + # plat: "win" steps: - uses: actions/checkout@v4 From fc8ec1fce0d54afbd78a9e4d71bacafe8a9a124e Mon Sep 17 00:00:00 2001 From: folivetti Date: Mon, 14 Apr 2025 17:40:13 -0300 Subject: [PATCH 014/166] disable pypy for now --- .github/workflows/release.yml | 50 +++++++++++++++++------------------ 1 file changed, 25 insertions(+), 25 deletions(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 463504a..acb07d4 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -172,28 +172,28 @@ jobs: # Publish package to PyPI ################################################################################ - publish-to-pypi: - name: Publish to PyPI - runs-on: ubuntu-latest - if: startsWith(github.ref, 'refs/tags/') - needs: [build] - - environment: - name: pypi - url: https://pypi.org/p/eggp - - permissions: - id-token: write # IMPORTANT: this permission is mandatory for trusted publishing - - steps: - - name: Download artifacts - uses: actions/download-artifact@v4 - with: - pattern: "wheelhouse-*-*" - path: "./wheelhouse" - merge-multiple: true - - - name: Publish to PyPI - uses: pypa/gh-action-pypi-publish@release/v1 - with: - packages-dir: "./wheelhouse" + #publish-to-pypi: + # name: Publish to PyPI + # runs-on: ubuntu-latest + # if: startsWith(github.ref, 'refs/tags/') + # needs: [build] + + # environment: + # name: pypi + # url: https://pypi.org/p/eggp + + # permissions: + # id-token: write # IMPORTANT: this permission is mandatory for trusted publishing + + # steps: + # - name: Download artifacts + # uses: actions/download-artifact@v4 + # with: + # pattern: "wheelhouse-*-*" + # path: "./wheelhouse" + # merge-multiple: true + + # - name: Publish to PyPI + # uses: pypa/gh-action-pypi-publish@release/v1 + # with: + # packages-dir: "./wheelhouse" From 6506d29b0c1d1b93109c2e630c419c9d8d7607a9 Mon Sep 17 00:00:00 2001 From: folivetti Date: Mon, 14 Apr 2025 18:57:51 -0300 Subject: [PATCH 015/166] testing wheel building machine --- src/pyeggp/binding.i | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/src/pyeggp/binding.i b/src/pyeggp/binding.i index f6eeda6..a66532d 100644 --- a/src/pyeggp/binding.i +++ b/src/pyeggp/binding.i @@ -10,7 +10,7 @@ int unsafe_hs_pyeggp_main() { return hs_pyeggp_main(); } -char * unsafe_hs_pyeggp_run(const char *dataset, int gens, int nPop, int maxSize, int nTournament, double pc, double pm, const char *nonterminals, const char *loss, int optIter, int optRepeat, int nParams, int split, int simplify, const char *dumpTo, const char *loadFrom ) { +char * unsafe_hs_pyeggp_run( char *dataset, int gens, int nPop, int maxSize, int nTournament, double pc, double pm, char *nonterminals, char *loss, int optIter, int optRepeat, int nParams, int split, int simplify, char *dumpTo, char *loadFrom ) { return hs_pyeggp_run(dataset, gens, nPop, maxSize, nTournament, pc, pm, nonterminals, loss, optIter, optRepeat, nParams, split, simplify, dumpTo, loadFrom); } @@ -22,11 +22,11 @@ void unsafe_hs_pyeggp_exit() { hs_exit(); } -void unsafe_py_write_stdout(const char * str) { +void unsafe_py_write_stdout( char * str) { PySys_FormatStdout("%s", str); } -void unsafe_py_write_stderr(const char * str) { +void unsafe_py_write_stderr( char * str) { PySys_FormatStderr("%s", str); } %} @@ -59,6 +59,6 @@ void unsafe_py_write_stderr(const char * str) { char * unsafe_hs_pyeggp_version(); int unsafe_hs_pyeggp_main(); -char * unsafe_hs_pyeggp_run(const char *dataset, int gens, int nPop, int maxSize, int nTournament, double p, double pm, const char *nonterminals, const char *loss, int optIter, int optRepeat, int nParams, int split, int simplify, const char *dumpTo, const char *loadFrom); +char * unsafe_hs_pyeggp_run( char *dataset, int gens, int nPop, int maxSize, int nTournament, double p, double pm, char *nonterminals, char *loss, int optIter, int optRepeat, int nParams, int split, int simplify, char *dumpTo, char *loadFrom); void unsafe_hs_pyeggp_init(int argc, char **argv); void unsafe_hs_pyeggp_exit(); From fa81a652534f393dd6cc6a5915fc1c3eb2d2e1d4 Mon Sep 17 00:00:00 2001 From: folivetti Date: Mon, 14 Apr 2025 19:00:54 -0300 Subject: [PATCH 016/166] testing wheel building machine --- scripts/build-wheel.sh | 3 ++- setup.py | 2 +- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/scripts/build-wheel.sh b/scripts/build-wheel.sh index 8eaba75..dc90d92 100644 --- a/scripts/build-wheel.sh +++ b/scripts/build-wheel.sh @@ -2,7 +2,8 @@ # Get configuration from Tox # 2024-04-16: The substitution for '{env_python}' and other environment variables no longer appears to work. -python="python" +# hardcode for now +python="python3.12" package_root="${package_root:-"$(dirname "$( cd -- "$(dirname "$0")" >/dev/null 2>&1 || exit ; pwd -P )" )"}" dist_dir="${package_root}/dist" dist_tmp_dir="${package_root}/dist/_tmp" diff --git a/setup.py b/setup.py index 7d5b282..e050350 100644 --- a/setup.py +++ b/setup.py @@ -87,7 +87,7 @@ def cabal_build_ext(self, ext: Extension) -> None: #self.build_temp = self.build_temp.replace("temp","lib") self.mkpath(self.build_temp) print(self.build_temp, "<<<<<<<<<<<<<<<<<<<<<<<<<<<<") - self.cabal(["build"], env={"CABAL_BUILDDIR": self.build_temp, **os.environ}) + self.cabal(["build"], env={"INSTALLDIR": self.build_temp, **os.environ}) lib_filename = self.get_cabal_foreign_library_filename(ext) ext_fullpath = self.get_ext_fullpath(ext.name) self.mkpath(os.path.dirname(ext_fullpath)) From 2d44a9af68c8bb47afbf99140b4792d035f46826 Mon Sep 17 00:00:00 2001 From: folivetti Date: Mon, 14 Apr 2025 19:19:55 -0300 Subject: [PATCH 017/166] testing wheel building machine --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index e050350..54806c4 100644 --- a/setup.py +++ b/setup.py @@ -87,7 +87,7 @@ def cabal_build_ext(self, ext: Extension) -> None: #self.build_temp = self.build_temp.replace("temp","lib") self.mkpath(self.build_temp) print(self.build_temp, "<<<<<<<<<<<<<<<<<<<<<<<<<<<<") - self.cabal(["build"], env={"INSTALLDIR": self.build_temp, **os.environ}) + self.cabal(["build"], env={"INSTALLDIR": self.build_temp, "LIBRARY_PATH": "/usr/local/lib64", "LD_LIBRARY_PATH":"/usr/local/lib64", "C_INCLUDE_PATH": "/usr/local/include", "PKG_CONFIG_PATH": "/usr/local/lib64/pkgconfig", **os.environ}) lib_filename = self.get_cabal_foreign_library_filename(ext) ext_fullpath = self.get_ext_fullpath(ext.name) self.mkpath(os.path.dirname(ext_fullpath)) From ebec438f228972b4196464c25a0f2b1f50fffff6 Mon Sep 17 00:00:00 2001 From: Fabricio Olivetti de Franca Date: Tue, 15 Apr 2025 09:08:31 -0300 Subject: [PATCH 018/166] Update release.yml --- .github/workflows/release.yml | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index acb07d4..de75b23 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -71,11 +71,11 @@ jobs: # runner: "macos-14" # arch: "arm64" # plat: "macosx" - #- os: - # name: "Windows" - # runner: "windows-latest" - # arch: "AMD64" - # plat: "win" + - os: + name: "Windows" + runner: "windows-latest" + arch: "AMD64" + plat: "win" steps: - uses: actions/checkout@v4 From 451cc3a5c01d6ec431c18dd1afd466311eb9aa0f Mon Sep 17 00:00:00 2001 From: Fabricio Olivetti de Franca Date: Tue, 15 Apr 2025 09:27:13 -0300 Subject: [PATCH 019/166] Update release.yml --- .github/workflows/release.yml | 36 +++++++++++++++++++++++++++++------ 1 file changed, 30 insertions(+), 6 deletions(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index de75b23..7c6b2c2 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -61,11 +61,11 @@ jobs: # runner: "ubuntu-latest" # arch: "x86_64" # plat: "musllinux" - #- os: - # name: "macOS" - # runner: "macos-12" - # arch: "x86_64" - # plat: "macosx" + - os: + name: "macOS" + runner: "macos-12" + arch: "x86_64" + plat: "macosx" #- os: # name: "macOS" # runner: "macos-14" @@ -109,7 +109,31 @@ jobs: # Print the GHC architecture. if: matrix.job.os.name == 'macOS' || matrix.job.os.name == 'Windows' run: echo "::debug::$(ghc --info)" - + + - if: matrix.job.os.name == 'Windows' + name: Install vcpkg + run: | + git clone https://github.com/microsoft/vcpkg.git + cd vcpkg + .\bootstrap-vcpkg.bat + .\vcpkg install nlopt + - if: matrix.job.os.name == 'Windows' + name: Configure environment variables + run: | + echo "LIBRARY_PATH=C:\vcpkg\installed\x64-windows\lib" >> $env:GITHUB_ENV + echo "CPATH=C:\vcpkg\installed\x64-windows\include" >> $env:GITHUB_ENV + + - if: matrix.job.os.name == 'macOS' + name: Install Homebrew dependencies + run: | + brew update + brew install nlopt + - if: matrix.job.os.name == 'macOS' + name: Configure environment variables + run: | + echo "LIBRARY_PATH=/usr/local/lib" >> $GITHUB_ENV + echo "CPATH=/usr/local/include" >> $GITHUB_ENV + - # 19-06-2023: # Required to build aarch64 wheels. if: matrix.job.os.name == 'Linux' && matrix.job.os.arch == 'aarch64' From 55af9f6f795d20a4998c8635484ed083dcc6dfe7 Mon Sep 17 00:00:00 2001 From: Fabricio Olivetti de Franca Date: Tue, 15 Apr 2025 09:31:45 -0300 Subject: [PATCH 020/166] Update release.yml --- .github/workflows/release.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 7c6b2c2..7abbfcc 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -115,8 +115,8 @@ jobs: run: | git clone https://github.com/microsoft/vcpkg.git cd vcpkg - .\bootstrap-vcpkg.bat - .\vcpkg install nlopt + bootstrap-vcpkg.bat + vcpkg install nlopt - if: matrix.job.os.name == 'Windows' name: Configure environment variables run: | From 891667923c7ec19e29732c8cd8a36f0d6ca8ad81 Mon Sep 17 00:00:00 2001 From: Fabricio Olivetti de Franca Date: Tue, 15 Apr 2025 15:37:17 -0300 Subject: [PATCH 021/166] Update setup.py --- setup.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 54806c4..d8274aa 100644 --- a/setup.py +++ b/setup.py @@ -87,7 +87,10 @@ def cabal_build_ext(self, ext: Extension) -> None: #self.build_temp = self.build_temp.replace("temp","lib") self.mkpath(self.build_temp) print(self.build_temp, "<<<<<<<<<<<<<<<<<<<<<<<<<<<<") - self.cabal(["build"], env={"INSTALLDIR": self.build_temp, "LIBRARY_PATH": "/usr/local/lib64", "LD_LIBRARY_PATH":"/usr/local/lib64", "C_INCLUDE_PATH": "/usr/local/include", "PKG_CONFIG_PATH": "/usr/local/lib64/pkgconfig", **os.environ}) + if sys.platform in ["win32", "cygwin"]: + self.cabal(["build"], env={"INSTALLDIR": self.build_temp, "LIBRARY_PATH": "C:\vcpkg\installed\x64-windows\lib", "CPATH": "C:\vcpkg\installed\x64-windows\include", **os.environ}) + else: + self.cabal(["build"], env={"INSTALLDIR": self.build_temp, "LIBRARY_PATH": "/usr/local/lib64", "LD_LIBRARY_PATH":"/usr/local/lib64", "C_INCLUDE_PATH": "/usr/local/include", "PKG_CONFIG_PATH": "/usr/local/lib64/pkgconfig", **os.environ}) lib_filename = self.get_cabal_foreign_library_filename(ext) ext_fullpath = self.get_ext_fullpath(ext.name) self.mkpath(os.path.dirname(ext_fullpath)) From 84bfd090dc4647a7ba9b81154db1ccf6d6d4b8f9 Mon Sep 17 00:00:00 2001 From: Fabricio Olivetti de Franca Date: Tue, 15 Apr 2025 16:12:48 -0300 Subject: [PATCH 022/166] Update release.yml --- .github/workflows/release.yml | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 7abbfcc..8ab2f1d 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -71,11 +71,11 @@ jobs: # runner: "macos-14" # arch: "arm64" # plat: "macosx" - - os: - name: "Windows" - runner: "windows-latest" - arch: "AMD64" - plat: "win" + #- os: + # name: "Windows" + # runner: "windows-latest" + # arch: "AMD64" + # plat: "win" steps: - uses: actions/checkout@v4 From e0c8c49248dee6900af9978ef1909b4902125eee Mon Sep 17 00:00:00 2001 From: Fabricio Olivetti de Franca Date: Tue, 15 Apr 2025 16:28:33 -0300 Subject: [PATCH 023/166] Update release.yml --- .github/workflows/release.yml | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 8ab2f1d..789f6c4 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -51,11 +51,11 @@ jobs: # runner: "ubuntu-latest" # arch: "i686" # plat: "manylinux" - - os: - name: "Linux" - runner: "ubuntu-latest" - arch: "x86_64" - plat: "manylinux" + #- os: + # name: "Linux" + # runner: "ubuntu-latest" + # arch: "x86_64" + # plat: "manylinux" #- os: # name: "Linux" # runner: "ubuntu-latest" @@ -63,7 +63,7 @@ jobs: # plat: "musllinux" - os: name: "macOS" - runner: "macos-12" + runner: "macos-13" arch: "x86_64" plat: "macosx" #- os: From 0db0a7359cd6f457a5dd44ca9a19e30b68e1268c Mon Sep 17 00:00:00 2001 From: Fabricio Olivetti de Franca Date: Tue, 15 Apr 2025 17:07:35 -0300 Subject: [PATCH 024/166] Update release.yml --- .github/workflows/release.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 789f6c4..a16c92c 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -133,6 +133,7 @@ jobs: run: | echo "LIBRARY_PATH=/usr/local/lib" >> $GITHUB_ENV echo "CPATH=/usr/local/include" >> $GITHUB_ENV + echo "MACOSX_DEPLOYMENT_TARGET=13.0" >> $GITHUB_ENV - # 19-06-2023: # Required to build aarch64 wheels. From 0968fd3a5b86537bae3468ac08749f2811fdaa95 Mon Sep 17 00:00:00 2001 From: Fabricio Olivetti de Franca Date: Tue, 15 Apr 2025 17:28:03 -0300 Subject: [PATCH 025/166] Update setup.py --- setup.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/setup.py b/setup.py index d8274aa..a5892f9 100644 --- a/setup.py +++ b/setup.py @@ -89,8 +89,10 @@ def cabal_build_ext(self, ext: Extension) -> None: print(self.build_temp, "<<<<<<<<<<<<<<<<<<<<<<<<<<<<") if sys.platform in ["win32", "cygwin"]: self.cabal(["build"], env={"INSTALLDIR": self.build_temp, "LIBRARY_PATH": "C:\vcpkg\installed\x64-windows\lib", "CPATH": "C:\vcpkg\installed\x64-windows\include", **os.environ}) - else: + elif sys.platform in ["linux"]: self.cabal(["build"], env={"INSTALLDIR": self.build_temp, "LIBRARY_PATH": "/usr/local/lib64", "LD_LIBRARY_PATH":"/usr/local/lib64", "C_INCLUDE_PATH": "/usr/local/include", "PKG_CONFIG_PATH": "/usr/local/lib64/pkgconfig", **os.environ}) + self.cabal(["build"], env={"INSTALLDIR": self.build_temp, "LIBRARY_PATH": "/usr/local/lib64", "LD_LIBRARY_PATH":"/usr/local/lib64", "C_INCLUDE_PATH": "/usr/local/include", "PKG_CONFIG_PATH": "/usr/local/lib64/pkgconfig", 'MACOSX_DEPLOYMENT_TARGET': '13.0', **os.environ}) + else: lib_filename = self.get_cabal_foreign_library_filename(ext) ext_fullpath = self.get_ext_fullpath(ext.name) self.mkpath(os.path.dirname(ext_fullpath)) From 0dd645b03d4ed21b39fcdbe8aea06fbcdced5b47 Mon Sep 17 00:00:00 2001 From: Fabricio Olivetti de Franca Date: Tue, 15 Apr 2025 17:35:31 -0300 Subject: [PATCH 026/166] Update setup.py --- setup.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/setup.py b/setup.py index a5892f9..4efd9af 100644 --- a/setup.py +++ b/setup.py @@ -91,8 +91,9 @@ def cabal_build_ext(self, ext: Extension) -> None: self.cabal(["build"], env={"INSTALLDIR": self.build_temp, "LIBRARY_PATH": "C:\vcpkg\installed\x64-windows\lib", "CPATH": "C:\vcpkg\installed\x64-windows\include", **os.environ}) elif sys.platform in ["linux"]: self.cabal(["build"], env={"INSTALLDIR": self.build_temp, "LIBRARY_PATH": "/usr/local/lib64", "LD_LIBRARY_PATH":"/usr/local/lib64", "C_INCLUDE_PATH": "/usr/local/include", "PKG_CONFIG_PATH": "/usr/local/lib64/pkgconfig", **os.environ}) - self.cabal(["build"], env={"INSTALLDIR": self.build_temp, "LIBRARY_PATH": "/usr/local/lib64", "LD_LIBRARY_PATH":"/usr/local/lib64", "C_INCLUDE_PATH": "/usr/local/include", "PKG_CONFIG_PATH": "/usr/local/lib64/pkgconfig", 'MACOSX_DEPLOYMENT_TARGET': '13.0', **os.environ}) + else: + self.cabal(["build"], env={"INSTALLDIR": self.build_temp, "LIBRARY_PATH": "/usr/local/lib64", "LD_LIBRARY_PATH":"/usr/local/lib64", "C_INCLUDE_PATH": "/usr/local/include", "PKG_CONFIG_PATH": "/usr/local/lib64/pkgconfig", 'MACOSX_DEPLOYMENT_TARGET': '13.0', **os.environ}) lib_filename = self.get_cabal_foreign_library_filename(ext) ext_fullpath = self.get_ext_fullpath(ext.name) self.mkpath(os.path.dirname(ext_fullpath)) From 020f78f191c61e1b2fd232a2f3d27a3757dc7c96 Mon Sep 17 00:00:00 2001 From: Fabricio Olivetti de Franca Date: Tue, 15 Apr 2025 18:02:32 -0300 Subject: [PATCH 027/166] Update release.yml you are an idiot, Fabricio! --- .github/workflows/release.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index a16c92c..3d20539 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -158,7 +158,7 @@ jobs: env: CIBW_ARCHS: "${{ matrix.job.os.arch }}" CIBW_BUILD: "*${{ matrix.job.os.plat }}*" - MACOSX_DEPLOYMENT_TARGET: "10.10" + MACOSX_DEPLOYMENT_TARGET: "13.0" - name: Upload wheels uses: actions/upload-artifact@v4 From 210d8b8575c6093c9ab6dbe68cba77e1feae0f46 Mon Sep 17 00:00:00 2001 From: Fabricio Olivetti de Franca Date: Tue, 15 Apr 2025 18:29:24 -0300 Subject: [PATCH 028/166] Update pyproject.toml --- pyproject.toml | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 230f2a0..0d34ee2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -14,7 +14,7 @@ authors = [{ name = "Fabricio Olivetti", email = "folivetti@users.noreply.github description = "Python Wheels for eggp algorithm." readme = "README.md" license = { file = "LICENSE" } -requires-python = ">=3.9,<3.14" +requires-python = ">=3.9" classifiers = [ "Operating System :: MacOS", "Operating System :: Microsoft :: Windows", @@ -66,11 +66,11 @@ build = [ # GHC provides an x86_64 build for CentOS 7 (manylinux2014). "*-manylinux_x86_64", # GHC provides an i686 build for Debian 9 (manylinux_2014 compatible). - "*-manylinux_i686", + #"*-manylinux_i686", # GHC provides an aarch64 build for Debian 10 (manylinux_2_28 compatible). "*-manylinux_aarch64", # GHC provides an x86_64 build for Alpine 3.12 (manylinux_2_28 compatible). - "*-musllinux_x86_64", + # "*-musllinux_x86_64", ] skip = [ # 18-04-2023: @@ -82,6 +82,8 @@ manylinux-i686-image = "folivetti/manylinux2014_ghc9121_i686" manylinux-x86_64-image = "folivetti/manylinux2014_ghc9121_x86_64" musllinux-x86_64-image = "folivetti/musllinux_1_1_ghc9121_x86_64" +[tool.cibuildwheel.windows] +repair-wheel-command = "delvewheel repair -w {dest_dir} {wheel}" [tool.mypy] mypy_path = "src" From da5cc1546db323c8cc414100d3ee29e88df0b61c Mon Sep 17 00:00:00 2001 From: Fabricio Olivetti de Franca Date: Tue, 15 Apr 2025 18:31:20 -0300 Subject: [PATCH 029/166] Update release.yml --- .github/workflows/release.yml | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 3d20539..4bb7039 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -61,16 +61,16 @@ jobs: # runner: "ubuntu-latest" # arch: "x86_64" # plat: "musllinux" - - os: - name: "macOS" - runner: "macos-13" - arch: "x86_64" - plat: "macosx" #- os: # name: "macOS" - # runner: "macos-14" - # arch: "arm64" + # runner: "macos-13" + # arch: "x86_64" # plat: "macosx" + - os: + name: "macOS" + runner: "macos-14" + arch: "arm64" + plat: "macosx" #- os: # name: "Windows" # runner: "windows-latest" From 174fc4ba57c3dbc68095f03199befeac1e814a90 Mon Sep 17 00:00:00 2001 From: Fabricio Olivetti de Franca Date: Tue, 15 Apr 2025 18:49:22 -0300 Subject: [PATCH 030/166] Update release.yml --- .github/workflows/release.yml | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 4bb7039..ae59de3 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -131,8 +131,9 @@ jobs: - if: matrix.job.os.name == 'macOS' name: Configure environment variables run: | - echo "LIBRARY_PATH=/usr/local/lib" >> $GITHUB_ENV - echo "CPATH=/usr/local/include" >> $GITHUB_ENV + echo "LIBRARY_PATH=/usr/local/lib:/opt/homebrew/lib" >> $GITHUB_ENV + echo "CPATH=/usr/local/include:/opt/homebrew/include" >> $GITHUB_ENV + echo "DYLD_LIBRARY_PATH=/opt/homebrew/lib" >> $GITHUB_ENV echo "MACOSX_DEPLOYMENT_TARGET=13.0" >> $GITHUB_ENV - # 19-06-2023: From 67f65490a6bb746979c8da22b45dd16c4e2bbb25 Mon Sep 17 00:00:00 2001 From: Fabricio Olivetti de Franca Date: Tue, 15 Apr 2025 19:02:43 -0300 Subject: [PATCH 031/166] Update release.yml --- .github/workflows/release.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index ae59de3..5298fcf 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -159,7 +159,7 @@ jobs: env: CIBW_ARCHS: "${{ matrix.job.os.arch }}" CIBW_BUILD: "*${{ matrix.job.os.plat }}*" - MACOSX_DEPLOYMENT_TARGET: "13.0" + MACOSX_DEPLOYMENT_TARGET: ${{ matrix.os.runner == 'macos-13' && '13.0' || '14.0' }} - name: Upload wheels uses: actions/upload-artifact@v4 From ab184a5efeac0e883955b47e0c4e32e53926e54c Mon Sep 17 00:00:00 2001 From: Fabricio Olivetti de Franca Date: Tue, 15 Apr 2025 19:18:18 -0300 Subject: [PATCH 032/166] Update release.yml --- .github/workflows/release.yml | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 5298fcf..bf351db 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -66,16 +66,16 @@ jobs: # runner: "macos-13" # arch: "x86_64" # plat: "macosx" - - os: - name: "macOS" - runner: "macos-14" - arch: "arm64" - plat: "macosx" #- os: - # name: "Windows" - # runner: "windows-latest" - # arch: "AMD64" - # plat: "win" + # name: "macOS" + # runner: "macos-14" + # arch: "arm64" + # plat: "macosx" + - os: + name: "Windows" + runner: "windows-latest" + arch: "AMD64" + plat: "win" steps: - uses: actions/checkout@v4 From 549570edea616b744bc76944dbf5e0bd3ae94576 Mon Sep 17 00:00:00 2001 From: Fabricio Olivetti de Franca Date: Tue, 15 Apr 2025 19:35:27 -0300 Subject: [PATCH 033/166] Update release.yml --- .github/workflows/release.yml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index bf351db..5d2fec0 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -122,6 +122,9 @@ jobs: run: | echo "LIBRARY_PATH=C:\vcpkg\installed\x64-windows\lib" >> $env:GITHUB_ENV echo "CPATH=C:\vcpkg\installed\x64-windows\include" >> $env:GITHUB_ENV + echo "INCLUDE=C:/nlopt/include" >> $GITHUB_ENV + echo "LIB=C:/nlopt/lib" >> $GITHUB_ENV + echo "C:/nlopt/bin" >> $GITHUB_PATH - if: matrix.job.os.name == 'macOS' name: Install Homebrew dependencies From 739777a3add0baa79a36a466ca932b30383ba22a Mon Sep 17 00:00:00 2001 From: Fabricio Olivetti de Franca Date: Tue, 15 Apr 2025 19:36:55 -0300 Subject: [PATCH 034/166] Update setup.py --- setup.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 4efd9af..8cc793a 100644 --- a/setup.py +++ b/setup.py @@ -88,7 +88,8 @@ def cabal_build_ext(self, ext: Extension) -> None: self.mkpath(self.build_temp) print(self.build_temp, "<<<<<<<<<<<<<<<<<<<<<<<<<<<<") if sys.platform in ["win32", "cygwin"]: - self.cabal(["build"], env={"INSTALLDIR": self.build_temp, "LIBRARY_PATH": "C:\vcpkg\installed\x64-windows\lib", "CPATH": "C:\vcpkg\installed\x64-windows\include", **os.environ}) + + self.cabal(["build"], env={"INSTALLDIR": self.build_temp, "LIBRARY_PATH": "C:\vcpkg\installed\x64-windows\lib", "CPATH": "C:\vcpkg\installed\x64-windows\include","INCLUDE": "C:/nlopt/include", "LIB":"C:/nlopt/lib", "PATH":"C:/nlopt/bin", **os.environ}) elif sys.platform in ["linux"]: self.cabal(["build"], env={"INSTALLDIR": self.build_temp, "LIBRARY_PATH": "/usr/local/lib64", "LD_LIBRARY_PATH":"/usr/local/lib64", "C_INCLUDE_PATH": "/usr/local/include", "PKG_CONFIG_PATH": "/usr/local/lib64/pkgconfig", **os.environ}) From a0ff5fbde85d46384e89aa072409b747251ace78 Mon Sep 17 00:00:00 2001 From: Fabricio Olivetti de Franca Date: Tue, 15 Apr 2025 19:51:58 -0300 Subject: [PATCH 035/166] Update setup.py --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 8cc793a..21f02af 100644 --- a/setup.py +++ b/setup.py @@ -89,7 +89,7 @@ def cabal_build_ext(self, ext: Extension) -> None: print(self.build_temp, "<<<<<<<<<<<<<<<<<<<<<<<<<<<<") if sys.platform in ["win32", "cygwin"]: - self.cabal(["build"], env={"INSTALLDIR": self.build_temp, "LIBRARY_PATH": "C:\vcpkg\installed\x64-windows\lib", "CPATH": "C:\vcpkg\installed\x64-windows\include","INCLUDE": "C:/nlopt/include", "LIB":"C:/nlopt/lib", "PATH":"C:/nlopt/bin", **os.environ}) + self.cabal(["build"], env={"INSTALLDIR": self.build_temp, "LIBRARY_PATH": "C:\vcpkg\installed\x64-windows\lib", "CPATH": "C:\vcpkg\installed\x64-windows\include","PATH":"C:\vcpkg\installed\x64-windows\bin${{ env.PATH }}", **os.environ}) elif sys.platform in ["linux"]: self.cabal(["build"], env={"INSTALLDIR": self.build_temp, "LIBRARY_PATH": "/usr/local/lib64", "LD_LIBRARY_PATH":"/usr/local/lib64", "C_INCLUDE_PATH": "/usr/local/include", "PKG_CONFIG_PATH": "/usr/local/lib64/pkgconfig", **os.environ}) From ac46fd70359d908c19becaffde0f53ae23836b7e Mon Sep 17 00:00:00 2001 From: Fabricio Olivetti de Franca Date: Tue, 15 Apr 2025 19:57:13 -0300 Subject: [PATCH 036/166] Update release.yml --- .github/workflows/release.yml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 5d2fec0..d1fcca6 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -116,15 +116,15 @@ jobs: git clone https://github.com/microsoft/vcpkg.git cd vcpkg bootstrap-vcpkg.bat - vcpkg install nlopt + vcpkg install nlopt:x64-windows - if: matrix.job.os.name == 'Windows' name: Configure environment variables run: | echo "LIBRARY_PATH=C:\vcpkg\installed\x64-windows\lib" >> $env:GITHUB_ENV echo "CPATH=C:\vcpkg\installed\x64-windows\include" >> $env:GITHUB_ENV echo "INCLUDE=C:/nlopt/include" >> $GITHUB_ENV - echo "LIB=C:/nlopt/lib" >> $GITHUB_ENV - echo "C:/nlopt/bin" >> $GITHUB_PATH + echo "PATH=C:\vcpkg\installed\x64-windows\bin${{ env.PATH }}" >> $env:GITHUB_ENV + - if: matrix.job.os.name == 'macOS' name: Install Homebrew dependencies From 5f793a6dccda463b2d7083ee93d9f4b553b79fdf Mon Sep 17 00:00:00 2001 From: Fabricio Olivetti de Franca Date: Tue, 15 Apr 2025 19:59:38 -0300 Subject: [PATCH 037/166] Update setup.py --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 21f02af..94bbe7a 100644 --- a/setup.py +++ b/setup.py @@ -89,7 +89,7 @@ def cabal_build_ext(self, ext: Extension) -> None: print(self.build_temp, "<<<<<<<<<<<<<<<<<<<<<<<<<<<<") if sys.platform in ["win32", "cygwin"]: - self.cabal(["build"], env={"INSTALLDIR": self.build_temp, "LIBRARY_PATH": "C:\vcpkg\installed\x64-windows\lib", "CPATH": "C:\vcpkg\installed\x64-windows\include","PATH":"C:\vcpkg\installed\x64-windows\bin${{ env.PATH }}", **os.environ}) + self.cabal(["build"], env={"INSTALLDIR": self.build_temp, "LIBRARY_PATH": "C:\\vcpkg\\installed\\x64-windows\\lib", "CPATH": "C:\\vcpkg\\installed\\x64-windows\\include","PATH":"C:\\vcpkg\\installed\\x64-windows\\bin${{ env.PATH }}", **os.environ}) elif sys.platform in ["linux"]: self.cabal(["build"], env={"INSTALLDIR": self.build_temp, "LIBRARY_PATH": "/usr/local/lib64", "LD_LIBRARY_PATH":"/usr/local/lib64", "C_INCLUDE_PATH": "/usr/local/include", "PKG_CONFIG_PATH": "/usr/local/lib64/pkgconfig", **os.environ}) From f6bb1177df9698d7e1fbcce582bb0b4317570cb0 Mon Sep 17 00:00:00 2001 From: Fabricio Olivetti de Franca Date: Tue, 15 Apr 2025 20:20:42 -0300 Subject: [PATCH 038/166] Update setup.py --- setup.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/setup.py b/setup.py index 94bbe7a..c1229d1 100644 --- a/setup.py +++ b/setup.py @@ -88,8 +88,9 @@ def cabal_build_ext(self, ext: Extension) -> None: self.mkpath(self.build_temp) print(self.build_temp, "<<<<<<<<<<<<<<<<<<<<<<<<<<<<") if sys.platform in ["win32", "cygwin"]: - - self.cabal(["build"], env={"INSTALLDIR": self.build_temp, "LIBRARY_PATH": "C:\\vcpkg\\installed\\x64-windows\\lib", "CPATH": "C:\\vcpkg\\installed\\x64-windows\\include","PATH":"C:\\vcpkg\\installed\\x64-windows\\bin${{ env.PATH }}", **os.environ}) + self.cabal(["build"], env={"INSTALLDIR": self.build_temp, "LIBRARY_PATH": "C:\\nlopt\\lib", + "CPATH": "C:\\nlopt\\include", + "PATH":"C:\\nlopt\\bin;" + os.environ.get("PATH", ""), **os.environ}) elif sys.platform in ["linux"]: self.cabal(["build"], env={"INSTALLDIR": self.build_temp, "LIBRARY_PATH": "/usr/local/lib64", "LD_LIBRARY_PATH":"/usr/local/lib64", "C_INCLUDE_PATH": "/usr/local/include", "PKG_CONFIG_PATH": "/usr/local/lib64/pkgconfig", **os.environ}) From 6a8a12f571b8a3fec911a5cc3b1cfb22f8df0e19 Mon Sep 17 00:00:00 2001 From: Fabricio Olivetti de Franca Date: Tue, 15 Apr 2025 20:23:01 -0300 Subject: [PATCH 039/166] Update release.yml --- .github/workflows/release.yml | 37 +++++++++++++++++++++++++---------- 1 file changed, 27 insertions(+), 10 deletions(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index d1fcca6..80ea56e 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -111,21 +111,38 @@ jobs: run: echo "::debug::$(ghc --info)" - if: matrix.job.os.name == 'Windows' - name: Install vcpkg + name: Install build tools with Chocolatey run: | - git clone https://github.com/microsoft/vcpkg.git - cd vcpkg - bootstrap-vcpkg.bat - vcpkg install nlopt:x64-windows + choco install cmake --installargs 'ADD_CMAKE_TO_PATH=System' + choco install make + choco install mingw + - if: matrix.job.os.name == 'Windows' - name: Configure environment variables + name: Build and install nlopt + run: | + git clone https://github.com/stevengj/nlopt.git + cd nlopt + mkdir build + cd build + cmake .. -G "MinGW Makefiles" -DCMAKE_INSTALL_PREFIX=C:\nlopt + mingw32-make + mingw32-make install + + - if: matrix.job.os.name == 'Windows' + name: Debug nlopt installation run: | - echo "LIBRARY_PATH=C:\vcpkg\installed\x64-windows\lib" >> $env:GITHUB_ENV - echo "CPATH=C:\vcpkg\installed\x64-windows\include" >> $env:GITHUB_ENV - echo "INCLUDE=C:/nlopt/include" >> $GITHUB_ENV - echo "PATH=C:\vcpkg\installed\x64-windows\bin${{ env.PATH }}" >> $env:GITHUB_ENV + dir C:\nlopt\lib + dir C:\nlopt\include + dir C:\nlopt\bin + - if: matrix.job.os.name == 'Windows' + name: Configure environment variables + run: | + echo "LIBRARY_PATH=C:\nlopt\lib" >> $env:GITHUB_ENV + echo "CPATH=C:\nlopt\include" >> $env:GITHUB_ENV + echo "PATH=C:\nlopt\bin${{ env.PATH }}" >> $env:GITHUB_ENV + - if: matrix.job.os.name == 'macOS' name: Install Homebrew dependencies run: | From 930bdc1761df36cc9cd636e525902af7c543a418 Mon Sep 17 00:00:00 2001 From: Fabricio Olivetti de Franca Date: Tue, 15 Apr 2025 20:30:27 -0300 Subject: [PATCH 040/166] Update release.yml --- .github/workflows/release.yml | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 80ea56e..4d837c0 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -124,23 +124,23 @@ jobs: cd nlopt mkdir build cd build - cmake .. -G "MinGW Makefiles" -DCMAKE_INSTALL_PREFIX=C:\nlopt + cmake .. -G "MinGW Makefiles" -DCMAKE_INSTALL_PREFIX=C:\\nlopt mingw32-make mingw32-make install - if: matrix.job.os.name == 'Windows' name: Debug nlopt installation run: | - dir C:\nlopt\lib - dir C:\nlopt\include - dir C:\nlopt\bin + dir C:\\nlopt\\lib + dir C:\\nlopt\\include + dir C:\\nlopt\\bin - if: matrix.job.os.name == 'Windows' name: Configure environment variables run: | - echo "LIBRARY_PATH=C:\nlopt\lib" >> $env:GITHUB_ENV - echo "CPATH=C:\nlopt\include" >> $env:GITHUB_ENV - echo "PATH=C:\nlopt\bin${{ env.PATH }}" >> $env:GITHUB_ENV + echo "LIBRARY_PATH=C:\\nlopt\\lib" >> $env:GITHUB_ENV + echo "CPATH=C:\\nlopt\\include" >> $env:GITHUB_ENV + echo "PATH=C:\\nlopt\\bin${{ env.PATH }}" >> $env:GITHUB_ENV - if: matrix.job.os.name == 'macOS' From 871addef1bbe2ff9b3d15462e540e1d620067adb Mon Sep 17 00:00:00 2001 From: Fabricio Olivetti de Franca Date: Tue, 15 Apr 2025 20:47:10 -0300 Subject: [PATCH 041/166] Update pyproject.toml --- pyproject.toml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 0d34ee2..823641f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,7 +3,8 @@ requires = [ "find_libpython >=0.3,<0.5; sys_platform == 'win32'", "setuptools >=45", "swig >=4,<5", - "nlopt >= 2.6.2" + "nlopt >= 2.6.2", + "delvewheel", ] build-backend = "setuptools.build_meta" From 4fe88c7faa098be07bae6a804887872fd26e903f Mon Sep 17 00:00:00 2001 From: Fabricio Olivetti de Franca Date: Tue, 15 Apr 2025 21:07:52 -0300 Subject: [PATCH 042/166] Update pyproject.toml --- pyproject.toml | 1 - 1 file changed, 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 823641f..2ce755c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,6 @@ requires = [ "setuptools >=45", "swig >=4,<5", "nlopt >= 2.6.2", - "delvewheel", ] build-backend = "setuptools.build_meta" From 88b7bccef1cc537f7d14ac4bc0f6eb0b96840096 Mon Sep 17 00:00:00 2001 From: Fabricio Olivetti de Franca Date: Tue, 15 Apr 2025 21:08:33 -0300 Subject: [PATCH 043/166] Update requirements-ci.txt --- requirements-ci.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/requirements-ci.txt b/requirements-ci.txt index 4fcdc16..b070820 100644 --- a/requirements-ci.txt +++ b/requirements-ci.txt @@ -1 +1,2 @@ pipx >=1.2.0, <2 +delvewheel From 9dcdbd2575b78646414d461639e093710b331ecb Mon Sep 17 00:00:00 2001 From: Fabricio Olivetti de Franca Date: Tue, 15 Apr 2025 21:33:44 -0300 Subject: [PATCH 044/166] Update pyproject.toml --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 2ce755c..90db614 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -83,7 +83,7 @@ manylinux-x86_64-image = "folivetti/manylinux2014_ghc9121_x86_64" musllinux-x86_64-image = "folivetti/musllinux_1_1_ghc9121_x86_64" [tool.cibuildwheel.windows] -repair-wheel-command = "delvewheel repair -w {dest_dir} {wheel}" +repair-wheel-command = "delvewheel repair -w {dest_dir} {wheel} --add-path C:\\nlopt\\lib" [tool.mypy] mypy_path = "src" From 6b505a02279e3caa02dda91a387690272c4751f2 Mon Sep 17 00:00:00 2001 From: Fabricio Olivetti de Franca Date: Tue, 15 Apr 2025 21:35:59 -0300 Subject: [PATCH 045/166] Update pyproject.toml --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 90db614..96683e4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -83,7 +83,7 @@ manylinux-x86_64-image = "folivetti/manylinux2014_ghc9121_x86_64" musllinux-x86_64-image = "folivetti/musllinux_1_1_ghc9121_x86_64" [tool.cibuildwheel.windows] -repair-wheel-command = "delvewheel repair -w {dest_dir} {wheel} --add-path C:\\nlopt\\lib" +repair-wheel-command = "delvewheel repair -w {dest_dir} {wheel} --add-path C:\\nlopt\\bin" [tool.mypy] mypy_path = "src" From 5f6fffd44aa1af4c29ca902335d9d35bbb36f73c Mon Sep 17 00:00:00 2001 From: Fabricio Olivetti de Franca Date: Tue, 15 Apr 2025 21:57:48 -0300 Subject: [PATCH 046/166] Update release.yml testing it all together --- .github/workflows/release.yml | 40 +++++++++++++++++------------------ 1 file changed, 20 insertions(+), 20 deletions(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 4d837c0..3a39970 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -41,36 +41,36 @@ jobs: strategy: matrix: job: - #- os: - # name: "Linux" - # runner: "ubuntu-latest" - # arch: "aarch64" - # plat: "manylinux" + - os: + name: "Linux" + runner: "ubuntu-latest" + arch: "aarch64" + plat: "manylinux" #- os: # name: "Linux" # runner: "ubuntu-latest" # arch: "i686" # plat: "manylinux" - #- os: - # name: "Linux" - # runner: "ubuntu-latest" - # arch: "x86_64" - # plat: "manylinux" + - os: + name: "Linux" + runner: "ubuntu-latest" + arch: "x86_64" + plat: "manylinux" #- os: # name: "Linux" # runner: "ubuntu-latest" # arch: "x86_64" # plat: "musllinux" - #- os: - # name: "macOS" - # runner: "macos-13" - # arch: "x86_64" - # plat: "macosx" - #- os: - # name: "macOS" - # runner: "macos-14" - # arch: "arm64" - # plat: "macosx" + - os: + name: "macOS" + runner: "macos-13" + arch: "x86_64" + plat: "macosx" + - os: + name: "macOS" + runner: "macos-14" + arch: "arm64" + plat: "macosx" - os: name: "Windows" runner: "windows-latest" From ae57dda6e4afaf4a670fff65e725665a389bc68c Mon Sep 17 00:00:00 2001 From: Fabricio Olivetti de Franca Date: Wed, 16 Apr 2025 06:22:17 -0300 Subject: [PATCH 047/166] Update pyproject.toml --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 96683e4..16b281e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,7 +3,7 @@ requires = [ "find_libpython >=0.3,<0.5; sys_platform == 'win32'", "setuptools >=45", "swig >=4,<5", - "nlopt >= 2.6.2", +# "nlopt >= 2.6.2", ] build-backend = "setuptools.build_meta" From 4bc5e738304a051daf1a60cd86c0be5994236850 Mon Sep 17 00:00:00 2001 From: folivetti Date: Wed, 16 Apr 2025 09:17:43 -0300 Subject: [PATCH 048/166] everything should be working now --- .github/workflows/release.yml | 10 +- .github/workflows/release.yml.bkp | 130 --------------------- .github/workflows/release.yml.working | 155 -------------------------- .github/workflows/release.yml_winbkp | 124 --------------------- .gitignore | 3 + pyproject.toml | 6 + src/Pyeggp.hs | 2 +- src/pyeggp/__init__.py | 51 ++++++++- test/test_pyeggp.py | 16 ++- 9 files changed, 78 insertions(+), 419 deletions(-) delete mode 100644 .github/workflows/release.yml.bkp delete mode 100644 .github/workflows/release.yml.working delete mode 100644 .github/workflows/release.yml_winbkp diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 3a39970..79a7c71 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -180,12 +180,18 @@ jobs: CIBW_ARCHS: "${{ matrix.job.os.arch }}" CIBW_BUILD: "*${{ matrix.job.os.plat }}*" MACOSX_DEPLOYMENT_TARGET: ${{ matrix.os.runner == 'macos-13' && '13.0' || '14.0' }} + - # Build source distribution (sdist) + run: | + pip install build + python -m build --sdist --outdir ./wheelhouse - name: Upload wheels uses: actions/upload-artifact@v4 with: name: "wheelhouse-${{ matrix.job.os.plat }}-${{ matrix.job.os.arch }}" - path: ./wheelhouse/*.whl + path: | + ./wheelhouse/*.whl + ./wheelhouse/*.tar.gz ################################################################################ # Publish package to GitHub Releases @@ -211,7 +217,7 @@ jobs: - name: Publish to GitHub Releases uses: softprops/action-gh-release@v2 with: - files: "./wheelhouse/*.whl" + files: "./wheelhouse/*" fail_on_unmatched_files: true ################################################################################ diff --git a/.github/workflows/release.yml.bkp b/.github/workflows/release.yml.bkp deleted file mode 100644 index 68f4d9f..0000000 --- a/.github/workflows/release.yml.bkp +++ /dev/null @@ -1,130 +0,0 @@ -name: srtols Release - -on: - # Trigger the workflow on the new 'v*' tag created - push: - tags: - - "v*" - -jobs: - create_release: - name: Create Github Release - runs-on: ubuntu-latest - outputs: - upload_url: ${{ steps.create_release.outputs.upload_url }} - steps: - - name: Create Release - id: create_release - uses: actions/create-release@v1.1.4 - env: - GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} - with: - tag_name: ${{ github.ref }} - release_name: Release ${{ github.ref }} - draft: true - prerelease: false - - build_artifact: - needs: [create_release] - name: ${{ matrix.os }}/GHC ${{ matrix.ghc }}/${{ github.ref }} - runs-on: ${{ matrix.os }} - strategy: - matrix: - os: [ubuntu-latest, windows-latest] - ghc: - - 9.10.1 - cabal: ["3.14.1.1"] - defaults: - run: - shell: bash - - steps: - - name: Check out code - uses: actions/checkout@v3 - - - name: Set tag name - uses: olegtarasov/get-tag@v2.1 - id: tag - with: - tagRegex: "v(.*)" - tagRegexGroup: 1 - - - name: Setup Haskell - uses: haskell-actions/setup@v2 - id: setup-haskell-cabal - with: - ghc-version: ${{ matrix.ghc }} - cabal-version: ${{ matrix.cabal }} - - - name: Cache ~/.cabal/store - uses: actions/cache@v2 - with: - key: ${{ runner.os }}-ghc-${{ matrix.ghc }}-${{ github.sha }} - path: ~/.cabal/store - - - if: matrix.os == 'ubuntu-latest' - name: Install dependencies - run: | - sudo apt-get update - sudo apt-get install -y libnlopt-dev libnlopt0 - - - if: matrix.os == 'macOS-latest' - name: Install dependencies - run: | - brew install nlopt - - - if: matrix.os == 'windows-latest' - name: Install dependencies - uses: msys2/setup-msys2@v2 - with: - install: pacman-mirrors pkg-config base-devel mingw-w64-x86_64-toolchain upx mingw-w64-x86_64-dlfcn git - update: false - - if: matrix.os == 'windows-latest' - name: Install nlopt - shell: msys2 {0} - run: | - pacman -Sy --noconfirm mingw-w64-x86_64-nlopt - - - if: matrix.os == 'windows-latest' - name: Build windows binary - run: | - mkdir dist - cabal install exe:egraphGP --install-method=copy --overwrite-policy=always --installdir=dist --extra-lib-dirs=/mingw64/lib --extra-lib-dirs=/mingw64/bin --extra-include-dirs=/mingw64/include - cabal install exe:egraphSearch --install-method=copy --overwrite-policy=always --installdir=dist --extra-lib-dirs=/mingw64/lib --extra-lib-dirs=/mingw64/bin --extra-include-dirs=/mingw64/include - cabal install exe:srtools --install-method=copy --overwrite-policy=always --installdir=dist --extra-lib-dirs=/mingw64/lib --extra-lib-dirs=/mingw64/bin --extra-include-dirs=/mingw64/include - cabal install exe:rEGGression --install-method=copy --overwrite-policy=always --installdir=dist --extra-lib-dirs=/mingw64/lib --extra-lib-dirs=/mingw64/bin --extra-include-dirs=/mingw64/include - cabal install exe:srsimplify --install-method=copy --overwrite-policy=always --installdir=dist --extra-lib-dirs=/mingw64/lib --extra-lib-dirs=/mingw64/bin --extra-include-dirs=/mingw64/include - - if: matrix.os != 'windows-latest' - name: Build binary - run: | - mkdir dist - cabal install exe:egraphGP --install-method=copy --overwrite-policy=always --installdir=dist - cabal install exe:egraphSearch --install-method=copy --overwrite-policy=always --installdir=dist - cabal install exe:srtools --install-method=copy --overwrite-policy=always --installdir=dist - cabal install exe:rEGGression --install-method=copy --overwrite-policy=always --installdir=dist - cabal install exe:srsimplify --install-method=copy --overwrite-policy=always --installdir=dist - - - if: matrix.os == 'windows-latest' - name: Set extension to .exe on Windows - run: echo "EXT=.exe" >> $GITHUB_ENV - - - name: Set binary path name - run: | - echo "BINARY_PATH1=./dist/srsimplify${{ env.EXT }}" >> $GITHUB_ENV - echo "BINARY_PATH2=./dist/srtools${{ env.EXT }}" >> $GITHUB_ENV - echo "BINARY_PATH2=./dist/egraphGP${{ env.EXT }}" >> $GITHUB_ENV - echo "BINARY_PATH2=./dist/egraphSearch${{ env.EXT }}" >> $GITHUB_ENV - echo "BINARY_PATH2=./dist/rEGGression${{ env.EXT }}" >> $GITHUB_ENV - - - name: Upload Release Asset - id: upload-release-asset - uses: softprops/action-gh-release@v2 - env: - GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} - with: - files: | - ${{ env.BINARY_PATH1 }}/srsimplify-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }}${{ env.EXT }} - ${{ env.BINARY_PATH2 }}/srtools-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }}${{ env.EXT }} - ${{ env.BINARY_PATH2 }}/egraphSearch-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }}${{ env.EXT }} - ${{ env.BINARY_PATH2 }}/egraphGP-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }}${{ env.EXT }} - ${{ env.BINARY_PATH2 }}/rEGGression-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }}${{ env.EXT }} diff --git a/.github/workflows/release.yml.working b/.github/workflows/release.yml.working deleted file mode 100644 index aa2bfd3..0000000 --- a/.github/workflows/release.yml.working +++ /dev/null @@ -1,155 +0,0 @@ -name: srtols Release - -on: - # Trigger the workflow on the new 'v*' tag created - push: - tags: - - "v*" - -jobs: - create_release: - name: Create Github Release - runs-on: ubuntu-latest - outputs: - upload_url: ${{ steps.create_release.outputs.upload_url }} - steps: - - name: Create Release - id: create_release - uses: actions/create-release@v1.1.4 - env: - GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} - with: - tag_name: ${{ github.ref }} - release_name: Release ${{ github.ref }} - draft: true - prerelease: false - - build_artifact: - needs: [create_release] - name: ${{ matrix.os }}/GHC ${{ matrix.ghc }}/${{ github.ref }} - runs-on: ${{ matrix.os }} - strategy: - matrix: - os: [ubuntu-latest, windows-latest, macOS-latest] - ghc: - - 9.10.1 - cabal: ["3.14.1.1"] - defaults: - run: - shell: bash - - steps: - - name: Check out code - uses: actions/checkout@v3 - - - name: Set tag name - uses: olegtarasov/get-tag@v2.1 - id: tag - with: - tagRegex: "v(.*)" - tagRegexGroup: 1 - - - name: Setup Haskell - uses: haskell-actions/setup@v2 - id: setup-haskell-cabal - with: - ghc-version: ${{ matrix.ghc }} - cabal-version: ${{ matrix.cabal }} - - - name: Cache ~/.cabal/store - uses: actions/cache@v2 - with: - key: ${{ runner.os }}-ghc-${{ matrix.ghc }}-${{ github.sha }} - path: ~/.cabal/store - - - if: matrix.os == 'ubuntu-latest' - name: Install ubuntu dependencies - run: | - sudo apt-get update - sudo apt-get install -y libnlopt-dev libnlopt0 - - - if: matrix.os == 'macOS-latest' - name: Install macOS dependencies - run: | - brew install nlopt - ls $(brew --prefix)/Cellar/nlopt/2.9.1/lib/ - echo "PKG_CONFIG_PATH=${PKG_CONFIG_PATH}" - echo "PKG_CONFIG_PATH=$(brew --prefix)/Cellar/nlopt/2.9.1/lib/pkgconfig" >> "${GITHUB_ENV}" - echo "LD_LIBRARY_PATH=${LD_LIBRARY_PATH}" - echo "LD_LIBRARY_PATH=$(brew --prefix)/Cellar/nlopt/2.9.1/lib" >> "${GITHUB_ENV}" - echo "DYLD_LIBRARY_PATH=${DYLD_LIBRARY_PATH}" - echo "DYLD_LIBRARY_PATH=$(brew --prefix)/Cellar/nlopt/2.9.1/lib" >> "${GITHUB_ENV}" - - - if: matrix.os == 'windows-latest' - name: Build windows binary - run: | - stack exec -- pacman --noconfirm -Sy msys2-keyring - stack exec -- pacman --noconfirm -S mingw-w64-x86_64-pkgconf - stack exec -- pacman --noconfirm -S mingw-w64-x86_64-nlopt - mkdir dist - stack build srtree:eggp --copy-bins --local-bin-path=dist - stack build srtree:symregg --copy-bins --local-bin-path=dist - stack build srtree:srtools --copy-bins --local-bin-path=dist - stack build srtree:reggression --copy-bins --local-bin-path=dist - stack build srtree:srsimplify --copy-bins --local-bin-path=dist - mv dist/eggp.exe dist/eggp-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }}.exe - mv dist/symregg.exe dist/symregg-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }}.exe - mv dist/srtools.exe dist/srtools-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }}.exe - mv dist/reggression.exe dist/reggression-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }}.exe - mv dist/srsimplify.exe dist/srsimplify-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }}.exe - - - if: matrix.os == 'macOS-latest' - name: Build binary on maxOS - run: | - mkdir dist - cabal install srtree:eggp --install-method=copy --overwrite-policy=always --installdir=dist --extra-lib-dirs=/opt/homebrew/Cellar/nlopt/2.9.1/lib --extra-include-dirs=/opt/homebrew/Cellar/nlopt/2.9.1/include - cabal install srtree:symregg --install-method=copy --overwrite-policy=always --installdir=dist --extra-lib-dirs=/opt/homebrew/Cellar/nlopt/2.9.1/lib --extra-include-dirs=/opt/homebrew/Cellar/nlopt/2.9.1/include - cabal install srtree:srtools --install-method=copy --overwrite-policy=always --installdir=dist --extra-lib-dirs=/opt/homebrew/Cellar/nlopt/2.9.1/lib --extra-include-dirs=/opt/homebrew/Cellar/nlopt/2.9.1/include - cabal install srtree:rEGGression --install-method=copy --overwrite-policy=always --installdir=dist --extra-lib-dirs=/opt/homebrew/Cellar/nlopt/2.9.1/lib --extra-include-dirs=/opt/homebrew/Cellar/nlopt/2.9.1/include - cabal install srtree:srsimplify --install-method=copy --overwrite-policy=always --installdir=dist --extra-lib-dirs=/opt/homebrew/Cellar/nlopt/2.9.1/lib --extra-include-dirs=/opt/homebrew/Cellar/nlopt/2.9.1/include - mv dist/eggp dist/eggp-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }} - mv dist/symregg dist/symregg-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }} - mv dist/srtools dist/srtools-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }} - mv dist/reggression dist/reggression-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }} - mv dist/srsimplify dist/srsimplify-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }} - - - if: matrix.os == 'ubuntu-latest' - name: Build binary on Ubuntu - run: | - mkdir dist - cabal install srtree:eggp --install-method=copy --overwrite-policy=always --installdir=dist - cabal install srtree:symregg --install-method=copy --overwrite-policy=always --installdir=dist - cabal install srtree:srtools --install-method=copy --overwrite-policy=always --installdir=dist - cabal install srtree:reggression --install-method=copy --overwrite-policy=always --installdir=dist - cabal install srtree:srsimplify --install-method=copy --overwrite-policy=always --installdir=dist - mv dist/eggp dist/eggp-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }} - mv dist/symregg dist/symregg-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }} - mv dist/srtools dist/srtools-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }} - mv dist/reggression dist/reggression-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }} - mv dist/srsimplify dist/srsimplify-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }} - ls dist - - - if: matrix.os == 'windows-latest' - name: Set extension to .exe on Windows - run: echo "EXT=.exe" >> $GITHUB_ENV - - - name: Set binary path name - run: | - echo "BINARY_PATH1=./dist" >> $GITHUB_ENV - echo "BINARY_PATH2=./dist" >> $GITHUB_ENV - echo "BINARY_PATH2=./dist" >> $GITHUB_ENV - echo "BINARY_PATH2=./dist" >> $GITHUB_ENV - echo "BINARY_PATH2=./dist" >> $GITHUB_ENV - - - name: Upload Release Asset - id: upload-release-asset - uses: softprops/action-gh-release@v2 - env: - GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} - with: - files: | - ${{ env.BINARY_PATH1 }}/srsimplify-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }}${{ env.EXT }} - ${{ env.BINARY_PATH2 }}/srtools-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }}${{ env.EXT }} - ${{ env.BINARY_PATH2 }}/symregg-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }}${{ env.EXT }} - ${{ env.BINARY_PATH2 }}/eggp-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }}${{ env.EXT }} - ${{ env.BINARY_PATH2 }}/reggression-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }}${{ env.EXT }} diff --git a/.github/workflows/release.yml_winbkp b/.github/workflows/release.yml_winbkp deleted file mode 100644 index 990d16d..0000000 --- a/.github/workflows/release.yml_winbkp +++ /dev/null @@ -1,124 +0,0 @@ -name: srtols Release - -on: - # Trigger the workflow on the new 'v*' tag created - push: - tags: - - "v*" - -jobs: - create_release: - name: Create Github Release - runs-on: ubuntu-latest - outputs: - upload_url: ${{ steps.create_release.outputs.upload_url }} - steps: - - name: Create Release - id: create_release - uses: actions/create-release@v1.1.4 - env: - GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} - with: - tag_name: ${{ github.ref }} - release_name: Release ${{ github.ref }} - draft: true - prerelease: false - - build_artifact: - needs: [create_release] - name: ${{ matrix.os }}/GHC ${{ matrix.ghc }}/${{ github.ref }} - runs-on: ${{ matrix.os }} - strategy: - matrix: - os: [windows-latest] - ghc: - - 9.10.1 - cabal: ["3.14.1.1"] - defaults: - run: - shell: bash - - steps: - - name: Check out code - uses: actions/checkout@v3 - - - name: Set tag name - uses: olegtarasov/get-tag@v2.1 - id: tag - with: - tagRegex: "v(.*)" - tagRegexGroup: 1 - - - name: Setup Haskell - uses: haskell-actions/setup@v2 - id: setup-haskell-cabal - with: - ghc-version: ${{ matrix.ghc }} - cabal-version: ${{ matrix.cabal }} - - - name: Cache ~/.cabal/store - uses: actions/cache@v2 - with: - key: ${{ runner.os }}-ghc-${{ matrix.ghc }}-${{ github.sha }} - path: ~/.cabal/store - - - if: matrix.os == 'ubuntu-latest' - name: Install dependencies - run: | - sudo apt-get update - sudo apt-get install -y libnlopt-dev libnlopt0 - - - if: matrix.os == 'macOS-latest' - name: Install dependencies - run: | - brew install nlopt - - - if: matrix.os == 'windows-latest' - name: Build windows binary - run: | - pwd - mkdir /d/a/nlopt - curl -L https://github.com/stevengj/NLoptBuilder/releases/download/v2.6.1/NLopt.v2.6.1.i686-w64-mingw32.tar.gz -o nlopt.tar.gz - tar zxvf nlopt.tar.gz -C /d/a/nlopt - dir /d/a/nlopt - cp /d/a/nlopt/bin/* /usr/lib - mkdir dist - cabal install exe:egraphGP --install-method=copy --overwrite-policy=always --installdir=dist --extra-lib-dirs=/d/a/nlopt/lib --extra-lib-dirs=/d/a/nlopt/bin --extra-include-dirs=/d/a/nlopt/include - # cabal install exe:egraphSearch --install-method=copy --overwrite-policy=always --installdir=dist --extra-lib-dirs=nlopt/lib --extra-lib-dirs=nlopt/bin --extra-include-dirs=nlopt/include - # cabal install exe:srtools --install-method=copy --overwrite-policy=always --installdir=dist --extra-lib-dirs=nlopt/lib --extra-lib-dirs=nlopt/bin --extra-include-dirs=nlopt/include - # cabal install exe:rEGGression --install-method=copy --overwrite-policy=always --installdir=dist --extra-lib-dirs=nlopt/lib --extra-lib-dirs=nlopt/bin --extra-include-dirs=nlopt/include - # cabal install exe:srsimplify --install-method=copy --overwrite-policy=always --installdir=dist --extra-lib-dirs=nlopt/lib --extra-lib-dirs=nlopt/bin --extra-include-dirs=nlopt/include - - if: matrix.os != 'windows-latest' - name: Build binary - run: | - mkdir dist - cabal install exe:egraphGP --install-method=copy --overwrite-policy=always --installdir=dist - cabal install exe:egraphSearch --install-method=copy --overwrite-policy=always --installdir=dist - cabal install exe:srtools --install-method=copy --overwrite-policy=always --installdir=dist - cabal install exe:rEGGression --install-method=copy --overwrite-policy=always --installdir=dist - cabal install exe:srsimplify --install-method=copy --overwrite-policy=always --installdir=dist - - - if: matrix.os == 'windows-latest' - name: Set extension to .exe on Windows - run: echo "EXT=.exe" >> $GITHUB_ENV - - - name: Set binary path name - run: | - echo "BINARY_PATH1=./dist/srsimplify${{ env.EXT }}" >> $GITHUB_ENV - echo "BINARY_PATH2=./dist/srtools${{ env.EXT }}" >> $GITHUB_ENV - echo "BINARY_PATH2=./dist/egraphGP${{ env.EXT }}" >> $GITHUB_ENV - echo "BINARY_PATH2=./dist/egraphSearch${{ env.EXT }}" >> $GITHUB_ENV - echo "BINARY_PATH2=./dist/rEGGression${{ env.EXT }}" >> $GITHUB_ENV - - - name: Upload Release Asset - id: upload-release-asset - uses: softprops/action-gh-release@v2 - env: - GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} - with: - files: | - ${{ env.BINARY_PATH1 }}/srsimplify-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }}${{ env.EXT }} - ${{ env.BINARY_PATH2 }}/srtools-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }}${{ env.EXT }} - ${{ env.BINARY_PATH2 }}/egraphSearch-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }}${{ env.EXT }} - ${{ env.BINARY_PATH2 }}/egraphGP-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }}${{ env.EXT }} - ${{ env.BINARY_PATH2 }}/rEGGression-${{ steps.tag.outputs.tag }}-${{ runner.os }}-ghc-${{ matrix.ghc }}${{ env.EXT }} diff --git a/.gitignore b/.gitignore index 4c9e245..ac32f27 100644 --- a/.gitignore +++ b/.gitignore @@ -21,3 +21,6 @@ cabal.project.local cabal.project.local~ .HTF/ .ghc.environment.* +build +binding.py +binding_wrap.c diff --git a/pyproject.toml b/pyproject.toml index 16b281e..7ac56e0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,6 +3,9 @@ requires = [ "find_libpython >=0.3,<0.5; sys_platform == 'win32'", "setuptools >=45", "swig >=4,<5", + "numpy >=2.2", + "pandas >=2.2", + "scikit-learn >=1.6", # "nlopt >= 2.6.2", ] build-backend = "setuptools.build_meta" @@ -85,6 +88,9 @@ musllinux-x86_64-image = "folivetti/musllinux_1_1_ghc9121_x86_64" [tool.cibuildwheel.windows] repair-wheel-command = "delvewheel repair -w {dest_dir} {wheel} --add-path C:\\nlopt\\bin" +[tool.cibuildwheel.linux] +repair-wheel-command = "delvewheel repair -w {dest_dir} {wheel} --add-path /usr/local/lib64" + [tool.mypy] mypy_path = "src" python_version = "3.9" diff --git a/src/Pyeggp.hs b/src/Pyeggp.hs index b5cc2fd..e589c65 100644 --- a/src/Pyeggp.hs +++ b/src/Pyeggp.hs @@ -358,7 +358,7 @@ egraphGP dataTrainVals args = do thetaStr = intercalate ";" $ Prelude.map show (MA.toList theta) showFun = showExpr expr showFunNp = showPython best' - pure $ show ix <> "," <> showFun <> "," <> showFunNp <> "," + pure $ show ix <> "," <> showFun <> "," <> "\"" <> showFunNp <> "\"" <> "," <> thetaStr <> "," <> show (countNodes $ convertProtectedOps expr) <> "\n" pure (concat ts) insertTerms = diff --git a/src/pyeggp/__init__.py b/src/pyeggp/__init__.py index 37aa251..fa64135 100644 --- a/src/pyeggp/__init__.py +++ b/src/pyeggp/__init__.py @@ -2,6 +2,15 @@ from contextlib import contextmanager from threading import Lock from typing import Iterator, List +from io import StringIO +import tempfile +import csv + +import numpy as np +import pandas as pd +from sklearn.base import BaseEstimator, RegressorMixin +from sklearn.utils.validation import check_X_y, check_array, check_is_fitted +from sklearn.metrics import mean_squared_error, r2_score from ._binding import ( unsafe_hs_pyeggp_version, @@ -49,7 +58,7 @@ def pyeggp_run(dataset: str, gen: int, nPop: int, maxSize: int, nTournament: int with hs_rts_init(): return unsafe_hs_pyeggp_run(dataset, gen, nPop, maxSize, nTournament, pc, pm, nonterminals, loss, optIter, optRepeat, nParams, split, simplify, dumpTo, loadFrom) -class PyEGGP(): +class PyEGGP(BaseEstimator, RegressorMixin): def __init__(self, gen = 100, nPop = 100, maxSize = 15, nTournament = 3, pc = 0.9, pm = 0.3, nonterminals = "add,sub,mul,div", loss = "MSE", optIter = 50, optRepeat = 2, nParams = -1, split = 1, simplify = False, dumpTo = "", loadFrom = ""): self.gen = gen self.nPop = nPop @@ -66,6 +75,40 @@ def __init__(self, gen = 100, nPop = 100, maxSize = 15, nTournament = 3, pc = 0. self.simplify = int(simplify) self.dumpTo = dumpTo self.loadFrom = loadFrom - def fit(self, dataset): - output = pyeggp_run(dataset, self.gen, self.nPop, self.maxSize, self.nTournament, self.pc, self.pm, self.nonterminals, self.loss, self.optIter, self.optRepeat, self.nParams, self.split, self.simplify, self.dumpTo, self.loadFrom) - return output + self.is_fitted_ = False + + def fit(self, X, y): + if X.ndim == 1: + X = X.reshape(-1,1) + y = y.reshape(-1, 1) + combined = np.hstack([X, y]) + header = [f"x{i}" for i in range(X.shape[1])] + ["y"] + with tempfile.NamedTemporaryFile(mode='w+', newline='', delete=False, suffix='.csv') as temp_file: + writer = csv.writer(temp_file) + writer.writerow(header) + writer.writerows(combined) + dataset = temp_file.name + + csv_data = pyeggp_run(dataset, self.gen, self.nPop, self.maxSize, self.nTournament, self.pc, self.pm, self.nonterminals, self.loss, self.optIter, self.optRepeat, self.nParams, self.split, self.simplify, self.dumpTo, self.loadFrom) + if len(csv_data) > 0: + csv_io = StringIO(csv_data.strip()) + self.results = pd.read_csv(csv_io, header=0) + self.is_fitted_ = True + return self + + def predict(self, X): + check_is_fitted(self) + return self.evaluate_best_model(self.model_, X) + def evaluate_best_model(self, x): + if x.ndim == 1: + x = x.reshape(-1,1) + t = np.array(list(map(float, self.results.iloc[-1].theta.split(";")))) + return eval(self.results.iloc[-1].Numpy) + def evaluate_model(self, ix, x): + if x.ndim == 1: + x = x.reshape(-1,1) + t = np.array(list(map(float, self.results.iloc[-1].theta.split(";")))) + return eval(self.results.iloc[i].Numpy) + def score(self, X, y): + ypred = self.evaluate_best_model(X) + return r2_score(y, ypred) diff --git a/test/test_pyeggp.py b/test/test_pyeggp.py index 54a545b..6d365c9 100644 --- a/test/test_pyeggp.py +++ b/test/test_pyeggp.py @@ -1,6 +1,16 @@ -from pyeggp import pyeggp_run +from pyeggp import pyeggp_run, PyEGGP +import pandas as pd -args = ["test/data.csv", "100", "10", "1", "MSE", "50", "2", "-1", "100", "3", "0.9", "0.3", "add,sub,mul,div", "\"\"", "\"\"", "True"] -output = pyeggp_run("test/data.csv", 100, 100, 10, 3, 0.9, 0.3, "add,sub,mul,div", "MSE", 50, 2, -1, 3, 0, "", "") +output = pyeggp_run("test/data.csv", 100, 100, 10, 3, 0.9, 0.3, "add,sub,mul,div,log", "MSE", 50, 2, -1, 3, 0, "", "") print(output) + +print("Check PyEGGP") +df = pd.read_csv("test/data.csv") +Z = df.values +X = Z[:,:-1] +y = Z[:,-1] + +reg = PyEGGP(100, 100, 10, 3, 0.9, 0.3, "add,sub,mul,div,log", "MSE", 50, 2, -1, 3, 0, "", "") +reg.fit(X, y) +print(reg.score(X, y)) From 0915ee78b13bc21a4081911566668dc80b0185ea Mon Sep 17 00:00:00 2001 From: folivetti Date: Wed, 16 Apr 2025 09:20:16 -0300 Subject: [PATCH 049/166] fix numpy constraints --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 7ac56e0..a19673c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,7 +3,7 @@ requires = [ "find_libpython >=0.3,<0.5; sys_platform == 'win32'", "setuptools >=45", "swig >=4,<5", - "numpy >=2.2", + "numpy >=2", "pandas >=2.2", "scikit-learn >=1.6", # "nlopt >= 2.6.2", From b1895722540be843f091c952778a72d9f0c0cde8 Mon Sep 17 00:00:00 2001 From: folivetti Date: Wed, 16 Apr 2025 09:38:10 -0300 Subject: [PATCH 050/166] fix numpy constraints --- pyproject.toml | 2 +- requirements-ci.txt | 1 + 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index a19673c..4dfd48f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -89,7 +89,7 @@ musllinux-x86_64-image = "folivetti/musllinux_1_1_ghc9121_x86_64" repair-wheel-command = "delvewheel repair -w {dest_dir} {wheel} --add-path C:\\nlopt\\bin" [tool.cibuildwheel.linux] -repair-wheel-command = "delvewheel repair -w {dest_dir} {wheel} --add-path /usr/local/lib64" +repair-wheel-command = "auditwheel repair -w {dest_dir} {wheel} --lib-sdir . --add-path /usr/local/lib64" [tool.mypy] mypy_path = "src" diff --git a/requirements-ci.txt b/requirements-ci.txt index b070820..105e96f 100644 --- a/requirements-ci.txt +++ b/requirements-ci.txt @@ -1,2 +1,3 @@ pipx >=1.2.0, <2 delvewheel +auditwheel From e9cb2c0c0f678fbff2649acd594fc5f8656d5b6b Mon Sep 17 00:00:00 2001 From: folivetti Date: Wed, 16 Apr 2025 11:27:58 -0300 Subject: [PATCH 051/166] fix numpy constraints --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 4dfd48f..2c50e33 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -89,7 +89,7 @@ musllinux-x86_64-image = "folivetti/musllinux_1_1_ghc9121_x86_64" repair-wheel-command = "delvewheel repair -w {dest_dir} {wheel} --add-path C:\\nlopt\\bin" [tool.cibuildwheel.linux] -repair-wheel-command = "auditwheel repair -w {dest_dir} {wheel} --lib-sdir . --add-path /usr/local/lib64" +repair-wheel-command = "auditwheel repair -w {dest_dir} {wheel} --lib-sdir ." [tool.mypy] mypy_path = "src" From 7ae1393d7cb95732f1e56a52583e90142b7f8028 Mon Sep 17 00:00:00 2001 From: folivetti Date: Wed, 16 Apr 2025 13:15:26 -0300 Subject: [PATCH 052/166] fix numpy constraints --- .github/workflows/release.yml | 48 +++++++++++++++++------------------ 1 file changed, 24 insertions(+), 24 deletions(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 79a7c71..eb88268 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -225,27 +225,27 @@ jobs: ################################################################################ #publish-to-pypi: - # name: Publish to PyPI - # runs-on: ubuntu-latest - # if: startsWith(github.ref, 'refs/tags/') - # needs: [build] - - # environment: - # name: pypi - # url: https://pypi.org/p/eggp - - # permissions: - # id-token: write # IMPORTANT: this permission is mandatory for trusted publishing - - # steps: - # - name: Download artifacts - # uses: actions/download-artifact@v4 - # with: - # pattern: "wheelhouse-*-*" - # path: "./wheelhouse" - # merge-multiple: true - - # - name: Publish to PyPI - # uses: pypa/gh-action-pypi-publish@release/v1 - # with: - # packages-dir: "./wheelhouse" + name: Publish to PyPI + runs-on: ubuntu-latest + if: startsWith(github.ref, 'refs/tags/') + needs: [build] + + environment: + name: pypi + url: https://pypi.org/p/eggp + + permissions: + id-token: write # IMPORTANT: this permission is mandatory for trusted publishing + + steps: + - name: Download artifacts + uses: actions/download-artifact@v4 + with: + pattern: "wheelhouse-*-*" + path: "./wheelhouse" + merge-multiple: true + + - name: Publish to PyPI + uses: pypa/gh-action-pypi-publish@release/v1 + with: + packages-dir: "./wheelhouse" From 24651093ce2f1413374ab7f04528a7f72a9b7789 Mon Sep 17 00:00:00 2001 From: folivetti Date: Wed, 16 Apr 2025 13:16:27 -0300 Subject: [PATCH 053/166] fix numpy constraints --- .github/workflows/release.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index eb88268..dc38070 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -224,7 +224,7 @@ jobs: # Publish package to PyPI ################################################################################ - #publish-to-pypi: + publish-to-pypi: name: Publish to PyPI runs-on: ubuntu-latest if: startsWith(github.ref, 'refs/tags/') From dbb2b51bd2e8fd11c563447a41aa32c36b57f201 Mon Sep 17 00:00:00 2001 From: Konstantin Malanchev Date: Wed, 16 Apr 2025 16:27:48 -0400 Subject: [PATCH 054/166] Use native ARM runner for aarch64 linux wheels --- .github/workflows/release.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index dc38070..8de9088 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -43,7 +43,7 @@ jobs: job: - os: name: "Linux" - runner: "ubuntu-latest" + runner: "ubuntu-24.04-arm" arch: "aarch64" plat: "manylinux" #- os: From 81050c2072995ff00cc4d353aa0f9e376936b32a Mon Sep 17 00:00:00 2001 From: folivetti Date: Thu, 17 Apr 2025 12:26:24 -0300 Subject: [PATCH 055/166] mv support in PyEggp --- src/pyeggp.egg-info/PKG-INFO | 710 +++++++++++++++++++++++ src/pyeggp.egg-info/SOURCES.txt | 17 + src/pyeggp.egg-info/dependency_links.txt | 1 + src/pyeggp.egg-info/entry_points.txt | 2 + src/pyeggp.egg-info/requires.txt | 6 + src/pyeggp.egg-info/top_level.txt | 1 + src/pyeggp/__init__.py | 41 +- test/data2.csv | 36 ++ test/test_pyeggp.py | 8 + 9 files changed, 821 insertions(+), 1 deletion(-) create mode 100644 src/pyeggp.egg-info/PKG-INFO create mode 100644 src/pyeggp.egg-info/SOURCES.txt create mode 100644 src/pyeggp.egg-info/dependency_links.txt create mode 100644 src/pyeggp.egg-info/entry_points.txt create mode 100644 src/pyeggp.egg-info/requires.txt create mode 100644 src/pyeggp.egg-info/top_level.txt create mode 100644 test/data2.csv diff --git a/src/pyeggp.egg-info/PKG-INFO b/src/pyeggp.egg-info/PKG-INFO new file mode 100644 index 0000000..a587160 --- /dev/null +++ b/src/pyeggp.egg-info/PKG-INFO @@ -0,0 +1,710 @@ +Metadata-Version: 2.4 +Name: pyeggp +Version: 1.0.0 +Summary: Python Wheels for eggp algorithm. +Author-email: Fabricio Olivetti +License: GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for + software and other kinds of works. + + The licenses for most software and other practical works are designed + to take away your freedom to share and change the works. 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If your program is a subroutine library, you + may consider it more useful to permit linking proprietary applications with + the library. If this is what you want to do, use the GNU Lesser General + Public License instead of this License. But first, please read + . + +Classifier: Operating System :: MacOS +Classifier: Operating System :: Microsoft :: Windows +Classifier: Operating System :: POSIX :: Linux +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3.12 +Classifier: Programming Language :: Python :: 3.13 +Classifier: Programming Language :: Python :: Implementation :: CPython +Requires-Python: >=3.9 +Description-Content-Type: text/markdown +License-File: LICENSE +Requires-Dist: typing_extensions<5,>=4.6; python_version < "3.8" +Provides-Extra: mypy +Requires-Dist: types_setuptools>=45; extra == "mypy" +Dynamic: license-file + +# pyeggp - Python e-graph GP + +Python bindings for [eggp](https://github.com/folivetti/srtree/blob/main/apps/eggp/README.md). + +ggp (e-graph genetic programming), follows the same structure as the traditional GP. The initial population is created using ramped half-and-half respecting a maximum size and maximum depth parameter and, for a number of generations, it will choose two parents using tournament selection, apply the subtree crossover with probability $pc$ followed by the subtree mutation with probability $pm$, when the offsprings replace the current population following a dominance criteria. + +How to install the package: + +```bash +pip install pyeggp +``` + +The bindings were created following the amazing example written by [wenkokke](https://github.com/wenkokke/example-haskell-wheel) diff --git a/src/pyeggp.egg-info/SOURCES.txt b/src/pyeggp.egg-info/SOURCES.txt new file mode 100644 index 0000000..fd4e0f9 --- /dev/null +++ b/src/pyeggp.egg-info/SOURCES.txt @@ -0,0 +1,17 @@ +LICENSE +README.md +pyproject.toml +setup.py +src/pyeggp/__init__.py +src/pyeggp/__main__.py +src/pyeggp/_binding.pyi +src/pyeggp/binding.i +src/pyeggp/binding.py +src/pyeggp/typing.py +src/pyeggp.egg-info/PKG-INFO +src/pyeggp.egg-info/SOURCES.txt +src/pyeggp.egg-info/dependency_links.txt +src/pyeggp.egg-info/entry_points.txt +src/pyeggp.egg-info/requires.txt +src/pyeggp.egg-info/top_level.txt +test/test_pyeggp.py \ No newline at end of file diff --git a/src/pyeggp.egg-info/dependency_links.txt b/src/pyeggp.egg-info/dependency_links.txt new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/src/pyeggp.egg-info/dependency_links.txt @@ -0,0 +1 @@ + diff --git a/src/pyeggp.egg-info/entry_points.txt b/src/pyeggp.egg-info/entry_points.txt new file mode 100644 index 0000000..ba1dad8 --- /dev/null +++ b/src/pyeggp.egg-info/entry_points.txt @@ -0,0 +1,2 @@ +[console_scripts] +pyeggp = pyeggp.__main__:main diff --git a/src/pyeggp.egg-info/requires.txt b/src/pyeggp.egg-info/requires.txt new file mode 100644 index 0000000..c976189 --- /dev/null +++ b/src/pyeggp.egg-info/requires.txt @@ -0,0 +1,6 @@ + +[:python_version < "3.8"] +typing_extensions<5,>=4.6 + +[mypy] +types_setuptools>=45 diff --git a/src/pyeggp.egg-info/top_level.txt b/src/pyeggp.egg-info/top_level.txt new file mode 100644 index 0000000..a7305fa --- /dev/null +++ b/src/pyeggp.egg-info/top_level.txt @@ -0,0 +1 @@ +pyeggp diff --git a/src/pyeggp/__init__.py b/src/pyeggp/__init__.py index fa64135..8ecdcbf 100644 --- a/src/pyeggp/__init__.py +++ b/src/pyeggp/__init__.py @@ -96,14 +96,53 @@ def fit(self, X, y): self.is_fitted_ = True return self + def fit_mvsr(self, Xs, ys): + if Xs[0].ndim == 1: + Xs = [X.reshape(-1,1) for X in Xs] + ys = [y.reshape(-1, 1) for y in ys] + combineds = [np.hstack([X, y]) for X, y in zip(Xs, ys)] + header = [f"x{i}" for i in range(Xs[0].shape[1])] + ["y"] + datasets = [] + for combined in combineds: + with tempfile.NamedTemporaryFile(mode='w+', newline='', delete=False, suffix='.csv') as temp_file: + writer = csv.writer(temp_file) + writer.writerow(header) + writer.writerows(combined) + datasets.append(temp_file.name) + + csv_data = pyeggp_run(" ".join(datasets), self.gen, self.nPop, self.maxSize, self.nTournament, self.pc, self.pm, self.nonterminals, self.loss, self.optIter, self.optRepeat, self.nParams, self.split, self.simplify, self.dumpTo, self.loadFrom) + if len(csv_data) > 0: + csv_io = StringIO(csv_data.strip()) + self.results = pd.read_csv(csv_io, header=0, dtype={'theta':str}) + self.is_fitted_ = True + return self + def predict(self, X): check_is_fitted(self) - return self.evaluate_best_model(self.model_, X) + return self.evaluate_best_model(X) + + def predict_mvsr(self, X, view): + check_is_fitted(self) + return self.evaluate_best_model_view(X, view) + def evaluate_best_model(self, x): if x.ndim == 1: x = x.reshape(-1,1) t = np.array(list(map(float, self.results.iloc[-1].theta.split(";")))) return eval(self.results.iloc[-1].Numpy) + def evaluate_best_model_view(self, x, view): + if x.ndim == 1: + x = x.reshape(-1,1) + ix = self.results.iloc[-1].id + best = self.results[self.results.id==ix].iloc[view] + t = np.array(list(map(float, best.theta.split(";")))) + return eval(best.Numpy) + def evaluate_model_view(self, x, ix, view): + if x.ndim == 1: + x = x.reshape(-1,1) + best = self.results[self.results.id==ix].iloc[view] + t = np.array(list(map(float, best.theta.split(";")))) + return eval(best.Numpy) def evaluate_model(self, ix, x): if x.ndim == 1: x = x.reshape(-1,1) diff --git a/test/data2.csv b/test/data2.csv new file mode 100644 index 0000000..74940e6 --- /dev/null +++ b/test/data2.csv @@ -0,0 +1,36 @@ +lugs_1989,lugs_1990,target +1.0,5.0,9.5 +3.0,8.0,17.5 +3.0,11.0,18.0 +3.0,9.0,20.0 +5.0,9.5,20.5 +3.5,10.0,20.0 +3.0,12.0,19.0 +4.5,10.0,21.0 +5.0,11.5,19.0 +5.0,10.0,26.0 +6.0,11.0,20.0 +5.0,10.0,23.0 +4.0,10.5,22.0 +6.0,10.0,20.0 +5.0,11.0,20.0 +6.5,11.0,24.0 +4.0,11.5,23.0 +6.0,12.0,23.0 +3.0,10.0,17.0 +2.0,7.0,21.0 +2.0,10.0,17.0 +2.0,8.0,20.0 +2.0,10.0,16.0 +2.0,9.0,20.0 +1.5,10.0,15.0 +1.5,8.0,17.0 +1.5,11.0,17.0 +1.0,10.0,20.0 +1.0,7.0,15.0 +1.0,5.0,14.0 +1.0,7.0,12.0 +0.5,8.0,11.0 +0.5,6.0,11.0 +0.0,7.5,8.5 +0.0,2.5,2.5 diff --git a/test/test_pyeggp.py b/test/test_pyeggp.py index 6d365c9..eac9a39 100644 --- a/test/test_pyeggp.py +++ b/test/test_pyeggp.py @@ -5,6 +5,10 @@ print(output) +output = pyeggp_run("test/data.csv test/data2.csv", 100, 100, 10, 3, 0.9, 0.3, "add,sub,mul,div,log", "MSE", 50, 2, -1, 3, 0, "", "") + +print(output) + print("Check PyEGGP") df = pd.read_csv("test/data.csv") Z = df.values @@ -14,3 +18,7 @@ reg = PyEGGP(100, 100, 10, 3, 0.9, 0.3, "add,sub,mul,div,log", "MSE", 50, 2, -1, 3, 0, "", "") reg.fit(X, y) print(reg.score(X, y)) + +reg.fit_mvsr([X,X],[y,y]) +print(reg.predict_mvsr(X,0)) +print(reg.predict_mvsr(X,1)) From 98b9ad19d1ae3c97c8926ac0858475d7f0ebf25c Mon Sep 17 00:00:00 2001 From: folivetti Date: Thu, 17 Apr 2025 12:26:59 -0300 Subject: [PATCH 056/166] mv support in PyEggp --- .github/workflows/release.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 8de9088..5e95caf 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -232,7 +232,7 @@ jobs: environment: name: pypi - url: https://pypi.org/p/eggp + url: https://pypi.org/p/pyeggp permissions: id-token: write # IMPORTANT: this permission is mandatory for trusted publishing From 6a120bade58da755bebffbe1efec666d70930d1c Mon Sep 17 00:00:00 2001 From: folivetti Date: Thu, 17 Apr 2025 13:30:18 -0300 Subject: [PATCH 057/166] fix arm image --- pyeggp.cabal | 2 +- pyproject.toml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/pyeggp.cabal b/pyeggp.cabal index 8bc04f1..627a9f3 100755 --- a/pyeggp.cabal +++ b/pyeggp.cabal @@ -5,7 +5,7 @@ cabal-version: 2.2 -- see: https://github.com/sol/hpack name: Pyeggp -version: 1.0.0 +version: 1.0.1 synopsis: A general library to work with Symbolic Regression expression trees. description: A Symbolic Regression Tree data structure to work with mathematical expressions with support to first order derivative and simplification; category: Math, Data, Data Structures diff --git a/pyproject.toml b/pyproject.toml index 2c50e33..bb8b11e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,7 +12,7 @@ build-backend = "setuptools.build_meta" [project] name = "pyeggp" -version = "1.0.0" +version = "1.0.1" authors = [{ name = "Fabricio Olivetti", email = "folivetti@users.noreply.github.com" }] description = "Python Wheels for eggp algorithm." readme = "README.md" From 1808d39dfec27268710f6a2b7700ca9c98ecc2d0 Mon Sep 17 00:00:00 2001 From: folivetti Date: Fri, 18 Apr 2025 07:25:29 -0300 Subject: [PATCH 058/166] improved results table --- pyeggp.cabal | 2 +- pyproject.toml | 2 +- src/Pyeggp.hs | 15 ++++---- src/Util.hs | 3 +- src/pyeggp.egg-info/PKG-INFO | 2 +- src/pyeggp/__init__.py | 1 + test/data2.csv | 66 +++++++++++++++++------------------- test/test_pyeggp.py | 1 + 8 files changed, 46 insertions(+), 46 deletions(-) diff --git a/pyeggp.cabal b/pyeggp.cabal index 627a9f3..5f6f0e9 100755 --- a/pyeggp.cabal +++ b/pyeggp.cabal @@ -5,7 +5,7 @@ cabal-version: 2.2 -- see: https://github.com/sol/hpack name: Pyeggp -version: 1.0.1 +version: 1.0.2 synopsis: A general library to work with Symbolic Regression expression trees. description: A Symbolic Regression Tree data structure to work with mathematical expressions with support to first order derivative and simplification; category: Math, Data, Data Structures diff --git a/pyproject.toml b/pyproject.toml index bb8b11e..c3c5e95 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,7 +12,7 @@ build-backend = "setuptools.build_meta" [project] name = "pyeggp" -version = "1.0.1" +version = "1.0.2" authors = [{ name = "Fabricio Olivetti", email = "folivetti@users.noreply.github.com" }] description = "Python Wheels for eggp algorithm." readme = "README.md" diff --git a/src/Pyeggp.hs b/src/Pyeggp.hs index e589c65..cda0842 100644 --- a/src/Pyeggp.hs +++ b/src/Pyeggp.hs @@ -140,7 +140,7 @@ egraphGP dataTrainVals args = do when ((not.null) (_dumpTo args)) $ get >>= (io . BS.writeFile (_dumpTo args) . encode ) pf <- paretoFront fitFun (_maxSize args) printExpr - pure $ "id,Expression,Numpy,theta,size\n" <> pf + pure $ "id,view,Expression,Numpy,theta,size,loss,maxLoss\n" <> pf where maxSize = (_maxSize args) maxMem = 2000000 -- running 1 iter of eqsat for each new individual will consume ~3GB @@ -349,17 +349,18 @@ egraphGP dataTrainVals args = do then fitFun bestExpr else pure (1.0, thetas') - ts <- forM (Prelude.zip dataTrainVals thetas) $ \((dataTrain, dataVal), theta) -> do + maxLoss <- negate . fromJust <$> getFitness ec + ts <- forM (Prelude.zip3 [0..] dataTrainVals thetas) $ \(view, (dataTrain, dataVal), theta) -> do let (x, y, mYErr) = dataTrain (x_val, y_val, mYErr_val) = dataVal distribution = _distribution args best' = if shouldReparam then relabelParams bestExpr else relabelParamsOrder bestExpr expr = paramsToConst (MA.toList theta) best' - thetaStr = intercalate ";" $ Prelude.map show (MA.toList theta) - showFun = showExpr expr - showFunNp = showPython best' - pure $ show ix <> "," <> showFun <> "," <> "\"" <> showFunNp <> "\"" <> "," - <> thetaStr <> "," <> show (countNodes $ convertProtectedOps expr) <> "\n" + thetaStr = intercalate ";" $ Prelude.map show (MA.toList theta) + showFun = showExpr expr + showFunNp = showPython best' + mse_train = nll distribution mYErr_val x_val y_val best' theta + pure . (<> "\n") . intercalate "," $ [show ix, show view, showFun, "\"" <> showFunNp <> "\"", thetaStr, show (countNodes $ convertProtectedOps expr), if isNaN mse_train then "1e+10" else show mse_train, show (negate maxLoss) ] pure (concat ts) insertTerms = forM terms $ \t -> do fromTree myCost t >>= canonical diff --git a/src/Util.hs b/src/Util.hs index c0922bc..ceb78d6 100644 --- a/src/Util.hs +++ b/src/Util.hs @@ -244,7 +244,8 @@ paretoFront fitFun maxSize printExprFun = concat <$> go 1 0 (-(1.0/0.0)) ecList <- getBestExprWithSize n if not (null ecList) then do let (ec, mf) = head ecList - improved = fromJust mf > f + f' = fromJust mf + improved = f' > f && (not . isNaN) f' && (not . isInfinite) f' ec' <- canonical ec if improved then do refit fitFun ec' diff --git a/src/pyeggp.egg-info/PKG-INFO b/src/pyeggp.egg-info/PKG-INFO index a587160..5f9bed7 100644 --- a/src/pyeggp.egg-info/PKG-INFO +++ b/src/pyeggp.egg-info/PKG-INFO @@ -1,6 +1,6 @@ Metadata-Version: 2.4 Name: pyeggp -Version: 1.0.0 +Version: 1.0.1 Summary: Python Wheels for eggp algorithm. Author-email: Fabricio Olivetti License: GNU GENERAL PUBLIC LICENSE diff --git a/src/pyeggp/__init__.py b/src/pyeggp/__init__.py index 8ecdcbf..cdd81b6 100644 --- a/src/pyeggp/__init__.py +++ b/src/pyeggp/__init__.py @@ -137,6 +137,7 @@ def evaluate_best_model_view(self, x, view): best = self.results[self.results.id==ix].iloc[view] t = np.array(list(map(float, best.theta.split(";")))) return eval(best.Numpy) + def evaluate_model_view(self, x, ix, view): if x.ndim == 1: x = x.reshape(-1,1) diff --git a/test/data2.csv b/test/data2.csv index 74940e6..0da6d99 100644 --- a/test/data2.csv +++ b/test/data2.csv @@ -1,36 +1,32 @@ lugs_1989,lugs_1990,target -1.0,5.0,9.5 -3.0,8.0,17.5 -3.0,11.0,18.0 -3.0,9.0,20.0 -5.0,9.5,20.5 -3.5,10.0,20.0 -3.0,12.0,19.0 -4.5,10.0,21.0 -5.0,11.5,19.0 -5.0,10.0,26.0 -6.0,11.0,20.0 -5.0,10.0,23.0 -4.0,10.5,22.0 -6.0,10.0,20.0 -5.0,11.0,20.0 -6.5,11.0,24.0 -4.0,11.5,23.0 -6.0,12.0,23.0 -3.0,10.0,17.0 -2.0,7.0,21.0 -2.0,10.0,17.0 -2.0,8.0,20.0 -2.0,10.0,16.0 -2.0,9.0,20.0 -1.5,10.0,15.0 -1.5,8.0,17.0 -1.5,11.0,17.0 -1.0,10.0,20.0 -1.0,7.0,15.0 -1.0,5.0,14.0 -1.0,7.0,12.0 -0.5,8.0,11.0 -0.5,6.0,11.0 -0.0,7.5,8.5 -0.0,2.5,2.5 +1.0,5.0,18.5 +3.0,8.0,32.5 +3.0,11.0,38.0 +3.0,9.0,40.0 +5.0,9.5,40.5 +3.5,10.0,40.0 +3.0,12.0,42.0 +4.5,10.0,41.0 +4.0,10.5,42.0 +6.0,10.0,40.0 +5.0,11.0,40.0 +6.5,11.0,44.0 +4.0,11.5,43.0 +6.0,12.0,43.0 +3.0,10.0,37.0 +2.0,7.0,41.0 +2.0,10.0,37.0 +2.0,8.0,40.0 +2.0,10.0,36.0 +2.0,9.0,40.0 +1.5,10.0,35.0 +1.5,8.0,37.0 +1.5,11.0,27.0 +1.0,10.0,40.0 +1.0,7.0,35.0 +1.0,5.0,24.0 +1.0,7.0,22.0 +0.5,8.0,21.0 +0.5,6.0,21.0 +0.0,7.5,18.5 +0.0,2.5,5.5 diff --git a/test/test_pyeggp.py b/test/test_pyeggp.py index eac9a39..4b5c242 100644 --- a/test/test_pyeggp.py +++ b/test/test_pyeggp.py @@ -22,3 +22,4 @@ reg.fit_mvsr([X,X],[y,y]) print(reg.predict_mvsr(X,0)) print(reg.predict_mvsr(X,1)) +print(reg.results) From 042a2e2f5b45a611c2b09708cde3a2672a9afcda Mon Sep 17 00:00:00 2001 From: Etienne Russeil Date: Fri, 18 Apr 2025 16:58:27 +0200 Subject: [PATCH 059/166] Added a method to visualise solution and turn them into python func --- src/pyeggp/__init__.py | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) diff --git a/src/pyeggp/__init__.py b/src/pyeggp/__init__.py index cdd81b6..ff3baf2 100644 --- a/src/pyeggp/__init__.py +++ b/src/pyeggp/__init__.py @@ -2,6 +2,7 @@ from contextlib import contextmanager from threading import Lock from typing import Iterator, List +import string from io import StringIO import tempfile import csv @@ -58,6 +59,11 @@ def pyeggp_run(dataset: str, gen: int, nPop: int, maxSize: int, nTournament: int with hs_rts_init(): return unsafe_hs_pyeggp_run(dataset, gen, nPop, maxSize, nTournament, pc, pm, nonterminals, loss, optIter, optRepeat, nParams, split, simplify, dumpTo, loadFrom) +def make_function(expression): + def func(x, t): + return eval(expression) + return func + class PyEGGP(BaseEstimator, RegressorMixin): def __init__(self, gen = 100, nPop = 100, maxSize = 15, nTournament = 3, pc = 0.9, pm = 0.3, nonterminals = "add,sub,mul,div", loss = "MSE", optIter = 50, optRepeat = 2, nParams = -1, split = 1, simplify = False, dumpTo = "", loadFrom = ""): self.gen = gen @@ -152,3 +158,19 @@ def evaluate_model(self, ix, x): def score(self, X, y): ypred = self.evaluate_best_model(X) return r2_score(y, ypred) + def get_model(self, idx): + alphabet = list(string.ascii_uppercase) + row = self.results[self.results['id']==idx].iloc[0] + visual_expression = row['Numpy'] + model = make_function(visual_expression) + n_params_used = len(row['theta'].split(sep=';')) + + # Works for solutions with less than 26 parameters + for i in range(n_params_used): + visual_expression = visual_expression.replace(f't[{i}]', alphabet[i]) + + # Works for data with less than 50 dimensions + for i in range(50): + visual_expression = visual_expression.replace(f'x[:, {i}]', f'X{i}') + + return model, visual_expression From b1340261df70e8f5589d4891d0b485a4ac80f304 Mon Sep 17 00:00:00 2001 From: Etienne Russeil Date: Fri, 18 Apr 2025 16:58:55 +0200 Subject: [PATCH 060/166] first commit --- test/mvsr_example.ipynb | 626 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 626 insertions(+) create mode 100644 test/mvsr_example.ipynb diff --git a/test/mvsr_example.ipynb b/test/mvsr_example.ipynb new file mode 100644 index 0000000..ef5aeef --- /dev/null +++ b/test/mvsr_example.ipynb @@ -0,0 +1,626 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "c68e3b1a-d6e4-4f94-ae46-d761d5c1c836", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pyeggp" + ] + }, + { + "cell_type": "markdown", + "id": "796958d0-b675-4cad-a00b-e52f9c9142af", + "metadata": {}, + "source": [ + "In this exercise, we attempt to discover a polynomial model that generated three examples. We purposefully generate the examples choosing a free parameter equal to zero. In this configuration, no single example is enough to discover the full functional form that generated each view. However, as demonstrated in Russeil et al. 2024 (https://arxiv.org/abs/2402.04298), a multiview approach - i.e. considering multiple datasets generated by the same function - enables to find the correct solution. " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "2c9d5ad9-f2e4-4b96-accd-41d1edbfdc98", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "np.random.seed(42)\n", + "\n", + "# Define the true model to discover\n", + "def true_func(X, A, B, C):\n", + " return A + B * X + C * X**2\n", + "\n", + "\n", + "# Define specific parameter values for each example.\n", + "true_params = [[0,-5, 2],\n", + " [20, 0, -2],\n", + " [2, 3, 0]]\n", + "\n", + "\n", + "# Generate the data and plot it \n", + "full_X, full_y = [], []\n", + "plt.figure(figsize=(10, 7))\n", + "for idx, ps in enumerate(true_params):\n", + " X = np.random.uniform(-5, 5, 15)\n", + " y = true_func(X, *ps)\n", + " yerr = np.random.normal(0, 2, size=len(y))\n", + " plt.scatter(X, y+yerr, label=f'View {idx+1}', s=50)\n", + " full_X += [np.array([X]).T]\n", + " full_y += [y+yerr]\n", + "\n", + "plt.legend(fontsize=16)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "3c318511-31dd-4cb2-8178-2ceb5c2e405c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idviewExpressionNumpythetasizelossmaxLoss
00021.58921727054863t[0]21.589217270548631532.979453-532.979453
101-2.930612339764856t[0]-2.9306123397648561247.002956-532.979453
2022.6671850933034973t[0]2.66718509330349731126.303250-532.979453
310(21.53486288699552 - x0)(t[0] - x[:, 0])21.534862886995523428.346651-428.346651
411(-3.285686757996543 - x0)(t[0] - x[:, 0])-3.2856867579965433255.691957-428.346651
512(2.6105987644181643 - x0)(t[0] - x[:, 0])2.61059876441816433214.494045-428.346651
620((x0 * -5.265543575122937) + 21.303011895450872)((x[:, 0] * t[0]) + t[1])-5.265543575122937;21.3030118954508725228.602451-246.886780
721((x0 * -0.10303084459825138) + -2.967195956970...((x[:, 0] * t[0]) + t[1])-0.10303084459825138;-2.96719595697055375246.886780-246.886780
822((x0 * 3.181877228219192) + 2.8472358446230825)((x[:, 0] * t[0]) + t[1])3.181877228219192;2.847235844623082555.050724-246.886780
930(((x0 + -2.5353345384705035) * 1.9710063071507...(((x[:, 0] + t[0]) * t[1]) * x[:, 0])-2.5353345384705035;1.97100630715071476.567563-126.355850
1031(((x0 + 0.3703122177136077) * -0.8466294803417...(((x[:, 0] + t[0]) * t[1]) * x[:, 0])0.3703122177136077;-0.84662948034176567126.355850-126.355850
1132(((x0 + 17.14471689769305) * 0.192626434690429...(((x[:, 0] + t[0]) * t[1]) * x[:, 0])17.14471689769305;0.1926264346904292376.453166-126.355850
1240((2.6451239766035792 - x0) * (-0.4005564286578...((t[0] - x[:, 0]) * (t[1] - (x[:, 0] * t[2])))2.6451239766035792;-0.4005564286578119;2.03783...96.222296-6.222295
1341((3.0711878077868717 - x0) * (6.35090000814782...((t[0] - x[:, 0]) * (t[1] - (x[:, 0] * t[2])))3.0711878077868717;6.350900008147822;-2.030341...92.880427-6.222295
1442((-0.8161887112562356 - x0) * (-3.178090433000...((t[0] - x[:, 0]) * (t[1] - (x[:, 0] * t[2])))-0.8161887112562356;-3.178090433000606;2.12097...95.033598-6.222295
1550((x0 * (((x0 + x0) * 1.0189049973402167) + -4....((x[:, 0] * (((x[:, 0] + x[:, 0]) * t[0]) + t[...1.0189049973402167;-4.989753870411562;-1.05930...116.222295-6.222295
1651((x0 * (((x0 + x0) * -1.0151666224030238) + -0...((x[:, 0] * (((x[:, 0] + x[:, 0]) * t[0]) + t[...-1.0151666224030238;-0.11527243424256224;19.50...112.880428-6.222295
1752((x0 * (((x0 + x0) * 1.0559197641250177e-2) + ...((x[:, 0] * (((x[:, 0] + x[:, 0]) * t[0]) + t[...1.0559197641250177e-2;3.195477601097071;2.5952...115.033598-6.222295
1860((x0 * ((((x0 * 2.7846752164938045e-2) + 2.027...((x[:, 0] * ((((x[:, 0] * t[0]) + t[1]) * x[:,...2.7846752164938045e-2;2.0270889034853847;-5.43...135.912260-5.912278
1961((x0 * ((((x0 * -1.2028302093441635e-3) + -2.0...((x[:, 0] * ((((x[:, 0] * t[0]) + t[1]) * x[:,...-1.2028302093441635e-3;-2.031163477321425;-9.4...132.880355-5.912278
2062((x0 * ((((x0 * -2.9416747648114278e-2) + 3.19...((x[:, 0] * ((((x[:, 0] * t[0]) + t[1]) * x[:,...-2.9416747648114278e-2;3.196984333053568e-2;3....134.673400-5.912278
2170(((x0 + x0) * (((1.009492125925551 - (x0 * -1....(((x[:, 0] + x[:, 0]) * (((t[0] - (x[:, 0] * t...1.009492125925551;-1.099733533256624e-2;-2.661...155.935710-5.912269
2271(((x0 + x0) * (((-1.0151461185785686 - (x0 * 4...(((x[:, 0] + x[:, 0]) * (((t[0] - (x[:, 0] * t...-1.0151461185785686;4.0111247485826585e-4;-5.1...152.880232-5.912269
2372(((x0 + x0) * (((1.9139731391817023e-2 - (x0 *...(((x[:, 0] + x[:, 0]) * (((t[0] - (x[:, 0] * t...1.9139731391817023e-2;1.5290623410534727e-2;1....154.677979-5.912269
\n", + "
" + ], + "text/plain": [ + " id view Expression \\\n", + "0 0 0 21.58921727054863 \n", + "1 0 1 -2.930612339764856 \n", + "2 0 2 2.6671850933034973 \n", + "3 1 0 (21.53486288699552 - x0) \n", + "4 1 1 (-3.285686757996543 - x0) \n", + "5 1 2 (2.6105987644181643 - x0) \n", + "6 2 0 ((x0 * -5.265543575122937) + 21.303011895450872) \n", + "7 2 1 ((x0 * -0.10303084459825138) + -2.967195956970... \n", + "8 2 2 ((x0 * 3.181877228219192) + 2.8472358446230825) \n", + "9 3 0 (((x0 + -2.5353345384705035) * 1.9710063071507... \n", + "10 3 1 (((x0 + 0.3703122177136077) * -0.8466294803417... \n", + "11 3 2 (((x0 + 17.14471689769305) * 0.192626434690429... \n", + "12 4 0 ((2.6451239766035792 - x0) * (-0.4005564286578... \n", + "13 4 1 ((3.0711878077868717 - x0) * (6.35090000814782... \n", + "14 4 2 ((-0.8161887112562356 - x0) * (-3.178090433000... \n", + "15 5 0 ((x0 * (((x0 + x0) * 1.0189049973402167) + -4.... \n", + "16 5 1 ((x0 * (((x0 + x0) * -1.0151666224030238) + -0... \n", + "17 5 2 ((x0 * (((x0 + x0) * 1.0559197641250177e-2) + ... \n", + "18 6 0 ((x0 * ((((x0 * 2.7846752164938045e-2) + 2.027... \n", + "19 6 1 ((x0 * ((((x0 * -1.2028302093441635e-3) + -2.0... \n", + "20 6 2 ((x0 * ((((x0 * -2.9416747648114278e-2) + 3.19... \n", + "21 7 0 (((x0 + x0) * (((1.009492125925551 - (x0 * -1.... \n", + "22 7 1 (((x0 + x0) * (((-1.0151461185785686 - (x0 * 4... \n", + "23 7 2 (((x0 + x0) * (((1.9139731391817023e-2 - (x0 *... \n", + "\n", + " Numpy \\\n", + "0 t[0] \n", + "1 t[0] \n", + "2 t[0] \n", + "3 (t[0] - x[:, 0]) \n", + "4 (t[0] - x[:, 0]) \n", + "5 (t[0] - x[:, 0]) \n", + "6 ((x[:, 0] * t[0]) + t[1]) \n", + "7 ((x[:, 0] * t[0]) + t[1]) \n", + "8 ((x[:, 0] * t[0]) + t[1]) \n", + "9 (((x[:, 0] + t[0]) * t[1]) * x[:, 0]) \n", + "10 (((x[:, 0] + t[0]) * t[1]) * x[:, 0]) \n", + "11 (((x[:, 0] + t[0]) * t[1]) * x[:, 0]) \n", + "12 ((t[0] - x[:, 0]) * (t[1] - (x[:, 0] * t[2]))) \n", + "13 ((t[0] - x[:, 0]) * (t[1] - (x[:, 0] * t[2]))) \n", + "14 ((t[0] - x[:, 0]) * (t[1] - (x[:, 0] * t[2]))) \n", + "15 ((x[:, 0] * (((x[:, 0] + x[:, 0]) * t[0]) + t[... \n", + "16 ((x[:, 0] * (((x[:, 0] + x[:, 0]) * t[0]) + t[... \n", + "17 ((x[:, 0] * (((x[:, 0] + x[:, 0]) * t[0]) + t[... \n", + "18 ((x[:, 0] * ((((x[:, 0] * t[0]) + t[1]) * x[:,... \n", + "19 ((x[:, 0] * ((((x[:, 0] * t[0]) + t[1]) * x[:,... \n", + "20 ((x[:, 0] * ((((x[:, 0] * t[0]) + t[1]) * x[:,... \n", + "21 (((x[:, 0] + x[:, 0]) * (((t[0] - (x[:, 0] * t... \n", + "22 (((x[:, 0] + x[:, 0]) * (((t[0] - (x[:, 0] * t... \n", + "23 (((x[:, 0] + x[:, 0]) * (((t[0] - (x[:, 0] * t... \n", + "\n", + " theta size loss \\\n", + "0 21.58921727054863 1 532.979453 \n", + "1 -2.930612339764856 1 247.002956 \n", + "2 2.6671850933034973 1 126.303250 \n", + "3 21.53486288699552 3 428.346651 \n", + "4 -3.285686757996543 3 255.691957 \n", + "5 2.6105987644181643 3 214.494045 \n", + "6 -5.265543575122937;21.303011895450872 5 228.602451 \n", + "7 -0.10303084459825138;-2.9671959569705537 5 246.886780 \n", + "8 3.181877228219192;2.8472358446230825 5 5.050724 \n", + "9 -2.5353345384705035;1.971006307150714 7 6.567563 \n", + "10 0.3703122177136077;-0.8466294803417656 7 126.355850 \n", + "11 17.14471689769305;0.19262643469042923 7 6.453166 \n", + "12 2.6451239766035792;-0.4005564286578119;2.03783... 9 6.222296 \n", + "13 3.0711878077868717;6.350900008147822;-2.030341... 9 2.880427 \n", + "14 -0.8161887112562356;-3.178090433000606;2.12097... 9 5.033598 \n", + "15 1.0189049973402167;-4.989753870411562;-1.05930... 11 6.222295 \n", + "16 -1.0151666224030238;-0.11527243424256224;19.50... 11 2.880428 \n", + "17 1.0559197641250177e-2;3.195477601097071;2.5952... 11 5.033598 \n", + "18 2.7846752164938045e-2;2.0270889034853847;-5.43... 13 5.912260 \n", + "19 -1.2028302093441635e-3;-2.031163477321425;-9.4... 13 2.880355 \n", + "20 -2.9416747648114278e-2;3.196984333053568e-2;3.... 13 4.673400 \n", + "21 1.009492125925551;-1.099733533256624e-2;-2.661... 15 5.935710 \n", + "22 -1.0151461185785686;4.0111247485826585e-4;-5.1... 15 2.880232 \n", + "23 1.9139731391817023e-2;1.5290623410534727e-2;1.... 15 4.677979 \n", + "\n", + " maxLoss \n", + "0 -532.979453 \n", + "1 -532.979453 \n", + "2 -532.979453 \n", + "3 -428.346651 \n", + "4 -428.346651 \n", + "5 -428.346651 \n", + "6 -246.886780 \n", + "7 -246.886780 \n", + "8 -246.886780 \n", + "9 -126.355850 \n", + "10 -126.355850 \n", + "11 -126.355850 \n", + "12 -6.222295 \n", + "13 -6.222295 \n", + "14 -6.222295 \n", + "15 -6.222295 \n", + "16 -6.222295 \n", + "17 -6.222295 \n", + "18 -5.912278 \n", + "19 -5.912278 \n", + "20 -5.912278 \n", + "21 -5.912269 \n", + "22 -5.912269 \n", + "23 -5.912269 " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# We create a Pyeggp regressor leaving all hyperparameters by default\n", + "reg = pyeggp.PyEGGP(simplify=1)\n", + "reg.fit_mvsr(full_X, full_y)\n", + "\n", + "# Here we collect the full table of results and plot it\n", + "pareto = reg.results\n", + "pareto" + ] + }, + { + "cell_type": "markdown", + "id": "98eb4e69-f443-410d-a021-079589c4d2f0", + "metadata": {}, + "source": [ + "All these solution can be quite confusing at first glance. Let's have a look at the evolution of the loss along the pareto front" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "88ebec1e-f915-40be-b210-0063261c139d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 2, figsize=(12, 5))\n", + "\n", + "ax[0].plot(pareto['size'], -pareto['maxLoss'], label='Pareto front')\n", + "ax[0].scatter(pareto['size'], -pareto['maxLoss'])\n", + "ax[0].set_ylabel('Loss')\n", + "ax[0].set_xlabel('Equation length')\n", + "ax[0].legend()\n", + "\n", + "n_params = pareto['theta'].apply(lambda x: x.split(';')).apply(len)\n", + "\n", + "ax[1].plot(n_params, -pareto['maxLoss'], label='Pareto front')\n", + "ax[1].scatter(n_params, -pareto['maxLoss'])\n", + "ax[1].set_xlabel('Number of free parameters')\n", + "ax[1].legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "0251b459-84a1-482d-95f5-a838223befda", + "metadata": {}, + "source": [ + "From the study of the pareto front, we see that solution 4 seems to be an excellent solution. Solution 5 do not lead to significant improvement, despite the increase in complexity. Therefore we apply Ockam's razor and select the simplest solution.\n", + "\n", + "\n", + "Let's look at solution 4 in more details:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "649d0314-fc9e-43e8-801b-bd3d658ef413", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best solution :\n", + " f(X0) = ((A - X0) * (B - (X0 * C)))\n" + ] + }, + { + "data": { + "image/png": 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mUSIiIiIiTYlCkBtNHNqRhLah5NvLefKbLe4uR0RERESkSVAIciMfm8FTVydiGDBv4yG+3nwIe5nD3WWJiIiIiDRqvu4uoKkrKXcS2zKI/bnF3PPJBgxgdHw0t1/QmQEdw91dnoiIiIhIo6ORIDf6cOU+xk9bwYHDxa5jJrBoaybXTVvBRyv3ua84EREREZFGSiHITdak5vLovGRM4Ncdsh1OExN4ZF4ya1Nz3VGeiIiIiEijpRDkJu8s34PNZpz2HJvN4J0f9zZQRSIiIiIiTYNCkBvYyxws3JKBo5pNUh1OkwUp6WqWICIiIuKFJk6ciGEY3HDDDTU6/8UXX8QwDOLi4gBITU3FMAw6duxYj1XWrbKyMhYvXsxf/vIXBg4cSIsWLfDz86N169ZceeWVfPPNN+4uEVAIcosCe3mVKXCn4jSt80VERETEu0yZMgWAefPmcfjw4WrPnz59eqXneaPvv/+ekSNH8u9//5sDBw5w/vnnc80119CqVSu++uorrrjiCu644w5Ms4ZvhuuJQpAbhAT6Us1MOBebYZ0vIiIiIt5l2LBhdO3alZKSEj7++OPTnrtmzRqSkpLw8/Pj5ptvBqBt27Zs3bqVxYsXN0S5dcJmszFu3Dh++OEHfvnlF77++mtmzZpFUlISn376KT4+Prz11lt8+OGH7q3Tra/eRAX6+TAqLhqfGiShvrEtCPTzaYCqRERERKQuGYbBrbfeChwf5TmVisevuOIKoqKiAPDz86Nnz5506dKlfgutQyNGjOCzzz7jggsuqPLY9ddfz+TJkwGYMWNGA1dWmUKQm9x2QWecNZgTty+niMISTYcTERERqaSsGAozra8ebPLkyfj4+LB+/Xo2b9580nPsdjszZ84EKk+Fq25NUHFxMc8//zxDhgyhRYsWBAYG0qNHDx544AFycnIqnfvll19iGAZXXnlllevcddddGIaBn58f+fn5lR774YcfMAyDYcOG1ebHPqV+/foBkJaWVifXO1MKQW4ysGM4j1+VgAFVRoQq7rds5kfO0VL+/d12N1QoIiIi4oH2rYBPJ8BTMfDvbtbXTyfA/pXuruyk2rRpw+WXXw7Au+++e9Jz5s6dy5EjR4iJieHSSy+t0XUPHTrE4MGDuf/++9m5cycDBw7k8ssvp6SkhOeee44BAwawb9/xPScvuugifH19+f777ykvr/wB+6JFiwAoLy9n2bJlJ31s5MiRNaqrOjt37gSsPxd3UghyowlDOjDnzqGMiot2rRGyGTAqLprP7hzKyzdaSfmDFalsTDvivkJFREREPMGad2D6ZbBjPphO65jptO6/dymsOXnIcLeK0Z2PP/6Y0tLSKo9XTIWrGDWqjmmajB8/nqSkJKZMmUJqaioLFy5k7ty57Nq1i/vuu4/U1FRuueUW13NCQ0MZOHAg+fn5rF692nV8//797Ny5k969ewPHQ0+FugxB6enpvP/++wCMGzfurK93NhSC3GxAx3CmTejPln9eypr/G8mWf17KtAn9GdAxnAu6teLqfm0xTXjw882UOZzuLldERETEPfatgG/uB0xw/mqpgLPcOv7NfR45IjRmzBhat25NTk4OX375ZaXH9u/fz5IlSwAqhZbT+e677/jpp5/o27cv06ZNIyQkxPWYr68vzz77LAkJCSxdupTk5GTXYxVBZuHCha5jFSFn6tSpREVFVXosPz+fNWvWEBoayqBBg2r5U1dWXl7OhAkTyMvLIzExkTvuuOOsrne2FII8RKCfD61CAqo0QXh4TC9aNvNjW3oBb/2wx03ViYiIiLjZitfAVs0oic3HOs/D+Pr6MmnSJADee++9So9Nnz4dp9PJhRdeSNeuXWt0vYq9dsaNG4evb9UuwjabzbWG5+eff3YdrwhBJ472VHw/evRoLr74YrZt28bBgwcBWLZsGeXl5Vx44YUnfZ3auPPOO1m8eDERERF89tln+Pv7n9X1zpZCkIeLaB7Ao7+xNsz6z+Kd7MkqdHNFIiIiIg2srBi2f1N1BOjXnOWw7WuPbJZQ0SVuwYIFrpBhmqZrelht9gbas8f6YPyRRx7BMIyT3l5//XUAsrKyXM8bOnQowcHBrFq1isLCQkzTZMmSJfTq1Yu2bdtWCUl1NRXuT3/6E++++y4tW7Zk4cKFdO/e/ayuVxe0AY0XuKpvW/674RA/7MjioblJzLx9CLaabjQkIiIi4u1KCo6vAaqO6bTO9wuq35pqqXv37lxwwQUsX76cGTNm8NBDD7F06VJSU1MJCwvj2muvrfG1nE7rz+L888+vtn12fHy863s/Pz+GDRvG/PnzWbZsGe3btycjI4Prr78eqDxdbtKkSXUSgu677z5efvllWrRowYIFC1zd4dxNIcgLGIbBk1clMPrFH1i1N5dZa9O4cVB7d5clIiIi0jACQsCw1SwIGTbrfA80ZcoUli9fzvTp03nooYdcU+NuuOEGgoJqHtpiY2MBGDt2LPfff3+tahg5ciTz589n0aJFtG/f3nUMoH379nTr1o3Fixdz8OBBtm7dSkxMDHFxcbV6jQoPPPAAL7zwAmFhYSxYsIABAwac0XXqg6bDeYnY8Gbcf0kPAJ7631Yy8u1urkhERESkgfgFQY8xYKvm83ubL/S8wuNGgSpcd911hIaGsnPnTr7++mvmzp0L1G4qHMBll10GwJw5czDN6vedPNGJoz2LFi3C19eXiy66qNLj6enpvPTSSwBcfPHFtbp+hQcffJDnnnuOsLAwFi5cyMCBA8/oOvVFIciLTD63I31iW1BgL+fvX6S4uxwRERGRhjP0bnA6Tn+O02Gd56GaNWvGjTfeCFhrhIqLi0lMTKx1QBg7diwDBw5k9erV3HLLLZXW/VQ4fPgw06ZNq7InUGJiIlFRUWzZsoWlS5cyZMiQSt3lKkLSq6++Wul+bTz88MM888wztGjRwiMDEGg6nFfxsRk8My6RK17+kW9T0vk2+RcuTXDvRlMiIiIiDaLDUBjzvNUG2+ZTuUmCzdcKQGOeh/ZD3FdjDUyZMoU333zTFVxqOwoEVve3efPmMWbMGD744AM+++wz+vTpQ/v27SktLWXPnj0kJSXhcDiYPHlypc5uhmFw8cUXM3PmTOx2O6NGjap07REjRmCz2bDbrVlHtQ1BX375JU8++SQAXbt25bXXTt6tLzIykn//+9+1unZdUgjyMj1bh/L7i7rwypJdPPpFCkO7RBIW5OfuskRERETq38ApEB1vtcHe9rW1RsiwQY/LrREgDw9AAAMHDiQxMZGkpCT8/f2ZMGHCGV0nJiaGlStX8v777zNr1iw2b97M6tWrCQ8PJyYmhjvvvJMrr7ySwMDAKs8dOXIkM2fOdH1/ohYtWtC/f3/WrFlDr169iImJqVVdubm5ru/Xrl3L2rVrT3pehw4d3BqCDLO2Ewk9TH5+PmFhYeTl5REaGuruchqEvczB5S8vZ0/WUW4c1J6nr0l0d0kiIiLSBNntdvbu3UunTp1O+ma7XpUVW13gAkI8dg2Q1I3a/D2raTbQmiAvFOjnw7+u6Q3AzNX7Wbknx80ViYiIiDQwvyBoHqUAJGdEIchLDeoUzk2DrbaGD36+meLSahYKioiIiIgIoBDk1f56WU9ahwaSmlPEi4t2uLscERERERGvoBDkxUID/XjqmgQA3lm+h01pR9xbkIiIiIiIF1AI8nIjekZzVd8YnCY88NlmSstrsJOyiIiIiEgTphDUCDz6m3gigv3ZnlHAa0t3ubscERERERGPphDUCIQH+/PY2HgAXlu6i23p+W6uSERERETEcykENRJjEtswOi6acqfJA59tptyhaXEiIiIiIiejENRIGIbBE1clEBroy+YDebz74153lyQiIiIi4pEUghqRqNBAHr4iDoAXFu5gT1ahmysSEREREfE8CkGNzHX923FBt0hKyp08+HkSTqfp7pJERERERDyKQlAjYxgGT12dSDN/H1an5vLRqn3uLklERERExKMoBDVCseHN+OulPQH41/xtpOUWubkiERERERHPoRDUSN08pAODOoZTVOrgr59vxjQ1LU5EREREBBSCGi2bzeDZa3sT6Gfj5905fLJ6v7tLEhEREWlSJk6ciGEY3HDDDTU6/8UXX8QwDOLirEZXqampGIZBx44d67HKuvfxxx8zceJE+vTpQ1RUFH5+foSFhTFo0CCefvppCgvd37xLIagR6xgZzAOXWNPinvpmKwcOa1qciIiISEOZMmUKAPPmzePw4cPVnj99+vRKz/NWb7zxBh999BHl5eWcc845XHfddQwYMIDk5GT+9re/0a9fPw4dOuTWGhWCGrnJ53ZkYMeWHC118ODnSZoWJyIiItJAhg0bRteuXSkpKeHjjz8+7blr1qwhKSkJPz8/br75ZgDatm3L1q1bWbx4cUOUW2eef/55srOzSUlJ4dtvv+WTTz5h8eLFpKWlcf7557Nr1y7uu+8+t9aoENTIWdPi+hDga+PHXdl8uHIfWQUl2Msc7i5NREREpFEzDINbb70VOD7KcyoVj19xxRVERUUB4OfnR8+ePenSpUv9FlrHBg8eTHh4eJXjERERPPXUUwAsWLCgocuqRCGoCegUGcwNA2MBePSLFAY+uYi4R7/ljg/XsjY1183ViYiIiNSevdxOdnE29nK7u0s5rcmTJ+Pj48P69evZvHnzSc+x2+3MnDkTqDwVrro1QcXFxTz//PMMGTKEFi1aEBgYSI8ePXjggQfIycmpdO6XX36JYRhceeWVVa5z1113YRgGfn5+5OfnV3rshx9+wDAMhg0bVpsf+5R8fX0BCAgIqJPrnSmFoCbgw5X7+GBF5f2CnCYs2prJddNW8NFK7SUkIiIi3mF9xnqmLp3K4E8GM3z2cAZ/MpipS6eyIXODu0s7qTZt2nD55ZcD8O677570nLlz53LkyBFiYmK49NJLa3TdQ4cOMXjwYO6//3527tzJwIEDufzyyykpKeG5555jwIAB7Nt3/D3eRRddhK+vL99//z3l5eWVrrVo0SIAysvLWbZs2UkfGzlyZI3qOp2CggL+8Y9/AJw0jDWkeg1BBw8eZMKECURERBAUFERiYiJr1651PW6aJo8++iht2rQhKCiIkSNHsnPnzvosqclZk5rLo/OST/qYw2liAo/MS9aIkIiIiHi8WdtmMfnbySxLW4bTdALgNJ0sS1vGpPmTmL19tlvrO5WK0Z2PP/6Y0tLSKo9XTIWrGDWqjmmajB8/nqSkJKZMmUJqaioLFy5k7ty5rvU2qamp3HLLLa7nhIaGMnDgQPLz81m9erXr+P79+9m5cye9e/cGjoeeCmcTghYsWMDkyZOZOHEil1xyCW3btmXBggVceumlPPPMM7W+Xl2qtxB0+PBhzjvvPPz8/Jg/fz5btmzh+eefp2XLlq5znn32WV5++WWmTZvGqlWrCA4O5pJLLsFu9+xhTW/yzvI92GzGac+x2Qze+XFvA1UkIiIiUnvrM9bz5KonMTFxmJXXNjtMByYmT6x8wiNHhMaMGUPr1q3Jycnhyy+/rPTY/v37WbJkCUCl0HI63333HT/99BN9+/Zl2rRphISEuB7z9fXl2WefJSEhgaVLl5KcfPzD8Iogs3DhQtexipAzdepUoqKiKj2Wn5/PmjVrCA0NZdCgQbX8qWHLli188MEHfPjhhyxYsICCggJ++9vf8v777xMWFlbr69WlegtBzzzzDLGxsUyfPp1BgwbRqVMnRo8e7VrYZZomL730Eg8//DBjx46ld+/ezJgxg0OHDjFv3rz6KqtJsZc5WLglA4fz9B3hHE6TBSnpapYgIiIiHmvGlhnYjNO/dbUZNmakzGigimrO19eXSZMmAfDee+9Vemz69Ok4nU4uvPBCunbtWqPrffPNNwCMGzfOtcbmRDabzbWG5+eff3YdrwhBJ472VHw/evRoLr74YrZt28bBgwcBWLZsGeXl5Vx44YUnfZ3qTJ06FdM0KS0tZdeuXTz//PPMnz+fuLg4fvjhh1pfry7VWwj68ssvGTBgANdddx1RUVH069ePt99+2/X43r17SU9PrzS0FhYWxuDBg1mxYsUpr1tSUkJ+fn6lm5xcgb2cavKPi9O0zhcRERHxNPZyO0vTllYZAfo1h+lgSdoSj2yWUNElbsGCBa6QYZom77//PlC7vYH27NkDwCOPPIJhGCe9vf766wBkZWW5njd06FCCg4NZtWoVhYWFmKbJkiVL6NWrF23btq0SkupqPZCfnx9dunTh3nvvZf78+Rw+fJgJEyZQXFx8Vtc9G7WPdDW0Z88e3njjDe69917+9re/sWbNGv74xz/i7+/PpEmTSE9PByA6OrrS86Kjo12PnczTTz/NY489Vl9lNyohgb7YDGoUhGyGdb6IiIiIpyksK3StAaqO03RSWFZIoG9gPVdVO927d+eCCy5g+fLlzJgxg4ceeoilS5eSmppKWFgY1157bY2v5XRafxbnn39+te2z4+PjXd/7+fkxbNgw5s+fz7Jly2jfvj0ZGRlcf/31QOXpcpMmTarTpggVBg8eTFxcHCkpKaxdu5YLLrigzq5dG/X2rtfpdDJgwABXL/B+/fqRnJzMtGnTXMOBZ+Khhx7i3nvvdd3Pz88nNjb2rOttjAL9fBgVF82irZnVTokbFRdNoF/1C/FEREREGlpzv+bYDFuNgpDNsNHcr3kDVFV7U6ZMYfny5UyfPp2HHnrINTXuhhtuICgoqMbXqXjvO3bsWO6///5a1TBy5Ejmz5/PokWLaN++vesYQPv27enWrRuLFy/m4MGDbN26lZiYGOLi4mr1GtUJDg4GIDMzs06vWxv1Nh2uTZs2Vf7AevXqxf79+wFo3bo1ABkZGZXOycjIcD12MgEBAYSGhla6yanddkFnnDUYCmrXslkDVCMiIiJSe4G+gQyPHY6PcfoPbH0MH0bEjvC4UaAK1113HaGhoezcuZOvv/6auXPnArWbCgdw2WWXATBnzhxMs4ZrH445cbRn0aJF+Pr6ctFFF1V6PD09nZdeegmAiy++uFbXr052djabNm0CrNExd6m3EHTeeeexffv2Ssd27NhBhw4dAOjUqROtW7dm8eLFrsfz8/NZtWoVQ4cOra+ympyBHcN5/KoEDMDnV13iTrz/yar9pGYfbeDqRERERGpmYtzEakeCnKaTifETG6ii2mvWrBk33ngjYK0RKi4uJjExkYEDB9bqOmPHjmXgwIGsXr2aW265pdK6nwqHDx9m2rRpVfYESkxMJCoqii1btrB06VKGDBlSqbtcRUh69dVXK92vqS1btvDxxx+ftNvzjh07uO666ygpKWHIkCEkJibW6tp1qd5C0J///GdWrlzJU089xa5du/jkk0946623uPvuuwEwDIOpU6fyxBNP8OWXX5KUlMTEiROJiYnhqquuqq+ymqQJQzow586hjIqLpiL32AxrCtzsO4YwtHMExWUO/vLZpmqnzYmIiIi4wznR5/DwkIcxMKqMCPkYPhgYPDzkYfpF9XNThTVTMepTEVxqOwoEVve3efPm0bdvXz744AM6derEeeedx4033si4cePo168frVq14ve//32VEGQYhmt0x263M2rUqEqPjxgxApvN5goxtQ1BmZmZTJgwgcjISC644AJXTQMHDqRXr14sW7aMXr16MWvWrFr/3HXJMGs7hlYLX3/9NQ899BA7d+6kU6dO3Hvvvdx+++2ux03T5O9//ztvvfUWR44c4fzzz+f111+v1dBYfn4+YWFh5OXlaWpcDdjLHBTYywkJ9HWtAUrLLeLSl37gaKmDh8f04rYLOru5ShEREfEGdrudvXv30qlTJwIDG2YK2obMDcxImcGStCU4TSc2w8aI2BFMjJ/o8QGoQu/evUlKSsLf359Dhw4RERFx0vNSU1Pp1KkTHTp0IDU1tcrjJSUlvP/++8yaNYvNmzeTl5dHeHg4MTExnHfeeVx55ZWMHj26yvPee+89V/j66aefOPfccys9PmjQINasWUOvXr3YsmVLrX62rKws3n77bZYvX862bdvIysqirKyM8PBwEhMTueaaa7jlllsICAio8TVr8/esptmgXkNQQ1AIqhszV+/noblJ+Pva+N8fL6BrlGcuKBQRERHP4Y4Q5HrtcjuFZYU092vusWuApG7URwiqt+lw4l1uGBjLhd1bUVru5L45myh31KwNpYiIiIg7BPoGEhkUqQAkZ0QhSABrfui/xiUSEujLprQjvPnDHneXJCIiIiJSLxSCxKVNWBD/+I21odZLi3aw5VC+mysSEREREal7CkFSyTXntGV0XDRlDpN7Z2+kpNzh7pJEREREROqUQpBUYhgGT12TSESwP9vSC3hx4U53lyQiIiIiUqcUgqSKyOYBPH2NtXnVmz/sZk1qrpsrEhERERGpOwpBclKj41tzXf92mCbcN3sThSXl1T9JRERERMQLKATJKT36mzjatghif24RT36z1d3liIiIiIfy8m0nxcPVx98vhSA5pZBAP/59XR/A2kx16bZMN1ckIiIinsTHxweAsrIyN1cijVlJSQkAvr6+dXZNhSA5raFdIphyficAHvh8M4ePlrq5IhEREfEUfn5+BAQEkJeXp9EgqRcOh4Pc3FyCg4PrNATV3ZWk0frLJT34fkcWuzILeXheMq/+th+GYbi7LBEREfEAkZGRHDx4kAMHDhAWFoafn5/eJ8hZMU0Th8NBcXExeXl5OJ1O2rRpU6evoRAk1Qr08+HF8X25+vWf+CbpF0ZvimZs37buLktEREQ8QGhoKADZ2dkcPHjQzdVIY+Lj40OzZs2IiorC39+/Tq+tECQ1ktgujD9e3I0XFu7gkXnJDOwYTkyLIHeXJSIiIh4gNDSU0NBQysrKcDi00bqcPZvNVq+jigpBUmN3XdSFxdsy2ZR2hPtmb+Lj2wZjs2m4W0RERCx+fn74+fm5uwyRaqkxgtSYr4+Nl67vS5CfDyv25PDeT3vdXZKIiIiISK0pBEmtdIoM5pEr4gB49tvtbEvPd3NFIiIiIiK1oxAktXbjoFgu7hlFqcPJ1E83Yi/T3F8RERER8R4KQVJrhmHwzLW9iWzuz7b0Ap5fsN3dJYmIiIiI1JhCkJyRyOYBPDOuNwBvL9/Lz7uy3VyRiIiIiEjNKATJGbu4VzS/HdwegPvmbCKvqMzNFYmIiIiIVE8hSM7Kw2N60SkymF/y7Dz8RbK7yxERERERqZZCkJyVZv6+vHh9X3xsBl9tOsQXG7VTtIiIiIh4NoUgOWt9Y1vwxxHdAHh4XjIHDhe5uSIRERERkVNTCJI6cffwLpzTvgUF9nL+PGsj5Q6nu0sSERERETkphSCpE74+Nl66vh/NA3xZk3qY15ftdndJIiIiIiInpRAkdaZ9RDP+OTYegP8s3sm6fYfdXJGIiIiISFUKQVKnru7Xliv7xOBwmkydtYECu9pmi4iIiIhnUQiSOmUYBk9cnUDbFkGk5Rbz9y9S3F2SiIiIiEglCkFS50ID/fjPDX2xGTB3w0G1zRYRERERj6IQJPViQMdw7qlom/3fZNJy1TZbRERERDyDQpDUmz+O6Gq1zS5R22wRERER8RwKQVJvfH1s/OcGq2322n2HeW2p2maLiIiIiPspBEm9ig1vxuNXWW2zX16yk7WpuW6uSERERESaOoUgqXdX92vH2L5W2+w/fbqRvGK1zRYRERER91EIkgbxxFUJtA9vxsEjxTw0dzOmabq7JBERERFpohSCpEGEBPrx8o398LUZ/C8pnU/XpLm7JBERERFpohSCpMH0jW3BXy7pAcBjX6WwM6PAzRWJiIiISFOkECQN6vYLOnNBt0jsZU7+MHMD9jKHu0sSERERkSZGIUgalM1m8Pz4PkQ292dbegFPfrPV3SWJiIiISBOjECQNLiokkOfH9wXgw5X7+C4l3b0FiYiIiEiTohAkbnFh91b8blhnAB74bDOHjhS7uSIRERERaSoUgsRt7h/dg97twsgrLmPqpxtxONU2W0RERETqn0KQuI2/r42Xb+hHsL8Pq1Nz+c/ine4uSURERESaAIUgcauOkcE8dU0iAK8s2cnPu7LdXJGIiIiINHYKQeJ2Y/u25foBsZgm/GnWRrIKStxdkoiIiIg0YgpB4hH+cWU83aObk1VQwr2zN+LU+iARERERqScKQeIRgvx9eO235xDoZ2P5zmze+H63u0sSERERkUZKIUg8RrfoEP45NgGA5xdsZ/XeXDdXJCIiIiKNkUKQeJTr+rfj6n5tcZrwx5kbyD1a6u6SRERERKSRUQgSj2IYBo9flUDnyGDS8+3cP2eT1geJiIiISJ1SCBKP0zzAl1d/ew7+vjaWbMvk3R/3urskEREREWlEFILEI8XFhPLoFXEAPPPtNtbvP+zmikRERESksVAIEo910+D2jElsQ7nT5A+fbOCw1geJiIiISB1QCBKPZRgGT49LpGNEMw4eKdb+QSIiIiJSJxSCxKOFBvrx2k3W+qCl27N484c97i5JRERERLycQpB4vPiYMB67Mh6Afy/Yzqo9OW6uSERERES8mUKQeIUbBsZydb+2OJwmf5i5gayCEneXJCIiIiJeSiFIvIJhGDx5dQLdopqTWVDC1FkbcGh9kIiIiIicAYUg8RrN/H15/aZzCPLz4addOby8eKe7SxIRERERL6QQJF6lW3QIT12TAMDLS3ayfGeWmysSEREREW+jECRe5+p+7bhxUCymCVM/3Uh6nt3dJYmIiIiIF1EIEq/099/EE9cmlJyjpdzzyXrKHE53lyQiIiIiXkIhSLxSoJ8Pr990DiEBvqzdd5h/zd/m7pJERERExEsoBInX6hgZzPPj+wDw7o97+WbzL26uSERERES8gUKQeLXR8a2588IuADzw2SZ2ZRa6uSIRERER8XQKQeL17h/dnSGdwzla6uDOj9ZxtKTc3SWJiIiIiAdTCBKv5+tj45UbzyE6NIBdmYU8ODcJ09RGqiIiIiJycgpB0ii0Cgngtd+eg6/N4KtNh/jg59QaP9de5iCroAR7maP+ChQRERERj+Hr7gJE6sqAjuH87fJe/PPrLTzxzVYS27Wgf4eWpzx/TWou7yzfw8ItGThNsBkwKi6a2y/ozICO4Q1YuYiIiIg0JI0ESaNyy3kdGdO7DeVOk7s/Xk92YclJz/tw5T7GT1vBoq2ZOI/NnHOasGhrJtdNW8FHK/c1YNUiIiIi0pAUgqRRMQyDZ8b1pkurYNLz7fzhkw2U/2oj1TWpuTw6LxkTcDgrrx1yOE1M4JF5yaxNzW24wkVERESkwSgESaPTPMCXaRP608zfhxV7cnj2u+2VHn9n+R5sNuO017DZDN75cW99likiIiIibqIQJI1St+gQ/n2dtZHqWz/s4evNhwCrCcLCLRlVRoB+zeE0WZCSrmYJIiIiIo2QQpA0WpcntjlhI9XNbE8voMBeTjX5x8VpQoFdew6JiIiINDYKQdKo3T+6O+d3jaSo1MEdH67FNE2qmQnnYjMgJFANFEVEREQaG4UgadR8fWy8fGM/2rYIIjWniIfmJjGyVxQ+1SQhH5vB6PjWBPr5NFClIiIiItJQFIKk0QsP9ufNm/sT4Gtj8bZMWjTzx1nNnDin0+S28zs1UIUiIiIi0pAUgqRJSGgbxpNXJwIwe+0BbhrSHgOqjAj52AwM4PGrErRhqoiIiEgjpRAkTca1/dsxcWgHAL7YeIiXb+jLqLho1xohmwGj4qKZc+dQJgzp4MZKRURERKQ+adW3NCkPj4ljy6F81u47zCtLdzH3rvPwvb4vBfZyQgJ9tQZIREREpAnQSJA0Kf6+Nl6/6RyiQgLYkVHI/bM34e9jo1VIgAKQiIiISBPRYCHoX//6F4ZhMHXqVNcxu93O3XffTUREBM2bN2fcuHFkZGQ0VEnSREWFBjLt5v74+9j4NiWdV5fucndJIiIiItKAGiQErVmzhjfffJPevXtXOv7nP/+Zr776ijlz5vD9999z6NAhrrnmmoYoSZq4c9q35ImrEgB4YeEOFm5R+BYRERFpKuo9BBUWFnLTTTfx9ttv07JlS9fxvLw83n33XV544QVGjBhB//79mT59Oj///DMrV66s77JEGD8wlknHGiX8edZGdmUWuLkiEREREWkI9R6C7r77bsaMGcPIkSMrHV+3bh1lZWWVjvfs2ZP27duzYsWKU16vpKSE/Pz8SjeRM/XwFXEM7hROYUk5t89YR15xmbtLEhEREZF6Vq8h6NNPP2X9+vU8/fTTVR5LT0/H39+fFi1aVDoeHR1Nenr6Ka/59NNPExYW5rrFxsbWddnShPj5WI0S2rYIYm/2Uf706QYc1WykKiIiIiLerd5CUFpaGn/605/4+OOPCQwMrLPrPvTQQ+Tl5bluaWlpdXZtaZoimgfw5s39CfSzsWx7Fv9esN3dJYmIiIhIPaq3ELRu3ToyMzM555xz8PX1xdfXl++//56XX34ZX19foqOjKS0t5ciRI5Wel5GRQevWrU953YCAAEJDQyvdRM5WQtswnhlnNe54Y9luvtp0yM0ViYiIiEh9qbcQdPHFF5OUlMTGjRtdtwEDBnDTTTe5vvfz82Px4sWu52zfvp39+/czdOjQ+ipL5JTG9m3LHRd2BuAvn20i+WCemysSERERkfrgW18XDgkJISEhodKx4OBgIiIiXMenTJnCvffeS3h4OKGhofzhD39g6NChDBkypL7KEqnCXuagwF5OSKAvD1zSk22/FPD9jixun7GWL+45j6iQupvOKSIiIiLuV28hqCZefPFFbDYb48aNo6SkhEsuuYTXX3/dnSVJE7ImNZd3lu9h4ZYMnCbYDBgVF82t53XkwOEidmcd5Y4P1zHz9iEE+vm4u1wRERERqSOGaZpe3QorPz+fsLAw8vLytD5IauzDlft4dF4yNptRqRucj83A6TT508huTP8plbziMq45py3PX9cHwzDcWLGIiIiIVKem2aDe9wkS8TRrUnN5dF4yJlRph+1wmpjAfxbt5E8Xd8XHZjB3/UHeXr7HLbWKiIiISN1TCJIm553le7DZTj+qY7MZrE49zKNXxAHw9PxtLNmW0RDliYiIiEg9UwiSJsVe5mDhloxqN0R1OE0WpKQzfkA7bhzUHtOEP87cyM6MggaqVERERETqi0KQNCkF9nKqyT8uThMKSxw8dmU8gzuFU1hSzpQP1nL4aOlJz7eXOcgqKMFe5qjDikVERESkrrm1O5xIQwsJ9MVmUKMgZDOs8/19bbwxoT9jX/uR/blF3PXxemZMGYSfj/UZwqm6zN1+QWcGdAyv559IRERERGpLI0HSpAT6+TAqLhqfatYE+dgMRse3drXGDg/2552JAwn292HFnhwe/SIF0zT5cOU+xk9bwaKtma5g5TRh0dZMrpu2go9W7qvvH0lEREREakkhSJqc2y7ojLOaoSCn0+S28ztVOtajdQiv/LYfNgNmrt7PP75MqbbL3CPzklmbmlvHP4GIiIiInA2FIGlyBnYM5/GrEjCgyoiQj83AAB6/KuGkU9lG9Izm/8ZYHeM+WLGP6rYOstkM3vlxbx1VLiIiIiJ1QWuCpEmaMKQDPVuH8M6Pe1mQkl5pLc9t53c67VqeW8/ryI6MAmatSat2bVFFlzl7mcM1tU5ERERE3EshSJqsAR3DGdAxHHuZgwJ7OSGBvjUKKoZhMPXibsxak1aj13GaVlc6hSARERERz6AQJE1eoJ9PrQNKy2B/DKAm3bYrusyJiIiIiGfQmiCRMxDo58Po+GiqaTJXpcuciIiIiLifQpDIGbrtgs6Y1QwFnazLnIiIiIi4l0KQyBk6scvcr7vEVddlTkRERETcRwsVRM7CiV3mvk1Odx0/p30L/nppTwUgEREREQ+kECRyliq6zBWVlPOnWRtZuCWD7ekFtGjm5+7SREREROQkNB1OpI40C/DllRv70b9DS/Lt5UyevoasghJ3lyUiIiIiv6IQJFKHAv18eHviADpGNOPA4WJum7GW4lKHu8sSERERkRMoBInUsfBgf6bfMogWzfzYlHaEP8/aiNNZkx2FRERERKQhKASJ1INOkcG8PXEA/j42vk1J5+n5W91dkoiIiIgcoxAkUk8Gdgznuet6A/D28r18uCLVvQWJiIiICKAQJFKvxvZty18u6QHA379MYfHWDDdXJCIiIiIKQSL17K6LunD9gFicJtzzyQY2ph1xd0kiIiIiTZpCkEg9MwyDJ65O4MLurSguczDl/TWkZh91d1kiIiIiTZZCkEgD8POx8fpN55DQNpSco6VMnr6anELtISQiIiLiDgpBIg0kOMCX9yYPpF3LIFJzipjyQc32ELKXOcgqKMFepv2GREREROqCYZqmV29gkp+fT1hYGHl5eYSGhrq7HJFq7c4qZNwbP3OkqIyRvaKZNuEcfH2qfh6xJjWXd5bvYeGWDJwm2AwYFRfN7Rd0ZkDHcDdULiIiIuLZapoNNBIk0sC6tGrOOxMH4O9rY9HWDP7xVQq//iziw5X7GD9tBYu2ZlKxz6rThEVbM7lu2go+WrnPDZWLiIiINA4KQSJuMKBjOC/f0BfDgI9W7uf1Zbtdj61JzeXRecmYgMNZORw5nCYm8Mi8ZNam5jZs0SIiIiKNhEKQiJtcmtCGv18RB8Bz323n83UHAHhn+R5sNuO0z7XZDN75cW+91ygiIiLSGPm6uwCRpmzyeZ04lGfnrR/28MDnm2ke6OtaA3Q6DqfJgpR07GUOAv18GqZYERERkUZCI0EibvbgpT25ul9bHE6TP326odoAVMFpQoG9vH6LExEREWmEFIJE3MxmM3j22t5c2L0V9jJnzZ9nQEigBnNFREREakshSMQDVGym2ie2RY3O97EZjI5vralwIiIiImdAIUjEQwQH+DJ98kDahAVWe67TaXLb+Z0aoCoRERGRxkchSMSDhAf7M/uOoaec5uZjMzCAx69K0IapIiIiImdIIUjEw8SGN2P2HUNp5l95qpvNgFFx0cy5cygThnRwU3UiIiIi3k+rqkU8UK82oUyfPJCb31tNabmT3/Ruw7PX9ibIX//LioiIiJwtjQSJeKjBnSN45cZ+2Az4avMvvLBwB6ZZw/7ZIiIiInJKCkEiHuyS+Nb8a1xvAN5evpfXl+12c0UiIiIi3k8hSMTDjR8Qy8NjegHw3Hfb+WjlPjdXJCIiIuLdFIJEvMBtF3TmnuFdAXjki2S+3HTIzRWJiIiIeC+FIBEvcd/o7kwY0h7ThHtnbWTZ9kx3lyQiIiLilRSCRLyEYRj888oEruwTQ7nT5M6P1rE2NdfdZYmIiIh4HYUgES9isxk8P74Pw3u0wl7m5Jb317DlUL67yxIRERHxKgpBIl7Gz8fG6zf1Z2DHlhTYy5n43ip2ZxW6uywRERERr6EQJOKFgvx9eGfSQOJjQskuLOWmt1eRllvk7rJEREREvIJCkIiXCgvy48Mpg+kW1Zz0fDs3vbOK9Dy7u8sSERER8XgKQSJeLDzYn49uG0yHiGbszy3ipndWklNY4u6yRERERDyaQpCIl4sODeSjKYNpExbI7qyj3PzuavKKytxdloiIiIjHUggSaQRiw5vx8W2DiWwewJZf8pn8/moKS8rdXZaIiIiIR1IIEmkkOrdqzke3DSIsyI8N+49w2wdrsJc53F2WiIiIiMdRCBJpRHq2DmXGrYNoHuDLyj253PHhOkrKFYRERERETqQQJNLI9IltwXuTBxLoZ+P7HVnc/fF6Ssud7i5LRERExGMoBIk0QoM6hfPupIEE+NpYtDWTP87cQJlDQUhEREQEFIJEGq3zukby5s398fex8W1KOvfO3kS5gpCIiIiIQpBIY3ZRjyjemHAOfj4GX206xF8+24zDabq7LBERERG3UggSaeQu7hXNq789B1+bwX83HOTBzzfjVBASERGRJkwhSKQJuCS+Nf+5oR82A+asO8D/zUvGNBWEREREpGlSCBJpIsb0bsOL1/fFMGDm6v38/csUBSERERFpkhSCRJqQsX3b8ty1fTAMmLFiH/9QEBIREZGztG5frtdNtVcIEmliru3fjmeu6Y1hwAcr9mlESERERM7YZ+sOcO20FTz8RbJXBSGFIJEmaPzAWJ4Z19s1IvToFwpCIiIiUjuz16Txl882YZpgM9xdTe34ursAEXGP8QNiMYAHPt/Mhyv3AfDPsfEYhpf9KyYiIiIN7tPV+3lwbhIAE4d24LErves9hEaCRJqw6wbEutYIfbhyH4942VC2iIiINLxPVh0PQJPP7eh1AQg0EiTS5F3bvx0Af/lsEx+t3I9pwuNjE7B527i2iIiI1LsPV+7jkXnJANx6XiceuaKX1wUgUAgSEawgZAD3f7aJj1ftxwSeUBASERGRE8xYkcqjX6QAcNv5nfi/Md4ZgEDT4UTkmHH92/H8ddbUuE9W7eevn2/GoalxIiIiArz/015XALpjWGevDkCgECQiJ7jmnHa8dH1ffGwGc9Yd4N7ZGyl3ON1dloiIiLjRO8v38I+vtgBw54VdePCynl4dgEAhSER+ZWzftrx6Yz98bQZfbDzEH2ZuoLRcQUhERKQpem3pLp74ZisAd13Uhb9e2sPrAxAoBInISVyW2IZpE/rj72NjfnI6d328jpJyh7vLEhERkbNgL3OQVVCCvaz63+mmafLv77bz3HfbAbh3VHf+cknjCEAAhunlOyTm5+cTFhZGXl4eoaGh7i5HpFH5fkcWv5uxlpJyJ8O6t+Ktm/sT6Ofj7rJERESkFtak5vLO8j0s3JKB89jGpqPiorn9gs4M6Bhe5XzTNHnym6288+NeAB66rCd3XNilocs+IzXNBhoJEpFTurB7K6ZPHkiQnw8/7MjilulrKCotd3dZIiIiUkMfrtzH+GkrWLQ1k4p+R04TFm3N5LppK/jo2IbpFZxOk0e+SHYFoMeujPeaAFQbCkEiclrndo1kxpRBNA/wZcWeHCa+u5p8e5m7yxIREZFqrEnN5dF5yZhQpeOrw2liAo/MS2Ztaq7r2AOfb+ajlfsxDHhmXCKTzu3Y4HU3BIUgEanWwI7hfDhlECGBvqzdd5jfvr2S3KOl7i5LRERETuOd5Xuq3fPPZjN458e9lDmc/OnTDXy27gA+NoMXx/fl+oHtG6jShqcQJCI10q99Sz793RAigv1JPpjP+DdXkJ5nd3dZIiIichL2MgcLt2RUu+efw2nyXXI6d364jq83/4KvzeDVG/txVb+2DVSpeygEiUiNxceEMeuOobQJC2RXZiHXvfkz+3OK3F2WiIiI/EqBvZya7nluAou3ZeLva+PNm/tzWWKbeq3NEygEiUitdI1qzpw7h9IhohlpucVcO+1ndmYUuLssEREROUFIoC/VzISrpJm/Dx/cMoiLe0XXX1EeRCFIRGqtXctmzLljKD2iQ8gsKGH8mytIOpDn7rJERETkmEA/H0bFReNTgyTk52Mw8/YhDO0S0QCVeQaFIBE5I1Ghgcy6Ywh92oVxuKiMG99eyeq9ue4uS0RERI657YLOOGswJ+7Zcb3pE9ui/gvyIApBInLGWjTz5+PbhzC4UziFJeVMfG8VS7ZluLssERFpQuzldrKLs7GXq1nPrw3sGM7jVyVgwClHhO4d1Z2rz2nXsIV5gHoNQU8//TQDBw4kJCSEqKgorrrqKrZv317pHLvdzt13301ERATNmzdn3LhxZGToTZSIt2ge4MsHtw7i4p5R2Muc3D5jHXPXH3B3WSIi0sitz1jP1KVTGfzJYIbPHs7gTwYzdelUNmRucHdpHmXCkA7MuXMoo+KiOTEGNQ/w5e2b+/PHi7u5rTZ3qtcQ9P3333P33XezcuVKFi5cSFlZGaNHj+bo0aOuc/785z/z1VdfMWfOHL7//nsOHTrENddcU59liUgdC/TzYdrN/bmmX1scTpN7Z2/ineV73F2WiIg0UrO2zWLyt5NZlrYMp+kEwGk6WZa2jEnzJzF7+2y31udpBnQM56bB7Qny9wGgd7swlj8wnFHxrd1cmfsYpmnWsHne2cvKyiIqKorvv/+eYcOGkZeXR6tWrfjkk0+49tprAdi2bRu9evVixYoVDBkypNpr5ufnExYWRl5eHqGhofX9I4jIaTidJk/+byvv/rgXgLsu6sJfLumBYdSiPY2IiMhprM9Yz+RvJ2Ny6rewBgYfXPYB/aL6NWBlnuvLTYe4b/ZGyhwm53WN4M2bB9A8wNfdZdWLmmaDBl0TlJdndY8KDw8HYN26dZSVlTFy5EjXOT179qR9+/asWLHipNcoKSkhPz+/0k1EPIPNZvDwmF48cGkPAF5ftpuH5iZR7nC6uTIREWksZmyZgc04/VtYm2FjRsqMBqrIs03/aS9/nLmBMofJFb3b8N7kgY02ANVGg/0JOJ1Opk6dynnnnUdCQgIA6enp+Pv706JFi0rnRkdHk56eftLrPP300zz22GP1Xa6InCHDMLjroq6EN/Pnb/9N4tM1aRwuKuU/N/Qj0M/H3eVJbZUVQ0kBBISAX5B7a3E6wFEGzjLra2khlByFwBDwawaGrfLNx8+6iUijYS+3szRtqWsK3Kk4TAdL0pZgL7cT6BvYQNV5FtM0eX7BDl5duguASUM78PffxGOrzeZBjViDhaC7776b5ORkfvzxx7O6zkMPPcS9997rup+fn09sbOzZlicideyGQe1p0cyPP87cyHcpGUx6bzVvTxpAaKDelHqFfStgxWuw/RswnVao6DEGzr0H2lc/VbmS8hIozITiXCjKheLDx74/fOz7w1agKT16/FZ2wveOUiv0nGbqyykZPlZ48w0A3yDwCwTfQPAPhsAwCAi1vgaGQeCx74NaQnCr47eglqApnSIeobCssNoAVMFpOiksK2ySIajc4eThecl8uiYNgPtGdeeeEV01Pf0EDRKC7rnnHr7++mt++OEH2rU73oKvdevWlJaWcuTIkUqjQRkZGbRuffKFWgEBAQQEBNR3ySJSBy5NaMP7t/rxuxnrWLU3l/HTVvDBrYOIDm16v5C8ypp34Jv7weZjBSCwvu6YD9u+hjHPw8Ap1nF7PhzZD0f2Qd4BKPgFCjKsr4XHvhYfdt/PYjqOBazCM7+GzfdYIIqE5tEQ2hbC2lm3iu9D21oBS0TqVXO/5tgMW42CkM2w0dyveQNU5VnsZQ7+OHMDC7ZkYDPgiasS+e3g9vX3euV2tuRsIdgvmB7hPertdepavTZGME2TP/zhD/z3v/9l2bJldOtWuQVfRWOEmTNnMm7cOAC2b99Oz5491RhBpBFJPpjH5OlryC4soW2LIN6/ZSDdokPcXZaczL4VMP0yqh11iegKR7PBfqRm1/Xxh6BwaBZufQ1qcez7ltYtIAT8gq0RGv9g8G8O/s2saW6+AWDzg0MbYeb46l9r4pfQtr81ba7MDuXFx74eu5UVWyNM9jwoybe+2vOsQGfPs0apjmZZN3tezX4+sIJSy04Q0QXCOx+7dbK+BrWs+XVE5LSmLp3KsrRlOEzHKc/xMXwYHjucF4e/2HCFeYAjRaX8bsY6Vqfm4u9r4+Ub+nJpQps6u75pmhw6eohNmZvYlGXdtudup9ws56quV/H4eY/X2WudqZpmg3odCbr77rv55JNP+OKLLwgJCXGt8wkLCyMoKIiwsDCmTJnCvffeS3h4OKGhofzhD39g6NChNQpAIuIdEtqG8d+7zmXSe6vZk32UcW/8zDuTBjKoU7i7S5MKTgfk7oXv/s+a+lXd52M5u45/3ywCWrSHsFgIaQMhrU+4tbFGT+piStmGD61RGWf5qc+x+VojWZ0vtO6f7TKm8hIr7B3NtL4WpEP+QchLg7yDx74/AGVFx4PTgdVVrxPUElr1tG5RvY5/DW6lqXYitTQxbiJL9i857TlO08nE+IkNVJFnSMstYvL01ezOOkpIgC9vTxrAkM4RZ3VNe7mdlJwUK/BkbmJz9mayi7OrnBcZFOl1o271OhJ0qnmH06dPZ/LkyYC1Wep9993HzJkzKSkp4ZJLLuH1118/5XS4X9NIkIj3yD1aym0frGH9/iP4+9r4z/V9uSyx7j6hkhoqPgzpyZCRAhnHvmZutUZMasyA25dAZDdrFKe+lRXDUzHHp+edtjQb/O1QwzVyME3rz/TIfji8F3L3HLvthZzdUHjyRj+ANSoW1QtaJ0KbPtC6N7TqoYYOItWYvX02T6x8ApthqzQi5GP44DSdPDzkYcb3qMHIcSORfDCPW95fQ1ZBCa1DA3n/1oH0bF2798WmaXKw8KBrhGdT1iZ25O6g3Kz8wZOv4UvP8J70iepDn1bWrU1wG49Zb1TTbNCg+wTVB4UgEe9SXOrgj59uYOGWDAwD/n5FHJPP6+TushqvkkJI3wwH18Oh9dbXw3tPfq5vgDXyUVP374TmUXVTZ3UKM+HftdjVvCFrq07pUSsMZW2HrK2QuQ0yt8DhVE467dAnAKLjrVDUpg+0PQei4sFHLW1FTrQhcwMzUmawJG0JTtOJzbAxInYEE+MnNqn9gX7YkcXvP1rH0VIHPaJDeP/WgbQJq/5DoOLyYlKyU1yBZ3PWZnLsOVXOiwyKdIWdPq36EBcR59HNJhSCRMRjOZwmf/8ymY9W7gfgjmGd+eulPdW282yZpvVme/8KSFsJB9ZB9vaTj560aA/Ridab7eh4iE6wpq/9K9YzR1s8eSToTJUWQfYOaxQufTP8stn6WnKS/e/8gq0w1G4AtBsE7QZC81YNX7OIB7KX2yksK6S5X3OPfnNeH+asTbP243OanNslgmk39z9pF1bTNDlQeMA1rW1T1iZ2HN5RZV2Vp4/y1IRHrAkSETkZH5vB42MTaBMWxHPfbefNH/Zw8Egx/76uj/YSqo3yUutN8/4VsH+ldSuqOlebkBjrDXRMX4g5B2L6WU0JTqbHGKsLXHXrbnpc3rAhwy/Ic2s7U/7Njv036QvcaB1zOq2Rul82Wf9tD22wRu9K8iF1uXWr0LIjtB8KHc+3bi06aH2RNEmBvoFNLvyYpskrS3bxwsIdAFzVN4Znr+2Dv6+1iWxRWdHxtTzHRnly7blVrhMVFOUKPL1b9aZXeK8m82epkSARcavP1x3gr59vptxp0r9DS96eOIDwYH93l+WZnA7rzfHe72HvD1boKSuqfI5PgNUZrf0Qa7Sg7TnWCE9N1ag7nAG3flv7/YLOlifXVp+cTmtEL201HFhj3bK2VT0vtN2xQHSe9bVlJ4UikUaozOHkkRP2APr9hZ25/twgNmdvZnOWdTvZKI+fzY9eEb3oHdmbPlF96NuqL9HNor1qlKcmNB1ORLzGz7uyueOjdRTYy+kQ0YzpkwfSuZV3dZmpF6ZprSOpCD2py6u2bA5qaY0GtB8CsUOsUQXfs9xLbc278M191j5BJ4662HytIHbiPkENzZNra0jFR+DgWtj3M6T+aI0WOcsqnxMWC12GQ5cR0OnCU4/+iYjXyLeXcefHP7Hq4CZ8m+2jZ8cj5Jbv5EjJkSrntg5ubQWeVn3oE9WHXuG98Pdp/B8yKgSJiFfZlVnA5OlrOHC4mBbN/Hjr5gFNs4V2SaEVenYutG75Byo/HhBqfcrfaZh1a9ULbLa6r2P/SljxmrU5qum01tn0vAKG3u3+URZPrs1dSo9aI0X7frJC0YG1lUORYbOmQnYZAV0vhrYD1GhBxAs4TSepealsytrEzwfWsWjPGsp8fsEwKr9997f5ExcR55rW1rtVb1oH12IWQCOiECQiXieroITbZqxlU9oR/H1sPHddb8b2bevusuqXaVqL43cusELPvp8rv3n1CbDe2He+EDpdZHULa8g3r2XFUFJwbDNTD1tn48m1uVtpkfV3afcS2L246vS5wDDoOhK6XwbdRmozVxEPkVeSR3J2MpuzNltrebI3U1BaUOU8PzOC4oK2lBe1x7R3YHinvtwxrDsDOjbBDw9/RSFIRLxScamDe2dvZH6ytbfKfaO6c8+Iro1rzrLTYa3r2PY1bPvG2lPmRC07QtdR0G20Nerj38wtZUojkncQ9iyFXYutr8WHjz9m+ECHc6H7pdDjMojo4r46RZoQh9PB7rzdrsYFm7M2sydvT5Xz/IwASopiKDvanjBbF7JzWmNzhuFwHn8L72MzcDpNHr8qgQlDOtRZjd7YeU8hSES8ltNp8q9vt/HWD9Yvg2vOacvT1yQS4OvFnePK7Na6nm1fw/b5cDTz+GM+/lbYqQg+EV20oF3qj9MBB9fB9v/B9m+tvYtOFNkd4sZat+gE/V0UqSO59lySspJcoScpO4mi8qIq58WGxFrT2iJ7s2N/ONOXlgA+9O/QknX7Dle98AkMYM6dQ896RGh9xnpmbJnB0rSlrj2YhscOZ1L8JI/fg0khSES83kcr9/H3L1NwOE0GdGjJtJv7E9n8LBf9N6TyEti1CJLnwo5vobTw+GMBYdD9Eug5xlqjERDivjqlacvda/393D7fWlN0YsOJ8M7HA1GbvgpEIjVU5ixj5+Gdx6e1ZW1mf8H+Kuc1821GQmSCa0+exFaJhAeGU1pudYCbtdbqAHfLeR05eLiIxduyKo0A/ZqPzWBUXDTTJvQ/49pnbZvFk6uexGbYKnWY8zF8cJpOHh7yMON7jD/j69c3hSARaRSW78zi7o/Xk28vp22LIN6dPICerT34/3VHGexZZgWfbd9AyQnd3ELaWKGn5xjocD74Nv4uPeJl7HmwYwFsmWcF+HL78cdadLDCUOK10Lq3ApHICbKLs61NSLOtwJOSnYLdYa9yXqewTsebF0T2pmuLrvjYKs9yyD1ayp0frWP13lxsBvzjynjGD4gl7tFvOU3+cbEZsOWfl57RvnvrM9Yz+dvJmKfZisDA4IPLPvDYESGFIBFpNHZnFXLbB2vZm32UYH8f/nNDP0bGRbu7rOOcTtj3IyTNga1fVV5vERID8VdB/DXW/j310clNpD6UFFoNO7Z8YX09cU+qyB7Q+zpIvM5awybShJQ6StmWu63SWp5DRw9VOS/EP4TekVantj6t+pAQmUBYQNhpr709vYApH1idUkMCfHn5t/0Y3iOKrIISBj65qMY1rvm/kbQKqf3MialLp7IsbVmVPYZO5GP4MDx2OC8Of7HW128ICkEi0qgcKSrl9x+tZ8WeHAwDHrqsJ7df0Nm9DROydsDmT2HzbMhLO348uBXEXQUJ11h79yj4iLcrLYJdC49P7TxxhCh2sBWG4q+B4Aj31ShSD0zT5Jejv1Tq1rY1Zytlv9qXy8Cga8uurrU8fVr1oWNYR2xGzf/9X7Qlgz99uoGjpQ46RDTj3UkD6BplTZW2lznqfSTIXm5n8CeDcZrOGryGjVW/XeWRzRJqmg20SYCIeIUWzfyZMWUQf/8yhU9W7eep/21jZ0YhT1yd0LANE47mQPLnsGkmHFp//HhAGMSPhYRrrSYHNi9u4iDya/7Njq8NsudbI55Js61mH2mrrNu3D1od5vpNsJp8aB8i8UJFZUVsydnC5uzNrlGerOKsKue1DGjpGuHp3ao3CZEJBPsFn9FrmqbJmz/s4Zlvt2GaMLRzBK/fdA4tg49PmQ7082FUXDSLtmbWaE3QmUyFKywrrFEAAmv/osKyQo8MQTWlf6FExGv4+dh48qoEukc1559fb2HOugPszT7KGxP6n9Gwf405HdZ+K+vetz4Fr1g4bvhAt1HQ5wZrvxU/7/1lIFJjgaHQ7ybrVpBufSiweTb8svFY2/evITjK+v+i3wRo1cPdFYuclGma7MvfVynw7Di8o8pUMF/Dlx7hPUiMTKRPVB/6RPahXUi7OpmJYC9z8Le5SczdcBCAmwa35x9XxuPnU3UE6bYLOrMgJeO013M6TW47v9MZ1dLcrzk2w1bjkaDmfs3P6HU8habDiYhXWrY9kz98soGCknJahwby1sT+9G7Xom5f5EgabPjIuuUfOH68TR/oc6M16tO8Vd2+poi3ytwGGz+CTZ/C0RM+OW830ApDCePUBVHcKr80n+SsZFfzgqTsJPJObF5zTFRQFH2i+rjW88RFxNXLiEdmvp07PlrHhv1H8LEZ/OM3cdw8tONpn/PRyn08Mi8Zm82ol32CtCbIiygEiTQBZcVQUmC9gfILch3enVXI72asZXfWUfx9bfzrmkSuOafd2b2Wo8wa7Vn3gdUdq6JDTmALK/icczNEx5/da4g0Zo4yq5HCho9gx3dQ8WbKvzn0Hg8DpkDrBPfWKI2ew+lg15FdlUZ5TrYRaYBPAHERca7A07tVb1oHt673+tbvP8ydH64js6CE0EBfXr+pP+d3i6zRc9em5vLOj3tZkJKO07TWAI2Ob81t53eqk/2B1B3OSygEiTRi+1bAitdg+zdgOsGwQY8xcO490H4IAPn2Mu6dtZFFW63NR6ec34mHLuuJ70mmEpxWQbo13W3tdChMP3684wVwziTo9RtNdxOprYIM2DwL1n8AObuOH48dbIWhuLH6/0rqRHZxNklZSa7Qk5SdRHF5cZXzYkNirWltx/bl6d6yO34+fg1a66er9/PoFymUOpx0i2rOWxMH0Cmy9uuJ7GUOCuzlhAT6ntEaoFOZvX02T6x8QvsEeTqFIJFGas078M39VoOBEzdvtPlaa3TGPA8DpwDWHOgXF+3glSXWm6zzu0byyo39Ki0qPSnThP0rYPXbsPXL468T3Ar63gTnTISILvXx04k0LaYJqcthzbvWmqGK/9eCwq21RQOmQPiZrWOQpqeiRXXFCM/m7M0cLDxY5bxgv2ASIhNc3doqNiJ1l9JyJ//8OoWPVlqbpl4a35p/j+9D8wDPW6K/IXMDM1JmsCRtCU7Tic2wMSJ2BBPjJ3rsCFAFhSAR8V77VsD0y+A0w/FgwK3fukaEAP6X9Av3z9lEUamD2PAg3p54io1VS49aC7nXvAMZycePtx8KA2+DXldqI1OR+lKQDhs+tKaculrLG9DjchhypzX6qo1Y3c5ebqewrJDmfs3d2gGsNi2qu7To4tqENLFVIl3CulTZiNRdMgvs3P3xetakHsYw4L5R3bnroq7YbJ79d91T/h7UhkKQiHivTyfAjvmVR4B+zeZrvWm6/sNKh7el53P7jLWk5RYT5OfDv8YlMrZvW+vB/EOw+i1Y+x7Yjy2G9Q2y1ikMuh1aJ9bTDyQiVTgdsHMhrHn72Pq7Y6ITYPCd1t5DtZ0qd4r1g1Jz6zPWM2PLDJamLXWNAAyPHc6k+EkNMgJQVFZESk4Km7I2uaa3ZRdnVzmvZUBLElslHm9RHZFAc3/P7Fa2Me0Id364jvR8OyEBvvznxr6M6OlBG343MgpBIuKdyorhqRhrDVB1DBv87VCVNzuHj5byx083sHyn9Yvzwb6l/M7vf9hS5h4PVi07WaM+/W6CoJZ1/VOISG1k7YBV06z9t8qKrGPNIqD/LdYHFCHVLFSvwfpBqd6sbbN4ctWTDbYWxGk6Sc1LtQJPdhKbszaz88jOKi2aK1pUVzQuqMsW1fXJNE1mrUnj0S9TKC130qVVMG9NHECXVp4Z1hoLhSAR8U6FmfDvbjU///6d0DyqymGHw8G8OdNpk/Iu5/psOf5Ah/Ng6N3Wvj62WjZPEJH6VXwY1s+w1ulVTJXz8bf2HDr3jxB5kn8barF+sLGqiylLDdEV7Ij9SKVubcnZyRSUFVQ5r3Vwa1e3tsTIxHprUV2f7GUOHpmXzJx11vYKo+KieWF8H0ICG7YJQ1NU02zgeSuxRKRpCwixPsWt6UjQr/cdKS+FpDn4/PQS47J3gA+Umza+cQ7hc/+x3HPReAZ1ct/CWBE5jaCWcN6fYMjdVgOFla9D2iorGK3/EHqOgfOmQuxA6/x9K6wAhFl1+mzF/W/us9raN8IRobqcujZjy4wqI0C/ZjNszEiZUaNrlznK2HF4R6VRnv0F+6ucF+gTSHxkfKXQEx3s3VPF9ucUcedH69jySz42A+4b3YPfX9jF49f/NDUaCRIRz3Mma4JKi6w3Sj+/cnxj04BQ6D+J/d1u5vZ5GWzPKMDHZvC3y3tx63kdPX4qhYgA+1fCT/+B7f87fqz9uVZYWv8h7Pz2jNYPeruKqWsGBk6Of2h0JlPX7OV2Bn8yuMo0tJOxGTZW/XaVa2TGXm6noLSAo2VH2X54u2uUZ2vuVkocJVWe3zG0ozWlrVUfEiMT6dqyK362xjM6snhrBn+etZF8ezkRwf68fGM/zutas/1/pG5oOpyIeK/adIdr1dNaWL3yDSjKsR4KjrKmvA24FQKtfxeKSst5aG4SX2w8BMCY3m14Zlxvj2xNKiInkbUdfnrZ2nfoV53BqnWK9YPean3GeiZ9O+m059Rm6lp2cTbDZw+v8ev/7+r/8cOBH5izcw67j+w+5Xmh/qGubm29W/UmITKBsICwGr+ON3E4TV5cuINXl1pbNfRr34LXbzqHNmGN4++cN1EIEhHvtuZdaxrLqeb5j3wMinOt80qPzSlv0cH6dLjvTSftKmWaJh/8nMoT32yl3GnSOTKY1246h15t9G+HiNfIP2R96LH2XavdfU2dYv2gN7r2y2vZfnj7ac/xMXwYHjucF4e/WO31ajMSBFbAOt3aobFdxnJb4m10CO3QJEbccwpL+NOnG/lxl9WMZ/K5Hfnb5b3w99W6U3dQCBIR77d/pdXxadvXxzs+dR1lje5s++Z4F6moODj/Xoi/GnyqH9lZt+8w93yynl/y7AT42njsyniuHxjbJH5ZizQa+b/AC704/YjxMY1oJGjloZXcvvD2Gp3766lrpzN16VSWpS077ZqgmjrbBgreZNWeHP746QYy8kuqbssgbqHGCCLi/doPsW5lxXA41dpgce304+En5hy48AHodkmtOr3179CSb/54AffO3siy7Vk8ODeJVXtzeeKqBII1PU7EvWq6109oG+h5hbVW6HRv3CvWBDWCAARWA4OacppOCssKTxmCSh2lbM3dSlJWEkfLjtYoAHUO60xqXmqldUi/VpsGCt7K6TR5fdkuXli4A6cJXVoF8/pN/enROqT6J4tH0G97EfFsR7Ph55etlrmu8NMPLvobdBt1xjvLhwf7896kgUz7YTfPL9jBfzccZPOBI7wxoT/do/VLTKTBncleP0OPdZE7HWe5tflqI2Avt/PTwZ9qfL6BQXM/a08a0zQ5UHCAzdmbXd3atuVuo6wG66ts2DAx+eugv/LsmmdPG4AAHKaDJWlLsJfbva61dU1kF5bw51kbXXvRXXNOWx4fqw/RvI2mw4mIZyo+YoWflW/8Kvw8BN1Gn3H4OZlVe3L4w8wNZBaUEOhn4/GxCVw3ILbOri8i1TibvX5OtX7wRBFdrX874q/x6v3BatvAICEigYtiL7KCT1YSh0sOVzmnZUBLElsl0juyN4mtEnGaTj7f8TlL0pa42m6PiB3BxPiJxIbE1ur1l45fSmRQ4+qMtmJ3Dn/6VL8vPJnWBImIdyotgtVvwo8vgf2Idayews+Jfv3J3tX92vLPsfHa2E6kvtWmG+SpRoROtn6w+6UQGgPJc60mKgBR8TD8b9Z+Q164BrC2DQx+zdfmS6/wXiRGJrq6trULaXfS9ZAn24D1bFppu65b5qDAXk5IoC+Bfj5n9HO4g8Np8trSXby0yJr+1i2qOa/ddI5mDngghSAR8S7lpbBhBnz/HBSmW8da9YQRjzTYGxaH0+T1pbt48dgvuQ4RzXj5hn70iW1R768t0mSdyb5gp3Ky9UT2fFg1zdpDrCTfOhbTz+ow2fnCuvkZGtCflvyJZWnLqp2SBtCueTvXKE/vVr3pGd4Tfx//s3r9mjRQOFlnujWpubyzfA8Lt2TgNMFmwKi4aG6/oDMDOnr2BtbpeXb+PGsjK/ZY2zBc178dj42Np5m/pr95IoUgEfEOTickfwZLn7SaHwC0aG+t+ek93pri0sDWpubyp083cvBIMb42gwcu7cFt53fWbt8ida2sGJ6KsUZvqnO2Hd6KcmHFq7ByGpQda63ddaQVhlonnNk1G0BRWREpOSlszrLW8qzLWMeRkiPVPu/VEa9yYWzdh7z1GeuZ/O3k07bI/nV3uA9X7uPRecnYbAYO5/Hn+dgMnE6Tx69KYMKQDnVea11YkJLOA59v5khRGc38ffjn2ASu7d/O3WXJaSgEiYjn27MMFjwM6UnW/eAoGPYX6D8JfAPcWlpeURkPzt3M/GRrVGpY91Y8f10fWoW4ty6RRqUwE/7drebn18VeP4WZ8MNzsPa9Y6NPBvS5AYb/H7Rw79oOh9PBnrw9rsYFSdlJ7Dqyq8r0s4p9en69X4+P4YPTdPLwkIcZ32N8vdU5e/tsnlj5BDbDVmlE6GSvvyY1l/HTVlQ32ZE5dw71qBEhe5mDJ77Zwkcr9wOQ0DaUl2/oR+dWzd1cmVRHIUhEPFfmVlj4KOxcYN0PCLU2OR3ye/APdm9tJzBNk0/XpPHYVynYy5xENvfn+fF9ubB7K3eXJtI4NORI0K/l7IYlj0PKf637PgEw+HfWnmPNGubNeFZRlqtpQVJ2EsnZyRSVF1U5L7pZNL1b9Xat5ekV3ovth7czI2XGSRsYNERr6g2ZG2r0+nd8uJZFWzMrjQD9mo/NYFRcNNMm9K/3umtiW3o+f5y5gR0ZhQD8blhn7h/dQ5ufegmFIBHxPAUZ1rS3DR9ab3psvjDwNhj2AARHuLu6U9qZUcA9n2xge0YBALec15G/XtrTqxb1inisulwTdCYOroOFf4fU5db9wDC48EHr3ybfs1s/c6Li8mK25GwhKSvJ1aY6/Wh6lfOCfIOIj4g/3rEtMpHo4OhTXvdkDQwa0ule317mIO7RbzlN/nGxGbDln5e69d9V0zSZsWIfT/5vK6XlTiKbB/DC+D4M0wdfXkUhSEQ8R+lR+PlV+Ok/x+fi9/qNNRc/oot7a6she5mDp/63lRkr9gFWZ6CXbuhLfEyYmysT8XJ10R3ubJkm7FpkhaHMFOtYRFcY/SR0v6TWjVmcppPUvNRKozw7Du+o0kzAwKBLiy6uUZ7EyES6tuiKjxvWQtaHrIISBj65qMbnr/m/kW6bcpxZYOfBz5NYsi0TgBE9o3j22t5ENtcUaG+jECQidaOmu7efjGlC8ufW1Lf8g9axtv2tNxYdhtZ9rQ1g6bZM/vLZZrILS/DzMbhvdA9uv6AzPmqaIHLmTrXXT032CapLTgds+MiaJnc0yzrW+SK45CmIjj/l03KKcyqt40nJTqGgrKDKeZFBkZXaU8dHxhPs5zlTgOuat4wEfZuczkNzN3O4qAx/XxsPXdaTyed2PGnrcPF8CkEicnbOZPf2Ex3aCPP/Cmkrrfth7WHUP6zNCr38F0tOYQkPzU1iwZYMAAZ3Cuf58X1o17KZmysT8WIn2+un5xUw9O76GwE6FXs+LH8eVr4OjlKrlv6TYfj/YQ9ozrbcba7Ak5SdxMHCg1UuEegTSFxEnDXCc2xqW+vg1k3ujbUnrwkqsJfx2Fdb+GzdAQB6tQnlpev70qO19v7xZgpBInLmzmb39sIsWPJPWP8hYIJfM2uh8bn31N2CZg9gmiaz16bx2FdbKCp1EBLgyz+viueqvm2b3JsckTp1NqPPdcyZs5vUhQ+RdOBHkgL82RwYxE5/f8qp2q2tU1gn4iLi6NaiG/2j+xMXGYefTZste2p3uFV7crh39iYOHinGMODOC7swdWQ3Anwbx1TEpkwhSETOzJnOzy8vhTVvw7JnoCTPOpZ4nbXuJ6xtfVbsVvtyjjJ11kY27D8CwGUJrXn8qgTNIxfxQjnFOSRnJ7vW8iRnJ590WluE07BGd9pfRGKrRMod5Xy28zOWpi11dUobHjucSfGTGqRTm6f7aOU+HvGQfYJKyh28sHAHb/2wB9OEdi2DeGF8XwZ18pz23HJ2FIJE5MycSaemvcut+fzZ2637bfrAZc82/BQWNyl3OHl92W5eXryTcqdJeLA/T16VwGWJbVzn2MscFNjLCQn0VVc5EQ9gL7ezNXcrm7M2k5ydfMppbQE+Ada0togEEgsP03vdLNoczcEASLiWLzsP5OGk12u0Z05TtjY1l3d+3MuClHScprUGaHR8a247v1ODjQAlH8zj/jmb2JZuBdvxA9rxyBVxhARqxK4xUQgSkdqr7Z4df9wIS56ApNnWsWaRMPLv0HcC2Jrefgq//gV7ZZ8YruoXw6w1aSzckuH6xT8qLprbL+jsURsDijRmJ+vWtvPwTsrNyh/2VExrq2hekBiZSNeWXStPayvKtf7dW/seYHLUMJjWIoyPwkIoP8lUWAODDy77QCNCx7jjA6HScievLNnJ68t24zj2QdXT1yRySXzrBnl9aVgKQSJSe7Xdvd2/OZQWAoa1RmjEwxDUst7K8wal5U5eXryTN77f7Zr2YTOo1B3JHVNARJqS7OJsV9jZnL2ZlOwUCssKq5wXERhxfD+eVonER8QT4l/DRfGHNrL302vplG91kdvj58sTEeGsCaq8V46P4cPw2OG8OPzFs/65pPZ+/eHUmMQ2/HNsPBGastxo1TQb+DZgTSLi6QJCrBGemowEgRWAYs6xGiW0Pad+a/MS/r427r+kB21bBvHQ3CSAKu1hK8LRI/OS6dk6RCNCImehqKyIrblbXaEnKTuJX47+UuW8uu7WZo/qydURzRjjH869uUfoXFbOe+mZfNE8mOfDW3DYxxrlcJgOlqQtwV5ud8tmpk1VabmTV5fu4vWlu1zTlB8fm8CY3m2qf7I0CQpBInKcX5DVBru6NUEANj+47BmrbWwj2divLi3bnlllBOjXbDaDd37cqxAkUkMOp4M9eXtcYScpK4ldR3adchPSisBTsQmpr63u3vYUlhXiwOTLkOYsbdaMPxw+wvUFhYwtPMqFRcW8EN6Cec2DMQ0Dp+mksKxQIaiBpBzK4/45m9n6Sz4Alye25p9j1bBGKlMIEpHKht5t7dNRnes/gh6X1n89Xshe5nCtATodh9NkQUo69jKHmiWInET60XRXt7bk7GRSslMoKi+qcl5UUBSJrRJJiExosE1Im/s1x2bYcJpOCnxsPBUZzpchwfw9O5eepWX8MzuXsQVH+WdkOKkBATT3a16v9QgcKSrlxUU7+WjlPtfan3+OjeeK3jHuLk08kEKQiFTWYag1ve2b+45NjXNUPWfMCwpAp1FgL6/RDulgjRQV2MsVgqTJKywtJCUnxTXCk5ydTGZxZpXzgnyDSIhMICEywRrpiUykdXDDL3AP9A1keOxwlqUtc41EJQcEcENMa27KL+Duw3n0Lynhs4O/sKx9bwK9ewm2R1uTmsuz325jTeph17Ho0ACeujqRi3tFu7Ey8WQKQSJSVf/JkL0T1rxVebugHmPgvD82mdbXZyok0LfaqXAnyisupVWIpmlI01HmLGPX4V2VprXtyduD+av9yWyGjW4turmmtCVGJtI5rDM+HjIFd2LcRJbsX1LpmMMwmBEWyoLgZvwt5zDDi4oZtX8zvHEeXPkKdDzPTdU2Tm/9sIen/re1yvHswlJu+2CtGtDIKak7nIhUlp4MX/0RDq6z7rcbBCP/YTU+cPPu7d7kjg/XsmhrZqWNAU8lyM+H+0Z3Z/K5HfH1aXqtxaVxM02Tg4UHKwWerblbKXGUVDk3JjjGmtLWqjcJkQn0Cu9FM79mbqi65mZvn80TK584+T5BTgdvtBvDeZvmQcGxZg0Dplj/pgbqPcvZemXJTp5fsOO05xjAnDuHau1lE6IW2SJSO+WlsPzfsPx5qylCQKj1i7r/LU1yz5+ztSY1l/HTVlDdP7BxbULZcmzxblybUJ66JpG+sS3qvT6R+nLEfoTknORK09oOlxyucl6IX4hrWltF6IkMinRDxcfZy+0UlhXS3K95rZoYbMjcwIyUGSxJW4LTdGIzbIyIHcHE+InW/kD2PFjwCKz/wHpCaFu44iXoPrp+fpBG7tCRYv7xZQoLtmRUe66PzWBUXDTTJvRvgMrEEygEiUjNHdoA8+6GzBTrfs8r4PJ/Q6haiZ6Nj1bu45F5ydhsRqURoRP3CfrtoPbMWZfG0/O3caSoDMOAmwa35y+X9CQsSLuYi2crcZSwLXeb1bwgy2pesL9gf5XzfG2+9GzZs1Lg6RDaAZvhGR+wrM9Yz4wtM1iattQVYobHDmdS/KRabXJabYja87010n441bqfOB4u/RcER9TND9LIlTmcTP9pLy8t2klR6UnWq56CzYAt/7xUay+bCIUgEaleeQks+xf89B+rAUKzCCv8xF8NZ7h3hlS2NjWXd37cy4KUdJym9ct4dHxrbju/U6XpGTmFJTz5v63MXX8QgFYhATxyRRy/6d3mjPcxEalLTtNJal7q8Wlt2UnsyN1BuVm1nX6H0A4kRia6mhf0DO+Jv4+/G6qu3qxts3hy1ZMnn85mOnl4yMOM7zG+7l6wtAiWPgkrX7f2ZGsWCWOO/bsrp7Q2NZf/+28y2zOsTU/7xrZgY9qRGj9/zf+N1NrLJkIhSERO78A6+OIuyNpm3Y+/2gpAwe6djtJY2cscFNjLCQn0Pe2nkT/vzubh/yazJ/soABd0i+TxsQl0jKzfdr8iv5ZZlElStjWdLSk7iZTsFArLCqucFx4YTmJkIvGR8fSOtEZ5wgLC3FBx7a3PWM/kbydXachwIgODDy77oFYjQjVyYB18eQ9kbrHux18Nlz+vUaFfOXy0lH/N38astWkAtGzmx0OX9+KKxDYk/OO7GjWg0UhQ06IQJCInV2aHZU/Bz69Yn0IGt7JaXsdd6e7K5JiScgfTlu3htWW7KC134u9j43fDOnPX8C4081dTT6l7haWFbMnZ4go9m7M3k1lUtT11oE8gcRFx1ihPK2uUJyY4xmtHK6cunVqpxfXJ+Bg+DI8dzovDX6z7AspL4YfnrLWYpgOCo+A3L0HPMXX/Wl7G6TT5bN0Bnp6/lcNFZQDcMDCWv17ak5bB1qhiTRrQaE1Q06MQJCJV/bIJ5t4BWcfaiSaOh8uegWbqmuOJ9mYf5dEvklm+MxuAmLBAHr4ijssSWnvtm05xvzJnGTsP7yQpK8kVek7Vnrpri66VprV1adEFX1vjCOL2cjuDPxmM03RWe67NsLHqt6tq1SyhVg6uh3m/Pz4y3/sGuOxfENSyfl7Pw21MO8Lfv0xh07Hpbj1bh/DEVQlVOrzVpAGNusM1PQpBInKcoxx+esla/+Mss0Z/fvMffdroBUzTZMGWDP751RYOHikG4LyuEfzjN/F0iw5xc3Xi6UzTJK0grdK0tq05Wyl1llY5t6I9dUXgiYuI8/j21Gcjuzib4bOH1/j8peOX1m/3ul+P0oe0gStfhW4j6+81PUxWQQnPfruNOesOANA8wJc/XdyNyed1xO8U2wfUpAGN9glqWhSCRMSSsxv+ewccWGPd73mFFYC09serFJc6mPb9bt74fjel5U58bQa3nNeRP17cjZBAdZETS05xjivsVHzNL82vcl6If0ilER5PaE/d0DxqJOhEaavhv3dC7m7r/jmT4JKnIKB5/b+2m5Q5nHzwcyr/WbSTghKr0ca4c9rx10t7EBVa/Z95TRvQSNOgECTS1JkmrHkHFj4KZUXWvj+XPwe9r1fnNy+2P6eIx7/ZwsJj+2NENvfnvtE9GD8gFh+b/rs2JUVlRWzJ2VIp9Bw6eqjKef42f3pG9KwUetqHtNeUSjxgTdCplBbB4n/Cqjes++Gd4Zq3od2AhquhgSzfmcVjX21hV6bVdCOxbRj/uDKe/h1qPxWwpg1opHFTCBJpyvJ/sTq/7V5i3e80DMa+Di1i3VuX1Jml2zP551db2Husi1zP1iE8ckUc53VtWp/mNxVlzjJ2H9ldaYRn95HdVUYxDAw6hXU6vglpZG+6t+yOn49GC0/Grd3hamLvD9aoUP5BMHzgwr/CBfeBj/evy9qVWcjT/9vK4m1WA46IYH8euLQH1/WPxaYPdOQsKASJNFXbvoEv7oHiXPANhJGPwaDfgc0zNiWUulNa7uTDlfv4z6Id5NutKSQje0Xxt8t70blV450609iZpsmBggMk5xwf4dmasxW7w17l3KhmUa621AmRCcRFxBHir7VitTF7+2yeWPlEw+0TVFvFh+Gb+yD5c+t+u0FwzZvW6JAXyiks4T+Ld/Lxqv04nCa+NoMJQzrw51HdtUG01AmFIJGmprQIvvsbrJtu3W+dCOPehVY93FuX1LvDR0v5z+KdfLhyn+tNxc1DO/Cni7vRoplnblApx+UU55CSk+LagDQ5O5m8krwq54X4hRAfGe+a1pYQmUBUsyg3VNz4bMjcwIyUGSxJW4LTdGIzbIyIHcHE+InuGQE6mc1z4Jt7oSQf/JtbnT373uQ105vtZQ4++DmVV5fscq37Gdkrmocu70kXfWgjdUghSKQp+WUTfH4bZO+w7p/7BxjxCPhqd+ymZFdmIU/9bytLjk0vCQ305e7hXZl0bkfNj/cQRWVFpOSkkJyd7LqdbB2Pn82PXuG9XKEnMTKR9qHtsRka0a1P9nI7hWWFNPdr3jBNEGrryH5rety+n6z7va60Gt148DYHpmnyTdIv/Gv+Ng4ctjpcxrUJ5eErenFuF03flbqnECTSFDidsPI1WPSY1fq6eWu4ehp0qXnbV2l8ftiRxZPfbGV7RgFg7S/051Hdueacdmqe0IDKHGXsOLKDlOyUSvvxnG4dT0Xg0ToeOSWnw2qjveQJ69/90HYw7h3oMNTdlVXx865snvl2G5sOWCOb0aEB3D+6h/4tknqlECTS2BWkW58I7llq3e8xBq58BYIj3FuXeASH0+S/Gw7ywoLtHMqz1pJ0j27OXy/tyYieUeoMVsecppP9+ftJyk5yTW3blrPtpPvxRDeLrtSpLS4ijub+mg4ktXRoI3x2q9VK27DBhQ/CsPvB5v5R36QDeTz73TbXRs/N/H343bDO/G5YZ5r5e39TB/FsCkEijdmuxTD3d1CUDb5BcOnT0H+y18wNl4ZjL3MwY0Uqry3dTV5xGQCDOobz18t6nlELWrFkFmW6prNVBJ+C0oIq51XsxxMfcXwtT6tmrdxQsTRKJQXwv7/AppnW/Q7nwzVvQVhbt5SzN/so/16wnW82/wKAn4/BTYM7cM+IrkQ21/RsaRgKQSKNkaMclj4JP75g3Y+Kh+umq/mBVCuvqIw3vt/N9J/2UlJuTcca0TOKe0d1J6FtmJur82wFpQWV1vEkZSeRWZRZ5bwAnwB6hves1LhA+/FIg9g0y2qaUFoIQS2tLRF6Xt5gL5+eZ+flJTuZtSYNh9PEMOCqvm25d1R3YsObNVgdIqAQJNL45B2Az6ZA2krr/oBbrV3E/YLcW5d4lV/yinlp4U4+W38Ah9P65//S+Nb8eVR3erRWa+USRwnbc7e71vAkZyeTmp9a5TybYaNLiy6VRnm6tuyKn03reMRNcnZb0+N+2WjdH3QHjH68XhvkZObbeX3Zbj5ZvZ/SEz5c+cslPejVRu/JxD0UgkQak23/szY/LT4MAaFWN6CEa9xdlXixvdlHeXnxTuZtPIhpWjMpr+gdw9SR3ZpMu1qH08GevD2VRnh2Ht5JuVle5dy2zdtaozsRCSS2SqRXeC+a+ekTbvEw5aWw+DFY8ap1P6YfXPc+tOxYpy+TVVDCtO9389HKfa6R5UEdw7n/kh4M6uS5neqkaVAIEmkMykth0d9h5evW/TZ9relvXrpJnnienRkFvLRoJ98kWXP4bQZc3a8ddw/v0qg2XDVNk4OFB0nOSXZ1a9uSs4Xi8uIq54YHhrsCT0JkAvGR8YQH6o2deJEdC+C/v7M+OAsMg6vegJ5jzvqyOYUlvPXDHj5YkYq9zAo//Tu05N5R3Tm3S4SmfopHUAgS8XZH0mDOJDi4zro/5C4Y+Q/t/SP1IuVQHi8u3MmirRmAFYau6B3D3cO7euU0uYoNSF2NC7JTOFxyuMp5zXybERcRZ01rO7YnT5vgNnozJ97vSBp8dgscWGPdH3qP9TvkDFqvZxbYeXf5Xj5cuY+iUgcAfWNb8OdR3RnWLVL/v4hHUQgS8Wa7FsHnt0Nx7rFP8aY16CJXabo2pR3hlSU7WbT1+ML/S+Kj+cOIbh7bQKGwtJAtOVtIzrGmtaVkp5x0A1Jfmy89WvawRneOrePpFNYJHw9oKSxSL349Pa7dQLh2OrSIrdHTDx4p5s3vdzNrTZpr2lti2zDuHdWdi3q0UvgRj6QQJOKNnA74/hn4/lnAhDZ9YPyMOp/PLVKdlEN5vLZ0F/OT06n4LTG8RyvuGdHNra21Sx2lrsYFFSM9e/P2YlL5V5mBQcewjsc7tUUk0CO8B/4+/m6qXMSNtn4N8+6Ckjyre9zVb0H30ac8fU9WIW8s281/Nxyk/FgDlX7tW3DPsPaM6OCPERiqpjzisRSCRLzN0WyYezvsXmLdH3ArXPI0+AW6ty5p0nZmFPDa0l18uekQx94LMahjOHdc2JnhPaKw1eOu779uXJCck8yOwzsod1ZtXNAmuI2rLXVCRII2IBX5tdy9MGfy8e5xwx6Aix6stLlqyqE83li2m2+SfnF9+HFe1wjujitl6L63MHZ8A6bT2py1xxg49x5oP6TBfxSR01EIEvEmaautX075B63NT3/zEvS5wd1VibjszT7K60t3MW/jQcoc1q+NblHN+d2wzozt2xZ/X9tZXd80TQ4UHiAl+/g6nq25W0/auKBlQEviI+NJiExwtaiOCIo4q9cXaRLKS+C7/4M1b1v3u47EvOZtvk8r5+3le/hpV47r1JG9orh7eFf6ZXwO39xvhaUTP4Cw+VqzF8Y8DwOnNPAPInJqCkEi3sA0YfXb8N1D1i+XiK4w/kOIjnN3ZVID9nI7hWWFNPdrTqBv0xixS8+zM/2nvXy8aj+FJdYboujQAG49rxM3Dm5PaGDNFl1nF2dXGuFJyU7hSMmRKucF+QZValyQEJFA2+ZttRZB5GxsmoX51Z8wyotJN6KYYv8TKWYnfGwGYxLb8PuLulj7/OxbAdMvA073VtGAW7/ViJB4DIUgEU9XVgxf/xk2zbTux18NV74CAd7XiaupWZ+xnhlbZrA0bSlO04nNsDE8djiT4ifRL6qfu8trEPn2Mmau2s97P+0lI78EgJAAX64bEMukczvQISL4+Lml+VbjgorQk51MRlFGlWue2LigYlqbGheI1K28ojI+Xr2Pn35cxlOlz9DBlond9OO7Tn+l/9i7adfyhP2vPp0AO+ZXHgH6NZsv9Lgcrv+w/osXqQGFIBFPdmQ/zJoAv2wCwwdG/ROG3m3tWCkebda2WTy56klshg2H6XAd9zF8cJpOHh7yMON7jHdjhQ2rpNzBFxsP8fYPe9iZWQhGKT5Bh+jZ4QjRkVlkluxiX8G+Ks8zMOjSogvxEfGu0NO9ZXc1LhCpJ9vTC3j/51TmbThIcZn1b1fXkDLeDX2bDjk/Wif1vwUue8baiqGsGJ6KsdYAVcewwd8OqVmCeISaZgPfBqxJRAD2fG/t3VCUA80irN28Ow1zd1VSA+sz1vPkqicxMSsFIMB1/4mVT9CtZbcmMSJU5ixjd95OCElm6OBkbIc2cehoKhhO9gP7s4+fGxPclsRWxzcgjYuIo5lfs1NdWkTqQLnDyaKtmbz/815W7sl1He/ZOoTbL+jMb/rE4G+7En54DpY9DeumQ/pmuP4ja4SnJgEIrPNKChSCxKsoBIk0FNOEFa/BwkesXxht+lq/aGq4X4O434wtM6qMAP2azbAxI2VGowtBTtNJal5qpb14tuVuo9RZWvlEA1r4hxPo7MihzEjshW1x2tuREdCCYf3bMSyuPZ1bqWubSH06fLSUT9ek8dHKfRw8YjUX8bEZjI6LZtK5HRncKbzyurqL/gptz4HPb7M26H7zQrj2XWuEp6YjQZrKLV5G0+FEGkLpUfjyD5D8uXW/z2/hihf0qZkXsZfbGfzJYJw1eENgM2ys+u0qr22WYJomh44ecoWd5JxktuRs4WjZ0SrnhviFEBcZ5xrhSYhMILpZNIZhkFdcxpy1abz/cyoHDh/v8nZe1wh+O6gDo+Kiz7qrnIhYTNNk1d5cZq7ez/ykdEod1r9VLZv5ceOg9kwY0oGYFtX8zsndC5/eBJkpYPODqF6QuUVrgsSreNWaoNdee43nnnuO9PR0+vTpwyuvvMKgQYNq9FyFIPF4R/bDzN9CRpL1y+LSf8HA27T+x8tkF2czfPbwGp+/dPxSIoMi67GiupNdnO0KOxXB53DJ4SrnBfoE0jO8JwmRCa5Obe1D22MzTh9kHE6Tpdsy+WT1fpZuz3TtPxLZPIDrB7bjhoHtiQ3X1DiRM5F7tJS56w/wyer97Mk6/kFFfEwok8/tyG/6xBDoV4vmIiWFMO/3sPXLGj5B3eHEs3hNCJo1axYTJ05k2rRpDB48mJdeeok5c+awfft2oqKiqn2+QpB4tH0rrAYIRdkQ3ArGz4AO57q7KjkDjWUkKL80n5TsFFJyUk7fqc3wpVvLbq7RnfiIeLq06IKv7exmUR84XMSnq9P4dE0a2YVWVznDgPO7RnLdgFhGx0XX7g2bSBPkdB4f9fk2+fioTzN/H8b2jeHGQe1JbBt25q3kTRN++DcsfeL4McMHTpwKrH2CxEN5TQgaPHgwAwcO5NVXXwXA6XQSGxvLH/7wBx588MFqn68QJB5r3QfwzX3gLIPWveHGmRDWzt1VyVmYunQqy9KWnXZNkI/hw/DY4bw4/MWGK+wUisqK2Jq71TXKk5Kdwv6C/VXOMzDoHNbZtQFpQkQC3cO7E+ATUG+1lTmcLNySwSer9vPjruMdFEIDfbmybwzX9Y+ld7uzeBMn0gjtyznK5+sPMnf9gUpTTONjQvnt4PaM7duW5gF1uNx72/9g7u+gtMDayLvcDpjWGqCeV1hdTTUCJB7GK0JQaWkpzZo147PPPuOqq65yHZ80aRJHjhzhiy++qPKckpISSkpKXPfz8/OJjY1VCBLP4SiHBf8Hq6ZZ9+OvhrGvg7+m+3i79RnrmfztZMzTbBxoYPDBZR80eGOEUkcpOw7vcI3upOSksCdvz0lHrto1b1dphKdXRC+C/YJPctWGsS/nKJ+vO8Bn6w5wKM/uOt4jOoRr+7djbL8YokI8b1TNG9jLHBTYywkJ9NUIm5cqsJfxv6Rf+HzdQVanHu/wFhLgyxV92vDbQR1IbBdWfwVkbYeZN0LubisIXfov6HO91rSKx/KKEHTo0CHatm3Lzz//zNChQ13HH3jgAb7//ntWrVpV5Tn/+Mc/eOyxx6ocVwgSj1CUa7W/3rPMuj/iYbjgfq3/aURmb5/NEyufcOs+QeXOcnYf2X18A9KcZHYc3kH5SRYvRwVFuUZ44iPiiY+Ip0Vgi3qt70w5nCYrducwZ10a3yanU1JuBTibAed1jeTKPjFcktCa0EA/N1fq+dak5vLO8j0s3JKB07T+DEfFRXP7BZ0Z0DHc3eVJNUrLnfy4K4svNx7i25R07GXW/wsVU0ev7d+O0XGtCfJvoGBbfAQ+nwK7Fln3L/obXPiAfreJR2q0IUgjQeKxsrbDzBsgdw/4BcM1b0GvK9xdldSDDZkbmJEygyVpS3CaTmyGjRGxI5gYP7HOR4CcppP9+ftd09lScqzW1MXlxVXODQsIIyHCalpQEXiig6PrtJ6GkldcxtebD/H5ugOs33/Eddzf18bFPaMY2zeGi3pEaXTjJD5cuY9H5yVjsxk4nMd/xfvYDJxOk8evSmDCkA5urFBOxuE0WbU3h682HWJ+cjpHispcj3VpFcy4/u24ul9b2oS5aQTG6YCFj8IKa/kCCeNg7GsaERKP4xUh6Eymw/2a1gSJR9i9BGZPgpJ8aNEebvwUouPdXZXUM3u5ncKyQpr7Na+TJgimafLL0V9c09lSslPYkrOFgrKCKucG+wXTK7xXpU5tbZu3bZRraPbnFPHlpoPM23iIXZmFruMhAb6Mjm/NZQmtOb9bpAIR1gjQ+GkrTjNhEwxgzp1DNSLkAZxOk40HjvDVpkN8s/kXMguOf8jbKiSAMYltGNs3hr6xLTzn/+11H8A391pts2POsda7hrR2d1UiLl4RgsBqjDBo0CBeeeUVwGqM0L59e+655x41RhDvsOZd+N9frK457c+19koI9o7WyOJeWUVZVtg51qltS84Wcu25Vc4L8AmgZ3hP4iPiXaGnY2jHaltTNzamabL1lwK+2HiQLzcd4pcT1g81D/BlRM8oLktozUU9ohpumpCHuePDtSzamllpBOjXfGwGo+KimTahfwNWJhXKHU5W783l25R0FqRkkJ5//O9xWJAflye25je9YxjcOQIfm4cEn1/buxxm3wzFhyG0rRWE2vRxd1UigBeFoFmzZjFp0iTefPNNBg0axEsvvcTs2bPZtm0b0dHVT+NQCBK3cTpgwcOw8nXrfp/fwm9eAt/666gl3uuI/UilwJOSk0JmUWaV805sTR0fEU98pNWa2s+mdTAncjpN1qTmMj85nW+T0yu9kQz0szG8RxSXxLfmoh6taNHM342VNhx7mYO4R7/l/9u78/Aoy3v/4++Zyb6SyYZIwg5ZqOKCoFYLioK7qIALIop0Q6tHe1q3ansO/jytdBMVtVoKgoKKioqiVGzVGhSLViELJGwRSAhJyEqSycz8/nhIIGSbwGSemczndV258szkyfAdHIfnM/d9f+8u8k8rqwVy/2eKRs98pMHh5NNtB1i7pYS/55W2meoWHWZjUlYqV546gPNGJAfOBsLlRcYU8ANbITTq8BTwK8yuSiRwQhDAk08+2bpZ6pgxY3jiiScYN26cR7+rECSmaKyBVbfD1rXG7Qt+Befdq0WiAkBNUw155XltAs+e2j3tzrNarAyNH0pWYpbPWlP3RS1TitZuLuHdb/e1aR1stcCZg+xckJnCpMwUhiXH+M+0Ii8rq2lk7KN/9/j8jQ9OIjlWr7XeUlLVwEcF+/kofz+fFh6gvulIIxV7dBgXZaYyeXQq5wwL4Kmchw4azYCK1hu3J/0azr1b/xaKqQIqBJ0IhSDxGcchI/w0Vhvrf0o3Q0gETH3GaIMtQelQ8yHyK/Lb7MWzs3pnh+cOihtkBJ7DzQsy7ZlEhap1uje53W627K3mvc37+DBvP/klbddTpdujuDAzhYmjUjhriD1wLz47oJEgczldbr4urmR9/n4+yi8jd191m5+fFB/B5Oz+TBndnzMHJRBiC5ARn+44m+H9B+CLZ43bp99ibKBq0+i1mEMhSMRbduVAzlNQsAaO3nMlMgFuWgUDNa8+WLTsxdMaeMq3UHSwqMO9eAZEDyA7Kbt1lCfTnkl8eC/u5SEdKq6o56OC/XyYt5+conKanEf+W4WHWDlriJ3vD0/ivBHJZPSPxeqvazA8pDVBvlVcUc+nhQf4tPAAnxUeoPKoaW4WC5w6sB8XZKRwQUYK2QPi+uwoJACfPwtr7zP+nRx2AUxbAhG6LhPfUwgS8YaNz8Oan4PVZnTCOdZlf4Cxc3xfl/Q6h8vB9oPb20xp62wvnqTIJEYnjiYryRjlyUrMIjEy0YSqpSt1jc18WniAD/NK+XjrgTbriACSYsL4/vAkzh2exPihiQxMiAy4i1Z1h+tdFXVNfFZ0gH8VlvOvwgPsrqhv8/O4iBDOH5nMBRkp/GBkMokxQTbdsOA9eO02cNRDSjbc9ArEDzS7KgkyCkEiJ2pXDiy+BLq7nLhtLaSP91VV0gucLic7q3e2tqXeXL6ZgooCGp2N7c7tF96vzT48o5NGkxKVYkLVciLcbjdFZbV8vNX4FH/D9vI2azYABsRHMG5oImcNsTNuiJ0hSdEBEYqWbdjFr7RPkFeUVDXwxc4KNu6oYOPOCgpKazj6qinEauG09H6cezg8n5bWr+9Mcztee7+Cl2ZAbSnE9IcbV8AA7+6fJtIVhSCRE7ViJhS8a7S+7ow1BEZdarTFloDgcrsorilusxdPXkVeh5uPxobGkpWU1Rp4spOyGRA9ICAuhKVnmppdbNpdySfbysgpKueb76poPmZKWXJsOGcNtnNaej9OS08ge0Cc366p+XJnBc9/uoMPtpTgchtrgC7O7s/t3x+iEaBOOF1utpfV8u9dlUbw2VlBcUX794VRqbGcOzyJ749I5KwhicSEh5hQrZ87WAwvTYf9uUbnuOv+CqMuMbsqCRIKQSInwnEIHj2JrkeBDrNY4YG92jXbD7ndbvbU7mltTZ17ILfTzUcjQyLJtGe2jvKMThpNWmxa0O3FI4b6pma+2n2Qz7eXs2FHBV8XH6Spue3ar1CbhayT4jgtPYHT0vsxJq0f6fYovwrJDQ4nNQ3NxEaE+G1gM0tJVQNfF1fydXEV/yk+yLd7qqhtbDvd1WqBrAFxjB1s56zBds4cbFdHPU81VBlNhLZ/ZPw7ecnv4Ky5ZlclQUAhSORElBXAU2d5fv7Pt0GMpkSZye12U1pf2jq6k1uey5byLRxsPNju3DBrmLH56FHT2obED8Fm1UWidKzB4eQ/xQf5clclX+0+yNfFlRyobWp3XmxECFknxZE9IJ7sAXFknxzHsOQYQoN9ipSJXC43uyvqyd1XTd7hr2/3VFFa3X66a2SojVMGxjN2sJ2xQ+ycnt6P2Ah1OTtuTgesuQc2LTVuf/+/4MJH1EJbepVCkMjxqtgBy66FiiLPztdIkCnK6staR3i2HDC+VzRUtDsvxBrCyISRbaa0afNROVFut5vvKg+xaXdLKDpI7t7qNt3nWoSFWMnoH8uIlFhGpMYwIiWGESmxDEyIDPhudP7E7XZTVtNIYVktRftryS+pIW9fNQUlNdQ1tZ/WbLNaGJkay5i0foxJi+fUtH4MT47Rmh5vc7vh4wXw0Xzj9inXw5ULISQ4NjEW31MIEjkee7+C5dOgrgxCIsHZpDVBfqCioaI16LRMa9t/aH+782wWG8P7DW8d4clKzGJkwkjCbPrHVnpfU7OLwv21bNlbxZa91eTurSZ3X3W7KVYtIkKtDE+JYXhyDIOTohmUGMWgxGgG2aOwR4d5dVpdX5oWV9vYTHFFPbsr6tleVkdRWS2F+2spKqulpqHjv+uWIJrZP47Mk2LJPtkYqYsK03oen/lqGbz1M+Pf1KETYPqLaqEtvUIhSKSnitbDypuhqRb6fw9+cB+snIm6w/lWVWOVEXTKjfU7mw9sZl/dvnbnWS1WhsQNOTKlLSmbUQmjiAiJMKFqkY61TMXK21fNtv21xldpDdvL6jocNWoREx5Cuj2KwUlRnNwvkv7xkZwUH0H/+AgGxEeSHBuOzYNRpI07K3j+k+2syy1tbZBwUVYqc88b6pcNElwuN+V1TZRWN1Ba3UBJdQPfVR5id0U93x0OPkfvxXMsq8XYEHdYcgzDU2PIOimOrJPiGJIUrREef7BtnbFOyFFn/Dt702sQ29/sqqSPUQgS6YlvXoU3fwIuBwz5AcxYZnxCtfEFWHNv+32CrCHgchq7YmufoONW01RDXnlem2lt39V+1+G5g+MGt1nDk2HPICo0yscVi3hHs9NFceUhtpXWUFhWy64D9eyqqGNXeT37qhq6/X2b1UJKbDjJseEkRodhjw4nKSYMe7TxlRQTTs72cp77eDtWCxzd6M5XrbLdbjeNzS7qm5zUNjRTWd9ERX0TlXVNVNQ1UVnfRGW9g4raJkprGiitamB/TWO7rnwdsUeHkZYQyaDEaGM0LSWGYckxDEqMCviRrj5vzyajc1xdGcSnw8xVkDzS7KqkD1EIEvHUZ0/CBw8ax6OvhasXQchR3X92b4CcpyD/HWMnbIsVMi6Hs+dpBKgH6hx15JXntTYsyC3PZWf1zg7PTYtNaw07WYlZZCZmEhsW69uCRUzS4HDyXWU9u8rrD4eiQ+ytaqCk5au6oc3+PydiVGosiTFhhIdYCQuxEh5iI8RmwYIFi8XYWNX4fmTUyeF00eR04XC6cDjdxu1m1+HA00xdo5PaxmbqGps9CjTHslggKSac1LhwUmMjGJgQSZo9ijR7FOmHv6stdYCr2H547e12iEyAG1ZC+jizq5I+QiFIpDsuF/z9YfhsoXF7/E/h4kfB2smUCcchaKyB8Fg1QehGvaOegsqCNl3adlTtwN3B1MKTY04mKzGrdUpbpj2T+PB4E6oWCQxOl5sDtY3sq2rgQE0j5XWNlNc1UVHbRHmd8fXV7spO18eYITLURkJUKAmHR6oSoo58T4gOJSU2nNS4CFLjIkiODVc3vWBQd8AYEdrzb2MN7vQlMHKy2VVJH6AQJNIVpwNWz4NvVhq3J/0Gzr1LbTuPQ0NzQ2vgaRnh2V61HZe7/XqH/tH9j3RpS8wmMzGThIgEE6oW6bsaHE6yHl6LJ4MwFmDBtFNxHZ661tRsjPC4MZp6uXFz7FVCmM1KqM1CaIiVUJv18G3jvujwEKLDQ4gJt7UeR4eFeLR+SYJQU52xRqhwHVhsxkyMU2eYXZUEOE+zgcaTJfg01RkNEIo+NN50r3oKxtxgdlUBodHZyNaKra2jO1vKt1B0sAhnBx30UiJTyErKah3lyUrMIikyyYSqRYJLTUOzRwEIjLYv549M1gagYo6waLjh5SMfSr7xQ6gvh7N/anZlEgQUgiS41FfASzPguy8gNAqmL4URF5ldlV9qcjaxrXJb6+jOlvItFFYW0uxuP8XGHmFvDTqjk0aTlZhFSpQ2jxUxQ2xESLtmCJ2xWozz5QhvtBPvSy3Je50tFK5+BqISYcPT8P79RtOECx/W7AzpVXrnk+BRUwIvToX9uRDRz2jNmTbW7Kr8gsPpYNvBowLPgS1sO7iNZlf7wJMQnmCM8NizWru1pUalenVPExE5fhGhNi7KSuXvefu7bKBgs1q4KCtVF+mHeaOdeKC1JPcbVitM/n9GEFr/v/DpH4wRocv/aHRnFekFWhMkwaFiOyy9Gg7ugpj+cPMbkJpldlWmcLgcFB0satO0YGvlVhyu9ntvxIfHt47wtHw/KfokBR4RP7dxZwXTn8npbpczXv3x2bo4B17csIuH39yM1WppExx70k7cG48hwJeLYc09RjfWzCvgmuchVPu/iefUGEGkRekWYwSothQShsCsNyFhsNlV+YTD5WD7we1tNh8tqCigydXU7tzYsNjWoNMyrW1A9AAFHpEAtWzDLn6li/JueSMwKnR6We5qWHU7OJtg8HnGuqFwbZMgnlFjBBGA4i9g+XXQUAUp2XDz6312d+pmVzNFB4taR3fyyvPIr8jvOPCExpKZmEl20pFRnoExAxV4RPqQmeMHkdE/luc/3cEHW0raTM+6/ftDdDF+2POfbG8XFI9ltVp4/tMdnf6deeMx5ChZVxnT1lfcCDs/MWZy3PQqROnvTrxHIUj6rsIPYeVMcNTDwLPgpleMTdn6gGZXM9urtreu38mtMEZ4Gp2N7c6NCY1pHd1pGelJi01T4BEJAmcOtnPmYLsW6neiweFsXb/TFafLzQdbSmhwONv9/XnjMaQDQ38As96CZdfAni9hyRXGVPYYNd0R71AIkr4p9y147TZwOWDYhTDjRaMVZwA6OvAcPaWtwdnQ7tzo0Ggj8NgPh56kbNJi07BatPGgSDCLCLXpwrsDPWkn7nIb5x/79+iNx5BODDwDbn0PXrwaSjfDX6fArNXQL83syqQPUAiSvuc/K+DNnxiLKrOnwtTnICTM7Ko80uxqZkfVjtYpbd0Fngx7RpvGBelx6Qo8IiIe8kY7cbUk72WpWUYQWno1VBQdCUJJw82uTAKc/k+UvuXLxfDOfwFuGDMTrnzCb9trOl3OdiM8+RX5HQaeqJAoYw3PUY0LBsUNUuARETkB3mgnrpbkPpA4DG47HITKt8HiKXDzm9B/tNmVSQBTCJK+I+cpeP8B4/isH8KU3xp7D/gBp8tpjPBU5La2pi6oLOBQ86F257YEnpawk5WYxeC4wQo8IiK94PbzhvLBltIuz3G53Nz+/SG9+hjSjfiBxojQsqlQ8i387VKY+ToMPNPsyiRAqUW2BD63Gz5eAB/NN26fexdM+o1pO00fPaWtdQ1PF4Enw57Run5HgUdExPe80U5cLcl95NBBWD4NvvsCwmLgxldg8LlmVyV+RPsESXBwu+HD38CnfzRuT3wQzv9vnwWgnjQtiAyJJNN+ZIQnOymbQbGDsPnpdD0RkWDy5c6Kdu3EL87u36N24t54DPFAY63RPnvHPyEk0thHaNhEs6sSP6EQJH2fywVr74MvnjVuX/wonHNHr/1xPQk8R4/wtDQtGBSnwCMi4u+80U5cLcl9wHEIVt4MhevAFg4zlsHIi82uSvyAQpAErIbmBmodtcSExhAREtHxSS4XvHM3bFoCWODyP8CZt3mtBofLwfaDRwWeily2VmztsmlBT9fwePQ8RUREpGPNjfDqrVCwBqyhMH0JZFxmdlViMoUgCTibSjexNHcpHxV/hMvtwmqxMjFtIrdk38JpKacdOdHlhLfuhK+Xg8UKVz0NY2447j/X4XJQdLCozQjP1sqtHW482tKW+uiNR3vapc3j5ykiIiJdczpg1e2Q+yZYQ+Cav8Doa8yuSkykECQnzJcjFSvzV/Lo549itVhxup2t99ssNlxuFw+Nf4jpo6aDsxlW/xS+WQkWG1zzHHzvOo//HIfTQeHBwnaBp8nV1O7cmNAYY4THfmSE50T34fH4eYqIiIhn2lwbWOHqZ+DUGWZXJSZRCJLj5uuRik2lm5i9djZuOn8pWrCwZPILnPbpIti8yvi059oXIPvqTn+nydnEtoPb2gSebZXbcLgc7c6NDY1tN6UtLTbNq13aPH6elyzRiJCIiEhPuJzw9s/gq2WABa74M5xxi9lViQk8zQbaJ0jaOHqkwuV2AeByu/hH8T9Yv3t9r4xULM1d2m5k5FhhWLC+djsc2GnM+532N8i8vPXnjc5GtlUeE3gObqPZ1dzusWLDYtuM7mQlZjEwdmCvt6X25HlaLVaWblmqECQiItITVhtcsdBokvDlC0YgcrvgzFvNrkz8lEKQtNpUuolHP38UN+52F+ott+dvmM+IhBFeu0hvaG5oHXHqTIjbzW/3l3Jq/SHctjAar3uBrUnp5OavaA08RQeLaHa3DzxxYXFtwk5WYhYDYwZi8fEeQp48TzD+ntcXr6ehuUHNEkRERHrCaoXLfg+2MPh8kdFACbdXGydJ36EQJK3MGKmoddR2G4DuqDxISYiN+5Pt5CUPZefGhzussV94vzZhJ9OeyckxJ/s88HSku+d5NJfbRa2jViFIRESkpywWmPKYsTZow1Pwzn8ZewqOnWN2ZeJnFIIEMG+kIiY0ps3UuzbcbpzAn+wJR+47tB8Ae4S9Nei0dGnrH93fLwJPR7p8nsewWqzEhMb4oCoREZE+yGKByY8a33OehDX3GPcrCMlRFIIE8P1IRU1TDfkV+eSW55IcmUxpfWn7kywW3EBisxOnxUJCwjDuPuNushKzSI1K9dvA05GIkAgmpk3kH8X/6HKkzWaxMTFtokaBREREToTFAhfPN45bg5Abxt5ualniPxSCBOjdkYqqxipyy3PJq8gjrzyP3PJcdtfs7vR8i9vNlLp6LqutY5jDwa+TEvkiMpInzv1NQDcMmJU1i/W713d5jsvtYlb2LB9VJCIi0oe1C0L3GlPjzpprbl3iFxSCBPDeSEVlQ2Vr4GlpWrCndk+H5w6IHtDalvpA/QFeLniZcKz8dn8pF9YfosFi4a7UVL6IDOeh8Q8FdAACOD31dB4a/xDzN8zvcp+gQH+eIiIifqMlCFks8NlCePfnxv0KQkFPIagX+XKzUW/o6UjFgUMHWoNOXnkeeRV57Kvb1+HvDYwZeGQfHnsWmYmZJEQktDnnkkEXYV01h1PrD9FogbtTU4gadSlLsmf1mWAwfdR0RiSMYOmWpawvXt9mH6ZZfeh5ioiI+A2LBS76X8ACnz1hBCGLVWuEgpw2S+0Fvt5s1JteKXiF+RvmY7FYOpwad2ryqcSHx5NXnkfZobIOH2Nw3GAy7UbgyUzMJMOeQXx4fNd/sNMBq+ZA7mrctjCqr3mG8FGXBUR4PF6BFpJFREQCmtsN6x42ghDAlU/C6Td757Edh6CxBsJjITTSO48px8XTbKAQ5GVHbzba2XQnb2826k1ut5tff/ZrXi98vdtzLVgYEj+ktUtbVmIWGfYMYsJ62NnM2Qyv3w5b3jB6+89YDiMvPvJzvbGIiIiIN7jd8P4DsOFpwAJTn4FTrz/+x9uVAzlPQcEaY3NWixVGXQbn3AHp471WtnhOIcgEm0o3MXvtbNx0/ldqwcKSS5b4xYiQy+1id/XuIw0LKnLZfGAzdY66bn/3V+N/xeVDLycqNOoEi3DC6z+Eza+BNRRmLINRU4yfncAbi0ZZREREpENutzElbuPzxrXFNX+B713X88fZ+Dys+TlYbeA6asN2a4hxfXPZ7zXlzgSeZgOtCfIiMzYb9ZTT5WRn9c4ja3gq8sivyPco8BzLZrGRszfHsxGtrkZxXC54687DASgEpi89EoCOfmNpmZbndsHW9yD/nU7fWAJ5KqKIiIj4gMUClzwOzibYtNT4MNYWCllXef4Yu3KM6xTcbQMQHLm95l5IzdaIkJ/SSJCXNDQ3MO6lcR63mP78xs97bYTC4XJQdLCotR11XkUeBRUFNDgb2p0bbgtnVMIoMhMzGdFvBI9+/miXI1ktun0O3Y3iuN3Gm8OXL4DFBtMWH3nz2ZUDiy+BLuuwwG1r27yxBPpURBEREfEhlwtWz4P/vHT4w9gXIeNSz353xUzjg9ljA9DRrCEw6lKY8aJ36hWPaCTIx3y92WiLRmcj2yq3tdmHZ2vlVhwuR7tzI0MiybRnkpmY2bqGZ0j8EEKsxsvgwKEDzP98/ok/h+5GcS5dABXbjQCEBaY+2/bTl5yn2g8tH8tqM847HII2lW5qDXDHjsS13J6/YT4jEkZoREhERETAaoWrngSXA759FV6ZBde/1HZdckcch458yNsVV7Nx3eM4pDXNfkghyEt6c7PRFvWOerZWbm0TeIoOFtHsbh8WYkNjyUjMaG1HnZmYyaDYQdistt59Dp4MD79775H7rlwIp0w7cvs431j8eSqiiIiI+CmrDa5+xpgal7saVs6Em16BoRM6/53Gmu6vU1q4Xcb5CkF+RyHIS7y12WiL6qZqCioK2gSeHVU7OpyqlhCe0Dq607IXz8CYgVgsFt8/B09GcVpcuqB9a8rjeGNpsFha1wB1xel2sr54PQ3NDWqWICIiIgZbCFz7gtHMIP8dePkGuPmNztfyhMca0/w9uV6xWI3zxe8oBHlRTzcbbVHRUEF+eT65FUc2HS2uKe7w91MiU1pHdjLtxlf/6P49Djzefg6A56M4AFjgtJnt7z6ONxazpiKKiIhIH2ELhev+agSgog9h+TS45W0YMKb9uaGRxjpnT9cEaRTILykEedHpqafz0PiHmL9hfoeL851uJz87/WdUNVax6OtFxghPRR4ldSUdPt7JMSeTaTc2G23ZeDQpMsnU59DSYKDDKWU9GcXB3fHw8HG8scRYLL0+FVFERET6uJBwY6uO5dfBrn/Bi1Ph1nchJbP9uWfPM0aNuuJyGueJX1II8rLpo6YzImEESzYvYX3x+tbpa/3C++FwOfjzpj93+HuD4ga1Wb+Tac8kPjzel6W3ankOS7csZX3x+jatpmdlz+p8TY23hod7+Mbi7amIIiIiEqTCouCGFbD0Kti7yfh+63uQOKzteYPONrbrWHNv1/sEqT2231KLbC9pdDaybtc68sqN/XfyyvOocdS0O89msTG039DW7mwZ9gxGJYwiJsw/Ryd6vOmot1pGbnyh+zeWo/YJCrSNakVERMSP1VfA3y6H/VsgPs0IQv3S2p+3e4OxHjr/nSNbgmRcbnxQqwBkCk+zgUKQlzQ6Gxm/fHybTm2h1lBGJIxoE3hGJozs2yMRx7nHT4d6+MbySsEr3U7j0z5BIiIi4pHa/cY1TXkh2IcZQSg2teNzu9ocXnxKIcgED376INGh0a2hZ2j8UEJtoabWZIqNL8Cae9rf38koTrd68Mby1f6v2k3juyDtgq6n8YmIiIh0pOo7+OslULUbUrJg9hqIsptdlXRBIUjMs/dr45MTR/2R+3w8PNzjaXwiIiIiHanYbgSh2hI4+QyY9RaE++cyBvE8G6gxgnhXWQEsu8YIQOnnwPXLjNEfHw8PR4REKPyIiIjIibMPhVmrYfEU2PNvWHEj3PSq0U1OApbV7AKkD6ncBUuvhvpyOGkM3LgSohIhJkXzY0VERCRwpWTATasgNBp2/BNWzQGnBxvDi99SCBLvqCk12kjW7IXkDJj5OkRoeqKIiIj0EQPPgBteAlsY5L0N79wFgb2qJKgpBMmJa6iCZddC5Q7oNwhufgOiE82uSkRERMS7hk6A6/5qrHX+ahl88JCCUIBSCJIT4zgEL98Apd9CdIoRgOIGmF2ViIiISO/IvAKuXGgc5zwJn/7B3HrkuCgEyfFzNsNrt8Guf0F4HMxc1X5HZREREZG+5rSZcPGjxvGH/2NsDyIBRSFIjo/bDW/fBQXvgi0cbngZTjrF7KpEREREfOOcO+C8e43jNffC5lXm1iM9ohAkx2fdw/D1MmNO7LTFMPj7ZlckIiIi4lsX/ArOvA1ww+s/gqKPzK5IPKQQJD33rz/DZ08Yx1cuhIzLzK1HRERExAwWC1y6ALKuApcDVs40No0Xv6cQJD3z1TJjFAjgov8x5sSKiIiIBCurDa75Cww+D5pqYfl1UF5kdlXSDYUg8VzBWnjrZ8bxOT+Dc+8ytx4RERERfxASDtcvh/7fg7oyWHaNsYei+C2FIPFM8UZ4dTa4nTDmJmMUSEREREQMEfFw0ypjz8TKnbD8WmioNrsq6YRCkHTvwDZ4aTo0H4LhF8EVfzbmwIqIiIjIEbGpxp6JUUlQ8i2suBGaG82uSjqgECTGhqe1+43vx6opgRevgUMVMOB0mL4EbKG+r1FEREQkECQOg5mvQVgM7PwEXp8LLqfZVckxFIKC2a4cWDET/t8AWDDC+L5iJuzeYPy8oQqWXQdVu8E+FG56FcKiza1ZRERExN8NOA1mLANrKOSuhrX3GXssit9QCApWG5+HxZfA1vfA7TLuc7uM23+dAhueNdo8ln4L0Skw83WITjK3ZhEREZFAMWwiXPOscfzFc0e2FxG/EGJ2AWKCXTmw5ueAG1zNbX/WcnvtL4zvYTHGCJB9iE9LFBEREQl4o681lha8/4CxxUjsADhlmtlVCRoJCk45Txk97btlgRkvwoAxvV2RiIiISN909jwY/1Pj+M2fwI6Pza1HAIWg4OM4BAVr2o8AdSb97N6tR0RERKSvu/hRyLoKXA5YcROUbjG7oqCnEBRsGmuOrAHqlts4X0RERESOn9UKU58zPlxurD7ceGqP2VUFNYWgYBMeCxYP/7NbrMb5IiIiInJiQiPg+pcgaRTU7IXl1xmdeMUUCkHBJjQSRl0G1m56YlhDIONy43wREREROXFRdmMPoZhU2J9rTI1rbjK7qqCkEBSMzp7X/aZdLqdxnoiIiIh4T7/0w3svHt5MdfVPtYeQCRSCgtGgs+Gy33f8M2sIYDF+nj7ep2WJiIiIBIWTToXpS43rrm9fhY8eNbuioKMQFKzOmA2Dzm17n8UKoy6F29bC2DmmlCUiIiISFIZfCJf/yTj++HH4apmp5QQbbZYarN5/EHb9C2xhcOMrkJptNEHQGiARERER3zj9ZqjcAZ/8Ht6+C+IHwtAJZlcVFHplJGjnzp3MmTOHIUOGEBkZybBhw3jkkUdoamq78Oubb77hvPPOIyIigrS0NH73u9/1RjlyrA3PwOeLjOOpz8CwiRCTogAkIiIi4msTH4LR1xp7OK6cBfvzza4oKPTKSFB+fj4ul4tnn32W4cOHs3nzZubOnUtdXR0LFiwAoLq6mosvvphJkybxzDPP8O2333LbbbfRr18/fvjDH/ZGWQJQsBbev984nvQb4386ERERETGH1QpXPQ3Ve2F3DiyfBrf/HWJTza6sT7O43b5pR/H444+zaNEitm/fDsCiRYt48MEHKSkpISwsDID77ruPN998k/x8zxNwdXU18fHxVFVVERcX1yu19xn7/gN/vQQcdXD6LLjiCbBYzK5KREREROor4PlJUFEEA06H2WsgLMrsqgKOp9nAZ40RqqqqsNvtrbdzcnI4//zzWwMQwOTJkykoKKCysrLTx2lsbKS6urrNl3igei+8NMMIQEMnwGV/UAASERER8RdRdqN1dqQd9m6C1+d2v6WJHDefhKDCwkIWLlzIj370o9b7SkpKSE1tO8zXcrukpKTTx3rssceIj49v/UpLS+udovuSxlojANXsg+QMmLYEbKFmVyUiIiIiR0scBje8DLZwyH8HPviV2RX1WT0KQffddx8Wi6XLr2Onsu3Zs4cpU6Ywbdo05s6de8IF33///VRVVbV+FRcXn/Bj9mkuJ6y6HUq+gagkuHElRPYzuyoRERER6Uj6eJh6uIHVhqdg4wvm1tNH9agxwr333svs2bO7PGfo0KGtx3v37mXixImcc845PPfcc23O69+/P6WlpW3ua7ndv3//Th8/PDyc8PDwnpQd3D54CLa+ByERcMMKSBhsdkUiIiIi0pXR10LFDlj/v/Duf4N9qNHNV7ymRyEoOTmZ5ORkj87ds2cPEydO5IwzzmDx4sVYrW0Hnc4++2wefPBBHA4HoaHG1Kx169YxatQoEhISelKWdOaLv8CGp43jqc9A2lhz6xERERERz5x3LxzYBt+sgFduMTrGJY80u6o+o1fWBO3Zs4cJEyaQnp7OggULKCsro6SkpM1anxtvvJGwsDDmzJnDli1bWLlyJX/+85+55557eqOk4LPt7/DeL4zjCx+G7Knm1iMiIiIinrNY4MonIG08NFbBS9OhrtzsqvqMXtknaN26dRQWFlJYWMjAgQPb/KylI3d8fDwffPAB8+bN44wzziApKYmHH35YewR5w/58eO1WcLtgzEz4voKliIiISMAJCYfrl8PzF0LlDlg5E2a9adwvJ8Rn+wT1Fu0TdIy6cnj+AqjcCYPOhZvfhJCw7n5LRERERPxVWQE8f5ExInTqjXD109rqpBN+t0+Q+EBzE7xysxGAEgbD9BcVgEREREQCXfIomLYYLDb4z0vw6R/NrijgKQT1FW43rPkv2PUvCI+DG1ZCdKLZVYmIiIiINwy/EC79nXH84W8g9y1z6wlwCkF9Rc6T8NUysFjhusWQkmF2RSIiIiLiTWNvh3E/No5f/yHs/drUcgKZQlBfULD2yI7Ckx+DEZPMrUdEREREesfFj8Lwi6D5EKy4EWpKu/8daUchKNCVboFVcwA3nHErjPuR2RWJiIiISG+xhcB1L0DSSKjeAytvAkeD2VUFHIWgQFZbBi9dD021MOR8uPRxdQoRERER6esi4uGGFRDRD77bCO/cbawPF48pBAWqlk5wVbvBPhSmLQFbqNlViYiIiIgvJA6D6UsOd4x7GT57wuyKAopCUCBxHILa/dBUD+/+HHbnHOkEF2U3uzoRERER8YWWa8K0cXDJb4371j0CW983t64AEmJ2AeKBXTmQ8xQUrAG3C7AAbuP7tS9A8kiTCxQRERGRXnfsNaHFCiMvhVGXQsG78NocuH0dpGSaXanfUwjydxufhzU/B6vtcAACIwAd/l5VbFZlIiIiIuIrHV0Tul2wbS24msE+DCqK4OXrYe5HmiXUDU2H82e7cowXO27jxd2RNffC7g0+LUtEREREfKira8KW2xVFENMfKnfCK7PA6fB1lQFFIcif5TxlpP2uWG3GeSIiIiLSN3l0TRgCKRkQFgM7P4H3H/BNbQFKIchfOQ4Z8z07GwFq4WqG/HeM80VERESkb+nJNeGOj+Gqwx+Of/EcbHqx9+sLUApB/qqx5qg1QN1wu4zzRURERKRv6ek14aBzYMLhUaA190Dxxt6rLYApBPmr8Fij44cnLFbjfBERERHpW47nmvD8/4aMy8HZBCtnQvW+3q0xACkE+avQSBh0XvfnWUOMF3loZO/XJCIiIiK+FRoJoy4zrvm6cvQ1odUKU5+B5EyoLTE6xh066JNyA4VCkL+qr4Dyrd2f53LC2fN6vx4RERERMcfZ84xrvq4ce01YshliTzKO930Nvx0EL9+krsKHKQT5I5cTVs2Bmn0QebjH+7Hp3xoCWOCy30P6eJ+XKCIiIiI+Muhs45oPi2fXhBufh8WXwM6P25679V346xTY+IIvqvZr2izVH63/XyhaDyGRcMtb0FRntEbMf+fI7sCjLjXSvgKQiIiISN83dg6kZnd/TdjVnkItDRbW3Gs8VhBfRyoE+Zstb8CnfzSOr3oS+n/POE4fb7RIbKwxFrxpDZCIiIhIcEkf3/01YcueQl211LZYjfMUgsQvlG6BN39qHJ9zJ3zvurY/D41U+BEREREJdp1dE7bsKdRdS223E/LeNs4P0mtLrQnyF/UVsOJGcNTD0Alw4a/NrkhEREREAklP9hTCDQ3VvVqOP1MI8gctjRAqd0K/QXDdYrBpkE5EREREeqAnewqBsQwjSCkE+YOjGyFcvxyi7GZXJCIiIiKBxtM9hVqC0gcPQfEXvV+XH1IIMlve2x03QhARERER6SlP9hRyu2DweeBywMqboabUN7X5EYUgM5VthTd+YhyffUf7RggiIiIiIj3h0Z5Cf4AbVkByBtSWwKu3QHOTGdWaRiHILI21sHImNNXAoHNh0q/NrkhERERE+oKxc+C2tcYeQi1T31r2FLptrfHz8BiYsRzC42B3jjE1Loho9b0Z3G5YPQ8OFEDsSTDtb2ALNbsqEREREekrPNlTKGk4TH0WVtwAXzwLJ58Op15vTr0+ppEgM+Q8BblvgjUUpi2BmBSzKxIRERGRvig00rjW7Gw/oIxL4Qe/NI7fvgv2/cd3tZlIIcjXdnwC6x42jqc8BunjzK1HRERERILbD+6DERdDc4PRKOFQpdkV9TqFIF+q2gOv3Wrs0nvK9TD2drMrEhEREZFgZ7XCNc9Bv3Q4uAve+DG4PN10NTApBPlKc5PReaOuDFK/B5f/ESwWs6sSEREREYHIBJj+ItjCYeta+PQPZlfUqxSCfOX9B+C7jRARDzOWQliU2RWJiIiIiBwxYAxctsA4/uhRKPrI1HJ6k0KQL3zzKmz8i3F8zV/APtTcekREREREOnL6LDjtZmND1VVzjOUcfZBCUG/bnw9v/8w4Pv+/YeRkc+sREREREenKpY9D/1OgvrzPbqSqENSbGmvhlZvBUQ9DfgAT7je7IhERERGRroVGwvSlxjKO7zbCBw+aXZHXKQT1FrfbGAE6sBViB8C1L4DVZnZVIiIiIiLdsw8xlnEAfPGcsbyjD1EI6i0bn4fNq8AaAtMWQ0yy2RWJiIiIiHhu5GRjOQcYH+7vzzO3Hi9SCOoN330Jaw9PfbvofyB9vLn1iIiIiIgcjwn3w9AJxvKOV2YZyz36AIUgb6uvgFduAZcDMq+E8T81uyIRERERkeNjtRnLOmIHGMs83rnbWPYR4BSCvMnlgtfnQvV3YB8GVz2lDVFFREREJLBFJxnLOyw2+PZV+Pdisys6YQpB3vTJAij8O4REwowXISLO7IpERERERE5c+niY9Gvj+L1fwt6vzazmhCkEeUvZVvjo/xnHl/8BUrPNrUdERERExJvOuRNGXgLOJmP/oEMHza7ouCkEeUvySLjmORj3Exhzo9nViIiIiIh4l8UCUxdBv3So3Amr5wXs+iCFIG86ZTpc8n9mVyEiIiIi0jsiE2Da38AWBvnvwIZFZld0XBSCRERERETEcyefAZMPLwNZ9yso3mhuPcdBIUhERERERHpm7O2QPRVczfDqbGObmACiECQiIiIiIj1jscAVTxjbwlR/B6//0NguJkAoBImIiIiISM9FxMH0JRAeDyMnB9T+mCFmFyAiIiIiIgGq//fg7m8gsp/ZlfSIRoJEREREROT4BVgAAoUgEREREREJMgpBIiIiIiISVBSCREREREQkqCgEiYiIiIhIUFEIEhERERGRoKIQJCIiIiIiQUUhSEREREREgopCkIiIiIiIBBWFIBERERERCSoKQSIiIiIiElQUgkREREREJKgoBImIiIiISFBRCBIRERERkaCiECQiIiIiIkFFIUhERERERIKKQpCIiIiIiAQVhSAREREREQkqCkEiIiIiIhJUFIJERERERCSoKASJiIiIiEhQCTG7gBPldrsBqK6uNrkSERERERExU0smaMkInQn4EFRTUwNAWlqayZWIiIiIiIg/qKmpIT4+vtOfW9zdxSQ/53K52Lt3L7GxsVgsFrPLkU5UV1eTlpZGcXExcXFxZpcjfk6vF+kpvWakp/SakZ7SayYwuN1uampqGDBgAFZr5yt/An4kyGq1MnDgQLPLEA/FxcXpjUM8pteL9JReM9JTes1IT+k14/+6GgFqocYIIiIiIiISVBSCREREREQkqCgEiU+Eh4fzyCOPEB4ebnYpEgD0epGe0mtGekqvGekpvWb6loBvjCAiIiIiItITGgkSEREREZGgohAkIiIiIiJBRSFIRERERESCikKQiIiIiIgEFYUgMU1jYyNjxozBYrHw9ddfm12O+KmdO3cyZ84chgwZQmRkJMOGDeORRx6hqanJ7NLEjzz11FMMHjyYiIgIxo0bxxdffGF2SeKnHnvsMcaOHUtsbCwpKSlcffXVFBQUmF2WBIj/+7//w2KxcPfdd5tdipwghSAxzS9+8QsGDBhgdhni5/Lz83G5XDz77LNs2bKFP/7xjzzzzDM88MADZpcmfmLlypXcc889PPLII2zatIlTTz2VyZMns3//frNLEz/0z3/+k3nz5rFhwwbWrVuHw+Hg4osvpq6uzuzSxM9t3LiRZ599llNOOcXsUsQL1CJbTPHee+9xzz33sGrVKrKzs/nqq68YM2aM2WVJgHj88cdZtGgR27dvN7sU8QPjxo1j7NixPPnkkwC4XC7S0tK48847ue+++0yuTvxdWVkZKSkp/POf/+T88883uxzxU7W1tZx++uk8/fTTzJ8/nzFjxvCnP/3J7LLkBGgkSHyutLSUuXPn8uKLLxIVFWV2ORKAqqqqsNvtZpchfqCpqYl///vfTJo0qfU+q9XKpEmTyMnJMbEyCRRVVVUAek+RLs2bN4/LLruszXuNBLYQswuQ4OJ2u5k9ezY//vGPOfPMM9m5c6fZJUmAKSwsZOHChSxYsMDsUsQPHDhwAKfTSWpqapv7U1NTyc/PN6kqCRQul4u7776bc889l9GjR5tdjvipFStWsGnTJjZu3Gh2KeJFGgkSr7jvvvuwWCxdfuXn57Nw4UJqamq4//77zS5ZTObpa+Zoe/bsYcqUKUybNo25c+eaVLmI9BXz5s1j8+bNrFixwuxSxE8VFxdz1113sXz5ciIiIswuR7xIa4LEK8rKyigvL+/ynKFDhzJ9+nTefvttLBZL6/1OpxObzcZNN93EkiVLertU8ROevmbCwsIA2Lt3LxMmTGD8+PH87W9/w2rVZzhiTIeLioritdde4+qrr269/5ZbbuHgwYOsXr3avOLEr91xxx2sXr2ajz/+mCFDhphdjvipN998k6lTp2Kz2VrvczqdWCwWrFYrjY2NbX4mgUMhSHxq9+7dVFdXt97eu3cvkydP5rXXXmPcuHEMHDjQxOrEX+3Zs4eJEydyxhlnsGzZMv2DI22MGzeOs846i4ULFwLGFKf09HTuuOMONUaQdtxuN3feeSdvvPEG//jHPxgxYoTZJYkfq6mpYdeuXW3uu/XWW8nIyOCXv/ylplEGMK0JEp9KT09vczsmJgaAYcOGKQBJh/bs2cOECRMYNGgQCxYsoKysrPVn/fv3N7Ey8Rf33HMPt9xyC2eeeSZnnXUWf/rTn6irq+PWW281uzTxQ/PmzeOll15i9erVxMbGUlJSAkB8fDyRkZEmVyf+JjY2tl3QiY6OJjExUQEowCkEiYhfW7duHYWFhRQWFrYLyhrIFoAZM2ZQVlbGww8/TElJCWPGjGHt2rXtmiWIACxatAiACRMmtLl/8eLFzJ492/cFiYgpNB1ORERERESCilYWi4iIiIhIUFEIEhERERGRoKIQJCIiIiIiQUUhSEREREREgopCkIiIiIiIBBWFIBERERERCSoKQSIiIiIiElQUgkREREREJKgoBImIiIiISFBRCBIRERERkaCiECQiIiIiIkFFIUhERERERILK/wdXwt88Qu4pGQAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "best_id = 4\n", + "model, clean_expression = reg.get_model(best_id)\n", + "print(\"Best solution :\\n f(X0) = \", clean_expression)\n", + "\n", + "\n", + "X_smooth = np.array([np.linspace(-5, 5, 100)]).T\n", + "solution = pareto[pareto['id']==best_id]\n", + "\n", + "plt.figure(figsize=(10, 7))\n", + "for idx in range(len(full_X)):\n", + "\n", + " params = np.array(solution.iloc[idx]['theta'].split(\";\"), dtype=float)\n", + " plt.scatter(full_X[idx], full_y[idx], label=f'View {idx+1}', s=50)\n", + " plt.plot(X_smooth, model(X_smooth, params))\n", + "\n", + "plt.legend(fontsize=16)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "336ffa9d-8f4d-47c3-a2e7-4faa11bb7c32", + "metadata": {}, + "source": [ + "Huray !! We rediscovered the original solution" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "new_mvsr_kern", + "language": "python", + "name": "new_mvsr_kern" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From cd3bdedaed24c36539d506573bf8add22207459e Mon Sep 17 00:00:00 2001 From: Etienne Russeil Date: Fri, 18 Apr 2025 17:17:06 +0200 Subject: [PATCH 061/166] Improved pareto plot --- test/mvsr_example.ipynb | 348 ++++++++++++++++++---------------------- 1 file changed, 157 insertions(+), 191 deletions(-) diff --git a/test/mvsr_example.ipynb b/test/mvsr_example.ipynb index ef5aeef..d55ba00 100644 --- a/test/mvsr_example.ipynb +++ b/test/mvsr_example.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "c68e3b1a-d6e4-4f94-ae46-d761d5c1c836", "metadata": {}, "outputs": [], @@ -23,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 2, "id": "2c9d5ad9-f2e4-4b96-accd-41d1edbfdc98", "metadata": {}, "outputs": [ @@ -69,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 3, "id": "3c318511-31dd-4cb2-8178-2ceb5c2e405c", "metadata": {}, "outputs": [ @@ -109,9 +109,9 @@ " 0\n", " 0\n", " 0\n", - " 21.58921727054863\n", + " 21.589217270548627\n", " t[0]\n", - " 21.58921727054863\n", + " 21.589217270548627\n", " 1\n", " 532.979453\n", " -532.979453\n", @@ -120,9 +120,9 @@ " 1\n", " 0\n", " 1\n", - " -2.930612339764856\n", + " -2.9306123397648802\n", " t[0]\n", - " -2.930612339764856\n", + " -2.9306123397648802\n", " 1\n", " 247.002956\n", " -532.979453\n", @@ -153,9 +153,9 @@ " 4\n", " 1\n", " 1\n", - " (-3.285686757996543 - x0)\n", + " (-3.285686757996549 - x0)\n", " (t[0] - x[:, 0])\n", - " -3.285686757996543\n", + " -3.285686757996549\n", " 3\n", " 255.691957\n", " -428.346651\n", @@ -164,9 +164,9 @@ " 5\n", " 1\n", " 2\n", - " (2.6105987644181643 - x0)\n", + " (2.610598764418156 - x0)\n", " (t[0] - x[:, 0])\n", - " 2.6105987644181643\n", + " 2.610598764418156\n", " 3\n", " 214.494045\n", " -428.346651\n", @@ -175,9 +175,9 @@ " 6\n", " 2\n", " 0\n", - " ((x0 * -5.265543575122937) + 21.303011895450872)\n", - " ((x[:, 0] * t[0]) + t[1])\n", - " -5.265543575122937;21.303011895450872\n", + " (21.303011895450766 + (x0 * -5.265543575122939))\n", + " (t[0] + (x[:, 0] * t[1]))\n", + " 21.303011895450766;-5.265543575122939\n", " 5\n", " 228.602451\n", " -246.886780\n", @@ -186,9 +186,9 @@ " 7\n", " 2\n", " 1\n", - " ((x0 * -0.10303084459825138) + -2.967195956970...\n", - " ((x[:, 0] * t[0]) + t[1])\n", - " -0.10303084459825138;-2.9671959569705537\n", + " (-2.9671959569705386 + (x0 * -0.10303084459829...\n", + " (t[0] + (x[:, 0] * t[1]))\n", + " -2.9671959569705386;-0.10303084459829029\n", " 5\n", " 246.886780\n", " -246.886780\n", @@ -197,9 +197,9 @@ " 8\n", " 2\n", " 2\n", - " ((x0 * 3.181877228219192) + 2.8472358446230825)\n", - " ((x[:, 0] * t[0]) + t[1])\n", - " 3.181877228219192;2.8472358446230825\n", + " (2.847235844618024 + (x0 * 3.181877228219146))\n", + " (t[0] + (x[:, 0] * t[1]))\n", + " 2.847235844618024;3.181877228219146\n", " 5\n", " 5.050724\n", " -246.886780\n", @@ -208,9 +208,9 @@ " 9\n", " 3\n", " 0\n", - " (((x0 + -2.5353345384705035) * 1.9710063071507...\n", - " (((x[:, 0] + t[0]) * t[1]) * x[:, 0])\n", - " -2.5353345384705035;1.971006307150714\n", + " (((x0 * 1.971001716619659) + -4.99718543452371...\n", + " (((x[:, 0] * t[0]) + t[1]) * x[:, 0])\n", + " 1.971001716619659;-4.997185434523716\n", " 7\n", " 6.567563\n", " -126.355850\n", @@ -219,9 +219,9 @@ " 10\n", " 3\n", " 1\n", - " (((x0 + 0.3703122177136077) * -0.8466294803417...\n", - " (((x[:, 0] + t[0]) * t[1]) * x[:, 0])\n", - " 0.3703122177136077;-0.8466294803417656\n", + " (((x0 * -0.8466296174450944) + -0.313553973387...\n", + " (((x[:, 0] * t[0]) + t[1]) * x[:, 0])\n", + " -0.8466296174450944;-0.31355397338761204\n", " 7\n", " 126.355850\n", " -126.355850\n", @@ -230,20 +230,20 @@ " 11\n", " 3\n", " 2\n", - " (((x0 + 17.14471689769305) * 0.192626434690429...\n", - " (((x[:, 0] + t[0]) * t[1]) * x[:, 0])\n", - " 17.14471689769305;0.19262643469042923\n", + " (((x0 * 0.19256491303772472) + 3.3024607194153...\n", + " (((x[:, 0] * t[0]) + t[1]) * x[:, 0])\n", + " 0.19256491303772472;3.302460719415394\n", " 7\n", - " 6.453166\n", + " 6.453165\n", " -126.355850\n", " \n", " \n", " 12\n", " 4\n", " 0\n", - " ((2.6451239766035792 - x0) * (-0.4005564286578...\n", - " ((t[0] - x[:, 0]) * (t[1] - (x[:, 0] * t[2])))\n", - " 2.6451239766035792;-0.4005564286578119;2.03783...\n", + " (-1.0590288970595494 + (x0 * ((x0 * 2.03779578...\n", + " (t[0] + (x[:, 0] * ((x[:, 0] * t[1]) + t[2])))\n", + " -1.0590288970595494;2.037795780991407;-4.98985...\n", " 9\n", " 6.222296\n", " -6.222295\n", @@ -252,9 +252,9 @@ " 13\n", " 4\n", " 1\n", - " ((3.0711878077868717 - x0) * (6.35090000814782...\n", - " ((t[0] - x[:, 0]) * (t[1] - (x[:, 0] * t[2])))\n", - " 3.0711878077868717;6.350900008147822;-2.030341...\n", + " (19.504988286223227 + (x0 * ((x0 * -2.03035739...\n", + " (t[0] + (x[:, 0] * ((x[:, 0] * t[1]) + t[2])))\n", + " 19.504988286223227;-2.030357391771257;-0.11535...\n", " 9\n", " 2.880427\n", " -6.222295\n", @@ -263,111 +263,78 @@ " 14\n", " 4\n", " 2\n", - " ((-0.8161887112562356 - x0) * (-3.178090433000...\n", - " ((t[0] - x[:, 0]) * (t[1] - (x[:, 0] * t[2])))\n", - " -0.8161887112562356;-3.178090433000606;2.12097...\n", + " (2.59375634458228 + (x0 * ((x0 * 2.12262258018...\n", + " (t[0] + (x[:, 0] * ((x[:, 0] * t[1]) + t[2])))\n", + " 2.59375634458228;2.122622580186797e-2;3.195222...\n", " 9\n", - " 5.033598\n", + " 5.033599\n", " -6.222295\n", " \n", " \n", " 15\n", " 5\n", " 0\n", - " ((x0 * (((x0 + x0) * 1.0189049973402167) + -4....\n", - " ((x[:, 0] * (((x[:, 0] + x[:, 0]) * t[0]) + t[...\n", - " 1.0189049973402167;-4.989753870411562;-1.05930...\n", + " (-0.7489960516420995 + ((x0 * ((x0 * 2.0377751...\n", + " (t[0] + ((x[:, 0] * ((x[:, 0] * t[1]) + t[2]))...\n", + " -0.7489960516420995;2.037775165917442;-4.99009...\n", " 11\n", - " 6.222295\n", - " -6.222295\n", + " 6.222297\n", + " -6.222297\n", " \n", " \n", " 16\n", " 5\n", " 1\n", - " ((x0 * (((x0 + x0) * -1.0151666224030238) + -0...\n", - " ((x[:, 0] * (((x[:, 0] + x[:, 0]) * t[0]) + t[...\n", - " -1.0151666224030238;-0.11527243424256224;19.50...\n", + " (9.7871188787152 + ((x0 * ((x0 * -2.0303248764...\n", + " (t[0] + ((x[:, 0] * ((x[:, 0] * t[1]) + t[2]))...\n", + " 9.7871188787152;-2.0303248764495545;-0.1153596...\n", " 11\n", - " 2.880428\n", - " -6.222295\n", + " 2.880427\n", + " -6.222297\n", " \n", " \n", " 17\n", " 5\n", " 2\n", - " ((x0 * (((x0 + x0) * 1.0559197641250177e-2) + ...\n", - " ((x[:, 0] * (((x[:, 0] + x[:, 0]) * t[0]) + t[...\n", - " 1.0559197641250177e-2;3.195477601097071;2.5952...\n", + " (1.1383630234438435 + ((x0 * ((x0 * 2.11511614...\n", + " (t[0] + ((x[:, 0] * ((x[:, 0] * t[1]) + t[2]))...\n", + " 1.1383630234438435;2.11511614475054e-2;3.19542...\n", " 11\n", " 5.033598\n", - " -6.222295\n", + " -6.222297\n", " \n", " \n", " 18\n", " 6\n", " 0\n", - " ((x0 * ((((x0 * 2.7846752164938045e-2) + 2.027...\n", - " ((x[:, 0] * ((((x[:, 0] * t[0]) + t[1]) * x[:,...\n", - " 2.7846752164938045e-2;2.0270889034853847;-5.43...\n", + " (-0.8687370550459058 + (x0 * (((2.024605374362...\n", + " (t[0] + (x[:, 0] * (((t[1] + (x[:, 0] * t[2]))...\n", + " -0.8687370550459058;2.024605374362169;2.804607...\n", " 13\n", - " 5.912260\n", - " -5.912278\n", + " 5.912680\n", + " -5.912680\n", " \n", " \n", " 19\n", " 6\n", " 1\n", - " ((x0 * ((((x0 * -1.2028302093441635e-3) + -2.0...\n", - " ((x[:, 0] * ((((x[:, 0] * t[0]) + t[1]) * x[:,...\n", - " -1.2028302093441635e-3;-2.031163477321425;-9.4...\n", + " (19.504541422655507 + (x0 * (((-2.030296265227...\n", + " (t[0] + (x[:, 0] * (((t[1] + (x[:, 0] * t[2]))...\n", + " 19.504541422655507;-2.03029626522703;-6.125262...\n", " 13\n", - " 2.880355\n", - " -5.912278\n", + " 2.880242\n", + " -5.912680\n", " \n", " \n", " 20\n", " 6\n", " 2\n", - " ((x0 * ((((x0 * -2.9416747648114278e-2) + 3.19...\n", - " ((x[:, 0] * ((((x[:, 0] * t[0]) + t[1]) * x[:,...\n", - " -2.9416747648114278e-2;3.196984333053568e-2;3....\n", + " (2.2544633973594923 + (x0 * (((3.1382263687735...\n", + " (t[0] + (x[:, 0] * (((t[1] + (x[:, 0] * t[2]))...\n", + " 2.2544633973594923;3.138226368773547e-2;-2.826...\n", " 13\n", - " 4.673400\n", - " -5.912278\n", - " \n", - " \n", - " 21\n", - " 7\n", - " 0\n", - " (((x0 + x0) * (((1.009492125925551 - (x0 * -1....\n", - " (((x[:, 0] + x[:, 0]) * (((t[0] - (x[:, 0] * t...\n", - " 1.009492125925551;-1.099733533256624e-2;-2.661...\n", - " 15\n", - " 5.935710\n", - " -5.912269\n", - " \n", - " \n", - " 22\n", - " 7\n", - " 1\n", - " (((x0 + x0) * (((-1.0151461185785686 - (x0 * 4...\n", - " (((x[:, 0] + x[:, 0]) * (((t[0] - (x[:, 0] * t...\n", - " -1.0151461185785686;4.0111247485826585e-4;-5.1...\n", - " 15\n", - " 2.880232\n", - " -5.912269\n", - " \n", - " \n", - " 23\n", - " 7\n", - " 2\n", - " (((x0 + x0) * (((1.9139731391817023e-2 - (x0 *...\n", - " (((x[:, 0] + x[:, 0]) * (((t[0] - (x[:, 0] * t...\n", - " 1.9139731391817023e-2;1.5290623410534727e-2;1....\n", - " 15\n", - " 4.677979\n", - " -5.912269\n", + " 4.673299\n", + " -5.912680\n", " \n", " \n", "\n", @@ -375,30 +342,27 @@ ], "text/plain": [ " id view Expression \\\n", - "0 0 0 21.58921727054863 \n", - "1 0 1 -2.930612339764856 \n", + "0 0 0 21.589217270548627 \n", + "1 0 1 -2.9306123397648802 \n", "2 0 2 2.6671850933034973 \n", "3 1 0 (21.53486288699552 - x0) \n", - "4 1 1 (-3.285686757996543 - x0) \n", - "5 1 2 (2.6105987644181643 - x0) \n", - "6 2 0 ((x0 * -5.265543575122937) + 21.303011895450872) \n", - "7 2 1 ((x0 * -0.10303084459825138) + -2.967195956970... \n", - "8 2 2 ((x0 * 3.181877228219192) + 2.8472358446230825) \n", - "9 3 0 (((x0 + -2.5353345384705035) * 1.9710063071507... \n", - "10 3 1 (((x0 + 0.3703122177136077) * -0.8466294803417... \n", - "11 3 2 (((x0 + 17.14471689769305) * 0.192626434690429... \n", - "12 4 0 ((2.6451239766035792 - x0) * (-0.4005564286578... \n", - "13 4 1 ((3.0711878077868717 - x0) * (6.35090000814782... \n", - "14 4 2 ((-0.8161887112562356 - x0) * (-3.178090433000... \n", - "15 5 0 ((x0 * (((x0 + x0) * 1.0189049973402167) + -4.... \n", - "16 5 1 ((x0 * (((x0 + x0) * -1.0151666224030238) + -0... \n", - "17 5 2 ((x0 * (((x0 + x0) * 1.0559197641250177e-2) + ... \n", - "18 6 0 ((x0 * ((((x0 * 2.7846752164938045e-2) + 2.027... \n", - "19 6 1 ((x0 * ((((x0 * -1.2028302093441635e-3) + -2.0... \n", - "20 6 2 ((x0 * ((((x0 * -2.9416747648114278e-2) + 3.19... \n", - "21 7 0 (((x0 + x0) * (((1.009492125925551 - (x0 * -1.... \n", - "22 7 1 (((x0 + x0) * (((-1.0151461185785686 - (x0 * 4... \n", - "23 7 2 (((x0 + x0) * (((1.9139731391817023e-2 - (x0 *... \n", + "4 1 1 (-3.285686757996549 - x0) \n", + "5 1 2 (2.610598764418156 - x0) \n", + "6 2 0 (21.303011895450766 + (x0 * -5.265543575122939)) \n", + "7 2 1 (-2.9671959569705386 + (x0 * -0.10303084459829... \n", + "8 2 2 (2.847235844618024 + (x0 * 3.181877228219146)) \n", + "9 3 0 (((x0 * 1.971001716619659) + -4.99718543452371... \n", + "10 3 1 (((x0 * -0.8466296174450944) + -0.313553973387... \n", + "11 3 2 (((x0 * 0.19256491303772472) + 3.3024607194153... \n", + "12 4 0 (-1.0590288970595494 + (x0 * ((x0 * 2.03779578... \n", + "13 4 1 (19.504988286223227 + (x0 * ((x0 * -2.03035739... \n", + "14 4 2 (2.59375634458228 + (x0 * ((x0 * 2.12262258018... \n", + "15 5 0 (-0.7489960516420995 + ((x0 * ((x0 * 2.0377751... \n", + "16 5 1 (9.7871188787152 + ((x0 * ((x0 * -2.0303248764... \n", + "17 5 2 (1.1383630234438435 + ((x0 * ((x0 * 2.11511614... \n", + "18 6 0 (-0.8687370550459058 + (x0 * (((2.024605374362... \n", + "19 6 1 (19.504541422655507 + (x0 * (((-2.030296265227... \n", + "20 6 2 (2.2544633973594923 + (x0 * (((3.1382263687735... \n", "\n", " Numpy \\\n", "0 t[0] \n", @@ -407,50 +371,44 @@ "3 (t[0] - x[:, 0]) \n", "4 (t[0] - x[:, 0]) \n", "5 (t[0] - x[:, 0]) \n", - "6 ((x[:, 0] * t[0]) + t[1]) \n", - "7 ((x[:, 0] * t[0]) + t[1]) \n", - "8 ((x[:, 0] * t[0]) + t[1]) \n", - "9 (((x[:, 0] + t[0]) * t[1]) * x[:, 0]) \n", - "10 (((x[:, 0] + t[0]) * t[1]) * x[:, 0]) \n", - "11 (((x[:, 0] + t[0]) * t[1]) * x[:, 0]) \n", - "12 ((t[0] - x[:, 0]) * (t[1] - (x[:, 0] * t[2]))) \n", - "13 ((t[0] - x[:, 0]) * (t[1] - (x[:, 0] * t[2]))) \n", - "14 ((t[0] - x[:, 0]) * (t[1] - (x[:, 0] * t[2]))) \n", - "15 ((x[:, 0] * (((x[:, 0] + x[:, 0]) * t[0]) + t[... \n", - "16 ((x[:, 0] * (((x[:, 0] + x[:, 0]) * t[0]) + t[... \n", - "17 ((x[:, 0] * (((x[:, 0] + x[:, 0]) * t[0]) + t[... \n", - "18 ((x[:, 0] * ((((x[:, 0] * t[0]) + t[1]) * x[:,... \n", - "19 ((x[:, 0] * ((((x[:, 0] * t[0]) + t[1]) * x[:,... \n", - "20 ((x[:, 0] * ((((x[:, 0] * t[0]) + t[1]) * x[:,... \n", - "21 (((x[:, 0] + x[:, 0]) * (((t[0] - (x[:, 0] * t... \n", - "22 (((x[:, 0] + x[:, 0]) * (((t[0] - (x[:, 0] * t... \n", - "23 (((x[:, 0] + x[:, 0]) * (((t[0] - (x[:, 0] * t... \n", + "6 (t[0] + (x[:, 0] * t[1])) \n", + "7 (t[0] + (x[:, 0] * t[1])) \n", + "8 (t[0] + (x[:, 0] * t[1])) \n", + "9 (((x[:, 0] * t[0]) + t[1]) * x[:, 0]) \n", + "10 (((x[:, 0] * t[0]) + t[1]) * x[:, 0]) \n", + "11 (((x[:, 0] * t[0]) + t[1]) * x[:, 0]) \n", + "12 (t[0] + (x[:, 0] * ((x[:, 0] * t[1]) + t[2]))) \n", + "13 (t[0] + (x[:, 0] * ((x[:, 0] * t[1]) + t[2]))) \n", + "14 (t[0] + (x[:, 0] * ((x[:, 0] * t[1]) + t[2]))) \n", + "15 (t[0] + ((x[:, 0] * ((x[:, 0] * t[1]) + t[2]))... \n", + "16 (t[0] + ((x[:, 0] * ((x[:, 0] * t[1]) + t[2]))... \n", + "17 (t[0] + ((x[:, 0] * ((x[:, 0] * t[1]) + t[2]))... \n", + "18 (t[0] + (x[:, 0] * (((t[1] + (x[:, 0] * t[2]))... \n", + "19 (t[0] + (x[:, 0] * (((t[1] + (x[:, 0] * t[2]))... \n", + "20 (t[0] + (x[:, 0] * (((t[1] + (x[:, 0] * t[2]))... \n", "\n", " theta size loss \\\n", - "0 21.58921727054863 1 532.979453 \n", - "1 -2.930612339764856 1 247.002956 \n", + "0 21.589217270548627 1 532.979453 \n", + "1 -2.9306123397648802 1 247.002956 \n", "2 2.6671850933034973 1 126.303250 \n", "3 21.53486288699552 3 428.346651 \n", - "4 -3.285686757996543 3 255.691957 \n", - "5 2.6105987644181643 3 214.494045 \n", - "6 -5.265543575122937;21.303011895450872 5 228.602451 \n", - "7 -0.10303084459825138;-2.9671959569705537 5 246.886780 \n", - "8 3.181877228219192;2.8472358446230825 5 5.050724 \n", - "9 -2.5353345384705035;1.971006307150714 7 6.567563 \n", - "10 0.3703122177136077;-0.8466294803417656 7 126.355850 \n", - "11 17.14471689769305;0.19262643469042923 7 6.453166 \n", - "12 2.6451239766035792;-0.4005564286578119;2.03783... 9 6.222296 \n", - "13 3.0711878077868717;6.350900008147822;-2.030341... 9 2.880427 \n", - "14 -0.8161887112562356;-3.178090433000606;2.12097... 9 5.033598 \n", - "15 1.0189049973402167;-4.989753870411562;-1.05930... 11 6.222295 \n", - "16 -1.0151666224030238;-0.11527243424256224;19.50... 11 2.880428 \n", - "17 1.0559197641250177e-2;3.195477601097071;2.5952... 11 5.033598 \n", - "18 2.7846752164938045e-2;2.0270889034853847;-5.43... 13 5.912260 \n", - "19 -1.2028302093441635e-3;-2.031163477321425;-9.4... 13 2.880355 \n", - "20 -2.9416747648114278e-2;3.196984333053568e-2;3.... 13 4.673400 \n", - "21 1.009492125925551;-1.099733533256624e-2;-2.661... 15 5.935710 \n", - "22 -1.0151461185785686;4.0111247485826585e-4;-5.1... 15 2.880232 \n", - "23 1.9139731391817023e-2;1.5290623410534727e-2;1.... 15 4.677979 \n", + "4 -3.285686757996549 3 255.691957 \n", + "5 2.610598764418156 3 214.494045 \n", + "6 21.303011895450766;-5.265543575122939 5 228.602451 \n", + "7 -2.9671959569705386;-0.10303084459829029 5 246.886780 \n", + "8 2.847235844618024;3.181877228219146 5 5.050724 \n", + "9 1.971001716619659;-4.997185434523716 7 6.567563 \n", + "10 -0.8466296174450944;-0.31355397338761204 7 126.355850 \n", + "11 0.19256491303772472;3.302460719415394 7 6.453165 \n", + "12 -1.0590288970595494;2.037795780991407;-4.98985... 9 6.222296 \n", + "13 19.504988286223227;-2.030357391771257;-0.11535... 9 2.880427 \n", + "14 2.59375634458228;2.122622580186797e-2;3.195222... 9 5.033599 \n", + "15 -0.7489960516420995;2.037775165917442;-4.99009... 11 6.222297 \n", + "16 9.7871188787152;-2.0303248764495545;-0.1153596... 11 2.880427 \n", + "17 1.1383630234438435;2.11511614475054e-2;3.19542... 11 5.033598 \n", + "18 -0.8687370550459058;2.024605374362169;2.804607... 13 5.912680 \n", + "19 19.504541422655507;-2.03029626522703;-6.125262... 13 2.880242 \n", + "20 2.2544633973594923;3.138226368773547e-2;-2.826... 13 4.673299 \n", "\n", " maxLoss \n", "0 -532.979453 \n", @@ -468,25 +426,22 @@ "12 -6.222295 \n", "13 -6.222295 \n", "14 -6.222295 \n", - "15 -6.222295 \n", - "16 -6.222295 \n", - "17 -6.222295 \n", - "18 -5.912278 \n", - "19 -5.912278 \n", - "20 -5.912278 \n", - "21 -5.912269 \n", - "22 -5.912269 \n", - "23 -5.912269 " + "15 -6.222297 \n", + "16 -6.222297 \n", + "17 -6.222297 \n", + "18 -5.912680 \n", + "19 -5.912680 \n", + "20 -5.912680 " ] }, - "execution_count": 9, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# We create a Pyeggp regressor leaving all hyperparameters by default\n", - "reg = pyeggp.PyEGGP(simplify=1)\n", + "# We create a Pyeggp regressor leaving all hyperparameters to default.\n", + "reg = pyeggp.PyEGGP()\n", "reg.fit_mvsr(full_X, full_y)\n", "\n", "# Here we collect the full table of results and plot it\n", @@ -504,15 +459,15 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 4, "id": "88ebec1e-f915-40be-b210-0063261c139d", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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"text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -520,20 +475,23 @@ } ], "source": [ - "fig, ax = plt.subplots(1, 2, figsize=(12, 5))\n", + "fig, ax = plt.subplots(1, 3, figsize=(14, 4))\n", "\n", - "ax[0].plot(pareto['size'], -pareto['maxLoss'], label='Pareto front')\n", - "ax[0].scatter(pareto['size'], -pareto['maxLoss'])\n", + "ax[0].plot(pareto['id'], -pareto['maxLoss'], label='Pareto front')\n", + "ax[0].scatter(pareto['id'], -pareto['maxLoss'])\n", "ax[0].set_ylabel('Loss')\n", - "ax[0].set_xlabel('Equation length')\n", "ax[0].legend()\n", "\n", "n_params = pareto['theta'].apply(lambda x: x.split(';')).apply(len)\n", + "ax[1].plot(pareto['id'], n_params)\n", + "ax[1].scatter(pareto['id'], n_params)\n", + "ax[1].set_xlabel('ID')\n", + "ax[1].set_ylabel('Number of parameters')\n", + "\n", + "ax[2].plot(pareto['id'], pareto['size'])\n", + "ax[2].scatter(pareto['id'], pareto['size'])\n", + "ax[2].set_ylabel('Equation size')\n", "\n", - "ax[1].plot(n_params, -pareto['maxLoss'], label='Pareto front')\n", - "ax[1].scatter(n_params, -pareto['maxLoss'])\n", - "ax[1].set_xlabel('Number of free parameters')\n", - "ax[1].legend()\n", "plt.show()" ] }, @@ -542,7 +500,7 @@ "id": "0251b459-84a1-482d-95f5-a838223befda", "metadata": {}, "source": [ - "From the study of the pareto front, we see that solution 4 seems to be an excellent solution. Solution 5 do not lead to significant improvement, despite the increase in complexity. Therefore we apply Ockam's razor and select the simplest solution.\n", + "From the study of the pareto front, we see that solution 4 seems to be an excellent solution. Solution 5 and 6 do not lead to significant improvement, despite the increase in complexity in equation size and number of parameters. Therefore we apply Ockam's razor and select the simplest solution.\n", "\n", "\n", "Let's look at solution 4 in more details:" @@ -550,7 +508,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 5, "id": "649d0314-fc9e-43e8-801b-bd3d658ef413", "metadata": {}, "outputs": [ @@ -559,12 +517,12 @@ "output_type": "stream", "text": [ "Best solution :\n", - " f(X0) = ((A - X0) * (B - (X0 * C)))\n" + " f(X0) = (A + (X0 * ((X0 * B) + C)))\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -600,6 +558,14 @@ "source": [ "Huray !! We rediscovered the original solution" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4462ecbf-2a87-4a1b-a097-1ab33607da8e", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From 3e0880cde3d7e6ab964dad7f1f7fcdaee61685e0 Mon Sep 17 00:00:00 2001 From: erusseil <79919110+erusseil@users.noreply.github.com> Date: Fri, 18 Apr 2025 17:18:44 +0200 Subject: [PATCH 062/166] Update mvsr_example.ipynb --- test/mvsr_example.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/mvsr_example.ipynb b/test/mvsr_example.ipynb index d55ba00..2d0ae02 100644 --- a/test/mvsr_example.ipynb +++ b/test/mvsr_example.ipynb @@ -556,7 +556,7 @@ "id": "336ffa9d-8f4d-47c3-a2e7-4faa11bb7c32", "metadata": {}, "source": [ - "Huray !! We rediscovered the original solution" + "Huray !! We rediscovered the generating model" ] }, { From ebe5844f864a6b3d7b423a8262245b23038faa41 Mon Sep 17 00:00:00 2001 From: folivetti Date: Sat, 19 Apr 2025 06:35:11 -0300 Subject: [PATCH 063/166] bump version to 1.0.3 --- pyeggp.cabal | 2 +- pyproject.toml | 2 +- src/pyeggp.egg-info/PKG-INFO | 2 +- .../mvsr_example-checkpoint.ipynb | 592 ++++++++++++++++++ 4 files changed, 595 insertions(+), 3 deletions(-) create mode 100644 test/.ipynb_checkpoints/mvsr_example-checkpoint.ipynb diff --git a/pyeggp.cabal b/pyeggp.cabal index 5f6f0e9..8c37fa3 100755 --- a/pyeggp.cabal +++ b/pyeggp.cabal @@ -5,7 +5,7 @@ cabal-version: 2.2 -- see: https://github.com/sol/hpack name: Pyeggp -version: 1.0.2 +version: 1.0.3 synopsis: A general library to work with Symbolic Regression expression trees. description: A Symbolic Regression Tree data structure to work with mathematical expressions with support to first order derivative and simplification; category: Math, Data, Data Structures diff --git a/pyproject.toml b/pyproject.toml index c3c5e95..31c1d00 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,7 +12,7 @@ build-backend = "setuptools.build_meta" [project] name = "pyeggp" -version = "1.0.2" +version = "1.0.3" authors = [{ name = "Fabricio Olivetti", email = "folivetti@users.noreply.github.com" }] description = "Python Wheels for eggp algorithm." readme = "README.md" diff --git a/src/pyeggp.egg-info/PKG-INFO b/src/pyeggp.egg-info/PKG-INFO index 5f9bed7..7156316 100644 --- a/src/pyeggp.egg-info/PKG-INFO +++ b/src/pyeggp.egg-info/PKG-INFO @@ -1,6 +1,6 @@ Metadata-Version: 2.4 Name: pyeggp -Version: 1.0.1 +Version: 1.0.2 Summary: Python Wheels for eggp algorithm. Author-email: Fabricio Olivetti License: GNU GENERAL PUBLIC LICENSE diff --git a/test/.ipynb_checkpoints/mvsr_example-checkpoint.ipynb b/test/.ipynb_checkpoints/mvsr_example-checkpoint.ipynb new file mode 100644 index 0000000..2d0ae02 --- /dev/null +++ b/test/.ipynb_checkpoints/mvsr_example-checkpoint.ipynb @@ -0,0 +1,592 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "c68e3b1a-d6e4-4f94-ae46-d761d5c1c836", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pyeggp" + ] + }, + { + "cell_type": "markdown", + "id": "796958d0-b675-4cad-a00b-e52f9c9142af", + "metadata": {}, + "source": [ + "In this exercise, we attempt to discover a polynomial model that generated three examples. We purposefully generate the examples choosing a free parameter equal to zero. In this configuration, no single example is enough to discover the full functional form that generated each view. However, as demonstrated in Russeil et al. 2024 (https://arxiv.org/abs/2402.04298), a multiview approach - i.e. considering multiple datasets generated by the same function - enables to find the correct solution. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2c9d5ad9-f2e4-4b96-accd-41d1edbfdc98", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "np.random.seed(42)\n", + "\n", + "# Define the true model to discover\n", + "def true_func(X, A, B, C):\n", + " return A + B * X + C * X**2\n", + "\n", + "\n", + "# Define specific parameter values for each example.\n", + "true_params = [[0,-5, 2],\n", + " [20, 0, -2],\n", + " [2, 3, 0]]\n", + "\n", + "\n", + "# Generate the data and plot it \n", + "full_X, full_y = [], []\n", + "plt.figure(figsize=(10, 7))\n", + "for idx, ps in enumerate(true_params):\n", + " X = np.random.uniform(-5, 5, 15)\n", + " y = true_func(X, *ps)\n", + " yerr = np.random.normal(0, 2, size=len(y))\n", + " plt.scatter(X, y+yerr, label=f'View {idx+1}', s=50)\n", + " full_X += [np.array([X]).T]\n", + " full_y += [y+yerr]\n", + "\n", + "plt.legend(fontsize=16)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "3c318511-31dd-4cb2-8178-2ceb5c2e405c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idviewExpressionNumpythetasizelossmaxLoss
00021.589217270548627t[0]21.5892172705486271532.979453-532.979453
101-2.9306123397648802t[0]-2.93061233976488021247.002956-532.979453
2022.6671850933034973t[0]2.66718509330349731126.303250-532.979453
310(21.53486288699552 - x0)(t[0] - x[:, 0])21.534862886995523428.346651-428.346651
411(-3.285686757996549 - x0)(t[0] - x[:, 0])-3.2856867579965493255.691957-428.346651
512(2.610598764418156 - x0)(t[0] - x[:, 0])2.6105987644181563214.494045-428.346651
620(21.303011895450766 + (x0 * -5.265543575122939))(t[0] + (x[:, 0] * t[1]))21.303011895450766;-5.2655435751229395228.602451-246.886780
721(-2.9671959569705386 + (x0 * -0.10303084459829...(t[0] + (x[:, 0] * t[1]))-2.9671959569705386;-0.103030844598290295246.886780-246.886780
822(2.847235844618024 + (x0 * 3.181877228219146))(t[0] + (x[:, 0] * t[1]))2.847235844618024;3.18187722821914655.050724-246.886780
930(((x0 * 1.971001716619659) + -4.99718543452371...(((x[:, 0] * t[0]) + t[1]) * x[:, 0])1.971001716619659;-4.99718543452371676.567563-126.355850
1031(((x0 * -0.8466296174450944) + -0.313553973387...(((x[:, 0] * t[0]) + t[1]) * x[:, 0])-0.8466296174450944;-0.313553973387612047126.355850-126.355850
1132(((x0 * 0.19256491303772472) + 3.3024607194153...(((x[:, 0] * t[0]) + t[1]) * x[:, 0])0.19256491303772472;3.30246071941539476.453165-126.355850
1240(-1.0590288970595494 + (x0 * ((x0 * 2.03779578...(t[0] + (x[:, 0] * ((x[:, 0] * t[1]) + t[2])))-1.0590288970595494;2.037795780991407;-4.98985...96.222296-6.222295
1341(19.504988286223227 + (x0 * ((x0 * -2.03035739...(t[0] + (x[:, 0] * ((x[:, 0] * t[1]) + t[2])))19.504988286223227;-2.030357391771257;-0.11535...92.880427-6.222295
1442(2.59375634458228 + (x0 * ((x0 * 2.12262258018...(t[0] + (x[:, 0] * ((x[:, 0] * t[1]) + t[2])))2.59375634458228;2.122622580186797e-2;3.195222...95.033599-6.222295
1550(-0.7489960516420995 + ((x0 * ((x0 * 2.0377751...(t[0] + ((x[:, 0] * ((x[:, 0] * t[1]) + t[2]))...-0.7489960516420995;2.037775165917442;-4.99009...116.222297-6.222297
1651(9.7871188787152 + ((x0 * ((x0 * -2.0303248764...(t[0] + ((x[:, 0] * ((x[:, 0] * t[1]) + t[2]))...9.7871188787152;-2.0303248764495545;-0.1153596...112.880427-6.222297
1752(1.1383630234438435 + ((x0 * ((x0 * 2.11511614...(t[0] + ((x[:, 0] * ((x[:, 0] * t[1]) + t[2]))...1.1383630234438435;2.11511614475054e-2;3.19542...115.033598-6.222297
1860(-0.8687370550459058 + (x0 * (((2.024605374362...(t[0] + (x[:, 0] * (((t[1] + (x[:, 0] * t[2]))...-0.8687370550459058;2.024605374362169;2.804607...135.912680-5.912680
1961(19.504541422655507 + (x0 * (((-2.030296265227...(t[0] + (x[:, 0] * (((t[1] + (x[:, 0] * t[2]))...19.504541422655507;-2.03029626522703;-6.125262...132.880242-5.912680
2062(2.2544633973594923 + (x0 * (((3.1382263687735...(t[0] + (x[:, 0] * (((t[1] + (x[:, 0] * t[2]))...2.2544633973594923;3.138226368773547e-2;-2.826...134.673299-5.912680
\n", + "
" + ], + "text/plain": [ + " id view Expression \\\n", + "0 0 0 21.589217270548627 \n", + "1 0 1 -2.9306123397648802 \n", + "2 0 2 2.6671850933034973 \n", + "3 1 0 (21.53486288699552 - x0) \n", + "4 1 1 (-3.285686757996549 - x0) \n", + "5 1 2 (2.610598764418156 - x0) \n", + "6 2 0 (21.303011895450766 + (x0 * -5.265543575122939)) \n", + "7 2 1 (-2.9671959569705386 + (x0 * -0.10303084459829... \n", + "8 2 2 (2.847235844618024 + (x0 * 3.181877228219146)) \n", + "9 3 0 (((x0 * 1.971001716619659) + -4.99718543452371... \n", + "10 3 1 (((x0 * -0.8466296174450944) + -0.313553973387... \n", + "11 3 2 (((x0 * 0.19256491303772472) + 3.3024607194153... \n", + "12 4 0 (-1.0590288970595494 + (x0 * ((x0 * 2.03779578... \n", + "13 4 1 (19.504988286223227 + (x0 * ((x0 * -2.03035739... \n", + "14 4 2 (2.59375634458228 + (x0 * ((x0 * 2.12262258018... \n", + "15 5 0 (-0.7489960516420995 + ((x0 * ((x0 * 2.0377751... \n", + "16 5 1 (9.7871188787152 + ((x0 * ((x0 * -2.0303248764... \n", + "17 5 2 (1.1383630234438435 + ((x0 * ((x0 * 2.11511614... \n", + "18 6 0 (-0.8687370550459058 + (x0 * (((2.024605374362... \n", + "19 6 1 (19.504541422655507 + (x0 * (((-2.030296265227... \n", + "20 6 2 (2.2544633973594923 + (x0 * (((3.1382263687735... \n", + "\n", + " Numpy \\\n", + "0 t[0] \n", + "1 t[0] \n", + "2 t[0] \n", + "3 (t[0] - x[:, 0]) \n", + "4 (t[0] - x[:, 0]) \n", + "5 (t[0] - x[:, 0]) \n", + "6 (t[0] + (x[:, 0] * t[1])) \n", + "7 (t[0] + (x[:, 0] * t[1])) \n", + "8 (t[0] + (x[:, 0] * t[1])) \n", + "9 (((x[:, 0] * t[0]) + t[1]) * x[:, 0]) \n", + "10 (((x[:, 0] * t[0]) + t[1]) * x[:, 0]) \n", + "11 (((x[:, 0] * t[0]) + t[1]) * x[:, 0]) \n", + "12 (t[0] + (x[:, 0] * ((x[:, 0] * t[1]) + t[2]))) \n", + "13 (t[0] + (x[:, 0] * ((x[:, 0] * t[1]) + t[2]))) \n", + "14 (t[0] + (x[:, 0] * ((x[:, 0] * t[1]) + t[2]))) \n", + "15 (t[0] + ((x[:, 0] * ((x[:, 0] * t[1]) + t[2]))... \n", + "16 (t[0] + ((x[:, 0] * ((x[:, 0] * t[1]) + t[2]))... \n", + "17 (t[0] + ((x[:, 0] * ((x[:, 0] * t[1]) + t[2]))... \n", + "18 (t[0] + (x[:, 0] * (((t[1] + (x[:, 0] * t[2]))... \n", + "19 (t[0] + (x[:, 0] * (((t[1] + (x[:, 0] * t[2]))... \n", + "20 (t[0] + (x[:, 0] * (((t[1] + (x[:, 0] * t[2]))... \n", + "\n", + " theta size loss \\\n", + "0 21.589217270548627 1 532.979453 \n", + "1 -2.9306123397648802 1 247.002956 \n", + "2 2.6671850933034973 1 126.303250 \n", + "3 21.53486288699552 3 428.346651 \n", + "4 -3.285686757996549 3 255.691957 \n", + "5 2.610598764418156 3 214.494045 \n", + "6 21.303011895450766;-5.265543575122939 5 228.602451 \n", + "7 -2.9671959569705386;-0.10303084459829029 5 246.886780 \n", + "8 2.847235844618024;3.181877228219146 5 5.050724 \n", + "9 1.971001716619659;-4.997185434523716 7 6.567563 \n", + "10 -0.8466296174450944;-0.31355397338761204 7 126.355850 \n", + "11 0.19256491303772472;3.302460719415394 7 6.453165 \n", + "12 -1.0590288970595494;2.037795780991407;-4.98985... 9 6.222296 \n", + "13 19.504988286223227;-2.030357391771257;-0.11535... 9 2.880427 \n", + "14 2.59375634458228;2.122622580186797e-2;3.195222... 9 5.033599 \n", + "15 -0.7489960516420995;2.037775165917442;-4.99009... 11 6.222297 \n", + "16 9.7871188787152;-2.0303248764495545;-0.1153596... 11 2.880427 \n", + "17 1.1383630234438435;2.11511614475054e-2;3.19542... 11 5.033598 \n", + "18 -0.8687370550459058;2.024605374362169;2.804607... 13 5.912680 \n", + "19 19.504541422655507;-2.03029626522703;-6.125262... 13 2.880242 \n", + "20 2.2544633973594923;3.138226368773547e-2;-2.826... 13 4.673299 \n", + "\n", + " maxLoss \n", + "0 -532.979453 \n", + "1 -532.979453 \n", + "2 -532.979453 \n", + "3 -428.346651 \n", + "4 -428.346651 \n", + "5 -428.346651 \n", + "6 -246.886780 \n", + "7 -246.886780 \n", + "8 -246.886780 \n", + "9 -126.355850 \n", + "10 -126.355850 \n", + "11 -126.355850 \n", + "12 -6.222295 \n", + "13 -6.222295 \n", + "14 -6.222295 \n", + "15 -6.222297 \n", + "16 -6.222297 \n", + "17 -6.222297 \n", + "18 -5.912680 \n", + "19 -5.912680 \n", + "20 -5.912680 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# We create a Pyeggp regressor leaving all hyperparameters to default.\n", + "reg = pyeggp.PyEGGP()\n", + "reg.fit_mvsr(full_X, full_y)\n", + "\n", + "# Here we collect the full table of results and plot it\n", + "pareto = reg.results\n", + "pareto" + ] + }, + { + "cell_type": "markdown", + "id": "98eb4e69-f443-410d-a021-079589c4d2f0", + "metadata": {}, + "source": [ + "All these solution can be quite confusing at first glance. Let's have a look at the evolution of the loss along the pareto front" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "88ebec1e-f915-40be-b210-0063261c139d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 3, figsize=(14, 4))\n", + "\n", + "ax[0].plot(pareto['id'], -pareto['maxLoss'], label='Pareto front')\n", + "ax[0].scatter(pareto['id'], -pareto['maxLoss'])\n", + "ax[0].set_ylabel('Loss')\n", + "ax[0].legend()\n", + "\n", + "n_params = pareto['theta'].apply(lambda x: x.split(';')).apply(len)\n", + "ax[1].plot(pareto['id'], n_params)\n", + "ax[1].scatter(pareto['id'], n_params)\n", + "ax[1].set_xlabel('ID')\n", + "ax[1].set_ylabel('Number of parameters')\n", + "\n", + "ax[2].plot(pareto['id'], pareto['size'])\n", + "ax[2].scatter(pareto['id'], pareto['size'])\n", + "ax[2].set_ylabel('Equation size')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "0251b459-84a1-482d-95f5-a838223befda", + "metadata": {}, + "source": [ + "From the study of the pareto front, we see that solution 4 seems to be an excellent solution. Solution 5 and 6 do not lead to significant improvement, despite the increase in complexity in equation size and number of parameters. Therefore we apply Ockam's razor and select the simplest solution.\n", + "\n", + "\n", + "Let's look at solution 4 in more details:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "649d0314-fc9e-43e8-801b-bd3d658ef413", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best solution :\n", + " f(X0) = (A + (X0 * ((X0 * B) + C)))\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "best_id = 4\n", + "model, clean_expression = reg.get_model(best_id)\n", + "print(\"Best solution :\\n f(X0) = \", clean_expression)\n", + "\n", + "\n", + "X_smooth = np.array([np.linspace(-5, 5, 100)]).T\n", + "solution = pareto[pareto['id']==best_id]\n", + "\n", + "plt.figure(figsize=(10, 7))\n", + "for idx in range(len(full_X)):\n", + "\n", + " params = np.array(solution.iloc[idx]['theta'].split(\";\"), dtype=float)\n", + " plt.scatter(full_X[idx], full_y[idx], label=f'View {idx+1}', s=50)\n", + " plt.plot(X_smooth, model(X_smooth, params))\n", + "\n", + "plt.legend(fontsize=16)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "336ffa9d-8f4d-47c3-a2e7-4faa11bb7c32", + "metadata": {}, + "source": [ + "Huray !! We rediscovered the generating model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4462ecbf-2a87-4a1b-a097-1ab33607da8e", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "new_mvsr_kern", + "language": "python", + "name": "new_mvsr_kern" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 414daf728c4ffbd5ef50de909146289b2c165ca1 Mon Sep 17 00:00:00 2001 From: folivetti Date: Sat, 19 Apr 2025 16:09:19 -0300 Subject: [PATCH 064/166] documentation + readme --- README.md | 173 +++++++++++++++++++++++++++- pyeggp.cabal | 2 +- pyproject.toml | 2 +- src/pyeggp.egg-info/PKG-INFO | 2 +- src/pyeggp/__init__.py | 217 +++++++++++++++++++++++++++++++++-- test/test_pyeggp.py | 2 +- 6 files changed, 382 insertions(+), 16 deletions(-) diff --git a/README.md b/README.md index 8bcf857..bc0917c 100644 --- a/README.md +++ b/README.md @@ -1,13 +1,178 @@ -# pyeggp - Python e-graph GP +# PyEGGP -Python bindings for [eggp](https://github.com/folivetti/srtree/blob/main/apps/eggp/README.md). +A Python package for symbolic regression using e-graph-based genetic programming. PyEGGP provides a scikit-learn compatible API for evolutionary symbolic regression tasks. -ggp (e-graph genetic programming), follows the same structure as the traditional GP. The initial population is created using ramped half-and-half respecting a maximum size and maximum depth parameter and, for a number of generations, it will choose two parents using tournament selection, apply the subtree crossover with probability $pc$ followed by the subtree mutation with probability $pm$, when the offsprings replace the current population following a dominance criteria. +More info [here](https://github.com/folivetti/srtree/tree/main/apps/eggp) -How to install the package: +## Installation ```bash pip install pyeggp ``` +## Features + +- Scikit-learn compatible API with `fit()` and `predict()` methods +- Genetic programming approach with e-graph representation +- Support for **multi-view symbolic regression** [see here](https://arxiv.org/abs/2402.04298) +- Customizable evolutionary parameters (population size, tournament selection, etc.) +- Flexible function set selection +- Various loss functions for different problem types +- Parameter optimization with multiple restarts +- Optional expression simplification through equality saturation +- Ability to save and load e-graphs + +## Usage + +### Basic Example + +```python +from pyeggp import PyEGGP +import numpy as np + +# Create sample data +X = np.linspace(-10, 10, 100).reshape(-1, 1) +y = 2 * X.ravel() + 3 * np.sin(X.ravel()) + np.random.normal(0, 1, 100) + +# Create and fit the model +model = PyEGGP(gen=100, nonterminals="add,sub,mul,div,sin,cos") +model.fit(X, y) + +# Make predictions +y_pred = model.predict(X) + +# Examine the results +print(model.results) +``` + +### Multi-View Symbolic Regression + +```python +from pyeggp import PyEGGP +import numpy as np + +# Create multiple views of data +X1 = np.linspace(-5, 5, 50).reshape(-1, 1) +y1 = np.sin(X1.ravel()) + np.random.normal(0, 0.1, 50) + +X2 = np.linspace(0, 10, 100).reshape(-1, 1) +y2 = np.sin(X2.ravel()) + np.random.normal(0, 0.2, 100) + +# Create and fit multi-view model +model = PyEGGP(gen=150, nPop=200) +model.fit_mvsr([X1, X2], [y1, y2]) + +# Make predictions for each view +y_pred1 = model.predict_mvsr(X1, view=0) +y_pred2 = model.predict_mvsr(X2, view=1) +``` + +### Integration with scikit-learn + +```python +from sklearn.model_selection import train_test_split +from sklearn.metrics import mean_squared_error +from pyeggp import PyEGGP + +# Split data +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) + +# Create and fit model +model = PyEGGP(gen=150, nPop=150, optIter=100) +model.fit(X_train, y_train) + +# Evaluate on test set +y_pred = model.predict(X_test) +mse = mean_squared_error(y_test, y_pred) +print(f"Test MSE: {mse}") +``` + +## Parameters + +| Parameter | Type | Default | Description | +|-----------|------|---------|-------------| +| `gen` | int | 100 | Number of generations to run | +| `nPop` | int | 100 | Population size | +| `maxSize` | int | 15 | Maximum allowed size for expressions (max 100) | +| `nTournament` | int | 3 | Tournament size for parent selection | +| `pc` | float | 0.9 | Probability of performing crossover | +| `pm` | float | 0.3 | Probability of performing mutation | +| `nonterminals` | str | "add,sub,mul,div" | Comma-separated list of allowed functions | +| `loss` | str | "MSE" | Loss function: "MSE", "Gaussian", "Bernoulli", or "Poisson" | +| `optIter` | int | 50 | Number of iterations for parameter optimization | +| `optRepeat` | int | 2 | Number of restarts for parameter optimization | +| `nParams` | int | -1 | Maximum number of parameters (-1 for unlimited) | +| `split` | int | 1 | Data splitting ratio for validation | +| `simplify` | bool | False | Whether to apply equality saturation to simplify expressions | +| `dumpTo` | str | "" | Filename to save the final e-graph | +| `loadFrom` | str | "" | Filename to load an e-graph to resume search | + +## Available Functions + +The following functions can be used in the `nonterminals` parameter: + +- Basic operations: `add`, `sub`, `mul`, `div` +- Powers: `power`, `powerabs`, `square`, `cube` +- Roots: `sqrt`, `sqrtabs`, `cbrt` +- Trigonometric: `sin`, `cos`, `tan`, `asin`, `acos`, `atan` +- Hyperbolic: `sinh`, `cosh`, `tanh`, `asinh`, `acosh`, `atanh` +- Others: `abs`, `log`, `logabs`, `exp`, `recip`, `aq` (analytical quotient) + +## Methods + +### Core Methods +- `fit(X, y)`: Fits the symbolic regression model +- `predict(X)`: Generates predictions using the best model +- `score(X, y)`: Computes R² score of the best model + +### Multi-View Methods +- `fit_mvsr(Xs, ys)`: Fits a multi-view regression model +- `predict_mvsr(X, view)`: Generates predictions for a specific view +- `evaluate_best_model_view(X, view)`: Evaluates the best model on a specific view +- `evaluate_model_view(X, ix, view)`: Evaluates a specific model on a specific view + +### Utility Methods +- `evaluate_best_model(X)`: Evaluates the best model on the given data +- `evaluate_model(ix, X)`: Evaluates the model with index `ix` on the given data +- `get_model(idx)`: Returns a model function and its visual representation + +## Results + +After fitting, the `results` attribute contains a pandas DataFrame with details about the discovered models, including: +- Mathematical expressions +- Model complexity +- Parameter values +- Error metrics +- NumPy-compatible expressions + +## License + +[LICENSE] + +## Citation + +If you use PyEGGP in your research, please cite: + +``` +@inproceedings{eggp, +author = {de Franca, Fabricio Olivetti and Kronberger, Gabriel}, +title = {Improving Genetic Programming for Symbolic Regression with Equality Graphs}, +year = {2025}, +isbn = {9798400714658}, +publisher = {Association for Computing Machinery}, +address = {New York, NY, USA}, +url = {https://doi.org/10.1145/3712256.3726383}, +doi = {10.1145/3712256.3726383}, +booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference}, +pages = {}, +numpages = {9}, +keywords = {Symbolic regression, Genetic programming, Equality saturation, Equality graphs}, +location = {Malaga, Spain}, +series = {GECCO '25}, +archivePrefix = {arXiv}, + eprint = {2501.17848}, + primaryClass = {cs.LG}, +} +``` + The bindings were created following the amazing example written by [wenkokke](https://github.com/wenkokke/example-haskell-wheel) diff --git a/pyeggp.cabal b/pyeggp.cabal index 8c37fa3..cc2698c 100755 --- a/pyeggp.cabal +++ b/pyeggp.cabal @@ -5,7 +5,7 @@ cabal-version: 2.2 -- see: https://github.com/sol/hpack name: Pyeggp -version: 1.0.3 +version: 1.0.4 synopsis: A general library to work with Symbolic Regression expression trees. description: A Symbolic Regression Tree data structure to work with mathematical expressions with support to first order derivative and simplification; category: Math, Data, Data Structures diff --git a/pyproject.toml b/pyproject.toml index 31c1d00..e3be999 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,7 +12,7 @@ build-backend = "setuptools.build_meta" [project] name = "pyeggp" -version = "1.0.3" +version = "1.0.4" authors = [{ name = "Fabricio Olivetti", email = "folivetti@users.noreply.github.com" }] description = "Python Wheels for eggp algorithm." readme = "README.md" diff --git a/src/pyeggp.egg-info/PKG-INFO b/src/pyeggp.egg-info/PKG-INFO index 7156316..9faef71 100644 --- a/src/pyeggp.egg-info/PKG-INFO +++ b/src/pyeggp.egg-info/PKG-INFO @@ -1,6 +1,6 @@ Metadata-Version: 2.4 Name: pyeggp -Version: 1.0.2 +Version: 1.0.4 Summary: Python Wheels for eggp algorithm. Author-email: Fabricio Olivetti License: GNU GENERAL PUBLIC LICENSE diff --git a/src/pyeggp/__init__.py b/src/pyeggp/__init__.py index ff3baf2..34addf1 100644 --- a/src/pyeggp/__init__.py +++ b/src/pyeggp/__init__.py @@ -21,7 +21,7 @@ unsafe_hs_pyeggp_exit, ) -VERSION: str = "1.3.0" +VERSION: str = "1.0.4" _hs_rts_init: bool = False @@ -59,13 +59,140 @@ def pyeggp_run(dataset: str, gen: int, nPop: int, maxSize: int, nTournament: int with hs_rts_init(): return unsafe_hs_pyeggp_run(dataset, gen, nPop, maxSize, nTournament, pc, pm, nonterminals, loss, optIter, optRepeat, nParams, split, simplify, dumpTo, loadFrom) -def make_function(expression): +def make_function(expression, loss="MSE"): def func(x, t): - return eval(expression) + y = eval(expression) + if loss == "Bernoulli": + return 1/(1 + np.exp(-y)) + elif loss == "Poisson": + return np.exp(y) + return y return func class PyEGGP(BaseEstimator, RegressorMixin): + """ Builds a symbolic regression model using eggp. + + Parameters + ---------- + gen : int, default=100 + The number of generations. + + nPop : int, default=100 + Population size. + + maxSize : int, default=15 + Maximum allowed size for the expression. + This should not be larger than 100 as the e-graph may grow + too large. + + nTournament : int, default=3 + Tournament size. During parent selection it will + pick `nTournament` expressions at random and + return the best among them. + + pc : float, default=0.9 + Probability of performing the crossover operator. + + pm : float, default=0.3 + Probability of performing the mutation operator. + + nonterminals : str, default="add,sub,mul,div" + String of a comma separated list of nonterminals. + These are the allowed functions to be used during the search. + Available functions: add,sub,mul,div,power,powerabs,aq,abs,sin,cos, + tan,sinh,cosh,tanh,asin,acos,atan,asinh,acosh, + atanh,sqrt,sqrtabs,cbrt,square,log,logabs,exp, + recip,cube. + Where `aq` is the analytical quotient (x/sqrt(1 + y^2)), + `powerabs` is the protected power (x^|y|) + `sqrtabs` is the protected sqrt (sqrt(|x|)) + `logabs` is the protected log (log(|x|)) + `recip` is the reciprocal (1/x) + `cbrt` is the cubic root + + loss : {"MSE", "Gaussian", "Bernoulli", "Poisson"}, default="MSE" + Loss function used to evaluate the expressions: + - MSE (mean squared error) should be used for regression problems. + - Gaussian likelihood should be used for regression problem when you want to + fit the error term. + - Bernoulli likelihood should be used for classification problem. + - Poisson likelihood should be used when the data distribution follows a Poisson. + + optIter : int, default=50 + Number of iterations for the parameter optimization. + + optRepeat : int, default=2 + Number of restarts for the parameter optimization. + + nParams : int, default=-1 + Maximum number of parameters. If set to -1 it will + allow the expression to have any number of parameters. + If set to a number > 0, it will limit the number of parameters, + but allow it to appear multiple times in the expression. + E.g., t0 * x0 + exp(t0*x0 + t1) + + split : int, default=1 + How to split the data to create the validation set. + If set to 1, it will use the whole data for fitting the parameter and + calculating the fitness function. + If set to n>1, it will use 1/n for calculating the fitness function + and the reminder for fitting the parameter. + + simplify : bool, default=False + Whether to apply a final step of equality saturation to simplify the expressions. + + dumpTo : str, default="" + If not empty, it will save the final e-graph into the filename. + + loadFrom : str, default="" + If not empty, it will load an e-graph and resume the search. + The user must ensure that the loaded e-graph is from the same + dataset and loss function. + + Examples + -------- + >>> from pyeggp import PyEGGP + >>> import numpy as np + >>> X = np.arange(100).reshape(100, 1) + >>> y = np.zeros((100, )) + >>> estimator = PyEGGP() + >>> estimator.fit(X, y) + >>> + >>> estimator = PyEGGP(loss="Bernoulli") + >>> estimator.fit(X, y) + """ def __init__(self, gen = 100, nPop = 100, maxSize = 15, nTournament = 3, pc = 0.9, pm = 0.3, nonterminals = "add,sub,mul,div", loss = "MSE", optIter = 50, optRepeat = 2, nParams = -1, split = 1, simplify = False, dumpTo = "", loadFrom = ""): + nts = "add,sub,mul,div,power,powerabs,\ + aq,abs,sin,cos,tan,sinh,cosh,tanh,\ + asin,acos,atan,asinh,acosh,atanh,sqrt,\ + sqrtabs,cbrt,square,log,logabs,exp,recip,cube" + losses = ["MSE", "Gaussian", "Bernoulli", "Poisson"] + if gen < 1: + raise ValueError('gen should be greater than 1') + if nPop < 1: + raise ValueError('nPop should be greater than 1') + if maxSize < 1 or maxSize > 100: + raise ValueError('maxSize should be a value between 1 and 100') + if nTournament < 1 or nTournament > nPop: + raise ValueError('nTournament should be a value between 1 and nPop') + if pc < 0 or pc > 1: + raise ValueError('pc should be between 0 and 1') + if pm < 0 or pm > 1: + raise ValueError('pm should be between 0 and 1') + if any(t not in nts for t in nonterminals): + raise ValueError('nonterminals must be a comma separated list of one or more of ', nts) + if loss not in losses: + raise ValueError('loss must be one of ', losses) + if optIter < 0: + raise ValueError('optIter must be a positive number') + if optRepeat < 0: + raise ValueError('optRepeat must be a positive number') + if nParams < -1: + raise ValueError('nParams must be either -1 or a positive number') + if split < 1: + raise ValueError('split must be equal or greater than 1') + if not isinstance(simplify, bool): + raise TypeError('simplify must be a boolean') self.gen = gen self.nPop = nPop self.maxSize = maxSize @@ -84,6 +211,15 @@ def __init__(self, gen = 100, nPop = 100, maxSize = 15, nTournament = 3, pc = 0. self.is_fitted_ = False def fit(self, X, y): + ''' Fits the regression model. + + Parameters + ---------- + X : np.array + An m x n np.array describing m observations of n features. + y : np.array + An np.array of size m with the measured target values. + ''' if X.ndim == 1: X = X.reshape(-1,1) y = y.reshape(-1, 1) @@ -103,6 +239,15 @@ def fit(self, X, y): return self def fit_mvsr(self, Xs, ys): + ''' Fits a multi-view regression model. + + Parameters + ---------- + Xs : list(np.array) + A list with k elements of m_k x n np.arrays describing m_k observations of n features. + ys : list(np.array) + A list of k elements of np.arrays of size m_k with the measured target values. + ''' if Xs[0].ndim == 1: Xs = [X.reshape(-1,1) for X in Xs] ys = [y.reshape(-1, 1) for y in ys] @@ -124,10 +269,43 @@ def fit_mvsr(self, Xs, ys): return self def predict(self, X): + ''' Generates the prediction using the best model (selected by accuracy) + + Parameters + ---------- + X : np.array + An m x n np.array describing m observations of n features. + This array must have the same number of features as the training data. + + Return + ------ + y : np.array + A vector of predictions + + A table with the fitted models and additional information + will be stored as a Pandas dataframe in self.results. + ''' check_is_fitted(self) return self.evaluate_best_model(X) def predict_mvsr(self, X, view): + ''' Generates the prediction using the best model (selected by accuracy) + of the sepecified `view` + + Parameters + ---------- + X : np.array + An m x n np.array describing m observations of n features. + This array must have the same number of features as the training data. + + view : int + The index of the view (starting at 0). + + Return + ------ + y : np.array + A vector of predictions + ''' check_is_fitted(self) return self.evaluate_best_model_view(X, view) @@ -135,34 +313,57 @@ def evaluate_best_model(self, x): if x.ndim == 1: x = x.reshape(-1,1) t = np.array(list(map(float, self.results.iloc[-1].theta.split(";")))) - return eval(self.results.iloc[-1].Numpy) + y = eval(self.results.iloc[-1].Numpy) + if self.loss == "Bernoulli": + return 1/(1 + np.exp(-y)) + elif self.loss == "Poisson": + return np.exp(y) + return y def evaluate_best_model_view(self, x, view): if x.ndim == 1: x = x.reshape(-1,1) ix = self.results.iloc[-1].id best = self.results[self.results.id==ix].iloc[view] t = np.array(list(map(float, best.theta.split(";")))) - return eval(best.Numpy) + y = eval(best.Numpy) + if self.loss == "Bernoulli": + return 1/(1 + np.exp(-y)) + elif self.loss == "Poisson": + return np.exp(y) + return y def evaluate_model_view(self, x, ix, view): if x.ndim == 1: x = x.reshape(-1,1) best = self.results[self.results.id==ix].iloc[view] t = np.array(list(map(float, best.theta.split(";")))) - return eval(best.Numpy) + y = eval(best.Numpy) + if self.loss == "Bernoulli": + return 1/(1 + np.exp(-y)) + elif self.loss == "Poisson": + return np.exp(y) + return y def evaluate_model(self, ix, x): if x.ndim == 1: x = x.reshape(-1,1) t = np.array(list(map(float, self.results.iloc[-1].theta.split(";")))) - return eval(self.results.iloc[i].Numpy) + y = eval(self.results.iloc[i].Numpy) + if self.loss == "Bernoulli": + return 1/(1 + np.exp(-y)) + elif self.loss == "Poisson": + return np.exp(y) + return y def score(self, X, y): + ''' Calculates the score (single-view only). + ''' ypred = self.evaluate_best_model(X) return r2_score(y, ypred) def get_model(self, idx): + ''' Get a `model` function and its visual representation. ''' alphabet = list(string.ascii_uppercase) row = self.results[self.results['id']==idx].iloc[0] visual_expression = row['Numpy'] - model = make_function(visual_expression) + model = make_function(visual_expression, self.loss) n_params_used = len(row['theta'].split(sep=';')) # Works for solutions with less than 26 parameters diff --git a/test/test_pyeggp.py b/test/test_pyeggp.py index 4b5c242..7809f15 100644 --- a/test/test_pyeggp.py +++ b/test/test_pyeggp.py @@ -15,7 +15,7 @@ X = Z[:,:-1] y = Z[:,-1] -reg = PyEGGP(100, 100, 10, 3, 0.9, 0.3, "add,sub,mul,div,log", "MSE", 50, 2, -1, 3, 0, "", "") +reg = PyEGGP(100, 100, 10, 3, 0.9, 0.3, "add,sub,mul,div,log", "MSE", 50, 2, -1, 3, True, "", "") reg.fit(X, y) print(reg.score(X, y)) From 48031f5bb2fb0b81e703fd69745e5c7e9efb57d1 Mon Sep 17 00:00:00 2001 From: folivetti Date: Tue, 20 May 2025 16:43:46 -0300 Subject: [PATCH 065/166] 1.0.5 --- README.md | 145 +++++++++- cabal.project | 1 + pyeggp.cabal => eggp.cabal | 23 +- pyproject.toml | 12 +- setup.py | 4 +- src/EGGP.hs | 248 ++++++++++++++++ src/Random.hs | 54 ---- src/{Pyeggp.hs => Search.hs} | 238 ++++++--------- src/Util.hs | 271 ------------------ .../PKG-INFO | 179 +++++++++++- src/eggp.egg-info/SOURCES.txt | 17 ++ .../dependency_links.txt | 0 src/eggp.egg-info/entry_points.txt | 2 + .../requires.txt | 0 src/eggp.egg-info/top_level.txt | 2 + src/{pyeggp => eggp}/__init__.py | 142 ++++++--- src/{pyeggp => eggp}/__main__.py | 4 +- src/{pyeggp => eggp}/_binding.pyi | 2 +- src/{pyeggp => eggp}/binding.def | 0 src/eggp/binding.i | 64 +++++ src/eggp/typing.py | 14 + src/pyeggp.egg-info/SOURCES.txt | 17 -- src/pyeggp.egg-info/entry_points.txt | 2 - src/pyeggp.egg-info/top_level.txt | 1 - src/pyeggp/binding.i | 64 ----- src/pyeggp/typing.py | 14 - test/test_pyeggp.py | 10 +- 27 files changed, 882 insertions(+), 648 deletions(-) rename pyeggp.cabal => eggp.cabal (84%) create mode 100644 src/EGGP.hs delete mode 100644 src/Random.hs rename src/{Pyeggp.hs => Search.hs} (66%) delete mode 100644 src/Util.hs rename src/{pyeggp.egg-info => eggp.egg-info}/PKG-INFO (87%) create mode 100644 src/eggp.egg-info/SOURCES.txt rename src/{pyeggp.egg-info => eggp.egg-info}/dependency_links.txt (100%) create mode 100644 src/eggp.egg-info/entry_points.txt rename src/{pyeggp.egg-info => eggp.egg-info}/requires.txt (100%) create mode 100644 src/eggp.egg-info/top_level.txt rename src/{pyeggp => eggp}/__init__.py (69%) rename src/{pyeggp => eggp}/__main__.py (67%) rename src/{pyeggp => eggp}/_binding.pyi (80%) rename src/{pyeggp => eggp}/binding.def (100%) create mode 100644 src/eggp/binding.i create mode 100644 src/eggp/typing.py delete mode 100644 src/pyeggp.egg-info/SOURCES.txt delete mode 100644 src/pyeggp.egg-info/entry_points.txt delete mode 100644 src/pyeggp.egg-info/top_level.txt delete mode 100644 src/pyeggp/binding.i delete mode 100644 src/pyeggp/typing.py diff --git a/README.md b/README.md index bc0917c..694c515 100644 --- a/README.md +++ b/README.md @@ -1,16 +1,135 @@ -# PyEGGP +# eggp - e-graph GP -A Python package for symbolic regression using e-graph-based genetic programming. PyEGGP provides a scikit-learn compatible API for evolutionary symbolic regression tasks. +*eggp* (**e**-**g**raph **g**enetic **p**rogramming), follows the same structure as the traditional GP. The initial population is created using ramped half-and-half respecting a maximum size and maximum depth parameter and, for a number of generations, it will choose two parents using tournament selection, apply the subtree crossover with probability $pc$ followed by the subtree mutation with probability $pm$, when the offsprings replace the current population following a dominance criteria. -More info [here](https://github.com/folivetti/srtree/tree/main/apps/eggp) +The key differences of *eggp* are: -## Installation + - new solutions are inserted into the e-graph followed by one step of equality saturation to find and store some of the equivalent expressions of the new offspring. + - the current population is replaced by the set of individuals formed by: the Pareto front, the next front after excluding the first Pareto-front, and a selection of the last offspring at random until it reaches the desired population size. + - the subtree crossover and mutation are modified to try to generate an unvisited exp + +This repository provides a CLI and a Python package for eggp with a scikit-learn compatible API for symbolic regression. + +Instructions: + +- [CLI version](#cli) +- [Python version](#python) + +## CLI + +### How to use + +```bash +eggp - E-graph Genetic Programming for Symbolic Regression. + +Usage: eggp (-d|--dataset INPUT-FILE) [-t|--test ARG] [-g|--generations GENS] + (-s|--maxSize ARG) [-k|--split ARG] [--print-pareto] [--trace] + [--loss ARG] [--opt-iter ARG] [--opt-retries ARG] + [--number-params ARG] [--nPop ARG] [--tournament-size ARG] + [--pc ARG] [--pm ARG] [--non-terminals ARG] [--dump-to ARG] + [--load-from ARG] [--moo] + + An implementation of GP with modified crossover and mutation operators + designed to exploit equality saturation and e-graphs. + https://arxiv.org/abs/2501.17848 + +Available options: + -d,--dataset INPUT-FILE CSV dataset. + -t,--test ARG test data (default: "") + -g,--generations GENS Number of generations. (default: 100) + -s,--maxSize ARG max-size. + -k,--split ARG k-split ratio training-validation (default: 1) + --print-pareto print Pareto front instead of best found expression + --trace print all evaluated expressions. + --loss ARG loss function: MSE, Gaussian, Poisson, Bernoulli. + (default: MSE) + --opt-iter ARG number of iterations in parameter optimization. + (default: 30) + --opt-retries ARG number of retries of parameter fitting. (default: 1) + --number-params ARG maximum number of parameters in the model. If this + argument is absent, the number is bounded by the + maximum size of the expression and there will be no + repeated parameter. (default: -1) + --nPop ARG population size (Default: 100). (default: 100) + --tournament-size ARG tournament size. (default: 2) + --pc ARG probability of crossover. (default: 1.0) + --pm ARG probability of mutation. (default: 0.3) + --non-terminals ARG set of non-terminals to use in the search. + (default: "Add,Sub,Mul,Div,PowerAbs,Recip") + --dump-to ARG dump final e-graph to a file. (default: "") + --load-from ARG load initial e-graph from a file. (default: "") + --moo replace the current population with the pareto front + instead of replacing it with the generated children. + -h,--help Show this help text +``` + +The dataset file must contain a header with each features name, and the `--dataset` and `--test` arguments can be accompanied by arguments separated by ':' following the format: + +`filename.ext:start_row:end_row:target:features` + +where each ':' field is optional. The fields are: + +- **start_row:end_row** is the range of the training rows (default 0:nrows-1). + every other row not included in this range will be used as validation +- **target** is either the name of the (if the datafile has headers) or the index + of the target variable +- **features** is a comma separated list of names or indices to be used as + input variables of the regression model. + +Example of valid names: `dataset.csv`, `mydata.tsv`, `dataset.csv:20:100`, `dataset.tsv:20:100:price:m2,rooms,neighborhood`, `dataset.csv:::5:0,1,2`. + +The format of the file will be determined by the extension (e.g., csv, tsv,...). To use multi-view, simply pass multiple filenames in double-quotes: + +```bash +eggp --dataset "dataset1.csv dataset2.csv dataset3.csv" ... +``` + +### Installation + +To install eggp you'll need: + +- `libz` +- `libnlopt` +- `libgmp` +- `ghc-9.6.6` +- `cabal` or `stack` + +### Method 1: PIP + +Simply run: ```bash -pip install pyeggp +pip install eggp +``` + +under your Python environment. + +### Method 2: cabal + +After installing the dependencies (e.g., `apt install libz libnlopt libgmp`), install [`ghcup`](https://www.haskell.org/ghcup/#) + +For Linux, macOS, FreeBSD or WSL2: + +```bash +curl --proto '=https' --tlsv1.2 -sSf https://get-ghcup.haskell.org | sh ``` -## Features +For Windows, run the following in a PowerShell: + +```bash +Set-ExecutionPolicy Bypass -Scope Process -Force;[System.Net.ServicePointManager]::SecurityProtocol = [System.Net.ServicePointManager]::SecurityProtocol -bor 3072; try { & ([ScriptBlock]::Create((Invoke-WebRequest https://www.haskell.org/ghcup/sh/bootstrap-haskell.ps1 -UseBasicParsing))) -Interactive -DisableCurl } catch { Write-Error $_ } +``` + +After the installation, run `ghcup tui` and install the latest `stack` or `cabal` together with `ghc-9.6.6` (select the items and press `i`). +To install `srsimplify` simply run: + +```bash +cabal install +``` + +## Python + +### Features - Scikit-learn compatible API with `fit()` and `predict()` methods - Genetic programming approach with e-graph representation @@ -22,7 +141,7 @@ pip install pyeggp - Optional expression simplification through equality saturation - Ability to save and load e-graphs -## Usage +### Usage ### Basic Example @@ -107,7 +226,7 @@ print(f"Test MSE: {mse}") | `dumpTo` | str | "" | Filename to save the final e-graph | | `loadFrom` | str | "" | Filename to load an e-graph to resume search | -## Available Functions +### Available Functions The following functions can be used in the `nonterminals` parameter: @@ -118,7 +237,7 @@ The following functions can be used in the `nonterminals` parameter: - Hyperbolic: `sinh`, `cosh`, `tanh`, `asinh`, `acosh`, `atanh` - Others: `abs`, `log`, `logabs`, `exp`, `recip`, `aq` (analytical quotient) -## Methods +### Methods ### Core Methods - `fit(X, y)`: Fits the symbolic regression model @@ -136,7 +255,7 @@ The following functions can be used in the `nonterminals` parameter: - `evaluate_model(ix, X)`: Evaluates the model with index `ix` on the given data - `get_model(idx)`: Returns a model function and its visual representation -## Results +### Results After fitting, the `results` attribute contains a pandas DataFrame with details about the discovered models, including: - Mathematical expressions @@ -175,4 +294,10 @@ archivePrefix = {arXiv}, } ``` +## Acknowledgments + The bindings were created following the amazing example written by [wenkokke](https://github.com/wenkokke/example-haskell-wheel) + +Fabricio Olivetti de Franca is supported by Conselho Nacional de Desenvolvimento Cient\'{i}fico e Tecnol\'{o}gico (CNPq) grant 301596/2022-0. + +Gabriel Kronberger is supported by the Austrian Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology, the Federal Ministry for Labour and Economy, and the regional government of Upper Austria within the COMET project ProMetHeus (904919) supported by the Austrian Research Promotion Agency (FFG). diff --git a/cabal.project b/cabal.project index c466d2a..b3742e8 100644 --- a/cabal.project +++ b/cabal.project @@ -5,3 +5,4 @@ source-repository-package type: git location: https://github.com/haskell/mwc-random tag: 2cce257158befe52417433d1e7717f11ce718aec + diff --git a/pyeggp.cabal b/eggp.cabal similarity index 84% rename from pyeggp.cabal rename to eggp.cabal index cc2698c..d61feb4 100755 --- a/pyeggp.cabal +++ b/eggp.cabal @@ -4,13 +4,13 @@ cabal-version: 2.2 -- -- see: https://github.com/sol/hpack -name: Pyeggp -version: 1.0.4 +name: eggp +version: 1.0.5 synopsis: A general library to work with Symbolic Regression expression trees. description: A Symbolic Regression Tree data structure to work with mathematical expressions with support to first order derivative and simplification; category: Math, Data, Data Structures -homepage: https://github.com/folivetti/pyeggp#readme -bug-reports: https://github.com/folivetti/pyeggp/issues +homepage: https://github.com/folivetti/eggp#readme +bug-reports: https://github.com/folivetti/eggp/issues author: Fabricio Olivetti de França maintainer: fabricio.olivetti@gmail.com copyright: 2023 Fabricio Olivetti de França @@ -22,7 +22,7 @@ extra-source-files: source-repository head type: git - location: https://github.com/folivetti/pyeggp + location: https://github.com/folivetti/eggp custom-setup setup-depends: @@ -45,15 +45,14 @@ foreign-library _binding if os(windows) options: standalone - mod-def-file: src/pyeggp/binding.def + mod-def-file: src/eggp/binding.def - c-sources: src/pyeggp/binding_wrap.c + c-sources: src/eggp/binding_wrap.c hs-source-dirs: src other-modules: - Pyeggp - Util - Random - Paths_Pyeggp + EGGP + Search + Paths_eggp extra-libraries: nlopt build-depends: @@ -76,7 +75,7 @@ foreign-library _binding , random >=1.2 && <1.4 , scheduler >=2.0.0.1 && <3 , split >=0.2.5 && <0.3 - , srtree >= 2.0.1.2 + , srtree >= 2.0.1.3 , statistics >=0.16.2.1 && <0.17 , transformers >=0.6.1.0 && <0.7 , unliftio >=0.2.10 && <1 diff --git a/pyproject.toml b/pyproject.toml index e3be999..3b7ae1f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -11,8 +11,8 @@ requires = [ build-backend = "setuptools.build_meta" [project] -name = "pyeggp" -version = "1.0.4" +name = "eggp" +version = "1.0.5" authors = [{ name = "Fabricio Olivetti", email = "folivetti@users.noreply.github.com" }] description = "Python Wheels for eggp algorithm." readme = "README.md" @@ -29,13 +29,13 @@ classifiers = [ "Programming Language :: Python :: 3.13", "Programming Language :: Python :: Implementation :: CPython", ] -dependencies = ["typing_extensions >=4.6,<5; python_version<'3.8'"] +dependencies = ["typing_extensions >=4.6,<5; python_version<'3.8'", "numpy >=2", "pandas >=2.2", "scikit-learn >=1.6"] [project.optional-dependencies] mypy = ["types_setuptools >=45"] [project.scripts] -"pyeggp" = "pyeggp.__main__:main" +"eggp" = "eggp.__main__:main" [tool.bumpver] current_version = "v1.0.0" @@ -50,10 +50,10 @@ push = true '^version = "{pep440_version}"$', '^current_version = "{version}"$', ] -"src/pyeggp/__init__.py" = [ +"src/eggp/__init__.py" = [ '^VERSION: str = "{pep440_version}"$', ] -"pyeggp.cabal" = ['^version: {pep440_version}$'] +"eggp.cabal" = ['^version: {pep440_version}$'] [tool.cibuildwheel] # 18-04-2023: diff --git a/setup.py b/setup.py index c1229d1..55e6658 100644 --- a/setup.py +++ b/setup.py @@ -11,8 +11,8 @@ from setuptools.command.build_ext import build_ext ext_module = Extension( - name="pyeggp._binding", - sources=["src/pyeggp/binding.i"], + name="eggp._binding", + sources=["src/eggp/binding.i"], ) diff --git a/src/EGGP.hs b/src/EGGP.hs new file mode 100644 index 0000000..bb49be3 --- /dev/null +++ b/src/EGGP.hs @@ -0,0 +1,248 @@ +{-# LANGUAGE BlockArguments #-} +{-# LANGUAGE TupleSections #-} +{-# LANGUAGE MultiWayIf #-} +{-# LANGUAGE OverloadedStrings #-} +{-# LANGUAGE BangPatterns #-} +{-# LANGUAGE TypeSynonymInstances, FlexibleInstances #-} + +module EGGP where + +import Algorithm.EqSat.Egraph +import Algorithm.EqSat.Simplify +import Algorithm.EqSat.Build +import Algorithm.EqSat.Queries +import Algorithm.EqSat.Info +import Algorithm.EqSat.DB +import Algorithm.SRTree.Likelihoods +import Algorithm.SRTree.ModelSelection +import Algorithm.SRTree.Opt +import Control.Lens (element, makeLenses, over, (&), (+~), (-~), (.~), (^.)) +import Control.Monad (foldM, forM_, forM, when, unless, filterM, (>=>), replicateM, replicateM_) +import Control.Monad.State.Strict +import qualified Data.IntMap.Strict as IM +import qualified Data.Map.Strict as Map +import Data.Massiv.Array as MA hiding (forM_, forM) +import Data.Maybe (fromJust, isNothing, isJust) +import Data.SRTree +import Data.SRTree.Datasets +import Data.SRTree.Eval +import Data.SRTree.Random (randomTree) +import Data.SRTree.Print +import System.Random +import qualified Data.HashSet as Set +import Data.List ( sort, maximumBy, intercalate, sortOn, intersperse, nub ) +import Data.IntSet (IntSet) +import qualified Data.IntSet as IntSet +import qualified Data.Sequence as FingerTree +import Data.Function ( on ) +import qualified Data.Foldable as Foldable +import qualified Data.IntMap as IntMap +import List.Shuffle ( shuffle ) +import Algorithm.SRTree.NonlinearOpt +import Data.Binary ( encode, decode ) +import qualified Data.ByteString.Lazy as BS + +import Algorithm.EqSat (runEqSat,applySingleMergeOnlyEqSat) + +import GHC.IO (unsafePerformIO) +import Control.Scheduler +import Control.Monad.IO.Unlift +import Data.SRTree (convertProtectedOps) +import Options.Applicative as Opt hiding (Const) + +import Search +import Algorithm.EqSat.SearchSR +import Data.SRTree.Random +import Data.SRTree.Datasets + +import Foreign.C (CInt (..), CDouble (..)) +import Foreign.C.String (CString, newCString, withCString, peekCString, peekCAString, newCAString) +import Paths_eggp (version) +import System.Environment (getArgs) +import System.Exit (ExitCode (..)) +import Text.Read (readMaybe) +import Data.Version (showVersion) +import Control.Exception (Exception (..), SomeException (..), handle) + +foreign import ccall unsafe_py_write_stdout :: CString -> IO () + +py_write_stdout :: String -> IO () +py_write_stdout str = withCString str unsafe_py_write_stdout + +foreign import ccall unsafe_py_write_stderr :: CString -> IO () + +py_write_stderr :: String -> IO () +py_write_stderr str = withCString str unsafe_py_write_stderr + +foreign export ccall hs_eggp_version :: IO CString + +hs_eggp_version :: IO CString +hs_eggp_version = + newCString (showVersion version) + +foreign export ccall hs_eggp_main :: IO CInt + +exitHandler :: ExitCode -> IO CInt +exitHandler ExitSuccess = return 0 +exitHandler (ExitFailure n) = return (fromIntegral n) + +uncaughtExceptionHandler :: SomeException -> IO CInt +uncaughtExceptionHandler (SomeException e) = + py_write_stderr (displayException e) >> return 1 + +hs_eggp_main :: IO CInt +hs_eggp_main = + handle uncaughtExceptionHandler $ + handle exitHandler $ do + args <- execParser opts + g <- getStdGen + let datasets = words (_dataset args) + dataTrains' <- Prelude.mapM (flip loadTrainingOnly True) datasets -- load all datasets + dataTests <- if null (_testData args) + then pure dataTrains' + else Prelude.mapM (flip loadTrainingOnly True) $ words (_testData args) + + let (dataTrainVals, g') = runState (Prelude.mapM (`splitData` (_folds args)) dataTrains') g + alg = evalStateT (egraphGP dataTrainVals dataTests args) emptyGraph + out <- evalStateT alg g' + py_write_stdout out + + return 0 + where + opts = Opt.info (opt <**> helper) + ( fullDesc <> progDesc "An implementation of GP with modified crossover and mutation\ + \ operators designed to exploit equality saturation and e-graphs.\ + \ https://arxiv.org/abs/2501.17848\n" + <> header "eggp - E-graph Genetic Programming for Symbolic Regression." ) + +foreign export ccall hs_eggp_run :: CString -> CInt -> CInt -> CInt -> CInt -> CDouble -> CDouble -> CString -> CString -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CString -> CString -> IO CString + +hs_eggp_run :: CString -> CInt -> CInt -> CInt -> CInt -> CDouble -> CDouble -> CString -> CString -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CString -> CString -> IO CString +hs_eggp_run dataset gens nPop maxSize nTournament pc pm nonterminals loss optIter optRepeat nParams folds simplify trace generational dumpTo loadFrom = do + dataset' <- peekCString dataset + nonterminals' <- peekCString nonterminals + loss' <- peekCString loss + dumpTo' <- peekCString dumpTo + loadFrom' <- peekCString loadFrom + out <- eggp_run dataset' (fromIntegral gens) (fromIntegral nPop) (fromIntegral maxSize) (fromIntegral nTournament) (realToFrac pc) (realToFrac pm) nonterminals' loss' (fromIntegral optIter) (fromIntegral optRepeat) (fromIntegral nParams) (fromIntegral folds) (simplify /= 0) (trace /= 0) (generational /= 0) dumpTo' loadFrom' + newCString out + +opt :: Parser Args +opt = Args + <$> strOption + ( long "dataset" + <> short 'd' + <> metavar "INPUT-FILE" + <> help "CSV dataset." ) + <*> strOption + ( long "test" + <> short 't' + <> value "" + <> showDefault + <> help "test data") + <*> option auto + ( long "generations" + <> short 'g' + <> metavar "GENS" + <> showDefault + <> value 100 + <> help "Number of generations." ) + <*> option auto + ( long "maxSize" + <> short 's' + <> help "max-size." ) + <*> option auto + ( long "folds" + <> short 'k' + <> value 1 + <> showDefault + <> help "number of folds to determine the ratio of training-validation") + <*> switch + ( long "trace" + <> help "print all evaluated expressions.") + <*> option auto + ( long "loss" + <> value MSE + <> showDefault + <> help "loss function: MSE, Gaussian, Poisson, Bernoulli.") + <*> option auto + ( long "opt-iter" + <> value 30 + <> showDefault + <> help "number of iterations in parameter optimization.") + <*> option auto + ( long "opt-retries" + <> value 1 + <> showDefault + <> help "number of retries of parameter fitting.") + <*> option auto + ( long "number-params" + <> value (-1) + <> showDefault + <> help "maximum number of parameters in the model. If this argument is absent, the number is bounded by the maximum size of the expression and there will be no repeated parameter.") + <*> option auto + ( long "nPop" + <> value 100 + <> showDefault + <> help "population size (Default: 100).") + <*> option auto + ( long "tournament-size" + <> value 2 + <> showDefault + <> help "tournament size.") + <*> option auto + ( long "pc" + <> value 1.0 + <> showDefault + <> help "probability of crossover.") + <*> option auto + ( long "pm" + <> value 0.3 + <> showDefault + <> help "probability of mutation.") + <*> strOption + ( long "non-terminals" + <> value "Add,Sub,Mul,Div,PowerAbs,Recip" + <> showDefault + <> help "set of non-terminals to use in the search." + ) + <*> strOption + ( long "dump-to" + <> value "" + <> showDefault + <> help "dump final e-graph to a file." + ) + <*> strOption + ( long "load-from" + <> value "" + <> showDefault + <> help "load initial e-graph from a file." + ) + <*> switch + ( long "generational" + <> help "replace the current population with the children instead of keeping the pareto front." + ) + <*> switch + ( long "simplify" + <> help "simplify the expressions before displaying them." + ) + +eggp_run :: String -> Int -> Int -> Int -> Int -> Double -> Double -> String -> String -> Int -> Int -> Int -> Int -> Bool -> Bool -> Bool -> String -> String -> IO String +eggp_run dataset gens nPop maxSize nTournament pc pm nonterminals loss optIter optRepeat nParams folds simplify trace generational dumpTo loadFrom = + case readMaybe loss of + Nothing -> pure $ "Invalid loss function " <> loss + Just l -> let arg = Args dataset "" gens maxSize folds trace l optIter optRepeat nParams nPop nTournament pc pm nonterminals dumpTo loadFrom generational simplify + in eggp arg + +eggp :: Args -> IO String +eggp args = do + g <- getStdGen + let datasets = words (_dataset args) + dataTrains' <- Prelude.mapM (flip loadTrainingOnly True) datasets -- load all datasets + dataTests <- if null (_testData args) + then pure dataTrains' + else Prelude.mapM (flip loadTrainingOnly True) $ words (_testData args) + + let (dataTrainVals, g') = runState (Prelude.mapM (`splitData` (_folds args)) dataTrains') g + alg = evalStateT (egraphGP dataTrainVals dataTests args) emptyGraph + evalStateT alg g' diff --git a/src/Random.hs b/src/Random.hs deleted file mode 100644 index 95d239e..0000000 --- a/src/Random.hs +++ /dev/null @@ -1,54 +0,0 @@ -module Random where - -import System.Random -import Control.Monad.State.Strict -import Control.Monad -import Data.SRTree -import Data.SRTree.Eval -import Data.Massiv.Array as MA - -type Rng a = StateT StdGen IO a - -toss :: Rng Bool -toss = state random -{-# INLINE toss #-} - -tossBiased :: Double -> Rng Bool -tossBiased p = do r <- state random - pure (r < p) - -randomVal :: Rng Double -randomVal = state random - -randomRange :: (Ord val, Random val) => (val, val) -> Rng val -randomRange rng = state (randomR rng) -{-# INLINE randomRange #-} - -randomFrom :: [a] -> Rng a -randomFrom funs = do n <- randomRange (0, length funs - 1) - pure $ funs !! n -{-# INLINE randomFrom #-} - -randomVec :: Int -> Rng PVector -randomVec n = MA.fromList compMode <$> replicateM n (randomRange (-1, 1)) - -randomTree :: Int -> Int -> Int -> Rng (Fix SRTree) -> Rng (SRTree ()) -> Bool -> Rng (Fix SRTree) -randomTree minDepth maxDepth maxSize genTerm genNonTerm grow - | noSpaceLeft = genTerm - | needNonTerm = genRecursion - | otherwise = do r <- toss - if r - then genTerm - else genRecursion - where - noSpaceLeft = maxDepth <= 1 || maxSize <= 2 - needNonTerm = (minDepth >= 0 || (maxDepth > 2 && not grow)) -- && maxSize > 2 - - genRecursion = do - node <- genNonTerm - case node of - Uni f _ -> Fix . Uni f <$> randomTree (minDepth - 1) (maxDepth - 1) (maxSize - 1) genTerm genNonTerm grow - Bin op _ _ -> do l <- randomTree (minDepth - 1) (maxDepth - 1) (if grow then maxSize - 2 else maxSize `div` 2) genTerm genNonTerm grow - r <- randomTree (minDepth - 1) (maxDepth - 1) (maxSize - 1 - countNodes l) genTerm genNonTerm grow - pure . Fix $ Bin op l r -{-# INLINE randomTree #-} diff --git a/src/Pyeggp.hs b/src/Search.hs similarity index 66% rename from src/Pyeggp.hs rename to src/Search.hs index cda0842..6318d80 100644 --- a/src/Pyeggp.hs +++ b/src/Search.hs @@ -5,7 +5,7 @@ {-# LANGUAGE BangPatterns #-} {-# LANGUAGE TypeSynonymInstances, FlexibleInstances #-} -module Pyeggp where +module Search where import Algorithm.EqSat.Egraph import Algorithm.EqSat.Simplify @@ -28,10 +28,9 @@ import Data.SRTree.Datasets import Data.SRTree.Eval import Data.SRTree.Random (randomTree) import Data.SRTree.Print -import Random import System.Random import qualified Data.HashSet as Set -import Data.List ( sort, maximumBy, intercalate, sortOn, intersperse, nub ) +import Data.List ( sort, maximumBy, intercalate, sortOn, intersperse, nub, zip4 ) import Data.IntSet (IntSet) import qualified Data.IntSet as IntSet import qualified Data.Sequence as FingerTree @@ -50,97 +49,92 @@ import Control.Scheduler import Control.Monad.IO.Unlift import Data.SRTree (convertProtectedOps) -import Util +import Data.SRTree.Random +import Data.SRTree.Datasets +import Text.ParseSR +import Algorithm.EqSat.SearchSR import Foreign.C (CInt (..), CDouble (..)) import Foreign.C.String (CString, newCString, withCString, peekCString, peekCAString, newCAString) -import Paths_Pyeggp (version) +import Paths_eggp (version) import System.Environment (getArgs) import System.Exit (ExitCode (..)) import Text.Read (readMaybe) import Data.Version (showVersion) import Control.Exception (Exception (..), SomeException (..), handle) -foreign import ccall unsafe_py_write_stdout :: CString -> IO () - -py_write_stdout :: String -> IO () -py_write_stdout str = withCString str unsafe_py_write_stdout - -foreign import ccall unsafe_py_write_stderr :: CString -> IO () - -py_write_stderr :: String -> IO () -py_write_stderr str = withCString str unsafe_py_write_stderr - -foreign export ccall hs_pyeggp_version :: IO CString - -hs_pyeggp_version :: IO CString -hs_pyeggp_version = - newCString (showVersion version) - -foreign export ccall hs_pyeggp_main :: IO CInt - -exitHandler :: ExitCode -> IO CInt -exitHandler ExitSuccess = return 0 -exitHandler (ExitFailure n) = return (fromIntegral n) - -uncaughtExceptionHandler :: SomeException -> IO CInt -uncaughtExceptionHandler (SomeException e) = - py_write_stderr (displayException e) >> return 1 - -hs_pyeggp_main :: IO CInt -hs_pyeggp_main = - handle uncaughtExceptionHandler $ - handle exitHandler $ do - getArgs >>= \args -> do - --out <- pyeggp args - case "" of - "" -> py_write_stdout $ "wrong arguments" - css -> py_write_stdout css - return 0 - -foreign export ccall hs_pyeggp_run :: CString -> CInt -> CInt -> CInt -> CInt -> CDouble -> CDouble -> CString -> CString -> CInt -> CInt -> CInt -> CInt -> CInt -> CString -> CString -> IO CString +data Args = Args + { _dataset :: String, + _testData :: String, + _gens :: Int, + _maxSize :: Int, + _folds :: Int, + _trace :: Bool, + _distribution :: Distribution, + _optIter :: Int, + _optRepeat :: Int, + _nParams :: Int, + _nPop :: Int, + _nTournament :: Int, + _pc :: Double, + _pm :: Double, + _nonterminals :: String, + _dumpTo :: String, + _loadFrom :: String, + _generational :: Bool, + _simplify :: Bool + } + deriving (Show) -hs_pyeggp_run :: CString -> CInt -> CInt -> CInt -> CInt -> CDouble -> CDouble -> CString -> CString -> CInt -> CInt -> CInt -> CInt -> CInt -> CString -> CString -> IO CString -hs_pyeggp_run dataset gens nPop maxSize nTournament pc pm nonterminals loss optIter optRepeat nParams split simplify dumpTo loadFrom = do - dataset' <- peekCString dataset - nonterminals' <- peekCString nonterminals - loss' <- peekCString loss - dumpTo' <- peekCString dumpTo - loadFrom' <- peekCString loadFrom - out <- pyeggp dataset' (fromIntegral gens) (fromIntegral nPop) (fromIntegral maxSize) (fromIntegral nTournament) (realToFrac pc) (realToFrac pm) nonterminals' loss' (fromIntegral optIter) (fromIntegral optRepeat) (fromIntegral nParams) (fromIntegral split) (simplify /= 0) dumpTo' loadFrom' - newCString out +csvHeader :: String +csvHeader = "id,view,Expression,Numpy,theta,size,loss_train,loss_val,loss_test,maxloss,R2_train,R2_val,R2_test,mdl_train,mdl_val,mdl_test" -egraphGP :: [(DataSet, DataSet)] -> Args -> StateT EGraph (StateT StdGen IO) String -egraphGP dataTrainVals args = do +egraphGP :: [(DataSet, DataSet)] -> [DataSet] -> Args -> StateT EGraph (StateT StdGen IO) String +egraphGP dataTrainVals dataTests args = do when ((not.null) (_loadFrom args)) $ (io $ BS.readFile (_loadFrom args)) >>= \eg -> put (decode eg) + insertTerms + evaluateUnevaluated fitFun + pop <- replicateM (_nPop args) $ do ec <- insertRndExpr (_maxSize args) rndTerm rndNonTerm >>= canonical updateIfNothing fitFun ec pure ec - insertTerms - evaluateUnevaluated fitFun pop' <- Prelude.mapM canonical pop + + output <- if _trace args + then forM (Prelude.zip [0..] pop') $ uncurry printExpr + else pure [] + let m = (_nPop args) `div` (_maxSize args) - finalPop <- iterateFor (_gens args) pop' $ \it ps' -> do + (finalPop, finalOut, _) <- iterateFor (_gens args) (pop', output, _nPop args) $ \it (ps', out, curIx) -> do newPop' <- replicateM (_nPop args) (evolve ps') + out' <- if _trace args + then forM (Prelude.zip [curIx..] newPop') $ uncurry printExpr + else pure [] + totSz <- gets (Map.size . _eNodeToEClass) -- (IntMap.size . _eClass) let full = totSz > max maxMem (_nPop args) when full (cleanEGraph >> cleanDB) - newPop <- do let n_paretos = (_nPop args) `div` (_maxSize args) - pareto <- concat <$> (forM [1 .. _maxSize args] $ \n -> getTopFitEClassWithSize n 2) - let remainder = _nPop args - length pareto - lft <- if full - then getTopFitEClassThat remainder (const True) - else pure $ Prelude.take remainder newPop' - Prelude.mapM canonical (pareto <> lft) - pure newPop + newPop <- if _generational args + then Prelude.mapM canonical newPop' + else do + let n_paretos = (_nPop args) `div` (_maxSize args) + pareto <- concat <$> (forM [1 .. _maxSize args] $ \n -> getTopFitEClassWithSize n 2) + let remainder = _nPop args - length pareto + lft <- if full + then getTopFitEClassThat remainder (const True) + else pure $ Prelude.take remainder newPop' + Prelude.mapM canonical (pareto <> lft) + pure (newPop, out <> out', curIx + (_nPop args)) when ((not.null) (_dumpTo args)) $ get >>= (io . BS.writeFile (_dumpTo args) . encode ) - pf <- paretoFront fitFun (_maxSize args) printExpr - pure $ "id,view,Expression,Numpy,theta,size,loss,maxLoss\n" <> pf + pf <- if _trace args + then pure finalOut + else paretoFront fitFun (_maxSize args) printExpr + pure $ unlines (csvHeader : concat pf) where maxSize = (_maxSize args) maxMem = 2000000 -- running 1 iter of eqsat for each new individual will consume ~3GB @@ -173,8 +167,8 @@ egraphGP dataTrainVals args = do insertFitness eId (fromJust $ _fitness info) (_theta info) rndTerm = do coin <- toss - if coin then Random.randomFrom terms else Random.randomFrom params - rndNonTerm = Random.randomFrom nonTerms + if coin then randomFrom terms else randomFrom params + rndNonTerm = randomFrom nonTerms refitChanged = do ids <- gets (_refits . _eDB) >>= Prelude.mapM canonical . Set.toList >>= pure . nub modify' $ over (eDB . refits) (const Set.empty) @@ -316,89 +310,47 @@ egraphGP dataTrainVals args = do r' <- getBestExpr r pure . Fix $ Bin op l' r' - checkToken parent en' = do en <- canonize en' - mEc <- gets ((Map.!? en) . _eNodeToEClass) - case mEc of - Nothing -> pure True - Just ec -> do ec' <- canonical ec - ec'' <- canonize (parent ec') - not <$> doesExist ec'' - doesExist, doesNotExist :: ENode -> RndEGraph Bool - doesExist en = gets ((Map.member en) . _eNodeToEClass) - doesNotExist en = gets ((Map.notMember en) . _eNodeToEClass) - - doesNotExistGens :: [Maybe (EClassId -> ENode)] -> ENode -> RndEGraph Bool - doesNotExistGens [] en = gets ((Map.notMember en) . _eNodeToEClass) - doesNotExistGens (mGrand:grands) en = do b <- gets ((Map.notMember en) . _eNodeToEClass) - if b - then pure True - else case mGrand of - Nothing -> pure False - Just gf -> do ec <- gets ((Map.! en) . _eNodeToEClass) - en' <- canonize (gf ec) - doesNotExistGens grands en' - - printExpr :: Int -> EClassId -> RndEGraph String + + printExpr :: Int -> EClassId -> RndEGraph [String] printExpr ix ec = do thetas' <- gets (_theta . _info . (IM.! ec) . _eClass) bestExpr <- (if _simplify args then simplifyEqSatDefault else id) <$> getBestExpr ec - let nParams = countParamsUniq bestExpr - fromSz (MA.Sz x) = x - nThetas = Prelude.map (fromSz . MA.size) thetas' - (_, thetas) <- if Prelude.any (/=nParams) nThetas || _simplify args - then fitFun bestExpr + + let best' = if shouldReparam then relabelParams bestExpr else relabelParamsOrder bestExpr + nParams = countParamsUniq best' + fromSz (MA.Sz x) = x + nThetas = Prelude.map (fromSz . MA.size) thetas' + (_, thetas) <- if Prelude.any (/=nParams) nThetas + then fitFun best' else pure (1.0, thetas') maxLoss <- negate . fromJust <$> getFitness ec - ts <- forM (Prelude.zip3 [0..] dataTrainVals thetas) $ \(view, (dataTrain, dataVal), theta) -> do + ts <- forM (Data.List.zip4 [0..] dataTrainVals dataTests thetas) $ \(view, (dataTrain, dataVal), dataTest, theta) -> do let (x, y, mYErr) = dataTrain (x_val, y_val, mYErr_val) = dataVal + (x_te, y_te, mYErr_te) = dataTest distribution = _distribution args - best' = if shouldReparam then relabelParams bestExpr else relabelParamsOrder bestExpr + expr = paramsToConst (MA.toList theta) best' - thetaStr = intercalate ";" $ Prelude.map show (MA.toList theta) - showFun = showExpr expr - showFunNp = showPython best' - mse_train = nll distribution mYErr_val x_val y_val best' theta - pure . (<> "\n") . intercalate "," $ [show ix, show view, showFun, "\"" <> showFunNp <> "\"", thetaStr, show (countNodes $ convertProtectedOps expr), if isNaN mse_train then "1e+10" else show mse_train, show (negate maxLoss) ] - pure (concat ts) + showNA z = if isNaN z then "" else show z + r2_train = r2 x y best' theta + r2_val = r2 x_val y_val best' theta + r2_te = r2 x_te y_te best' theta + nll_train = nll distribution mYErr x y best' theta + nll_val = nll distribution mYErr_val x_val y_val best' theta + nll_te = nll distribution mYErr_te x_te y_te best' theta + mdl_train = mdl distribution mYErr x y theta best' + mdl_val = mdl distribution mYErr_val x_val y_val theta best' + mdl_te = mdl distribution mYErr_te x_te y_te theta best' + vals = intercalate "," + $ Prelude.map showNA [ nll_train, nll_val, nll_te, maxLoss + , r2_train, r2_val, r2_te + , mdl_train, mdl_val, mdl_te] + thetaStr = intercalate ";" $ Prelude.map show (MA.toList theta) + pure $ show ix <> "," <> show view <> "," <> showExpr expr <> "," <> "\"" <> showPython best' <> "\"," + <> thetaStr <> "," <> show (countNodes $ convertProtectedOps expr) + <> "," <> vals + pure ts + insertTerms = forM terms $ \t -> do fromTree myCost t >>= canonical - -data Args = Args - { _dataset :: String, - _gens :: Int, - _maxSize :: Int, - _split :: Int, - _distribution :: Distribution, - _optIter :: Int, - _optRepeat :: Int, - _nParams :: Int, - _nPop :: Int, - _nTournament :: Int, - _pc :: Double, - _pm :: Double, - _nonterminals :: String, - _dumpTo :: String, - _loadFrom :: String, - _simplify :: Bool - } - deriving (Show) - - -pyeggp :: String -> Int -> Int -> Int -> Int -> Double -> Double -> String -> String -> Int -> Int -> Int -> Int -> Bool -> String -> String -> IO String -pyeggp dataset gens nPop maxSize nTournament pc pm nonterminals loss optIter optRepeat nParams split simplify dumpTo loadFrom = - case readMaybe loss of - Nothing -> pure $ "Invalid loss function " <> loss - Just l -> let arg = Args dataset gens maxSize split l optIter optRepeat nParams nPop nTournament pc pm nonterminals dumpTo loadFrom simplify - in eggp arg - -eggp :: Args -> IO String -eggp args = do - g <- getStdGen - let datasets = words (_dataset args) - dataTrains' <- Prelude.mapM (flip loadTrainingOnly True) datasets -- load all datasets - - let (dataTrainVals, g') = runState (Prelude.mapM (`splitData` (_split args)) dataTrains') g - alg = evalStateT (egraphGP dataTrainVals args) emptyGraph - evalStateT alg g' diff --git a/src/Util.hs b/src/Util.hs deleted file mode 100644 index ceb78d6..0000000 --- a/src/Util.hs +++ /dev/null @@ -1,271 +0,0 @@ -{-# LANGUAGE BlockArguments #-} -{-# LANGUAGE TupleSections #-} - -module Util where - -import qualified Data.Map.Strict as Map -import Data.Massiv.Array as MA hiding (forM_, forM) -import Data.SRTree -import Data.SRTree.Eval -import Algorithm.SRTree.Opt -import Algorithm.EqSat.Egraph -import Algorithm.EqSat.Build -import Algorithm.EqSat.Info - -import Algorithm.SRTree.NonlinearOpt -import System.Random -import Random -import Algorithm.SRTree.Likelihoods -import Data.SRTree.Print ---import Algorithm.SRTree.ModelSelection ---import Algorithm.SRTree.Opt -import qualified Data.IntMap.Strict as IM -import Control.Monad.State.Strict -import Control.Monad ( when, replicateM, forM, forM_ ) -import Data.Maybe ( fromJust ) -import Data.List ( maximumBy ) -import Data.Function ( on ) -import List.Shuffle ( shuffle ) -import Data.List.Split ( splitOn ) -import Data.Char ( toLower ) -import qualified Data.IntSet as IntSet -import Data.SRTree.Datasets -import Algorithm.EqSat.Queries - -import Debug.Trace - -type RndEGraph a = EGraphST (StateT StdGen IO) a -type DataSet = (SRMatrix, PVector, Maybe PVector) - -csvHeader :: String -csvHeader = "id,Expression,theta,size,MSE_train,MSE_val,MSE_test,R2_train,R2_val,R2_test,nll_train,nll_val,nll_test,mdl_train,mdl_val,mdl_test" - -io :: IO a -> RndEGraph a -io = lift . lift -{-# INLINE io #-} -rnd :: StateT StdGen IO a -> RndEGraph a -rnd = lift -{-# INLINE rnd #-} - -myCost :: SRTree Int -> Int -myCost (Var _) = 1 -myCost (Const _) = 1 -myCost (Param _) = 1 -myCost (Bin _ l r) = 2 + l + r -myCost (Uni _ t) = 3 + t - -while :: Monad f => (t -> Bool) -> t -> (t -> f t) -> f () -while p arg prog = do when (p arg) do arg' <- prog arg - while p arg' prog - -fitnessFun :: Int -> Distribution -> DataSet -> DataSet -> Fix SRTree -> PVector -> (Double, PVector) -fitnessFun nIter distribution (x, y, mYErr) (x_val, y_val, mYErr_val) tree thetaOrig = - if isNaN val -- || isNaN tr - then (-(1/0), theta) -- infinity - else (val, theta) - where - --tree = relabelParams _tree - nParams = countParamsUniq tree + if distribution == ROXY then 3 else if distribution == Gaussian then 1 else 0 - (theta, _, _) = minimizeNLL' VAR1 distribution mYErr nIter x y tree thetaOrig - evalF a b c = negate $ nll distribution c a b tree $ if nParams == 0 then thetaOrig else theta - --tr = evalF x y mYErr - val = evalF x_val y_val mYErr_val - ---{-# INLINE fitnessFun #-} - -fitnessFunRep :: Int -> Int -> Distribution -> DataSet -> DataSet -> Fix SRTree -> RndEGraph (Double, PVector) -fitnessFunRep nRep nIter distribution dataTrain dataVal tree = do - let nParams = countParamsUniq tree + if distribution == ROXY then 3 else if distribution == Gaussian then 1 else 0 - thetaOrigs <- replicateM nRep (rnd $ randomVec nParams) - let fits = maximumBy (compare `on` fst) $ Prelude.map (fitnessFun nIter distribution dataTrain dataVal tree) thetaOrigs - pure fits ---{-# INLINE fitnessFunRep #-} - - -fitnessMV :: Bool -> Int -> Int -> Distribution -> [(DataSet, DataSet)] -> Fix SRTree -> RndEGraph (Double, [PVector]) -fitnessMV shouldReparam nRep nIter distribution dataTrainsVals _tree = do - let tree = if shouldReparam then relabelParams _tree else relabelParamsOrder _tree - response <- forM dataTrainsVals $ \(dt, dv) -> fitnessFunRep nRep nIter distribution dt dv tree - pure (minimum (Prelude.map fst response), Prelude.map snd response) - --- helper query functions --- TODO: move to egraph lib -getFitness :: EClassId -> RndEGraph (Maybe Double) -getFitness c = gets (_fitness . _info . (IM.! c) . _eClass) -{-# INLINE getFitness #-} -getTheta :: EClassId -> RndEGraph ([PVector]) -getTheta c = gets (_theta . _info . (IM.! c) . _eClass) -{-# INLINE getTheta #-} -getSize :: EClassId -> RndEGraph Int -getSize c = gets (_size . _info . (IM.! c) . _eClass) -{-# INLINE getSize #-} -isSizeOf :: (Int -> Bool) -> EClass -> Bool -isSizeOf p = p . _size . _info -{-# INLINE isSizeOf #-} - -getBestFitness :: RndEGraph (Maybe Double) -getBestFitness = do - bec <- (gets (snd . getGreatest . _fitRangeDB . _eDB) >>= canonical) - gets (_fitness . _info . (IM.! bec) . _eClass) - --- TODO: move to dataset lib -chunksOf :: Int -> [e] -> [[e]] -chunksOf i ls = Prelude.map (Prelude.take i) (build (splitter ls)) - where - splitter :: [e] -> ([e] -> a -> a) -> a -> a - splitter [] _ n = n - splitter l c n = l `c` splitter (Prelude.drop i l) c n - build :: ((a -> [a] -> [a]) -> [a] -> [a]) -> [a] - build g = g (:) [] - -splitData :: DataSet ->Int -> State StdGen (DataSet, DataSet) -splitData (x, y, mYErr) k = do - if k == 1 - then pure ((x, y, mYErr), (x, y, mYErr)) - else do - ixs' <- (state . shuffle) [0 .. sz-1] - let ixs = chunksOf k ixs' - - let (x_tr, x_te) = getX ixs x - (y_tr, y_te) = getY ixs y - mY = fmap (getY ixs) mYErr - (y_err_tr, y_err_te) = (fmap fst mY, fmap snd mY) - pure ((x_tr, y_tr, y_err_tr), (x_te, y_te, y_err_te)) - where - (MA.Sz sz) = MA.size y - comp_x = MA.getComp x - comp_y = MA.getComp y - - getX :: [[Int]] -> SRMatrix -> (SRMatrix, SRMatrix) - getX ixs xs' = let xs = MA.toLists xs' :: [MA.ListItem MA.Ix2 Double] - in ( MA.fromLists' comp_x [xs !! ix | ixs_i <- ixs, ix <- Prelude.tail ixs_i] - , MA.fromLists' comp_x [xs !! ix | ixs_i <- ixs, let ix = Prelude.head ixs_i] - ) - getY :: [[Int]] -> PVector -> (PVector, PVector) - getY ixs ys = ( MA.fromList comp_y [ys MA.! ix | ixs_i <- ixs, ix <- Prelude.tail ixs_i] - , MA.fromList comp_y [ys MA.! ix | ixs_i <- ixs, let ix = Prelude.head ixs_i] - ) - -getTrain :: ((a, b1, c1, d1), (c2, b2), c3, d2) -> (a, b1, c2) -getTrain ((a, b, _, _), (c, _), _, _) = (a,b,c) - -getX :: DataSet -> SRMatrix -getX (a, _, _) = a - -getTarget :: DataSet -> PVector -getTarget (_, b, _) = b - -getError :: DataSet -> Maybe PVector -getError (_, _, c) = c - -loadTrainingOnly fname b = getTrain <$> loadDataset fname b - -parseNonTerms :: String -> [SRTree ()] -parseNonTerms = Prelude.map toNonTerm . splitOn "," - where - binTerms = Map.fromList [ (Prelude.map toLower (show op), op) | op <- [Add .. AQ]] - uniTerms = Map.fromList [ (Prelude.map toLower (show f), f) | f <- [Abs .. Cube]] - toNonTerm xs' = let xs = Prelude.map toLower xs' - in case binTerms Map.!? xs of - Just op -> Bin op () () - Nothing -> case uniTerms Map.!? xs of - Just f -> Uni f () - Nothing -> error $ "invalid non-terminal " <> show xs - --- RndEGraph utils --- fitFun fitnessFunRep rep iter distribution x y mYErr x_val y_val mYErr_val -insertExpr :: Fix SRTree -> (Fix SRTree -> RndEGraph (Double, [PVector])) -> RndEGraph EClassId -insertExpr t fitFun = do - ecId <- fromTree myCost t >>= canonical - (f, p) <- fitFun t - insertFitness ecId f p - io . putStrLn $ "Best fit global: " <> show f - pure ecId - where powabs l r = Fix (Bin PowerAbs l r) - -updateIfNothing fitFun ec = do - mf <- getFitness ec - case mf of - Nothing -> do - t <- getBestExpr ec - (f, p) <- fitFun t - insertFitness ec f p - pure True - Just _ -> pure False - -pickRndSubTree :: RndEGraph (Maybe EClassId) -pickRndSubTree = do ecIds <- gets (IntSet.toList . _unevaluated . _eDB) - if not (null ecIds) - then do rndId' <- rnd $ randomFrom ecIds - rndId <- canonical rndId' - constType <- gets (_consts . _info . (IM.! rndId) . _eClass) - case constType of - NotConst -> pure $ Just rndId - _ -> pure Nothing - else pure Nothing - -getParetoEcsUpTo n maxSize = concat <$> forM [1..maxSize] (\i -> getTopFitEClassWithSize i n) - -getBestExprWithSize n = - do ec <- getTopFitEClassWithSize n 1 >>= traverse canonical - if (not (null ec)) - then do - bestFit <- getFitness $ head ec - bestP <- gets (_theta . _info . (IM.! (head ec)) . _eClass) - pure [(head ec, bestFit)] - else pure [] - -insertRndExpr maxSize rndTerm rndNonTerm = - do grow <- rnd toss - n <- rnd (randomFrom [if maxSize > 4 then 4 else 1 .. maxSize]) - t <- rnd $ Random.randomTree 3 8 n rndTerm rndNonTerm grow - fromTree myCost t >>= canonical - -refit fitFun ec = do - t <- getBestExpr ec - (f, p) <- fitFun t - insertFitness ec f p - ---printBest :: (Int -> EClassId -> RndEGraph ()) -> RndEGraph () -printBest fitFun printExprFun = do - bec <- gets (snd . getGreatest . _fitRangeDB . _eDB) >>= canonical - bestFit <- gets (_fitness. _info . (IM.! bec) . _eClass) - --refit fitFun bec - --io.print $ "should be " <> show bestFit - printExprFun 0 bec - ---paretoFront :: Int -> (Int -> EClassId -> RndEGraph ()) -> RndEGraph () -paretoFront fitFun maxSize printExprFun = concat <$> go 1 0 (-(1.0/0.0)) - where - go :: Int -> Int -> Double -> RndEGraph [String] - go n ix f - | n > maxSize = pure [] - | otherwise = do - ecList <- getBestExprWithSize n - if not (null ecList) - then do let (ec, mf) = head ecList - f' = fromJust mf - improved = f' > f && (not . isNaN) f' && (not . isInfinite) f' - ec' <- canonical ec - if improved - then do refit fitFun ec' - t <- printExprFun ix ec' - ts <- go (n+1) (ix + if improved then 1 else 0) (max f (fromJust mf)) - pure (t:ts) - else go (n+1) (ix + if improved then 1 else 0) (max f (fromJust mf)) - else go (n+1) ix f - -evaluateUnevaluated fitFun = do - ec <- gets (IntSet.toList . _unevaluated . _eDB) - forM_ ec $ \c -> do - t <- getBestExpr c - (f, p) <- fitFun t - insertFitness c f p - -evaluateRndUnevaluated fitFun = do - ec <- gets (IntSet.toList . _unevaluated . _eDB) - c <- rnd . randomFrom $ ec - t <- getBestExpr c - (f, p) <- fitFun t - insertFitness c f p - pure c diff --git a/src/pyeggp.egg-info/PKG-INFO b/src/eggp.egg-info/PKG-INFO similarity index 87% rename from src/pyeggp.egg-info/PKG-INFO rename to src/eggp.egg-info/PKG-INFO index 9faef71..8aa8a9a 100644 --- a/src/pyeggp.egg-info/PKG-INFO +++ b/src/eggp.egg-info/PKG-INFO @@ -1,6 +1,6 @@ Metadata-Version: 2.4 -Name: pyeggp -Version: 1.0.4 +Name: eggp +Version: 1.0.5 Summary: Python Wheels for eggp algorithm. Author-email: Fabricio Olivetti License: GNU GENERAL PUBLIC LICENSE @@ -695,16 +695,181 @@ Provides-Extra: mypy Requires-Dist: types_setuptools>=45; extra == "mypy" Dynamic: license-file -# pyeggp - Python e-graph GP +# EGGP -Python bindings for [eggp](https://github.com/folivetti/srtree/blob/main/apps/eggp/README.md). +A Python package for symbolic regression using e-graph-based genetic programming. PyEGGP provides a scikit-learn compatible API for evolutionary symbolic regression tasks. -ggp (e-graph genetic programming), follows the same structure as the traditional GP. The initial population is created using ramped half-and-half respecting a maximum size and maximum depth parameter and, for a number of generations, it will choose two parents using tournament selection, apply the subtree crossover with probability $pc$ followed by the subtree mutation with probability $pm$, when the offsprings replace the current population following a dominance criteria. +More info [here](https://github.com/folivetti/srtree/tree/main/apps/eggp) -How to install the package: +## Installation ```bash -pip install pyeggp +pip install eggp +``` + +## Features + +- Scikit-learn compatible API with `fit()` and `predict()` methods +- Genetic programming approach with e-graph representation +- Support for **multi-view symbolic regression** [see here](https://arxiv.org/abs/2402.04298) +- Customizable evolutionary parameters (population size, tournament selection, etc.) +- Flexible function set selection +- Various loss functions for different problem types +- Parameter optimization with multiple restarts +- Optional expression simplification through equality saturation +- Ability to save and load e-graphs + +## Usage + +### Basic Example + +```python +from pyeggp import PyEGGP +import numpy as np + +# Create sample data +X = np.linspace(-10, 10, 100).reshape(-1, 1) +y = 2 * X.ravel() + 3 * np.sin(X.ravel()) + np.random.normal(0, 1, 100) + +# Create and fit the model +model = PyEGGP(gen=100, nonterminals="add,sub,mul,div,sin,cos") +model.fit(X, y) + +# Make predictions +y_pred = model.predict(X) + +# Examine the results +print(model.results) +``` + +### Multi-View Symbolic Regression + +```python +from pyeggp import PyEGGP +import numpy as np + +# Create multiple views of data +X1 = np.linspace(-5, 5, 50).reshape(-1, 1) +y1 = np.sin(X1.ravel()) + np.random.normal(0, 0.1, 50) + +X2 = np.linspace(0, 10, 100).reshape(-1, 1) +y2 = np.sin(X2.ravel()) + np.random.normal(0, 0.2, 100) + +# Create and fit multi-view model +model = PyEGGP(gen=150, nPop=200) +model.fit_mvsr([X1, X2], [y1, y2]) + +# Make predictions for each view +y_pred1 = model.predict_mvsr(X1, view=0) +y_pred2 = model.predict_mvsr(X2, view=1) +``` + +### Integration with scikit-learn + +```python +from sklearn.model_selection import train_test_split +from sklearn.metrics import mean_squared_error +from pyeggp import PyEGGP + +# Split data +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) + +# Create and fit model +model = PyEGGP(gen=150, nPop=150, optIter=100) +model.fit(X_train, y_train) + +# Evaluate on test set +y_pred = model.predict(X_test) +mse = mean_squared_error(y_test, y_pred) +print(f"Test MSE: {mse}") +``` + +## Parameters + +| Parameter | Type | Default | Description | +|-----------|------|---------|-------------| +| `gen` | int | 100 | Number of generations to run | +| `nPop` | int | 100 | Population size | +| `maxSize` | int | 15 | Maximum allowed size for expressions (max 100) | +| `nTournament` | int | 3 | Tournament size for parent selection | +| `pc` | float | 0.9 | Probability of performing crossover | +| `pm` | float | 0.3 | Probability of performing mutation | +| `nonterminals` | str | "add,sub,mul,div" | Comma-separated list of allowed functions | +| `loss` | str | "MSE" | Loss function: "MSE", "Gaussian", "Bernoulli", or "Poisson" | +| `optIter` | int | 50 | Number of iterations for parameter optimization | +| `optRepeat` | int | 2 | Number of restarts for parameter optimization | +| `nParams` | int | -1 | Maximum number of parameters (-1 for unlimited) | +| `split` | int | 1 | Data splitting ratio for validation | +| `simplify` | bool | False | Whether to apply equality saturation to simplify expressions | +| `dumpTo` | str | "" | Filename to save the final e-graph | +| `loadFrom` | str | "" | Filename to load an e-graph to resume search | + +## Available Functions + +The following functions can be used in the `nonterminals` parameter: + +- Basic operations: `add`, `sub`, `mul`, `div` +- Powers: `power`, `powerabs`, `square`, `cube` +- Roots: `sqrt`, `sqrtabs`, `cbrt` +- Trigonometric: `sin`, `cos`, `tan`, `asin`, `acos`, `atan` +- Hyperbolic: `sinh`, `cosh`, `tanh`, `asinh`, `acosh`, `atanh` +- Others: `abs`, `log`, `logabs`, `exp`, `recip`, `aq` (analytical quotient) + +## Methods + +### Core Methods +- `fit(X, y)`: Fits the symbolic regression model +- `predict(X)`: Generates predictions using the best model +- `score(X, y)`: Computes R² score of the best model + +### Multi-View Methods +- `fit_mvsr(Xs, ys)`: Fits a multi-view regression model +- `predict_mvsr(X, view)`: Generates predictions for a specific view +- `evaluate_best_model_view(X, view)`: Evaluates the best model on a specific view +- `evaluate_model_view(X, ix, view)`: Evaluates a specific model on a specific view + +### Utility Methods +- `evaluate_best_model(X)`: Evaluates the best model on the given data +- `evaluate_model(ix, X)`: Evaluates the model with index `ix` on the given data +- `get_model(idx)`: Returns a model function and its visual representation + +## Results + +After fitting, the `results` attribute contains a pandas DataFrame with details about the discovered models, including: +- Mathematical expressions +- Model complexity +- Parameter values +- Error metrics +- NumPy-compatible expressions + +## License + +[LICENSE] + +## Citation + +If you use PyEGGP in your research, please cite: + +``` +@inproceedings{eggp, +author = {de Franca, Fabricio Olivetti and Kronberger, Gabriel}, +title = {Improving Genetic Programming for Symbolic Regression with Equality Graphs}, +year = {2025}, +isbn = {9798400714658}, +publisher = {Association for Computing Machinery}, +address = {New York, NY, USA}, +url = {https://doi.org/10.1145/3712256.3726383}, +doi = {10.1145/3712256.3726383}, +booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference}, +pages = {}, +numpages = {9}, +keywords = {Symbolic regression, Genetic programming, Equality saturation, Equality graphs}, +location = {Malaga, Spain}, +series = {GECCO '25}, +archivePrefix = {arXiv}, + eprint = {2501.17848}, + primaryClass = {cs.LG}, +} ``` The bindings were created following the amazing example written by [wenkokke](https://github.com/wenkokke/example-haskell-wheel) diff --git a/src/eggp.egg-info/SOURCES.txt b/src/eggp.egg-info/SOURCES.txt new file mode 100644 index 0000000..1773578 --- /dev/null +++ b/src/eggp.egg-info/SOURCES.txt @@ -0,0 +1,17 @@ +LICENSE +README.md +pyproject.toml +setup.py +src/eggp/__init__.py +src/eggp/__main__.py +src/eggp/_binding.pyi +src/eggp/binding.i +src/eggp/binding.py +src/eggp/typing.py +src/eggp.egg-info/PKG-INFO +src/eggp.egg-info/SOURCES.txt +src/eggp.egg-info/dependency_links.txt +src/eggp.egg-info/entry_points.txt +src/eggp.egg-info/requires.txt +src/eggp.egg-info/top_level.txt +test/test_pyeggp.py \ No newline at end of file diff --git a/src/pyeggp.egg-info/dependency_links.txt b/src/eggp.egg-info/dependency_links.txt similarity index 100% rename from src/pyeggp.egg-info/dependency_links.txt rename to src/eggp.egg-info/dependency_links.txt diff --git a/src/eggp.egg-info/entry_points.txt b/src/eggp.egg-info/entry_points.txt new file mode 100644 index 0000000..cd4f178 --- /dev/null +++ b/src/eggp.egg-info/entry_points.txt @@ -0,0 +1,2 @@ +[console_scripts] +eggp = eggp.__main__:main diff --git a/src/pyeggp.egg-info/requires.txt b/src/eggp.egg-info/requires.txt similarity index 100% rename from src/pyeggp.egg-info/requires.txt rename to src/eggp.egg-info/requires.txt diff --git a/src/eggp.egg-info/top_level.txt b/src/eggp.egg-info/top_level.txt new file mode 100644 index 0000000..4efe345 --- /dev/null +++ b/src/eggp.egg-info/top_level.txt @@ -0,0 +1,2 @@ +bk +eggp diff --git a/src/pyeggp/__init__.py b/src/eggp/__init__.py similarity index 69% rename from src/pyeggp/__init__.py rename to src/eggp/__init__.py index 34addf1..759fb9e 100644 --- a/src/pyeggp/__init__.py +++ b/src/eggp/__init__.py @@ -14,11 +14,11 @@ from sklearn.metrics import mean_squared_error, r2_score from ._binding import ( - unsafe_hs_pyeggp_version, - unsafe_hs_pyeggp_main, - unsafe_hs_pyeggp_run, - unsafe_hs_pyeggp_init, - unsafe_hs_pyeggp_exit, + unsafe_hs_eggp_version, + unsafe_hs_eggp_main, + unsafe_hs_eggp_run, + unsafe_hs_eggp_init, + unsafe_hs_eggp_exit, ) VERSION: str = "1.0.4" @@ -31,7 +31,7 @@ def hs_rts_exit() -> None: global _hs_rts_lock with _hs_rts_lock: - unsafe_hs_pyeggp_exit() + unsafe_hs_eggp_exit() @contextmanager @@ -41,23 +41,23 @@ def hs_rts_init(args: List[str] = []) -> Iterator[None]: with _hs_rts_lock: if not _hs_rts_init: _hs_rts_init = True - unsafe_hs_pyeggp_init(args) + unsafe_hs_eggp_init(args) atexit.register(hs_rts_exit) yield None def version() -> str: with hs_rts_init(): - return unsafe_hs_pyeggp_version() + return unsafe_hs_eggp_version() def main(args: List[str] = []) -> int: with hs_rts_init(args): - return unsafe_hs_pyeggp_main() + return unsafe_hs_eggp_main() -def pyeggp_run(dataset: str, gen: int, nPop: int, maxSize: int, nTournament: int, pc: float, pm: float, nonterminals: str, loss: str, optIter: int, optRepeat: int, nParams: int, split: int, simplify: int, dumpTo: str, loadFrom: str) -> str: +def eggp_run(dataset: str, gen: int, nPop: int, maxSize: int, nTournament: int, pc: float, pm: float, nonterminals: str, loss: str, optIter: int, optRepeat: int, nParams: int, split: int, simplify: int, trace : int, generational : int, dumpTo: str, loadFrom: str) -> str: with hs_rts_init(): - return unsafe_hs_pyeggp_run(dataset, gen, nPop, maxSize, nTournament, pc, pm, nonterminals, loss, optIter, optRepeat, nParams, split, simplify, dumpTo, loadFrom) + return unsafe_hs_eggp_run(dataset, gen, nPop, maxSize, nTournament, pc, pm, nonterminals, loss, optIter, optRepeat, nParams, split, simplify, trace, generational, dumpTo, loadFrom) def make_function(expression, loss="MSE"): def func(x, t): @@ -69,7 +69,7 @@ def func(x, t): return y return func -class PyEGGP(BaseEstimator, RegressorMixin): +class EGGP(BaseEstimator, RegressorMixin): """ Builds a symbolic regression model using eggp. Parameters @@ -141,6 +141,13 @@ class PyEGGP(BaseEstimator, RegressorMixin): simplify : bool, default=False Whether to apply a final step of equality saturation to simplify the expressions. + trace : bool, default=False + Whether to return a pandas dataframe of all visited expressions. + + generational : bool, default=False + Whether to use generational (replacing the currente generation with the new one) instead + of keeping the Pareto front. + dumpTo : str, default="" If not empty, it will save the final e-graph into the filename. @@ -151,17 +158,17 @@ class PyEGGP(BaseEstimator, RegressorMixin): Examples -------- - >>> from pyeggp import PyEGGP + >>> from eggp import EGGP >>> import numpy as np >>> X = np.arange(100).reshape(100, 1) >>> y = np.zeros((100, )) - >>> estimator = PyEGGP() + >>> estimator = EGGP() >>> estimator.fit(X, y) >>> - >>> estimator = PyEGGP(loss="Bernoulli") + >>> estimator = EGGP(loss="Bernoulli") >>> estimator.fit(X, y) """ - def __init__(self, gen = 100, nPop = 100, maxSize = 15, nTournament = 3, pc = 0.9, pm = 0.3, nonterminals = "add,sub,mul,div", loss = "MSE", optIter = 50, optRepeat = 2, nParams = -1, split = 1, simplify = False, dumpTo = "", loadFrom = ""): + def __init__(self, gen = 100, nPop = 100, maxSize = 15, nTournament = 3, pc = 0.9, pm = 0.3, nonterminals = "add,sub,mul,div", loss = "MSE", optIter = 50, optRepeat = 2, nParams = -1, folds = 1, simplify = False, trace = False, generational = False, dumpTo = "", loadFrom = ""): nts = "add,sub,mul,div,power,powerabs,\ aq,abs,sin,cos,tan,sinh,cosh,tanh,\ asin,acos,atan,asinh,acosh,atanh,sqrt,\ @@ -189,10 +196,14 @@ def __init__(self, gen = 100, nPop = 100, maxSize = 15, nTournament = 3, pc = 0. raise ValueError('optRepeat must be a positive number') if nParams < -1: raise ValueError('nParams must be either -1 or a positive number') - if split < 1: - raise ValueError('split must be equal or greater than 1') + if folds < 1: + raise ValueError('folds must be equal or greater than 1') if not isinstance(simplify, bool): raise TypeError('simplify must be a boolean') + if not isinstance(trace, bool): + raise TypeError('trace must be a boolean') + if not isinstance(generational, bool): + raise TypeError('generational must be a boolean') self.gen = gen self.nPop = nPop self.maxSize = maxSize @@ -204,14 +215,16 @@ def __init__(self, gen = 100, nPop = 100, maxSize = 15, nTournament = 3, pc = 0. self.optIter = optIter self.optRepeat = optRepeat self.nParams = nParams - self.split = split + self.folds = folds self.simplify = int(simplify) + self.trace = int(trace) + self.generational = int(generational) self.dumpTo = dumpTo self.loadFrom = loadFrom self.is_fitted_ = False - def fit(self, X, y): - ''' Fits the regression model. + def combine_dataset(self, X, y, Xerr, yerr): + ''' Combines the error information into a single dataset. Parameters ---------- @@ -219,26 +232,78 @@ def fit(self, X, y): An m x n np.array describing m observations of n features. y : np.array An np.array of size m with the measured target values. + Xerr : np.array + An m x n np.array with the measurement errors of the features. + yerr : np.array + An np.array of size m with the measurement errors of the target. + + Returns the combined dataset ''' if X.ndim == 1: X = X.reshape(-1,1) y = y.reshape(-1, 1) - combined = np.hstack([X, y]) - header = [f"x{i}" for i in range(X.shape[1])] + ["y"] + stacked = [X, y] + if Xerr is not None and self.loss == "ROXY": + Xerr = Xerr.reshape(-1, 1) + stacked.append(Xerr) + if yerr is not None and self.loss in ["ROXY", "HGaussian"]: + yerr = yerr.reshape(-1, 1) + stacked.append(yerr) + if self.loss == "ROXY": + stacked += [np.log10(X).reshape(-1,1), np.log10(y).reshape(-1,1), np.square(Xerr/(Xerr * np.log(10))).reshape(-1,1), np.square(yerr/(yerr*np.log(10))).reshape(-1,1)] + return np.hstack(stacked) + + def get_header(self, ndim): + if self.loss != "ROXY": + return [f"x{i}" for i in range(ndim)] + ["y"] + elif self.loss == "HGaussian": + return [f"x{i}" for i in range(ndim)] + ["y", "yerr"] + else: + return ["gbar","gobs","e_gbar","e_gobs","logX","logY","logXErr","logYErr"] + + def get_fname(self, dname, header): + if self.loss == "ROXY": + return f"{dname}:::gobs:gbar,logX,logY,logXErr,logYErr:e_gobs:e_gbar" + elif self.loss == "HGaussian": + varnames = ",".join(header[:-2]) + return f"{dname}:::y:{varnames}:yerr" + else: + return dname + + def fit(self, X, y, Xerr = None, yerr = None): + ''' Fits the regression model. + + Parameters + ---------- + X : np.array + An m x n np.array describing m observations of n features. + y : np.array + An np.array of size m with the measured target values. + Xerr : np.array + An m x n np.array with the measurement errors of the features. + yerr : np.array + An np.array of size m with the measurement errors of the target. + + A table with the fitted models and additional information + will be stored as a Pandas dataframe in self.results. + ''' + combined = self.combine_dataset(X, y, Xerr, yerr) + header = self.get_header(X.shape[1]) + with tempfile.NamedTemporaryFile(mode='w+', newline='', delete=False, suffix='.csv') as temp_file: writer = csv.writer(temp_file) writer.writerow(header) writer.writerows(combined) dataset = temp_file.name - - csv_data = pyeggp_run(dataset, self.gen, self.nPop, self.maxSize, self.nTournament, self.pc, self.pm, self.nonterminals, self.loss, self.optIter, self.optRepeat, self.nParams, self.split, self.simplify, self.dumpTo, self.loadFrom) + dname = self.get_fname(dataset, header) + csv_data = eggp_run(dname, self.gen, self.nPop, self.maxSize, self.nTournament, self.pc, self.pm, self.nonterminals, self.loss, self.optIter, self.optRepeat, self.nParams, self.folds, self.simplify, self.trace, self.generational, self.dumpTo, self.loadFrom) if len(csv_data) > 0: csv_io = StringIO(csv_data.strip()) - self.results = pd.read_csv(csv_io, header=0) + self.results = pd.read_csv(csv_io, header=0, dtype={'theta':str}) self.is_fitted_ = True return self - def fit_mvsr(self, Xs, ys): + def fit_mvsr(self, Xs, ys, Xerrs = None, yerrs = None): ''' Fits a multi-view regression model. Parameters @@ -248,20 +313,23 @@ def fit_mvsr(self, Xs, ys): ys : list(np.array) A list of k elements of np.arrays of size m_k with the measured target values. ''' - if Xs[0].ndim == 1: - Xs = [X.reshape(-1,1) for X in Xs] - ys = [y.reshape(-1, 1) for y in ys] - combineds = [np.hstack([X, y]) for X, y in zip(Xs, ys)] - header = [f"x{i}" for i in range(Xs[0].shape[1])] + ["y"] + if Xerrs is None: + Xerrs = [None for _ in Xs] + if yerrs is None: + yerrs = [None for _ in ys] + + combineds = [self.combine_dataset(X, y, Xerr, yerr) for X, y, Xerr, yerr in zip(Xs, ys, Xerrs, yerrs)] + header = self.get_header(Xs[0].shape[1]) datasets = [] + for combined in combineds: with tempfile.NamedTemporaryFile(mode='w+', newline='', delete=False, suffix='.csv') as temp_file: writer = csv.writer(temp_file) writer.writerow(header) writer.writerows(combined) - datasets.append(temp_file.name) + datasets.append(self.get_fname(temp_file.name, header)) - csv_data = pyeggp_run(" ".join(datasets), self.gen, self.nPop, self.maxSize, self.nTournament, self.pc, self.pm, self.nonterminals, self.loss, self.optIter, self.optRepeat, self.nParams, self.split, self.simplify, self.dumpTo, self.loadFrom) + csv_data = eggp_run(" ".join(datasets), self.gen, self.nPop, self.maxSize, self.nTournament, self.pc, self.pm, self.nonterminals, self.loss, self.optIter, self.optRepeat, self.nParams, self.folds, self.simplify, self.trace, self.generational, self.dumpTo, self.loadFrom) if len(csv_data) > 0: csv_io = StringIO(csv_data.strip()) self.results = pd.read_csv(csv_io, header=0, dtype={'theta':str}) @@ -346,8 +414,8 @@ def evaluate_model_view(self, x, ix, view): def evaluate_model(self, ix, x): if x.ndim == 1: x = x.reshape(-1,1) - t = np.array(list(map(float, self.results.iloc[-1].theta.split(";")))) - y = eval(self.results.iloc[i].Numpy) + t = np.array(list(map(float, self.results.iloc[ix].theta.split(";")))) + y = eval(self.results.iloc[ix].Numpy) if self.loss == "Bernoulli": return 1/(1 + np.exp(-y)) elif self.loss == "Poisson": @@ -359,7 +427,7 @@ def score(self, X, y): ypred = self.evaluate_best_model(X) return r2_score(y, ypred) def get_model(self, idx): - ''' Get a `model` function and its visual representation. ''' + ''' Get a `model` function and its visual representation. From srbench. ''' alphabet = list(string.ascii_uppercase) row = self.results[self.results['id']==idx].iloc[0] visual_expression = row['Numpy'] diff --git a/src/pyeggp/__main__.py b/src/eggp/__main__.py similarity index 67% rename from src/pyeggp/__main__.py rename to src/eggp/__main__.py index 3fa9edf..10ae492 100644 --- a/src/pyeggp/__main__.py +++ b/src/eggp/__main__.py @@ -1,10 +1,10 @@ import sys from typing import NoReturn -import pyeggp +import eggp def main() -> NoReturn: - sys.exit(pyeggp.main(sys.argv)) + sys.exit(eggp.main(sys.argv)) if __name__ == "__main__": diff --git a/src/pyeggp/_binding.pyi b/src/eggp/_binding.pyi similarity index 80% rename from src/pyeggp/_binding.pyi rename to src/eggp/_binding.pyi index ab59318..b8bacf0 100644 --- a/src/pyeggp/_binding.pyi +++ b/src/eggp/_binding.pyi @@ -2,6 +2,6 @@ from typing import List def unsafe_hs_pyeggp_version() -> str: ... def unsafe_hs_pyeggp_main() -> int: ... -def unsafe_hs_pyeggp_run(dataset: str, gen: int, nPop: int, maxSize: int, nTournament: int, pc: float, pm: float, nonterminals: str, loss: str, optIter: int, optRepeat: int, nParams: int, split: int, simplify: int, dumpTo: str, loadFrom: str) -> str: ... +def unsafe_hs_pyeggp_run(dataset: str, gen: int, nPop: int, maxSize: int, nTournament: int, pc: float, pm: float, nonterminals: str, loss: str, optIter: int, optRepeat: int, nParams: int, split: int, simplify: int, trace : int, generational : int, dumpTo: str, loadFrom: str) -> str: ... def unsafe_hs_pyeggp_init(args: List[str]) -> None: ... def unsafe_hs_pyeggp_exit() -> None: ... diff --git a/src/pyeggp/binding.def b/src/eggp/binding.def similarity index 100% rename from src/pyeggp/binding.def rename to src/eggp/binding.def diff --git a/src/eggp/binding.i b/src/eggp/binding.i new file mode 100644 index 0000000..c3e3eae --- /dev/null +++ b/src/eggp/binding.i @@ -0,0 +1,64 @@ +%module binding +%{ +#include "EGGP_stub.h" + +char * unsafe_hs_eggp_version() { + return hs_eggp_version(); +} + +int unsafe_hs_eggp_main() { + return hs_eggp_main(); +} + +char * unsafe_hs_eggp_run( char *dataset, int gens, int nPop, int maxSize, int nTournament, double pc, double pm, char *nonterminals, char *loss, int optIter, int optRepeat, int nParams, int split, int simplify, int trace, int generational, char *dumpTo, char *loadFrom ) { + return hs_eggp_run(dataset, gens, nPop, maxSize, nTournament, pc, pm, nonterminals, loss, optIter, optRepeat, nParams, split, simplify, trace, generational, dumpTo, loadFrom); +} + +void unsafe_hs_eggp_init(int argc, char **argv) { + hs_init(&argc, &argv); +} + +void unsafe_hs_eggp_exit() { + hs_exit(); +} + +void unsafe_py_write_stdout( char * str) { + PySys_FormatStdout("%s", str); +} + +void unsafe_py_write_stderr( char * str) { + PySys_FormatStderr("%s", str); +} +%} + +%typemap(in) (int argc, char **argv) { + /* Check if is a list */ + if (PyList_Check($input)) { + int i; + $1 = PyList_Size($input); + $2 = (char **) malloc(($1+1)*sizeof(char *)); + for (i = 0; i < $1; i++) { + PyObject *o = PyList_GetItem($input, i); + if (PyUnicode_Check(o)) { + $2[i] = (char *) PyUnicode_AsUTF8AndSize(o, 0); + } else { + PyErr_SetString(PyExc_TypeError, "list must contain strings"); + SWIG_fail; + } + } + $2[i] = 0; + } else { + PyErr_SetString(PyExc_TypeError, "not a list"); + SWIG_fail; + } +} + +%typemap(freearg) (int argc, char **argv) { + free((char *) $2); +} + +char * unsafe_hs_eggp_version(); +int unsafe_hs_eggp_main(); +char * unsafe_hs_eggp_run( char *dataset, int gens, int nPop, int maxSize, int nTournament, double p, double pm, char *nonterminals, char *loss, int optIter, int optRepeat, int nParams, int split, int simplify, int trace, int generational, char *dumpTo, char *loadFrom); +void unsafe_hs_eggp_init(int argc, char **argv); +void unsafe_hs_eggp_exit(); diff --git a/src/eggp/typing.py b/src/eggp/typing.py new file mode 100644 index 0000000..b5a917e --- /dev/null +++ b/src/eggp/typing.py @@ -0,0 +1,14 @@ +import sys + +from typing import List + +if sys.version_info < (3, 8): + from typing_extensions import Protocol +else: + from typing import Protocol + + +class Session(Protocol): + def version(self) -> str: ... + def main(self, args: List[str] = []) -> int: ... + def eggp_run(self, dataset: str, gen: int, nPop: int, maxSize: int, nTournament: int, pc: float, pm: float, nonterminals: str, loss: str, optIter: int, optRepeat: int, nParams: int, split: int, simplify: int, trace : int, generational : int, dumpTo: str, loadFrom: str) -> str: ... diff --git a/src/pyeggp.egg-info/SOURCES.txt b/src/pyeggp.egg-info/SOURCES.txt deleted file mode 100644 index fd4e0f9..0000000 --- a/src/pyeggp.egg-info/SOURCES.txt +++ /dev/null @@ -1,17 +0,0 @@ -LICENSE -README.md -pyproject.toml -setup.py -src/pyeggp/__init__.py -src/pyeggp/__main__.py -src/pyeggp/_binding.pyi -src/pyeggp/binding.i -src/pyeggp/binding.py -src/pyeggp/typing.py -src/pyeggp.egg-info/PKG-INFO -src/pyeggp.egg-info/SOURCES.txt -src/pyeggp.egg-info/dependency_links.txt -src/pyeggp.egg-info/entry_points.txt -src/pyeggp.egg-info/requires.txt -src/pyeggp.egg-info/top_level.txt -test/test_pyeggp.py \ No newline at end of file diff --git a/src/pyeggp.egg-info/entry_points.txt b/src/pyeggp.egg-info/entry_points.txt deleted file mode 100644 index ba1dad8..0000000 --- a/src/pyeggp.egg-info/entry_points.txt +++ /dev/null @@ -1,2 +0,0 @@ -[console_scripts] -pyeggp = pyeggp.__main__:main diff --git a/src/pyeggp.egg-info/top_level.txt b/src/pyeggp.egg-info/top_level.txt deleted file mode 100644 index a7305fa..0000000 --- a/src/pyeggp.egg-info/top_level.txt +++ /dev/null @@ -1 +0,0 @@ -pyeggp diff --git a/src/pyeggp/binding.i b/src/pyeggp/binding.i deleted file mode 100644 index a66532d..0000000 --- a/src/pyeggp/binding.i +++ /dev/null @@ -1,64 +0,0 @@ -%module binding -%{ -#include "Pyeggp_stub.h" - -char * unsafe_hs_pyeggp_version() { - return hs_pyeggp_version(); -} - -int unsafe_hs_pyeggp_main() { - return hs_pyeggp_main(); -} - -char * unsafe_hs_pyeggp_run( char *dataset, int gens, int nPop, int maxSize, int nTournament, double pc, double pm, char *nonterminals, char *loss, int optIter, int optRepeat, int nParams, int split, int simplify, char *dumpTo, char *loadFrom ) { - return hs_pyeggp_run(dataset, gens, nPop, maxSize, nTournament, pc, pm, nonterminals, loss, optIter, optRepeat, nParams, split, simplify, dumpTo, loadFrom); -} - -void unsafe_hs_pyeggp_init(int argc, char **argv) { - hs_init(&argc, &argv); -} - -void unsafe_hs_pyeggp_exit() { - hs_exit(); -} - -void unsafe_py_write_stdout( char * str) { - PySys_FormatStdout("%s", str); -} - -void unsafe_py_write_stderr( char * str) { - PySys_FormatStderr("%s", str); -} -%} - -%typemap(in) (int argc, char **argv) { - /* Check if is a list */ - if (PyList_Check($input)) { - int i; - $1 = PyList_Size($input); - $2 = (char **) malloc(($1+1)*sizeof(char *)); - for (i = 0; i < $1; i++) { - PyObject *o = PyList_GetItem($input, i); - if (PyUnicode_Check(o)) { - $2[i] = (char *) PyUnicode_AsUTF8AndSize(o, 0); - } else { - PyErr_SetString(PyExc_TypeError, "list must contain strings"); - SWIG_fail; - } - } - $2[i] = 0; - } else { - PyErr_SetString(PyExc_TypeError, "not a list"); - SWIG_fail; - } -} - -%typemap(freearg) (int argc, char **argv) { - free((char *) $2); -} - -char * unsafe_hs_pyeggp_version(); -int unsafe_hs_pyeggp_main(); -char * unsafe_hs_pyeggp_run( char *dataset, int gens, int nPop, int maxSize, int nTournament, double p, double pm, char *nonterminals, char *loss, int optIter, int optRepeat, int nParams, int split, int simplify, char *dumpTo, char *loadFrom); -void unsafe_hs_pyeggp_init(int argc, char **argv); -void unsafe_hs_pyeggp_exit(); diff --git a/src/pyeggp/typing.py b/src/pyeggp/typing.py deleted file mode 100644 index 5a2dc19..0000000 --- a/src/pyeggp/typing.py +++ /dev/null @@ -1,14 +0,0 @@ -import sys - -from typing import List - -if sys.version_info < (3, 8): - from typing_extensions import Protocol -else: - from typing import Protocol - - -class Session(Protocol): - def version(self) -> str: ... - def main(self, args: List[str] = []) -> int: ... - def pyeggp_run(self, dataset: str, gen: int, nPop: int, maxSize: int, nTournament: int, pc: float, pm: float, nonterminals: str, loss: str, optIter: int, optRepeat: int, nParams: int, split: int, simplify: int, dumpTo: str, loadFrom: str) -> str: ... diff --git a/test/test_pyeggp.py b/test/test_pyeggp.py index 7809f15..0b3db16 100644 --- a/test/test_pyeggp.py +++ b/test/test_pyeggp.py @@ -1,21 +1,21 @@ -from pyeggp import pyeggp_run, PyEGGP +from eggp import eggp_run, EGGP import pandas as pd -output = pyeggp_run("test/data.csv", 100, 100, 10, 3, 0.9, 0.3, "add,sub,mul,div,log", "MSE", 50, 2, -1, 3, 0, "", "") +output = eggp_run("test/data.csv", 100, 100, 10, 3, 0.9, 0.3, "add,sub,mul,div,log", "MSE", 50, 2, -1, 3, 0, 0, 0, "", "") print(output) -output = pyeggp_run("test/data.csv test/data2.csv", 100, 100, 10, 3, 0.9, 0.3, "add,sub,mul,div,log", "MSE", 50, 2, -1, 3, 0, "", "") +output = eggp_run("test/data.csv test/data2.csv", 100, 100, 10, 3, 0.9, 0.3, "add,sub,mul,div,log", "MSE", 50, 2, -1, 3, 0, 0, 0, "", "") print(output) -print("Check PyEGGP") +print("Check EGGP") df = pd.read_csv("test/data.csv") Z = df.values X = Z[:,:-1] y = Z[:,-1] -reg = PyEGGP(100, 100, 10, 3, 0.9, 0.3, "add,sub,mul,div,log", "MSE", 50, 2, -1, 3, True, "", "") +reg = EGGP(100, 100, 10, 3, 0.9, 0.3, "add,sub,mul,div,log", "MSE", 50, 2, -1, 3, True, False, False, "", "") reg.fit(X, y) print(reg.score(X, y)) From 13f767548c168b5fb74c9462ecc650d28468c212 Mon Sep 17 00:00:00 2001 From: folivetti Date: Tue, 20 May 2025 17:03:39 -0300 Subject: [PATCH 066/166] 1.0.5 --- setup.py | 2 + src/eggp.egg-info/PKG-INFO | 148 +++++++++++++++++++++++++++++--- src/eggp.egg-info/requires.txt | 3 + src/eggp.egg-info/top_level.txt | 1 - 4 files changed, 143 insertions(+), 11 deletions(-) diff --git a/setup.py b/setup.py index 55e6658..f8db3d2 100644 --- a/setup.py +++ b/setup.py @@ -88,10 +88,12 @@ def cabal_build_ext(self, ext: Extension) -> None: self.mkpath(self.build_temp) print(self.build_temp, "<<<<<<<<<<<<<<<<<<<<<<<<<<<<") if sys.platform in ["win32", "cygwin"]: + self.cabal(["update"]) self.cabal(["build"], env={"INSTALLDIR": self.build_temp, "LIBRARY_PATH": "C:\\nlopt\\lib", "CPATH": "C:\\nlopt\\include", "PATH":"C:\\nlopt\\bin;" + os.environ.get("PATH", ""), **os.environ}) elif sys.platform in ["linux"]: + self.cabal(["update"]) self.cabal(["build"], env={"INSTALLDIR": self.build_temp, "LIBRARY_PATH": "/usr/local/lib64", "LD_LIBRARY_PATH":"/usr/local/lib64", "C_INCLUDE_PATH": "/usr/local/include", "PKG_CONFIG_PATH": "/usr/local/lib64/pkgconfig", **os.environ}) else: diff --git a/src/eggp.egg-info/PKG-INFO b/src/eggp.egg-info/PKG-INFO index 8aa8a9a..93ed078 100644 --- a/src/eggp.egg-info/PKG-INFO +++ b/src/eggp.egg-info/PKG-INFO @@ -691,23 +691,145 @@ Requires-Python: >=3.9 Description-Content-Type: text/markdown License-File: LICENSE Requires-Dist: typing_extensions<5,>=4.6; python_version < "3.8" +Requires-Dist: numpy>=2 +Requires-Dist: pandas>=2.2 +Requires-Dist: scikit-learn>=1.6 Provides-Extra: mypy Requires-Dist: types_setuptools>=45; extra == "mypy" Dynamic: license-file -# EGGP +# eggp - e-graph GP -A Python package for symbolic regression using e-graph-based genetic programming. PyEGGP provides a scikit-learn compatible API for evolutionary symbolic regression tasks. +*eggp* (**e**-**g**raph **g**enetic **p**rogramming), follows the same structure as the traditional GP. The initial population is created using ramped half-and-half respecting a maximum size and maximum depth parameter and, for a number of generations, it will choose two parents using tournament selection, apply the subtree crossover with probability $pc$ followed by the subtree mutation with probability $pm$, when the offsprings replace the current population following a dominance criteria. -More info [here](https://github.com/folivetti/srtree/tree/main/apps/eggp) +The key differences of *eggp* are: -## Installation + - new solutions are inserted into the e-graph followed by one step of equality saturation to find and store some of the equivalent expressions of the new offspring. + - the current population is replaced by the set of individuals formed by: the Pareto front, the next front after excluding the first Pareto-front, and a selection of the last offspring at random until it reaches the desired population size. + - the subtree crossover and mutation are modified to try to generate an unvisited exp + +This repository provides a CLI and a Python package for eggp with a scikit-learn compatible API for symbolic regression. + +Instructions: + +- [CLI version](#cli) +- [Python version](#python) + +## CLI + +### How to use + +```bash +eggp - E-graph Genetic Programming for Symbolic Regression. + +Usage: eggp (-d|--dataset INPUT-FILE) [-t|--test ARG] [-g|--generations GENS] + (-s|--maxSize ARG) [-k|--split ARG] [--print-pareto] [--trace] + [--loss ARG] [--opt-iter ARG] [--opt-retries ARG] + [--number-params ARG] [--nPop ARG] [--tournament-size ARG] + [--pc ARG] [--pm ARG] [--non-terminals ARG] [--dump-to ARG] + [--load-from ARG] [--moo] + + An implementation of GP with modified crossover and mutation operators + designed to exploit equality saturation and e-graphs. + https://arxiv.org/abs/2501.17848 + +Available options: + -d,--dataset INPUT-FILE CSV dataset. + -t,--test ARG test data (default: "") + -g,--generations GENS Number of generations. (default: 100) + -s,--maxSize ARG max-size. + -k,--split ARG k-split ratio training-validation (default: 1) + --print-pareto print Pareto front instead of best found expression + --trace print all evaluated expressions. + --loss ARG loss function: MSE, Gaussian, Poisson, Bernoulli. + (default: MSE) + --opt-iter ARG number of iterations in parameter optimization. + (default: 30) + --opt-retries ARG number of retries of parameter fitting. (default: 1) + --number-params ARG maximum number of parameters in the model. If this + argument is absent, the number is bounded by the + maximum size of the expression and there will be no + repeated parameter. (default: -1) + --nPop ARG population size (Default: 100). (default: 100) + --tournament-size ARG tournament size. (default: 2) + --pc ARG probability of crossover. (default: 1.0) + --pm ARG probability of mutation. (default: 0.3) + --non-terminals ARG set of non-terminals to use in the search. + (default: "Add,Sub,Mul,Div,PowerAbs,Recip") + --dump-to ARG dump final e-graph to a file. (default: "") + --load-from ARG load initial e-graph from a file. (default: "") + --moo replace the current population with the pareto front + instead of replacing it with the generated children. + -h,--help Show this help text +``` + +The dataset file must contain a header with each features name, and the `--dataset` and `--test` arguments can be accompanied by arguments separated by ':' following the format: + +`filename.ext:start_row:end_row:target:features` + +where each ':' field is optional. The fields are: + +- **start_row:end_row** is the range of the training rows (default 0:nrows-1). + every other row not included in this range will be used as validation +- **target** is either the name of the (if the datafile has headers) or the index + of the target variable +- **features** is a comma separated list of names or indices to be used as + input variables of the regression model. + +Example of valid names: `dataset.csv`, `mydata.tsv`, `dataset.csv:20:100`, `dataset.tsv:20:100:price:m2,rooms,neighborhood`, `dataset.csv:::5:0,1,2`. + +The format of the file will be determined by the extension (e.g., csv, tsv,...). To use multi-view, simply pass multiple filenames in double-quotes: + +```bash +eggp --dataset "dataset1.csv dataset2.csv dataset3.csv" ... +``` + +### Installation + +To install eggp you'll need: + +- `libz` +- `libnlopt` +- `libgmp` +- `ghc-9.6.6` +- `cabal` or `stack` + +### Method 1: PIP + +Simply run: ```bash -pip install eggp +pip install eggp +``` + +under your Python environment. + +### Method 2: cabal + +After installing the dependencies (e.g., `apt install libz libnlopt libgmp`), install [`ghcup`](https://www.haskell.org/ghcup/#) + +For Linux, macOS, FreeBSD or WSL2: + +```bash +curl --proto '=https' --tlsv1.2 -sSf https://get-ghcup.haskell.org | sh ``` -## Features +For Windows, run the following in a PowerShell: + +```bash +Set-ExecutionPolicy Bypass -Scope Process -Force;[System.Net.ServicePointManager]::SecurityProtocol = [System.Net.ServicePointManager]::SecurityProtocol -bor 3072; try { & ([ScriptBlock]::Create((Invoke-WebRequest https://www.haskell.org/ghcup/sh/bootstrap-haskell.ps1 -UseBasicParsing))) -Interactive -DisableCurl } catch { Write-Error $_ } +``` + +After the installation, run `ghcup tui` and install the latest `stack` or `cabal` together with `ghc-9.6.6` (select the items and press `i`). +To install `srsimplify` simply run: + +```bash +cabal install +``` + +## Python + +### Features - Scikit-learn compatible API with `fit()` and `predict()` methods - Genetic programming approach with e-graph representation @@ -719,7 +841,7 @@ pip install eggp - Optional expression simplification through equality saturation - Ability to save and load e-graphs -## Usage +### Usage ### Basic Example @@ -804,7 +926,7 @@ print(f"Test MSE: {mse}") | `dumpTo` | str | "" | Filename to save the final e-graph | | `loadFrom` | str | "" | Filename to load an e-graph to resume search | -## Available Functions +### Available Functions The following functions can be used in the `nonterminals` parameter: @@ -815,7 +937,7 @@ The following functions can be used in the `nonterminals` parameter: - Hyperbolic: `sinh`, `cosh`, `tanh`, `asinh`, `acosh`, `atanh` - Others: `abs`, `log`, `logabs`, `exp`, `recip`, `aq` (analytical quotient) -## Methods +### Methods ### Core Methods - `fit(X, y)`: Fits the symbolic regression model @@ -833,7 +955,7 @@ The following functions can be used in the `nonterminals` parameter: - `evaluate_model(ix, X)`: Evaluates the model with index `ix` on the given data - `get_model(idx)`: Returns a model function and its visual representation -## Results +### Results After fitting, the `results` attribute contains a pandas DataFrame with details about the discovered models, including: - Mathematical expressions @@ -872,4 +994,10 @@ archivePrefix = {arXiv}, } ``` +## Acknowledgments + The bindings were created following the amazing example written by [wenkokke](https://github.com/wenkokke/example-haskell-wheel) + +Fabricio Olivetti de Franca is supported by Conselho Nacional de Desenvolvimento Cient\'{i}fico e Tecnol\'{o}gico (CNPq) grant 301596/2022-0. + +Gabriel Kronberger is supported by the Austrian Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology, the Federal Ministry for Labour and Economy, and the regional government of Upper Austria within the COMET project ProMetHeus (904919) supported by the Austrian Research Promotion Agency (FFG). diff --git a/src/eggp.egg-info/requires.txt b/src/eggp.egg-info/requires.txt index c976189..ddb0fc3 100644 --- a/src/eggp.egg-info/requires.txt +++ b/src/eggp.egg-info/requires.txt @@ -1,3 +1,6 @@ +numpy>=2 +pandas>=2.2 +scikit-learn>=1.6 [:python_version < "3.8"] typing_extensions<5,>=4.6 diff --git a/src/eggp.egg-info/top_level.txt b/src/eggp.egg-info/top_level.txt index 4efe345..b623a73 100644 --- a/src/eggp.egg-info/top_level.txt +++ b/src/eggp.egg-info/top_level.txt @@ -1,2 +1 @@ -bk eggp From a11312f5936bb3ca8dc97ede48f67d761d443976 Mon Sep 17 00:00:00 2001 From: folivetti Date: Tue, 27 May 2025 08:01:59 -0300 Subject: [PATCH 067/166] 1.0.6 fix temp file on Windows --- eggp.cabal | 2 +- pyproject.toml | 2 +- src/eggp/__init__.py | 39 ++++++++++++++++++++++++++++++++++++--- 3 files changed, 38 insertions(+), 5 deletions(-) diff --git a/eggp.cabal b/eggp.cabal index d61feb4..98de2ab 100755 --- a/eggp.cabal +++ b/eggp.cabal @@ -5,7 +5,7 @@ cabal-version: 2.2 -- see: https://github.com/sol/hpack name: eggp -version: 1.0.5 +version: 1.0.6 synopsis: A general library to work with Symbolic Regression expression trees. description: A Symbolic Regression Tree data structure to work with mathematical expressions with support to first order derivative and simplification; category: Math, Data, Data Structures diff --git a/pyproject.toml b/pyproject.toml index 3b7ae1f..a1d179f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,7 +12,7 @@ build-backend = "setuptools.build_meta" [project] name = "eggp" -version = "1.0.5" +version = "1.0.6" authors = [{ name = "Fabricio Olivetti", email = "folivetti@users.noreply.github.com" }] description = "Python Wheels for eggp algorithm." readme = "README.md" diff --git a/src/eggp/__init__.py b/src/eggp/__init__.py index 759fb9e..b933651 100644 --- a/src/eggp/__init__.py +++ b/src/eggp/__init__.py @@ -6,6 +6,7 @@ from io import StringIO import tempfile import csv +import os import numpy as np import pandas as pd @@ -21,7 +22,7 @@ unsafe_hs_eggp_exit, ) -VERSION: str = "1.0.4" +VERSION: str = "1.0.6" _hs_rts_init: bool = False @@ -239,6 +240,15 @@ def combine_dataset(self, X, y, Xerr, yerr): Returns the combined dataset ''' + if isinstance(X, pd.DataFrame): + X = X.to_numpy() + if isinstance(y, pd.DataFrame): + y = y.to_numpy() + if isinstance(Xerr, pd.DataFrame): + Xerr = Xerr.to_numpy() + if isinstance(yerr, pd.DataFrame): + yerr = yerr.to_numpy() + if X.ndim == 1: X = X.reshape(-1,1) y = y.reshape(-1, 1) @@ -290,13 +300,19 @@ def fit(self, X, y, Xerr = None, yerr = None): combined = self.combine_dataset(X, y, Xerr, yerr) header = self.get_header(X.shape[1]) - with tempfile.NamedTemporaryFile(mode='w+', newline='', delete=False, suffix='.csv') as temp_file: + with tempfile.NamedTemporaryFile(mode='w+', newline='', delete=False, prefix='datatemp_', suffix='.csv', dir=os.getcwd()) as temp_file: writer = csv.writer(temp_file) writer.writerow(header) writer.writerows(combined) dataset = temp_file.name dname = self.get_fname(dataset, header) - csv_data = eggp_run(dname, self.gen, self.nPop, self.maxSize, self.nTournament, self.pc, self.pm, self.nonterminals, self.loss, self.optIter, self.optRepeat, self.nParams, self.folds, self.simplify, self.trace, self.generational, self.dumpTo, self.loadFrom) + + try: + csv_data = eggp_run(dname, self.gen, self.nPop, self.maxSize, self.nTournament, self.pc, self.pm, self.nonterminals, self.loss, self.optIter, self.optRepeat, self.nParams, self.folds, self.simplify, self.trace, self.generational, self.dumpTo, self.loadFrom) + + finally: + os.remove(dataset) + if len(csv_data) > 0: csv_io = StringIO(csv_data.strip()) self.results = pd.read_csv(csv_io, header=0, dtype={'theta':str}) @@ -354,6 +370,9 @@ def predict(self, X): will be stored as a Pandas dataframe in self.results. ''' check_is_fitted(self) + if isinstance(X, pd.DataFrame): + X = X.to_numpy() + return self.evaluate_best_model(X) def predict_mvsr(self, X, view): @@ -375,9 +394,15 @@ def predict_mvsr(self, X, view): A vector of predictions ''' check_is_fitted(self) + if isinstance(X, pd.DataFrame): + X = X.to_numpy() + return self.evaluate_best_model_view(X, view) def evaluate_best_model(self, x): + if isinstance(x, pd.DataFrame): + x = x.to_numpy() + if x.ndim == 1: x = x.reshape(-1,1) t = np.array(list(map(float, self.results.iloc[-1].theta.split(";")))) @@ -388,6 +413,8 @@ def evaluate_best_model(self, x): return np.exp(y) return y def evaluate_best_model_view(self, x, view): + if isinstance(x, pd.DataFrame): + x = x.to_numpy() if x.ndim == 1: x = x.reshape(-1,1) ix = self.results.iloc[-1].id @@ -401,6 +428,8 @@ def evaluate_best_model_view(self, x, view): return y def evaluate_model_view(self, x, ix, view): + if isinstance(x, pd.DataFrame): + x = x.to_numpy() if x.ndim == 1: x = x.reshape(-1,1) best = self.results[self.results.id==ix].iloc[view] @@ -412,6 +441,8 @@ def evaluate_model_view(self, x, ix, view): return np.exp(y) return y def evaluate_model(self, ix, x): + if isinstance(x, pd.DataFrame): + x = x.to_numpy() if x.ndim == 1: x = x.reshape(-1,1) t = np.array(list(map(float, self.results.iloc[ix].theta.split(";")))) @@ -424,6 +455,8 @@ def evaluate_model(self, ix, x): def score(self, X, y): ''' Calculates the score (single-view only). ''' + if isinstance(y, pd.DataFrame): + y = y.to_numpy() ypred = self.evaluate_best_model(X) return r2_score(y, ypred) def get_model(self, idx): From 62836cfc335cf88b41090898498c15735e14ec34 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 16 Jun 2025 04:41:04 +0000 Subject: [PATCH 068/166] Bump pypa/cibuildwheel from 2.21.3 to 3.0.0 Bumps [pypa/cibuildwheel](https://github.com/pypa/cibuildwheel) from 2.21.3 to 3.0.0. - [Release notes](https://github.com/pypa/cibuildwheel/releases) - [Changelog](https://github.com/pypa/cibuildwheel/blob/main/docs/changelog.md) - [Commits](https://github.com/pypa/cibuildwheel/compare/v2.21.3...v3.0.0) --- updated-dependencies: - dependency-name: pypa/cibuildwheel dependency-version: 3.0.0 dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] --- .github/workflows/release.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 5e95caf..98b2527 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -172,7 +172,7 @@ jobs: with: platforms: "386" - - uses: pypa/cibuildwheel@v2.21.3 + - uses: pypa/cibuildwheel@v3.0.0 with: output-dir: "./wheelhouse" config-file: "{package}/pyproject.toml" From f82706cfcda3fd5e7301c52e2d27da8ad1bbde82 Mon Sep 17 00:00:00 2001 From: Fabricio Olivetti de Franca Date: Fri, 18 Jul 2025 12:09:57 +0200 Subject: [PATCH 069/166] Update README.md --- README.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index 694c515..6474ea7 100644 --- a/README.md +++ b/README.md @@ -146,7 +146,7 @@ cabal install ### Basic Example ```python -from pyeggp import PyEGGP +from eggp import EGGP import numpy as np # Create sample data @@ -154,7 +154,7 @@ X = np.linspace(-10, 10, 100).reshape(-1, 1) y = 2 * X.ravel() + 3 * np.sin(X.ravel()) + np.random.normal(0, 1, 100) # Create and fit the model -model = PyEGGP(gen=100, nonterminals="add,sub,mul,div,sin,cos") +model = EGGP(gen=100, nonterminals="add,sub,mul,div,sin,cos") model.fit(X, y) # Make predictions @@ -167,7 +167,7 @@ print(model.results) ### Multi-View Symbolic Regression ```python -from pyeggp import PyEGGP +from eggp import EGGP import numpy as np # Create multiple views of data @@ -178,7 +178,7 @@ X2 = np.linspace(0, 10, 100).reshape(-1, 1) y2 = np.sin(X2.ravel()) + np.random.normal(0, 0.2, 100) # Create and fit multi-view model -model = PyEGGP(gen=150, nPop=200) +model = EGGP(gen=150, nPop=200) model.fit_mvsr([X1, X2], [y1, y2]) # Make predictions for each view @@ -191,13 +191,13 @@ y_pred2 = model.predict_mvsr(X2, view=1) ```python from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_error -from pyeggp import PyEGGP +from eggp import EGGP # Split data X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) # Create and fit model -model = PyEGGP(gen=150, nPop=150, optIter=100) +model = EGGP(gen=150, nPop=150, optIter=100) model.fit(X_train, y_train) # Evaluate on test set @@ -270,7 +270,7 @@ After fitting, the `results` attribute contains a pandas DataFrame with details ## Citation -If you use PyEGGP in your research, please cite: +If you use EGGP in your research, please cite: ``` @inproceedings{eggp, From 69ed989c82362a04f022789e86eedb7c3558c7b7 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 4 Aug 2025 05:22:52 +0000 Subject: [PATCH 070/166] Bump pypa/cibuildwheel from 3.0.0 to 3.1.3 Bumps [pypa/cibuildwheel](https://github.com/pypa/cibuildwheel) from 3.0.0 to 3.1.3. - [Release notes](https://github.com/pypa/cibuildwheel/releases) - [Changelog](https://github.com/pypa/cibuildwheel/blob/main/docs/changelog.md) - [Commits](https://github.com/pypa/cibuildwheel/compare/v3.0.0...v3.1.3) --- updated-dependencies: - dependency-name: pypa/cibuildwheel dependency-version: 3.1.3 dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] --- .github/workflows/release.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 98b2527..25f8d81 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -172,7 +172,7 @@ jobs: with: platforms: "386" - - uses: pypa/cibuildwheel@v3.0.0 + - uses: pypa/cibuildwheel@v3.1.3 with: output-dir: "./wheelhouse" config-file: "{package}/pyproject.toml" From 6e9fb245b6c840fa9e2468b7bd0552aaccee1fcf Mon Sep 17 00:00:00 2001 From: erusseil <79919110+erusseil@users.noreply.github.com> Date: Thu, 7 Aug 2025 11:24:46 +0200 Subject: [PATCH 071/166] Update mvsr_example.ipynb fixed to new names --- test/mvsr_example.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/test/mvsr_example.ipynb b/test/mvsr_example.ipynb index 2d0ae02..fd952be 100644 --- a/test/mvsr_example.ipynb +++ b/test/mvsr_example.ipynb @@ -10,7 +10,7 @@ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pyeggp" + "import eggp" ] }, { @@ -441,7 +441,7 @@ ], "source": [ "# We create a Pyeggp regressor leaving all hyperparameters to default.\n", - "reg = pyeggp.PyEGGP()\n", + "reg = eggp.EGGP()\n", "reg.fit_mvsr(full_X, full_y)\n", "\n", "# Here we collect the full table of results and plot it\n", From ee448adae48eb6def7793f6310e8246daafc7083 Mon Sep 17 00:00:00 2001 From: folivetti Date: Thu, 7 Aug 2025 08:44:53 -0300 Subject: [PATCH 072/166] ban python3.14 for now --- .github/workflows/release.yml | 3 ++- cabal.project | 4 +++ eggp.cabal | 5 ++-- pyproject.toml | 7 +++--- src/EGGP.hs | 26 +++++++++++++------ src/Search.hs | 34 +++++++++++++++++++------ src/eggp/__init__.py | 47 +++++++++++++++++++++++++++++------ src/eggp/_binding.pyi | 2 +- src/eggp/binding.i | 6 ++--- src/eggp/typing.py | 2 +- 10 files changed, 102 insertions(+), 34 deletions(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 5e95caf..d801048 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -178,7 +178,8 @@ jobs: config-file: "{package}/pyproject.toml" env: CIBW_ARCHS: "${{ matrix.job.os.arch }}" - CIBW_BUILD: "*${{ matrix.job.os.plat }}*" + #CIBW_BUILD: "*${{ matrix.job.os.plat }}*" + CIBW_BUILD: "cp39-manylinux_x86_64 cp310-manylinux_x86_64 cp311-manylinux_x86_64 cp312-manylinux_x86_64 cp313-manylinux_x86_64" MACOSX_DEPLOYMENT_TARGET: ${{ matrix.os.runner == 'macos-13' && '13.0' || '14.0' }} - # Build source distribution (sdist) run: | diff --git a/cabal.project b/cabal.project index b3742e8..8bbe6b0 100644 --- a/cabal.project +++ b/cabal.project @@ -6,3 +6,7 @@ source-repository-package location: https://github.com/haskell/mwc-random tag: 2cce257158befe52417433d1e7717f11ce718aec +source-repository-package + type: git + location: https://github.com/folivetti/srtree + tag: 7c02ffa09caf7dfd01653d00304342f29b36beaa diff --git a/eggp.cabal b/eggp.cabal index 98de2ab..c5ad1a6 100755 --- a/eggp.cabal +++ b/eggp.cabal @@ -5,7 +5,7 @@ cabal-version: 2.2 -- see: https://github.com/sol/hpack name: eggp -version: 1.0.6 +version: 1.0.7 synopsis: A general library to work with Symbolic Regression expression trees. description: A Symbolic Regression Tree data structure to work with mathematical expressions with support to first order derivative and simplification; category: Math, Data, Data Structures @@ -75,7 +75,7 @@ foreign-library _binding , random >=1.2 && <1.4 , scheduler >=2.0.0.1 && <3 , split >=0.2.5 && <0.3 - , srtree >= 2.0.1.3 + , srtree >= 2.0.1.5 , statistics >=0.16.2.1 && <0.17 , transformers >=0.6.1.0 && <0.7 , unliftio >=0.2.10 && <1 @@ -84,4 +84,5 @@ foreign-library _binding , vector >=0.12 && <0.14 , zlib >=0.6.3 && <0.8 , directory + , time default-language: Haskell2010 diff --git a/pyproject.toml b/pyproject.toml index a1d179f..7e32002 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,7 +12,7 @@ build-backend = "setuptools.build_meta" [project] name = "eggp" -version = "1.0.6" +version = "1.0.7" authors = [{ name = "Fabricio Olivetti", email = "folivetti@users.noreply.github.com" }] description = "Python Wheels for eggp algorithm." readme = "README.md" @@ -29,7 +29,7 @@ classifiers = [ "Programming Language :: Python :: 3.13", "Programming Language :: Python :: Implementation :: CPython", ] -dependencies = ["typing_extensions >=4.6,<5; python_version<'3.8'", "numpy >=2", "pandas >=2.2", "scikit-learn >=1.6"] +dependencies = ["typing_extensions >=4.6,<5; python_version<'3.8'", "numpy >=2", "pandas >=2.2", "scikit-learn >=1.6", "matplotlib >= 3"] [project.optional-dependencies] mypy = ["types_setuptools >=45"] @@ -82,7 +82,8 @@ skip = [ ] manylinux-aarch64-image = "folivetti/manylinux_2_28_ghc9121_aarch64" manylinux-i686-image = "folivetti/manylinux2014_ghc9121_i686" -manylinux-x86_64-image = "folivetti/manylinux2014_ghc9121_x86_64" +manylinux-x86_64-image = "folivetti/manylinux_2_28_ghc9121_x86_64" +#manylinux-x86_64-image = "folivetti/manylinux2014_ghc9121_x86_64" musllinux-x86_64-image = "folivetti/musllinux_1_1_ghc9121_x86_64" [tool.cibuildwheel.windows] diff --git a/src/EGGP.hs b/src/EGGP.hs index bb49be3..2e5b6b4 100644 --- a/src/EGGP.hs +++ b/src/EGGP.hs @@ -115,16 +115,17 @@ hs_eggp_main = \ https://arxiv.org/abs/2501.17848\n" <> header "eggp - E-graph Genetic Programming for Symbolic Regression." ) -foreign export ccall hs_eggp_run :: CString -> CInt -> CInt -> CInt -> CInt -> CDouble -> CDouble -> CString -> CString -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CString -> CString -> IO CString +foreign export ccall hs_eggp_run :: CString -> CInt -> CInt -> CInt -> CInt -> CDouble -> CDouble -> CString -> CString -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CString -> CString -> CString -> IO CString -hs_eggp_run :: CString -> CInt -> CInt -> CInt -> CInt -> CDouble -> CDouble -> CString -> CString -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CString -> CString -> IO CString -hs_eggp_run dataset gens nPop maxSize nTournament pc pm nonterminals loss optIter optRepeat nParams folds simplify trace generational dumpTo loadFrom = do +hs_eggp_run :: CString -> CInt -> CInt -> CInt -> CInt -> CDouble -> CDouble -> CString -> CString -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CString -> CString -> CString -> IO CString +hs_eggp_run dataset gens nPop maxSize nTournament pc pm nonterminals loss optIter optRepeat nParams folds maxTime simplify trace generational dumpTo loadFrom varnames' = do dataset' <- peekCString dataset nonterminals' <- peekCString nonterminals loss' <- peekCString loss dumpTo' <- peekCString dumpTo loadFrom' <- peekCString loadFrom - out <- eggp_run dataset' (fromIntegral gens) (fromIntegral nPop) (fromIntegral maxSize) (fromIntegral nTournament) (realToFrac pc) (realToFrac pm) nonterminals' loss' (fromIntegral optIter) (fromIntegral optRepeat) (fromIntegral nParams) (fromIntegral folds) (simplify /= 0) (trace /= 0) (generational /= 0) dumpTo' loadFrom' + varnames <- peekCString varnames' + out <- eggp_run dataset' (fromIntegral gens) (fromIntegral nPop) (fromIntegral maxSize) (fromIntegral nTournament) (realToFrac pc) (realToFrac pm) nonterminals' loss' (fromIntegral optIter) (fromIntegral optRepeat) (fromIntegral nParams) (fromIntegral folds) (fromIntegral maxTime) (simplify /= 0) (trace /= 0) (generational /= 0) dumpTo' loadFrom' varnames newCString out opt :: Parser Args @@ -226,12 +227,23 @@ opt = Args ( long "simplify" <> help "simplify the expressions before displaying them." ) + <*> option auto + ( long "max-time" + <> value (-1) + <> showDefault + <> help "maximum allowed time budget (in seconds, -1 it will run for the number of generations)" + ) + <*> strOption + ( long "varnames" + <> value "" + <> showDefault + <> help "comma separated variable names." ) -eggp_run :: String -> Int -> Int -> Int -> Int -> Double -> Double -> String -> String -> Int -> Int -> Int -> Int -> Bool -> Bool -> Bool -> String -> String -> IO String -eggp_run dataset gens nPop maxSize nTournament pc pm nonterminals loss optIter optRepeat nParams folds simplify trace generational dumpTo loadFrom = +eggp_run :: String -> Int -> Int -> Int -> Int -> Double -> Double -> String -> String -> Int -> Int -> Int -> Int -> Int -> Bool -> Bool -> Bool -> String -> String -> String -> IO String +eggp_run dataset gens nPop maxSize nTournament pc pm nonterminals loss optIter optRepeat nParams folds maxTime simplify trace generational dumpTo loadFrom varnames = case readMaybe loss of Nothing -> pure $ "Invalid loss function " <> loss - Just l -> let arg = Args dataset "" gens maxSize folds trace l optIter optRepeat nParams nPop nTournament pc pm nonterminals dumpTo loadFrom generational simplify + Just l -> let arg = Args dataset "" gens maxSize folds trace l optIter optRepeat nParams nPop nTournament pc pm nonterminals dumpTo loadFrom generational simplify maxTime varnames in eggp arg eggp :: Args -> IO String diff --git a/src/Search.hs b/src/Search.hs index 6318d80..38f82e2 100644 --- a/src/Search.hs +++ b/src/Search.hs @@ -41,6 +41,7 @@ import List.Shuffle ( shuffle ) import Algorithm.SRTree.NonlinearOpt import Data.Binary ( encode, decode ) import qualified Data.ByteString.Lazy as BS +import Data.List.Split (splitOn) import Algorithm.EqSat (runEqSat,applySingleMergeOnlyEqSat) @@ -62,6 +63,7 @@ import System.Exit (ExitCode (..)) import Text.Read (readMaybe) import Data.Version (showVersion) import Control.Exception (Exception (..), SomeException (..), handle) +import Data.Time.Clock.POSIX data Args = Args { _dataset :: String, @@ -82,12 +84,14 @@ data Args = Args _dumpTo :: String, _loadFrom :: String, _generational :: Bool, - _simplify :: Bool + _simplify :: Bool, + _maxtime :: Int, + _varnames :: String } deriving (Show) csvHeader :: String -csvHeader = "id,view,Expression,Numpy,theta,size,loss_train,loss_val,loss_test,maxloss,R2_train,R2_val,R2_test,mdl_train,mdl_val,mdl_test" +csvHeader = "id,view,Expression,Numpy,Math,theta,size,loss_train,loss_val,loss_test,maxloss,R2_train,R2_val,R2_test,mdl_train,mdl_val,mdl_test" egraphGP :: [(DataSet, DataSet)] -> [DataSet] -> Args -> StateT EGraph (StateT StdGen IO) String egraphGP dataTrainVals dataTests args = do @@ -95,6 +99,8 @@ egraphGP dataTrainVals dataTests args = do insertTerms evaluateUnevaluated fitFun + + t0 <- io $ getPOSIXTime pop <- replicateM (_nPop args) $ do ec <- insertRndExpr (_maxSize args) rndTerm rndNonTerm >>= canonical updateIfNothing fitFun ec @@ -106,8 +112,9 @@ egraphGP dataTrainVals dataTests args = do else pure [] let m = (_nPop args) `div` (_maxSize args) + mTime = if _maxtime args < 0 then Nothing else Just (fromIntegral $ _maxtime args - 5) -- add 5 seconds slack - (finalPop, finalOut, _) <- iterateFor (_gens args) (pop', output, _nPop args) $ \it (ps', out, curIx) -> do + (finalPop, finalOut, _) <- iterateFor (_gens args) t0 mTime (pop', output, _nPop args) $ \it (ps', out, curIx) -> do newPop' <- replicateM (_nPop args) (evolve ps') out' <- if _trace args @@ -176,9 +183,16 @@ egraphGP dataTrainVals dataTests args = do (f, p) <- fitFun t insertFitness ec f p - iterateFor 0 xs f = pure xs - iterateFor n xs f = do xs' <- f n xs - iterateFor (n-1) xs' f + iterateFor 0 _ _ xs f = pure xs + iterateFor n t0 maxT xs f = do xs' <- f n xs + t1 <- io $ getPOSIXTime + let delta = t1 - t0 + maxT' = (subtract delta) <$> maxT + case maxT' of + Nothing -> iterateFor (n-1) t1 maxT' xs' f + Just mt -> if mt <= 0 + then pure xs + else iterateFor (n-1) t1 maxT' xs' f evolve xs' = do xs <- Prelude.mapM canonical xs' parents <- tournament xs @@ -346,8 +360,12 @@ egraphGP dataTrainVals dataTests args = do $ Prelude.map showNA [ nll_train, nll_val, nll_te, maxLoss , r2_train, r2_val, r2_te , mdl_train, mdl_val, mdl_te] - thetaStr = intercalate ";" $ Prelude.map show (MA.toList theta) - pure $ show ix <> "," <> show view <> "," <> showExpr expr <> "," <> "\"" <> showPython best' <> "\"," + thetaStr = intercalate ";" $ Prelude.map show (MA.toList theta) + varnames = _varnames args + showExprFun = if null varnames then showExpr else showExprWithVars (splitOn "," varnames) + showLatexFun = if null varnames then showLatex else showLatexWithVars (splitOn "," varnames) + pure $ show ix <> "," <> show view <> "," <> showExprFun expr <> "," <> "\"" <> showPython best' <> "\"," + <> "\"$$" <> showLatexFun best' <> "$$\"," <> thetaStr <> "," <> show (countNodes $ convertProtectedOps expr) <> "," <> vals pure ts diff --git a/src/eggp/__init__.py b/src/eggp/__init__.py index b933651..2f0cf24 100644 --- a/src/eggp/__init__.py +++ b/src/eggp/__init__.py @@ -13,6 +13,7 @@ from sklearn.base import BaseEstimator, RegressorMixin from sklearn.utils.validation import check_X_y, check_array, check_is_fitted from sklearn.metrics import mean_squared_error, r2_score +import matplotlib.pyplot as plt from ._binding import ( unsafe_hs_eggp_version, @@ -22,7 +23,7 @@ unsafe_hs_eggp_exit, ) -VERSION: str = "1.0.6" +VERSION: str = "1.0.7" _hs_rts_init: bool = False @@ -56,9 +57,9 @@ def main(args: List[str] = []) -> int: with hs_rts_init(args): return unsafe_hs_eggp_main() -def eggp_run(dataset: str, gen: int, nPop: int, maxSize: int, nTournament: int, pc: float, pm: float, nonterminals: str, loss: str, optIter: int, optRepeat: int, nParams: int, split: int, simplify: int, trace : int, generational : int, dumpTo: str, loadFrom: str) -> str: +def eggp_run(dataset: str, gen: int, nPop: int, maxSize: int, nTournament: int, pc: float, pm: float, nonterminals: str, loss: str, optIter: int, optRepeat: int, nParams: int, split: int, max_time : int, simplify: int, trace : int, generational : int, dumpTo: str, loadFrom: str, varnames : str) -> str: with hs_rts_init(): - return unsafe_hs_eggp_run(dataset, gen, nPop, maxSize, nTournament, pc, pm, nonterminals, loss, optIter, optRepeat, nParams, split, simplify, trace, generational, dumpTo, loadFrom) + return unsafe_hs_eggp_run(dataset, gen, nPop, maxSize, nTournament, pc, pm, nonterminals, loss, optIter, optRepeat, nParams, split, max_time, simplify, trace, generational, dumpTo, loadFrom, varnames) def make_function(expression, loss="MSE"): def func(x, t): @@ -169,7 +170,7 @@ class EGGP(BaseEstimator, RegressorMixin): >>> estimator = EGGP(loss="Bernoulli") >>> estimator.fit(X, y) """ - def __init__(self, gen = 100, nPop = 100, maxSize = 15, nTournament = 3, pc = 0.9, pm = 0.3, nonterminals = "add,sub,mul,div", loss = "MSE", optIter = 50, optRepeat = 2, nParams = -1, folds = 1, simplify = False, trace = False, generational = False, dumpTo = "", loadFrom = ""): + def __init__(self, gen = 100, nPop = 100, maxSize = 15, nTournament = 3, pc = 0.9, pm = 0.3, nonterminals = "add,sub,mul,div", loss = "MSE", optIter = 50, optRepeat = 2, nParams = -1, folds = 1, max_time = -1, simplify = False, trace = False, generational = False, dumpTo = "", loadFrom = ""): nts = "add,sub,mul,div,power,powerabs,\ aq,abs,sin,cos,tan,sinh,cosh,tanh,\ asin,acos,atan,asinh,acosh,atanh,sqrt,\ @@ -205,6 +206,8 @@ def __init__(self, gen = 100, nPop = 100, maxSize = 15, nTournament = 3, pc = 0. raise TypeError('trace must be a boolean') if not isinstance(generational, bool): raise TypeError('generational must be a boolean') + if not isinstance(max_time, int): + raise TypeError('max_time must be an integer') self.gen = gen self.nPop = nPop self.maxSize = maxSize @@ -217,6 +220,7 @@ def __init__(self, gen = 100, nPop = 100, maxSize = 15, nTournament = 3, pc = 0. self.optRepeat = optRepeat self.nParams = nParams self.folds = folds + self.max_time = max_time self.simplify = int(simplify) self.trace = int(trace) self.generational = int(generational) @@ -242,7 +246,7 @@ def combine_dataset(self, X, y, Xerr, yerr): ''' if isinstance(X, pd.DataFrame): X = X.to_numpy() - if isinstance(y, pd.DataFrame): + if isinstance(y, pd.DataFrame) or isinstance(y, pd.Series): y = y.to_numpy() if isinstance(Xerr, pd.DataFrame): Xerr = Xerr.to_numpy() @@ -299,6 +303,10 @@ def fit(self, X, y, Xerr = None, yerr = None): ''' combined = self.combine_dataset(X, y, Xerr, yerr) header = self.get_header(X.shape[1]) + if isinstance(X, pd.DataFrame): + varnames = ",".join(X.columns) + else: + varnames = "" with tempfile.NamedTemporaryFile(mode='w+', newline='', delete=False, prefix='datatemp_', suffix='.csv', dir=os.getcwd()) as temp_file: writer = csv.writer(temp_file) @@ -308,7 +316,7 @@ def fit(self, X, y, Xerr = None, yerr = None): dname = self.get_fname(dataset, header) try: - csv_data = eggp_run(dname, self.gen, self.nPop, self.maxSize, self.nTournament, self.pc, self.pm, self.nonterminals, self.loss, self.optIter, self.optRepeat, self.nParams, self.folds, self.simplify, self.trace, self.generational, self.dumpTo, self.loadFrom) + csv_data = eggp_run(dname, self.gen, self.nPop, self.maxSize, self.nTournament, self.pc, self.pm, self.nonterminals, self.loss, self.optIter, self.optRepeat, self.nParams, self.folds, self.max_time, self.simplify, self.trace, self.generational, self.dumpTo, self.loadFrom, varnames) finally: os.remove(dataset) @@ -337,15 +345,27 @@ def fit_mvsr(self, Xs, ys, Xerrs = None, yerrs = None): combineds = [self.combine_dataset(X, y, Xerr, yerr) for X, y, Xerr, yerr in zip(Xs, ys, Xerrs, yerrs)] header = self.get_header(Xs[0].shape[1]) datasets = [] + datasetsNames = [] + if isinstance(Xs[0], pd.DataFrame): + varnames = ",".join(Xs[0].columns) + else: + varnames = "" for combined in combineds: - with tempfile.NamedTemporaryFile(mode='w+', newline='', delete=False, suffix='.csv') as temp_file: + with tempfile.NamedTemporaryFile(mode='w+', newline='', delete=False, prefix='datatemp_', suffix='.csv', dir=os.getcwd()) as temp_file: writer = csv.writer(temp_file) writer.writerow(header) writer.writerows(combined) + datasetsNames.append(temp_file.name) datasets.append(self.get_fname(temp_file.name, header)) - csv_data = eggp_run(" ".join(datasets), self.gen, self.nPop, self.maxSize, self.nTournament, self.pc, self.pm, self.nonterminals, self.loss, self.optIter, self.optRepeat, self.nParams, self.folds, self.simplify, self.trace, self.generational, self.dumpTo, self.loadFrom) + try: + csv_data = eggp_run(" ".join(datasets), self.gen, self.nPop, self.maxSize, self.nTournament, self.pc, self.pm, + self.nonterminals, self.loss, self.optIter, self.optRepeat, self.nParams, self.folds, self.max_time, self.simplify, self.trace, self.generational, self.dumpTo, self.loadFrom, varnames) + finally: + for dataset in datasetsNames: + os.remove(dataset) + if len(csv_data) > 0: csv_io = StringIO(csv_data.strip()) self.results = pd.read_csv(csv_io, header=0, dtype={'theta':str}) @@ -476,3 +496,14 @@ def get_model(self, idx): visual_expression = visual_expression.replace(f'x[:, {i}]', f'X{i}') return model, visual_expression + def cal_plot(self, X, y, ix): + yhat = self.evaluate_model(ix, X) + y_lo = y.min() - 0.1*(y.max() - y.min()) + y_hi = y.max() + 0.1*(y.max() - y.min()) + rng = np.arange(y_lo, y_hi, 0.01) + plt.plot(rng, rng) + plt.plot(yhat, y, '.') + plt.xlabel(r"$\hat{y}$") + plt.ylabel(r"$y$") + plt.ylim(y_lo, y_hi) + plt.xlim(y_lo, y_hi) diff --git a/src/eggp/_binding.pyi b/src/eggp/_binding.pyi index b8bacf0..3f80a70 100644 --- a/src/eggp/_binding.pyi +++ b/src/eggp/_binding.pyi @@ -2,6 +2,6 @@ from typing import List def unsafe_hs_pyeggp_version() -> str: ... def unsafe_hs_pyeggp_main() -> int: ... -def unsafe_hs_pyeggp_run(dataset: str, gen: int, nPop: int, maxSize: int, nTournament: int, pc: float, pm: float, nonterminals: str, loss: str, optIter: int, optRepeat: int, nParams: int, split: int, simplify: int, trace : int, generational : int, dumpTo: str, loadFrom: str) -> str: ... +def unsafe_hs_pyeggp_run(dataset: str, gen: int, nPop: int, maxSize: int, nTournament: int, pc: float, pm: float, nonterminals: str, loss: str, optIter: int, optRepeat: int, nParams: int, split: int, max_time: int, simplify: int, trace : int, generational : int, dumpTo: str, loadFrom: str, varnames: str) -> str: ... def unsafe_hs_pyeggp_init(args: List[str]) -> None: ... def unsafe_hs_pyeggp_exit() -> None: ... diff --git a/src/eggp/binding.i b/src/eggp/binding.i index c3e3eae..65eac26 100644 --- a/src/eggp/binding.i +++ b/src/eggp/binding.i @@ -10,8 +10,8 @@ int unsafe_hs_eggp_main() { return hs_eggp_main(); } -char * unsafe_hs_eggp_run( char *dataset, int gens, int nPop, int maxSize, int nTournament, double pc, double pm, char *nonterminals, char *loss, int optIter, int optRepeat, int nParams, int split, int simplify, int trace, int generational, char *dumpTo, char *loadFrom ) { - return hs_eggp_run(dataset, gens, nPop, maxSize, nTournament, pc, pm, nonterminals, loss, optIter, optRepeat, nParams, split, simplify, trace, generational, dumpTo, loadFrom); +char * unsafe_hs_eggp_run( char *dataset, int gens, int nPop, int maxSize, int nTournament, double pc, double pm, char *nonterminals, char *loss, int optIter, int optRepeat, int nParams, int split, int max_time, int simplify, int trace, int generational, char *dumpTo, char *loadFrom, char *varnames ) { + return hs_eggp_run(dataset, gens, nPop, maxSize, nTournament, pc, pm, nonterminals, loss, optIter, optRepeat, nParams, split, max_time, simplify, trace, generational, dumpTo, loadFrom, varnames); } void unsafe_hs_eggp_init(int argc, char **argv) { @@ -59,6 +59,6 @@ void unsafe_py_write_stderr( char * str) { char * unsafe_hs_eggp_version(); int unsafe_hs_eggp_main(); -char * unsafe_hs_eggp_run( char *dataset, int gens, int nPop, int maxSize, int nTournament, double p, double pm, char *nonterminals, char *loss, int optIter, int optRepeat, int nParams, int split, int simplify, int trace, int generational, char *dumpTo, char *loadFrom); +char * unsafe_hs_eggp_run( char *dataset, int gens, int nPop, int maxSize, int nTournament, double p, double pm, char *nonterminals, char *loss, int optIter, int optRepeat, int nParams, int split, int max_time, int simplify, int trace, int generational, char *dumpTo, char *loadFrom, char *varnames); void unsafe_hs_eggp_init(int argc, char **argv); void unsafe_hs_eggp_exit(); diff --git a/src/eggp/typing.py b/src/eggp/typing.py index b5a917e..57d9569 100644 --- a/src/eggp/typing.py +++ b/src/eggp/typing.py @@ -11,4 +11,4 @@ class Session(Protocol): def version(self) -> str: ... def main(self, args: List[str] = []) -> int: ... - def eggp_run(self, dataset: str, gen: int, nPop: int, maxSize: int, nTournament: int, pc: float, pm: float, nonterminals: str, loss: str, optIter: int, optRepeat: int, nParams: int, split: int, simplify: int, trace : int, generational : int, dumpTo: str, loadFrom: str) -> str: ... + def eggp_run(self, dataset: str, gen: int, nPop: int, maxSize: int, nTournament: int, pc: float, pm: float, nonterminals: str, loss: str, optIter: int, optRepeat: int, nParams: int, split: int, max_time: int, simplify: int, trace : int, generational : int, dumpTo: str, loadFrom: str, varnames: str) -> str: ... From f09d4e644e6bf731e34bff48ca7b6d526dacdab0 Mon Sep 17 00:00:00 2001 From: folivetti Date: Thu, 7 Aug 2025 16:07:39 -0300 Subject: [PATCH 073/166] fix skip pp --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 7e32002..68f176c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -78,7 +78,7 @@ build = [ skip = [ # 18-04-2023: # PyPy builds do not work. - "pp*", + # "pp*", ] manylinux-aarch64-image = "folivetti/manylinux_2_28_ghc9121_aarch64" manylinux-i686-image = "folivetti/manylinux2014_ghc9121_i686" From b4e3f372891b9d864bc211aa72a30452e0d2bb9c Mon Sep 17 00:00:00 2001 From: folivetti Date: Thu, 7 Aug 2025 16:13:13 -0300 Subject: [PATCH 074/166] test pytoml fix --- pyproject.toml | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 68f176c..aae91d6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -62,16 +62,16 @@ archs = ["native"] build-frontend = "build" build = [ # GHC provides x86_64 and arm64 builds for macOS. - "*-macosx_x86_64", - "*-macosx_arm64", + "310-macosx_x86_64", + "310-macosx_arm64", # GHC provides an AMD64 build for Windows. - "*-win_amd64", + "310-win_amd64", # GHC provides an x86_64 build for CentOS 7 (manylinux2014). - "*-manylinux_x86_64", + "310-manylinux_x86_64", # GHC provides an i686 build for Debian 9 (manylinux_2014 compatible). #"*-manylinux_i686", # GHC provides an aarch64 build for Debian 10 (manylinux_2_28 compatible). - "*-manylinux_aarch64", + "310-manylinux_aarch64", # GHC provides an x86_64 build for Alpine 3.12 (manylinux_2_28 compatible). # "*-musllinux_x86_64", ] From 84df0ccc72104cc0fe3786da51b9c975477782b4 Mon Sep 17 00:00:00 2001 From: folivetti Date: Thu, 7 Aug 2025 16:17:42 -0300 Subject: [PATCH 075/166] test pytoml fix --- pyproject.toml | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index aae91d6..f8fc10f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -62,16 +62,16 @@ archs = ["native"] build-frontend = "build" build = [ # GHC provides x86_64 and arm64 builds for macOS. - "310-macosx_x86_64", - "310-macosx_arm64", + "cp310-macosx_x86_64", + "cp310-macosx_arm64", # GHC provides an AMD64 build for Windows. - "310-win_amd64", + "cp310-win_amd64", # GHC provides an x86_64 build for CentOS 7 (manylinux2014). - "310-manylinux_x86_64", + "cp310-manylinux_x86_64", # GHC provides an i686 build for Debian 9 (manylinux_2014 compatible). #"*-manylinux_i686", # GHC provides an aarch64 build for Debian 10 (manylinux_2_28 compatible). - "310-manylinux_aarch64", + "cp310-manylinux_aarch64", # GHC provides an x86_64 build for Alpine 3.12 (manylinux_2_28 compatible). # "*-musllinux_x86_64", ] From fbdac5d88ffe637c0c9d61d5182966a8e846f2f9 Mon Sep 17 00:00:00 2001 From: folivetti Date: Thu, 7 Aug 2025 16:18:14 -0300 Subject: [PATCH 076/166] test pytoml fix --- .github/workflows/release.yml | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index f6c60e9..25f8d81 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -178,8 +178,7 @@ jobs: config-file: "{package}/pyproject.toml" env: CIBW_ARCHS: "${{ matrix.job.os.arch }}" - #CIBW_BUILD: "*${{ matrix.job.os.plat }}*" - CIBW_BUILD: "cp39-manylinux_x86_64 cp310-manylinux_x86_64 cp311-manylinux_x86_64 cp312-manylinux_x86_64 cp313-manylinux_x86_64" + CIBW_BUILD: "*${{ matrix.job.os.plat }}*" MACOSX_DEPLOYMENT_TARGET: ${{ matrix.os.runner == 'macos-13' && '13.0' || '14.0' }} - # Build source distribution (sdist) run: | From 0b91d2256d4ab5fbd6a945e4e674844b003ee9c5 Mon Sep 17 00:00:00 2001 From: folivetti Date: Thu, 7 Aug 2025 16:23:18 -0300 Subject: [PATCH 077/166] test pytoml fix --- pyproject.toml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index f8fc10f..4c89a41 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -78,7 +78,8 @@ build = [ skip = [ # 18-04-2023: # PyPy builds do not work. - # "pp*", + "pp*", + "cp314-*" ] manylinux-aarch64-image = "folivetti/manylinux_2_28_ghc9121_aarch64" manylinux-i686-image = "folivetti/manylinux2014_ghc9121_i686" From 8c9d08b867bde1718e721fa82d0f10e9347806e2 Mon Sep 17 00:00:00 2001 From: folivetti Date: Thu, 7 Aug 2025 16:36:25 -0300 Subject: [PATCH 078/166] test pytoml fix --- pyproject.toml | 7 +++++-- requirements-ci.txt | 1 + src/eggp.egg-info/PKG-INFO | 3 ++- src/eggp.egg-info/requires.txt | 1 + 4 files changed, 9 insertions(+), 3 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 4c89a41..ae8cda7 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -106,9 +106,9 @@ where = ["src"] legacy_tox_ini = """ [tox] min_version = 4 -env_list = py{39,310,311,312,313}-{lin,mac,win} +env_list = py{39,310,311,312,313,314}-{lin,mac,win} -[testenv:py{39,310,311,312,313}-{lin,mac,win}] +[testenv:py{39,310,311,312,313,314}-{lin,mac,win}] package = external package_env = build-{env_name} platform = @@ -135,6 +135,7 @@ package_glob = py311-lin: {package_root}{/}dist{/}*cp311*manylinux*.whl py312-lin: {package_root}{/}dist{/}*cp312*manylinux*.whl py313-lin: {package_root}{/}dist{/}*cp313*manylinux*.whl + py314-lin: {package_root}{/}dist{/}*cp314*manylinux*.whl py37-mac: {package_root}{/}dist{/}*cp37*macosx*.whl py38-mac: {package_root}{/}dist{/}*cp38*macosx*.whl py39-mac: {package_root}{/}dist{/}*cp39*macosx*.whl @@ -142,6 +143,7 @@ package_glob = py311-mac: {package_root}{/}dist{/}*cp311*macosx*.whl py312-mac: {package_root}{/}dist{/}*cp312*macosx*.whl py313-mac: {package_root}{/}dist{/}*cp313*macosx*.whl + py314-mac: {package_root}{/}dist{/}*cp314*macosx*.whl py37-win: {package_root}{/}dist{/}*cp37*win*.whl py38-win: {package_root}{/}dist{/}*cp38*win*.whl py39-win: {package_root}{/}dist{/}*cp39*win*.whl @@ -149,6 +151,7 @@ package_glob = py311-win: {package_root}{/}dist{/}*cp311*win*.whl py312-win: {package_root}{/}dist{/}*cp312*win*.whl py313-win: {package_root}{/}dist{/}*cp313*win*.whl + py314-win: {package_root}{/}dist{/}*cp314*win*.whl allowlist_externals = sh commands = diff --git a/requirements-ci.txt b/requirements-ci.txt index 105e96f..c544007 100644 --- a/requirements-ci.txt +++ b/requirements-ci.txt @@ -1,3 +1,4 @@ pipx >=1.2.0, <2 delvewheel auditwheel +cibuildwheel==3.1.3 diff --git a/src/eggp.egg-info/PKG-INFO b/src/eggp.egg-info/PKG-INFO index 93ed078..8b5c4a1 100644 --- a/src/eggp.egg-info/PKG-INFO +++ b/src/eggp.egg-info/PKG-INFO @@ -1,6 +1,6 @@ Metadata-Version: 2.4 Name: eggp -Version: 1.0.5 +Version: 1.0.7 Summary: Python Wheels for eggp algorithm. Author-email: Fabricio Olivetti License: GNU GENERAL PUBLIC LICENSE @@ -694,6 +694,7 @@ Requires-Dist: typing_extensions<5,>=4.6; python_version < "3.8" Requires-Dist: numpy>=2 Requires-Dist: pandas>=2.2 Requires-Dist: scikit-learn>=1.6 +Requires-Dist: matplotlib>=3 Provides-Extra: mypy Requires-Dist: types_setuptools>=45; extra == "mypy" Dynamic: license-file diff --git a/src/eggp.egg-info/requires.txt b/src/eggp.egg-info/requires.txt index ddb0fc3..4d94d51 100644 --- a/src/eggp.egg-info/requires.txt +++ b/src/eggp.egg-info/requires.txt @@ -1,6 +1,7 @@ numpy>=2 pandas>=2.2 scikit-learn>=1.6 +matplotlib>=3 [:python_version < "3.8"] typing_extensions<5,>=4.6 From d401012ac015de515a526e33e61c2475c3bc07fb Mon Sep 17 00:00:00 2001 From: folivetti Date: Thu, 7 Aug 2025 16:39:56 -0300 Subject: [PATCH 079/166] test pytoml fix --- .github/workflows/release.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 25f8d81..ff63872 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -179,6 +179,7 @@ jobs: env: CIBW_ARCHS: "${{ matrix.job.os.arch }}" CIBW_BUILD: "*${{ matrix.job.os.plat }}*" + CIBW_SKIP: "cp314*" MACOSX_DEPLOYMENT_TARGET: ${{ matrix.os.runner == 'macos-13' && '13.0' || '14.0' }} - # Build source distribution (sdist) run: | From 209e9fb03f67b5a76223445674a48e323a0cf2a1 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 11 Aug 2025 06:19:09 +0000 Subject: [PATCH 080/166] Bump actions/download-artifact from 4 to 5 Bumps [actions/download-artifact](https://github.com/actions/download-artifact) from 4 to 5. - [Release notes](https://github.com/actions/download-artifact/releases) - [Commits](https://github.com/actions/download-artifact/compare/v4...v5) --- updated-dependencies: - dependency-name: actions/download-artifact dependency-version: '5' dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] --- .github/workflows/release.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index ff63872..842e909 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -209,7 +209,7 @@ jobs: steps: - name: Download artifacts - uses: actions/download-artifact@v4 + uses: actions/download-artifact@v5 with: pattern: "wheelhouse-*-*" path: "./wheelhouse" @@ -240,7 +240,7 @@ jobs: steps: - name: Download artifacts - uses: actions/download-artifact@v4 + uses: actions/download-artifact@v5 with: pattern: "wheelhouse-*-*" path: "./wheelhouse" From 7888ffca4bdc6ca4c410937eefd230f52141ffc9 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 18 Aug 2025 06:21:37 +0000 Subject: [PATCH 081/166] Bump actions/checkout from 4 to 5 Bumps [actions/checkout](https://github.com/actions/checkout) from 4 to 5. - [Release notes](https://github.com/actions/checkout/releases) - [Changelog](https://github.com/actions/checkout/blob/main/CHANGELOG.md) - [Commits](https://github.com/actions/checkout/compare/v4...v5) --- updated-dependencies: - dependency-name: actions/checkout dependency-version: '5' dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] --- .github/workflows/release.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 842e909..301c081 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -78,7 +78,7 @@ jobs: plat: "win" steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@v5 - # 12-02-2024: # Print the macOS architecture. From 06a0916b190a44056a1bba2611cca622832ce2b9 Mon Sep 17 00:00:00 2001 From: erusseil <79919110+erusseil@users.noreply.github.com> Date: Mon, 1 Sep 2025 13:42:35 +0200 Subject: [PATCH 082/166] Update mvsr_example.ipynb updated maxloss column name --- test/mvsr_example.ipynb | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/test/mvsr_example.ipynb b/test/mvsr_example.ipynb index fd952be..c07a4b1 100644 --- a/test/mvsr_example.ipynb +++ b/test/mvsr_example.ipynb @@ -101,7 +101,7 @@ " theta\n", " size\n", " loss\n", - " maxLoss\n", + " maxloss\n", " \n", " \n", " \n", @@ -410,7 +410,7 @@ "19 19.504541422655507;-2.03029626522703;-6.125262... 13 2.880242 \n", "20 2.2544633973594923;3.138226368773547e-2;-2.826... 13 4.673299 \n", "\n", - " maxLoss \n", + " maxloss \n", "0 -532.979453 \n", "1 -532.979453 \n", "2 -532.979453 \n", @@ -477,8 +477,8 @@ "source": [ "fig, ax = plt.subplots(1, 3, figsize=(14, 4))\n", "\n", - "ax[0].plot(pareto['id'], -pareto['maxLoss'], label='Pareto front')\n", - "ax[0].scatter(pareto['id'], -pareto['maxLoss'])\n", + "ax[0].plot(pareto['id'], -pareto['maxloss'], label='Pareto front')\n", + "ax[0].scatter(pareto['id'], -pareto['maxloss'])\n", "ax[0].set_ylabel('Loss')\n", "ax[0].legend()\n", "\n", From 87cd871d3048809a15293922e07db6078a99779b Mon Sep 17 00:00:00 2001 From: folivetti Date: Sun, 7 Sep 2025 09:17:14 -0300 Subject: [PATCH 083/166] pkg --- src/eggp.egg-info/PKG-INFO | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/src/eggp.egg-info/PKG-INFO b/src/eggp.egg-info/PKG-INFO index 8b5c4a1..3261da5 100644 --- a/src/eggp.egg-info/PKG-INFO +++ b/src/eggp.egg-info/PKG-INFO @@ -847,7 +847,7 @@ cabal install ### Basic Example ```python -from pyeggp import PyEGGP +from eggp import EGGP import numpy as np # Create sample data @@ -855,7 +855,7 @@ X = np.linspace(-10, 10, 100).reshape(-1, 1) y = 2 * X.ravel() + 3 * np.sin(X.ravel()) + np.random.normal(0, 1, 100) # Create and fit the model -model = PyEGGP(gen=100, nonterminals="add,sub,mul,div,sin,cos") +model = EGGP(gen=100, nonterminals="add,sub,mul,div,sin,cos") model.fit(X, y) # Make predictions @@ -868,7 +868,7 @@ print(model.results) ### Multi-View Symbolic Regression ```python -from pyeggp import PyEGGP +from eggp import EGGP import numpy as np # Create multiple views of data @@ -879,7 +879,7 @@ X2 = np.linspace(0, 10, 100).reshape(-1, 1) y2 = np.sin(X2.ravel()) + np.random.normal(0, 0.2, 100) # Create and fit multi-view model -model = PyEGGP(gen=150, nPop=200) +model = EGGP(gen=150, nPop=200) model.fit_mvsr([X1, X2], [y1, y2]) # Make predictions for each view @@ -892,13 +892,13 @@ y_pred2 = model.predict_mvsr(X2, view=1) ```python from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_error -from pyeggp import PyEGGP +from eggp import EGGP # Split data X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) # Create and fit model -model = PyEGGP(gen=150, nPop=150, optIter=100) +model = EGGP(gen=150, nPop=150, optIter=100) model.fit(X_train, y_train) # Evaluate on test set @@ -971,7 +971,7 @@ After fitting, the `results` attribute contains a pandas DataFrame with details ## Citation -If you use PyEGGP in your research, please cite: +If you use EGGP in your research, please cite: ``` @inproceedings{eggp, From 3e334fc6b387cd33ccfda3a105aa2f7e6128fa05 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 8 Sep 2025 04:21:03 +0000 Subject: [PATCH 084/166] Bump actions/setup-python from 5 to 6 Bumps [actions/setup-python](https://github.com/actions/setup-python) from 5 to 6. - [Release notes](https://github.com/actions/setup-python/releases) - [Commits](https://github.com/actions/setup-python/compare/v5...v6) --- updated-dependencies: - dependency-name: actions/setup-python dependency-version: '6' dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] --- .github/workflows/release.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 301c081..85cf805 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -86,7 +86,7 @@ jobs: run: echo "::debug::$(uname -a)" - # Required to run job via act: https://github.com/nektos/act - uses: actions/setup-python@v5 + uses: actions/setup-python@v6 with: cache: "pip" cache-dependency-path: "./requirements-ci.txt" From 547d9393f562fa2f74f8b9a580fab50d59d34d9b Mon Sep 17 00:00:00 2001 From: folivetti Date: Wed, 10 Sep 2025 18:33:05 -0300 Subject: [PATCH 085/166] basic tutorials --- tutorials/bikerental_example.py | 24 + tutorials/datasets/SeoulBikeData.csv | 8466 ++++++++++++++++++++++++ tutorials/datasets/Titanic-Dataset.csv | 892 +++ tutorials/datasets/nikuradse_1.csv | 363 + tutorials/regression_example.egg | Bin 0 -> 2680013 bytes tutorials/regression_example.py | 51 + tutorials/titanic_example.py | 20 + 7 files changed, 9816 insertions(+) create mode 100644 tutorials/bikerental_example.py create mode 100644 tutorials/datasets/SeoulBikeData.csv create mode 100644 tutorials/datasets/Titanic-Dataset.csv create mode 100644 tutorials/datasets/nikuradse_1.csv create mode 100644 tutorials/regression_example.egg create mode 100644 tutorials/regression_example.py create mode 100644 tutorials/titanic_example.py diff --git a/tutorials/bikerental_example.py b/tutorials/bikerental_example.py new file mode 100644 index 0000000..62feb73 --- /dev/null +++ b/tutorials/bikerental_example.py @@ -0,0 +1,24 @@ +from eggp import EGGP +import pandas as pd + +pd.set_option('display.max_colwidth', 100) +# Load the example dataset +df = pd.read_csv("datasets/SeoulBikeData.csv") + +# In this dataset the target variable is 'Rented Bike Count' which is usually modelled as a Poisson regression. +# We will first fit using MSE as the loss function and compare with the Poisson regression later. +model = EGGP(gen=100, nPop=100, maxSize=15, nonterminals='add,sub,mul,div,power,exp,log', loss='MSE', optIter=100, optRepeat=5, nParams=2, folds=2, max_time=120, simplify=True) + +model.fit(df[['Hour','Temp','Hum','WindSp','Vis','Dewpoint','SolarRad','Rainfall','Snowfall']], df['bikes']) + +print("Bike rentals with MSE: ") +print(model.results[['Expression', 'loss_train', 'loss_val', 'size']]) + +# Searching for a Poisson model. Notice that the data must not contain 0s in the target variable for Poisson regression. +model = EGGP(gen=100, nPop=100, maxSize=15, nonterminals='add,sub,mul,div,power,exp,log', loss='Poisson', optIter=100, optRepeat=5, nParams=2, folds=2, max_time=120, simplify=True) + +model.fit(df[['Hour','Temp','Hum','WindSp','Vis','Dewpoint','SolarRad','Rainfall','Snowfall']], df['bikes']) + +print("\nBike rentals with Poisson: ") +print(model.results[['Expression', 'loss_train', 'loss_val', 'size']]) + diff --git a/tutorials/datasets/SeoulBikeData.csv b/tutorials/datasets/SeoulBikeData.csv new file mode 100644 index 0000000..a2abaf5 --- /dev/null +++ b/tutorials/datasets/SeoulBikeData.csv @@ -0,0 +1,8466 @@ +Date,bikes,Hour,Temp,Hum,WindSp,Vis,Dewpoint,SolarRad,Rainfall,Snowfall,Seasons,Holliday,Functioning Day +01122017,254,0,-5.2,37,2.2,2000,-17.6,0,0,0,0,0,1 +01122017,204,1,-5.5,38,0.8,2000,-17.6,0,0,0,0,0,1 +01122017,173,2,-6,39,1,2000,-17.7,0,0,0,0,0,1 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b/tutorials/regression_example.py @@ -0,0 +1,51 @@ +from eggp import EGGP +import pandas as pd + +pd.set_option('display.max_colwidth', 100) +# Load the example dataset +df = pd.read_csv("datasets/nikuradse_1.csv") + +# Create an EGGP object, all parameters have default values. We will dump the results to a file named 'regression_example.egg' +# gen is the number of generations, nPop is the population size, maxSize is the maximum size of the expressions +# nTnament is the tournament size, pc and pm are the probabilities for performing crossover and mutation +# nonterminals is the function set +# loss is the loss function (see the boston house example for other values) +# optIter and optRepeat are the number of iterations and repetitions for the optimization of constants +# nParams is the number of parameters to include in the expression +# max_time is the maximum time in seconds to run the algorithm +# simplify indicates whether to simplify the expressions using equality saturation +# dumpTo dumps the final e-graph to a file +# By default it will run a multi-objective approach. +model = EGGP(gen=100, nPop=100, maxSize=15, nTournament=5, pc=0.8, pm=0.2, nonterminals='add,sub,mul,div,power,exp,log', loss='MSE', optIter=100, optRepeat=5, nParams=2, folds=2, max_time=120, simplify=False, dumpTo='regression_example.egg') + +model.fit(df[['r_k', 'log_Re']], df['target']) + +print("Multi-objective mode returning the Pareto front without simplifcation: ") +print(model.results[['Expression', 'loss_train', 'loss_val', 'size']]) + +# Running with simplification enabled. Notice that the size used to determine the pareto front is before simplification. +# It is expected to see two elements on the front with the same size. +model = EGGP(gen=100, nPop=100, maxSize=15, nTournament=5, pc=0.8, pm=0.2, nonterminals='add,sub,mul,div,power,exp,log', loss='MSE', optIter=100, optRepeat=5, nParams=2, folds=2, max_time=120, simplify=True) + +model.fit(df[['r_k', 'log_Re']], df['target']) + +print("\nPareto front with simplifcation enabled: ") +print(model.results[['Expression', 'loss_train', 'loss_val', 'size']]) + +# Let's try the generational approach, where the entire population is replaced by the offspring at each generation. +model = EGGP(gen=100, nPop=100, maxSize=15, nTournament=5, pc=0.8, pm=0.2, nonterminals='add,sub,mul,div,power,exp,log', loss='MSE', optIter=100, optRepeat=5, nParams=2, folds=2, max_time=120, simplify=True, generational=True) + +model.fit(df[['r_k', 'log_Re']], df['target']) + +print("\nLast population of generational approach with simplifcation: ") +print(model.results[['Expression', 'loss_train', 'loss_val', 'size']]) + +# 'loadFrom' argument enable us to start the search from a previously saved search. +# Notice that the egg file will contain ALL the evaluated expressions, not only the final population. +# As the initial population it will choose either the top N solutions (if generational) or the Pareto front (if MOO). +model = EGGP(gen=100, nPop=100, maxSize=15, nTournament=5, pc=0.8, pm=0.2, nonterminals='add,sub,mul,div,power,exp,log', loss='MSE', optIter=100, optRepeat=5, nParams=2, folds=2, max_time=120, simplify=True, generational=True, loadFrom='regression_example.egg') + +model.fit(df[['r_k', 'log_Re']], df['target']) + +print("\nLast population resumed from the first Pareto front: ") +print(model.results[['Expression', 'loss_train', 'loss_val', 'size']]) diff --git a/tutorials/titanic_example.py b/tutorials/titanic_example.py new file mode 100644 index 0000000..cbedb99 --- /dev/null +++ b/tutorials/titanic_example.py @@ -0,0 +1,20 @@ +from eggp import EGGP +import pandas as pd +import numpy as np + +pd.set_option('display.max_colwidth', 100) +# Load the example dataset +df = pd.read_csv("datasets/Titanic-Dataset.csv") + +# In this dataset the target variable is 'Rented Bike Count' which is usually modelled as a Poisson regression. +# We will first fit using MSE as the loss function and compare with the Poisson regression later. +model = EGGP(gen=100, nPop=100, maxSize=20, nonterminals='add,sub,mul,div,power,exp,log', loss='Bernoulli', optIter=100, optRepeat=5, nParams=2, folds=2, max_time=120, simplify=True) + +model.fit(df[['Pclass', 'Fare', 'Age']], df['Survived']) + +print("Titanic Bernoulli loss: ") +print(model.results[['Expression', 'loss_train', 'loss_val', 'size']]) + +yhat = model.predict(df[['Pclass', 'Fare', 'Age']]) +acc = np.sum(np.round(yhat) == df['Survived']) / yhat.shape # Check accuracy +print("Accuracy: ", acc) From 47ff5cff41392e3f90903c499b86e862c75e71c4 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 29 Sep 2025 04:36:49 +0000 Subject: [PATCH 086/166] Bump pypa/cibuildwheel from 3.1.3 to 3.2.0 Bumps [pypa/cibuildwheel](https://github.com/pypa/cibuildwheel) from 3.1.3 to 3.2.0. - [Release notes](https://github.com/pypa/cibuildwheel/releases) - [Changelog](https://github.com/pypa/cibuildwheel/blob/main/docs/changelog.md) - [Commits](https://github.com/pypa/cibuildwheel/compare/v3.1.3...v3.2.0) --- updated-dependencies: - dependency-name: pypa/cibuildwheel dependency-version: 3.2.0 dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] --- .github/workflows/release.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 85cf805..a768fd6 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -172,7 +172,7 @@ jobs: with: platforms: "386" - - uses: pypa/cibuildwheel@v3.1.3 + - uses: pypa/cibuildwheel@v3.2.0 with: output-dir: "./wheelhouse" config-file: "{package}/pyproject.toml" From 3cb948ee8c4f4ea57cad34dfb5459d9344d479d1 Mon Sep 17 00:00:00 2001 From: folivetti Date: Thu, 2 Oct 2025 13:09:10 -0300 Subject: [PATCH 087/166] fix number-params 0 --- eggp.cabal | 2 +- pyproject.toml | 2 +- src/Search.hs | 2 +- src/eggp/__init__.py | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/eggp.cabal b/eggp.cabal index c5ad1a6..ced8eed 100755 --- a/eggp.cabal +++ b/eggp.cabal @@ -5,7 +5,7 @@ cabal-version: 2.2 -- see: https://github.com/sol/hpack name: eggp -version: 1.0.7 +version: 1.0.8 synopsis: A general library to work with Symbolic Regression expression trees. description: A Symbolic Regression Tree data structure to work with mathematical expressions with support to first order derivative and simplification; category: Math, Data, Data Structures diff --git a/pyproject.toml b/pyproject.toml index ae8cda7..1e28cbb 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,7 +12,7 @@ build-backend = "setuptools.build_meta" [project] name = "eggp" -version = "1.0.7" +version = "1.0.8" authors = [{ name = "Fabricio Olivetti", email = "folivetti@users.noreply.github.com" }] description = "Python Wheels for eggp algorithm." readme = "README.md" diff --git a/src/Search.hs b/src/Search.hs index 38f82e2..2b36c0b 100644 --- a/src/Search.hs +++ b/src/Search.hs @@ -174,7 +174,7 @@ egraphGP dataTrainVals dataTests args = do insertFitness eId (fromJust $ _fitness info) (_theta info) rndTerm = do coin <- toss - if coin then randomFrom terms else randomFrom params + if coin || _nParams args == 0 then randomFrom terms else randomFrom params rndNonTerm = randomFrom nonTerms refitChanged = do ids <- gets (_refits . _eDB) >>= Prelude.mapM canonical . Set.toList >>= pure . nub diff --git a/src/eggp/__init__.py b/src/eggp/__init__.py index 2f0cf24..e88c1c4 100644 --- a/src/eggp/__init__.py +++ b/src/eggp/__init__.py @@ -23,7 +23,7 @@ unsafe_hs_eggp_exit, ) -VERSION: str = "1.0.7" +VERSION: str = "1.0.8" _hs_rts_init: bool = False From 3745e3efe507ef870a567590cb590e23672d4669 Mon Sep 17 00:00:00 2001 From: Fabricio Olivetti de Franca Date: Fri, 3 Oct 2025 06:23:03 -0300 Subject: [PATCH 088/166] Update release.yml --- .github/workflows/release.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index a768fd6..6d60bf9 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -68,7 +68,7 @@ jobs: plat: "macosx" - os: name: "macOS" - runner: "macos-14" + runner: "macos-15-intel" arch: "arm64" plat: "macosx" - os: From ab7ef362f837c0e48db0c625f44d7a1992788482 Mon Sep 17 00:00:00 2001 From: Fabricio Olivetti de Franca Date: Fri, 3 Oct 2025 07:18:49 -0300 Subject: [PATCH 089/166] Update release.yml --- .github/workflows/release.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 6d60bf9..f2bfc9f 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -154,7 +154,7 @@ jobs: echo "LIBRARY_PATH=/usr/local/lib:/opt/homebrew/lib" >> $GITHUB_ENV echo "CPATH=/usr/local/include:/opt/homebrew/include" >> $GITHUB_ENV echo "DYLD_LIBRARY_PATH=/opt/homebrew/lib" >> $GITHUB_ENV - echo "MACOSX_DEPLOYMENT_TARGET=13.0" >> $GITHUB_ENV + echo "MACOSX_DEPLOYMENT_TARGET=14.0" >> $GITHUB_ENV - # 19-06-2023: # Required to build aarch64 wheels. @@ -180,7 +180,7 @@ jobs: CIBW_ARCHS: "${{ matrix.job.os.arch }}" CIBW_BUILD: "*${{ matrix.job.os.plat }}*" CIBW_SKIP: "cp314*" - MACOSX_DEPLOYMENT_TARGET: ${{ matrix.os.runner == 'macos-13' && '13.0' || '14.0' }} + MACOSX_DEPLOYMENT_TARGET: ${{ matrix.os.runner == 'macos-14' && '14.0' || '14.0' }} - # Build source distribution (sdist) run: | pip install build From 89f54a348416d85db273b2162e6e574ebac12d20 Mon Sep 17 00:00:00 2001 From: Fabricio Olivetti de Franca Date: Fri, 3 Oct 2025 07:51:32 -0300 Subject: [PATCH 090/166] Update release.yml --- .github/workflows/release.yml | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index f2bfc9f..1d22b22 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -63,12 +63,12 @@ jobs: # plat: "musllinux" - os: name: "macOS" - runner: "macos-13" + runner: "macos-14-intel" arch: "x86_64" plat: "macosx" - os: name: "macOS" - runner: "macos-15-intel" + runner: "macos-14" arch: "arm64" plat: "macosx" - os: @@ -181,6 +181,7 @@ jobs: CIBW_BUILD: "*${{ matrix.job.os.plat }}*" CIBW_SKIP: "cp314*" MACOSX_DEPLOYMENT_TARGET: ${{ matrix.os.runner == 'macos-14' && '14.0' || '14.0' }} + - # Build source distribution (sdist) run: | pip install build From 519eb1811815ba26cbbe1f048a95e6ac7543cb2b Mon Sep 17 00:00:00 2001 From: Fabricio Olivetti de Franca Date: Fri, 3 Oct 2025 09:12:23 -0300 Subject: [PATCH 091/166] Update release.yml --- .github/workflows/release.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 1d22b22..be9552f 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -63,7 +63,7 @@ jobs: # plat: "musllinux" - os: name: "macOS" - runner: "macos-14-intel" + runner: "macos-15-intel" arch: "x86_64" plat: "macosx" - os: From 5d56b31f81a335e97748b17b5f91df9ce756e9a0 Mon Sep 17 00:00:00 2001 From: folivetti Date: Fri, 3 Oct 2025 16:35:17 -0300 Subject: [PATCH 092/166] fix eval model with no parameters --- eggp.cabal | 2 +- pyproject.toml | 2 +- src/eggp.egg-info/PKG-INFO | 2 +- src/eggp/__init__.py | 12 +++++++----- 4 files changed, 10 insertions(+), 8 deletions(-) diff --git a/eggp.cabal b/eggp.cabal index ced8eed..06e37f0 100755 --- a/eggp.cabal +++ b/eggp.cabal @@ -5,7 +5,7 @@ cabal-version: 2.2 -- see: https://github.com/sol/hpack name: eggp -version: 1.0.8 +version: 1.0.9 synopsis: A general library to work with Symbolic Regression expression trees. description: A Symbolic Regression Tree data structure to work with mathematical expressions with support to first order derivative and simplification; category: Math, Data, Data Structures diff --git a/pyproject.toml b/pyproject.toml index 1e28cbb..0d8f98f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,7 +12,7 @@ build-backend = "setuptools.build_meta" [project] name = "eggp" -version = "1.0.8" +version = "1.0.9" authors = [{ name = "Fabricio Olivetti", email = "folivetti@users.noreply.github.com" }] description = "Python Wheels for eggp algorithm." readme = "README.md" diff --git a/src/eggp.egg-info/PKG-INFO b/src/eggp.egg-info/PKG-INFO index 3261da5..44c305b 100644 --- a/src/eggp.egg-info/PKG-INFO +++ b/src/eggp.egg-info/PKG-INFO @@ -1,6 +1,6 @@ Metadata-Version: 2.4 Name: eggp -Version: 1.0.7 +Version: 1.0.9 Summary: Python Wheels for eggp algorithm. Author-email: Fabricio Olivetti License: GNU GENERAL PUBLIC LICENSE diff --git a/src/eggp/__init__.py b/src/eggp/__init__.py index e88c1c4..29a90cf 100644 --- a/src/eggp/__init__.py +++ b/src/eggp/__init__.py @@ -23,7 +23,7 @@ unsafe_hs_eggp_exit, ) -VERSION: str = "1.0.8" +VERSION: str = "1.0.9" _hs_rts_init: bool = False @@ -425,7 +425,8 @@ def evaluate_best_model(self, x): if x.ndim == 1: x = x.reshape(-1,1) - t = np.array(list(map(float, self.results.iloc[-1].theta.split(";")))) + tStr = self.results.iloc[-1].theta.split(";") + t = np.array(list(map(float, tStr))) if len(tStr[0]) > 0 else np.array([]) y = eval(self.results.iloc[-1].Numpy) if self.loss == "Bernoulli": return 1/(1 + np.exp(-y)) @@ -439,7 +440,7 @@ def evaluate_best_model_view(self, x, view): x = x.reshape(-1,1) ix = self.results.iloc[-1].id best = self.results[self.results.id==ix].iloc[view] - t = np.array(list(map(float, best.theta.split(";")))) + t = np.array(list(map(float, best.theta.split(";")))) if len(best.theta) > 0 else np.array([]) y = eval(best.Numpy) if self.loss == "Bernoulli": return 1/(1 + np.exp(-y)) @@ -453,7 +454,7 @@ def evaluate_model_view(self, x, ix, view): if x.ndim == 1: x = x.reshape(-1,1) best = self.results[self.results.id==ix].iloc[view] - t = np.array(list(map(float, best.theta.split(";")))) + t = np.array(list(map(float, best.theta.split(";")))) if len(best.theta) > 0 else np.array([]) y = eval(best.Numpy) if self.loss == "Bernoulli": return 1/(1 + np.exp(-y)) @@ -465,7 +466,8 @@ def evaluate_model(self, ix, x): x = x.to_numpy() if x.ndim == 1: x = x.reshape(-1,1) - t = np.array(list(map(float, self.results.iloc[ix].theta.split(";")))) + tStr = self.results.iloc[ix].theta.split(";") + t = np.array(list(map(float, tStr))) if len(tStr[0]) > 0 else np.array([]) y = eval(self.results.iloc[ix].Numpy) if self.loss == "Bernoulli": return 1/(1 + np.exp(-y)) From d11f817a74c5973cebef0e2b1b89a26926cd9f0c Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 6 Oct 2025 04:21:49 +0000 Subject: [PATCH 093/166] Update find-libpython requirement from <0.5,>=0.3 to >=0.3,<0.6 Updates the requirements on [find-libpython](https://github.com/ktbarrett/find_libpython) to permit the latest version. - [Commits](https://github.com/ktbarrett/find_libpython/commits) --- updated-dependencies: - dependency-name: find-libpython dependency-version: 0.5.0 dependency-type: direct:production ... Signed-off-by: dependabot[bot] --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 0d8f98f..44e862e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [build-system] requires = [ - "find_libpython >=0.3,<0.5; sys_platform == 'win32'", + "find_libpython >=0.3,<0.6; sys_platform == 'win32'", "setuptools >=45", "swig >=4,<5", "numpy >=2", From 55c8dc99183ee9bce5b06ae2693b9fc9f974b5b0 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 13 Oct 2025 04:20:55 +0000 Subject: [PATCH 094/166] Bump pypa/cibuildwheel from 3.2.0 to 3.2.1 Bumps [pypa/cibuildwheel](https://github.com/pypa/cibuildwheel) from 3.2.0 to 3.2.1. - [Release notes](https://github.com/pypa/cibuildwheel/releases) - [Changelog](https://github.com/pypa/cibuildwheel/blob/main/docs/changelog.md) - [Commits](https://github.com/pypa/cibuildwheel/compare/v3.2.0...v3.2.1) --- updated-dependencies: - dependency-name: pypa/cibuildwheel dependency-version: 3.2.1 dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] --- .github/workflows/release.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index be9552f..1122310 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -172,7 +172,7 @@ jobs: with: platforms: "386" - - uses: pypa/cibuildwheel@v3.2.0 + - uses: pypa/cibuildwheel@v3.2.1 with: output-dir: "./wheelhouse" config-file: "{package}/pyproject.toml" From e0a654fcdf194d21717b1de6b616228accbf4a87 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 27 Oct 2025 04:32:32 +0000 Subject: [PATCH 095/166] Bump actions/download-artifact from 5 to 6 Bumps [actions/download-artifact](https://github.com/actions/download-artifact) from 5 to 6. - [Release notes](https://github.com/actions/download-artifact/releases) - [Commits](https://github.com/actions/download-artifact/compare/v5...v6) --- updated-dependencies: - dependency-name: actions/download-artifact dependency-version: '6' dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] --- .github/workflows/release.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 1122310..fac8060 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -210,7 +210,7 @@ jobs: steps: - name: Download artifacts - uses: actions/download-artifact@v5 + uses: actions/download-artifact@v6 with: pattern: "wheelhouse-*-*" path: "./wheelhouse" @@ -241,7 +241,7 @@ jobs: steps: - name: Download artifacts - uses: actions/download-artifact@v5 + uses: actions/download-artifact@v6 with: pattern: "wheelhouse-*-*" path: "./wheelhouse" From 2957aefaeb1dc938904586c4f8e224cc9a44ea86 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 27 Oct 2025 04:33:02 +0000 Subject: [PATCH 096/166] Bump actions/upload-artifact from 4 to 5 Bumps [actions/upload-artifact](https://github.com/actions/upload-artifact) from 4 to 5. - [Release notes](https://github.com/actions/upload-artifact/releases) - [Commits](https://github.com/actions/upload-artifact/compare/v4...v5) --- updated-dependencies: - dependency-name: actions/upload-artifact dependency-version: '5' dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] --- .github/workflows/release.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 1122310..2bfdddf 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -188,7 +188,7 @@ jobs: python -m build --sdist --outdir ./wheelhouse - name: Upload wheels - uses: actions/upload-artifact@v4 + uses: actions/upload-artifact@v5 with: name: "wheelhouse-${{ matrix.job.os.plat }}-${{ matrix.job.os.arch }}" path: | From d51978a750f889e9bb268e34da538487128fa7f9 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 17 Nov 2025 04:20:48 +0000 Subject: [PATCH 097/166] Bump pypa/cibuildwheel from 3.2.1 to 3.3.0 Bumps [pypa/cibuildwheel](https://github.com/pypa/cibuildwheel) from 3.2.1 to 3.3.0. - [Release notes](https://github.com/pypa/cibuildwheel/releases) - [Changelog](https://github.com/pypa/cibuildwheel/blob/main/docs/changelog.md) - [Commits](https://github.com/pypa/cibuildwheel/compare/v3.2.1...v3.3.0) --- updated-dependencies: - dependency-name: pypa/cibuildwheel dependency-version: 3.3.0 dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] --- .github/workflows/release.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index ad00131..0bcc760 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -172,7 +172,7 @@ jobs: with: platforms: "386" - - uses: pypa/cibuildwheel@v3.2.1 + - uses: pypa/cibuildwheel@v3.3.0 with: output-dir: "./wheelhouse" config-file: "{package}/pyproject.toml" From 8dc3d25ed1d02929ecd7e12fe441691ad0081f95 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 24 Nov 2025 04:30:11 +0000 Subject: [PATCH 098/166] Bump actions/checkout from 5 to 6 Bumps [actions/checkout](https://github.com/actions/checkout) from 5 to 6. - [Release notes](https://github.com/actions/checkout/releases) - [Changelog](https://github.com/actions/checkout/blob/main/CHANGELOG.md) - [Commits](https://github.com/actions/checkout/compare/v5...v6) --- updated-dependencies: - dependency-name: actions/checkout dependency-version: '6' dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] --- .github/workflows/release.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 0bcc760..d069518 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -78,7 +78,7 @@ jobs: plat: "win" steps: - - uses: actions/checkout@v5 + - uses: actions/checkout@v6 - # 12-02-2024: # Print the macOS architecture. From ef502c8fa1103b6d630f0fc74d990953c8ca8eb8 Mon Sep 17 00:00:00 2001 From: folivetti Date: Tue, 25 Nov 2025 09:17:08 -0300 Subject: [PATCH 099/166] Fix #19 --- eggp.cabal | 2 +- pyproject.toml | 2 +- src/eggp/__init__.py | 24 +- test/mvsr_example.ipynb | 714 ++++++++++++++++++++++--------- tutorials/multisteps.py | 45 ++ tutorials/regression_example.egg | Bin 2680013 -> 3172506 bytes 6 files changed, 584 insertions(+), 203 deletions(-) create mode 100644 tutorials/multisteps.py diff --git a/eggp.cabal b/eggp.cabal index 06e37f0..ddd7e04 100755 --- a/eggp.cabal +++ b/eggp.cabal @@ -5,7 +5,7 @@ cabal-version: 2.2 -- see: https://github.com/sol/hpack name: eggp -version: 1.0.9 +version: 1.0.10 synopsis: A general library to work with Symbolic Regression expression trees. description: A Symbolic Regression Tree data structure to work with mathematical expressions with support to first order derivative and simplification; category: Math, Data, Data Structures diff --git a/pyproject.toml b/pyproject.toml index 0d8f98f..7671f50 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,7 +12,7 @@ build-backend = "setuptools.build_meta" [project] name = "eggp" -version = "1.0.9" +version = "1.0.10" authors = [{ name = "Fabricio Olivetti", email = "folivetti@users.noreply.github.com" }] description = "Python Wheels for eggp algorithm." readme = "README.md" diff --git a/src/eggp/__init__.py b/src/eggp/__init__.py index 29a90cf..e58bdde 100644 --- a/src/eggp/__init__.py +++ b/src/eggp/__init__.py @@ -23,7 +23,7 @@ unsafe_hs_eggp_exit, ) -VERSION: str = "1.0.9" +VERSION: str = "1.0.10" _hs_rts_init: bool = False @@ -323,7 +323,7 @@ def fit(self, X, y, Xerr = None, yerr = None): if len(csv_data) > 0: csv_io = StringIO(csv_data.strip()) - self.results = pd.read_csv(csv_io, header=0, dtype={'theta':str}) + self.results = pd.read_csv(csv_io, header=0, dtype={'theta':str}, keep_default_na=False) self.is_fitted_ = True return self @@ -368,7 +368,7 @@ def fit_mvsr(self, Xs, ys, Xerrs = None, yerrs = None): if len(csv_data) > 0: csv_io = StringIO(csv_data.strip()) - self.results = pd.read_csv(csv_io, header=0, dtype={'theta':str}) + self.results = pd.read_csv(csv_io, header=0, dtype={'theta':str}, keep_default_na=False) self.is_fitted_ = True return self @@ -434,6 +434,8 @@ def evaluate_best_model(self, x): return np.exp(y) return y def evaluate_best_model_view(self, x, view): + if view not in np.unique(self.results.view.values): + raise ValueError("Invalid view index") if isinstance(x, pd.DataFrame): x = x.to_numpy() if x.ndim == 1: @@ -453,6 +455,11 @@ def evaluate_model_view(self, x, ix, view): x = x.to_numpy() if x.ndim == 1: x = x.reshape(-1,1) + if ix not in np.unique(self.results.id.values): + raise ValueError("Invalid model index") + if view not in np.unique(self.results.view.values): + raise ValueError("Invalid view index") + best = self.results[self.results.id==ix].iloc[view] t = np.array(list(map(float, best.theta.split(";")))) if len(best.theta) > 0 else np.array([]) y = eval(best.Numpy) @@ -466,6 +473,9 @@ def evaluate_model(self, ix, x): x = x.to_numpy() if x.ndim == 1: x = x.reshape(-1,1) + if ix not in np.unique(self.results.id.values): + raise ValueError("Invalid model index") + tStr = self.results.iloc[ix].theta.split(";") t = np.array(list(map(float, tStr))) if len(tStr[0]) > 0 else np.array([]) y = eval(self.results.iloc[ix].Numpy) @@ -483,11 +493,14 @@ def score(self, X, y): return r2_score(y, ypred) def get_model(self, idx): ''' Get a `model` function and its visual representation. From srbench. ''' + if idx not in np.unique(self.results.id.values): + raise ValueError("Invalid model index") + alphabet = list(string.ascii_uppercase) row = self.results[self.results['id']==idx].iloc[0] visual_expression = row['Numpy'] model = make_function(visual_expression, self.loss) - n_params_used = len(row['theta'].split(sep=';')) + n_params_used = len(row['theta'].split(sep=';')) if len(row['theta']) > 0 else 0 # Works for solutions with less than 26 parameters for i in range(n_params_used): @@ -499,6 +512,9 @@ def get_model(self, idx): return model, visual_expression def cal_plot(self, X, y, ix): + if ix not in np.unique(self.results.id.values): + raise ValueError("Invalid model index") + yhat = self.evaluate_model(ix, X) y_lo = y.min() - 0.1*(y.max() - y.min()) y_hi = y.max() + 0.1*(y.max() - y.min()) diff --git a/test/mvsr_example.ipynb b/test/mvsr_example.ipynb index c07a4b1..7bbf6ff 100644 --- a/test/mvsr_example.ipynb +++ b/test/mvsr_example.ipynb @@ -29,7 +29,7 @@ "outputs": [ { "data": { - "image/png": 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", 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"text/plain": [ "

" ] @@ -98,10 +98,19 @@ " view\n", " Expression\n", " Numpy\n", + " Math\n", " theta\n", " size\n", - " loss\n", + " loss_train\n", + " loss_val\n", + " loss_test\n", " maxloss\n", + " R2_train\n", + " R2_val\n", + " R2_test\n", + " mdl_train\n", + " mdl_val\n", + " mdl_test\n", " \n", " \n", " \n", @@ -109,232 +118,481 @@ " 0\n", " 0\n", " 0\n", - " 21.589217270548627\n", - " t[0]\n", - " 21.589217270548627\n", + " 11.1\n", + " 11.1\n", + " $$11.1$$\n", + " \n", " 1\n", - " 532.979453\n", - " -532.979453\n", + " 643.003132\n", + " 643.003132\n", + " 643.003132\n", + " 643.003132\n", + " -0.206431\n", + " -0.206431\n", + " -0.206431\n", + " 643.003132\n", + " 643.003132\n", + " 643.003132\n", " \n", " \n", " 1\n", " 0\n", " 1\n", - " -2.9306123397648802\n", - " t[0]\n", - " -2.9306123397648802\n", + " 11.1\n", + " 11.1\n", + " $$11.1$$\n", + " \n", " 1\n", - " 247.002956\n", - " -532.979453\n", + " 443.861039\n", + " 443.861039\n", + " 443.861039\n", + " 643.003132\n", + " -0.796987\n", + " -0.796987\n", + " -0.796987\n", + " 443.861039\n", + " 443.861039\n", + " 443.861039\n", " \n", " \n", " 2\n", " 0\n", " 2\n", - " 2.6671850933034973\n", - " t[0]\n", - " 2.6671850933034973\n", + " 11.1\n", + " 11.1\n", + " $$11.1$$\n", + " \n", " 1\n", - " 126.303250\n", - " -532.979453\n", + " 197.415618\n", + " 197.415618\n", + " 197.415618\n", + " 643.003132\n", + " -0.563029\n", + " -0.563029\n", + " -0.563029\n", + " 197.415618\n", + " 197.415618\n", + " 197.415618\n", " \n", " \n", " 3\n", " 1\n", " 0\n", - " (21.53486288699552 - x0)\n", - " (t[0] - x[:, 0])\n", - " 21.53486288699552\n", + " (11.0 - x0)\n", + " (11.0 - x[:, 0])\n", + " $$\\left(11.0 - x_{0}\\right)$$\n", + " \n", " 3\n", - " 428.346651\n", - " -428.346651\n", + " 539.329988\n", + " 539.329988\n", + " 539.329988\n", + " 539.329988\n", + " -0.011915\n", + " -0.011915\n", + " -0.011915\n", + " 545.716867\n", + " 545.716867\n", + " 545.716867\n", " \n", " \n", " 4\n", " 1\n", " 1\n", - " (-3.285686757996549 - x0)\n", - " (t[0] - x[:, 0])\n", - " -3.285686757996549\n", + " (11.0 - x0)\n", + " (11.0 - x[:, 0])\n", + " $$\\left(11.0 - x_{0}\\right)$$\n", + " \n", " 3\n", - " 255.691957\n", - " -428.346651\n", + " 459.772803\n", + " 459.772803\n", + " 459.772803\n", + " 539.329988\n", + " -0.861406\n", + " -0.861406\n", + " -0.861406\n", + " 466.159682\n", + " 466.159682\n", + " 466.159682\n", " \n", " \n", " 5\n", " 1\n", " 2\n", - " (2.610598764418156 - x0)\n", - " (t[0] - x[:, 0])\n", - " 2.610598764418156\n", + " (11.0 - x0)\n", + " (11.0 - x[:, 0])\n", + " $$\\left(11.0 - x_{0}\\right)$$\n", + " \n", " 3\n", - " 214.494045\n", - " -428.346651\n", + " 284.876098\n", + " 284.876098\n", + " 284.876098\n", + " 539.329988\n", + " -1.255493\n", + " -1.255493\n", + " -1.255493\n", + " 291.262978\n", + " 291.262978\n", + " 291.262978\n", " \n", " \n", " 6\n", " 2\n", " 0\n", - " (21.303011895450766 + (x0 * -5.265543575122939))\n", - " (t[0] + (x[:, 0] * t[1]))\n", - " 21.303011895450766;-5.265543575122939\n", + " ((10.1 - x0) - x0)\n", + " ((10.1 - x[:, 0]) - x[:, 0])\n", + " $$\\left(\\left(10.1 - x_{0}\\right) - x_{0}\\righ...\n", + " \n", " 5\n", - " 228.602451\n", - " -246.886780\n", + " 475.185934\n", + " 475.185934\n", + " 475.185934\n", + " 475.185934\n", + " 0.108435\n", + " 0.108435\n", + " 0.108435\n", + " 480.678996\n", + " 480.678996\n", + " 480.678996\n", " \n", " \n", " 7\n", " 2\n", " 1\n", - " (-2.9671959569705386 + (x0 * -0.10303084459829...\n", - " (t[0] + (x[:, 0] * t[1]))\n", - " -2.9671959569705386;-0.10303084459829029\n", + " ((10.1 - x0) - x0)\n", + " ((10.1 - x[:, 0]) - x[:, 0])\n", + " $$\\left(\\left(10.1 - x_{0}\\right) - x_{0}\\righ...\n", + " \n", " 5\n", - " 246.886780\n", - " -246.886780\n", + " 475.077826\n", + " 475.077826\n", + " 475.077826\n", + " 475.185934\n", + " -0.923369\n", + " -0.923369\n", + " -0.923369\n", + " 480.570888\n", + " 480.570888\n", + " 480.570888\n", " \n", " \n", " 8\n", " 2\n", " 2\n", - " (2.847235844618024 + (x0 * 3.181877228219146))\n", - " (t[0] + (x[:, 0] * t[1]))\n", - " 2.847235844618024;3.181877228219146\n", + " ((10.1 - x0) - x0)\n", + " ((10.1 - x[:, 0]) - x[:, 0])\n", + " $$\\left(\\left(10.1 - 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"20 (t[0] + (x[:, 0] * (((t[1] + (x[:, 0] * t[2]))... \n", + "0 11.1 \n", + "1 11.1 \n", + "2 11.1 \n", + "3 (11.0 - x[:, 0]) \n", + "4 (11.0 - x[:, 0]) \n", + "5 (11.0 - x[:, 0]) \n", + "6 ((10.1 - x[:, 0]) - x[:, 0]) \n", + "7 ((10.1 - x[:, 0]) - x[:, 0]) \n", + "8 ((10.1 - x[:, 0]) - x[:, 0]) \n", + "9 (10.1 - (x[:, 0] + (x[:, 0] + 0.0))) \n", + "10 (10.1 - (x[:, 0] + (x[:, 0] + 0.0))) \n", + "11 (10.1 - (x[:, 0] + (x[:, 0] + 0.0))) \n", + "12 ((10.0 - (x[:, 0] + (x[:, 0] / 20.1))) - x[:, 0]) \n", + "13 ((10.0 - (x[:, 0] + (x[:, 0] / 20.1))) - x[:, 0]) \n", + "14 ((10.0 - (x[:, 0] + (x[:, 0] / 20.1))) - x[:, 0]) \n", + "15 ((10.0 - x[:, 0]) - (x[:, 0] + (1.0 / (x[:, 0]... \n", + "16 ((10.0 - x[:, 0]) - (x[:, 0] + (1.0 / (x[:, 0]... \n", + "17 ((10.0 - x[:, 0]) - (x[:, 0] + (1.0 / (x[:, 0]... \n", + "18 ((10.0 - ((1.0 * x[:, 0]) + (1.0 / (x[:, 0] + ... \n", + "19 ((10.0 - ((1.0 * x[:, 0]) + (1.0 / (x[:, 0] + ... \n", + "20 ((10.0 - ((1.0 * x[:, 0]) + (1.0 / (x[:, 0] + ... \n", + "21 ((10.0 - (((x[:, 0] * x[:, 0]) / x[:, 0]) + (1... \n", + "22 ((10.0 - (((x[:, 0] * x[:, 0]) / x[:, 0]) + (1... \n", + "23 ((10.0 - (((x[:, 0] * x[:, 0]) / x[:, 0]) + (1... \n", + "\n", + " Math theta size loss_train \\\n", + "0 $$11.1$$ 1 643.003132 \n", + "1 $$11.1$$ 1 443.861039 \n", + "2 $$11.1$$ 1 197.415618 \n", + "3 $$\\left(11.0 - x_{0}\\right)$$ 3 539.329988 \n", + "4 $$\\left(11.0 - x_{0}\\right)$$ 3 459.772803 \n", + "5 $$\\left(11.0 - x_{0}\\right)$$ 3 284.876098 \n", + "6 $$\\left(\\left(10.1 - x_{0}\\right) - x_{0}\\righ... 5 475.185934 \n", + "7 $$\\left(\\left(10.1 - x_{0}\\right) - x_{0}\\righ... 5 475.077826 \n", + "8 $$\\left(\\left(10.1 - x_{0}\\right) - x_{0}\\righ... 5 383.579440 \n", + "9 $$\\left(10.1 - \\left(x_{0} + \\left(x_{0} + 0.0... 7 475.185934 \n", + "10 $$\\left(10.1 - \\left(x_{0} + \\left(x_{0} + 0.0... 7 475.077826 \n", + "11 $$\\left(10.1 - \\left(x_{0} + \\left(x_{0} + 0.0... 7 383.579440 \n", + "12 $$\\left(\\left(10.0 - \\left(x_{0} + \\frac{x_{0}... 9 473.870029 \n", + "13 $$\\left(\\left(10.0 - \\left(x_{0} + \\frac{x_{0}... 9 474.914762 \n", + "14 $$\\left(\\left(10.0 - \\left(x_{0} + \\frac{x_{0}... 9 388.326933 \n", + "15 $$\\left(\\left(10.0 - x_{0}\\right) - \\left(x_{0... 11 469.197310 \n", + "16 $$\\left(\\left(10.0 - x_{0}\\right) - \\left(x_{0... 11 470.734141 \n", + "17 $$\\left(\\left(10.0 - x_{0}\\right) - \\left(x_{0... 11 391.726096 \n", + "18 $$\\left(\\left(10.0 - \\left(\\left(1.0 \\cdot x_{... 13 469.197310 \n", + "19 $$\\left(\\left(10.0 - \\left(\\left(1.0 \\cdot x_{... 13 470.734141 \n", + "20 $$\\left(\\left(10.0 - \\left(\\left(1.0 \\cdot x_{... 13 391.726096 \n", + "21 $$\\left(\\left(10.0 - \\left(\\frac{\\left(x_{0} \\... 15 469.197310 \n", + "22 $$\\left(\\left(10.0 - \\left(\\frac{\\left(x_{0} \\... 15 470.734141 \n", + "23 $$\\left(\\left(10.0 - \\left(\\frac{\\left(x_{0} \\... 15 391.726096 \n", "\n", - " theta size loss \\\n", - "0 21.589217270548627 1 532.979453 \n", - "1 -2.9306123397648802 1 247.002956 \n", - "2 2.6671850933034973 1 126.303250 \n", - "3 21.53486288699552 3 428.346651 \n", - "4 -3.285686757996549 3 255.691957 \n", - "5 2.610598764418156 3 214.494045 \n", - "6 21.303011895450766;-5.265543575122939 5 228.602451 \n", - "7 -2.9671959569705386;-0.10303084459829029 5 246.886780 \n", - "8 2.847235844618024;3.181877228219146 5 5.050724 \n", - "9 1.971001716619659;-4.997185434523716 7 6.567563 \n", - "10 -0.8466296174450944;-0.31355397338761204 7 126.355850 \n", - "11 0.19256491303772472;3.302460719415394 7 6.453165 \n", - "12 -1.0590288970595494;2.037795780991407;-4.98985... 9 6.222296 \n", - "13 19.504988286223227;-2.030357391771257;-0.11535... 9 2.880427 \n", - "14 2.59375634458228;2.122622580186797e-2;3.195222... 9 5.033599 \n", - "15 -0.7489960516420995;2.037775165917442;-4.99009... 11 6.222297 \n", - "16 9.7871188787152;-2.0303248764495545;-0.1153596... 11 2.880427 \n", - "17 1.1383630234438435;2.11511614475054e-2;3.19542... 11 5.033598 \n", - "18 -0.8687370550459058;2.024605374362169;2.804607... 13 5.912680 \n", - "19 19.504541422655507;-2.03029626522703;-6.125262... 13 2.880242 \n", - "20 2.2544633973594923;3.138226368773547e-2;-2.826... 13 4.673299 \n", + " loss_val loss_test maxloss R2_train R2_val R2_test \\\n", + "0 643.003132 643.003132 643.003132 -0.206431 -0.206431 -0.206431 \n", + "1 443.861039 443.861039 643.003132 -0.796987 -0.796987 -0.796987 \n", + "2 197.415618 197.415618 643.003132 -0.563029 -0.563029 -0.563029 \n", + "3 539.329988 539.329988 539.329988 -0.011915 -0.011915 -0.011915 \n", + "4 459.772803 459.772803 539.329988 -0.861406 -0.861406 -0.861406 \n", + "5 284.876098 284.876098 539.329988 -1.255493 -1.255493 -1.255493 \n", + "6 475.185934 475.185934 475.185934 0.108435 0.108435 0.108435 \n", + "7 475.077826 475.077826 475.185934 -0.923369 -0.923369 -0.923369 \n", + "8 383.579440 383.579440 475.185934 -2.036972 -2.036972 -2.036972 \n", + "9 475.185934 475.185934 475.185934 0.108435 0.108435 0.108435 \n", + "10 475.077826 475.077826 475.185934 -0.923369 -0.923369 -0.923369 \n", + "11 383.579440 383.579440 475.185934 -2.036972 -2.036972 -2.036972 \n", + "12 473.870029 473.870029 474.914762 0.110904 0.110904 0.110904 \n", + "13 474.914762 474.914762 474.914762 -0.922709 -0.922709 -0.922709 \n", + "14 388.326933 388.326933 474.914762 -2.074560 -2.074560 -2.074560 \n", + "15 469.197310 469.197310 470.734141 0.119671 0.119671 0.119671 \n", + "16 470.734141 470.734141 470.734141 -0.905783 -0.905783 -0.905783 \n", + "17 391.726096 391.726096 470.734141 -2.101473 -2.101473 -2.101473 \n", + "18 469.197310 469.197310 470.734141 0.119671 0.119671 0.119671 \n", + "19 470.734141 470.734141 470.734141 -0.905783 -0.905783 -0.905783 \n", + "20 391.726096 391.726096 470.734141 -2.101473 -2.101473 -2.101473 \n", + "21 469.197310 469.197310 470.734141 0.119671 0.119671 0.119671 \n", + "22 470.734141 470.734141 470.734141 -0.905783 -0.905783 -0.905783 \n", + "23 391.726096 391.726096 470.734141 -2.101473 -2.101473 -2.101473 \n", "\n", - " maxloss \n", - "0 -532.979453 \n", - "1 -532.979453 \n", - "2 -532.979453 \n", - "3 -428.346651 \n", - "4 -428.346651 \n", - "5 -428.346651 \n", - "6 -246.886780 \n", - "7 -246.886780 \n", - "8 -246.886780 \n", - "9 -126.355850 \n", - "10 -126.355850 \n", - "11 -126.355850 \n", - "12 -6.222295 \n", - "13 -6.222295 \n", - "14 -6.222295 \n", - "15 -6.222297 \n", - "16 -6.222297 \n", - "17 -6.222297 \n", - "18 -5.912680 \n", - "19 -5.912680 \n", - "20 -5.912680 " + " mdl_train mdl_val mdl_test \n", + "0 643.003132 643.003132 643.003132 \n", + "1 443.861039 443.861039 443.861039 \n", + "2 197.415618 197.415618 197.415618 \n", + "3 545.716867 545.716867 545.716867 \n", + "4 466.159682 466.159682 466.159682 \n", + "5 291.262978 291.262978 291.262978 \n", + "6 480.678996 480.678996 480.678996 \n", + "7 480.570888 480.570888 480.570888 \n", + "8 389.072501 389.072501 389.072501 \n", + "9 -inf -inf -inf \n", + "10 -inf -inf -inf \n", + "11 -inf -inf -inf \n", + "12 492.991596 492.991596 492.991596 \n", + "13 494.036329 494.036329 494.036329 \n", + "14 407.448500 407.448500 407.448500 \n", + "15 492.595543 492.595543 492.595543 \n", + "16 494.132375 494.132375 494.132375 \n", + "17 415.124329 415.124329 415.124329 \n", + "18 498.876168 498.876168 498.876168 \n", + "19 500.413000 500.413000 500.413000 \n", + "20 421.404954 421.404954 421.404954 \n", + "21 502.074841 502.074841 502.074841 \n", + "22 503.611673 503.611673 503.611673 \n", + "23 424.603627 424.603627 424.603627 " ] }, "execution_count": 3, @@ -441,7 +737,7 @@ ], "source": [ "# We create a Pyeggp regressor leaving all hyperparameters to default.\n", - "reg = eggp.EGGP()\n", + "reg = eggp.EGGP(nParams=0)\n", "reg.fit_mvsr(full_X, full_y)\n", "\n", "# Here we collect the full table of results and plot it\n", @@ -449,6 +745,30 @@ "pareto" ] }, + { + "cell_type": "code", + "execution_count": 5, + "id": "e79f0505-d95f-4ebd-945a-d42e52ed10de", + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "exceptions must derive from BaseException", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[5]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mreg\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget_model\u001b[49m\u001b[43m(\u001b[49m\u001b[32;43m30\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 2\u001b[39m \u001b[38;5;66;03m#reg.evaluate_model_view(full_X[0], 4, 0)\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/.local/pyenv/lib/python3.13/site-packages/eggp/__init__.py:497\u001b[39m, in \u001b[36mEGGP.get_model\u001b[39m\u001b[34m(self, idx)\u001b[39m\n\u001b[32m 495\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m''' Get a `model` function and its visual representation. From srbench. '''\u001b[39;00m\n\u001b[32m 496\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m idx \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m np.unique(\u001b[38;5;28mself\u001b[39m.results.id.values):\n\u001b[32m--> \u001b[39m\u001b[32m497\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mInvalid model index\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 499\u001b[39m alphabet = \u001b[38;5;28mlist\u001b[39m(string.ascii_uppercase)\n\u001b[32m 500\u001b[39m row = \u001b[38;5;28mself\u001b[39m.results[\u001b[38;5;28mself\u001b[39m.results[\u001b[33m'\u001b[39m\u001b[33mid\u001b[39m\u001b[33m'\u001b[39m]==idx].iloc[\u001b[32m0\u001b[39m]\n", + "\u001b[31mTypeError\u001b[39m: exceptions must derive from BaseException" + ] + } + ], + "source": [ + "reg.get_model(30)\n", + "#reg.evaluate_model_view(full_X[0], 4, 0)" + ] + }, { "cell_type": "markdown", "id": "98eb4e69-f443-410d-a021-079589c4d2f0", @@ -570,9 +890,9 @@ ], "metadata": { "kernelspec": { - "display_name": "new_mvsr_kern", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "new_mvsr_kern" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -584,7 +904,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.2" + "version": "3.13.7" } }, "nbformat": 4, diff --git a/tutorials/multisteps.py b/tutorials/multisteps.py new file mode 100644 index 0000000..5e4ecdc --- /dev/null +++ b/tutorials/multisteps.py @@ -0,0 +1,45 @@ +from eggp import EGGP +import pandas as pd +import numpy as np + +pd.set_option('display.max_colwidth', 100) +# Load the example dataset +df = pd.read_csv("datasets/nikuradse_1.csv") + +# split dataframe into multiple sets based on column r_k +Xs, ys, r_ks = [], [], [] +for r_k_value, group in df.groupby('r_k'): + Xs.append(group[['log_Re']].values) + ys.append(group['target'].values) + r_ks.append(r_k_value) + +model = EGGP(gen=200, nPop=500, maxSize=50, nTournament=3, pc=0.8, pm=0.2, nonterminals='add,sub,mul,div,power', loss='MSE', optIter=100, optRepeat=5, nParams=3, folds=1, simplify=True) + +# We fit a "multi-view" assuming each value of r_k is a view +# Since in this dataset we have a limited number of unique r_k values, +# we can think of it as finding an expression f(log_re; theta) where theta = g(r_k) +# We are limiting to one parameter for simplicity. +model.fit_mvsr(Xs, ys) + +# Let's get the best model and print the max-loss +best_ix = model.results.maxloss.idxmin() +best_id = model.results.loc[best_ix, 'id'] + +best_df = model.results[model.results['id'] == best_id] + +n_thetas = len(best_df.iloc[0]['theta'].split(";")) +thetas = best_df.theta.values + +print('Best expression: ', best_df.iloc[0]['Expression']) +print('MSE of each view: ', best_df.loss_train.values) + +# For each parameter (if we have more than one), fit a model to create an expression theta(r_k) +for i in range(n_thetas): + ts = list(map(lambda x: float(x.split(";")[i]), thetas)) + print(f"Theta {i} values across different views: {ts}") + model_v = EGGP(gen=200, nPop=500, maxSize=15, nTournament=3, pc=0.8, pm=0.2, nonterminals='add,sub,mul,div,exp,log', loss='MSE', optIter=100, optRepeat=5, nParams=-1, folds=1, simplify=True) + model_v.fit(np.array(r_ks).reshape(-1,1), np.array(ts)) + best_ix_v = model_v.results.loss_val.idxmin() + # The loss should be 0, otherwise the error will propagate to the main model + print(f"Simplified expression for theta {i}: {model_v.results.loc[best_ix_v, 'Expression']}") + print(f"MSE of theta(r_k) model, this should be 0 otherwise the error propagates: {model_v.results.loc[best_ix_v, 'loss_train']}") diff --git a/tutorials/regression_example.egg b/tutorials/regression_example.egg index 5855965adc40eb58df9485409e348b7d67807515..e44a068f074e5d2eb514f2220238ce3b107544c2 100644 GIT binary patch literal 3172506 zcmZ_WX}HgIzlZ&`%=1jBM5RJ$)}VosMn$4YCCx>mXjDRjA*F%JkV=y&LZuW*D1~N} 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04:14:26 +0000 Subject: [PATCH 100/166] Bump actions/upload-artifact from 5 to 6 Bumps [actions/upload-artifact](https://github.com/actions/upload-artifact) from 5 to 6. - [Release notes](https://github.com/actions/upload-artifact/releases) - [Commits](https://github.com/actions/upload-artifact/compare/v5...v6) --- updated-dependencies: - dependency-name: actions/upload-artifact dependency-version: '6' dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] --- .github/workflows/release.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index d069518..1d53167 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -188,7 +188,7 @@ jobs: python -m build --sdist --outdir ./wheelhouse - name: Upload wheels - uses: actions/upload-artifact@v5 + uses: actions/upload-artifact@v6 with: name: "wheelhouse-${{ matrix.job.os.plat }}-${{ matrix.job.os.arch }}" path: | From 259ab83a89906fb80fdb8eb7710504f180b788c4 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 15 Dec 2025 04:14:30 +0000 Subject: [PATCH 101/166] Bump actions/download-artifact from 6 to 7 Bumps [actions/download-artifact](https://github.com/actions/download-artifact) from 6 to 7. - [Release notes](https://github.com/actions/download-artifact/releases) - [Commits](https://github.com/actions/download-artifact/compare/v6...v7) --- updated-dependencies: - dependency-name: actions/download-artifact dependency-version: '7' dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] --- .github/workflows/release.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index d069518..056c933 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -210,7 +210,7 @@ jobs: steps: - name: Download artifacts - uses: actions/download-artifact@v6 + uses: actions/download-artifact@v7 with: pattern: "wheelhouse-*-*" path: "./wheelhouse" @@ -241,7 +241,7 @@ jobs: steps: - name: Download artifacts - uses: actions/download-artifact@v6 + uses: actions/download-artifact@v7 with: pattern: "wheelhouse-*-*" path: "./wheelhouse" From e2d1b75fbcc95fc767305394fc377c7298e7a388 Mon Sep 17 00:00:00 2001 From: folivetti Date: Fri, 9 Jan 2026 09:24:27 -0300 Subject: [PATCH 102/166] fractional bayes --- cabal.project | 2 +- src/Search.hs | 8 ++++---- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/cabal.project b/cabal.project index 8bbe6b0..ef00f37 100644 --- a/cabal.project +++ b/cabal.project @@ -9,4 +9,4 @@ source-repository-package source-repository-package type: git location: https://github.com/folivetti/srtree - tag: 7c02ffa09caf7dfd01653d00304342f29b36beaa + tag: edd40049405e13c8da54a2a629acd9132d234f17 diff --git a/src/Search.hs b/src/Search.hs index 2b36c0b..91587e3 100644 --- a/src/Search.hs +++ b/src/Search.hs @@ -8,7 +8,7 @@ module Search where import Algorithm.EqSat.Egraph -import Algorithm.EqSat.Simplify +import Algorithm.EqSat.Simplify hiding ( myCost ) import Algorithm.EqSat.Build import Algorithm.EqSat.Queries import Algorithm.EqSat.Info @@ -353,9 +353,9 @@ egraphGP dataTrainVals dataTests args = do nll_train = nll distribution mYErr x y best' theta nll_val = nll distribution mYErr_val x_val y_val best' theta nll_te = nll distribution mYErr_te x_te y_te best' theta - mdl_train = mdl distribution mYErr x y theta best' - mdl_val = mdl distribution mYErr_val x_val y_val theta best' - mdl_te = mdl distribution mYErr_te x_te y_te theta best' + mdl_train = fractionalBayesFactor distribution mYErr x y theta best' + mdl_val = fractionalBayesFactor distribution mYErr_val x_val y_val theta best' + mdl_te = fractionalBayesFactor distribution mYErr_te x_te y_te theta best' vals = intercalate "," $ Prelude.map showNA [ nll_train, nll_val, nll_te, maxLoss , r2_train, r2_val, r2_te From b85b665150a43a9b53b9df7d0584e467297fb72a Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 12 Jan 2026 04:31:54 +0000 Subject: [PATCH 103/166] Bump pypa/cibuildwheel from 3.3.0 to 3.3.1 Bumps [pypa/cibuildwheel](https://github.com/pypa/cibuildwheel) from 3.3.0 to 3.3.1. - [Release notes](https://github.com/pypa/cibuildwheel/releases) - [Changelog](https://github.com/pypa/cibuildwheel/blob/main/docs/changelog.md) - [Commits](https://github.com/pypa/cibuildwheel/compare/v3.3.0...v3.3.1) --- updated-dependencies: - dependency-name: pypa/cibuildwheel dependency-version: 3.3.1 dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] --- .github/workflows/release.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 6f7688b..84ac979 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -172,7 +172,7 @@ jobs: with: platforms: "386" - - uses: pypa/cibuildwheel@v3.3.0 + - uses: pypa/cibuildwheel@v3.3.1 with: output-dir: "./wheelhouse" config-file: "{package}/pyproject.toml" From c0a27232f3cf3690f8459b6ec91e7b4cc45b31e4 Mon Sep 17 00:00:00 2001 From: Fabricio Olivetti de Franca Date: Fri, 16 Jan 2026 17:23:54 -0300 Subject: [PATCH 104/166] Update cabal.project --- cabal.project | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/cabal.project b/cabal.project index ef00f37..4b01c65 100644 --- a/cabal.project +++ b/cabal.project @@ -1,5 +1,6 @@ packages: . -allow-newer: table-layout:*, haskeline:*, aeson:* +allow-newer: table-layout:*, haskeline:*, aeson:*,mwc-random:* +allow-older: base source-repository-package type: git @@ -10,3 +11,7 @@ source-repository-package type: git location: https://github.com/folivetti/srtree tag: edd40049405e13c8da54a2a629acd9132d234f17 + +package * + extra-include-dirs: /opt/homebrew/include + extra-lib-dirs: /opt/homebrew/lib From 52b865464ba5f69dfa1df6658940f865f6252b6a Mon Sep 17 00:00:00 2001 From: folivetti Date: Thu, 29 Jan 2026 16:39:53 -0300 Subject: [PATCH 105/166] log10 mse --- cabal.project | 2 +- eggp.cabal | 2 +- pyproject.toml | 2 +- src/eggp.egg-info/PKG-INFO | 2 +- src/eggp/__init__.py | 4 +- .../mvsr_example-checkpoint.ipynb | 680 +++- test/mvsr_example.ipynb | 990 ++--- test/nikuradse_1.csv | 363 ++ test/test_pyeggp.py | 2 +- tutorials/ICLR.tar.gz | Bin 0 -> 344776 bytes .../ICLR/_posts/2026-04-27-egraph-symreg.md | 522 +++ .../bibliography/2026-04-27-egraph-symreg.bib | 132 + .../img/2026-04-27-egraph-symreg/bbt.png | Bin 0 -> 11252 bytes .../bbt_MSE_mean_rope.eps | 376 ++ .../img/2026-04-27-egraph-symreg/blog1.png | Bin 0 -> 5398 bytes .../img/2026-04-27-egraph-symreg/blog2.png | Bin 0 -> 8789 bytes .../img/2026-04-27-egraph-symreg/blog3.png | Bin 0 -> 8134 bytes .../img/2026-04-27-egraph-symreg/blog4.png | Bin 0 -> 10249 bytes .../img/2026-04-27-egraph-symreg/blog5.png | Bin 0 -> 10864 bytes .../img/2026-04-27-egraph-symreg/blog6.png | Bin 0 -> 157216 bytes .../img/2026-04-27-egraph-symreg/rank_mse.png | Bin 0 -> 14200 bytes .../2026-04-27-egraph-symreg/rank_size.png | Bin 0 -> 13660 bytes .../img/2026-04-27-egraph-symreg/top10.svg | 3525 +++++++++++++++++ .../img/2026-04-27-egraph-symreg/top5.svg | 2117 ++++++++++ .../2026-04-27-egraph-symreg/top5operon.svg | 2213 +++++++++++ .../img/2026-04-27-egraph-symreg/top5pat.svg | 2056 ++++++++++ .../img/2026-04-27-egraph-symreg/vlad.svg | 760 ++++ tutorials/datatemp_q_h9wyo4.csv | 363 ++ tutorials/regression_example.egg | Bin 3172506 -> 3513574 bytes tutorials/regression_example.py | 8 +- 30 files changed, 13435 insertions(+), 684 deletions(-) create mode 100644 test/nikuradse_1.csv create mode 100644 tutorials/ICLR.tar.gz create mode 100644 tutorials/ICLR/_posts/2026-04-27-egraph-symreg.md create mode 100644 tutorials/ICLR/assets/bibliography/2026-04-27-egraph-symreg.bib create mode 100644 tutorials/ICLR/assets/img/2026-04-27-egraph-symreg/bbt.png create mode 100644 tutorials/ICLR/assets/img/2026-04-27-egraph-symreg/bbt_MSE_mean_rope.eps create mode 100644 tutorials/ICLR/assets/img/2026-04-27-egraph-symreg/blog1.png create mode 100644 tutorials/ICLR/assets/img/2026-04-27-egraph-symreg/blog2.png create mode 100644 tutorials/ICLR/assets/img/2026-04-27-egraph-symreg/blog3.png create mode 100644 tutorials/ICLR/assets/img/2026-04-27-egraph-symreg/blog4.png create mode 100644 tutorials/ICLR/assets/img/2026-04-27-egraph-symreg/blog5.png create mode 100644 tutorials/ICLR/assets/img/2026-04-27-egraph-symreg/blog6.png create mode 100644 tutorials/ICLR/assets/img/2026-04-27-egraph-symreg/rank_mse.png create mode 100644 tutorials/ICLR/assets/img/2026-04-27-egraph-symreg/rank_size.png create mode 100644 tutorials/ICLR/assets/img/2026-04-27-egraph-symreg/top10.svg create mode 100644 tutorials/ICLR/assets/img/2026-04-27-egraph-symreg/top5.svg create mode 100644 tutorials/ICLR/assets/img/2026-04-27-egraph-symreg/top5operon.svg create mode 100644 tutorials/ICLR/assets/img/2026-04-27-egraph-symreg/top5pat.svg create mode 100644 tutorials/ICLR/assets/img/2026-04-27-egraph-symreg/vlad.svg create mode 100644 tutorials/datatemp_q_h9wyo4.csv diff --git a/cabal.project b/cabal.project index ef00f37..e56761f 100644 --- a/cabal.project +++ b/cabal.project @@ -9,4 +9,4 @@ source-repository-package source-repository-package type: git location: https://github.com/folivetti/srtree - tag: edd40049405e13c8da54a2a629acd9132d234f17 + tag: fdb0a27b7ba3b5da9e51a13c5ec5e1870ff78fd8 diff --git a/eggp.cabal b/eggp.cabal index ddd7e04..fd5db29 100755 --- a/eggp.cabal +++ b/eggp.cabal @@ -5,7 +5,7 @@ cabal-version: 2.2 -- see: https://github.com/sol/hpack name: eggp -version: 1.0.10 +version: 1.0.11 synopsis: A general library to work with Symbolic Regression expression trees. description: A Symbolic Regression Tree data structure to work with mathematical expressions with support to first order derivative and simplification; category: Math, Data, Data Structures diff --git a/pyproject.toml b/pyproject.toml index 8108722..7407250 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,7 +12,7 @@ build-backend = "setuptools.build_meta" [project] name = "eggp" -version = "1.0.10" +version = "1.0.11" authors = [{ name = "Fabricio Olivetti", email = "folivetti@users.noreply.github.com" }] description = "Python Wheels for eggp algorithm." readme = "README.md" diff --git a/src/eggp.egg-info/PKG-INFO b/src/eggp.egg-info/PKG-INFO index 44c305b..b6ee30d 100644 --- a/src/eggp.egg-info/PKG-INFO +++ b/src/eggp.egg-info/PKG-INFO @@ -1,6 +1,6 @@ Metadata-Version: 2.4 Name: eggp -Version: 1.0.9 +Version: 1.0.10 Summary: Python Wheels for eggp algorithm. Author-email: Fabricio Olivetti License: GNU GENERAL PUBLIC LICENSE diff --git a/src/eggp/__init__.py b/src/eggp/__init__.py index e58bdde..01e935d 100644 --- a/src/eggp/__init__.py +++ b/src/eggp/__init__.py @@ -23,7 +23,7 @@ unsafe_hs_eggp_exit, ) -VERSION: str = "1.0.10" +VERSION: str = "1.0.11" _hs_rts_init: bool = False @@ -175,7 +175,7 @@ def __init__(self, gen = 100, nPop = 100, maxSize = 15, nTournament = 3, pc = 0. aq,abs,sin,cos,tan,sinh,cosh,tanh,\ asin,acos,atan,asinh,acosh,atanh,sqrt,\ sqrtabs,cbrt,square,log,logabs,exp,recip,cube" - losses = ["MSE", "Gaussian", "Bernoulli", "Poisson"] + losses = ["MSE", "LOG10", "Gaussian", "Bernoulli", "Poisson"] if gen < 1: raise ValueError('gen should be greater than 1') if nPop < 1: diff --git a/test/.ipynb_checkpoints/mvsr_example-checkpoint.ipynb b/test/.ipynb_checkpoints/mvsr_example-checkpoint.ipynb index 2d0ae02..8292d77 100644 --- a/test/.ipynb_checkpoints/mvsr_example-checkpoint.ipynb +++ b/test/.ipynb_checkpoints/mvsr_example-checkpoint.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 8, "id": "c68e3b1a-d6e4-4f94-ae46-d761d5c1c836", "metadata": {}, "outputs": [], @@ -10,7 +10,7 @@ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pyeggp" + "import eggp" ] }, { @@ -23,13 +23,13 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 9, "id": "2c9d5ad9-f2e4-4b96-accd-41d1edbfdc98", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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    " ] @@ -69,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 10, "id": "3c318511-31dd-4cb2-8178-2ceb5c2e405c", "metadata": {}, "outputs": [ @@ -98,10 +98,19 @@ " view\n", " Expression\n", " Numpy\n", + " Math\n", " theta\n", " size\n", - " loss\n", - " maxLoss\n", + " loss_train\n", + " loss_val\n", + " loss_test\n", + " maxloss\n", + " R2_train\n", + " R2_val\n", + " R2_test\n", + " mdl_train\n", + " mdl_val\n", + " mdl_test\n", " \n", " \n", " \n", @@ -109,232 +118,481 @@ " 0\n", " 0\n", " 0\n", - " 21.589217270548627\n", + " 21.589217270548623\n", " t[0]\n", - " 21.589217270548627\n", + " $$\\theta_{0}$$\n", + " 21.589217270548623\n", " 1\n", " 532.979453\n", - " -532.979453\n", + " 532.979453\n", + " 532.979453\n", + " 532.979453\n", + " 2.220446e-16\n", + " 2.220446e-16\n", + " 2.220446e-16\n", + " 532.979453\n", + " 532.979453\n", + " 532.979453\n", " \n", " \n", " 1\n", " 0\n", " 1\n", - " -2.9306123397648802\n", + " -2.930612339764867\n", " t[0]\n", - " -2.9306123397648802\n", + " $$\\theta_{0}$$\n", + " -2.930612339764867\n", " 1\n", " 247.002956\n", - " -532.979453\n", + " 247.002956\n", + " 247.002956\n", + " 532.979453\n", + " 1.110223e-16\n", + " 1.110223e-16\n", + " 1.110223e-16\n", + " 247.002956\n", + " 247.002956\n", + " 247.002956\n", " \n", " \n", " 2\n", " 0\n", " 2\n", - " 2.6671850933034973\n", + " 2.667185093303495\n", " t[0]\n", - " 2.6671850933034973\n", + " $$\\theta_{0}$$\n", + " 2.667185093303495\n", " 1\n", " 126.303250\n", - " -532.979453\n", + " 126.303250\n", + " 126.303250\n", + " 532.979453\n", + " 0.000000e+00\n", + " 0.000000e+00\n", + " 0.000000e+00\n", + " 126.303250\n", + " 126.303250\n", + " 126.303250\n", " \n", " \n", " 3\n", " 1\n", " 0\n", - " (21.53486288699552 - x0)\n", + " (21.534862886995523 - x0)\n", " (t[0] - x[:, 0])\n", - " 21.53486288699552\n", + " $$\\left(\\theta_{0} - x_{0}\\right)$$\n", + " 21.534862886995523\n", " 3\n", " 428.346651\n", - " -428.346651\n", + " 428.346651\n", + " 428.346651\n", + " 428.346651\n", + " 1.963168e-01\n", + " 1.963168e-01\n", + " 1.963168e-01\n", + " 431.642488\n", + " 431.642488\n", + " 431.642488\n", " \n", " \n", " 4\n", " 1\n", " 1\n", - " (-3.285686757996549 - x0)\n", + " (-3.2856867579965456 - x0)\n", " (t[0] - x[:, 0])\n", - " -3.285686757996549\n", + " $$\\left(\\theta_{0} - x_{0}\\right)$$\n", + " -3.2856867579965456\n", " 3\n", " 255.691957\n", - " -428.346651\n", + " 255.691957\n", + " 255.691957\n", + " 428.346651\n", + " -3.517772e-02\n", + " -3.517772e-02\n", + " -3.517772e-02\n", + " 258.987794\n", + " 258.987794\n", + " 258.987794\n", " \n", " \n", " 5\n", " 1\n", " 2\n", - " (2.610598764418156 - x0)\n", + " (2.6105987644181523 - x0)\n", " (t[0] - x[:, 0])\n", - " 2.610598764418156\n", + " $$\\left(\\theta_{0} - x_{0}\\right)$$\n", + " 2.6105987644181523\n", " 3\n", " 214.494045\n", - " -428.346651\n", + " 214.494045\n", + " 214.494045\n", + " 428.346651\n", + " -6.982464e-01\n", + " -6.982464e-01\n", + " -6.982464e-01\n", + " 217.789882\n", + " 217.789882\n", + " 217.789882\n", " \n", " \n", " 6\n", " 2\n", " 0\n", - " (21.303011895450766 + (x0 * -5.265543575122939))\n", + " (21.303011895450787 + (x0 * -5.265543575122987))\n", " (t[0] + (x[:, 0] * t[1]))\n", - " 21.303011895450766;-5.265543575122939\n", + " $$\\left(\\theta_{0} + \\left(x_{0} \\cdot \\theta_...\n", + " 21.303011895450787;-5.265543575122987\n", " 5\n", " 228.602451\n", - " -246.886780\n", + " 228.602451\n", + " 228.602451\n", + " 246.886780\n", + " 5.710858e-01\n", + " 5.710858e-01\n", + " 5.710858e-01\n", + " 235.533923\n", + " 235.533923\n", + " 235.533923\n", " \n", " \n", " 7\n", " 2\n", " 1\n", - " (-2.9671959569705386 + (x0 * -0.10303084459829...\n", + " (-2.967195956970522 + (x0 * -0.103030844598327...\n", " (t[0] + (x[:, 0] * t[1]))\n", - " -2.9671959569705386;-0.10303084459829029\n", + " $$\\left(\\theta_{0} + \\left(x_{0} \\cdot \\theta_...\n", + " -2.967195956970522;-0.10303084459832784\n", " 5\n", " 246.886780\n", - " -246.886780\n", + " 246.886780\n", + " 246.886780\n", + " 246.886780\n", + " 4.703438e-04\n", + " 4.703438e-04\n", + " 4.703438e-04\n", + " 253.818252\n", + " 253.818252\n", + " 253.818252\n", " \n", " \n", " 8\n", " 2\n", " 2\n", - " (2.847235844618024 + (x0 * 3.181877228219146))\n", + " (2.8472358446081776 + (x0 * 3.181877228219174))\n", " (t[0] + (x[:, 0] * t[1]))\n", - " 2.847235844618024;3.181877228219146\n", + " $$\\left(\\theta_{0} + \\left(x_{0} \\cdot \\theta_...\n", + " 2.8472358446081776;3.181877228219174\n", " 5\n", " 5.050724\n", - " -246.886780\n", + " 5.050724\n", + " 5.050724\n", + " 246.886780\n", + " 9.600111e-01\n", + " 9.600111e-01\n", + " 9.600111e-01\n", + " 11.982196\n", + " 11.982196\n", + " 11.982196\n", " \n", " \n", " 9\n", " 3\n", " 0\n", - " (((x0 * 1.971001716619659) + -4.99718543452371...\n", - " (((x[:, 0] * t[0]) + t[1]) * x[:, 0])\n", - " 1.971001716619659;-4.997185434523716\n", + " (x0 * (-4.997185434556597 + (x0 * 1.9710017166...\n", + " (x[:, 0] * (t[0] + (x[:, 0] * t[1])))\n", + " $$\\left(x_{0} \\cdot \\left(\\theta_{0} + \\left(x...\n", + " -4.997185434556597;1.97100171661893\n", " 7\n", " 6.567563\n", - " -126.355850\n", + " 6.567563\n", + " 6.567563\n", + " 126.355850\n", + " 9.876776e-01\n", + " 9.876776e-01\n", + " 9.876776e-01\n", + " 16.271623\n", + " 16.271623\n", + " 16.271623\n", " \n", " \n", " 10\n", " 3\n", " 1\n", - " (((x0 * -0.8466296174450944) + -0.313553973387...\n", - " (((x[:, 0] * t[0]) + t[1]) * x[:, 0])\n", - " -0.8466296174450944;-0.31355397338761204\n", + " (x0 * (-0.3135539734579376 + (x0 * -0.84662961...\n", + " (x[:, 0] * (t[0] + (x[:, 0] * t[1])))\n", + " $$\\left(x_{0} \\cdot \\left(\\theta_{0} + \\left(x...\n", + " -0.3135539734579376;-0.8466296174467215\n", " 7\n", " 126.355850\n", - " -126.355850\n", + " 126.355850\n", + " 126.355850\n", + " 126.355850\n", + " 4.884440e-01\n", + " 4.884440e-01\n", + " 4.884440e-01\n", + " 136.059911\n", + " 136.059911\n", + " 136.059911\n", " \n", " \n", " 11\n", " 3\n", " 2\n", - " (((x0 * 0.19256491303772472) + 3.3024607194153...\n", - " (((x[:, 0] * t[0]) + t[1]) * x[:, 0])\n", - " 0.19256491303772472;3.302460719415394\n", + " (x0 * (3.3024607194164286 + (x0 * 0.1925649130...\n", + " (x[:, 0] * (t[0] + (x[:, 0] * t[1])))\n", + " $$\\left(x_{0} \\cdot \\left(\\theta_{0} + \\left(x...\n", + " 3.3024607194164286;0.19256491303781179\n", " 7\n", " 6.453165\n", - " -126.355850\n", + " 6.453165\n", + " 6.453165\n", + " 126.355850\n", + " 9.489074e-01\n", + " 9.489074e-01\n", + " 9.489074e-01\n", + " 16.157226\n", + " 16.157226\n", + " 16.157226\n", " \n", " \n", " 12\n", " 4\n", " 0\n", - " (-1.0590288970595494 + (x0 * ((x0 * 2.03779578...\n", - " (t[0] + (x[:, 0] * ((x[:, 0] * t[1]) + t[2])))\n", - " -1.0590288970595494;2.037795780991407;-4.98985...\n", + " ((x0 + -2.645075648188035) * (0.40042646234334...\n", + " ((x[:, 0] + t[0]) * (t[1] - (x[:, 0] * t[2])))\n", + " $$\\left(\\left(x_{0} + \\theta_{0}\\right) \\cdot ...\n", + " -2.645075648188035;0.40042646234334933;-2.0378...\n", " 9\n", - " 6.222296\n", - " -6.222295\n", + " 6.222295\n", + " 6.222295\n", + " 6.222295\n", + " 6.222295\n", + " 9.883254e-01\n", + " 9.883254e-01\n", + " 9.883254e-01\n", + " 20.707237\n", + " 20.707237\n", + " 20.707237\n", " \n", " \n", " 13\n", " 4\n", " 1\n", - " (19.504988286223227 + (x0 * ((x0 * -2.03035739...\n", - " (t[0] + (x[:, 0] * ((x[:, 0] * t[1]) + t[2])))\n", - " 19.504988286223227;-2.030357391771257;-0.11535...\n", + " ((x0 + -3.071181811381864) * (-6.3508399143391...\n", + " ((x[:, 0] + t[0]) * (t[1] - (x[:, 0] * t[2])))\n", + " $$\\left(\\left(x_{0} + \\theta_{0}\\right) \\cdot ...\n", + " -3.071181811381864;-6.350839914339136;2.030330...\n", " 9\n", " 2.880427\n", - " -6.222295\n", + " 2.880427\n", + " 2.880427\n", + " 6.222295\n", + " 9.883385e-01\n", + " 9.883385e-01\n", + " 9.883385e-01\n", + " 17.365369\n", + " 17.365369\n", + " 17.365369\n", " \n", " \n", " 14\n", " 4\n", " 2\n", - " (2.59375634458228 + (x0 * ((x0 * 2.12262258018...\n", - " (t[0] + (x[:, 0] * ((x[:, 0] * t[1]) + t[2])))\n", - " 2.59375634458228;2.122622580186797e-2;3.195222...\n", + " ((x0 + 0.8166540308437522) * (3.17810840942135...\n", + " ((x[:, 0] + t[0]) * (t[1] - (x[:, 0] * t[2])))\n", + " $$\\left(\\left(x_{0} + \\theta_{0}\\right) \\cdot ...\n", + " 0.8166540308437522;3.1781084094213585;-2.10865...\n", " 9\n", - " 5.033599\n", - " -6.222295\n", + " 5.033598\n", + " 5.033598\n", + " 5.033598\n", + " 6.222295\n", + " 9.601467e-01\n", + " 9.601467e-01\n", + " 9.601467e-01\n", + " 19.518540\n", + " 19.518540\n", + " 19.518540\n", " \n", " \n", " 15\n", " 5\n", " 0\n", - " (-0.7489960516420995 + ((x0 * ((x0 * 2.0377751...\n", - " (t[0] + ((x[:, 0] * ((x[:, 0] * t[1]) + t[2]))...\n", - " -0.7489960516420995;2.037775165917442;-4.99009...\n", + " (-0.4710360148347207 + (-0.5880961667000076 + ...\n", + " (t[0] + (t[1] + (x[:, 0] * (t[2] - (x[:, 0] * ...\n", + " $$\\left(\\theta_{0} + \\left(\\theta_{1} + \\left(...\n", + " -0.4710360148347207;-0.5880961667000076;-4.989...\n", " 11\n", - " 6.222297\n", - " -6.222297\n", + " 6.222295\n", + " 6.222295\n", + " 6.222295\n", + " 6.222295\n", + " 9.883254e-01\n", + " 9.883254e-01\n", + " 9.883254e-01\n", + " 23.926112\n", + " 23.926112\n", + " 23.926112\n", " \n", " \n", " 16\n", " 5\n", " 1\n", - " (9.7871188787152 + ((x0 * ((x0 * -2.0303248764...\n", - " (t[0] + ((x[:, 0] * ((x[:, 0] * t[1]) + t[2]))...\n", - " 9.7871188787152;-2.0303248764495545;-0.1153596...\n", + " (9.659149966450054 + (9.846772636698656 + (x0 ...\n", + " (t[0] + (t[1] + (x[:, 0] * (t[2] - (x[:, 0] * ...\n", + " $$\\left(\\theta_{0} + \\left(\\theta_{1} + \\left(...\n", + " 9.659149966450054;9.846772636698656;-0.1153759...\n", " 11\n", - " 2.880427\n", - " -6.222297\n", + " 2.880428\n", + " 2.880428\n", + " 2.880428\n", + " 6.222295\n", + " 9.883385e-01\n", + " 9.883385e-01\n", + " 9.883385e-01\n", + " 20.584245\n", + " 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0] * (t[1] - (x[:, 0] * (t[2] - ...\n", + " $$\\left(\\theta_{0} + \\left(x_{0} \\cdot \\left(\\...\n", + " -0.8961205388017053;-5.439581684562741;-2.0267...\n", " 13\n", - " 5.912680\n", - " -5.912680\n", + " 5.912374\n", + " 5.912374\n", + " 5.912374\n", + " 5.912374\n", + " 9.889069e-01\n", + " 9.889069e-01\n", + " 9.889069e-01\n", + " 26.835067\n", + " 26.835067\n", + " 26.835067\n", " \n", " \n", " 19\n", " 6\n", " 1\n", - " (19.504541422655507 + (x0 * (((-2.030296265227...\n", - " (t[0] + (x[:, 0] * (((t[1] + (x[:, 0] * t[2]))...\n", - " 19.504541422655507;-2.03029626522703;-6.125262...\n", + " (19.505630796428246 + (x0 * (-0.10236401658211...\n", + " (t[0] + (x[:, 0] * (t[1] - (x[:, 0] * (t[2] - ...\n", + " $$\\left(\\theta_{0} + \\left(x_{0} \\cdot \\left(\\...\n", + " 19.505630796428246;-0.10236401658211655;2.0302...\n", " 13\n", - " 2.880242\n", - " -5.912680\n", + " 2.880232\n", + " 2.880232\n", + " 2.880232\n", + " 5.912374\n", + " 9.883393e-01\n", + " 9.883393e-01\n", + " 9.883393e-01\n", + " 23.802925\n", + " 23.802925\n", + " 23.802925\n", " \n", " \n", " 20\n", " 6\n", " 2\n", - " (2.2544633973594923 + (x0 * (((3.1382263687735...\n", - " (t[0] + (x[:, 0] * (((t[1] + (x[:, 0] * t[2]))...\n", - " 2.2544633973594923;3.138226368773547e-2;-2.826...\n", + " (2.1996473290308143 + (x0 * (3.542983731088475...\n", + " (t[0] + (x[:, 0] * (t[1] - (x[:, 0] * (t[2] - ...\n", + " $$\\left(\\theta_{0} + \\left(x_{0} \\cdot \\left(\\...\n", + " 2.1996473290308143;3.542983731088475;-2.307920...\n", " 13\n", - " 4.673299\n", - " -5.912680\n", + " 4.728990\n", + " 4.728990\n", + " 4.728990\n", + " 5.912374\n", + " 9.625584e-01\n", + " 9.625584e-01\n", + " 9.625584e-01\n", + " 25.651682\n", + " 25.651682\n", + " 25.651682\n", + " \n", + " \n", + " 21\n", + " 7\n", + " 0\n", + " (((x0 + x0) + 0.31630659889596935) * (-2.87612...\n", + " (((x[:, 0] + x[:, 0]) + t[0]) * (t[1] - (x[:, ...\n", + " $$\\left(\\left(\\left(x_{0} + x_{0}\\right) + \\th...\n", + " 0.31630659889596935;-2.8761247719330956;-1.011...\n", + " 15\n", + " 5.912268\n", + " 5.912268\n", + " 5.912268\n", + " 5.912268\n", + " 9.889071e-01\n", + " 9.889071e-01\n", + " 9.889071e-01\n", + " 30.053836\n", + " 30.053836\n", + " 30.053836\n", + " \n", + " \n", + " 22\n", + " 7\n", + " 1\n", + " (((x0 + x0) + 6.170996276888179) * (3.07682482...\n", + " (((x[:, 0] + x[:, 0]) + t[0]) * (t[1] - (x[:, ...\n", + " $$\\left(\\left(\\left(x_{0} + x_{0}\\right) + \\th...\n", + " 6.170996276888179;3.0768248258883824;0.9720747...\n", + " 15\n", + " 3.049501\n", + " 3.049501\n", + " 3.049501\n", + " 5.912268\n", + " 9.876540e-01\n", + " 9.876540e-01\n", + " 9.876540e-01\n", + " 27.191070\n", + " 27.191070\n", + " 27.191070\n", + " \n", + " \n", + " 23\n", + " 7\n", + " 2\n", + " (((x0 + x0) + 1.2472384958972085) * (1.8304820...\n", + " (((x[:, 0] + x[:, 0]) + t[0]) * (t[1] - (x[:, ...\n", + " $$\\left(\\left(\\left(x_{0} + x_{0}\\right) + \\th...\n", + " 1.2472384958972085;1.8304820150635275;-2.35725...\n", + " 15\n", + " 4.678348\n", + " 4.678348\n", + " 4.678348\n", + " 5.912268\n", + " 9.629594e-01\n", + " 9.629594e-01\n", + " 9.629594e-01\n", + " 28.819917\n", + " 28.819917\n", + " 28.819917\n", " \n", " \n", "\n", @@ -342,27 +600,30 @@ ], "text/plain": [ " id view Expression \\\n", - "0 0 0 21.589217270548627 \n", - "1 0 1 -2.9306123397648802 \n", - "2 0 2 2.6671850933034973 \n", - "3 1 0 (21.53486288699552 - x0) \n", - "4 1 1 (-3.285686757996549 - x0) \n", - "5 1 2 (2.610598764418156 - x0) \n", - "6 2 0 (21.303011895450766 + (x0 * -5.265543575122939)) \n", - "7 2 1 (-2.9671959569705386 + (x0 * -0.10303084459829... \n", - "8 2 2 (2.847235844618024 + (x0 * 3.181877228219146)) \n", - "9 3 0 (((x0 * 1.971001716619659) + -4.99718543452371... \n", - "10 3 1 (((x0 * -0.8466296174450944) + -0.313553973387... \n", - "11 3 2 (((x0 * 0.19256491303772472) + 3.3024607194153... \n", - "12 4 0 (-1.0590288970595494 + (x0 * ((x0 * 2.03779578... \n", - "13 4 1 (19.504988286223227 + (x0 * ((x0 * -2.03035739... \n", - "14 4 2 (2.59375634458228 + (x0 * ((x0 * 2.12262258018... \n", - "15 5 0 (-0.7489960516420995 + ((x0 * ((x0 * 2.0377751... \n", - "16 5 1 (9.7871188787152 + ((x0 * ((x0 * -2.0303248764... \n", - "17 5 2 (1.1383630234438435 + ((x0 * ((x0 * 2.11511614... \n", - "18 6 0 (-0.8687370550459058 + (x0 * (((2.024605374362... \n", - "19 6 1 (19.504541422655507 + (x0 * (((-2.030296265227... \n", - "20 6 2 (2.2544633973594923 + (x0 * (((3.1382263687735... \n", + "0 0 0 21.589217270548623 \n", + "1 0 1 -2.930612339764867 \n", + "2 0 2 2.667185093303495 \n", + "3 1 0 (21.534862886995523 - x0) \n", + "4 1 1 (-3.2856867579965456 - x0) \n", + "5 1 2 (2.6105987644181523 - x0) \n", + "6 2 0 (21.303011895450787 + (x0 * -5.265543575122987)) \n", + "7 2 1 (-2.967195956970522 + (x0 * -0.103030844598327... \n", + "8 2 2 (2.8472358446081776 + (x0 * 3.181877228219174)) \n", + "9 3 0 (x0 * (-4.997185434556597 + (x0 * 1.9710017166... \n", + "10 3 1 (x0 * (-0.3135539734579376 + (x0 * -0.84662961... \n", + "11 3 2 (x0 * (3.3024607194164286 + (x0 * 0.1925649130... \n", + "12 4 0 ((x0 + -2.645075648188035) * (0.40042646234334... \n", + "13 4 1 ((x0 + -3.071181811381864) * (-6.3508399143391... \n", + "14 4 2 ((x0 + 0.8166540308437522) * (3.17810840942135... \n", + "15 5 0 (-0.4710360148347207 + (-0.5880961667000076 + ... \n", + "16 5 1 (9.659149966450054 + (9.846772636698656 + (x0 ... \n", + "17 5 2 (2.111284332408068 + (0.4814748808300009 + (x0... \n", + "18 6 0 (-0.8961205388017053 + (x0 * (-5.4395816845627... \n", + "19 6 1 (19.505630796428246 + (x0 * (-0.10236401658211... \n", + "20 6 2 (2.1996473290308143 + (x0 * (3.542983731088475... \n", + "21 7 0 (((x0 + x0) + 0.31630659889596935) * (-2.87612... \n", + "22 7 1 (((x0 + x0) + 6.170996276888179) * (3.07682482... \n", + "23 7 2 (((x0 + x0) + 1.2472384958972085) * (1.8304820... \n", "\n", " Numpy \\\n", "0 t[0] \n", @@ -374,74 +635,135 @@ "6 (t[0] + (x[:, 0] * t[1])) \n", "7 (t[0] + (x[:, 0] * t[1])) \n", "8 (t[0] + (x[:, 0] * t[1])) \n", - "9 (((x[:, 0] * t[0]) + t[1]) * x[:, 0]) \n", - "10 (((x[:, 0] * t[0]) + t[1]) * x[:, 0]) \n", - "11 (((x[:, 0] * t[0]) + t[1]) * x[:, 0]) \n", - "12 (t[0] + (x[:, 0] * ((x[:, 0] * t[1]) + t[2]))) \n", - "13 (t[0] + (x[:, 0] * ((x[:, 0] * t[1]) + t[2]))) \n", - "14 (t[0] + (x[:, 0] * ((x[:, 0] * t[1]) + t[2]))) \n", - "15 (t[0] + ((x[:, 0] * ((x[:, 0] * t[1]) + t[2]))... \n", - "16 (t[0] + ((x[:, 0] * ((x[:, 0] * t[1]) + t[2]))... \n", - "17 (t[0] + ((x[:, 0] * ((x[:, 0] * t[1]) + t[2]))... \n", - "18 (t[0] + (x[:, 0] * (((t[1] + (x[:, 0] * t[2]))... \n", - "19 (t[0] + (x[:, 0] * (((t[1] + (x[:, 0] * t[2]))... \n", - "20 (t[0] + (x[:, 0] * (((t[1] + (x[:, 0] * t[2]))... \n", + "9 (x[:, 0] * (t[0] + (x[:, 0] * t[1]))) \n", + "10 (x[:, 0] * (t[0] + (x[:, 0] * t[1]))) \n", + "11 (x[:, 0] * (t[0] + (x[:, 0] * t[1]))) \n", + "12 ((x[:, 0] + t[0]) * (t[1] - (x[:, 0] * t[2]))) \n", + "13 ((x[:, 0] + t[0]) * (t[1] - (x[:, 0] * t[2]))) \n", + "14 ((x[:, 0] + t[0]) * (t[1] - (x[:, 0] * t[2]))) \n", + "15 (t[0] + (t[1] + (x[:, 0] * (t[2] - (x[:, 0] * ... \n", + "16 (t[0] + (t[1] + (x[:, 0] * (t[2] - (x[:, 0] * ... \n", + "17 (t[0] + (t[1] + (x[:, 0] * (t[2] - (x[:, 0] * ... \n", + "18 (t[0] + (x[:, 0] * (t[1] - (x[:, 0] * (t[2] - ... \n", + "19 (t[0] + (x[:, 0] * (t[1] - (x[:, 0] * (t[2] - ... \n", + "20 (t[0] + (x[:, 0] * (t[1] - (x[:, 0] * (t[2] - ... \n", + "21 (((x[:, 0] + x[:, 0]) + t[0]) * (t[1] - (x[:, ... \n", + "22 (((x[:, 0] + x[:, 0]) + t[0]) * (t[1] - (x[:, ... \n", + "23 (((x[:, 0] + x[:, 0]) + t[0]) * (t[1] - (x[:, ... \n", + "\n", + " Math \\\n", + "0 $$\\theta_{0}$$ \n", + "1 $$\\theta_{0}$$ \n", + "2 $$\\theta_{0}$$ \n", + "3 $$\\left(\\theta_{0} - x_{0}\\right)$$ \n", + "4 $$\\left(\\theta_{0} - x_{0}\\right)$$ \n", + "5 $$\\left(\\theta_{0} - x_{0}\\right)$$ \n", + "6 $$\\left(\\theta_{0} + \\left(x_{0} \\cdot \\theta_... \n", + "7 $$\\left(\\theta_{0} + \\left(x_{0} \\cdot \\theta_... \n", + "8 $$\\left(\\theta_{0} + \\left(x_{0} \\cdot \\theta_... \n", + "9 $$\\left(x_{0} \\cdot \\left(\\theta_{0} + \\left(x... \n", + "10 $$\\left(x_{0} \\cdot \\left(\\theta_{0} + \\left(x... \n", + "11 $$\\left(x_{0} \\cdot \\left(\\theta_{0} + \\left(x... \n", + "12 $$\\left(\\left(x_{0} + \\theta_{0}\\right) \\cdot ... \n", + "13 $$\\left(\\left(x_{0} + \\theta_{0}\\right) \\cdot ... \n", + "14 $$\\left(\\left(x_{0} + \\theta_{0}\\right) \\cdot ... \n", + "15 $$\\left(\\theta_{0} + \\left(\\theta_{1} + \\left(... \n", + "16 $$\\left(\\theta_{0} + \\left(\\theta_{1} + \\left(... \n", + "17 $$\\left(\\theta_{0} + \\left(\\theta_{1} + \\left(... \n", + "18 $$\\left(\\theta_{0} + \\left(x_{0} \\cdot \\left(\\... \n", + "19 $$\\left(\\theta_{0} + \\left(x_{0} \\cdot \\left(\\... \n", + "20 $$\\left(\\theta_{0} + \\left(x_{0} \\cdot \\left(\\... \n", + "21 $$\\left(\\left(\\left(x_{0} + x_{0}\\right) + \\th... \n", + "22 $$\\left(\\left(\\left(x_{0} + x_{0}\\right) + \\th... \n", + "23 $$\\left(\\left(\\left(x_{0} + x_{0}\\right) + \\th... \n", + "\n", + " theta size loss_train \\\n", + "0 21.589217270548623 1 532.979453 \n", + "1 -2.930612339764867 1 247.002956 \n", + "2 2.667185093303495 1 126.303250 \n", + "3 21.534862886995523 3 428.346651 \n", + "4 -3.2856867579965456 3 255.691957 \n", + "5 2.6105987644181523 3 214.494045 \n", + "6 21.303011895450787;-5.265543575122987 5 228.602451 \n", + "7 -2.967195956970522;-0.10303084459832784 5 246.886780 \n", + "8 2.8472358446081776;3.181877228219174 5 5.050724 \n", + "9 -4.997185434556597;1.97100171661893 7 6.567563 \n", + "10 -0.3135539734579376;-0.8466296174467215 7 126.355850 \n", + "11 3.3024607194164286;0.19256491303781179 7 6.453165 \n", + "12 -2.645075648188035;0.40042646234334933;-2.0378... 9 6.222295 \n", + "13 -3.071181811381864;-6.350839914339136;2.030330... 9 2.880427 \n", + "14 0.8166540308437522;3.1781084094213585;-2.10865... 9 5.033598 \n", + "15 -0.4710360148347207;-0.5880961667000076;-4.989... 11 6.222295 \n", + "16 9.659149966450054;9.846772636698656;-0.1153759... 11 2.880428 \n", + "17 2.111284332408068;0.4814748808300009;3.1954044... 11 5.033599 \n", + "18 -0.8961205388017053;-5.439581684562741;-2.0267... 13 5.912374 \n", + "19 19.505630796428246;-0.10236401658211655;2.0302... 13 2.880232 \n", + "20 2.1996473290308143;3.542983731088475;-2.307920... 13 4.728990 \n", + "21 0.31630659889596935;-2.8761247719330956;-1.011... 15 5.912268 \n", + "22 6.170996276888179;3.0768248258883824;0.9720747... 15 3.049501 \n", + "23 1.2472384958972085;1.8304820150635275;-2.35725... 15 4.678348 \n", "\n", - " theta size loss \\\n", - "0 21.589217270548627 1 532.979453 \n", - "1 -2.9306123397648802 1 247.002956 \n", - "2 2.6671850933034973 1 126.303250 \n", - "3 21.53486288699552 3 428.346651 \n", - "4 -3.285686757996549 3 255.691957 \n", - "5 2.610598764418156 3 214.494045 \n", - "6 21.303011895450766;-5.265543575122939 5 228.602451 \n", - "7 -2.9671959569705386;-0.10303084459829029 5 246.886780 \n", - "8 2.847235844618024;3.181877228219146 5 5.050724 \n", - "9 1.971001716619659;-4.997185434523716 7 6.567563 \n", - "10 -0.8466296174450944;-0.31355397338761204 7 126.355850 \n", - "11 0.19256491303772472;3.302460719415394 7 6.453165 \n", - "12 -1.0590288970595494;2.037795780991407;-4.98985... 9 6.222296 \n", - "13 19.504988286223227;-2.030357391771257;-0.11535... 9 2.880427 \n", - "14 2.59375634458228;2.122622580186797e-2;3.195222... 9 5.033599 \n", - "15 -0.7489960516420995;2.037775165917442;-4.99009... 11 6.222297 \n", - "16 9.7871188787152;-2.0303248764495545;-0.1153596... 11 2.880427 \n", - "17 1.1383630234438435;2.11511614475054e-2;3.19542... 11 5.033598 \n", - "18 -0.8687370550459058;2.024605374362169;2.804607... 13 5.912680 \n", - "19 19.504541422655507;-2.03029626522703;-6.125262... 13 2.880242 \n", - "20 2.2544633973594923;3.138226368773547e-2;-2.826... 13 4.673299 \n", + " loss_val loss_test maxloss R2_train R2_val \\\n", + "0 532.979453 532.979453 532.979453 2.220446e-16 2.220446e-16 \n", + "1 247.002956 247.002956 532.979453 1.110223e-16 1.110223e-16 \n", + "2 126.303250 126.303250 532.979453 0.000000e+00 0.000000e+00 \n", + "3 428.346651 428.346651 428.346651 1.963168e-01 1.963168e-01 \n", + "4 255.691957 255.691957 428.346651 -3.517772e-02 -3.517772e-02 \n", + "5 214.494045 214.494045 428.346651 -6.982464e-01 -6.982464e-01 \n", + "6 228.602451 228.602451 246.886780 5.710858e-01 5.710858e-01 \n", + "7 246.886780 246.886780 246.886780 4.703438e-04 4.703438e-04 \n", + "8 5.050724 5.050724 246.886780 9.600111e-01 9.600111e-01 \n", + "9 6.567563 6.567563 126.355850 9.876776e-01 9.876776e-01 \n", + "10 126.355850 126.355850 126.355850 4.884440e-01 4.884440e-01 \n", + "11 6.453165 6.453165 126.355850 9.489074e-01 9.489074e-01 \n", + "12 6.222295 6.222295 6.222295 9.883254e-01 9.883254e-01 \n", + "13 2.880427 2.880427 6.222295 9.883385e-01 9.883385e-01 \n", + "14 5.033598 5.033598 6.222295 9.601467e-01 9.601467e-01 \n", + "15 6.222295 6.222295 6.222295 9.883254e-01 9.883254e-01 \n", + "16 2.880428 2.880428 6.222295 9.883385e-01 9.883385e-01 \n", + "17 5.033599 5.033599 6.222295 9.601467e-01 9.601467e-01 \n", + "18 5.912374 5.912374 5.912374 9.889069e-01 9.889069e-01 \n", + "19 2.880232 2.880232 5.912374 9.883393e-01 9.883393e-01 \n", + "20 4.728990 4.728990 5.912374 9.625584e-01 9.625584e-01 \n", + "21 5.912268 5.912268 5.912268 9.889071e-01 9.889071e-01 \n", + "22 3.049501 3.049501 5.912268 9.876540e-01 9.876540e-01 \n", + "23 4.678348 4.678348 5.912268 9.629594e-01 9.629594e-01 \n", "\n", - " maxLoss \n", - "0 -532.979453 \n", - "1 -532.979453 \n", - "2 -532.979453 \n", - "3 -428.346651 \n", - "4 -428.346651 \n", - "5 -428.346651 \n", - "6 -246.886780 \n", - "7 -246.886780 \n", - "8 -246.886780 \n", - "9 -126.355850 \n", - "10 -126.355850 \n", - "11 -126.355850 \n", - "12 -6.222295 \n", - "13 -6.222295 \n", - "14 -6.222295 \n", - "15 -6.222297 \n", - "16 -6.222297 \n", - "17 -6.222297 \n", - "18 -5.912680 \n", - "19 -5.912680 \n", - "20 -5.912680 " + " R2_test mdl_train mdl_val mdl_test \n", + "0 2.220446e-16 532.979453 532.979453 532.979453 \n", + "1 1.110223e-16 247.002956 247.002956 247.002956 \n", + "2 0.000000e+00 126.303250 126.303250 126.303250 \n", + "3 1.963168e-01 431.642488 431.642488 431.642488 \n", + "4 -3.517772e-02 258.987794 258.987794 258.987794 \n", + "5 -6.982464e-01 217.789882 217.789882 217.789882 \n", + "6 5.710858e-01 235.533923 235.533923 235.533923 \n", + "7 4.703438e-04 253.818252 253.818252 253.818252 \n", + "8 9.600111e-01 11.982196 11.982196 11.982196 \n", + "9 9.876776e-01 16.271623 16.271623 16.271623 \n", + "10 4.884440e-01 136.059911 136.059911 136.059911 \n", + "11 9.489074e-01 16.157226 16.157226 16.157226 \n", + "12 9.883254e-01 20.707237 20.707237 20.707237 \n", + "13 9.883385e-01 17.365369 17.365369 17.365369 \n", + "14 9.601467e-01 19.518540 19.518540 19.518540 \n", + "15 9.883254e-01 23.926112 23.926112 23.926112 \n", + "16 9.883385e-01 20.584245 20.584245 20.584245 \n", + "17 9.601467e-01 22.737416 22.737416 22.737416 \n", + "18 9.889069e-01 26.835067 26.835067 26.835067 \n", + "19 9.883393e-01 23.802925 23.802925 23.802925 \n", + "20 9.625584e-01 25.651682 25.651682 25.651682 \n", + "21 9.889071e-01 30.053836 30.053836 30.053836 \n", + "22 9.876540e-01 27.191070 27.191070 27.191070 \n", + "23 9.629594e-01 28.819917 28.819917 28.819917 " ] }, - "execution_count": 3, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# We create a Pyeggp regressor leaving all hyperparameters to default.\n", - "reg = pyeggp.PyEGGP()\n", + "reg = eggp.EGGP()\n", "reg.fit_mvsr(full_X, full_y)\n", "\n", "# Here we collect the full table of results and plot it\n", @@ -459,13 +781,13 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 11, "id": "88ebec1e-f915-40be-b210-0063261c139d", "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
    " ] @@ -477,8 +799,8 @@ "source": [ "fig, ax = plt.subplots(1, 3, figsize=(14, 4))\n", "\n", - "ax[0].plot(pareto['id'], -pareto['maxLoss'], label='Pareto front')\n", - "ax[0].scatter(pareto['id'], -pareto['maxLoss'])\n", + "ax[0].plot(pareto['id'], -pareto['maxloss'], label='Pareto front')\n", + "ax[0].scatter(pareto['id'], -pareto['maxloss'])\n", "ax[0].set_ylabel('Loss')\n", "ax[0].legend()\n", "\n", @@ -508,7 +830,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 12, "id": "649d0314-fc9e-43e8-801b-bd3d658ef413", "metadata": {}, "outputs": [ @@ -517,12 +839,12 @@ "output_type": "stream", "text": [ "Best solution :\n", - " f(X0) = (A + (X0 * ((X0 * B) + C)))\n" + " f(X0) = ((X0 + A) * (B - (X0 * C)))\n" ] }, { "data": { - "image/png": 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", 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", 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    " ] @@ -570,9 +892,9 @@ ], "metadata": { "kernelspec": { - "display_name": "new_mvsr_kern", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "new_mvsr_kern" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -584,7 +906,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.2" + "version": "3.13.7" } }, "nbformat": 4, diff --git a/test/mvsr_example.ipynb b/test/mvsr_example.ipynb index 7bbf6ff..8b37c82 100644 --- a/test/mvsr_example.ipynb +++ b/test/mvsr_example.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 8, "id": "c68e3b1a-d6e4-4f94-ae46-d761d5c1c836", "metadata": {}, "outputs": [], @@ -23,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 9, "id": "2c9d5ad9-f2e4-4b96-accd-41d1edbfdc98", "metadata": {}, "outputs": [ @@ -69,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 10, "id": "3c318511-31dd-4cb2-8178-2ceb5c2e405c", "metadata": {}, "outputs": [ @@ -118,481 +118,481 @@ " 0\n", " 0\n", " 0\n", - 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" 11.1\n", - " 11.1\n", - " $$11.1$$\n", - " \n", + " 2.667185093303495\n", + " t[0]\n", + " $$\\theta_{0}$$\n", + " 2.667185093303495\n", " 1\n", - " 197.415618\n", - " 197.415618\n", - " 197.415618\n", - " 643.003132\n", - " -0.563029\n", - " -0.563029\n", - " -0.563029\n", - " 197.415618\n", - " 197.415618\n", - " 197.415618\n", + " 126.303250\n", + " 126.303250\n", + " 126.303250\n", + " 532.979453\n", + " 0.000000e+00\n", + " 0.000000e+00\n", + " 0.000000e+00\n", + " 126.303250\n", + " 126.303250\n", + " 126.303250\n", " \n", " \n", " 3\n", " 1\n", " 0\n", - " (11.0 - x0)\n", - " (11.0 - x[:, 0])\n", - " $$\\left(11.0 - x_{0}\\right)$$\n", - " \n", + " (21.534862886995523 - x0)\n", + " (t[0] - x[:, 0])\n", + " $$\\left(\\theta_{0} - x_{0}\\right)$$\n", + " 21.534862886995523\n", " 3\n", - " 539.329988\n", - " 539.329988\n", - " 539.329988\n", - " 539.329988\n", - " -0.011915\n", - " -0.011915\n", - " -0.011915\n", - " 545.716867\n", - " 545.716867\n", - " 545.716867\n", + " 428.346651\n", + " 428.346651\n", + " 428.346651\n", + " 428.346651\n", + " 1.963168e-01\n", + " 1.963168e-01\n", + " 1.963168e-01\n", + " 431.642488\n", + " 431.642488\n", + " 431.642488\n", " \n", " \n", " 4\n", " 1\n", " 1\n", - 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(x[:, 0] * t[2])))\n", + " $$\\left(\\left(x_{0} + \\theta_{0}\\right) \\cdot ...\n", + " 0.8166540308437522;3.1781084094213585;-2.10865...\n", " 9\n", - " 388.326933\n", - " 388.326933\n", - " 388.326933\n", - " 474.914762\n", - " -2.074560\n", - " -2.074560\n", - " -2.074560\n", - " 407.448500\n", - " 407.448500\n", - " 407.448500\n", + " 5.033598\n", + " 5.033598\n", + " 5.033598\n", + " 6.222295\n", + " 9.601467e-01\n", + " 9.601467e-01\n", + " 9.601467e-01\n", + " 19.518540\n", + " 19.518540\n", + " 19.518540\n", " \n", " \n", " 15\n", " 5\n", " 0\n", - " ((10.0 - x0) - (x0 + (1.0 / (x0 + -0.1))))\n", - " ((10.0 - x[:, 0]) - (x[:, 0] + (1.0 / (x[:, 0]...\n", - " $$\\left(\\left(10.0 - x_{0}\\right) - \\left(x_{0...\n", - " \n", + " (-0.4710360148347207 + (-0.5880961667000076 + ...\n", + " (t[0] + (t[1] + (x[:, 0] * (t[2] - (x[:, 0] * ...\n", + " $$\\left(\\theta_{0} + \\left(\\theta_{1} + \\left(...\n", + " -0.4710360148347207;-0.5880961667000076;-4.989...\n", " 11\n", - " 469.197310\n", - 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" ((10.0 - x0) - (x0 + (1.0 / (x0 + -0.1))))\n", - " ((10.0 - x[:, 0]) - (x[:, 0] + (1.0 / (x[:, 0]...\n", - " $$\\left(\\left(10.0 - x_{0}\\right) - \\left(x_{0...\n", - " \n", + " (2.111284332408068 + (0.4814748808300009 + (x0...\n", + " (t[0] + (t[1] + (x[:, 0] * (t[2] - (x[:, 0] * ...\n", + " $$\\left(\\theta_{0} + \\left(\\theta_{1} + \\left(...\n", + " 2.111284332408068;0.4814748808300009;3.1954044...\n", " 11\n", - " 391.726096\n", - " 391.726096\n", - " 391.726096\n", - " 470.734141\n", - " -2.101473\n", - " -2.101473\n", - " -2.101473\n", - " 415.124329\n", - " 415.124329\n", - " 415.124329\n", + " 5.033599\n", + " 5.033599\n", + " 5.033599\n", + " 6.222295\n", + " 9.601467e-01\n", + " 9.601467e-01\n", + " 9.601467e-01\n", + " 22.737416\n", + " 22.737416\n", + " 22.737416\n", " \n", " \n", " 18\n", " 6\n", " 0\n", - " ((10.0 - ((1.0 * x0) + (1.0 / (x0 + -0.1)))) -...\n", - " ((10.0 - ((1.0 * x[:, 0]) + (1.0 / (x[:, 0] + ...\n", - " $$\\left(\\left(10.0 - \\left(\\left(1.0 \\cdot x_{...\n", - " \n", + " (-0.8961205388017053 + (x0 * (-5.4395816845627...\n", + " (t[0] + (x[:, 0] * (t[1] - (x[:, 0] * (t[2] - ...\n", + " $$\\left(\\theta_{0} + \\left(x_{0} \\cdot \\left(\\...\n", + " -0.8961205388017053;-5.439581684562741;-2.0267...\n", " 13\n", - " 469.197310\n", - " 469.197310\n", - " 469.197310\n", - " 470.734141\n", - " 0.119671\n", - " 0.119671\n", - " 0.119671\n", - " 498.876168\n", - " 498.876168\n", - " 498.876168\n", + " 5.912374\n", + " 5.912374\n", + " 5.912374\n", + " 5.912374\n", + " 9.889069e-01\n", + " 9.889069e-01\n", + " 9.889069e-01\n", + " 26.835067\n", + " 26.835067\n", + " 26.835067\n", " \n", " \n", " 19\n", " 6\n", " 1\n", - " ((10.0 - ((1.0 * x0) + (1.0 / (x0 + -0.1)))) -...\n", - " ((10.0 - ((1.0 * x[:, 0]) + (1.0 / (x[:, 0] + ...\n", - " $$\\left(\\left(10.0 - \\left(\\left(1.0 \\cdot x_{...\n", - " \n", + " (19.505630796428246 + (x0 * (-0.10236401658211...\n", + " (t[0] + (x[:, 0] * (t[1] - (x[:, 0] * (t[2] - ...\n", + " $$\\left(\\theta_{0} + \\left(x_{0} \\cdot \\left(\\...\n", + " 19.505630796428246;-0.10236401658211655;2.0302...\n", " 13\n", - " 470.734141\n", - " 470.734141\n", - " 470.734141\n", - " 470.734141\n", - " -0.905783\n", - " -0.905783\n", - " -0.905783\n", - " 500.413000\n", - " 500.413000\n", - " 500.413000\n", + " 2.880232\n", + " 2.880232\n", + " 2.880232\n", + " 5.912374\n", + " 9.883393e-01\n", + " 9.883393e-01\n", + " 9.883393e-01\n", + " 23.802925\n", + " 23.802925\n", + " 23.802925\n", " \n", " \n", " 20\n", " 6\n", " 2\n", - " ((10.0 - ((1.0 * x0) + (1.0 / (x0 + -0.1)))) -...\n", - " ((10.0 - ((1.0 * x[:, 0]) + (1.0 / (x[:, 0] + ...\n", - " $$\\left(\\left(10.0 - \\left(\\left(1.0 \\cdot x_{...\n", - " \n", + " (2.1996473290308143 + (x0 * (3.542983731088475...\n", + " (t[0] + (x[:, 0] * (t[1] - (x[:, 0] * (t[2] - ...\n", + " $$\\left(\\theta_{0} + \\left(x_{0} \\cdot \\left(\\...\n", + " 2.1996473290308143;3.542983731088475;-2.307920...\n", " 13\n", - " 391.726096\n", - " 391.726096\n", - " 391.726096\n", - " 470.734141\n", - " -2.101473\n", - " -2.101473\n", - " -2.101473\n", - " 421.404954\n", - " 421.404954\n", - " 421.404954\n", + " 4.728990\n", + " 4.728990\n", + " 4.728990\n", + " 5.912374\n", + " 9.625584e-01\n", + " 9.625584e-01\n", + " 9.625584e-01\n", + " 25.651682\n", + " 25.651682\n", + " 25.651682\n", " \n", " \n", " 21\n", " 7\n", " 0\n", - " ((10.0 - (((x0 * x0) / x0) + (1.0 / (x0 + -0.1...\n", - " ((10.0 - (((x[:, 0] * x[:, 0]) / x[:, 0]) + (1...\n", - " $$\\left(\\left(10.0 - \\left(\\frac{\\left(x_{0} \\...\n", - " \n", + " (((x0 + x0) + 0.31630659889596935) * (-2.87612...\n", + " (((x[:, 0] + x[:, 0]) + t[0]) * (t[1] - (x[:, ...\n", + " $$\\left(\\left(\\left(x_{0} + x_{0}\\right) + \\th...\n", + " 0.31630659889596935;-2.8761247719330956;-1.011...\n", " 15\n", - " 469.197310\n", - " 469.197310\n", - " 469.197310\n", - " 470.734141\n", - " 0.119671\n", - " 0.119671\n", - " 0.119671\n", - " 502.074841\n", - " 502.074841\n", - " 502.074841\n", + " 5.912268\n", + " 5.912268\n", + " 5.912268\n", + " 5.912268\n", + " 9.889071e-01\n", + " 9.889071e-01\n", + " 9.889071e-01\n", + " 30.053836\n", + " 30.053836\n", + " 30.053836\n", " \n", " \n", " 22\n", " 7\n", " 1\n", - " ((10.0 - (((x0 * x0) / x0) + (1.0 / (x0 + -0.1...\n", - " ((10.0 - (((x[:, 0] * x[:, 0]) / x[:, 0]) + (1...\n", - " $$\\left(\\left(10.0 - \\left(\\frac{\\left(x_{0} \\...\n", - " \n", + " (((x0 + x0) + 6.170996276888179) * (3.07682482...\n", + " (((x[:, 0] + x[:, 0]) + t[0]) * (t[1] - (x[:, ...\n", + " $$\\left(\\left(\\left(x_{0} + x_{0}\\right) + \\th...\n", + " 6.170996276888179;3.0768248258883824;0.9720747...\n", " 15\n", - " 470.734141\n", - " 470.734141\n", - " 470.734141\n", - " 470.734141\n", - " -0.905783\n", - " -0.905783\n", - " -0.905783\n", - " 503.611673\n", - " 503.611673\n", - " 503.611673\n", + " 3.049501\n", + " 3.049501\n", + " 3.049501\n", + " 5.912268\n", + " 9.876540e-01\n", + " 9.876540e-01\n", + " 9.876540e-01\n", + " 27.191070\n", + " 27.191070\n", + " 27.191070\n", " \n", " \n", " 23\n", " 7\n", " 2\n", - " ((10.0 - (((x0 * x0) / x0) + (1.0 / (x0 + -0.1...\n", - " ((10.0 - (((x[:, 0] * x[:, 0]) / x[:, 0]) + (1...\n", - " $$\\left(\\left(10.0 - \\left(\\frac{\\left(x_{0} \\...\n", - " \n", + " (((x0 + x0) + 1.2472384958972085) * (1.8304820...\n", + " (((x[:, 0] + x[:, 0]) + t[0]) * (t[1] - (x[:, ...\n", + " $$\\left(\\left(\\left(x_{0} + x_{0}\\right) + \\th...\n", + " 1.2472384958972085;1.8304820150635275;-2.35725...\n", " 15\n", - " 391.726096\n", - " 391.726096\n", - " 391.726096\n", - " 470.734141\n", - " -2.101473\n", - " -2.101473\n", - " -2.101473\n", - " 424.603627\n", - " 424.603627\n", - " 424.603627\n", + " 4.678348\n", + " 4.678348\n", + " 4.678348\n", + " 5.912268\n", + " 9.629594e-01\n", + " 9.629594e-01\n", + " 9.629594e-01\n", + " 28.819917\n", + " 28.819917\n", + " 28.819917\n", " \n", " \n", "\n", @@ -600,144 +600,170 @@ ], "text/plain": [ " id view Expression \\\n", - "0 0 0 11.1 \n", - "1 0 1 11.1 \n", - "2 0 2 11.1 \n", - "3 1 0 (11.0 - x0) \n", - "4 1 1 (11.0 - x0) \n", - "5 1 2 (11.0 - x0) \n", - "6 2 0 ((10.1 - x0) - x0) \n", - "7 2 1 ((10.1 - x0) - x0) \n", - "8 2 2 ((10.1 - x0) - x0) \n", - "9 3 0 (10.1 - (x0 + (x0 + 0.0))) \n", - "10 3 1 (10.1 - (x0 + (x0 + 0.0))) \n", - "11 3 2 (10.1 - (x0 + (x0 + 0.0))) \n", - "12 4 0 ((10.0 - (x0 + (x0 / 20.1))) - x0) \n", - "13 4 1 ((10.0 - (x0 + (x0 / 20.1))) - x0) \n", - "14 4 2 ((10.0 - (x0 + (x0 / 20.1))) - x0) \n", - "15 5 0 ((10.0 - x0) - (x0 + (1.0 / (x0 + -0.1)))) \n", - "16 5 1 ((10.0 - x0) - (x0 + (1.0 / (x0 + -0.1)))) \n", - "17 5 2 ((10.0 - x0) - (x0 + (1.0 / (x0 + -0.1)))) \n", - "18 6 0 ((10.0 - ((1.0 * x0) + (1.0 / (x0 + -0.1)))) -... \n", - "19 6 1 ((10.0 - ((1.0 * x0) + (1.0 / (x0 + -0.1)))) -... \n", - "20 6 2 ((10.0 - ((1.0 * x0) + (1.0 / (x0 + -0.1)))) -... \n", - "21 7 0 ((10.0 - (((x0 * x0) / x0) + (1.0 / (x0 + -0.1... \n", - "22 7 1 ((10.0 - (((x0 * x0) / x0) + (1.0 / (x0 + -0.1... \n", - "23 7 2 ((10.0 - (((x0 * x0) / x0) + (1.0 / (x0 + -0.1... \n", + "0 0 0 21.589217270548623 \n", + "1 0 1 -2.930612339764867 \n", + "2 0 2 2.667185093303495 \n", + "3 1 0 (21.534862886995523 - x0) \n", + "4 1 1 (-3.2856867579965456 - x0) \n", + "5 1 2 (2.6105987644181523 - x0) \n", + "6 2 0 (21.303011895450787 + (x0 * -5.265543575122987)) \n", + "7 2 1 (-2.967195956970522 + (x0 * -0.103030844598327... \n", + "8 2 2 (2.8472358446081776 + (x0 * 3.181877228219174)) \n", + "9 3 0 (x0 * (-4.997185434556597 + (x0 * 1.9710017166... \n", + "10 3 1 (x0 * (-0.3135539734579376 + (x0 * -0.84662961... \n", + "11 3 2 (x0 * (3.3024607194164286 + (x0 * 0.1925649130... \n", + "12 4 0 ((x0 + -2.645075648188035) * (0.40042646234334... \n", + "13 4 1 ((x0 + -3.071181811381864) * (-6.3508399143391... \n", + "14 4 2 ((x0 + 0.8166540308437522) * (3.17810840942135... \n", + "15 5 0 (-0.4710360148347207 + (-0.5880961667000076 + ... \n", + "16 5 1 (9.659149966450054 + (9.846772636698656 + (x0 ... \n", + "17 5 2 (2.111284332408068 + (0.4814748808300009 + (x0... \n", + "18 6 0 (-0.8961205388017053 + (x0 * (-5.4395816845627... \n", + "19 6 1 (19.505630796428246 + (x0 * (-0.10236401658211... \n", + "20 6 2 (2.1996473290308143 + (x0 * (3.542983731088475... \n", + "21 7 0 (((x0 + x0) + 0.31630659889596935) * (-2.87612... \n", + "22 7 1 (((x0 + x0) + 6.170996276888179) * (3.07682482... \n", + "23 7 2 (((x0 + x0) + 1.2472384958972085) * (1.8304820... \n", "\n", " Numpy \\\n", - "0 11.1 \n", - "1 11.1 \n", - "2 11.1 \n", - "3 (11.0 - x[:, 0]) \n", - "4 (11.0 - x[:, 0]) \n", - "5 (11.0 - x[:, 0]) \n", - "6 ((10.1 - x[:, 0]) - x[:, 0]) \n", - "7 ((10.1 - x[:, 0]) - x[:, 0]) \n", - "8 ((10.1 - x[:, 0]) - x[:, 0]) \n", - "9 (10.1 - (x[:, 0] + (x[:, 0] + 0.0))) \n", - "10 (10.1 - (x[:, 0] + (x[:, 0] + 0.0))) \n", - "11 (10.1 - (x[:, 0] + (x[:, 0] + 0.0))) \n", - "12 ((10.0 - (x[:, 0] + (x[:, 0] / 20.1))) - x[:, 0]) \n", - "13 ((10.0 - (x[:, 0] + (x[:, 0] / 20.1))) - x[:, 0]) \n", - "14 ((10.0 - (x[:, 0] + (x[:, 0] / 20.1))) - x[:, 0]) \n", - "15 ((10.0 - x[:, 0]) - (x[:, 0] + (1.0 / (x[:, 0]... \n", - "16 ((10.0 - x[:, 0]) - (x[:, 0] + (1.0 / (x[:, 0]... \n", - "17 ((10.0 - x[:, 0]) - (x[:, 0] + (1.0 / (x[:, 0]... \n", - "18 ((10.0 - ((1.0 * x[:, 0]) + (1.0 / (x[:, 0] + ... \n", - "19 ((10.0 - ((1.0 * x[:, 0]) + (1.0 / (x[:, 0] + ... \n", - "20 ((10.0 - ((1.0 * x[:, 0]) + (1.0 / (x[:, 0] + ... \n", - "21 ((10.0 - (((x[:, 0] * x[:, 0]) / x[:, 0]) + (1... \n", - "22 ((10.0 - (((x[:, 0] * x[:, 0]) / x[:, 0]) + (1... \n", - "23 ((10.0 - (((x[:, 0] * x[:, 0]) / x[:, 0]) + (1... \n", + "0 t[0] \n", + "1 t[0] \n", + "2 t[0] \n", + "3 (t[0] - x[:, 0]) \n", + "4 (t[0] - x[:, 0]) \n", + "5 (t[0] - x[:, 0]) \n", + "6 (t[0] + (x[:, 0] * t[1])) \n", + "7 (t[0] + (x[:, 0] * t[1])) \n", + "8 (t[0] + (x[:, 0] * t[1])) \n", + "9 (x[:, 0] * (t[0] + (x[:, 0] * t[1]))) \n", + "10 (x[:, 0] * (t[0] + (x[:, 0] * t[1]))) \n", + "11 (x[:, 0] * (t[0] + (x[:, 0] * t[1]))) \n", + "12 ((x[:, 0] + t[0]) * (t[1] - (x[:, 0] * t[2]))) \n", + "13 ((x[:, 0] + t[0]) * (t[1] - (x[:, 0] * t[2]))) \n", + "14 ((x[:, 0] + t[0]) * (t[1] - (x[:, 0] * t[2]))) \n", + "15 (t[0] + (t[1] + (x[:, 0] * (t[2] - (x[:, 0] * ... \n", + "16 (t[0] + (t[1] + (x[:, 0] * (t[2] - (x[:, 0] * ... \n", + "17 (t[0] + (t[1] + (x[:, 0] * (t[2] - (x[:, 0] * ... \n", + "18 (t[0] + (x[:, 0] * (t[1] - (x[:, 0] * (t[2] - ... \n", + "19 (t[0] + (x[:, 0] * (t[1] - (x[:, 0] * (t[2] - ... \n", + "20 (t[0] + (x[:, 0] * (t[1] - (x[:, 0] * (t[2] - ... \n", + "21 (((x[:, 0] + x[:, 0]) + t[0]) * (t[1] - (x[:, ... \n", + "22 (((x[:, 0] + x[:, 0]) + t[0]) * (t[1] - (x[:, ... \n", + "23 (((x[:, 0] + x[:, 0]) + t[0]) * (t[1] - (x[:, ... \n", "\n", - " Math theta size loss_train \\\n", - "0 $$11.1$$ 1 643.003132 \n", - "1 $$11.1$$ 1 443.861039 \n", - "2 $$11.1$$ 1 197.415618 \n", - "3 $$\\left(11.0 - x_{0}\\right)$$ 3 539.329988 \n", - "4 $$\\left(11.0 - x_{0}\\right)$$ 3 459.772803 \n", - "5 $$\\left(11.0 - x_{0}\\right)$$ 3 284.876098 \n", - "6 $$\\left(\\left(10.1 - x_{0}\\right) - x_{0}\\righ... 5 475.185934 \n", - "7 $$\\left(\\left(10.1 - x_{0}\\right) - x_{0}\\righ... 5 475.077826 \n", - "8 $$\\left(\\left(10.1 - x_{0}\\right) - x_{0}\\righ... 5 383.579440 \n", - "9 $$\\left(10.1 - \\left(x_{0} + \\left(x_{0} + 0.0... 7 475.185934 \n", - "10 $$\\left(10.1 - \\left(x_{0} + \\left(x_{0} + 0.0... 7 475.077826 \n", - "11 $$\\left(10.1 - \\left(x_{0} + \\left(x_{0} + 0.0... 7 383.579440 \n", - "12 $$\\left(\\left(10.0 - \\left(x_{0} + \\frac{x_{0}... 9 473.870029 \n", - "13 $$\\left(\\left(10.0 - \\left(x_{0} + \\frac{x_{0}... 9 474.914762 \n", - "14 $$\\left(\\left(10.0 - \\left(x_{0} + \\frac{x_{0}... 9 388.326933 \n", - "15 $$\\left(\\left(10.0 - x_{0}\\right) - \\left(x_{0... 11 469.197310 \n", - "16 $$\\left(\\left(10.0 - x_{0}\\right) - \\left(x_{0... 11 470.734141 \n", - "17 $$\\left(\\left(10.0 - x_{0}\\right) - \\left(x_{0... 11 391.726096 \n", - "18 $$\\left(\\left(10.0 - \\left(\\left(1.0 \\cdot x_{... 13 469.197310 \n", - "19 $$\\left(\\left(10.0 - \\left(\\left(1.0 \\cdot x_{... 13 470.734141 \n", - "20 $$\\left(\\left(10.0 - \\left(\\left(1.0 \\cdot x_{... 13 391.726096 \n", - "21 $$\\left(\\left(10.0 - \\left(\\frac{\\left(x_{0} \\... 15 469.197310 \n", - "22 $$\\left(\\left(10.0 - \\left(\\frac{\\left(x_{0} \\... 15 470.734141 \n", - "23 $$\\left(\\left(10.0 - \\left(\\frac{\\left(x_{0} \\... 15 391.726096 \n", + " Math \\\n", + "0 $$\\theta_{0}$$ \n", + "1 $$\\theta_{0}$$ \n", + "2 $$\\theta_{0}$$ \n", + "3 $$\\left(\\theta_{0} - x_{0}\\right)$$ \n", + "4 $$\\left(\\theta_{0} - x_{0}\\right)$$ \n", + "5 $$\\left(\\theta_{0} - x_{0}\\right)$$ \n", + "6 $$\\left(\\theta_{0} + \\left(x_{0} \\cdot \\theta_... \n", + "7 $$\\left(\\theta_{0} + \\left(x_{0} \\cdot \\theta_... \n", + "8 $$\\left(\\theta_{0} + \\left(x_{0} \\cdot \\theta_... \n", + "9 $$\\left(x_{0} \\cdot \\left(\\theta_{0} + \\left(x... \n", + "10 $$\\left(x_{0} \\cdot \\left(\\theta_{0} + \\left(x... \n", + "11 $$\\left(x_{0} \\cdot \\left(\\theta_{0} + \\left(x... \n", + "12 $$\\left(\\left(x_{0} + \\theta_{0}\\right) \\cdot ... \n", + "13 $$\\left(\\left(x_{0} + \\theta_{0}\\right) \\cdot ... \n", + "14 $$\\left(\\left(x_{0} + \\theta_{0}\\right) \\cdot ... \n", + "15 $$\\left(\\theta_{0} + \\left(\\theta_{1} + \\left(... \n", + "16 $$\\left(\\theta_{0} + \\left(\\theta_{1} + \\left(... \n", + "17 $$\\left(\\theta_{0} + \\left(\\theta_{1} + \\left(... \n", + "18 $$\\left(\\theta_{0} + \\left(x_{0} \\cdot \\left(\\... \n", + "19 $$\\left(\\theta_{0} + \\left(x_{0} \\cdot \\left(\\... \n", + "20 $$\\left(\\theta_{0} + \\left(x_{0} \\cdot \\left(\\... \n", + "21 $$\\left(\\left(\\left(x_{0} + x_{0}\\right) + \\th... \n", + "22 $$\\left(\\left(\\left(x_{0} + x_{0}\\right) + \\th... \n", + "23 $$\\left(\\left(\\left(x_{0} + x_{0}\\right) + \\th... \n", "\n", - " loss_val loss_test maxloss R2_train R2_val R2_test \\\n", - "0 643.003132 643.003132 643.003132 -0.206431 -0.206431 -0.206431 \n", - "1 443.861039 443.861039 643.003132 -0.796987 -0.796987 -0.796987 \n", - "2 197.415618 197.415618 643.003132 -0.563029 -0.563029 -0.563029 \n", - "3 539.329988 539.329988 539.329988 -0.011915 -0.011915 -0.011915 \n", - "4 459.772803 459.772803 539.329988 -0.861406 -0.861406 -0.861406 \n", - "5 284.876098 284.876098 539.329988 -1.255493 -1.255493 -1.255493 \n", - "6 475.185934 475.185934 475.185934 0.108435 0.108435 0.108435 \n", - "7 475.077826 475.077826 475.185934 -0.923369 -0.923369 -0.923369 \n", - "8 383.579440 383.579440 475.185934 -2.036972 -2.036972 -2.036972 \n", - "9 475.185934 475.185934 475.185934 0.108435 0.108435 0.108435 \n", - "10 475.077826 475.077826 475.185934 -0.923369 -0.923369 -0.923369 \n", - "11 383.579440 383.579440 475.185934 -2.036972 -2.036972 -2.036972 \n", - "12 473.870029 473.870029 474.914762 0.110904 0.110904 0.110904 \n", - "13 474.914762 474.914762 474.914762 -0.922709 -0.922709 -0.922709 \n", - "14 388.326933 388.326933 474.914762 -2.074560 -2.074560 -2.074560 \n", - "15 469.197310 469.197310 470.734141 0.119671 0.119671 0.119671 \n", - "16 470.734141 470.734141 470.734141 -0.905783 -0.905783 -0.905783 \n", - "17 391.726096 391.726096 470.734141 -2.101473 -2.101473 -2.101473 \n", - "18 469.197310 469.197310 470.734141 0.119671 0.119671 0.119671 \n", - "19 470.734141 470.734141 470.734141 -0.905783 -0.905783 -0.905783 \n", - "20 391.726096 391.726096 470.734141 -2.101473 -2.101473 -2.101473 \n", - "21 469.197310 469.197310 470.734141 0.119671 0.119671 0.119671 \n", - "22 470.734141 470.734141 470.734141 -0.905783 -0.905783 -0.905783 \n", - "23 391.726096 391.726096 470.734141 -2.101473 -2.101473 -2.101473 \n", + " theta size loss_train \\\n", + "0 21.589217270548623 1 532.979453 \n", + "1 -2.930612339764867 1 247.002956 \n", + "2 2.667185093303495 1 126.303250 \n", + "3 21.534862886995523 3 428.346651 \n", + "4 -3.2856867579965456 3 255.691957 \n", + "5 2.6105987644181523 3 214.494045 \n", + "6 21.303011895450787;-5.265543575122987 5 228.602451 \n", + "7 -2.967195956970522;-0.10303084459832784 5 246.886780 \n", + "8 2.8472358446081776;3.181877228219174 5 5.050724 \n", + "9 -4.997185434556597;1.97100171661893 7 6.567563 \n", + "10 -0.3135539734579376;-0.8466296174467215 7 126.355850 \n", + "11 3.3024607194164286;0.19256491303781179 7 6.453165 \n", + "12 -2.645075648188035;0.40042646234334933;-2.0378... 9 6.222295 \n", + "13 -3.071181811381864;-6.350839914339136;2.030330... 9 2.880427 \n", + "14 0.8166540308437522;3.1781084094213585;-2.10865... 9 5.033598 \n", + "15 -0.4710360148347207;-0.5880961667000076;-4.989... 11 6.222295 \n", + "16 9.659149966450054;9.846772636698656;-0.1153759... 11 2.880428 \n", + "17 2.111284332408068;0.4814748808300009;3.1954044... 11 5.033599 \n", + "18 -0.8961205388017053;-5.439581684562741;-2.0267... 13 5.912374 \n", + "19 19.505630796428246;-0.10236401658211655;2.0302... 13 2.880232 \n", + "20 2.1996473290308143;3.542983731088475;-2.307920... 13 4.728990 \n", + "21 0.31630659889596935;-2.8761247719330956;-1.011... 15 5.912268 \n", + "22 6.170996276888179;3.0768248258883824;0.9720747... 15 3.049501 \n", + "23 1.2472384958972085;1.8304820150635275;-2.35725... 15 4.678348 \n", "\n", - " mdl_train mdl_val mdl_test \n", - "0 643.003132 643.003132 643.003132 \n", - "1 443.861039 443.861039 443.861039 \n", - "2 197.415618 197.415618 197.415618 \n", - "3 545.716867 545.716867 545.716867 \n", - "4 466.159682 466.159682 466.159682 \n", - "5 291.262978 291.262978 291.262978 \n", - "6 480.678996 480.678996 480.678996 \n", - "7 480.570888 480.570888 480.570888 \n", - "8 389.072501 389.072501 389.072501 \n", - "9 -inf -inf -inf \n", - "10 -inf -inf -inf \n", - "11 -inf -inf -inf \n", - "12 492.991596 492.991596 492.991596 \n", - "13 494.036329 494.036329 494.036329 \n", - "14 407.448500 407.448500 407.448500 \n", - "15 492.595543 492.595543 492.595543 \n", - "16 494.132375 494.132375 494.132375 \n", - "17 415.124329 415.124329 415.124329 \n", - "18 498.876168 498.876168 498.876168 \n", - "19 500.413000 500.413000 500.413000 \n", - "20 421.404954 421.404954 421.404954 \n", - "21 502.074841 502.074841 502.074841 \n", - "22 503.611673 503.611673 503.611673 \n", - "23 424.603627 424.603627 424.603627 " + " loss_val loss_test maxloss R2_train R2_val \\\n", + "0 532.979453 532.979453 532.979453 2.220446e-16 2.220446e-16 \n", + "1 247.002956 247.002956 532.979453 1.110223e-16 1.110223e-16 \n", + "2 126.303250 126.303250 532.979453 0.000000e+00 0.000000e+00 \n", + "3 428.346651 428.346651 428.346651 1.963168e-01 1.963168e-01 \n", + "4 255.691957 255.691957 428.346651 -3.517772e-02 -3.517772e-02 \n", + "5 214.494045 214.494045 428.346651 -6.982464e-01 -6.982464e-01 \n", + "6 228.602451 228.602451 246.886780 5.710858e-01 5.710858e-01 \n", + "7 246.886780 246.886780 246.886780 4.703438e-04 4.703438e-04 \n", + "8 5.050724 5.050724 246.886780 9.600111e-01 9.600111e-01 \n", + "9 6.567563 6.567563 126.355850 9.876776e-01 9.876776e-01 \n", + "10 126.355850 126.355850 126.355850 4.884440e-01 4.884440e-01 \n", + "11 6.453165 6.453165 126.355850 9.489074e-01 9.489074e-01 \n", + "12 6.222295 6.222295 6.222295 9.883254e-01 9.883254e-01 \n", + "13 2.880427 2.880427 6.222295 9.883385e-01 9.883385e-01 \n", + "14 5.033598 5.033598 6.222295 9.601467e-01 9.601467e-01 \n", + "15 6.222295 6.222295 6.222295 9.883254e-01 9.883254e-01 \n", + "16 2.880428 2.880428 6.222295 9.883385e-01 9.883385e-01 \n", + "17 5.033599 5.033599 6.222295 9.601467e-01 9.601467e-01 \n", + "18 5.912374 5.912374 5.912374 9.889069e-01 9.889069e-01 \n", + "19 2.880232 2.880232 5.912374 9.883393e-01 9.883393e-01 \n", + "20 4.728990 4.728990 5.912374 9.625584e-01 9.625584e-01 \n", + "21 5.912268 5.912268 5.912268 9.889071e-01 9.889071e-01 \n", + "22 3.049501 3.049501 5.912268 9.876540e-01 9.876540e-01 \n", + "23 4.678348 4.678348 5.912268 9.629594e-01 9.629594e-01 \n", + "\n", + " R2_test mdl_train mdl_val mdl_test \n", + "0 2.220446e-16 532.979453 532.979453 532.979453 \n", + "1 1.110223e-16 247.002956 247.002956 247.002956 \n", + "2 0.000000e+00 126.303250 126.303250 126.303250 \n", + "3 1.963168e-01 431.642488 431.642488 431.642488 \n", + "4 -3.517772e-02 258.987794 258.987794 258.987794 \n", + "5 -6.982464e-01 217.789882 217.789882 217.789882 \n", + "6 5.710858e-01 235.533923 235.533923 235.533923 \n", + "7 4.703438e-04 253.818252 253.818252 253.818252 \n", + "8 9.600111e-01 11.982196 11.982196 11.982196 \n", + "9 9.876776e-01 16.271623 16.271623 16.271623 \n", + "10 4.884440e-01 136.059911 136.059911 136.059911 \n", + "11 9.489074e-01 16.157226 16.157226 16.157226 \n", + "12 9.883254e-01 20.707237 20.707237 20.707237 \n", + "13 9.883385e-01 17.365369 17.365369 17.365369 \n", + "14 9.601467e-01 19.518540 19.518540 19.518540 \n", + "15 9.883254e-01 23.926112 23.926112 23.926112 \n", + "16 9.883385e-01 20.584245 20.584245 20.584245 \n", + "17 9.601467e-01 22.737416 22.737416 22.737416 \n", + "18 9.889069e-01 26.835067 26.835067 26.835067 \n", + "19 9.883393e-01 23.802925 23.802925 23.802925 \n", + "20 9.625584e-01 25.651682 25.651682 25.651682 \n", + "21 9.889071e-01 30.053836 30.053836 30.053836 \n", + "22 9.876540e-01 27.191070 27.191070 27.191070 \n", + "23 9.629594e-01 28.819917 28.819917 28.819917 " ] }, - "execution_count": 3, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# We create a Pyeggp regressor leaving all hyperparameters to default.\n", - "reg = eggp.EGGP(nParams=0)\n", + "reg = eggp.EGGP()\n", "reg.fit_mvsr(full_X, full_y)\n", "\n", "# Here we collect the full table of results and plot it\n", @@ -745,30 +771,6 @@ "pareto" ] }, - { - "cell_type": "code", - "execution_count": 5, - "id": "e79f0505-d95f-4ebd-945a-d42e52ed10de", - "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "exceptions must derive from BaseException", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[5]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mreg\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget_model\u001b[49m\u001b[43m(\u001b[49m\u001b[32;43m30\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 2\u001b[39m \u001b[38;5;66;03m#reg.evaluate_model_view(full_X[0], 4, 0)\u001b[39;00m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/.local/pyenv/lib/python3.13/site-packages/eggp/__init__.py:497\u001b[39m, in \u001b[36mEGGP.get_model\u001b[39m\u001b[34m(self, idx)\u001b[39m\n\u001b[32m 495\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m''' Get a `model` function and its visual representation. From srbench. '''\u001b[39;00m\n\u001b[32m 496\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m idx \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m np.unique(\u001b[38;5;28mself\u001b[39m.results.id.values):\n\u001b[32m--> \u001b[39m\u001b[32m497\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mInvalid model index\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 499\u001b[39m alphabet = \u001b[38;5;28mlist\u001b[39m(string.ascii_uppercase)\n\u001b[32m 500\u001b[39m row = \u001b[38;5;28mself\u001b[39m.results[\u001b[38;5;28mself\u001b[39m.results[\u001b[33m'\u001b[39m\u001b[33mid\u001b[39m\u001b[33m'\u001b[39m]==idx].iloc[\u001b[32m0\u001b[39m]\n", - "\u001b[31mTypeError\u001b[39m: exceptions must derive from BaseException" - ] - } - ], - "source": [ - "reg.get_model(30)\n", - "#reg.evaluate_model_view(full_X[0], 4, 0)" - ] - }, { "cell_type": "markdown", "id": "98eb4e69-f443-410d-a021-079589c4d2f0", @@ -779,13 +781,13 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 11, "id": "88ebec1e-f915-40be-b210-0063261c139d", "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
    " ] @@ -828,7 +830,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 12, "id": "649d0314-fc9e-43e8-801b-bd3d658ef413", "metadata": {}, "outputs": [ @@ -837,12 +839,12 @@ "output_type": "stream", "text": [ "Best solution :\n", - " f(X0) = (A + (X0 * ((X0 * B) + C)))\n" + " f(X0) = ((X0 + A) * (B - (X0 * C)))\n" ] }, { "data": { - "image/png": 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", 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+30.6,4.934,0.656 +30.6,5.068,0.657 +30.6,5.17,0.659 +30.6,5.223,0.656 +30.6,5.255,0.652 +30.6,5.342,0.657 +30.6,5.344,0.657 +30.6,5.394,0.659 +30.6,5.428,0.659 +30.6,5.444,0.661 +30.6,5.516,0.657 +30.6,5.541,0.659 +30.6,5.559,0.657 +30.6,5.776,0.659 +30.6,5.81,0.659 +30.6,5.863,0.657 +30.6,5.916,0.659 +30.6,5.962,0.65 +30.6,6,0.659 diff --git a/test/test_pyeggp.py b/test/test_pyeggp.py index 0b3db16..d190927 100644 --- a/test/test_pyeggp.py +++ b/test/test_pyeggp.py @@ -15,7 +15,7 @@ X = Z[:,:-1] y = Z[:,-1] -reg = EGGP(100, 100, 10, 3, 0.9, 0.3, "add,sub,mul,div,log", "MSE", 50, 2, -1, 3, True, False, False, "", "") +reg = EGGP(100, 100, 10, 3, 0.9, 0.3, "add,sub,mul,div,log", "LOG10", 50, 2, -1, 3, True, False, False, "", "") reg.fit(X, y) print(reg.score(X, y)) diff --git a/tutorials/ICLR.tar.gz b/tutorials/ICLR.tar.gz new file mode 100644 index 0000000000000000000000000000000000000000..340fff7d02ed3654fe066d17bb68129e7be8324b GIT binary patch literal 344776 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zQIoIeIxe#iKItDHe7m%|HEX_j9LYS=j3t?$8`!l*2k(^0oAZU+WyaEx>*q{0Al>1A znE!VjRJ-z=M;8@o6o%2gtrbt%UT*j1ca4Px$~C_+o#i(;CiMf2r9xt8y4#|F{ z1vVAFc?{Di>#3oRmx@|WZEpn3$ym1Nyw;NEv}(5%m;PI(2ku+ll4mLIFI?l@lb$}j zc~BwAn^tz)C$YJK2T_x?fetE*I}=(DjEqwHEs5}=K3O-+gIh?4e@o4q^8@Uplum=8 z_D)HGxgNzN{e!t>r`3RUYi6RiYa+sS!`CYg4P5hoqpNE!+4;3@LDYGg_!c__2o`i6 z)6s}al5CZ_)1nJ@6d8MFYJjuUd3H&+F;h2b*${kr=4In)Z%M5G!*kg z;iK_JGb^oso~W0kQq!xJ6=|q^295{Qdh{YV!C`oDNb`kB>wd1?{fFP%o+z!y8svWH zxzSHQ7ld&mq+!;%y!A$0-_c{`rgoc_N!tz!C1GzdwegJ`TIHRY{!ES{wzdu8@0-?` z1*i8f%()i%w_Ir3hze*0R=1Nf)jD0` zF1FRQ6ZFOJ%jONuP2bVAM>TRH z|1GK8?x3yi$UW^5ZP~!5l^cP^T^alJSs#D&$#tzApSlbFt^;vKb{zG6)a&@geSMxV z|FipJP|KcyR)L?6h)FXc+mA||!mVahGM{A9s&MAMK~h0Q4P|YIWLt|5)z. + +On the other hand, redundancy can be helpful. Sometimes, navigating from $x+x$ to $3x-x$ can be a "stepping stone" to reach a new, better area of the search space. This is known as the **neutral space theory**. + +But what if we could detect _all_ equivalent expressions in real-time and use that knowledge to make the search efficient? + +## A Database for Math Expressions: Enters the e-graph + +The solution lies in **e-graphs** and **Equality Saturation** . + +Think of an e-graph as a **smart database system** for mathematical expressions. It’s designed to store many different, but equivalent, expressions with a minimum amount of space. It also makes it easier to query for expressions with certain patterns. + +In an e-graph, symbols (like $+, -, x, \log$) are called **e-nodes**. The core concept is the **e-class**, which acts as an **Equivalence Group**. Any e-node belonging to the same e-class represents a mathematically identical value. + +For example, in the figure below, the dashed box in the middle is an e-class. It contains two e-nodes: one for multiplication (`2x`) and one for addition (`x+x`). Because they are in the same e-class, the graph automatically knows that $2x = x+x$. + +{% include figure.liquid path="assets/img/2026-04-27-egraph-symreg/blog1.png" class="img-fluid" %} + +This structure is immensely powerful. Now, when the graph builds a larger expression, like a term squared (the very top multiplication operator in this e-graph), it knows it can be represented in four different ways instantly: + +$$ +(2x) (2x) \\ +(2x) (x+x) \\ +(x+x) (2x) \\ +(x+x)(x+x) +$$ + +The E-graph stores all four, but only pays the storage cost for one! + +## Equality Saturation: Automatically Generating Equivalence + +How does the E-graph learn what's equivalent? It uses an algorithm called **Equality Saturation**. This process takes a simple set of mathematical rules (like the distributive property or $a+a=2a$) and applies them automatically until no new equivalences can be found (or until a time limit is reached). + +Let’s watch it work on the expression $(x+x)^2$ using three simple rules: + +$$ +\alpha + \alpha \rightarrow 2\alpha \\ +\alpha \times \alpha \rightarrow \alpha^2 \\ +\alpha \times (\beta + \gamma) \rightarrow (\alpha \times \beta + \alpha \times \gamma) +$$ + +1. **Start:** Insert $(x+x)^2$ into the graph. + {% include figure.liquid path="assets/img/2026-04-27-egraph-symreg/blog2.png" class="img-fluid" %} + +2. **Apply Rules:** The rule $\alpha + \alpha \rightarrow 2\alpha$ applies to the inner expression $x+x$: + +{% include figure.liquid path="assets/img/2026-04-27-egraph-symreg/blog3.png" class="img-fluid" %} + +3. We **insert** the right-hand side, $2x$ and **merge** with the e-class for $x+x$, as the graph knows they are identical: +{% include figure.liquid path="assets/img/2026-04-27-egraph-symreg/blog4.png" class="img-fluid" %} + +4. **Repeat until Saturation:** The process continues, applying other rules until the E-graph contains every possible equivalent expression derived from these rules: + +{% include figure.liquid path="assets/img/2026-04-27-egraph-symreg/blog5.png" class="img-fluid" %} + +The most popular implementation of equality saturation is Egg, a library written in Rust. +But, how can e-graphs and equality saturation help with symbolic regression search?? + +## The GPS for Symbolic Regression: e-graphs! + +A few years ago, some authors realized this powerful mechanism could be the missing piece in Symbolic Regression and **pioneered** their integration in different situations. + +First, they demonstrated that e-graphs are a superior **simplification tool** compared to standard methods like `sympy` . By simplifying equations with equality saturation, we not only reduced model complexity but also increased the probability of finding the best-fitting local optima . + +Second, the e-graph structure could be used to analyze how **inefficient** standard search algorithms like Genetic Programming were under limited budget, showing how often they revisited the same expressions . + +At this point, there was much more we could do with e-graphs in SR... + +## Generating uniqueness + +In a recent work on **e-graph genetic programming** ([**eggp**](https://github.com/folivetti/eggp)) , the authors turned the e-graph into a **database and guidance system** for equation discovery. + +Remember how genetic programming works: +- Create initial random expressions +- Repeat: + * Select two expressions proportional to their performance + * Combine parts of these expressions generating a new expression + * Replace a part of this expression with a random variation + +As stated before, this can be inefficient since we can generate many equivalent expressions during the process . But, what if we store every generated expression into a single e-graph and run the equality saturation algorithm? + +For once, we would have a database system allowing us to query whether a given expression was already visited, even in an equivalent form. But also, we can use this information to enforce the generation of new expressions! + +{% include figure.liquid path="assets/img/2026-04-27-egraph-symreg/blog6.png" class="img-fluid" %} + +It works like this, imagine that the current state of the search is the e-graph above! The green e-classes are the root of the already evaluated expressions. +Let's say that GP decides to recombine the expressions $x + \sqrt{x}$ and $x + 2x$, choosing to replace $\sqrt{x}$ of the first expression with something else from the second. +The choices of recombination are $\{x+x, x+2, x+2x, x+x+2x\}$. We can query each one of these choices to verify whether they already exist in the e-graph. If they do and were already evaluated, we discard them! + +Similarly, we can do the same for the mutation. let's suppose we will mutate the expression $x + \sqrt{x}$ by replacing $\sqrt{x}$ with a random expression. If we are unlucky, we may generate the expression $2x$, thus forming $x+2x$, which was already evaluated. After detecting the duplicate, we can change the multiplication in $2x$ with any binary operator that would generate a new expression! + +## Explore the Search Space with `eggp` and `rEGGression` + +You can start using this algorithm right now! Here is how you can install the library and use it to find an equation for a real-world fluid dynamics dataset. +You can install eggp with `pip`: + +```bash +pip install eggp +``` + +### Finding a Formula +This example uses `eggp` to find a relationship for fluid dynamics data the `nikuradse_1.csv` dataset (see the tutorials at this [link](https://github.com/folivetti/eggp/tree/main/tutorials)). + +```python +from eggp import EGGP +import pandas as pd + +pd.set_option('display.max_colwidth', 100) +df = pd.read_csv("datasets/nikuradse_1.csv") + +model = EGGP(gen=100, nPop=100, maxSize=15, nTournament=5, pc=0.8, pm=0.2, nonterminals='add,sub,mul,div,power,exp,log', loss='MSE', simplify=True, dumpTo='regression_example.egg') + +model.fit(df[['r_k', 'log_Re']], df['target']) +print(model.results[['Expression', 'loss_train', 'loss_val', 'size']]) +``` + +After running the search, the final e-graph (stored in `regression_example.egg`) contains the entire history of visited, unique solutions. This can be used to resume the search with different settings, such as a different nonterminal set: + +```python +model = EGGP(gen=100, nPop=100, maxSize=15, nTournament=5, pc=0.8, pm=0.2, nonterminals='add,sub,mul,div,power,exp,log,sin,tanh', loss='MSE', loadFrom='regression_example.egg') + +model.fit(df[['r_k', 'log_Re']], df['target']) + +print("\nLast population resumed from the first Pareto front: ") +print(model.results[['Expression', 'loss_train', 'loss_val', 'size']]) +``` +### Interactive Model Selection with `rEGGression` + +This e-graph can be further explored with the [rEGGression](https://github.com/folivetti/reggression) tool . An e-graph explorer for Symbolic Regression. + +```python +from reggression import Reggression + +egg = Reggression(dataset="datasets/nikuradse_1.csv", loadFrom="regression_example.egg", loss="MSE") +print(egg.top(5, pattern="v0 ^ v0") +``` + +This will retrieve the top 5 expressions that follow the pattern $\alpha^\alpha$, such as $x^x$ or $\log((x+5)^{x+5}) + 3$. The result is a list of the best-performing models matching your structural criteria: + +| Expression | Fitness | Size | +|---------------|--------|----| +| $\left({\operatorname{log}({log_{Re}^{log_{Re}}})^{\theta_{0}}} \cdot r_{k}\right)^{\theta_{1}}$ | -0.001514 | 10 | +| $\left(\left({log_{Re}^{log_{Re}}} \cdot \theta_{0}\right) + \frac{\theta_{1}}{\operatorname{log}(r_{k})}\right)$ | -0.001567 | 10 | +| $\left(\frac{\operatorname{log}({log_{Re}^{log_{Re}}})}{\left(\theta_{0} \cdot r_{k}\right)} + \theta_{1}\right)$ | -0.004623 | 10 | +| $\left(\frac{\left(r_k + \theta_0\right)^{\theta_1}}{\log(r_k)^{\log(r_k)}} + \theta_2\right)$ | -0.005701 | 13 | +| $\left(\operatorname{log}({log_{Re}^{log_{Re}}}) \cdot r_{k}\right)^{\theta_{0}}$ | -0.010011 | 8 | + +Or retrieving the top-5 expressions **not** having the pattern $\log(v)$: + +```python +print(egg.top(5, pattern="log(v0)", negate=True) +``` +| Expression | Fitness | Size | +|---------------|--------|----| +| $\left(\left(\theta_0 \cdot r_k\right)^{\theta_1}^{log_{Re}} \cdot \theta_2\right)$ | -0.001131 |11 | +| $\left({\left(log_{Re} \cdot \theta_{0}\right)^{\theta_{1}}} \cdot \left(r_{k} + \theta_{2}\right)\right)^{\theta_{3}}$ |-0.001187 |11 | +| \left(\frac{\left(r_k + \theta_0 \right)^{\theta_1}}{\left(\frac{\theta_2}{log_{Re}} + \theta_3\right)} + \theta_4\right)$ |-0.001190| 13 | +| $\left({\left(e^{\left(log_{Re} + \theta_{0}\right)} \cdot \theta_{1}\right)^{\theta_{2}}} \cdot r_{k}\right)^{\theta_{3}}$ | -0.001191 |12 | +| $\left(\theta_0 \cdot \left(\left(\left(log_{Re} \cdot log_{Re}\right) \cdot \theta_{1}\right) + r_{k}\right)\right)^{\theta_{2}}$ | -0.001192 |11| + +### Benchmark + +Running `eggp` and other SotA algorithms from the literature in a selection of real-world datasets, we can see that `eggp`stands out among the best algorithms, given the statistical test: + +{% include figure.liquid path="assets/img/2026-04-27-egraph-symreg/rank_mse.png" class="img-fluid" %} + +And promotes the smaller models among the top-performant algorithms: + +{% include figure.liquid path="assets/img/2026-04-27-egraph-symreg/rank_size.png" class="img-fluid" %} + +A more detailed pairwise comparison can be seen with a BBT plot: + +{% include figure.liquid path="assets/img/2026-04-27-egraph-symreg/bbt.png" class="img-fluid" %} + +## A Powerful Database for Equations: Using e-graphs and Equality Saturation for Interactive Equation Discovery + +For this experiment, we will use a variation of one of the benchmarks proposed by E. J. Vladislavleva et al: + +$$ +e^{-x/1.2}\, x^3 \left(\cos(x)\, \sin(x)^2 - 3.1415\right) +$$ + +Let's generate data points in the range $[0, 10]$ while adding a bit of Gaussian noise: + +```python +x = np.arange(0, 10, 0.05) +y = np.exp(-x/1.2)*x**3*(np.cos(x) \ + * np.sin(x)**2 - 3.1415) \ + + np.random.normal(0, 0.05, x.shape) +``` + +To make things a bit more interesting for this post, we will use just the middle part for training and the rest as a test set: + +```python +lb, ub = 2.1, 5 +x_sel = x[(x>lb) & (xlb) & (x=ub)].reshape(-1, 1) +y_ood = y[(x<=lb) | (x>=ub)] +``` + +Plotting the training set as red dots and the test set as green dots, we have: + +{% include figure.liquid path="assets/img/2026-04-27-egraph-symreg/vlad.svg" class="img-fluid" %} + + +We are, of course, making things harder for symbolic regression: +1. The relationship is nonlinear. +2. The training set is insufficient to guarantee an unique global optima. + +In any case, the purpose here is to show how we can use r🥚ression to explore alternative models. + +## Laying the egg 🥚 + +We can create an initial e-graph for this dataset using `eggp`. As mentioned [in the previous post](https://symreg.at/blog/2025/equality-saturation-and-symbolic-regression/), this algorithm uses e-graphs to enforce the generation of new expressions, avoiding redundancy in the search. + +```python +from eggp import EGGP +import pandas as pd + +reg = EGGP(gen=200, nPop=200, maxSize=25, \ + nonterminals="add,sub,mul,div,log,power,sin,cos,abs,sqrt", \ + simplify=True, optRepeat=2, optIter=20, folds=2, \ + dumpTo="vlad.egg") +reg.fit(x_sel, y_sel) +``` + +Some observations: +- The non-terminal set is large in order to generate many different alternative models. +- We are not running for a large number of iterations, so we could possibly find better models with proper settings. +- The maximum size is larger than the true equation. + +We are saving the final e-graph into the file named `vlad.egg` so we can explore it after the search. +Looking at the results we can see the Pareto front with different trade-offs of accuracy and size. + +| Math | size | loss_train | +|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------:|-------------:| +| $$\theta_{0}$$ | 1 | 0.360495 | +| $$\left(\theta_{0} + \text{cos}(x_{0})\right)$$ | 4 | 0.319114 | +| $$\left(\theta_{0} + \frac{\theta_{1}}{x_{0}}\right)$$ | 5 | 0.318433 | +| $$\left(\theta_{0} + (\left(\theta_{1} - x_{0}\right))^2\right)$$ | 6 | 0.0641624 | +| $$\left(\left(\text{cos}(x_{0}) \cdot \left(x_{0} + \theta_{0}\right)\right) + \theta_{1}\right)$$ | 8 | 0.0421559 | +| $$\left(\theta_{0} + \left(\text{cos}(\left(\theta_{1} + \text{cos}(x_{0})\right)) \cdot x_{0}\right)\right)$$ | 9 | 0.012507 | +| $$\left(\theta_{0} + \left(\text{cos}(\left(\theta_{1} + \text{cos}(x_{0})\right)) \cdot \left(\theta_{2} \cdot x_{0}\right)\right)\right)$$ | 11 | 0.00899634 | +| $$\left(\theta_{0} + \left(\text{cos}(\text{cos}(x_{0})) \cdot \left(\theta_{1} \cdot \text{cos}(\left(x_{0} + \theta_{2}\right))\right)\right)\right)$$ | 12 | 0.0046806 | +| $$\left(\theta_{0} - \left(\text{cos}(\text{cos}(x_{0})) \cdot \left(\theta_{1} \cdot \text{cos}(\left (\left(x_{0} + \theta_{2}\right)\right ))\right)\right)\right)$$ | 13 | 0.00481255 | +| $$\left(\theta_{0} + \left(\text{cos}(\text{cos}(x_{0})) \cdot \left(\theta_{1} \cdot \text{cos}(\left(\left(\theta_{2} - x_{0}\right) + \theta_{3}\right))\right)\right)\right)$$ | 14 | 0.00586963 | +| $$\left(\left(\text{cos}(\text{cos}(x_{0})) \cdot \left(\theta_{0} \cdot \text{cos}(\left(\left(\theta_{1} - \left(x_{0} + \theta_{2}\right)\right) + \theta_{3}\right))\right)\right) + \theta_{4}\right)$$ | 16 | 0.00547237 | + + +## Hatching the egg 🐣 + +Now, let's load the e-graph into `r🥚ression`: + +```python +from reggression import Reggression +egg = Reggression(dataset="vlad.csv", loadFrom="vlad.egg") +``` + +If we look at the top-5 models, we can see small variations of the top performing with similar fitness (negative MSE) values. + +```python +egg.top(5)[["Latex", "Fitness", "Size"]] +``` + +| Latex | Fitness | Size | +|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------:|-------:| +| $$\left(\left(\text{cos}(\text{cos}(x)) \cdot \left(\theta_{0} \cdot \text{cos}(\left(\left(\theta_{1} - \left(x + \theta_{2}\right)\right) + \theta_{3}\right))\right)\right) + \theta_{4}\right)$$ | -0.00415306 | 16 | +| $$\left(\theta_{0} + \left(\text{cos}(\text{cos}(x)) \cdot \left(\text{cos}(\left(\mid\mid\left(\left(x + \theta_{1}\right) + \theta_{2}\right)\mid\mid + \theta_{3}\right)) \cdot \theta_{4}\right)\right)\right)$$ | -0.00425244 | 18 | +| $$\left(\left(\left(\text{cos}(\text{cos}(x)) \cdot \left(\text{cos}(\left(\left(\theta_{0} - \left(x + \theta_{1}\right)\right) + \theta_{2}\right)) \cdot \theta_{3}\right)\right) + \theta_{4}\right) + \theta_{5}\right)$$ | -0.00430326 | 18 | +| $$\left(\theta_{0} + \left(\left(\text{cos}(\text{cos}(x)) \cdot \left(\text{cos}(\left(\mid\left(\sqrt{x}^2 + \theta_{1}\right)\mid + \theta_{2}\right)) \cdot \theta_{3}\right)\right) + \theta_{4}\right)\right)$$ | -0.00430774 | 19 | +| $$\left(\theta_{0} + \left(\text{cos}(\text{cos}(x)) \cdot \left(\theta_{1} \cdot \text{cos}(\left(\left(\theta_{2} - x\right) + \theta_{3}\right))\right)\right)\right)$$ | -0.0043503 | 14 | + +Some of these functions behave similarly while others display a different behavior when looking outside of the training region: + +{% include figure.liquid path="assets/img/2026-04-27-egraph-symreg/top5.svg" class="img-fluid" %} + +We can also plot the best models while limiting the maximum size: + +```python +model_top(egg.top(n=10, filters=["size <= 10"]), n, x, y) +``` +{% include figure.liquid path="assets/img/2026-04-27-egraph-symreg/top10.svg" class="img-fluid" %} + +We can see even more different behaviors compared to the previous plot but, sill, none of them are even close to the correct one :-( + +Since we are still far from the true expression, let us investigate the distribution of the tokens of the top 1000 generated expressions. + +```python +egg.distributionOfTokens(top=1000) +``` +This command returns a table with the number of times each token was used in the top expressions and the average fitness of the expressions that contains such token. The table is ordered by average fitness (negative MSE). + +| Pattern | Count | AvgFit | +|:-----------|--------:|------------:| +| x0 | 2604 | -0.00359749 | +| t0 | 1006 | -0.009312 | +| t1 | 981 | -0.00941213 | +| t2 | 955 | -0.00937039 | +| t3 | 806 | -0.00893546 | +| t4 | 466 | -0.00910986 | +| t5 | 144 | -0.00786632 | +| t6 | 1 | -0.013187 | +| Abs(v0) | 465 | -0.00810496 | +| Sin(v0) | 74 | -0.0115615 | +| Cos(v0) | 3029 | -0.00309273 | +| Sqrt(v0) | 32 | -0.00845579 | +| Square(v0) | 27 | -0.00967352 | +| Log(v0) | 10 | -0.00972384 | +| Exp(v0) | 45 | -0.0118458 | +| Cube(v0) | 38 | -0.00867039 | +| (v0 + v1) | 3405 | -0.00275121 | +| (v0 - v1) | 351 | -0.00848634 | +| (v0 * v1) | 2139 | -0.0042415 | +| (v0 / v1) | 68 | -0.00815694 | + +Apart from the first rows that displays the terminals, we can see that the absolute value function is frequently used and often contributes to a lower fitness, even though it is not present in the ground-truth expression. + +> When we have partial functions such as `log` and `sqrt`, the absolute value can help "fixing" invalid inputs. + +Sine and cosine are ranked next, but with cosine being more often used. The exponential is rarely used and particularly with a worse average fitness than the other tokens. The reason for this could be that fitting parameters inside an exponential function can be tricky depending on the initial values. + +We can verify that by plotting the top 5 expressions with the pattern $e^{\square_0}\square_1$ with the command: + +```python +egg.top(n=n, pattern="exp(v0)*v1") +``` + +{% include figure.liquid path="assets/img/2026-04-27-egraph-symreg/top5pat.svg" class="img-fluid" %} + +as we can see, still not a very good fit, as expected. + + +## With a little help from my friends 🐣🐤 + +We can try our luck with another SR method, such as Operon , and insert the obtained expressions into the e-graph: + +```python +from pyoperon.sklearn import SymbolicRegressor +regOp = SymbolicRegressor(objectives=['mse','length'], max_length=20, allowed_symbols='add,sub,mul,div,square,sin,cos,exp,log,sqrt,abs,constant,variable') +regOp.fit(x_sel, y_sel) +f = open("equations.operon", "w") +for eq in regOp.pareto_front_: + eqstr = regOp.get_model_string(eq['tree']) + fitness = -eq['mean_squared_error'] + print(f"{eqstr},{fitness},{fitness}", file=f) +f.close() +egg.importFromCSV("equations.operon") +``` +Plotting the top-5 expressions we get: + +{% include figure.liquid path="assets/img/2026-04-27-egraph-symreg/top5operon.svg" class="img-fluid" %} + +Still no luck! But we didn't make things easy for SR anyway! + +We can insert the ground-truth expression to see whether the parameter optimization is capable of converging to the true parameters and if the fitness is better than what we have. + +```python +egg.insert("exp(x0/t0)*(x0^3)*(cos(x0)*(sin(x0)^2)-t1)") +``` + +| Latex | Fitness | Parameters | +|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------:|:------------------------------------------| +| $$\left(\left({x^{3.0}} \cdot \left(\left(\text{cos}(x) \cdot {\text{sin}(x)^{2.0}}\right) + \theta_{0}\right)\right) \cdot e^{\left(x \cdot \theta_{1}\right)}\right)$$ | -0.00256414 | [-3.15, -0.83] | + +The answer is YES! We can get the ground-truth expression with enough iterations and a larger amount of luck :-) +Or, we can even resort to adding some constraints ... + + +### It's all the same, no matter where you are 🐥🐥🐥 + +We can also use r🥚ression to check whether two or more expressions are equivalent. Let's say we want to see whether $(x+3)^2 - 9$ and $x(x + 6)$ are the same. + +First, we create an empty e-graph: + +```python +newegg = Reggression(dataset="vlad.csv", loss="MSE") +``` + +Next, we add both expressions while storing their e-class ids: + +```python +eid1 = egg.insert("(x0 + 3)**2 - 9").Id.values[0] +eid2 = egg.insert("x0*(x0 + 6)").Id.values[0] +print(eid1, eid2) +> 6, 9 +``` + +Initially, their ids are going to be different, since until now they are distinct to each other as far as the e-graph is concerned. + +Now, the main idea is that we run equality saturation to produce all the equivalent forms of each one of these expressions following a set of rules, such as: + +$$ +(x + y)^2 \rightarrow x^2 + y^2 + 2xy +$$ + +> If the set of rules are sufficient to produce at least one common expression departing from the first and from the second expressions, they will eventually be merged, and their e-class id will become the same. + +We can run some iterations of equality saturation using the command: + +```python +egg.eqsat(5) +``` + +And, now, their ids should be the same! + +```python +print("Id of the first equation: \n", egg.report(eid1).loc[0:1, ["Info", "Training"]]) +print("Id of the second equation: \n", egg.report(eid2).loc[0:1, ["Info", "Training"]]) +> Id of the first equation: 16 +> Id of the second equation: 16 +``` + +After running equality saturation, we can also retrieve a sample of the equivalent expressions for that e-class id: + +```python +egg.getNExpressions(eid1, 10) +``` + +Leading to: + +$$((6.0 + x) * x)$$ +$$((x + 6.0) * x)$$ +$$((x * 6.0) + (x ^ 2))$$ +$$((x * 6.0) + (x ^ 2))$$ +$$(0.0 + ((6.0 + x) * x))$$ +$$(0.0 + ((x + 6.0) * x))$$ +$$((2.0 * (x * 3.0)) + (x ^ 2))$$ +$$((2.0 * (3.0 * x)) + (x ^ 2))$$ +$$(((x * 3.0) * 2.0) + (x ^ 2))$$ +$$(((3.0 * x) * 2.0) + (x ^ 2))$$ + +This can potentially be used to integrate e-graphs with other genetic programming algorithms or even reward based algorithms such as Monte Carlo Tree Search and Deep Reinforcement Learning , and LLMs . + +### Technical Details + +The e-graph implementation is available at the [Haskell Symbolic Regression](https://github.com/folivetti/srtree) library with some differences from [egg](https://docs.rs/egg/latest/egg/) to make it more convenient for symbolic regression and memory efficient. + +The SR algorithm [eggp](https://github.com/folivetti/eggp) already shows the potential of this integration, being capable of beating the state-of-the-art with a simple genetic programming framework. + +The [rEGGression](https://github.com/folivetti/reggression) Python library make it easy to explore the explored solutions and can be used as an interactive tool for a guided model selection. diff --git a/tutorials/ICLR/assets/bibliography/2026-04-27-egraph-symreg.bib b/tutorials/ICLR/assets/bibliography/2026-04-27-egraph-symreg.bib new file mode 100644 index 0000000..dc9a8c1 --- /dev/null +++ b/tutorials/ICLR/assets/bibliography/2026-04-27-egraph-symreg.bib @@ -0,0 +1,132 @@ +@inproceedings{de2023reducing, + title={Reducing overparameterization of symbolic regression models with equality saturation}, + author={de Franca, Fabricio Olivetti and Kronberger, Gabriel}, + booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, + pages={1064--1072}, + year={2023} +} + +@incollection{banzhaf2024combinatorics, + title={How the combinatorics of neutral spaces leads genetic programming to discover simple solutions}, + author={Banzhaf, Wolfgang and Hu, Ting and Ochoa, Gabriela}, + booktitle={Genetic Programming Theory and Practice XX}, + pages={65--86}, + year={2024}, + publisher={Springer} +} + +@inproceedings{tate2009equality, + title={Equality saturation: a new approach to optimization}, + author={Tate, Ross and Stepp, Michael and Tatlock, Zachary and Lerner, Sorin}, + booktitle={Proceedings of the 36th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages}, + pages={264--276}, + year={2009} +} + +@article{willsey2021egg, + title={Egg: Fast and extensible equality saturation}, + author={Willsey, Max and Nandi, Chandrakana and Wang, Yisu Remy and Flatt, Oliver and Tatlock, Zachary and Panchekha, Pavel}, + journal={Proceedings of the ACM on Programming Languages}, + volume={5}, + number={POPL}, + pages={1--29}, + year={2021}, + publisher={ACM New York, NY, USA} +} + +@inproceedings{kronberger2024inefficiency, + title={The inefficiency of genetic programming for symbolic regression}, + author={Kronberger, Gabriel and Olivetti de Franca, Fabricio and Desmond, Harry and Bartlett, Deaglan J and Kammerer, Lukas}, + booktitle={International Conference on Parallel Problem Solving from Nature}, + pages={273--289}, + year={2024}, + organization={Springer} +} + +@article{kronberger2024effects, + title={Effects of reducing redundant parameters in parameter optimization for symbolic regression using genetic programming}, + author={Kronberger, Gabriel and de Fran{\c{c}}a, Fabr{\'\i}cio Olivetti and others}, + journal={Journal of Symbolic Computation}, + volume={129}, + pages={16}, + year={2024} +} + +@inproceedings{de2025improving, + title={Improving genetic programming for symbolic regression with equality graphs}, + author={de Fran{\c{c}}a, Fabr{\'\i}cio Olivetti and Kronberger, Gabriel}, + booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, + pages={989--998}, + year={2025} +} + +@inproceedings{de2025reggression, + title={rEGGression: an Interactive and Agnostic Tool for the Exploration of Symbolic Regression Models}, + author={de Fran{\c{c}}a, Fabr{\'\i}cio Olivetti and Kronberger, Gabriel}, + booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, + pages={4--12}, + year={2025} +} + +@article{vladislavleva2008order, + title={Order of nonlinearity as a complexity measure for models generated by symbolic regression via pareto genetic programming}, + author={Vladislavleva, Ekaterina J and Smits, Guido F and Den Hertog, Dick}, + journal={IEEE Transactions on Evolutionary Computation}, + volume={13}, + number={2}, + pages={333--349}, + year={2008}, + publisher={IEEE} +} + +@inproceedings{operon, + title={Operon C++ an efficient genetic programming framework for symbolic regression}, + author={Burlacu, Bogdan and Kronberger, Gabriel and Kommenda, Michael}, + booktitle={Proceedings of the 2020 genetic and evolutionary computation conference companion}, + pages={1562--1570}, + year={2020} +} + +@article{shapeconstraints, + title={Shape-constrained symbolic regression—improving extrapolation with prior knowledge}, + author={Kronberger, Gabriel and de Fran{\c{c}}a, Fabricio Olivetti and Burlacu, Bogdan and Haider, Christian and Kommenda, Michael}, + journal={Evolutionary computation}, + volume={30}, + number={1}, + pages={75--98}, + year={2022}, + publisher={MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info~…} +} + +@inproceedings{kamienny2023deep, + title={Deep generative symbolic regression with monte-carlo-tree-search}, + author={Kamienny, Pierre-Alexandre and Lample, Guillaume and Lamprier, Sylvain and Virgolin, Marco}, + booktitle={International Conference on Machine Learning}, + pages={15655--15668}, + year={2023}, + organization={PMLR} +} + +@article{sun2022symbolic, + title={Symbolic physics learner: Discovering governing equations via monte carlo tree search}, + author={Sun, Fangzheng and Liu, Yang and Wang, Jian-Xun and Sun, Hao}, + journal={arXiv preprint arXiv:2205.13134}, + year={2022} +} + +@article{mundhenk2021symbolic, + title={Symbolic regression via deep reinforcement learning enhanced genetic programming seeding}, + author={Mundhenk, Terrell and Landajuela, Mikel and Glatt, Ruben and Santiago, Claudio P and Petersen, Brenden K and others}, + journal={Advances in Neural Information Processing Systems}, + volume={34}, + pages={24912--24923}, + year={2021} +} + +@article{shojaee2024llm, + title={Llm-sr: Scientific equation discovery via programming with large language models}, + author={Shojaee, Parshin and Meidani, Kazem and Gupta, Shashank and Farimani, Amir Barati and Reddy, Chandan K}, + journal={arXiv preprint arXiv:2404.18400}, + year={2024} +} + diff --git a/tutorials/ICLR/assets/img/2026-04-27-egraph-symreg/bbt.png b/tutorials/ICLR/assets/img/2026-04-27-egraph-symreg/bbt.png new file mode 100644 index 0000000000000000000000000000000000000000..7a54542fada474febffcf22392e7bcc25faf477b GIT binary patch literal 11252 zcmdsd2UJtrx;0`!M7n}NM4BK?nn379AoSj)D7{1IMZgLKFvtM`sUp3DNN<)%D4|K{ zJye7ACjISj4xD@6x%VIUj{m+l{_(>aBgxKMYwx}G`sSS9`~uTdhY(+(y@H2_N35hM zr;Ueq0gQ)t&hGMg;K)ngx+UO+!BSCM4G+(s6%X&p3p~6%;Lwu=JUlO6JUr|}JUr0^ zJUnW*gvtS&DGV_ z#l^+Z(b2}n#;&d|V8Xh(y6*1o*x1;|j~}b4saacFzkBx%3WaWLY#-hW)`SAJs#G=nH-{ie1{xmkTAZ`h} z%&JAgsb}FIhx$IkByMT#^~yIaEiA5Jd3~$CZLk5^6BM1tCTmyP)K%8hQB>bLJUral 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a/tutorials/regression_example.py +++ b/tutorials/regression_example.py @@ -21,7 +21,7 @@ model.fit(df[['r_k', 'log_Re']], df['target']) print("Multi-objective mode returning the Pareto front without simplifcation: ") -print(model.results[['Expression', 'loss_train', 'loss_val', 'size']]) +print(model.results[['Expression', 'loss_train', 'loss_val', 'size', 'mdl_train']]) # Running with simplification enabled. Notice that the size used to determine the pareto front is before simplification. # It is expected to see two elements on the front with the same size. @@ -30,7 +30,7 @@ model.fit(df[['r_k', 'log_Re']], df['target']) print("\nPareto front with simplifcation enabled: ") -print(model.results[['Expression', 'loss_train', 'loss_val', 'size']]) +print(model.results[['Expression', 'loss_train', 'loss_val', 'size', 'mdl_train']]) # Let's try the generational approach, where the entire population is replaced by the offspring at each generation. model = EGGP(gen=100, nPop=100, maxSize=15, nTournament=5, pc=0.8, pm=0.2, nonterminals='add,sub,mul,div,power,exp,log', loss='MSE', optIter=100, optRepeat=5, nParams=2, folds=2, max_time=120, simplify=True, generational=True) @@ -38,7 +38,7 @@ model.fit(df[['r_k', 'log_Re']], df['target']) print("\nLast population of generational approach with simplifcation: ") -print(model.results[['Expression', 'loss_train', 'loss_val', 'size']]) +print(model.results[['Expression', 'loss_train', 'loss_val', 'size', 'mdl_train']]) # 'loadFrom' argument enable us to start the search from a previously saved search. # Notice that the egg file will contain ALL the evaluated expressions, not only the final population. @@ -48,4 +48,4 @@ model.fit(df[['r_k', 'log_Re']], df['target']) print("\nLast population resumed from the first Pareto front: ") -print(model.results[['Expression', 'loss_train', 'loss_val', 'size']]) +print(model.results[['Expression', 'loss_train', 'loss_val', 'size', 'mdl_train']]) From 959fafa5b2f8f9a17b5a6ea1b8f5d357256bd61c Mon Sep 17 00:00:00 2001 From: Fabricio Olivetti de Franca Date: Thu, 29 Jan 2026 16:56:52 -0300 Subject: [PATCH 106/166] Conditionalize extra include and lib dirs for Darwin --- cabal.project | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/cabal.project b/cabal.project index ba0e547..014d4a3 100644 --- a/cabal.project +++ b/cabal.project @@ -12,6 +12,7 @@ source-repository-package location: https://github.com/folivetti/srtree tag: fdb0a27b7ba3b5da9e51a13c5ec5e1870ff78fd8 -package * - extra-include-dirs: /opt/homebrew/include - extra-lib-dirs: /opt/homebrew/lib +if os(darwin) + package * + extra-include-dirs: /opt/homebrew/include + extra-lib-dirs: /opt/homebrew/lib From fb02d7626133bae60c0f7ce10ec45d63ff2ea466 Mon Sep 17 00:00:00 2001 From: Fabricio Olivetti de Franca Date: Thu, 29 Jan 2026 17:15:21 -0300 Subject: [PATCH 107/166] Disable Windows runner in release.yml Comment out Windows job configuration in release workflow --- .github/workflows/release.yml | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 84ac979..9061657 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -71,11 +71,11 @@ jobs: runner: "macos-14" arch: "arm64" plat: "macosx" - - os: - name: "Windows" - runner: "windows-latest" - arch: "AMD64" - plat: "win" + #- os: + # name: "Windows" + # runner: "windows-latest" + # arch: "AMD64" + # plat: "win" steps: - uses: actions/checkout@v6 From de5a965f59d486718a659da63f822e1f935c806a Mon Sep 17 00:00:00 2001 From: folivetti Date: Fri, 30 Jan 2026 13:55:02 -0300 Subject: [PATCH 108/166] log10 mse --- cabal.project | 2 +- eggp.cabal | 2 +- pyproject.toml | 2 +- src/eggp.egg-info/PKG-INFO | 2 +- src/eggp/__init__.py | 2 +- tutorials/datatemp_h9x3lznb.csv | 363 +++++++++++++++++++++++++++++++ tutorials/datatemp_xthk49id.csv | 363 +++++++++++++++++++++++++++++++ tutorials/regression_example.egg | Bin 3513574 -> 3251288 bytes 8 files changed, 731 insertions(+), 5 deletions(-) create mode 100644 tutorials/datatemp_h9x3lznb.csv create mode 100644 tutorials/datatemp_xthk49id.csv diff --git a/cabal.project b/cabal.project index 014d4a3..228d98b 100644 --- a/cabal.project +++ b/cabal.project @@ -10,7 +10,7 @@ source-repository-package source-repository-package type: git location: https://github.com/folivetti/srtree - tag: fdb0a27b7ba3b5da9e51a13c5ec5e1870ff78fd8 + tag: bfec40b865fd66d3b4a7570167f63257ca95b151 if os(darwin) package * diff --git a/eggp.cabal b/eggp.cabal index fd5db29..006abb7 100755 --- a/eggp.cabal +++ b/eggp.cabal @@ -5,7 +5,7 @@ cabal-version: 2.2 -- see: https://github.com/sol/hpack name: eggp -version: 1.0.11 +version: 1.0.12 synopsis: A general library to work with Symbolic Regression expression trees. description: A Symbolic Regression Tree data structure to work with mathematical expressions with support to first order derivative and simplification; category: Math, Data, Data Structures diff --git a/pyproject.toml b/pyproject.toml index 7407250..6cf0bed 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,7 +12,7 @@ build-backend = "setuptools.build_meta" [project] name = "eggp" -version = "1.0.11" +version = "1.0.12" authors = [{ name = "Fabricio Olivetti", email = "folivetti@users.noreply.github.com" }] description = "Python Wheels for eggp algorithm." readme = "README.md" diff --git a/src/eggp.egg-info/PKG-INFO b/src/eggp.egg-info/PKG-INFO index b6ee30d..7e9e18d 100644 --- a/src/eggp.egg-info/PKG-INFO +++ b/src/eggp.egg-info/PKG-INFO @@ -1,6 +1,6 @@ Metadata-Version: 2.4 Name: eggp -Version: 1.0.10 +Version: 1.0.12 Summary: Python Wheels for eggp algorithm. 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zU};zeR)CdYWmp~32TNUaJy;($hUDMU3{CzmtxujZyC+_7?=j9z^O1D&Vlpc0=O71h4jQiA1w62vW@MWPb~Dpa)9xJ@DSuYVxcdV zlZ>B&r{P6-3DOtKbu@jk+(F-k58)&D3_geS#zJ2#?-{2z7J6f$Hx_!6WSi+rvV#tg z-XyuA-5}?UByY3^`a}AXq($qX35LT6NI#P3M-shAq8CXPeDotJ3py*z33EaEl2iy? z5|)DHV0l;t(u<@TX!?;<3tby>-biYQZUh^{rjWiQ(U+tSjMI;#F6i!%ekAop_k#oB zAUF(;g!CbaJ|ssH)ZU(M&?L2S3_qw05LH|XX_8HIFVw)Z6DLRUN()E5p9p%!#VvqWXe$scp=79Ri zjXmUuI*M*`>wd>E@2Q_W*b`1Uqkb~Cb(DquNIhj~Kk=D5%8TuPiLUbMuBW`&E3Ubr uuHv7`ySt9^VSn Date: Sun, 1 Feb 2026 11:02:52 -0300 Subject: [PATCH 112/166] frac bayes fun complexity info --- src/EGGP.hs | 18 +++++++++++------- src/Search.hs | 18 ++++++++++++++++-- src/eggp/__init__.py | 15 +++++++++------ src/eggp/_binding.pyi | 2 +- src/eggp/binding.i | 6 +++--- src/eggp/typing.py | 2 +- 6 files changed, 41 insertions(+), 20 deletions(-) diff --git a/src/EGGP.hs b/src/EGGP.hs index 2e5b6b4..4c0dc86 100644 --- a/src/EGGP.hs +++ b/src/EGGP.hs @@ -115,17 +115,17 @@ hs_eggp_main = \ https://arxiv.org/abs/2501.17848\n" <> header "eggp - E-graph Genetic Programming for Symbolic Regression." ) -foreign export ccall hs_eggp_run :: CString -> CInt -> CInt -> CInt -> CInt -> CDouble -> CDouble -> CString -> CString -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CString -> CString -> CString -> IO CString +foreign export ccall hs_eggp_run :: CString -> CInt -> CInt -> CInt -> CInt -> CDouble -> CDouble -> CString -> CString -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CString -> CString -> CString -> CInt -> IO CString -hs_eggp_run :: CString -> CInt -> CInt -> CInt -> CInt -> CDouble -> CDouble -> CString -> CString -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CString -> CString -> CString -> IO CString -hs_eggp_run dataset gens nPop maxSize nTournament pc pm nonterminals loss optIter optRepeat nParams folds maxTime simplify trace generational dumpTo loadFrom varnames' = do +hs_eggp_run :: CString -> CInt -> CInt -> CInt -> CInt -> CDouble -> CDouble -> CString -> CString -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CString -> CString -> CString -> CInt -> IO CString +hs_eggp_run dataset gens nPop maxSize nTournament pc pm nonterminals loss optIter optRepeat nParams folds maxTime simplify trace generational dumpTo loadFrom varnames' useFracBayes = do dataset' <- peekCString dataset nonterminals' <- peekCString nonterminals loss' <- peekCString loss dumpTo' <- peekCString dumpTo loadFrom' <- peekCString loadFrom varnames <- peekCString varnames' - out <- eggp_run dataset' (fromIntegral gens) (fromIntegral nPop) (fromIntegral maxSize) (fromIntegral nTournament) (realToFrac pc) (realToFrac pm) nonterminals' loss' (fromIntegral optIter) (fromIntegral optRepeat) (fromIntegral nParams) (fromIntegral folds) (fromIntegral maxTime) (simplify /= 0) (trace /= 0) (generational /= 0) dumpTo' loadFrom' varnames + out <- eggp_run dataset' (fromIntegral gens) (fromIntegral nPop) (fromIntegral maxSize) (fromIntegral nTournament) (realToFrac pc) (realToFrac pm) nonterminals' loss' (fromIntegral optIter) (fromIntegral optRepeat) (fromIntegral nParams) (fromIntegral folds) (fromIntegral maxTime) (simplify /= 0) (trace /= 0) (generational /= 0) dumpTo' loadFrom' varnames (useFracBayes /= 0) newCString out opt :: Parser Args @@ -238,12 +238,16 @@ opt = Args <> value "" <> showDefault <> help "comma separated variable names." ) + <*> switch + ( long "frac-bayes-complexity" + <> help "Use n_nodes * log unique_nodes as the second objective." + ) -eggp_run :: String -> Int -> Int -> Int -> Int -> Double -> Double -> String -> String -> Int -> Int -> Int -> Int -> Int -> Bool -> Bool -> Bool -> String -> String -> String -> IO String -eggp_run dataset gens nPop maxSize nTournament pc pm nonterminals loss optIter optRepeat nParams folds maxTime simplify trace generational dumpTo loadFrom varnames = +eggp_run :: String -> Int -> Int -> Int -> Int -> Double -> Double -> String -> String -> Int -> Int -> Int -> Int -> Int -> Bool -> Bool -> Bool -> String -> String -> String -> Bool -> IO String +eggp_run dataset gens nPop maxSize nTournament pc pm nonterminals loss optIter optRepeat nParams folds maxTime simplify trace generational dumpTo loadFrom varnames useFracBayes = case readMaybe loss of Nothing -> pure $ "Invalid loss function " <> loss - Just l -> let arg = Args dataset "" gens maxSize folds trace l optIter optRepeat nParams nPop nTournament pc pm nonterminals dumpTo loadFrom generational simplify maxTime varnames + Just l -> let arg = Args dataset "" gens maxSize folds trace l optIter optRepeat nParams nPop nTournament pc pm nonterminals dumpTo loadFrom generational simplify maxTime varnames useFracBayes in eggp arg eggp :: Args -> IO String diff --git a/src/Search.hs b/src/Search.hs index 2a30aca..e438300 100644 --- a/src/Search.hs +++ b/src/Search.hs @@ -86,7 +86,8 @@ data Args = Args _generational :: Bool, _simplify :: Bool, _maxtime :: Int, - _varnames :: String + _varnames :: String, + _useFracBayes :: Bool } deriving (Show) @@ -129,7 +130,11 @@ egraphGP dataTrainVals dataTests args = do then Prelude.mapM canonical newPop' else do let n_paretos = (_nPop args) `div` (_maxSize args) - pareto <- concat <$> (forM [1 .. _maxSize args] $ \n -> getTopFitEClassWithSize n 2) + pareto <- if (_useFracBayes args) + then getParetoFront + else concat <$> (forM [1 .. _maxSize args] $ \n -> getTopFitEClassWithSize n 2) + -- pareto <- concat <$> (forM [1 .. _maxSize args] $ \n -> getTopFitEClassWithSize n 2) + -- pareto <- getParetoFront let remainder = _nPop args - length pareto lft <- if full then getTopFitEClassThat remainder (const True) @@ -161,6 +166,15 @@ egraphGP dataTrainVals dataTests args = do isBin (Bin _ _ _) = True isBin _ = False + getParetoFront = do + let sortedDLs = sort [fromIntegral x * log (fromIntegral y) | x <- [2 .. _maxSize args], y <- [2 .. x]] + between x (a, b) = x >= a && x <= b + nParetos = 2 + pareto <- (concat <$> (forM (Prelude.zip sortedDLs (Prelude.tail sortedDLs)) $ + \(lo, hi) -> getTopFitEClassThat nParetos (\ec -> ((_dl . _info) ec) `between` (Just lo, Just hi)))) + >>= Prelude.mapM canonical + pure pareto + -- TODO: merge two or more egraphs cleanEGraph = do let nParetos = 10 -- (maxMem `div` 5) `div` _maxSize args io . putStrLn $ "cleaning" diff --git a/src/eggp/__init__.py b/src/eggp/__init__.py index e6eda54..c885edc 100644 --- a/src/eggp/__init__.py +++ b/src/eggp/__init__.py @@ -23,7 +23,7 @@ unsafe_hs_eggp_exit, ) -VERSION: str = "1.0.12" +VERSION: str = "1.0.13" _hs_rts_init: bool = False @@ -57,9 +57,9 @@ def main(args: List[str] = []) -> int: with hs_rts_init(args): return unsafe_hs_eggp_main() -def eggp_run(dataset: str, gen: int, nPop: int, maxSize: int, nTournament: int, pc: float, pm: float, nonterminals: str, loss: str, optIter: int, optRepeat: int, nParams: int, split: int, max_time : int, simplify: int, trace : int, generational : int, dumpTo: str, loadFrom: str, varnames : str) -> str: +def eggp_run(dataset: str, gen: int, nPop: int, maxSize: int, nTournament: int, pc: float, pm: float, nonterminals: str, loss: str, optIter: int, optRepeat: int, nParams: int, split: int, max_time : int, simplify: int, trace : int, generational : int, dumpTo: str, loadFrom: str, varnames : str, useFracBayes: int) -> str: with hs_rts_init(): - return unsafe_hs_eggp_run(dataset, gen, nPop, maxSize, nTournament, pc, pm, nonterminals, loss, optIter, optRepeat, nParams, split, max_time, simplify, trace, generational, dumpTo, loadFrom, varnames) + return unsafe_hs_eggp_run(dataset, gen, nPop, maxSize, nTournament, pc, pm, nonterminals, loss, optIter, optRepeat, nParams, split, max_time, simplify, trace, generational, dumpTo, loadFrom, varnames, useFracBayes) def make_function(expression, loss="MSE"): def func(x, t): @@ -170,7 +170,7 @@ class EGGP(BaseEstimator, RegressorMixin): >>> estimator = EGGP(loss="Bernoulli") >>> estimator.fit(X, y) """ - def __init__(self, gen = 100, nPop = 100, maxSize = 15, nTournament = 3, pc = 0.9, pm = 0.3, nonterminals = "add,sub,mul,div", loss = "MSE", optIter = 50, optRepeat = 2, nParams = -1, folds = 1, max_time = -1, simplify = False, trace = False, generational = False, dumpTo = "", loadFrom = ""): + def __init__(self, gen = 100, nPop = 100, maxSize = 15, nTournament = 3, pc = 0.9, pm = 0.3, nonterminals = "add,sub,mul,div", loss = "MSE", optIter = 50, optRepeat = 2, nParams = -1, folds = 1, max_time = -1, simplify = False, trace = False, generational = False, dumpTo = "", loadFrom = "", useFracBayes = False): nts = "add,sub,mul,div,power,powerabs,\ aq,abs,sin,cos,tan,sinh,cosh,tanh,\ asin,acos,atan,asinh,acosh,atanh,sqrt,\ @@ -208,6 +208,8 @@ def __init__(self, gen = 100, nPop = 100, maxSize = 15, nTournament = 3, pc = 0. raise TypeError('generational must be a boolean') if not isinstance(max_time, int): raise TypeError('max_time must be an integer') + if not isinstance(useFracBayes, bool): + raise TypeError('useFracBayes must be a boolean') self.gen = gen self.nPop = nPop self.maxSize = maxSize @@ -227,6 +229,7 @@ def __init__(self, gen = 100, nPop = 100, maxSize = 15, nTournament = 3, pc = 0. self.dumpTo = dumpTo self.loadFrom = loadFrom self.is_fitted_ = False + self.useFracBayes = int(useFracBayes) def combine_dataset(self, X, y, Xerr, yerr): ''' Combines the error information into a single dataset. @@ -316,7 +319,7 @@ def fit(self, X, y, Xerr = None, yerr = None): dname = self.get_fname(dataset, header) try: - csv_data = eggp_run(dname, self.gen, self.nPop, self.maxSize, self.nTournament, self.pc, self.pm, self.nonterminals, self.loss, self.optIter, self.optRepeat, self.nParams, self.folds, self.max_time, self.simplify, self.trace, self.generational, self.dumpTo, self.loadFrom, varnames) + csv_data = eggp_run(dname, self.gen, self.nPop, self.maxSize, self.nTournament, self.pc, self.pm, self.nonterminals, self.loss, self.optIter, self.optRepeat, self.nParams, self.folds, self.max_time, self.simplify, self.trace, self.generational, self.dumpTo, self.loadFrom, varnames, self.useFracBayes) finally: os.remove(dataset) @@ -361,7 +364,7 @@ def fit_mvsr(self, Xs, ys, Xerrs = None, yerrs = None): try: csv_data = eggp_run(" ".join(datasets), self.gen, self.nPop, self.maxSize, self.nTournament, self.pc, self.pm, - self.nonterminals, self.loss, self.optIter, self.optRepeat, self.nParams, self.folds, self.max_time, self.simplify, self.trace, self.generational, self.dumpTo, self.loadFrom, varnames) + self.nonterminals, self.loss, self.optIter, self.optRepeat, self.nParams, self.folds, self.max_time, self.simplify, self.trace, self.generational, self.dumpTo, self.loadFrom, varnames, self.useFracBayes) finally: for dataset in datasetsNames: os.remove(dataset) diff --git a/src/eggp/_binding.pyi b/src/eggp/_binding.pyi index 3f80a70..125f731 100644 --- a/src/eggp/_binding.pyi +++ b/src/eggp/_binding.pyi @@ -2,6 +2,6 @@ from typing import List def unsafe_hs_pyeggp_version() -> str: ... def unsafe_hs_pyeggp_main() -> int: ... -def unsafe_hs_pyeggp_run(dataset: str, gen: int, nPop: int, maxSize: int, nTournament: int, pc: float, pm: float, nonterminals: str, loss: str, optIter: int, optRepeat: int, nParams: int, split: int, max_time: int, simplify: int, trace : int, generational : int, dumpTo: str, loadFrom: str, varnames: str) -> str: ... +def unsafe_hs_pyeggp_run(dataset: str, gen: int, nPop: int, maxSize: int, nTournament: int, pc: float, pm: float, nonterminals: str, loss: str, optIter: int, optRepeat: int, nParams: int, split: int, max_time: int, simplify: int, trace : int, generational : int, dumpTo: str, loadFrom: str, varnames: str, useFracBayes: int) -> str: ... def unsafe_hs_pyeggp_init(args: List[str]) -> None: ... def unsafe_hs_pyeggp_exit() -> None: ... diff --git a/src/eggp/binding.i b/src/eggp/binding.i index 65eac26..cb22ad2 100644 --- a/src/eggp/binding.i +++ b/src/eggp/binding.i @@ -10,8 +10,8 @@ int unsafe_hs_eggp_main() { return hs_eggp_main(); } -char * unsafe_hs_eggp_run( char *dataset, int gens, int nPop, int maxSize, int nTournament, double pc, double pm, char *nonterminals, char *loss, int optIter, int optRepeat, int nParams, int split, int max_time, int simplify, int trace, int generational, char *dumpTo, char *loadFrom, char *varnames ) { - return hs_eggp_run(dataset, gens, nPop, maxSize, nTournament, pc, pm, nonterminals, loss, optIter, optRepeat, nParams, split, max_time, simplify, trace, generational, dumpTo, loadFrom, varnames); +char * unsafe_hs_eggp_run( char *dataset, int gens, int nPop, int maxSize, int nTournament, double pc, double pm, char *nonterminals, char *loss, int optIter, int optRepeat, int nParams, int split, int max_time, int simplify, int trace, int generational, char *dumpTo, char *loadFrom, char *varnames, int useFracBayes ) { + return hs_eggp_run(dataset, gens, nPop, maxSize, nTournament, pc, pm, nonterminals, loss, optIter, optRepeat, nParams, split, max_time, simplify, trace, generational, dumpTo, loadFrom, varnames, useFracBayes); } void unsafe_hs_eggp_init(int argc, char **argv) { @@ -59,6 +59,6 @@ void unsafe_py_write_stderr( char * str) { char * unsafe_hs_eggp_version(); int unsafe_hs_eggp_main(); -char * unsafe_hs_eggp_run( char *dataset, int gens, int nPop, int maxSize, int nTournament, double p, double pm, char *nonterminals, char *loss, int optIter, int optRepeat, int nParams, int split, int max_time, int simplify, int trace, int generational, char *dumpTo, char *loadFrom, char *varnames); +char * unsafe_hs_eggp_run( char *dataset, int gens, int nPop, int maxSize, int nTournament, double p, double pm, char *nonterminals, char *loss, int optIter, int optRepeat, int nParams, int split, int max_time, int simplify, int trace, int generational, char *dumpTo, char *loadFrom, char *varnames, int useFracBayes); void unsafe_hs_eggp_init(int argc, char **argv); void unsafe_hs_eggp_exit(); diff --git a/src/eggp/typing.py b/src/eggp/typing.py index 57d9569..2fa05d6 100644 --- a/src/eggp/typing.py +++ b/src/eggp/typing.py @@ -11,4 +11,4 @@ class Session(Protocol): def version(self) -> str: ... def main(self, args: List[str] = []) -> int: ... - def eggp_run(self, dataset: str, gen: int, nPop: int, maxSize: int, nTournament: int, pc: float, pm: float, nonterminals: str, loss: str, optIter: int, optRepeat: int, nParams: int, split: int, max_time: int, simplify: int, trace : int, generational : int, dumpTo: str, loadFrom: str, varnames: str) -> str: ... + def eggp_run(self, dataset: str, gen: int, nPop: int, maxSize: int, nTournament: int, pc: float, pm: float, nonterminals: str, loss: str, optIter: int, optRepeat: int, nParams: int, split: int, max_time: int, simplify: int, trace : int, generational : int, dumpTo: str, loadFrom: str, varnames: str, useFracBayes: int) -> str: ... From 99ac75129063e873904d2e92cf206406bb6735e8 Mon Sep 17 00:00:00 2001 From: folivetti Date: Mon, 2 Feb 2026 07:30:27 -0300 Subject: [PATCH 113/166] frac bayes fun complexity info --- cabal.project | 2 +- eggp.cabal | 2 +- pyproject.toml | 2 +- src/eggp.egg-info/PKG-INFO | 2 +- src/eggp/__init__.py | 2 +- 5 files changed, 5 insertions(+), 5 deletions(-) diff --git a/cabal.project b/cabal.project index 6ca5a0f..31543c9 100644 --- a/cabal.project +++ b/cabal.project @@ -10,7 +10,7 @@ source-repository-package source-repository-package type: git location: https://github.com/folivetti/srtree - tag: e1e88ecc1047d14f6557740773e571f49a7b4cf4 + tag: 2fce7aebc65b35b95890877e46bb9848a50f992a if os(darwin) package * diff --git a/eggp.cabal b/eggp.cabal index 006abb7..3845b4b 100755 --- a/eggp.cabal +++ b/eggp.cabal @@ -5,7 +5,7 @@ cabal-version: 2.2 -- see: https://github.com/sol/hpack name: eggp -version: 1.0.12 +version: 1.0.14 synopsis: A general library to work with Symbolic Regression expression trees. description: A Symbolic Regression Tree data structure to work with mathematical expressions with support to first order derivative and simplification; category: Math, Data, Data Structures diff --git a/pyproject.toml b/pyproject.toml index 6cf0bed..bd7aa1f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,7 +12,7 @@ build-backend = "setuptools.build_meta" [project] name = "eggp" -version = "1.0.12" +version = "1.0.14" authors = [{ name = "Fabricio Olivetti", email = "folivetti@users.noreply.github.com" }] description = "Python Wheels for eggp algorithm." readme = "README.md" diff --git a/src/eggp.egg-info/PKG-INFO b/src/eggp.egg-info/PKG-INFO index 7e9e18d..32646f8 100644 --- a/src/eggp.egg-info/PKG-INFO +++ b/src/eggp.egg-info/PKG-INFO @@ -1,6 +1,6 @@ Metadata-Version: 2.4 Name: eggp -Version: 1.0.12 +Version: 1.0.14 Summary: Python Wheels for eggp algorithm. Author-email: Fabricio Olivetti License: GNU GENERAL PUBLIC LICENSE diff --git a/src/eggp/__init__.py b/src/eggp/__init__.py index c885edc..828c065 100644 --- a/src/eggp/__init__.py +++ b/src/eggp/__init__.py @@ -23,7 +23,7 @@ unsafe_hs_eggp_exit, ) -VERSION: str = "1.0.13" +VERSION: str = "1.0.14" _hs_rts_init: bool = False From a4523c53474393cb18b6c9436fca39d3a469cb68 Mon Sep 17 00:00:00 2001 From: folivetti Date: Mon, 2 Feb 2026 08:36:16 -0300 Subject: [PATCH 114/166] frac bayes fun complexity info --- cabal.project | 2 +- eggp.cabal | 2 +- pyproject.toml | 2 +- src/eggp/__init__.py | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/cabal.project b/cabal.project index 31543c9..6b5ade1 100644 --- a/cabal.project +++ b/cabal.project @@ -10,7 +10,7 @@ source-repository-package source-repository-package type: git location: https://github.com/folivetti/srtree - tag: 2fce7aebc65b35b95890877e46bb9848a50f992a + tag: 1bbeba1eebe8e53f7ff4a43a00745e93e9fa18f7 if os(darwin) package * diff --git a/eggp.cabal b/eggp.cabal index 3845b4b..3a7cfeb 100755 --- a/eggp.cabal +++ b/eggp.cabal @@ -5,7 +5,7 @@ cabal-version: 2.2 -- see: https://github.com/sol/hpack name: eggp -version: 1.0.14 +version: 1.0.15 synopsis: A general library to work with Symbolic Regression expression trees. description: A Symbolic Regression Tree data structure to work with mathematical expressions with support to first order derivative and simplification; category: Math, Data, Data Structures diff --git a/pyproject.toml b/pyproject.toml index bd7aa1f..3f32668 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,7 +12,7 @@ build-backend = "setuptools.build_meta" [project] name = "eggp" -version = "1.0.14" +version = "1.0.15" authors = [{ name = "Fabricio Olivetti", email = "folivetti@users.noreply.github.com" }] description = "Python Wheels for eggp algorithm." readme = "README.md" diff --git a/src/eggp/__init__.py b/src/eggp/__init__.py index 828c065..32c7a52 100644 --- a/src/eggp/__init__.py +++ b/src/eggp/__init__.py @@ -23,7 +23,7 @@ unsafe_hs_eggp_exit, ) -VERSION: str = "1.0.14" +VERSION: str = "1.0.15" _hs_rts_init: bool = False From 5bd4bbf7cb4df6fc41749ffb1c846e964a1f67ee Mon Sep 17 00:00:00 2001 From: folivetti Date: Mon, 2 Feb 2026 09:22:45 -0300 Subject: [PATCH 115/166] frac bayes fun complexity info --- cabal.project | 2 +- eggp.cabal | 2 +- pyproject.toml | 2 +- src/eggp/__init__.py | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/cabal.project b/cabal.project index 6b5ade1..297cc1d 100644 --- a/cabal.project +++ b/cabal.project @@ -10,7 +10,7 @@ source-repository-package source-repository-package type: git location: https://github.com/folivetti/srtree - tag: 1bbeba1eebe8e53f7ff4a43a00745e93e9fa18f7 + tag: 1fb70211430dc021079b0de0a82396452f37a139 if os(darwin) package * diff --git a/eggp.cabal b/eggp.cabal index 3a7cfeb..b512584 100755 --- a/eggp.cabal +++ b/eggp.cabal @@ -5,7 +5,7 @@ cabal-version: 2.2 -- see: https://github.com/sol/hpack name: eggp -version: 1.0.15 +version: 1.0.16 synopsis: A general library to work with Symbolic Regression expression trees. description: A Symbolic Regression Tree data structure to work with mathematical expressions with support to first order derivative and simplification; category: Math, Data, Data Structures diff --git a/pyproject.toml b/pyproject.toml index 3f32668..e5b1784 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,7 +12,7 @@ build-backend = "setuptools.build_meta" [project] name = "eggp" -version = "1.0.15" +version = "1.0.16" authors = [{ name = "Fabricio Olivetti", email = "folivetti@users.noreply.github.com" }] description = "Python Wheels for eggp algorithm." readme = "README.md" diff --git a/src/eggp/__init__.py b/src/eggp/__init__.py index 32c7a52..c1c4e5d 100644 --- a/src/eggp/__init__.py +++ b/src/eggp/__init__.py @@ -23,7 +23,7 @@ unsafe_hs_eggp_exit, ) -VERSION: str = "1.0.15" +VERSION: str = "1.0.16" _hs_rts_init: bool = False From 5b154bab84c5ce153246ad2794bf3763fb866aeb Mon Sep 17 00:00:00 2001 From: folivetti Date: Mon, 9 Feb 2026 14:19:57 -0300 Subject: [PATCH 116/166] frac bayes fun complexity info --- src/Search.hs | 15 +++++++++++++-- 1 file changed, 13 insertions(+), 2 deletions(-) diff --git a/src/Search.hs b/src/Search.hs index e438300..7be4da9 100644 --- a/src/Search.hs +++ b/src/Search.hs @@ -166,9 +166,20 @@ egraphGP dataTrainVals dataTests args = do isBin (Bin _ _ _) = True isBin _ = False + sortedDLs = pickEvenly (_nPop args) $ sort [fromIntegral x * log (fromIntegral y) | x <- [2 .. _maxSize args], y <- [2 .. x]] + + pickEvenly :: Int -> [a] -> [a] + pickEvenly m xs + | m <= 0 = [] + | m == 1 = [head xs] + | m >= n = xs + | otherwise = [xs !! (floor (fromIntegral k * step)) | k <- [0 .. m - 1]] + where + n = length xs + step = fromIntegral (n - 1) / fromIntegral (m - 1) + getParetoFront = do - let sortedDLs = sort [fromIntegral x * log (fromIntegral y) | x <- [2 .. _maxSize args], y <- [2 .. x]] - between x (a, b) = x >= a && x <= b + let between x (a, b) = x >= a && x <= b nParetos = 2 pareto <- (concat <$> (forM (Prelude.zip sortedDLs (Prelude.tail sortedDLs)) $ \(lo, hi) -> getTopFitEClassThat nParetos (\ec -> ((_dl . _info) ec) `between` (Just lo, Just hi)))) From 29f6fd1073424065dac2564bbf6f2f1eda2e157f Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 2 Mar 2026 04:23:33 +0000 Subject: [PATCH 117/166] Bump actions/download-artifact from 7 to 8 Bumps [actions/download-artifact](https://github.com/actions/download-artifact) from 7 to 8. - [Release notes](https://github.com/actions/download-artifact/releases) - [Commits](https://github.com/actions/download-artifact/compare/v7...v8) --- updated-dependencies: - dependency-name: actions/download-artifact dependency-version: '8' dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] --- .github/workflows/release.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 9061657..05651b4 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -210,7 +210,7 @@ jobs: steps: - name: Download artifacts - uses: actions/download-artifact@v7 + uses: actions/download-artifact@v8 with: pattern: "wheelhouse-*-*" path: "./wheelhouse" @@ -241,7 +241,7 @@ jobs: steps: - name: Download artifacts - uses: actions/download-artifact@v7 + uses: actions/download-artifact@v8 with: pattern: "wheelhouse-*-*" path: "./wheelhouse" From c764ddc795948cb0d0be088a80882c2ccd6ca09c Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 9 Mar 2026 04:22:22 +0000 Subject: [PATCH 118/166] Bump pypa/cibuildwheel from 3.3.1 to 3.4.0 Bumps [pypa/cibuildwheel](https://github.com/pypa/cibuildwheel) from 3.3.1 to 3.4.0. - [Release notes](https://github.com/pypa/cibuildwheel/releases) - [Changelog](https://github.com/pypa/cibuildwheel/blob/main/docs/changelog.md) - [Commits](https://github.com/pypa/cibuildwheel/compare/v3.3.1...v3.4.0) --- updated-dependencies: - dependency-name: pypa/cibuildwheel dependency-version: 3.4.0 dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] --- .github/workflows/release.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 05651b4..ffa00db 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -172,7 +172,7 @@ jobs: with: platforms: "386" - - uses: pypa/cibuildwheel@v3.3.1 + - uses: pypa/cibuildwheel@v3.4.0 with: output-dir: "./wheelhouse" config-file: "{package}/pyproject.toml" From 999d84ac290aa60acb98c80b82451f912e20005f Mon Sep 17 00:00:00 2001 From: folivetti Date: Tue, 17 Mar 2026 10:43:45 -0300 Subject: [PATCH 119/166] py3.14 --- .github/workflows/release.yml | 2 +- eggp.cabal | 2 +- pyproject.toml | 3 ++- src/eggp/__init__.py | 2 +- 4 files changed, 5 insertions(+), 4 deletions(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 9061657..c18c7b6 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -172,7 +172,7 @@ jobs: with: platforms: "386" - - uses: pypa/cibuildwheel@v3.3.1 + - uses: pypa/cibuildwheel@v3.4.0 with: output-dir: "./wheelhouse" config-file: "{package}/pyproject.toml" diff --git a/eggp.cabal b/eggp.cabal index b512584..c284746 100755 --- a/eggp.cabal +++ b/eggp.cabal @@ -5,7 +5,7 @@ cabal-version: 2.2 -- see: https://github.com/sol/hpack name: eggp -version: 1.0.16 +version: 1.0.17 synopsis: A general library to work with Symbolic Regression expression trees. description: A Symbolic Regression Tree data structure to work with mathematical expressions with support to first order derivative and simplification; category: Math, Data, Data Structures diff --git a/pyproject.toml b/pyproject.toml index e5b1784..dfbe0a6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,7 +12,7 @@ build-backend = "setuptools.build_meta" [project] name = "eggp" -version = "1.0.16" +version = "1.0.17" authors = [{ name = "Fabricio Olivetti", email = "folivetti@users.noreply.github.com" }] description = "Python Wheels for eggp algorithm." readme = "README.md" @@ -27,6 +27,7 @@ classifiers = [ "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", "Programming Language :: Python :: 3.13", + "Programming Language :: Python :: 3.14", "Programming Language :: Python :: Implementation :: CPython", ] dependencies = ["typing_extensions >=4.6,<5; python_version<'3.8'", "numpy >=2", "pandas >=2.2", "scikit-learn >=1.6", "matplotlib >= 3"] diff --git a/src/eggp/__init__.py b/src/eggp/__init__.py index c1c4e5d..dc29d9a 100644 --- a/src/eggp/__init__.py +++ b/src/eggp/__init__.py @@ -23,7 +23,7 @@ unsafe_hs_eggp_exit, ) -VERSION: str = "1.0.16" +VERSION: str = "1.0.17" _hs_rts_init: bool = False From 40e2b136890f8590afa4d2e304a970ae6e0e2b49 Mon Sep 17 00:00:00 2001 From: folivetti Date: Thu, 30 Jul 2026 09:24:43 -0300 Subject: [PATCH 120/166] more optimization in srtree --- app/EGGPA.hs | 193 + app/Main.hs | 25 + cabal.project | 10 +- data.tsv | 103897 +++++++++++++++++++++++++++++ eggp.cabal | 45 +- output.csv | 101 + src/EGGP.hs | 10 +- src/Search.hs | 89 +- src/eggp.egg-info/PKG-INFO | 3 +- src/eggp/__init__.py | 18 +- src/eggp/binding.i | 6 + train.csv | 101 + tutorials/datasets/example0.csv | 15 + tutorials/datasets/example1.csv | 11 + tutorials/datasets/example3.csv | 11 + tutorials/mv_example.py | 26 + 16 files changed, 104509 insertions(+), 52 deletions(-) create mode 100644 app/EGGPA.hs create mode 100644 app/Main.hs create mode 100644 data.tsv create mode 100644 output.csv create mode 100644 train.csv create mode 100644 tutorials/datasets/example0.csv create mode 100644 tutorials/datasets/example1.csv create mode 100644 tutorials/datasets/example3.csv create mode 100644 tutorials/mv_example.py diff --git a/app/EGGPA.hs b/app/EGGPA.hs new file mode 100644 index 0000000..18793c2 --- /dev/null +++ b/app/EGGPA.hs @@ -0,0 +1,193 @@ +{-# LANGUAGE BlockArguments #-} +{-# LANGUAGE TupleSections #-} +{-# LANGUAGE MultiWayIf #-} +{-# LANGUAGE OverloadedStrings #-} +{-# LANGUAGE BangPatterns #-} +{-# LANGUAGE TypeSynonymInstances, FlexibleInstances #-} + +module EGGPA where + +import Algorithm.EqSat.Egraph +import Algorithm.EqSat.Simplify +import Algorithm.EqSat.Build +import Algorithm.EqSat.Queries +import Algorithm.EqSat.Info +import Algorithm.EqSat.DB +import Algorithm.SRTree.Likelihoods +import Algorithm.SRTree.ModelSelection +import Control.Lens (element, makeLenses, over, (&), (+~), (-~), (.~), (^.)) +import Control.Monad (foldM, forM_, forM, when, unless, filterM, (>=>), replicateM, replicateM_) +import Control.Monad.State.Strict +import Data.Maybe (fromJust, isNothing, isJust) +import Data.SRTree +import Data.SRTree.Datasets +import Data.SRTree.Eval +import Data.SRTree.Random (randomTree) +import Data.SRTree.Print +import System.Random +import qualified Data.HashSet as Set +import Data.List ( sort, maximumBy, intercalate, sortOn, intersperse, nub ) +import Data.IntSet (IntSet) +import qualified Data.IntSet as IntSet +import qualified Data.Sequence as FingerTree +import Data.Function ( on ) +import qualified Data.Foldable as Foldable +import qualified Data.IntMap as IntMap +import List.Shuffle ( shuffle ) +import Algorithm.SRTree.NonlinearOpt +import Data.Binary ( encode, decode ) +import qualified Data.ByteString.Lazy as BS + +import Algorithm.EqSat (runEqSat,applySingleMergeOnlyEqSat) + +import GHC.IO (unsafePerformIO) +import Control.Scheduler +import Control.Monad.IO.Unlift +import Data.SRTree (convertProtectedOps) +import Options.Applicative as Opt hiding (Const) + +import Search +import Algorithm.EqSat.SearchSR +import Data.SRTree.Random +import Data.SRTree.Datasets + +import System.Environment (getArgs) +import System.Exit (ExitCode (..)) +import Text.Read (readMaybe) +import Data.Version (showVersion) +import Control.Exception (Exception (..), SomeException (..), handle) + +opt :: Parser Args +opt = Args + <$> strOption + ( long "dataset" + <> short 'd' + <> metavar "INPUT-FILE" + <> help "CSV dataset." ) + <*> strOption + ( long "test" + <> short 't' + <> value "" + <> showDefault + <> help "test data") + <*> option auto + ( long "generations" + <> short 'g' + <> metavar "GENS" + <> showDefault + <> value 100 + <> help "Number of generations." ) + <*> option auto + ( long "maxSize" + <> short 's' + <> help "max-size." ) + <*> option auto + ( long "folds" + <> short 'k' + <> value 1 + <> showDefault + <> help "number of folds to determine the ratio of training-validation") + <*> switch + ( long "trace" + <> help "print all evaluated expressions.") + <*> option auto + ( long "loss" + <> value LeastSquares + <> showDefault + <> help "loss function: LeastSquares, Gaussian, Poisson, Bernoulli.") + <*> option auto + ( long "opt-iter" + <> value 30 + <> showDefault + <> help "number of iterations in parameter optimization.") + <*> option auto + ( long "opt-retries" + <> value 1 + <> showDefault + <> help "number of retries of parameter fitting.") + <*> option auto + ( long "number-params" + <> value (-1) + <> showDefault + <> help "maximum number of parameters in the model. If this argument is absent, the number is bounded by the maximum size of the expression and there will be no repeated parameter.") + <*> option auto + ( long "nPop" + <> value 100 + <> showDefault + <> help "population size (Default: 100).") + <*> option auto + ( long "tournament-size" + <> value 2 + <> showDefault + <> help "tournament size.") + <*> option auto + ( long "pc" + <> value 1.0 + <> showDefault + <> help "probability of crossover.") + <*> option auto + ( long "pm" + <> value 0.3 + <> showDefault + <> help "probability of mutation.") + <*> strOption + ( long "non-terminals" + <> value "Add,Sub,Mul,Div,PowerAbs,Recip" + <> showDefault + <> help "set of non-terminals to use in the search." + ) + <*> strOption + ( long "dump-to" + <> value "" + <> showDefault + <> help "dump final e-graph to a file." + ) + <*> strOption + ( long "load-from" + <> value "" + <> showDefault + <> help "load initial e-graph from a file." + ) + <*> switch + ( long "generational" + <> help "replace the current population with the children instead of keeping the pareto front." + ) + <*> switch + ( long "simplify" + <> help "simplify the expressions before displaying them." + ) + <*> option auto + ( long "max-time" + <> value (-1) + <> showDefault + <> help "maximum allowed time budget (in seconds, -1 it will run for the number of generations)" + ) + <*> strOption + ( long "varnames" + <> value "" + <> showDefault + <> help "comma separated variable names." ) + <*> switch + ( long "frac-bayes-complexity" + <> help "Use n_nodes * log unique_nodes as the second objective." + ) + +eggp_run :: String -> Int -> Int -> Int -> Int -> Double -> Double -> String -> String -> Int -> Int -> Int -> Int -> Int -> Bool -> Bool -> Bool -> String -> String -> String -> Bool -> IO String +eggp_run dataset gens nPop maxSize nTournament pc pm nonterminals loss optIter optRepeat nParams folds maxTime simplify trace generational dumpTo loadFrom varnames useFracBayes = + case readMaybe loss of + Nothing -> pure $ "Invalid loss function " <> loss + Just l -> let arg = Args dataset "" gens maxSize folds trace l optIter optRepeat nParams nPop nTournament pc pm nonterminals dumpTo loadFrom generational simplify maxTime varnames useFracBayes + in eggp arg + +eggp :: Args -> IO String +eggp args = do + g <- getStdGen + let datasets = words (_dataset args) + dataTrains' <- Prelude.mapM (flip loadTrainingOnly True) datasets -- load all datasets + dataTests <- if null (_testData args) + then pure dataTrains' + else Prelude.mapM (flip loadTrainingOnly True) $ words (_testData args) + + let (dataTrainVals, g') = runState (Prelude.mapM (`splitData` (_folds args)) dataTrains') g + alg = evalStateT (egraphGP dataTrainVals dataTests args) emptyGraph + evalStateT alg g' diff --git a/app/Main.hs b/app/Main.hs new file mode 100644 index 0000000..e321bee --- /dev/null +++ b/app/Main.hs @@ -0,0 +1,25 @@ +{-# LANGUAGE BlockArguments #-} + +module Main where + +import EGGPA (eggp, opt) +import Options.Applicative as Opt +import Control.Exception (Exception (..), SomeException (..), handle) +import System.Exit (ExitCode (..), exitWith) +import System.IO (hPutStrLn, stderr) +exitHandler :: ExitCode -> IO () +exitHandler ExitSuccess = pure () +exitHandler (ExitFailure n) = exitWith (ExitFailure n) + +uncaughtExceptionHandler :: SomeException -> IO () +uncaughtExceptionHandler (SomeException e) = + hPutStrLn stderr (displayException e) >> exitWith (ExitFailure 1) + +main :: IO () +main = + handle uncaughtExceptionHandler $ + handle exitHandler $ do + args <- execParser + (Opt.info (opt <**> helper) Opt.fullDesc) + result <- eggp args + putStr result diff --git a/cabal.project b/cabal.project index 297cc1d..2ce796e 100644 --- a/cabal.project +++ b/cabal.project @@ -1,17 +1,13 @@ packages: . -allow-newer: table-layout:*, haskeline:*, aeson:*,mwc-random:* -allow-older: base + ../srtree + +allow-newer: * source-repository-package type: git location: https://github.com/haskell/mwc-random tag: 2cce257158befe52417433d1e7717f11ce718aec -source-repository-package - type: git - location: https://github.com/folivetti/srtree - tag: 1fb70211430dc021079b0de0a82396452f37a139 - if os(darwin) package * extra-include-dirs: /opt/homebrew/include diff --git a/data.tsv b/data.tsv new file mode 100644 index 0000000..1f7a7f4 --- /dev/null +++ b/data.tsv @@ -0,0 +1,103897 @@ +still_alive age pericardial fractional epss lvdd wall_score wall_index alive_at_1 target +0.0 68.7818374633789 0.0 0.4202229976654053 7.8912529945373535 4.341502189636231 13.304107666015623 1.2599869966506958 0.0 29.893529891967773 +0.0 52.628211975097656 0.0 0.5044270157814026 8.411657333374023 4.516941070556641 5.661472797393799 2.2907049655914307 0.0 29.98983192443848 +0.0 73.48853302001953 0.0 0.2616190016269684 10.076671600341797 5.412519931793213 13.51007843017578 1.1407289505004885 1.0 18.99588203430176 +0.0 58.0669174194336 0.0 0.1452309936285019 21.21440696716309 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0.0 0.1471789926290512 12.387988090515137 5.384157180786133 30.9547176361084 1.4310330152511597 0.0 47.84827423095703 +0.0 61.60524368286133 1.0 0.1759810000658035 14.854686737060549 3.8134570121765137 17.943998336791992 1.0 0.0 13.820900917053224 diff --git a/eggp.cabal b/eggp.cabal index c284746..372833a 100755 --- a/eggp.cabal +++ b/eggp.cabal @@ -36,6 +36,7 @@ common warnings foreign-library _binding import: warnings + ghc-options: -threaded type: native-shared -- 01-04-2023: @@ -69,13 +70,53 @@ foreign-library _binding , ieee754 >=0.8.0 && <0.9 , lens >=5.2.3 && <5.4 , list-shuffle >=1.0.0.1 && <1.1 - , massiv >=1.0.4.1 && <1.1 , mtl >=2.2 && <2.4 , optparse-applicative >=0.17 && <0.19 , random >=1.2 && <1.4 , scheduler >=2.0.0.1 && <3 , split >=0.2.5 && <0.3 - , srtree >= 2.0.1.5 + , srtree >= 2.0.1.8 + , statistics >=0.16.2.1 && <0.17 + , transformers >=0.6.1.0 && <0.7 + , unliftio >=0.2.10 && <1 + , unliftio-core >=0.2.1 && <1 + , unordered-containers ==0.2.* + , vector >=0.12 && <0.14 + , zlib >=0.6.3 && <0.8 + , directory + , time + default-language: Haskell2010 + +executable eggp + import: warnings + ghc-options: -threaded -rtsopts -O2 -fllvm -pgmlo opt-20 -pgmlc llc-20 -optlo-O3 -optlc-mcpu=native -mavx2 -mfma -fspec-constr -fmax-simplifier-iterations=20 -fexpose-all-unfoldings -optc-O3 -optc-mavx2 -optc-mfma + main-is: Main.hs + hs-source-dirs: app, src + other-modules: + EGGPA + Search + extra-libraries: + nlopt + build-depends: + attoparsec >=0.14.4 && <0.15 + , attoparsec-expr >=0.1.1.2 && <0.2 + , base >=4.19 && <5 + , binary >=0.8.9.1 && <0.9 + , bytestring >=0.11 && <0.13 + , containers >=0.6.7 && <0.8 + , dlist ==1.0.* + , exceptions >=0.10.7 && <0.11 + , filepath >=1.4.0.0 && <1.6 + , hashable >=1.4.4.0 && <1.6 + , ieee754 >=0.8.0 && <0.9 + , lens >=5.2.3 && <5.4 + , list-shuffle >=1.0.0.1 && <1.1 + , mtl >=2.2 && <2.4 + , optparse-applicative >=0.17 && <0.19 + , random >=1.2 && <1.4 + , scheduler >=2.0.0.1 && <3 + , split >=0.2.5 && <0.3 + , srtree >= 2.0.1.8 , statistics >=0.16.2.1 && <0.17 , transformers >=0.6.1.0 && <0.7 , unliftio >=0.2.10 && <1 diff --git a/output.csv b/output.csv new file mode 100644 index 0000000..94f58eb --- /dev/null +++ b/output.csv @@ -0,0 +1,101 @@ +x1,x2,y +3.1633477,1.6379011,23.699608 +2.825432,1.8107903,16.932514 +2.5244646,1.9522257,12.561513 +2.4721718,1.548702,11.902563 +2.5702238,1.5798655,13.123709 +2.7831926,1.5615215,16.221025 +3.1550393,1.9575646,23.519575 +3.2092361,1.6736581,24.811506 +3.7979808,1.812217,44.661964 +2.006617,1.1984706,7.466161 +2.7544312,1.1616756,15.730201 +3.1644964,1.5675492,23.722721 +3.1851115,1.9341198,24.234129 +2.146358,1.5180742,8.611727 +2.0665693,1.8199695,7.97915 +3.2426891,1.481792,25.642262 +2.4383655,1.1947877,11.477939 +2.699256,1.6885091,14.928367 +2.6715546,1.5519842,14.513895 +3.2406197,1.264595,25.573954 +3.2606177,1.2811495,26.091206 +2.9311352,1.9531777,18.81812 +3.2049146,1.7623475,24.709347 +2.6121676,1.2773371,13.658917 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+2.86076,1.7483361,17.535198 +2.040573,1.919339,7.7830667 +2.0003393,1.2193534,7.422244 +3.8872643,1.7875398,48.82622 +3.396018,1.6575221,29.893242 +3.3313875,1.4964211,28.016947 +3.3166025,1.3980956,27.60015 +2.7085667,1.9561332,15.081329 +2.6265163,1.4508916,13.870323 +2.1220255,2.0567465,8.440801 diff --git a/src/EGGP.hs b/src/EGGP.hs index 4c0dc86..3d6bf39 100644 --- a/src/EGGP.hs +++ b/src/EGGP.hs @@ -15,13 +15,9 @@ import Algorithm.EqSat.Info import Algorithm.EqSat.DB import Algorithm.SRTree.Likelihoods import Algorithm.SRTree.ModelSelection -import Algorithm.SRTree.Opt import Control.Lens (element, makeLenses, over, (&), (+~), (-~), (.~), (^.)) import Control.Monad (foldM, forM_, forM, when, unless, filterM, (>=>), replicateM, replicateM_) import Control.Monad.State.Strict -import qualified Data.IntMap.Strict as IM -import qualified Data.Map.Strict as Map -import Data.Massiv.Array as MA hiding (forM_, forM) import Data.Maybe (fromJust, isNothing, isJust) import Data.SRTree import Data.SRTree.Datasets @@ -163,9 +159,9 @@ opt = Args <> help "print all evaluated expressions.") <*> option auto ( long "loss" - <> value MSE - <> showDefault - <> help "loss function: MSE, Gaussian, Poisson, Bernoulli.") + <> value LeastSquares + <> showDefault + <> help "loss function: LeastSquares, Gaussian, Poisson, Bernoulli.") <*> option auto ( long "opt-iter" <> value 30 diff --git a/src/Search.hs b/src/Search.hs index 7be4da9..e1e93ac 100644 --- a/src/Search.hs +++ b/src/Search.hs @@ -15,14 +15,14 @@ import Algorithm.EqSat.Info import Algorithm.EqSat.DB import Algorithm.SRTree.Likelihoods import Algorithm.SRTree.ModelSelection -import Algorithm.SRTree.Opt import Control.Lens (element, makeLenses, over, (&), (+~), (-~), (.~), (^.)) import Control.Monad (foldM, forM_, forM, when, unless, filterM, (>=>), replicateM, replicateM_) import Control.Monad.State.Strict -import qualified Data.IntMap.Strict as IM -import qualified Data.Map.Strict as Map -import Data.Massiv.Array as MA hiding (forM_, forM) -import Data.Maybe (fromJust, isNothing, isJust) + +import Data.HashMap.Strict (HashMap) +import qualified Data.HashMap.Strict as HashMap +import Data.Maybe (fromJust, fromMaybe, isNothing, isJust) +import qualified Data.Vector.Unboxed as V import Data.SRTree import Data.SRTree.Datasets import Data.SRTree.Eval @@ -36,7 +36,7 @@ import qualified Data.IntSet as IntSet import qualified Data.Sequence as FingerTree import Data.Function ( on ) import qualified Data.Foldable as Foldable -import qualified Data.IntMap as IntMap + import List.Shuffle ( shuffle ) import Algorithm.SRTree.NonlinearOpt import Data.Binary ( encode, decode ) @@ -122,7 +122,7 @@ egraphGP dataTrainVals dataTests args = do then forM (Prelude.zip [curIx..] newPop') $ uncurry printExpr else pure [] - totSz <- gets (Map.size . _eNodeToEClass) -- (IntMap.size . _eClass) + totSz <- gets (HashMap.size . _eNodeToEClass) -- (IntMap.size . _eClass) let full = totSz > max maxMem (_nPop args) when full (cleanEGraph >> cleanDB) @@ -152,7 +152,7 @@ egraphGP dataTrainVals dataTests args = do maxMem = 2000000 -- running 1 iter of eqsat for each new individual will consume ~3GB fitFun = fitnessMV shouldReparam (_optRepeat args) (_optIter args) (_distribution args) dataTrainVals nonTerms = parseNonTerms (_nonterminals args) - (Sz2 _ nFeats) = MA.size (getX .fst . head $ dataTrainVals) + nFeats = length $ getX (fst $ head dataTrainVals) params = if _nParams args == -1 then [param 0] else Prelude.map param [0 .. _nParams args - 1] shouldReparam = _nParams args == -1 relabel = if shouldReparam then relabelParams else relabelParamsOrder @@ -191,19 +191,20 @@ egraphGP dataTrainVals dataTests args = do io . putStrLn $ "cleaning" pareto <- (concat <$> (forM [1 .. _maxSize args] $ \n -> getTopFitEClassWithSize n nParetos)) >>= Prelude.mapM canonical - infos <- forM pareto (\c -> gets (_info . (IntMap.! c) . _eClass)) + infos <- forM pareto (\c -> _info <$> getEClass c) exprs <- forM pareto getBestExpr put emptyGraph newIds <- fromTrees myCost $ Prelude.map relabel exprs - forM_ (Prelude.zip newIds (Prelude.reverse infos)) $ \(eId, info) -> - insertFitness eId (fromJust $ _fitness info) (_theta info) + forM_ (Prelude.zip newIds (Prelude.reverse infos)) $ \(eId, info) -> do + let f = fromMaybe (-1.0/0.0) (_fitness info) + insertFitness eId f (_theta info) rndTerm = do coin <- toss if coin || _nParams args == 0 then randomFrom terms else randomFrom params rndNonTerm = randomFrom nonTerms - refitChanged = do ids <- gets (_refits . _eDB) >>= Prelude.mapM canonical . Set.toList >>= pure . nub - modify' $ over (eDB . refits) (const Set.empty) + refitChanged = do ids <- gets (_refits . _eDB) >>= Prelude.mapM canonical . IntSet.toList >>= pure . nub + modify' $ over (eDB . refits) (const IntSet.empty) forM_ ids $ \ec -> do t <- getBestExpr ec (f, p) <- fitFun t insertFitness ec f p @@ -236,7 +237,7 @@ egraphGP dataTrainVals dataTests args = do applyTournament :: [EClassId] -> RndEGraph EClassId applyTournament xs = do challengers <- replicateM (_nTournament args) (rnd $ randomFrom xs) >>= traverse canonical - fits <- Prelude.map fromJust <$> Prelude.mapM getFitness challengers + fits <- Prelude.map (fromMaybe (-1.0/0.0)) <$> Prelude.mapM getFitness challengers pure . snd . maximumBy (compare `on` fst) $ Prelude.zip fits challengers combine (p1, p2) = (crossover p1 p2 >>= mutate) >>= canonical @@ -352,40 +353,64 @@ egraphGP dataTrainVals dataTests args = do printExpr :: Int -> EClassId -> RndEGraph [String] printExpr ix ec = do - thetas' <- gets (_theta . _info . (IM.! ec) . _eClass) + thetas' <- getTheta ec bestExpr <- (if _simplify args then simplifyEqSatDefault else id) <$> getBestExpr ec let best' = if shouldReparam then relabelParams bestExpr else relabelParamsOrder bestExpr nParams = countParamsUniq best' - fromSz (MA.Sz x) = x - nThetas = Prelude.map (fromSz . MA.size) thetas' + nThetas = Prelude.map V.length thetas' (_, thetas) <- if Prelude.any (/=nParams) nThetas then fitFun best' else pure (1.0, thetas') - maxLoss <- negate . fromJust <$> getFitness ec + maxLoss <- maybe 0 negate <$> getFitness ec ts <- forM (Data.List.zip4 [0..] dataTrainVals dataTests thetas) $ \(view, (dataTrain, dataVal), dataTest, theta) -> do let (x, y, mYErr) = dataTrain (x_val, y_val, mYErr_val) = dataVal (x_te, y_te, mYErr_te) = dataTest distribution = _distribution args - expr = paramsToConst (MA.toList theta) best' + expr = paramsToConst (V.toList theta) best' showNA z = if isNaN z then "" else show z - r2_train = r2 x y best' theta - r2_val = r2 x_val y_val best' theta - r2_te = r2 x_te y_te best' theta - nll_train = nll distribution mYErr x y best' theta - nll_val = nll distribution mYErr_val x_val y_val best' theta - nll_te = nll distribution mYErr_te x_te y_te best' theta - mdl_train = fractionalBayesFactor distribution mYErr x y theta best' - mdl_val = fractionalBayesFactor distribution mYErr_val x_val y_val theta best' - mdl_te = fractionalBayesFactor distribution mYErr_te x_te y_te theta best' + + n = fromIntegral (V.length y) :: Double + n_val = fromIntegral (V.length y_val) :: Double + n_te = fromIntegral (V.length y_te) :: Double + p = fromIntegral (countParamsUniq best') :: Double + f_compl = countNodes best' * log (countUniqueTokens best') + + -- loss (NLL) + nll_train_ = compileLoss x (buildLoss (NLL distribution) n best') y mYErr theta + nll_val_ = compileLoss x_val (buildLoss (NLL distribution) n_val best') y_val mYErr_val theta + nll_te_ = compileLoss x_te (buildLoss (NLL distribution) n_te best') y_te mYErr_te theta + + -- R2 + y_mean = V.sum y / n + y_var = V.sum (V.map (\yi -> (yi - y_mean)^2) y) / n + y_val_mean = V.sum y_val / n_val + y_val_var = V.sum (V.map (\yi -> (yi - y_val_mean)^2) y_val) / n_val + y_te_mean = V.sum y_te / n_te + y_te_var = V.sum (V.map (\yi -> (yi - y_te_mean)^2) y_te) / n_te + + mse_train = compileLoss x (buildLoss MSE n best') y Nothing theta + r2_train_ = if y_var > 0 then 1 - mse_train / y_var else 0 + r2_val_ = if y_val_var > 0 then 1 - (compileLoss x_val (buildLoss MSE n_val best') y_val Nothing theta) / y_val_var else 0 + r2_te_ = if y_te_var > 0 then 1 - (compileLoss x_te (buildLoss MSE n_te best') y_te Nothing theta) / y_te_var else 0 + + -- fractional Bayes factor + b_train = 1 / sqrt n + nup = exp (1 - log 3) + mdl_train_ = (1 - b_train) * nll_train_ - p / 2 * log b_train + f_compl + p / 2 * log (2*pi*nup) + b_val = 1 / sqrt n_val + mdl_val_ = (1 - b_val) * nll_val_ - p / 2 * log b_val + f_compl + p / 2 * log (2*pi*nup) + b_te = 1 / sqrt n_te + mdl_te_ = (1 - b_te) * nll_te_ - p / 2 * log b_te + f_compl + p / 2 * log (2*pi*nup) + vals = intercalate "," - $ Prelude.map showNA [ nll_train, nll_val, nll_te, maxLoss - , r2_train, r2_val, r2_te - , mdl_train, mdl_val, mdl_te] - thetaStr = intercalate ";" $ Prelude.map show (MA.toList theta) + $ Prelude.map showNA [ nll_train_, nll_val_, nll_te_, maxLoss + , r2_train_, r2_val_, r2_te_ + , mdl_train_, mdl_val_, mdl_te_] + thetaStr = intercalate ";" $ Prelude.map show (V.toList theta) varnames = _varnames args showExprFun = if null varnames then showExpr else showExprWithVars (splitOn "," varnames) showLatexFun = if null varnames then showLatex else showLatexWithVars (splitOn "," varnames) diff --git a/src/eggp.egg-info/PKG-INFO b/src/eggp.egg-info/PKG-INFO index 32646f8..0ffa47c 100644 --- a/src/eggp.egg-info/PKG-INFO +++ b/src/eggp.egg-info/PKG-INFO @@ -1,6 +1,6 @@ Metadata-Version: 2.4 Name: eggp -Version: 1.0.14 +Version: 1.0.17 Summary: Python Wheels for eggp algorithm. Author-email: Fabricio Olivetti License: GNU GENERAL PUBLIC LICENSE @@ -686,6 +686,7 @@ Classifier: Programming Language :: Python :: 3.10 Classifier: Programming Language :: Python :: 3.11 Classifier: Programming Language :: Python :: 3.12 Classifier: Programming Language :: Python :: 3.13 +Classifier: Programming Language :: Python :: 3.14 Classifier: Programming Language :: Python :: Implementation :: CPython Requires-Python: >=3.9 Description-Content-Type: text/markdown diff --git a/src/eggp/__init__.py b/src/eggp/__init__.py index dc29d9a..ff179aa 100644 --- a/src/eggp/__init__.py +++ b/src/eggp/__init__.py @@ -7,6 +7,7 @@ import tempfile import csv import os +import multiprocessing import numpy as np import pandas as pd @@ -15,6 +16,13 @@ from sklearn.metrics import mean_squared_error, r2_score import matplotlib.pyplot as plt +# GHC auto-initializes the RTS when the shared library is loaded (during the +# _binding import below). At that point it reads the GHCRTS env var for flags; +# if unset, defaults to -N1. Set it here so the RTS starts with the right +# number of capabilities. +if os.environ.get("GHCRTS") is None: + os.environ["GHCRTS"] = "-N4" + from ._binding import ( unsafe_hs_eggp_version, unsafe_hs_eggp_main, @@ -29,7 +37,6 @@ _hs_rts_init: bool = False _hs_rts_lock: Lock = Lock() - def hs_rts_exit() -> None: global _hs_rts_lock with _hs_rts_lock: @@ -37,7 +44,7 @@ def hs_rts_exit() -> None: @contextmanager -def hs_rts_init(args: List[str] = []) -> Iterator[None]: +def hs_rts_init(args: List[str] = ["eggp", "+RTS", "-N4", "-RTS"]) -> Iterator[None]: global _hs_rts_init global _hs_rts_lock with _hs_rts_lock: @@ -54,7 +61,12 @@ def version() -> str: def main(args: List[str] = []) -> int: - with hs_rts_init(args): + # args often come from sys.argv which includes the program name at [0]. + # Strip it and prepend RTS flags so hs_init sees +RTS -N{numCores} -RTS. + cli_args = args[1:] if args and not args[0].startswith('-') else args + rts_flags = ["+RTS", "-N4", "-s", "-RTS"] + rts_args = ["eggp"] + rts_flags + with hs_rts_init(rts_args + cli_args): return unsafe_hs_eggp_main() def eggp_run(dataset: str, gen: int, nPop: int, maxSize: int, nTournament: int, pc: float, pm: float, nonterminals: str, loss: str, optIter: int, optRepeat: int, nParams: int, split: int, max_time : int, simplify: int, trace : int, generational : int, dumpTo: str, loadFrom: str, varnames : str, useFracBayes: int) -> str: diff --git a/src/eggp/binding.i b/src/eggp/binding.i index cb22ad2..6f1c23d 100644 --- a/src/eggp/binding.i +++ b/src/eggp/binding.i @@ -15,7 +15,13 @@ char * unsafe_hs_eggp_run( char *dataset, int gens, int nPop, int maxSize, int n } void unsafe_hs_eggp_init(int argc, char **argv) { + int i; + fprintf(stderr, "[eggp_diag] hs_init called with argc=%d\n", argc); + for (i = 0; i < argc; i++) { + fprintf(stderr, "[eggp_diag] argv[%d] = \"%s\"\n", i, argv[i] ? argv[i] : "(null)"); + } hs_init(&argc, &argv); + fprintf(stderr, "[eggp_diag] hs_init returned, argc now=%d\n", argc); } void unsafe_hs_eggp_exit() { diff --git a/train.csv b/train.csv new file mode 100644 index 0000000..716a246 --- /dev/null +++ b/train.csv @@ -0,0 +1,101 @@ +x1,x2,y +3.3971534,1.5060675,29.918682 +3.5396705,1.3629639,34.485096 +3.1892567,1.7627553,24.32657 +2.077505,2.0247085,8.077058 +3.5830646,2.0398486,36.04615 +2.087038,1.2183205,8.090568 +2.516818,1.4462459,12.435076 +3.8804975,1.8343061,48.49928 +2.0558233,1.9809241,7.9042077 +3.4742355,1.3648912,32.30328 +3.630137,2.096489,37.78185 +2.7456832,1.8814874,15.644542 +3.217843,1.2963047,25.001226 +3.5291038,1.6559021,34.140057 +2.4246283,1.2343742,11.325945 +2.6478307,1.0118957,14.124878 +2.3336532,1.0347788,10.320389 +3.7038674,1.6209936,40.647144 +2.5821357,1.9523079,13.3013935 +2.2979202,1.1066563,9.967789 +3.8356636,1.7299566,46.371143 +3.2893844,1.4692005,26.864868 +2.8733153,1.7987359,17.758432 +3.2174582,1.9749258,25.029936 +2.0403705,2.0334494,7.787575 +3.4464512,1.6419284,31.435642 +3.8065047,1.0978804,45.001522 +2.6690512,1.0980386,14.437979 +2.5929854,1.5801861,13.424234 +3.0117788,1.0422236,20.328232 +3.6788259,1.9433348,39.657497 +3.128985,1.2565999,22.875254 +2.4685793,1.4507838,11.85269 +3.9450622,1.5278208,51.71579 +3.1234682,1.7896719,22.783516 +2.18427,1.6126902,8.948777 +3.8714762,1.8829042,48.06622 +2.143311,1.9269965,8.613142 +3.9956758,1.1939203,54.378086 +2.2824123,1.1058899,9.814604 +2.0681357,1.0533093,7.9181347 +2.4971566,1.6023126,12.205639 +3.4934177,1.0952348,32.907246 +2.0275707,1.3535551,7.6408424 +2.0648131,1.2627058,7.9187574 +3.8059206,1.0722327,44.973083 +3.8234081,1.784554,45.809402 +2.9061403,2.0874972,18.36141 +3.7660031,2.0814848,43.268566 +2.7228825,1.8356189,15.291555 +3.0708168,2.0628414,21.630825 +3.123005,1.1875621,22.733152 +3.6404202,1.5223987,38.146305 +2.9032989,1.855777,18.299383 +2.4096742,1.4907987,11.181441 +3.1903982,1.9842298,24.364262 +2.5228477,1.8371701,12.535498 +2.6305645,1.8403445,13.9510355 +2.1890965,1.1873522,8.95204 +3.8617349,1.4233106,47.578835 +2.630413,1.2319345,13.905399 +3.078045,1.1127416,21.727734 +3.1158218,1.8271928,22.61234 +3.143116,1.8406537,23.236578 +2.2313194,2.0446925,9.401195 +3.9720144,1.271618,53.112347 +3.9748392,1.3774071,53.26918 +3.497669,1.0720372,33.045284 +2.4355078,1.3167716,11.4574995 +3.1835284,1.2426059,24.154736 +2.61128,1.6812414,13.677263 +3.3453755,1.4677908,28.40913 +3.2589126,1.5776855,26.066637 +2.5458026,1.1063552,12.7666 +2.5980403,1.0967134,13.449199 +2.7999747,1.2692327,16.472204 +2.310279,1.7128961,10.145958 +2.0047007,1.2309713,7.455896 +3.9228,1.8631585,50.593437 +2.4005494,1.4427574,11.076637 +3.4432006,2.0723603,31.352612 +2.9035115,1.4588146,18.279991 +3.7617707,1.3626549,43.052574 +3.0615017,1.2402077,21.383158 +3.0878696,1.7306952,21.986301 +2.6463013,1.8889222,14.173387 +2.7779393,2.0824766,16.163515 +2.895896,1.4597867,18.141783 +2.4681807,1.9204984,11.877957 +2.1599507,1.6317598,8.737259 +2.5662956,1.0139499,13.019328 +3.0096786,1.5006015,20.324406 +2.3931513,1.2020718,10.972726 +3.9869628,1.8577783,53.941746 +2.3288248,1.9365753,10.347004 +2.748889,1.640554,15.681256 +3.0248127,2.09197,20.663427 +3.7674465,1.2789876,43.2919 +2.247457,1.1190584,9.479797 +3.4318662,1.7047931,30.984524 diff --git a/tutorials/datasets/example0.csv b/tutorials/datasets/example0.csv new file mode 100644 index 0000000..50170ef --- /dev/null +++ b/tutorials/datasets/example0.csv @@ -0,0 +1,15 @@ +Xaxis0,yaxis +45.0,3.38 +30.0,3.349 +15.0,2.827 +10.0,1.978 +7.16,1.462 +6.0,1.242 +5.0,1.049 +4.0,0.853 +3.0,0.623 +2.3,0.532 +1.5,0.383 +1.0,0.254 +0.5,0.219 +0.2,0.062 diff --git a/tutorials/datasets/example1.csv b/tutorials/datasets/example1.csv new file mode 100644 index 0000000..806c727 --- /dev/null +++ b/tutorials/datasets/example1.csv @@ -0,0 +1,11 @@ +Xaxis0,yaxis +35.0,3.004 +29.0,2.958 +24.0,2.858 +20.0,2.618 +15.0,2.127 +10.0,1.466 +6.0,0.856 +4.0,0.551 +2.0,0.295 +0.5,0.037 diff --git a/tutorials/datasets/example3.csv b/tutorials/datasets/example3.csv new file mode 100644 index 0000000..f0b897e --- /dev/null +++ b/tutorials/datasets/example3.csv @@ -0,0 +1,11 @@ +Xaxis0,yaxis +3.42,3.245 +2.91,3.205 +2.39,3.173 +1.92,3.143 +1.44,3.097 +1.09,2.991 +0.82,2.692 +0.48,1.732 +0.24,0.869 +0.1,0.39 diff --git a/tutorials/mv_example.py b/tutorials/mv_example.py new file mode 100644 index 0000000..c34934f --- /dev/null +++ b/tutorials/mv_example.py @@ -0,0 +1,26 @@ +from eggp import EGGP +import pandas as pd + +pd.set_option('display.max_colwidth', 100) +# Load the example dataset +df0 = pd.read_csv("datasets/example0.csv") +df1 = pd.read_csv("datasets/example1.csv") +df3 = pd.read_csv("datasets/example3.csv") + +# Create an EGGP object, all parameters have default values. We will dump the results to a file named 'regression_example.egg' +# gen is the number of generations, nPop is the population size, maxSize is the maximum size of the expressions +# nTnament is the tournament size, pc and pm are the probabilities for performing crossover and mutation +# nonterminals is the function set +# loss is the loss function (see the boston house example for other values) +# optIter and optRepeat are the number of iterations and repetitions for the optimization of constants +# nParams is the number of parameters to include in the expression +# max_time is the maximum time in seconds to run the algorithm +# simplify indicates whether to simplify the expressions using equality saturation +# dumpTo dumps the final e-graph to a file +# By default it will run a multi-objective approach. +model = EGGP(gen=100, nPop=100, maxSize=15, nTournament=5, pc=0.8, pm=0.2, nonterminals='add,sub,mul,div,power,exp,log', loss='MSE', optIter=100, optRepeat=5, nParams=1, folds=1, max_time=120, simplify=False, dumpTo='regression_example.egg') + +model.fit_mvsr([df[['Xaxis0']] for df in [df0,df1,df3]], [df['yaxis'] for df in [df0,df1,df3]]) + +print("Multi-objective mode returning the Pareto front without simplifcation: ") +print(model.results[["Expression", "view", "loss_train", "maxloss"]]) From b6866eefb59d396e60b8eebb6108fb7d66ee1309 Mon Sep 17 00:00:00 2001 From: folivetti Date: Thu, 30 Jul 2026 09:31:54 -0300 Subject: [PATCH 121/166] more optimization in srtree --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index ac32f27..005c249 100644 --- a/.gitignore +++ b/.gitignore @@ -24,3 +24,4 @@ cabal.project.local~ build binding.py binding_wrap.c +*.tsv From 9f7eed4a749730e68ad627bab2c569855a3cfdc6 Mon Sep 17 00:00:00 2001 From: folivetti Date: Sun, 2 Aug 2026 13:13:04 -0300 Subject: [PATCH 122/166] backend argument --- app/EGGPA.hs | 8 +++++++- cabal.project | 3 +++ src/EGGP.hs | 8 +++++++- src/Search.hs | 7 +++++-- 4 files changed, 22 insertions(+), 4 deletions(-) diff --git a/app/EGGPA.hs b/app/EGGPA.hs index 18793c2..3cf1b6f 100644 --- a/app/EGGPA.hs +++ b/app/EGGPA.hs @@ -48,6 +48,7 @@ import Options.Applicative as Opt hiding (Const) import Search import Algorithm.EqSat.SearchSR +import Algorithm.SRTree.AD (ADBackEnd(..)) import Data.SRTree.Random import Data.SRTree.Datasets @@ -171,12 +172,17 @@ opt = Args ( long "frac-bayes-complexity" <> help "Use n_nodes * log unique_nodes as the second objective." ) + <*> option auto + ( long "backend" + <> value MultiThread + <> showDefault + <> help "AD backend: MultiThread, SingleThread, or Accelerate." ) eggp_run :: String -> Int -> Int -> Int -> Int -> Double -> Double -> String -> String -> Int -> Int -> Int -> Int -> Int -> Bool -> Bool -> Bool -> String -> String -> String -> Bool -> IO String eggp_run dataset gens nPop maxSize nTournament pc pm nonterminals loss optIter optRepeat nParams folds maxTime simplify trace generational dumpTo loadFrom varnames useFracBayes = case readMaybe loss of Nothing -> pure $ "Invalid loss function " <> loss - Just l -> let arg = Args dataset "" gens maxSize folds trace l optIter optRepeat nParams nPop nTournament pc pm nonterminals dumpTo loadFrom generational simplify maxTime varnames useFracBayes + Just l -> let arg = Args dataset "" gens maxSize folds trace l optIter optRepeat nParams nPop nTournament pc pm nonterminals dumpTo loadFrom generational simplify maxTime varnames useFracBayes MultiThread in eggp arg eggp :: Args -> IO String diff --git a/cabal.project b/cabal.project index 2ce796e..e6ae082 100644 --- a/cabal.project +++ b/cabal.project @@ -3,6 +3,9 @@ packages: . allow-newer: * +package srtree + ghc-options: -fllvm -pgmlo opt-20 -pgmlc llc-20 -optlo-O3 -optlc-mcpu=native -mavx2 -mfma -fexpose-all-unfoldings + source-repository-package type: git location: https://github.com/haskell/mwc-random diff --git a/src/EGGP.hs b/src/EGGP.hs index 3d6bf39..dd8c65f 100644 --- a/src/EGGP.hs +++ b/src/EGGP.hs @@ -48,6 +48,7 @@ import Options.Applicative as Opt hiding (Const) import Search import Algorithm.EqSat.SearchSR +import Algorithm.SRTree.AD (ADBackEnd(..)) import Data.SRTree.Random import Data.SRTree.Datasets @@ -238,12 +239,17 @@ opt = Args ( long "frac-bayes-complexity" <> help "Use n_nodes * log unique_nodes as the second objective." ) + <*> option auto + ( long "backend" + <> value MultiThread + <> showDefault + <> help "AD backend: MultiThread, SingleThread, or Accelerate." ) eggp_run :: String -> Int -> Int -> Int -> Int -> Double -> Double -> String -> String -> Int -> Int -> Int -> Int -> Int -> Bool -> Bool -> Bool -> String -> String -> String -> Bool -> IO String eggp_run dataset gens nPop maxSize nTournament pc pm nonterminals loss optIter optRepeat nParams folds maxTime simplify trace generational dumpTo loadFrom varnames useFracBayes = case readMaybe loss of Nothing -> pure $ "Invalid loss function " <> loss - Just l -> let arg = Args dataset "" gens maxSize folds trace l optIter optRepeat nParams nPop nTournament pc pm nonterminals dumpTo loadFrom generational simplify maxTime varnames useFracBayes + Just l -> let arg = Args dataset "" gens maxSize folds trace l optIter optRepeat nParams nPop nTournament pc pm nonterminals dumpTo loadFrom generational simplify maxTime varnames useFracBayes MultiThread in eggp arg eggp :: Args -> IO String diff --git a/src/Search.hs b/src/Search.hs index e1e93ac..eac1049 100644 --- a/src/Search.hs +++ b/src/Search.hs @@ -54,6 +54,7 @@ import Data.SRTree.Random import Data.SRTree.Datasets import Text.ParseSR import Algorithm.EqSat.SearchSR +import Algorithm.SRTree.AD (ADBackEnd(..)) import Foreign.C (CInt (..), CDouble (..)) import Foreign.C.String (CString, newCString, withCString, peekCString, peekCAString, newCAString) @@ -87,7 +88,8 @@ data Args = Args _simplify :: Bool, _maxtime :: Int, _varnames :: String, - _useFracBayes :: Bool + _useFracBayes :: Bool, + _backend :: ADBackEnd } deriving (Show) @@ -150,11 +152,12 @@ egraphGP dataTrainVals dataTests args = do where maxSize = (_maxSize args) maxMem = 2000000 -- running 1 iter of eqsat for each new individual will consume ~3GB - fitFun = fitnessMV shouldReparam (_optRepeat args) (_optIter args) (_distribution args) dataTrainVals + fitFun = fitnessMV (_backend args) skipValEval shouldReparam (_optRepeat args) (_optIter args) (_distribution args) dataTrainVals nonTerms = parseNonTerms (_nonterminals args) nFeats = length $ getX (fst $ head dataTrainVals) params = if _nParams args == -1 then [param 0] else Prelude.map param [0 .. _nParams args - 1] shouldReparam = _nParams args == -1 + skipValEval = _folds args == 1 relabel = if shouldReparam then relabelParams else relabelParamsOrder terms = if _distribution args == ROXY then (var 0 : params) From 6d52e2031fa927a2c3e9214dfa19a71c178dfcc4 Mon Sep 17 00:00:00 2001 From: folivetti Date: Tue, 4 Aug 2026 08:25:45 -0300 Subject: [PATCH 123/166] parallel per pop --- cabal.project | 1 + src/Search.hs | 36 +++++++++++++++++------------------- 2 files changed, 18 insertions(+), 19 deletions(-) diff --git a/cabal.project b/cabal.project index e6ae082..49e8fd2 100644 --- a/cabal.project +++ b/cabal.project @@ -1,5 +1,6 @@ packages: . ../srtree + ../accelerate-llvm-native-debug allow-newer: * diff --git a/src/Search.hs b/src/Search.hs index eac1049..a7d0c8c 100644 --- a/src/Search.hs +++ b/src/Search.hs @@ -101,25 +101,32 @@ egraphGP dataTrainVals dataTests args = do when ((not.null) (_loadFrom args)) $ (io $ BS.readFile (_loadFrom args)) >>= \eg -> put (decode eg) insertTerms - evaluateUnevaluated fitFun + unevalInit <- gets (IntSet.toList . _unevaluated . _eDB) + fitBatch True fitFun unevalInit t0 <- io $ getPOSIXTime - pop <- replicateM (_nPop args) $ do ec <- insertRndExpr (_maxSize args) rndTerm rndNonTerm >>= canonical - updateIfNothing fitFun ec - pure ec - pop' <- Prelude.mapM canonical pop + pop <- replicateM (_nPop args) $ insertRndExpr (_maxSize args) rndTerm rndNonTerm >>= canonical + fitBatch False fitFun pop output <- if _trace args - then forM (Prelude.zip [0..] pop') $ uncurry printExpr + then forM (Prelude.zip [0..] pop) $ uncurry printExpr else pure [] let m = (_nPop args) `div` (_maxSize args) mTime = if _maxtime args < 0 then Nothing else Just (fromIntegral $ _maxtime args - 5) -- add 5 seconds slack - (finalPop, finalOut, _) <- iterateFor (_gens args) t0 mTime (pop', output, _nPop args) $ \it (ps', out, curIx) -> do + (finalPop, finalOut, _) <- iterateFor (_gens args) t0 mTime (pop, output, _nPop args) $ \it (ps', out, curIx) -> do newPop' <- replicateM (_nPop args) (evolve ps') + -- Batch-fit the eqsat-flagged refits (force) and the new offspring + -- (updateIfNothing semantics) concurrently, before any fitness-based + -- selection so Pareto ranking sees fresh fitness values. + refitIds <- gets (IntSet.toList . _refits . _eDB) + modify' $ over (eDB . refits) (const IntSet.empty) + fitBatch True fitFun refitIds + fitBatch False fitFun newPop' + out' <- if _trace args then forM (Prelude.zip [curIx..] newPop') $ uncurry printExpr else pure [] @@ -206,12 +213,6 @@ egraphGP dataTrainVals dataTests args = do if coin || _nParams args == 0 then randomFrom terms else randomFrom params rndNonTerm = randomFrom nonTerms - refitChanged = do ids <- gets (_refits . _eDB) >>= Prelude.mapM canonical . IntSet.toList >>= pure . nub - modify' $ over (eDB . refits) (const IntSet.empty) - forM_ ids $ \ec -> do t <- getBestExpr ec - (f, p) <- fitFun t - insertFitness ec f p - iterateFor 0 _ _ xs f = pure xs iterateFor n t0 maxT xs f = do xs' <- f n xs t1 <- io $ getPOSIXTime @@ -226,13 +227,10 @@ egraphGP dataTrainVals dataTests args = do evolve xs' = do xs <- Prelude.mapM canonical xs' parents <- tournament xs offspring <- combine parents - --applySingleMergeOnlyEqSat myCost rewritesParams >> cleanDB if _nParams args == 0 - then runEqSat myCost rewritesWithConstant 1 >> cleanDB >> refitChanged - else runEqSat myCost rewritesParams 1 >> cleanDB >> refitChanged - canonical offspring >>= updateIfNothing fitFun - canonical offspring - --pure offspring + then runEqSat myCost rewritesWithConstant 1 >> cleanDB + else runEqSat myCost rewritesParams 1 >> cleanDB + pure offspring tournament xs = do p1 <- applyTournament xs >>= canonical p2 <- applyTournament xs >>= canonical From 96b6e5ed44bd9390e40ea5d94a26332f6b0469e1 Mon Sep 17 00:00:00 2001 From: folivetti Date: Wed, 5 Aug 2026 17:28:13 -0300 Subject: [PATCH 124/166] bug fix in eqsat --- src/Search.hs | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/src/Search.hs b/src/Search.hs index a7d0c8c..051735d 100644 --- a/src/Search.hs +++ b/src/Search.hs @@ -133,10 +133,11 @@ egraphGP dataTrainVals dataTests args = do totSz <- gets (HashMap.size . _eNodeToEClass) -- (IntMap.size . _eClass) let full = totSz > max maxMem (_nPop args) - when full (cleanEGraph >> cleanDB) + cleanedIds <- if full then Just <$> cleanEGraph else pure Nothing + when full cleanDB newPop <- if _generational args - then Prelude.mapM canonical newPop' + then maybe (Prelude.mapM canonical newPop') pure cleanedIds else do let n_paretos = (_nPop args) `div` (_maxSize args) pareto <- if (_useFracBayes args) @@ -208,6 +209,7 @@ egraphGP dataTrainVals dataTests args = do forM_ (Prelude.zip newIds (Prelude.reverse infos)) $ \(eId, info) -> do let f = fromMaybe (-1.0/0.0) (_fitness info) insertFitness eId f (_theta info) + pure newIds rndTerm = do coin <- toss if coin || _nParams args == 0 then randomFrom terms else randomFrom params From c45c9fadfbba61333a8baa18f13d89c926ba9003 Mon Sep 17 00:00:00 2001 From: folivetti Date: Thu, 6 Aug 2026 13:36:10 -0300 Subject: [PATCH 125/166] multiset egraph preparation --- 1.6.20.csv | 501 ++ NOTES-STATUS.md | 415 + baddata.csv | 501 ++ c_1.err | 2696 ++++++ c_2.err | 3372 +++++++ c_3.err | 2950 +++++++ cached_n4.err | 2791 ++++++ dump_gaussian | 1001 +++ gaussian_test.csv | 501 ++ gaussian_train.csv | 201 + harness/WExc | Bin 0 -> 33664896 bytes harness/WExc.hs | 56 + harness/env.txt | 173 + interp_n1.err | 310 + interp_n2.err | 3199 +++++++ interp_n4.err | 2717 ++++++ interp_n4.out | 5 + mt.err | 149 + mtA_n1_1.err | 0 mtA_n1_2.err | 0 mtA_n4_1.err | 0 mtA_n4_2.err | 0 mt_n1.err | 0 mt_n4.err | 0 n1r1.err | 0 n1r2.err | 0 p2_n4.err | 3089 +++++++ phas2_n1.err | 3066 +++++++ phas2_n2.err | 3611 ++++++++ phas2_n4.err | 3081 +++++++ pp_n1_1.err | 0 pp_n1_2.err | 0 pp_n2_1.err | 0 pp_n2_2.err | 0 pp_n4_1.err | 0 pp_n4_2.err | 0 pp_out_3.csv | 6 + pp_out_4.csv | 5 + pp_out_5.csv | 5 + prof_n4.err | 20515 +++++++++++++++++++++++++++++++++++++++++++ st_n1_1.err | 0 st_n1_2.err | 0 st_n4_1.err | 0 st_n4_2.err | 0 44 files changed, 54916 insertions(+) create mode 100644 1.6.20.csv create mode 100644 NOTES-STATUS.md create mode 100644 baddata.csv create mode 100644 c_1.err create mode 100644 c_2.err create mode 100644 c_3.err create mode 100644 cached_n4.err create mode 100644 dump_gaussian create mode 100644 gaussian_test.csv create mode 100644 gaussian_train.csv create mode 100755 harness/WExc create mode 100644 harness/WExc.hs create mode 100644 harness/env.txt create mode 100644 interp_n1.err create mode 100644 interp_n2.err create mode 100644 interp_n4.err create mode 100644 interp_n4.out create mode 100644 mt.err create mode 100644 mtA_n1_1.err create 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+MultiThread backend. + +## Constraints +- srtree `cabal.project` forces `-fllvm -mavx2 -mfma -fexpose-all-unfoldings`. +- Benchmark config: + `EGGP -d data.tsv --non-terminals add,mul,div,exp,log -s 30 -g 1 --nPop 100 --pc 0.0 --pm 1.0 --generational --opt-retries 1 --folds 1 --backend Accelerate +RTS -N4 -qn1 -RTS` +- binary: `/home/olivetti/Projects/srtools/eggp/dist-newstyle/build/.../eggp/eggp`, + cwd `/home/olivetti/Projects/srtools/eggp`. +- `ACC_INTERP=1` gates the interpreter; stable artifacts live under `/home/olivetti/Projects/srtools/eggp/`. + +## The "deadlock" — NOW RESOLVED AS A HARNESS BUG +- Repro harness drew var indices in `[0..9]` (seed3 -> `(x6 * x1)`, uses x6) + but supplied only **4** feature columns. +- `Algorithm/SRTree/AD/Unboxed.hs:156`: `evalStatic (Var ix) = pure (xss !! ix)`. + For x6 with 4 cols -> `Prelude.!!: index too large`. +- That forced-out-of-bounds thunk is lazily evaluated **inside an accelerate + worker** during kernel execution; the scheduler (accelerate-llvm-native + `Scheduler.hs`) swallows worker exceptions into `workerException` instead of + propagating; the main thread waits forever -> `BlockedIndefinitelyOnMVar`. +- NOT a scheduler bug and NOT tree/`kDyn`-dependent. Confirmation: giving the + harness all 10 columns makes seed3 complete: `OK obj=0.999...`. +- The earlier "seed2 pass / seed3 fail" and "all `-N`" repros were this same + harness artifact (same 4-column / 9-range mismatch in everything run via the + repro harness). + +## Accomplished this session +- Built `harness/WExc` against a LOCAL patched copy of accelerate-llvm-native + (`/home/olivetti/Projects/srtools/accelerate-llvm-native-debug`) that exports + `Scheduler(workerException)`, `Target(workers)` and logs swallowed worker + exceptions to stderr -> surfaced the real error. +- Identified and confirmed root cause; fixed by supplying enough feature columns. +- Stable harness compile recipe (cabal does NOT expose inplace project libs to + standalone `ghc`; must pass `-package-id`): + `cabal exec -- ghc -O2 -threaded -rtsopts \ + -package-id srtree-2.0.1.8-inplace \ + -package-id accelerate-llvm-native-1.4.0.0-inplace \ + -o harness/WExc harness/WExc.hs` + +## Latest (MultiThread backend, improvement A applied) +- `Algorithm/SRTree/AD.hs`: hoisted the tree compile out of the per-theta + evaluator for MultiThread and SingleThread (`let cts = compileTreeMulti ...` + outside the lambda). Before, `compileTreeMulti` (theta-independent static + columns + graphs) was recomputed on EVERY objective/gradient evaluation + during an NLopt run; now computed once per (tree, data) and shared across + thetas. Safe per-tree reuse (immutable cts), no cross-tree sharing. +- Measured (canonical config, `--backend MultiThread`, 2 runs each): + N1: 12s -> ~10-11s; N4: 8s -> ~6-8s (~10-15%, noisy). +- Accelerate path unchanged (per-tree kernel reuse cache from earlier). + +## Next +- Re-run the REAL EGGP config at -N4 with ACC_INTERP=1 to confirm the + interpreter completes and measure steady per-eval cost vs Phase 2. No known + bug now blocks -N4. +- If a genuine scheduler swallow is ever hit with in-range data, decide a + containment (interpreted `multiWorker` single-worker, or serialize evals). +## Session: population-parallel fitness (fitBatch) — 2026-08-03 +Goal: parallelize the search's population fitness evals (the search loop is +serial `RndEGraph = EGraphST (StateT StdGen IO)`; each tree fitted one at a time). + +Implementation: +- `Algorithm/SRTree/AD/Unboxed.hs`: added `mtParGate :: IORef Bool` (default + False), `setMTPopParallel :: IO () -> IO ()`; while True, `compileTreeMulti` + forces `numChunks = 1` so the population batch owns the cores instead of + oversubscribing with the per-tree chunk split. Exported. +- `Algorithm/EqSat/SearchSR.hs`: new `fitBatch :: Bool -> (Fix SRTree -> RndEGraph + (Double,[Target])) -> [EClassId] -> RndEGraph ()`. Canonicalizes ecs, drops + already-fitted unless `force`, snapshots the EGraph, splits the shared StdGen + once into per-worker generators (global RNG draw order changes, accepted), + `setMTPopParallel True` -> `mapConcurrently (mapM (runJob eg fitFun)) (chunk + nCaps jobsG)` (round-robin chunks) -> `setMTPopParallel False`, then serially + `insertFitness` the results (single serial writer, correctness preserved). +- `Search.hs`: all four fitness call sites converted to fitBatch — initial + terms (`evaluateUnevaluated` replacement), initial `replicateM nPop` pop, + generation loop refits (`force=True`, drained from `_refits`) + offspring + (`force=False` = updateIfNothing semantics), run before Pareto selection so + ranking sees fresh fitness. Removed dead `refitChanged`; evolve no longer fits. + +CRITICAL BUG FOUND AND FIXED: the batch measured ~0.001s wall at N4 with 4 +chunks running and NO speedup, because `runRndEGraph` returns the fitness as a +LAZY thunk. `insertFitness` forced it later, serially, in the main thread, so +all NLopt work happened on one core regardless of -N. Fix: in `runJob`, force +results to NF inside the worker: `evaluate (DeepSeq.force f)` / +`evaluate (DeepSeq.force p)` (Target = Vector Double, both NFData). After the +fix the batch wall reflects the real work and scales. + +Results (real config, data.tsv, 100 pop, 1 gen, opt-retries 1): +- MultiThread: N1 ~10-13s, N2 ~7-9s, N4 ~6-7s (was ~12/N1, 6-8/N4 — modest + gain; MT already chunk-parallel per tree internally). +- SingleThread (clean test, no inner parallelism): N1 ~27-28s -> N4 ~11-12s, + i.e. ~2.4x scaling from the population parallelism (was flat 24s -> 23s). +- All runs rc=0, output sane (losses/R2 in normal range). + +## Session: why "per-tree" parallelism shows little gain — 2026-08-04 +User repro config (48s old MT -N1): `-d data.tsv --non-terminals add,mul,div,exp,log,power +-s 50 -g 1 --nPop 300 --pc 0.0 --pm 1.0 --generational --opt-retries 1 --opt-iter 100 +--folds 1`. + +Measured (current build, with fitBatch): +- MT -N1: 56-61s, MT -N8: 28s (2x) +- ST -N1: 160s, ST -N8: 44-51s (3x) +So ST at -N8 (~44s) only matches MT at -N1 (~56-61s) — confirms user's observation. +MT's value is its vectorized per-even eval, not multi-core. Across-tree concurrency is a +weak lever because NLopt is serial WITHIN a tree (can only parallelize each eval call). + +Phase breakdown (PHASE_DEBUG instrumentation, MT -N8): popFit ~3.6s, gen1Evolve ~12s +(serial), FitRefits ~6s, FitNew ~3.3s. The serial `gen1Evolve` cap is ENTIRELY one +`runEqSat myCost rewrites 1` call = 12.6s on the first evolve (saturating the ~300-tree +egraph); subsequent invocations are 1-3ms. runEqSat is pure serial state over the shared +egraph (Algorithm/EqSat.hs:106-135); tournament/combine are negligible (~1ms). + +Strategy A/B at -N8 (env gates, since removed): round-robin chunk (current) 26-28s == +per-tree-thread flat 28-30s, and population-serial + MT inner-parallel (old model) is +WORSE 35-37s. So round-robin across trees + single-chunk-per-tree (fitBatch) is best. +Fit-batch scaling ceiling ~3x is inherent (NLopt serial iterations + memory BW). + +Conclusion: feature works and is beneficial for ST (160->44) and gives MT -N8=28s +(faster than old MT -N1). Do NOT expect larger per-tree speedup: the remaining wall is +the serial one-shot eqsat (12.6s) and the serial gen loop, neither parallelizable without +redesigning eqsat/egraph. + +## Session: fixed the one-shot eqsat serial cost (variable ordering) — 2026-08-04 (cont.) + +Root cause of the 12.6s serial `runEqSat myCost rewrites 1` call: the egraph is SHARED +across the whole population (`StateT EGraph` in `egraphGP`, all 300 initial trees +inserted at `Search.hs:109`), so the FIRST `runEqSat` call matches over the full +300-tree egraph while every later call matches a much-reduced/saturated one (~20-25 +matches, ~0s). The `>1500`-eclass throttle never engages because `runEqSat ... 1` is +called with `maxIter=1`, and `go`'s `it == 1 || not changed` branch always returns after +exactly one iteration — throttling is only reachable at `it > 1`. + +Instrumented `runEqSat`/`matchCached` (env-gated, since removed) and found the cost is +NOT in `applyMatch`/`rebuild` (~0.06s) but in match ENUMERATION +(`matchCached`/`genericJoin`, srtree/src/Algorithm/EqSat/DB.hs): the first call produces +~1750 matches, ~90% from the two simplest identity rules `x*y` and `x+y` (any mul/add +node matches). Root cause of the enumeration cost: `DB.hs:orderedVars` breaks ties (equal +`varCost`) by variable id, which puts PATTERN-VARIABLE leaves (low ids, e.g. x=0, y=1) +before the query ROOT (high id, freshly assigned, e.g. v=256). Since the root indexes the +operator trie directly, matching leaves first forces `domainX`/`intersectTries` to fold +over the ENTIRE operator trie once per candidate of the earlier variables (`O(candidates +x nodes)`), and — per `intersectTries`'s single-occurrence shortcut (DB.hs:298-304) — +the root's own domain, computed last, ignores the already-bound children entirely, +returning ALL nodes of that operator regardless of consistency. + +Fix (srtree/src/Algorithm/EqSat/DB.hs, `orderedVars`): break ties by preferring a +variable that is the ROOT of some atom (an "atom header") over pattern-variable leaves, +so `domainX` on the root does ONE fold over the trie (the true set of candidate nodes), +and children are then resolved by direct trie descent instead of repeated whole-trie +folds. This only changes the TIE-BREAK order among equal-`varCost` variables — it does +not change the frequency-based primary heuristic that already handles multi-atom +patterns (shared subterms still sort first). + +IMPORTANT CAVEATS actually verified (not assumed): +- The two orderings do NOT enumerate an identical match SET on the same call (verified + via per-rule match counts on the same seed: ~21/46 rules differ in count on the first + call, e.g. `x*y` 1738->1787 total). This means the OLD ordering was itself not a + faithful/complete enumeration (the shortcut described above lets it over/under count + depending on trie shape) — reordering is not a neutral optimization, so it was + quality-gated empirically rather than assumed safe. +- Quality gate (median best `loss_train`, same seed `-s 50`, 5 runs each, MT -N8; NB: MT + is not run-to-run deterministic due to thread scheduling / parallel fitBatch order): + old ordering: median 153.01, range [146.11, 155.72] (spread 9.6) + new ordering: median 151.80, range [149.88, 153.01] (spread 3.1) + -> not worse (marginally better median, much tighter spread) - PASSES the gate. +- Wall clock (user config, clean build, single run each): + MT -N1: 56-64s -> 49s | MT -N8: 28-31s -> 17s (was ~2x scaling, now ~2.9x) + ST -N1: 160s -> 125s | ST -N8: 44-51s -> 35s (was ~3x scaling, now ~3.6x) + Shrinking the serial one-shot-eqsat bottleneck let the existing fitBatch parallelism + scale better too (not just a flat-time win). + +Change is a single ~10-line tie-break in `orderedVars`; no callers changed, no env +gates left in the tree (all debug instrumentation used to diagnose this was added and +then fully removed again). + +## Session: unified SingleThread onto the vectorized AD kernel — 2026-08-04 (cont.) + +Investigated remaining speedup levers after the eqsat fix. Found that the `SingleThread` +ADBackEnd was not just "MultiThread minus threads" — it called a genuinely slower +algorithm: +- `compileFunAndGrad SingleThread` -> `evalGrad` (`srtree/src/Algorithm/SRTree/AD/Unboxed.hs:206-272`, + row-fused): outer loop over data ROWS, re-dispatching the node-kind `case` on + EVERY row -> scalar/branchy, no SIMD across rows. +- `compileFunAndGrad MultiThread` -> `evalGradMulti` -> always `evalGradVec` + (`Unboxed.hs:293-377`, node-outer), even at a single chunk/capability: processes rows in + 1024-row chunks, dispatching the opcode ONCE per node then running a tight straight-line + loop over the chunk (`fwdBin`/`bwdBin`/etc, lines 494-690) with a literal lambda + operator -- exactly what LLVM can auto-vectorize under the forced `-fllvm -mavx2 -mfma` + build flags. (Initially assumed this kernel allocates `O(nodes*m)`; verified from source + that it's already chunked to `stride*1024` buffers, same memory profile as `evalGrad` -- + no memory-cost tradeoff, purely a loop-shape/vectorization difference.) +- Both operate on the identical `CompiledTree` produced by `compileTree`; `evalGrad` had + exactly one caller (the `SingleThread` branch) plus one dead commented-out reference -- + very small blast radius. + +Fix (`srtree/src/Algorithm/SRTree/AD.hs`): `compileFunAndGrad SingleThread` now calls +`evalGradVec` instead of `evalGrad` (drop-in, since both consume the same `CompiledTree`). + +Caveat verified (not assumed): summation order differs slightly between row-at-a-time +(`evalGrad`) and chunk-of-1024 (`evalGradVec`) accumulation -- same left-to-right row +order, but grouped into 1024-row partial sums rather than one running total, so results +are not bit-identical (though should be equally or more numerically stable). Quality-gated +empirically: +- 5 runs each, same seed `-s 50`, `--backend SingleThread +RTS -N1`: + before: median best `loss_train` 153.01, range [152.83, 154.44] + after: median best `loss_train` 153.01 (identical median), range across 8 runs + [148.91, 161.51] -- one high outlier balanced by a new best-low; verified it's + not a correctness bug (no NaNs, converged population, just a different local + optimum -- normal GP stochastic variance from fitBatch's async scheduling). + -> PASSES the gate (median unchanged, spread comparable). +- Wall clock (user config, clean build): + ST -N1: 122-147s -> 48-54s (~61% faster) + ST -N8: 35s -> 16-18s, now equal to MT -N8 (18s) -- the ST/MT gap is fully closed, + since population-level cross-tree parallelism (`fitBatch`) already benefited both + backends equally; only the per-tree kernel differed. + MT -N1 (46-48s) and MT -N8 (17-18s) unaffected, as expected (untouched code path). + +## Session: phase-timed the post-fix generation loop — 2026-08-04 (cont.) + +With eqsat's one-shot cost fixed, re-profiled `egraphGP` (user config, MT -N8, ~18s +total) with temporary real-IO wall-clock timers (via the existing `io :: IO a -> +RndEGraph a` escape hatch -- no `unsafePerformIO`/CSE issues like the eqsat +micro-timing, since `RndEGraph` already runs in `IO`). Instrumentation was added, +measured once, and fully removed afterward (`git diff` on `Search.hs` is empty). + +Phase breakdown (single run, MT -N8): +``` +insertTerms+fitBatchUneval 2.97s +insertRndExpr+fitBatchPop 3.54s +evolveLoop (300x tournament/crossover/mutate/eqsat/cleanDB) 0.64s <- was ~12s pre-fix +fitBatchRefits 8.01s <- now the single largest phase +fitBatchNewPop 2.99s +cleanEGraph / selection / paretoFront ~0.26s (not triggered / cheap for -g 1) +``` +Confirms the eqsat fix worked (evolveLoop dropped from ~12s to 0.64s) and reveals a new +dominant cost: `fitBatchRefits` (8.0s, ~44% of total). Investigated why it costs more +than `fitBatchNewPop` (which fits the 300 new offspring): `refitIds` (eclasses flagged +dirty by eqsat merges during the evolve loop, via `_refits . _eDB`) numbered **868** +vs 300 new offspring -- each evolve call's one-shot eqsat pass merges/touches ~2.9 +*other* existing egraph classes on average (not just the offspring), so nearly 3x more +individuals get NLopt-refit than are newly created. This is a real, quantified next +optimization target (not yet acted on) -- e.g. deduplicating/capping the refit set, or +checking whether a refit's fitness actually needs recomputing before paying for NLopt. + +No code changes from this session (purely diagnostic); `Search.hs` is back to its +original committed state. + +## Session: fixed `Map.!` crash from egraph cleaning — 2026-08-05 + +**Bug:** intermittent `Map.!: given key is not an element in the map`, only with +`--generational` and only once the shared egraph crosses `maxMem = 2000000` enodes. + +**Root cause:** `Search.hs` `cleanEGraph` wipes the egraph (`put emptyGraph`) when +`totSz > max maxMem (_nPop args)` and re-inserts only the per-size pareto exprs via +`fromTrees`, but returned no ids. The `--generational` branch then ran +`Prelude.mapM canonical newPop'` on pre-wipe offspring ids against the fresh empty +`_canonicalMap` -> missing key in `canonical` (`srtree/.../Egraph.hs:242` +`m IntMap.! eclassId`). Non-generational survived because when `full` it re-derives +all ids from the fresh graph (`getTopFitEClassThat`). + +**Fix (src/Search.hs only):** +- `cleanEGraph` now returns `newIds` (the fresh re-inserted eclass ids). +- Generational branch uses them when `full`: + `maybe (Prelude.mapM canonical newPop') pure cleanedIds`. + Population after a clean = the re-inserted pareto ids (10 x maxSize); the next + generation's `evolve` regenerates the full `_nPop`. Pareto-only survival, matching + the existing non-generational clean semantics. + +**Validation:** temporarily lowered `maxMem` to 1 to force the clean path; both +`--generational` and default branches ran 3 generations with cleaning every +generation, exit 0, valid CSV, no crash. Threshold reverted to 2000000 and rebuilt. +Real config (`-s 50 -g 1 --nPop 300 --generational`, 54s @ -N1): no cleaning, exit 0. + +**Noted, not fixed:** `cleanMaps` (`srtree/.../Build.hs`) has the same latent +id-invalidation hazard but is unreachable today (only via `throttle`, which needs +`maxIter > 1`; the loop runs one-shot eqsat). + +## Session: shrink `fitBatchRefits` (refit only when the class best changed) — 2026-08-05 (cont.) + +Follow-up on the 2026-08-04 phase profile that showed `fitBatchRefits` at 8.0s of ~18s +(868 refits/gen). Goal per approved scope: reduce refit work (Tier 1: fewer fits, Tier 2: +warm-starts) without changing `joinData` semantics; every change gated with 5-run A/B +quality tests. All measurement instrumentation was env-gated (`EGGP_STATS=1`) and fully +removed afterward (`git diff src/Search.hs` is back to the `Map.!` fix only). + +**Step 0 — instrumentation + baseline (single run, MT -N8, user config).** Counters at the +three dirty sites in `srtree/.../Build.hs` (merge / repairAnalysis / modifyEClass) classify +each refit flag by cause; `srtree/.../SearchSR.hs` counts NLopt evals and dedup. Baseline: +``` +refits=744 merge(best=99,other=17) repair(best=275,other=384) modEC=1 +refitJobs=744 distinctBest=738 evals=4718 +``` +Findings: +- Only **375/744 (50%)** refits had a changed `_best`; the other 401 were cost/size/ + `maxMaybe` churn with an unchanged best tree. Refitting those recomputes an identical + fitness (theta is tied to `_best`) -> pure waste. +- Dedup negligible (738/744 distinct) -> abandoned idea (dedup the refit set). +- ~6.3 evals/refit -> NLopt (TNEWTON) converges far before `opt-iter=100` -> abandoned the + idea of a `--refit-iter` budget. + +**Idea 4 (kept): refit iff `_best` changed AND the class already has a fitness.** +`Build.hs` repairAnalysis + mergeClasses now insert into `_refits` only when +`bestChanged && isJust (_fitness old-class)`. The `isJust` clause drops never-fitted +intermediates: they are invisible to ranking (absent from `_fitRangeDB`) and never reported +(`paretoFront` re-verifies via safe `refit`), so skipping them is lossless. First attempt +used `bestChanged || isNothing` (744->650, kept intermediates); sharpened to +`&& isJust` (-> ~24-43). + +**Gate (5 runs each, MT -N8, same config):** median best `loss_train` (proper CSV parse; +`awk` is broken by commas inside the quoted Numpy/Math columns): +``` +baseline (always-refit): wall 15.96-18.33s, refits 655-910, median loss_train 152.52 +idea 4 (best-changed): wall 11.0-12.8s, refits ~35-43, median loss_train 153.01 +final clean build: wall 11.7-14.0s, median loss_train 153.01 +``` +~30% faster; loss delta +0.49 is within the documented -N8 scheduling noise (historical +range spreads 9.6; idea4 median exactly equals the older 153.01 baseline median). PASSES. +`joinData` untouched (`Info.hs:41-64`). + +**Warm-start (idea 1) — evaluated, NOT implemented.** With refit volume collapsed to ~40, +the refit phase is now ~0.3s/gen; warm-starting those fits (or the final `paretoFront` +re-verify, ~0.2s) would save <2% wall. Not worth the semantic change + a gate. + +**Post-idea4 phase profile (MT -N8, ~12s, phase timers added then removed):** +``` +insertTerms 1.68s (eqsat build of initial DB) +unevalFit (9 vars) 1.78s (0 NLopt evals; 9 bare vars, ~200ms each = compile+eval + over the 103,897-row data.tsv) +initPop (300 fits) 3.7s (1570 evals) +evolveLoop 0.66s (tournament/crossover/mutate/one-shot eqsat) +fitBatchRefits 0.32s (111 evals) <- was 8.0s +fitBatchNewPop 3.8s (1420 evals) +paretoFront 0.17s +``` +Remaining wall is dominated by the required population fits (600 trees/gen) over the +~104k-row dataset (~20ms per NLopt eval); no further easy lever within the approved scope +(subsampling/batching would be a search-semantics change, out of scope). + +**Files changed (final state):** `srtree/src/Algorithm/EqSat/Build.hs` (the two idea-4 +refit-flag conditions; counters removed); `eggp/src/Search.hs` (`Map.!` fix only — the +refit cause/evals/phase instrumentation was removed). No env gates left in the tree. + + +## Session: intermittent `Map.!: given key is not an element in the map` crash — 2026-08-05 + +**Symptom:** steady-state (non-generational) run with `--simplify` crashed intermittently +with `Map.!: given key is not an element in the map` on: +`-d gaussian_train.csv:::y:0,1 -t gaussian_test.csv:::y:0,1 -s 15 -g 1000 --nPop 200 +--non-terminals add,mul,div,log,exp --opt-retries 20 --opt-iter 10 --simplify +RTS -N8 -RTS`. +Not reproduced across 4 full runs of that config (RNG-dependent — only fires when the +simplified front expression's e-graph hits specific shapes). + +**Method:** wrote a standalone stress harness (`ghc -O1 -package srtree`, not part of the +repo) generating random small trees and hammering 4 code paths per tree: `simplifyEqSatDefault` +(the `--simplify` path), `eqSat` with `rewritesParams` (the search's `evolve` path), +raw `runEqSat` + `recalculateBest`, and AD `compileFunAndGrad MultiThread`. This reproduced +the crash on the very first adversarial tree (nested `log`/`exp`, e.g. +`Log((((Exp(x1) / (x1 + 2.0)) / (x1 * x1)) * (x1 * x1)))`), then two more bugs after each fix, +in the `--simplify` path only (`simplifyEqSatDefault t = eqSat t rewrites myCost 30`, +maxIt=30 — the search's own `runEqSat myCost rewritesParams 1` never triggers any of these +because `it == 1` short-circuits before the code paths below run). + +**Bug 1 — `recalculateBest`/`fillUpCosts` (`srtree/.../EqSat.hs`):** `log(exp x) :==: exp(log x)` +-style rules can create a cycle in the e-graph reachable from the root. `fillUpCosts`'s +bottom-up fixpoint never assigns a cost to a class in a cycle (no leaf anchor), so +`costs Map.! eid'` (the user's exact error) crashes on the root. Fix: made `nodeCost` total — +a missing child (still uncosted/cyclic) contributes cost 0 and a `Const 0` placeholder +expression, so every class gets a finite cost and expression by the end of the fixpoint. + +**Bug 2 — `getChildrenData`/`getChildrenMinHeight` (`srtree/.../Info.hs`):** looked up raw +(non-canonicalized) e-node child ids directly in `_eClass`. `repairAnalysis` calls +`makeAnalysis` on an e-node whose children may have been merged away since it was queued; +the raw child id is no longer a key in `_eClass` -> `IntMap.!` crash. Fix: `canonical` the +child ids first. + +**Bug 3 — `cleanMaps` (`srtree/.../Build.hs`), triggered by `runEqSat`'s `throttle` +(`srtree/.../EqSat.hs`) when the e-graph exceeds 1500 classes during a `maxIt > 1` run:** +`cleanMaps` replaced `_canonicalMap` with **only the identity entries** +(`IntMap.filterWithKey (\k v -> k == v)`) while `_nextId` keeps counting and `_patDB`/ +`_worklist`/`_analysis`/`_unevaluated`/`_refits` still reference now-untracked stale ids. +Any subsequent `canonical` call on such a stale id crashes (`IntMap.!` on `_canonicalMap`). +This was previously noted as "latent, unreachable" (see the `--generational` session above) +because the search's `runEqSat ... 1` never throttles, but `--simplify`'s `maxIt=30` does. +Fix: `cleanMaps` now keeps `_canonicalMap` **complete** (drop the identity filter) so +`canonical` stays total for every id ever created, while `_eClass` is still pruned to live +representatives (the actual memory-saving part of `cleanMaps`). + +**Bug 4 — AD `insertKey` (`srtree/.../SRTree/AD/Unboxed.hs`):** eqsat constant folding can +produce `Const NaN` nodes (e.g. `Infinity/Infinity`), which `--simplify`'s output can then +contain. `Data.Map`'s `Ord Double` is not a valid total order for `NaN` in a way that keeps +`member`/`lookup` consistent — empirically, `Map.insert (Const NaN) v m; Map.member (Const NaN) m` +can return `False` right after the insert (verified with a standalone repro: inserting two +different `NaN`-producing `Const` expressions, `member`/`lookup` on either key returned +`Nothing`/`False` even immediately after insertion). `insertKey`'s `member`-then-`lookup` +pattern hit this and crashed with `UNBOXED_GETKEYS_MISSING` (`a Map.! k`, `Map.!:` in the +original). Fix: do a single `Map.lookup`; on a miss, take the fresh id `insEntry` assigns +directly instead of looking the key back up. Repeated `NaN` nodes just get separate ids +(no CSE for them), which is harmless — their static value is recomputed identically either way. + +**Validation:** custom stress harness (`ghc -O1 -package srtree`, 60 random trees x 4 exercise +paths) ran clean (`STRESS OK: no crash`) after all 4 fixes. Real user config re-run 3x +steady-state + 1x `--generational`, all `exit=0`, empty stderr, valid CSV output. + +**Files changed:** `srtree/src/Algorithm/EqSat.hs` (`recalculateBest`/`fillUpCosts` total +`nodeCost`), `srtree/src/Algorithm/EqSat/Info.hs` (`getChildrenData`/`getChildrenMinHeight` +canonicalize), `srtree/src/Algorithm/EqSat/Build.hs` (`cleanMaps` keeps `_canonicalMap` +complete), `srtree/src/Algorithm/SRTree/AD/Unboxed.hs` (`insertKey` NaN-safe lookup). Also +added defensive `error`-with-context in place of a few other bare `Map.!`/`IntMap.!` calls +that were audited and found always-safe today (`matchCached`, `reprPrat`, `constrainOnVal`, +`canonical`, `getEClass`) so any future regression fails with a diagnosable message instead +of the bare containers error; added `HasCallStack` to the eqsat call chain for the same +reason. No behavior change on any path that doesn't hit the four bugs above. diff --git a/baddata.csv b/baddata.csv new file mode 100644 index 0000000..f16dac8 --- /dev/null +++ b/baddata.csv @@ -0,0 +1,501 @@ +x1,x2,y +1.2393644,-4.643872,1.252991 +0.26108742,2.8663802,1.5509158 +0.6849086,-1.5439928,1.3824545 +1.079956,2.8306918,3.4226975 +0.048182845,-3.950752,1.5020465 +1.0227747,3.3030548,3.5600586 +-1.4265628,3.8440218,7.824298 +1.0359294,3.4447632,3.7799976 +0.46859014,2.2893467,1.6396097 +1.4089837,2.3426332,4.6835737 +1.1939023,-0.34855604,1.413047 +-0.615285,-0.356462,1.4756235 +0.03748989,-3.0320704,1.503132 +1.1581023,0.9097123,1.9297155 +0.68176436,-4.744624,1.2837676 +1.2758427,-0.9455843,1.3122584 +-1.3078312,-1.1782937,1.290133 +-1.4334493,0.54502106,1.881682 +0.5607505,4.7922354,2.0810168 +-0.30201483,-1.43928,1.476357 +0.42699075,-3.7207985,1.3881521 +0.54088545,1.4012766,1.6107864 +1.3066447,-4.270253,1.2529578 +0.8262739,-3.4292054,1.2798806 +1.2570257,4.7544746,7.51452 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+[acc] eval 4.126577e-3 s +[acc] eval 4.402668e-3 s +[acc] eval 5.471951e-3 s +[acc] eval 4.310164e-3 s +[acc] eval 3.64803e-3 s +[acc] eval 4.163857e-3 s +[acc] eval 3.145944e-3 s +[acc] eval 3.37815e-3 s +[acc] eval 3.587017e-3 s +[acc] eval 3.832397e-3 s +[acc] eval 4.030346e-3 s +[acc] eval 3.955417e-3 s +[acc] eval 3.744949e-3 s +[acc] eval 3.808688e-3 s +[acc] eval 3.555163e-3 s +[acc] eval 3.083458e-3 s +[acc] eval 3.080156e-3 s +[acc] eval 3.046579e-3 s +[acc] eval 4.571993e-3 s +[acc] eval 4.301965e-3 s +[acc] eval 3.262411e-3 s +[acc] eval 4.232172e-3 s +[acc] eval 0.329695191 s +[acc] compile 0.119902571 s +[acc] eval 1.4481623e-2 s +[acc] eval 1.1050019e-2 s +[acc] eval 1.3591664e-2 s +[acc] eval 2.0517458e-2 s +[acc] eval 6.911327e-3 s +[acc] eval 7.775254e-3 s +[acc] eval 4.332085e-3 s +[acc] eval 5.058598e-3 s +[acc] eval 6.099726e-3 s +[acc] eval 7.830303e-3 s +[acc] eval 4.085327e-3 s +[acc] eval 3.51176e-3 s +[acc] eval 0.230281629 s +[acc] compile 0.165436383 s +[acc] eval 2.8061207e-2 s +[acc] eval 0.196048836 s +[acc] compile 0.243277694 s +[acc] eval 1.2584704e-2 s +[acc] eval 3.193267e-3 s +[acc] eval 2.921975e-3 s +[acc] eval 2.759682e-3 s +[acc] eval 2.719907e-3 s +[acc] eval 2.602856e-3 s +[acc] eval 2.870885e-3 s +[acc] eval 3.219554e-3 s +[acc] eval 3.515545e-3 s +[acc] eval 3.305954e-3 s +[acc] eval 3.074495e-3 s +[acc] eval 2.889658e-3 s +[acc] eval 0.292922555 s +[acc] compile 0.285565289 s +[acc] eval 3.5415885e-2 s +[acc] eval 0.325630188 s +[acc] compile 8.506327e-3 s +[acc] eval 6.353497e-3 s +[acc] eval 1.9032171e-2 s +[acc] compile 0.464056285 s +[acc] eval 2.4872293e-2 s +[acc] eval 0.501603539 s +[acc] compile 5.584367e-3 s +[acc] eval 3.652177e-3 s +[acc] eval 8.60399e-4 s +[acc] eval 7.22918e-4 s +[acc] eval 1.2334728e-2 s +[acc] compile 6.851658e-2 s +[acc] eval 1.1895182e-2 s +[acc] eval 3.44143e-3 s +[acc] eval 2.891196e-3 s +[acc] eval 1.2105097e-2 s +[acc] eval 3.125261e-3 s +[acc] eval 2.799408e-3 s +[acc] eval 2.635464e-3 s +[acc] eval 2.188526e-3 s +[acc] eval 1.966764e-3 s +[acc] eval 1.883703e-3 s +[acc] eval 1.960095e-3 s +[acc] eval 1.884817e-3 s +[acc] eval 0.120572774 s +[acc] compile 0.222781961 s +[acc] eval 2.7588178e-2 s +[acc] eval 0.258037335 s +[acc] compile 5.64818e-4 s +[acc] eval 4.168983e-3 s +[acc] compile 8.0610242e-2 s +[acc] eval 2.6167117e-2 s +[acc] eval 1.4929928e-2 s +[acc] eval 5.264696e-3 s +[acc] eval 5.455828e-3 s +[acc] eval 3.770974e-3 s +[acc] eval 3.548499e-3 s +[acc] eval 3.495597e-3 s +[acc] eval 3.66193e-3 s +[acc] eval 3.861396e-3 s +[acc] eval 3.78664e-3 s +[acc] eval 3.271083e-3 s +[acc] eval 3.590075e-3 s +[acc] eval 3.687003e-3 s +[acc] eval 4.152938e-3 s +[acc] eval 4.070433e-3 s +[acc] eval 4.231572e-3 s +[acc] eval 4.086356e-3 s +[acc] eval 3.870793e-3 s +[acc] eval 0.19180584 s +[acc] compile 5.39915e-3 s +[acc] eval 6.287036e-3 s +[acc] eval 9.90159e-4 s +[acc] eval 7.18955e-4 s +[acc] eval 9.59251e-4 s +[acc] eval 6.30828e-4 s +[acc] eval 1.223263e-3 s +[acc] eval 1.498927e-3 s +[acc] eval 1.212158e-3 s +[acc] eval 1.242757e-3 s +[acc] eval 9.38253e-4 s +[acc] eval 1.078514e-3 s +[acc] eval 1.24908e-3 s +[acc] eval 2.6151357e-2 s +[acc] compile 8.26792e-4 s +[acc] eval 1.4912595e-2 s +[acc] compile 1.1293708e-2 s +[acc] eval 2.6998483e-2 s +[acc] eval 4.3415135e-2 s +[acc] compile 6.40759e-4 s +[acc] eval 1.5494592e-2 s +[acc] compile 0.17870293 s +[acc] eval 3.6086484e-2 s +[acc] eval 0.217382967 s +[acc] compile 0.545726914 s +[acc] eval 2.9581419e-2 s +[acc] eval 1.4233681e-2 s +[acc] eval 5.103915e-3 s +[acc] eval 5.334376e-3 s +[acc] eval 4.460919e-3 s +[acc] eval 4.65382e-3 s +[acc] eval 4.642266e-3 s +[acc] eval 1.1636899e-2 s +[acc] eval 4.200995e-3 s +[acc] eval 4.461385e-3 s +[acc] eval 4.538405e-3 s +[acc] eval 8.107146e-3 s +[acc] eval 0.652658966 s +[acc] compile 6.6502386e-2 s +[acc] eval 1.0223352e-2 s +[acc] eval 7.9596703e-2 s +[acc] compile 1.0073007e-2 s +[acc] eval 2.1258545e-2 s +[acc] eval 3.3869584e-2 s +[acc] compile 3.29921e-4 s +[acc] eval 8.791268e-3 s +[acc] compile 0.114167173 s +[acc] eval 1.7145795e-2 s +[acc] eval 0.132638463 s +[acc] compile 4.84754e-4 s +[acc] eval 5.330605e-3 s +[acc] compile 0.117500256 s +[acc] eval 1.3539218e-2 s +[acc] eval 0.141116163 s +[acc] compile 4.254437e-3 s +[acc] eval 7.459071e-3 s +[acc] eval 1.158759e-3 s +[acc] eval 7.81739e-4 s +[acc] eval 9.88293e-4 s +[acc] eval 1.5918987e-2 s +[acc] compile 3.220134e-3 s +[acc] eval 5.686216e-3 s +[acc] eval 1.206104e-3 s +[acc] eval 1.088272e-3 s +[acc] eval 1.282669e-3 s +[acc] eval 1.4131215e-2 s +[acc] compile 0.11399323 s +[acc] eval 3.1861852e-2 s +[acc] eval 3.852394e-3 s +[acc] eval 3.532524e-3 s +[acc] eval 3.653495e-3 s +[acc] eval 3.463906e-3 s +[acc] eval 3.220279e-3 s +[acc] eval 3.343395e-3 s +[acc] eval 3.357728e-3 s +[acc] eval 3.388012e-3 s +[acc] eval 3.384681e-3 s +[acc] eval 3.23591e-3 s +[acc] eval 4.015376e-3 s +[acc] eval 0.190561991 s +[acc] compile 5.04686e-3 s +[acc] eval 2.003058e-2 s +[acc] eval 1.938341e-3 s +[acc] eval 2.064931e-3 s +[acc] eval 2.055943e-3 s +[acc] eval 2.043797e-3 s +[acc] eval 2.11748e-3 s +[acc] eval 2.060453e-3 s +[acc] eval 2.059847e-3 s +[acc] eval 1.975089e-3 s +[acc] eval 1.983429e-3 s +[acc] eval 1.928988e-3 s +[acc] eval 2.016567e-3 s +[acc] eval 1.818492e-3 s +[acc] eval 1.839011e-3 s +[acc] eval 1.922565e-3 s +[acc] eval 1.696822e-3 s +[acc] eval 1.906203e-3 s +[acc] eval 1.688329e-3 s +[acc] eval 1.816939e-3 s +[acc] eval 6.3763368e-2 s +[acc] compile 0.158744342 s +[acc] eval 2.3475886e-2 s +[acc] eval 0.191502449 s +[acc] compile 0.229168037 s +[acc] eval 1.3944717e-2 s +[acc] eval 5.374335e-3 s +[acc] eval 1.0682683e-2 s +[acc] eval 3.436089e-3 s +[acc] eval 3.361181e-3 s +[acc] eval 3.210325e-3 s +[acc] eval 2.973792e-3 s +[acc] eval 2.736913e-3 s +[acc] eval 3.045365e-3 s +[acc] eval 3.113105e-3 s +[acc] eval 3.816242e-3 s +[acc] eval 3.82341e-3 s +[acc] eval 3.64617e-3 s +[acc] eval 3.796553e-3 s +[acc] eval 3.830543e-3 s +[acc] eval 4.607128e-3 s +[acc] eval 3.630282e-3 s +[acc] eval 3.610084e-3 s +[acc] eval 3.523413e-3 s +[acc] eval 4.294179e-3 s +[acc] eval 3.114969e-3 s +[acc] eval 2.83781e-3 s +[acc] eval 2.693154e-3 s +[acc] eval 0.33351737 s +[acc] compile 2.924868e-3 s +[acc] eval 4.715161e-3 s +[acc] eval 9.09941e-4 s +[acc] eval 6.69125e-4 s +[acc] eval 1.0565171e-2 s +[acc] compile 0.134062512 s +[acc] eval 1.7344054e-2 s +[acc] eval 0.154016846 s +[acc] compile 4.102476e-3 s +[acc] eval 1.0534754e-2 s +[acc] eval 1.6609308e-2 s +[acc] compile 4.9602361e-2 s +[acc] eval 4.632135e-3 s +[acc] eval 5.5667752e-2 s +[acc] compile 3.901107e-3 s +[acc] eval 1.0532394e-2 s +[acc] eval 1.665925e-2 s +[acc] compile 6.6605437e-2 s +[acc] eval 1.5894176e-2 s +[acc] eval 8.5114833e-2 s +[acc] compile 5.883742e-3 s +[acc] eval 6.084221e-3 s +[acc] eval 1.4535255e-2 s +[acc] compile 8.692838e-3 s +[acc] eval 5.899596e-3 s +[acc] eval 3.080722e-3 s +[acc] eval 9.345559e-3 s +[acc] eval 2.430178e-3 s +[acc] eval 2.287606e-3 s +[acc] eval 2.372276e-3 s +[acc] eval 2.217708e-3 s +[acc] eval 2.217867e-3 s +[acc] eval 2.385989e-3 s +[acc] eval 2.553789e-3 s +[acc] eval 2.372734e-3 s +[acc] eval 1.941626e-3 s +[acc] eval 5.1087623e-2 s +[acc] compile 0.124188196 s +[acc] eval 8.159378e-3 s +[acc] eval 1.686049e-3 s +[acc] eval 1.450106e-3 s +[acc] eval 1.493952e-3 s +[acc] eval 0.139054866 s +[acc] compile 0.343404947 s +[acc] eval 2.9404791e-2 s +[acc] eval 0.376077619 s +[acc] compile 0.105083609 s +[acc] eval 7.456852e-3 s +[acc] eval 0.11521114 s +[acc] compile 4.815967e-3 s +[acc] eval 5.497849e-3 s +[acc] eval 1.2611342e-2 s +[acc] compile 5.728258e-3 s +[acc] eval 4.30278e-3 s +[acc] eval 1.390517e-3 s +[acc] eval 8.118396e-3 s +[acc] eval 1.486093e-3 s +[acc] eval 1.077059e-3 s +[acc] eval 9.87473e-4 s +[acc] eval 9.33471e-4 s +[acc] eval 1.115346e-3 s +[acc] eval 1.436897e-3 s +[acc] eval 1.161464e-3 s +[acc] eval 1.200775e-3 s +[acc] eval 1.19887e-3 s +[acc] eval 1.298597e-3 s +[acc] eval 3.3689737e-2 s +[acc] compile 0.203141886 s +[acc] eval 2.2651299e-2 s +[acc] eval 0.229476147 s +[acc] compile 0.169741066 s +[acc] eval 1.3469254e-2 s +[acc] eval 0.186737905 s +[acc] compile 0.133668371 s +[acc] eval 1.7098756e-2 s +[acc] eval 5.469513e-3 s +[acc] eval 4.65889e-3 s +[acc] eval 3.747247e-3 s +[acc] eval 1.1279011e-2 s +[acc] eval 2.865603e-3 s +[acc] eval 3.038019e-3 s +[acc] eval 2.81446e-3 s +[acc] eval 2.685626e-3 s +[acc] eval 2.628333e-3 s +[acc] eval 2.564492e-3 s +[acc] eval 2.770876e-3 s +[acc] eval 0.199532604 s +[acc] compile 0.2842744 s +[acc] eval 3.393513e-2 s +[acc] eval 0.321945042 s +[acc] compile 0.202490283 s +[acc] eval 8.107871e-3 s +[acc] eval 0.214125305 s +[acc] compile 3.974706e-3 s +[acc] eval 8.14155e-3 s +[acc] eval 2.2330795e-2 s +[acc] compile 0.13364131 s +[acc] eval 2.2010983e-2 s +[acc] eval 0.158398918 s +[acc] compile 0.346321869 s +[acc] eval 2.5000164e-2 s +[acc] eval 0.375333905 s +[acc] compile 5.32536e-4 s +[acc] eval 7.021225e-3 s +[acc] compile 0.184578299 s +[acc] eval 1.4060459e-2 s +[acc] eval 0.201345255 s +[acc] compile 5.26714e-4 s +[acc] eval 5.476714e-3 s +[acc] compile 8.1662531e-2 s +[acc] eval 5.786265e-3 s +[acc] eval 9.0805721e-2 s +[acc] compile 4.311294e-3 s +[acc] eval 5.421245e-3 s +[acc] eval 2.316546e-3 s +[acc] eval 1.1845836e-2 s +[acc] eval 1.316254e-3 s +[acc] eval 1.704715e-3 s +[acc] eval 1.520584e-3 s +[acc] eval 1.311585e-3 s +[acc] eval 1.259988e-3 s +[acc] eval 3.3118802e-2 s +[acc] compile 6.043084e-3 s +[acc] eval 7.740604e-3 s +[acc] eval 1.862281e-3 s +[acc] eval 1.974016e-3 s +[acc] eval 2.072639e-3 s +[acc] eval 1.967142e-3 s +[acc] eval 1.986669e-3 s +[acc] eval 2.178767e-3 s +[acc] eval 9.389768e-3 s +[acc] eval 1.622715e-3 s +[acc] eval 1.534125e-3 s +[acc] eval 1.837087e-3 s +[acc] eval 1.942066e-3 s +[acc] eval 1.796089e-3 s +[acc] eval 1.732284e-3 s +[acc] eval 1.454563e-3 s +[acc] eval 1.410479e-3 s +[acc] eval 1.430384e-3 s +[acc] eval 1.38534e-3 s +[acc] eval 1.34856e-3 s +[acc] eval 1.422284e-3 s +[acc] eval 1.362915e-3 s +[acc] eval 1.474823e-3 s +[acc] eval 1.411495e-3 s +[acc] eval 1.472462e-3 s +[acc] eval 1.503977e-3 s +[acc] eval 1.948056e-3 s +[acc] eval 2.317545e-3 s +[acc] eval 2.262312e-3 s +[acc] eval 2.17059e-3 s +[acc] eval 1.981636e-3 s +[acc] eval 7.6773442e-2 s +[acc] compile 7.135427e-3 s +[acc] eval 1.319298e-2 s +[acc] eval 2.735932e-3 s +[acc] eval 6.937111e-3 s +[acc] eval 2.415306e-3 s +[acc] eval 2.171217e-3 s +[acc] eval 2.306298e-3 s +[acc] eval 2.193012e-3 s +[acc] eval 1.982365e-3 s +[acc] eval 1.821409e-3 s +[acc] eval 1.927415e-3 s +[acc] eval 1.821146e-3 s +[acc] eval 1.847734e-3 s +[acc] eval 1.946721e-3 s +[acc] eval 5.4001326e-2 s +[acc] compile 0.187537063 s +[acc] eval 2.2746036e-2 s +[acc] eval 8.444172e-3 s +[acc] eval 2.995191e-3 s +[acc] eval 2.691806e-3 s +[acc] eval 2.894228e-3 s +[acc] eval 3.505049e-3 s +[acc] eval 3.027457e-3 s +[acc] eval 3.669519e-3 s +[acc] eval 3.100664e-3 s +[acc] eval 3.060051e-3 s +[acc] eval 2.950389e-3 s +[acc] eval 2.779032e-3 s +[acc] eval 2.773255e-3 s +[acc] eval 2.660928e-3 s +[acc] eval 2.677015e-3 s +[acc] eval 2.709435e-3 s +[acc] eval 2.983502e-3 s +[acc] eval 2.981518e-3 s +[acc] eval 2.735807e-3 s +[acc] eval 2.613691e-3 s +[acc] eval 2.826759e-3 s +[acc] eval 3.199826e-3 s +[acc] eval 3.159922e-3 s +[acc] eval 3.191827e-3 s +[acc] eval 3.346114e-3 s +[acc] eval 3.498179e-3 s +[acc] eval 3.070892e-3 s +[acc] eval 3.136746e-3 s +[acc] eval 3.26299e-3 s +[acc] eval 3.289996e-3 s +[acc] eval 3.679037e-3 s +[acc] eval 3.175846e-3 s +[acc] eval 3.175872e-3 s +[acc] eval 3.432081e-3 s +[acc] eval 3.42619e-3 s +[acc] eval 3.250348e-3 s +[acc] eval 3.006658e-3 s +[acc] eval 2.71449e-3 s +[acc] eval 3.117551e-3 s +[acc] eval 3.15594e-3 s +[acc] eval 2.92897e-3 s +[acc] eval 3.002171e-3 s +[acc] eval 0.348060953 s +[acc] compile 0.347751544 s +[acc] eval 2.9631221e-2 s +[acc] eval 3.889875e-3 s +[acc] eval 3.465485e-3 s +[acc] eval 2.481962e-3 s +[acc] eval 2.447605e-3 s +[acc] eval 2.420066e-3 s +[acc] eval 2.581369e-3 s +[acc] eval 2.898856e-3 s +[acc] eval 2.520788e-3 s +[acc] eval 2.417924e-3 s +[acc] eval 2.458684e-3 s +[acc] eval 2.391716e-3 s +[acc] eval 2.64122e-3 s +[acc] eval 2.669566e-3 s +[acc] eval 2.547043e-3 s +[acc] eval 2.590513e-3 s +[acc] eval 1.1465011e-2 s +[acc] eval 2.819097e-3 s +[acc] eval 2.870423e-3 s +[acc] eval 2.809209e-3 s +[acc] eval 2.665421e-3 s +[acc] eval 2.630735e-3 s +[acc] eval 2.621076e-3 s +[acc] eval 2.58475e-3 s +[acc] eval 2.57202e-3 s +[acc] eval 2.538439e-3 s +[acc] eval 7.562828e-3 s +[acc] eval 2.548231e-3 s +[acc] eval 2.510309e-3 s +[acc] eval 3.154377e-3 s +[acc] eval 0.476612253 s +[acc] compile 2.975102e-3 s +[acc] eval 1.0832413e-2 s +[acc] eval 8.60086e-4 s +[acc] eval 6.72956e-4 s +[acc] eval 1.023465e-3 s +[acc] eval 1.75226e-2 s +[acc] compile 5.44541e-4 s +[acc] eval 1.8599684e-2 s +[acc] compile 0.125325947 s +[acc] eval 1.6968712e-2 s +[acc] eval 0.145627885 s +[acc] compile 3.23738e-3 s +[acc] eval 5.611382e-3 s +[acc] eval 1.0114942e-2 s +[acc] compile 0.220580962 s +[acc] eval 1.3489933e-2 s +[acc] eval 0.237165726 s +[acc] compile 0.364838505 s +[acc] eval 1.9151783e-2 s +[acc] eval 0.402306535 s +[acc] compile 5.69312e-4 s +[acc] eval 7.252652e-3 s +[acc] compile 6.1261159e-2 s +[acc] eval 1.8917058e-2 s +[acc] eval 9.3009077e-2 s +[acc] compile 0.412999827 s +[acc] eval 2.6991507e-2 s +[acc] eval 0.451015851 s +[acc] compile 0.141373241 s +[acc] eval 1.166052e-2 s +[acc] eval 1.421383e-2 s +[acc] eval 3.827949e-3 s +[acc] eval 4.529579e-3 s +[acc] eval 3.977691e-3 s +[acc] eval 3.749046e-3 s +[acc] eval 3.644249e-3 s +[acc] eval 3.590663e-3 s +[acc] eval 3.533954e-3 s +[acc] eval 4.349081e-3 s +[acc] eval 3.982254e-3 s +[acc] eval 3.874173e-3 s +[acc] eval 3.844182e-3 s +[acc] eval 4.365683e-3 s +[acc] eval 3.501362e-3 s +[acc] eval 3.13889e-3 s +[acc] eval 3.199014e-3 s +[acc] eval 3.693582e-3 s +[acc] eval 0.234476515 s +[acc] compile 6.726544e-3 s +[acc] eval 6.230242e-3 s +[acc] eval 7.243781e-3 s +[acc] eval 1.151859e-3 s +[acc] eval 1.326977e-3 s +[acc] eval 2.4443896e-2 s +[acc] compile 8.252354e-3 s +[acc] eval 1.0118876e-2 s +[acc] eval 2.2030849e-2 s +[acc] compile 0.384375299 s +[acc] eval 2.5566748e-2 s +[acc] eval 0.414063889 s +[acc] compile 0.302813677 s +[acc] eval 1.2509951e-2 s +[acc] eval 0.320080019 s +[acc] compile 0.185058536 s +[acc] eval 2.1819269e-2 s +[acc] eval 0.20982304 s +[acc] compile 0.31241283 s +[acc] eval 1.9977182e-2 s +[acc] eval 5.09684e-3 s +[acc] eval 5.535301e-3 s +[acc] eval 1.3302824e-2 s +[acc] eval 3.702888e-3 s +[acc] eval 3.927763e-3 s +[acc] eval 3.902628e-3 s +[acc] eval 3.716536e-3 s +[acc] eval 3.187697e-3 s +[acc] eval 3.484674e-3 s +[acc] eval 3.140529e-3 s +[acc] eval 3.010326e-3 s +[acc] eval 2.93858e-3 s +[acc] eval 3.024604e-3 s +[acc] eval 3.064413e-3 s +[acc] eval 2.971454e-3 s +[acc] eval 2.944002e-3 s +[acc] eval 3.440606e-3 s +[acc] eval 3.435068e-3 s +[acc] eval 3.334427e-3 s +[acc] eval 3.310136e-3 s +[acc] eval 3.228792e-3 s +[acc] eval 3.184269e-3 s +[acc] eval 3.010452e-3 s +[acc] eval 3.202384e-3 s +[acc] eval 4.084596e-3 s +[acc] eval 3.165374e-3 s +[acc] eval 2.973573e-3 s +[acc] eval 3.002709e-3 s +[acc] eval 2.939311e-3 s +[acc] eval 3.184416e-3 s +[acc] eval 4.050587e-3 s +[acc] eval 3.949137e-3 s +[acc] eval 3.947585e-3 s +[acc] eval 4.059677e-3 s +[acc] eval 0.469224695 s +[acc] compile 5.82832e-4 s +[acc] eval 1.1583288e-2 s +[acc] compile 3.52976e-4 s +[acc] eval 2.5524288e-2 s +[acc] compile 3.001059e-3 s +[acc] eval 5.605826e-3 s +[acc] eval 1.0002543e-2 s +[acc] compile 0.107199931 s +[acc] eval 1.1686178e-2 s +[acc] eval 0.126113278 s +[acc] compile 0.139279499 s +[acc] eval 1.9241803e-2 s +[acc] eval 0.161576004 s +[acc] compile 0.141423186 s +[acc] eval 1.4072818e-2 s +[acc] eval 0.159561504 s +[acc] compile 0.225471976 s +[acc] eval 3.2293674e-2 s +[acc] eval 0.261783043 s +[acc] compile 1.039872e-3 s +[acc] eval 9.654457e-3 s +[acc] compile 0.175290023 s +[acc] eval 1.4113521e-2 s +[acc] eval 0.191482003 s +[acc] compile 0.13494955 s +[acc] eval 3.1492388e-2 s +[acc] eval 5.46579e-3 s +[acc] eval 5.883105e-3 s +[acc] eval 6.88651e-3 s +[acc] eval 5.540427e-3 s +[acc] eval 4.063648e-3 s +[acc] eval 3.156853e-3 s +[acc] eval 3.488093e-3 s +[acc] eval 2.958079e-3 s +[acc] eval 2.806493e-3 s +[acc] eval 2.735009e-3 s +[acc] eval 2.610251e-3 s +[acc] eval 2.794785e-3 s +[acc] eval 2.823216e-3 s +[acc] eval 2.884531e-3 s +[acc] eval 2.910474e-3 s +[acc] eval 3.009444e-3 s +[acc] eval 2.928921e-3 s +[acc] eval 2.977493e-3 s +[acc] eval 2.917403e-3 s +[acc] eval 2.756112e-3 s +[acc] eval 2.79573e-3 s +[acc] eval 3.871719e-3 s +[acc] eval 3.01918e-3 s +[acc] eval 2.693788e-3 s +[acc] eval 2.868717e-3 s +[acc] eval 2.860137e-3 s +[acc] eval 2.739552e-3 s +[acc] eval 2.671878e-3 s +[acc] eval 2.86564e-3 s +[acc] eval 0.269786305 s +[acc] eval 2.094995e-3 s +[acc] eval 5.74192e-4 s +[acc] eval 8.34092e-4 s +[acc] eval 4.334459e-3 s +[acc] eval 3.885526e-3 s +[acc] eval 8.40752e-4 s +[acc] eval 6.676e-4 s +[acc] eval 6.470856e-3 s +[acc] compile 6.1392362e-2 s +[acc] eval 7.222726e-3 s +[acc] eval 2.842302e-3 s +[acc] eval 2.45042e-3 s +[acc] eval 2.49201e-3 s +[acc] eval 2.521481e-3 s +[acc] eval 2.294646e-3 s +[acc] eval 1.48486e-2 s +[acc] eval 1.637854e-3 s +[acc] eval 1.824385e-3 s +[acc] eval 1.549697e-3 s +[acc] eval 1.315289e-3 s +[acc] eval 1.240708e-3 s +[acc] eval 0.106145074 s +[acc] compile 0.17838127 s +[acc] eval 1.2266768e-2 s +[acc] eval 3.048244e-3 s +[acc] eval 2.39629e-3 s +[acc] eval 2.026799e-3 s +[acc] eval 1.0296434e-2 s +[acc] eval 1.713604e-3 s +[acc] eval 1.602372e-3 s +[acc] eval 1.536347e-3 s +[acc] eval 1.590279e-3 s +[acc] eval 1.50979e-3 s +[acc] eval 1.45922e-3 s +[acc] eval 1.849066e-3 s +[acc] eval 0.222776979 s +[acc] compile 0.139279869 s +[acc] eval 2.0952984e-2 s +[acc] eval 3.052215e-3 s +[acc] eval 9.343986e-3 s +[acc] eval 2.461848e-3 s +[acc] eval 2.3684e-3 s +[acc] eval 2.360691e-3 s +[acc] eval 2.308775e-3 s +[acc] eval 2.345117e-3 s +[acc] eval 2.296514e-3 s +[acc] eval 2.255476e-3 s +[acc] eval 2.093558e-3 s +[acc] eval 2.124718e-3 s +[acc] eval 0.196590402 s +[acc] compile 0.22468478 s +[acc] eval 1.1770071e-2 s +[acc] eval 3.269471e-3 s +[acc] eval 3.216764e-3 s +[acc] eval 1.0771399e-2 s +[acc] eval 3.078527e-3 s +[acc] eval 2.552394e-3 s +[acc] eval 2.245565e-3 s +[acc] eval 1.961399e-3 s +[acc] eval 2.116578e-3 s +[acc] eval 2.287445e-3 s +[acc] eval 0.2713276 s +[acc] compile 3.39567e-4 s +[acc] eval 5.870957e-3 s +[acc] compile 4.4526122e-2 s +[acc] eval 5.760334e-3 s +[acc] eval 1.082686e-3 s +[acc] eval 1.148064e-3 s +[acc] eval 9.57818e-4 s +[acc] eval 9.15993e-4 s +[acc] eval 1.054459e-3 s +[acc] eval 1.027618e-3 s +[acc] eval 9.88101e-4 s +[acc] eval 8.32634e-4 s +[acc] eval 1.042791e-3 s +[acc] eval 1.119164e-3 s +[acc] eval 1.251e-3 s +[acc] eval 1.34295e-3 s +[acc] eval 1.693993e-3 s +[acc] eval 7.698222e-3 s +[acc] eval 1.019921e-3 s +[acc] eval 1.435102e-3 s +[acc] eval 7.8300989e-2 s +[acc] compile 5.40387e-4 s +[acc] eval 4.784196e-3 s +[acc] compile 9.820756e-3 s +[acc] eval 1.7782728e-2 s +[acc] eval 9.83716e-4 s +[acc] eval 1.493677e-3 s +[acc] eval 3.1793051e-2 s +[acc] compile 6.496503e-3 s +[acc] eval 1.0428184e-2 s +[acc] eval 2.5940994e-2 s +[acc] compile 3.220357e-3 s +[acc] eval 8.092109e-3 s +[acc] eval 1.3008346e-2 s +[acc] compile 3.833141e-3 s +[acc] eval 7.910607e-3 s +[acc] eval 1.082423e-3 s +[acc] eval 9.51673e-4 s +[acc] eval 1.350094e-3 s +[acc] eval 9.1389e-4 s +[acc] eval 1.00594e-3 s +[acc] eval 1.068033e-3 s +[acc] eval 9.28926e-4 s +[acc] eval 1.072984e-3 s +[acc] eval 7.58706e-4 s +[acc] eval 9.56542e-4 s +[acc] eval 9.35606e-4 s +[acc] eval 7.61455e-4 s +[acc] eval 7.650061e-3 s +[acc] eval 9.41865e-4 s +[acc] eval 9.71145e-4 s +[acc] eval 8.82871e-4 s +[acc] eval 8.32352e-4 s +[acc] eval 8.35588e-4 s +[acc] eval 7.16462e-4 s +[acc] eval 7.87879e-4 s +[acc] eval 1.006725e-3 s +[acc] eval 1.029673e-3 s +[acc] eval 9.9769e-4 s +[acc] eval 9.04771e-4 s +[acc] eval 9.08807e-4 s +[acc] eval 8.58906e-4 s +[acc] eval 9.66817e-4 s +[acc] eval 7.38285e-4 s +[acc] eval 6.6931e-4 s +[acc] eval 4.8102116e-2 s +[acc] compile 2.849008e-3 s +[acc] eval 3.411444e-3 s +[acc] eval 7.60504e-4 s +[acc] eval 8.06667e-4 s +[acc] eval 9.424683e-3 s +[acc] compile 6.188701e-3 s +[acc] eval 1.0598008e-2 s +[acc] eval 9.198531e-3 s +[acc] eval 2.046407e-3 s +[acc] eval 1.9049e-3 s +[acc] eval 2.007694e-3 s +[acc] eval 2.081179e-3 s +[acc] eval 2.413062e-3 s +[acc] eval 1.998283e-3 s +[acc] eval 1.889948e-3 s +[acc] eval 1.92311e-3 s +[acc] eval 1.917648e-3 s +[acc] eval 2.193039e-3 s +[acc] eval 1.994931e-3 s +[acc] eval 1.972794e-3 s +[acc] eval 1.911357e-3 s +[acc] eval 1.932574e-3 s +[acc] eval 1.948976e-3 s +[acc] eval 2.046172e-3 s +[acc] eval 1.935901e-3 s +[acc] eval 1.924991e-3 s +[acc] eval 1.813888e-3 s +[acc] eval 1.892126e-3 s +[acc] eval 2.245774e-3 s +[acc] eval 2.746119e-3 s +[acc] eval 2.443565e-3 s +[acc] eval 1.925205e-3 s +[acc] eval 1.992043e-3 s +[acc] eval 1.784226e-3 s +[acc] eval 1.774867e-3 s +[acc] eval 1.785962e-3 s +[acc] eval 8.7022124e-2 s +[acc] compile 5.841841e-3 s +[acc] eval 1.3478895e-2 s +[acc] eval 2.2452602e-2 s +[acc] compile 3.043963e-3 s +[acc] eval 4.534117e-3 s +[acc] eval 1.5672423e-2 s +[acc] compile 0.183073295 s +[acc] eval 1.2298532e-2 s +[acc] eval 0.197417047 s +[acc] compile 4.833239e-3 s +[acc] eval 5.301377e-3 s +[acc] eval 1.1612165e-2 s +[acc] compile 5.6192237e-2 s +[acc] eval 1.9259917e-2 s +[acc] eval 2.158665e-3 s +[acc] eval 2.199052e-3 s +[acc] eval 1.752163e-3 s +[acc] eval 1.716117e-3 s +[acc] eval 1.634226e-3 s +[acc] eval 1.911885e-3 s +[acc] eval 1.87378e-3 s +[acc] eval 2.080218e-3 s +[acc] eval 2.030925e-3 s +[acc] eval 2.179967e-3 s +[acc] eval 2.426894e-3 s +[acc] eval 0.100973665 s +[acc] compile 5.8556083e-2 s +[acc] eval 8.779638e-3 s +[acc] eval 2.443475e-3 s +[acc] eval 3.812108e-3 s +[acc] eval 1.1319262e-2 s +[acc] eval 1.833201e-3 s +[acc] eval 1.97334e-3 s +[acc] eval 2.567264e-3 s +[acc] eval 1.828747e-3 s +[acc] eval 1.839707e-3 s +[acc] eval 1.860437e-3 s +[acc] eval 1.823156e-3 s +[acc] eval 1.792187e-3 s +[acc] eval 1.676755e-3 s +[acc] eval 1.693423e-3 s +[acc] eval 1.659444e-3 s +[acc] eval 1.67188e-3 s +[acc] eval 1.836465e-3 s +[acc] eval 1.769852e-3 s +[acc] eval 1.783281e-3 s +[acc] eval 2.196065e-3 s +[acc] eval 1.975929e-3 s +[acc] eval 2.306859e-3 s +[acc] eval 2.300725e-3 s +[acc] eval 1.889975e-3 s +[acc] eval 1.65324e-3 s +[acc] eval 1.921539e-3 s +[acc] eval 2.171032e-3 s +[acc] eval 2.34925e-3 s +[acc] eval 2.175228e-3 s +[acc] eval 2.171857e-3 s +[acc] eval 0.139972373 s +[acc] compile 0.23364967 s +[acc] eval 1.114504e-2 s +[acc] eval 0.258559514 s +[acc] compile 0.104446115 s +[acc] eval 5.303159e-3 s +[acc] eval 0.112325145 s +[acc] compile 6.16943e-3 s +[acc] eval 1.8038796e-2 s +[acc] eval 2.6475561e-2 s +[acc] compile 0.122018688 s +[acc] eval 3.281874e-2 s +[acc] eval 0.157394525 s +[acc] compile 2.98095e-3 s +[acc] eval 5.659473e-3 s +[acc] eval 1.0243496e-2 s +[acc] compile 4.915402e-3 s +[acc] eval 6.187235e-3 s +[acc] eval 1.2437991e-2 s +[acc] compile 0.119670209 s +[acc] eval 9.912472e-3 s +[acc] eval 0.134365377 s +[acc] compile 6.8153053e-2 s +[acc] eval 6.016172e-3 s +[acc] eval 1.122042e-3 s +[acc] eval 9.17603e-4 s +[acc] eval 7.778142e-2 s +[acc] compile 9.2925154e-2 s +[acc] eval 1.9962566e-2 s +[acc] eval 0.115567925 s +[acc] compile 3.389623e-3 s +[acc] eval 1.2285398e-2 s +[acc] eval 5.94243e-4 s +[acc] eval 7.1001e-4 s +[acc] eval 1.7887618e-2 s +[acc] compile 5.9281436e-2 s +[acc] eval 6.595748e-3 s +[acc] eval 9.560778e-3 s +[acc] eval 2.688819e-3 s +[acc] eval 2.418313e-3 s +[acc] eval 2.862356e-3 s +[acc] eval 3.299001e-3 s +[acc] eval 2.645608e-3 s +[acc] eval 2.657868e-3 s +[acc] eval 2.755332e-3 s +[acc] eval 2.973483e-3 s +[acc] eval 2.620306e-3 s +[acc] eval 2.278462e-3 s +[acc] eval 2.204541e-3 s +[acc] eval 2.200573e-3 s +[acc] eval 2.327774e-3 s +[acc] eval 2.454363e-3 s +[acc] eval 2.359576e-3 s +[acc] eval 2.475051e-3 s +[acc] eval 2.334869e-3 s +[acc] eval 2.320619e-3 s +[acc] eval 2.274802e-3 s +[acc] eval 2.317317e-3 s +[acc] eval 2.478207e-3 s +[acc] eval 2.692504e-3 s +[acc] eval 2.680182e-3 s +[acc] eval 2.603602e-3 s +[acc] eval 2.810504e-3 s +[acc] eval 2.970376e-3 s +[acc] eval 2.927721e-3 s +[acc] eval 2.689056e-3 s +[acc] eval 2.649741e-3 s +[acc] eval 2.607929e-3 s +[acc] eval 2.529745e-3 s +[acc] eval 0.160929017 s +[acc] compile 4.544504e-3 s +[acc] eval 6.374319e-3 s +[acc] eval 2.569183e-3 s +[acc] eval 2.403178e-3 s +[acc] eval 2.435664e-3 s +[acc] eval 2.369013e-3 s +[acc] eval 8.105742e-3 s +[acc] eval 2.170703e-3 s +[acc] eval 2.086292e-3 s +[acc] eval 2.090218e-3 s +[acc] eval 3.822801e-2 s +[acc] compile 0.264362751 s +[acc] eval 2.4301705e-2 s +[acc] eval 9.5011e-3 s +[acc] eval 3.331441e-3 s +[acc] eval 3.760086e-3 s +[acc] eval 3.197273e-3 s +[acc] eval 2.51731e-3 s +[acc] eval 2.799274e-3 s +[acc] eval 3.3996e-3 s +[acc] eval 3.178259e-3 s +[acc] eval 3.291875e-3 s +[acc] eval 3.429908e-3 s +[acc] eval 3.279405e-3 s +[acc] eval 3.329727e-3 s +[acc] eval 3.188201e-3 s +[acc] eval 3.188861e-3 s +[acc] eval 3.097044e-3 s +[acc] eval 2.765773e-3 s +[acc] eval 2.528638e-3 s +[acc] eval 2.659042e-3 s +[acc] eval 2.785459e-3 s +[acc] eval 2.898654e-3 s +[acc] eval 2.873331e-3 s +[acc] eval 2.825399e-3 s +[acc] eval 2.687876e-3 s +[acc] eval 2.557812e-3 s +[acc] eval 2.700571e-3 s +[acc] eval 3.265359e-3 s +[acc] eval 1.141121e-2 s +[acc] eval 3.057207e-3 s +[acc] eval 3.177915e-3 s +[acc] eval 0.397196642 s +[acc] compile 3.43057e-4 s +[acc] eval 1.0348129e-2 s +[acc] compile 4.594139e-3 s +[acc] eval 7.269454e-3 s +[acc] eval 1.114644e-3 s +[acc] eval 1.363762e-3 s +[acc] eval 1.008666e-3 s +[acc] eval 9.09162e-4 s +[acc] eval 1.052811e-3 s +[acc] eval 1.4588349e-2 s +[acc] eval 1.183795e-3 s +[acc] eval 1.238918e-3 s +[acc] eval 1.273906e-3 s +[acc] eval 3.7874606e-2 s +[acc] compile 5.9625911e-2 s +[acc] eval 8.830522e-3 s +[acc] eval 7.1407761e-2 s +[acc] compile 6.127554e-3 s +[acc] eval 9.329725e-3 s +[acc] eval 3.565207e-3 s +[acc] eval 2.793926e-3 s +[acc] eval 2.5224479e-2 s +[acc] compile 4.24334e-3 s +[acc] eval 1.6985423e-2 s +[acc] eval 3.3838183e-2 s +[acc] compile 6.33591e-4 s +[acc] eval 1.488622e-2 s +[acc] compile 3.512802e-3 s +[acc] eval 5.422424e-3 s +[acc] eval 1.2020569e-2 s +[acc] compile 0.135605317 s +[acc] eval 1.5431575e-2 s +[acc] eval 1.890559e-3 s +[acc] eval 1.79328e-3 s +[acc] eval 1.786998e-3 s +[acc] eval 1.761186e-3 s +[acc] eval 2.022688e-3 s +[acc] eval 1.849887e-3 s +[acc] eval 2.026803e-3 s +[acc] eval 2.095084e-3 s +[acc] eval 2.045665e-3 s +[acc] eval 1.526289e-3 s +[acc] eval 1.620337e-3 s +[acc] eval 2.066032e-3 s +[acc] eval 2.284776e-3 s +[acc] eval 2.221606e-3 s +[acc] eval 2.183353e-3 s +[acc] eval 1.91406e-3 s +[acc] eval 1.394369e-3 s +[acc] eval 1.65659e-3 s +[acc] eval 1.409625e-3 s +[acc] eval 1.431568e-3 s +[acc] eval 2.039388e-3 s +[acc] eval 2.055612e-3 s +[acc] eval 2.085203e-3 s +[acc] eval 1.54444e-3 s +[acc] eval 1.460788e-3 s +[acc] eval 1.379761e-3 s +[acc] eval 1.310332e-3 s +[acc] eval 1.3943e-3 s +[acc] eval 1.47166e-3 s +[acc] eval 1.370783e-3 s +[acc] eval 2.08417e-3 s +[acc] eval 2.088463e-3 s +[acc] eval 0.213116106 s +[acc] compile 7.523192e-3 s +[acc] eval 8.240626e-3 s +[acc] eval 3.543613e-3 s +[acc] eval 3.714603e-3 s +[acc] eval 3.109945e-3 s +[acc] eval 1.023705e-2 s +[acc] eval 2.965774e-3 s +[acc] eval 2.4116e-3 s +[acc] eval 2.410112e-3 s +[acc] eval 2.182315e-3 s +[acc] eval 2.29825e-3 s +[acc] eval 1.967859e-3 s +[acc] eval 2.230884e-3 s +[acc] eval 2.584194e-3 s +[acc] eval 2.245339e-3 s +[acc] eval 2.238858e-3 s +[acc] eval 2.274608e-3 s +[acc] eval 1.975695e-3 s +[acc] eval 2.170781e-3 s +[acc] eval 2.020598e-3 s +[acc] eval 1.839239e-3 s +[acc] eval 1.854312e-3 s +[acc] eval 1.965316e-3 s +[acc] eval 1.83372e-3 s +[acc] eval 2.13734e-3 s +[acc] eval 2.337931e-3 s +[acc] eval 2.402776e-3 s +[acc] eval 2.069462e-3 s +[acc] eval 1.785024e-3 s +[acc] eval 1.738043e-3 s +[acc] eval 1.804487e-3 s +[acc] eval 1.758647e-3 s +[acc] eval 1.999121e-3 s +[acc] eval 1.915797e-3 s +[acc] eval 2.046697e-3 s +[acc] eval 1.814036e-3 s +[acc] eval 1.78793e-3 s +[acc] eval 1.760677e-3 s +[acc] eval 1.778391e-3 s +[acc] eval 1.998835e-3 s +[acc] eval 2.385733e-3 s +[acc] eval 2.323654e-3 s +[acc] eval 2.572829e-3 s +[acc] eval 2.505338e-3 s +[acc] eval 2.516102e-3 s +[acc] eval 0.125463231 s +[acc] compile 0.258946171 s +[acc] eval 1.8271225e-2 s +[acc] eval 4.255251e-3 s +[acc] eval 1.0463675e-2 s +[acc] eval 2.836898e-3 s +[acc] eval 2.787086e-3 s +[acc] eval 2.473372e-3 s +[acc] eval 3.182481e-3 s +[acc] eval 3.616933e-3 s +[acc] eval 2.987705e-3 s +[acc] eval 3.075449e-3 s +[acc] eval 3.083104e-3 s +[acc] eval 3.077115e-3 s +[acc] eval 4.240847e-3 s +[acc] eval 4.116549e-3 s +[acc] eval 3.965675e-3 s +[acc] eval 3.965123e-3 s +[acc] eval 4.506298e-3 s +[acc] eval 3.989704e-3 s +[acc] eval 3.330476e-3 s +[acc] eval 3.193085e-3 s +[acc] eval 3.006396e-3 s +[acc] eval 3.142855e-3 s +[acc] eval 2.985239e-3 s +[acc] eval 2.964659e-3 s +[acc] eval 2.872641e-3 s +[acc] eval 2.794628e-3 s +[acc] eval 2.381451e-3 s +[acc] eval 2.686455e-3 s +[acc] eval 3.261822e-3 s +[acc] eval 4.917785e-3 s +[acc] eval 0.387921045 s +[acc] compile 3.30225e-4 s +[acc] eval 8.322934e-3 s +[acc] compile 0.102700742 s +[acc] eval 1.0958231e-2 s +[acc] eval 9.74712e-3 s +[acc] eval 1.312571e-3 s +[acc] eval 1.580029e-3 s +[acc] eval 1.639263e-3 s +[acc] eval 1.554492e-3 s +[acc] eval 1.265846e-3 s +[acc] eval 1.078153e-3 s +[acc] eval 0.13391851 s +[acc] compile 0.314721308 s +[acc] eval 3.3422364e-2 s +[acc] eval 0.362752833 s +[acc] compile 0.171014643 s +[acc] eval 2.1621924e-2 s +[acc] eval 0.194919137 s +[acc] compile 0.141273883 s +[acc] eval 1.7574306e-2 s +[acc] eval 9.150539e-3 s +[acc] eval 6.891647e-3 s +[acc] eval 7.308745e-3 s +[acc] eval 7.138043e-3 s +[acc] eval 3.12415e-3 s +[acc] eval 4.896994e-3 s +[acc] eval 7.147996e-3 s +[acc] eval 2.519455e-3 s +[acc] eval 3.548705e-3 s +[acc] eval 4.103871e-3 s +[acc] eval 6.794643e-3 s +[acc] eval 0.225254475 s +[acc] compile 7.180155e-3 s +[acc] eval 6.656376e-3 s +[acc] eval 1.6558255e-2 s +[acc] compile 0.102574236 s +[acc] eval 2.0185067e-2 s +[acc] eval 0.134722207 s +[acc] compile 0.181984029 s +[acc] eval 1.2331755e-2 s +[acc] eval 0.199447371 s +[acc] compile 0.155541651 s +[acc] eval 3.0741464e-2 s +[acc] eval 0.194413151 s +[acc] compile 0.120668147 s +[acc] eval 4.479438e-3 s +[acc] eval 0.12703316 s +[acc] compile 0.226296515 s +[acc] eval 1.420226e-2 s +[acc] eval 0.247133496 s +[acc] compile 7.6786421e-2 s +[acc] eval 8.971176e-3 s +[acc] eval 3.652891e-3 s +[acc] eval 2.361356e-3 s +[acc] eval 2.299449e-3 s +[acc] eval 1.994382e-3 s +[acc] eval 1.85043e-3 s +[acc] eval 1.859525e-3 s +[acc] eval 2.071567e-3 s +[acc] eval 1.1175032e-2 s +[acc] eval 1.739409e-3 s +[acc] eval 1.607135e-3 s +[acc] eval 1.494185e-3 s +[acc] eval 1.511954e-3 s +[acc] eval 1.686505e-3 s +[acc] eval 1.435908e-3 s +[acc] eval 1.491764e-3 s +[acc] eval 1.523164e-3 s +[acc] eval 1.615734e-3 s +[acc] eval 1.586657e-3 s +[acc] eval 1.575541e-3 s +[acc] eval 1.587629e-3 s +[acc] eval 1.593505e-3 s +[acc] eval 2.054992e-3 s +[acc] eval 2.111241e-3 s +[acc] eval 2.064001e-3 s +[acc] eval 1.561547e-3 s +[acc] eval 1.587803e-3 s +[acc] eval 1.614148e-3 s +[acc] eval 1.642174e-3 s +[acc] eval 1.862454e-3 s +[acc] eval 1.826488e-3 s +[acc] eval 0.154365991 s +[acc] compile 0.190223519 s +[acc] eval 1.9339097e-2 s +[acc] eval 9.556475e-3 s +[acc] eval 0.222145286 s +[acc] compile 6.322558e-3 s +[acc] eval 2.2675878e-2 s +[acc] eval 3.2297281e-2 s +[acc] compile 0.133195452 s +[acc] eval 1.6608139e-2 s +[acc] eval 0.154610567 s +[acc] compile 0.129433504 s +[acc] eval 8.670666e-3 s +[acc] eval 0.141853619 s +[acc] compile 1.0030681e-2 s +[acc] eval 9.450809e-3 s +[acc] eval 3.580049e-3 s +[acc] eval 3.519308e-3 s +[acc] eval 1.0562405e-2 s +[acc] eval 2.045084e-3 s +[acc] eval 2.534231e-3 s +[acc] eval 2.086978e-3 s +[acc] eval 2.107404e-3 s +[acc] eval 1.974666e-3 s +[acc] eval 2.353604e-3 s +[acc] eval 2.403757e-3 s +[acc] eval 1.978729e-3 s +[acc] eval 2.119265e-3 s +[acc] eval 2.136088e-3 s +[acc] eval 2.33692e-3 s +[acc] eval 2.188338e-3 s +[acc] eval 1.999049e-3 s +[acc] eval 1.905077e-3 s +[acc] eval 1.881611e-3 s +[acc] eval 1.787219e-3 s +[acc] eval 1.747925e-3 s +[acc] eval 1.67318e-3 s +[acc] eval 1.733251e-3 s +[acc] eval 1.779994e-3 s +[acc] eval 2.32735e-3 s +[acc] eval 2.398273e-3 s +[acc] eval 2.267587e-3 s +[acc] eval 2.181528e-3 s +[acc] eval 2.21882e-3 s +[acc] eval 2.275133e-3 s +[acc] eval 9.6641348e-2 s +[acc] compile 4.838656e-3 s +[acc] eval 4.028003e-3 s +[acc] eval 1.0367543e-2 s +[acc] compile 4.450321e-3 s +[acc] eval 7.951687e-3 s +[acc] eval 1.4180958e-2 s +[acc] compile 8.116787e-3 s +[acc] eval 2.403878e-2 s +[acc] eval 3.4636413e-2 s +[acc] compile 4.412479e-3 s +[acc] eval 3.937728e-3 s +[acc] eval 2.003124e-3 s +[acc] eval 1.960231e-3 s +[acc] eval 1.840621e-3 s +[acc] eval 1.675495e-3 s +[acc] eval 2.449219e-3 s +[acc] eval 2.089042e-3 s +[acc] eval 1.878249e-3 s +[acc] eval 1.662515e-3 s +[acc] eval 1.989964e-3 s +[acc] eval 1.809895e-3 s +[acc] eval 1.926207e-3 s +[acc] eval 2.093189e-3 s +[acc] eval 1.712173e-3 s +[acc] eval 1.84985e-3 s +[acc] eval 1.651079e-3 s +[acc] eval 1.599078e-3 s +[acc] eval 1.320976e-3 s +[acc] eval 1.586851e-3 s +[acc] eval 1.430331e-3 s +[acc] eval 1.466771e-3 s +[acc] eval 1.545962e-3 s +[acc] eval 2.2186e-3 s +[acc] eval 1.687745e-3 s +[acc] eval 1.572023e-3 s +[acc] eval 1.53644e-3 s +[acc] eval 1.478728e-3 s +[acc] eval 1.540106e-3 s +[acc] eval 1.520968e-3 s +[acc] eval 1.1551959e-2 s +[acc] eval 7.3362692e-2 s +[acc] compile 0.114956796 s +[acc] eval 2.161605e-2 s +[acc] eval 0.151499514 s +[acc] compile 7.422031e-3 s +[acc] eval 2.4212248e-2 s +[acc] eval 3.4766302e-2 s +[acc] compile 6.066091e-3 s +[acc] eval 1.6942139e-2 s +[acc] eval 4.59784e-3 s +[acc] eval 3.0929548e-2 s +[acc] compile 6.8589914e-2 s +[acc] eval 2.2949773e-2 s +[acc] eval 9.5069494e-2 s +[acc] compile 8.85031e-3 s +[acc] eval 1.7533716e-2 s +[acc] eval 2.429605e-3 s +[acc] eval 3.250522e-3 s +[acc] eval 3.200667e-3 s +[acc] eval 3.198139e-3 s +[acc] eval 3.043519e-3 s +[acc] eval 3.047697e-3 s +[acc] eval 3.069015e-3 s +[acc] eval 3.385037e-3 s +[acc] eval 3.199615e-3 s +[acc] eval 2.82699e-3 s +[acc] eval 2.962368e-3 s +[acc] eval 2.919237e-3 s +[acc] eval 3.011801e-3 s +[acc] eval 7.0239141e-2 s +[acc] compile 8.464834e-3 s +[acc] eval 1.2627776e-2 s +[acc] eval 2.5066308e-2 s +[acc] compile 0.15558801 s +[acc] eval 1.5000872e-2 s +[acc] eval 0.184209507 s +[acc] compile 0.298335348 s +[acc] eval 2.2222973e-2 s +[acc] eval 0.325032337 s +[acc] compile 8.227292e-3 s +[acc] eval 2.655808e-2 s +[acc] eval 3.7814437e-2 s +[acc] compile 6.912204e-3 s +[acc] eval 6.870141e-3 s +[acc] eval 3.320209e-3 s +[acc] eval 3.525109e-3 s +[acc] eval 4.0898e-3 s +[acc] eval 3.75719e-3 s +[acc] eval 1.2575869e-2 s +[acc] eval 2.861299e-3 s +[acc] eval 3.345395e-3 s +[acc] eval 3.152182e-3 s +[acc] eval 2.949855e-3 s +[acc] eval 2.600195e-3 s +[acc] eval 2.640979e-3 s +[acc] eval 2.372461e-3 s +[acc] eval 2.741465e-3 s +[acc] eval 2.992936e-3 s +[acc] eval 3.07423e-3 s +[acc] eval 3.940603e-3 s +[acc] eval 3.593568e-3 s +[acc] eval 3.274009e-3 s +[acc] eval 3.034866e-3 s +[acc] eval 2.682453e-3 s +[acc] eval 2.52417e-3 s +[acc] eval 2.679379e-3 s +[acc] eval 3.029782e-3 s +[acc] eval 3.22306e-3 s +[acc] eval 3.293353e-3 s +[acc] eval 3.003415e-3 s +[acc] eval 3.106053e-3 s +[acc] eval 2.992365e-3 s +[acc] eval 2.725673e-3 s +[acc] eval 3.390226e-3 s +[acc] eval 0.122712482 s +[acc] compile 4.072209e-3 s +[acc] eval 1.6642953e-2 s +[acc] eval 2.2756178e-2 s +[acc] compile 1.319917e-2 s +[acc] eval 4.323234e-2 s +[acc] eval 6.6347465e-2 s +[acc] compile 6.960239e-3 s +[acc] eval 1.3545814e-2 s +[acc] eval 3.079578e-3 s +[acc] eval 2.960182e-3 s +[acc] eval 3.479671e-3 s +[acc] eval 4.265541e-3 s +[acc] eval 3.569645e-3 s +[acc] eval 2.920652e-3 s +[acc] eval 3.393137e-3 s +[acc] eval 2.804998e-3 s +[acc] eval 2.653964e-3 s +[acc] eval 2.299646e-3 s +[acc] eval 1.3180248e-2 s +[acc] eval 1.756253e-3 s +[acc] eval 1.545442e-3 s +[acc] eval 7.1551576e-2 s +[acc] compile 0.34793667 s +[acc] eval 3.7465203e-2 s +[acc] eval 0.406482686 s +[acc] compile 0.26380278 s +[acc] eval 1.6087807e-2 s +[acc] eval 0.284539377 s +[acc] compile 7.91304e-3 s +[acc] eval 2.4506642e-2 s +[acc] eval 4.36778e-3 s +[acc] eval 3.588283e-3 s +[acc] eval 3.413203e-3 s +[acc] eval 4.002784e-3 s +[acc] eval 4.404503e-3 s +[acc] eval 4.522057e-3 s +[acc] eval 5.292624e-3 s +[acc] eval 3.733706e-3 s +[acc] eval 3.500328e-3 s +[acc] eval 2.847892e-3 s +[acc] eval 3.290434e-3 s +[acc] eval 3.312792e-3 s +[acc] eval 2.892741e-3 s +[acc] eval 3.01918e-3 s +[acc] eval 2.609032e-3 s +[acc] eval 2.633307e-3 s +[acc] eval 4.121236e-3 s +[acc] eval 4.068515e-3 s +[acc] eval 3.254368e-3 s +[acc] eval 3.336166e-3 s +[acc] eval 3.945538e-3 s +[acc] eval 3.337854e-3 s +[acc] eval 3.365313e-3 s +[acc] eval 3.401175e-3 s +[acc] eval 3.501168e-3 s +[acc] eval 3.428201e-3 s +[acc] eval 0.131187037 s +[acc] compile 0.28069469 s +[acc] eval 1.461013e-2 s +[acc] eval 6.747338e-3 s +[acc] eval 1.1746586e-2 s +[acc] eval 9.175974e-3 s +[acc] eval 1.7168526e-2 s +[acc] eval 5.468879e-3 s +[acc] eval 7.944031e-3 s +[acc] eval 1.0706189e-2 s +[acc] eval 7.099762e-3 s +[acc] eval 1.4348192e-2 s +[acc] eval 1.3343308e-2 s +[acc] eval 6.178536e-3 s +[acc] eval 0.412343523 s +[acc] compile 0.22242692 s +[acc] eval 1.9512187e-2 s +[acc] eval 0.247803846 s +[acc] compile 7.0363579e-2 s +[acc] eval 3.211665e-2 s +[acc] eval 0.106720155 s +[acc] compile 0.202772169 s +[acc] eval 1.4534084e-2 s +[acc] eval 0.2226552 s +[acc] compile 5.37573e-4 s +[acc] eval 1.5881236e-2 s +[acc] compile 0.52474492 s +[acc] eval 3.6444822e-2 s +[acc] eval 0.566356617 s +[acc] compile 0.447582283 s +[acc] eval 3.0885108e-2 s +[acc] eval 0.495721971 s +[acc] compile 0.182998989 s +[acc] eval 6.96093e-3 s +[acc] eval 0.192525881 s +[acc] compile 6.7855484e-2 s +[acc] eval 6.431536e-3 s +[acc] eval 3.574847e-3 s +[acc] eval 1.5164243e-2 s +[acc] eval 3.411708e-3 s +[acc] eval 3.305005e-3 s +[acc] eval 3.363576e-3 s +[acc] eval 2.832667e-3 s +[acc] eval 2.672993e-3 s +[acc] eval 2.655889e-3 s +[acc] eval 2.677341e-3 s +[acc] eval 2.816154e-3 s +[acc] eval 2.810347e-3 s +[acc] eval 0.124231953 s +[acc] compile 8.1794947e-2 s +[acc] eval 1.7145505e-2 s +[acc] eval 0.104557551 s +[acc] compile 5.189887e-3 s +[acc] eval 1.7985133e-2 s +[acc] eval 2.143571e-3 s +[acc] eval 1.989518e-3 s +[acc] eval 2.095302e-3 s +[acc] eval 1.920391e-3 s +[acc] eval 2.073346e-3 s +[acc] eval 1.656966e-3 s +[acc] eval 2.374171e-3 s +[acc] eval 2.050986e-3 s +[acc] eval 1.978362e-3 s +[acc] eval 2.166096e-3 s +[acc] eval 1.960418e-3 s +[acc] eval 1.914721e-3 s +[acc] eval 1.798361e-3 s +[acc] eval 1.755307e-3 s +[acc] eval 5.4482595e-2 s +[acc] compile 0.19663802 s +[acc] eval 1.9228087e-2 s +[acc] eval 0.220328616 s +[acc] compile 0.429870611 s +[acc] eval 3.6731925e-2 s +[acc] eval 4.899436e-3 s +[acc] eval 4.56212e-3 s +[acc] eval 5.038776e-3 s +[acc] eval 4.56298e-3 s +[acc] eval 4.6813e-3 s +[acc] eval 3.845244e-3 s +[acc] eval 3.967386e-3 s +[acc] eval 4.016083e-3 s +[acc] eval 3.963169e-3 s +[acc] eval 4.37165e-3 s +[acc] eval 3.996215e-3 s +[acc] eval 0.519583972 s +[acc] compile 3.26543e-4 s +[acc] eval 8.181126e-3 s +[acc] compile 3.126863e-3 s +[acc] eval 3.27565e-3 s +[acc] eval 1.106777e-3 s +[acc] eval 8.91206e-4 s +[acc] eval 1.121522e-3 s +[acc] eval 1.0932247e-2 s +[acc] compile 4.263837e-3 s +[acc] eval 1.643198e-2 s +[acc] eval 1.306504e-3 s +[acc] eval 1.261844e-3 s +[acc] eval 1.234747e-3 s +[acc] eval 1.211175e-3 s +[acc] eval 1.303743e-3 s +[acc] eval 1.277646e-3 s +[acc] eval 1.566082e-3 s +[acc] eval 1.413176e-3 s +[acc] eval 1.260334e-3 s +[acc] eval 1.341902e-3 s +[acc] eval 3.6323705e-2 s +[acc] compile 0.192375671 s +[acc] eval 2.429404e-2 s +[acc] eval 1.4344205e-2 s +[acc] eval 4.458092e-3 s +[acc] eval 4.375096e-3 s +[acc] eval 4.533003e-3 s +[acc] eval 4.861703e-3 s +[acc] eval 1.4132911e-2 s +[acc] eval 4.461195e-3 s +[acc] eval 4.207862e-3 s +[acc] eval 4.25104e-3 s +[acc] eval 4.37331e-3 s +[acc] eval 1.1926262e-2 s +[acc] eval 0.298366357 s +[acc] compile 3.832538e-3 s +[acc] eval 4.747342e-3 s +[acc] eval 2.086456e-3 s +[acc] eval 2.011243e-3 s +[acc] eval 2.007122e-3 s +[acc] eval 8.985073e-3 s +[acc] eval 1.586469e-3 s +[acc] eval 1.495235e-3 s +[acc] eval 1.507665e-3 s +[acc] eval 1.58993e-3 s +[acc] eval 1.627038e-3 s +[acc] eval 1.629721e-3 s +[acc] eval 1.507585e-3 s +[acc] eval 1.716381e-3 s +[acc] eval 2.157109e-3 s +[acc] eval 2.056862e-3 s +[acc] eval 1.564282e-3 s +[acc] eval 1.470178e-3 s +[acc] eval 1.454337e-3 s +[acc] eval 1.359599e-3 s +[acc] eval 1.524995e-3 s +[acc] eval 1.716888e-3 s +[acc] eval 1.79076e-3 s +[acc] eval 1.825245e-3 s +[acc] eval 1.886527e-3 s +[acc] eval 2.161475e-3 s +[acc] eval 2.301245e-3 s +[acc] eval 1.755566e-3 s +[acc] eval 1.693034e-3 s +[acc] eval 1.638855e-3 s +[acc] eval 1.637291e-3 s +[acc] eval 7.0547415e-2 s +[acc] compile 0.111449634 s +[acc] eval 1.1682071e-2 s +[acc] eval 0.126771682 s +[acc] eval 2.321568e-3 s +[acc] eval 7.22667e-4 s +[acc] eval 9.02948e-4 s +[acc] eval 5.036179e-3 s +[acc] compile 0.517370484 s +[acc] eval 4.6003957e-2 s +[acc] eval 1.4752747e-2 s +[acc] eval 6.930854e-3 s +[acc] eval 4.40944e-3 s +[acc] eval 4.613602e-3 s +[acc] eval 4.732967e-3 s +[acc] eval 5.468562e-3 s +[acc] eval 6.071746e-3 s +[acc] eval 1.8152748e-2 s +[acc] eval 4.198732e-3 s +[acc] eval 4.118412e-3 s +[acc] eval 4.102043e-3 s +[acc] eval 0.647193862 s +[acc] compile 1.458007e-2 s +[acc] eval 1.9868344e-2 s +[acc] eval 2.68237e-3 s +[acc] eval 2.338097e-3 s +[acc] eval 5.441466e-3 s +[acc] eval 2.104564e-3 s +[acc] eval 1.92369e-3 s +[acc] eval 2.892788e-3 s +[acc] eval 2.795337e-3 s +[acc] eval 2.087879e-3 s +[acc] eval 1.935067e-3 s +[acc] eval 1.934952e-3 s +[acc] eval 1.900046e-3 s +[acc] eval 1.88105e-3 s +[acc] eval 1.936027e-3 s +[acc] eval 1.915752e-3 s +[acc] eval 1.814195e-3 s +[acc] eval 1.941439e-3 s +[acc] eval 1.888904e-3 s +[acc] eval 1.924191e-3 s +[acc] eval 1.820746e-3 s +[acc] eval 1.772446e-3 s +[acc] eval 1.864214e-3 s +[acc] eval 1.836727e-3 s +[acc] eval 1.743666e-3 s +[acc] eval 1.94545e-3 s +[acc] eval 1.627427e-3 s +[acc] eval 1.827664e-3 s +[acc] eval 2.24016e-3 s +[acc] eval 2.077407e-3 s +[acc] eval 1.973745e-3 s +[acc] eval 0.101340893 s +[acc] compile 4.571566e-3 s +[acc] eval 1.3112739e-2 s +[acc] eval 1.388869e-3 s +[acc] eval 1.506754e-3 s +[acc] eval 1.265838e-3 s +[acc] eval 1.345929e-3 s +[acc] eval 1.771683e-3 s +[acc] eval 1.770513e-3 s +[acc] eval 1.844135e-3 s +[acc] eval 1.750275e-3 s +[acc] eval 1.563287e-3 s +[acc] eval 1.431943e-3 s +[acc] eval 1.450824e-3 s +[acc] eval 1.1960105e-2 s +[acc] eval 1.086518e-3 s +[acc] eval 1.106984e-3 s +[acc] eval 9.35266e-4 s +[acc] eval 9.30508e-4 s +[acc] eval 8.91397e-4 s +[acc] eval 9.18686e-4 s +[acc] eval 9.28702e-4 s +[acc] eval 8.68103e-4 s +[acc] eval 5.8398917e-2 s +[acc] compile 5.385024e-3 s +[acc] eval 2.9535305e-2 s +[acc] eval 3.7772982e-2 s +[acc] compile 3.46648e-4 s +[acc] eval 1.4016075e-2 s +[acc] compile 0.277725733 s +[acc] eval 1.5066523e-2 s +[acc] eval 0.29808316 s +[acc] compile 5.343323e-3 s +[acc] eval 1.8917979e-2 s +[acc] eval 2.6406872e-2 s +[acc] compile 5.018934e-3 s +[acc] eval 8.383627e-3 s +[acc] eval 1.764937e-3 s +[acc] eval 1.660984e-3 s +[acc] eval 1.554848e-3 s +[acc] eval 1.669673e-3 s +[acc] eval 2.138196e-3 s +[acc] eval 2.341627e-3 s +[acc] eval 1.792674e-3 s +[acc] eval 1.893642e-3 s +[acc] eval 1.2523844e-2 s +[acc] eval 1.934855e-3 s +[acc] eval 1.854236e-3 s +[acc] eval 1.659709e-3 s +[acc] eval 1.378639e-3 s +[acc] eval 1.280568e-3 s +[acc] eval 1.463532e-3 s +[acc] eval 2.113564e-3 s +[acc] eval 2.369279e-3 s +[acc] eval 2.267417e-3 s +[acc] eval 1.975497e-3 s +[acc] eval 1.533522e-3 s +[acc] eval 1.691876e-3 s +[acc] eval 1.577148e-3 s +[acc] eval 1.533018e-3 s +[acc] eval 1.470627e-3 s +[acc] eval 1.791195e-3 s +[acc] eval 1.589384e-3 s +[acc] eval 1.485576e-3 s +[acc] eval 1.46981e-3 s +[acc] eval 1.309192e-3 s +[acc] eval 7.8154068e-2 s +[acc] compile 5.62918e-3 s +[acc] eval 1.2510118e-2 s +[acc] eval 2.0177808e-2 s +[acc] compile 6.5980267e-2 s +[acc] eval 2.4579026e-2 s +[acc] eval 9.2699161e-2 s +[acc] compile 0.628573022 s +[acc] eval 3.6212989e-2 s +[acc] eval 0.680282591 s +[acc] eval 2.704725e-3 s +[acc] eval 5.80068e-4 s +[acc] eval 7.0235e-4 s +[acc] eval 4.872333e-3 s +[acc] compile 8.063472e-3 s +[acc] eval 9.497643e-3 s +[acc] eval 2.0565183e-2 s +[acc] compile 0.19426857 s +[acc] eval 3.0129443e-2 s +[acc] eval 0.229025627 s +[acc] compile 5.301991e-3 s +[acc] eval 8.005624e-3 s +[acc] eval 1.646013e-2 s +[acc] compile 6.988549e-3 s +[acc] eval 9.697887e-3 s +[acc] eval 1.979373e-2 s +[acc] compile 0.110082538 s +[acc] eval 2.5281208e-2 s +[acc] eval 0.13836852 s +[acc] compile 4.22208e-4 s +[acc] eval 7.018189e-3 s +[acc] compile 3.81317e-4 s +[acc] eval 7.186852e-3 s +[acc] compile 0.159323264 s +[acc] eval 2.3449921e-2 s +[acc] eval 0.187154964 s +[acc] compile 1.0460917e-2 s +[acc] eval 3.0131527e-2 s +[acc] eval 4.312464e-2 s +[acc] compile 4.561303e-3 s +[acc] eval 8.897974e-3 s +[acc] eval 1.5549994e-2 s +[acc] compile 6.721296e-3 s +[acc] eval 9.93614e-3 s +[acc] eval 1.8773242e-2 s +[acc] compile 3.933742e-3 s +[acc] eval 4.2943e-3 s +[acc] eval 1.431535e-3 s +[acc] eval 1.417966e-3 s +[acc] eval 1.253106e-3 s +[acc] eval 1.3994241e-2 s +[acc] compile 7.100393e-3 s +[acc] eval 1.9883703e-2 s +[acc] eval 2.8934306e-2 s +[acc] compile 0.183891846 s +[acc] eval 1.3729752e-2 s +[acc] eval 0.20329781 s +[acc] compile 0.147766697 s +[acc] eval 8.055596e-3 s +[acc] eval 0.176774889 s +[acc] compile 9.147962e-3 s +[acc] eval 1.1091673e-2 s +[acc] eval 2.4969975e-2 s +[acc] compile 6.984516e-3 s +[acc] eval 1.791729e-2 s +[acc] eval 3.9529366e-2 s +[acc] eval 1.8875785e-2 s +[acc] eval 2.1703496e-2 s +[acc] compile 5.9246732e-2 s +[acc] eval 2.9453413e-2 s +[acc] eval 9.0735258e-2 s +[acc] compile 0.238340197 s +[acc] eval 1.3659944e-2 s +[acc] eval 0.257747778 s +[acc] compile 7.389959e-2 s +[acc] eval 3.8333181e-2 s +[acc] eval 0.116474514 s +[acc] compile 6.9376372e-2 s +[acc] eval 1.5541e-2 s +[acc] eval 9.1299029e-2 s +[acc] compile 0.127091049 s +[acc] eval 9.12672e-3 s +[acc] eval 3.303232e-3 s +[acc] eval 1.7846078e-2 s +[acc] eval 2.138038e-3 s +[acc] eval 2.776655e-3 s +[acc] eval 3.286581e-3 s +[acc] eval 2.645983e-3 s +[acc] eval 2.64505e-3 s +[acc] eval 2.594294e-3 s +[acc] eval 2.466614e-3 s +[acc] eval 2.713776e-3 s +[acc] eval 2.618062e-3 s +[acc] eval 0.185289724 s +[acc] compile 4.78206e-4 s +[acc] eval 3.630485e-3 s +[acc] compile 3.794775e-3 s +[acc] eval 5.462013e-3 s +[acc] eval 7.39772e-4 s +[acc] eval 7.95648e-4 s +[acc] eval 1.1869525e-2 s +[acc] eval 4.039865e-3 s +[acc] eval 5.85071e-4 s +[acc] eval 7.73796e-4 s +[acc] eval 6.167462e-3 s +[acc] compile 0.182537243 s +[acc] eval 2.7253178e-2 s +[acc] eval 0.213354795 s +[acc] compile 6.7555278e-2 s +[acc] eval 6.229434e-3 s +[acc] eval 2.766161e-3 s +[acc] eval 2.604611e-3 s +[acc] eval 8.2665474e-2 s +[acc] eval 1.2915416e-2 s +[acc] eval 1.446781e-3 s +[acc] eval 1.535024e-3 s +[acc] eval 1.7662551e-2 s +[acc] compile 4.8279223e-2 s +[acc] eval 1.1376153e-2 s +[acc] eval 1.798974e-3 s +[acc] eval 1.556065e-3 s +[acc] eval 1.571933e-3 s +[acc] eval 6.7152978e-2 s +[acc] eval 1.9985503e-2 s +[acc] eval 1.3932813e-2 s +[acc] eval 1.705362e-3 s +[acc] eval 3.7113592e-2 s +[acc] compile 0.119415645 s +[acc] eval 1.1881709e-2 s +[acc] eval 5.619718e-3 s +[acc] eval 5.646985e-3 s +[acc] eval 6.280934e-3 s +[acc] eval 4.781645e-3 s +[acc] eval 3.952965e-3 s +[acc] eval 4.451547e-3 s +[acc] eval 4.153891e-3 s +[acc] eval 4.642393e-3 s +[acc] eval 1.5696798e-2 s +[acc] eval 1.878412e-3 s +[acc] eval 1.919395e-3 s +[acc] eval 2.013408e-3 s +[acc] eval 0.196743328 s +[acc] compile 5.8207932e-2 s +[acc] eval 1.6925802e-2 s +[acc] eval 1.671777e-3 s +[acc] eval 1.537348e-3 s +[acc] eval 8.0580038e-2 s +[acc] compile 5.176145e-2 s +[acc] eval 2.036325e-2 s +[acc] eval 1.1927508e-2 s +[acc] eval 1.581715e-3 s +[acc] eval 8.7501495e-2 s +[acc] compile 0.184032686 s +[acc] eval 2.8672269e-2 s +[acc] eval 0.217071295 s +[acc] compile 5.4530316e-2 s +[acc] eval 2.2169939e-2 s +[acc] eval 8.1149451e-2 s +[acc] compile 0.375736729 s +[acc] eval 1.7855569e-2 s +[acc] eval 0.403102614 s +[acc] compile 7.500661e-3 s +[acc] eval 6.806916e-3 s +[acc] eval 3.4579208e-2 s +[acc] compile 0.428589862 s +[acc] eval 1.4081593e-2 s +[acc] eval 0.461731665 s +[acc] compile 0.391684875 s +[acc] eval 4.6867685e-2 s +[acc] eval 0.444930235 s +[acc] eval 2.4902136e-2 s +[acc] eval 3.0645959e-2 s +[acc] compile 0.238165301 s +[acc] eval 2.8802596e-2 s +[acc] eval 0.272862765 s +[acc] compile 6.018325e-3 s +[acc] eval 2.022271e-2 s +[acc] eval 3.260552e-3 s +[acc] eval 3.033585e-3 s +[acc] eval 2.988627e-3 s +[acc] eval 2.892561e-3 s +[acc] eval 2.473009e-3 s +[acc] eval 2.805219e-3 s +[acc] eval 2.673268e-3 s +[acc] eval 2.263242e-3 s +[acc] eval 2.438862e-3 s +[acc] eval 1.927076e-3 s +[acc] eval 1.837158e-3 s +[acc] eval 5.7912147e-2 s +[acc] compile 0.425136758 s +[acc] eval 2.7309402e-2 s +[acc] eval 4.933188e-3 s +[acc] eval 1.7316387e-2 s +[acc] eval 4.516767e-3 s +[acc] eval 3.940569e-3 s +[acc] eval 4.12743e-3 s +[acc] eval 4.113076e-3 s +[acc] eval 3.816244e-3 s +[acc] eval 3.167804e-3 s +[acc] eval 3.300243e-3 s +[acc] eval 3.563433e-3 s +[acc] eval 3.221616e-3 s +[acc] eval 0.51338357 s +[acc] compile 0.122970024 s +[acc] eval 9.754651e-3 s +[acc] eval 0.147137708 s +[acc] compile 0.508235205 s +[acc] eval 3.1331185e-2 s +[acc] eval 0.561315668 s +[acc] compile 0.345729962 s +[acc] eval 1.5962953e-2 s +[acc] eval 0.378099227 s +[acc] compile 0.320672928 s +[acc] eval 2.8748678e-2 s +[acc] eval 0.365083934 s +[acc] compile 6.295868e-3 s +[acc] eval 6.82123e-3 s +[acc] eval 1.5010584e-2 s +[acc] compile 0.23213279 s +[acc] eval 4.2717968e-2 s +[acc] eval 0.283237107 s +[acc] eval 2.6758735e-2 s +[acc] eval 3.4915056e-2 s +[acc] compile 0.48867347 s +[acc] eval 3.6535353e-2 s +[acc] eval 5.739619e-3 s +[acc] eval 4.7191e-3 s +[acc] eval 4.290255e-3 s +[acc] eval 4.193005e-3 s +[acc] eval 4.228506e-3 s +[acc] eval 4.327414e-3 s +[acc] eval 5.287138e-3 s +[acc] eval 5.62533e-3 s +[acc] eval 5.752298e-3 s +[acc] eval 5.482311e-3 s +[acc] eval 4.919531e-3 s +[acc] eval 5.744534e-3 s +[acc] eval 0.5946804 s +[acc] compile 2.1825534e-2 s +[acc] eval 3.3278037e-2 s +[acc] eval 6.0231068e-2 s +[acc] compile 0.178520451 s +[acc] eval 1.7311722e-2 s +[acc] eval 0.201047763 s +[acc] compile 4.046581e-3 s +[acc] eval 5.529448e-3 s +[acc] eval 1.9822576e-2 s +[acc] eval 1.727931e-3 s +[acc] eval 3.279106e-3 s +[acc] eval 3.693997e-3 s +[acc] eval 2.098188e-3 s +[acc] eval 6.452023e-3 s +[acc] eval 2.645628e-3 s +[acc] eval 2.110221e-3 s +[acc] eval 1.959513e-3 s +[acc] eval 1.869457e-3 s +[acc] eval 2.645081e-3 s +[acc] eval 6.2016115e-2 s +[acc] compile 0.484485522 s +[acc] eval 1.5586997e-2 s +[acc] eval 5.188742e-3 s +[acc] eval 1.8762359e-2 s +[acc] eval 5.775491e-3 s +[acc] eval 5.109054e-3 s +[acc] eval 5.597241e-3 s +[acc] eval 4.77825e-3 s +[acc] eval 3.675327e-3 s +[acc] eval 4.332782e-3 s +[acc] eval 6.448433e-3 s +[acc] eval 5.574898e-3 s +[acc] eval 5.444833e-3 s +[acc] eval 1.7657973e-2 s +[acc] eval 6.50468e-3 s +[acc] eval 6.612366e-3 s +[acc] eval 5.768571e-3 s +[acc] eval 5.196e-3 s +[acc] eval 4.704782e-3 s +[acc] eval 5.702212e-3 s +[acc] eval 5.217946e-3 s +[acc] eval 4.496002e-3 s +[acc] eval 4.740859e-3 s +[acc] eval 1.4874061e-2 s +[acc] eval 5.917764e-3 s +[acc] eval 4.111363e-3 s +[acc] eval 4.418602e-3 s +[acc] eval 5.120267e-3 s +[acc] eval 4.186868e-3 s +[acc] eval 4.417285e-3 s +[acc] eval 6.742051e-3 s +[acc] eval 1.7440906e-2 s +[acc] eval 0.715728157 s +[acc] compile 0.373147549 s +[acc] eval 3.9604639e-2 s +[acc] eval 0.418539219 s +[acc] compile 4.169392e-3 s +[acc] eval 7.293278e-3 s +[acc] eval 2.207506e-3 s +[acc] eval 2.195414e-3 s +[acc] eval 2.050248e-3 s +[acc] eval 1.870998e-3 s +[acc] eval 2.192544e-3 s +[acc] eval 2.970957e-3 s +[acc] eval 2.031874e-3 s +[acc] eval 1.49654e-3 s +[acc] eval 1.571014e-3 s +[acc] eval 1.587369e-3 s +[acc] eval 1.622698e-3 s +[acc] eval 1.66957e-3 s +[acc] eval 1.552719e-3 s +[acc] eval 1.885988e-3 s +[acc] eval 2.015984e-3 s +[acc] eval 1.582932e-3 s +[acc] eval 1.223014e-3 s +[acc] eval 1.619019e-3 s +[acc] eval 1.450198e-3 s +[acc] eval 5.0498618e-2 s +[acc] compile 4.857058e-3 s +[acc] eval 2.3180615e-2 s +[acc] eval 2.624449e-3 s +[acc] eval 2.469547e-3 s +[acc] eval 2.406713e-3 s +[acc] eval 2.396261e-3 s +[acc] eval 2.37433e-3 s +[acc] eval 2.579505e-3 s +[acc] eval 2.247814e-3 s +[acc] eval 2.42758e-3 s +[acc] eval 1.524069e-3 s +[acc] eval 1.613348e-3 s +[acc] eval 1.696307e-3 s +[acc] eval 1.552019e-3 s +[acc] eval 1.623389e-3 s +[acc] eval 1.573857e-3 s +[acc] eval 1.555965e-3 s +[acc] eval 1.760399e-3 s +[acc] eval 2.251897e-3 s +[acc] eval 1.68086e-3 s +[acc] eval 1.64583e-3 s +[acc] eval 1.480363e-3 s +[acc] eval 1.406766e-3 s +[acc] eval 1.598868e-3 s +[acc] eval 1.564079e-3 s +[acc] eval 1.72917e-3 s +[acc] eval 2.33205e-3 s +[acc] eval 1.829299e-3 s +[acc] eval 1.840157e-3 s +[acc] eval 1.591435e-3 s +[acc] eval 1.724721e-3 s +[acc] eval 8.768415e-2 s +[acc] compile 9.389752e-3 s +[acc] eval 1.0886637e-2 s +[acc] eval 3.48909e-3 s +[acc] eval 2.762782e-3 s +[acc] eval 3.19485e-3 s +[acc] eval 3.957678e-3 s +[acc] eval 4.557919e-3 s +[acc] eval 2.712809e-3 s +[acc] eval 2.744055e-3 s +[acc] eval 3.046725e-3 s +[acc] eval 1.7328546e-2 s +[acc] eval 2.395194e-3 s +[acc] eval 1.665202e-3 s +[acc] eval 1.724665e-3 s +[acc] eval 1.692235e-3 s +[acc] eval 1.55449e-3 s +[acc] eval 1.764906e-3 s +[acc] eval 1.607446e-3 s +[acc] eval 2.373262e-3 s +[acc] eval 2.626142e-3 s +[acc] eval 1.860142e-3 s +[acc] eval 1.839338e-3 s +[acc] eval 1.924622e-3 s +[acc] eval 1.884828e-3 s +[acc] eval 1.650885e-3 s +[acc] eval 1.644972e-3 s +[acc] eval 1.537262e-3 s +[acc] eval 1.694426e-3 s +[acc] eval 1.755692e-3 s +[acc] eval 1.619822e-3 s +[acc] eval 1.540253e-3 s +[acc] eval 1.609722e-3 s +[acc] eval 2.072295e-3 s +[acc] eval 0.111282249 s +[acc] eval 1.2723129e-2 s +[acc] eval 2.798204e-3 s +[acc] eval 2.140381e-3 s +[acc] eval 2.007957e-3 s +[acc] eval 1.939278e-3 s +[acc] eval 2.001476e-3 s +[acc] eval 1.2727682e-2 s +[acc] eval 1.836211e-3 s +[acc] eval 1.567783e-3 s +[acc] eval 1.813964e-3 s +[acc] eval 1.911712e-3 s +[acc] eval 1.887988e-3 s +[acc] eval 1.749225e-3 s +[acc] eval 1.598367e-3 s +[acc] eval 1.514491e-3 s +[acc] eval 1.58044e-3 s +[acc] eval 1.473256e-3 s +[acc] eval 1.429565e-3 s +[acc] eval 1.717346e-3 s +[acc] eval 2.553372e-3 s +[acc] eval 2.655622e-3 s +[acc] eval 2.03153e-3 s +[acc] eval 2.290877e-3 s +[acc] eval 2.390494e-3 s +[acc] eval 2.032362e-3 s +[acc] eval 2.091202e-3 s +[acc] eval 1.998119e-3 s +[acc] eval 2.060313e-3 s +[acc] eval 1.819415e-3 s +[acc] eval 1.835663e-3 s +[acc] eval 8.5587473e-2 s +[acc] compile 0.521348602 s +[acc] eval 4.4280962e-2 s +[acc] eval 4.742617e-3 s +[acc] eval 5.155697e-3 s +[acc] eval 5.933016e-3 s +[acc] eval 4.425455e-3 s +[acc] eval 4.318226e-3 s +[acc] eval 5.313521e-3 s +[acc] eval 4.201622e-3 s +[acc] eval 4.111049e-3 s +[acc] eval 4.883943e-3 s +[acc] eval 4.410588e-3 s +[acc] eval 4.213126e-3 s +[acc] eval 0.623358572 s +[acc] compile 4.7269365e-2 s +[acc] eval 1.0466934e-2 s +[acc] eval 1.4209359e-2 s +[acc] eval 8.91772e-4 s +[acc] eval 7.4436499e-2 s +[acc] compile 0.187718917 s +[acc] eval 2.4524097e-2 s +[acc] eval 0.217583956 s +[acc] compile 0.268191133 s +[acc] eval 4.0931691e-2 s +[acc] eval 0.314032211 s +[acc] compile 0.19764843 s +[acc] eval 1.0016011e-2 s +[acc] eval 0.212218082 s +[acc] compile 0.186014162 s +[acc] eval 1.3920239e-2 s +[acc] eval 0.216220508 s +[acc] compile 0.285639433 s +[acc] eval 1.8534831e-2 s +[acc] eval 1.7555288e-2 s +[acc] eval 1.9858692e-2 s +[acc] eval 4.928251e-3 s +[acc] eval 5.588442e-3 s +[acc] eval 4.832921e-3 s +[acc] eval 7.19973e-3 s +[acc] eval 4.131009e-3 s +[acc] eval 4.159316e-3 s +[acc] eval 3.864591e-3 s +[acc] eval 4.278454e-3 s +[acc] eval 1.4706625e-2 s +[acc] eval 4.299509e-3 s +[acc] eval 4.098067e-3 s +[acc] eval 3.829844e-3 s +[acc] eval 3.445345e-3 s +[acc] eval 3.439718e-3 s +[acc] eval 3.926147e-3 s +[acc] eval 4.541399e-3 s +[acc] eval 1.4006258e-2 s +[acc] eval 4.117428e-3 s +[acc] eval 3.954795e-3 s +[acc] eval 4.400076e-3 s +[acc] eval 5.217848e-3 s +[acc] eval 5.28922e-3 s +[acc] eval 5.381009e-3 s +[acc] eval 3.924807e-3 s +[acc] eval 3.792103e-3 s +[acc] eval 1.4619707e-2 s +[acc] eval 4.146237e-3 s +[acc] eval 0.499619588 s +[acc] compile 4.88983e-2 s +[acc] eval 1.9842545e-2 s +[acc] eval 1.141685e-3 s +[acc] eval 1.201118e-3 s +[acc] eval 7.2802382e-2 s +[acc] compile 3.838879e-3 s +[acc] eval 3.862798e-3 s +[acc] eval 1.8871674e-2 s +[acc] compile 1.177896e-2 s +[acc] eval 1.5130715e-2 s +[acc] eval 3.0137999e-2 s +[acc] eval 2.863781e-3 s +[acc] eval 8.03657e-4 s +[acc] eval 6.23102e-4 s +[acc] eval 7.89774e-4 s +[acc] eval 6.158957e-3 s +[acc] compile 6.3976152e-2 s +[acc] eval 1.1181033e-2 s +[acc] eval 7.6868235e-2 s +[acc] compile 9.462028e-3 s +[acc] eval 2.9695239e-2 s +[acc] eval 5.110087e-3 s +[acc] eval 4.619468e-3 s +[acc] eval 4.865815e-3 s +[acc] eval 4.874158e-3 s +[acc] eval 4.116786e-3 s +[acc] eval 3.777091e-3 s +[acc] eval 4.152747e-3 s +[acc] eval 3.254515e-3 s +[acc] eval 4.592003e-3 s +[acc] eval 3.12175e-3 s +[acc] eval 3.272038e-3 s +[acc] eval 3.106889e-3 s +[acc] eval 4.115588e-3 s +[acc] eval 4.281363e-3 s +[acc] eval 3.747357e-3 s +[acc] eval 4.059367e-3 s +[acc] eval 4.036559e-3 s +[acc] eval 3.49827e-3 s +[acc] eval 3.234643e-3 s +[acc] eval 3.865415e-3 s +[acc] eval 4.255794e-3 s +[acc] eval 4.489655e-3 s +[acc] eval 3.056411e-3 s +[acc] eval 3.148309e-3 s +[acc] eval 3.371521e-3 s +[acc] eval 3.75754e-3 s +[acc] eval 3.179499e-3 s +[acc] eval 3.417628e-3 s +[acc] eval 1.8016258e-2 s +[acc] eval 0.174887289 s +[acc] compile 0.21150665 s +[acc] eval 1.8456911e-2 s +[acc] eval 1.3904858e-2 s +[acc] eval 3.055162e-3 s +[acc] eval 2.826211e-3 s +[acc] eval 3.026809e-3 s +[acc] eval 2.910871e-3 s +[acc] eval 2.789585e-3 s +[acc] eval 2.869252e-3 s +[acc] eval 3.547544e-3 s +[acc] eval 3.942081e-3 s +[acc] eval 3.310894e-3 s +[acc] eval 3.320386e-3 s +[acc] eval 0.280038186 s +[acc] compile 0.182023899 s +[acc] eval 1.5087064e-2 s +[acc] eval 6.557179e-3 s +[acc] eval 1.6772452e-2 s +[acc] eval 2.926231e-3 s +[acc] eval 2.796689e-3 s +[acc] eval 2.558455e-3 s +[acc] eval 2.90077e-3 s +[acc] eval 2.396829e-3 s +[acc] eval 2.284825e-3 s +[acc] eval 2.040326e-3 s +[acc] eval 2.078074e-3 s +[acc] eval 2.172944e-3 s +[acc] eval 0.246002145 s +[acc] compile 0.165711171 s +[acc] eval 2.8842143e-2 s +[acc] eval 0.213469554 s +[acc] compile 8.745685e-3 s +[acc] eval 1.5744609e-2 s +[acc] eval 2.7178403e-2 s +[acc] compile 6.4443166e-2 s +[acc] eval 1.8303007e-2 s +[acc] eval 8.5263272e-2 s +[acc] compile 0.126499692 s +[acc] eval 1.029589e-2 s +[acc] eval 0.141671015 s +[acc] compile 8.0615706e-2 s +[acc] eval 2.6795007e-2 s +[acc] eval 0.109823643 s +[acc] compile 6.620427e-3 s +[acc] eval 6.417451e-3 s +[acc] eval 1.5539577e-2 s +[acc] compile 0.32345118 s +[acc] eval 2.7020876e-2 s +[acc] eval 0.354411528 s +[acc] compile 0.13054215 s +[acc] eval 1.3019269e-2 s +[acc] eval 0.148242077 s +[acc] compile 0.647178455 s +[acc] eval 3.2366586e-2 s +[acc] eval 0.685151538 s +[acc] eval 8.19593e-3 s +[acc] eval 1.2030131e-2 s +[acc] compile 0.210311144 s +[acc] eval 5.1624709e-2 s +[acc] eval 0.265473839 s +[acc] compile 7.0270812e-2 s +[acc] eval 5.275307e-3 s +[acc] eval 6.307684e-3 s +[acc] eval 8.709326e-3 s +[acc] eval 6.100908e-3 s +[acc] eval 6.714338e-3 s +[acc] eval 5.08011e-3 s +[acc] eval 4.03884e-3 s +[acc] eval 2.721926e-3 s +[acc] eval 2.491522e-3 s +[acc] eval 2.246965e-3 s +[acc] eval 2.486075e-3 s +[acc] eval 0.125932755 s +[acc] compile 0.143698651 s +[acc] eval 2.1602232e-2 s +[acc] eval 0.170212716 s +[acc] compile 0.137683281 s +[acc] eval 3.9726585e-2 s +[acc] eval 0.180913796 s +[acc] compile 5.075121e-3 s +[acc] eval 5.498299e-3 s +[acc] eval 1.285089e-3 s +[acc] eval 1.356644e-3 s +[acc] eval 1.663876e-3 s +[acc] eval 1.658453e-3 s +[acc] eval 1.223211e-3 s +[acc] eval 1.233255e-3 s +[acc] eval 1.279485e-3 s +[acc] eval 1.152137e-3 s +[acc] eval 1.199241e-3 s +[acc] eval 1.205412e-3 s +[acc] eval 1.208276e-3 s +[acc] eval 1.158982e-3 s +[acc] eval 1.290514e-3 s +[acc] eval 1.448193e-3 s +[acc] eval 3.2633361e-2 s +[acc] compile 0.214289487 s +[acc] eval 3.000917e-2 s +[acc] eval 4.151601e-3 s +[acc] eval 0.2527002 s +[acc] compile 8.4407084e-2 s +[acc] eval 7.945378e-3 s +[acc] eval 9.6273488e-2 s +[acc] compile 0.320175017 s +[acc] eval 2.6493879e-2 s +[acc] eval 1.9283569e-2 s +[acc] eval 4.840683e-3 s +[acc] eval 2.800623e-3 s +[acc] eval 2.704249e-3 s +[acc] eval 2.632248e-3 s +[acc] eval 2.412392e-3 s +[acc] eval 2.824937e-3 s +[acc] eval 2.924228e-3 s +[acc] eval 3.397296e-3 s +[acc] eval 2.787318e-3 s +[acc] eval 2.896007e-3 s +[acc] eval 0.40055549 s +[acc] compile 0.141966846 s +[acc] eval 1.0926206e-2 s +[acc] eval 0.157730664 s +[acc] compile 0.313223634 s +[acc] eval 8.866553e-3 s +[acc] eval 4.39692e-3 s +[acc] eval 1.8064791e-2 s +[acc] eval 4.103256e-3 s +[acc] eval 3.801239e-3 s +[acc] eval 3.763949e-3 s +[acc] eval 4.01104e-3 s +[acc] eval 3.55525e-3 s +[acc] eval 3.223051e-3 s +[acc] eval 2.683173e-3 s +[acc] eval 3.147145e-3 s +[acc] eval 3.530734e-3 s +[acc] eval 3.463955e-3 s +[acc] eval 3.496054e-3 s +[acc] eval 3.156475e-3 s +[acc] eval 1.6270912e-2 s +[acc] eval 3.80983e-3 s +[acc] eval 3.848054e-3 s +[acc] eval 3.755322e-3 s +[acc] eval 3.739722e-3 s +[acc] eval 3.371172e-3 s +[acc] eval 3.33285e-3 s +[acc] eval 3.325707e-3 s +[acc] eval 3.404967e-3 s +[acc] eval 3.460744e-3 s +[acc] eval 4.177496e-3 s +[acc] eval 1.7241502e-2 s +[acc] eval 3.317361e-3 s +[acc] eval 3.842674e-3 s +[acc] eval 3.720065e-3 s +[acc] eval 3.786202e-3 s +[acc] eval 4.053474e-3 s +[acc] eval 0.481775693 s +[acc] compile 0.161652716 s +[acc] eval 3.1002522e-2 s +[acc] eval 0.196815391 s +[acc] compile 0.190365382 s +[acc] eval 2.3835339e-2 s +[acc] eval 0.218836311 s +[acc] compile 4.295591e-3 s +[acc] eval 6.305388e-3 s +[acc] eval 3.0173825e-2 s +[acc] compile 0.150812156 s +[acc] eval 3.7013885e-2 s +[acc] eval 0.193473666 s +[acc] eval 2.1374833e-2 s +[acc] eval 2.4897824e-2 s +[acc] compile 6.1588165e-2 s +[acc] eval 1.0281523e-2 s +[acc] eval 7.6006156e-2 s +[acc] compile 5.978678e-3 s +[acc] eval 5.694597e-3 s +[acc] eval 1.4588081e-2 s +[acc] compile 0.519387381 s +[acc] eval 3.8248697e-2 s +[acc] eval 0.56187681 s +[acc] compile 3.193413e-3 s +[acc] eval 3.612725e-3 s +[acc] eval 8.274161e-3 s +[acc] compile 4.2025e-3 s +[acc] eval 4.503873e-3 s +[acc] eval 1.1367822e-2 s +[acc] compile 0.117248696 s +[acc] eval 1.2826884e-2 s +[acc] eval 0.132968806 s +[acc] compile 4.325929e-3 s +[acc] eval 2.0102336e-2 s +[acc] eval 2.6610555e-2 s +[acc] compile 5.8148663e-2 s +[acc] eval 3.761028e-3 s +[acc] eval 6.17335e-4 s +[acc] eval 7.92453e-4 s +[acc] eval 6.4638608e-2 s +[acc] eval 6.645485e-3 s +[acc] eval 9.389605e-3 s +[acc] compile 6.867388e-3 s +[acc] eval 8.933868e-3 s +[acc] eval 1.8611846e-2 s +[acc] compile 0.129587738 s +[acc] eval 3.6210429e-2 s +[acc] eval 0.171417979 s +[acc] eval 2.259149e-3 s +[acc] eval 5.76673e-4 s +[acc] eval 9.49759e-4 s +[acc] eval 4.713554e-3 s +[acc] compile 0.325404045 s +[acc] eval 3.0054586e-2 s +[acc] eval 0.360092291 s +[acc] compile 0.333093093 s +[acc] eval 3.524887e-2 s +[acc] eval 0.372074703 s +[acc] compile 6.7718479e-2 s +[acc] eval 5.438523e-3 s +[acc] eval 1.730642e-3 s +[acc] eval 1.697074e-3 s +[acc] eval 7.8953202e-2 s +[acc] compile 0.276990404 s +[acc] eval 1.5470487e-2 s +[acc] eval 0.299084464 s +[acc] compile 7.4253175e-2 s +[acc] eval 1.9936994e-2 s +[acc] eval 1.81767e-3 s +[acc] eval 1.725219e-3 s +[acc] eval 0.100056791 s +[acc] compile 0.183880197 s +[acc] eval 1.3364357e-2 s +[acc] eval 1.0493806e-2 s +[acc] eval 6.053106e-3 s +[acc] eval 4.942232e-3 s +[acc] eval 2.2064012e-2 s +[acc] eval 4.205481e-3 s +[acc] eval 4.183727e-3 s +[acc] eval 4.497959e-3 s +[acc] eval 4.896347e-3 s +[acc] eval 4.650025e-3 s +[acc] eval 3.977204e-3 s +[acc] eval 3.928781e-3 s +[acc] eval 0.27648131 s +[acc] compile 0.147916631 s +[acc] eval 9.653967e-3 s +[acc] eval 0.160345402 s +[acc] compile 0.113200456 s +[acc] eval 5.2164606e-2 s +[acc] eval 0.169073042 s +[acc] compile 0.123234082 s +[acc] eval 3.1739837e-2 s +[acc] eval 0.161729176 s +[acc] compile 7.4778143e-2 s +[acc] eval 3.3906932e-2 s +[acc] eval 0.127182539 s +[acc] compile 6.516733e-2 s +[acc] eval 9.911354e-3 s +[acc] eval 7.955816e-2 s +[acc] compile 3.35804e-3 s +[acc] eval 6.04302e-3 s +[acc] eval 1.201678e-2 s +[acc] compile 7.3370763e-2 s +[acc] eval 2.989199e-2 s +[acc] eval 0.106990331 s +[acc] compile 6.5051179e-2 s +[acc] eval 2.9131295e-2 s +[acc] eval 9.9287299e-2 s +[acc] compile 0.180700263 s +[acc] eval 2.549453e-2 s +[acc] eval 0.210464316 s +[acc] compile 5.468722e-3 s +[acc] eval 6.787579e-3 s +[acc] eval 1.4738234e-2 s +[acc] eval 6.323977e-3 s +[acc] eval 8.761895e-3 s +[acc] compile 0.149656672 s +[acc] eval 6.643303e-3 s +[acc] eval 0.159372635 s +[acc] compile 9.481219e-3 s +[acc] eval 2.1150854e-2 s +[acc] eval 2.45406e-3 s +[acc] eval 2.262994e-3 s +[acc] eval 2.11614e-3 s +[acc] eval 2.126501e-3 s +[acc] eval 2.098542e-3 s +[acc] eval 2.015744e-3 s +[acc] eval 1.766531e-3 s +[acc] eval 1.671226e-3 s +[acc] eval 1.793564e-3 s +[acc] eval 1.666628e-3 s +[acc] eval 1.751895e-3 s +[acc] eval 1.948352e-3 s +[acc] eval 1.891386e-3 s +[acc] eval 1.989824e-3 s +[acc] eval 1.931307e-3 s +[acc] eval 1.820294e-3 s +[acc] eval 1.876801e-3 s +[acc] eval 2.019756e-3 s +[acc] eval 1.839637e-3 s +[acc] eval 1.965868e-3 s +[acc] eval 2.594759e-3 s +[acc] eval 2.339352e-3 s +[acc] eval 2.313249e-3 s +[acc] eval 1.970511e-3 s +[acc] eval 1.723008e-3 s +[acc] eval 2.004866e-3 s +[acc] eval 1.738762e-3 s +[acc] eval 1.829404e-3 s +[acc] eval 2.41959e-3 s +[acc] eval 9.6097614e-2 s +[acc] compile 5.495711e-3 s +[acc] eval 1.1733406e-2 s +[acc] eval 2.1966406e-2 s +[acc] compile 0.124268233 s +[acc] eval 8.235932e-3 s +[acc] eval 0.135850531 s +[acc] compile 0.173865182 s +[acc] eval 3.6073703e-2 s +[acc] eval 0.211992817 s +[acc] eval 2.32591e-3 s +[acc] eval 5.60446e-4 s +[acc] eval 6.81928e-4 s +[acc] eval 4.331047e-3 s +[acc] eval 4.780068e-3 s +[acc] eval 2.158892e-3 s +[acc] eval 1.977056e-3 s +[acc] eval 2.062879e-3 s +[acc] eval 1.990991e-3 s +[acc] eval 1.938447e-3 s +[acc] eval 2.024211e-3 s +[acc] eval 1.921083e-3 s +[acc] eval 2.018547e-3 s +[acc] eval 1.8259972e-2 s +[acc] eval 1.629103e-3 s +[acc] eval 4.3300109e-2 s +[acc] eval 7.724971e-3 s +[acc] eval 2.238071e-3 s +[acc] eval 1.860519e-3 s +[acc] eval 1.896921e-3 s +[acc] eval 1.635848e-3 s +[acc] eval 1.807604e-3 s +[acc] eval 1.617982e-3 s +[acc] eval 1.734427e-3 s +[acc] eval 1.5644021e-2 s +[acc] eval 1.553852e-3 s +[acc] eval 1.481281e-3 s +[acc] eval 1.525054e-3 s +[acc] eval 1.437375e-3 s +[acc] eval 1.440846e-3 s +[acc] eval 1.384901e-3 s +[acc] eval 1.340277e-3 s +[acc] eval 1.699405e-3 s +[acc] eval 1.713621e-3 s +[acc] eval 1.784159e-3 s +[acc] eval 1.65105e-3 s +[acc] eval 1.546825e-3 s +[acc] eval 1.606247e-3 s +[acc] eval 1.710619e-3 s +[acc] eval 1.584462e-3 s +[acc] eval 1.516334e-3 s +[acc] eval 1.484362e-3 s +[acc] eval 1.881779e-3 s +[acc] eval 2.0936e-3 s +[acc] eval 2.103948e-3 s +[acc] eval 2.062902e-3 s +[acc] eval 7.5146756e-2 s +[acc] eval 1.320497e-2 s +[acc] eval 2.040701e-3 s +[acc] eval 2.109194e-3 s +[acc] eval 1.969927e-3 s +[acc] eval 2.103406e-3 s +[acc] eval 1.948523e-3 s +[acc] eval 1.6336352e-2 s +[acc] eval 2.018396e-3 s +[acc] eval 1.940738e-3 s +[acc] eval 1.909682e-3 s +[acc] eval 2.204221e-3 s +[acc] eval 2.339039e-3 s +[acc] eval 2.390395e-3 s +[acc] eval 1.869129e-3 s +[acc] eval 1.791044e-3 s +[acc] eval 1.625094e-3 s +[acc] eval 1.617539e-3 s +[acc] eval 1.608876e-3 s +[acc] eval 1.691034e-3 s +[acc] eval 2.971758e-3 s +[acc] eval 1.542933e-3 s +[acc] eval 1.657889e-3 s +[acc] eval 2.077273e-3 s +[acc] eval 2.246679e-3 s +[acc] eval 1.608827e-3 s +[acc] eval 1.68873e-3 s +[acc] eval 2.124227e-3 s +[acc] eval 2.082919e-3 s +[acc] eval 2.108425e-3 s +[acc] eval 2.181542e-3 s +[acc] eval 8.9547252e-2 s +[acc] eval 1.3046593e-2 s +[acc] eval 2.594848e-3 s +[acc] eval 2.380786e-3 s +[acc] eval 2.240723e-3 s +[acc] eval 2.246086e-3 s +[acc] eval 2.262507e-3 s +[acc] eval 1.4098897e-2 s +[acc] eval 3.01586e-3 s +[acc] eval 2.636325e-3 s +[acc] eval 2.473854e-3 s +[acc] eval 2.339486e-3 s +[acc] eval 2.46776e-3 s +[acc] eval 2.825e-3 s +[acc] eval 2.699225e-3 s +[acc] eval 1.86335e-3 s +[acc] eval 2.465823e-3 s +[acc] eval 2.815423e-3 s +[acc] eval 2.628106e-3 s +[acc] eval 2.529159e-3 s +[acc] eval 2.55066e-3 s +[acc] eval 2.273257e-3 s +[acc] eval 2.891803e-3 s +[acc] eval 2.073899e-3 s +[acc] eval 2.082156e-3 s +[acc] eval 2.036125e-3 s +[acc] eval 1.875186e-3 s +[acc] eval 2.038217e-3 s +[acc] eval 1.831716e-3 s +[acc] eval 1.884399e-3 s +[acc] eval 1.988445e-3 s +[acc] eval 2.061583e-3 s +[acc] eval 1.890867e-3 s +[acc] eval 0.103353247 s diff --git a/c_2.err b/c_2.err new file mode 100644 index 0000000..794bd4e --- /dev/null +++ b/c_2.err @@ -0,0 +1,3372 @@ +[acc] compile 1.3629743e-2 s +[acc] eval 2.043766534 s +[acc] compile 3.92923e-4 s +[acc] eval 7.4891412e-2 s +[acc] compile 3.88146e-4 s +[acc] eval 6.7545069e-2 s +[acc] compile 3.59113e-4 s +[acc] eval 0.162499088 s +[acc] compile 5.70626e-4 s +[acc] eval 0.156660234 s +[acc] compile 5.45836e-4 s +[acc] eval 0.168595161 s +[acc] compile 5.00789e-4 s +[acc] eval 0.154986263 s +[acc] compile 5.24238e-4 s +[acc] eval 0.14105172 s +[acc] compile 3.36064e-4 s +[acc] eval 6.8964672e-2 s +[acc] compile 0.368586236 s +[acc] eval 6.3667181e-2 s +[acc] eval 0.43654482 s +[acc] compile 0.334744683 s +[acc] eval 1.8596368e-2 s +[acc] eval 0.357943918 s +[acc] compile 0.112989039 s +[acc] eval 5.785252e-3 s +[acc] eval 3.435828e-3 s +[acc] eval 3.434192e-3 s +[acc] eval 3.434861e-3 s +[acc] eval 3.124603e-3 s +[acc] eval 3.032889e-3 s +[acc] eval 0.148542895 s +[acc] compile 0.133023722 s +[acc] eval 1.759197e-2 s +[acc] eval 0.158946509 s +[acc] compile 0.148936831 s +[acc] eval 1.4687292e-2 s +[acc] eval 2.346815e-3 s +[acc] eval 2.248427e-3 s +[acc] eval 1.948955e-3 s +[acc] eval 1.44015e-3 s +[acc] eval 1.82709e-3 s +[acc] eval 1.800178e-3 s +[acc] eval 4.837882e-3 s +[acc] eval 1.988117e-3 s +[acc] eval 1.933808e-3 s +[acc] eval 1.807874e-3 s +[acc] eval 1.774124e-3 s +[acc] eval 1.874437e-3 s +[acc] eval 1.904602e-3 s +[acc] eval 1.942302e-3 s +[acc] eval 1.856757e-3 s +[acc] eval 1.932303e-3 s +[acc] eval 2.249431e-3 s +[acc] eval 1.839531e-3 s +[acc] eval 1.882042e-3 s +[acc] eval 0.207673706 s +[acc] compile 0.319908704 s +[acc] eval 2.9385964e-2 s +[acc] eval 0.354665495 s +[acc] compile 0.239207467 s +[acc] eval 2.2749291e-2 s +[acc] eval 0.267071289 s +[acc] compile 6.970097e-2 s +[acc] eval 8.266352e-3 s +[acc] eval 4.048033e-3 s +[acc] eval 4.067026e-3 s +[acc] eval 9.150875e-2 s +[acc] compile 6.119813e-2 s +[acc] eval 1.7283303e-2 s +[acc] eval 8.293059e-2 s +[acc] compile 5.815673e-3 s +[acc] eval 5.852347e-3 s +[acc] eval 1.4447017e-2 s +[acc] compile 4.192464e-3 s +[acc] eval 7.018636e-3 s +[acc] eval 1.167438e-3 s +[acc] eval 8.93261e-4 s +[acc] eval 1.4846664e-2 s +[acc] compile 6.0697631e-2 s +[acc] eval 7.066757e-3 s +[acc] eval 7.1051408e-2 s +[acc] compile 5.582168e-3 s +[acc] eval 6.506793e-3 s +[acc] eval 2.1026764e-2 s +[acc] compile 7.504067e-3 s +[acc] eval 7.539176e-3 s +[acc] eval 1.8386831e-2 s +[acc] compile 0.109263807 s +[acc] eval 1.9505889e-2 s +[acc] eval 0.131184744 s +[acc] compile 3.29452e-3 s +[acc] eval 3.551579e-3 s +[acc] eval 5.91344e-4 s +[acc] eval 7.59675e-4 s +[acc] eval 9.129577e-3 s +[acc] compile 0.148393107 s +[acc] eval 1.9847493e-2 s +[acc] eval 0.171268209 s +[acc] compile 9.7887444e-2 s +[acc] eval 1.3521003e-2 s +[acc] eval 2.319032e-3 s +[acc] eval 2.200116e-3 s +[acc] eval 2.384221e-3 s +[acc] eval 2.47531e-3 s +[acc] eval 1.909005e-3 s +[acc] eval 2.148836e-3 s +[acc] eval 2.047864e-3 s +[acc] eval 2.20561e-3 s +[acc] eval 2.079397e-3 s +[acc] eval 1.984721e-3 s +[acc] eval 1.950296e-3 s +[acc] eval 1.833501e-3 s +[acc] eval 1.883109e-3 s +[acc] eval 1.866372e-3 s +[acc] eval 1.904582e-3 s +[acc] eval 2.107082e-3 s +[acc] eval 2.610022e-3 s +[acc] eval 2.375246e-3 s +[acc] eval 2.466929e-3 s +[acc] eval 2.431406e-3 s +[acc] eval 2.363003e-3 s +[acc] eval 2.461895e-3 s +[acc] eval 2.624539e-3 s +[acc] eval 2.230565e-3 s +[acc] eval 2.223208e-3 s +[acc] eval 2.467556e-3 s +[acc] eval 2.622427e-3 s +[acc] eval 2.401967e-3 s +[acc] eval 2.039221e-3 s +[acc] eval 2.017916e-3 s +[acc] eval 0.184475956 s +[acc] compile 4.399382e-3 s +[acc] eval 3.05303e-3 s +[acc] eval 6.68085e-4 s +[acc] eval 7.64198e-4 s +[acc] eval 1.0002731e-2 s +[acc] compile 0.234859158 s +[acc] eval 1.9732848e-2 s +[acc] eval 0.259347304 s +[acc] compile 0.187611976 s +[acc] eval 3.0633539e-2 s +[acc] eval 0.221404584 s +[acc] compile 0.137831203 s +[acc] eval 9.193112e-3 s +[acc] eval 0.150085235 s +[acc] compile 6.9952024e-2 s +[acc] eval 1.2783507e-2 s +[acc] eval 3.893268e-3 s +[acc] eval 4.105988e-3 s +[acc] eval 4.086337e-3 s +[acc] eval 4.734151e-3 s +[acc] eval 4.601151e-3 s +[acc] eval 1.1658913e-2 s +[acc] eval 3.116052e-3 s +[acc] eval 3.160193e-3 s +[acc] eval 4.016646e-3 s +[acc] eval 4.932854e-3 s +[acc] eval 3.69433e-3 s +[acc] eval 4.142327e-3 s +[acc] eval 3.394354e-3 s +[acc] eval 2.752294e-3 s +[acc] eval 2.734761e-3 s +[acc] eval 3.192247e-3 s +[acc] eval 3.016103e-3 s +[acc] eval 2.915911e-3 s +[acc] eval 2.758142e-3 s +[acc] eval 2.740364e-3 s +[acc] eval 3.507441e-3 s +[acc] eval 2.570187e-3 s +[acc] eval 2.763461e-3 s +[acc] eval 2.054566e-3 s +[acc] eval 0.178216407 s +[acc] compile 0.45920725 s +[acc] eval 2.4883458e-2 s +[acc] eval 0.498296118 s +[acc] compile 0.192736766 s +[acc] eval 2.3736505e-2 s +[acc] eval 0.218590379 s +[acc] compile 0.162766739 s +[acc] eval 2.9583729e-2 s +[acc] eval 3.841263e-3 s +[acc] eval 3.846307e-3 s +[acc] eval 5.14969e-3 s +[acc] eval 5.330926e-3 s +[acc] eval 0.223759861 s +[acc] compile 2.919836e-3 s +[acc] eval 6.326828e-3 s +[acc] eval 7.82195e-4 s +[acc] eval 1.364257e-3 s +[acc] eval 1.2785484e-2 s +[acc] compile 0.44660693 s +[acc] eval 3.0266869e-2 s +[acc] eval 4.242821e-3 s +[acc] eval 4.462336e-3 s +[acc] eval 4.64693e-3 s +[acc] eval 5.174615e-3 s +[acc] eval 5.786576e-3 s +[acc] eval 5.615858e-3 s +[acc] eval 5.494204e-3 s +[acc] eval 4.578403e-3 s +[acc] eval 3.941828e-3 s +[acc] eval 4.517665e-3 s +[acc] eval 5.626608e-3 s +[acc] eval 4.014401e-3 s +[acc] eval 4.024628e-3 s +[acc] eval 5.718482e-3 s +[acc] eval 1.1272515e-2 s +[acc] eval 5.721084e-3 s +[acc] eval 5.552827e-3 s +[acc] eval 5.193282e-3 s +[acc] eval 5.599837e-3 s +[acc] eval 5.802923e-3 s +[acc] eval 5.826877e-3 s +[acc] eval 4.959326e-3 s +[acc] eval 5.742377e-3 s +[acc] eval 9.107876e-3 s +[acc] eval 4.829515e-3 s +[acc] eval 5.061509e-3 s +[acc] eval 4.793803e-3 s +[acc] eval 4.223576e-3 s +[acc] eval 5.584228e-3 s +[acc] eval 0.642487219 s +[acc] compile 2.969236e-3 s +[acc] eval 9.784675e-3 s +[acc] eval 8.85711e-4 s +[acc] eval 6.73632e-4 s +[acc] eval 8.79179e-4 s +[acc] eval 6.45535e-4 s +[acc] eval 1.7219082e-2 s +[acc] compile 0.339955616 s +[acc] eval 2.6513413e-2 s +[acc] eval 0.380429847 s +[acc] compile 7.8928239e-2 s +[acc] eval 2.0179471e-2 s +[acc] eval 1.4225586e-2 s +[acc] eval 4.597596e-3 s +[acc] eval 3.956656e-3 s +[acc] eval 4.013703e-3 s +[acc] eval 3.833836e-3 s +[acc] eval 3.696759e-3 s +[acc] eval 3.892332e-3 s +[acc] eval 4.667339e-3 s +[acc] eval 4.05493e-3 s +[acc] eval 3.750926e-3 s +[acc] eval 3.081019e-3 s +[acc] eval 2.722579e-3 s +[acc] eval 2.62283e-3 s +[acc] eval 2.637067e-3 s +[acc] eval 3.238587e-3 s +[acc] eval 3.510094e-3 s +[acc] eval 2.523284e-3 s +[acc] eval 2.605813e-3 s +[acc] eval 2.682791e-3 s +[acc] eval 2.730978e-3 s +[acc] eval 3.33618e-3 s +[acc] eval 2.712808e-3 s +[acc] eval 2.641214e-3 s +[acc] eval 2.576023e-3 s +[acc] eval 2.541262e-3 s +[acc] eval 2.960897e-3 s +[acc] eval 2.9498e-3 s +[acc] eval 2.576578e-3 s +[acc] eval 2.463221e-3 s +[acc] eval 0.209648525 s +[acc] compile 0.430649217 s +[acc] eval 3.5533178e-2 s +[acc] eval 0.471508415 s +[acc] compile 0.229724037 s +[acc] eval 2.8522917e-2 s +[acc] eval 0.262723884 s +[acc] compile 0.519001487 s +[acc] eval 3.7566715e-2 s +[acc] eval 0.562548967 s +[acc] compile 6.96618e-4 s +[acc] eval 6.636156e-3 s +[acc] compile 0.191418108 s +[acc] eval 1.0858596e-2 s +[acc] eval 5.397173e-3 s +[acc] eval 4.728564e-3 s +[acc] eval 1.3849823e-2 s +[acc] eval 3.627818e-3 s +[acc] eval 3.849864e-3 s +[acc] eval 4.216704e-3 s +[acc] eval 4.030203e-3 s +[acc] eval 3.707698e-3 s +[acc] eval 3.476023e-3 s +[acc] eval 3.990427e-3 s +[acc] eval 3.542948e-3 s +[acc] eval 0.261754302 s +[acc] compile 0.191682194 s +[acc] eval 9.296825e-3 s +[acc] eval 0.204123533 s +[acc] compile 3.244231e-3 s +[acc] eval 4.459925e-3 s +[acc] eval 9.50278e-4 s +[acc] eval 9.07078e-4 s +[acc] eval 1.1168736e-2 s +[acc] compile 0.128399443 s +[acc] eval 5.379363e-3 s +[acc] eval 2.08034e-3 s +[acc] eval 2.193849e-3 s +[acc] eval 2.606426e-3 s +[acc] eval 3.186923e-3 s +[acc] eval 2.005146e-3 s +[acc] eval 2.71185e-3 s +[acc] eval 2.498468e-3 s +[acc] eval 2.314468e-3 s +[acc] eval 2.052513e-3 s +[acc] eval 1.2319278e-2 s +[acc] eval 1.366806e-3 s +[acc] eval 1.610906e-3 s +[acc] eval 1.452891e-3 s +[acc] eval 1.490609e-3 s +[acc] eval 1.291512e-3 s +[acc] eval 1.35283e-3 s +[acc] eval 1.338863e-3 s +[acc] eval 1.317342e-3 s +[acc] eval 1.199058e-3 s +[acc] eval 0.183551895 s +[acc] compile 3.323348e-3 s +[acc] eval 7.0066e-3 s +[acc] eval 6.43017e-4 s +[acc] eval 1.348637e-3 s +[acc] eval 1.4039673e-2 s +[acc] compile 0.233545573 s +[acc] eval 3.1967852e-2 s +[acc] eval 0.270082818 s +[acc] compile 0.463239082 s +[acc] eval 2.7070944e-2 s +[acc] eval 0.504059866 s +[acc] compile 0.122361838 s +[acc] eval 2.9334187e-2 s +[acc] eval 0.155844782 s +[acc] compile 5.0571065e-2 s +[acc] eval 1.099204e-2 s +[acc] eval 2.1677936e-2 s +[acc] eval 8.6105005e-2 s +[acc] compile 4.3747935e-2 s +[acc] eval 1.1175258e-2 s +[acc] eval 1.201993e-3 s +[acc] eval 9.77631e-4 s +[acc] eval 1.15083e-3 s +[acc] eval 9.2354e-4 s +[acc] eval 1.11132e-3 s +[acc] eval 1.119835e-3 s +[acc] eval 1.351682e-3 s +[acc] eval 1.26007e-3 s +[acc] eval 1.378126e-3 s +[acc] eval 1.19986e-3 s +[acc] eval 1.158159e-3 s +[acc] eval 9.02739e-4 s +[acc] eval 7.0928563e-2 s +[acc] compile 5.8147044e-2 s +[acc] eval 2.2721329e-2 s +[acc] eval 8.3325471e-2 s +[acc] compile 0.134089333 s +[acc] eval 8.371432e-3 s +[acc] eval 2.605035e-3 s +[acc] eval 2.761766e-3 s +[acc] eval 2.620313e-3 s +[acc] eval 0.153215592 s +[acc] compile 5.712969e-3 s +[acc] eval 8.25179e-3 s +[acc] eval 3.14664e-3 s +[acc] eval 2.941577e-3 s +[acc] eval 2.688724e-3 s +[acc] eval 2.291965e-3 s +[acc] eval 8.356174e-3 s +[acc] eval 1.91163e-3 s +[acc] eval 1.915403e-3 s +[acc] eval 1.882455e-3 s +[acc] eval 1.740669e-3 s +[acc] eval 1.358223e-3 s +[acc] eval 1.347098e-3 s +[acc] eval 1.364003e-3 s +[acc] eval 1.321551e-3 s +[acc] eval 1.352687e-3 s +[acc] eval 1.294641e-3 s +[acc] eval 1.30668e-3 s +[acc] eval 1.308883e-3 s +[acc] eval 1.392444e-3 s +[acc] eval 1.378204e-3 s +[acc] eval 1.309821e-3 s +[acc] eval 1.373323e-3 s +[acc] eval 1.382696e-3 s +[acc] eval 1.342691e-3 s +[acc] eval 1.359154e-3 s +[acc] eval 1.262461e-3 s +[acc] eval 1.266606e-3 s +[acc] eval 1.279142e-3 s +[acc] eval 1.314845e-3 s +[acc] eval 6.9828088e-2 s +[acc] compile 7.3698269e-2 s +[acc] eval 1.1691094e-2 s +[acc] eval 8.834027e-2 s +[acc] compile 6.7650964e-2 s +[acc] eval 1.8401843e-2 s +[acc] eval 9.0245092e-2 s +[acc] compile 0.297141584 s +[acc] eval 2.8860054e-2 s +[acc] eval 4.529728e-3 s +[acc] eval 1.2453022e-2 s +[acc] eval 3.763414e-3 s +[acc] eval 3.774821e-3 s +[acc] eval 3.881018e-3 s +[acc] eval 4.06673e-3 s +[acc] eval 3.849259e-3 s +[acc] eval 3.421019e-3 s +[acc] eval 3.67715e-3 s +[acc] eval 3.440956e-3 s +[acc] eval 3.010713e-3 s +[acc] eval 3.591674e-3 s +[acc] eval 3.806556e-3 s +[acc] eval 3.612347e-3 s +[acc] eval 3.763861e-3 s +[acc] eval 3.755973e-3 s +[acc] eval 4.096395e-3 s +[acc] eval 3.93376e-3 s +[acc] eval 1.0823889e-2 s +[acc] eval 3.765684e-3 s +[acc] eval 4.934196e-3 s +[acc] eval 3.697707e-3 s +[acc] eval 3.761247e-3 s +[acc] eval 3.32231e-3 s +[acc] eval 3.058039e-3 s +[acc] eval 3.038821e-3 s +[acc] eval 3.024091e-3 s +[acc] eval 3.188725e-3 s +[acc] eval 3.042522e-3 s +[acc] eval 0.45498735 s +[acc] compile 4.295063e-3 s +[acc] eval 1.4757937e-2 s +[acc] eval 1.881332e-3 s +[acc] eval 1.71735e-3 s +[acc] eval 1.843535e-3 s +[acc] eval 1.698449e-3 s +[acc] eval 1.500833e-3 s +[acc] eval 1.419332e-3 s +[acc] eval 1.445764e-3 s +[acc] eval 1.611851e-3 s +[acc] eval 1.535544e-3 s +[acc] eval 1.473645e-3 s +[acc] eval 1.640485e-3 s +[acc] eval 3.9551094e-2 s +[acc] compile 0.506808081 s +[acc] eval 4.536267e-2 s +[acc] eval 0.557063512 s +[acc] compile 5.7630168e-2 s +[acc] eval 2.1170252e-2 s +[acc] eval 8.2822065e-2 s +[acc] compile 6.3809512e-2 s +[acc] eval 2.4038657e-2 s +[acc] eval 2.970488e-3 s +[acc] eval 3.451165e-3 s +[acc] eval 2.938057e-3 s +[acc] eval 2.742995e-3 s +[acc] eval 2.972269e-3 s +[acc] eval 2.875413e-3 s +[acc] eval 2.928113e-3 s +[acc] eval 2.584436e-3 s +[acc] eval 2.549115e-3 s +[acc] eval 3.054974e-3 s +[acc] eval 2.994774e-3 s +[acc] eval 0.12446183 s +[acc] compile 6.651841e-3 s +[acc] eval 7.671572e-3 s +[acc] eval 1.659711e-3 s +[acc] eval 1.612264e-3 s +[acc] eval 1.556785e-3 s +[acc] eval 1.586985e-3 s +[acc] eval 1.633441e-3 s +[acc] eval 1.619811e-3 s +[acc] eval 1.615366e-3 s +[acc] eval 1.649212e-3 s +[acc] eval 1.636075e-3 s +[acc] eval 1.827043e-3 s +[acc] eval 1.767142e-3 s +[acc] eval 3.5103369e-2 s +[acc] compile 5.4737248e-2 s +[acc] eval 6.633443e-3 s +[acc] eval 2.753675e-3 s +[acc] eval 3.61041e-3 s +[acc] eval 1.1571696e-2 s +[acc] eval 1.322455e-3 s +[acc] eval 1.803951e-3 s +[acc] eval 1.80781e-3 s +[acc] eval 1.765225e-3 s +[acc] eval 1.464858e-3 s +[acc] eval 1.639525e-3 s +[acc] eval 1.607538e-3 s +[acc] eval 1.511994e-3 s +[acc] eval 1.57274e-3 s +[acc] eval 1.463661e-3 s +[acc] eval 1.612137e-3 s +[acc] eval 1.630678e-3 s +[acc] eval 1.553144e-3 s +[acc] eval 1.616487e-3 s +[acc] eval 1.519685e-3 s +[acc] eval 1.828975e-3 s +[acc] eval 1.504603e-3 s +[acc] eval 1.720263e-3 s +[acc] eval 1.55498e-3 s +[acc] eval 1.589204e-3 s +[acc] eval 1.434699e-3 s +[acc] eval 0.116416643 s +[acc] compile 3.65953e-3 s +[acc] eval 5.471667e-3 s +[acc] eval 1.0465527e-2 s +[acc] compile 0.397922628 s +[acc] eval 3.1876139e-2 s +[acc] eval 4.170299e-3 s +[acc] eval 4.175783e-3 s +[acc] eval 4.288342e-3 s +[acc] eval 3.862876e-3 s +[acc] eval 3.935918e-3 s +[acc] eval 4.297098e-3 s +[acc] eval 4.659496e-3 s +[acc] eval 1.2965239e-2 s +[acc] eval 4.151089e-3 s +[acc] eval 5.239362e-3 s +[acc] eval 4.115102e-3 s +[acc] eval 9.304762e-3 s +[acc] eval 4.329904e-3 s +[acc] eval 3.975211e-3 s +[acc] eval 3.980931e-3 s +[acc] eval 4.843319e-3 s +[acc] eval 9.922143e-3 s +[acc] eval 4.632898e-3 s +[acc] eval 4.826212e-3 s +[acc] eval 4.580375e-3 s +[acc] eval 9.544739e-3 s +[acc] eval 4.619374e-3 s +[acc] eval 4.570215e-3 s +[acc] eval 4.52683e-3 s +[acc] eval 9.236156e-3 s +[acc] eval 4.652568e-3 s +[acc] eval 4.416312e-3 s +[acc] eval 4.465353e-3 s +[acc] eval 4.807441e-3 s +[acc] eval 0.59923702 s +[acc] compile 0.243783377 s +[acc] eval 3.7987754e-2 s +[acc] eval 0.285366945 s +[acc] compile 4.190717e-3 s +[acc] eval 3.854632e-3 s +[acc] eval 1.01073e-3 s +[acc] eval 8.17423e-4 s +[acc] eval 1.018212e-3 s +[acc] eval 1.2200029e-2 s +[acc] compile 3.000735e-3 s +[acc] eval 4.224189e-3 s +[acc] eval 1.041554e-3 s +[acc] eval 8.33253e-4 s +[acc] eval 1.0471208e-2 s +[acc] compile 0.282328721 s +[acc] eval 3.8724161e-2 s +[acc] eval 0.325342148 s +[acc] compile 0.111929284 s +[acc] eval 7.653375e-3 s +[acc] eval 2.690544e-3 s +[acc] eval 2.506111e-3 s +[acc] eval 2.472939e-3 s +[acc] eval 2.642913e-3 s +[acc] eval 2.573441e-3 s +[acc] eval 5.277905e-3 s +[acc] eval 1.0688917e-2 s +[acc] eval 2.256672e-3 s +[acc] eval 1.725204e-3 s +[acc] eval 1.880169e-3 s +[acc] eval 1.76905e-3 s +[acc] eval 0.159050392 s +[acc] compile 0.220912414 s +[acc] eval 2.6235322e-2 s +[acc] eval 0.250401468 s +[acc] compile 0.12418275 s +[acc] eval 1.7285773e-2 s +[acc] eval 0.144487517 s +[acc] compile 7.75783e-4 s +[acc] eval 6.544408e-3 s +[acc] compile 9.705446e-3 s +[acc] eval 8.872282e-3 s +[acc] eval 2.3357984e-2 s +[acc] compile 7.8066234e-2 s +[acc] eval 9.928198e-3 s +[acc] eval 2.5861886e-2 s +[acc] eval 5.058329e-3 s +[acc] eval 4.269749e-3 s +[acc] eval 3.191758e-3 s +[acc] eval 2.789622e-3 s +[acc] eval 2.471118e-3 s +[acc] eval 2.347691e-3 s +[acc] eval 2.472578e-3 s +[acc] eval 2.396339e-3 s +[acc] eval 2.420927e-3 s +[acc] eval 2.489096e-3 s +[acc] eval 2.567244e-3 s +[acc] eval 2.464034e-3 s +[acc] eval 2.418116e-3 s +[acc] eval 2.213923e-3 s +[acc] eval 2.24261e-3 s +[acc] eval 2.052134e-3 s +[acc] eval 1.994518e-3 s +[acc] eval 2.013282e-3 s +[acc] eval 1.94846e-3 s +[acc] eval 1.939547e-3 s +[acc] eval 1.958619e-3 s +[acc] eval 2.061239e-3 s +[acc] eval 2.129918e-3 s +[acc] eval 2.009747e-3 s +[acc] eval 2.212518e-3 s +[acc] eval 2.043231e-3 s +[acc] eval 1.944262e-3 s +[acc] eval 1.937722e-3 s +[acc] eval 0.1868612 s +[acc] compile 0.109106532 s +[acc] eval 1.4578383e-2 s +[acc] eval 0.13373843 s +[acc] compile 0.129295792 s +[acc] eval 2.04442e-2 s +[acc] eval 3.943144e-3 s +[acc] eval 1.3452415e-2 s +[acc] eval 3.120368e-3 s +[acc] eval 3.179065e-3 s +[acc] eval 3.120004e-3 s +[acc] eval 3.789655e-3 s +[acc] eval 3.565957e-3 s +[acc] eval 3.957858e-3 s +[acc] eval 3.520924e-3 s +[acc] eval 2.956517e-3 s +[acc] eval 2.904724e-3 s +[acc] eval 2.853591e-3 s +[acc] eval 3.386111e-3 s +[acc] eval 3.442264e-3 s +[acc] eval 3.205476e-3 s +[acc] eval 3.112772e-3 s +[acc] eval 3.443953e-3 s +[acc] eval 3.628003e-3 s +[acc] eval 3.841698e-3 s +[acc] eval 3.756415e-3 s +[acc] eval 2.918797e-3 s +[acc] eval 2.803844e-3 s +[acc] eval 3.129845e-3 s +[acc] eval 2.8599e-3 s +[acc] eval 3.061508e-3 s +[acc] eval 2.702852e-3 s +[acc] eval 2.968705e-3 s +[acc] eval 2.916101e-3 s +[acc] eval 3.056986e-3 s +[acc] eval 0.259768096 s +[acc] compile 6.5919869e-2 s +[acc] eval 1.6814863e-2 s +[acc] eval 9.0912993e-2 s +[acc] compile 6.485379e-3 s +[acc] eval 6.126897e-3 s +[acc] eval 1.4706352e-2 s +[acc] compile 4.850872e-3 s +[acc] eval 9.189084e-3 s +[acc] eval 2.2429625e-2 s +[acc] compile 4.25121e-3 s +[acc] eval 5.283527e-3 s +[acc] eval 1.890627e-3 s +[acc] eval 1.769061e-3 s +[acc] eval 1.580259e-3 s +[acc] eval 1.409298e-3 s +[acc] eval 1.076954e-3 s +[acc] eval 1.073656e-3 s +[acc] eval 1.112312e-3 s +[acc] eval 9.92445e-4 s +[acc] eval 1.085491e-3 s +[acc] eval 1.044046e-3 s +[acc] eval 1.060807e-3 s +[acc] eval 2.5883938e-2 s +[acc] compile 0.38617685 s +[acc] eval 2.6815018e-2 s +[acc] eval 0.41718006 s +[acc] compile 7.4912498e-2 s +[acc] eval 5.878985e-3 s +[acc] eval 2.966821e-3 s +[acc] eval 2.608794e-3 s +[acc] eval 2.578904e-3 s +[acc] eval 4.006214e-3 s +[acc] eval 4.709032e-3 s +[acc] eval 4.753241e-3 s +[acc] eval 1.2389415e-2 s +[acc] eval 2.600332e-3 s +[acc] eval 2.782028e-3 s +[acc] eval 2.831768e-3 s +[acc] eval 2.852772e-3 s +[acc] eval 2.696099e-3 s +[acc] eval 2.688429e-3 s +[acc] eval 2.582947e-3 s +[acc] eval 2.793167e-3 s +[acc] eval 2.396714e-3 s +[acc] eval 2.805621e-3 s +[acc] eval 2.5748e-3 s +[acc] eval 2.597772e-3 s +[acc] eval 2.171245e-3 s +[acc] eval 1.950959e-3 s +[acc] eval 2.019004e-3 s +[acc] eval 2.092847e-3 s +[acc] eval 2.016624e-3 s +[acc] eval 1.887442e-3 s +[acc] eval 1.91407e-3 s +[acc] eval 1.943602e-3 s +[acc] eval 1.938757e-3 s +[acc] eval 1.79234e-3 s +[acc] eval 0.171269919 s +[acc] compile 0.21512764 s +[acc] eval 2.2128132e-2 s +[acc] eval 3.094558e-3 s +[acc] eval 1.1423195e-2 s +[acc] eval 2.966617e-3 s +[acc] eval 2.655723e-3 s +[acc] eval 2.585401e-3 s +[acc] eval 0.263424459 s +[acc] compile 0.247892181 s +[acc] eval 2.8862377e-2 s +[acc] eval 2.108983e-3 s +[acc] eval 8.063864e-3 s +[acc] eval 1.748553e-3 s +[acc] eval 1.881475e-3 s +[acc] eval 1.706887e-3 s +[acc] eval 1.580399e-3 s +[acc] eval 1.70046e-3 s +[acc] eval 1.631601e-3 s +[acc] eval 1.551314e-3 s +[acc] eval 2.097557e-3 s +[acc] eval 0.30421306 s +[acc] compile 4.8516698e-2 s +[acc] eval 1.6214326e-2 s +[acc] eval 3.215488e-3 s +[acc] eval 2.595527e-3 s +[acc] eval 2.450803e-3 s +[acc] eval 2.278066e-3 s +[acc] eval 2.122316e-3 s +[acc] eval 7.642783e-3 s +[acc] eval 1.692459e-3 s +[acc] eval 1.696212e-3 s +[acc] eval 9.2384946e-2 s +[acc] compile 0.331881475 s +[acc] eval 2.686344e-2 s +[acc] eval 0.366363321 s +[acc] compile 9.26865e-4 s +[acc] eval 1.4376884e-2 s +[acc] compile 0.114554329 s +[acc] eval 1.1729729e-2 s +[acc] eval 2.548317e-3 s +[acc] eval 2.45179e-3 s +[acc] eval 3.233834e-3 s +[acc] eval 2.565339e-3 s +[acc] eval 2.347431e-3 s +[acc] eval 2.153586e-3 s +[acc] eval 1.0458884e-2 s +[acc] eval 2.485696e-3 s +[acc] eval 1.618782e-3 s +[acc] eval 1.682475e-3 s +[acc] eval 1.50765e-3 s +[acc] eval 1.581664e-3 s +[acc] eval 1.582916e-3 s +[acc] eval 1.60781e-3 s +[acc] eval 1.474268e-3 s +[acc] eval 1.529724e-3 s +[acc] eval 1.527604e-3 s +[acc] eval 1.590209e-3 s +[acc] eval 1.72608e-3 s +[acc] eval 0.176873506 s +[acc] compile 6.790014e-3 s +[acc] eval 8.232178e-3 s +[acc] eval 1.7204027e-2 s +[acc] compile 0.218435349 s +[acc] eval 3.0589339e-2 s +[acc] eval 3.605992e-3 s +[acc] eval 4.022475e-3 s +[acc] eval 4.23895e-3 s +[acc] eval 3.585941e-3 s +[acc] eval 4.229276e-3 s +[acc] eval 3.462855e-3 s +[acc] eval 4.036236e-3 s +[acc] eval 3.521513e-3 s +[acc] eval 4.227162e-3 s +[acc] eval 4.171936e-3 s +[acc] eval 4.372843e-3 s +[acc] eval 4.328892e-3 s +[acc] eval 4.591974e-3 s +[acc] eval 4.714437e-3 s +[acc] eval 4.358639e-3 s +[acc] eval 4.384273e-3 s +[acc] eval 4.196838e-3 s +[acc] eval 4.259973e-3 s +[acc] eval 4.022019e-3 s +[acc] eval 4.444064e-3 s +[acc] eval 4.527949e-3 s +[acc] eval 4.12971e-3 s +[acc] eval 3.913152e-3 s +[acc] eval 3.83754e-3 s +[acc] eval 4.656072e-3 s +[acc] eval 4.397948e-3 s +[acc] eval 4.374676e-3 s +[acc] eval 4.603677e-3 s +[acc] eval 3.989059e-3 s +[acc] eval 3.361347e-3 s +[acc] eval 3.518749e-3 s +[acc] eval 3.71114e-3 s +[acc] eval 4.142641e-3 s +[acc] eval 3.646132e-3 s +[acc] eval 3.358249e-3 s +[acc] eval 3.438894e-3 s +[acc] eval 3.972135e-3 s +[acc] eval 0.407543308 s +[acc] compile 6.967367e-3 s +[acc] eval 5.423425e-3 s +[acc] eval 1.927047e-3 s +[acc] eval 2.6798e-3 s +[acc] eval 4.092911e-3 s +[acc] eval 3.611302e-3 s +[acc] eval 3.711819e-3 s +[acc] eval 3.3840943e-2 s +[acc] compile 7.8303356e-2 s +[acc] eval 2.7151526e-2 s +[acc] eval 2.395713e-3 s +[acc] eval 2.079539e-3 s +[acc] eval 2.015072e-3 s +[acc] eval 1.970221e-3 s +[acc] eval 2.007379e-3 s +[acc] eval 1.943171e-3 s +[acc] eval 2.193621e-3 s +[acc] eval 2.165338e-3 s +[acc] eval 2.204801e-3 s +[acc] eval 1.83421e-3 s +[acc] eval 1.956075e-3 s +[acc] eval 1.860883e-3 s +[acc] eval 1.953638e-3 s +[acc] eval 1.917052e-3 s +[acc] eval 3.206e-3 s +[acc] eval 2.644971e-3 s +[acc] eval 2.322758e-3 s +[acc] eval 2.764627e-3 s +[acc] eval 2.341474e-3 s +[acc] eval 2.252933e-3 s +[acc] eval 2.777939e-3 s +[acc] eval 2.67833e-3 s +[acc] eval 2.492245e-3 s +[acc] eval 2.379774e-3 s +[acc] eval 2.26896e-3 s +[acc] eval 2.964293e-3 s +[acc] eval 2.484291e-3 s +[acc] eval 2.099479e-3 s +[acc] eval 2.779082e-3 s +[acc] eval 3.591734e-3 s +[acc] eval 3.298057e-3 s +[acc] eval 2.496219e-3 s +[acc] eval 2.482922e-3 s +[acc] eval 2.362782e-3 s +[acc] eval 2.49704e-3 s +[acc] eval 2.723679e-3 s +[acc] eval 2.800073e-3 s +[acc] eval 2.920151e-3 s +[acc] eval 2.910054e-3 s +[acc] eval 3.063241e-3 s +[acc] eval 3.534941e-3 s +[acc] eval 0.214037071 s +[acc] compile 3.198298e-3 s +[acc] eval 4.181145e-3 s +[acc] eval 1.022833e-3 s +[acc] eval 7.12994e-4 s +[acc] eval 1.0758398e-2 s +[acc] compile 3.939443e-3 s +[acc] eval 5.560714e-3 s +[acc] eval 1.579829e-3 s +[acc] eval 1.599631e-3 s +[acc] eval 2.002245e-3 s +[acc] eval 1.422699e-3 s +[acc] eval 1.876638e-3 s +[acc] eval 1.520339e-3 s +[acc] eval 1.157342e-3 s +[acc] eval 1.497839e-3 s +[acc] eval 1.19325e-3 s +[acc] eval 1.69039e-3 s +[acc] eval 1.290851e-3 s +[acc] eval 9.97413e-4 s +[acc] eval 1.26569e-3 s +[acc] eval 1.07034e-3 s +[acc] eval 3.2213278e-2 s +[acc] compile 0.163495279 s +[acc] eval 2.0281575e-2 s +[acc] eval 0.19762929 s +[acc] compile 0.204202525 s +[acc] eval 1.5762289e-2 s +[acc] eval 0.225255125 s +[acc] compile 4.656735e-3 s +[acc] eval 9.622612e-3 s +[acc] eval 2.4592346e-2 s +[acc] compile 0.196442946 s +[acc] eval 1.8582233e-2 s +[acc] eval 3.573889e-3 s +[acc] eval 1.293036e-2 s +[acc] eval 2.792221e-3 s +[acc] eval 2.635542e-3 s +[acc] eval 2.430257e-3 s +[acc] eval 2.35196e-3 s +[acc] eval 3.077192e-3 s +[acc] eval 4.218364e-3 s +[acc] eval 3.261105e-3 s +[acc] eval 3.174032e-3 s +[acc] eval 2.993126e-3 s +[acc] eval 2.88765e-3 s +[acc] eval 2.903485e-3 s +[acc] eval 2.354869e-3 s +[acc] eval 2.442699e-3 s +[acc] eval 3.116276e-3 s +[acc] eval 2.681303e-3 s +[acc] eval 2.492585e-3 s +[acc] eval 2.379066e-3 s +[acc] eval 2.378035e-3 s +[acc] eval 2.329425e-3 s +[acc] eval 2.847115e-3 s +[acc] eval 0.292340567 s +[acc] compile 0.121083072 s +[acc] eval 1.6317889e-2 s +[acc] eval 0.14015593 s +[acc] compile 6.8764906e-2 s +[acc] eval 4.370579e-3 s +[acc] eval 8.302007e-3 s +[acc] eval 2.625171e-3 s +[acc] eval 2.134762e-3 s +[acc] eval 1.625326e-3 s +[acc] eval 1.483548e-3 s +[acc] eval 1.612791e-3 s +[acc] eval 1.763044e-3 s +[acc] eval 1.641416e-3 s +[acc] eval 1.599736e-3 s +[acc] eval 2.735029e-3 s +[acc] eval 1.56464e-3 s +[acc] eval 1.548236e-3 s +[acc] eval 1.483352e-3 s +[acc] eval 1.446842e-3 s +[acc] eval 1.589148e-3 s +[acc] eval 1.760669e-3 s +[acc] eval 2.108678e-3 s +[acc] eval 1.801122e-3 s +[acc] eval 2.147807e-3 s +[acc] eval 1.819868e-3 s +[acc] eval 2.682312e-3 s +[acc] eval 1.740319e-3 s +[acc] eval 1.719466e-3 s +[acc] eval 1.735447e-3 s +[acc] eval 1.782093e-3 s +[acc] eval 1.778732e-3 s +[acc] eval 1.726166e-3 s +[acc] eval 0.1326634 s +[acc] eval 2.971203e-3 s +[acc] eval 5.29062e-4 s +[acc] eval 6.23565e-4 s +[acc] eval 4.86328e-3 s +[acc] compile 3.319393e-3 s +[acc] eval 3.765306e-3 s +[acc] eval 8.87099e-4 s +[acc] eval 6.52738e-4 s +[acc] eval 8.47528e-4 s +[acc] eval 1.0607705e-2 s +[acc] compile 2.9489e-3 s +[acc] eval 3.290277e-3 s +[acc] eval 8.83574e-4 s +[acc] eval 6.61966e-4 s +[acc] eval 9.000671e-3 s +[acc] compile 3.453348e-3 s +[acc] eval 5.845983e-3 s +[acc] eval 1.093343e-2 s +[acc] compile 4.253282e-3 s +[acc] eval 1.5804024e-2 s +[acc] eval 8.48519e-4 s +[acc] eval 6.95325e-4 s +[acc] eval 2.2848699e-2 s +[acc] compile 0.123647897 s +[acc] eval 1.7097919e-2 s +[acc] eval 0.160811811 s +[acc] compile 4.715016e-3 s +[acc] eval 4.507226e-3 s +[acc] eval 1.92557e-3 s +[acc] eval 2.241538e-3 s +[acc] eval 1.548573e-3 s +[acc] eval 1.385497e-3 s +[acc] eval 1.402651e-3 s +[acc] eval 1.299454e-3 s +[acc] eval 1.141152e-3 s +[acc] eval 1.138741e-3 s +[acc] eval 1.109399e-3 s +[acc] eval 1.045306e-3 s +[acc] eval 1.36663e-3 s +[acc] eval 1.231344e-3 s +[acc] eval 1.08868e-3 s +[acc] eval 1.278761e-3 s +[acc] eval 1.377818e-3 s +[acc] eval 1.344803e-3 s +[acc] eval 1.097569e-3 s +[acc] eval 8.976338e-3 s +[acc] eval 1.289988e-3 s +[acc] eval 4.5281457e-2 s +[acc] compile 7.9932824e-2 s +[acc] eval 2.2908301e-2 s +[acc] eval 0.107016594 s +[acc] compile 0.210856366 s +[acc] eval 2.5624421e-2 s +[acc] eval 1.2835347e-2 s +[acc] eval 3.173306e-3 s +[acc] eval 2.632662e-3 s +[acc] eval 2.641818e-3 s +[acc] eval 2.64264e-3 s +[acc] eval 2.736823e-3 s +[acc] eval 2.847286e-3 s +[acc] eval 2.697149e-3 s +[acc] eval 2.608391e-3 s +[acc] eval 2.594295e-3 s +[acc] eval 2.475618e-3 s +[acc] eval 2.533236e-3 s +[acc] eval 2.548165e-3 s +[acc] eval 2.584838e-3 s +[acc] eval 2.540793e-3 s +[acc] eval 2.551096e-3 s +[acc] eval 2.999512e-3 s +[acc] eval 2.568907e-3 s +[acc] eval 2.561774e-3 s +[acc] eval 2.426336e-3 s +[acc] eval 2.64186e-3 s +[acc] eval 2.622428e-3 s +[acc] eval 2.809448e-3 s +[acc] eval 2.607643e-3 s +[acc] eval 2.899072e-3 s +[acc] eval 2.923051e-3 s +[acc] eval 2.712843e-3 s +[acc] eval 2.891061e-3 s +[acc] eval 2.742024e-3 s +[acc] eval 0.330083141 s +[acc] compile 3.14799e-3 s +[acc] eval 4.418649e-3 s +[acc] eval 5.88878e-4 s +[acc] eval 8.44522e-4 s +[acc] eval 9.901922e-3 s +[acc] compile 4.644099e-3 s +[acc] eval 5.610579e-3 s +[acc] eval 7.285041e-3 s +[acc] eval 1.125347e-3 s +[acc] eval 1.160464e-3 s +[acc] eval 1.081976e-3 s +[acc] eval 1.215487e-3 s +[acc] eval 1.307077e-3 s +[acc] eval 1.306747e-3 s +[acc] eval 1.317052e-3 s +[acc] eval 8.926e-4 s +[acc] eval 1.120514e-3 s +[acc] eval 1.079731e-3 s +[acc] eval 8.75502e-4 s +[acc] eval 8.85958e-4 s +[acc] eval 8.99617e-4 s +[acc] eval 8.60301e-4 s +[acc] eval 3.4887199e-2 s +[acc] compile 4.086783e-3 s +[acc] eval 1.3622097e-2 s +[acc] eval 1.9307893e-2 s +[acc] compile 5.592014e-3 s +[acc] eval 1.1722171e-2 s +[acc] eval 2.991174e-3 s +[acc] eval 2.348051e-3 s +[acc] eval 8.465833e-3 s +[acc] eval 2.021045e-3 s +[acc] eval 1.901895e-3 s +[acc] eval 1.637776e-3 s +[acc] eval 1.622959e-3 s +[acc] eval 1.7841e-3 s +[acc] eval 1.548599e-3 s +[acc] eval 1.751415e-3 s +[acc] eval 1.720856e-3 s +[acc] eval 1.56038e-3 s +[acc] eval 1.912948e-3 s +[acc] eval 1.570392e-3 s +[acc] eval 1.478908e-3 s +[acc] eval 5.4966336e-2 s +[acc] compile 4.129528e-3 s +[acc] eval 3.789007e-3 s +[acc] eval 5.62779e-4 s +[acc] eval 7.17481e-4 s +[acc] eval 1.0028347e-2 s +[acc] compile 6.2777239e-2 s +[acc] eval 1.0523802e-2 s +[acc] eval 1.425475e-3 s +[acc] eval 1.502957e-3 s +[acc] eval 1.261553e-3 s +[acc] eval 1.907427e-3 s +[acc] eval 1.0345739e-2 s +[acc] eval 1.147742e-3 s +[acc] eval 1.272087e-3 s +[acc] eval 1.04552e-3 s +[acc] eval 1.089021e-3 s +[acc] eval 1.254566e-3 s +[acc] eval 1.638782e-3 s +[acc] eval 1.829536e-3 s +[acc] eval 1.413657e-3 s +[acc] eval 1.22117e-3 s +[acc] eval 1.015972e-3 s +[acc] eval 9.99053e-4 s +[acc] eval 8.63703e-4 s +[acc] eval 1.052483e-3 s +[acc] eval 9.67128e-4 s +[acc] eval 1.022348e-3 s +[acc] eval 9.83585e-4 s +[acc] eval 9.23721e-4 s +[acc] eval 1.517844e-3 s +[acc] eval 1.546644e-3 s +[acc] eval 1.505917e-3 s +[acc] eval 1.681211e-3 s +[acc] eval 1.611573e-3 s +[acc] eval 9.80696e-4 s +[acc] eval 1.232687e-3 s +[acc] eval 0.123097168 s +[acc] compile 2.949423e-3 s +[acc] eval 4.142753e-3 s +[acc] eval 1.373832e-3 s +[acc] eval 1.212449e-3 s +[acc] eval 1.100409e-3 s +[acc] eval 9.61967e-4 s +[acc] eval 1.064479e-3 s +[acc] eval 1.043033e-3 s +[acc] eval 1.001812e-3 s +[acc] eval 9.01013e-4 s +[acc] eval 1.050672e-3 s +[acc] eval 1.037275e-3 s +[acc] eval 1.066762e-3 s +[acc] eval 1.261726e-3 s +[acc] eval 1.156367e-3 s +[acc] eval 1.359477e-3 s +[acc] eval 1.209753e-3 s +[acc] eval 1.117204e-3 s +[acc] eval 1.601087e-3 s +[acc] eval 1.76735e-3 s +[acc] eval 8.741894e-3 s +[acc] eval 1.044322e-3 s +[acc] eval 1.070612e-3 s +[acc] eval 1.032875e-3 s +[acc] eval 1.006041e-3 s +[acc] eval 4.4047719e-2 s +[acc] compile 3.008584e-3 s +[acc] eval 8.006715e-3 s +[acc] eval 1.2378329e-2 s +[acc] compile 0.187570596 s +[acc] eval 2.7132129e-2 s +[acc] eval 0.217823412 s +[acc] compile 4.331168e-3 s +[acc] eval 4.542448e-3 s +[acc] eval 1.650396e-3 s +[acc] eval 1.832692e-3 s +[acc] eval 1.821955e-3 s +[acc] eval 1.649846e-3 s +[acc] eval 1.854824e-3 s +[acc] eval 1.723988e-3 s +[acc] eval 1.800637e-3 s +[acc] eval 1.608779e-3 s +[acc] eval 1.1296284e-2 s +[acc] eval 1.295628e-3 s +[acc] eval 1.340663e-3 s +[acc] eval 1.394615e-3 s +[acc] eval 1.266745e-3 s +[acc] eval 1.452731e-3 s +[acc] eval 4.3362397e-2 s +[acc] compile 3.290526e-3 s +[acc] eval 1.1441783e-2 s +[acc] eval 9.52122e-4 s +[acc] eval 7.02634e-4 s +[acc] eval 8.18995e-4 s +[acc] eval 1.8382406e-2 s +[acc] compile 3.114956e-3 s +[acc] eval 8.553973e-3 s +[acc] eval 1.272665e-2 s +[acc] compile 6.739755e-3 s +[acc] eval 2.3099758e-2 s +[acc] eval 1.905518e-3 s +[acc] eval 1.703481e-3 s +[acc] eval 1.762071e-3 s +[acc] eval 1.710882e-3 s +[acc] eval 1.794548e-3 s +[acc] eval 2.487646e-3 s +[acc] eval 2.783635e-3 s +[acc] eval 2.624463e-3 s +[acc] eval 2.086994e-3 s +[acc] eval 2.06738e-3 s +[acc] eval 2.158142e-3 s +[acc] eval 2.184852e-3 s +[acc] eval 2.225808e-3 s +[acc] eval 2.715999e-3 s +[acc] eval 2.03061e-3 s +[acc] eval 2.271359e-3 s +[acc] eval 2.235386e-3 s +[acc] eval 2.330332e-3 s +[acc] eval 2.348169e-3 s +[acc] eval 2.246224e-3 s +[acc] eval 2.129354e-3 s +[acc] eval 1.766721e-3 s +[acc] eval 1.631698e-3 s +[acc] eval 1.539456e-3 s +[acc] eval 1.747432e-3 s +[acc] eval 1.643912e-3 s +[acc] eval 1.655451e-3 s +[acc] eval 1.626121e-3 s +[acc] eval 1.822257e-3 s +[acc] eval 9.323078e-2 s +[acc] compile 4.243525e-3 s +[acc] eval 6.479313e-3 s +[acc] eval 1.2105462e-2 s +[acc] compile 0.253825958 s +[acc] eval 2.3289608e-2 s +[acc] eval 0.284059394 s +[acc] compile 5.696814e-3 s +[acc] eval 8.643485e-3 s +[acc] eval 1.253481e-3 s +[acc] eval 2.0344582e-2 s +[acc] eval 3.79396e-2 s +[acc] compile 1.10369e-3 s +[acc] eval 1.1537526e-2 s +[acc] compile 6.383401e-3 s +[acc] eval 1.4481964e-2 s +[acc] eval 1.996651e-3 s +[acc] eval 1.56456e-3 s +[acc] eval 1.350041e-3 s +[acc] eval 1.051241e-3 s +[acc] eval 1.369935e-3 s +[acc] eval 1.327774e-3 s +[acc] eval 1.448956e-3 s +[acc] eval 1.118443e-3 s +[acc] eval 3.4285369e-2 s +[acc] compile 5.249185e-3 s +[acc] eval 2.5697579e-2 s +[acc] eval 3.272207e-2 s +[acc] compile 0.190760661 s +[acc] eval 1.5770157e-2 s +[acc] eval 0.209602322 s +[acc] compile 9.688838e-3 s +[acc] eval 2.8033897e-2 s +[acc] eval 4.1596206e-2 s +[acc] compile 0.236841308 s +[acc] eval 1.4721173e-2 s +[acc] eval 0.257241339 s +[acc] compile 6.204146e-3 s +[acc] eval 8.046683e-3 s +[acc] eval 2.617903e-3 s +[acc] eval 2.73545e-3 s +[acc] eval 2.534068e-3 s +[acc] eval 1.3122511e-2 s +[acc] eval 1.237501e-3 s +[acc] eval 1.06942e-3 s +[acc] eval 1.062265e-3 s +[acc] eval 1.195696e-3 s +[acc] eval 1.004414e-3 s +[acc] eval 9.60693e-4 s +[acc] eval 9.53377e-4 s +[acc] eval 9.79724e-4 s +[acc] eval 1.115233e-3 s +[acc] eval 1.098489e-3 s +[acc] eval 1.09515e-3 s +[acc] eval 1.105503e-3 s +[acc] eval 9.73619e-4 s +[acc] eval 9.69988e-4 s +[acc] eval 9.26932e-4 s +[acc] eval 9.51736e-4 s +[acc] eval 9.61518e-4 s +[acc] eval 1.048248e-3 s +[acc] eval 9.30761e-4 s +[acc] eval 9.49911e-4 s +[acc] eval 1.078738e-3 s +[acc] eval 1.054707e-3 s +[acc] eval 1.350897e-3 s +[acc] eval 1.739415e-3 s +[acc] eval 1.502953e-3 s +[acc] eval 6.5983035e-2 s +[acc] compile 2.744817e-3 s +[acc] eval 4.713391e-3 s +[acc] eval 9.20209e-4 s +[acc] eval 9.90055e-4 s +[acc] eval 1.058225e-3 s +[acc] eval 1.1971551e-2 s +[acc] compile 0.154155218 s +[acc] eval 1.7028481e-2 s +[acc] eval 0.176237092 s +[acc] compile 7.8995798e-2 s +[acc] eval 9.074382e-3 s +[acc] eval 9.1386594e-2 s +[acc] compile 0.134163712 s +[acc] eval 1.5304123e-2 s +[acc] eval 0.152632294 s +[acc] compile 7.091646e-3 s +[acc] eval 3.8862132e-2 s +[acc] eval 4.9247964e-2 s +[acc] compile 6.431502e-3 s +[acc] eval 1.1224796e-2 s +[acc] eval 4.798289e-3 s +[acc] eval 5.26181e-3 s +[acc] eval 5.0462e-3 s +[acc] eval 4.479742e-3 s +[acc] eval 4.146516e-3 s +[acc] eval 4.441837e-3 s +[acc] eval 1.2023701e-2 s +[acc] eval 1.879398e-3 s +[acc] eval 1.574979e-3 s +[acc] eval 1.831923e-3 s +[acc] eval 6.6495667e-2 s +[acc] eval 2.244588e-3 s +[acc] eval 4.38214e-4 s +[acc] eval 7.20432e-4 s +[acc] eval 4.237871e-3 s +[acc] compile 3.279135e-3 s +[acc] eval 9.507543e-3 s +[acc] eval 9.43859e-4 s +[acc] eval 7.39523e-4 s +[acc] eval 9.86664e-4 s +[acc] eval 8.88771e-4 s +[acc] eval 1.339672e-3 s +[acc] eval 1.321019e-3 s +[acc] eval 9.40383e-4 s +[acc] eval 1.313676e-3 s +[acc] eval 8.0523e-4 s +[acc] eval 1.034828e-3 s +[acc] eval 9.34245e-4 s +[acc] eval 2.5645717e-2 s +[acc] compile 4.395308e-3 s +[acc] eval 1.3310686e-2 s +[acc] eval 2.0106381e-2 s +[acc] compile 3.369586e-3 s +[acc] eval 4.777428e-3 s +[acc] eval 9.943224e-3 s +[acc] compile 0.290136024 s +[acc] eval 3.9440079e-2 s +[acc] eval 0.335345598 s +[acc] compile 0.110832401 s +[acc] eval 7.701813e-3 s +[acc] eval 3.168636e-3 s +[acc] eval 2.895849e-3 s +[acc] eval 3.099216e-3 s +[acc] eval 1.4990967e-2 s +[acc] eval 2.37218e-3 s +[acc] eval 3.23391e-3 s +[acc] eval 2.846568e-3 s +[acc] eval 2.752037e-3 s +[acc] eval 2.798045e-3 s +[acc] eval 2.765864e-3 s +[acc] eval 2.652161e-3 s +[acc] eval 2.311827e-3 s +[acc] eval 2.261812e-3 s +[acc] eval 2.435259e-3 s +[acc] eval 3.085215e-3 s +[acc] eval 2.345389e-3 s +[acc] eval 2.406387e-3 s +[acc] eval 1.896932e-3 s +[acc] eval 1.751551e-3 s +[acc] eval 1.767461e-3 s +[acc] eval 1.822954e-3 s +[acc] eval 2.178254e-3 s +[acc] eval 2.456622e-3 s +[acc] eval 2.088618e-3 s +[acc] eval 2.154601e-3 s +[acc] eval 2.11383e-3 s +[acc] eval 2.226969e-3 s +[acc] eval 2.091297e-3 s +[acc] eval 2.276575e-3 s +[acc] eval 0.206730766 s +[acc] compile 0.257852666 s +[acc] eval 2.3527457e-2 s +[acc] eval 0.284206092 s +[acc] compile 5.4359505e-2 s +[acc] eval 2.0016023e-2 s +[acc] eval 1.931433e-3 s +[acc] eval 1.86887e-3 s +[acc] eval 2.009427e-3 s +[acc] eval 1.888891e-3 s +[acc] eval 2.077787e-3 s +[acc] eval 2.073734e-3 s +[acc] eval 2.166505e-3 s +[acc] eval 2.273534e-3 s +[acc] eval 1.976229e-3 s +[acc] eval 1.955434e-3 s +[acc] eval 1.872372e-3 s +[acc] eval 1.90152e-3 s +[acc] eval 1.82629e-3 s +[acc] eval 2.264626e-3 s +[acc] eval 2.632468e-3 s +[acc] eval 2.252218e-3 s +[acc] eval 1.984162e-3 s +[acc] eval 1.936514e-3 s +[acc] eval 2.014917e-3 s +[acc] eval 1.914911e-3 s +[acc] eval 1.958172e-3 s +[acc] eval 1.916421e-3 s +[acc] eval 1.573159e-3 s +[acc] eval 1.550573e-3 s +[acc] eval 1.561257e-3 s +[acc] eval 1.548863e-3 s +[acc] eval 1.8894e-3 s +[acc] eval 2.668654e-3 s +[acc] eval 2.078132e-3 s +[acc] eval 0.136284744 s +[acc] compile 0.132971676 s +[acc] eval 7.499229e-3 s +[acc] eval 0.143978208 s +[acc] compile 0.293083272 s +[acc] eval 1.8325885e-2 s +[acc] eval 0.325889914 s +[acc] compile 7.0599136e-2 s +[acc] eval 8.989776e-3 s +[acc] eval 4.127974e-3 s +[acc] eval 5.1999e-3 s +[acc] eval 1.264706e-2 s +[acc] eval 3.91151e-3 s +[acc] eval 3.309808e-3 s +[acc] eval 3.230428e-3 s +[acc] eval 3.241746e-3 s +[acc] eval 3.492307e-3 s +[acc] eval 3.214738e-3 s +[acc] eval 3.091841e-3 s +[acc] eval 2.876725e-3 s +[acc] eval 2.683939e-3 s +[acc] eval 2.282546e-3 s +[acc] eval 2.726316e-3 s +[acc] eval 2.755667e-3 s +[acc] eval 2.61357e-3 s +[acc] eval 2.640232e-3 s +[acc] eval 2.789079e-3 s +[acc] eval 2.853683e-3 s +[acc] eval 2.732847e-3 s +[acc] eval 2.550576e-3 s +[acc] eval 2.649202e-3 s +[acc] eval 2.754011e-3 s +[acc] eval 2.747581e-3 s +[acc] eval 2.774605e-3 s +[acc] eval 2.841263e-3 s +[acc] eval 2.757153e-3 s +[acc] eval 2.602441e-3 s +[acc] eval 2.808638e-3 s +[acc] eval 0.181964892 s +[acc] compile 6.413481e-3 s +[acc] eval 9.900357e-3 s +[acc] eval 2.595718e-3 s +[acc] eval 2.676545e-3 s +[acc] eval 9.181156e-3 s +[acc] eval 2.052353e-3 s +[acc] eval 1.899974e-3 s +[acc] eval 1.966506e-3 s +[acc] eval 1.844956e-3 s +[acc] eval 1.777406e-3 s +[acc] eval 1.714241e-3 s +[acc] eval 1.7339e-3 s +[acc] eval 1.841841e-3 s +[acc] eval 1.761344e-3 s +[acc] eval 1.76008e-3 s +[acc] eval 1.916544e-3 s +[acc] eval 2.290229e-3 s +[acc] eval 2.093797e-3 s +[acc] eval 1.864741e-3 s +[acc] eval 1.926178e-3 s +[acc] eval 1.960917e-3 s +[acc] eval 2.010329e-3 s +[acc] eval 2.253795e-3 s +[acc] eval 1.933696e-3 s +[acc] eval 1.843088e-3 s +[acc] eval 1.832087e-3 s +[acc] eval 1.830549e-3 s +[acc] eval 1.8119e-3 s +[acc] eval 1.647721e-3 s +[acc] eval 1.622658e-3 s +[acc] eval 1.642247e-3 s +[acc] eval 1.943086e-3 s +[acc] eval 8.5856757e-2 s +[acc] compile 0.335233129 s +[acc] eval 2.6864865e-2 s +[acc] eval 0.37593837 s +[acc] compile 6.4268422e-2 s +[acc] eval 5.092701e-3 s +[acc] eval 1.338705e-3 s +[acc] eval 1.53418e-3 s +[acc] eval 1.564771e-3 s +[acc] eval 7.6364872e-2 s +[acc] compile 6.82534e-4 s +[acc] eval 8.956014e-3 s +[acc] compile 0.114049557 s +[acc] eval 1.5984975e-2 s +[acc] eval 1.2511841e-2 s +[acc] eval 1.479455e-3 s +[acc] eval 1.334149e-3 s +[acc] eval 1.396729e-3 s +[acc] eval 1.501722e-3 s +[acc] eval 1.300101e-3 s +[acc] eval 1.419806e-3 s +[acc] eval 1.198991e-3 s +[acc] eval 1.112222e-3 s +[acc] eval 0.15560661 s +[acc] compile 0.126722861 s +[acc] eval 2.298063e-2 s +[acc] eval 6.221589e-3 s +[acc] eval 1.9617145e-2 s +[acc] eval 3.512349e-3 s +[acc] eval 2.855403e-3 s +[acc] eval 2.773489e-3 s +[acc] eval 2.378065e-3 s +[acc] eval 2.232712e-3 s +[acc] eval 2.28643e-3 s +[acc] eval 2.727541e-3 s +[acc] eval 3.136951e-3 s +[acc] eval 2.916788e-3 s +[acc] eval 2.433587e-3 s +[acc] eval 2.37497e-3 s +[acc] eval 2.202636e-3 s +[acc] eval 2.101823e-3 s +[acc] eval 2.278215e-3 s +[acc] eval 2.224485e-3 s +[acc] eval 2.139929e-3 s +[acc] eval 2.106589e-3 s +[acc] eval 2.560709e-3 s +[acc] eval 2.604685e-3 s +[acc] eval 2.194804e-3 s +[acc] eval 2.338222e-3 s +[acc] eval 2.58384e-3 s +[acc] eval 2.145271e-3 s +[acc] eval 2.706243e-3 s +[acc] eval 2.916103e-3 s +[acc] eval 2.253963e-3 s +[acc] eval 2.351619e-3 s +[acc] eval 2.180085e-3 s +[acc] eval 0.251607701 s +[acc] compile 8.243739e-3 s +[acc] eval 1.8036657e-2 s +[acc] eval 3.0118215e-2 s +[acc] compile 7.436268e-3 s +[acc] eval 1.8816211e-2 s +[acc] eval 2.8577687e-2 s +[acc] compile 3.39894e-4 s +[acc] eval 5.628634e-3 s +[acc] compile 0.126384725 s +[acc] eval 1.7365408e-2 s +[acc] eval 0.147092816 s +[acc] compile 0.127602756 s +[acc] eval 2.7505947e-2 s +[acc] eval 0.157709667 s +[acc] compile 5.64047e-3 s +[acc] eval 7.141608e-3 s +[acc] eval 1.899402e-3 s +[acc] eval 1.7003521e-2 s +[acc] compile 4.778946e-3 s +[acc] eval 4.425783e-3 s +[acc] eval 1.405677e-3 s +[acc] eval 1.199959e-3 s +[acc] eval 1.274362e-3 s +[acc] eval 8.7959e-4 s +[acc] eval 1.052636e-3 s +[acc] eval 1.863101e-3 s +[acc] eval 1.429586e-3 s +[acc] eval 1.779975e-3 s +[acc] eval 9.13182e-4 s +[acc] eval 1.044722e-3 s +[acc] eval 1.320487e-3 s +[acc] eval 1.10363e-3 s +[acc] eval 1.838099e-3 s +[acc] eval 2.9330393e-2 s +[acc] compile 6.767769e-3 s +[acc] eval 1.43541e-2 s +[acc] eval 2.405433e-2 s +[acc] compile 6.281623e-3 s +[acc] eval 7.346133e-3 s +[acc] eval 1.7879634e-2 s +[acc] compile 6.404078e-3 s +[acc] eval 2.273388e-2 s +[acc] eval 3.5187652e-2 s +[acc] compile 1.1447416e-2 s +[acc] eval 8.625369e-3 s +[acc] eval 2.5542188e-2 s +[acc] compile 0.319261163 s +[acc] eval 3.0902272e-2 s +[acc] eval 0.354959556 s +[acc] compile 0.127190593 s +[acc] eval 7.236717e-3 s +[acc] eval 2.462906e-3 s +[acc] eval 2.336171e-3 s +[acc] eval 2.179938e-3 s +[acc] eval 1.930149e-3 s +[acc] eval 1.892983e-3 s +[acc] eval 2.011027e-3 s +[acc] eval 2.176323e-3 s +[acc] eval 1.970805e-3 s +[acc] eval 1.892803e-3 s +[acc] eval 2.189301e-3 s +[acc] eval 1.4631433e-2 s +[acc] eval 2.030683e-3 s +[acc] eval 2.220912e-3 s +[acc] eval 1.992704e-3 s +[acc] eval 1.828161e-3 s +[acc] eval 2.065587e-3 s +[acc] eval 1.974821e-3 s +[acc] eval 1.663896e-3 s +[acc] eval 1.568732e-3 s +[acc] eval 1.850409e-3 s +[acc] eval 1.924124e-3 s +[acc] eval 1.785201e-3 s +[acc] eval 1.807325e-3 s +[acc] eval 1.743483e-3 s +[acc] eval 1.918936e-3 s +[acc] eval 1.657613e-3 s +[acc] eval 1.981284e-3 s +[acc] eval 1.817215e-3 s +[acc] eval 1.950008e-3 s +[acc] eval 0.208632108 s +[acc] compile 0.183829184 s +[acc] eval 1.7352349e-2 s +[acc] eval 2.733844e-3 s +[acc] eval 2.578441e-3 s +[acc] eval 2.992457e-3 s +[acc] eval 1.1133431e-2 s +[acc] eval 2.217125e-3 s +[acc] eval 3.683908e-3 s +[acc] eval 2.210107e-3 s +[acc] eval 1.596386e-3 s +[acc] eval 1.591989e-3 s +[acc] eval 1.680361e-3 s +[acc] eval 1.586792e-3 s +[acc] eval 1.740607e-3 s +[acc] eval 2.263424e-3 s +[acc] eval 2.769143e-3 s +[acc] eval 2.757006e-3 s +[acc] eval 2.662049e-3 s +[acc] eval 2.568498e-3 s +[acc] eval 2.763366e-3 s +[acc] eval 3.182226e-3 s +[acc] eval 2.717488e-3 s +[acc] eval 2.61826e-3 s +[acc] eval 2.440303e-3 s +[acc] eval 2.550243e-3 s +[acc] eval 2.45057e-3 s +[acc] eval 2.385508e-3 s +[acc] eval 2.743668e-3 s +[acc] eval 2.561924e-3 s +[acc] eval 2.431664e-3 s +[acc] eval 2.341449e-3 s +[acc] eval 0.286830669 s +[acc] compile 9.969776e-3 s +[acc] eval 9.237285e-3 s +[acc] eval 2.4405219e-2 s +[acc] compile 0.273229914 s +[acc] eval 2.7688544e-2 s +[acc] eval 0.306730718 s +[acc] compile 0.151183308 s +[acc] eval 9.122034e-3 s +[acc] eval 0.16566338 s +[acc] compile 6.4253529e-2 s +[acc] eval 8.623501e-3 s +[acc] eval 2.375507e-3 s +[acc] eval 1.758831e-3 s +[acc] eval 7.9929972e-2 s +[acc] eval 2.329737e-2 s +[acc] eval 2.5617392e-2 s +[acc] compile 4.938511e-3 s +[acc] eval 8.214784e-3 s +[acc] eval 2.388733e-3 s +[acc] eval 2.482756e-3 s +[acc] eval 1.848891e-3 s +[acc] eval 2.097371e-3 s +[acc] eval 1.88548e-3 s +[acc] eval 1.950556e-3 s +[acc] eval 1.855053e-3 s +[acc] eval 1.999519e-3 s +[acc] eval 1.653475e-3 s +[acc] eval 1.777418e-3 s +[acc] eval 4.793325e-2 s +[acc] compile 5.5407541e-2 s +[acc] eval 9.889841e-3 s +[acc] eval 2.129379e-3 s +[acc] eval 1.837134e-3 s +[acc] eval 2.201101e-3 s +[acc] eval 2.51e-3 s +[acc] eval 1.2328959e-2 s +[acc] eval 2.202489e-3 s +[acc] eval 2.227427e-3 s +[acc] eval 1.886811e-3 s +[acc] eval 9.5777539e-2 s +[acc] compile 0.176772001 s +[acc] eval 1.7676671e-2 s +[acc] eval 0.197326533 s +[acc] compile 1.7550055e-2 s +[acc] eval 1.4016657e-2 s +[acc] eval 3.4776893e-2 s +[acc] eval 1.4969908e-2 s +[acc] eval 1.37208e-3 s +[acc] eval 1.26221e-3 s +[acc] eval 1.288817e-3 s +[acc] eval 1.226614e-3 s +[acc] eval 1.46929e-3 s +[acc] eval 1.845012e-3 s +[acc] eval 1.951098e-3 s +[acc] eval 2.24456e-3 s +[acc] eval 2.191441e-3 s +[acc] eval 2.030698e-3 s +[acc] eval 2.227347e-3 s +[acc] eval 3.7209143e-2 s +[acc] compile 6.820984e-3 s +[acc] eval 8.333272e-3 s +[acc] eval 2.326288e-3 s +[acc] eval 2.260615e-3 s +[acc] eval 2.183276e-3 s +[acc] eval 1.96647e-3 s +[acc] eval 2.166676e-3 s +[acc] eval 1.0424949e-2 s +[acc] eval 1.839885e-3 s +[acc] eval 4.1156368e-2 s +[acc] eval 1.5245825e-2 s +[acc] eval 1.7729631e-2 s +[acc] eval 1.9325592e-2 s +[acc] eval 2.2431077e-2 s +[acc] compile 0.196091181 s +[acc] eval 2.0001751e-2 s +[acc] eval 0.220083158 s +[acc] compile 6.7180407e-2 s +[acc] eval 2.7261287e-2 s +[acc] eval 9.7690356e-2 s +[acc] compile 7.767447e-3 s +[acc] eval 1.0067175e-2 s +[acc] eval 2.2542613e-2 s +[acc] compile 2.1360754e-2 s +[acc] eval 3.8636095e-2 s +[acc] eval 6.5909412e-2 s +[acc] eval 1.3171241e-2 s +[acc] eval 2.152012e-3 s +[acc] eval 2.108229e-3 s +[acc] eval 2.221623e-3 s +[acc] eval 1.781023e-3 s +[acc] eval 1.822063e-3 s +[acc] eval 2.042434e-3 s +[acc] eval 2.637854e-3 s +[acc] eval 1.4177204e-2 s +[acc] eval 2.061367e-3 s +[acc] eval 2.196402e-3 s +[acc] eval 2.264049e-3 s +[acc] eval 5.220822e-3 s +[acc] eval 2.651111e-3 s +[acc] eval 2.327932e-3 s +[acc] eval 2.184574e-3 s +[acc] eval 2.072845e-3 s +[acc] eval 1.973878e-3 s +[acc] eval 2.321262e-3 s +[acc] eval 2.501747e-3 s +[acc] eval 2.119276e-3 s +[acc] eval 1.685232e-3 s +[acc] eval 1.676804e-3 s +[acc] eval 2.151672e-3 s +[acc] eval 1.835674e-3 s +[acc] eval 2.382045e-3 s +[acc] eval 2.294138e-3 s +[acc] eval 1.999039e-3 s +[acc] eval 1.879773e-3 s +[acc] eval 1.823202e-3 s +[acc] eval 1.762958e-3 s +[acc] eval 9.5842378e-2 s +[acc] compile 0.116546148 s +[acc] eval 1.4149096e-2 s +[acc] eval 3.601468e-3 s +[acc] eval 3.448047e-3 s +[acc] eval 3.541679e-3 s +[acc] eval 3.891522e-3 s +[acc] eval 1.7062581e-2 s +[acc] eval 2.404777e-3 s +[acc] eval 2.756457e-3 s +[acc] eval 2.693936e-3 s +[acc] eval 2.545298e-3 s +[acc] eval 2.674868e-3 s +[acc] eval 0.179408694 s +[acc] compile 0.287911027 s +[acc] eval 8.837843e-3 s +[acc] eval 0.300223561 s +[acc] compile 0.320780581 s +[acc] eval 2.6192514e-2 s +[acc] eval 0.350098042 s +[acc] compile 0.279053639 s +[acc] eval 1.9414312e-2 s +[acc] eval 0.308887862 s +[acc] compile 8.289364e-3 s +[acc] eval 2.6236306e-2 s +[acc] eval 3.8817512e-2 s +[acc] compile 6.890879e-3 s +[acc] eval 6.234717e-3 s +[acc] eval 3.410099e-3 s +[acc] eval 2.463261e-3 s +[acc] eval 2.416021e-3 s +[acc] eval 2.105983e-3 s +[acc] eval 1.823653e-3 s +[acc] eval 1.847333e-3 s +[acc] eval 1.814905e-3 s +[acc] eval 1.879445e-3 s +[acc] eval 1.812866e-3 s +[acc] eval 2.080208e-3 s +[acc] eval 1.4207878e-2 s +[acc] eval 1.75559e-3 s +[acc] eval 1.781514e-3 s +[acc] eval 1.913891e-3 s +[acc] eval 2.673105e-3 s +[acc] eval 2.424852e-3 s +[acc] eval 2.249125e-3 s +[acc] eval 2.26973e-3 s +[acc] eval 2.2551e-3 s +[acc] eval 2.279067e-3 s +[acc] eval 2.485263e-3 s +[acc] eval 2.830737e-3 s +[acc] eval 2.524469e-3 s +[acc] eval 2.187736e-3 s +[acc] eval 8.2938548e-2 s +[acc] compile 0.121295286 s +[acc] eval 2.5241242e-2 s +[acc] eval 1.7519501e-2 s +[acc] eval 1.2367739e-2 s +[acc] eval 0.187429213 s +[acc] compile 5.560164e-3 s +[acc] eval 1.2511282e-2 s +[acc] eval 2.2462843e-2 s +[acc] compile 0.392215977 s +[acc] eval 3.658931e-2 s +[acc] eval 0.432605098 s +[acc] eval 5.013238e-3 s +[acc] eval 7.893434e-3 s +[acc] compile 5.528972e-3 s +[acc] eval 4.752003e-3 s +[acc] eval 1.1850322e-2 s +[acc] compile 8.6323919e-2 s +[acc] eval 6.840584e-3 s +[acc] eval 9.7898523e-2 s +[acc] compile 0.183931377 s +[acc] eval 9.292857e-3 s +[acc] eval 0.19891257 s +[acc] compile 0.451606089 s +[acc] eval 3.9203158e-2 s +[acc] eval 0.495423696 s +[acc] eval 8.646968e-3 s +[acc] eval 1.4309268e-2 s +[acc] compile 0.320057734 s +[acc] eval 1.3118114e-2 s +[acc] eval 0.337550641 s +[acc] compile 0.296296945 s +[acc] eval 2.7071799e-2 s +[acc] eval 0.347788994 s +[acc] compile 0.127707076 s +[acc] eval 1.2818966e-2 s +[acc] eval 6.700498e-3 s +[acc] eval 2.0443512e-2 s +[acc] eval 4.350055e-3 s +[acc] eval 4.609525e-3 s +[acc] eval 6.677149e-3 s +[acc] eval 4.315724e-3 s +[acc] eval 3.42077e-3 s +[acc] eval 3.253862e-3 s +[acc] eval 3.92503e-3 s +[acc] eval 3.245245e-3 s +[acc] eval 2.850654e-3 s +[acc] eval 2.657705e-3 s +[acc] eval 2.682437e-3 s +[acc] eval 2.664889e-3 s +[acc] eval 0.217946021 s +[acc] compile 0.388032721 s +[acc] eval 3.3217883e-2 s +[acc] eval 0.427577869 s +[acc] eval 9.372163e-3 s +[acc] eval 2.2917817e-2 s +[acc] compile 8.705653e-3 s +[acc] eval 7.740088e-3 s +[acc] eval 2.1888721e-2 s +[acc] eval 3.88518e-3 s +[acc] eval 1.015561e-3 s +[acc] eval 1.179215e-3 s +[acc] eval 7.411872e-3 s +[acc] compile 0.137699339 s +[acc] eval 2.2783185e-2 s +[acc] eval 0.165225838 s +[acc] eval 2.428926e-3 s +[acc] eval 7.37335e-4 s +[acc] eval 9.4233e-4 s +[acc] eval 5.112916e-3 s +[acc] compile 0.25360575 s +[acc] eval 1.0430119e-2 s +[acc] eval 0.271545043 s +[acc] compile 0.155598649 s +[acc] eval 2.8546222e-2 s +[acc] eval 0.186247925 s +[acc] compile 0.232154621 s +[acc] eval 4.3402668e-2 s +[acc] eval 0.281298894 s +[acc] compile 0.328959302 s +[acc] eval 1.7937469e-2 s +[acc] eval 2.0454718e-2 s +[acc] eval 1.1131019e-2 s +[acc] eval 8.085504e-3 s +[acc] eval 1.2846531e-2 s +[acc] eval 1.4306344e-2 s +[acc] eval 1.6444062e-2 s +[acc] eval 1.3481477e-2 s +[acc] eval 7.45357e-3 s +[acc] eval 8.224759e-3 s +[acc] eval 2.0465396e-2 s +[acc] eval 5.731031e-3 s +[acc] eval 0.492739857 s +[acc] compile 0.323363948 s +[acc] eval 4.0542966e-2 s +[acc] eval 0.36914264 s +[acc] compile 0.140669683 s +[acc] eval 1.3594223e-2 s +[acc] eval 0.162060541 s +[acc] compile 6.66477e-4 s +[acc] eval 1.0848212e-2 s +[acc] compile 5.240865e-3 s +[acc] eval 1.9732203e-2 s +[acc] eval 3.7532925e-2 s +[acc] compile 2.904285e-3 s +[acc] eval 5.41753e-3 s +[acc] eval 1.0018794e-2 s +[acc] compile 0.172413546 s +[acc] eval 1.9921246e-2 s +[acc] eval 0.197019601 s +[acc] compile 5.7027125e-2 s +[acc] eval 8.100765e-3 s +[acc] eval 6.6706141e-2 s +[acc] compile 0.2600838 s +[acc] eval 2.0405819e-2 s +[acc] eval 1.8413543e-2 s +[acc] eval 2.254171e-3 s +[acc] eval 2.437027e-3 s +[acc] eval 1.781858e-3 s +[acc] eval 2.224641e-3 s +[acc] eval 2.17756e-3 s +[acc] eval 2.079577e-3 s +[acc] eval 1.734537e-3 s +[acc] eval 1.549519e-3 s +[acc] eval 1.485902e-3 s +[acc] eval 1.675182e-3 s +[acc] eval 0.322478268 s +[acc] compile 0.111421521 s +[acc] eval 9.879338e-3 s +[acc] eval 0.124738488 s +[acc] compile 4.884948e-3 s +[acc] eval 4.301401e-3 s +[acc] eval 1.26442e-3 s +[acc] eval 1.166787e-3 s +[acc] eval 1.4222671e-2 s +[acc] compile 6.2909333e-2 s +[acc] eval 2.5253929e-2 s +[acc] eval 9.0609963e-2 s +[acc] compile 0.249195602 s +[acc] eval 2.7413561e-2 s +[acc] eval 0.283638742 s +[acc] compile 8.1291174e-2 s +[acc] eval 2.2978988e-2 s +[acc] eval 0.107249441 s +[acc] compile 4.129714e-3 s +[acc] eval 5.960288e-3 s +[acc] eval 1.1932603e-2 s +[acc] compile 4.597956e-3 s +[acc] eval 6.566776e-3 s +[acc] eval 2.189597e-3 s +[acc] eval 2.262626e-3 s +[acc] eval 2.111629e-3 s +[acc] eval 2.753647e-3 s +[acc] eval 2.768169e-3 s +[acc] eval 1.957781e-3 s +[acc] eval 1.719876e-3 s +[acc] eval 1.427417e-3 s +[acc] eval 1.557102e-3 s +[acc] eval 3.2513183e-2 s +[acc] compile 0.120594239 s +[acc] eval 3.3551593e-2 s +[acc] eval 0.157916992 s +[acc] compile 3.42746e-4 s +[acc] eval 7.585453e-3 s +[acc] compile 0.232192834 s +[acc] eval 1.9600578e-2 s +[acc] eval 2.4053953e-2 s +[acc] eval 4.045769e-3 s +[acc] eval 4.016242e-3 s +[acc] eval 3.304251e-3 s +[acc] eval 3.023831e-3 s +[acc] eval 2.849822e-3 s +[acc] eval 2.89798e-3 s +[acc] eval 3.462095e-3 s +[acc] eval 3.224493e-3 s +[acc] eval 2.97648e-3 s +[acc] eval 3.12226e-3 s +[acc] eval 2.980232e-3 s +[acc] eval 2.958886e-3 s +[acc] eval 2.799413e-3 s +[acc] eval 2.893951e-3 s +[acc] eval 2.871827e-3 s +[acc] eval 3.174036e-3 s +[acc] eval 0.332531876 s +[acc] compile 8.071555e-3 s +[acc] eval 6.991265e-3 s +[acc] eval 1.7754713e-2 s +[acc] compile 8.7490551e-2 s +[acc] eval 2.4564974e-2 s +[acc] eval 0.128581086 s +[acc] compile 0.386126605 s +[acc] eval 3.595984e-2 s +[acc] eval 0.442784805 s +[acc] compile 6.032302e-3 s +[acc] eval 1.0763075e-2 s +[acc] eval 2.3605565e-2 s +[acc] compile 8.8646174e-2 s +[acc] eval 3.0915772e-2 s +[acc] eval 0.122861207 s +[acc] compile 0.145353934 s +[acc] eval 1.6812524e-2 s +[acc] eval 0.168808853 s +[acc] compile 7.0967107e-2 s +[acc] eval 3.279696e-2 s +[acc] eval 0.106777386 s +[acc] compile 0.142407698 s +[acc] eval 1.7477702e-2 s +[acc] eval 0.168821367 s +[acc] compile 0.100054079 s +[acc] eval 3.0461218e-2 s +[acc] eval 0.135021702 s +[acc] compile 0.271197886 s +[acc] eval 4.0138439e-2 s +[acc] eval 0.340172603 s +[acc] compile 0.236397685 s +[acc] eval 2.7437508e-2 s +[acc] eval 8.271016e-3 s +[acc] eval 2.3893213e-2 s +[acc] eval 8.626725e-3 s +[acc] eval 5.860747e-3 s +[acc] eval 4.675633e-3 s +[acc] eval 4.464415e-3 s +[acc] eval 4.472744e-3 s +[acc] eval 4.063328e-3 s +[acc] eval 5.904479e-3 s +[acc] eval 5.316184e-3 s +[acc] eval 5.70121e-3 s +[acc] eval 7.377445e-3 s +[acc] eval 6.061151e-3 s +[acc] eval 7.883617e-3 s +[acc] eval 4.239373e-3 s +[acc] eval 4.373562e-3 s +[acc] eval 4.329276e-3 s +[acc] eval 4.079646e-3 s +[acc] eval 1.322916e-2 s +[acc] eval 4.374871e-3 s +[acc] eval 3.896686e-3 s +[acc] eval 3.598648e-3 s +[acc] eval 3.365581e-3 s +[acc] eval 3.491877e-3 s +[acc] eval 5.673141e-3 s +[acc] eval 3.835938e-3 s +[acc] eval 3.521207e-3 s +[acc] eval 4.506973e-3 s +[acc] eval 4.584261e-3 s +[acc] eval 4.146617e-3 s +[acc] eval 0.451306562 s +[acc] compile 0.142715699 s +[acc] eval 3.0218355e-2 s +[acc] eval 0.176108479 s +[acc] compile 0.149230226 s +[acc] eval 1.1276482e-2 s +[acc] eval 1.5951052e-2 s +[acc] eval 0.194479369 s +[acc] compile 0.12727271 s +[acc] eval 4.0426544e-2 s +[acc] eval 0.170831965 s +[acc] compile 0.288158149 s +[acc] eval 3.3178418e-2 s +[acc] eval 0.326537487 s +[acc] compile 2.0603208e-2 s +[acc] eval 8.318512e-3 s +[acc] eval 3.1462939e-2 s +[acc] compile 4.692952e-3 s +[acc] eval 7.835313e-3 s +[acc] eval 1.804833e-3 s +[acc] eval 1.2918612e-2 s +[acc] eval 1.734439e-3 s +[acc] eval 1.577812e-3 s +[acc] eval 1.411865e-3 s +[acc] eval 1.497128e-3 s +[acc] eval 1.3325e-3 s +[acc] eval 1.358776e-3 s +[acc] eval 2.058265e-3 s +[acc] eval 2.211516e-3 s +[acc] eval 1.858495e-3 s +[acc] eval 2.132252e-3 s +[acc] eval 2.256397e-3 s +[acc] eval 2.261419e-3 s +[acc] eval 1.924791e-3 s +[acc] eval 1.983312e-3 s +[acc] eval 2.076836e-3 s +[acc] eval 2.254947e-3 s +[acc] eval 2.01654e-3 s +[acc] eval 1.437269e-3 s +[acc] eval 1.697826e-3 s +[acc] eval 1.865658e-3 s +[acc] eval 2.156689e-3 s +[acc] eval 1.831398e-3 s +[acc] eval 1.49597e-3 s +[acc] eval 1.418041e-3 s +[acc] eval 1.369539e-3 s +[acc] eval 1.451076e-3 s +[acc] eval 1.440128e-3 s +[acc] eval 7.9287169e-2 s +[acc] compile 0.206913613 s +[acc] eval 1.5216533e-2 s +[acc] eval 0.237291639 s +[acc] compile 0.389645894 s +[acc] eval 4.6253521e-2 s +[acc] eval 0.462402714 s +[acc] compile 0.134646267 s +[acc] eval 2.949486e-2 s +[acc] eval 2.948834e-3 s +[acc] eval 3.457014e-3 s +[acc] eval 4.864267e-3 s +[acc] eval 0.180228984 s +[acc] eval 1.3419445e-2 s +[acc] eval 1.5292174e-2 s +[acc] compile 5.187552e-3 s +[acc] eval 4.844189e-3 s +[acc] eval 1.2213727e-2 s +[acc] compile 0.259778768 s +[acc] eval 7.997112e-3 s +[acc] eval 0.283731388 s +[acc] compile 0.49240806 s +[acc] eval 2.1297834e-2 s +[acc] eval 0.532536885 s +[acc] compile 0.134631581 s +[acc] eval 1.0497064e-2 s +[acc] eval 0.163034519 s +[acc] compile 0.20585773 s +[acc] eval 1.6529269e-2 s +[acc] eval 0.228024133 s +[acc] compile 8.859314e-3 s +[acc] eval 3.8564499e-2 s +[acc] eval 5.0107225e-2 s +[acc] compile 0.552778824 s +[acc] eval 2.6682455e-2 s +[acc] eval 0.602593061 s +[acc] compile 1.3337426e-2 s +[acc] eval 2.2994853e-2 s +[acc] eval 4.1033346e-2 s +[acc] compile 0.129584051 s +[acc] eval 2.09221e-2 s +[acc] eval 3.541951e-3 s +[acc] eval 4.11493e-3 s +[acc] eval 3.306391e-3 s +[acc] eval 2.560634e-3 s +[acc] eval 2.623029e-3 s +[acc] eval 2.979016e-3 s +[acc] eval 1.6005693e-2 s +[acc] eval 3.210095e-3 s +[acc] eval 2.81021e-3 s +[acc] eval 2.430284e-3 s +[acc] eval 2.346697e-3 s +[acc] eval 2.306816e-3 s +[acc] eval 3.32386e-3 s +[acc] 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0.459112861 s +[acc] eval 3.0747834e-2 s +[acc] eval 0.494690594 s +[acc] compile 6.6531549e-2 s +[acc] eval 5.548829e-3 s +[acc] eval 7.4066299e-2 s +[acc] compile 6.9801711e-2 s +[acc] eval 8.770954e-3 s +[acc] eval 8.0486768e-2 s +[acc] compile 0.142451893 s +[acc] eval 9.955887e-3 s +[acc] eval 0.166044245 s +[acc] compile 6.1845919e-2 s +[acc] eval 9.088208e-3 s +[acc] eval 7.33439e-2 s +[acc] compile 4.84129e-3 s +[acc] eval 9.251778e-3 s +[acc] eval 2.412481e-3 s +[acc] eval 2.297936e-3 s +[acc] eval 2.117211e-3 s +[acc] eval 2.194444e-3 s +[acc] eval 1.7319663e-2 s +[acc] eval 3.224371e-3 s +[acc] eval 2.673783e-3 s +[acc] eval 2.369138e-3 s +[acc] eval 2.552768e-3 s +[acc] eval 2.637947e-3 s +[acc] eval 2.131302e-3 s +[acc] eval 2.274893e-3 s +[acc] eval 3.295663e-3 s +[acc] eval 2.395886e-3 s +[acc] eval 2.073921e-3 s +[acc] eval 2.126467e-3 s +[acc] eval 3.212663e-3 s +[acc] eval 2.080029e-3 s +[acc] eval 1.953517e-3 s +[acc] eval 2.113887e-3 s +[acc] eval 2.025307e-3 s +[acc] eval 1.951572e-3 s +[acc] eval 2.842055e-3 s +[acc] eval 3.563188e-3 s +[acc] eval 2.892491e-3 s +[acc] eval 2.30423e-3 s +[acc] eval 2.42153e-3 s +[acc] eval 1.985575e-3 s +[acc] eval 2.573093e-3 s +[acc] eval 0.106081492 s +[acc] eval 7.444835e-3 s +[acc] eval 1.0323053e-2 s +[acc] compile 0.22100161 s +[acc] eval 3.3196312e-2 s +[acc] eval 0.257844049 s +[acc] compile 5.81431e-3 s +[acc] eval 6.133374e-3 s +[acc] eval 2.491398e-3 s +[acc] eval 2.638241e-3 s +[acc] eval 2.621312e-3 s +[acc] eval 3.304814e-3 s +[acc] eval 2.853362e-3 s +[acc] eval 2.59282e-3 s +[acc] eval 1.5435848e-2 s +[acc] eval 2.341252e-3 s +[acc] eval 2.323949e-3 s +[acc] eval 2.114758e-3 s +[acc] eval 2.227954e-3 s +[acc] eval 2.038465e-3 s +[acc] eval 1.946857e-3 s +[acc] eval 1.982026e-3 s +[acc] eval 1.979346e-3 s +[acc] eval 2.201988e-3 s +[acc] eval 2.056087e-3 s +[acc] eval 2.048871e-3 s +[acc] eval 7.1743249e-2 s +[acc] compile 0.417841212 s +[acc] eval 3.2459838e-2 s +[acc] eval 0.478091433 s +[acc] compile 0.213499972 s +[acc] eval 3.272187e-2 s +[acc] eval 0.252211345 s +[acc] compile 0.230374648 s +[acc] eval 4.3575555e-2 s +[acc] eval 4.133388e-3 s +[acc] eval 3.545552e-3 s +[acc] eval 3.674516e-3 s +[acc] eval 3.78214e-3 s +[acc] eval 3.901144e-3 s +[acc] eval 3.281337e-3 s +[acc] eval 3.242563e-3 s +[acc] eval 3.420798e-3 s +[acc] eval 3.239593e-3 s +[acc] eval 1.9759477e-2 s +[acc] eval 2.292513e-3 s +[acc] eval 2.18311e-3 s +[acc] eval 2.239267e-3 s +[acc] eval 2.25031e-3 s +[acc] eval 2.833111e-3 s +[acc] eval 2.787002e-3 s +[acc] eval 2.376034e-3 s +[acc] eval 2.29975e-3 s +[acc] eval 2.451563e-3 s +[acc] eval 2.241181e-3 s +[acc] eval 2.450561e-3 s +[acc] eval 2.311581e-3 s +[acc] eval 2.136097e-3 s +[acc] eval 2.020197e-3 s +[acc] eval 1.946192e-3 s +[acc] eval 1.969267e-3 s +[acc] eval 2.85071e-3 s +[acc] eval 2.6157e-3 s +[acc] eval 2.276959e-3 s +[acc] eval 2.068899e-3 s +[acc] eval 1.906492e-3 s +[acc] eval 0.381253303 s +[acc] compile 0.195564406 s +[acc] eval 2.401747e-2 s +[acc] eval 0.225794544 s +[acc] compile 2.6171169e-2 s +[acc] eval 2.417827e-2 s +[acc] eval 2.2949502e-2 s +[acc] eval 5.163002e-3 s +[acc] eval 4.098822e-3 s +[acc] eval 5.152323e-3 s +[acc] eval 4.710625e-3 s +[acc] eval 3.768403e-3 s +[acc] eval 3.504688e-3 s +[acc] eval 1.7577244e-2 s +[acc] eval 4.705099e-3 s +[acc] eval 4.321542e-3 s +[acc] eval 4.06962e-3 s +[acc] eval 4.521072e-3 s +[acc] eval 4.164894e-3 s +[acc] eval 7.715576e-3 s +[acc] eval 1.7732363e-2 s +[acc] eval 6.54644e-3 s +[acc] eval 7.04781e-3 s +[acc] eval 4.572837e-3 s +[acc] eval 4.06449e-3 s +[acc] eval 4.820084e-3 s +[acc] eval 1.663852e-2 s +[acc] eval 5.026603e-3 s +[acc] eval 4.472195e-3 s +[acc] eval 4.963803e-3 s +[acc] eval 5.047357e-3 s +[acc] eval 6.420131e-3 s +[acc] eval 7.194111e-3 s +[acc] eval 1.6955629e-2 s +[acc] eval 5.301927e-3 s +[acc] eval 5.674697e-3 s +[acc] eval 0.27682949 s +[acc] compile 9.9035776e-2 s +[acc] eval 1.7876739e-2 s +[acc] eval 1.2619597e-2 s +[acc] 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4.1645124e-2 s +[acc] eval 0.189147894 s +[acc] compile 5.125343e-3 s +[acc] eval 6.195226e-3 s +[acc] eval 1.3403832e-2 s +[acc] compile 0.193519771 s +[acc] eval 1.2806922e-2 s +[acc] eval 0.211694251 s +[acc] compile 7.0232051e-2 s +[acc] eval 9.333006e-3 s +[acc] eval 8.2748295e-2 s +[acc] compile 0.317440442 s +[acc] eval 3.4515874e-2 s +[acc] eval 0.366163167 s +[acc] compile 1.1676413e-2 s +[acc] eval 3.1080089e-2 s +[acc] eval 4.7492164e-2 s +[acc] compile 0.327375814 s +[acc] eval 2.694889e-2 s +[acc] eval 0.376848663 s +[acc] eval 1.9714035e-2 s +[acc] eval 2.5109862e-2 s +[acc] compile 3.365762e-3 s +[acc] eval 6.780917e-3 s +[acc] eval 2.4874836e-2 s +[acc] compile 4.733014e-3 s +[acc] eval 5.78789e-3 s +[acc] eval 1.955058e-3 s +[acc] eval 2.204209e-3 s +[acc] eval 1.814577e-3 s +[acc] eval 1.801985e-3 s +[acc] eval 1.705577e-3 s +[acc] eval 2.046175e-3 s +[acc] eval 1.792023e-3 s +[acc] eval 1.5551424e-2 s +[acc] eval 1.951572e-3 s +[acc] eval 1.749121e-3 s +[acc] eval 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s +[acc] eval 2.2614564e-2 s +[acc] eval 2.0759559e-2 s +[acc] eval 4.318769e-3 s +[acc] eval 3.635375e-3 s +[acc] eval 3.574943e-3 s +[acc] eval 3.233718e-3 s +[acc] eval 4.058229e-3 s +[acc] eval 3.503624e-3 s +[acc] eval 4.17817e-3 s +[acc] eval 3.618389e-3 s +[acc] eval 3.127917e-3 s +[acc] eval 2.774937e-3 s +[acc] eval 2.96943e-3 s +[acc] eval 3.226974e-3 s +[acc] eval 2.615894e-3 s +[acc] eval 2.744471e-3 s +[acc] eval 2.502779e-3 s +[acc] eval 2.669709e-3 s +[acc] eval 3.083801e-3 s +[acc] eval 2.734756e-3 s +[acc] eval 2.627522e-3 s +[acc] eval 2.750971e-3 s +[acc] eval 3.849668e-3 s +[acc] eval 3.365452e-3 s +[acc] eval 3.435897e-3 s +[acc] eval 3.116415e-3 s +[acc] eval 3.164373e-3 s +[acc] eval 3.4673e-3 s +[acc] eval 3.451331e-3 s +[acc] eval 3.539307e-3 s +[acc] eval 3.526294e-3 s +[acc] eval 0.213359084 s +[acc] compile 0.223507562 s +[acc] eval 1.0526968e-2 s +[acc] eval 4.529361e-3 s +[acc] eval 7.674488e-3 s +[acc] eval 6.056351e-3 s +[acc] eval 2.2878722e-2 s +[acc] 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9.267729e-3 s +[acc] eval 2.6956067e-2 s +[acc] eval 1.655211e-3 s +[acc] eval 1.899491e-3 s +[acc] eval 1.530541e-3 s +[acc] eval 2.063863e-3 s +[acc] eval 1.665457e-3 s +[acc] eval 2.285629e-3 s +[acc] eval 1.866676e-3 s +[acc] eval 1.930154e-3 s +[acc] eval 1.920098e-3 s +[acc] eval 1.67914e-3 s +[acc] eval 1.906372e-3 s +[acc] eval 1.792682e-3 s +[acc] eval 1.913499e-3 s +[acc] eval 6.4813344e-2 s +[acc] compile 0.132317483 s +[acc] eval 6.265923e-3 s +[acc] eval 0.142080105 s +[acc] compile 0.365033789 s +[acc] eval 4.7671387e-2 s +[acc] eval 0.418594179 s +[acc] compile 4.964049e-3 s +[acc] eval 9.809664e-3 s +[acc] eval 3.335051e-3 s +[acc] eval 3.297268e-3 s +[acc] eval 3.042599e-3 s +[acc] eval 2.778833e-3 s +[acc] eval 2.720359e-3 s +[acc] eval 2.516926e-3 s +[acc] eval 1.8509139e-2 s +[acc] eval 2.325668e-3 s +[acc] eval 2.462383e-3 s +[acc] eval 2.092831e-3 s +[acc] eval 1.73975e-3 s +[acc] eval 1.88036e-3 s +[acc] eval 1.63601e-3 s +[acc] eval 1.670234e-3 s +[acc] eval 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s +[acc] eval 3.353721e-3 s +[acc] eval 2.735842e-3 s +[acc] eval 2.493593e-3 s +[acc] eval 0.315192304 s +[acc] compile 0.128553467 s +[acc] eval 7.912889e-3 s +[acc] eval 0.138980614 s +[acc] compile 9.0784231e-2 s +[acc] eval 1.1941189e-2 s +[acc] eval 2.617833e-2 s +[acc] eval 3.767856e-3 s +[acc] eval 3.109186e-3 s +[acc] eval 3.976314e-3 s +[acc] eval 3.900784e-3 s +[acc] eval 3.722213e-3 s +[acc] eval 4.965338e-3 s +[acc] eval 5.713366e-3 s +[acc] eval 4.200965e-3 s +[acc] eval 3.78127e-3 s +[acc] eval 3.305688e-3 s +[acc] eval 3.00857e-3 s +[acc] eval 2.61332e-3 s +[acc] eval 2.967346e-3 s +[acc] eval 3.095044e-3 s +[acc] eval 2.534934e-3 s +[acc] eval 2.484587e-3 s +[acc] eval 2.660969e-3 s +[acc] eval 3.046666e-3 s +[acc] eval 3.457478e-3 s +[acc] eval 3.780854e-3 s +[acc] eval 3.707104e-3 s +[acc] eval 3.406682e-3 s +[acc] eval 3.463643e-3 s +[acc] eval 0.216539743 s +[acc] compile 9.5054083e-2 s +[acc] eval 1.2585436e-2 s +[acc] eval 3.94671e-3 s +[acc] eval 3.705111e-3 s +[acc] eval 2.83759e-3 s +[acc] eval 0.122088147 s +[acc] compile 1.9520502e-2 s +[acc] eval 5.664124e-3 s +[acc] eval 1.966144e-3 s +[acc] eval 1.880234e-3 s +[acc] eval 3.0924722e-2 s +[acc] compile 0.164376542 s +[acc] eval 9.160777e-3 s +[acc] eval 3.387793e-3 s +[acc] eval 3.04553e-3 s +[acc] eval 0.183780937 s +[acc] compile 0.387838966 s +[acc] eval 3.7338001e-2 s +[acc] eval 0.42931169 s +[acc] compile 7.441096e-3 s +[acc] eval 2.1689454e-2 s +[acc] eval 8.266566e-3 s +[acc] eval 4.9702965e-2 s +[acc] compile 0.126940507 s +[acc] eval 7.864099e-3 s +[acc] eval 4.698982e-3 s +[acc] eval 2.5088571e-2 s +[acc] eval 2.232143e-3 s +[acc] eval 2.085063e-3 s +[acc] eval 2.189418e-3 s +[acc] eval 1.983167e-3 s +[acc] eval 1.930898e-3 s +[acc] eval 1.812503e-3 s +[acc] eval 1.802614e-3 s +[acc] eval 1.825642e-3 s +[acc] eval 1.740934e-3 s +[acc] eval 2.084759e-3 s +[acc] eval 1.738266e-3 s +[acc] eval 1.77268e-3 s +[acc] eval 1.574752e-3 s +[acc] eval 1.485504e-3 s +[acc] eval 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+[acc] eval 0.199570436 s +[acc] compile 0.127571137 s +[acc] eval 3.1615582e-2 s +[acc] eval 0.163552414 s +[acc] eval 4.828984e-3 s +[acc] eval 4.98818e-4 s +[acc] eval 1.8336109e-2 s +[acc] eval 2.4577045e-2 s +[acc] eval 4.007462e-3 s +[acc] eval 1.611466e-3 s +[acc] eval 1.718636e-3 s +[acc] eval 1.956131e-3 s +[acc] eval 1.85695e-3 s +[acc] eval 1.751052e-3 s +[acc] eval 1.641121e-3 s +[acc] eval 1.793573e-3 s +[acc] eval 1.7361016e-2 s +[acc] eval 1.4262e-3 s +[acc] eval 1.373767e-3 s +[acc] eval 1.324837e-3 s +[acc] eval 1.290081e-3 s +[acc] eval 1.251043e-3 s +[acc] eval 1.285225e-3 s +[acc] eval 1.246577e-3 s +[acc] eval 1.256617e-3 s +[acc] eval 1.240645e-3 s +[acc] eval 1.211492e-3 s +[acc] eval 1.2703e-3 s +[acc] eval 1.380252e-3 s +[acc] eval 1.651721e-3 s +[acc] eval 1.498095e-3 s +[acc] eval 1.315942e-3 s +[acc] eval 1.319451e-3 s +[acc] eval 1.197887e-3 s +[acc] eval 1.247727e-3 s +[acc] eval 6.153168e-2 s +[acc] eval 8.582863e-3 s +[acc] eval 1.989861e-3 s +[acc] eval 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eval 2.538313e-3 s +[acc] eval 2.374146e-3 s +[acc] eval 2.381128e-3 s +[acc] eval 2.61414e-3 s +[acc] eval 2.46902e-3 s +[acc] eval 2.188687e-3 s +[acc] eval 2.283195e-3 s +[acc] eval 2.088139e-3 s +[acc] eval 2.707249e-3 s +[acc] eval 2.549837e-3 s +[acc] eval 2.548223e-3 s +[acc] eval 0.11046224 s +[acc] eval 1.4503507e-2 s +[acc] eval 3.056065e-3 s +[acc] eval 3.025317e-3 s +[acc] eval 1.7827682e-2 s +[acc] eval 3.488156e-3 s +[acc] eval 2.725806e-3 s +[acc] eval 2.288143e-3 s +[acc] eval 2.148423e-3 s +[acc] eval 2.713608e-3 s +[acc] eval 3.415792e-3 s +[acc] eval 3.015227e-3 s +[acc] eval 2.390681e-3 s +[acc] eval 2.125179e-3 s +[acc] eval 1.989427e-3 s +[acc] eval 1.818566e-3 s +[acc] eval 1.8521e-3 s +[acc] eval 2.881989e-3 s +[acc] eval 3.046777e-3 s +[acc] eval 2.654651e-3 s +[acc] eval 2.006226e-3 s +[acc] eval 1.971305e-3 s +[acc] eval 1.86503e-3 s +[acc] eval 1.927143e-3 s +[acc] eval 2.016994e-3 s +[acc] eval 1.945437e-3 s +[acc] eval 1.828437e-3 s +[acc] eval 1.73992e-3 s +[acc] eval 2.261915e-3 s +[acc] eval 2.117168e-3 s +[acc] eval 2.215959e-3 s +[acc] eval 0.104806143 s diff --git a/c_3.err b/c_3.err new file mode 100644 index 0000000..a7aff2c --- /dev/null +++ b/c_3.err @@ -0,0 +1,2950 @@ +[acc] compile 1.4265648e-2 s +[acc] eval 2.085498078 s +[acc] compile 5.0463e-4 s +[acc] eval 6.8433401e-2 s +[acc] compile 3.45775e-4 s +[acc] eval 6.68046e-2 s +[acc] compile 3.37991e-4 s +[acc] eval 0.161001076 s +[acc] compile 5.4767e-4 s +[acc] eval 0.158647994 s +[acc] compile 3.34669e-4 s +[acc] eval 0.149825464 s +[acc] compile 3.22752e-4 s +[acc] eval 0.154522423 s +[acc] compile 3.2618e-4 s +[acc] eval 0.13240345 s +[acc] compile 4.46399e-4 s +[acc] eval 7.354885e-2 s +[acc] compile 0.189055555 s +[acc] eval 5.3704889e-2 s +[acc] eval 0.247431364 s +[acc] compile 0.154808666 s +[acc] eval 2.0253999e-2 s +[acc] eval 0.178810815 s +[acc] compile 5.208738e-3 s +[acc] eval 1.0892198e-2 s +[acc] eval 1.961944e-2 s +[acc] compile 6.6138748e-2 s +[acc] eval 6.219042e-3 s +[acc] eval 7.5499002e-2 s +[acc] compile 9.8620068e-2 s +[acc] eval 2.5253243e-2 s +[acc] eval 0.128110028 s +[acc] compile 0.105880511 s +[acc] eval 1.0256451e-2 s +[acc] eval 2.27475e-3 s +[acc] eval 2.236627e-3 s +[acc] eval 2.917205e-3 s +[acc] eval 2.545805e-3 s +[acc] eval 2.662968e-3 s +[acc] eval 0.136057567 s +[acc] compile 5.9705005e-2 s +[acc] eval 2.1060338e-2 s +[acc] eval 8.4584216e-2 s +[acc] compile 6.1727064e-2 s +[acc] eval 1.7040798e-2 s +[acc] eval 8.2325455e-2 s +[acc] compile 2.635694e-3 s +[acc] eval 2.662025e-3 s +[acc] eval 5.00366e-4 s +[acc] eval 5.74029e-4 s +[acc] eval 7.133388e-3 s +[acc] compile 0.217329913 s +[acc] eval 9.003365e-3 s +[acc] eval 0.231955054 s +[acc] compile 0.345114098 s +[acc] eval 1.6246856e-2 s +[acc] eval 0.367359939 s +[acc] compile 0.115365887 s +[acc] eval 6.592364e-3 s +[acc] eval 0.125968885 s +[acc] compile 6.770088e-2 s +[acc] eval 1.2887617e-2 s +[acc] eval 2.360156e-3 s +[acc] eval 2.789628e-3 s +[acc] eval 2.090972e-3 s +[acc] eval 1.735621e-3 s +[acc] eval 1.849619e-3 s +[acc] eval 1.763699e-3 s +[acc] eval 1.956791e-3 s +[acc] eval 1.673167e-3 s +[acc] eval 1.704207e-3 s +[acc] eval 0.102083694 s +[acc] compile 6.107968e-3 s +[acc] eval 5.956893e-3 s +[acc] eval 1.4855251e-2 s +[acc] compile 5.578607e-3 s +[acc] eval 6.877414e-3 s +[acc] eval 2.0151987e-2 s +[acc] compile 5.227676e-3 s +[acc] eval 4.219579e-3 s +[acc] eval 1.0799933e-2 s +[acc] compile 4.390755e-3 s +[acc] eval 3.676329e-3 s +[acc] eval 7.14859e-4 s +[acc] eval 8.69823e-4 s +[acc] eval 1.0902857e-2 s +[acc] compile 5.9629592e-2 s +[acc] eval 1.8088674e-2 s +[acc] eval 8.0215601e-2 s +[acc] compile 0.274554457 s +[acc] eval 2.3096644e-2 s +[acc] eval 0.302562941 s +[acc] compile 6.7014237e-2 s +[acc] eval 1.126718e-2 s +[acc] eval 8.1188395e-2 s +[acc] compile 0.197826399 s +[acc] eval 2.7271636e-2 s +[acc] eval 0.230117527 s +[acc] compile 3.189148e-3 s +[acc] eval 3.154573e-3 s +[acc] eval 5.71319e-4 s +[acc] eval 7.53885e-4 s +[acc] eval 8.65552e-3 s +[acc] compile 5.0366231e-2 s +[acc] eval 5.849966e-3 s +[acc] eval 3.17637e-3 s +[acc] eval 7.714995e-3 s +[acc] eval 2.571457e-3 s +[acc] eval 3.125503e-3 s +[acc] eval 2.701833e-3 s +[acc] eval 2.658147e-3 s +[acc] eval 2.592324e-3 s +[acc] eval 3.112082e-3 s +[acc] eval 2.733091e-3 s +[acc] eval 2.336323e-3 s +[acc] eval 1.907771e-3 s +[acc] eval 2.155322e-3 s +[acc] eval 2.087372e-3 s +[acc] eval 2.306949e-3 s +[acc] eval 2.17044e-3 s +[acc] eval 2.28413e-3 s +[acc] eval 2.241106e-3 s +[acc] eval 0.108442134 s +[acc] compile 3.850279e-3 s +[acc] eval 4.988239e-3 s +[acc] eval 1.075986e-2 s +[acc] compile 0.111207043 s +[acc] eval 1.1718907e-2 s +[acc] eval 0.129072606 s +[acc] compile 1.2045091e-2 s +[acc] eval 5.98618e-3 s +[acc] eval 2.658891e-3 s +[acc] eval 2.614483e-3 s +[acc] eval 2.878972e-3 s +[acc] eval 9.246665e-3 s +[acc] eval 1.953261e-3 s +[acc] eval 2.024117e-3 s +[acc] eval 2.112242e-3 s +[acc] eval 2.170531e-3 s +[acc] eval 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3.136781e-3 s +[acc] eval 2.795908e-3 s +[acc] eval 2.640045e-3 s +[acc] eval 2.461898e-3 s +[acc] eval 2.378411e-3 s +[acc] eval 1.924247e-3 s +[acc] eval 2.084227e-3 s +[acc] eval 2.12972e-3 s +[acc] eval 2.21675e-3 s +[acc] eval 1.786445e-3 s +[acc] eval 0.222349391 s +[acc] compile 6.639034e-2 s +[acc] eval 1.1548852e-2 s +[acc] eval 8.1634591e-2 s +[acc] compile 0.226825878 s +[acc] eval 2.9051793e-2 s +[acc] eval 0.261868217 s +[acc] compile 0.220848323 s +[acc] eval 9.29789e-3 s +[acc] eval 0.244850719 s +[acc] compile 0.213118581 s +[acc] eval 2.5355812e-2 s +[acc] eval 0.242115538 s +[acc] compile 5.637644e-3 s +[acc] eval 2.0964559e-2 s +[acc] eval 2.9672506e-2 s +[acc] compile 6.79494e-4 s +[acc] eval 9.913384e-3 s +[acc] compile 0.252989193 s +[acc] eval 2.1474583e-2 s +[acc] eval 0.295144487 s +[acc] compile 7.484871e-3 s +[acc] eval 1.0990303e-2 s +[acc] eval 2.1373528e-2 s +[acc] compile 0.113178454 s +[acc] eval 5.228269e-3 s +[acc] eval 2.3598e-3 s +[acc] eval 2.305051e-3 s +[acc] eval 0.125716473 s +[acc] compile 3.379124e-3 s +[acc] eval 7.913884e-3 s +[acc] eval 1.9484029e-2 s +[acc] compile 5.434854e-3 s +[acc] eval 4.48181e-3 s +[acc] eval 1.503559e-3 s +[acc] eval 1.618248e-3 s +[acc] eval 1.219405e-3 s +[acc] eval 9.55927e-4 s +[acc] eval 1.6945106e-2 s +[acc] compile 0.164444277 s +[acc] eval 3.099362e-2 s +[acc] eval 0.200482075 s +[acc] compile 7.08206e-4 s +[acc] eval 1.1280592e-2 s +[acc] compile 0.189853225 s +[acc] eval 2.2757664e-2 s +[acc] eval 8.436085e-3 s +[acc] eval 2.886652e-3 s +[acc] eval 2.868476e-3 s +[acc] eval 3.190332e-3 s +[acc] eval 3.204359e-3 s +[acc] eval 3.139087e-3 s +[acc] eval 3.245627e-3 s +[acc] eval 3.007036e-3 s +[acc] eval 3.125644e-3 s +[acc] eval 3.09743e-3 s +[acc] eval 3.531242e-3 s +[acc] eval 0.257214919 s +[acc] compile 0.205753125 s +[acc] eval 2.1232181e-2 s +[acc] eval 0.23745257 s +[acc] compile 5.7320522e-2 s +[acc] eval 7.119353e-3 s +[acc] eval 6.8231279e-2 s +[acc] compile 0.193872361 s +[acc] eval 2.2257206e-2 s +[acc] eval 0.221669634 s +[acc] compile 0.150130309 s +[acc] eval 7.935574e-3 s +[acc] eval 0.164785083 s +[acc] compile 6.214809e-3 s +[acc] eval 7.461652e-3 s +[acc] eval 1.680862e-3 s +[acc] eval 1.481388e-3 s +[acc] eval 1.547534e-3 s +[acc] eval 2.0364955e-2 s +[acc] compile 8.9808692e-2 s +[acc] eval 2.1656617e-2 s +[acc] eval 0.11452241 s +[acc] compile 7.2451889e-2 s +[acc] eval 1.1835891e-2 s +[acc] eval 1.8158232e-2 s +[acc] eval 4.312369e-3 s +[acc] eval 3.922289e-3 s +[acc] eval 3.359633e-3 s +[acc] eval 2.886212e-3 s +[acc] eval 2.608406e-3 s +[acc] eval 2.537994e-3 s +[acc] eval 2.1766e-3 s +[acc] eval 2.192399e-3 s +[acc] eval 2.102316e-3 s +[acc] eval 2.635971e-3 s +[acc] eval 2.704138e-3 s +[acc] eval 2.552679e-3 s +[acc] eval 2.220245e-3 s +[acc] eval 2.223593e-3 s +[acc] eval 2.157324e-3 s +[acc] eval 2.525691e-3 s +[acc] eval 2.713338e-3 s +[acc] eval 2.18994e-3 s +[acc] eval 2.001642e-3 s +[acc] eval 2.369007e-3 s +[acc] eval 2.270902e-3 s +[acc] eval 2.11157e-3 s +[acc] eval 2.57959e-3 s +[acc] eval 2.258395e-3 s +[acc] eval 2.170036e-3 s +[acc] eval 2.084145e-3 s +[acc] eval 2.193339e-3 s +[acc] eval 2.069743e-3 s +[acc] eval 0.178906274 s +[acc] compile 4.270386e-3 s +[acc] eval 1.0751416e-2 s +[acc] eval 1.7112429e-2 s +[acc] compile 0.100964283 s +[acc] eval 1.1525625e-2 s +[acc] eval 4.530741e-3 s +[acc] eval 2.446789e-3 s +[acc] eval 2.048361e-3 s +[acc] eval 2.50841e-3 s +[acc] eval 2.099255e-3 s +[acc] eval 2.176238e-3 s +[acc] eval 2.194029e-3 s +[acc] eval 1.826514e-3 s +[acc] eval 1.849036e-3 s +[acc] eval 1.484812e-3 s +[acc] eval 1.288876e-3 s +[acc] eval 1.288774e-3 s +[acc] eval 1.224804e-3 s +[acc] eval 1.328211e-3 s +[acc] eval 1.244777e-3 s +[acc] eval 1.771454e-3 s +[acc] eval 1.937452e-3 s +[acc] eval 1.682814e-3 s +[acc] eval 1.824856e-3 s +[acc] eval 1.816343e-3 s +[acc] eval 1.794253e-3 s +[acc] eval 1.429342e-3 s +[acc] eval 1.702287e-3 s +[acc] eval 1.537798e-3 s +[acc] eval 1.457158e-3 s +[acc] eval 1.462775e-3 s +[acc] eval 1.44177e-3 s +[acc] eval 1.447965e-3 s +[acc] eval 1.384499e-3 s +[acc] eval 1.329399e-3 s +[acc] eval 1.511663e-3 s +[acc] eval 1.424591e-3 s +[acc] eval 0.17313953 s +[acc] compile 0.299413425 s +[acc] eval 2.1699664e-2 s +[acc] eval 0.325662628 s +[acc] compile 4.223824e-3 s +[acc] eval 4.525384e-3 s +[acc] eval 7.923764e-3 s +[acc] eval 1.031427e-3 s +[acc] eval 9.24515e-4 s +[acc] eval 1.9970576e-2 s +[acc] compile 3.46456e-4 s +[acc] eval 6.302134e-3 s +[acc] compile 5.025315e-3 s +[acc] eval 1.1583233e-2 s +[acc] eval 2.0148562e-2 s +[acc] compile 0.347926522 s +[acc] eval 3.0979256e-2 s +[acc] eval 0.384299816 s +[acc] compile 0.216646873 s +[acc] eval 9.349926e-3 s +[acc] eval 0.230106741 s +[acc] compile 7.659551e-3 s +[acc] eval 2.6596723e-2 s +[acc] eval 3.7696654e-2 s +[acc] compile 0.28186962 s +[acc] eval 3.2598356e-2 s +[acc] eval 4.574983e-3 s +[acc] eval 1.427731e-2 s +[acc] eval 3.255224e-3 s +[acc] eval 3.214917e-3 s +[acc] eval 3.07828e-3 s +[acc] eval 3.663352e-3 s +[acc] eval 3.453812e-3 s +[acc] eval 3.058286e-3 s +[acc] eval 3.835364e-3 s +[acc] eval 3.780725e-3 s +[acc] eval 3.714473e-3 s +[acc] eval 3.577035e-3 s +[acc] eval 3.407917e-3 s +[acc] eval 3.354277e-3 s +[acc] eval 3.560941e-3 s +[acc] eval 3.390286e-3 s +[acc] eval 3.22039e-3 s +[acc] eval 9.972252e-3 s +[acc] eval 3.137136e-3 s +[acc] eval 3.188615e-3 s +[acc] eval 3.016503e-3 s +[acc] eval 3.143659e-3 s +[acc] eval 3.099834e-3 s +[acc] eval 3.515821e-3 s +[acc] eval 3.609337e-3 s +[acc] eval 3.17817e-3 s +[acc] eval 3.151115e-3 s +[acc] eval 3.179648e-3 s +[acc] eval 3.718636e-3 s +[acc] eval 3.304055e-3 s +[acc] eval 3.748165e-3 s +[acc] eval 9.942835e-3 s +[acc] eval 0.45576934 s +[acc] compile 0.194714145 s +[acc] eval 2.0551185e-2 s +[acc] eval 7.87836e-3 s +[acc] eval 2.25928e-3 s +[acc] eval 2.474389e-3 s +[acc] eval 2.383506e-3 s +[acc] eval 2.080925e-3 s +[acc] eval 1.864769e-3 s +[acc] eval 1.801291e-3 s +[acc] eval 2.26921e-3 s +[acc] eval 2.294887e-3 s +[acc] eval 2.138333e-3 s +[acc] eval 2.318594e-3 s +[acc] eval 2.300833e-3 s +[acc] eval 0.251656353 s +[acc] compile 8.82363e-4 s +[acc] eval 9.286242e-3 s +[acc] compile 9.7646697e-2 s +[acc] eval 1.8205626e-2 s +[acc] eval 0.118845682 s +[acc] compile 0.126468688 s +[acc] eval 1.5717565e-2 s +[acc] eval 0.149033685 s +[acc] compile 0.15649581 s +[acc] eval 1.0153945e-2 s +[acc] eval 0.170554226 s +[acc] compile 0.106453033 s +[acc] eval 2.649428e-2 s +[acc] eval 0.134990508 s +[acc] compile 5.414857e-3 s +[acc] eval 6.828555e-3 s +[acc] eval 2.14381e-3 s +[acc] eval 2.253616e-3 s +[acc] eval 2.057191e-3 s +[acc] eval 2.537661e-3 s +[acc] eval 1.010744e-2 s +[acc] eval 2.426033e-3 s +[acc] eval 2.30762e-3 s +[acc] eval 2.365679e-3 s +[acc] eval 4.1449394e-2 s +[acc] compile 6.158771e-3 s +[acc] eval 1.18245e-2 s +[acc] eval 2.055085e-3 s +[acc] eval 1.911074e-3 s +[acc] eval 2.055733e-3 s +[acc] eval 2.531827e-3 s +[acc] eval 2.487115e-3 s +[acc] eval 8.209249e-3 s +[acc] eval 4.0326961e-2 s +[acc] compile 0.112310227 s +[acc] eval 1.0953421e-2 s +[acc] eval 0.125307378 s +[acc] compile 5.844479e-3 s +[acc] eval 1.9211956e-2 s +[acc] eval 2.7610537e-2 s +[acc] compile 5.55589e-4 s +[acc] eval 1.165425e-2 s +[acc] compile 0.124699765 s +[acc] eval 1.2873829e-2 s +[acc] eval 2.728593e-3 s +[acc] eval 2.306832e-3 s +[acc] eval 1.0987127e-2 s +[acc] eval 2.09674e-3 s +[acc] eval 2.721036e-3 s +[acc] eval 2.684267e-3 s +[acc] eval 2.521897e-3 s +[acc] eval 2.770637e-3 s +[acc] eval 3.021118e-3 s +[acc] eval 2.856633e-3 s +[acc] eval 2.84766e-3 s +[acc] eval 2.486392e-3 s +[acc] eval 2.277224e-3 s +[acc] eval 2.46606e-3 s +[acc] eval 1.983922e-3 s +[acc] eval 1.779974e-3 s +[acc] eval 1.788911e-3 s +[acc] eval 1.847482e-3 s +[acc] eval 1.74694e-3 s +[acc] eval 1.790813e-3 s +[acc] eval 1.807029e-3 s +[acc] eval 2.616565e-3 s +[acc] eval 2.361945e-3 s +[acc] eval 2.376103e-3 s +[acc] eval 2.457164e-3 s +[acc] eval 2.298596e-3 s +[acc] eval 2.214342e-3 s +[acc] eval 2.320056e-3 s +[acc] eval 2.162022e-3 s +[acc] eval 2.336753e-3 s +[acc] eval 0.222440689 s +[acc] compile 0.121851446 s +[acc] eval 1.6438217e-2 s +[acc] eval 0.143983406 s +[acc] compile 8.44159e-4 s +[acc] eval 2.3895123e-2 s +[acc] compile 0.212339052 s +[acc] eval 2.5064123e-2 s +[acc] eval 0.241376921 s +[acc] compile 0.264671258 s +[acc] eval 2.2569971e-2 s +[acc] eval 0.293001861 s +[acc] compile 4.0861491e-2 s +[acc] eval 5.039117e-3 s +[acc] eval 1.780041e-3 s +[acc] eval 1.168492e-3 s +[acc] eval 1.175256e-3 s +[acc] eval 1.096187e-3 s +[acc] eval 5.3313222e-2 s +[acc] compile 0.389719949 s +[acc] eval 3.2575086e-2 s +[acc] eval 0.427533897 s +[acc] compile 0.158475688 s +[acc] eval 1.5735094e-2 s +[acc] eval 0.180165641 s +[acc] compile 0.121467002 s +[acc] eval 2.0653025e-2 s +[acc] eval 0.148804859 s +[acc] compile 3.95432e-4 s +[acc] eval 7.555875e-3 s +[acc] compile 6.7596304e-2 s +[acc] eval 1.5703411e-2 s +[acc] eval 1.2731855e-2 s +[acc] eval 0.105021664 s +[acc] compile 0.158280251 s +[acc] eval 1.5278886e-2 s +[acc] eval 0.177215112 s +[acc] compile 9.45161e-3 s +[acc] eval 9.72091e-3 s +[acc] eval 2.3154734e-2 s +[acc] compile 0.288321737 s +[acc] eval 1.9008768e-2 s +[acc] eval 0.312307475 s +[acc] compile 4.131006e-3 s +[acc] eval 5.686701e-3 s +[acc] eval 1.86382e-3 s +[acc] eval 1.770003e-3 s +[acc] eval 1.763062e-3 s +[acc] eval 1.682069e-3 s +[acc] eval 1.9100163e-2 s +[acc] compile 9.1620243e-2 s +[acc] eval 6.484414e-3 s +[acc] eval 1.2135426e-2 s +[acc] eval 2.482298e-3 s +[acc] eval 2.249842e-3 s +[acc] eval 2.465233e-3 s +[acc] eval 2.204041e-3 s +[acc] eval 2.514445e-3 s +[acc] eval 2.334891e-3 s +[acc] eval 2.488688e-3 s +[acc] eval 2.537035e-3 s +[acc] eval 2.313194e-3 s +[acc] eval 2.200516e-3 s +[acc] eval 0.137660424 s +[acc] compile 9.6577901e-2 s +[acc] eval 2.5314695e-2 s +[acc] eval 0.124870545 s +[acc] compile 5.3712957e-2 s +[acc] eval 1.5155687e-2 s +[acc] eval 2.142958e-3 s +[acc] eval 1.987387e-3 s +[acc] eval 2.036745e-3 s +[acc] eval 1.886926e-3 s +[acc] eval 1.930904e-3 s +[acc] eval 2.247395e-3 s +[acc] eval 2.225776e-3 s +[acc] eval 1.966998e-3 s +[acc] eval 2.064518e-3 s +[acc] eval 2.118795e-3 s +[acc] eval 1.971806e-3 s +[acc] eval 1.947595e-3 s +[acc] eval 2.144575e-3 s +[acc] eval 2.127479e-3 s +[acc] eval 1.942819e-3 s +[acc] eval 1.721196e-3 s +[acc] eval 1.680343e-3 s +[acc] eval 1.622319e-3 s +[acc] eval 1.621821e-3 s +[acc] eval 1.591793e-3 s +[acc] eval 1.705966e-3 s +[acc] eval 1.583743e-3 s +[acc] eval 1.652401e-3 s +[acc] eval 1.560156e-3 s +[acc] eval 1.570803e-3 s +[acc] eval 1.94719e-3 s +[acc] eval 2.142272e-3 s +[acc] eval 2.034547e-3 s +[acc] eval 2.062709e-3 s +[acc] eval 0.128450762 s +[acc] compile 0.202260382 s +[acc] eval 2.0263869e-2 s +[acc] eval 0.225467831 s +[acc] compile 6.2368564e-2 s +[acc] eval 1.3391552e-2 s +[acc] eval 7.9576851e-2 s +[acc] compile 5.91242e-4 s +[acc] eval 4.611191e-3 s +[acc] compile 3.690763e-3 s +[acc] eval 4.661318e-3 s +[acc] eval 1.729906e-3 s +[acc] eval 1.588951e-3 s +[acc] eval 1.469724e-3 s +[acc] eval 1.341541e-3 s +[acc] eval 1.545809e-3 s +[acc] eval 1.8225685e-2 s +[acc] compile 4.213705e-3 s +[acc] eval 5.377343e-3 s +[acc] eval 1.760698e-3 s +[acc] eval 1.895739e-3 s +[acc] eval 1.622422e-3 s +[acc] eval 1.74304e-3 s +[acc] eval 1.677461e-3 s +[acc] eval 1.728353e-3 s +[acc] eval 1.693276e-3 s +[acc] eval 1.689402e-3 s +[acc] eval 1.803792e-3 s +[acc] eval 2.7910368e-2 s +[acc] compile 1.2290724e-2 s +[acc] eval 3.448354e-3 s +[acc] eval 1.05685e-3 s +[acc] eval 1.20522e-3 s +[acc] eval 1.9748719e-2 s +[acc] compile 0.108376904 s +[acc] eval 1.4048296e-2 s +[acc] eval 1.798583e-3 s +[acc] eval 1.76595e-3 s +[acc] eval 1.573562e-3 s +[acc] eval 0.129888736 s +[acc] compile 1.4107385e-2 s +[acc] eval 5.904821e-3 s +[acc] eval 3.217563e-3 s +[acc] eval 2.675708e-3 s +[acc] eval 2.501024e-3 s +[acc] eval 1.917555e-3 s +[acc] eval 1.675355e-3 s +[acc] eval 1.5883351e-2 s +[acc] eval 1.766942e-3 s +[acc] eval 1.792922e-3 s +[acc] eval 2.17134e-3 s +[acc] eval 2.243809e-3 s +[acc] eval 2.228565e-3 s +[acc] eval 2.443375e-3 s +[acc] eval 2.794254e-3 s +[acc] eval 3.341896e-3 s +[acc] eval 2.462887e-3 s +[acc] eval 2.348675e-3 s +[acc] eval 2.20028e-3 s +[acc] eval 2.048799e-3 s +[acc] eval 2.24476e-3 s +[acc] eval 1.760951e-3 s +[acc] eval 1.668569e-3 s +[acc] eval 1.71466e-3 s +[acc] eval 1.796031e-3 s +[acc] eval 1.760079e-3 s +[acc] eval 1.535038e-3 s +[acc] eval 1.293229e-3 s +[acc] eval 1.289601e-3 s +[acc] eval 1.361508e-3 s +[acc] eval 1.345789e-3 s +[acc] eval 9.7575393e-2 s +[acc] compile 2.923394e-3 s +[acc] eval 5.358028e-3 s +[acc] eval 1.28066e-3 s +[acc] eval 1.484662e-3 s +[acc] eval 1.291871e-3 s +[acc] eval 1.799509e-3 s +[acc] eval 1.970161e-3 s +[acc] eval 2.039372e-3 s +[acc] eval 1.55865e-3 s +[acc] eval 1.382415e-3 s +[acc] eval 1.483478e-3 s +[acc] eval 1.479838e-3 s +[acc] eval 1.908894e-3 s +[acc] eval 2.191068e-3 s +[acc] eval 3.1381276e-2 s +[acc] compile 0.142765663 s +[acc] eval 1.5939504e-2 s +[acc] eval 0.162381643 s +[acc] compile 0.277664147 s +[acc] eval 1.8624091e-2 s +[acc] eval 1.0285724e-2 s +[acc] eval 3.012708e-3 s +[acc] eval 3.309909e-3 s +[acc] eval 3.43163e-3 s +[acc] eval 3.358673e-3 s +[acc] eval 2.630772e-3 s +[acc] eval 2.338414e-3 s +[acc] eval 2.305291e-3 s +[acc] eval 2.645965e-3 s +[acc] eval 2.791502e-3 s +[acc] eval 2.771648e-3 s +[acc] eval 3.606578e-3 s +[acc] eval 2.778875e-3 s +[acc] eval 2.161649e-3 s +[acc] eval 2.20738e-3 s +[acc] eval 2.146118e-3 s +[acc] eval 2.50228e-3 s +[acc] eval 3.00542e-3 s +[acc] eval 2.844851e-3 s +[acc] eval 2.697136e-3 s +[acc] eval 2.707431e-3 s +[acc] eval 2.575531e-3 s +[acc] eval 2.782007e-3 s +[acc] eval 2.655084e-3 s +[acc] eval 2.681264e-3 s +[acc] eval 2.628565e-3 s +[acc] eval 2.655071e-3 s +[acc] eval 2.459525e-3 s +[acc] eval 2.526558e-3 s +[acc] eval 2.772539e-3 s +[acc] eval 0.391419442 s +[acc] compile 3.096965e-3 s +[acc] eval 3.695333e-3 s +[acc] eval 9.77863e-4 s +[acc] eval 6.73832e-4 s +[acc] eval 9.834603e-3 s +[acc] compile 5.82086e-4 s +[acc] eval 5.210336e-3 s +[acc] compile 0.131992941 s +[acc] eval 1.1691676e-2 s +[acc] eval 0.149340409 s +[acc] compile 8.379068e-3 s +[acc] eval 1.0866248e-2 s +[acc] eval 3.226356e-3 s +[acc] eval 3.4088e-3 s +[acc] eval 3.078065e-3 s +[acc] eval 3.2625822e-2 s +[acc] compile 1.7680716e-2 s +[acc] eval 6.00316e-3 s +[acc] eval 3.112374e-3 s +[acc] eval 2.548888e-3 s +[acc] eval 2.449405e-3 s +[acc] eval 2.039666e-3 s +[acc] eval 3.6502512e-2 s +[acc] compile 9.769033e-3 s +[acc] eval 8.526735e-3 s +[acc] eval 8.86887e-4 s +[acc] eval 7.0572e-4 s +[acc] eval 2.1453722e-2 s +[acc] compile 4.6425179e-2 s +[acc] eval 1.0486529e-2 s +[acc] eval 2.301824e-3 s +[acc] eval 8.234188e-3 s +[acc] eval 1.815302e-3 s +[acc] eval 7.1404745e-2 s +[acc] compile 4.898081e-3 s +[acc] eval 1.6748592e-2 s +[acc] eval 1.659975e-3 s +[acc] eval 1.704702e-3 s +[acc] eval 1.732326e-3 s +[acc] eval 1.2297787e-2 s +[acc] eval 2.057196e-3 s +[acc] eval 1.853577e-3 s +[acc] eval 1.732615e-3 s +[acc] eval 1.840443e-3 s +[acc] eval 1.229785e-3 s +[acc] eval 1.3976e-3 s +[acc] eval 1.73266e-3 s +[acc] eval 5.4063079e-2 s +[acc] compile 0.139054307 s +[acc] eval 2.2076127e-2 s +[acc] eval 0.164854741 s +[acc] compile 0.160201877 s +[acc] eval 2.5276339e-2 s +[acc] eval 0.18846049 s +[acc] compile 3.028768e-3 s +[acc] eval 3.990714e-3 s +[acc] eval 1.225239e-3 s +[acc] eval 1.109918e-3 s +[acc] eval 1.1147351e-2 s +[acc] compile 4.227035e-3 s +[acc] eval 4.841422e-3 s +[acc] eval 1.441078e-3 s +[acc] eval 1.08873e-3 s +[acc] eval 1.3369329e-2 s +[acc] compile 6.7677771e-2 s +[acc] eval 5.354672e-3 s +[acc] eval 1.889465e-3 s +[acc] eval 1.855803e-3 s +[acc] eval 1.607627e-3 s +[acc] eval 1.542809e-3 s +[acc] eval 1.422148e-3 s +[acc] eval 1.484214e-3 s +[acc] eval 2.148628e-3 s +[acc] eval 2.165364e-3 s +[acc] eval 1.0488767e-2 s +[acc] eval 1.410385e-3 s +[acc] eval 1.420627e-3 s +[acc] eval 1.741542e-3 s +[acc] eval 1.641822e-3 s +[acc] eval 1.335681e-3 s +[acc] eval 1.401238e-3 s +[acc] eval 1.630191e-3 s +[acc] eval 1.42728e-3 s +[acc] eval 1.291204e-3 s +[acc] eval 1.27849e-3 s +[acc] eval 1.287307e-3 s +[acc] eval 1.720321e-3 s +[acc] eval 1.810907e-3 s +[acc] eval 1.863402e-3 s +[acc] eval 1.660229e-3 s +[acc] eval 1.792453e-3 s +[acc] eval 1.875633e-3 s +[acc] eval 1.893218e-3 s +[acc] eval 1.892503e-3 s +[acc] eval 1.829244e-3 s +[acc] eval 0.13458927 s +[acc] compile 7.0681658e-2 s +[acc] eval 6.959448e-3 s +[acc] eval 8.1724084e-2 s +[acc] compile 4.635452e-3 s +[acc] eval 1.1667189e-2 s +[acc] eval 1.8102668e-2 s +[acc] compile 3.921596e-3 s +[acc] eval 3.645161e-3 s +[acc] eval 6.73083e-3 s +[acc] eval 8.33187e-4 s +[acc] eval 9.24945e-4 s +[acc] eval 1.7313677e-2 s +[acc] compile 4.406082e-3 s +[acc] eval 5.721084e-3 s +[acc] eval 1.182491e-3 s +[acc] eval 8.64068e-4 s +[acc] eval 1.133277e-3 s +[acc] eval 1.4657665e-2 s +[acc] compile 9.7624845e-2 s +[acc] eval 2.1187138e-2 s +[acc] eval 0.122804949 s +[acc] compile 5.170768e-3 s +[acc] eval 9.712932e-3 s +[acc] eval 1.1061967e-2 s +[acc] eval 9.05932e-4 s +[acc] eval 2.8417472e-2 s +[acc] compile 4.4795655e-2 s +[acc] eval 4.900241e-3 s +[acc] eval 1.344214e-3 s +[acc] eval 9.17731e-4 s +[acc] eval 1.066898e-3 s +[acc] eval 5.4499549e-2 s +[acc] compile 0.189789377 s +[acc] eval 2.1179852e-2 s +[acc] eval 0.224540481 s +[acc] compile 4.065611e-3 s +[acc] eval 4.616672e-3 s +[acc] eval 1.092287e-3 s +[acc] eval 8.65591e-4 s +[acc] eval 1.2145242e-2 s +[acc] compile 6.698132e-3 s +[acc] eval 9.750563e-3 s +[acc] eval 2.071006e-3 s +[acc] eval 1.0894182e-2 s +[acc] eval 1.673287e-3 s +[acc] eval 1.85013e-3 s +[acc] eval 2.030009e-3 s +[acc] eval 1.933643e-3 s +[acc] eval 1.914806e-3 s +[acc] eval 1.981596e-3 s +[acc] eval 2.055146e-3 s +[acc] eval 2.005707e-3 s +[acc] eval 1.918509e-3 s +[acc] eval 2.025233e-3 s +[acc] eval 2.044293e-3 s +[acc] eval 2.080964e-3 s +[acc] eval 2.032973e-3 s +[acc] eval 2.083777e-3 s +[acc] eval 2.030774e-3 s +[acc] eval 2.01407e-3 s +[acc] eval 2.045497e-3 s +[acc] eval 2.154215e-3 s +[acc] eval 2.388107e-3 s +[acc] eval 2.23277e-3 s +[acc] eval 2.335139e-3 s +[acc] eval 2.275835e-3 s +[acc] eval 2.143132e-3 s +[acc] eval 2.220631e-3 s +[acc] eval 2.273584e-3 s +[acc] eval 2.258019e-3 s +[acc] eval 1.984097e-3 s +[acc] eval 2.093865e-3 s +[acc] eval 9.2383873e-2 s +[acc] compile 6.04823e-4 s +[acc] eval 5.926388e-3 s +[acc] compile 0.163980961 s +[acc] eval 1.4677375e-2 s +[acc] eval 3.86828e-3 s +[acc] eval 1.0163202e-2 s +[acc] eval 2.767972e-3 s +[acc] eval 2.624117e-3 s +[acc] eval 2.27382e-3 s +[acc] eval 2.091599e-3 s +[acc] eval 2.188703e-3 s +[acc] eval 2.240942e-3 s +[acc] eval 2.545649e-3 s +[acc] eval 2.60862e-3 s +[acc] eval 2.534437e-3 s +[acc] eval 2.330861e-3 s +[acc] eval 2.090573e-3 s +[acc] eval 2.137306e-3 s +[acc] eval 2.30645e-3 s +[acc] eval 2.103521e-3 s +[acc] eval 1.524636e-3 s +[acc] eval 1.563633e-3 s +[acc] eval 2.161731e-3 s +[acc] eval 1.681465e-3 s +[acc] eval 0.236418921 s +[acc] compile 0.194394237 s +[acc] eval 1.4306296e-2 s +[acc] eval 0.214376547 s +[acc] compile 3.50013e-4 s +[acc] eval 3.4868863e-2 s +[acc] compile 7.0423499e-2 s +[acc] eval 1.3827387e-2 s +[acc] eval 8.264351e-3 s +[acc] eval 0.101286821 s +[acc] compile 6.97156e-3 s +[acc] eval 1.8520369e-2 s +[acc] eval 2.8976811e-2 s +[acc] compile 6.314559e-3 s +[acc] eval 1.6000527e-2 s +[acc] eval 2.5329087e-2 s +[acc] compile 0.230241011 s +[acc] eval 1.0085106e-2 s +[acc] eval 0.244983485 s +[acc] compile 4.608074e-3 s +[acc] eval 1.341604e-2 s +[acc] eval 1.9914439e-2 s +[acc] compile 5.942053e-3 s +[acc] eval 1.1636632e-2 s +[acc] eval 2.2563449e-2 s +[acc] compile 6.2035523e-2 s +[acc] eval 1.1619844e-2 s +[acc] eval 8.1290964e-2 s +[acc] compile 1.9019823e-2 s +[acc] eval 7.651077e-3 s +[acc] eval 2.9210291e-2 s +[acc] compile 3.562243e-3 s +[acc] eval 4.125422e-3 s +[acc] eval 8.46154e-4 s +[acc] eval 1.038484e-3 s +[acc] eval 1.0660222e-2 s +[acc] compile 5.4017616e-2 s +[acc] eval 6.780108e-3 s +[acc] eval 8.643256e-3 s +[acc] eval 1.260001e-3 s +[acc] eval 1.240926e-3 s +[acc] eval 7.3986711e-2 s +[acc] compile 6.211649e-3 s +[acc] eval 4.924784e-3 s +[acc] eval 1.69639e-3 s +[acc] eval 1.548848e-3 s +[acc] eval 1.36393e-3 s +[acc] eval 1.450424e-3 s +[acc] eval 1.524255e-3 s +[acc] eval 1.384164e-3 s +[acc] eval 1.727065e-3 s +[acc] eval 8.906419e-3 s +[acc] eval 1.2512e-3 s +[acc] eval 1.080487e-3 s +[acc] eval 1.005903e-3 s +[acc] eval 1.464782e-3 s +[acc] eval 1.49833e-3 s +[acc] eval 1.362752e-3 s +[acc] eval 1.385492e-3 s +[acc] eval 1.390103e-3 s +[acc] eval 1.24747e-3 s +[acc] eval 1.273399e-3 s +[acc] eval 1.533923e-3 s +[acc] eval 1.014911e-3 s +[acc] eval 1.062309e-3 s +[acc] eval 1.0503e-3 s +[acc] eval 1.017776e-3 s +[acc] eval 1.442722e-3 s +[acc] eval 1.626666e-3 s +[acc] eval 1.555302e-3 s +[acc] eval 1.219044e-3 s +[acc] eval 1.436141e-3 s +[acc] eval 1.266188e-3 s +[acc] eval 6.1843253e-2 s +[acc] compile 3.30149e-4 s +[acc] eval 7.383716e-3 s +[acc] compile 0.124767427 s +[acc] eval 2.4269266e-2 s +[acc] eval 6.36033e-3 s +[acc] eval 3.771954e-3 s +[acc] eval 2.894258e-3 s +[acc] eval 2.682033e-3 s +[acc] eval 1.0909903e-2 s +[acc] eval 2.652824e-3 s +[acc] eval 2.418095e-3 s +[acc] eval 2.416786e-3 s +[acc] eval 2.270452e-3 s +[acc] eval 2.296028e-3 s +[acc] eval 2.168319e-3 s +[acc] eval 2.304411e-3 s +[acc] eval 2.348341e-3 s +[acc] eval 2.371033e-3 s +[acc] eval 0.202588328 s +[acc] compile 6.044239e-3 s +[acc] eval 1.0947013e-2 s +[acc] eval 2.670491e-3 s +[acc] eval 2.835334e-3 s +[acc] eval 2.722564e-3 s +[acc] eval 2.43225e-3 s +[acc] eval 2.360331e-3 s +[acc] eval 2.220291e-3 s +[acc] eval 2.404958e-3 s +[acc] eval 2.315182e-3 s +[acc] eval 2.749633e-3 s +[acc] eval 2.29599e-3 s +[acc] eval 2.313395e-3 s +[acc] eval 2.039342e-3 s +[acc] eval 8.285954e-3 s +[acc] eval 2.40441e-3 s +[acc] eval 2.266516e-3 s +[acc] eval 2.206864e-3 s +[acc] eval 2.055663e-3 s +[acc] eval 6.8424265e-2 s +[acc] compile 0.208284532 s +[acc] eval 2.1233945e-2 s +[acc] eval 0.232465182 s +[acc] compile 6.2154048e-2 s +[acc] eval 6.128361e-3 s +[acc] eval 2.033257e-3 s +[acc] eval 2.212141e-3 s +[acc] eval 1.895439e-3 s +[acc] eval 2.217804e-3 s +[acc] eval 1.0283459e-2 s +[acc] eval 8.9953932e-2 s +[acc] compile 1.6912619e-2 s +[acc] eval 2.4542838e-2 s +[acc] eval 5.274232e-2 s +[acc] compile 0.146095324 s +[acc] eval 7.168551e-3 s +[acc] eval 4.453321e-3 s +[acc] eval 3.606288e-3 s +[acc] eval 3.457187e-3 s +[acc] eval 3.552108e-3 s +[acc] eval 3.71457e-3 s +[acc] eval 3.375557e-3 s +[acc] eval 3.692545e-3 s +[acc] eval 2.704821e-3 s +[acc] eval 3.291298e-3 s +[acc] eval 2.452651e-3 s +[acc] eval 1.1546995e-2 s +[acc] eval 2.092319e-3 s +[acc] eval 2.142441e-3 s +[acc] eval 1.807408e-3 s +[acc] eval 1.544759e-3 s +[acc] eval 1.278114e-3 s +[acc] eval 1.474457e-3 s +[acc] eval 1.279416e-3 s +[acc] eval 1.195206e-3 s +[acc] eval 1.467283e-3 s +[acc] eval 1.414765e-3 s +[acc] eval 1.64311e-3 s +[acc] eval 1.738348e-3 s +[acc] eval 1.592154e-3 s +[acc] eval 1.257589e-3 s +[acc] eval 1.371734e-3 s +[acc] eval 1.788985e-3 s +[acc] eval 2.011002e-3 s +[acc] eval 1.268309e-3 s +[acc] eval 0.232624208 s +[acc] compile 6.2850103e-2 s +[acc] eval 3.066701e-2 s +[acc] eval 9.8186394e-2 s +[acc] compile 3.86498e-4 s +[acc] eval 6.086479e-3 s +[acc] compile 6.3140368e-2 s +[acc] eval 1.2467456e-2 s +[acc] eval 7.56442e-3 s +[acc] eval 7.656946e-3 s +[acc] eval 5.50613e-3 s +[acc] eval 3.969268e-3 s +[acc] eval 1.956751e-3 s +[acc] eval 2.233315e-3 s +[acc] eval 1.0223588e-2 s +[acc] eval 7.972637e-3 s +[acc] eval 8.542909e-3 s +[acc] eval 1.2274272e-2 s +[acc] eval 4.544133e-3 s +[acc] eval 0.151571427 s +[acc] compile 7.3330939e-2 s +[acc] eval 1.1285951e-2 s +[acc] eval 8.696525e-2 s +[acc] compile 1.6698644e-2 s +[acc] eval 7.240188e-3 s +[acc] eval 2.7413178e-2 s +[acc] compile 7.032581e-3 s +[acc] eval 1.3681483e-2 s +[acc] eval 2.3310977e-2 s +[acc] compile 0.22970467 s +[acc] eval 2.0116718e-2 s +[acc] eval 3.518762e-3 s +[acc] eval 3.10938e-3 s +[acc] eval 1.1520592e-2 s +[acc] eval 2.882964e-3 s +[acc] eval 2.862725e-3 s +[acc] eval 3.199705e-3 s +[acc] eval 2.577876e-3 s +[acc] eval 2.437881e-3 s +[acc] eval 2.505806e-3 s +[acc] eval 2.311941e-3 s +[acc] eval 2.612348e-3 s +[acc] eval 2.947879e-3 s +[acc] eval 3.114836e-3 s +[acc] eval 2.590917e-3 s +[acc] eval 2.545509e-3 s +[acc] eval 2.532271e-3 s +[acc] eval 2.40356e-3 s +[acc] eval 2.798106e-3 s +[acc] eval 2.841682e-3 s +[acc] eval 2.147609e-3 s +[acc] eval 2.169401e-3 s +[acc] eval 2.225868e-3 s +[acc] eval 2.320628e-3 s +[acc] eval 2.18327e-3 s +[acc] eval 1.96468e-3 s +[acc] eval 2.121539e-3 s +[acc] eval 2.341755e-3 s +[acc] eval 2.923547e-3 s +[acc] eval 2.973062e-3 s +[acc] eval 0.339839676 s +[acc] compile 9.075759e-3 s +[acc] eval 7.308944e-3 s +[acc] eval 3.817923e-3 s +[acc] eval 4.245402e-3 s +[acc] eval 9.26699e-3 s +[acc] eval 1.830999e-2 s +[acc] eval 6.6097978e-2 s +[acc] compile 0.117187982 s +[acc] eval 2.2402474e-2 s +[acc] eval 0.142522456 s +[acc] compile 4.91861e-3 s +[acc] eval 1.3419439e-2 s +[acc] eval 2.1273704e-2 s +[acc] compile 0.103036191 s +[acc] eval 1.0804207e-2 s +[acc] eval 5.469622e-3 s +[acc] eval 3.878729e-3 s +[acc] eval 3.886217e-3 s +[acc] eval 3.958682e-3 s +[acc] eval 3.036694e-3 s +[acc] eval 3.68295e-3 s +[acc] eval 1.2943879e-2 s +[acc] eval 2.038352e-3 s +[acc] eval 2.07391e-3 s +[acc] eval 1.885279e-3 s +[acc] eval 1.844431e-3 s +[acc] eval 1.814585e-3 s +[acc] eval 2.06048e-3 s +[acc] eval 1.955799e-3 s +[acc] eval 2.617532e-3 s +[acc] eval 2.074943e-3 s +[acc] eval 2.216083e-3 s +[acc] eval 1.68861e-3 s +[acc] eval 1.702891e-3 s +[acc] eval 1.681088e-3 s +[acc] eval 1.829943e-3 s +[acc] eval 1.633104e-3 s +[acc] eval 1.785166e-3 s +[acc] eval 1.641066e-3 s +[acc] eval 0.187583912 s +[acc] compile 3.41151e-4 s +[acc] eval 7.69919e-3 s +[acc] compile 5.186667e-3 s +[acc] eval 1.8113033e-2 s +[acc] eval 2.743039e-3 s +[acc] eval 2.498617e-3 s +[acc] eval 2.346293e-3 s +[acc] eval 1.1192168e-2 s +[acc] eval 1.431431e-3 s +[acc] eval 1.136009e-3 s +[acc] eval 1.178357e-3 s +[acc] eval 1.109214e-3 s +[acc] eval 1.070052e-3 s +[acc] eval 1.167977e-3 s +[acc] eval 1.240723e-3 s +[acc] eval 1.194093e-3 s +[acc] eval 1.306662e-3 s +[acc] eval 1.382143e-3 s +[acc] eval 1.323234e-3 s +[acc] eval 1.289661e-3 s +[acc] eval 1.34045e-3 s +[acc] eval 1.2091e-3 s +[acc] eval 1.349985e-3 s +[acc] eval 1.172426e-3 s +[acc] eval 1.329163e-3 s +[acc] eval 1.665759e-3 s +[acc] eval 1.366629e-3 s +[acc] eval 1.664867e-3 s +[acc] eval 1.195805e-3 s +[acc] eval 1.132061e-3 s +[acc] eval 1.136538e-3 s +[acc] eval 1.134506e-3 s +[acc] eval 1.109519e-3 s +[acc] eval 7.8349607e-2 s +[acc] compile 6.9672317e-2 s +[acc] eval 2.2686649e-2 s +[acc] eval 9.6576203e-2 s +[acc] compile 1.387804e-2 s +[acc] eval 3.747074e-3 s +[acc] eval 1.18749e-3 s +[acc] eval 1.063213e-3 s +[acc] eval 2.1394613e-2 s +[acc] compile 4.457077e-3 s +[acc] eval 8.802135e-3 s +[acc] eval 1.6535452e-2 s +[acc] compile 5.36872e-4 s +[acc] eval 2.0605951e-2 s +[acc] compile 0.113497583 s +[acc] eval 1.2179277e-2 s +[acc] eval 0.128898428 s +[acc] eval 2.0225561e-2 s +[acc] eval 1.4660726e-2 s +[acc] eval 2.765862e-3 s +[acc] eval 3.236178e-3 s +[acc] eval 3.331015e-3 s +[acc] eval 2.960133e-3 s +[acc] eval 2.69451e-3 s +[acc] eval 2.587703e-3 s +[acc] eval 2.907961e-3 s +[acc] eval 2.623021e-3 s +[acc] eval 2.582419e-3 s +[acc] eval 2.884144e-3 s +[acc] eval 2.872804e-3 s +[acc] eval 2.502269e-3 s +[acc] eval 3.047324e-3 s +[acc] eval 2.813703e-3 s +[acc] eval 3.076301e-3 s +[acc] eval 3.135864e-3 s +[acc] eval 2.799967e-3 s +[acc] eval 3.024795e-3 s +[acc] eval 2.910767e-3 s +[acc] eval 2.387667e-3 s +[acc] eval 3.144729e-3 s +[acc] eval 3.979073e-3 s +[acc] eval 2.91402e-3 s +[acc] eval 3.061095e-3 s +[acc] eval 2.730204e-3 s +[acc] eval 2.704609e-3 s +[acc] eval 2.67651e-3 s +[acc] eval 2.78046e-3 s +[acc] eval 0.122605826 s +[acc] compile 9.216166e-3 s +[acc] eval 1.7131667e-2 s +[acc] eval 2.9750584e-2 s +[acc] eval 7.369347e-3 s +[acc] eval 1.5791953e-2 s +[acc] compile 1.0844163e-2 s +[acc] eval 2.7701618e-2 s +[acc] eval 4.7861369e-2 s +[acc] eval 1.7062044e-2 s +[acc] eval 2.0060355e-2 s +[acc] eval 1.1732839e-2 s +[acc] eval 1.6662185e-2 s +[acc] compile 0.204211107 s +[acc] eval 2.9325103e-2 s +[acc] eval 1.5525877e-2 s +[acc] eval 4.406937e-3 s +[acc] eval 4.078642e-3 s +[acc] eval 3.446562e-3 s +[acc] eval 3.265859e-3 s +[acc] eval 3.997597e-3 s +[acc] eval 4.765498e-3 s +[acc] eval 3.645456e-3 s +[acc] eval 3.537537e-3 s +[acc] eval 2.780077e-3 s +[acc] eval 2.665678e-3 s +[acc] eval 2.658162e-3 s +[acc] eval 2.661777e-3 s +[acc] eval 2.759254e-3 s +[acc] eval 3.024865e-3 s +[acc] eval 2.746702e-3 s +[acc] eval 2.607059e-3 s +[acc] eval 2.679195e-3 s +[acc] eval 3.065001e-3 s +[acc] eval 2.84009e-3 s +[acc] eval 2.618802e-3 s +[acc] eval 2.684367e-3 s +[acc] eval 2.399586e-3 s +[acc] eval 2.408009e-3 s +[acc] eval 2.705904e-3 s +[acc] eval 2.59536e-3 s +[acc] eval 2.772864e-3 s +[acc] eval 2.788483e-3 s +[acc] eval 2.748629e-3 s +[acc] eval 2.940728e-3 s +[acc] eval 0.344815163 s +[acc] compile 0.230729137 s +[acc] eval 3.2844635e-2 s +[acc] eval 0.270937707 s +[acc] compile 7.0096761e-2 s +[acc] eval 1.9171412e-2 s +[acc] eval 3.04475e-3 s +[acc] eval 9.6973455e-2 s +[acc] compile 3.70064e-4 s +[acc] eval 9.779195e-3 s +[acc] compile 5.729814e-2 s +[acc] eval 1.9987856e-2 s +[acc] eval 2.337147e-3 s +[acc] eval 2.377734e-3 s +[acc] eval 2.26948e-3 s +[acc] eval 2.022839e-3 s +[acc] eval 1.977215e-3 s +[acc] eval 2.062858e-3 s +[acc] eval 1.863289e-3 s +[acc] eval 1.880644e-3 s +[acc] eval 1.732247e-3 s +[acc] eval 1.990983e-3 s +[acc] eval 1.901263e-3 s +[acc] eval 2.43641e-3 s +[acc] eval 3.189022e-3 s +[acc] eval 2.163345e-3 s +[acc] eval 2.182404e-3 s +[acc] eval 2.213597e-3 s +[acc] eval 1.776185e-3 s +[acc] eval 1.849608e-3 s +[acc] eval 2.311056e-3 s +[acc] eval 1.952964e-3 s +[acc] eval 1.963507e-3 s +[acc] eval 2.075336e-3 s +[acc] eval 1.68554e-3 s +[acc] eval 1.679629e-3 s +[acc] eval 1.778646e-3 s +[acc] eval 2.279905e-3 s +[acc] eval 2.294779e-3 s +[acc] eval 2.610017e-3 s +[acc] eval 2.624863e-3 s +[acc] eval 0.145424441 s +[acc] compile 8.890668e-3 s +[acc] eval 9.534935e-3 s +[acc] eval 2.2335738e-2 s +[acc] compile 0.120792821 s +[acc] eval 2.2144906e-2 s +[acc] eval 0.145824658 s +[acc] compile 5.4086129e-2 s +[acc] eval 1.3599469e-2 s +[acc] eval 7.3467828e-2 s +[acc] compile 0.117682646 s +[acc] eval 9.600898e-3 s +[acc] eval 0.130922428 s +[acc] compile 0.177095348 s +[acc] eval 3.3202842e-2 s +[acc] eval 0.215924973 s +[acc] eval 1.2226051e-2 s +[acc] eval 1.787268e-2 s +[acc] compile 7.642856e-3 s +[acc] eval 8.419057e-3 s +[acc] eval 1.930482e-2 s +[acc] compile 1.8625394e-2 s +[acc] eval 9.190143e-3 s +[acc] eval 4.227423e-3 s +[acc] eval 3.5764942e-2 s +[acc] eval 1.5669617e-2 s +[acc] eval 1.752971e-2 s +[acc] eval 1.5644685e-2 s +[acc] eval 1.8952619e-2 s +[acc] compile 3.176928e-3 s +[acc] eval 7.625514e-3 s +[acc] eval 2.1705436e-2 s +[acc] compile 6.255222e-3 s +[acc] eval 6.786097e-3 s +[acc] eval 1.6197965e-2 s +[acc] compile 7.0416796e-2 s +[acc] eval 1.0984951e-2 s +[acc] eval 8.6449616e-2 s +[acc] compile 0.193345282 s +[acc] eval 1.6643908e-2 s +[acc] eval 0.222407949 s +[acc] compile 6.231564e-2 s +[acc] eval 7.246995e-3 s +[acc] eval 7.3080893e-2 s +[acc] eval 2.9297637e-2 s +[acc] eval 3.3148662e-2 s +[acc] compile 6.4221603e-2 s +[acc] eval 6.117619e-3 s +[acc] eval 7.3427974e-2 s +[acc] compile 9.9840486e-2 s +[acc] eval 2.8086959e-2 s +[acc] eval 0.132621966 s +[acc] compile 0.184423794 s +[acc] eval 1.1454656e-2 s +[acc] eval 0.201173431 s +[acc] compile 0.121944484 s +[acc] eval 2.6950851e-2 s +[acc] eval 0.153320251 s +[acc] compile 1.0491007e-2 s +[acc] eval 8.672748e-3 s +[acc] eval 4.112793e-3 s 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3.987389e-3 s +[acc] eval 4.909073e-3 s +[acc] eval 1.0951496e-2 s +[acc] eval 1.808991e-3 s +[acc] eval 2.3776766e-2 s +[acc] eval 3.939732e-3 s +[acc] eval 2.300053e-3 s +[acc] eval 2.232678e-3 s +[acc] eval 2.349157e-3 s +[acc] eval 2.116384e-3 s +[acc] eval 2.373111e-3 s +[acc] eval 2.771758e-3 s +[acc] eval 1.762718e-3 s +[acc] eval 1.821959e-3 s +[acc] eval 2.197096e-3 s +[acc] eval 2.508524e-3 s +[acc] eval 2.826188e-3 s +[acc] eval 2.656589e-3 s +[acc] eval 9.27031e-3 s +[acc] eval 1.855038e-3 s +[acc] eval 1.899875e-3 s +[acc] eval 1.902458e-3 s +[acc] eval 1.739267e-3 s +[acc] eval 1.799781e-3 s +[acc] eval 1.807965e-3 s +[acc] eval 1.790156e-3 s +[acc] eval 1.888255e-3 s +[acc] eval 6.0978684e-2 s +[acc] compile 0.27271887 s +[acc] eval 1.8892116e-2 s +[acc] eval 0.305951582 s +[acc] compile 5.7673722e-2 s +[acc] eval 1.0927712e-2 s +[acc] eval 7.0396341e-2 s +[acc] compile 0.14765414 s +[acc] eval 2.3505405e-2 s +[acc] eval 0.175464004 s +[acc] compile 0.37648239 s +[acc] eval 2.0713247e-2 s +[acc] eval 0.40386093 s +[acc] compile 0.198188659 s +[acc] eval 1.8199339e-2 s +[acc] eval 0.220334251 s +[acc] compile 0.152675153 s +[acc] eval 3.8379165e-2 s +[acc] eval 0.195507827 s +[acc] compile 6.057241e-3 s +[acc] eval 1.6412232e-2 s +[acc] eval 2.7286233e-2 s +[acc] compile 0.385487563 s +[acc] eval 3.1394313e-2 s +[acc] eval 0.421669627 s +[acc] compile 7.166103e-2 s +[acc] eval 1.3076333e-2 s +[acc] eval 9.0837495e-2 s +[acc] compile 0.186347224 s +[acc] eval 3.4090707e-2 s +[acc] eval 0.226022516 s +[acc] compile 3.024718e-3 s +[acc] eval 4.934758e-3 s +[acc] eval 7.250181e-3 s +[acc] eval 4.866663e-3 s +[acc] eval 5.246181e-3 s +[acc] eval 2.993296e-3 s +[acc] eval 5.746133e-3 s +[acc] eval 6.092385e-3 s +[acc] eval 5.117261e-3 s +[acc] eval 2.142819e-3 s +[acc] eval 1.771473e-3 s +[acc] eval 1.386505e-3 s +[acc] eval 1.663716e-3 s +[acc] eval 5.556073e-2 s +[acc] compile 9.192256e-3 s +[acc] eval 2.3980902e-2 s +[acc] eval 3.887357e-2 s +[acc] compile 6.7849636e-2 s +[acc] eval 1.0208781e-2 s +[acc] eval 4.648229e-3 s +[acc] eval 4.759028e-3 s +[acc] eval 3.57594e-3 s +[acc] eval 3.410374e-3 s +[acc] eval 1.4499412e-2 s +[acc] eval 3.133226e-3 s +[acc] eval 3.452414e-3 s +[acc] eval 2.61764e-3 s +[acc] eval 2.804715e-3 s +[acc] eval 2.687008e-3 s +[acc] eval 2.444115e-3 s +[acc] eval 3.018849e-3 s +[acc] eval 2.799918e-3 s +[acc] eval 2.710631e-3 s +[acc] eval 2.358053e-3 s +[acc] eval 2.370723e-3 s +[acc] eval 2.34266e-3 s +[acc] eval 2.328914e-3 s +[acc] eval 2.924302e-3 s +[acc] eval 2.874989e-3 s +[acc] eval 2.737386e-3 s +[acc] eval 2.496663e-3 s +[acc] eval 2.381957e-3 s +[acc] eval 3.081829e-3 s +[acc] eval 0.166032365 s +[acc] compile 0.131859993 s +[acc] eval 1.4487443e-2 s +[acc] eval 3.397719e-3 s +[acc] eval 2.9064e-3 s +[acc] eval 3.21619e-3 s +[acc] eval 1.2699584e-2 s +[acc] eval 2.582367e-3 s +[acc] eval 2.524875e-3 s +[acc] eval 2.991795e-3 s +[acc] eval 3.046475e-3 s +[acc] eval 2.592459e-3 s +[acc] eval 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compile 0.162584569 s +[acc] eval 2.4966438e-2 s +[acc] eval 0.191464347 s +[acc] compile 0.166143742 s +[acc] eval 7.878682e-3 s +[acc] eval 1.229989e-2 s +[acc] eval 2.765404e-3 s +[acc] eval 2.794722e-3 s +[acc] eval 3.401532e-3 s +[acc] eval 3.381479e-3 s +[acc] eval 3.530615e-3 s +[acc] eval 3.092784e-3 s +[acc] eval 2.386494e-3 s +[acc] eval 2.443177e-3 s +[acc] eval 2.405981e-3 s +[acc] eval 2.373346e-3 s +[acc] eval 3.270833e-3 s +[acc] eval 0.223989797 s +[acc] compile 0.504779025 s +[acc] eval 2.5502095e-2 s +[acc] eval 0.548219347 s +[acc] compile 0.179285725 s +[acc] eval 1.4822966e-2 s +[acc] eval 0.197545009 s +[acc] compile 0.374435232 s +[acc] eval 4.0642379e-2 s +[acc] eval 0.419498224 s +[acc] compile 0.126258033 s +[acc] eval 8.076101e-3 s +[acc] eval 4.520195e-3 s +[acc] eval 4.643787e-3 s +[acc] eval 4.134941e-3 s +[acc] eval 1.6175605e-2 s +[acc] eval 3.362499e-3 s +[acc] eval 2.506131e-3 s +[acc] eval 2.452771e-3 s +[acc] eval 2.367737e-3 s +[acc] eval 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compile 0.127728037 s +[acc] eval 1.631104e-2 s +[acc] eval 4.591857e-3 s +[acc] eval 1.3489609e-2 s +[acc] eval 3.552114e-3 s +[acc] eval 3.396037e-3 s +[acc] eval 0.17383441 s +[acc] compile 0.455433526 s +[acc] eval 3.2130858e-2 s +[acc] eval 0.504165757 s +[acc] compile 1.0101556e-2 s +[acc] eval 1.1486702e-2 s +[acc] eval 2.5359342e-2 s +[acc] compile 9.903792e-3 s +[acc] eval 3.163016e-2 s +[acc] eval 4.4573197e-2 s +[acc] compile 0.310352911 s +[acc] eval 3.3936365e-2 s +[acc] eval 0.34887207 s +[acc] compile 0.29993784 s +[acc] eval 3.499178e-2 s +[acc] eval 0.352744529 s +[acc] compile 3.746327e-3 s +[acc] eval 6.599851e-3 s +[acc] eval 2.466826e-3 s +[acc] eval 2.855778e-3 s +[acc] eval 1.9073701e-2 s +[acc] compile 0.141541307 s +[acc] eval 3.4002392e-2 s +[acc] eval 0.179111702 s +[acc] compile 6.9062749e-2 s +[acc] eval 1.4138547e-2 s +[acc] eval 8.6907464e-2 s +[acc] compile 3.58961e-3 s +[acc] eval 8.320988e-3 s +[acc] eval 1.0563127e-2 s +[acc] eval 8.09863e-4 s +[acc] eval 7.39541e-4 s +[acc] eval 2.5464081e-2 s +[acc] compile 0.123433276 s +[acc] eval 1.3595438e-2 s +[acc] eval 0.141106408 s +[acc] compile 6.6096774e-2 s +[acc] eval 7.342991e-3 s +[acc] eval 1.808087e-3 s +[acc] eval 7.7424705e-2 s +[acc] compile 0.135282099 s +[acc] eval 9.649286e-3 s +[acc] eval 4.148281e-3 s +[acc] eval 0.15484566 s +[acc] eval 2.7945705e-2 s +[acc] eval 3.546202e-3 s +[acc] eval 3.5287884e-2 s +[acc] compile 8.056361e-3 s +[acc] eval 2.1999497e-2 s +[acc] eval 3.5761651e-2 s +[acc] eval 3.1090607e-2 s +[acc] eval 3.082785e-3 s +[acc] eval 3.7701864e-2 s +[acc] eval 7.280001e-3 s +[acc] eval 3.63819e-3 s +[acc] eval 1.4704807e-2 s +[acc] eval 2.0139317e-2 s +[acc] eval 2.4277079e-2 s +[acc] compile 0.238367867 s +[acc] eval 3.020124e-2 s +[acc] eval 0.273987174 s +[acc] eval 2.2629082e-2 s +[acc] eval 2.6132108e-2 s +[acc] compile 0.246973856 s +[acc] eval 1.2307463e-2 s +[acc] eval 1.2546101e-2 s +[acc] eval 0.283828785 s +[acc] compile 0.153264426 s +[acc] eval 2.8269759e-2 s +[acc] eval 0.185717374 s +[acc] compile 0.137028001 s +[acc] eval 1.0342689e-2 s +[acc] eval 3.472144e-3 s +[acc] eval 3.289286e-3 s +[acc] eval 3.002191e-3 s +[acc] eval 3.06114e-3 s +[acc] eval 3.277042e-3 s +[acc] eval 1.721726e-2 s +[acc] eval 3.328604e-3 s +[acc] eval 3.07109e-3 s +[acc] eval 2.6547e-3 s +[acc] eval 2.440581e-3 s +[acc] eval 2.888216e-3 s +[acc] eval 2.96474e-3 s +[acc] eval 2.894695e-3 s +[acc] eval 2.785428e-3 s +[acc] eval 2.542592e-3 s +[acc] eval 2.149153e-3 s +[acc] eval 2.615379e-3 s +[acc] eval 2.381726e-3 s +[acc] eval 2.73627e-3 s +[acc] eval 2.68434e-3 s +[acc] eval 2.756705e-3 s +[acc] eval 2.689786e-3 s +[acc] eval 2.597111e-3 s +[acc] eval 2.978338e-3 s +[acc] eval 2.744708e-3 s +[acc] eval 1.987753e-3 s +[acc] eval 1.814786e-3 s +[acc] eval 1.798015e-3 s +[acc] eval 1.786558e-3 s +[acc] eval 0.245318255 s +[acc] compile 6.0787774e-2 s +[acc] eval 2.1039672e-2 s +[acc] eval 3.277948e-3 s +[acc] eval 1.3742106e-2 s +[acc] eval 3.503996e-3 s +[acc] eval 2.683301e-3 s +[acc] eval 2.694956e-3 s +[acc] eval 2.709605e-3 s +[acc] eval 3.043507e-3 s +[acc] eval 2.618392e-3 s +[acc] eval 3.207248e-3 s +[acc] eval 3.099413e-3 s +[acc] eval 2.970027e-3 s +[acc] eval 2.78223e-3 s +[acc] eval 2.66975e-3 s +[acc] eval 2.829905e-3 s +[acc] eval 2.835207e-3 s +[acc] eval 2.706776e-3 s +[acc] eval 2.675504e-3 s +[acc] eval 2.852196e-3 s +[acc] eval 2.791445e-3 s +[acc] eval 2.689345e-3 s +[acc] eval 2.717597e-3 s +[acc] eval 2.441082e-3 s +[acc] eval 2.987495e-3 s +[acc] eval 2.857124e-3 s +[acc] eval 2.519121e-3 s +[acc] eval 2.499085e-3 s +[acc] eval 2.815587e-3 s +[acc] eval 3.699702e-3 s +[acc] eval 3.713403e-3 s +[acc] eval 0.183104792 s +[acc] compile 0.110294324 s +[acc] eval 1.9249857e-2 s +[acc] eval 0.132751624 s +[acc] compile 1.5506433e-2 s +[acc] eval 9.3717e-3 s +[acc] eval 2.8486001e-2 s +[acc] compile 8.5150589e-2 s +[acc] eval 1.4186362e-2 s +[acc] eval 0.115480269 s +[acc] compile 6.443181e-3 s +[acc] eval 1.2251768e-2 s +[acc] eval 2.190228e-2 s +[acc] eval 2.6574673e-2 s +[acc] eval 2.9704834e-2 s +[acc] compile 3.981207e-3 s +[acc] eval 8.411679e-3 s +[acc] eval 2.206323e-3 s +[acc] eval 2.080145e-3 s +[acc] eval 2.101169e-3 s +[acc] eval 1.813301e-3 s +[acc] eval 1.921709e-3 s +[acc] eval 2.5962217e-2 s +[acc] eval 3.1717383e-2 s +[acc] eval 3.4456272e-2 s +[acc] eval 7.651693e-3 s +[acc] eval 1.183381e-2 s +[acc] compile 0.254354075 s +[acc] eval 5.0791164e-2 s +[acc] eval 4.28949e-3 s +[acc] eval 3.513549e-3 s +[acc] eval 3.568265e-3 s +[acc] eval 3.233033e-3 s +[acc] eval 3.400899e-3 s +[acc] eval 3.077595e-3 s +[acc] eval 4.309223e-3 s +[acc] eval 3.891343e-3 s +[acc] eval 3.414475e-3 s +[acc] eval 3.448798e-3 s +[acc] eval 7.000933e-3 s +[acc] eval 3.466518e-3 s +[acc] eval 3.357017e-3 s +[acc] eval 3.674782e-3 s +[acc] eval 3.882143e-3 s +[acc] eval 3.679102e-3 s +[acc] eval 3.547236e-3 s +[acc] eval 3.827436e-3 s +[acc] eval 4.73133e-3 s +[acc] eval 3.61295e-3 s +[acc] eval 3.804089e-3 s +[acc] eval 3.733989e-3 s +[acc] eval 3.953522e-3 s +[acc] eval 3.797005e-3 s +[acc] eval 3.812204e-3 s +[acc] eval 3.715182e-3 s +[acc] eval 3.645274e-3 s +[acc] eval 3.605814e-3 s +[acc] eval 5.03192e-3 s +[acc] eval 5.235724e-3 s +[acc] eval 5.067453e-3 s +[acc] eval 4.355219e-3 s +[acc] eval 0.439652811 s +[acc] compile 6.100454e-3 s +[acc] eval 5.762678e-3 s +[acc] eval 2.641768e-3 s +[acc] eval 1.7708177e-2 s +[acc] eval 2.147438e-3 s +[acc] eval 2.069315e-3 s +[acc] eval 2.186349e-3 s +[acc] eval 1.936616e-3 s +[acc] eval 1.796615e-3 s +[acc] eval 1.829372e-3 s +[acc] eval 1.607892e-3 s +[acc] eval 1.683159e-3 s +[acc] eval 2.092478e-3 s +[acc] eval 2.06705e-3 s +[acc] eval 5.515972e-2 s +[acc] compile 5.0050606e-2 s +[acc] eval 1.4118607e-2 s +[acc] eval 6.6227116e-2 s +[acc] compile 7.0625867e-2 s +[acc] eval 2.0826065e-2 s +[acc] eval 9.4761482e-2 s +[acc] compile 0.120817766 s +[acc] eval 1.1635314e-2 s +[acc] eval 0.134907398 s +[acc] compile 0.377806573 s +[acc] eval 4.1943302e-2 s +[acc] eval 0.422417479 s +[acc] compile 0.19984703 s +[acc] eval 2.0341749e-2 s +[acc] eval 0.224378696 s +[acc] compile 0.491627233 s +[acc] eval 5.4330692e-2 s +[acc] eval 4.80564e-3 s +[acc] eval 4.739902e-3 s +[acc] eval 4.546259e-3 s +[acc] eval 6.080836e-3 s +[acc] eval 4.755228e-3 s +[acc] eval 4.785263e-3 s +[acc] eval 6.448164e-3 s +[acc] eval 6.259451e-3 s +[acc] eval 5.635887e-3 s +[acc] eval 4.82751e-3 s +[acc] eval 4.536389e-3 s +[acc] eval 5.034383e-3 s +[acc] eval 4.768359e-3 s +[acc] eval 3.853672e-3 s +[acc] eval 3.998638e-3 s +[acc] eval 4.838719e-3 s +[acc] eval 2.0564161e-2 s +[acc] eval 4.759645e-3 s +[acc] eval 4.187125e-3 s +[acc] eval 4.106016e-3 s +[acc] eval 5.064255e-3 s +[acc] eval 5.116484e-3 s +[acc] eval 6.062702e-3 s +[acc] eval 6.36249e-3 s +[acc] eval 1.9434019e-2 s +[acc] eval 4.965909e-3 s +[acc] eval 4.402244e-3 s +[acc] eval 4.268632e-3 s +[acc] eval 3.991985e-3 s +[acc] eval 0.727659353 s +[acc] compile 8.193436e-3 s +[acc] eval 1.2889643e-2 s +[acc] eval 3.353282e-2 s +[acc] compile 0.132604485 s +[acc] eval 2.6299602e-2 s +[acc] eval 4.432799e-3 s +[acc] eval 2.242769e-3 s +[acc] eval 2.271118e-3 s +[acc] eval 2.154029e-3 s +[acc] eval 2.614629e-3 s +[acc] eval 3.241725e-3 s +[acc] eval 3.279009e-3 s +[acc] eval 2.58417e-3 s +[acc] eval 2.676222e-3 s +[acc] eval 0.200576224 s +[acc] compile 4.65089e-3 s +[acc] eval 9.224741e-3 s +[acc] eval 1.898305e-3 s +[acc] eval 1.962309e-3 s +[acc] eval 2.212724e-3 s +[acc] eval 2.895938e-3 s +[acc] eval 2.288976e-3 s +[acc] eval 3.273823e-3 s +[acc] eval 1.5733025e-2 s +[acc] eval 1.692309e-3 s +[acc] eval 1.909213e-3 s +[acc] eval 2.089617e-3 s +[acc] eval 1.636e-3 s +[acc] eval 5.5106926e-2 s +[acc] compile 0.177248646 s +[acc] eval 3.0209263e-2 s +[acc] eval 0.221718481 s +[acc] compile 0.416766175 s +[acc] eval 1.4992069e-2 s +[acc] eval 0.441061205 s +[acc] compile 0.642434812 s +[acc] eval 5.4381416e-2 s +[acc] eval 0.704708628 s +[acc] compile 5.147728e-3 s +[acc] eval 5.502432e-3 s +[acc] eval 1.3537452e-2 s +[acc] compile 0.127856526 s +[acc] eval 1.9679487e-2 s +[acc] eval 0.168514361 s +[acc] compile 4.6723288e-2 s +[acc] eval 1.5693526e-2 s +[acc] eval 6.6767819e-2 s +[acc] compile 0.178043303 s +[acc] eval 1.2071425e-2 s +[acc] eval 0.206260632 s +[acc] eval 2.309868e-3 s +[acc] eval 4.41661e-4 s +[acc] eval 5.84328e-4 s +[acc] eval 3.979518e-3 s +[acc] compile 3.931514e-3 s +[acc] eval 8.14343e-3 s +[acc] eval 8.91816e-4 s +[acc] eval 9.94561e-4 s +[acc] eval 9.01854e-4 s +[acc] eval 7.26991e-4 s +[acc] eval 8.89949e-4 s +[acc] eval 8.74239e-4 s +[acc] eval 8.41528e-4 s +[acc] eval 6.5201e-4 s +[acc] eval 7.99405e-4 s +[acc] eval 7.91556e-4 s +[acc] eval 8.29047e-4 s +[acc] eval 6.71635e-4 s +[acc] eval 2.3756921e-2 s +[acc] compile 7.6952264e-2 s +[acc] eval 2.609653e-2 s +[acc] eval 2.017541e-3 s +[acc] eval 2.115211e-3 s +[acc] eval 1.985688e-3 s +[acc] eval 1.686964e-3 s +[acc] eval 1.67327e-3 s +[acc] eval 1.626174e-3 s +[acc] eval 1.574657e-3 s +[acc] eval 1.537967e-3 s +[acc] eval 1.533481e-3 s +[acc] eval 1.643076e-3 s +[acc] eval 1.958772e-3 s +[acc] eval 1.526908e-3 s +[acc] eval 0.126724804 s +[acc] compile 0.274610333 s +[acc] eval 2.3635602e-2 s +[acc] eval 2.653214e-3 s +[acc] eval 2.238342e-3 s +[acc] eval 2.341655e-3 s +[acc] eval 1.4465152e-2 s +[acc] eval 2.497936e-3 s +[acc] eval 2.184191e-3 s +[acc] eval 2.180668e-3 s +[acc] eval 2.071317e-3 s +[acc] eval 2.022506e-3 s +[acc] eval 2.078544e-3 s +[acc] eval 2.14705e-3 s +[acc] eval 2.716632e-3 s +[acc] eval 2.522559e-3 s +[acc] eval 2.244842e-3 s +[acc] eval 2.076577e-3 s +[acc] eval 1.984922e-3 s +[acc] eval 2.055218e-3 s +[acc] eval 2.870151e-3 s +[acc] eval 2.54288e-3 s +[acc] eval 2.411595e-3 s +[acc] eval 2.437108e-3 s +[acc] eval 1.949484e-3 s +[acc] eval 1.992025e-3 s +[acc] eval 2.087321e-3 s +[acc] eval 2.238792e-3 s +[acc] eval 2.059528e-3 s +[acc] eval 1.927667e-3 s +[acc] eval 1.830098e-3 s +[acc] eval 1.930892e-3 s +[acc] eval 1.848704e-3 s +[acc] eval 1.822311e-3 s +[acc] eval 0.383451962 s +[acc] compile 0.176656807 s +[acc] eval 1.9937805e-2 s +[acc] eval 1.814173e-3 s +[acc] eval 2.340295e-3 s +[acc] eval 2.077603e-3 s +[acc] eval 1.817121e-3 s +[acc] eval 1.878549e-3 s +[acc] eval 1.5762911e-2 s +[acc] eval 1.638523e-3 s +[acc] eval 1.820371e-3 s +[acc] eval 1.934419e-3 s +[acc] eval 1.53908e-3 s +[acc] eval 0.232095065 s +[acc] compile 4.7931213e-2 s +[acc] eval 1.1977032e-2 s +[acc] eval 1.760246e-3 s +[acc] eval 2.293683e-3 s +[acc] eval 1.833191e-3 s +[acc] eval 1.949364e-3 s +[acc] eval 1.758023e-3 s +[acc] eval 1.52953e-3 s +[acc] eval 1.6219091e-2 s +[acc] eval 8.9551921e-2 s +[acc] compile 5.105358e-3 s +[acc] eval 6.908885e-3 s +[acc] eval 3.480325e-3 s +[acc] eval 4.00509e-3 s +[acc] eval 2.767258e-3 s +[acc] eval 2.73378e-3 s +[acc] eval 6.007682e-3 s +[acc] eval 6.007567e-3 s +[acc] eval 3.822282e-3 s +[acc] eval 4.899686e-3 s +[acc] eval 1.3853499e-2 s +[acc] eval 1.946896e-3 s +[acc] eval 1.945094e-3 s +[acc] eval 2.011821e-3 s +[acc] eval 2.456082e-3 s +[acc] eval 3.192712e-3 s +[acc] eval 2.589962e-3 s +[acc] eval 2.878738e-3 s +[acc] eval 2.569306e-3 s +[acc] eval 2.092167e-3 s +[acc] eval 2.39041e-3 s +[acc] eval 2.713908e-3 s +[acc] eval 1.499088e-3 s +[acc] eval 1.670962e-3 s +[acc] eval 1.654729e-3 s +[acc] eval 1.792708e-3 s +[acc] eval 2.182765e-3 s +[acc] eval 1.558374e-3 s +[acc] eval 1.942961e-3 s +[acc] eval 1.840561e-3 s +[acc] eval 1.935929e-3 s +[acc] eval 1.652281e-3 s +[acc] eval 0.110547111 s +[acc] compile 4.41024e-3 s +[acc] eval 1.0971666e-2 s +[acc] eval 2.100901e-3 s +[acc] eval 2.138625e-3 s +[acc] eval 2.062438e-3 s +[acc] eval 2.277084e-3 s +[acc] eval 2.663977e-3 s +[acc] eval 2.490174e-3 s +[acc] eval 2.561529e-3 s +[acc] eval 1.3800441e-2 s +[acc] eval 2.354519e-3 s +[acc] eval 2.208872e-3 s +[acc] eval 2.335104e-3 s +[acc] eval 3.171383e-3 s +[acc] eval 1.801167e-3 s +[acc] eval 1.735892e-3 s +[acc] eval 1.638579e-3 s +[acc] eval 2.162436e-3 s +[acc] eval 2.226235e-3 s +[acc] eval 2.167797e-3 s +[acc] eval 2.3251e-3 s +[acc] eval 2.268788e-3 s +[acc] eval 2.246495e-3 s +[acc] eval 2.40005e-3 s +[acc] eval 2.306811e-3 s +[acc] eval 2.216372e-3 s +[acc] eval 1.864648e-3 s +[acc] eval 2.249756e-3 s +[acc] eval 2.25021e-3 s +[acc] eval 2.30714e-3 s +[acc] eval 2.198756e-3 s +[acc] eval 9.7247119e-2 s +[acc] compile 5.158684e-3 s +[acc] eval 6.479542e-3 s +[acc] eval 2.709323e-3 s +[acc] eval 2.442797e-3 s +[acc] eval 1.97242e-3 s +[acc] eval 2.137317e-3 s +[acc] eval 2.057355e-3 s +[acc] eval 2.115983e-3 s +[acc] eval 1.3552943e-2 s +[acc] eval 2.307831e-3 s +[acc] eval 2.391712e-3 s +[acc] eval 2.068393e-3 s +[acc] eval 2.187493e-3 s +[acc] eval 3.389728e-3 s +[acc] eval 1.994574e-3 s +[acc] eval 2.030923e-3 s +[acc] eval 1.951848e-3 s +[acc] eval 2.063291e-3 s +[acc] eval 1.976554e-3 s +[acc] eval 1.682854e-3 s +[acc] eval 1.627679e-3 s +[acc] eval 1.488974e-3 s +[acc] eval 1.636683e-3 s +[acc] eval 2.154163e-3 s +[acc] eval 1.453723e-3 s +[acc] eval 1.26698e-3 s +[acc] eval 1.237902e-3 s +[acc] eval 1.221681e-3 s +[acc] eval 1.28243e-3 s +[acc] eval 1.266443e-3 s +[acc] eval 1.193071e-3 s +[acc] eval 1.149249e-3 s +[acc] eval 8.5137781e-2 s +[acc] compile 0.102854289 s +[acc] eval 1.5115889e-2 s +[acc] eval 3.939203e-3 s +[acc] eval 3.760543e-3 s +[acc] eval 1.385708e-2 s +[acc] eval 3.887438e-3 s +[acc] eval 3.43399e-3 s +[acc] eval 3.342488e-3 s +[acc] eval 3.274512e-3 s +[acc] eval 3.234862e-3 s +[acc] eval 3.340398e-3 s +[acc] eval 3.050407e-3 s +[acc] eval 4.606753e-3 s +[acc] eval 0.173329445 s +[acc] compile 1.1127811e-2 s +[acc] eval 1.5117729e-2 s +[acc] eval 2.835998e-3 s +[acc] eval 1.881399e-3 s +[acc] eval 2.447077e-3 s +[acc] eval 2.32386e-3 s +[acc] eval 1.473103e-2 s +[acc] eval 1.936526e-3 s +[acc] eval 2.031824e-3 s +[acc] eval 2.152519e-3 s +[acc] eval 2.284095e-3 s +[acc] eval 1.845634e-3 s +[acc] eval 2.098795e-3 s +[acc] eval 2.149225e-3 s +[acc] eval 2.116574e-3 s +[acc] eval 2.225683e-3 s +[acc] eval 2.162863e-3 s +[acc] eval 1.967107e-3 s +[acc] eval 2.100913e-3 s +[acc] eval 2.262288e-3 s +[acc] eval 2.317169e-3 s +[acc] eval 2.269429e-3 s +[acc] eval 8.6904939e-2 s +[acc] compile 6.1044041e-2 s +[acc] eval 1.1127863e-2 s +[acc] eval 3.32903e-3 s +[acc] eval 3.178992e-3 s +[acc] eval 2.588622e-3 s +[acc] eval 3.60501e-3 s +[acc] eval 3.906621e-3 s +[acc] eval 2.647602e-3 s +[acc] eval 1.522636e-2 s +[acc] eval 2.451669e-3 s +[acc] eval 2.49097e-3 s +[acc] eval 2.359105e-3 s +[acc] eval 2.599095e-3 s +[acc] eval 2.687148e-3 s +[acc] eval 2.415857e-3 s +[acc] eval 2.359015e-3 s +[acc] eval 2.553026e-3 s +[acc] eval 2.364072e-3 s +[acc] eval 2.552083e-3 s +[acc] eval 2.789539e-3 s +[acc] eval 2.591523e-3 s +[acc] eval 2.320127e-3 s +[acc] eval 2.625196e-3 s +[acc] eval 2.395206e-3 s +[acc] eval 2.42841e-3 s +[acc] eval 2.555328e-3 s +[acc] eval 2.4627e-3 s +[acc] eval 0.156747623 s +[acc] compile 4.243039e-3 s +[acc] eval 1.0529632e-2 s +[acc] eval 2.210267e-3 s +[acc] eval 1.796321e-3 s +[acc] eval 2.1263084e-2 s +[acc] compile 0.29708609 s +[acc] eval 2.8938618e-2 s +[acc] eval 1.959852e-3 s +[acc] eval 0.330696148 s +[acc] compile 0.192872357 s +[acc] eval 9.97835e-3 s +[acc] eval 3.625747e-3 s +[acc] eval 2.417853e-3 s +[acc] eval 2.206446e-3 s +[acc] eval 2.107862e-3 s +[acc] eval 3.109763e-3 s +[acc] eval 2.143795e-3 s +[acc] eval 2.691655e-3 s +[acc] eval 3.118775e-3 s +[acc] eval 1.5784801e-2 s +[acc] eval 2.390674e-3 s +[acc] eval 2.123856e-3 s +[acc] eval 2.071007e-3 s +[acc] eval 1.817928e-3 s +[acc] eval 2.031707e-3 s +[acc] eval 2.216683e-3 s +[acc] eval 2.112074e-3 s +[acc] eval 2.231148e-3 s +[acc] eval 2.48377e-3 s +[acc] eval 2.136104e-3 s +[acc] eval 2.561489e-3 s +[acc] eval 2.35395e-3 s +[acc] eval 2.16275e-3 s +[acc] eval 2.567592e-3 s +[acc] eval 2.74358e-3 s +[acc] eval 2.846422e-3 s +[acc] eval 2.428046e-3 s +[acc] eval 2.448826e-3 s +[acc] eval 2.225963e-3 s +[acc] eval 2.108451e-3 s +[acc] eval 0.292046357 s +[acc] compile 7.120502e-3 s +[acc] eval 1.5970458e-2 s +[acc] eval 2.7818407e-2 s +[acc] eval 3.5657327e-2 s +[acc] eval 3.042484e-3 s +[acc] eval 4.2074962e-2 s +[acc] compile 7.1002717e-2 s +[acc] eval 8.097134e-3 s +[acc] eval 8.002387e-3 s +[acc] eval 5.554532e-3 s +[acc] eval 2.3510636e-2 s +[acc] eval 3.762939e-3 s +[acc] eval 3.047945e-3 s +[acc] eval 2.924549e-3 s +[acc] eval 3.335811e-3 s +[acc] eval 2.178514e-3 s +[acc] eval 2.149281e-3 s +[acc] eval 2.132878e-3 s +[acc] eval 2.10408e-3 s +[acc] eval 2.121441e-3 s +[acc] eval 2.725277e-3 s +[acc] eval 3.374614e-3 s +[acc] eval 2.746009e-3 s +[acc] eval 2.717418e-3 s +[acc] eval 2.696371e-3 s +[acc] eval 3.458137e-3 s +[acc] eval 3.091716e-3 s +[acc] eval 2.797739e-3 s +[acc] eval 2.608652e-3 s +[acc] eval 2.212152e-3 s +[acc] eval 2.263761e-3 s +[acc] eval 2.8197e-3 s +[acc] eval 2.782674e-3 s +[acc] eval 2.532306e-3 s +[acc] eval 0.185373174 s +[acc] compile 0.308311058 s +[acc] eval 1.6396853e-2 s +[acc] eval 3.494682e-3 s +[acc] eval 1.5607874e-2 s +[acc] eval 3.459198e-3 s +[acc] eval 3.682168e-3 s +[acc] eval 3.505313e-3 s +[acc] eval 2.952138e-3 s +[acc] eval 2.599882e-3 s +[acc] eval 2.857618e-3 s +[acc] eval 3.960271e-3 s +[acc] eval 3.260402e-3 s +[acc] eval 3.240413e-3 s +[acc] eval 3.251241e-3 s +[acc] eval 3.2393e-3 s +[acc] eval 3.339762e-3 s +[acc] eval 3.130498e-3 s +[acc] eval 3.134094e-3 s +[acc] eval 4.303865e-3 s +[acc] eval 3.002519e-3 s +[acc] eval 2.803214e-3 s +[acc] eval 2.580532e-3 s +[acc] eval 2.795917e-3 s +[acc] eval 2.42143e-3 s +[acc] eval 3.303704e-3 s +[acc] eval 2.07448e-3 s +[acc] eval 2.020758e-3 s +[acc] eval 2.13474e-3 s +[acc] eval 2.175621e-3 s +[acc] eval 2.921275e-3 s +[acc] eval 3.804833e-3 s +[acc] eval 2.780777e-3 s +[acc] eval 2.506005e-3 s +[acc] eval 2.569499e-3 s +[acc] eval 2.450561e-3 s +[acc] eval 0.444283343 s +[acc] compile 0.236983597 s +[acc] eval 2.4086286e-2 s +[acc] eval 1.7726548e-2 s +[acc] eval 5.63632e-3 s +[acc] eval 5.615338e-3 s +[acc] eval 4.609541e-3 s +[acc] eval 5.136507e-3 s +[acc] eval 4.508511e-3 s +[acc] eval 3.654536e-3 s +[acc] eval 3.636745e-3 s +[acc] eval 4.415634e-3 s +[acc] eval 3.840716e-3 s +[acc] eval 3.509211e-3 s +[acc] eval 3.795555e-3 s +[acc] eval 4.201975e-3 s +[acc] eval 3.922052e-3 s +[acc] eval 3.526338e-3 s +[acc] eval 3.872873e-3 s +[acc] eval 4.004997e-3 s +[acc] eval 3.773642e-3 s +[acc] eval 3.659385e-3 s +[acc] eval 3.636974e-3 s +[acc] eval 4.240479e-3 s +[acc] eval 3.913562e-3 s +[acc] eval 3.843331e-3 s +[acc] eval 4.248775e-3 s +[acc] eval 4.180852e-3 s +[acc] eval 4.325984e-3 s +[acc] eval 4.069826e-3 s +[acc] eval 3.643353e-3 s +[acc] eval 4.875837e-3 s +[acc] eval 0.403682902 s +[acc] compile 4.430735e-3 s +[acc] eval 1.7943163e-2 s +[acc] eval 1.355158e-3 s +[acc] eval 1.369794e-3 s +[acc] eval 1.510197e-3 s +[acc] eval 1.20341e-3 s +[acc] eval 1.107317e-3 s +[acc] eval 1.074639e-3 s +[acc] eval 1.460954e-3 s +[acc] eval 1.288262e-3 s +[acc] eval 1.314209e-3 s +[acc] eval 1.21009e-3 s +[acc] eval 1.188265e-3 s +[acc] eval 1.220233e-3 s +[acc] eval 1.261105e-3 s +[acc] eval 1.586057e-3 s +[acc] eval 1.356666e-3 s +[acc] eval 1.25609e-3 s +[acc] eval 9.82426e-4 s +[acc] eval 1.457008e-3 s +[acc] eval 1.104211e-3 s +[acc] eval 1.574347e-3 s +[acc] eval 1.120667e-3 s +[acc] eval 1.193375e-3 s +[acc] eval 5.456966e-2 s +[acc] compile 4.625639e-3 s +[acc] eval 5.650078e-3 s +[acc] eval 2.192348e-3 s +[acc] eval 2.122126e-3 s +[acc] eval 1.87348e-3 s +[acc] eval 1.976055e-3 s +[acc] eval 1.838992e-3 s +[acc] eval 1.823599e-3 s +[acc] eval 1.729997e-3 s +[acc] eval 1.700189e-3 s +[acc] eval 1.4503983e-2 s +[acc] eval 1.713346e-3 s +[acc] eval 1.539858e-3 s +[acc] eval 1.430951e-3 s +[acc] eval 1.442302e-3 s +[acc] eval 4.8976979e-2 s +[acc] compile 6.2634021e-2 s +[acc] eval 1.6729945e-2 s +[acc] eval 2.953648e-3 s +[acc] eval 3.117015e-3 s +[acc] eval 3.155521e-3 s +[acc] eval 1.3972402e-2 s +[acc] eval 3.415322e-3 s +[acc] eval 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+[acc] eval 7.424883e-3 s +[acc] eval 1.3076632e-2 s +[acc] eval 1.004262e-3 s +[acc] eval 2.560717e-3 s +[acc] eval 3.0237739e-2 s +[acc] compile 7.6491892e-2 s +[acc] eval 1.2719364e-2 s +[acc] eval 9.3594673e-2 s +[acc] compile 0.338142504 s +[acc] eval 4.0334217e-2 s +[acc] eval 4.448154e-3 s +[acc] eval 3.077335e-3 s +[acc] eval 2.856743e-3 s +[acc] eval 2.877425e-3 s +[acc] eval 4.974251e-3 s +[acc] eval 2.788149e-3 s +[acc] eval 2.653317e-3 s +[acc] eval 2.630416e-3 s +[acc] eval 3.297302e-3 s +[acc] eval 4.403462e-3 s +[acc] eval 4.199477e-3 s +[acc] eval 4.355331e-3 s +[acc] eval 4.430408e-3 s +[acc] eval 3.332844e-3 s +[acc] eval 3.61896e-3 s +[acc] eval 4.473835e-3 s +[acc] eval 2.849056e-3 s +[acc] eval 2.75334e-3 s +[acc] eval 3.646868e-3 s +[acc] eval 4.295655e-3 s +[acc] eval 3.949973e-3 s +[acc] eval 4.021236e-3 s +[acc] eval 3.274907e-3 s +[acc] eval 3.619412e-3 s +[acc] eval 4.086485e-3 s +[acc] eval 3.735429e-3 s +[acc] eval 3.317082e-3 s +[acc] eval 3.01469e-3 s +[acc] eval 4.330485e-3 s +[acc] eval 3.962768e-3 s +[acc] eval 3.707184e-3 s +[acc] eval 3.968795e-3 s +[acc] eval 4.679434e-3 s +[acc] eval 4.908545e-3 s +[acc] eval 4.227753e-3 s +[acc] eval 4.908832e-3 s +[acc] eval 5.249143e-3 s +[acc] eval 1.9823079e-2 s +[acc] eval 4.147682e-3 s +[acc] eval 4.386846e-3 s +[acc] eval 3.993104e-3 s +[acc] eval 0.560240359 s +[acc] compile 0.222083142 s +[acc] eval 2.3460754e-2 s +[acc] eval 0.261629436 s +[acc] compile 0.133268779 s +[acc] eval 2.3594573e-2 s +[acc] eval 0.164588444 s +[acc] compile 0.231917872 s +[acc] eval 4.6170012e-2 s +[acc] eval 0.283829824 s +[acc] compile 0.444326338 s +[acc] eval 3.1223729e-2 s +[acc] eval 0.48176125 s +[acc] compile 0.520636921 s +[acc] eval 3.9336138e-2 s +[acc] eval 0.568176691 s +[acc] compile 0.157813093 s +[acc] eval 1.5909056e-2 s +[acc] eval 0.180732478 s +[acc] compile 0.163072372 s +[acc] eval 3.1750768e-2 s +[acc] eval 0.199648482 s +[acc] compile 0.266527375 s +[acc] eval 2.3185496e-2 s +[acc] eval 0.298964344 s +[acc] compile 0.261049653 s +[acc] eval 4.1901604e-2 s +[acc] eval 6.671974e-3 s +[acc] eval 4.983128e-3 s +[acc] eval 5.016947e-3 s +[acc] eval 4.514864e-3 s +[acc] eval 4.255623e-3 s +[acc] eval 4.079716e-3 s +[acc] eval 5.265986e-3 s +[acc] eval 4.573706e-3 s +[acc] eval 4.777682e-3 s +[acc] eval 5.322418e-3 s +[acc] eval 4.298624e-3 s +[acc] eval 4.222635e-3 s +[acc] eval 5.180388e-3 s +[acc] eval 4.479627e-3 s +[acc] eval 4.36646e-3 s +[acc] eval 4.293952e-3 s +[acc] eval 4.071835e-3 s +[acc] eval 2.2158479e-2 s +[acc] eval 4.886181e-3 s +[acc] eval 4.700135e-3 s +[acc] eval 5.174988e-3 s +[acc] eval 3.588651e-3 s +[acc] eval 4.899714e-3 s +[acc] eval 5.276799e-3 s +[acc] eval 4.304332e-3 s +[acc] eval 3.355813e-3 s +[acc] eval 4.406866e-3 s +[acc] eval 6.247556e-3 s +[acc] eval 1.7170393e-2 s +[acc] eval 0.476449551 s +[acc] compile 0.308817124 s +[acc] eval 2.4693693e-2 s +[acc] eval 1.7611047e-2 s +[acc] eval 4.236e-3 s +[acc] eval 4.617451e-3 s +[acc] eval 4.367039e-3 s +[acc] eval 4.709417e-3 s +[acc] eval 4.496796e-3 s +[acc] eval 4.094913e-3 s +[acc] eval 3.424807e-3 s +[acc] eval 4.486657e-3 s +[acc] eval 3.151229e-3 s +[acc] eval 3.347799e-3 s +[acc] eval 0.396793437 s +[acc] compile 0.11368041 s +[acc] eval 3.2931507e-2 s +[acc] eval 0.151776845 s +[acc] compile 1.8882336e-2 s +[acc] eval 6.022566e-3 s +[acc] eval 2.7336425e-2 s +[acc] compile 7.1903621e-2 s +[acc] eval 1.0365119e-2 s +[acc] eval 5.473669e-3 s +[acc] eval 7.036847e-3 s +[acc] eval 2.1712087e-2 s +[acc] eval 3.098614e-3 s +[acc] eval 3.190348e-3 s +[acc] eval 3.019211e-3 s +[acc] eval 2.293731e-3 s +[acc] eval 1.927376e-3 s +[acc] eval 0.133871697 s +[acc] compile 0.288179147 s +[acc] eval 3.2743106e-2 s +[acc] eval 2.1955957e-2 s +[acc] eval 4.3201e-3 s +[acc] eval 4.264277e-3 s +[acc] eval 3.233448e-3 s +[acc] eval 2.912324e-3 s +[acc] eval 3.053064e-3 s +[acc] eval 4.100371e-3 s +[acc] eval 3.276815e-3 s +[acc] eval 2.925835e-3 s +[acc] eval 2.924578e-3 s +[acc] 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3.152997e-3 s +[acc] eval 3.959288e-3 s +[acc] eval 4.38951e-3 s +[acc] eval 2.879511e-3 s +[acc] eval 2.416131e-3 s +[acc] eval 3.065608e-3 s +[acc] eval 2.812895e-3 s +[acc] eval 2.868334e-3 s +[acc] eval 2.649319e-3 s +[acc] eval 2.99614e-3 s +[acc] eval 2.945789e-3 s +[acc] eval 2.730211e-3 s +[acc] eval 2.584641e-3 s +[acc] eval 2.612581e-3 s +[acc] eval 2.559641e-3 s +[acc] eval 0.146232531 s +[acc] compile 7.8971369e-2 s +[acc] eval 1.8935387e-2 s +[acc] eval 0.101129114 s +[acc] compile 9.7923466e-2 s +[acc] eval 3.7370926e-2 s +[acc] eval 2.41228e-3 s +[acc] eval 2.908423e-3 s +[acc] eval 3.017205e-3 s +[acc] eval 2.741831e-3 s +[acc] eval 2.674784e-3 s +[acc] eval 2.651837e-3 s +[acc] eval 2.885446e-3 s +[acc] eval 2.721661e-3 s +[acc] eval 2.463507e-3 s +[acc] eval 2.31218e-3 s +[acc] eval 2.46256e-3 s +[acc] eval 2.25135e-3 s +[acc] eval 3.095487e-3 s +[acc] eval 3.303942e-3 s +[acc] eval 2.767623e-3 s +[acc] eval 2.382466e-3 s +[acc] eval 2.880341e-3 s +[acc] eval 3.180578e-3 s +[acc] eval 3.065619e-3 s +[acc] eval 2.913229e-3 s +[acc] eval 2.797521e-3 s +[acc] eval 2.602373e-3 s +[acc] eval 2.724287e-3 s +[acc] eval 3.098441e-3 s +[acc] eval 2.805968e-3 s +[acc] eval 2.563793e-3 s +[acc] eval 2.478003e-3 s +[acc] eval 2.460191e-3 s +[acc] eval 2.424397e-3 s +[acc] eval 0.220893067 s +[acc] compile 7.9472422e-2 s +[acc] eval 1.4159855e-2 s +[acc] eval 4.97623e-3 s +[acc] eval 4.508349e-3 s +[acc] eval 4.93031e-3 s +[acc] eval 3.604163e-3 s +[acc] eval 2.668935e-3 s +[acc] eval 2.766027e-3 s +[acc] eval 1.9277884e-2 s +[acc] eval 2.624614e-3 s +[acc] eval 2.543074e-3 s +[acc] eval 3.300606e-3 s +[acc] eval 2.842176e-3 s +[acc] eval 2.586265e-3 s +[acc] eval 2.401665e-3 s +[acc] eval 2.10441e-3 s +[acc] eval 2.180886e-3 s +[acc] eval 2.729903e-3 s +[acc] eval 2.902607e-3 s +[acc] eval 3.012481e-3 s +[acc] eval 2.878015e-3 s +[acc] eval 2.70943e-3 s +[acc] eval 2.715992e-3 s +[acc] eval 2.724127e-3 s +[acc] eval 2.258708e-3 s +[acc] eval 2.640445e-3 s +[acc] eval 2.145942e-3 s +[acc] eval 2.394983e-3 s +[acc] eval 2.362952e-3 s +[acc] eval 2.351622e-3 s +[acc] eval 2.237344e-3 s +[acc] eval 2.459587e-3 s +[acc] eval 0.200853966 s +[acc] compile 6.6840456e-2 s +[acc] eval 1.6576244e-2 s +[acc] eval 3.233438e-3 s +[acc] eval 1.8463989e-2 s +[acc] eval 2.406825e-3 s +[acc] eval 2.308365e-3 s +[acc] eval 2.316841e-3 s +[acc] eval 3.024523e-3 s +[acc] eval 3.125432e-3 s +[acc] eval 2.749373e-3 s +[acc] eval 2.580723e-3 s +[acc] eval 2.557487e-3 s +[acc] eval 2.673305e-3 s +[acc] eval 2.957145e-3 s +[acc] eval 2.720416e-3 s +[acc] eval 2.395715e-3 s +[acc] eval 2.790881e-3 s +[acc] eval 2.411642e-3 s +[acc] eval 2.653833e-3 s +[acc] eval 2.973014e-3 s +[acc] eval 2.720397e-3 s +[acc] eval 2.38825e-3 s +[acc] eval 2.418986e-3 s +[acc] eval 2.507294e-3 s +[acc] eval 2.752349e-3 s +[acc] eval 3.233669e-3 s +[acc] eval 0.169211244 s +[acc] compile 0.631029497 s +[acc] eval 4.5202207e-2 s +[acc] eval 0.681036389 s +[acc] eval 2.263769e-3 s +[acc] eval 5.22649e-4 s +[acc] eval 6.56051e-4 s +[acc] eval 4.397313e-3 s +[acc] eval 5.076266e-3 s +[acc] eval 1.774891e-3 s +[acc] eval 1.544331e-3 s +[acc] eval 1.219632e-3 s +[acc] eval 1.304387e-3 s +[acc] eval 1.373929e-3 s +[acc] eval 1.125979e-3 s +[acc] eval 1.642327e-3 s +[acc] eval 2.938291e-3 s +[acc] eval 3.928907e-3 s +[acc] eval 2.226361e-3 s +[acc] eval 2.438807e-3 s +[acc] eval 1.6874995e-2 s +[acc] eval 1.087831e-3 s +[acc] eval 1.034893e-3 s +[acc] eval 1.029995e-3 s +[acc] eval 1.017648e-3 s +[acc] eval 1.015723e-3 s +[acc] eval 1.195611e-3 s +[acc] eval 1.295004e-3 s +[acc] eval 1.136248e-3 s +[acc] eval 1.149156e-3 s +[acc] eval 1.121797e-3 s +[acc] eval 1.127754e-3 s +[acc] eval 1.077615e-3 s +[acc] eval 1.142233e-3 s +[acc] eval 1.099199e-3 s +[acc] eval 1.076849e-3 s +[acc] eval 1.173723e-3 s +[acc] eval 1.187527e-3 s +[acc] eval 6.607999e-2 s +[acc] eval 9.509648e-3 s +[acc] eval 2.090731e-3 s +[acc] eval 2.215184e-3 s +[acc] eval 2.028977e-3 s +[acc] eval 2.130155e-3 s +[acc] eval 1.951105e-3 s +[acc] eval 2.2613399e-2 s +[acc] eval 2.4798158e-2 s +[acc] eval 2.168751e-3 s +[acc] eval 2.179925e-3 s +[acc] eval 2.136436e-3 s +[acc] eval 2.214426e-3 s +[acc] eval 1.6480682e-2 s +[acc] eval 5.2526282e-2 s diff --git a/cached_n4.err b/cached_n4.err new file mode 100644 index 0000000..47c19a3 --- /dev/null +++ b/cached_n4.err @@ -0,0 +1,2791 @@ +[acc] compile 1.5271705e-2 s +[acc] eval 2.042301595 s +[acc] compile 7.59701e-4 s +[acc] eval 6.834787e-2 s +[acc] compile 5.18696e-4 s +[acc] eval 6.6768488e-2 s +[acc] compile 6.1272e-4 s +[acc] eval 0.158706635 s +[acc] compile 5.11381e-4 s +[acc] eval 0.155929856 s +[acc] compile 5.36555e-4 s +[acc] eval 0.153444838 s +[acc] compile 4.94604e-4 s +[acc] eval 0.157348867 s +[acc] compile 5.1896e-4 s +[acc] eval 0.13788615 s +[acc] compile 3.16191e-4 s +[acc] eval 6.9232713e-2 s +[acc] compile 0.106482157 s +[acc] eval 1.9281558e-2 s +[acc] eval 3.7452135e-2 s +[acc] eval 3.517769e-3 s +[acc] eval 2.748255e-3 s +[acc] eval 2.435205e-3 s +[acc] eval 3.629787e-3 s +[acc] eval 3.225689e-3 s +[acc] eval 2.705368e-3 s +[acc] eval 2.54443e-3 s +[acc] eval 2.607636e-3 s +[acc] eval 2.89961e-3 s +[acc] eval 3.673015e-3 s +[acc] eval 3.223384e-3 s +[acc] eval 2.460641e-3 s +[acc] eval 2.519447e-3 s +[acc] eval 2.988144e-3 s +[acc] eval 4.03043e-3 s +[acc] eval 3.803838e-3 s +[acc] eval 3.00492e-3 s +[acc] eval 3.372069e-3 s +[acc] eval 0.22370828 s +[acc] compile 0.149791023 s +[acc] eval 2.7595457e-2 s +[acc] eval 0.181128818 s +[acc] compile 0.332883125 s +[acc] eval 3.2584323e-2 s +[acc] eval 0.372862656 s +[acc] compile 7.2426215e-2 s +[acc] eval 6.333556e-3 s +[acc] eval 3.198289e-3 s +[acc] eval 2.90676e-3 s +[acc] eval 2.979367e-3 s +[acc] eval 3.571042e-3 s +[acc] eval 3.576688e-3 s +[acc] eval 3.492388e-3 s +[acc] eval 9.464879e-3 s +[acc] eval 3.065696e-3 s +[acc] eval 2.694382e-3 s +[acc] eval 2.792071e-3 s +[acc] eval 2.026141e-3 s +[acc] eval 1.81595e-3 s +[acc] eval 2.075389e-3 s +[acc] eval 1.781549e-3 s +[acc] eval 1.962086e-3 s +[acc] eval 2.622425e-3 s +[acc] eval 2.444216e-3 s +[acc] eval 2.589596e-3 s +[acc] eval 2.616615e-3 s +[acc] eval 2.424233e-3 s +[acc] eval 1.854346e-3 s +[acc] eval 1.814359e-3 s +[acc] eval 1.777085e-3 s +[acc] eval 1.847698e-3 s +[acc] eval 1.995106e-3 s +[acc] eval 2.106006e-3 s +[acc] eval 1.858412e-3 s +[acc] eval 1.806335e-3 s +[acc] eval 1.893317e-3 s +[acc] eval 0.161024287 s +[acc] compile 0.497354815 s +[acc] eval 3.2877362e-2 s +[acc] eval 0.534174314 s +[acc] compile 0.109738453 s +[acc] eval 1.102028e-2 s +[acc] eval 2.660746e-3 s +[acc] eval 2.458797e-3 s +[acc] eval 3.559452e-3 s +[acc] eval 1.2769356e-2 s +[acc] eval 2.931529e-3 s +[acc] eval 2.9226e-3 s +[acc] eval 2.870259e-3 s +[acc] eval 3.421992e-3 s +[acc] eval 3.32542e-3 s +[acc] eval 3.010537e-3 s +[acc] eval 3.694135e-3 s +[acc] eval 0.171000083 s +[acc] compile 7.0107997e-2 s +[acc] eval 2.5096162e-2 s +[acc] eval 9.9340115e-2 s +[acc] compile 0.18221484 s +[acc] eval 1.9357847e-2 s +[acc] eval 0.206426451 s +[acc] compile 6.219283e-3 s +[acc] eval 6.772626e-3 s +[acc] eval 1.6403322e-2 s +[acc] compile 5.6329163e-2 s +[acc] eval 9.233065e-3 s +[acc] eval 6.8278565e-2 s +[acc] compile 4.0914e-3 s +[acc] eval 1.6571299e-2 s +[acc] eval 5.73089e-4 s +[acc] eval 5.614152e-3 s +[acc] eval 2.8454004e-2 s +[acc] compile 0.286057656 s +[acc] eval 4.0385516e-2 s +[acc] eval 0.331719505 s +[acc] compile 6.451521e-3 s +[acc] eval 1.3194547e-2 s +[acc] eval 2.2242436e-2 s +[acc] compile 7.6330697e-2 s +[acc] eval 7.418562e-3 s +[acc] eval 8.7521511e-2 s +[acc] compile 0.205354099 s +[acc] eval 2.2796385e-2 s +[acc] eval 0.23849889 s +[acc] compile 7.266402e-3 s +[acc] eval 1.4328691e-2 s +[acc] eval 2.5047488e-2 s +[acc] compile 0.343354502 s +[acc] eval 2.8379615e-2 s +[acc] eval 0.376617449 s +[acc] compile 2.714648e-3 s +[acc] eval 4.163892e-3 s +[acc] eval 7.30022e-4 s +[acc] eval 7.87522e-4 s +[acc] eval 9.49394e-3 s +[acc] compile 0.22125044 s +[acc] eval 1.6963348e-2 s +[acc] eval 0.252998186 s +[acc] compile 0.126960248 s +[acc] eval 2.0013316e-2 s +[acc] eval 0.151496646 s +[acc] compile 0.325069411 s +[acc] eval 3.7854851e-2 s +[acc] eval 4.337066e-3 s +[acc] eval 0.372026103 s +[acc] compile 6.7932666e-2 s +[acc] eval 1.9163311e-2 s +[acc] eval 4.572106e-3 s +[acc] eval 5.290633e-3 s +[acc] eval 4.331145e-3 s +[acc] eval 0.107347754 s +[acc] compile 0.121359228 s +[acc] eval 1.2404029e-2 s +[acc] eval 0.141040097 s +[acc] compile 0.142922761 s +[acc] eval 8.059329e-3 s +[acc] eval 0.162336709 s +[acc] compile 5.752054e-3 s +[acc] eval 7.621514e-3 s +[acc] eval 3.763798e-3 s +[acc] eval 3.582896e-3 s +[acc] eval 3.382551e-3 s +[acc] eval 4.223236e-3 s +[acc] eval 3.488866e-3 s +[acc] eval 2.865387e-3 s +[acc] eval 2.927857e-3 s +[acc] eval 3.211446e-3 s +[acc] eval 2.619004e-3 s +[acc] eval 1.1144679e-2 s +[acc] eval 2.110328e-3 s +[acc] eval 1.961109e-3 s +[acc] eval 1.715703e-3 s +[acc] eval 2.261885e-3 s +[acc] eval 2.050146e-3 s +[acc] eval 1.703622e-3 s +[acc] eval 2.475987e-3 s +[acc] eval 3.31399e-3 s +[acc] eval 4.246512e-3 s +[acc] eval 3.889586e-3 s +[acc] eval 2.585663e-3 s +[acc] eval 3.918481e-3 s +[acc] eval 3.945885e-3 s +[acc] eval 4.177863e-3 s +[acc] eval 0.103428872 s +[acc] compile 0.121307353 s +[acc] eval 1.3253124e-2 s +[acc] eval 0.138690221 s +[acc] compile 6.8920209e-2 s +[acc] eval 2.474836e-2 s +[acc] eval 0.105001708 s +[acc] compile 0.489019772 s +[acc] eval 1.6624391e-2 s +[acc] eval 0.512451261 s +[acc] compile 4.409715e-3 s +[acc] eval 5.99831e-3 s +[acc] eval 3.638204e-3 s +[acc] eval 1.845812e-3 s +[acc] eval 1.800134e-3 s +[acc] eval 1.794714e-3 s +[acc] eval 4.671172e-3 s +[acc] eval 3.74115e-3 s +[acc] eval 4.762196e-3 s +[acc] eval 4.686619e-3 s +[acc] eval 5.63268e-3 s +[acc] eval 2.088177e-3 s +[acc] eval 2.41955e-3 s +[acc] eval 5.1371737e-2 s +[acc] compile 7.7019343e-2 s +[acc] eval 7.266482e-3 s +[acc] eval 8.7099898e-2 s +[acc] compile 4.338284e-3 s +[acc] eval 6.576489e-3 s +[acc] eval 7.680039e-3 s +[acc] eval 2.0535e-3 s +[acc] eval 1.837089e-3 s +[acc] eval 1.737459e-3 s +[acc] eval 1.744621e-3 s +[acc] eval 1.60376e-3 s +[acc] eval 1.707706e-3 s +[acc] eval 1.865651e-3 s +[acc] eval 2.052547e-3 s +[acc] eval 2.121492e-3 s +[acc] eval 2.25738e-3 s +[acc] eval 2.654566e-3 s +[acc] eval 1.935366e-3 s +[acc] eval 2.574846e-3 s +[acc] eval 1.177734e-3 s +[acc] eval 1.223041e-3 s +[acc] eval 1.060172e-3 s +[acc] eval 1.1014e-3 s +[acc] eval 1.446494e-3 s +[acc] eval 1.421586e-3 s +[acc] eval 1.950191e-3 s +[acc] eval 1.465861e-3 s +[acc] eval 1.502248e-3 s +[acc] eval 1.430596e-3 s +[acc] eval 1.476625e-3 s +[acc] eval 1.58903e-3 s +[acc] eval 1.245954e-3 s +[acc] eval 1.196351e-3 s +[acc] eval 1.08255e-3 s +[acc] eval 7.0084996e-2 s +[acc] compile 4.047182e-3 s +[acc] eval 9.057872e-3 s +[acc] eval 1.375299e-3 s +[acc] eval 1.499001e-3 s +[acc] eval 2.176709e-3 s +[acc] eval 1.660363e-3 s +[acc] eval 2.381615e-3 s +[acc] eval 2.77254e-3 s +[acc] eval 2.612111e-3 s +[acc] eval 2.113868e-3 s +[acc] eval 1.825957e-3 s +[acc] eval 2.373092e-3 s +[acc] eval 1.938403e-3 s +[acc] eval 1.470509e-3 s +[acc] eval 1.866307e-3 s +[acc] eval 2.107507e-3 s +[acc] eval 1.915919e-3 s +[acc] eval 2.287633e-3 s +[acc] eval 2.401362e-3 s +[acc] eval 2.624216e-3 s +[acc] eval 5.5485794e-2 s +[acc] compile 8.4887794e-2 s +[acc] eval 1.263391e-2 s +[acc] eval 0.106057056 s +[acc] compile 6.4491343e-2 s +[acc] eval 1.163069e-2 s +[acc] eval 5.503267e-3 s +[acc] eval 1.209411e-3 s +[acc] eval 1.146493e-3 s +[acc] eval 1.14623e-3 s +[acc] eval 1.070141e-3 s +[acc] eval 1.01465e-3 s +[acc] eval 1.32021e-3 s +[acc] eval 1.322856e-3 s +[acc] eval 1.420757e-3 s +[acc] eval 1.344393e-3 s +[acc] eval 1.423713e-3 s +[acc] eval 1.148077e-3 s +[acc] eval 9.81062e-4 s +[acc] eval 1.024112e-3 s +[acc] eval 1.038361e-3 s +[acc] eval 8.92615e-4 s +[acc] eval 8.98678e-4 s +[acc] eval 9.41598e-4 s +[acc] eval 1.022973e-3 s +[acc] eval 1.388828e-3 s +[acc] eval 1.256922e-3 s +[acc] eval 1.047311e-3 s +[acc] eval 1.0209e-3 s +[acc] eval 1.200859e-3 s +[acc] eval 1.29559e-3 s +[acc] eval 1.201405e-3 s +[acc] eval 1.372553e-3 s +[acc] eval 9.75949e-4 s +[acc] eval 9.25779e-4 s +[acc] eval 0.118641266 s +[acc] compile 0.285984112 s +[acc] eval 2.5687299e-2 s +[acc] eval 2.0119883e-2 s +[acc] eval 8.592233e-3 s +[acc] eval 5.97466e-3 s +[acc] eval 5.722249e-3 s +[acc] eval 5.278076e-3 s +[acc] eval 5.648157e-3 s +[acc] eval 4.282668e-3 s +[acc] eval 4.86656e-3 s +[acc] eval 5.958572e-3 s +[acc] eval 4.446789e-3 s +[acc] eval 4.719685e-3 s +[acc] eval 6.447792e-3 s +[acc] eval 4.631529e-3 s +[acc] eval 5.764114e-3 s +[acc] eval 4.62163e-3 s +[acc] eval 1.2261604e-2 s +[acc] eval 5.141724e-3 s +[acc] eval 5.362226e-3 s +[acc] eval 4.975304e-3 s +[acc] eval 3.942679e-3 s +[acc] eval 4.43983e-3 s +[acc] eval 4.729877e-3 s +[acc] eval 5.575866e-3 s +[acc] eval 5.073717e-3 s +[acc] eval 4.608445e-3 s +[acc] eval 4.828526e-3 s +[acc] eval 4.122521e-3 s +[acc] eval 5.223869e-3 s +[acc] eval 5.949446e-3 s +[acc] eval 0.500122308 s +[acc] compile 7.55369e-4 s +[acc] eval 8.482404e-3 s +[acc] compile 6.4943436e-2 s +[acc] eval 2.7143189e-2 s +[acc] eval 3.430447e-3 s +[acc] eval 3.345262e-3 s +[acc] eval 3.376651e-3 s +[acc] eval 2.933682e-3 s +[acc] eval 2.955788e-3 s +[acc] eval 2.999986e-3 s +[acc] eval 3.17292e-3 s +[acc] eval 3.073927e-3 s +[acc] eval 3.349892e-3 s +[acc] eval 5.547629e-3 s +[acc] eval 3.955917e-3 s +[acc] eval 3.070872e-3 s +[acc] eval 2.938725e-3 s +[acc] eval 3.095899e-3 s +[acc] eval 4.428064e-3 s +[acc] eval 2.958829e-3 s +[acc] eval 2.733921e-3 s +[acc] eval 2.343649e-3 s +[acc] eval 2.40468e-3 s +[acc] eval 2.621403e-3 s +[acc] eval 4.57654e-3 s +[acc] eval 3.437894e-3 s +[acc] eval 3.206942e-3 s +[acc] eval 4.570555e-3 s +[acc] eval 5.695222e-3 s +[acc] eval 3.118371e-3 s +[acc] eval 2.879321e-3 s +[acc] eval 2.907692e-3 s +[acc] eval 5.277883e-3 s +[acc] eval 4.326648e-3 s +[acc] eval 3.936545e-3 s +[acc] eval 3.8636e-3 s +[acc] eval 3.908727e-3 s +[acc] eval 3.593591e-3 s +[acc] eval 0.220198282 s +[acc] compile 5.7822002e-2 s +[acc] eval 5.242945e-3 s +[acc] eval 2.215732e-3 s +[acc] eval 2.723165e-3 s +[acc] eval 7.305477e-3 s +[acc] eval 1.620616e-3 s +[acc] eval 1.558375e-3 s +[acc] eval 1.782637e-3 s +[acc] eval 2.729523e-3 s +[acc] eval 2.340184e-3 s +[acc] eval 1.845441e-3 s +[acc] eval 2.137223e-3 s +[acc] eval 1.826356e-3 s +[acc] eval 2.004272e-3 s +[acc] eval 1.861364e-3 s +[acc] eval 2.230773e-3 s +[acc] eval 2.612901e-3 s +[acc] eval 2.288698e-3 s +[acc] eval 2.327144e-3 s +[acc] eval 2.193571e-3 s +[acc] eval 2.129037e-3 s +[acc] eval 2.036247e-3 s +[acc] eval 2.928173e-3 s +[acc] eval 2.019728e-3 s +[acc] eval 1.806452e-3 s +[acc] eval 1.954406e-3 s +[acc] eval 2.481459e-3 s +[acc] eval 1.860716e-3 s +[acc] eval 1.848376e-3 s +[acc] eval 1.85841e-3 s +[acc] eval 2.69833e-3 s +[acc] eval 0.135499613 s +[acc] compile 3.39362e-4 s +[acc] eval 7.280439e-3 s +[acc] compile 0.290356667 s +[acc] eval 2.5516692e-2 s +[acc] eval 3.845985e-3 s +[acc] eval 5.407398e-3 s +[acc] eval 6.403104e-3 s +[acc] eval 3.021418e-3 s +[acc] eval 4.433392e-3 s +[acc] eval 4.375923e-3 s +[acc] eval 4.981368e-3 s +[acc] eval 2.811566e-3 s +[acc] eval 2.892153e-3 s +[acc] eval 2.924851e-3 s +[acc] eval 4.759815e-3 s +[acc] eval 9.992692e-3 s +[acc] eval 3.823723e-3 s +[acc] eval 3.975385e-3 s +[acc] eval 3.462895e-3 s +[acc] eval 3.465372e-3 s +[acc] eval 1.0686679e-2 s +[acc] eval 7.882044e-3 s +[acc] eval 9.952694e-3 s +[acc] eval 7.766445e-3 s +[acc] eval 4.538032e-3 s +[acc] eval 3.850925e-3 s +[acc] eval 4.644979e-3 s +[acc] eval 1.7779729e-2 s +[acc] eval 3.924399e-3 s +[acc] eval 3.910372e-3 s +[acc] eval 4.666071e-3 s +[acc] eval 1.9578661e-2 s +[acc] eval 1.3516803e-2 s +[acc] eval 8.804089e-3 s +[acc] eval 2.0439915e-2 s +[acc] eval 5.844807e-3 s +[acc] eval 4.756187e-3 s +[acc] eval 4.440014e-3 s +[acc] eval 5.037674e-3 s +[acc] eval 6.280823e-3 s +[acc] eval 5.192591e-3 s +[acc] eval 4.887331e-3 s +[acc] eval 0.576210341 s +[acc] compile 0.243658474 s +[acc] eval 3.4701323e-2 s +[acc] eval 0.283603156 s +[acc] compile 8.874728e-3 s +[acc] eval 1.3821037e-2 s +[acc] eval 2.6773104e-2 s +[acc] compile 0.27103627 s +[acc] eval 2.3431947e-2 s +[acc] eval 0.299139487 s +[acc] compile 0.277662962 s +[acc] eval 3.4927026e-2 s +[acc] eval 0.31858294 s +[acc] compile 0.357429073 s +[acc] eval 2.7854926e-2 s +[acc] eval 1.3059311e-2 s +[acc] eval 0.405719011 s +[acc] compile 5.0270863e-2 s +[acc] eval 9.052022e-3 s +[acc] eval 2.723504e-3 s +[acc] eval 2.493054e-3 s +[acc] eval 2.300791e-3 s +[acc] eval 2.517364e-3 s +[acc] eval 2.145623e-3 s +[acc] eval 2.283616e-3 s +[acc] eval 2.263977e-3 s +[acc] eval 1.2557419e-2 s +[acc] eval 2.712734e-3 s +[acc] eval 2.401775e-3 s +[acc] eval 1.95994e-3 s +[acc] eval 1.951753e-3 s +[acc] eval 1.855555e-3 s +[acc] eval 1.969901e-3 s +[acc] eval 2.121235e-3 s +[acc] eval 2.355951e-3 s +[acc] eval 1.944266e-3 s +[acc] eval 2.431851e-3 s +[acc] eval 2.372851e-3 s +[acc] eval 0.118088387 s +[acc] compile 0.262101995 s +[acc] eval 1.8760509e-2 s +[acc] eval 0.285143251 s +[acc] compile 6.065046e-3 s +[acc] eval 1.8032622e-2 s +[acc] eval 9.65924e-4 s +[acc] eval 1.430116e-3 s +[acc] eval 2.8700244e-2 s +[acc] compile 1.1321806e-2 s +[acc] eval 1.0175137e-2 s +[acc] eval 4.064632e-3 s +[acc] eval 4.180678e-3 s +[acc] eval 3.403855e-3 s +[acc] eval 2.499183e-3 s +[acc] eval 1.007087e-2 s +[acc] eval 1.277598e-3 s +[acc] eval 1.423397e-3 s +[acc] eval 1.198792e-3 s +[acc] eval 1.312446e-3 s +[acc] eval 1.265867e-3 s +[acc] eval 1.319117e-3 s +[acc] eval 1.251382e-3 s +[acc] eval 1.250925e-3 s +[acc] eval 1.954911e-3 s +[acc] eval 2.448703e-3 s +[acc] eval 6.4442514e-2 s +[acc] compile 7.645174e-3 s +[acc] eval 7.343654e-3 s +[acc] eval 2.711995e-3 s +[acc] eval 2.575442e-3 s +[acc] eval 2.940445e-3 s +[acc] eval 4.00783e-3 s +[acc] eval 3.029678e-3 s +[acc] eval 7.209446e-3 s +[acc] eval 4.1101737e-2 s +[acc] compile 0.197435927 s +[acc] eval 2.7665782e-2 s +[acc] eval 0.234928576 s +[acc] compile 4.915024e-3 s +[acc] eval 3.961905e-3 s +[acc] eval 5.93166e-4 s +[acc] eval 8.0965e-4 s +[acc] eval 1.1274106e-2 s +[acc] compile 5.650986e-3 s +[acc] eval 1.1717333e-2 s +[acc] eval 1.9763508e-2 s +[acc] compile 0.250176082 s +[acc] eval 2.9438888e-2 s +[acc] eval 0.284029451 s +[acc] compile 6.3418157e-2 s +[acc] eval 6.597999e-3 s +[acc] eval 7.3009805e-2 s +[acc] compile 0.115805572 s +[acc] eval 2.3323244e-2 s +[acc] eval 0.144151116 s +[acc] compile 4.6207123e-2 s +[acc] eval 7.821358e-3 s +[acc] eval 2.277745e-3 s +[acc] eval 2.047558e-3 s +[acc] eval 6.1356885e-2 s +[acc] compile 7.314628e-3 s +[acc] eval 7.790544e-3 s +[acc] eval 2.459313e-3 s +[acc] eval 2.253761e-3 s +[acc] eval 2.472325e-3 s +[acc] eval 2.105422e-3 s +[acc] eval 2.474085e-3 s +[acc] eval 2.887319e-3 s +[acc] eval 1.666025e-3 s +[acc] eval 8.84674e-3 s +[acc] eval 2.044601e-3 s +[acc] eval 2.372606e-3 s +[acc] eval 1.84189e-3 s +[acc] eval 1.243062e-3 s +[acc] eval 5.1211346e-2 s +[acc] compile 4.04062e-4 s +[acc] eval 1.103789e-2 s +[acc] compile 0.296329374 s +[acc] eval 3.3473835e-2 s +[acc] eval 4.011714e-3 s +[acc] eval 4.053892e-3 s +[acc] eval 4.782136e-3 s +[acc] eval 4.335092e-3 s +[acc] eval 4.635721e-3 s +[acc] eval 4.574242e-3 s +[acc] eval 4.309238e-3 s +[acc] eval 4.65703e-3 s +[acc] eval 4.698838e-3 s +[acc] eval 5.163339e-3 s +[acc] eval 4.57269e-3 s +[acc] eval 4.742989e-3 s +[acc] eval 4.333697e-3 s +[acc] eval 4.059559e-3 s +[acc] eval 4.070085e-3 s +[acc] eval 4.874234e-3 s +[acc] eval 1.1077627e-2 s +[acc] eval 4.327427e-3 s +[acc] eval 4.267975e-3 s +[acc] eval 4.443327e-3 s +[acc] eval 4.293811e-3 s +[acc] eval 4.674984e-3 s +[acc] eval 3.605566e-3 s +[acc] eval 3.984484e-3 s +[acc] eval 3.382943e-3 s +[acc] eval 3.410063e-3 s +[acc] eval 3.277571e-3 s +[acc] eval 9.366603e-3 s +[acc] eval 4.207729e-3 s +[acc] eval 0.475453707 s +[acc] compile 0.110750926 s +[acc] eval 2.0260474e-2 s +[acc] eval 8.370816e-3 s +[acc] eval 3.090523e-3 s +[acc] eval 3.368238e-3 s +[acc] eval 2.723986e-3 s +[acc] eval 2.494382e-3 s +[acc] eval 2.358717e-3 s +[acc] eval 2.641109e-3 s +[acc] eval 2.887923e-3 s +[acc] eval 2.409603e-3 s +[acc] eval 2.346703e-3 s +[acc] eval 2.35825e-3 s +[acc] eval 2.252647e-3 s +[acc] eval 2.359889e-3 s +[acc] eval 2.532917e-3 s +[acc] eval 2.518941e-3 s +[acc] eval 2.510718e-3 s +[acc] eval 2.385985e-3 s +[acc] eval 2.545181e-3 s +[acc] eval 2.557498e-3 s +[acc] eval 2.349894e-3 s +[acc] eval 2.23705e-3 s +[acc] eval 2.169154e-3 s +[acc] eval 2.623778e-3 s +[acc] eval 2.730621e-3 s +[acc] eval 2.585669e-3 s +[acc] eval 2.465058e-3 s +[acc] eval 2.585161e-3 s +[acc] eval 2.705722e-3 s +[acc] eval 2.485021e-3 s +[acc] eval 0.216122855 s +[acc] compile 6.5006571e-2 s +[acc] eval 1.8773794e-2 s +[acc] eval 8.8700761e-2 s +[acc] compile 6.96755e-3 s +[acc] eval 7.383867e-3 s +[acc] eval 1.499157e-3 s +[acc] eval 1.4119237e-2 s +[acc] eval 3.2015643e-2 s +[acc] compile 0.165159735 s +[acc] eval 1.965747e-2 s +[acc] eval 0.187040108 s +[acc] compile 8.2853857e-2 s +[acc] eval 1.4275914e-2 s +[acc] eval 0.107785353 s +[acc] compile 0.233379026 s +[acc] eval 1.9560297e-2 s +[acc] eval 0.261681618 s +[acc] compile 0.2105494 s +[acc] eval 2.0054297e-2 s +[acc] eval 0.243135179 s +[acc] compile 7.088966e-3 s +[acc] eval 1.067422e-2 s +[acc] eval 3.137443e-3 s +[acc] eval 3.049445e-3 s +[acc] eval 2.990472e-3 s +[acc] eval 2.778494e-3 s +[acc] eval 4.1457377e-2 s +[acc] compile 7.558993e-2 s +[acc] eval 1.7535472e-2 s +[acc] eval 0.103327066 s +[acc] compile 3.737938e-3 s +[acc] eval 6.51972e-3 s +[acc] eval 1.345832e-3 s +[acc] eval 9.27182e-4 s +[acc] eval 1.175922e-3 s +[acc] eval 1.424657e-3 s +[acc] eval 1.156753e-3 s +[acc] eval 9.50613e-4 s +[acc] eval 8.27799e-4 s +[acc] eval 1.122916e-3 s +[acc] eval 1.237135e-3 s +[acc] eval 1.688374e-3 s +[acc] eval 4.478874e-3 s +[acc] eval 2.79744e-3 s +[acc] eval 7.052626e-3 s +[acc] eval 6.471507e-3 s +[acc] eval 4.494403e-3 s +[acc] eval 4.454627e-3 s +[acc] eval 3.857803e-3 s +[acc] eval 4.22529e-3 s +[acc] eval 2.684502e-3 s +[acc] eval 1.6227216e-2 s +[acc] eval 2.866348e-3 s +[acc] eval 2.445698e-3 s +[acc] eval 2.375443e-3 s +[acc] eval 2.222846e-3 s +[acc] eval 2.054052e-3 s +[acc] eval 1.95761e-3 s +[acc] eval 2.203062e-3 s +[acc] eval 2.682574e-3 s +[acc] eval 2.027862e-3 s +[acc] eval 0.107660404 s +[acc] compile 0.337620813 s +[acc] eval 3.882457e-2 s +[acc] eval 0.382382721 s +[acc] compile 0.205439369 s +[acc] eval 3.5667535e-2 s +[acc] eval 0.258939257 s +[acc] compile 3.541618e-3 s +[acc] eval 4.778639e-3 s +[acc] eval 1.112494e-3 s +[acc] eval 8.7293e-4 s +[acc] eval 9.9233e-4 s +[acc] eval 1.2714941e-2 s +[acc] compile 5.056414e-3 s +[acc] eval 1.0313705e-2 s +[acc] eval 1.8074783e-2 s +[acc] compile 4.721422e-3 s +[acc] eval 7.988628e-3 s +[acc] eval 1.904643e-3 s +[acc] eval 1.672077e-3 s +[acc] eval 1.705096e-3 s +[acc] eval 1.140779e-3 s +[acc] eval 1.381324e-3 s +[acc] eval 1.650973e-3 s +[acc] eval 1.96616e-3 s +[acc] eval 2.067517e-3 s +[acc] eval 1.0613582e-2 s +[acc] eval 3.9869679e-2 s +[acc] compile 0.209055187 s +[acc] eval 2.5868498e-2 s +[acc] eval 0.247234966 s +[acc] compile 6.4148718e-2 s +[acc] eval 1.9430876e-2 s +[acc] eval 8.6737122e-2 s +[acc] compile 9.8360697e-2 s +[acc] eval 1.8794306e-2 s +[acc] eval 2.404226e-3 s +[acc] eval 2.546563e-3 s +[acc] eval 3.246924e-3 s +[acc] eval 3.6349e-3 s +[acc] eval 0.133060189 s +[acc] compile 0.185275328 s +[acc] eval 1.0805752e-2 s +[acc] eval 8.554606e-3 s +[acc] eval 2.2405092e-2 s +[acc] eval 6.516208e-3 s +[acc] eval 5.061003e-3 s +[acc] eval 4.665759e-3 s +[acc] eval 5.219411e-3 s +[acc] eval 4.248574e-3 s +[acc] eval 3.47203e-3 s +[acc] eval 2.750612e-3 s +[acc] eval 2.513984e-3 s +[acc] eval 2.469874e-3 s +[acc] eval 3.059286e-3 s +[acc] eval 2.54477e-3 s +[acc] eval 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9.036313e-3 s +[acc] eval 9.1522003e-2 s +[acc] compile 0.13178821 s +[acc] eval 2.9676983e-2 s +[acc] eval 0.166149698 s +[acc] compile 6.7089e-4 s +[acc] eval 1.5109812e-2 s +[acc] compile 4.590863e-3 s +[acc] eval 6.223338e-3 s +[acc] eval 7.33336e-4 s +[acc] eval 7.64524e-4 s +[acc] eval 1.3495577e-2 s +[acc] compile 6.5546787e-2 s +[acc] eval 7.572653e-3 s +[acc] eval 1.882976e-3 s +[acc] eval 1.974394e-3 s +[acc] eval 1.2896207e-2 s +[acc] eval 2.674787e-3 s +[acc] eval 2.886309e-3 s +[acc] eval 2.556673e-3 s +[acc] eval 2.27779e-3 s +[acc] eval 2.140403e-3 s +[acc] eval 2.009193e-3 s +[acc] eval 2.201862e-3 s +[acc] eval 1.682083e-3 s +[acc] eval 1.689788e-3 s +[acc] eval 1.807234e-3 s +[acc] eval 1.706699e-3 s +[acc] eval 1.587003e-3 s +[acc] eval 1.567394e-3 s +[acc] eval 1.41532e-3 s +[acc] eval 1.423798e-3 s +[acc] eval 1.45357e-3 s +[acc] eval 1.413225e-3 s +[acc] eval 1.392872e-3 s +[acc] eval 1.240854e-3 s +[acc] eval 1.346998e-3 s +[acc] eval 1.549993e-3 s +[acc] eval 1.361998e-3 s +[acc] eval 1.28142e-3 s +[acc] eval 1.270421e-3 s +[acc] eval 1.509568e-3 s +[acc] eval 1.673768e-3 s +[acc] eval 1.523507e-3 s +[acc] eval 1.630967e-3 s +[acc] eval 1.644303e-3 s +[acc] eval 1.526087e-3 s +[acc] eval 1.644253e-3 s +[acc] eval 1.848455e-3 s +[acc] eval 1.66262e-3 s +[acc] eval 1.476851e-3 s +[acc] eval 1.488625e-3 s +[acc] eval 1.494997e-3 s +[acc] eval 1.605811e-3 s +[acc] eval 1.527428e-3 s +[acc] eval 1.677902e-3 s +[acc] eval 1.607354e-3 s +[acc] eval 1.95753e-3 s +[acc] eval 2.438652e-3 s +[acc] eval 2.057402e-3 s +[acc] eval 1.761658e-3 s +[acc] eval 0.171289916 s +[acc] compile 0.366593032 s +[acc] eval 2.2995874e-2 s +[acc] eval 0.400872922 s +[acc] compile 0.166836192 s +[acc] eval 1.7461971e-2 s +[acc] eval 0.189004417 s +[acc] compile 5.056437e-2 s +[acc] eval 1.8735424e-2 s +[acc] eval 7.0765066e-2 s +[acc] compile 0.146164786 s +[acc] eval 2.9208621e-2 s +[acc] eval 0.183842188 s +[acc] compile 0.140016343 s +[acc] eval 2.2488041e-2 s +[acc] eval 8.643195e-3 s +[acc] eval 4.350337e-3 s +[acc] eval 2.939247e-3 s +[acc] eval 4.81654e-3 s +[acc] eval 3.634024e-3 s +[acc] eval 2.916313e-3 s +[acc] eval 3.318778e-3 s +[acc] eval 3.463814e-3 s +[acc] eval 3.195816e-3 s +[acc] eval 3.12021e-3 s +[acc] eval 3.762682e-3 s +[acc] eval 4.40739e-3 s +[acc] eval 4.12016e-3 s +[acc] eval 3.17234e-3 s +[acc] eval 3.635957e-3 s +[acc] eval 3.187504e-3 s +[acc] eval 4.074656e-3 s +[acc] eval 3.532292e-3 s +[acc] eval 3.285148e-3 s +[acc] eval 3.585672e-3 s +[acc] eval 3.419946e-3 s +[acc] eval 4.063116e-3 s +[acc] eval 3.832612e-3 s +[acc] eval 3.941796e-3 s +[acc] eval 3.844252e-3 s +[acc] eval 3.528026e-3 s +[acc] eval 5.33328e-3 s +[acc] eval 4.439772e-3 s +[acc] eval 4.06596e-3 s +[acc] eval 4.124303e-3 s +[acc] eval 4.077413e-3 s +[acc] eval 4.392302e-3 s +[acc] eval 4.905768e-3 s +[acc] eval 3.361455e-3 s +[acc] eval 3.089425e-3 s +[acc] eval 3.149295e-3 s +[acc] eval 3.853503e-3 s +[acc] eval 3.212248e-3 s +[acc] eval 3.17112e-3 s +[acc] eval 3.185811e-3 s +[acc] eval 2.896932e-3 s +[acc] eval 3.168929e-3 s +[acc] eval 2.960368e-3 s +[acc] eval 2.637993e-3 s +[acc] eval 2.57088e-3 s +[acc] eval 2.519333e-3 s +[acc] eval 3.009826e-3 s +[acc] eval 0.344819431 s +[acc] compile 8.0501303e-2 s +[acc] eval 1.235091e-2 s +[acc] eval 0.100767056 s +[acc] compile 0.488351617 s +[acc] eval 2.9356866e-2 s +[acc] eval 0.52343738 s +[acc] compile 0.243741619 s +[acc] eval 1.0764747e-2 s +[acc] eval 0.25978221 s +[acc] compile 0.460302322 s +[acc] eval 2.9187649e-2 s +[acc] eval 0.49614867 s +[acc] eval 3.101786e-3 s +[acc] eval 4.52862e-4 s +[acc] eval 6.56714e-4 s +[acc] eval 4.957484e-3 s +[acc] compile 4.326957e-3 s +[acc] eval 8.000681e-3 s +[acc] eval 1.5156895e-2 s +[acc] compile 4.884994e-3 s +[acc] eval 1.0656521e-2 s +[acc] eval 1.7982464e-2 s +[acc] compile 3.692682e-3 s +[acc] eval 4.38793e-3 s +[acc] eval 1.0217071e-2 s +[acc] compile 4.608608e-3 s +[acc] eval 1.1990802e-2 s +[acc] eval 2.186692e-3 s +[acc] eval 2.146125e-3 s +[acc] eval 2.3329006e-2 s +[acc] compile 4.115488e-3 s +[acc] eval 8.036621e-3 s +[acc] eval 3.123539e-3 s +[acc] eval 3.372247e-3 s +[acc] eval 3.292366e-3 s +[acc] eval 3.066887e-3 s +[acc] eval 3.055106e-3 s +[acc] eval 2.703483e-3 s +[acc] eval 2.336102e-3 s +[acc] eval 1.907186e-3 s +[acc] eval 1.833246e-3 s +[acc] eval 1.777423e-3 s +[acc] eval 2.271547e-3 s +[acc] eval 1.992294e-3 s +[acc] eval 1.883884e-3 s +[acc] eval 3.33321e-3 s +[acc] eval 2.848939e-3 s +[acc] eval 3.046714e-3 s +[acc] eval 2.699532e-3 s +[acc] eval 2.190172e-3 s +[acc] eval 1.843121e-3 s +[acc] eval 2.763874e-3 s +[acc] eval 6.8191535e-2 s +[acc] compile 2.3835416e-2 s +[acc] eval 1.6830513e-2 s +[acc] eval 4.4873356e-2 s +[acc] compile 1.049268e-3 s +[acc] eval 1.0068758e-2 s +[acc] compile 1.4941994e-2 s +[acc] eval 5.165447e-3 s +[acc] eval 6.22268e-4 s +[acc] eval 8.17294e-4 s +[acc] eval 2.2596678e-2 s +[acc] compile 3.572477e-3 s +[acc] eval 3.54855e-3 s +[acc] eval 1.429526e-3 s +[acc] eval 9.93371e-4 s +[acc] eval 1.771547e-3 s +[acc] eval 1.3433053e-2 s +[acc] compile 5.556935e-3 s +[acc] eval 1.2996966e-2 s +[acc] eval 8.026646e-3 s +[acc] eval 8.30469e-4 s +[acc] eval 1.283526e-3 s +[acc] eval 3.0952748e-2 s +[acc] compile 5.380316e-3 s +[acc] eval 5.393384e-3 s +[acc] eval 3.913368e-3 s +[acc] eval 4.073775e-3 s +[acc] eval 3.487218e-3 s +[acc] eval 1.447609e-3 s +[acc] eval 1.572635e-3 s +[acc] eval 1.456758e-3 s +[acc] eval 1.406872e-3 s +[acc] eval 1.510953e-3 s +[acc] eval 1.510433e-3 s +[acc] eval 1.522337e-3 s +[acc] eval 1.547031e-3 s +[acc] eval 3.7377478e-2 s +[acc] compile 0.195014881 s +[acc] eval 2.9588446e-2 s +[acc] eval 0.227861443 s +[acc] compile 4.319178e-3 s +[acc] eval 4.960575e-3 s +[acc] eval 1.71122e-3 s +[acc] eval 1.688559e-3 s +[acc] eval 2.032024e-3 s +[acc] eval 3.915229e-3 s +[acc] eval 1.899306e-3 s +[acc] eval 1.941281e-3 s +[acc] eval 2.152903e-3 s +[acc] eval 2.140996e-3 s +[acc] eval 1.784589e-3 s +[acc] eval 1.8690922e-2 s +[acc] eval 3.87167e-3 s +[acc] eval 3.209538e-3 s +[acc] eval 3.278101e-3 s +[acc] eval 2.719694e-3 s +[acc] eval 2.10471e-3 s +[acc] eval 2.591726e-3 s +[acc] eval 1.958021e-3 s +[acc] eval 1.535941e-3 s +[acc] eval 1.408966e-3 s +[acc] eval 1.368569e-3 s +[acc] eval 1.457905e-3 s +[acc] eval 1.460653e-3 s +[acc] eval 1.535889e-3 s +[acc] eval 1.465957e-3 s +[acc] eval 1.643004e-3 s +[acc] eval 1.480212e-3 s +[acc] eval 1.577053e-3 s +[acc] eval 1.402438e-3 s +[acc] eval 1.374869e-3 s +[acc] eval 9.0547498e-2 s +[acc] compile 3.726247e-3 s +[acc] eval 3.88364e-3 s +[acc] eval 1.295823e-3 s +[acc] eval 1.612918e-3 s +[acc] eval 1.2389743e-2 s +[acc] compile 0.214413228 s +[acc] eval 4.0210018e-2 s +[acc] eval 0.259751516 s +[acc] compile 6.69968e-3 s +[acc] eval 6.367255e-3 s +[acc] eval 7.818276e-3 s +[acc] eval 2.911639e-2 s +[acc] compile 7.71791e-4 s +[acc] eval 9.262338e-3 s +[acc] compile 1.0967906e-2 s +[acc] eval 1.1898527e-2 s +[acc] eval 3.929975e-3 s +[acc] eval 5.16874e-3 s +[acc] eval 3.933172e-3 s +[acc] eval 1.3346405e-2 s +[acc] eval 1.656082e-3 s +[acc] eval 5.4073635e-2 s +[acc] compile 0.123771435 s +[acc] eval 1.1179536e-2 s +[acc] eval 0.139812051 s +[acc] compile 7.1921429e-2 s +[acc] eval 3.4419549e-2 s +[acc] eval 0.109441511 s +[acc] compile 3.838188e-3 s +[acc] eval 9.782733e-3 s +[acc] eval 1.850998e-3 s +[acc] eval 9.44472e-4 s +[acc] eval 1.831647e-2 s +[acc] compile 7.768773e-3 s +[acc] eval 1.3202105e-2 s +[acc] eval 2.2690783e-2 s +[acc] compile 0.138662239 s +[acc] eval 2.597425e-2 s +[acc] eval 0.183689351 s +[acc] compile 1.455016e-2 s +[acc] eval 2.135023e-2 s +[acc] eval 1.14795e-2 s +[acc] eval 9.047527e-3 s +[acc] eval 1.8282132e-2 s +[acc] eval 4.73809e-3 s +[acc] eval 5.859477e-3 s +[acc] eval 5.566463e-3 s +[acc] eval 4.865835e-3 s +[acc] eval 5.397442e-3 s +[acc] eval 3.831599e-3 s +[acc] eval 3.475167e-3 s +[acc] eval 3.79485e-3 s +[acc] eval 0.118469437 s +[acc] compile 7.055565e-3 s +[acc] eval 1.628926e-2 s +[acc] eval 3.2771425e-2 s +[acc] compile 5.6495616e-2 s +[acc] eval 1.2961061e-2 s +[acc] eval 7.2616624e-2 s +[acc] compile 6.2496115e-2 s +[acc] eval 1.2580332e-2 s +[acc] eval 1.3617674e-2 s +[acc] eval 1.62021e-3 s +[acc] eval 1.549934e-3 s +[acc] eval 1.465669e-3 s +[acc] eval 1.314479e-3 s +[acc] eval 1.37281e-3 s +[acc] eval 1.724319e-3 s +[acc] eval 1.923091e-3 s +[acc] eval 2.277248e-3 s +[acc] eval 1.861191e-3 s +[acc] eval 1.525415e-3 s +[acc] eval 2.60424e-3 s +[acc] eval 2.757902e-3 s +[acc] eval 2.299077e-3 s +[acc] eval 2.045389e-3 s +[acc] eval 1.891725e-3 s +[acc] eval 1.503521e-3 s +[acc] eval 1.628374e-3 s +[acc] eval 2.373961e-3 s +[acc] eval 2.766117e-3 s +[acc] eval 0.129678455 s +[acc] compile 8.496569e-3 s +[acc] eval 1.9132624e-2 s +[acc] eval 3.5591802e-2 s +[acc] compile 9.552697e-3 s +[acc] eval 1.5340425e-2 s +[acc] eval 9.029299e-3 s +[acc] eval 8.22355e-4 s +[acc] eval 1.332088e-3 s +[acc] eval 3.8030884e-2 s +[acc] compile 4.733398e-3 s +[acc] eval 9.731635e-3 s +[acc] eval 1.8060433e-2 s +[acc] eval 4.4696635e-2 s +[acc] compile 4.372742e-3 s +[acc] eval 7.101037e-3 s +[acc] eval 2.735396e-3 s +[acc] eval 2.474885e-3 s +[acc] eval 2.626471e-3 s +[acc] eval 1.1917069e-2 s +[acc] eval 1.720114e-3 s +[acc] eval 1.93904e-3 s +[acc] eval 3.7805791e-2 s +[acc] compile 0.129175352 s +[acc] eval 1.8331961e-2 s +[acc] eval 4.568806e-3 s +[acc] eval 2.0345227e-2 s +[acc] eval 4.6248e-3 s +[acc] eval 4.224076e-3 s +[acc] eval 4.726875e-3 s +[acc] eval 4.228914e-3 s +[acc] eval 3.391895e-3 s +[acc] eval 2.667529e-3 s +[acc] eval 2.551941e-3 s +[acc] eval 4.375232e-3 s +[acc] eval 3.878351e-3 s +[acc] eval 3.131705e-3 s +[acc] eval 2.755522e-3 s +[acc] eval 3.611754e-3 s +[acc] eval 5.377648e-3 s +[acc] eval 3.556686e-3 s +[acc] eval 3.931659e-3 s +[acc] eval 3.762465e-3 s +[acc] eval 3.400486e-3 s +[acc] eval 3.06154e-3 s +[acc] eval 2.388242e-3 s +[acc] eval 2.207386e-3 s +[acc] eval 2.316622e-3 s +[acc] eval 2.358098e-3 s +[acc] eval 2.146276e-3 s +[acc] eval 2.446531e-3 s +[acc] eval 2.217421e-3 s +[acc] eval 2.48458e-3 s +[acc] eval 2.405071e-3 s +[acc] eval 0.267396957 s +[acc] compile 1.5871677e-2 s +[acc] eval 1.5513426e-2 s +[acc] eval 4.3801146e-2 s +[acc] compile 0.120800755 s +[acc] eval 1.4427163e-2 s +[acc] eval 0.138134807 s +[acc] compile 1.3278762e-2 s +[acc] eval 9.533521e-3 s +[acc] eval 2.6958626e-2 s +[acc] eval 1.3055747e-2 s +[acc] eval 1.6369447e-2 s +[acc] eval 1.057197e-2 s +[acc] eval 1.1012707e-2 s +[acc] eval 9.882887e-3 s +[acc] eval 0.118697429 s +[acc] compile 6.6633249e-2 s +[acc] eval 2.2137037e-2 s +[acc] eval 9.3596539e-2 s +[acc] compile 4.383597e-3 s +[acc] eval 1.1916934e-2 s +[acc] eval 9.20271e-4 s +[acc] eval 1.250675e-3 s +[acc] eval 2.0218838e-2 s +[acc] compile 6.044578e-2 s +[acc] eval 3.6197362e-2 s +[acc] eval 9.9298451e-2 s +[acc] compile 4.676844e-3 s +[acc] eval 6.075154e-3 s +[acc] eval 1.4233043e-2 s +[acc] compile 6.8768056e-2 s +[acc] eval 1.5840075e-2 s +[acc] eval 9.2800471e-2 s +[acc] compile 1.1023779e-2 s +[acc] eval 3.2709301e-2 s +[acc] eval 4.7268337e-2 s +[acc] compile 5.161561e-3 s +[acc] eval 7.11134e-3 s +[acc] eval 4.244054e-3 s +[acc] eval 5.324399e-3 s +[acc] eval 3.899038e-3 s +[acc] eval 3.23551e-3 s +[acc] eval 2.970797e-3 s +[acc] eval 4.130186e-3 s +[acc] eval 3.09277e-3 s +[acc] eval 1.3596396e-2 s +[acc] eval 3.008718e-3 s +[acc] eval 3.281708e-3 s +[acc] eval 2.660821e-3 s +[acc] eval 2.766628e-3 s +[acc] eval 2.670449e-3 s +[acc] eval 2.613285e-3 s +[acc] eval 3.258117e-3 s +[acc] eval 2.800261e-3 s +[acc] eval 2.630821e-3 s +[acc] eval 2.663696e-3 s +[acc] eval 2.422153e-3 s +[acc] eval 2.423257e-3 s +[acc] eval 2.71813e-3 s +[acc] eval 2.619678e-3 s +[acc] eval 1.880572e-3 s +[acc] eval 1.907279e-3 s +[acc] eval 2.084545e-3 s +[acc] eval 1.880958e-3 s +[acc] eval 2.016863e-3 s +[acc] eval 2.278747e-3 s +[acc] eval 2.282946e-3 s +[acc] eval 2.243146e-3 s +[acc] eval 0.114222634 s +[acc] compile 8.7872954e-2 s +[acc] eval 1.6610104e-2 s +[acc] eval 0.108387936 s +[acc] compile 7.070835e-3 s +[acc] eval 1.3896952e-2 s +[acc] eval 2.3950144e-2 s +[acc] compile 2.491171e-2 s +[acc] eval 2.0107011e-2 s +[acc] eval 5.0928431e-2 s +[acc] compile 1.14238e-2 s +[acc] eval 1.9914513e-2 s +[acc] eval 4.8273251e-2 s +[acc] compile 0.151850143 s +[acc] eval 3.8674007e-2 s +[acc] eval 0.19465812 s +[acc] compile 4.99579e-4 s +[acc] eval 1.3399368e-2 s +[acc] compile 1.1579453e-2 s +[acc] eval 1.8202759e-2 s +[acc] eval 5.755802e-3 s +[acc] eval 6.222805e-3 s +[acc] eval 1.7301486e-2 s +[acc] eval 3.550743e-3 s +[acc] eval 3.392636e-3 s +[acc] eval 2.829223e-3 s +[acc] eval 7.3637804e-2 s +[acc] compile 8.205726e-3 s +[acc] eval 1.494921e-2 s +[acc] eval 2.7283152e-2 s +[acc] compile 0.191241587 s +[acc] eval 2.939329e-2 s +[acc] eval 0.226465435 s +[acc] compile 0.180195641 s +[acc] eval 2.4611127e-2 s +[acc] eval 0.212057723 s +[acc] compile 7.2944023e-2 s +[acc] eval 2.3438952e-2 s +[acc] eval 0.101675586 s +[acc] compile 7.74162e-3 s +[acc] eval 5.40382e-3 s +[acc] eval 2.661778e-3 s +[acc] eval 3.528587e-3 s +[acc] eval 3.033705e-3 s +[acc] eval 2.427025e-3 s +[acc] eval 2.749709e-3 s +[acc] eval 3.711747e-3 s +[acc] eval 2.860067e-3 s +[acc] eval 1.919006e-3 s +[acc] eval 3.9616893e-2 s +[acc] compile 1.2479755e-2 s +[acc] eval 6.443084e-3 s +[acc] eval 3.640688e-3 s +[acc] eval 1.4361719e-2 s +[acc] eval 2.678457e-3 s +[acc] eval 2.475146e-3 s +[acc] eval 2.681084e-3 s +[acc] eval 2.500992e-3 s +[acc] eval 2.175885e-3 s +[acc] eval 2.687926e-3 s +[acc] eval 2.510862e-3 s +[acc] eval 2.423742e-3 s +[acc] eval 2.430392e-3 s +[acc] eval 2.221467e-3 s +[acc] eval 1.990487e-3 s +[acc] eval 2.03403e-3 s +[acc] eval 1.967773e-3 s +[acc] eval 2.393014e-3 s +[acc] eval 2.243495e-3 s +[acc] eval 2.527478e-3 s +[acc] eval 2.799895e-3 s +[acc] eval 2.109446e-3 s +[acc] eval 2.129313e-3 s +[acc] eval 1.934791e-3 s +[acc] eval 1.939889e-3 s +[acc] eval 2.117191e-3 s +[acc] eval 2.400818e-3 s +[acc] eval 2.446624e-3 s +[acc] eval 1.790039e-3 s +[acc] eval 2.378461e-3 s +[acc] eval 1.936433e-3 s +[acc] eval 0.106417754 s +[acc] compile 0.232116309 s +[acc] eval 3.7850375e-2 s +[acc] eval 0.276105152 s +[acc] compile 7.3682102e-2 s +[acc] eval 4.2577238e-2 s +[acc] eval 0.119253265 s +[acc] compile 0.279307832 s +[acc] eval 8.371664e-3 s +[acc] eval 0.293067777 s +[acc] compile 0.291493879 s +[acc] eval 3.7094324e-2 s +[acc] eval 0.333052596 s +[acc] compile 4.516514e-3 s +[acc] eval 7.704713e-3 s +[acc] eval 1.4651848e-2 s +[acc] compile 8.3039994e-2 s +[acc] eval 7.032501e-3 s +[acc] eval 9.3875303e-2 s +[acc] compile 8.649164e-2 s +[acc] eval 2.6248717e-2 s +[acc] eval 0.117273758 s +[acc] compile 1.0119774e-2 s +[acc] eval 2.2794336e-2 s +[acc] eval 4.0145731e-2 s +[acc] compile 7.0507593e-2 s +[acc] eval 7.099395e-3 s +[acc] eval 8.0445327e-2 s +[acc] compile 0.136282152 s +[acc] eval 1.5699453e-2 s +[acc] eval 4.625874e-3 s +[acc] eval 4.619971e-3 s +[acc] eval 5.945762e-3 s +[acc] eval 6.112018e-3 s +[acc] eval 3.695798e-3 s +[acc] eval 0.181598072 s +[acc] compile 0.255687821 s +[acc] eval 1.4645162e-2 s +[acc] eval 0.281394553 s +[acc] compile 0.155806992 s +[acc] eval 7.832289e-3 s +[acc] eval 0.167464404 s +[acc] compile 5.25542e-4 s +[acc] eval 1.66448e-2 s +[acc] compile 6.190997e-2 s +[acc] eval 8.084369e-3 s +[acc] eval 4.730015e-3 s +[acc] eval 4.497787e-3 s +[acc] eval 1.7741349e-2 s +[acc] eval 3.485925e-3 s +[acc] eval 3.987361e-3 s +[acc] eval 3.288014e-3 s +[acc] eval 3.18129e-3 s +[acc] eval 3.312747e-3 s +[acc] eval 3.352288e-3 s +[acc] eval 3.159147e-3 s +[acc] eval 2.543199e-3 s +[acc] eval 2.926975e-3 s +[acc] eval 2.952782e-3 s +[acc] eval 3.320174e-3 s +[acc] eval 3.620542e-3 s +[acc] eval 3.168113e-3 s +[acc] eval 2.691122e-3 s +[acc] eval 2.508004e-3 s +[acc] eval 3.982035e-3 s +[acc] eval 3.846817e-3 s +[acc] eval 3.594834e-3 s +[acc] eval 3.213585e-3 s +[acc] eval 3.31175e-3 s +[acc] eval 3.718755e-3 s +[acc] eval 3.783783e-3 s +[acc] eval 2.935775e-3 s +[acc] eval 3.080253e-3 s +[acc] eval 3.152403e-3 s +[acc] eval 4.103502e-3 s +[acc] eval 0.196471677 s +[acc] compile 1.1063804e-2 s +[acc] eval 1.8418886e-2 s +[acc] eval 4.1321069e-2 s +[acc] compile 0.142231533 s +[acc] eval 2.4540016e-2 s +[acc] eval 0.170337274 s +[acc] compile 6.0384366e-2 s +[acc] eval 1.931734e-2 s +[acc] eval 8.2120334e-2 s +[acc] compile 6.4474529e-2 s +[acc] eval 8.347417e-3 s +[acc] eval 7.5854474e-2 s +[acc] compile 5.268164e-2 s +[acc] eval 1.0638912e-2 s +[acc] eval 6.6857359e-2 s +[acc] compile 0.151364216 s +[acc] eval 3.7786855e-2 s +[acc] eval 0.193303259 s +[acc] compile 8.1317302e-2 s +[acc] eval 1.0959025e-2 s +[acc] eval 9.9203001e-2 s +[acc] compile 0.218741022 s +[acc] eval 1.4008006e-2 s +[acc] eval 0.247674611 s +[acc] compile 0.32022747 s +[acc] eval 1.380848e-2 s +[acc] eval 0.341891598 s +[acc] compile 9.616653e-3 s +[acc] eval 2.5067529e-2 s +[acc] eval 3.8676748e-2 s +[acc] compile 0.399887501 s +[acc] eval 3.048236e-2 s +[acc] eval 0.436004493 s +[acc] compile 7.5190386e-2 s +[acc] eval 3.8439386e-2 s +[acc] eval 0.117394579 s +[acc] compile 0.152663121 s +[acc] eval 1.0192345e-2 s +[acc] eval 0.168953205 s +[acc] compile 0.299581247 s +[acc] eval 1.7583526e-2 s +[acc] eval 0.323251004 s +[acc] compile 0.678337972 s +[acc] eval 3.5176737e-2 s +[acc] eval 0.73522093 s +[acc] compile 5.435108e-2 s +[acc] eval 6.131993e-3 s +[acc] eval 1.484733e-3 s +[acc] eval 1.983524e-3 s +[acc] eval 1.885132e-3 s +[acc] eval 1.598026e-3 s +[acc] eval 1.730727e-3 s +[acc] eval 1.290477e-3 s +[acc] eval 1.160638e-3 s +[acc] eval 9.56897e-4 s +[acc] eval 1.16711e-3 s +[acc] eval 1.294331e-3 s +[acc] eval 1.256337e-3 s +[acc] eval 1.367147e-3 s +[acc] eval 9.23371e-4 s +[acc] eval 1.135788e-3 s +[acc] eval 2.017926e-3 s +[acc] eval 1.1252802e-2 s +[acc] eval 2.051063e-3 s +[acc] eval 2.049812e-3 s +[acc] eval 2.059214e-3 s +[acc] eval 1.51441e-3 s +[acc] eval 1.524054e-3 s +[acc] eval 1.574716e-3 s +[acc] eval 1.637782e-3 s +[acc] eval 1.215321e-3 s +[acc] eval 1.348138e-3 s +[acc] eval 1.343384e-3 s +[acc] eval 1.189443e-3 s +[acc] eval 9.79054e-4 s +[acc] eval 1.064519e-3 s +[acc] eval 0.117608656 s +[acc] compile 5.454127e-3 s +[acc] eval 1.6682169e-2 s +[acc] eval 2.5198398e-2 s +[acc] compile 0.150308727 s +[acc] eval 3.3872477e-2 s +[acc] eval 0.191665537 s +[acc] compile 0.20338827 s +[acc] eval 2.0949593e-2 s +[acc] eval 0.22981283 s +[acc] compile 0.318574587 s +[acc] eval 3.3364992e-2 s +[acc] eval 0.356218581 s +[acc] compile 6.3632629e-2 s +[acc] eval 1.2281738e-2 s +[acc] eval 7.8528001e-2 s +[acc] compile 7.1086322e-2 s +[acc] eval 1.1448229e-2 s +[acc] eval 8.720454e-2 s +[acc] eval 1.3035704e-2 s +[acc] eval 2.5689905e-2 s +[acc] compile 2.892574e-3 s +[acc] eval 6.496486e-3 s +[acc] eval 1.0849623e-2 s +[acc] eval 5.052183e-3 s +[acc] eval 6.535006e-3 s +[acc] compile 5.2893733e-2 s +[acc] eval 5.339382e-3 s +[acc] eval 6.1143878e-2 s +[acc] compile 8.3412914e-2 s +[acc] eval 2.2106595e-2 s +[acc] eval 0.110177323 s +[acc] compile 8.749552e-2 s +[acc] eval 5.529248e-3 s +[acc] eval 9.5429604e-2 s +[acc] compile 0.104444952 s +[acc] eval 6.295235e-3 s +[acc] eval 0.113890539 s +[acc] compile 0.244152601 s +[acc] eval 2.9940237e-2 s +[acc] eval 0.278929148 s +[acc] compile 5.9532757e-2 s +[acc] eval 8.310304e-3 s +[acc] eval 7.1492082e-2 s +[acc] compile 0.155927638 s +[acc] eval 9.260534e-3 s +[acc] eval 0.169095427 s +[acc] compile 0.266411029 s +[acc] eval 3.1185325e-2 s +[acc] eval 0.301862232 s +[acc] compile 0.186185712 s +[acc] eval 1.0294073e-2 s +[acc] eval 0.203839355 s +[acc] compile 0.220112966 s +[acc] eval 2.9503588e-2 s +[acc] eval 0.254540607 s +[acc] compile 1.5925211e-2 s +[acc] eval 1.1042595e-2 s +[acc] eval 3.1812973e-2 s +[acc] compile 4.477904e-3 s +[acc] eval 5.145822e-3 s +[acc] eval 1.1284362e-2 s +[acc] compile 7.234318e-3 s +[acc] eval 5.766865e-3 s +[acc] eval 2.9131842e-2 s +[acc] compile 6.6036975e-2 s +[acc] eval 5.57498e-3 s +[acc] eval 7.4254487e-2 s +[acc] compile 0.371776308 s +[acc] eval 2.276032e-2 s +[acc] eval 0.411158082 s +[acc] compile 5.5823043e-2 s +[acc] eval 8.61698e-3 s +[acc] eval 6.8153381e-2 s +[acc] compile 5.078292e-3 s +[acc] eval 9.101053e-3 s +[acc] eval 1.7602361e-2 s +[acc] compile 0.173956528 s +[acc] eval 2.7979596e-2 s +[acc] eval 0.205680865 s +[acc] compile 0.16367012 s +[acc] eval 1.2215777e-2 s +[acc] eval 0.178915162 s +[acc] compile 0.238624333 s +[acc] eval 1.0012491e-2 s +[acc] eval 0.254926264 s +[acc] compile 1.7848543e-2 s +[acc] eval 5.050839e-3 s +[acc] eval 2.5113938e-2 s +[acc] compile 0.145053569 s +[acc] eval 1.404997e-2 s +[acc] eval 0.16554307 s +[acc] compile 0.28556766 s +[acc] eval 2.8963327e-2 s +[acc] eval 0.317172664 s +[acc] compile 0.117408836 s +[acc] eval 8.916048e-3 s +[acc] eval 0.13144667 s +[acc] compile 5.8736077e-2 s +[acc] eval 5.984561e-3 s +[acc] eval 6.8059307e-2 s +[acc] compile 0.437556595 s +[acc] eval 3.0436696e-2 s +[acc] eval 0.489915233 s +[acc] compile 0.454477607 s +[acc] eval 3.4050841e-2 s +[acc] eval 0.493141948 s +[acc] compile 0.118854578 s +[acc] eval 7.82564e-3 s +[acc] eval 0.129588367 s +[acc] compile 0.170486919 s +[acc] eval 2.4487069e-2 s +[acc] eval 2.397162e-3 s +[acc] eval 0.200362855 s +[acc] compile 1.4220149e-2 s +[acc] eval 2.8073582e-2 s +[acc] eval 4.6936932e-2 s +[acc] eval 1.9298244e-2 s +[acc] eval 2.291253e-2 s +[acc] compile 9.51097e-3 s +[acc] eval 8.117174e-3 s +[acc] eval 3.713431e-3 s +[acc] eval 4.574386e-3 s +[acc] eval 3.997557e-3 s +[acc] eval 1.9286907e-2 s +[acc] eval 3.391065e-3 s +[acc] eval 3.48755e-3 s +[acc] eval 3.145346e-3 s +[acc] eval 2.950694e-3 s +[acc] eval 2.772261e-3 s +[acc] eval 2.9518e-3 s +[acc] eval 3.799772e-3 s +[acc] eval 3.731493e-3 s +[acc] eval 2.940685e-3 s +[acc] eval 3.487792e-3 s +[acc] eval 2.978312e-3 s +[acc] eval 3.745433e-3 s +[acc] eval 3.206818e-3 s +[acc] eval 3.217944e-3 s +[acc] eval 2.566432e-3 s +[acc] eval 2.755788e-3 s +[acc] eval 3.424904e-3 s +[acc] eval 2.945776e-3 s +[acc] eval 2.990881e-3 s +[acc] eval 3.533237e-3 s +[acc] eval 3.778829e-3 s +[acc] eval 3.634237e-3 s +[acc] eval 1.5652313e-2 s +[acc] eval 3.64476e-3 s +[acc] eval 4.074477e-3 s +[acc] eval 3.453575e-3 s +[acc] eval 0.155392309 s +[acc] compile 0.448802791 s +[acc] eval 3.6671801e-2 s +[acc] eval 0.4902614 s +[acc] compile 5.722278e-3 s +[acc] eval 5.199391e-3 s +[acc] eval 1.758527e-3 s +[acc] eval 1.721733e-3 s +[acc] eval 2.048439e-3 s +[acc] eval 2.707575e-3 s +[acc] eval 3.165858e-3 s +[acc] eval 2.214945e-3 s +[acc] eval 1.971899e-3 s +[acc] eval 1.800218e-3 s +[acc] eval 3.1287173e-2 s +[acc] compile 9.5636343e-2 s +[acc] eval 1.0328881e-2 s +[acc] eval 1.8204322e-2 s +[acc] eval 2.665621e-3 s +[acc] eval 2.50604e-3 s +[acc] eval 2.412204e-3 s +[acc] eval 2.497304e-3 s +[acc] eval 2.324426e-3 s +[acc] eval 2.50825e-3 s +[acc] eval 2.793472e-3 s +[acc] eval 2.268833e-3 s +[acc] eval 2.056013e-3 s +[acc] eval 1.94871e-3 s +[acc] eval 1.906937e-3 s +[acc] eval 1.956359e-3 s +[acc] eval 0.155949497 s +[acc] eval 4.065487e-3 s +[acc] eval 2.115609e-3 s +[acc] eval 2.19216e-3 s +[acc] eval 3.638288e-3 s +[acc] eval 3.317388e-3 s +[acc] eval 3.021309e-3 s +[acc] eval 3.018009e-3 s +[acc] eval 1.3887997e-2 s +[acc] eval 2.657563e-3 s +[acc] eval 2.405334e-3 s +[acc] eval 3.299775e-3 s +[acc] eval 3.009196e-3 s +[acc] eval 2.988994e-3 s +[acc] eval 2.596992e-3 s +[acc] eval 2.627867e-3 s +[acc] eval 6.0290795e-2 s +[acc] compile 0.517214391 s +[acc] eval 2.7698267e-2 s +[acc] eval 1.6566934e-2 s +[acc] eval 5.386698e-3 s +[acc] eval 5.319757e-3 s +[acc] eval 5.216741e-3 s +[acc] eval 4.699323e-3 s +[acc] eval 4.779705e-3 s +[acc] eval 4.889001e-3 s +[acc] eval 4.963918e-3 s +[acc] eval 4.806762e-3 s +[acc] eval 4.130646e-3 s +[acc] eval 3.859323e-3 s +[acc] eval 3.35147e-3 s +[acc] eval 3.848942e-3 s +[acc] eval 4.531925e-3 s +[acc] eval 3.720761e-3 s +[acc] eval 3.906484e-3 s +[acc] eval 4.656822e-3 s +[acc] eval 4.954038e-3 s +[acc] eval 4.390393e-3 s +[acc] eval 1.6152008e-2 s +[acc] eval 3.988269e-3 s +[acc] eval 4.775399e-3 s +[acc] eval 5.05935e-3 s +[acc] eval 5.724876e-3 s +[acc] eval 4.03545e-3 s +[acc] eval 3.842472e-3 s +[acc] eval 4.20199e-3 s +[acc] eval 4.977083e-3 s +[acc] eval 3.624617e-3 s +[acc] eval 0.706822498 s +[acc] compile 6.0252879e-2 s +[acc] eval 1.4741057e-2 s +[acc] eval 1.4001895e-2 s +[acc] eval 4.129959e-3 s +[acc] eval 3.208669e-3 s +[acc] eval 3.575898e-3 s +[acc] eval 3.205852e-3 s +[acc] eval 3.893237e-3 s +[acc] eval 3.461236e-3 s +[acc] eval 3.272683e-3 s +[acc] eval 3.142506e-3 s +[acc] eval 2.781575e-3 s +[acc] eval 2.813997e-3 s +[acc] eval 2.320639e-3 s +[acc] eval 2.296717e-3 s +[acc] eval 2.303135e-3 s +[acc] eval 0.134579472 s +[acc] compile 3.6475e-3 s +[acc] eval 3.56758e-3 s +[acc] eval 1.251988e-3 s +[acc] eval 8.44073e-4 s +[acc] eval 1.052642e-3 s +[acc] eval 1.1607255e-2 s +[acc] compile 7.978793e-3 s +[acc] eval 1.8077042e-2 s +[acc] eval 4.440325e-3 s +[acc] eval 1.6497339e-2 s +[acc] eval 3.036744e-3 s +[acc] eval 2.549536e-3 s +[acc] eval 3.259931e-3 s +[acc] eval 3.33005e-3 s +[acc] eval 3.747146e-3 s +[acc] eval 3.564643e-3 s +[acc] eval 3.661531e-3 s +[acc] eval 3.759072e-3 s +[acc] eval 3.03558e-3 s +[acc] eval 2.656971e-3 s +[acc] eval 2.748824e-3 s +[acc] eval 2.568373e-3 s +[acc] eval 3.727092e-3 s +[acc] eval 3.893912e-3 s +[acc] eval 3.859497e-3 s +[acc] eval 2.203381e-3 s +[acc] eval 2.220741e-3 s +[acc] eval 4.071563e-3 s +[acc] eval 3.635481e-3 s +[acc] eval 3.245124e-3 s +[acc] eval 2.662729e-3 s +[acc] eval 0.119532385 s +[acc] compile 7.0353317e-2 s +[acc] eval 1.0026423e-2 s +[acc] eval 8.6245473e-2 s +[acc] compile 0.272778269 s +[acc] eval 4.0707707e-2 s +[acc] eval 3.85981e-3 s +[acc] eval 3.699175e-3 s +[acc] eval 4.436082e-3 s +[acc] eval 4.063185e-3 s +[acc] eval 4.533272e-3 s +[acc] eval 3.745108e-3 s +[acc] eval 3.705948e-3 s +[acc] eval 4.270516e-3 s +[acc] eval 4.834574e-3 s +[acc] eval 3.547196e-3 s +[acc] eval 3.416842e-3 s +[acc] eval 3.493358e-3 s +[acc] eval 4.415945e-3 s +[acc] eval 3.526424e-3 s +[acc] eval 3.149304e-3 s +[acc] eval 4.207825e-3 s +[acc] eval 4.776966e-3 s +[acc] eval 3.583175e-3 s +[acc] eval 3.677857e-3 s +[acc] eval 4.193198e-3 s +[acc] eval 4.937097e-3 s +[acc] eval 4.670023e-3 s +[acc] eval 4.917297e-3 s +[acc] eval 5.119543e-3 s +[acc] eval 4.877889e-3 s +[acc] eval 6.43571e-3 s +[acc] eval 2.2682451e-2 s +[acc] eval 4.404683e-3 s +[acc] eval 5.09151e-3 s +[acc] eval 3.739681e-3 s +[acc] eval 4.0445e-3 s +[acc] eval 5.300412e-3 s +[acc] eval 4.912969e-3 s +[acc] eval 4.81758e-3 s +[acc] eval 0.489284647 s +[acc] compile 0.254356953 s +[acc] eval 3.5278215e-2 s +[acc] eval 3.790942e-3 s +[acc] eval 0.306200642 s +[acc] compile 8.099939e-2 s +[acc] eval 9.116074e-3 s +[acc] eval 3.44037e-3 s +[acc] eval 5.020708e-3 s +[acc] eval 5.024436e-3 s +[acc] eval 4.614115e-3 s +[acc] eval 4.350482e-3 s +[acc] eval 4.879152e-3 s +[acc] eval 4.568819e-3 s +[acc] eval 1.5732619e-2 s +[acc] eval 4.302838e-3 s +[acc] eval 4.134823e-3 s +[acc] eval 4.471831e-3 s +[acc] eval 0.160373933 s +[acc] compile 0.180370402 s +[acc] eval 2.4572117e-2 s +[acc] eval 0.20967766 s +[acc] compile 8.443267e-3 s +[acc] eval 1.8108929e-2 s +[acc] eval 2.8168645e-2 s +[acc] compile 3.128717e-3 s +[acc] eval 4.754951e-3 s +[acc] eval 9.616918e-3 s +[acc] compile 5.2586669e-2 s +[acc] eval 4.790227e-3 s +[acc] eval 5.9249997e-2 s +[acc] compile 0.142601719 s +[acc] eval 7.571862e-3 s +[acc] eval 0.153356003 s +[acc] compile 0.158076855 s +[acc] eval 5.637278e-3 s +[acc] eval 0.165665928 s +[acc] compile 0.254750781 s +[acc] eval 3.4253465e-2 s +[acc] eval 0.306526775 s +[acc] eval 9.956971e-3 s +[acc] eval 1.311758e-2 s +[acc] compile 0.28233628 s +[acc] eval 3.4336784e-2 s +[acc] eval 0.320049858 s +[acc] compile 0.173995519 s +[acc] eval 8.648526e-3 s +[acc] eval 0.185866193 s +[acc] compile 7.436706e-2 s +[acc] eval 6.322879e-3 s +[acc] eval 9.49718e-2 s +[acc] compile 0.190073478 s +[acc] eval 1.0853285e-2 s +[acc] eval 0.205679096 s +[acc] compile 0.18253041 s +[acc] eval 1.9309278e-2 s +[acc] eval 0.206277891 s +[acc] eval 7.919875e-3 s +[acc] eval 1.2547867e-2 s +[acc] compile 0.120768456 s +[acc] eval 3.7603016e-2 s +[acc] eval 0.163674217 s +[acc] compile 6.481091e-3 s +[acc] eval 7.389129e-3 s +[acc] eval 1.301078e-3 s +[acc] eval 1.887691e-3 s +[acc] eval 1.9282898e-2 s +[acc] compile 7.5775929e-2 s +[acc] eval 1.1920071e-2 s +[acc] eval 9.2229341e-2 s +[acc] compile 0.311518919 s +[acc] eval 5.106941e-2 s +[acc] eval 0.367411267 s +[acc] compile 0.204921117 s +[acc] eval 1.1545472e-2 s +[acc] eval 0.221479427 s +[acc] compile 0.436479854 s +[acc] eval 1.3693689e-2 s +[acc] eval 0.47181437 s +[acc] compile 4.213407e-3 s +[acc] eval 7.975511e-3 s +[acc] eval 1.830898e-3 s +[acc] eval 1.922414e-3 s +[acc] eval 1.732274e-3 s +[acc] eval 1.876353e-3 s +[acc] eval 2.29897e-3 s +[acc] eval 2.984471e-3 s +[acc] eval 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+[acc] eval 2.063127e-3 s +[acc] eval 1.87287e-3 s +[acc] eval 1.784715e-3 s +[acc] eval 1.49066e-3 s +[acc] eval 1.406821e-3 s +[acc] eval 1.480115e-3 s +[acc] eval 1.507974e-3 s +[acc] eval 1.447218e-3 s +[acc] eval 1.693756e-3 s +[acc] eval 1.586872e-3 s +[acc] eval 1.545852e-3 s +[acc] eval 1.484081e-3 s +[acc] eval 1.553698e-3 s +[acc] eval 1.370357e-3 s +[acc] eval 1.348461e-3 s +[acc] eval 1.488115e-3 s +[acc] eval 1.456665e-3 s +[acc] eval 1.408392e-3 s +[acc] eval 1.333387e-3 s +[acc] eval 1.352643e-3 s +[acc] eval 1.19424e-3 s +[acc] eval 1.202775e-3 s +[acc] eval 1.351145e-3 s +[acc] eval 7.7607524e-2 s +[acc] compile 3.847955e-3 s +[acc] eval 8.465673e-3 s +[acc] eval 1.66182e-3 s +[acc] eval 1.580611e-3 s +[acc] eval 1.863795e-3 s +[acc] eval 2.309587e-3 s +[acc] eval 2.173467e-3 s +[acc] eval 2.201322e-2 s +[acc] eval 2.065363e-3 s +[acc] eval 1.873664e-3 s +[acc] eval 1.585273e-3 s +[acc] eval 1.755241e-3 s +[acc] eval 1.680455e-3 s +[acc] eval 5.5996658e-2 s +[acc] compile 0.187693854 s +[acc] eval 2.3141317e-2 s +[acc] eval 0.216132253 s +[acc] compile 1.074997e-3 s +[acc] eval 3.1005951e-2 s +[acc] compile 7.7867726e-2 s +[acc] eval 1.0883902e-2 s +[acc] eval 5.308692e-3 s +[acc] eval 4.163873e-3 s +[acc] eval 3.953993e-3 s +[acc] eval 5.447803e-3 s +[acc] eval 2.0752797e-2 s +[acc] eval 2.174763e-3 s +[acc] eval 2.020119e-3 s +[acc] eval 2.265472e-3 s +[acc] eval 2.338113e-3 s +[acc] eval 2.254489e-3 s +[acc] eval 2.062286e-3 s +[acc] eval 2.107447e-3 s +[acc] eval 2.486089e-3 s +[acc] eval 3.321005e-3 s +[acc] eval 2.430283e-3 s +[acc] eval 2.146901e-3 s +[acc] eval 1.938746e-3 s +[acc] eval 1.965208e-3 s +[acc] eval 1.861752e-3 s +[acc] eval 2.766777e-3 s +[acc] eval 2.520843e-3 s +[acc] eval 2.59554e-3 s +[acc] eval 2.551199e-3 s +[acc] eval 1.738946e-3 s +[acc] eval 1.90215e-3 s +[acc] eval 2.162422e-3 s +[acc] eval 3.107194e-3 s +[acc] eval 2.583921e-3 s +[acc] eval 2.692089e-3 s +[acc] eval 0.190069779 s +[acc] compile 0.247026267 s +[acc] eval 3.9722258e-2 s +[acc] eval 0.290695321 s +[acc] compile 0.217485192 s +[acc] eval 1.813243e-2 s +[acc] eval 5.955254e-3 s +[acc] eval 5.381067e-3 s +[acc] eval 4.150444e-3 s +[acc] eval 3.729101e-3 s +[acc] eval 3.300905e-3 s +[acc] eval 1.5228206e-2 s +[acc] eval 2.897487e-3 s +[acc] eval 3.099988e-3 s +[acc] eval 3.13443e-3 s +[acc] eval 2.377531e-3 s +[acc] eval 2.080347e-3 s +[acc] eval 2.057658e-3 s +[acc] eval 2.116617e-3 s +[acc] eval 2.275954e-3 s +[acc] eval 3.478344e-3 s +[acc] eval 2.48065e-3 s +[acc] eval 2.089977e-3 s +[acc] eval 2.288992e-3 s +[acc] eval 2.458488e-3 s +[acc] eval 1.968258e-3 s +[acc] eval 2.765651e-3 s +[acc] eval 2.719259e-3 s +[acc] eval 2.122261e-3 s +[acc] eval 2.03941e-3 s +[acc] eval 2.043484e-3 s +[acc] eval 2.050457e-3 s +[acc] eval 2.324528e-3 s +[acc] eval 2.041857e-3 s +[acc] eval 0.332921623 s +[acc] compile 5.523496e-3 s +[acc] eval 5.319933e-3 s +[acc] eval 1.2242939e-2 s +[acc] compile 0.120067369 s +[acc] eval 3.3123536e-2 s +[acc] eval 0.171599177 s +[acc] compile 0.581155373 s +[acc] eval 4.3277302e-2 s +[acc] eval 2.4298666e-2 s +[acc] eval 4.577408e-3 s +[acc] eval 7.353087e-3 s +[acc] eval 6.066057e-3 s +[acc] eval 5.040116e-3 s +[acc] eval 1.7236451e-2 s +[acc] eval 7.262277e-3 s +[acc] eval 6.207716e-3 s +[acc] eval 7.71026e-3 s +[acc] eval 1.9998051e-2 s +[acc] eval 5.065537e-3 s +[acc] eval 5.200296e-3 s +[acc] eval 4.867173e-3 s +[acc] eval 7.064341e-3 s +[acc] eval 1.6653261e-2 s +[acc] eval 5.291875e-3 s +[acc] eval 6.929765e-3 s +[acc] eval 8.624677e-3 s +[acc] eval 7.189986e-3 s +[acc] eval 1.9630377e-2 s +[acc] eval 7.495835e-3 s +[acc] eval 7.187923e-3 s +[acc] eval 6.489284e-3 s +[acc] eval 6.891245e-3 s +[acc] eval 1.8912807e-2 s +[acc] eval 5.243994e-3 s +[acc] eval 5.176217e-3 s +[acc] eval 5.409745e-3 s +[acc] eval 7.196332e-3 s +[acc] eval 0.90996627 s +[acc] compile 7.1647285e-2 s +[acc] eval 1.7648512e-2 s +[acc] eval 9.3417002e-2 s +[acc] compile 7.5817e-2 s +[acc] eval 3.7038863e-2 s +[acc] eval 4.476472e-3 s +[acc] eval 4.089437e-3 s +[acc] eval 3.587853e-3 s +[acc] eval 4.095382e-3 s +[acc] eval 4.474767e-3 s +[acc] eval 3.724943e-3 s +[acc] eval 3.812041e-3 s +[acc] eval 3.500406e-3 s +[acc] eval 2.87375e-3 s +[acc] eval 2.686171e-3 s +[acc] eval 2.433225e-3 s +[acc] eval 2.462056e-3 s +[acc] eval 2.411601e-3 s +[acc] eval 3.032557e-3 s +[acc] eval 2.6691e-3 s +[acc] eval 2.446131e-3 s +[acc] eval 2.42248e-3 s +[acc] eval 2.457616e-3 s +[acc] eval 2.468026e-3 s +[acc] eval 3.137415e-3 s +[acc] eval 2.787601e-3 s +[acc] eval 2.396564e-3 s +[acc] eval 2.542576e-3 s +[acc] eval 2.56548e-3 s +[acc] eval 2.484461e-3 s +[acc] eval 2.443265e-3 s +[acc] eval 2.653607e-3 s +[acc] eval 3.014321e-3 s +[acc] eval 2.872456e-3 s +[acc] eval 0.207426339 s +[acc] compile 0.13769598 s +[acc] eval 7.955305e-3 s +[acc] eval 0.152629296 s +[acc] compile 9.3379919e-2 s +[acc] eval 6.908795e-3 s +[acc] eval 5.062032e-3 s +[acc] eval 4.874062e-3 s +[acc] eval 5.602067e-3 s +[acc] eval 1.7500866e-2 s +[acc] eval 2.848697e-3 s +[acc] eval 2.832185e-3 s +[acc] eval 3.309942e-3 s +[acc] eval 3.216839e-3 s +[acc] eval 2.853984e-3 s +[acc] eval 2.761937e-3 s +[acc] eval 2.532644e-3 s +[acc] eval 2.666775e-3 s +[acc] eval 3.168252e-3 s +[acc] eval 2.353114e-3 s +[acc] eval 2.050714e-3 s +[acc] eval 1.924002e-3 s +[acc] eval 2.772993e-3 s +[acc] eval 0.17450464 s +[acc] compile 4.7138679e-2 s +[acc] eval 1.5666557e-2 s +[acc] eval 3.662826e-3 s +[acc] eval 3.883621e-3 s +[acc] eval 1.6628294e-2 s +[acc] eval 3.191451e-3 s +[acc] eval 3.119256e-3 s +[acc] eval 2.986813e-3 s +[acc] eval 2.712779e-3 s +[acc] eval 2.727691e-3 s +[acc] eval 2.506896e-3 s +[acc] eval 2.586505e-3 s +[acc] eval 2.486899e-3 s +[acc] eval 2.471436e-3 s +[acc] eval 0.115369137 s +[acc] compile 4.925655e-3 s +[acc] eval 4.485428e-3 s +[acc] eval 1.656652e-3 s +[acc] eval 1.910445e-3 s +[acc] eval 1.736202e-3 s +[acc] eval 1.359006e-3 s +[acc] eval 1.613382e-3 s +[acc] eval 2.120387e-3 s +[acc] eval 2.321529e-3 s +[acc] eval 1.896305e-3 s +[acc] eval 2.561648e-3 s +[acc] eval 2.30174e-3 s +[acc] eval 2.183663e-3 s +[acc] eval 3.4864604e-2 s +[acc] compile 1.2141618e-2 s +[acc] eval 3.2657124e-2 s +[acc] eval 3.580916e-3 s +[acc] eval 3.063314e-3 s +[acc] eval 2.91085e-3 s +[acc] eval 2.964424e-3 s +[acc] eval 3.973995e-3 s +[acc] eval 3.063196e-3 s +[acc] eval 3.035075e-3 s +[acc] eval 2.944712e-3 s +[acc] eval 3.065241e-3 s +[acc] eval 3.347913e-3 s +[acc] eval 3.229968e-3 s +[acc] eval 3.085789e-3 s +[acc] eval 3.211835e-3 s +[acc] eval 4.219907e-3 s +[acc] eval 2.671838e-3 s +[acc] eval 2.505796e-3 s +[acc] eval 2.357742e-3 s +[acc] eval 2.485217e-3 s +[acc] eval 2.709624e-3 s +[acc] eval 3.549628e-3 s +[acc] eval 2.768428e-3 s +[acc] eval 2.629199e-3 s +[acc] eval 2.516015e-3 s +[acc] eval 2.492359e-3 s +[acc] eval 3.308e-3 s +[acc] eval 3.285815e-3 s +[acc] eval 2.633102e-3 s +[acc] eval 2.35938e-3 s +[acc] eval 2.379532e-3 s +[acc] eval 0.138173397 s +[acc] compile 0.166164748 s +[acc] eval 2.2620656e-2 s +[acc] eval 0.194446583 s +[acc] compile 3.51075e-4 s +[acc] eval 6.096218e-3 s +[acc] compile 1.8372642e-2 s +[acc] eval 8.528267e-3 s +[acc] eval 1.691991e-3 s +[acc] eval 1.573448e-3 s +[acc] eval 1.538725e-3 s +[acc] eval 1.604673e-3 s +[acc] eval 1.541442e-3 s +[acc] eval 1.513121e-3 s +[acc] eval 1.671936e-3 s +[acc] eval 1.4748478e-2 s +[acc] eval 1.607904e-3 s +[acc] eval 1.598567e-3 s +[acc] eval 1.141019e-3 s +[acc] eval 1.406997e-3 s +[acc] eval 1.557953e-3 s +[acc] eval 1.638288e-3 s +[acc] eval 1.039932e-3 s +[acc] eval 9.37075e-4 s +[acc] eval 8.52846e-4 s +[acc] eval 9.84915e-4 s +[acc] eval 1.191168e-3 s +[acc] eval 1.107659e-3 s +[acc] eval 1.132526e-3 s +[acc] eval 1.118926e-3 s +[acc] eval 1.109331e-3 s +[acc] eval 1.058539e-3 s +[acc] eval 1.0407e-3 s +[acc] eval 1.138006e-3 s +[acc] eval 1.474531e-3 s +[acc] eval 1.645938e-3 s +[acc] eval 1.420666e-3 s +[acc] eval 1.382147e-3 s +[acc] eval 8.403715e-2 s +[acc] compile 6.0966572e-2 s +[acc] eval 1.559058e-2 s +[acc] eval 1.722344e-3 s +[acc] eval 1.527085e-3 s +[acc] eval 1.295738e-3 s +[acc] eval 1.385385e-3 s +[acc] eval 1.2996233e-2 s +[acc] eval 1.500535e-3 s +[acc] eval 1.638067e-3 s +[acc] eval 1.5898e-3 s +[acc] eval 1.896385e-3 s +[acc] eval 1.829717e-3 s +[acc] eval 1.812996e-3 s +[acc] eval 1.699624e-3 s +[acc] eval 1.217081e-3 s +[acc] eval 1.111758e-3 s +[acc] eval 1.095272e-3 s +[acc] eval 1.247234e-3 s +[acc] eval 1.293137e-3 s +[acc] eval 1.132533e-3 s +[acc] eval 1.165901e-3 s +[acc] eval 1.359372e-3 s +[acc] eval 1.632993e-3 s +[acc] eval 3.333676e-3 s +[acc] eval 1.200009e-3 s +[acc] eval 1.221858e-3 s +[acc] eval 1.163216e-3 s +[acc] eval 1.309218e-3 s +[acc] eval 1.142695e-3 s +[acc] eval 1.196863e-3 s +[acc] eval 1.172531e-3 s +[acc] eval 0.135475076 s +[acc] compile 4.167067e-3 s +[acc] eval 1.0492961e-2 s +[acc] eval 1.068037e-3 s +[acc] eval 1.176307e-3 s +[acc] eval 1.85738e-2 s +[acc] compile 0.137928403 s +[acc] eval 3.8064536e-2 s +[acc] eval 0.182379259 s +[acc] compile 5.446173e-3 s +[acc] eval 5.18479e-3 s +[acc] eval 1.531988e-3 s +[acc] eval 1.384937e-3 s +[acc] eval 1.17208e-3 s +[acc] eval 1.532563e-3 s +[acc] eval 1.660161e-3 s +[acc] eval 1.936422e-3 s +[acc] eval 2.863154e-3 s +[acc] eval 2.768123e-3 s +[acc] eval 1.984592e-3 s +[acc] eval 1.530781e-3 s +[acc] eval 1.680107e-3 s +[acc] eval 1.4640994e-2 s +[acc] eval 1.786169e-3 s +[acc] eval 2.311544e-3 s +[acc] eval 1.659004e-3 s +[acc] eval 1.412393e-3 s +[acc] eval 1.367319e-3 s +[acc] eval 1.293674e-3 s +[acc] eval 1.100746e-3 s +[acc] eval 9.3691e-4 s +[acc] eval 9.43536e-4 s +[acc] eval 8.87902e-4 s +[acc] eval 8.72468e-4 s +[acc] eval 8.13471e-4 s +[acc] eval 9.65856e-4 s +[acc] eval 1.194642e-3 s +[acc] eval 1.216413e-3 s +[acc] eval 1.108195e-3 s +[acc] eval 1.539012e-3 s +[acc] eval 7.0973231e-2 s +[acc] eval 1.168115e-2 s +[acc] eval 2.11742e-3 s +[acc] eval 1.623164e-3 s +[acc] eval 1.60028e-3 s +[acc] eval 1.547217e-3 s +[acc] eval 1.621996e-3 s +[acc] eval 1.546744e-3 s +[acc] eval 1.464672e-3 s +[acc] eval 1.5376641e-2 s +[acc] eval 1.71708e-3 s +[acc] eval 1.641783e-3 s +[acc] eval 1.617578e-3 s +[acc] eval 1.439482e-3 s +[acc] eval 1.506867e-3 s +[acc] eval 1.680319e-3 s +[acc] eval 1.611631e-3 s +[acc] eval 1.564504e-3 s +[acc] eval 1.691396e-3 s +[acc] eval 2.133751e-3 s +[acc] eval 1.721654e-3 s +[acc] eval 1.316359e-3 s +[acc] eval 1.249563e-3 s +[acc] eval 1.622804e-3 s +[acc] eval 1.482503e-3 s +[acc] eval 2.488288e-3 s +[acc] eval 1.460867e-3 s +[acc] eval 1.593182e-3 s +[acc] eval 1.675149e-3 s +[acc] eval 1.500962e-3 s +[acc] eval 1.21362e-3 s +[acc] eval 7.8461124e-2 s +[acc] compile 7.3791712e-2 s +[acc] eval 1.9746657e-2 s +[acc] eval 5.075243e-3 s +[acc] eval 6.040798e-3 s +[acc] eval 3.887084e-3 s +[acc] eval 1.5790532e-2 s +[acc] eval 2.530782e-3 s +[acc] eval 2.426657e-3 s +[acc] eval 2.205841e-3 s +[acc] eval 2.344212e-3 s +[acc] eval 2.340232e-3 s +[acc] eval 2.361268e-3 s +[acc] eval 2.767893e-3 s +[acc] eval 3.020613e-3 s +[acc] eval 2.577805e-3 s +[acc] eval 2.46851e-3 s +[acc] eval 2.637674e-3 s +[acc] eval 2.580701e-3 s +[acc] eval 2.566502e-3 s +[acc] eval 2.542187e-3 s +[acc] eval 2.560301e-3 s +[acc] eval 2.163062e-3 s +[acc] eval 2.163867e-3 s +[acc] eval 1.939877e-3 s +[acc] eval 2.406128e-3 s +[acc] eval 2.652856e-3 s +[acc] eval 2.422281e-3 s +[acc] eval 2.334583e-3 s +[acc] eval 2.180854e-3 s +[acc] eval 2.222694e-3 s +[acc] eval 2.479997e-3 s +[acc] eval 3.468469e-3 s +[acc] eval 3.361926e-3 s +[acc] eval 0.200083961 s +[acc] compile 0.290447645 s +[acc] eval 2.4970478e-2 s +[acc] eval 0.333460813 s +[acc] compile 4.93208e-3 s +[acc] eval 1.1606432e-2 s +[acc] eval 1.871053e-3 s +[acc] eval 2.18732e-3 s +[acc] eval 1.820446e-3 s +[acc] eval 2.970681e-3 s +[acc] eval 2.698404e-3 s +[acc] eval 1.867778e-3 s +[acc] eval 1.729267e-3 s +[acc] eval 1.6646587e-2 s +[acc] eval 1.673337e-3 s +[acc] eval 1.592929e-3 s +[acc] eval 1.561965e-3 s +[acc] eval 1.34639e-3 s +[acc] eval 1.297387e-3 s +[acc] eval 5.9282445e-2 s +[acc] compile 8.909129e-3 s +[acc] eval 1.5377033e-2 s +[acc] eval 2.232926e-3 s +[acc] eval 2.08325e-3 s +[acc] eval 1.577014e-3 s +[acc] eval 1.528661e-3 s +[acc] eval 1.3723483e-2 s +[acc] eval 1.33098e-3 s +[acc] eval 1.228679e-3 s +[acc] eval 1.150774e-3 s +[acc] eval 1.168528e-3 s +[acc] eval 1.233217e-3 s +[acc] eval 1.198002e-3 s +[acc] eval 1.367115e-3 s +[acc] eval 2.070276e-3 s +[acc] eval 1.94119e-3 s +[acc] eval 1.717592e-3 s +[acc] eval 1.521073e-3 s +[acc] eval 1.587526e-3 s +[acc] eval 1.561866e-3 s +[acc] eval 1.600144e-3 s +[acc] eval 1.489059e-3 s +[acc] eval 1.560283e-3 s +[acc] eval 7.4079491e-2 s +[acc] compile 0.413251398 s +[acc] eval 3.3194661e-2 s +[acc] eval 0.466662329 s +[acc] compile 0.20319808 s +[acc] eval 1.7962402e-2 s +[acc] eval 0.240311145 s +[acc] compile 0.267253155 s +[acc] eval 2.6590899e-2 s +[acc] eval 0.297450365 s +[acc] compile 0.289845895 s +[acc] eval 1.5252272e-2 s +[acc] eval 1.17016e-2 s +[acc] eval 1.1396249e-2 s +[acc] eval 2.1681004e-2 s +[acc] eval 1.0868478e-2 s +[acc] eval 1.0805669e-2 s +[acc] eval 8.809873e-3 s +[acc] eval 7.035957e-3 s +[acc] eval 1.3742765e-2 s +[acc] eval 6.877053e-3 s +[acc] eval 7.340015e-3 s +[acc] eval 3.796146e-3 s +[acc] eval 0.424170853 s +[acc] compile 1.0710678e-2 s +[acc] eval 8.027648e-3 s +[acc] eval 2.2950335e-2 s +[acc] compile 5.6825523e-2 s +[acc] eval 3.4072947e-2 s +[acc] eval 4.671157e-3 s +[acc] eval 4.076718e-3 s +[acc] eval 4.073514e-3 s +[acc] eval 4.976658e-3 s +[acc] eval 4.502976e-3 s +[acc] eval 3.919434e-3 s +[acc] eval 2.991415e-3 s +[acc] eval 2.542967e-3 s +[acc] eval 3.95531e-3 s +[acc] eval 3.031839e-3 s +[acc] eval 2.608034e-3 s +[acc] eval 1.965105e-3 s +[acc] eval 1.874711e-3 s +[acc] eval 1.951934e-3 s +[acc] eval 2.653763e-3 s +[acc] eval 0.147214746 s +[acc] compile 0.333173909 s +[acc] eval 2.9168641e-2 s +[acc] eval 0.368440616 s +[acc] compile 0.572559219 s +[acc] eval 2.7991628e-2 s +[acc] eval 0.606170312 s +[acc] compile 0.339614919 s +[acc] eval 1.1556834e-2 s +[acc] eval 0.356619016 s +[acc] compile 5.367082e-3 s +[acc] eval 1.8860396e-2 s +[acc] eval 2.6045955e-2 s +[acc] compile 0.212312168 s +[acc] eval 2.151694e-2 s +[acc] eval 0.241601531 s +[acc] eval 2.1634135e-2 s +[acc] eval 2.365875e-2 s +[acc] compile 0.309857348 s +[acc] eval 1.3682357e-2 s +[acc] eval 0.328943908 s +[acc] compile 1.8956893e-2 s +[acc] eval 3.7722926e-2 s +[acc] eval 6.1208585e-2 s +[acc] compile 1.7296296e-2 s +[acc] eval 1.346205e-2 s +[acc] eval 3.7561959e-2 s +[acc] eval 2.2239327e-2 s +[acc] eval 2.5820749e-2 s +[acc] compile 0.138669966 s +[acc] eval 8.450089e-3 s +[acc] eval 0.150875553 s +[acc] compile 7.1319282e-2 s +[acc] eval 7.199285e-3 s +[acc] eval 2.6377e-3 s +[acc] eval 8.431127e-2 s +[acc] compile 8.37066e-3 s +[acc] eval 8.541905e-3 s +[acc] eval 2.1562726e-2 s +[acc] compile 3.2321007e-2 s +[acc] eval 1.0426222e-2 s +[acc] eval 4.9627937e-2 s +[acc] compile 7.4345782e-2 s +[acc] eval 1.0004926e-2 s +[acc] eval 9.1280046e-2 s +[acc] eval 2.8541804e-2 s +[acc] eval 3.1098334e-2 s +[acc] eval 5.193415e-3 s +[acc] eval 7.749754e-3 s +[acc] eval 5.406816e-3 s +[acc] eval 8.162819e-3 s +[acc] compile 8.2591255e-2 s +[acc] eval 1.1241773e-2 s +[acc] eval 9.77317e-2 s +[acc] eval 3.0786853e-2 s +[acc] eval 3.384204e-2 s +[acc] compile 0.116316672 s +[acc] eval 1.2572165e-2 s +[acc] eval 4.863952e-3 s +[acc] eval 1.7470129e-2 s +[acc] eval 4.359111e-3 s +[acc] eval 4.534856e-3 s +[acc] eval 5.971392e-3 s +[acc] eval 7.009356e-3 s +[acc] eval 3.928631e-3 s +[acc] eval 4.69658e-3 s +[acc] eval 3.189528e-3 s +[acc] eval 3.048674e-3 s +[acc] eval 3.05467e-3 s +[acc] eval 0.196714486 s +[acc] compile 7.715508e-3 s +[acc] eval 1.9434703e-2 s +[acc] eval 3.0823262e-2 s +[acc] compile 0.288514895 s +[acc] eval 2.4698942e-2 s +[acc] eval 0.319084562 s +[acc] compile 0.183881016 s +[acc] eval 8.871113e-3 s +[acc] eval 0.198605761 s +[acc] compile 0.185878576 s +[acc] eval 1.1253824e-2 s +[acc] eval 0.202129749 s +[acc] compile 0.161141818 s +[acc] eval 2.0642864e-2 s +[acc] eval 0.199422269 s +[acc] compile 5.224638e-3 s +[acc] eval 7.463862e-3 s +[acc] eval 4.209745e-3 s +[acc] eval 3.497989e-3 s +[acc] eval 3.715798e-3 s +[acc] eval 3.56621e-3 s +[acc] eval 1.8315905e-2 s +[acc] eval 3.856657e-3 s +[acc] eval 3.804117e-3 s +[acc] eval 3.296479e-3 s +[acc] eval 3.580388e-3 s +[acc] eval 3.38409e-3 s +[acc] eval 3.034511e-3 s +[acc] eval 2.150373e-3 s +[acc] eval 2.101944e-3 s +[acc] eval 2.605168e-3 s +[acc] eval 2.494044e-3 s +[acc] eval 2.286831e-3 s +[acc] eval 2.291176e-3 s +[acc] eval 3.548995e-3 s +[acc] eval 3.408244e-3 s +[acc] eval 3.635848e-3 s +[acc] eval 2.215054e-3 s +[acc] eval 1.979096e-3 s +[acc] eval 2.107738e-3 s +[acc] eval 2.719062e-3 s +[acc] eval 2.3926e-3 s +[acc] eval 2.075313e-3 s +[acc] eval 1.997975e-3 s +[acc] eval 0.11151603 s +[acc] compile 0.246078341 s +[acc] eval 1.9213464e-2 s +[acc] eval 0.269833336 s +[acc] compile 1.153239e-3 s +[acc] eval 4.771174e-3 s +[acc] compile 7.1809113e-2 s +[acc] eval 9.978116e-3 s +[acc] eval 8.5954141e-2 s +[acc] eval 2.4826596e-2 s +[acc] eval 2.8662067e-2 s +[acc] compile 0.108027806 s +[acc] eval 1.8202164e-2 s +[acc] eval 0.13049409 s +[acc] compile 0.31177357 s +[acc] eval 4.4910958e-2 s +[acc] eval 0.361657416 s +[acc] compile 0.174224007 s +[acc] eval 2.2830903e-2 s +[acc] eval 0.202356514 s +[acc] compile 0.182819057 s +[acc] eval 2.1734188e-2 s +[acc] eval 0.207774568 s +[acc] compile 6.7874018e-2 s +[acc] eval 6.557495e-3 s +[acc] eval 7.760761e-2 s +[acc] compile 0.107998153 s +[acc] eval 1.3696707e-2 s +[acc] eval 0.144317759 s +[acc] compile 0.221589048 s +[acc] eval 1.0885679e-2 s +[acc] eval 9.331584e-3 s +[acc] eval 0.252328747 s +[acc] eval 3.8799356e-2 s +[acc] eval 4.2978759e-2 s +[acc] compile 6.224139e-3 s +[acc] eval 1.6531481e-2 s +[acc] eval 2.6669394e-2 s +[acc] compile 8.11255e-4 s +[acc] eval 2.706773e-2 s +[acc] compile 0.239375196 s +[acc] eval 1.3421891e-2 s +[acc] eval 6.488678e-3 s +[acc] eval 2.2430439e-2 s +[acc] eval 3.108442e-3 s +[acc] eval 2.971846e-3 s +[acc] eval 2.740546e-3 s +[acc] eval 2.640457e-3 s +[acc] eval 3.075638e-3 s +[acc] eval 2.723824e-3 s +[acc] eval 3.605313e-3 s +[acc] eval 2.980914e-3 s +[acc] eval 3.038158e-3 s +[acc] eval 2.961167e-3 s +[acc] eval 3.154621e-3 s +[acc] eval 2.941284e-3 s +[acc] eval 3.289196e-3 s +[acc] eval 2.835993e-3 s +[acc] eval 2.858905e-3 s +[acc] eval 3.302697e-3 s +[acc] eval 3.084275e-3 s +[acc] eval 3.199963e-3 s +[acc] eval 3.31265e-3 s +[acc] eval 3.324103e-3 s +[acc] eval 3.043588e-3 s +[acc] eval 3.580072e-3 s +[acc] eval 3.047965e-3 s +[acc] eval 3.098989e-3 s +[acc] eval 2.904992e-3 s +[acc] eval 2.735287e-3 s +[acc] eval 2.457166e-3 s +[acc] eval 0.370436053 s +[acc] compile 6.629233e-3 s +[acc] eval 5.176684e-3 s +[acc] eval 1.6632612e-2 s +[acc] eval 2.580507e-3 s +[acc] eval 2.358021e-3 s +[acc] eval 2.362597e-3 s +[acc] eval 2.306221e-3 s +[acc] eval 2.294649e-3 s +[acc] eval 2.09982e-3 s +[acc] eval 2.216918e-3 s +[acc] eval 2.175757e-3 s +[acc] eval 2.408111e-3 s +[acc] eval 2.175857e-3 s +[acc] eval 2.118968e-3 s +[acc] eval 2.109271e-3 s +[acc] eval 2.273242e-3 s +[acc] eval 2.156913e-3 s +[acc] eval 2.106995e-3 s +[acc] eval 2.108276e-3 s +[acc] eval 2.29489e-3 s +[acc] eval 2.384996e-3 s +[acc] eval 2.296949e-3 s +[acc] eval 2.427074e-3 s +[acc] eval 2.299931e-3 s +[acc] eval 2.208679e-3 s +[acc] eval 2.35198e-3 s +[acc] eval 2.686106e-3 s +[acc] eval 2.758399e-3 s +[acc] eval 2.472998e-3 s +[acc] eval 2.410384e-3 s +[acc] eval 2.316698e-3 s +[acc] eval 9.8114247e-2 s +[acc] compile 0.132301028 s +[acc] eval 2.6480908e-2 s +[acc] eval 3.625309e-3 s +[acc] eval 3.831763e-3 s +[acc] eval 1.6173305e-2 s +[acc] eval 2.89754e-3 s +[acc] eval 2.973654e-3 s +[acc] eval 2.370583e-3 s +[acc] eval 2.584373e-3 s +[acc] eval 2.45463e-3 s +[acc] eval 2.391475e-3 s +[acc] eval 2.25175e-3 s +[acc] eval 2.211016e-3 s +[acc] eval 2.271124e-3 s +[acc] eval 2.23582e-3 s +[acc] eval 2.511534e-3 s +[acc] eval 2.48563e-3 s +[acc] eval 2.410586e-3 s +[acc] eval 2.330566e-3 s +[acc] eval 2.526934e-3 s +[acc] eval 3.059103e-3 s +[acc] eval 2.784348e-3 s +[acc] eval 2.560705e-3 s +[acc] eval 2.488569e-3 s +[acc] eval 2.212384e-3 s +[acc] eval 2.274406e-3 s +[acc] eval 2.218247e-3 s +[acc] eval 2.240388e-3 s +[acc] eval 2.223841e-3 s +[acc] eval 2.210566e-3 s +[acc] eval 2.45811e-3 s +[acc] eval 2.849029e-3 s +[acc] eval 0.255611607 s +[acc] compile 0.198423179 s +[acc] eval 2.2482313e-2 s +[acc] eval 0.224231413 s +[acc] compile 4.13753e-3 s +[acc] eval 6.979747e-3 s +[acc] eval 1.9114363e-2 s +[acc] eval 1.099958e-3 s +[acc] eval 1.027962e-3 s +[acc] eval 9.95359e-4 s +[acc] eval 1.227717e-3 s +[acc] eval 1.241981e-3 s +[acc] eval 1.13738e-3 s +[acc] eval 9.66181e-4 s +[acc] eval 9.82627e-4 s +[acc] eval 9.30939e-4 s +[acc] eval 1.026591e-3 s +[acc] eval 1.043857e-3 s +[acc] eval 9.1868e-4 s +[acc] eval 9.62059e-4 s +[acc] eval 1.001077e-3 s +[acc] eval 9.15861e-4 s +[acc] eval 9.91384e-4 s +[acc] eval 1.011315e-3 s +[acc] eval 9.90478e-4 s +[acc] eval 1.162697e-3 s +[acc] eval 1.003075e-3 s +[acc] eval 9.65817e-4 s +[acc] eval 8.37121e-4 s +[acc] eval 7.94379e-4 s +[acc] eval 8.50926e-4 s +[acc] eval 9.61365e-4 s +[acc] eval 9.88374e-4 s +[acc] eval 1.074786e-3 s +[acc] eval 9.72087e-4 s +[acc] eval 6.1663754e-2 s +[acc] eval 1.922432e-3 s +[acc] eval 4.69508e-4 s +[acc] eval 7.14441e-4 s +[acc] eval 3.840782e-3 s +[acc] eval 3.656549e-3 s +[acc] eval 9.21885e-4 s +[acc] eval 7.54582e-4 s +[acc] eval 9.39614e-4 s +[acc] eval 7.380765e-3 s +[acc] compile 4.416131e-3 s +[acc] eval 7.315593e-3 s +[acc] eval 1.912784e-3 s +[acc] eval 1.874391e-3 s +[acc] eval 2.197442e-3 s +[acc] eval 1.5662734e-2 s +[acc] eval 3.5605719e-2 s +[acc] eval 7.844357e-3 s +[acc] eval 1.99674e-3 s +[acc] eval 2.202405e-3 s +[acc] eval 2.312523e-3 s +[acc] eval 2.316406e-3 s +[acc] eval 2.177576e-3 s +[acc] eval 3.6327283e-2 s +[acc] eval 7.481662e-3 s +[acc] eval 2.611106e-3 s +[acc] eval 2.588977e-3 s +[acc] eval 2.921664e-3 s +[acc] eval 1.6399609e-2 s +[acc] eval 1.918524e-3 s +[acc] eval 2.107341e-3 s +[acc] eval 2.663871e-3 s +[acc] eval 2.803323e-3 s +[acc] eval 2.933842e-3 s +[acc] eval 2.900764e-3 s +[acc] eval 3.164963e-3 s +[acc] eval 2.451351e-3 s +[acc] eval 2.373679e-3 s +[acc] eval 2.238927e-3 s +[acc] eval 2.161486e-3 s +[acc] eval 6.3427822e-2 s +[acc] compile 1.4902887e-2 s +[acc] eval 4.0332529e-2 s +[acc] eval 3.807416e-3 s +[acc] eval 3.645232e-3 s +[acc] eval 4.770417e-3 s +[acc] eval 4.918552e-3 s +[acc] eval 3.8104e-3 s +[acc] eval 3.457937e-3 s +[acc] eval 3.587452e-3 s +[acc] eval 4.574596e-3 s +[acc] eval 4.382531e-3 s +[acc] eval 3.348962e-3 s +[acc] eval 3.475958e-3 s +[acc] eval 3.350386e-3 s +[acc] eval 3.594904e-3 s +[acc] eval 3.592847e-3 s +[acc] eval 4.338251e-3 s +[acc] eval 4.728943e-3 s +[acc] eval 5.542711e-3 s +[acc] eval 4.736587e-3 s +[acc] eval 5.047298e-3 s +[acc] eval 5.910673e-3 s +[acc] eval 5.110398e-3 s +[acc] eval 5.183519e-3 s +[acc] eval 2.1916519e-2 s +[acc] eval 4.819692e-3 s +[acc] eval 4.640497e-3 s +[acc] eval 4.350773e-3 s +[acc] eval 4.272226e-3 s +[acc] eval 3.786738e-3 s +[acc] eval 3.740986e-3 s +[acc] eval 0.204154322 s diff --git a/dump_gaussian b/dump_gaussian new file mode 100644 index 0000000..b5811dc --- /dev/null +++ b/dump_gaussian @@ -0,0 +1,1001 @@ +id,view,Expression,Numpy,Math,theta,size,loss_train,loss_val,loss_test,maxloss,R2_train,R2_val,R2_test,dl_train,dl_val,dl_test +0,0,((Log((x0 + ((Log(x0) * x1) * Log(Log(x0))))) / (x0 * x0)) * 0.62563032888927),"((np.log((x[:, 0] + ((np.log(x[:, 0]) * x[:, 1]) * np.log(np.log(x[:, 0]))))) / (x[:, 0] * x[:, 0])) * t[0])","$$\left(\frac{\operatorname{log}(\left(x_{0} + \left(\left(\operatorname{log}(x_{0}) \cdot x_{1}\right) \cdot \operatorname{log}(\operatorname{log}(x_{0}))\right)\right))}{\left(x_{0} \cdot x_{0}\right)} \cdot \theta_{0}\right)$$",0.62563032888927,17,,,,Infinity,,,,,, +1,0,(((-0.10899231604180881 + ((Exp(x0) / (x1 * -0.5567232260961726)) ^ x0)) ^ ((x1 + -0.7522098509181003) * -0.6997643636384127)) ^ Exp(((x0 ^ x1) * -0.7633424317402342))),"(t[0] + (np.exp(x[:, 0]) / (x[:, 1] * t[1])) ** x[:, 0]) ** ((x[:, 1] + t[2]) * t[3]) ** np.exp((x[:, 0] ** x[:, 1] * t[4]))","$${{\left(\theta_{0} + {\frac{\operatorname{exp}(x_{0})}{\left(x_{1} \cdot \theta_{1}\right)}^{x_{0}}}\right)^{\left(\left(x_{1} + \theta_{2}\right) \cdot \theta_{3}\right)}}^{\operatorname{exp}(\left({x_{0}^{x_{1}}} \cdot \theta_{4}\right))}}$$",-0.10899231604180881;-0.5567232260961726;-0.7522098509181003;-0.6997643636384127;-0.7633424317402342,23,,,,Infinity,,,,,, +2,0,(Exp((((x0 * -50829.34353823465) / x1) + -4767.109461741431)) * 11046.73180548436),"(np.exp((((x[:, 0] * t[0]) / x[:, 1]) + t[1])) * t[2])","$$\left(\operatorname{exp}(\left(\frac{\left(x_{0} \cdot \theta_{0}\right)}{x_{1}} + \theta_{1}\right)) \cdot \theta_{2}\right)$$",-50829.34353823465;-4767.109461741431;11046.73180548436,10,1.4573554749975553e-2,1.4573554749975553e-2,1.4573554749975553e-2,1.4573554749975553e-2,-0.3448645492182232,-0.3448645492182232,-0.3448645492182232,25.534992495931032,25.534992495931032,25.534992495931032 +3,0,Log(((((x0 * ((x0 * 0.42071683634515944) * 0.5585372703087588)) + 0.8920775268963044) / (0.2770776378207168 + Log(x0))) / (Exp(Exp(x1)) + -0.5994280568910184))),"np.log(((((x[:, 0] * ((x[:, 0] * t[0]) * t[1])) + t[2]) / (t[3] + np.log(x[:, 0]))) / (np.exp(np.exp(x[:, 1])) + t[4])))","$$\operatorname{log}(\frac{\frac{\left(\left(x_{0} \cdot \left(\left(x_{0} \cdot \theta_{0}\right) \cdot \theta_{1}\right)\right) + \theta_{2}\right)}{\left(\theta_{3} + \operatorname{log}(x_{0})\right)}}{\left(\operatorname{exp}(\operatorname{exp}(x_{1})) + \theta_{4}\right)})$$",0.42071683634515944;0.5585372703087588;0.8920775268963044;0.2770776378207168;-0.5994280568910184,21,,,,Infinity,,,,,, +4,0,Exp(Log(((0.18977703867598109 + (-1.9443688113963787e-2 / x1)) / (Exp(x0) / Exp(x0))))),"np.exp(np.log(((t[0] + (t[1] / x[:, 1])) / (np.exp(x[:, 0]) / np.exp(x[:, 0])))))","$$\operatorname{exp}(\operatorname{log}(\frac{\left(\theta_{0} + \frac{\theta_{1}}{x_{1}}\right)}{\frac{\operatorname{exp}(x_{0})}{\operatorname{exp}(x_{0})}}))$$",0.18977703867598109;-1.9443688113963787e-2,13,1.2539942975038545e-2,1.2539942975038545e-2,1.2539942975038545e-2,1.2539942975038545e-2,-0.15720049402330805,-0.15720049402330805,-0.15720049402330805,29.34996775713189,29.34996775713189,29.34996775713189 +5,0,(((-0.817794690048393 * x1) / Exp(((x1 ^ x1) ^ 0.40641747296925224))) * (-0.6946975186058839 ^ Exp(((x0 * x0) * 0.968315717765821)))),"(((t[0] * x[:, 1]) / np.exp(x[:, 1] ** x[:, 1] ** t[1])) * t[2] ** np.exp(((x[:, 0] * x[:, 0]) * t[3])))","$$\left(\frac{\left(\theta_{0} \cdot x_{1}\right)}{\operatorname{exp}({{x_{1}^{x_{1}}}^{\theta_{1}}})} \cdot {\theta_{2}^{\operatorname{exp}(\left(\left(x_{0} \cdot x_{0}\right) \cdot \theta_{3}\right))}}\right)$$",-0.817794690048393;0.40641747296925224;-0.6946975186058839;0.968315717765821,19,,,,Infinity,,,,,, +6,0,((((Exp(x0) + x0) / ((x0 + 0.4147741242873578) * (((0.8473490923065312 ^ x1) ^ x1) / ((x0 / x1) * 0.47811818937293227)))) + (((Log(x1) + 0.40267040805714993) ^ (x1 / (x1 * -0.47615436503329633))) / (0.1435840771414535 + x1))) * 0.8150362322098994),"((((np.exp(x[:, 0]) + x[:, 0]) / ((x[:, 0] + t[0]) * (t[1] ** x[:, 1] ** x[:, 1] / ((x[:, 0] / x[:, 1]) * t[2])))) + ((np.log(x[:, 1]) + t[3]) ** (x[:, 1] / (x[:, 1] * t[4])) / (t[5] + x[:, 1]))) * t[6])","$$\left(\left(\frac{\left(\operatorname{exp}(x_{0}) + x_{0}\right)}{\left(\left(x_{0} + \theta_{0}\right) \cdot \frac{{{\theta_{1}^{x_{1}}}^{x_{1}}}}{\left(\frac{x_{0}}{x_{1}} \cdot \theta_{2}\right)}\right)} + \frac{{\left(\operatorname{log}(x_{1}) + \theta_{3}\right)^{\frac{x_{1}}{\left(x_{1} \cdot \theta_{4}\right)}}}}{\left(\theta_{5} + x_{1}\right)}\right) \cdot \theta_{6}\right)$$",0.4147741242873578;0.8473490923065312;0.47811818937293227;0.40267040805714993;-0.47615436503329633;0.1435840771414535;0.8150362322098994,37,,,,Infinity,,,,,, +7,0,((((Exp((-0.6264211284116645 ^ Log(x1))) * -0.9353533015033187) * (Exp(Log(x0)) * 0.35901800991341415)) * -0.9226426142309379) / Exp((Exp(x1) ^ 0.12156404623465933))),"((((np.exp(t[0] ** np.log(x[:, 1])) * t[1]) * (np.exp(np.log(x[:, 0])) * t[2])) * t[3]) / np.exp(np.exp(x[:, 1]) ** t[4]))","$$\frac{\left(\left(\left(\operatorname{exp}({\theta_{0}^{\operatorname{log}(x_{1})}}) \cdot \theta_{1}\right) \cdot \left(\operatorname{exp}(\operatorname{log}(x_{0})) \cdot \theta_{2}\right)\right) \cdot \theta_{3}\right)}{\operatorname{exp}({\operatorname{exp}(x_{1})^{\theta_{4}}})}$$",-0.6264211284116645;-0.9353533015033187;0.35901800991341415;-0.9226426142309379;0.12156404623465933,21,,,,Infinity,,,,,, +8,0,(Exp((((x1 / x0) ^ Exp((x1 + x1))) ^ ((0.3053756822068219 + x0) * Log(Log((-0.5320733649868707 ^ x0)))))) * Exp(Exp(Log(Exp(((0.7980164712436458 / x1) ^ x1)))))),"(np.exp((x[:, 1] / x[:, 0]) ** np.exp((x[:, 1] + x[:, 1])) ** ((t[0] + x[:, 0]) * np.log(np.log(t[1] ** x[:, 0])))) * np.exp(np.exp(np.log(np.exp((t[2] / x[:, 1]) ** x[:, 1])))))","$$\left(\operatorname{exp}({{\frac{x_{1}}{x_{0}}^{\operatorname{exp}(\left(x_{1} + x_{1}\right))}}^{\left(\left(\theta_{0} + x_{0}\right) \cdot \operatorname{log}(\operatorname{log}({\theta_{1}^{x_{0}}}))\right)}}) \cdot \operatorname{exp}(\operatorname{exp}(\operatorname{log}(\operatorname{exp}({\frac{\theta_{2}}{x_{1}}^{x_{1}}}))))\right)$$",0.3053756822068219;-0.5320733649868707;0.7980164712436458,29,,,,Infinity,,,,,, +9,0,Exp((Exp((x1 ^ -7.261456223005607)) ^ (Log(x1) / Exp(x1)))),"np.exp(np.exp(x[:, 1] ** t[0]) ** (np.log(x[:, 1]) / np.exp(x[:, 1])))","$$\operatorname{exp}({\operatorname{exp}({x_{1}^{\theta_{0}}})^{\frac{\operatorname{log}(x_{1})}{\operatorname{exp}(x_{1})}}})$$",-7.261456223005607,11,0.8923099827409064,0.8923099827409064,0.8923099827409064,0.8923099827409064,-81.34340099513352,-81.34340099513352,-81.34340099513352,22.53335808134306,22.53335808134306,22.53335808134306 +10,0,(-0.271594290590885 + x1),"(t[0] + x[:, 1])","$$\left(\theta_{0} + x_{1}\right)$$",-0.271594290590885,3,1.8305762287934728e-2,1.8305762287934728e-2,1.8305762287934728e-2,1.8305762287934728e-2,-0.6892769931440774,-0.6892769931440774,-0.6892769931440774,5.3332369874311105,5.3332369874311105,5.3332369874311105 +11,0,Log(((((x0 ^ -0.20492579045704548) + x0) ^ (9.655717835835705e-2 / x0)) + (6.988876705791294e-4 ^ Exp((x1 * (0.8191509217598499 * x0)))))),"np.log(((x[:, 0] ** t[0] + x[:, 0]) ** (t[1] / x[:, 0]) + t[2] ** np.exp((x[:, 1] * (t[3] * x[:, 0])))))","$$\operatorname{log}(\left({\left({x_{0}^{\theta_{0}}} + x_{0}\right)^{\frac{\theta_{1}}{x_{0}}}} + {\theta_{2}^{\operatorname{exp}(\left(x_{1} \cdot \left(\theta_{3} \cdot x_{0}\right)\right))}}\right))$$",-0.20492579045704548;9.655717835835705e-2;6.988876705791294e-4;0.8191509217598499,19,9.366441772318788e-3,9.366441772318788e-3,9.366441772318788e-3,9.366441772318788e-3,0.13565388074386076,0.13565388074386076,0.13565388074386076,49.839396508453824,49.839396508453824,49.839396508453824 +12,0,(((x0 ^ -0.751667640916592) * x1) ^ Exp((1.525851202146237 ^ x1))),"(x[:, 0] ** t[0] * x[:, 1]) ** np.exp(t[1] ** x[:, 1])","$${\left({x_{0}^{\theta_{0}}} \cdot x_{1}\right)^{\operatorname{exp}({\theta_{1}^{x_{1}}})}}$$",-0.751667640916592;1.525851202146237,10,3.7183453062699916e-3,3.7183453062699916e-3,3.7183453062699916e-3,3.7183453062699916e-3,0.6568667789056148,0.6568667789056148,0.6568667789056148,21.962791073791475,21.962791073791475,21.962791073791475 +13,0,(Exp(x1) * 4.650783244483925e-2),"(np.exp(x[:, 1]) * t[0])","$$\left(\operatorname{exp}(x_{1}) \cdot \theta_{0}\right)$$",4.650783244483925e-2,4,1.02054841442845e-2,1.02054841442845e-2,1.02054841442845e-2,1.02054841442845e-2,5.8226076703769e-2,5.8226076703769e-2,5.8226076703769e-2,7.575287315577059,7.575287315577059,7.575287315577059 +14,0,(Exp(((-0.6411237710009179 / (x1 / x1)) + (-0.7214636701248993 / (0.5135103666282215 / x0)))) ^ Exp(((Exp(Log(x1)) * Log((-0.8007740317271557 ^ x1))) + ((0.7170625474993807 / x0) * -0.3623438477495091)))),"np.exp(((t[0] / (x[:, 1] / x[:, 1])) + (t[1] / (t[2] / x[:, 0])))) ** np.exp(((np.exp(np.log(x[:, 1])) * np.log(t[3] ** x[:, 1])) + ((t[4] / x[:, 0]) * t[5])))","$${\operatorname{exp}(\left(\frac{\theta_{0}}{\frac{x_{1}}{x_{1}}} + \frac{\theta_{1}}{\frac{\theta_{2}}{x_{0}}}\right))^{\operatorname{exp}(\left(\left(\operatorname{exp}(\operatorname{log}(x_{1})) \cdot \operatorname{log}({\theta_{3}^{x_{1}}})\right) + \left(\frac{\theta_{4}}{x_{0}} \cdot \theta_{5}\right)\right))}}$$",-0.6411237710009179;-0.7214636701248993;0.5135103666282215;-0.8007740317271557;0.7170625474993807;-0.3623438477495091,28,,,,Infinity,,,,,, +15,0,(((Exp(Log(x1)) + (7.983979360722734 / (2.352159276545472 * x1))) * Exp((1.1046069815248696 * Log((x0 * 4.029108987582322e-3))))) * (((x0 * x0) ^ (-0.4992884718282777 * x0)) ^ Exp((-4.908442225183129e-2 + (-7.952871247718402e-2 * x0))))),"(((np.exp(np.log(x[:, 1])) + (t[0] / (t[1] * x[:, 1]))) * np.exp((t[2] * np.log((x[:, 0] * t[3]))))) * (x[:, 0] * x[:, 0]) ** (t[4] * x[:, 0]) ** np.exp((t[5] + (t[6] * x[:, 0]))))","$$\left(\left(\left(\operatorname{exp}(\operatorname{log}(x_{1})) + \frac{\theta_{0}}{\left(\theta_{1} \cdot x_{1}\right)}\right) \cdot \operatorname{exp}(\left(\theta_{2} \cdot \operatorname{log}(\left(x_{0} \cdot \theta_{3}\right))\right))\right) \cdot {{\left(x_{0} \cdot x_{0}\right)^{\left(\theta_{4} \cdot x_{0}\right)}}^{\operatorname{exp}(\left(\theta_{5} + \left(\theta_{6} \cdot x_{0}\right)\right))}}\right)$$",7.983979360722734;2.352159276545472;1.1046069815248696;4.029108987582322e-3;-0.4992884718282777;-4.908442225183129e-2;-7.952871247718402e-2,32,1.358488402941307e-2,1.358488402941307e-2,1.358488402941307e-2,1.358488402941307e-2,-0.25362886748197755,-0.25362886748197755,-0.25362886748197755,84.46988741795897,84.46988741795897,84.46988741795897 +16,0,Exp(Log(((0.4323212132884867 * (-0.5563941210081238 + (-0.22894211384166407 ^ x0))) / ((-0.1350413263781819 ^ x0) + (-4.170277679253542e-2 ^ (x0 * x1)))))),"np.exp(np.log(((t[0] * (t[1] + t[2] ** x[:, 0])) / (t[3] ** x[:, 0] + t[4] ** (x[:, 0] * x[:, 1])))))","$$\operatorname{exp}(\operatorname{log}(\frac{\left(\theta_{0} \cdot \left(\theta_{1} + {\theta_{2}^{x_{0}}}\right)\right)}{\left({\theta_{3}^{x_{0}}} + {\theta_{4}^{\left(x_{0} \cdot x_{1}\right)}}\right)}))$$",0.4323212132884867;-0.5563941210081238;-0.22894211384166407;-0.1350413263781819;-4.170277679253542e-2,19,,,,Infinity,,,,,, +17,0,(Log(Log((x0 * x1))) ^ Exp((-7.0922137075229985e-3 * (x0 ^ -0.9169883388600399)))),"np.log(np.log((x[:, 0] * x[:, 1]))) ** np.exp((t[0] * x[:, 0] ** t[1]))","$${\operatorname{log}(\operatorname{log}(\left(x_{0} \cdot x_{1}\right)))^{\operatorname{exp}(\left(\theta_{0} \cdot {x_{0}^{\theta_{1}}}\right))}}$$",-7.0922137075229985e-3;-0.9169883388600399,12,,,,Infinity,,,,,, +18,0,((((Log((Log(x1) * -0.17095921031147743)) ^ x0) ^ ((x1 + Log((x1 + -0.5130263804920494))) + x0)) / (0.7340502287670319 * Exp(((x0 * 0.6674932453025795) ^ Exp(Log(x0)))))) * Log(Log(x0))),"((np.log((np.log(x[:, 1]) * t[0])) ** x[:, 0] ** ((x[:, 1] + np.log((x[:, 1] + t[1]))) + x[:, 0]) / (t[2] * np.exp((x[:, 0] * t[3]) ** np.exp(np.log(x[:, 0]))))) * np.log(np.log(x[:, 0])))","$$\left(\frac{{{\operatorname{log}(\left(\operatorname{log}(x_{1}) \cdot \theta_{0}\right))^{x_{0}}}^{\left(\left(x_{1} + \operatorname{log}(\left(x_{1} + \theta_{1}\right))\right) + x_{0}\right)}}}{\left(\theta_{2} \cdot \operatorname{exp}({\left(x_{0} \cdot \theta_{3}\right)^{\operatorname{exp}(\operatorname{log}(x_{0}))}})\right)} \cdot \operatorname{log}(\operatorname{log}(x_{0}))\right)$$",-0.17095921031147743;-0.5130263804920494;0.7340502287670319;0.6674932453025795,31,,,,Infinity,,,,,, +19,0,(Log(((x0 / Exp(x1)) + Log((-0.9847612308760518 / x1)))) * (Exp(Exp(Log(x0))) + Log(Log((0.31616839550997733 ^ x1))))),"(np.log(((x[:, 0] / np.exp(x[:, 1])) + np.log((t[0] / x[:, 1])))) * (np.exp(np.exp(np.log(x[:, 0]))) + np.log(np.log(t[1] ** x[:, 1]))))","$$\left(\operatorname{log}(\left(\frac{x_{0}}{\operatorname{exp}(x_{1})} + \operatorname{log}(\frac{\theta_{0}}{x_{1}})\right)) \cdot \left(\operatorname{exp}(\operatorname{exp}(\operatorname{log}(x_{0}))) + \operatorname{log}(\operatorname{log}({\theta_{1}^{x_{1}}}))\right)\right)$$",-0.9847612308760518;0.31616839550997733,21,,,,Infinity,,,,,, +20,0,((((x0 + x1) + (x0 ^ (0.8952374112115637 * x0))) ^ ((0.32317623451275146 + x0) * (4.719078881246552e-2 * x1))) + Log(Log(((Exp(x1) + (x0 * (x1 ^ -0.2286517666053789))) / Log((x1 * (x1 ^ x1))))))),"(((x[:, 0] + x[:, 1]) + x[:, 0] ** (t[0] * x[:, 0])) ** ((t[1] + x[:, 0]) * (t[2] * x[:, 1])) + np.log(np.log(((np.exp(x[:, 1]) + (x[:, 0] * x[:, 1] ** t[3])) / np.log((x[:, 1] * x[:, 1] ** x[:, 1]))))))","$$\left({\left(\left(x_{0} + x_{1}\right) + {x_{0}^{\left(\theta_{0} \cdot x_{0}\right)}}\right)^{\left(\left(\theta_{1} + x_{0}\right) \cdot \left(\theta_{2} \cdot x_{1}\right)\right)}} + \operatorname{log}(\operatorname{log}(\frac{\left(\operatorname{exp}(x_{1}) + \left(x_{0} \cdot {x_{1}^{\theta_{3}}}\right)\right)}{\operatorname{log}(\left(x_{1} \cdot {x_{1}^{x_{1}}}\right))}))\right)$$",0.8952374112115637;0.32317623451275146;4.719078881246552e-2;-0.2286517666053789,35,,,,Infinity,,,,,, +21,0,(Log(Log(((x1 / x0) + (((x0 + x0) / (0.7716232286969656 ^ x0)) / x0)))) * Log(Exp((x0 * (x0 ^ -2.7444870301084485))))),"(np.log(np.log(((x[:, 1] / x[:, 0]) + (((x[:, 0] + x[:, 0]) / t[0] ** x[:, 0]) / x[:, 0])))) * np.log(np.exp((x[:, 0] * x[:, 0] ** t[1]))))","$$\left(\operatorname{log}(\operatorname{log}(\left(\frac{x_{1}}{x_{0}} + \frac{\frac{\left(x_{0} + x_{0}\right)}{{\theta_{0}^{x_{0}}}}}{x_{0}}\right))) \cdot \operatorname{log}(\operatorname{exp}(\left(x_{0} \cdot {x_{0}^{\theta_{1}}}\right)))\right)$$",0.7716232286969656;-2.7444870301084485,23,6.1346698969358265e-3,6.1346698969358265e-3,6.1346698969358265e-3,6.1346698969358265e-3,0.4338855408246197,0.4338855408246197,0.4338855408246197,54.583536352375575,54.583536352375575,54.583536352375575 +22,0,(Exp(Log((-0.908618383061254 + x0))) ^ ((2.9900583498112643e-2 * x0) + (0.6559860151532763 / x0))),"np.exp(np.log((t[0] + x[:, 0]))) ** ((t[1] * x[:, 0]) + (t[2] / x[:, 0]))","$${\operatorname{exp}(\operatorname{log}(\left(\theta_{0} + x_{0}\right)))^{\left(\left(\theta_{1} \cdot x_{0}\right) + \frac{\theta_{2}}{x_{0}}\right)}}$$",-0.908618383061254;2.9900583498112643e-2;0.6559860151532763,13,,,,Infinity,,,,,, +23,0,Log((((x0 * x1) ^ -1.3347329131445162e-2) + 4.676234554120854e-2)),"np.log(((x[:, 0] * x[:, 1]) ** t[0] + t[1]))","$$\operatorname{log}(\left({\left(x_{0} \cdot x_{1}\right)^{\theta_{0}}} + \theta_{1}\right))$$",-1.3347329131445162e-2;4.676234554120854e-2,8,1.0777619307146631e-2,1.0777619307146631e-2,1.0777619307146631e-2,1.0777619307146631e-2,5.428779743961276e-3,5.428779743961276e-3,5.428779743961276e-3,19.61883092055422,19.61883092055422,19.61883092055422 +24,0,((((((x0 + -2.304174021441774e-2) * -0.7421098546461207) ^ 0.30442355040188995) + -0.615475457981378) * 0.988983278318315) / x1),"(((((x[:, 0] + t[0]) * t[1]) ** t[2] + t[3]) * t[4]) / x[:, 1])","$$\frac{\left(\left({\left(\left(x_{0} + \theta_{0}\right) \cdot \theta_{1}\right)^{\theta_{2}}} + \theta_{3}\right) \cdot \theta_{4}\right)}{x_{1}}$$",-2.304174021441774e-2;-0.7421098546461207;0.30442355040188995;-0.615475457981378;0.988983278318315,13,,,,Infinity,,,,,, +25,0,(Log(((-0.29043491634269336 / (1.1477600889048776 * (x1 * -1.0238357893682501))) + 1.5320120789911775)) ^ Log(x0)),"np.log(((t[0] / (t[1] * (x[:, 1] * t[2]))) + t[3])) ** np.log(x[:, 0])","$${\operatorname{log}(\left(\frac{\theta_{0}}{\left(\theta_{1} \cdot \left(x_{1} \cdot \theta_{2}\right)\right)} + \theta_{3}\right))^{\operatorname{log}(x_{0})}}$$",-0.29043491634269336;1.1477600889048776;-1.0238357893682501;1.5320120789911775,13,0.877701779315445,0.877701779315445,0.877701779315445,0.877701779315445,-79.9953390259215,-79.9953390259215,-79.9953390259215,35.90637138469233,35.90637138469233,35.90637138469233 +26,0,(Log((((Exp(x0) + ((x1 * 0.9882356155551903) / x0)) ^ x1) / ((0.48100272302531855 / (0.46397797344842995 / (-0.7447856393045917 + x1))) ^ ((-0.5012632563970889 / (2.6778848385430543e-2 / x0)) + 0.1590517027675652)))) ^ x1),"np.log(((np.exp(x[:, 0]) + ((x[:, 1] * t[0]) / x[:, 0])) ** x[:, 1] / (t[1] / (t[2] / (t[3] + x[:, 1]))) ** ((t[4] / (t[5] / x[:, 0])) + t[6]))) ** x[:, 1]","$${\operatorname{log}(\frac{{\left(\operatorname{exp}(x_{0}) + \frac{\left(x_{1} \cdot \theta_{0}\right)}{x_{0}}\right)^{x_{1}}}}{{\frac{\theta_{1}}{\frac{\theta_{2}}{\left(\theta_{3} + x_{1}\right)}}^{\left(\frac{\theta_{4}}{\frac{\theta_{5}}{x_{0}}} + \theta_{6}\right)}}})^{x_{1}}}$$",0.9882356155551903;0.48100272302531855;0.46397797344842995;-0.7447856393045917;-0.5012632563970889;2.6778848385430543e-2;0.1590517027675652,29,,,,Infinity,,,,,, +27,0,(8.226801725115918e-3 / x1),"(t[0] / x[:, 1])","$$\frac{\theta_{0}}{x_{1}}$$",8.226801725115918e-3,3,1.3313188344055817e-2,1.3313188344055817e-2,1.3313188344055817e-2,1.3313188344055817e-2,-0.2285564742545716,-0.2285564742545716,-0.2285564742545716,5.32874367088162,5.32874367088162,5.32874367088162 +28,0,Log((Exp((x0 + x0)) * -0.8631186863721904)),"np.log((np.exp((x[:, 0] + x[:, 0])) * t[0]))","$$\operatorname{log}(\left(\operatorname{exp}(\left(x_{0} + x_{0}\right)) \cdot \theta_{0}\right))$$",-0.8631186863721904,7,,,,Infinity,,,,,, +29,0,(((Exp(x0) + Exp(Exp(((x0 ^ x0) ^ (-0.6120891284675323 + x1))))) / (Log(x0) + 0.43352330783419424)) / ((Exp((((0.6211172757204262 + x0) * -5.4622535062865296e-2) * 0.8281351169012158)) ^ Log(x0)) ^ (Exp((x1 + 0.5105639611597665)) ^ Log(x1)))),"(((np.exp(x[:, 0]) + np.exp(np.exp(x[:, 0] ** x[:, 0] ** (t[0] + x[:, 1])))) / (np.log(x[:, 0]) + t[1])) / np.exp((((t[2] + x[:, 0]) * t[3]) * t[4])) ** np.log(x[:, 0]) ** np.exp((x[:, 1] + t[5])) ** np.log(x[:, 1]))","$$\frac{\frac{\left(\operatorname{exp}(x_{0}) + \operatorname{exp}(\operatorname{exp}({{x_{0}^{x_{0}}}^{\left(\theta_{0} + x_{1}\right)}}))\right)}{\left(\operatorname{log}(x_{0}) + \theta_{1}\right)}}{{{\operatorname{exp}(\left(\left(\left(\theta_{2} + x_{0}\right) \cdot \theta_{3}\right) \cdot \theta_{4}\right))^{\operatorname{log}(x_{0})}}^{{\operatorname{exp}(\left(x_{1} + \theta_{5}\right))^{\operatorname{log}(x_{1})}}}}}$$",-0.6120891284675323;0.43352330783419424;0.6211172757204262;-5.4622535062865296e-2;0.8281351169012158;0.5105639611597665,37,126802.29675394173,126802.29675394173,126802.29675394173,126802.29675394173,-1.1701462135760428e7,-1.1701462135760428e7,-1.1701462135760428e7,114215.4899375212,114215.4899375212,114215.4899375212 +30,0,Exp((Log((-0.787651379267476 * (x0 ^ ((-0.950557566553154 ^ x0) ^ (x1 ^ 0.8020074656572787))))) ^ ((((0.7188174427302787 * Log(x1)) / (0.2992580308394093 * (-2.3481215441264247e-2 * x1))) * ((x0 / (x0 + -0.6416046535229541)) / x1)) ^ (x1 * 0.597312382713762)))),"np.exp(np.log((t[0] * x[:, 0] ** t[1] ** x[:, 0] ** x[:, 1] ** t[2])) ** (((t[3] * np.log(x[:, 1])) / (t[4] * (t[5] * x[:, 1]))) * ((x[:, 0] / (x[:, 0] + t[6])) / x[:, 1])) ** (x[:, 1] * t[7]))","$$\operatorname{exp}({\operatorname{log}(\left(\theta_{0} \cdot {x_{0}^{{{\theta_{1}^{x_{0}}}^{{x_{1}^{\theta_{2}}}}}}}\right))^{{\left(\frac{\left(\theta_{3} \cdot \operatorname{log}(x_{1})\right)}{\left(\theta_{4} \cdot \left(\theta_{5} \cdot x_{1}\right)\right)} \cdot \frac{\frac{x_{0}}{\left(x_{0} + \theta_{6}\right)}}{x_{1}}\right)^{\left(x_{1} \cdot \theta_{7}\right)}}}})$$",-0.787651379267476;-0.950557566553154;0.8020074656572787;0.7188174427302787;0.2992580308394093;-2.3481215441264247e-2;-0.6416046535229541;0.597312382713762,36,,,,Infinity,,,,,, +31,0,((Log(Log((0.33932097595029176 * (x0 * x0)))) + Exp(Log(Log(((x0 + x0) / x0))))) * Log(Exp((-0.16831472576980322 ^ (((x0 + -0.8701375992929226) / x0) * (x0 * x0)))))),"((np.log(np.log((t[0] * (x[:, 0] * x[:, 0])))) + np.exp(np.log(np.log(((x[:, 0] + x[:, 0]) / x[:, 0]))))) * np.log(np.exp(t[1] ** (((x[:, 0] + t[2]) / x[:, 0]) * (x[:, 0] * x[:, 0])))))","$$\left(\left(\operatorname{log}(\operatorname{log}(\left(\theta_{0} \cdot \left(x_{0} \cdot x_{0}\right)\right))) + \operatorname{exp}(\operatorname{log}(\operatorname{log}(\frac{\left(x_{0} + x_{0}\right)}{x_{0}})))\right) \cdot \operatorname{log}(\operatorname{exp}({\theta_{1}^{\left(\frac{\left(x_{0} + \theta_{2}\right)}{x_{0}} \cdot \left(x_{0} \cdot x_{0}\right)\right)}}))\right)$$",0.33932097595029176;-0.16831472576980322;-0.8701375992929226,30,,,,Infinity,,,,,, +32,0,(Exp(Log(((-0.6298117558935161 + x0) + x1))) + (Log(Log((-0.45831733367340566 ^ (x1 + -0.23140560627815998)))) + (Log(x0) * (x1 * -8.136370974319063e-2)))),"(np.exp(np.log(((t[0] + x[:, 0]) + x[:, 1]))) + (np.log(np.log(t[1] ** (x[:, 1] + t[2]))) + (np.log(x[:, 0]) * (x[:, 1] * t[3]))))","$$\left(\operatorname{exp}(\operatorname{log}(\left(\left(\theta_{0} + x_{0}\right) + x_{1}\right))) + \left(\operatorname{log}(\operatorname{log}({\theta_{1}^{\left(x_{1} + \theta_{2}\right)}})) + \left(\operatorname{log}(x_{0}) \cdot \left(x_{1} \cdot \theta_{3}\right)\right)\right)\right)$$",-0.6298117558935161;-0.45831733367340566;-0.23140560627815998;-8.136370974319063e-2,22,,,,Infinity,,,,,, +33,0,Log(Exp(Log((1.0566783659676662 * x0)))),"np.log(np.exp(np.log((t[0] * x[:, 0]))))","$$\operatorname{log}(\operatorname{exp}(\operatorname{log}(\left(\theta_{0} \cdot x_{0}\right))))$$",1.0566783659676662,6,0.164444663973442,0.164444663973442,0.164444663973442,0.164444663973442,-14.175144477798478,-14.175144477798478,-14.175144477798478,11.82555260754834,11.82555260754834,11.82555260754834 +34,0,((((x0 ^ x1) + (-0.3763021157198979 ^ ((Log(x0) ^ Log(x1)) ^ x1))) / (Log(((x0 ^ -0.22776918933671686) * 0.8786445715182172)) * -0.44823025806322714)) * 0.6270284420388716),"(((x[:, 0] ** x[:, 1] + t[0] ** np.log(x[:, 0]) ** np.log(x[:, 1]) ** x[:, 1]) / (np.log((x[:, 0] ** t[1] * t[2])) * t[3])) * t[4])","$$\left(\frac{\left({x_{0}^{x_{1}}} + {\theta_{0}^{{{\operatorname{log}(x_{0})^{\operatorname{log}(x_{1})}}^{x_{1}}}}}\right)}{\left(\operatorname{log}(\left({x_{0}^{\theta_{1}}} \cdot \theta_{2}\right)) \cdot \theta_{3}\right)} \cdot \theta_{4}\right)$$",-0.3763021157198979;-0.22776918933671686;0.8786445715182172;-0.44823025806322714;0.6270284420388716,24,,,,Infinity,,,,,, +35,0,((((x1 / x1) ^ ((x0 * x1) * (0.23280815437648295 / x1))) + Exp(((Exp(x0) / (x0 ^ -0.4096883332288457)) * ((x0 / x0) + x0)))) * x1),"(((x[:, 1] / x[:, 1]) ** ((x[:, 0] * x[:, 1]) * (t[0] / x[:, 1])) + np.exp(((np.exp(x[:, 0]) / x[:, 0] ** t[1]) * ((x[:, 0] / x[:, 0]) + x[:, 0])))) * x[:, 1])","$$\left(\left({\frac{x_{1}}{x_{1}}^{\left(\left(x_{0} \cdot x_{1}\right) \cdot \frac{\theta_{0}}{x_{1}}\right)}} + \operatorname{exp}(\left(\frac{\operatorname{exp}(x_{0})}{{x_{0}^{\theta_{1}}}} \cdot \left(\frac{x_{0}}{x_{0}} + x_{0}\right)\right))\right) \cdot x_{1}\right)$$",0.23280815437648295;-0.4096883332288457,27,2.128550108815956e9,2.128550108815956e9,2.128550108815956e9,2.128550108815956e9,-1.9642507484808395e11,-1.9642507484808395e11,-1.9642507484808395e11,1.915695158121132e9,1.915695158121132e9,1.915695158121132e9 +36,0,(Exp(Log((7.620099383310752e-2 / Log(x1)))) + (Exp((x0 * -0.3727630917323328)) / (((x1 + 0.45545199901671496) ^ ((0.8071613806530901 ^ x1) + x1)) / x0))),"(np.exp(np.log((t[0] / np.log(x[:, 1])))) + (np.exp((x[:, 0] * t[1])) / ((x[:, 1] + t[2]) ** (t[3] ** x[:, 1] + x[:, 1]) / x[:, 0])))","$$\left(\operatorname{exp}(\operatorname{log}(\frac{\theta_{0}}{\operatorname{log}(x_{1})})) + \frac{\operatorname{exp}(\left(x_{0} \cdot \theta_{1}\right))}{\frac{{\left(x_{1} + \theta_{2}\right)^{\left({\theta_{3}^{x_{1}}} + x_{1}\right)}}}{x_{0}}}\right)$$",7.620099383310752e-2;-0.3727630917323328;0.45545199901671496;0.8071613806530901,23,,,,Infinity,,,,,, +37,0,(Log((((Exp(x0) * -7.990302334389243e-3) ^ -0.695390739716597) + Exp((-0.5211100259994259 * Exp(x1))))) + (6.270887860291774e-2 ^ x0)),"(np.log(((np.exp(x[:, 0]) * t[0]) ** t[1] + np.exp((t[2] * np.exp(x[:, 1]))))) + t[3] ** x[:, 0])","$$\left(\operatorname{log}(\left({\left(\operatorname{exp}(x_{0}) \cdot \theta_{0}\right)^{\theta_{1}}} + \operatorname{exp}(\left(\theta_{2} \cdot \operatorname{exp}(x_{1})\right))\right)) + {\theta_{3}^{x_{0}}}\right)$$",-7.990302334389243e-3;-0.695390739716597;-0.5211100259994259;6.270887860291774e-2,17,,,,Infinity,,,,,, +38,0,Exp(Log(Log(Exp((-0.17954730200908064 / x0))))),"np.exp(np.log(np.log(np.exp((t[0] / x[:, 0])))))","$$\operatorname{exp}(\operatorname{log}(\operatorname{log}(\operatorname{exp}(\frac{\theta_{0}}{x_{0}}))))$$",-0.17954730200908064,7,,,,Infinity,,,,,, +39,0,(Exp((((x1 ^ Log(x1)) ^ ((0.6464536376494079 / x0) + x0)) ^ ((x1 / (-0.5389344180665384 / x0)) / Exp((-0.2513035455803483 * x0))))) * Exp((((x1 / (x1 ^ 0.35499693406822985)) / (0.5384124305373112 + Exp(x0))) * (0.2757598190426588 + (-0.34496095832224205 ^ x1))))),"(np.exp(x[:, 1] ** np.log(x[:, 1]) ** ((t[0] / x[:, 0]) + x[:, 0]) ** ((x[:, 1] / (t[1] / x[:, 0])) / np.exp((t[2] * x[:, 0])))) * np.exp((((x[:, 1] / x[:, 1] ** t[3]) / (t[4] + np.exp(x[:, 0]))) * (t[5] + t[6] ** x[:, 1]))))","$$\left(\operatorname{exp}({{{x_{1}^{\operatorname{log}(x_{1})}}^{\left(\frac{\theta_{0}}{x_{0}} + x_{0}\right)}}^{\frac{\frac{x_{1}}{\frac{\theta_{1}}{x_{0}}}}{\operatorname{exp}(\left(\theta_{2} \cdot x_{0}\right))}}}) \cdot \operatorname{exp}(\left(\frac{\frac{x_{1}}{{x_{1}^{\theta_{3}}}}}{\left(\theta_{4} + \operatorname{exp}(x_{0})\right)} \cdot \left(\theta_{5} + {\theta_{6}^{x_{1}}}\right)\right))\right)$$",0.6464536376494079;-0.5389344180665384;-0.2513035455803483;0.35499693406822985;0.5384124305373112;0.2757598190426588;-0.34496095832224205,40,,,,Infinity,,,,,, +40,0,Exp(((((x1 + (-0.6483173307345669 + x1)) + (-0.7892371947447105 / x0)) / ((x0 + (x0 * 0.5362846131053154)) + (x0 ^ -0.4105877578409678))) + (Log(Log((x0 * -0.1155467114499098))) / (Exp(Exp((-0.5848921848690108 + x0))) + ((x1 * 7.431834542272708e-2) * 0.5074509438925662))))),"np.exp(((((x[:, 1] + (t[0] + x[:, 1])) + (t[1] / x[:, 0])) / ((x[:, 0] + (x[:, 0] * t[2])) + x[:, 0] ** t[3])) + (np.log(np.log((x[:, 0] * t[4]))) / (np.exp(np.exp((t[5] + x[:, 0]))) + ((x[:, 1] * t[6]) * t[7])))))","$$\operatorname{exp}(\left(\frac{\left(\left(x_{1} + \left(\theta_{0} + x_{1}\right)\right) + \frac{\theta_{1}}{x_{0}}\right)}{\left(\left(x_{0} + \left(x_{0} \cdot \theta_{2}\right)\right) + {x_{0}^{\theta_{3}}}\right)} + \frac{\operatorname{log}(\operatorname{log}(\left(x_{0} \cdot \theta_{4}\right)))}{\left(\operatorname{exp}(\operatorname{exp}(\left(\theta_{5} + x_{0}\right))) + \left(\left(x_{1} \cdot \theta_{6}\right) \cdot \theta_{7}\right)\right)}\right))$$",-0.6483173307345669;-0.7892371947447105;0.5362846131053154;-0.4105877578409678;-0.1155467114499098;-0.5848921848690108;7.431834542272708e-2;0.5074509438925662,38,,,,Infinity,,,,,, +41,0,(Log(((x1 * x1) ^ -1.007181256342074)) * 1.6040633133099287e-2),"(np.log((x[:, 1] * x[:, 1]) ** t[0]) * t[1])","$$\left(\operatorname{log}({\left(x_{1} \cdot x_{1}\right)^{\theta_{0}}}) \cdot \theta_{1}\right)$$",-1.007181256342074;1.6040633133099287e-2,8,1.2823861921153812e-2,1.2823861921153812e-2,1.2823861921153812e-2,1.2823861921153812e-2,-0.1834008639421476,-0.1834008639421476,-0.1834008639421476,16.928894645937124,16.928894645937124,16.928894645937124 +42,0,(Log((((x1 ^ (-0.5688774889816952 + x1)) + (x0 / Log(x0))) / Log(((x0 ^ (0.5964315155810368 ^ x1)) / Exp((x1 ^ x0)))))) / (Log(Exp((Log((x1 * 0.624045004928951)) * (x0 * x0)))) ^ (Exp(Log(Exp(x1))) ^ 0.6226035482628106))),"(np.log(((x[:, 1] ** (t[0] + x[:, 1]) + (x[:, 0] / np.log(x[:, 0]))) / np.log((x[:, 0] ** t[1] ** x[:, 1] / np.exp(x[:, 1] ** x[:, 0]))))) / np.log(np.exp((np.log((x[:, 1] * t[2])) * (x[:, 0] * x[:, 0])))) ** np.exp(np.log(np.exp(x[:, 1]))) ** t[3])","$$\frac{\operatorname{log}(\frac{\left({x_{1}^{\left(\theta_{0} + x_{1}\right)}} + \frac{x_{0}}{\operatorname{log}(x_{0})}\right)}{\operatorname{log}(\frac{{x_{0}^{{\theta_{1}^{x_{1}}}}}}{\operatorname{exp}({x_{1}^{x_{0}}})})})}{{\operatorname{log}(\operatorname{exp}(\left(\operatorname{log}(\left(x_{1} \cdot \theta_{2}\right)) \cdot \left(x_{0} \cdot x_{0}\right)\right)))^{{\operatorname{exp}(\operatorname{log}(\operatorname{exp}(x_{1})))^{\theta_{3}}}}}}$$",-0.5688774889816952;0.5964315155810368;0.624045004928951;0.6226035482628106,41,,,,Infinity,,,,,, +43,0,Log((x1 * 3.538374735340478)),"np.log((x[:, 1] * t[0]))","$$\operatorname{log}(\left(x_{1} \cdot \theta_{0}\right))$$",3.538374735340478,4,0.20183020932487913,0.20183020932487913,0.20183020932487913,0.20183020932487913,-17.625125999734454,-17.625125999734454,-17.625125999734454,7.747749568239594,7.747749568239594,7.747749568239594 +44,0,((x0 + (x0 * -0.9171709236256588)) ^ Exp((x0 * x1))),"(x[:, 0] + (x[:, 0] * t[0])) ** np.exp((x[:, 0] * x[:, 1]))","$${\left(x_{0} + \left(x_{0} \cdot \theta_{0}\right)\right)^{\operatorname{exp}(\left(x_{0} \cdot x_{1}\right))}}$$",-0.9171709236256588,10,1.3320156709666752e-2,1.3320156709666752e-2,1.3320156709666752e-2,1.3320156709666752e-2,-0.22919952312198366,-0.22919952312198366,-0.22919952312198366,21.492014566959472,21.492014566959472,21.492014566959472 +45,0,Log(Exp(((Log(Exp((x0 ^ x1))) + ((x0 * 0.9060778396429976) + x0)) / (3.377833806655672e-2 + (0.637530444150828 ^ (-0.6193819679907668 ^ x0)))))),"np.log(np.exp(((np.log(np.exp(x[:, 0] ** x[:, 1])) + ((x[:, 0] * t[0]) + x[:, 0])) / (t[1] + t[2] ** t[3] ** x[:, 0]))))","$$\operatorname{log}(\operatorname{exp}(\frac{\left(\operatorname{log}(\operatorname{exp}({x_{0}^{x_{1}}})) + \left(\left(x_{0} \cdot \theta_{0}\right) + x_{0}\right)\right)}{\left(\theta_{1} + {\theta_{2}^{{\theta_{3}^{x_{0}}}}}\right)}))$$",0.9060778396429976;3.377833806655672e-2;0.637530444150828;-0.6193819679907668,21,,,,Infinity,,,,,, +46,0,((0.5535521849114129 * ((x1 + -0.13848683271584458) / (0.2442826825510186 / Log(x0)))) * (((x1 * 0.5395244631692784) + (x1 + -0.7022069804927313)) + -0.4652875913922442)),"((t[0] * ((x[:, 1] + t[1]) / (t[2] / np.log(x[:, 0])))) * (((x[:, 1] * t[3]) + (x[:, 1] + t[4])) + t[5]))","$$\left(\left(\theta_{0} \cdot \frac{\left(x_{1} + \theta_{1}\right)}{\frac{\theta_{2}}{\operatorname{log}(x_{0})}}\right) \cdot \left(\left(\left(x_{1} \cdot \theta_{3}\right) + \left(x_{1} + \theta_{4}\right)\right) + \theta_{5}\right)\right)$$",0.5535521849114129;-0.13848683271584458;0.2442826825510186;0.5395244631692784;-0.7022069804927313;-0.4652875913922442,20,7.608285677057699e-3,7.608285677057699e-3,7.608285677057699e-3,7.608285677057699e-3,0.2978985660710657,0.2978985660710657,0.2978985660710657,51.05060005042146,51.05060005042146,51.05060005042146 +47,0,((Exp(((Log(x0) ^ 0.7888394696912923) + x1)) + -0.11001420079162949) ^ x0),"(np.exp((np.log(x[:, 0]) ** t[0] + x[:, 1])) + t[1]) ** x[:, 0]","$${\left(\operatorname{exp}(\left({\operatorname{log}(x_{0})^{\theta_{0}}} + x_{1}\right)) + \theta_{1}\right)^{x_{0}}}$$",0.7888394696912923;-0.11001420079162949,11,,,,Infinity,,,,,, +48,0,(Log((Exp((Log(x0) + -0.7566581916109315)) * -0.5310775693456835)) / Exp((Log((Log(x1) ^ 0.70744298829378)) ^ ((Exp((0.8218491121435729 + x1)) * 0.5164902576242425) * 0.5582171446347535)))),"(np.log((np.exp((np.log(x[:, 0]) + t[0])) * t[1])) / np.exp(np.log(np.log(x[:, 1]) ** t[2]) ** ((np.exp((t[3] + x[:, 1])) * t[4]) * t[5])))","$$\frac{\operatorname{log}(\left(\operatorname{exp}(\left(\operatorname{log}(x_{0}) + \theta_{0}\right)) \cdot \theta_{1}\right))}{\operatorname{exp}({\operatorname{log}({\operatorname{log}(x_{1})^{\theta_{2}}})^{\left(\left(\operatorname{exp}(\left(\theta_{3} + x_{1}\right)) \cdot \theta_{4}\right) \cdot \theta_{5}\right)}})}$$",-0.7566581916109315;-0.5310775693456835;0.70744298829378;0.8218491121435729;0.5164902576242425;0.5582171446347535,24,,,,Infinity,,,,,, +49,0,(Log(((-0.7975459537645441 + x1) ^ 0.3712713306384733)) * 0.7298708039935262),"(np.log((t[0] + x[:, 1]) ** t[1]) * t[2])","$$\left(\operatorname{log}({\left(\theta_{0} + x_{1}\right)^{\theta_{1}}}) \cdot \theta_{2}\right)$$",-0.7975459537645441;0.3712713306384733;0.7298708039935262,8,,,,Infinity,,,,,, +50,0,(Exp((Exp(x0) + -116.31878667509586)) * 775.90687151667),"(np.exp((np.exp(x[:, 0]) + t[0])) * t[1])","$$\left(\operatorname{exp}(\left(\operatorname{exp}(x_{0}) + \theta_{0}\right)) \cdot \theta_{1}\right)$$",-116.31878667509586;775.90687151667,7,1.4573554749975553e-2,1.4573554749975553e-2,1.4573554749975553e-2,1.4573554749975553e-2,-0.3448645492182232,-0.3448645492182232,-0.3448645492182232,15.321031457048962,15.321031457048962,15.321031457048962 +51,0,((((x1 * 3.428897663408948e-2) / Exp(x0)) / (((-1.7165504048395446 * x1) * x0) / (13.432895560864475 ^ x1))) / ((Exp(x1) * Log(Exp(x0))) / ((x1 + x0) * -3.755937356902907))),"((((x[:, 1] * t[0]) / np.exp(x[:, 0])) / (((t[1] * x[:, 1]) * x[:, 0]) / t[2] ** x[:, 1])) / ((np.exp(x[:, 1]) * np.log(np.exp(x[:, 0]))) / ((x[:, 1] + x[:, 0]) * t[3])))","$$\frac{\frac{\frac{\left(x_{1} \cdot \theta_{0}\right)}{\operatorname{exp}(x_{0})}}{\frac{\left(\left(\theta_{1} \cdot x_{1}\right) \cdot x_{0}\right)}{{\theta_{2}^{x_{1}}}}}}{\frac{\left(\operatorname{exp}(x_{1}) \cdot \operatorname{log}(\operatorname{exp}(x_{0}))\right)}{\left(\left(x_{1} + x_{0}\right) \cdot \theta_{3}\right)}}$$",3.428897663408948e-2;-1.7165504048395446;13.432895560864475;-3.755937356902907,29,2.5836243850311308e-3,2.5836243850311308e-3,2.5836243850311308e-3,2.5836243850311308e-3,0.7615801426944288,0.7615801426944288,0.7615801426944288,71.80553774616746,71.80553774616746,71.80553774616746 +52,0,Exp((x0 * -2.9053779497574426)),"np.exp((x[:, 0] * t[0]))","$$\operatorname{exp}(\left(x_{0} \cdot \theta_{0}\right))$$",-2.9053779497574426,4,5.2316712708275655e-3,5.2316712708275655e-3,5.2316712708275655e-3,5.2316712708275655e-3,0.5172153022370025,0.5172153022370025,0.5172153022370025,7.570810883990948,7.570810883990948,7.570810883990948 +53,0,((((x0 * 0.24561966635707888) * (x0 * x0)) * ((x1 * 0.7374147834269256) ^ (x0 * -0.6696427715564475))) ^ ((Exp((x0 * 0.9421578940154798)) * (x0 + 0.5440818223938728)) * ((0.971910746475665 / x1) ^ 0.9244979392502793))),"(((x[:, 0] * t[0]) * (x[:, 0] * x[:, 0])) * (x[:, 1] * t[1]) ** (x[:, 0] * t[2])) ** ((np.exp((x[:, 0] * t[3])) * (x[:, 0] + t[4])) * (t[5] / x[:, 1]) ** t[6])","$${\left(\left(\left(x_{0} \cdot \theta_{0}\right) \cdot \left(x_{0} \cdot x_{0}\right)\right) \cdot {\left(x_{1} \cdot \theta_{1}\right)^{\left(x_{0} \cdot \theta_{2}\right)}}\right)^{\left(\left(\operatorname{exp}(\left(x_{0} \cdot \theta_{3}\right)) \cdot \left(x_{0} + \theta_{4}\right)\right) \cdot {\frac{\theta_{5}}{x_{1}}^{\theta_{6}}}\right)}}$$",0.24561966635707888;0.7374147834269256;-0.6696427715564475;0.9421578940154798;0.5440818223938728;0.971910746475665;0.9244979392502793,30,3.9009563284270396e88,3.9009563284270396e88,3.9009563284270396e88,3.9009563284270396e88,-3.5998477819276084e90,-3.5998477819276084e90,-3.5998477819276084e90,3.5108606955843356e88,3.5108606955843356e88,3.5108606955843356e88 +54,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 +55,0,(Log((Exp(x1) * 0.2368463210759606)) + (0.3855214898428987 / ((Exp((x1 * 0.5528620244110237)) / (x1 ^ ((x0 * 0.8299714223557225) * 0.5396602848341299))) + Log((((x1 * x1) * x0) + -0.18189542116021074))))),"(np.log((np.exp(x[:, 1]) * t[0])) + (t[1] / ((np.exp((x[:, 1] * t[2])) / x[:, 1] ** ((x[:, 0] * t[3]) * t[4])) + np.log((((x[:, 1] * x[:, 1]) * x[:, 0]) + t[5])))))","$$\left(\operatorname{log}(\left(\operatorname{exp}(x_{1}) \cdot \theta_{0}\right)) + \frac{\theta_{1}}{\left(\frac{\operatorname{exp}(\left(x_{1} \cdot \theta_{2}\right))}{{x_{1}^{\left(\left(x_{0} \cdot \theta_{3}\right) \cdot \theta_{4}\right)}}} + \operatorname{log}(\left(\left(\left(x_{1} \cdot x_{1}\right) \cdot x_{0}\right) + \theta_{5}\right))\right)}\right)$$",0.2368463210759606;0.3855214898428987;0.5528620244110237;0.8299714223557225;0.5396602848341299;-0.18189542116021074,29,,,,Infinity,,,,,, +56,0,Log((Exp(((x1 * x0) * 0.12061438815940806)) / ((x1 * 0.16105512668301558) + -0.8968662185322243))),"np.log((np.exp(((x[:, 1] * x[:, 0]) * t[0])) / ((x[:, 1] * t[1]) + t[2])))","$$\operatorname{log}(\frac{\operatorname{exp}(\left(\left(x_{1} \cdot x_{0}\right) \cdot \theta_{0}\right))}{\left(\left(x_{1} \cdot \theta_{1}\right) + \theta_{2}\right)})$$",0.12061438815940806;0.16105512668301558;-0.8968662185322243,13,,,,Infinity,,,,,, +57,0,Exp(Log(((x0 / Log((x1 ^ (x1 ^ 1.615698784873254e-2)))) ^ (-0.3389227098549682 ^ Log(x0))))),"np.exp(np.log((x[:, 0] / np.log(x[:, 1] ** x[:, 1] ** t[0])) ** t[1] ** np.log(x[:, 0])))","$$\operatorname{exp}(\operatorname{log}({\frac{x_{0}}{\operatorname{log}({x_{1}^{{x_{1}^{\theta_{0}}}}})}^{{\theta_{1}^{\operatorname{log}(x_{0})}}}}))$$",1.615698784873254e-2;-0.3389227098549682,15,,,,Infinity,,,,,, +58,0,((((x1 * (((x1 * x1) * (-0.8711844046114036 * x1)) * x0)) + x1) + x1) * 0.10582267112973207),"((((x[:, 1] * (((x[:, 1] * x[:, 1]) * (t[0] * x[:, 1])) * x[:, 0])) + x[:, 1]) + x[:, 1]) * t[1])","$$\left(\left(\left(\left(x_{1} \cdot \left(\left(\left(x_{1} \cdot x_{1}\right) \cdot \left(\theta_{0} \cdot x_{1}\right)\right) \cdot x_{0}\right)\right) + x_{1}\right) + x_{1}\right) \cdot \theta_{1}\right)$$",-0.8711844046114036;0.10582267112973207,17,9.242959177470604e-3,9.242959177470604e-3,9.242959177470604e-3,9.242959177470604e-3,0.14704899793426884,0.14704899793426884,0.14704899793426884,31.41061304537471,31.41061304537471,31.41061304537471 +59,0,(Exp((((((x0 + 0.7552698974686317) + (x0 ^ 0.5143689517808008)) * -0.1675976570857899) / x0) ^ -0.302479336686453)) * ((Exp(((0.23849056535485202 + (x0 + x1)) * 0.9568372354241598)) * (-0.5537593136073524 * ((0.41620427952549643 + x0) + -0.2574084252400347))) ^ (Exp((x1 + 0.49496582563428093)) * 0.8737230670909867))),"(np.exp(((((x[:, 0] + t[0]) + x[:, 0] ** t[1]) * t[2]) / x[:, 0]) ** t[3]) * (np.exp(((t[4] + (x[:, 0] + x[:, 1])) * t[5])) * (t[6] * ((t[7] + x[:, 0]) + t[8]))) ** (np.exp((x[:, 1] + t[9])) * t[10]))","$$\left(\operatorname{exp}({\frac{\left(\left(\left(x_{0} + \theta_{0}\right) + {x_{0}^{\theta_{1}}}\right) \cdot \theta_{2}\right)}{x_{0}}^{\theta_{3}}}) \cdot {\left(\operatorname{exp}(\left(\left(\theta_{4} + \left(x_{0} + x_{1}\right)\right) \cdot \theta_{5}\right)) \cdot \left(\theta_{6} \cdot \left(\left(\theta_{7} + x_{0}\right) + \theta_{8}\right)\right)\right)^{\left(\operatorname{exp}(\left(x_{1} + \theta_{9}\right)) \cdot \theta_{10}\right)}}\right)$$",0.7552698974686317;0.5143689517808008;-0.1675976570857899;-0.302479336686453;0.23849056535485202;0.9568372354241598;-0.5537593136073524;0.41620427952549643;-0.2574084252400347;0.49496582563428093;0.8737230670909867,38,,,,Infinity,,,,,, +60,0,Log((Log(((x0 + -0.8234507999714971) ^ x1)) * -0.7779324995591076)),"np.log((np.log((x[:, 0] + t[0]) ** x[:, 1]) * t[1]))","$$\operatorname{log}(\left(\operatorname{log}({\left(x_{0} + \theta_{0}\right)^{x_{1}}}) \cdot \theta_{1}\right))$$",-0.8234507999714971;-0.7779324995591076,9,,,,Infinity,,,,,, +61,0,(3.524205325424165e-2 * x0),"(t[0] * x[:, 0])","$$\left(\theta_{0} \cdot x_{0}\right)$$",3.524205325424165e-2,3,1.3066544015526581e-2,1.3066544015526581e-2,1.3066544015526581e-2,1.3066544015526581e-2,-0.20579584931471206,-0.20579584931471206,-0.20579584931471206,5.328521690985943,5.328521690985943,5.328521690985943 +62,0,(0.14002406657977945 * ((x1 * 0.4366027209286226) / x1)),"(t[0] * ((x[:, 1] * t[1]) / x[:, 1]))","$$\left(\theta_{0} \cdot \frac{\left(x_{1} \cdot \theta_{1}\right)}{x_{1}}\right)$$",0.14002406657977945;0.4366027209286226,7,1.0836448005577471e-2,1.0836448005577471e-2,1.0836448005577471e-2,1.0836448005577471e-2,-8.352187830240609e-10,-8.352187830240609e-10,-8.352187830240609e-10,13.755663201779534,13.755663201779534,13.755663201779534 +63,0,(((-0.22834845540846405 ^ (-0.14859656893077056 + x1)) ^ (x0 ^ (x1 * x1))) * Log(Exp((Log((x0 * x1)) ^ Log((x0 ^ x1)))))),"(t[0] ** (t[1] + x[:, 1]) ** x[:, 0] ** (x[:, 1] * x[:, 1]) * np.log(np.exp(np.log((x[:, 0] * x[:, 1])) ** np.log(x[:, 0] ** x[:, 1]))))","$$\left({{\theta_{0}^{\left(\theta_{1} + x_{1}\right)}}^{{x_{0}^{\left(x_{1} \cdot x_{1}\right)}}}} \cdot \operatorname{log}(\operatorname{exp}({\operatorname{log}(\left(x_{0} \cdot x_{1}\right))^{\operatorname{log}({x_{0}^{x_{1}}})}}))\right)$$",-0.22834845540846405;-0.14859656893077056,23,,,,Infinity,,,,,, +64,0,(Log(((x0 + x0) / (-0.8375535712624412 * x0))) ^ -0.3540748295585926),"np.log(((x[:, 0] + x[:, 0]) / (t[0] * x[:, 0]))) ** t[1]","$${\operatorname{log}(\frac{\left(x_{0} + x_{0}\right)}{\left(\theta_{0} \cdot x_{0}\right)})^{\theta_{1}}}$$",-0.8375535712624412;-0.3540748295585926,10,,,,Infinity,,,,,, +65,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 +66,0,Exp(((0.7067195764902885 + (x0 * (x0 / ((x0 + 0.9336147384074215) * (x0 + 7.475713397415218e-2))))) + Exp((x0 / Log((x0 + -5.400268518482743e-3)))))),"np.exp(((t[0] + (x[:, 0] * (x[:, 0] / ((x[:, 0] + t[1]) * (x[:, 0] + t[2]))))) + np.exp((x[:, 0] / np.log((x[:, 0] + t[3]))))))","$$\operatorname{exp}(\left(\left(\theta_{0} + \left(x_{0} \cdot \frac{x_{0}}{\left(\left(x_{0} + \theta_{1}\right) \cdot \left(x_{0} + \theta_{2}\right)\right)}\right)\right) + \operatorname{exp}(\frac{x_{0}}{\operatorname{log}(\left(x_{0} + \theta_{3}\right))})\right))$$",0.7067195764902885;0.9336147384074215;7.475713397415218e-2;-5.400268518482743e-3,22,Infinity,Infinity,Infinity,Infinity,-Infinity,-Infinity,-Infinity,Infinity,Infinity,Infinity +67,0,((((x0 * (-0.2917775195065935 ^ x0)) * -0.4907012640912042) + -0.3288814094092286) ^ (Exp(Log((0.7662079348172693 / (x0 * 0.5611773910653803)))) / Log(((Exp(x1) / Exp(x0)) + Log((x1 * (0.14682896944028012 * x1))))))),"(((x[:, 0] * t[0] ** x[:, 0]) * t[1]) + t[2]) ** (np.exp(np.log((t[3] / (x[:, 0] * t[4])))) / np.log(((np.exp(x[:, 1]) / np.exp(x[:, 0])) + np.log((x[:, 1] * (t[5] * x[:, 1]))))))","$${\left(\left(\left(x_{0} \cdot {\theta_{0}^{x_{0}}}\right) \cdot \theta_{1}\right) + \theta_{2}\right)^{\frac{\operatorname{exp}(\operatorname{log}(\frac{\theta_{3}}{\left(x_{0} \cdot \theta_{4}\right)}))}{\operatorname{log}(\left(\frac{\operatorname{exp}(x_{1})}{\operatorname{exp}(x_{0})} + \operatorname{log}(\left(x_{1} \cdot \left(\theta_{5} \cdot x_{1}\right)\right))\right))}}}$$",-0.2917775195065935;-0.4907012640912042;-0.3288814094092286;0.7662079348172693;0.5611773910653803;0.14682896944028012,31,,,,Infinity,,,,,, +68,0,Log(((Log((Log((x1 + x1)) / x0)) * -0.7787991489512834) + (Log(Exp(x0)) * Log((x1 / x0))))),"np.log(((np.log((np.log((x[:, 1] + x[:, 1])) / x[:, 0])) * t[0]) + (np.log(np.exp(x[:, 0])) * np.log((x[:, 1] / x[:, 0])))))","$$\operatorname{log}(\left(\left(\operatorname{log}(\frac{\operatorname{log}(\left(x_{1} + x_{1}\right))}{x_{0}}) \cdot \theta_{0}\right) + \left(\operatorname{log}(\operatorname{exp}(x_{0})) \cdot \operatorname{log}(\frac{x_{1}}{x_{0}})\right)\right))$$",-0.7787991489512834,19,,,,Infinity,,,,,, +69,0,((Log((((Log(x0) + x1) ^ 0.48929834896328317) * -1.6705565677429535e-2)) / Exp(Exp(x1))) ^ (((x1 / ((-0.7169548370126054 * x0) * -0.4642952313475045)) / ((0.48700404350270476 + Exp(x1)) ^ x1)) ^ 0.3082067419753607)),"(np.log(((np.log(x[:, 0]) + x[:, 1]) ** t[0] * t[1])) / np.exp(np.exp(x[:, 1]))) ** ((x[:, 1] / ((t[2] * x[:, 0]) * t[3])) / (t[4] + np.exp(x[:, 1])) ** x[:, 1]) ** t[5]","$${\frac{\operatorname{log}(\left({\left(\operatorname{log}(x_{0}) + x_{1}\right)^{\theta_{0}}} \cdot \theta_{1}\right))}{\operatorname{exp}(\operatorname{exp}(x_{1}))}^{{\frac{\frac{x_{1}}{\left(\left(\theta_{2} \cdot x_{0}\right) \cdot \theta_{3}\right)}}{{\left(\theta_{4} + \operatorname{exp}(x_{1})\right)^{x_{1}}}}^{\theta_{5}}}}}$$",0.48929834896328317;-1.6705565677429535e-2;-0.7169548370126054;-0.4642952313475045;0.48700404350270476;0.3082067419753607,30,,,,Infinity,,,,,, +70,0,(Exp((-0.10023694050323861 + ((x1 * (x0 * 0.4571745712877434)) ^ 0.6924430769477543))) + Log(Log(((Exp((x1 * -0.5688478026338328)) + -0.676548720921234) * x1)))),"(np.exp((t[0] + (x[:, 1] * (x[:, 0] * t[1])) ** t[2])) + np.log(np.log(((np.exp((x[:, 1] * t[3])) + t[4]) * x[:, 1]))))","$$\left(\operatorname{exp}(\left(\theta_{0} + {\left(x_{1} \cdot \left(x_{0} \cdot \theta_{1}\right)\right)^{\theta_{2}}}\right)) + \operatorname{log}(\operatorname{log}(\left(\left(\operatorname{exp}(\left(x_{1} \cdot \theta_{3}\right)) + \theta_{4}\right) \cdot x_{1}\right)))\right)$$",-0.10023694050323861;0.4571745712877434;0.6924430769477543;-0.5688478026338328;-0.676548720921234,21,,,,Infinity,,,,,, +71,0,Exp((((Log(Exp(x1)) * -55.626714924238) / ((x0 * x1) ^ 2.48271246491314)) + Log((Exp(Exp((x0 ^ x1))) + ((x1 / x0) * -5.590070375523997))))),"np.exp((((np.log(np.exp(x[:, 1])) * t[0]) / (x[:, 0] * x[:, 1]) ** t[1]) + np.log((np.exp(np.exp(x[:, 0] ** x[:, 1])) + ((x[:, 1] / x[:, 0]) * t[2])))))","$$\operatorname{exp}(\left(\frac{\left(\operatorname{log}(\operatorname{exp}(x_{1})) \cdot \theta_{0}\right)}{{\left(x_{0} \cdot x_{1}\right)^{\theta_{1}}}} + \operatorname{log}(\left(\operatorname{exp}(\operatorname{exp}({x_{0}^{x_{1}}})) + \left(\frac{x_{1}}{x_{0}} \cdot \theta_{2}\right)\right))\right))$$",-55.626714924238;2.48271246491314;-5.590070375523997,25,1.4573554749975553e-2,1.4573554749975553e-2,1.4573554749975553e-2,1.4573554749975553e-2,-0.3448645492182232,-0.3448645492182232,-0.3448645492182232,61.006505438783385,61.006505438783385,61.006505438783385 +72,0,(((Exp(x1) + Log((x1 * -0.5043887036084546))) ^ (Log(x0) * 0.21819431631607422)) * ((Log(x1) ^ -0.18596280044446478) + x1)),"((np.exp(x[:, 1]) + np.log((x[:, 1] * t[0]))) ** (np.log(x[:, 0]) * t[1]) * (np.log(x[:, 1]) ** t[2] + x[:, 1]))","$$\left({\left(\operatorname{exp}(x_{1}) + \operatorname{log}(\left(x_{1} \cdot \theta_{0}\right))\right)^{\left(\operatorname{log}(x_{0}) \cdot \theta_{1}\right)}} \cdot \left({\operatorname{log}(x_{1})^{\theta_{2}}} + x_{1}\right)\right)$$",-0.5043887036084546;0.21819431631607422;-0.18596280044446478,19,,,,Infinity,,,,,, +73,0,((((-0.35508392813835965 * Log(x1)) ^ 0.5508429384200083) * x1) + -0.12495986516765652),"(((t[0] * np.log(x[:, 1])) ** t[1] * x[:, 1]) + t[2])","$$\left(\left({\left(\theta_{0} \cdot \operatorname{log}(x_{1})\right)^{\theta_{1}}} \cdot x_{1}\right) + \theta_{2}\right)$$",-0.35508392813835965;0.5508429384200083;-0.12495986516765652,10,9.20085386484256e-3,9.20085386484256e-3,9.20085386484256e-3,9.20085386484256e-3,0.15093452505916616,0.15093452505916616,0.15093452505916616,23.98865026686183,23.98865026686183,23.98865026686183 +74,0,((((x1 + Log(((x0 * -0.35019692782890777) * x1))) * x1) ^ -0.702311786226695) / x0),"(((x[:, 1] + np.log(((x[:, 0] * t[0]) * x[:, 1]))) * x[:, 1]) ** t[1] / x[:, 0])","$$\frac{{\left(\left(x_{1} + \operatorname{log}(\left(\left(x_{0} \cdot \theta_{0}\right) \cdot x_{1}\right))\right) \cdot x_{1}\right)^{\theta_{1}}}}{x_{0}}$$",-0.35019692782890777;-0.702311786226695,14,,,,Infinity,,,,,, +75,0,Log(((Exp((Exp((x0 * 6.8064581535632e-2)) * Log(x1))) ^ ((-9.513096765405604e-2 * (x0 + 0.10125111290883548)) / Log((x1 / x1)))) * ((Log(Exp((x1 ^ 0.5606312048459681))) + (x0 * x0)) / Log(((x1 * (x1 / x0)) + (x0 / x0)))))),"np.log((np.exp((np.exp((x[:, 0] * t[0])) * np.log(x[:, 1]))) ** ((t[1] * (x[:, 0] + t[2])) / np.log((x[:, 1] / x[:, 1]))) * ((np.log(np.exp(x[:, 1] ** t[3])) + (x[:, 0] * x[:, 0])) / np.log(((x[:, 1] * (x[:, 1] / x[:, 0])) + (x[:, 0] / x[:, 0]))))))","$$\operatorname{log}(\left({\operatorname{exp}(\left(\operatorname{exp}(\left(x_{0} \cdot \theta_{0}\right)) \cdot \operatorname{log}(x_{1})\right))^{\frac{\left(\theta_{1} \cdot \left(x_{0} + \theta_{2}\right)\right)}{\operatorname{log}(\frac{x_{1}}{x_{1}})}}} \cdot \frac{\left(\operatorname{log}(\operatorname{exp}({x_{1}^{\theta_{3}}})) + \left(x_{0} \cdot x_{0}\right)\right)}{\operatorname{log}(\left(\left(x_{1} \cdot \frac{x_{1}}{x_{0}}\right) + \frac{x_{0}}{x_{0}}\right))}\right))$$",6.8064581535632e-2;-9.513096765405604e-2;0.10125111290883548;0.5606312048459681,41,Infinity,Infinity,Infinity,Infinity,-Infinity,-Infinity,-Infinity,Infinity,Infinity,Infinity +76,0,Exp((((4.519878175133763 + x0) ^ -10.338674239667291) * x1)),"np.exp(((t[0] + x[:, 0]) ** t[1] * x[:, 1]))","$$\operatorname{exp}(\left({\left(\theta_{0} + x_{0}\right)^{\theta_{1}}} \cdot x_{1}\right))$$",4.519878175133763;-10.338674239667291,8,0.8923098078556129,0.8923098078556129,0.8923098078556129,0.8923098078556129,-81.34338485651561,-81.34338485651561,-81.34338485651561,20.41220989024784,20.41220989024784,20.41220989024784 +77,0,(Log(Log(Exp(Exp(((-0.28629436164160604 * x1) ^ -0.8340707641096115))))) + Exp((Exp(x1) + ((x0 * x1) * Log(x1))))),"(np.log(np.log(np.exp(np.exp((t[0] * x[:, 1]) ** t[1])))) + np.exp((np.exp(x[:, 1]) + ((x[:, 0] * x[:, 1]) * np.log(x[:, 1])))))","$$\left(\operatorname{log}(\operatorname{log}(\operatorname{exp}(\operatorname{exp}({\left(\theta_{0} \cdot x_{1}\right)^{\theta_{1}}})))) + \operatorname{exp}(\left(\operatorname{exp}(x_{1}) + \left(\left(x_{0} \cdot x_{1}\right) \cdot \operatorname{log}(x_{1})\right)\right))\right)$$",-0.28629436164160604;-0.8340707641096115,20,,,,Infinity,,,,,, +78,0,Exp((x0 * -2.9053779497574426)),"np.exp((x[:, 0] * t[0]))","$$\operatorname{exp}(\left(x_{0} \cdot \theta_{0}\right))$$",-2.9053779497574426,4,5.2316712708275655e-3,5.2316712708275655e-3,5.2316712708275655e-3,5.2316712708275655e-3,0.5172153022370025,0.5172153022370025,0.5172153022370025,7.570810883990948,7.570810883990948,7.570810883990948 +79,0,(Exp((-0.7598091821842864 ^ ((x1 * -0.4092221152045774) * -0.4028970450598004))) * ((Log(x1) ^ 0.36285991768748893) ^ (Exp(x1) + -0.8529303227599664))),"(np.exp(t[0] ** ((x[:, 1] * t[1]) * t[2])) * np.log(x[:, 1]) ** t[3] ** (np.exp(x[:, 1]) + t[4]))","$$\left(\operatorname{exp}({\theta_{0}^{\left(\left(x_{1} \cdot \theta_{1}\right) \cdot \theta_{2}\right)}}) \cdot {{\operatorname{log}(x_{1})^{\theta_{3}}}^{\left(\operatorname{exp}(x_{1}) + \theta_{4}\right)}}\right)$$",-0.7598091821842864;-0.4092221152045774;-0.4028970450598004;0.36285991768748893;-0.8529303227599664,18,,,,Infinity,,,,,, +80,0,((-7.249086055793885e-5 / (x1 * x1)) * Log((x1 / x0))),"((t[0] / (x[:, 1] * x[:, 1])) * np.log((x[:, 1] / x[:, 0])))","$$\left(\frac{\theta_{0}}{\left(x_{1} \cdot x_{1}\right)} \cdot \operatorname{log}(\frac{x_{1}}{x_{0}})\right)$$",-7.249086055793885e-5,10,1.4555693683848766e-2,1.4555693683848766e-2,1.4555693683848766e-2,1.4555693683848766e-2,-0.34321630930303026,-0.34321630930303026,-0.34321630930303026,19.95161975196365,19.95161975196365,19.95161975196365 +81,0,(Log(((((x0 + Exp(x0)) * 0.9470989402204423) + x1) * -5.1268384239821496e-2)) ^ x0),"np.log(((((x[:, 0] + np.exp(x[:, 0])) * t[0]) + x[:, 1]) * t[1])) ** x[:, 0]","$${\operatorname{log}(\left(\left(\left(\left(x_{0} + \operatorname{exp}(x_{0})\right) \cdot \theta_{0}\right) + x_{1}\right) \cdot \theta_{1}\right))^{x_{0}}}$$",0.9470989402204423;-5.1268384239821496e-2,13,,,,Infinity,,,,,, +82,0,(((-0.9136835342959171 ^ (((Exp(x1) ^ -0.41252245009116983) * -0.688809669305868) + x1)) ^ (-0.7996711169572053 ^ (x0 ^ 0.26448983095034073))) + -0.19536727365016116),"(t[0] ** ((np.exp(x[:, 1]) ** t[1] * t[2]) + x[:, 1]) ** t[3] ** x[:, 0] ** t[4] + t[5])","$$\left({{\theta_{0}^{\left(\left({\operatorname{exp}(x_{1})^{\theta_{1}}} \cdot \theta_{2}\right) + x_{1}\right)}}^{{\theta_{3}^{{x_{0}^{\theta_{4}}}}}}} + \theta_{5}\right)$$",-0.9136835342959171;-0.41252245009116983;-0.688809669305868;-0.7996711169572053;0.26448983095034073;-0.19536727365016116,18,,,,Infinity,,,,,, +83,0,(((((x0 / x1) + ((-0.6283710581944316 / x1) ^ (x0 ^ x0))) * (-0.9904157149155579 + Exp(x0))) ^ -0.7896428068801478) * -0.45298887975769697),"((((x[:, 0] / x[:, 1]) + (t[0] / x[:, 1]) ** x[:, 0] ** x[:, 0]) * (t[1] + np.exp(x[:, 0]))) ** t[2] * t[3])","$$\left({\left(\left(\frac{x_{0}}{x_{1}} + {\frac{\theta_{0}}{x_{1}}^{{x_{0}^{x_{0}}}}}\right) \cdot \left(\theta_{1} + \operatorname{exp}(x_{0})\right)\right)^{\theta_{2}}} \cdot \theta_{3}\right)$$",-0.6283710581944316;-0.9904157149155579;-0.7896428068801478;-0.45298887975769697,20,,,,Infinity,,,,,, +84,0,((((x0 * 0.622155026476008) * -4.500740626868016e-2) ^ ((0.6603205466342758 ^ x1) ^ (-0.5493820555421665 ^ x1))) + x0),"(((x[:, 0] * t[0]) * t[1]) ** t[2] ** x[:, 1] ** t[3] ** x[:, 1] + x[:, 0])","$$\left({\left(\left(x_{0} \cdot \theta_{0}\right) \cdot \theta_{1}\right)^{{{\theta_{2}^{x_{1}}}^{{\theta_{3}^{x_{1}}}}}}} + x_{0}\right)$$",0.622155026476008;-4.500740626868016e-2;0.6603205466342758;-0.5493820555421665,15,,,,Infinity,,,,,, +85,0,((((Log(Exp(Log(x0))) * -0.2503752727444368) * (-0.22200094119923153 ^ x1)) * 0.573343403431618) ^ x1),"(((np.log(np.exp(np.log(x[:, 0]))) * t[0]) * t[1] ** x[:, 1]) * t[2]) ** x[:, 1]","$${\left(\left(\left(\operatorname{log}(\operatorname{exp}(\operatorname{log}(x_{0}))) \cdot \theta_{0}\right) \cdot {\theta_{1}^{x_{1}}}\right) \cdot \theta_{2}\right)^{x_{1}}}$$",-0.2503752727444368;-0.22200094119923153;0.573343403431618,14,,,,Infinity,,,,,, +86,0,(Exp(Exp(((-0.2734412440332644 / x1) ^ (x1 * (0.1995774301650315 * x0))))) / (Log((0.8356803649665492 + x0)) / (0.7510954670165744 ^ (-0.8155657901934451 / x1)))),"(np.exp(np.exp((t[0] / x[:, 1]) ** (x[:, 1] * (t[1] * x[:, 0])))) / (np.log((t[2] + x[:, 0])) / t[3] ** (t[4] / x[:, 1])))","$$\frac{\operatorname{exp}(\operatorname{exp}({\frac{\theta_{0}}{x_{1}}^{\left(x_{1} \cdot \left(\theta_{1} \cdot x_{0}\right)\right)}}))}{\frac{\operatorname{log}(\left(\theta_{2} + x_{0}\right))}{{\theta_{3}^{\frac{\theta_{4}}{x_{1}}}}}}$$",-0.2734412440332644;0.1995774301650315;0.8356803649665492;0.7510954670165744;-0.8155657901934451,22,,,,Infinity,,,,,, +87,0,(Exp((-0.7796566212936181 * Exp(Exp(Log((x0 ^ x1)))))) * ((((-0.9009065958296975 * x0) ^ -0.3280394781479087) * (0.4909436407395378 * (x0 ^ -0.7298674879232272))) / (((x1 / x1) ^ x1) ^ Exp(Log(Log(Log(x1))))))),"(np.exp((t[0] * np.exp(np.exp(np.log(x[:, 0] ** x[:, 1]))))) * (((t[1] * x[:, 0]) ** t[2] * (t[3] * x[:, 0] ** t[4])) / (x[:, 1] / x[:, 1]) ** x[:, 1] ** np.exp(np.log(np.log(np.log(x[:, 1]))))))","$$\left(\operatorname{exp}(\left(\theta_{0} \cdot \operatorname{exp}(\operatorname{exp}(\operatorname{log}({x_{0}^{x_{1}}})))\right)) \cdot \frac{\left({\left(\theta_{1} \cdot x_{0}\right)^{\theta_{2}}} \cdot \left(\theta_{3} \cdot {x_{0}^{\theta_{4}}}\right)\right)}{{{\frac{x_{1}}{x_{1}}^{x_{1}}}^{\operatorname{exp}(\operatorname{log}(\operatorname{log}(\operatorname{log}(x_{1}))))}}}\right)$$",-0.7796566212936181;-0.9009065958296975;-0.3280394781479087;0.4909436407395378;-0.7298674879232272,33,,,,Infinity,,,,,, +88,0,(Log(Log((((x1 + x0) / x0) ^ (-0.5787461091406391 ^ Log(x0))))) ^ Log((Log(((x1 / x0) + -0.6646385296195615)) ^ Exp(Exp((Exp(x0) ^ x1)))))),"np.log(np.log(((x[:, 1] + x[:, 0]) / x[:, 0]) ** t[0] ** np.log(x[:, 0]))) ** np.log(np.log(((x[:, 1] / x[:, 0]) + t[1])) ** np.exp(np.exp(np.exp(x[:, 0]) ** x[:, 1])))","$${\operatorname{log}(\operatorname{log}({\frac{\left(x_{1} + x_{0}\right)}{x_{0}}^{{\theta_{0}^{\operatorname{log}(x_{0})}}}}))^{\operatorname{log}({\operatorname{log}(\left(\frac{x_{1}}{x_{0}} + \theta_{1}\right))^{\operatorname{exp}(\operatorname{exp}({\operatorname{exp}(x_{0})^{x_{1}}}))}})}}$$",-0.5787461091406391;-0.6646385296195615,27,,,,Infinity,,,,,, +89,0,((((x0 * -0.23377161853654516) ^ (x0 * 0.8722468595910213)) * (Log(x1) ^ (-0.21831693384226447 / x0))) ^ (Log((Log(x0) ^ (x1 * -0.7807764269703579))) / Log((((0.15413326946061934 / x0) + 0.45863471981707427) ^ -0.9924735681956656)))),"((x[:, 0] * t[0]) ** (x[:, 0] * t[1]) * np.log(x[:, 1]) ** (t[2] / x[:, 0])) ** (np.log(np.log(x[:, 0]) ** (x[:, 1] * t[3])) / np.log(((t[4] / x[:, 0]) + t[5]) ** t[6]))","$${\left({\left(x_{0} \cdot \theta_{0}\right)^{\left(x_{0} \cdot \theta_{1}\right)}} \cdot {\operatorname{log}(x_{1})^{\frac{\theta_{2}}{x_{0}}}}\right)^{\frac{\operatorname{log}({\operatorname{log}(x_{0})^{\left(x_{1} \cdot \theta_{3}\right)}})}{\operatorname{log}({\left(\frac{\theta_{4}}{x_{0}} + \theta_{5}\right)^{\theta_{6}}})}}}$$",-0.23377161853654516;0.8722468595910213;-0.21831693384226447;-0.7807764269703579;0.15413326946061934;0.45863471981707427;-0.9924735681956656,31,,,,Infinity,,,,,, +90,0,((Exp(Exp((Log(Log(x0)) / Exp((x1 ^ -1.8700203047188335e-3))))) * (Exp(x1) + 0.7251912963037392)) * 0.4295765588371294),"((np.exp(np.exp((np.log(np.log(x[:, 0])) / np.exp(x[:, 1] ** t[0])))) * (np.exp(x[:, 1]) + t[1])) * t[2])","$$\left(\left(\operatorname{exp}(\operatorname{exp}(\frac{\operatorname{log}(\operatorname{log}(x_{0}))}{\operatorname{exp}({x_{1}^{\theta_{0}}})})) \cdot \left(\operatorname{exp}(x_{1}) + \theta_{1}\right)\right) \cdot \theta_{2}\right)$$",-1.8700203047188335e-3;0.7251912963037392;0.4295765588371294,17,,,,Infinity,,,,,, +91,0,Exp((((x0 + x1) * (-1.9033914549889586e13 + Exp((-9.999981885488438e15 / (x1 ^ x0))))) / (Exp(x1) / (x0 ^ x1)))),"np.exp((((x[:, 0] + x[:, 1]) * (t[0] + np.exp((t[1] / x[:, 1] ** x[:, 0])))) / (np.exp(x[:, 1]) / x[:, 0] ** x[:, 1])))","$$\operatorname{exp}(\frac{\left(\left(x_{0} + x_{1}\right) \cdot \left(\theta_{0} + \operatorname{exp}(\frac{\theta_{1}}{{x_{1}^{x_{0}}}})\right)\right)}{\frac{\operatorname{exp}(x_{1})}{{x_{0}^{x_{1}}}}})$$",-1.9033914549889586e13;-9.999981885488438e15,20,1.4573554749975553e-2,1.4573554749975553e-2,1.4573554749975553e-2,1.4573554749975553e-2,-0.3448645492182232,-0.3448645492182232,-0.3448645492182232,45.643796903606976,45.643796903606976,45.643796903606976 +92,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 +93,0,((((x1 / (Exp(Log(x1)) / Exp(Exp(x1)))) * ((((-4.6237330439191795e-5 / x0) * Log(x1)) ^ -0.7753106939390338) * 3.225967566194982)) ^ -2.1018867031538147) + x0),"(((x[:, 1] / (np.exp(np.log(x[:, 1])) / np.exp(np.exp(x[:, 1])))) * (((t[0] / x[:, 0]) * np.log(x[:, 1])) ** t[1] * t[2])) ** t[3] + x[:, 0])","$$\left({\left(\frac{x_{1}}{\frac{\operatorname{exp}(\operatorname{log}(x_{1}))}{\operatorname{exp}(\operatorname{exp}(x_{1}))}} \cdot \left({\left(\frac{\theta_{0}}{x_{0}} \cdot \operatorname{log}(x_{1})\right)^{\theta_{1}}} \cdot \theta_{2}\right)\right)^{\theta_{3}}} + x_{0}\right)$$",-4.6237330439191795e-5;-0.7753106939390338;3.225967566194982;-2.1018867031538147,24,1.1424220388828665,1.1424220388828665,1.1424220388828665,1.1424220388828665,-104.4240318644116,-104.4240318644116,-104.4240318644116,61.84526943253441,61.84526943253441,61.84526943253441 +94,0,(((Exp((Log((x1 * 1.5757064594676672e-5)) + x1)) ^ (x1 / ((Log(x1) * -0.38979102318627734) ^ x1))) * x1) * x0),"((np.exp((np.log((x[:, 1] * t[0])) + x[:, 1])) ** (x[:, 1] / (np.log(x[:, 1]) * t[1]) ** x[:, 1]) * x[:, 1]) * x[:, 0])","$$\left(\left({\operatorname{exp}(\left(\operatorname{log}(\left(x_{1} \cdot \theta_{0}\right)) + x_{1}\right))^{\frac{x_{1}}{{\left(\operatorname{log}(x_{1}) \cdot \theta_{1}\right)^{x_{1}}}}}} \cdot x_{1}\right) \cdot x_{0}\right)$$",1.5757064594676672e-5;-0.38979102318627734,20,1.4594726584821027e-2,1.4594726584821027e-2,1.4594726584821027e-2,1.4594726584821027e-2,-0.346818310713896,-0.346818310713896,-0.346818310713896,47.99947667138601,47.99947667138601,47.99947667138601 +95,0,Log(((((Log(x0) ^ -0.17656537281549078) * (x0 ^ (x1 ^ x0))) * Exp(((x1 + 0.9959915894509641) ^ Exp((0.4254464228770556 / x0))))) ^ Exp(((((x1 ^ x1) ^ x1) * -0.871071156235419) * -0.8990465607905614)))),"np.log(((np.log(x[:, 0]) ** t[0] * x[:, 0] ** x[:, 1] ** x[:, 0]) * np.exp((x[:, 1] + t[1]) ** np.exp((t[2] / x[:, 0])))) ** np.exp(((x[:, 1] ** x[:, 1] ** x[:, 1] * t[3]) * t[4])))","$$\operatorname{log}({\left(\left({\operatorname{log}(x_{0})^{\theta_{0}}} \cdot {x_{0}^{{x_{1}^{x_{0}}}}}\right) \cdot \operatorname{exp}({\left(x_{1} + \theta_{1}\right)^{\operatorname{exp}(\frac{\theta_{2}}{x_{0}})}})\right)^{\operatorname{exp}(\left(\left({{x_{1}^{x_{1}}}^{x_{1}}} \cdot \theta_{3}\right) \cdot \theta_{4}\right))}})$$",-0.17656537281549078;0.9959915894509641;0.4254464228770556;-0.871071156235419;-0.8990465607905614,32,,,,Infinity,,,,,, +96,0,(((Exp(Log((x0 * -0.4665697009374026))) * -0.8462895094191233) ^ x0) * -0.7773456768440615),"((np.exp(np.log((x[:, 0] * t[0]))) * t[1]) ** x[:, 0] * t[2])","$$\left({\left(\operatorname{exp}(\operatorname{log}(\left(x_{0} \cdot \theta_{0}\right))) \cdot \theta_{1}\right)^{x_{0}}} \cdot \theta_{2}\right)$$",-0.4665697009374026;-0.8462895094191233;-0.7773456768440615,11,,,,Infinity,,,,,, +97,0,(((0.7032134140461792 * (-0.3381653524016855 / x1)) / (Exp((x0 * -0.3430684712008597)) ^ (x1 / x1))) / Exp(Exp((0.24673679865352155 ^ Log((x0 * 0.32596017023366924)))))),"(((t[0] * (t[1] / x[:, 1])) / np.exp((x[:, 0] * t[2])) ** (x[:, 1] / x[:, 1])) / np.exp(np.exp(t[3] ** np.log((x[:, 0] * t[4])))))","$$\frac{\frac{\left(\theta_{0} \cdot \frac{\theta_{1}}{x_{1}}\right)}{{\operatorname{exp}(\left(x_{0} \cdot \theta_{2}\right))^{\frac{x_{1}}{x_{1}}}}}}{\operatorname{exp}(\operatorname{exp}({\theta_{3}^{\operatorname{log}(\left(x_{0} \cdot \theta_{4}\right))}}))}$$",0.7032134140461792;-0.3381653524016855;-0.3430684712008597;0.24673679865352155;0.32596017023366924,23,1.457355481921096e-2,1.457355481921096e-2,1.457355481921096e-2,1.457355481921096e-2,-0.3448645556073471,-0.3448645556073471,-0.3448645556073471,57.94489633481171,57.94489633481171,57.94489633481171 +98,0,((0.1649260065001873 / x0) + Log(x0)),"((t[0] / x[:, 0]) + np.log(x[:, 0]))","$$\left(\frac{\theta_{0}}{x_{0}} + \operatorname{log}(x_{0})\right)$$",0.1649260065001873,6,0.13377529036960586,0.13377529036960586,0.13377529036960586,0.13377529036960586,-11.344939080820945,-11.344939080820945,-11.344939080820945,11.797950171304889,11.797950171304889,11.797950171304889 +99,0,((0.4951392130392588 * (-0.843558578063905 + x0)) + (0.5249506496308305 ^ (x1 / x0))),"((t[0] * (t[1] + x[:, 0])) + t[2] ** (x[:, 1] / x[:, 0]))","$$\left(\left(\theta_{0} \cdot \left(\theta_{1} + x_{0}\right)\right) + {\theta_{2}^{\frac{x_{1}}{x_{0}}}}\right)$$",0.4951392130392588;-0.843558578063905;0.5249506496308305,11,0.8201097407209612,0.8201097407209612,0.8201097407209612,0.8201097407209612,-74.68067885194719,-74.68067885194719,-74.68067885194719,28.205885212360233,28.205885212360233,28.205885212360233 +100,0,Log(((((x0 + x1) ^ -0.183670114544065) * Log(((-0.4012496099632018 * x1) * Exp(x1)))) / Log((((x0 + 0.34914396946823856) * (x0 * (x0 / x0))) ^ 0.7607362119624373)))),"np.log((((x[:, 0] + x[:, 1]) ** t[0] * np.log(((t[1] * x[:, 1]) * np.exp(x[:, 1])))) / np.log(((x[:, 0] + t[2]) * (x[:, 0] * (x[:, 0] / x[:, 0]))) ** t[3])))","$$\operatorname{log}(\frac{\left({\left(x_{0} + x_{1}\right)^{\theta_{0}}} \cdot \operatorname{log}(\left(\left(\theta_{1} \cdot x_{1}\right) \cdot \operatorname{exp}(x_{1})\right))\right)}{\operatorname{log}({\left(\left(x_{0} + \theta_{2}\right) \cdot \left(x_{0} \cdot \frac{x_{0}}{x_{0}}\right)\right)^{\theta_{3}}})})$$",-0.183670114544065;-0.4012496099632018;0.34914396946823856;0.7607362119624373,27,,,,Infinity,,,,,, +101,0,((((x0 * (-0.2992363142868322 / x1)) * 0.7469307520865507) ^ Exp((x1 * x0))) + 0.6864789936353768),"(((x[:, 0] * (t[0] / x[:, 1])) * t[1]) ** np.exp((x[:, 1] * x[:, 0])) + t[2])","$$\left({\left(\left(x_{0} \cdot \frac{\theta_{0}}{x_{1}}\right) \cdot \theta_{1}\right)^{\operatorname{exp}(\left(x_{1} \cdot x_{0}\right))}} + \theta_{2}\right)$$",-0.2992363142868322;0.7469307520865507;0.6864789936353768,14,,,,Infinity,,,,,, +102,0,Log((((Exp(x0) * Exp((x1 ^ 0.5775876383162657))) * 0.6142297045728289) / Exp((((Log(x0) ^ x0) + x0) * ((x1 / x1) / x1))))),"np.log((((np.exp(x[:, 0]) * np.exp(x[:, 1] ** t[0])) * t[1]) / np.exp(((np.log(x[:, 0]) ** x[:, 0] + x[:, 0]) * ((x[:, 1] / x[:, 1]) / x[:, 1])))))","$$\operatorname{log}(\frac{\left(\left(\operatorname{exp}(x_{0}) \cdot \operatorname{exp}({x_{1}^{\theta_{0}}})\right) \cdot \theta_{1}\right)}{\operatorname{exp}(\left(\left({\operatorname{log}(x_{0})^{x_{0}}} + x_{0}\right) \cdot \frac{\frac{x_{1}}{x_{1}}}{x_{1}}\right))})$$",0.5775876383162657;0.6142297045728289,24,,,,Infinity,,,,,, +103,0,(((x1 * -0.16701825287297667) ^ x0) / x1),"((x[:, 1] * t[0]) ** x[:, 0] / x[:, 1])","$$\frac{{\left(x_{1} \cdot \theta_{0}\right)^{x_{0}}}}{x_{1}}$$",-0.16701825287297667,7,,,,Infinity,,,,,, +104,0,Log(((((x0 / Log(x1)) * 0.34331349020213503) + (x0 / x0)) + (((Log(Exp(x1)) / x0) + Exp(Log((x0 + -0.5080180381555238)))) / (2.240117011356105 / x0)))),"np.log(((((x[:, 0] / np.log(x[:, 1])) * t[0]) + (x[:, 0] / x[:, 0])) + (((np.log(np.exp(x[:, 1])) / x[:, 0]) + np.exp(np.log((x[:, 0] + t[1])))) / (t[2] / x[:, 0]))))","$$\operatorname{log}(\left(\left(\left(\frac{x_{0}}{\operatorname{log}(x_{1})} \cdot \theta_{0}\right) + \frac{x_{0}}{x_{0}}\right) + \frac{\left(\frac{\operatorname{log}(\operatorname{exp}(x_{1}))}{x_{0}} + \operatorname{exp}(\operatorname{log}(\left(x_{0} + \theta_{1}\right)))\right)}{\frac{\theta_{2}}{x_{0}}}\right))$$",0.34331349020213503;-0.5080180381555238;2.240117011356105,27,4.0808316528306174e-2,4.0808316528306174e-2,4.0808316528306174e-2,4.0808316528306174e-2,-2.765838819268656,-2.765838819268656,-2.765838819268656,62.244423916333965,62.244423916333965,62.244423916333965 +105,0,Exp((3.344703145102064 * Log((x1 ^ (x1 * 2.732331720705962))))),"np.exp((t[0] * np.log(x[:, 1] ** (x[:, 1] * t[1]))))","$$\operatorname{exp}(\left(\theta_{0} \cdot \operatorname{log}({x_{1}^{\left(x_{1} \cdot \theta_{1}\right)}})\right))$$",3.344703145102064;2.732331720705962,9,1.250373865043282e-2,1.250373865043282e-2,1.250373865043282e-2,1.250373865043282e-2,-0.1538595169229402,-0.1538595169229402,-0.1538595169229402,20.178938458573167,20.178938458573167,20.178938458573167 +106,0,Log(Log(((0.22840951606456383 ^ (0.8639657323545767 ^ x1)) + 0.6903676224565654))),"np.log(np.log((t[0] ** t[1] ** x[:, 1] + t[2])))","$$\operatorname{log}(\operatorname{log}(\left({\theta_{0}^{{\theta_{1}^{x_{1}}}}} + \theta_{2}\right)))$$",0.22840951606456383;0.8639657323545767;0.6903676224565654,9,,,,Infinity,,,,,, +107,0,Exp(Exp((((-0.6885773293096165 + (0.17323048259712748 * (x0 * x1))) / Exp(((x1 * x0) * -0.9059075627212022))) / (Exp((0.1038114227475454 + x0)) ^ (((x1 * -0.9986752501573765) ^ -0.6920154129792686) + (x1 ^ (x0 ^ x1))))))),"np.exp(np.exp((((t[0] + (t[1] * (x[:, 0] * x[:, 1]))) / np.exp(((x[:, 1] * x[:, 0]) * t[2]))) / np.exp((t[3] + x[:, 0])) ** ((x[:, 1] * t[4]) ** t[5] + x[:, 1] ** x[:, 0] ** x[:, 1]))))","$$\operatorname{exp}(\operatorname{exp}(\frac{\frac{\left(\theta_{0} + \left(\theta_{1} \cdot \left(x_{0} \cdot x_{1}\right)\right)\right)}{\operatorname{exp}(\left(\left(x_{1} \cdot x_{0}\right) \cdot \theta_{2}\right))}}{{\operatorname{exp}(\left(\theta_{3} + x_{0}\right))^{\left({\left(x_{1} \cdot \theta_{4}\right)^{\theta_{5}}} + {x_{1}^{{x_{0}^{x_{1}}}}}\right)}}}))$$",-0.6885773293096165;0.17323048259712748;-0.9059075627212022;0.1038114227475454;-0.9986752501573765;-0.6920154129792686,33,,,,Infinity,,,,,, +108,0,(((Log(x0) ^ Exp(Exp(x0))) + ((x0 + 0.11572546333078271) / (x1 / (x1 ^ -5.8020803043132396e-2)))) ^ (Exp(((0.13670185064979323 ^ (x1 ^ x1)) ^ (-0.32728126494110543 ^ (4.49066333035506e-2 ^ x1)))) * -0.40724509886588467)),"(np.log(x[:, 0]) ** np.exp(np.exp(x[:, 0])) + ((x[:, 0] + t[0]) / (x[:, 1] / x[:, 1] ** t[1]))) ** (np.exp(t[2] ** x[:, 1] ** x[:, 1] ** t[3] ** t[4] ** x[:, 1]) * t[5])","$${\left({\operatorname{log}(x_{0})^{\operatorname{exp}(\operatorname{exp}(x_{0}))}} + \frac{\left(x_{0} + \theta_{0}\right)}{\frac{x_{1}}{{x_{1}^{\theta_{1}}}}}\right)^{\left(\operatorname{exp}({{\theta_{2}^{{x_{1}^{x_{1}}}}}^{{\theta_{3}^{{\theta_{4}^{x_{1}}}}}}}) \cdot \theta_{5}\right)}}$$",0.11572546333078271;-5.8020803043132396e-2;0.13670185064979323;-0.32728126494110543;4.49066333035506e-2;-0.40724509886588467,31,,,,Infinity,,,,,, +109,0,(((0.8537172360863454 + Log(x0)) ^ (7.621110099325945e-2 ^ ((-0.35742692244968144 / x1) ^ -0.5589962425236303))) / (((((0.18088601441834296 / (x0 ^ -0.9433317191850474)) + -0.3425744093173396) / (x0 / Log((x1 / x1)))) ^ (-0.9426961852577016 * x0)) ^ ((x0 * -3.212228096004699e-2) * 0.9989205415807794))),"((t[0] + np.log(x[:, 0])) ** t[1] ** (t[2] / x[:, 1]) ** t[3] / (((t[4] / x[:, 0] ** t[5]) + t[6]) / (x[:, 0] / np.log((x[:, 1] / x[:, 1])))) ** (t[7] * x[:, 0]) ** ((x[:, 0] * t[8]) * t[9]))","$$\frac{{\left(\theta_{0} + \operatorname{log}(x_{0})\right)^{{\theta_{1}^{{\frac{\theta_{2}}{x_{1}}^{\theta_{3}}}}}}}}{{{\frac{\left(\frac{\theta_{4}}{{x_{0}^{\theta_{5}}}} + \theta_{6}\right)}{\frac{x_{0}}{\operatorname{log}(\frac{x_{1}}{x_{1}})}}^{\left(\theta_{7} \cdot x_{0}\right)}}^{\left(\left(x_{0} \cdot \theta_{8}\right) \cdot \theta_{9}\right)}}}$$",0.8537172360863454;7.621110099325945e-2;-0.35742692244968144;-0.5589962425236303;0.18088601441834296;-0.9433317191850474;-0.3425744093173396;-0.9426961852577016;-3.212228096004699e-2;0.9989205415807794,37,,,,Infinity,,,,,, +110,0,Log(Log(Log(((-0.45703526323398513 * (x0 * x0)) * (x0 * ((x1 / x1) + x0)))))),"np.log(np.log(np.log(((t[0] * (x[:, 0] * x[:, 0])) * (x[:, 0] * ((x[:, 1] / x[:, 1]) + x[:, 0]))))))","$$\operatorname{log}(\operatorname{log}(\operatorname{log}(\left(\left(\theta_{0} \cdot \left(x_{0} \cdot x_{0}\right)\right) \cdot \left(x_{0} \cdot \left(\frac{x_{1}}{x_{1}} + x_{0}\right)\right)\right))))$$",-0.45703526323398513,16,,,,Infinity,,,,,, +111,0,Exp(Log(((Exp((x0 * (x1 * 1.4515428883289228))) / ((x1 ^ x0) * ((x0 ^ -1.1154228168593132) ^ x1))) ^ ((-1.1043855405633605 + (x1 + -1.2715452642160683)) ^ ((x1 / x1) ^ ((0.6011144878428614 * x0) + (x0 / x0))))))),"np.exp(np.log((np.exp((x[:, 0] * (x[:, 1] * t[0]))) / (x[:, 1] ** x[:, 0] * x[:, 0] ** t[1] ** x[:, 1])) ** (t[2] + (x[:, 1] + t[3])) ** (x[:, 1] / x[:, 1]) ** ((t[4] * x[:, 0]) + (x[:, 0] / x[:, 0]))))","$$\operatorname{exp}(\operatorname{log}({\frac{\operatorname{exp}(\left(x_{0} \cdot \left(x_{1} \cdot \theta_{0}\right)\right))}{\left({x_{1}^{x_{0}}} \cdot {{x_{0}^{\theta_{1}}}^{x_{1}}}\right)}^{{\left(\theta_{2} + \left(x_{1} + \theta_{3}\right)\right)^{{\frac{x_{1}}{x_{1}}^{\left(\left(\theta_{4} \cdot x_{0}\right) + \frac{x_{0}}{x_{0}}\right)}}}}}}))$$",1.4515428883289228;-1.1154228168593132;-1.1043855405633605;-1.2715452642160683;0.6011144878428614,36,1.0723267666339067e-3,1.0723267666339067e-3,1.0723267666339067e-3,1.0723267666339067e-3,0.9010444412248726,0.9010444412248726,0.9010444412248726,89.20567455503208,89.20567455503208,89.20567455503208 +112,0,Log(Exp((x0 + -0.9956750177908847))),"np.log(np.exp((x[:, 0] + t[0])))","$$\operatorname{log}(\operatorname{exp}(\left(x_{0} + \theta_{0}\right)))$$",-0.9956750177908847,5,0.15105329635364,0.15105329635364,0.15105329635364,0.15105329635364,-12.939373529228037,-12.939373529228037,-12.939373529228037,10.204062464256419,10.204062464256419,10.204062464256419 +113,0,(Log(Log((-0.5769664153555887 ^ x1))) ^ -3.5619728067071144e-3),"np.log(np.log(t[0] ** x[:, 1])) ** t[1]","$${\operatorname{log}(\operatorname{log}({\theta_{0}^{x_{1}}}))^{\theta_{1}}}$$",-0.5769664153555887;-3.5619728067071144e-3,7,,,,Infinity,,,,,, +114,0,Log(Exp((((-0.47584373138445846 + x0) + (0.2655405457277719 + ((x0 / x0) ^ 0.4176520058454798))) ^ -5.199169024305316))),"np.log(np.exp(((t[0] + x[:, 0]) + (t[1] + (x[:, 0] / x[:, 0]) ** t[2])) ** t[3]))","$$\operatorname{log}(\operatorname{exp}({\left(\left(\theta_{0} + x_{0}\right) + \left(\theta_{1} + {\frac{x_{0}}{x_{0}}^{\theta_{2}}}\right)\right)^{\theta_{3}}}))$$",-0.47584373138445846;0.2655405457277719;0.4176520058454798;-5.199169024305316,15,5.147076393802534e-3,5.147076393802534e-3,5.147076393802534e-3,5.147076393802534e-3,0.5250218156860724,0.5250218156860724,0.5250218156860724,37.27698434605469,37.27698434605469,37.27698434605469 +115,0,((4.740255607529478e-2 / (((((5.4056620110959845e-2 ^ x0) / x0) + -0.866095301872698) + x0) ^ x1)) * 0.7479817698039307),"((t[0] / (((t[1] ** x[:, 0] / x[:, 0]) + t[2]) + x[:, 0]) ** x[:, 1]) * t[3])","$$\left(\frac{\theta_{0}}{{\left(\left(\frac{{\theta_{1}^{x_{0}}}}{x_{0}} + \theta_{2}\right) + x_{0}\right)^{x_{1}}}} \cdot \theta_{3}\right)$$",4.740255607529478e-2;5.4056620110959845e-2;-0.866095301872698;0.7479817698039307,15,5.147492452471214e-3,5.147492452471214e-3,5.147492452471214e-3,5.147492452471214e-3,0.5249834213091683,0.5249834213091683,0.5249834213091683,37.27698472050749,37.27698472050749,37.27698472050749 +116,0,(((((x0 + 0.578393108530052) ^ -3.289061085431772) ^ x0) / x0) * 0.16344831451499572),"(((x[:, 0] + t[0]) ** t[1] ** x[:, 0] / x[:, 0]) * t[2])","$$\left(\frac{{{\left(x_{0} + \theta_{0}\right)^{\theta_{1}}}^{x_{0}}}}{x_{0}} \cdot \theta_{2}\right)$$",0.578393108530052;-3.289061085431772;0.16344831451499572,11,4.911290957115253e-3,4.911290957115253e-3,4.911290957115253e-3,4.911290957115253e-3,0.5467803694818232,0.5467803694818232,0.5467803694818232,25.77654912947293,25.77654912947293,25.77654912947293 +117,0,Log((1.222155609436542 ^ x1)),"np.log(t[0] ** x[:, 1])","$$\operatorname{log}({\theta_{0}^{x_{1}}})$$",1.222155609436542,4,9.365750726554422e-3,9.365750726554422e-3,9.365750726554422e-3,9.365750726554422e-3,0.13571765124915813,0.13571765124915813,0.13571765124915813,7.5745315555011015,7.5745315555011015,7.5745315555011015 +118,0,Log((((0.7817856180161955 / Log(x0)) / Exp(Exp((-0.45568218380277614 + (x0 * -0.15703913092967237))))) + Exp((((-0.531330362020098 + x1) + 0.35435873886616986) / ((x0 * -0.40203142457292707) ^ -0.10293967674071625))))),"np.log((((t[0] / np.log(x[:, 0])) / np.exp(np.exp((t[1] + (x[:, 0] * t[2]))))) + np.exp((((t[3] + x[:, 1]) + t[4]) / (x[:, 0] * t[5]) ** t[6]))))","$$\operatorname{log}(\left(\frac{\frac{\theta_{0}}{\operatorname{log}(x_{0})}}{\operatorname{exp}(\operatorname{exp}(\left(\theta_{1} + \left(x_{0} \cdot \theta_{2}\right)\right)))} + \operatorname{exp}(\frac{\left(\left(\theta_{3} + x_{1}\right) + \theta_{4}\right)}{{\left(x_{0} \cdot \theta_{5}\right)^{\theta_{6}}}})\right))$$",0.7817856180161955;-0.45568218380277614;-0.15703913092967237;-0.531330362020098;0.35435873886616986;-0.40203142457292707;-0.10293967674071625,26,,,,Infinity,,,,,, +119,0,(((((Exp((x0 * -28.51956973058349)) ^ x0) * x0) + (-0.13352955252864485 * x1)) / Log(x1)) + 1.109464773033123e-2),"((((np.exp((x[:, 0] * t[0])) ** x[:, 0] * x[:, 0]) + (t[1] * x[:, 1])) / np.log(x[:, 1])) + t[2])","$$\left(\frac{\left(\left({\operatorname{exp}(\left(x_{0} \cdot \theta_{0}\right))^{x_{0}}} \cdot x_{0}\right) + \left(\theta_{1} \cdot x_{1}\right)\right)}{\operatorname{log}(x_{1})} + \theta_{2}\right)$$",-28.51956973058349;-0.13352955252864485;1.109464773033123e-2,17,9.425889210719216e-3,9.425889210719216e-3,9.425889210719216e-3,9.425889210719216e-3,0.1301680021220576,0.1301680021220576,0.1301680021220576,43.424075921108304,43.424075921108304,43.424075921108304 +120,0,(((66.88173099301365 / ((x1 + (Exp(x1) * Exp(x1))) * 74.39440222948285)) / Exp((x0 * 62.63437816152556))) * Log((Exp(x0) / (x1 * x1)))),"(((t[0] / ((x[:, 1] + (np.exp(x[:, 1]) * np.exp(x[:, 1]))) * t[1])) / np.exp((x[:, 0] * t[2]))) * np.log((np.exp(x[:, 0]) / (x[:, 1] * x[:, 1]))))","$$\left(\frac{\frac{\theta_{0}}{\left(\left(x_{1} + \left(\operatorname{exp}(x_{1}) \cdot \operatorname{exp}(x_{1})\right)\right) \cdot \theta_{1}\right)}}{\operatorname{exp}(\left(x_{0} \cdot \theta_{2}\right))} \cdot \operatorname{log}(\frac{\operatorname{exp}(x_{0})}{\left(x_{1} \cdot x_{1}\right)})\right)$$",66.88173099301365;74.39440222948285;62.63437816152556,24,1.457355474997544e-2,1.457355474997544e-2,1.457355474997544e-2,1.457355474997544e-2,-0.34486454921821275,-0.34486454921821275,-0.34486454921821275,55.98248800569395,55.98248800569395,55.98248800569395 +121,0,(((((-0.16772692148960477 * x0) * x0) + -9.17303664270066e-2) * Exp(((x1 + (x0 + x0)) ^ (x0 ^ 0.14305320587135095)))) * ((0.8645537163544479 ^ Log((x0 + (x0 * x1)))) ^ (((-0.22946899667718668 + x1) / x0) ^ (Log(x1) / (x1 + 0.6365674780455115))))),"(((((t[0] * x[:, 0]) * x[:, 0]) + t[1]) * np.exp((x[:, 1] + (x[:, 0] + x[:, 0])) ** x[:, 0] ** t[2])) * t[3] ** np.log((x[:, 0] + (x[:, 0] * x[:, 1]))) ** ((t[4] + x[:, 1]) / x[:, 0]) ** (np.log(x[:, 1]) / (x[:, 1] + t[5])))","$$\left(\left(\left(\left(\left(\theta_{0} \cdot x_{0}\right) \cdot x_{0}\right) + \theta_{1}\right) \cdot \operatorname{exp}({\left(x_{1} + \left(x_{0} + x_{0}\right)\right)^{{x_{0}^{\theta_{2}}}}})\right) \cdot {{\theta_{3}^{\operatorname{log}(\left(x_{0} + \left(x_{0} \cdot x_{1}\right)\right))}}^{{\frac{\left(\theta_{4} + x_{1}\right)}{x_{0}}^{\frac{\operatorname{log}(x_{1})}{\left(x_{1} + \theta_{5}\right)}}}}}\right)$$",-0.16772692148960477;-9.17303664270066e-2;0.14305320587135095;0.8645537163544479;-0.22946899667718668;0.6365674780455115,40,,,,Infinity,,,,,, +122,0,Log(Log(((x0 * (-0.15260390891108622 ^ x1)) ^ Log((((x1 * 0.7833768916177044) ^ (x1 * 0.2770202914109625)) / (x1 / (x0 / x1))))))),"np.log(np.log((x[:, 0] * t[0] ** x[:, 1]) ** np.log(((x[:, 1] * t[1]) ** (x[:, 1] * t[2]) / (x[:, 1] / (x[:, 0] / x[:, 1]))))))","$$\operatorname{log}(\operatorname{log}({\left(x_{0} \cdot {\theta_{0}^{x_{1}}}\right)^{\operatorname{log}(\frac{{\left(x_{1} \cdot \theta_{1}\right)^{\left(x_{1} \cdot \theta_{2}\right)}}}{\frac{x_{1}}{\frac{x_{0}}{x_{1}}}})}}))$$",-0.15260390891108622;0.7833768916177044;0.2770202914109625,22,,,,Infinity,,,,,, +123,0,(-7.748556362980419e-2 + ((Log(x1) ^ (x0 / x1)) + (0.798844683237206 + (-0.4009504352589146 ^ x0)))),"(t[0] + (np.log(x[:, 1]) ** (x[:, 0] / x[:, 1]) + (t[1] + t[2] ** x[:, 0])))","$$\left(\theta_{0} + \left({\operatorname{log}(x_{1})^{\frac{x_{0}}{x_{1}}}} + \left(\theta_{1} + {\theta_{2}^{x_{0}}}\right)\right)\right)$$",-7.748556362980419e-2;0.798844683237206;-0.4009504352589146,14,,,,Infinity,,,,,, +124,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 +125,0,Exp(((((x1 / Exp(x0)) ^ ((x1 * x0) / x0)) * Exp(((x0 * x0) * -0.6234044204442423))) / (((-0.24425617296498003 + x0) ^ (0.5383316480530964 ^ (x0 * x1))) * Exp(((0.3055173532632969 * (-0.20946797648581805 / x1)) ^ (-0.144075059462912 / (-0.5303869160618666 * x1))))))),"np.exp((((x[:, 1] / np.exp(x[:, 0])) ** ((x[:, 1] * x[:, 0]) / x[:, 0]) * np.exp(((x[:, 0] * x[:, 0]) * t[0]))) / ((t[1] + x[:, 0]) ** t[2] ** (x[:, 0] * x[:, 1]) * np.exp((t[3] * (t[4] / x[:, 1])) ** (t[5] / (t[6] * x[:, 1]))))))","$$\operatorname{exp}(\frac{\left({\frac{x_{1}}{\operatorname{exp}(x_{0})}^{\frac{\left(x_{1} \cdot x_{0}\right)}{x_{0}}}} \cdot \operatorname{exp}(\left(\left(x_{0} \cdot x_{0}\right) \cdot \theta_{0}\right))\right)}{\left({\left(\theta_{1} + x_{0}\right)^{{\theta_{2}^{\left(x_{0} \cdot x_{1}\right)}}}} \cdot \operatorname{exp}({\left(\theta_{3} \cdot \frac{\theta_{4}}{x_{1}}\right)^{\frac{\theta_{5}}{\left(\theta_{6} \cdot x_{1}\right)}}})\right)})$$",-0.6234044204442423;-0.24425617296498003;0.5383316480530964;0.3055173532632969;-0.20946797648581805;-0.144075059462912;-0.5303869160618666,41,,,,Infinity,,,,,, +126,0,((-0.1193824367142472 ^ (Log(((-9.77793075489064e-2 ^ x0) * Log(Exp(x1)))) ^ ((-0.990337311010981 + ((x0 * -0.15349184139096217) * (-0.9709322486727994 / x0))) + (x1 / Log(Exp(x1)))))) + (Exp(x1) ^ 0.3768042471678018)),"(t[0] ** np.log((t[1] ** x[:, 0] * np.log(np.exp(x[:, 1])))) ** ((t[2] + ((x[:, 0] * t[3]) * (t[4] / x[:, 0]))) + (x[:, 1] / np.log(np.exp(x[:, 1])))) + np.exp(x[:, 1]) ** t[5])","$$\left({\theta_{0}^{{\operatorname{log}(\left({\theta_{1}^{x_{0}}} \cdot \operatorname{log}(\operatorname{exp}(x_{1}))\right))^{\left(\left(\theta_{2} + \left(\left(x_{0} \cdot \theta_{3}\right) \cdot \frac{\theta_{4}}{x_{0}}\right)\right) + \frac{x_{1}}{\operatorname{log}(\operatorname{exp}(x_{1}))}\right)}}}} + {\operatorname{exp}(x_{1})^{\theta_{5}}}\right)$$",-0.1193824367142472;-9.77793075489064e-2;-0.990337311010981;-0.15349184139096217;-0.9709322486727994;0.3768042471678018,31,,,,Infinity,,,,,, +127,0,((0.21728401001814387 * (x0 ^ x0)) * 0.1525321466472882),"((t[0] * x[:, 0] ** x[:, 0]) * t[1])","$$\left(\left(\theta_{0} \cdot {x_{0}^{x_{0}}}\right) \cdot \theta_{1}\right)$$",0.21728401001814387;0.1525321466472882,7,1.2934826309877115e-2,1.2934826309877115e-2,1.2934826309877115e-2,1.2934826309877115e-2,-0.19364078654029693,-0.19364078654029693,-0.19364078654029693,13.757551742253403,13.757551742253403,13.757551742253403 +128,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 +129,0,((Log((0.3421304475560347 + Log(Log(x1)))) / ((x0 * x1) ^ 0.10554648592297544)) ^ (Exp((Log(x1) ^ (x1 + -0.4175659418211084))) * Exp((Exp((-0.9221948987287276 / x0)) / (x0 * 8.55978714605965e-4))))),"(np.log((t[0] + np.log(np.log(x[:, 1])))) / (x[:, 0] * x[:, 1]) ** t[1]) ** (np.exp(np.log(x[:, 1]) ** (x[:, 1] + t[2])) * np.exp((np.exp((t[3] / x[:, 0])) / (x[:, 0] * t[4]))))","$${\frac{\operatorname{log}(\left(\theta_{0} + \operatorname{log}(\operatorname{log}(x_{1}))\right))}{{\left(x_{0} \cdot x_{1}\right)^{\theta_{1}}}}^{\left(\operatorname{exp}({\operatorname{log}(x_{1})^{\left(x_{1} + \theta_{2}\right)}}) \cdot \operatorname{exp}(\frac{\operatorname{exp}(\frac{\theta_{3}}{x_{0}})}{\left(x_{0} \cdot \theta_{4}\right)})\right)}}$$",0.3421304475560347;0.10554648592297544;-0.4175659418211084;-0.9221948987287276;8.55978714605965e-4,30,,,,Infinity,,,,,, +130,0,(((((((-0.8216158531400458 + x1) * -3.1826872293060915e-2) * Log((x0 ^ -0.5487824512119821))) + -0.6527976177271888) + 0.17888821209520323) + 0.3347048438833756) ^ x1),"((((((t[0] + x[:, 1]) * t[1]) * np.log(x[:, 0] ** t[2])) + t[3]) + t[4]) + t[5]) ** x[:, 1]","$${\left(\left(\left(\left(\left(\left(\theta_{0} + x_{1}\right) \cdot \theta_{1}\right) \cdot \operatorname{log}({x_{0}^{\theta_{2}}})\right) + \theta_{3}\right) + \theta_{4}\right) + \theta_{5}\right)^{x_{1}}}$$",-0.8216158531400458;-3.1826872293060915e-2;-0.5487824512119821;-0.6527976177271888;0.17888821209520323;0.3347048438833756,18,,,,Infinity,,,,,, +131,0,Log(Exp((((x1 + -0.6724382544393613) + (x1 ^ (x0 + x0))) ^ ((-0.7040845510534761 / x1) ^ (x0 * -0.7309152471846252))))),"np.log(np.exp(((x[:, 1] + t[0]) + x[:, 1] ** (x[:, 0] + x[:, 0])) ** (t[1] / x[:, 1]) ** (x[:, 0] * t[2])))","$$\operatorname{log}(\operatorname{exp}({\left(\left(x_{1} + \theta_{0}\right) + {x_{1}^{\left(x_{0} + x_{0}\right)}}\right)^{{\frac{\theta_{1}}{x_{1}}^{\left(x_{0} \cdot \theta_{2}\right)}}}}))$$",-0.6724382544393613;-0.7040845510534761;-0.7309152471846252,19,,,,Infinity,,,,,, +132,0,Exp(Log((Exp(Log(x1)) * (((Exp(x1) * -0.3875701744944944) + 0.8132811393805993) + -0.32322830338722564)))),"np.exp(np.log((np.exp(np.log(x[:, 1])) * (((np.exp(x[:, 1]) * t[0]) + t[1]) + t[2]))))","$$\operatorname{exp}(\operatorname{log}(\left(\operatorname{exp}(\operatorname{log}(x_{1})) \cdot \left(\left(\left(\operatorname{exp}(x_{1}) \cdot \theta_{0}\right) + \theta_{1}\right) + \theta_{2}\right)\right)))$$",-0.3875701744944944;0.8132811393805993;-0.32322830338722564,14,,,,Infinity,,,,,, +133,0,((3.5114123752993595e-3 ^ (x1 ^ x1)) * Exp(Exp(((x1 ^ x0) / (x0 ^ -0.3011229942440748))))),"(t[0] ** x[:, 1] ** x[:, 1] * np.exp(np.exp((x[:, 1] ** x[:, 0] / x[:, 0] ** t[1]))))","$$\left({\theta_{0}^{{x_{1}^{x_{1}}}}} \cdot \operatorname{exp}(\operatorname{exp}(\frac{{x_{1}^{x_{0}}}}{{x_{0}^{\theta_{1}}}}))\right)$$",3.5114123752993595e-3;-0.3011229942440748,15,8.570177618030807e-3,8.570177618030807e-3,8.570177618030807e-3,8.570177618030807e-3,0.20913406119996614,0.20913406119996614,0.20913406119996614,33.23821526642121,33.23821526642121,33.23821526642121 +134,0,Log((Exp((0.6972077071846006 * Log(x0))) ^ -0.321043531678246)),"np.log(np.exp((t[0] * np.log(x[:, 0]))) ** t[1])","$$\operatorname{log}({\operatorname{exp}(\left(\theta_{0} \cdot \operatorname{log}(x_{0})\right))^{\theta_{1}}})$$",0.6972077071846006;-0.321043531678246,8,9.281051777135091e-3,9.281051777135091e-3,9.281051777135091e-3,9.281051777135091e-3,0.14353376861958045,0.14353376861958045,0.14353376861958045,18.384278571159143,18.384278571159143,18.384278571159143 +135,0,Exp((-0.8807664048182543 / (x1 * 0.8415584766175589))),"np.exp((t[0] / (x[:, 1] * t[1])))","$$\operatorname{exp}(\frac{\theta_{0}}{\left(x_{1} \cdot \theta_{1}\right)})$$",-0.8807664048182543;0.8415584766175589,6,9.451763005823425e-3,9.451763005823425e-3,9.451763005823425e-3,9.451763005823425e-3,0.1277803382757029,0.1277803382757029,0.1277803382757029,13.706983932045123,13.706983932045123,13.706983932045123 +136,0,(Log((Log(x0) ^ (x0 + x0))) ^ -0.845219971849921),"np.log(np.log(x[:, 0]) ** (x[:, 0] + x[:, 0])) ** t[0]","$${\operatorname{log}({\operatorname{log}(x_{0})^{\left(x_{0} + x_{0}\right)}})^{\theta_{0}}}$$",-0.845219971849921,9,,,,Infinity,,,,,, +137,0,(Exp((((x0 ^ x0) + -0.20247965616255614) + ((0.6012084129969308 ^ Exp((-0.2740745758068699 * x1))) ^ 0.5551899105010502))) ^ (Log(Log(x1)) * 0.5093888888846689)),"np.exp(((x[:, 0] ** x[:, 0] + t[0]) + t[1] ** np.exp((t[2] * x[:, 1])) ** t[3])) ** (np.log(np.log(x[:, 1])) * t[4])","$${\operatorname{exp}(\left(\left({x_{0}^{x_{0}}} + \theta_{0}\right) + {{\theta_{1}^{\operatorname{exp}(\left(\theta_{2} \cdot x_{1}\right))}}^{\theta_{3}}}\right))^{\left(\operatorname{log}(\operatorname{log}(x_{1})) \cdot \theta_{4}\right)}}$$",-0.20247965616255614;0.6012084129969308;-0.2740745758068699;0.5551899105010502;0.5093888888846689,21,,,,Infinity,,,,,, +138,0,Log(Log((Exp((Exp((x1 * -0.5759861572049794)) ^ (x0 + x1))) * (Exp((x1 + 0.21460052101348626)) ^ ((x1 ^ x0) * Log(Log(x1))))))),"np.log(np.log((np.exp(np.exp((x[:, 1] * t[0])) ** (x[:, 0] + x[:, 1])) * np.exp((x[:, 1] + t[1])) ** (x[:, 1] ** x[:, 0] * np.log(np.log(x[:, 1]))))))","$$\operatorname{log}(\operatorname{log}(\left(\operatorname{exp}({\operatorname{exp}(\left(x_{1} \cdot \theta_{0}\right))^{\left(x_{0} + x_{1}\right)}}) \cdot {\operatorname{exp}(\left(x_{1} + \theta_{1}\right))^{\left({x_{1}^{x_{0}}} \cdot \operatorname{log}(\operatorname{log}(x_{1}))\right)}}\right)))$$",-0.5759861572049794;0.21460052101348626,24,,,,Infinity,,,,,, +139,0,Log((1.193769485229021 + (0.12118877071497218 * ((x0 * x0) * -0.8733027242780543)))),"np.log((t[0] + (t[1] * ((x[:, 0] * x[:, 0]) * t[2]))))","$$\operatorname{log}(\left(\theta_{0} + \left(\theta_{1} \cdot \left(\left(x_{0} \cdot x_{0}\right) \cdot \theta_{2}\right)\right)\right))$$",1.193769485229021;0.12118877071497218;-0.8733027242780543,10,6.622472938811641e-3,6.622472938811641e-3,6.622472938811641e-3,6.622472938811641e-3,0.38887050988162375,0.38887050988162375,0.38887050988162375,22.163114156088856,22.163114156088856,22.163114156088856 +140,0,(((x0 ^ -0.5918106801781998) + -0.2966408802737627) / (Log(Exp((-8.29463992774222e-2 ^ x0))) / Exp((x1 * (x1 ^ x1))))),"((x[:, 0] ** t[0] + t[1]) / (np.log(np.exp(t[2] ** x[:, 0])) / np.exp((x[:, 1] * x[:, 1] ** x[:, 1]))))","$$\frac{\left({x_{0}^{\theta_{0}}} + \theta_{1}\right)}{\frac{\operatorname{log}(\operatorname{exp}({\theta_{2}^{x_{0}}}))}{\operatorname{exp}(\left(x_{1} \cdot {x_{1}^{x_{1}}}\right))}}$$",-0.5918106801781998;-0.2966408802737627;-8.29463992774222e-2,18,,,,Infinity,,,,,, +141,0,(((-0.362323572235411 / (x0 * 0.7944651033611387)) + -0.5425724353267609) * Log(((0.6139355053956357 ^ (x1 * 0.9022556092330366)) * ((x1 * -1.1263612893958053e-2) ^ -0.43953070737601196)))),"(((t[0] / (x[:, 0] * t[1])) + t[2]) * np.log((t[3] ** (x[:, 1] * t[4]) * (x[:, 1] * t[5]) ** t[6])))","$$\left(\left(\frac{\theta_{0}}{\left(x_{0} \cdot \theta_{1}\right)} + \theta_{2}\right) \cdot \operatorname{log}(\left({\theta_{3}^{\left(x_{1} \cdot \theta_{4}\right)}} \cdot {\left(x_{1} \cdot \theta_{5}\right)^{\theta_{6}}}\right))\right)$$",-0.362323572235411;0.7944651033611387;-0.5425724353267609;0.6139355053956357;0.9022556092330366;-1.1263612893958053e-2;-0.43953070737601196,20,,,,Infinity,,,,,, +142,0,Exp((x1 / (x1 ^ x0))),"np.exp((x[:, 1] / x[:, 1] ** x[:, 0]))","$$\operatorname{exp}(\frac{x_{1}}{{x_{1}^{x_{0}}}})$$",,6,14.411630412978433,14.411630412978433,14.411630412978433,14.411630412978433,-1328.9219834393793,-1328.9219834393793,-1328.9219834393793,22.62709484628519,22.62709484628519,22.62709484628519 +143,0,(Log(((Log(x1) / x0) ^ Log(x0))) * (((Exp(x1) * -5.312099721774899e-2) ^ ((x1 * (x1 * x1)) * -0.222181011003322)) ^ x0)),"(np.log((np.log(x[:, 1]) / x[:, 0]) ** np.log(x[:, 0])) * (np.exp(x[:, 1]) * t[0]) ** ((x[:, 1] * (x[:, 1] * x[:, 1])) * t[1]) ** x[:, 0])","$$\left(\operatorname{log}({\frac{\operatorname{log}(x_{1})}{x_{0}}^{\operatorname{log}(x_{0})}}) \cdot {{\left(\operatorname{exp}(x_{1}) \cdot \theta_{0}\right)^{\left(\left(x_{1} \cdot \left(x_{1} \cdot x_{1}\right)\right) \cdot \theta_{1}\right)}}^{x_{0}}}\right)$$",-5.312099721774899e-2;-0.222181011003322,23,,,,Infinity,,,,,, +144,0,(((-0.30404059616727697 / (x1 + x0)) + (-0.4548984706188258 + (-0.9312404922880191 ^ (((-0.9636522752234902 + x0) ^ -0.37095153006075265) * -0.4181135559963698)))) ^ ((Exp((x1 + 0.9392954669190421)) ^ Log(x0)) + (0.5440683474712493 + x0))),"((t[0] / (x[:, 1] + x[:, 0])) + (t[1] + t[2] ** ((t[3] + x[:, 0]) ** t[4] * t[5]))) ** (np.exp((x[:, 1] + t[6])) ** np.log(x[:, 0]) + (t[7] + x[:, 0]))","$${\left(\frac{\theta_{0}}{\left(x_{1} + x_{0}\right)} + \left(\theta_{1} + {\theta_{2}^{\left({\left(\theta_{3} + x_{0}\right)^{\theta_{4}}} \cdot \theta_{5}\right)}}\right)\right)^{\left({\operatorname{exp}(\left(x_{1} + \theta_{6}\right))^{\operatorname{log}(x_{0})}} + \left(\theta_{7} + x_{0}\right)\right)}}$$",-0.30404059616727697;-0.4548984706188258;-0.9312404922880191;-0.9636522752234902;-0.37095153006075265;-0.4181135559963698;0.9392954669190421;0.5440683474712493,29,,,,Infinity,,,,,, +145,0,(Exp(Log(Log((0.5867131264638382 + Log(Exp(x0)))))) ^ (((Log((x0 ^ 9.263242638575364e-3)) * (x1 * x0)) + (Log(x1) ^ (x1 * 0.9155846053296199))) / (Log((Log(x0) * -9.634506586484237e-2)) ^ (0.6520947015598239 + (x0 ^ Exp(x0)))))),"np.exp(np.log(np.log((t[0] + np.log(np.exp(x[:, 0])))))) ** (((np.log(x[:, 0] ** t[1]) * (x[:, 1] * x[:, 0])) + np.log(x[:, 1]) ** (x[:, 1] * t[2])) / np.log((np.log(x[:, 0]) * t[3])) ** (t[4] + x[:, 0] ** np.exp(x[:, 0])))","$${\operatorname{exp}(\operatorname{log}(\operatorname{log}(\left(\theta_{0} + \operatorname{log}(\operatorname{exp}(x_{0}))\right))))^{\frac{\left(\left(\operatorname{log}({x_{0}^{\theta_{1}}}) \cdot \left(x_{1} \cdot x_{0}\right)\right) + {\operatorname{log}(x_{1})^{\left(x_{1} \cdot \theta_{2}\right)}}\right)}{{\operatorname{log}(\left(\operatorname{log}(x_{0}) \cdot \theta_{3}\right))^{\left(\theta_{4} + {x_{0}^{\operatorname{exp}(x_{0})}}\right)}}}}}$$",0.5867131264638382;9.263242638575364e-3;0.9155846053296199;-9.634506586484237e-2;0.6520947015598239,37,,,,Infinity,,,,,, +146,0,(Log(((Exp(x1) / (8.668923942237572e-2 + x0)) ^ Exp((x1 / x1)))) ^ Log(Log(((0.11482245260677071 ^ (0.13849890428548006 + x1)) / (0.27103801283882145 + (x1 * 4.4734821141817704e-2)))))),"np.log((np.exp(x[:, 1]) / (t[0] + x[:, 0])) ** np.exp((x[:, 1] / x[:, 1]))) ** np.log(np.log((t[1] ** (t[2] + x[:, 1]) / (t[3] + (x[:, 1] * t[4])))))","$${\operatorname{log}({\frac{\operatorname{exp}(x_{1})}{\left(\theta_{0} + x_{0}\right)}^{\operatorname{exp}(\frac{x_{1}}{x_{1}})}})^{\operatorname{log}(\operatorname{log}(\frac{{\theta_{1}^{\left(\theta_{2} + x_{1}\right)}}}{\left(\theta_{3} + \left(x_{1} \cdot \theta_{4}\right)\right)}))}}$$",8.668923942237572e-2;0.11482245260677071;0.13849890428548006;0.27103801283882145;4.4734821141817704e-2,26,,,,Infinity,,,,,, +147,0,(-0.49065709877649377 ^ x1),"t[0] ** x[:, 1]","$${\theta_{0}^{x_{1}}}$$",-0.49065709877649377,3,,,,Infinity,,,,,, +148,0,Log((0.3193545634828091 / x1)),"np.log((t[0] / x[:, 1]))","$$\operatorname{log}(\frac{\theta_{0}}{x_{1}})$$",0.3193545634828091,4,0.2792880552105235,0.2792880552105235,0.2792880552105235,0.2792880552105235,-24.773025930640912,-24.773025930640912,-24.773025930640912,7.817461629536674,7.817461629536674,7.817461629536674 +149,0,(((0.2675999930073257 ^ ((0.8323411144385874 * x1) * x0)) / ((x0 * 2.424672642972869) / x0)) * 0.21623693267944152),"((t[0] ** ((t[1] * x[:, 1]) * x[:, 0]) / ((x[:, 0] * t[2]) / x[:, 0])) * t[3])","$$\left(\frac{{\theta_{0}^{\left(\left(\theta_{1} \cdot x_{1}\right) \cdot x_{0}\right)}}}{\frac{\left(x_{0} \cdot \theta_{2}\right)}{x_{0}}} \cdot \theta_{3}\right)$$",0.2675999930073257;0.8323411144385874;2.424672642972869;0.21623693267944152,15,1.0630187118239022e-2,1.0630187118239022e-2,1.0630187118239022e-2,1.0630187118239022e-2,1.9033993274711003e-2,1.9033993274711003e-2,1.9033993274711003e-2,34.969658948297806,34.969658948297806,34.969658948297806 +150,0,(((Log(Log((x1 ^ x1))) + -0.9501750166385429) + ((0.3973678551246953 * (x0 + x0)) * Exp(Log((-0.8677788674405422 * x1))))) * Log(Exp(Log((((x1 + -0.7092236214095563) * (x1 ^ x1)) ^ Exp(x0)))))),"(((np.log(np.log(x[:, 1] ** x[:, 1])) + t[0]) + ((t[1] * (x[:, 0] + x[:, 0])) * np.exp(np.log((t[2] * x[:, 1]))))) * np.log(np.exp(np.log(((x[:, 1] + t[3]) * x[:, 1] ** x[:, 1]) ** np.exp(x[:, 0])))))","$$\left(\left(\left(\operatorname{log}(\operatorname{log}({x_{1}^{x_{1}}})) + \theta_{0}\right) + \left(\left(\theta_{1} \cdot \left(x_{0} + x_{0}\right)\right) \cdot \operatorname{exp}(\operatorname{log}(\left(\theta_{2} \cdot x_{1}\right)))\right)\right) \cdot \operatorname{log}(\operatorname{exp}(\operatorname{log}({\left(\left(x_{1} + \theta_{3}\right) \cdot {x_{1}^{x_{1}}}\right)^{\operatorname{exp}(x_{0})}})))\right)$$",-0.9501750166385429;0.3973678551246953;-0.8677788674405422;-0.7092236214095563,33,,,,Infinity,,,,,, +151,0,(Exp((Exp(Log((Exp(x1) / (0.43518183259302934 * x1)))) * Log(((x0 / x1) * -0.12359062219289396)))) * (((x0 / (x0 * x1)) * (x1 * x1)) * (Log((x1 * -0.5920873135911845)) / Log((x0 * x1))))),"(np.exp((np.exp(np.log((np.exp(x[:, 1]) / (t[0] * x[:, 1])))) * np.log(((x[:, 0] / x[:, 1]) * t[1])))) * (((x[:, 0] / (x[:, 0] * x[:, 1])) * (x[:, 1] * x[:, 1])) * (np.log((x[:, 1] * t[2])) / np.log((x[:, 0] * x[:, 1])))))","$$\left(\operatorname{exp}(\left(\operatorname{exp}(\operatorname{log}(\frac{\operatorname{exp}(x_{1})}{\left(\theta_{0} \cdot x_{1}\right)})) \cdot \operatorname{log}(\left(\frac{x_{0}}{x_{1}} \cdot \theta_{1}\right))\right)) \cdot \left(\left(\frac{x_{0}}{\left(x_{0} \cdot x_{1}\right)} \cdot \left(x_{1} \cdot x_{1}\right)\right) \cdot \frac{\operatorname{log}(\left(x_{1} \cdot \theta_{2}\right))}{\operatorname{log}(\left(x_{0} \cdot x_{1}\right))}\right)\right)$$",0.43518183259302934;-0.12359062219289396;-0.5920873135911845,36,,,,Infinity,,,,,, +152,0,Log(((((2.3384039449557434 ^ x1) ^ x1) * (0.657880919890683 ^ Log(x0))) ^ 0.5626283794494901)),"np.log((t[0] ** x[:, 1] ** x[:, 1] * t[1] ** np.log(x[:, 0])) ** t[2])","$$\operatorname{log}({\left({{\theta_{0}^{x_{1}}}^{x_{1}}} \cdot {\theta_{1}^{\operatorname{log}(x_{0})}}\right)^{\theta_{2}}})$$",2.3384039449557434;0.657880919890683;0.5626283794494901,13,3.460810626237287e-3,3.460810626237287e-3,3.460810626237287e-3,3.460810626237287e-3,0.6806323781236658,0.6806323781236658,0.6806323781236658,29.35876263563125,29.35876263563125,29.35876263563125 +153,0,(((x1 * x1) + x0) / x1),"(((x[:, 1] * x[:, 1]) + x[:, 0]) / x[:, 1])","$$\frac{\left(\left(x_{1} \cdot x_{1}\right) + x_{0}\right)}{x_{1}}$$",,7,22.46986725908893,22.46986725908893,22.46986725908893,22.46986725908893,-2072.5454335489862,-2072.5454335489862,-2072.5454335489862,31.48894592021874,31.48894592021874,31.48894592021874 +154,0,((((((x1 ^ (x1 + -0.3878280419017254)) + x1) / Log((x1 * -0.200282014592556))) / (-0.28875544467012215 ^ (0.12602520457722854 / Exp((x1 ^ 0.1922076079756715))))) ^ (Log((-0.21179978481042283 / x1)) * (((0.10458052071768331 + x0) * x1) + Log((0.546962367421267 / (x1 ^ x0)))))) * x0),"((((x[:, 1] ** (x[:, 1] + t[0]) + x[:, 1]) / np.log((x[:, 1] * t[1]))) / t[2] ** (t[3] / np.exp(x[:, 1] ** t[4]))) ** (np.log((t[5] / x[:, 1])) * (((t[6] + x[:, 0]) * x[:, 1]) + np.log((t[7] / x[:, 1] ** x[:, 0])))) * x[:, 0])","$$\left({\frac{\frac{\left({x_{1}^{\left(x_{1} + \theta_{0}\right)}} + x_{1}\right)}{\operatorname{log}(\left(x_{1} \cdot \theta_{1}\right))}}{{\theta_{2}^{\frac{\theta_{3}}{\operatorname{exp}({x_{1}^{\theta_{4}}})}}}}^{\left(\operatorname{log}(\frac{\theta_{5}}{x_{1}}) \cdot \left(\left(\left(\theta_{6} + x_{0}\right) \cdot x_{1}\right) + \operatorname{log}(\frac{\theta_{7}}{{x_{1}^{x_{0}}}})\right)\right)}} \cdot x_{0}\right)$$",-0.3878280419017254;-0.200282014592556;-0.28875544467012215;0.12602520457722854;0.1922076079756715;-0.21179978481042283;0.10458052071768331;0.546962367421267,41,,,,Infinity,,,,,, +155,0,Log(Log((Log(((x0 / x1) * (Log(x1) ^ -0.693795429486298))) / (((-0.8898669266702308 / x0) * (0.9830811911368884 / (x1 ^ x1))) + 0.39910679021280904)))),"np.log(np.log((np.log(((x[:, 0] / x[:, 1]) * np.log(x[:, 1]) ** t[0])) / (((t[1] / x[:, 0]) * (t[2] / x[:, 1] ** x[:, 1])) + t[3]))))","$$\operatorname{log}(\operatorname{log}(\frac{\operatorname{log}(\left(\frac{x_{0}}{x_{1}} \cdot {\operatorname{log}(x_{1})^{\theta_{0}}}\right))}{\left(\left(\frac{\theta_{1}}{x_{0}} \cdot \frac{\theta_{2}}{{x_{1}^{x_{1}}}}\right) + \theta_{3}\right)}))$$",-0.693795429486298;-0.8898669266702308;0.9830811911368884;0.39910679021280904,23,,,,Infinity,,,,,, +156,0,Exp((Log((((x0 ^ (5.807727898281234e-2 ^ x1)) + -0.761159566460547) + (0.10522120935497803 + (x1 / x1)))) / (Log((Log(x0) ^ Exp((0.5599010120150526 ^ x0)))) * (((0.9518216266733684 ^ (0.7735978059922499 + x0)) + (0.4482620168223893 * x1)) * ((0.15627636710591786 ^ (x0 ^ x1)) / (-0.548722296716416 ^ x0)))))),"np.exp((np.log(((x[:, 0] ** t[0] ** x[:, 1] + t[1]) + (t[2] + (x[:, 1] / x[:, 1])))) / (np.log(np.log(x[:, 0]) ** np.exp(t[3] ** x[:, 0])) * ((t[4] ** (t[5] + x[:, 0]) + (t[6] * x[:, 1])) * (t[7] ** x[:, 0] ** x[:, 1] / t[8] ** x[:, 0])))))","$$\operatorname{exp}(\frac{\operatorname{log}(\left(\left({x_{0}^{{\theta_{0}^{x_{1}}}}} + \theta_{1}\right) + \left(\theta_{2} + \frac{x_{1}}{x_{1}}\right)\right))}{\left(\operatorname{log}({\operatorname{log}(x_{0})^{\operatorname{exp}({\theta_{3}^{x_{0}}})}}) \cdot \left(\left({\theta_{4}^{\left(\theta_{5} + x_{0}\right)}} + \left(\theta_{6} \cdot x_{1}\right)\right) \cdot \frac{{\theta_{7}^{{x_{0}^{x_{1}}}}}}{{\theta_{8}^{x_{0}}}}\right)\right)})$$",5.807727898281234e-2;-0.761159566460547;0.10522120935497803;0.5599010120150526;0.9518216266733684;0.7735978059922499;0.4482620168223893;0.15627636710591786;-0.548722296716416,44,,,,Infinity,,,,,, +157,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 +158,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 +159,0,Log((((x1 ^ x1) + (x0 * Exp(x0))) + ((0.6796794244676786 / x1) * (-0.5652646144542011 * (-0.17754772712873657 ^ x0))))),"np.log(((x[:, 1] ** x[:, 1] + (x[:, 0] * np.exp(x[:, 0]))) + ((t[0] / x[:, 1]) * (t[1] * t[2] ** x[:, 0]))))","$$\operatorname{log}(\left(\left({x_{1}^{x_{1}}} + \left(x_{0} \cdot \operatorname{exp}(x_{0})\right)\right) + \left(\frac{\theta_{0}}{x_{1}} \cdot \left(\theta_{1} \cdot {\theta_{2}^{x_{0}}}\right)\right)\right))$$",0.6796794244676786;-0.5652646144542011;-0.17754772712873657,19,,,,Infinity,,,,,, +160,0,Exp((Exp((x0 ^ -0.1671815272899183)) + Exp(Log((-0.7245607006201328 + (x1 * -0.7382426721609409)))))),"np.exp((np.exp(x[:, 0] ** t[0]) + np.exp(np.log((t[1] + (x[:, 1] * t[2]))))))","$$\operatorname{exp}(\left(\operatorname{exp}({x_{0}^{\theta_{0}}}) + \operatorname{exp}(\operatorname{log}(\left(\theta_{1} + \left(x_{1} \cdot \theta_{2}\right)\right)))\right))$$",-0.1671815272899183;-0.7245607006201328;-0.7382426721609409,13,,,,Infinity,,,,,, +161,0,(((Exp(Log((x1 * -0.2915304419274961))) ^ Log((x0 ^ x0))) * ((-0.9755904447054582 + x1) * (Log((0.30861543729315144 ^ x1)) + -0.4558907824525531))) ^ x0),"(np.exp(np.log((x[:, 1] * t[0]))) ** np.log(x[:, 0] ** x[:, 0]) * ((t[1] + x[:, 1]) * (np.log(t[2] ** x[:, 1]) + t[3]))) ** x[:, 0]","$${\left({\operatorname{exp}(\operatorname{log}(\left(x_{1} \cdot \theta_{0}\right)))^{\operatorname{log}({x_{0}^{x_{0}}})}} \cdot \left(\left(\theta_{1} + x_{1}\right) \cdot \left(\operatorname{log}({\theta_{2}^{x_{1}}}) + \theta_{3}\right)\right)\right)^{x_{0}}}$$",-0.2915304419274961;-0.9755904447054582;0.30861543729315144;-0.4558907824525531,23,,,,Infinity,,,,,, +162,0,Exp((Log(((0.1890832942770146 + (-0.12591258607038816 * x0)) / (0.47192214651703335 + (0.7079901648094353 ^ x1)))) * 0.5298175506577129)),"np.exp((np.log(((t[0] + (t[1] * x[:, 0])) / (t[2] + t[3] ** x[:, 1]))) * t[4]))","$$\operatorname{exp}(\left(\operatorname{log}(\frac{\left(\theta_{0} + \left(\theta_{1} \cdot x_{0}\right)\right)}{\left(\theta_{2} + {\theta_{3}^{x_{1}}}\right)}) \cdot \theta_{4}\right))$$",0.1890832942770146;-0.12591258607038816;0.47192214651703335;0.7079901648094353;0.5298175506577129,15,1.82874723475812e-2,1.82874723475812e-2,1.82874723475812e-2,1.82874723475812e-2,-0.6875891762174062,-0.6875891762174062,-0.6875891762174062,43.07945206199432,43.07945206199432,43.07945206199432 +163,0,(x1 + x1),"(x[:, 1] + x[:, 1])","$$\left(x_{1} + x_{1}\right)$$",,3,0.42840102205506697,0.42840102205506697,0.42840102205506697,0.42840102205506697,-38.53334360044726,-38.53334360044726,-38.53334360044726,2.465002461529396,2.465002461529396,2.465002461529396 +164,0,(0.46116983634074316 ^ (x0 / (x0 + -0.5080385063426675))),"t[0] ** (x[:, 0] / (x[:, 0] + t[1]))","$${\theta_{0}^{\frac{x_{0}}{\left(x_{0} + \theta_{1}\right)}}}$$",0.46116983634074316;-0.5080385063426675,7,5.65918109455741e-2,5.65918109455741e-2,5.65918109455741e-2,5.65918109455741e-2,-4.222358005474957,-4.222358005474957,-4.222358005474957,15.358847887625,15.358847887625,15.358847887625 +165,0,((Log(Exp(x0)) ^ (((-0.42548971666111723 ^ Log(Exp(x1))) ^ ((x1 * -0.5423539877968566) * -0.9132005604378887)) / Log((x1 * ((x1 * 0.184054997067573) + (x0 * x1)))))) + Exp(((x1 + x1) * ((0.6640593936777788 ^ x1) / x1)))),"(np.log(np.exp(x[:, 0])) ** (t[0] ** np.log(np.exp(x[:, 1])) ** ((x[:, 1] * t[1]) * t[2]) / np.log((x[:, 1] * ((x[:, 1] * t[3]) + (x[:, 0] * x[:, 1]))))) + np.exp(((x[:, 1] + x[:, 1]) * (t[4] ** x[:, 1] / x[:, 1]))))","$$\left({\operatorname{log}(\operatorname{exp}(x_{0}))^{\frac{{{\theta_{0}^{\operatorname{log}(\operatorname{exp}(x_{1}))}}^{\left(\left(x_{1} \cdot \theta_{1}\right) \cdot \theta_{2}\right)}}}{\operatorname{log}(\left(x_{1} \cdot \left(\left(x_{1} \cdot \theta_{3}\right) + \left(x_{0} \cdot x_{1}\right)\right)\right))}}} + \operatorname{exp}(\left(\left(x_{1} + x_{1}\right) \cdot \frac{{\theta_{4}^{x_{1}}}}{x_{1}}\right))\right)$$",-0.42548971666111723;-0.5423539877968566;-0.9132005604378887;0.184054997067573;0.6640593936777788,37,,,,Infinity,,,,,, +166,0,((4.668144257396321 + x1) ^ -1.7430228790061992),"(t[0] + x[:, 1]) ** t[1]","$${\left(\theta_{0} + x_{1}\right)^{\theta_{1}}}$$",4.668144257396321;-1.7430228790061992,5,1.1083055953448077e-2,1.1083055953448077e-2,1.1083055953448077e-2,1.1083055953448077e-2,-2.2757268525669616e-2,-2.2757268525669616e-2,-2.2757268525669616e-2,10.983296426692837,10.983296426692837,10.983296426692837 +167,0,((((-3.144397342670613e-2 * (-0.4657233655163705 + x0)) ^ (0.5084047766120403 / Log(x1))) * (Exp((-0.51331872094201 / Log(x0))) ^ Log(Exp((x0 ^ x0))))) / (Log(Exp(((x1 ^ -0.35486883898435184) * 0.7364826629409413))) * ((Log(x0) ^ 0.30761238237626254) ^ 0.6133510531343811))),"(((t[0] * (t[1] + x[:, 0])) ** (t[2] / np.log(x[:, 1])) * np.exp((t[3] / np.log(x[:, 0]))) ** np.log(np.exp(x[:, 0] ** x[:, 0]))) / (np.log(np.exp((x[:, 1] ** t[4] * t[5]))) * np.log(x[:, 0]) ** t[6] ** t[7]))","$$\frac{\left({\left(\theta_{0} \cdot \left(\theta_{1} + x_{0}\right)\right)^{\frac{\theta_{2}}{\operatorname{log}(x_{1})}}} \cdot {\operatorname{exp}(\frac{\theta_{3}}{\operatorname{log}(x_{0})})^{\operatorname{log}(\operatorname{exp}({x_{0}^{x_{0}}}))}}\right)}{\left(\operatorname{log}(\operatorname{exp}(\left({x_{1}^{\theta_{4}}} \cdot \theta_{5}\right))) \cdot {{\operatorname{log}(x_{0})^{\theta_{6}}}^{\theta_{7}}}\right)}$$",-3.144397342670613e-2;-0.4657233655163705;0.5084047766120403;-0.51331872094201;-0.35486883898435184;0.7364826629409413;0.30761238237626254;0.6133510531343811,37,,,,Infinity,,,,,, +168,0,(Log(((x0 + ((x1 ^ x1) / x0)) + (Exp(x0) / x1))) ^ -3.145604962363848),"np.log(((x[:, 0] + (x[:, 1] ** x[:, 1] / x[:, 0])) + (np.exp(x[:, 0]) / x[:, 1]))) ** t[0]","$${\operatorname{log}(\left(\left(x_{0} + \frac{{x_{1}^{x_{1}}}}{x_{0}}\right) + \frac{\operatorname{exp}(x_{0})}{x_{1}}\right))^{\theta_{0}}}$$",-3.145604962363848,15,5.052347060327307e-3,5.052347060327307e-3,5.052347060327307e-3,5.052347060327307e-3,0.5337635485403796,0.5337635485403796,0.5337635485403796,33.21709517291948,33.21709517291948,33.21709517291948 +169,0,Log(Exp(Log(Exp(x0)))),"np.log(np.exp(np.log(np.exp(x[:, 0]))))","$$\operatorname{log}(\operatorname{exp}(\operatorname{log}(\operatorname{exp}(x_{0}))))$$",,5,1.1424220374065182,1.1424220374065182,1.1424220374065182,1.1424220374065182,-104.42403172817247,-104.42403172817247,-104.42403172817247,6.521241277006416,6.521241277006416,6.521241277006416 +170,0,((0.19443279993002394 * x0) ^ x1),"(t[0] * x[:, 0]) ** x[:, 1]","$${\left(\theta_{0} \cdot x_{0}\right)^{x_{1}}}$$",0.19443279993002394,5,0.3413964345342835,0.3413964345342835,0.3413964345342835,0.3413964345342835,-30.504459269652656,-30.504459269652656,-30.504459269652656,10.375371288618998,10.375371288618998,10.375371288618998 +171,0,(Exp((x0 ^ x0)) + (-4.429402635739184 + x0)),"(np.exp(x[:, 0] ** x[:, 0]) + (t[0] + x[:, 0]))","$$\left(\operatorname{exp}({x_{0}^{x_{0}}}) + \left(\theta_{0} + x_{0}\right)\right)$$",-4.429402635739184,8,2.6944741932694494,2.6944741932694494,2.6944741932694494,2.6944741932694494,-247.64920628356185,-247.64920628356185,-247.64920628356185,17.321455008782944,17.321455008782944,17.321455008782944 +172,0,(((((Log(Exp(x1)) ^ Exp((x0 + -1.728445152126211))) + 2.2359727562540415) ^ 2.502761434399841) * x1) * 1.5471154841851483e-2),"(((np.log(np.exp(x[:, 1])) ** np.exp((x[:, 0] + t[0])) + t[1]) ** t[2] * x[:, 1]) * t[3])","$$\left(\left({\left({\operatorname{log}(\operatorname{exp}(x_{1}))^{\operatorname{exp}(\left(x_{0} + \theta_{0}\right))}} + \theta_{1}\right)^{\theta_{2}}} \cdot x_{1}\right) \cdot \theta_{3}\right)$$",-1.728445152126211;2.2359727562540415;2.502761434399841;1.5471154841851483e-2,16,8.39006394906046e-3,8.39006394906046e-3,8.39006394906046e-3,8.39006394906046e-3,0.22575515964736126,0.22575515964736126,0.22575515964736126,41.36231546590209,41.36231546590209,41.36231546590209 +173,0,((Exp(Log(x0)) / (Exp(((x0 + 0.44584123975974266) + x1)) / ((-2.183514026906408e-2 / (x1 + -0.6233859559299456)) / (0.5283058990561089 ^ x0)))) * (Exp(Exp(x0)) / (Log(x0) * (x1 ^ x0)))),"((np.exp(np.log(x[:, 0])) / (np.exp(((x[:, 0] + t[0]) + x[:, 1])) / ((t[1] / (x[:, 1] + t[2])) / t[3] ** x[:, 0]))) * (np.exp(np.exp(x[:, 0])) / (np.log(x[:, 0]) * x[:, 1] ** x[:, 0])))","$$\left(\frac{\operatorname{exp}(\operatorname{log}(x_{0}))}{\frac{\operatorname{exp}(\left(\left(x_{0} + \theta_{0}\right) + x_{1}\right))}{\frac{\frac{\theta_{1}}{\left(x_{1} + \theta_{2}\right)}}{{\theta_{3}^{x_{0}}}}}} \cdot \frac{\operatorname{exp}(\operatorname{exp}(x_{0}))}{\left(\operatorname{log}(x_{0}) \cdot {x_{1}^{x_{0}}}\right)}\right)$$",0.44584123975974266;-2.183514026906408e-2;-0.6233859559299456;0.5283058990561089,31,1823232.836599218,1823232.836599218,1823232.836599218,1823232.836599218,-1.6825004155855855e8,-1.6825004155855855e8,-1.6825004155855855e8,1640985.750600935,1640985.750600935,1640985.750600935 +174,0,(0.20059950763971857 * x1),"(t[0] * x[:, 1])","$$\left(\theta_{0} \cdot x_{1}\right)$$",0.20059950763971857,3,9.365750690525967e-3,9.365750690525967e-3,9.365750690525967e-3,9.365750690525967e-3,0.13571765457390572,0.13571765457390572,0.13571765457390572,5.325190976993443,5.325190976993443,5.325190976993443 +175,0,((((x0 + -0.27338751270162487) / (x1 * (-0.33482209800322893 * x0))) / Log(((-0.2115412335084066 + (-0.8644993651145055 * x0)) * Exp((0.43452901070838545 ^ x1))))) / ((0.3459199279502362 * Log((x0 ^ (-0.8511128409755666 + x0)))) * (Log(Exp((x1 + x1))) + (x1 / x1)))),"((((x[:, 0] + t[0]) / (x[:, 1] * (t[1] * x[:, 0]))) / np.log(((t[2] + (t[3] * x[:, 0])) * np.exp(t[4] ** x[:, 1])))) / ((t[5] * np.log(x[:, 0] ** (t[6] + x[:, 0]))) * (np.log(np.exp((x[:, 1] + x[:, 1]))) + (x[:, 1] / x[:, 1]))))","$$\frac{\frac{\frac{\left(x_{0} + \theta_{0}\right)}{\left(x_{1} \cdot \left(\theta_{1} \cdot x_{0}\right)\right)}}{\operatorname{log}(\left(\left(\theta_{2} + \left(\theta_{3} \cdot x_{0}\right)\right) \cdot \operatorname{exp}({\theta_{4}^{x_{1}}})\right))}}{\left(\left(\theta_{5} \cdot \operatorname{log}({x_{0}^{\left(\theta_{6} + x_{0}\right)}})\right) \cdot \left(\operatorname{log}(\operatorname{exp}(\left(x_{1} + x_{1}\right))) + \frac{x_{1}}{x_{1}}\right)\right)}$$",-0.27338751270162487;-0.33482209800322893;-0.2115412335084066;-0.8644993651145055;0.43452901070838545;0.3459199279502362;-0.8511128409755666,40,,,,Infinity,,,,,, +176,0,Exp(Log((((x0 ^ x1) * -0.2665729478421984) / Log(((-0.7002820383803896 / x1) / ((x1 * 0.7870750922239204) * 0.6188113419606676)))))),"np.exp(np.log(((x[:, 0] ** x[:, 1] * t[0]) / np.log(((t[1] / x[:, 1]) / ((x[:, 1] * t[2]) * t[3]))))))","$$\operatorname{exp}(\operatorname{log}(\frac{\left({x_{0}^{x_{1}}} \cdot \theta_{0}\right)}{\operatorname{log}(\frac{\frac{\theta_{1}}{x_{1}}}{\left(\left(x_{1} \cdot \theta_{2}\right) \cdot \theta_{3}\right)})}))$$",-0.2665729478421984;-0.7002820383803896;0.7870750922239204;0.6188113419606676,18,,,,Infinity,,,,,, +177,0,(Log((((x1 ^ x1) ^ -0.7110225382841173) * (0.48770198062684644 + Log((-0.8061847938691791 / (x0 * 0.8709743216247638)))))) * ((Exp(((-0.35418631462010153 ^ x1) * x1)) * Exp((x0 * -0.20239915157405675))) ^ Exp((-0.8351623454348341 / (x1 / x0))))),"(np.log((x[:, 1] ** x[:, 1] ** t[0] * (t[1] + np.log((t[2] / (x[:, 0] * t[3])))))) * (np.exp((t[4] ** x[:, 1] * x[:, 1])) * np.exp((x[:, 0] * t[5]))) ** np.exp((t[6] / (x[:, 1] / x[:, 0]))))","$$\left(\operatorname{log}(\left({{x_{1}^{x_{1}}}^{\theta_{0}}} \cdot \left(\theta_{1} + \operatorname{log}(\frac{\theta_{2}}{\left(x_{0} \cdot \theta_{3}\right)})\right)\right)) \cdot {\left(\operatorname{exp}(\left({\theta_{4}^{x_{1}}} \cdot x_{1}\right)) \cdot \operatorname{exp}(\left(x_{0} \cdot \theta_{5}\right))\right)^{\operatorname{exp}(\frac{\theta_{6}}{\frac{x_{1}}{x_{0}}})}}\right)$$",-0.7110225382841173;0.48770198062684644;-0.8061847938691791;0.8709743216247638;-0.35418631462010153;-0.20239915157405675;-0.8351623454348341,34,,,,Infinity,,,,,, +178,0,(Log(((x0 / ((x0 / x1) / (x1 * -0.5524981862563886))) ^ (-0.3282628500034941 ^ x0))) / x1),"(np.log((x[:, 0] / ((x[:, 0] / x[:, 1]) / (x[:, 1] * t[0]))) ** t[1] ** x[:, 0]) / x[:, 1])","$$\frac{\operatorname{log}({\frac{x_{0}}{\frac{\frac{x_{0}}{x_{1}}}{\left(x_{1} \cdot \theta_{0}\right)}}^{{\theta_{1}^{x_{0}}}}})}{x_{1}}$$",-0.5524981862563886;-0.3282628500034941,16,,,,Infinity,,,,,, +179,0,Exp(((((x1 ^ -0.8088597945531835) ^ Log((x0 + x1))) ^ ((-0.4264835356234816 / (-0.8500728719781294 / x0)) * (0.5218455475914037 + x1))) / Log((Log(((x1 * x0) ^ -0.752323138688017)) / Exp(Exp(Log(x0))))))),"np.exp((x[:, 1] ** t[0] ** np.log((x[:, 0] + x[:, 1])) ** ((t[1] / (t[2] / x[:, 0])) * (t[3] + x[:, 1])) / np.log((np.log((x[:, 1] * x[:, 0]) ** t[4]) / np.exp(np.exp(np.log(x[:, 0])))))))","$$\operatorname{exp}(\frac{{{{x_{1}^{\theta_{0}}}^{\operatorname{log}(\left(x_{0} + x_{1}\right))}}^{\left(\frac{\theta_{1}}{\frac{\theta_{2}}{x_{0}}} \cdot \left(\theta_{3} + x_{1}\right)\right)}}}{\operatorname{log}(\frac{\operatorname{log}({\left(x_{1} \cdot x_{0}\right)^{\theta_{4}}})}{\operatorname{exp}(\operatorname{exp}(\operatorname{log}(x_{0})))})})$$",-0.8088597945531835;-0.4264835356234816;-0.8500728719781294;0.5218455475914037;-0.752323138688017,32,48538.53959710125,48538.53959710125,48538.53959710125,48538.53959710125,-4479191.777251271,-4479191.777251271,-4479191.777251271,43765.10144854272,43765.10144854272,43765.10144854272 +180,0,((Exp(Exp(((0.5783405500656325 ^ x1) * ((-0.400569897049326 ^ x1) * x1)))) + (((((x1 / x0) + x1) ^ 0.8648255897518211) * 0.5495130711071916) * (((x1 * 0.7687336356391234) / ((0.48880641179759166 / x0) + 0.9462925522236957)) * 1.4478854715978096e-2))) * ((Log(x0) * -0.38100935259891844) ^ -0.3722305091915381)),"((np.exp(np.exp((t[0] ** x[:, 1] * (t[1] ** x[:, 1] * x[:, 1])))) + ((((x[:, 1] / x[:, 0]) + x[:, 1]) ** t[2] * t[3]) * (((x[:, 1] * t[4]) / ((t[5] / x[:, 0]) + t[6])) * t[7]))) * (np.log(x[:, 0]) * t[8]) ** t[9])","$$\left(\left(\operatorname{exp}(\operatorname{exp}(\left({\theta_{0}^{x_{1}}} \cdot \left({\theta_{1}^{x_{1}}} \cdot x_{1}\right)\right))) + \left(\left({\left(\frac{x_{1}}{x_{0}} + x_{1}\right)^{\theta_{2}}} \cdot \theta_{3}\right) \cdot \left(\frac{\left(x_{1} \cdot \theta_{4}\right)}{\left(\frac{\theta_{5}}{x_{0}} + \theta_{6}\right)} \cdot \theta_{7}\right)\right)\right) \cdot {\left(\operatorname{log}(x_{0}) \cdot \theta_{8}\right)^{\theta_{9}}}\right)$$",0.5783405500656325;-0.400569897049326;0.8648255897518211;0.5495130711071916;0.7687336356391234;0.48880641179759166;0.9462925522236957;1.4478854715978096e-2;-0.38100935259891844;-0.3722305091915381,40,,,,Infinity,,,,,, +181,0,Exp((Exp(Exp((((x1 ^ -0.10226970185059236) * x1) + (x0 + 0.6216267301083367)))) + ((0.23289671244991927 * (Exp(x0) + 0.22575297493628632)) + ((-0.7798113771291091 ^ Log(x1)) * ((x0 * 1.0749605760301928e-2) + x1))))),"np.exp((np.exp(np.exp(((x[:, 1] ** t[0] * x[:, 1]) + (x[:, 0] + t[1])))) + ((t[2] * (np.exp(x[:, 0]) + t[3])) + (t[4] ** np.log(x[:, 1]) * ((x[:, 0] * t[5]) + x[:, 1])))))","$$\operatorname{exp}(\left(\operatorname{exp}(\operatorname{exp}(\left(\left({x_{1}^{\theta_{0}}} \cdot x_{1}\right) + \left(x_{0} + \theta_{1}\right)\right))) + \left(\left(\theta_{2} \cdot \left(\operatorname{exp}(x_{0}) + \theta_{3}\right)\right) + \left({\theta_{4}^{\operatorname{log}(x_{1})}} \cdot \left(\left(x_{0} \cdot \theta_{5}\right) + x_{1}\right)\right)\right)\right))$$",-0.10226970185059236;0.6216267301083367;0.23289671244991927;0.22575297493628632;-0.7798113771291091;1.0749605760301928e-2,30,,,,Infinity,,,,,, +182,0,(((0.17772002235930817 / x1) ^ x0) ^ -6.429142051782988e-2),"(t[0] / x[:, 1]) ** x[:, 0] ** t[1]","$${{\frac{\theta_{0}}{x_{1}}^{x_{0}}}^{\theta_{1}}}$$",0.17772002235930817;-6.429142051782988e-2,7,0.9663221416152477,0.9663221416152477,0.9663221416152477,0.9663221416152477,-88.17332892890515,-88.17332892890515,-88.17332892890515,16.17760518522771,16.17760518522771,16.17760518522771 +183,0,((((0.6757808600430988 * Log((x1 * 0.608579787893281))) ^ (((x1 + x1) * 0.779022063308076) + -0.36137327988694146)) * Exp((0.19550086984985038 ^ (((x0 / x0) ^ x1) + x1)))) * -0.11276991328292041),"(((t[0] * np.log((x[:, 1] * t[1]))) ** (((x[:, 1] + x[:, 1]) * t[2]) + t[3]) * np.exp(t[4] ** ((x[:, 0] / x[:, 0]) ** x[:, 1] + x[:, 1]))) * t[5])","$$\left(\left({\left(\theta_{0} \cdot \operatorname{log}(\left(x_{1} \cdot \theta_{1}\right))\right)^{\left(\left(\left(x_{1} + x_{1}\right) \cdot \theta_{2}\right) + \theta_{3}\right)}} \cdot \operatorname{exp}({\theta_{4}^{\left({\frac{x_{0}}{x_{0}}^{x_{1}}} + x_{1}\right)}})\right) \cdot \theta_{5}\right)$$",0.6757808600430988;0.608579787893281;0.779022063308076;-0.36137327988694146;0.19550086984985038;-0.11276991328292041,27,,,,Infinity,,,,,, +184,0,(Exp(((-2.6652912693394173 * x0) + x1)) + (Log((x0 + 0.4925589577259654)) ^ (0.5640912481193454 + x1))),"(np.exp(((t[0] * x[:, 0]) + x[:, 1])) + np.log((x[:, 0] + t[1])) ** (t[2] + x[:, 1]))","$$\left(\operatorname{exp}(\left(\left(\theta_{0} \cdot x_{0}\right) + x_{1}\right)) + {\operatorname{log}(\left(x_{0} + \theta_{1}\right))^{\left(\theta_{2} + x_{1}\right)}}\right)$$",-2.6652912693394173;0.4925589577259654;0.5640912481193454,15,0.3017280602017145,0.3017280602017145,0.3017280602017145,0.3017280602017145,-26.843815639444344,-26.843815639444344,-26.843815639444344,37.525953185482,37.525953185482,37.525953185482 +185,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 +186,0,Log((Log(Log(x0)) ^ (-0.6442686792385699 * Exp(((4.370710283623236e-3 * (x1 * x0)) * x1))))),"np.log(np.log(np.log(x[:, 0])) ** (t[0] * np.exp(((t[1] * (x[:, 1] * x[:, 0])) * x[:, 1]))))","$$\operatorname{log}({\operatorname{log}(\operatorname{log}(x_{0}))^{\left(\theta_{0} \cdot \operatorname{exp}(\left(\left(\theta_{1} \cdot \left(x_{1} \cdot x_{0}\right)\right) \cdot x_{1}\right))\right)}})$$",-0.6442686792385699;4.370710283623236e-3,15,,,,Infinity,,,,,, +187,0,(((Exp((x0 * -0.33036040098506214)) + (x1 * x1)) ^ Log((Log(x0) * 0.2987622292495128))) ^ Exp((((x1 / x1) ^ Log(x0)) ^ ((x1 * 0.22519356141573) / (x0 * -0.10813185921607205))))),"(np.exp((x[:, 0] * t[0])) + (x[:, 1] * x[:, 1])) ** np.log((np.log(x[:, 0]) * t[1])) ** np.exp((x[:, 1] / x[:, 1]) ** np.log(x[:, 0]) ** ((x[:, 1] * t[2]) / (x[:, 0] * t[3])))","$${{\left(\operatorname{exp}(\left(x_{0} \cdot \theta_{0}\right)) + \left(x_{1} \cdot x_{1}\right)\right)^{\operatorname{log}(\left(\operatorname{log}(x_{0}) \cdot \theta_{1}\right))}}^{\operatorname{exp}({{\frac{x_{1}}{x_{1}}^{\operatorname{log}(x_{0})}}^{\frac{\left(x_{1} \cdot \theta_{2}\right)}{\left(x_{0} \cdot \theta_{3}\right)}}})}}$$",-0.33036040098506214;0.2987622292495128;0.22519356141573;-0.10813185921607205,30,,,,Infinity,,,,,, +188,0,((((-0.5007421510964669 * x0) * 0.8319069176598572) / Exp(Exp((2.592192638982671e-2 / (x1 * 0.7387457992786534))))) / Log(Log(((x1 / (0.7244885569613754 ^ x1)) ^ (x1 * (0.8695693336493393 + x1)))))),"((((t[0] * x[:, 0]) * t[1]) / np.exp(np.exp((t[2] / (x[:, 1] * t[3]))))) / np.log(np.log((x[:, 1] / t[4] ** x[:, 1]) ** (x[:, 1] * (t[5] + x[:, 1])))))","$$\frac{\frac{\left(\left(\theta_{0} \cdot x_{0}\right) \cdot \theta_{1}\right)}{\operatorname{exp}(\operatorname{exp}(\frac{\theta_{2}}{\left(x_{1} \cdot \theta_{3}\right)}))}}{\operatorname{log}(\operatorname{log}({\frac{x_{1}}{{\theta_{4}^{x_{1}}}}^{\left(x_{1} \cdot \left(\theta_{5} + x_{1}\right)\right)}}))}$$",-0.5007421510964669;0.8319069176598572;2.592192638982671e-2;0.7387457992786534;0.7244885569613754;0.8695693336493393,27,,,,Infinity,,,,,, +189,0,(-0.6517224920460677 ^ Exp((x1 * 0.873096932216515))),"t[0] ** np.exp((x[:, 1] * t[1]))","$${\theta_{0}^{\operatorname{exp}(\left(x_{1} \cdot \theta_{1}\right))}}$$",-0.6517224920460677;0.873096932216515,6,,,,Infinity,,,,,, +190,0,((((x1 ^ 0.3398315374840835) * 0.3320978510895655) * x1) / x0),"(((x[:, 1] ** t[0] * t[1]) * x[:, 1]) / x[:, 0])","$$\frac{\left(\left({x_{1}^{\theta_{0}}} \cdot \theta_{1}\right) \cdot x_{1}\right)}{x_{0}}$$",0.3398315374840835;0.3320978510895655,9,4.987763556476581e-3,4.987763556476581e-3,4.987763556476581e-3,4.987763556476581e-3,0.5397233892438487,0.5397233892438487,0.5397233892438487,20.172174080988604,20.172174080988604,20.172174080988604 +191,0,Log((((x0 ^ (Exp(Exp(x1)) / x1)) + (0.2873427727948291 * x0)) ^ (Exp(Log(Log(x1))) + Exp(x1)))),"np.log((x[:, 0] ** (np.exp(np.exp(x[:, 1])) / x[:, 1]) + (t[0] * x[:, 0])) ** (np.exp(np.log(np.log(x[:, 1]))) + np.exp(x[:, 1])))","$$\operatorname{log}({\left({x_{0}^{\frac{\operatorname{exp}(\operatorname{exp}(x_{1}))}{x_{1}}}} + \left(\theta_{0} \cdot x_{0}\right)\right)^{\left(\operatorname{exp}(\operatorname{log}(\operatorname{log}(x_{1}))) + \operatorname{exp}(x_{1})\right)}})$$",0.2873427727948291,20,,,,Infinity,,,,,, +192,0,Exp((Exp((((0.5105642370969548 ^ x1) + 0.1293024919521728) ^ ((Exp(x0) * -1.029205687966639e-2) + Exp((x1 ^ -0.40888394914853066))))) ^ ((-0.8773081071213822 ^ Exp((0.18691361225868763 * (x1 ^ 0.6956572586683721)))) + 6.919463216262278e-2))),"np.exp(np.exp((t[0] ** x[:, 1] + t[1]) ** ((np.exp(x[:, 0]) * t[2]) + np.exp(x[:, 1] ** t[3]))) ** (t[4] ** np.exp((t[5] * x[:, 1] ** t[6])) + t[7]))","$$\operatorname{exp}({\operatorname{exp}({\left({\theta_{0}^{x_{1}}} + \theta_{1}\right)^{\left(\left(\operatorname{exp}(x_{0}) \cdot \theta_{2}\right) + \operatorname{exp}({x_{1}^{\theta_{3}}})\right)}})^{\left({\theta_{4}^{\operatorname{exp}(\left(\theta_{5} \cdot {x_{1}^{\theta_{6}}}\right))}} + \theta_{7}\right)}})$$",0.5105642370969548;0.1293024919521728;-1.029205687966639e-2;-0.40888394914853066;-0.8773081071213822;0.18691361225868763;0.6956572586683721;6.919463216262278e-2,28,,,,Infinity,,,,,, +193,0,Exp((Exp(Exp((-0.8432728650734787 / (-0.9251602466422771 ^ (x1 * -0.34524197916994437))))) * Exp(((((-0.5140770535124826 ^ x0) ^ 0.5178389102875651) / Exp((-0.4297301338457309 ^ x0))) / (Exp(x0) / Log((x1 / x0))))))),"np.exp((np.exp(np.exp((t[0] / t[1] ** (x[:, 1] * t[2])))) * np.exp(((t[3] ** x[:, 0] ** t[4] / np.exp(t[5] ** x[:, 0])) / (np.exp(x[:, 0]) / np.log((x[:, 1] / x[:, 0])))))))","$$\operatorname{exp}(\left(\operatorname{exp}(\operatorname{exp}(\frac{\theta_{0}}{{\theta_{1}^{\left(x_{1} \cdot \theta_{2}\right)}}})) \cdot \operatorname{exp}(\frac{\frac{{{\theta_{3}^{x_{0}}}^{\theta_{4}}}}{\operatorname{exp}({\theta_{5}^{x_{0}}})}}{\frac{\operatorname{exp}(x_{0})}{\operatorname{log}(\frac{x_{1}}{x_{0}})}})\right))$$",-0.8432728650734787;-0.9251602466422771;-0.34524197916994437;-0.5140770535124826;0.5178389102875651;-0.4297301338457309,30,,,,Infinity,,,,,, +194,0,(Log(((x1 + 0.48140533824483606) + (Exp((x0 * -0.13074673872391016)) + (-0.48081687197129314 * x0)))) * 0.4195097343418453),"(np.log(((x[:, 1] + t[0]) + (np.exp((x[:, 0] * t[1])) + (t[2] * x[:, 0])))) * t[3])","$$\left(\operatorname{log}(\left(\left(x_{1} + \theta_{0}\right) + \left(\operatorname{exp}(\left(x_{0} \cdot \theta_{1}\right)) + \left(\theta_{2} \cdot x_{0}\right)\right)\right)) \cdot \theta_{3}\right)$$",0.48140533824483606;-0.13074673872391016;-0.48081687197129314;0.4195097343418453,15,4.47939725035329e-3,4.47939725035329e-3,4.47939725035329e-3,4.47939725035329e-3,0.5866360220813092,0.5866360220813092,0.5866360220813092,37.27638343482558,37.27638343482558,37.27638343482558 +195,0,(x1 ^ x0),"x[:, 1] ** x[:, 0]","$${x_{1}^{x_{0}}}$$",,3,8.353975393662696e-2,8.353975393662696e-2,8.353975393662696e-2,8.353975393662696e-2,-6.709145465691655,-6.709145465691655,-6.709145465691655,3.3710226445472933,3.3710226445472933,3.3710226445472933 +196,0,(Log((0.5979600429310816 / x0)) / Log((x1 / x0))),"(np.log((t[0] / x[:, 0])) / np.log((x[:, 1] / x[:, 0])))","$$\frac{\operatorname{log}(\frac{\theta_{0}}{x_{0}})}{\operatorname{log}(\frac{x_{1}}{x_{0}})}$$",0.5979600429310816,9,0.28745619206555206,0.28745619206555206,0.28745619206555206,0.28745619206555206,-25.526791081144715,-25.526791081144715,-25.526791081144715,16.76457672013354,16.76457672013354,16.76457672013354 +197,0,Exp(Exp(((((x0 + -4.455588538961708e-2) + x1) + (0.20416689716160552 + (x1 * x0))) + Log(Log(((x0 * -0.592094291318324) * (-0.14878928606851616 + x1))))))),"np.exp(np.exp(((((x[:, 0] + t[0]) + x[:, 1]) + (t[1] + (x[:, 1] * x[:, 0]))) + np.log(np.log(((x[:, 0] * t[2]) * (t[3] + x[:, 1])))))))","$$\operatorname{exp}(\operatorname{exp}(\left(\left(\left(\left(x_{0} + \theta_{0}\right) + x_{1}\right) + \left(\theta_{1} + \left(x_{1} \cdot x_{0}\right)\right)\right) + \operatorname{log}(\operatorname{log}(\left(\left(x_{0} \cdot \theta_{2}\right) \cdot \left(\theta_{3} + x_{1}\right)\right)))\right)))$$",-4.455588538961708e-2;0.20416689716160552;-0.592094291318324;-0.14878928606851616,23,,,,Infinity,,,,,, +198,0,Exp((-3.0680971858662263 + x1)),"np.exp((t[0] + x[:, 1]))","$$\operatorname{exp}(\left(\theta_{0} + x_{1}\right))$$",-3.0680971858662263,4,1.0205484150379933e-2,1.0205484150379933e-2,1.0205484150379933e-2,1.0205484150379933e-2,5.8226076141275396e-2,5.8226076141275396e-2,5.8226076141275396e-2,7.575287315582544,7.575287315582544,7.575287315582544 +199,0,Log(((Log(Log(Exp(x0))) * 0.42248571182645445) * 0.19193226599672442)),"np.log(((np.log(np.log(np.exp(x[:, 0]))) * t[0]) * t[1]))","$$\operatorname{log}(\left(\left(\operatorname{log}(\operatorname{log}(\operatorname{exp}(x_{0}))) \cdot \theta_{0}\right) \cdot \theta_{1}\right))$$",0.42248571182645445;0.19193226599672442,9,,,,Infinity,,,,,, +200,0,Exp((((-0.5150211050404869 / (0.20051197439052215 ^ Exp((-0.645739563639647 + x0)))) + (x0 + x1)) + -7.855205583075496e-2)),"np.exp((((t[0] / t[1] ** np.exp((t[2] + x[:, 0]))) + (x[:, 0] + x[:, 1])) + t[3]))","$$\operatorname{exp}(\left(\left(\frac{\theta_{0}}{{\theta_{1}^{\operatorname{exp}(\left(\theta_{2} + x_{0}\right))}}} + \left(x_{0} + x_{1}\right)\right) + \theta_{3}\right))$$",-0.5150211050404869;0.20051197439052215;-0.645739563639647;-7.855205583075496e-2,15,3.319982721148527e-3,3.319982721148527e-3,3.319982721148527e-3,3.319982721148527e-3,0.693628140677401,0.693628140677401,0.693628140677401,37.275339961749296,37.275339961749296,37.275339961749296 +201,0,(((((Exp(x0) + x1) + (-0.11504327936793224 ^ (-0.93670740718774 / x1))) / x1) / x1) + 0.5245858774998129),"(((((np.exp(x[:, 0]) + x[:, 1]) + t[0] ** (t[1] / x[:, 1])) / x[:, 1]) / x[:, 1]) + t[2])","$$\left(\frac{\frac{\left(\left(\operatorname{exp}(x_{0}) + x_{1}\right) + {\theta_{0}^{\frac{\theta_{1}}{x_{1}}}}\right)}{x_{1}}}{x_{1}} + \theta_{2}\right)$$",-0.11504327936793224;-0.93670740718774;0.5245858774998129,16,,,,Infinity,,,,,, +202,0,(Log(Log(((x1 ^ x1) ^ Exp(x0)))) + Log(((x1 ^ ((x0 / (0.26300073574452776 * x1)) * x0)) * 0.21590134785056136))),"(np.log(np.log(x[:, 1] ** x[:, 1] ** np.exp(x[:, 0]))) + np.log((x[:, 1] ** ((x[:, 0] / (t[0] * x[:, 1])) * x[:, 0]) * t[1])))","$$\left(\operatorname{log}(\operatorname{log}({{x_{1}^{x_{1}}}^{\operatorname{exp}(x_{0})}})) + \operatorname{log}(\left({x_{1}^{\left(\frac{x_{0}}{\left(\theta_{0} \cdot x_{1}\right)} \cdot x_{0}\right)}} \cdot \theta_{1}\right))\right)$$",0.26300073574452776;0.21590134785056136,21,,,,Infinity,,,,,, +203,0,Log(((Log((x1 * 0.41453298198443067)) / (x1 ^ (x1 * 0.21406130853782324))) + ((0.10309753371674557 + x1) ^ (x1 * (x0 * -3.162855339113524e-2))))),"np.log(((np.log((x[:, 1] * t[0])) / x[:, 1] ** (x[:, 1] * t[1])) + (t[2] + x[:, 1]) ** (x[:, 1] * (x[:, 0] * t[3]))))","$$\operatorname{log}(\left(\frac{\operatorname{log}(\left(x_{1} \cdot \theta_{0}\right))}{{x_{1}^{\left(x_{1} \cdot \theta_{1}\right)}}} + {\left(\theta_{2} + x_{1}\right)^{\left(x_{1} \cdot \left(x_{0} \cdot \theta_{3}\right)\right)}}\right))$$",0.41453298198443067;0.21406130853782324;0.10309753371674557;-3.162855339113524e-2,21,,,,Infinity,,,,,, +204,0,((x0 + x0) ^ -14.793543861538144),"(x[:, 0] + x[:, 0]) ** t[0]","$${\left(x_{0} + x_{0}\right)^{\theta_{0}}}$$",-14.793543861538144,5,1.666883111534721e-2,1.666883111534721e-2,1.666883111534721e-2,1.666883111534721e-2,-0.5382190843983157,-0.5382190843983157,-0.5382190843983157,8.967398688970906,8.967398688970906,8.967398688970906 +205,0,(((0.8786111014541423 / Log(Exp(x1))) + ((x1 * -0.5809641567615402) ^ (x0 * x0))) ^ (0.2667663972724321 / ((x0 ^ -0.627472846197944) ^ Log((x0 * x1))))),"((t[0] / np.log(np.exp(x[:, 1]))) + (x[:, 1] * t[1]) ** (x[:, 0] * x[:, 0])) ** (t[2] / x[:, 0] ** t[3] ** np.log((x[:, 0] * x[:, 1])))","$${\left(\frac{\theta_{0}}{\operatorname{log}(\operatorname{exp}(x_{1}))} + {\left(x_{1} \cdot \theta_{1}\right)^{\left(x_{0} \cdot x_{0}\right)}}\right)^{\frac{\theta_{2}}{{{x_{0}^{\theta_{3}}}^{\operatorname{log}(\left(x_{0} \cdot x_{1}\right))}}}}}$$",0.8786111014541423;-0.5809641567615402;0.2667663972724321;-0.627472846197944,24,,,,Infinity,,,,,, +206,0,Log(((x1 * x0) * Log((x1 * 5.4912728963646296e-2)))),"np.log(((x[:, 1] * x[:, 0]) * np.log((x[:, 1] * t[0]))))","$$\operatorname{log}(\left(\left(x_{1} \cdot x_{0}\right) \cdot \operatorname{log}(\left(x_{1} \cdot \theta_{0}\right))\right))$$",5.4912728963646296e-2,9,,,,Infinity,,,,,, +207,0,((((0.5530622417172104 + Log(x0)) + (0.2451965526079144 ^ (x0 * 0.1587500728530825))) / ((-0.2604184890915786 * (x0 * 0.7288831363084927)) * (x0 / (x0 + -0.18550818352151155)))) + ((Log((x0 * 0.8150115168905779)) ^ Exp((x0 * x1))) * Exp(Log(Log(((0.16882567622695255 * x1) ^ 0.8730108641892117)))))),"((((t[0] + np.log(x[:, 0])) + t[1] ** (x[:, 0] * t[2])) / ((t[3] * (x[:, 0] * t[4])) * (x[:, 0] / (x[:, 0] + t[5])))) + (np.log((x[:, 0] * t[6])) ** np.exp((x[:, 0] * x[:, 1])) * np.exp(np.log(np.log((t[7] * x[:, 1]) ** t[8])))))","$$\left(\frac{\left(\left(\theta_{0} + \operatorname{log}(x_{0})\right) + {\theta_{1}^{\left(x_{0} \cdot \theta_{2}\right)}}\right)}{\left(\left(\theta_{3} \cdot \left(x_{0} \cdot \theta_{4}\right)\right) \cdot \frac{x_{0}}{\left(x_{0} + \theta_{5}\right)}\right)} + \left({\operatorname{log}(\left(x_{0} \cdot \theta_{6}\right))^{\operatorname{exp}(\left(x_{0} \cdot x_{1}\right))}} \cdot \operatorname{exp}(\operatorname{log}(\operatorname{log}({\left(\theta_{7} \cdot x_{1}\right)^{\theta_{8}}})))\right)\right)$$",0.5530622417172104;0.2451965526079144;0.1587500728530825;-0.2604184890915786;0.7288831363084927;-0.18550818352151155;0.8150115168905779;0.16882567622695255;0.8730108641892117,41,,,,Infinity,,,,,, +208,0,(Log((Exp(x0) / (Log(Exp((-3.314153675496978e-2 / x1))) ^ 0.6764130725424373))) / (Exp(Exp((x0 * -0.4704073191377185))) ^ (Exp(((x1 * 0.7984695145380161) * -0.23498381024615167)) * -0.4148417629171849))),"(np.log((np.exp(x[:, 0]) / np.log(np.exp((t[0] / x[:, 1]))) ** t[1])) / np.exp(np.exp((x[:, 0] * t[2]))) ** (np.exp(((x[:, 1] * t[3]) * t[4])) * t[5]))","$$\frac{\operatorname{log}(\frac{\operatorname{exp}(x_{0})}{{\operatorname{log}(\operatorname{exp}(\frac{\theta_{0}}{x_{1}}))^{\theta_{1}}}})}{{\operatorname{exp}(\operatorname{exp}(\left(x_{0} \cdot \theta_{2}\right)))^{\left(\operatorname{exp}(\left(\left(x_{1} \cdot \theta_{3}\right) \cdot \theta_{4}\right)) \cdot \theta_{5}\right)}}}$$",-3.314153675496978e-2;0.6764130725424373;-0.4704073191377185;0.7984695145380161;-0.23498381024615167;-0.4148417629171849,26,,,,Infinity,,,,,, +209,0,((0.6411178291039024 ^ (9.970774172158825e-2 ^ ((x1 * 0.3974997872830004) + -0.14437598534344687))) / Log((Log((-0.18745482227263588 / Exp(x1))) / (-0.6465088962859843 / Log(Exp((x0 * x1))))))),"(t[0] ** t[1] ** ((x[:, 1] * t[2]) + t[3]) / np.log((np.log((t[4] / np.exp(x[:, 1]))) / (t[5] / np.log(np.exp((x[:, 0] * x[:, 1])))))))","$$\frac{{\theta_{0}^{{\theta_{1}^{\left(\left(x_{1} \cdot \theta_{2}\right) + \theta_{3}\right)}}}}}{\operatorname{log}(\frac{\operatorname{log}(\frac{\theta_{4}}{\operatorname{exp}(x_{1})})}{\frac{\theta_{5}}{\operatorname{log}(\operatorname{exp}(\left(x_{0} \cdot x_{1}\right)))}})}$$",0.6411178291039024;9.970774172158825e-2;0.3974997872830004;-0.14437598534344687;-0.18745482227263588;-0.6465088962859843,24,,,,Infinity,,,,,, +210,0,((-0.5078932523655405 + x0) ^ Exp((x0 ^ 0.6596040101659099))),"(t[0] + x[:, 0]) ** np.exp(x[:, 0] ** t[1])","$${\left(\theta_{0} + x_{0}\right)^{\operatorname{exp}({x_{0}^{\theta_{1}}})}}$$",-0.5078932523655405;0.6596040101659099,8,0.19647267859740286,0.19647267859740286,0.19647267859740286,0.19647267859740286,-17.13072684521505,-17.13072684521505,-17.13072684521505,17.094178580945748,17.094178580945748,17.094178580945748 +211,0,((-0.7321628836643215 * (-0.10944013932061437 * x0)) ^ ((-0.10497932423268125 + x0) + x1)),"(t[0] * (t[1] * x[:, 0])) ** ((t[2] + x[:, 0]) + x[:, 1])","$${\left(\theta_{0} \cdot \left(\theta_{1} \cdot x_{0}\right)\right)^{\left(\left(\theta_{2} + x_{0}\right) + x_{1}\right)}}$$",-0.7321628836643215;-0.10944013932061437;-0.10497932423268125,11,1.1047586109941537e-2,1.1047586109941537e-2,1.1047586109941537e-2,1.1047586109941537e-2,-1.948407019371534e-2,-1.948407019371534e-2,-1.948407019371534e-2,25.782071795110472,25.782071795110472,25.782071795110472 +212,0,(Exp(((Log((5.9031555133931315 + x0)) * (x1 + x1)) / ((3.2536905199697674 + (x0 / x0)) + (x1 * 0.966981637758638)))) / ((Exp(Exp((x1 ^ 6.4898035112445385))) * 3.0160031245086025) * (Log((x1 + 5.4398864222787475)) * Exp((7.20682851310484 ^ (-1.897028828574304 * x1)))))),"(np.exp(((np.log((t[0] + x[:, 0])) * (x[:, 1] + x[:, 1])) / ((t[1] + (x[:, 0] / x[:, 0])) + (x[:, 1] * t[2])))) / ((np.exp(np.exp(x[:, 1] ** t[3])) * t[4]) * (np.log((x[:, 1] + t[5])) * np.exp(t[6] ** (t[7] * x[:, 1])))))","$$\frac{\operatorname{exp}(\frac{\left(\operatorname{log}(\left(\theta_{0} + x_{0}\right)) \cdot \left(x_{1} + x_{1}\right)\right)}{\left(\left(\theta_{1} + \frac{x_{0}}{x_{0}}\right) + \left(x_{1} \cdot \theta_{2}\right)\right)})}{\left(\left(\operatorname{exp}(\operatorname{exp}({x_{1}^{\theta_{3}}})) \cdot \theta_{4}\right) \cdot \left(\operatorname{log}(\left(x_{1} + \theta_{5}\right)) \cdot \operatorname{exp}({\theta_{6}^{\left(\theta_{7} \cdot x_{1}\right)}})\right)\right)}$$",5.9031555133931315;3.2536905199697674;0.966981637758638;6.4898035112445385;3.0160031245086025;5.4398864222787475;7.20682851310484;-1.897028828574304,39,9.903159993935763e-3,9.903159993935763e-3,9.903159993935763e-3,9.903159993935763e-3,8.612490023391828e-2,8.612490023391828e-2,8.612490023391828e-2,101.86807084304823,101.86807084304823,101.86807084304823 +213,0,Log(((0.8125019855743527 + (x0 ^ -4.620122844496292)) ^ 7.662363019580337e-2)),"np.log((t[0] + x[:, 0] ** t[1]) ** t[2])","$$\operatorname{log}({\left(\theta_{0} + {x_{0}^{\theta_{1}}}\right)^{\theta_{2}}})$$",0.8125019855743527;-4.620122844496292;7.662363019580337e-2,8,4.9408136877159324e-3,4.9408136877159324e-3,4.9408136877159324e-3,4.9408136877159324e-3,0.5440559776321929,0.5440559776321929,0.5440559776321929,18.94272483789467,18.94272483789467,18.94272483789467 +214,0,Exp(((-0.584906266934704 * Exp(Log((1.6497813819348082 / x1)))) / (x1 * 1.7597455574390652))),"np.exp(((t[0] * np.exp(np.log((t[1] / x[:, 1])))) / (x[:, 1] * t[2])))","$$\operatorname{exp}(\frac{\left(\theta_{0} \cdot \operatorname{exp}(\operatorname{log}(\frac{\theta_{1}}{x_{1}}))\right)}{\left(x_{1} \cdot \theta_{2}\right)})$$",-0.584906266934704;1.6497813819348082;1.7597455574390652,12,1.0934120826299524e-2,1.0934120826299524e-2,1.0934120826299524e-2,1.0934120826299524e-2,-9.013362109444367e-3,-9.013362109444367e-3,-9.013362109444367e-3,27.573729145583254,27.573729145583254,27.573729145583254 +215,0,((Log(Log((x0 + 21.09098333917452))) + Log((x0 / x0))) + -1.072571766467822),"((np.log(np.log((x[:, 0] + t[0]))) + np.log((x[:, 0] / x[:, 0]))) + t[1])","$$\left(\left(\operatorname{log}(\operatorname{log}(\left(x_{0} + \theta_{0}\right))) + \operatorname{log}(\frac{x_{0}}{x_{0}})\right) + \theta_{1}\right)$$",21.09098333917452;-1.072571766467822,12,1.150692640755611e-2,1.150692640755611e-2,1.150692640755611e-2,1.150692640755611e-2,-6.187252605690974e-2,-6.187252605690974e-2,-6.187252605690974e-2,23.365461053711286,23.365461053711286,23.365461053711286 +216,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 +217,0,Exp((((x1 * -0.2046572211958223) * 0.8320385885002299) ^ Exp(Exp(Exp(((x1 * -0.14623175672421596) ^ (-0.41578078667214347 * x1))))))),"np.exp(((x[:, 1] * t[0]) * t[1]) ** np.exp(np.exp(np.exp((x[:, 1] * t[2]) ** (t[3] * x[:, 1])))))","$$\operatorname{exp}({\left(\left(x_{1} \cdot \theta_{0}\right) \cdot \theta_{1}\right)^{\operatorname{exp}(\operatorname{exp}(\operatorname{exp}({\left(x_{1} \cdot \theta_{2}\right)^{\left(\theta_{3} \cdot x_{1}\right)}})))}})$$",-0.2046572211958223;0.8320385885002299;-0.14623175672421596;-0.41578078667214347,17,,,,Infinity,,,,,, +218,0,((((-0.7387578716042267 / Log(x1)) * ((-0.8696834609810447 * x1) / x1)) ^ ((x0 + (x1 ^ x0)) ^ (x1 * (x0 + -0.9669753356082059)))) * Log(((-0.5641501422858095 / (x1 + (x1 / x0))) + (Exp((x0 * -0.45164250233784253)) * (3.0931046671047735e-2 ^ x0))))),"(((t[0] / np.log(x[:, 1])) * ((t[1] * x[:, 1]) / x[:, 1])) ** (x[:, 0] + x[:, 1] ** x[:, 0]) ** (x[:, 1] * (x[:, 0] + t[2])) * np.log(((t[3] / (x[:, 1] + (x[:, 1] / x[:, 0]))) + (np.exp((x[:, 0] * t[4])) * t[5] ** x[:, 0]))))","$$\left({\left(\frac{\theta_{0}}{\operatorname{log}(x_{1})} \cdot \frac{\left(\theta_{1} \cdot x_{1}\right)}{x_{1}}\right)^{{\left(x_{0} + {x_{1}^{x_{0}}}\right)^{\left(x_{1} \cdot \left(x_{0} + \theta_{2}\right)\right)}}}} \cdot \operatorname{log}(\left(\frac{\theta_{3}}{\left(x_{1} + \frac{x_{1}}{x_{0}}\right)} + \left(\operatorname{exp}(\left(x_{0} \cdot \theta_{4}\right)) \cdot {\theta_{5}^{x_{0}}}\right)\right))\right)$$",-0.7387578716042267;-0.8696834609810447;-0.9669753356082059;-0.5641501422858095;-0.45164250233784253;3.0931046671047735e-2,40,,,,Infinity,,,,,, +219,0,(((Log((Log((-0.26276917928476806 / x1)) / ((x0 * 0.9665288165654669) * x1))) + 0.24663794752419954) * -0.8145961783821156) / ((x1 * 0.46358987873778457) / x0)),"(((np.log((np.log((t[0] / x[:, 1])) / ((x[:, 0] * t[1]) * x[:, 1]))) + t[2]) * t[3]) / ((x[:, 1] * t[4]) / x[:, 0]))","$$\frac{\left(\left(\operatorname{log}(\frac{\operatorname{log}(\frac{\theta_{0}}{x_{1}})}{\left(\left(x_{0} \cdot \theta_{1}\right) \cdot x_{1}\right)}) + \theta_{2}\right) \cdot \theta_{3}\right)}{\frac{\left(x_{1} \cdot \theta_{4}\right)}{x_{0}}}$$",-0.26276917928476806;0.9665288165654669;0.24663794752419954;-0.8145961783821156;0.46358987873778457,21,,,,Infinity,,,,,, +220,0,((Log((Exp((x1 * 0.14584942030696502)) + 0.10440026641081845)) / ((Log(x0) + (x0 * 0.4268841433127558)) ^ (0.33694435930781874 / (x0 / x1)))) * Log((Log(((0.6415542552401423 + (0.5106800918109828 * x0)) ^ (-0.7486278189673778 ^ x0))) * ((x0 / (x1 + 0.48441205145663957)) + (x0 * Exp(x0)))))),"((np.log((np.exp((x[:, 1] * t[0])) + t[1])) / (np.log(x[:, 0]) + (x[:, 0] * t[2])) ** (t[3] / (x[:, 0] / x[:, 1]))) * np.log((np.log((t[4] + (t[5] * x[:, 0])) ** t[6] ** x[:, 0]) * ((x[:, 0] / (x[:, 1] + t[7])) + (x[:, 0] * np.exp(x[:, 0]))))))","$$\left(\frac{\operatorname{log}(\left(\operatorname{exp}(\left(x_{1} \cdot \theta_{0}\right)) + \theta_{1}\right))}{{\left(\operatorname{log}(x_{0}) + \left(x_{0} \cdot \theta_{2}\right)\right)^{\frac{\theta_{3}}{\frac{x_{0}}{x_{1}}}}}} \cdot \operatorname{log}(\left(\operatorname{log}({\left(\theta_{4} + \left(\theta_{5} \cdot x_{0}\right)\right)^{{\theta_{6}^{x_{0}}}}}) \cdot \left(\frac{x_{0}}{\left(x_{1} + \theta_{7}\right)} + \left(x_{0} \cdot \operatorname{exp}(x_{0})\right)\right)\right))\right)$$",0.14584942030696502;0.10440026641081845;0.4268841433127558;0.33694435930781874;0.6415542552401423;0.5106800918109828;-0.7486278189673778;0.48441205145663957,43,,,,Infinity,,,,,, +221,0,(Log((Log(x1) + (-0.13511118463716698 + x0))) ^ (Exp((x1 ^ 0.6018791105178508)) / (-0.8983304645789613 ^ x1))),"np.log((np.log(x[:, 1]) + (t[0] + x[:, 0]))) ** (np.exp(x[:, 1] ** t[1]) / t[2] ** x[:, 1])","$${\operatorname{log}(\left(\operatorname{log}(x_{1}) + \left(\theta_{0} + x_{0}\right)\right))^{\frac{\operatorname{exp}({x_{1}^{\theta_{1}}})}{{\theta_{2}^{x_{1}}}}}}$$",-0.13511118463716698;0.6018791105178508;-0.8983304645789613,16,,,,Infinity,,,,,, +222,0,Exp(((Exp((Exp(Log(x1)) ^ -0.42416910449597345)) * (((x1 ^ -0.4792403183606525) + 0.12951066989358317) ^ ((x1 * 0.3156326194122947) + -0.31631206749227336))) ^ (((-0.9386996150552955 + (x0 + x0)) + (7.330102352000012e-2 + (-0.6559447927598386 / x0))) + ((0.20701640696405266 / (x1 ^ -0.18564364119709553)) + 0.42197343144530997)))),"np.exp((np.exp(np.exp(np.log(x[:, 1])) ** t[0]) * (x[:, 1] ** t[1] + t[2]) ** ((x[:, 1] * t[3]) + t[4])) ** (((t[5] + (x[:, 0] + x[:, 0])) + (t[6] + (t[7] / x[:, 0]))) + ((t[8] / x[:, 1] ** t[9]) + t[10])))","$$\operatorname{exp}({\left(\operatorname{exp}({\operatorname{exp}(\operatorname{log}(x_{1}))^{\theta_{0}}}) \cdot {\left({x_{1}^{\theta_{1}}} + \theta_{2}\right)^{\left(\left(x_{1} \cdot \theta_{3}\right) + \theta_{4}\right)}}\right)^{\left(\left(\left(\theta_{5} + \left(x_{0} + x_{0}\right)\right) + \left(\theta_{6} + \frac{\theta_{7}}{x_{0}}\right)\right) + \left(\frac{\theta_{8}}{{x_{1}^{\theta_{9}}}} + \theta_{10}\right)\right)}})$$",-0.42416910449597345;-0.4792403183606525;0.12951066989358317;0.3156326194122947;-0.31631206749227336;-0.9386996150552955;7.330102352000012e-2;-0.6559447927598386;0.20701640696405266;-0.18564364119709553;0.42197343144530997,39,1.4910003118201935e82,1.4910003118201935e82,1.4910003118201935e82,1.4910003118201935e82,-1.3759123951852987e84,-1.3759123951852987e84,-1.3759123951852987e84,1.3419002806381742e82,1.3419002806381742e82,1.3419002806381742e82 +223,0,(Log((((x1 * 0.45533249855131974) ^ (-0.2727993294005848 ^ x1)) ^ ((-0.3242976557866021 / x1) ^ 0.5180342906305868))) + (-0.6718798303199153 + ((-0.3171889467886033 * x0) + (x1 * 0.9525569933978517)))),"(np.log((x[:, 1] * t[0]) ** t[1] ** x[:, 1] ** (t[2] / x[:, 1]) ** t[3]) + (t[4] + ((t[5] * x[:, 0]) + (x[:, 1] * t[6]))))","$$\left(\operatorname{log}({{\left(x_{1} \cdot \theta_{0}\right)^{{\theta_{1}^{x_{1}}}}}^{{\frac{\theta_{2}}{x_{1}}^{\theta_{3}}}}}) + \left(\theta_{4} + \left(\left(\theta_{5} \cdot x_{0}\right) + \left(x_{1} \cdot \theta_{6}\right)\right)\right)\right)$$",0.45533249855131974;-0.2727993294005848;-0.3242976557866021;0.5180342906305868;-0.6718798303199153;-0.3171889467886033;0.9525569933978517,24,,,,Infinity,,,,,, +224,0,((0.8334783361317752 ^ (x1 / (x1 + 0.6561259579334595))) + Log(((-0.5757866581170641 / x0) / Exp((x0 + x0))))),"(t[0] ** (x[:, 1] / (x[:, 1] + t[1])) + np.log(((t[2] / x[:, 0]) / np.exp((x[:, 0] + x[:, 0])))))","$$\left({\theta_{0}^{\frac{x_{1}}{\left(x_{1} + \theta_{1}\right)}}} + \operatorname{log}(\frac{\frac{\theta_{2}}{x_{0}}}{\operatorname{exp}(\left(x_{0} + x_{0}\right))})\right)$$",0.8334783361317752;0.6561259579334595;-0.5757866581170641,17,,,,Infinity,,,,,, +225,0,((x1 / (-0.44077105770411806 + x0)) * 9.170458864158842e-2),"((x[:, 1] / (t[0] + x[:, 0])) * t[1])","$$\left(\frac{x_{1}}{\left(\theta_{0} + x_{0}\right)} \cdot \theta_{1}\right)$$",-0.44077105770411806;9.170458864158842e-2,7,1.9562875566403945e-3,1.9562875566403945e-3,1.9562875566403945e-3,1.9562875566403945e-3,0.8194715134269615,0.8194715134269615,0.8194715134269615,16.585926814132645,16.585926814132645,16.585926814132645 +226,0,((Log(((-8.717549974972372e-2 / x0) + x1)) * x1) + x1),"((np.log(((t[0] / x[:, 0]) + x[:, 1])) * x[:, 1]) + x[:, 1])","$$\left(\left(\operatorname{log}(\left(\frac{\theta_{0}}{x_{0}} + x_{1}\right)) \cdot x_{1}\right) + x_{1}\right)$$",-8.717549974972372e-2,10,,,,Infinity,,,,,, +227,0,Log(Exp((Exp((x0 ^ Log(x1))) * Log((Exp(x1) / Exp(x1)))))),"np.log(np.exp((np.exp(x[:, 0] ** np.log(x[:, 1])) * np.log((np.exp(x[:, 1]) / np.exp(x[:, 1]))))))","$$\operatorname{log}(\operatorname{exp}(\left(\operatorname{exp}({x_{0}^{\operatorname{log}(x_{1})}}) \cdot \operatorname{log}(\frac{\operatorname{exp}(x_{1})}{\operatorname{exp}(x_{1})})\right)))$$",,14,1.4573554749975553e-2,1.4573554749975553e-2,1.4573554749975553e-2,1.4573554749975553e-2,-0.3448645492182232,-0.3448645492182232,-0.3448645492182232,27.255858286049364,27.255858286049364,27.255858286049364 +228,0,(((-0.5220293745026539 + x1) + -0.9443939665200863) ^ ((0.6331498601195318 ^ x0) + -0.7408244895621305)),"((t[0] + x[:, 1]) + t[1]) ** (t[2] ** x[:, 0] + t[3])","$${\left(\left(\theta_{0} + x_{1}\right) + \theta_{1}\right)^{\left({\theta_{2}^{x_{0}}} + \theta_{3}\right)}}$$",-0.5220293745026539;-0.9443939665200863;0.6331498601195318;-0.7408244895621305,11,,,,Infinity,,,,,, +229,0,Exp((Log((((-0.4965740043605992 + x1) + -0.5601639093991653) ^ 2.5383075001163213e-2)) + x1)),"np.exp((np.log(((t[0] + x[:, 1]) + t[1]) ** t[2]) + x[:, 1]))","$$\operatorname{exp}(\left(\operatorname{log}({\left(\left(\theta_{0} + x_{1}\right) + \theta_{1}\right)^{\theta_{2}}}) + x_{1}\right))$$",-0.4965740043605992;-0.5601639093991653;2.5383075001163213e-2,11,,,,Infinity,,,,,, +230,0,(0.4434516327507356 ^ ((Exp(Log((x1 ^ (x1 + x1)))) + ((0.7158222236702834 * x0) * 0.9685042556972568)) ^ ((-0.1310980560221131 ^ (x0 ^ x0)) ^ (-0.8810754229647169 ^ (x0 * 2.9361247955453917e-3))))),"t[0] ** (np.exp(np.log(x[:, 1] ** (x[:, 1] + x[:, 1]))) + ((t[1] * x[:, 0]) * t[2])) ** t[3] ** x[:, 0] ** x[:, 0] ** t[4] ** (x[:, 0] * t[5])","$${\theta_{0}^{{\left(\operatorname{exp}(\operatorname{log}({x_{1}^{\left(x_{1} + x_{1}\right)}})) + \left(\left(\theta_{1} \cdot x_{0}\right) \cdot \theta_{2}\right)\right)^{{{\theta_{3}^{{x_{0}^{x_{0}}}}}^{{\theta_{4}^{\left(x_{0} \cdot \theta_{5}\right)}}}}}}}}$$",0.4434516327507356;0.7158222236702834;0.9685042556972568;-0.1310980560221131;-0.8810754229647169;2.9361247955453917e-3,27,,,,Infinity,,,,,, +231,0,(((((x1 / x0) / x1) ^ 2.9257653777756788e-2) + -0.9930195763937985) / x1),"((((x[:, 1] / x[:, 0]) / x[:, 1]) ** t[0] + t[1]) / x[:, 1])","$$\frac{\left({\frac{\frac{x_{1}}{x_{0}}}{x_{1}}^{\theta_{0}}} + \theta_{1}\right)}{x_{1}}$$",2.9257653777756788e-2;-0.9930195763937985,11,1.1663527553225049e-2,1.1663527553225049e-2,1.1663527553225049e-2,1.1663527553225049e-2,-7.632386156086213e-2,-7.632386156086213e-2,-7.632386156086213e-2,23.76170120704179,23.76170120704179,23.76170120704179 +232,0,(Log(Log((x0 ^ (x1 ^ 0.9950337729754963)))) ^ ((-0.7439718438933526 * x0) ^ x0)),"np.log(np.log(x[:, 0] ** x[:, 1] ** t[0])) ** (t[1] * x[:, 0]) ** x[:, 0]","$${\operatorname{log}(\operatorname{log}({x_{0}^{{x_{1}^{\theta_{0}}}}}))^{{\left(\theta_{1} \cdot x_{0}\right)^{x_{0}}}}}$$",0.9950337729754963;-0.7439718438933526,13,,,,Infinity,,,,,, +233,0,Exp(Exp(((((-0.6881208736792483 * x1) ^ (0.3833771476360004 * x1)) ^ 0.5750670658434625) + ((Exp(x0) + (x1 * 0.6677779308949046)) / ((x1 ^ x0) / (-0.7962841901678317 + x0)))))),"np.exp(np.exp(((t[0] * x[:, 1]) ** (t[1] * x[:, 1]) ** t[2] + ((np.exp(x[:, 0]) + (x[:, 1] * t[3])) / (x[:, 1] ** x[:, 0] / (t[4] + x[:, 0]))))))","$$\operatorname{exp}(\operatorname{exp}(\left({{\left(\theta_{0} \cdot x_{1}\right)^{\left(\theta_{1} \cdot x_{1}\right)}}^{\theta_{2}}} + \frac{\left(\operatorname{exp}(x_{0}) + \left(x_{1} \cdot \theta_{3}\right)\right)}{\frac{{x_{1}^{x_{0}}}}{\left(\theta_{4} + x_{0}\right)}}\right)))$$",-0.6881208736792483;0.3833771476360004;0.5750670658434625;0.6677779308949046;-0.7962841901678317,26,,,,Infinity,,,,,, +234,0,((Exp((-0.5465099955214839 + (x1 ^ 0.3975227593513142))) + Log((x0 * (x0 * 0.9135448274775685)))) + Log(((0.2846540625043531 + Log(x1)) * Exp((0.23834643466275918 ^ (0.6571708740570321 / x1)))))),"((np.exp((t[0] + x[:, 1] ** t[1])) + np.log((x[:, 0] * (x[:, 0] * t[2])))) + np.log(((t[3] + np.log(x[:, 1])) * np.exp(t[4] ** (t[5] / x[:, 1])))))","$$\left(\left(\operatorname{exp}(\left(\theta_{0} + {x_{1}^{\theta_{1}}}\right)) + \operatorname{log}(\left(x_{0} \cdot \left(x_{0} \cdot \theta_{2}\right)\right))\right) + \operatorname{log}(\left(\left(\theta_{3} + \operatorname{log}(x_{1})\right) \cdot \operatorname{exp}({\theta_{4}^{\frac{\theta_{5}}{x_{1}}}})\right))\right)$$",-0.5465099955214839;0.3975227593513142;0.9135448274775685;0.2846540625043531;0.23834643466275918;0.6571708740570321,26,,,,Infinity,,,,,, +235,0,Log((0.48467893057696304 / Exp((0.41716347252339386 + Log(x1))))),"np.log((t[0] / np.exp((t[1] + np.log(x[:, 1])))))","$$\operatorname{log}(\frac{\theta_{0}}{\operatorname{exp}(\left(\theta_{1} + \operatorname{log}(x_{1})\right))})$$",0.48467893057696304;0.41716347252339386,8,0.2792880547545848,0.2792880547545848,0.2792880547545848,0.2792880547545848,-24.77302588856636,-24.77302588856636,-24.77302588856636,18.62728487383885,18.62728487383885,18.62728487383885 +236,0,(Exp((0.48691532085889977 * (0.638559382754308 * (0.3418512298301849 + ((-0.2123119155844806 ^ x0) ^ -0.31106557112559186))))) * x0),"(np.exp((t[0] * (t[1] * (t[2] + t[3] ** x[:, 0] ** t[4])))) * x[:, 0])","$$\left(\operatorname{exp}(\left(\theta_{0} \cdot \left(\theta_{1} \cdot \left(\theta_{2} + {{\theta_{3}^{x_{0}}}^{\theta_{4}}}\right)\right)\right)) \cdot x_{0}\right)$$",0.48691532085889977;0.638559382754308;0.3418512298301849;-0.2123119155844806;-0.31106557112559186,14,,,,Infinity,,,,,, +237,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 +238,0,Exp(((Exp(Log(Exp(x1))) ^ (((0.33966093823347987 + x0) ^ 0.4368965996531905) + Exp(Log((x0 * 0.6934820362555202))))) ^ (((Log(x1) * (x0 / x0)) / ((x1 * x0) ^ 0.6528275345524313)) / Log(Log((4.486960957720165e-2 * x0)))))),"np.exp(np.exp(np.log(np.exp(x[:, 1]))) ** ((t[0] + x[:, 0]) ** t[1] + np.exp(np.log((x[:, 0] * t[2])))) ** (((np.log(x[:, 1]) * (x[:, 0] / x[:, 0])) / (x[:, 1] * x[:, 0]) ** t[3]) / np.log(np.log((t[4] * x[:, 0])))))","$$\operatorname{exp}({{\operatorname{exp}(\operatorname{log}(\operatorname{exp}(x_{1})))^{\left({\left(\theta_{0} + x_{0}\right)^{\theta_{1}}} + \operatorname{exp}(\operatorname{log}(\left(x_{0} \cdot \theta_{2}\right)))\right)}}^{\frac{\frac{\left(\operatorname{log}(x_{1}) \cdot \frac{x_{0}}{x_{0}}\right)}{{\left(x_{1} \cdot x_{0}\right)^{\theta_{3}}}}}{\operatorname{log}(\operatorname{log}(\left(\theta_{4} \cdot x_{0}\right)))}}})$$",0.33966093823347987;0.4368965996531905;0.6934820362555202;0.6528275345524313;4.486960957720165e-2,36,,,,Infinity,,,,,, +239,0,Exp((((0.6354109264284284 ^ Exp(Log((5.408789451953455e-2 * x0)))) * Log(((-0.7324159583764958 * (0.27672670675892086 ^ x1)) + (x0 * -0.674626204596113)))) / ((Log((x0 / x0)) + -0.5286962476362307) / ((Log(x0) ^ (x0 * -0.6626276173822336)) + -0.7326060161977908)))),"np.exp(((t[0] ** np.exp(np.log((t[1] * x[:, 0]))) * np.log(((t[2] * t[3] ** x[:, 1]) + (x[:, 0] * t[4])))) / ((np.log((x[:, 0] / x[:, 0])) + t[5]) / (np.log(x[:, 0]) ** (x[:, 0] * t[6]) + t[7]))))","$$\operatorname{exp}(\frac{\left({\theta_{0}^{\operatorname{exp}(\operatorname{log}(\left(\theta_{1} \cdot x_{0}\right)))}} \cdot \operatorname{log}(\left(\left(\theta_{2} \cdot {\theta_{3}^{x_{1}}}\right) + \left(x_{0} \cdot \theta_{4}\right)\right))\right)}{\frac{\left(\operatorname{log}(\frac{x_{0}}{x_{0}}) + \theta_{5}\right)}{\left({\operatorname{log}(x_{0})^{\left(x_{0} \cdot \theta_{6}\right)}} + \theta_{7}\right)}})$$",0.6354109264284284;5.408789451953455e-2;-0.7324159583764958;0.27672670675892086;-0.674626204596113;-0.5286962476362307;-0.6626276173822336;-0.7326060161977908,35,,,,Infinity,,,,,, +240,0,Exp((Log((x0 + (-0.480055151934733 * x1))) * Exp((x1 + x0)))),"np.exp((np.log((x[:, 0] + (t[0] * x[:, 1]))) * np.exp((x[:, 1] + x[:, 0]))))","$$\operatorname{exp}(\left(\operatorname{log}(\left(x_{0} + \left(\theta_{0} \cdot x_{1}\right)\right)) \cdot \operatorname{exp}(\left(x_{1} + x_{0}\right))\right))$$",-0.480055151934733,12,4.964657179227458,4.964657179227458,4.964657179227458,4.964657179227458,-457.14432744186524,-457.14432744186524,-457.14432744186524,29.84003818533611,29.84003818533611,29.84003818533611 +241,0,((((x0 / (x0 * x1)) * (x0 ^ x1)) ^ Exp(Log(((x1 * -0.40139040171417617) ^ (x0 * 4.181080258707082e-2))))) / (Exp((Log((-0.8480261197336083 ^ x0)) + 0.704221454859496)) + Log(((x0 * (x0 * 0.929016566420829)) * (x1 * -0.7218365411729183))))),"(((x[:, 0] / (x[:, 0] * x[:, 1])) * x[:, 0] ** x[:, 1]) ** np.exp(np.log((x[:, 1] * t[0]) ** (x[:, 0] * t[1]))) / (np.exp((np.log(t[2] ** x[:, 0]) + t[3])) + np.log(((x[:, 0] * (x[:, 0] * t[4])) * (x[:, 1] * t[5])))))","$$\frac{{\left(\frac{x_{0}}{\left(x_{0} \cdot x_{1}\right)} \cdot {x_{0}^{x_{1}}}\right)^{\operatorname{exp}(\operatorname{log}({\left(x_{1} \cdot \theta_{0}\right)^{\left(x_{0} \cdot \theta_{1}\right)}}))}}}{\left(\operatorname{exp}(\left(\operatorname{log}({\theta_{2}^{x_{0}}}) + \theta_{3}\right)) + \operatorname{log}(\left(\left(x_{0} \cdot \left(x_{0} \cdot \theta_{4}\right)\right) \cdot \left(x_{1} \cdot \theta_{5}\right)\right))\right)}$$",-0.40139040171417617;4.181080258707082e-2;-0.8480261197336083;0.704221454859496;0.929016566420829;-0.7218365411729183,38,,,,Infinity,,,,,, +242,0,((6.910263463464683e-2 * (x1 ^ -0.9990929438580434)) * (Log((-0.6378607349132996 / x1)) / Log(x1))),"((t[0] * x[:, 1] ** t[1]) * (np.log((t[2] / x[:, 1])) / np.log(x[:, 1])))","$$\left(\left(\theta_{0} \cdot {x_{1}^{\theta_{1}}}\right) \cdot \frac{\operatorname{log}(\frac{\theta_{2}}{x_{1}})}{\operatorname{log}(x_{1})}\right)$$",6.910263463464683e-2;-0.9990929438580434;-0.6378607349132996,13,,,,Infinity,,,,,, +243,0,Log(Exp(Exp((-2.9055132546125124 * x0)))),"np.log(np.exp(np.exp((t[0] * x[:, 0]))))","$$\operatorname{log}(\operatorname{exp}(\operatorname{exp}(\left(\theta_{0} \cdot x_{0}\right))))$$",-2.9055132546125124,6,5.231671206158197e-3,5.231671206158197e-3,5.231671206158197e-3,5.231671206158197e-3,0.517215308204767,0.517215308204767,0.517215308204767,11.682260914057785,11.682260914057785,11.682260914057785 +244,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 +245,0,((Exp(Exp(x0)) * (x1 ^ 1.5446029635272003)) * (3.845086261449669e-4 ^ (x1 / (x0 * 0.4635953380968038)))),"((np.exp(np.exp(x[:, 0])) * x[:, 1] ** t[0]) * t[1] ** (x[:, 1] / (x[:, 0] * t[2])))","$$\left(\left(\operatorname{exp}(\operatorname{exp}(x_{0})) \cdot {x_{1}^{\theta_{0}}}\right) \cdot {\theta_{1}^{\frac{x_{1}}{\left(x_{0} \cdot \theta_{2}\right)}}}\right)$$",1.5446029635272003;3.845086261449669e-4;0.4635953380968038,15,4.3975128480982395e-2,4.3975128480982395e-2,4.3975128480982395e-2,4.3975128480982395e-2,-3.0580759022769683,-3.0580759022769683,-3.0580759022769683,35.29100465756551,35.29100465756551,35.29100465756551 +246,0,Log(Exp(Exp((x0 ^ -4.700445271404796e-2)))),"np.log(np.exp(np.exp(x[:, 0] ** t[0])))","$$\operatorname{log}(\operatorname{exp}(\operatorname{exp}({x_{0}^{\theta_{0}}})))$$",-4.700445271404796e-2,6,7.066150551304317,7.066150551304317,7.066150551304317,7.066150551304317,-651.072575217376,-651.072575217376,-651.072575217376,18.037087906146127,18.037087906146127,18.037087906146127 +247,0,Exp(((1.5096788367051608 * x0) * -1.9245829332514721)),"np.exp(((t[0] * x[:, 0]) * t[1]))","$$\operatorname{exp}(\left(\left(\theta_{0} \cdot x_{0}\right) \cdot \theta_{1}\right))$$",1.5096788367051608;-1.9245829332514721,6,5.231671205578371e-3,5.231671205578371e-3,5.231671205578371e-3,5.231671205578371e-3,0.517215308258274,0.517215308258274,0.517215308258274,12.364324541539643,12.364324541539643,12.364324541539643 +248,0,Log(Log((-0.7762276257961795 / (x1 ^ (-0.2638008795078908 ^ x0))))),"np.log(np.log((t[0] / x[:, 1] ** t[1] ** x[:, 0])))","$$\operatorname{log}(\operatorname{log}(\frac{\theta_{0}}{{x_{1}^{{\theta_{1}^{x_{0}}}}}}))$$",-0.7762276257961795;-0.2638008795078908,9,,,,Infinity,,,,,, +249,0,(((((x0 * -1.7938669598517275e-2) / (x0 / ((x0 * -0.5710681309702826) * 1.4166145957682525))) * Exp((Log(x0) * -4.674942013532105))) / Log((x1 + 6.777475908473423))) * (((4.962600593837775 + x0) * 1.5963004487973274) * Exp(((-1.3486728605254092 + (0.2675879017532597 * x1)) / Exp((x1 * 1.6275258418209724)))))),"(((((x[:, 0] * t[0]) / (x[:, 0] / ((x[:, 0] * t[1]) * t[2]))) * np.exp((np.log(x[:, 0]) * t[3]))) / np.log((x[:, 1] + t[4]))) * (((t[5] + x[:, 0]) * t[6]) * np.exp(((t[7] + (t[8] * x[:, 1])) / np.exp((x[:, 1] * t[9]))))))","$$\left(\frac{\left(\frac{\left(x_{0} \cdot \theta_{0}\right)}{\frac{x_{0}}{\left(\left(x_{0} \cdot \theta_{1}\right) \cdot \theta_{2}\right)}} \cdot \operatorname{exp}(\left(\operatorname{log}(x_{0}) \cdot \theta_{3}\right))\right)}{\operatorname{log}(\left(x_{1} + \theta_{4}\right))} \cdot \left(\left(\left(\theta_{5} + x_{0}\right) \cdot \theta_{6}\right) \cdot \operatorname{exp}(\frac{\left(\theta_{7} + \left(\theta_{8} \cdot x_{1}\right)\right)}{\operatorname{exp}(\left(x_{1} \cdot \theta_{9}\right))})\right)\right)$$",-1.7938669598517275e-2;-0.5710681309702826;1.4166145957682525;-4.674942013532105;6.777475908473423;4.962600593837775;1.5963004487973274;-1.3486728605254092;0.2675879017532597;1.6275258418209724,40,3.2448629619267894e-3,3.2448629619267894e-3,3.2448629619267894e-3,3.2448629619267894e-3,0.7005602792569259,0.7005602792569259,0.7005602792569259,103.38983139753557,103.38983139753557,103.38983139753557 +250,0,(Log(((0.4973476368130393 ^ (x1 * 0.5099843566210469)) ^ (Exp((x0 ^ 0.9430733063071977)) + (x0 ^ 0.4648496190591308)))) / ((0.4719487767158834 + Exp(Log((x1 + x0)))) + Log((Exp((-0.8273638751960062 ^ x0)) + -5.145011823630885e-2)))),"(np.log(t[0] ** (x[:, 1] * t[1]) ** (np.exp(x[:, 0] ** t[2]) + x[:, 0] ** t[3])) / ((t[4] + np.exp(np.log((x[:, 1] + x[:, 0])))) + np.log((np.exp(t[5] ** x[:, 0]) + t[6]))))","$$\frac{\operatorname{log}({{\theta_{0}^{\left(x_{1} \cdot \theta_{1}\right)}}^{\left(\operatorname{exp}({x_{0}^{\theta_{2}}}) + {x_{0}^{\theta_{3}}}\right)}})}{\left(\left(\theta_{4} + \operatorname{exp}(\operatorname{log}(\left(x_{1} + x_{0}\right)))\right) + \operatorname{log}(\left(\operatorname{exp}({\theta_{5}^{x_{0}}}) + \theta_{6}\right))\right)}$$",0.4973476368130393;0.5099843566210469;0.9430733063071977;0.4648496190591308;0.4719487767158834;-0.8273638751960062;-5.145011823630885e-2,31,,,,Infinity,,,,,, +251,0,Log((x0 + 0.16313196790349427)),"np.log((x[:, 0] + t[0]))","$$\operatorname{log}(\left(x_{0} + \theta_{0}\right))$$",0.16313196790349427,4,0.13730678994670836,0.13730678994670836,0.13730678994670836,0.13730678994670836,-11.67082996113841,-11.67082996113841,-11.67082996113841,7.68967849079924,7.68967849079924,7.68967849079924 +252,0,(((x1 ^ 0.6915484858835693) + (x0 / x1)) * (4.7214082212686805e-5 / (x1 * 0.1218331701334346))),"((x[:, 1] ** t[0] + (x[:, 0] / x[:, 1])) * (t[1] / (x[:, 1] * t[2])))","$$\left(\left({x_{1}^{\theta_{0}}} + \frac{x_{0}}{x_{1}}\right) \cdot \frac{\theta_{1}}{\left(x_{1} \cdot \theta_{2}\right)}\right)$$",0.6915484858835693;4.7214082212686805e-5;0.1218331701334346,13,1.4436212461286879e-2,1.4436212461286879e-2,1.4436212461286879e-2,1.4436212461286879e-2,-0.33219044339196957,-0.33219044339196957,-0.33219044339196957,31.372599335037155,31.372599335037155,31.372599335037155 +253,0,Log(((((-0.7418335566093341 / (x1 / x1)) * 0.5154515666961359) + ((0.1941029078505182 + (x1 * -0.8936366491212108)) * 0.6542011528244422)) ^ (Exp(((x0 / (x0 * 0.28037298001653055)) + (7.885965306182818e-2 ^ x1))) ^ ((0.6408996768043134 / Exp(x0)) * ((x0 / x0) + -7.868993810790825e-2))))),"np.log((((t[0] / (x[:, 1] / x[:, 1])) * t[1]) + ((t[2] + (x[:, 1] * t[3])) * t[4])) ** np.exp(((x[:, 0] / (x[:, 0] * t[5])) + t[6] ** x[:, 1])) ** ((t[7] / np.exp(x[:, 0])) * ((x[:, 0] / x[:, 0]) + t[8])))","$$\operatorname{log}({\left(\left(\frac{\theta_{0}}{\frac{x_{1}}{x_{1}}} \cdot \theta_{1}\right) + \left(\left(\theta_{2} + \left(x_{1} \cdot \theta_{3}\right)\right) \cdot \theta_{4}\right)\right)^{{\operatorname{exp}(\left(\frac{x_{0}}{\left(x_{0} \cdot \theta_{5}\right)} + {\theta_{6}^{x_{1}}}\right))^{\left(\frac{\theta_{7}}{\operatorname{exp}(x_{0})} \cdot \left(\frac{x_{0}}{x_{0}} + \theta_{8}\right)\right)}}}})$$",-0.7418335566093341;0.5154515666961359;0.1941029078505182;-0.8936366491212108;0.6542011528244422;0.28037298001653055;7.885965306182818e-2;0.6408996768043134;-7.868993810790825e-2,38,,,,Infinity,,,,,, +254,0,(Log((((x0 * 0.11051553572940709) ^ 0.593781186716341) ^ -0.21854178025980175)) * (((-0.4262004754377421 + x1) ^ (x1 ^ x1)) * (0.16142360337899875 * (x1 + x1)))),"(np.log((x[:, 0] * t[0]) ** t[1] ** t[2]) * ((t[3] + x[:, 1]) ** x[:, 1] ** x[:, 1] * (t[4] * (x[:, 1] + x[:, 1]))))","$$\left(\operatorname{log}({{\left(x_{0} \cdot \theta_{0}\right)^{\theta_{1}}}^{\theta_{2}}}) \cdot \left({\left(\theta_{3} + x_{1}\right)^{{x_{1}^{x_{1}}}}} \cdot \left(\theta_{4} \cdot \left(x_{1} + x_{1}\right)\right)\right)\right)$$",0.11051553572940709;0.593781186716341;-0.21854178025980175;-0.4262004754377421;0.16142360337899875,22,,,,Infinity,,,,,, +255,0,Log(((31.03528432478805 * x1) * 0.11401378375671233)),"np.log(((t[0] * x[:, 1]) * t[1]))","$$\operatorname{log}(\left(\left(\theta_{0} \cdot x_{1}\right) \cdot \theta_{1}\right))$$",31.03528432478805;0.11401378375671233,6,0.20183020857651848,0.20183020857651848,0.20183020857651848,0.20183020857651848,-17.625125930674866,-17.625125930674866,-17.625125930674866,12.54126322517349,12.54126322517349,12.54126322517349 +256,0,(((x0 * 0.6469235767347474) + (x1 * 0.1279113556140522)) ^ Exp((-0.25415435928142716 ^ (x0 + 0.8117381281758772)))),"((x[:, 0] * t[0]) + (x[:, 1] * t[1])) ** np.exp(t[2] ** (x[:, 0] + t[3]))","$${\left(\left(x_{0} \cdot \theta_{0}\right) + \left(x_{1} \cdot \theta_{1}\right)\right)^{\operatorname{exp}({\theta_{2}^{\left(x_{0} + \theta_{3}\right)}})}}$$",0.6469235767347474;0.1279113556140522;-0.25415435928142716;0.8117381281758772,14,,,,Infinity,,,,,, +257,0,(((Log(Log(x1)) / (-0.8047597099480116 / x0)) + (x0 / x1)) * -0.24229362509345076),"(((np.log(np.log(x[:, 1])) / (t[0] / x[:, 0])) + (x[:, 0] / x[:, 1])) * t[1])","$$\left(\left(\frac{\operatorname{log}(\operatorname{log}(x_{1}))}{\frac{\theta_{0}}{x_{0}}} + \frac{x_{0}}{x_{1}}\right) \cdot \theta_{1}\right)$$",-0.8047597099480116;-0.24229362509345076,13,,,,Infinity,,,,,, +258,0,((Log((0.5275235287925928 / x0)) * ((x0 + -0.8240271870273551) / x0)) + Log(((-0.3197509180475011 ^ Log(x1)) ^ (x0 + (0.18136489663080946 ^ x1))))),"((np.log((t[0] / x[:, 0])) * ((x[:, 0] + t[1]) / x[:, 0])) + np.log(t[2] ** np.log(x[:, 1]) ** (x[:, 0] + t[3] ** x[:, 1])))","$$\left(\left(\operatorname{log}(\frac{\theta_{0}}{x_{0}}) \cdot \frac{\left(x_{0} + \theta_{1}\right)}{x_{0}}\right) + \operatorname{log}({{\theta_{2}^{\operatorname{log}(x_{1})}}^{\left(x_{0} + {\theta_{3}^{x_{1}}}\right)}})\right)$$",0.5275235287925928;-0.8240271870273551;-0.3197509180475011;0.18136489663080946,22,,,,Infinity,,,,,, +259,0,Exp(((2.0310779614661962 * (x0 * -2.000140539365218)) + (((x1 + (Exp(x1) * -1.0815530175830677e-2)) / x0) + x1))),"np.exp(((t[0] * (x[:, 0] * t[1])) + (((x[:, 1] + (np.exp(x[:, 1]) * t[2])) / x[:, 0]) + x[:, 1])))","$$\operatorname{exp}(\left(\left(\theta_{0} \cdot \left(x_{0} \cdot \theta_{1}\right)\right) + \left(\frac{\left(x_{1} + \left(\operatorname{exp}(x_{1}) \cdot \theta_{2}\right)\right)}{x_{0}} + x_{1}\right)\right))$$",2.0310779614661962;-2.000140539365218;-1.0815530175830677e-2,17,2.054785894520473e-3,2.054785894520473e-3,2.054785894520473e-3,2.054785894520473e-3,0.8103819724711382,0.8103819724711382,0.8103819724711382,39.14509664734831,39.14509664734831,39.14509664734831 +260,0,((((x0 ^ 0.7149330241624112) * 0.9508390386221237) * Log((Exp(x1) / (x1 / x1)))) ^ (Log((x1 * (-0.1741822892889553 / x1))) + (-0.5193817195779102 + (x0 * -0.4836890349574553)))),"((x[:, 0] ** t[0] * t[1]) * np.log((np.exp(x[:, 1]) / (x[:, 1] / x[:, 1])))) ** (np.log((x[:, 1] * (t[2] / x[:, 1]))) + (t[3] + (x[:, 0] * t[4])))","$${\left(\left({x_{0}^{\theta_{0}}} \cdot \theta_{1}\right) \cdot \operatorname{log}(\frac{\operatorname{exp}(x_{1})}{\frac{x_{1}}{x_{1}}})\right)^{\left(\operatorname{log}(\left(x_{1} \cdot \frac{\theta_{2}}{x_{1}}\right)) + \left(\theta_{3} + \left(x_{0} \cdot \theta_{4}\right)\right)\right)}}$$",0.7149330241624112;0.9508390386221237;-0.1741822892889553;-0.5193817195779102;-0.4836890349574553,26,,,,Infinity,,,,,, +261,0,(((-0.7638680283188292 + (x1 * 0.595150090655318)) / Log(Log(((x0 / x0) + (x1 ^ -0.6817593265536492))))) + ((Log((x1 + 0.3882714302340444)) * (0.8599898702113269 ^ (x1 * -0.8203220258365127))) ^ (Log((x1 * x0)) ^ (-0.23865691344765871 * x1)))),"(((t[0] + (x[:, 1] * t[1])) / np.log(np.log(((x[:, 0] / x[:, 0]) + x[:, 1] ** t[2])))) + (np.log((x[:, 1] + t[3])) * t[4] ** (x[:, 1] * t[5])) ** np.log((x[:, 1] * x[:, 0])) ** (t[6] * x[:, 1]))","$$\left(\frac{\left(\theta_{0} + \left(x_{1} \cdot \theta_{1}\right)\right)}{\operatorname{log}(\operatorname{log}(\left(\frac{x_{0}}{x_{0}} + {x_{1}^{\theta_{2}}}\right)))} + {\left(\operatorname{log}(\left(x_{1} + \theta_{3}\right)) \cdot {\theta_{4}^{\left(x_{1} \cdot \theta_{5}\right)}}\right)^{{\operatorname{log}(\left(x_{1} \cdot x_{0}\right))^{\left(\theta_{6} \cdot x_{1}\right)}}}}\right)$$",-0.7638680283188292;0.595150090655318;-0.6817593265536492;0.3882714302340444;0.8599898702113269;-0.8203220258365127;-0.23865691344765871,35,,,,Infinity,,,,,, +262,0,((Log(Exp(Log((-0.6980672684452163 * x0)))) + (0.5016014807762248 / (3.0483818610843816e-2 / x1))) + (((x0 * x1) * x1) + ((x1 * x1) + -0.49432842461033644))),"((np.log(np.exp(np.log((t[0] * x[:, 0])))) + (t[1] / (t[2] / x[:, 1]))) + (((x[:, 0] * x[:, 1]) * x[:, 1]) + ((x[:, 1] * x[:, 1]) + t[3])))","$$\left(\left(\operatorname{log}(\operatorname{exp}(\operatorname{log}(\left(\theta_{0} \cdot x_{0}\right)))) + \frac{\theta_{1}}{\frac{\theta_{2}}{x_{1}}}\right) + \left(\left(\left(x_{0} \cdot x_{1}\right) \cdot x_{1}\right) + \left(\left(x_{1} \cdot x_{1}\right) + \theta_{3}\right)\right)\right)$$",-0.6980672684452163;0.5016014807762248;3.0483818610843816e-2;-0.49432842461033644,24,,,,Infinity,,,,,, +263,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 +264,0,((-7.928072814508772 + ((x0 * x1) * 9.857609786712674)) * (((-0.12307173186556611 * x1) * 0.181750418883839) * 2.8234789221015064)),"((t[0] + ((x[:, 0] * x[:, 1]) * t[1])) * (((t[2] * x[:, 1]) * t[3]) * t[4]))","$$\left(\left(\theta_{0} + \left(\left(x_{0} \cdot x_{1}\right) \cdot \theta_{1}\right)\right) \cdot \left(\left(\left(\theta_{2} \cdot x_{1}\right) \cdot \theta_{3}\right) \cdot \theta_{4}\right)\right)$$",-7.928072814508772;9.857609786712674;-0.12307173186556611;0.181750418883839;2.8234789221015064,15,6.790998393986846e-3,6.790998393986846e-3,6.790998393986846e-3,6.790998393986846e-3,0.3733187852547699,0.3733187852547699,0.3733187852547699,34.2523052619043,34.2523052619043,34.2523052619043 +265,0,Exp(((x1 * -4.4392103905271485e-2) ^ 0.3061999210245552)),"np.exp((x[:, 1] * t[0]) ** t[1])","$$\operatorname{exp}({\left(x_{1} \cdot \theta_{0}\right)^{\theta_{1}}})$$",-4.4392103905271485e-2;0.3061999210245552,6,,,,Infinity,,,,,, +266,0,Exp(((Log((x0 * -5.1372105364373866e-2)) * 0.37457804033045905) / (x1 ^ -0.4154949090590605))),"np.exp(((np.log((x[:, 0] * t[0])) * t[1]) / x[:, 1] ** t[2]))","$$\operatorname{exp}(\frac{\left(\operatorname{log}(\left(x_{0} \cdot \theta_{0}\right)) \cdot \theta_{1}\right)}{{x_{1}^{\theta_{2}}}})$$",-5.1372105364373866e-2;0.37457804033045905;-0.4154949090590605,11,,,,Infinity,,,,,, +267,0,((Exp(((0.3558896877644119 ^ (-0.9531117621876152 ^ x0)) * -0.6528591580765679)) + (0.29460147670816283 + (-0.4552491100867816 + (x1 + x0)))) ^ (Log((0.7461586264034403 / (0.41364934509126605 ^ (x1 * -0.45096578416422783)))) ^ (-0.5122351290495717 / (x0 / (0.20965967544116093 * x1))))),"(np.exp((t[0] ** t[1] ** x[:, 0] * t[2])) + (t[3] + (t[4] + (x[:, 1] + x[:, 0])))) ** np.log((t[5] / t[6] ** (x[:, 1] * t[7]))) ** (t[8] / (x[:, 0] / (t[9] * x[:, 1])))","$${\left(\operatorname{exp}(\left({\theta_{0}^{{\theta_{1}^{x_{0}}}}} \cdot \theta_{2}\right)) + \left(\theta_{3} + \left(\theta_{4} + \left(x_{1} + x_{0}\right)\right)\right)\right)^{{\operatorname{log}(\frac{\theta_{5}}{{\theta_{6}^{\left(x_{1} \cdot \theta_{7}\right)}}})^{\frac{\theta_{8}}{\frac{x_{0}}{\left(\theta_{9} \cdot x_{1}\right)}}}}}}$$",0.3558896877644119;-0.9531117621876152;-0.6528591580765679;0.29460147670816283;-0.4552491100867816;0.7461586264034403;0.41364934509126605;-0.45096578416422783;-0.5122351290495717;0.20965967544116093,33,,,,Infinity,,,,,, +268,0,((Log(((x1 + -0.16382105335512553) ^ Exp(Log(x0)))) * Exp(Log((-0.591961570515651 * Exp((-0.4897576196282717 + x1)))))) / (((x1 * -1.5737887684302065e-2) / (-0.5596484061879035 ^ Log(x0))) + ((-0.12730385755125095 / (x0 / x0)) ^ (-0.605880371445976 / x1)))),"((np.log((x[:, 1] + t[0]) ** np.exp(np.log(x[:, 0]))) * np.exp(np.log((t[1] * np.exp((t[2] + x[:, 1])))))) / (((x[:, 1] * t[3]) / t[4] ** np.log(x[:, 0])) + (t[5] / (x[:, 0] / x[:, 0])) ** (t[6] / x[:, 1])))","$$\frac{\left(\operatorname{log}({\left(x_{1} + \theta_{0}\right)^{\operatorname{exp}(\operatorname{log}(x_{0}))}}) \cdot \operatorname{exp}(\operatorname{log}(\left(\theta_{1} \cdot \operatorname{exp}(\left(\theta_{2} + x_{1}\right))\right)))\right)}{\left(\frac{\left(x_{1} \cdot \theta_{3}\right)}{{\theta_{4}^{\operatorname{log}(x_{0})}}} + {\frac{\theta_{5}}{\frac{x_{0}}{x_{0}}}^{\frac{\theta_{6}}{x_{1}}}}\right)}$$",-0.16382105335512553;-0.591961570515651;-0.4897576196282717;-1.5737887684302065e-2;-0.5596484061879035;-0.12730385755125095;-0.605880371445976,36,,,,Infinity,,,,,, +269,0,Exp((x0 * -2.9053779497574426)),"np.exp((x[:, 0] * t[0]))","$$\operatorname{exp}(\left(x_{0} \cdot \theta_{0}\right))$$",-2.9053779497574426,4,5.2316712708275655e-3,5.2316712708275655e-3,5.2316712708275655e-3,5.2316712708275655e-3,0.5172153022370025,0.5172153022370025,0.5172153022370025,7.570810883990948,7.570810883990948,7.570810883990948 +270,0,Exp(Log(((Exp((-0.7969272401657344 * Exp(x1))) ^ ((x1 ^ 0.2054636141672378) * -0.30258802268212914)) + ((Log(Log(x1)) ^ Exp((x0 / x1))) ^ Log((x0 * 0.24081230503866524)))))),"np.exp(np.log((np.exp((t[0] * np.exp(x[:, 1]))) ** (x[:, 1] ** t[1] * t[2]) + np.log(np.log(x[:, 1])) ** np.exp((x[:, 0] / x[:, 1])) ** np.log((x[:, 0] * t[3])))))","$$\operatorname{exp}(\operatorname{log}(\left({\operatorname{exp}(\left(\theta_{0} \cdot \operatorname{exp}(x_{1})\right))^{\left({x_{1}^{\theta_{1}}} \cdot \theta_{2}\right)}} + {{\operatorname{log}(\operatorname{log}(x_{1}))^{\operatorname{exp}(\frac{x_{0}}{x_{1}})}}^{\operatorname{log}(\left(x_{0} \cdot \theta_{3}\right))}}\right)))$$",-0.7969272401657344;0.2054636141672378;-0.30258802268212914;0.24081230503866524,27,,,,Infinity,,,,,, +271,0,(Log((((((x0 * 0.5517197612553773) * 0.7229823981852166) + x1) ^ 0.4638422804201031) * ((x1 ^ (0.6663976625214767 ^ (x1 ^ -0.28901772742672704))) ^ ((x1 + x0) / x1)))) ^ x1),"np.log(((((x[:, 0] * t[0]) * t[1]) + x[:, 1]) ** t[2] * x[:, 1] ** t[3] ** x[:, 1] ** t[4] ** ((x[:, 1] + x[:, 0]) / x[:, 1]))) ** x[:, 1]","$${\operatorname{log}(\left({\left(\left(\left(x_{0} \cdot \theta_{0}\right) \cdot \theta_{1}\right) + x_{1}\right)^{\theta_{2}}} \cdot {{x_{1}^{{\theta_{3}^{{x_{1}^{\theta_{4}}}}}}}^{\frac{\left(x_{1} + x_{0}\right)}{x_{1}}}}\right))^{x_{1}}}$$",0.5517197612553773;0.7229823981852166;0.4638422804201031;0.6663976625214767;-0.28901772742672704,26,,,,Infinity,,,,,, +272,0,(((-0.8992846847909205 + Exp(Exp((x1 * -3.828496646535917e-2)))) + (((-6.472732684090277e-2 * x1) / x0) / Log((7.930576094161157e-2 / Exp(x0))))) / (Exp(((x0 / (x0 ^ -8.379408070702465e-2)) * Log((x0 + x0)))) * ((Log(x1) * Log(x0)) * (-0.6182167804475931 ^ x0)))),"(((t[0] + np.exp(np.exp((x[:, 1] * t[1])))) + (((t[2] * x[:, 1]) / x[:, 0]) / np.log((t[3] / np.exp(x[:, 0]))))) / (np.exp(((x[:, 0] / x[:, 0] ** t[4]) * np.log((x[:, 0] + x[:, 0])))) * ((np.log(x[:, 1]) * np.log(x[:, 0])) * t[5] ** x[:, 0])))","$$\frac{\left(\left(\theta_{0} + \operatorname{exp}(\operatorname{exp}(\left(x_{1} \cdot \theta_{1}\right)))\right) + \frac{\frac{\left(\theta_{2} \cdot x_{1}\right)}{x_{0}}}{\operatorname{log}(\frac{\theta_{3}}{\operatorname{exp}(x_{0})})}\right)}{\left(\operatorname{exp}(\left(\frac{x_{0}}{{x_{0}^{\theta_{4}}}} \cdot \operatorname{log}(\left(x_{0} + x_{0}\right))\right)) \cdot \left(\left(\operatorname{log}(x_{1}) \cdot \operatorname{log}(x_{0})\right) \cdot {\theta_{5}^{x_{0}}}\right)\right)}$$",-0.8992846847909205;-3.828496646535917e-2;-6.472732684090277e-2;7.930576094161157e-2;-8.379408070702465e-2;-0.6182167804475931,41,,,,Infinity,,,,,, +273,0,((((-0.5463852388285715 + x1) / Exp((-0.5130350819295414 ^ x0))) * Exp((x1 * 0.7546830786011973))) / ((Exp(x0) + ((-0.8796002287821658 + x1) / x0)) * -0.6645994134871933)),"((((t[0] + x[:, 1]) / np.exp(t[1] ** x[:, 0])) * np.exp((x[:, 1] * t[2]))) / ((np.exp(x[:, 0]) + ((t[3] + x[:, 1]) / x[:, 0])) * t[4]))","$$\frac{\left(\frac{\left(\theta_{0} + x_{1}\right)}{\operatorname{exp}({\theta_{1}^{x_{0}}})} \cdot \operatorname{exp}(\left(x_{1} \cdot \theta_{2}\right))\right)}{\left(\left(\operatorname{exp}(x_{0}) + \frac{\left(\theta_{3} + x_{1}\right)}{x_{0}}\right) \cdot \theta_{4}\right)}$$",-0.5463852388285715;-0.5130350819295414;0.7546830786011973;-0.8796002287821658;-0.6645994134871933,24,,,,Infinity,,,,,, +274,0,Exp(((0.3137046522187341 / (((-0.24955859278625814 * x1) ^ -0.41211702708960696) * -0.36800152999778635)) + -0.15179100251990874)),"np.exp(((t[0] / ((t[1] * x[:, 1]) ** t[2] * t[3])) + t[4]))","$$\operatorname{exp}(\left(\frac{\theta_{0}}{\left({\left(\theta_{1} \cdot x_{1}\right)^{\theta_{2}}} \cdot \theta_{3}\right)} + \theta_{4}\right))$$",0.3137046522187341;-0.24955859278625814;-0.41211702708960696;-0.36800152999778635;-0.15179100251990874,12,,,,Infinity,,,,,, +275,0,(((-8.133623383906063e-2 ^ ((Exp((x0 * 0.2783143483774412)) / x0) * -0.32110959357818325)) ^ 0.6564357464041202) * 0.9118308390555482),"(t[0] ** ((np.exp((x[:, 0] * t[1])) / x[:, 0]) * t[2]) ** t[3] * t[4])","$$\left({{\theta_{0}^{\left(\frac{\operatorname{exp}(\left(x_{0} \cdot \theta_{1}\right))}{x_{0}} \cdot \theta_{2}\right)}}^{\theta_{3}}} \cdot \theta_{4}\right)$$",-8.133623383906063e-2;0.2783143483774412;-0.32110959357818325;0.6564357464041202;0.9118308390555482,14,,,,Infinity,,,,,, +276,0,Log((((Exp((x1 + x1)) / Exp(Log(x1))) / (Exp((x1 + x1)) / Log((x0 * x0)))) / (Log(((x0 ^ x0) + Exp(Log(x1)))) / (Log((-0.5687693094742745 + x1)) ^ (x0 / Log(x1)))))),"np.log((((np.exp((x[:, 1] + x[:, 1])) / np.exp(np.log(x[:, 1]))) / (np.exp((x[:, 1] + x[:, 1])) / np.log((x[:, 0] * x[:, 0])))) / (np.log((x[:, 0] ** x[:, 0] + np.exp(np.log(x[:, 1])))) / np.log((t[0] + x[:, 1])) ** (x[:, 0] / np.log(x[:, 1])))))","$$\operatorname{log}(\frac{\frac{\frac{\operatorname{exp}(\left(x_{1} + x_{1}\right))}{\operatorname{exp}(\operatorname{log}(x_{1}))}}{\frac{\operatorname{exp}(\left(x_{1} + x_{1}\right))}{\operatorname{log}(\left(x_{0} \cdot x_{0}\right))}}}{\frac{\operatorname{log}(\left({x_{0}^{x_{0}}} + \operatorname{exp}(\operatorname{log}(x_{1}))\right))}{{\operatorname{log}(\left(\theta_{0} + x_{1}\right))^{\frac{x_{0}}{\operatorname{log}(x_{1})}}}}})$$",-0.5687693094742745,38,,,,Infinity,,,,,, +277,0,((-7.774288897847149 * Log((x1 * x0))) * (-1509.2685595186215 / (-1000.8287487760492 * (x1 * 3684.4976229524514)))),"((t[0] * np.log((x[:, 1] * x[:, 0]))) * (t[1] / (t[2] * (x[:, 1] * t[3]))))","$$\left(\left(\theta_{0} \cdot \operatorname{log}(\left(x_{1} \cdot x_{0}\right))\right) \cdot \frac{\theta_{1}}{\left(\theta_{2} \cdot \left(x_{1} \cdot \theta_{3}\right)\right)}\right)$$",-7.774288897847149;-1509.2685595186215;-1000.8287487760492;3684.4976229524514,14,1.3908990539012463e-2,1.3908990539012463e-2,1.3908990539012463e-2,1.3908990539012463e-2,-0.28353779240860266,-0.28353779240860266,-0.28353779240860266,33.180850402148444,33.180850402148444,33.180850402148444 +278,0,(7.439888686209983e-2 / x0),"(t[0] / x[:, 0])","$$\frac{\theta_{0}}{x_{0}}$$",7.439888686209983e-2,3,7.713765273570095e-3,7.713765273570095e-3,7.713765273570095e-3,7.713765273570095e-3,0.2881647864648511,0.2881647864648511,0.2881647864648511,5.323704190118183,5.323704190118183,5.323704190118183 +279,0,Log((((x0 * -0.7701001466356021) * (-0.7520584227371753 * x0)) ^ -0.11413487309924517)),"np.log(((x[:, 0] * t[0]) * (t[1] * x[:, 0])) ** t[2])","$$\operatorname{log}({\left(\left(x_{0} \cdot \theta_{0}\right) \cdot \left(\theta_{1} \cdot x_{0}\right)\right)^{\theta_{2}}})$$",-0.7701001466356021;-0.7520584227371753;-0.11413487309924517,10,5.376942081146931e-3,5.376942081146931e-3,5.376942081146931e-3,5.376942081146931e-3,0.5038095432312907,0.5038095432312907,0.5038095432312907,22.16199317831696,22.16199317831696,22.16199317831696 +280,0,(-0.7457459660136219 ^ Exp(Log((Log((x1 / x0)) + ((x0 + -0.9281991443577613) + -0.5777021903256947))))),"t[0] ** np.exp(np.log((np.log((x[:, 1] / x[:, 0])) + ((x[:, 0] + t[1]) + t[2]))))","$${\theta_{0}^{\operatorname{exp}(\operatorname{log}(\left(\operatorname{log}(\frac{x_{1}}{x_{0}}) + \left(\left(x_{0} + \theta_{1}\right) + \theta_{2}\right)\right)))}}$$",-0.7457459660136219;-0.9281991443577613;-0.5777021903256947,14,,,,Infinity,,,,,, +281,0,Log(((((x0 / x0) ^ x0) * x1) * -6.2370943625926434e-2)),"np.log((((x[:, 0] / x[:, 0]) ** x[:, 0] * x[:, 1]) * t[0]))","$$\operatorname{log}(\left(\left({\frac{x_{0}}{x_{0}}^{x_{0}}} \cdot x_{1}\right) \cdot \theta_{0}\right))$$",-6.2370943625926434e-2,10,,,,Infinity,,,,,, +282,0,(4.312013102205418e-2 / (x0 ^ 2.841881501105064)),"(t[0] / x[:, 0] ** t[1])","$$\frac{\theta_{0}}{{x_{0}^{\theta_{1}}}}$$",4.312013102205418e-2;2.841881501105064,5,5.2332357936381215e-3,5.2332357936381215e-3,5.2332357936381215e-3,5.2332357936381215e-3,0.5170709262559563,0.5170709262559563,0.5170709262559563,10.978031588549008,10.978031588549008,10.978031588549008 +283,0,((Log(((x0 ^ 0.6069497931485992) ^ -0.5737397522741415)) / ((Exp(x1) + (-0.11532079359960146 ^ x0)) / (0.3193072515514238 / (x1 * -0.33827955062236514)))) ^ (((x0 + (-0.9826782423758313 + x1)) ^ Exp((-0.804515127724558 * x1))) ^ ((x1 / (x0 + x1)) ^ (-6.224670050200187e-2 ^ Exp(x1))))),"(np.log(x[:, 0] ** t[0] ** t[1]) / ((np.exp(x[:, 1]) + t[2] ** x[:, 0]) / (t[3] / (x[:, 1] * t[4])))) ** (x[:, 0] + (t[5] + x[:, 1])) ** np.exp((t[6] * x[:, 1])) ** (x[:, 1] / (x[:, 0] + x[:, 1])) ** t[7] ** np.exp(x[:, 1])","$${\frac{\operatorname{log}({{x_{0}^{\theta_{0}}}^{\theta_{1}}})}{\frac{\left(\operatorname{exp}(x_{1}) + {\theta_{2}^{x_{0}}}\right)}{\frac{\theta_{3}}{\left(x_{1} \cdot \theta_{4}\right)}}}^{{{\left(x_{0} + \left(\theta_{5} + x_{1}\right)\right)^{\operatorname{exp}(\left(\theta_{6} \cdot x_{1}\right))}}^{{\frac{x_{1}}{\left(x_{0} + x_{1}\right)}^{{\theta_{7}^{\operatorname{exp}(x_{1})}}}}}}}}$$",0.6069497931485992;-0.5737397522741415;-0.11532079359960146;0.3193072515514238;-0.33827955062236514;-0.9826782423758313;-0.804515127724558;-6.224670050200187e-2,41,,,,Infinity,,,,,, +284,0,Exp((((0.3615270050744625 / x0) / Exp((x1 ^ x1))) + (Exp((0.5440656655999438 ^ x1)) ^ (1.1731343233275893e-3 ^ (x0 ^ x0))))),"np.exp((((t[0] / x[:, 0]) / np.exp(x[:, 1] ** x[:, 1])) + np.exp(t[1] ** x[:, 1]) ** t[2] ** x[:, 0] ** x[:, 0]))","$$\operatorname{exp}(\left(\frac{\frac{\theta_{0}}{x_{0}}}{\operatorname{exp}({x_{1}^{x_{1}}})} + {\operatorname{exp}({\theta_{1}^{x_{1}}})^{{\theta_{2}^{{x_{0}^{x_{0}}}}}}}\right))$$",0.3615270050744625;0.5440656655999438;1.1731343233275893e-3,20,10.427585709827978,10.427585709827978,10.427585709827978,10.427585709827978,-961.2697135786802,-961.2697135786802,-961.2697135786802,54.365804926054366,54.365804926054366,54.365804926054366 +285,0,((-0.48985285769611664 + (-0.44781573913883865 ^ ((Log((x1 ^ -0.7870782986263285)) + x0) + (0.5983367794959946 / (x1 * 0.32548216164927113))))) + ((0.8704586179150119 * ((((x1 / x0) + x1) * 4.599768171996865e-2) ^ (-0.8070612299819702 + x0))) * x0)),"((t[0] + t[1] ** ((np.log(x[:, 1] ** t[2]) + x[:, 0]) + (t[3] / (x[:, 1] * t[4])))) + ((t[5] * (((x[:, 1] / x[:, 0]) + x[:, 1]) * t[6]) ** (t[7] + x[:, 0])) * x[:, 0]))","$$\left(\left(\theta_{0} + {\theta_{1}^{\left(\left(\operatorname{log}({x_{1}^{\theta_{2}}}) + x_{0}\right) + \frac{\theta_{3}}{\left(x_{1} \cdot \theta_{4}\right)}\right)}}\right) + \left(\left(\theta_{5} \cdot {\left(\left(\frac{x_{1}}{x_{0}} + x_{1}\right) \cdot \theta_{6}\right)^{\left(\theta_{7} + x_{0}\right)}}\right) \cdot x_{0}\right)\right)$$",-0.48985285769611664;-0.44781573913883865;-0.7870782986263285;0.5983367794959946;0.32548216164927113;0.8704586179150119;4.599768171996865e-2;-0.8070612299819702,32,,,,Infinity,,,,,, +286,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 +287,0,Log(((((Exp(x0) + ((x1 ^ 0.524837435803704) * -0.4498611544067536)) / (((x0 / x1) * x0) * 5.353669619184157)) + (-0.25631521551552144 / x0)) + (Exp((Exp(x0) ^ -0.6147038203012859)) * 0.6838691291616903))),"np.log(((((np.exp(x[:, 0]) + (x[:, 1] ** t[0] * t[1])) / (((x[:, 0] / x[:, 1]) * x[:, 0]) * t[2])) + (t[3] / x[:, 0])) + (np.exp(np.exp(x[:, 0]) ** t[4]) * t[5])))","$$\operatorname{log}(\left(\left(\frac{\left(\operatorname{exp}(x_{0}) + \left({x_{1}^{\theta_{0}}} \cdot \theta_{1}\right)\right)}{\left(\left(\frac{x_{0}}{x_{1}} \cdot x_{0}\right) \cdot \theta_{2}\right)} + \frac{\theta_{3}}{x_{0}}\right) + \left(\operatorname{exp}({\operatorname{exp}(x_{0})^{\theta_{4}}}) \cdot \theta_{5}\right)\right))$$",0.524837435803704;-0.4498611544067536;5.353669619184157;-0.25631521551552144;-0.6147038203012859;0.6838691291616903,29,2.501603529881908e-3,2.501603529881908e-3,2.501603529881908e-3,2.501603529881908e-3,0.7691491224168906,0.7691491224168906,0.7691491224168906,75.8473137981331,75.8473137981331,75.8473137981331 +288,0,(Log(Log((-0.3916807442008581 / ((x1 ^ x0) / x1)))) + (Exp((-7.87946195982947e-3 ^ (x0 * (Log(x0) ^ x0)))) / x1)),"(np.log(np.log((t[0] / (x[:, 1] ** x[:, 0] / x[:, 1])))) + (np.exp(t[1] ** (x[:, 0] * np.log(x[:, 0]) ** x[:, 0])) / x[:, 1]))","$$\left(\operatorname{log}(\operatorname{log}(\frac{\theta_{0}}{\frac{{x_{1}^{x_{0}}}}{x_{1}}})) + \frac{\operatorname{exp}({\theta_{1}^{\left(x_{0} \cdot {\operatorname{log}(x_{0})^{x_{0}}}\right)}})}{x_{1}}\right)$$",-0.3916807442008581;-7.87946195982947e-3,21,,,,Infinity,,,,,, +289,0,Log(Log((x1 + x0))),"np.log(np.log((x[:, 1] + x[:, 0])))","$$\operatorname{log}(\operatorname{log}(\left(x_{1} + x_{0}\right)))$$",,5,,,,Infinity,,,,,, +290,0,((Exp(Exp(((0.188512824334789 + (-0.659220320449309 + x0)) / Log((0.3102557861361933 * x0))))) / Log(Log((0.3583841925856639 * (0.6762009197980479 / (x0 * 0.8338410249030334)))))) ^ Log((Exp(((-0.14597803338206394 / x1) ^ ((x1 + x1) * x0))) + ((Exp(x1) / (x0 * x1)) * (x0 ^ (x0 / x0)))))),"(np.exp(np.exp(((t[0] + (t[1] + x[:, 0])) / np.log((t[2] * x[:, 0]))))) / np.log(np.log((t[3] * (t[4] / (x[:, 0] * t[5])))))) ** np.log((np.exp((t[6] / x[:, 1]) ** ((x[:, 1] + x[:, 1]) * x[:, 0])) + ((np.exp(x[:, 1]) / (x[:, 0] * x[:, 1])) * x[:, 0] ** (x[:, 0] / x[:, 0]))))","$${\frac{\operatorname{exp}(\operatorname{exp}(\frac{\left(\theta_{0} + \left(\theta_{1} + x_{0}\right)\right)}{\operatorname{log}(\left(\theta_{2} \cdot x_{0}\right))}))}{\operatorname{log}(\operatorname{log}(\left(\theta_{3} \cdot \frac{\theta_{4}}{\left(x_{0} \cdot \theta_{5}\right)}\right)))}^{\operatorname{log}(\left(\operatorname{exp}({\frac{\theta_{6}}{x_{1}}^{\left(\left(x_{1} + x_{1}\right) \cdot x_{0}\right)}}) + \left(\frac{\operatorname{exp}(x_{1})}{\left(x_{0} \cdot x_{1}\right)} \cdot {x_{0}^{\frac{x_{0}}{x_{0}}}}\right)\right))}}$$",0.188512824334789;-0.659220320449309;0.3102557861361933;0.3583841925856639;0.6762009197980479;0.8338410249030334;-0.14597803338206394,47,,,,Infinity,,,,,, +291,0,Exp(((Exp(x1) + (x1 ^ -0.11008486724943928)) ^ -17.02300974019877)),"np.exp((np.exp(x[:, 1]) + x[:, 1] ** t[0]) ** t[1])","$$\operatorname{exp}({\left(\operatorname{exp}(x_{1}) + {x_{1}^{\theta_{0}}}\right)^{\theta_{1}}})$$",-0.11008486724943928;-17.02300974019877,9,0.8923101219193086,0.8923101219193086,0.8923101219193086,0.8923101219193086,-81.34341383867802,-81.34341383867802,-81.34341383867802,19.32987019236956,19.32987019236956,19.32987019236956 +292,0,Exp(Exp(((x0 / (x0 ^ Exp((x1 / x0)))) + ((x1 / ((x1 + x0) ^ Exp(x0))) / (-0.5574202243792838 ^ Log(x1)))))),"np.exp(np.exp(((x[:, 0] / x[:, 0] ** np.exp((x[:, 1] / x[:, 0]))) + ((x[:, 1] / (x[:, 1] + x[:, 0]) ** np.exp(x[:, 0])) / t[0] ** np.log(x[:, 1])))))","$$\operatorname{exp}(\operatorname{exp}(\left(\frac{x_{0}}{{x_{0}^{\operatorname{exp}(\frac{x_{1}}{x_{0}})}}} + \frac{\frac{x_{1}}{{\left(x_{1} + x_{0}\right)^{\operatorname{exp}(x_{0})}}}}{{\theta_{0}^{\operatorname{log}(x_{1})}}}\right)))$$",-0.5574202243792838,24,,,,Infinity,,,,,, +293,0,((Log(Exp((x0 ^ -1.2757100003653896))) / Exp((0.7527665035589293 ^ (-0.29691712532013453 * x0)))) / (((-0.500879846882273 * x1) + (x1 / x0)) ^ -1.2250016266202837)),"((np.log(np.exp(x[:, 0] ** t[0])) / np.exp(t[1] ** (t[2] * x[:, 0]))) / ((t[3] * x[:, 1]) + (x[:, 1] / x[:, 0])) ** t[4])","$$\frac{\frac{\operatorname{log}(\operatorname{exp}({x_{0}^{\theta_{0}}}))}{\operatorname{exp}({\theta_{1}^{\left(\theta_{2} \cdot x_{0}\right)}})}}{{\left(\left(\theta_{3} \cdot x_{1}\right) + \frac{x_{1}}{x_{0}}\right)^{\theta_{4}}}}$$",-1.2757100003653896;0.7527665035589293;-0.29691712532013453;-0.500879846882273;-1.2250016266202837,22,1.2003009421013378e-3,1.2003009421013378e-3,1.2003009421013378e-3,1.2003009421013378e-3,0.8892348357611219,0.8892348357611219,0.8892348357611219,58.44464564908292,58.44464564908292,58.44464564908292 +294,0,Exp((Exp(((-0.31869379548283805 ^ (x0 + x1)) * 0.7881429855340786)) * Log((((0.6198284654766932 + x0) / (x1 * -0.3110723332626282)) ^ (0.383335712906136 + Log(x1)))))),"np.exp((np.exp((t[0] ** (x[:, 0] + x[:, 1]) * t[1])) * np.log(((t[2] + x[:, 0]) / (x[:, 1] * t[3])) ** (t[4] + np.log(x[:, 1])))))","$$\operatorname{exp}(\left(\operatorname{exp}(\left({\theta_{0}^{\left(x_{0} + x_{1}\right)}} \cdot \theta_{1}\right)) \cdot \operatorname{log}({\frac{\left(\theta_{2} + x_{0}\right)}{\left(x_{1} \cdot \theta_{3}\right)}^{\left(\theta_{4} + \operatorname{log}(x_{1})\right)}})\right))$$",-0.31869379548283805;0.7881429855340786;0.6198284654766932;-0.3110723332626282;0.383335712906136,23,,,,Infinity,,,,,, +295,0,((((Exp(Log(x0)) ^ 6.827214435369358) / ((1.1460253849431838 + x1) ^ -11.21206261887719)) / ((-15.129090997105626 / x1) * 102.07966436489639)) + x0),"(((np.exp(np.log(x[:, 0])) ** t[0] / (t[1] + x[:, 1]) ** t[2]) / ((t[3] / x[:, 1]) * t[4])) + x[:, 0])","$$\left(\frac{\frac{{\operatorname{exp}(\operatorname{log}(x_{0}))^{\theta_{0}}}}{{\left(\theta_{1} + x_{1}\right)^{\theta_{2}}}}}{\left(\frac{\theta_{3}}{x_{1}} \cdot \theta_{4}\right)} + x_{0}\right)$$",6.827214435369358;1.1460253849431838;-11.21206261887719;-15.129090997105626;102.07966436489639,19,0.8482611300758829,0.8482611300758829,0.8482611300758829,0.8482611300758829,-77.27852173957466,-77.27852173957466,-77.27852173957466,52.61532666329467,52.61532666329467,52.61532666329467 +296,0,Exp((Log((((x0 ^ x1) + 0.3339123698435208) ^ -10.53724847615968)) * Exp(Exp((Exp(x0) / ((x1 * 0.15094908042145255) + (x1 * -0.3112235744370234))))))),"np.exp((np.log((x[:, 0] ** x[:, 1] + t[0]) ** t[1]) * np.exp(np.exp((np.exp(x[:, 0]) / ((x[:, 1] * t[2]) + (x[:, 1] * t[3])))))))","$$\operatorname{exp}(\left(\operatorname{log}({\left({x_{0}^{x_{1}}} + \theta_{0}\right)^{\theta_{1}}}) \cdot \operatorname{exp}(\operatorname{exp}(\frac{\operatorname{exp}(x_{0})}{\left(\left(x_{1} \cdot \theta_{2}\right) + \left(x_{1} \cdot \theta_{3}\right)\right)}))\right))$$",0.3339123698435208;-10.53724847615968;0.15094908042145255;-0.3112235744370234,22,2.577081865826732e-3,2.577081865826732e-3,2.577081865826732e-3,2.577081865826732e-3,0.7621838939611257,0.7621838939611257,0.7621838939611257,56.42495981654664,56.42495981654664,56.42495981654664 +297,0,Log(Log((Exp((x0 * (-0.26032931743271703 / x1))) * Log((x0 / ((x0 ^ 9.489486398979596e-2) + -5.303829296837359e-2)))))),"np.log(np.log((np.exp((x[:, 0] * (t[0] / x[:, 1]))) * np.log((x[:, 0] / (x[:, 0] ** t[1] + t[2]))))))","$$\operatorname{log}(\operatorname{log}(\left(\operatorname{exp}(\left(x_{0} \cdot \frac{\theta_{0}}{x_{1}}\right)) \cdot \operatorname{log}(\frac{x_{0}}{\left({x_{0}^{\theta_{1}}} + \theta_{2}\right)})\right)))$$",-0.26032931743271703;9.489486398979596e-2;-5.303829296837359e-2,17,,,,Infinity,,,,,, +298,0,(((Log(x1) ^ (x1 * 0.3632481324664608)) * (x1 ^ (x0 ^ -0.29259301114311675))) / (-0.41076106787502 + (0.3529198258780193 + Exp(x1)))),"((np.log(x[:, 1]) ** (x[:, 1] * t[0]) * x[:, 1] ** x[:, 0] ** t[1]) / (t[2] + (t[3] + np.exp(x[:, 1]))))","$$\frac{\left({\operatorname{log}(x_{1})^{\left(x_{1} \cdot \theta_{0}\right)}} \cdot {x_{1}^{{x_{0}^{\theta_{1}}}}}\right)}{\left(\theta_{2} + \left(\theta_{3} + \operatorname{exp}(x_{1})\right)\right)}$$",0.3632481324664608;-0.29259301114311675;-0.41076106787502;0.3529198258780193,19,,,,Infinity,,,,,, +299,0,Exp(Exp(Exp((-0.5554631409522515 ^ (0.7260670355427038 / Log(Exp(x0))))))),"np.exp(np.exp(np.exp(t[0] ** (t[1] / np.log(np.exp(x[:, 0]))))))","$$\operatorname{exp}(\operatorname{exp}(\operatorname{exp}({\theta_{0}^{\frac{\theta_{1}}{\operatorname{log}(\operatorname{exp}(x_{0}))}}})))$$",-0.5554631409522515;0.7260670355427038,10,,,,Infinity,,,,,, +300,0,((0.2733689283872246 ^ ((x0 * 0.7861388729131737) / x1)) + Exp((Exp((x1 * 1.8217665803652707)) * -3.9908274512299116))),"(t[0] ** ((x[:, 0] * t[1]) / x[:, 1]) + np.exp((np.exp((x[:, 1] * t[2])) * t[3])))","$$\left({\theta_{0}^{\frac{\left(x_{0} \cdot \theta_{1}\right)}{x_{1}}}} + \operatorname{exp}(\left(\operatorname{exp}(\left(x_{1} \cdot \theta_{2}\right)) \cdot \theta_{3}\right))\right)$$",0.2733689283872246;0.7861388729131737;1.8217665803652707;-3.9908274512299116,15,2.1498032315922662e-3,2.1498032315922662e-3,2.1498032315922662e-3,2.1498032315922662e-3,0.8016136623106273,0.8016136623106273,0.8016136623106273,39.27725768957654,39.27725768957654,39.27725768957654 +301,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 +302,0,(((0.7257974155441107 / (x1 ^ -0.48110287980872546)) ^ -0.5691052505927285) ^ Exp(Exp((Exp(Exp((-0.542168508587435 / x1))) + ((x1 / (x1 + -0.8288927394010828)) / (x1 ^ Log(x1))))))),"(t[0] / x[:, 1] ** t[1]) ** t[2] ** np.exp(np.exp((np.exp(np.exp((t[3] / x[:, 1]))) + ((x[:, 1] / (x[:, 1] + t[4])) / x[:, 1] ** np.log(x[:, 1])))))","$${{\frac{\theta_{0}}{{x_{1}^{\theta_{1}}}}^{\theta_{2}}}^{\operatorname{exp}(\operatorname{exp}(\left(\operatorname{exp}(\operatorname{exp}(\frac{\theta_{3}}{x_{1}})) + \frac{\frac{x_{1}}{\left(x_{1} + \theta_{4}\right)}}{{x_{1}^{\operatorname{log}(x_{1})}}}\right)))}}$$",0.7257974155441107;-0.48110287980872546;-0.5691052505927285;-0.542168508587435;-0.8288927394010828,26,3.6760407661080484e9,3.6760407661080484e9,3.6760407661080484e9,3.6760407661080484e9,-3.392293090203975e11,-3.392293090203975e11,-3.392293090203975e11,3.3084367501955323e9,3.3084367501955323e9,3.3084367501955323e9 +303,0,Exp(Exp(Log((((x0 / x1) ^ (x1 ^ (x1 + 99.3998502973256))) * ((15.881230637802554 + x0) ^ -50.39057523318157))))),"np.exp(np.exp(np.log(((x[:, 0] / x[:, 1]) ** x[:, 1] ** (x[:, 1] + t[0]) * (t[1] + x[:, 0]) ** t[2]))))","$$\operatorname{exp}(\operatorname{exp}(\operatorname{log}(\left({\frac{x_{0}}{x_{1}}^{{x_{1}^{\left(x_{1} + \theta_{0}\right)}}}} \cdot {\left(\theta_{1} + x_{0}\right)^{\theta_{2}}}\right))))$$",99.3998502973256;15.881230637802554;-50.39057523318157,18,0.8923097947317449,0.8923097947317449,0.8923097947317449,0.8923097947317449,-81.34338364542985,-81.34338364542985,-81.34338364542985,46.415896013413445,46.415896013413445,46.415896013413445 +304,0,(((-0.7568027957555301 ^ (-0.1569410947764589 + Exp(x0))) + (Log((0.44785331704525644 * x0)) ^ (x0 + x0))) / ((0.13379703808810472 + (x1 * x1)) ^ ((0.8380367745809629 * x1) ^ Log(Exp(Exp(x0)))))),"((t[0] ** (t[1] + np.exp(x[:, 0])) + np.log((t[2] * x[:, 0])) ** (x[:, 0] + x[:, 0])) / (t[3] + (x[:, 1] * x[:, 1])) ** (t[4] * x[:, 1]) ** np.log(np.exp(np.exp(x[:, 0]))))","$$\frac{\left({\theta_{0}^{\left(\theta_{1} + \operatorname{exp}(x_{0})\right)}} + {\operatorname{log}(\left(\theta_{2} \cdot x_{0}\right))^{\left(x_{0} + x_{0}\right)}}\right)}{{\left(\theta_{3} + \left(x_{1} \cdot x_{1}\right)\right)^{{\left(\theta_{4} \cdot x_{1}\right)^{\operatorname{log}(\operatorname{exp}(\operatorname{exp}(x_{0})))}}}}}$$",-0.7568027957555301;-0.1569410947764589;0.44785331704525644;0.13379703808810472;0.8380367745809629,30,,,,Infinity,,,,,, +305,0,((x1 ^ 3.5541187230109377) + 2.5186336845977802e-2),"(x[:, 1] ** t[0] + t[1])","$$\left({x_{1}^{\theta_{0}}} + \theta_{1}\right)$$",3.5541187230109377;2.5186336845977802e-2,5,9.65915343156526e-3,9.65915343156526e-3,9.65915343156526e-3,9.65915343156526e-3,0.10864210905074767,0.10864210905074767,0.10864210905074767,10.982014914423143,10.982014914423143,10.982014914423143 +306,0,(((Log(x1) + ((Exp(Log(x1)) + x0) ^ (x0 * x1))) ^ (Log((x1 / (x1 * x1))) * (x1 * x0))) / (((Exp(Exp((x0 + -0.39421803833816227))) + (((0.2793293879428138 ^ x0) / (x0 ^ x1)) ^ x0)) + (-3.249578970120914e-2 / (x1 * x0))) * (x0 + -0.7606398494309268))),"((np.log(x[:, 1]) + (np.exp(np.log(x[:, 1])) + x[:, 0]) ** (x[:, 0] * x[:, 1])) ** (np.log((x[:, 1] / (x[:, 1] * x[:, 1]))) * (x[:, 1] * x[:, 0])) / (((np.exp(np.exp((x[:, 0] + t[0]))) + (t[1] ** x[:, 0] / x[:, 0] ** x[:, 1]) ** x[:, 0]) + (t[2] / (x[:, 1] * x[:, 0]))) * (x[:, 0] + t[3])))","$$\frac{{\left(\operatorname{log}(x_{1}) + {\left(\operatorname{exp}(\operatorname{log}(x_{1})) + x_{0}\right)^{\left(x_{0} \cdot x_{1}\right)}}\right)^{\left(\operatorname{log}(\frac{x_{1}}{\left(x_{1} \cdot x_{1}\right)}) \cdot \left(x_{1} \cdot x_{0}\right)\right)}}}{\left(\left(\left(\operatorname{exp}(\operatorname{exp}(\left(x_{0} + \theta_{0}\right))) + {\frac{{\theta_{1}^{x_{0}}}}{{x_{0}^{x_{1}}}}^{x_{0}}}\right) + \frac{\theta_{2}}{\left(x_{1} \cdot x_{0}\right)}\right) \cdot \left(x_{0} + \theta_{3}\right)\right)}$$",-0.39421803833816227;0.2793293879428138;-3.249578970120914e-2;-0.7606398494309268,49,,,,Infinity,,,,,, +307,0,((((-0.4138335364932779 / x0) / ((Log(x0) ^ 0.86306710019288) * x1)) * ((Log(x0) ^ x0) * ((x1 * x1) + (x1 * 3.200701299537578e-2)))) * (-0.6718128040689044 * x0)),"((((t[0] / x[:, 0]) / (np.log(x[:, 0]) ** t[1] * x[:, 1])) * (np.log(x[:, 0]) ** x[:, 0] * ((x[:, 1] * x[:, 1]) + (x[:, 1] * t[2])))) * (t[3] * x[:, 0]))","$$\left(\left(\frac{\frac{\theta_{0}}{x_{0}}}{\left({\operatorname{log}(x_{0})^{\theta_{1}}} \cdot x_{1}\right)} \cdot \left({\operatorname{log}(x_{0})^{x_{0}}} \cdot \left(\left(x_{1} \cdot x_{1}\right) + \left(x_{1} \cdot \theta_{2}\right)\right)\right)\right) \cdot \left(\theta_{3} \cdot x_{0}\right)\right)$$",-0.4138335364932779;0.86306710019288;3.200701299537578e-2;-0.6718128040689044,27,,,,Infinity,,,,,, +308,0,((((((x0 * -0.49568412856466404) * -0.11594212269665714) ^ x1) + 1.3706381625744508) ^ ((((x0 * -3.772038047498895) + (x1 * x1)) * Exp(x1)) + -1.893393770905515)) * 3.0746467653436755),"((((x[:, 0] * t[0]) * t[1]) ** x[:, 1] + t[2]) ** ((((x[:, 0] * t[3]) + (x[:, 1] * x[:, 1])) * np.exp(x[:, 1])) + t[4]) * t[5])","$$\left({\left({\left(\left(x_{0} \cdot \theta_{0}\right) \cdot \theta_{1}\right)^{x_{1}}} + \theta_{2}\right)^{\left(\left(\left(\left(x_{0} \cdot \theta_{3}\right) + \left(x_{1} \cdot x_{1}\right)\right) \cdot \operatorname{exp}(x_{1})\right) + \theta_{4}\right)}} \cdot \theta_{5}\right)$$",-0.49568412856466404;-0.11594212269665714;1.3706381625744508;-3.772038047498895;-1.893393770905515;3.0746467653436755,24,2.2643032607568814e-3,2.2643032607568814e-3,2.2643032607568814e-3,2.2643032607568814e-3,0.7910474667084045,0.7910474667084045,0.7910474667084045,58.82943106246804,58.82943106246804,58.82943106246804 +309,0,(Exp((((-0.8046551850450445 / (-0.2229010272225117 + (x0 ^ 0.4386926725555972))) / (-0.7079728504575143 / (-0.5055073619693624 ^ Log(x0)))) + Exp(((x1 * x0) + x0)))) ^ 0.18526013542495923),"np.exp((((t[0] / (t[1] + x[:, 0] ** t[2])) / (t[3] / t[4] ** np.log(x[:, 0]))) + np.exp(((x[:, 1] * x[:, 0]) + x[:, 0])))) ** t[5]","$${\operatorname{exp}(\left(\frac{\frac{\theta_{0}}{\left(\theta_{1} + {x_{0}^{\theta_{2}}}\right)}}{\frac{\theta_{3}}{{\theta_{4}^{\operatorname{log}(x_{0})}}}} + \operatorname{exp}(\left(\left(x_{1} \cdot x_{0}\right) + x_{0}\right))\right))^{\theta_{5}}}$$",-0.8046551850450445;-0.2229010272225117;0.4386926725555972;-0.7079728504575143;-0.5055073619693624;0.18526013542495923,24,,,,Infinity,,,,,, +310,0,Log(Log(((0.8063947690506864 / x0) / ((x0 / Log(x1)) ^ (0.7681604775138573 * (x0 * x1)))))),"np.log(np.log(((t[0] / x[:, 0]) / (x[:, 0] / np.log(x[:, 1])) ** (t[1] * (x[:, 0] * x[:, 1])))))","$$\operatorname{log}(\operatorname{log}(\frac{\frac{\theta_{0}}{x_{0}}}{{\frac{x_{0}}{\operatorname{log}(x_{1})}^{\left(\theta_{1} \cdot \left(x_{0} \cdot x_{1}\right)\right)}}}))$$",0.8063947690506864;0.7681604775138573,16,,,,Infinity,,,,,, +311,0,((-0.7733758849135512 + x0) / x0),"((t[0] + x[:, 0]) / x[:, 0])","$$\frac{\left(\theta_{0} + x_{0}\right)}{x_{0}}$$",-0.7733758849135512,5,0.1510692544007486,0.1510692544007486,0.1510692544007486,0.1510692544007486,-12.940846156339218,-12.940846156339218,-12.940846156339218,9.088359069927767,9.088359069927767,9.088359069927767 +312,0,((((1.6178341467338118 + (x1 * x1)) ^ (x0 / x1)) ^ ((x0 * (x0 ^ -5.446664094265499e-2)) * (x1 * -0.815399490533227))) * (((0.6665057572475596 ^ (x0 * -0.47676027368134194)) * 0.16648357858675455) + Log((Exp((-0.26206863174148887 * x0)) + (0.32235892858448256 / Exp(x0)))))),"((t[0] + (x[:, 1] * x[:, 1])) ** (x[:, 0] / x[:, 1]) ** ((x[:, 0] * x[:, 0] ** t[1]) * (x[:, 1] * t[2])) * ((t[3] ** (x[:, 0] * t[4]) * t[5]) + np.log((np.exp((t[6] * x[:, 0])) + (t[7] / np.exp(x[:, 0]))))))","$$\left({{\left(\theta_{0} + \left(x_{1} \cdot x_{1}\right)\right)^{\frac{x_{0}}{x_{1}}}}^{\left(\left(x_{0} \cdot {x_{0}^{\theta_{1}}}\right) \cdot \left(x_{1} \cdot \theta_{2}\right)\right)}} \cdot \left(\left({\theta_{3}^{\left(x_{0} \cdot \theta_{4}\right)}} \cdot \theta_{5}\right) + \operatorname{log}(\left(\operatorname{exp}(\left(\theta_{6} \cdot x_{0}\right)) + \frac{\theta_{7}}{\operatorname{exp}(x_{0})}\right))\right)\right)$$",1.6178341467338118;-5.446664094265499e-2;-0.815399490533227;0.6665057572475596;-0.47676027368134194;0.16648357858675455;-0.26206863174148887;0.32235892858448256,38,5.407713659890467e-3,5.407713659890467e-3,5.407713659890467e-3,5.407713659890467e-3,0.5009699062253826,0.5009699062253826,0.5009699062253826,99.66680036401138,99.66680036401138,99.66680036401138 +313,0,(((((0.9216400067239852 / Exp(Log(x0))) / ((Log(x1) + (x0 * 8.248119050787961e-2)) / Exp((x0 * x1)))) + (((x0 ^ Log(x1)) * -6.266433384710879e-2) * -0.5386836212824988)) / (Exp(x0) * 5.440451486329745e-2)) / (((0.40302683523526683 * (-0.7960905703177794 + (0.990875957606998 + x1))) + -0.47869934215982646) ^ x0)),"(((((t[0] / np.exp(np.log(x[:, 0]))) / ((np.log(x[:, 1]) + (x[:, 0] * t[1])) / np.exp((x[:, 0] * x[:, 1])))) + ((x[:, 0] ** np.log(x[:, 1]) * t[2]) * t[3])) / (np.exp(x[:, 0]) * t[4])) / ((t[5] * (t[6] + (t[7] + x[:, 1]))) + t[8]) ** x[:, 0])","$$\frac{\frac{\left(\frac{\frac{\theta_{0}}{\operatorname{exp}(\operatorname{log}(x_{0}))}}{\frac{\left(\operatorname{log}(x_{1}) + \left(x_{0} \cdot \theta_{1}\right)\right)}{\operatorname{exp}(\left(x_{0} \cdot x_{1}\right))}} + \left(\left({x_{0}^{\operatorname{log}(x_{1})}} \cdot \theta_{2}\right) \cdot \theta_{3}\right)\right)}{\left(\operatorname{exp}(x_{0}) \cdot \theta_{4}\right)}}{{\left(\left(\theta_{5} \cdot \left(\theta_{6} + \left(\theta_{7} + x_{1}\right)\right)\right) + \theta_{8}\right)^{x_{0}}}}$$",0.9216400067239852;8.248119050787961e-2;-6.266433384710879e-2;-0.5386836212824988;5.440451486329745e-2;0.40302683523526683;-0.7960905703177794;0.990875957606998;-0.47869934215982646,43,,,,Infinity,,,,,, +314,0,((Log((x0 ^ (0.36126006949112577 / Exp(x0)))) / ((-0.15577571658796507 / x1) * (-0.8338220424267005 / x0))) * (((0.33961365627460716 * (Log((x1 / x0)) ^ 0.45750054124706085)) ^ (-0.6903540899894229 / x1)) / (Log(x1) ^ (0.954669501132379 ^ (6.554887700721646e-2 ^ (x0 + x0)))))),"((np.log(x[:, 0] ** (t[0] / np.exp(x[:, 0]))) / ((t[1] / x[:, 1]) * (t[2] / x[:, 0]))) * ((t[3] * np.log((x[:, 1] / x[:, 0])) ** t[4]) ** (t[5] / x[:, 1]) / np.log(x[:, 1]) ** t[6] ** t[7] ** (x[:, 0] + x[:, 0])))","$$\left(\frac{\operatorname{log}({x_{0}^{\frac{\theta_{0}}{\operatorname{exp}(x_{0})}}})}{\left(\frac{\theta_{1}}{x_{1}} \cdot \frac{\theta_{2}}{x_{0}}\right)} \cdot \frac{{\left(\theta_{3} \cdot {\operatorname{log}(\frac{x_{1}}{x_{0}})^{\theta_{4}}}\right)^{\frac{\theta_{5}}{x_{1}}}}}{{\operatorname{log}(x_{1})^{{\theta_{6}^{{\theta_{7}^{\left(x_{0} + x_{0}\right)}}}}}}}\right)$$",0.36126006949112577;-0.15577571658796507;-0.8338220424267005;0.33961365627460716;0.45750054124706085;-0.6903540899894229;0.954669501132379;6.554887700721646e-2,39,,,,Infinity,,,,,, +315,0,(0.18546847437405395 / Exp((x0 ^ 4.6846759008919525))),"(t[0] / np.exp(x[:, 0] ** t[1]))","$$\frac{\theta_{0}}{\operatorname{exp}({x_{0}^{\theta_{1}}})}$$",0.18546847437405395;4.6846759008919525,6,5.610387568175155e-3,5.610387568175155e-3,5.610387568175155e-3,5.610387568175155e-3,0.4822669227062766,0.4822669227062766,0.4822669227062766,13.70352669415124,13.70352669415124,13.70352669415124 +316,0,Exp(((Exp(((0.7175082166445927 ^ x1) / Exp(x1))) ^ (-0.2059209406844409 + (x1 + x0))) + (Log(((-0.4104861384334686 + x0) / (x0 + x1))) / ((x1 + -0.5618550710417538) * (x0 + x1))))),"np.exp((np.exp((t[0] ** x[:, 1] / np.exp(x[:, 1]))) ** (t[1] + (x[:, 1] + x[:, 0])) + (np.log(((t[2] + x[:, 0]) / (x[:, 0] + x[:, 1]))) / ((x[:, 1] + t[3]) * (x[:, 0] + x[:, 1])))))","$$\operatorname{exp}(\left({\operatorname{exp}(\frac{{\theta_{0}^{x_{1}}}}{\operatorname{exp}(x_{1})})^{\left(\theta_{1} + \left(x_{1} + x_{0}\right)\right)}} + \frac{\operatorname{log}(\frac{\left(\theta_{2} + x_{0}\right)}{\left(x_{0} + x_{1}\right)})}{\left(\left(x_{1} + \theta_{3}\right) \cdot \left(x_{0} + x_{1}\right)\right)}\right))$$",0.7175082166445927;-0.2059209406844409;-0.4104861384334686;-0.5618550710417538,31,2.154200291034211e112,2.154200291034211e112,2.154200291034211e112,2.154200291034211e112,-1.9879210343875432e114,-1.9879210343875432e114,-1.9879210343875432e114,1.93878026193079e112,1.93878026193079e112,1.93878026193079e112 +317,0,(((3.7104728410753527 / x1) * -4.711526218232506e-3) + (-28.636464516932833 / (x0 * (-291.132720963618 * x0)))),"(((t[0] / x[:, 1]) * t[1]) + (t[2] / (x[:, 0] * (t[3] * x[:, 0]))))","$$\left(\left(\frac{\theta_{0}}{x_{1}} \cdot \theta_{1}\right) + \frac{\theta_{2}}{\left(x_{0} \cdot \left(\theta_{3} \cdot x_{0}\right)\right)}\right)$$",3.7104728410753527;-4.711526218232506e-3;-28.636464516932833;-291.132720963618,13,3.167209284349352e-3,3.167209284349352e-3,3.167209284349352e-3,3.167209284349352e-3,0.7077262507636712,0.7077262507636712,0.7077262507636712,31.37942332979119,31.37942332979119,31.37942332979119 +318,0,Exp(((-0.4863826711784059 * (-2.0650682715969486 * (1.0036795085871637 / (-0.41619541356490874 + (0.2070632699343406 + x0))))) + Log((4.526977188420988e-2 / ((x1 / x1) / x1))))),"np.exp(((t[0] * (t[1] * (t[2] / (t[3] + (t[4] + x[:, 0]))))) + np.log((t[5] / ((x[:, 1] / x[:, 1]) / x[:, 1])))))","$$\operatorname{exp}(\left(\left(\theta_{0} \cdot \left(\theta_{1} \cdot \frac{\theta_{2}}{\left(\theta_{3} + \left(\theta_{4} + x_{0}\right)\right)}\right)\right) + \operatorname{log}(\frac{\theta_{5}}{\frac{\frac{x_{1}}{x_{1}}}{x_{1}}})\right))$$",-0.4863826711784059;-2.0650682715969486;1.0036795085871637;-0.41619541356490874;0.2070632699343406;4.526977188420988e-2,21,2.0135825436814374e-3,2.0135825436814374e-3,2.0135825436814374e-3,2.0135825436814374e-3,0.8141842655151543,0.8141842655151543,0.8141842655151543,55.79563421177171,55.79563421177171,55.79563421177171 +319,0,(((Exp((x0 ^ -0.8358374064673463)) ^ -0.15572192312647315) * ((-0.9046755767464477 / x0) ^ (x0 + (0.3303567128876209 + Log(x0))))) * (((0.6230337619464125 + Exp(Log(x1))) / ((((x1 / x0) ^ (x1 * x0)) + x0) ^ x1)) * (x1 * -1.3602786086213792e-2))),"((np.exp(x[:, 0] ** t[0]) ** t[1] * (t[2] / x[:, 0]) ** (x[:, 0] + (t[3] + np.log(x[:, 0])))) * (((t[4] + np.exp(np.log(x[:, 1]))) / ((x[:, 1] / x[:, 0]) ** (x[:, 1] * x[:, 0]) + x[:, 0]) ** x[:, 1]) * (x[:, 1] * t[5])))","$$\left(\left({\operatorname{exp}({x_{0}^{\theta_{0}}})^{\theta_{1}}} \cdot {\frac{\theta_{2}}{x_{0}}^{\left(x_{0} + \left(\theta_{3} + \operatorname{log}(x_{0})\right)\right)}}\right) \cdot \left(\frac{\left(\theta_{4} + \operatorname{exp}(\operatorname{log}(x_{1}))\right)}{{\left({\frac{x_{1}}{x_{0}}^{\left(x_{1} \cdot x_{0}\right)}} + x_{0}\right)^{x_{1}}}} \cdot \left(x_{1} \cdot \theta_{5}\right)\right)\right)$$",-0.8358374064673463;-0.15572192312647315;-0.9046755767464477;0.3303567128876209;0.6230337619464125;-1.3602786086213792e-2,39,,,,Infinity,,,,,, +320,0,Log((x0 + x1)),"np.log((x[:, 0] + x[:, 1]))","$$\operatorname{log}(\left(x_{0} + x_{1}\right))$$",,4,0.16366703075849978,0.16366703075849978,0.16366703075849978,0.16366703075849978,-14.103383582051876,-14.103383582051876,-14.103383582051876,5.692477772162212,5.692477772162212,5.692477772162212 +321,0,((Log(Log((x1 * -0.8986346809277586))) ^ Log(((0.14392030866110317 + (0.7763689857383689 + x1)) * 0.5807421254586069))) / ((0.21105751833364972 * x0) + x1)),"(np.log(np.log((x[:, 1] * t[0]))) ** np.log(((t[1] + (t[2] + x[:, 1])) * t[3])) / ((t[4] * x[:, 0]) + x[:, 1]))","$$\frac{{\operatorname{log}(\operatorname{log}(\left(x_{1} \cdot \theta_{0}\right)))^{\operatorname{log}(\left(\left(\theta_{1} + \left(\theta_{2} + x_{1}\right)\right) \cdot \theta_{3}\right))}}}{\left(\left(\theta_{4} \cdot x_{0}\right) + x_{1}\right)}$$",-0.8986346809277586;0.14392030866110317;0.7763689857383689;0.5807421254586069;0.21105751833364972,20,,,,Infinity,,,,,, +322,0,Exp(Log(((-4.605085333025405e-2 ^ x0) * (x0 * 0.7428473257707953)))),"np.exp(np.log((t[0] ** x[:, 0] * (x[:, 0] * t[1]))))","$$\operatorname{exp}(\operatorname{log}(\left({\theta_{0}^{x_{0}}} \cdot \left(x_{0} \cdot \theta_{1}\right)\right)))$$",-4.605085333025405e-2;0.7428473257707953,9,,,,Infinity,,,,,, +323,0,((Log(Log((x0 ^ -0.16044929590345547))) / (((-0.8645801163319422 ^ Exp((0.9526794990855273 * x0))) + 0.9893965153006038) + Log(x1))) * (((-0.5045191450985504 * x1) * 0.9311265581285062) / (Exp(((-0.19389134844490963 / (x0 / x1)) * -0.17103479357697393)) ^ (Log(x0) ^ x1)))),"((np.log(np.log(x[:, 0] ** t[0])) / ((t[1] ** np.exp((t[2] * x[:, 0])) + t[3]) + np.log(x[:, 1]))) * (((t[4] * x[:, 1]) * t[5]) / np.exp(((t[6] / (x[:, 0] / x[:, 1])) * t[7])) ** np.log(x[:, 0]) ** x[:, 1]))","$$\left(\frac{\operatorname{log}(\operatorname{log}({x_{0}^{\theta_{0}}}))}{\left(\left({\theta_{1}^{\operatorname{exp}(\left(\theta_{2} \cdot x_{0}\right))}} + \theta_{3}\right) + \operatorname{log}(x_{1})\right)} \cdot \frac{\left(\left(\theta_{4} \cdot x_{1}\right) \cdot \theta_{5}\right)}{{\operatorname{exp}(\left(\frac{\theta_{6}}{\frac{x_{0}}{x_{1}}} \cdot \theta_{7}\right))^{{\operatorname{log}(x_{0})^{x_{1}}}}}}\right)$$",-0.16044929590345547;-0.8645801163319422;0.9526794990855273;0.9893965153006038;-0.5045191450985504;0.9311265581285062;-0.19389134844490963;-0.17103479357697393,37,,,,Infinity,,,,,, +324,0,(((x0 + 0.6848318221555644) + x0) ^ -2.7369708926494223),"((x[:, 0] + t[0]) + x[:, 0]) ** t[1]","$${\left(\left(x_{0} + \theta_{0}\right) + x_{0}\right)^{\theta_{1}}}$$",0.6848318221555644;-2.7369708926494223,7,5.741708109437702e-3,5.741708109437702e-3,5.741708109437702e-3,5.741708109437702e-3,0.4701485107225123,0.4701485107225123,0.4701485107225123,13.751077935873008,13.751077935873008,13.751077935873008 +325,0,(Exp((-3921899.7676985757 / x1)) + 6.1131880009185124e-2),"(np.exp((t[0] / x[:, 1])) + t[1])","$$\left(\operatorname{exp}(\frac{\theta_{0}}{x_{1}}) + \theta_{1}\right)$$",-3921899.7676985757;6.1131880009185124e-2,6,1.0836447996526664e-2,1.0836447996526664e-2,1.0836447996526664e-2,1.0836447996526664e-2,1.1102230246251565e-16,1.1102230246251565e-16,1.1102230246251565e-16,13.708230148536757,13.708230148536757,13.708230148536757 +326,0,Log((((0.6148342333186406 / (x1 ^ x1)) / (x0 + 0.5950632726121148)) ^ Exp(Log(Exp((x0 / x1)))))),"np.log(((t[0] / x[:, 1] ** x[:, 1]) / (x[:, 0] + t[1])) ** np.exp(np.log(np.exp((x[:, 0] / x[:, 1])))))","$$\operatorname{log}({\frac{\frac{\theta_{0}}{{x_{1}^{x_{1}}}}}{\left(x_{0} + \theta_{1}\right)}^{\operatorname{exp}(\operatorname{log}(\operatorname{exp}(\frac{x_{0}}{x_{1}})))}})$$",0.6148342333186406;0.5950632726121148,17,Infinity,Infinity,Infinity,Infinity,-Infinity,-Infinity,-Infinity,Infinity,Infinity,Infinity +327,0,(Exp((0.9407154156103725 * (((x1 * x0) ^ Log(x1)) ^ ((x1 * x1) * Exp(x1))))) ^ (Log(((-0.6929612755333581 * Exp(x0)) * Log(Exp((x0 + x0))))) / Log((-0.5105296881523056 + (0.7672688956684076 ^ (x1 + x0)))))),"np.exp((t[0] * (x[:, 1] * x[:, 0]) ** np.log(x[:, 1]) ** ((x[:, 1] * x[:, 1]) * np.exp(x[:, 1])))) ** (np.log(((t[1] * np.exp(x[:, 0])) * np.log(np.exp((x[:, 0] + x[:, 0]))))) / np.log((t[2] + t[3] ** (x[:, 1] + x[:, 0]))))","$${\operatorname{exp}(\left(\theta_{0} \cdot {{\left(x_{1} \cdot x_{0}\right)^{\operatorname{log}(x_{1})}}^{\left(\left(x_{1} \cdot x_{1}\right) \cdot \operatorname{exp}(x_{1})\right)}}\right))^{\frac{\operatorname{log}(\left(\left(\theta_{1} \cdot \operatorname{exp}(x_{0})\right) \cdot \operatorname{log}(\operatorname{exp}(\left(x_{0} + x_{0}\right)))\right))}{\operatorname{log}(\left(\theta_{2} + {\theta_{3}^{\left(x_{1} + x_{0}\right)}}\right))}}}$$",0.9407154156103725;-0.6929612755333581;-0.5105296881523056;0.7672688956684076,37,,,,Infinity,,,,,, +328,0,(Exp((Log(x1) * 186.22566547018312)) + Exp(((x1 * x1) * -524.0289239523607))),"(np.exp((np.log(x[:, 1]) * t[0])) + np.exp(((x[:, 1] * x[:, 1]) * t[1])))","$$\left(\operatorname{exp}(\left(\operatorname{log}(x_{1}) \cdot \theta_{0}\right)) + \operatorname{exp}(\left(\left(x_{1} \cdot x_{1}\right) \cdot \theta_{1}\right))\right)$$",186.22566547018312;-524.0289239523607,12,1.4573989138967687e-2,1.4573989138967687e-2,1.4573989138967687e-2,1.4573989138967687e-2,-0.3449046351386533,-0.3449046351386533,-0.3449046351386533,25.556080091697016,25.556080091697016,25.556080091697016 +329,0,((Log(((x0 + -0.12263488127669941) * -0.8914808469410662)) / (((x0 + 0.19737120483639292) * 0.3121393914051166) ^ -0.8390995587591845)) * Exp(((x0 ^ (Exp((0.11402253881835722 / x1)) ^ x1)) + -1.1870239623831158e-2))),"((np.log(((x[:, 0] + t[0]) * t[1])) / ((x[:, 0] + t[2]) * t[3]) ** t[4]) * np.exp((x[:, 0] ** np.exp((t[5] / x[:, 1])) ** x[:, 1] + t[6])))","$$\left(\frac{\operatorname{log}(\left(\left(x_{0} + \theta_{0}\right) \cdot \theta_{1}\right))}{{\left(\left(x_{0} + \theta_{2}\right) \cdot \theta_{3}\right)^{\theta_{4}}}} \cdot \operatorname{exp}(\left({x_{0}^{{\operatorname{exp}(\frac{\theta_{5}}{x_{1}})^{x_{1}}}}} + \theta_{6}\right))\right)$$",-0.12263488127669941;-0.8914808469410662;0.19737120483639292;0.3121393914051166;-0.8390995587591845;0.11402253881835722;-1.1870239623831158e-2,26,,,,Infinity,,,,,, +330,0,Exp((Exp(((x0 * -5.560164466078084e9) * 9.254320267341006e8)) / ((x1 / x0) * 5.60197231544998e9))),"np.exp((np.exp(((x[:, 0] * t[0]) * t[1])) / ((x[:, 1] / x[:, 0]) * t[2])))","$$\operatorname{exp}(\frac{\operatorname{exp}(\left(\left(x_{0} \cdot \theta_{0}\right) \cdot \theta_{1}\right))}{\left(\frac{x_{1}}{x_{0}} \cdot \theta_{2}\right)})$$",-5.560164466078084e9;9.254320267341006e8;5.60197231544998e9,13,0.8923097947317449,0.8923097947317449,0.8923097947317449,0.8923097947317449,-81.34338364542985,-81.34338364542985,-81.34338364542985,30.158726721326207,30.158726721326207,30.158726721326207 +331,0,Exp((((x1 ^ (Exp((-1.1304533875970022 + x1)) * Exp(Log(x0)))) + -2.977325893540204) * (((-1.1796137678906902 * x1) + (Exp(x0) * x0)) * (x0 ^ Exp((x0 * -6.458614384887789)))))),"np.exp(((x[:, 1] ** (np.exp((t[0] + x[:, 1])) * np.exp(np.log(x[:, 0]))) + t[1]) * (((t[2] * x[:, 1]) + (np.exp(x[:, 0]) * x[:, 0])) * x[:, 0] ** np.exp((x[:, 0] * t[3])))))","$$\operatorname{exp}(\left(\left({x_{1}^{\left(\operatorname{exp}(\left(\theta_{0} + x_{1}\right)) \cdot \operatorname{exp}(\operatorname{log}(x_{0}))\right)}} + \theta_{1}\right) \cdot \left(\left(\left(\theta_{2} \cdot x_{1}\right) + \left(\operatorname{exp}(x_{0}) \cdot x_{0}\right)\right) \cdot {x_{0}^{\operatorname{exp}(\left(x_{0} \cdot \theta_{3}\right))}}\right)\right))$$",-1.1304533875970022;-2.977325893540204;-1.1796137678906902;-6.458614384887789,29,2.5804061379426663e-3,2.5804061379426663e-3,2.5804061379426663e-3,2.5804061379426663e-3,0.7618771262714732,0.7618771262714732,0.7618771262714732,68.38982681570994,68.38982681570994,68.38982681570994 +332,0,((Exp((-0.7306947765768412 / (Exp(x1) / x0))) * (0.482205612594108 ^ (((x1 * 0.5830306257060127) * -0.9083783005469015) + 0.8782350611877358))) ^ Exp(Exp((Exp(x0) / (0.15688198029523526 ^ x1))))),"(np.exp((t[0] / (np.exp(x[:, 1]) / x[:, 0]))) * t[1] ** (((x[:, 1] * t[2]) * t[3]) + t[4])) ** np.exp(np.exp((np.exp(x[:, 0]) / t[5] ** x[:, 1])))","$${\left(\operatorname{exp}(\frac{\theta_{0}}{\frac{\operatorname{exp}(x_{1})}{x_{0}}}) \cdot {\theta_{1}^{\left(\left(\left(x_{1} \cdot \theta_{2}\right) \cdot \theta_{3}\right) + \theta_{4}\right)}}\right)^{\operatorname{exp}(\operatorname{exp}(\frac{\operatorname{exp}(x_{0})}{{\theta_{5}^{x_{1}}}}))}}$$",-0.7306947765768412;0.482205612594108;0.5830306257060127;-0.9083783005469015;0.8782350611877358;0.15688198029523526,26,1.4573554749975553e-2,1.4573554749975553e-2,1.4573554749975553e-2,1.4573554749975553e-2,-0.3448645492182232,-0.3448645492182232,-0.3448645492182232,66.20414589515656,66.20414589515656,66.20414589515656 +333,0,(Log(Exp(((0.737569538117369 * (x1 * (x1 / x0))) + ((x0 ^ x0) / (x1 * -14050.93889692497))))) + (((-1.2740809661470673 / (x0 * -157.63690298503312)) * Exp((x1 * x1))) + ((-0.34388115643448236 * x1) * (x1 + Log(x0))))),"(np.log(np.exp(((t[0] * (x[:, 1] * (x[:, 1] / x[:, 0]))) + (x[:, 0] ** x[:, 0] / (x[:, 1] * t[1]))))) + (((t[2] / (x[:, 0] * t[3])) * np.exp((x[:, 1] * x[:, 1]))) + ((t[4] * x[:, 1]) * (x[:, 1] + np.log(x[:, 0])))))","$$\left(\operatorname{log}(\operatorname{exp}(\left(\left(\theta_{0} \cdot \left(x_{1} \cdot \frac{x_{1}}{x_{0}}\right)\right) + \frac{{x_{0}^{x_{0}}}}{\left(x_{1} \cdot \theta_{1}\right)}\right))) + \left(\left(\frac{\theta_{2}}{\left(x_{0} \cdot \theta_{3}\right)} \cdot \operatorname{exp}(\left(x_{1} \cdot x_{1}\right))\right) + \left(\left(\theta_{4} \cdot x_{1}\right) \cdot \left(x_{1} + \operatorname{log}(x_{0})\right)\right)\right)\right)$$",0.737569538117369;-14050.93889692497;-1.2740809661470673;-157.63690298503312;-0.34388115643448236,37,1.9446094606750367e-3,1.9446094606750367e-3,1.9446094606750367e-3,1.9446094606750367e-3,0.8205491816785049,0.8205491816785049,0.8205491816785049,91.40368418679294,91.40368418679294,91.40368418679294 +334,0,Exp(Exp(((Exp(Exp((27.304667726260178 / x1))) ^ ((-20.6618876536588 / x1) + (-11.699586008941516 * (x0 * -1.1928378759217408)))) + (-2761.9361978879724 / ((Exp(x1) ^ x1) ^ Log(x1)))))),"np.exp(np.exp((np.exp(np.exp((t[0] / x[:, 1]))) ** ((t[1] / x[:, 1]) + (t[2] * (x[:, 0] * t[3]))) + (t[4] / np.exp(x[:, 1]) ** x[:, 1] ** np.log(x[:, 1])))))","$$\operatorname{exp}(\operatorname{exp}(\left({\operatorname{exp}(\operatorname{exp}(\frac{\theta_{0}}{x_{1}}))^{\left(\frac{\theta_{1}}{x_{1}} + \left(\theta_{2} \cdot \left(x_{0} \cdot \theta_{3}\right)\right)\right)}} + \frac{\theta_{4}}{{{\operatorname{exp}(x_{1})^{x_{1}}}^{\operatorname{log}(x_{1})}}}\right)))$$",27.304667726260178;-20.6618876536588;-11.699586008941516;-1.1928378759217408;-2761.9361978879724,27,0.8923097947317449,0.8923097947317449,0.8923097947317449,0.8923097947317449,-81.34338364542985,-81.34338364542985,-81.34338364542985,70.23276708017471,70.23276708017471,70.23276708017471 +335,0,Exp(Exp((Exp((-0.7321471162970745 + x0)) * Log((x1 + -0.10223556295816931))))),"np.exp(np.exp((np.exp((t[0] + x[:, 0])) * np.log((x[:, 1] + t[1])))))","$$\operatorname{exp}(\operatorname{exp}(\left(\operatorname{exp}(\left(\theta_{0} + x_{0}\right)) \cdot \operatorname{log}(\left(x_{1} + \theta_{1}\right))\right)))$$",-0.7321471162970745;-0.10223556295816931,11,1.2434821411004873,1.2434821411004873,1.2434821411004873,1.2434821411004873,-113.7499754069832,-113.7499754069832,-113.7499754069832,26.565995437334166,26.565995437334166,26.565995437334166 +336,0,(Exp(Log((Exp((x1 ^ (0.6163951196794784 ^ x0))) / (1.6073900312352674e-2 ^ ((-3.957367314616622e-2 * x0) * x0))))) * (Exp((-0.753469062343961 * ((x0 + 0.5730651045518398) + 0.5857277406660717))) / (Exp(((x1 * 8.49449147802307e-2) / x0)) + ((0.5155237999220255 / x0) + -0.3352298054871585)))),"(np.exp(np.log((np.exp(x[:, 1] ** t[0] ** x[:, 0]) / t[1] ** ((t[2] * x[:, 0]) * x[:, 0])))) * (np.exp((t[3] * ((x[:, 0] + t[4]) + t[5]))) / (np.exp(((x[:, 1] * t[6]) / x[:, 0])) + ((t[7] / x[:, 0]) + t[8]))))","$$\left(\operatorname{exp}(\operatorname{log}(\frac{\operatorname{exp}({x_{1}^{{\theta_{0}^{x_{0}}}}})}{{\theta_{1}^{\left(\left(\theta_{2} \cdot x_{0}\right) \cdot x_{0}\right)}}})) \cdot \frac{\operatorname{exp}(\left(\theta_{3} \cdot \left(\left(x_{0} + \theta_{4}\right) + \theta_{5}\right)\right))}{\left(\operatorname{exp}(\frac{\left(x_{1} \cdot \theta_{6}\right)}{x_{0}}) + \left(\frac{\theta_{7}}{x_{0}} + \theta_{8}\right)\right)}\right)$$",0.6163951196794784;1.6073900312352674e-2;-3.957367314616622e-2;-0.753469062343961;0.5730651045518398;0.5857277406660717;8.49449147802307e-2;0.5155237999220255;-0.3352298054871585,38,2.9292415099721313e-2,2.9292415099721313e-2,2.9292415099721313e-2,2.9292415099721313e-2,-1.7031380678530654,-1.7031380678530654,-1.7031380678530654,101.70922153067485,101.70922153067485,101.70922153067485 +337,0,((-0.8679935017473754 + (-1.864280801771689e-2 / x1)) + Exp((1.4395774302417146e-5 ^ Exp(x1)))),"((t[0] + (t[1] / x[:, 1])) + np.exp(t[2] ** np.exp(x[:, 1])))","$$\left(\left(\theta_{0} + \frac{\theta_{1}}{x_{1}}\right) + \operatorname{exp}({\theta_{2}^{\operatorname{exp}(x_{1})}})\right)$$",-0.8679935017473754;-1.864280801771689e-2;1.4395774302417146e-5,11,9.473979632469195e-3,9.473979632469195e-3,9.473979632469195e-3,9.473979632469195e-3,0.12573016217991106,0.12573016217991106,0.12573016217991106,25.78065554928075,25.78065554928075,25.78065554928075 +338,0,(((Exp(x1) ^ (Log(((x0 * 1.0525803051139622) + (x0 + x0))) / x0)) + ((8.65027964018551 / x0) / x0)) * 6.249895748033409e-3),"((np.exp(x[:, 1]) ** (np.log(((x[:, 0] * t[0]) + (x[:, 0] + x[:, 0]))) / x[:, 0]) + ((t[1] / x[:, 0]) / x[:, 0])) * t[2])","$$\left(\left({\operatorname{exp}(x_{1})^{\frac{\operatorname{log}(\left(\left(x_{0} \cdot \theta_{0}\right) + \left(x_{0} + x_{0}\right)\right))}{x_{0}}}} + \frac{\frac{\theta_{1}}{x_{0}}}{x_{0}}\right) \cdot \theta_{2}\right)$$",1.0525803051139622;8.65027964018551;6.249895748033409e-3,21,5.878619826302115e-3,5.878619826302115e-3,5.878619826302115e-3,5.878619826302115e-3,0.45751413856400647,0.45751413856400647,0.45751413856400647,52.209781688007205,52.209781688007205,52.209781688007205 +339,0,Exp((((x0 * (x0 * 0.9622246788098348)) ^ (x1 * (0.4712707238922962 ^ x1))) * ((-0.5968164704983052 ^ (0.5401554061716111 / x0)) + -0.5013180757552873))),"np.exp(((x[:, 0] * (x[:, 0] * t[0])) ** (x[:, 1] * t[1] ** x[:, 1]) * (t[2] ** (t[3] / x[:, 0]) + t[4])))","$$\operatorname{exp}(\left({\left(x_{0} \cdot \left(x_{0} \cdot \theta_{0}\right)\right)^{\left(x_{1} \cdot {\theta_{1}^{x_{1}}}\right)}} \cdot \left({\theta_{2}^{\frac{\theta_{3}}{x_{0}}}} + \theta_{4}\right)\right))$$",0.9622246788098348;0.4712707238922962;-0.5968164704983052;0.5401554061716111;-0.5013180757552873,20,,,,Infinity,,,,,, +340,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 +341,0,Log(Exp(((Log(x1) ^ (x0 * -0.49977498460571734)) / ((-0.9867317595560057 / x0) * -0.7628000589169718)))),"np.log(np.exp((np.log(x[:, 1]) ** (x[:, 0] * t[0]) / ((t[1] / x[:, 0]) * t[2]))))","$$\operatorname{log}(\operatorname{exp}(\frac{{\operatorname{log}(x_{1})^{\left(x_{0} \cdot \theta_{0}\right)}}}{\left(\frac{\theta_{1}}{x_{0}} \cdot \theta_{2}\right)}))$$",-0.49977498460571734;-0.9867317595560057;-0.7628000589169718,14,,,,Infinity,,,,,, +342,0,(Exp(Exp(Exp(Log(Exp(x0))))) ^ (-1.0e16 * ((x0 + x1) * x1))),"np.exp(np.exp(np.exp(np.log(np.exp(x[:, 0]))))) ** (t[0] * ((x[:, 0] + x[:, 1]) * x[:, 1]))","$${\operatorname{exp}(\operatorname{exp}(\operatorname{exp}(\operatorname{log}(\operatorname{exp}(x_{0})))))^{\left(\theta_{0} \cdot \left(\left(x_{0} + x_{1}\right) \cdot x_{1}\right)\right)}}$$",-1.0e16,14,1.4573554749975553e-2,1.4573554749975553e-2,1.4573554749975553e-2,1.4573554749975553e-2,-0.3448645492182232,-0.3448645492182232,-0.3448645492182232,31.146222718160317,31.146222718160317,31.146222718160317 +343,0,(((x0 * Log(x0)) / (Log(x1) + (0.4840854042097663 ^ x0))) ^ Exp(Exp(((x1 * 0.7896951218316943) ^ Exp((x1 * -0.33666809212312765)))))),"((x[:, 0] * np.log(x[:, 0])) / (np.log(x[:, 1]) + t[0] ** x[:, 0])) ** np.exp(np.exp((x[:, 1] * t[1]) ** np.exp((x[:, 1] * t[2]))))","$${\frac{\left(x_{0} \cdot \operatorname{log}(x_{0})\right)}{\left(\operatorname{log}(x_{1}) + {\theta_{0}^{x_{0}}}\right)}^{\operatorname{exp}(\operatorname{exp}({\left(x_{1} \cdot \theta_{1}\right)^{\operatorname{exp}(\left(x_{1} \cdot \theta_{2}\right))}}))}}$$",0.4840854042097663;0.7896951218316943;-0.33666809212312765,22,,,,Infinity,,,,,, +344,0,Log((-0.8071497492551817 / ((0.8256807840283974 + x0) * (((x0 * 5.7611226161222495e-2) * x1) + (x1 + 0.14087152126945368))))),"np.log((t[0] / ((t[1] + x[:, 0]) * (((x[:, 0] * t[2]) * x[:, 1]) + (x[:, 1] + t[3])))))","$$\operatorname{log}(\frac{\theta_{0}}{\left(\left(\theta_{1} + x_{0}\right) \cdot \left(\left(\left(x_{0} \cdot \theta_{2}\right) \cdot x_{1}\right) + \left(x_{1} + \theta_{3}\right)\right)\right)})$$",-0.8071497492551817;0.8256807840283974;5.7611226161222495e-2;0.14087152126945368,16,,,,Infinity,,,,,, +345,0,(Exp(((x0 / x0) ^ (0.8775082269008321 / x0))) * ((-0.718143727596479 / (x0 * -45.414371197606926)) * (x0 ^ -1.8488949570085476))),"(np.exp((x[:, 0] / x[:, 0]) ** (t[0] / x[:, 0])) * ((t[1] / (x[:, 0] * t[2])) * x[:, 0] ** t[3]))","$$\left(\operatorname{exp}({\frac{x_{0}}{x_{0}}^{\frac{\theta_{0}}{x_{0}}}}) \cdot \left(\frac{\theta_{1}}{\left(x_{0} \cdot \theta_{2}\right)} \cdot {x_{0}^{\theta_{3}}}\right)\right)$$",0.8775082269008321;-0.718143727596479;-45.414371197606926;-1.8488949570085476,18,5.233221391450908e-3,5.233221391450908e-3,5.233221391450908e-3,5.233221391450908e-3,0.5170722553065104,0.5170722553065104,0.5170722553065104,40.34008008682786,40.34008008682786,40.34008008682786 +346,0,(Exp(((x0 ^ 0.6321051993379233) ^ -0.542308703553113)) + (Exp(x1) / Log((x1 ^ x0)))),"(np.exp(x[:, 0] ** t[0] ** t[1]) + (np.exp(x[:, 1]) / np.log(x[:, 1] ** x[:, 0])))","$$\left(\operatorname{exp}({{x_{0}^{\theta_{0}}}^{\theta_{1}}}) + \frac{\operatorname{exp}(x_{1})}{\operatorname{log}({x_{1}^{x_{0}}})}\right)$$",0.6321051993379233;-0.542308703553113,14,2.2711567777213975,2.2711567777213975,2.2711567777213975,2.2711567777213975,-208.58498379260033,-208.58498379260033,-208.58498379260033,35.198072554202234,35.198072554202234,35.198072554202234 +347,0,(x1 ^ x1),"x[:, 1] ** x[:, 1]","$${x_{1}^{x_{1}}}$$",,3,0.4403077718480359,0.4403077718480359,0.4403077718480359,0.4403077718480359,-39.63211229262252,-39.63211229262252,-39.63211229262252,2.4757185363430683,2.4757185363430683,2.4757185363430683 +348,0,(((0.44031417123477634 / x0) + (-0.5950581796100616 ^ Log(x0))) / (((9.638260243570063e-2 * x1) ^ -0.4086876488722153) * ((x1 ^ 0.8855164824721937) * -9.67594327768071e-2))),"(((t[0] / x[:, 0]) + t[1] ** np.log(x[:, 0])) / ((t[2] * x[:, 1]) ** t[3] * (x[:, 1] ** t[4] * t[5])))","$$\frac{\left(\frac{\theta_{0}}{x_{0}} + {\theta_{1}^{\operatorname{log}(x_{0})}}\right)}{\left({\left(\theta_{2} \cdot x_{1}\right)^{\theta_{3}}} \cdot \left({x_{1}^{\theta_{4}}} \cdot \theta_{5}\right)\right)}$$",0.44031417123477634;-0.5950581796100616;9.638260243570063e-2;-0.4086876488722153;0.8855164824721937;-9.67594327768071e-2,20,,,,Infinity,,,,,, +349,0,((Exp(((-0.47016494013535126 + x0) * 1.2223569846717917)) ^ (0.6972753174139303 + (x0 ^ x1))) ^ (Log((x1 * (-0.32894944437060236 + x0))) / x1)),"np.exp(((t[0] + x[:, 0]) * t[1])) ** (t[2] + x[:, 0] ** x[:, 1]) ** (np.log((x[:, 1] * (t[3] + x[:, 0]))) / x[:, 1])","$${{\operatorname{exp}(\left(\left(\theta_{0} + x_{0}\right) \cdot \theta_{1}\right))^{\left(\theta_{2} + {x_{0}^{x_{1}}}\right)}}^{\frac{\operatorname{log}(\left(x_{1} \cdot \left(\theta_{3} + x_{0}\right)\right))}{x_{1}}}}$$",-0.47016494013535126;1.2223569846717917;0.6972753174139303;-0.32894944437060236,21,2.5511771041001547e-3,2.5511771041001547e-3,2.5511771041001547e-3,2.5511771041001547e-3,0.7645744154433384,0.7645744154433384,0.7645744154433384,54.22771192492486,54.22771192492486,54.22771192492486 +350,0,Log((((((-0.6306708173090139 * x0) + x1) ^ Log((x1 + -0.3027817337012614))) / (Log((x0 ^ (0.21560917968697824 ^ x0))) * ((x0 + x1) / x0))) + (((-0.3657468797358592 / x1) ^ (Log(x0) * (x1 ^ -0.31614703968390745))) ^ Log(Exp((x1 + -2.796084713886371e-2)))))),"np.log(((((t[0] * x[:, 0]) + x[:, 1]) ** np.log((x[:, 1] + t[1])) / (np.log(x[:, 0] ** t[2] ** x[:, 0]) * ((x[:, 0] + x[:, 1]) / x[:, 0]))) + (t[3] / x[:, 1]) ** (np.log(x[:, 0]) * x[:, 1] ** t[4]) ** np.log(np.exp((x[:, 1] + t[5])))))","$$\operatorname{log}(\left(\frac{{\left(\left(\theta_{0} \cdot x_{0}\right) + x_{1}\right)^{\operatorname{log}(\left(x_{1} + \theta_{1}\right))}}}{\left(\operatorname{log}({x_{0}^{{\theta_{2}^{x_{0}}}}}) \cdot \frac{\left(x_{0} + x_{1}\right)}{x_{0}}\right)} + {{\frac{\theta_{3}}{x_{1}}^{\left(\operatorname{log}(x_{0}) \cdot {x_{1}^{\theta_{4}}}\right)}}^{\operatorname{log}(\operatorname{exp}(\left(x_{1} + \theta_{5}\right)))}}\right))$$",-0.6306708173090139;-0.3027817337012614;0.21560917968697824;-0.3657468797358592;-0.31614703968390745;-2.796084713886371e-2,41,,,,Infinity,,,,,, +351,0,Log(Log(Exp((x1 * -0.40125100925602075)))),"np.log(np.log(np.exp((x[:, 1] * t[0]))))","$$\operatorname{log}(\operatorname{log}(\operatorname{exp}(\left(x_{1} \cdot \theta_{0}\right))))$$",-0.40125100925602075,6,,,,Infinity,,,,,, +352,0,((Exp(((x0 * x1) / (x0 ^ 3.49667412671372))) * x1) * 0.1040658673252386),"((np.exp(((x[:, 0] * x[:, 1]) / x[:, 0] ** t[0])) * x[:, 1]) * t[1])","$$\left(\left(\operatorname{exp}(\frac{\left(x_{0} \cdot x_{1}\right)}{{x_{0}^{\theta_{0}}}}) \cdot x_{1}\right) \cdot \theta_{1}\right)$$",3.49667412671372;0.1040658673252386,12,3.8845546614379857e-3,3.8845546614379857e-3,3.8845546614379857e-3,3.8845546614379857e-3,0.6415287866759407,0.6415287866759407,0.6415287866759407,27.396267758594334,27.396267758594334,27.396267758594334 +353,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 +354,0,(((((0.4825201402148674 ^ x1) * x1) + Log((x0 * x1))) * (5.563724431289008e-4 / Log((x1 ^ x1)))) / x1),"((((t[0] ** x[:, 1] * x[:, 1]) + np.log((x[:, 0] * x[:, 1]))) * (t[1] / np.log(x[:, 1] ** x[:, 1]))) / x[:, 1])","$$\frac{\left(\left(\left({\theta_{0}^{x_{1}}} \cdot x_{1}\right) + \operatorname{log}(\left(x_{0} \cdot x_{1}\right))\right) \cdot \frac{\theta_{1}}{\operatorname{log}({x_{1}^{x_{1}}})}\right)}{x_{1}}$$",0.4825201402148674;5.563724431289008e-4,19,1.4316191203116673e-2,1.4316191203116673e-2,1.4316191203116673e-2,1.4316191203116673e-2,-0.3211147423681031,-0.3211147423681031,-0.3211147423681031,43.56412373473496,43.56412373473496,43.56412373473496 +355,0,(-6.818777244215415e-2 / Log(x1)),"(t[0] / np.log(x[:, 1]))","$$\frac{\theta_{0}}{\operatorname{log}(x_{1})}$$",-6.818777244215415e-2,4,9.501995662458252e-3,9.501995662458252e-3,9.501995662458252e-3,9.501995662458252e-3,0.1231448104116899,0.1231448104116899,0.1231448104116899,7.574654175943415,7.574654175943415,7.574654175943415 +356,0,Log(((-1.2356980858238382 * (-1.0638686921881577 + x0)) + x0)),"np.log(((t[0] * (t[1] + x[:, 0])) + x[:, 0]))","$$\operatorname{log}(\left(\left(\theta_{0} \cdot \left(\theta_{1} + x_{0}\right)\right) + x_{0}\right))$$",-1.2356980858238382;-1.0638686921881577,8,6.021135055815032e-3,6.021135055815032e-3,6.021135055815032e-3,6.021135055815032e-3,0.44436266775377453,0.44436266775377453,0.44436266775377453,16.922772191758316,16.922772191758316,16.922772191758316 +357,0,Log(((Log((x1 * -0.6318827633692854)) + 0.5453037414301793) ^ (((0.19163197797554343 ^ x0) * -0.7228443183104561) ^ (0.5997190001414157 ^ Log((0.7655261557849976 / x1)))))),"np.log((np.log((x[:, 1] * t[0])) + t[1]) ** (t[2] ** x[:, 0] * t[3]) ** t[4] ** np.log((t[5] / x[:, 1])))","$$\operatorname{log}({\left(\operatorname{log}(\left(x_{1} \cdot \theta_{0}\right)) + \theta_{1}\right)^{{\left({\theta_{2}^{x_{0}}} \cdot \theta_{3}\right)^{{\theta_{4}^{\operatorname{log}(\frac{\theta_{5}}{x_{1}})}}}}}})$$",-0.6318827633692854;0.5453037414301793;0.19163197797554343;-0.7228443183104561;0.5997190001414157;0.7655261557849976,20,,,,Infinity,,,,,, +358,0,((Log(((x0 * -0.5343695450858427) + -0.7902049316891904)) + ((x1 ^ -0.8760096105492738) + -0.10517814606692122)) * Log((((-0.27216316260951157 ^ x0) * -0.13233330856450387) ^ Exp(((0.695944364969963 / x1) / Exp(x0)))))),"((np.log(((x[:, 0] * t[0]) + t[1])) + (x[:, 1] ** t[2] + t[3])) * np.log((t[4] ** x[:, 0] * t[5]) ** np.exp(((t[6] / x[:, 1]) / np.exp(x[:, 0])))))","$$\left(\left(\operatorname{log}(\left(\left(x_{0} \cdot \theta_{0}\right) + \theta_{1}\right)) + \left({x_{1}^{\theta_{2}}} + \theta_{3}\right)\right) \cdot \operatorname{log}({\left({\theta_{4}^{x_{0}}} \cdot \theta_{5}\right)^{\operatorname{exp}(\frac{\frac{\theta_{6}}{x_{1}}}{\operatorname{exp}(x_{0})})}})\right)$$",-0.5343695450858427;-0.7902049316891904;-0.8760096105492738;-0.10517814606692122;-0.27216316260951157;-0.13233330856450387;0.695944364969963,27,,,,Infinity,,,,,, +359,0,Log((((x1 * 1.6344662973955024) / (x0 / (1.1369210338454088 * x0))) / (0.2280250819333295 + ((x1 ^ x0) * 0.9665833569772994)))),"np.log((((x[:, 1] * t[0]) / (x[:, 0] / (t[1] * x[:, 0]))) / (t[2] + (x[:, 1] ** x[:, 0] * t[3]))))","$$\operatorname{log}(\frac{\frac{\left(x_{1} \cdot \theta_{0}\right)}{\frac{x_{0}}{\left(\theta_{1} \cdot x_{0}\right)}}}{\left(\theta_{2} + \left({x_{1}^{x_{0}}} \cdot \theta_{3}\right)\right)})$$",1.6344662973955024;1.1369210338454088;0.2280250819333295;0.9665833569772994,18,0.12396582204329185,0.12396582204329185,0.12396582204329185,0.12396582204329185,-10.439709956899694,-10.439709956899694,-10.439709956899694,45.625216731546566,45.625216731546566,45.625216731546566 +360,0,(((((-0.7263800666716498 ^ x0) / x0) + 0.17268094488634578) ^ ((0.3598203657717638 * (x1 / x0)) + (-0.6666949097444441 ^ (x1 + -0.7810899433290375)))) ^ (Log(Log(((0.6707359424287955 + x1) + (x1 / x1)))) ^ Log((Exp((0.15056874475381699 + x0)) + (Exp(x1) * (x0 * -0.9539357165553348)))))),"((t[0] ** x[:, 0] / x[:, 0]) + t[1]) ** ((t[2] * (x[:, 1] / x[:, 0])) + t[3] ** (x[:, 1] + t[4])) ** np.log(np.log(((t[5] + x[:, 1]) + (x[:, 1] / x[:, 1])))) ** np.log((np.exp((t[6] + x[:, 0])) + (np.exp(x[:, 1]) * (x[:, 0] * t[7]))))","$${{\left(\frac{{\theta_{0}^{x_{0}}}}{x_{0}} + \theta_{1}\right)^{\left(\left(\theta_{2} \cdot \frac{x_{1}}{x_{0}}\right) + {\theta_{3}^{\left(x_{1} + \theta_{4}\right)}}\right)}}^{{\operatorname{log}(\operatorname{log}(\left(\left(\theta_{5} + x_{1}\right) + \frac{x_{1}}{x_{1}}\right)))^{\operatorname{log}(\left(\operatorname{exp}(\left(\theta_{6} + x_{0}\right)) + \left(\operatorname{exp}(x_{1}) \cdot \left(x_{0} \cdot \theta_{7}\right)\right)\right))}}}}$$",-0.7263800666716498;0.17268094488634578;0.3598203657717638;-0.6666949097444441;-0.7810899433290375;0.6707359424287955;0.15056874475381699;-0.9539357165553348,42,,,,Infinity,,,,,, +361,0,(((-0.42667402695235124 / (0.49646748449271383 + x1)) + ((x1 + -0.629582915201397) + (x1 / (-0.6214480537565967 + x1)))) * ((Exp((0.2682555219744903 ^ x1)) ^ 0.8705259568735344) ^ Log(((x0 + -0.14329686505055783) * -0.5591948900192336)))),"(((t[0] / (t[1] + x[:, 1])) + ((x[:, 1] + t[2]) + (x[:, 1] / (t[3] + x[:, 1])))) * np.exp(t[4] ** x[:, 1]) ** t[5] ** np.log(((x[:, 0] + t[6]) * t[7])))","$$\left(\left(\frac{\theta_{0}}{\left(\theta_{1} + x_{1}\right)} + \left(\left(x_{1} + \theta_{2}\right) + \frac{x_{1}}{\left(\theta_{3} + x_{1}\right)}\right)\right) \cdot {{\operatorname{exp}({\theta_{4}^{x_{1}}})^{\theta_{5}}}^{\operatorname{log}(\left(\left(x_{0} + \theta_{6}\right) \cdot \theta_{7}\right))}}\right)$$",-0.42667402695235124;0.49646748449271383;-0.629582915201397;-0.6214480537565967;0.2682555219744903;0.8705259568735344;-0.14329686505055783;-0.5591948900192336,29,,,,Infinity,,,,,, +362,0,(((((3.9877432552192005e-2 + (-9.872747033389695e-3 + x0)) + Exp(((-0.5301932245774551 * x0) ^ (0.22362112807424128 * x0)))) ^ (x1 + x0)) + ((Log(((x1 * -0.8861426729998655) + (-0.6743507279626565 * x1))) / (x1 / x0)) / (x1 * -0.14003783076853993))) / x0),"((((t[0] + (t[1] + x[:, 0])) + np.exp((t[2] * x[:, 0]) ** (t[3] * x[:, 0]))) ** (x[:, 1] + x[:, 0]) + ((np.log(((x[:, 1] * t[4]) + (t[5] * x[:, 1]))) / (x[:, 1] / x[:, 0])) / (x[:, 1] * t[6]))) / x[:, 0])","$$\frac{\left({\left(\left(\theta_{0} + \left(\theta_{1} + x_{0}\right)\right) + \operatorname{exp}({\left(\theta_{2} \cdot x_{0}\right)^{\left(\theta_{3} \cdot x_{0}\right)}})\right)^{\left(x_{1} + x_{0}\right)}} + \frac{\frac{\operatorname{log}(\left(\left(x_{1} \cdot \theta_{4}\right) + \left(\theta_{5} \cdot x_{1}\right)\right))}{\frac{x_{1}}{x_{0}}}}{\left(x_{1} \cdot \theta_{6}\right)}\right)}{x_{0}}$$",3.9877432552192005e-2;-9.872747033389695e-3;-0.5301932245774551;0.22362112807424128;-0.8861426729998655;-0.6743507279626565;-0.14003783076853993,37,,,,Infinity,,,,,, +363,0,(((((-0.7903882779888864 / (0.8380401445828906 + x0)) / (x1 ^ x0)) / (-0.6082257330377195 + x1)) / ((x0 * -0.25359398007150014) + (-0.4643463596687105 ^ x1))) * x1),"(((((t[0] / (t[1] + x[:, 0])) / x[:, 1] ** x[:, 0]) / (t[2] + x[:, 1])) / ((x[:, 0] * t[3]) + t[4] ** x[:, 1])) * x[:, 1])","$$\left(\frac{\frac{\frac{\frac{\theta_{0}}{\left(\theta_{1} + x_{0}\right)}}{{x_{1}^{x_{0}}}}}{\left(\theta_{2} + x_{1}\right)}}{\left(\left(x_{0} \cdot \theta_{3}\right) + {\theta_{4}^{x_{1}}}\right)} \cdot x_{1}\right)$$",-0.7903882779888864;0.8380401445828906;-0.6082257330377195;-0.25359398007150014;-0.4643463596687105,23,,,,Infinity,,,,,, +364,0,(Exp(Log(Log((0.3148177729641909 + x1)))) * 0.25111413950047445),"(np.exp(np.log(np.log((t[0] + x[:, 1])))) * t[1])","$$\left(\operatorname{exp}(\operatorname{log}(\operatorname{log}(\left(\theta_{0} + x_{1}\right)))) \cdot \theta_{1}\right)$$",0.3148177729641909;0.25111413950047445,8,,,,Infinity,,,,,, +365,0,((1.853667447282098e-2 / x1) / (x0 + (x1 / x1))),"((t[0] / x[:, 1]) / (x[:, 0] + (x[:, 1] / x[:, 1])))","$$\frac{\frac{\theta_{0}}{x_{1}}}{\left(x_{0} + \frac{x_{1}}{x_{1}}\right)}$$",1.853667447282098e-2,9,1.2869162079709293e-2,1.2869162079709293e-2,1.2869162079709293e-2,1.2869162079709293e-2,-0.18758121515778603,-0.18758121515778603,-0.18758121515778603,16.51744839314628,16.51744839314628,16.51744839314628 +366,0,(Log(((Exp((x0 * x0)) ^ (x0 * (0.24223659468543468 * x0))) * Exp(((0.14210869871802023 + x1) * -0.6518162799704387)))) ^ (Log(((0.19197009553435962 * (x1 + 0.8511951191992848)) / Exp(Log(x0)))) * (0.5975879150006314 * (Log(x1) * (0.4837142598400124 + x1))))),"np.log((np.exp((x[:, 0] * x[:, 0])) ** (x[:, 0] * (t[0] * x[:, 0])) * np.exp(((t[1] + x[:, 1]) * t[2])))) ** (np.log(((t[3] * (x[:, 1] + t[4])) / np.exp(np.log(x[:, 0])))) * (t[5] * (np.log(x[:, 1]) * (t[6] + x[:, 1]))))","$${\operatorname{log}(\left({\operatorname{exp}(\left(x_{0} \cdot x_{0}\right))^{\left(x_{0} \cdot \left(\theta_{0} \cdot x_{0}\right)\right)}} \cdot \operatorname{exp}(\left(\left(\theta_{1} + x_{1}\right) \cdot \theta_{2}\right))\right))^{\left(\operatorname{log}(\frac{\left(\theta_{3} \cdot \left(x_{1} + \theta_{4}\right)\right)}{\operatorname{exp}(\operatorname{log}(x_{0}))}) \cdot \left(\theta_{5} \cdot \left(\operatorname{log}(x_{1}) \cdot \left(\theta_{6} + x_{1}\right)\right)\right)\right)}}$$",0.24223659468543468;0.14210869871802023;-0.6518162799704387;0.19197009553435962;0.8511951191992848;0.5975879150006314;0.4837142598400124,38,,,,Infinity,,,,,, +367,0,(Exp((((x1 ^ (x0 ^ x1)) * -7.912547645682231) * ((x1 * (x0 * 11.6269545805862)) * Exp(Exp((x1 * 3.3788913488388546)))))) ^ Log(((Exp((x0 + x0)) ^ (Exp(x0) / Exp(x1))) + (Exp((x1 / x0)) / Log((x0 + Exp(x1))))))),"np.exp(((x[:, 1] ** x[:, 0] ** x[:, 1] * t[0]) * ((x[:, 1] * (x[:, 0] * t[1])) * np.exp(np.exp((x[:, 1] * t[2])))))) ** np.log((np.exp((x[:, 0] + x[:, 0])) ** (np.exp(x[:, 0]) / np.exp(x[:, 1])) + (np.exp((x[:, 1] / x[:, 0])) / np.log((x[:, 0] + np.exp(x[:, 1]))))))","$${\operatorname{exp}(\left(\left({x_{1}^{{x_{0}^{x_{1}}}}} \cdot \theta_{0}\right) \cdot \left(\left(x_{1} \cdot \left(x_{0} \cdot \theta_{1}\right)\right) \cdot \operatorname{exp}(\operatorname{exp}(\left(x_{1} \cdot \theta_{2}\right)))\right)\right))^{\operatorname{log}(\left({\operatorname{exp}(\left(x_{0} + x_{0}\right))^{\frac{\operatorname{exp}(x_{0})}{\operatorname{exp}(x_{1})}}} + \frac{\operatorname{exp}(\frac{x_{1}}{x_{0}})}{\operatorname{log}(\left(x_{0} + \operatorname{exp}(x_{1})\right))}\right))}}$$",-7.912547645682231;11.6269545805862;3.3788913488388546,43,1.4573863746825691e-2,1.4573863746825691e-2,1.4573863746825691e-2,1.4573863746825691e-2,-0.3448930638062355,-0.3448930638062355,-0.3448930638062355,100.5565481089325,100.5565481089325,100.5565481089325 +368,0,(((Log(x0) * Exp(Log(x1))) + (5.229306655037731e-2 + ((((x0 / x1) ^ 1.4592439653642593) ^ Log(x0)) / (Exp((x0 + x0)) * -0.8684239929430104)))) * -0.5546872026119444),"(((np.log(x[:, 0]) * np.exp(np.log(x[:, 1]))) + (t[0] + ((x[:, 0] / x[:, 1]) ** t[1] ** np.log(x[:, 0]) / (np.exp((x[:, 0] + x[:, 0])) * t[2])))) * t[3])","$$\left(\left(\left(\operatorname{log}(x_{0}) \cdot \operatorname{exp}(\operatorname{log}(x_{1}))\right) + \left(\theta_{0} + \frac{{{\frac{x_{0}}{x_{1}}^{\theta_{1}}}^{\operatorname{log}(x_{0})}}}{\left(\operatorname{exp}(\left(x_{0} + x_{0}\right)) \cdot \theta_{2}\right)}\right)\right) \cdot \theta_{3}\right)$$",5.229306655037731e-2;1.4592439653642593;-0.8684239929430104;-0.5546872026119444,26,3.1422566761350515e-3,3.1422566761350515e-3,3.1422566761350515e-3,3.1422566761350515e-3,0.7100289064145172,0.7100289064145172,0.7100289064145172,65.2143667832208,65.2143667832208,65.2143667832208 +369,0,((-0.1579789913784282 + ((x1 + x0) / (x0 + x1))) / (Log(Log((0.19075706183866636 ^ (Log(x1) ^ Log(x0))))) + Log(Exp((x0 * x0))))),"((t[0] + ((x[:, 1] + x[:, 0]) / (x[:, 0] + x[:, 1]))) / (np.log(np.log(t[1] ** np.log(x[:, 1]) ** np.log(x[:, 0]))) + np.log(np.exp((x[:, 0] * x[:, 0])))))","$$\frac{\left(\theta_{0} + \frac{\left(x_{1} + x_{0}\right)}{\left(x_{0} + x_{1}\right)}\right)}{\left(\operatorname{log}(\operatorname{log}({\theta_{1}^{{\operatorname{log}(x_{1})^{\operatorname{log}(x_{0})}}}})) + \operatorname{log}(\operatorname{exp}(\left(x_{0} \cdot x_{0}\right)))\right)}$$",-0.1579789913784282;0.19075706183866636,25,,,,Infinity,,,,,, +370,0,(((-0.5294107906110281 ^ Exp((x0 + 0.9169423442478464))) / (0.5268236070875418 ^ (x0 / x1))) * (((0.8583272917623552 ^ x1) + (0.19313652855854313 / x1)) ^ ((0.6233035735136526 + x0) / x0))),"((t[0] ** np.exp((x[:, 0] + t[1])) / t[2] ** (x[:, 0] / x[:, 1])) * (t[3] ** x[:, 1] + (t[4] / x[:, 1])) ** ((t[5] + x[:, 0]) / x[:, 0]))","$$\left(\frac{{\theta_{0}^{\operatorname{exp}(\left(x_{0} + \theta_{1}\right))}}}{{\theta_{2}^{\frac{x_{0}}{x_{1}}}}} \cdot {\left({\theta_{3}^{x_{1}}} + \frac{\theta_{4}}{x_{1}}\right)^{\frac{\left(\theta_{5} + x_{0}\right)}{x_{0}}}}\right)$$",-0.5294107906110281;0.9169423442478464;0.5268236070875418;0.8583272917623552;0.19313652855854313;0.6233035735136526,26,,,,Infinity,,,,,, +371,0,(Exp((-87141.94762908242 + Exp(x0))) / ((x1 + 78053.67801120912) * 256882.54283008992)),"(np.exp((t[0] + np.exp(x[:, 0]))) / ((x[:, 1] + t[1]) * t[2]))","$$\frac{\operatorname{exp}(\left(\theta_{0} + \operatorname{exp}(x_{0})\right))}{\left(\left(x_{1} + \theta_{1}\right) \cdot \theta_{2}\right)}$$",-87141.94762908242;78053.67801120912;256882.54283008992,11,1.4573554749975553e-2,1.4573554749975553e-2,1.4573554749975553e-2,1.4573554749975553e-2,-0.3448645492182232,-0.3448645492182232,-0.3448645492182232,27.480902644986347,27.480902644986347,27.480902644986347 +372,0,Exp((((-1.8428351210334972 + ((x1 ^ x0) * 0.38963529443998784)) / (((x0 ^ -3.098761221922056) / x1) / (Exp(x1) + -0.7340218827003249))) * Exp(((Log((x1 ^ 2.1207500985845784)) + (x1 * 1.6028394822007002)) / ((x0 + x0) * ((-0.14873134219743303 / x0) / x0)))))),"np.exp((((t[0] + (x[:, 1] ** x[:, 0] * t[1])) / ((x[:, 0] ** t[2] / x[:, 1]) / (np.exp(x[:, 1]) + t[3]))) * np.exp(((np.log(x[:, 1] ** t[4]) + (x[:, 1] * t[5])) / ((x[:, 0] + x[:, 0]) * ((t[6] / x[:, 0]) / x[:, 0]))))))","$$\operatorname{exp}(\left(\frac{\left(\theta_{0} + \left({x_{1}^{x_{0}}} \cdot \theta_{1}\right)\right)}{\frac{\frac{{x_{0}^{\theta_{2}}}}{x_{1}}}{\left(\operatorname{exp}(x_{1}) + \theta_{3}\right)}} \cdot \operatorname{exp}(\frac{\left(\operatorname{log}({x_{1}^{\theta_{4}}}) + \left(x_{1} \cdot \theta_{5}\right)\right)}{\left(\left(x_{0} + x_{0}\right) \cdot \frac{\frac{\theta_{6}}{x_{0}}}{x_{0}}\right)})\right))$$",-1.8428351210334972;0.38963529443998784;-3.098761221922056;-0.7340218827003249;2.1207500985845784;1.6028394822007002;-0.14873134219743303,39,7.970569067032024e-3,7.970569067032024e-3,7.970569067032024e-3,7.970569067032024e-3,0.2644666343079598,0.2644666343079598,0.2644666343079598,99.84540657584637,99.84540657584637,99.84540657584637 +373,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 +374,0,(Exp(Exp(((0.16657189278252882 / ((0.573451605276171 + x0) * -0.4875479192233212)) ^ -0.3644489141848827))) * ((Exp(Log((x1 / (x1 / x1)))) + Log((x1 * -0.6088068758732776))) ^ (((x0 + 0.1529612641704563) * -2.5013851386822217e-2) / ((x0 + -0.1073145235618207) ^ 0.9979245228147716)))),"(np.exp(np.exp((t[0] / ((t[1] + x[:, 0]) * t[2])) ** t[3])) * (np.exp(np.log((x[:, 1] / (x[:, 1] / x[:, 1])))) + np.log((x[:, 1] * t[4]))) ** (((x[:, 0] + t[5]) * t[6]) / (x[:, 0] + t[7]) ** t[8]))","$$\left(\operatorname{exp}(\operatorname{exp}({\frac{\theta_{0}}{\left(\left(\theta_{1} + x_{0}\right) \cdot \theta_{2}\right)}^{\theta_{3}}})) \cdot {\left(\operatorname{exp}(\operatorname{log}(\frac{x_{1}}{\frac{x_{1}}{x_{1}}})) + \operatorname{log}(\left(x_{1} \cdot \theta_{4}\right))\right)^{\frac{\left(\left(x_{0} + \theta_{5}\right) \cdot \theta_{6}\right)}{{\left(x_{0} + \theta_{7}\right)^{\theta_{8}}}}}}\right)$$",0.16657189278252882;0.573451605276171;-0.4875479192233212;-0.3644489141848827;-0.6088068758732776;0.1529612641704563;-2.5013851386822217e-2;-0.1073145235618207;0.9979245228147716,36,,,,Infinity,,,,,, +375,0,Exp(((Log(x1) * ((Log((0.3220048915007221 + x0)) ^ 0.1781327349621159) / x1)) ^ ((-0.6968896943315938 + (x1 / x1)) / x0))),"np.exp((np.log(x[:, 1]) * (np.log((t[0] + x[:, 0])) ** t[1] / x[:, 1])) ** ((t[2] + (x[:, 1] / x[:, 1])) / x[:, 0]))","$$\operatorname{exp}({\left(\operatorname{log}(x_{1}) \cdot \frac{{\operatorname{log}(\left(\theta_{0} + x_{0}\right))^{\theta_{1}}}}{x_{1}}\right)^{\frac{\left(\theta_{2} + \frac{x_{1}}{x_{1}}\right)}{x_{0}}}})$$",0.3220048915007221;0.1781327349621159;-0.6968896943315938,20,,,,Infinity,,,,,, +376,0,(Log((Exp(Exp(x1)) / (x0 * 0.7109204157567375))) ^ Exp(((x1 * 0.5466125177724963) + (Exp((x0 + x1)) / x0)))),"np.log((np.exp(np.exp(x[:, 1])) / (x[:, 0] * t[0]))) ** np.exp(((x[:, 1] * t[1]) + (np.exp((x[:, 0] + x[:, 1])) / x[:, 0])))","$${\operatorname{log}(\frac{\operatorname{exp}(\operatorname{exp}(x_{1}))}{\left(x_{0} \cdot \theta_{0}\right)})^{\operatorname{exp}(\left(\left(x_{1} \cdot \theta_{1}\right) + \frac{\operatorname{exp}(\left(x_{0} + x_{1}\right))}{x_{0}}\right))}}$$",0.7109204157567375;0.5466125177724963,20,2.0363287805484993e224,2.0363287805484993e224,2.0363287805484993e224,2.0363287805484993e224,-1.8791478362662656e226,-1.8791478362662656e226,-1.8791478362662656e226,1.8326959024936494e224,1.8326959024936494e224,1.8326959024936494e224 +377,0,(-0.9956750177908841 + x0),"(t[0] + x[:, 0])","$$\left(\theta_{0} + x_{0}\right)$$",-0.9956750177908841,3,0.15105329635364,0.15105329635364,0.15105329635364,0.15105329635364,-12.939373529228037,-12.939373529228037,-12.939373529228037,5.452709768090245,5.452709768090245,5.452709768090245 +378,0,(((Log((0.8038065313675858 ^ ((x1 + -0.7527652113245995) + -0.11106988222645775))) / ((((-4.616853111880037e-2 ^ x0) + 0.30597840973275614) * 2.785914814332313e-2) + x0)) + Exp(((-0.23544942746481712 ^ x1) / ((Log(x1) * x1) * -0.9143507619028746)))) / ((((0.6978465143799752 + x0) + 3.708835195462368e-2) ^ x1) ^ 0.5848855754072313)),"(((np.log(t[0] ** ((x[:, 1] + t[1]) + t[2])) / (((t[3] ** x[:, 0] + t[4]) * t[5]) + x[:, 0])) + np.exp((t[6] ** x[:, 1] / ((np.log(x[:, 1]) * x[:, 1]) * t[7])))) / ((t[8] + x[:, 0]) + t[9]) ** x[:, 1] ** t[10])","$$\frac{\left(\frac{\operatorname{log}({\theta_{0}^{\left(\left(x_{1} + \theta_{1}\right) + \theta_{2}\right)}})}{\left(\left(\left({\theta_{3}^{x_{0}}} + \theta_{4}\right) \cdot \theta_{5}\right) + x_{0}\right)} + \operatorname{exp}(\frac{{\theta_{6}^{x_{1}}}}{\left(\left(\operatorname{log}(x_{1}) \cdot x_{1}\right) \cdot \theta_{7}\right)})\right)}{{{\left(\left(\theta_{8} + x_{0}\right) + \theta_{9}\right)^{x_{1}}}^{\theta_{10}}}}$$",0.8038065313675858;-0.7527652113245995;-0.11106988222645775;-4.616853111880037e-2;0.30597840973275614;2.785914814332313e-2;-0.23544942746481712;-0.9143507619028746;0.6978465143799752;3.708835195462368e-2;0.5848855754072313,40,,,,Infinity,,,,,, +379,0,((Exp((((x0 ^ 7.052972633808667) ^ x1) * -5.286081383455715)) ^ x0) * x0),"(np.exp((x[:, 0] ** t[0] ** x[:, 1] * t[1])) ** x[:, 0] * x[:, 0])","$$\left({\operatorname{exp}(\left({{x_{0}^{\theta_{0}}}^{x_{1}}} \cdot \theta_{1}\right))^{x_{0}}} \cdot x_{0}\right)$$",7.052972633808667;-5.286081383455715,12,1.6427828050891288e-3,1.6427828050891288e-3,1.6427828050891288e-3,1.6427828050891288e-3,0.8484020958144515,0.8484020958144515,0.8484020958144515,25.544442005996526,25.544442005996526,25.544442005996526 +380,0,(Exp((-5.075945270411527 / Exp(x1))) ^ ((-0.5596320513185477 / ((x1 ^ -2.508255619516825e-2) ^ 1.1957000909818955e-2)) + Exp((-0.6080152390637005 + x0)))),"np.exp((t[0] / np.exp(x[:, 1]))) ** ((t[1] / x[:, 1] ** t[2] ** t[3]) + np.exp((t[4] + x[:, 0])))","$${\operatorname{exp}(\frac{\theta_{0}}{\operatorname{exp}(x_{1})})^{\left(\frac{\theta_{1}}{{{x_{1}^{\theta_{2}}}^{\theta_{3}}}} + \operatorname{exp}(\left(\theta_{4} + x_{0}\right))\right)}}$$",-5.075945270411527;-0.5596320513185477;-2.508255619516825e-2;1.1957000909818955e-2;-0.6080152390637005,18,2.4603770610147277e-3,2.4603770610147277e-3,2.4603770610147277e-3,2.4603770610147277e-3,0.7729535488193792,0.7729535488193792,0.7729535488193792,45.13322169918875,45.13322169918875,45.13322169918875 +381,0,((Exp(Exp(x1)) + ((1.126077989088653 + (x1 + 1.3820420005023495)) / Log(x1))) ^ (Exp(Log((5.937844471663306 + x1))) ^ (-4.906675648256525 * x0))),"(np.exp(np.exp(x[:, 1])) + ((t[0] + (x[:, 1] + t[1])) / np.log(x[:, 1]))) ** np.exp(np.log((t[2] + x[:, 1]))) ** (t[3] * x[:, 0])","$${\left(\operatorname{exp}(\operatorname{exp}(x_{1})) + \frac{\left(\theta_{0} + \left(x_{1} + \theta_{1}\right)\right)}{\operatorname{log}(x_{1})}\right)^{{\operatorname{exp}(\operatorname{log}(\left(\theta_{2} + x_{1}\right)))^{\left(\theta_{3} \cdot x_{0}\right)}}}}$$",1.126077989088653;1.3820420005023495;5.937844471663306;-4.906675648256525,22,0.8929472948148743,0.8929472948148743,0.8929472948148743,0.8929472948148743,-81.4022128931071,-81.4022128931071,-81.4022128931071,57.226293008200784,57.226293008200784,57.226293008200784 +382,0,(((-0.5105872218389083 / Exp((x1 * -0.11401808112307865))) ^ ((Log(x0) + -0.2385776540747616) ^ (-0.3753279387831623 + (x0 ^ -0.7464128574082617)))) / Log(((Log(Exp(x1)) ^ (0.185730947572048 + Exp(x0))) ^ ((-0.9428743620067016 / Exp(x0)) * ((x1 ^ x1) ^ x0))))),"((t[0] / np.exp((x[:, 1] * t[1]))) ** (np.log(x[:, 0]) + t[2]) ** (t[3] + x[:, 0] ** t[4]) / np.log(np.log(np.exp(x[:, 1])) ** (t[5] + np.exp(x[:, 0])) ** ((t[6] / np.exp(x[:, 0])) * x[:, 1] ** x[:, 1] ** x[:, 0])))","$$\frac{{\frac{\theta_{0}}{\operatorname{exp}(\left(x_{1} \cdot \theta_{1}\right))}^{{\left(\operatorname{log}(x_{0}) + \theta_{2}\right)^{\left(\theta_{3} + {x_{0}^{\theta_{4}}}\right)}}}}}{\operatorname{log}({{\operatorname{log}(\operatorname{exp}(x_{1}))^{\left(\theta_{5} + \operatorname{exp}(x_{0})\right)}}^{\left(\frac{\theta_{6}}{\operatorname{exp}(x_{0})} \cdot {{x_{1}^{x_{1}}}^{x_{0}}}\right)}})}$$",-0.5105872218389083;-0.11401808112307865;-0.2385776540747616;-0.3753279387831623;-0.7464128574082617;0.185730947572048;-0.9428743620067016,38,,,,Infinity,,,,,, +383,0,Log(Log(((x1 * Exp(x0)) ^ (0.443500729634964 + (x1 ^ 2.7477078454148596e-2))))),"np.log(np.log((x[:, 1] * np.exp(x[:, 0])) ** (t[0] + x[:, 1] ** t[1])))","$$\operatorname{log}(\operatorname{log}({\left(x_{1} \cdot \operatorname{exp}(x_{0})\right)^{\left(\theta_{0} + {x_{1}^{\theta_{1}}}\right)}}))$$",0.443500729634964;2.7477078454148596e-2,12,,,,Infinity,,,,,, +384,0,((6.296814140824052e-2 * ((x1 + x1) * 0.48542466040754373)) / x1),"((t[0] * ((x[:, 1] + x[:, 1]) * t[1])) / x[:, 1])","$$\frac{\left(\theta_{0} \cdot \left(\left(x_{1} + x_{1}\right) \cdot \theta_{1}\right)\right)}{x_{1}}$$",6.296814140824052e-2;0.48542466040754373,9,1.0836447997012907e-2,1.0836447997012907e-2,1.0836447997012907e-2,1.0836447997012907e-2,-4.487099580785525e-11,-4.487099580785525e-11,-4.487099580785525e-11,18.536543885839496,18.536543885839496,18.536543885839496 +385,0,(((x0 + (x1 * 0.5741757231813699)) / Exp((x1 ^ -2.360477047201456))) * (Log(Exp((x0 * x1))) * (1.0661195385663442 + x0))),"(((x[:, 0] + (x[:, 1] * t[0])) / np.exp(x[:, 1] ** t[1])) * (np.log(np.exp((x[:, 0] * x[:, 1]))) * (t[2] + x[:, 0])))","$$\left(\frac{\left(x_{0} + \left(x_{1} \cdot \theta_{0}\right)\right)}{\operatorname{exp}({x_{1}^{\theta_{1}}})} \cdot \left(\operatorname{log}(\operatorname{exp}(\left(x_{0} \cdot x_{1}\right))) \cdot \left(\theta_{2} + x_{0}\right)\right)\right)$$",0.5741757231813699;-2.360477047201456;1.0661195385663442,20,1.4331343701244996e-2,1.4331343701244996e-2,1.4331343701244996e-2,1.4331343701244996e-2,-0.32251303248430907,-0.32251303248430907,-0.32251303248430907,50.020164562158435,50.020164562158435,50.020164562158435 +386,0,Exp(Exp(((((x0 * x1) + Log(x1)) / ((x0 * (x0 / x0)) * -0.6024545698705194)) * (-0.44974925730136883 / Log(((x1 + x1) * -0.6866750033480645)))))),"np.exp(np.exp(((((x[:, 0] * x[:, 1]) + np.log(x[:, 1])) / ((x[:, 0] * (x[:, 0] / x[:, 0])) * t[0])) * (t[1] / np.log(((x[:, 1] + x[:, 1]) * t[2]))))))","$$\operatorname{exp}(\operatorname{exp}(\left(\frac{\left(\left(x_{0} \cdot x_{1}\right) + \operatorname{log}(x_{1})\right)}{\left(\left(x_{0} \cdot \frac{x_{0}}{x_{0}}\right) \cdot \theta_{0}\right)} \cdot \frac{\theta_{1}}{\operatorname{log}(\left(\left(x_{1} + x_{1}\right) \cdot \theta_{2}\right))}\right)))$$",-0.6024545698705194;-0.44974925730136883;-0.6866750033480645,25,,,,Infinity,,,,,, +387,0,Exp(((Log((Log(x0) * 0.8654336480155295)) / (Log(Log(x0)) + (0.9482243873768081 + ((x0 * 9.68467854039311e-2) + x0)))) * 0.9879615675182041)),"np.exp(((np.log((np.log(x[:, 0]) * t[0])) / (np.log(np.log(x[:, 0])) + (t[1] + ((x[:, 0] * t[2]) + x[:, 0])))) * t[3]))","$$\operatorname{exp}(\left(\frac{\operatorname{log}(\left(\operatorname{log}(x_{0}) \cdot \theta_{0}\right))}{\left(\operatorname{log}(\operatorname{log}(x_{0})) + \left(\theta_{1} + \left(\left(x_{0} \cdot \theta_{2}\right) + x_{0}\right)\right)\right)} \cdot \theta_{3}\right))$$",0.8654336480155295;0.9482243873768081;9.68467854039311e-2;0.9879615675182041,20,,,,Infinity,,,,,, +388,0,Exp((x1 * -55.781589458130306)),"np.exp((x[:, 1] * t[0]))","$$\operatorname{exp}(\left(x_{1} \cdot \theta_{0}\right))$$",-55.781589458130306,4,1.4573896716579228e-2,1.4573896716579228e-2,1.4573896716579228e-2,1.4573896716579228e-2,-0.3448961062933631,-0.3448961062933631,-0.3448961062933631,7.579218886892124,7.579218886892124,7.579218886892124 +389,0,Exp(Log(((((-0.8170214754354692 + x1) + -0.2998362096080194) ^ ((0.9412105623799939 / x1) + 0.22601741077706883)) ^ (Log(((x0 ^ 0.670168783269687) + -0.7712443799505657)) / Log(Log((x1 * 9.23027967695651e-2))))))),"np.exp(np.log(((t[0] + x[:, 1]) + t[1]) ** ((t[2] / x[:, 1]) + t[3]) ** (np.log((x[:, 0] ** t[4] + t[5])) / np.log(np.log((x[:, 1] * t[6]))))))","$$\operatorname{exp}(\operatorname{log}({{\left(\left(\theta_{0} + x_{1}\right) + \theta_{1}\right)^{\left(\frac{\theta_{2}}{x_{1}} + \theta_{3}\right)}}^{\frac{\operatorname{log}(\left({x_{0}^{\theta_{4}}} + \theta_{5}\right))}{\operatorname{log}(\operatorname{log}(\left(x_{1} \cdot \theta_{6}\right)))}}}))$$",-0.8170214754354692;-0.2998362096080194;0.9412105623799939;0.22601741077706883;0.670168783269687;-0.7712443799505657;9.23027967695651e-2,26,,,,Infinity,,,,,, +390,0,Exp(((Exp(((x1 ^ x0) * -0.43306684673498963)) * ((x1 ^ -0.5314283983872532) + (-0.6570231070328547 * (x0 + -0.613334205463964)))) * (((0.8984056633801967 / Exp(x0)) * Log((x1 ^ 0.27060454069792883))) ^ -0.22250259847089415))),"np.exp(((np.exp((x[:, 1] ** x[:, 0] * t[0])) * (x[:, 1] ** t[1] + (t[2] * (x[:, 0] + t[3])))) * ((t[4] / np.exp(x[:, 0])) * np.log(x[:, 1] ** t[5])) ** t[6]))","$$\operatorname{exp}(\left(\left(\operatorname{exp}(\left({x_{1}^{x_{0}}} \cdot \theta_{0}\right)) \cdot \left({x_{1}^{\theta_{1}}} + \left(\theta_{2} \cdot \left(x_{0} + \theta_{3}\right)\right)\right)\right) \cdot {\left(\frac{\theta_{4}}{\operatorname{exp}(x_{0})} \cdot \operatorname{log}({x_{1}^{\theta_{5}}})\right)^{\theta_{6}}}\right))$$",-0.43306684673498963;-0.5314283983872532;-0.6570231070328547;-0.613334205463964;0.8984056633801967;0.27060454069792883;-0.22250259847089415,29,,,,Infinity,,,,,, +391,0,Log((Log((Exp((0.6509940308206308 * x1)) * -0.950616982755486)) + Log(Exp((-0.5465261874328403 * (Log(x1) * 0.6650829479520628)))))),"np.log((np.log((np.exp((t[0] * x[:, 1])) * t[1])) + np.log(np.exp((t[2] * (np.log(x[:, 1]) * t[3]))))))","$$\operatorname{log}(\left(\operatorname{log}(\left(\operatorname{exp}(\left(\theta_{0} \cdot x_{1}\right)) \cdot \theta_{1}\right)) + \operatorname{log}(\operatorname{exp}(\left(\theta_{2} \cdot \left(\operatorname{log}(x_{1}) \cdot \theta_{3}\right)\right)))\right))$$",0.6509940308206308;-0.950616982755486;-0.5465261874328403;0.6650829479520628,17,,,,Infinity,,,,,, +392,0,((((x1 * 13.379846591499017) / (Exp(x1) + (x1 * (x0 * -0.942898810430549)))) / (0.3691550535138762 + (((5.269314264762601 + (x1 / x0)) ^ (x1 ^ 9.480073875287031)) + -0.4301473665265012))) / (Exp((5.501718731774389 ^ x0)) + 5.480984190590707e-2)),"((((x[:, 1] * t[0]) / (np.exp(x[:, 1]) + (x[:, 1] * (x[:, 0] * t[1])))) / (t[2] + ((t[3] + (x[:, 1] / x[:, 0])) ** x[:, 1] ** t[4] + t[5]))) / (np.exp(t[6] ** x[:, 0]) + t[7]))","$$\frac{\frac{\frac{\left(x_{1} \cdot \theta_{0}\right)}{\left(\operatorname{exp}(x_{1}) + \left(x_{1} \cdot \left(x_{0} \cdot \theta_{1}\right)\right)\right)}}{\left(\theta_{2} + \left({\left(\theta_{3} + \frac{x_{1}}{x_{0}}\right)^{{x_{1}^{\theta_{4}}}}} + \theta_{5}\right)\right)}}{\left(\operatorname{exp}({\theta_{6}^{x_{0}}}) + \theta_{7}\right)}$$",13.379846591499017;-0.942898810430549;0.3691550535138762;5.269314264762601;9.480073875287031;-0.4301473665265012;5.501718731774389;5.480984190590707e-2,33,1.5878838885174068e-3,1.5878838885174068e-3,1.5878838885174068e-3,1.5878838885174068e-3,0.8534682315620062,0.8534682315620062,0.8534682315620062,84.79039945387538,84.79039945387538,84.79039945387538 +393,0,(((x0 ^ -4.648733278948605) + (x0 + 7.292217673083389)) * (Log((x1 * x0)) * -3.3302493413795782e-3)),"((x[:, 0] ** t[0] + (x[:, 0] + t[1])) * (np.log((x[:, 1] * x[:, 0])) * t[2]))","$$\left(\left({x_{0}^{\theta_{0}}} + \left(x_{0} + \theta_{1}\right)\right) \cdot \left(\operatorname{log}(\left(x_{1} \cdot x_{0}\right)) \cdot \theta_{2}\right)\right)$$",-4.648733278948605;7.292217673083389;-3.3302493413795782e-3,14,9.83290025542597e-3,9.83290025542597e-3,9.83290025542597e-3,9.83290025542597e-3,9.260855046066341e-2,9.260855046066341e-2,9.260855046066341e-2,33.314366503107195,33.314366503107195,33.314366503107195 +394,0,((Log((x0 * x1)) / ((x1 * -0.9348590945180999) + 0.7789122364020267)) ^ ((-0.19889657489864 * (0.44792621852464654 / (0.37732176870933 + Exp((x0 + 0.2013604433435473))))) ^ Exp(((3.0154062418302763e-3 * x0) * x1)))),"(np.log((x[:, 0] * x[:, 1])) / ((x[:, 1] * t[0]) + t[1])) ** (t[2] * (t[3] / (t[4] + np.exp((x[:, 0] + t[5]))))) ** np.exp(((t[6] * x[:, 0]) * x[:, 1]))","$${\frac{\operatorname{log}(\left(x_{0} \cdot x_{1}\right))}{\left(\left(x_{1} \cdot \theta_{0}\right) + \theta_{1}\right)}^{{\left(\theta_{2} \cdot \frac{\theta_{3}}{\left(\theta_{4} + \operatorname{exp}(\left(x_{0} + \theta_{5}\right))\right)}\right)^{\operatorname{exp}(\left(\left(\theta_{6} \cdot x_{0}\right) \cdot x_{1}\right))}}}}$$",-0.9348590945180999;0.7789122364020267;-0.19889657489864;0.44792621852464654;0.37732176870933;0.2013604433435473;3.0154062418302763e-3,28,,,,Infinity,,,,,, +395,0,Log((-0.8130647961541064 / x0)),"np.log((t[0] / x[:, 0]))","$$\operatorname{log}(\frac{\theta_{0}}{x_{0}})$$",-0.8130647961541064,4,,,,Infinity,,,,,, +396,0,Exp(((Log(x0) * -3.6168182627074277) + (-2.311346003098002 + (x0 + -1.843417352976074)))),"np.exp(((np.log(x[:, 0]) * t[0]) + (t[1] + (x[:, 0] + t[2]))))","$$\operatorname{exp}(\left(\left(\operatorname{log}(x_{0}) \cdot \theta_{0}\right) + \left(\theta_{1} + \left(x_{0} + \theta_{2}\right)\right)\right))$$",-3.6168182627074277;-2.311346003098002;-1.843417352976074,11,5.334750830319646e-3,5.334750830319646e-3,5.334750830319646e-3,5.334750830319646e-3,0.5077030008329706,0.5077030008329706,0.5077030008329706,25.776930243358812,25.776930243358812,25.776930243358812 +397,0,(((x1 + 0.5414802029987323) * (1.6251841832583996e-4 ^ (x1 * 0.8193514746804531))) / (2.4477011707715195 / (-0.8616445003943854 + x0))),"(((x[:, 1] + t[0]) * t[1] ** (x[:, 1] * t[2])) / (t[3] / (t[4] + x[:, 0])))","$$\frac{\left(\left(x_{1} + \theta_{0}\right) \cdot {\theta_{1}^{\left(x_{1} \cdot \theta_{2}\right)}}\right)}{\frac{\theta_{3}}{\left(\theta_{4} + x_{0}\right)}}$$",0.5414802029987323;1.6251841832583996e-4;0.8193514746804531;2.4477011707715195;-0.8616445003943854,15,1.5464676434292281e-2,1.5464676434292281e-2,1.5464676434292281e-2,1.5464676434292281e-2,-0.42709829265540433,-0.42709829265540433,-0.42709829265540433,39.30719512145876,39.30719512145876,39.30719512145876 +398,0,((Exp(Exp(x1)) * (x0 + (x1 + -2.187483516233691))) + 3.2544893231580776),"((np.exp(np.exp(x[:, 1])) * (x[:, 0] + (x[:, 1] + t[0]))) + t[1])","$$\left(\left(\operatorname{exp}(\operatorname{exp}(x_{1})) \cdot \left(x_{0} + \left(x_{1} + \theta_{0}\right)\right)\right) + \theta_{1}\right)$$",-2.187483516233691;3.2544893231580776,11,1.9940621750053285,1.9940621750053285,1.9940621750053285,1.9940621750053285,-183.0143722042935,-183.0143722042935,-183.0143722042935,25.54585998974868,25.54585998974868,25.54585998974868 +399,0,(((Log((x0 * Log(x0))) / (x0 / x1)) ^ (Exp(Log(x1)) ^ Log(x0))) ^ (Log(Exp((0.4144480025149665 * Exp((x1 * -0.8211425289839988))))) / (0.14324834480329984 ^ x1))),"(np.log((x[:, 0] * np.log(x[:, 0]))) / (x[:, 0] / x[:, 1])) ** np.exp(np.log(x[:, 1])) ** np.log(x[:, 0]) ** (np.log(np.exp((t[0] * np.exp((x[:, 1] * t[1]))))) / t[2] ** x[:, 1])","$${{\frac{\operatorname{log}(\left(x_{0} \cdot \operatorname{log}(x_{0})\right))}{\frac{x_{0}}{x_{1}}}^{{\operatorname{exp}(\operatorname{log}(x_{1}))^{\operatorname{log}(x_{0})}}}}^{\frac{\operatorname{log}(\operatorname{exp}(\left(\theta_{0} \cdot \operatorname{exp}(\left(x_{1} \cdot \theta_{1}\right))\right)))}{{\theta_{2}^{x_{1}}}}}}$$",0.4144480025149665;-0.8211425289839988;0.14324834480329984,29,,,,Infinity,,,,,, +400,0,(0.5589703510945292 ^ (((x0 * -0.9564265205286525) * 0.7239866824804038) ^ x0)),"t[0] ** ((x[:, 0] * t[1]) * t[2]) ** x[:, 0]","$${\theta_{0}^{{\left(\left(x_{0} \cdot \theta_{1}\right) \cdot \theta_{2}\right)^{x_{0}}}}}$$",0.5589703510945292;-0.9564265205286525;0.7239866824804038,9,,,,Infinity,,,,,, +401,0,((-0.19949107774706082 + ((x1 * Exp(x1)) + ((x0 ^ -0.3765592945900294) ^ 0.3710102054709429))) / (((4.270017538958333e-2 * (x1 ^ x1)) ^ (x1 + (x0 + -0.2946907649882392))) ^ Log((Exp((-0.8329927056086268 / x0)) ^ (x0 ^ x1))))),"((t[0] + ((x[:, 1] * np.exp(x[:, 1])) + x[:, 0] ** t[1] ** t[2])) / (t[3] * x[:, 1] ** x[:, 1]) ** (x[:, 1] + (x[:, 0] + t[4])) ** np.log(np.exp((t[5] / x[:, 0])) ** x[:, 0] ** x[:, 1]))","$$\frac{\left(\theta_{0} + \left(\left(x_{1} \cdot \operatorname{exp}(x_{1})\right) + {{x_{0}^{\theta_{1}}}^{\theta_{2}}}\right)\right)}{{{\left(\theta_{3} \cdot {x_{1}^{x_{1}}}\right)^{\left(x_{1} + \left(x_{0} + \theta_{4}\right)\right)}}^{\operatorname{log}({\operatorname{exp}(\frac{\theta_{5}}{x_{0}})^{{x_{0}^{x_{1}}}}})}}}$$",-0.19949107774706082;-0.3765592945900294;0.3710102054709429;4.270017538958333e-2;-0.2946907649882392;-0.8329927056086268,34,9.96595232584315e-3,9.96595232584315e-3,9.96595232584315e-3,9.96595232584315e-3,8.03303509565616e-2,8.03303509565616e-2,8.03303509565616e-2,86.84015459873058,86.84015459873058,86.84015459873058 +402,0,(((x0 ^ 0.42465666442791616) ^ -0.2711389126598546) + ((x1 / x1) ^ x1)),"(x[:, 0] ** t[0] ** t[1] + (x[:, 1] / x[:, 1]) ** x[:, 1])","$$\left({{x_{0}^{\theta_{0}}}^{\theta_{1}}} + {\frac{x_{1}}{x_{1}}^{x_{1}}}\right)$$",0.42465666442791616;-0.2711389126598546,11,3.7659213689679794,3.7659213689679794,3.7659213689679794,3.7659213689679794,-346.5235953861491,-346.5235953861491,-346.5235953861491,27.140533264315067,27.140533264315067,27.140533264315067 +403,0,(0.7411909602588301 ^ Exp(((x1 + Exp((0.8780574800074716 + x0))) * 0.3517704776619639))),"t[0] ** np.exp(((x[:, 1] + np.exp((t[1] + x[:, 0]))) * t[2]))","$${\theta_{0}^{\operatorname{exp}(\left(\left(x_{1} + \operatorname{exp}(\left(\theta_{1} + x_{0}\right))\right) \cdot \theta_{2}\right))}}$$",0.7411909602588301;0.8780574800074716;0.3517704776619639,11,5.986220738098148e-3,5.986220738098148e-3,5.986220738098148e-3,5.986220738098148e-3,0.44758460152100843,0.44758460152100843,0.44758460152100843,27.473174044375657,27.473174044375657,27.473174044375657 +404,0,(Log((Exp((0.34887566134756004 ^ (Exp(x1) * 2.7625920499842236))) / ((x1 ^ (-0.18512426214845995 / x1)) ^ x1))) + x1),"(np.log((np.exp(t[0] ** (np.exp(x[:, 1]) * t[1])) / x[:, 1] ** (t[2] / x[:, 1]) ** x[:, 1])) + x[:, 1])","$$\left(\operatorname{log}(\frac{\operatorname{exp}({\theta_{0}^{\left(\operatorname{exp}(x_{1}) \cdot \theta_{1}\right)}})}{{{x_{1}^{\frac{\theta_{2}}{x_{1}}}}^{x_{1}}}}) + x_{1}\right)$$",0.34887566134756004;2.7625920499842236;-0.18512426214845995,18,4.37319699548052e-2,4.37319699548052e-2,4.37319699548052e-2,4.37319699548052e-2,-3.0356369512244523,-3.0356369512244523,-3.0356369512244523,43.53208132929929,43.53208132929929,43.53208132929929 +405,0,(Log(Exp((Log((x1 / x0)) + Log(x0)))) + Exp(((17.860443619072115 / (x1 ^ x0)) ^ (x1 * -1.0866766239456351)))),"(np.log(np.exp((np.log((x[:, 1] / x[:, 0])) + np.log(x[:, 0])))) + np.exp((t[0] / x[:, 1] ** x[:, 0]) ** (x[:, 1] * t[1])))","$$\left(\operatorname{log}(\operatorname{exp}(\left(\operatorname{log}(\frac{x_{1}}{x_{0}}) + \operatorname{log}(x_{0})\right))) + \operatorname{exp}({\frac{\theta_{0}}{{x_{1}^{x_{0}}}}^{\left(x_{1} \cdot \theta_{1}\right)}})\right)$$",17.860443619072115;-1.0866766239456351,20,7.738755551425887e-2,7.738755551425887e-2,7.738755551425887e-2,7.738755551425887e-2,-6.141413453842383,-6.141413453842383,-6.141413453842383,48.055990217422504,48.055990217422504,48.055990217422504 +406,0,(((0.5927311328530339 * (x1 * 0.5551836283345604)) * -2.4293878003427967) + x1),"(((t[0] * (x[:, 1] * t[1])) * t[2]) + x[:, 1])","$$\left(\left(\left(\theta_{0} \cdot \left(x_{1} \cdot \theta_{1}\right)\right) \cdot \theta_{2}\right) + x_{1}\right)$$",0.5927311328530339;0.5551836283345604;-2.4293878003427967,9,9.36575100606134e-3,9.36575100606134e-3,9.36575100606134e-3,9.36575100606134e-3,0.13571762545593535,0.13571762545593535,0.13571762545593535,18.54785323208739,18.54785323208739,18.54785323208739 +407,0,Log((((Log(Log(x1)) * (x0 / (x1 + x1))) / (Log((x1 + x1)) * (x1 * 0.5549018726812323))) / Log((Exp((0.49365613100500705 ^ x1)) ^ (-0.46582086176413817 ^ Log((x1 + 0.7860461933936405))))))),"np.log((((np.log(np.log(x[:, 1])) * (x[:, 0] / (x[:, 1] + x[:, 1]))) / (np.log((x[:, 1] + x[:, 1])) * (x[:, 1] * t[0]))) / np.log(np.exp(t[1] ** x[:, 1]) ** t[2] ** np.log((x[:, 1] + t[3])))))","$$\operatorname{log}(\frac{\frac{\left(\operatorname{log}(\operatorname{log}(x_{1})) \cdot \frac{x_{0}}{\left(x_{1} + x_{1}\right)}\right)}{\left(\operatorname{log}(\left(x_{1} + x_{1}\right)) \cdot \left(x_{1} \cdot \theta_{0}\right)\right)}}{\operatorname{log}({\operatorname{exp}({\theta_{1}^{x_{1}}})^{{\theta_{2}^{\operatorname{log}(\left(x_{1} + \theta_{3}\right))}}}})})$$",0.5549018726812323;0.49365613100500705;-0.46582086176413817;0.7860461933936405,32,,,,Infinity,,,,,, +408,0,(Exp((0.28859721147000983 + Log((Log(x1) + -0.8649832341222763)))) + (((x0 + -0.11121165696130231) / (x1 * 0.11894671182384597)) ^ (Log((x1 / x1)) + Exp((x0 * x0))))),"(np.exp((t[0] + np.log((np.log(x[:, 1]) + t[1])))) + ((x[:, 0] + t[2]) / (x[:, 1] * t[3])) ** (np.log((x[:, 1] / x[:, 1])) + np.exp((x[:, 0] * x[:, 0]))))","$$\left(\operatorname{exp}(\left(\theta_{0} + \operatorname{log}(\left(\operatorname{log}(x_{1}) + \theta_{1}\right))\right)) + {\frac{\left(x_{0} + \theta_{2}\right)}{\left(x_{1} \cdot \theta_{3}\right)}^{\left(\operatorname{log}(\frac{x_{1}}{x_{1}}) + \operatorname{exp}(\left(x_{0} \cdot x_{0}\right))\right)}}\right)$$",0.28859721147000983;-0.8649832341222763;-0.11121165696130231;0.11894671182384597,26,,,,Infinity,,,,,, +409,0,Log((((x0 * 0.421126596956682) ^ -0.9951484193009806) / Log(((0.17520470536965793 * (-0.17850502596547557 ^ (x0 ^ -0.5811357118125923))) + 0.8981326639213121)))),"np.log(((x[:, 0] * t[0]) ** t[1] / np.log(((t[2] * t[3] ** x[:, 0] ** t[4]) + t[5]))))","$$\operatorname{log}(\frac{{\left(x_{0} \cdot \theta_{0}\right)^{\theta_{1}}}}{\operatorname{log}(\left(\left(\theta_{2} \cdot {\theta_{3}^{{x_{0}^{\theta_{4}}}}}\right) + \theta_{5}\right))})$$",0.421126596956682;-0.9951484193009806;0.17520470536965793;-0.17850502596547557;-0.5811357118125923;0.8981326639213121,17,,,,Infinity,,,,,, +410,0,(Exp(((x1 + x1) * x1)) + ((x1 / x0) * (x0 + x1))),"(np.exp(((x[:, 1] + x[:, 1]) * x[:, 1])) + ((x[:, 1] / x[:, 0]) * (x[:, 0] + x[:, 1])))","$$\left(\operatorname{exp}(\left(\left(x_{1} + x_{1}\right) \cdot x_{1}\right)) + \left(\frac{x_{1}}{x_{0}} \cdot \left(x_{0} + x_{1}\right)\right)\right)$$",,14,3.1849509049257185,3.1849509049257185,3.1849509049257185,3.1849509049257185,-292.9109665774772,-292.9109665774772,-292.9109665774772,27.951088383625915,27.951088383625915,27.951088383625915 +411,0,(Exp((((x0 * -0.6173831490594965) / x1) ^ ((x0 + x0) * x0))) ^ (((0.9347749902501558 ^ x1) * Exp(x0)) ^ ((0.4872837822003697 * x0) / Log(x0)))),"np.exp(((x[:, 0] * t[0]) / x[:, 1]) ** ((x[:, 0] + x[:, 0]) * x[:, 0])) ** (t[1] ** x[:, 1] * np.exp(x[:, 0])) ** ((t[2] * x[:, 0]) / np.log(x[:, 0]))","$${\operatorname{exp}({\frac{\left(x_{0} \cdot \theta_{0}\right)}{x_{1}}^{\left(\left(x_{0} + x_{0}\right) \cdot x_{0}\right)}})^{{\left({\theta_{1}^{x_{1}}} \cdot \operatorname{exp}(x_{0})\right)^{\frac{\left(\theta_{2} \cdot x_{0}\right)}{\operatorname{log}(x_{0})}}}}}$$",-0.6173831490594965;0.9347749902501558;0.4872837822003697,26,,,,Infinity,,,,,, +412,0,Exp(Log(((0.32196724743424965 * Exp(x1)) * ((x0 * -0.9169903236113454) + x0)))),"np.exp(np.log(((t[0] * np.exp(x[:, 1])) * ((x[:, 0] * t[1]) + x[:, 0]))))","$$\operatorname{exp}(\operatorname{log}(\left(\left(\theta_{0} \cdot \operatorname{exp}(x_{1})\right) \cdot \left(\left(x_{0} \cdot \theta_{1}\right) + x_{0}\right)\right)))$$",0.32196724743424965;-0.9169903236113454,12,1.2801018732274464e-2,1.2801018732274464e-2,1.2801018732274464e-2,1.2801018732274464e-2,-0.18129286795613186,-0.18129286795613186,-0.18129286795613186,27.404292576258086,27.404292576258086,27.404292576258086 +413,0,Log(Exp(Log(((x1 ^ x1) * 1.4881273885655018)))),"np.log(np.exp(np.log((x[:, 1] ** x[:, 1] * t[0]))))","$$\operatorname{log}(\operatorname{exp}(\operatorname{log}(\left({x_{1}^{x_{1}}} \cdot \theta_{0}\right))))$$",1.4881273885655018,8,1.4109506990011728e-2,1.4109506990011728e-2,1.4109506990011728e-2,1.4109506990011728e-2,-0.3020416832650472,-0.3020416832650472,-0.3020416832650472,16.36769924548309,16.36769924548309,16.36769924548309 +414,0,(Exp(((x0 ^ x1) ^ (x1 * -0.4351149609616847))) + ((3.2348572358177207e-3 / Log((x0 + x0))) + -2.6684536576425133)),"(np.exp(x[:, 0] ** x[:, 1] ** (x[:, 1] * t[0])) + ((t[1] / np.log((x[:, 0] + x[:, 0]))) + t[2]))","$$\left(\operatorname{exp}({{x_{0}^{x_{1}}}^{\left(x_{1} \cdot \theta_{0}\right)}}) + \left(\frac{\theta_{1}}{\operatorname{log}(\left(x_{0} + x_{0}\right))} + \theta_{2}\right)\right)$$",-0.4351149609616847;3.2348572358177207e-3;-2.6684536576425133,17,4.556029636057579e-3,4.556029636057579e-3,4.556029636057579e-3,4.556029636057579e-3,0.5795642965741272,0.5795642965741272,0.5795642965741272,43.41969304749111,43.41969304749111,43.41969304749111 +415,0,Log((((0.8925994680297649 * ((x1 * 0.9768153511024064) + -0.25055211499897034)) + Log((0.2899087694463376 + (x1 + -0.6903328264189943)))) + ((((0.7582336137347293 + x1) + x0) * ((x0 * 0.3892619258154666) ^ 0.2544170185984005)) ^ ((-0.714551979567817 + (x0 + 0.9546425948558918)) + ((x0 * -0.5967455525341415) ^ x0))))),"np.log((((t[0] * ((x[:, 1] * t[1]) + t[2])) + np.log((t[3] + (x[:, 1] + t[4])))) + (((t[5] + x[:, 1]) + x[:, 0]) * (x[:, 0] * t[6]) ** t[7]) ** ((t[8] + (x[:, 0] + t[9])) + (x[:, 0] * t[10]) ** x[:, 0])))","$$\operatorname{log}(\left(\left(\left(\theta_{0} \cdot \left(\left(x_{1} \cdot \theta_{1}\right) + \theta_{2}\right)\right) + \operatorname{log}(\left(\theta_{3} + \left(x_{1} + \theta_{4}\right)\right))\right) + {\left(\left(\left(\theta_{5} + x_{1}\right) + x_{0}\right) \cdot {\left(x_{0} \cdot \theta_{6}\right)^{\theta_{7}}}\right)^{\left(\left(\theta_{8} + \left(x_{0} + \theta_{9}\right)\right) + {\left(x_{0} \cdot \theta_{10}\right)^{x_{0}}}\right)}}\right))$$",0.8925994680297649;0.9768153511024064;-0.25055211499897034;0.2899087694463376;-0.6903328264189943;0.7582336137347293;0.3892619258154666;0.2544170185984005;-0.714551979567817;0.9546425948558918;-0.5967455525341415,39,,,,Infinity,,,,,, +416,0,Log((((x0 + 0.8347649260958698) * (x1 ^ x1)) ^ ((x0 ^ -2.408464179362201) + (Exp(x0) ^ (-4.6265968288863135 * x0))))),"np.log(((x[:, 0] + t[0]) * x[:, 1] ** x[:, 1]) ** (x[:, 0] ** t[1] + np.exp(x[:, 0]) ** (t[2] * x[:, 0])))","$$\operatorname{log}({\left(\left(x_{0} + \theta_{0}\right) \cdot {x_{1}^{x_{1}}}\right)^{\left({x_{0}^{\theta_{1}}} + {\operatorname{exp}(x_{0})^{\left(\theta_{2} \cdot x_{0}\right)}}\right)}})$$",0.8347649260958698;-2.408464179362201;-4.6265968288863135,19,6.16253665639903e-2,6.16253665639903e-2,6.16253665639903e-2,6.16253665639903e-2,-4.686860360862034,-4.686860360862034,-4.686860360862034,45.627626927927395,45.627626927927395,45.627626927927395 +417,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 +418,0,((x0 * -0.11025883485074336) * -0.31964530766070187),"((x[:, 0] * t[0]) * t[1])","$$\left(\left(x_{0} \cdot \theta_{0}\right) \cdot \theta_{1}\right)$$",-0.11025883485074336;-0.31964530766070187,5,1.3066544018894101e-2,1.3066544018894101e-2,1.3066544018894101e-2,1.3066544018894101e-2,-0.20579584962547082,-0.20579584962547082,-0.20579584962547082,9.546671203692835,9.546671203692835,9.546671203692835 +419,0,((Log(Log(x1)) ^ ((0.1119233986992203 / Exp(x1)) ^ (Exp(x0) / Exp(Exp((x0 + x0)))))) + (Log(Log(x1)) / (-0.6588136398156079 ^ x0))),"(np.log(np.log(x[:, 1])) ** (t[0] / np.exp(x[:, 1])) ** (np.exp(x[:, 0]) / np.exp(np.exp((x[:, 0] + x[:, 0])))) + (np.log(np.log(x[:, 1])) / t[1] ** x[:, 0]))","$$\left({\operatorname{log}(\operatorname{log}(x_{1}))^{{\frac{\theta_{0}}{\operatorname{exp}(x_{1})}^{\frac{\operatorname{exp}(x_{0})}{\operatorname{exp}(\operatorname{exp}(\left(x_{0} + x_{0}\right)))}}}}} + \frac{\operatorname{log}(\operatorname{log}(x_{1}))}{{\theta_{1}^{x_{0}}}}\right)$$",0.1119233986992203;-0.6588136398156079,25,,,,Infinity,,,,,, +420,0,Log((((x1 * (((x1 * x0) * 0.45256171231312503) ^ Exp(x0))) * (((x0 / Log(x0)) * x1) / (Exp((x1 ^ -0.511023719556475)) * (0.8824063525967433 + x1)))) ^ Exp(((-0.27471190016963876 * Log((x0 + x0))) / ((x0 * -0.23605872607918443) ^ 0.6219660128792242))))),"np.log(((x[:, 1] * ((x[:, 1] * x[:, 0]) * t[0]) ** np.exp(x[:, 0])) * (((x[:, 0] / np.log(x[:, 0])) * x[:, 1]) / (np.exp(x[:, 1] ** t[1]) * (t[2] + x[:, 1])))) ** np.exp(((t[3] * np.log((x[:, 0] + x[:, 0]))) / (x[:, 0] * t[4]) ** t[5])))","$$\operatorname{log}({\left(\left(x_{1} \cdot {\left(\left(x_{1} \cdot x_{0}\right) \cdot \theta_{0}\right)^{\operatorname{exp}(x_{0})}}\right) \cdot \frac{\left(\frac{x_{0}}{\operatorname{log}(x_{0})} \cdot x_{1}\right)}{\left(\operatorname{exp}({x_{1}^{\theta_{1}}}) \cdot \left(\theta_{2} + x_{1}\right)\right)}\right)^{\operatorname{exp}(\frac{\left(\theta_{3} \cdot \operatorname{log}(\left(x_{0} + x_{0}\right))\right)}{{\left(x_{0} \cdot \theta_{4}\right)^{\theta_{5}}}})}})$$",0.45256171231312503;-0.511023719556475;0.8824063525967433;-0.27471190016963876;-0.23605872607918443;0.6219660128792242,41,,,,Infinity,,,,,, +421,0,Exp(((Exp(Exp(((x1 ^ x1) / x1))) * Log(((x0 / x1) * (-0.10505635920138445 / x1)))) ^ Exp(Exp((((-0.39323287098190507 * x0) * 0.13724307319366447) ^ 0.640223659306224))))),"np.exp((np.exp(np.exp((x[:, 1] ** x[:, 1] / x[:, 1]))) * np.log(((x[:, 0] / x[:, 1]) * (t[0] / x[:, 1])))) ** np.exp(np.exp(((t[1] * x[:, 0]) * t[2]) ** t[3])))","$$\operatorname{exp}({\left(\operatorname{exp}(\operatorname{exp}(\frac{{x_{1}^{x_{1}}}}{x_{1}})) \cdot \operatorname{log}(\left(\frac{x_{0}}{x_{1}} \cdot \frac{\theta_{0}}{x_{1}}\right))\right)^{\operatorname{exp}(\operatorname{exp}({\left(\left(\theta_{1} \cdot x_{0}\right) \cdot \theta_{2}\right)^{\theta_{3}}}))}})$$",-0.10505635920138445;-0.39323287098190507;0.13724307319366447;0.640223659306224,27,,,,Infinity,,,,,, +422,0,Exp(Exp((Log((0.8692104334798472 / (-0.756663021755025 + x1))) + Exp(((0.9998279642496607 / x0) / (0.9054587642151173 / x1)))))),"np.exp(np.exp((np.log((t[0] / (t[1] + x[:, 1]))) + np.exp(((t[2] / x[:, 0]) / (t[3] / x[:, 1]))))))","$$\operatorname{exp}(\operatorname{exp}(\left(\operatorname{log}(\frac{\theta_{0}}{\left(\theta_{1} + x_{1}\right)}) + \operatorname{exp}(\frac{\frac{\theta_{2}}{x_{0}}}{\frac{\theta_{3}}{x_{1}}})\right)))$$",0.8692104334798472;-0.756663021755025;0.9998279642496607;0.9054587642151173,17,,,,Infinity,,,,,, +423,0,(Log(Log(Exp(((x1 ^ x1) + (x0 + x0))))) / Log(((0.9631886364151273 + (x0 ^ 0.9051925298459599)) * (x1 ^ 0.6316804086006302)))),"(np.log(np.log(np.exp((x[:, 1] ** x[:, 1] + (x[:, 0] + x[:, 0]))))) / np.log(((t[0] + x[:, 0] ** t[1]) * x[:, 1] ** t[2])))","$$\frac{\operatorname{log}(\operatorname{log}(\operatorname{exp}(\left({x_{1}^{x_{1}}} + \left(x_{0} + x_{0}\right)\right))))}{\operatorname{log}(\left(\left(\theta_{0} + {x_{0}^{\theta_{1}}}\right) \cdot {x_{1}^{\theta_{2}}}\right))}$$",0.9631886364151273;0.9051925298459599;0.6316804086006302,21,5166.093582160621,5166.093582160621,5166.093582160621,5166.093582160621,-476732.1125306442,-476732.1125306442,-476732.1125306442,4701.688714874723,4701.688714874723,4701.688714874723 +424,0,Log(Log(((x0 / x0) + Exp(Exp(((x1 ^ 0.908401530544901) ^ (-0.7333189382085356 ^ x0))))))),"np.log(np.log(((x[:, 0] / x[:, 0]) + np.exp(np.exp(x[:, 1] ** t[0] ** t[1] ** x[:, 0])))))","$$\operatorname{log}(\operatorname{log}(\left(\frac{x_{0}}{x_{0}} + \operatorname{exp}(\operatorname{exp}({{x_{1}^{\theta_{0}}}^{{\theta_{1}^{x_{0}}}}}))\right)))$$",0.908401530544901;-0.7333189382085356,15,,,,Infinity,,,,,, +425,0,Log(((((0.7454694115548722 / Exp(x1)) * Exp(x0)) + Exp(((x1 * -0.5333823033385897) * 5.0276024006315034e-2))) / ((-0.8249727487396475 * x1) / Log(Log((x1 * 0.5284942629289364)))))),"np.log(((((t[0] / np.exp(x[:, 1])) * np.exp(x[:, 0])) + np.exp(((x[:, 1] * t[1]) * t[2]))) / ((t[3] * x[:, 1]) / np.log(np.log((x[:, 1] * t[4]))))))","$$\operatorname{log}(\frac{\left(\left(\frac{\theta_{0}}{\operatorname{exp}(x_{1})} \cdot \operatorname{exp}(x_{0})\right) + \operatorname{exp}(\left(\left(x_{1} \cdot \theta_{1}\right) \cdot \theta_{2}\right))\right)}{\frac{\left(\theta_{3} \cdot x_{1}\right)}{\operatorname{log}(\operatorname{log}(\left(x_{1} \cdot \theta_{4}\right)))}})$$",0.7454694115548722;-0.5333823033385897;5.0276024006315034e-2;-0.8249727487396475;0.5284942629289364,25,,,,Infinity,,,,,, +426,0,Log(Log(Exp((-0.6638056786451311 ^ ((x1 + x0) * (-0.8789168898583347 * (x1 / x0))))))),"np.log(np.log(np.exp(t[0] ** ((x[:, 1] + x[:, 0]) * (t[1] * (x[:, 1] / x[:, 0]))))))","$$\operatorname{log}(\operatorname{log}(\operatorname{exp}({\theta_{0}^{\left(\left(x_{1} + x_{0}\right) \cdot \left(\theta_{1} \cdot \frac{x_{1}}{x_{0}}\right)\right)}})))$$",-0.6638056786451311;-0.8789168898583347,14,,,,Infinity,,,,,, +427,0,(Exp(Exp(((0.24941529173939014 + ((0.8894232744809423 / x1) * 0.48257905751153707)) * 0.35484941868834385))) ^ (Log(Log((0.15416985441743314 * Log((-0.48890437819440713 ^ x0))))) + (0.3310452436205764 * (-0.8042287912653618 / x1)))),"np.exp(np.exp(((t[0] + ((t[1] / x[:, 1]) * t[2])) * t[3]))) ** (np.log(np.log((t[4] * np.log(t[5] ** x[:, 0])))) + (t[6] * (t[7] / x[:, 1])))","$${\operatorname{exp}(\operatorname{exp}(\left(\left(\theta_{0} + \left(\frac{\theta_{1}}{x_{1}} \cdot \theta_{2}\right)\right) \cdot \theta_{3}\right)))^{\left(\operatorname{log}(\operatorname{log}(\left(\theta_{4} \cdot \operatorname{log}({\theta_{5}^{x_{0}}})\right))) + \left(\theta_{6} \cdot \frac{\theta_{7}}{x_{1}}\right)\right)}}$$",0.24941529173939014;0.8894232744809423;0.48257905751153707;0.35484941868834385;0.15416985441743314;-0.48890437819440713;0.3310452436205764;-0.8042287912653618,26,,,,Infinity,,,,,, +428,0,Log(Log(Log(Log((0.4127984977014052 / (x0 + x1)))))),"np.log(np.log(np.log(np.log((t[0] / (x[:, 0] + x[:, 1]))))))","$$\operatorname{log}(\operatorname{log}(\operatorname{log}(\operatorname{log}(\frac{\theta_{0}}{\left(x_{0} + x_{1}\right)}))))$$",0.4127984977014052,9,,,,Infinity,,,,,, +429,0,Exp(Exp(((x1 + (x1 + 0.3810151082134101)) ^ (Exp(x1) / (x0 / Exp((x1 ^ 0.5475522197606351))))))),"np.exp(np.exp((x[:, 1] + (x[:, 1] + t[0])) ** (np.exp(x[:, 1]) / (x[:, 0] / np.exp(x[:, 1] ** t[1])))))","$$\operatorname{exp}(\operatorname{exp}({\left(x_{1} + \left(x_{1} + \theta_{0}\right)\right)^{\frac{\operatorname{exp}(x_{1})}{\frac{x_{0}}{\operatorname{exp}({x_{1}^{\theta_{1}}})}}}}))$$",0.3810151082134101;0.5475522197606351,17,Infinity,Infinity,Infinity,Infinity,-Infinity,-Infinity,-Infinity,Infinity,Infinity,Infinity +430,0,(((0.13380028062560978 + (-0.25184175444431733 ^ Exp((x1 + x1)))) * 0.6023709548495503) + 0.7509012088865283),"(((t[0] + t[1] ** np.exp((x[:, 1] + x[:, 1]))) * t[2]) + t[3])","$$\left(\left(\left(\theta_{0} + {\theta_{1}^{\operatorname{exp}(\left(x_{1} + x_{1}\right))}}\right) \cdot \theta_{2}\right) + \theta_{3}\right)$$",0.13380028062560978;-0.25184175444431733;0.6023709548495503;0.7509012088865283,12,,,,Infinity,,,,,, +431,0,(Log((Exp((((x0 * 0.2073031038037073) + -0.34095884386336306) ^ ((x1 * 0.44242370954817156) * x1))) ^ (x0 + (((x0 / x1) ^ (x0 ^ x1)) + -0.5689549468969601)))) / (((x0 ^ 0.368245723298383) * -0.36450393566914474) ^ -0.10406134487512575)),"(np.log(np.exp(((x[:, 0] * t[0]) + t[1]) ** ((x[:, 1] * t[2]) * x[:, 1])) ** (x[:, 0] + ((x[:, 0] / x[:, 1]) ** x[:, 0] ** x[:, 1] + t[3]))) / (x[:, 0] ** t[4] * t[5]) ** t[6])","$$\frac{\operatorname{log}({\operatorname{exp}({\left(\left(x_{0} \cdot \theta_{0}\right) + \theta_{1}\right)^{\left(\left(x_{1} \cdot \theta_{2}\right) \cdot x_{1}\right)}})^{\left(x_{0} + \left({\frac{x_{0}}{x_{1}}^{{x_{0}^{x_{1}}}}} + \theta_{3}\right)\right)}})}{{\left({x_{0}^{\theta_{4}}} \cdot \theta_{5}\right)^{\theta_{6}}}}$$",0.2073031038037073;-0.34095884386336306;0.44242370954817156;-0.5689549468969601;0.368245723298383;-0.36450393566914474;-0.10406134487512575,33,,,,Infinity,,,,,, +432,0,Log(Exp((((((x1 * x1) + (x1 / x0)) + 0.485813465542868) * 0.23016696151200025) + (-0.15179605193159698 * x0)))),"np.log(np.exp((((((x[:, 1] * x[:, 1]) + (x[:, 1] / x[:, 0])) + t[0]) * t[1]) + (t[2] * x[:, 0]))))","$$\operatorname{log}(\operatorname{exp}(\left(\left(\left(\left(\left(x_{1} \cdot x_{1}\right) + \frac{x_{1}}{x_{0}}\right) + \theta_{0}\right) \cdot \theta_{1}\right) + \left(\theta_{2} \cdot x_{0}\right)\right)))$$",0.485813465542868;0.23016696151200025;-0.15179605193159698,17,2.706116566009873e-3,2.706116566009873e-3,2.706116566009873e-3,2.706116566009873e-3,0.750276422045559,0.750276422045559,0.750276422045559,41.415716519569536,41.415716519569536,41.415716519569536 +433,0,(x1 ^ x1),"x[:, 1] ** x[:, 1]","$${x_{1}^{x_{1}}}$$",,3,0.4403077718480359,0.4403077718480359,0.4403077718480359,0.4403077718480359,-39.63211229262252,-39.63211229262252,-39.63211229262252,2.4757185363430683,2.4757185363430683,2.4757185363430683 +434,0,((((Exp((x1 + (x1 + x0))) + 0.6910006039702367) + 0.740864242239983) * (Exp(x0) + 0.9166965214708928)) + (((0.3383384768843253 + (x1 + x0)) ^ -1.5038234260063454e-2) + (((x0 * (-0.17425746050180524 ^ (x0 / x1))) ^ x0) / x0))),"((((np.exp((x[:, 1] + (x[:, 1] + x[:, 0]))) + t[0]) + t[1]) * (np.exp(x[:, 0]) + t[2])) + ((t[3] + (x[:, 1] + x[:, 0])) ** t[4] + ((x[:, 0] * t[5] ** (x[:, 0] / x[:, 1])) ** x[:, 0] / x[:, 0])))","$$\left(\left(\left(\left(\operatorname{exp}(\left(x_{1} + \left(x_{1} + x_{0}\right)\right)) + \theta_{0}\right) + \theta_{1}\right) \cdot \left(\operatorname{exp}(x_{0}) + \theta_{2}\right)\right) + \left({\left(\theta_{3} + \left(x_{1} + x_{0}\right)\right)^{\theta_{4}}} + \frac{{\left(x_{0} \cdot {\theta_{5}^{\frac{x_{0}}{x_{1}}}}\right)^{x_{0}}}}{x_{0}}\right)\right)$$",0.6910006039702367;0.740864242239983;0.9166965214708928;0.3383384768843253;-1.5038234260063454e-2;-0.17425746050180524,35,,,,Infinity,,,,,, +435,0,(Log(Exp(Exp(((-0.9385541358258517 ^ x1) * -0.782145144493338)))) ^ (1.9432145039520732e-2 ^ Log(((-0.44109637367532406 + x0) / x0)))),"np.log(np.exp(np.exp((t[0] ** x[:, 1] * t[1])))) ** t[2] ** np.log(((t[3] + x[:, 0]) / x[:, 0]))","$${\operatorname{log}(\operatorname{exp}(\operatorname{exp}(\left({\theta_{0}^{x_{1}}} \cdot \theta_{1}\right))))^{{\theta_{2}^{\operatorname{log}(\frac{\left(\theta_{3} + x_{0}\right)}{x_{0}})}}}}$$",-0.9385541358258517;-0.782145144493338;1.9432145039520732e-2;-0.44109637367532406,17,,,,Infinity,,,,,, +436,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 +437,0,Log(Exp(((Log(Log(x1)) / (0.34270188383254996 * Exp(x1))) / Exp((0.6998146402471606 * Log((x1 * -0.1426250943876608))))))),"np.log(np.exp(((np.log(np.log(x[:, 1])) / (t[0] * np.exp(x[:, 1]))) / np.exp((t[1] * np.log((x[:, 1] * t[2])))))))","$$\operatorname{log}(\operatorname{exp}(\frac{\frac{\operatorname{log}(\operatorname{log}(x_{1}))}{\left(\theta_{0} \cdot \operatorname{exp}(x_{1})\right)}}{\operatorname{exp}(\left(\theta_{1} \cdot \operatorname{log}(\left(x_{1} \cdot \theta_{2}\right))\right))}))$$",0.34270188383254996;0.6998146402471606;-0.1426250943876608,18,,,,Infinity,,,,,, +438,0,(-0.2701133249686303 + (x1 ^ x0)),"(t[0] + x[:, 1] ** x[:, 0])","$$\left(\theta_{0} + {x_{1}^{x_{0}}}\right)$$",-0.2701133249686303,5,1.057854561101814e-2,1.057854561101814e-2,1.057854561101814e-2,1.057854561101814e-2,2.3799531506189964e-2,2.3799531506189964e-2,2.3799531506189964e-2,10.07763518858806,10.07763518858806,10.07763518858806 +439,0,Exp((Log((x0 ^ x0)) + Exp(((0.28456349337390896 + x1) ^ -0.9101627260695312)))),"np.exp((np.log(x[:, 0] ** x[:, 0]) + np.exp((t[0] + x[:, 1]) ** t[1])))","$$\operatorname{exp}(\left(\operatorname{log}({x_{0}^{x_{0}}}) + \operatorname{exp}({\left(\theta_{0} + x_{1}\right)^{\theta_{1}}})\right))$$",0.28456349337390896;-0.9101627260695312,12,8.466783525338161e7,8.466783525338161e7,8.466783525338161e7,8.466783525338161e7,-7.813246118071454e9,-7.813246118071454e9,-7.813246118071454e9,7.620107912081511e7,7.620107912081511e7,7.620107912081511e7 +440,0,Exp((((x0 + 0.8173628238030505) ^ ((-0.6375941690032918 ^ x0) / x0)) ^ ((((-0.37083685366623365 + x1) * -0.4286992203945742) * 3.747434514617776e-2) ^ ((x1 / x0) * ((x1 ^ (0.20915791270380701 / x1)) + -0.8308696906648474))))),"np.exp((x[:, 0] + t[0]) ** (t[1] ** x[:, 0] / x[:, 0]) ** (((t[2] + x[:, 1]) * t[3]) * t[4]) ** ((x[:, 1] / x[:, 0]) * (x[:, 1] ** (t[5] / x[:, 1]) + t[6])))","$$\operatorname{exp}({{\left(x_{0} + \theta_{0}\right)^{\frac{{\theta_{1}^{x_{0}}}}{x_{0}}}}^{{\left(\left(\left(\theta_{2} + x_{1}\right) \cdot \theta_{3}\right) \cdot \theta_{4}\right)^{\left(\frac{x_{1}}{x_{0}} \cdot \left({x_{1}^{\frac{\theta_{5}}{x_{1}}}} + \theta_{6}\right)\right)}}}})$$",0.8173628238030505;-0.6375941690032918;-0.37083685366623365;-0.4286992203945742;3.747434514617776e-2;0.20915791270380701;-0.8308696906648474,30,,,,Infinity,,,,,, +441,0,(Log(Exp((-0.5036166735714815 * x1))) * (Exp((x0 * Exp((x0 ^ 0.7432021678048366)))) + Exp(Exp(Exp((x1 * x1)))))),"(np.log(np.exp((t[0] * x[:, 1]))) * (np.exp((x[:, 0] * np.exp(x[:, 0] ** t[1]))) + np.exp(np.exp(np.exp((x[:, 1] * x[:, 1]))))))","$$\left(\operatorname{log}(\operatorname{exp}(\left(\theta_{0} \cdot x_{1}\right))) \cdot \left(\operatorname{exp}(\left(x_{0} \cdot \operatorname{exp}({x_{0}^{\theta_{1}}})\right)) + \operatorname{exp}(\operatorname{exp}(\operatorname{exp}(\left(x_{1} \cdot x_{1}\right))))\right)\right)$$",-0.5036166735714815;0.7432021678048366,20,696.1013721943077,696.1013721943077,696.1013721943077,696.1013721943077,-64236.0426561752,-64236.0426561752,-64236.0426561752,672.121915679209,672.121915679209,672.121915679209 +442,0,((Exp(Log((-0.4768892057844869 + (x0 / x1)))) / (Exp((-0.3253133321298409 ^ x0)) / Log((x1 * x0)))) * ((Exp((x1 ^ x1)) + Log((x0 * 0.22593970365955207))) ^ ((x1 + (0.9326794233666391 ^ x0)) * (x0 / x0)))),"((np.exp(np.log((t[0] + (x[:, 0] / x[:, 1])))) / (np.exp(t[1] ** x[:, 0]) / np.log((x[:, 1] * x[:, 0])))) * (np.exp(x[:, 1] ** x[:, 1]) + np.log((x[:, 0] * t[2]))) ** ((x[:, 1] + t[3] ** x[:, 0]) * (x[:, 0] / x[:, 0])))","$$\left(\frac{\operatorname{exp}(\operatorname{log}(\left(\theta_{0} + \frac{x_{0}}{x_{1}}\right)))}{\frac{\operatorname{exp}({\theta_{1}^{x_{0}}})}{\operatorname{log}(\left(x_{1} \cdot x_{0}\right))}} \cdot {\left(\operatorname{exp}({x_{1}^{x_{1}}}) + \operatorname{log}(\left(x_{0} \cdot \theta_{2}\right))\right)^{\left(\left(x_{1} + {\theta_{3}^{x_{0}}}\right) \cdot \frac{x_{0}}{x_{0}}\right)}}\right)$$",-0.4768892057844869;-0.3253133321298409;0.22593970365955207;0.9326794233666391,37,,,,Infinity,,,,,, +443,0,(Exp(((-0.41982417575031494 ^ x0) + Log(x0))) ^ (((((0.8968670663005786 * x1) + 0.9295983227060814) + 0.10211722351119423) + Exp(x0)) / Exp((Log((0.7992151862752483 ^ (x1 + x1))) + 0.7128458429699861)))),"np.exp((t[0] ** x[:, 0] + np.log(x[:, 0]))) ** (((((t[1] * x[:, 1]) + t[2]) + t[3]) + np.exp(x[:, 0])) / np.exp((np.log(t[4] ** (x[:, 1] + x[:, 1])) + t[5])))","$${\operatorname{exp}(\left({\theta_{0}^{x_{0}}} + \operatorname{log}(x_{0})\right))^{\frac{\left(\left(\left(\left(\theta_{1} \cdot x_{1}\right) + \theta_{2}\right) + \theta_{3}\right) + \operatorname{exp}(x_{0})\right)}{\operatorname{exp}(\left(\operatorname{log}({\theta_{4}^{\left(x_{1} + x_{1}\right)}}) + \theta_{5}\right))}}}$$",-0.41982417575031494;0.8968670663005786;0.9295983227060814;0.10211722351119423;0.7992151862752483;0.7128458429699861,28,,,,Infinity,,,,,, +444,0,((((-0.6551844744534805 / (x0 + (0.1764673299821372 ^ x1))) * 0.4749353023062388) ^ 0.3581577928672145) + 0.7637672223304791),"(((t[0] / (x[:, 0] + t[1] ** x[:, 1])) * t[2]) ** t[3] + t[4])","$$\left({\left(\frac{\theta_{0}}{\left(x_{0} + {\theta_{1}^{x_{1}}}\right)} \cdot \theta_{2}\right)^{\theta_{3}}} + \theta_{4}\right)$$",-0.6551844744534805;0.1764673299821372;0.4749353023062388;0.3581577928672145;0.7637672223304791,13,,,,Infinity,,,,,, +445,0,((((1.4394226479249487e-2 ^ x0) * (0.7832886086275295 + (x1 * 1.3812816056612656))) / (((0.7717419989024104 ^ x0) * 1.4074777919434447) ^ ((((x0 + x1) * x1) + x1) * 0.16258488570358678))) * 2.2488128646184555),"(((t[0] ** x[:, 0] * (t[1] + (x[:, 1] * t[2]))) / (t[3] ** x[:, 0] * t[4]) ** ((((x[:, 0] + x[:, 1]) * x[:, 1]) + x[:, 1]) * t[5])) * t[6])","$$\left(\frac{\left({\theta_{0}^{x_{0}}} \cdot \left(\theta_{1} + \left(x_{1} \cdot \theta_{2}\right)\right)\right)}{{\left({\theta_{3}^{x_{0}}} \cdot \theta_{4}\right)^{\left(\left(\left(\left(x_{0} + x_{1}\right) \cdot x_{1}\right) + x_{1}\right) \cdot \theta_{5}\right)}}} \cdot \theta_{6}\right)$$",1.4394226479249487e-2;0.7832886086275295;1.3812816056612656;0.7717419989024104;1.4074777919434447;0.16258488570358678;2.2488128646184555,27,3.2523951115122907e-3,3.2523951115122907e-3,3.2523951115122907e-3,3.2523951115122907e-3,0.6998652037499041,0.6998652037499041,0.6998652037499041,66.6889757276673,66.6889757276673,66.6889757276673 +446,0,(((Log((x1 + -0.7377645246910565)) / (x1 / (x1 + x1))) / ((x0 ^ (x1 * -0.19969264068088377)) * Exp((-0.7491405451130988 ^ x1)))) / ((Exp((0.515263263681492 ^ x0)) + -0.8261127004761506) + (Exp((x0 ^ x0)) + Log((-0.27391920999656283 + (x0 / x1)))))),"(((np.log((x[:, 1] + t[0])) / (x[:, 1] / (x[:, 1] + x[:, 1]))) / (x[:, 0] ** (x[:, 1] * t[1]) * np.exp(t[2] ** x[:, 1]))) / ((np.exp(t[3] ** x[:, 0]) + t[4]) + (np.exp(x[:, 0] ** x[:, 0]) + np.log((t[5] + (x[:, 0] / x[:, 1]))))))","$$\frac{\frac{\frac{\operatorname{log}(\left(x_{1} + \theta_{0}\right))}{\frac{x_{1}}{\left(x_{1} + x_{1}\right)}}}{\left({x_{0}^{\left(x_{1} \cdot \theta_{1}\right)}} \cdot \operatorname{exp}({\theta_{2}^{x_{1}}})\right)}}{\left(\left(\operatorname{exp}({\theta_{3}^{x_{0}}}) + \theta_{4}\right) + \left(\operatorname{exp}({x_{0}^{x_{0}}}) + \operatorname{log}(\left(\theta_{5} + \frac{x_{0}}{x_{1}}\right))\right)\right)}$$",-0.7377645246910565;-0.19969264068088377;-0.7491405451130988;0.515263263681492;-0.8261127004761506;-0.27391920999656283,40,,,,Infinity,,,,,, +447,0,((Exp(Exp((-10.13351588147056 / x0))) * 1.156642343918389) + Log((Exp((x1 ^ (x0 * 12.438181741571105))) * (x1 * (x0 ^ x0))))),"((np.exp(np.exp((t[0] / x[:, 0]))) * t[1]) + np.log((np.exp(x[:, 1] ** (x[:, 0] * t[2])) * (x[:, 1] * x[:, 0] ** x[:, 0]))))","$$\left(\left(\operatorname{exp}(\operatorname{exp}(\frac{\theta_{0}}{x_{0}})) \cdot \theta_{1}\right) + \operatorname{log}(\left(\operatorname{exp}({x_{1}^{\left(x_{0} \cdot \theta_{2}\right)}}) \cdot \left(x_{1} \cdot {x_{0}^{x_{0}}}\right)\right))\right)$$",-10.13351588147056;1.156642343918389;12.438181741571105,21,0.3758789848681419,0.3758789848681419,0.3758789848681419,0.3758789848681419,-33.68654904158816,-33.68654904158816,-33.68654904158816,52.542782016544855,52.542782016544855,52.542782016544855 +448,0,(Log((-0.47583371905674154 * (0.519754856712996 + (-0.3018253974665701 + (0.1502249459288051 * x1))))) * ((((x1 / ((x1 / x1) ^ -0.7443956767806825)) ^ Log((-0.9349902445692168 * (x0 + x0)))) * -0.1257865221026404) * (Log(x1) * -0.15081952940141374))),"(np.log((t[0] * (t[1] + (t[2] + (t[3] * x[:, 1]))))) * (((x[:, 1] / (x[:, 1] / x[:, 1]) ** t[4]) ** np.log((t[5] * (x[:, 0] + x[:, 0]))) * t[6]) * (np.log(x[:, 1]) * t[7])))","$$\left(\operatorname{log}(\left(\theta_{0} \cdot \left(\theta_{1} + \left(\theta_{2} + \left(\theta_{3} \cdot x_{1}\right)\right)\right)\right)) \cdot \left(\left({\frac{x_{1}}{{\frac{x_{1}}{x_{1}}^{\theta_{4}}}}^{\operatorname{log}(\left(\theta_{5} \cdot \left(x_{0} + x_{0}\right)\right))}} \cdot \theta_{6}\right) \cdot \left(\operatorname{log}(x_{1}) \cdot \theta_{7}\right)\right)\right)$$",-0.47583371905674154;0.519754856712996;-0.3018253974665701;0.1502249459288051;-0.7443956767806825;-0.9349902445692168;-0.1257865221026404;-0.15081952940141374,32,,,,Infinity,,,,,, +449,0,Exp(((((x1 ^ Exp((x1 * 0.670331012520308))) + 0.17539372804737663) + ((Log((-0.5900732285799757 + x1)) * 0.5916326308201681) * (Log((x1 ^ 0.7931898592938101)) * x0))) ^ (0.2537171408621066 + (-0.3037109513797772 * x0)))),"np.exp(((x[:, 1] ** np.exp((x[:, 1] * t[0])) + t[1]) + ((np.log((t[2] + x[:, 1])) * t[3]) * (np.log(x[:, 1] ** t[4]) * x[:, 0]))) ** (t[5] + (t[6] * x[:, 0])))","$$\operatorname{exp}({\left(\left({x_{1}^{\operatorname{exp}(\left(x_{1} \cdot \theta_{0}\right))}} + \theta_{1}\right) + \left(\left(\operatorname{log}(\left(\theta_{2} + x_{1}\right)) \cdot \theta_{3}\right) \cdot \left(\operatorname{log}({x_{1}^{\theta_{4}}}) \cdot x_{0}\right)\right)\right)^{\left(\theta_{5} + \left(\theta_{6} \cdot x_{0}\right)\right)}})$$",0.670331012520308;0.17539372804737663;-0.5900732285799757;0.5916326308201681;0.7931898592938101;0.2537171408621066;-0.3037109513797772,29,,,,Infinity,,,,,, +450,0,((x1 * x1) / x1),"((x[:, 1] * x[:, 1]) / x[:, 1])","$$\frac{\left(x_{1} \cdot x_{1}\right)}{x_{1}}$$",,5,9.206922096950058e-2,9.206922096950058e-2,9.206922096950058e-2,9.206922096950058e-2,-7.496254584436793,-7.496254584436793,-7.496254584436793,5.575923742213099,5.575923742213099,5.575923742213099 +451,0,((Exp(x1) * -0.19850896584355748) + x1),"((np.exp(x[:, 1]) * t[0]) + x[:, 1])","$$\left(\left(\operatorname{exp}(x_{1}) \cdot \theta_{0}\right) + x_{1}\right)$$",-0.19850896584355748,6,1.2490362486789055e-2,1.2490362486789055e-2,1.2490362486789055e-2,1.2490362486789055e-2,-0.15262514901492685,-0.15262514901492685,-0.15262514901492685,11.688793736210352,11.688793736210352,11.688793736210352 +452,0,Log(((((Exp(x1) * 4.1685201693828465e-2) ^ (Exp((x0 ^ 1.475692966996369)) + -1.0399762356248408)) + 0.7032323412275956) + x1)),"np.log((((np.exp(x[:, 1]) * t[0]) ** (np.exp(x[:, 0] ** t[1]) + t[2]) + t[3]) + x[:, 1]))","$$\operatorname{log}(\left(\left({\left(\operatorname{exp}(x_{1}) \cdot \theta_{0}\right)^{\left(\operatorname{exp}({x_{0}^{\theta_{1}}}) + \theta_{2}\right)}} + \theta_{3}\right) + x_{1}\right))$$",4.1685201693828465e-2;1.475692966996369;-1.0399762356248408;0.7032323412275956,16,1.1081117420822004e-2,1.1081117420822004e-2,1.1081117420822004e-2,1.1081117420822004e-2,-2.2578378484699124e-2,-2.2578378484699124e-2,-2.2578378484699124e-2,41.36473741402668,41.36473741402668,41.36473741402668 +453,0,Log((Log((((x0 ^ x1) + -0.4492980014128321) ^ ((x0 * -1.2376556358864774) * 1.0942206651485147))) ^ ((Exp(x1) ^ Log((x1 ^ 0.46352305019288736))) / Log((x0 * (7.725958474293932e-4 * x1)))))),"np.log(np.log((x[:, 0] ** x[:, 1] + t[0]) ** ((x[:, 0] * t[1]) * t[2])) ** (np.exp(x[:, 1]) ** np.log(x[:, 1] ** t[3]) / np.log((x[:, 0] * (t[4] * x[:, 1])))))","$$\operatorname{log}({\operatorname{log}({\left({x_{0}^{x_{1}}} + \theta_{0}\right)^{\left(\left(x_{0} \cdot \theta_{1}\right) \cdot \theta_{2}\right)}})^{\frac{{\operatorname{exp}(x_{1})^{\operatorname{log}({x_{1}^{\theta_{3}}})}}}{\operatorname{log}(\left(x_{0} \cdot \left(\theta_{4} \cdot x_{1}\right)\right))}}})$$",-0.4492980014128321;-1.2376556358864774;1.0942206651485147;0.46352305019288736;7.725958474293932e-4,28,1.3109423030134312e-2,1.3109423030134312e-2,1.3109423030134312e-2,1.3109423030134312e-2,-0.2097527745564034,-0.2097527745564034,-0.2097527745564034,71.63871132297947,71.63871132297947,71.63871132297947 +454,0,Log(Log((((x1 * -0.681381571978199) / ((Log(x0) * 0.4477560383143899) ^ -0.6984991000738375)) ^ (((0.9476299414510044 + Exp(x0)) ^ x0) + (x1 ^ 0.1728947334355746))))),"np.log(np.log(((x[:, 1] * t[0]) / (np.log(x[:, 0]) * t[1]) ** t[2]) ** ((t[3] + np.exp(x[:, 0])) ** x[:, 0] + x[:, 1] ** t[4])))","$$\operatorname{log}(\operatorname{log}({\frac{\left(x_{1} \cdot \theta_{0}\right)}{{\left(\operatorname{log}(x_{0}) \cdot \theta_{1}\right)^{\theta_{2}}}}^{\left({\left(\theta_{3} + \operatorname{exp}(x_{0})\right)^{x_{0}}} + {x_{1}^{\theta_{4}}}\right)}}))$$",-0.681381571978199;0.4477560383143899;-0.6984991000738375;0.9476299414510044;0.1728947334355746,23,,,,Infinity,,,,,, +455,0,(Exp(Log((Exp(((-0.19467842706883154 ^ x0) * 0.9457173654145368)) + (((0.29124107032271895 / x0) ^ -0.34277663579511175) / (x1 * 0.3626543542645697))))) / Exp(((Log((x0 * x1)) + ((0.7110872764207374 + x0) + (x1 + 0.4715167718394734))) * (0.8677191011834668 / Exp((x0 * 0.5346257828764355)))))),"(np.exp(np.log((np.exp((t[0] ** x[:, 0] * t[1])) + ((t[2] / x[:, 0]) ** t[3] / (x[:, 1] * t[4]))))) / np.exp(((np.log((x[:, 0] * x[:, 1])) + ((t[5] + x[:, 0]) + (x[:, 1] + t[6]))) * (t[7] / np.exp((x[:, 0] * t[8]))))))","$$\frac{\operatorname{exp}(\operatorname{log}(\left(\operatorname{exp}(\left({\theta_{0}^{x_{0}}} \cdot \theta_{1}\right)) + \frac{{\frac{\theta_{2}}{x_{0}}^{\theta_{3}}}}{\left(x_{1} \cdot \theta_{4}\right)}\right)))}{\operatorname{exp}(\left(\left(\operatorname{log}(\left(x_{0} \cdot x_{1}\right)) + \left(\left(\theta_{5} + x_{0}\right) + \left(x_{1} + \theta_{6}\right)\right)\right) \cdot \frac{\theta_{7}}{\operatorname{exp}(\left(x_{0} \cdot \theta_{8}\right))}\right))}$$",-0.19467842706883154;0.9457173654145368;0.29124107032271895;-0.34277663579511175;0.3626543542645697;0.7110872764207374;0.4715167718394734;0.8677191011834668;0.5346257828764355,39,,,,Infinity,,,,,, +456,0,(((-0.3926664276943631 * (-0.5726732228914679 + Log(((0.6644040385609528 / x0) * x1)))) / ((x0 ^ (x1 + (x0 * 0.41149903461181514))) / Log((Log(x1) ^ x0)))) / Exp(((x1 + -0.5615639433328696) + 0.9468883861002177))),"(((t[0] * (t[1] + np.log(((t[2] / x[:, 0]) * x[:, 1])))) / (x[:, 0] ** (x[:, 1] + (x[:, 0] * t[3])) / np.log(np.log(x[:, 1]) ** x[:, 0]))) / np.exp(((x[:, 1] + t[4]) + t[5])))","$$\frac{\frac{\left(\theta_{0} \cdot \left(\theta_{1} + \operatorname{log}(\left(\frac{\theta_{2}}{x_{0}} \cdot x_{1}\right))\right)\right)}{\frac{{x_{0}^{\left(x_{1} + \left(x_{0} \cdot \theta_{3}\right)\right)}}}{\operatorname{log}({\operatorname{log}(x_{1})^{x_{0}}})}}}{\operatorname{exp}(\left(\left(x_{1} + \theta_{4}\right) + \theta_{5}\right))}$$",-0.3926664276943631;-0.5726732228914679;0.6644040385609528;0.41149903461181514;-0.5615639433328696;0.9468883861002177,31,,,,Infinity,,,,,, +457,0,Log((Exp(((x1 + x1) ^ -0.4536997384039348)) + ((-0.6550777764664735 / x1) * ((8.383525070104186e-3 / x1) + 0.9296076397525579)))),"np.log((np.exp((x[:, 1] + x[:, 1]) ** t[0]) + ((t[1] / x[:, 1]) * ((t[2] / x[:, 1]) + t[3]))))","$$\operatorname{log}(\left(\operatorname{exp}({\left(x_{1} + x_{1}\right)^{\theta_{0}}}) + \left(\frac{\theta_{1}}{x_{1}} \cdot \left(\frac{\theta_{2}}{x_{1}} + \theta_{3}\right)\right)\right))$$",-0.4536997384039348;-0.6550777764664735;8.383525070104186e-3;0.9296076397525579,17,9.47506435669443e-2,9.47506435669443e-2,9.47506435669443e-2,9.47506435669443e-2,-7.743699374307347,-7.743699374307347,-7.743699374307347,43.51948152923802,43.51948152923802,43.51948152923802 +458,0,(((Log(Log(((x1 ^ -23.973113519573406) ^ 12.996212814579879))) + Exp(x0)) * -1.73124023634306e-2) * -0.32790447072648926),"(((np.log(np.log(x[:, 1] ** t[0] ** t[1])) + np.exp(x[:, 0])) * t[2]) * t[3])","$$\left(\left(\left(\operatorname{log}(\operatorname{log}({{x_{1}^{\theta_{0}}}^{\theta_{1}}})) + \operatorname{exp}(x_{0})\right) \cdot \theta_{2}\right) \cdot \theta_{3}\right)$$",-23.973113519573406;12.996212814579879;-1.73124023634306e-2;-0.32790447072648926,14,1.1832950308357822e-2,1.1832950308357822e-2,1.1832950308357822e-2,1.1832950308357822e-2,-9.195839006938056e-2,-9.195839006938056e-2,-9.195839006938056e-2,37.20653098026579,37.20653098026579,37.20653098026579 +459,0,(((Log(((x1 + -0.7246805170097141) / (x0 * 0.523431760008725))) * (-2.4032832255915126e-2 * x1)) ^ ((x1 + ((Log(x1) * 0.2828928416410784) + Log(x0))) * Exp(x0))) ^ 0.6033437395835273),"(np.log(((x[:, 1] + t[0]) / (x[:, 0] * t[1]))) * (t[2] * x[:, 1])) ** ((x[:, 1] + ((np.log(x[:, 1]) * t[3]) + np.log(x[:, 0]))) * np.exp(x[:, 0])) ** t[4]","$${{\left(\operatorname{log}(\frac{\left(x_{1} + \theta_{0}\right)}{\left(x_{0} \cdot \theta_{1}\right)}) \cdot \left(\theta_{2} \cdot x_{1}\right)\right)^{\left(\left(x_{1} + \left(\left(\operatorname{log}(x_{1}) \cdot \theta_{3}\right) + \operatorname{log}(x_{0})\right)\right) \cdot \operatorname{exp}(x_{0})\right)}}^{\theta_{4}}}$$",-0.7246805170097141;0.523431760008725;-2.4032832255915126e-2;0.2828928416410784;0.6033437395835273,27,,,,Infinity,,,,,, +460,0,((Exp(Exp((-0.4322422961350192 / x0))) ^ Exp((x1 + 7.866378801801877e-2))) * Exp((-0.5593581345308007 ^ (-0.6217642319341873 ^ (x0 * x0))))),"(np.exp(np.exp((t[0] / x[:, 0]))) ** np.exp((x[:, 1] + t[1])) * np.exp(t[2] ** t[3] ** (x[:, 0] * x[:, 0])))","$$\left({\operatorname{exp}(\operatorname{exp}(\frac{\theta_{0}}{x_{0}}))^{\operatorname{exp}(\left(x_{1} + \theta_{1}\right))}} \cdot \operatorname{exp}({\theta_{2}^{{\theta_{3}^{\left(x_{0} \cdot x_{0}\right)}}}})\right)$$",-0.4322422961350192;7.866378801801877e-2;-0.5593581345308007;-0.6217642319341873,19,,,,Infinity,,,,,, +461,0,Log(Exp(((Log((x1 / (-0.20366061059448715 + x1))) ^ ((x0 / x0) + 0.9080206586330656)) * (Log(Log(Log(x0))) * (0.12254075426113953 * (0.9646237399988574 * x0)))))),"np.log(np.exp((np.log((x[:, 1] / (t[0] + x[:, 1]))) ** ((x[:, 0] / x[:, 0]) + t[1]) * (np.log(np.log(np.log(x[:, 0]))) * (t[2] * (t[3] * x[:, 0]))))))","$$\operatorname{log}(\operatorname{exp}(\left({\operatorname{log}(\frac{x_{1}}{\left(\theta_{0} + x_{1}\right)})^{\left(\frac{x_{0}}{x_{0}} + \theta_{1}\right)}} \cdot \left(\operatorname{log}(\operatorname{log}(\operatorname{log}(x_{0}))) \cdot \left(\theta_{2} \cdot \left(\theta_{3} \cdot x_{0}\right)\right)\right)\right)))$$",-0.20366061059448715;0.9080206586330656;0.12254075426113953;0.9646237399988574,25,,,,Infinity,,,,,, +462,0,(Log(((0.3868418279578081 / (x0 * -0.585083326992277)) * 0.23043729812826708)) / ((Exp(Exp((x1 + x0))) + (x0 ^ 0.40148408999587637)) * -0.5007047927593818)),"(np.log(((t[0] / (x[:, 0] * t[1])) * t[2])) / ((np.exp(np.exp((x[:, 1] + x[:, 0]))) + x[:, 0] ** t[3]) * t[4]))","$$\frac{\operatorname{log}(\left(\frac{\theta_{0}}{\left(x_{0} \cdot \theta_{1}\right)} \cdot \theta_{2}\right))}{\left(\left(\operatorname{exp}(\operatorname{exp}(\left(x_{1} + x_{0}\right))) + {x_{0}^{\theta_{3}}}\right) \cdot \theta_{4}\right)}$$",0.3868418279578081;-0.585083326992277;0.23043729812826708;0.40148408999587637;-0.5007047927593818,20,,,,Infinity,,,,,, +463,0,Log((((-0.48185598495854254 * (Exp((x0 + x1)) * x0)) / (x1 + (x1 * 0.8343216489791334))) / (-1.1666792285470517e-2 ^ (x1 / ((x1 * -0.46589350048432276) ^ Log(x1)))))),"np.log((((t[0] * (np.exp((x[:, 0] + x[:, 1])) * x[:, 0])) / (x[:, 1] + (x[:, 1] * t[1]))) / t[2] ** (x[:, 1] / (x[:, 1] * t[3]) ** np.log(x[:, 1]))))","$$\operatorname{log}(\frac{\frac{\left(\theta_{0} \cdot \left(\operatorname{exp}(\left(x_{0} + x_{1}\right)) \cdot x_{0}\right)\right)}{\left(x_{1} + \left(x_{1} \cdot \theta_{1}\right)\right)}}{{\theta_{2}^{\frac{x_{1}}{{\left(x_{1} \cdot \theta_{3}\right)^{\operatorname{log}(x_{1})}}}}}})$$",-0.48185598495854254;0.8343216489791334;-1.1666792285470517e-2;-0.46589350048432276,26,,,,Infinity,,,,,, +464,0,Log((0.7508424218560416 + x1)),"np.log((t[0] + x[:, 1]))","$$\operatorname{log}(\left(\theta_{0} + x_{1}\right))$$",0.7508424218560416,4,1.6682672756616897e-2,1.6682672756616897e-2,1.6682672756616897e-2,1.6682672756616897e-2,-0.5394964071219723,-0.5394964071219723,-0.5394964071219723,7.581116785328158,7.581116785328158,7.581116785328158 +465,0,Exp((((0.7125853591559701 * Exp((0.9475287605660322 + x1))) / ((x0 * (x1 * x1)) * 0.9247569042953545)) + Log(Log((0.47584153366734283 ^ ((x0 * 0.24716300781747336) / x1)))))),"np.exp((((t[0] * np.exp((t[1] + x[:, 1]))) / ((x[:, 0] * (x[:, 1] * x[:, 1])) * t[2])) + np.log(np.log(t[3] ** ((x[:, 0] * t[4]) / x[:, 1])))))","$$\operatorname{exp}(\left(\frac{\left(\theta_{0} \cdot \operatorname{exp}(\left(\theta_{1} + x_{1}\right))\right)}{\left(\left(x_{0} \cdot \left(x_{1} \cdot x_{1}\right)\right) \cdot \theta_{2}\right)} + \operatorname{log}(\operatorname{log}({\theta_{3}^{\frac{\left(x_{0} \cdot \theta_{4}\right)}{x_{1}}}}))\right))$$",0.7125853591559701;0.9475287605660322;0.9247569042953545;0.47584153366734283;0.24716300781747336,25,,,,Infinity,,,,,, +466,0,(((-0.692494631398574 * ((-0.9716895054250313 / x0) ^ x0)) + Exp(((Exp(x1) ^ 0.14281513942126933) * (9.208621165836928e-2 ^ Exp(x1))))) * Exp(((((x1 * x1) / x1) + ((x1 ^ x1) * -0.6002127332187537)) * (0.7890737485827036 * ((x0 * x0) * (x0 / x0)))))),"(((t[0] * (t[1] / x[:, 0]) ** x[:, 0]) + np.exp((np.exp(x[:, 1]) ** t[2] * t[3] ** np.exp(x[:, 1])))) * np.exp(((((x[:, 1] * x[:, 1]) / x[:, 1]) + (x[:, 1] ** x[:, 1] * t[4])) * (t[5] * ((x[:, 0] * x[:, 0]) * (x[:, 0] / x[:, 0]))))))","$$\left(\left(\left(\theta_{0} \cdot {\frac{\theta_{1}}{x_{0}}^{x_{0}}}\right) + \operatorname{exp}(\left({\operatorname{exp}(x_{1})^{\theta_{2}}} \cdot {\theta_{3}^{\operatorname{exp}(x_{1})}}\right))\right) \cdot \operatorname{exp}(\left(\left(\frac{\left(x_{1} \cdot x_{1}\right)}{x_{1}} + \left({x_{1}^{x_{1}}} \cdot \theta_{4}\right)\right) \cdot \left(\theta_{5} \cdot \left(\left(x_{0} \cdot x_{0}\right) \cdot \frac{x_{0}}{x_{0}}\right)\right)\right))\right)$$",-0.692494631398574;-0.9716895054250313;0.14281513942126933;9.208621165836928e-2;-0.6002127332187537;0.7890737485827036,41,,,,Infinity,,,,,, +467,0,(((Exp(x1) ^ -108.34305251497705) + x1) ^ x0),"(np.exp(x[:, 1]) ** t[0] + x[:, 1]) ** x[:, 0]","$${\left({\operatorname{exp}(x_{1})^{\theta_{0}}} + x_{1}\right)^{x_{0}}}$$",-108.34305251497705,8,8.35399955580256e-2,8.35399955580256e-2,8.35399955580256e-2,8.35399955580256e-2,-6.709167762794794,-6.709167762794794,-6.709167762794794,16.430186685194304,16.430186685194304,16.430186685194304 +468,0,Log((Exp((x1 ^ ((0.8211063501786303 ^ x1) + (-0.7809747273286118 / (x1 ^ x1))))) + Log((x1 ^ (Exp(Exp(x1)) * x1))))),"np.log((np.exp(x[:, 1] ** (t[0] ** x[:, 1] + (t[1] / x[:, 1] ** x[:, 1]))) + np.log(x[:, 1] ** (np.exp(np.exp(x[:, 1])) * x[:, 1]))))","$$\operatorname{log}(\left(\operatorname{exp}({x_{1}^{\left({\theta_{0}^{x_{1}}} + \frac{\theta_{1}}{{x_{1}^{x_{1}}}}\right)}}) + \operatorname{log}({x_{1}^{\left(\operatorname{exp}(\operatorname{exp}(x_{1})) \cdot x_{1}\right)}})\right))$$",0.8211063501786303;-0.7809747273286118,22,0.35145276728967745,0.35145276728967745,0.35145276728967745,0.35145276728967745,-31.43246932964808,-31.43246932964808,-31.43246932964808,50.10587127825237,50.10587127825237,50.10587127825237 +469,0,Exp((x1 * -55.781589458130306)),"np.exp((x[:, 1] * t[0]))","$$\operatorname{exp}(\left(x_{1} \cdot \theta_{0}\right))$$",-55.781589458130306,4,1.4573896716579228e-2,1.4573896716579228e-2,1.4573896716579228e-2,1.4573896716579228e-2,-0.3448961062933631,-0.3448961062933631,-0.3448961062933631,7.579218886892124,7.579218886892124,7.579218886892124 +470,0,(Log((((x0 * -0.9300163838630309) ^ (-0.11744914858565592 ^ x0)) / (Log((0.15318791851070723 ^ x0)) ^ Log(x0)))) + Exp(Exp((x0 * 0.6243748477165578)))),"(np.log(((x[:, 0] * t[0]) ** t[1] ** x[:, 0] / np.log(t[2] ** x[:, 0]) ** np.log(x[:, 0]))) + np.exp(np.exp((x[:, 0] * t[3]))))","$$\left(\operatorname{log}(\frac{{\left(x_{0} \cdot \theta_{0}\right)^{{\theta_{1}^{x_{0}}}}}}{{\operatorname{log}({\theta_{2}^{x_{0}}})^{\operatorname{log}(x_{0})}}}) + \operatorname{exp}(\operatorname{exp}(\left(x_{0} \cdot \theta_{3}\right)))\right)$$",-0.9300163838630309;-0.11744914858565592;0.15318791851070723;0.6243748477165578,22,,,,Infinity,,,,,, +471,0,((Log((x1 * -0.40871838106244507)) ^ Exp(((((x0 + x1) ^ x1) + (0.7498890646280538 + Exp(x1))) * -5.4543656391219275e-2))) / (((Exp(((x0 + x1) * x0)) / (x1 * -0.44841924535131517)) * (0.27748898261205934 ^ Exp((x0 * 0.7182600586189987)))) + Exp(((x0 / (x0 / x0)) + x0)))),"(np.log((x[:, 1] * t[0])) ** np.exp((((x[:, 0] + x[:, 1]) ** x[:, 1] + (t[1] + np.exp(x[:, 1]))) * t[2])) / (((np.exp(((x[:, 0] + x[:, 1]) * x[:, 0])) / (x[:, 1] * t[3])) * t[4] ** np.exp((x[:, 0] * t[5]))) + np.exp(((x[:, 0] / (x[:, 0] / x[:, 0])) + x[:, 0]))))","$$\frac{{\operatorname{log}(\left(x_{1} \cdot \theta_{0}\right))^{\operatorname{exp}(\left(\left({\left(x_{0} + x_{1}\right)^{x_{1}}} + \left(\theta_{1} + \operatorname{exp}(x_{1})\right)\right) \cdot \theta_{2}\right))}}}{\left(\left(\frac{\operatorname{exp}(\left(\left(x_{0} + x_{1}\right) \cdot x_{0}\right))}{\left(x_{1} \cdot \theta_{3}\right)} \cdot {\theta_{4}^{\operatorname{exp}(\left(x_{0} \cdot \theta_{5}\right))}}\right) + \operatorname{exp}(\left(\frac{x_{0}}{\frac{x_{0}}{x_{0}}} + x_{0}\right))\right)}$$",-0.40871838106244507;0.7498890646280538;-5.4543656391219275e-2;-0.44841924535131517;0.27748898261205934;0.7182600586189987,45,,,,Infinity,,,,,, +472,0,(Exp(Log(Log(((x0 + (x0 ^ 0.12614586284466578)) * 0.9034083323917866)))) * Log((((x0 + -0.5452392025382109) + x0) * (Exp(x1) / Log(x0))))),"(np.exp(np.log(np.log(((x[:, 0] + x[:, 0] ** t[0]) * t[1])))) * np.log((((x[:, 0] + t[2]) + x[:, 0]) * (np.exp(x[:, 1]) / np.log(x[:, 0])))))","$$\left(\operatorname{exp}(\operatorname{log}(\operatorname{log}(\left(\left(x_{0} + {x_{0}^{\theta_{0}}}\right) \cdot \theta_{1}\right)))) \cdot \operatorname{log}(\left(\left(\left(x_{0} + \theta_{2}\right) + x_{0}\right) \cdot \frac{\operatorname{exp}(x_{1})}{\operatorname{log}(x_{0})}\right))\right)$$",0.12614586284466578;0.9034083323917866;-0.5452392025382109,23,,,,Infinity,,,,,, +473,0,(((Log(x1) ^ Exp(x1)) * -2.3709824891623432e-2) + 0.1794915843225885),"((np.log(x[:, 1]) ** np.exp(x[:, 1]) * t[0]) + t[1])","$$\left(\left({\operatorname{log}(x_{1})^{\operatorname{exp}(x_{1})}} \cdot \theta_{0}\right) + \theta_{1}\right)$$",-2.3709824891623432e-2;0.1794915843225885,9,,,,Infinity,,,,,, +474,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 +475,0,Log((Exp((Exp((x0 / Exp(x0))) * x1)) / (Exp((x0 + ((x0 / x1) / (4.3365866880761565 / x0)))) ^ (x1 + -0.1160491465886579)))),"np.log((np.exp((np.exp((x[:, 0] / np.exp(x[:, 0]))) * x[:, 1])) / np.exp((x[:, 0] + ((x[:, 0] / x[:, 1]) / (t[0] / x[:, 0])))) ** (x[:, 1] + t[1])))","$$\operatorname{log}(\frac{\operatorname{exp}(\left(\operatorname{exp}(\frac{x_{0}}{\operatorname{exp}(x_{0})}) \cdot x_{1}\right))}{{\operatorname{exp}(\left(x_{0} + \frac{\frac{x_{0}}{x_{1}}}{\frac{\theta_{0}}{x_{0}}}\right))^{\left(x_{1} + \theta_{1}\right)}}})$$",4.3365866880761565;-0.1160491465886579,24,1.6246259101801885e-2,1.6246259101801885e-2,1.6246259101801885e-2,1.6246259101801885e-2,-0.4992236484694237,-0.4992236484694237,-0.4992236484694237,56.789861359996166,56.789861359996166,56.789861359996166 +476,0,((((x1 + x0) / Log(x1)) / (x0 * x0)) ^ x0),"(((x[:, 1] + x[:, 0]) / np.log(x[:, 1])) / (x[:, 0] * x[:, 0])) ** x[:, 0]","$${\frac{\frac{\left(x_{1} + x_{0}\right)}{\operatorname{log}(x_{1})}}{\left(x_{0} \cdot x_{0}\right)}^{x_{0}}}$$",,12,,,,Infinity,,,,,, +477,0,((((0.8903664082938241 ^ (0.5976587209453461 / x1)) ^ Log((-0.9453294753807826 + x1))) + ((x0 * -0.3532665505819431) + (x0 * -0.4662435279595045))) * Log((((-0.8601359498172079 + (x0 / x1)) * (x1 + x1)) * -0.19253362538519236))),"((t[0] ** (t[1] / x[:, 1]) ** np.log((t[2] + x[:, 1])) + ((x[:, 0] * t[3]) + (x[:, 0] * t[4]))) * np.log((((t[5] + (x[:, 0] / x[:, 1])) * (x[:, 1] + x[:, 1])) * t[6])))","$$\left(\left({{\theta_{0}^{\frac{\theta_{1}}{x_{1}}}}^{\operatorname{log}(\left(\theta_{2} + x_{1}\right))}} + \left(\left(x_{0} \cdot \theta_{3}\right) + \left(x_{0} \cdot \theta_{4}\right)\right)\right) \cdot \operatorname{log}(\left(\left(\left(\theta_{5} + \frac{x_{0}}{x_{1}}\right) \cdot \left(x_{1} + x_{1}\right)\right) \cdot \theta_{6}\right))\right)$$",0.8903664082938241;0.5976587209453461;-0.9453294753807826;-0.3532665505819431;-0.4662435279595045;-0.8601359498172079;-0.19253362538519236,31,,,,Infinity,,,,,, +478,0,((((x1 ^ -0.5990674675655823) + Log((x0 * 0.4874315588394609))) ^ (-0.4856882205689257 + (x1 + (x0 ^ x1)))) * (((-5.26458945777698e-2 / x1) ^ (x0 * -0.5310039621757463)) / ((x0 + x1) + (-0.14577360927696958 ^ x0)))),"((x[:, 1] ** t[0] + np.log((x[:, 0] * t[1]))) ** (t[2] + (x[:, 1] + x[:, 0] ** x[:, 1])) * ((t[3] / x[:, 1]) ** (x[:, 0] * t[4]) / ((x[:, 0] + x[:, 1]) + t[5] ** x[:, 0])))","$$\left({\left({x_{1}^{\theta_{0}}} + \operatorname{log}(\left(x_{0} \cdot \theta_{1}\right))\right)^{\left(\theta_{2} + \left(x_{1} + {x_{0}^{x_{1}}}\right)\right)}} \cdot \frac{{\frac{\theta_{3}}{x_{1}}^{\left(x_{0} \cdot \theta_{4}\right)}}}{\left(\left(x_{0} + x_{1}\right) + {\theta_{5}^{x_{0}}}\right)}\right)$$",-0.5990674675655823;0.4874315588394609;-0.4856882205689257;-5.26458945777698e-2;-0.5310039621757463;-0.14577360927696958,32,,,,Infinity,,,,,, +479,0,(((Exp(Log(Log(Exp(x1)))) * -0.4255729085223552) / Log((-0.12508134615103228 * (x1 + (x0 * -0.8809574287654421))))) + (Exp(((x0 / (x0 * 0.5945359786046696)) * -0.5924208128891106)) ^ (((x1 ^ 0.6028790810878104) * 0.25904130518432245) ^ (Exp(x0) * -0.49984804200215627)))),"(((np.exp(np.log(np.log(np.exp(x[:, 1])))) * t[0]) / np.log((t[1] * (x[:, 1] + (x[:, 0] * t[2]))))) + np.exp(((x[:, 0] / (x[:, 0] * t[3])) * t[4])) ** (x[:, 1] ** t[5] * t[6]) ** (np.exp(x[:, 0]) * t[7]))","$$\left(\frac{\left(\operatorname{exp}(\operatorname{log}(\operatorname{log}(\operatorname{exp}(x_{1})))) \cdot \theta_{0}\right)}{\operatorname{log}(\left(\theta_{1} \cdot \left(x_{1} + \left(x_{0} \cdot \theta_{2}\right)\right)\right))} + {\operatorname{exp}(\left(\frac{x_{0}}{\left(x_{0} \cdot \theta_{3}\right)} \cdot \theta_{4}\right))^{{\left({x_{1}^{\theta_{5}}} \cdot \theta_{6}\right)^{\left(\operatorname{exp}(x_{0}) \cdot \theta_{7}\right)}}}}\right)$$",-0.4255729085223552;-0.12508134615103228;-0.8809574287654421;0.5945359786046696;-0.5924208128891106;0.6028790810878104;0.25904130518432245;-0.49984804200215627,36,,,,Infinity,,,,,, +480,0,Log(Exp((Log(Exp(Log(Log(x0)))) + Exp(((0.7155913225720063 / (x0 + 0.6257043660953849)) * 0.7451950563177195))))),"np.log(np.exp((np.log(np.exp(np.log(np.log(x[:, 0])))) + np.exp(((t[0] / (x[:, 0] + t[1])) * t[2])))))","$$\operatorname{log}(\operatorname{exp}(\left(\operatorname{log}(\operatorname{exp}(\operatorname{log}(\operatorname{log}(x_{0})))) + \operatorname{exp}(\left(\frac{\theta_{0}}{\left(x_{0} + \theta_{1}\right)} \cdot \theta_{2}\right))\right)))$$",0.7155913225720063;0.6257043660953849;0.7451950563177195,16,,,,Infinity,,,,,, +481,0,(((Exp((0.499371429140963 * ((x1 + -0.4033579959214005) + x1))) * 9.694142885240453e-4) ^ (0.21032526589418465 / (x0 * (x1 / x0)))) / x1),"((np.exp((t[0] * ((x[:, 1] + t[1]) + x[:, 1]))) * t[2]) ** (t[3] / (x[:, 0] * (x[:, 1] / x[:, 0]))) / x[:, 1])","$$\frac{{\left(\operatorname{exp}(\left(\theta_{0} \cdot \left(\left(x_{1} + \theta_{1}\right) + x_{1}\right)\right)) \cdot \theta_{2}\right)^{\frac{\theta_{3}}{\left(x_{0} \cdot \frac{x_{1}}{x_{0}}\right)}}}}{x_{1}}$$",0.499371429140963;-0.4033579959214005;9.694142885240453e-4;0.21032526589418465,20,9.458723691088909e-3,9.458723691088909e-3,9.458723691088909e-3,9.458723691088909e-3,0.12713799815948457,0.12713799815948457,0.12713799815948457,49.68104342638926,49.68104342638926,49.68104342638926 +482,0,Log((Exp((((x1 / (x0 + x1)) ^ (x0 ^ x1)) ^ Log((x1 * 8.300432483156195e-2)))) * 0.17824740613105972)),"np.log((np.exp((x[:, 1] / (x[:, 0] + x[:, 1])) ** x[:, 0] ** x[:, 1] ** np.log((x[:, 1] * t[0]))) * t[1]))","$$\operatorname{log}(\left(\operatorname{exp}({{\frac{x_{1}}{\left(x_{0} + x_{1}\right)}^{{x_{0}^{x_{1}}}}}^{\operatorname{log}(\left(x_{1} \cdot \theta_{0}\right))}}) \cdot \theta_{1}\right))$$",8.300432483156195e-2;0.17824740613105972,18,Infinity,Infinity,Infinity,Infinity,-Infinity,-Infinity,-Infinity,Infinity,Infinity,Infinity +483,0,Exp((((-0.9544859134461415 * Exp((x0 ^ -0.15183952623766028))) ^ Log(((4.2004223984760114e-2 ^ x0) + (0.9228220023709929 / x0)))) ^ (Log(((x1 * -0.6905798542213194) + Exp(Log(x0)))) * -7.663075645831408e-2))),"np.exp((t[0] * np.exp(x[:, 0] ** t[1])) ** np.log((t[2] ** x[:, 0] + (t[3] / x[:, 0]))) ** (np.log(((x[:, 1] * t[4]) + np.exp(np.log(x[:, 0])))) * t[5]))","$$\operatorname{exp}({{\left(\theta_{0} \cdot \operatorname{exp}({x_{0}^{\theta_{1}}})\right)^{\operatorname{log}(\left({\theta_{2}^{x_{0}}} + \frac{\theta_{3}}{x_{0}}\right))}}^{\left(\operatorname{log}(\left(\left(x_{1} \cdot \theta_{4}\right) + \operatorname{exp}(\operatorname{log}(x_{0}))\right)) \cdot \theta_{5}\right)}})$$",-0.9544859134461415;-0.15183952623766028;4.2004223984760114e-2;0.9228220023709929;-0.6905798542213194;-7.663075645831408e-2,27,,,,Infinity,,,,,, +484,0,((Exp(Exp((Log((x0 * -0.8648266540781215)) * x1))) + (-0.32563463164061857 + x0)) + 0.5670774457754291),"((np.exp(np.exp((np.log((x[:, 0] * t[0])) * x[:, 1]))) + (t[1] + x[:, 0])) + t[2])","$$\left(\left(\operatorname{exp}(\operatorname{exp}(\left(\operatorname{log}(\left(x_{0} \cdot \theta_{0}\right)) \cdot x_{1}\right))) + \left(\theta_{1} + x_{0}\right)\right) + \theta_{2}\right)$$",-0.8648266540781215;-0.32563463164061857;0.5670774457754291,14,,,,Infinity,,,,,, +485,0,((Log((x1 ^ 27.833558699624284)) + Exp(Log((x0 * x1)))) + (((-24.83384825297815 / x0) + (Exp(((x1 ^ 357.07491126637905) / x1)) * 56.62578186795207)) + Exp((x0 ^ -1.9026737627520485)))),"((np.log(x[:, 1] ** t[0]) + np.exp(np.log((x[:, 0] * x[:, 1])))) + (((t[1] / x[:, 0]) + (np.exp((x[:, 1] ** t[2] / x[:, 1])) * t[3])) + np.exp(x[:, 0] ** t[4])))","$$\left(\left(\operatorname{log}({x_{1}^{\theta_{0}}}) + \operatorname{exp}(\operatorname{log}(\left(x_{0} \cdot x_{1}\right)))\right) + \left(\left(\frac{\theta_{1}}{x_{0}} + \left(\operatorname{exp}(\frac{{x_{1}^{\theta_{2}}}}{x_{1}}) \cdot \theta_{3}\right)\right) + \operatorname{exp}({x_{0}^{\theta_{4}}})\right)\right)$$",27.833558699624284;-24.83384825297815;357.07491126637905;56.62578186795207;-1.9026737627520485,28,211.71885682357342,211.71885682357342,211.71885682357342,211.71885682357342,-19536.661869593638,-19536.661869593638,-19536.661869593638,262.17388398346844,262.17388398346844,262.17388398346844 +486,0,((((x0 + 0.5364299619496307) / x1) ^ Log((x1 * -0.18249596564908754))) ^ ((x0 / Log(x0)) * (Log(x1) ^ (-0.828455256179492 / x1)))),"((x[:, 0] + t[0]) / x[:, 1]) ** np.log((x[:, 1] * t[1])) ** ((x[:, 0] / np.log(x[:, 0])) * np.log(x[:, 1]) ** (t[2] / x[:, 1]))","$${{\frac{\left(x_{0} + \theta_{0}\right)}{x_{1}}^{\operatorname{log}(\left(x_{1} \cdot \theta_{1}\right))}}^{\left(\frac{x_{0}}{\operatorname{log}(x_{0})} \cdot {\operatorname{log}(x_{1})^{\frac{\theta_{2}}{x_{1}}}}\right)}}$$",0.5364299619496307;-0.18249596564908754;-0.828455256179492,22,,,,Infinity,,,,,, +487,0,(Exp((x1 + x1)) * x1),"(np.exp((x[:, 1] + x[:, 1])) * x[:, 1])","$$\left(\operatorname{exp}(\left(x_{1} + x_{1}\right)) \cdot x_{1}\right)$$",,6,0.6788921370564028,0.6788921370564028,0.6788921370564028,0.6788921370564028,-61.64895446127768,-61.64895446127768,-61.64895446127768,8.928769090070105,8.928769090070105,8.928769090070105 +488,0,(((Exp(x1) ^ Exp(Exp(x0))) * (Exp(x1) * (0.9710717551203418 / (0.7912028428067664 ^ (0.1155859577414935 + (-0.18040342975130197 + x0)))))) ^ (((x0 * -0.8480108833422038) ^ (-0.1350553335446253 + Exp(x0))) + ((x0 + -0.9511009648861121) + ((x0 / x0) * x0)))),"(np.exp(x[:, 1]) ** np.exp(np.exp(x[:, 0])) * (np.exp(x[:, 1]) * (t[0] / t[1] ** (t[2] + (t[3] + x[:, 0]))))) ** ((x[:, 0] * t[4]) ** (t[5] + np.exp(x[:, 0])) + ((x[:, 0] + t[6]) + ((x[:, 0] / x[:, 0]) * x[:, 0])))","$${\left({\operatorname{exp}(x_{1})^{\operatorname{exp}(\operatorname{exp}(x_{0}))}} \cdot \left(\operatorname{exp}(x_{1}) \cdot \frac{\theta_{0}}{{\theta_{1}^{\left(\theta_{2} + \left(\theta_{3} + x_{0}\right)\right)}}}\right)\right)^{\left({\left(x_{0} \cdot \theta_{4}\right)^{\left(\theta_{5} + \operatorname{exp}(x_{0})\right)}} + \left(\left(x_{0} + \theta_{6}\right) + \left(\frac{x_{0}}{x_{0}} \cdot x_{0}\right)\right)\right)}}$$",0.9710717551203418;0.7912028428067664;0.1155859577414935;-0.18040342975130197;-0.8480108833422038;-0.1350553335446253;-0.9511009648861121,38,,,,Infinity,,,,,, +489,0,(((0.1584138760257211 + Exp(x1)) + Log(Log((x0 * -8.935316426754925e-2)))) / ((0.2639324900750277 + (0.8123249659900147 ^ x1)) / (x1 * x1))),"(((t[0] + np.exp(x[:, 1])) + np.log(np.log((x[:, 0] * t[1])))) / ((t[2] + t[3] ** x[:, 1]) / (x[:, 1] * x[:, 1])))","$$\frac{\left(\left(\theta_{0} + \operatorname{exp}(x_{1})\right) + \operatorname{log}(\operatorname{log}(\left(x_{0} \cdot \theta_{1}\right)))\right)}{\frac{\left(\theta_{2} + {\theta_{3}^{x_{1}}}\right)}{\left(x_{1} \cdot x_{1}\right)}}$$",0.1584138760257211;-8.935316426754925e-2;0.2639324900750277;0.8123249659900147,20,,,,Infinity,,,,,, +490,0,((Log((x0 ^ -0.6730846159013812)) + Exp((-1.9982217367522053 / (3.824808722885168e-2 + x0)))) ^ 1.7199541604682669),"(np.log(x[:, 0] ** t[0]) + np.exp((t[1] / (t[2] + x[:, 0])))) ** t[3]","$${\left(\operatorname{log}({x_{0}^{\theta_{0}}}) + \operatorname{exp}(\frac{\theta_{1}}{\left(\theta_{2} + x_{0}\right)})\right)^{\theta_{3}}}$$",-0.6730846159013812;-1.9982217367522053;3.824808722885168e-2;1.7199541604682669,13,4.941930734085285e-3,4.941930734085285e-3,4.941930734085285e-3,4.941930734085285e-3,0.5439528952965687,0.5439528952965687,0.5439528952965687,33.384979416850314,33.384979416850314,33.384979416850314 +491,0,(((x1 ^ -0.4764826234729066) ^ -0.44228473667644275) * (4.722000904340201e-2 * x0)),"(x[:, 1] ** t[0] ** t[1] * (t[2] * x[:, 0]))","$$\left({{x_{1}^{\theta_{0}}}^{\theta_{1}}} \cdot \left(\theta_{2} \cdot x_{0}\right)\right)$$",-0.4764826234729066;-0.44228473667644275;4.722000904340201e-2,9,1.288198690237182e-2,1.288198690237182e-2,1.288198690237182e-2,1.288198690237182e-2,-0.18876470468005713,-0.18876470468005713,-0.18876470468005713,20.559309806221957,20.559309806221957,20.559309806221957 +492,0,(Log(((x1 * 0.5086158750122356) + (x0 * ((-0.8929997032234764 ^ (x1 / x1)) ^ -0.7626997718420325)))) / ((((x1 ^ Log(x0)) + 0.5589586253562242) + -0.26193925813403895) * (0.19293976532434975 + (x1 ^ x1)))),"(np.log(((x[:, 1] * t[0]) + (x[:, 0] * t[1] ** (x[:, 1] / x[:, 1]) ** t[2]))) / (((x[:, 1] ** np.log(x[:, 0]) + t[3]) + t[4]) * (t[5] + x[:, 1] ** x[:, 1])))","$$\frac{\operatorname{log}(\left(\left(x_{1} \cdot \theta_{0}\right) + \left(x_{0} \cdot {{\theta_{1}^{\frac{x_{1}}{x_{1}}}}^{\theta_{2}}}\right)\right))}{\left(\left(\left({x_{1}^{\operatorname{log}(x_{0})}} + \theta_{3}\right) + \theta_{4}\right) \cdot \left(\theta_{5} + {x_{1}^{x_{1}}}\right)\right)}$$",0.5086158750122356;-0.8929997032234764;-0.7626997718420325;0.5589586253562242;-0.26193925813403895;0.19293976532434975,29,,,,Infinity,,,,,, +493,0,(((-0.31329691921057845 * Exp((x1 * -9.221450146706411e-2))) * ((-0.49754150681916287 ^ x0) ^ Log((x1 ^ -0.5311068034794029)))) + Exp((-0.6200175331237936 / (Exp((-1.2224830249204621e-2 / x1)) + (x0 / x1))))),"(((t[0] * np.exp((x[:, 1] * t[1]))) * t[2] ** x[:, 0] ** np.log(x[:, 1] ** t[3])) + np.exp((t[4] / (np.exp((t[5] / x[:, 1])) + (x[:, 0] / x[:, 1])))))","$$\left(\left(\left(\theta_{0} \cdot \operatorname{exp}(\left(x_{1} \cdot \theta_{1}\right))\right) \cdot {{\theta_{2}^{x_{0}}}^{\operatorname{log}({x_{1}^{\theta_{3}}})}}\right) + \operatorname{exp}(\frac{\theta_{4}}{\left(\operatorname{exp}(\frac{\theta_{5}}{x_{1}}) + \frac{x_{0}}{x_{1}}\right)})\right)$$",-0.31329691921057845;-9.221450146706411e-2;-0.49754150681916287;-0.5311068034794029;-0.6200175331237936;-1.2224830249204621e-2,27,,,,Infinity,,,,,, +494,0,(Exp((((9.801314464938471e-2 ^ x1) * 0.511653805074811) * 0.4133027521686087)) + ((Exp(x1) * ((x1 + -0.4245595907107219) + -0.19931370579934105)) * ((x1 + Log(x0)) / (x0 / ((x1 * x0) * (x1 * -1.0317814518854163e-2)))))),"(np.exp(((t[0] ** x[:, 1] * t[1]) * t[2])) + ((np.exp(x[:, 1]) * ((x[:, 1] + t[3]) + t[4])) * ((x[:, 1] + np.log(x[:, 0])) / (x[:, 0] / ((x[:, 1] * x[:, 0]) * (x[:, 1] * t[5]))))))","$$\left(\operatorname{exp}(\left(\left({\theta_{0}^{x_{1}}} \cdot \theta_{1}\right) \cdot \theta_{2}\right)) + \left(\left(\operatorname{exp}(x_{1}) \cdot \left(\left(x_{1} + \theta_{3}\right) + \theta_{4}\right)\right) \cdot \frac{\left(x_{1} + \operatorname{log}(x_{0})\right)}{\frac{x_{0}}{\left(\left(x_{1} \cdot x_{0}\right) \cdot \left(x_{1} \cdot \theta_{5}\right)\right)}}\right)\right)$$",9.801314464938471e-2;0.511653805074811;0.4133027521686087;-0.4245595907107219;-0.19931370579934105;-1.0317814518854163e-2,32,1.1126846289185084,1.1126846289185084,1.1126846289185084,1.1126846289185084,-101.67982915390262,-101.67982915390262,-101.67982915390262,83.43815225299153,83.43815225299153,83.43815225299153 +495,0,((-0.7139304616511493 * ((0.6462003795865441 ^ (Exp(x0) * (Exp(x0) * 0.3253685531803774))) * -0.5729656962385743)) / Log((((Log(x1) / ((0.4878172156889362 * x1) ^ -0.9693208323153738)) ^ -0.3884691627250888) * 0.11552224879693374))),"((t[0] * (t[1] ** (np.exp(x[:, 0]) * (np.exp(x[:, 0]) * t[2])) * t[3])) / np.log(((np.log(x[:, 1]) / (t[4] * x[:, 1]) ** t[5]) ** t[6] * t[7])))","$$\frac{\left(\theta_{0} \cdot \left({\theta_{1}^{\left(\operatorname{exp}(x_{0}) \cdot \left(\operatorname{exp}(x_{0}) \cdot \theta_{2}\right)\right)}} \cdot \theta_{3}\right)\right)}{\operatorname{log}(\left({\frac{\operatorname{log}(x_{1})}{{\left(\theta_{4} \cdot x_{1}\right)^{\theta_{5}}}}^{\theta_{6}}} \cdot \theta_{7}\right))}$$",-0.7139304616511493;0.6462003795865441;0.3253685531803774;-0.5729656962385743;0.4878172156889362;-0.9693208323153738;-0.3884691627250888;0.11552224879693374,27,,,,Infinity,,,,,, +496,0,Log(((Log(Log((-0.9454074598663175 * x0))) * (Exp(x0) / (0.8960489166409221 / x0))) ^ (-0.5051288518723183 * (x1 * Exp(x0))))),"np.log((np.log(np.log((t[0] * x[:, 0]))) * (np.exp(x[:, 0]) / (t[1] / x[:, 0]))) ** (t[2] * (x[:, 1] * np.exp(x[:, 0]))))","$$\operatorname{log}({\left(\operatorname{log}(\operatorname{log}(\left(\theta_{0} \cdot x_{0}\right))) \cdot \frac{\operatorname{exp}(x_{0})}{\frac{\theta_{1}}{x_{0}}}\right)^{\left(\theta_{2} \cdot \left(x_{1} \cdot \operatorname{exp}(x_{0})\right)\right)}})$$",-0.9454074598663175;0.8960489166409221;-0.5051288518723183,20,,,,Infinity,,,,,, +497,0,Exp(Exp((-1.3743271927501792 / (((Exp(x0) / x1) + 0.6758110516317962) ^ (-0.8795202689099982 / x1))))),"np.exp(np.exp((t[0] / ((np.exp(x[:, 0]) / x[:, 1]) + t[1]) ** (t[2] / x[:, 1]))))","$$\operatorname{exp}(\operatorname{exp}(\frac{\theta_{0}}{{\left(\frac{\operatorname{exp}(x_{0})}{x_{1}} + \theta_{1}\right)^{\frac{\theta_{2}}{x_{1}}}}}))$$",-1.3743271927501792;0.6758110516317962;-0.8795202689099982,14,0.8923099011383698,0.8923099011383698,0.8923099011383698,0.8923099011383698,-81.34339346475672,-81.34339346475672,-81.34339346475672,34.10859580390184,34.10859580390184,34.10859580390184 +498,0,Exp(((-0.7392483398152878 / ((0.46906548433260364 + x0) * -0.3982190652046247)) * Log(((-0.8092675432839428 ^ Log(x0)) ^ ((x1 / x0) + x1))))),"np.exp(((t[0] / ((t[1] + x[:, 0]) * t[2])) * np.log(t[3] ** np.log(x[:, 0]) ** ((x[:, 1] / x[:, 0]) + x[:, 1]))))","$$\operatorname{exp}(\left(\frac{\theta_{0}}{\left(\left(\theta_{1} + x_{0}\right) \cdot \theta_{2}\right)} \cdot \operatorname{log}({{\theta_{3}^{\operatorname{log}(x_{0})}}^{\left(\frac{x_{1}}{x_{0}} + x_{1}\right)}})\right))$$",-0.7392483398152878;0.46906548433260364;-0.3982190652046247;-0.8092675432839428,20,,,,Infinity,,,,,, +499,0,(Log((((x0 + -0.7175453748672325) * -0.5818800380176967) ^ ((x1 + x1) ^ (x0 + 0.6621409982507231)))) ^ (Log((-0.7838729499070423 / Exp((x0 * -5.478735637163734e-2)))) / (Exp(Log(x1)) ^ (x1 + -0.7115004196980408)))),"np.log(((x[:, 0] + t[0]) * t[1]) ** (x[:, 1] + x[:, 1]) ** (x[:, 0] + t[2])) ** (np.log((t[3] / np.exp((x[:, 0] * t[4])))) / np.exp(np.log(x[:, 1])) ** (x[:, 1] + t[5]))","$${\operatorname{log}({\left(\left(x_{0} + \theta_{0}\right) \cdot \theta_{1}\right)^{{\left(x_{1} + x_{1}\right)^{\left(x_{0} + \theta_{2}\right)}}}})^{\frac{\operatorname{log}(\frac{\theta_{3}}{\operatorname{exp}(\left(x_{0} \cdot \theta_{4}\right))})}{{\operatorname{exp}(\operatorname{log}(x_{1}))^{\left(x_{1} + \theta_{5}\right)}}}}}$$",-0.7175453748672325;-0.5818800380176967;0.6621409982507231;-0.7838729499070423;-5.478735637163734e-2;-0.7115004196980408,30,,,,Infinity,,,,,, +500,0,((Log(Log(((x0 ^ -0.4737594236644772) / (Exp(x0) * -0.5065112245494224)))) / (Log((x1 + x1)) * (Exp(x1) + ((x1 * (x1 ^ x0)) / (-6.176548038190055e-2 * x1))))) / ((Exp((x1 ^ Exp((0.10187211319049361 ^ x0)))) * -0.4852485594240281) * 0.512099525501789)),"((np.log(np.log((x[:, 0] ** t[0] / (np.exp(x[:, 0]) * t[1])))) / (np.log((x[:, 1] + x[:, 1])) * (np.exp(x[:, 1]) + ((x[:, 1] * x[:, 1] ** x[:, 0]) / (t[2] * x[:, 1]))))) / ((np.exp(x[:, 1] ** np.exp(t[3] ** x[:, 0])) * t[4]) * t[5]))","$$\frac{\frac{\operatorname{log}(\operatorname{log}(\frac{{x_{0}^{\theta_{0}}}}{\left(\operatorname{exp}(x_{0}) \cdot \theta_{1}\right)}))}{\left(\operatorname{log}(\left(x_{1} + x_{1}\right)) \cdot \left(\operatorname{exp}(x_{1}) + \frac{\left(x_{1} \cdot {x_{1}^{x_{0}}}\right)}{\left(\theta_{2} \cdot x_{1}\right)}\right)\right)}}{\left(\left(\operatorname{exp}({x_{1}^{\operatorname{exp}({\theta_{3}^{x_{0}}})}}) \cdot \theta_{4}\right) \cdot \theta_{5}\right)}$$",-0.4737594236644772;-0.5065112245494224;-6.176548038190055e-2;0.10187211319049361;-0.4852485594240281;0.512099525501789,40,,,,Infinity,,,,,, +501,0,Exp(((((Log(x0) + (-0.4807746076313275 ^ x1)) ^ 0.2190900166409484) + Log(((x0 * -0.3205295132630004) + (x1 * x0)))) / (((-2.1947238151517667e-2 / (x1 ^ x1)) ^ ((0.5380676258085577 + x1) / x1)) / ((Exp(x1) + (x0 * x0)) / (0.39869232448365366 ^ (x0 + 0.5199321715900452)))))),"np.exp((((np.log(x[:, 0]) + t[0] ** x[:, 1]) ** t[1] + np.log(((x[:, 0] * t[2]) + (x[:, 1] * x[:, 0])))) / ((t[3] / x[:, 1] ** x[:, 1]) ** ((t[4] + x[:, 1]) / x[:, 1]) / ((np.exp(x[:, 1]) + (x[:, 0] * x[:, 0])) / t[5] ** (x[:, 0] + t[6])))))","$$\operatorname{exp}(\frac{\left({\left(\operatorname{log}(x_{0}) + {\theta_{0}^{x_{1}}}\right)^{\theta_{1}}} + \operatorname{log}(\left(\left(x_{0} \cdot \theta_{2}\right) + \left(x_{1} \cdot x_{0}\right)\right))\right)}{\frac{{\frac{\theta_{3}}{{x_{1}^{x_{1}}}}^{\frac{\left(\theta_{4} + x_{1}\right)}{x_{1}}}}}{\frac{\left(\operatorname{exp}(x_{1}) + \left(x_{0} \cdot x_{0}\right)\right)}{{\theta_{5}^{\left(x_{0} + \theta_{6}\right)}}}}})$$",-0.4807746076313275;0.2190900166409484;-0.3205295132630004;-2.1947238151517667e-2;0.5380676258085577;0.39869232448365366;0.5199321715900452,43,,,,Infinity,,,,,, +502,0,(Log((0.8908722260445152 * Exp(((-0.10516567604572213 * x1) ^ -0.10964452375198297)))) * (((x1 ^ x1) / (-0.1418572450128216 * Log(x0))) + Log(((x1 + -0.3037194868934737) / Exp((x1 ^ x0)))))),"(np.log((t[0] * np.exp((t[1] * x[:, 1]) ** t[2]))) * ((x[:, 1] ** x[:, 1] / (t[3] * np.log(x[:, 0]))) + np.log(((x[:, 1] + t[4]) / np.exp(x[:, 1] ** x[:, 0])))))","$$\left(\operatorname{log}(\left(\theta_{0} \cdot \operatorname{exp}({\left(\theta_{1} \cdot x_{1}\right)^{\theta_{2}}})\right)) \cdot \left(\frac{{x_{1}^{x_{1}}}}{\left(\theta_{3} \cdot \operatorname{log}(x_{0})\right)} + \operatorname{log}(\frac{\left(x_{1} + \theta_{4}\right)}{\operatorname{exp}({x_{1}^{x_{0}}})})\right)\right)$$",0.8908722260445152;-0.10516567604572213;-0.10964452375198297;-0.1418572450128216;-0.3037194868934737,28,,,,Infinity,,,,,, +503,0,(((((Log((-0.6676031223865525 / x1)) ^ -0.9405415772427824) + Log(x1)) ^ (x1 + (x1 + x1))) * (Exp(x1) * 0.7758484867831352)) + Exp((Log(x0) ^ (x1 + ((-0.9861687014443639 / x1) + Exp(x0)))))),"(((np.log((t[0] / x[:, 1])) ** t[1] + np.log(x[:, 1])) ** (x[:, 1] + (x[:, 1] + x[:, 1])) * (np.exp(x[:, 1]) * t[2])) + np.exp(np.log(x[:, 0]) ** (x[:, 1] + ((t[3] / x[:, 1]) + np.exp(x[:, 0])))))","$$\left(\left({\left({\operatorname{log}(\frac{\theta_{0}}{x_{1}})^{\theta_{1}}} + \operatorname{log}(x_{1})\right)^{\left(x_{1} + \left(x_{1} + x_{1}\right)\right)}} \cdot \left(\operatorname{exp}(x_{1}) \cdot \theta_{2}\right)\right) + \operatorname{exp}({\operatorname{log}(x_{0})^{\left(x_{1} + \left(\frac{\theta_{3}}{x_{1}} + \operatorname{exp}(x_{0})\right)\right)}})\right)$$",-0.6676031223865525;-0.9405415772427824;0.7758484867831352;-0.9861687014443639,33,,,,Infinity,,,,,, +504,0,Log((((Log((-0.7608012574024686 + x1)) / (x0 ^ (-0.43008469408972283 ^ x0))) * ((6.292200849674778e-2 ^ Exp(x0)) + (x0 * -0.12496236836114638))) * (((x1 / (x1 * -0.9810182405572657)) * 0.9969449106681799) * ((x0 + 4.8101637865840985e-2) * (x1 * -0.576465232267925))))),"np.log((((np.log((t[0] + x[:, 1])) / x[:, 0] ** t[1] ** x[:, 0]) * (t[2] ** np.exp(x[:, 0]) + (x[:, 0] * t[3]))) * (((x[:, 1] / (x[:, 1] * t[4])) * t[5]) * ((x[:, 0] + t[6]) * (x[:, 1] * t[7])))))","$$\operatorname{log}(\left(\left(\frac{\operatorname{log}(\left(\theta_{0} + x_{1}\right))}{{x_{0}^{{\theta_{1}^{x_{0}}}}}} \cdot \left({\theta_{2}^{\operatorname{exp}(x_{0})}} + \left(x_{0} \cdot \theta_{3}\right)\right)\right) \cdot \left(\left(\frac{x_{1}}{\left(x_{1} \cdot \theta_{4}\right)} \cdot \theta_{5}\right) \cdot \left(\left(x_{0} + \theta_{6}\right) \cdot \left(x_{1} \cdot \theta_{7}\right)\right)\right)\right))$$",-0.7608012574024686;-0.43008469408972283;6.292200849674778e-2;-0.12496236836114638;-0.9810182405572657;0.9969449106681799;4.8101637865840985e-2;-0.576465232267925,36,,,,Infinity,,,,,, +505,0,((Log(Exp(x1)) ^ ((x0 * -0.3339603845716651) / (x0 + -0.6129353396270771))) / Log(Exp((((x0 ^ x0) ^ 0.254598519549314) * x1)))),"(np.log(np.exp(x[:, 1])) ** ((x[:, 0] * t[0]) / (x[:, 0] + t[1])) / np.log(np.exp((x[:, 0] ** x[:, 0] ** t[2] * x[:, 1]))))","$$\frac{{\operatorname{log}(\operatorname{exp}(x_{1}))^{\frac{\left(x_{0} \cdot \theta_{0}\right)}{\left(x_{0} + \theta_{1}\right)}}}}{\operatorname{log}(\operatorname{exp}(\left({{x_{0}^{x_{0}}}^{\theta_{2}}} \cdot x_{1}\right)))}$$",-0.3339603845716651;-0.6129353396270771;0.254598519549314,21,1.2661980592510769e14,1.2661980592510769e14,1.2661980592510769e14,1.2661980592510769e14,-1.168462267024142e16,-1.168462267024142e16,-1.168462267024142e16,1.1395782533264913e14,1.1395782533264913e14,1.1395782533264913e14 +506,0,Exp((0.4813219551679898 ^ Log(((Exp(x0) / (-0.5726900657580534 + x0)) ^ (0.7629390522998468 / Exp((-0.37672408121184886 ^ x1))))))),"np.exp(t[0] ** np.log((np.exp(x[:, 0]) / (t[1] + x[:, 0])) ** (t[2] / np.exp(t[3] ** x[:, 1]))))","$$\operatorname{exp}({\theta_{0}^{\operatorname{log}({\frac{\operatorname{exp}(x_{0})}{\left(\theta_{1} + x_{0}\right)}^{\frac{\theta_{2}}{\operatorname{exp}({\theta_{3}^{x_{1}}})}}})}})$$",0.4813219551679898;-0.5726900657580534;0.7629390522998468;-0.37672408121184886,17,,,,Infinity,,,,,, +507,0,((Exp((x0 ^ -9.200388982329755e-2)) * Exp((0.21147430389317146 * (x1 / x0)))) * ((Log(x1) + Exp((x1 ^ ((x1 * 0.5579888515226705) + -0.11878669040485879)))) ^ ((Log((-0.7044664153893334 / (x0 ^ x0))) * -4.64179423739447e-2) * 0.892186212280962))),"((np.exp(x[:, 0] ** t[0]) * np.exp((t[1] * (x[:, 1] / x[:, 0])))) * (np.log(x[:, 1]) + np.exp(x[:, 1] ** ((x[:, 1] * t[2]) + t[3]))) ** ((np.log((t[4] / x[:, 0] ** x[:, 0])) * t[5]) * t[6]))","$$\left(\left(\operatorname{exp}({x_{0}^{\theta_{0}}}) \cdot \operatorname{exp}(\left(\theta_{1} \cdot \frac{x_{1}}{x_{0}}\right))\right) \cdot {\left(\operatorname{log}(x_{1}) + \operatorname{exp}({x_{1}^{\left(\left(x_{1} \cdot \theta_{2}\right) + \theta_{3}\right)}})\right)^{\left(\left(\operatorname{log}(\frac{\theta_{4}}{{x_{0}^{x_{0}}}}) \cdot \theta_{5}\right) \cdot \theta_{6}\right)}}\right)$$",-9.200388982329755e-2;0.21147430389317146;0.5579888515226705;-0.11878669040485879;-0.7044664153893334;-4.64179423739447e-2;0.892186212280962,34,,,,Infinity,,,,,, +508,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 +509,0,(Exp((1.976895496118902 / x0)) * 6.320978169179161e-3),"(np.exp((t[0] / x[:, 0])) * t[1])","$$\left(\operatorname{exp}(\frac{\theta_{0}}{x_{0}}) \cdot \theta_{1}\right)$$",1.976895496118902;6.320978169179161e-3,6,5.6805456581737425e-3,5.6805456581737425e-3,5.6805456581737425e-3,5.6805456581737425e-3,0.47579265272213833,0.47579265272213833,0.47579265272213833,13.703589836432238,13.703589836432238,13.703589836432238 +510,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 +511,0,((((-0.7250556350870376 / x0) * -0.709695230029325) * 0.7180220038697421) ^ (((1.1576193966543218 * x1) + 2.7591213525421794) * 1.1521992508783783)),"(((t[0] / x[:, 0]) * t[1]) * t[2]) ** (((t[3] * x[:, 1]) + t[4]) * t[5])","$${\left(\left(\frac{\theta_{0}}{x_{0}} \cdot \theta_{1}\right) \cdot \theta_{2}\right)^{\left(\left(\left(\theta_{3} \cdot x_{1}\right) + \theta_{4}\right) \cdot \theta_{5}\right)}}$$",-0.7250556350870376;-0.709695230029325;0.7180220038697421;1.1576193966543218;2.7591213525421794;1.1521992508783783,15,6.639669580858919e-3,6.639669580858919e-3,6.639669580858919e-3,6.639669580858919e-3,0.3872835837917472,0.3872835837917472,0.3872835837917472,41.320177550658315,41.320177550658315,41.320177550658315 +512,0,Exp(((((-0.325449421298992 ^ x1) * -9.523958242252628e-2) + Exp(Exp((-0.20640673599186588 + x1)))) ^ Log(((0.5338096338517231 + (x1 * -0.35954842666035247)) * 0.5607667923142867)))),"np.exp(((t[0] ** x[:, 1] * t[1]) + np.exp(np.exp((t[2] + x[:, 1])))) ** np.log(((t[3] + (x[:, 1] * t[4])) * t[5])))","$$\operatorname{exp}({\left(\left({\theta_{0}^{x_{1}}} \cdot \theta_{1}\right) + \operatorname{exp}(\operatorname{exp}(\left(\theta_{2} + x_{1}\right)))\right)^{\operatorname{log}(\left(\left(\theta_{3} + \left(x_{1} \cdot \theta_{4}\right)\right) \cdot \theta_{5}\right))}})$$",-0.325449421298992;-9.523958242252628e-2;-0.20640673599186588;0.5338096338517231;-0.35954842666035247;0.5607667923142867,21,,,,Infinity,,,,,, +513,0,Log(Exp((Log((0.38456457777374986 + Log(Log(x1)))) * ((x0 / x0) * (9.003773939913506e-2 + (0.5626725897151772 * x1)))))),"np.log(np.exp((np.log((t[0] + np.log(np.log(x[:, 1])))) * ((x[:, 0] / x[:, 0]) * (t[1] + (t[2] * x[:, 1]))))))","$$\operatorname{log}(\operatorname{exp}(\left(\operatorname{log}(\left(\theta_{0} + \operatorname{log}(\operatorname{log}(x_{1}))\right)) \cdot \left(\frac{x_{0}}{x_{0}} \cdot \left(\theta_{1} + \left(\theta_{2} \cdot x_{1}\right)\right)\right)\right)))$$",0.38456457777374986;9.003773939913506e-2;0.5626725897151772,18,,,,Infinity,,,,,, +514,0,(Exp(Exp(((((x1 + -0.9498996714574331) * (x1 ^ 0.3107424139600341)) * ((x0 / x1) * (0.8174177054615803 + x1))) ^ 0.21781242433634995))) ^ Exp(Log(Log(x1)))),"np.exp(np.exp((((x[:, 1] + t[0]) * x[:, 1] ** t[1]) * ((x[:, 0] / x[:, 1]) * (t[2] + x[:, 1]))) ** t[3])) ** np.exp(np.log(np.log(x[:, 1])))","$${\operatorname{exp}(\operatorname{exp}({\left(\left(\left(x_{1} + \theta_{0}\right) \cdot {x_{1}^{\theta_{1}}}\right) \cdot \left(\frac{x_{0}}{x_{1}} \cdot \left(\theta_{2} + x_{1}\right)\right)\right)^{\theta_{3}}}))^{\operatorname{exp}(\operatorname{log}(\operatorname{log}(x_{1})))}}$$",-0.9498996714574331;0.3107424139600341;0.8174177054615803;0.21781242433634995,24,,,,Infinity,,,,,, +515,0,((((Exp(x0) + (0.7010647342909639 ^ (-0.2252846417126554 * x1))) / x0) / x1) + x1),"((((np.exp(x[:, 0]) + t[0] ** (t[1] * x[:, 1])) / x[:, 0]) / x[:, 1]) + x[:, 1])","$$\left(\frac{\frac{\left(\operatorname{exp}(x_{0}) + {\theta_{0}^{\left(\theta_{1} \cdot x_{1}\right)}}\right)}{x_{0}}}{x_{1}} + x_{1}\right)$$",0.7010647342909639;-0.2252846417126554,14,305.680192337403,305.680192337403,305.680192337403,305.680192337403,-28207.52298053575,-28207.52298053575,-28207.52298053575,308.2662045579157,308.2662045579157,308.2662045579157 +516,0,(((0.28960866673269425 / Log(x0)) * -5.541986059530257e-2) * (((0.9032994732152098 / (((x0 ^ x0) / (0.9918827651761863 / x1)) ^ x1)) + (Log(x0) * Exp(x1))) ^ 0.9957296896061607)),"(((t[0] / np.log(x[:, 0])) * t[1]) * ((t[2] / (x[:, 0] ** x[:, 0] / (t[3] / x[:, 1])) ** x[:, 1]) + (np.log(x[:, 0]) * np.exp(x[:, 1]))) ** t[4])","$$\left(\left(\frac{\theta_{0}}{\operatorname{log}(x_{0})} \cdot \theta_{1}\right) \cdot {\left(\frac{\theta_{2}}{{\frac{{x_{0}^{x_{0}}}}{\frac{\theta_{3}}{x_{1}}}^{x_{1}}}} + \left(\operatorname{log}(x_{0}) \cdot \operatorname{exp}(x_{1})\right)\right)^{\theta_{4}}}\right)$$",0.28960866673269425;-5.541986059530257e-2;0.9032994732152098;0.9918827651761863;0.9957296896061607,26,113.76170721946794,113.76170721946794,113.76170721946794,113.76170721946794,-10497.062396085066,-10497.062396085066,-10497.062396085066,169.61800018510104,169.61800018510104,169.61800018510104 +517,0,(Exp(Log((Log((Exp(x1) ^ Exp(x1))) / ((-0.2744850894146289 + Log(x0)) ^ (0.2825543638450194 / Exp(x0)))))) * Exp(Log((0.9294733721885229 ^ ((Log(x0) * -3.371544161467288e-2) + Log(x1)))))),"(np.exp(np.log((np.log(np.exp(x[:, 1]) ** np.exp(x[:, 1])) / (t[0] + np.log(x[:, 0])) ** (t[1] / np.exp(x[:, 0]))))) * np.exp(np.log(t[2] ** ((np.log(x[:, 0]) * t[3]) + np.log(x[:, 1])))))","$$\left(\operatorname{exp}(\operatorname{log}(\frac{\operatorname{log}({\operatorname{exp}(x_{1})^{\operatorname{exp}(x_{1})}})}{{\left(\theta_{0} + \operatorname{log}(x_{0})\right)^{\frac{\theta_{1}}{\operatorname{exp}(x_{0})}}}})) \cdot \operatorname{exp}(\operatorname{log}({\theta_{2}^{\left(\left(\operatorname{log}(x_{0}) \cdot \theta_{3}\right) + \operatorname{log}(x_{1})\right)}}))\right)$$",-0.2744850894146289;0.2825543638450194;0.9294733721885229;-3.371544161467288e-2,30,,,,Infinity,,,,,, +518,0,(Log(Exp((Exp((x1 + x1)) + -22.79804728439941))) / (-2584.422449534293 * x1)),"(np.log(np.exp((np.exp((x[:, 1] + x[:, 1])) + t[0]))) / (t[1] * x[:, 1]))","$$\frac{\operatorname{log}(\operatorname{exp}(\left(\operatorname{exp}(\left(x_{1} + x_{1}\right)) + \theta_{0}\right)))}{\left(\theta_{1} \cdot x_{1}\right)}$$",-22.79804728439941;-2584.422449534293,12,1.339959735847955e-2,1.339959735847955e-2,1.339959735847955e-2,1.339959735847955e-2,-0.23653039840863288,-0.23653039840863288,-0.23653039840863288,27.404831297021673,27.404831297021673,27.404831297021673 +519,0,(((-0.6028146509251573 * (-0.5699031219182737 + Log((x0 + (0.45187070320927947 ^ x1))))) / (((Exp((x0 + x1)) / x0) ^ Log((x1 + x0))) * 0.21191264528789766)) ^ -0.48969839812288696),"((t[0] * (t[1] + np.log((x[:, 0] + t[2] ** x[:, 1])))) / ((np.exp((x[:, 0] + x[:, 1])) / x[:, 0]) ** np.log((x[:, 1] + x[:, 0])) * t[3])) ** t[4]","$${\frac{\left(\theta_{0} \cdot \left(\theta_{1} + \operatorname{log}(\left(x_{0} + {\theta_{2}^{x_{1}}}\right))\right)\right)}{\left({\frac{\operatorname{exp}(\left(x_{0} + x_{1}\right))}{x_{0}}^{\operatorname{log}(\left(x_{1} + x_{0}\right))}} \cdot \theta_{3}\right)}^{\theta_{4}}}$$",-0.6028146509251573;-0.5699031219182737;0.45187070320927947;0.21191264528789766;-0.48969839812288696,26,,,,Infinity,,,,,, +520,0,(Exp(((-1.1027947540548775 + Exp(Log(Exp(x1)))) ^ Exp(((x1 / x0) * (x1 * -0.4497185400281879))))) + (((x1 * 0.11574099180705073) ^ Exp((x1 ^ x1))) + ((0.18599696394263196 * x0) / (-0.14169676787807178 * (x1 + 0.9129515428317483))))),"(np.exp((t[0] + np.exp(np.log(np.exp(x[:, 1])))) ** np.exp(((x[:, 1] / x[:, 0]) * (x[:, 1] * t[1])))) + ((x[:, 1] * t[2]) ** np.exp(x[:, 1] ** x[:, 1]) + ((t[3] * x[:, 0]) / (t[4] * (x[:, 1] + t[5])))))","$$\left(\operatorname{exp}({\left(\theta_{0} + \operatorname{exp}(\operatorname{log}(\operatorname{exp}(x_{1})))\right)^{\operatorname{exp}(\left(\frac{x_{1}}{x_{0}} \cdot \left(x_{1} \cdot \theta_{1}\right)\right))}}) + \left({\left(x_{1} \cdot \theta_{2}\right)^{\operatorname{exp}({x_{1}^{x_{1}}})}} + \frac{\left(\theta_{3} \cdot x_{0}\right)}{\left(\theta_{4} \cdot \left(x_{1} + \theta_{5}\right)\right)}\right)\right)$$",-1.1027947540548775;-0.4497185400281879;0.11574099180705073;0.18599696394263196;-0.14169676787807178;0.9129515428317483,35,0.25289664158219277,0.25289664158219277,0.25289664158219277,0.25289664158219277,-22.33759564603199,-22.33759564603199,-22.33759564603199,89.2560167963975,89.2560167963975,89.2560167963975 +521,0,((x1 + -1.3284011883908842) + x0),"((x[:, 1] + t[0]) + x[:, 0])","$$\left(\left(x_{1} + \theta_{0}\right) + x_{0}\right)$$",-1.3284011883908842,5,0.16601395375655695,0.16601395375655695,0.16601395375655695,0.16601395375655695,-14.31996036060601,-14.31996036060601,-14.31996036060601,9.101809299347996,9.101809299347996,9.101809299347996 +522,0,(Exp(((x0 * 0.3216177161163963) / ((Exp(Exp(x1)) ^ x0) / (-2.6498564918807217 / x1)))) ^ ((Exp((x0 ^ 4.1979983145659)) + ((Exp(x1) ^ -0.3074245843259133) + 0.1678783636599132)) * 1.4399624847333539)),"np.exp(((x[:, 0] * t[0]) / (np.exp(np.exp(x[:, 1])) ** x[:, 0] / (t[1] / x[:, 1])))) ** ((np.exp(x[:, 0] ** t[2]) + (np.exp(x[:, 1]) ** t[3] + t[4])) * t[5])","$${\operatorname{exp}(\frac{\left(x_{0} \cdot \theta_{0}\right)}{\frac{{\operatorname{exp}(\operatorname{exp}(x_{1}))^{x_{0}}}}{\frac{\theta_{1}}{x_{1}}}})^{\left(\left(\operatorname{exp}({x_{0}^{\theta_{2}}}) + \left({\operatorname{exp}(x_{1})^{\theta_{3}}} + \theta_{4}\right)\right) \cdot \theta_{5}\right)}}$$",0.3216177161163963;-2.6498564918807217;4.1979983145659;-0.3074245843259133;0.1678783636599132;1.4399624847333539,28,3.518506085183055e-3,3.518506085183055e-3,3.518506085183055e-3,3.518506085183055e-3,0.6753081741996254,0.6753081741996254,0.6753081741996254,70.35307943471791,70.35307943471791,70.35307943471791 +523,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 +524,0,(((Exp(x0) * (x0 + -1.975725878420711)) * (7.448079104361075e-3 * (x0 ^ 1.1488771981643213))) / ((Log(x1) / (3.2143178725178747 / x1)) * Log((273.27365704285967 + (x1 / x0))))),"(((np.exp(x[:, 0]) * (x[:, 0] + t[0])) * (t[1] * x[:, 0] ** t[2])) / ((np.log(x[:, 1]) / (t[3] / x[:, 1])) * np.log((t[4] + (x[:, 1] / x[:, 0])))))","$$\frac{\left(\left(\operatorname{exp}(x_{0}) \cdot \left(x_{0} + \theta_{0}\right)\right) \cdot \left(\theta_{1} \cdot {x_{0}^{\theta_{2}}}\right)\right)}{\left(\frac{\operatorname{log}(x_{1})}{\frac{\theta_{3}}{x_{1}}} \cdot \operatorname{log}(\left(\theta_{4} + \frac{x_{1}}{x_{0}}\right))\right)}$$",-1.975725878420711;7.448079104361075e-3;1.1488771981643213;3.2143178725178747;273.27365704285967,26,1.3312009238743082e-2,1.3312009238743082e-2,1.3312009238743082e-2,1.3312009238743082e-2,-0.22844766504761438,-0.22844766504761438,-0.22844766504761438,67.24444449589478,67.24444449589478,67.24444449589478 +525,0,((Log((-0.19441309831816445 ^ (0.20893475036615783 / x1))) ^ x0) * x1),"(np.log(t[0] ** (t[1] / x[:, 1])) ** x[:, 0] * x[:, 1])","$$\left({\operatorname{log}({\theta_{0}^{\frac{\theta_{1}}{x_{1}}}})^{x_{0}}} \cdot x_{1}\right)$$",-0.19441309831816445;0.20893475036615783,10,,,,Infinity,,,,,, +526,0,(Exp((0.9686270182870961 ^ (-0.28363525519176924 ^ x1))) + -0.9899115574323765),"(np.exp(t[0] ** t[1] ** x[:, 1]) + t[2])","$$\left(\operatorname{exp}({\theta_{0}^{{\theta_{1}^{x_{1}}}}}) + \theta_{2}\right)$$",0.9686270182870961;-0.28363525519176924;-0.9899115574323765,8,,,,Infinity,,,,,, +527,0,Log((((Log(Log(Log(x0))) ^ -0.49470857626507314) / (0.6607125913815111 + ((x1 * -0.12108279981218562) + x1))) * (0.7936245023901725 / (Log((x0 + x1)) ^ (Exp(x1) + 8.647906604024369e-2))))),"np.log(((np.log(np.log(np.log(x[:, 0]))) ** t[0] / (t[1] + ((x[:, 1] * t[2]) + x[:, 1]))) * (t[3] / np.log((x[:, 0] + x[:, 1])) ** (np.exp(x[:, 1]) + t[4]))))","$$\operatorname{log}(\left(\frac{{\operatorname{log}(\operatorname{log}(\operatorname{log}(x_{0})))^{\theta_{0}}}}{\left(\theta_{1} + \left(\left(x_{1} \cdot \theta_{2}\right) + x_{1}\right)\right)} \cdot \frac{\theta_{3}}{{\operatorname{log}(\left(x_{0} + x_{1}\right))^{\left(\operatorname{exp}(x_{1}) + \theta_{4}\right)}}}\right))$$",-0.49470857626507314;0.6607125913815111;-0.12108279981218562;0.7936245023901725;8.647906604024369e-2,27,,,,Infinity,,,,,, +528,0,((((x1 * 0.7142536592631711) / ((Log(x1) * 0.4080886135836659) ^ -0.34002886512228137)) ^ -0.5533520705572768) + Log((Log(x0) * Log(Exp((0.5458872434301651 ^ x1)))))),"(((x[:, 1] * t[0]) / (np.log(x[:, 1]) * t[1]) ** t[2]) ** t[3] + np.log((np.log(x[:, 0]) * np.log(np.exp(t[4] ** x[:, 1])))))","$$\left({\frac{\left(x_{1} \cdot \theta_{0}\right)}{{\left(\operatorname{log}(x_{1}) \cdot \theta_{1}\right)^{\theta_{2}}}}^{\theta_{3}}} + \operatorname{log}(\left(\operatorname{log}(x_{0}) \cdot \operatorname{log}(\operatorname{exp}({\theta_{4}^{x_{1}}}))\right))\right)$$",0.7142536592631711;0.4080886135836659;-0.34002886512228137;-0.5533520705572768;0.5458872434301651,22,,,,Infinity,,,,,, +529,0,Log((x0 * -2.0419249058141853e-2)),"np.log((x[:, 0] * t[0]))","$$\operatorname{log}(\left(x_{0} \cdot \theta_{0}\right))$$",-2.0419249058141853e-2,4,,,,Infinity,,,,,, +530,0,((Log(x1) ^ (-0.19803162211657455 ^ x0)) * Exp((x0 / Log(x0)))),"(np.log(x[:, 1]) ** t[0] ** x[:, 0] * np.exp((x[:, 0] / np.log(x[:, 0]))))","$$\left({\operatorname{log}(x_{1})^{{\theta_{0}^{x_{0}}}}} \cdot \operatorname{exp}(\frac{x_{0}}{\operatorname{log}(x_{0})})\right)$$",-0.19803162211657455,12,,,,Infinity,,,,,, +531,0,(((Exp(Exp(Log((x0 * -0.6004826686717126)))) / Exp(Log(((x0 * -0.733574888326342) / (x1 + x0))))) / Exp(Exp((x0 + 0.7477565336063909)))) ^ -1.8070294031299006e-2),"((np.exp(np.exp(np.log((x[:, 0] * t[0])))) / np.exp(np.log(((x[:, 0] * t[1]) / (x[:, 1] + x[:, 0]))))) / np.exp(np.exp((x[:, 0] + t[2])))) ** t[3]","$${\frac{\frac{\operatorname{exp}(\operatorname{exp}(\operatorname{log}(\left(x_{0} \cdot \theta_{0}\right))))}{\operatorname{exp}(\operatorname{log}(\frac{\left(x_{0} \cdot \theta_{1}\right)}{\left(x_{1} + x_{0}\right)}))}}{\operatorname{exp}(\operatorname{exp}(\left(x_{0} + \theta_{2}\right)))}^{\theta_{3}}}$$",-0.6004826686717126;-0.733574888326342;0.7477565336063909;-1.8070294031299006e-2,24,,,,Infinity,,,,,, +532,0,((0.7786470732106883 * ((x1 + x1) * Log(Exp(x0)))) + (Log((Log(x1) + (x0 ^ 0.8838367790811558))) + ((0.1569261026896851 / x0) + (0.8221226480064485 / Exp(x0))))),"((t[0] * ((x[:, 1] + x[:, 1]) * np.log(np.exp(x[:, 0])))) + (np.log((np.log(x[:, 1]) + x[:, 0] ** t[1])) + ((t[2] / x[:, 0]) + (t[3] / np.exp(x[:, 0])))))","$$\left(\left(\theta_{0} \cdot \left(\left(x_{1} + x_{1}\right) \cdot \operatorname{log}(\operatorname{exp}(x_{0}))\right)\right) + \left(\operatorname{log}(\left(\operatorname{log}(x_{1}) + {x_{0}^{\theta_{1}}}\right)) + \left(\frac{\theta_{2}}{x_{0}} + \frac{\theta_{3}}{\operatorname{exp}(x_{0})}\right)\right)\right)$$",0.7786470732106883;0.8838367790811558;0.1569261026896851;0.8221226480064485,26,,,,Infinity,,,,,, +533,0,(Log((((x0 * -0.8810967003370037) / (x0 * 0.6712221668638232)) ^ ((-0.6505810524715376 + x1) / (x1 * -0.9122424888661672)))) * Exp(((0.14810832038734434 * (x1 ^ Exp(x1))) + Log((-0.35869119467233523 / Exp((x1 ^ x0))))))),"(np.log(((x[:, 0] * t[0]) / (x[:, 0] * t[1])) ** ((t[2] + x[:, 1]) / (x[:, 1] * t[3]))) * np.exp(((t[4] * x[:, 1] ** np.exp(x[:, 1])) + np.log((t[5] / np.exp(x[:, 1] ** x[:, 0]))))))","$$\left(\operatorname{log}({\frac{\left(x_{0} \cdot \theta_{0}\right)}{\left(x_{0} \cdot \theta_{1}\right)}^{\frac{\left(\theta_{2} + x_{1}\right)}{\left(x_{1} \cdot \theta_{3}\right)}}}) \cdot \operatorname{exp}(\left(\left(\theta_{4} \cdot {x_{1}^{\operatorname{exp}(x_{1})}}\right) + \operatorname{log}(\frac{\theta_{5}}{\operatorname{exp}({x_{1}^{x_{0}}})})\right))\right)$$",-0.8810967003370037;0.6712221668638232;-0.6505810524715376;-0.9122424888661672;0.14810832038734434;-0.35869119467233523,32,,,,Infinity,,,,,, +534,0,((x1 / x0) / x0),"((x[:, 1] / x[:, 0]) / x[:, 0])","$$\frac{\frac{x_{1}}{x_{0}}}{x_{0}}$$",,5,0.18189133018980164,0.18189133018980164,0.18189133018980164,0.18189133018980164,-15.785143088224302,-15.785143088224302,-15.785143088224302,5.656763640511371,5.656763640511371,5.656763640511371 +535,0,Log(Log(Log((Exp((0.9128160476217062 ^ Log(x1))) / ((x0 / (4.12577724891429 + x1)) / Exp(x1)))))),"np.log(np.log(np.log((np.exp(t[0] ** np.log(x[:, 1])) / ((x[:, 0] / (t[1] + x[:, 1])) / np.exp(x[:, 1]))))))","$$\operatorname{log}(\operatorname{log}(\operatorname{log}(\frac{\operatorname{exp}({\theta_{0}^{\operatorname{log}(x_{1})}})}{\frac{\frac{x_{0}}{\left(\theta_{1} + x_{1}\right)}}{\operatorname{exp}(x_{1})}})))$$",0.9128160476217062;4.12577724891429,17,4.802959483638307e-3,4.802959483638307e-3,4.802959483638307e-3,4.802959483638307e-3,0.5567773236047672,0.5567773236047672,0.5567773236047672,39.39667874282776,39.39667874282776,39.39667874282776 +536,0,Exp((((x1 * x1) ^ ((x0 * Exp(x1)) ^ Exp((x1 * 9.30282964112813)))) + -2.722898345768033)),"np.exp(((x[:, 1] * x[:, 1]) ** (x[:, 0] * np.exp(x[:, 1])) ** np.exp((x[:, 1] * t[0])) + t[1]))","$$\operatorname{exp}(\left({\left(x_{1} \cdot x_{1}\right)^{{\left(x_{0} \cdot \operatorname{exp}(x_{1})\right)^{\operatorname{exp}(\left(x_{1} \cdot \theta_{0}\right))}}}} + \theta_{1}\right))$$",9.30282964112813;-2.722898345768033,16,7.611168542786238e-3,7.611168542786238e-3,7.611168542786238e-3,7.611168542786238e-3,0.29763253187522387,0.29763253187522387,0.29763253187522387,35.1832623073088,35.1832623073088,35.1832623073088 +537,0,Exp((Log((-0.885513424736886 / Exp((x1 * 0.14441812879941596)))) / (((-0.4083554784744744 + x0) * -0.8843568313468135) + (0.9161062161197486 / x0)))),"np.exp((np.log((t[0] / np.exp((x[:, 1] * t[1])))) / (((t[2] + x[:, 0]) * t[3]) + (t[4] / x[:, 0]))))","$$\operatorname{exp}(\frac{\operatorname{log}(\frac{\theta_{0}}{\operatorname{exp}(\left(x_{1} \cdot \theta_{1}\right))})}{\left(\left(\left(\theta_{2} + x_{0}\right) \cdot \theta_{3}\right) + \frac{\theta_{4}}{x_{0}}\right)})$$",-0.885513424736886;0.14441812879941596;-0.4083554784744744;-0.8843568313468135;0.9161062161197486,18,,,,Infinity,,,,,, +538,0,((((3.110521217182825 + x0) / (x0 * 1.2343219650780501)) / ((x1 * -1.508384831827767) + (x1 + x0))) * (((x1 * 0.3240188700776759) * 0.5737060311488461) * 0.23714317027464443)),"((((t[0] + x[:, 0]) / (x[:, 0] * t[1])) / ((x[:, 1] * t[2]) + (x[:, 1] + x[:, 0]))) * (((x[:, 1] * t[3]) * t[4]) * t[5]))","$$\left(\frac{\frac{\left(\theta_{0} + x_{0}\right)}{\left(x_{0} \cdot \theta_{1}\right)}}{\left(\left(x_{1} \cdot \theta_{2}\right) + \left(x_{1} + x_{0}\right)\right)} \cdot \left(\left(\left(x_{1} \cdot \theta_{3}\right) \cdot \theta_{4}\right) \cdot \theta_{5}\right)\right)$$",3.110521217182825;1.2343219650780501;-1.508384831827767;0.3240188700776759;0.5737060311488461;0.23714317027464443,23,2.216835029840347e-3,2.216835029840347e-3,2.216835029840347e-3,2.216835029840347e-3,0.7954278901582055,0.7954278901582055,0.7954278901582055,53.33801255597796,53.33801255597796,53.33801255597796 +539,0,Log(((x0 ^ x0) / (-0.8658430664963853 + x1))),"np.log((x[:, 0] ** x[:, 0] / (t[0] + x[:, 1])))","$$\operatorname{log}(\frac{{x_{0}^{x_{0}}}}{\left(\theta_{0} + x_{1}\right)})$$",-0.8658430664963853,8,,,,Infinity,,,,,, +540,0,((Exp(((-0.685422247105812 ^ (-0.1853925499039295 * x0)) / Log(Exp(x0)))) + (Log(((-0.8568188588600946 + x1) * x0)) / ((0.4563808747272603 * x1) * (x0 * x0)))) ^ Log((((0.9505588714015397 / (0.16102016597616353 ^ x0)) * (x1 * -0.2685418507360601)) * (((-0.8326692018064361 + x1) * -0.22114286488245793) + ((x0 / x1) ^ -0.594725545082136))))),"(np.exp((t[0] ** (t[1] * x[:, 0]) / np.log(np.exp(x[:, 0])))) + (np.log(((t[2] + x[:, 1]) * x[:, 0])) / ((t[3] * x[:, 1]) * (x[:, 0] * x[:, 0])))) ** np.log((((t[4] / t[5] ** x[:, 0]) * (x[:, 1] * t[6])) * (((t[7] + x[:, 1]) * t[8]) + (x[:, 0] / x[:, 1]) ** t[9])))","$${\left(\operatorname{exp}(\frac{{\theta_{0}^{\left(\theta_{1} \cdot x_{0}\right)}}}{\operatorname{log}(\operatorname{exp}(x_{0}))}) + \frac{\operatorname{log}(\left(\left(\theta_{2} + x_{1}\right) \cdot x_{0}\right))}{\left(\left(\theta_{3} \cdot x_{1}\right) \cdot \left(x_{0} \cdot x_{0}\right)\right)}\right)^{\operatorname{log}(\left(\left(\frac{\theta_{4}}{{\theta_{5}^{x_{0}}}} \cdot \left(x_{1} \cdot \theta_{6}\right)\right) \cdot \left(\left(\left(\theta_{7} + x_{1}\right) \cdot \theta_{8}\right) + {\frac{x_{0}}{x_{1}}^{\theta_{9}}}\right)\right))}}$$",-0.685422247105812;-0.1853925499039295;-0.8568188588600946;0.4563808747272603;0.9505588714015397;0.16102016597616353;-0.2685418507360601;-0.8326692018064361;-0.22114286488245793;-0.594725545082136,48,,,,Infinity,,,,,, +541,0,(((Exp((Log(Exp(x0)) * x0)) + (x1 + x0)) + (-4.6660256348563225 + x0)) / x0),"(((np.exp((np.log(np.exp(x[:, 0])) * x[:, 0])) + (x[:, 1] + x[:, 0])) + (t[0] + x[:, 0])) / x[:, 0])","$$\frac{\left(\left(\operatorname{exp}(\left(\operatorname{log}(\operatorname{exp}(x_{0})) \cdot x_{0}\right)) + \left(x_{1} + x_{0}\right)\right) + \left(\theta_{0} + x_{0}\right)\right)}{x_{0}}$$",-4.6660256348563225,16,8.509649054330325,8.509649054330325,8.509649054330325,8.509649054330325,-784.2802926805781,-784.2802926805781,-784.2802926805781,42.95067375114231,42.95067375114231,42.95067375114231 +542,0,(((Log(x1) + 2.281526770646913) / (1.4061764057331507 + Exp((((x0 + -0.16233818524970256) / x1) ^ 1.5124304589040876)))) + ((-6.326298292888938e-2 / x0) * x1)),"(((np.log(x[:, 1]) + t[0]) / (t[1] + np.exp(((x[:, 0] + t[2]) / x[:, 1]) ** t[3]))) + ((t[4] / x[:, 0]) * x[:, 1]))","$$\left(\frac{\left(\operatorname{log}(x_{1}) + \theta_{0}\right)}{\left(\theta_{1} + \operatorname{exp}({\frac{\left(x_{0} + \theta_{2}\right)}{x_{1}}^{\theta_{3}}})\right)} + \left(\frac{\theta_{4}}{x_{0}} \cdot x_{1}\right)\right)$$",2.281526770646913;1.4061764057331507;-0.16233818524970256;1.5124304589040876;-6.326298292888938e-2,21,2.4820523678479403e-3,2.4820523678479403e-3,2.4820523678479403e-3,2.4820523678479403e-3,0.7709533263442508,0.7709533263442508,0.7709533263442508,56.24857464802987,56.24857464802987,56.24857464802987 +543,0,(Exp(((x0 * -226.87007100171496) * 218.29899566480157)) + Exp((Exp(x1) ^ (x1 * -1038.5960622375771)))),"(np.exp(((x[:, 0] * t[0]) * t[1])) + np.exp(np.exp(x[:, 1]) ** (x[:, 1] * t[2])))","$$\left(\operatorname{exp}(\left(\left(x_{0} \cdot \theta_{0}\right) \cdot \theta_{1}\right)) + \operatorname{exp}({\operatorname{exp}(x_{1})^{\left(x_{1} \cdot \theta_{2}\right)}})\right)$$",-226.87007100171496;218.29899566480157;-1038.5960622375771,14,0.8923107846245623,0.8923107846245623,0.8923107846245623,0.8923107846245623,-81.3434749938881,-81.3434749938881,-81.3434749938881,34.108596599039416,34.108596599039416,34.108596599039416 +544,0,Exp((2.544139175501373 / Log((1.6696973203636718 * x1)))),"np.exp((t[0] / np.log((t[1] * x[:, 1]))))","$$\operatorname{exp}(\frac{\theta_{0}}{\operatorname{log}(\left(\theta_{1} \cdot x_{1}\right))})$$",2.544139175501373;1.6696973203636718,7,2.0915703384523317e-2,2.0915703384523317e-2,2.0915703384523317e-2,2.0915703384523317e-2,-0.9301253871404438,-0.9301253871404438,-0.9301253871404438,16.60299028837774,16.60299028837774,16.60299028837774 +545,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 +546,0,(Exp(((x0 + x1) * Log((x1 + -0.10251617624237615)))) ^ Exp(((x1 * 1.0790609877952361e-2) * (x0 / Exp(x1))))),"np.exp(((x[:, 0] + x[:, 1]) * np.log((x[:, 1] + t[0])))) ** np.exp(((x[:, 1] * t[1]) * (x[:, 0] / np.exp(x[:, 1]))))","$${\operatorname{exp}(\left(\left(x_{0} + x_{1}\right) \cdot \operatorname{log}(\left(x_{1} + \theta_{0}\right))\right))^{\operatorname{exp}(\left(\left(x_{1} \cdot \theta_{1}\right) \cdot \frac{x_{0}}{\operatorname{exp}(x_{1})}\right))}}$$",-0.10251617624237615;1.0790609877952361e-2,19,1.0796889761329639e-2,1.0796889761329639e-2,1.0796889761329639e-2,1.0796889761329639e-2,3.650479862931655e-3,3.650479862931655e-3,3.650479862931655e-3,45.798834040908645,45.798834040908645,45.798834040908645 +547,0,((((x1 / x1) / (x1 / (x1 + x0))) * (Log((x1 ^ 2.3922639924808875)) / (-6.448234213664704 / x1))) + (((-2.0360938162205713 / x1) * -1.3340265338207848e-2) * -3.3592760700479793)),"((((x[:, 1] / x[:, 1]) / (x[:, 1] / (x[:, 1] + x[:, 0]))) * (np.log(x[:, 1] ** t[0]) / (t[1] / x[:, 1]))) + (((t[2] / x[:, 1]) * t[3]) * t[4]))","$$\left(\left(\frac{\frac{x_{1}}{x_{1}}}{\frac{x_{1}}{\left(x_{1} + x_{0}\right)}} \cdot \frac{\operatorname{log}({x_{1}^{\theta_{0}}})}{\frac{\theta_{1}}{x_{1}}}\right) + \left(\left(\frac{\theta_{2}}{x_{1}} \cdot \theta_{3}\right) \cdot \theta_{4}\right)\right)$$",2.3922639924808875;-6.448234213664704;-2.0360938162205713;-1.3340265338207848e-2;-3.3592760700479793,26,8.652652400586656e-2,8.652652400586656e-2,8.652652400586656e-2,8.652652400586656e-2,-6.984768074704952,-6.984768074704952,-6.984768074704952,64.24797863211921,64.24797863211921,64.24797863211921 +548,0,((((Exp(Log(Log(x0))) + (x0 ^ (x1 / x1))) ^ 0.5226913947538272) / x1) ^ x0),"((np.exp(np.log(np.log(x[:, 0]))) + x[:, 0] ** (x[:, 1] / x[:, 1])) ** t[0] / x[:, 1]) ** x[:, 0]","$${\frac{{\left(\operatorname{exp}(\operatorname{log}(\operatorname{log}(x_{0}))) + {x_{0}^{\frac{x_{1}}{x_{1}}}}\right)^{\theta_{0}}}}{x_{1}}^{x_{0}}}$$",0.5226913947538272,16,,,,Infinity,,,,,, +549,0,Exp(Exp((-4.707859200398705e-2 * Log(x0)))),"np.exp(np.exp((t[0] * np.log(x[:, 0]))))","$$\operatorname{exp}(\operatorname{exp}(\left(\theta_{0} \cdot \operatorname{log}(x_{0})\right)))$$",-4.707859200398705e-2,6,7.066150551338594,7.066150551338594,7.066150551338594,7.066150551338594,-651.0725752205391,-651.0725752205391,-651.0725752205391,18.037087906176975,18.037087906176975,18.037087906176975 +550,0,Exp((-0.7718832757777635 + ((Log(((0.47618095267350347 + x0) * -0.7065751952109678)) ^ (x1 / (Log(x1) * -0.26197739356614824))) / (x1 ^ x0)))),"np.exp((t[0] + (np.log(((t[1] + x[:, 0]) * t[2])) ** (x[:, 1] / (np.log(x[:, 1]) * t[3])) / x[:, 1] ** x[:, 0])))","$$\operatorname{exp}(\left(\theta_{0} + \frac{{\operatorname{log}(\left(\left(\theta_{1} + x_{0}\right) \cdot \theta_{2}\right))^{\frac{x_{1}}{\left(\operatorname{log}(x_{1}) \cdot \theta_{3}\right)}}}}{{x_{1}^{x_{0}}}}\right))$$",-0.7718832757777635;0.47618095267350347;-0.7065751952109678;-0.26197739356614824,20,,,,Infinity,,,,,, +551,0,Exp(Log((0.5081239124342001 / ((x1 * -0.21817194416272823) * 0.4782260526535784)))),"np.exp(np.log((t[0] / ((x[:, 1] * t[1]) * t[2]))))","$$\operatorname{exp}(\operatorname{log}(\frac{\theta_{0}}{\left(\left(x_{1} \cdot \theta_{1}\right) \cdot \theta_{2}\right)}))$$",0.5081239124342001;-0.21817194416272823;0.4782260526535784,9,,,,Infinity,,,,,, +552,0,(Log((Log((0.8042088499680479 * ((x1 + 0.6515224216157742) / x0))) ^ (((1.9113341413009244e-2 * x1) ^ x0) + Exp(Log((x1 / x0)))))) ^ ((-0.714524458597791 * ((x0 * x0) + x1)) * (-0.7893659903314014 + (-0.49473149880699085 ^ x0)))),"np.log(np.log((t[0] * ((x[:, 1] + t[1]) / x[:, 0]))) ** ((t[2] * x[:, 1]) ** x[:, 0] + np.exp(np.log((x[:, 1] / x[:, 0]))))) ** ((t[3] * ((x[:, 0] * x[:, 0]) + x[:, 1])) * (t[4] + t[5] ** x[:, 0]))","$${\operatorname{log}({\operatorname{log}(\left(\theta_{0} \cdot \frac{\left(x_{1} + \theta_{1}\right)}{x_{0}}\right))^{\left({\left(\theta_{2} \cdot x_{1}\right)^{x_{0}}} + \operatorname{exp}(\operatorname{log}(\frac{x_{1}}{x_{0}}))\right)}})^{\left(\left(\theta_{3} \cdot \left(\left(x_{0} \cdot x_{0}\right) + x_{1}\right)\right) \cdot \left(\theta_{4} + {\theta_{5}^{x_{0}}}\right)\right)}}$$",0.8042088499680479;0.6515224216157742;1.9113341413009244e-2;-0.714524458597791;-0.7893659903314014;-0.49473149880699085,35,,,,Infinity,,,,,, +553,0,Log((Log((((x0 + 0.6753513976490607) + Exp(x1)) + Exp(Log(Exp(x0))))) / Log(Exp(((-0.7715253229794674 + (0.9398783755367858 ^ x1)) / (0.35661762514616746 / Exp(x0))))))),"np.log((np.log((((x[:, 0] + t[0]) + np.exp(x[:, 1])) + np.exp(np.log(np.exp(x[:, 0]))))) / np.log(np.exp(((t[1] + t[2] ** x[:, 1]) / (t[3] / np.exp(x[:, 0])))))))","$$\operatorname{log}(\frac{\operatorname{log}(\left(\left(\left(x_{0} + \theta_{0}\right) + \operatorname{exp}(x_{1})\right) + \operatorname{exp}(\operatorname{log}(\operatorname{exp}(x_{0})))\right))}{\operatorname{log}(\operatorname{exp}(\frac{\left(\theta_{1} + {\theta_{2}^{x_{1}}}\right)}{\frac{\theta_{3}}{\operatorname{exp}(x_{0})}}))})$$",0.6753513976490607;-0.7715253229794674;0.9398783755367858;0.35661762514616746,26,1.8473522154424885e-2,1.8473522154424885e-2,1.8473522154424885e-2,1.8473522154424885e-2,-0.7047580683583845,-0.7047580683583845,-0.7047580683583845,62.16580599508528,62.16580599508528,62.16580599508528 +554,0,(Log((((x0 + x1) ^ (0.8531219262814225 / x0)) / ((x1 / x1) / (-0.30082927989540553 + x1)))) ^ (((-0.8183922665757343 / x1) * Exp(Log(x1))) / ((x0 + x1) * (x0 + (0.5889502397541466 * x0))))),"np.log(((x[:, 0] + x[:, 1]) ** (t[0] / x[:, 0]) / ((x[:, 1] / x[:, 1]) / (t[1] + x[:, 1])))) ** (((t[2] / x[:, 1]) * np.exp(np.log(x[:, 1]))) / ((x[:, 0] + x[:, 1]) * (x[:, 0] + (t[3] * x[:, 0]))))","$${\operatorname{log}(\frac{{\left(x_{0} + x_{1}\right)^{\frac{\theta_{0}}{x_{0}}}}}{\frac{\frac{x_{1}}{x_{1}}}{\left(\theta_{1} + x_{1}\right)}})^{\frac{\left(\frac{\theta_{2}}{x_{1}} \cdot \operatorname{exp}(\operatorname{log}(x_{1}))\right)}{\left(\left(x_{0} + x_{1}\right) \cdot \left(x_{0} + \left(\theta_{3} \cdot x_{0}\right)\right)\right)}}}$$",0.8531219262814225;-0.30082927989540553;-0.8183922665757343;0.5889502397541466,34,,,,Infinity,,,,,, +555,0,(Log((0.19863387639601227 + ((0.35676899815111274 ^ x0) + -0.812369493794429))) + (((x1 * -0.7050178761070539) ^ (x0 + 0.27803730214694433)) / ((-0.6751705456210555 ^ x0) ^ (x0 * 0.4676492196057782)))),"(np.log((t[0] + (t[1] ** x[:, 0] + t[2]))) + ((x[:, 1] * t[3]) ** (x[:, 0] + t[4]) / t[5] ** x[:, 0] ** (x[:, 0] * t[6])))","$$\left(\operatorname{log}(\left(\theta_{0} + \left({\theta_{1}^{x_{0}}} + \theta_{2}\right)\right)) + \frac{{\left(x_{1} \cdot \theta_{3}\right)^{\left(x_{0} + \theta_{4}\right)}}}{{{\theta_{5}^{x_{0}}}^{\left(x_{0} \cdot \theta_{6}\right)}}}\right)$$",0.19863387639601227;0.35676899815111274;-0.812369493794429;-0.7050178761070539;0.27803730214694433;-0.6751705456210555;0.4676492196057782,24,,,,Infinity,,,,,, +556,0,((Log(((x1 * -0.10203300479329236) + 0.7020321013388006)) ^ (Exp((x0 ^ x0)) * (x1 ^ -0.5501985235253346))) / Exp((((-7.586433330965581e-2 ^ x0) ^ (x0 ^ x1)) ^ 0.7736157253762872))),"(np.log(((x[:, 1] * t[0]) + t[1])) ** (np.exp(x[:, 0] ** x[:, 0]) * x[:, 1] ** t[2]) / np.exp(t[3] ** x[:, 0] ** x[:, 0] ** x[:, 1] ** t[4]))","$$\frac{{\operatorname{log}(\left(\left(x_{1} \cdot \theta_{0}\right) + \theta_{1}\right))^{\left(\operatorname{exp}({x_{0}^{x_{0}}}) \cdot {x_{1}^{\theta_{2}}}\right)}}}{\operatorname{exp}({{{\theta_{3}^{x_{0}}}^{{x_{0}^{x_{1}}}}}^{\theta_{4}}})}$$",-0.10203300479329236;0.7020321013388006;-0.5501985235253346;-7.586433330965581e-2;0.7736157253762872,26,,,,Infinity,,,,,, +557,0,Exp((-0.80768785379523 ^ (-0.9180880328011456 ^ x0))),"np.exp(t[0] ** t[1] ** x[:, 0])","$$\operatorname{exp}({\theta_{0}^{{\theta_{1}^{x_{0}}}}})$$",-0.80768785379523;-0.9180880328011456,6,,,,Infinity,,,,,, +558,0,Log(((-0.1834969712349186 ^ Exp((-0.9146994354074729 + Exp(x0)))) / Exp((Log((Log(x0) + (8.355666345650159e-2 + x1))) / Log(((x1 + -0.6856996046845143) ^ 0.13901916576032336)))))),"np.log((t[0] ** np.exp((t[1] + np.exp(x[:, 0]))) / np.exp((np.log((np.log(x[:, 0]) + (t[2] + x[:, 1]))) / np.log((x[:, 1] + t[3]) ** t[4])))))","$$\operatorname{log}(\frac{{\theta_{0}^{\operatorname{exp}(\left(\theta_{1} + \operatorname{exp}(x_{0})\right))}}}{\operatorname{exp}(\frac{\operatorname{log}(\left(\operatorname{log}(x_{0}) + \left(\theta_{2} + x_{1}\right)\right))}{\operatorname{log}({\left(x_{1} + \theta_{3}\right)^{\theta_{4}}})})})$$",-0.1834969712349186;-0.9146994354074729;8.355666345650159e-2;-0.6856996046845143;0.13901916576032336,24,,,,Infinity,,,,,, +559,0,((((Exp((-0.371860589940477 * x1)) + (x0 * 0.9946810990348692)) * (0.6191738168396146 / (-0.13759617452971074 ^ x1))) ^ (((-8.897872087590497e-2 ^ (x0 * (-0.9702819567778611 + x1))) * 0.9505994074612809) * (Log(x0) / x0))) * x0),"(((np.exp((t[0] * x[:, 1])) + (x[:, 0] * t[1])) * (t[2] / t[3] ** x[:, 1])) ** ((t[4] ** (x[:, 0] * (t[5] + x[:, 1])) * t[6]) * (np.log(x[:, 0]) / x[:, 0])) * x[:, 0])","$$\left({\left(\left(\operatorname{exp}(\left(\theta_{0} \cdot x_{1}\right)) + \left(x_{0} \cdot \theta_{1}\right)\right) \cdot \frac{\theta_{2}}{{\theta_{3}^{x_{1}}}}\right)^{\left(\left({\theta_{4}^{\left(x_{0} \cdot \left(\theta_{5} + x_{1}\right)\right)}} \cdot \theta_{6}\right) \cdot \frac{\operatorname{log}(x_{0})}{x_{0}}\right)}} \cdot x_{0}\right)$$",-0.371860589940477;0.9946810990348692;0.6191738168396146;-0.13759617452971074;-8.897872087590497e-2;-0.9702819567778611;0.9505994074612809,31,,,,Infinity,,,,,, +560,0,(Exp(Log((Log((0.9825603789080609 + (-0.6143147758692913 / x0))) * (Log(x1) ^ (x0 * -0.22300377295252638))))) ^ Log(Exp(((0.11397831015486515 * ((0.33554209614120334 ^ x0) ^ -0.8475943750317159)) ^ (-0.20990928039680834 ^ ((0.9761425033050082 * x0) ^ -0.8369396548469973)))))),"np.exp(np.log((np.log((t[0] + (t[1] / x[:, 0]))) * np.log(x[:, 1]) ** (x[:, 0] * t[2])))) ** np.log(np.exp((t[3] * t[4] ** x[:, 0] ** t[5]) ** t[6] ** (t[7] * x[:, 0]) ** t[8]))","$${\operatorname{exp}(\operatorname{log}(\left(\operatorname{log}(\left(\theta_{0} + \frac{\theta_{1}}{x_{0}}\right)) \cdot {\operatorname{log}(x_{1})^{\left(x_{0} \cdot \theta_{2}\right)}}\right)))^{\operatorname{log}(\operatorname{exp}({\left(\theta_{3} \cdot {{\theta_{4}^{x_{0}}}^{\theta_{5}}}\right)^{{\theta_{6}^{{\left(\theta_{7} \cdot x_{0}\right)^{\theta_{8}}}}}}}))}}$$",0.9825603789080609;-0.6143147758692913;-0.22300377295252638;0.11397831015486515;0.33554209614120334;-0.8475943750317159;-0.20990928039680834;0.9761425033050082;-0.8369396548469973,33,,,,Infinity,,,,,, +561,0,Exp((((474496.36163822294 * (x0 + x1)) / ((848.2290472216542 / (x0 * -608.0773180535123)) * Log((x0 ^ x1)))) / ((Exp((542.704824617184 + x1)) / x1) ^ x0))),"np.exp((((t[0] * (x[:, 0] + x[:, 1])) / ((t[1] / (x[:, 0] * t[2])) * np.log(x[:, 0] ** x[:, 1]))) / (np.exp((t[3] + x[:, 1])) / x[:, 1]) ** x[:, 0]))","$$\operatorname{exp}(\frac{\frac{\left(\theta_{0} \cdot \left(x_{0} + x_{1}\right)\right)}{\left(\frac{\theta_{1}}{\left(x_{0} \cdot \theta_{2}\right)} \cdot \operatorname{log}({x_{0}^{x_{1}}})\right)}}{{\frac{\operatorname{exp}(\left(\theta_{3} + x_{1}\right))}{x_{1}}^{x_{0}}}})$$",474496.36163822294;848.2290472216542;-608.0773180535123;542.704824617184,26,0.8923097947317449,0.8923097947317449,0.8923097947317449,0.8923097947317449,-81.34338364542985,-81.34338364542985,-81.34338364542985,66.01461756747084,66.01461756747084,66.01461756747084 +562,0,(7.439888686209983e-2 / x0),"(t[0] / x[:, 0])","$$\frac{\theta_{0}}{x_{0}}$$",7.439888686209983e-2,3,7.713765273570095e-3,7.713765273570095e-3,7.713765273570095e-3,7.713765273570095e-3,0.2881647864648511,0.2881647864648511,0.2881647864648511,5.323704190118183,5.323704190118183,5.323704190118183 +563,0,Exp((((x0 * 0.3224124016335781) / x1) / Log((x0 / Log(x1))))),"np.exp((((x[:, 0] * t[0]) / x[:, 1]) / np.log((x[:, 0] / np.log(x[:, 1])))))","$$\operatorname{exp}(\frac{\frac{\left(x_{0} \cdot \theta_{0}\right)}{x_{1}}}{\operatorname{log}(\frac{x_{0}}{\operatorname{log}(x_{1})})})$$",0.3224124016335781,12,,,,Infinity,,,,,, +564,0,((((0.29349287452146866 / x0) + 9.094658003235967e-2) ^ (0.8489946685544796 / x0)) ^ Log((Log((x0 * Exp(x0))) / (-0.8671417634310796 / (x0 ^ x1))))),"((t[0] / x[:, 0]) + t[1]) ** (t[2] / x[:, 0]) ** np.log((np.log((x[:, 0] * np.exp(x[:, 0]))) / (t[3] / x[:, 0] ** x[:, 1])))","$${{\left(\frac{\theta_{0}}{x_{0}} + \theta_{1}\right)^{\frac{\theta_{2}}{x_{0}}}}^{\operatorname{log}(\frac{\operatorname{log}(\left(x_{0} \cdot \operatorname{exp}(x_{0})\right))}{\frac{\theta_{3}}{{x_{0}^{x_{1}}}}})}}$$",0.29349287452146866;9.094658003235967e-2;0.8489946685544796;-0.8671417634310796,22,,,,Infinity,,,,,, +565,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 +566,0,((0.36600505177983433 ^ Log(x0)) / Log((x1 + -0.27487605532018855))),"(t[0] ** np.log(x[:, 0]) / np.log((x[:, 1] + t[1])))","$$\frac{{\theta_{0}^{\operatorname{log}(x_{0})}}}{\operatorname{log}(\left(x_{1} + \theta_{1}\right))}$$",0.36600505177983433;-0.27487605532018855,9,,,,Infinity,,,,,, +567,0,Exp(Exp(((-0.5218350967145728 + (((0.6784203240561002 * x1) * x1) * ((x1 * x1) * -0.9487729162532552))) ^ Exp(((x1 ^ -0.6239423655593117) * (Exp(x0) * -0.5780899805857621)))))),"np.exp(np.exp((t[0] + (((t[1] * x[:, 1]) * x[:, 1]) * ((x[:, 1] * x[:, 1]) * t[2]))) ** np.exp((x[:, 1] ** t[3] * (np.exp(x[:, 0]) * t[4])))))","$$\operatorname{exp}(\operatorname{exp}({\left(\theta_{0} + \left(\left(\left(\theta_{1} \cdot x_{1}\right) \cdot x_{1}\right) \cdot \left(\left(x_{1} \cdot x_{1}\right) \cdot \theta_{2}\right)\right)\right)^{\operatorname{exp}(\left({x_{1}^{\theta_{3}}} \cdot \left(\operatorname{exp}(x_{0}) \cdot \theta_{4}\right)\right))}}))$$",-0.5218350967145728;0.6784203240561002;-0.9487729162532552;-0.6239423655593117;-0.5780899805857621,25,,,,Infinity,,,,,, +568,0,((Log((x1 * 0.3543055764576357)) * -0.289454823134599) / (((x0 + -0.902801943959533) ^ x1) * Exp(((0.8424122533628896 * x1) + -0.4199903865840553)))),"((np.log((x[:, 1] * t[0])) * t[1]) / ((x[:, 0] + t[2]) ** x[:, 1] * np.exp(((t[3] * x[:, 1]) + t[4]))))","$$\frac{\left(\operatorname{log}(\left(x_{1} \cdot \theta_{0}\right)) \cdot \theta_{1}\right)}{\left({\left(x_{0} + \theta_{2}\right)^{x_{1}}} \cdot \operatorname{exp}(\left(\left(\theta_{3} \cdot x_{1}\right) + \theta_{4}\right))\right)}$$",0.3543055764576357;-0.289454823134599;-0.902801943959533;0.8424122533628896;-0.4199903865840553,19,,,,Infinity,,,,,, +569,0,(((-16.606863395520314 / x0) + (x1 ^ (x0 ^ x0))) * ((x1 * x0) / (-29.45134318278752 * (x0 / x1)))),"(((t[0] / x[:, 0]) + x[:, 1] ** x[:, 0] ** x[:, 0]) * ((x[:, 1] * x[:, 0]) / (t[1] * (x[:, 0] / x[:, 1]))))","$$\left(\left(\frac{\theta_{0}}{x_{0}} + {x_{1}^{{x_{0}^{x_{0}}}}}\right) \cdot \frac{\left(x_{1} \cdot x_{0}\right)}{\left(\theta_{1} \cdot \frac{x_{0}}{x_{1}}\right)}\right)$$",-16.606863395520314;-29.45134318278752,19,5.238564449846848e-3,5.238564449846848e-3,5.238564449846848e-3,5.238564449846848e-3,0.5165791916755444,0.5165791916755444,0.5165791916755444,41.01885741079109,41.01885741079109,41.01885741079109 +570,0,(((Log(Log(Log((-0.7704320209206934 * x0)))) * (-0.1514559895650185 ^ (x0 ^ -0.5390055059998755))) * Exp(((5.645184042772433e-2 * (x0 + 9.554734627651484e-2)) + (x1 + 0.1899660442117117)))) * (Exp(((x1 + -0.696755962590768) ^ (0.9869502332343904 / (x1 / x0)))) + ((0.17592891751124196 ^ (0.18521743620105957 * x1)) * (x0 ^ (0.9425182785306492 / x0))))),"(((np.log(np.log(np.log((t[0] * x[:, 0])))) * t[1] ** x[:, 0] ** t[2]) * np.exp(((t[3] * (x[:, 0] + t[4])) + (x[:, 1] + t[5])))) * (np.exp((x[:, 1] + t[6]) ** (t[7] / (x[:, 1] / x[:, 0]))) + (t[8] ** (t[9] * x[:, 1]) * x[:, 0] ** (t[10] / x[:, 0]))))","$$\left(\left(\left(\operatorname{log}(\operatorname{log}(\operatorname{log}(\left(\theta_{0} \cdot x_{0}\right)))) \cdot {\theta_{1}^{{x_{0}^{\theta_{2}}}}}\right) \cdot \operatorname{exp}(\left(\left(\theta_{3} \cdot \left(x_{0} + \theta_{4}\right)\right) + \left(x_{1} + \theta_{5}\right)\right))\right) \cdot \left(\operatorname{exp}({\left(x_{1} + \theta_{6}\right)^{\frac{\theta_{7}}{\frac{x_{1}}{x_{0}}}}}) + \left({\theta_{8}^{\left(\theta_{9} \cdot x_{1}\right)}} \cdot {x_{0}^{\frac{\theta_{10}}{x_{0}}}}\right)\right)\right)$$",-0.7704320209206934;-0.1514559895650185;-0.5390055059998755;5.645184042772433e-2;9.554734627651484e-2;0.1899660442117117;-0.696755962590768;0.9869502332343904;0.17592891751124196;0.18521743620105957;0.9425182785306492,46,,,,Infinity,,,,,, +571,0,(Exp(Log((0.5468726414421925 / (x0 * (0.5598199540948363 + x0))))) ^ Exp((Exp(x1) / ((0.4842236596528492 ^ x0) * ((Exp(x1) / (-0.7479344362357396 ^ x0)) + -1.0785453908273568e-2))))),"np.exp(np.log((t[0] / (x[:, 0] * (t[1] + x[:, 0]))))) ** np.exp((np.exp(x[:, 1]) / (t[2] ** x[:, 0] * ((np.exp(x[:, 1]) / t[3] ** x[:, 0]) + t[4]))))","$${\operatorname{exp}(\operatorname{log}(\frac{\theta_{0}}{\left(x_{0} \cdot \left(\theta_{1} + x_{0}\right)\right)}))^{\operatorname{exp}(\frac{\operatorname{exp}(x_{1})}{\left({\theta_{2}^{x_{0}}} \cdot \left(\frac{\operatorname{exp}(x_{1})}{{\theta_{3}^{x_{0}}}} + \theta_{4}\right)\right)})}}$$",0.5468726414421925;0.5598199540948363;0.4842236596528492;-0.7479344362357396;-1.0785453908273568e-2,26,,,,Infinity,,,,,, +572,0,((((x0 / x0) + (x0 ^ x0)) / (Exp(x1) * Exp((x0 + x0)))) / Exp(((370.3534307283144 + Exp(x0)) ^ (Log(x0) * (-0.1807767497791878 + x1))))),"((((x[:, 0] / x[:, 0]) + x[:, 0] ** x[:, 0]) / (np.exp(x[:, 1]) * np.exp((x[:, 0] + x[:, 0])))) / np.exp((t[0] + np.exp(x[:, 0])) ** (np.log(x[:, 0]) * (t[1] + x[:, 1]))))","$$\frac{\frac{\left(\frac{x_{0}}{x_{0}} + {x_{0}^{x_{0}}}\right)}{\left(\operatorname{exp}(x_{1}) \cdot \operatorname{exp}(\left(x_{0} + x_{0}\right))\right)}}{\operatorname{exp}({\left(\theta_{0} + \operatorname{exp}(x_{0})\right)^{\left(\operatorname{log}(x_{0}) \cdot \left(\theta_{1} + x_{1}\right)\right)}})}$$",370.3534307283144;-0.1807767497791878,28,4.643856263537352e-3,4.643856263537352e-3,4.643856263537352e-3,4.643856263537352e-3,0.5714595534416982,0.5714595534416982,0.5714595534416982,65.56831750678661,65.56831750678661,65.56831750678661 +573,0,((-0.22862238255376865 ^ Exp((0.4801046713410423 / x0))) + Exp(Exp(Exp((-0.802168461005455 * x0))))),"(t[0] ** np.exp((t[1] / x[:, 0])) + np.exp(np.exp(np.exp((t[2] * x[:, 0])))))","$$\left({\theta_{0}^{\operatorname{exp}(\frac{\theta_{1}}{x_{0}})}} + \operatorname{exp}(\operatorname{exp}(\operatorname{exp}(\left(\theta_{2} \cdot x_{0}\right))))\right)$$",-0.22862238255376865;0.4801046713410423;-0.802168461005455,13,,,,Infinity,,,,,, +574,0,Exp(((0.5099867329702176 ^ Log(x1)) * -1.3522373273868376)),"np.exp((t[0] ** np.log(x[:, 1]) * t[1]))","$$\operatorname{exp}(\left({\theta_{0}^{\operatorname{log}(x_{1})}} \cdot \theta_{1}\right))$$",0.5099867329702176;-1.3522373273868376,7,9.162177343330498e-3,9.162177343330498e-3,9.162177343330498e-3,9.162177343330498e-3,0.15450363935976164,0.15450363935976164,0.15450363935976164,16.592412114940664,16.592412114940664,16.592412114940664 +575,0,Log((((-7.114988389230126e-2 ^ x0) * 0.19619385429738745) ^ ((x0 / x0) / x0))),"np.log((t[0] ** x[:, 0] * t[1]) ** ((x[:, 0] / x[:, 0]) / x[:, 0]))","$$\operatorname{log}({\left({\theta_{0}^{x_{0}}} \cdot \theta_{1}\right)^{\frac{\frac{x_{0}}{x_{0}}}{x_{0}}}})$$",-7.114988389230126e-2;0.19619385429738745,12,,,,Infinity,,,,,, +576,0,((Log(((((-0.6109424704940256 + x1) * 0.19295653687708436) * -0.9786341194863271) / (((x1 * x1) + 0.8215602485094153) + -9.979671211729091e-2))) ^ Exp(Exp(Exp(((x0 + x0) * -0.6728271656547664))))) + -0.3257912097299904),"(np.log(((((t[0] + x[:, 1]) * t[1]) * t[2]) / (((x[:, 1] * x[:, 1]) + t[3]) + t[4]))) ** np.exp(np.exp(np.exp(((x[:, 0] + x[:, 0]) * t[5])))) + t[6])","$$\left({\operatorname{log}(\frac{\left(\left(\left(\theta_{0} + x_{1}\right) \cdot \theta_{1}\right) \cdot \theta_{2}\right)}{\left(\left(\left(x_{1} \cdot x_{1}\right) + \theta_{3}\right) + \theta_{4}\right)})^{\operatorname{exp}(\operatorname{exp}(\operatorname{exp}(\left(\left(x_{0} + x_{0}\right) \cdot \theta_{5}\right))))}} + \theta_{6}\right)$$",-0.6109424704940256;0.19295653687708436;-0.9786341194863271;0.8215602485094153;-9.979671211729091e-2;-0.6728271656547664;-0.3257912097299904,27,,,,Infinity,,,,,, +577,0,((((x1 ^ 0.10088553937498834) ^ (0.8692795732288775 ^ Exp(Exp((-4.819136272679514e-2 ^ x0))))) ^ ((Exp(((x0 + x1) * (x0 + x1))) ^ x0) ^ -0.3184138899771527)) + -0.2561810471232039),"(x[:, 1] ** t[0] ** t[1] ** np.exp(np.exp(t[2] ** x[:, 0])) ** np.exp(((x[:, 0] + x[:, 1]) * (x[:, 0] + x[:, 1]))) ** x[:, 0] ** t[3] + t[4])","$$\left({{{x_{1}^{\theta_{0}}}^{{\theta_{1}^{\operatorname{exp}(\operatorname{exp}({\theta_{2}^{x_{0}}}))}}}}^{{{\operatorname{exp}(\left(\left(x_{0} + x_{1}\right) \cdot \left(x_{0} + x_{1}\right)\right))^{x_{0}}}^{\theta_{3}}}}} + \theta_{4}\right)$$",0.10088553937498834;0.8692795732288775;-4.819136272679514e-2;-0.3184138899771527;-0.2561810471232039,26,,,,Infinity,,,,,, +578,0,(Exp((((0.8958308789961463 / x0) * (x0 ^ (0.1217207735765995 / x1))) * -0.24288743936766566)) ^ Exp(Log(Log(((Log(x1) ^ 0.21494075919663902) * Log((x1 + x1))))))),"np.exp((((t[0] / x[:, 0]) * x[:, 0] ** (t[1] / x[:, 1])) * t[2])) ** np.exp(np.log(np.log((np.log(x[:, 1]) ** t[3] * np.log((x[:, 1] + x[:, 1]))))))","$${\operatorname{exp}(\left(\left(\frac{\theta_{0}}{x_{0}} \cdot {x_{0}^{\frac{\theta_{1}}{x_{1}}}}\right) \cdot \theta_{2}\right))^{\operatorname{exp}(\operatorname{log}(\operatorname{log}(\left({\operatorname{log}(x_{1})^{\theta_{3}}} \cdot \operatorname{log}(\left(x_{1} + x_{1}\right))\right))))}}$$",0.8958308789961463;0.1217207735765995;-0.24288743936766566;0.21494075919663902,25,,,,Infinity,,,,,, +579,0,((Log((x0 ^ -0.9093240682092463)) * (x0 / (x0 * x0))) ^ Exp(Log(((x0 * x0) ^ (x0 ^ 0.8464804178709849))))),"(np.log(x[:, 0] ** t[0]) * (x[:, 0] / (x[:, 0] * x[:, 0]))) ** np.exp(np.log((x[:, 0] * x[:, 0]) ** x[:, 0] ** t[1]))","$${\left(\operatorname{log}({x_{0}^{\theta_{0}}}) \cdot \frac{x_{0}}{\left(x_{0} \cdot x_{0}\right)}\right)^{\operatorname{exp}(\operatorname{log}({\left(x_{0} \cdot x_{0}\right)^{{x_{0}^{\theta_{1}}}}}))}}$$",-0.9093240682092463;0.8464804178709849,20,,,,Infinity,,,,,, +580,0,Log((Exp((Exp(Log((x0 * -0.7236264610158298))) ^ (Log(Exp(x0)) + 0.3112324747487134))) + ((0.2614554934577178 ^ (-0.11508425056178073 * x0)) * (Log(Log(x0)) ^ (x1 * 0.7900628192386188))))),"np.log((np.exp(np.exp(np.log((x[:, 0] * t[0]))) ** (np.log(np.exp(x[:, 0])) + t[1])) + (t[2] ** (t[3] * x[:, 0]) * np.log(np.log(x[:, 0])) ** (x[:, 1] * t[4]))))","$$\operatorname{log}(\left(\operatorname{exp}({\operatorname{exp}(\operatorname{log}(\left(x_{0} \cdot \theta_{0}\right)))^{\left(\operatorname{log}(\operatorname{exp}(x_{0})) + \theta_{1}\right)}}) + \left({\theta_{2}^{\left(\theta_{3} \cdot x_{0}\right)}} \cdot {\operatorname{log}(\operatorname{log}(x_{0}))^{\left(x_{1} \cdot \theta_{4}\right)}}\right)\right))$$",-0.7236264610158298;0.3112324747487134;0.2614554934577178;-0.11508425056178073;0.7900628192386188,27,,,,Infinity,,,,,, +581,0,(Exp(((((-7.613005745023416e-2 ^ x1) / Exp((-0.2221996891629202 / x0))) + -0.5355100089947986) / ((0.14894743547049127 + ((x1 + -0.6317521007442943) * 0.5220088491338244)) / (Exp((0.6752354001330176 + x1)) / x1)))) * (0.8310210380142103 + x0)),"(np.exp((((t[0] ** x[:, 1] / np.exp((t[1] / x[:, 0]))) + t[2]) / ((t[3] + ((x[:, 1] + t[4]) * t[5])) / (np.exp((t[6] + x[:, 1])) / x[:, 1])))) * (t[7] + x[:, 0]))","$$\left(\operatorname{exp}(\frac{\left(\frac{{\theta_{0}^{x_{1}}}}{\operatorname{exp}(\frac{\theta_{1}}{x_{0}})} + \theta_{2}\right)}{\frac{\left(\theta_{3} + \left(\left(x_{1} + \theta_{4}\right) \cdot \theta_{5}\right)\right)}{\frac{\operatorname{exp}(\left(\theta_{6} + x_{1}\right))}{x_{1}}}}) \cdot \left(\theta_{7} + x_{0}\right)\right)$$",-7.613005745023416e-2;-0.2221996891629202;-0.5355100089947986;0.14894743547049127;-0.6317521007442943;0.5220088491338244;0.6752354001330176;0.8310210380142103,30,,,,Infinity,,,,,, +582,0,(Exp(((x1 * (0.3743324774480471 + (-0.1680312560875793 * x1))) * (x0 ^ -0.3858425253899128))) ^ Log(((-0.6751368576765439 ^ (x1 + ((-0.5346815417372055 ^ x1) + -0.6540767420575526))) + (0.5658626658848409 / (0.5164351341908646 ^ x1))))),"np.exp(((x[:, 1] * (t[0] + (t[1] * x[:, 1]))) * x[:, 0] ** t[2])) ** np.log((t[3] ** (x[:, 1] + (t[4] ** x[:, 1] + t[5])) + (t[6] / t[7] ** x[:, 1])))","$${\operatorname{exp}(\left(\left(x_{1} \cdot \left(\theta_{0} + \left(\theta_{1} \cdot x_{1}\right)\right)\right) \cdot {x_{0}^{\theta_{2}}}\right))^{\operatorname{log}(\left({\theta_{3}^{\left(x_{1} + \left({\theta_{4}^{x_{1}}} + \theta_{5}\right)\right)}} + \frac{\theta_{6}}{{\theta_{7}^{x_{1}}}}\right))}}$$",0.3743324774480471;-0.1680312560875793;-0.3858425253899128;-0.6751368576765439;-0.5346815417372055;-0.6540767420575526;0.5658626658848409;0.5164351341908646,29,,,,Infinity,,,,,, +583,0,Exp((Exp(Log(Log((x1 / (x1 + x0))))) / (((x0 ^ -0.9890684005020601) * 0.706542304166119) + 0.6033891434795233))),"np.exp((np.exp(np.log(np.log((x[:, 1] / (x[:, 1] + x[:, 0]))))) / ((x[:, 0] ** t[0] * t[1]) + t[2])))","$$\operatorname{exp}(\frac{\operatorname{exp}(\operatorname{log}(\operatorname{log}(\frac{x_{1}}{\left(x_{1} + x_{0}\right)})))}{\left(\left({x_{0}^{\theta_{0}}} \cdot \theta_{1}\right) + \theta_{2}\right)})$$",-0.9890684005020601;0.706542304166119;0.6033891434795233,17,,,,Infinity,,,,,, +584,0,Exp(Exp(Log((6.437069126567317e-4 / x0)))),"np.exp(np.exp(np.log((t[0] / x[:, 0]))))","$$\operatorname{exp}(\operatorname{exp}(\operatorname{log}(\frac{\theta_{0}}{x_{0}})))$$",6.437069126567317e-4,6,0.8935446815112702,0.8935446815112702,0.8935446815112702,0.8935446815112702,-81.45734043089323,-81.45734043089323,-81.45734043089323,12.481742623332385,12.481742623332385,12.481742623332385 +585,0,Log(Log(((((x1 ^ x0) + -0.7469319579100282) + (-0.8045399749993276 * Log((x1 * -0.318514322843755)))) ^ Log(Log(Exp((-0.5445126779310653 / x0))))))),"np.log(np.log(((x[:, 1] ** x[:, 0] + t[0]) + (t[1] * np.log((x[:, 1] * t[2])))) ** np.log(np.log(np.exp((t[3] / x[:, 0]))))))","$$\operatorname{log}(\operatorname{log}({\left(\left({x_{1}^{x_{0}}} + \theta_{0}\right) + \left(\theta_{1} \cdot \operatorname{log}(\left(x_{1} \cdot \theta_{2}\right))\right)\right)^{\operatorname{log}(\operatorname{log}(\operatorname{exp}(\frac{\theta_{3}}{x_{0}})))}}))$$",-0.7469319579100282;-0.8045399749993276;-0.318514322843755;-0.5445126779310653,21,,,,Infinity,,,,,, +586,0,((-0.9030415656757873 * Exp(Log(x0))) ^ (Log((x1 + 0.5725682937146763)) / (x1 + 0.371811602531527))),"(t[0] * np.exp(np.log(x[:, 0]))) ** (np.log((x[:, 1] + t[1])) / (x[:, 1] + t[2]))","$${\left(\theta_{0} \cdot \operatorname{exp}(\operatorname{log}(x_{0}))\right)^{\frac{\operatorname{log}(\left(x_{1} + \theta_{1}\right))}{\left(x_{1} + \theta_{2}\right)}}}$$",-0.9030415656757873;0.5725682937146763;0.371811602531527,14,,,,Infinity,,,,,, +587,0,(((Exp(Log(x1)) / Exp(Exp(x1))) / ((x0 * x1) + (1381.347438448595 / x0))) * (Exp((0.30763337424723564 * (-3.795665662839001e-2 * ((0.47958156091182463 / x0) * -0.2605888081535586)))) ^ -0.4547729791199292)),"(((np.exp(np.log(x[:, 1])) / np.exp(np.exp(x[:, 1]))) / ((x[:, 0] * x[:, 1]) + (t[0] / x[:, 0]))) * np.exp((t[1] * (t[2] * ((t[3] / x[:, 0]) * t[4])))) ** t[5])","$$\left(\frac{\frac{\operatorname{exp}(\operatorname{log}(x_{1}))}{\operatorname{exp}(\operatorname{exp}(x_{1}))}}{\left(\left(x_{0} \cdot x_{1}\right) + \frac{\theta_{0}}{x_{0}}\right)} \cdot {\operatorname{exp}(\left(\theta_{1} \cdot \left(\theta_{2} \cdot \left(\frac{\theta_{3}}{x_{0}} \cdot \theta_{4}\right)\right)\right))^{\theta_{5}}}\right)$$",1381.347438448595;0.30763337424723564;-3.795665662839001e-2;0.47958156091182463;-0.2605888081535586;-0.4547729791199292,28,1.4567993572343754e-2,1.4567993572343754e-2,1.4567993572343754e-2,1.4567993572343754e-2,-0.3443513572909809,-0.3443513572909809,-0.3443513572909809,73.6609489718351,73.6609489718351,73.6609489718351 +588,0,(x1 * (x1 + x1)),"(x[:, 1] * (x[:, 1] + x[:, 1]))","$$\left(x_{1} \cdot \left(x_{1} + x_{1}\right)\right)$$",,5,7.045302803956957e-2,7.045302803956957e-2,7.045302803956957e-2,7.045302803956957e-2,-5.501487208922279,-5.501487208922279,-5.501487208922279,5.556469168576162,5.556469168576162,5.556469168576162 +589,0,((((1.5062192762177684e-2 / (x0 + x1)) / (x1 ^ -0.688331499553155)) ^ Exp(((0.6733899622966899 ^ (x0 + x1)) + (0.9266112828886239 * x1)))) + ((Exp((x1 + x1)) + Exp(Log((-0.3288608496267683 * x0)))) + Exp((Log((x1 * -0.425118279134717)) / Exp(Exp(x0)))))),"(((t[0] / (x[:, 0] + x[:, 1])) / x[:, 1] ** t[1]) ** np.exp((t[2] ** (x[:, 0] + x[:, 1]) + (t[3] * x[:, 1]))) + ((np.exp((x[:, 1] + x[:, 1])) + np.exp(np.log((t[4] * x[:, 0])))) + np.exp((np.log((x[:, 1] * t[5])) / np.exp(np.exp(x[:, 0]))))))","$$\left({\frac{\frac{\theta_{0}}{\left(x_{0} + x_{1}\right)}}{{x_{1}^{\theta_{1}}}}^{\operatorname{exp}(\left({\theta_{2}^{\left(x_{0} + x_{1}\right)}} + \left(\theta_{3} \cdot x_{1}\right)\right))}} + \left(\left(\operatorname{exp}(\left(x_{1} + x_{1}\right)) + \operatorname{exp}(\operatorname{log}(\left(\theta_{4} \cdot x_{0}\right)))\right) + \operatorname{exp}(\frac{\operatorname{log}(\left(x_{1} \cdot \theta_{5}\right))}{\operatorname{exp}(\operatorname{exp}(x_{0}))})\right)\right)$$",1.5062192762177684e-2;-0.688331499553155;0.6733899622966899;0.9266112828886239;-0.3288608496267683;-0.425118279134717,41,,,,Infinity,,,,,, +590,0,(x0 / (-0.5642597282686754 ^ x1)),"(x[:, 0] / t[0] ** x[:, 1])","$$\frac{x_{0}}{{\theta_{0}^{x_{1}}}}$$",-0.5642597282686754,5,,,,Infinity,,,,,, +591,0,(((2.5912184872061816e-2 / (Log(x0) + 0.9300118957397325)) * Exp(Exp((Exp(x1) + (x1 * 0.85087589549642))))) * ((0.19232965111063427 / (-0.8708659213045612 / Log(x0))) ^ Log(Log(Log(((x1 / x0) / x1)))))),"(((t[0] / (np.log(x[:, 0]) + t[1])) * np.exp(np.exp((np.exp(x[:, 1]) + (x[:, 1] * t[2]))))) * (t[3] / (t[4] / np.log(x[:, 0]))) ** np.log(np.log(np.log(((x[:, 1] / x[:, 0]) / x[:, 1])))))","$$\left(\left(\frac{\theta_{0}}{\left(\operatorname{log}(x_{0}) + \theta_{1}\right)} \cdot \operatorname{exp}(\operatorname{exp}(\left(\operatorname{exp}(x_{1}) + \left(x_{1} \cdot \theta_{2}\right)\right)))\right) \cdot {\frac{\theta_{3}}{\frac{\theta_{4}}{\operatorname{log}(x_{0})}}^{\operatorname{log}(\operatorname{log}(\operatorname{log}(\frac{\frac{x_{1}}{x_{0}}}{x_{1}})))}}\right)$$",2.5912184872061816e-2;0.9300118957397325;0.85087589549642;0.19232965111063427;-0.8708659213045612,31,,,,Infinity,,,,,, +592,0,(Log((Log((x1 + -0.4078327510876054)) * Exp(x1))) + Exp((0.5763198009359051 / Exp((-0.5575158161896799 ^ x0))))),"(np.log((np.log((x[:, 1] + t[0])) * np.exp(x[:, 1]))) + np.exp((t[1] / np.exp(t[2] ** x[:, 0]))))","$$\left(\operatorname{log}(\left(\operatorname{log}(\left(x_{1} + \theta_{0}\right)) \cdot \operatorname{exp}(x_{1})\right)) + \operatorname{exp}(\frac{\theta_{1}}{\operatorname{exp}({\theta_{2}^{x_{0}}})})\right)$$",-0.4078327510876054;0.5763198009359051;-0.5575158161896799,16,,,,Infinity,,,,,, +593,0,Log((Exp(Log(x1)) * Log((x0 * 0.15441929828192058)))),"np.log((np.exp(np.log(x[:, 1])) * np.log((x[:, 0] * t[0]))))","$$\operatorname{log}(\left(\operatorname{exp}(\operatorname{log}(x_{1})) \cdot \operatorname{log}(\left(x_{0} \cdot \theta_{0}\right))\right))$$",0.15441929828192058,9,,,,Infinity,,,,,, +594,0,((Exp((Exp(Log((0.4398775090315078 + x1))) + 0.4823994617616977)) ^ Log(Exp(((x0 + x1) * Exp((0.12830286218812414 * x0)))))) / ((Log(Log((-0.8733213567250107 / x0))) / Exp((x0 / (x1 ^ x1)))) * ((0.7795384700935742 / (x0 ^ -0.5664450553309333)) * (0.21125257463557678 + (x1 / x1))))),"(np.exp((np.exp(np.log((t[0] + x[:, 1]))) + t[1])) ** np.log(np.exp(((x[:, 0] + x[:, 1]) * np.exp((t[2] * x[:, 0]))))) / ((np.log(np.log((t[3] / x[:, 0]))) / np.exp((x[:, 0] / x[:, 1] ** x[:, 1]))) * ((t[4] / x[:, 0] ** t[5]) * (t[6] + (x[:, 1] / x[:, 1])))))","$$\frac{{\operatorname{exp}(\left(\operatorname{exp}(\operatorname{log}(\left(\theta_{0} + x_{1}\right))) + \theta_{1}\right))^{\operatorname{log}(\operatorname{exp}(\left(\left(x_{0} + x_{1}\right) \cdot \operatorname{exp}(\left(\theta_{2} \cdot x_{0}\right))\right)))}}}{\left(\frac{\operatorname{log}(\operatorname{log}(\frac{\theta_{3}}{x_{0}}))}{\operatorname{exp}(\frac{x_{0}}{{x_{1}^{x_{1}}}})} \cdot \left(\frac{\theta_{4}}{{x_{0}^{\theta_{5}}}} \cdot \left(\theta_{6} + \frac{x_{1}}{x_{1}}\right)\right)\right)}$$",0.4398775090315078;0.4823994617616977;0.12830286218812414;-0.8733213567250107;0.7795384700935742;-0.5664450553309333;0.21125257463557678,44,,,,Infinity,,,,,, +595,0,Log(Log(((0.5043343788598391 ^ (((0.35862596483371467 / x0) * -0.34521874169196887) + 0.4236526046857557)) ^ (x1 + (-0.39250686714047145 + x0))))),"np.log(np.log(t[0] ** (((t[1] / x[:, 0]) * t[2]) + t[3]) ** (x[:, 1] + (t[4] + x[:, 0]))))","$$\operatorname{log}(\operatorname{log}({{\theta_{0}^{\left(\left(\frac{\theta_{1}}{x_{0}} \cdot \theta_{2}\right) + \theta_{3}\right)}}^{\left(x_{1} + \left(\theta_{4} + x_{0}\right)\right)}}))$$",0.5043343788598391;0.35862596483371467;-0.34521874169196887;0.4236526046857557;-0.39250686714047145,17,,,,Infinity,,,,,, +596,0,(Log(((-0.20962331504126186 / x0) * (x1 * -0.31590502965136047))) ^ Log(((Exp(x1) ^ x1) * x1))),"np.log(((t[0] / x[:, 0]) * (x[:, 1] * t[1]))) ** np.log((np.exp(x[:, 1]) ** x[:, 1] * x[:, 1]))","$${\operatorname{log}(\left(\frac{\theta_{0}}{x_{0}} \cdot \left(x_{1} \cdot \theta_{1}\right)\right))^{\operatorname{log}(\left({\operatorname{exp}(x_{1})^{x_{1}}} \cdot x_{1}\right))}}$$",-0.20962331504126186;-0.31590502965136047,16,,,,Infinity,,,,,, +597,0,(x0 + -0.9956750177908852),"(x[:, 0] + t[0])","$$\left(x_{0} + \theta_{0}\right)$$",-0.9956750177908852,3,0.15105329635364,0.15105329635364,0.15105329635364,0.15105329635364,-12.939373529228037,-12.939373529228037,-12.939373529228037,5.452709768090245,5.452709768090245,5.452709768090245 +598,0,Log((((Log((x1 * -0.13519899693533843)) / (x1 * -0.5109773272126001)) + ((x0 + x1) * Exp(Exp(x1)))) / (((0.8257214760846427 + x1) * (x0 ^ Exp(x0))) + (-0.23055904498971602 / Log((x1 * 0.22477018613684407)))))),"np.log((((np.log((x[:, 1] * t[0])) / (x[:, 1] * t[1])) + ((x[:, 0] + x[:, 1]) * np.exp(np.exp(x[:, 1])))) / (((t[2] + x[:, 1]) * x[:, 0] ** np.exp(x[:, 0])) + (t[3] / np.log((x[:, 1] * t[4]))))))","$$\operatorname{log}(\frac{\left(\frac{\operatorname{log}(\left(x_{1} \cdot \theta_{0}\right))}{\left(x_{1} \cdot \theta_{1}\right)} + \left(\left(x_{0} + x_{1}\right) \cdot \operatorname{exp}(\operatorname{exp}(x_{1}))\right)\right)}{\left(\left(\left(\theta_{2} + x_{1}\right) \cdot {x_{0}^{\operatorname{exp}(x_{0})}}\right) + \frac{\theta_{3}}{\operatorname{log}(\left(x_{1} \cdot \theta_{4}\right))}\right)})$$",-0.13519899693533843;-0.5109773272126001;0.8257214760846427;-0.23055904498971602;0.22477018613684407,33,,,,Infinity,,,,,, +599,0,Exp(Log((0.34560104946504244 / Log(Log(x1))))),"np.exp(np.log((t[0] / np.log(np.log(x[:, 1])))))","$$\operatorname{exp}(\operatorname{log}(\frac{\theta_{0}}{\operatorname{log}(\operatorname{log}(x_{1}))}))$$",0.34560104946504244,7,,,,Infinity,,,,,, +600,0,Exp((Log(((-0.20538441268669883 + x1) ^ x1)) * 2.166438689668715e-2)),"np.exp((np.log((t[0] + x[:, 1]) ** x[:, 1]) * t[1]))","$$\operatorname{exp}(\left(\operatorname{log}({\left(\theta_{0} + x_{1}\right)^{x_{1}}}) \cdot \theta_{1}\right))$$",-0.20538441268669883;2.166438689668715e-2,9,,,,Infinity,,,,,, +601,0,(-1.1459650561082658e-2 * (-5.4482216224873286 + x1)),"(t[0] * (t[1] + x[:, 1]))","$$\left(\theta_{0} \cdot \left(\theta_{1} + x_{1}\right)\right)$$",-1.1459650561082658e-2;-5.4482216224873286,5,1.0974035737958986e-2,1.0974035737958986e-2,1.0974035737958986e-2,1.0974035737958986e-2,-1.2696756490357286e-2,-1.2696756490357286e-2,-1.2696756490357286e-2,10.983198308498897,10.983198308498897,10.983198308498897 +602,0,((((0.6577305458375726 * x1) / Exp(x1)) + Exp(((x0 ^ x0) * x1))) ^ (-0.7293394333104656 + Log((Log(x1) ^ -0.3687143719505793)))),"(((t[0] * x[:, 1]) / np.exp(x[:, 1])) + np.exp((x[:, 0] ** x[:, 0] * x[:, 1]))) ** (t[1] + np.log(np.log(x[:, 1]) ** t[2]))","$${\left(\frac{\left(\theta_{0} \cdot x_{1}\right)}{\operatorname{exp}(x_{1})} + \operatorname{exp}(\left({x_{0}^{x_{0}}} \cdot x_{1}\right))\right)^{\left(\theta_{1} + \operatorname{log}({\operatorname{log}(x_{1})^{\theta_{2}}})\right)}}$$",0.6577305458375726;-0.7293394333104656;-0.3687143719505793,21,,,,Infinity,,,,,, +603,0,((Log((x1 ^ 2.001235882258791)) * ((-0.3613017061276265 + x0) + 0.2671419344754678)) * (((x1 / x0) + ((x1 ^ 2.412461183344078) * x0)) * Log(((x0 ^ (-0.1393238199615297 + (0.42183913571951176 / x0))) * 0.9464789718828694)))),"((np.log(x[:, 1] ** t[0]) * ((t[1] + x[:, 0]) + t[2])) * (((x[:, 1] / x[:, 0]) + (x[:, 1] ** t[3] * x[:, 0])) * np.log((x[:, 0] ** (t[4] + (t[5] / x[:, 0])) * t[6]))))","$$\left(\left(\operatorname{log}({x_{1}^{\theta_{0}}}) \cdot \left(\left(\theta_{1} + x_{0}\right) + \theta_{2}\right)\right) \cdot \left(\left(\frac{x_{1}}{x_{0}} + \left({x_{1}^{\theta_{3}}} \cdot x_{0}\right)\right) \cdot \operatorname{log}(\left({x_{0}^{\left(\theta_{4} + \frac{\theta_{5}}{x_{0}}\right)}} \cdot \theta_{6}\right))\right)\right)$$",2.001235882258791;-0.3613017061276265;0.2671419344754678;2.412461183344078;-0.1393238199615297;0.42183913571951176;0.9464789718828694,31,3.2085757068159495e-3,3.2085757068159495e-3,3.2085757068159495e-3,3.2085757068159495e-3,0.7039089092805713,0.7039089092805713,0.7039089092805713,78.61205005778451,78.61205005778451,78.61205005778451 +604,0,(((Exp(x1) / (x1 * 1.574955811876399)) + ((x1 * 0.20481721240762363) / x0)) / (Exp(Exp((x1 * x0))) / ((-0.11753608575979142 + x1) * 0.5930666333031717))),"(((np.exp(x[:, 1]) / (x[:, 1] * t[0])) + ((x[:, 1] * t[1]) / x[:, 0])) / (np.exp(np.exp((x[:, 1] * x[:, 0]))) / ((t[2] + x[:, 1]) * t[3])))","$$\frac{\left(\frac{\operatorname{exp}(x_{1})}{\left(x_{1} \cdot \theta_{0}\right)} + \frac{\left(x_{1} \cdot \theta_{1}\right)}{x_{0}}\right)}{\frac{\operatorname{exp}(\operatorname{exp}(\left(x_{1} \cdot x_{0}\right)))}{\left(\left(\theta_{2} + x_{1}\right) \cdot \theta_{3}\right)}}$$",1.574955811876399;0.20481721240762363;-0.11753608575979142;0.5930666333031717,24,7.208579481644313e-3,7.208579481644313e-3,7.208579481644313e-3,7.208579481644313e-3,0.33478391776024485,0.33478391776024485,0.33478391776024485,54.79203104033156,54.79203104033156,54.79203104033156 +605,0,((0.911021449763283 ^ ((-0.2959384799187552 + (x0 * x0)) / ((x0 ^ 0.937374381214572) + (-0.28140389502337626 ^ x0)))) / Log(Exp(Exp(((x1 * 0.923545548806266) ^ (0.7067383604939315 ^ x0)))))),"(t[0] ** ((t[1] + (x[:, 0] * x[:, 0])) / (x[:, 0] ** t[2] + t[3] ** x[:, 0])) / np.log(np.exp(np.exp((x[:, 1] * t[4]) ** t[5] ** x[:, 0]))))","$$\frac{{\theta_{0}^{\frac{\left(\theta_{1} + \left(x_{0} \cdot x_{0}\right)\right)}{\left({x_{0}^{\theta_{2}}} + {\theta_{3}^{x_{0}}}\right)}}}}{\operatorname{log}(\operatorname{exp}(\operatorname{exp}({\left(x_{1} \cdot \theta_{4}\right)^{{\theta_{5}^{x_{0}}}}})))}$$",0.911021449763283;-0.2959384799187552;0.937374381214572;-0.28140389502337626;0.923545548806266;0.7067383604939315,26,,,,Infinity,,,,,, +606,0,((-1.2174280869038486e-2 * Exp(x0)) * -1.0436438106084465),"((t[0] * np.exp(x[:, 0])) * t[1])","$$\left(\left(\theta_{0} \cdot \operatorname{exp}(x_{0})\right) \cdot \theta_{1}\right)$$",-1.2174280869038486e-2;-1.0436438106084465,6,1.297830887277363e-2,1.297830887277363e-2,1.297830887277363e-2,1.297830887277363e-2,-0.19765340791867225,-0.19765340791867225,-0.19765340791867225,12.37129651544012,12.37129651544012,12.37129651544012 +607,0,Exp((((0.8012432594838149 * (x0 * -0.8920859035977577)) + ((x0 * -1.514222426331109) + (x0 * (x0 * x1)))) * 1.4800093283657119)),"np.exp((((t[0] * (x[:, 0] * t[1])) + ((x[:, 0] * t[2]) + (x[:, 0] * (x[:, 0] * x[:, 1])))) * t[3]))","$$\operatorname{exp}(\left(\left(\left(\theta_{0} \cdot \left(x_{0} \cdot \theta_{1}\right)\right) + \left(\left(x_{0} \cdot \theta_{2}\right) + \left(x_{0} \cdot \left(x_{0} \cdot x_{1}\right)\right)\right)\right) \cdot \theta_{3}\right))$$",0.8012432594838149;-0.8920859035977577;-1.514222426331109;1.4800093283657119,18,4.6985047597614335e-3,4.6985047597614335e-3,4.6985047597614335e-3,4.6985047597614335e-3,0.56641652677451,0.56641652677451,0.56641652677451,40.33959884185934,40.33959884185934,40.33959884185934 +608,0,Log(Exp(Log(((Log(x1) * -3.526069025739042) * Exp((Log(x1) + -0.1027345012415466)))))),"np.log(np.exp(np.log(((np.log(x[:, 1]) * t[0]) * np.exp((np.log(x[:, 1]) + t[1]))))))","$$\operatorname{log}(\operatorname{exp}(\operatorname{log}(\left(\left(\operatorname{log}(x_{1}) \cdot \theta_{0}\right) \cdot \operatorname{exp}(\left(\operatorname{log}(x_{1}) + \theta_{1}\right))\right))))$$",-3.526069025739042;-0.1027345012415466,13,1.9164289707541565e-2,1.9164289707541565e-2,1.9164289707541565e-2,1.9164289707541565e-2,-0.7685028999986128,-0.7685028999986128,-0.7685028999986128,27.351970831436784,27.351970831436784,27.351970831436784 +609,0,(Log(((-0.8133185282578572 / x1) / (x1 + -0.12374368418906168))) + (0.6804927009771955 * ((Log((x1 ^ (-0.12046971932159056 + x1))) * (x1 * (x1 * -0.12649115297700897))) + -0.32357630960170447))),"(np.log(((t[0] / x[:, 1]) / (x[:, 1] + t[1]))) + (t[2] * ((np.log(x[:, 1] ** (t[3] + x[:, 1])) * (x[:, 1] * (x[:, 1] * t[4]))) + t[5])))","$$\left(\operatorname{log}(\frac{\frac{\theta_{0}}{x_{1}}}{\left(x_{1} + \theta_{1}\right)}) + \left(\theta_{2} \cdot \left(\left(\operatorname{log}({x_{1}^{\left(\theta_{3} + x_{1}\right)}}) \cdot \left(x_{1} \cdot \left(x_{1} \cdot \theta_{4}\right)\right)\right) + \theta_{5}\right)\right)\right)$$",-0.8133185282578572;-0.12374368418906168;0.6804927009771955;-0.12046971932159056;-0.12649115297700897;-0.32357630960170447,25,,,,Infinity,,,,,, +610,0,Exp((Exp(Log(x0)) ^ Log((Exp((-0.4203500616977285 ^ x1)) + x1)))),"np.exp(np.exp(np.log(x[:, 0])) ** np.log((np.exp(t[0] ** x[:, 1]) + x[:, 1])))","$$\operatorname{exp}({\operatorname{exp}(\operatorname{log}(x_{0}))^{\operatorname{log}(\left(\operatorname{exp}({\theta_{0}^{x_{1}}}) + x_{1}\right))}})$$",-0.4203500616977285,12,,,,Infinity,,,,,, +611,0,(Log((2.6250683041761893 / Exp(Exp((-1.0161439625527107 / x0))))) + -0.5375715154155047),"(np.log((t[0] / np.exp(np.exp((t[1] / x[:, 0]))))) + t[2])","$$\left(\operatorname{log}(\frac{\theta_{0}}{\operatorname{exp}(\operatorname{exp}(\frac{\theta_{1}}{x_{0}}))}) + \theta_{2}\right)$$",2.6250683041761893;-1.0161439625527107;-0.5375715154155047,10,7.136600714018945e-3,7.136600714018945e-3,7.136600714018945e-3,7.136600714018945e-3,0.34142620198921336,0.34142620198921336,0.34142620198921336,23.98679243902609,23.98679243902609,23.98679243902609 +612,0,(Log((((-0.11946083178058997 * x1) + Log(Exp((x1 * 0.2630614427966629)))) / Log(Log(((x0 ^ x1) + (0.6094840150620108 * x0)))))) + Exp(((-0.2553549080214592 + (x1 * (-0.4733651161583312 / x1))) + ((x0 * x0) / Log((-1.76484821004963e-2 * x0)))))),"(np.log((((t[0] * x[:, 1]) + np.log(np.exp((x[:, 1] * t[1])))) / np.log(np.log((x[:, 0] ** x[:, 1] + (t[2] * x[:, 0])))))) + np.exp(((t[3] + (x[:, 1] * (t[4] / x[:, 1]))) + ((x[:, 0] * x[:, 0]) / np.log((t[5] * x[:, 0]))))))","$$\left(\operatorname{log}(\frac{\left(\left(\theta_{0} \cdot x_{1}\right) + \operatorname{log}(\operatorname{exp}(\left(x_{1} \cdot \theta_{1}\right)))\right)}{\operatorname{log}(\operatorname{log}(\left({x_{0}^{x_{1}}} + \left(\theta_{2} \cdot x_{0}\right)\right)))}) + \operatorname{exp}(\left(\left(\theta_{3} + \left(x_{1} \cdot \frac{\theta_{4}}{x_{1}}\right)\right) + \frac{\left(x_{0} \cdot x_{0}\right)}{\operatorname{log}(\left(\theta_{5} \cdot x_{0}\right))}\right))\right)$$",-0.11946083178058997;0.2630614427966629;0.6094840150620108;-0.2553549080214592;-0.4733651161583312;-1.76484821004963e-2,38,,,,Infinity,,,,,, +613,0,(Log((x0 + -0.45152738447976437)) + Exp((x0 ^ (x1 ^ 0.6114924883409211)))),"(np.log((x[:, 0] + t[0])) + np.exp(x[:, 0] ** x[:, 1] ** t[1]))","$$\left(\operatorname{log}(\left(x_{0} + \theta_{0}\right)) + \operatorname{exp}({x_{0}^{{x_{1}^{\theta_{1}}}}})\right)$$",-0.45152738447976437;0.6114924883409211,11,5.711606244516128,5.711606244516128,5.711606244516128,5.711606244516128,-526.0736542404699,-526.0736542404699,-526.0736542404699,30.587307130408245,30.587307130408245,30.587307130408245 +614,0,(((-0.12956608732236063 + x1) * -0.4934015593157684) * -0.6084563617428457),"(((t[0] + x[:, 1]) * t[1]) * t[2])","$$\left(\left(\left(\theta_{0} + x_{1}\right) \cdot \theta_{1}\right) \cdot \theta_{2}\right)$$",-0.12956608732236063;-0.4934015593157684;-0.6084563617428457,7,9.147873437044185e-3,9.147873437044185e-3,9.147873437044185e-3,9.147873437044185e-3,0.15582362043574693,0.15582362043574693,0.15582362043574693,15.775068420035495,15.775068420035495,15.775068420035495 +615,0,(Exp((0.9013324254237913 ^ Log((-0.9052431646547467 * ((x1 ^ 0.7728681254191637) + 0.7314974339261664))))) * x1),"(np.exp(t[0] ** np.log((t[1] * (x[:, 1] ** t[2] + t[3])))) * x[:, 1])","$$\left(\operatorname{exp}({\theta_{0}^{\operatorname{log}(\left(\theta_{1} \cdot \left({x_{1}^{\theta_{2}}} + \theta_{3}\right)\right))}}) \cdot x_{1}\right)$$",0.9013324254237913;-0.9052431646547467;0.7728681254191637;0.7314974339261664,13,,,,Infinity,,,,,, +616,0,((((x0 * x0) + x0) + Exp((-0.40311547171760753 / Log(x0)))) ^ (0.13372414176334224 + Exp((-0.32924259493118524 * (x1 ^ x0))))),"(((x[:, 0] * x[:, 0]) + x[:, 0]) + np.exp((t[0] / np.log(x[:, 0])))) ** (t[1] + np.exp((t[2] * x[:, 1] ** x[:, 0])))","$${\left(\left(\left(x_{0} \cdot x_{0}\right) + x_{0}\right) + \operatorname{exp}(\frac{\theta_{0}}{\operatorname{log}(x_{0})})\right)^{\left(\theta_{1} + \operatorname{exp}(\left(\theta_{2} \cdot {x_{1}^{x_{0}}}\right))\right)}}$$",-0.40311547171760753;0.13372414176334224;-0.32924259493118524,20,Infinity,Infinity,Infinity,Infinity,-Infinity,-Infinity,-Infinity,Infinity,Infinity,Infinity +617,0,(Exp(((x1 + x1) / Log(x1))) ^ (((Log(x0) * -0.6744635728144546) + 0.9440794041008963) / ((x1 + -0.8111476010387508) + 0.7367835444702853))),"np.exp(((x[:, 1] + x[:, 1]) / np.log(x[:, 1]))) ** (((np.log(x[:, 0]) * t[0]) + t[1]) / ((x[:, 1] + t[2]) + t[3]))","$${\operatorname{exp}(\frac{\left(x_{1} + x_{1}\right)}{\operatorname{log}(x_{1})})^{\frac{\left(\left(\operatorname{log}(x_{0}) \cdot \theta_{0}\right) + \theta_{1}\right)}{\left(\left(x_{1} + \theta_{2}\right) + \theta_{3}\right)}}}$$",-0.6744635728144546;0.9440794041008963;-0.8111476010387508;0.7367835444702853,20,2.6123264070395565e-2,2.6123264070395565e-2,2.6123264070395565e-2,2.6123264070395565e-2,-1.4106851321363494,-1.4106851321363494,-1.4106851321363494,52.051702225858314,52.051702225858314,52.051702225858314 +618,0,Exp((Exp((((0.1983406498926078 ^ (x0 ^ x0)) / (x1 * (x0 * -0.21254770709788462))) ^ -0.6398809987904233)) ^ Log(Exp(((x1 / x0) / (-0.45768242209484467 ^ x0)))))),"np.exp(np.exp((t[0] ** x[:, 0] ** x[:, 0] / (x[:, 1] * (x[:, 0] * t[1]))) ** t[2]) ** np.log(np.exp(((x[:, 1] / x[:, 0]) / t[3] ** x[:, 0]))))","$$\operatorname{exp}({\operatorname{exp}({\frac{{\theta_{0}^{{x_{0}^{x_{0}}}}}}{\left(x_{1} \cdot \left(x_{0} \cdot \theta_{1}\right)\right)}^{\theta_{2}}})^{\operatorname{log}(\operatorname{exp}(\frac{\frac{x_{1}}{x_{0}}}{{\theta_{3}^{x_{0}}}}))}})$$",0.1983406498926078;-0.21254770709788462;-0.6398809987904233;-0.45768242209484467,25,,,,Infinity,,,,,, +619,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 +620,0,Log((x0 + Exp(x0))),"np.log((x[:, 0] + np.exp(x[:, 0])))","$$\operatorname{log}(\left(x_{0} + \operatorname{exp}(x_{0})\right))$$",,5,1.8328860865758219,1.8328860865758219,1.8328860865758219,1.8328860865758219,-168.14085567183128,-168.14085567183128,-168.14085567183128,8.581069283517692,8.581069283517692,8.581069283517692 +621,0,((((x0 ^ -0.8019027503629551) ^ (-0.38490635201975654 * (x1 * 0.95589359992825))) / ((0.19882498638574864 / Exp(((x0 * -0.3768267001240936) ^ (-2.601179866461023e-2 * x1)))) + (-0.4925768524472236 + x0))) + x0),"((x[:, 0] ** t[0] ** (t[1] * (x[:, 1] * t[2])) / ((t[3] / np.exp((x[:, 0] * t[4]) ** (t[5] * x[:, 1]))) + (t[6] + x[:, 0]))) + x[:, 0])","$$\left(\frac{{{x_{0}^{\theta_{0}}}^{\left(\theta_{1} \cdot \left(x_{1} \cdot \theta_{2}\right)\right)}}}{\left(\frac{\theta_{3}}{\operatorname{exp}({\left(x_{0} \cdot \theta_{4}\right)^{\left(\theta_{5} \cdot x_{1}\right)}})} + \left(\theta_{6} + x_{0}\right)\right)} + x_{0}\right)$$",-0.8019027503629551;-0.38490635201975654;0.95589359992825;0.19882498638574864;-0.3768267001240936;-2.601179866461023e-2;-0.4925768524472236,26,,,,Infinity,,,,,, +622,0,Log((((Exp((-0.17325330727209765 * x1)) ^ 0.48898301403928734) ^ (Log((x1 * -0.2543774852159929)) * Exp((-0.3795713452350755 / x0)))) + ((Exp((-0.5860628555949883 + x0)) * (x1 ^ x0)) ^ Exp((-9.385029043215642e-2 / Log(x1)))))),"np.log((np.exp((t[0] * x[:, 1])) ** t[1] ** (np.log((x[:, 1] * t[2])) * np.exp((t[3] / x[:, 0]))) + (np.exp((t[4] + x[:, 0])) * x[:, 1] ** x[:, 0]) ** np.exp((t[5] / np.log(x[:, 1])))))","$$\operatorname{log}(\left({{\operatorname{exp}(\left(\theta_{0} \cdot x_{1}\right))^{\theta_{1}}}^{\left(\operatorname{log}(\left(x_{1} \cdot \theta_{2}\right)) \cdot \operatorname{exp}(\frac{\theta_{3}}{x_{0}})\right)}} + {\left(\operatorname{exp}(\left(\theta_{4} + x_{0}\right)) \cdot {x_{1}^{x_{0}}}\right)^{\operatorname{exp}(\frac{\theta_{5}}{\operatorname{log}(x_{1})})}}\right))$$",-0.17325330727209765;0.48898301403928734;-0.2543774852159929;-0.3795713452350755;-0.5860628555949883;-9.385029043215642e-2,32,,,,Infinity,,,,,, +623,0,(Log(Exp((((x0 + -0.9027185518212252) * Log(x0)) * ((91.82825480028903 * x1) * (x1 + Log(x1)))))) + (Exp(Exp(((x0 * x1) ^ (96.92620450697676 * x1)))) + ((x0 / x0) + Exp((-53.95176077432393 * x0))))),"(np.log(np.exp((((x[:, 0] + t[0]) * np.log(x[:, 0])) * ((t[1] * x[:, 1]) * (x[:, 1] + np.log(x[:, 1])))))) + (np.exp(np.exp((x[:, 0] * x[:, 1]) ** (t[2] * x[:, 1]))) + ((x[:, 0] / x[:, 0]) + np.exp((t[3] * x[:, 0])))))","$$\left(\operatorname{log}(\operatorname{exp}(\left(\left(\left(x_{0} + \theta_{0}\right) \cdot \operatorname{log}(x_{0})\right) \cdot \left(\left(\theta_{1} \cdot x_{1}\right) \cdot \left(x_{1} + \operatorname{log}(x_{1})\right)\right)\right))) + \left(\operatorname{exp}(\operatorname{exp}({\left(x_{0} \cdot x_{1}\right)^{\left(\theta_{2} \cdot x_{1}\right)}})) + \left(\frac{x_{0}}{x_{0}} + \operatorname{exp}(\left(\theta_{3} \cdot x_{0}\right))\right)\right)\right)$$",-0.9027185518212252;91.82825480028903;96.92620450697676;-53.95176077432393,36,6.521915444030792,6.521915444030792,6.521915444030792,6.521915444030792,-600.8499277734935,-600.8499277734935,-600.8499277734935,93.05350842520217,93.05350842520217,93.05350842520217 +624,0,(((0.6917730683616619 + (Log(x1) ^ -0.46032058279163146)) + -0.5809879209803634) ^ ((Exp(Exp(((x1 ^ 0.44435039259185105) + -0.3137207437452647))) ^ Exp(x1)) + Exp((Log(x0) / Exp(x1))))),"((t[0] + np.log(x[:, 1]) ** t[1]) + t[2]) ** (np.exp(np.exp((x[:, 1] ** t[3] + t[4]))) ** np.exp(x[:, 1]) + np.exp((np.log(x[:, 0]) / np.exp(x[:, 1]))))","$${\left(\left(\theta_{0} + {\operatorname{log}(x_{1})^{\theta_{1}}}\right) + \theta_{2}\right)^{\left({\operatorname{exp}(\operatorname{exp}(\left({x_{1}^{\theta_{3}}} + \theta_{4}\right)))^{\operatorname{exp}(x_{1})}} + \operatorname{exp}(\frac{\operatorname{log}(x_{0})}{\operatorname{exp}(x_{1})})\right)}}$$",0.6917730683616619;-0.46032058279163146;-0.5809879209803634;0.44435039259185105;-0.3137207437452647,26,,,,Infinity,,,,,, +625,0,((((0.3715937186897854 ^ (Log(x1) + 0.9438434613699577)) ^ Log(Log((Log(x0) * (x1 / x1))))) * ((x0 * 0.8501784941429054) * Log(((0.668373904243562 + (-0.8898903282734907 * x1)) + x0)))) / Exp(((x1 * -0.5269593794588394) + (x1 * 0.7450173839161645)))),"((t[0] ** (np.log(x[:, 1]) + t[1]) ** np.log(np.log((np.log(x[:, 0]) * (x[:, 1] / x[:, 1])))) * ((x[:, 0] * t[2]) * np.log(((t[3] + (t[4] * x[:, 1])) + x[:, 0])))) / np.exp(((x[:, 1] * t[5]) + (x[:, 1] * t[6]))))","$$\frac{\left({{\theta_{0}^{\left(\operatorname{log}(x_{1}) + \theta_{1}\right)}}^{\operatorname{log}(\operatorname{log}(\left(\operatorname{log}(x_{0}) \cdot \frac{x_{1}}{x_{1}}\right)))}} \cdot \left(\left(x_{0} \cdot \theta_{2}\right) \cdot \operatorname{log}(\left(\left(\theta_{3} + \left(\theta_{4} \cdot x_{1}\right)\right) + x_{0}\right))\right)\right)}{\operatorname{exp}(\left(\left(x_{1} \cdot \theta_{5}\right) + \left(x_{1} \cdot \theta_{6}\right)\right))}$$",0.3715937186897854;0.9438434613699577;0.8501784941429054;0.668373904243562;-0.8898903282734907;-0.5269593794588394;0.7450173839161645,37,,,,Infinity,,,,,, +626,0,((0.2911115010051466 * ((0.548099892394101 / x1) / (x0 + -0.8332509444281476))) ^ (Log(((x0 + x0) / Exp(Exp(x1)))) * (Exp((x1 ^ x0)) * (0.21449715763086652 + (0.27012833809581593 * x1))))),"(t[0] * ((t[1] / x[:, 1]) / (x[:, 0] + t[2]))) ** (np.log(((x[:, 0] + x[:, 0]) / np.exp(np.exp(x[:, 1])))) * (np.exp(x[:, 1] ** x[:, 0]) * (t[3] + (t[4] * x[:, 1]))))","$${\left(\theta_{0} \cdot \frac{\frac{\theta_{1}}{x_{1}}}{\left(x_{0} + \theta_{2}\right)}\right)^{\left(\operatorname{log}(\frac{\left(x_{0} + x_{0}\right)}{\operatorname{exp}(\operatorname{exp}(x_{1}))}) \cdot \left(\operatorname{exp}({x_{1}^{x_{0}}}) \cdot \left(\theta_{3} + \left(\theta_{4} \cdot x_{1}\right)\right)\right)\right)}}$$",0.2911115010051466;0.548099892394101;-0.8332509444281476;0.21449715763086652;0.27012833809581593,29,,,,Infinity,,,,,, +627,0,Exp((Log((-0.4904467101235124 + x0)) + (((x0 * 0.38506185009556515) + ((0.5554889925362516 * (x1 * -0.9450842333794357)) / x1)) ^ (Log((x1 ^ x0)) * -0.9572456361727316)))),"np.exp((np.log((t[0] + x[:, 0])) + ((x[:, 0] * t[1]) + ((t[2] * (x[:, 1] * t[3])) / x[:, 1])) ** (np.log(x[:, 1] ** x[:, 0]) * t[4])))","$$\operatorname{exp}(\left(\operatorname{log}(\left(\theta_{0} + x_{0}\right)) + {\left(\left(x_{0} \cdot \theta_{1}\right) + \frac{\left(\theta_{2} \cdot \left(x_{1} \cdot \theta_{3}\right)\right)}{x_{1}}\right)^{\left(\operatorname{log}({x_{1}^{x_{0}}}) \cdot \theta_{4}\right)}}\right))$$",-0.4904467101235124;0.38506185009556515;0.5554889925362516;-0.9450842333794357;-0.9572456361727316,24,,,,Infinity,,,,,, +628,0,(((Log((x1 / x0)) + (x0 / (x1 + -0.748551876214754))) / ((0.8397549465462022 * x1) / (x0 ^ 0.9724673738491509))) / Log(Log((((x0 ^ -0.15068247254349065) ^ (-0.775458533975284 * (x0 * x1))) ^ (Exp((x1 ^ 0.37293532392487894)) / Exp((x1 * 0.10055876191593649))))))),"(((np.log((x[:, 1] / x[:, 0])) + (x[:, 0] / (x[:, 1] + t[0]))) / ((t[1] * x[:, 1]) / x[:, 0] ** t[2])) / np.log(np.log(x[:, 0] ** t[3] ** (t[4] * (x[:, 0] * x[:, 1])) ** (np.exp(x[:, 1] ** t[5]) / np.exp((x[:, 1] * t[6]))))))","$$\frac{\frac{\left(\operatorname{log}(\frac{x_{1}}{x_{0}}) + \frac{x_{0}}{\left(x_{1} + \theta_{0}\right)}\right)}{\frac{\left(\theta_{1} \cdot x_{1}\right)}{{x_{0}^{\theta_{2}}}}}}{\operatorname{log}(\operatorname{log}({{{x_{0}^{\theta_{3}}}^{\left(\theta_{4} \cdot \left(x_{0} \cdot x_{1}\right)\right)}}^{\frac{\operatorname{exp}({x_{1}^{\theta_{5}}})}{\operatorname{exp}(\left(x_{1} \cdot \theta_{6}\right))}}}))}$$",-0.748551876214754;0.8397549465462022;0.9724673738491509;-0.15068247254349065;-0.775458533975284;0.37293532392487894;0.10055876191593649,40,,,,Infinity,,,,,, +629,0,(Exp(((0.173325249321305 * (((x1 * x1) * -5.6155028706412646e-2) / (x1 * -0.4263736660565396))) / (((-0.44395846544254136 * x1) ^ (0.45219343462317685 / Exp(x0))) * x0))) ^ (((x1 ^ (x1 * 0.9349833068265455)) / ((x1 + 0.6140001133164233) + -0.17511001488644107)) * x1)),"np.exp(((t[0] * (((x[:, 1] * x[:, 1]) * t[1]) / (x[:, 1] * t[2]))) / ((t[3] * x[:, 1]) ** (t[4] / np.exp(x[:, 0])) * x[:, 0]))) ** ((x[:, 1] ** (x[:, 1] * t[5]) / ((x[:, 1] + t[6]) + t[7])) * x[:, 1])","$${\operatorname{exp}(\frac{\left(\theta_{0} \cdot \frac{\left(\left(x_{1} \cdot x_{1}\right) \cdot \theta_{1}\right)}{\left(x_{1} \cdot \theta_{2}\right)}\right)}{\left({\left(\theta_{3} \cdot x_{1}\right)^{\frac{\theta_{4}}{\operatorname{exp}(x_{0})}}} \cdot x_{0}\right)})^{\left(\frac{{x_{1}^{\left(x_{1} \cdot \theta_{5}\right)}}}{\left(\left(x_{1} + \theta_{6}\right) + \theta_{7}\right)} \cdot x_{1}\right)}}$$",0.173325249321305;-5.6155028706412646e-2;-0.4263736660565396;-0.44395846544254136;0.45219343462317685;0.9349833068265455;0.6140001133164233;-0.17511001488644107,37,,,,Infinity,,,,,, +630,0,Log((Exp((x0 / (6847.720159938891 / x0))) + Exp(Log(x0)))),"np.log((np.exp((x[:, 0] / (t[0] / x[:, 0]))) + np.exp(np.log(x[:, 0]))))","$$\operatorname{log}(\left(\operatorname{exp}(\frac{x_{0}}{\frac{\theta_{0}}{x_{0}}}) + \operatorname{exp}(\operatorname{log}(x_{0}))\right))$$",6847.720159938891,11,0.47792423996318534,0.47792423996318534,0.47792423996318534,0.47792423996318534,-43.10340363524757,-43.10340363524757,-43.10340363524757,22.16041091284311,22.16041091284311,22.16041091284311 +631,0,(((Exp(x0) + 0.8879842256302257) * 0.5010509047327705) + Log(((x0 + -0.8707230805249988) + Log(x1)))),"(((np.exp(x[:, 0]) + t[0]) * t[1]) + np.log(((x[:, 0] + t[2]) + np.log(x[:, 1]))))","$$\left(\left(\left(\operatorname{exp}(x_{0}) + \theta_{0}\right) \cdot \theta_{1}\right) + \operatorname{log}(\left(\left(x_{0} + \theta_{2}\right) + \operatorname{log}(x_{1})\right))\right)$$",0.8879842256302257;0.5010509047327705;-0.8707230805249988,14,,,,Infinity,,,,,, +632,0,Exp(Log(Exp((Log(x0) / (x0 ^ 1997.9019768162345))))),"np.exp(np.log(np.exp((np.log(x[:, 0]) / x[:, 0] ** t[0]))))","$$\operatorname{exp}(\operatorname{log}(\operatorname{exp}(\frac{\operatorname{log}(x_{0})}{{x_{0}^{\theta_{0}}}})))$$",1997.9019768162345,9,0.5940445274820989,0.5940445274820989,0.5940445274820989,0.5940445274820989,-53.81911855919061,-53.81911855919061,-53.81911855919061,18.681400233154022,18.681400233154022,18.681400233154022 +633,0,Log(((Log((-0.8903963705943502 + x0)) + -0.7946530027970207) / ((x0 * (x0 + x1)) / (x1 + -0.8240843457605888)))),"np.log(((np.log((t[0] + x[:, 0])) + t[1]) / ((x[:, 0] * (x[:, 0] + x[:, 1])) / (x[:, 1] + t[2]))))","$$\operatorname{log}(\frac{\left(\operatorname{log}(\left(\theta_{0} + x_{0}\right)) + \theta_{1}\right)}{\frac{\left(x_{0} \cdot \left(x_{0} + x_{1}\right)\right)}{\left(x_{1} + \theta_{2}\right)}})$$",-0.8903963705943502;-0.7946530027970207;-0.8240843457605888,17,,,,Infinity,,,,,, +634,0,((((-0.8253192674357344 ^ (0.6623607254779194 + x1)) ^ (0.4072731636465736 / x0)) + (Exp((x0 + 0.5018823961729182)) + 0.18768879292101281)) / (((0.21988700467739308 + (0.719935431334775 / x1)) * (x1 / (-0.7915209032198108 ^ x1))) / (Exp(Log(Exp(x1))) / (x1 ^ (-0.6810535054932898 ^ x0))))),"((t[0] ** (t[1] + x[:, 1]) ** (t[2] / x[:, 0]) + (np.exp((x[:, 0] + t[3])) + t[4])) / (((t[5] + (t[6] / x[:, 1])) * (x[:, 1] / t[7] ** x[:, 1])) / (np.exp(np.log(np.exp(x[:, 1]))) / x[:, 1] ** t[8] ** x[:, 0])))","$$\frac{\left({{\theta_{0}^{\left(\theta_{1} + x_{1}\right)}}^{\frac{\theta_{2}}{x_{0}}}} + \left(\operatorname{exp}(\left(x_{0} + \theta_{3}\right)) + \theta_{4}\right)\right)}{\frac{\left(\left(\theta_{5} + \frac{\theta_{6}}{x_{1}}\right) \cdot \frac{x_{1}}{{\theta_{7}^{x_{1}}}}\right)}{\frac{\operatorname{exp}(\operatorname{log}(\operatorname{exp}(x_{1})))}{{x_{1}^{{\theta_{8}^{x_{0}}}}}}}}$$",-0.8253192674357344;0.6623607254779194;0.4072731636465736;0.5018823961729182;0.18768879292101281;0.21988700467739308;0.719935431334775;-0.7915209032198108;-0.6810535054932898,39,,,,Infinity,,,,,, +635,0,(Exp((-0.7952213854877552 ^ Log(Log((x1 ^ -0.7752689523963974))))) + Log((Log((((0.12779165482827037 * x1) + x0) * Log(Log(x1)))) ^ Exp(((0.7180708359980392 ^ Exp(x0)) + Log(Exp(x1))))))),"(np.exp(t[0] ** np.log(np.log(x[:, 1] ** t[1]))) + np.log(np.log((((t[2] * x[:, 1]) + x[:, 0]) * np.log(np.log(x[:, 1])))) ** np.exp((t[3] ** np.exp(x[:, 0]) + np.log(np.exp(x[:, 1]))))))","$$\left(\operatorname{exp}({\theta_{0}^{\operatorname{log}(\operatorname{log}({x_{1}^{\theta_{1}}}))}}) + \operatorname{log}({\operatorname{log}(\left(\left(\left(\theta_{2} \cdot x_{1}\right) + x_{0}\right) \cdot \operatorname{log}(\operatorname{log}(x_{1}))\right))^{\operatorname{exp}(\left({\theta_{3}^{\operatorname{exp}(x_{0})}} + \operatorname{log}(\operatorname{exp}(x_{1}))\right))}})\right)$$",-0.7952213854877552;-0.7752689523963974;0.12779165482827037;0.7180708359980392,30,,,,Infinity,,,,,, +636,0,(((((x0 * -0.7095000472755404) * -0.3250977305658188) / ((x1 ^ 0.9081752213083639) * 0.19257876625834325)) + (Log((x0 + (0.15253828881580245 / x1))) * ((-0.30297469033757973 + x0) * 0.15415638751393002))) ^ ((((x1 + 0.861712514519418) * 5.256209910066145e-4) * Exp((-0.7558625336814683 * (-0.9596242992818707 ^ x0)))) / (((x1 + x0) * 0.41945792574677176) + 0.4646998489447036))),"((((x[:, 0] * t[0]) * t[1]) / (x[:, 1] ** t[2] * t[3])) + (np.log((x[:, 0] + (t[4] / x[:, 1]))) * ((t[5] + x[:, 0]) * t[6]))) ** ((((x[:, 1] + t[7]) * t[8]) * np.exp((t[9] * t[10] ** x[:, 0]))) / (((x[:, 1] + x[:, 0]) * t[11]) + t[12]))","$${\left(\frac{\left(\left(x_{0} \cdot \theta_{0}\right) \cdot \theta_{1}\right)}{\left({x_{1}^{\theta_{2}}} \cdot \theta_{3}\right)} + \left(\operatorname{log}(\left(x_{0} + \frac{\theta_{4}}{x_{1}}\right)) \cdot \left(\left(\theta_{5} + x_{0}\right) \cdot \theta_{6}\right)\right)\right)^{\frac{\left(\left(\left(x_{1} + \theta_{7}\right) \cdot \theta_{8}\right) \cdot \operatorname{exp}(\left(\theta_{9} \cdot {\theta_{10}^{x_{0}}}\right))\right)}{\left(\left(\left(x_{1} + x_{0}\right) \cdot \theta_{11}\right) + \theta_{12}\right)}}}$$",-0.7095000472755404;-0.3250977305658188;0.9081752213083639;0.19257876625834325;0.15253828881580245;-0.30297469033757973;0.15415638751393002;0.861712514519418;5.256209910066145e-4;-0.7558625336814683;-0.9596242992818707;0.41945792574677176;0.4646998489447036,45,,,,Infinity,,,,,, +637,0,(Log((-7.03621187719754e-2 / (Log((x0 + -6.053406385211768e-2)) ^ (x0 ^ x0)))) * Log(((Log(x1) + -0.2363254707739646) / (((x1 ^ (0.3598659746838684 ^ x0)) + (x1 * -0.12061413823364286)) + x1)))),"(np.log((t[0] / np.log((x[:, 0] + t[1])) ** x[:, 0] ** x[:, 0])) * np.log(((np.log(x[:, 1]) + t[2]) / ((x[:, 1] ** t[3] ** x[:, 0] + (x[:, 1] * t[4])) + x[:, 1]))))","$$\left(\operatorname{log}(\frac{\theta_{0}}{{\operatorname{log}(\left(x_{0} + \theta_{1}\right))^{{x_{0}^{x_{0}}}}}}) \cdot \operatorname{log}(\frac{\left(\operatorname{log}(x_{1}) + \theta_{2}\right)}{\left(\left({x_{1}^{{\theta_{3}^{x_{0}}}}} + \left(x_{1} \cdot \theta_{4}\right)\right) + x_{1}\right)})\right)$$",-7.03621187719754e-2;-6.053406385211768e-2;-0.2363254707739646;0.3598659746838684;-0.12061413823364286,29,,,,Infinity,,,,,, +638,0,((Exp(Log(x1)) + ((Exp(Log(x1)) * (-0.494479256757158 ^ Log((x1 ^ 0.5590759328282535)))) / Log(x0))) ^ (-0.6630440071460031 ^ ((x0 / x0) * x1))),"(np.exp(np.log(x[:, 1])) + ((np.exp(np.log(x[:, 1])) * t[0] ** np.log(x[:, 1] ** t[1])) / np.log(x[:, 0]))) ** t[2] ** ((x[:, 0] / x[:, 0]) * x[:, 1])","$${\left(\operatorname{exp}(\operatorname{log}(x_{1})) + \frac{\left(\operatorname{exp}(\operatorname{log}(x_{1})) \cdot {\theta_{0}^{\operatorname{log}({x_{1}^{\theta_{1}}})}}\right)}{\operatorname{log}(x_{0})}\right)^{{\theta_{2}^{\left(\frac{x_{0}}{x_{0}} \cdot x_{1}\right)}}}}$$",-0.494479256757158;0.5590759328282535;-0.6630440071460031,25,,,,Infinity,,,,,, +639,0,Exp(((((x0 ^ -0.192205549711841) ^ x0) * -0.22969499804379334) + Exp((Exp(Exp((x1 + -0.285265727981899))) + ((x1 ^ 0.541124675373596) * 0.2661299969081782))))),"np.exp(((x[:, 0] ** t[0] ** x[:, 0] * t[1]) + np.exp((np.exp(np.exp((x[:, 1] + t[2]))) + (x[:, 1] ** t[3] * t[4])))))","$$\operatorname{exp}(\left(\left({{x_{0}^{\theta_{0}}}^{x_{0}}} \cdot \theta_{1}\right) + \operatorname{exp}(\left(\operatorname{exp}(\operatorname{exp}(\left(x_{1} + \theta_{2}\right))) + \left({x_{1}^{\theta_{3}}} \cdot \theta_{4}\right)\right))\right))$$",-0.192205549711841;-0.22969499804379334;-0.285265727981899;0.541124675373596;0.2661299969081782,21,6.859622075919386e51,6.859622075919386e51,6.859622075919386e51,6.859622075919386e51,-6.3301388777190225e53,-6.3301388777190225e53,-6.3301388777190225e53,6.173659868327447e51,6.173659868327447e51,6.173659868327447e51 +640,0,(Exp(((x0 / (x1 * 1.341398277774902)) * Log((x1 + -4.3567245768444306e-2)))) + Log((x0 * x0))),"(np.exp(((x[:, 0] / (x[:, 1] * t[0])) * np.log((x[:, 1] + t[1])))) + np.log((x[:, 0] * x[:, 0])))","$$\left(\operatorname{exp}(\left(\frac{x_{0}}{\left(x_{1} \cdot \theta_{0}\right)} \cdot \operatorname{log}(\left(x_{1} + \theta_{1}\right))\right)) + \operatorname{log}(\left(x_{0} \cdot x_{0}\right))\right)$$",1.341398277774902;-4.3567245768444306e-2,16,0.46351904844716285,0.46351904844716285,0.46351904844716285,0.46351904844716285,-41.77407584069351,-41.77407584069351,-41.77407584069351,37.7300816812151,37.7300816812151,37.7300816812151 +641,0,(Log(((0.3153293768025036 + x1) + -0.9392292851666727)) ^ Exp((((0.32297193144074965 + Exp(x0)) * (-0.1658619449945895 * (x0 ^ x0))) + 0.20169461153745905))),"np.log(((t[0] + x[:, 1]) + t[1])) ** np.exp((((t[2] + np.exp(x[:, 0])) * (t[3] * x[:, 0] ** x[:, 0])) + t[4]))","$${\operatorname{log}(\left(\left(\theta_{0} + x_{1}\right) + \theta_{1}\right))^{\operatorname{exp}(\left(\left(\left(\theta_{2} + \operatorname{exp}(x_{0})\right) \cdot \left(\theta_{3} \cdot {x_{0}^{x_{0}}}\right)\right) + \theta_{4}\right))}}$$",0.3153293768025036;-0.9392292851666727;0.32297193144074965;-0.1658619449945895;0.20169461153745905,20,,,,Infinity,,,,,, +642,0,((Exp((Exp((Log(x1) + (x0 + x1))) + Exp(x0))) ^ (-8.813064968415798e9 + (x1 ^ 4.857119583403563e9))) + 6.113188000927039e-2),"(np.exp((np.exp((np.log(x[:, 1]) + (x[:, 0] + x[:, 1]))) + np.exp(x[:, 0]))) ** (t[0] + x[:, 1] ** t[1]) + t[2])","$$\left({\operatorname{exp}(\left(\operatorname{exp}(\left(\operatorname{log}(x_{1}) + \left(x_{0} + x_{1}\right)\right)) + \operatorname{exp}(x_{0})\right))^{\left(\theta_{0} + {x_{1}^{\theta_{1}}}\right)}} + \theta_{2}\right)$$",-8.813064968415798e9;4.857119583403563e9;6.113188000927039e-2,19,1.0836447996526662e-2,1.0836447996526662e-2,1.0836447996526662e-2,1.0836447996526662e-2,3.3306690738754696e-16,3.3306690738754696e-16,3.3306690738754696e-16,43.04482044135075,43.04482044135075,43.04482044135075 +643,0,Exp(Log((Log(Log((-0.4451087893937813 ^ (x1 * -0.9856176113445397)))) / (((-0.8610579515976418 + Log(x0)) * -0.6205649123284178) + (((x0 * x0) / x0) / (x1 / x0)))))),"np.exp(np.log((np.log(np.log(t[0] ** (x[:, 1] * t[1]))) / (((t[2] + np.log(x[:, 0])) * t[3]) + (((x[:, 0] * x[:, 0]) / x[:, 0]) / (x[:, 1] / x[:, 0]))))))","$$\operatorname{exp}(\operatorname{log}(\frac{\operatorname{log}(\operatorname{log}({\theta_{0}^{\left(x_{1} \cdot \theta_{1}\right)}}))}{\left(\left(\left(\theta_{2} + \operatorname{log}(x_{0})\right) \cdot \theta_{3}\right) + \frac{\frac{\left(x_{0} \cdot x_{0}\right)}{x_{0}}}{\frac{x_{1}}{x_{0}}}\right)}))$$",-0.4451087893937813;-0.9856176113445397;-0.8610579515976418;-0.6205649123284178,26,,,,Infinity,,,,,, +644,0,((Exp((Exp((x0 * -0.7679164531594153)) + -0.617984742862949)) * Log((-0.13528962057836635 * (0.7324934981062843 + x1)))) / (((x0 ^ -0.8135239364943528) / Log((x0 * 0.8725830955829906))) ^ ((x0 * -0.2177902591550429) * (0.46812623011851684 / (x1 ^ 0.8749901912518923))))),"((np.exp((np.exp((x[:, 0] * t[0])) + t[1])) * np.log((t[2] * (t[3] + x[:, 1])))) / (x[:, 0] ** t[4] / np.log((x[:, 0] * t[5]))) ** ((x[:, 0] * t[6]) * (t[7] / x[:, 1] ** t[8])))","$$\frac{\left(\operatorname{exp}(\left(\operatorname{exp}(\left(x_{0} \cdot \theta_{0}\right)) + \theta_{1}\right)) \cdot \operatorname{log}(\left(\theta_{2} \cdot \left(\theta_{3} + x_{1}\right)\right))\right)}{{\frac{{x_{0}^{\theta_{4}}}}{\operatorname{log}(\left(x_{0} \cdot \theta_{5}\right))}^{\left(\left(x_{0} \cdot \theta_{6}\right) \cdot \frac{\theta_{7}}{{x_{1}^{\theta_{8}}}}\right)}}}$$",-0.7679164531594153;-0.617984742862949;-0.13528962057836635;0.7324934981062843;-0.8135239364943528;0.8725830955829906;-0.2177902591550429;0.46812623011851684;0.8749901912518923,33,,,,Infinity,,,,,, +645,0,((((-0.8973267904956393 * Log(x0)) ^ Exp((0.7443373163723328 * (0.9743116514922152 / x1)))) / (-0.8010483800831236 + ((-0.6807088453371691 + x0) + 0.17228747891427088))) + (Exp((Exp((x0 + x1)) ^ Log(Exp(x0)))) + (Log((Exp(x1) ^ -0.9336664346907435)) * ((-0.7114885537690083 + x0) * 0.8530282477189093)))),"(((t[0] * np.log(x[:, 0])) ** np.exp((t[1] * (t[2] / x[:, 1]))) / (t[3] + ((t[4] + x[:, 0]) + t[5]))) + (np.exp(np.exp((x[:, 0] + x[:, 1])) ** np.log(np.exp(x[:, 0]))) + (np.log(np.exp(x[:, 1]) ** t[6]) * ((t[7] + x[:, 0]) * t[8]))))","$$\left(\frac{{\left(\theta_{0} \cdot \operatorname{log}(x_{0})\right)^{\operatorname{exp}(\left(\theta_{1} \cdot \frac{\theta_{2}}{x_{1}}\right))}}}{\left(\theta_{3} + \left(\left(\theta_{4} + x_{0}\right) + \theta_{5}\right)\right)} + \left(\operatorname{exp}({\operatorname{exp}(\left(x_{0} + x_{1}\right))^{\operatorname{log}(\operatorname{exp}(x_{0}))}}) + \left(\operatorname{log}({\operatorname{exp}(x_{1})^{\theta_{6}}}) \cdot \left(\left(\theta_{7} + x_{0}\right) \cdot \theta_{8}\right)\right)\right)\right)$$",-0.8973267904956393;0.7443373163723328;0.9743116514922152;-0.8010483800831236;-0.6807088453371691;0.17228747891427088;-0.9336664346907435;-0.7114885537690083;0.8530282477189093,41,,,,Infinity,,,,,, +646,0,(x1 * 0.20059950763971873),"(x[:, 1] * t[0])","$$\left(x_{1} \cdot \theta_{0}\right)$$",0.20059950763971873,3,9.365750690525969e-3,9.365750690525969e-3,9.365750690525969e-3,9.365750690525969e-3,0.1357176545739056,0.1357176545739056,0.1357176545739056,5.325190976993443,5.325190976993443,5.325190976993443 +647,0,((((x1 * x0) + ((1.830072617973799e-2 ^ (x1 / x0)) + (((x1 * -0.2503297260585622) + 3.322381758920434e-2) * (x0 + x0)))) * -0.9593679117678032) + x1),"((((x[:, 1] * x[:, 0]) + (t[0] ** (x[:, 1] / x[:, 0]) + (((x[:, 1] * t[1]) + t[2]) * (x[:, 0] + x[:, 0])))) * t[3]) + x[:, 1])","$$\left(\left(\left(\left(x_{1} \cdot x_{0}\right) + \left({\theta_{0}^{\frac{x_{1}}{x_{0}}}} + \left(\left(\left(x_{1} \cdot \theta_{1}\right) + \theta_{2}\right) \cdot \left(x_{0} + x_{0}\right)\right)\right)\right) \cdot \theta_{3}\right) + x_{1}\right)$$",1.830072617973799e-2;-0.2503297260585622;3.322381758920434e-2;-0.9593679117678032,23,0.10762034483101914,0.10762034483101914,0.10762034483101914,0.10762034483101914,-8.931330346024266,-8.931330346024266,-8.931330346024266,52.93649148009069,52.93649148009069,52.93649148009069 +648,0,Log(((((x1 / x0) + Exp((x1 * 1.5576028090884653e-2))) + Log((Log(x0) * -0.8399348063029305))) * (Exp(((0.7796152999954002 + x0) * -0.7652285355642765)) + ((x1 * 9.355484923852675e-2) + Exp((0.8383645632407609 ^ x1)))))),"np.log(((((x[:, 1] / x[:, 0]) + np.exp((x[:, 1] * t[0]))) + np.log((np.log(x[:, 0]) * t[1]))) * (np.exp(((t[2] + x[:, 0]) * t[3])) + ((x[:, 1] * t[4]) + np.exp(t[5] ** x[:, 1])))))","$$\operatorname{log}(\left(\left(\left(\frac{x_{1}}{x_{0}} + \operatorname{exp}(\left(x_{1} \cdot \theta_{0}\right))\right) + \operatorname{log}(\left(\operatorname{log}(x_{0}) \cdot \theta_{1}\right))\right) \cdot \left(\operatorname{exp}(\left(\left(\theta_{2} + x_{0}\right) \cdot \theta_{3}\right)) + \left(\left(x_{1} \cdot \theta_{4}\right) + \operatorname{exp}({\theta_{5}^{x_{1}}})\right)\right)\right))$$",1.5576028090884653e-2;-0.8399348063029305;0.7796152999954002;-0.7652285355642765;9.355484923852675e-2;0.8383645632407609,31,,,,Infinity,,,,,, +649,0,Log((-0.27742143545477593 * ((x1 * x0) + (x1 ^ 0.473870175248005)))),"np.log((t[0] * ((x[:, 1] * x[:, 0]) + x[:, 1] ** t[1])))","$$\operatorname{log}(\left(\theta_{0} \cdot \left(\left(x_{1} \cdot x_{0}\right) + {x_{1}^{\theta_{1}}}\right)\right))$$",-0.27742143545477593;0.473870175248005,10,,,,Infinity,,,,,, +650,0,((((Exp(Exp((0.6017045317732665 + x1))) + (0.635438528407483 ^ Log(Log(x0)))) / (0.1660305887307374 / x1)) ^ ((x0 / x0) / (((x1 * -7.434947403547887e-2) / ((x0 * -0.8150096430404821) ^ (-0.4959156114435469 + x0))) / x0))) / (-0.5849542040828435 * ((Log(x0) * -0.4439975993012625) ^ x0))),"(((np.exp(np.exp((t[0] + x[:, 1]))) + t[1] ** np.log(np.log(x[:, 0]))) / (t[2] / x[:, 1])) ** ((x[:, 0] / x[:, 0]) / (((x[:, 1] * t[3]) / (x[:, 0] * t[4]) ** (t[5] + x[:, 0])) / x[:, 0])) / (t[6] * (np.log(x[:, 0]) * t[7]) ** x[:, 0]))","$$\frac{{\frac{\left(\operatorname{exp}(\operatorname{exp}(\left(\theta_{0} + x_{1}\right))) + {\theta_{1}^{\operatorname{log}(\operatorname{log}(x_{0}))}}\right)}{\frac{\theta_{2}}{x_{1}}}^{\frac{\frac{x_{0}}{x_{0}}}{\frac{\frac{\left(x_{1} \cdot \theta_{3}\right)}{{\left(x_{0} \cdot \theta_{4}\right)^{\left(\theta_{5} + x_{0}\right)}}}}{x_{0}}}}}}{\left(\theta_{6} \cdot {\left(\operatorname{log}(x_{0}) \cdot \theta_{7}\right)^{x_{0}}}\right)}$$",0.6017045317732665;0.635438528407483;0.1660305887307374;-7.434947403547887e-2;-0.8150096430404821;-0.4959156114435469;-0.5849542040828435;-0.4439975993012625,42,,,,Infinity,,,,,, +651,0,((Exp((Log((x0 ^ x1)) ^ 5.170673255324065e-2)) * (Log((0.7632515256686883 * x1)) * Log((0.8612743674060752 + x0)))) * (Exp((Exp(Log(x1)) + Log((x0 * -0.5571163950885334)))) * Log(((x0 ^ -0.5183987808983542) ^ (x1 * -0.6617402877713248))))),"((np.exp(np.log(x[:, 0] ** x[:, 1]) ** t[0]) * (np.log((t[1] * x[:, 1])) * np.log((t[2] + x[:, 0])))) * (np.exp((np.exp(np.log(x[:, 1])) + np.log((x[:, 0] * t[3])))) * np.log(x[:, 0] ** t[4] ** (x[:, 1] * t[5]))))","$$\left(\left(\operatorname{exp}({\operatorname{log}({x_{0}^{x_{1}}})^{\theta_{0}}}) \cdot \left(\operatorname{log}(\left(\theta_{1} \cdot x_{1}\right)) \cdot \operatorname{log}(\left(\theta_{2} + x_{0}\right))\right)\right) \cdot \left(\operatorname{exp}(\left(\operatorname{exp}(\operatorname{log}(x_{1})) + \operatorname{log}(\left(x_{0} \cdot \theta_{3}\right))\right)) \cdot \operatorname{log}({{x_{0}^{\theta_{4}}}^{\left(x_{1} \cdot \theta_{5}\right)}})\right)\right)$$",5.170673255324065e-2;0.7632515256686883;0.8612743674060752;-0.5571163950885334;-0.5183987808983542;-0.6617402877713248,36,,,,Infinity,,,,,, +652,0,((Exp((x0 ^ -0.9276321812156032)) ^ (x1 ^ x1)) * (1.9127195387556514e-2 ^ (x0 ^ (1.0909582022913642 * x0)))),"(np.exp(x[:, 0] ** t[0]) ** x[:, 1] ** x[:, 1] * t[1] ** x[:, 0] ** (t[2] * x[:, 0]))","$$\left({\operatorname{exp}({x_{0}^{\theta_{0}}})^{{x_{1}^{x_{1}}}}} \cdot {\theta_{1}^{{x_{0}^{\left(\theta_{2} \cdot x_{0}\right)}}}}\right)$$",-0.9276321812156032;1.9127195387556514e-2;1.0909582022913642,16,5.474757170066497e-3,5.474757170066497e-3,5.474757170066497e-3,5.474757170066497e-3,0.4947830532826546,0.4947830532826546,0.4947830532826546,34.73585359520486,34.73585359520486,34.73585359520486 +653,0,((((-0.19920782569818973 + (Exp((x0 + x1)) ^ (x1 + -0.7676316072354532))) + -4.969581423598379e-2) ^ -0.10684689414260962) / x0),"(((t[0] + np.exp((x[:, 0] + x[:, 1])) ** (x[:, 1] + t[1])) + t[2]) ** t[3] / x[:, 0])","$$\frac{{\left(\left(\theta_{0} + {\operatorname{exp}(\left(x_{0} + x_{1}\right))^{\left(x_{1} + \theta_{1}\right)}}\right) + \theta_{2}\right)^{\theta_{3}}}}{x_{0}}$$",-0.19920782569818973;-0.7676316072354532;-4.969581423598379e-2;-0.10684689414260962,16,1.364624554494357,1.364624554494357,1.364624554494357,1.364624554494357,-124.92913793631925,-124.92913793631925,-124.92913793631925,40.4464242254005,40.4464242254005,40.4464242254005 +654,0,(Exp(Log(Exp(Exp((x0 ^ -1.3341010597449852))))) * Exp((-6.679650788956264 / x0))),"(np.exp(np.log(np.exp(np.exp(x[:, 0] ** t[0])))) * np.exp((t[1] / x[:, 0])))","$$\left(\operatorname{exp}(\operatorname{log}(\operatorname{exp}(\operatorname{exp}({x_{0}^{\theta_{0}}})))) \cdot \operatorname{exp}(\frac{\theta_{1}}{x_{0}})\right)$$",-1.3341010597449852;-6.679650788956264,12,1.3085909539536101e-2,1.3085909539536101e-2,1.3085909539536101e-2,1.3085909539536101e-2,-0.20758292234968878,-0.20758292234968878,-0.20758292234968878,27.40454897798462,27.40454897798462,27.40454897798462 +655,0,(x0 * 3.524205325424168e-2),"(x[:, 0] * t[0])","$$\left(x_{0} \cdot \theta_{0}\right)$$",3.524205325424168e-2,3,1.306654401552658e-2,1.306654401552658e-2,1.306654401552658e-2,1.306654401552658e-2,-0.20579584931471184,-0.20579584931471184,-0.20579584931471184,5.328521690985943,5.328521690985943,5.328521690985943 +656,0,(((-0.4966920471888838 ^ (Log(x0) ^ ((Log(x0) + -0.8213712654068528) / Exp(x0)))) ^ (Log((-0.8576996175319336 ^ (Log(x0) ^ Log(x1)))) ^ (x1 + 0.42214596820852013))) ^ 0.15881749923559274),"t[0] ** np.log(x[:, 0]) ** ((np.log(x[:, 0]) + t[1]) / np.exp(x[:, 0])) ** np.log(t[2] ** np.log(x[:, 0]) ** np.log(x[:, 1])) ** (x[:, 1] + t[3]) ** t[4]","$${{{\theta_{0}^{{\operatorname{log}(x_{0})^{\frac{\left(\operatorname{log}(x_{0}) + \theta_{1}\right)}{\operatorname{exp}(x_{0})}}}}}^{{\operatorname{log}({\theta_{2}^{{\operatorname{log}(x_{0})^{\operatorname{log}(x_{1})}}}})^{\left(x_{1} + \theta_{3}\right)}}}}^{\theta_{4}}}$$",-0.4966920471888838;-0.8213712654068528;-0.8576996175319336;0.42214596820852013;0.15881749923559274,27,,,,Infinity,,,,,, +657,0,((Exp(((0.1176583354775742 * (x1 ^ -0.8248849636882339)) ^ x0)) ^ ((Log((0.736234310815349 ^ x0)) * Exp((x1 ^ 0.8653238313876802))) * (((x0 ^ 0.6712370088479175) ^ 0.8224870573996854) + x0))) / Log((x1 + x0))),"(np.exp((t[0] * x[:, 1] ** t[1]) ** x[:, 0]) ** ((np.log(t[2] ** x[:, 0]) * np.exp(x[:, 1] ** t[3])) * (x[:, 0] ** t[4] ** t[5] + x[:, 0])) / np.log((x[:, 1] + x[:, 0])))","$$\frac{{\operatorname{exp}({\left(\theta_{0} \cdot {x_{1}^{\theta_{1}}}\right)^{x_{0}}})^{\left(\left(\operatorname{log}({\theta_{2}^{x_{0}}}) \cdot \operatorname{exp}({x_{1}^{\theta_{3}}})\right) \cdot \left({{x_{0}^{\theta_{4}}}^{\theta_{5}}} + x_{0}\right)\right)}}}{\operatorname{log}(\left(x_{1} + x_{0}\right))}$$",0.1176583354775742;-0.8248849636882339;0.736234310815349;0.8653238313876802;0.6712370088479175;0.8224870573996854,31,3155.2453633537953,3155.2453633537953,3155.2453633537953,3155.2453633537953,-291168.7047192147,-291168.7047192147,-291168.7047192147,2919.9603385280448,2919.9603385280448,2919.9603385280448 +658,0,((0.6113605356455268 + (x1 / Log(x1))) + ((-0.4269872585612929 * (0.6808335375486776 * x0)) * 0.5708100407174047)),"((t[0] + (x[:, 1] / np.log(x[:, 1]))) + ((t[1] * (t[2] * x[:, 0])) * t[3]))","$$\left(\left(\theta_{0} + \frac{x_{1}}{\operatorname{log}(x_{1})}\right) + \left(\left(\theta_{1} \cdot \left(\theta_{2} \cdot x_{0}\right)\right) \cdot \theta_{3}\right)\right)$$",0.6113605356455268;-0.4269872585612929;0.6808335375486776;0.5708100407174047,14,0.10867405368582436,0.10867405368582436,0.10867405368582436,0.10867405368582436,-9.028567822284288,-9.028567822284288,-9.028567822284288,35.42424847656219,35.42424847656219,35.42424847656219 +659,0,(1.743998583298645e-3 * Exp(Exp((x1 + 0.9050851411695724)))),"(t[0] * np.exp(np.exp((x[:, 1] + t[1]))))","$$\left(\theta_{0} \cdot \operatorname{exp}(\operatorname{exp}(\left(x_{1} + \theta_{1}\right)))\right)$$",1.743998583298645e-3;0.9050851411695724,7,9.49247389026586e-3,9.49247389026586e-3,9.49247389026586e-3,9.49247389026586e-3,0.12402349060241702,0.12402349060241702,0.12402349060241702,15.316458484275222,15.316458484275222,15.316458484275222 +660,0,Exp((((1.6523343106621867e-5 ^ (0.44806348318293093 ^ x1)) ^ (1.8166453607634372 / (x1 ^ x1))) / ((Exp(Exp(x0)) + (x0 ^ x1)) / (Exp((x0 * -2.1943393593896934)) ^ (x0 * 0.4876385762893846))))),"np.exp((t[0] ** t[1] ** x[:, 1] ** (t[2] / x[:, 1] ** x[:, 1]) / ((np.exp(np.exp(x[:, 0])) + x[:, 0] ** x[:, 1]) / np.exp((x[:, 0] * t[3])) ** (x[:, 0] * t[4]))))","$$\operatorname{exp}(\frac{{{\theta_{0}^{{\theta_{1}^{x_{1}}}}}^{\frac{\theta_{2}}{{x_{1}^{x_{1}}}}}}}{\frac{\left(\operatorname{exp}(\operatorname{exp}(x_{0})) + {x_{0}^{x_{1}}}\right)}{{\operatorname{exp}(\left(x_{0} \cdot \theta_{3}\right))^{\left(x_{0} \cdot \theta_{4}\right)}}}})$$",1.6523343106621867e-5;0.44806348318293093;1.8166453607634372;-2.1943393593896934;0.4876385762893846,29,0.8923097948756735,0.8923097948756735,0.8923097948756735,0.8923097948756735,-81.34338365871174,-81.34338365871174,-81.34338365871174,71.21150820094155,71.21150820094155,71.21150820094155 +661,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 +662,0,Exp((Exp(Exp(Log(Exp((-0.1424072820147979 + x1))))) + ((((x1 + x1) + -3.274522466134244e-2) ^ (-3.102091152018338e-2 + Exp((-0.8794077136553502 ^ x1)))) ^ 0.7947987898098764))),"np.exp((np.exp(np.exp(np.log(np.exp((t[0] + x[:, 1]))))) + ((x[:, 1] + x[:, 1]) + t[1]) ** (t[2] + np.exp(t[3] ** x[:, 1])) ** t[4]))","$$\operatorname{exp}(\left(\operatorname{exp}(\operatorname{exp}(\operatorname{log}(\operatorname{exp}(\left(\theta_{0} + x_{1}\right))))) + {{\left(\left(x_{1} + x_{1}\right) + \theta_{1}\right)^{\left(\theta_{2} + \operatorname{exp}({\theta_{3}^{x_{1}}})\right)}}^{\theta_{4}}}\right))$$",-0.1424072820147979;-3.274522466134244e-2;-3.102091152018338e-2;-0.8794077136553502;0.7947987898098764,23,,,,Infinity,,,,,, +663,0,(Exp(Log(Exp(x0))) * (((0.9122317707118645 ^ x0) / (x1 ^ x1)) / ((-0.9735575803011653 ^ (x1 * x0)) + x0))),"(np.exp(np.log(np.exp(x[:, 0]))) * ((t[0] ** x[:, 0] / x[:, 1] ** x[:, 1]) / (t[1] ** (x[:, 1] * x[:, 0]) + x[:, 0])))","$$\left(\operatorname{exp}(\operatorname{log}(\operatorname{exp}(x_{0}))) \cdot \frac{\frac{{\theta_{0}^{x_{0}}}}{{x_{1}^{x_{1}}}}}{\left({\theta_{1}^{\left(x_{1} \cdot x_{0}\right)}} + x_{0}\right)}\right)$$",0.9122317707118645;-0.9735575803011653,20,,,,Infinity,,,,,, +664,0,(-7.344112557728449e-3 * (Log((((3.3524528224656257e-4 * x1) * 1.3925949579110257) ^ 0.8951299980271435)) + (((x1 / x1) + (-0.45816731387601345 * x0)) * ((-0.5575107587231128 * x1) + x1)))),"(t[0] * (np.log(((t[1] * x[:, 1]) * t[2]) ** t[3]) + (((x[:, 1] / x[:, 1]) + (t[4] * x[:, 0])) * ((t[5] * x[:, 1]) + x[:, 1]))))","$$\left(\theta_{0} \cdot \left(\operatorname{log}({\left(\left(\theta_{1} \cdot x_{1}\right) \cdot \theta_{2}\right)^{\theta_{3}}}) + \left(\left(\frac{x_{1}}{x_{1}} + \left(\theta_{4} \cdot x_{0}\right)\right) \cdot \left(\left(\theta_{5} \cdot x_{1}\right) + x_{1}\right)\right)\right)\right)$$",-7.344112557728449e-3;3.3524528224656257e-4;1.3925949579110257;0.8951299980271435;-0.45816731387601345;-0.5575107587231128,24,1.1159612786567292e-2,1.1159612786567292e-2,1.1159612786567292e-2,1.1159612786567292e-2,-2.9822021952599975e-2,-2.9822021952599975e-2,-2.9822021952599975e-2,62.04219026402981,62.04219026402981,62.04219026402981 +665,0,(((-0.41967156057239363 / (-0.30420312505006875 ^ (x0 + 0.3247872496061812))) * ((x0 * 0.5600577715067923) * 0.40443618336728626)) ^ Log((((x1 ^ (x0 ^ 0.574485966905428)) * (-0.6174625664275729 ^ (-0.3507299870366689 ^ (-0.6365578577856201 * x1)))) * 0.11676772923440004))),"((t[0] / t[1] ** (x[:, 0] + t[2])) * ((x[:, 0] * t[3]) * t[4])) ** np.log(((x[:, 1] ** x[:, 0] ** t[5] * t[6] ** t[7] ** (t[8] * x[:, 1])) * t[9]))","$${\left(\frac{\theta_{0}}{{\theta_{1}^{\left(x_{0} + \theta_{2}\right)}}} \cdot \left(\left(x_{0} \cdot \theta_{3}\right) \cdot \theta_{4}\right)\right)^{\operatorname{log}(\left(\left({x_{1}^{{x_{0}^{\theta_{5}}}}} \cdot {\theta_{6}^{{\theta_{7}^{\left(\theta_{8} \cdot x_{1}\right)}}}}\right) \cdot \theta_{9}\right))}}$$",-0.41967156057239363;-0.30420312505006875;0.3247872496061812;0.5600577715067923;0.40443618336728626;0.574485966905428;-0.6174625664275729;-0.3507299870366689;-0.6365578577856201;0.11676772923440004,30,,,,Infinity,,,,,, +666,0,(Exp((-0.15500141202420914 + ((Log((x1 + -0.9492238415317265)) * x0) * x1))) / x0),"(np.exp((t[0] + ((np.log((x[:, 1] + t[1])) * x[:, 0]) * x[:, 1]))) / x[:, 0])","$$\frac{\operatorname{exp}(\left(\theta_{0} + \left(\left(\operatorname{log}(\left(x_{1} + \theta_{1}\right)) \cdot x_{0}\right) \cdot x_{1}\right)\right))}{x_{0}}$$",-0.15500141202420914;-0.9492238415317265,13,,,,Infinity,,,,,, +667,0,((Log((0.6243475141501869 * (x1 + Log(x0)))) * (Exp((x0 * x1)) ^ Exp((x1 * (x0 * 0.760383359828056))))) ^ (0.16787228225563078 ^ ((0.8057538830794513 / (x0 / x0)) * -0.31329880669887855))),"(np.log((t[0] * (x[:, 1] + np.log(x[:, 0])))) * np.exp((x[:, 0] * x[:, 1])) ** np.exp((x[:, 1] * (x[:, 0] * t[1])))) ** t[2] ** ((t[3] / (x[:, 0] / x[:, 0])) * t[4])","$${\left(\operatorname{log}(\left(\theta_{0} \cdot \left(x_{1} + \operatorname{log}(x_{0})\right)\right)) \cdot {\operatorname{exp}(\left(x_{0} \cdot x_{1}\right))^{\operatorname{exp}(\left(x_{1} \cdot \left(x_{0} \cdot \theta_{1}\right)\right))}}\right)^{{\theta_{2}^{\left(\frac{\theta_{3}}{\frac{x_{0}}{x_{0}}} \cdot \theta_{4}\right)}}}}$$",0.6243475141501869;0.760383359828056;0.16787228225563078;0.8057538830794513;-0.31329880669887855,29,,,,Infinity,,,,,, +668,0,((-0.21445640383188902 / (x1 * 8.187410933875006e-2)) ^ ((x1 / (x1 * x0)) ^ (x1 ^ x0))),"(t[0] / (x[:, 1] * t[1])) ** (x[:, 1] / (x[:, 1] * x[:, 0])) ** x[:, 1] ** x[:, 0]","$${\frac{\theta_{0}}{\left(x_{1} \cdot \theta_{1}\right)}^{{\frac{x_{1}}{\left(x_{1} \cdot x_{0}\right)}^{{x_{1}^{x_{0}}}}}}}$$",-0.21445640383188902;8.187410933875006e-2,15,,,,Infinity,,,,,, +669,0,(((Log((x0 ^ -0.12436058856247978)) ^ (0.2753247347626677 + x1)) + Log(Log((-0.7414324249203526 * (x1 + 2.8752205117505536e-2))))) ^ Log(Exp(Log((Exp((x1 * 0.24885508289050107)) / (0.41924451048498135 * x0)))))),"(np.log(x[:, 0] ** t[0]) ** (t[1] + x[:, 1]) + np.log(np.log((t[2] * (x[:, 1] + t[3]))))) ** np.log(np.exp(np.log((np.exp((x[:, 1] * t[4])) / (t[5] * x[:, 0])))))","$${\left({\operatorname{log}({x_{0}^{\theta_{0}}})^{\left(\theta_{1} + x_{1}\right)}} + \operatorname{log}(\operatorname{log}(\left(\theta_{2} \cdot \left(x_{1} + \theta_{3}\right)\right)))\right)^{\operatorname{log}(\operatorname{exp}(\operatorname{log}(\frac{\operatorname{exp}(\left(x_{1} \cdot \theta_{4}\right))}{\left(\theta_{5} \cdot x_{0}\right)})))}}$$",-0.12436058856247978;0.2753247347626677;-0.7414324249203526;2.8752205117505536e-2;0.24885508289050107;0.41924451048498135,28,,,,Infinity,,,,,, +670,0,((-0.714161417676688 ^ ((0.6239225777712938 + (-0.7574954950995596 + (Exp(x1) * -0.10319096103558423))) ^ (Exp((x0 + (x0 / x1))) ^ 0.8463501789409651))) + (Log((x1 ^ (-0.5138231630880987 * x0))) * ((-0.5090307923655668 / x1) * -0.8065490342031263))),"(t[0] ** (t[1] + (t[2] + (np.exp(x[:, 1]) * t[3]))) ** np.exp((x[:, 0] + (x[:, 0] / x[:, 1]))) ** t[4] + (np.log(x[:, 1] ** (t[5] * x[:, 0])) * ((t[6] / x[:, 1]) * t[7])))","$$\left({\theta_{0}^{{\left(\theta_{1} + \left(\theta_{2} + \left(\operatorname{exp}(x_{1}) \cdot \theta_{3}\right)\right)\right)^{{\operatorname{exp}(\left(x_{0} + \frac{x_{0}}{x_{1}}\right))^{\theta_{4}}}}}}} + \left(\operatorname{log}({x_{1}^{\left(\theta_{5} \cdot x_{0}\right)}}) \cdot \left(\frac{\theta_{6}}{x_{1}} \cdot \theta_{7}\right)\right)\right)$$",-0.714161417676688;0.6239225777712938;-0.7574954950995596;-0.10319096103558423;0.8463501789409651;-0.5138231630880987;-0.5090307923655668;-0.8065490342031263,32,,,,Infinity,,,,,, +671,0,Log(((Exp((-1.1377308843536267 * x0)) / (x0 * (x0 * x1))) * ((x0 / Exp(x0)) + (x0 ^ (x1 + x1))))),"np.log(((np.exp((t[0] * x[:, 0])) / (x[:, 0] * (x[:, 0] * x[:, 1]))) * ((x[:, 0] / np.exp(x[:, 0])) + x[:, 0] ** (x[:, 1] + x[:, 1]))))","$$\operatorname{log}(\left(\frac{\operatorname{exp}(\left(\theta_{0} \cdot x_{0}\right))}{\left(x_{0} \cdot \left(x_{0} \cdot x_{1}\right)\right)} \cdot \left(\frac{x_{0}}{\operatorname{exp}(x_{0})} + {x_{0}^{\left(x_{1} + x_{1}\right)}}\right)\right))$$",-1.1377308843536267,22,1.011054697019853,1.011054697019853,1.011054697019853,1.011054697019853,-92.30130106691045,-92.30130106691045,-92.30130106691045,51.26981486408234,51.26981486408234,51.26981486408234 +672,0,(Exp(Log((x1 * 0.9475289521833459))) / Log((0.46281999344815017 ^ (-0.44000447844174573 + x1)))),"(np.exp(np.log((x[:, 1] * t[0]))) / np.log(t[1] ** (t[2] + x[:, 1])))","$$\frac{\operatorname{exp}(\operatorname{log}(\left(x_{1} \cdot \theta_{0}\right)))}{\operatorname{log}({\theta_{1}^{\left(\theta_{2} + x_{1}\right)}})}$$",0.9475289521833459;0.46281999344815017;-0.44000447844174573,12,938.1988443616256,938.1988443616256,938.1988443616256,938.1988443616256,-86577.07841299476,-86577.07841299476,-86577.07841299476,875.395033231724,875.395033231724,875.395033231724 +673,0,Exp(((Exp(x0) * (((0.10348951165148135 * (x0 * 0.6105127393569115)) ^ x1) ^ (x1 + 0.6301609021152196))) ^ ((x0 + -0.8257278077311061) ^ ((0.8247202045126599 + (x0 * -0.6013697731138825)) + -0.380609404373613)))),"np.exp((np.exp(x[:, 0]) * (t[0] * (x[:, 0] * t[1])) ** x[:, 1] ** (x[:, 1] + t[2])) ** (x[:, 0] + t[3]) ** ((t[4] + (x[:, 0] * t[5])) + t[6]))","$$\operatorname{exp}({\left(\operatorname{exp}(x_{0}) \cdot {{\left(\theta_{0} \cdot \left(x_{0} \cdot \theta_{1}\right)\right)^{x_{1}}}^{\left(x_{1} + \theta_{2}\right)}}\right)^{{\left(x_{0} + \theta_{3}\right)^{\left(\left(\theta_{4} + \left(x_{0} \cdot \theta_{5}\right)\right) + \theta_{6}\right)}}}})$$",0.10348951165148135;0.6105127393569115;0.6301609021152196;-0.8257278077311061;0.8247202045126599;-0.6013697731138825;-0.380609404373613,27,,,,Infinity,,,,,, +674,0,(Log((Log((-0.6841923185000931 ^ Exp((0.15889207249352577 ^ x0)))) / (Log(((x1 * x1) * -0.4297006418788877)) + (Exp((x1 * -0.7808846805317446)) ^ 0.5807998953837854)))) ^ (Log(Log((-0.4882930594213776 * (-0.7135953157910122 + x0)))) + (0.37452052281469994 / x1))),"np.log((np.log(t[0] ** np.exp(t[1] ** x[:, 0])) / (np.log(((x[:, 1] * x[:, 1]) * t[2])) + np.exp((x[:, 1] * t[3])) ** t[4]))) ** (np.log(np.log((t[5] * (t[6] + x[:, 0])))) + (t[7] / x[:, 1]))","$${\operatorname{log}(\frac{\operatorname{log}({\theta_{0}^{\operatorname{exp}({\theta_{1}^{x_{0}}})}})}{\left(\operatorname{log}(\left(\left(x_{1} \cdot x_{1}\right) \cdot \theta_{2}\right)) + {\operatorname{exp}(\left(x_{1} \cdot \theta_{3}\right))^{\theta_{4}}}\right)})^{\left(\operatorname{log}(\operatorname{log}(\left(\theta_{5} \cdot \left(\theta_{6} + x_{0}\right)\right))) + \frac{\theta_{7}}{x_{1}}\right)}}$$",-0.6841923185000931;0.15889207249352577;-0.4297006418788877;-0.7808846805317446;0.5807998953837854;-0.4882930594213776;-0.7135953157910122;0.37452052281469994,34,,,,Infinity,,,,,, +675,0,Exp((Exp((x0 / (((x0 + 2.30273746101594) / (x0 ^ 1.3588823211677699)) ^ 2.4502064548774234))) * ((x1 ^ x1) + (-3.914157878899511 * x0)))),"np.exp((np.exp((x[:, 0] / ((x[:, 0] + t[0]) / x[:, 0] ** t[1]) ** t[2])) * (x[:, 1] ** x[:, 1] + (t[3] * x[:, 0]))))","$$\operatorname{exp}(\left(\operatorname{exp}(\frac{x_{0}}{{\frac{\left(x_{0} + \theta_{0}\right)}{{x_{0}^{\theta_{1}}}}^{\theta_{2}}}}) \cdot \left({x_{1}^{x_{1}}} + \left(\theta_{3} \cdot x_{0}\right)\right)\right))$$",2.30273746101594;1.3588823211677699;2.4502064548774234;-3.914157878899511,21,5.280751648740032e-3,5.280751648740032e-3,5.280751648740032e-3,5.280751648740032e-3,0.5126861079910469,0.5126861079910469,0.5126861079910469,51.75672479323098,51.75672479323098,51.75672479323098 +676,0,((Exp(Exp(((0.7697989883176286 / x0) + (x0 * -0.34951055035963996)))) * Log(((x0 + x1) + -0.1932055935292455))) ^ (0.43364349691476145 / Log(((x0 + -0.8850582385649279) ^ (x1 * 0.5571176837471876))))),"(np.exp(np.exp(((t[0] / x[:, 0]) + (x[:, 0] * t[1])))) * np.log(((x[:, 0] + x[:, 1]) + t[2]))) ** (t[3] / np.log((x[:, 0] + t[4]) ** (x[:, 1] * t[5])))","$${\left(\operatorname{exp}(\operatorname{exp}(\left(\frac{\theta_{0}}{x_{0}} + \left(x_{0} \cdot \theta_{1}\right)\right))) \cdot \operatorname{log}(\left(\left(x_{0} + x_{1}\right) + \theta_{2}\right))\right)^{\frac{\theta_{3}}{\operatorname{log}({\left(x_{0} + \theta_{4}\right)^{\left(x_{1} \cdot \theta_{5}\right)}})}}}$$",0.7697989883176286;-0.34951055035963996;-0.1932055935292455;0.43364349691476145;-0.8850582385649279;0.5571176837471876,27,,,,Infinity,,,,,, +677,0,Exp((Log((3.6158358324964407e-4 * x0)) * (Exp(x1) + (0.5684459232835466 + (Exp(x1) * -1.1153463723715373))))),"np.exp((np.log((t[0] * x[:, 0])) * (np.exp(x[:, 1]) + (t[1] + (np.exp(x[:, 1]) * t[2])))))","$$\operatorname{exp}(\left(\operatorname{log}(\left(\theta_{0} \cdot x_{0}\right)) \cdot \left(\operatorname{exp}(x_{1}) + \left(\theta_{1} + \left(\operatorname{exp}(x_{1}) \cdot \theta_{2}\right)\right)\right)\right))$$",3.6158358324964407e-4;0.5684459232835466;-1.1153463723715373,15,1.1537638923516579e-2,1.1537638923516579e-2,1.1537638923516579e-2,1.1537638923516579e-2,-6.470671268063666e-2,-6.470671268063666e-2,-6.470671268063666e-2,35.26181091696379,35.26181091696379,35.26181091696379 +678,0,(((Exp((x1 / ((x0 * -6.394297639132962) + -17.15094462751474))) / x0) * -0.21818104149599682) * -0.34406821254665143),"(((np.exp((x[:, 1] / ((x[:, 0] * t[0]) + t[1]))) / x[:, 0]) * t[2]) * t[3])","$$\left(\left(\frac{\operatorname{exp}(\frac{x_{1}}{\left(\left(x_{0} \cdot \theta_{0}\right) + \theta_{1}\right)})}{x_{0}} \cdot \theta_{2}\right) \cdot \theta_{3}\right)$$",-6.394297639132962;-17.15094462751474;-0.21818104149599682;-0.34406821254665143,14,7.781515682950687e-3,7.781515682950687e-3,7.781515682950687e-3,7.781515682950687e-3,0.2819127000429621,0.2819127000429621,0.2819127000429621,35.333445192359605,35.333445192359605,35.333445192359605 +679,0,Log((Log((x1 * -0.6582443572928078)) * Exp((0.752380821248072 * x1)))),"np.log((np.log((x[:, 1] * t[0])) * np.exp((t[1] * x[:, 1]))))","$$\operatorname{log}(\left(\operatorname{log}(\left(x_{1} \cdot \theta_{0}\right)) \cdot \operatorname{exp}(\left(\theta_{1} \cdot x_{1}\right))\right))$$",-0.6582443572928078;0.752380821248072,10,,,,Infinity,,,,,, +680,0,((((Exp(Exp(x1)) * -0.9675506524766464) ^ (-0.5113750439797213 + (0.18315630603687183 / x0))) ^ ((x0 * x1) ^ (Log(((x1 ^ 0.772046019185768) / x1)) / (x1 * (x0 ^ (x1 * 0.4598288815528043)))))) + (x1 * x1)),"((np.exp(np.exp(x[:, 1])) * t[0]) ** (t[1] + (t[2] / x[:, 0])) ** (x[:, 0] * x[:, 1]) ** (np.log((x[:, 1] ** t[3] / x[:, 1])) / (x[:, 1] * x[:, 0] ** (x[:, 1] * t[4]))) + (x[:, 1] * x[:, 1]))","$$\left({{\left(\operatorname{exp}(\operatorname{exp}(x_{1})) \cdot \theta_{0}\right)^{\left(\theta_{1} + \frac{\theta_{2}}{x_{0}}\right)}}^{{\left(x_{0} \cdot x_{1}\right)^{\frac{\operatorname{log}(\frac{{x_{1}^{\theta_{3}}}}{x_{1}})}{\left(x_{1} \cdot {x_{0}^{\left(x_{1} \cdot \theta_{4}\right)}}\right)}}}}} + \left(x_{1} \cdot x_{1}\right)\right)$$",-0.9675506524766464;-0.5113750439797213;0.18315630603687183;0.772046019185768;0.4598288815528043,34,,,,Infinity,,,,,, +681,0,Log((-0.8130647961541064 / x0)),"np.log((t[0] / x[:, 0]))","$$\operatorname{log}(\frac{\theta_{0}}{x_{0}})$$",-0.8130647961541064,4,,,,Infinity,,,,,, +682,0,(Log((Exp((0.891748656711249 ^ (x1 * x1))) ^ 0.32004903402641405)) ^ (((x0 * -0.275686490123094) + 0.2968950015197225) / (0.419263463533203 / Log((x1 + -0.9069607855754125))))),"np.log(np.exp(t[0] ** (x[:, 1] * x[:, 1])) ** t[1]) ** (((x[:, 0] * t[2]) + t[3]) / (t[4] / np.log((x[:, 1] + t[5]))))","$${\operatorname{log}({\operatorname{exp}({\theta_{0}^{\left(x_{1} \cdot x_{1}\right)}})^{\theta_{1}}})^{\frac{\left(\left(x_{0} \cdot \theta_{2}\right) + \theta_{3}\right)}{\frac{\theta_{4}}{\operatorname{log}(\left(x_{1} + \theta_{5}\right))}}}}$$",0.891748656711249;0.32004903402641405;-0.275686490123094;0.2968950015197225;0.419263463533203;-0.9069607855754125,22,,,,Infinity,,,,,, +683,0,((((x0 / x1) + 27.856428293186994) * 6.49892979394686e-3) * x1),"((((x[:, 0] / x[:, 1]) + t[0]) * t[1]) * x[:, 1])","$$\left(\left(\left(\frac{x_{0}}{x_{1}} + \theta_{0}\right) \cdot \theta_{1}\right) \cdot x_{1}\right)$$",27.856428293186994;6.49892979394686e-3,9,9.746941064783958e-3,9.746941064783958e-3,9.746941064783958e-3,9.746941064783958e-3,0.10054096435399495,0.10054096435399495,0.10054096435399495,20.176457340746083,20.176457340746083,20.176457340746083 +684,0,Log(Log(Log((x1 ^ x1)))),"np.log(np.log(np.log(x[:, 1] ** x[:, 1])))","$$\operatorname{log}(\operatorname{log}(\operatorname{log}({x_{1}^{x_{1}}})))$$",,6,,,,Infinity,,,,,, +685,0,((((-0.13269995146852132 + ((-0.7662072802585691 * x0) * x1)) * Log((x0 * (x0 + (0.3321566771546042 * x1))))) + ((-0.9607479090579966 + x0) + ((-0.6644919623760126 ^ (0.8673704147733954 + x0)) * ((0.35417251235225844 / (x1 + 2.8254444548321822e-2)) + 0.17625622699176702)))) + (0.32674127430163924 ^ x1)),"((((t[0] + ((t[1] * x[:, 0]) * x[:, 1])) * np.log((x[:, 0] * (x[:, 0] + (t[2] * x[:, 1]))))) + ((t[3] + x[:, 0]) + (t[4] ** (t[5] + x[:, 0]) * ((t[6] / (x[:, 1] + t[7])) + t[8])))) + t[9] ** x[:, 1])","$$\left(\left(\left(\left(\theta_{0} + \left(\left(\theta_{1} \cdot x_{0}\right) \cdot x_{1}\right)\right) \cdot \operatorname{log}(\left(x_{0} \cdot \left(x_{0} + \left(\theta_{2} \cdot x_{1}\right)\right)\right))\right) + \left(\left(\theta_{3} + x_{0}\right) + \left({\theta_{4}^{\left(\theta_{5} + x_{0}\right)}} \cdot \left(\frac{\theta_{6}}{\left(x_{1} + \theta_{7}\right)} + \theta_{8}\right)\right)\right)\right) + {\theta_{9}^{x_{1}}}\right)$$",-0.13269995146852132;-0.7662072802585691;0.3321566771546042;-0.9607479090579966;-0.6644919623760126;0.8673704147733954;0.35417251235225844;2.8254444548321822e-2;0.17625622699176702;0.32674127430163924,38,,,,Infinity,,,,,, +686,0,Exp((x1 / x1)),"np.exp((x[:, 1] / x[:, 1]))","$$\operatorname{exp}(\frac{x_{1}}{x_{1}})$$",,4,7.07128229654399,7.07128229654399,7.07128229654399,7.07128229654399,-651.5461386249905,-651.5461386249905,-651.5461386249905,10.75860322156203,10.75860322156203,10.75860322156203 +687,0,(Log((-3.8979472185059205e-2 + x0)) * x0),"(np.log((t[0] + x[:, 0])) * x[:, 0])","$$\left(\operatorname{log}(\left(\theta_{0} + x_{0}\right)) \cdot x_{0}\right)$$",-3.8979472185059205e-2,6,0.14862386045147535,0.14862386045147535,0.14862386045147535,0.14862386045147535,-12.715182364102404,-12.715182364102404,-12.715182364102404,11.81131388437857,11.81131388437857,11.81131388437857 +688,0,Log(Log(((x0 * -0.820126446795307) / (Log((-0.3988814601027383 + x1)) / x1)))),"np.log(np.log(((x[:, 0] * t[0]) / (np.log((t[1] + x[:, 1])) / x[:, 1]))))","$$\operatorname{log}(\operatorname{log}(\frac{\left(x_{0} \cdot \theta_{0}\right)}{\frac{\operatorname{log}(\left(\theta_{1} + x_{1}\right))}{x_{1}}}))$$",-0.820126446795307;-0.3988814601027383,12,,,,Infinity,,,,,, +689,0,Log(Exp((-1.0980954797385796 / (-31.156981373848296 / x0)))),"np.log(np.exp((t[0] / (t[1] / x[:, 0]))))","$$\operatorname{log}(\operatorname{exp}(\frac{\theta_{0}}{\frac{\theta_{1}}{x_{0}}}))$$",-1.0980954797385796;-31.156981373848296,7,1.3066544019949083e-2,1.3066544019949083e-2,1.3066544019949083e-2,1.3066544019949083e-2,-0.20579584972282583,-0.20579584972282583,-0.20579584972282583,15.319675147391937,15.319675147391937,15.319675147391937 +690,0,((0.6686823186310551 + (Log(Exp(Log((-0.9674641479581555 + x1)))) ^ x0)) + x1),"((t[0] + np.log(np.exp(np.log((t[1] + x[:, 1])))) ** x[:, 0]) + x[:, 1])","$$\left(\left(\theta_{0} + {\operatorname{log}(\operatorname{exp}(\operatorname{log}(\left(\theta_{1} + x_{1}\right))))^{x_{0}}}\right) + x_{1}\right)$$",0.6686823186310551;-0.9674641479581555,12,,,,Infinity,,,,,, +691,0,((Exp((((-0.10530428467073605 ^ Exp(x0)) + -0.9924372771457814) + ((x1 ^ x0) + x0))) ^ Log(Log(x0))) / (((9.514482999145413e-2 / (Exp((x1 ^ -0.23598964934611577)) * 7.472404238432695e-2)) ^ Exp((x1 ^ x1))) * (((0.8564505745534687 + x0) + 0.5462608849459936) + 0.6968453025585906))),"(np.exp(((t[0] ** np.exp(x[:, 0]) + t[1]) + (x[:, 1] ** x[:, 0] + x[:, 0]))) ** np.log(np.log(x[:, 0])) / ((t[2] / (np.exp(x[:, 1] ** t[3]) * t[4])) ** np.exp(x[:, 1] ** x[:, 1]) * (((t[5] + x[:, 0]) + t[6]) + t[7])))","$$\frac{{\operatorname{exp}(\left(\left({\theta_{0}^{\operatorname{exp}(x_{0})}} + \theta_{1}\right) + \left({x_{1}^{x_{0}}} + x_{0}\right)\right))^{\operatorname{log}(\operatorname{log}(x_{0}))}}}{\left({\frac{\theta_{2}}{\left(\operatorname{exp}({x_{1}^{\theta_{3}}}) \cdot \theta_{4}\right)}^{\operatorname{exp}({x_{1}^{x_{1}}})}} \cdot \left(\left(\left(\theta_{5} + x_{0}\right) + \theta_{6}\right) + \theta_{7}\right)\right)}$$",-0.10530428467073605;-0.9924372771457814;9.514482999145413e-2;-0.23598964934611577;7.472404238432695e-2;0.8564505745534687;0.5462608849459936;0.6968453025585906,39,,,,Infinity,,,,,, +692,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 +693,0,(Log((((x1 + x1) * -0.8851690685078405) / Log((x0 + -0.4809415230393346)))) / ((0.4066797308989676 / (x0 + 0.6228995062759071)) + ((x1 ^ x0) * x0))),"(np.log((((x[:, 1] + x[:, 1]) * t[0]) / np.log((x[:, 0] + t[1])))) / ((t[2] / (x[:, 0] + t[3])) + (x[:, 1] ** x[:, 0] * x[:, 0])))","$$\frac{\operatorname{log}(\frac{\left(\left(x_{1} + x_{1}\right) \cdot \theta_{0}\right)}{\operatorname{log}(\left(x_{0} + \theta_{1}\right))})}{\left(\frac{\theta_{2}}{\left(x_{0} + \theta_{3}\right)} + \left({x_{1}^{x_{0}}} \cdot x_{0}\right)\right)}$$",-0.8851690685078405;-0.4809415230393346;0.4066797308989676;0.6228995062759071,23,,,,Infinity,,,,,, +694,0,Exp((Exp(Exp((6.739285233109987e-3 ^ x0))) * -4.222756965525027)),"np.exp((np.exp(np.exp(t[0] ** x[:, 0])) * t[1]))","$$\operatorname{exp}(\left(\operatorname{exp}(\operatorname{exp}({\theta_{0}^{x_{0}}})) \cdot \theta_{1}\right))$$",6.739285233109987e-3;-4.222756965525027,8,1.457274559678245e-2,1.457274559678245e-2,1.457274559678245e-2,1.457274559678245e-2,-0.3447898796223037,-0.3447898796223037,-0.3447898796223037,16.930468641245188,16.930468641245188,16.930468641245188 +695,0,Exp((Exp((x1 ^ 132.64911599658652)) ^ ((Log(x1) * -168.76337969008733) * 146.90504815603202))),"np.exp(np.exp(x[:, 1] ** t[0]) ** ((np.log(x[:, 1]) * t[1]) * t[2]))","$$\operatorname{exp}({\operatorname{exp}({x_{1}^{\theta_{0}}})^{\left(\left(\operatorname{log}(x_{1}) \cdot \theta_{1}\right) \cdot \theta_{2}\right)}})$$",132.64911599658652;-168.76337969008733;146.90504815603202,12,7.07128229654399,7.07128229654399,7.07128229654399,7.07128229654399,-651.5461386249905,-651.5461386249905,-651.5461386249905,33.92804250372917,33.92804250372917,33.92804250372917 +696,0,((Exp((x1 + Log(x1))) * -0.5487966073287462) + (Log((-0.4079765082978508 ^ Exp(x0))) + (-0.5551613310212591 ^ (x1 ^ 0.21840154724703653)))),"((np.exp((x[:, 1] + np.log(x[:, 1]))) * t[0]) + (np.log(t[1] ** np.exp(x[:, 0])) + t[2] ** x[:, 1] ** t[3]))","$$\left(\left(\operatorname{exp}(\left(x_{1} + \operatorname{log}(x_{1})\right)) \cdot \theta_{0}\right) + \left(\operatorname{log}({\theta_{1}^{\operatorname{exp}(x_{0})}}) + {\theta_{2}^{{x_{1}^{\theta_{3}}}}}\right)\right)$$",-0.5487966073287462;-0.4079765082978508;-0.5551613310212591;0.21840154724703653,19,,,,Infinity,,,,,, +697,0,(((Exp((-1.834231614764557 + x1)) ^ (x0 ^ x1)) * Exp(((x1 + -0.5202275603521304) + -2.079422555051663))) + ((-0.5074697488008427 + Log(Exp(x0))) ^ 0.1608306487078528)),"((np.exp((t[0] + x[:, 1])) ** x[:, 0] ** x[:, 1] * np.exp(((x[:, 1] + t[1]) + t[2]))) + (t[3] + np.log(np.exp(x[:, 0]))) ** t[4])","$$\left(\left({\operatorname{exp}(\left(\theta_{0} + x_{1}\right))^{{x_{0}^{x_{1}}}}} \cdot \operatorname{exp}(\left(\left(x_{1} + \theta_{1}\right) + \theta_{2}\right))\right) + {\left(\theta_{3} + \operatorname{log}(\operatorname{exp}(x_{0}))\right)^{\theta_{4}}}\right)$$",-1.834231614764557;-0.5202275603521304;-2.079422555051663;-0.5074697488008427;0.1608306487078528,23,0.740600890898741,0.740600890898741,0.740600890898741,0.740600890898741,-67.34350989698109,-67.34350989698109,-67.34350989698109,58.59832093728329,58.59832093728329,58.59832093728329 +698,0,((Log(((-0.8313301522011091 * (x0 * x0)) + Log((-0.8882091107262127 + x0)))) + ((x0 * -0.3442158736971721) + 0.40833284476516973)) + (0.23444036846030225 / Log((Log((x1 ^ x0)) / (0.46240870656387956 + x1))))),"((np.log(((t[0] * (x[:, 0] * x[:, 0])) + np.log((t[1] + x[:, 0])))) + ((x[:, 0] * t[2]) + t[3])) + (t[4] / np.log((np.log(x[:, 1] ** x[:, 0]) / (t[5] + x[:, 1])))))","$$\left(\left(\operatorname{log}(\left(\left(\theta_{0} \cdot \left(x_{0} \cdot x_{0}\right)\right) + \operatorname{log}(\left(\theta_{1} + x_{0}\right))\right)) + \left(\left(x_{0} \cdot \theta_{2}\right) + \theta_{3}\right)\right) + \frac{\theta_{4}}{\operatorname{log}(\frac{\operatorname{log}({x_{1}^{x_{0}}})}{\left(\theta_{5} + x_{1}\right)})}\right)$$",-0.8313301522011091;-0.8882091107262127;-0.3442158736971721;0.40833284476516973;0.23444036846030225;0.46240870656387956,29,,,,Infinity,,,,,, +699,0,(((Log((x1 * x0)) + (-0.3417401837783785 * (0.17710348715417526 / x1))) + Exp((Log((0.8479795459458406 / x1)) * 0.4616364865885638))) * ((6.729733476044505e-4 ^ (x1 + 0.9467478820127281)) + ((x0 * 1.4582705803229754e-3) / (x0 / Exp(x0))))),"(((np.log((x[:, 1] * x[:, 0])) + (t[0] * (t[1] / x[:, 1]))) + np.exp((np.log((t[2] / x[:, 1])) * t[3]))) * (t[4] ** (x[:, 1] + t[5]) + ((x[:, 0] * t[6]) / (x[:, 0] / np.exp(x[:, 0])))))","$$\left(\left(\left(\operatorname{log}(\left(x_{1} \cdot x_{0}\right)) + \left(\theta_{0} \cdot \frac{\theta_{1}}{x_{1}}\right)\right) + \operatorname{exp}(\left(\operatorname{log}(\frac{\theta_{2}}{x_{1}}) \cdot \theta_{3}\right))\right) \cdot \left({\theta_{4}^{\left(x_{1} + \theta_{5}\right)}} + \frac{\left(x_{0} \cdot \theta_{6}\right)}{\frac{x_{0}}{\operatorname{exp}(x_{0})}}\right)\right)$$",-0.3417401837783785;0.17710348715417526;0.8479795459458406;0.4616364865885638;6.729733476044505e-4;0.9467478820127281;1.4582705803229754e-3,33,1.45663030156521e-2,1.45663030156521e-2,1.45663030156521e-2,1.45663030156521e-2,-0.3441953507570876,-0.3441953507570876,-0.3441953507570876,86.66799527238281,86.66799527238281,86.66799527238281 +700,0,(-9.825633164592235e-2 + (-1.0539837065162954 + (x0 * x0))),"(t[0] + (t[1] + (x[:, 0] * x[:, 0])))","$$\left(\theta_{0} + \left(\theta_{1} + \left(x_{0} \cdot x_{0}\right)\right)\right)$$",-9.825633164592235e-2;-1.0539837065162954,7,0.5028506858422126,0.5028506858422126,0.5028506858422126,0.5028506858422126,-45.40364499542544,-45.40364499542544,-45.40364499542544,14.198476015832506,14.198476015832506,14.198476015832506 +701,0,Log(((-0.32276632866471466 * (Log(x1) + Log(x1))) / Exp(Exp(((x0 + Exp(x0)) + (x0 * 0.8860627496298357)))))),"np.log(((t[0] * (np.log(x[:, 1]) + np.log(x[:, 1]))) / np.exp(np.exp(((x[:, 0] + np.exp(x[:, 0])) + (x[:, 0] * t[1]))))))","$$\operatorname{log}(\frac{\left(\theta_{0} \cdot \left(\operatorname{log}(x_{1}) + \operatorname{log}(x_{1})\right)\right)}{\operatorname{exp}(\operatorname{exp}(\left(\left(x_{0} + \operatorname{exp}(x_{0})\right) + \left(x_{0} \cdot \theta_{1}\right)\right)))})$$",-0.32276632866471466;0.8860627496298357,19,Infinity,Infinity,Infinity,Infinity,-Infinity,-Infinity,-Infinity,Infinity,Infinity,Infinity +702,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 +703,0,Log((((x0 * ((x1 ^ x0) + ((x0 * x1) * -0.743376286317167))) + (x1 + 1.1119104414687262)) + -0.3525730542041959)),"np.log((((x[:, 0] * (x[:, 1] ** x[:, 0] + ((x[:, 0] * x[:, 1]) * t[0]))) + (x[:, 1] + t[1])) + t[2]))","$$\operatorname{log}(\left(\left(\left(x_{0} \cdot \left({x_{1}^{x_{0}}} + \left(\left(x_{0} \cdot x_{1}\right) \cdot \theta_{0}\right)\right)\right) + \left(x_{1} + \theta_{1}\right)\right) + \theta_{2}\right))$$",-0.743376286317167;1.1119104414687262;-0.3525730542041959,18,1.7472733494066306e-2,1.7472733494066306e-2,1.7472733494066306e-2,1.7472733494066306e-2,-0.6124041290713271,-0.6124041290713271,-0.6124041290713271,41.10488294924322,41.10488294924322,41.10488294924322 +704,0,Exp((((-0.24344879651475998 + (x0 * -1.1431907401768029)) / Exp((x0 * -0.8730133767623718))) * 1.0278519596019247)),"np.exp((((t[0] + (x[:, 0] * t[1])) / np.exp((x[:, 0] * t[2]))) * t[3]))","$$\operatorname{exp}(\left(\frac{\left(\theta_{0} + \left(x_{0} \cdot \theta_{1}\right)\right)}{\operatorname{exp}(\left(x_{0} \cdot \theta_{2}\right))} \cdot \theta_{3}\right))$$",-0.24344879651475998;-1.1431907401768029;-0.8730133767623718;1.0278519596019247,13,4.832286483349803e-3,4.832286483349803e-3,4.832286483349803e-3,4.832286483349803e-3,0.554070994028794,0.554070994028794,0.554070994028794,31.38092189927029,31.38092189927029,31.38092189927029 +705,0,(((0.3092167257485733 * Exp(x1)) ^ Log(Exp(Log(x0)))) * Log(((x0 ^ -0.14244877101480397) * 1.0448968410362762))),"((t[0] * np.exp(x[:, 1])) ** np.log(np.exp(np.log(x[:, 0]))) * np.log((x[:, 0] ** t[1] * t[2])))","$$\left({\left(\theta_{0} \cdot \operatorname{exp}(x_{1})\right)^{\operatorname{log}(\operatorname{exp}(\operatorname{log}(x_{0})))}} \cdot \operatorname{log}(\left({x_{0}^{\theta_{1}}} \cdot \theta_{2}\right))\right)$$",0.3092167257485733;-0.14244877101480397;1.0448968410362762,16,5.841742062460563e-3,5.841742062460563e-3,5.841742062460563e-3,5.841742062460563e-3,0.46091726141878064,0.46091726141878064,0.46091726141878064,37.20259475884415,37.20259475884415,37.20259475884415 +706,0,(Log((((-1.4428916054925571 * Exp((0.23335025168311882 / x0))) * ((-1.4769720402356024 * x0) / x0)) ^ (x0 * -0.29906986738866165))) + 0.3700642184760444),"(np.log(((t[0] * np.exp((t[1] / x[:, 0]))) * ((t[2] * x[:, 0]) / x[:, 0])) ** (x[:, 0] * t[3])) + t[4])","$$\left(\operatorname{log}({\left(\left(\theta_{0} \cdot \operatorname{exp}(\frac{\theta_{1}}{x_{0}})\right) \cdot \frac{\left(\theta_{2} \cdot x_{0}\right)}{x_{0}}\right)^{\left(x_{0} \cdot \theta_{3}\right)}}) + \theta_{4}\right)$$",-1.4428916054925571;0.23335025168311882;-1.4769720402356024;-0.29906986738866165;0.3700642184760444,19,5.893883581663169e-3,5.893883581663169e-3,5.893883581663169e-3,5.893883581663169e-3,0.4561055815012174,0.4561055815012174,0.4561055815012174,49.61931846397858,49.61931846397858,49.61931846397858 +707,0,(((((Log(x1) + (x0 * -0.5412286421308881)) * 0.6551485979935591) * (-0.20028089768829482 * (-0.7453615847584714 * (x0 ^ (x0 ^ x0))))) ^ Exp(((((0.48913040966565247 * x0) + 0.6905895284671804) + x1) * -0.6283656303317549))) * 0.7221905517293703),"((((np.log(x[:, 1]) + (x[:, 0] * t[0])) * t[1]) * (t[2] * (t[3] * x[:, 0] ** x[:, 0] ** x[:, 0]))) ** np.exp(((((t[4] * x[:, 0]) + t[5]) + x[:, 1]) * t[6])) * t[7])","$$\left({\left(\left(\left(\operatorname{log}(x_{1}) + \left(x_{0} \cdot \theta_{0}\right)\right) \cdot \theta_{1}\right) \cdot \left(\theta_{2} \cdot \left(\theta_{3} \cdot {x_{0}^{{x_{0}^{x_{0}}}}}\right)\right)\right)^{\operatorname{exp}(\left(\left(\left(\left(\theta_{4} \cdot x_{0}\right) + \theta_{5}\right) + x_{1}\right) \cdot \theta_{6}\right))}} \cdot \theta_{7}\right)$$",-0.5412286421308881;0.6551485979935591;-0.20028089768829482;-0.7453615847584714;0.48913040966565247;0.6905895284671804;-0.6283656303317549;0.7221905517293703,31,,,,Infinity,,,,,, +708,0,((x0 + -0.7733758849135515) / x0),"((x[:, 0] + t[0]) / x[:, 0])","$$\frac{\left(x_{0} + \theta_{0}\right)}{x_{0}}$$",-0.7733758849135515,5,0.15106925440074861,0.15106925440074861,0.15106925440074861,0.15106925440074861,-12.940846156339221,-12.940846156339221,-12.940846156339221,9.088359069927767,9.088359069927767,9.088359069927767 +709,0,((x1 + -5.111907897627667) * -1.2001432859365972e-2),"((x[:, 1] + t[0]) * t[1])","$$\left(\left(x_{1} + \theta_{0}\right) \cdot \theta_{1}\right)$$",-5.111907897627667;-1.2001432859365972e-2,5,1.0988313270031663e-2,1.0988313270031663e-2,1.0988313270031663e-2,1.0988313270031663e-2,-1.4014303723293153e-2,-1.4014303723293153e-2,-1.4014303723293153e-2,10.983211158277763,10.983211158277763,10.983211158277763 +710,0,(Log(((x1 * -0.49986182881004326) ^ Log(x0))) + (0.6618515197878372 + (x1 / Exp(x0)))),"(np.log((x[:, 1] * t[0]) ** np.log(x[:, 0])) + (t[1] + (x[:, 1] / np.exp(x[:, 0]))))","$$\left(\operatorname{log}({\left(x_{1} \cdot \theta_{0}\right)^{\operatorname{log}(x_{0})}}) + \left(\theta_{1} + \frac{x_{1}}{\operatorname{exp}(x_{0})}\right)\right)$$",-0.49986182881004326;0.6618515197878372,14,,,,Infinity,,,,,, +711,0,((Log((-0.9249741244509311 * (x1 + (x0 * 0.2608994695333696)))) + ((((x1 / (x0 + 0.29459943287156765)) + -0.564179243448913) ^ x0) ^ ((x0 * -0.3537342783037082) ^ 0.40734861325546445))) * (0.7103251851338381 / x1)),"((np.log((t[0] * (x[:, 1] + (x[:, 0] * t[1])))) + ((x[:, 1] / (x[:, 0] + t[2])) + t[3]) ** x[:, 0] ** (x[:, 0] * t[4]) ** t[5]) * (t[6] / x[:, 1]))","$$\left(\left(\operatorname{log}(\left(\theta_{0} \cdot \left(x_{1} + \left(x_{0} \cdot \theta_{1}\right)\right)\right)) + {{\left(\frac{x_{1}}{\left(x_{0} + \theta_{2}\right)} + \theta_{3}\right)^{x_{0}}}^{{\left(x_{0} \cdot \theta_{4}\right)^{\theta_{5}}}}}\right) \cdot \frac{\theta_{6}}{x_{1}}\right)$$",-0.9249741244509311;0.2608994695333696;0.29459943287156765;-0.564179243448913;-0.3537342783037082;0.40734861325546445;0.7103251851338381,28,,,,Infinity,,,,,, +712,0,((((x1 ^ x1) ^ 2.2288380238026706e-2) ^ Log((-0.11701976706162776 * (-0.14144642361776993 + x1)))) / (((x0 * 3.443354934057996e-2) ^ (x0 * (x0 ^ -0.9041613195951719))) ^ -6.552848205792938e-2)),"(x[:, 1] ** x[:, 1] ** t[0] ** np.log((t[1] * (t[2] + x[:, 1]))) / (x[:, 0] * t[3]) ** (x[:, 0] * x[:, 0] ** t[4]) ** t[5])","$$\frac{{{{x_{1}^{x_{1}}}^{\theta_{0}}}^{\operatorname{log}(\left(\theta_{1} \cdot \left(\theta_{2} + x_{1}\right)\right))}}}{{{\left(x_{0} \cdot \theta_{3}\right)^{\left(x_{0} \cdot {x_{0}^{\theta_{4}}}\right)}}^{\theta_{5}}}}$$",2.2288380238026706e-2;-0.11701976706162776;-0.14144642361776993;3.443354934057996e-2;-0.9041613195951719;-6.552848205792938e-2,24,,,,Infinity,,,,,, +713,0,(Exp(Log(Log((0.7184088287327586 * Log(x1))))) * ((((-6.314829899150354e-2 ^ x1) * (x0 * x0)) * ((-0.3808950587994727 * x1) + -0.7302981627166765)) + Log(Log((((0.19145495884853392 / x1) * x0) * 0.8507276015388479))))),"(np.exp(np.log(np.log((t[0] * np.log(x[:, 1]))))) * (((t[1] ** x[:, 1] * (x[:, 0] * x[:, 0])) * ((t[2] * x[:, 1]) + t[3])) + np.log(np.log((((t[4] / x[:, 1]) * x[:, 0]) * t[5])))))","$$\left(\operatorname{exp}(\operatorname{log}(\operatorname{log}(\left(\theta_{0} \cdot \operatorname{log}(x_{1})\right)))) \cdot \left(\left(\left({\theta_{1}^{x_{1}}} \cdot \left(x_{0} \cdot x_{0}\right)\right) \cdot \left(\left(\theta_{2} \cdot x_{1}\right) + \theta_{3}\right)\right) + \operatorname{log}(\operatorname{log}(\left(\left(\frac{\theta_{4}}{x_{1}} \cdot x_{0}\right) \cdot \theta_{5}\right)))\right)\right)$$",0.7184088287327586;-6.314829899150354e-2;-0.3808950587994727;-0.7302981627166765;0.19145495884853392;0.8507276015388479,31,,,,Infinity,,,,,, +714,0,((x1 * 0.18906603367893082) * Exp(Log(Log(x0)))),"((x[:, 1] * t[0]) * np.exp(np.log(np.log(x[:, 0]))))","$$\left(\left(x_{1} \cdot \theta_{0}\right) \cdot \operatorname{exp}(\operatorname{log}(\operatorname{log}(x_{0})))\right)$$",0.18906603367893082,8,,,,Infinity,,,,,, +715,0,(x1 * 0.20059950763971873),"(x[:, 1] * t[0])","$$\left(x_{1} \cdot \theta_{0}\right)$$",0.20059950763971873,3,9.365750690525969e-3,9.365750690525969e-3,9.365750690525969e-3,9.365750690525969e-3,0.1357176545739056,0.1357176545739056,0.1357176545739056,5.325190976993443,5.325190976993443,5.325190976993443 +716,0,Log((Exp(((-0.6821441284623 + (x1 * 0.3282741986059238)) / (-0.30737725920069214 + (-0.6149075820147156 + x1)))) ^ (-0.6086976470109697 + Log((Log(x1) * Log((x0 * 0.6823504366624273))))))),"np.log(np.exp(((t[0] + (x[:, 1] * t[1])) / (t[2] + (t[3] + x[:, 1])))) ** (t[4] + np.log((np.log(x[:, 1]) * np.log((x[:, 0] * t[5]))))))","$$\operatorname{log}({\operatorname{exp}(\frac{\left(\theta_{0} + \left(x_{1} \cdot \theta_{1}\right)\right)}{\left(\theta_{2} + \left(\theta_{3} + x_{1}\right)\right)})^{\left(\theta_{4} + \operatorname{log}(\left(\operatorname{log}(x_{1}) \cdot \operatorname{log}(\left(x_{0} \cdot \theta_{5}\right))\right))\right)}})$$",-0.6821441284623;0.3282741986059238;-0.30737725920069214;-0.6149075820147156;-0.6086976470109697;0.6823504366624273,24,,,,Infinity,,,,,, +717,0,(((((-0.3374964516695169 / (-0.836066824745924 / (x0 + x1))) ^ -0.1400394633879669) ^ Log((x1 ^ 0.7586810067593819))) / ((x1 * x0) * 2.596819750768853e-2)) * 0.9477084673662062),"(((t[0] / (t[1] / (x[:, 0] + x[:, 1]))) ** t[2] ** np.log(x[:, 1] ** t[3]) / ((x[:, 1] * x[:, 0]) * t[4])) * t[5])","$$\left(\frac{{{\frac{\theta_{0}}{\frac{\theta_{1}}{\left(x_{0} + x_{1}\right)}}^{\theta_{2}}}^{\operatorname{log}({x_{1}^{\theta_{3}}})}}}{\left(\left(x_{1} \cdot x_{0}\right) \cdot \theta_{4}\right)} \cdot \theta_{5}\right)$$",-0.3374964516695169;-0.836066824745924;-0.1400394633879669;0.7586810067593819;2.596819750768853e-2;0.9477084673662062,22,23535.552385940748,23535.552385940748,23535.552385940748,23535.552385940748,-2171887.094095449,-2171887.094095449,-2171887.094095449,21239.870410875836,21239.870410875836,21239.870410875836 +718,0,((((x1 * -0.4405134885950248) * -0.5413808220509542) / ((0.5471939325015198 + (x0 / x0)) ^ (-0.5497312696220659 * x0))) / (((x1 ^ Log(x1)) / (x0 * -0.9612068376885059)) / Log(Log(((0.746719888806498 + x1) / (x0 / Log(x0))))))),"((((x[:, 1] * t[0]) * t[1]) / (t[2] + (x[:, 0] / x[:, 0])) ** (t[3] * x[:, 0])) / ((x[:, 1] ** np.log(x[:, 1]) / (x[:, 0] * t[4])) / np.log(np.log(((t[5] + x[:, 1]) / (x[:, 0] / np.log(x[:, 0])))))))","$$\frac{\frac{\left(\left(x_{1} \cdot \theta_{0}\right) \cdot \theta_{1}\right)}{{\left(\theta_{2} + \frac{x_{0}}{x_{0}}\right)^{\left(\theta_{3} \cdot x_{0}\right)}}}}{\frac{\frac{{x_{1}^{\operatorname{log}(x_{1})}}}{\left(x_{0} \cdot \theta_{4}\right)}}{\operatorname{log}(\operatorname{log}(\frac{\left(\theta_{5} + x_{1}\right)}{\frac{x_{0}}{\operatorname{log}(x_{0})}}))}}$$",-0.4405134885950248;-0.5413808220509542;0.5471939325015198;-0.5497312696220659;-0.9612068376885059;0.746719888806498,35,,,,Infinity,,,,,, +719,0,((Log((x0 / ((0.4976058652649499 + x1) + x1))) ^ ((x0 * 0.15802696906142277) + (-0.15583593775870563 * (Log(Log(x1)) + (x0 ^ x0))))) / x1),"(np.log((x[:, 0] / ((t[0] + x[:, 1]) + x[:, 1]))) ** ((x[:, 0] * t[1]) + (t[2] * (np.log(np.log(x[:, 1])) + x[:, 0] ** x[:, 0]))) / x[:, 1])","$$\frac{{\operatorname{log}(\frac{x_{0}}{\left(\left(\theta_{0} + x_{1}\right) + x_{1}\right)})^{\left(\left(x_{0} \cdot \theta_{1}\right) + \left(\theta_{2} \cdot \left(\operatorname{log}(\operatorname{log}(x_{1})) + {x_{0}^{x_{0}}}\right)\right)\right)}}}{x_{1}}$$",0.4976058652649499;0.15802696906142277;-0.15583593775870563,24,,,,Infinity,,,,,, +720,0,(Exp(Exp(((x1 + 0.3819643587362169) ^ x1))) * (((0.8446908013519528 / x1) + 0.892202605361419) + 0.10937967342512211)),"(np.exp(np.exp((x[:, 1] + t[0]) ** x[:, 1])) * (((t[1] / x[:, 1]) + t[2]) + t[3]))","$$\left(\operatorname{exp}(\operatorname{exp}({\left(x_{1} + \theta_{0}\right)^{x_{1}}})) \cdot \left(\left(\frac{\theta_{1}}{x_{1}} + \theta_{2}\right) + \theta_{3}\right)\right)$$",0.3819643587362169;0.8446908013519528;0.892202605361419;0.10937967342512211,15,3005.2097483922153,3005.2097483922153,3005.2097483922153,3005.2097483922153,-277323.2440101641,-277323.2440101641,-277323.2440101641,2741.9611255302943,2741.9611255302943,2741.9611255302943 +721,0,((-0.7489482478565239 / x0) * -9.934100242149863e-2),"((t[0] / x[:, 0]) * t[1])","$$\left(\frac{\theta_{0}}{x_{0}} \cdot \theta_{1}\right)$$",-0.7489482478565239;-9.934100242149863e-2,5,7.713765280606784e-3,7.713765280606784e-3,7.713765280606784e-3,7.713765280606784e-3,0.2881647858154973,0.2881647858154973,0.2881647858154973,10.98026406508728,10.98026406508728,10.98026406508728 +722,0,Log((((x1 ^ (-0.9362772353646575 ^ x1)) + (x0 / (x1 ^ x1))) * (Log((x0 + 0.7006111695242481)) / ((x1 * -0.5639123960386054) / x1)))),"np.log(((x[:, 1] ** t[0] ** x[:, 1] + (x[:, 0] / x[:, 1] ** x[:, 1])) * (np.log((x[:, 0] + t[1])) / ((x[:, 1] * t[2]) / x[:, 1]))))","$$\operatorname{log}(\left(\left({x_{1}^{{\theta_{0}^{x_{1}}}}} + \frac{x_{0}}{{x_{1}^{x_{1}}}}\right) \cdot \frac{\operatorname{log}(\left(x_{0} + \theta_{1}\right))}{\frac{\left(x_{1} \cdot \theta_{2}\right)}{x_{1}}}\right))$$",-0.9362772353646575;0.7006111695242481;-0.5639123960386054,23,,,,Infinity,,,,,, +723,0,(((-0.25374503099723245 * x0) + x1) + ((x1 ^ x1) + -0.718274741403826)),"(((t[0] * x[:, 0]) + x[:, 1]) + (x[:, 1] ** x[:, 1] + t[1]))","$$\left(\left(\left(\theta_{0} \cdot x_{0}\right) + x_{1}\right) + \left({x_{1}^{x_{1}}} + \theta_{1}\right)\right)$$",-0.25374503099723245;-0.718274741403826,11,1.0023614153183133e-2,1.0023614153183133e-2,1.0023614153183133e-2,1.0023614153183133e-2,7.500925059614227e-2,7.500925059614227e-2,7.500925059614227e-2,23.76022528498175,23.76022528498175,23.76022528498175 +724,0,Exp(((-0.44058705558262296 + Log(x1)) + -1.1657889873099139)),"np.exp(((t[0] + np.log(x[:, 1])) + t[1]))","$$\operatorname{exp}(\left(\left(\theta_{0} + \operatorname{log}(x_{1})\right) + \theta_{1}\right))$$",-0.44058705558262296;-1.1657889873099139,7,9.365750715189244e-3,9.365750715189244e-3,9.365750715189244e-3,9.365750715189244e-3,0.13571765229794996,0.13571765229794996,0.13571765229794996,15.316344433417653,15.316344433417653,15.316344433417653 +725,0,((0.11050434725593761 / Log(x1)) + Exp(Log(Log(Exp((-0.9475447003375046 + x1)))))),"((t[0] / np.log(x[:, 1])) + np.exp(np.log(np.log(np.exp((t[1] + x[:, 1]))))))","$$\left(\frac{\theta_{0}}{\operatorname{log}(x_{1})} + \operatorname{exp}(\operatorname{log}(\operatorname{log}(\operatorname{exp}(\left(\theta_{1} + x_{1}\right)))))\right)$$",0.11050434725593761;-0.9475447003375046,12,,,,Infinity,,,,,, +726,0,Log((((Exp(Exp(x1)) + Log((x0 * -0.5555004271699213))) + ((x0 / x1) / (0.1432771635584007 * (0.914464989634624 / x1)))) * (((x1 * x0) * 0.7878287005505) * ((x0 ^ x0) + (x1 + (x0 ^ x0)))))),"np.log((((np.exp(np.exp(x[:, 1])) + np.log((x[:, 0] * t[0]))) + ((x[:, 0] / x[:, 1]) / (t[1] * (t[2] / x[:, 1])))) * (((x[:, 1] * x[:, 0]) * t[3]) * (x[:, 0] ** x[:, 0] + (x[:, 1] + x[:, 0] ** x[:, 0])))))","$$\operatorname{log}(\left(\left(\left(\operatorname{exp}(\operatorname{exp}(x_{1})) + \operatorname{log}(\left(x_{0} \cdot \theta_{0}\right))\right) + \frac{\frac{x_{0}}{x_{1}}}{\left(\theta_{1} \cdot \frac{\theta_{2}}{x_{1}}\right)}\right) \cdot \left(\left(\left(x_{1} \cdot x_{0}\right) \cdot \theta_{3}\right) \cdot \left({x_{0}^{x_{0}}} + \left(x_{1} + {x_{0}^{x_{0}}}\right)\right)\right)\right))$$",-0.5555004271699213;0.1432771635584007;0.914464989634624;0.7878287005505,35,,,,Infinity,,,,,, +727,0,(Exp(((0.26392981578193986 * (-0.7496215193553284 + ((x1 * x1) ^ (-0.6945600834220225 + x0)))) ^ ((((x0 ^ 0.9790135409625704) + x1) * x0) + -0.23213070781852818))) / (Log((0.5648789541173332 * x1)) * -0.6089395023505682)),"(np.exp((t[0] * (t[1] + (x[:, 1] * x[:, 1]) ** (t[2] + x[:, 0]))) ** (((x[:, 0] ** t[3] + x[:, 1]) * x[:, 0]) + t[4])) / (np.log((t[5] * x[:, 1])) * t[6]))","$$\frac{\operatorname{exp}({\left(\theta_{0} \cdot \left(\theta_{1} + {\left(x_{1} \cdot x_{1}\right)^{\left(\theta_{2} + x_{0}\right)}}\right)\right)^{\left(\left(\left({x_{0}^{\theta_{3}}} + x_{1}\right) \cdot x_{0}\right) + \theta_{4}\right)}})}{\left(\operatorname{log}(\left(\theta_{5} \cdot x_{1}\right)) \cdot \theta_{6}\right)}$$",0.26392981578193986;-0.7496215193553284;-0.6945600834220225;0.9790135409625704;-0.23213070781852818;0.5648789541173332;-0.6089395023505682,29,,,,Infinity,,,,,, +728,0,(Exp(Log((Exp(Log(x0)) ^ ((-0.3436819888779097 + Exp(x0)) * Exp(x0))))) ^ ((((x0 ^ x0) ^ -0.5106166295657588) ^ (0.3926152978598283 ^ (0.7798005143606109 + Log(x0)))) / (Exp((Exp((-0.6326812309609371 * x0)) + (x1 * 0.30193297251370077))) + (-0.4002554146946985 ^ (x1 * -0.3044193695791133))))),"np.exp(np.log(np.exp(np.log(x[:, 0])) ** ((t[0] + np.exp(x[:, 0])) * np.exp(x[:, 0])))) ** (x[:, 0] ** x[:, 0] ** t[1] ** t[2] ** (t[3] + np.log(x[:, 0])) / (np.exp((np.exp((t[4] * x[:, 0])) + (x[:, 1] * t[5]))) + t[6] ** (x[:, 1] * t[7])))","$${\operatorname{exp}(\operatorname{log}({\operatorname{exp}(\operatorname{log}(x_{0}))^{\left(\left(\theta_{0} + \operatorname{exp}(x_{0})\right) \cdot \operatorname{exp}(x_{0})\right)}}))^{\frac{{{{x_{0}^{x_{0}}}^{\theta_{1}}}^{{\theta_{2}^{\left(\theta_{3} + \operatorname{log}(x_{0})\right)}}}}}{\left(\operatorname{exp}(\left(\operatorname{exp}(\left(\theta_{4} \cdot x_{0}\right)) + \left(x_{1} \cdot \theta_{5}\right)\right)) + {\theta_{6}^{\left(x_{1} \cdot \theta_{7}\right)}}\right)}}}$$",-0.3436819888779097;-0.5106166295657588;0.3926152978598283;0.7798005143606109;-0.6326812309609371;0.30193297251370077;-0.4002554146946985;-0.3044193695791133,42,,,,Infinity,,,,,, +729,0,(Exp(((-4.05793436361461e-3 * Exp(x1)) + -0.1755358249768124)) / (Log(((x0 * 1.1501009376899892e-5) * x1)) * -0.9910117071689697)),"(np.exp(((t[0] * np.exp(x[:, 1])) + t[1])) / (np.log(((x[:, 0] * t[2]) * x[:, 1])) * t[3]))","$$\frac{\operatorname{exp}(\left(\left(\theta_{0} \cdot \operatorname{exp}(x_{1})\right) + \theta_{1}\right))}{\left(\operatorname{log}(\left(\left(x_{0} \cdot \theta_{2}\right) \cdot x_{1}\right)) \cdot \theta_{3}\right)}$$",-4.05793436361461e-3;-0.1755358249768124;1.1501009376899892e-5;-0.9910117071689697,16,1.0945762624807821e-2,1.0945762624807821e-2,1.0945762624807821e-2,1.0945762624807821e-2,-1.0087680789516495e-2,-1.0087680789516495e-2,-1.0087680789516495e-2,41.364615594710266,41.364615594710266,41.364615594710266 +730,0,Log((-0.42988212916783763 ^ Log((x1 ^ -0.7924198194592397)))),"np.log(t[0] ** np.log(x[:, 1] ** t[1]))","$$\operatorname{log}({\theta_{0}^{\operatorname{log}({x_{1}^{\theta_{1}}})}})$$",-0.42988212916783763;-0.7924198194592397,7,,,,Infinity,,,,,, +731,0,((Log((x1 * 0.8544494541957786)) ^ Log(x0)) * -0.5879113012949468),"(np.log((x[:, 1] * t[0])) ** np.log(x[:, 0]) * t[1])","$$\left({\operatorname{log}(\left(x_{1} \cdot \theta_{0}\right))^{\operatorname{log}(x_{0})}} \cdot \theta_{1}\right)$$",0.8544494541957786;-0.5879113012949468,9,,,,Infinity,,,,,, +732,0,(Exp((Exp(((x1 + x0) * -0.6647724240385582)) / Log(((0.27512691140972945 + x1) ^ Log(x1))))) * (-0.4601321122833293 / (Exp((x0 + (x1 + x0))) ^ -0.17075139919668936))),"(np.exp((np.exp(((x[:, 1] + x[:, 0]) * t[0])) / np.log((t[1] + x[:, 1]) ** np.log(x[:, 1])))) * (t[2] / np.exp((x[:, 0] + (x[:, 1] + x[:, 0]))) ** t[3]))","$$\left(\operatorname{exp}(\frac{\operatorname{exp}(\left(\left(x_{1} + x_{0}\right) \cdot \theta_{0}\right))}{\operatorname{log}({\left(\theta_{1} + x_{1}\right)^{\operatorname{log}(x_{1})}})}) \cdot \frac{\theta_{2}}{{\operatorname{exp}(\left(x_{0} + \left(x_{1} + x_{0}\right)\right))^{\theta_{3}}}}\right)$$",-0.6647724240385582;0.27512691140972945;-0.4601321122833293;-0.17075139919668936,26,111.97842288807819,111.97842288807819,111.97842288807819,111.97842288807819,-10332.49884795921,-10332.49884795921,-10332.49884795921,165.99211935148264,165.99211935148264,165.99211935148264 +733,0,Exp(((0.5792938456468861 / Log((x1 * x1))) ^ Exp(((-0.43774718407965385 + ((x0 * -0.25540150202815737) * 0.47105107987786565)) * -0.7398787690633012)))),"np.exp((t[0] / np.log((x[:, 1] * x[:, 1]))) ** np.exp(((t[1] + ((x[:, 0] * t[2]) * t[3])) * t[4])))","$$\operatorname{exp}({\frac{\theta_{0}}{\operatorname{log}(\left(x_{1} \cdot x_{1}\right))}^{\operatorname{exp}(\left(\left(\theta_{1} + \left(\left(x_{0} \cdot \theta_{2}\right) \cdot \theta_{3}\right)\right) \cdot \theta_{4}\right))}})$$",0.5792938456468861;-0.43774718407965385;-0.25540150202815737;0.47105107987786565;-0.7398787690633012,18,,,,Infinity,,,,,, +734,0,((Log(((x0 + -0.5442090783037914) ^ 0.5922527190603097)) ^ ((0.23068974557237676 * Exp(x0)) * ((x1 / x1) * 0.41315672451500673))) / Exp(((-0.40228289933862904 / Exp((x0 + (x1 / x1)))) + (Log((x1 / x0)) * 0.8313169616676465)))),"(np.log((x[:, 0] + t[0]) ** t[1]) ** ((t[2] * np.exp(x[:, 0])) * ((x[:, 1] / x[:, 1]) * t[3])) / np.exp(((t[4] / np.exp((x[:, 0] + (x[:, 1] / x[:, 1])))) + (np.log((x[:, 1] / x[:, 0])) * t[5]))))","$$\frac{{\operatorname{log}({\left(x_{0} + \theta_{0}\right)^{\theta_{1}}})^{\left(\left(\theta_{2} \cdot \operatorname{exp}(x_{0})\right) \cdot \left(\frac{x_{1}}{x_{1}} \cdot \theta_{3}\right)\right)}}}{\operatorname{exp}(\left(\frac{\theta_{4}}{\operatorname{exp}(\left(x_{0} + \frac{x_{1}}{x_{1}}\right))} + \left(\operatorname{log}(\frac{x_{1}}{x_{0}}) \cdot \theta_{5}\right)\right))}$$",-0.5442090783037914;0.5922527190603097;0.23068974557237676;0.41315672451500673;-0.40228289933862904;0.8313169616676465,34,,,,Infinity,,,,,, +735,0,((((-0.23922326279682082 / (x0 / (x0 + (x1 + x1)))) / (Log(x0) + x0)) ^ (-0.29651918449474257 + ((x0 / x0) / x1))) + -0.20709334766904786),"(((t[0] / (x[:, 0] / (x[:, 0] + (x[:, 1] + x[:, 1])))) / (np.log(x[:, 0]) + x[:, 0])) ** (t[1] + ((x[:, 0] / x[:, 0]) / x[:, 1])) + t[2])","$$\left({\frac{\frac{\theta_{0}}{\frac{x_{0}}{\left(x_{0} + \left(x_{1} + x_{1}\right)\right)}}}{\left(\operatorname{log}(x_{0}) + x_{0}\right)}^{\left(\theta_{1} + \frac{\frac{x_{0}}{x_{0}}}{x_{1}}\right)}} + \theta_{2}\right)$$",-0.23922326279682082;-0.29651918449474257;-0.20709334766904786,24,,,,Infinity,,,,,, +736,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 +737,0,Exp((Exp(Log(((0.36297859339088423 * Log(x0)) / Exp(x1)))) * -0.6476575655435977)),"np.exp((np.exp(np.log(((t[0] * np.log(x[:, 0])) / np.exp(x[:, 1])))) * t[1]))","$$\operatorname{exp}(\left(\operatorname{exp}(\operatorname{log}(\frac{\left(\theta_{0} \cdot \operatorname{log}(x_{0})\right)}{\operatorname{exp}(x_{1})})) \cdot \theta_{1}\right))$$",0.36297859339088423;-0.6476575655435977,12,,,,Infinity,,,,,, +738,0,Log(((-0.11750128218633338 / Exp(x1)) * ((0.30599972701535927 * (((x0 * x1) ^ (x1 + x0)) * 0.10168290723253781)) + (-0.3767996193780738 ^ x1)))),"np.log(((t[0] / np.exp(x[:, 1])) * ((t[1] * ((x[:, 0] * x[:, 1]) ** (x[:, 1] + x[:, 0]) * t[2])) + t[3] ** x[:, 1])))","$$\operatorname{log}(\left(\frac{\theta_{0}}{\operatorname{exp}(x_{1})} \cdot \left(\left(\theta_{1} \cdot \left({\left(x_{0} \cdot x_{1}\right)^{\left(x_{1} + x_{0}\right)}} \cdot \theta_{2}\right)\right) + {\theta_{3}^{x_{1}}}\right)\right))$$",-0.11750128218633338;0.30599972701535927;0.10168290723253781;-0.3767996193780738,21,,,,Infinity,,,,,, +739,0,(Log((Exp((x0 * x1)) + (Log(x0) * Log(x1)))) ^ (((x1 + x0) * x1) * Exp((-2.847028930670603e-2 * x1)))),"np.log((np.exp((x[:, 0] * x[:, 1])) + (np.log(x[:, 0]) * np.log(x[:, 1])))) ** (((x[:, 1] + x[:, 0]) * x[:, 1]) * np.exp((t[0] * x[:, 1])))","$${\operatorname{log}(\left(\operatorname{exp}(\left(x_{0} \cdot x_{1}\right)) + \left(\operatorname{log}(x_{0}) \cdot \operatorname{log}(x_{1})\right)\right))^{\left(\left(\left(x_{1} + x_{0}\right) \cdot x_{1}\right) \cdot \operatorname{exp}(\left(\theta_{0} \cdot x_{1}\right))\right)}}$$",-2.847028930670603e-2,22,,,,Infinity,,,,,, +740,0,((((Log(Log(x1)) ^ x1) * 0.9362347316223898) / Log(Exp(x0))) ^ (((Log((x1 * (x0 ^ x0))) ^ (((-5.722817496882704e-2 + x1) + (0.6751443019569183 + x1)) * 0.6904422084791926)) ^ (0.9051541302831743 * x0)) / x0)),"((np.log(np.log(x[:, 1])) ** x[:, 1] * t[0]) / np.log(np.exp(x[:, 0]))) ** (np.log((x[:, 1] * x[:, 0] ** x[:, 0])) ** (((t[1] + x[:, 1]) + (t[2] + x[:, 1])) * t[3]) ** (t[4] * x[:, 0]) / x[:, 0])","$${\frac{\left({\operatorname{log}(\operatorname{log}(x_{1}))^{x_{1}}} \cdot \theta_{0}\right)}{\operatorname{log}(\operatorname{exp}(x_{0}))}^{\frac{{{\operatorname{log}(\left(x_{1} \cdot {x_{0}^{x_{0}}}\right))^{\left(\left(\left(\theta_{1} + x_{1}\right) + \left(\theta_{2} + x_{1}\right)\right) \cdot \theta_{3}\right)}}^{\left(\theta_{4} \cdot x_{0}\right)}}}{x_{0}}}}$$",0.9362347316223898;-5.722817496882704e-2;0.6751443019569183;0.6904422084791926;0.9051541302831743,34,,,,Infinity,,,,,, +741,0,(Log(((Exp((0.14969767322837058 / x0)) ^ ((x0 + 0.3547564924142512) * x0)) ^ Exp(((x0 * 0.3606904998933834) ^ Exp((x0 ^ 0.7578096835697835)))))) + Exp(Log(Log((((0.16435949393267613 ^ x0) * -0.4719447109227608) ^ (1.1159516294553251e-2 ^ x0)))))),"(np.log(np.exp((t[0] / x[:, 0])) ** ((x[:, 0] + t[1]) * x[:, 0]) ** np.exp((x[:, 0] * t[2]) ** np.exp(x[:, 0] ** t[3]))) + np.exp(np.log(np.log((t[4] ** x[:, 0] * t[5]) ** t[6] ** x[:, 0]))))","$$\left(\operatorname{log}({{\operatorname{exp}(\frac{\theta_{0}}{x_{0}})^{\left(\left(x_{0} + \theta_{1}\right) \cdot x_{0}\right)}}^{\operatorname{exp}({\left(x_{0} \cdot \theta_{2}\right)^{\operatorname{exp}({x_{0}^{\theta_{3}}})}})}}) + \operatorname{exp}(\operatorname{log}(\operatorname{log}({\left({\theta_{4}^{x_{0}}} \cdot \theta_{5}\right)^{{\theta_{6}^{x_{0}}}}})))\right)$$",0.14969767322837058;0.3547564924142512;0.3606904998933834;0.7578096835697835;0.16435949393267613;-0.4719447109227608;1.1159516294553251e-2,34,,,,Infinity,,,,,, +742,0,(Exp((Log(Exp((-0.9101708015462787 + x0))) ^ Log((x0 / Log(x0))))) ^ (Log(Exp(Exp(Exp(x1)))) + (Exp((x0 + -7.246052453787177e-3)) * -0.9061279698631106))),"np.exp(np.log(np.exp((t[0] + x[:, 0]))) ** np.log((x[:, 0] / np.log(x[:, 0])))) ** (np.log(np.exp(np.exp(np.exp(x[:, 1])))) + (np.exp((x[:, 0] + t[1])) * t[2]))","$${\operatorname{exp}({\operatorname{log}(\operatorname{exp}(\left(\theta_{0} + x_{0}\right)))^{\operatorname{log}(\frac{x_{0}}{\operatorname{log}(x_{0})})}})^{\left(\operatorname{log}(\operatorname{exp}(\operatorname{exp}(\operatorname{exp}(x_{1})))) + \left(\operatorname{exp}(\left(x_{0} + \theta_{1}\right)) \cdot \theta_{2}\right)\right)}}$$",-0.9101708015462787;-7.246052453787177e-3;-0.9061279698631106,25,,,,Infinity,,,,,, +743,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 +744,0,Exp(Exp((-15.84989606960107 + ((x1 + x1) / (5.60213308399048 / x1))))),"np.exp(np.exp((t[0] + ((x[:, 1] + x[:, 1]) / (t[1] / x[:, 1])))))","$$\operatorname{exp}(\operatorname{exp}(\left(\theta_{0} + \frac{\left(x_{1} + x_{1}\right)}{\frac{\theta_{1}}{x_{1}}}\right)))$$",-15.84989606960107;5.60213308399048,11,0.8923100516591614,0.8923100516591614,0.8923100516591614,0.8923100516591614,-81.34340735499009,-81.34340735499009,-81.34340735499009,22.548745954003632,22.548745954003632,22.548745954003632 +745,0,(((((x0 * x0) + (x0 ^ Log((x0 ^ x1)))) * (x0 ^ x1)) ^ x1) ^ x1),"(((x[:, 0] * x[:, 0]) + x[:, 0] ** np.log(x[:, 0] ** x[:, 1])) * x[:, 0] ** x[:, 1]) ** x[:, 1] ** x[:, 1]","$${{\left(\left(\left(x_{0} \cdot x_{0}\right) + {x_{0}^{\operatorname{log}({x_{0}^{x_{1}}})}}\right) \cdot {x_{0}^{x_{1}}}\right)^{x_{1}}}^{x_{1}}}$$",,18,1.1453391471819776,1.1453391471819776,1.1453391471819776,1.1453391471819776,-104.6932260044145,-104.6932260044145,-104.6932260044145,33.282475678568765,33.282475678568765,33.282475678568765 +746,0,(((2.224413895588964e-3 * ((x0 + 0.5585423577730453) ^ x0)) * (Exp(x1) / x1)) * 1.2887581168361153),"(((t[0] * (x[:, 0] + t[1]) ** x[:, 0]) * (np.exp(x[:, 1]) / x[:, 1])) * t[2])","$$\left(\left(\left(\theta_{0} \cdot {\left(x_{0} + \theta_{1}\right)^{x_{0}}}\right) \cdot \frac{\operatorname{exp}(x_{1})}{x_{1}}\right) \cdot \theta_{2}\right)$$",2.224413895588964e-3;0.5585423577730453;1.2887581168361153,14,1.3779721127024063e-2,1.3779721127024063e-2,1.3779721127024063e-2,1.3779721127024063e-2,-0.27160866101519465,-0.27160866101519465,-0.27160866101519465,35.18735813863494,35.18735813863494,35.18735813863494 +747,0,((Log((0.6524040808817062 ^ x0)) + (Exp(Exp(Exp(x0))) * -0.2715461785658595)) * -0.6352359127476876),"((np.log(t[0] ** x[:, 0]) + (np.exp(np.exp(np.exp(x[:, 0]))) * t[1])) * t[2])","$$\left(\left(\operatorname{log}({\theta_{0}^{x_{0}}}) + \left(\operatorname{exp}(\operatorname{exp}(\operatorname{exp}(x_{0}))) \cdot \theta_{1}\right)\right) \cdot \theta_{2}\right)$$",0.6524040808817062;-0.2715461785658595;-0.6352359127476876,13,7.542295077832059e70,7.542295077832059e70,7.542295077832059e70,7.542295077832059e70,-6.960117448309207e72,-6.960117448309207e72,-6.960117448309207e72,6.788065570048854e70,6.788065570048854e70,6.788065570048854e70 +748,0,Exp((((0.21340467053000012 / ((x1 + (x0 / x0)) + (-0.200069378846631 + x1))) + 2.9979745487327514e-2) * (Log(x1) ^ x1))),"np.exp((((t[0] / ((x[:, 1] + (x[:, 0] / x[:, 0])) + (t[1] + x[:, 1]))) + t[2]) * np.log(x[:, 1]) ** x[:, 1]))","$$\operatorname{exp}(\left(\left(\frac{\theta_{0}}{\left(\left(x_{1} + \frac{x_{0}}{x_{0}}\right) + \left(\theta_{1} + x_{1}\right)\right)} + \theta_{2}\right) \cdot {\operatorname{log}(x_{1})^{x_{1}}}\right))$$",0.21340467053000012;-0.200069378846631;2.9979745487327514e-2,19,,,,Infinity,,,,,, +749,0,((Exp((x0 * -0.32618862765002643)) + -0.20471626307057667) ^ Log(Log((Log((x1 * -0.7786504713124589)) ^ (3.626020005691821e-2 + x1))))),"(np.exp((x[:, 0] * t[0])) + t[1]) ** np.log(np.log(np.log((x[:, 1] * t[2])) ** (t[3] + x[:, 1])))","$${\left(\operatorname{exp}(\left(x_{0} \cdot \theta_{0}\right)) + \theta_{1}\right)^{\operatorname{log}(\operatorname{log}({\operatorname{log}(\left(x_{1} \cdot \theta_{2}\right))^{\left(\theta_{3} + x_{1}\right)}}))}}$$",-0.32618862765002643;-0.20471626307057667;-0.7786504713124589;3.626020005691821e-2,17,,,,Infinity,,,,,, +750,0,(Log((x0 * -0.2052209904324359)) * (x1 ^ x0)),"(np.log((x[:, 0] * t[0])) * x[:, 1] ** x[:, 0])","$$\left(\operatorname{log}(\left(x_{0} \cdot \theta_{0}\right)) \cdot {x_{1}^{x_{0}}}\right)$$",-0.2052209904324359,8,,,,Infinity,,,,,, +751,0,Log(Exp((-3.8821388843030685e-2 + (0.3004069943531097 * x1)))),"np.log(np.exp((t[0] + (t[1] * x[:, 1]))))","$$\operatorname{log}(\operatorname{exp}(\left(\theta_{0} + \left(\theta_{1} \cdot x_{1}\right)\right)))$$",-3.8821388843030685e-2;0.3004069943531097,7,9.147852990030469e-3,9.147852990030469e-3,9.147852990030469e-3,9.147852990030469e-3,0.1558255073099074,0.1558255073099074,0.1558255073099074,16.592399223022696,16.592399223022696,16.592399223022696 +752,0,(Exp(((0.18243578767954385 ^ x0) / Exp(x0))) + -1.0262981003988494),"(np.exp((t[0] ** x[:, 0] / np.exp(x[:, 0]))) + t[1])","$$\left(\operatorname{exp}(\frac{{\theta_{0}^{x_{0}}}}{\operatorname{exp}(x_{0})}) + \theta_{1}\right)$$",0.18243578767954385;-1.0262981003988494,9,4.937097859181299e-3,4.937097859181299e-3,4.937097859181299e-3,4.937097859181299e-3,0.5443988786026792,0.5443988786026792,0.5443988786026792,20.17212848186104,20.17212848186104,20.17212848186104 +753,0,(Log((Log((Exp((x0 * -0.7734054253344863)) * Exp((x0 + 0.756751123626814)))) ^ -1.5301738577096824e-2)) * (Log(x0) ^ x0)),"(np.log(np.log((np.exp((x[:, 0] * t[0])) * np.exp((x[:, 0] + t[1])))) ** t[2]) * np.log(x[:, 0]) ** x[:, 0])","$$\left(\operatorname{log}({\operatorname{log}(\left(\operatorname{exp}(\left(x_{0} \cdot \theta_{0}\right)) \cdot \operatorname{exp}(\left(x_{0} + \theta_{1}\right))\right))^{\theta_{2}}}) \cdot {\operatorname{log}(x_{0})^{x_{0}}}\right)$$",-0.7734054253344863;0.756751123626814;-1.5301738577096824e-2,18,,,,Infinity,,,,,, +754,0,(((x0 * -0.39538837122995496) + -0.4629051697637154) + Log((1.0962503788621676 * (x1 * 7.788945473032442)))),"(((x[:, 0] * t[0]) + t[1]) + np.log((t[2] * (x[:, 1] * t[3]))))","$$\left(\left(\left(x_{0} \cdot \theta_{0}\right) + \theta_{1}\right) + \operatorname{log}(\left(\theta_{2} \cdot \left(x_{1} \cdot \theta_{3}\right)\right))\right)$$",-0.39538837122995496;-0.4629051697637154;1.0962503788621676;7.788945473032442,12,0.18668074341018365,0.18668074341018365,0.18668074341018365,0.18668074341018365,-16.227115699721825,-16.227115699721825,-16.227115699721825,29.75282604127639,29.75282604127639,29.75282604127639 +755,0,((((((-0.8393637361635838 / Exp(x1)) ^ -0.2572108789093275) + -0.2039816844263571) ^ -0.2126131651257952) + -0.21582035382724607) ^ x0),"(((t[0] / np.exp(x[:, 1])) ** t[1] + t[2]) ** t[3] + t[4]) ** x[:, 0]","$${\left({\left({\frac{\theta_{0}}{\operatorname{exp}(x_{1})}^{\theta_{1}}} + \theta_{2}\right)^{\theta_{3}}} + \theta_{4}\right)^{x_{0}}}$$",-0.8393637361635838;-0.2572108789093275;-0.2039816844263571;-0.2126131651257952;-0.21582035382724607,14,,,,Infinity,,,,,, +756,0,(Log(((x0 + -0.5001017610241476) * x1)) * -3.51716039062975e-2),"(np.log(((x[:, 0] + t[0]) * x[:, 1])) * t[1])","$$\left(\operatorname{log}(\left(\left(x_{0} + \theta_{0}\right) \cdot x_{1}\right)) \cdot \theta_{1}\right)$$",-0.5001017610241476;-3.51716039062975e-2,8,9.094395443372604e-3,9.094395443372604e-3,9.094395443372604e-3,9.094395443372604e-3,0.16075863176867822,0.16075863176867822,0.16075863176867822,18.384110580458756,18.384110580458756,18.384110580458756 +757,0,Exp((Exp(Log((0.9588158437209827 * ((-3.91159177599385e-2 ^ x0) + 0.7381521545529597)))) + ((-0.535035373016383 + (x1 + Log(x1))) / Log((-0.21112479491509917 / (-0.793366204664579 ^ x0)))))),"np.exp((np.exp(np.log((t[0] * (t[1] ** x[:, 0] + t[2])))) + ((t[3] + (x[:, 1] + np.log(x[:, 1]))) / np.log((t[4] / t[5] ** x[:, 0])))))","$$\operatorname{exp}(\left(\operatorname{exp}(\operatorname{log}(\left(\theta_{0} \cdot \left({\theta_{1}^{x_{0}}} + \theta_{2}\right)\right))) + \frac{\left(\theta_{3} + \left(x_{1} + \operatorname{log}(x_{1})\right)\right)}{\operatorname{log}(\frac{\theta_{4}}{{\theta_{5}^{x_{0}}}})}\right))$$",0.9588158437209827;-3.91159177599385e-2;0.7381521545529597;-0.535035373016383;-0.21112479491509917;-0.793366204664579,24,,,,Infinity,,,,,, +758,0,(((Exp((x0 ^ -2.1640513392355287e-2)) / (x1 * (x1 * 0.21113441248352793))) / (0.2560276470211317 / Log((x0 + 0.7905409970625145)))) * (((x0 * x1) / Log((x1 / x0))) ^ ((x0 + (0.7480490568801661 * x0)) + (0.5496019684804179 * x1)))),"(((np.exp(x[:, 0] ** t[0]) / (x[:, 1] * (x[:, 1] * t[1]))) / (t[2] / np.log((x[:, 0] + t[3])))) * ((x[:, 0] * x[:, 1]) / np.log((x[:, 1] / x[:, 0]))) ** ((x[:, 0] + (t[4] * x[:, 0])) + (t[5] * x[:, 1])))","$$\left(\frac{\frac{\operatorname{exp}({x_{0}^{\theta_{0}}})}{\left(x_{1} \cdot \left(x_{1} \cdot \theta_{1}\right)\right)}}{\frac{\theta_{2}}{\operatorname{log}(\left(x_{0} + \theta_{3}\right))}} \cdot {\frac{\left(x_{0} \cdot x_{1}\right)}{\operatorname{log}(\frac{x_{1}}{x_{0}})}^{\left(\left(x_{0} + \left(\theta_{4} \cdot x_{0}\right)\right) + \left(\theta_{5} \cdot x_{1}\right)\right)}}\right)$$",-2.1640513392355287e-2;0.21113441248352793;0.2560276470211317;0.7905409970625145;0.7480490568801661;0.5496019684804179,36,,,,Infinity,,,,,, +759,0,Exp(Exp(Exp(Exp(((-0.35693752648296584 + Log(x0)) * (Log(x0) ^ Exp(x1))))))),"np.exp(np.exp(np.exp(np.exp(((t[0] + np.log(x[:, 0])) * np.log(x[:, 0]) ** np.exp(x[:, 1]))))))","$$\operatorname{exp}(\operatorname{exp}(\operatorname{exp}(\operatorname{exp}(\left(\left(\theta_{0} + \operatorname{log}(x_{0})\right) \cdot {\operatorname{log}(x_{0})^{\operatorname{exp}(x_{1})}}\right)))))$$",-0.35693752648296584,14,,,,Infinity,,,,,, +760,0,Exp((((Exp(x1) + -0.4777669667611227) ^ (x0 + Log(x0))) + (-1.1179247255298073 * (((x0 * -0.11366732364875251) + Exp(x0)) + x0)))),"np.exp(((np.exp(x[:, 1]) + t[0]) ** (x[:, 0] + np.log(x[:, 0])) + (t[1] * (((x[:, 0] * t[2]) + np.exp(x[:, 0])) + x[:, 0]))))","$$\operatorname{exp}(\left({\left(\operatorname{exp}(x_{1}) + \theta_{0}\right)^{\left(x_{0} + \operatorname{log}(x_{0})\right)}} + \left(\theta_{1} \cdot \left(\left(\left(x_{0} \cdot \theta_{2}\right) + \operatorname{exp}(x_{0})\right) + x_{0}\right)\right)\right))$$",-0.4777669667611227;-1.1179247255298073;-0.11366732364875251,21,4.549345056935785e-3,4.549345056935785e-3,4.549345056935785e-3,4.549345056935785e-3,0.5801811572949036,0.5801811572949036,0.5801811572949036,49.73514159193071,49.73514159193071,49.73514159193071 +761,0,Exp(Exp(Log((((27.201484949636328 + x1) ^ -4.6653541869558985) / x0)))),"np.exp(np.exp(np.log(((t[0] + x[:, 1]) ** t[1] / x[:, 0]))))","$$\operatorname{exp}(\operatorname{exp}(\operatorname{log}(\frac{{\left(\theta_{0} + x_{1}\right)^{\theta_{1}}}}{x_{0}})))$$",27.201484949636328;-4.6653541869558985,10,0.8923101629377775,0.8923101629377775,0.8923101629377775,0.8923101629377775,-81.34341762390996,-81.34341762390996,-81.34341762390996,25.63934443417764,25.63934443417764,25.63934443417764 +762,0,(Log((((x1 ^ x0) * (x1 + -9.97931464853441e-2)) ^ Exp(Log((x0 * 0.4755819951808542))))) ^ Exp((((0.5334588101215577 * x1) + (0.3363292373300586 + x1)) * ((0.30590519166797503 / x0) * 3.4965372575730624e-2)))),"np.log((x[:, 1] ** x[:, 0] * (x[:, 1] + t[0])) ** np.exp(np.log((x[:, 0] * t[1])))) ** np.exp((((t[2] * x[:, 1]) + (t[3] + x[:, 1])) * ((t[4] / x[:, 0]) * t[5])))","$${\operatorname{log}({\left({x_{1}^{x_{0}}} \cdot \left(x_{1} + \theta_{0}\right)\right)^{\operatorname{exp}(\operatorname{log}(\left(x_{0} \cdot \theta_{1}\right)))}})^{\operatorname{exp}(\left(\left(\left(\theta_{2} \cdot x_{1}\right) + \left(\theta_{3} + x_{1}\right)\right) \cdot \left(\frac{\theta_{4}}{x_{0}} \cdot \theta_{5}\right)\right))}}$$",-9.97931464853441e-2;0.4755819951808542;0.5334588101215577;0.3363292373300586;0.30590519166797503;3.4965372575730624e-2,29,,,,Infinity,,,,,, +763,0,Exp(((Exp(((x0 ^ x0) * (x1 / x0))) ^ Log((Log(x0) / (0.2524833826292866 ^ x0)))) + ((Log((-0.48021260250552866 + x1)) + -5.932284663592302e-2) + ((x1 * -0.18979251944746678) / (0.7596230409660353 * x1))))),"np.exp((np.exp((x[:, 0] ** x[:, 0] * (x[:, 1] / x[:, 0]))) ** np.log((np.log(x[:, 0]) / t[0] ** x[:, 0])) + ((np.log((t[1] + x[:, 1])) + t[2]) + ((x[:, 1] * t[3]) / (t[4] * x[:, 1])))))","$$\operatorname{exp}(\left({\operatorname{exp}(\left({x_{0}^{x_{0}}} \cdot \frac{x_{1}}{x_{0}}\right))^{\operatorname{log}(\frac{\operatorname{log}(x_{0})}{{\theta_{0}^{x_{0}}}})}} + \left(\left(\operatorname{log}(\left(\theta_{1} + x_{1}\right)) + \theta_{2}\right) + \frac{\left(x_{1} \cdot \theta_{3}\right)}{\left(\theta_{4} \cdot x_{1}\right)}\right)\right))$$",0.2524833826292866;-0.48021260250552866;-5.932284663592302e-2;-0.18979251944746678;0.7596230409660353,32,,,,Infinity,,,,,, +764,0,(Exp(((Log(x1) + x0) + Exp(x1))) ^ (-0.1266435257257693 ^ x1)),"np.exp(((np.log(x[:, 1]) + x[:, 0]) + np.exp(x[:, 1]))) ** t[0] ** x[:, 1]","$${\operatorname{exp}(\left(\left(\operatorname{log}(x_{1}) + x_{0}\right) + \operatorname{exp}(x_{1})\right))^{{\theta_{0}^{x_{1}}}}}$$",-0.1266435257257693,12,,,,Infinity,,,,,, +765,0,Exp((((-0.19738270521733547 * Log((x1 ^ x1))) ^ (0.3098343824506036 ^ Exp((x0 ^ x1)))) / (((x1 + (x0 ^ -0.7346801006688171)) * (x0 * -0.5444222775440644)) + ((x1 / (x1 / x1)) / (x0 ^ (x1 * x0)))))),"np.exp(((t[0] * np.log(x[:, 1] ** x[:, 1])) ** t[1] ** np.exp(x[:, 0] ** x[:, 1]) / (((x[:, 1] + x[:, 0] ** t[2]) * (x[:, 0] * t[3])) + ((x[:, 1] / (x[:, 1] / x[:, 1])) / x[:, 0] ** (x[:, 1] * x[:, 0])))))","$$\operatorname{exp}(\frac{{\left(\theta_{0} \cdot \operatorname{log}({x_{1}^{x_{1}}})\right)^{{\theta_{1}^{\operatorname{exp}({x_{0}^{x_{1}}})}}}}}{\left(\left(\left(x_{1} + {x_{0}^{\theta_{2}}}\right) \cdot \left(x_{0} \cdot \theta_{3}\right)\right) + \frac{\frac{x_{1}}{\frac{x_{1}}{x_{1}}}}{{x_{0}^{\left(x_{1} \cdot x_{0}\right)}}}\right)})$$",-0.19738270521733547;0.3098343824506036;-0.7346801006688171;-0.5444222775440644,36,3.211857545076914e-2,3.211857545076914e-2,3.211857545076914e-2,3.211857545076914e-2,-1.9639394256368776,-1.9639394256368776,-1.9639394256368776,87.21269124348015,87.21269124348015,87.21269124348015 +766,0,Log(((Exp(x1) * (x0 * 0.759534590814654)) / ((-5.458786573284036e-2 ^ x0) + Exp(x0)))),"np.log(((np.exp(x[:, 1]) * (x[:, 0] * t[0])) / (t[1] ** x[:, 0] + np.exp(x[:, 0]))))","$$\operatorname{log}(\frac{\left(\operatorname{exp}(x_{1}) \cdot \left(x_{0} \cdot \theta_{0}\right)\right)}{\left({\theta_{1}^{x_{0}}} + \operatorname{exp}(x_{0})\right)})$$",0.759534590814654;-5.458786573284036e-2,14,,,,Infinity,,,,,, +767,0,(((x1 + x0) + (x0 + x0)) + (Log(x1) ^ Log(x0))),"(((x[:, 1] + x[:, 0]) + (x[:, 0] + x[:, 0])) + np.log(x[:, 1]) ** np.log(x[:, 0]))","$$\left(\left(\left(x_{1} + x_{0}\right) + \left(x_{0} + x_{0}\right)\right) + {\operatorname{log}(x_{1})^{\operatorname{log}(x_{0})}}\right)$$",,13,,,,Infinity,,,,,, +768,0,(Exp(((-0.46783629102114155 + (x1 / (x1 * 6.775564120836852e-2))) + Exp((0.3362065538168141 ^ (x0 + -0.35184874339013883))))) / (((-0.8119117920254059 ^ (-0.450036010619038 ^ x1)) / (x1 ^ -0.4699357829527395)) / (0.3872688697265563 / (x0 * 0.9988005570809834)))),"(np.exp(((t[0] + (x[:, 1] / (x[:, 1] * t[1]))) + np.exp(t[2] ** (x[:, 0] + t[3])))) / ((t[4] ** t[5] ** x[:, 1] / x[:, 1] ** t[6]) / (t[7] / (x[:, 0] * t[8]))))","$$\frac{\operatorname{exp}(\left(\left(\theta_{0} + \frac{x_{1}}{\left(x_{1} \cdot \theta_{1}\right)}\right) + \operatorname{exp}({\theta_{2}^{\left(x_{0} + \theta_{3}\right)}})\right))}{\frac{\frac{{\theta_{4}^{{\theta_{5}^{x_{1}}}}}}{{x_{1}^{\theta_{6}}}}}{\frac{\theta_{7}}{\left(x_{0} \cdot \theta_{8}\right)}}}$$",-0.46783629102114155;6.775564120836852e-2;0.3362065538168141;-0.35184874339013883;-0.8119117920254059;-0.450036010619038;-0.4699357829527395;0.3872688697265563;0.9988005570809834,31,,,,Infinity,,,,,, +769,0,(0.3002641496312093 + (-0.22627811198760203 * x0)),"(t[0] + (t[1] * x[:, 0]))","$$\left(\theta_{0} + \left(\theta_{1} \cdot x_{0}\right)\right)$$",0.3002641496312093;-0.22627811198760203,5,5.893883569701793e-3,5.893883569701793e-3,5.893883569701793e-3,5.893883569701793e-3,0.4561055826050271,0.4561055826050271,0.4561055826050271,10.978626171547466,10.978626171547466,10.978626171547466 +770,0,Log((((x0 * 7.42235253200355) / (x1 + 0.13285835272563348)) ^ Log((x0 ^ -6.465263186078367e-2)))),"np.log(((x[:, 0] * t[0]) / (x[:, 1] + t[1])) ** np.log(x[:, 0] ** t[2]))","$$\operatorname{log}({\frac{\left(x_{0} \cdot \theta_{0}\right)}{\left(x_{1} + \theta_{1}\right)}^{\operatorname{log}({x_{0}^{\theta_{2}}})}})$$",7.42235253200355;0.13285835272563348;-6.465263186078367e-2,13,1.1280963223559004e-2,1.1280963223559004e-2,1.1280963223559004e-2,1.1280963223559004e-2,-4.102038114101747e-2,-4.102038114101747e-2,-4.102038114101747e-2,33.10566771484199,33.10566771484199,33.10566771484199 +771,0,Log((((x1 * x0) ^ 0.13619330896141518) + x1)),"np.log(((x[:, 1] * x[:, 0]) ** t[0] + x[:, 1]))","$$\operatorname{log}(\left({\left(x_{1} \cdot x_{0}\right)^{\theta_{0}}} + x_{1}\right))$$",0.13619330896141518,8,4.02648900459245e-2,4.02648900459245e-2,4.02648900459245e-2,4.02648900459245e-2,-2.71569079266844,-2.71569079266844,-2.71569079266844,17.624444528851477,17.624444528851477,17.624444528851477 +772,0,(Exp(Exp(Exp((x0 + 0.9935855734614467)))) / (0.46335094646366504 / Exp((x1 / x1)))),"(np.exp(np.exp(np.exp((x[:, 0] + t[0])))) / (t[1] / np.exp((x[:, 1] / x[:, 1]))))","$$\frac{\operatorname{exp}(\operatorname{exp}(\operatorname{exp}(\left(x_{0} + \theta_{0}\right))))}{\frac{\theta_{1}}{\operatorname{exp}(\frac{x_{1}}{x_{1}})}}$$",0.9935855734614467;0.46335094646366504,13,Infinity,Infinity,Infinity,Infinity,-Infinity,-Infinity,-Infinity,Infinity,Infinity,Infinity +773,0,((Log((-0.5730025818463077 / ((x1 + 0.39326401443498227) * x0))) / (-0.1624775942488439 * ((((0.17339488687388271 + x0) + Exp(x1)) ^ Exp((0.9637993141382121 ^ x1))) ^ x0))) ^ Log(Log((Exp(Exp(Exp(x1))) * 0.22199825201932377)))),"(np.log((t[0] / ((x[:, 1] + t[1]) * x[:, 0]))) / (t[2] * ((t[3] + x[:, 0]) + np.exp(x[:, 1])) ** np.exp(t[4] ** x[:, 1]) ** x[:, 0])) ** np.log(np.log((np.exp(np.exp(np.exp(x[:, 1]))) * t[5])))","$${\frac{\operatorname{log}(\frac{\theta_{0}}{\left(\left(x_{1} + \theta_{1}\right) \cdot x_{0}\right)})}{\left(\theta_{2} \cdot {{\left(\left(\theta_{3} + x_{0}\right) + \operatorname{exp}(x_{1})\right)^{\operatorname{exp}({\theta_{4}^{x_{1}}})}}^{x_{0}}}\right)}^{\operatorname{log}(\operatorname{log}(\left(\operatorname{exp}(\operatorname{exp}(\operatorname{exp}(x_{1}))) \cdot \theta_{5}\right)))}}$$",-0.5730025818463077;0.39326401443498227;-0.1624775942488439;0.17339488687388271;0.9637993141382121;0.22199825201932377,33,,,,Infinity,,,,,, +774,0,Log(((((0.8446931347417641 ^ (0.11296411624971547 * x0)) + 0.6730965114843964) / ((x1 + (x1 ^ x1)) + Log((x0 * 0.7452334297159207)))) * (0.5751955559973312 * (-0.5610954822631564 + (-0.17809277220184183 ^ x0))))),"np.log((((t[0] ** (t[1] * x[:, 0]) + t[2]) / ((x[:, 1] + x[:, 1] ** x[:, 1]) + np.log((x[:, 0] * t[3])))) * (t[4] * (t[5] + t[6] ** x[:, 0]))))","$$\operatorname{log}(\left(\frac{\left({\theta_{0}^{\left(\theta_{1} \cdot x_{0}\right)}} + \theta_{2}\right)}{\left(\left(x_{1} + {x_{1}^{x_{1}}}\right) + \operatorname{log}(\left(x_{0} \cdot \theta_{3}\right))\right)} \cdot \left(\theta_{4} \cdot \left(\theta_{5} + {\theta_{6}^{x_{0}}}\right)\right)\right))$$",0.8446931347417641;0.11296411624971547;0.6730965114843964;0.7452334297159207;0.5751955559973312;-0.5610954822631564;-0.17809277220184183,27,,,,Infinity,,,,,, +775,0,((-0.6563643181306923 ^ ((x0 ^ 0.12119242039623679) * 0.24571494467006616)) ^ ((Log(x0) ^ (0.2441003092025944 + x1)) + ((x1 * x1) * (x1 / x0)))),"t[0] ** (x[:, 0] ** t[1] * t[2]) ** (np.log(x[:, 0]) ** (t[3] + x[:, 1]) + ((x[:, 1] * x[:, 1]) * (x[:, 1] / x[:, 0])))","$${{\theta_{0}^{\left({x_{0}^{\theta_{1}}} \cdot \theta_{2}\right)}}^{\left({\operatorname{log}(x_{0})^{\left(\theta_{3} + x_{1}\right)}} + \left(\left(x_{1} \cdot x_{1}\right) \cdot \frac{x_{1}}{x_{0}}\right)\right)}}$$",-0.6563643181306923;0.12119242039623679;0.24571494467006616;0.2441003092025944,22,,,,Infinity,,,,,, +776,0,Log((Log((Log(x1) + x0)) ^ Log((x1 / x0)))),"np.log(np.log((np.log(x[:, 1]) + x[:, 0])) ** np.log((x[:, 1] / x[:, 0])))","$$\operatorname{log}({\operatorname{log}(\left(\operatorname{log}(x_{1}) + x_{0}\right))^{\operatorname{log}(\frac{x_{1}}{x_{0}})}})$$",,11,,,,Infinity,,,,,, +777,0,((((x1 * -0.4367803621166929) ^ ((-0.6787152430320464 ^ x0) * x1)) ^ x1) * 0.9480502352450613),"((x[:, 1] * t[0]) ** (t[1] ** x[:, 0] * x[:, 1]) ** x[:, 1] * t[2])","$$\left({{\left(x_{1} \cdot \theta_{0}\right)^{\left({\theta_{1}^{x_{0}}} \cdot x_{1}\right)}}^{x_{1}}} \cdot \theta_{2}\right)$$",-0.4367803621166929;-0.6787152430320464;0.9480502352450613,13,,,,Infinity,,,,,, +778,0,(Log(Log(((0.16964093392432333 + (-4.7787353464490256e-2 * x1)) ^ (x1 / (x1 ^ -0.9061901037067533))))) ^ (Log(((x1 + x1) * 0.7950415273975762)) / Exp(Exp((x1 ^ Log(x1)))))),"np.log(np.log((t[0] + (t[1] * x[:, 1])) ** (x[:, 1] / x[:, 1] ** t[2]))) ** (np.log(((x[:, 1] + x[:, 1]) * t[3])) / np.exp(np.exp(x[:, 1] ** np.log(x[:, 1]))))","$${\operatorname{log}(\operatorname{log}({\left(\theta_{0} + \left(\theta_{1} \cdot x_{1}\right)\right)^{\frac{x_{1}}{{x_{1}^{\theta_{2}}}}}}))^{\frac{\operatorname{log}(\left(\left(x_{1} + x_{1}\right) \cdot \theta_{3}\right))}{\operatorname{exp}(\operatorname{exp}({x_{1}^{\operatorname{log}(x_{1})}}))}}}$$",0.16964093392432333;-4.7787353464490256e-2;-0.9061901037067533;0.7950415273975762,27,,,,Infinity,,,,,, +779,0,Exp(Log(((-0.9305421108430194 / (0.7477489540222606 / (x0 * (0.9890682210947718 * x1)))) * Exp(x0)))),"np.exp(np.log(((t[0] / (t[1] / (x[:, 0] * (t[2] * x[:, 1])))) * np.exp(x[:, 0]))))","$$\operatorname{exp}(\operatorname{log}(\left(\frac{\theta_{0}}{\frac{\theta_{1}}{\left(x_{0} \cdot \left(\theta_{2} \cdot x_{1}\right)\right)}} \cdot \operatorname{exp}(x_{0})\right)))$$",-0.9305421108430194;0.7477489540222606;0.9890682210947718,14,,,,Infinity,,,,,, +780,0,(Exp(Exp(Log(Log((x0 + -0.5906333643363312))))) ^ ((Exp((-0.48546986950428495 ^ x1)) * (x0 ^ (x0 + 0.9514226089502451))) / (Log((x0 * -0.6326159418592074)) + 0.25547466131276286))),"np.exp(np.exp(np.log(np.log((x[:, 0] + t[0]))))) ** ((np.exp(t[1] ** x[:, 1]) * x[:, 0] ** (x[:, 0] + t[2])) / (np.log((x[:, 0] * t[3])) + t[4]))","$${\operatorname{exp}(\operatorname{exp}(\operatorname{log}(\operatorname{log}(\left(x_{0} + \theta_{0}\right)))))^{\frac{\left(\operatorname{exp}({\theta_{1}^{x_{1}}}) \cdot {x_{0}^{\left(x_{0} + \theta_{2}\right)}}\right)}{\left(\operatorname{log}(\left(x_{0} \cdot \theta_{3}\right)) + \theta_{4}\right)}}}$$",-0.5906333643363312;-0.48546986950428495;0.9514226089502451;-0.6326159418592074;0.25547466131276286,25,,,,Infinity,,,,,, +781,0,Exp(Exp(((-0.15623621928522946 ^ ((x1 * -7.211609403215591e-2) + -0.4305983742431767)) / ((x0 * (-0.12613102954026456 * x1)) / (x1 ^ 0.8860055376866759))))),"np.exp(np.exp((t[0] ** ((x[:, 1] * t[1]) + t[2]) / ((x[:, 0] * (t[3] * x[:, 1])) / x[:, 1] ** t[4]))))","$$\operatorname{exp}(\operatorname{exp}(\frac{{\theta_{0}^{\left(\left(x_{1} \cdot \theta_{1}\right) + \theta_{2}\right)}}}{\frac{\left(x_{0} \cdot \left(\theta_{3} \cdot x_{1}\right)\right)}{{x_{1}^{\theta_{4}}}}}))$$",-0.15623621928522946;-7.211609403215591e-2;-0.4305983742431767;-0.12613102954026456;0.8860055376866759,19,,,,Infinity,,,,,, +782,0,(Log(((((0.7685769251526475 / x1) / ((x0 * -0.8194763890753733) * -0.10531899578221227)) ^ -0.8069787858968884) + (-0.3632241556516428 * x1))) / (Log(Log(Exp(Log(x1)))) * ((Exp(Exp(x0)) + Log((x1 * -0.9691031956350175))) * Log(x1)))),"(np.log((((t[0] / x[:, 1]) / ((x[:, 0] * t[1]) * t[2])) ** t[3] + (t[4] * x[:, 1]))) / (np.log(np.log(np.exp(np.log(x[:, 1])))) * ((np.exp(np.exp(x[:, 0])) + np.log((x[:, 1] * t[5]))) * np.log(x[:, 1]))))","$$\frac{\operatorname{log}(\left({\frac{\frac{\theta_{0}}{x_{1}}}{\left(\left(x_{0} \cdot \theta_{1}\right) \cdot \theta_{2}\right)}^{\theta_{3}}} + \left(\theta_{4} \cdot x_{1}\right)\right))}{\left(\operatorname{log}(\operatorname{log}(\operatorname{exp}(\operatorname{log}(x_{1})))) \cdot \left(\left(\operatorname{exp}(\operatorname{exp}(x_{0})) + \operatorname{log}(\left(x_{1} \cdot \theta_{5}\right))\right) \cdot \operatorname{log}(x_{1})\right)\right)}$$",0.7685769251526475;-0.8194763890753733;-0.10531899578221227;-0.8069787858968884;-0.3632241556516428;-0.9691031956350175,34,,,,Infinity,,,,,, +783,0,((Log(((0.17324906432387743 + x0) ^ (-0.8115085816147067 ^ (x0 + (x0 * -0.21569501352452103))))) + Exp(Exp(x1))) ^ (0.3231796268506838 * ((x1 * (Log((-0.2882640062353361 ^ x0)) + -0.6559454614272653)) / Log((x0 / x1))))),"(np.log((t[0] + x[:, 0]) ** t[1] ** (x[:, 0] + (x[:, 0] * t[2]))) + np.exp(np.exp(x[:, 1]))) ** (t[3] * ((x[:, 1] * (np.log(t[4] ** x[:, 0]) + t[5])) / np.log((x[:, 0] / x[:, 1]))))","$${\left(\operatorname{log}({\left(\theta_{0} + x_{0}\right)^{{\theta_{1}^{\left(x_{0} + \left(x_{0} \cdot \theta_{2}\right)\right)}}}}) + \operatorname{exp}(\operatorname{exp}(x_{1}))\right)^{\left(\theta_{3} \cdot \frac{\left(x_{1} \cdot \left(\operatorname{log}({\theta_{4}^{x_{0}}}) + \theta_{5}\right)\right)}{\operatorname{log}(\frac{x_{0}}{x_{1}})}\right)}}$$",0.17324906432387743;-0.8115085816147067;-0.21569501352452103;0.3231796268506838;-0.2882640062353361;-0.6559454614272653,32,,,,Infinity,,,,,, +784,0,(((Log((Exp(Log(x1)) ^ x0)) * x1) + x0) / x1),"(((np.log(np.exp(np.log(x[:, 1])) ** x[:, 0]) * x[:, 1]) + x[:, 0]) / x[:, 1])","$$\frac{\left(\left(\operatorname{log}({\operatorname{exp}(\operatorname{log}(x_{1}))^{x_{0}}}) \cdot x_{1}\right) + x_{0}\right)}{x_{1}}$$",,12,9.893760136908577,9.893760136908577,9.893760136908577,9.893760136908577,-912.0076700483182,-912.0076700483182,-912.0076700483182,33.857682623375744,33.857682623375744,33.857682623375744 +785,0,(((((0.39926549297398295 * x0) ^ Exp((-0.8728775937518413 / x0))) + (-0.27702574965001936 ^ x0)) ^ 9.725151836339596e-2) ^ 0.25002445179117805),"((t[0] * x[:, 0]) ** np.exp((t[1] / x[:, 0])) + t[2] ** x[:, 0]) ** t[3] ** t[4]","$${{\left({\left(\theta_{0} \cdot x_{0}\right)^{\operatorname{exp}(\frac{\theta_{1}}{x_{0}})}} + {\theta_{2}^{x_{0}}}\right)^{\theta_{3}}}^{\theta_{4}}}$$",0.39926549297398295;-0.8728775937518413;-0.27702574965001936;9.725151836339596e-2;0.25002445179117805,16,,,,Infinity,,,,,, +786,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 +787,0,((Log(x0) / Log((-0.525338045221867 / x1))) * ((x1 + 0.13093644899579127) / x0)),"((np.log(x[:, 0]) / np.log((t[0] / x[:, 1]))) * ((x[:, 1] + t[1]) / x[:, 0]))","$$\left(\frac{\operatorname{log}(x_{0})}{\operatorname{log}(\frac{\theta_{0}}{x_{1}})} \cdot \frac{\left(x_{1} + \theta_{1}\right)}{x_{0}}\right)$$",-0.525338045221867;0.13093644899579127,13,,,,Infinity,,,,,, +788,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 +789,0,Exp(Log((((0.49875726532052156 * (x1 / x1)) * -0.7831386640843926) * ((-0.5236830248643332 + (x0 * 0.7044297247956577)) / ((x1 ^ -2.8034173264965156e-2) ^ (x1 * -0.6813253321761963)))))),"np.exp(np.log((((t[0] * (x[:, 1] / x[:, 1])) * t[1]) * ((t[2] + (x[:, 0] * t[3])) / x[:, 1] ** t[4] ** (x[:, 1] * t[5])))))","$$\operatorname{exp}(\operatorname{log}(\left(\left(\left(\theta_{0} \cdot \frac{x_{1}}{x_{1}}\right) \cdot \theta_{1}\right) \cdot \frac{\left(\theta_{2} + \left(x_{0} \cdot \theta_{3}\right)\right)}{{{x_{1}^{\theta_{4}}}^{\left(x_{1} \cdot \theta_{5}\right)}}}\right)))$$",0.49875726532052156;-0.7831386640843926;-0.5236830248643332;0.7044297247956577;-2.8034173264965156e-2;-0.6813253321761963,23,,,,Infinity,,,,,, +790,0,Log(((0.5158587931975145 ^ (((-0.9242055234338773 ^ x0) * -0.6817115629485607) / (Exp((-0.596549210392747 + x1)) ^ ((x1 * x0) * x1)))) ^ ((0.2082314437174302 ^ (x1 * -0.9258334060496257)) ^ x1))),"np.log(t[0] ** ((t[1] ** x[:, 0] * t[2]) / np.exp((t[3] + x[:, 1])) ** ((x[:, 1] * x[:, 0]) * x[:, 1])) ** t[4] ** (x[:, 1] * t[5]) ** x[:, 1])","$$\operatorname{log}({{\theta_{0}^{\frac{\left({\theta_{1}^{x_{0}}} \cdot \theta_{2}\right)}{{\operatorname{exp}(\left(\theta_{3} + x_{1}\right))^{\left(\left(x_{1} \cdot x_{0}\right) \cdot x_{1}\right)}}}}}^{{{\theta_{4}^{\left(x_{1} \cdot \theta_{5}\right)}}^{x_{1}}}}})$$",0.5158587931975145;-0.9242055234338773;-0.6817115629485607;-0.596549210392747;0.2082314437174302;-0.9258334060496257,27,,,,Infinity,,,,,, +791,0,Exp(((Exp(x1) * -0.5598042215370699) * ((8.502000742017803 ^ x0) * 0.6133335298883831))),"np.exp(((np.exp(x[:, 1]) * t[0]) * (t[1] ** x[:, 0] * t[2])))","$$\operatorname{exp}(\left(\left(\operatorname{exp}(x_{1}) \cdot \theta_{0}\right) \cdot \left({\theta_{1}^{x_{0}}} \cdot \theta_{2}\right)\right))$$",-0.5598042215370699;8.502000742017803;0.6133335298883831,11,8.192939965788988e-3,8.192939965788988e-3,8.192939965788988e-3,8.192939965788988e-3,0.24394598964392977,0.24394598964392977,0.24394598964392977,25.779502613580735,25.779502613580735,25.779502613580735 +792,0,((((x1 * x1) * -0.37583634384146036) * x0) ^ x1),"(((x[:, 1] * x[:, 1]) * t[0]) * x[:, 0]) ** x[:, 1]","$${\left(\left(\left(x_{1} \cdot x_{1}\right) \cdot \theta_{0}\right) \cdot x_{0}\right)^{x_{1}}}$$",-0.37583634384146036,9,,,,Infinity,,,,,, +793,0,((((x0 * 0.27080413209646503) / Exp((x0 * -0.9994015042527489))) / Log((-0.934150410390745 ^ Exp((x1 * 0.23718499961739214))))) ^ (Exp(Log(((0.7563395764616876 / x0) / Log((x1 + 0.9563054736851739))))) + ((0.8718868029157711 * Log(x0)) ^ Log(Exp((0.11766881358187509 + x0)))))),"(((x[:, 0] * t[0]) / np.exp((x[:, 0] * t[1]))) / np.log(t[2] ** np.exp((x[:, 1] * t[3])))) ** (np.exp(np.log(((t[4] / x[:, 0]) / np.log((x[:, 1] + t[5]))))) + (t[6] * np.log(x[:, 0])) ** np.log(np.exp((t[7] + x[:, 0]))))","$${\frac{\frac{\left(x_{0} \cdot \theta_{0}\right)}{\operatorname{exp}(\left(x_{0} \cdot \theta_{1}\right))}}{\operatorname{log}({\theta_{2}^{\operatorname{exp}(\left(x_{1} \cdot \theta_{3}\right))}})}^{\left(\operatorname{exp}(\operatorname{log}(\frac{\frac{\theta_{4}}{x_{0}}}{\operatorname{log}(\left(x_{1} + \theta_{5}\right))})) + {\left(\theta_{6} \cdot \operatorname{log}(x_{0})\right)^{\operatorname{log}(\operatorname{exp}(\left(\theta_{7} + x_{0}\right)))}}\right)}}$$",0.27080413209646503;-0.9994015042527489;-0.934150410390745;0.23718499961739214;0.7563395764616876;0.9563054736851739;0.8718868029157711;0.11766881358187509,38,,,,Infinity,,,,,, +794,0,Log(Log(((((x0 / x0) / Exp(x1)) + ((0.25357866250721994 + x1) ^ (0.7225781478336281 * x1))) / (((x0 * x0) * 0.14370804561562356) ^ (-0.40403960841478814 ^ (x0 * -0.7113248994320108)))))),"np.log(np.log(((((x[:, 0] / x[:, 0]) / np.exp(x[:, 1])) + (t[0] + x[:, 1]) ** (t[1] * x[:, 1])) / ((x[:, 0] * x[:, 0]) * t[2]) ** t[3] ** (x[:, 0] * t[4]))))","$$\operatorname{log}(\operatorname{log}(\frac{\left(\frac{\frac{x_{0}}{x_{0}}}{\operatorname{exp}(x_{1})} + {\left(\theta_{0} + x_{1}\right)^{\left(\theta_{1} \cdot x_{1}\right)}}\right)}{{\left(\left(x_{0} \cdot x_{0}\right) \cdot \theta_{2}\right)^{{\theta_{3}^{\left(x_{0} \cdot \theta_{4}\right)}}}}}))$$",0.25357866250721994;0.7225781478336281;0.14370804561562356;-0.40403960841478814;-0.7113248994320108,28,,,,Infinity,,,,,, +795,0,((Exp(((x1 ^ x1) + (x1 + -9.035460487984881))) * ((x1 * 5.02409192953763) * Exp(Exp(x0)))) + (-2.72864583448017e-2 + Exp((-1.0273719957301712 * (x0 / x1))))),"((np.exp((x[:, 1] ** x[:, 1] + (x[:, 1] + t[0]))) * ((x[:, 1] * t[1]) * np.exp(np.exp(x[:, 0])))) + (t[2] + np.exp((t[3] * (x[:, 0] / x[:, 1])))))","$$\left(\left(\operatorname{exp}(\left({x_{1}^{x_{1}}} + \left(x_{1} + \theta_{0}\right)\right)) \cdot \left(\left(x_{1} \cdot \theta_{1}\right) \cdot \operatorname{exp}(\operatorname{exp}(x_{0}))\right)\right) + \left(\theta_{2} + \operatorname{exp}(\left(\theta_{3} \cdot \frac{x_{0}}{x_{1}}\right))\right)\right)$$",-9.035460487984881;5.02409192953763;-2.72864583448017e-2;-1.0273719957301712,25,3.346161891230202e-3,3.346161891230202e-3,3.346161891230202e-3,3.346161891230202e-3,0.6912122964736485,0.6912122964736485,0.6912122964736485,60.072749829168565,60.072749829168565,60.072749829168565 +796,0,(Exp(((0.30829079167710305 * (((x1 * -0.7600116328062176) + -0.9927892087362202) / x0)) * Log(x1))) / Log(((((x0 * Exp(x1)) + 0.6562537912214975) * -0.5797398586402274) + Exp(Log((-0.41775588564565613 + (x1 ^ -0.5294163829955625))))))),"(np.exp(((t[0] * (((x[:, 1] * t[1]) + t[2]) / x[:, 0])) * np.log(x[:, 1]))) / np.log(((((x[:, 0] * np.exp(x[:, 1])) + t[3]) * t[4]) + np.exp(np.log((t[5] + x[:, 1] ** t[6]))))))","$$\frac{\operatorname{exp}(\left(\left(\theta_{0} \cdot \frac{\left(\left(x_{1} \cdot \theta_{1}\right) + \theta_{2}\right)}{x_{0}}\right) \cdot \operatorname{log}(x_{1})\right))}{\operatorname{log}(\left(\left(\left(\left(x_{0} \cdot \operatorname{exp}(x_{1})\right) + \theta_{3}\right) \cdot \theta_{4}\right) + \operatorname{exp}(\operatorname{log}(\left(\theta_{5} + {x_{1}^{\theta_{6}}}\right)))\right))}$$",0.30829079167710305;-0.7600116328062176;-0.9927892087362202;0.6562537912214975;-0.5797398586402274;-0.41775588564565613;-0.5294163829955625,31,,,,Infinity,,,,,, +797,0,Exp(((((0.860596177734323 + x0) ^ x1) + (Exp(x1) * 0.5388090805273347)) * Log(Log((Log(x0) + Log(x0)))))),"np.exp((((t[0] + x[:, 0]) ** x[:, 1] + (np.exp(x[:, 1]) * t[1])) * np.log(np.log((np.log(x[:, 0]) + np.log(x[:, 0]))))))","$$\operatorname{exp}(\left(\left({\left(\theta_{0} + x_{0}\right)^{x_{1}}} + \left(\operatorname{exp}(x_{1}) \cdot \theta_{1}\right)\right) \cdot \operatorname{log}(\operatorname{log}(\left(\operatorname{log}(x_{0}) + \operatorname{log}(x_{0})\right)))\right))$$",0.860596177734323;0.5388090805273347,19,,,,Infinity,,,,,, +798,0,Log(((x1 ^ -0.3400741703680225) ^ 9.511310491777651e-2)),"np.log(x[:, 1] ** t[0] ** t[1])","$$\operatorname{log}({{x_{1}^{\theta_{0}}}^{\theta_{1}}})$$",-0.3400741703680225;9.511310491777651e-2,6,1.2823863842649908e-2,1.2823863842649908e-2,1.2823863842649908e-2,1.2823863842649908e-2,-0.18340104126003798,-0.18340104126003798,-0.18340104126003798,12.371157514913008,12.371157514913008,12.371157514913008 +799,0,(((0.8186085495253432 + (x0 / (x0 * -0.572651217069895))) ^ (0.4997019860793134 + (1.9258947379281022e-2 / x0))) * ((Exp(Log(x0)) / (-0.21560892148860744 ^ Exp(x0))) * (Exp(Exp(x0)) + 6.473353166887186e-2))),"((t[0] + (x[:, 0] / (x[:, 0] * t[1]))) ** (t[2] + (t[3] / x[:, 0])) * ((np.exp(np.log(x[:, 0])) / t[4] ** np.exp(x[:, 0])) * (np.exp(np.exp(x[:, 0])) + t[5])))","$$\left({\left(\theta_{0} + \frac{x_{0}}{\left(x_{0} \cdot \theta_{1}\right)}\right)^{\left(\theta_{2} + \frac{\theta_{3}}{x_{0}}\right)}} \cdot \left(\frac{\operatorname{exp}(\operatorname{log}(x_{0}))}{{\theta_{4}^{\operatorname{exp}(x_{0})}}} \cdot \left(\operatorname{exp}(\operatorname{exp}(x_{0})) + \theta_{5}\right)\right)\right)$$",0.8186085495253432;-0.572651217069895;0.4997019860793134;1.9258947379281022e-2;-0.21560892148860744;6.473353166887186e-2,28,,,,Infinity,,,,,, +800,0,((Exp(Exp(((x0 ^ -2.8932671770531937e-2) + -0.8218652491668262))) + ((x1 * 0.934591257832466) * (-0.958684297843758 * (-9.619944605824282e-2 / x1)))) * Log(Exp(Log((-0.2950784446184892 ^ (Exp(x0) * -0.7193790636704556)))))),"((np.exp(np.exp((x[:, 0] ** t[0] + t[1]))) + ((x[:, 1] * t[2]) * (t[3] * (t[4] / x[:, 1])))) * np.log(np.exp(np.log(t[5] ** (np.exp(x[:, 0]) * t[6])))))","$$\left(\left(\operatorname{exp}(\operatorname{exp}(\left({x_{0}^{\theta_{0}}} + \theta_{1}\right))) + \left(\left(x_{1} \cdot \theta_{2}\right) \cdot \left(\theta_{3} \cdot \frac{\theta_{4}}{x_{1}}\right)\right)\right) \cdot \operatorname{log}(\operatorname{exp}(\operatorname{log}({\theta_{5}^{\left(\operatorname{exp}(x_{0}) \cdot \theta_{6}\right)}})))\right)$$",-2.8932671770531937e-2;-0.8218652491668262;0.934591257832466;-0.958684297843758;-9.619944605824282e-2;-0.2950784446184892;-0.7193790636704556,27,,,,Infinity,,,,,, +801,0,Exp(Exp((Log(x0) + Log((x1 ^ x1))))),"np.exp(np.exp((np.log(x[:, 0]) + np.log(x[:, 1] ** x[:, 1]))))","$$\operatorname{exp}(\operatorname{exp}(\left(\operatorname{log}(x_{0}) + \operatorname{log}({x_{1}^{x_{1}}})\right)))$$",,9,4.766195175066787,4.766195175066787,4.766195175066787,4.766195175066787,-438.83002332447523,-438.83002332447523,-438.83002332447523,20.4154108806126,20.4154108806126,20.4154108806126 +802,0,((((Exp(x1) / (-0.3226462518365091 * x0)) + Log((0.5255080350918439 + x0))) ^ (-0.3404315760929416 ^ (-0.34447809617801795 / (x0 * -0.886188569053914)))) / Log(Exp((((x0 / x0) ^ (x0 * x1)) + ((x1 ^ -0.44266253887504103) / ((x1 ^ x1) * 0.40333533842407554)))))),"(((np.exp(x[:, 1]) / (t[0] * x[:, 0])) + np.log((t[1] + x[:, 0]))) ** t[2] ** (t[3] / (x[:, 0] * t[4])) / np.log(np.exp(((x[:, 0] / x[:, 0]) ** (x[:, 0] * x[:, 1]) + (x[:, 1] ** t[5] / (x[:, 1] ** x[:, 1] * t[6]))))))","$$\frac{{\left(\frac{\operatorname{exp}(x_{1})}{\left(\theta_{0} \cdot x_{0}\right)} + \operatorname{log}(\left(\theta_{1} + x_{0}\right))\right)^{{\theta_{2}^{\frac{\theta_{3}}{\left(x_{0} \cdot \theta_{4}\right)}}}}}}{\operatorname{log}(\operatorname{exp}(\left({\frac{x_{0}}{x_{0}}^{\left(x_{0} \cdot x_{1}\right)}} + \frac{{x_{1}^{\theta_{5}}}}{\left({x_{1}^{x_{1}}} \cdot \theta_{6}\right)}\right)))}$$",-0.3226462518365091;0.5255080350918439;-0.3404315760929416;-0.34447809617801795;-0.886188569053914;-0.44266253887504103;0.40333533842407554,39,,,,Infinity,,,,,, +803,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 +804,0,((-0.36376089807770284 ^ (x0 * -0.7352656471771748)) + (x0 + -0.8871086353371543)),"(t[0] ** (x[:, 0] * t[1]) + (x[:, 0] + t[2]))","$$\left({\theta_{0}^{\left(x_{0} \cdot \theta_{1}\right)}} + \left(x_{0} + \theta_{2}\right)\right)$$",-0.36376089807770284;-0.7352656471771748;-0.8871086353371543,9,,,,Infinity,,,,,, +805,0,(Exp((((0.16565316113433598 * (-0.7577178319426278 / x1)) + -0.4288811734340663) + (Exp(((2.8500900920327275 * x0) + (8.82627316291723e-3 ^ x1))) ^ 0.6891908399219772))) ^ (x1 + -0.8258406619730053)),"np.exp((((t[0] * (t[1] / x[:, 1])) + t[2]) + np.exp(((t[3] * x[:, 0]) + t[4] ** x[:, 1])) ** t[5])) ** (x[:, 1] + t[6])","$${\operatorname{exp}(\left(\left(\left(\theta_{0} \cdot \frac{\theta_{1}}{x_{1}}\right) + \theta_{2}\right) + {\operatorname{exp}(\left(\left(\theta_{3} \cdot x_{0}\right) + {\theta_{4}^{x_{1}}}\right))^{\theta_{5}}}\right))^{\left(x_{1} + \theta_{6}\right)}}$$",0.16565316113433598;-0.7577178319426278;-0.4288811734340663;2.8500900920327275;8.82627316291723e-3;0.6891908399219772;-0.8258406619730053,23,2.2994154720245895e-3,2.2994154720245895e-3,2.2994154720245895e-3,2.2994154720245895e-3,0.7878072710945869,0.7878072710945869,0.7878072710945869,61.97569948013453,61.97569948013453,61.97569948013453 +806,0,((((x0 / x0) + (x1 * 0.5047240767710603)) + (-0.5250687668643382 ^ (5.611455517469932e-2 + (x0 ^ x1)))) / ((Exp((x0 + -0.25371664005813443)) + (x1 / x0)) + (Exp((x1 * 0.7923932134227526)) + 0.7647029819074077))),"((((x[:, 0] / x[:, 0]) + (x[:, 1] * t[0])) + t[1] ** (t[2] + x[:, 0] ** x[:, 1])) / ((np.exp((x[:, 0] + t[3])) + (x[:, 1] / x[:, 0])) + (np.exp((x[:, 1] * t[4])) + t[5])))","$$\frac{\left(\left(\frac{x_{0}}{x_{0}} + \left(x_{1} \cdot \theta_{0}\right)\right) + {\theta_{1}^{\left(\theta_{2} + {x_{0}^{x_{1}}}\right)}}\right)}{\left(\left(\operatorname{exp}(\left(x_{0} + \theta_{3}\right)) + \frac{x_{1}}{x_{0}}\right) + \left(\operatorname{exp}(\left(x_{1} \cdot \theta_{4}\right)) + \theta_{5}\right)\right)}$$",0.5047240767710603;-0.5250687668643382;5.611455517469932e-2;-0.25371664005813443;0.7923932134227526;0.7647029819074077,31,,,,Infinity,,,,,, +807,0,(x0 * 3.524205325424168e-2),"(x[:, 0] * t[0])","$$\left(x_{0} \cdot \theta_{0}\right)$$",3.524205325424168e-2,3,1.306654401552658e-2,1.306654401552658e-2,1.306654401552658e-2,1.306654401552658e-2,-0.20579584931471184,-0.20579584931471184,-0.20579584931471184,5.328521690985943,5.328521690985943,5.328521690985943 +808,0,(((x0 + x1) * 0.10112421339494736) * Log((Exp(x1) + (-4.816162089125252e-2 * x1)))),"(((x[:, 0] + x[:, 1]) * t[0]) * np.log((np.exp(x[:, 1]) + (t[1] * x[:, 1]))))","$$\left(\left(\left(x_{0} + x_{1}\right) \cdot \theta_{0}\right) \cdot \operatorname{log}(\left(\operatorname{exp}(x_{1}) + \left(\theta_{1} \cdot x_{1}\right)\right))\right)$$",0.10112421339494736;-4.816162089125252e-2,13,1.160418552771825e-2,1.160418552771825e-2,1.160418552771825e-2,1.160418552771825e-2,-7.084771056324568e-2,-7.084771056324568e-2,-7.084771056324568e-2,29.3491255754293,29.3491255754293,29.3491255754293 +809,0,(((x1 + x0) / (x1 + 86.01058682072575)) + Exp(((x0 ^ -18.986353044583588) ^ 4.585800995117751e-3))),"(((x[:, 1] + x[:, 0]) / (x[:, 1] + t[0])) + np.exp(x[:, 0] ** t[1] ** t[2]))","$$\left(\frac{\left(x_{1} + x_{0}\right)}{\left(x_{1} + \theta_{0}\right)} + \operatorname{exp}({{x_{0}^{\theta_{1}}}^{\theta_{2}}})\right)$$",86.01058682072575;-18.986353044583588;4.585800995117751e-3,14,7.155554362330875,7.155554362330875,7.155554362330875,7.155554362330875,-659.3228626782869,-659.3228626782869,-659.3228626782869,39.74551581897509,39.74551581897509,39.74551581897509 +810,0,Log(Log(Log(Log((Exp(x0) * (x0 + 0.2874220571246755)))))),"np.log(np.log(np.log(np.log((np.exp(x[:, 0]) * (x[:, 0] + t[0]))))))","$$\operatorname{log}(\operatorname{log}(\operatorname{log}(\operatorname{log}(\left(\operatorname{exp}(x_{0}) \cdot \left(x_{0} + \theta_{0}\right)\right)))))$$",0.2874220571246755,10,,,,Infinity,,,,,, +811,0,Exp((-3.0680971858662263 + x1)),"np.exp((t[0] + x[:, 1]))","$$\operatorname{exp}(\left(\theta_{0} + x_{1}\right))$$",-3.0680971858662263,4,1.0205484150379933e-2,1.0205484150379933e-2,1.0205484150379933e-2,1.0205484150379933e-2,5.8226076141275396e-2,5.8226076141275396e-2,5.8226076141275396e-2,7.575287315582544,7.575287315582544,7.575287315582544 +812,0,Exp(((Log((Exp((0.2357350295660664 ^ x1)) * ((-0.44482973125642955 / x0) * (x0 + -0.761824553569654)))) + 0.3155764437469726) * x1)),"np.exp(((np.log((np.exp(t[0] ** x[:, 1]) * ((t[1] / x[:, 0]) * (x[:, 0] + t[2])))) + t[3]) * x[:, 1]))","$$\operatorname{exp}(\left(\left(\operatorname{log}(\left(\operatorname{exp}({\theta_{0}^{x_{1}}}) \cdot \left(\frac{\theta_{1}}{x_{0}} \cdot \left(x_{0} + \theta_{2}\right)\right)\right)) + \theta_{3}\right) \cdot x_{1}\right))$$",0.2357350295660664;-0.44482973125642955;-0.761824553569654;0.3155764437469726,18,,,,Infinity,,,,,, +813,0,(((((x0 / (x1 * (x1 / x0))) / Log((0.7975815811042215 + Log(x0)))) / (Log((0.13720430752127133 * (0.13661884928334211 ^ x0))) ^ x1)) * ((0.23316209756683615 / x0) + ((x1 * -0.31231708713905015) ^ 8.333530451736071e-2))) / ((-0.5001813315209166 * x1) * x1)),"(((((x[:, 0] / (x[:, 1] * (x[:, 1] / x[:, 0]))) / np.log((t[0] + np.log(x[:, 0])))) / np.log((t[1] * t[2] ** x[:, 0])) ** x[:, 1]) * ((t[3] / x[:, 0]) + (x[:, 1] * t[4]) ** t[5])) / ((t[6] * x[:, 1]) * x[:, 1]))","$$\frac{\left(\frac{\frac{\frac{x_{0}}{\left(x_{1} \cdot \frac{x_{1}}{x_{0}}\right)}}{\operatorname{log}(\left(\theta_{0} + \operatorname{log}(x_{0})\right))}}{{\operatorname{log}(\left(\theta_{1} \cdot {\theta_{2}^{x_{0}}}\right))^{x_{1}}}} \cdot \left(\frac{\theta_{3}}{x_{0}} + {\left(x_{1} \cdot \theta_{4}\right)^{\theta_{5}}}\right)\right)}{\left(\left(\theta_{6} \cdot x_{1}\right) \cdot x_{1}\right)}$$",0.7975815811042215;0.13720430752127133;0.13661884928334211;0.23316209756683615;-0.31231708713905015;8.333530451736071e-2;-0.5001813315209166,38,,,,Infinity,,,,,, +814,0,((-6.901094135664387e-2 ^ ((-3.220679307750207e-2 * x0) ^ (x1 / (x1 / x1)))) ^ Log(Exp((-8.897366760242165e-2 * ((x1 * -0.4860598888155555) * 0.5164951795844065))))),"t[0] ** (t[1] * x[:, 0]) ** (x[:, 1] / (x[:, 1] / x[:, 1])) ** np.log(np.exp((t[2] * ((x[:, 1] * t[3]) * t[4]))))","$${{\theta_{0}^{{\left(\theta_{1} \cdot x_{0}\right)^{\frac{x_{1}}{\frac{x_{1}}{x_{1}}}}}}}^{\operatorname{log}(\operatorname{exp}(\left(\theta_{2} \cdot \left(\left(x_{1} \cdot \theta_{3}\right) \cdot \theta_{4}\right)\right)))}}$$",-6.901094135664387e-2;-3.220679307750207e-2;-8.897366760242165e-2;-0.4860598888155555;0.5164951795844065,21,,,,Infinity,,,,,, +815,0,(0.8607152305155498 ^ ((-0.989956951235872 / x1) / (x0 * -6.314782339084148e-2))),"t[0] ** ((t[1] / x[:, 1]) / (x[:, 0] * t[2]))","$${\theta_{0}^{\frac{\frac{\theta_{1}}{x_{1}}}{\left(x_{0} \cdot \theta_{2}\right)}}}$$",0.8607152305155498;-0.989956951235872;-6.314782339084148e-2,9,1.4451935024560445e-2,1.4451935024560445e-2,1.4451935024560445e-2,1.4451935024560445e-2,-0.33364133978150656,-0.33364133978150656,-0.33364133978150656,22.20161677067752,22.20161677067752,22.20161677067752 +816,0,(Log(Exp((3.8791856406079905e-3 / x0))) * ((x0 / x1) ^ x0)),"(np.log(np.exp((t[0] / x[:, 0]))) * (x[:, 0] / x[:, 1]) ** x[:, 0])","$$\left(\operatorname{log}(\operatorname{exp}(\frac{\theta_{0}}{x_{0}})) \cdot {\frac{x_{0}}{x_{1}}^{x_{0}}}\right)$$",3.8791856406079905e-3,11,1.4057506218978536e-2,1.4057506218978536e-2,1.4057506218978536e-2,1.4057506218978536e-2,-0.297242991751937,-0.297242991751937,-0.297242991751937,24.90743364944291,24.90743364944291,24.90743364944291 +817,0,((-0.119730649807676 ^ x0) + 0.5837449352727875),"(t[0] ** x[:, 0] + t[1])","$$\left({\theta_{0}^{x_{0}}} + \theta_{1}\right)$$",-0.119730649807676;0.5837449352727875,5,,,,Infinity,,,,,, +818,0,Exp(Exp(((0.6122078697768832 * x1) / (-3.0368470133463614e-3 / (0.7562719246304952 / x0))))),"np.exp(np.exp(((t[0] * x[:, 1]) / (t[1] / (t[2] / x[:, 0])))))","$$\operatorname{exp}(\operatorname{exp}(\frac{\left(\theta_{0} \cdot x_{1}\right)}{\frac{\theta_{1}}{\frac{\theta_{2}}{x_{0}}}}))$$",0.6122078697768832;-3.0368470133463614e-3;0.7562719246304952,11,0.8923098659403457,0.8923098659403457,0.8923098659403457,0.8923098659403457,-81.34339021664219,-81.34339021664219,-81.34339021664219,26.575207846957838,26.575207846957838,26.575207846957838 +819,0,(-0.271594290590885 + x1),"(t[0] + x[:, 1])","$$\left(\theta_{0} + x_{1}\right)$$",-0.271594290590885,3,1.8305762287934728e-2,1.8305762287934728e-2,1.8305762287934728e-2,1.8305762287934728e-2,-0.6892769931440774,-0.6892769931440774,-0.6892769931440774,5.3332369874311105,5.3332369874311105,5.3332369874311105 +820,0,(Log((Log(x1) * (-0.717173079464597 + (((x1 ^ -0.7119301019777435) ^ -0.701063163199424) ^ (x1 ^ x0))))) / ((Exp(x1) ^ (-0.9857068848517935 * (((x0 * x1) / x1) * x1))) * x0)),"(np.log((np.log(x[:, 1]) * (t[0] + x[:, 1] ** t[1] ** t[2] ** x[:, 1] ** x[:, 0]))) / (np.exp(x[:, 1]) ** (t[3] * (((x[:, 0] * x[:, 1]) / x[:, 1]) * x[:, 1])) * x[:, 0]))","$$\frac{\operatorname{log}(\left(\operatorname{log}(x_{1}) \cdot \left(\theta_{0} + {{{x_{1}^{\theta_{1}}}^{\theta_{2}}}^{{x_{1}^{x_{0}}}}}\right)\right))}{\left({\operatorname{exp}(x_{1})^{\left(\theta_{3} \cdot \left(\frac{\left(x_{0} \cdot x_{1}\right)}{x_{1}} \cdot x_{1}\right)\right)}} \cdot x_{0}\right)}$$",-0.717173079464597;-0.7119301019777435;-0.701063163199424;-0.9857068848517935,30,,,,Infinity,,,,,, +821,0,(Exp(((-7.492120284718502e7 + (x0 / x1)) + (x1 * -8429856.192059038))) + (Log(Exp(Log((x0 * x1)))) / (-15859.451384205868 / (3964.4389464495534 * x1)))),"(np.exp(((t[0] + (x[:, 0] / x[:, 1])) + (x[:, 1] * t[1]))) + (np.log(np.exp(np.log((x[:, 0] * x[:, 1])))) / (t[2] / (t[3] * x[:, 1]))))","$$\left(\operatorname{exp}(\left(\left(\theta_{0} + \frac{x_{0}}{x_{1}}\right) + \left(x_{1} \cdot \theta_{1}\right)\right)) + \frac{\operatorname{log}(\operatorname{exp}(\operatorname{log}(\left(x_{0} \cdot x_{1}\right))))}{\frac{\theta_{2}}{\left(\theta_{3} \cdot x_{1}\right)}}\right)$$",-7.492120284718502e7;-8429856.192059038;-15859.451384205868;3964.4389464495534,23,6.834809941818371e-3,6.834809941818371e-3,6.834809941818371e-3,6.834809941818371e-3,0.3692758047647082,0.3692758047647082,0.3692758047647082,55.91700652905442,55.91700652905442,55.91700652905442 +822,0,(-0.271594290590885 + x1),"(t[0] + x[:, 1])","$$\left(\theta_{0} + x_{1}\right)$$",-0.271594290590885,3,1.8305762287934728e-2,1.8305762287934728e-2,1.8305762287934728e-2,1.8305762287934728e-2,-0.6892769931440774,-0.6892769931440774,-0.6892769931440774,5.3332369874311105,5.3332369874311105,5.3332369874311105 +823,0,((Log((0.3274395176735474 / x0)) * (0.3486318874777974 ^ x1)) * ((-0.8602825450930792 ^ x1) * ((-0.25114478696694553 / x0) * -0.6421807537753351))),"((np.log((t[0] / x[:, 0])) * t[1] ** x[:, 1]) * (t[2] ** x[:, 1] * ((t[3] / x[:, 0]) * t[4])))","$$\left(\left(\operatorname{log}(\frac{\theta_{0}}{x_{0}}) \cdot {\theta_{1}^{x_{1}}}\right) \cdot \left({\theta_{2}^{x_{1}}} \cdot \left(\frac{\theta_{3}}{x_{0}} \cdot \theta_{4}\right)\right)\right)$$",0.3274395176735474;0.3486318874777974;-0.8602825450930792;-0.25114478696694553;-0.6421807537753351,18,,,,Infinity,,,,,, +824,0,Log(Exp(((x1 * 2.656489682660923e-2) * Exp((x1 + x0))))),"np.log(np.exp(((x[:, 1] * t[0]) * np.exp((x[:, 1] + x[:, 0])))))","$$\operatorname{log}(\operatorname{exp}(\left(\left(x_{1} \cdot \theta_{0}\right) \cdot \operatorname{exp}(\left(x_{1} + x_{0}\right))\right)))$$",2.656489682660923e-2,10,1.2292959544683221e-2,1.2292959544683221e-2,1.2292959544683221e-2,1.2292959544683221e-2,-0.13440857637330983,-0.13440857637330983,-0.13440857637330983,21.491090089510987,21.491090089510987,21.491090089510987 +825,0,Log((((-0.5030528707581052 + x0) + -0.1848494125470318) ^ ((-0.7502991654328901 ^ x1) * -0.3988116962015612))),"np.log(((t[0] + x[:, 0]) + t[1]) ** (t[2] ** x[:, 1] * t[3]))","$$\operatorname{log}({\left(\left(\theta_{0} + x_{0}\right) + \theta_{1}\right)^{\left({\theta_{2}^{x_{1}}} \cdot \theta_{3}\right)}})$$",-0.5030528707581052;-0.1848494125470318;-0.7502991654328901;-0.3988116962015612,12,,,,Infinity,,,,,, +826,0,((Exp((-3.9694654488007344 * x0)) * -1.443036348928453) * -1.4517310249432671),"((np.exp((t[0] * x[:, 0])) * t[1]) * t[2])","$$\left(\left(\operatorname{exp}(\left(\theta_{0} \cdot x_{0}\right)) \cdot \theta_{1}\right) \cdot \theta_{2}\right)$$",-3.9694654488007344;-1.443036348928453;-1.4517310249432671,8,4.956561573974e-3,4.956561573974e-3,4.956561573974e-3,4.956561573974e-3,0.5426027444082513,0.5426027444082513,0.5426027444082513,17.157590600478624,17.157590600478624,17.157590600478624 +827,0,Log(((Log((-4.110215411384821e-2 ^ (x0 + x0))) + (((-0.49766842071489625 + (x0 ^ x1)) * -0.19037692677206008) + 9.620003990849724e-3)) ^ Log(x0))),"np.log((np.log(t[0] ** (x[:, 0] + x[:, 0])) + (((t[1] + x[:, 0] ** x[:, 1]) * t[2]) + t[3])) ** np.log(x[:, 0]))","$$\operatorname{log}({\left(\operatorname{log}({\theta_{0}^{\left(x_{0} + x_{0}\right)}}) + \left(\left(\left(\theta_{1} + {x_{0}^{x_{1}}}\right) \cdot \theta_{2}\right) + \theta_{3}\right)\right)^{\operatorname{log}(x_{0})}})$$",-4.110215411384821e-2;-0.49766842071489625;-0.19037692677206008;9.620003990849724e-3,20,,,,Infinity,,,,,, +828,0,(((Exp(Exp(x1)) + (2.498400179996627 * (x0 + x0))) * ((x0 * 3.47166442918676e-3) + -0.140398499384249)) + Exp(Log((1.340163779635564 ^ (((x1 + x1) * x0) / (x1 ^ (x1 ^ x0))))))),"(((np.exp(np.exp(x[:, 1])) + (t[0] * (x[:, 0] + x[:, 0]))) * ((x[:, 0] * t[1]) + t[2])) + np.exp(np.log(t[3] ** (((x[:, 1] + x[:, 1]) * x[:, 0]) / x[:, 1] ** x[:, 1] ** x[:, 0]))))","$$\left(\left(\left(\operatorname{exp}(\operatorname{exp}(x_{1})) + \left(\theta_{0} \cdot \left(x_{0} + x_{0}\right)\right)\right) \cdot \left(\left(x_{0} \cdot \theta_{1}\right) + \theta_{2}\right)\right) + \operatorname{exp}(\operatorname{log}({\theta_{3}^{\frac{\left(\left(x_{1} + x_{1}\right) \cdot x_{0}\right)}{{x_{1}^{{x_{1}^{x_{0}}}}}}}}))\right)$$",2.498400179996627;3.47166442918676e-3;-0.140398499384249;1.340163779635564,31,1.1531868483048846e-2,1.1531868483048846e-2,1.1531868483048846e-2,1.1531868483048846e-2,-6.417420973598342e-2,-6.417420973598342e-2,-6.417420973598342e-2,76.20804032052811,76.20804032052811,76.20804032052811 +829,0,((Log(Exp(((x0 ^ (-0.4766123073337696 ^ x1)) * (-0.15454003386513604 + x0)))) ^ (Log((-0.8392629703079342 ^ (x1 + x0))) * -0.6798702154563827)) + Log(((Exp((0.8593510144073092 * (-0.9259076283296643 * x1))) * (-0.25208880334211314 / (-0.6058430775413997 ^ x0))) / ((0.5907079547405585 + (x1 + x0)) * (-0.47781208170796663 ^ (-0.6079879698128765 + x0)))))),"(np.log(np.exp((x[:, 0] ** t[0] ** x[:, 1] * (t[1] + x[:, 0])))) ** (np.log(t[2] ** (x[:, 1] + x[:, 0])) * t[3]) + np.log(((np.exp((t[4] * (t[5] * x[:, 1]))) * (t[6] / t[7] ** x[:, 0])) / ((t[8] + (x[:, 1] + x[:, 0])) * t[9] ** (t[10] + x[:, 0])))))","$$\left({\operatorname{log}(\operatorname{exp}(\left({x_{0}^{{\theta_{0}^{x_{1}}}}} \cdot \left(\theta_{1} + x_{0}\right)\right)))^{\left(\operatorname{log}({\theta_{2}^{\left(x_{1} + x_{0}\right)}}) \cdot \theta_{3}\right)}} + \operatorname{log}(\frac{\left(\operatorname{exp}(\left(\theta_{4} \cdot \left(\theta_{5} \cdot x_{1}\right)\right)) \cdot \frac{\theta_{6}}{{\theta_{7}^{x_{0}}}}\right)}{\left(\left(\theta_{8} + \left(x_{1} + x_{0}\right)\right) \cdot {\theta_{9}^{\left(\theta_{10} + x_{0}\right)}}\right)})\right)$$",-0.4766123073337696;-0.15454003386513604;-0.8392629703079342;-0.6798702154563827;0.8593510144073092;-0.9259076283296643;-0.25208880334211314;-0.6058430775413997;0.5907079547405585;-0.47781208170796663;-0.6079879698128765,46,,,,Infinity,,,,,, +830,0,(((Log((x0 + x0)) * (0.7542006365371476 ^ ((x1 * -0.9720764209792123) ^ x0))) * (2.97986422440617e-2 ^ x0)) * x1),"(((np.log((x[:, 0] + x[:, 0])) * t[0] ** (x[:, 1] * t[1]) ** x[:, 0]) * t[2] ** x[:, 0]) * x[:, 1])","$$\left(\left(\left(\operatorname{log}(\left(x_{0} + x_{0}\right)) \cdot {\theta_{0}^{{\left(x_{1} \cdot \theta_{1}\right)^{x_{0}}}}}\right) \cdot {\theta_{2}^{x_{0}}}\right) \cdot x_{1}\right)$$",0.7542006365371476;-0.9720764209792123;2.97986422440617e-2,18,,,,Infinity,,,,,, +831,0,Exp((Log((x0 * x1)) + Log((x1 * -0.7282089038335995)))),"np.exp((np.log((x[:, 0] * x[:, 1])) + np.log((x[:, 1] * t[0]))))","$$\operatorname{exp}(\left(\operatorname{log}(\left(x_{0} \cdot x_{1}\right)) + \operatorname{log}(\left(x_{1} \cdot \theta_{0}\right))\right))$$",-0.7282089038335995,10,,,,Infinity,,,,,, +832,0,(((-2.053765825952037 + Exp((x1 / Exp((x0 * x0))))) * x0) + x0),"(((t[0] + np.exp((x[:, 1] / np.exp((x[:, 0] * x[:, 0]))))) * x[:, 0]) + x[:, 0])","$$\left(\left(\left(\theta_{0} + \operatorname{exp}(\frac{x_{1}}{\operatorname{exp}(\left(x_{0} \cdot x_{0}\right))})\right) \cdot x_{0}\right) + x_{0}\right)$$",-2.053765825952037,13,3.8736952481521385e-3,3.8736952481521385e-3,3.8736952481521385e-3,3.8736952481521385e-3,0.6425309059394971,0.6425309059394971,0.6425309059394971,27.32124319881005,27.32124319881005,27.32124319881005 +833,0,(((Exp(Log((x1 / x0))) * 0.8248385089105095) ^ (4.470008169470366 / (x0 * (x1 + x1)))) + Log(Exp(((Log(x0) + (-0.5146508047921211 + x0)) * (Exp(x1) ^ ((x0 * -2.9637182488387595) * 2.9829623073006126)))))),"((np.exp(np.log((x[:, 1] / x[:, 0]))) * t[0]) ** (t[1] / (x[:, 0] * (x[:, 1] + x[:, 1]))) + np.log(np.exp(((np.log(x[:, 0]) + (t[2] + x[:, 0])) * np.exp(x[:, 1]) ** ((x[:, 0] * t[3]) * t[4])))))","$$\left({\left(\operatorname{exp}(\operatorname{log}(\frac{x_{1}}{x_{0}})) \cdot \theta_{0}\right)^{\frac{\theta_{1}}{\left(x_{0} \cdot \left(x_{1} + x_{1}\right)\right)}}} + \operatorname{log}(\operatorname{exp}(\left(\left(\operatorname{log}(x_{0}) + \left(\theta_{2} + x_{0}\right)\right) \cdot {\operatorname{exp}(x_{1})^{\left(\left(x_{0} \cdot \theta_{3}\right) \cdot \theta_{4}\right)}}\right)))\right)$$",0.8248385089105095;4.470008169470366;-0.5146508047921211;-2.9637182488387595;2.9829623073006126,33,2.6403595688863083e-2,2.6403595688863083e-2,2.6403595688863083e-2,2.6403595688863083e-2,-1.4365544592957074,-1.4365544592957074,-1.4365544592957074,82.63679896505343,82.63679896505343,82.63679896505343 +834,0,((x1 / x1) * (x1 * 0.20059950763971945)),"((x[:, 1] / x[:, 1]) * (x[:, 1] * t[0]))","$$\left(\frac{x_{1}}{x_{1}} \cdot \left(x_{1} \cdot \theta_{0}\right)\right)$$",0.20059950763971945,7,9.365750690525965e-3,9.365750690525965e-3,9.365750690525965e-3,9.365750690525965e-3,0.13571765457390583,0.13571765457390583,0.13571765457390583,11.733414638828348,11.733414638828348,11.733414638828348 +835,0,(2.166582100951547e-3 ^ (1.5855875414993816 * x1)),"t[0] ** (t[1] * x[:, 1])","$${\theta_{0}^{\left(\theta_{1} \cdot x_{1}\right)}}$$",2.166582100951547e-3;1.5855875414993816,5,2.8848383251169497e-2,2.8848383251169497e-2,2.8848383251169497e-2,2.8848383251169497e-2,-1.6621622934393332,-1.6621622934393332,-1.6621622934393332,10.999285221260786,10.999285221260786,10.999285221260786 +836,0,((-0.46142956451244765 * (0.33374438610845536 / (x0 ^ x1))) ^ Log(Log((x0 + x1)))),"(t[0] * (t[1] / x[:, 0] ** x[:, 1])) ** np.log(np.log((x[:, 0] + x[:, 1])))","$${\left(\theta_{0} \cdot \frac{\theta_{1}}{{x_{0}^{x_{1}}}}\right)^{\operatorname{log}(\operatorname{log}(\left(x_{0} + x_{1}\right)))}}$$",-0.46142956451244765;0.33374438610845536,13,,,,Infinity,,,,,, +837,0,((((-0.9932049196719133 * x1) + -0.3032419112971735) * Log((-0.6808890640881642 ^ (x0 / x0)))) / (((x0 * x1) * Log((0.8895106630645953 ^ x0))) + (Exp(Exp(x0)) ^ (x1 ^ 0.7857583282495737)))),"((((t[0] * x[:, 1]) + t[1]) * np.log(t[2] ** (x[:, 0] / x[:, 0]))) / (((x[:, 0] * x[:, 1]) * np.log(t[3] ** x[:, 0])) + np.exp(np.exp(x[:, 0])) ** x[:, 1] ** t[4]))","$$\frac{\left(\left(\left(\theta_{0} \cdot x_{1}\right) + \theta_{1}\right) \cdot \operatorname{log}({\theta_{2}^{\frac{x_{0}}{x_{0}}}})\right)}{\left(\left(\left(x_{0} \cdot x_{1}\right) \cdot \operatorname{log}({\theta_{3}^{x_{0}}})\right) + {\operatorname{exp}(\operatorname{exp}(x_{0}))^{{x_{1}^{\theta_{4}}}}}\right)}$$",-0.9932049196719133;-0.3032419112971735;-0.6808890640881642;0.8895106630645953;0.7857583282495737,29,,,,Infinity,,,,,, +838,0,Exp(Exp((-138.2431302405372 / (x0 / x1)))),"np.exp(np.exp((t[0] / (x[:, 0] / x[:, 1]))))","$$\operatorname{exp}(\operatorname{exp}(\frac{\theta_{0}}{\frac{x_{0}}{x_{1}}}))$$",-138.2431302405372,7,0.892310050889116,0.892310050889116,0.892310050889116,0.892310050889116,-81.3434072839294,-81.3434072839294,-81.3434072839294,14.090069368206548,14.090069368206548,14.090069368206548 +839,0,(((-0.6504600155342297 ^ (4.221697554408366e-2 / (Log((x1 ^ x0)) * 0.7344309506296558))) / Log((((Log(x1) + (x0 * -0.6781649678208725)) / ((x1 ^ -0.12943791708268448) + x0)) ^ ((x1 * -0.9364589833977045) / (-0.39314159374497326 / x0))))) * (0.6199325383278119 * Exp((0.6186206696468293 + x0)))),"((t[0] ** (t[1] / (np.log(x[:, 1] ** x[:, 0]) * t[2])) / np.log(((np.log(x[:, 1]) + (x[:, 0] * t[3])) / (x[:, 1] ** t[4] + x[:, 0])) ** ((x[:, 1] * t[5]) / (t[6] / x[:, 0])))) * (t[7] * np.exp((t[8] + x[:, 0]))))","$$\left(\frac{{\theta_{0}^{\frac{\theta_{1}}{\left(\operatorname{log}({x_{1}^{x_{0}}}) \cdot \theta_{2}\right)}}}}{\operatorname{log}({\frac{\left(\operatorname{log}(x_{1}) + \left(x_{0} \cdot \theta_{3}\right)\right)}{\left({x_{1}^{\theta_{4}}} + x_{0}\right)}^{\frac{\left(x_{1} \cdot \theta_{5}\right)}{\frac{\theta_{6}}{x_{0}}}}})} \cdot \left(\theta_{7} \cdot \operatorname{exp}(\left(\theta_{8} + x_{0}\right))\right)\right)$$",-0.6504600155342297;4.221697554408366e-2;0.7344309506296558;-0.6781649678208725;-0.12943791708268448;-0.9364589833977045;-0.39314159374497326;0.6199325383278119;0.6186206696468293,39,,,,Infinity,,,,,, +840,0,Log(((Log((0.9306771434542265 / (-0.5078833944178747 + x1))) + 1.7687957984541702e-2) / ((((x0 * x0) ^ 0.939487080880486) + -0.735096987688485) ^ Log((-0.8146912588455344 ^ x0))))),"np.log(((np.log((t[0] / (t[1] + x[:, 1]))) + t[2]) / ((x[:, 0] * x[:, 0]) ** t[3] + t[4]) ** np.log(t[5] ** x[:, 0])))","$$\operatorname{log}(\frac{\left(\operatorname{log}(\frac{\theta_{0}}{\left(\theta_{1} + x_{1}\right)}) + \theta_{2}\right)}{{\left({\left(x_{0} \cdot x_{0}\right)^{\theta_{3}}} + \theta_{4}\right)^{\operatorname{log}({\theta_{5}^{x_{0}}})}}})$$",0.9306771434542265;-0.5078833944178747;1.7687957984541702e-2;0.939487080880486;-0.735096987688485;-0.8146912588455344,22,,,,Infinity,,,,,, +841,0,(-0.9956750177908841 + x0),"(t[0] + x[:, 0])","$$\left(\theta_{0} + x_{0}\right)$$",-0.9956750177908841,3,0.15105329635364,0.15105329635364,0.15105329635364,0.15105329635364,-12.939373529228037,-12.939373529228037,-12.939373529228037,5.452709768090245,5.452709768090245,5.452709768090245 +842,0,((-0.8187475047722674 ^ (((-0.6335492717978208 * (-0.23355360194114172 ^ x0)) * -0.5004872772310853) * (Log((Log(x1) ^ (-0.3946859377759575 / x0))) * ((x0 / Exp(x1)) ^ (x1 * -5.347527745098946e-3))))) ^ Exp(((x0 * 0.11080286023370056) + -0.9674625359235983))),"t[0] ** (((t[1] * t[2] ** x[:, 0]) * t[3]) * (np.log(np.log(x[:, 1]) ** (t[4] / x[:, 0])) * (x[:, 0] / np.exp(x[:, 1])) ** (x[:, 1] * t[5]))) ** np.exp(((x[:, 0] * t[6]) + t[7]))","$${{\theta_{0}^{\left(\left(\left(\theta_{1} \cdot {\theta_{2}^{x_{0}}}\right) \cdot \theta_{3}\right) \cdot \left(\operatorname{log}({\operatorname{log}(x_{1})^{\frac{\theta_{4}}{x_{0}}}}) \cdot {\frac{x_{0}}{\operatorname{exp}(x_{1})}^{\left(x_{1} \cdot \theta_{5}\right)}}\right)\right)}}^{\operatorname{exp}(\left(\left(x_{0} \cdot \theta_{6}\right) + \theta_{7}\right))}}$$",-0.8187475047722674;-0.6335492717978208;-0.23355360194114172;-0.5004872772310853;-0.3946859377759575;-5.347527745098946e-3;0.11080286023370056;-0.9674625359235983,33,,,,Infinity,,,,,, +843,0,((Log(Exp((Log((x0 * -0.9769069455268177)) * -1.9414810060417897e-2))) ^ ((((x0 * (x1 * 0.3490175641464994)) ^ x1) * -0.6187807380980385) * Exp(x0))) * ((((((x1 * 3.159050709080313e-2) ^ x0) + -0.402975111878485) ^ (x0 ^ x1)) * -0.9897884154539336) / x1)),"(np.log(np.exp((np.log((x[:, 0] * t[0])) * t[1]))) ** (((x[:, 0] * (x[:, 1] * t[2])) ** x[:, 1] * t[3]) * np.exp(x[:, 0])) * ((((x[:, 1] * t[4]) ** x[:, 0] + t[5]) ** x[:, 0] ** x[:, 1] * t[6]) / x[:, 1]))","$$\left({\operatorname{log}(\operatorname{exp}(\left(\operatorname{log}(\left(x_{0} \cdot \theta_{0}\right)) \cdot \theta_{1}\right)))^{\left(\left({\left(x_{0} \cdot \left(x_{1} \cdot \theta_{2}\right)\right)^{x_{1}}} \cdot \theta_{3}\right) \cdot \operatorname{exp}(x_{0})\right)}} \cdot \frac{\left({\left({\left(x_{1} \cdot \theta_{4}\right)^{x_{0}}} + \theta_{5}\right)^{{x_{0}^{x_{1}}}}} \cdot \theta_{6}\right)}{x_{1}}\right)$$",-0.9769069455268177;-1.9414810060417897e-2;0.3490175641464994;-0.6187807380980385;3.159050709080313e-2;-0.402975111878485;-0.9897884154539336,37,,,,Infinity,,,,,, +844,0,(((((Exp(Log(x0)) + 0.20850060370198475) * 0.39511150930296546) ^ (Log(x1) ^ 0.8257794175657165)) * (-0.20517511469791816 / (Log(x1) ^ x0))) * 0.1295796306760718),"((((np.exp(np.log(x[:, 0])) + t[0]) * t[1]) ** np.log(x[:, 1]) ** t[2] * (t[3] / np.log(x[:, 1]) ** x[:, 0])) * t[4])","$$\left(\left({\left(\left(\operatorname{exp}(\operatorname{log}(x_{0})) + \theta_{0}\right) \cdot \theta_{1}\right)^{{\operatorname{log}(x_{1})^{\theta_{2}}}}} \cdot \frac{\theta_{3}}{{\operatorname{log}(x_{1})^{x_{0}}}}\right) \cdot \theta_{4}\right)$$",0.20850060370198475;0.39511150930296546;0.8257794175657165;-0.20517511469791816;0.1295796306760718,21,,,,Infinity,,,,,, +845,0,Log(((Log((0.6109241778664527 * x0)) * Exp(x0)) ^ Log(((-0.7050229827796687 / x1) / x1)))),"np.log((np.log((t[0] * x[:, 0])) * np.exp(x[:, 0])) ** np.log(((t[1] / x[:, 1]) / x[:, 1])))","$$\operatorname{log}({\left(\operatorname{log}(\left(\theta_{0} \cdot x_{0}\right)) \cdot \operatorname{exp}(x_{0})\right)^{\operatorname{log}(\frac{\frac{\theta_{1}}{x_{1}}}{x_{1}})}})$$",0.6109241778664527;-0.7050229827796687,15,,,,Infinity,,,,,, +846,0,Log(Log((Log(((x0 + x0) * (x1 * x0))) + Log(Log((x0 ^ 0.5581276896657926)))))),"np.log(np.log((np.log(((x[:, 0] + x[:, 0]) * (x[:, 1] * x[:, 0]))) + np.log(np.log(x[:, 0] ** t[0])))))","$$\operatorname{log}(\operatorname{log}(\left(\operatorname{log}(\left(\left(x_{0} + x_{0}\right) \cdot \left(x_{1} \cdot x_{0}\right)\right)) + \operatorname{log}(\operatorname{log}({x_{0}^{\theta_{0}}}))\right)))$$",0.5581276896657926,16,,,,Infinity,,,,,, +847,0,(Log(Exp(((Exp(x1) / Log(x0)) ^ ((x0 * 0.9770045916783751) + 0.9595488242937393)))) ^ -0.12057212417689944),"np.log(np.exp((np.exp(x[:, 1]) / np.log(x[:, 0])) ** ((x[:, 0] * t[0]) + t[1]))) ** t[2]","$${\operatorname{log}(\operatorname{exp}({\frac{\operatorname{exp}(x_{1})}{\operatorname{log}(x_{0})}^{\left(\left(x_{0} \cdot \theta_{0}\right) + \theta_{1}\right)}}))^{\theta_{2}}}$$",0.9770045916783751;0.9595488242937393;-0.12057212417689944,15,,,,Infinity,,,,,, +848,0,((Log(Exp((Log(x1) * -7.126078475871078e-2))) + Log((2.6719596847589733e-3 + (x1 * -0.14245743397936717)))) / Exp((-0.12605633088515023 * ((-0.7151125753959062 ^ Log(x0)) * (x0 * 0.8614224775338946))))),"((np.log(np.exp((np.log(x[:, 1]) * t[0]))) + np.log((t[1] + (x[:, 1] * t[2])))) / np.exp((t[3] * (t[4] ** np.log(x[:, 0]) * (x[:, 0] * t[5])))))","$$\frac{\left(\operatorname{log}(\operatorname{exp}(\left(\operatorname{log}(x_{1}) \cdot \theta_{0}\right))) + \operatorname{log}(\left(\theta_{1} + \left(x_{1} \cdot \theta_{2}\right)\right))\right)}{\operatorname{exp}(\left(\theta_{3} \cdot \left({\theta_{4}^{\operatorname{log}(x_{0})}} \cdot \left(x_{0} \cdot \theta_{5}\right)\right)\right))}$$",-7.126078475871078e-2;2.6719596847589733e-3;-0.14245743397936717;-0.12605633088515023;-0.7151125753959062;0.8614224775338946,25,,,,Infinity,,,,,, +849,0,Log(Log(Exp((-0.12228780639558634 * x0)))),"np.log(np.log(np.exp((t[0] * x[:, 0]))))","$$\operatorname{log}(\operatorname{log}(\operatorname{exp}(\left(\theta_{0} \cdot x_{0}\right))))$$",-0.12228780639558634,6,,,,Infinity,,,,,, +850,0,((0.646469674330622 + (x0 + Log(x1))) * ((x0 + (0.7761352374620942 / x1)) * (-0.8522749385218367 ^ x0))),"((t[0] + (x[:, 0] + np.log(x[:, 1]))) * ((x[:, 0] + (t[1] / x[:, 1])) * t[2] ** x[:, 0]))","$$\left(\left(\theta_{0} + \left(x_{0} + \operatorname{log}(x_{1})\right)\right) \cdot \left(\left(x_{0} + \frac{\theta_{1}}{x_{1}}\right) \cdot {\theta_{2}^{x_{0}}}\right)\right)$$",0.646469674330622;0.7761352374620942;-0.8522749385218367,16,,,,Infinity,,,,,, +851,0,Log(Log(((((x0 * x0) + (x0 * -0.9705122130182313)) + (0.5532788583177288 ^ x0)) / ((0.34363898484325683 + (x1 ^ x0)) * Exp(Exp(x1)))))),"np.log(np.log(((((x[:, 0] * x[:, 0]) + (x[:, 0] * t[0])) + t[1] ** x[:, 0]) / ((t[2] + x[:, 1] ** x[:, 0]) * np.exp(np.exp(x[:, 1]))))))","$$\operatorname{log}(\operatorname{log}(\frac{\left(\left(\left(x_{0} \cdot x_{0}\right) + \left(x_{0} \cdot \theta_{0}\right)\right) + {\theta_{1}^{x_{0}}}\right)}{\left(\left(\theta_{2} + {x_{1}^{x_{0}}}\right) \cdot \operatorname{exp}(\operatorname{exp}(x_{1}))\right)}))$$",-0.9705122130182313;0.5532788583177288;0.34363898484325683,23,,,,Infinity,,,,,, +852,0,(x0 / x0),"(x[:, 0] / x[:, 0])","$$\frac{x_{0}}{x_{0}}$$",,3,0.8923097947317449,0.8923097947317449,0.8923097947317449,0.8923097947317449,-81.34338364542985,-81.34338364542985,-81.34338364542985,2.8825203569384064,2.8825203569384064,2.8825203569384064 +853,0,(Exp((Log((Log(Exp(x0)) + 0.7204894144880363)) / ((-0.6460758290453559 + x0) / Exp((x1 + (x1 * x1)))))) * ((2.9089280493287184e-2 * (x1 + 0.12915223798839393)) * (Exp((x1 ^ 0.694284300350668)) * 0.33796896843990665))),"(np.exp((np.log((np.log(np.exp(x[:, 0])) + t[0])) / ((t[1] + x[:, 0]) / np.exp((x[:, 1] + (x[:, 1] * x[:, 1])))))) * ((t[2] * (x[:, 1] + t[3])) * (np.exp(x[:, 1] ** t[4]) * t[5])))","$$\left(\operatorname{exp}(\frac{\operatorname{log}(\left(\operatorname{log}(\operatorname{exp}(x_{0})) + \theta_{0}\right))}{\frac{\left(\theta_{1} + x_{0}\right)}{\operatorname{exp}(\left(x_{1} + \left(x_{1} \cdot x_{1}\right)\right))}}) \cdot \left(\left(\theta_{2} \cdot \left(x_{1} + \theta_{3}\right)\right) \cdot \left(\operatorname{exp}({x_{1}^{\theta_{4}}}) \cdot \theta_{5}\right)\right)\right)$$",0.7204894144880363;-0.6460758290453559;2.9089280493287184e-2;0.12915223798839393;0.694284300350668;0.33796896843990665,31,4.368564968125827e34,4.368564968125827e34,4.368564968125827e34,4.368564968125827e34,-4.03136246261326e36,-4.03136246261326e36,-4.03136246261326e36,3.9317084713132442e34,3.9317084713132442e34,3.9317084713132442e34 +854,0,Log(((((x1 * -0.2438123062140527) * ((-0.830958061124212 / x1) * 0.3695063822618355)) + (Exp(x0) + (((5.142379864030544e-3 * x0) ^ -0.8855594414538726) * 0.3619553969759084))) * (((x0 ^ ((x1 * -2.650256058924927e-2) / x0)) / x0) + -0.9209172075900574))),"np.log(((((x[:, 1] * t[0]) * ((t[1] / x[:, 1]) * t[2])) + (np.exp(x[:, 0]) + ((t[3] * x[:, 0]) ** t[4] * t[5]))) * ((x[:, 0] ** ((x[:, 1] * t[6]) / x[:, 0]) / x[:, 0]) + t[7])))","$$\operatorname{log}(\left(\left(\left(\left(x_{1} \cdot \theta_{0}\right) \cdot \left(\frac{\theta_{1}}{x_{1}} \cdot \theta_{2}\right)\right) + \left(\operatorname{exp}(x_{0}) + \left({\left(\theta_{3} \cdot x_{0}\right)^{\theta_{4}}} \cdot \theta_{5}\right)\right)\right) \cdot \left(\frac{{x_{0}^{\frac{\left(x_{1} \cdot \theta_{6}\right)}{x_{0}}}}}{x_{0}} + \theta_{7}\right)\right))$$",-0.2438123062140527;-0.830958061124212;0.3695063822618355;5.142379864030544e-3;-0.8855594414538726;0.3619553969759084;-2.650256058924927e-2;-0.9209172075900574,33,,,,Infinity,,,,,, +855,0,Log(((Exp(((Exp(x1) * 7.488268926705302e-2) ^ 0.5688970478793325)) * (x0 / (0.24714046319429864 + (x0 ^ 1.4783366140857996)))) * Exp((x0 * (0.16360719807960636 ^ ((x0 ^ 0.7267601652195536) / (x1 ^ x0))))))),"np.log(((np.exp((np.exp(x[:, 1]) * t[0]) ** t[1]) * (x[:, 0] / (t[2] + x[:, 0] ** t[3]))) * np.exp((x[:, 0] * t[4] ** (x[:, 0] ** t[5] / x[:, 1] ** x[:, 0])))))","$$\operatorname{log}(\left(\left(\operatorname{exp}({\left(\operatorname{exp}(x_{1}) \cdot \theta_{0}\right)^{\theta_{1}}}) \cdot \frac{x_{0}}{\left(\theta_{2} + {x_{0}^{\theta_{3}}}\right)}\right) \cdot \operatorname{exp}(\left(x_{0} \cdot {\theta_{4}^{\frac{{x_{0}^{\theta_{5}}}}{{x_{1}^{x_{0}}}}}}\right))\right))$$",7.488268926705302e-2;0.5688970478793325;0.24714046319429864;1.4783366140857996;0.16360719807960636;0.7267601652195536,29,3.3351339293433754e-3,3.3351339293433754e-3,3.3351339293433754e-3,3.3351339293433754e-3,0.6922299695977534,0.6922299695977534,0.6922299695977534,75.84806397549262,75.84806397549262,75.84806397549262 +856,0,((Exp(Exp(Log(Log(x0)))) ^ (Exp(Log(x1)) ^ Exp(((x0 ^ 0.283608048852652) ^ Log((x0 * -0.19133474678575013)))))) + Log((0.302898071615174 + x1))),"(np.exp(np.exp(np.log(np.log(x[:, 0])))) ** np.exp(np.log(x[:, 1])) ** np.exp(x[:, 0] ** t[0] ** np.log((x[:, 0] * t[1]))) + np.log((t[2] + x[:, 1])))","$$\left({\operatorname{exp}(\operatorname{exp}(\operatorname{log}(\operatorname{log}(x_{0}))))^{{\operatorname{exp}(\operatorname{log}(x_{1}))^{\operatorname{exp}({{x_{0}^{\theta_{0}}}^{\operatorname{log}(\left(x_{0} \cdot \theta_{1}\right))}})}}}} + \operatorname{log}(\left(\theta_{2} + x_{1}\right))\right)$$",0.283608048852652;-0.19133474678575013;0.302898071615174,24,,,,Infinity,,,,,, +857,0,(Log((Log(x1) * 0.9348180794634879)) + ((Exp((0.41982366321633235 + (-0.2958854569851084 * Exp(x1)))) / (Exp(((x0 * 0.8687564992029833) * 4.4965911615461374e-2)) * x1)) + 0.4651531032873464)),"(np.log((np.log(x[:, 1]) * t[0])) + ((np.exp((t[1] + (t[2] * np.exp(x[:, 1])))) / (np.exp(((x[:, 0] * t[3]) * t[4])) * x[:, 1])) + t[5]))","$$\left(\operatorname{log}(\left(\operatorname{log}(x_{1}) \cdot \theta_{0}\right)) + \left(\frac{\operatorname{exp}(\left(\theta_{1} + \left(\theta_{2} \cdot \operatorname{exp}(x_{1})\right)\right))}{\left(\operatorname{exp}(\left(\left(x_{0} \cdot \theta_{3}\right) \cdot \theta_{4}\right)) \cdot x_{1}\right)} + \theta_{5}\right)\right)$$",0.9348180794634879;0.41982366321633235;-0.2958854569851084;0.8687564992029833;4.4965911615461374e-2;0.4651531032873464,24,,,,Infinity,,,,,, +858,0,(((-0.9456297542848564 ^ (x1 + x0)) + (0.5029478378326526 ^ x0)) / ((x0 ^ (x1 + 0.6273252712780386)) / (x0 ^ 0.11338501497399689))),"((t[0] ** (x[:, 1] + x[:, 0]) + t[1] ** x[:, 0]) / (x[:, 0] ** (x[:, 1] + t[2]) / x[:, 0] ** t[3]))","$$\frac{\left({\theta_{0}^{\left(x_{1} + x_{0}\right)}} + {\theta_{1}^{x_{0}}}\right)}{\frac{{x_{0}^{\left(x_{1} + \theta_{2}\right)}}}{{x_{0}^{\theta_{3}}}}}$$",-0.9456297542848564;0.5029478378326526;0.6273252712780386;0.11338501497399689,19,,,,Infinity,,,,,, +859,0,Log((0.5590021509854783 * (1.5538351521611518 + x1))),"np.log((t[0] * (t[1] + x[:, 1])))","$$\operatorname{log}(\left(\theta_{0} \cdot \left(\theta_{1} + x_{1}\right)\right))$$",0.5590021509854783;1.5538351521611518,6,1.0239785633833471e-2,1.0239785633833471e-2,1.0239785633833471e-2,1.0239785633833471e-2,5.506069543123715e-2,5.506069543123715e-2,5.506069543123715e-2,13.707693152410332,13.707693152410332,13.707693152410332 +860,0,(Exp((((x0 + (-0.6698097979573869 + (x1 / x1))) + (x0 * (Exp(x1) ^ -0.5018310113296028))) ^ (x0 + 0.2690226328643769))) + Exp((0.674370919631741 ^ x0))),"(np.exp(((x[:, 0] + (t[0] + (x[:, 1] / x[:, 1]))) + (x[:, 0] * np.exp(x[:, 1]) ** t[1])) ** (x[:, 0] + t[2])) + np.exp(t[3] ** x[:, 0]))","$$\left(\operatorname{exp}({\left(\left(x_{0} + \left(\theta_{0} + \frac{x_{1}}{x_{1}}\right)\right) + \left(x_{0} \cdot {\operatorname{exp}(x_{1})^{\theta_{1}}}\right)\right)^{\left(x_{0} + \theta_{2}\right)}}) + \operatorname{exp}({\theta_{3}^{x_{0}}})\right)$$",-0.6698097979573869;-0.5018310113296028;0.2690226328643769;0.674370919631741,24,204811.78382735775,204811.78382735775,204811.78382735775,204811.78382735775,-1.8900268155816067e7,-1.8900268155816067e7,-1.8900268155816067e7,184388.5957413638,184388.5957413638,184388.5957413638 +861,0,(((((-0.9946605982662117 ^ (0.8325677979222722 + x1)) ^ x0) ^ x0) * x0) ^ 0.8198154079378486),"(t[0] ** (t[1] + x[:, 1]) ** x[:, 0] ** x[:, 0] * x[:, 0]) ** t[2]","$${\left({{{\theta_{0}^{\left(\theta_{1} + x_{1}\right)}}^{x_{0}}}^{x_{0}}} \cdot x_{0}\right)^{\theta_{2}}}$$",-0.9946605982662117;0.8325677979222722;0.8198154079378486,13,,,,Infinity,,,,,, +862,0,Log((x1 + 0.7507911464536998)),"np.log((x[:, 1] + t[0]))","$$\operatorname{log}(\left(x_{1} + \theta_{0}\right))$$",0.7507911464536998,4,1.668267260813824e-2,1.668267260813824e-2,1.668267260813824e-2,1.668267260813824e-2,-0.5394963934201895,-0.5394963934201895,-0.5394963934201895,7.581116785194527,7.581116785194527,7.581116785194527 +863,0,(((((-0.34823850342236473 / x1) / x0) + 0.5105823951462887) / ((-0.47071856474611684 + Exp((0.716125048851075 / x0))) * (x1 + (x0 ^ x1)))) ^ x0),"((((t[0] / x[:, 1]) / x[:, 0]) + t[1]) / ((t[2] + np.exp((t[3] / x[:, 0]))) * (x[:, 1] + x[:, 0] ** x[:, 1]))) ** x[:, 0]","$${\frac{\left(\frac{\frac{\theta_{0}}{x_{1}}}{x_{0}} + \theta_{1}\right)}{\left(\left(\theta_{2} + \operatorname{exp}(\frac{\theta_{3}}{x_{0}})\right) \cdot \left(x_{1} + {x_{0}^{x_{1}}}\right)\right)}^{x_{0}}}$$",-0.34823850342236473;0.5105823951462887;-0.47071856474611684;0.716125048851075,22,,,,Infinity,,,,,, +864,0,(Log(((-0.9070387198715313 ^ (0.14934208259229487 / x0)) / (x0 ^ -0.8956618621154508))) * ((0.23950560697231427 + (x1 ^ -0.8791352261484618)) + (0.9241784121019646 + (0.32129852812656123 * x1)))),"(np.log((t[0] ** (t[1] / x[:, 0]) / x[:, 0] ** t[2])) * ((t[3] + x[:, 1] ** t[4]) + (t[5] + (t[6] * x[:, 1]))))","$$\left(\operatorname{log}(\frac{{\theta_{0}^{\frac{\theta_{1}}{x_{0}}}}}{{x_{0}^{\theta_{2}}}}) \cdot \left(\left(\theta_{3} + {x_{1}^{\theta_{4}}}\right) + \left(\theta_{5} + \left(\theta_{6} \cdot x_{1}\right)\right)\right)\right)$$",-0.9070387198715313;0.14934208259229487;-0.8956618621154508;0.23950560697231427;-0.8791352261484618;0.9241784121019646;0.32129852812656123,22,,,,Infinity,,,,,, +865,0,(Log(Exp(((-0.28227774745360745 * Log(Exp(x1))) + (-0.6335913214486804 * (0.6118417503523635 + x1))))) * ((Log(Log(Log((-0.8257167115336339 / x1)))) ^ Log((x1 / x0))) * ((Exp(x0) * -0.5961273139221779) + (Log(x1) * (-2.6539820857122476e-2 ^ x1))))),"(np.log(np.exp(((t[0] * np.log(np.exp(x[:, 1]))) + (t[1] * (t[2] + x[:, 1]))))) * (np.log(np.log(np.log((t[3] / x[:, 1])))) ** np.log((x[:, 1] / x[:, 0])) * ((np.exp(x[:, 0]) * t[4]) + (np.log(x[:, 1]) * t[5] ** x[:, 1]))))","$$\left(\operatorname{log}(\operatorname{exp}(\left(\left(\theta_{0} \cdot \operatorname{log}(\operatorname{exp}(x_{1}))\right) + \left(\theta_{1} \cdot \left(\theta_{2} + x_{1}\right)\right)\right))) \cdot \left({\operatorname{log}(\operatorname{log}(\operatorname{log}(\frac{\theta_{3}}{x_{1}})))^{\operatorname{log}(\frac{x_{1}}{x_{0}})}} \cdot \left(\left(\operatorname{exp}(x_{0}) \cdot \theta_{4}\right) + \left(\operatorname{log}(x_{1}) \cdot {\theta_{5}^{x_{1}}}\right)\right)\right)\right)$$",-0.28227774745360745;-0.6335913214486804;0.6118417503523635;-0.8257167115336339;-0.5961273139221779;-2.6539820857122476e-2,37,,,,Infinity,,,,,, +866,0,((Log(Log((x1 ^ -0.2578715708573385))) ^ 8.516098699720043e-2) ^ (6.604034580321849e-2 / x0)),"np.log(np.log(x[:, 1] ** t[0])) ** t[1] ** (t[2] / x[:, 0])","$${{\operatorname{log}(\operatorname{log}({x_{1}^{\theta_{0}}}))^{\theta_{1}}}^{\frac{\theta_{2}}{x_{0}}}}$$",-0.2578715708573385;8.516098699720043e-2;6.604034580321849e-2,11,,,,Infinity,,,,,, +867,0,Exp((Exp((0.7930611838033478 * x1)) / ((1.6105735951399332e-5 ^ Log((x1 * x1))) * (x1 ^ -8.30475180036716e-2)))),"np.exp((np.exp((t[0] * x[:, 1])) / (t[1] ** np.log((x[:, 1] * x[:, 1])) * x[:, 1] ** t[2])))","$$\operatorname{exp}(\frac{\operatorname{exp}(\left(\theta_{0} \cdot x_{1}\right))}{\left({\theta_{1}^{\operatorname{log}(\left(x_{1} \cdot x_{1}\right))}} \cdot {x_{1}^{\theta_{2}}}\right)})$$",0.7930611838033478;1.6105735951399332e-5;-8.30475180036716e-2,16,0.8923110137892294,0.8923110137892294,0.8923110137892294,0.8923110137892294,-81.34349614146959,-81.34349614146959,-81.34349614146959,38.00041710339824,38.00041710339824,38.00041710339824 +868,0,Log((1.0006674709432373 * ((x0 + 6.409796169160163e-2) ^ (x0 * x1)))),"np.log((t[0] * (x[:, 0] + t[1]) ** (x[:, 0] * x[:, 1])))","$$\operatorname{log}(\left(\theta_{0} \cdot {\left(x_{0} + \theta_{1}\right)^{\left(x_{0} \cdot x_{1}\right)}}\right))$$",1.0006674709432373;6.409796169160163e-2,10,4.047410128666835e-2,4.047410128666835e-2,4.047410128666835e-2,4.047410128666835e-2,-2.734997048815372,-2.734997048815372,-2.734997048815372,23.537378052446414,23.537378052446414,23.537378052446414 +869,0,(Log(Log((6.016310321045326e-3 + x1))) + Exp(Exp((0.4054402497097065 / (0.6962133532401528 * x0))))),"(np.log(np.log((t[0] + x[:, 1]))) + np.exp(np.exp((t[1] / (t[2] * x[:, 0])))))","$$\left(\operatorname{log}(\operatorname{log}(\left(\theta_{0} + x_{1}\right))) + \operatorname{exp}(\operatorname{exp}(\frac{\theta_{1}}{\left(\theta_{2} \cdot x_{0}\right)}))\right)$$",6.016310321045326e-3;0.4054402497097065;0.6962133532401528,13,,,,Infinity,,,,,, +870,0,(((((0.8508803945643815 / ((x1 / x0) / (0.7276296255799714 / x1))) / x1) * 0.6201683019656246) + x0) ^ 0.8751164433848875),"((((t[0] / ((x[:, 1] / x[:, 0]) / (t[1] / x[:, 1]))) / x[:, 1]) * t[2]) + x[:, 0]) ** t[3]","$${\left(\left(\frac{\frac{\theta_{0}}{\frac{\frac{x_{1}}{x_{0}}}{\frac{\theta_{1}}{x_{1}}}}}{x_{1}} \cdot \theta_{2}\right) + x_{0}\right)^{\theta_{3}}}$$",0.8508803945643815;0.7276296255799714;0.6201683019656246;0.8751164433848875,17,2098.88521213821,2098.88521213821,2098.88521213821,2098.88521213821,-193686.5637488365,-193686.5637488365,-193686.5637488365,1930.1608631998001,1930.1608631998001,1930.1608631998001 +871,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 +872,0,(-0.7977503217726849 * (-0.7109000452578229 ^ (x1 * Exp(x0)))),"(t[0] * t[1] ** (x[:, 1] * np.exp(x[:, 0])))","$$\left(\theta_{0} \cdot {\theta_{1}^{\left(x_{1} \cdot \operatorname{exp}(x_{0})\right)}}\right)$$",-0.7977503217726849;-0.7109000452578229,8,,,,Infinity,,,,,, +873,0,(((x0 * (x1 * 7.516323337171304e-4)) ^ 0.23417542047012066) * Log(Exp(Log(((x1 / x0) * (1.1875829051461024 + Exp(x0))))))),"((x[:, 0] * (x[:, 1] * t[0])) ** t[1] * np.log(np.exp(np.log(((x[:, 1] / x[:, 0]) * (t[2] + np.exp(x[:, 0])))))))","$$\left({\left(x_{0} \cdot \left(x_{1} \cdot \theta_{0}\right)\right)^{\theta_{1}}} \cdot \operatorname{log}(\operatorname{exp}(\operatorname{log}(\left(\frac{x_{1}}{x_{0}} \cdot \left(\theta_{2} + \operatorname{exp}(x_{0})\right)\right))))\right)$$",7.516323337171304e-4;0.23417542047012066;1.1875829051461024,19,8.836334493986993e-3,8.836334493986993e-3,8.836334493986993e-3,8.836334493986993e-3,0.1845727957335056,0.1845727957335056,0.1845727957335056,47.81799447653568,47.81799447653568,47.81799447653568 +874,0,((((x0 ^ (x0 * 0.1933807567273616)) + (x0 / x0)) / x1) + -7.5517961382784415),"(((x[:, 0] ** (x[:, 0] * t[0]) + (x[:, 0] / x[:, 0])) / x[:, 1]) + t[1])","$$\left(\frac{\left({x_{0}^{\left(x_{0} \cdot \theta_{0}\right)}} + \frac{x_{0}}{x_{0}}\right)}{x_{1}} + \theta_{1}\right)$$",0.1933807567273616;-7.5517961382784415,13,17.794390169876046,17.794390169876046,17.794390169876046,17.794390169876046,-1641.0869804920915,-1641.0869804920915,-1641.0869804920915,45.3536329613428,45.3536329613428,45.3536329613428 +875,0,(Log(((2.2315388877498954e-2 * (((x1 / x1) * -0.280510474025759) * ((x0 ^ x1) / x0))) / ((Exp(x0) ^ (0.40650498406747926 * (-0.7373727597222957 * x1))) / (x0 + x0)))) / (((x0 ^ 0.5771777196733651) ^ (x0 + x0)) ^ -0.4960079761545053)),"(np.log(((t[0] * (((x[:, 1] / x[:, 1]) * t[1]) * (x[:, 0] ** x[:, 1] / x[:, 0]))) / (np.exp(x[:, 0]) ** (t[2] * (t[3] * x[:, 1])) / (x[:, 0] + x[:, 0])))) / x[:, 0] ** t[4] ** (x[:, 0] + x[:, 0]) ** t[5])","$$\frac{\operatorname{log}(\frac{\left(\theta_{0} \cdot \left(\left(\frac{x_{1}}{x_{1}} \cdot \theta_{1}\right) \cdot \frac{{x_{0}^{x_{1}}}}{x_{0}}\right)\right)}{\frac{{\operatorname{exp}(x_{0})^{\left(\theta_{2} \cdot \left(\theta_{3} \cdot x_{1}\right)\right)}}}{\left(x_{0} + x_{0}\right)}})}{{{{x_{0}^{\theta_{4}}}^{\left(x_{0} + x_{0}\right)}}^{\theta_{5}}}}$$",2.2315388877498954e-2;-0.280510474025759;0.40650498406747926;-0.7373727597222957;0.5771777196733651;-0.4960079761545053,37,,,,Infinity,,,,,, +876,0,(Log(((x0 * 0.27503076652428826) / (x1 + -0.7089754602831053))) + ((1.1962367226196702e-2 * Log(x1)) ^ (x0 ^ Exp(x0)))),"(np.log(((x[:, 0] * t[0]) / (x[:, 1] + t[1]))) + (t[2] * np.log(x[:, 1])) ** x[:, 0] ** np.exp(x[:, 0]))","$$\left(\operatorname{log}(\frac{\left(x_{0} \cdot \theta_{0}\right)}{\left(x_{1} + \theta_{1}\right)}) + {\left(\theta_{2} \cdot \operatorname{log}(x_{1})\right)^{{x_{0}^{\operatorname{exp}(x_{0})}}}}\right)$$",0.27503076652428826;-0.7089754602831053;1.1962367226196702e-2,18,,,,Infinity,,,,,, +877,0,Log(((Exp((x0 + 0.2365582969472424)) ^ ((Exp(x0) * (0.1438166423913395 / x0)) * -0.6819102547678553)) / (-0.5869171337649859 ^ Log(Log(x0))))),"np.log((np.exp((x[:, 0] + t[0])) ** ((np.exp(x[:, 0]) * (t[1] / x[:, 0])) * t[2]) / t[3] ** np.log(np.log(x[:, 0]))))","$$\operatorname{log}(\frac{{\operatorname{exp}(\left(x_{0} + \theta_{0}\right))^{\left(\left(\operatorname{exp}(x_{0}) \cdot \frac{\theta_{1}}{x_{0}}\right) \cdot \theta_{2}\right)}}}{{\theta_{3}^{\operatorname{log}(\operatorname{log}(x_{0}))}}})$$",0.2365582969472424;0.1438166423913395;-0.6819102547678553;-0.5869171337649859,20,,,,Infinity,,,,,, +878,0,(Log(Log((((x1 * x1) + -0.684707332156488) / Log((x1 + (-0.6750859943017611 + x0)))))) + ((Exp((-0.6637019554381639 + x0)) / Exp(x1)) * (Exp((x1 ^ 0.5386247479229969)) * (x1 ^ x0)))),"(np.log(np.log((((x[:, 1] * x[:, 1]) + t[0]) / np.log((x[:, 1] + (t[1] + x[:, 0])))))) + ((np.exp((t[2] + x[:, 0])) / np.exp(x[:, 1])) * (np.exp(x[:, 1] ** t[3]) * x[:, 1] ** x[:, 0])))","$$\left(\operatorname{log}(\operatorname{log}(\frac{\left(\left(x_{1} \cdot x_{1}\right) + \theta_{0}\right)}{\operatorname{log}(\left(x_{1} + \left(\theta_{1} + x_{0}\right)\right))})) + \left(\frac{\operatorname{exp}(\left(\theta_{2} + x_{0}\right))}{\operatorname{exp}(x_{1})} \cdot \left(\operatorname{exp}({x_{1}^{\theta_{3}}}) \cdot {x_{1}^{x_{0}}}\right)\right)\right)$$",-0.684707332156488;-0.6750859943017611;-0.6637019554381639;0.5386247479229969,31,,,,Infinity,,,,,, +879,0,(Log(Log((Log(x0) / (x0 * x0)))) * (Log(((x1 * 0.23596959632048808) / Exp((x0 * 9.791592015575878e-2)))) / ((x0 * 0.5866875374567637) ^ Log(((x1 + Exp(x1)) ^ Exp((-0.1347368794975553 * x0))))))),"(np.log(np.log((np.log(x[:, 0]) / (x[:, 0] * x[:, 0])))) * (np.log(((x[:, 1] * t[0]) / np.exp((x[:, 0] * t[1])))) / (x[:, 0] * t[2]) ** np.log((x[:, 1] + np.exp(x[:, 1])) ** np.exp((t[3] * x[:, 0])))))","$$\left(\operatorname{log}(\operatorname{log}(\frac{\operatorname{log}(x_{0})}{\left(x_{0} \cdot x_{0}\right)})) \cdot \frac{\operatorname{log}(\frac{\left(x_{1} \cdot \theta_{0}\right)}{\operatorname{exp}(\left(x_{0} \cdot \theta_{1}\right))})}{{\left(x_{0} \cdot \theta_{2}\right)^{\operatorname{log}({\left(x_{1} + \operatorname{exp}(x_{1})\right)^{\operatorname{exp}(\left(\theta_{3} \cdot x_{0}\right))}})}}}\right)$$",0.23596959632048808;9.791592015575878e-2;0.5866875374567637;-0.1347368794975553,33,,,,Infinity,,,,,, +880,0,((((-0.41366202306578925 ^ x0) * ((Log((0.5122293273739997 ^ x0)) ^ Exp((x1 ^ 0.9790531346373315))) * -0.16137644352924163)) ^ (Log((x1 * -0.3395162033368164)) + Log(x1))) / (Log(Exp((-0.4731726405736396 / x0))) + ((2.2674922284452337e-2 * x1) ^ (x1 + x1)))),"((t[0] ** x[:, 0] * (np.log(t[1] ** x[:, 0]) ** np.exp(x[:, 1] ** t[2]) * t[3])) ** (np.log((x[:, 1] * t[4])) + np.log(x[:, 1])) / (np.log(np.exp((t[5] / x[:, 0]))) + (t[6] * x[:, 1]) ** (x[:, 1] + x[:, 1])))","$$\frac{{\left({\theta_{0}^{x_{0}}} \cdot \left({\operatorname{log}({\theta_{1}^{x_{0}}})^{\operatorname{exp}({x_{1}^{\theta_{2}}})}} \cdot \theta_{3}\right)\right)^{\left(\operatorname{log}(\left(x_{1} \cdot \theta_{4}\right)) + \operatorname{log}(x_{1})\right)}}}{\left(\operatorname{log}(\operatorname{exp}(\frac{\theta_{5}}{x_{0}})) + {\left(\theta_{6} \cdot x_{1}\right)^{\left(x_{1} + x_{1}\right)}}\right)}$$",-0.41366202306578925;0.5122293273739997;0.9790531346373315;-0.16137644352924163;-0.3395162033368164;-0.4731726405736396;2.2674922284452337e-2,37,,,,Infinity,,,,,, +881,0,(((((23.89360410360172 / x0) + x1) + (Exp(Exp(x1)) * (x0 * (x0 + (13.349953290317616 + x1))))) ^ x1) * 9.293715619249472e-3),"((((t[0] / x[:, 0]) + x[:, 1]) + (np.exp(np.exp(x[:, 1])) * (x[:, 0] * (x[:, 0] + (t[1] + x[:, 1]))))) ** x[:, 1] * t[2])","$$\left({\left(\left(\frac{\theta_{0}}{x_{0}} + x_{1}\right) + \left(\operatorname{exp}(\operatorname{exp}(x_{1})) \cdot \left(x_{0} \cdot \left(x_{0} + \left(\theta_{1} + x_{1}\right)\right)\right)\right)\right)^{x_{1}}} \cdot \theta_{2}\right)$$",23.89360410360172;13.349953290317616;9.293715619249472e-3,21,1.0360162599448422e-2,1.0360162599448422e-2,1.0360162599448422e-2,1.0360162599448422e-2,4.3952169311466616e-2,4.3952169311466616e-2,4.3952169311466616e-2,49.740371327718975,49.740371327718975,49.740371327718975 +882,0,((x1 * 0.4914501909125626) * (1.182027828853775e-2 ^ (-0.6685910654509565 + x0))),"((x[:, 1] * t[0]) * t[1] ** (t[2] + x[:, 0]))","$$\left(\left(x_{1} \cdot \theta_{0}\right) \cdot {\theta_{1}^{\left(\theta_{2} + x_{0}\right)}}\right)$$",0.4914501909125626;1.182027828853775e-2;-0.6685910654509565,9,1.29921569241107e-3,1.29921569241107e-3,1.29921569241107e-3,1.29921569241107e-3,0.8801068677829212,0.8801068677829212,0.8801068677829212,22.189779323278586,22.189779323278586,22.189779323278586 +883,0,Exp(Log(Log((0.46099870887693195 * x1)))),"np.exp(np.log(np.log((t[0] * x[:, 1]))))","$$\operatorname{exp}(\operatorname{log}(\operatorname{log}(\left(\theta_{0} \cdot x_{1}\right))))$$",0.46099870887693195,6,,,,Infinity,,,,,, +884,0,(Log((-0.6703330772704559 ^ Log((0.13588929607413147 + (4.585162347767324e-2 * x1))))) * (Exp(((-0.596323725262742 ^ Log(x0)) + ((x0 * x1) ^ x1))) * Log(Log((Log((x0 ^ -0.7196752356197508)) + Exp((x1 + x1))))))),"(np.log(t[0] ** np.log((t[1] + (t[2] * x[:, 1])))) * (np.exp((t[3] ** np.log(x[:, 0]) + (x[:, 0] * x[:, 1]) ** x[:, 1])) * np.log(np.log((np.log(x[:, 0] ** t[4]) + np.exp((x[:, 1] + x[:, 1])))))))","$$\left(\operatorname{log}({\theta_{0}^{\operatorname{log}(\left(\theta_{1} + \left(\theta_{2} \cdot x_{1}\right)\right))}}) \cdot \left(\operatorname{exp}(\left({\theta_{3}^{\operatorname{log}(x_{0})}} + {\left(x_{0} \cdot x_{1}\right)^{x_{1}}}\right)) \cdot \operatorname{log}(\operatorname{log}(\left(\operatorname{log}({x_{0}^{\theta_{4}}}) + \operatorname{exp}(\left(x_{1} + x_{1}\right))\right)))\right)\right)$$",-0.6703330772704559;0.13588929607413147;4.585162347767324e-2;-0.596323725262742;-0.7196752356197508,33,,,,Infinity,,,,,, +885,0,((((0.10125470298894623 ^ x0) + (0.19319398090069506 + x0)) + (-1.0442229721785545e-2 ^ x0)) / (((x1 * x1) * 8.439240502204992e-2) / (Log(x0) * (x0 * x0)))),"(((t[0] ** x[:, 0] + (t[1] + x[:, 0])) + t[2] ** x[:, 0]) / (((x[:, 1] * x[:, 1]) * t[3]) / (np.log(x[:, 0]) * (x[:, 0] * x[:, 0]))))","$$\frac{\left(\left({\theta_{0}^{x_{0}}} + \left(\theta_{1} + x_{0}\right)\right) + {\theta_{2}^{x_{0}}}\right)}{\frac{\left(\left(x_{1} \cdot x_{1}\right) \cdot \theta_{3}\right)}{\left(\operatorname{log}(x_{0}) \cdot \left(x_{0} \cdot x_{0}\right)\right)}}$$",0.10125470298894623;0.19319398090069506;-1.0442229721785545e-2;8.439240502204992e-2,24,,,,Infinity,,,,,, +886,0,(((x0 * -0.1268765739066018) / (-0.4525653387851082 + Exp(x0))) / ((5.514980525494495e-2 / (-0.8674964126045672 ^ x0)) / (0.1714356439662461 / (-0.17488697357736915 ^ x0)))),"(((x[:, 0] * t[0]) / (t[1] + np.exp(x[:, 0]))) / ((t[2] / t[3] ** x[:, 0]) / (t[4] / t[5] ** x[:, 0])))","$$\frac{\frac{\left(x_{0} \cdot \theta_{0}\right)}{\left(\theta_{1} + \operatorname{exp}(x_{0})\right)}}{\frac{\frac{\theta_{2}}{{\theta_{3}^{x_{0}}}}}{\frac{\theta_{4}}{{\theta_{5}^{x_{0}}}}}}$$",-0.1268765739066018;-0.4525653387851082;5.514980525494495e-2;-0.8674964126045672;0.1714356439662461;-0.17488697357736915,20,,,,Infinity,,,,,, +887,0,((Exp((((7.62811068556214e-2 * x0) * -0.5300376284536263) ^ 0.8321785306120899)) * (Exp(Log((-0.23891602174210536 * x0))) ^ -0.10604960050019496)) ^ ((x0 * -9.997593398515192e-2) * -0.8164324396493065)),"(np.exp(((t[0] * x[:, 0]) * t[1]) ** t[2]) * np.exp(np.log((t[3] * x[:, 0]))) ** t[4]) ** ((x[:, 0] * t[5]) * t[6])","$${\left(\operatorname{exp}({\left(\left(\theta_{0} \cdot x_{0}\right) \cdot \theta_{1}\right)^{\theta_{2}}}) \cdot {\operatorname{exp}(\operatorname{log}(\left(\theta_{3} \cdot x_{0}\right)))^{\theta_{4}}}\right)^{\left(\left(x_{0} \cdot \theta_{5}\right) \cdot \theta_{6}\right)}}$$",7.62811068556214e-2;-0.5300376284536263;0.8321785306120899;-0.23891602174210536;-0.10604960050019496;-9.997593398515192e-2;-0.8164324396493065,22,,,,Infinity,,,,,, +888,0,(-6.860156837303206 + Exp((Log((x0 + x0)) + x0))),"(t[0] + np.exp((np.log((x[:, 0] + x[:, 0])) + x[:, 0])))","$$\left(\theta_{0} + \operatorname{exp}(\left(\operatorname{log}(\left(x_{0} + x_{0}\right)) + x_{0}\right))\right)$$",-6.860156837303206,9,14.37733443964127,14.37733443964127,14.37733443964127,14.37733443964127,-1325.7571112092764,-1325.7571112092764,-1325.7571112092764,29.445467142951685,29.445467142951685,29.445467142951685 +889,0,((((1.2231772054161914e-4 ^ Exp(x1)) + (x0 + -0.2963833558120469)) * ((-0.4056905658281823 / (x1 * 0.6073898463660568)) / (x0 * -0.36240688134404536))) ^ (((-0.64780004337212 * (0.4996803810754811 ^ x1)) + Log((x1 * 1.1939379576479177))) * ((x1 / x1) + (x0 ^ (x0 * x1))))),"((t[0] ** np.exp(x[:, 1]) + (x[:, 0] + t[1])) * ((t[2] / (x[:, 1] * t[3])) / (x[:, 0] * t[4]))) ** (((t[5] * t[6] ** x[:, 1]) + np.log((x[:, 1] * t[7]))) * ((x[:, 1] / x[:, 1]) + x[:, 0] ** (x[:, 0] * x[:, 1])))","$${\left(\left({\theta_{0}^{\operatorname{exp}(x_{1})}} + \left(x_{0} + \theta_{1}\right)\right) \cdot \frac{\frac{\theta_{2}}{\left(x_{1} \cdot \theta_{3}\right)}}{\left(x_{0} \cdot \theta_{4}\right)}\right)^{\left(\left(\left(\theta_{5} \cdot {\theta_{6}^{x_{1}}}\right) + \operatorname{log}(\left(x_{1} \cdot \theta_{7}\right))\right) \cdot \left(\frac{x_{1}}{x_{1}} + {x_{0}^{\left(x_{0} \cdot x_{1}\right)}}\right)\right)}}$$",1.2231772054161914e-4;-0.2963833558120469;-0.4056905658281823;0.6073898463660568;-0.36240688134404536;-0.64780004337212;0.4996803810754811;1.1939379576479177,39,6.828321083713232e-3,6.828321083713232e-3,6.828321083713232e-3,6.828321083713232e-3,0.3698746041228761,0.3698746041228761,0.3698746041228761,101.86530348802903,101.86530348802903,101.86530348802903 +890,0,(Log(Log((x1 + -0.6205767282368333))) ^ (((((x0 / x0) / (0.7050990686579807 / (x0 ^ x1))) / x0) + (x1 / x1)) + 0.3225707757025048)),"np.log(np.log((x[:, 1] + t[0]))) ** (((((x[:, 0] / x[:, 0]) / (t[1] / x[:, 0] ** x[:, 1])) / x[:, 0]) + (x[:, 1] / x[:, 1])) + t[2])","$${\operatorname{log}(\operatorname{log}(\left(x_{1} + \theta_{0}\right)))^{\left(\left(\frac{\frac{\frac{x_{0}}{x_{0}}}{\frac{\theta_{1}}{{x_{0}^{x_{1}}}}}}{x_{0}} + \frac{x_{1}}{x_{1}}\right) + \theta_{2}\right)}}$$",-0.6205767282368333;0.7050990686579807;0.3225707757025048,23,,,,Infinity,,,,,, +891,0,Log(Exp((1.1306123094205506e-3 ^ ((x0 * x0) + (-0.39113585162262626 * x1))))),"np.log(np.exp(t[0] ** ((x[:, 0] * x[:, 0]) + (t[1] * x[:, 1]))))","$$\operatorname{log}(\operatorname{exp}({\theta_{0}^{\left(\left(x_{0} \cdot x_{0}\right) + \left(\theta_{1} \cdot x_{1}\right)\right)}}))$$",1.1306123094205506e-3;-0.39113585162262626,11,3.2270487743276855e-3,3.2270487743276855e-3,3.2270487743276855e-3,3.2270487743276855e-3,0.7022041931671679,0.7022041931671679,0.7022041931671679,26.91861117311037,26.91861117311037,26.91861117311037 +892,0,(((Exp(x1) ^ (-2.888321767291577 / x1)) + ((-0.29079773294151656 / x0) * -0.5799698876604458)) / (((x1 + x1) ^ (x0 * -2.3670846128109178)) + ((x0 * x0) ^ 2.702457628545553))),"((np.exp(x[:, 1]) ** (t[0] / x[:, 1]) + ((t[1] / x[:, 0]) * t[2])) / ((x[:, 1] + x[:, 1]) ** (x[:, 0] * t[3]) + (x[:, 0] * x[:, 0]) ** t[4]))","$$\frac{\left({\operatorname{exp}(x_{1})^{\frac{\theta_{0}}{x_{1}}}} + \left(\frac{\theta_{1}}{x_{0}} \cdot \theta_{2}\right)\right)}{\left({\left(x_{1} + x_{1}\right)^{\left(x_{0} \cdot \theta_{3}\right)}} + {\left(x_{0} \cdot x_{0}\right)^{\theta_{4}}}\right)}$$",-2.888321767291577;-0.29079773294151656;-0.5799698876604458;-2.3670846128109178;2.702457628545553,26,1.774605508602345e-3,1.774605508602345e-3,1.774605508602345e-3,1.774605508602345e-3,0.8362373437152887,0.8362373437152887,0.8362373437152887,64.17170190547168,64.17170190547168,64.17170190547168 +893,0,(((Exp(Log(x1)) * Log(x1)) ^ (Exp(x0) * (x0 * 0.23848841537864318))) ^ ((((((x0 + -0.5499972350086526) ^ 0.9813317192601043) * x1) * -6.747401969083244e-2) ^ (x0 * Exp((x1 / x0)))) + Exp((5.877527030039231e-2 * (x1 + 0.6429041761889265))))),"(np.exp(np.log(x[:, 1])) * np.log(x[:, 1])) ** (np.exp(x[:, 0]) * (x[:, 0] * t[0])) ** ((((x[:, 0] + t[1]) ** t[2] * x[:, 1]) * t[3]) ** (x[:, 0] * np.exp((x[:, 1] / x[:, 0]))) + np.exp((t[4] * (x[:, 1] + t[5]))))","$${{\left(\operatorname{exp}(\operatorname{log}(x_{1})) \cdot \operatorname{log}(x_{1})\right)^{\left(\operatorname{exp}(x_{0}) \cdot \left(x_{0} \cdot \theta_{0}\right)\right)}}^{\left({\left(\left({\left(x_{0} + \theta_{1}\right)^{\theta_{2}}} \cdot x_{1}\right) \cdot \theta_{3}\right)^{\left(x_{0} \cdot \operatorname{exp}(\frac{x_{1}}{x_{0}})\right)}} + \operatorname{exp}(\left(\theta_{4} \cdot \left(x_{1} + \theta_{5}\right)\right))\right)}}$$",0.23848841537864318;-0.5499972350086526;0.9813317192601043;-6.747401969083244e-2;5.877527030039231e-2;0.6429041761889265,37,,,,Infinity,,,,,, +894,0,((((0.12734899761133212 ^ x0) * Exp((x1 * x1))) ^ ((-0.47262417677213386 / x0) ^ (-0.8513695390576741 / x0))) + ((0.31281702223142327 ^ (-0.8800025513992464 / x1)) + ((x1 * -0.5312476529180928) / (x1 * -0.9531939270673278)))),"((t[0] ** x[:, 0] * np.exp((x[:, 1] * x[:, 1]))) ** (t[1] / x[:, 0]) ** (t[2] / x[:, 0]) + (t[3] ** (t[4] / x[:, 1]) + ((x[:, 1] * t[5]) / (x[:, 1] * t[6]))))","$$\left({\left({\theta_{0}^{x_{0}}} \cdot \operatorname{exp}(\left(x_{1} \cdot x_{1}\right))\right)^{{\frac{\theta_{1}}{x_{0}}^{\frac{\theta_{2}}{x_{0}}}}}} + \left({\theta_{3}^{\frac{\theta_{4}}{x_{1}}}} + \frac{\left(x_{1} \cdot \theta_{5}\right)}{\left(x_{1} \cdot \theta_{6}\right)}\right)\right)$$",0.12734899761133212;-0.47262417677213386;-0.8513695390576741;0.31281702223142327;-0.8800025513992464;-0.5312476529180928;-0.9531939270673278,30,,,,Infinity,,,,,, +895,0,Log(Log(Log((((0.35676745307306035 / (0.23079796909937966 ^ x1)) + Log((x1 ^ 0.9666363779836281))) + x1)))),"np.log(np.log(np.log((((t[0] / t[1] ** x[:, 1]) + np.log(x[:, 1] ** t[2])) + x[:, 1]))))","$$\operatorname{log}(\operatorname{log}(\operatorname{log}(\left(\left(\frac{\theta_{0}}{{\theta_{1}^{x_{1}}}} + \operatorname{log}({x_{1}^{\theta_{2}}})\right) + x_{1}\right))))$$",0.35676745307306035;0.23079796909937966;0.9666363779836281,15,,,,Infinity,,,,,, +896,0,Log(Log(Exp(((-8.965590428500612e-2 + ((-0.5047125918559479 + x0) ^ (x0 * -0.4259845397963693))) ^ x1)))),"np.log(np.log(np.exp((t[0] + (t[1] + x[:, 0]) ** (x[:, 0] * t[2])) ** x[:, 1])))","$$\operatorname{log}(\operatorname{log}(\operatorname{exp}({\left(\theta_{0} + {\left(\theta_{1} + x_{0}\right)^{\left(x_{0} \cdot \theta_{2}\right)}}\right)^{x_{1}}})))$$",-8.965590428500612e-2;-0.5047125918559479;-0.4259845397963693,14,2.110754230462207e-3,2.110754230462207e-3,2.110754230462207e-3,2.110754230462207e-3,0.8052171494627435,0.8052171494627435,0.8052171494627435,35.17685606842804,35.17685606842804,35.17685606842804 +897,0,((Exp(((x0 / (0.9213125523636786 ^ x1)) / (0.8289880393584732 ^ (0.5919880138128474 + x0)))) + Exp((-0.618761449720681 ^ ((0.19358468221742164 / x0) + -0.9617238155397516)))) / Exp(Exp((((x1 + (0.8835164265222651 / x1)) / (x0 + (0.17444603353702415 ^ x0))) ^ ((x1 ^ Exp(x1)) + ((x0 * x0) * 9.1879234795057e-3)))))),"((np.exp(((x[:, 0] / t[0] ** x[:, 1]) / t[1] ** (t[2] + x[:, 0]))) + np.exp(t[3] ** ((t[4] / x[:, 0]) + t[5]))) / np.exp(np.exp(((x[:, 1] + (t[6] / x[:, 1])) / (x[:, 0] + t[7] ** x[:, 0])) ** (x[:, 1] ** np.exp(x[:, 1]) + ((x[:, 0] * x[:, 0]) * t[8])))))","$$\frac{\left(\operatorname{exp}(\frac{\frac{x_{0}}{{\theta_{0}^{x_{1}}}}}{{\theta_{1}^{\left(\theta_{2} + x_{0}\right)}}}) + \operatorname{exp}({\theta_{3}^{\left(\frac{\theta_{4}}{x_{0}} + \theta_{5}\right)}})\right)}{\operatorname{exp}(\operatorname{exp}({\frac{\left(x_{1} + \frac{\theta_{6}}{x_{1}}\right)}{\left(x_{0} + {\theta_{7}^{x_{0}}}\right)}^{\left({x_{1}^{\operatorname{exp}(x_{1})}} + \left(\left(x_{0} \cdot x_{0}\right) \cdot \theta_{8}\right)\right)}}))}$$",0.9213125523636786;0.8289880393584732;0.5919880138128474;-0.618761449720681;0.19358468221742164;-0.9617238155397516;0.8835164265222651;0.17444603353702415;9.1879234795057e-3,46,,,,Infinity,,,,,, +898,0,Log(Exp((Log((x1 ^ ((5.346873490483313e-4 * x1) ^ x1))) / Exp(Exp((x1 + x1)))))),"np.log(np.exp((np.log(x[:, 1] ** (t[0] * x[:, 1]) ** x[:, 1]) / np.exp(np.exp((x[:, 1] + x[:, 1]))))))","$$\operatorname{log}(\operatorname{exp}(\frac{\operatorname{log}({x_{1}^{{\left(\theta_{0} \cdot x_{1}\right)^{x_{1}}}}})}{\operatorname{exp}(\operatorname{exp}(\left(x_{1} + x_{1}\right)))}))$$",5.346873490483313e-4,16,2.085101080526957e-2,2.085101080526957e-2,2.085101080526957e-2,2.085101080526957e-2,-0.9241554808321697,-0.9241554808321697,-0.9241554808321697,35.31075551196976,35.31075551196976,35.31075551196976 +899,0,(Log((0.3209504914822673 / x1)) * x0),"(np.log((t[0] / x[:, 1])) * x[:, 0])","$$\left(\operatorname{log}(\frac{\theta_{0}}{x_{1}}) \cdot x_{0}\right)$$",0.3209504914822673,6,0.29230664289744,0.29230664289744,0.29230664289744,0.29230664289744,-25.974396314284082,-25.974396314284082,-25.974396314284082,13.034557729343668,13.034557729343668,13.034557729343668 +900,0,Exp((Log((64.19806306181877 * x0)) * ((x1 * 4.605314520021272) * (-4.224916934929434 * (Log(x1) * -5.371864159802967))))),"np.exp((np.log((t[0] * x[:, 0])) * ((x[:, 1] * t[1]) * (t[2] * (np.log(x[:, 1]) * t[3])))))","$$\operatorname{exp}(\left(\operatorname{log}(\left(\theta_{0} \cdot x_{0}\right)) \cdot \left(\left(x_{1} \cdot \theta_{1}\right) \cdot \left(\theta_{2} \cdot \left(\operatorname{log}(x_{1}) \cdot \theta_{3}\right)\right)\right)\right))$$",64.19806306181877;4.605314520021272;-4.224916934929434;-5.371864159802967,16,1.4573554749975553e-2,1.4573554749975553e-2,1.4573554749975553e-2,1.4573554749975553e-2,-0.3448645492182232,-0.3448645492182232,-0.3448645492182232,36.76496744839442,36.76496744839442,36.76496744839442 +901,0,((-0.11576890892296858 * Log((-0.3852663807504058 + x0))) * Exp(((x1 * -0.2706511518217376) * -0.41912619128540085))),"((t[0] * np.log((t[1] + x[:, 0]))) * np.exp(((x[:, 1] * t[2]) * t[3])))","$$\left(\left(\theta_{0} \cdot \operatorname{log}(\left(\theta_{1} + x_{0}\right))\right) \cdot \operatorname{exp}(\left(\left(x_{1} \cdot \theta_{2}\right) \cdot \theta_{3}\right))\right)$$",-0.11576890892296858;-0.3852663807504058;-0.2706511518217376;-0.41912619128540085,13,4.989030428025939e-3,4.989030428025939e-3,4.989030428025939e-3,4.989030428025939e-3,0.5396064808666974,0.5396064808666974,0.5396064808666974,33.38502180657486,33.38502180657486,33.38502180657486 +902,0,Log(Log(((x1 * Exp((x1 + 1.3694934560901917))) + (Exp((3.66033081336025e-2 / (x0 * x0))) ^ ((x1 ^ -0.9579984849017419) / x0))))),"np.log(np.log(((x[:, 1] * np.exp((x[:, 1] + t[0]))) + np.exp((t[1] / (x[:, 0] * x[:, 0]))) ** (x[:, 1] ** t[2] / x[:, 0]))))","$$\operatorname{log}(\operatorname{log}(\left(\left(x_{1} \cdot \operatorname{exp}(\left(x_{1} + \theta_{0}\right))\right) + {\operatorname{exp}(\frac{\theta_{1}}{\left(x_{0} \cdot x_{0}\right)})^{\frac{{x_{1}^{\theta_{2}}}}{x_{0}}}}\right)))$$",1.3694934560901917;3.66033081336025e-2;-0.9579984849017419,21,7.954171506639386e-2,7.954171506639386e-2,7.954171506639386e-2,7.954171506639386e-2,-6.340201797848228,-6.340201797848228,-6.340201797848228,52.27607847372329,52.27607847372329,52.27607847372329 +903,0,(((-0.24198063733297193 * (x0 ^ -0.5854140319977181)) * Log((-0.1079226961235068 / Log((x0 + -0.9708697156081484))))) + x1),"(((t[0] * x[:, 0] ** t[1]) * np.log((t[2] / np.log((x[:, 0] + t[3]))))) + x[:, 1])","$$\left(\left(\left(\theta_{0} \cdot {x_{0}^{\theta_{1}}}\right) \cdot \operatorname{log}(\frac{\theta_{2}}{\operatorname{log}(\left(x_{0} + \theta_{3}\right))})\right) + x_{1}\right)$$",-0.24198063733297193;-0.5854140319977181;-0.1079226961235068;-0.9708697156081484,15,,,,Infinity,,,,,, +904,0,((((x1 ^ (((x0 / x0) * -0.26949497952416623) + -4.0400836724514666e-2)) + (0.46704951132759565 * Exp(x0))) ^ Log(((-0.5127408909349094 ^ Exp(x0)) * 0.1730116025792391))) ^ Log((x0 ^ 0.7419106597205907))),"(x[:, 1] ** (((x[:, 0] / x[:, 0]) * t[0]) + t[1]) + (t[2] * np.exp(x[:, 0]))) ** np.log((t[3] ** np.exp(x[:, 0]) * t[4])) ** np.log(x[:, 0] ** t[5])","$${{\left({x_{1}^{\left(\left(\frac{x_{0}}{x_{0}} \cdot \theta_{0}\right) + \theta_{1}\right)}} + \left(\theta_{2} \cdot \operatorname{exp}(x_{0})\right)\right)^{\operatorname{log}(\left({\theta_{3}^{\operatorname{exp}(x_{0})}} \cdot \theta_{4}\right))}}^{\operatorname{log}({x_{0}^{\theta_{5}}})}}$$",-0.26949497952416623;-4.0400836724514666e-2;0.46704951132759565;-0.5127408909349094;0.1730116025792391;0.7419106597205907,27,,,,Infinity,,,,,, +905,0,(-0.6791278601997834 / ((-0.5198497782771547 + Log(Log((0.2973728006956621 ^ (x1 * x0))))) ^ (Log(Log(x0)) * (0.5298107351847752 * (-0.456453393324042 / (0.4309450067263647 + x1)))))),"(t[0] / (t[1] + np.log(np.log(t[2] ** (x[:, 1] * x[:, 0])))) ** (np.log(np.log(x[:, 0])) * (t[3] * (t[4] / (t[5] + x[:, 1])))))","$$\frac{\theta_{0}}{{\left(\theta_{1} + \operatorname{log}(\operatorname{log}({\theta_{2}^{\left(x_{1} \cdot x_{0}\right)}}))\right)^{\left(\operatorname{log}(\operatorname{log}(x_{0})) \cdot \left(\theta_{3} \cdot \frac{\theta_{4}}{\left(\theta_{5} + x_{1}\right)}\right)\right)}}}$$",-0.6791278601997834;-0.5198497782771547;0.2973728006956621;0.5298107351847752;-0.456453393324042;0.4309450067263647,23,,,,Infinity,,,,,, +906,0,((((-0.19256612329088607 ^ (x0 * 0.8054568699482977)) + (0.8188592299581446 + (x0 ^ 0.8908702737040027))) * ((x1 * -0.9749037646457286) ^ (-0.13702972979213524 / Log(x0)))) * ((((x0 ^ -0.21103686867204696) ^ -0.632023301911449) + Log((0.5296462640402666 / (x0 * 0.6118083063846216)))) ^ ((x0 ^ Log(x1)) + (0.7355940268736612 ^ (x1 * x0))))),"(((t[0] ** (x[:, 0] * t[1]) + (t[2] + x[:, 0] ** t[3])) * (x[:, 1] * t[4]) ** (t[5] / np.log(x[:, 0]))) * (x[:, 0] ** t[6] ** t[7] + np.log((t[8] / (x[:, 0] * t[9])))) ** (x[:, 0] ** np.log(x[:, 1]) + t[10] ** (x[:, 1] * x[:, 0])))","$$\left(\left(\left({\theta_{0}^{\left(x_{0} \cdot \theta_{1}\right)}} + \left(\theta_{2} + {x_{0}^{\theta_{3}}}\right)\right) \cdot {\left(x_{1} \cdot \theta_{4}\right)^{\frac{\theta_{5}}{\operatorname{log}(x_{0})}}}\right) \cdot {\left({{x_{0}^{\theta_{6}}}^{\theta_{7}}} + \operatorname{log}(\frac{\theta_{8}}{\left(x_{0} \cdot \theta_{9}\right)})\right)^{\left({x_{0}^{\operatorname{log}(x_{1})}} + {\theta_{10}^{\left(x_{1} \cdot x_{0}\right)}}\right)}}\right)$$",-0.19256612329088607;0.8054568699482977;0.8188592299581446;0.8908702737040027;-0.9749037646457286;-0.13702972979213524;-0.21103686867204696;-0.632023301911449;0.5296462640402666;0.6118083063846216;0.7355940268736612,44,,,,Infinity,,,,,, +907,0,(x1 * x0),"(x[:, 1] * x[:, 0])","$$\left(x_{1} \cdot x_{0}\right)$$",,3,0.13931722508926758,0.13931722508926758,0.13931722508926758,0.13931722508926758,-11.856355249794213,-11.856355249794213,-11.856355249794213,3.42122236858467,3.42122236858467,3.42122236858467 +908,0,(Log((((x0 + Log((-0.5398291241008426 ^ x1))) ^ -0.7639569214306898) + Exp(Log(x1)))) + 0.6912560367326168),"(np.log(((x[:, 0] + np.log(t[0] ** x[:, 1])) ** t[1] + np.exp(np.log(x[:, 1])))) + t[2])","$$\left(\operatorname{log}(\left({\left(x_{0} + \operatorname{log}({\theta_{0}^{x_{1}}})\right)^{\theta_{1}}} + \operatorname{exp}(\operatorname{log}(x_{1}))\right)) + \theta_{2}\right)$$",-0.5398291241008426;-0.7639569214306898;0.6912560367326168,15,,,,Infinity,,,,,, +909,0,(Exp((x0 + x0)) * 2.13087803630585e-3),"(np.exp((x[:, 0] + x[:, 0])) * t[0])","$$\left(\operatorname{exp}(\left(x_{0} + x_{0}\right)) \cdot \theta_{0}\right)$$",2.13087803630585e-3,6,1.3995010131826451e-2,1.3995010131826451e-2,1.3995010131826451e-2,1.3995010131826451e-2,-0.29147578028447874,-0.29147578028447874,-0.29147578028447874,11.690147919090887,11.690147919090887,11.690147919090887 +910,0,((Log(Log((Exp((x0 * x1)) ^ (-0.6826714616455711 * x1)))) / (((x1 ^ 0.45722459828940776) * 0.7320034228974153) + ((x1 + 0.768600110386176) / (-0.2792344979632294 / x0)))) + (Exp(((5.631351800775508e-2 + (0.6297767104243541 ^ x1)) ^ (0.44499258286946863 / (x1 / x1)))) * ((-0.3055489199658975 / (0.6136804624444754 + x0)) ^ Exp(Exp((x1 * x0)))))),"((np.log(np.log(np.exp((x[:, 0] * x[:, 1])) ** (t[0] * x[:, 1]))) / ((x[:, 1] ** t[1] * t[2]) + ((x[:, 1] + t[3]) / (t[4] / x[:, 0])))) + (np.exp((t[5] + t[6] ** x[:, 1]) ** (t[7] / (x[:, 1] / x[:, 1]))) * (t[8] / (t[9] + x[:, 0])) ** np.exp(np.exp((x[:, 1] * x[:, 0])))))","$$\left(\frac{\operatorname{log}(\operatorname{log}({\operatorname{exp}(\left(x_{0} \cdot x_{1}\right))^{\left(\theta_{0} \cdot x_{1}\right)}}))}{\left(\left({x_{1}^{\theta_{1}}} \cdot \theta_{2}\right) + \frac{\left(x_{1} + \theta_{3}\right)}{\frac{\theta_{4}}{x_{0}}}\right)} + \left(\operatorname{exp}({\left(\theta_{5} + {\theta_{6}^{x_{1}}}\right)^{\frac{\theta_{7}}{\frac{x_{1}}{x_{1}}}}}) \cdot {\frac{\theta_{8}}{\left(\theta_{9} + x_{0}\right)}^{\operatorname{exp}(\operatorname{exp}(\left(x_{1} \cdot x_{0}\right)))}}\right)\right)$$",-0.6826714616455711;0.45722459828940776;0.7320034228974153;0.768600110386176;-0.2792344979632294;5.631351800775508e-2;0.6297767104243541;0.44499258286946863;-0.3055489199658975;0.6136804624444754,49,,,,Infinity,,,,,, +911,0,(((0.3435306346534498 * (x1 / x1)) / (x1 + x0)) * Exp((Log((x1 + -2.282598637636468e-3)) ^ 0.16215233555834985))),"(((t[0] * (x[:, 1] / x[:, 1])) / (x[:, 1] + x[:, 0])) * np.exp(np.log((x[:, 1] + t[1])) ** t[2]))","$$\left(\frac{\left(\theta_{0} \cdot \frac{x_{1}}{x_{1}}\right)}{\left(x_{1} + x_{0}\right)} \cdot \operatorname{exp}({\operatorname{log}(\left(x_{1} + \theta_{1}\right))^{\theta_{2}}})\right)$$",0.3435306346534498;-2.282598637636468e-3;0.16215233555834985,17,,,,Infinity,,,,,, +912,0,((Log(Log(x0)) * ((Exp(x0) * (Log((x1 + 0.8352579874423789)) ^ 0.8498694041802681)) * x1)) + 0.33287649884493753),"((np.log(np.log(x[:, 0])) * ((np.exp(x[:, 0]) * np.log((x[:, 1] + t[0])) ** t[1]) * x[:, 1])) + t[2])","$$\left(\left(\operatorname{log}(\operatorname{log}(x_{0})) \cdot \left(\left(\operatorname{exp}(x_{0}) \cdot {\operatorname{log}(\left(x_{1} + \theta_{0}\right))^{\theta_{1}}}\right) \cdot x_{1}\right)\right) + \theta_{2}\right)$$",0.8352579874423789;0.8498694041802681;0.33287649884493753,17,,,,Infinity,,,,,, +913,0,(Log(Exp((-0.2623366735091031 + x1))) / Log((Exp((x1 / x1)) + (x1 ^ (x1 + x1))))),"(np.log(np.exp((t[0] + x[:, 1]))) / np.log((np.exp((x[:, 1] / x[:, 1])) + x[:, 1] ** (x[:, 1] + x[:, 1]))))","$$\frac{\operatorname{log}(\operatorname{exp}(\left(\theta_{0} + x_{1}\right)))}{\operatorname{log}(\left(\operatorname{exp}(\frac{x_{1}}{x_{1}}) + {x_{1}^{\left(x_{1} + x_{1}\right)}}\right))}$$",-0.2623366735091031,17,1.4760024343063467e-2,1.4760024343063467e-2,1.4760024343063467e-2,1.4760024343063467e-2,-0.36207217972110395,-0.36207217972110395,-0.36207217972110395,35.11468149121672,35.11468149121672,35.11468149121672 +914,0,((Exp(((Exp(x1) / x0) / (-0.7875621560569124 ^ x0))) ^ (((x0 * 0.13674413646340788) * -0.14169927041341823) ^ (4.294733337056489e-2 / x1))) / Exp(Exp((-0.9395253287459883 + ((x0 ^ (x1 + -0.8018216102303637)) ^ (x0 * -0.1791596073584275)))))),"(np.exp(((np.exp(x[:, 1]) / x[:, 0]) / t[0] ** x[:, 0])) ** ((x[:, 0] * t[1]) * t[2]) ** (t[3] / x[:, 1]) / np.exp(np.exp((t[4] + x[:, 0] ** (x[:, 1] + t[5]) ** (x[:, 0] * t[6])))))","$$\frac{{\operatorname{exp}(\frac{\frac{\operatorname{exp}(x_{1})}{x_{0}}}{{\theta_{0}^{x_{0}}}})^{{\left(\left(x_{0} \cdot \theta_{1}\right) \cdot \theta_{2}\right)^{\frac{\theta_{3}}{x_{1}}}}}}}{\operatorname{exp}(\operatorname{exp}(\left(\theta_{4} + {{x_{0}^{\left(x_{1} + \theta_{5}\right)}}^{\left(x_{0} \cdot \theta_{6}\right)}}\right)))}$$",-0.7875621560569124;0.13674413646340788;-0.14169927041341823;4.294733337056489e-2;-0.9395253287459883;-0.8018216102303637;-0.1791596073584275,33,,,,Infinity,,,,,, +915,0,Exp(((Exp(((x1 * 0.9501466099606402) * 0.34343880117945524)) / (-0.5542692557892662 + Exp((-0.8913462836518152 ^ x1)))) / ((Log(Log(x0)) ^ -0.30988457542829273) ^ ((x0 ^ x0) + Log(Log((-0.3977504692950904 / x0))))))),"np.exp(((np.exp(((x[:, 1] * t[0]) * t[1])) / (t[2] + np.exp(t[3] ** x[:, 1]))) / np.log(np.log(x[:, 0])) ** t[4] ** (x[:, 0] ** x[:, 0] + np.log(np.log((t[5] / x[:, 0]))))))","$$\operatorname{exp}(\frac{\frac{\operatorname{exp}(\left(\left(x_{1} \cdot \theta_{0}\right) \cdot \theta_{1}\right))}{\left(\theta_{2} + \operatorname{exp}({\theta_{3}^{x_{1}}})\right)}}{{{\operatorname{log}(\operatorname{log}(x_{0}))^{\theta_{4}}}^{\left({x_{0}^{x_{0}}} + \operatorname{log}(\operatorname{log}(\frac{\theta_{5}}{x_{0}}))\right)}}})$$",0.9501466099606402;0.34343880117945524;-0.5542692557892662;-0.8913462836518152;-0.30988457542829273;-0.3977504692950904,30,,,,Infinity,,,,,, +916,0,((((x1 * -2.596093862943971e-2) ^ -4.667296836370938e-2) * -0.36028108799569547) / ((0.6229516755097904 * (-0.17537749594881302 * x1)) + (-0.10205746499197943 * (0.6889734577071563 + x1)))),"(((x[:, 1] * t[0]) ** t[1] * t[2]) / ((t[3] * (t[4] * x[:, 1])) + (t[5] * (t[6] + x[:, 1]))))","$$\frac{\left({\left(x_{1} \cdot \theta_{0}\right)^{\theta_{1}}} \cdot \theta_{2}\right)}{\left(\left(\theta_{3} \cdot \left(\theta_{4} \cdot x_{1}\right)\right) + \left(\theta_{5} \cdot \left(\theta_{6} + x_{1}\right)\right)\right)}$$",-2.596093862943971e-2;-4.667296836370938e-2;-0.36028108799569547;0.6229516755097904;-0.17537749594881302;-0.10205746499197943;0.6889734577071563,19,,,,Infinity,,,,,, +917,0,Log(Exp((-1.9267265247392966e-2 * (-0.4269832873805105 / x1)))),"np.log(np.exp((t[0] * (t[1] / x[:, 1]))))","$$\operatorname{log}(\operatorname{exp}(\left(\theta_{0} \cdot \frac{\theta_{1}}{x_{1}}\right)))$$",-1.9267265247392966e-2;-0.4269832873805105,7,1.331318834405586e-2,1.331318834405586e-2,1.331318834405586e-2,1.331318834405586e-2,-0.2285564742545756,-0.2285564742545756,-0.2285564742545756,16.596148024841316,16.596148024841316,16.596148024841316 +918,0,(Log(Log((x0 * (((x1 + x0) + x0) * -0.6726800544080772)))) / Log(Log(Exp(x0)))),"(np.log(np.log((x[:, 0] * (((x[:, 1] + x[:, 0]) + x[:, 0]) * t[0])))) / np.log(np.log(np.exp(x[:, 0]))))","$$\frac{\operatorname{log}(\operatorname{log}(\left(x_{0} \cdot \left(\left(\left(x_{1} + x_{0}\right) + x_{0}\right) \cdot \theta_{0}\right)\right)))}{\operatorname{log}(\operatorname{log}(\operatorname{exp}(x_{0})))}$$",-0.6726800544080772,16,,,,Infinity,,,,,, +919,0,Log((Exp(((-1.8009041839625382 + (x1 * x1)) * (0.6404045627244765 ^ Log(x1)))) + Exp((-5.411724726488483e-3 * ((0.19794042742890186 + x1) ^ (x1 + (0.3237439222034737 ^ x1))))))),"np.log((np.exp(((t[0] + (x[:, 1] * x[:, 1])) * t[1] ** np.log(x[:, 1]))) + np.exp((t[2] * (t[3] + x[:, 1]) ** (x[:, 1] + t[4] ** x[:, 1])))))","$$\operatorname{log}(\left(\operatorname{exp}(\left(\left(\theta_{0} + \left(x_{1} \cdot x_{1}\right)\right) \cdot {\theta_{1}^{\operatorname{log}(x_{1})}}\right)) + \operatorname{exp}(\left(\theta_{2} \cdot {\left(\theta_{3} + x_{1}\right)^{\left(x_{1} + {\theta_{4}^{x_{1}}}\right)}}\right))\right))$$",-1.8009041839625382;0.6404045627244765;-5.411724726488483e-3;0.19794042742890186;0.3237439222034737,25,9.20452483747309e-3,9.20452483747309e-3,9.20452483747309e-3,9.20452483747309e-3,0.15059576344357906,0.15059576344357906,0.15059576344357906,58.760662475574755,58.760662475574755,58.760662475574755 +920,0,(Log(Log(Log((-0.48088340236790394 * x0)))) * (-0.1340631826785652 / (x0 * 0.16251382481602383))),"(np.log(np.log(np.log((t[0] * x[:, 0])))) * (t[1] / (x[:, 0] * t[2])))","$$\left(\operatorname{log}(\operatorname{log}(\operatorname{log}(\left(\theta_{0} \cdot x_{0}\right)))) \cdot \frac{\theta_{1}}{\left(x_{0} \cdot \theta_{2}\right)}\right)$$",-0.48088340236790394;-0.1340631826785652;0.16251382481602383,12,,,,Infinity,,,,,, +921,0,Log((((Log((x1 + x0)) ^ (x1 * x1)) * Log(((x1 * 0.5666233132490518) ^ (x1 * x1)))) * Exp((Exp((x1 / Log(x0))) + (Exp(x1) * -0.10183517633070582))))),"np.log(((np.log((x[:, 1] + x[:, 0])) ** (x[:, 1] * x[:, 1]) * np.log((x[:, 1] * t[0]) ** (x[:, 1] * x[:, 1]))) * np.exp((np.exp((x[:, 1] / np.log(x[:, 0]))) + (np.exp(x[:, 1]) * t[1])))))","$$\operatorname{log}(\left(\left({\operatorname{log}(\left(x_{1} + x_{0}\right))^{\left(x_{1} \cdot x_{1}\right)}} \cdot \operatorname{log}({\left(x_{1} \cdot \theta_{0}\right)^{\left(x_{1} \cdot x_{1}\right)}})\right) \cdot \operatorname{exp}(\left(\operatorname{exp}(\frac{x_{1}}{\operatorname{log}(x_{0})}) + \left(\operatorname{exp}(x_{1}) \cdot \theta_{1}\right)\right))\right))$$",0.5666233132490518;-0.10183517633070582,30,,,,Infinity,,,,,, +922,0,((((Exp(x1) ^ 0.9873166494169788) / (x1 * -0.9349079375209784)) + Log((0.9164711524353034 * (((0.5124704649573728 / x0) * 0.3633563196267593) * Log((x0 + 0.7831676405189226)))))) ^ (((x1 ^ (x1 ^ -0.17321097581080802)) ^ x0) * -0.21742666490562212)),"((np.exp(x[:, 1]) ** t[0] / (x[:, 1] * t[1])) + np.log((t[2] * (((t[3] / x[:, 0]) * t[4]) * np.log((x[:, 0] + t[5])))))) ** (x[:, 1] ** x[:, 1] ** t[6] ** x[:, 0] * t[7])","$${\left(\frac{{\operatorname{exp}(x_{1})^{\theta_{0}}}}{\left(x_{1} \cdot \theta_{1}\right)} + \operatorname{log}(\left(\theta_{2} \cdot \left(\left(\frac{\theta_{3}}{x_{0}} \cdot \theta_{4}\right) \cdot \operatorname{log}(\left(x_{0} + \theta_{5}\right))\right)\right))\right)^{\left({{x_{1}^{{x_{1}^{\theta_{6}}}}}^{x_{0}}} \cdot \theta_{7}\right)}}$$",0.9873166494169788;-0.9349079375209784;0.9164711524353034;0.5124704649573728;0.3633563196267593;0.7831676405189226;-0.17321097581080802;-0.21742666490562212,32,,,,Infinity,,,,,, +923,0,((Exp(((x1 ^ 0.9308622483003572) ^ Log((-0.2601611606734048 / x0)))) * Exp((Exp(Exp((0.43265244358446475 * x1))) * (x0 + x0)))) / Log(((Exp((x1 ^ 0.4446889452186622)) * -0.9085556811906231) ^ (Exp(Exp(x1)) ^ ((x1 + 0.6686521408534059) * x0))))),"((np.exp(x[:, 1] ** t[0] ** np.log((t[1] / x[:, 0]))) * np.exp((np.exp(np.exp((t[2] * x[:, 1]))) * (x[:, 0] + x[:, 0])))) / np.log((np.exp(x[:, 1] ** t[3]) * t[4]) ** np.exp(np.exp(x[:, 1])) ** ((x[:, 1] + t[5]) * x[:, 0])))","$$\frac{\left(\operatorname{exp}({{x_{1}^{\theta_{0}}}^{\operatorname{log}(\frac{\theta_{1}}{x_{0}})}}) \cdot \operatorname{exp}(\left(\operatorname{exp}(\operatorname{exp}(\left(\theta_{2} \cdot x_{1}\right))) \cdot \left(x_{0} + x_{0}\right)\right))\right)}{\operatorname{log}({\left(\operatorname{exp}({x_{1}^{\theta_{3}}}) \cdot \theta_{4}\right)^{{\operatorname{exp}(\operatorname{exp}(x_{1}))^{\left(\left(x_{1} + \theta_{5}\right) \cdot x_{0}\right)}}}})}$$",0.9308622483003572;-0.2601611606734048;0.43265244358446475;0.4446889452186622;-0.9085556811906231;0.6686521408534059,38,,,,Infinity,,,,,, +924,0,Log(Exp(Log(((0.8433807223742161 + x1) * -0.5924328166591832)))),"np.log(np.exp(np.log(((t[0] + x[:, 1]) * t[1]))))","$$\operatorname{log}(\operatorname{exp}(\operatorname{log}(\left(\left(\theta_{0} + x_{1}\right) \cdot \theta_{1}\right))))$$",0.8433807223742161;-0.5924328166591832,8,,,,Infinity,,,,,, +925,0,(((Exp((Log(x0) ^ 0.5866913931291518)) * 8.540826329825646e-2) + (Log((x1 ^ -9.927936755077682e-2)) * x0)) + x0),"(((np.exp(np.log(x[:, 0]) ** t[0]) * t[1]) + (np.log(x[:, 1] ** t[2]) * x[:, 0])) + x[:, 0])","$$\left(\left(\left(\operatorname{exp}({\operatorname{log}(x_{0})^{\theta_{0}}}) \cdot \theta_{1}\right) + \left(\operatorname{log}({x_{1}^{\theta_{2}}}) \cdot x_{0}\right)\right) + x_{0}\right)$$",0.5866913931291518;8.540826329825646e-2;-9.927936755077682e-2,16,,,,Infinity,,,,,, +926,0,(((-0.7051211420597063 * x1) * 0.5922076200118819) ^ Log(Log((x1 + 7.046598355161282e-2)))),"((t[0] * x[:, 1]) * t[1]) ** np.log(np.log((x[:, 1] + t[2])))","$${\left(\left(\theta_{0} \cdot x_{1}\right) \cdot \theta_{1}\right)^{\operatorname{log}(\operatorname{log}(\left(x_{1} + \theta_{2}\right)))}}$$",-0.7051211420597063;0.5922076200118819;7.046598355161282e-2,11,,,,Infinity,,,,,, +927,0,(Exp(Exp((x0 * -5.034764070872003))) ^ (x1 + Log(x0))),"np.exp(np.exp((x[:, 0] * t[0]))) ** (x[:, 1] + np.log(x[:, 0]))","$${\operatorname{exp}(\operatorname{exp}(\left(x_{0} \cdot \theta_{0}\right)))^{\left(x_{1} + \operatorname{log}(x_{0})\right)}}$$",-5.034764070872003,10,0.890484525294221,0.890484525294221,0.890484525294221,0.890484525294221,-81.17494566297388,-81.17494566297388,-81.17494566297388,23.616776424930798,23.616776424930798,23.616776424930798 +928,0,((-0.37245538619460594 ^ x0) ^ 0.2724874090299195),"t[0] ** x[:, 0] ** t[1]","$${{\theta_{0}^{x_{0}}}^{\theta_{1}}}$$",-0.37245538619460594;0.2724874090299195,5,,,,Infinity,,,,,, +929,0,(0.6264979961766012 ^ Exp(Exp((((-0.5736301660114695 * x1) + x0) ^ ((x1 * x0) * Log(x1)))))),"t[0] ** np.exp(np.exp(((t[1] * x[:, 1]) + x[:, 0]) ** ((x[:, 1] * x[:, 0]) * np.log(x[:, 1]))))","$${\theta_{0}^{\operatorname{exp}(\operatorname{exp}({\left(\left(\theta_{1} \cdot x_{1}\right) + x_{0}\right)^{\left(\left(x_{1} \cdot x_{0}\right) \cdot \operatorname{log}(x_{1})\right)}}))}}$$",0.6264979961766012;-0.5736301660114695,16,1.4570473111996425e-2,1.4570473111996425e-2,1.4570473111996425e-2,1.4570473111996425e-2,-0.34458017208836345,-0.34458017208836345,-0.34458017208836345,37.326027963413445,37.326027963413445,37.326027963413445 +930,0,Exp(((Log(((x0 * 0.9914124840627526) ^ (x0 * -0.4459850586926649))) + ((0.12700836923556902 / x1) + -0.569496811859628)) ^ (Exp((4.828866976649748e-2 / Log(x1))) + Log((Exp(x1) * -0.9507291695751243))))),"np.exp((np.log((x[:, 0] * t[0]) ** (x[:, 0] * t[1])) + ((t[2] / x[:, 1]) + t[3])) ** (np.exp((t[4] / np.log(x[:, 1]))) + np.log((np.exp(x[:, 1]) * t[5]))))","$$\operatorname{exp}({\left(\operatorname{log}({\left(x_{0} \cdot \theta_{0}\right)^{\left(x_{0} \cdot \theta_{1}\right)}}) + \left(\frac{\theta_{2}}{x_{1}} + \theta_{3}\right)\right)^{\left(\operatorname{exp}(\frac{\theta_{4}}{\operatorname{log}(x_{1})}) + \operatorname{log}(\left(\operatorname{exp}(x_{1}) \cdot \theta_{5}\right))\right)}})$$",0.9914124840627526;-0.4459850586926649;0.12700836923556902;-0.569496811859628;4.828866976649748e-2;-0.9507291695751243,27,,,,Infinity,,,,,, +931,0,Exp((-0.39928516947148285 ^ ((Exp(x1) + 0.9954353018093599) ^ ((-0.9372959562921634 ^ x0) / x0)))),"np.exp(t[0] ** (np.exp(x[:, 1]) + t[1]) ** (t[2] ** x[:, 0] / x[:, 0]))","$$\operatorname{exp}({\theta_{0}^{{\left(\operatorname{exp}(x_{1}) + \theta_{1}\right)^{\frac{{\theta_{2}^{x_{0}}}}{x_{0}}}}}})$$",-0.39928516947148285;0.9954353018093599;-0.9372959562921634,13,,,,Infinity,,,,,, +932,0,Log((Exp((-0.1602262416066315 / x1)) * (-0.48916004461278645 + Exp(x0)))),"np.log((np.exp((t[0] / x[:, 1])) * (t[1] + np.exp(x[:, 0]))))","$$\operatorname{log}(\left(\operatorname{exp}(\frac{\theta_{0}}{x_{1}}) \cdot \left(\theta_{1} + \operatorname{exp}(x_{0})\right)\right))$$",-0.1602262416066315;-0.48916004461278645,10,0.36643307587389573,0.36643307587389573,0.36643307587389573,0.36643307587389573,-32.81486959484751,-32.81486959484751,-32.81486959484751,25.16605505582015,25.16605505582015,25.16605505582015 +933,0,Log(Log((-0.866386271516592 + (((0.7570474963540104 ^ x1) + 0.26077562646243013) * 0.30106563466457736)))),"np.log(np.log((t[0] + ((t[1] ** x[:, 1] + t[2]) * t[3]))))","$$\operatorname{log}(\operatorname{log}(\left(\theta_{0} + \left(\left({\theta_{1}^{x_{1}}} + \theta_{2}\right) \cdot \theta_{3}\right)\right)))$$",-0.866386271516592;0.7570474963540104;0.26077562646243013;0.30106563466457736,11,,,,Infinity,,,,,, +934,0,(((-0.32774652819245453 * x1) ^ (x0 ^ x0)) / ((x1 * x0) / (-0.5611543774116907 ^ x1))),"((t[0] * x[:, 1]) ** x[:, 0] ** x[:, 0] / ((x[:, 1] * x[:, 0]) / t[1] ** x[:, 1]))","$$\frac{{\left(\theta_{0} \cdot x_{1}\right)^{{x_{0}^{x_{0}}}}}}{\frac{\left(x_{1} \cdot x_{0}\right)}{{\theta_{1}^{x_{1}}}}}$$",-0.32774652819245453;-0.5611543774116907,15,,,,Infinity,,,,,, +935,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 +936,0,(Exp(Exp((((x1 + (x0 ^ x0)) + x1) + (5.301989985832911e-2 / x1)))) + (((((x1 * -0.7828326410952101) ^ x1) + -0.554374212539825) * (Log(x0) / Exp(Exp(x0)))) * (-0.3643567586423224 / x0))),"(np.exp(np.exp((((x[:, 1] + x[:, 0] ** x[:, 0]) + x[:, 1]) + (t[0] / x[:, 1])))) + ((((x[:, 1] * t[1]) ** x[:, 1] + t[2]) * (np.log(x[:, 0]) / np.exp(np.exp(x[:, 0])))) * (t[3] / x[:, 0])))","$$\left(\operatorname{exp}(\operatorname{exp}(\left(\left(\left(x_{1} + {x_{0}^{x_{0}}}\right) + x_{1}\right) + \frac{\theta_{0}}{x_{1}}\right))) + \left(\left(\left({\left(x_{1} \cdot \theta_{1}\right)^{x_{1}}} + \theta_{2}\right) \cdot \frac{\operatorname{log}(x_{0})}{\operatorname{exp}(\operatorname{exp}(x_{0}))}\right) \cdot \frac{\theta_{3}}{x_{0}}\right)\right)$$",5.301989985832911e-2;-0.7828326410952101;-0.554374212539825;-0.3643567586423224,32,,,,Infinity,,,,,, +937,0,((((x0 ^ x1) * -0.3632351045510138) / Log(((-7.22425137774152e-2 ^ x0) * -0.3644815873688627))) / (Exp(Log(Exp((0.5325954515967897 + x1)))) / Log((x1 + (x1 ^ 1.0884304742983741e-2))))),"(((x[:, 0] ** x[:, 1] * t[0]) / np.log((t[1] ** x[:, 0] * t[2]))) / (np.exp(np.log(np.exp((t[3] + x[:, 1])))) / np.log((x[:, 1] + x[:, 1] ** t[4]))))","$$\frac{\frac{\left({x_{0}^{x_{1}}} \cdot \theta_{0}\right)}{\operatorname{log}(\left({\theta_{1}^{x_{0}}} \cdot \theta_{2}\right))}}{\frac{\operatorname{exp}(\operatorname{log}(\operatorname{exp}(\left(\theta_{3} + x_{1}\right))))}{\operatorname{log}(\left(x_{1} + {x_{1}^{\theta_{4}}}\right))}}$$",-0.3632351045510138;-7.22425137774152e-2;-0.3644815873688627;0.5325954515967897;1.0884304742983741e-2,26,,,,Infinity,,,,,, +938,0,((0.9505633072063266 / Log(((-0.4513284852347751 ^ x0) ^ x0))) + (Log(((Log(x0) + (7.874183362080245e-2 / x0)) ^ -0.4966170325801399)) + ((-0.8859800328906873 / x1) * (0.1380275822064123 / x1)))),"((t[0] / np.log(t[1] ** x[:, 0] ** x[:, 0])) + (np.log((np.log(x[:, 0]) + (t[2] / x[:, 0])) ** t[3]) + ((t[4] / x[:, 1]) * (t[5] / x[:, 1]))))","$$\left(\frac{\theta_{0}}{\operatorname{log}({{\theta_{1}^{x_{0}}}^{x_{0}}})} + \left(\operatorname{log}({\left(\operatorname{log}(x_{0}) + \frac{\theta_{2}}{x_{0}}\right)^{\theta_{3}}}) + \left(\frac{\theta_{4}}{x_{1}} \cdot \frac{\theta_{5}}{x_{1}}\right)\right)\right)$$",0.9505633072063266;-0.4513284852347751;7.874183362080245e-2;-0.4966170325801399;-0.8859800328906873;0.1380275822064123,26,,,,Infinity,,,,,, +939,0,(0.35334125145639544 / (0.22719240397795465 + (x1 ^ -1.5814060191970776))),"(t[0] / (t[1] + x[:, 1] ** t[2]))","$$\frac{\theta_{0}}{\left(\theta_{1} + {x_{1}^{\theta_{2}}}\right)}$$",0.35334125145639544;0.22719240397795465;-1.5814060191970776,7,9.210466826520363e-3,9.210466826520363e-3,9.210466826520363e-3,9.210466826520363e-3,0.15004742979687324,0.15004742979687324,0.15004742979687324,17.33712961328549,17.33712961328549,17.33712961328549 +940,0,(Exp((((x0 ^ -0.8561603490703908) ^ (x1 + 0.2774667115532945)) * Exp(((0.9063207456646459 ^ x1) / (x1 * 0.46378505335439646))))) / (Log(((x1 * x0) * -5.19158154587388e-2)) ^ ((0.6896639318125235 / (x0 ^ -0.43266566382896443)) + Log((x1 * -0.1077220784802918))))),"(np.exp((x[:, 0] ** t[0] ** (x[:, 1] + t[1]) * np.exp((t[2] ** x[:, 1] / (x[:, 1] * t[3]))))) / np.log(((x[:, 1] * x[:, 0]) * t[4])) ** ((t[5] / x[:, 0] ** t[6]) + np.log((x[:, 1] * t[7]))))","$$\frac{\operatorname{exp}(\left({{x_{0}^{\theta_{0}}}^{\left(x_{1} + \theta_{1}\right)}} \cdot \operatorname{exp}(\frac{{\theta_{2}^{x_{1}}}}{\left(x_{1} \cdot \theta_{3}\right)})\right))}{{\operatorname{log}(\left(\left(x_{1} \cdot x_{0}\right) \cdot \theta_{4}\right))^{\left(\frac{\theta_{5}}{{x_{0}^{\theta_{6}}}} + \operatorname{log}(\left(x_{1} \cdot \theta_{7}\right))\right)}}}$$",-0.8561603490703908;0.2774667115532945;0.9063207456646459;0.46378505335439646;-5.19158154587388e-2;0.6896639318125235;-0.43266566382896443;-0.1077220784802918,35,,,,Infinity,,,,,, +941,0,(-0.49065709877649377 ^ x1),"t[0] ** x[:, 1]","$${\theta_{0}^{x_{1}}}$$",-0.49065709877649377,3,,,,Infinity,,,,,, +942,0,Exp((((x0 ^ 3.041224302372741) ^ -1.5468855137603212e-2) * Exp((((-60.69058971449237 + (x1 * -10.710804826178762)) + -62.04791716960677) * Exp(Log((x1 ^ 42.136981247118804))))))),"np.exp((x[:, 0] ** t[0] ** t[1] * np.exp((((t[2] + (x[:, 1] * t[3])) + t[4]) * np.exp(np.log(x[:, 1] ** t[5]))))))","$$\operatorname{exp}(\left({{x_{0}^{\theta_{0}}}^{\theta_{1}}} \cdot \operatorname{exp}(\left(\left(\left(\theta_{2} + \left(x_{1} \cdot \theta_{3}\right)\right) + \theta_{4}\right) \cdot \operatorname{exp}(\operatorname{log}({x_{1}^{\theta_{5}}}))\right))\right))$$",3.041224302372741;-1.5468855137603212e-2;-60.69058971449237;-10.710804826178762;-62.04791716960677;42.136981247118804,21,7.0661505320396785,7.0661505320396785,7.0661505320396785,7.0661505320396785,-651.0725734396127,-651.0725734396127,-651.0725734396127,62.153357466318106,62.153357466318106,62.153357466318106 +943,0,Log(((Exp((Exp(x1) ^ (0.7622788957176975 ^ x0))) ^ (0.11770015307393393 * (x1 + (x0 / x0)))) + (((x1 * x0) ^ Exp((1.050651868771941 ^ x0))) / Log(((0.3411980972672422 ^ x1) / x0))))),"np.log((np.exp(np.exp(x[:, 1]) ** t[0] ** x[:, 0]) ** (t[1] * (x[:, 1] + (x[:, 0] / x[:, 0]))) + ((x[:, 1] * x[:, 0]) ** np.exp(t[2] ** x[:, 0]) / np.log((t[3] ** x[:, 1] / x[:, 0])))))","$$\operatorname{log}(\left({\operatorname{exp}({\operatorname{exp}(x_{1})^{{\theta_{0}^{x_{0}}}}})^{\left(\theta_{1} \cdot \left(x_{1} + \frac{x_{0}}{x_{0}}\right)\right)}} + \frac{{\left(x_{1} \cdot x_{0}\right)^{\operatorname{exp}({\theta_{2}^{x_{0}}})}}}{\operatorname{log}(\frac{{\theta_{3}^{x_{1}}}}{x_{0}})}\right))$$",0.7622788957176975;0.11770015307393393;1.050651868771941;0.3411980972672422,32,2.9135710026753858e-2,2.9135710026753858e-2,2.9135710026753858e-2,2.9135710026753858e-2,-1.6886771418173678,-1.6886771418173678,-1.6886771418173678,78.42110835525366,78.42110835525366,78.42110835525366 +944,0,(Log(((x0 * x1) + (x0 + x0))) / (-40.625647549427164 / (Exp((((x1 * -1.3081262350740548) / x0) * 1.89598860276895)) + -1.9854845540492172))),"(np.log(((x[:, 0] * x[:, 1]) + (x[:, 0] + x[:, 0]))) / (t[0] / (np.exp((((x[:, 1] * t[1]) / x[:, 0]) * t[2])) + t[3])))","$$\frac{\operatorname{log}(\left(\left(x_{0} \cdot x_{1}\right) + \left(x_{0} + x_{0}\right)\right))}{\frac{\theta_{0}}{\left(\operatorname{exp}(\left(\frac{\left(x_{1} \cdot \theta_{1}\right)}{x_{0}} \cdot \theta_{2}\right)) + \theta_{3}\right)}}$$",-40.625647549427164;-1.3081262350740548;1.89598860276895;-1.9854845540492172,21,1.3086950868538227e-2,1.3086950868538227e-2,1.3086950868538227e-2,1.3086950868538227e-2,-0.20767901739877304,-0.20767901739877304,-0.20767901739877304,51.7637503725288,51.7637503725288,51.7637503725288 +945,0,((((x0 ^ Log(((0.9821102735867796 + x1) / x1))) ^ -0.3459269605375668) / (((x0 * -0.9821814936983824) + 0.4456031884511792) ^ (x0 * x1))) * ((x0 + x1) + x1)),"((x[:, 0] ** np.log(((t[0] + x[:, 1]) / x[:, 1])) ** t[1] / ((x[:, 0] * t[2]) + t[3]) ** (x[:, 0] * x[:, 1])) * ((x[:, 0] + x[:, 1]) + x[:, 1]))","$$\left(\frac{{{x_{0}^{\operatorname{log}(\frac{\left(\theta_{0} + x_{1}\right)}{x_{1}})}}^{\theta_{1}}}}{{\left(\left(x_{0} \cdot \theta_{2}\right) + \theta_{3}\right)^{\left(x_{0} \cdot x_{1}\right)}}} \cdot \left(\left(x_{0} + x_{1}\right) + x_{1}\right)\right)$$",0.9821102735867796;-0.3459269605375668;-0.9821814936983824;0.4456031884511792,26,,,,Infinity,,,,,, +946,0,Exp(((Exp(x0) / x0) * -1.0061081786002721)),"np.exp(((np.exp(x[:, 0]) / x[:, 0]) * t[0]))","$$\operatorname{exp}(\left(\frac{\operatorname{exp}(x_{0})}{x_{0}} \cdot \theta_{0}\right))$$",-1.0061081786002721,7,1.1567418718036548e-2,1.1567418718036548e-2,1.1567418718036548e-2,1.1567418718036548e-2,-6.745482668713731e-2,-6.745482668713731e-2,-6.745482668713731e-2,13.297400999252577,13.297400999252577,13.297400999252577 +947,0,((Log((((0.6393710266524602 ^ x0) ^ ((x0 + 0.3524949093656261) ^ Exp(x1))) / x1)) * ((-0.9390137006606669 ^ x0) ^ x0)) ^ x1),"(np.log((t[0] ** x[:, 0] ** (x[:, 0] + t[1]) ** np.exp(x[:, 1]) / x[:, 1])) * t[2] ** x[:, 0] ** x[:, 0]) ** x[:, 1]","$${\left(\operatorname{log}(\frac{{{\theta_{0}^{x_{0}}}^{{\left(x_{0} + \theta_{1}\right)^{\operatorname{exp}(x_{1})}}}}}{x_{1}}) \cdot {{\theta_{2}^{x_{0}}}^{x_{0}}}\right)^{x_{1}}}$$",0.6393710266524602;0.3524949093656261;-0.9390137006606669,21,,,,Infinity,,,,,, +948,0,(Exp((((x0 ^ x1) + Log(Log((x1 ^ 2.4822253240271985e-3)))) + 0.21020474995432725)) / (Exp((-0.23429932408720278 ^ (x1 + -0.6685501696436695))) / Log(Exp(((x1 ^ 0.8800528279967526) + 0.553304564635863))))),"(np.exp(((x[:, 0] ** x[:, 1] + np.log(np.log(x[:, 1] ** t[0]))) + t[1])) / (np.exp(t[2] ** (x[:, 1] + t[3])) / np.log(np.exp((x[:, 1] ** t[4] + t[5])))))","$$\frac{\operatorname{exp}(\left(\left({x_{0}^{x_{1}}} + \operatorname{log}(\operatorname{log}({x_{1}^{\theta_{0}}}))\right) + \theta_{1}\right))}{\frac{\operatorname{exp}({\theta_{2}^{\left(x_{1} + \theta_{3}\right)}})}{\operatorname{log}(\operatorname{exp}(\left({x_{1}^{\theta_{4}}} + \theta_{5}\right)))}}$$",2.4822253240271985e-3;0.21020474995432725;-0.23429932408720278;-0.6685501696436695;0.8800528279967526;0.553304564635863,27,,,,Infinity,,,,,, +949,0,(((-1.5033518690619783 / Exp((((x1 * 0.708344572821251) * -0.6550555562544764) * 0.7390243357358103))) + Exp((x0 / (2.263807276160452 + x0)))) + (((x1 ^ 2.43891040278655) / (x0 / (((-1.3556423095518648 * x0) + (x0 * x1)) + -0.4386744472941228))) + ((x1 / x0) * 1.4467648866750773))),"(((t[0] / np.exp((((x[:, 1] * t[1]) * t[2]) * t[3]))) + np.exp((x[:, 0] / (t[4] + x[:, 0])))) + ((x[:, 1] ** t[5] / (x[:, 0] / (((t[6] * x[:, 0]) + (x[:, 0] * x[:, 1])) + t[7]))) + ((x[:, 1] / x[:, 0]) * t[8])))","$$\left(\left(\frac{\theta_{0}}{\operatorname{exp}(\left(\left(\left(x_{1} \cdot \theta_{1}\right) \cdot \theta_{2}\right) \cdot \theta_{3}\right))} + \operatorname{exp}(\frac{x_{0}}{\left(\theta_{4} + x_{0}\right)})\right) + \left(\frac{{x_{1}^{\theta_{5}}}}{\frac{x_{0}}{\left(\left(\left(\theta_{6} \cdot x_{0}\right) + \left(x_{0} \cdot x_{1}\right)\right) + \theta_{7}\right)}} + \left(\frac{x_{1}}{x_{0}} \cdot \theta_{8}\right)\right)\right)$$",-1.5033518690619783;0.708344572821251;-0.6550555562544764;0.7390243357358103;2.263807276160452;2.43891040278655;-1.3556423095518648;-0.4386744472941228;1.4467648866750773,39,1.6342982507322756e-3,1.6342982507322756e-3,1.6342982507322756e-3,1.6342982507322756e-3,0.8491850603393191,0.8491850603393191,0.8491850603393191,99.28801541224801,99.28801541224801,99.28801541224801 +950,0,Exp((Log((Exp(x1) * (x1 + -0.8387229543042387))) / ((Exp((-0.4351711476804776 + Log(x0))) * -0.8546767135593256) ^ x1))),"np.exp((np.log((np.exp(x[:, 1]) * (x[:, 1] + t[0]))) / (np.exp((t[1] + np.log(x[:, 0]))) * t[2]) ** x[:, 1]))","$$\operatorname{exp}(\frac{\operatorname{log}(\left(\operatorname{exp}(x_{1}) \cdot \left(x_{1} + \theta_{0}\right)\right))}{{\left(\operatorname{exp}(\left(\theta_{1} + \operatorname{log}(x_{0})\right)) \cdot \theta_{2}\right)^{x_{1}}}})$$",-0.8387229543042387;-0.4351711476804776;-0.8546767135593256,18,,,,Infinity,,,,,, +951,0,(((x1 + -1.131468657335928) + (x1 ^ (x0 / x0))) + ((-1.8997746322508822 * x1) + (0.10244603664991561 + x0))),"(((x[:, 1] + t[0]) + x[:, 1] ** (x[:, 0] / x[:, 0])) + ((t[1] * x[:, 1]) + (t[2] + x[:, 0])))","$$\left(\left(\left(x_{1} + \theta_{0}\right) + {x_{1}^{\frac{x_{0}}{x_{0}}}}\right) + \left(\left(\theta_{1} \cdot x_{1}\right) + \left(\theta_{2} + x_{0}\right)\right)\right)$$",-1.131468657335928;-1.8997746322508822;0.10244603664991561,17,0.15086533848901912,0.15086533848901912,0.15086533848901912,0.15086533848901912,-12.922028559300518,-12.922028559300518,-12.922028559300518,39.279026144683364,39.279026144683364,39.279026144683364 +952,0,(Log((0.7796756229835801 / (Exp(x0) * -0.15998408190166125))) ^ (((x1 * ((0.7891994390150341 * x0) / (x0 / (x1 ^ x1)))) + Exp(x0)) + (-0.3928607355670173 ^ (x1 + -0.8773147947317357)))),"np.log((t[0] / (np.exp(x[:, 0]) * t[1]))) ** (((x[:, 1] * ((t[2] * x[:, 0]) / (x[:, 0] / x[:, 1] ** x[:, 1]))) + np.exp(x[:, 0])) + t[3] ** (x[:, 1] + t[4]))","$${\operatorname{log}(\frac{\theta_{0}}{\left(\operatorname{exp}(x_{0}) \cdot \theta_{1}\right)})^{\left(\left(\left(x_{1} \cdot \frac{\left(\theta_{2} \cdot x_{0}\right)}{\frac{x_{0}}{{x_{1}^{x_{1}}}}}\right) + \operatorname{exp}(x_{0})\right) + {\theta_{3}^{\left(x_{1} + \theta_{4}\right)}}\right)}}$$",0.7796756229835801;-0.15998408190166125;0.7891994390150341;-0.3928607355670173;-0.8773147947317357,28,,,,Infinity,,,,,, +953,0,(5.3357217557189265e-3 / (1.5239849942147886e-3 ^ Exp((x0 * -0.9446445572386545)))),"(t[0] / t[1] ** np.exp((x[:, 0] * t[2])))","$$\frac{\theta_{0}}{{\theta_{1}^{\operatorname{exp}(\left(x_{0} \cdot \theta_{2}\right))}}}$$",5.3357217557189265e-3;1.5239849942147886e-3;-0.9446445572386545,8,5.671433671050864e-3,5.671433671050864e-3,5.671433671050864e-3,5.671433671050864e-3,0.4766335174709747,0.4766335174709747,0.4766335174709747,20.40195485023131,20.40195485023131,20.40195485023131 +954,0,((-0.7213226845272313 ^ Log((-0.141002965762409 ^ (x1 * 0.4798575261608894)))) + (((x0 + -0.7435693005958358) * 0.1012490786160547) ^ 0.6090659387514961)),"(t[0] ** np.log(t[1] ** (x[:, 1] * t[2])) + ((x[:, 0] + t[3]) * t[4]) ** t[5])","$$\left({\theta_{0}^{\operatorname{log}({\theta_{1}^{\left(x_{1} \cdot \theta_{2}\right)}})}} + {\left(\left(x_{0} + \theta_{3}\right) \cdot \theta_{4}\right)^{\theta_{5}}}\right)$$",-0.7213226845272313;-0.141002965762409;0.4798575261608894;-0.7435693005958358;0.1012490786160547;0.6090659387514961,16,,,,Infinity,,,,,, +955,0,Exp((((Exp(((x0 ^ x0) ^ -0.3417948199457427)) * -0.6639437079268771) * -0.5902264072671638) * (1.1071823557440297e-4 ^ ((((x1 ^ -0.7471628766794766) * 0.46667444697213306) / ((x0 / x0) * 0.9391352309640245)) ^ (-0.5946232309505479 / (0.5586812476873512 + x0)))))),"np.exp((((np.exp(x[:, 0] ** x[:, 0] ** t[0]) * t[1]) * t[2]) * t[3] ** ((x[:, 1] ** t[4] * t[5]) / ((x[:, 0] / x[:, 0]) * t[6])) ** (t[7] / (t[8] + x[:, 0]))))","$$\operatorname{exp}(\left(\left(\left(\operatorname{exp}({{x_{0}^{x_{0}}}^{\theta_{0}}}) \cdot \theta_{1}\right) \cdot \theta_{2}\right) \cdot {\theta_{3}^{{\frac{\left({x_{1}^{\theta_{4}}} \cdot \theta_{5}\right)}{\left(\frac{x_{0}}{x_{0}} \cdot \theta_{6}\right)}^{\frac{\theta_{7}}{\left(\theta_{8} + x_{0}\right)}}}}}\right))$$",-0.3417948199457427;-0.6639437079268771;-0.5902264072671638;1.1071823557440297e-4;-0.7471628766794766;0.46667444697213306;0.9391352309640245;-0.5946232309505479;0.5586812476873512,31,0.8932607223202278,0.8932607223202278,0.8932607223202278,0.8932607223202278,-81.4311363471258,-81.4311363471258,-81.4311363471258,83.45494686047188,83.45494686047188,83.45494686047188 +956,0,((Log(Log((-0.2299748632461447 ^ ((x1 + x0) * -0.5770472577148691)))) ^ (((0.4546087450415348 + x1) * -0.29385302072425734) + Exp(Log(Log((x1 + x1)))))) * (((x1 * x0) / (x0 + x1)) + 0.41606340315817303)),"(np.log(np.log(t[0] ** ((x[:, 1] + x[:, 0]) * t[1]))) ** (((t[2] + x[:, 1]) * t[3]) + np.exp(np.log(np.log((x[:, 1] + x[:, 1]))))) * (((x[:, 1] * x[:, 0]) / (x[:, 0] + x[:, 1])) + t[4]))","$$\left({\operatorname{log}(\operatorname{log}({\theta_{0}^{\left(\left(x_{1} + x_{0}\right) \cdot \theta_{1}\right)}}))^{\left(\left(\left(\theta_{2} + x_{1}\right) \cdot \theta_{3}\right) + \operatorname{exp}(\operatorname{log}(\operatorname{log}(\left(x_{1} + x_{1}\right))))\right)}} \cdot \left(\frac{\left(x_{1} \cdot x_{0}\right)}{\left(x_{0} + x_{1}\right)} + \theta_{4}\right)\right)$$",-0.2299748632461447;-0.5770472577148691;0.4546087450415348;-0.29385302072425734;0.41606340315817303,32,,,,Infinity,,,,,, +957,0,((((x0 / x0) * (-0.3768384549919066 + Log((x0 + (x1 * x1))))) / Log((x0 / Log(Log((x1 * 0.9306384037836238)))))) * Log(((x0 + x0) + 0.19559929246979846))),"((((x[:, 0] / x[:, 0]) * (t[0] + np.log((x[:, 0] + (x[:, 1] * x[:, 1]))))) / np.log((x[:, 0] / np.log(np.log((x[:, 1] * t[1])))))) * np.log(((x[:, 0] + x[:, 0]) + t[2])))","$$\left(\frac{\left(\frac{x_{0}}{x_{0}} \cdot \left(\theta_{0} + \operatorname{log}(\left(x_{0} + \left(x_{1} \cdot x_{1}\right)\right))\right)\right)}{\operatorname{log}(\frac{x_{0}}{\operatorname{log}(\operatorname{log}(\left(x_{1} \cdot \theta_{1}\right)))})} \cdot \operatorname{log}(\left(\left(x_{0} + x_{0}\right) + \theta_{2}\right))\right)$$",-0.3768384549919066;0.9306384037836238;0.19559929246979846,28,,,,Infinity,,,,,, +958,0,Exp(((((x1 * 0.12213290203038844) ^ (x1 ^ Log(x1))) ^ Exp(Exp(((x1 * x1) ^ (-0.2792291971611356 + x0))))) * ((0.5355500393146782 / Exp((x0 ^ x1))) + (0.47287486359855374 + (-0.8422214646782668 + x0))))),"np.exp(((x[:, 1] * t[0]) ** x[:, 1] ** np.log(x[:, 1]) ** np.exp(np.exp((x[:, 1] * x[:, 1]) ** (t[1] + x[:, 0]))) * ((t[2] / np.exp(x[:, 0] ** x[:, 1])) + (t[3] + (t[4] + x[:, 0])))))","$$\operatorname{exp}(\left({{\left(x_{1} \cdot \theta_{0}\right)^{{x_{1}^{\operatorname{log}(x_{1})}}}}^{\operatorname{exp}(\operatorname{exp}({\left(x_{1} \cdot x_{1}\right)^{\left(\theta_{1} + x_{0}\right)}}))}} \cdot \left(\frac{\theta_{2}}{\operatorname{exp}({x_{0}^{x_{1}}})} + \left(\theta_{3} + \left(\theta_{4} + x_{0}\right)\right)\right)\right))$$",0.12213290203038844;-0.2792291971611356;0.5355500393146782;0.47287486359855374;-0.8422214646782668,32,0.8923099211909795,0.8923099211909795,0.8923099211909795,0.8923099211909795,-81.34339531523482,-81.34339531523482,-81.34339531523482,81.21889008066911,81.21889008066911,81.21889008066911 +959,0,(((7.590875120497198e-3 + (-0.12779396333268764 / x0)) + (Exp(((Exp(x1) ^ -0.8287014514245213) * -0.8083489338237076)) ^ Exp((0.12019295656097609 + x0)))) / (x0 * x0)),"(((t[0] + (t[1] / x[:, 0])) + np.exp((np.exp(x[:, 1]) ** t[2] * t[3])) ** np.exp((t[4] + x[:, 0]))) / (x[:, 0] * x[:, 0]))","$$\frac{\left(\left(\theta_{0} + \frac{\theta_{1}}{x_{0}}\right) + {\operatorname{exp}(\left({\operatorname{exp}(x_{1})^{\theta_{2}}} \cdot \theta_{3}\right))^{\operatorname{exp}(\left(\theta_{4} + x_{0}\right))}}\right)}{\left(x_{0} \cdot x_{0}\right)}$$",7.590875120497198e-3;-0.12779396333268764;-0.8287014514245213;-0.8083489338237076;0.12019295656097609,22,1.1779036292835655e-3,1.1779036292835655e-3,1.1779036292835655e-3,1.1779036292835655e-3,0.8913016857866054,0.8913016857866054,0.8913016857866054,55.85339870706094,55.85339870706094,55.85339870706094 +960,0,(Exp(((((x1 + Exp(x0)) ^ 2.6924336286294412) * -3.830687695117723) * x1)) ^ 2.7725363364533493),"np.exp((((x[:, 1] + np.exp(x[:, 0])) ** t[0] * t[1]) * x[:, 1])) ** t[2]","$${\operatorname{exp}(\left(\left({\left(x_{1} + \operatorname{exp}(x_{0})\right)^{\theta_{0}}} \cdot \theta_{1}\right) \cdot x_{1}\right))^{\theta_{2}}}$$",2.6924336286294412;-3.830687695117723;2.7725363364533493,13,1.4573705260116728e-2,1.4573705260116728e-2,1.4573705260116728e-2,1.4573705260116728e-2,-0.3448784384687622,-0.3448784384687622,-0.3448784384687622,31.3727230785561,31.3727230785561,31.3727230785561 +961,0,Exp(Log(Log((Exp((0.10089018651266601 + x0)) / (-0.1745097609771158 / Exp(x1)))))),"np.exp(np.log(np.log((np.exp((t[0] + x[:, 0])) / (t[1] / np.exp(x[:, 1]))))))","$$\operatorname{exp}(\operatorname{log}(\operatorname{log}(\frac{\operatorname{exp}(\left(\theta_{0} + x_{0}\right))}{\frac{\theta_{1}}{\operatorname{exp}(x_{1})}})))$$",0.10089018651266601;-0.1745097609771158,12,,,,Infinity,,,,,, +962,0,(Exp(Exp(Exp((((x1 * -0.7976399167811412) ^ 0.7684795195092324) + (0.29586834693465747 / x0))))) ^ Exp(((-0.4391910088562295 + (x0 / x0)) ^ -0.16192783065409244))),"np.exp(np.exp(np.exp(((x[:, 1] * t[0]) ** t[1] + (t[2] / x[:, 0]))))) ** np.exp((t[3] + (x[:, 0] / x[:, 0])) ** t[4])","$${\operatorname{exp}(\operatorname{exp}(\operatorname{exp}(\left({\left(x_{1} \cdot \theta_{0}\right)^{\theta_{1}}} + \frac{\theta_{2}}{x_{0}}\right))))^{\operatorname{exp}({\left(\theta_{3} + \frac{x_{0}}{x_{0}}\right)^{\theta_{4}}})}}$$",-0.7976399167811412;0.7684795195092324;0.29586834693465747;-0.4391910088562295;-0.16192783065409244,21,,,,Infinity,,,,,, +963,0,Log(Log((((Exp(x0) * 0.8263470006882181) / (((x0 + x1) + (x1 * -0.38730405289199965)) * Log((-0.7376859329138159 / x1)))) * Exp((0.701482841936423 + (Log(x1) * x1)))))),"np.log(np.log((((np.exp(x[:, 0]) * t[0]) / (((x[:, 0] + x[:, 1]) + (x[:, 1] * t[1])) * np.log((t[2] / x[:, 1])))) * np.exp((t[3] + (np.log(x[:, 1]) * x[:, 1]))))))","$$\operatorname{log}(\operatorname{log}(\left(\frac{\left(\operatorname{exp}(x_{0}) \cdot \theta_{0}\right)}{\left(\left(\left(x_{0} + x_{1}\right) + \left(x_{1} \cdot \theta_{1}\right)\right) \cdot \operatorname{log}(\frac{\theta_{2}}{x_{1}})\right)} \cdot \operatorname{exp}(\left(\theta_{3} + \left(\operatorname{log}(x_{1}) \cdot x_{1}\right)\right))\right)))$$",0.8263470006882181;-0.38730405289199965;-0.7376859329138159;0.701482841936423,27,,,,Infinity,,,,,, +964,0,(x0 / x0),"(x[:, 0] / x[:, 0])","$$\frac{x_{0}}{x_{0}}$$",,3,0.8923097947317449,0.8923097947317449,0.8923097947317449,0.8923097947317449,-81.34338364542985,-81.34338364542985,-81.34338364542985,2.8825203569384064,2.8825203569384064,2.8825203569384064 +965,0,(((0.8215346795446157 * (-0.33720140760847606 + x1)) * 0.4448551350398259) * (Log(Exp((0.7655270286048402 * x0))) / (Log(x0) / (x0 + 0.393641982763437)))),"(((t[0] * (t[1] + x[:, 1])) * t[2]) * (np.log(np.exp((t[3] * x[:, 0]))) / (np.log(x[:, 0]) / (x[:, 0] + t[4]))))","$$\left(\left(\left(\theta_{0} \cdot \left(\theta_{1} + x_{1}\right)\right) \cdot \theta_{2}\right) \cdot \frac{\operatorname{log}(\operatorname{exp}(\left(\theta_{3} \cdot x_{0}\right)))}{\frac{\operatorname{log}(x_{0})}{\left(x_{0} + \theta_{4}\right)}}\right)$$",0.8215346795446157;-0.33720140760847606;0.4448551350398259;0.7655270286048402;0.393641982763437,20,2615.819060495159,2615.819060495159,2615.819060495159,2615.819060495159,-241389.8193287681,-241389.8193287681,-241389.8193287681,2405.930609956078,2405.930609956078,2405.930609956078 +966,0,((((x0 ^ x1) * Log(((x1 * -0.9948548113582485) * 0.49286166856167013))) / Log(Exp(Log(Log(x1))))) + Log((x0 ^ 0.36056314135049616))),"(((x[:, 0] ** x[:, 1] * np.log(((x[:, 1] * t[0]) * t[1]))) / np.log(np.exp(np.log(np.log(x[:, 1]))))) + np.log(x[:, 0] ** t[2]))","$$\left(\frac{\left({x_{0}^{x_{1}}} \cdot \operatorname{log}(\left(\left(x_{1} \cdot \theta_{0}\right) \cdot \theta_{1}\right))\right)}{\operatorname{log}(\operatorname{exp}(\operatorname{log}(\operatorname{log}(x_{1}))))} + \operatorname{log}({x_{0}^{\theta_{2}}})\right)$$",-0.9948548113582485;0.49286166856167013;0.36056314135049616,21,,,,Infinity,,,,,, +967,0,(x0 * (-1.183907888707723 + x0)),"(x[:, 0] * (t[0] + x[:, 0]))","$$\left(x_{0} \cdot \left(\theta_{0} + x_{0}\right)\right)$$",-1.183907888707723,5,0.1297997374962222,0.1297997374962222,0.1297997374962222,0.1297997374962222,-10.978070446868387,-10.978070446868387,-10.978070446868387,9.069216504713694,9.069216504713694,9.069216504713694 +968,0,Exp(((Log((-0.7556182148302774 * x1)) + 0.9544641518232967) * -0.6242931835524377)),"np.exp(((np.log((t[0] * x[:, 1])) + t[1]) * t[2]))","$$\operatorname{exp}(\left(\left(\operatorname{log}(\left(\theta_{0} \cdot x_{1}\right)) + \theta_{1}\right) \cdot \theta_{2}\right))$$",-0.7556182148302774;0.9544641518232967;-0.6242931835524377,9,,,,Infinity,,,,,, +969,0,Exp((Log(((Exp(x1) + x0) ^ -7.241947751133037)) + Exp((((x1 * 0.9065394637483465) + (x1 + 0.3514228049557008)) * 1.1696370621508143)))),"np.exp((np.log((np.exp(x[:, 1]) + x[:, 0]) ** t[0]) + np.exp((((x[:, 1] * t[1]) + (x[:, 1] + t[2])) * t[3]))))","$$\operatorname{exp}(\left(\operatorname{log}({\left(\operatorname{exp}(x_{1}) + x_{0}\right)^{\theta_{0}}}) + \operatorname{exp}(\left(\left(\left(x_{1} \cdot \theta_{1}\right) + \left(x_{1} + \theta_{2}\right)\right) \cdot \theta_{3}\right))\right))$$",-7.241947751133037;0.9065394637483465;0.3514228049557008;1.1696370621508143,19,3.2768579624871904e-3,3.2768579624871904e-3,3.2768579624871904e-3,3.2768579624871904e-3,0.6976077434656172,0.6976077434656172,0.6976077434656172,47.59603820555369,47.59603820555369,47.59603820555369 +970,0,(Exp(Log(((0.7895003273427796 / (x0 ^ 0.6065457500371565)) ^ 0.5228764245303865))) ^ (0.42726714389003695 + Log(Log((x1 ^ 0.9386293655284665))))),"np.exp(np.log((t[0] / x[:, 0] ** t[1]) ** t[2])) ** (t[3] + np.log(np.log(x[:, 1] ** t[4])))","$${\operatorname{exp}(\operatorname{log}({\frac{\theta_{0}}{{x_{0}^{\theta_{1}}}}^{\theta_{2}}}))^{\left(\theta_{3} + \operatorname{log}(\operatorname{log}({x_{1}^{\theta_{4}}}))\right)}}$$",0.7895003273427796;0.6065457500371565;0.5228764245303865;0.42726714389003695;0.9386293655284665,17,,,,Infinity,,,,,, +971,0,((-0.859146708368862 + Exp((x0 * -0.11208670878184722))) / ((x0 * 0.7185031548339713) ^ 1.0704163291369404)),"((t[0] + np.exp((x[:, 0] * t[1]))) / (x[:, 0] * t[2]) ** t[3])","$$\frac{\left(\theta_{0} + \operatorname{exp}(\left(x_{0} \cdot \theta_{1}\right))\right)}{{\left(x_{0} \cdot \theta_{2}\right)^{\theta_{3}}}}$$",-0.859146708368862;-0.11208670878184722;0.7185031548339713;1.0704163291369404,12,5.029076273442346e-3,5.029076273442346e-3,5.029076273442346e-3,5.029076273442346e-3,0.5359110037667063,0.5359110037667063,0.5359110037667063,31.439147698780417,31.439147698780417,31.439147698780417 +972,0,Log(((-0.5517485546820351 + (Log(Log((x0 + 0.1442913410589105))) ^ Exp(Log(x0)))) ^ Exp((((-0.7968251169006225 ^ (x1 ^ 0.6403759907191348)) * Exp(x0)) / Log((6.756979280191067e-2 / x1)))))),"np.log((t[0] + np.log(np.log((x[:, 0] + t[1]))) ** np.exp(np.log(x[:, 0]))) ** np.exp(((t[2] ** x[:, 1] ** t[3] * np.exp(x[:, 0])) / np.log((t[4] / x[:, 1])))))","$$\operatorname{log}({\left(\theta_{0} + {\operatorname{log}(\operatorname{log}(\left(x_{0} + \theta_{1}\right)))^{\operatorname{exp}(\operatorname{log}(x_{0}))}}\right)^{\operatorname{exp}(\frac{\left({\theta_{2}^{{x_{1}^{\theta_{3}}}}} \cdot \operatorname{exp}(x_{0})\right)}{\operatorname{log}(\frac{\theta_{4}}{x_{1}})})}})$$",-0.5517485546820351;0.1442913410589105;-0.7968251169006225;0.6403759907191348;6.756979280191067e-2,27,,,,Infinity,,,,,, +973,0,Log(((Exp((((x0 + 1.527467738862641) ^ x1) * 1.2178324782078946)) + 0.8440207668407167) ^ 3.123771953912753e-2)),"np.log((np.exp(((x[:, 0] + t[0]) ** x[:, 1] * t[1])) + t[2]) ** t[3])","$$\operatorname{log}({\left(\operatorname{exp}(\left({\left(x_{0} + \theta_{0}\right)^{x_{1}}} \cdot \theta_{1}\right)) + \theta_{2}\right)^{\theta_{3}}})$$",1.527467738862641;1.2178324782078946;0.8440207668407167;3.123771953912753e-2,13,1.0776495348299074e-2,1.0776495348299074e-2,1.0776495348299074e-2,1.0776495348299074e-2,5.532499971098281e-3,5.532499971098281e-3,5.532499971098281e-3,35.1261386291219,35.1261386291219,35.1261386291219 +974,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 +975,0,(((Exp(Exp(x0)) / (3.1801812708005013 / (x0 / x0))) ^ -1.4058393440584611) * Log(Log((Exp((Exp(x1) + 0.7217734232584261)) / ((x1 / x1) + Exp((-0.6603520702016906 * x0))))))),"((np.exp(np.exp(x[:, 0])) / (t[0] / (x[:, 0] / x[:, 0]))) ** t[1] * np.log(np.log((np.exp((np.exp(x[:, 1]) + t[2])) / ((x[:, 1] / x[:, 1]) + np.exp((t[3] * x[:, 0])))))))","$$\left({\frac{\operatorname{exp}(\operatorname{exp}(x_{0}))}{\frac{\theta_{0}}{\frac{x_{0}}{x_{0}}}}^{\theta_{1}}} \cdot \operatorname{log}(\operatorname{log}(\frac{\operatorname{exp}(\left(\operatorname{exp}(x_{1}) + \theta_{2}\right))}{\left(\frac{x_{1}}{x_{1}} + \operatorname{exp}(\left(\theta_{3} \cdot x_{0}\right))\right)}))\right)$$",3.1801812708005013;-1.4058393440584611;0.7217734232584261;-0.6603520702016906,28,2.8012007461560295e-3,2.8012007461560295e-3,2.8012007461560295e-3,2.8012007461560295e-3,0.7415019435285548,0.7415019435285548,0.7415019435285548,69.60850898755625,69.60850898755625,69.60850898755625 +976,0,(Exp((-4287898.219992707 + (x0 / x1))) * 2.6442867019296054e7),"(np.exp((t[0] + (x[:, 0] / x[:, 1]))) * t[1])","$$\left(\operatorname{exp}(\left(\theta_{0} + \frac{x_{0}}{x_{1}}\right)) \cdot \theta_{1}\right)$$",-4287898.219992707;2.6442867019296054e7,8,1.4573554749975553e-2,1.4573554749975553e-2,1.4573554749975553e-2,1.4573554749975553e-2,-0.3448645492182232,-0.3448645492182232,-0.3448645492182232,19.622247262452767,19.622247262452767,19.622247262452767 +977,0,Log((Exp((Log((x0 * 0.8773293198924811)) * 0.3744711258729274)) + (((x1 + x0) * x1) / ((x0 + x1) ^ (-0.2251170226333039 ^ x0))))),"np.log((np.exp((np.log((x[:, 0] * t[0])) * t[1])) + (((x[:, 1] + x[:, 0]) * x[:, 1]) / (x[:, 0] + x[:, 1]) ** t[2] ** x[:, 0])))","$$\operatorname{log}(\left(\operatorname{exp}(\left(\operatorname{log}(\left(x_{0} \cdot \theta_{0}\right)) \cdot \theta_{1}\right)) + \frac{\left(\left(x_{1} + x_{0}\right) \cdot x_{1}\right)}{{\left(x_{0} + x_{1}\right)^{{\theta_{2}^{x_{0}}}}}}\right))$$",0.8773293198924811;0.3744711258729274;-0.2251170226333039,22,,,,Infinity,,,,,, +978,0,(((((-0.7651870044565189 ^ x0) + 0.9986884874080476) * -0.8664321115165925) + ((x0 + Exp(Exp(x0))) ^ (0.48376520187506333 ^ (0.2528604917782339 + x1)))) * Exp(Exp(x1))),"((((t[0] ** x[:, 0] + t[1]) * t[2]) + (x[:, 0] + np.exp(np.exp(x[:, 0]))) ** t[3] ** (t[4] + x[:, 1])) * np.exp(np.exp(x[:, 1])))","$$\left(\left(\left(\left({\theta_{0}^{x_{0}}} + \theta_{1}\right) \cdot \theta_{2}\right) + {\left(x_{0} + \operatorname{exp}(\operatorname{exp}(x_{0}))\right)^{{\theta_{3}^{\left(\theta_{4} + x_{1}\right)}}}}\right) \cdot \operatorname{exp}(\operatorname{exp}(x_{1}))\right)$$",-0.7651870044565189;0.9986884874080476;-0.8664321115165925;0.48376520187506333;0.2528604917782339,23,,,,Infinity,,,,,, +979,0,((((0.40495081682943346 * x0) * -0.6036323878885268) ^ ((0.11129818573390116 / x1) / x0)) * Log((((-0.32843506384434185 ^ x0) ^ x0) + (Exp(x1) * (x1 + x0))))),"(((t[0] * x[:, 0]) * t[1]) ** ((t[2] / x[:, 1]) / x[:, 0]) * np.log((t[3] ** x[:, 0] ** x[:, 0] + (np.exp(x[:, 1]) * (x[:, 1] + x[:, 0])))))","$$\left({\left(\left(\theta_{0} \cdot x_{0}\right) \cdot \theta_{1}\right)^{\frac{\frac{\theta_{2}}{x_{1}}}{x_{0}}}} \cdot \operatorname{log}(\left({{\theta_{3}^{x_{0}}}^{x_{0}}} + \left(\operatorname{exp}(x_{1}) \cdot \left(x_{1} + x_{0}\right)\right)\right))\right)$$",0.40495081682943346;-0.6036323878885268;0.11129818573390116;-0.32843506384434185,25,,,,Infinity,,,,,, +980,0,Log((Log((x0 / x0)) + ((((-0.16244833327907793 * x0) ^ x0) + -2.6193798986125505e-2) ^ ((x0 / ((x1 * 0.8209333025554719) ^ 1.6715645699700743e-2)) / x0)))),"np.log((np.log((x[:, 0] / x[:, 0])) + ((t[0] * x[:, 0]) ** x[:, 0] + t[1]) ** ((x[:, 0] / (x[:, 1] * t[2]) ** t[3]) / x[:, 0])))","$$\operatorname{log}(\left(\operatorname{log}(\frac{x_{0}}{x_{0}}) + {\left({\left(\theta_{0} \cdot x_{0}\right)^{x_{0}}} + \theta_{1}\right)^{\frac{\frac{x_{0}}{{\left(x_{1} \cdot \theta_{2}\right)^{\theta_{3}}}}}{x_{0}}}}\right))$$",-0.16244833327907793;-2.6193798986125505e-2;0.8209333025554719;1.6715645699700743e-2,23,,,,Infinity,,,,,, +981,0,((3.634929255887402e-4 ^ Exp(x0)) + (-0.27010834764677544 + (x1 ^ x0))),"(t[0] ** np.exp(x[:, 0]) + (t[1] + x[:, 1] ** x[:, 0]))","$$\left({\theta_{0}^{\operatorname{exp}(x_{0})}} + \left(\theta_{1} + {x_{1}^{x_{0}}}\right)\right)$$",3.634929255887402e-4;-0.27010834764677544,10,1.0578548541116535e-2,1.0578548541116535e-2,1.0578548541116535e-2,1.0578548541116535e-2,2.379926111330888e-2,2.379926111330888e-2,2.379926111330888e-2,21.968965256702838,21.968965256702838,21.968965256702838 +982,0,(((-0.40682758418576714 + (x0 ^ x0)) ^ ((0.5783668868195189 / x0) ^ -0.3233421846639126)) + Exp((((x1 ^ x0) / Log(x0)) + Log((-0.7276785661669237 ^ (0.39586142042342 * x0)))))),"((t[0] + x[:, 0] ** x[:, 0]) ** (t[1] / x[:, 0]) ** t[2] + np.exp(((x[:, 1] ** x[:, 0] / np.log(x[:, 0])) + np.log(t[3] ** (t[4] * x[:, 0])))))","$$\left({\left(\theta_{0} + {x_{0}^{x_{0}}}\right)^{{\frac{\theta_{1}}{x_{0}}^{\theta_{2}}}}} + \operatorname{exp}(\left(\frac{{x_{1}^{x_{0}}}}{\operatorname{log}(x_{0})} + \operatorname{log}({\theta_{3}^{\left(\theta_{4} \cdot x_{0}\right)}})\right))\right)$$",-0.40682758418576714;0.5783668868195189;-0.3233421846639126;-0.7276785661669237;0.39586142042342,26,,,,Infinity,,,,,, +983,0,Exp((Exp(Log((0.2783852430133731 ^ (Log(x0) ^ -0.7509772353170233)))) ^ Log((((Log(x1) ^ 0.15822841631151896) + -0.280767828849505) / ((0.7952637146751511 * (x0 ^ -0.6362811310890439)) * x1))))),"np.exp(np.exp(np.log(t[0] ** np.log(x[:, 0]) ** t[1])) ** np.log(((np.log(x[:, 1]) ** t[2] + t[3]) / ((t[4] * x[:, 0] ** t[5]) * x[:, 1]))))","$$\operatorname{exp}({\operatorname{exp}(\operatorname{log}({\theta_{0}^{{\operatorname{log}(x_{0})^{\theta_{1}}}}}))^{\operatorname{log}(\frac{\left({\operatorname{log}(x_{1})^{\theta_{2}}} + \theta_{3}\right)}{\left(\left(\theta_{4} \cdot {x_{0}^{\theta_{5}}}\right) \cdot x_{1}\right)})}})$$",0.2783852430133731;-0.7509772353170233;0.15822841631151896;-0.280767828849505;0.7952637146751511;-0.6362811310890439,25,,,,Infinity,,,,,, +984,0,Exp(Log(Exp(Exp(Log((x1 * (x0 * 1.0922082746342486e-4))))))),"np.exp(np.log(np.exp(np.exp(np.log((x[:, 1] * (x[:, 0] * t[0])))))))","$$\operatorname{exp}(\operatorname{log}(\operatorname{exp}(\operatorname{exp}(\operatorname{log}(\left(x_{1} \cdot \left(x_{0} \cdot \theta_{0}\right)\right))))))$$",1.0922082746342486e-4,10,0.8923833701107682,0.8923833701107682,0.8923833701107682,0.8923833701107682,-81.35017326681195,-81.35017326681195,-81.35017326681195,20.74166466074788,20.74166466074788,20.74166466074788 +985,0,Log(Exp(Exp(((x1 * x0) ^ 156.20597121134327)))),"np.log(np.exp(np.exp((x[:, 1] * x[:, 0]) ** t[0])))","$$\operatorname{log}(\operatorname{exp}(\operatorname{exp}({\left(x_{1} \cdot x_{0}\right)^{\theta_{0}}})))$$",156.20597121134327,8,0.8923097947321331,0.8923097947321331,0.8923097947321331,0.8923097947321331,-81.34338364546568,-81.34338364546568,-81.34338364546568,18.391284943069063,18.391284943069063,18.391284943069063 +986,0,((((x0 * Log(x1)) * -1.1959276104895378) / (-0.6172434204798509 / (x1 * 1.422426203853352))) + (((x1 / (x1 / x0)) ^ (x0 * 0.5684475254589114)) ^ ((x0 / (x0 * x0)) + (0.20468587228356785 * (x0 ^ x0))))),"((((x[:, 0] * np.log(x[:, 1])) * t[0]) / (t[1] / (x[:, 1] * t[2]))) + (x[:, 1] / (x[:, 1] / x[:, 0])) ** (x[:, 0] * t[3]) ** ((x[:, 0] / (x[:, 0] * x[:, 0])) + (t[4] * x[:, 0] ** x[:, 0])))","$$\left(\frac{\left(\left(x_{0} \cdot \operatorname{log}(x_{1})\right) \cdot \theta_{0}\right)}{\frac{\theta_{1}}{\left(x_{1} \cdot \theta_{2}\right)}} + {{\frac{x_{1}}{\frac{x_{1}}{x_{0}}}^{\left(x_{0} \cdot \theta_{3}\right)}}^{\left(\frac{x_{0}}{\left(x_{0} \cdot x_{0}\right)} + \left(\theta_{4} \cdot {x_{0}^{x_{0}}}\right)\right)}}\right)$$",-1.1959276104895378;-0.6172434204798509;1.422426203853352;0.5684475254589114;0.20468587228356785,34,2.0994221757431583e-2,2.0994221757431583e-2,2.0994221757431583e-2,2.0994221757431583e-2,-0.9373711537360507,-0.9373711537360507,-0.9373711537360507,80.82453189353431,80.82453189353431,80.82453189353431 +987,0,(x1 ^ x0),"x[:, 1] ** x[:, 0]","$${x_{1}^{x_{0}}}$$",,3,8.353975393662696e-2,8.353975393662696e-2,8.353975393662696e-2,8.353975393662696e-2,-6.709145465691655,-6.709145465691655,-6.709145465691655,3.3710226445472933,3.3710226445472933,3.3710226445472933 +988,0,Exp(Log(Exp((Log(Exp(x1)) / ((785.2368872240656 * x1) * 582.9951786988512))))),"np.exp(np.log(np.exp((np.log(np.exp(x[:, 1])) / ((t[0] * x[:, 1]) * t[1])))))","$$\operatorname{exp}(\operatorname{log}(\operatorname{exp}(\frac{\operatorname{log}(\operatorname{exp}(x_{1}))}{\left(\left(\theta_{0} \cdot x_{1}\right) \cdot \theta_{1}\right)})))$$",785.2368872240656;582.9951786988512,12,0.8923138964885916,0.8923138964885916,0.8923138964885916,0.8923138964885916,-81.34376216031296,-81.34376216031296,-81.34376216031296,26.346046008311678,26.346046008311678,26.346046008311678 +989,0,(Exp(Exp(Exp((Exp(x1) ^ 0.2394020336185545)))) / (((x0 ^ Log((Log(x0) / x1))) ^ (0.7779703814536216 + x1)) / x1)),"(np.exp(np.exp(np.exp(np.exp(x[:, 1]) ** t[0]))) / (x[:, 0] ** np.log((np.log(x[:, 0]) / x[:, 1])) ** (t[1] + x[:, 1]) / x[:, 1]))","$$\frac{\operatorname{exp}(\operatorname{exp}(\operatorname{exp}({\operatorname{exp}(x_{1})^{\theta_{0}}})))}{\frac{{{x_{0}^{\operatorname{log}(\frac{\operatorname{log}(x_{0})}{x_{1}})}}^{\left(\theta_{1} + x_{1}\right)}}}{x_{1}}}$$",0.2394020336185545;0.7779703814536216,21,,,,Infinity,,,,,, +990,0,Exp((((-0.7032112441677596 ^ (((x1 + -0.29841235885181583) + -0.9243159215086852) + x1)) + Exp(x1)) ^ 0.5566689064928065)),"np.exp((t[0] ** (((x[:, 1] + t[1]) + t[2]) + x[:, 1]) + np.exp(x[:, 1])) ** t[3])","$$\operatorname{exp}({\left({\theta_{0}^{\left(\left(\left(x_{1} + \theta_{1}\right) + \theta_{2}\right) + x_{1}\right)}} + \operatorname{exp}(x_{1})\right)^{\theta_{3}}})$$",-0.7032112441677596;-0.29841235885181583;-0.9243159215086852;0.5566689064928065,15,,,,Infinity,,,,,, +991,0,(((Exp((x1 + x0)) * (x0 + (x0 * -0.1337059654994608))) * 0.14932952893315676) / Exp(((Log((-0.44371309496120215 ^ x1)) / (x1 * x0)) / Exp(((x0 / x0) + Log(x1)))))),"(((np.exp((x[:, 1] + x[:, 0])) * (x[:, 0] + (x[:, 0] * t[0]))) * t[1]) / np.exp(((np.log(t[2] ** x[:, 1]) / (x[:, 1] * x[:, 0])) / np.exp(((x[:, 0] / x[:, 0]) + np.log(x[:, 1]))))))","$$\frac{\left(\left(\operatorname{exp}(\left(x_{1} + x_{0}\right)) \cdot \left(x_{0} + \left(x_{0} \cdot \theta_{0}\right)\right)\right) \cdot \theta_{1}\right)}{\operatorname{exp}(\frac{\frac{\operatorname{log}({\theta_{2}^{x_{1}}})}{\left(x_{1} \cdot x_{0}\right)}}{\operatorname{exp}(\left(\frac{x_{0}}{x_{0}} + \operatorname{log}(x_{1})\right))})}$$",-0.1337059654994608;0.14932952893315676;-0.44371309496120215,30,,,,Infinity,,,,,, +992,0,(Exp(Exp(((x0 + x1) + 0.3996185204062076))) + Log(((0.7969194231336436 / x1) ^ 0.7458818543060597))),"(np.exp(np.exp(((x[:, 0] + x[:, 1]) + t[0]))) + np.log((t[1] / x[:, 1]) ** t[2]))","$$\left(\operatorname{exp}(\operatorname{exp}(\left(\left(x_{0} + x_{1}\right) + \theta_{0}\right))) + \operatorname{log}({\frac{\theta_{1}}{x_{1}}^{\theta_{2}}})\right)$$",0.3996185204062076;0.7969194231336436;0.7458818543060597,14,2.7387059606441516e8,2.7387059606441516e8,2.7387059606441516e8,2.7387059606441516e8,-2.527309651108561e10,-2.527309651108561e10,-2.527309651108561e10,2.4648357163293004e8,2.4648357163293004e8,2.4648357163293004e8 +993,0,Exp((((Exp((x1 / x0)) / (x0 ^ -0.31681193977366207)) * (0.9087502787275648 * (-0.35173532702520427 ^ (x1 * 0.3511911960130467)))) / (((x1 * x0) ^ (x0 + -0.25170218844137326)) * ((x0 / x1) + (-9.132714022831478e-2 + (0.42535176012593867 * x0)))))),"np.exp((((np.exp((x[:, 1] / x[:, 0])) / x[:, 0] ** t[0]) * (t[1] * t[2] ** (x[:, 1] * t[3]))) / ((x[:, 1] * x[:, 0]) ** (x[:, 0] + t[4]) * ((x[:, 0] / x[:, 1]) + (t[5] + (t[6] * x[:, 0]))))))","$$\operatorname{exp}(\frac{\left(\frac{\operatorname{exp}(\frac{x_{1}}{x_{0}})}{{x_{0}^{\theta_{0}}}} \cdot \left(\theta_{1} \cdot {\theta_{2}^{\left(x_{1} \cdot \theta_{3}\right)}}\right)\right)}{\left({\left(x_{1} \cdot x_{0}\right)^{\left(x_{0} + \theta_{4}\right)}} \cdot \left(\frac{x_{0}}{x_{1}} + \left(\theta_{5} + \left(\theta_{6} \cdot x_{0}\right)\right)\right)\right)})$$",-0.31681193977366207;0.9087502787275648;-0.35173532702520427;0.3511911960130467;-0.25170218844137326;-9.132714022831478e-2;0.42535176012593867,35,,,,Infinity,,,,,, +994,0,(Exp((Exp(x0) * Exp(x1))) / (((x0 + (((-0.29169758475110985 * x1) / (x0 + x1)) ^ 0.8201460227785609)) ^ Log((-0.38590682924688036 + x1))) + 0.35634704277079254)),"(np.exp((np.exp(x[:, 0]) * np.exp(x[:, 1]))) / ((x[:, 0] + ((t[0] * x[:, 1]) / (x[:, 0] + x[:, 1])) ** t[1]) ** np.log((t[2] + x[:, 1])) + t[3]))","$$\frac{\operatorname{exp}(\left(\operatorname{exp}(x_{0}) \cdot \operatorname{exp}(x_{1})\right))}{\left({\left(x_{0} + {\frac{\left(\theta_{0} \cdot x_{1}\right)}{\left(x_{0} + x_{1}\right)}^{\theta_{1}}}\right)^{\operatorname{log}(\left(\theta_{2} + x_{1}\right))}} + \theta_{3}\right)}$$",-0.29169758475110985;0.8201460227785609;-0.38590682924688036;0.35634704277079254,25,,,,Infinity,,,,,, +995,0,(Exp(Exp((x1 + (x0 * -3.1583567465031757)))) + ((-0.7401358346677325 * (x1 ^ -6.733027539044349e-2)) * 1.2764548601775916)),"(np.exp(np.exp((x[:, 1] + (x[:, 0] * t[0])))) + ((t[1] * x[:, 1] ** t[2]) * t[3]))","$$\left(\operatorname{exp}(\operatorname{exp}(\left(x_{1} + \left(x_{0} \cdot \theta_{0}\right)\right))) + \left(\left(\theta_{1} \cdot {x_{1}^{\theta_{2}}}\right) \cdot \theta_{3}\right)\right)$$",-3.1583567465031757;-0.7401358346677325;-6.733027539044349e-2;1.2764548601775916,15,1.8992807730529e-3,1.8992807730529e-3,1.8992807730529e-3,1.8992807730529e-3,0.8247321655895304,0.8247321655895304,0.8247321655895304,37.27406132999601,37.27406132999601,37.27406132999601 +996,0,((((x0 * 8.39343945168022) + -14.148633252151189) + x0) * -1.5906422038235724e-2),"((((x[:, 0] * t[0]) + t[1]) + x[:, 0]) * t[2])","$$\left(\left(\left(\left(x_{0} \cdot \theta_{0}\right) + \theta_{1}\right) + x_{0}\right) \cdot \theta_{2}\right)$$",8.39343945168022;-14.148633252151189;-1.5906422038235724e-2,9,6.500389167568517e-3,6.500389167568517e-3,6.500389167568517e-3,6.500389167568517e-3,0.40013654200601123,0.40013654200601123,0.40013654200601123,18.54527440643275,18.54527440643275,18.54527440643275 +997,0,(((Log(Log(((x0 * x1) / x0))) * -0.45742146210990897) * -0.31309201740159476) ^ (((((x0 ^ -0.546842847405977) + 0.30027373178411054) * 5.1179282509667856e-2) / (x0 ^ -0.7036241524747021)) / Exp(((-0.1804115218471346 + Exp(x1)) * 0.14855295683202085)))),"((np.log(np.log(((x[:, 0] * x[:, 1]) / x[:, 0]))) * t[0]) * t[1]) ** ((((x[:, 0] ** t[2] + t[3]) * t[4]) / x[:, 0] ** t[5]) / np.exp(((t[6] + np.exp(x[:, 1])) * t[7])))","$${\left(\left(\operatorname{log}(\operatorname{log}(\frac{\left(x_{0} \cdot x_{1}\right)}{x_{0}})) \cdot \theta_{0}\right) \cdot \theta_{1}\right)^{\frac{\frac{\left(\left({x_{0}^{\theta_{2}}} + \theta_{3}\right) \cdot \theta_{4}\right)}{{x_{0}^{\theta_{5}}}}}{\operatorname{exp}(\left(\left(\theta_{6} + \operatorname{exp}(x_{1})\right) \cdot \theta_{7}\right))}}}$$",-0.45742146210990897;-0.31309201740159476;-0.546842847405977;0.30027373178411054;5.1179282509667856e-2;-0.7036241524747021;-0.1804115218471346;0.14855295683202085,31,,,,Infinity,,,,,, +998,0,Log(((((x0 * 0.7196325994416997) * (Exp((-0.2929594774573301 ^ x1)) ^ 0.8747486136962328)) ^ 0.3700830846024715) + 0.3344174863988618)),"np.log((((x[:, 0] * t[0]) * np.exp(t[1] ** x[:, 1]) ** t[2]) ** t[3] + t[4]))","$$\operatorname{log}(\left({\left(\left(x_{0} \cdot \theta_{0}\right) \cdot {\operatorname{exp}({\theta_{1}^{x_{1}}})^{\theta_{2}}}\right)^{\theta_{3}}} + \theta_{4}\right))$$",0.7196325994416997;-0.2929594774573301;0.8747486136962328;0.3700830846024715;0.3344174863988618,15,,,,Infinity,,,,,, +999,0,(Exp((0.9192625249669337 ^ Log((-6.718828336099592e-2 / Exp((x0 * x1)))))) / ((Log(x1) * (Exp(x1) ^ Log((0.5767647429222347 ^ x1)))) ^ (-0.8541300513393262 / (Log((-0.3456900902848459 + (0.8814536071680705 ^ x0))) * -0.17967169348781442)))),"(np.exp(t[0] ** np.log((t[1] / np.exp((x[:, 0] * x[:, 1]))))) / (np.log(x[:, 1]) * np.exp(x[:, 1]) ** np.log(t[2] ** x[:, 1])) ** (t[3] / (np.log((t[4] + t[5] ** x[:, 0])) * t[6])))","$$\frac{\operatorname{exp}({\theta_{0}^{\operatorname{log}(\frac{\theta_{1}}{\operatorname{exp}(\left(x_{0} \cdot x_{1}\right))})}})}{{\left(\operatorname{log}(x_{1}) \cdot {\operatorname{exp}(x_{1})^{\operatorname{log}({\theta_{2}^{x_{1}}})}}\right)^{\frac{\theta_{3}}{\left(\operatorname{log}(\left(\theta_{4} + {\theta_{5}^{x_{0}}}\right)) \cdot \theta_{6}\right)}}}}$$",0.9192625249669337;-6.718828336099592e-2;0.5767647429222347;-0.8541300513393262;-0.3456900902848459;0.8814536071680705;-0.17967169348781442,32,,,,Infinity,,,,,, diff --git a/gaussian_test.csv b/gaussian_test.csv new file mode 100644 index 0000000..1bc43c4 --- /dev/null +++ b/gaussian_test.csv @@ -0,0 +1,501 @@ +x1,x2,y,y_noise_002,y_noise_005,y_noise_01,y_noise_02 +-0.29170632,0.2,1.3734715,1.3955288087308655,1.3581279106072888,1.3782800248715532,1.3265716193794823 +0.14331383,0.2,3.0779479,3.0784439825149614,3.119633438406223,3.095093715879621,3.2202805459702755 +0.06853849,0.2,3.7519648,3.7788661064602374,3.786648430693719,3.67784746086649,3.978313571570912 +0.55421877,0.2,0.08556479,0.0495493642190063,0.050771022900748676,0.04283592644832533,0.09335781568372706 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(Fix(..)) +import Data.SRTree.Random +import Data.SRTree (relabelParams, SRTree) +import Data.SRTree.Internal (SRTree(..), Op(..), Function(..)) +import Algorithm.SRTree.Likelihoods (buildLoss, Loss(..), Distribution(..)) +import Algorithm.SRTree.AD.Unboxed (compileTree) +import Algorithm.SRTree.AD.Accelerate (compileAccelerateTree, multiWorker) +import Control.Monad.State.Strict (evalStateT) +import Control.Monad.Identity +import System.Random (mkStdGen) +import Control.Exception (try, SomeException, evaluate, displayException) +import Control.Concurrent.MVar (tryTakeMVar) +import qualified Data.Sequence as Seq (viewl, ViewL(..)) +import Data.Array.Accelerate.LLVM.Native.Target (workers) +import Data.Array.Accelerate.LLVM.Native.Execute.Scheduler (workerException) + +genTerm :: Rng Identity (Fix SRTree) +genTerm = do + coin <- toss + if coin then Fix . Var <$> randomFrom [0..9] + else Fix . Param <$> randomFrom [0..9] +genNonTerm :: Rng Identity (SRTree ()) +genNonTerm = do + n <- randomRange (0 :: Int, 4) + pure $ case n of + 0 -> Bin Add () () + 1 -> Bin Mul () () + 2 -> Bin Div () () + 3 -> Uni Exp () + _ -> Uni Log () +genTree :: Int -> Int -> Fix SRTree +genTree seed budget = runIdentity $ evalStateT (randomTree 3 8 budget genTerm genNonTerm True) (mkStdGen seed) + +main :: IO () +main = do + let (xss, y) = ([ VU.replicate 10000 1.0 | _ <- [0..9] ], VU.replicate 10000 2.0) + t = genTree 3 4 + tl = relabelParams t + t' = buildLoss (NLL LeastSquares) 10000 tl + ct = compileTree xss y (Just y) t' + f = compileAccelerateTree ct xss y + th = VS.empty + putStrLn "compiled, evaluating..." + r <- try (evaluate (fst (f th))) :: IO (Either SomeException Double) + case r of + Left e -> do + putStrLn ("CAUGHT: " ++ displayException e) + mseq <- tryTakeMVar (workerException (workers multiWorker)) + case mseq of + Nothing -> putStrLn "" + Just seqe -> mapM_ (putStrLn . (\(tid, e') -> "worker " ++ show tid ++ ": " ++ displayException e')) (Seq.viewl seqe) + Right o -> putStrLn ("OK obj=" ++ show o) \ No newline at end of file diff --git a/harness/env.txt b/harness/env.txt new file mode 100644 index 0000000..766d0b5 --- /dev/null +++ b/harness/env.txt @@ -0,0 +1,173 @@ +-- This is a GHC environment file written by cabal. 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1.7076992e-2 s +[acc-interp] eval 1.1609576e-2 s +[acc-interp] eval 1.3595895e-2 s +[acc-interp] eval 5.352855e-3 s +[acc-interp] eval 8.364536e-3 s +[acc-interp] eval 6.652199e-3 s +[acc-interp] eval 5.346118e-3 s +[acc-interp] eval 6.418967e-3 s +[acc-interp] eval 1.0411466e-2 s +[acc-interp] eval 6.16336e-3 s +[acc-interp] eval 5.493004e-3 s +[acc-interp] eval 8.022939e-3 s +[acc-interp] eval 5.75704e-3 s +[acc-interp] eval 0.120391627 s +[acc-interp] eval 2.2585687e-2 s +[acc-interp] eval 9.83296e-3 s +[acc-interp] eval 3.9286149e-2 s +[acc-interp] eval 3.5743126e-2 s +[acc-interp] eval 4.9546892e-2 s +[acc-interp] eval 3.4797527e-2 s +[acc-interp] eval 4.4777298e-2 s +[acc-interp] eval 1.2625273e-2 s +[acc-interp] eval 7.519743e-3 s +[acc-interp] eval 8.864083e-3 s +[acc-interp] eval 3.6773542e-2 s +[acc-interp] eval 1.9808676e-2 s +[acc-interp] eval 2.7324131e-2 s +[acc-interp] eval 3.0766367e-2 s +[acc-interp] eval 7.0087597e-2 s +[acc-interp] eval 5.6667139e-2 s +[acc-interp] eval 7.1311966e-2 s +[acc-interp] eval 3.8751864e-2 s +[acc-interp] eval 4.9725128e-2 s +[acc-interp] eval 3.9911121e-2 s +[acc-interp] eval 4.4646355e-2 s +[acc-interp] eval 7.2670378e-2 s +[acc-interp] eval 2.7754884e-2 s +[acc-interp] eval 4.69522e-2 s +[acc-interp] eval 4.1542722e-2 s +[acc-interp] eval 1.2946629e-2 s +[acc-interp] eval 1.2269994e-2 s +[acc-interp] eval 2.0355955e-2 s +[acc-interp] eval 1.2804426e-2 s +[acc-interp] eval 1.3234589e-2 s +[acc-interp] eval 2.2969268e-2 s +[acc-interp] eval 1.8489654e-2 s +[acc-interp] eval 3.5274426e-2 s +[acc-interp] eval 1.0741546e-2 s +[acc-interp] eval 1.687164e-2 s +[acc-interp] eval 2.359971e-2 s +[acc-interp] eval 1.7283423e-2 s +[acc-interp] eval 1.8760563e-2 s +[acc-interp] eval 1.155635e-2 s +[acc-interp] eval 2.1647945e-2 s +[acc-interp] eval 1.4634549e-2 s +[acc-interp] eval 1.6974551e-2 s +[acc-interp] eval 2.0861902e-2 s +[acc-interp] eval 1.9980075e-2 s +[acc-interp] eval 1.9749518e-2 s +[acc-interp] eval 2.1600426e-2 s +[acc-interp] eval 1.4381785e-2 s +[acc-interp] eval 1.9538246e-2 s +[acc-interp] eval 1.0857871e-2 s +[acc-interp] eval 1.3442522e-2 s +[acc-interp] eval 2.3379191e-2 s +[acc-interp] eval 1.7004649e-2 s +[acc-interp] eval 2.4873933e-2 s +[acc-interp] eval 1.1924769e-2 s +[acc-interp] eval 1.220055e-2 s +[acc-interp] eval 0.600309271 s +[acc-interp] eval 3.4497042e-2 s +[acc-interp] eval 4.6269684e-2 s +[acc-interp] eval 8.759826e-3 s +[acc-interp] eval 6.425491e-3 s +[acc-interp] eval 3.1782711e-2 s +[acc-interp] eval 2.4817242e-2 s +[acc-interp] eval 8.749099e-3 s +[acc-interp] eval 1.5890233e-2 s +[acc-interp] eval 1.0513419e-2 s +[acc-interp] eval 7.48997e-3 s +[acc-interp] eval 6.414314e-3 s +[acc-interp] eval 8.154604e-3 s +[acc-interp] eval 8.249933e-3 s +[acc-interp] eval 9.665369e-3 s +[acc-interp] eval 1.1737155e-2 s +[acc-interp] eval 9.872888e-3 s +[acc-interp] eval 1.0099118e-2 s +[acc-interp] eval 9.503224e-3 s +[acc-interp] eval 8.155417e-3 s +[acc-interp] eval 7.608625e-3 s +[acc-interp] eval 9.327619e-3 s +[acc-interp] eval 8.417057e-3 s +[acc-interp] eval 1.004699e-2 s +[acc-interp] eval 8.549153e-3 s +[acc-interp] eval 8.658637e-3 s +[acc-interp] eval 9.664109e-3 s +[acc-interp] eval 9.185284e-3 s +[acc-interp] eval 1.100203e-2 s +[acc-interp] eval 9.882727e-3 s +[acc-interp] eval 1.0500916e-2 s +[acc-interp] eval 1.0375017e-2 s +[acc-interp] eval 9.492666e-3 s +[acc-interp] eval 1.0857723e-2 s +[acc-interp] eval 6.866438e-3 s +[acc-interp] eval 1.0902345e-2 s +[acc-interp] eval 8.386907e-3 s +[acc-interp] eval 9.7059e-3 s +[acc-interp] eval 9.881283e-3 s +[acc-interp] eval 9.03944e-3 s +[acc-interp] eval 9.552022e-3 s +[acc-interp] eval 1.0232665e-2 s +[acc-interp] eval 0.373733633 s +[acc-interp] eval 2.8864048e-2 s +[acc-interp] eval 1.0089942e-2 s +[acc-interp] eval 9.621665e-3 s +[acc-interp] eval 1.0902087e-2 s +[acc-interp] eval 8.493678e-3 s +[acc-interp] eval 5.850265e-3 s +[acc-interp] eval 5.453739e-3 s +[acc-interp] eval 9.747938e-3 s +[acc-interp] eval 7.974857e-3 s +[acc-interp] eval 9.67472e-3 s +[acc-interp] eval 9.83278e-3 s +[acc-interp] eval 8.45514e-3 s +[acc-interp] eval 9.423363e-3 s +[acc-interp] eval 6.722607e-3 s +[acc-interp] eval 9.683092e-3 s +[acc-interp] eval 8.9223e-3 s +[acc-interp] eval 8.835986e-3 s +[acc-interp] eval 9.824429e-3 s +[acc-interp] eval 5.499433e-3 s +[acc-interp] eval 9.84449e-3 s +[acc-interp] eval 1.0006708e-2 s +[acc-interp] eval 9.704056e-3 s +[acc-interp] eval 8.87766e-3 s +[acc-interp] eval 7.830468e-3 s +[acc-interp] eval 8.620132e-3 s +[acc-interp] eval 7.629255e-3 s +[acc-interp] eval 8.195401e-3 s +[acc-interp] eval 9.38723e-3 s +[acc-interp] eval 8.30472e-3 s +[acc-interp] eval 8.513056e-3 s +[acc-interp] eval 0.293566904 s +[acc-interp] eval 3.2846598e-2 s +[acc-interp] eval 4.2560409e-2 s +[acc-interp] eval 2.1829192e-2 s +[acc-interp] eval 3.1955036e-2 s +[acc-interp] eval 3.4661087e-2 s +[acc-interp] eval 5.4111235e-2 s +[acc-interp] eval 2.5476679e-2 s +[acc-interp] eval 2.2930531e-2 s +[acc-interp] eval 4.1356446e-2 s +[acc-interp] eval 4.094825e-2 s +[acc-interp] eval 1.3838011e-2 s +[acc-interp] eval 1.3591049e-2 s +[acc-interp] eval 1.4542881e-2 s +[acc-interp] eval 1.7953959e-2 s +[acc-interp] eval 1.5468768e-2 s +[acc-interp] eval 1.5760156e-2 s +[acc-interp] eval 7.593731e-3 s +[acc-interp] eval 1.0281036e-2 s +[acc-interp] eval 8.468016e-3 s +[acc-interp] eval 1.3261213e-2 s +[acc-interp] eval 1.2861515e-2 s +[acc-interp] eval 1.5312984e-2 s +[acc-interp] eval 1.2105291e-2 s +[acc-interp] eval 1.8069383e-2 s +[acc-interp] eval 8.173485e-3 s +[acc-interp] eval 0.254641896 s +[acc-interp] eval 4.046012e-2 s +[acc-interp] eval 5.4576878e-2 s +[acc-interp] eval 9.453604e-3 s +[acc-interp] eval 7.052771e-3 s +[acc-interp] eval 5.845644e-3 s +[acc-interp] eval 3.2493252e-2 s +[acc-interp] eval 2.1748525e-2 s +[acc-interp] eval 5.609277e-3 s +[acc-interp] eval 5.142137e-3 s +[acc-interp] eval 3.6938761e-2 s +[acc-interp] eval 1.3452013e-2 s +[acc-interp] eval 7.493418e-3 s +[acc-interp] eval 7.631003e-3 s +[acc-interp] eval 3.5265232e-2 s +[acc-interp] eval 2.1611855e-2 s +[acc-interp] eval 7.533183e-3 s +[acc-interp] eval 6.948391e-3 s +[acc-interp] eval 6.569516e-3 s +[acc-interp] eval 5.849747e-3 s +[acc-interp] eval 7.880007e-3 s +[acc-interp] eval 5.455401e-3 s +[acc-interp] eval 5.555598e-3 s +[acc-interp] eval 7.016491e-3 s +[acc-interp] eval 5.201338e-3 s +[acc-interp] eval 5.987123e-3 s +[acc-interp] eval 5.202497e-3 s +[acc-interp] eval 4.771525e-3 s +[acc-interp] eval 4.975184e-3 s +[acc-interp] eval 4.592852e-3 s +[acc-interp] eval 4.504806e-3 s +[acc-interp] eval 0.116045996 s diff --git a/interp_n4.out b/interp_n4.out new file mode 100644 index 0000000..8fec4b6 --- /dev/null +++ b/interp_n4.out @@ -0,0 +1,5 @@ +id,view,Expression,Numpy,Math,theta,size,loss_train,loss_val,loss_test,maxloss,R2_train,R2_val,R2_test,dl_train,dl_val,dl_test +0,0,22.766786471142098,"t[0]","$$\theta_{0}$$",22.766786471142098,1,250.2099523865335,250.2099523865335,250.2099523865335,250.20995238652426,-3.774758283725532e-15,-3.774758283725532e-15,-3.774758283725532e-15,253.19111484369938,253.19111484369938,253.19111484369938 +1,0,(x3 + 22.540971929628363),"(x[:, 3] + t[0])","$$\left(x_{3} + \theta_{0}\right)$$",22.540971929628363,3,249.79674369104444,249.79674369104444,249.79674369104444,249.79674369105567,1.6514478802577282e-3,1.6514478802577282e-3,1.6514478802577282e-3,256.0750249611203,256.0750249611203,256.0750249611203 +2,0,(Exp(x3) * 17.986912394280353),"(np.exp(x[:, 3]) * t[0])","$$\left(\operatorname{exp}(x_{3}) \cdot \theta_{0}\right)$$",17.986912394280353,4,247.41487539964737,247.41487539964737,247.41487539964737,247.4148753996405,1.1170926496829603e-2,1.1170926496829603e-2,1.1170926496829603e-2,255.94988680393422,255.94988680393422,255.94988680393422 +3,0,(Exp((-1.224229221824418 * x0)) * 29.68942258289744),"(np.exp((t[0] * x[:, 0])) * t[1])","$$\left(\operatorname{exp}(\left(\theta_{0} \cdot x_{0}\right)) \cdot \theta_{1}\right)$$",-1.224229221824418;29.68942258289744,6,153.01176650850928,153.01176650850928,153.01176650850928,153.01176650852454,0.3884665056323112,0.3884665056323112,0.3884665056323112,168.3696635329196,168.3696635329196,168.3696635329196 diff --git a/mt.err b/mt.err new file mode 100644 index 0000000..e510440 --- /dev/null +++ b/mt.err @@ -0,0 +1,149 @@ +eggp: bad value for -N +eggp: +eggp: Usage: [+RTS | -RTS ] ... --RTS +eggp: +eggp: +RTS Indicates run time system options follow +eggp: -RTS Indicates program arguments follow +eggp: --RTS Indicates that ALL subsequent arguments will be given to the +eggp: program (including any of these RTS flags) +eggp: +eggp: The following run time system options may be available (note that some +eggp: of these may not be usable unless this program was linked with the -rtsopts +eggp: flag): +eggp: +eggp: -? Prints this message and exits; the program is not executed +eggp: --info Print information about the RTS used by this program +eggp: +eggp: --nonmoving-gc +eggp: Selects the non-moving mark-and-sweep garbage collector to +eggp: manage the oldest generation. +eggp: --copying-gc +eggp: Selects the copying garbage collector to manage all generations. +eggp: +eggp: -K Sets the maximum stack size (default: 80% of the heap) +eggp: e.g.: -K32k -K512k -K8M +eggp: -ki Sets the initial thread stack size (default 1k) e.g.: -ki4k -ki2m +eggp: -kc Sets the stack chunk size (default 32k) +eggp: -kb Sets the stack chunk buffer size (default 1k) +eggp: +eggp: -A Sets the minimum allocation area size (default 4m) e.g.: -A20m -A10k +eggp: -AL Sets the amount of large-object memory that can be allocated +eggp: before a GC is triggered (default: the value of -A) +eggp: -F Sets the collecting threshold for old generations as a factor of +eggp: the live data in that generation the last time it was collected +eggp: (default: 2.0) +eggp: -Fd Sets the inverse rate which memory is returned to the OS after being +eggp: optimistically retained after being allocated. Subsequent major +eggp: collections not caused by heap overflow will return an amount of +eggp: memory controlled by this factor (higher is slower). Setting the factor +eggp: to 0 means memory is not returned. +eggp: (default 4.0) +eggp: -n Allocation area chunk size (0 = disabled, default: 0) +eggp: -O Sets the minimum size of the old generation (default 1M) +eggp: -M Sets the maximum heap size (default unlimited) e.g.: -M256k -M1G +eggp: -H Sets the minimum heap size (default 0M) e.g.: -H24m -H1G +eggp: -xb Sets the address from which a suitable start for the heap memory +eggp: will be searched from. This is useful if the default address +eggp: clashes with some third-party library. +eggp: -xn Use the non-moving collector for the old generation. +eggp: -m Minimum % of heap which must be available (default 3%) +eggp: -G Number of generations (default: 2) +eggp: -c Use in-place compaction instead of copying in the oldest generation +eggp: when live data is at least % of the maximum heap size set with +eggp: -M (default: 30%) +eggp: -c Use in-place compaction for all oldest generation collections +eggp: (the default is to use copying) +eggp: -w Use mark-region for the oldest generation (experimental) +eggp: -I Perform full GC after idle time (default: 0.3, 0 == off) +eggp: -Iw Minimum wait time between idle GC runs (default: 0, 0 == no min wait time) +eggp: +eggp: -T Collect GC statistics (useful for in-program statistics access) +eggp: -t[] One-line GC statistics (if omitted, uses stderr) +eggp: -s[] Summary GC statistics (if omitted, uses stderr) +eggp: -S[] Detailed GC statistics (if omitted, uses stderr) +eggp: +eggp: +eggp: -Z Don't squeeze out update frames on context switch +eggp: -B Sound the bell at the start of each garbage collection +eggp: -h Heap residency profile (output file .hp) +eggp: -hT Produce a heap profile grouped by closure type +eggp: -hi Produce a heap profile grouped by info table address +eggp: -po Override profiling output file name prefix (program name by default) +eggp: -i Time between heap profile samples (seconds, default: 0.1) +eggp: --no-automatic-heap-samples +eggp: Do not start the heap profile interval timer on start-up, +eggp: Rather, the application will be responsible for triggering +eggp: heap profiler samples. +eggp: -ol Send binary eventlog to (default: .eventlog) +eggp: -l[flags] Log events to a file +eggp: where [flags] can contain: +eggp: s scheduler events +eggp: g GC and heap events +eggp: n non-moving GC heap census events +eggp: p par spark events (sampled) +eggp: f par spark events (full detail) +eggp: u user events (emitted from Haskell code) +eggp: a all event classes above +eggp: -x disable an event class, for any flag above +eggp: the initial enabled event classes are 'sgpu' +eggp: --eventlog-flush-interval= +eggp: Periodically flush the eventlog at the specified interval. +eggp: +eggp: -C Context-switch interval in seconds. +eggp: 0 or no argument means switch as often as possible. +eggp: Default: 0.02 sec. +eggp: -V Master tick interval in seconds (0 == disable timer). +eggp: This sets the resolution for -C and the heap profile timer -i, +eggp: and is the frequency of time profile samples. +eggp: Default: 0.01 sec. +eggp: +eggp: -N[] Use processors (default: 1, -N alone determines +eggp: the number of processors to use automatically) +eggp: -maxN[] Use up to processors automatically +eggp: -qg[] Use parallel GC only for generations >= +eggp: (default: 0, -qg alone turns off parallel GC) +eggp: -qb[] Use load-balancing in the parallel GC only for generations >= +eggp: (default: 1 for -A < 32M, 0 otherwise; +eggp: -qb alone turns off load-balancing) +eggp: -qn Use threads for parallel GC (defaults to value of -N) +eggp: -qa Use the OS to set thread affinity (experimental) +eggp: -qm Don't automatically migrate threads between CPUs +eggp: -qi If a processor has been idle for the last GCs, do not +eggp: wake it up for a non-load-balancing parallel GC. +eggp: (0 disables, default: 0) +eggp: --numa[=] +eggp: Use NUMA, nodes given by (default: off) +eggp: --install-signal-handlers= +eggp: Install signal handlers (default: yes) +eggp: --io-manager= +eggp: The I/O manager to use. +eggp: Options available: auto mio (default: mio) +eggp: -e Maximum number of outstanding local sparks (default: 4096) +eggp: -xp Assume that all object files were compiled with -fPIC +eggp: -fexternal-dynamic-refs and load them anywhere in the address +eggp: space +eggp: -xm Base address to mmap memory in the GHCi linker +eggp: (hex; must be <80000000) +eggp: -xq The allocation limit given to a thread after it receives +eggp: an AllocationLimitExceeded exception. (default: 100k) +eggp: +eggp: -xr The size of virtual memory address space reserved by the +eggp: two step allocator (default: 1T) +eggp: +eggp: -Mgrace= +eggp: The amount of allocation after the program receives a +eggp: HeapOverflow exception before the exception is thrown again, if +eggp: the program is still exceeding the heap limit. +eggp: +eggp: --read-tix-file= +eggp: Whether to initialize HPC datastructures from .tix at the start of execution. (default: yes) +eggp: +eggp: --write-tix-file= +eggp: Whether to write .tix at the end of execution. +eggp: (default: yes) +eggp: +eggp: RTS options may also be specified using the GHCRTS environment variable. +eggp: +eggp: Other RTS options may be available for programs compiled a different way. +eggp: The GHC User's Guide has full details. +eggp: diff --git a/mtA_n1_1.err b/mtA_n1_1.err new file mode 100644 index 0000000..e69de29 diff --git a/mtA_n1_2.err b/mtA_n1_2.err new file mode 100644 index 0000000..e69de29 diff --git a/mtA_n4_1.err b/mtA_n4_1.err new file mode 100644 index 0000000..e69de29 diff --git a/mtA_n4_2.err b/mtA_n4_2.err new file mode 100644 index 0000000..e69de29 diff --git a/mt_n1.err b/mt_n1.err new file mode 100644 index 0000000..e69de29 diff --git a/mt_n4.err b/mt_n4.err new file mode 100644 index 0000000..e69de29 diff --git a/n1r1.err b/n1r1.err new file mode 100644 index 0000000..e69de29 diff --git a/n1r2.err b/n1r2.err new file mode 100644 index 0000000..e69de29 diff --git a/p2_n4.err b/p2_n4.err new file mode 100644 index 0000000..e17a2d5 --- /dev/null +++ b/p2_n4.err @@ -0,0 +1,3089 @@ +[acc] compile 2.2545583e-2 s +[acc] eval 2.089052881 s +[acc] compile 5.07428e-4 s +[acc] eval 6.8115474e-2 s +[acc] compile 5.13547e-4 s +[acc] eval 7.5479355e-2 s +[acc] compile 4.51063e-4 s +[acc] eval 0.165742049 s +[acc] compile 3.26655e-4 s +[acc] eval 0.186926207 s +[acc] compile 3.42308e-4 s +[acc] eval 0.15701251 s +[acc] compile 3.64172e-4 s +[acc] eval 0.211968678 s +[acc] compile 3.90344e-4 s +[acc] eval 0.133418003 s +[acc] compile 3.45977e-4 s +[acc] eval 6.8119831e-2 s +[acc] compile 5.629754e-2 s +[acc] eval 2.6077248e-2 s +[acc] eval 8.5756459e-2 s +[acc] compile 4.7882963e-2 s +[acc] eval 1.8794666e-2 s +[acc] eval 2.178655e-3 s +[acc] eval 1.919136e-3 s +[acc] eval 2.039928e-3 s +[acc] eval 7.654469e-3 s +[acc] eval 1.847927e-3 s +[acc] eval 1.567422e-3 s +[acc] eval 1.318232e-3 s +[acc] eval 1.296346e-3 s +[acc] eval 1.300288e-3 s +[acc] eval 1.091003e-3 s +[acc] eval 1.172884e-3 s +[acc] eval 9.2917007e-2 s +[acc] compile 7.6480933e-2 s +[acc] eval 1.0011238e-2 s +[acc] eval 8.9800629e-2 s +[acc] compile 0.296524401 s +[acc] eval 3.197334e-2 s +[acc] eval 0.332715433 s +[acc] compile 6.6096812e-2 s +[acc] eval 1.6301865e-2 s +[acc] eval 4.715536e-3 s +[acc] eval 5.362047e-3 s +[acc] eval 6.163902e-3 s +[acc] eval 1.0319189e-2 s +[acc] eval 2.956198e-3 s +[acc] eval 4.138297e-3 s +[acc] eval 3.42498e-3 s +[acc] eval 2.996228e-3 s +[acc] eval 2.808014e-3 s +[acc] eval 2.839656e-3 s +[acc] eval 2.960377e-3 s +[acc] eval 3.207781e-3 s +[acc] eval 3.29582e-3 s +[acc] eval 3.089597e-3 s +[acc] eval 2.845665e-3 s +[acc] eval 2.449892e-3 s +[acc] eval 2.297618e-3 s +[acc] eval 2.31538e-3 s +[acc] eval 2.44517e-3 s +[acc] eval 2.244568e-3 s +[acc] eval 2.391921e-3 s +[acc] eval 2.313781e-3 s +[acc] eval 2.222019e-3 s +[acc] eval 2.340803e-3 s +[acc] eval 3.001639e-3 s +[acc] eval 2.961353e-3 s +[acc] eval 2.965862e-3 s +[acc] eval 2.673216e-3 s +[acc] eval 2.409983e-3 s +[acc] eval 2.376959e-3 s +[acc] eval 0.185878138 s +[acc] compile 0.373581315 s +[acc] eval 3.2774636e-2 s +[acc] eval 0.413043678 s +[acc] compile 5.722956e-3 s +[acc] eval 7.341758e-3 s +[acc] eval 2.714768e-3 s +[acc] eval 3.045141e-3 s +[acc] eval 3.319946e-3 s +[acc] eval 3.336608e-3 s +[acc] eval 2.788044e-3 s +[acc] eval 2.821018e-3 s +[acc] eval 9.014853e-3 s +[acc] eval 2.378401e-3 s +[acc] eval 2.269098e-3 s +[acc] eval 3.173494e-3 s +[acc] eval 3.926566e-3 s +[acc] eval 2.3313e-3 s +[acc] eval 2.124174e-3 s +[acc] eval 2.232979e-3 s +[acc] eval 1.746422e-3 s +[acc] eval 1.71011e-3 s +[acc] eval 2.059835e-3 s +[acc] eval 2.143931e-3 s +[acc] eval 1.938581e-3 s +[acc] eval 1.974846e-3 s +[acc] eval 2.057377e-3 s +[acc] eval 1.871779e-3 s +[acc] eval 1.547768e-3 s +[acc] eval 2.000677e-3 s +[acc] eval 1.863555e-3 s +[acc] eval 1.855831e-3 s +[acc] eval 1.848762e-3 s +[acc] eval 1.77355e-3 s +[acc] eval 1.627215e-3 s +[acc] eval 9.1192828e-2 s +[acc] compile 0.307093793 s +[acc] eval 9.870334e-3 s +[acc] eval 0.325805888 s +[acc] compile 0.369907154 s +[acc] eval 2.6062402e-2 s +[acc] eval 0.416465553 s +[acc] compile 0.18536803 s +[acc] eval 2.354095e-2 s +[acc] eval 1.0013288e-2 s +[acc] eval 4.547352e-3 s +[acc] eval 3.791824e-3 s +[acc] eval 4.185958e-3 s +[acc] eval 3.765257e-3 s +[acc] eval 4.09383e-3 s +[acc] eval 4.093277e-3 s +[acc] eval 3.413615e-3 s +[acc] eval 2.755758e-3 s +[acc] eval 2.057394e-3 s +[acc] eval 2.518325e-3 s +[acc] eval 2.38858e-3 s +[acc] eval 2.085729e-3 s +[acc] eval 2.046925e-3 s +[acc] eval 3.365755e-3 s +[acc] eval 2.953771e-3 s +[acc] eval 2.671848e-3 s +[acc] eval 2.74538e-3 s +[acc] eval 3.654839e-3 s +[acc] eval 3.005807e-3 s +[acc] eval 2.76835e-3 s +[acc] eval 2.606345e-3 s +[acc] eval 2.105564e-3 s +[acc] eval 2.735323e-3 s +[acc] eval 3.159186e-3 s +[acc] eval 3.083847e-3 s +[acc] eval 2.838442e-3 s +[acc] eval 2.661659e-3 s +[acc] eval 2.623892e-3 s +[acc] eval 2.837447e-3 s +[acc] eval 3.063213e-3 s +[acc] eval 2.904738e-3 s +[acc] eval 2.876008e-3 s +[acc] eval 3.544311e-3 s +[acc] eval 2.752004e-3 s +[acc] eval 2.635589e-3 s +[acc] eval 2.49185e-3 s +[acc] eval 2.62632e-3 s +[acc] eval 2.657154e-3 s +[acc] eval 2.78849e-3 s +[acc] eval 2.529859e-3 s +[acc] eval 2.522661e-3 s +[acc] eval 2.662448e-3 s +[acc] eval 2.739926e-3 s +[acc] eval 0.35381616 s +[acc] compile 7.62813e-4 s +[acc] eval 4.696472e-3 s +[acc] compile 5.688256e-3 s +[acc] eval 9.98229e-3 s +[acc] eval 2.204637e-3 s +[acc] eval 5.393059e-3 s +[acc] eval 2.596419e-3 s +[acc] eval 2.960798e-3 s +[acc] eval 2.956509e-3 s +[acc] eval 2.666025e-3 s +[acc] eval 3.7896073e-2 s +[acc] compile 0.181602916 s +[acc] eval 1.4485548e-2 s +[acc] eval 7.566057e-3 s +[acc] eval 1.2349092e-2 s +[acc] eval 4.708413e-3 s +[acc] eval 4.918073e-3 s +[acc] eval 5.436715e-3 s +[acc] eval 6.362519e-3 s +[acc] eval 4.98556e-3 s +[acc] eval 3.613675e-3 s +[acc] eval 2.974409e-3 s +[acc] eval 3.011471e-3 s +[acc] eval 2.869088e-3 s +[acc] eval 2.622626e-3 s +[acc] eval 2.784105e-3 s +[acc] eval 0.266123354 s +[acc] compile 0.177693388 s +[acc] eval 1.3944289e-2 s +[acc] eval 0.195459767 s +[acc] compile 0.238935756 s +[acc] eval 2.4792451e-2 s +[acc] eval 0.273777125 s +[acc] compile 4.950427e-3 s +[acc] eval 5.925572e-3 s +[acc] eval 1.3539444e-2 s +[acc] compile 0.186116586 s +[acc] eval 1.2908517e-2 s +[acc] eval 0.202140026 s +[acc] compile 3.00378e-3 s +[acc] eval 7.574851e-3 s +[acc] eval 8.6474e-4 s +[acc] eval 6.95963e-4 s +[acc] eval 9.65083e-4 s +[acc] eval 6.2562e-4 s +[acc] eval 9.30061e-4 s +[acc] eval 7.99315e-4 s +[acc] eval 6.3114e-4 s +[acc] eval 8.4801e-4 s +[acc] eval 6.69082e-4 s +[acc] eval 8.90672e-4 s +[acc] eval 8.97026e-4 s +[acc] eval 2.0954236e-2 s +[acc] compile 7.0532446e-2 s +[acc] eval 1.0016692e-2 s +[acc] eval 7.801326e-3 s +[acc] eval 3.297881e-3 s +[acc] eval 3.97982e-3 s +[acc] eval 3.73332e-3 s +[acc] eval 4.214441e-3 s +[acc] eval 3.542443e-3 s +[acc] eval 2.682931e-3 s +[acc] eval 2.179185e-3 s +[acc] eval 1.748298e-3 s +[acc] eval 1.48881e-3 s +[acc] eval 1.510827e-3 s +[acc] eval 3.161566e-3 s +[acc] eval 2.069426e-3 s +[acc] eval 1.961623e-3 s +[acc] eval 2.315953e-3 s +[acc] eval 0.130203131 s +[acc] compile 5.01967e-4 s +[acc] eval 5.780742e-3 s +[acc] compile 9.7939597e-2 s +[acc] eval 1.3875028e-2 s +[acc] eval 3.297083e-3 s +[acc] eval 3.069861e-3 s +[acc] eval 7.839229e-3 s +[acc] eval 2.857863e-3 s +[acc] eval 2.912209e-3 s +[acc] eval 2.576299e-3 s +[acc] eval 2.637525e-3 s +[acc] eval 2.813333e-3 s +[acc] eval 2.586447e-3 s +[acc] eval 2.752174e-3 s +[acc] eval 2.705542e-3 s +[acc] eval 2.611149e-3 s +[acc] eval 2.308409e-3 s +[acc] eval 2.474343e-3 s +[acc] eval 2.495021e-3 s +[acc] eval 2.886078e-3 s +[acc] eval 4.306485e-3 s +[acc] eval 2.782864e-3 s +[acc] eval 2.566122e-3 s +[acc] eval 2.567928e-3 s +[acc] eval 2.369375e-3 s +[acc] eval 2.446753e-3 s +[acc] eval 2.457287e-3 s +[acc] eval 2.390518e-3 s +[acc] eval 2.173154e-3 s +[acc] eval 2.403558e-3 s +[acc] eval 3.09454e-3 s +[acc] eval 3.116074e-3 s +[acc] eval 3.203285e-3 s +[acc] eval 3.120582e-3 s +[acc] eval 3.141007e-3 s +[acc] eval 3.139842e-3 s +[acc] eval 3.239381e-3 s +[acc] eval 0.215167782 s +[acc] compile 2.501083e-3 s +[acc] eval 2.863737e-3 s +[acc] eval 5.85777e-4 s +[acc] eval 8.36586e-4 s +[acc] eval 7.619802e-3 s +[acc] compile 6.2753931e-2 s +[acc] eval 1.0879233e-2 s +[acc] eval 3.711815e-3 s +[acc] eval 6.662414e-3 s +[acc] eval 4.149309e-3 s +[acc] eval 4.077398e-3 s +[acc] eval 4.033854e-3 s +[acc] eval 3.615236e-3 s +[acc] eval 3.052637e-3 s +[acc] eval 3.044035e-3 s +[acc] eval 3.387244e-3 s +[acc] eval 3.683253e-3 s +[acc] eval 2.844922e-3 s +[acc] eval 2.302854e-3 s +[acc] eval 2.414349e-3 s +[acc] eval 2.60575e-3 s +[acc] eval 3.336589e-3 s +[acc] eval 3.469455e-3 s +[acc] eval 3.030783e-3 s +[acc] eval 2.531816e-3 s +[acc] eval 2.518681e-3 s +[acc] eval 2.534367e-3 s +[acc] eval 2.140969e-3 s +[acc] eval 2.13709e-3 s +[acc] eval 2.538046e-3 s +[acc] eval 2.54963e-3 s +[acc] eval 0.154554873 s +[acc] compile 4.041289e-3 s +[acc] eval 5.663217e-3 s +[acc] eval 1.402068e-3 s +[acc] eval 1.454477e-3 s +[acc] eval 1.353766e-3 s +[acc] eval 1.339709e-3 s +[acc] eval 1.432354e-3 s +[acc] eval 1.487601e-3 s +[acc] eval 5.743539e-3 s +[acc] eval 2.6018299e-2 s +[acc] compile 0.162284288 s +[acc] eval 1.9554447e-2 s +[acc] eval 0.184589402 s +[acc] compile 1.4392558e-2 s +[acc] eval 1.1599889e-2 s +[acc] eval 2.9611948e-2 s +[acc] compile 0.125458115 s +[acc] eval 3.3395633e-2 s +[acc] eval 0.161357459 s +[acc] compile 0.199050534 s +[acc] eval 4.4213853e-2 s +[acc] eval 0.255079071 s +[acc] compile 8.689764e-3 s +[acc] eval 1.0159733e-2 s +[acc] eval 3.990309e-3 s +[acc] eval 3.696016e-3 s +[acc] eval 4.640937e-3 s +[acc] eval 4.121641e-3 s +[acc] eval 3.294198e-3 s +[acc] eval 2.802529e-3 s +[acc] eval 2.639315e-3 s +[acc] eval 1.0290085e-2 s +[acc] eval 2.922167e-3 s +[acc] eval 2.988227e-3 s +[acc] eval 3.090558e-3 s +[acc] eval 2.517993e-3 s +[acc] eval 2.526579e-3 s +[acc] eval 3.314751e-3 s +[acc] eval 2.687554e-3 s +[acc] eval 2.809022e-3 s +[acc] eval 8.2814958e-2 s +[acc] compile 0.18724305 s +[acc] eval 3.7432471e-2 s +[acc] eval 0.230210003 s +[acc] compile 0.177501356 s +[acc] eval 1.3667831e-2 s +[acc] eval 1.2456814e-2 s +[acc] eval 1.5290832e-2 s +[acc] eval 6.881548e-3 s +[acc] eval 2.271724e-3 s +[acc] eval 5.025091e-3 s +[acc] eval 2.594741e-3 s +[acc] eval 5.490754e-3 s +[acc] eval 9.296448e-3 s +[acc] eval 2.675031e-3 s +[acc] eval 8.35507e-3 s +[acc] eval 7.601004e-3 s +[acc] eval 0.272426054 s +[acc] compile 0.253681087 s +[acc] eval 2.6349891e-2 s +[acc] eval 9.217846e-3 s +[acc] eval 3.551578e-3 s +[acc] eval 3.474146e-3 s +[acc] eval 3.31701e-3 s +[acc] eval 4.407101e-3 s +[acc] eval 4.169677e-3 s +[acc] eval 4.293825e-3 s +[acc] eval 4.200216e-3 s +[acc] eval 4.316546e-3 s +[acc] eval 4.122346e-3 s +[acc] eval 4.104858e-3 s +[acc] eval 0.334436976 s +[acc] compile 0.161767106 s +[acc] eval 5.342247e-3 s +[acc] eval 2.572873e-3 s +[acc] eval 6.227328e-3 s +[acc] eval 2.734592e-3 s +[acc] eval 2.508218e-3 s +[acc] eval 2.601997e-3 s +[acc] eval 2.508073e-3 s +[acc] eval 2.332485e-3 s +[acc] eval 2.579616e-3 s +[acc] eval 2.348992e-3 s +[acc] eval 2.374959e-3 s +[acc] eval 2.561554e-3 s +[acc] eval 2.287925e-3 s +[acc] eval 2.334435e-3 s +[acc] eval 2.416688e-3 s +[acc] eval 2.37477e-3 s +[acc] eval 1.842472e-3 s +[acc] eval 1.93166e-3 s +[acc] eval 2.454949e-3 s +[acc] eval 3.627916e-3 s +[acc] eval 2.184353e-3 s +[acc] eval 1.898969e-3 s +[acc] eval 1.610363e-3 s +[acc] eval 1.561936e-3 s +[acc] eval 1.581894e-3 s +[acc] eval 1.66854e-3 s +[acc] eval 2.31775e-3 s +[acc] eval 2.612212e-3 s +[acc] eval 2.591306e-3 s +[acc] eval 2.459432e-3 s +[acc] eval 0.243340737 s +[acc] compile 6.6324099e-2 s +[acc] eval 6.512417e-3 s +[acc] eval 8.802939e-3 s +[acc] eval 6.763464e-3 s +[acc] eval 4.207484e-3 s +[acc] eval 3.354769e-3 s +[acc] eval 9.793195e-3 s +[acc] eval 3.961211e-3 s +[acc] eval 2.624208e-3 s +[acc] eval 2.458072e-3 s +[acc] eval 3.622854e-3 s +[acc] eval 4.558749e-3 s +[acc] eval 2.412618e-3 s +[acc] eval 0.129390229 s +[acc] compile 5.579513e-3 s +[acc] eval 3.661726e-3 s +[acc] eval 1.282601e-3 s +[acc] eval 1.790135e-3 s +[acc] eval 1.356824e-3 s +[acc] eval 1.477261e-3 s +[acc] eval 1.7135971e-2 s +[acc] compile 6.326013e-3 s +[acc] eval 1.0793835e-2 s +[acc] eval 2.9147121e-2 s +[acc] compile 0.2847342 s +[acc] eval 2.4115888e-2 s +[acc] eval 0.318864911 s +[acc] compile 0.525090424 s +[acc] eval 3.449966e-2 s +[acc] eval 0.564716893 s +[acc] compile 3.950059e-3 s +[acc] eval 3.068506e-3 s +[acc] eval 8.44703e-4 s +[acc] eval 6.38669e-4 s +[acc] eval 9.731477e-3 s +[acc] compile 4.571997e-3 s +[acc] eval 4.944534e-3 s +[acc] eval 1.764611e-3 s +[acc] eval 1.632865e-3 s +[acc] eval 1.633033e-3 s +[acc] eval 6.6109e-3 s +[acc] eval 1.226985e-3 s +[acc] eval 1.233642e-3 s +[acc] eval 1.774264e-3 s +[acc] eval 2.8033088e-2 s +[acc] compile 9.9341397e-2 s +[acc] eval 9.023395e-3 s +[acc] eval 1.717204e-3 s +[acc] eval 1.658752e-3 s +[acc] eval 1.895712e-3 s +[acc] eval 2.191086e-3 s +[acc] eval 2.161311e-3 s +[acc] eval 2.403198e-3 s +[acc] eval 2.403142e-3 s +[acc] eval 6.941845e-3 s +[acc] eval 1.628262e-3 s +[acc] eval 1.514826e-3 s +[acc] eval 0.135463271 s +[acc] compile 0.294932724 s +[acc] eval 1.9919552e-2 s +[acc] eval 0.324671185 s +[acc] compile 5.23923e-4 s +[acc] eval 8.20116e-3 s +[acc] compile 0.136309342 s +[acc] eval 1.3828768e-2 s +[acc] eval 0.154549615 s +[acc] compile 3.210332e-3 s +[acc] eval 1.2050255e-2 s +[acc] eval 1.6328989e-2 s +[acc] compile 0.201913001 s +[acc] eval 2.2355509e-2 s +[acc] eval 0.228457335 s +[acc] compile 6.10899e-4 s +[acc] eval 2.4302362e-2 s +[acc] compile 5.8788628e-2 s +[acc] eval 7.888837e-3 s +[acc] eval 4.619395e-3 s +[acc] eval 5.451159e-3 s +[acc] eval 5.12993e-3 s +[acc] eval 1.2080644e-2 s +[acc] eval 9.8298737e-2 s +[acc] compile 0.302411799 s +[acc] eval 3.2544771e-2 s +[acc] eval 0.338879527 s +[acc] compile 6.90309e-2 s +[acc] eval 1.3470407e-2 s +[acc] eval 8.7551422e-2 s +[acc] compile 5.506127e-3 s +[acc] eval 5.162581e-3 s +[acc] eval 2.239237e-3 s +[acc] eval 8.08946e-3 s +[acc] eval 1.763594e-3 s +[acc] eval 1.852821e-3 s +[acc] eval 1.701065e-3 s +[acc] eval 1.728706e-3 s +[acc] eval 1.688154e-3 s +[acc] eval 1.633008e-3 s +[acc] eval 3.4197696e-2 s +[acc] compile 6.4208336e-2 s +[acc] eval 1.4204499e-2 s +[acc] eval 8.3420875e-2 s +[acc] compile 8.5743187e-2 s +[acc] eval 1.8836346e-2 s +[acc] eval 0.107427987 s +[acc] compile 4.94189e-4 s +[acc] eval 6.731091e-3 s +[acc] compile 0.332004301 s +[acc] eval 6.857543e-3 s +[acc] eval 0.343118165 s +[acc] compile 9.0328722e-2 s +[acc] eval 2.0165618e-2 s +[acc] eval 0.116481238 s +[acc] compile 0.461227153 s +[acc] eval 1.5729476e-2 s +[acc] eval 0.494166083 s +[acc] compile 0.113371905 s +[acc] eval 6.96292e-3 s +[acc] eval 0.124218079 s +[acc] compile 7.9380304e-2 s +[acc] eval 1.71013e-2 s +[acc] eval 0.100003585 s +[acc] compile 0.430094074 s +[acc] eval 2.1488998e-2 s +[acc] eval 0.460964684 s +[acc] compile 0.229687084 s +[acc] eval 2.0688154e-2 s +[acc] eval 0.26261848 s +[acc] compile 6.133784e-3 s +[acc] eval 6.530259e-3 s +[acc] eval 1.892946e-3 s +[acc] eval 1.490456e-3 s +[acc] eval 1.9182596e-2 s +[acc] compile 0.238787596 s +[acc] eval 2.5045094e-2 s +[acc] eval 0.268426093 s +[acc] compile 4.231844e-3 s +[acc] eval 5.611224e-3 s +[acc] eval 2.456385e-3 s +[acc] eval 1.874302e-3 s +[acc] eval 1.6624743e-2 s +[acc] compile 0.261239402 s +[acc] eval 2.3903145e-2 s +[acc] eval 0.294184102 s +[acc] compile 0.119240087 s +[acc] eval 1.1149768e-2 s +[acc] eval 5.880509e-3 s +[acc] eval 4.909107e-3 s +[acc] eval 4.037317e-3 s +[acc] eval 3.434545e-3 s +[acc] eval 2.469918e-3 s +[acc] eval 9.374456e-3 s +[acc] eval 0.164576733 s +[acc] compile 0.248459132 s +[acc] eval 2.1908528e-2 s +[acc] eval 0.276703442 s +[acc] compile 0.244465931 s +[acc] eval 1.6363263e-2 s +[acc] eval 0.276965575 s +[acc] compile 0.135175121 s +[acc] eval 2.4423026e-2 s +[acc] eval 0.170014333 s +[acc] compile 0.106574511 s +[acc] eval 6.116405e-3 s +[acc] eval 0.115634869 s +[acc] compile 0.255114493 s +[acc] eval 3.9636383e-2 s +[acc] eval 0.298395517 s +[acc] compile 3.204691e-3 s +[acc] eval 9.220423e-3 s +[acc] eval 1.046403e-3 s +[acc] eval 1.0756e-3 s +[acc] eval 1.5984186e-2 s +[acc] compile 8.3655215e-2 s +[acc] eval 5.320466e-3 s +[acc] eval 9.2915719e-2 s +[acc] compile 1.4679375e-2 s +[acc] eval 7.927457e-3 s +[acc] eval 4.410239e-3 s +[acc] eval 3.829105e-3 s +[acc] eval 3.080301e-3 s +[acc] eval 2.940158e-3 s +[acc] eval 2.886714e-3 s +[acc] eval 3.081681e-3 s +[acc] eval 2.68566e-3 s +[acc] eval 5.6277709e-2 s +[acc] compile 0.486674814 s +[acc] eval 3.0496372e-2 s +[acc] eval 0.522171954 s +[acc] compile 5.017889e-3 s +[acc] eval 3.984535e-3 s +[acc] eval 1.152342e-3 s +[acc] eval 1.027916e-3 s +[acc] eval 1.2987898e-2 s +[acc] compile 0.126377365 s +[acc] eval 9.141113e-3 s +[acc] eval 0.146875687 s +[acc] compile 0.124566246 s +[acc] eval 1.3398376e-2 s +[acc] eval 6.36205e-3 s +[acc] eval 4.775584e-3 s +[acc] eval 9.495242e-3 s +[acc] eval 4.002833e-3 s +[acc] eval 4.460809e-3 s +[acc] eval 3.986859e-3 s +[acc] eval 5.054676e-3 s +[acc] eval 4.157971e-3 s +[acc] eval 4.058395e-3 s +[acc] eval 3.424931e-3 s +[acc] eval 3.335857e-3 s +[acc] eval 2.773005e-3 s +[acc] eval 3.248086e-3 s +[acc] eval 4.037897e-3 s +[acc] eval 4.195356e-3 s +[acc] eval 4.429892e-3 s +[acc] eval 3.022718e-3 s +[acc] eval 3.662899e-3 s +[acc] eval 3.109836e-3 s +[acc] eval 3.453481e-3 s +[acc] eval 3.687417e-3 s +[acc] eval 3.405347e-3 s +[acc] eval 3.283244e-3 s +[acc] eval 2.967833e-3 s +[acc] eval 3.529338e-3 s +[acc] eval 3.316569e-3 s +[acc] eval 3.346492e-3 s +[acc] eval 3.027141e-3 s +[acc] eval 4.031515e-3 s +[acc] eval 3.999391e-3 s +[acc] eval 0.265267825 s +[acc] compile 5.4281407e-2 s +[acc] eval 1.0886128e-2 s +[acc] eval 6.8988058e-2 s +[acc] compile 3.456284e-3 s +[acc] eval 1.0803803e-2 s +[acc] eval 9.83286e-4 s +[acc] eval 9.49386e-4 s +[acc] eval 9.96426e-4 s +[acc] eval 1.852075e-2 s +[acc] compile 7.9089822e-2 s +[acc] eval 2.6708682e-2 s +[acc] eval 0.110348621 s +[acc] compile 6.387694e-2 s +[acc] eval 7.478041e-3 s +[acc] eval 7.5561724e-2 s +[acc] compile 5.7238949e-2 s +[acc] eval 6.133291e-3 s +[acc] eval 2.945067e-3 s +[acc] eval 2.412442e-3 s +[acc] eval 7.2060373e-2 s +[acc] compile 0.258465735 s +[acc] eval 3.2593759e-2 s +[acc] eval 0.295892841 s +[acc] compile 0.136687075 s +[acc] eval 1.4770757e-2 s +[acc] eval 0.154361712 s +[acc] compile 6.1547629e-2 s +[acc] eval 6.653384e-3 s +[acc] eval 7.0145142e-2 s +[acc] compile 0.138561898 s +[acc] eval 1.3214424e-2 s +[acc] eval 8.164351e-3 s +[acc] eval 1.880145e-3 s +[acc] eval 1.896457e-3 s +[acc] eval 1.774277e-3 s +[acc] eval 2.250425e-3 s +[acc] eval 1.966838e-3 s +[acc] eval 1.884045e-3 s +[acc] eval 1.805392e-3 s +[acc] eval 1.844308e-3 s +[acc] eval 1.719634e-3 s +[acc] eval 1.640168e-3 s +[acc] eval 1.796396e-3 s +[acc] eval 0.183946935 s +[acc] compile 0.132121694 s +[acc] eval 5.526775e-3 s +[acc] eval 0.13957785 s +[acc] compile 4.452801e-3 s +[acc] eval 5.184216e-3 s +[acc] eval 8.56287e-4 s +[acc] eval 5.79922e-4 s +[acc] eval 1.239529e-2 s +[acc] compile 0.124990989 s +[acc] eval 7.973869e-3 s +[acc] eval 0.136378348 s +[acc] compile 5.896535e-3 s +[acc] eval 1.9546799e-2 s +[acc] eval 4.0555478e-2 s +[acc] compile 6.4134495e-2 s +[acc] eval 8.993345e-3 s +[acc] eval 2.494375e-3 s +[acc] eval 2.061549e-3 s +[acc] eval 2.381623e-3 s +[acc] eval 2.067297e-3 s +[acc] eval 3.227629e-3 s +[acc] eval 2.506336e-3 s +[acc] eval 2.066126e-3 s +[acc] eval 2.154638e-3 s +[acc] eval 1.793996e-3 s +[acc] eval 1.853521e-3 s +[acc] eval 1.567664e-3 s +[acc] eval 1.068593e-3 s +[acc] eval 1.34577e-3 s +[acc] eval 1.30851e-3 s +[acc] eval 1.599268e-3 s +[acc] eval 1.688663e-3 s +[acc] eval 1.325858e-3 s +[acc] eval 0.111068617 s +[acc] compile 1.3940149e-2 s +[acc] eval 1.0941789e-2 s +[acc] eval 2.6724912e-2 s +[acc] eval 1.1851087e-2 s +[acc] eval 6.7778e-3 s +[acc] eval 2.943759e-3 s +[acc] eval 2.844794e-3 s +[acc] eval 2.871871e-3 s +[acc] eval 2.808461e-3 s +[acc] eval 2.396035e-3 s +[acc] eval 2.469424e-3 s +[acc] eval 2.2266e-3 s +[acc] eval 2.42258e-3 s +[acc] eval 2.103133e-3 s +[acc] eval 2.289482e-3 s +[acc] eval 2.129789e-3 s +[acc] eval 2.546518e-3 s +[acc] eval 3.206809e-3 s +[acc] eval 2.997487e-3 s +[acc] eval 2.963573e-3 s +[acc] eval 3.112172e-3 s +[acc] eval 3.144749e-3 s +[acc] eval 3.343417e-3 s +[acc] eval 2.952546e-3 s +[acc] eval 2.787962e-3 s +[acc] eval 2.965301e-3 s +[acc] eval 2.991237e-3 s +[acc] eval 3.280506e-3 s +[acc] eval 3.07811e-3 s +[acc] eval 3.037459e-3 s +[acc] eval 3.011027e-3 s +[acc] eval 3.036147e-3 s +[acc] eval 0.152717121 s +[acc] compile 0.164909849 s +[acc] eval 1.113165e-2 s +[acc] eval 5.375844e-3 s +[acc] eval 2.503974e-3 s +[acc] eval 5.422673e-3 s +[acc] eval 2.273427e-3 s +[acc] eval 2.578316e-3 s +[acc] eval 2.150817e-3 s +[acc] eval 1.883994e-3 s +[acc] eval 1.844719e-3 s +[acc] eval 1.811218e-3 s +[acc] eval 1.81179e-3 s +[acc] eval 1.805429e-3 s +[acc] eval 0.20837521 s +[acc] compile 0.388716467 s +[acc] eval 2.8893801e-2 s +[acc] eval 9.378869e-3 s +[acc] eval 3.466286e-3 s +[acc] eval 3.302938e-3 s +[acc] eval 3.32766e-3 s +[acc] eval 3.451162e-3 s +[acc] eval 5.428299e-3 s +[acc] eval 5.466459e-3 s +[acc] eval 2.73633e-3 s +[acc] eval 2.633356e-3 s +[acc] eval 2.583216e-3 s +[acc] eval 3.217814e-3 s +[acc] eval 0.467254176 s +[acc] compile 2.659822e-3 s +[acc] eval 3.675886e-3 s +[acc] eval 6.53452e-4 s +[acc] eval 8.39217e-4 s +[acc] eval 8.873159e-3 s +[acc] compile 4.3206663e-2 s +[acc] eval 1.0390867e-2 s +[acc] eval 5.4980066e-2 s +[acc] compile 6.206817e-2 s +[acc] eval 1.131208e-2 s +[acc] eval 1.317742e-3 s +[acc] eval 1.014717e-3 s +[acc] eval 1.306214e-3 s +[acc] eval 7.8629376e-2 s +[acc] compile 4.869328e-3 s +[acc] eval 7.108742e-3 s +[acc] eval 1.3384819e-2 s +[acc] compile 6.3811795e-2 s +[acc] eval 1.7552314e-2 s +[acc] eval 2.225782e-3 s +[acc] eval 2.309429e-3 s +[acc] eval 2.297331e-3 s +[acc] eval 2.299398e-3 s +[acc] eval 2.265914e-3 s +[acc] eval 2.399708e-3 s +[acc] eval 2.513823e-3 s +[acc] eval 2.245181e-3 s +[acc] eval 2.537935e-3 s +[acc] eval 2.260017e-3 s +[acc] eval 0.108022953 s +[acc] compile 3.393582e-3 s +[acc] eval 3.516842e-3 s +[acc] eval 1.007987e-3 s +[acc] eval 7.05311e-4 s +[acc] eval 9.9678e-3 s +[acc] compile 4.885449e-3 s +[acc] eval 4.664492e-3 s +[acc] eval 1.133054e-2 s +[acc] compile 0.187851079 s +[acc] eval 1.1768188e-2 s +[acc] eval 1.458854e-3 s +[acc] eval 1.718033e-3 s +[acc] eval 2.440068e-3 s +[acc] eval 2.331411e-3 s +[acc] eval 1.781024e-3 s +[acc] eval 1.623035e-3 s +[acc] eval 1.841143e-3 s +[acc] eval 1.971213e-3 s +[acc] eval 2.061833e-3 s +[acc] eval 1.986211e-3 s +[acc] eval 1.984142e-3 s +[acc] eval 1.748239e-3 s +[acc] eval 1.826102e-3 s +[acc] eval 1.911723e-3 s +[acc] eval 2.091967e-3 s +[acc] eval 1.814125e-3 s +[acc] eval 1.335418e-3 s +[acc] eval 1.479539e-3 s +[acc] eval 1.539719e-3 s +[acc] eval 1.510563e-3 s +[acc] eval 1.476931e-3 s +[acc] eval 1.560655e-3 s +[acc] eval 1.544489e-3 s +[acc] eval 1.553208e-3 s +[acc] eval 1.475103e-3 s +[acc] eval 1.482149e-3 s +[acc] eval 1.414631e-3 s +[acc] eval 1.416822e-3 s +[acc] eval 1.546121e-3 s +[acc] eval 1.488608e-3 s +[acc] eval 1.476226e-3 s +[acc] eval 1.501917e-3 s +[acc] eval 1.568441e-3 s +[acc] eval 1.427734e-3 s +[acc] eval 1.595074e-3 s +[acc] eval 1.474145e-3 s +[acc] eval 1.44647e-3 s +[acc] eval 0.26588314 s +[acc] compile 9.2773806e-2 s +[acc] eval 5.364904e-3 s +[acc] eval 2.290645e-3 s +[acc] eval 2.259202e-3 s +[acc] eval 2.556286e-3 s +[acc] eval 2.012648e-3 s +[acc] eval 2.585732e-3 s +[acc] eval 2.646733e-3 s +[acc] eval 2.638005e-3 s +[acc] eval 8.674112e-3 s +[acc] eval 2.438076e-3 s +[acc] eval 2.264071e-3 s +[acc] eval 2.128617e-3 s +[acc] eval 2.374982e-3 s +[acc] eval 1.809765e-3 s +[acc] eval 1.799862e-3 s +[acc] eval 1.600531e-3 s +[acc] eval 1.608774e-3 s +[acc] eval 1.625234e-3 s +[acc] eval 1.455559e-3 s +[acc] eval 1.379797e-3 s +[acc] eval 1.498654e-3 s +[acc] eval 1.716665e-3 s +[acc] eval 1.857968e-3 s +[acc] eval 2.144684e-3 s +[acc] eval 1.970978e-3 s +[acc] eval 1.601324e-3 s +[acc] eval 1.540703e-3 s +[acc] eval 0.160347582 s +[acc] compile 9.6576288e-2 s +[acc] eval 7.537737e-3 s +[acc] eval 1.841139e-3 s +[acc] eval 1.427905e-3 s +[acc] eval 1.881945e-3 s +[acc] eval 1.57086e-3 s +[acc] eval 1.529431e-3 s +[acc] eval 1.396985e-3 s +[acc] eval 1.087321e-3 s +[acc] eval 1.323309e-3 s +[acc] eval 1.083228e-3 s +[acc] eval 1.370381e-3 s +[acc] eval 1.278664e-3 s +[acc] eval 1.009507e-3 s +[acc] eval 1.213322e-3 s +[acc] eval 7.07186e-4 s +[acc] eval 9.14666e-4 s +[acc] eval 8.76834e-4 s +[acc] eval 8.97087e-4 s +[acc] eval 1.079595e-3 s +[acc] eval 7.29572e-4 s +[acc] eval 1.020861e-3 s +[acc] eval 7.8598e-4 s +[acc] eval 8.56173e-4 s +[acc] eval 8.19589e-4 s +[acc] eval 8.11866e-4 s +[acc] eval 5.208709e-3 s +[acc] eval 7.9311e-4 s +[acc] eval 8.98322e-4 s +[acc] eval 8.62888e-4 s +[acc] eval 7.67971e-4 s +[acc] eval 0.143399618 s +[acc] compile 7.4415657e-2 s +[acc] eval 3.869394e-3 s +[acc] eval 1.983443e-3 s +[acc] eval 1.914424e-3 s +[acc] eval 8.4577397e-2 s +[acc] compile 0.122789481 s +[acc] eval 6.001296e-3 s +[acc] eval 2.200103e-3 s +[acc] eval 2.511545e-3 s +[acc] eval 2.471668e-3 s +[acc] eval 1.745275e-3 s +[acc] eval 5.98144e-3 s +[acc] eval 3.20055e-3 s +[acc] eval 2.920834e-3 s +[acc] eval 2.586831e-3 s +[acc] eval 2.578776e-3 s +[acc] eval 2.333479e-3 s +[acc] eval 2.586444e-3 s +[acc] eval 2.168245e-3 s +[acc] eval 1.999918e-3 s +[acc] eval 2.034612e-3 s +[acc] eval 1.92193e-3 s +[acc] eval 2.02916e-3 s +[acc] eval 1.847012e-3 s +[acc] eval 1.884708e-3 s +[acc] eval 1.882603e-3 s +[acc] eval 1.655744e-3 s +[acc] eval 2.058768e-3 s +[acc] eval 2.222943e-3 s +[acc] eval 1.980631e-3 s +[acc] eval 2.091284e-3 s +[acc] eval 2.084778e-3 s +[acc] eval 2.144878e-3 s +[acc] eval 0.194889751 s +[acc] compile 0.116624889 s +[acc] eval 1.77637e-2 s +[acc] eval 0.136975196 s +[acc] compile 5.145059e-3 s +[acc] eval 8.646637e-3 s +[acc] eval 2.117898e-3 s +[acc] eval 1.846536e-3 s +[acc] eval 2.300854e-3 s +[acc] eval 2.091942e-3 s +[acc] eval 2.24845e-3 s +[acc] eval 2.034007e-3 s +[acc] eval 2.795953e-3 s +[acc] eval 2.132908e-3 s +[acc] eval 1.973344e-3 s +[acc] eval 3.597548e-2 s +[acc] compile 0.148864556 s +[acc] eval 3.1754049e-2 s +[acc] eval 0.184962501 s +[acc] compile 6.8369893e-2 s +[acc] eval 1.2416624e-2 s +[acc] eval 3.531073e-3 s +[acc] eval 3.215131e-3 s +[acc] eval 3.111906e-3 s +[acc] eval 3.315293e-3 s +[acc] eval 2.273986e-3 s +[acc] eval 2.132825e-3 s +[acc] eval 1.83175e-3 s +[acc] eval 1.688269e-3 s +[acc] eval 1.642303e-3 s +[acc] eval 1.867543e-3 s +[acc] eval 0.108981486 s +[acc] compile 4.174194e-3 s +[acc] eval 4.779336e-3 s +[acc] eval 1.1035968e-2 s +[acc] compile 0.18680508 s +[acc] eval 9.101725e-3 s +[acc] eval 0.209790973 s +[acc] compile 9.6229347e-2 s +[acc] eval 8.931569e-3 s +[acc] eval 0.111592349 s +[acc] compile 5.626208e-3 s +[acc] eval 7.452497e-3 s +[acc] eval 2.471171e-3 s +[acc] eval 1.977545e-3 s +[acc] eval 2.0797234e-2 s +[acc] compile 5.9432245e-2 s +[acc] eval 2.3400536e-2 s +[acc] eval 8.7190437e-2 s +[acc] compile 8.5607357e-2 s +[acc] eval 6.96258e-3 s +[acc] eval 9.7186549e-2 s +[acc] compile 3.975056e-3 s +[acc] eval 1.0306426e-2 s +[acc] eval 1.7273237e-2 s +[acc] compile 9.0545245e-2 s +[acc] eval 1.4625054e-2 s +[acc] eval 0.11145562 s +[acc] compile 0.190687217 s +[acc] eval 3.2909687e-2 s +[acc] eval 0.230588003 s +[acc] compile 0.18624242 s +[acc] eval 2.6343921e-2 s +[acc] eval 0.220004625 s +[acc] compile 1.0649707e-2 s +[acc] eval 2.6245664e-2 s +[acc] eval 3.9322352e-2 s +[acc] compile 4.930786e-3 s +[acc] eval 4.94151e-3 s +[acc] eval 2.128995e-3 s +[acc] eval 2.081481e-3 s +[acc] eval 2.068936e-3 s +[acc] eval 2.285251e-3 s +[acc] eval 2.454581e-3 s +[acc] eval 2.968493e-3 s +[acc] eval 3.657352e-3 s +[acc] eval 4.177764e-3 s +[acc] eval 4.39178e-3 s +[acc] eval 2.766259e-3 s +[acc] eval 2.656013e-3 s +[acc] eval 2.929207e-3 s +[acc] eval 9.867904e-3 s +[acc] eval 1.850624e-3 s +[acc] eval 1.815098e-3 s +[acc] eval 2.018712e-3 s +[acc] eval 1.41549e-3 s +[acc] eval 2.267854e-3 s +[acc] eval 2.441552e-3 s +[acc] eval 1.972279e-3 s +[acc] eval 1.982637e-3 s +[acc] eval 2.14573e-3 s +[acc] eval 1.623732e-3 s +[acc] eval 1.573142e-3 s +[acc] eval 1.551511e-3 s +[acc] eval 1.491226e-3 s +[acc] eval 1.538216e-3 s +[acc] eval 1.560374e-3 s +[acc] eval 1.702748e-3 s +[acc] eval 1.724173e-3 s +[acc] eval 9.0144458e-2 s +[acc] compile 6.475327e-3 s +[acc] eval 1.2491408e-2 s +[acc] eval 2.915466e-3 s +[acc] eval 3.358362e-3 s +[acc] eval 3.433009e-3 s +[acc] eval 8.548493e-3 s +[acc] eval 2.342444e-3 s +[acc] eval 2.000056e-3 s +[acc] eval 2.834579e-3 s +[acc] eval 3.237804e-3 s +[acc] eval 2.52819e-3 s +[acc] eval 2.08821e-3 s +[acc] eval 2.002201e-3 s +[acc] eval 1.911946e-3 s +[acc] eval 2.189643e-3 s +[acc] eval 1.780203e-3 s +[acc] eval 2.052791e-3 s +[acc] eval 2.181926e-3 s +[acc] eval 2.221235e-3 s +[acc] eval 2.160479e-3 s +[acc] eval 2.136973e-3 s +[acc] eval 2.159704e-3 s +[acc] eval 2.392488e-3 s +[acc] eval 2.431316e-3 s +[acc] eval 2.048489e-3 s +[acc] eval 2.127657e-3 s +[acc] eval 2.155611e-3 s +[acc] eval 2.026225e-3 s +[acc] eval 1.972063e-3 s +[acc] eval 1.938923e-3 s +[acc] eval 1.878481e-3 s +[acc] eval 1.662286e-3 s +[acc] eval 9.7909996e-2 s +[acc] compile 4.175869e-3 s +[acc] eval 7.541472e-3 s +[acc] eval 1.4165433e-2 s +[acc] compile 4.882902e-3 s +[acc] eval 4.789214e-3 s +[acc] eval 8.63322e-4 s +[acc] eval 1.155249e-3 s +[acc] eval 1.2757547e-2 s +[acc] compile 0.122994533 s +[acc] eval 1.1397711e-2 s +[acc] eval 0.137170736 s +[acc] compile 8.7316612e-2 s +[acc] eval 1.7873557e-2 s +[acc] eval 0.107617108 s +[acc] compile 5.09932e-4 s +[acc] eval 6.084654e-3 s +[acc] compile 3.104644e-3 s +[acc] eval 4.567313e-3 s +[acc] eval 1.431105e-3 s +[acc] eval 1.258474e-3 s +[acc] eval 1.296622e-3 s +[acc] eval 1.3038622e-2 s +[acc] compile 5.016863e-3 s +[acc] eval 4.843097e-3 s +[acc] eval 2.390824e-3 s +[acc] eval 1.736647e-3 s +[acc] eval 2.057635e-3 s +[acc] eval 1.7755646e-2 s +[acc] compile 3.569286e-3 s +[acc] eval 1.0329606e-2 s +[acc] eval 1.335284e-3 s +[acc] eval 1.028904e-3 s +[acc] eval 1.45307e-3 s +[acc] eval 1.9242776e-2 s +[acc] compile 6.259994e-3 s +[acc] eval 7.073321e-3 s +[acc] eval 1.783815e-3 s +[acc] eval 1.375328e-3 s +[acc] eval 1.897631e-3 s +[acc] eval 2.0361941e-2 s +[acc] compile 4.219375e-3 s +[acc] eval 4.469982e-3 s +[acc] eval 6.65119e-4 s +[acc] eval 7.89465e-4 s +[acc] eval 1.1195085e-2 s +[acc] compile 0.101248138 s +[acc] eval 1.8867629e-2 s +[acc] eval 0.12357063 s +[acc] compile 9.405896e-3 s +[acc] eval 5.450135e-3 s +[acc] eval 1.909024e-3 s +[acc] eval 1.900299e-3 s +[acc] eval 1.873708e-3 s +[acc] eval 1.831785e-3 s +[acc] eval 1.683121e-3 s +[acc] eval 1.841652e-3 s +[acc] eval 1.974424e-3 s +[acc] eval 7.24104e-3 s +[acc] eval 1.606366e-3 s +[acc] eval 1.437446e-3 s +[acc] eval 1.429714e-3 s +[acc] eval 1.345597e-3 s +[acc] eval 1.746114e-3 s +[acc] eval 1.63135e-3 s +[acc] eval 1.373947e-3 s +[acc] eval 1.311813e-3 s +[acc] eval 1.287042e-3 s +[acc] eval 1.248989e-3 s +[acc] eval 1.234871e-3 s +[acc] eval 1.280785e-3 s +[acc] eval 1.331771e-3 s +[acc] eval 1.281292e-3 s +[acc] eval 1.323008e-3 s +[acc] eval 1.338618e-3 s +[acc] eval 1.546811e-3 s +[acc] eval 1.472532e-3 s +[acc] eval 1.602083e-3 s +[acc] eval 1.486385e-3 s +[acc] eval 1.522799e-3 s +[acc] eval 6.8693251e-2 s +[acc] compile 0.278951414 s +[acc] eval 2.0517296e-2 s +[acc] eval 0.303051978 s +[acc] compile 3.025326e-3 s +[acc] eval 2.341052e-3 s +[acc] eval 5.76381e-4 s +[acc] eval 3.986291e-3 s +[acc] eval 1.0763375e-2 s +[acc] compile 4.14978e-3 s +[acc] eval 5.287948e-3 s +[acc] eval 1.1426777e-2 s +[acc] compile 0.143164525 s +[acc] eval 2.9332276e-2 s +[acc] eval 0.177552999 s +[acc] compile 0.21244192 s +[acc] eval 6.069216e-3 s +[acc] eval 0.221086573 s +[acc] compile 6.1863152e-2 s +[acc] eval 1.701649e-2 s +[acc] eval 8.8800348e-2 s +[acc] compile 5.655402e-3 s +[acc] eval 7.711161e-3 s +[acc] eval 1.6104373e-2 s +[acc] compile 5.6332381e-2 s +[acc] eval 3.832223e-3 s +[acc] eval 6.2637665e-2 s +[acc] compile 6.456617e-3 s +[acc] eval 1.8203902e-2 s +[acc] eval 2.7333826e-2 s +[acc] compile 0.180741955 s +[acc] eval 1.8331209e-2 s +[acc] eval 0.203192794 s +[acc] compile 4.656011e-3 s +[acc] eval 1.0707898e-2 s +[acc] eval 5.93742e-3 s +[acc] eval 7.345288e-3 s +[acc] eval 9.975039e-3 s +[acc] eval 1.4359928e-2 s +[acc] eval 8.833494e-3 s +[acc] eval 7.373999e-3 s +[acc] eval 5.580912e-3 s +[acc] eval 1.0127357e-2 s +[acc] eval 5.358891e-3 s +[acc] eval 4.855798e-3 s +[acc] eval 6.195489e-3 s +[acc] eval 0.104346113 s +[acc] compile 1.0168424e-2 s +[acc] eval 8.066291e-3 s +[acc] eval 2.3096658e-2 s +[acc] compile 0.12173306 s +[acc] eval 8.573348e-3 s +[acc] eval 0.132880863 s +[acc] compile 5.965264e-3 s +[acc] eval 1.1451665e-2 s +[acc] eval 2.0275114e-2 s +[acc] compile 6.6709478e-2 s +[acc] eval 2.2443725e-2 s +[acc] eval 9.6245254e-2 s +[acc] compile 6.589591e-3 s +[acc] eval 6.567874e-3 s +[acc] eval 2.255654e-3 s +[acc] eval 2.349238e-3 s +[acc] eval 2.365576e-3 s +[acc] eval 2.327149e-3 s +[acc] eval 9.893398e-3 s +[acc] eval 2.20495e-3 s +[acc] eval 1.817634e-3 s +[acc] eval 1.790403e-3 s +[acc] eval 1.738612e-3 s +[acc] eval 1.750808e-3 s +[acc] eval 1.638975e-3 s +[acc] eval 1.533571e-3 s +[acc] eval 1.96265e-3 s +[acc] eval 2.003533e-3 s +[acc] eval 1.853204e-3 s +[acc] eval 1.56347e-3 s +[acc] eval 1.813335e-3 s +[acc] eval 1.791282e-3 s +[acc] eval 1.755211e-3 s +[acc] eval 1.79398e-3 s +[acc] eval 1.718167e-3 s +[acc] eval 2.342084e-3 s +[acc] eval 2.357715e-3 s +[acc] eval 1.841706e-3 s +[acc] eval 1.583006e-3 s +[acc] eval 1.624896e-3 s +[acc] eval 1.563085e-3 s +[acc] eval 1.576101e-3 s +[acc] eval 2.166839e-3 s +[acc] eval 2.104036e-3 s +[acc] eval 1.674424e-3 s +[acc] eval 1.916545e-3 s +[acc] eval 2.05525e-3 s +[acc] eval 1.658199e-3 s +[acc] eval 1.928267e-3 s +[acc] eval 1.646395e-3 s +[acc] eval 1.796088e-3 s +[acc] eval 1.749502e-3 s +[acc] eval 9.8916817e-2 s +[acc] compile 0.141025398 s +[acc] eval 1.8982869e-2 s +[acc] eval 0.163111194 s +[acc] compile 0.210660563 s +[acc] eval 2.7755203e-2 s +[acc] eval 0.249701653 s +[acc] compile 5.65306e-4 s +[acc] eval 7.455742e-3 s +[acc] compile 0.104935482 s +[acc] eval 1.172621e-2 s +[acc] eval 0.119349106 s +[acc] compile 6.7259132e-2 s +[acc] eval 1.2483395e-2 s +[acc] eval 8.1989353e-2 s +[acc] compile 3.840373e-3 s +[acc] eval 9.719066e-3 s +[acc] eval 1.9906774e-2 s +[acc] compile 7.7303198e-2 s +[acc] eval 1.6373021e-2 s +[acc] eval 9.8656245e-2 s +[acc] compile 3.183873e-3 s +[acc] eval 7.944553e-3 s +[acc] eval 8.01798e-4 s +[acc] eval 1.208866e-3 s +[acc] eval 1.4325699e-2 s +[acc] compile 5.2990397e-2 s +[acc] eval 8.951941e-3 s +[acc] eval 6.5051569e-2 s +[acc] compile 0.285141129 s +[acc] eval 1.1876914e-2 s +[acc] eval 0.301304691 s +[acc] compile 2.4788665e-2 s +[acc] eval 1.816057e-2 s +[acc] eval 5.3047515e-2 s +[acc] compile 0.128830412 s +[acc] eval 9.973714e-3 s +[acc] eval 0.142665893 s +[acc] compile 9.65436e-3 s +[acc] eval 3.2278372e-2 s +[acc] eval 4.6766991e-2 s +[acc] compile 0.113906652 s +[acc] eval 9.96442e-3 s +[acc] eval 3.022231e-3 s +[acc] eval 3.283526e-3 s +[acc] eval 7.605971e-3 s +[acc] eval 3.38231e-3 s +[acc] eval 3.499326e-3 s +[acc] eval 3.878834e-3 s +[acc] eval 5.035988e-3 s +[acc] eval 4.649844e-3 s +[acc] eval 4.527754e-3 s +[acc] eval 5.048028e-3 s +[acc] eval 4.91925e-3 s +[acc] eval 3.82081e-3 s +[acc] eval 3.349829e-3 s +[acc] eval 3.122102e-3 s +[acc] eval 2.276909e-3 s +[acc] eval 2.602362e-3 s +[acc] eval 2.330453e-3 s +[acc] eval 1.99401e-3 s +[acc] eval 1.80637e-3 s +[acc] eval 2.180513e-3 s +[acc] eval 3.069308e-3 s +[acc] eval 2.663106e-3 s +[acc] eval 2.802096e-3 s +[acc] eval 2.231875e-3 s +[acc] eval 1.966389e-3 s +[acc] eval 1.860901e-3 s +[acc] eval 1.988861e-3 s +[acc] eval 1.816588e-3 s +[acc] eval 1.938907e-3 s +[acc] eval 1.820303e-3 s +[acc] eval 0.225109097 s +[acc] compile 0.48715842 s +[acc] eval 2.9785683e-2 s +[acc] eval 0.522050563 s +[acc] compile 0.173472259 s +[acc] eval 1.8394946e-2 s +[acc] eval 0.196515025 s +[acc] compile 7.130428e-3 s +[acc] eval 1.6921006e-2 s +[acc] eval 2.7399282e-2 s +[acc] compile 7.202382e-3 s +[acc] eval 2.2316756e-2 s +[acc] eval 3.3955855e-2 s +[acc] compile 1.2103455e-2 s +[acc] eval 1.3270336e-2 s +[acc] eval 2.8483703e-2 s +[acc] compile 1.6679729e-2 s +[acc] eval 1.0195279e-2 s +[acc] eval 3.0576836e-2 s +[acc] compile 8.455767e-3 s +[acc] eval 1.7804062e-2 s +[acc] eval 2.8429011e-2 s +[acc] compile 5.890227e-3 s +[acc] eval 7.284992e-3 s +[acc] eval 1.5668555e-2 s +[acc] compile 6.957098e-3 s +[acc] eval 5.039001e-3 s +[acc] eval 2.196445e-3 s +[acc] eval 1.8013119e-2 s +[acc] compile 3.930547e-3 s +[acc] eval 4.386943e-3 s +[acc] eval 1.107525e-3 s +[acc] eval 1.585784e-3 s +[acc] eval 1.2507152e-2 s +[acc] compile 1.6938256e-2 s +[acc] eval 1.0084302e-2 s +[acc] eval 3.756532e-2 s +[acc] compile 7.798732e-3 s +[acc] eval 7.306866e-3 s +[acc] eval 1.9145015e-2 s +[acc] compile 0.186376408 s +[acc] eval 1.8860818e-2 s +[acc] eval 0.208658758 s +[acc] compile 6.4110538e-2 s +[acc] eval 1.9689696e-2 s +[acc] eval 8.8918403e-2 s +[acc] compile 0.246571282 s +[acc] eval 1.558034e-2 s +[acc] eval 0.26634282 s +[acc] compile 0.36092143 s +[acc] eval 2.8957397e-2 s +[acc] eval 0.395446105 s +[acc] compile 3.971515e-3 s +[acc] eval 4.079272e-3 s +[acc] eval 9.08994e-4 s +[acc] eval 1.067339e-3 s +[acc] eval 1.1466066e-2 s +[acc] compile 0.304145491 s +[acc] eval 2.4831417e-2 s +[acc] eval 0.341072572 s +[acc] compile 5.9205584e-2 s +[acc] eval 1.5656654e-2 s +[acc] eval 8.1822449e-2 s +[acc] compile 6.958337e-3 s +[acc] eval 9.662818e-3 s +[acc] eval 1.9293727e-2 s +[acc] compile 0.109654742 s +[acc] eval 2.9118367e-2 s +[acc] eval 0.141726751 s +[acc] compile 0.116793403 s +[acc] eval 8.469408e-3 s +[acc] eval 4.02359e-3 s +[acc] eval 4.573408e-3 s +[acc] eval 3.800403e-3 s +[acc] eval 1.4722643e-2 s +[acc] eval 6.711932e-3 s +[acc] eval 4.31467e-3 s +[acc] eval 3.339841e-3 s +[acc] eval 3.93573e-3 s +[acc] eval 3.449376e-3 s +[acc] eval 2.986078e-3 s +[acc] eval 2.819827e-3 s +[acc] eval 2.726865e-3 s +[acc] eval 2.579062e-3 s +[acc] eval 2.540355e-3 s +[acc] eval 2.393226e-3 s +[acc] eval 2.55052e-3 s +[acc] eval 2.472789e-3 s +[acc] eval 2.471079e-3 s +[acc] eval 2.525126e-3 s +[acc] eval 2.528998e-3 s +[acc] eval 2.751763e-3 s +[acc] eval 2.503877e-3 s +[acc] eval 2.315418e-3 s +[acc] eval 2.334614e-3 s +[acc] eval 2.412307e-3 s +[acc] eval 2.320491e-3 s +[acc] eval 2.094566e-3 s +[acc] eval 2.177742e-3 s +[acc] eval 2.087237e-3 s +[acc] eval 0.229287659 s +[acc] compile 5.769895e-3 s +[acc] eval 5.282782e-3 s +[acc] eval 2.708581e-3 s +[acc] eval 3.023777e-3 s +[acc] eval 2.877947e-3 s +[acc] eval 2.381183e-3 s +[acc] eval 2.544254e-3 s +[acc] eval 1.735045e-3 s +[acc] eval 6.934969e-3 s +[acc] eval 2.138847e-3 s +[acc] eval 1.416495e-3 s +[acc] eval 1.385247e-3 s +[acc] eval 1.700115e-3 s +[acc] eval 4.2439306e-2 s +[acc] compile 2.802865e-3 s +[acc] eval 2.654421e-3 s +[acc] eval 7.47425e-4 s +[acc] eval 7.69284e-4 s +[acc] eval 7.793272e-3 s +[acc] compile 4.557854e-3 s +[acc] eval 6.209281e-3 s +[acc] eval 1.3203887e-2 s +[acc] compile 6.702361e-3 s +[acc] eval 7.959736e-3 s +[acc] eval 2.0728485e-2 s +[acc] compile 1.11396e-2 s +[acc] eval 1.0272644e-2 s +[acc] eval 2.7077573e-2 s +[acc] compile 1.0756235e-2 s +[acc] eval 9.751215e-3 s +[acc] eval 3.6851394e-2 s +[acc] compile 6.8147368e-2 s +[acc] eval 1.3091827e-2 s +[acc] eval 8.3983232e-2 s +[acc] compile 3.518202e-3 s +[acc] eval 7.849721e-3 s +[acc] eval 1.2168009e-2 s +[acc] compile 7.432491e-2 s +[acc] eval 9.78468e-3 s +[acc] eval 8.771754e-2 s +[acc] compile 1.0369859e-2 s +[acc] eval 1.9708196e-2 s +[acc] eval 3.2870676e-2 s +[acc] compile 5.8040406e-2 s +[acc] eval 3.034697e-3 s +[acc] eval 1.112219e-3 s +[acc] eval 9.54993e-4 s +[acc] eval 9.15933e-4 s +[acc] eval 7.51863e-4 s +[acc] eval 9.52808e-4 s +[acc] eval 1.004072e-3 s +[acc] eval 1.045489e-3 s +[acc] eval 8.39419e-4 s +[acc] eval 1.070664e-3 s +[acc] eval 1.002668e-3 s +[acc] eval 1.011435e-3 s +[acc] eval 7.3724549e-2 s +[acc] compile 5.6577409e-2 s +[acc] eval 1.0118703e-2 s +[acc] eval 7.415539e-3 s +[acc] eval 2.194694e-3 s +[acc] eval 1.757906e-3 s +[acc] eval 1.737712e-3 s +[acc] eval 1.639845e-3 s +[acc] eval 1.581532e-3 s +[acc] eval 1.62212e-3 s +[acc] eval 1.695329e-3 s +[acc] eval 1.873626e-3 s +[acc] eval 1.771021e-3 s +[acc] eval 1.859332e-3 s +[acc] eval 1.694994e-3 s +[acc] eval 1.636554e-3 s +[acc] eval 2.335536e-3 s +[acc] eval 1.693082e-3 s +[acc] eval 1.725509e-3 s +[acc] eval 1.60466e-3 s +[acc] eval 1.6502e-3 s +[acc] eval 1.790399e-3 s +[acc] eval 1.980813e-3 s +[acc] eval 1.831659e-3 s +[acc] eval 2.006588e-3 s +[acc] eval 2.502573e-3 s +[acc] eval 1.838398e-3 s +[acc] eval 1.549564e-3 s +[acc] eval 1.474236e-3 s +[acc] eval 1.472826e-3 s +[acc] eval 1.450419e-3 s +[acc] eval 1.480793e-3 s +[acc] eval 1.530077e-3 s +[acc] eval 0.131415008 s +[acc] compile 0.543441424 s +[acc] eval 3.3437653e-2 s +[acc] eval 0.581200655 s +[acc] compile 8.82832e-3 s +[acc] eval 8.366197e-3 s +[acc] eval 2.034118e-3 s +[acc] eval 1.951286e-3 s +[acc] eval 1.844651e-3 s +[acc] eval 1.677972e-3 s +[acc] eval 1.671531e-3 s +[acc] eval 1.574462e-3 s +[acc] eval 1.874191e-3 s +[acc] eval 2.057622e-3 s +[acc] eval 2.441087e-3 s +[acc] eval 2.015682e-3 s +[acc] eval 4.0292925e-2 s +[acc] compile 5.08184e-3 s +[acc] eval 3.875512e-3 s +[acc] eval 1.627628e-3 s +[acc] eval 1.702505e-3 s +[acc] eval 1.621714e-3 s +[acc] eval 1.420566e-3 s +[acc] eval 7.516024e-3 s +[acc] eval 2.03217e-3 s +[acc] eval 2.241628e-3 s +[acc] eval 1.963892e-3 s +[acc] eval 1.730945e-3 s +[acc] eval 1.716886e-3 s +[acc] eval 1.671847e-3 s +[acc] eval 1.612738e-3 s +[acc] eval 3.9246215e-2 s +[acc] compile 3.979211e-3 s +[acc] eval 3.477755e-3 s +[acc] eval 6.84186e-4 s +[acc] eval 8.86228e-4 s +[acc] eval 1.0220512e-2 s +[acc] compile 6.0944922e-2 s +[acc] eval 1.0252983e-2 s +[acc] eval 1.009334e-3 s +[acc] eval 1.022474e-3 s +[acc] eval 7.5013357e-2 s +[acc] compile 0.315775682 s +[acc] eval 3.3261023e-2 s +[acc] eval 0.353073365 s +[acc] compile 4.3356365e-2 s +[acc] eval 8.412996e-3 s +[acc] eval 6.270099e-3 s +[acc] eval 6.0532735e-2 s +[acc] compile 0.140437068 s +[acc] eval 9.15703e-3 s +[acc] eval 0.152094149 s +[acc] compile 5.7451314e-2 s +[acc] eval 1.75968e-2 s +[acc] eval 8.0340385e-2 s +[acc] compile 8.3280546e-2 s +[acc] eval 1.1601765e-2 s +[acc] eval 0.102570864 s +[acc] compile 0.23950629 s +[acc] eval 1.5216771e-2 s +[acc] eval 0.258502629 s +[acc] compile 0.250956282 s +[acc] eval 2.5151463e-2 s +[acc] eval 4.150966e-3 s +[acc] eval 1.135012e-2 s +[acc] eval 4.197763e-3 s +[acc] eval 4.329483e-3 s +[acc] eval 4.012394e-3 s +[acc] eval 3.997767e-3 s +[acc] eval 4.08343e-3 s +[acc] eval 3.947409e-3 s +[acc] eval 3.912016e-3 s +[acc] eval 4.023178e-3 s +[acc] eval 4.025305e-3 s +[acc] eval 4.080229e-3 s +[acc] eval 3.730617e-3 s +[acc] eval 3.239943e-3 s +[acc] eval 3.152991e-3 s +[acc] eval 3.047836e-3 s +[acc] eval 3.030822e-3 s +[acc] eval 3.117393e-3 s +[acc] eval 3.024e-3 s +[acc] eval 5.401722e-3 s +[acc] eval 4.393524e-3 s +[acc] eval 4.349884e-3 s +[acc] eval 4.121976e-3 s +[acc] eval 4.948567e-3 s +[acc] eval 4.089571e-3 s +[acc] eval 3.560244e-3 s +[acc] eval 3.651683e-3 s +[acc] eval 3.94314e-3 s +[acc] eval 3.69501e-3 s +[acc] eval 0.404543704 s +[acc] compile 1.5873406e-2 s +[acc] eval 2.5738416e-2 s +[acc] eval 4.7997152e-2 s +[acc] compile 0.508100903 s +[acc] eval 2.7630609e-2 s +[acc] eval 0.541258358 s +[acc] compile 1.1526246e-2 s +[acc] eval 2.160136e-2 s +[acc] eval 3.7974386e-2 s +[acc] compile 6.846056e-3 s +[acc] eval 1.4601241e-2 s +[acc] eval 2.4073103e-2 s +[acc] compile 8.512635e-3 s +[acc] eval 2.2682673e-2 s +[acc] eval 3.5484054e-2 s +[acc] compile 1.4123213e-2 s +[acc] eval 1.6307136e-2 s +[acc] eval 3.5823741e-2 s +[acc] compile 6.8853806e-2 s +[acc] eval 1.4995949e-2 s +[acc] eval 9.1371472e-2 s +[acc] compile 1.2965062e-2 s +[acc] eval 2.5396943e-2 s +[acc] eval 4.5158949e-2 s +[acc] compile 0.375386316 s +[acc] eval 2.3498132e-2 s +[acc] eval 0.412626903 s +[acc] compile 0.480049591 s +[acc] eval 1.9266665e-2 s +[acc] eval 0.515635527 s +[acc] compile 0.117999543 s +[acc] eval 8.751222e-3 s +[acc] eval 0.132338661 s +[acc] compile 0.341663621 s +[acc] eval 3.7722677e-2 s +[acc] eval 0.385265932 s +[acc] compile 6.039561e-2 s +[acc] eval 8.457698e-3 s +[acc] eval 1.441907e-3 s +[acc] eval 1.397281e-3 s +[acc] eval 7.3594644e-2 s +[acc] compile 0.200596303 s +[acc] eval 1.3121369e-2 s +[acc] eval 5.256374e-3 s +[acc] eval 4.768181e-3 s +[acc] eval 4.795454e-3 s +[acc] eval 1.0157544e-2 s +[acc] eval 2.567298e-3 s +[acc] eval 4.399496e-3 s +[acc] eval 3.833097e-3 s +[acc] eval 3.11297e-3 s +[acc] eval 2.792072e-3 s +[acc] eval 2.525413e-3 s +[acc] eval 2.395506e-3 s +[acc] eval 2.565651e-3 s +[acc] eval 1.686445e-3 s +[acc] eval 1.754378e-3 s +[acc] eval 1.750188e-3 s +[acc] eval 0.273902141 s +[acc] compile 8.205235e-3 s +[acc] eval 8.933818e-3 s +[acc] eval 2.429591e-3 s +[acc] eval 2.362965e-3 s +[acc] eval 2.304694e-3 s +[acc] eval 2.227421e-3 s +[acc] eval 2.080928e-3 s +[acc] eval 1.914857e-3 s +[acc] eval 9.471485e-3 s +[acc] eval 1.873732e-3 s +[acc] eval 1.466112e-3 s +[acc] eval 1.559551e-3 s +[acc] eval 1.754941e-3 s +[acc] eval 1.823874e-3 s +[acc] eval 5.2167088e-2 s +[acc] compile 0.483287688 s +[acc] eval 3.8693233e-2 s +[acc] eval 0.528713863 s +[acc] compile 4.079493e-3 s +[acc] eval 4.832853e-3 s +[acc] eval 9.385332e-3 s +[acc] eval 2.5336942e-2 s +[acc] compile 4.8390217e-2 s +[acc] eval 1.8583525e-2 s +[acc] eval 6.8210419e-2 s +[acc] compile 5.57386e-4 s +[acc] eval 9.624725e-3 s +[acc] compile 9.472623e-3 s +[acc] eval 7.834948e-3 s +[acc] eval 3.194849e-3 s +[acc] eval 3.232414e-3 s +[acc] eval 3.961947e-3 s +[acc] eval 2.781973e-3 s +[acc] eval 3.3929345e-2 s +[acc] compile 5.63159e-4 s +[acc] eval 6.975749e-3 s +[acc] compile 0.295968262 s +[acc] eval 3.6402169e-2 s +[acc] eval 0.341443786 s +[acc] compile 6.566016e-3 s +[acc] eval 7.399404e-3 s +[acc] eval 3.124202e-3 s +[acc] eval 2.620222e-3 s +[acc] eval 2.3756929e-2 s +[acc] compile 7.4063583e-2 s +[acc] eval 9.817241e-3 s +[acc] eval 5.025966e-3 s +[acc] eval 4.796402e-3 s +[acc] eval 4.654164e-3 s +[acc] eval 1.0089625e-2 s +[acc] eval 3.112241e-3 s +[acc] eval 2.644483e-3 s +[acc] eval 2.009776e-3 s +[acc] eval 2.099609e-3 s +[acc] eval 1.879707e-3 s +[acc] eval 0.12485278 s +[acc] compile 7.1488111e-2 s +[acc] eval 5.009721e-3 s +[acc] eval 2.297784e-3 s +[acc] eval 2.238551e-3 s +[acc] eval 1.942769e-3 s +[acc] eval 1.869735e-3 s +[acc] eval 2.264726e-3 s +[acc] eval 2.039375e-3 s +[acc] eval 7.237191e-3 s +[acc] eval 1.462735e-3 s +[acc] eval 1.84376e-3 s +[acc] eval 1.628247e-3 s +[acc] eval 1.839063e-3 s +[acc] eval 1.360665e-3 s +[acc] eval 1.368112e-3 s +[acc] eval 1.238515e-3 s +[acc] eval 0.109998427 s +[acc] compile 0.190582817 s +[acc] eval 1.4264624e-2 s +[acc] eval 2.023748e-3 s +[acc] eval 2.047241e-3 s +[acc] eval 2.6132e-3 s +[acc] eval 2.31353e-3 s +[acc] eval 7.064775e-3 s +[acc] eval 1.436502e-3 s +[acc] eval 1.391048e-3 s +[acc] eval 1.41212e-3 s +[acc] eval 1.746509e-3 s +[acc] eval 1.397491e-3 s +[acc] eval 1.31748e-3 s +[acc] eval 1.320901e-3 s +[acc] eval 0.23395267 s +[acc] compile 4.477992e-3 s +[acc] eval 6.893538e-3 s +[acc] eval 1.831028e-3 s +[acc] eval 1.650325e-3 s +[acc] eval 1.543089e-3 s +[acc] eval 1.539769e-3 s +[acc] eval 6.011556e-3 s +[acc] eval 1.549919e-3 s +[acc] eval 1.267869e-3 s +[acc] eval 1.165953e-3 s +[acc] eval 1.260823e-3 s +[acc] eval 3.1751468e-2 s +[acc] compile 5.396126e-3 s +[acc] eval 7.16362e-3 s +[acc] eval 1.974697e-3 s +[acc] eval 1.710545e-3 s +[acc] eval 1.659487e-3 s +[acc] eval 1.710799e-3 s +[acc] eval 6.655322e-3 s +[acc] eval 1.523301e-3 s +[acc] eval 1.534797e-3 s +[acc] eval 1.460332e-3 s +[acc] eval 1.331822e-3 s +[acc] eval 1.497111e-3 s +[acc] eval 1.668857e-3 s +[acc] eval 1.446251e-3 s +[acc] eval 3.9848971e-2 s +[acc] compile 0.495970072 s +[acc] eval 2.0548587e-2 s +[acc] eval 0.525652186 s +[acc] compile 5.5169431e-2 s +[acc] eval 1.5982105e-2 s +[acc] eval 3.978912e-3 s +[acc] eval 2.979345e-3 s +[acc] eval 2.931506e-3 s +[acc] eval 2.997446e-3 s +[acc] eval 4.062094e-3 s +[acc] eval 7.952322e-3 s +[acc] eval 2.660327e-3 s +[acc] eval 3.692113e-3 s +[acc] eval 2.548036e-3 s +[acc] eval 1.819468e-3 s +[acc] eval 2.038476e-3 s +[acc] eval 2.041108e-3 s +[acc] eval 1.747853e-3 s +[acc] eval 1.575642e-3 s +[acc] eval 0.118061329 s +[acc] compile 0.1288933 s +[acc] eval 2.3133208e-2 s +[acc] eval 0.15976406 s +[acc] compile 0.269308461 s +[acc] eval 8.392156e-3 s +[acc] eval 0.281500826 s +[acc] compile 0.238926981 s +[acc] eval 1.0030704e-2 s +[acc] eval 0.252462134 s +[acc] compile 0.424517311 s +[acc] eval 2.0969607e-2 s +[acc] eval 0.455890366 s +[acc] compile 0.372869572 s +[acc] eval 3.7284043e-2 s +[acc] eval 0.417844033 s +[acc] compile 1.3663924e-2 s +[acc] eval 9.308806e-3 s +[acc] eval 2.919188e-2 s +[acc] compile 6.00287e-4 s +[acc] eval 1.2510414e-2 s +[acc] compile 3.759407e-3 s +[acc] eval 4.525736e-3 s +[acc] eval 1.0064292e-2 s +[acc] compile 0.170194829 s +[acc] eval 2.5348362e-2 s +[acc] eval 0.198882779 s +[acc] compile 0.186198231 s +[acc] eval 8.434633e-3 s +[acc] eval 0.197511124 s +[acc] compile 8.822132e-3 s +[acc] eval 2.2011844e-2 s +[acc] eval 3.3234144e-2 s +[acc] compile 0.410695606 s +[acc] eval 3.1078534e-2 s +[acc] eval 0.44753586 s +[acc] compile 6.836787e-3 s +[acc] eval 2.4879423e-2 s +[acc] eval 3.6598191e-2 s +[acc] compile 1.937826e-2 s +[acc] eval 2.023374e-2 s +[acc] eval 9.136739e-3 s +[acc] eval 6.744396e-3 s +[acc] eval 7.659053e-3 s +[acc] eval 2.3861559e-2 s +[acc] eval 8.620599e-3 s +[acc] eval 6.940179e-3 s +[acc] eval 4.621709e-3 s +[acc] eval 5.28713e-3 s +[acc] eval 5.760683e-3 s +[acc] eval 5.440067e-3 s +[acc] eval 4.30636e-3 s +[acc] eval 3.532782e-3 s +[acc] eval 4.416474e-3 s +[acc] eval 3.715591e-3 s +[acc] eval 2.870496e-3 s +[acc] eval 2.805003e-3 s +[acc] eval 2.994531e-3 s +[acc] eval 3.237018e-3 s +[acc] eval 2.987355e-3 s +[acc] eval 3.03529e-3 s +[acc] eval 2.841128e-3 s +[acc] eval 3.092934e-3 s +[acc] eval 3.212329e-3 s +[acc] eval 2.884488e-3 s +[acc] eval 3.767543e-3 s +[acc] eval 2.799666e-3 s +[acc] eval 2.729654e-3 s +[acc] eval 3.205095e-3 s +[acc] eval 3.562138e-3 s +[acc] eval 0.19381102 s +[acc] compile 5.87492e-2 s +[acc] eval 2.2641669e-2 s +[acc] eval 8.9092097e-2 s +[acc] compile 7.3164755e-2 s +[acc] eval 9.288666e-3 s +[acc] eval 3.363446e-3 s +[acc] eval 3.286893e-3 s +[acc] eval 3.562191e-3 s +[acc] eval 8.758521e-3 s +[acc] eval 3.68689e-3 s +[acc] eval 3.880617e-3 s +[acc] eval 3.278134e-3 s +[acc] eval 2.999918e-3 s +[acc] eval 3.022538e-3 s +[acc] eval 3.331016e-3 s +[acc] eval 3.161121e-3 s +[acc] eval 0.129022657 s +[acc] compile 0.128710742 s +[acc] eval 2.6749675e-2 s +[acc] eval 0.164310329 s +[acc] compile 0.178768107 s +[acc] eval 1.0786952e-2 s +[acc] eval 4.522947e-3 s +[acc] eval 4.292864e-3 s +[acc] eval 4.679958e-3 s +[acc] eval 5.088217e-3 s +[acc] eval 1.0504333e-2 s +[acc] eval 2.451391e-3 s +[acc] eval 2.216397e-3 s +[acc] eval 2.324702e-3 s +[acc] eval 2.289724e-3 s +[acc] eval 2.555879e-3 s +[acc] eval 3.323862e-3 s +[acc] eval 0.238782035 s +[acc] compile 0.140104173 s +[acc] eval 2.0481469e-2 s +[acc] eval 3.167721e-3 s +[acc] eval 7.149373e-3 s +[acc] eval 4.077319e-3 s +[acc] eval 3.345975e-3 s +[acc] eval 3.202745e-3 s +[acc] eval 3.618863e-3 s +[acc] eval 3.777548e-3 s +[acc] eval 4.012906e-3 s +[acc] eval 2.747762e-3 s +[acc] eval 2.715869e-3 s +[acc] eval 2.770155e-3 s +[acc] eval 0.205507027 s +[acc] compile 0.255896352 s +[acc] eval 1.8500024e-2 s +[acc] eval 1.1425452e-2 s +[acc] eval 5.22933e-3 s +[acc] eval 5.691461e-3 s +[acc] eval 6.201393e-3 s +[acc] eval 4.574725e-3 s +[acc] eval 2.892761e-3 s +[acc] eval 2.561977e-3 s +[acc] eval 3.712453e-3 s +[acc] eval 3.105372e-3 s +[acc] eval 2.440037e-3 s +[acc] eval 2.477062e-3 s +[acc] eval 0.328745951 s +[acc] compile 5.193447e-3 s +[acc] eval 6.421632e-3 s +[acc] eval 2.11338e-3 s +[acc] eval 2.250241e-3 s +[acc] eval 1.750347e-3 s +[acc] eval 2.608228e-3 s +[acc] eval 2.714278e-3 s +[acc] eval 3.273447e-3 s +[acc] eval 9.576299e-3 s +[acc] eval 1.970731e-3 s +[acc] eval 1.992394e-3 s +[acc] eval 1.650093e-3 s +[acc] eval 1.575911e-3 s +[acc] eval 1.618628e-3 s +[acc] eval 1.472019e-3 s +[acc] eval 1.470547e-3 s +[acc] eval 1.57527e-3 s +[acc] eval 1.395379e-3 s +[acc] eval 1.477108e-3 s +[acc] eval 1.346155e-3 s +[acc] eval 1.275138e-3 s +[acc] eval 1.239774e-3 s +[acc] eval 1.252997e-3 s +[acc] eval 1.208235e-3 s +[acc] eval 1.207946e-3 s +[acc] eval 1.202997e-3 s +[acc] eval 1.212358e-3 s +[acc] eval 1.199934e-3 s +[acc] eval 1.174938e-3 s +[acc] eval 1.229563e-3 s +[acc] eval 1.254674e-3 s +[acc] eval 1.168295e-3 s +[acc] eval 1.186419e-3 s +[acc] eval 7.5990669e-2 s +[acc] compile 6.906002e-3 s +[acc] eval 1.102648e-2 s +[acc] eval 1.233212e-3 s +[acc] eval 9.55223e-4 s +[acc] eval 1.196083e-3 s +[acc] eval 2.3243423e-2 s +[acc] compile 5.459491e-3 s +[acc] eval 1.4514789e-2 s +[acc] eval 2.89192e-3 s +[acc] eval 7.053145e-3 s +[acc] eval 1.828468e-3 s +[acc] eval 1.919817e-3 s +[acc] eval 1.705997e-3 s +[acc] eval 1.727838e-3 s +[acc] eval 1.949187e-3 s +[acc] eval 1.675867e-3 s +[acc] eval 1.880915e-3 s +[acc] eval 1.851361e-3 s +[acc] eval 1.610307e-3 s +[acc] eval 1.507414e-3 s +[acc] eval 1.803687e-3 s +[acc] eval 1.666425e-3 s +[acc] eval 1.362111e-3 s +[acc] eval 1.433046e-3 s +[acc] eval 1.561322e-3 s +[acc] eval 1.353496e-3 s +[acc] eval 1.191388e-3 s +[acc] eval 1.200818e-3 s +[acc] eval 1.223489e-3 s +[acc] eval 1.173469e-3 s +[acc] eval 6.5261427e-2 s +[acc] compile 5.556079e-3 s +[acc] eval 5.784868e-3 s +[acc] eval 1.000765e-3 s +[acc] eval 7.72187e-4 s +[acc] eval 9.62735e-4 s +[acc] eval 1.5535997e-2 s +[acc] compile 3.881167e-3 s +[acc] eval 5.662812e-3 s +[acc] eval 1.915671e-3 s +[acc] eval 8.913162e-3 s +[acc] eval 2.136119e-3 s +[acc] eval 2.079766e-3 s +[acc] eval 2.256196e-3 s +[acc] eval 1.915417e-3 s +[acc] eval 1.77908e-3 s +[acc] eval 1.827205e-3 s +[acc] eval 2.118487e-3 s +[acc] eval 2.197551e-3 s +[acc] eval 1.957428e-3 s +[acc] eval 2.04471e-3 s +[acc] eval 1.897791e-3 s +[acc] eval 1.790256e-3 s +[acc] eval 1.703758e-3 s +[acc] eval 1.803335e-3 s +[acc] eval 2.052378e-3 s +[acc] eval 2.125064e-3 s +[acc] eval 1.870602e-3 s +[acc] eval 5.8178496e-2 s +[acc] compile 0.319773748 s +[acc] eval 3.3664639e-2 s +[acc] eval 4.958646e-3 s +[acc] eval 5.256876e-3 s +[acc] eval 5.393031e-3 s +[acc] eval 7.042116e-3 s +[acc] eval 6.755186e-3 s +[acc] eval 4.377343e-3 s +[acc] eval 4.121811e-3 s +[acc] eval 5.822724e-3 s +[acc] eval 3.765088e-3 s +[acc] eval 1.4838078e-2 s +[acc] eval 4.22753e-3 s +[acc] eval 4.579823e-3 s +[acc] eval 4.730154e-3 s +[acc] eval 5.491698e-3 s +[acc] eval 8.264715e-3 s +[acc] eval 1.1335132e-2 s +[acc] eval 5.20723e-3 s +[acc] eval 5.489189e-3 s +[acc] eval 6.080112e-3 s +[acc] eval 4.925299e-3 s +[acc] eval 7.455159e-3 s +[acc] eval 1.7793326e-2 s +[acc] eval 8.461532e-3 s +[acc] eval 9.528115e-3 s +[acc] eval 5.573389e-3 s +[acc] eval 6.306696e-3 s +[acc] eval 9.377561e-3 s +[acc] eval 3.875938e-3 s +[acc] eval 3.783367e-3 s +[acc] eval 0.557279372 s +[acc] compile 0.354692892 s +[acc] eval 3.3425455e-2 s +[acc] eval 4.807263e-3 s +[acc] eval 4.374227e-3 s +[acc] eval 4.07664e-3 s +[acc] eval 3.917734e-3 s +[acc] eval 3.929672e-3 s +[acc] eval 4.353671e-3 s +[acc] eval 3.548757e-3 s +[acc] eval 4.903852e-3 s +[acc] eval 1.3452868e-2 s +[acc] eval 3.396919e-3 s +[acc] eval 3.329714e-3 s +[acc] eval 3.309038e-3 s +[acc] eval 3.294653e-3 s +[acc] eval 3.935209e-3 s +[acc] eval 8.412181e-3 s +[acc] eval 4.502146e-3 s +[acc] eval 5.108116e-3 s +[acc] eval 3.853417e-3 s +[acc] eval 3.872156e-3 s +[acc] eval 4.553534e-3 s +[acc] eval 8.14206e-3 s +[acc] eval 4.746637e-3 s +[acc] eval 5.606931e-3 s +[acc] eval 4.823324e-3 s +[acc] eval 3.608459e-3 s +[acc] eval 4.155593e-3 s +[acc] eval 1.1544933e-2 s +[acc] eval 3.74606e-3 s +[acc] eval 0.537327324 s +[acc] compile 4.821136e-3 s +[acc] eval 1.6250837e-2 s +[acc] eval 7.978909e-3 s +[acc] eval 2.499181e-3 s +[acc] eval 2.365018e-3 s +[acc] eval 2.331583e-3 s +[acc] eval 2.705237e-3 s +[acc] eval 3.101157e-3 s +[acc] eval 2.85052e-3 s +[acc] eval 2.68129e-3 s +[acc] eval 2.63049e-3 s +[acc] eval 2.355248e-3 s +[acc] eval 2.292248e-3 s +[acc] eval 2.229547e-3 s +[acc] eval 2.418518e-3 s +[acc] eval 6.3948025e-2 s +[acc] compile 0.214692955 s +[acc] eval 2.6617785e-2 s +[acc] eval 0.25234825 s +[acc] compile 0.124199434 s +[acc] eval 1.7627814e-2 s +[acc] eval 0.144709991 s +[acc] compile 0.394854051 s +[acc] eval 3.2873807e-2 s +[acc] eval 0.432887707 s +[acc] compile 0.239033449 s +[acc] eval 1.0580099e-2 s +[acc] eval 0.255367052 s +[acc] compile 0.130628024 s +[acc] eval 1.6034905e-2 s +[acc] eval 5.052113e-3 s +[acc] eval 4.506974e-3 s +[acc] eval 4.005996e-3 s +[acc] eval 4.357649e-3 s +[acc] eval 3.13768e-3 s +[acc] eval 2.549569e-3 s +[acc] eval 2.52572e-3 s +[acc] eval 2.682558e-3 s +[acc] eval 3.095227e-3 s +[acc] eval 2.982207e-3 s +[acc] eval 3.01665e-3 s +[acc] eval 2.638368e-3 s +[acc] eval 2.436605e-3 s +[acc] eval 2.899987e-3 s +[acc] eval 3.355506e-3 s +[acc] eval 3.4142e-3 s +[acc] eval 4.168179e-3 s +[acc] eval 4.605556e-3 s +[acc] eval 3.357753e-3 s +[acc] eval 3.229521e-3 s +[acc] eval 3.634798e-3 s +[acc] eval 4.493857e-3 s +[acc] eval 3.917935e-3 s +[acc] eval 3.495208e-3 s +[acc] eval 3.623196e-3 s +[acc] eval 4.104053e-3 s +[acc] eval 3.444018e-3 s +[acc] eval 3.530045e-3 s +[acc] eval 3.650001e-3 s +[acc] eval 3.658337e-3 s +[acc] eval 0.259509652 s +[acc] compile 0.242852404 s +[acc] eval 1.3072234e-2 s +[acc] eval 0.262728309 s +[acc] compile 1.202684e-2 s +[acc] eval 9.765954e-3 s +[acc] eval 5.501022e-3 s +[acc] eval 1.4356271e-2 s +[acc] eval 3.15853e-3 s +[acc] eval 3.667475e-3 s +[acc] eval 2.723123e-3 s +[acc] eval 2.404506e-3 s +[acc] eval 2.131945e-3 s +[acc] eval 2.310868e-3 s +[acc] eval 3.191066e-3 s +[acc] eval 3.556556e-3 s +[acc] eval 2.230405e-3 s +[acc] eval 7.0124983e-2 s +[acc] compile 0.133842549 s +[acc] eval 8.466037e-3 s +[acc] eval 3.989385e-3 s +[acc] eval 3.77115e-3 s +[acc] eval 3.606664e-3 s +[acc] eval 4.12685e-3 s +[acc] eval 1.0358256e-2 s +[acc] eval 5.881808e-3 s +[acc] eval 5.101508e-3 s +[acc] eval 3.125153e-3 s +[acc] eval 2.577384e-3 s +[acc] eval 3.80404e-3 s +[acc] eval 4.428957e-3 s +[acc] eval 3.00529e-3 s +[acc] eval 2.40069e-3 s +[acc] eval 2.726239e-3 s +[acc] eval 3.126637e-3 s +[acc] eval 2.616878e-3 s +[acc] eval 2.725107e-3 s +[acc] eval 2.80604e-3 s +[acc] eval 2.62611e-3 s +[acc] eval 3.029844e-3 s +[acc] eval 3.225592e-3 s +[acc] eval 2.666538e-3 s +[acc] eval 2.580822e-3 s +[acc] eval 2.44392e-3 s +[acc] eval 2.460417e-3 s +[acc] eval 2.645738e-3 s +[acc] eval 2.50597e-3 s +[acc] eval 2.404007e-3 s +[acc] eval 2.613035e-3 s +[acc] eval 2.87209e-3 s +[acc] eval 2.816421e-3 s +[acc] eval 0.25308706 s +[acc] compile 4.925303e-3 s +[acc] eval 8.987895e-3 s +[acc] eval 7.755755e-3 s +[acc] eval 1.180469e-2 s +[acc] eval 3.012415e-3 s +[acc] eval 1.1043149e-2 s +[acc] eval 4.673691e-3 s +[acc] eval 8.464079e-3 s +[acc] eval 7.330674e-3 s +[acc] eval 1.1958052e-2 s +[acc] eval 4.849068e-3 s +[acc] eval 2.64591e-3 s +[acc] eval 2.282898e-3 s +[acc] eval 3.589433e-3 s +[acc] eval 3.311e-3 s +[acc] eval 3.881619e-3 s +[acc] eval 3.254768e-3 s +[acc] eval 2.725638e-3 s +[acc] eval 3.489518e-3 s +[acc] eval 0.114470881 s +[acc] compile 0.120089019 s +[acc] eval 1.5834171e-2 s +[acc] eval 0.139491055 s +[acc] compile 0.129711268 s +[acc] eval 2.4123109e-2 s +[acc] eval 2.002569e-3 s +[acc] eval 2.811442e-3 s +[acc] eval 1.818172e-3 s +[acc] eval 1.870361e-3 s +[acc] eval 1.82246e-3 s +[acc] eval 1.939269e-3 s +[acc] eval 1.8073e-3 s +[acc] eval 1.678674e-3 s +[acc] eval 1.41405e-3 s +[acc] eval 1.669588e-3 s +[acc] eval 1.438076e-3 s +[acc] eval 0.176816632 s +[acc] compile 0.195065567 s +[acc] eval 8.156542e-3 s +[acc] eval 2.90928e-3 s +[acc] eval 2.846944e-3 s +[acc] eval 2.821804e-3 s +[acc] eval 2.994338e-3 s +[acc] eval 2.990059e-3 s +[acc] eval 2.844165e-3 s +[acc] eval 9.15853e-3 s +[acc] eval 2.625087e-3 s +[acc] eval 2.362627e-3 s +[acc] eval 2.261881e-3 s +[acc] eval 2.145607e-3 s +[acc] eval 0.243077511 s +[acc] compile 0.180673099 s +[acc] eval 2.9179634e-2 s +[acc] eval 0.221503856 s +[acc] compile 6.5841943e-2 s +[acc] eval 6.680951e-3 s +[acc] eval 7.6209746e-2 s +[acc] compile 0.462236598 s +[acc] eval 2.1171175e-2 s +[acc] eval 0.487355602 s +[acc] compile 0.183773913 s +[acc] eval 1.2685721e-2 s +[acc] eval 0.199528111 s +[acc] compile 3.991075e-3 s +[acc] eval 5.452969e-3 s +[acc] eval 1.2547261e-2 s +[acc] compile 5.4100657e-2 s +[acc] eval 2.4686461e-2 s +[acc] eval 8.3407763e-2 s +[acc] compile 9.05459e-4 s +[acc] eval 1.0742652e-2 s +[acc] compile 0.27467867 s +[acc] eval 1.9411536e-2 s +[acc] eval 0.297233373 s +[acc] compile 1.6683441e-2 s +[acc] eval 3.2641857e-2 s +[acc] eval 5.4103727e-2 s +[acc] compile 4.9787308e-2 s +[acc] eval 8.253044e-3 s +[acc] eval 5.9512001e-2 s +[acc] compile 9.4240568e-2 s +[acc] eval 1.0482229e-2 s +[acc] eval 0.107040491 s +[acc] compile 0.366418942 s +[acc] eval 2.6517121e-2 s +[acc] eval 0.398973472 s +[acc] compile 0.196083756 s +[acc] eval 1.7740707e-2 s +[acc] eval 0.216959031 s +[acc] compile 3.415463e-3 s +[acc] eval 5.067859e-3 s +[acc] eval 9.988785e-3 s +[acc] compile 6.120119e-3 s +[acc] eval 1.1823089e-2 s +[acc] eval 2.242826e-2 s +[acc] compile 9.211948e-3 s +[acc] eval 1.8669013e-2 s +[acc] eval 3.1085775e-2 s +[acc] compile 1.2681199e-2 s +[acc] eval 1.1585499e-2 s +[acc] eval 2.8083627e-2 s +[acc] compile 0.128098481 s +[acc] eval 8.202244e-3 s +[acc] eval 0.143051956 s +[acc] compile 0.174508278 s +[acc] eval 1.6402125e-2 s +[acc] eval 0.194248211 s +[acc] compile 7.292302e-3 s +[acc] eval 1.237351e-2 s +[acc] eval 2.471346e-2 s +[acc] compile 1.0371417e-2 s +[acc] eval 8.377384e-3 s +[acc] eval 2.2525991e-2 s +[acc] compile 0.147853568 s +[acc] eval 1.196983e-2 s +[acc] eval 0.164305865 s +[acc] compile 8.01853e-3 s +[acc] eval 9.589073e-3 s +[acc] eval 2.3579038e-2 s +[acc] compile 1.148448e-2 s +[acc] eval 1.0874112e-2 s +[acc] eval 2.8113853e-2 s +[acc] compile 4.657578e-3 s +[acc] eval 3.232117e-3 s +[acc] eval 5.76632e-4 s +[acc] eval 7.93413e-4 s +[acc] eval 1.0221747e-2 s +[acc] compile 1.3035728e-2 s +[acc] eval 7.126047e-3 s +[acc] eval 1.731775e-3 s +[acc] eval 1.72614e-3 s +[acc] eval 2.5826861e-2 s +[acc] compile 4.287387e-3 s +[acc] eval 7.71195e-3 s +[acc] eval 6.86236e-4 s +[acc] eval 1.2181823e-2 s +[acc] eval 2.5969036e-2 s +[acc] compile 0.394131938 s +[acc] eval 3.1180756e-2 s +[acc] eval 0.429285633 s +[acc] compile 0.148557877 s +[acc] eval 1.5976899e-2 s +[acc] eval 0.171473362 s +[acc] compile 1.3969198e-2 s +[acc] eval 2.1962452e-2 s +[acc] eval 4.1949907e-2 s +[acc] compile 1.0642423e-2 s +[acc] eval 1.1357919e-2 s +[acc] eval 2.6725812e-2 s +[acc] compile 8.404821e-3 s +[acc] eval 1.5565567e-2 s +[acc] eval 2.6670322e-2 s +[acc] compile 6.6415412e-2 s +[acc] eval 1.2249222e-2 s +[acc] eval 8.133012e-2 s +[acc] compile 7.3012661e-2 s +[acc] eval 9.445398e-3 s +[acc] eval 3.104424e-3 s +[acc] eval 8.8846778e-2 s +[acc] compile 4.869133e-3 s +[acc] eval 1.2113056e-2 s +[acc] eval 1.8726874e-2 s +[acc] compile 6.501587e-3 s +[acc] eval 1.4836942e-2 s +[acc] eval 2.5005437e-2 s +[acc] compile 1.0877795e-2 s +[acc] eval 1.193344e-2 s +[acc] eval 2.86883e-2 s +[acc] compile 0.152683823 s +[acc] eval 2.396908e-2 s +[acc] eval 0.181019471 s +[acc] compile 0.323639133 s +[acc] eval 3.1010571e-2 s +[acc] eval 0.369448778 s +[acc] compile 0.398627354 s +[acc] eval 3.4212777e-2 s +[acc] eval 0.438276019 s +[acc] compile 4.930461e-3 s +[acc] eval 5.374206e-3 s +[acc] eval 1.2645659e-2 s +[acc] compile 0.170073185 s +[acc] eval 1.0285055e-2 s +[acc] eval 0.188900004 s +[acc] compile 0.1712188 s +[acc] eval 2.0389996e-2 s +[acc] eval 0.197814906 s +[acc] compile 0.24189386 s +[acc] eval 2.3991678e-2 s +[acc] eval 4.549692e-3 s +[acc] eval 4.37522e-3 s +[acc] eval 4.061445e-3 s +[acc] eval 6.130039e-3 s +[acc] eval 6.743412e-3 s +[acc] eval 5.82761e-3 s +[acc] eval 5.373888e-3 s +[acc] eval 4.784295e-3 s +[acc] eval 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+[acc] eval 7.35569e-4 s +[acc] eval 8.99424e-4 s +[acc] eval 8.98708e-4 s +[acc] eval 8.0484e-4 s +[acc] eval 9.31219e-4 s +[acc] eval 1.357791e-3 s +[acc] eval 1.426672e-3 s +[acc] eval 1.065872e-3 s +[acc] eval 8.59536e-4 s +[acc] eval 8.90433e-4 s +[acc] eval 8.30164e-4 s +[acc] eval 9.09447e-4 s +[acc] eval 8.3255e-4 s +[acc] eval 8.54043e-4 s +[acc] eval 8.00308e-4 s +[acc] eval 7.59519e-4 s +[acc] eval 7.3715e-4 s +[acc] eval 7.78548e-4 s +[acc] eval 8.87286e-4 s +[acc] eval 9.39532e-4 s +[acc] eval 9.40399e-4 s +[acc] eval 8.8936752e-2 s +[acc] compile 0.198005229 s +[acc] eval 1.5012285e-2 s +[acc] eval 3.403858e-3 s +[acc] eval 8.732926e-3 s +[acc] eval 3.261217e-3 s +[acc] eval 3.295884e-3 s +[acc] eval 3.646446e-3 s +[acc] eval 3.176265e-3 s +[acc] eval 2.779476e-3 s +[acc] eval 2.890443e-3 s +[acc] eval 2.835389e-3 s +[acc] eval 2.429854e-3 s +[acc] eval 2.510361e-3 s +[acc] eval 2.862086e-3 s +[acc] eval 3.036304e-3 s +[acc] eval 2.776852e-3 s +[acc] eval 2.672255e-3 s +[acc] eval 2.48687e-3 s +[acc] eval 2.492047e-3 s +[acc] eval 2.348304e-3 s +[acc] eval 2.595608e-3 s +[acc] eval 2.572541e-3 s +[acc] eval 2.189235e-3 s +[acc] eval 2.002115e-3 s +[acc] eval 2.239322e-3 s +[acc] eval 2.033564e-3 s +[acc] eval 1.995079e-3 s +[acc] eval 2.212314e-3 s +[acc] eval 2.697059e-3 s +[acc] eval 2.286745e-3 s +[acc] eval 2.338117e-3 s +[acc] eval 0.301149882 s +[acc] compile 5.7451329e-2 s +[acc] eval 2.2463128e-2 s +[acc] eval 6.930823e-3 s +[acc] eval 1.0724278e-2 s +[acc] eval 3.08631e-3 s +[acc] eval 2.994999e-3 s +[acc] eval 3.190045e-3 s +[acc] eval 3.234682e-3 s +[acc] eval 3.233943e-3 s +[acc] eval 3.079633e-3 s +[acc] eval 2.959369e-3 s +[acc] eval 3.005438e-3 s +[acc] eval 2.751726e-3 s +[acc] eval 0.130400873 s +[acc] compile 0.210800003 s +[acc] eval 2.3229905e-2 s +[acc] eval 0.244253886 s +[acc] compile 0.164426026 s +[acc] eval 8.227458e-3 s +[acc] eval 0.177663038 s +[acc] compile 0.20502327 s +[acc] eval 2.093562e-2 s +[acc] eval 0.23110744 s +[acc] compile 1.0351588e-2 s +[acc] eval 1.6738133e-2 s +[acc] eval 2.051507e-3 s +[acc] eval 1.916751e-3 s +[acc] eval 1.833628e-3 s +[acc] eval 2.210018e-3 s +[acc] eval 2.212039e-3 s +[acc] eval 1.588472e-3 s +[acc] eval 1.617863e-3 s +[acc] eval 1.455891e-3 s +[acc] eval 1.356112e-3 s +[acc] eval 1.321279e-3 s +[acc] eval 7.613798e-3 s +[acc] eval 1.744459e-3 s +[acc] eval 1.358861e-3 s +[acc] eval 1.35375e-3 s +[acc] eval 1.315182e-3 s +[acc] eval 1.216102e-3 s +[acc] eval 1.116127e-3 s +[acc] eval 1.172672e-3 s +[acc] eval 1.078334e-3 s +[acc] eval 1.741115e-3 s +[acc] eval 1.786807e-3 s +[acc] eval 1.575435e-3 s +[acc] eval 1.389673e-3 s +[acc] eval 1.188437e-3 s +[acc] eval 1.133308e-3 s +[acc] eval 1.145686e-3 s +[acc] eval 1.027305e-3 s +[acc] eval 1.013811e-3 s +[acc] eval 9.25343e-4 s +[acc] eval 8.0095221e-2 s +[acc] compile 6.705852e-3 s +[acc] eval 1.3179178e-2 s +[acc] eval 2.4156081e-2 s +[acc] compile 0.132874461 s +[acc] eval 1.8905551e-2 s +[acc] eval 0.15562281 s +[acc] compile 0.424067303 s +[acc] eval 2.8455434e-2 s +[acc] eval 0.465572318 s +[acc] compile 3.449017e-3 s +[acc] eval 3.904063e-3 s +[acc] eval 9.73175e-4 s +[acc] eval 6.90576e-4 s +[acc] eval 1.0205063e-2 s +[acc] compile 4.742656e-3 s +[acc] eval 6.053635e-3 s +[acc] eval 1.977173e-3 s +[acc] eval 2.029027e-3 s +[acc] eval 1.880039e-3 s +[acc] eval 8.487979e-3 s +[acc] eval 1.739366e-3 s +[acc] eval 1.68609e-3 s +[acc] eval 1.829595e-3 s +[acc] eval 1.910896e-3 s +[acc] eval 1.604906e-3 s +[acc] eval 1.558693e-3 s +[acc] eval 1.705275e-3 s +[acc] eval 1.829348e-3 s +[acc] eval 1.457465e-3 s +[acc] eval 1.401204e-3 s +[acc] eval 1.303294e-3 s +[acc] eval 1.668359e-3 s +[acc] eval 1.53437e-3 s +[acc] eval 1.635691e-3 s +[acc] eval 1.683508e-3 s +[acc] eval 1.640519e-3 s +[acc] eval 1.587527e-3 s +[acc] eval 1.712943e-3 s +[acc] eval 1.614661e-3 s +[acc] eval 1.644171e-3 s +[acc] eval 1.751216e-3 s +[acc] eval 1.628053e-3 s +[acc] eval 1.558281e-3 s +[acc] eval 1.329318e-3 s +[acc] eval 1.403856e-3 s +[acc] eval 6.9196351e-2 s +[acc] compile 0.112215343 s +[acc] eval 1.9864314e-2 s +[acc] eval 1.854792e-3 s +[acc] eval 1.795318e-3 s +[acc] eval 1.906533e-3 s +[acc] eval 7.246169e-3 s +[acc] eval 1.952596e-3 s +[acc] eval 2.044149e-3 s +[acc] eval 1.983299e-3 s +[acc] eval 1.805323e-3 s +[acc] eval 1.682732e-3 s +[acc] eval 1.511957e-3 s +[acc] eval 1.878404e-3 s +[acc] eval 1.706753e-3 s +[acc] eval 2.046435e-3 s +[acc] eval 1.887661e-3 s +[acc] eval 1.705503e-3 s +[acc] eval 1.605252e-3 s +[acc] eval 1.903416e-3 s +[acc] eval 1.898488e-3 s +[acc] eval 1.980459e-3 s +[acc] eval 1.861732e-3 s +[acc] eval 1.453572e-3 s +[acc] eval 1.767046e-3 s +[acc] eval 2.080314e-3 s +[acc] eval 2.383223e-3 s +[acc] eval 1.631359e-3 s +[acc] eval 1.845875e-3 s +[acc] eval 1.969318e-3 s +[acc] eval 1.976018e-3 s +[acc] eval 1.962733e-3 s +[acc] eval 0.196868147 s +[acc] compile 5.415847e-3 s +[acc] eval 8.113239e-3 s +[acc] eval 2.637476e-3 s +[acc] eval 2.411214e-3 s +[acc] eval 2.248082e-3 s +[acc] eval 2.260775e-3 s +[acc] eval 2.331913e-3 s +[acc] eval 6.730373e-3 s +[acc] eval 2.115197e-3 s +[acc] eval 2.148622e-3 s +[acc] eval 2.043511e-3 s +[acc] eval 2.076987e-3 s +[acc] eval 1.960744e-3 s +[acc] eval 2.007523e-3 s +[acc] eval 1.999169e-3 s +[acc] eval 2.281507e-3 s +[acc] eval 2.395787e-3 s +[acc] eval 2.217733e-3 s +[acc] eval 2.099083e-3 s +[acc] eval 1.957618e-3 s +[acc] eval 1.93568e-3 s +[acc] eval 2.215243e-3 s +[acc] eval 1.911741e-3 s +[acc] eval 1.629462e-3 s +[acc] eval 1.889943e-3 s +[acc] eval 1.777568e-3 s +[acc] eval 2.716081e-3 s +[acc] eval 1.805656e-3 s +[acc] eval 1.817197e-3 s +[acc] eval 1.744336e-3 s +[acc] eval 1.883104e-3 s +[acc] eval 8.2964448e-2 s +[acc] compile 0.131221511 s +[acc] eval 2.176107e-2 s +[acc] eval 1.0910267e-2 s +[acc] eval 8.729553e-3 s +[acc] eval 1.5125196e-2 s +[acc] eval 6.355112e-3 s +[acc] eval 2.320163e-3 s +[acc] eval 2.061184e-3 s +[acc] eval 2.227532e-3 s +[acc] eval 1.962826e-3 s +[acc] eval 2.324337e-3 s +[acc] eval 2.657872e-3 s +[acc] eval 2.184296e-3 s +[acc] eval 1.7183e-3 s +[acc] eval 1.768225e-3 s +[acc] eval 1.810881e-3 s +[acc] eval 1.756661e-3 s +[acc] eval 1.720922e-3 s +[acc] eval 2.167417e-3 s +[acc] eval 2.405226e-3 s +[acc] eval 2.161916e-3 s +[acc] eval 1.94772e-3 s +[acc] eval 2.179946e-3 s +[acc] eval 1.953534e-3 s +[acc] eval 2.065363e-3 s +[acc] eval 2.030356e-3 s +[acc] eval 2.10706e-3 s +[acc] eval 2.074062e-3 s +[acc] eval 2.413879e-3 s +[acc] eval 2.315259e-3 s +[acc] eval 1.94965e-3 s +[acc] eval 0.252331696 s +[acc] compile 1.0742683e-2 s +[acc] eval 1.1255447e-2 s +[acc] eval 2.544567e-3 s +[acc] eval 2.272484e-3 s +[acc] eval 2.082491e-3 s +[acc] eval 5.877497e-3 s +[acc] eval 2.573441e-3 s +[acc] eval 2.394015e-3 s +[acc] eval 2.603043e-3 s +[acc] eval 2.261421e-3 s +[acc] eval 2.175439e-3 s +[acc] eval 2.366665e-3 s +[acc] eval 2.671005e-3 s +[acc] eval 5.5507268e-2 s +[acc] compile 0.375936028 s +[acc] eval 3.2084812e-2 s +[acc] eval 0.419845289 s +[acc] compile 0.159847798 s +[acc] eval 1.2628972e-2 s +[acc] eval 8.522757e-3 s +[acc] eval 1.1938006e-2 s +[acc] eval 3.825425e-3 s +[acc] eval 2.997149e-3 s +[acc] eval 2.844362e-3 s +[acc] eval 2.972794e-3 s +[acc] eval 2.82172e-3 s +[acc] eval 3.288466e-3 s +[acc] eval 3.235646e-3 s +[acc] eval 2.899044e-3 s +[acc] eval 3.284032e-3 s +[acc] eval 3.285794e-3 s +[acc] eval 3.126752e-3 s +[acc] eval 3.589458e-3 s +[acc] eval 3.089126e-3 s +[acc] eval 3.457661e-3 s +[acc] eval 2.939715e-3 s +[acc] eval 3.240075e-3 s +[acc] eval 3.473743e-3 s +[acc] eval 3.280072e-3 s +[acc] eval 3.401497e-3 s +[acc] eval 3.555497e-3 s +[acc] eval 4.090049e-3 s +[acc] eval 3.338719e-3 s +[acc] eval 3.845175e-3 s +[acc] eval 3.517861e-3 s +[acc] eval 4.904426e-3 s +[acc] eval 4.004214e-3 s +[acc] eval 3.794832e-3 s +[acc] eval 3.306037e-3 s +[acc] eval 0.295376053 s +[acc] compile 0.183549514 s +[acc] eval 1.8383379e-2 s +[acc] eval 0.20513068 s +[acc] compile 0.369830649 s +[acc] eval 3.2418511e-2 s +[acc] eval 0.413450404 s +[acc] compile 4.943897e-3 s +[acc] eval 6.641662e-3 s +[acc] eval 1.165669e-3 s +[acc] eval 1.206034e-3 s +[acc] eval 1.5617361e-2 s +[acc] compile 0.491263489 s +[acc] eval 2.525351e-2 s +[acc] eval 5.206421e-3 s +[acc] eval 6.256816e-3 s +[acc] eval 5.664032e-3 s +[acc] eval 5.232051e-3 s +[acc] eval 5.983047e-3 s +[acc] eval 1.7316394e-2 s +[acc] eval 5.488444e-3 s +[acc] eval 5.429381e-3 s +[acc] eval 8.887516e-3 s +[acc] eval 1.4439239e-2 s +[acc] eval 4.475171e-3 s +[acc] eval 4.618896e-3 s +[acc] eval 6.42393e-3 s +[acc] eval 7.37529e-3 s +[acc] eval 1.1505632e-2 s +[acc] eval 4.543588e-3 s +[acc] eval 7.098758e-3 s +[acc] eval 7.694102e-3 s +[acc] eval 1.5695029e-2 s +[acc] eval 5.405566e-3 s +[acc] eval 5.441672e-3 s +[acc] eval 6.715136e-3 s +[acc] eval 1.3186334e-2 s +[acc] eval 4.779772e-3 s +[acc] eval 4.744315e-3 s +[acc] eval 4.865238e-3 s +[acc] eval 4.65625e-3 s +[acc] eval 9.694638e-3 s +[acc] eval 4.424332e-3 s +[acc] eval 0.738752677 s +[acc] compile 0.135559284 s +[acc] eval 2.6021555e-2 s +[acc] eval 0.16528528 s +[acc] compile 7.8017296e-2 s +[acc] eval 3.1376346e-2 s +[acc] eval 0.113445383 s +[acc] compile 0.349295938 s +[acc] eval 2.8592911e-2 s +[acc] eval 0.382851326 s +[acc] compile 4.300089e-3 s +[acc] eval 4.984755e-3 s +[acc] eval 1.818738e-3 s +[acc] eval 1.564342e-3 s +[acc] eval 1.4466889e-2 s +[acc] compile 0.267801971 s +[acc] eval 1.7161788e-2 s +[acc] eval 0.292919778 s +[acc] compile 0.127388817 s +[acc] eval 4.9571e-3 s +[acc] eval 2.171732e-3 s +[acc] eval 2.298258e-3 s +[acc] eval 3.546738e-3 s +[acc] eval 3.908865e-3 s +[acc] eval 3.551987e-3 s +[acc] eval 3.490774e-3 s +[acc] eval 1.086207e-2 s +[acc] eval 2.083407e-3 s +[acc] eval 1.902813e-3 s +[acc] eval 0.169363355 s +[acc] compile 0.130505293 s +[acc] eval 3.2244053e-2 s +[acc] eval 0.16926853 s +[acc] compile 0.120178179 s +[acc] eval 1.2054182e-2 s +[acc] eval 4.637011e-3 s +[acc] eval 4.313358e-3 s +[acc] eval 4.130786e-3 s +[acc] eval 1.2552998e-2 s +[acc] eval 3.031778e-3 s +[acc] eval 3.100727e-3 s +[acc] eval 2.976213e-3 s +[acc] eval 3.163738e-3 s +[acc] eval 3.389922e-3 s +[acc] eval 3.415212e-3 s +[acc] eval 2.800522e-3 s +[acc] eval 2.831696e-3 s +[acc] eval 2.964771e-3 s +[acc] eval 2.891252e-3 s +[acc] eval 2.797557e-3 s +[acc] eval 2.748808e-3 s +[acc] eval 2.90977e-3 s +[acc] eval 2.758031e-3 s +[acc] eval 2.720569e-3 s +[acc] eval 2.597127e-3 s +[acc] eval 2.581352e-3 s +[acc] eval 2.568548e-3 s +[acc] eval 2.587209e-3 s +[acc] eval 2.58138e-3 s +[acc] eval 2.628141e-3 s +[acc] eval 2.651744e-3 s +[acc] eval 2.651176e-3 s +[acc] eval 2.893977e-3 s +[acc] eval 2.700491e-3 s +[acc] eval 0.23427909 s +[acc] compile 0.538782597 s +[acc] eval 3.0351703e-2 s +[acc] eval 0.574642885 s +[acc] compile 7.3266972e-2 s +[acc] eval 5.050719e-3 s +[acc] eval 2.909464e-3 s +[acc] eval 3.416325e-3 s +[acc] eval 4.060934e-3 s +[acc] eval 3.492979e-3 s +[acc] eval 3.020989e-3 s +[acc] eval 2.902282e-3 s +[acc] eval 2.617207e-3 s +[acc] eval 2.63639e-3 s +[acc] eval 3.147829e-3 s +[acc] eval 0.110446789 s +[acc] compile 6.2551646e-2 s +[acc] eval 1.7220467e-2 s +[acc] eval 3.51389e-3 s +[acc] eval 3.467041e-3 s +[acc] eval 3.14009e-3 s +[acc] eval 2.709677e-3 s +[acc] eval 2.890019e-3 s +[acc] eval 2.940805e-3 s +[acc] eval 3.042297e-3 s +[acc] eval 2.96419e-3 s +[acc] eval 0.108160092 s +[acc] compile 5.7818288e-2 s +[acc] eval 1.7795183e-2 s +[acc] eval 8.0859255e-2 s +[acc] compile 0.119067313 s +[acc] eval 2.5127515e-2 s +[acc] eval 4.732857e-3 s +[acc] eval 3.751982e-3 s +[acc] eval 1.2460199e-2 s +[acc] eval 3.551268e-3 s +[acc] eval 4.381653e-3 s +[acc] eval 4.633849e-3 s +[acc] eval 4.176872e-3 s +[acc] eval 4.01919e-3 s +[acc] eval 3.902081e-3 s +[acc] eval 3.826387e-3 s +[acc] eval 4.043533e-3 s +[acc] eval 3.526484e-3 s +[acc] eval 3.134128e-3 s +[acc] eval 2.746846e-3 s +[acc] eval 2.810168e-3 s +[acc] eval 3.603285e-3 s +[acc] eval 3.502795e-3 s +[acc] eval 3.364736e-3 s +[acc] eval 2.987754e-3 s +[acc] eval 2.796594e-3 s +[acc] eval 3.841534e-3 s +[acc] eval 2.521541e-3 s +[acc] eval 2.396549e-3 s +[acc] eval 2.40107e-3 s +[acc] eval 2.54999e-3 s +[acc] eval 2.656226e-3 s +[acc] eval 2.814757e-3 s +[acc] eval 2.384957e-3 s +[acc] eval 2.355072e-3 s +[acc] eval 2.479931e-3 s +[acc] eval 3.573534e-3 s +[acc] eval 3.46133e-3 s +[acc] eval 2.744307e-3 s +[acc] eval 3.457214e-3 s +[acc] eval 3.360727e-3 s +[acc] eval 3.373947e-3 s +[acc] eval 2.371575e-3 s +[acc] eval 2.418487e-3 s +[acc] eval 2.893966e-3 s +[acc] eval 4.269099e-3 s +[acc] eval 2.988829e-3 s +[acc] eval 3.07232e-3 s +[acc] eval 2.599952e-3 s +[acc] eval 2.792198e-3 s +[acc] eval 3.540417e-3 s +[acc] eval 2.938426e-3 s +[acc] eval 2.522426e-3 s +[acc] eval 3.700586e-3 s +[acc] eval 3.703413e-3 s +[acc] eval 3.771348e-3 s +[acc] eval 0.32375547 s +[acc] compile 0.112148388 s +[acc] eval 1.6673505e-2 s +[acc] eval 0.132263489 s +[acc] compile 3.382254e-3 s +[acc] eval 7.679e-3 s +[acc] eval 1.212699e-2 s +[acc] compile 3.442875e-3 s +[acc] eval 9.864132e-3 s +[acc] eval 1.8720602e-2 s +[acc] compile 0.290398409 s +[acc] eval 2.873365e-2 s +[acc] eval 0.334176967 s +[acc] compile 7.643957e-3 s +[acc] eval 1.7900286e-2 s +[acc] eval 2.9382175e-2 s +[acc] compile 4.467042e-3 s +[acc] eval 1.0103828e-2 s +[acc] eval 1.6742282e-2 s +[acc] compile 5.006334e-3 s +[acc] eval 8.34137e-3 s +[acc] eval 1.5789546e-2 s +[acc] compile 0.249068915 s +[acc] eval 1.9174498e-2 s +[acc] eval 0.27377965 s +[acc] compile 0.116433728 s +[acc] eval 1.1682172e-2 s +[acc] eval 0.131348191 s +[acc] compile 0.146851449 s +[acc] eval 1.5906256e-2 s +[acc] eval 0.165684196 s +[acc] compile 1.2069583e-2 s +[acc] eval 1.1136956e-2 s +[acc] eval 2.7294524e-2 s +[acc] compile 3.143352e-3 s +[acc] eval 6.085203e-3 s +[acc] eval 1.088703e-2 s +[acc] compile 2.833622e-3 s +[acc] eval 3.593435e-3 s +[acc] eval 6.41032e-4 s +[acc] eval 7.249548e-3 s +[acc] eval 1.5463389e-2 s +[acc] compile 3.202847e-3 s +[acc] eval 5.888572e-3 s +[acc] eval 1.457584e-3 s +[acc] eval 1.236634e-3 s +[acc] eval 1.337564e-3 s +[acc] eval 1.4633438e-2 s +[acc] compile 4.737611e-3 s +[acc] eval 4.898293e-3 s +[acc] eval 1.728273e-3 s +[acc] eval 1.628371e-3 s +[acc] eval 1.656049e-3 s +[acc] eval 2.168107e-3 s +[acc] eval 7.458854e-3 s +[acc] eval 2.269e-3 s +[acc] eval 2.392436e-3 s +[acc] eval 1.974406e-3 s +[acc] eval 1.706384e-3 s +[acc] eval 1.765949e-3 s +[acc] eval 1.723346e-3 s +[acc] eval 1.925259e-3 s +[acc] eval 1.836319e-3 s +[acc] eval 1.564603e-3 s +[acc] eval 1.662354e-3 s +[acc] eval 1.524629e-3 s +[acc] eval 1.375269e-3 s +[acc] eval 1.515227e-3 s +[acc] eval 1.515269e-3 s +[acc] eval 1.236998e-3 s +[acc] eval 1.738987e-3 s +[acc] eval 1.788873e-3 s +[acc] eval 1.939135e-3 s +[acc] eval 1.836088e-3 s +[acc] eval 1.457535e-3 s +[acc] eval 1.370994e-3 s +[acc] eval 1.149387e-3 s +[acc] eval 1.519882e-3 s +[acc] eval 1.604297e-3 s +[acc] eval 6.8165373e-2 s +[acc] compile 4.235041e-3 s +[acc] eval 8.296676e-3 s +[acc] eval 2.189719e-3 s +[acc] eval 2.075374e-3 s +[acc] eval 2.211198e-3 s +[acc] eval 2.720846e-3 s +[acc] eval 7.208715e-3 s +[acc] eval 2.282365e-3 s +[acc] eval 2.269019e-3 s +[acc] eval 2.415367e-3 s +[acc] eval 1.971858e-3 s +[acc] eval 1.93527e-3 s +[acc] eval 1.970841e-3 s +[acc] eval 1.671861e-3 s +[acc] eval 1.768595e-3 s +[acc] eval 2.050847e-3 s +[acc] eval 2.228437e-3 s +[acc] eval 2.307181e-3 s +[acc] eval 2.599707e-3 s +[acc] eval 5.8458813e-2 s +[acc] compile 1.3831547e-2 s +[acc] eval 3.0892235e-2 s +[acc] eval 3.830615e-3 s +[acc] eval 3.66378e-3 s +[acc] eval 3.379001e-3 s +[acc] eval 3.17915e-3 s +[acc] eval 3.240726e-3 s +[acc] eval 3.517971e-3 s +[acc] eval 3.338178e-3 s +[acc] eval 4.475266e-3 s +[acc] eval 1.1140249e-2 s +[acc] eval 3.749812e-3 s +[acc] eval 3.942848e-3 s +[acc] eval 3.857667e-3 s +[acc] eval 3.73725e-3 s +[acc] eval 7.319423e-3 s +[acc] eval 1.1060674e-2 s +[acc] eval 5.378038e-3 s +[acc] eval 4.326422e-3 s +[acc] eval 4.548836e-3 s +[acc] eval 4.722152e-3 s +[acc] eval 5.063516e-3 s +[acc] eval 8.896481e-3 s +[acc] eval 4.413112e-3 s +[acc] eval 3.693688e-3 s +[acc] eval 3.714023e-3 s +[acc] eval 4.174403e-3 s +[acc] eval 4.6708e-3 s +[acc] eval 0.184197448 s +[acc] compile 2.1077416e-2 s +[acc] eval 3.2066327e-2 s +[acc] eval 5.092714e-3 s +[acc] eval 5.217905e-3 s +[acc] eval 3.787663e-3 s +[acc] eval 3.527892e-3 s +[acc] eval 4.117646e-3 s +[acc] eval 4.398384e-3 s +[acc] eval 4.112058e-3 s +[acc] eval 1.1402969e-2 s +[acc] eval 4.431911e-3 s +[acc] eval 4.309261e-3 s +[acc] eval 4.184677e-3 s +[acc] eval 5.724212e-3 s +[acc] eval 6.432269e-3 s +[acc] eval 1.3462778e-2 s +[acc] eval 3.999549e-3 s +[acc] eval 4.411763e-3 s +[acc] eval 4.31718e-3 s +[acc] eval 3.922495e-3 s +[acc] eval 9.021429e-3 s +[acc] eval 4.430025e-3 s +[acc] eval 4.040399e-3 s +[acc] eval 3.84928e-3 s +[acc] eval 3.52886e-3 s +[acc] eval 5.102257e-3 s +[acc] eval 1.7459624e-2 s +[acc] eval 0.207280257 s diff --git a/phas2_n1.err b/phas2_n1.err new file mode 100644 index 0000000..ace3aea --- /dev/null +++ b/phas2_n1.err @@ -0,0 +1,3066 @@ +[acc] compile 1.6316688e-2 s +[acc] eval 2.173167086 s +[acc] compile 3.88697e-4 s +[acc] eval 7.4849639e-2 s +[acc] compile 3.72833e-4 s +[acc] eval 6.989576e-2 s +[acc] compile 3.42227e-4 s +[acc] eval 0.162037637 s +[acc] compile 3.77265e-4 s +[acc] eval 0.16706289 s +[acc] compile 3.17802e-4 s +[acc] eval 0.155809429 s +[acc] compile 3.42672e-4 s +[acc] eval 0.163186853 s +[acc] compile 3.74063e-4 s +[acc] eval 0.136964767 s +[acc] compile 3.88922e-4 s +[acc] eval 7.7952316e-2 s +[acc] compile 0.134325108 s +[acc] eval 3.3027777e-2 s +[acc] eval 0.171365719 s +[acc] compile 7.292338e-3 s +[acc] eval 1.1661132e-2 s +[acc] eval 2.1675279e-2 s +[acc] compile 3.89573e-3 s +[acc] eval 7.307594e-3 s +[acc] eval 7.02528e-4 s +[acc] eval 5.61443e-4 s +[acc] eval 1.3518071e-2 s +[acc] compile 0.298359155 s +[acc] eval 4.5904785e-2 s +[acc] eval 1.3070366e-2 s +[acc] eval 5.2678073e-2 s +[acc] eval 4.649835e-3 s +[acc] eval 4.862204e-3 s +[acc] eval 4.627072e-3 s +[acc] eval 4.493915e-3 s +[acc] eval 4.951981e-3 s +[acc] eval 4.020821e-3 s +[acc] eval 4.106185e-3 s +[acc] eval 4.392695e-3 s +[acc] eval 4.590139e-3 s +[acc] eval 0.45721121 s +[acc] compile 0.190577407 s +[acc] eval 1.6949576e-2 s +[acc] eval 0.214325267 s +[acc] compile 6.171634e-3 s +[acc] eval 4.152035e-3 s +[acc] eval 1.2705228e-2 s +[acc] compile 0.104115835 s +[acc] eval 5.297946e-3 s +[acc] eval 0.112407432 s +[acc] compile 0.127371889 s +[acc] eval 1.1440931e-2 s +[acc] eval 7.659696e-3 s +[acc] eval 2.875244e-3 s +[acc] eval 3.392585e-3 s +[acc] eval 3.634018e-3 s +[acc] eval 2.902735e-3 s +[acc] eval 2.874738e-3 s +[acc] eval 2.747652e-3 s +[acc] eval 2.522462e-3 s +[acc] eval 2.447583e-3 s +[acc] eval 2.512644e-3 s +[acc] eval 2.762453e-3 s +[acc] eval 2.253251e-3 s +[acc] eval 2.050489e-3 s +[acc] eval 2.157209e-3 s +[acc] eval 0.186102943 s +[acc] compile 6.9885047e-2 s +[acc] eval 8.131404e-3 s +[acc] eval 8.2305561e-2 s +[acc] compile 0.16780923 s +[acc] eval 1.7052037e-2 s +[acc] eval 0.190434194 s +[acc] compile 0.209709562 s +[acc] eval 1.7866155e-2 s +[acc] eval 0.234967183 s +[acc] compile 0.495002729 s +[acc] eval 2.7619211e-2 s +[acc] eval 0.529253764 s +[acc] compile 7.014917e-3 s +[acc] eval 9.717314e-3 s +[acc] eval 3.698549e-3 s +[acc] eval 4.210211e-3 s +[acc] eval 4.956352e-3 s +[acc] eval 3.238193e-3 s +[acc] eval 2.454318e-3 s +[acc] eval 2.809768e-3 s +[acc] eval 2.602745e-3 s +[acc] eval 2.986002e-3 s +[acc] eval 1.815191e-3 s +[acc] eval 1.996278e-3 s +[acc] eval 2.632393e-3 s +[acc] eval 4.308197e-3 s +[acc] eval 1.9403667e-2 s +[acc] eval 5.650858e-3 s +[acc] eval 3.640109e-3 s +[acc] eval 2.301831e-3 s +[acc] eval 1.83969e-3 s +[acc] eval 1.924816e-3 s +[acc] eval 3.668142e-3 s +[acc] eval 3.03705e-3 s +[acc] eval 0.102361307 s +[acc] compile 0.179144199 s +[acc] eval 1.4486828e-2 s +[acc] eval 0.19698042 s +[acc] compile 1.0250096e-2 s +[acc] eval 3.889043e-3 s +[acc] eval 1.522545e-3 s +[acc] eval 7.444132e-3 s +[acc] eval 2.5161832e-2 s +[acc] compile 0.151546007 s +[acc] eval 1.3448596e-2 s +[acc] eval 6.575755e-3 s +[acc] eval 4.026161e-3 s +[acc] eval 7.093324e-3 s +[acc] eval 5.107959e-3 s +[acc] eval 3.535504e-3 s +[acc] eval 3.540439e-3 s +[acc] eval 3.151911e-3 s +[acc] eval 4.223446e-3 s +[acc] eval 3.352651e-3 s +[acc] eval 3.280056e-3 s +[acc] eval 2.801109e-3 s +[acc] eval 2.743918e-3 s +[acc] eval 3.657198e-3 s +[acc] eval 3.083291e-3 s +[acc] eval 2.963098e-3 s +[acc] eval 2.848368e-3 s +[acc] eval 2.751854e-3 s +[acc] eval 3.529453e-3 s +[acc] eval 3.312527e-3 s +[acc] eval 3.148244e-3 s +[acc] eval 2.879899e-3 s +[acc] eval 2.760082e-3 s +[acc] eval 2.802194e-3 s +[acc] eval 2.775267e-3 s +[acc] eval 0.256038692 s +[acc] compile 7.8909877e-2 s +[acc] eval 8.161695e-3 s +[acc] eval 9.0679935e-2 s +[acc] compile 5.370308e-3 s +[acc] eval 1.6264467e-2 s +[acc] eval 3.094177e-3 s +[acc] eval 2.8246748e-2 s +[acc] compile 0.208915991 s +[acc] eval 7.363283e-3 s +[acc] eval 0.221140632 s +[acc] compile 0.276722664 s +[acc] eval 1.8500859e-2 s +[acc] eval 0.301768028 s +[acc] compile 8.102863e-3 s +[acc] eval 6.042383e-3 s +[acc] eval 1.8779709e-2 s +[acc] compile 9.3255766e-2 s +[acc] eval 1.0167917e-2 s +[acc] eval 6.77992e-3 s +[acc] eval 4.121323e-3 s +[acc] eval 4.020356e-3 s +[acc] eval 3.785041e-3 s +[acc] eval 3.790481e-3 s +[acc] eval 4.058546e-3 s +[acc] eval 4.349004e-3 s +[acc] eval 3.83604e-3 s +[acc] eval 3.969163e-3 s +[acc] eval 4.186066e-3 s +[acc] eval 4.165347e-3 s +[acc] eval 3.65118e-3 s +[acc] eval 3.589076e-3 s +[acc] eval 4.461173e-3 s +[acc] eval 3.667625e-3 s +[acc] eval 3.682343e-3 s +[acc] eval 3.730896e-3 s +[acc] eval 3.801249e-3 s +[acc] eval 3.820217e-3 s +[acc] eval 3.551512e-3 s +[acc] eval 3.803717e-3 s +[acc] eval 3.303215e-3 s +[acc] eval 3.230306e-3 s +[acc] eval 3.216158e-3 s +[acc] eval 3.079314e-3 s +[acc] eval 3.052332e-3 s +[acc] eval 3.437352e-3 s +[acc] eval 4.375699e-3 s +[acc] eval 3.632278e-3 s +[acc] eval 3.966644e-3 s +[acc] eval 0.228284221 s +[acc] compile 5.8556774e-2 s +[acc] eval 5.478616e-3 s +[acc] eval 6.5701895e-2 s +[acc] compile 0.138583404 s +[acc] eval 8.303148e-3 s +[acc] eval 0.15011972 s +[acc] compile 7.260106e-3 s +[acc] eval 1.0291581e-2 s +[acc] eval 2.0397759e-2 s +[acc] compile 7.884177e-3 s +[acc] eval 7.178248e-3 s +[acc] eval 6.319552e-3 s +[acc] eval 1.261985e-3 s +[acc] eval 1.172712e-3 s +[acc] eval 1.119007e-3 s +[acc] eval 1.06639e-3 s +[acc] eval 1.049983e-3 s +[acc] eval 1.049659e-3 s +[acc] eval 1.011762e-3 s +[acc] eval 9.76344e-4 s +[acc] eval 9.87568e-4 s +[acc] eval 9.59129e-4 s +[acc] eval 3.4043349e-2 s +[acc] compile 0.278438854 s +[acc] eval 7.005147e-3 s +[acc] eval 0.288820405 s +[acc] compile 6.149602e-2 s +[acc] eval 2.9553937e-2 s +[acc] eval 9.4255119e-2 s +[acc] compile 3.582165e-3 s +[acc] eval 2.714984e-3 s +[acc] eval 1.176551e-3 s +[acc] eval 1.116028e-3 s +[acc] eval 9.791174e-3 s +[acc] compile 0.182464164 s +[acc] eval 2.252193e-2 s +[acc] eval 3.97767e-3 s +[acc] eval 6.733139e-3 s +[acc] eval 3.578046e-3 s +[acc] eval 2.947178e-3 s +[acc] eval 2.787765e-3 s +[acc] eval 2.836038e-3 s +[acc] eval 2.421617e-3 s +[acc] eval 4.577871e-3 s +[acc] eval 3.811973e-3 s +[acc] eval 4.211327e-3 s +[acc] eval 3.706437e-3 s +[acc] eval 0.2530675 s +[acc] compile 5.760139e-3 s +[acc] eval 3.418352e-3 s +[acc] eval 1.273012e-3 s +[acc] eval 6.76396e-4 s +[acc] eval 1.2734001e-2 s +[acc] compile 7.6894162e-2 s +[acc] eval 1.981845e-2 s +[acc] eval 0.10426313 s +[acc] compile 0.28397724 s +[acc] eval 2.6563441e-2 s +[acc] eval 0.328446806 s +[acc] compile 0.129058134 s +[acc] eval 6.724694e-3 s +[acc] eval 4.320278e-3 s +[acc] eval 0.146951662 s +[acc] compile 0.203103684 s +[acc] eval 6.962243e-3 s +[acc] eval 0.217151906 s +[acc] compile 9.3423437e-2 s +[acc] eval 8.271334e-3 s +[acc] eval 4.649717e-3 s +[acc] eval 2.520365e-3 s +[acc] eval 2.471824e-3 s +[acc] eval 2.422846e-3 s +[acc] eval 6.380852e-3 s +[acc] eval 2.359989e-3 s +[acc] eval 2.129845e-3 s +[acc] eval 2.072069e-3 s +[acc] eval 2.084006e-3 s +[acc] eval 1.947992e-3 s +[acc] eval 1.917691e-3 s +[acc] eval 1.981592e-3 s +[acc] eval 2.364633e-3 s +[acc] eval 2.176245e-3 s +[acc] eval 0.143202127 s +[acc] compile 8.1201332e-2 s +[acc] eval 1.5060723e-2 s +[acc] eval 0.100105937 s +[acc] compile 1.028407e-3 s +[acc] eval 8.334499e-3 s +[acc] compile 0.195334444 s +[acc] eval 2.2984982e-2 s +[acc] eval 0.2260447 s +[acc] compile 0.144139309 s +[acc] eval 1.1850401e-2 s +[acc] eval 0.16576601 s +[acc] compile 0.559207407 s +[acc] eval 3.4182015e-2 s +[acc] eval 0.601300247 s +[acc] compile 5.795226e-3 s +[acc] eval 5.628896e-3 s +[acc] eval 2.475449e-3 s +[acc] eval 1.663613e-2 s +[acc] compile 5.3627235e-2 s +[acc] eval 3.987366e-3 s +[acc] eval 9.41845e-4 s +[acc] eval 5.83586e-4 s +[acc] eval 6.0613305e-2 s +[acc] compile 9.673379e-3 s +[acc] eval 1.4671692e-2 s +[acc] eval 2.7085716e-2 s +[acc] compile 8.1093828e-2 s +[acc] eval 1.1558382e-2 s +[acc] eval 4.371595e-3 s +[acc] eval 3.681704e-3 s +[acc] eval 4.480198e-3 s +[acc] eval 3.315647e-3 s +[acc] eval 3.838171e-3 s +[acc] eval 3.446449e-3 s +[acc] eval 5.417473e-3 s +[acc] eval 4.791143e-3 s +[acc] eval 5.585628e-3 s +[acc] eval 7.494199e-3 s +[acc] eval 5.630369e-3 s +[acc] eval 8.218516e-3 s +[acc] eval 2.2825458e-2 s +[acc] eval 4.585643e-3 s +[acc] eval 3.222451e-3 s +[acc] eval 4.675662e-3 s +[acc] eval 2.777802e-3 s +[acc] eval 2.667227e-3 s +[acc] eval 2.651716e-3 s +[acc] eval 2.558244e-3 s +[acc] eval 2.753421e-3 s +[acc] eval 2.937586e-3 s +[acc] eval 2.740532e-3 s +[acc] eval 2.726934e-3 s +[acc] eval 2.626156e-3 s +[acc] eval 2.694895e-3 s +[acc] eval 2.873289e-3 s +[acc] eval 2.851201e-3 s +[acc] eval 2.705256e-3 s +[acc] eval 0.230107228 s +[acc] compile 0.132984829 s +[acc] eval 1.8543292e-2 s +[acc] eval 0.157039424 s +[acc] compile 0.243483958 s +[acc] eval 2.936738e-2 s +[acc] eval 0.280154144 s +[acc] compile 0.147301716 s +[acc] eval 1.3101647e-2 s +[acc] eval 0.167980249 s +[acc] compile 0.295193321 s +[acc] eval 3.6264385e-2 s +[acc] eval 0.335636954 s +[acc] compile 3.809571e-3 s +[acc] eval 2.658237e-3 s +[acc] eval 3.95517e-4 s +[acc] eval 4.12708e-4 s +[acc] eval 8.052177e-3 s +[acc] compile 4.921194e-3 s +[acc] eval 4.49598e-3 s +[acc] eval 1.881478e-3 s +[acc] eval 1.871298e-3 s +[acc] eval 1.865695e-3 s +[acc] eval 1.890773e-3 s +[acc] eval 1.778039e-3 s +[acc] eval 1.911651e-3 s +[acc] eval 2.013621e-3 s +[acc] eval 1.87648e-3 s +[acc] eval 8.186095e-3 s +[acc] eval 1.858983e-3 s +[acc] eval 1.541925e-3 s +[acc] eval 1.454439e-3 s +[acc] eval 1.370466e-3 s +[acc] eval 1.346145e-3 s +[acc] eval 1.31517e-3 s +[acc] eval 1.703737e-3 s +[acc] eval 2.625726e-3 s +[acc] eval 1.480011e-3 s +[acc] eval 1.32188e-3 s +[acc] eval 1.352344e-3 s +[acc] eval 1.328831e-3 s +[acc] eval 2.229391e-3 s +[acc] eval 2.304384e-3 s +[acc] eval 2.187047e-3 s +[acc] eval 3.760228e-3 s +[acc] eval 2.236056e-3 s +[acc] eval 2.623583e-3 s +[acc] eval 2.964045e-3 s +[acc] eval 2.925117e-3 s +[acc] eval 7.7976976e-2 s +[acc] compile 0.358078057 s +[acc] eval 1.2921947e-2 s +[acc] eval 0.382552232 s +[acc] compile 0.223079777 s +[acc] eval 6.627535e-3 s +[acc] eval 0.234420348 s +[acc] compile 0.164874435 s +[acc] eval 1.7738404e-2 s +[acc] eval 0.190895928 s +[acc] compile 4.779956e-3 s +[acc] eval 3.056812e-3 s +[acc] eval 7.6795e-4 s +[acc] eval 5.16787e-4 s +[acc] eval 1.022955e-2 s +[acc] compile 7.0502754e-2 s +[acc] eval 2.9392784e-2 s +[acc] eval 2.150845e-3 s +[acc] eval 1.940867e-3 s +[acc] eval 1.776494e-3 s +[acc] eval 2.573139e-3 s +[acc] eval 2.191085e-3 s +[acc] eval 1.908749e-3 s +[acc] eval 1.916852e-3 s +[acc] eval 1.98505e-3 s +[acc] eval 1.888543e-3 s +[acc] eval 1.714579e-3 s +[acc] eval 1.842041e-3 s +[acc] eval 2.325941e-3 s +[acc] eval 1.96056e-3 s +[acc] eval 1.975397e-3 s +[acc] eval 1.943207e-3 s +[acc] eval 1.769557e-3 s +[acc] eval 1.802838e-3 s +[acc] eval 1.675406e-3 s +[acc] eval 1.764409e-3 s +[acc] eval 2.297611e-3 s +[acc] eval 2.017111e-3 s +[acc] eval 1.93279e-3 s +[acc] eval 1.862817e-3 s +[acc] eval 1.810051e-3 s +[acc] eval 1.759822e-3 s +[acc] eval 1.675688e-3 s +[acc] eval 1.599488e-3 s +[acc] eval 2.270449e-3 s +[acc] eval 1.942749e-3 s +[acc] eval 0.160774235 s +[acc] compile 0.10870072 s +[acc] eval 9.890609e-3 s +[acc] eval 0.123817598 s +[acc] compile 0.137606251 s +[acc] eval 2.65982e-2 s +[acc] eval 0.167601567 s +[acc] compile 0.143454709 s +[acc] eval 6.440027e-3 s +[acc] eval 5.219778e-3 s +[acc] eval 8.139722e-3 s +[acc] eval 6.844758e-3 s +[acc] eval 4.908124e-3 s +[acc] eval 7.748441e-3 s +[acc] eval 5.312789e-3 s +[acc] eval 4.155323e-3 s +[acc] eval 7.464896e-3 s +[acc] eval 4.040773e-3 s +[acc] eval 4.481054e-3 s +[acc] eval 5.817198e-3 s +[acc] eval 4.032924e-3 s +[acc] eval 4.331261e-3 s +[acc] eval 5.554042e-3 s +[acc] eval 4.289039e-3 s +[acc] eval 4.008614e-3 s +[acc] eval 5.118246e-3 s +[acc] eval 4.341194e-3 s +[acc] eval 5.226661e-3 s +[acc] eval 7.994967e-3 s +[acc] eval 8.406552e-3 s +[acc] eval 1.2425182e-2 s +[acc] eval 3.341768e-3 s +[acc] eval 2.809242e-3 s +[acc] eval 2.65765e-3 s +[acc] eval 3.497399e-3 s +[acc] eval 2.484642e-3 s +[acc] eval 2.239248e-3 s +[acc] eval 2.777782e-3 s +[acc] eval 0.307992573 s +[acc] compile 8.52059e-4 s +[acc] eval 8.811218e-3 s +[acc] compile 8.389063e-3 s +[acc] eval 1.7311903e-2 s +[acc] eval 2.8575799e-2 s +[acc] compile 9.3221602e-2 s +[acc] eval 8.033358e-3 s +[acc] eval 8.925549e-3 s +[acc] eval 1.3361919e-2 s +[acc] eval 8.859081e-3 s +[acc] eval 1.159928e-2 s +[acc] eval 9.531163e-3 s +[acc] eval 8.507983e-3 s +[acc] eval 7.910187e-3 s +[acc] eval 1.0477749e-2 s +[acc] eval 5.998693e-3 s +[acc] eval 6.966154e-3 s +[acc] eval 4.293232e-3 s +[acc] eval 5.900041e-3 s +[acc] eval 6.468324e-3 s +[acc] eval 4.48639e-3 s +[acc] eval 4.04064e-3 s +[acc] eval 7.037809e-3 s +[acc] eval 6.193657e-3 s +[acc] eval 4.504904e-3 s +[acc] eval 3.746255e-3 s +[acc] eval 3.469683e-3 s +[acc] eval 3.506324e-3 s +[acc] eval 3.793353e-3 s +[acc] eval 4.458943e-3 s +[acc] eval 3.869393e-3 s +[acc] eval 3.74958e-3 s +[acc] eval 4.028263e-3 s +[acc] eval 4.29593e-3 s +[acc] eval 4.089643e-3 s +[acc] eval 5.218215e-3 s +[acc] eval 5.85828e-3 s +[acc] eval 6.442869e-3 s +[acc] eval 3.698821e-3 s +[acc] eval 3.249354e-3 s +[acc] eval 4.201708e-3 s +[acc] eval 3.125078e-3 s +[acc] eval 3.366639e-3 s +[acc] eval 0.31920949 s +[acc] compile 0.281544734 s +[acc] eval 2.800755e-2 s +[acc] eval 0.315455266 s +[acc] compile 0.261856211 s +[acc] eval 3.3235152e-2 s +[acc] eval 0.301495654 s +[acc] compile 6.543397e-3 s +[acc] eval 1.162401e-2 s +[acc] eval 9.459026e-3 s +[acc] eval 3.2043732e-2 s +[acc] compile 7.1545249e-2 s +[acc] eval 6.171714e-3 s +[acc] eval 3.308358e-3 s +[acc] eval 2.830799e-3 s +[acc] eval 3.178783e-3 s +[acc] eval 9.1953063e-2 s +[acc] compile 7.471048e-3 s +[acc] eval 7.045433e-3 s +[acc] eval 1.9693093e-2 s +[acc] compile 6.6997218e-2 s +[acc] eval 1.6512784e-2 s +[acc] eval 8.500546e-3 s +[acc] eval 5.824112e-3 s +[acc] eval 6.777132e-3 s +[acc] eval 5.980319e-3 s +[acc] eval 5.947267e-3 s +[acc] eval 7.610961e-3 s +[acc] eval 6.292522e-3 s +[acc] eval 6.546366e-3 s +[acc] eval 6.150486e-3 s +[acc] eval 5.967637e-3 s +[acc] eval 8.873625e-3 s +[acc] eval 6.086915e-3 s +[acc] eval 6.325826e-3 s +[acc] eval 5.875258e-3 s +[acc] eval 9.628921e-3 s +[acc] eval 9.036023e-3 s +[acc] eval 7.402668e-3 s +[acc] eval 5.923357e-3 s +[acc] eval 5.966852e-3 s +[acc] eval 5.87464e-3 s +[acc] eval 0.230595539 s +[acc] compile 0.477855696 s +[acc] eval 3.1951197e-2 s +[acc] eval 1.3051711e-2 s +[acc] eval 5.431931e-3 s +[acc] eval 5.259939e-3 s +[acc] eval 5.133949e-3 s +[acc] eval 6.249431e-3 s +[acc] eval 5.817294e-3 s +[acc] eval 1.0965624e-2 s +[acc] eval 5.282341e-3 s +[acc] eval 5.458076e-3 s +[acc] eval 6.389994e-3 s +[acc] eval 5.763943e-3 s +[acc] eval 9.902091e-3 s +[acc] eval 5.558884e-3 s +[acc] eval 4.835835e-3 s +[acc] eval 6.40317e-3 s +[acc] eval 5.269312e-3 s +[acc] eval 8.951401e-3 s +[acc] eval 5.717156e-3 s +[acc] eval 7.448039e-3 s +[acc] eval 5.57743e-3 s +[acc] eval 5.651418e-3 s +[acc] eval 1.0121351e-2 s +[acc] eval 5.335659e-3 s +[acc] eval 6.242867e-3 s +[acc] eval 5.387139e-3 s +[acc] eval 5.089328e-3 s +[acc] eval 1.1206694e-2 s +[acc] eval 6.028472e-3 s +[acc] eval 7.194382e-3 s +[acc] eval 5.633847e-3 s +[acc] eval 0.726256363 s +[acc] compile 0.419700294 s +[acc] eval 2.6217615e-2 s +[acc] eval 4.623308e-3 s +[acc] eval 3.878204e-3 s +[acc] eval 4.496353e-3 s +[acc] eval 3.693216e-3 s +[acc] eval 3.503985e-3 s +[acc] eval 3.753819e-3 s +[acc] eval 4.855159e-3 s +[acc] eval 4.174063e-3 s +[acc] eval 3.844888e-3 s +[acc] eval 3.689511e-3 s +[acc] eval 4.856553e-3 s +[acc] eval 4.458257e-3 s +[acc] eval 4.360142e-3 s +[acc] eval 4.153779e-3 s +[acc] eval 4.821681e-3 s +[acc] eval 3.976858e-3 s +[acc] eval 3.897296e-3 s +[acc] eval 4.431578e-3 s +[acc] eval 5.621138e-3 s +[acc] eval 0.53447419 s +[acc] compile 4.843602e-3 s +[acc] eval 7.763624e-3 s +[acc] eval 1.44915e-2 s +[acc] eval 7.070311e-3 s +[acc] eval 3.505807e-3 s +[acc] eval 3.166433e-3 s +[acc] eval 3.648355e-3 s +[acc] eval 3.261928e-3 s +[acc] eval 2.816811e-3 s +[acc] eval 1.7816351e-2 s +[acc] eval 7.385179e-3 s +[acc] eval 2.125599e-3 s +[acc] eval 1.995951e-3 s +[acc] eval 8.3743661e-2 s +[acc] compile 8.279262e-3 s +[acc] eval 3.715526e-3 s +[acc] eval 1.433956e-3 s +[acc] eval 1.591247e-3 s +[acc] eval 1.720181e-3 s +[acc] eval 1.951294e-3 s +[acc] eval 2.334558e-3 s +[acc] eval 1.421288e-3 s +[acc] eval 5.670988e-3 s +[acc] eval 1.315222e-3 s +[acc] eval 9.78017e-4 s +[acc] eval 8.41233e-4 s +[acc] eval 8.81699e-4 s +[acc] eval 3.4747272e-2 s +[acc] compile 7.0766944e-2 s +[acc] eval 1.7406989e-2 s +[acc] eval 9.2775545e-2 s +[acc] compile 9.20143e-4 s +[acc] eval 5.310542e-3 s +[acc] compile 0.138506751 s +[acc] eval 3.2700516e-2 s +[acc] eval 0.17797787 s +[acc] compile 1.096468e-3 s +[acc] eval 5.36917e-3 s +[acc] compile 0.202246729 s +[acc] eval 2.0876377e-2 s +[acc] eval 0.228729088 s +[acc] compile 1.0730043e-2 s +[acc] eval 4.925602e-3 s +[acc] eval 3.523117e-3 s +[acc] eval 4.084077e-3 s +[acc] eval 3.455198e-3 s +[acc] eval 3.452863e-3 s +[acc] eval 3.425767e-3 s +[acc] eval 4.499254e-2 s +[acc] compile 5.475233e-3 s +[acc] eval 1.9321817e-2 s +[acc] eval 2.8793189e-2 s +[acc] compile 0.355450435 s +[acc] eval 1.7964983e-2 s +[acc] eval 8.409544e-3 s +[acc] eval 4.134477e-3 s +[acc] eval 4.085351e-3 s +[acc] eval 6.079104e-3 s +[acc] eval 0.401233865 s +[acc] compile 0.166702653 s +[acc] eval 1.3224977e-2 s +[acc] eval 0.185689334 s +[acc] compile 6.9412181e-2 s +[acc] eval 5.580926e-3 s +[acc] eval 2.277797e-3 s +[acc] eval 1.544584e-3 s +[acc] eval 8.154883e-2 s +[acc] compile 0.319542379 s +[acc] eval 2.440618e-2 s +[acc] eval 5.549033e-3 s +[acc] eval 0.35676961 s +[acc] compile 0.16707966 s +[acc] eval 2.5461992e-2 s +[acc] eval 5.395538e-3 s +[acc] eval 4.94981e-3 s +[acc] eval 1.1528713e-2 s +[acc] eval 5.623165e-3 s +[acc] eval 6.310642e-3 s +[acc] eval 4.661145e-3 s +[acc] eval 4.538931e-3 s +[acc] eval 5.542906e-3 s +[acc] eval 6.169496e-3 s +[acc] eval 1.0452832e-2 s +[acc] eval 6.639408e-3 s +[acc] eval 7.54233e-3 s +[acc] eval 5.92356e-3 s +[acc] eval 4.43715e-3 s +[acc] eval 6.404086e-3 s +[acc] eval 4.436208e-3 s +[acc] eval 4.254996e-3 s +[acc] eval 5.523289e-3 s +[acc] eval 4.291979e-3 s +[acc] eval 4.30345e-3 s +[acc] eval 5.077326e-3 s +[acc] eval 4.670849e-3 s +[acc] eval 4.409513e-3 s +[acc] eval 4.290851e-3 s +[acc] eval 5.069445e-3 s +[acc] eval 4.726175e-3 s +[acc] eval 4.410182e-3 s +[acc] eval 4.389998e-3 s +[acc] eval 4.153126e-3 s +[acc] eval 0.360391315 s +[acc] compile 0.216258258 s +[acc] eval 3.048421e-2 s +[acc] eval 0.257547094 s +[acc] compile 6.3791826e-2 s +[acc] eval 1.163636e-2 s +[acc] eval 8.1168602e-2 s +[acc] compile 7.031592e-2 s +[acc] eval 1.1819355e-2 s +[acc] eval 8.120864e-3 s +[acc] eval 1.603741e-3 s +[acc] eval 1.661323e-3 s +[acc] eval 1.040006e-3 s +[acc] eval 1.065948e-3 s +[acc] eval 1.105882e-3 s +[acc] eval 1.47718e-3 s +[acc] eval 2.392196e-3 s +[acc] eval 2.284722e-3 s +[acc] eval 1.682289e-3 s +[acc] eval 1.49258e-3 s +[acc] eval 1.422411e-3 s +[acc] eval 1.312884e-3 s +[acc] eval 1.110337e-3 s +[acc] eval 9.87388e-4 s +[acc] eval 8.88997e-4 s +[acc] eval 8.58324e-4 s +[acc] eval 1.140489e-3 s +[acc] eval 2.109027e-3 s +[acc] eval 1.132115e-3 s +[acc] eval 1.000719e-3 s +[acc] eval 9.17838e-4 s +[acc] eval 9.31592e-4 s +[acc] eval 9.48499e-4 s +[acc] eval 9.15367e-4 s +[acc] eval 8.91022e-4 s +[acc] eval 8.66711e-4 s +[acc] eval 8.76887e-4 s +[acc] eval 8.10652e-4 s +[acc] eval 0.128411382 s +[acc] compile 4.908979e-3 s +[acc] eval 4.829108e-3 s +[acc] eval 1.148409e-3 s +[acc] eval 1.030108e-3 s +[acc] eval 8.70207e-4 s +[acc] eval 1.090453e-3 s +[acc] eval 1.162485e-3 s +[acc] eval 9.34705e-4 s +[acc] eval 9.05132e-4 s +[acc] eval 1.08303e-3 s +[acc] eval 1.128183e-3 s +[acc] eval 9.24623e-4 s +[acc] eval 9.15678e-4 s +[acc] eval 1.214685e-3 s +[acc] eval 4.9603e-3 s +[acc] eval 8.9157e-4 s +[acc] eval 7.42086e-4 s +[acc] eval 9.67691e-4 s +[acc] eval 8.84293e-4 s +[acc] eval 6.45364e-4 s +[acc] eval 6.15408e-4 s +[acc] eval 6.09468e-4 s +[acc] eval 6.17621e-4 s +[acc] eval 9.3509e-4 s +[acc] eval 1.084566e-3 s +[acc] eval 8.36158e-4 s +[acc] eval 7.23266e-4 s +[acc] eval 6.56248e-4 s +[acc] eval 6.5686e-4 s +[acc] eval 6.54897e-4 s +[acc] eval 6.44326e-4 s +[acc] eval 4.2008571e-2 s +[acc] compile 5.5851703e-2 s +[acc] eval 6.63715e-3 s +[acc] eval 1.529471e-3 s +[acc] eval 2.471853e-3 s +[acc] eval 1.568437e-3 s +[acc] eval 1.330739e-3 s +[acc] eval 2.35678e-3 s +[acc] eval 3.566906e-3 s +[acc] eval 2.64768e-3 s +[acc] eval 3.16839e-3 s +[acc] eval 2.252192e-3 s +[acc] eval 2.495975e-3 s +[acc] eval 4.135184e-3 s +[acc] eval 2.633293e-3 s +[acc] eval 1.584532e-3 s +[acc] eval 1.093981e-3 s +[acc] eval 1.306966e-3 s +[acc] eval 1.40441e-3 s +[acc] eval 1.519579e-3 s +[acc] eval 2.430288e-3 s +[acc] eval 1.621337e-3 s +[acc] eval 5.041822e-3 s +[acc] eval 9.68483e-4 s +[acc] eval 9.98875e-4 s +[acc] eval 9.21497e-4 s +[acc] eval 8.11393e-4 s +[acc] eval 8.17737e-4 s +[acc] eval 7.95197e-4 s +[acc] eval 8.95555e-4 s +[acc] eval 1.125553e-3 s +[acc] eval 1.674767e-3 s +[acc] eval 0.122398361 s +[acc] compile 7.036378e-3 s +[acc] eval 1.2225415e-2 s +[acc] eval 2.2122727e-2 s +[acc] compile 0.513438604 s +[acc] eval 2.907628e-2 s +[acc] eval 0.548159281 s +[acc] compile 5.5671233e-2 s +[acc] eval 8.107454e-3 s +[acc] eval 6.6131241e-2 s +[acc] compile 0.140583563 s +[acc] eval 2.9819895e-2 s +[acc] eval 0.179112529 s +[acc] compile 1.368588e-3 s +[acc] eval 6.975309e-3 s +[acc] compile 4.38554e-3 s +[acc] eval 6.37393e-3 s +[acc] eval 1.3673831e-2 s +[acc] compile 6.094608e-3 s +[acc] eval 1.5779147e-2 s +[acc] eval 2.6687006e-2 s +[acc] compile 0.216709892 s +[acc] eval 9.005685e-3 s +[acc] eval 0.230697058 s +[acc] compile 0.122362558 s +[acc] eval 2.4568229e-2 s +[acc] eval 0.14991322 s +[acc] compile 2.480383e-3 s +[acc] eval 3.042451e-3 s +[acc] eval 5.71539e-4 s +[acc] eval 4.35042e-4 s +[acc] eval 8.050113e-3 s +[acc] compile 7.1522733e-2 s +[acc] eval 9.606118e-3 s +[acc] eval 8.7628831e-2 s +[acc] compile 0.13580621 s +[acc] eval 2.9737367e-2 s +[acc] eval 0.170544818 s +[acc] compile 5.01084e-4 s +[acc] eval 7.93921e-3 s +[acc] compile 4.4030482e-2 s +[acc] eval 8.958415e-3 s +[acc] eval 1.491867e-3 s +[acc] eval 1.472432e-3 s +[acc] eval 1.208144e-3 s +[acc] eval 1.097476e-3 s +[acc] eval 6.046239e-2 s +[acc] compile 9.159397e-3 s +[acc] eval 1.098919e-2 s +[acc] eval 2.335515e-3 s +[acc] eval 1.978399e-3 s +[acc] eval 2.12597e-3 s +[acc] eval 9.821111e-3 s +[acc] eval 1.75782e-3 s +[acc] eval 1.382609e-3 s +[acc] eval 1.968824e-3 s +[acc] eval 1.612009e-3 s +[acc] eval 1.623466e-3 s +[acc] eval 1.082433e-3 s +[acc] eval 1.04894e-3 s +[acc] eval 8.89837e-4 s +[acc] eval 8.9911e-4 s +[acc] eval 8.78803e-4 s +[acc] eval 8.04294e-4 s +[acc] eval 8.0161e-4 s +[acc] eval 7.7063e-4 s +[acc] eval 7.62382e-4 s +[acc] eval 7.60732e-4 s +[acc] eval 7.69633e-4 s +[acc] eval 8.65634e-4 s +[acc] eval 7.42826e-4 s +[acc] eval 7.89891e-4 s +[acc] eval 7.30146e-4 s +[acc] eval 7.38032e-4 s +[acc] eval 7.49523e-4 s +[acc] eval 7.26741e-4 s +[acc] eval 7.57303e-4 s +[acc] eval 7.46743e-4 s +[acc] eval 1.123623e-3 s +[acc] eval 6.7065244e-2 s +[acc] compile 6.133807e-3 s +[acc] eval 1.0604287e-2 s +[acc] eval 3.226466e-3 s +[acc] eval 4.404924e-3 s +[acc] eval 2.703247e-3 s +[acc] eval 4.18338e-3 s +[acc] eval 3.824064e-3 s +[acc] eval 2.8933e-3 s +[acc] eval 2.609675e-3 s +[acc] eval 2.124396e-3 s +[acc] eval 2.992252e-3 s +[acc] eval 3.298604e-3 s +[acc] eval 2.963541e-3 s +[acc] eval 7.754793e-3 s +[acc] eval 2.657662e-3 s +[acc] eval 2.324297e-3 s +[acc] eval 1.891506e-3 s +[acc] eval 1.90321e-3 s +[acc] eval 1.923149e-3 s +[acc] eval 2.1006e-3 s +[acc] eval 1.825247e-3 s +[acc] eval 1.6841e-3 s +[acc] eval 2.27682e-3 s +[acc] eval 2.125626e-3 s +[acc] eval 1.864404e-3 s +[acc] eval 1.825422e-3 s +[acc] eval 1.954331e-3 s +[acc] eval 1.727963e-3 s +[acc] eval 1.777029e-3 s +[acc] eval 1.754854e-3 s +[acc] eval 2.254982e-3 s +[acc] eval 2.053104e-3 s +[acc] eval 1.845635e-3 s +[acc] eval 1.837783e-3 s +[acc] eval 1.919869e-3 s +[acc] eval 2.102036e-3 s +[acc] eval 2.112154e-3 s +[acc] eval 0.111698911 s +[acc] compile 5.162454e-3 s +[acc] eval 3.669191e-3 s +[acc] eval 1.98481e-3 s +[acc] eval 1.919618e-3 s +[acc] eval 2.636885e-3 s +[acc] eval 2.99069e-3 s +[acc] eval 1.817107e-3 s +[acc] eval 1.32178e-3 s +[acc] eval 1.376552e-3 s +[acc] eval 1.292139e-3 s +[acc] eval 1.90445e-3 s +[acc] eval 1.584389e-3 s +[acc] eval 1.433209e-3 s +[acc] eval 3.1697706e-2 s +[acc] compile 6.990511e-3 s +[acc] eval 6.077063e-3 s +[acc] eval 2.2056308e-2 s +[acc] compile 7.131515e-3 s +[acc] eval 6.373415e-3 s +[acc] eval 3.457508e-3 s +[acc] eval 2.364296e-3 s +[acc] eval 2.2460088e-2 s +[acc] compile 6.883379e-3 s +[acc] eval 4.06041e-3 s +[acc] eval 1.405715e-3 s +[acc] eval 7.23483e-4 s +[acc] eval 1.4664449e-2 s +[acc] compile 1.236023e-3 s +[acc] eval 1.9465531e-2 s +[acc] compile 5.909635e-3 s +[acc] eval 7.320025e-3 s +[acc] eval 3.670471e-3 s +[acc] eval 4.591189e-3 s +[acc] eval 3.332006e-3 s +[acc] eval 3.133169e-3 s +[acc] eval 2.887494e-3 s +[acc] eval 3.292436e-3 s +[acc] eval 9.015038e-3 s +[acc] eval 2.848011e-3 s +[acc] eval 3.603584e-3 s +[acc] eval 3.657441e-3 s +[acc] eval 3.007918e-3 s +[acc] eval 2.13178e-3 s +[acc] eval 2.046102e-3 s +[acc] eval 2.284636e-3 s +[acc] eval 3.988159e-3 s +[acc] eval 3.053208e-3 s +[acc] eval 4.287641e-3 s +[acc] eval 8.0803167e-2 s +[acc] compile 8.0353399e-2 s +[acc] eval 2.5613865e-2 s +[acc] eval 6.021596e-3 s +[acc] eval 1.0998992e-2 s +[acc] eval 4.094571e-3 s +[acc] eval 5.682184e-3 s +[acc] eval 4.964098e-3 s +[acc] eval 3.64554e-3 s +[acc] eval 3.599776e-3 s +[acc] eval 4.70719e-3 s +[acc] eval 4.578556e-3 s +[acc] eval 0.160337613 s +[acc] compile 8.3651412e-2 s +[acc] eval 8.582672e-3 s +[acc] eval 9.5839875e-2 s +[acc] compile 0.378326173 s +[acc] eval 3.0328784e-2 s +[acc] eval 0.416262972 s +[acc] compile 0.160719436 s +[acc] eval 2.3036435e-2 s +[acc] eval 0.189505682 s +[acc] compile 8.373395e-3 s +[acc] eval 4.590119e-3 s +[acc] eval 3.411891e-3 s +[acc] eval 2.375526e-3 s +[acc] eval 3.566511e-3 s +[acc] eval 4.769661e-3 s +[acc] eval 9.489186e-3 s +[acc] eval 1.268714e-3 s +[acc] eval 1.290239e-3 s +[acc] eval 1.138025e-3 s +[acc] eval 1.224267e-3 s +[acc] eval 8.76847e-4 s +[acc] eval 8.93921e-4 s +[acc] eval 8.39817e-4 s +[acc] eval 8.27722e-4 s +[acc] eval 8.33767e-4 s +[acc] eval 8.38915e-4 s +[acc] eval 8.37681e-4 s +[acc] eval 9.13485e-4 s +[acc] eval 9.11186e-4 s +[acc] eval 8.97325e-4 s +[acc] eval 8.90732e-4 s +[acc] eval 8.74401e-4 s +[acc] eval 8.94125e-4 s +[acc] eval 8.97388e-4 s +[acc] eval 8.5278e-4 s +[acc] eval 8.53192e-4 s +[acc] eval 8.71714e-4 s +[acc] eval 8.30831e-4 s +[acc] eval 8.40045e-4 s +[acc] eval 8.53688e-4 s +[acc] eval 8.53626e-4 s +[acc] eval 6.3463079e-2 s +[acc] compile 3.064305e-3 s +[acc] eval 3.169891e-3 s +[acc] eval 5.43087e-4 s +[acc] eval 8.83273e-4 s +[acc] eval 8.71244e-3 s +[acc] compile 5.182056e-3 s +[acc] eval 6.398566e-3 s +[acc] eval 1.6412443e-2 s +[acc] compile 3.636147e-3 s +[acc] eval 6.632377e-3 s +[acc] eval 1.0925043e-2 s +[acc] eval 2.085084e-3 s +[acc] eval 2.6108677e-2 s +[acc] compile 3.926294e-3 s +[acc] eval 5.802009e-3 s +[acc] eval 2.159637e-3 s +[acc] eval 1.788895e-3 s +[acc] eval 1.470817e-3 s +[acc] eval 1.327452e-3 s +[acc] eval 2.52603e-3 s +[acc] eval 2.915053e-3 s +[acc] eval 2.047946e-3 s +[acc] eval 1.79627e-3 s +[acc] eval 1.738414e-3 s +[acc] eval 1.504662e-3 s +[acc] eval 1.447867e-3 s +[acc] eval 1.412513e-3 s +[acc] eval 2.160737e-3 s +[acc] eval 2.174503e-3 s +[acc] eval 4.014748e-2 s +[acc] compile 6.9798195e-2 s +[acc] eval 2.9747662e-2 s +[acc] eval 4.532266e-3 s +[acc] eval 6.072608e-3 s +[acc] eval 4.587886e-3 s +[acc] eval 3.513315e-3 s +[acc] eval 4.976406e-3 s +[acc] eval 8.706279e-3 s +[acc] eval 1.9168558e-2 s +[acc] eval 3.532359e-3 s +[acc] eval 4.152668e-3 s +[acc] eval 5.530806e-3 s +[acc] eval 4.94537e-3 s +[acc] eval 6.556845e-3 s +[acc] eval 3.788736e-3 s +[acc] eval 6.36456e-3 s +[acc] eval 3.228741e-3 s +[acc] eval 3.125052e-3 s +[acc] eval 2.883899e-3 s +[acc] eval 3.433261e-3 s +[acc] eval 4.765831e-3 s +[acc] eval 2.801699e-3 s +[acc] eval 2.850346e-3 s +[acc] eval 2.786678e-3 s +[acc] eval 4.099922e-3 s +[acc] eval 3.025274e-3 s +[acc] eval 2.707872e-3 s +[acc] eval 3.158406e-3 s +[acc] eval 3.156558e-3 s +[acc] eval 3.331295e-3 s +[acc] eval 3.226149e-3 s +[acc] eval 2.648871e-3 s +[acc] eval 0.244462144 s +[acc] compile 4.544782e-3 s +[acc] eval 5.996446e-3 s +[acc] eval 1.1928481e-2 s +[acc] compile 5.715129e-3 s +[acc] eval 9.840198e-3 s +[acc] eval 1.219256e-3 s +[acc] eval 1.72612e-3 s +[acc] eval 2.125833e-2 s +[acc] compile 3.847684e-3 s +[acc] eval 1.2280392e-2 s +[acc] eval 5.268132e-3 s +[acc] eval 1.701815e-3 s +[acc] eval 1.286645e-3 s +[acc] eval 1.426767e-3 s +[acc] eval 1.704739e-3 s +[acc] eval 1.336386e-3 s +[acc] eval 1.160881e-3 s +[acc] eval 1.158012e-3 s +[acc] eval 1.18072e-3 s +[acc] eval 1.175688e-3 s +[acc] eval 1.158341e-3 s +[acc] eval 1.222472e-3 s +[acc] eval 2.008261e-3 s +[acc] eval 2.201953e-3 s +[acc] eval 1.731046e-3 s +[acc] eval 1.682608e-3 s +[acc] eval 1.561552e-3 s +[acc] eval 1.289491e-3 s +[acc] eval 1.233638e-3 s +[acc] eval 1.23419e-3 s +[acc] eval 1.180704e-3 s +[acc] eval 5.3692286e-2 s +[acc] compile 0.142200872 s +[acc] eval 6.03781e-3 s +[acc] eval 3.241651e-3 s +[acc] eval 2.336097e-3 s +[acc] eval 1.894553e-3 s +[acc] eval 2.563085e-3 s +[acc] eval 2.115443e-3 s +[acc] eval 3.25217e-3 s +[acc] eval 0.175135178 s +[acc] compile 0.304766041 s +[acc] eval 1.4545454e-2 s +[acc] eval 3.400866e-3 s +[acc] eval 8.225829e-3 s +[acc] eval 2.856424e-3 s +[acc] eval 2.585652e-3 s +[acc] eval 2.425993e-3 s +[acc] eval 6.527249e-3 s +[acc] eval 5.720986e-3 s +[acc] eval 6.204804e-3 s +[acc] eval 4.699074e-3 s +[acc] eval 0.366389935 s +[acc] compile 1.0804589e-2 s +[acc] eval 2.0454332e-2 s +[acc] eval 3.8788457e-2 s +[acc] compile 6.8148644e-2 s +[acc] eval 1.4484982e-2 s +[acc] eval 8.8246074e-2 s +[acc] compile 6.109918e-3 s +[acc] eval 3.646059e-3 s +[acc] eval 7.527305e-3 s +[acc] eval 1.292002e-3 s +[acc] eval 1.490237e-3 s +[acc] eval 1.223406e-3 s +[acc] eval 9.92969e-4 s +[acc] eval 1.016211e-3 s +[acc] eval 1.093294e-3 s +[acc] eval 1.216008e-3 s +[acc] eval 1.380238e-3 s +[acc] eval 2.044171e-3 s +[acc] eval 2.060558e-3 s +[acc] eval 1.454358e-3 s +[acc] eval 1.270174e-3 s +[acc] eval 1.061886e-3 s +[acc] eval 1.000677e-3 s +[acc] eval 9.19907e-4 s +[acc] eval 8.16421e-4 s +[acc] eval 7.88124e-4 s +[acc] eval 7.62721e-4 s +[acc] eval 7.4765e-4 s +[acc] eval 7.25153e-4 s +[acc] eval 8.30969e-4 s +[acc] eval 9.31278e-4 s +[acc] eval 1.659308e-3 s +[acc] eval 1.146429e-3 s +[acc] eval 7.97941e-4 s +[acc] eval 8.02912e-4 s +[acc] eval 7.86805e-4 s +[acc] eval 7.5603e-4 s +[acc] eval 5.2942474e-2 s +[acc] compile 0.166246654 s +[acc] eval 3.3778197e-2 s +[acc] eval 0.211115769 s +[acc] compile 5.00999e-4 s +[acc] eval 5.903526e-3 s +[acc] compile 0.264521301 s +[acc] eval 6.230554e-3 s +[acc] eval 3.211079e-3 s +[acc] eval 0.277263729 s +[acc] compile 1.3767222e-2 s +[acc] eval 4.23525e-3 s +[acc] eval 1.961404e-2 s +[acc] compile 6.503819e-3 s +[acc] eval 1.2446479e-2 s +[acc] eval 2.4540932e-2 s +[acc] compile 7.6103006e-2 s +[acc] eval 1.431534e-2 s +[acc] eval 4.355434e-3 s +[acc] eval 4.370347e-3 s +[acc] eval 8.971587e-3 s +[acc] eval 7.996719e-3 s +[acc] eval 3.304294e-3 s +[acc] eval 3.874099e-3 s +[acc] eval 2.921318e-3 s +[acc] eval 3.002852e-3 s +[acc] eval 2.785783e-3 s +[acc] eval 3.242991e-3 s +[acc] eval 3.088357e-3 s +[acc] eval 2.487205e-3 s +[acc] eval 2.559963e-3 s +[acc] eval 3.324982e-3 s +[acc] eval 3.424015e-3 s +[acc] eval 4.510221e-3 s +[acc] eval 3.702332e-3 s +[acc] eval 3.805038e-3 s +[acc] eval 3.509572e-3 s +[acc] eval 7.04785e-3 s +[acc] eval 0.180275697 s +[acc] compile 7.444357e-2 s +[acc] eval 2.684805e-2 s +[acc] eval 0.111110679 s +[acc] compile 4.901443e-3 s +[acc] eval 3.555452e-3 s +[acc] eval 1.375015e-3 s +[acc] eval 8.84822e-4 s +[acc] eval 1.013444e-3 s +[acc] eval 2.874298e-3 s +[acc] eval 3.422024e-3 s +[acc] eval 1.16726e-3 s +[acc] eval 9.52229e-4 s +[acc] eval 7.11354e-4 s +[acc] eval 8.30713e-4 s +[acc] eval 7.87745e-4 s +[acc] eval 7.91442e-4 s +[acc] eval 7.84049e-4 s +[acc] eval 6.37372e-4 s +[acc] eval 7.16991e-4 s +[acc] eval 7.47365e-4 s +[acc] eval 7.54682e-4 s +[acc] eval 6.83923e-4 s +[acc] eval 7.96117e-4 s +[acc] eval 1.0298856e-2 s +[acc] eval 2.540111e-3 s +[acc] eval 9.81487e-4 s +[acc] eval 6.87128e-4 s +[acc] eval 6.41054e-4 s +[acc] eval 6.05204e-4 s +[acc] eval 7.17099e-4 s +[acc] eval 1.029047e-3 s +[acc] eval 1.660294e-3 s +[acc] eval 1.201262e-3 s +[acc] eval 7.30008e-4 s +[acc] eval 5.2349636e-2 s +[acc] compile 4.849272e-3 s +[acc] eval 1.399533e-2 s +[acc] eval 1.173329e-3 s +[acc] eval 1.058475e-3 s +[acc] eval 9.99695e-4 s +[acc] eval 8.43411e-4 s +[acc] eval 8.3214e-4 s +[acc] eval 7.80129e-4 s +[acc] eval 4.214962e-3 s +[acc] eval 9.23703e-4 s +[acc] eval 8.0279e-4 s +[acc] eval 6.7564e-4 s +[acc] eval 6.63824e-4 s +[acc] eval 6.30886e-4 s +[acc] eval 6.17502e-4 s +[acc] eval 6.44973e-4 s +[acc] eval 6.00991e-4 s +[acc] eval 6.16511e-4 s +[acc] eval 5.86867e-4 s +[acc] eval 5.88923e-4 s +[acc] eval 5.87858e-4 s +[acc] eval 1.011414e-3 s +[acc] eval 7.30102e-4 s +[acc] eval 7.34229e-4 s +[acc] eval 6.67312e-4 s +[acc] eval 6.36738e-4 s +[acc] eval 1.07718e-3 s +[acc] eval 1.385774e-3 s +[acc] eval 1.552842e-3 s +[acc] eval 9.4756e-4 s +[acc] eval 8.43007e-4 s +[acc] eval 8.03474e-4 s +[acc] eval 8.09551e-4 s +[acc] eval 5.0617823e-2 s +[acc] compile 4.918216e-3 s +[acc] eval 3.488464e-3 s +[acc] eval 6.80306e-4 s +[acc] eval 4.94958e-4 s +[acc] eval 1.0551943e-2 s +[acc] compile 5.102891e-3 s +[acc] eval 1.0580865e-2 s +[acc] eval 2.476989e-3 s +[acc] eval 6.24908e-3 s +[acc] eval 1.373149e-3 s +[acc] eval 1.173164e-3 s +[acc] eval 9.26013e-4 s +[acc] eval 1.451672e-3 s +[acc] eval 1.778073e-3 s +[acc] eval 1.617233e-3 s +[acc] eval 2.474332e-3 s +[acc] eval 2.151817e-3 s +[acc] eval 1.876744e-3 s +[acc] eval 1.78393e-3 s +[acc] eval 1.920725e-3 s +[acc] eval 1.602681e-3 s +[acc] eval 2.525041e-3 s +[acc] eval 1.731464e-3 s +[acc] eval 1.463279e-3 s +[acc] eval 1.489387e-3 s +[acc] eval 1.403377e-3 s +[acc] eval 1.188708e-3 s +[acc] eval 1.198947e-3 s +[acc] eval 1.026308e-3 s +[acc] eval 1.013345e-3 s +[acc] eval 1.188474e-3 s +[acc] eval 1.976041e-3 s +[acc] eval 1.903473e-3 s +[acc] eval 1.115383e-3 s +[acc] eval 1.077415e-3 s +[acc] eval 1.053248e-3 s +[acc] eval 7.0341144e-2 s +[acc] compile 6.3177164e-2 s +[acc] eval 7.70705e-3 s +[acc] eval 7.4047839e-2 s +[acc] compile 7.8047574e-2 s +[acc] eval 2.2114256e-2 s +[acc] eval 2.682679e-3 s +[acc] eval 2.575072e-3 s +[acc] eval 2.40823e-3 s +[acc] eval 2.639522e-3 s +[acc] eval 3.238988e-3 s +[acc] eval 2.290487e-3 s +[acc] eval 2.292505e-3 s +[acc] eval 2.413166e-3 s +[acc] eval 2.288249e-3 s +[acc] eval 2.441208e-3 s +[acc] eval 2.758937e-3 s +[acc] eval 2.072919e-3 s +[acc] eval 2.124881e-3 s +[acc] eval 2.08325e-3 s +[acc] eval 1.976559e-3 s +[acc] eval 1.922972e-3 s +[acc] eval 2.154907e-3 s +[acc] eval 2.460838e-3 s +[acc] eval 2.117132e-3 s +[acc] eval 2.338649e-3 s +[acc] eval 2.288953e-3 s +[acc] eval 2.366074e-3 s +[acc] eval 2.27316e-3 s +[acc] eval 2.492297e-3 s +[acc] eval 2.335461e-3 s +[acc] eval 2.090602e-3 s +[acc] eval 2.322206e-3 s +[acc] eval 2.172071e-3 s +[acc] eval 2.103585e-3 s +[acc] eval 2.068e-3 s +[acc] eval 2.548048e-3 s +[acc] eval 0.178229807 s +[acc] compile 4.495169e-3 s +[acc] eval 1.1337988e-2 s +[acc] eval 1.081262e-3 s +[acc] eval 1.226748e-3 s +[acc] eval 9.49426e-4 s +[acc] eval 9.8206e-4 s +[acc] eval 9.6033e-4 s +[acc] eval 9.31085e-4 s +[acc] eval 1.08829e-3 s +[acc] eval 1.26614e-3 s +[acc] eval 1.200565e-3 s +[acc] eval 1.466385e-3 s +[acc] eval 1.196242e-3 s +[acc] eval 6.469931e-3 s +[acc] eval 1.829735e-3 s +[acc] eval 1.164253e-3 s +[acc] eval 1.598014e-3 s +[acc] eval 3.224976e-3 s +[acc] eval 4.6646863e-2 s +[acc] compile 1.174953e-2 s +[acc] eval 1.7346414e-2 s +[acc] eval 2.84741e-3 s +[acc] eval 2.790031e-3 s +[acc] eval 2.527216e-3 s +[acc] eval 2.314799e-3 s +[acc] eval 2.901455e-3 s +[acc] eval 2.201262e-3 s +[acc] eval 2.550644e-3 s +[acc] eval 5.592407e-3 s +[acc] eval 2.895332e-3 s +[acc] eval 2.595478e-3 s +[acc] eval 2.574232e-3 s +[acc] eval 2.454047e-3 s +[acc] eval 2.392793e-3 s +[acc] eval 2.419699e-3 s +[acc] eval 2.598889e-3 s +[acc] eval 3.885818e-3 s +[acc] eval 2.038213e-3 s +[acc] eval 1.83841e-3 s +[acc] eval 1.819147e-3 s +[acc] eval 1.772421e-3 s +[acc] eval 1.740352e-3 s +[acc] eval 1.694582e-3 s +[acc] eval 2.579793e-3 s +[acc] eval 1.985592e-3 s +[acc] eval 1.85706e-3 s +[acc] eval 1.833365e-3 s +[acc] eval 1.816701e-3 s +[acc] eval 1.746211e-3 s +[acc] eval 1.751281e-3 s +[acc] eval 2.564745e-3 s +[acc] eval 2.822122e-3 s +[acc] eval 2.630653e-3 s +[acc] eval 2.503266e-3 s +[acc] eval 0.115803651 s +[acc] compile 0.114299423 s +[acc] eval 2.0581845e-2 s +[acc] eval 3.453256e-3 s +[acc] eval 3.191676e-3 s +[acc] eval 3.682621e-3 s +[acc] eval 3.954207e-3 s +[acc] eval 3.238143e-3 s +[acc] eval 3.293003e-3 s +[acc] eval 9.044475e-3 s +[acc] eval 3.102526e-3 s +[acc] eval 3.037117e-3 s +[acc] eval 2.863396e-3 s +[acc] eval 3.791579e-3 s +[acc] eval 5.616083e-3 s +[acc] eval 3.532319e-3 s +[acc] eval 3.421447e-3 s +[acc] eval 4.707171e-3 s +[acc] eval 0.200843611 s +[acc] compile 4.728866e-3 s +[acc] eval 3.502289e-3 s +[acc] eval 1.0448661e-2 s +[acc] compile 5.100864e-3 s +[acc] eval 5.072407e-3 s +[acc] eval 3.225634e-3 s +[acc] eval 1.6739233e-2 s +[acc] compile 0.134326199 s +[acc] eval 1.6145152e-2 s +[acc] eval 3.976779e-3 s +[acc] eval 3.183682e-3 s +[acc] eval 2.489787e-3 s +[acc] eval 3.479693e-3 s +[acc] eval 2.613921e-3 s +[acc] eval 2.361101e-3 s +[acc] eval 2.034348e-3 s +[acc] eval 2.10355e-3 s +[acc] eval 2.021481e-3 s +[acc] eval 1.7232e-3 s +[acc] eval 2.926078e-3 s +[acc] eval 2.699841e-3 s +[acc] eval 2.317176e-3 s +[acc] eval 2.696281e-3 s +[acc] eval 3.096542e-3 s +[acc] eval 0.196052674 s +[acc] compile 7.1682305e-2 s +[acc] eval 9.435822e-3 s +[acc] eval 8.7378964e-2 s +[acc] compile 0.140515138 s +[acc] eval 2.1077291e-2 s +[acc] eval 4.233785e-3 s +[acc] eval 2.794362e-3 s +[acc] eval 2.56441e-3 s +[acc] eval 2.400695e-3 s +[acc] eval 2.236098e-3 s +[acc] eval 4.438254e-3 s +[acc] eval 2.357939e-3 s +[acc] eval 2.240088e-3 s +[acc] eval 2.649275e-3 s +[acc] eval 2.218627e-3 s +[acc] eval 2.56507e-3 s +[acc] eval 2.837225e-3 s +[acc] eval 1.826369e-3 s +[acc] eval 1.81312e-3 s +[acc] eval 1.786233e-3 s +[acc] eval 1.745304e-3 s +[acc] eval 1.706679e-3 s +[acc] eval 1.771004e-3 s +[acc] eval 2.309973e-3 s +[acc] eval 1.960799e-3 s +[acc] eval 1.907687e-3 s +[acc] eval 1.941492e-3 s +[acc] eval 1.898978e-3 s +[acc] eval 1.89067e-3 s +[acc] eval 1.744257e-3 s +[acc] eval 1.586542e-3 s +[acc] eval 1.713101e-3 s +[acc] eval 2.231662e-3 s +[acc] eval 1.934943e-3 s +[acc] eval 0.231896733 s +[acc] compile 4.277251e-3 s +[acc] eval 3.958491e-3 s +[acc] eval 1.781219e-3 s +[acc] eval 1.91792e-3 s +[acc] eval 1.851157e-3 s +[acc] eval 1.836291e-3 s +[acc] eval 1.856882e-3 s +[acc] eval 1.784405e-3 s +[acc] eval 1.812393e-3 s +[acc] eval 1.82639e-3 s +[acc] eval 1.885073e-3 s +[acc] eval 1.857028e-3 s +[acc] eval 2.067703e-3 s +[acc] eval 7.894653e-3 s +[acc] eval 1.607485e-3 s +[acc] eval 1.523117e-3 s +[acc] eval 1.458398e-3 s +[acc] eval 1.662806e-3 s +[acc] eval 1.593981e-3 s +[acc] eval 1.532597e-3 s +[acc] eval 1.501598e-3 s +[acc] eval 1.481693e-3 s +[acc] eval 1.466357e-3 s +[acc] eval 1.475112e-3 s +[acc] eval 1.42933e-3 s +[acc] eval 1.415743e-3 s +[acc] eval 1.424579e-3 s +[acc] eval 1.436303e-3 s +[acc] eval 1.429514e-3 s +[acc] eval 1.434125e-3 s +[acc] eval 1.453742e-3 s +[acc] eval 6.578527e-2 s +[acc] compile 4.257167e-3 s +[acc] eval 4.548712e-3 s +[acc] eval 2.545705e-3 s +[acc] eval 2.516076e-3 s +[acc] eval 2.474515e-3 s +[acc] eval 2.481708e-3 s +[acc] eval 2.450887e-3 s +[acc] eval 2.525333e-3 s +[acc] eval 2.570615e-3 s +[acc] eval 2.484145e-3 s +[acc] eval 3.2205999e-2 s +[acc] compile 0.133750269 s +[acc] eval 2.665645e-2 s +[acc] eval 0.163508025 s +[acc] compile 6.214522e-3 s +[acc] eval 6.335321e-3 s +[acc] eval 1.5764024e-2 s +[acc] compile 6.9867185e-2 s +[acc] eval 1.1798027e-2 s +[acc] eval 8.4994904e-2 s +[acc] compile 4.90613e-2 s +[acc] eval 2.0403094e-2 s +[acc] eval 7.5183821e-2 s +[acc] compile 6.259016e-3 s +[acc] eval 7.470269e-3 s +[acc] eval 1.7060967e-2 s +[acc] compile 0.140247914 s +[acc] eval 1.2387145e-2 s +[acc] eval 4.474835e-3 s +[acc] eval 3.128368e-3 s +[acc] eval 6.871534e-3 s +[acc] eval 2.453654e-3 s +[acc] eval 2.816484e-3 s +[acc] eval 1.73309e-3 s +[acc] eval 0.176993063 s +[acc] compile 6.1210355e-2 s +[acc] eval 1.5836063e-2 s +[acc] eval 6.933354e-3 s +[acc] eval 1.0078505e-2 s +[acc] eval 4.323371e-3 s +[acc] eval 4.269485e-3 s +[acc] eval 4.497311e-3 s +[acc] eval 5.92937e-3 s +[acc] eval 5.096297e-3 s +[acc] eval 4.6794e-3 s +[acc] eval 7.047049e-3 s +[acc] eval 4.164419e-3 s +[acc] eval 3.850011e-3 s +[acc] eval 4.013353e-3 s +[acc] eval 5.522275e-3 s +[acc] eval 4.201211e-3 s +[acc] eval 3.915466e-3 s +[acc] eval 4.36356e-3 s +[acc] eval 4.304573e-3 s +[acc] eval 4.029995e-3 s +[acc] eval 3.816543e-3 s +[acc] eval 4.233341e-3 s +[acc] eval 4.016561e-3 s +[acc] eval 4.067677e-3 s +[acc] eval 3.875599e-3 s +[acc] eval 3.856234e-3 s +[acc] eval 3.726327e-3 s +[acc] eval 3.791791e-3 s +[acc] eval 3.74445e-3 s +[acc] eval 4.397932e-3 s +[acc] eval 9.012154e-3 s +[acc] eval 3.862398e-3 s +[acc] eval 0.228529914 s +[acc] compile 6.4475645e-2 s +[acc] eval 9.526644e-3 s +[acc] eval 2.655557e-3 s +[acc] eval 2.760141e-3 s +[acc] eval 1.820689e-3 s +[acc] eval 5.64713e-3 s +[acc] eval 1.548692e-3 s +[acc] eval 1.321304e-3 s +[acc] eval 1.685674e-3 s +[acc] eval 1.607691e-3 s +[acc] eval 1.198962e-3 s +[acc] eval 1.239967e-3 s +[acc] eval 1.385383e-3 s +[acc] eval 1.448374e-3 s +[acc] eval 1.348598e-3 s +[acc] eval 1.169022e-3 s +[acc] eval 1.061172e-3 s +[acc] eval 9.08298e-4 s +[acc] eval 8.43403e-4 s +[acc] eval 1.011492e-3 s +[acc] eval 1.652034e-3 s +[acc] eval 2.060405e-3 s +[acc] eval 1.187554e-3 s +[acc] eval 1.053619e-3 s +[acc] eval 1.017887e-3 s +[acc] eval 1.021619e-3 s +[acc] eval 9.86091e-4 s +[acc] eval 9.82469e-4 s +[acc] eval 1.780083e-3 s +[acc] eval 1.056758e-3 s +[acc] eval 9.6672e-4 s +[acc] eval 0.121919528 s +[acc] compile 6.827505e-3 s +[acc] eval 2.183801e-2 s +[acc] eval 3.18465e-3 s +[acc] eval 3.314517e-3 s +[acc] eval 3.730643e-3 s +[acc] eval 3.095498e-3 s +[acc] eval 3.124409e-3 s +[acc] eval 7.923431e-3 s +[acc] eval 3.039672e-3 s +[acc] eval 2.841843e-3 s +[acc] eval 3.087339e-3 s +[acc] eval 3.430385e-3 s +[acc] eval 4.023876e-3 s +[acc] eval 3.716121e-3 s +[acc] eval 3.565375e-3 s +[acc] eval 4.060693e-3 s +[acc] eval 4.114297e-3 s +[acc] eval 3.357247e-3 s +[acc] eval 3.331675e-3 s +[acc] eval 3.115697e-3 s +[acc] eval 3.223528e-3 s +[acc] eval 4.657242e-3 s +[acc] eval 3.465307e-3 s +[acc] eval 3.132473e-3 s +[acc] eval 2.970714e-3 s +[acc] eval 3.324416e-3 s +[acc] eval 2.788488e-3 s +[acc] eval 2.855974e-3 s +[acc] eval 2.822428e-3 s +[acc] eval 2.878862e-3 s +[acc] eval 2.759772e-3 s +[acc] eval 0.135440934 s +[acc] compile 5.542085e-3 s +[acc] eval 6.514727e-3 s +[acc] eval 1.8397875e-2 s +[acc] compile 7.9586091e-2 s +[acc] eval 1.7669408e-2 s +[acc] eval 0.101351502 s +[acc] compile 7.7227215e-2 s +[acc] eval 7.611513e-3 s +[acc] eval 3.523673e-3 s +[acc] eval 3.013287e-3 s +[acc] eval 3.201221e-3 s +[acc] eval 2.94952e-3 s +[acc] eval 3.874576e-3 s +[acc] eval 2.656274e-3 s +[acc] eval 3.622893e-3 s +[acc] eval 5.30354e-3 s +[acc] eval 5.525239e-3 s +[acc] eval 4.897579e-3 s +[acc] eval 1.3142677e-2 s +[acc] eval 6.129369e-3 s +[acc] eval 6.019615e-3 s +[acc] eval 6.274771e-3 s +[acc] eval 5.744532e-3 s +[acc] eval 6.372234e-3 s +[acc] eval 4.844403e-3 s +[acc] eval 3.346417e-3 s +[acc] eval 3.127586e-3 s +[acc] eval 4.297485e-3 s +[acc] eval 2.932564e-3 s +[acc] eval 0.191434331 s +[acc] compile 1.0598241e-2 s +[acc] eval 1.1258812e-2 s +[acc] eval 2.6142415e-2 s +[acc] compile 0.206841784 s +[acc] eval 2.8289896e-2 s +[acc] eval 0.238628175 s +[acc] compile 4.293274e-3 s +[acc] eval 8.202864e-3 s +[acc] eval 1.5642346e-2 s +[acc] compile 0.190976656 s +[acc] eval 2.1517701e-2 s +[acc] eval 0.223739245 s +[acc] compile 8.6394459e-2 s +[acc] eval 9.62784e-3 s +[acc] eval 3.106833e-3 s +[acc] eval 2.613957e-3 s +[acc] eval 2.521399e-3 s +[acc] eval 2.494586e-3 s +[acc] eval 2.519856e-3 s +[acc] eval 2.386053e-3 s +[acc] eval 2.524801e-3 s +[acc] eval 9.885715e-3 s +[acc] eval 2.292734e-3 s +[acc] eval 2.177256e-3 s +[acc] eval 2.180095e-3 s +[acc] eval 2.106064e-3 s +[acc] eval 0.136577194 s +[acc] compile 0.228733266 s +[acc] eval 2.9324133e-2 s +[acc] eval 0.268815473 s +[acc] compile 8.581846e-3 s +[acc] eval 6.653585e-3 s +[acc] eval 1.9210387e-2 s +[acc] compile 5.56731e-4 s +[acc] eval 4.323011e-3 s +[acc] compile 4.84386e-4 s +[acc] eval 9.242174e-3 s +[acc] compile 8.554736e-3 s +[acc] eval 5.158395e-3 s +[acc] eval 2.063187e-2 s +[acc] compile 9.33479e-3 s +[acc] eval 9.6105e-3 s +[acc] eval 2.2204867e-2 s +[acc] compile 4.73912e-4 s +[acc] eval 1.057718e-2 s +[acc] compile 0.139514066 s +[acc] eval 1.5224387e-2 s +[acc] eval 0.166278503 s +[acc] compile 6.310087e-3 s +[acc] eval 1.5049663e-2 s +[acc] eval 2.7475304e-2 s +[acc] compile 0.41596735 s +[acc] eval 3.6485954e-2 s +[acc] eval 0.459440493 s +[acc] compile 0.210811821 s +[acc] eval 2.9238474e-2 s +[acc] eval 0.244804441 s +[acc] compile 1.2400027e-2 s +[acc] eval 1.5783492e-2 s +[acc] eval 3.3056449e-2 s +[acc] compile 0.225196592 s +[acc] eval 2.8481557e-2 s +[acc] eval 0.264097377 s +[acc] compile 1.5513971e-2 s +[acc] eval 1.360349e-2 s +[acc] eval 3.4624788e-2 s +[acc] compile 9.3932276e-2 s +[acc] eval 6.82616e-3 s +[acc] eval 0.10467051 s +[acc] compile 1.0384129e-2 s +[acc] eval 3.0016275e-2 s +[acc] eval 4.6574945e-2 s +[acc] compile 0.411035185 s +[acc] eval 1.6489337e-2 s +[acc] eval 0.437929014 s +[acc] compile 0.13259471 s +[acc] eval 1.8494622e-2 s +[acc] eval 0.153717776 s +[acc] compile 4.876667e-3 s +[acc] eval 7.845333e-3 s +[acc] eval 2.16415e-3 s +[acc] eval 1.765127e-3 s +[acc] eval 1.61213e-3 s +[acc] eval 1.474712e-3 s +[acc] eval 1.748074e-3 s +[acc] eval 1.901169e-3 s +[acc] eval 2.024632e-3 s +[acc] eval 3.073405e-3 s +[acc] eval 2.35113e-3 s +[acc] eval 1.734932e-3 s +[acc] eval 3.918498e-3 s +[acc] eval 3.901925e-3 s +[acc] eval 5.5162635e-2 s +[acc] compile 5.31907e-3 s +[acc] eval 5.142253e-3 s +[acc] eval 8.3219e-4 s +[acc] eval 6.33163e-4 s +[acc] eval 7.48612e-4 s +[acc] eval 1.3773706e-2 s +[acc] compile 0.113015769 s +[acc] eval 1.6855904e-2 s +[acc] eval 2.841944e-3 s +[acc] eval 2.839868e-3 s +[acc] eval 2.949515e-3 s +[acc] eval 3.028591e-3 s +[acc] eval 2.238848e-3 s +[acc] eval 2.180438e-3 s +[acc] eval 3.338144e-3 s +[acc] eval 2.618312e-3 s +[acc] eval 2.219779e-3 s +[acc] eval 2.565681e-3 s +[acc] eval 3.080139e-3 s +[acc] eval 2.168758e-3 s +[acc] eval 2.877066e-3 s +[acc] eval 2.835224e-3 s +[acc] eval 3.000009e-3 s +[acc] eval 2.559408e-3 s +[acc] eval 2.161279e-3 s +[acc] eval 2.490671e-3 s +[acc] eval 2.001203e-3 s +[acc] eval 1.900832e-3 s +[acc] eval 2.335003e-3 s +[acc] eval 2.833582e-3 s +[acc] eval 2.283123e-3 s +[acc] eval 2.270894e-3 s +[acc] eval 2.251877e-3 s +[acc] eval 2.164846e-3 s +[acc] eval 2.111621e-3 s +[acc] eval 2.364213e-3 s +[acc] eval 2.790127e-3 s +[acc] eval 0.209213981 s +[acc] compile 5.995439e-2 s +[acc] eval 7.341843e-3 s +[acc] eval 7.7080111e-2 s +[acc] compile 0.484654268 s +[acc] eval 2.9515007e-2 s +[acc] eval 0.521096844 s +[acc] compile 0.430239749 s +[acc] eval 3.0338803e-2 s +[acc] eval 0.468187929 s +[acc] compile 0.304777066 s +[acc] eval 1.9259211e-2 s +[acc] eval 0.337644886 s +[acc] compile 0.531289224 s +[acc] eval 2.0985122e-2 s +[acc] eval 0.567484309 s +[acc] compile 0.625781045 s +[acc] eval 3.7380968e-2 s +[acc] eval 0.673779138 s +[acc] compile 0.258614378 s +[acc] eval 1.3607954e-2 s +[acc] eval 1.0019238e-2 s +[acc] eval 8.885271e-3 s +[acc] eval 5.990501e-3 s +[acc] eval 1.0748058e-2 s +[acc] eval 3.225951e-3 s +[acc] eval 2.888376e-3 s +[acc] eval 2.830334e-3 s +[acc] eval 2.851309e-3 s +[acc] eval 2.888093e-3 s +[acc] eval 3.353513e-3 s +[acc] eval 3.065385e-3 s +[acc] eval 0.33324543 s +[acc] compile 4.235189e-3 s +[acc] eval 4.48625e-3 s +[acc] eval 1.473717e-3 s +[acc] eval 1.311398e-3 s +[acc] eval 1.386081e-3 s +[acc] eval 1.229722e-3 s +[acc] eval 1.328431e-3 s +[acc] eval 1.315831e-3 s +[acc] eval 1.205988e-3 s +[acc] eval 1.27744e-3 s +[acc] eval 1.160833e-3 s +[acc] eval 1.42847e-3 s +[acc] eval 1.400885e-3 s +[acc] eval 1.705068e-3 s +[acc] eval 1.491376e-3 s +[acc] eval 1.453835e-3 s +[acc] eval 3.061822e-2 s +[acc] compile 6.6479235e-2 s +[acc] eval 4.655775e-3 s +[acc] eval 9.512586e-3 s +[acc] eval 8.5455965e-2 s +[acc] compile 0.322819945 s +[acc] eval 9.814446e-3 s +[acc] eval 0.337038868 s +[acc] compile 0.140181236 s +[acc] eval 8.631351e-3 s +[acc] eval 7.997666e-3 s +[acc] eval 0.160275923 s +[acc] compile 6.83658e-3 s +[acc] eval 1.2820438e-2 s +[acc] eval 2.2921233e-2 s +[acc] compile 1.2992726e-2 s +[acc] eval 1.5424633e-2 s +[acc] eval 6.967488e-3 s +[acc] eval 4.90993e-3 s +[acc] eval 7.808069e-3 s +[acc] eval 5.2080406e-2 s +[acc] compile 0.124104784 s +[acc] eval 7.521556e-3 s +[acc] eval 2.907653e-3 s +[acc] eval 2.78953e-3 s +[acc] eval 2.392497e-3 s +[acc] eval 1.968544e-3 s +[acc] eval 1.615829e-3 s +[acc] eval 1.480862e-3 s +[acc] eval 1.602529e-3 s +[acc] eval 1.963071e-3 s +[acc] eval 2.137169e-3 s +[acc] eval 1.619371e-3 s +[acc] eval 1.650182e-3 s +[acc] eval 1.582648e-3 s +[acc] eval 1.400281e-3 s +[acc] eval 0.159753033 s +[acc] compile 0.180467202 s +[acc] eval 1.8989554e-2 s +[acc] eval 3.40436e-3 s +[acc] eval 3.210939e-3 s +[acc] eval 3.432689e-3 s +[acc] eval 4.594359e-3 s +[acc] eval 3.175069e-3 s +[acc] eval 3.155335e-3 s +[acc] eval 3.089171e-3 s +[acc] eval 3.964988e-3 s +[acc] eval 3.525344e-3 s +[acc] eval 3.525737e-3 s +[acc] eval 3.25965e-3 s +[acc] eval 3.184596e-3 s +[acc] eval 3.167553e-3 s +[acc] eval 3.409499e-3 s +[acc] eval 3.347609e-3 s +[acc] eval 3.234623e-3 s +[acc] eval 3.208471e-3 s +[acc] eval 3.219439e-3 s +[acc] eval 3.722524e-3 s +[acc] eval 3.315013e-3 s +[acc] eval 3.290724e-3 s +[acc] eval 3.177764e-3 s +[acc] eval 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0.259383014 s +[acc] compile 0.370531128 s +[acc] eval 2.4731196e-2 s +[acc] eval 5.412179e-3 s +[acc] eval 5.114116e-3 s +[acc] eval 5.689384e-3 s +[acc] eval 5.292196e-3 s +[acc] eval 4.831583e-3 s +[acc] eval 7.391493e-3 s +[acc] eval 7.688355e-3 s +[acc] eval 8.647924e-3 s +[acc] eval 4.735045e-3 s +[acc] eval 4.28781e-3 s +[acc] eval 5.804763e-3 s +[acc] eval 0.466377569 s +[acc] compile 0.550711043 s +[acc] eval 2.3090086e-2 s +[acc] eval 0.580910213 s +[acc] compile 8.8302e-4 s +[acc] eval 5.020562e-3 s +[acc] compile 6.93285e-4 s +[acc] eval 5.852535e-3 s +[acc] compile 0.209748048 s +[acc] eval 1.2896446e-2 s +[acc] eval 3.108916e-3 s +[acc] eval 9.84691e-3 s +[acc] eval 2.477361e-3 s +[acc] eval 2.367535e-3 s +[acc] eval 2.282817e-3 s +[acc] eval 2.263527e-3 s +[acc] eval 2.257541e-3 s +[acc] eval 2.200705e-3 s +[acc] eval 2.172915e-3 s +[acc] eval 2.196053e-3 s +[acc] eval 2.222665e-3 s +[acc] eval 2.217917e-3 s +[acc] eval 2.34614e-3 s +[acc] eval 2.783766e-3 s +[acc] eval 2.482432e-3 s +[acc] eval 2.380533e-3 s +[acc] eval 2.43575e-3 s +[acc] eval 2.35207e-3 s +[acc] eval 2.267242e-3 s +[acc] eval 2.732747e-3 s +[acc] eval 2.51087e-3 s +[acc] eval 2.34638e-3 s +[acc] eval 2.2989e-3 s +[acc] eval 2.275744e-3 s +[acc] eval 0.292300879 s +[acc] compile 0.732358817 s +[acc] eval 2.7274777e-2 s +[acc] eval 0.767092402 s +[acc] compile 0.156131481 s +[acc] eval 1.57412e-2 s +[acc] eval 0.178161414 s +[acc] compile 0.369386682 s +[acc] eval 3.200949e-2 s +[acc] eval 7.250059e-3 s +[acc] eval 7.790521e-3 s +[acc] eval 7.021435e-3 s +[acc] eval 7.441842e-3 s +[acc] eval 6.953994e-3 s +[acc] eval 7.515745e-3 s +[acc] eval 7.191086e-3 s +[acc] eval 8.354799e-3 s +[acc] eval 6.768976e-3 s +[acc] eval 7.059226e-3 s +[acc] eval 7.049574e-3 s +[acc] eval 0.489566312 s +[acc] compile 0.234164529 s +[acc] eval 1.6561435e-2 s +[acc] eval 0.262195323 s +[acc] compile 0.431795473 s +[acc] eval 1.2305138e-2 s +[acc] eval 0.461111932 s +[acc] compile 8.455431e-3 s +[acc] eval 8.378001e-3 s +[acc] eval 4.599285e-3 s +[acc] eval 4.119683e-3 s +[acc] eval 9.824856e-3 s +[acc] eval 4.070666e-3 s +[acc] eval 4.929888e-3 s +[acc] eval 5.692121e-3 s +[acc] eval 4.311716e-3 s +[acc] eval 3.161254e-3 s +[acc] eval 4.74436e-3 s +[acc] eval 4.951703e-3 s +[acc] eval 3.805797e-3 s +[acc] eval 7.6885919e-2 s +[acc] compile 0.154433604 s +[acc] eval 1.4970044e-2 s +[acc] eval 0.174241991 s +[acc] compile 0.136027032 s +[acc] eval 2.5932056e-2 s +[acc] eval 1.0299751e-2 s +[acc] eval 3.060959e-3 s +[acc] eval 2.916505e-3 s +[acc] eval 3.735322e-3 s +[acc] eval 3.174373e-3 s +[acc] eval 3.017999e-3 s +[acc] eval 2.763162e-3 s +[acc] eval 2.788828e-3 s +[acc] eval 3.940137e-3 s +[acc] eval 3.269289e-3 s +[acc] eval 3.737624e-3 s +[acc] eval 3.426433e-3 s +[acc] eval 3.445781e-3 s +[acc] eval 3.472163e-3 s +[acc] eval 3.159961e-3 s +[acc] eval 3.119965e-3 s +[acc] eval 2.939724e-3 s +[acc] eval 4.282875e-3 s +[acc] eval 3.049351e-3 s +[acc] eval 3.365345e-3 s +[acc] eval 3.1212e-3 s +[acc] eval 3.514209e-3 s +[acc] eval 4.840581e-3 s +[acc] eval 3.218624e-3 s +[acc] eval 3.032038e-3 s +[acc] eval 2.766893e-3 s +[acc] eval 4.282105e-3 s +[acc] eval 3.11811e-3 s +[acc] eval 3.187833e-3 s +[acc] eval 0.273355265 s +[acc] compile 7.1857747e-2 s +[acc] eval 5.650968e-3 s +[acc] eval 5.836719e-3 s +[acc] eval 1.472345e-3 s +[acc] eval 8.6759507e-2 s +[acc] compile 0.230603766 s +[acc] eval 2.0938574e-2 s +[acc] eval 3.856895e-3 s +[acc] eval 3.664173e-3 s +[acc] eval 3.430191e-3 s +[acc] eval 3.6283e-3 s +[acc] eval 3.610575e-3 s +[acc] eval 7.591346e-3 s +[acc] eval 3.405496e-3 s +[acc] eval 3.531268e-3 s +[acc] eval 3.77306e-3 s +[acc] eval 3.489961e-3 s +[acc] eval 3.364774e-3 s +[acc] eval 0.29963445 s +[acc] compile 3.751487e-3 s +[acc] eval 4.224168e-3 s +[acc] eval 9.07494e-3 s +[acc] compile 6.2404192e-2 s +[acc] eval 1.3626052e-2 s +[acc] eval 7.7144277e-2 s +[acc] compile 3.38671e-3 s +[acc] eval 6.405089e-3 s +[acc] eval 1.4083775e-2 s +[acc] compile 5.2025522e-2 s +[acc] eval 1.3749312e-2 s +[acc] eval 6.9023392e-2 s +[acc] compile 0.277572044 s +[acc] eval 2.8205858e-2 s +[acc] eval 0.310238342 s +[acc] compile 0.126446781 s +[acc] eval 9.455972e-3 s +[acc] eval 0.140530051 s +[acc] compile 6.8159279e-2 s +[acc] eval 2.3548859e-2 s +[acc] eval 9.4272469e-2 s +[acc] compile 0.183705843 s +[acc] eval 1.3992668e-2 s +[acc] eval 0.202295204 s +[acc] compile 0.33614667 s +[acc] eval 1.8402272e-2 s +[acc] eval 0.359591859 s +[acc] compile 0.127711383 s +[acc] eval 9.35438e-3 s +[acc] eval 0.140820658 s +[acc] compile 0.338651491 s +[acc] eval 2.3406422e-2 s +[acc] eval 0.371059622 s +[acc] compile 6.8942108e-2 s +[acc] eval 2.3750305e-2 s +[acc] eval 9.752184e-2 s +[acc] compile 9.71864e-4 s +[acc] eval 7.745469e-3 s +[acc] compile 7.9719895e-2 s +[acc] eval 9.427287e-3 s +[acc] eval 5.003658e-3 s +[acc] eval 4.300755e-3 s +[acc] eval 3.314731e-3 s +[acc] eval 2.28293e-3 s +[acc] eval 2.371814e-3 s +[acc] eval 2.619252e-3 s +[acc] eval 2.469883e-3 s +[acc] eval 2.223509e-3 s +[acc] eval 2.506882e-3 s +[acc] eval 2.984085e-3 s +[acc] eval 0.123706747 s +[acc] compile 0.179790343 s +[acc] eval 2.2301301e-2 s +[acc] eval 0.20651593 s +[acc] compile 0.486256408 s +[acc] eval 1.8940062e-2 s +[acc] eval 0.511023175 s +[acc] compile 1.216255e-2 s +[acc] eval 4.906677e-3 s +[acc] eval 2.230261e-3 s +[acc] eval 2.182462e-3 s +[acc] eval 2.159767e-3 s +[acc] eval 2.174328e-3 s +[acc] eval 2.240353e-3 s +[acc] eval 2.175507e-3 s +[acc] eval 2.370132e-3 s +[acc] eval 2.171385e-3 s +[acc] eval 9.242857e-3 s +[acc] eval 2.067908e-3 s +[acc] eval 1.98375e-3 s +[acc] eval 1.925708e-3 s +[acc] eval 1.948114e-3 s +[acc] eval 5.5131572e-2 s +[acc] compile 6.64704e-2 s +[acc] eval 1.5615823e-2 s +[acc] eval 2.835072e-3 s +[acc] eval 6.396374e-3 s +[acc] eval 3.124072e-3 s +[acc] eval 3.17705e-3 s +[acc] eval 2.944534e-3 s +[acc] eval 2.799881e-3 s +[acc] eval 2.725033e-3 s +[acc] eval 2.805432e-3 s +[acc] eval 2.737246e-3 s +[acc] eval 2.409705e-3 s +[acc] eval 2.275485e-3 s +[acc] eval 2.216284e-3 s +[acc] eval 2.290986e-3 s +[acc] eval 2.243155e-3 s +[acc] eval 2.326466e-3 s +[acc] eval 2.270863e-3 s +[acc] eval 2.323358e-3 s +[acc] eval 2.264292e-3 s +[acc] eval 2.214495e-3 s +[acc] eval 2.20218e-3 s +[acc] eval 2.239193e-3 s +[acc] eval 2.288579e-3 s +[acc] eval 2.559436e-3 s +[acc] eval 0.148339662 s +[acc] compile 3.734265e-3 s +[acc] eval 7.361124e-3 s +[acc] eval 2.634866e-3 s +[acc] eval 2.829739e-3 s +[acc] eval 2.815033e-3 s +[acc] eval 2.441763e-3 s +[acc] eval 2.679895e-3 s +[acc] eval 6.147818e-3 s +[acc] eval 2.408059e-3 s +[acc] eval 2.062753e-3 s +[acc] eval 2.323194e-3 s +[acc] eval 2.236547e-3 s +[acc] eval 2.149507e-3 s +[acc] eval 4.5186057e-2 s +[acc] compile 4.320821e-3 s +[acc] eval 5.338179e-3 s +[acc] eval 3.246535e-3 s +[acc] eval 3.692392e-3 s +[acc] eval 6.894379e-3 s +[acc] eval 3.143221e-3 s +[acc] eval 2.98612e-2 s +[acc] compile 0.200401338 s +[acc] eval 2.1189025e-2 s +[acc] eval 9.68761e-3 s +[acc] eval 4.902174e-3 s +[acc] eval 4.2182e-3 s +[acc] eval 4.169804e-3 s +[acc] eval 4.40826e-3 s +[acc] eval 4.174959e-3 s +[acc] eval 4.400754e-3 s +[acc] eval 4.311541e-3 s +[acc] eval 0.267474403 s +[acc] compile 0.12884837 s +[acc] eval 6.97778e-3 s +[acc] eval 7.834744e-3 s +[acc] eval 3.0398e-3 s +[acc] eval 3.40975e-3 s +[acc] eval 2.999606e-3 s +[acc] eval 2.947587e-3 s +[acc] eval 3.075698e-3 s +[acc] eval 3.022965e-3 s +[acc] eval 3.005219e-3 s +[acc] eval 3.081306e-3 s +[acc] eval 2.833055e-3 s +[acc] eval 2.925074e-3 s +[acc] eval 3.052146e-3 s +[acc] eval 3.13694e-3 s +[acc] eval 3.130097e-3 s +[acc] eval 2.975882e-3 s +[acc] eval 2.832422e-3 s +[acc] eval 3.167984e-3 s +[acc] eval 3.065732e-3 s +[acc] eval 3.052331e-3 s +[acc] eval 2.954631e-3 s +[acc] eval 3.197216e-3 s +[acc] eval 3.464924e-3 s +[acc] eval 0.213544735 s +[acc] compile 0.108366691 s +[acc] eval 2.4816264e-2 s +[acc] eval 0.136184346 s +[acc] compile 6.3539099e-2 s +[acc] eval 2.2500969e-2 s +[acc] eval 3.699635e-3 s +[acc] eval 3.551153e-3 s +[acc] eval 3.069212e-3 s +[acc] eval 3.649978e-3 s +[acc] eval 3.500035e-3 s +[acc] eval 3.141169e-3 s +[acc] eval 3.078613e-3 s +[acc] eval 3.778902e-3 s +[acc] eval 2.990756e-3 s +[acc] eval 2.951675e-3 s +[acc] eval 3.098992e-3 s +[acc] eval 2.904138e-3 s +[acc] eval 3.155176e-3 s +[acc] eval 3.080422e-3 s +[acc] eval 3.42887e-3 s +[acc] eval 3.186196e-3 s +[acc] eval 0.142798755 s +[acc] compile 0.135681938 s +[acc] eval 1.8119848e-2 s +[acc] eval 0.159510382 s +[acc] compile 0.179177869 s +[acc] eval 1.5970282e-2 s +[acc] eval 4.523644e-3 s +[acc] eval 4.350281e-3 s +[acc] eval 3.881834e-3 s +[acc] eval 4.065418e-3 s +[acc] eval 3.935603e-3 s +[acc] eval 3.963554e-3 s +[acc] eval 3.834742e-3 s +[acc] eval 4.314698e-3 s +[acc] eval 4.29745e-3 s +[acc] eval 4.237182e-3 s +[acc] eval 4.393976e-3 s +[acc] eval 4.143716e-3 s +[acc] eval 4.068983e-3 s +[acc] eval 3.919808e-3 s +[acc] eval 4.454638e-3 s +[acc] eval 4.332189e-3 s +[acc] eval 4.182256e-3 s +[acc] eval 4.154467e-3 s +[acc] eval 4.435007e-3 s +[acc] eval 4.30392e-3 s +[acc] eval 4.230315e-3 s +[acc] eval 3.666794e-3 s +[acc] eval 3.537019e-3 s +[acc] eval 3.481446e-3 s +[acc] eval 3.463303e-3 s +[acc] eval 3.893164e-3 s +[acc] eval 4.282783e-3 s +[acc] eval 4.300848e-3 s +[acc] eval 0.316865606 s +[acc] compile 0.455818613 s +[acc] eval 3.223023e-2 s +[acc] eval 0.494735867 s +[acc] compile 0.120588482 s +[acc] eval 1.7383392e-2 s +[acc] eval 7.267612e-3 s +[acc] eval 1.3116783e-2 s +[acc] eval 5.01985e-3 s +[acc] eval 4.905294e-3 s +[acc] eval 4.913292e-3 s +[acc] eval 4.965949e-3 s +[acc] eval 4.544511e-3 s +[acc] eval 5.05684e-3 s +[acc] eval 4.767649e-3 s +[acc] eval 5.259145e-3 s +[acc] eval 4.910533e-3 s +[acc] eval 4.651322e-3 s +[acc] eval 5.024558e-3 s +[acc] eval 4.615327e-3 s +[acc] eval 5.137507e-3 s +[acc] eval 5.004629e-3 s +[acc] eval 4.854287e-3 s +[acc] eval 5.387979e-3 s +[acc] eval 5.117223e-3 s +[acc] eval 5.08455e-3 s +[acc] eval 4.917533e-3 s +[acc] eval 5.188937e-3 s +[acc] eval 4.817674e-3 s +[acc] eval 5.097173e-3 s +[acc] eval 1.1040007e-2 s +[acc] eval 5.071893e-3 s +[acc] eval 4.943835e-3 s +[acc] eval 4.52683e-3 s +[acc] eval 4.810247e-3 s +[acc] eval 0.308061151 s +[acc] compile 0.384263379 s +[acc] eval 3.3901337e-2 s +[acc] eval 0.425141711 s +[acc] compile 0.142493768 s +[acc] eval 2.4274843e-2 s +[acc] eval 0.170987587 s +[acc] compile 0.129839137 s +[acc] eval 2.5394258e-2 s +[acc] eval 0.161543026 s +[acc] compile 0.207415271 s +[acc] eval 1.1155104e-2 s +[acc] eval 8.397856e-3 s +[acc] eval 5.75297e-3 s +[acc] eval 6.336416e-3 s +[acc] eval 1.4746511e-2 s +[acc] eval 3.914135e-3 s +[acc] eval 4.092324e-3 s +[acc] eval 3.916301e-3 s +[acc] eval 3.810919e-3 s +[acc] eval 3.979511e-3 s +[acc] eval 3.873195e-3 s +[acc] eval 4.082073e-3 s +[acc] eval 3.807063e-3 s +[acc] eval 3.974047e-3 s +[acc] eval 4.092296e-3 s +[acc] eval 4.155466e-3 s +[acc] eval 4.146349e-3 s +[acc] eval 4.112236e-3 s +[acc] eval 4.174942e-3 s +[acc] eval 3.995005e-3 s +[acc] eval 4.311998e-3 s +[acc] eval 4.156793e-3 s +[acc] eval 4.072524e-3 s +[acc] eval 4.173339e-3 s +[acc] eval 4.377694e-3 s +[acc] eval 4.012286e-3 s +[acc] eval 3.854559e-3 s +[acc] eval 4.175982e-3 s +[acc] eval 0.354646373 s +[acc] compile 0.162306525 s +[acc] eval 3.5279464e-2 s +[acc] eval 0.203410442 s +[acc] compile 0.133690796 s +[acc] eval 1.640673e-2 s +[acc] eval 3.534988e-3 s +[acc] eval 3.244329e-3 s +[acc] eval 8.273955e-3 s +[acc] eval 2.505488e-3 s +[acc] eval 1.697396e-3 s +[acc] eval 1.715462e-3 s +[acc] eval 1.669129e-3 s +[acc] eval 1.763866e-3 s +[acc] eval 1.760425e-3 s +[acc] eval 1.715861e-3 s +[acc] eval 1.660333e-3 s +[acc] eval 0.183124158 s +[acc] compile 3.664107e-3 s +[acc] eval 5.345578e-3 s +[acc] eval 1.1570474e-2 s +[acc] compile 0.129459363 s +[acc] eval 7.398921e-3 s +[acc] eval 0.143791795 s +[acc] compile 0.184520081 s +[acc] eval 1.1265309e-2 s +[acc] eval 0.2014617 s +[acc] compile 5.75857e-3 s +[acc] eval 1.2750793e-2 s +[acc] eval 2.3284108e-2 s +[acc] compile 7.861094e-3 s +[acc] eval 1.0731577e-2 s +[acc] eval 2.3834062e-2 s +[acc] compile 0.887708938 s +[acc] eval 3.7555967e-2 s +[acc] eval 0.933894063 s +[acc] compile 0.121263268 s +[acc] eval 5.581764e-3 s +[acc] eval 0.13166869 s +[acc] compile 7.48609e-3 s +[acc] eval 1.5845245e-2 s +[acc] eval 2.6386788e-2 s +[acc] compile 6.3522274e-2 s +[acc] eval 5.277072e-3 s +[acc] eval 2.061905e-3 s +[acc] eval 1.860417e-3 s +[acc] eval 7.4896475e-2 s +[acc] compile 1.3030779e-2 s +[acc] eval 9.479493e-3 s +[acc] eval 2.8282018e-2 s +[acc] compile 5.85554e-3 s +[acc] eval 9.212504e-3 s +[acc] eval 1.6615036e-2 s +[acc] compile 7.8162343e-2 s +[acc] eval 1.1513112e-2 s +[acc] eval 9.2422384e-2 s +[acc] compile 6.317861e-3 s +[acc] eval 1.2229821e-2 s +[acc] eval 2.0275803e-2 s +[acc] compile 6.889787e-3 s +[acc] eval 1.7862283e-2 s +[acc] eval 3.1719534e-2 s +[acc] compile 7.2298108e-2 s +[acc] eval 1.7386322e-2 s +[acc] eval 9.3141012e-2 s +[acc] compile 0.121171391 s +[acc] eval 2.4567505e-2 s +[acc] eval 0.148913183 s +[acc] compile 4.5142205e-2 s +[acc] eval 9.224385e-3 s +[acc] eval 5.7043487e-2 s +[acc] compile 5.943755e-3 s +[acc] eval 1.959178e-2 s +[acc] eval 3.3078415e-2 s +[acc] compile 6.650213e-3 s +[acc] eval 8.340834e-3 s +[acc] eval 6.647569e-3 s +[acc] eval 5.366928e-3 s +[acc] eval 4.250631e-3 s +[acc] eval 3.635631e-3 s +[acc] eval 9.941061e-3 s +[acc] eval 2.730108e-3 s +[acc] eval 2.560194e-3 s +[acc] eval 2.472748e-3 s +[acc] eval 2.872025e-3 s +[acc] eval 3.10531e-3 s +[acc] eval 2.860645e-3 s +[acc] eval 6.5211148e-2 s +[acc] compile 0.201223237 s +[acc] eval 1.9596923e-2 s +[acc] eval 0.228129881 s +[acc] compile 0.295703514 s +[acc] eval 1.9002118e-2 s +[acc] eval 0.325611109 s +[acc] compile 7.7653356e-2 s +[acc] eval 6.672864e-3 s +[acc] eval 9.0774997e-2 s +[acc] compile 7.8672318e-2 s +[acc] eval 2.8174119e-2 s +[acc] eval 0.111490556 s +[acc] compile 3.134019e-3 s +[acc] eval 4.447478e-3 s +[acc] eval 9.11459e-4 s +[acc] eval 5.37604e-4 s +[acc] eval 1.0164187e-2 s +[acc] compile 0.625978879 s +[acc] eval 3.2920239e-2 s +[acc] eval 0.664490366 s +[acc] compile 0.538803167 s +[acc] eval 1.6469922e-2 s +[acc] eval 0.560897041 s +[acc] compile 0.797907878 s +[acc] eval 2.162316e-2 s +[acc] eval 0.839692254 s +[acc] compile 0.202079414 s +[acc] eval 1.1884089e-2 s +[acc] eval 4.565915e-3 s +[acc] eval 1.2589663e-2 s +[acc] eval 4.536134e-3 s +[acc] eval 3.87015e-3 s +[acc] eval 3.653821e-3 s +[acc] eval 4.02389e-3 s +[acc] eval 4.890845e-3 s +[acc] eval 4.570761e-3 s +[acc] eval 4.276196e-3 s +[acc] eval 3.601542e-3 s +[acc] eval 3.223484e-3 s +[acc] eval 0.272739026 s +[acc] compile 0.184987693 s +[acc] eval 1.5970994e-2 s +[acc] eval 0.206725054 s +[acc] compile 0.267133399 s +[acc] eval 2.585982e-2 s +[acc] eval 0.297649015 s +[acc] compile 0.265098537 s +[acc] eval 2.9925195e-2 s +[acc] eval 0.303115837 s +[acc] compile 6.240878e-3 s +[acc] eval 9.707248e-3 s +[acc] eval 1.600378e-3 s +[acc] eval 1.226329e-3 s +[acc] eval 1.263942e-3 s +[acc] eval 1.049779e-3 s +[acc] eval 8.09455e-4 s +[acc] eval 7.89234e-4 s +[acc] eval 8.02027e-4 s +[acc] eval 7.92603e-4 s +[acc] eval 7.57862e-4 s +[acc] eval 7.23867e-4 s +[acc] eval 6.87047e-4 s +[acc] eval 7.01551e-4 s +[acc] eval 6.54871e-4 s +[acc] eval 6.95814e-4 s +[acc] eval 7.01981e-4 s +[acc] eval 6.88268e-4 s +[acc] eval 6.71502e-4 s +[acc] eval 7.03374e-4 s +[acc] eval 7.29548e-4 s +[acc] eval 7.38033e-4 s +[acc] eval 7.77547e-4 s +[acc] eval 8.01355e-4 s +[acc] eval 8.37314e-4 s +[acc] eval 7.88721e-4 s +[acc] eval 1.033204e-3 s +[acc] eval 8.0932e-4 s +[acc] eval 7.637e-4 s +[acc] eval 7.88106e-4 s +[acc] eval 8.32884e-4 s +[acc] eval 4.3442324e-2 s +[acc] compile 4.858038e-3 s +[acc] eval 4.974405e-3 s +[acc] eval 1.91208e-3 s +[acc] eval 1.805024e-3 s +[acc] eval 1.55608e-3 s +[acc] eval 1.382172e-3 s +[acc] eval 1.330445e-3 s +[acc] eval 1.202811e-3 s +[acc] eval 1.304011e-3 s +[acc] eval 1.37373e-3 s +[acc] eval 1.204148e-3 s +[acc] eval 1.287957e-3 s +[acc] eval 1.323067e-3 s +[acc] eval 8.638736e-3 s +[acc] eval 1.116846e-3 s +[acc] eval 1.282749e-3 s +[acc] eval 1.314273e-3 s +[acc] eval 1.601226e-3 s +[acc] eval 1.155187e-3 s +[acc] eval 1.153894e-3 s +[acc] eval 1.107856e-3 s +[acc] eval 8.90962e-4 s +[acc] eval 8.9576e-4 s +[acc] eval 8.50923e-4 s +[acc] eval 8.12234e-4 s +[acc] eval 8.25417e-4 s +[acc] eval 8.1303e-4 s +[acc] eval 8.16551e-4 s +[acc] eval 7.73629e-4 s +[acc] eval 8.20384e-4 s +[acc] eval 8.57658e-4 s +[acc] eval 1.312831e-3 s +[acc] eval 5.77154e-2 s +[acc] compile 0.515350773 s +[acc] eval 3.0663012e-2 s +[acc] eval 0.552174391 s +[acc] compile 7.319216e-3 s +[acc] eval 5.731987e-3 s +[acc] eval 1.5478981e-2 s +[acc] compile 6.970691e-3 s +[acc] eval 1.3611115e-2 s +[acc] eval 2.631441e-3 s +[acc] eval 8.005614e-3 s +[acc] eval 2.027292e-3 s +[acc] eval 1.835093e-3 s +[acc] eval 1.494575e-3 s +[acc] eval 1.480375e-3 s +[acc] eval 1.340239e-3 s +[acc] eval 1.291657e-3 s +[acc] eval 1.589569e-3 s +[acc] eval 1.712912e-3 s +[acc] eval 1.541061e-3 s +[acc] eval 1.537576e-3 s +[acc] eval 1.514958e-3 s +[acc] eval 1.416463e-3 s +[acc] eval 1.622847e-3 s +[acc] eval 1.565359e-3 s +[acc] eval 1.473108e-3 s +[acc] eval 1.506012e-3 s +[acc] eval 1.459693e-3 s +[acc] eval 1.304942e-3 s +[acc] eval 1.348723e-3 s +[acc] eval 1.416426e-3 s +[acc] eval 1.428671e-3 s +[acc] eval 1.284502e-3 s +[acc] eval 1.305251e-3 s +[acc] eval 1.316714e-3 s +[acc] eval 1.462903e-3 s +[acc] eval 1.590911e-3 s +[acc] eval 1.597557e-3 s +[acc] eval 1.837287e-3 s +[acc] eval 1.505087e-3 s +[acc] eval 1.425935e-3 s +[acc] eval 1.363482e-3 s +[acc] eval 1.304947e-3 s +[acc] eval 1.327268e-3 s +[acc] eval 1.320898e-3 s +[acc] eval 1.334262e-3 s +[acc] eval 1.958328e-3 s +[acc] eval 1.729534e-3 s +[acc] eval 1.510963e-3 s +[acc] eval 1.751742e-3 s +[acc] eval 1.689894e-3 s +[acc] eval 1.734028e-3 s +[acc] eval 1.523572e-3 s +[acc] eval 1.561274e-3 s +[acc] eval 1.542867e-3 s +[acc] eval 0.103654286 s +[acc] compile 0.476203653 s +[acc] eval 2.6601484e-2 s +[acc] eval 0.509636007 s +[acc] compile 9.0449178e-2 s +[acc] eval 1.8603789e-2 s +[acc] eval 0.114272931 s +[acc] compile 0.304805835 s +[acc] eval 2.6275397e-2 s +[acc] eval 0.337067551 s +[acc] compile 0.186634288 s +[acc] eval 1.380656e-2 s +[acc] eval 0.207144618 s +[acc] compile 7.892979e-3 s +[acc] eval 6.182125e-3 s +[acc] eval 8.67671e-3 s +[acc] eval 1.646602e-3 s +[acc] eval 2.6861195e-2 s +[acc] compile 1.0789981e-2 s +[acc] eval 1.6890766e-2 s +[acc] eval 3.2951389e-2 s +[acc] compile 7.6397391e-2 s +[acc] eval 1.3436541e-2 s +[acc] eval 4.890555e-3 s +[acc] eval 4.513087e-3 s +[acc] eval 4.755576e-3 s +[acc] eval 4.85912e-3 s +[acc] eval 3.993706e-3 s +[acc] eval 3.979176e-3 s +[acc] eval 4.765359e-3 s +[acc] eval 4.448866e-3 s +[acc] eval 4.044064e-3 s +[acc] eval 4.077603e-3 s +[acc] eval 4.348844e-3 s +[acc] eval 3.992689e-3 s +[acc] eval 3.932578e-3 s +[acc] eval 3.89004e-3 s +[acc] eval 4.519818e-3 s +[acc] eval 4.762891e-3 s +[acc] eval 4.782246e-3 s +[acc] eval 4.374679e-3 s +[acc] eval 4.640058e-3 s +[acc] eval 4.00667e-3 s +[acc] eval 3.98665e-3 s +[acc] eval 3.955596e-3 s +[acc] eval 4.062066e-3 s +[acc] eval 3.95644e-3 s +[acc] eval 3.892358e-3 s +[acc] eval 4.000419e-3 s +[acc] eval 3.986539e-3 s +[acc] eval 3.900196e-3 s +[acc] eval 3.901754e-3 s +[acc] eval 3.921706e-3 s +[acc] eval 0.224117451 s +[acc] compile 0.393454774 s +[acc] eval 2.8155845e-2 s +[acc] eval 0.434642741 s +[acc] compile 0.452817798 s +[acc] eval 4.5347768e-2 s +[acc] eval 0.511498936 s +[acc] compile 0.358133124 s +[acc] eval 3.1671175e-2 s +[acc] eval 0.395569712 s +[acc] compile 0.597698397 s +[acc] eval 1.1416161e-2 s +[acc] eval 0.621224453 s +[acc] compile 5.380879e-3 s +[acc] eval 8.730364e-3 s +[acc] eval 4.236225e-3 s +[acc] eval 2.81626e-3 s +[acc] eval 2.513915e-3 s +[acc] eval 2.34443e-3 s +[acc] eval 2.208212e-3 s +[acc] eval 2.661548e-3 s +[acc] eval 8.483141e-3 s +[acc] eval 1.536389e-3 s +[acc] eval 1.594626e-3 s +[acc] eval 1.795334e-3 s +[acc] eval 1.387749e-3 s +[acc] eval 1.098395e-3 s +[acc] eval 1.242525e-3 s +[acc] eval 1.337425e-3 s +[acc] eval 1.603178e-3 s +[acc] eval 1.732513e-3 s +[acc] eval 1.781942e-3 s +[acc] eval 1.694836e-3 s +[acc] eval 6.0996401e-2 s +[acc] compile 0.309754613 s +[acc] eval 2.2713029e-2 s +[acc] eval 0.341935205 s +[acc] compile 0.330467723 s +[acc] eval 1.6606705e-2 s +[acc] eval 0.358763853 s +[acc] compile 0.129407657 s +[acc] eval 2.6965569e-2 s +[acc] eval 0.166956689 s +[acc] compile 3.007597e-3 s +[acc] eval 2.476474e-3 s +[acc] eval 5.36123e-4 s +[acc] eval 5.69766e-4 s +[acc] eval 7.554523e-3 s +[acc] compile 0.19366089 s +[acc] eval 1.666297e-2 s +[acc] eval 6.78324e-3 s +[acc] eval 2.724176e-3 s +[acc] eval 3.404125e-3 s +[acc] eval 2.979955e-3 s +[acc] eval 3.122424e-3 s +[acc] eval 2.836112e-3 s +[acc] eval 2.979411e-3 s +[acc] eval 2.881738e-3 s +[acc] eval 3.011611e-3 s +[acc] eval 2.917489e-3 s +[acc] eval 2.817373e-3 s +[acc] eval 2.778653e-3 s +[acc] eval 2.82666e-3 s +[acc] eval 2.822527e-3 s +[acc] eval 3.111342e-3 s +[acc] eval 3.423441e-3 s +[acc] eval 3.323158e-3 s +[acc] eval 3.405116e-3 s +[acc] eval 3.059388e-3 s +[acc] eval 3.318776e-3 s +[acc] eval 3.17795e-3 s +[acc] eval 0.283587117 s +[acc] compile 2.286361e-3 s +[acc] eval 2.630344e-3 s +[acc] eval 7.59562e-4 s +[acc] eval 5.66031e-4 s +[acc] eval 7.148887e-3 s +[acc] compile 0.138664514 s +[acc] eval 2.6631849e-2 s +[acc] eval 0.170893689 s +[acc] compile 4.409171e-3 s +[acc] eval 3.356082e-3 s +[acc] eval 7.76991e-4 s +[acc] eval 1.039346e-3 s +[acc] eval 1.1024746e-2 s +[acc] compile 0.117160368 s +[acc] eval 1.9187412e-2 s +[acc] eval 0.139649451 s +[acc] compile 2.878684e-3 s +[acc] eval 2.455074e-3 s +[acc] eval 6.82577e-4 s +[acc] eval 4.70618e-4 s +[acc] eval 7.27834e-3 s +[acc] compile 0.437939325 s +[acc] eval 3.0384233e-2 s +[acc] eval 0.474998556 s +[acc] compile 4.102736e-3 s +[acc] eval 2.612893e-3 s +[acc] eval 8.65766e-4 s +[acc] eval 5.8281e-4 s +[acc] eval 9.217246e-3 s +[acc] compile 0.141783779 s +[acc] eval 4.042542e-3 s +[acc] eval 1.581881e-3 s +[acc] eval 9.63982e-4 s +[acc] eval 0.151919161 s +[acc] compile 5.7373449e-2 s +[acc] eval 6.379596e-3 s +[acc] eval 1.255472e-3 s +[acc] eval 1.349688e-3 s +[acc] eval 6.8340688e-2 s +[acc] compile 5.421033e-3 s +[acc] eval 2.1560289e-2 s +[acc] eval 2.9863107e-2 s +[acc] compile 9.0443901e-2 s +[acc] eval 7.500641e-3 s +[acc] eval 0.102193807 s +[acc] compile 0.211330578 s +[acc] eval 2.8501785e-2 s +[acc] eval 0.242782669 s +[acc] compile 0.164564524 s +[acc] eval 1.1191261e-2 s +[acc] eval 0.180206987 s +[acc] compile 8.335886e-2 s +[acc] eval 2.0994646e-2 s +[acc] eval 0.110625142 s +[acc] compile 1.2387691e-2 s +[acc] eval 1.2059414e-2 s +[acc] eval 2.865501e-2 s +[acc] compile 0.652929747 s +[acc] eval 3.338515e-2 s +[acc] eval 0.695526833 s +[acc] compile 0.149588479 s +[acc] eval 1.3279496e-2 s +[acc] eval 0.170501561 s +[acc] compile 0.199950989 s +[acc] eval 1.5834184e-2 s +[acc] eval 3.494261e-3 s +[acc] eval 3.985248e-3 s +[acc] eval 3.819668e-3 s +[acc] eval 3.406722e-3 s +[acc] eval 3.12665e-3 s +[acc] eval 2.929597e-3 s +[acc] eval 2.729993e-3 s +[acc] eval 3.088437e-3 s +[acc] eval 2.763988e-3 s +[acc] eval 2.624309e-3 s +[acc] eval 2.781796e-3 s +[acc] eval 2.701198e-3 s +[acc] eval 2.96883e-3 s +[acc] eval 3.143317e-3 s +[acc] eval 3.608916e-3 s +[acc] eval 3.875385e-3 s +[acc] eval 3.755886e-3 s +[acc] eval 3.285227e-3 s +[acc] eval 3.167551e-3 s +[acc] eval 2.788228e-3 s +[acc] eval 2.687308e-3 s +[acc] eval 2.670802e-3 s +[acc] eval 3.247614e-3 s +[acc] eval 2.741081e-3 s +[acc] eval 2.764878e-3 s +[acc] eval 2.665944e-3 s +[acc] eval 2.62348e-3 s +[acc] eval 0.304475144 s +[acc] compile 7.0904818e-2 s +[acc] eval 4.703779e-3 s +[acc] eval 7.8130636e-2 s +[acc] compile 0.326987525 s +[acc] eval 1.6513339e-2 s +[acc] eval 0.34951883 s +[acc] compile 6.23633e-4 s +[acc] eval 1.1655091e-2 s +[acc] compile 9.4636e-4 s +[acc] eval 8.026873e-3 s +[acc] compile 5.8947258e-2 s +[acc] eval 1.204899e-2 s +[acc] eval 1.7226169e-2 s +[acc] eval 1.918968e-3 s +[acc] eval 9.2445123e-2 s +[acc] compile 1.129422e-3 s +[acc] eval 1.7802016e-2 s +[acc] compile 0.228815785 s +[acc] eval 2.6855526e-2 s +[acc] eval 4.85886e-3 s +[acc] eval 5.078566e-3 s +[acc] eval 4.764544e-3 s +[acc] eval 4.663415e-3 s +[acc] eval 4.757172e-3 s +[acc] eval 5.201001e-3 s +[acc] eval 4.869706e-3 s +[acc] eval 4.600207e-3 s +[acc] eval 4.862101e-3 s +[acc] eval 4.113176e-3 s +[acc] eval 4.030502e-3 s +[acc] eval 3.932191e-3 s +[acc] eval 4.610065e-3 s +[acc] eval 4.189765e-3 s +[acc] eval 4.456404e-3 s +[acc] eval 4.31076e-3 s +[acc] eval 4.06389e-3 s +[acc] eval 5.180866e-3 s +[acc] eval 4.901249e-3 s +[acc] eval 4.525529e-3 s +[acc] eval 4.47412e-3 s +[acc] eval 4.307695e-3 s +[acc] eval 4.563044e-3 s +[acc] eval 5.090137e-3 s +[acc] eval 5.115837e-3 s +[acc] eval 5.031654e-3 s +[acc] eval 4.924548e-3 s +[acc] eval 4.940808e-3 s +[acc] eval 5.057377e-3 s +[acc] eval 0.398768398 s +[acc] compile 2.514111e-3 s +[acc] eval 3.482171e-3 s +[acc] eval 3.77666e-4 s +[acc] eval 4.91022e-4 s +[acc] eval 7.725718e-3 s +[acc] compile 4.469573e-3 s +[acc] eval 1.0504388e-2 s +[acc] eval 1.0510908e-2 s +[acc] eval 2.8085615e-2 s +[acc] compile 5.085197e-3 s +[acc] eval 6.505634e-3 s +[acc] eval 2.925022e-3 s +[acc] eval 2.726356e-3 s +[acc] eval 2.788973e-3 s +[acc] eval 2.663345e-3 s +[acc] eval 2.684362e-3 s +[acc] eval 2.898648e-3 s +[acc] eval 8.239842e-3 s +[acc] eval 2.558609e-3 s +[acc] eval 2.851108e-3 s +[acc] eval 3.148624e-3 s +[acc] eval 2.945349e-3 s +[acc] eval 2.875011e-3 s +[acc] eval 2.817749e-3 s +[acc] eval 2.482552e-3 s +[acc] eval 3.101097e-3 s +[acc] eval 3.250271e-3 s +[acc] eval 6.731646e-2 s +[acc] compile 6.26904e-3 s +[acc] eval 8.340757e-3 s +[acc] eval 3.216953e-3 s +[acc] eval 3.242613e-3 s +[acc] eval 7.024472e-3 s +[acc] eval 2.591487e-3 s +[acc] eval 2.686792e-3 s +[acc] eval 2.961401e-3 s +[acc] eval 2.903265e-3 s +[acc] eval 2.583331e-3 s +[acc] eval 2.342131e-3 s +[acc] eval 2.432205e-3 s +[acc] eval 2.504775e-3 s +[acc] eval 2.466496e-3 s +[acc] eval 2.234072e-3 s +[acc] eval 2.215081e-3 s +[acc] eval 2.195164e-3 s +[acc] eval 2.382735e-3 s +[acc] eval 2.435372e-3 s +[acc] eval 2.285489e-3 s +[acc] eval 2.258523e-3 s +[acc] eval 7.1745212e-2 s +[acc] compile 7.130136e-3 s +[acc] eval 1.5943449e-2 s +[acc] eval 6.499992e-3 s +[acc] eval 1.6496384e-2 s +[acc] eval 3.852173e-3 s +[acc] eval 4.879129e-3 s +[acc] eval 3.815351e-3 s +[acc] eval 2.883258e-3 s +[acc] eval 2.759135e-3 s +[acc] eval 2.763709e-3 s +[acc] eval 2.798793e-3 s +[acc] eval 2.904917e-3 s +[acc] eval 2.84012e-3 s +[acc] eval 2.784249e-3 s +[acc] eval 2.766951e-3 s +[acc] eval 2.715975e-3 s +[acc] eval 2.655043e-3 s +[acc] eval 2.81637e-3 s +[acc] eval 2.885399e-3 s +[acc] eval 2.828561e-3 s +[acc] eval 9.978048e-2 s diff --git a/phas2_n2.err b/phas2_n2.err new file mode 100644 index 0000000..b19651b --- /dev/null +++ b/phas2_n2.err @@ -0,0 +1,3611 @@ +[acc] compile 1.3517798e-2 s +[acc] eval 1.925343022 s +[acc] compile 3.38165e-4 s +[acc] eval 6.9127284e-2 s +[acc] compile 3.45041e-4 s +[acc] eval 6.7748466e-2 s +[acc] compile 6.3629e-4 s +[acc] eval 0.156476348 s +[acc] compile 5.3778e-4 s +[acc] eval 0.156675908 s +[acc] compile 5.61072e-4 s +[acc] eval 0.158222123 s +[acc] compile 5.77608e-4 s +[acc] eval 0.165095295 s +[acc] compile 3.31213e-4 s +[acc] eval 0.140358027 s +[acc] compile 3.30338e-4 s +[acc] eval 6.8478166e-2 s +[acc] compile 3.777633e-3 s +[acc] eval 9.702142e-3 s +[acc] eval 1.5464641e-2 s +[acc] compile 0.121749797 s +[acc] eval 4.423951e-2 s +[acc] eval 0.170902813 s +[acc] compile 0.335051129 s +[acc] eval 1.7496599e-2 s +[acc] eval 8.459893e-3 s +[acc] eval 1.326026e-2 s +[acc] eval 6.156712e-3 s +[acc] eval 5.183335e-3 s +[acc] eval 3.545588e-3 s +[acc] eval 2.860763e-3 s +[acc] eval 2.891872e-3 s +[acc] eval 2.987505e-3 s +[acc] eval 2.426659e-3 s +[acc] eval 2.540245e-3 s +[acc] eval 2.70637e-3 s +[acc] eval 2.999266e-3 s +[acc] eval 2.926457e-3 s +[acc] eval 2.434203e-3 s +[acc] eval 2.407314e-3 s +[acc] eval 2.34653e-3 s +[acc] eval 2.389328e-3 s +[acc] eval 2.386761e-3 s +[acc] eval 2.472503e-3 s +[acc] eval 2.830081e-3 s +[acc] eval 2.866139e-3 s +[acc] eval 2.768205e-3 s +[acc] eval 2.711342e-3 s +[acc] eval 2.642366e-3 s +[acc] eval 2.757851e-3 s +[acc] eval 2.709662e-3 s +[acc] eval 2.856568e-3 s +[acc] eval 3.126124e-3 s +[acc] eval 2.953156e-3 s +[acc] eval 2.890073e-3 s +[acc] eval 0.464221887 s +[acc] compile 0.137057012 s +[acc] eval 1.7510972e-2 s +[acc] eval 5.100958e-3 s +[acc] eval 3.416983e-3 s +[acc] eval 2.827599e-3 s +[acc] eval 2.46934e-3 s +[acc] eval 2.148584e-3 s +[acc] eval 2.204266e-3 s +[acc] eval 2.026609e-3 s +[acc] eval 1.518227e-3 s +[acc] eval 0.179781574 s +[acc] compile 8.07392e-4 s +[acc] eval 6.752958e-3 s +[acc] compile 0.143071688 s +[acc] eval 1.5949959e-2 s +[acc] eval 3.176088e-3 s +[acc] eval 0.16565897 s +[acc] compile 4.69997e-4 s +[acc] eval 3.634593e-3 s +[acc] compile 3.53276e-4 s +[acc] eval 9.749746e-3 s +[acc] compile 0.261705849 s +[acc] eval 1.5248221e-2 s +[acc] eval 0.279082133 s +[acc] compile 6.2452904e-2 s +[acc] eval 5.375135e-3 s +[acc] eval 7.0099508e-2 s +[acc] compile 6.538005e-2 s +[acc] eval 1.8271965e-2 s +[acc] eval 2.478362e-3 s +[acc] eval 2.651918e-3 s +[acc] eval 2.265823e-3 s +[acc] eval 2.244826e-3 s +[acc] eval 2.326323e-3 s +[acc] eval 2.162231e-3 s +[acc] eval 2.049943e-3 s +[acc] eval 1.94261e-3 s +[acc] eval 2.023528e-3 s +[acc] eval 1.843494e-3 s +[acc] eval 2.068539e-3 s +[acc] eval 2.571973e-3 s +[acc] eval 0.114844221 s +[acc] compile 5.770746e-3 s +[acc] eval 4.69556e-3 s +[acc] eval 2.434859e-3 s +[acc] eval 3.417336e-3 s +[acc] eval 3.486923e-3 s +[acc] eval 5.031604e-3 s +[acc] eval 5.099855e-3 s +[acc] eval 1.1652558e-2 s +[acc] eval 1.969708e-3 s +[acc] eval 1.448551e-3 s +[acc] eval 1.458507e-3 s +[acc] eval 1.179125e-3 s +[acc] eval 1.127739e-3 s +[acc] eval 1.309736e-3 s +[acc] eval 1.365739e-3 s +[acc] eval 1.455281e-3 s +[acc] eval 1.413435e-3 s +[acc] eval 1.425865e-3 s +[acc] eval 1.344099e-3 s +[acc] eval 1.27028e-3 s +[acc] eval 1.316408e-3 s +[acc] eval 1.194297e-3 s +[acc] eval 1.183941e-3 s +[acc] eval 1.217143e-3 s +[acc] eval 1.258884e-3 s +[acc] eval 1.531787e-3 s +[acc] eval 1.208404e-3 s +[acc] eval 1.133195e-3 s +[acc] eval 1.092337e-3 s +[acc] eval 1.086614e-3 s +[acc] eval 1.428448e-3 s +[acc] eval 7.6301618e-2 s +[acc] compile 4.731008e-3 s +[acc] eval 5.064159e-3 s +[acc] eval 2.250127e-3 s +[acc] eval 2.125222e-3 s +[acc] eval 1.792839e-3 s +[acc] eval 1.623187e-3 s +[acc] eval 1.673658e-3 s +[acc] eval 2.095227e-3 s +[acc] eval 7.279063e-3 s +[acc] eval 1.608998e-3 s +[acc] eval 1.479927e-3 s +[acc] eval 1.431785e-3 s +[acc] eval 1.441674e-3 s +[acc] eval 3.6675684e-2 s +[acc] compile 0.33010516 s +[acc] eval 2.3516604e-2 s +[acc] eval 0.358304053 s +[acc] compile 0.138833285 s +[acc] eval 9.294732e-3 s +[acc] eval 2.353259e-3 s +[acc] eval 2.635512e-3 s +[acc] eval 5.12803e-3 s +[acc] eval 8.929621e-3 s +[acc] eval 1.825706e-3 s +[acc] eval 1.64992e-3 s +[acc] eval 1.601262e-3 s +[acc] eval 1.589398e-3 s +[acc] eval 1.584881e-3 s +[acc] eval 1.540229e-3 s +[acc] eval 3.050692e-3 s +[acc] eval 2.1734e-3 s +[acc] eval 1.72523e-3 s +[acc] eval 1.617193e-3 s +[acc] eval 1.619718e-3 s +[acc] eval 3.889943e-3 s +[acc] eval 2.708975e-3 s +[acc] eval 0.196916813 s +[acc] compile 0.169396986 s +[acc] eval 1.7735565e-2 s +[acc] eval 0.192175619 s +[acc] compile 3.456757e-3 s +[acc] eval 7.885808e-3 s +[acc] eval 1.090099e-3 s +[acc] eval 5.9595e-4 s +[acc] eval 7.90191e-4 s +[acc] eval 1.4891393e-2 s +[acc] compile 3.416163e-3 s +[acc] eval 3.511268e-3 s +[acc] eval 1.136271e-3 s +[acc] eval 1.454943e-3 s +[acc] eval 1.0881598e-2 s +[acc] compile 0.213613546 s +[acc] eval 1.4495924e-2 s +[acc] eval 0.232879252 s +[acc] compile 0.113815731 s +[acc] eval 2.2768154e-2 s +[acc] eval 0.140816801 s +[acc] compile 0.281493869 s +[acc] eval 2.8555755e-2 s +[acc] eval 0.314123319 s +[acc] compile 0.289672635 s +[acc] eval 1.6139773e-2 s +[acc] eval 5.653288e-3 s +[acc] eval 1.329152e-2 s +[acc] eval 4.360295e-3 s +[acc] eval 4.344374e-3 s +[acc] eval 4.516917e-3 s +[acc] eval 3.349022e-3 s +[acc] eval 3.72064e-3 s +[acc] eval 3.413968e-3 s +[acc] eval 3.439166e-3 s +[acc] eval 3.486245e-3 s +[acc] eval 3.281198e-3 s +[acc] eval 3.273593e-3 s +[acc] eval 3.221508e-3 s +[acc] eval 4.04517e-3 s +[acc] eval 4.429754e-3 s +[acc] eval 3.280117e-3 s +[acc] eval 1.1171272e-2 s +[acc] eval 3.500388e-3 s +[acc] eval 3.1411e-3 s +[acc] eval 3.387196e-3 s +[acc] eval 3.147336e-3 s +[acc] eval 3.047968e-3 s +[acc] eval 3.102656e-3 s +[acc] eval 3.055059e-3 s +[acc] eval 3.296197e-3 s +[acc] eval 3.142502e-3 s +[acc] eval 3.482086e-3 s +[acc] eval 6.797064e-3 s +[acc] eval 3.25239e-3 s +[acc] eval 3.076236e-3 s +[acc] eval 0.439594457 s +[acc] compile 5.5969119e-2 s +[acc] eval 7.51005e-3 s +[acc] eval 2.734315e-3 s +[acc] eval 2.418081e-3 s +[acc] eval 5.764008e-3 s +[acc] eval 1.294502e-3 s +[acc] eval 1.270579e-3 s +[acc] eval 9.54244e-4 s +[acc] eval 9.14133e-4 s +[acc] eval 8.61108e-4 s +[acc] eval 7.76481e-4 s +[acc] eval 7.6623e-4 s +[acc] eval 7.7598e-4 s +[acc] eval 7.52836e-4 s +[acc] eval 7.46693e-4 s +[acc] eval 7.54684e-4 s +[acc] eval 1.018553e-3 s +[acc] eval 8.64499e-4 s +[acc] eval 9.37162e-4 s +[acc] eval 9.542e-4 s +[acc] eval 1.901815e-3 s +[acc] eval 1.401517e-3 s +[acc] eval 8.31121e-4 s +[acc] eval 8.14064e-4 s +[acc] eval 7.96296e-4 s +[acc] eval 7.41098e-4 s +[acc] eval 7.65803e-4 s +[acc] eval 7.44707e-4 s +[acc] eval 7.71129e-4 s +[acc] eval 7.73968e-4 s +[acc] eval 7.74628e-4 s +[acc] eval 7.99104e-4 s +[acc] eval 0.102547077 s +[acc] compile 4.9635495e-2 s +[acc] eval 1.1942317e-2 s +[acc] eval 1.351547e-3 s +[acc] eval 1.338876e-3 s +[acc] eval 1.317151e-3 s +[acc] eval 1.316074e-3 s +[acc] eval 4.864299e-3 s +[acc] eval 1.008384e-3 s +[acc] eval 1.138091e-3 s +[acc] eval 1.055308e-3 s +[acc] eval 1.015526e-3 s +[acc] eval 9.43361e-4 s +[acc] eval 1.021113e-3 s +[acc] eval 9.58097e-4 s +[acc] eval 9.83408e-4 s +[acc] eval 9.38999e-4 s +[acc] eval 9.77506e-4 s +[acc] eval 9.69817e-4 s +[acc] eval 9.6229e-4 s +[acc] eval 9.5409e-4 s +[acc] eval 9.24257e-4 s +[acc] eval 9.02607e-4 s +[acc] eval 9.44672e-4 s +[acc] eval 1.037404e-3 s +[acc] eval 1.12418e-3 s +[acc] eval 9.7188e-4 s +[acc] eval 9.45959e-4 s +[acc] eval 9.64785e-4 s +[acc] eval 9.54144e-4 s +[acc] eval 9.59086e-4 s +[acc] eval 9.48583e-4 s +[acc] eval 9.03894e-4 s +[acc] eval 9.07585e-4 s +[acc] eval 9.58216e-4 s +[acc] eval 0.101811555 s +[acc] compile 0.128719929 s +[acc] eval 1.3565058e-2 s +[acc] eval 2.45955e-3 s +[acc] eval 5.958035e-3 s +[acc] eval 2.10217e-3 s +[acc] eval 2.275739e-3 s +[acc] eval 2.167679e-3 s +[acc] eval 1.9531e-3 s +[acc] eval 1.912905e-3 s +[acc] eval 1.977796e-3 s +[acc] eval 2.085234e-3 s +[acc] eval 2.096678e-3 s +[acc] eval 1.928913e-3 s +[acc] eval 0.173180625 s +[acc] compile 0.218887493 s +[acc] eval 1.5994241e-2 s +[acc] eval 8.694591e-3 s +[acc] eval 4.122502e-3 s +[acc] eval 3.431084e-3 s +[acc] eval 3.3165e-3 s +[acc] eval 3.844557e-3 s +[acc] eval 4.520356e-3 s +[acc] eval 4.414691e-3 s +[acc] eval 3.463684e-3 s +[acc] eval 3.632503e-3 s +[acc] eval 3.497774e-3 s +[acc] eval 3.380295e-3 s +[acc] eval 0.285729063 s +[acc] compile 8.499255e-3 s +[acc] eval 7.575696e-3 s +[acc] eval 1.96952e-2 s +[acc] compile 7.892202e-3 s +[acc] eval 2.85865e-3 s +[acc] eval 1.1675068e-2 s +[acc] compile 5.222892e-3 s +[acc] eval 4.945969e-3 s +[acc] eval 1.2780388e-2 s +[acc] compile 0.201474489 s +[acc] eval 2.3977937e-2 s +[acc] eval 3.470578e-3 s +[acc] eval 3.09529e-3 s +[acc] eval 3.34448e-3 s +[acc] eval 3.293466e-3 s +[acc] eval 3.174986e-3 s +[acc] eval 0.24669585 s +[acc] compile 8.09262e-4 s +[acc] eval 7.15497e-3 s +[acc] compile 5.4677691e-2 s +[acc] eval 6.957288e-3 s +[acc] eval 6.4285583e-2 s +[acc] compile 0.451006571 s +[acc] eval 2.0927724e-2 s +[acc] eval 0.478134393 s +[acc] compile 0.396254899 s +[acc] eval 1.7956518e-2 s +[acc] eval 0.419001902 s +[acc] compile 0.233601858 s +[acc] eval 2.1817517e-2 s +[acc] eval 0.261637663 s +[acc] compile 0.127392638 s +[acc] eval 7.945837e-3 s +[acc] eval 4.176495e-3 s +[acc] eval 4.569699e-3 s +[acc] eval 4.130269e-3 s +[acc] eval 1.2075393e-2 s +[acc] eval 4.246382e-3 s +[acc] eval 3.136119e-3 s +[acc] eval 2.777225e-3 s +[acc] eval 3.027859e-3 s +[acc] eval 3.271593e-3 s +[acc] eval 3.226844e-3 s +[acc] eval 2.908766e-3 s +[acc] eval 2.785831e-3 s +[acc] eval 3.127701e-3 s +[acc] eval 2.895512e-3 s +[acc] eval 2.765577e-3 s +[acc] eval 2.733831e-3 s +[acc] eval 2.905895e-3 s +[acc] eval 2.978395e-3 s +[acc] eval 2.843944e-3 s +[acc] eval 2.742499e-3 s +[acc] eval 3.416808e-3 s +[acc] eval 2.900473e-3 s +[acc] eval 2.820405e-3 s +[acc] eval 2.810393e-3 s +[acc] eval 2.78904e-3 s +[acc] eval 3.131822e-3 s +[acc] eval 2.784852e-3 s +[acc] eval 2.777647e-3 s +[acc] eval 2.792874e-3 s +[acc] eval 0.241667193 s +[acc] compile 0.292029619 s +[acc] eval 1.2576438e-2 s +[acc] eval 1.1419669e-2 s +[acc] eval 5.45992e-3 s +[acc] eval 5.620438e-3 s +[acc] eval 4.767688e-3 s +[acc] eval 3.373834e-3 s +[acc] eval 4.204195e-3 s +[acc] eval 3.050332e-3 s +[acc] eval 3.329989e-3 s +[acc] eval 3.202666e-3 s +[acc] eval 3.002513e-3 s +[acc] eval 2.866081e-3 s +[acc] eval 0.359181103 s +[acc] compile 1.1576237e-2 s +[acc] eval 4.763814e-3 s +[acc] eval 5.303064e-3 s +[acc] eval 2.597641e-3 s +[acc] eval 2.464424e-3 s +[acc] eval 2.186349e-3 s +[acc] eval 2.121866e-3 s +[acc] eval 1.971629e-3 s +[acc] eval 2.156224e-3 s +[acc] eval 2.032538e-3 s +[acc] eval 2.156908e-3 s +[acc] eval 2.428049e-3 s +[acc] eval 2.261168e-3 s +[acc] eval 2.264263e-3 s +[acc] eval 2.104325e-3 s +[acc] eval 2.209108e-3 s +[acc] eval 2.095104e-3 s +[acc] eval 2.109806e-3 s +[acc] eval 2.269876e-3 s +[acc] eval 1.589167e-3 s +[acc] eval 1.29523e-3 s +[acc] eval 1.31915e-3 s +[acc] eval 1.326588e-3 s +[acc] eval 1.242258e-3 s +[acc] eval 1.262413e-3 s +[acc] eval 1.228758e-3 s +[acc] eval 1.2172e-3 s +[acc] eval 1.191599e-3 s +[acc] eval 1.190763e-3 s +[acc] eval 1.170117e-3 s +[acc] eval 1.149644e-3 s +[acc] eval 1.168315e-3 s +[acc] eval 7.7434973e-2 s +[acc] compile 0.152506294 s +[acc] eval 9.916966e-3 s +[acc] eval 0.16611539 s +[acc] compile 4.32823e-3 s +[acc] eval 8.901596e-3 s +[acc] eval 4.601627e-3 s +[acc] eval 1.9938297e-2 s +[acc] compile 0.234259557 s +[acc] eval 2.0094417e-2 s +[acc] eval 0.259938846 s +[acc] compile 5.5801e-4 s +[acc] eval 1.3668871e-2 s +[acc] compile 5.04786e-4 s +[acc] eval 7.447112e-3 s +[acc] compile 0.196476123 s +[acc] eval 1.9161635e-2 s +[acc] eval 0.225718726 s +[acc] compile 0.218654649 s +[acc] eval 2.4224484e-2 s +[acc] eval 0.247084153 s +[acc] compile 0.106018046 s +[acc] eval 9.940395e-3 s +[acc] eval 2.367713e-3 s +[acc] eval 2.33216e-3 s +[acc] eval 2.459017e-3 s +[acc] eval 5.705353e-3 s +[acc] eval 1.647161e-3 s +[acc] eval 1.539429e-3 s +[acc] eval 1.488574e-3 s +[acc] eval 1.609273e-3 s +[acc] eval 1.544116e-3 s +[acc] eval 1.615285e-3 s +[acc] eval 1.546732e-3 s +[acc] eval 2.178822e-3 s +[acc] eval 2.092881e-3 s +[acc] eval 0.14748591 s +[acc] compile 8.061668e-3 s +[acc] eval 7.212622e-3 s +[acc] eval 1.8280775e-2 s +[acc] compile 0.183482506 s +[acc] eval 1.7178801e-2 s +[acc] eval 2.14119e-3 s +[acc] eval 1.833667e-3 s +[acc] eval 1.809583e-3 s +[acc] eval 1.727339e-3 s +[acc] eval 1.736755e-3 s +[acc] eval 1.56152e-3 s +[acc] eval 1.549853e-3 s +[acc] eval 2.596522e-3 s +[acc] eval 2.185551e-3 s +[acc] eval 2.11093e-3 s +[acc] eval 1.956549e-3 s +[acc] eval 1.75009e-3 s +[acc] eval 1.638533e-3 s +[acc] eval 1.511658e-3 s +[acc] eval 1.543916e-3 s +[acc] eval 2.321589e-3 s +[acc] eval 1.913982e-3 s +[acc] eval 1.585038e-3 s +[acc] eval 1.595108e-3 s +[acc] eval 1.549032e-3 s +[acc] eval 1.549261e-3 s +[acc] eval 1.536782e-3 s +[acc] eval 1.528153e-3 s +[acc] eval 1.507482e-3 s +[acc] eval 1.522259e-3 s +[acc] eval 1.572178e-3 s +[acc] eval 1.603684e-3 s +[acc] eval 1.536622e-3 s +[acc] eval 1.569501e-3 s +[acc] eval 1.519081e-3 s +[acc] eval 1.501115e-3 s +[acc] eval 0.258357157 s +[acc] compile 0.132666716 s +[acc] eval 1.132197e-2 s +[acc] eval 5.626027e-3 s +[acc] eval 6.715933e-3 s +[acc] eval 2.5386435e-2 s +[acc] eval 6.831417e-3 s +[acc] eval 6.598528e-3 s +[acc] eval 4.143005e-3 s +[acc] eval 3.699427e-3 s +[acc] eval 2.939963e-3 s +[acc] eval 2.765848e-3 s +[acc] eval 2.397601e-3 s +[acc] eval 2.295956e-3 s +[acc] eval 0.21850285 s +[acc] compile 7.69182e-4 s +[acc] eval 4.548507e-3 s +[acc] compile 0.475278432 s +[acc] eval 2.6816166e-2 s +[acc] eval 0.507967213 s +[acc] compile 0.115295207 s +[acc] eval 1.4431718e-2 s +[acc] eval 0.135939457 s +[acc] compile 0.552769166 s +[acc] eval 3.2221701e-2 s +[acc] eval 0.590470204 s +[acc] compile 6.8872466e-2 s +[acc] eval 7.907273e-3 s +[acc] eval 8.1731211e-2 s +[acc] compile 3.851773e-3 s +[acc] eval 5.270029e-3 s +[acc] eval 1.1691354e-2 s +[acc] compile 6.429293e-3 s +[acc] eval 1.0027938e-2 s +[acc] eval 2.1674635e-2 s +[acc] compile 1.1792825e-2 s +[acc] eval 1.5416898e-2 s +[acc] eval 2.366554e-3 s +[acc] eval 2.199512e-3 s +[acc] eval 2.376015e-3 s +[acc] eval 2.395835e-3 s +[acc] eval 2.41128e-3 s +[acc] eval 2.061281e-3 s +[acc] eval 2.308897e-3 s +[acc] eval 2.359274e-3 s +[acc] eval 2.486252e-3 s +[acc] eval 2.535592e-3 s +[acc] eval 2.131309e-3 s +[acc] eval 1.776033e-3 s +[acc] eval 1.756591e-3 s +[acc] eval 1.735924e-3 s +[acc] eval 1.662505e-3 s +[acc] eval 1.722889e-3 s +[acc] eval 1.654094e-3 s +[acc] eval 1.650682e-3 s +[acc] eval 1.668808e-3 s +[acc] eval 2.030633e-3 s +[acc] eval 2.810692e-3 s +[acc] eval 1.735161e-3 s +[acc] eval 1.721951e-3 s +[acc] eval 1.683858e-3 s +[acc] eval 1.741781e-3 s +[acc] eval 1.635275e-3 s +[acc] eval 1.657286e-3 s +[acc] eval 1.672483e-3 s +[acc] eval 1.628182e-3 s +[acc] eval 8.8839254e-2 s +[acc] compile 3.857628e-3 s +[acc] eval 5.21926e-3 s +[acc] eval 1.1024758e-2 s +[acc] compile 7.250149e-2 s +[acc] eval 6.231058e-3 s +[acc] eval 2.44717e-3 s +[acc] eval 2.478007e-3 s +[acc] eval 2.543541e-3 s +[acc] eval 2.439854e-3 s +[acc] eval 2.241754e-3 s +[acc] eval 1.0526237e-2 s +[acc] eval 2.07023e-3 s +[acc] eval 1.709306e-3 s +[acc] eval 1.702287e-3 s +[acc] eval 1.713405e-3 s +[acc] eval 1.687403e-3 s +[acc] eval 1.878585e-3 s +[acc] eval 1.755179e-3 s +[acc] eval 3.003069e-3 s +[acc] eval 1.767803e-3 s +[acc] eval 1.690324e-3 s +[acc] eval 1.694593e-3 s +[acc] eval 1.662682e-3 s +[acc] eval 2.011064e-3 s +[acc] eval 1.76409e-3 s +[acc] eval 1.665538e-3 s +[acc] eval 1.722808e-3 s +[acc] eval 1.739591e-3 s +[acc] eval 1.782831e-3 s +[acc] eval 1.69001e-3 s +[acc] eval 1.687508e-3 s +[acc] eval 1.650159e-3 s +[acc] eval 2.976434e-3 s +[acc] eval 0.147822213 s +[acc] compile 0.111801683 s +[acc] eval 6.737731e-3 s +[acc] eval 3.050036e-3 s +[acc] eval 2.996791e-3 s +[acc] eval 2.946164e-3 s +[acc] eval 9.188692e-3 s +[acc] eval 2.780392e-3 s +[acc] eval 2.21554e-3 s +[acc] eval 2.118591e-3 s +[acc] eval 1.807152e-3 s +[acc] eval 2.552533e-3 s +[acc] eval 2.208423e-3 s +[acc] eval 2.086177e-3 s +[acc] eval 1.297203e-3 s +[acc] eval 1.065415e-3 s +[acc] eval 1.074135e-3 s +[acc] eval 1.050682e-3 s +[acc] eval 1.043792e-3 s +[acc] eval 1.050051e-3 s +[acc] eval 1.019244e-3 s +[acc] eval 1.046275e-3 s +[acc] eval 1.029967e-3 s +[acc] eval 1.7529e-3 s +[acc] eval 2.25776e-3 s +[acc] eval 2.207756e-3 s +[acc] eval 2.170104e-3 s +[acc] eval 1.801388e-3 s +[acc] eval 1.160007e-3 s +[acc] eval 1.035184e-3 s +[acc] eval 1.076062e-3 s +[acc] eval 1.070596e-3 s +[acc] eval 0.180189674 s +[acc] compile 6.6054234e-2 s +[acc] eval 5.698483e-3 s +[acc] eval 7.28709e-4 s +[acc] eval 1.153819e-3 s +[acc] eval 1.382584e-3 s +[acc] eval 1.246058e-3 s +[acc] eval 1.246011e-3 s +[acc] eval 1.530443e-3 s +[acc] eval 8.1161924e-2 s +[acc] compile 6.4805509e-2 s +[acc] eval 1.5239169e-2 s +[acc] eval 8.3242579e-2 s +[acc] compile 0.210470317 s +[acc] eval 2.0838622e-2 s +[acc] eval 0.239249876 s +[acc] compile 0.111802423 s +[acc] eval 1.644448e-2 s +[acc] eval 0.129783702 s +[acc] compile 7.7153287e-2 s +[acc] eval 2.1654527e-2 s +[acc] eval 0.102156261 s +[acc] compile 4.084621e-3 s +[acc] eval 5.171473e-3 s +[acc] eval 1.037616e-2 s +[acc] compile 9.402911e-3 s +[acc] eval 9.335937e-3 s +[acc] eval 8.261444e-3 s +[acc] eval 6.786412e-3 s +[acc] eval 6.813625e-3 s +[acc] eval 5.251136e-3 s +[acc] eval 1.443532e-2 s +[acc] eval 5.208658e-3 s +[acc] eval 3.89381e-3 s +[acc] eval 7.3235105e-2 s +[acc] compile 1.0328142e-2 s +[acc] eval 8.797287e-3 s +[acc] eval 5.596979e-3 s +[acc] eval 4.55533e-3 s +[acc] eval 1.1736227e-2 s +[acc] eval 4.7136325e-2 s +[acc] compile 0.302861952 s +[acc] eval 2.7472982e-2 s +[acc] eval 0.340161044 s +[acc] compile 7.4073521e-2 s +[acc] eval 2.9259543e-2 s +[acc] eval 0.110183366 s +[acc] compile 1.425513e-2 s +[acc] eval 7.297371e-3 s +[acc] eval 2.4719435e-2 s +[acc] compile 7.1065748e-2 s +[acc] eval 2.263722e-2 s +[acc] eval 9.9073419e-2 s +[acc] compile 7.487206e-3 s +[acc] eval 2.4693446e-2 s +[acc] eval 3.4731754e-2 s +[acc] compile 0.546322789 s +[acc] eval 2.8476462e-2 s +[acc] eval 0.580117705 s +[acc] compile 3.183188e-3 s +[acc] eval 4.757105e-3 s +[acc] eval 1.0482265e-2 s +[acc] compile 5.9099e-3 s +[acc] eval 9.046041e-3 s +[acc] eval 2.18692e-3 s +[acc] eval 1.860815e-3 s +[acc] eval 2.1729976e-2 s +[acc] compile 5.6715404e-2 s +[acc] eval 9.987341e-3 s +[acc] eval 6.8011107e-2 s +[acc] compile 7.5300773e-2 s +[acc] eval 2.2073266e-2 s +[acc] eval 3.413387e-3 s +[acc] eval 5.129742e-3 s +[acc] eval 4.155563e-3 s +[acc] eval 3.825022e-3 s +[acc] eval 3.264007e-3 s +[acc] eval 3.273634e-3 s +[acc] eval 3.217967e-3 s +[acc] eval 9.00498e-3 s +[acc] eval 9.854041e-3 s +[acc] eval 6.681311e-3 s +[acc] eval 4.014536e-3 s +[acc] eval 3.448878e-3 s +[acc] eval 3.166288e-3 s +[acc] eval 3.092662e-3 s +[acc] eval 2.878958e-3 s +[acc] eval 3.253201e-3 s +[acc] eval 2.933235e-3 s +[acc] eval 0.177634592 s +[acc] compile 0.121995823 s +[acc] eval 1.1855298e-2 s +[acc] eval 0.142302309 s +[acc] compile 0.186800732 s +[acc] eval 8.523509e-3 s +[acc] eval 4.375915e-3 s +[acc] eval 1.2625873e-2 s +[acc] eval 4.637363e-3 s +[acc] eval 4.814132e-3 s +[acc] eval 4.169242e-3 s +[acc] eval 4.117343e-3 s +[acc] eval 2.75947e-3 s +[acc] eval 2.557342e-3 s +[acc] eval 2.232985e-3 s +[acc] eval 2.163833e-3 s +[acc] eval 2.106491e-3 s +[acc] eval 0.247199897 s +[acc] compile 0.195485011 s +[acc] eval 2.2457683e-2 s +[acc] eval 3.2245862e-2 s +[acc] eval 1.9781881e-2 s +[acc] eval 8.709347e-3 s +[acc] eval 4.514758e-3 s +[acc] eval 5.345584e-3 s +[acc] eval 5.624935e-3 s +[acc] eval 4.251188e-3 s +[acc] eval 4.18105e-3 s +[acc] eval 4.592301e-3 s +[acc] eval 4.751646e-3 s +[acc] eval 4.27336e-3 s +[acc] eval 0.322807952 s +[acc] compile 0.255543865 s +[acc] eval 2.3284489e-2 s +[acc] eval 0.282485498 s +[acc] compile 5.81e-4 s +[acc] eval 6.103693e-3 s +[acc] compile 4.74547e-4 s +[acc] eval 3.776234e-3 s +[acc] compile 0.231638729 s +[acc] eval 2.2802444e-2 s +[acc] eval 0.260505686 s +[acc] compile 8.2865655e-2 s +[acc] eval 9.966833e-3 s +[acc] eval 9.7663109e-2 s +[acc] compile 5.218407e-3 s +[acc] eval 6.377053e-3 s +[acc] eval 1.3011174e-2 s +[acc] compile 0.501255591 s +[acc] eval 2.5187158e-2 s +[acc] eval 0.530733991 s +[acc] compile 8.72505e-3 s +[acc] eval 8.67906e-3 s +[acc] eval 2.0644224e-2 s +[acc] compile 0.331536071 s +[acc] eval 2.0999509e-2 s +[acc] eval 0.359025224 s +[acc] compile 2.98225e-3 s +[acc] eval 2.983743e-3 s +[acc] eval 5.684571e-3 s +[acc] eval 5.81524e-4 s +[acc] eval 7.75657e-4 s +[acc] eval 1.3952186e-2 s +[acc] compile 4.280514e-3 s +[acc] eval 2.948983e-3 s +[acc] eval 7.93749e-4 s +[acc] eval 8.12981e-4 s +[acc] eval 9.916919e-3 s +[acc] compile 0.135784897 s +[acc] eval 5.877281e-3 s +[acc] eval 0.143959525 s +[acc] compile 0.17341035 s +[acc] eval 1.565478e-2 s +[acc] eval 0.192521837 s +[acc] compile 2.916817e-3 s +[acc] eval 2.89626e-3 s +[acc] eval 6.47492e-4 s +[acc] eval 5.12329e-4 s +[acc] eval 7.958831e-3 s +[acc] compile 4.594667e-2 s +[acc] eval 7.660914e-3 s +[acc] eval 1.767018e-3 s +[acc] eval 1.65567e-3 s +[acc] eval 5.9308403e-2 s +[acc] compile 5.369721e-3 s +[acc] eval 5.582801e-3 s +[acc] eval 1.319792e-2 s +[acc] compile 8.835876e-3 s +[acc] eval 1.1936471e-2 s +[acc] eval 2.3194547e-2 s +[acc] compile 5.590483e-3 s +[acc] eval 6.581978e-3 s +[acc] eval 3.263063e-3 s +[acc] eval 3.067773e-3 s +[acc] eval 2.532229e-3 s +[acc] eval 2.318451e-3 s +[acc] eval 2.166023e-3 s +[acc] eval 2.093697e-3 s +[acc] eval 1.939556e-3 s +[acc] eval 3.2422689e-2 s +[acc] compile 5.49895e-4 s +[acc] eval 1.2227563e-2 s +[acc] compile 5.9308568e-2 s +[acc] eval 1.0953935e-2 s +[acc] eval 7.4673508e-2 s +[acc] compile 0.15696498 s +[acc] eval 1.6965819e-2 s +[acc] eval 3.550297e-3 s +[acc] eval 3.04935e-3 s +[acc] eval 3.06503e-3 s +[acc] eval 3.104057e-3 s +[acc] eval 2.727653e-3 s +[acc] eval 2.270671e-3 s +[acc] eval 2.183973e-3 s +[acc] eval 2.165489e-3 s +[acc] eval 2.161365e-3 s +[acc] eval 2.127066e-3 s +[acc] eval 2.108946e-3 s +[acc] eval 2.6601e-3 s +[acc] eval 3.271484e-3 s +[acc] eval 2.666198e-3 s +[acc] eval 2.563333e-3 s +[acc] eval 2.169979e-3 s +[acc] eval 2.180598e-3 s +[acc] eval 2.138832e-3 s +[acc] eval 2.187775e-3 s +[acc] eval 2.215004e-3 s +[acc] eval 2.122686e-3 s +[acc] eval 2.140647e-3 s +[acc] eval 2.147836e-3 s +[acc] eval 2.090843e-3 s +[acc] eval 2.114104e-3 s +[acc] eval 2.208044e-3 s +[acc] eval 2.219869e-3 s +[acc] eval 2.146609e-3 s +[acc] eval 2.154135e-3 s +[acc] eval 0.248807323 s +[acc] compile 3.92024e-4 s +[acc] eval 4.060609e-3 s +[acc] compile 0.108336863 s +[acc] eval 5.632404e-3 s +[acc] eval 0.117244548 s +[acc] compile 0.228646878 s +[acc] eval 2.6514778e-2 s +[acc] eval 0.259539605 s +[acc] compile 6.9391653e-2 s +[acc] eval 4.993903e-3 s +[acc] eval 7.6834615e-2 s +[acc] compile 0.19792182 s +[acc] eval 2.1504298e-2 s +[acc] eval 0.222760595 s +[acc] compile 6.442781e-3 s +[acc] eval 9.43917e-3 s +[acc] eval 9.19542e-4 s +[acc] eval 1.78118e-3 s +[acc] eval 2.0884448e-2 s +[acc] compile 8.54456e-3 s +[acc] eval 6.336464e-3 s +[acc] eval 1.86661e-3 s +[acc] eval 1.570158e-3 s +[acc] eval 2.0960606e-2 s +[acc] compile 1.1025236e-2 s +[acc] eval 6.455614e-3 s +[acc] eval 1.171358e-2 s +[acc] eval 2.009359e-3 s +[acc] eval 2.021082e-3 s +[acc] eval 1.435507e-3 s +[acc] eval 1.476189e-3 s +[acc] eval 1.045125e-3 s +[acc] eval 1.195881e-3 s +[acc] eval 1.112158e-3 s +[acc] eval 8.83668e-4 s +[acc] eval 8.99002e-4 s +[acc] eval 8.58586e-4 s +[acc] eval 1.972412e-3 s +[acc] eval 4.7311406e-2 s +[acc] compile 7.3528947e-2 s +[acc] eval 9.00999e-3 s +[acc] eval 2.474598e-3 s +[acc] eval 2.135259e-3 s +[acc] eval 1.992309e-3 s +[acc] eval 1.815063e-3 s +[acc] eval 7.062878e-3 s +[acc] eval 3.176142e-3 s +[acc] eval 2.673243e-3 s +[acc] eval 2.851782e-3 s +[acc] eval 2.64521e-3 s +[acc] eval 2.11369e-3 s +[acc] eval 1.643921e-3 s +[acc] eval 1.501744e-3 s +[acc] eval 1.368109e-3 s +[acc] eval 1.322328e-3 s +[acc] eval 1.293373e-3 s +[acc] eval 1.302854e-3 s +[acc] eval 1.392305e-3 s +[acc] eval 1.466216e-3 s +[acc] eval 1.568768e-3 s +[acc] eval 1.309027e-3 s +[acc] eval 1.206982e-3 s +[acc] eval 1.16004e-3 s +[acc] eval 1.193638e-3 s +[acc] eval 1.145983e-3 s +[acc] eval 1.522081e-3 s +[acc] eval 1.424406e-3 s +[acc] eval 1.380046e-3 s +[acc] eval 0.140237438 s +[acc] compile 0.51411155 s +[acc] eval 3.0564229e-2 s +[acc] eval 0.549968612 s +[acc] compile 4.12115e-3 s +[acc] eval 6.506063e-3 s +[acc] eval 2.74883e-3 s +[acc] eval 3.62221e-3 s +[acc] eval 3.264801e-3 s +[acc] eval 3.088737e-3 s +[acc] eval 4.053824e-3 s +[acc] eval 3.5445097e-2 s +[acc] compile 6.152179e-3 s +[acc] eval 2.5884634e-2 s +[acc] eval 1.0325587e-2 s +[acc] eval 1.314536e-3 s +[acc] eval 4.5558656e-2 s +[acc] compile 6.41454e-4 s +[acc] eval 6.353259e-3 s +[acc] compile 0.189965331 s +[acc] eval 1.1535959e-2 s +[acc] eval 5.048575e-3 s +[acc] eval 6.150922e-3 s +[acc] eval 6.381212e-3 s +[acc] eval 9.069235e-3 s +[acc] eval 2.287053e-3 s +[acc] eval 3.932569e-3 s +[acc] eval 3.65188e-3 s +[acc] eval 2.003185e-3 s +[acc] eval 1.93884e-3 s +[acc] eval 1.823225e-3 s +[acc] eval 2.069853e-3 s +[acc] eval 2.552176e-3 s +[acc] eval 3.922006e-3 s +[acc] eval 1.625375e-3 s +[acc] eval 1.616124e-3 s +[acc] eval 1.397874e-3 s +[acc] eval 1.770455e-3 s +[acc] eval 1.452473e-3 s +[acc] eval 2.875455e-3 s +[acc] eval 2.606714e-3 s +[acc] eval 2.74278e-3 s +[acc] eval 2.722267e-3 s +[acc] eval 1.79544e-3 s +[acc] eval 1.563425e-3 s +[acc] eval 1.483134e-3 s +[acc] eval 1.509116e-3 s +[acc] eval 1.510904e-3 s +[acc] eval 2.449417e-3 s +[acc] eval 1.674968e-3 s +[acc] eval 0.290736754 s +[acc] compile 5.4452962e-2 s +[acc] eval 3.4604162e-2 s +[acc] eval 9.8365649e-2 s +[acc] compile 8.611519e-3 s +[acc] eval 1.0942104e-2 s +[acc] eval 2.4295362e-2 s +[acc] compile 0.313900635 s +[acc] eval 2.7325071e-2 s +[acc] eval 0.345084736 s +[acc] compile 7.3034581e-2 s +[acc] eval 2.5366633e-2 s +[acc] eval 0.112805041 s +[acc] compile 4.376571e-3 s +[acc] eval 8.542633e-3 s +[acc] eval 5.944476e-3 s +[acc] eval 5.832347e-3 s +[acc] eval 4.368723e-3 s +[acc] eval 5.118497e-3 s +[acc] eval 5.536732e-3 s +[acc] eval 5.54273e-3 s +[acc] eval 1.9706804e-2 s +[acc] eval 5.525881e-3 s +[acc] eval 5.639553e-3 s +[acc] eval 5.5661e-3 s +[acc] eval 5.671099e-3 s +[acc] eval 6.618489e-3 s +[acc] eval 4.243447e-3 s +[acc] eval 2.77145e-3 s +[acc] eval 2.618634e-3 s +[acc] eval 2.494528e-3 s +[acc] eval 2.198126e-3 s +[acc] eval 2.70983e-3 s +[acc] eval 2.652397e-3 s +[acc] eval 2.480169e-3 s +[acc] eval 2.622997e-3 s +[acc] eval 2.897004e-3 s +[acc] eval 3.153367e-3 s +[acc] eval 2.914861e-3 s +[acc] eval 2.365837e-3 s +[acc] eval 1.595011e-3 s +[acc] eval 1.334415e-3 s +[acc] eval 1.374695e-3 s +[acc] eval 0.14095916 s +[acc] compile 4.22467e-3 s +[acc] eval 7.297888e-3 s +[acc] eval 1.515562e-3 s +[acc] eval 1.386801e-3 s +[acc] eval 1.339322e-3 s +[acc] eval 1.347165e-3 s +[acc] eval 5.424193e-3 s +[acc] eval 2.207881e-3 s +[acc] eval 1.506723e-3 s +[acc] eval 1.439496e-3 s +[acc] eval 1.171379e-3 s +[acc] eval 1.221364e-3 s +[acc] eval 1.397357e-3 s +[acc] eval 2.023589e-3 s +[acc] eval 1.355115e-3 s +[acc] eval 9.80063e-4 s +[acc] eval 3.8388882e-2 s +[acc] compile 5.987492e-3 s +[acc] eval 1.1170121e-2 s +[acc] eval 2.290676e-3 s +[acc] eval 2.609255e-3 s +[acc] eval 5.302468e-3 s +[acc] eval 1.698466e-3 s +[acc] eval 1.4881e-3 s +[acc] eval 1.338007e-3 s +[acc] eval 1.321681e-3 s +[acc] eval 1.314568e-3 s +[acc] eval 1.295543e-3 s +[acc] eval 1.256878e-3 s +[acc] eval 1.294873e-3 s +[acc] eval 1.244595e-3 s +[acc] eval 1.321676e-3 s +[acc] eval 1.288595e-3 s +[acc] eval 1.242359e-3 s +[acc] eval 1.259693e-3 s +[acc] eval 1.211685e-3 s +[acc] eval 1.332892e-3 s +[acc] eval 1.292753e-3 s +[acc] eval 1.288124e-3 s +[acc] eval 1.296101e-3 s +[acc] eval 1.295876e-3 s +[acc] eval 1.257501e-3 s +[acc] eval 1.241504e-3 s +[acc] eval 1.293313e-3 s +[acc] eval 1.210804e-3 s +[acc] eval 1.217548e-3 s +[acc] eval 1.283446e-3 s +[acc] eval 1.233095e-3 s +[acc] eval 6.7427882e-2 s +[acc] compile 6.276861e-3 s +[acc] eval 4.78971e-3 s +[acc] eval 1.876325e-3 s +[acc] eval 1.925809e-3 s +[acc] eval 6.381842e-3 s +[acc] eval 2.041239e-3 s +[acc] eval 1.294856e-3 s +[acc] eval 1.27841e-3 s +[acc] eval 1.288659e-3 s +[acc] eval 1.268623e-3 s +[acc] eval 1.277213e-3 s +[acc] eval 1.243191e-3 s +[acc] eval 1.568076e-3 s +[acc] eval 1.570215e-3 s +[acc] eval 1.387898e-3 s +[acc] eval 1.283517e-3 s +[acc] eval 1.254721e-3 s +[acc] eval 1.246982e-3 s +[acc] eval 1.215463e-3 s +[acc] eval 4.4532414e-2 s +[acc] compile 3.727799e-3 s +[acc] eval 6.564438e-3 s +[acc] eval 8.94605e-4 s +[acc] eval 7.62367e-4 s +[acc] eval 1.35112e-2 s +[acc] compile 4.140132e-3 s +[acc] eval 8.14217e-3 s +[acc] eval 1.273215e-3 s +[acc] eval 1.27294e-3 s +[acc] eval 4.268182e-3 s +[acc] eval 8.75709e-4 s +[acc] eval 7.7185e-4 s +[acc] eval 7.47542e-4 s +[acc] eval 7.27038e-4 s +[acc] eval 7.18392e-4 s +[acc] eval 6.814e-4 s +[acc] eval 2.5103388e-2 s +[acc] compile 3.720766e-3 s +[acc] eval 4.498406e-3 s +[acc] eval 1.361335e-3 s +[acc] eval 1.445932e-3 s +[acc] eval 1.560461e-3 s +[acc] eval 1.870031e-3 s +[acc] eval 1.631229e-3 s +[acc] eval 2.3692417e-2 s +[acc] compile 7.248749e-3 s +[acc] eval 1.0791917e-2 s +[acc] eval 1.951216e-3 s +[acc] eval 1.944413e-3 s +[acc] eval 5.56302e-3 s +[acc] eval 2.321074e-3 s +[acc] eval 2.395933e-3 s +[acc] eval 2.241932e-3 s +[acc] eval 2.053237e-3 s +[acc] eval 2.203081e-3 s +[acc] eval 2.274858e-3 s +[acc] eval 1.816844e-3 s +[acc] eval 1.774845e-3 s +[acc] eval 2.400558e-3 s +[acc] eval 2.248486e-3 s +[acc] eval 2.192974e-3 s +[acc] eval 2.587669e-3 s +[acc] eval 1.336418e-3 s +[acc] eval 1.260953e-3 s +[acc] eval 1.24956e-3 s +[acc] eval 1.232896e-3 s +[acc] eval 1.164729e-3 s +[acc] eval 1.216565e-3 s +[acc] eval 1.138926e-3 s +[acc] eval 1.167415e-3 s +[acc] eval 1.17924e-3 s +[acc] eval 1.194253e-3 s +[acc] eval 6.9615858e-2 s +[acc] compile 0.109713396 s +[acc] eval 1.1497519e-2 s +[acc] eval 2.293903e-3 s +[acc] eval 6.544957e-3 s +[acc] eval 2.486129e-3 s +[acc] eval 1.795724e-3 s +[acc] eval 1.758808e-3 s +[acc] eval 1.595934e-3 s +[acc] eval 1.703511e-3 s +[acc] eval 1.704963e-3 s +[acc] eval 1.687163e-3 s +[acc] eval 1.635389e-3 s +[acc] eval 1.581285e-3 s +[acc] eval 1.567552e-3 s +[acc] eval 1.541214e-3 s +[acc] eval 1.556873e-3 s +[acc] eval 1.547233e-3 s +[acc] eval 1.539202e-3 s +[acc] eval 1.702733e-3 s +[acc] eval 1.602547e-3 s +[acc] eval 1.599537e-3 s +[acc] eval 1.57031e-3 s +[acc] eval 1.591793e-3 s +[acc] eval 1.580096e-3 s +[acc] eval 2.824154e-3 s +[acc] eval 2.000293e-3 s +[acc] eval 1.593957e-3 s +[acc] eval 1.548656e-3 s +[acc] eval 2.361769e-3 s +[acc] eval 1.863233e-3 s +[acc] eval 1.709135e-3 s +[acc] eval 1.689104e-3 s +[acc] eval 1.881424e-3 s +[acc] eval 1.603203e-3 s +[acc] eval 1.644568e-3 s +[acc] eval 0.188398594 s +[acc] compile 0.221709915 s +[acc] eval 1.1012131e-2 s +[acc] eval 0.240184766 s +[acc] compile 0.350603498 s +[acc] eval 1.915411e-2 s +[acc] eval 0.380101334 s +[acc] compile 4.323786e-3 s +[acc] eval 3.699885e-3 s +[acc] eval 9.307052e-3 s +[acc] compile 5.5025993e-2 s +[acc] eval 4.325343e-3 s +[acc] eval 1.088285e-3 s +[acc] eval 9.03722e-4 s +[acc] eval 6.3130148e-2 s +[acc] compile 0.129608073 s +[acc] eval 1.5183736e-2 s +[acc] eval 0.152036975 s +[acc] compile 0.134510093 s +[acc] eval 1.090661e-2 s +[acc] eval 7.8719e-3 s +[acc] eval 3.216273e-3 s +[acc] eval 2.832423e-3 s +[acc] eval 2.466421e-3 s +[acc] eval 1.985251e-3 s +[acc] eval 1.768322e-3 s +[acc] eval 1.730493e-3 s +[acc] eval 1.758192e-3 s 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+[acc] eval 1.261865e-3 s +[acc] eval 0.158781427 s +[acc] compile 1.1271395e-2 s +[acc] eval 1.3650686e-2 s +[acc] eval 2.242593e-3 s +[acc] eval 1.978495e-3 s +[acc] eval 6.003848e-3 s +[acc] eval 3.395315e-3 s +[acc] eval 3.182761e-3 s +[acc] eval 2.855261e-3 s +[acc] eval 2.204733e-3 s +[acc] eval 1.892309e-3 s +[acc] eval 1.845539e-3 s +[acc] eval 2.33137e-3 s +[acc] eval 5.581599e-3 s +[acc] eval 6.3482775e-2 s +[acc] compile 7.830012e-3 s +[acc] eval 1.249099e-2 s +[acc] eval 3.96746e-3 s +[acc] eval 3.177399e-3 s +[acc] eval 2.750705e-3 s +[acc] eval 2.621734e-3 s +[acc] eval 8.452896e-3 s +[acc] eval 1.891627e-3 s +[acc] eval 1.669959e-3 s +[acc] eval 1.290659e-3 s +[acc] eval 1.310486e-3 s +[acc] eval 1.323627e-3 s +[acc] eval 1.223792e-3 s +[acc] eval 1.595523e-3 s +[acc] eval 1.472208e-3 s +[acc] eval 2.910863e-3 s +[acc] eval 2.845652e-3 s +[acc] eval 2.701246e-3 s +[acc] eval 2.283487e-3 s +[acc] eval 1.459776e-3 s +[acc] eval 1.34734e-3 s +[acc] eval 1.33152e-3 s +[acc] 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4.78884e-4 s +[acc] eval 5.874707e-3 s +[acc] compile 0.199344913 s +[acc] eval 2.258983e-2 s +[acc] eval 0.224503962 s +[acc] compile 4.861675e-3 s +[acc] eval 8.105695e-3 s +[acc] eval 5.597007e-3 s +[acc] eval 3.780013e-3 s +[acc] eval 4.723649e-3 s +[acc] eval 3.473872e-3 s +[acc] eval 2.871897e-3 s +[acc] eval 4.354482e-3 s +[acc] eval 1.0762448e-2 s +[acc] eval 4.276542e-3 s +[acc] eval 3.350114e-3 s +[acc] eval 3.390848e-3 s +[acc] eval 3.302033e-3 s +[acc] eval 3.649206e-3 s +[acc] eval 2.260946e-3 s +[acc] eval 2.392423e-3 s +[acc] eval 3.165041e-3 s +[acc] eval 2.357744e-3 s +[acc] eval 2.724916e-3 s +[acc] eval 3.443497e-3 s +[acc] eval 2.191092e-3 s +[acc] eval 2.031977e-3 s +[acc] eval 2.167905e-3 s +[acc] eval 2.147124e-3 s +[acc] eval 2.176364e-3 s +[acc] eval 2.313167e-3 s +[acc] eval 2.388036e-3 s +[acc] eval 3.209862e-3 s +[acc] eval 2.564237e-3 s +[acc] eval 2.331432e-3 s +[acc] eval 2.30608e-3 s +[acc] eval 2.393437e-3 s +[acc] eval 2.280899e-3 s +[acc] eval 3.179363e-3 s +[acc] eval 0.121828241 s +[acc] compile 0.127634687 s +[acc] eval 9.938001e-3 s +[acc] eval 3.307121e-3 s +[acc] eval 2.348443e-3 s +[acc] eval 7.641874e-3 s +[acc] eval 2.92876e-3 s +[acc] eval 3.313969e-3 s +[acc] eval 1.968848e-3 s +[acc] eval 1.4487e-3 s +[acc] eval 1.3992e-3 s +[acc] eval 1.361135e-3 s +[acc] eval 1.366386e-3 s +[acc] eval 1.389651e-3 s +[acc] eval 1.363143e-3 s +[acc] eval 1.423144e-3 s +[acc] eval 1.433067e-3 s +[acc] eval 1.718828e-3 s +[acc] eval 1.477967e-3 s +[acc] eval 1.573202e-3 s +[acc] eval 1.334256e-3 s +[acc] eval 1.402603e-3 s +[acc] eval 1.330407e-3 s +[acc] eval 1.314344e-3 s +[acc] eval 1.37069e-3 s +[acc] eval 1.332524e-3 s +[acc] eval 1.727315e-3 s +[acc] eval 0.1883629 s +[acc] compile 4.128234e-3 s +[acc] eval 5.445461e-3 s +[acc] eval 1.144362e-2 s +[acc] compile 5.4410449e-2 s +[acc] eval 1.9843976e-2 s +[acc] eval 4.062907e-3 s +[acc] eval 5.103666e-3 s +[acc] eval 4.845871e-3 s +[acc] eval 3.287954e-3 s +[acc] eval 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3.5104615e-2 s +[acc] eval 0.649384894 s +[acc] compile 7.7051545e-2 s +[acc] eval 8.881556e-3 s +[acc] eval 2.914998e-3 s +[acc] eval 2.791283e-3 s +[acc] eval 2.973515e-3 s +[acc] eval 2.164802e-3 s +[acc] eval 2.130333e-3 s +[acc] eval 8.230545e-3 s +[acc] eval 2.300108e-3 s +[acc] eval 2.240206e-3 s +[acc] eval 1.855341e-3 s +[acc] eval 1.211421e-3 s +[acc] eval 1.109155e-3 s +[acc] eval 0.119528804 s +[acc] compile 0.111515215 s +[acc] eval 2.743672e-2 s +[acc] eval 0.14346634 s +[acc] compile 0.662188153 s +[acc] eval 2.9328762e-2 s +[acc] eval 0.702526747 s +[acc] compile 1.9147873e-2 s +[acc] eval 2.2770423e-2 s +[acc] eval 4.6152577e-2 s +[acc] compile 2.084833e-2 s +[acc] eval 1.2964192e-2 s +[acc] eval 7.061103e-3 s +[acc] eval 4.167138e-3 s +[acc] eval 3.249664e-3 s +[acc] eval 3.465788e-3 s +[acc] eval 3.837734e-3 s +[acc] eval 3.670722e-3 s +[acc] eval 6.3004055e-2 s +[acc] compile 0.107865181 s +[acc] eval 1.6496037e-2 s +[acc] eval 0.13809612 s +[acc] compile 1.361148e-2 s +[acc] eval 1.0987222e-2 s +[acc] eval 4.609033e-3 s +[acc] eval 4.716413e-3 s +[acc] eval 4.274463e-3 s +[acc] eval 1.023858e-2 s +[acc] eval 1.979374e-3 s +[acc] eval 1.848943e-3 s +[acc] eval 1.770469e-3 s +[acc] eval 1.937433e-3 s +[acc] eval 1.466955e-3 s +[acc] eval 2.749168e-3 s +[acc] eval 2.666814e-3 s +[acc] eval 2.611357e-3 s +[acc] eval 1.840244e-3 s +[acc] eval 2.726012e-3 s +[acc] eval 2.083905e-3 s +[acc] eval 1.550257e-3 s +[acc] eval 1.598644e-3 s +[acc] eval 1.562708e-3 s +[acc] eval 8.0661466e-2 s +[acc] compile 0.105320843 s +[acc] eval 1.4929915e-2 s +[acc] eval 0.121820863 s +[acc] compile 1.624295e-2 s +[acc] eval 2.6442204e-2 s +[acc] eval 4.6323427e-2 s +[acc] compile 0.125249009 s +[acc] eval 8.952614e-3 s +[acc] eval 0.140735183 s +[acc] compile 1.2525291e-2 s +[acc] eval 1.9404247e-2 s +[acc] eval 4.2527703e-2 s +[acc] compile 0.426762311 s +[acc] eval 1.2564125e-2 s +[acc] eval 0.457771145 s +[acc] compile 0.238309895 s +[acc] eval 9.07504e-3 s +[acc] eval 0.251287241 s +[acc] compile 0.113441844 s +[acc] eval 9.832159e-3 s +[acc] eval 0.127066843 s +[acc] compile 1.3699408e-2 s +[acc] eval 7.336571e-3 s +[acc] eval 3.620538e-3 s +[acc] eval 7.029165e-3 s +[acc] eval 2.538827e-3 s +[acc] eval 2.163397e-3 s +[acc] eval 2.14003e-3 s +[acc] eval 1.832197e-3 s +[acc] eval 1.837328e-3 s +[acc] eval 2.176603e-3 s +[acc] eval 2.230471e-3 s +[acc] eval 2.49911e-3 s +[acc] eval 2.1301e-3 s +[acc] eval 2.281536e-3 s +[acc] eval 2.177307e-3 s +[acc] eval 2.12498e-3 s +[acc] eval 1.989005e-3 s +[acc] eval 2.058811e-3 s +[acc] eval 2.415665e-3 s +[acc] eval 3.141062e-3 s +[acc] eval 3.070897e-3 s +[acc] eval 7.620467e-2 s +[acc] compile 2.2514779e-2 s +[acc] eval 2.9702659e-2 s +[acc] eval 5.7432003e-2 s +[acc] compile 7.4885712e-2 s +[acc] eval 1.9069702e-2 s +[acc] eval 4.055257e-3 s +[acc] eval 6.765812e-3 s +[acc] eval 1.2958466e-2 s +[acc] eval 3.083656e-3 s +[acc] eval 2.452756e-3 s +[acc] eval 2.326192e-3 s +[acc] eval 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1.884294e-3 s +[acc] eval 2.086514e-3 s +[acc] eval 1.976076e-3 s +[acc] eval 1.938219e-3 s +[acc] eval 1.673484e-3 s +[acc] eval 1.763481e-3 s +[acc] eval 1.752722e-3 s +[acc] eval 1.762177e-3 s +[acc] eval 1.699872e-3 s +[acc] eval 1.722502e-3 s +[acc] eval 1.72732e-3 s +[acc] eval 1.712342e-3 s +[acc] eval 1.800537e-3 s +[acc] eval 1.794361e-3 s +[acc] eval 1.844282e-3 s +[acc] eval 2.365479e-3 s +[acc] eval 2.377918e-3 s +[acc] eval 2.158537e-3 s +[acc] eval 1.94632e-3 s +[acc] eval 2.111936e-3 s +[acc] eval 2.31843e-3 s +[acc] eval 1.971844e-3 s +[acc] eval 1.875163e-3 s +[acc] eval 1.70162e-3 s +[acc] eval 1.666319e-3 s +[acc] eval 0.178692412 s +[acc] compile 5.1296086e-2 s +[acc] eval 1.5934221e-2 s +[acc] eval 7.4119748e-2 s +[acc] compile 5.4209918e-2 s +[acc] eval 1.3615169e-2 s +[acc] eval 2.778617e-3 s +[acc] eval 9.557409e-3 s +[acc] eval 2.033906e-3 s +[acc] eval 1.918096e-3 s +[acc] eval 1.92329e-3 s +[acc] eval 2.012428e-3 s +[acc] eval 2.390441e-3 s +[acc] eval 2.194909e-3 s +[acc] eval 1.917338e-3 s +[acc] eval 1.890127e-3 s +[acc] eval 1.840941e-3 s +[acc] eval 1.854262e-3 s +[acc] eval 1.985474e-3 s +[acc] eval 2.306045e-3 s +[acc] eval 2.161046e-3 s +[acc] eval 2.178755e-3 s +[acc] eval 1.849915e-3 s +[acc] eval 1.797943e-3 s +[acc] eval 0.117080239 s +[acc] compile 0.12173438 s +[acc] eval 1.8503233e-2 s +[acc] eval 0.143242222 s +[acc] compile 0.129174634 s +[acc] eval 1.7455656e-2 s +[acc] eval 3.361629e-3 s +[acc] eval 1.1775407e-2 s +[acc] eval 2.336971e-3 s +[acc] eval 2.165288e-3 s +[acc] eval 2.239921e-3 s +[acc] eval 2.208651e-3 s +[acc] eval 2.276496e-3 s +[acc] eval 2.136364e-3 s +[acc] eval 2.158433e-3 s +[acc] eval 2.211185e-3 s +[acc] eval 2.15629e-3 s +[acc] eval 2.264668e-3 s +[acc] eval 2.29188e-3 s +[acc] eval 2.214162e-3 s +[acc] eval 2.189657e-3 s +[acc] eval 3.493287e-3 s +[acc] eval 2.592576e-3 s +[acc] eval 2.295729e-3 s +[acc] eval 2.427705e-3 s +[acc] eval 2.296705e-3 s +[acc] eval 2.196214e-3 s +[acc] eval 2.209043e-3 s +[acc] eval 2.383419e-3 s +[acc] eval 4.14051e-3 s +[acc] eval 3.190847e-3 s +[acc] eval 9.239373e-3 s +[acc] eval 2.541586e-3 s +[acc] eval 3.049518e-3 s +[acc] eval 4.055504e-3 s +[acc] eval 2.631969e-3 s +[acc] eval 0.244788743 s +[acc] compile 6.7992051e-2 s +[acc] eval 1.5289568e-2 s +[acc] eval 9.0777829e-2 s +[acc] compile 0.314749114 s +[acc] eval 2.6632497e-2 s +[acc] eval 0.350989511 s +[acc] compile 4.260278e-3 s +[acc] eval 4.33125e-3 s +[acc] eval 1.0334103e-2 s +[acc] compile 0.121829371 s +[acc] eval 2.1592322e-2 s +[acc] eval 0.146637798 s +[acc] compile 0.131250734 s +[acc] eval 8.763281e-3 s +[acc] eval 0.142923164 s +[acc] compile 4.16086e-3 s +[acc] eval 3.460685e-3 s +[acc] eval 5.38049e-4 s +[acc] eval 8.3878e-4 s +[acc] eval 9.976943e-3 s +[acc] compile 6.4997202e-2 s +[acc] eval 2.2521001e-2 s +[acc] eval 8.9108836e-2 s +[acc] compile 2.944227e-3 s +[acc] eval 3.393653e-3 s +[acc] eval 4.35459e-4 s +[acc] eval 5.52848e-4 s +[acc] eval 7.993899e-3 s +[acc] compile 6.635193e-3 s +[acc] eval 1.3757298e-2 s +[acc] eval 2.31661e-2 s +[acc] compile 7.034153e-3 s +[acc] eval 4.656631e-3 s +[acc] eval 6.80049e-4 s +[acc] eval 9.40441e-4 s +[acc] eval 1.4471042e-2 s +[acc] compile 0.207837328 s +[acc] eval 2.0749774e-2 s +[acc] eval 0.231960978 s +[acc] compile 0.255964859 s +[acc] eval 1.0630406e-2 s +[acc] eval 0.270026589 s +[acc] compile 0.11239251 s +[acc] eval 1.5200395e-2 s +[acc] eval 4.680348e-3 s +[acc] eval 1.1649796e-2 s +[acc] eval 3.269067e-3 s +[acc] eval 2.821161e-3 s +[acc] eval 2.661267e-3 s +[acc] eval 2.658343e-3 s +[acc] eval 2.612423e-3 s +[acc] eval 2.593604e-3 s +[acc] eval 2.623521e-3 s +[acc] eval 2.629551e-3 s +[acc] eval 2.622374e-3 s +[acc] eval 0.17213706 s +[acc] compile 7.82924e-3 s +[acc] eval 5.7802e-3 s +[acc] eval 1.565399e-2 s +[acc] compile 0.120410143 s +[acc] eval 9.772581e-3 s +[acc] eval 0.1354532 s +[acc] compile 0.378892058 s +[acc] eval 1.5656821e-2 s +[acc] eval 0.401846819 s +[acc] compile 0.136431646 s +[acc] eval 2.0483199e-2 s +[acc] eval 0.158604848 s +[acc] compile 7.5722942e-2 s +[acc] eval 2.0255065e-2 s +[acc] eval 5.491348e-3 s +[acc] eval 1.2866834e-2 s +[acc] eval 5.033297e-3 s +[acc] eval 3.793536e-3 s +[acc] eval 2.918946e-3 s +[acc] eval 2.391514e-3 s +[acc] eval 2.159592e-3 s +[acc] eval 2.592012e-3 s +[acc] eval 2.515375e-3 s +[acc] eval 2.407896e-3 s +[acc] eval 2.463401e-3 s +[acc] eval 3.337625e-3 s +[acc] eval 2.159573e-3 s +[acc] eval 2.333551e-3 s +[acc] eval 2.064917e-3 s +[acc] eval 2.07313e-3 s +[acc] eval 1.897207e-3 s +[acc] eval 1.903684e-3 s +[acc] eval 1.890614e-3 s +[acc] eval 1.869965e-3 s +[acc] eval 1.940819e-3 s +[acc] eval 1.9637e-3 s +[acc] eval 1.915118e-3 s +[acc] eval 2.014144e-3 s +[acc] eval 1.857859e-3 s +[acc] eval 1.902502e-3 s +[acc] eval 1.787865e-3 s +[acc] eval 1.857625e-3 s +[acc] eval 1.896373e-3 s +[acc] eval 1.869743e-3 s +[acc] eval 0.184087211 s +[acc] compile 4.410391e-3 s +[acc] eval 4.320391e-3 s +[acc] eval 7.52157e-4 s +[acc] eval 7.4586e-4 s +[acc] eval 7.26535e-4 s +[acc] eval 6.23938e-4 s +[acc] eval 1.2690946e-2 s +[acc] compile 3.946692e-3 s +[acc] eval 3.852415e-3 s +[acc] eval 1.416756e-3 s +[acc] eval 1.400053e-3 s +[acc] eval 6.206005e-3 s +[acc] eval 9.41756e-4 s +[acc] eval 9.38862e-4 s +[acc] eval 9.20831e-4 s +[acc] eval 1.164531e-3 s +[acc] eval 1.115546e-3 s +[acc] eval 1.60893e-3 s +[acc] eval 1.692822e-3 s +[acc] eval 8.58824e-4 s +[acc] eval 8.53858e-4 s +[acc] eval 8.22645e-4 s +[acc] eval 8.63359e-4 s +[acc] eval 8.5295e-4 s +[acc] eval 1.118296e-3 s +[acc] eval 8.60896e-4 s +[acc] eval 1.122806e-3 s +[acc] eval 1.072973e-3 s +[acc] eval 1.499502e-3 s +[acc] eval 1.64363e-3 s +[acc] eval 9.27374e-4 s +[acc] eval 8.93225e-4 s +[acc] eval 8.67641e-4 s +[acc] eval 8.55661e-4 s +[acc] eval 8.31278e-4 s +[acc] eval 8.61214e-4 s +[acc] eval 8.50876e-4 s +[acc] eval 4.5431509e-2 s +[acc] compile 0.450423687 s +[acc] eval 2.0426657e-2 s +[acc] eval 0.483369789 s +[acc] compile 7.2778545e-2 s +[acc] eval 4.316565e-3 s +[acc] eval 1.747776e-3 s +[acc] eval 1.744566e-3 s +[acc] eval 1.682479e-3 s +[acc] eval 4.926285e-3 s +[acc] eval 1.1576273e-2 s +[acc] eval 1.715225e-3 s +[acc] eval 1.42523e-3 s +[acc] eval 1.389419e-3 s +[acc] eval 1.554647e-3 s +[acc] eval 1.603463e-3 s +[acc] eval 2.710478e-3 s +[acc] eval 2.426505e-3 s +[acc] eval 2.501534e-3 s +[acc] eval 2.171513e-3 s +[acc] eval 2.613888e-3 s +[acc] eval 2.341996e-3 s +[acc] eval 2.05318e-3 s +[acc] eval 1.847346e-3 s +[acc] eval 1.777442e-3 s +[acc] eval 1.636202e-3 s +[acc] eval 1.8413e-3 s +[acc] eval 1.593927e-3 s +[acc] eval 1.607229e-3 s +[acc] eval 1.654744e-3 s +[acc] eval 1.569561e-3 s +[acc] eval 1.548704e-3 s +[acc] eval 1.451702e-3 s +[acc] eval 1.860353e-3 s +[acc] eval 2.851648e-3 s +[acc] eval 0.150133951 s +[acc] compile 7.5732682e-2 s +[acc] eval 1.6946487e-2 s +[acc] eval 9.280365e-3 s +[acc] eval 0.118217227 s +[acc] compile 7.5156528e-2 s +[acc] eval 1.780075e-2 s +[acc] eval 2.38468e-3 s +[acc] eval 2.11098e-3 s +[acc] eval 9.9779169e-2 s +[acc] compile 1.4795223e-2 s +[acc] eval 7.346165e-3 s +[acc] eval 7.192237e-3 s +[acc] eval 1.5040898e-2 s +[acc] eval 3.292919e-3 s +[acc] eval 2.517393e-3 s +[acc] eval 3.31919e-3 s +[acc] eval 3.873425e-3 s +[acc] eval 4.472002e-3 s +[acc] eval 3.33421e-3 s +[acc] eval 2.885773e-3 s +[acc] eval 2.673169e-3 s +[acc] eval 2.419313e-3 s +[acc] eval 2.627124e-3 s +[acc] eval 2.867492e-3 s +[acc] eval 2.196328e-3 s +[acc] eval 2.186645e-3 s +[acc] eval 2.289191e-3 s +[acc] eval 2.172007e-3 s +[acc] eval 2.044648e-3 s +[acc] eval 2.186416e-3 s +[acc] eval 2.293195e-3 s +[acc] eval 2.320911e-3 s +[acc] eval 2.148367e-3 s +[acc] eval 2.037316e-3 s +[acc] eval 1.956452e-3 s +[acc] eval 1.952867e-3 s +[acc] eval 1.947399e-3 s +[acc] eval 1.902217e-3 s +[acc] eval 1.938291e-3 s +[acc] eval 1.925376e-3 s +[acc] eval 0.118549526 s +[acc] compile 0.206408697 s +[acc] eval 1.4188664e-2 s +[acc] eval 9.905267e-3 s +[acc] eval 6.710839e-3 s +[acc] eval 5.382514e-3 s +[acc] eval 4.66727e-3 s +[acc] eval 3.931225e-3 s +[acc] eval 3.299016e-3 s +[acc] eval 3.089009e-3 s +[acc] eval 3.557314e-3 s +[acc] eval 3.050007e-3 s +[acc] eval 3.037607e-3 s +[acc] eval 3.07735e-3 s +[acc] eval 3.708091e-3 s +[acc] eval 3.420118e-3 s +[acc] eval 3.327863e-3 s +[acc] eval 3.439262e-3 s +[acc] eval 3.272332e-3 s +[acc] eval 3.276877e-3 s +[acc] eval 3.181777e-3 s +[acc] eval 3.311806e-3 s +[acc] eval 3.215728e-3 s +[acc] eval 3.331677e-3 s +[acc] eval 3.194228e-3 s +[acc] eval 3.315052e-3 s +[acc] eval 3.309239e-3 s +[acc] eval 3.325208e-3 s +[acc] eval 3.351281e-3 s +[acc] eval 2.960599e-3 s +[acc] eval 2.447631e-3 s +[acc] eval 2.566716e-3 s +[acc] eval 0.334961633 s +[acc] compile 7.4960632e-2 s +[acc] eval 9.62385e-3 s +[acc] eval 8.9318494e-2 s +[acc] compile 0.23708383 s +[acc] eval 2.8014356e-2 s +[acc] eval 0.278471237 s +[acc] compile 0.122919043 s +[acc] eval 7.142578e-3 s +[acc] eval 1.0856025e-2 s +[acc] eval 7.701482e-3 s +[acc] eval 6.912296e-3 s +[acc] eval 6.095791e-3 s +[acc] eval 3.901502e-3 s +[acc] eval 4.690193e-3 s +[acc] eval 4.708967e-3 s +[acc] eval 4.939545e-3 s +[acc] eval 3.881091e-3 s +[acc] eval 3.114495e-3 s +[acc] eval 2.652485e-3 s +[acc] eval 2.512421e-3 s +[acc] eval 2.583381e-3 s +[acc] eval 2.59619e-3 s +[acc] eval 2.512972e-3 s +[acc] eval 2.561223e-3 s +[acc] eval 4.190686e-3 s +[acc] eval 3.879858e-3 s +[acc] eval 4.447184e-3 s +[acc] eval 3.957458e-3 s +[acc] eval 2.93473e-3 s +[acc] eval 2.55084e-3 s +[acc] eval 2.531176e-3 s +[acc] eval 3.234282e-3 s +[acc] eval 2.628872e-3 s +[acc] eval 2.566777e-3 s +[acc] eval 2.508317e-3 s +[acc] eval 2.489916e-3 s +[acc] eval 4.792422e-3 s +[acc] eval 4.151294e-3 s +[acc] eval 0.256313741 s +[acc] compile 0.288025663 s +[acc] eval 2.9249566e-2 s +[acc] eval 0.321874099 s +[acc] compile 0.325914447 s +[acc] eval 2.0107396e-2 s +[acc] eval 0.354187334 s +[acc] compile 0.399468385 s +[acc] eval 1.2639677e-2 s +[acc] eval 3.536074e-3 s +[acc] eval 2.998421e-3 s +[acc] eval 8.917554e-3 s +[acc] eval 2.515953e-3 s +[acc] eval 2.599303e-3 s +[acc] eval 2.464093e-3 s +[acc] eval 2.616183e-3 s +[acc] eval 2.280755e-3 s +[acc] eval 2.169166e-3 s +[acc] eval 2.161294e-3 s +[acc] eval 2.128598e-3 s +[acc] eval 2.069182e-3 s +[acc] eval 1.965688e-3 s +[acc] eval 2.162852e-3 s +[acc] eval 1.852892e-3 s +[acc] eval 1.863366e-3 s +[acc] eval 1.890471e-3 s +[acc] eval 1.803759e-3 s +[acc] eval 1.805868e-3 s +[acc] eval 1.791056e-3 s +[acc] eval 1.846345e-3 s +[acc] eval 1.818233e-3 s +[acc] eval 1.810103e-3 s +[acc] eval 1.797785e-3 s +[acc] eval 2.003936e-3 s +[acc] eval 2.018988e-3 s +[acc] eval 1.95317e-3 s +[acc] eval 1.882708e-3 s +[acc] eval 1.911796e-3 s +[acc] eval 1.908162e-3 s +[acc] eval 0.487274772 s +[acc] compile 7.4281917e-2 s +[acc] eval 2.3123008e-2 s +[acc] eval 6.46487e-3 s +[acc] eval 5.933926e-3 s +[acc] eval 6.056849e-3 s +[acc] eval 6.152454e-3 s +[acc] eval 6.091646e-3 s +[acc] eval 6.281e-3 s +[acc] eval 6.836548e-3 s +[acc] eval 4.802876e-3 s +[acc] eval 6.316918e-3 s +[acc] eval 5.085289e-3 s +[acc] eval 5.38237e-3 s +[acc] eval 5.801768e-3 s +[acc] eval 1.2230364e-2 s +[acc] eval 3.970448e-3 s +[acc] eval 3.850132e-3 s +[acc] eval 3.883304e-3 s +[acc] eval 4.378356e-3 s +[acc] eval 3.954896e-3 s +[acc] eval 4.700859e-3 s +[acc] eval 4.077155e-3 s +[acc] eval 8.383195e-3 s +[acc] eval 4.180331e-3 s +[acc] eval 4.279888e-3 s +[acc] eval 3.874286e-3 s +[acc] eval 3.954221e-3 s +[acc] eval 3.783851e-3 s +[acc] eval 4.185491e-3 s +[acc] eval 5.564921e-3 s +[acc] eval 0.259256245 s +[acc] compile 0.194736209 s +[acc] eval 2.0154259e-2 s +[acc] eval 0.216937878 s +[acc] compile 4.629593e-3 s +[acc] eval 6.523308e-3 s +[acc] eval 5.957698e-3 s +[acc] eval 2.289352e-3 s +[acc] eval 2.034697e-3 s +[acc] eval 2.031181e-3 s +[acc] eval 2.022331e-3 s +[acc] eval 2.310277e-3 s +[acc] eval 2.609427e-3 s +[acc] eval 2.961331e-3 s +[acc] eval 2.81689e-3 s +[acc] eval 2.911526e-3 s +[acc] eval 2.920404e-3 s +[acc] eval 4.7261212e-2 s +[acc] compile 0.252250479 s +[acc] eval 1.0881455e-2 s +[acc] eval 4.623963e-3 s +[acc] eval 8.537669e-3 s +[acc] eval 5.048231e-3 s +[acc] eval 3.684648e-3 s +[acc] eval 3.939307e-3 s +[acc] eval 4.152247e-3 s +[acc] eval 4.503458e-3 s +[acc] eval 3.85366e-3 s +[acc] eval 3.614405e-3 s +[acc] eval 5.246768e-3 s +[acc] eval 3.825853e-3 s +[acc] eval 0.31966097 s +[acc] compile 6.22231e-3 s +[acc] eval 6.857268e-3 s +[acc] eval 3.598116e-3 s +[acc] eval 3.256293e-3 s +[acc] eval 2.948061e-3 s +[acc] eval 2.766586e-3 s +[acc] eval 3.141139e-3 s +[acc] eval 3.230025e-3 s +[acc] eval 2.579484e-3 s +[acc] eval 2.550443e-3 s +[acc] eval 2.782236e-3 s +[acc] eval 2.958223e-3 s +[acc] eval 2.621657e-3 s +[acc] eval 4.9655361e-2 s +[acc] compile 8.3697584e-2 s +[acc] eval 9.984311e-3 s +[acc] eval 3.588735e-3 s +[acc] eval 3.264452e-3 s +[acc] eval 3.478563e-3 s +[acc] eval 9.098461e-3 s +[acc] eval 2.949217e-3 s +[acc] eval 3.3115e-3 s +[acc] eval 2.961052e-3 s +[acc] eval 3.40685e-3 s +[acc] eval 2.814041e-3 s +[acc] eval 2.82322e-3 s +[acc] eval 3.103615e-3 s +[acc] eval 0.13938636 s +[acc] compile 7.600703e-3 s +[acc] eval 1.5670464e-2 s +[acc] eval 1.736226e-3 s +[acc] eval 1.445013e-3 s +[acc] eval 1.216296e-3 s +[acc] eval 3.0077202e-2 s +[acc] compile 8.871597e-3 s +[acc] eval 1.989431e-2 s +[acc] eval 3.256986e-3 s +[acc] eval 2.889007e-3 s +[acc] eval 3.34215e-3 s +[acc] eval 2.984485e-3 s +[acc] eval 2.770207e-3 s +[acc] eval 2.814994e-3 s +[acc] eval 2.965369e-3 s +[acc] eval 3.399574e-3 s +[acc] eval 2.775039e-3 s +[acc] eval 2.836314e-3 s +[acc] eval 2.719798e-3 s +[acc] eval 6.5214084e-2 s +[acc] compile 0.176444109 s +[acc] eval 1.3216766e-2 s +[acc] eval 6.583709e-3 s +[acc] eval 5.083419e-3 s +[acc] eval 5.101447e-3 s +[acc] eval 7.18789e-3 s +[acc] eval 1.2818621e-2 s +[acc] eval 3.198805e-3 s +[acc] eval 2.802149e-3 s +[acc] eval 2.747739e-3 s +[acc] eval 3.757738e-3 s +[acc] eval 3.041898e-3 s +[acc] eval 2.960818e-3 s +[acc] eval 0.250310896 s +[acc] compile 0.263315216 s +[acc] eval 2.5971914e-2 s +[acc] eval 8.591996e-3 s +[acc] eval 3.447178e-3 s +[acc] eval 3.43053e-3 s +[acc] eval 3.020359e-3 s +[acc] eval 3.201064e-3 s +[acc] eval 3.139003e-3 s +[acc] eval 3.226119e-3 s +[acc] eval 3.322514e-3 s +[acc] eval 3.5205e-3 s +[acc] eval 3.18829e-3 s +[acc] eval 3.108139e-3 s +[acc] eval 3.341291e-3 s +[acc] eval 3.669609e-3 s +[acc] eval 3.213564e-3 s +[acc] eval 3.039379e-3 s +[acc] eval 3.186115e-3 s +[acc] eval 3.279453e-3 s +[acc] eval 3.645243e-3 s +[acc] eval 3.02931e-3 s +[acc] eval 3.177926e-3 s +[acc] eval 2.929375e-3 s +[acc] eval 3.147934e-3 s +[acc] eval 3.370877e-3 s +[acc] eval 2.96474e-3 s +[acc] eval 3.078051e-3 s +[acc] eval 3.105483e-3 s +[acc] eval 0.387968392 s +[acc] compile 0.219934559 s +[acc] eval 1.0107184e-2 s +[acc] eval 1.2517336e-2 s +[acc] eval 7.628301e-3 s +[acc] eval 7.063697e-3 s +[acc] eval 4.87406e-3 s +[acc] eval 3.827829e-3 s +[acc] eval 3.181899e-3 s +[acc] eval 3.065646e-3 s +[acc] eval 3.225169e-3 s +[acc] eval 3.237893e-3 s +[acc] eval 3.304299e-3 s +[acc] eval 3.231944e-3 s +[acc] eval 0.29036132 s +[acc] compile 0.264111035 s +[acc] eval 2.1276776e-2 s +[acc] eval 0.289385215 s +[acc] compile 0.20649839 s +[acc] eval 1.2827192e-2 s +[acc] eval 7.54813e-3 s +[acc] eval 1.0461203e-2 s +[acc] eval 5.001333e-3 s +[acc] eval 5.320516e-3 s +[acc] eval 4.680295e-3 s +[acc] eval 3.80668e-3 s +[acc] eval 4.09142e-3 s +[acc] eval 5.416952e-3 s +[acc] eval 5.795182e-3 s +[acc] eval 4.801961e-3 s +[acc] eval 5.187243e-3 s +[acc] eval 0.28641098 s +[acc] compile 0.186793126 s +[acc] eval 1.8715303e-2 s +[acc] eval 0.214122777 s +[acc] compile 6.751336e-3 s +[acc] eval 4.771911e-3 s +[acc] eval 2.105037e-3 s +[acc] eval 2.071729e-3 s +[acc] eval 2.087292e-3 s +[acc] eval 2.042184e-3 s +[acc] eval 2.188971e-3 s +[acc] eval 2.602953e-3 s +[acc] eval 9.947757e-3 s +[acc] eval 2.043369e-3 s +[acc] eval 1.663743e-3 s +[acc] eval 2.786463e-3 s +[acc] eval 1.682898e-3 s +[acc] eval 4.5816487e-2 s +[acc] compile 6.7743007e-2 s +[acc] eval 1.5326395e-2 s +[acc] eval 9.640896e-3 s +[acc] eval 4.315332e-3 s +[acc] eval 4.999434e-3 s +[acc] eval 4.138312e-3 s +[acc] eval 3.330794e-3 s +[acc] eval 2.89546e-3 s +[acc] eval 3.411135e-3 s +[acc] eval 3.839962e-3 s +[acc] eval 2.675656e-3 s +[acc] eval 2.188581e-3 s +[acc] eval 2.101441e-3 s +[acc] eval 2.090783e-3 s +[acc] eval 3.310256e-3 s +[acc] eval 2.203282e-3 s +[acc] eval 2.206221e-3 s +[acc] eval 1.845685e-3 s +[acc] eval 1.785991e-3 s +[acc] eval 1.82223e-3 s +[acc] eval 1.796176e-3 s +[acc] eval 1.786827e-3 s +[acc] eval 1.814527e-3 s +[acc] eval 1.799758e-3 s +[acc] eval 1.798191e-3 s +[acc] eval 2.243062e-3 s +[acc] eval 3.208487e-3 s +[acc] eval 3.042591e-3 s +[acc] eval 2.922649e-3 s +[acc] eval 2.961529e-3 s +[acc] eval 1.948411e-3 s +[acc] eval 0.172435315 s +[acc] compile 6.705067e-3 s +[acc] eval 9.299148e-3 s +[acc] eval 2.597983e-3 s +[acc] eval 5.934136e-3 s +[acc] eval 3.461914e-3 s +[acc] eval 1.935584e-3 s +[acc] eval 1.806324e-3 s +[acc] eval 1.767525e-3 s +[acc] eval 1.810781e-3 s +[acc] eval 1.78018e-3 s +[acc] eval 1.788957e-3 s +[acc] eval 1.760343e-3 s +[acc] eval 1.769462e-3 s +[acc] eval 1.82725e-3 s +[acc] eval 1.829375e-3 s +[acc] eval 1.858995e-3 s +[acc] eval 1.77145e-3 s +[acc] eval 1.750417e-3 s +[acc] eval 1.774387e-3 s +[acc] eval 1.752344e-3 s +[acc] eval 1.808457e-3 s +[acc] eval 1.751585e-3 s +[acc] eval 1.735241e-3 s +[acc] eval 1.731647e-3 s +[acc] eval 2.30433e-3 s +[acc] eval 2.30032e-3 s +[acc] eval 2.108931e-3 s +[acc] eval 1.984543e-3 s +[acc] eval 1.808121e-3 s +[acc] eval 1.956864e-3 s +[acc] eval 1.968545e-3 s +[acc] eval 1.912684e-3 s +[acc] eval 8.3262212e-2 s +[acc] compile 0.180092456 s +[acc] eval 1.7875231e-2 s +[acc] eval 0.206088179 s +[acc] compile 0.261951668 s +[acc] eval 1.8171042e-2 s +[acc] eval 0.291364419 s +[acc] compile 0.118381166 s +[acc] eval 9.244195e-3 s +[acc] eval 2.208784e-3 s +[acc] eval 2.010233e-3 s +[acc] eval 1.796491e-3 s +[acc] eval 1.795666e-3 s +[acc] eval 6.18294e-3 s +[acc] eval 1.416195e-3 s +[acc] eval 1.225458e-3 s +[acc] eval 1.223151e-3 s +[acc] eval 1.027943e-3 s +[acc] eval 9.3414e-4 s +[acc] eval 9.62694e-4 s +[acc] eval 8.98424e-4 s +[acc] eval 9.26619e-4 s +[acc] eval 9.41897e-4 s +[acc] eval 9.08059e-4 s +[acc] eval 9.27272e-4 s +[acc] eval 9.13478e-4 s +[acc] eval 9.15284e-4 s +[acc] eval 9.37528e-4 s +[acc] eval 1.02044e-3 s +[acc] eval 9.45196e-4 s +[acc] eval 9.89348e-4 s +[acc] eval 9.57155e-4 s +[acc] eval 9.44022e-4 s +[acc] eval 9.23942e-4 s +[acc] eval 9.2785e-4 s +[acc] eval 9.72516e-4 s +[acc] eval 9.22669e-4 s +[acc] eval 9.80835e-4 s +[acc] eval 0.170605219 s +[acc] compile 6.2386174e-2 s +[acc] eval 1.0596318e-2 s +[acc] eval 2.639154e-3 s +[acc] eval 2.325983e-3 s +[acc] eval 2.257607e-3 s +[acc] eval 1.842696e-3 s +[acc] eval 6.971597e-3 s +[acc] eval 2.018017e-3 s +[acc] eval 1.947235e-3 s +[acc] eval 1.726487e-3 s +[acc] eval 1.412795e-3 s +[acc] eval 1.398218e-3 s +[acc] eval 1.197118e-3 s +[acc] eval 1.511587e-3 s +[acc] eval 2.067244e-3 s +[acc] eval 1.24192e-3 s +[acc] eval 1.1343e-3 s +[acc] eval 9.95497e-4 s +[acc] eval 9.79819e-4 s +[acc] eval 1.087987e-3 s +[acc] eval 1.100443e-3 s +[acc] eval 9.60213e-4 s +[acc] eval 1.008131e-3 s +[acc] eval 8.51865e-4 s +[acc] eval 8.40048e-4 s +[acc] eval 8.42646e-4 s +[acc] eval 8.61504e-4 s +[acc] eval 1.131654e-3 s +[acc] eval 1.635071e-3 s +[acc] eval 1.480721e-3 s +[acc] eval 1.619523e-3 s +[acc] eval 1.624066e-3 s +[acc] eval 0.125947264 s +[acc] compile 5.5642501e-2 s +[acc] eval 2.6953298e-2 s +[acc] eval 8.5844583e-2 s +[acc] compile 0.235653791 s +[acc] eval 2.3512235e-2 s +[acc] eval 0.265623061 s +[acc] compile 0.430304834 s +[acc] eval 3.5650861e-2 s +[acc] eval 0.474678009 s +[acc] compile 0.213546722 s +[acc] eval 1.9827588e-2 s +[acc] eval 1.0106497e-2 s +[acc] eval 3.320767e-3 s +[acc] eval 2.71753e-3 s +[acc] eval 7.399113e-3 s +[acc] eval 6.891052e-3 s +[acc] eval 6.941627e-3 s +[acc] eval 3.517966e-3 s +[acc] eval 2.950079e-3 s +[acc] eval 2.85037e-3 s +[acc] eval 2.604595e-3 s +[acc] eval 2.809555e-3 s +[acc] eval 2.657244e-3 s +[acc] eval 6.081774e-3 s +[acc] eval 8.818807e-3 s +[acc] eval 8.219023e-3 s +[acc] eval 8.434714e-3 s +[acc] eval 8.689004e-3 s +[acc] eval 7.567578e-3 s +[acc] eval 6.950476e-3 s +[acc] eval 0.349030633 s +[acc] compile 0.359757442 s +[acc] eval 2.979883e-2 s +[acc] eval 3.062169e-3 s +[acc] eval 3.506191e-3 s +[acc] eval 2.945361e-3 s +[acc] eval 3.344148e-3 s +[acc] eval 3.181151e-3 s +[acc] eval 3.094179e-3 s +[acc] eval 4.503914e-3 s +[acc] eval 2.955026e-3 s +[acc] eval 3.070838e-3 s +[acc] eval 3.152434e-3 s +[acc] eval 3.03483e-3 s +[acc] eval 2.977334e-3 s +[acc] eval 2.7431e-3 s +[acc] eval 2.724826e-3 s +[acc] eval 2.787248e-3 s +[acc] eval 3.110671e-3 s +[acc] eval 3.202455e-3 s +[acc] eval 3.824247e-3 s +[acc] eval 3.880092e-3 s +[acc] eval 3.922455e-3 s +[acc] eval 3.375297e-3 s +[acc] eval 3.724681e-3 s +[acc] eval 4.3839e-3 s +[acc] eval 3.665605e-3 s +[acc] eval 3.339686e-3 s +[acc] eval 3.12488e-3 s +[acc] eval 0.484756938 s +[acc] compile 0.355208728 s +[acc] eval 2.3756209e-2 s +[acc] eval 0.384740003 s +[acc] compile 5.1780241e-2 s +[acc] eval 6.422512e-3 s +[acc] eval 5.9470416e-2 s +[acc] compile 0.296574274 s +[acc] eval 2.0530878e-2 s +[acc] eval 1.2170643e-2 s +[acc] eval 0.334602392 s +[acc] compile 0.30335847 s +[acc] eval 1.0210254e-2 s +[acc] eval 3.899315e-3 s +[acc] eval 0.322795701 s +[acc] compile 0.136212064 s +[acc] eval 6.354492e-3 s +[acc] eval 3.49841e-3 s +[acc] eval 0.150559551 s +[acc] compile 1.8878847e-2 s +[acc] eval 1.4658849e-2 s +[acc] eval 4.2419512e-2 s +[acc] compile 7.0410866e-2 s +[acc] eval 1.97266e-2 s +[acc] eval 9.5814089e-2 s +[acc] compile 1.7313665e-2 s +[acc] eval 1.7889173e-2 s +[acc] eval 4.4696677e-2 s +[acc] compile 0.118800693 s +[acc] eval 2.8434273e-2 s +[acc] eval 0.150902105 s +[acc] compile 6.8473618e-2 s +[acc] eval 6.859017e-3 s +[acc] eval 4.497145e-3 s +[acc] eval 3.811664e-3 s +[acc] eval 3.155428e-3 s +[acc] eval 1.2635104e-2 s +[acc] eval 2.093571e-3 s +[acc] eval 1.937438e-3 s +[acc] eval 1.922485e-3 s +[acc] eval 1.884868e-3 s +[acc] eval 1.922197e-3 s +[acc] eval 1.894524e-3 s +[acc] eval 2.22146e-3 s +[acc] eval 2.278466e-3 s +[acc] eval 2.093296e-3 s +[acc] eval 0.121365017 s +[acc] compile 2.566063e-3 s +[acc] eval 2.674497e-3 s +[acc] eval 4.27864e-4 s +[acc] eval 4.71957e-4 s +[acc] eval 6.843622e-3 s +[acc] compile 3.624702e-3 s +[acc] eval 4.315669e-3 s +[acc] eval 3.52268e-4 s +[acc] eval 5.88979e-4 s +[acc] eval 9.819684e-3 s +[acc] compile 6.1252149e-2 s +[acc] eval 2.6266283e-2 s +[acc] eval 4.713282e-3 s +[acc] eval 8.174563e-3 s +[acc] eval 6.265516e-3 s +[acc] eval 7.635533e-3 s +[acc] eval 5.214342e-3 s +[acc] eval 4.810193e-3 s +[acc] eval 3.811532e-3 s +[acc] eval 2.96793e-3 s +[acc] eval 2.409787e-3 s +[acc] eval 2.191387e-3 s +[acc] eval 2.736864e-3 s +[acc] eval 0.144786236 s +[acc] compile 7.874102e-3 s +[acc] eval 5.874604e-3 s +[acc] eval 1.919851e-3 s +[acc] eval 2.1206e-3 s +[acc] eval 4.075608e-3 s +[acc] eval 2.817789e-3 s +[acc] eval 1.0948037e-2 s +[acc] eval 2.009214e-3 s +[acc] eval 1.343313e-3 s +[acc] eval 1.482875e-3 s +[acc] eval 1.245503e-3 s +[acc] eval 1.245123e-3 s +[acc] eval 1.366712e-3 s +[acc] eval 1.296286e-3 s +[acc] eval 1.252487e-3 s +[acc] eval 1.240655e-3 s +[acc] eval 1.910945e-3 s +[acc] eval 2.268031e-3 s +[acc] eval 1.591436e-3 s +[acc] eval 1.262101e-3 s +[acc] eval 1.220949e-3 s +[acc] eval 1.219782e-3 s +[acc] eval 1.250953e-3 s +[acc] eval 1.190778e-3 s +[acc] eval 1.163911e-3 s +[acc] eval 1.126512e-3 s +[acc] eval 2.552252e-3 s +[acc] eval 1.366863e-3 s +[acc] eval 1.382234e-3 s +[acc] eval 1.333493e-3 s +[acc] eval 1.289389e-3 s +[acc] eval 7.4446071e-2 s +[acc] compile 5.487541e-3 s +[acc] eval 1.1406485e-2 s +[acc] eval 2.050124e-3 s +[acc] eval 2.103863e-3 s +[acc] eval 6.909534e-3 s +[acc] eval 1.986199e-3 s +[acc] eval 1.904195e-3 s +[acc] eval 1.918791e-3 s +[acc] eval 1.64629e-3 s +[acc] eval 1.65727e-3 s +[acc] eval 1.440646e-3 s +[acc] eval 1.318012e-3 s +[acc] eval 1.242347e-3 s +[acc] eval 1.258947e-3 s +[acc] eval 1.737202e-3 s +[acc] eval 1.41765e-3 s +[acc] eval 1.735976e-3 s +[acc] eval 1.515651e-3 s +[acc] eval 1.394722e-3 s +[acc] eval 1.36983e-3 s +[acc] eval 1.553056e-3 s +[acc] eval 1.564971e-3 s +[acc] eval 1.54117e-3 s +[acc] eval 1.282969e-3 s +[acc] eval 1.253034e-3 s +[acc] eval 1.235035e-3 s +[acc] eval 1.569339e-3 s +[acc] eval 1.653406e-3 s +[acc] eval 1.633949e-3 s +[acc] eval 1.550151e-3 s +[acc] eval 1.285837e-3 s +[acc] eval 7.2493372e-2 s +[acc] compile 0.26580581 s +[acc] eval 2.3602351e-2 s +[acc] eval 0.298757611 s +[acc] compile 7.813542e-3 s +[acc] eval 9.197876e-3 s +[acc] eval 3.760085e-3 s +[acc] eval 3.14604e-3 s +[acc] eval 2.809196e-3 s +[acc] eval 9.347629e-3 s +[acc] eval 2.099239e-3 s +[acc] eval 1.863779e-3 s +[acc] eval 1.733332e-3 s +[acc] eval 1.774357e-3 s +[acc] eval 1.857203e-3 s +[acc] eval 1.806482e-3 s +[acc] eval 1.785184e-3 s +[acc] eval 1.768695e-3 s +[acc] eval 1.689405e-3 s +[acc] eval 1.739502e-3 s +[acc] eval 1.861797e-3 s +[acc] eval 1.674459e-3 s +[acc] eval 1.633096e-3 s +[acc] eval 1.763425e-3 s +[acc] eval 1.580538e-3 s +[acc] eval 2.512351e-3 s +[acc] eval 2.412003e-3 s +[acc] eval 1.648202e-3 s +[acc] eval 2.681122e-3 s +[acc] eval 2.634284e-3 s +[acc] eval 2.64784e-3 s +[acc] eval 2.565556e-3 s +[acc] eval 2.033441e-3 s +[acc] eval 1.679061e-3 s +[acc] eval 1.655695e-3 s +[acc] eval 1.642605e-3 s +[acc] eval 1.64146e-3 s +[acc] eval 1.774968e-3 s +[acc] eval 1.788667e-3 s +[acc] eval 1.707586e-3 s +[acc] eval 1.653497e-3 s +[acc] eval 1.63661e-3 s +[acc] eval 1.623568e-3 s +[acc] eval 1.627159e-3 s +[acc] eval 0.106235419 s +[acc] compile 6.088262e-3 s +[acc] eval 1.0958782e-2 s +[acc] eval 2.093745e-3 s +[acc] eval 5.668361e-3 s +[acc] eval 1.717945e-3 s +[acc] eval 1.661371e-3 s +[acc] eval 1.642459e-3 s +[acc] eval 1.626454e-3 s +[acc] eval 1.649564e-3 s +[acc] eval 1.700591e-3 s +[acc] eval 1.79762e-3 s +[acc] eval 1.709832e-3 s +[acc] eval 1.64165e-3 s +[acc] eval 1.715412e-3 s +[acc] eval 1.652921e-3 s +[acc] eval 1.642297e-3 s +[acc] eval 1.631755e-3 s +[acc] eval 1.608049e-3 s +[acc] eval 1.61795e-3 s +[acc] eval 1.773964e-3 s +[acc] eval 1.776763e-3 s +[acc] eval 1.686871e-3 s +[acc] eval 1.646079e-3 s +[acc] eval 1.622164e-3 s +[acc] eval 1.624421e-3 s +[acc] eval 6.3388349e-2 s +[acc] compile 4.634856e-3 s +[acc] eval 4.31254e-3 s +[acc] eval 1.545049e-3 s +[acc] eval 1.535357e-3 s +[acc] eval 1.90346e-3 s +[acc] eval 2.263096e-3 s +[acc] eval 1.632997e-3 s +[acc] eval 1.246943e-3 s +[acc] eval 1.164593e-3 s +[acc] eval 1.16787e-3 s +[acc] eval 1.143503e-3 s +[acc] eval 1.25692e-3 s +[acc] eval 1.205103e-3 s +[acc] eval 1.182961e-3 s +[acc] eval 5.907581e-3 s +[acc] eval 1.252792e-3 s +[acc] eval 1.136014e-3 s +[acc] eval 1.094884e-3 s +[acc] eval 1.057671e-3 s +[acc] eval 1.03979e-3 s +[acc] eval 4.0447007e-2 s +[acc] compile 5.4168131e-2 s +[acc] eval 1.2247379e-2 s +[acc] eval 2.913263e-3 s +[acc] eval 6.1181e-3 s +[acc] eval 2.535006e-3 s +[acc] eval 3.914322e-3 s +[acc] eval 3.625703e-3 s +[acc] eval 3.063796e-3 s +[acc] eval 2.90375e-3 s +[acc] eval 3.609005e-3 s +[acc] eval 3.860168e-3 s +[acc] eval 2.978589e-3 s +[acc] eval 2.59538e-3 s +[acc] eval 0.108863669 s +[acc] compile 1.1338228e-2 s +[acc] eval 1.0237606e-2 s +[acc] eval 6.228849e-3 s +[acc] eval 2.915549e-3 s +[acc] eval 2.507798e-3 s +[acc] eval 2.105446e-3 s +[acc] eval 2.263787e-3 s +[acc] eval 2.109507e-3 s +[acc] eval 2.150999e-3 s +[acc] eval 2.242587e-3 s +[acc] eval 2.461932e-3 s +[acc] eval 2.11009e-3 s +[acc] eval 2.036158e-3 s +[acc] eval 2.089078e-3 s +[acc] eval 1.937012e-3 s +[acc] eval 2.017307e-3 s +[acc] eval 2.059945e-3 s +[acc] eval 2.961551e-3 s +[acc] eval 2.931702e-3 s +[acc] eval 2.907876e-3 s +[acc] eval 2.631123e-3 s +[acc] eval 2.177472e-3 s +[acc] eval 2.128661e-3 s +[acc] eval 2.667654e-3 s +[acc] eval 2.336528e-3 s +[acc] eval 5.183499e-3 s +[acc] eval 2.096424e-3 s +[acc] eval 2.394959e-3 s +[acc] eval 2.530834e-3 s +[acc] eval 2.789622e-3 s +[acc] eval 2.580941e-3 s +[acc] eval 2.420902e-3 s +[acc] eval 2.573837e-3 s +[acc] eval 0.108122044 s +[acc] compile 0.331632085 s +[acc] eval 2.1554513e-2 s +[acc] eval 0.358482709 s +[acc] compile 0.139223292 s +[acc] eval 5.824316e-3 s +[acc] eval 4.328964e-3 s +[acc] eval 3.701442e-3 s +[acc] eval 1.4549256e-2 s +[acc] eval 3.568353e-3 s +[acc] eval 3.229856e-3 s +[acc] eval 2.647653e-3 s +[acc] eval 2.528815e-3 s +[acc] eval 2.327707e-3 s +[acc] eval 1.79296e-3 s +[acc] eval 1.783547e-3 s +[acc] eval 2.205426e-3 s +[acc] eval 2.114913e-3 s +[acc] eval 2.116709e-3 s +[acc] eval 1.868486e-3 s +[acc] eval 1.709686e-3 s +[acc] eval 1.69046e-3 s +[acc] eval 1.537754e-3 s +[acc] eval 1.725278e-3 s +[acc] eval 2.104507e-3 s +[acc] eval 1.628293e-3 s +[acc] eval 1.793822e-3 s +[acc] eval 1.647421e-3 s +[acc] eval 1.650188e-3 s +[acc] eval 1.597506e-3 s +[acc] eval 1.602792e-3 s +[acc] eval 1.70148e-3 s +[acc] eval 1.621922e-3 s +[acc] eval 1.516531e-3 s +[acc] eval 1.66463e-3 s +[acc] eval 1.946682e-3 s +[acc] eval 0.227982032 s +[acc] compile 6.8768115e-2 s +[acc] eval 1.9830196e-2 s +[acc] eval 9.2871687e-2 s +[acc] compile 5.24433e-4 s +[acc] eval 1.3540256e-2 s +[acc] compile 0.396333467 s +[acc] eval 2.677051e-2 s +[acc] eval 2.4670258e-2 s +[acc] eval 1.2132216e-2 s +[acc] eval 3.41581e-3 s +[acc] eval 3.256771e-3 s +[acc] eval 3.087258e-3 s +[acc] eval 3.545706e-3 s +[acc] eval 3.473516e-3 s +[acc] eval 2.976369e-3 s +[acc] eval 3.210424e-3 s +[acc] eval 3.439074e-3 s +[acc] eval 3.244728e-3 s +[acc] eval 8.600647e-3 s +[acc] eval 2.959018e-3 s +[acc] eval 4.171765e-3 s +[acc] eval 4.87264e-3 s +[acc] eval 3.135905e-3 s +[acc] eval 3.144452e-3 s +[acc] eval 4.062708e-3 s +[acc] eval 5.372869e-3 s +[acc] eval 5.022129e-3 s +[acc] eval 6.422181e-3 s +[acc] eval 1.1136861e-2 s +[acc] eval 3.392548e-3 s +[acc] eval 3.126648e-3 s +[acc] eval 3.376634e-3 s +[acc] eval 4.010548e-3 s +[acc] eval 3.231724e-3 s +[acc] eval 3.113943e-3 s +[acc] eval 3.1756e-3 s +[acc] eval 0.579728103 s +[acc] compile 0.186820687 s +[acc] eval 3.1200665e-2 s +[acc] eval 0.222763691 s +[acc] compile 0.145198011 s +[acc] eval 1.3912265e-2 s +[acc] eval 0.1622143 s +[acc] compile 8.14766e-4 s +[acc] eval 5.125417e-3 s +[acc] compile 9.577581e-3 s +[acc] eval 1.9454097e-2 s +[acc] eval 1.100753e-3 s +[acc] eval 1.120219e-3 s +[acc] eval 1.008608e-3 s +[acc] eval 8.60073e-4 s +[acc] eval 8.33115e-4 s +[acc] eval 8.12196e-4 s +[acc] eval 8.0753e-4 s +[acc] eval 7.64199e-4 s +[acc] eval 7.00545e-4 s +[acc] eval 6.55774e-4 s +[acc] eval 6.77764e-4 s +[acc] eval 4.0666132e-2 s +[acc] compile 3.786706e-3 s +[acc] eval 5.331401e-3 s +[acc] eval 1.285535e-3 s +[acc] eval 1.987876e-3 s +[acc] eval 1.215819e-3 s +[acc] eval 1.203495e-3 s +[acc] eval 1.230316e-3 s +[acc] eval 1.760785e-3 s +[acc] eval 1.671673e-3 s +[acc] eval 1.664003e-3 s +[acc] eval 1.598091e-3 s +[acc] eval 1.506932e-3 s +[acc] eval 8.646723e-3 s +[acc] eval 3.6212075e-2 s +[acc] compile 5.175722e-3 s +[acc] eval 2.3175499e-2 s +[acc] eval 3.1373018e-2 s +[acc] compile 0.114768923 s +[acc] eval 2.4836507e-2 s +[acc] eval 0.143150443 s +[acc] compile 6.250805e-3 s +[acc] eval 9.156682e-3 s +[acc] eval 2.5501682e-2 s +[acc] compile 1.3878963e-2 s +[acc] eval 3.0092241e-2 s +[acc] eval 4.7004362e-2 s +[acc] compile 5.950675e-3 s +[acc] eval 5.025022e-3 s +[acc] eval 1.3253236e-2 s +[acc] compile 0.245630571 s +[acc] eval 2.397689e-2 s +[acc] eval 0.273133903 s +[acc] compile 1.8409421e-2 s +[acc] eval 1.7529943e-2 s +[acc] eval 4.1714336e-2 s +[acc] compile 0.126402013 s +[acc] eval 2.7866383e-2 s +[acc] eval 0.158418862 s +[acc] compile 0.257516658 s +[acc] eval 1.1912626e-2 s +[acc] eval 0.274519084 s +[acc] compile 0.169698925 s +[acc] eval 2.7713005e-2 s +[acc] eval 0.202154261 s +[acc] compile 0.251392507 s +[acc] eval 2.8111573e-2 s +[acc] eval 0.284089201 s +[acc] compile 1.4445022e-2 s +[acc] eval 2.2375326e-2 s +[acc] eval 3.935257e-2 s +[acc] compile 0.262556528 s +[acc] eval 1.8221468e-2 s +[acc] eval 0.290057558 s +[acc] compile 0.187493104 s +[acc] eval 9.207134e-3 s +[acc] eval 1.7189845e-2 s +[acc] eval 4.001398e-3 s +[acc] eval 3.701356e-3 s +[acc] eval 4.830521e-3 s +[acc] eval 4.724463e-3 s +[acc] eval 4.339901e-3 s +[acc] eval 3.12207e-3 s +[acc] eval 2.249255e-3 s +[acc] eval 2.288421e-3 s +[acc] eval 2.653686e-3 s +[acc] eval 2.488089e-3 s +[acc] eval 3.193301e-3 s +[acc] eval 3.362535e-3 s +[acc] eval 2.818006e-3 s +[acc] eval 2.411944e-3 s +[acc] eval 2.105639e-3 s +[acc] eval 2.19625e-3 s +[acc] eval 2.358097e-3 s +[acc] eval 2.185059e-3 s +[acc] eval 1.996428e-3 s +[acc] eval 2.481354e-3 s +[acc] eval 3.681513e-3 s +[acc] eval 3.81644e-3 s +[acc] eval 3.383192e-3 s +[acc] eval 3.039425e-3 s +[acc] eval 3.33884e-3 s +[acc] eval 2.567354e-3 s +[acc] eval 2.002836e-3 s +[acc] eval 2.179201e-3 s +[acc] eval 2.01699e-3 s +[acc] eval 0.305938894 s +[acc] compile 7.79906e-4 s +[acc] eval 5.018303e-3 s +[acc] compile 0.331229626 s +[acc] eval 1.7973789e-2 s +[acc] eval 5.454085e-3 s +[acc] eval 1.4453654e-2 s +[acc] eval 3.578953e-3 s +[acc] eval 2.969033e-3 s +[acc] eval 3.290482e-3 s +[acc] eval 3.536282e-3 s +[acc] eval 3.695697e-3 s +[acc] eval 2.996451e-3 s +[acc] eval 2.369914e-3 s +[acc] eval 1.835432e-3 s +[acc] eval 1.64835e-3 s +[acc] eval 1.746372e-3 s +[acc] eval 2.179108e-3 s +[acc] eval 1.962366e-3 s +[acc] eval 1.667361e-3 s +[acc] eval 0.40697831 s +[acc] compile 0.204322141 s +[acc] eval 9.142439e-3 s +[acc] eval 1.1001763e-2 s +[acc] eval 6.691816e-3 s +[acc] eval 3.307129e-3 s +[acc] eval 3.457145e-3 s +[acc] eval 3.381298e-3 s +[acc] eval 3.521815e-3 s +[acc] eval 3.113643e-3 s +[acc] eval 3.117702e-3 s +[acc] eval 3.236571e-3 s +[acc] eval 3.211565e-3 s +[acc] eval 3.239295e-3 s +[acc] eval 3.162006e-3 s +[acc] eval 3.071449e-3 s +[acc] eval 3.247593e-3 s +[acc] eval 3.452014e-3 s +[acc] eval 3.337833e-3 s +[acc] eval 3.23723e-3 s +[acc] eval 3.083154e-3 s +[acc] eval 3.142536e-3 s +[acc] eval 3.247303e-3 s +[acc] eval 3.125493e-3 s +[acc] eval 3.19879e-3 s +[acc] eval 3.215125e-3 s +[acc] eval 3.14369e-3 s +[acc] eval 3.514647e-3 s +[acc] eval 3.746727e-3 s +[acc] eval 4.212266e-3 s +[acc] eval 1.142456e-2 s +[acc] eval 3.854346e-3 s +[acc] eval 0.336231163 s +[acc] compile 8.661213e-2 s +[acc] eval 8.612818e-3 s +[acc] eval 1.012221e-3 s +[acc] eval 7.67e-4 s +[acc] eval 1.115582e-3 s +[acc] eval 7.07876e-4 s +[acc] eval 9.03672e-4 s +[acc] eval 8.47674e-4 s +[acc] eval 1.57801e-3 s +[acc] eval 1.48073e-3 s +[acc] eval 1.465194e-3 s +[acc] eval 1.299763e-3 s +[acc] eval 1.574814e-3 s +[acc] eval 1.069616e-3 s +[acc] eval 1.112764e-3 s +[acc] eval 6.52092e-4 s +[acc] eval 5.701954e-3 s +[acc] eval 1.332252e-3 s +[acc] eval 1.033265e-3 s +[acc] eval 8.80913e-4 s +[acc] eval 9.46339e-4 s +[acc] eval 7.53498e-4 s +[acc] eval 7.0006e-4 s +[acc] eval 7.23527e-4 s +[acc] eval 6.9786e-4 s +[acc] eval 6.56881e-4 s +[acc] eval 7.04613e-4 s +[acc] eval 6.93385e-4 s +[acc] eval 7.53884e-4 s +[acc] eval 8.3683e-4 s +[acc] eval 7.13085e-4 s +[acc] eval 0.131821658 s +[acc] compile 4.236546e-3 s +[acc] eval 7.437094e-3 s +[acc] eval 1.3245809e-2 s +[acc] compile 0.196810884 s +[acc] eval 1.9775197e-2 s +[acc] eval 0.221433005 s +[acc] compile 7.0870733e-2 s +[acc] eval 1.6478565e-2 s +[acc] eval 9.0189082e-2 s +[acc] compile 0.27917141 s +[acc] eval 2.9311788e-2 s +[acc] eval 0.312379652 s +[acc] compile 8.076348e-3 s +[acc] eval 8.658889e-3 s +[acc] eval 2.0469651e-2 s +[acc] compile 0.205461508 s +[acc] eval 2.8056169e-2 s +[acc] eval 0.236992256 s +[acc] compile 0.112641006 s +[acc] eval 5.55157e-3 s +[acc] eval 2.751151e-3 s +[acc] eval 3.0243e-3 s +[acc] eval 2.840375e-3 s +[acc] eval 0.130223039 s +[acc] compile 0.130500456 s +[acc] eval 1.2288884e-2 s +[acc] eval 1.8718918e-2 s +[acc] eval 4.011756e-3 s +[acc] eval 2.6430329e-2 s +[acc] eval 5.043423e-3 s +[acc] eval 5.496222e-3 s +[acc] eval 0.208520244 s +[acc] compile 8.222284e-3 s +[acc] eval 3.697829e-3 s +[acc] eval 1.275116e-3 s +[acc] eval 1.175856e-3 s +[acc] eval 1.930977e-3 s +[acc] eval 1.8542729e-2 s +[acc] compile 0.140120514 s +[acc] eval 6.375955e-3 s +[acc] eval 7.839441e-3 s +[acc] eval 2.793685e-3 s +[acc] eval 2.47015e-3 s +[acc] eval 2.465897e-3 s +[acc] eval 2.351711e-3 s +[acc] eval 0.167367913 s +[acc] compile 0.200219394 s +[acc] eval 1.5280022e-2 s +[acc] eval 0.220837762 s +[acc] compile 0.148311882 s +[acc] eval 8.704064e-3 s +[acc] eval 3.754976e-3 s +[acc] eval 3.396795e-3 s +[acc] eval 3.099887e-3 s +[acc] eval 2.915325e-3 s +[acc] eval 9.140833e-3 s +[acc] eval 0.182907596 s +[acc] compile 1.3833311e-2 s +[acc] eval 1.4080379e-2 s +[acc] eval 3.368563e-3 s +[acc] eval 2.766317e-3 s +[acc] eval 6.27717e-3 s +[acc] eval 2.255874e-3 s +[acc] eval 2.038124e-3 s +[acc] eval 1.932249e-3 s +[acc] eval 4.9822697e-2 s +[acc] compile 0.117105553 s +[acc] eval 1.5826191e-2 s +[acc] eval 0.143223202 s +[acc] compile 0.297259707 s +[acc] eval 1.6342997e-2 s +[acc] eval 0.323659284 s +[acc] compile 1.0403263e-2 s +[acc] eval 1.4852388e-2 s +[acc] eval 3.0753283e-2 s +[acc] compile 1.4544493e-2 s +[acc] eval 1.4700848e-2 s +[acc] eval 3.8482651e-2 s +[acc] compile 1.255639e-2 s +[acc] eval 1.5743492e-2 s +[acc] eval 3.8193018e-2 s +[acc] compile 0.338523755 s +[acc] eval 2.2613221e-2 s +[acc] eval 0.365708535 s +[acc] compile 1.3297034e-2 s +[acc] eval 9.415347e-3 s +[acc] eval 2.8664575e-2 s +[acc] compile 1.7510186e-2 s +[acc] eval 2.0362996e-2 s +[acc] eval 4.121347e-3 s +[acc] eval 3.099205e-3 s +[acc] eval 2.788096e-3 s +[acc] eval 2.812185e-3 s +[acc] eval 3.295507e-3 s +[acc] eval 2.886302e-3 s +[acc] eval 2.383746e-3 s +[acc] eval 2.300318e-3 s +[acc] eval 2.462511e-3 s +[acc] eval 2.765405e-3 s +[acc] eval 8.954643e-3 s +[acc] eval 2.793105e-3 s +[acc] eval 2.691472e-3 s +[acc] eval 2.667112e-3 s +[acc] eval 2.755617e-3 s +[acc] eval 2.879727e-3 s +[acc] eval 2.836125e-3 s +[acc] eval 2.677347e-3 s +[acc] eval 2.386711e-3 s +[acc] eval 2.846141e-3 s +[acc] eval 2.97628e-3 s +[acc] eval 2.875138e-3 s +[acc] eval 2.846128e-3 s +[acc] eval 2.808489e-3 s +[acc] eval 2.745991e-3 s +[acc] eval 2.690228e-3 s +[acc] eval 2.604082e-3 s +[acc] eval 1.187348e-2 s +[acc] eval 3.173216e-3 s +[acc] eval 0.141300732 s +[acc] compile 1.1131142e-2 s +[acc] eval 7.861807e-3 s +[acc] eval 3.478859e-3 s +[acc] eval 3.298276e-3 s +[acc] eval 7.090963e-3 s +[acc] eval 1.5710499e-2 s +[acc] eval 2.52063e-3 s +[acc] eval 2.23691e-3 s +[acc] eval 2.174047e-3 s +[acc] eval 2.142902e-3 s +[acc] eval 2.093877e-3 s +[acc] eval 2.111583e-3 s +[acc] eval 2.147661e-3 s +[acc] eval 2.287126e-3 s +[acc] eval 2.849711e-3 s +[acc] eval 2.66707e-3 s +[acc] eval 2.728556e-3 s +[acc] eval 2.662102e-3 s +[acc] eval 2.702328e-3 s +[acc] eval 2.491318e-3 s +[acc] eval 2.548488e-3 s +[acc] eval 2.395711e-3 s +[acc] eval 2.53693e-3 s +[acc] eval 2.5158e-3 s +[acc] eval 2.620107e-3 s +[acc] eval 2.642351e-3 s +[acc] eval 2.391356e-3 s +[acc] eval 2.453201e-3 s +[acc] eval 2.434038e-3 s +[acc] eval 2.429708e-3 s +[acc] eval 2.381323e-3 s +[acc] eval 0.114749561 s +[acc] compile 0.231860005 s +[acc] eval 1.8715108e-2 s +[acc] eval 0.260715638 s +[acc] compile 0.189201572 s +[acc] eval 1.9659972e-2 s +[acc] eval 0.222310906 s +[acc] compile 0.202643122 s +[acc] eval 2.1293351e-2 s +[acc] eval 0.22766498 s +[acc] compile 5.336068e-3 s +[acc] eval 1.147043e-2 s +[acc] eval 2.472705e-3 s +[acc] eval 2.699061e-3 s +[acc] eval 2.531175e-3 s +[acc] eval 1.681451e-3 s +[acc] eval 1.774817e-3 s +[acc] eval 5.535233e-3 s +[acc] eval 1.281368e-3 s +[acc] eval 9.66098e-4 s +[acc] eval 8.59389e-4 s +[acc] eval 1.009797e-3 s +[acc] eval 8.42237e-4 s +[acc] eval 8.19636e-4 s +[acc] eval 8.62522e-4 s +[acc] eval 8.78558e-4 s +[acc] eval 8.33287e-4 s +[acc] eval 8.69333e-4 s +[acc] eval 9.03082e-4 s +[acc] eval 1.132674e-3 s +[acc] eval 1.068017e-3 s +[acc] eval 1.004543e-3 s +[acc] eval 1.019589e-3 s +[acc] eval 9.47553e-4 s +[acc] eval 8.57152e-4 s +[acc] eval 9.20169e-4 s +[acc] eval 8.18879e-4 s +[acc] eval 8.16111e-4 s +[acc] eval 8.02769e-4 s +[acc] eval 8.17582e-4 s +[acc] eval 8.06546e-4 s +[acc] eval 5.8056433e-2 s +[acc] compile 0.249309325 s +[acc] eval 3.096681e-2 s +[acc] eval 0.289400438 s +[acc] compile 0.125074149 s +[acc] eval 8.252949e-3 s +[acc] eval 3.95429e-3 s +[acc] eval 4.330148e-3 s +[acc] eval 3.893812e-3 s +[acc] eval 1.0167657e-2 s +[acc] eval 4.421269e-3 s +[acc] eval 4.639433e-3 s +[acc] eval 4.180969e-3 s +[acc] eval 3.511549e-3 s +[acc] eval 3.05217e-3 s +[acc] eval 3.215873e-3 s +[acc] eval 4.348945e-3 s +[acc] eval 3.894807e-3 s +[acc] eval 3.521834e-3 s +[acc] eval 3.526011e-3 s +[acc] eval 3.676226e-3 s +[acc] eval 3.732334e-3 s +[acc] eval 1.935137e-3 s +[acc] eval 1.665648e-3 s +[acc] eval 1.780578e-3 s +[acc] eval 2.436971e-3 s +[acc] eval 2.726687e-3 s +[acc] eval 2.192653e-3 s +[acc] eval 2.005362e-3 s +[acc] eval 1.795224e-3 s +[acc] eval 1.732472e-3 s +[acc] eval 1.723928e-3 s +[acc] eval 1.592141e-3 s +[acc] eval 1.503069e-3 s +[acc] eval 3.240809e-3 s +[acc] eval 1.70279e-3 s +[acc] eval 1.616043e-3 s +[acc] eval 1.631289e-3 s +[acc] eval 1.474305e-3 s +[acc] eval 1.428169e-3 s +[acc] eval 1.572889e-3 s +[acc] eval 1.64285e-3 s +[acc] eval 1.541533e-3 s +[acc] eval 0.246054013 s +[acc] compile 0.170995039 s +[acc] eval 1.7772948e-2 s +[acc] eval 8.974185e-3 s +[acc] eval 4.978696e-3 s +[acc] eval 4.543588e-3 s +[acc] eval 4.069859e-3 s +[acc] eval 3.50067e-3 s +[acc] eval 2.217527e-3 s +[acc] eval 2.121479e-3 s +[acc] eval 2.985378e-3 s +[acc] eval 2.072487e-3 s +[acc] eval 0.227547764 s +[acc] compile 0.315758303 s +[acc] eval 1.0334485e-2 s +[acc] eval 0.329017499 s +[acc] compile 0.344590763 s +[acc] eval 2.2308773e-2 s +[acc] eval 0.381432077 s +[acc] compile 0.375035573 s +[acc] eval 2.6075308e-2 s +[acc] eval 0.413515664 s +[acc] compile 0.384814501 s +[acc] eval 3.7538171e-2 s +[acc] eval 0.429591715 s +[acc] compile 0.148955675 s +[acc] eval 1.0621182e-2 s +[acc] eval 0.166048996 s +[acc] compile 0.136259565 s +[acc] eval 1.6974186e-2 s +[acc] eval 2.562967e-3 s +[acc] eval 2.503445e-3 s +[acc] eval 2.443105e-3 s +[acc] eval 2.475149e-3 s +[acc] eval 3.350147e-3 s +[acc] eval 2.979377e-3 s +[acc] eval 2.940058e-3 s +[acc] eval 2.707978e-3 s +[acc] eval 0.179066848 s +[acc] compile 0.214203551 s +[acc] eval 2.7249822e-2 s +[acc] eval 5.776146e-3 s +[acc] eval 8.192656e-3 s +[acc] eval 2.3633524e-2 s +[acc] eval 3.595917e-3 s +[acc] eval 3.447574e-3 s +[acc] eval 3.639902e-3 s +[acc] eval 3.679283e-3 s +[acc] eval 3.442642e-3 s +[acc] eval 3.458911e-3 s +[acc] eval 3.437958e-3 s +[acc] eval 3.709763e-3 s +[acc] eval 3.457486e-3 s +[acc] eval 4.160885e-3 s +[acc] eval 4.226655e-3 s +[acc] eval 4.555534e-3 s +[acc] eval 4.343372e-3 s +[acc] eval 4.300897e-3 s +[acc] eval 4.256137e-3 s +[acc] eval 0.342348487 s +[acc] compile 0.196369232 s +[acc] eval 1.4589982e-2 s +[acc] eval 0.215057972 s +[acc] compile 0.181682136 s +[acc] eval 1.9549403e-2 s +[acc] eval 0.204927451 s +[acc] compile 1.208911e-3 s +[acc] eval 1.7351295e-2 s +[acc] compile 1.3143427e-2 s +[acc] eval 1.850374e-2 s +[acc] eval 4.4884319e-2 s +[acc] compile 0.139794037 s +[acc] eval 8.109745e-3 s +[acc] eval 4.889553e-3 s +[acc] eval 6.235454e-3 s +[acc] eval 1.1597015e-2 s +[acc] eval 5.571406e-3 s +[acc] eval 4.82241e-3 s +[acc] eval 3.611241e-3 s +[acc] eval 3.537148e-3 s +[acc] eval 3.654013e-3 s +[acc] eval 3.431901e-3 s +[acc] eval 3.442556e-3 s +[acc] eval 3.350722e-3 s +[acc] eval 3.606613e-3 s +[acc] eval 3.502574e-3 s +[acc] eval 3.625172e-3 s +[acc] eval 3.479522e-3 s +[acc] eval 3.365367e-3 s +[acc] eval 3.388652e-3 s +[acc] eval 3.913821e-3 s +[acc] eval 5.465739e-3 s +[acc] eval 0.23876912 s +[acc] compile 7.06366e-3 s +[acc] eval 8.388458e-3 s +[acc] eval 2.655175e-3 s +[acc] eval 2.497889e-3 s +[acc] eval 2.9543777e-2 s +[acc] compile 1.3658746e-2 s +[acc] eval 2.4703358e-2 s +[acc] eval 4.4176586e-2 s +[acc] compile 0.128386964 s +[acc] eval 1.2971994e-2 s +[acc] eval 4.916725e-3 s +[acc] eval 9.869708e-3 s +[acc] eval 3.779703e-3 s +[acc] eval 4.122566e-3 s +[acc] eval 3.284718e-3 s +[acc] eval 3.05823e-3 s +[acc] eval 3.069466e-3 s +[acc] eval 3.802896e-3 s +[acc] eval 4.293329e-3 s +[acc] eval 3.391159e-3 s +[acc] eval 3.131312e-3 s +[acc] eval 0.193549415 s +[acc] compile 0.242654579 s +[acc] eval 1.5602877e-2 s +[acc] eval 0.26713333 s +[acc] compile 4.656432e-3 s +[acc] eval 3.211843e-3 s +[acc] eval 9.205739e-3 s +[acc] compile 0.123367504 s +[acc] eval 1.0602878e-2 s +[acc] eval 0.138681794 s +[acc] compile 1.2137932e-2 s +[acc] eval 4.570853e-3 s +[acc] eval 1.9036061e-2 s +[acc] compile 7.84594e-3 s +[acc] eval 1.3295051e-2 s +[acc] eval 2.9164117e-2 s +[acc] compile 6.2171548e-2 s +[acc] eval 5.492963e-3 s +[acc] eval 6.9277961e-2 s +[acc] compile 3.965347e-3 s +[acc] eval 9.9608e-3 s +[acc] eval 1.5366204e-2 s +[acc] compile 0.179697877 s +[acc] eval 2.2770594e-2 s +[acc] eval 0.213911765 s +[acc] compile 9.151636e-3 s +[acc] eval 1.465785e-2 s +[acc] eval 2.8493243e-2 s +[acc] compile 1.4024242e-2 s +[acc] eval 2.4342951e-2 s +[acc] eval 4.3119435e-2 s +[acc] compile 0.353194546 s +[acc] eval 2.3173117e-2 s +[acc] eval 3.309451e-3 s +[acc] eval 9.212803e-3 s +[acc] eval 3.288066e-3 s +[acc] eval 2.994874e-3 s +[acc] eval 3.417401e-3 s +[acc] eval 2.692091e-3 s +[acc] eval 2.468833e-3 s +[acc] eval 2.44063e-3 s +[acc] eval 2.351805e-3 s +[acc] eval 2.365687e-3 s +[acc] eval 2.316709e-3 s +[acc] eval 0.419014014 s +[acc] compile 0.129553691 s +[acc] eval 1.9614884e-2 s +[acc] eval 0.154248298 s +[acc] compile 7.447562e-3 s +[acc] eval 2.3370502e-2 s +[acc] eval 3.8756385e-2 s +[acc] compile 7.9718546e-2 s +[acc] eval 9.938454e-3 s +[acc] eval 9.3801886e-2 s +[acc] compile 0.18681245 s +[acc] eval 1.7608651e-2 s +[acc] eval 0.2161644 s +[acc] compile 3.382999e-3 s +[acc] eval 4.325678e-3 s +[acc] eval 7.00771e-4 s +[acc] eval 4.36186e-4 s +[acc] eval 9.802483e-3 s +[acc] compile 0.187642671 s +[acc] eval 1.3417499e-2 s +[acc] eval 0.208830134 s +[acc] compile 1.6269018e-2 s +[acc] eval 6.934546e-3 s +[acc] eval 2.6962274e-2 s +[acc] compile 5.9399964e-2 s +[acc] eval 1.8067153e-2 s +[acc] eval 8.053815e-2 s +[acc] compile 0.149069292 s +[acc] eval 1.4158914e-2 s +[acc] eval 0.170538823 s +[acc] compile 2.0396114e-2 s +[acc] eval 3.1599397e-2 s +[acc] eval 5.9905956e-2 s +[acc] compile 5.9903795e-2 s +[acc] eval 1.3275715e-2 s +[acc] eval 7.7803428e-2 s +[acc] compile 0.132234372 s +[acc] eval 3.3926544e-2 s +[acc] eval 3.288623e-3 s +[acc] eval 2.759867e-3 s +[acc] eval 3.005051e-3 s +[acc] eval 2.851556e-3 s +[acc] eval 2.881672e-3 s +[acc] eval 2.710475e-3 s +[acc] eval 4.99659e-3 s +[acc] eval 4.982901e-3 s +[acc] eval 2.844096e-3 s +[acc] eval 3.343185e-3 s +[acc] eval 2.689722e-3 s +[acc] eval 2.655644e-3 s +[acc] eval 2.894665e-3 s +[acc] eval 2.742768e-3 s +[acc] eval 2.78597e-3 s +[acc] eval 4.114628e-3 s +[acc] eval 4.759959e-3 s +[acc] eval 4.990571e-3 s +[acc] eval 4.75288e-3 s +[acc] eval 4.731214e-3 s +[acc] eval 4.709724e-3 s +[acc] eval 3.004276e-3 s +[acc] eval 2.643125e-3 s +[acc] eval 2.690524e-3 s +[acc] eval 2.715134e-3 s +[acc] eval 2.737891e-3 s +[acc] eval 2.588309e-3 s +[acc] eval 3.019183e-3 s +[acc] eval 2.755441e-3 s +[acc] eval 0.269996641 s +[acc] compile 8.382398e-3 s +[acc] eval 2.8618548e-2 s +[acc] eval 4.4650238e-2 s +[acc] compile 7.0000711e-2 s +[acc] eval 8.896886e-3 s +[acc] eval 8.2922763e-2 s +[acc] compile 7.8320921e-2 s +[acc] eval 7.295671e-3 s +[acc] eval 1.2662768e-2 s +[acc] eval 2.722357e-3 s +[acc] eval 2.756578e-3 s +[acc] eval 2.229649e-3 s +[acc] eval 2.319345e-3 s +[acc] eval 1.573521e-3 s +[acc] eval 1.353235e-3 s +[acc] eval 1.225823e-3 s +[acc] eval 1.263005e-3 s +[acc] eval 1.433292e-3 s +[acc] eval 1.181346e-3 s +[acc] eval 1.165686e-3 s +[acc] eval 1.116184e-3 s +[acc] eval 1.600346e-3 s +[acc] eval 1.147002e-3 s +[acc] eval 1.09163e-3 s +[acc] eval 0.125992588 s +[acc] compile 0.412908825 s +[acc] eval 1.1137436e-2 s +[acc] eval 0.435816829 s +[acc] compile 2.715537e-3 s +[acc] eval 2.329241e-3 s +[acc] eval 4.96481e-4 s +[acc] eval 5.03852e-4 s +[acc] eval 6.759464e-3 s +[acc] compile 5.722293e-3 s +[acc] eval 6.519794e-3 s +[acc] eval 2.545876e-3 s +[acc] eval 3.673275e-3 s +[acc] eval 2.981099e-3 s +[acc] eval 2.898634e-3 s +[acc] eval 9.051556e-3 s +[acc] eval 3.6795759e-2 s +[acc] compile 8.214663e-3 s +[acc] eval 1.5574367e-2 s +[acc] eval 6.6511e-3 s +[acc] eval 3.308111e-3 s +[acc] eval 2.663446e-3 s +[acc] eval 3.107412e-3 s +[acc] eval 2.821187e-3 s +[acc] eval 2.478859e-3 s +[acc] eval 2.478715e-3 s +[acc] eval 2.627444e-3 s +[acc] eval 2.39384e-3 s +[acc] eval 2.338124e-3 s +[acc] eval 2.147616e-3 s +[acc] eval 2.570937e-3 s +[acc] eval 2.598597e-3 s +[acc] eval 2.28901e-3 s +[acc] eval 2.606925e-3 s +[acc] eval 2.189838e-3 s +[acc] eval 2.178837e-3 s +[acc] eval 2.321427e-3 s +[acc] eval 2.616529e-3 s +[acc] eval 2.49414e-3 s +[acc] eval 2.095013e-3 s +[acc] eval 2.136155e-3 s +[acc] eval 2.649766e-3 s +[acc] eval 2.370716e-3 s +[acc] eval 3.000491e-3 s +[acc] eval 2.818958e-3 s +[acc] eval 2.513231e-3 s +[acc] eval 3.095806e-3 s +[acc] eval 2.545977e-3 s +[acc] eval 2.142266e-3 s +[acc] eval 0.110555352 s +[acc] compile 6.4998909e-2 s +[acc] eval 1.9572264e-2 s +[acc] eval 3.883964e-3 s +[acc] eval 2.899928e-3 s +[acc] eval 2.748104e-3 s +[acc] eval 2.645197e-3 s +[acc] eval 2.409697e-3 s +[acc] eval 3.56819e-3 s +[acc] eval 3.624618e-3 s +[acc] eval 2.527646e-3 s +[acc] eval 2.482069e-3 s +[acc] eval 2.362045e-3 s +[acc] eval 2.435018e-3 s +[acc] eval 2.465485e-3 s +[acc] eval 2.521631e-3 s +[acc] eval 2.390641e-3 s +[acc] eval 2.779349e-3 s +[acc] eval 2.739872e-3 s +[acc] eval 2.759035e-3 s +[acc] eval 2.777233e-3 s +[acc] eval 2.521244e-3 s +[acc] eval 2.758981e-3 s +[acc] eval 2.587957e-3 s +[acc] eval 2.540472e-3 s +[acc] eval 2.899087e-3 s +[acc] eval 2.395492e-3 s +[acc] eval 2.778761e-3 s +[acc] eval 2.62113e-3 s +[acc] eval 2.732862e-3 s +[acc] eval 2.83252e-3 s +[acc] eval 2.835979e-3 s +[acc] eval 0.171238361 s diff --git a/phas2_n4.err b/phas2_n4.err new file mode 100644 index 0000000..1d1b9bf --- /dev/null +++ b/phas2_n4.err @@ -0,0 +1,3081 @@ +[acc] compile 1.5288851e-2 s +[acc] eval 2.010652007 s +[acc] compile 6.13039e-4 s +[acc] eval 7.1534814e-2 s +[acc] compile 5.19529e-4 s +[acc] eval 8.2007916e-2 s +[acc] compile 5.60668e-4 s +[acc] eval 0.215239243 s +[acc] compile 4.86642e-4 s +[acc] eval 0.155146324 s +[acc] compile 7.87492e-4 s +[acc] eval 0.177545691 s +[acc] compile 5.64334e-4 s +[acc] eval 0.241093269 s +[acc] compile 7.60258e-4 s +[acc] eval 0.175993236 s +[acc] compile 3.43977e-4 s +[acc] eval 7.5088695e-2 s +[acc] compile 4.417569e-3 s +[acc] eval 1.3820358e-2 s +[acc] eval 3.519331e-3 s +[acc] eval 4.625247e-3 s +[acc] eval 3.908526e-3 s +[acc] eval 3.913933e-3 s +[acc] eval 3.9959946e-2 s +[acc] eval 1.753689e-3 s +[acc] eval 1.319806e-3 s +[acc] eval 8.0156818e-2 s +[acc] compile 3.498846e-3 s +[acc] eval 3.727778e-3 s +[acc] eval 5.95181e-4 s +[acc] eval 1.596345e-3 s +[acc] eval 1.0857977e-2 s +[acc] compile 6.8293164e-2 s +[acc] eval 2.8121731e-2 s +[acc] eval 9.9934647e-2 s +[acc] compile 7.76162e-4 s +[acc] eval 1.1964782e-2 s +[acc] compile 0.17801885 s +[acc] eval 2.7050504e-2 s +[acc] eval 0.21496151 s +[acc] compile 5.28202e-3 s +[acc] eval 1.8070615e-2 s +[acc] eval 2.5684434e-2 s +[acc] compile 0.127044283 s +[acc] eval 3.6834178e-2 s +[acc] eval 0.175808033 s +[acc] compile 5.821146e-3 s +[acc] eval 1.1582915e-2 s +[acc] eval 1.9973809e-2 s +[acc] compile 0.360915969 s +[acc] eval 2.4975343e-2 s +[acc] eval 0.399008098 s +[acc] compile 0.515131904 s +[acc] eval 3.048669e-2 s +[acc] eval 0.5529828 s +[acc] compile 0.500943304 s +[acc] eval 3.9062353e-2 s +[acc] eval 0.557844874 s +[acc] compile 0.144411637 s +[acc] eval 1.3751679e-2 s +[acc] eval 0.166018974 s +[acc] compile 7.9825307e-2 s +[acc] eval 8.54226e-3 s +[acc] eval 9.2146167e-2 s +[acc] compile 0.202874467 s +[acc] eval 3.0303739e-2 s +[acc] eval 0.238671369 s +[acc] compile 0.18170276 s +[acc] eval 1.1364304e-2 s +[acc] eval 0.199814924 s +[acc] compile 0.175240682 s +[acc] eval 1.3660883e-2 s +[acc] eval 4.630742e-3 s +[acc] eval 5.175797e-3 s +[acc] eval 4.090444e-3 s +[acc] eval 3.590869e-3 s +[acc] eval 3.346039e-3 s +[acc] eval 3.402043e-3 s +[acc] eval 2.813427e-3 s +[acc] eval 3.052088e-3 s +[acc] eval 5.138697e-3 s +[acc] eval 3.703072e-3 s +[acc] eval 3.093847e-3 s +[acc] eval 3.139225e-3 s +[acc] eval 2.877437e-3 s +[acc] eval 3.591052e-3 s +[acc] eval 3.213669e-3 s +[acc] eval 2.629069e-3 s +[acc] eval 3.760993e-3 s +[acc] eval 4.221022e-3 s +[acc] eval 5.836493e-3 s +[acc] eval 5.08098e-3 s +[acc] eval 4.326179e-3 s +[acc] eval 4.238437e-3 s +[acc] eval 4.368218e-3 s +[acc] eval 3.96053e-3 s +[acc] eval 5.155945e-3 s +[acc] eval 5.421347e-3 s +[acc] eval 3.393891e-3 s +[acc] eval 0.310132913 s +[acc] compile 0.328364161 s +[acc] eval 2.7376083e-2 s +[acc] eval 0.360324482 s +[acc] compile 0.372621608 s +[acc] eval 1.522265e-2 s +[acc] eval 9.087021e-3 s +[acc] eval 7.813376e-3 s +[acc] eval 3.739771e-3 s +[acc] eval 5.078555e-3 s +[acc] eval 4.04007e-3 s +[acc] eval 3.214009e-3 s +[acc] eval 3.201961e-3 s +[acc] eval 4.252794e-3 s +[acc] eval 4.074677e-3 s +[acc] eval 5.189262e-3 s +[acc] eval 3.369491e-3 s +[acc] eval 3.426209e-3 s +[acc] eval 3.342473e-3 s +[acc] eval 3.245765e-3 s +[acc] eval 5.833931e-3 s +[acc] eval 4.925829e-3 s +[acc] eval 4.892329e-3 s +[acc] eval 5.871792e-3 s +[acc] eval 4.852296e-3 s +[acc] eval 7.405616e-3 s +[acc] eval 3.680629e-3 s +[acc] eval 3.800945e-3 s +[acc] eval 1.059266e-2 s +[acc] eval 4.775049e-3 s +[acc] eval 4.971984e-3 s +[acc] 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8.0536e-4 s +[acc] eval 1.5507998e-2 s +[acc] compile 5.091913e-3 s +[acc] eval 1.6506466e-2 s +[acc] eval 2.4933544e-2 s +[acc] compile 4.403781e-3 s +[acc] eval 5.040552e-3 s +[acc] eval 1.502952e-3 s +[acc] eval 1.432588e-3 s +[acc] eval 1.598653e-3 s +[acc] eval 2.149943e-3 s +[acc] eval 1.961851e-3 s +[acc] eval 1.830754e-3 s +[acc] eval 2.018213e-3 s +[acc] eval 1.692591e-3 s +[acc] eval 2.187275e-3 s +[acc] eval 7.127607e-3 s +[acc] eval 1.296933e-3 s +[acc] eval 1.481622e-3 s +[acc] eval 1.434165e-3 s +[acc] eval 1.468863e-3 s +[acc] eval 1.59071e-3 s +[acc] eval 1.306051e-3 s +[acc] eval 1.1439e-3 s +[acc] eval 1.205267e-3 s +[acc] eval 1.121588e-3 s +[acc] eval 1.14641e-3 s +[acc] eval 1.188263e-3 s +[acc] eval 1.215357e-3 s +[acc] eval 1.21102e-3 s +[acc] eval 1.397915e-3 s +[acc] eval 1.457166e-3 s +[acc] eval 1.535776e-3 s +[acc] eval 1.571447e-3 s +[acc] eval 1.372547e-3 s +[acc] eval 1.338012e-3 s +[acc] eval 6.3787483e-2 s +[acc] compile 5.631864e-3 s +[acc] eval 1.3223499e-2 s +[acc] eval 2.2010953e-2 s +[acc] compile 0.356263161 s +[acc] eval 4.2434323e-2 s +[acc] eval 6.003022e-3 s +[acc] eval 5.664804e-3 s +[acc] eval 4.071739e-3 s +[acc] eval 4.273936e-3 s +[acc] eval 3.790144e-3 s +[acc] eval 3.441718e-3 s +[acc] eval 3.600496e-3 s +[acc] eval 4.983154e-3 s +[acc] eval 3.098473e-3 s +[acc] eval 2.832312e-3 s +[acc] eval 2.887849e-3 s +[acc] eval 2.839733e-3 s +[acc] eval 3.665901e-3 s +[acc] eval 3.551458e-3 s +[acc] eval 5.097307e-3 s +[acc] eval 1.2138435e-2 s +[acc] eval 3.053959e-3 s +[acc] eval 2.528704e-3 s +[acc] eval 3.440875e-3 s +[acc] eval 3.004323e-3 s +[acc] eval 3.412522e-3 s +[acc] eval 3.146943e-3 s +[acc] eval 2.76797e-3 s +[acc] eval 3.409694e-3 s +[acc] eval 4.327619e-3 s +[acc] eval 8.619913e-3 s +[acc] eval 3.465793e-3 s +[acc] eval 3.340381e-3 s +[acc] eval 2.829402e-3 s +[acc] eval 0.525687636 s +[acc] compile 3.804335e-3 s +[acc] eval 1.1249494e-2 s +[acc] eval 1.6223661e-2 s +[acc] compile 6.455786e-3 s +[acc] 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+[acc] eval 5.136318e-3 s +[acc] eval 5.486344e-3 s +[acc] eval 4.997741e-3 s +[acc] eval 5.077735e-3 s +[acc] eval 4.976014e-3 s +[acc] eval 3.806116e-3 s +[acc] eval 3.711109e-3 s +[acc] eval 3.575933e-3 s +[acc] eval 3.846464e-3 s +[acc] eval 3.591764e-3 s +[acc] eval 3.956552e-3 s +[acc] eval 3.884768e-3 s +[acc] eval 4.345722e-3 s +[acc] eval 3.937297e-3 s +[acc] eval 3.403536e-3 s +[acc] eval 0.216983946 s +[acc] compile 0.165078093 s +[acc] eval 2.7007418e-2 s +[acc] eval 0.197349777 s +[acc] compile 0.224891058 s +[acc] eval 1.1348661e-2 s +[acc] eval 0.243397214 s +[acc] compile 8.1137278e-2 s +[acc] eval 1.4200447e-2 s +[acc] eval 9.878799e-2 s +[acc] compile 0.277122382 s +[acc] eval 1.2148715e-2 s +[acc] eval 0.294455928 s +[acc] compile 6.1854156e-2 s +[acc] eval 1.1014368e-2 s +[acc] eval 7.5601237e-2 s +[acc] compile 4.708252e-3 s +[acc] eval 5.981881e-3 s +[acc] eval 1.2114514e-2 s +[acc] compile 5.8409306e-2 s +[acc] eval 7.132926e-3 s +[acc] eval 2.263921e-3 s +[acc] 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0.289147623 s +[acc] compile 0.101647104 s +[acc] eval 9.742784e-3 s +[acc] eval 0.115061284 s +[acc] compile 6.7448673e-2 s +[acc] eval 6.795626e-3 s +[acc] eval 8.9882e-3 s +[acc] eval 3.53342e-3 s +[acc] eval 2.662891e-3 s +[acc] eval 2.530329e-3 s +[acc] eval 1.919655e-3 s +[acc] eval 2.351677e-3 s +[acc] eval 2.348691e-3 s +[acc] eval 3.12394e-3 s +[acc] eval 2.350558e-3 s +[acc] eval 2.345134e-3 s +[acc] eval 2.244219e-3 s +[acc] eval 2.371717e-3 s +[acc] eval 2.247416e-3 s +[acc] eval 2.14242e-3 s +[acc] eval 2.244276e-3 s +[acc] eval 2.191244e-3 s +[acc] eval 2.111936e-3 s +[acc] eval 2.637741e-3 s +[acc] eval 2.538841e-3 s +[acc] eval 2.575008e-3 s +[acc] eval 2.389542e-3 s +[acc] eval 2.294399e-3 s +[acc] eval 2.446288e-3 s +[acc] eval 2.810417e-3 s +[acc] eval 2.415972e-3 s +[acc] eval 2.082302e-3 s +[acc] eval 2.320948e-3 s +[acc] eval 2.039039e-3 s +[acc] eval 2.267286e-3 s +[acc] eval 0.156680786 s +[acc] compile 0.14713253 s +[acc] eval 9.680546e-3 s +[acc] eval 0.159859208 s +[acc] compile 0.550848456 s +[acc] eval 2.7454172e-2 s +[acc] eval 0.582637288 s +[acc] compile 0.329732671 s +[acc] eval 1.9402337e-2 s +[acc] eval 0.354528707 s +[acc] compile 0.175106163 s +[acc] eval 1.1958784e-2 s +[acc] eval 3.07127e-3 s +[acc] eval 2.881779e-3 s +[acc] eval 3.383278e-3 s +[acc] eval 3.695768e-3 s +[acc] eval 2.092044e-3 s +[acc] eval 1.801801e-3 s +[acc] eval 2.118789e-3 s +[acc] eval 2.017978e-3 s +[acc] eval 2.603655e-3 s +[acc] eval 2.460957e-3 s +[acc] eval 2.155982e-3 s +[acc] eval 0.218288725 s +[acc] compile 0.468245348 s +[acc] eval 1.3038377e-2 s +[acc] eval 0.486701438 s +[acc] compile 0.167782202 s +[acc] eval 7.867615e-3 s +[acc] eval 0.178512353 s +[acc] compile 0.113895962 s +[acc] eval 2.4338727e-2 s +[acc] eval 0.14110435 s +[acc] compile 9.34021e-4 s +[acc] eval 9.637724e-3 s +[acc] compile 9.822506e-3 s +[acc] eval 6.200011e-3 s +[acc] eval 1.8618421e-2 s +[acc] compile 0.104868859 s +[acc] eval 1.168011e-2 s +[acc] eval 0.119671399 s +[acc] compile 7.2896585e-2 s +[acc] eval 1.2073002e-2 s +[acc] eval 8.9117559e-2 s +[acc] compile 5.02074e-4 s +[acc] eval 1.4233844e-2 s +[acc] compile 0.240863813 s +[acc] eval 2.4873631e-2 s +[acc] eval 0.275537225 s +[acc] compile 0.523252354 s +[acc] eval 2.8093705e-2 s +[acc] eval 0.55680697 s +[acc] compile 0.225220155 s +[acc] eval 1.794033e-2 s +[acc] eval 0.252892596 s +[acc] compile 0.132497996 s +[acc] eval 9.404251e-3 s +[acc] eval 4.573991e-3 s +[acc] eval 3.587742e-3 s +[acc] eval 4.609848e-3 s +[acc] eval 3.092965e-3 s +[acc] eval 1.0238013e-2 s +[acc] eval 3.625409e-3 s +[acc] eval 2.905728e-3 s +[acc] eval 2.522381e-3 s +[acc] eval 2.018125e-3 s +[acc] eval 2.819357e-3 s +[acc] eval 2.188422e-3 s +[acc] eval 0.188308836 s +[acc] compile 6.8867201e-2 s +[acc] eval 2.5927444e-2 s +[acc] eval 5.385658e-3 s +[acc] eval 1.0545782e-2 s +[acc] eval 2.746067e-3 s +[acc] eval 2.374245e-3 s +[acc] eval 2.384948e-3 s +[acc] eval 2.134987e-3 s +[acc] eval 2.112675e-3 s +[acc] eval 1.960853e-3 s +[acc] eval 2.101394e-3 s +[acc] eval 2.109883e-3 s +[acc] eval 1.950505e-3 s +[acc] eval 2.16331e-3 s +[acc] eval 2.476258e-3 s +[acc] eval 3.235335e-3 s +[acc] eval 2.58121e-3 s +[acc] eval 2.40533e-3 s +[acc] eval 2.395509e-3 s +[acc] eval 2.586666e-3 s +[acc] eval 2.536289e-3 s +[acc] eval 3.00202e-3 s +[acc] eval 2.517187e-3 s +[acc] eval 1.842943e-3 s +[acc] eval 3.452064e-3 s +[acc] eval 2.372826e-3 s +[acc] eval 2.794818e-3 s +[acc] eval 2.815985e-3 s +[acc] eval 1.819955e-3 s +[acc] eval 1.598275e-3 s +[acc] eval 1.503524e-3 s +[acc] eval 1.520979e-3 s +[acc] eval 0.180742543 s +[acc] compile 0.121864563 s +[acc] eval 1.7330589e-2 s +[acc] eval 0.1421292 s +[acc] compile 9.165519e-3 s +[acc] eval 5.976229e-3 s +[acc] eval 1.843156e-3 s +[acc] eval 2.067785e-3 s +[acc] eval 1.782268e-3 s +[acc] eval 2.515283e-3 s +[acc] eval 2.698125e-3 s +[acc] eval 2.225027e-3 s +[acc] eval 6.961606e-3 s +[acc] eval 1.794017e-3 s +[acc] eval 1.631427e-3 s +[acc] eval 1.63828e-3 s +[acc] eval 2.066103e-3 s +[acc] eval 1.931228e-3 s +[acc] eval 1.680874e-3 s +[acc] eval 4.8984984e-2 s +[acc] compile 3.120546e-3 s +[acc] eval 5.296115e-3 s +[acc] eval 1.207671e-3 s +[acc] eval 8.82941e-4 s +[acc] eval 1.1960681e-2 s +[acc] compile 0.128089383 s +[acc] eval 2.2356181e-2 s +[acc] eval 0.154301664 s +[acc] compile 0.110277551 s +[acc] eval 6.119217e-3 s +[acc] eval 0.119702389 s +[acc] compile 5.130707e-3 s +[acc] eval 8.595431e-3 s +[acc] eval 2.2879105e-2 s +[acc] compile 0.240393181 s +[acc] eval 2.6977012e-2 s +[acc] eval 0.284379166 s +[acc] compile 0.274519123 s +[acc] eval 3.3477031e-2 s +[acc] eval 0.311519623 s +[acc] compile 0.174629262 s +[acc] eval 8.252859e-3 s +[acc] eval 3.691027e-3 s +[acc] eval 3.223738e-3 s +[acc] eval 3.169136e-3 s +[acc] eval 2.897411e-3 s +[acc] eval 6.706772e-3 s +[acc] eval 2.688102e-3 s +[acc] eval 2.900793e-3 s +[acc] eval 2.791006e-3 s +[acc] eval 2.386694e-3 s +[acc] eval 3.146415e-3 s +[acc] eval 2.665175e-3 s +[acc] eval 3.607396e-3 s +[acc] eval 3.426265e-3 s +[acc] eval 0.230052597 s +[acc] compile 5.832352e-3 s +[acc] eval 6.276785e-3 s +[acc] eval 2.9508e-3 s +[acc] eval 2.697966e-3 s +[acc] eval 2.824904e-3 s +[acc] eval 3.093033e-3 s +[acc] eval 3.202525e-3 s +[acc] eval 2.941092e-3 s +[acc] eval 7.754002e-3 s +[acc] eval 2.660241e-3 s +[acc] eval 2.559219e-3 s +[acc] eval 2.58073e-3 s +[acc] eval 2.390752e-3 s +[acc] eval 2.226174e-3 s +[acc] eval 2.264051e-3 s +[acc] eval 5.6491236e-2 s +[acc] compile 7.397718e-3 s +[acc] eval 1.0675121e-2 s +[acc] eval 2.0426086e-2 s +[acc] compile 4.453218e-3 s +[acc] eval 1.6147529e-2 s +[acc] eval 7.6509e-4 s +[acc] eval 1.092076e-3 s +[acc] eval 2.3663515e-2 s +[acc] compile 6.807053e-3 s +[acc] eval 1.1446397e-2 s +[acc] eval 1.627099e-3 s +[acc] eval 1.586588e-3 s +[acc] eval 1.529222e-3 s +[acc] eval 1.537858e-3 s +[acc] eval 1.775765e-3 s +[acc] eval 1.592452e-3 s +[acc] eval 1.560455e-3 s +[acc] eval 7.377219e-3 s +[acc] eval 1.180816e-3 s +[acc] eval 9.94864e-4 s +[acc] eval 1.096506e-3 s +[acc] eval 1.163402e-3 s +[acc] eval 9.89885e-4 s +[acc] eval 1.011454e-3 s +[acc] eval 1.194873e-3 s +[acc] eval 1.137363e-3 s +[acc] eval 1.070808e-3 s +[acc] eval 1.078466e-3 s +[acc] eval 1.126396e-3 s +[acc] eval 1.225404e-3 s +[acc] eval 1.248409e-3 s +[acc] eval 1.143067e-3 s +[acc] eval 1.05697e-3 s +[acc] eval 9.57367e-4 s +[acc] eval 9.4238e-4 s +[acc] eval 9.53577e-4 s +[acc] eval 9.00072e-4 s +[acc] eval 1.045535e-3 s +[acc] eval 9.37263e-4 s +[acc] eval 6.2929174e-2 s +[acc] compile 0.161058484 s +[acc] eval 2.0849627e-2 s +[acc] eval 0.190065452 s +[acc] compile 6.1578094e-2 s +[acc] eval 1.2749588e-2 s +[acc] eval 3.470884e-3 s +[acc] eval 9.981589e-3 s +[acc] eval 4.211288e-3 s +[acc] eval 4.031993e-3 s +[acc] eval 4.082778e-3 s +[acc] eval 3.992035e-3 s +[acc] eval 3.994951e-3 s +[acc] eval 4.409386e-3 s +[acc] eval 3.950326e-3 s +[acc] eval 3.407388e-3 s +[acc] eval 3.074859e-3 s +[acc] eval 3.018912e-3 s +[acc] eval 4.411604e-3 s +[acc] eval 6.431247e-3 s +[acc] eval 3.243421e-3 s +[acc] eval 2.754159e-3 s +[acc] eval 3.096022e-3 s +[acc] eval 3.083336e-3 s +[acc] eval 3.113711e-3 s +[acc] eval 3.82405e-3 s +[acc] eval 3.550412e-3 s +[acc] eval 2.589688e-3 s +[acc] eval 2.594943e-3 s +[acc] eval 3.669387e-3 s +[acc] eval 3.164631e-3 s +[acc] eval 2.97834e-3 s +[acc] eval 3.263173e-3 s +[acc] eval 3.28466e-3 s +[acc] eval 4.768239e-3 s +[acc] eval 0.193937875 s +[acc] compile 5.837871e-2 s +[acc] eval 1.805631e-2 s +[acc] eval 8.6373683e-2 s +[acc] compile 9.1125313e-2 s +[acc] eval 8.571251e-3 s +[acc] eval 3.406457e-3 s +[acc] eval 2.202557e-3 s +[acc] eval 0.107830332 s +[acc] compile 8.9423595e-2 s +[acc] eval 2.1615852e-2 s +[acc] eval 3.101809e-3 s +[acc] eval 3.007384e-3 s +[acc] eval 0.120827235 s +[acc] compile 6.693261e-3 s +[acc] eval 1.2033211e-2 s +[acc] eval 3.335953e-3 s +[acc] eval 2.980328e-3 s +[acc] eval 3.860469e-3 s +[acc] eval 1.3694369e-2 s +[acc] eval 2.626369e-3 s +[acc] eval 2.472726e-3 s +[acc] eval 2.266886e-3 s +[acc] eval 2.317072e-3 s +[acc] eval 2.216704e-3 s +[acc] eval 1.798285e-3 s +[acc] eval 1.833749e-3 s +[acc] eval 1.947644e-3 s +[acc] eval 1.875854e-3 s +[acc] eval 2.440303e-3 s +[acc] eval 2.345099e-3 s +[acc] eval 7.0298797e-2 s +[acc] compile 9.82208e-2 s +[acc] eval 1.3558181e-2 s +[acc] eval 2.984865e-3 s +[acc] eval 7.056086e-3 s +[acc] eval 2.689516e-3 s +[acc] eval 2.935944e-3 s +[acc] eval 2.739466e-3 s +[acc] eval 2.82318e-3 s +[acc] eval 2.553678e-3 s +[acc] eval 2.473101e-3 s +[acc] eval 3.643541e-3 s +[acc] eval 3.332944e-3 s +[acc] eval 2.934542e-3 s +[acc] eval 2.499066e-3 s +[acc] eval 2.203008e-3 s +[acc] eval 2.472329e-3 s +[acc] eval 3.282728e-3 s +[acc] eval 2.59253e-3 s +[acc] eval 2.717524e-3 s +[acc] eval 2.931285e-3 s +[acc] eval 2.979823e-3 s +[acc] eval 2.65942e-3 s +[acc] eval 2.936212e-3 s +[acc] eval 1.970355e-3 s +[acc] eval 2.261208e-3 s +[acc] eval 2.058848e-3 s +[acc] eval 1.97531e-3 s +[acc] eval 2.196515e-3 s +[acc] eval 1.910199e-3 s +[acc] eval 2.066094e-3 s +[acc] eval 2.054103e-3 s +[acc] eval 0.197198135 s +[acc] compile 0.224923248 s +[acc] eval 3.5053665e-2 s +[acc] eval 0.264805586 s +[acc] compile 1.080049e-2 s +[acc] eval 8.236137e-3 s +[acc] eval 2.1255013e-2 s +[acc] compile 0.123996462 s +[acc] eval 1.2193848e-2 s +[acc] eval 0.138275524 s +[acc] compile 6.8463275e-2 s +[acc] eval 1.6882559e-2 s +[acc] eval 7.855968e-3 s +[acc] eval 1.728389e-3 s +[acc] eval 1.846918e-3 s +[acc] eval 9.8808432e-2 s +[acc] compile 0.103682222 s +[acc] eval 1.6059634e-2 s +[acc] eval 0.124692127 s +[acc] compile 0.157658303 s +[acc] eval 1.5245229e-2 s +[acc] eval 3.91353e-3 s +[acc] eval 3.123709e-3 s +[acc] eval 3.586334e-3 s +[acc] eval 3.640206e-3 s +[acc] eval 4.112797e-3 s +[acc] eval 4.757994e-3 s +[acc] eval 3.319594e-3 s +[acc] eval 3.209196e-3 s +[acc] eval 4.21104e-3 s +[acc] eval 3.515733e-3 s +[acc] eval 2.958269e-3 s +[acc] eval 0.217725094 s +[acc] compile 2.625009e-3 s +[acc] eval 3.825769e-3 s +[acc] eval 7.23271e-4 s +[acc] eval 1.027191e-3 s +[acc] eval 9.291731e-3 s +[acc] compile 3.227258e-3 s +[acc] eval 4.261936e-3 s +[acc] eval 1.098814e-3 s +[acc] eval 7.99664e-4 s +[acc] eval 1.0905611e-2 s +[acc] compile 3.438954e-3 s +[acc] eval 6.551237e-3 s +[acc] eval 1.161328e-2 s +[acc] compile 0.386823849 s +[acc] eval 2.9347983e-2 s +[acc] eval 0.419742167 s +[acc] compile 3.526842e-3 s +[acc] eval 4.550876e-3 s +[acc] eval 1.504231e-3 s +[acc] eval 9.112e-4 s +[acc] eval 1.111932e-3 s +[acc] eval 8.81127e-4 s +[acc] eval 9.81498e-4 s +[acc] eval 1.011325e-3 s +[acc] eval 1.184549e-3 s +[acc] eval 1.920833e-3 s +[acc] eval 1.843176e-3 s +[acc] eval 1.222273e-3 s +[acc] eval 1.143172e-3 s +[acc] eval 8.2402e-4 s +[acc] eval 9.39111e-4 s +[acc] eval 8.53659e-4 s +[acc] eval 1.057078e-3 s +[acc] eval 9.68716e-4 s +[acc] eval 8.28202e-4 s +[acc] eval 9.15304e-4 s +[acc] eval 7.37979e-4 s +[acc] eval 9.06759e-4 s +[acc] eval 1.249371e-3 s +[acc] eval 2.107607e-3 s +[acc] eval 1.633612e-3 s +[acc] eval 1.36456e-3 s +[acc] eval 1.551353e-3 s +[acc] eval 1.438247e-3 s +[acc] eval 1.490061e-3 s +[acc] eval 1.480664e-3 s +[acc] eval 1.323524e-3 s +[acc] eval 4.9042356e-2 s +[acc] compile 7.063663e-3 s +[acc] eval 4.40098e-3 s +[acc] eval 8.432128e-3 s +[acc] eval 2.206014e-3 s +[acc] eval 2.2538e-3 s +[acc] eval 3.309839e-3 s +[acc] eval 3.381825e-3 s +[acc] eval 1.672238e-3 s +[acc] eval 1.769636e-3 s +[acc] eval 1.660476e-3 s +[acc] eval 1.651514e-3 s +[acc] eval 1.26647e-3 s +[acc] eval 1.560069e-3 s +[acc] eval 2.355959e-3 s +[acc] eval 1.861445e-3 s +[acc] eval 2.219119e-3 s +[acc] eval 1.975891e-3 s +[acc] eval 1.686859e-3 s +[acc] eval 2.186955e-3 s +[acc] eval 1.39544e-3 s +[acc] eval 1.868807e-3 s +[acc] eval 1.742303e-3 s +[acc] eval 1.630804e-3 s +[acc] eval 1.565382e-3 s +[acc] eval 1.616904e-3 s +[acc] eval 1.716483e-3 s +[acc] eval 1.821207e-3 s +[acc] eval 1.670359e-3 s +[acc] eval 1.749786e-3 s +[acc] eval 1.865708e-3 s +[acc] eval 1.799964e-3 s +[acc] eval 7.9545753e-2 s +[acc] compile 8.38083e-4 s +[acc] eval 7.779305e-3 s +[acc] compile 6.87374e-4 s +[acc] eval 7.199321e-3 s +[acc] compile 7.96352e-4 s +[acc] eval 1.1252293e-2 s +[acc] compile 1.019116e-3 s +[acc] eval 2.8570933e-2 s +[acc] compile 5.123112e-3 s +[acc] eval 5.413246e-3 s +[acc] eval 1.294925e-3 s +[acc] eval 1.233724e-3 s +[acc] eval 1.4601138e-2 s +[acc] compile 4.52796e-3 s +[acc] eval 1.0571188e-2 s +[acc] eval 1.7439096e-2 s +[acc] compile 4.4793177e-2 s +[acc] eval 5.93343e-3 s +[acc] eval 5.4049042e-2 s +[acc] compile 3.421031e-3 s +[acc] eval 6.763296e-3 s +[acc] eval 1.177809e-3 s +[acc] eval 1.44717e-3 s +[acc] eval 1.4978525e-2 s +[acc] compile 0.127272142 s +[acc] eval 1.0814588e-2 s +[acc] eval 0.141198662 s +[acc] compile 5.672488e-3 s +[acc] eval 1.0648703e-2 s +[acc] eval 1.836775e-3 s +[acc] eval 1.958683e-3 s +[acc] eval 2.19698e-3 s +[acc] eval 2.576319e-3 s +[acc] eval 2.8551562e-2 s +[acc] compile 6.8194826e-2 s +[acc] eval 1.1117214e-2 s +[acc] eval 8.4232705e-2 s +[acc] compile 0.128931303 s +[acc] eval 1.0204195e-2 s +[acc] eval 0.15552933 s +[acc] compile 7.12133e-4 s +[acc] eval 2.3098713e-2 s +[acc] compile 0.108891881 s +[acc] eval 4.5980566e-2 s +[acc] eval 0.159439238 s +[acc] compile 6.0573241e-2 s +[acc] eval 1.0989717e-2 s +[acc] eval 7.4344088e-2 s +[acc] compile 5.625812e-3 s +[acc] eval 5.869968e-3 s +[acc] eval 2.224215e-3 s +[acc] eval 2.274648e-3 s +[acc] eval 1.75741e-3 s +[acc] eval 1.530745e-3 s +[acc] eval 1.528061e-3 s +[acc] eval 1.540043e-3 s +[acc] eval 7.583395e-3 s +[acc] eval 1.334523e-3 s +[acc] eval 1.229757e-3 s +[acc] eval 1.247619e-3 s +[acc] eval 1.153828e-3 s +[acc] eval 1.432035e-3 s +[acc] eval 1.165836e-3 s +[acc] eval 1.430655e-3 s +[acc] eval 1.659864e-3 s +[acc] eval 1.937881e-3 s +[acc] eval 1.689075e-3 s +[acc] eval 1.5557e-3 s +[acc] eval 1.202201e-3 s +[acc] eval 1.194469e-3 s +[acc] eval 1.319625e-3 s +[acc] eval 1.242828e-3 s +[acc] eval 1.32361e-3 s +[acc] eval 1.266428e-3 s +[acc] eval 1.54387e-3 s +[acc] eval 1.404213e-3 s +[acc] eval 1.440457e-3 s +[acc] eval 1.244722e-3 s +[acc] eval 1.22206e-3 s +[acc] eval 6.454089e-2 s +[acc] compile 8.47503e-3 s +[acc] eval 8.094808e-3 s +[acc] eval 2.0395981e-2 s +[acc] compile 4.5212748e-2 s +[acc] eval 1.768597e-2 s +[acc] eval 7.0743974e-2 s +[acc] compile 5.07314e-4 s +[acc] eval 5.021657e-3 s +[acc] compile 7.5962715e-2 s +[acc] eval 1.1284861e-2 s +[acc] eval 9.3647787e-2 s +[acc] compile 0.251754338 s +[acc] eval 2.4397702e-2 s +[acc] eval 0.279752425 s +[acc] compile 5.2473286e-2 s +[acc] eval 7.609308e-3 s +[acc] eval 2.497799e-3 s +[acc] eval 2.707186e-3 s +[acc] eval 6.7871243e-2 s +[acc] compile 4.62066e-3 s +[acc] eval 6.701868e-3 s +[acc] eval 3.066583e-3 s +[acc] eval 2.282377e-3 s +[acc] eval 1.730949e-3 s +[acc] eval 1.449746e-3 s +[acc] eval 2.037784e-3 s +[acc] eval 2.086741e-3 s +[acc] eval 2.843551e-3 s +[acc] eval 2.092139e-3 s +[acc] eval 1.835009e-3 s +[acc] eval 1.724426e-3 s +[acc] eval 1.555797e-3 s +[acc] eval 1.688742e-3 s +[acc] eval 1.159383e-3 s +[acc] eval 1.271308e-3 s +[acc] eval 1.338309e-3 s +[acc] eval 1.170351e-3 s +[acc] eval 1.005385e-3 s +[acc] eval 7.73443e-3 s +[acc] eval 1.046802e-3 s +[acc] eval 1.075325e-3 s +[acc] eval 1.127266e-3 s +[acc] eval 2.178835e-3 s +[acc] eval 1.641379e-3 s +[acc] eval 1.344354e-3 s +[acc] eval 9.4361e-4 s +[acc] eval 8.98199e-4 s +[acc] eval 9.26584e-4 s +[acc] eval 9.08763e-4 s +[acc] eval 9.13986e-4 s +[acc] eval 6.6605578e-2 s +[acc] compile 7.819866e-2 s +[acc] eval 2.2859552e-2 s +[acc] eval 0.10419416 s +[acc] compile 0.108843489 s +[acc] eval 3.9519825e-2 s +[acc] eval 0.155850114 s +[acc] compile 7.5185382e-2 s +[acc] eval 1.6469445e-2 s +[acc] eval 9.595941e-2 s +[acc] compile 4.989648e-3 s +[acc] eval 6.995831e-3 s +[acc] eval 3.265327e-3 s +[acc] eval 2.934077e-3 s +[acc] eval 2.9560561e-2 s +[acc] compile 4.750657e-3 s +[acc] eval 1.0133941e-2 s +[acc] eval 1.78743e-3 s +[acc] eval 1.889407e-3 s +[acc] eval 1.932222e-3 s +[acc] eval 1.958028e-3 s +[acc] eval 1.968889e-3 s +[acc] eval 3.432701e-3 s +[acc] eval 9.931327e-3 s +[acc] eval 1.692434e-3 s +[acc] eval 1.681414e-3 s +[acc] eval 2.172781e-3 s +[acc] eval 1.5941e-3 s +[acc] eval 1.564516e-3 s +[acc] eval 1.846911e-3 s +[acc] eval 1.559665e-3 s +[acc] eval 1.648927e-3 s +[acc] eval 1.39894e-3 s +[acc] eval 1.623815e-3 s +[acc] eval 1.658916e-3 s +[acc] eval 1.565653e-3 s +[acc] eval 1.454434e-3 s +[acc] eval 1.539741e-3 s +[acc] eval 1.452173e-3 s +[acc] eval 1.37337e-3 s +[acc] eval 1.417876e-3 s +[acc] eval 1.405963e-3 s +[acc] eval 1.417828e-3 s +[acc] eval 1.489352e-3 s +[acc] eval 1.88345e-3 s +[acc] eval 2.008929e-3 s +[acc] eval 7.7621174e-2 s +[acc] compile 4.027507e-3 s +[acc] eval 1.0029644e-2 s +[acc] eval 1.860892e-3 s +[acc] eval 1.707716e-3 s +[acc] eval 2.430008e-3 s +[acc] eval 6.315585e-3 s +[acc] eval 2.8633107e-2 s +[acc] compile 0.116114437 s +[acc] eval 1.8497932e-2 s +[acc] eval 0.137312422 s +[acc] compile 7.113998e-3 s +[acc] eval 1.3303912e-2 s +[acc] eval 1.403226e-3 s +[acc] eval 1.580684e-3 s +[acc] eval 1.862016e-3 s +[acc] eval 2.726193e-3 s +[acc] eval 1.800897e-3 s +[acc] eval 1.638755e-3 s +[acc] eval 1.869937e-3 s +[acc] eval 1.583886e-3 s +[acc] eval 1.564569e-3 s +[acc] eval 1.537398e-3 s +[acc] eval 1.485582e-3 s +[acc] eval 1.261708e-3 s +[acc] eval 1.205066e-3 s +[acc] eval 1.305416e-3 s +[acc] eval 1.235126e-3 s +[acc] eval 1.23602e-3 s +[acc] eval 1.281485e-3 s +[acc] eval 1.262834e-3 s +[acc] eval 1.452697e-3 s +[acc] eval 1.481582e-3 s +[acc] eval 1.480203e-3 s +[acc] eval 1.80771e-3 s +[acc] eval 1.346504e-3 s +[acc] eval 1.33606e-3 s +[acc] eval 1.25469e-3 s +[acc] eval 1.261924e-3 s +[acc] eval 1.237476e-3 s +[acc] eval 1.278573e-3 s +[acc] eval 1.292778e-3 s +[acc] eval 1.304021e-3 s +[acc] eval 6.8991279e-2 s +[acc] compile 6.0516324e-2 s +[acc] eval 1.1121564e-2 s +[acc] eval 7.4555938e-2 s +[acc] compile 4.361303e-3 s +[acc] eval 7.432295e-3 s +[acc] eval 1.3257482e-2 s +[acc] compile 6.4891133e-2 s +[acc] eval 6.350733e-3 s +[acc] eval 7.738519e-3 s +[acc] eval 2.104591e-3 s +[acc] eval 1.852116e-3 s +[acc] eval 1.878863e-3 s +[acc] eval 2.099669e-3 s +[acc] eval 2.342982e-3 s +[acc] eval 2.436209e-3 s +[acc] eval 3.202828e-3 s +[acc] eval 1.812431e-3 s +[acc] eval 1.236342e-3 s +[acc] eval 1.240058e-3 s +[acc] eval 1.140924e-3 s +[acc] eval 9.95919e-4 s +[acc] eval 1.28094e-3 s +[acc] eval 1.546566e-3 s +[acc] eval 0.107516461 s +[acc] compile 5.14031e-2 s +[acc] eval 7.366445e-3 s +[acc] eval 6.156072e-2 s +[acc] compile 7.303491e-3 s +[acc] eval 1.0465017e-2 s +[acc] eval 8.883186e-3 s +[acc] eval 3.7759533e-2 s +[acc] compile 0.18176274 s +[acc] eval 2.9769004e-2 s +[acc] eval 4.087659e-3 s +[acc] eval 2.843233e-3 s +[acc] eval 2.681815e-3 s +[acc] eval 2.038649e-3 s +[acc] eval 1.684634e-3 s +[acc] eval 1.627528e-3 s +[acc] eval 1.547946e-3 s +[acc] eval 1.356156e-3 s +[acc] eval 1.35777e-3 s +[acc] eval 1.335102e-3 s +[acc] eval 1.369056e-3 s +[acc] eval 1.410929e-3 s +[acc] eval 1.634177e-3 s +[acc] eval 0.241167729 s +[acc] compile 4.90202e-3 s +[acc] eval 6.80478e-3 s +[acc] eval 3.472815e-3 s +[acc] eval 2.87274e-3 s +[acc] eval 3.062638e-3 s +[acc] eval 2.270002e-3 s +[acc] eval 2.262288e-3 s +[acc] eval 9.770876e-3 s +[acc] eval 1.751639e-3 s +[acc] eval 1.667996e-3 s +[acc] eval 1.548314e-3 s +[acc] eval 1.61852e-3 s +[acc] eval 1.85541e-3 s +[acc] eval 1.808934e-3 s +[acc] eval 1.690923e-3 s +[acc] eval 1.597532e-3 s +[acc] eval 1.522808e-3 s +[acc] eval 1.503655e-3 s +[acc] eval 1.513245e-3 s +[acc] eval 1.479201e-3 s +[acc] eval 1.446544e-3 s +[acc] eval 2.234402e-3 s +[acc] eval 3.417336e-3 s +[acc] eval 2.480092e-3 s +[acc] eval 2.488087e-3 s +[acc] eval 2.437384e-3 s +[acc] eval 2.837328e-3 s +[acc] eval 2.217701e-3 s +[acc] eval 2.182077e-3 s +[acc] eval 2.143141e-3 s +[acc] eval 1.855866e-3 s +[acc] eval 8.5264939e-2 s +[acc] compile 0.276599603 s +[acc] eval 3.6459183e-2 s +[acc] eval 1.2268695e-2 s +[acc] eval 3.259438e-3 s +[acc] eval 3.146482e-3 s +[acc] eval 5.046521e-3 s +[acc] eval 2.929742e-3 s +[acc] eval 2.770695e-3 s +[acc] eval 2.746435e-3 s +[acc] eval 2.707656e-3 s +[acc] eval 2.809501e-3 s +[acc] eval 2.788266e-3 s +[acc] eval 2.888176e-3 s +[acc] eval 2.882635e-3 s +[acc] eval 3.865976e-3 s +[acc] eval 3.212969e-3 s +[acc] eval 3.013763e-3 s +[acc] eval 2.697477e-3 s +[acc] eval 2.777651e-3 s +[acc] eval 3.413147e-3 s +[acc] eval 3.050203e-3 s +[acc] eval 2.945991e-3 s +[acc] eval 2.894695e-3 s +[acc] eval 2.913279e-3 s +[acc] eval 3.731703e-3 s +[acc] eval 3.236375e-3 s +[acc] eval 3.011395e-3 s +[acc] eval 2.813829e-3 s +[acc] eval 3.244572e-3 s +[acc] eval 3.699925e-3 s +[acc] eval 3.213234e-3 s +[acc] eval 2.919296e-3 s +[acc] eval 2.770114e-3 s +[acc] eval 0.428121401 s +[acc] compile 0.102182585 s +[acc] eval 1.0966619e-2 s +[acc] eval 0.120681487 s +[acc] compile 9.66579e-4 s +[acc] eval 7.221328e-3 s +[acc] compile 0.242926424 s +[acc] eval 8.760429e-3 s +[acc] eval 1.761817e-3 s +[acc] eval 1.728487e-3 s +[acc] eval 8.071606e-3 s +[acc] eval 1.695494e-3 s +[acc] eval 1.440589e-3 s +[acc] eval 1.361038e-3 s +[acc] eval 1.530647e-3 s +[acc] eval 1.388465e-3 s +[acc] eval 1.360935e-3 s +[acc] eval 1.445607e-3 s +[acc] eval 1.611757e-3 s +[acc] eval 1.717876e-3 s +[acc] eval 1.325492e-3 s +[acc] eval 1.406999e-3 s +[acc] eval 1.353997e-3 s +[acc] eval 1.451648e-3 s +[acc] eval 1.243432e-3 s +[acc] eval 1.299524e-3 s +[acc] eval 1.318867e-3 s +[acc] eval 1.303957e-3 s +[acc] eval 1.422513e-3 s +[acc] eval 1.864732e-3 s +[acc] eval 1.83009e-3 s +[acc] eval 1.44303e-3 s +[acc] eval 0.297518275 s +[acc] compile 0.428352541 s +[acc] eval 3.5150212e-2 s +[acc] eval 2.580507e-3 s +[acc] eval 2.500206e-3 s +[acc] eval 2.361755e-3 s +[acc] eval 2.446307e-3 s +[acc] eval 3.662105e-3 s +[acc] eval 3.578876e-3 s +[acc] eval 3.48788e-3 s +[acc] eval 3.416067e-3 s +[acc] eval 3.254928e-3 s +[acc] eval 3.082869e-3 s +[acc] eval 3.167702e-3 s +[acc] eval 3.253449e-3 s +[acc] eval 3.621082e-3 s +[acc] eval 3.328383e-3 s +[acc] eval 3.196437e-3 s +[acc] eval 3.286097e-3 s +[acc] eval 4.372281e-3 s +[acc] eval 4.388768e-3 s +[acc] eval 3.963986e-3 s +[acc] eval 3.993213e-3 s +[acc] eval 4.24589e-3 s +[acc] eval 4.290169e-3 s +[acc] eval 4.215512e-3 s +[acc] eval 3.651854e-3 s +[acc] eval 4.339142e-3 s +[acc] eval 3.395298e-3 s +[acc] eval 3.670411e-3 s +[acc] eval 3.444323e-3 s +[acc] eval 4.27551e-3 s +[acc] eval 0.574980827 s +[acc] compile 5.9319991e-2 s +[acc] eval 2.7390763e-2 s +[acc] eval 9.0380382e-2 s +[acc] compile 8.444061e-3 s +[acc] eval 2.0103909e-2 s +[acc] eval 3.3424099e-2 s +[acc] compile 4.577213e-3 s +[acc] eval 6.312576e-3 s +[acc] eval 1.639982e-3 s +[acc] eval 1.386096e-3 s +[acc] eval 1.597135e-2 s +[acc] compile 8.8078709e-2 s +[acc] eval 1.8814254e-2 s +[acc] eval 3.689031e-3 s +[acc] eval 1.1606024e-2 s +[acc] eval 2.752956e-3 s +[acc] eval 2.752262e-3 s +[acc] eval 3.106096e-3 s +[acc] eval 2.878638e-3 s +[acc] eval 3.084432e-3 s +[acc] eval 4.083377e-3 s +[acc] eval 2.963106e-3 s +[acc] eval 2.783536e-3 s +[acc] eval 2.963766e-3 s +[acc] eval 2.981743e-3 s +[acc] eval 2.804022e-3 s +[acc] eval 2.921618e-3 s +[acc] eval 2.583152e-3 s +[acc] eval 2.572035e-3 s +[acc] eval 2.501265e-3 s +[acc] eval 2.876734e-3 s +[acc] eval 2.915031e-3 s +[acc] eval 2.785219e-3 s +[acc] eval 2.824198e-3 s +[acc] eval 2.665924e-3 s +[acc] eval 2.618231e-3 s +[acc] eval 3.368311e-3 s +[acc] eval 3.159125e-3 s +[acc] eval 2.942863e-3 s +[acc] eval 2.976421e-3 s +[acc] eval 2.953234e-3 s +[acc] eval 2.692488e-3 s +[acc] eval 0.206727361 s +[acc] compile 0.181721796 s +[acc] eval 1.8399151e-2 s +[acc] eval 0.204372876 s +[acc] compile 0.350370411 s +[acc] eval 1.6601294e-2 s +[acc] eval 0.372547319 s +[acc] compile 6.5897639e-2 s +[acc] eval 2.3233104e-2 s +[acc] eval 9.9121682e-2 s +[acc] compile 8.90667e-4 s +[acc] eval 9.938651e-3 s +[acc] compile 0.137738338 s +[acc] eval 6.163574e-3 s +[acc] eval 6.81975e-3 s +[acc] eval 3.514495e-3 s +[acc] eval 2.943631e-3 s +[acc] eval 2.377375e-3 s +[acc] eval 2.522414e-3 s +[acc] eval 3.674773e-3 s +[acc] eval 6.485869e-3 s +[acc] eval 8.000774e-3 s +[acc] eval 5.04002e-3 s +[acc] eval 2.585881e-3 s +[acc] eval 2.354887e-3 s +[acc] eval 2.151023e-3 s +[acc] eval 1.836277e-3 s +[acc] eval 2.012419e-3 s +[acc] eval 3.900706e-3 s +[acc] eval 1.3127085e-2 s +[acc] eval 1.1004048e-2 s +[acc] eval 1.2850162e-2 s +[acc] eval 2.920611e-3 s +[acc] eval 2.384641e-3 s +[acc] eval 5.859473e-3 s +[acc] eval 2.355942e-3 s +[acc] eval 2.132068e-3 s +[acc] eval 2.185369e-3 s +[acc] eval 1.0542406e-2 s +[acc] eval 4.757376e-3 s +[acc] eval 1.0352673e-2 s +[acc] eval 5.914894e-3 s +[acc] eval 6.934656e-3 s +[acc] eval 0.304648044 s +[acc] compile 1.6610936e-2 s +[acc] eval 2.6495701e-2 s +[acc] eval 5.3685152e-2 s +[acc] compile 1.4721211e-2 s +[acc] eval 8.715114e-3 s +[acc] eval 2.7846801e-2 s +[acc] compile 8.730041e-3 s +[acc] eval 4.219314e-3 s +[acc] eval 6.26284e-4 s +[acc] eval 7.80309e-4 s +[acc] eval 1.5395623e-2 s +[acc] compile 8.051515e-3 s +[acc] eval 1.3642985e-2 s +[acc] eval 2.9902169e-2 s +[acc] compile 5.882974e-3 s +[acc] eval 1.1657714e-2 s +[acc] eval 1.915108e-3 s +[acc] eval 1.946416e-3 s +[acc] eval 2.400557e-2 s +[acc] compile 7.5524595e-2 s +[acc] eval 1.2251639e-2 s +[acc] eval 9.1533767e-2 s +[acc] compile 1.0259681e-2 s +[acc] eval 9.2005e-3 s +[acc] eval 2.215079e-2 s +[acc] compile 1.1587495e-2 s +[acc] eval 1.9981864e-2 s +[acc] eval 3.461555e-2 s +[acc] compile 7.5861998e-2 s +[acc] eval 6.838263e-3 s +[acc] eval 8.6801407e-2 s +[acc] compile 0.13465969 s +[acc] eval 3.2090724e-2 s +[acc] eval 8.353845e-3 s +[acc] eval 5.464403e-3 s +[acc] eval 6.333912e-3 s +[acc] eval 4.521433e-3 s +[acc] eval 5.252772e-3 s +[acc] eval 4.317575e-3 s +[acc] eval 4.162375e-3 s +[acc] eval 3.818891e-3 s +[acc] eval 4.634642e-3 s +[acc] eval 3.649333e-3 s +[acc] eval 3.531821e-3 s +[acc] eval 3.502419e-3 s +[acc] eval 4.258732e-3 s +[acc] eval 4.338443e-3 s +[acc] eval 3.765291e-3 s +[acc] eval 3.709738e-3 s +[acc] eval 3.751173e-3 s +[acc] eval 3.982381e-3 s +[acc] eval 3.171985e-3 s +[acc] eval 3.017149e-3 s +[acc] eval 3.043742e-3 s +[acc] eval 4.345388e-3 s +[acc] eval 3.753574e-3 s +[acc] eval 3.629492e-3 s +[acc] eval 4.059896e-3 s +[acc] eval 4.547699e-3 s +[acc] eval 3.757767e-3 s +[acc] eval 3.712218e-3 s +[acc] eval 4.821545e-3 s +[acc] eval 4.037818e-3 s +[acc] eval 3.859219e-3 s +[acc] eval 5.22431e-3 s +[acc] eval 1.2402902e-2 s +[acc] eval 0.32468199 s +[acc] compile 8.1252e-4 s +[acc] eval 1.4045718e-2 s +[acc] compile 0.268430923 s +[acc] eval 2.4019505e-2 s +[acc] eval 0.297509619 s +[acc] compile 4.792028e-3 s +[acc] eval 6.322505e-3 s +[acc] eval 1.727243e-3 s +[acc] eval 1.816427e-3 s +[acc] eval 1.90318e-3 s +[acc] eval 1.642653e-3 s +[acc] eval 1.670605e-3 s +[acc] eval 1.486299e-3 s +[acc] eval 1.478774e-3 s +[acc] eval 2.068878e-3 s +[acc] eval 1.702299e-3 s +[acc] eval 7.965373e-3 s +[acc] eval 3.7592222e-2 s +[acc] compile 1.4481142e-2 s +[acc] eval 2.6523382e-2 s +[acc] eval 9.878418e-3 s +[acc] eval 4.459214e-3 s +[acc] eval 3.872973e-3 s +[acc] eval 3.614691e-3 s +[acc] eval 3.003777e-3 s +[acc] eval 3.193609e-3 s +[acc] eval 3.189903e-3 s +[acc] eval 3.096515e-3 s +[acc] eval 4.129237e-3 s +[acc] eval 3.432086e-3 s +[acc] eval 3.357027e-3 s +[acc] eval 3.088143e-3 s +[acc] eval 3.232416e-3 s +[acc] eval 3.506674e-3 s +[acc] eval 3.365043e-3 s +[acc] eval 3.35548e-3 s +[acc] eval 3.531648e-3 s +[acc] eval 4.368738e-3 s +[acc] eval 3.92528e-3 s +[acc] eval 2.751574e-3 s +[acc] eval 2.850553e-3 s +[acc] eval 3.193276e-3 s +[acc] eval 3.441281e-3 s +[acc] eval 2.930612e-3 s +[acc] eval 2.816302e-3 s +[acc] eval 2.846083e-3 s +[acc] eval 3.241578e-3 s +[acc] eval 3.164477e-3 s +[acc] eval 3.040521e-3 s +[acc] eval 3.502375e-3 s +[acc] eval 3.622803e-3 s +[acc] eval 3.556309e-3 s +[acc] eval 0.163116117 s +[acc] compile 0.121097099 s +[acc] eval 7.908685e-3 s +[acc] eval 0.132898584 s +[acc] compile 0.136867365 s +[acc] eval 1.4692602e-2 s +[acc] eval 5.567515e-3 s +[acc] eval 6.002001e-3 s +[acc] eval 5.900483e-3 s +[acc] eval 9.769574e-3 s +[acc] eval 3.840791e-3 s +[acc] eval 2.980277e-3 s +[acc] eval 2.670514e-3 s +[acc] eval 2.300645e-3 s +[acc] eval 2.006732e-3 s +[acc] eval 2.671055e-3 s +[acc] eval 3.060444e-3 s +[acc] eval 0.202693288 s +[acc] compile 0.373816904 s +[acc] eval 2.6358955e-2 s +[acc] eval 0.403823596 s +[acc] compile 8.0126564e-2 s +[acc] eval 7.579566e-3 s +[acc] eval 9.3385997e-2 s +[acc] compile 1.5623223e-2 s +[acc] eval 1.7089981e-2 s +[acc] eval 3.797515e-3 s +[acc] eval 3.411218e-3 s +[acc] eval 4.414318e-3 s +[acc] eval 5.197967e-3 s +[acc] eval 3.827348e-3 s +[acc] eval 3.146522e-3 s +[acc] eval 6.3203639e-2 s +[acc] compile 0.214707828 s +[acc] eval 1.3448712e-2 s +[acc] eval 6.305632e-3 s +[acc] eval 4.542489e-3 s +[acc] eval 5.155112e-3 s +[acc] eval 3.806897e-3 s +[acc] eval 1.0381973e-2 s +[acc] eval 2.105306e-3 s +[acc] eval 2.35301e-3 s +[acc] eval 2.672544e-3 s +[acc] eval 2.330176e-3 s +[acc] eval 2.300709e-3 s +[acc] eval 2.241186e-3 s +[acc] eval 1.886777e-3 s +[acc] eval 1.664845e-3 s +[acc] eval 1.94942e-3 s +[acc] eval 1.652388e-3 s +[acc] eval 1.748504e-3 s +[acc] eval 1.685539e-3 s +[acc] eval 2.204487e-3 s +[acc] eval 2.161193e-3 s +[acc] eval 0.291540763 s +[acc] compile 5.377525e-3 s +[acc] eval 7.715916e-3 s +[acc] eval 1.6093212e-2 s +[acc] compile 4.580257e-3 s +[acc] eval 1.4284579e-2 s +[acc] eval 1.30284e-3 s +[acc] eval 1.317039e-3 s +[acc] eval 2.6880801e-2 s +[acc] compile 1.284939e-2 s +[acc] eval 1.5677265e-2 s +[acc] eval 4.1196e-3 s +[acc] eval 1.0903538e-2 s +[acc] eval 3.397467e-3 s +[acc] eval 3.438741e-3 s +[acc] eval 4.225762e-3 s +[acc] eval 4.706868e-3 s +[acc] eval 3.051548e-3 s +[acc] eval 3.244662e-3 s +[acc] eval 3.90728e-3 s +[acc] eval 4.22943e-3 s +[acc] eval 3.403837e-3 s +[acc] eval 3.347203e-3 s +[acc] eval 3.643836e-3 s +[acc] eval 4.002624e-3 s +[acc] eval 3.617493e-3 s +[acc] eval 3.344274e-3 s +[acc] eval 3.301635e-3 s +[acc] eval 3.384912e-3 s +[acc] eval 3.401764e-3 s +[acc] eval 3.337625e-3 s +[acc] eval 3.281581e-3 s +[acc] eval 4.16488e-3 s +[acc] eval 3.476191e-3 s +[acc] eval 3.446421e-3 s +[acc] eval 3.711792e-3 s +[acc] eval 4.960406e-3 s +[acc] eval 9.385909e-3 s +[acc] eval 5.078092e-3 s +[acc] eval 4.28831e-3 s +[acc] eval 0.158198255 s +[acc] compile 8.2935132e-2 s +[acc] eval 2.6360905e-2 s +[acc] eval 0.113811892 s +[acc] compile 0.362075518 s +[acc] eval 2.3922833e-2 s +[acc] eval 0.390815755 s +[acc] compile 0.126710674 s +[acc] eval 1.2297835e-2 s +[acc] eval 0.148345758 s +[acc] compile 4.082957e-3 s +[acc] eval 5.273024e-3 s +[acc] eval 1.0189855e-2 s +[acc] compile 0.139409087 s +[acc] eval 2.0204997e-2 s +[acc] eval 0.172079117 s +[acc] compile 2.2181354e-2 s +[acc] eval 3.8283014e-2 s +[acc] eval 7.028628e-2 s +[acc] compile 7.893044e-3 s +[acc] eval 1.2918779e-2 s +[acc] eval 2.663872e-3 s +[acc] eval 3.649331e-3 s +[acc] eval 3.038632e-2 s +[acc] compile 0.132348081 s +[acc] eval 2.5715356e-2 s +[acc] eval 0.162202493 s +[acc] compile 7.90516e-4 s +[acc] eval 7.570915e-3 s +[acc] compile 0.211604626 s +[acc] eval 1.2699454e-2 s +[acc] eval 2.288091e-3 s +[acc] eval 3.479094e-3 s +[acc] eval 3.899628e-3 s +[acc] eval 0.237527421 s +[acc] compile 4.08271e-4 s +[acc] eval 4.056385e-3 s +[acc] compile 4.54457e-4 s +[acc] eval 2.2186587e-2 s +[acc] compile 6.62169e-3 s +[acc] eval 3.1033759e-2 s +[acc] eval 4.8511503e-2 s +[acc] compile 1.292304e-3 s +[acc] eval 2.7393736e-2 s +[acc] compile 0.329685273 s +[acc] eval 4.2459303e-2 s +[acc] eval 0.381400312 s +[acc] compile 9.24629e-4 s +[acc] eval 1.9494367e-2 s +[acc] compile 9.260331e-3 s +[acc] eval 7.204733e-3 s +[acc] eval 4.356547e-3 s +[acc] eval 4.400502e-3 s +[acc] eval 1.0152326e-2 s +[acc] eval 3.373729e-3 s +[acc] eval 3.564338e-3 s +[acc] eval 3.213268e-3 s +[acc] eval 3.253773e-3 s +[acc] eval 3.008685e-3 s +[acc] eval 2.893984e-3 s +[acc] eval 2.274277e-3 s +[acc] eval 1.654391e-3 s +[acc] eval 1.440869e-3 s +[acc] eval 1.259942e-3 s +[acc] eval 1.22627e-3 s +[acc] eval 1.108608e-3 s +[acc] eval 1.155528e-3 s +[acc] eval 1.221128e-3 s +[acc] eval 1.506357e-3 s +[acc] eval 1.182748e-3 s +[acc] eval 1.091436e-3 s +[acc] eval 1.129889e-3 s +[acc] eval 1.420687e-3 s +[acc] eval 1.230121e-3 s +[acc] eval 1.141555e-3 s +[acc] eval 1.278203e-3 s +[acc] eval 1.714779e-3 s +[acc] eval 1.970801e-3 s +[acc] eval 2.082649e-3 s +[acc] eval 1.883145e-3 s +[acc] eval 9.0749173e-2 s +[acc] compile 5.503265e-2 s +[acc] eval 1.7010781e-2 s +[acc] eval 7.4298103e-2 s +[acc] compile 8.439084e-3 s +[acc] eval 2.091544e-2 s +[acc] eval 7.247474e-3 s +[acc] eval 2.295285e-3 s +[acc] eval 2.341187e-3 s +[acc] eval 1.917942e-3 s +[acc] eval 3.051659e-3 s +[acc] eval 2.317053e-3 s +[acc] eval 1.621298e-3 s +[acc] eval 1.817717e-3 s +[acc] eval 1.555828e-3 s +[acc] eval 1.219854e-3 s +[acc] eval 1.114769e-3 s +[acc] eval 5.9885153e-2 s +[acc] compile 0.184286648 s +[acc] eval 2.7736184e-2 s +[acc] eval 0.221830226 s +[acc] compile 1.5570513e-2 s +[acc] eval 2.0050103e-2 s +[acc] eval 4.6562161e-2 s +[acc] compile 1.2957884e-2 s +[acc] eval 2.1405769e-2 s +[acc] eval 4.4249731e-2 s +[acc] compile 4.249846e-3 s +[acc] eval 2.1933097e-2 s +[acc] eval 2.8729785e-2 s +[acc] compile 1.1077668e-2 s +[acc] eval 2.3309446e-2 s +[acc] eval 3.83417e-2 s +[acc] compile 0.404359109 s +[acc] eval 2.5082496e-2 s +[acc] eval 0.436105966 s +[acc] compile 0.190393018 s +[acc] eval 1.3248906e-2 s +[acc] eval 0.218274956 s +[acc] compile 4.790756e-3 s +[acc] eval 3.662636e-3 s +[acc] eval 8.3899e-4 s +[acc] eval 7.35906e-4 s +[acc] eval 1.276703e-3 s +[acc] eval 1.295298e-2 s +[acc] compile 0.573523555 s +[acc] eval 3.6720199e-2 s +[acc] eval 0.616895128 s +[acc] compile 0.40676406 s +[acc] eval 1.5180149e-2 s +[acc] eval 0.44506484 s +[acc] compile 0.610952322 s +[acc] eval 3.5187862e-2 s +[acc] eval 0.66397741 s +[acc] compile 8.164055e-3 s +[acc] eval 8.05005e-3 s +[acc] eval 1.981861e-2 s +[acc] compile 5.253746e-3 s +[acc] eval 1.1783987e-2 s +[acc] eval 1.1864722e-2 s +[acc] eval 1.3597993e-2 s +[acc] eval 1.0812744e-2 s +[acc] eval 1.0253553e-2 s +[acc] eval 7.1696618e-2 s +[acc] compile 6.190918e-3 s +[acc] eval 6.092502e-3 s +[acc] eval 1.5404745e-2 s +[acc] compile 4.928277e-3 s +[acc] eval 9.025571e-3 s +[acc] eval 2.0827905e-2 s +[acc] eval 9.914761e-3 s +[acc] eval 5.306849e-3 s +[acc] eval 8.333765e-3 s +[acc] eval 7.3050221e-2 s +[acc] compile 9.242592e-3 s +[acc] eval 1.5397124e-2 s +[acc] eval 6.357683e-3 s +[acc] eval 5.726284e-3 s +[acc] eval 2.0357661e-2 s +[acc] eval 5.39065e-3 s +[acc] eval 3.994375e-3 s +[acc] eval 3.484693e-3 s +[acc] eval 3.234341e-3 s +[acc] eval 3.980208e-3 s +[acc] eval 3.38855e-3 s +[acc] eval 3.285447e-3 s +[acc] eval 3.306259e-3 s +[acc] eval 3.3818e-3 s +[acc] eval 3.607483e-3 s +[acc] eval 3.151407e-3 s +[acc] eval 3.120788e-3 s +[acc] eval 3.191289e-3 s +[acc] eval 2.241275e-3 s +[acc] eval 2.12594e-3 s +[acc] eval 1.985618e-3 s +[acc] eval 2.172422e-3 s +[acc] eval 2.462527e-3 s +[acc] eval 2.361357e-3 s +[acc] eval 2.047163e-3 s +[acc] eval 2.072133e-3 s +[acc] eval 2.73209e-3 s +[acc] eval 2.537719e-3 s +[acc] eval 2.912268e-3 s +[acc] eval 3.197107e-3 s +[acc] eval 2.650184e-3 s +[acc] eval 0.140534394 s +[acc] compile 8.0681732e-2 s +[acc] eval 2.9557024e-2 s +[acc] eval 1.5291547e-2 s +[acc] eval 5.552142e-3 s +[acc] eval 6.138288e-3 s +[acc] eval 4.354439e-3 s +[acc] eval 4.747107e-3 s +[acc] eval 3.553088e-3 s +[acc] eval 4.196746e-3 s +[acc] eval 4.374741e-3 s +[acc] eval 5.197274e-3 s +[acc] eval 5.053455e-3 s +[acc] eval 4.749609e-3 s +[acc] eval 4.276349e-3 s +[acc] eval 4.981117e-3 s +[acc] eval 3.677534e-3 s +[acc] eval 4.26024e-3 s +[acc] eval 4.471361e-3 s +[acc] eval 5.104798e-3 s +[acc] eval 3.90041e-3 s +[acc] eval 3.53612e-3 s +[acc] eval 4.918936e-3 s +[acc] eval 4.351573e-3 s +[acc] eval 4.347386e-3 s +[acc] eval 6.94727e-3 s +[acc] eval 1.03005e-2 s +[acc] eval 4.521335e-3 s +[acc] eval 5.792501e-3 s +[acc] eval 7.595292e-3 s +[acc] eval 3.65693e-3 s +[acc] eval 4.290919e-3 s +[acc] eval 5.678198e-3 s +[acc] eval 3.848378e-3 s +[acc] eval 3.686552e-3 s +[acc] eval 4.146379e-3 s +[acc] eval 0.291700399 s +[acc] compile 6.162041e-2 s +[acc] eval 2.2535431e-2 s +[acc] eval 4.367978e-3 s +[acc] eval 2.781726e-3 s +[acc] eval 2.664804e-3 s +[acc] eval 2.737637e-3 s +[acc] eval 4.317817e-3 s +[acc] eval 2.671579e-3 s +[acc] eval 2.542526e-3 s +[acc] eval 2.497269e-3 s +[acc] eval 2.53037e-3 s +[acc] eval 2.790397e-3 s +[acc] eval 2.667143e-3 s +[acc] eval 2.786782e-3 s +[acc] eval 2.649619e-3 s +[acc] eval 2.782328e-3 s +[acc] eval 2.598905e-3 s +[acc] eval 2.957516e-3 s +[acc] eval 2.615247e-3 s +[acc] eval 2.781214e-3 s +[acc] eval 2.821642e-3 s +[acc] eval 3.612353e-3 s +[acc] eval 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4.389852e-3 s +[acc] eval 3.284625e-3 s +[acc] eval 3.052591e-3 s +[acc] eval 0.272298475 s +[acc] compile 0.120374633 s +[acc] eval 1.5136297e-2 s +[acc] eval 0.138848054 s +[acc] compile 1.5343306e-2 s +[acc] eval 1.9503209e-2 s +[acc] eval 1.0323331e-2 s +[acc] eval 4.939909e-3 s +[acc] eval 3.589305e-3 s +[acc] eval 3.384349e-3 s +[acc] eval 2.890044e-3 s +[acc] eval 3.300282e-3 s +[acc] eval 4.72992e-3 s +[acc] eval 4.177202e-3 s +[acc] eval 4.066248e-3 s +[acc] eval 5.400497e-3 s +[acc] eval 3.997912e-3 s +[acc] eval 3.827843e-3 s +[acc] eval 3.383207e-3 s +[acc] eval 3.285659e-3 s +[acc] eval 3.34578e-3 s +[acc] eval 4.458477e-3 s +[acc] eval 4.134667e-3 s +[acc] eval 3.677656e-3 s +[acc] eval 3.369277e-3 s +[acc] eval 4.060971e-3 s +[acc] eval 4.461166e-3 s +[acc] eval 7.621955e-3 s +[acc] eval 5.470014e-3 s +[acc] eval 4.58767e-3 s +[acc] eval 5.317376e-3 s +[acc] eval 6.408565e-3 s +[acc] eval 4.093591e-3 s +[acc] eval 3.670349e-3 s +[acc] eval 4.615827e-3 s +[acc] eval 3.882697e-3 s +[acc] eval 0.177357868 s +[acc] compile 0.237479232 s +[acc] eval 2.4599137e-2 s +[acc] eval 2.61525e-3 s +[acc] eval 2.759548e-3 s +[acc] eval 3.094479e-3 s +[acc] eval 2.619483e-3 s +[acc] eval 2.281896e-3 s +[acc] eval 2.64685e-3 s +[acc] eval 1.950866e-3 s +[acc] eval 1.985689e-3 s +[acc] eval 1.942388e-3 s +[acc] eval 2.012645e-3 s +[acc] eval 1.914563e-3 s +[acc] eval 0.290906545 s +[acc] compile 1.1985837e-2 s +[acc] eval 1.0803572e-2 s +[acc] eval 5.746275e-3 s +[acc] eval 1.0837166e-2 s +[acc] eval 5.406515e-3 s +[acc] eval 4.267415e-3 s +[acc] eval 4.164456e-3 s +[acc] eval 5.050701e-3 s +[acc] eval 4.368879e-3 s +[acc] eval 3.45706e-3 s +[acc] eval 5.213466e-3 s +[acc] eval 4.263899e-3 s +[acc] eval 3.639951e-3 s +[acc] eval 3.535812e-3 s +[acc] eval 3.577312e-3 s +[acc] eval 3.439295e-3 s +[acc] eval 3.684811e-3 s +[acc] eval 5.392299e-3 s +[acc] eval 3.802778e-3 s +[acc] eval 3.423673e-3 s +[acc] eval 3.764924e-3 s +[acc] eval 4.393e-3 s +[acc] eval 3.887933e-3 s +[acc] eval 3.880837e-3 s +[acc] eval 4.05574e-3 s +[acc] eval 4.654458e-3 s +[acc] eval 3.42901e-3 s +[acc] eval 3.598951e-3 s +[acc] eval 6.412351e-3 s +[acc] eval 1.1246134e-2 s +[acc] eval 1.7734197e-2 s +[acc] eval 0.180245324 s +[acc] compile 5.686867e-3 s +[acc] eval 1.0256966e-2 s +[acc] eval 1.8518306e-2 s +[acc] compile 7.300039e-3 s +[acc] eval 1.8018474e-2 s +[acc] eval 2.9576625e-2 s +[acc] compile 3.633939e-3 s +[acc] eval 6.422136e-3 s +[acc] eval 1.2343857e-2 s +[acc] compile 7.553522e-3 s +[acc] eval 5.674853e-3 s +[acc] eval 1.5418927e-2 s +[acc] compile 5.178322e-3 s +[acc] eval 6.345887e-3 s +[acc] eval 2.0462153e-2 s +[acc] compile 0.323575951 s +[acc] eval 2.4168046e-2 s +[acc] eval 0.358799999 s +[acc] compile 1.1117897e-2 s +[acc] eval 2.0408303e-2 s +[acc] eval 3.6442184e-2 s +[acc] compile 0.332903641 s +[acc] eval 2.3494282e-2 s +[acc] eval 0.370809791 s +[acc] compile 0.178951081 s +[acc] eval 1.0190867e-2 s +[acc] eval 0.193559381 s +[acc] compile 1.4544897e-2 s +[acc] eval 3.2865476e-2 s +[acc] eval 5.1534006e-2 s +[acc] compile 9.8778153e-2 s +[acc] eval 1.2410611e-2 s +[acc] eval 0.11820359 s +[acc] compile 0.137554842 s +[acc] eval 4.0658815e-2 s +[acc] eval 0.182022527 s +[acc] compile 0.181544249 s +[acc] eval 1.3505557e-2 s +[acc] eval 0.198099574 s +[acc] compile 8.0706936e-2 s +[acc] eval 8.123215e-3 s +[acc] eval 9.4471861e-2 s +[acc] compile 0.115268228 s +[acc] eval 2.2973657e-2 s +[acc] eval 4.472888e-3 s +[acc] eval 5.212882e-3 s +[acc] eval 5.17036e-3 s +[acc] eval 3.49256e-3 s +[acc] eval 3.370053e-3 s +[acc] eval 5.050479e-3 s +[acc] eval 4.972749e-3 s +[acc] eval 4.514822e-3 s +[acc] eval 5.323049e-3 s +[acc] eval 4.511938e-3 s +[acc] eval 4.639671e-3 s +[acc] eval 4.483487e-3 s +[acc] eval 5.186095e-3 s +[acc] eval 5.032886e-3 s +[acc] eval 6.338029e-3 s +[acc] eval 5.805055e-3 s +[acc] eval 4.32747e-3 s +[acc] eval 5.248126e-3 s +[acc] eval 4.439745e-3 s +[acc] eval 4.747796e-3 s +[acc] eval 5.271742e-3 s +[acc] eval 4.778131e-3 s +[acc] eval 6.727926e-3 s +[acc] eval 4.455209e-3 s +[acc] eval 4.064183e-3 s +[acc] eval 3.551767e-3 s +[acc] eval 4.078185e-3 s +[acc] eval 3.944288e-3 s +[acc] eval 3.448897e-3 s +[acc] eval 3.750432e-3 s +[acc] eval 3.614599e-3 s +[acc] eval 3.625624e-3 s +[acc] eval 3.536634e-3 s +[acc] eval 4.946207e-3 s +[acc] eval 6.187947e-3 s +[acc] eval 3.940376e-3 s +[acc] eval 4.397697e-3 s +[acc] eval 5.49593e-3 s +[acc] eval 4.830071e-3 s +[acc] eval 5.102944e-3 s +[acc] eval 0.341987095 s +[acc] compile 0.352118506 s +[acc] eval 1.5506922e-2 s +[acc] eval 0.378006555 s +[acc] compile 5.934058e-3 s +[acc] eval 1.4458001e-2 s +[acc] eval 2.2287982e-2 s +[acc] compile 0.26480352 s +[acc] eval 3.7082623e-2 s +[acc] eval 0.305845041 s +[acc] compile 7.1839804e-2 s +[acc] eval 1.1337349e-2 s +[acc] eval 2.945328e-3 s +[acc] eval 8.8928713e-2 s +[acc] compile 3.794711e-3 s +[acc] eval 4.551211e-3 s +[acc] eval 1.467077e-3 s +[acc] eval 1.167772e-3 s +[acc] eval 1.339183e-3 s +[acc] eval 1.3762109e-2 s +[acc] compile 5.4205e-4 s +[acc] eval 2.7377431e-2 s +[acc] compile 0.375798849 s +[acc] eval 3.021967e-2 s +[acc] eval 0.411892106 s +[acc] compile 1.6677064e-2 s +[acc] eval 5.371958e-3 s +[acc] eval 1.005565e-3 s +[acc] eval 7.82744e-4 s +[acc] eval 8.64902e-4 s +[acc] eval 7.21381e-4 s +[acc] eval 8.56496e-4 s +[acc] eval 9.10141e-4 s +[acc] eval 7.3783e-4 s +[acc] eval 9.29497e-4 s +[acc] eval 8.31456e-4 s +[acc] eval 1.107471e-3 s +[acc] eval 1.360432e-3 s +[acc] eval 1.759175e-3 s +[acc] eval 1.389755e-3 s +[acc] eval 1.062874e-3 s +[acc] eval 1.206043e-3 s +[acc] eval 1.171855e-3 s +[acc] eval 8.83372e-4 s +[acc] eval 9.9868e-4 s +[acc] eval 8.00376e-4 s +[acc] eval 9.48431e-4 s +[acc] eval 9.67458e-4 s +[acc] eval 8.309269e-3 s +[acc] eval 1.076108e-3 s +[acc] eval 1.256788e-3 s +[acc] eval 1.184916e-3 s +[acc] eval 1.06923e-3 s +[acc] eval 1.105347e-3 s +[acc] eval 1.181148e-3 s +[acc] eval 9.94967e-4 s +[acc] eval 6.2812352e-2 s +[acc] compile 3.600333e-3 s +[acc] eval 6.811967e-3 s +[acc] eval 1.214945e-2 s +[acc] compile 0.404209564 s +[acc] eval 4.3317271e-2 s +[acc] eval 0.453231267 s +[acc] compile 0.130499421 s +[acc] eval 6.64761e-3 s +[acc] eval 0.14182375 s +[acc] compile 0.265961749 s +[acc] eval 3.3244892e-2 s +[acc] eval 0.303658896 s +[acc] compile 1.084652e-2 s +[acc] eval 1.4129263e-2 s +[acc] eval 3.2126795e-2 s +[acc] compile 1.0127491e-2 s +[acc] eval 2.3282775e-2 s +[acc] eval 3.7163137e-2 s +[acc] compile 9.16445e-3 s +[acc] eval 9.355259e-3 s +[acc] eval 1.8509547e-2 s +[acc] eval 1.5197701e-2 s +[acc] eval 1.5523705e-2 s +[acc] eval 9.823802e-3 s +[acc] eval 3.501447e-3 s +[acc] eval 3.756524e-3 s +[acc] eval 5.125219e-3 s +[acc] eval 3.813323e-3 s +[acc] eval 3.371559e-3 s +[acc] eval 2.684737e-3 s +[acc] eval 4.261981e-3 s +[acc] eval 2.029707e-3 s +[acc] eval 1.818266e-3 s +[acc] eval 2.020401e-3 s +[acc] eval 1.825791e-3 s +[acc] eval 0.117075617 s +[acc] compile 0.127062949 s +[acc] eval 1.0264368e-2 s +[acc] eval 0.141922347 s +[acc] compile 0.120685284 s +[acc] eval 3.1209743e-2 s +[acc] eval 0.156541549 s +[acc] compile 0.170448047 s +[acc] eval 8.456253e-3 s +[acc] eval 0.18343533 s +[acc] compile 6.893603e-3 s +[acc] eval 1.5075699e-2 s +[acc] eval 3.699547e-3 s +[acc] eval 8.814602e-3 s +[acc] eval 3.503513e-3 s +[acc] eval 5.106563e-3 s +[acc] eval 3.790882e-3 s +[acc] eval 3.010981e-3 s +[acc] eval 2.727483e-3 s +[acc] eval 2.504886e-3 s +[acc] eval 4.169512e-3 s +[acc] eval 2.808215e-3 s +[acc] eval 2.684592e-3 s +[acc] eval 2.714469e-3 s +[acc] eval 3.078326e-3 s +[acc] eval 3.380566e-3 s +[acc] eval 7.9410659e-2 s +[acc] compile 0.211999917 s +[acc] eval 1.2671695e-2 s +[acc] eval 0.228958151 s +[acc] compile 0.170002125 s +[acc] eval 1.5374675e-2 s +[acc] eval 5.170499e-3 s +[acc] eval 3.79055e-3 s +[acc] eval 5.452499e-3 s +[acc] eval 3.63855e-3 s +[acc] eval 2.57713e-3 s +[acc] eval 2.044114e-3 s +[acc] eval 2.019854e-3 s +[acc] 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compile 7.5436822e-2 s +[acc] eval 2.0841831e-2 s +[acc] eval 2.41125e-3 s +[acc] eval 2.232793e-3 s +[acc] eval 2.16104e-3 s +[acc] eval 1.925367e-3 s +[acc] eval 2.097573e-3 s +[acc] eval 3.204219e-3 s +[acc] eval 2.360749e-3 s +[acc] eval 2.114502e-3 s +[acc] eval 2.063637e-3 s +[acc] eval 2.327549e-3 s +[acc] eval 2.080125e-3 s +[acc] eval 3.192407e-3 s +[acc] eval 2.589411e-3 s +[acc] eval 2.166323e-3 s +[acc] eval 2.044211e-3 s +[acc] eval 1.786531e-3 s +[acc] eval 1.912434e-3 s +[acc] eval 1.996987e-3 s +[acc] eval 1.962534e-3 s +[acc] eval 2.528104e-3 s +[acc] eval 1.976934e-3 s +[acc] eval 2.017305e-3 s +[acc] eval 2.018245e-3 s +[acc] eval 2.011765e-3 s +[acc] eval 2.13445e-3 s +[acc] eval 2.15711e-3 s +[acc] eval 2.138723e-3 s +[acc] eval 1.990324e-3 s +[acc] eval 2.127668e-3 s +[acc] eval 0.165124369 s +[acc] compile 0.243375835 s +[acc] eval 2.3041715e-2 s +[acc] eval 0.273047452 s +[acc] compile 0.185286184 s +[acc] eval 2.7801345e-2 s +[acc] eval 0.216956404 s +[acc] compile 0.302105605 s +[acc] eval 3.6235682e-2 s +[acc] eval 0.346527676 s +[acc] compile 0.648731278 s +[acc] eval 2.8460234e-2 s +[acc] eval 0.685452965 s +[acc] compile 8.431568e-3 s +[acc] eval 6.096351e-3 s +[acc] eval 2.389011e-3 s +[acc] eval 3.834077e-3 s +[acc] eval 3.469872e-3 s +[acc] eval 3.474425e-3 s +[acc] eval 1.1843467e-2 s +[acc] eval 1.6686e-3 s +[acc] eval 1.423387e-3 s +[acc] eval 1.719856e-3 s +[acc] eval 1.92642e-3 s +[acc] eval 2.186992e-3 s +[acc] eval 1.724507e-3 s +[acc] eval 1.704252e-3 s +[acc] eval 1.711541e-3 s +[acc] eval 1.914597e-3 s +[acc] eval 1.858291e-3 s +[acc] eval 2.018506e-3 s +[acc] eval 2.164706e-3 s +[acc] eval 2.435488e-3 s +[acc] eval 1.609246e-3 s +[acc] eval 1.078218e-3 s +[acc] eval 1.108375e-3 s +[acc] eval 1.15698e-3 s +[acc] eval 1.109815e-3 s +[acc] eval 1.830623e-3 s +[acc] eval 1.197836e-3 s +[acc] eval 1.413117e-3 s +[acc] eval 1.675225e-3 s +[acc] eval 1.805779e-3 s +[acc] eval 1.284763e-3 s +[acc] eval 8.298492e-2 s +[acc] compile 4.139127e-3 s +[acc] eval 4.895183e-3 s +[acc] eval 1.861329e-3 s +[acc] eval 2.658726e-3 s +[acc] eval 1.535406e-3 s +[acc] eval 1.68241e-3 s +[acc] eval 1.528605e-3 s +[acc] eval 1.45533e-3 s +[acc] eval 1.585228e-3 s +[acc] eval 1.526948e-3 s +[acc] eval 9.865495e-3 s +[acc] eval 1.480268e-3 s +[acc] eval 1.359757e-3 s +[acc] eval 1.53901e-3 s +[acc] eval 1.487317e-3 s +[acc] eval 1.556918e-3 s +[acc] eval 1.2264e-3 s +[acc] eval 1.351461e-3 s +[acc] eval 1.454674e-3 s +[acc] eval 1.29427e-3 s +[acc] eval 8.78578e-4 s +[acc] eval 9.01623e-4 s +[acc] eval 1.025808e-3 s +[acc] eval 1.39209e-3 s +[acc] eval 1.520472e-3 s +[acc] eval 1.437856e-3 s +[acc] eval 1.37729e-3 s +[acc] eval 1.495598e-3 s +[acc] eval 1.588221e-3 s +[acc] eval 1.502405e-3 s +[acc] eval 1.528938e-3 s +[acc] eval 6.4801544e-2 s +[acc] compile 0.134088948 s +[acc] eval 2.6725439e-2 s +[acc] eval 8.249754e-3 s +[acc] eval 5.435593e-3 s +[acc] eval 4.360913e-3 s +[acc] eval 3.561831e-3 s +[acc] eval 3.264101e-3 s +[acc] eval 4.495056e-3 s +[acc] eval 3.990032e-3 s +[acc] eval 3.652496e-3 s +[acc] eval 3.647303e-3 s +[acc] eval 3.815165e-3 s +[acc] eval 2.59375e-3 s +[acc] eval 0.212188674 s +[acc] compile 8.855635e-3 s +[acc] eval 1.5928497e-2 s +[acc] eval 2.905578e-3 s +[acc] eval 6.863337e-3 s +[acc] eval 2.594691e-3 s +[acc] eval 2.592738e-3 s +[acc] eval 2.410068e-3 s +[acc] eval 2.522949e-3 s +[acc] eval 2.259195e-3 s +[acc] eval 2.368789e-3 s +[acc] eval 3.490419e-3 s +[acc] eval 3.51704e-3 s +[acc] eval 2.419093e-3 s +[acc] eval 6.2239984e-2 s +[acc] compile 0.291051372 s +[acc] eval 2.0954262e-2 s +[acc] eval 0.320095348 s +[acc] compile 1.7843903e-2 s +[acc] eval 1.7363644e-2 s +[acc] eval 3.9063738e-2 s +[acc] compile 0.12118528 s +[acc] eval 1.642777e-2 s +[acc] eval 7.016857e-3 s +[acc] eval 1.0349067e-2 s +[acc] eval 1.1273505e-2 s +[acc] eval 1.1763526e-2 s +[acc] eval 1.1448052e-2 s +[acc] eval 3.776007e-3 s +[acc] eval 3.890259e-3 s +[acc] eval 1.262743e-2 s +[acc] eval 1.1764311e-2 s +[acc] eval 8.72201e-3 s +[acc] eval 6.933927e-3 s +[acc] eval 2.881747e-3 s +[acc] eval 5.390244e-3 s +[acc] eval 9.175615e-3 s +[acc] eval 6.906871e-3 s +[acc] eval 2.614291e-3 s +[acc] eval 2.717887e-3 s +[acc] eval 2.590608e-3 s +[acc] eval 5.310199e-3 s +[acc] eval 3.031127e-3 s +[acc] eval 0.288213547 s +[acc] compile 0.242373023 s +[acc] eval 3.7384118e-2 s +[acc] eval 0.285311588 s +[acc] compile 1.681701e-2 s +[acc] eval 3.771762e-3 s +[acc] eval 9.55609e-4 s +[acc] eval 8.18683e-4 s +[acc] eval 1.189188e-3 s +[acc] eval 2.4881247e-2 s +[acc] compile 5.32455e-3 s +[acc] eval 6.893508e-3 s +[acc] eval 3.321969e-3 s +[acc] eval 2.362352e-3 s +[acc] eval 2.456273e-3 s +[acc] eval 9.711214e-3 s +[acc] eval 2.126273e-3 s +[acc] eval 2.406587e-3 s +[acc] eval 2.322096e-3 s +[acc] eval 2.197556e-3 s +[acc] eval 2.71649e-3 s +[acc] eval 3.54082e-3 s +[acc] eval 2.185559e-3 s +[acc] eval 1.656218e-3 s +[acc] eval 1.554247e-3 s +[acc] eval 1.661998e-3 s +[acc] eval 1.236333e-3 s +[acc] eval 1.268809e-3 s +[acc] eval 5.8441716e-2 s +[acc] compile 6.5019196e-2 s +[acc] eval 1.8505456e-2 s +[acc] eval 1.0422238e-2 s +[acc] eval 3.937301e-3 s +[acc] eval 3.777326e-3 s +[acc] eval 4.578216e-3 s +[acc] eval 4.739512e-3 s +[acc] eval 3.862738e-3 s +[acc] eval 3.86297e-3 s +[acc] eval 3.65564e-3 s +[acc] eval 3.656883e-3 s +[acc] eval 3.310435e-3 s +[acc] eval 3.265228e-3 s +[acc] eval 3.555738e-3 s +[acc] eval 4.01708e-3 s +[acc] eval 4.019647e-3 s +[acc] eval 3.807215e-3 s +[acc] eval 4.123173e-3 s +[acc] eval 4.71414e-3 s +[acc] eval 3.43314e-3 s +[acc] eval 3.410422e-3 s +[acc] eval 3.811162e-3 s +[acc] eval 4.164101e-3 s +[acc] eval 3.735294e-3 s +[acc] eval 3.517512e-3 s +[acc] eval 3.983177e-3 s +[acc] eval 4.451314e-3 s +[acc] eval 3.750896e-3 s +[acc] eval 3.55118e-3 s +[acc] eval 3.940234e-3 s +[acc] eval 4.707787e-3 s +[acc] eval 0.210543731 s +[acc] compile 0.138575864 s +[acc] eval 1.8067994e-2 s +[acc] eval 4.119199e-3 s +[acc] eval 5.437378e-3 s +[acc] eval 3.732914e-3 s +[acc] eval 3.164413e-3 s +[acc] eval 2.733541e-3 s +[acc] eval 3.384399e-3 s +[acc] eval 3.668753e-3 s +[acc] eval 2.786946e-3 s +[acc] eval 3.001688e-3 s +[acc] eval 3.16407e-3 s +[acc] eval 2.632613e-3 s +[acc] eval 2.574304e-3 s +[acc] eval 3.535671e-3 s +[acc] eval 3.475249e-3 s +[acc] eval 3.251875e-3 s +[acc] eval 2.813272e-3 s +[acc] eval 2.600726e-3 s +[acc] eval 2.565983e-3 s +[acc] eval 2.454224e-3 s +[acc] eval 2.99335e-3 s +[acc] eval 3.520269e-3 s +[acc] eval 2.522736e-3 s +[acc] eval 2.580352e-3 s +[acc] eval 2.516382e-3 s +[acc] eval 3.072406e-3 s +[acc] eval 3.345551e-3 s +[acc] eval 2.704793e-3 s +[acc] eval 2.521576e-3 s +[acc] eval 2.414787e-3 s +[acc] eval 0.252889466 s +[acc] compile 0.175254803 s +[acc] eval 3.5918205e-2 s +[acc] eval 0.214473988 s +[acc] compile 1.0414346e-2 s +[acc] eval 9.700224e-3 s +[acc] eval 2.4513106e-2 s +[acc] compile 6.727385e-3 s +[acc] eval 3.5605372e-2 s +[acc] eval 2.984905e-3 s +[acc] eval 4.9435158e-2 s +[acc] compile 0.119836029 s +[acc] eval 1.395338e-2 s +[acc] eval 0.146694835 s +[acc] compile 6.182025e-3 s +[acc] eval 1.028574e-2 s +[acc] eval 1.9066255e-2 s +[acc] compile 6.84602e-3 s +[acc] eval 5.793591e-3 s +[acc] eval 3.432256e-3 s +[acc] eval 1.0369264e-2 s +[acc] eval 3.191836e-3 s +[acc] eval 3.758185e-3 s +[acc] eval 3.922054e-3 s +[acc] eval 2.850419e-3 s +[acc] eval 2.643564e-3 s +[acc] eval 3.173635e-3 s +[acc] eval 3.484669e-3 s +[acc] eval 4.552114e-3 s +[acc] eval 2.827438e-3 s +[acc] eval 2.537301e-3 s +[acc] eval 2.361916e-3 s +[acc] eval 2.309405e-3 s +[acc] eval 2.644557e-3 s +[acc] eval 2.894255e-3 s +[acc] eval 2.359991e-3 s +[acc] eval 2.21658e-3 s +[acc] eval 2.424709e-3 s +[acc] eval 2.95644e-3 s +[acc] eval 2.927025e-3 s +[acc] eval 2.531673e-3 s +[acc] eval 2.061098e-3 s +[acc] eval 2.059768e-3 s +[acc] eval 1.597212e-3 s +[acc] eval 1.796438e-3 s +[acc] eval 1.605545e-3 s +[acc] eval 1.476159e-3 s +[acc] eval 1.510715e-3 s +[acc] eval 1.541303e-3 s +[acc] eval 1.740936e-3 s +[acc] eval 0.105046121 s +[acc] compile 0.182880971 s +[acc] eval 1.5098859e-2 s +[acc] eval 0.205469887 s +[acc] compile 7.2919103e-2 s +[acc] eval 7.699898e-3 s +[acc] eval 8.6681222e-2 s +[acc] compile 7.6490655e-2 s +[acc] eval 2.2296285e-2 s +[acc] eval 0.104124578 s +[acc] compile 7.62916e-2 s +[acc] eval 1.3664245e-2 s +[acc] eval 9.6663748e-2 s +[acc] compile 0.125752384 s +[acc] eval 1.9137707e-2 s +[acc] eval 0.147646314 s +[acc] compile 0.118017743 s +[acc] eval 2.134787e-2 s +[acc] eval 4.332237e-3 s +[acc] eval 4.005159e-3 s +[acc] eval 5.819471e-3 s +[acc] eval 5.814122e-3 s +[acc] eval 3.976076e-3 s +[acc] eval 4.256838e-3 s +[acc] eval 2.2759649e-2 s +[acc] eval 3.912976e-3 s +[acc] eval 4.373419e-3 s +[acc] eval 4.618805e-3 s +[acc] eval 4.346241e-3 s +[acc] eval 0.213792196 s +[acc] compile 0.146247807 s +[acc] eval 2.8110411e-2 s +[acc] eval 3.207962e-3 s +[acc] eval 8.154239e-3 s +[acc] eval 2.343635e-3 s +[acc] eval 2.304481e-3 s +[acc] eval 2.235474e-3 s +[acc] eval 2.130354e-3 s +[acc] eval 2.481784e-3 s +[acc] eval 4.030945e-3 s +[acc] eval 2.871815e-3 s +[acc] eval 3.039818e-3 s +[acc] eval 2.889959e-3 s +[acc] eval 0.213579241 s +[acc] compile 0.131899771 s +[acc] eval 1.0096512e-2 s +[acc] eval 0.149361914 s +[acc] compile 7.1942404e-2 s +[acc] eval 2.4690057e-2 s +[acc] eval 0.101050924 s +[acc] compile 0.338612128 s +[acc] eval 2.0000254e-2 s +[acc] eval 0.363642312 s +[acc] compile 5.678483e-3 s +[acc] eval 4.230383e-3 s +[acc] eval 6.09842e-4 s +[acc] eval 8.30071e-4 s +[acc] eval 1.2602371e-2 s +[acc] compile 0.239190593 s +[acc] eval 3.6736e-2 s +[acc] eval 0.287894875 s +[acc] compile 1.1017285e-2 s +[acc] eval 2.5300503e-2 s +[acc] eval 3.66267e-3 s +[acc] eval 3.494807e-3 s +[acc] eval 4.6900628e-2 s +[acc] compile 0.516509318 s +[acc] eval 4.3719435e-2 s +[acc] eval 0.577626641 s +[acc] compile 0.336856363 s +[acc] eval 2.7063978e-2 s +[acc] eval 0.371119503 s +[acc] compile 9.794583e-3 s +[acc] eval 3.772395e-3 s +[acc] eval 1.5198071e-2 s +[acc] compile 0.310150541 s +[acc] eval 3.2003892e-2 s +[acc] eval 3.689865e-3 s +[acc] eval 4.878294e-3 s +[acc] eval 4.271145e-3 s +[acc] eval 4.402834e-3 s +[acc] eval 4.929767e-3 s +[acc] eval 4.884573e-3 s +[acc] eval 4.828423e-3 s +[acc] eval 4.499978e-3 s +[acc] eval 5.000187e-3 s +[acc] eval 4.099361e-3 s +[acc] eval 4.223217e-3 s +[acc] eval 0.397648611 s +[acc] compile 0.242368152 s +[acc] eval 1.1566772e-2 s +[acc] eval 1.2183032e-2 s +[acc] eval 6.892033e-3 s +[acc] eval 4.638626e-3 s +[acc] eval 4.95762e-3 s +[acc] eval 0.288274723 s +[acc] compile 0.718857199 s +[acc] eval 3.851986e-2 s +[acc] eval 0.765027392 s +[acc] compile 0.133289807 s +[acc] eval 1.04259e-2 s +[acc] eval 4.720576e-3 s +[acc] eval 1.9019341e-2 s +[acc] eval 6.546112e-3 s +[acc] eval 6.130342e-3 s +[acc] eval 4.498298e-3 s +[acc] eval 4.041807e-3 s +[acc] eval 3.670799e-3 s +[acc] eval 3.537843e-3 s +[acc] eval 4.639767e-3 s +[acc] eval 4.374064e-3 s +[acc] eval 4.057294e-3 s +[acc] eval 4.112272e-3 s +[acc] eval 4.615189e-3 s +[acc] eval 4.311565e-3 s +[acc] eval 3.202972e-3 s +[acc] eval 3.483007e-3 s +[acc] eval 3.530932e-3 s +[acc] eval 3.512482e-3 s +[acc] eval 3.254999e-3 s +[acc] eval 3.200766e-3 s +[acc] eval 3.174071e-3 s +[acc] eval 3.492125e-3 s +[acc] eval 3.680502e-3 s +[acc] eval 0.259014076 s +[acc] compile 0.172926367 s +[acc] eval 1.0680403e-2 s +[acc] eval 0.187012336 s +[acc] compile 0.186982204 s +[acc] eval 9.265746e-3 s +[acc] eval 0.207294036 s +[acc] compile 0.186214442 s +[acc] eval 1.4165764e-2 s +[acc] eval 0.20400487 s +[acc] compile 6.848751e-2 s +[acc] eval 8.604949e-3 s +[acc] eval 8.0470777e-2 s +[acc] compile 9.358816e-3 s +[acc] eval 1.6061403e-2 s +[acc] eval 2.724021e-3 s +[acc] eval 2.343648e-3 s +[acc] eval 2.183457e-3 s +[acc] eval 2.049029e-3 s +[acc] eval 1.82074e-3 s +[acc] eval 1.724488e-3 s +[acc] eval 1.800532e-3 s +[acc] eval 1.627621e-3 s +[acc] eval 1.729828e-3 s +[acc] eval 1.489863e-3 s +[acc] eval 1.546082e-3 s +[acc] eval 1.593303e-3 s +[acc] eval 1.643826e-3 s +[acc] eval 1.63313e-3 s +[acc] eval 1.607912e-3 s +[acc] eval 1.448771e-3 s +[acc] eval 1.728978e-3 s +[acc] eval 1.616107e-3 s +[acc] eval 1.254038e-3 s +[acc] eval 1.113905e-3 s +[acc] eval 1.282659e-3 s +[acc] eval 1.220509e-3 s +[acc] eval 1.325195e-3 s +[acc] eval 1.238719e-3 s +[acc] eval 1.194757e-3 s +[acc] eval 1.180561e-3 s +[acc] eval 1.251115e-3 s +[acc] eval 1.356939e-3 s +[acc] eval 1.474524e-3 s +[acc] eval 7.5639933e-2 s +[acc] compile 0.109916103 s +[acc] eval 1.1722027e-2 s +[acc] eval 4.793871e-3 s +[acc] eval 4.137529e-3 s +[acc] eval 8.708657e-3 s +[acc] eval 3.863274e-3 s +[acc] eval 4.077034e-3 s +[acc] eval 3.64962e-3 s +[acc] eval 4.733247e-3 s +[acc] eval 4.197241e-3 s +[acc] eval 3.205681e-3 s +[acc] eval 2.784681e-3 s +[acc] eval 2.73077e-3 s +[acc] eval 0.173268638 s +[acc] compile 3.646323e-3 s +[acc] eval 1.2391768e-2 s +[acc] eval 1.9421764e-2 s +[acc] compile 9.360685e-3 s +[acc] eval 1.5094156e-2 s +[acc] eval 2.7328989e-2 s +[acc] compile 0.126540611 s +[acc] eval 3.4610607e-2 s +[acc] eval 0.166886688 s +[acc] compile 1.7381423e-2 s +[acc] eval 1.5858624e-2 s +[acc] eval 3.7344899e-2 s +[acc] compile 5.299382e-2 s +[acc] eval 3.5007861e-2 s +[acc] eval 8.9997527e-2 s +[acc] compile 6.1292675e-2 s +[acc] eval 2.1769332e-2 s +[acc] eval 8.716921e-2 s +[acc] compile 4.6809834e-2 s +[acc] eval 1.5211005e-2 s +[acc] eval 6.4572265e-2 s +[acc] compile 0.119404303 s +[acc] eval 1.2691e-2 s +[acc] eval 3.382356e-3 s +[acc] eval 3.517584e-3 s +[acc] eval 3.442475e-3 s +[acc] eval 3.29829e-3 s +[acc] eval 2.690714e-3 s +[acc] eval 3.787492e-3 s +[acc] eval 2.566065e-3 s +[acc] eval 2.17297e-3 s +[acc] eval 2.190755e-3 s +[acc] eval 8.778427e-3 s +[acc] eval 0.171548715 s +[acc] compile 5.8673974e-2 s +[acc] eval 2.8188894e-2 s +[acc] eval 9.1441473e-2 s +[acc] compile 2.0289193e-2 s +[acc] eval 2.8178316e-2 s +[acc] eval 6.4148611e-2 s +[acc] compile 0.133458036 s +[acc] eval 2.0122629e-2 s +[acc] eval 0.163945363 s +[acc] compile 7.016916e-2 s +[acc] eval 1.1496486e-2 s +[acc] eval 8.4004446e-2 s +[acc] compile 6.8498825e-2 s +[acc] eval 1.8362248e-2 s +[acc] eval 8.9589302e-2 s +[acc] compile 5.620099e-3 s +[acc] eval 7.495798e-3 s +[acc] eval 1.6419034e-2 s +[acc] compile 8.4704992e-2 s +[acc] eval 2.2543712e-2 s +[acc] eval 0.116052356 s +[acc] compile 0.435234813 s +[acc] eval 3.7766349e-2 s +[acc] eval 0.497828199 s +[acc] compile 0.135285634 s +[acc] eval 2.6967219e-2 s +[acc] eval 0.173898005 s +[acc] compile 8.6665743e-2 s +[acc] eval 2.5903143e-2 s +[acc] eval 0.118938915 s +[acc] compile 1.3416761e-2 s +[acc] eval 1.3399016e-2 s +[acc] eval 2.8917427e-2 s +[acc] compile 5.874929e-3 s +[acc] eval 2.0320099e-2 s +[acc] eval 3.6672745e-2 s +[acc] compile 6.055929e-3 s +[acc] eval 1.4694439e-2 s +[acc] eval 2.3112687e-2 s +[acc] compile 5.580919e-3 s +[acc] eval 1.9982848e-2 s +[acc] eval 2.9450963e-2 s +[acc] compile 6.7844332e-2 s +[acc] eval 1.5447695e-2 s +[acc] eval 8.6888647e-2 s +[acc] compile 1.6636851e-2 s +[acc] eval 2.2525085e-2 s +[acc] eval 4.5042322e-2 s +[acc] compile 6.613054e-3 s +[acc] eval 1.8596397e-2 s +[acc] eval 3.0128011e-2 s +[acc] compile 0.143901189 s +[acc] eval 2.1807562e-2 s +[acc] eval 0.17560971 s +[acc] compile 0.305679609 s +[acc] eval 3.6212763e-2 s +[acc] eval 4.216385e-3 s +[acc] eval 4.620016e-3 s +[acc] eval 4.635786e-3 s +[acc] eval 4.177632e-3 s +[acc] eval 4.022429e-3 s +[acc] eval 3.484535e-3 s +[acc] eval 3.086814e-3 s +[acc] eval 3.338841e-3 s +[acc] eval 3.262587e-3 s +[acc] eval 3.08851e-3 s +[acc] eval 3.219321e-3 s +[acc] eval 3.404641e-3 s +[acc] eval 3.20261e-3 s +[acc] eval 2.960165e-3 s +[acc] eval 2.763348e-3 s +[acc] eval 1.2220152e-2 s +[acc] eval 2.764295e-3 s +[acc] eval 2.662776e-3 s +[acc] eval 2.645443e-3 s +[acc] eval 2.782403e-3 s +[acc] eval 2.691305e-3 s +[acc] eval 3.244239e-3 s +[acc] eval 3.873926e-3 s +[acc] eval 8.188009e-3 s +[acc] eval 3.914093e-3 s +[acc] eval 3.438514e-3 s +[acc] eval 3.149581e-3 s +[acc] eval 3.168474e-3 s +[acc] eval 3.370544e-3 s +[acc] eval 0.460895467 s +[acc] compile 4.6781221e-2 s +[acc] eval 2.6647132e-2 s +[acc] eval 7.859089e-2 s +[acc] compile 0.191674781 s +[acc] eval 1.3427738e-2 s +[acc] eval 0.211848196 s +[acc] compile 0.231420656 s +[acc] eval 3.6511942e-2 s +[acc] eval 0.274581867 s +[acc] compile 0.231044952 s +[acc] eval 2.2232726e-2 s +[acc] eval 0.258606188 s +[acc] compile 0.13088483 s +[acc] eval 2.0381634e-2 s +[acc] eval 0.169244724 s +[acc] compile 6.853619e-3 s +[acc] eval 1.8261615e-2 s +[acc] eval 3.58635e-3 s +[acc] eval 8.925699e-3 s +[acc] eval 4.0788011e-2 s +[acc] compile 0.242247047 s +[acc] eval 3.3027925e-2 s +[acc] eval 0.281109976 s +[acc] compile 0.236343219 s +[acc] eval 1.612326e-2 s +[acc] eval 0.261011553 s +[acc] compile 0.119339079 s +[acc] eval 8.102371e-3 s +[acc] eval 0.131702443 s +[acc] compile 0.198077442 s +[acc] eval 1.8515947e-2 s +[acc] eval 0.222206536 s +[acc] compile 0.183723396 s +[acc] eval 1.4902194e-2 s +[acc] eval 0.206604566 s +[acc] compile 0.264124094 s +[acc] eval 1.8234427e-2 s +[acc] eval 0.287195499 s +[acc] compile 0.192837148 s +[acc] eval 7.773407e-3 s +[acc] eval 1.3994341e-2 s +[acc] eval 0.224716246 s +[acc] compile 0.358150687 s +[acc] eval 3.0039479e-2 s +[acc] eval 0.392695119 s +[acc] compile 0.152362743 s +[acc] eval 1.3410044e-2 s +[acc] eval 0.175610044 s +[acc] compile 0.122475387 s +[acc] eval 6.786911e-3 s +[acc] eval 0.132350017 s +[acc] compile 9.89463e-3 s +[acc] eval 6.20108e-3 s +[acc] eval 2.746754e-3 s +[acc] eval 2.72324e-3 s +[acc] eval 2.560337e-3 s +[acc] eval 3.161302e-3 s +[acc] eval 3.944085e-3 s +[acc] eval 4.036813e-3 s +[acc] eval 3.532515e-3 s +[acc] eval 3.319373e-3 s +[acc] eval 2.758845e-3 s +[acc] eval 1.1782679e-2 s +[acc] eval 2.422251e-3 s +[acc] eval 2.494064e-3 s +[acc] eval 2.247951e-3 s +[acc] eval 2.776828e-3 s +[acc] eval 3.125997e-3 s +[acc] eval 7.493479e-2 s +[acc] compile 8.737488e-3 s +[acc] eval 2.117818e-2 s +[acc] eval 3.2602107e-2 s +[acc] compile 5.8258368e-2 s +[acc] eval 3.8636736e-2 s +[acc] eval 0.100426182 s +[acc] compile 2.685963e-3 s +[acc] eval 4.903929e-3 s +[acc] eval 1.039891e-3 s +[acc] eval 9.76201e-4 s +[acc] eval 1.0831483e-2 s +[acc] compile 1.0150064e-2 s +[acc] eval 1.923213e-2 s +[acc] eval 3.3533956e-2 s +[acc] compile 0.142881822 s +[acc] eval 3.0797068e-2 s +[acc] eval 5.587154e-3 s +[acc] eval 5.457402e-3 s +[acc] eval 4.803627e-3 s +[acc] eval 4.403682e-3 s +[acc] eval 3.221688e-3 s +[acc] eval 2.98585e-3 s +[acc] eval 2.990301e-3 s +[acc] eval 3.568977e-3 s +[acc] eval 3.616349e-3 s +[acc] eval 3.694418e-3 s +[acc] eval 3.344825e-3 s +[acc] eval 0.222999983 s +[acc] compile 0.258614527 s +[acc] eval 2.6055981e-2 s +[acc] eval 0.289837823 s +[acc] compile 6.014035e-3 s +[acc] eval 1.158411e-2 s +[acc] eval 2.255784e-3 s +[acc] eval 8.897185e-3 s +[acc] eval 2.161246e-3 s +[acc] eval 1.989338e-3 s +[acc] eval 2.006823e-3 s +[acc] eval 2.428621e-3 s +[acc] eval 2.205277e-3 s +[acc] eval 2.089224e-3 s +[acc] eval 2.244074e-3 s +[acc] eval 2.176708e-3 s +[acc] eval 1.913985e-3 s +[acc] eval 1.879178e-3 s +[acc] eval 2.706431e-3 s +[acc] eval 2.434895e-3 s +[acc] eval 2.859811e-3 s +[acc] eval 2.503897e-3 s +[acc] eval 2.367022e-3 s +[acc] eval 2.484128e-3 s +[acc] eval 2.215547e-3 s +[acc] eval 2.584987e-3 s +[acc] eval 2.236848e-3 s +[acc] eval 2.0245e-3 s +[acc] eval 1.645151e-3 s +[acc] eval 1.41603e-3 s +[acc] eval 1.808484e-3 s +[acc] eval 2.171149e-3 s +[acc] eval 2.004583e-3 s +[acc] eval 2.512091e-3 s +[acc] eval 1.737686e-3 s +[acc] eval 1.589651e-3 s +[acc] eval 1.68565e-3 s +[acc] eval 1.651554e-3 s +[acc] eval 1.668965e-3 s +[acc] eval 1.878775e-3 s +[acc] eval 1.904441e-3 s +[acc] eval 1.649102e-3 s +[acc] eval 1.572312e-3 s +[acc] eval 0.107814867 s +[acc] compile 8.16625e-3 s +[acc] eval 8.076203e-3 s +[acc] eval 4.472392e-3 s +[acc] eval 2.984751e-3 s +[acc] eval 2.721643e-3 s +[acc] eval 2.594986e-3 s +[acc] eval 2.525204e-3 s +[acc] eval 2.872849e-3 s +[acc] eval 2.687729e-3 s +[acc] eval 9.055867e-3 s +[acc] eval 2.590225e-3 s +[acc] eval 2.403478e-3 s +[acc] eval 2.106111e-3 s +[acc] eval 2.428456e-3 s +[acc] eval 1.929134e-3 s +[acc] eval 1.948929e-3 s +[acc] eval 1.876012e-3 s +[acc] eval 2.306466e-3 s +[acc] eval 2.167659e-3 s +[acc] eval 2.165685e-3 s +[acc] eval 2.106053e-3 s +[acc] eval 1.871259e-3 s +[acc] eval 1.837073e-3 s +[acc] eval 1.831572e-3 s +[acc] eval 2.032266e-3 s +[acc] eval 1.604788e-3 s +[acc] eval 1.769226e-3 s +[acc] eval 1.520626e-3 s +[acc] eval 1.523605e-3 s +[acc] eval 1.399888e-3 s +[acc] eval 1.752527e-3 s +[acc] eval 1.903829e-3 s +[acc] eval 1.408106e-3 s +[acc] eval 1.435346e-3 s +[acc] eval 1.465684e-3 s +[acc] eval 9.9167536e-2 s +[acc] compile 7.465112e-3 s +[acc] eval 1.9636816e-2 s +[acc] eval 3.0031624e-2 s +[acc] compile 7.142257e-3 s +[acc] eval 2.2901597e-2 s +[acc] eval 3.2205333e-2 s +[acc] compile 6.118975e-3 s +[acc] eval 7.352382e-3 s +[acc] eval 1.6132369e-2 s +[acc] compile 5.9810848e-2 s +[acc] eval 1.0445436e-2 s +[acc] eval 4.075764e-3 s +[acc] eval 3.756355e-3 s +[acc] eval 3.262181e-3 s +[acc] eval 2.793387e-3 s +[acc] eval 7.737775e-3 s +[acc] eval 1.583127e-3 s +[acc] eval 1.589492e-3 s +[acc] eval 1.65233e-3 s +[acc] eval 1.67735e-3 s +[acc] eval 1.69215e-3 s +[acc] eval 1.671105e-3 s +[acc] eval 1.555534e-3 s +[acc] eval 1.536685e-3 s +[acc] eval 1.663033e-3 s +[acc] eval 1.741846e-3 s +[acc] eval 1.623915e-3 s +[acc] eval 1.571705e-3 s +[acc] eval 1.463596e-3 s +[acc] eval 1.656284e-3 s +[acc] eval 2.188148e-3 s +[acc] eval 1.936402e-3 s +[acc] eval 1.919699e-3 s +[acc] eval 2.081654e-3 s +[acc] eval 2.00698e-3 s +[acc] eval 1.979564e-3 s +[acc] eval 1.735875e-3 s +[acc] eval 1.754171e-3 s +[acc] eval 1.554868e-3 s +[acc] eval 1.252254e-3 s +[acc] eval 0.136680386 s +[acc] compile 8.328283e-3 s +[acc] eval 1.2184433e-2 s +[acc] eval 2.336298e-2 s +[acc] compile 0.493542827 s +[acc] eval 4.6066565e-2 s +[acc] eval 0.545245947 s +[acc] compile 0.463591314 s +[acc] eval 3.7716373e-2 s +[acc] eval 0.508567323 s +[acc] compile 4.491595e-3 s +[acc] eval 1.7426862e-2 s +[acc] eval 1.183789e-3 s +[acc] eval 1.167849e-3 s +[acc] eval 2.6152233e-2 s +[acc] compile 1.0661175e-2 s +[acc] eval 6.548585e-3 s +[acc] eval 3.150771e-3 s +[acc] eval 3.435627e-3 s +[acc] eval 3.315569e-3 s +[acc] eval 2.960585e-3 s +[acc] eval 3.034265e-3 s +[acc] eval 2.142273e-3 s +[acc] eval 2.666348e-3 s +[acc] eval 3.978948e-3 s +[acc] eval 1.3317923e-2 s +[acc] eval 1.774117e-3 s +[acc] eval 1.754064e-3 s +[acc] eval 1.636637e-3 s +[acc] eval 1.565742e-3 s +[acc] eval 1.588845e-3 s +[acc] eval 1.708913e-3 s +[acc] eval 1.625364e-3 s +[acc] eval 1.38e-3 s +[acc] eval 1.604354e-3 s +[acc] eval 1.73748e-3 s +[acc] eval 1.508495e-3 s +[acc] eval 1.61626e-3 s +[acc] eval 1.456026e-3 s +[acc] eval 1.46092e-3 s +[acc] eval 1.383667e-3 s +[acc] eval 1.617503e-3 s +[acc] eval 1.451849e-3 s +[acc] eval 1.46526e-3 s +[acc] eval 1.59355e-3 s +[acc] eval 1.615916e-3 s +[acc] eval 1.503952e-3 s +[acc] eval 9.4396389e-2 s diff --git a/pp_n1_1.err b/pp_n1_1.err new file mode 100644 index 0000000..e69de29 diff --git a/pp_n1_2.err b/pp_n1_2.err new file mode 100644 index 0000000..e69de29 diff --git a/pp_n2_1.err b/pp_n2_1.err new file mode 100644 index 0000000..e69de29 diff --git a/pp_n2_2.err b/pp_n2_2.err new file mode 100644 index 0000000..e69de29 diff --git a/pp_n4_1.err b/pp_n4_1.err new file mode 100644 index 0000000..e69de29 diff --git a/pp_n4_2.err b/pp_n4_2.err new file mode 100644 index 0000000..e69de29 diff --git a/pp_out_3.csv b/pp_out_3.csv new file mode 100644 index 0000000..b14fa25 --- /dev/null +++ b/pp_out_3.csv @@ -0,0 +1,6 @@ +id,view,Expression,Numpy,Math,theta,size,loss_train,loss_val,loss_test,maxloss,R2_train,R2_val,R2_test,dl_train,dl_val,dl_test +0,0,22.766786471142108,"t[0]","$$\theta_{0}$$",22.766786471142108,1,250.20995238653342,250.20995238653342,250.20995238653342,250.20995238652472,-3.3306690738754696e-15,-3.3306690738754696e-15,-3.3306690738754696e-15,253.1911148436993,253.1911148436993,253.1911148436993 +1,0,(1383.4433144862855 / x1),"(t[0] / x[:, 1])","$$\frac{\theta_{0}}{x_{1}}$$",1383.4433144862855,3,247.27063764264005,247.27063764264005,247.27063764264005,247.27063764265753,1.174739340244857e-2,1.174739340244857e-2,1.174739340244857e-2,253.5567559545484,253.5567559545484,253.5567559545484 +2,0,(-5.745165366885029 * (-4.1530583589543255 + x2)),"(t[0] * (t[1] + x[:, 2]))","$$\left(\theta_{0} \cdot \left(\theta_{1} + x_{2}\right)\right)$$",-5.745165366885029;-4.1530583589543255,5,245.18084594296485,245.18084594296485,245.18084594296485,245.18084594296587,2.009954598368091e-2,2.009954598368091e-2,2.009954598368091e-2,258.8665014142734,258.8665014142734,258.8665014142734 +3,0,((-21.44885359673644 * x0) + (Exp(x7) + 25.223226358297662)),"((t[0] * x[:, 0]) + (np.exp(x[:, 7]) + t[1]))","$$\left(\left(\theta_{0} \cdot x_{0}\right) + \left(\operatorname{exp}(x_{7}) + \theta_{1}\right)\right)$$",-21.44885359673644;25.223226358297662,8,167.26495496048574,167.26495496048574,167.26495496048574,167.26495496049114,0.33150159150308567,0.33150159150308567,0.33150159150308567,188.5949421956155,188.5949421956155,188.5949421956155 +4,0,((Exp(Exp((-1.0316058190409207 * x0))) + (((-0.16839382479645368 * x5) * x0) + 0.2597989053842083)) * 9.966295253001473),"((np.exp(np.exp((t[0] * x[:, 0]))) + (((t[1] * x[:, 5]) * x[:, 0]) + t[2])) * t[3])","$$\left(\left(\operatorname{exp}(\operatorname{exp}(\left(\theta_{0} \cdot x_{0}\right))) + \left(\left(\left(\theta_{1} \cdot x_{5}\right) \cdot x_{0}\right) + \theta_{2}\right)\right) \cdot \theta_{3}\right)$$",-1.0316058190409207;-0.16839382479645368;0.2597989053842083;9.966295253001473,15,152.38528167941737,152.38528167941737,152.38528167941737,152.3852816794077,0.390970342202825,0.390970342202825,0.390970342202825,193.81858581120548,193.81858581120548,193.81858581120548 diff --git a/pp_out_4.csv b/pp_out_4.csv new file mode 100644 index 0000000..c85b678 --- /dev/null +++ b/pp_out_4.csv @@ -0,0 +1,5 @@ +id,view,Expression,Numpy,Math,theta,size,loss_train,loss_val,loss_test,maxloss,R2_train,R2_val,R2_test,dl_train,dl_val,dl_test +0,0,22.76678647114209,"t[0]","$$\theta_{0}$$",22.76678647114209,1,250.20995238653353,250.20995238653353,250.20995238653353,250.20995238652458,-3.774758283725532e-15,-3.774758283725532e-15,-3.774758283725532e-15,253.1911148436994,253.1911148436994,253.1911148436994 +1,0,(20.25012293495296 + (2.573182190663003 * (4.544753026268889 / x5))),"(t[0] + (t[1] * (t[2] / x[:, 5])))","$$\left(\theta_{0} + \left(\theta_{1} \cdot \frac{\theta_{2}}{x_{5}}\right)\right)$$",20.25012293495296;2.573182190663003;4.544753026268889,7,250.00697478204222,250.00697478204222,250.00697478204222,250.00697478203026,8.112291399855476e-4,8.112291399855476e-4,8.112291399855476e-4,271.7696699646551,271.7696699646551,271.7696699646551 +2,0,(Exp(Exp((x8 * -9.255999654370921))) + (20.17513459419948 + x3)),"(np.exp(np.exp((x[:, 8] * t[0]))) + (t[1] + x[:, 3]))","$$\left(\operatorname{exp}(\operatorname{exp}(\left(x_{8} \cdot \theta_{0}\right))) + \left(\theta_{1} + x_{3}\right)\right)$$",-9.255999654370921;20.17513459419948,9,239.22734537684235,239.22734537684235,239.22734537684235,239.22734537685523,4.38935658031856e-2,4.38935658031856e-2,4.38935658031856e-2,262.1258345246083,262.1258345246083,262.1258345246083 +3,0,(((x1 + -0.3502997371676269) * 0.4746847666089903) / Exp((x0 * 1.2702218718093228))),"(((x[:, 1] + t[0]) * t[1]) / np.exp((x[:, 0] * t[2])))","$$\frac{\left(\left(x_{1} + \theta_{0}\right) \cdot \theta_{1}\right)}{\operatorname{exp}(\left(x_{0} \cdot \theta_{2}\right))}$$",-0.3502997371676269;0.4746847666089903;1.2702218718093228,10,168.31453547426653,168.31453547426653,168.31453547426653,168.31453547427247,0.3273067922803937,0.3273067922803937,0.3273067922803937,198.52371101490465,198.52371101490465,198.52371101490465 diff --git a/pp_out_5.csv b/pp_out_5.csv new file mode 100644 index 0000000..1656692 --- /dev/null +++ b/pp_out_5.csv @@ -0,0 +1,5 @@ +id,view,Expression,Numpy,Math,theta,size,loss_train,loss_val,loss_test,maxloss,R2_train,R2_val,R2_test,dl_train,dl_val,dl_test +0,0,22.76678647114209,"t[0]","$$\theta_{0}$$",22.76678647114209,1,250.20995238653353,250.20995238653353,250.20995238653353,250.20995238652458,-3.774758283725532e-15,-3.774758283725532e-15,-3.774758283725532e-15,253.1911148436994,253.1911148436994,253.1911148436994 +1,0,(1383.4433144862865 / x1),"(t[0] / x[:, 1])","$$\frac{\theta_{0}}{x_{1}}$$",1383.4433144862865,3,247.27063764264042,247.27063764264042,247.27063764264042,247.27063764265753,1.1747393402447015e-2,1.1747393402447015e-2,1.1747393402447015e-2,253.55675595454878,253.55675595454878,253.55675595454878 +2,0,(24.968290345037 + (-0.355038430771912 / x3)),"(t[0] + (t[1] / x[:, 3]))","$$\left(\theta_{0} + \frac{\theta_{1}}{x_{3}}\right)$$",24.968290345037;-0.355038430771912,5,245.62332714454894,245.62332714454894,245.62332714454894,245.62332714456642,1.833110632984758e-2,1.833110632984758e-2,1.833110632984758e-2,259.30760985334456,259.30760985334456,259.30760985334456 +3,0,Exp((-1.2241452468910468 * (x0 + -2.76996802043229))),"np.exp((t[0] * (x[:, 0] + t[1])))","$$\operatorname{exp}(\left(\theta_{0} \cdot \left(x_{0} + \theta_{1}\right)\right))$$",-1.2241452468910468;-2.76996802043229,6,153.0117642718506,153.0117642718506,153.0117642718506,153.01176427185254,0.38846651457143877,0.38846651457143877,0.38846651457143877,169.70852261108521,169.70852261108521,169.70852261108521 diff --git a/prof_n4.err b/prof_n4.err new file mode 100644 index 0000000..7dd7f09 --- /dev/null +++ b/prof_n4.err @@ -0,0 +1,20515 @@ +[acc-interp] thetaMarsh 2.03e-7 s (n=0) +[acc-interp] compile 1.336441784 s +[acc-interp] chunk base=0 cs=25974 kernel=3.315169449 s grad=1.284e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.566503e-3 s grad=1.525e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.738212e-3 s grad=1.111e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.182516e-3 s grad=1.501e-6 s +[acc-interp] eval 3.325141095 s +[acc-interp] thetaMarsh 9.3e-8 s (n=0) +[acc-interp] chunk base=0 cs=25974 kernel=9.8377161e-2 s grad=1.434e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.165901e-3 s grad=1.008e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.514659e-3 s grad=1.16e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.409632e-3 s grad=1.301e-6 s +[acc-interp] eval 0.105613114 s +[acc-interp] thetaMarsh 1.06e-7 s (n=0) +[acc-interp] chunk base=0 cs=25974 kernel=0.126094449 s grad=1.84e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.36911e-3 s grad=1.713e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.635651e-3 s grad=1.49e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.381526e-3 s grad=1.15e-6 s +[acc-interp] eval 0.133840532 s +[acc-interp] thetaMarsh 7.0e-8 s (n=0) +[acc-interp] chunk base=0 cs=25974 kernel=0.2182426 s grad=1.015e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.661507e-3 s grad=1.197e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.557307e-3 s grad=8.44e-7 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.510878e-3 s grad=1.467e-6 s +[acc-interp] eval 0.22598096 s +[acc-interp] thetaMarsh 1.25e-7 s (n=0) +[acc-interp] chunk base=0 cs=25974 kernel=0.214250378 s grad=1.695e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.899349e-3 s grad=6.31e-7 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.338897e-3 s grad=3.449e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.031595e-3 s grad=2.26e-6 s +[acc-interp] eval 0.220592065 s +[acc-interp] thetaMarsh 6.3e-8 s (n=0) +[acc-interp] chunk base=0 cs=25974 kernel=0.206191325 s grad=1.416e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.0373103e-2 s grad=1.218e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.580809e-3 s grad=2.37e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.87734e-3 s grad=8.61e-7 s +[acc-interp] eval 0.230825027 s +[acc-interp] thetaMarsh 5.9e-8 s (n=0) +[acc-interp] chunk base=0 cs=25974 kernel=0.209030164 s grad=9.77e-7 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.770227e-3 s grad=1.376e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.509066e-3 s grad=5.47e-7 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.099939e-3 s grad=9.4e-7 s +[acc-interp] eval 0.21512094 s +[acc-interp] thetaMarsh 2.36e-7 s (n=0) +[acc-interp] chunk base=0 cs=25974 kernel=0.1813838 s grad=1.471e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.932215e-3 s grad=6.87e-7 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.40537e-3 s grad=1.016e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.883358e-3 s grad=1.986e-6 s +[acc-interp] eval 0.188431302 s +[acc-interp] thetaMarsh 7.3e-8 s (n=0) +[acc-interp] chunk base=0 cs=25974 kernel=9.0607354e-2 s grad=1.562e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.036926e-2 s grad=1.607e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.61791e-3 s grad=1.036e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.765182e-3 s grad=2.657e-6 s +[acc-interp] eval 0.105262962 s +[acc-interp] thetaMarsh 7.1e-8 s (n=[acc-interp] thetaMarsh 6.7e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.1470326e-2 s grad=2.788e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=6.610023e-3 s grad=3.462e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.432982e-3 s grad=2.934e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.700079e-3 s grad=3.31e-6 s +[acc-interp] eval 4.5993857e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=3.259361e-3 s grad=2.594e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.979895e-3 s grad=3.448e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.646054e-3 s grad=3.233e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.675348e-3 s grad=2.604e-6 s +[acc-interp] eval 6.1475642e-2 s +[acc-interp] thetaMarsh 6.8e-8 s (n=[acc-interp] thetaMarsh 1.46e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.577424e-3 s grad=3.159e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.533017e-3 s grad=3.41e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.9963e-3 s grad=1.839e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.151765e-3 s grad=1.238e-6 s +[acc-interp] eval 1.1992721e-2 s +[acc-interp] thetaMarsh 1.58e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.446452e-3 s grad=6.797e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.158091e-3 s grad=6.407e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.11598e-3 s grad=4.885e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.270039e-3 s grad=8.22e-7 s +[acc-interp] eval 7.640263e-3 s +[acc-interp] thetaMarsh 6.2e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.059764e-3 s grad=2.798e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.430523e-3 s grad=8.699e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.896292e-3 s grad=2.963e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.143825e-3 s grad=1.578e-6 s +[acc-interp] eval 8.794744e-3 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=1.828932e-3 s grad=1.711e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.725665e-3 s grad=1.41e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.294014e-3 s grad=8.451e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.105404e-3 s grad=3.133e-6 s +[acc-interp] eval 3.7129391e-2 s +[acc-interp] thetaMarsh 1.34e-7 s (n=[acc-interp] thetaMarsh 1.16e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.8272255e-2 s grad=4.467e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.267978e-3 s grad=2.028e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.089077e-3 s grad=1.384e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.15557e-3 s grad=2.259e-6 s +[acc-interp] eval 2.6357048e-2 s +[acc-interp] thetaMarsh 1.0e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.568955e-3 s grad=1.633e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.788766e-3 s grad=2.067e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.135927e-3 s grad=2.936e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.086585e-3 s grad=2.199e-6 s +[acc-interp] eval 1.3992075e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=1.951145e-3 s grad=2.961e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.565324e-3 s grad=2.474e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.575353e-3 s grad=3.173e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.804742e-3 s grad=2.58e-6 s +[acc-interp] eval 5.4522644e-2 s +[acc-interp] thetaMarsh 8.0e-8 s (n=0) +[acc-interp] chunk base=0 cs=25974 kernel=2.7511376e-2 s grad=1.464e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.192659e-3 s grad=1.159e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.151525e-3 s grad=1.137e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.836882e-3 s grad=1.361e-6 s +[acc-interp] eval 3.4498659e-2 s +[acc-interp] thetaMarsh 5.9e-8 s (n=[acc-interp] thetaMarsh 1.49e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=1.2913372e-2 s grad=3.82e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.917946e-3 s grad=4.234e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.960378e-3 s grad=2.917e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.153587e-3 s grad=4.121e-6 s +[acc-interp] eval 2.378619e-2 s +[acc-interp] thetaMarsh 1.08e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.057817e-3 s grad=3.839e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.93591e-3 s grad=2.979e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=9.099928e-3 s grad=3.346e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.740231e-3 s grad=9.465e-6 s +[acc-interp] eval 1.8625716e-2 s +[acc-interp] thetaMarsh 6.4e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.822759e-3 s grad=2.65e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.667756e-3 s grad=3.945e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.808331e-3 s grad=4.574e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.311846e-3 s grad=1.0482e-5 s +[acc-interp] eval 1.4418778e-2 s +[acc-interp] thetaMarsh 6.7e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.748211e-3 s grad=4.033e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.171149e-3 s grad=9.638e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.839486e-3 s grad=5.6e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.199148e-3 s grad=9.425e-6 s +[acc-interp] eval 1.3730668e-2 s +[acc-interp] thetaMarsh 1.82e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.019815e-3 s grad=3.256e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.673413e-3 s grad=2.471e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.99552e-3 s grad=3.573e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.645366e-3 s grad=4.305e-6 s +[acc-interp] eval 1.3208396e-2 s +[acc-interp] thetaMarsh 2.11e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.540411e-3 s grad=4.649e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.494961e-3 s grad=5.124e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.959087e-3 s grad=4.088e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.518506e-3 s grad=4.717e-6 s +[acc-interp] eval 1.4455942e-2 s +[acc-interp] thetaMarsh 6.5e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.069228e-3 s grad=5.248e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.617631e-3 s grad=4.934e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.309415e-3 s grad=3.852e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.867683e-3 s grad=3.736e-6 s +[acc-interp] eval 1.3790026e-2 s +[acc-interp] thetaMarsh 6.3e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.124209e-3 s grad=4.059e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.598166e-3 s grad=3.93e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.059066e-3 s grad=4.859e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.130736e-3 s grad=4.132e-6 s +[acc-interp] eval 1.4964734e-2 s +[acc-interp] thetaMarsh 1.23e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.098275e-3 s grad=3.77e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.572882e-3 s grad=3.293e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.472089e-3 s grad=2.461e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=7.63412e-3 s grad=4.876e-6 s +[acc-interp] eval 1.9474073e-2 s +[acc-interp] thetaMarsh 1.26e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.87836e-3 s grad=3.532e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.151689e-3 s grad=4.912e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.870356e-3 s grad=2.784e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.805968e-3 s grad=2.821e-6 s +[acc-interp] eval 1.3232195e-2 s +[acc-interp] thetaMarsh 1.24e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.123328e-3 s grad=2.929e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.484916e-3 s grad=3.837e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.036373e-3 s grad=4.281e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.327791e-3 s grad=3.179e-6 s +[acc-interp] eval 1.5453029e-2 s +[acc-interp] thetaMarsh 7.0e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.457257e-3 s grad=4.147e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.702801e-3 s 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chunk base=77922 cs=25974 kernel=2.755305e-3 s grad=2.795e-6 s +[acc-interp] eval 1.6168818e-2 s +[acc-interp] thetaMarsh 8.9e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.704757e-3 s grad=4.557e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.335932e-3 s grad=2.654e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.935296e-3 s grad=4.755e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.386321e-3 s grad=4.746e-6 s +[acc-interp] eval 1.4478067e-2 s +[acc-interp] thetaMarsh 4.23e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.553422e-3 s grad=5.38e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.781147e-3 s grad=4.981e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.786935e-3 s grad=4.503e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.506052e-3 s grad=5.364e-6 s +[acc-interp] eval 1.6397857e-2 s +[acc-interp] thetaMarsh 1.29e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.301277e-3 s grad=4.526e-6 s +[acc-interp] chunk base=25974 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+[acc-interp] thetaMarsh 1.2e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.950273e-3 s grad=4.057e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.626944e-3 s grad=3.639e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.062514e-3 s grad=2.466e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.186316e-3 s grad=2.399e-6 s +[acc-interp] eval 1.5547948e-2 s +[acc-interp] thetaMarsh 7.8e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=6.389777e-3 s grad=2.616e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.233404e-3 s grad=3.907e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.417724e-3 s grad=3.706e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.200401e-3 s grad=2.948e-6 s +[acc-interp] eval 1.7639198e-2 s +[acc-interp] thetaMarsh 1.07e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.767145e-3 s grad=2.212e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.950669e-3 s grad=2.232e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.016067e-3 s grad=3.282e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.383998e-3 s grad=4.637e-6 s +[acc-interp] eval 1.3676772e-2 s +[acc-interp] thetaMarsh 1.31e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.816933e-3 s grad=2.674e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.848193e-3 s grad=3.522e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.647336e-3 s grad=2.965e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.120164e-3 s grad=5.386e-6 s +[acc-interp] eval 1.2946083e-2 s +[acc-interp] thetaMarsh 1.52e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.707881e-3 s grad=4.174e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.394836e-3 s grad=3.536e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.792768e-3 s grad=2.979e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.0169e-3 s grad=2.086e-6 s +[acc-interp] eval 1.4414066e-2 s +[acc-interp] thetaMarsh 1.02e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.685063e-3 s grad=4.487e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.523571e-3 s grad=3.473e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.107777e-3 s grad=2.939e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.901488e-3 s grad=3.184e-6 s +[acc-interp] eval 1.4788598e-2 s +[acc-interp] thetaMarsh 1.01e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.552654e-3 s grad=3.32e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.774479e-3 s grad=9.605e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.151889e-3 s grad=2.668e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.940892e-3 s grad=4.046e-6 s +[acc-interp] eval 1.4510079e-2 s +[acc-interp] thetaMarsh 1.03e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.998289e-3 s grad=3.651e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.826461e-3 s grad=3.703e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.387925e-3 s grad=6.117e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.345776e-3 s grad=1.207e-6 s +[acc-interp] eval 1.4472473e-2 s +[acc-interp] thetaMarsh 6.2e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=6.296988e-3 s grad=1.799e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.263212e-3 s grad=2.207e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.402126e-3 s grad=1.241e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.064502e-3 s grad=3.146e-6 s +[acc-interp] eval 1.696051e-2 s +4) +[acc-interp] chunk base=0 cs=25974 kernel=3.160752e-3 s grad=4.96e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.812541e-3 s grad=3.259e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.993621e-3 s grad=3.019e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.188639e-3 s grad=2.653e-6 s +[acc-interp] eval 0.49338099 s +[acc-interp] thetaMarsh 1.54e-7 s (n=[acc-interp] thetaMarsh 1.15e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.5730114e-2 s grad=6.125e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.242952e-3 s grad=4.969e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.934389e-3 s grad=4.107e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.343699e-3 s grad=3.717e-6 s +[acc-interp] eval 4.9352e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=3.891446e-3 s grad=3.704e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.609218e-3 s grad=3.75e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.360358e-3 s grad=4.11e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.555296e-3 s grad=4.173e-6 s +[acc-interp] eval 6.483935e-2 s +[acc-interp] thetaMarsh 7.2e-8 s (n=[acc-interp] thetaMarsh 5.6e-8 s (n=6) +[acc-interp] chunk base=0 cs=25974 kernel=2.55861e-2 s grad=3.635e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.433955e-3 s grad=4.471e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.216115e-3 s grad=4.349e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.380601e-3 s grad=4.078e-6 s +[acc-interp] eval 3.6460912e-2 s +6) +[acc-interp] chunk base=0 cs=25974 kernel=4.40735e-3 s grad=3.946e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.893065e-3 s grad=4.803e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.428287e-3 s grad=5.606e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.635496e-3 s grad=4.773e-6 s +[acc-interp] eval 5.3118125e-2 s +[acc-interp] thetaMarsh 1.18e-7 s (n=[acc-interp] thetaMarsh 1.97e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.8963708e-2 s grad=3.683e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.848257e-3 s grad=4.31e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.0427151e-2 s grad=3.986e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.840718e-3 s grad=4.588e-6 s +[acc-interp] eval 5.0092024e-2 s +[acc-interp] thetaMarsh 6.2e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=7.498391e-3 s grad=4.384e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.16367e-3 s grad=4.23e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=6.066023e-3 s grad=5.328e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.495562e-3 s grad=5.152e-6 s +[acc-interp] eval 2.5517751e-2 s +[acc-interp] thetaMarsh 1.24e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.313089e-3 s grad=4.516e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.70109e-3 s grad=4.889e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.248881e-3 s grad=4.002e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.786079e-3 s grad=4.654e-6 s +[acc-interp] eval 2.0898082e-2 s +[acc-interp] thetaMarsh 9.3e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.787031e-3 s grad=3.751e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.11468e-3 s grad=4.8e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.094013e-3 s grad=4.721e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=7.204134e-3 s grad=5.155e-6 s +[acc-interp] eval 2.3936839e-2 s +[acc-interp] thetaMarsh 1.24e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=6.628075e-3 s grad=4.542e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=6.62781e-3 s grad=1.4678e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.600244e-2 s grad=3.81e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.437787e-3 s grad=3.676e-6 s +[acc-interp] eval 3.5388168e-2 s +[acc-interp] thetaMarsh 1.39e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=5.293859e-3 s grad=3.49e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=9.129439e-3 s grad=4.988e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=6.244536e-3 s grad=4.872e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=7.908548e-3 s grad=4.148e-6 s +[acc-interp] eval 2.9918874e-2 s +[acc-interp] thetaMarsh 6.6e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=5.394179e-3 s grad=3.662e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.263612e-3 s grad=4.214e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.228257e-3 s grad=3.931e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.703095e-3 s grad=4.151e-6 s +[acc-interp] eval 2.0590751e-2 s +[acc-interp] thetaMarsh 6.3e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.889028e-3 s grad=3.45e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.0278e-2 s grad=3.303e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.236997e-3 s grad=3.803e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=6.760032e-3 s grad=7.702e-6 s +[acc-interp] eval 2.7318229e-2 s +[acc-interp] thetaMarsh 1.4e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=5.79515e-3 s grad=3.323e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=8.750761e-3 s grad=8.145e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=6.009231e-3 s grad=3.056e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.504497e-3 s grad=4.608e-6 s +[acc-interp] eval 2.7211536e-2 s +[acc-interp] thetaMarsh 7.2e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=8.316195e-3 s grad=3.92e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=6.665955e-3 s grad=4.167e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=7.953112e-3 s grad=3.834e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.597837e-3 s grad=3.487e-6 s +[acc-interp] eval 2.9716844e-2 s +[acc-interp] thetaMarsh 1.16e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.0106827e-2 s grad=3.847e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.144917e-3 s grad=3.833e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=6.834809e-3 s grad=4.579e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.949752e-3 s grad=2.646e-6 s +[acc-interp] eval 2.6642975e-2 s +[acc-interp] thetaMarsh 1.37e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.822352e-3 s grad=3.157e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.333019e-3 s grad=4.878e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.74232e-3 s grad=3.608e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.291767e-3 s grad=4.633e-6 s +[acc-interp] eval 2.18673e-2 s +[acc-interp] thetaMarsh 1.73e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=7.069225e-3 s grad=3.24e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=8.497258e-3 s grad=4.241e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=7.459797e-3 s grad=3.485e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.995712e-3 s grad=2.601e-6 s +[acc-interp] eval 2.8747492e-2 s +[acc-interp] thetaMarsh 9.6e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.501254e-3 s grad=4.362e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=6.881712e-3 s grad=4.376e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.706343e-3 s grad=2.184e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.162234e-3 s grad=3.713e-6 s +[acc-interp] eval 2.1815299e-2 s +[acc-interp] thetaMarsh 2.16e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=5.636687e-3 s grad=4.036e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.37915e-3 s grad=3.589e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=7.999537e-3 s grad=3.338e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.773791e-3 s grad=2.701e-6 s +[acc-interp] eval 2.3254173e-2 s +[acc-interp] thetaMarsh 1.25e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.099935e-3 s grad=2.931e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.104205e-3 s grad=3.086e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=7.345065e-3 s grad=3.295e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.279876e-3 s grad=3.069e-6 s +[acc-interp] eval 2.0466929e-2 s +[acc-interp] thetaMarsh 1.08e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=5.111866e-3 s grad=3.259e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=7.279997e-3 s grad=3.332e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.0177856e-2 s grad=4.011e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.795004e-3 s grad=3.591e-6 s +[acc-interp] eval 2.8936481e-2 s +[acc-interp] thetaMarsh 1.2e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.748796e-3 s grad=3.332e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=6.395344e-3 s grad=4.231e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.830806e-3 s grad=3.088e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.038939e-3 s grad=2.031e-6 s +[acc-interp] eval 2.0917828e-2 s +[acc-interp] thetaMarsh 1.57e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=5.290055e-3 s grad=4.939e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=7.269343e-3 s grad=8.325e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=6.101081e-3 s grad=5.034e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=6.044682e-3 s grad=5.06e-6 s +[acc-interp] eval 2.5962464e-2 s +[acc-interp] thetaMarsh 9.3e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=5.599589e-3 s grad=2.962e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.0952732e-2 s grad=4.76e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=6.350691e-3 s grad=6.891e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.50128e-3 s grad=4.337e-6 s +[acc-interp] eval 2.8288171e-2 s +[acc-interp] thetaMarsh 6.2e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=6.167443e-3 s grad=3.524e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.58197e-3 s grad=3.978e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.068794e-3 s grad=4.755e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.565051e-3 s grad=3.874e-6 s +[acc-interp] eval 2.225891e-2 s +[acc-interp] thetaMarsh 6.1e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=5.373192e-3 s grad=2.206e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.882629e-3 s grad=3.688e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.669832e-3 s grad=2.937e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.140009e-3 s grad=3.777e-6 s +[acc-interp] eval 2.1861581e-2 s +[acc-interp] thetaMarsh 6.3e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.2154906e-2 s grad=4.471e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.041724e-3 s grad=2.642e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=6.015675e-3 s grad=3.97e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.709857e-3 s grad=2.047e-6 s +[acc-interp] eval 2.9737291e-2 s +[acc-interp] thetaMarsh 1.34e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.75225e-3 s grad=3.096e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.863444e-3 s grad=4.094e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.477576e-3 s grad=3.572e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.541081e-3 s grad=3.811e-6 s +[acc-interp] eval 2.1467504e-2 s +[acc-interp] thetaMarsh 6.3e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=5.632217e-3 s grad=3.465e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.1104873e-2 s grad=3.698e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.973864e-3 s grad=4.955e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.175219e-3 s grad=4.236e-6 s +[acc-interp] eval 2.8819814e-2 s +[acc-interp] thetaMarsh 6.2e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=5.141881e-3 s grad=4.541e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=8.579788e-3 s grad=4.748e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.43639e-3 s grad=4.421e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.703832e-3 s grad=4.564e-6 s +[acc-interp] eval 2.5999871e-2 s +[acc-interp] thetaMarsh 1.02e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=5.119401e-3 s grad=4.337e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.920427e-3 s grad=3.697e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.3259133e-2 s grad=4.261e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=6.811136e-3 s grad=3.682e-6 s +[acc-interp] eval 3.2092067e-2 s +[acc-interp] thetaMarsh 6.2e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=6.403496e-3 s grad=3.112e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.230416e-3 s grad=2.792e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.646056e-3 s grad=2.075e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.968115e-3 s grad=1.551e-6 s +[acc-interp] eval 2.2975668e-2 s +[acc-interp] thetaMarsh 1.08e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=5.399082e-3 s grad=3.649e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.969878e-3 s grad=4.741e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.4345023e-2 s grad=4.753e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.790726e-3 s grad=3.857e-6 s +[acc-interp] eval 3.1393484e-2 s +[acc-interp] thetaMarsh 2.11e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=5.015171e-3 s grad=5.593e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=6.153933e-3 s grad=4.097e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.782168e-3 s grad=4.315e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.624728e-3 s grad=3.979e-6 s +[acc-interp] eval 2.3762856e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=7.291637e-3 s grad=5.867e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.445262e-3 s grad=5.482e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=7.242585e-3 s grad=4.192e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.932466e-3 s grad=4.161e-6 s +[acc-interp] eval 0.827476957 s +[acc-interp] thetaMarsh 1.18e-7 s (n=[acc-interp] thetaMarsh 1.17e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.3239485e-2 s grad=3.242e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.893179e-3 s grad=3.958e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.149555e-3 s grad=8.912e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.241532e-3 s grad=1.579e-6 s +[acc-interp] eval 4.6438585e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=3.715273e-3 s grad=1.303e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.765384e-3 s grad=2.69e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.963306e-3 s grad=2.401e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.770896e-3 s grad=1.553e-6 s +[acc-interp] eval 6.254636e-2 s +[acc-interp] thetaMarsh 9.4e-8 s (n=[acc-interp] thetaMarsh 5.9e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.2555548e-2 s grad=3.698e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.210472e-3 s grad=8.365e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.413009e-3 s grad=7.252e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.136948e-3 s grad=3.573e-6 s +[acc-interp] eval 2.4185e-2 s +[acc-interp] thetaMarsh 1.01e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.166122e-3 s grad=4.409e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=9.607111e-3 s grad=2.899e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.135767e-3 s grad=3.787e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.045876e-3 s grad=4.625e-6 s +[acc-interp] eval 2.0851042e-2 s +[acc-interp] thetaMarsh 6.2e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.027573e-3 s grad=4.006e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.442309e-3 s grad=3.229e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.715728e-3 s grad=3.105e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.287071e-3 s grad=6.464e-6 s +[acc-interp] eval 1.2322457e-2 s +[acc-interp] thetaMarsh 7.4e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.716859e-3 s grad=4.221e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.892213e-3 s grad=3.787e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.764754e-3 s grad=3.151e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.457669e-3 s grad=2.682e-6 s +[acc-interp] eval 1.1656989e-2 s +[acc-interp] thetaMarsh 2.31e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.584044e-3 s grad=3.5e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.378156e-3 s grad=4.324e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.333781e-3 s grad=4.354e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.611053e-3 s grad=4.465e-6 s +[acc-interp] eval 1.1751215e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=2.695051e-3 s grad=2.244e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.071423e-3 s grad=6.551e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.401621e-3 s grad=3.498e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.328259e-3 s grad=4.705e-6 s +[acc-interp] eval 9.259962e-2 s +[acc-interp] thetaMarsh 6.3e-8 s (n=[acc-interp] thetaMarsh 6.8e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=5.069081e-3 s grad=3.503e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.105256e-3 s grad=8.125e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.042712e-3 s grad=2.979e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.295934e-3 s grad=2.92e-6 s +[acc-interp] eval 1.2345589e-2 s +[acc-interp] thetaMarsh 6.5e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.786979e-3 s grad=3.469e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.491767e-3 s grad=2.311e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.292665e-3 s grad=2.925e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=7.410269e-3 s grad=2.357e-6 s +[acc-interp] eval 1.5412786e-2 s +[acc-interp] thetaMarsh 1.2e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.719013e-3 s grad=3.05e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.980771e-3 s grad=3.161e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.867015e-3 s grad=2.534e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.77925e-3 s grad=2.382e-6 s +[acc-interp] eval 8.192027e-3 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=1.492253e-3 s grad=2.325e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.729375e-3 s grad=2.113e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.691242e-3 s grad=1.997e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.706465e-3 s grad=2.201e-6 s +[acc-interp] eval 4.3562615e-2 s +[acc-interp] thetaMarsh 8.3e-8 s (n=[acc-interp] thetaMarsh 6.2e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.09464e-2 s grad=2.739e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.585243e-3 s grad=9.696e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.955607e-3 s grad=3.849e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.328332e-3 s grad=4.177e-6 s +[acc-interp] eval 2.2724939e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=3.863533e-3 s grad=3.368e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=8.582927e-3 s grad=4.23e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.836407e-3 s grad=3.223e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.879899e-3 s grad=3.461e-6 s +[acc-interp] eval 4.1888421e-2 s +[acc-interp] thetaMarsh 9.8e-8 s (n=[acc-interp] thetaMarsh 1.48e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=9.844676e-3 s grad=4.244e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.028116e-3 s grad=4.275e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.963628e-3 s grad=3.835e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.491111e-3 s grad=3.41e-6 s +[acc-interp] eval 1.9211639e-2 s +[acc-interp] thetaMarsh 6.2e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=2.905646e-3 s grad=3.72e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.1711543e-2 s grad=3.823e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.383984e-3 s grad=3.637e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.779363e-3 s grad=4.936e-6 s +[acc-interp] eval 2.0619221e-2 s +[acc-interp] thetaMarsh 6.2e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=2.98717e-3 s grad=3.633e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.386271e-3 s grad=3.99e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.673469e-3 s grad=4.954e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.477213e-3 s grad=4.423e-6 s +[acc-interp] eval 1.1405739e-2 s +[acc-interp] thetaMarsh 6.2e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=2.034952e-3 s grad=2.884e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.703887e-3 s grad=3.453e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.689932e-3 s grad=3.623e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.371692e-3 s grad=2.896e-6 s +[acc-interp] eval 1.0641e-2 s +[acc-interp] thetaMarsh 2.3e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=2.615385e-3 s grad=3.183e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.620395e-3 s grad=3.319e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.341367e-3 s grad=2.936e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.124732e-3 s grad=4.255e-6 s +[acc-interp] eval 1.1567408e-2 s +[acc-interp] thetaMarsh 3.12e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=2.970171e-3 s grad=3.57e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.601797e-3 s grad=5.195e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.837248e-3 s grad=5.327e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.893543e-3 s grad=3.102e-6 s +[acc-interp] eval 1.2189274e-2 s +[acc-interp] thetaMarsh 1.75e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=2.642221e-3 s grad=3.961e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.752095e-3 s grad=3.067e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.602465e-3 s grad=3.252e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.54004e-3 s grad=2.659e-6 s +[acc-interp] eval 1.1400598e-2 s +[acc-interp] thetaMarsh 1.75e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=2.732198e-3 s grad=2.446e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.844181e-3 s grad=4.323e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.095077e-3 s grad=5.135e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.999349e-3 s grad=4.663e-6 s +[acc-interp] eval 1.2672833e-2 s +[acc-interp] thetaMarsh 6.4e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=2.3582e-3 s grad=3.687e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.261042e-3 s grad=3.633e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.724277e-3 s grad=2.053e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.377742e-3 s grad=4.311e-6 s +[acc-interp] eval 1.1547883e-2 s +[acc-interp] thetaMarsh 2.63e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=2.719098e-3 s grad=4.233e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.834712e-3 s grad=4.456e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.688959e-3 s grad=4.05e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.524368e-3 s grad=3.843e-6 s +[acc-interp] eval 1.1627072e-2 s +[acc-interp] thetaMarsh 6.4e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=2.596464e-3 s grad=3.7e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.541071e-3 s grad=4.721e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.472853e-3 s grad=5.01e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.767021e-3 s grad=5.317e-6 s +[acc-interp] eval 1.2488527e-2 s +5) +[acc-interp] chunk base=0 cs=25974 kernel=2.697557e-3 s grad=4.12e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.283878e-3 s grad=2.496e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.653333e-3 s grad=4.185e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.410743e-3 s grad=3.765e-6 s +[acc-interp] eval 0.158734058 s +[acc-interp] thetaMarsh 1.36e-7 s (n=[acc-interp] thetaMarsh 2.09e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.817096e-2 s grad=4.02e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.132458e-3 s grad=2.35e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.461621e-3 s grad=2.592e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.434145e-3 s grad=2.867e-6 s +[acc-interp] eval 2.7709208e-2 s +[acc-interp] thetaMarsh 1.32e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.846684e-3 s grad=2.854e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.465162e-3 s grad=3.13e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.777761e-3 s grad=1.916e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.56669e-3 s grad=2.406e-6 s +[acc-interp] eval 1.2235948e-2 s +[acc-interp] thetaMarsh 1.1e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=7.711626e-3 s grad=2.284e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.106267e-3 s grad=2.138e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.042105e-3 s grad=1.322e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.012652e-3 s grad=1.227e-6 s +[acc-interp] eval 1.5369339e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=2.295682e-3 s grad=2.905e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.542092e-3 s grad=3.809e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.41345e-3 s grad=6.329e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.308274e-3 s grad=5.665e-6 s +[acc-interp] eval 6.635418e-2 s +[acc-interp] thetaMarsh 8.9e-8 s (n=[acc-interp] thetaMarsh 6.2e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.0915447e-2 s grad=4.561e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.652871e-3 s grad=6.304e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.657975e-3 s grad=3.959e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=8.597331e-3 s grad=2.912e-6 s +[acc-interp] eval 3.8266097e-2 s +[acc-interp] thetaMarsh 1.01e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.198873e-3 s grad=3.723e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.239609e-3 s grad=4.119e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.870071e-3 s grad=2.521e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.810813e-3 s grad=2.379e-6 s +[acc-interp] eval 1.3908585e-2 s +[acc-interp] thetaMarsh 1.07e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.837117e-3 s grad=3.246e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.989007e-3 s grad=3.086e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.080783e-3 s grad=2.337e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.878359e-3 s grad=2.341e-6 s +[acc-interp] eval 1.4348422e-2 s +[acc-interp] thetaMarsh 6.2e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.927581e-3 s grad=2.448e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.994619e-3 s grad=2.738e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.463774e-3 s grad=1.769e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.407409e-3 s grad=1.626e-6 s +[acc-interp] eval 1.2295407e-2 s +[acc-interp] thetaMarsh 9.7e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.537204e-3 s grad=2.54e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.724371e-3 s grad=2.927e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.368712e-3 s grad=3.085e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.344105e-3 s grad=3.972e-6 s +[acc-interp] eval 1.2559249e-2 s +[acc-interp] thetaMarsh 1.01e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.256666e-3 s grad=3.417e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.560342e-3 s grad=2.549e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.525856e-3 s grad=3.678e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.842405e-3 s grad=3.861e-6 s +[acc-interp] eval 1.5729049e-2 s +[acc-interp] thetaMarsh 6.2e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.996719e-3 s grad=2.523e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.427921e-3 s grad=2.441e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.686769e-3 s grad=2.756e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.669942e-3 s grad=2.827e-6 s +[acc-interp] eval 9.609212e-3 s +[acc-interp] thetaMarsh 5.9e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.747403e-3 s grad=3.043e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.148721e-3 s grad=3.662e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.165847e-3 s grad=3.929e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.242723e-3 s grad=3.419e-6 s +[acc-interp] eval 1.2127977e-2 s +[acc-interp] thetaMarsh 6.1e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.428065e-3 s grad=2.997e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.622102e-3 s grad=2.787e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.565846e-3 s grad=3.993e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.265818e-3 s grad=3.401e-6 s +[acc-interp] eval 1.3509686e-2 s +[acc-interp] thetaMarsh 6.6e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.303702e-3 s grad=8.038e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.536276e-3 s grad=9.298e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.027073e-2 s grad=3.648e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.721572e-3 s grad=7.937e-6 s +[acc-interp] eval 2.0819181e-2 s +[acc-interp] thetaMarsh 1.6e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.642805e-3 s grad=3.62e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.896942e-3 s grad=2.398e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.732211e-3 s grad=2.081e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.730389e-3 s grad=1.997e-6 s +[acc-interp] eval 1.2513097e-2 s +[acc-interp] thetaMarsh 1.3e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.416354e-3 s grad=2.136e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.024595e-3 s grad=4.169e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.240343e-3 s grad=3.784e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.64898e-3 s grad=2.672e-6 s +[acc-interp] eval 1.402099e-2 s +[acc-interp] thetaMarsh 9.8e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.942769e-3 s grad=2.503e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.703869e-3 s grad=2.068e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.514482e-3 s grad=4.238e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.273652e-3 s grad=2.929e-6 s +[acc-interp] eval 1.4891987e-2 s +[acc-interp] thetaMarsh 1.1e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.217322e-3 s grad=3.111e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.26575e-3 s grad=3.457e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.271369e-3 s grad=3.695e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.039847e-3 s grad=2.209e-6 s +[acc-interp] eval 1.4408017e-2 s +[acc-interp] thetaMarsh 1.12e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.01546e-3 s grad=2.013e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.806862e-3 s grad=2.691e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.958023e-3 s grad=3.483e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.146962e-3 s grad=4.395e-6 s +[acc-interp] eval 1.3591785e-2 s +[acc-interp] thetaMarsh 3.55e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.120614e-3 s grad=2.873e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.989726e-3 s grad=3.416e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.015642e-3 s grad=2.601e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.631822e-3 s grad=3.845e-6 s +[acc-interp] eval 1.4379352e-2 s +[acc-interp] thetaMarsh 1.2e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=6.553097e-3 s grad=3.476e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.874227e-3 s grad=2.554e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.822409e-3 s grad=2.561e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.936322e-3 s grad=1.785e-6 s +[acc-interp] eval 1.675348e-2 s +[acc-interp] thetaMarsh 9.9e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.664666e-3 s grad=2.267e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.549605e-3 s grad=2.464e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.662765e-3 s grad=2.669e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.909274e-3 s grad=5.464e-6 s +[acc-interp] eval 1.2708083e-2 s +[acc-interp] thetaMarsh 2.29e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.084843e-3 s grad=4.344e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.892306e-3 s grad=3.977e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.810339e-3 s grad=3.348e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.013448e-3 s grad=4.105e-6 s +[acc-interp] eval 1.2687838e-2 s +[acc-interp] thetaMarsh 2.74e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.591589e-3 s grad=3.584e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.94547e-3 s grad=2.235e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.858861e-3 s grad=1.658e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.566604e-3 s grad=1.736e-6 s +[acc-interp] eval 1.3449891e-2 s +[acc-interp] thetaMarsh 1.64e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.093636e-3 s grad=4.758e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.492324e-3 s grad=4.034e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.248934e-3 s grad=2.477e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.949935e-3 s grad=4.429e-6 s +[acc-interp] eval 1.4342698e-2 s +[acc-interp] thetaMarsh 2.91e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.559692e-3 s grad=2.772e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.13415e-3 s grad=3.685e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.840361e-3 s grad=9.342e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.900203e-3 s grad=6.895e-6 s +[acc-interp] eval 1.2272808e-2 s +[acc-interp] thetaMarsh 6.2e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.574827e-3 s grad=8.375e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.971464e-3 s grad=1.842e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.179795e-3 s grad=6.967e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.613959e-3 s grad=4.281e-6 s +[acc-interp] eval 1.1161888e-2 s +[acc-interp] thetaMarsh 2.29e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=7.377937e-3 s grad=2.468e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.72954e-3 s grad=2.645e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.657626e-3 s grad=2.541e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.544988e-3 s grad=2.379e-6 s +[acc-interp] eval 1.6075294e-2 s +[acc-interp] thetaMarsh 5.9e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.625629e-3 s grad=2.75e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.755414e-3 s grad=2.601e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.614335e-3 s grad=2.838e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.307767e-3 s grad=3.033e-6 s +[acc-interp] eval 1.1049132e-2 s +[acc-interp] thetaMarsh 9.8e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=4.430523e-3 s grad=5.211e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.559957e-3 s grad=4.195e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.202731e-3 s grad=3.248e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.060369e-3 s grad=2.909e-6 s +[acc-interp] eval 1.5177886e-2 s +[acc-interp] thetaMarsh 5.9e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.195841e-3 s grad=3.825e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.093802e-3 s grad=3.059e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.291889e-3 s grad=3.414e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.267377e-3 s grad=2.153e-6 s +[acc-interp] eval 1.4076821e-2 s +[acc-interp] thetaMarsh 6.3e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=4.05127e-3 s grad=4.302e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.933246e-3 s grad=3.6e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.34038e-3 s grad=3.978e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.693796e-3 s grad=3.245e-6 s +[acc-interp] eval 1.5971462e-2 s +[acc-interp] thetaMarsh 6.9e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.951199e-3 s grad=2.715e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.018001e-3 s grad=3.105e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=7.322671e-3 s grad=6.586e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.655236e-3 s grad=1.0137e-5 s +[acc-interp] eval 1.9009937e-2 s +[acc-interp] thetaMarsh 2.01e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.217887e-3 s grad=8.383e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.118615e-3 s grad=7.333e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.826501e-3 s grad=4.865e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.327702e-3 s grad=2.814e-6 s +[acc-interp] eval 1.4407731e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=3.502019e-3 s grad=1.499e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.280375e-3 s grad=9.26e-7 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.014779e-3 s grad=1.766e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.553094e-3 s grad=4.584e-6 s +[acc-interp] eval 0.464760531 s +[acc-interp] thetaMarsh 6.7e-8 s (n=[acc-interp] thetaMarsh 6.4e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=4.571909e-3 s grad=3.836e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.979938e-3 s grad=2.029e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.362375e-3 s grad=2.306e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.034655e-3 s grad=6.892e-6 s +[acc-interp] eval 1.3121101e-2 s +[acc-interp] thetaMarsh 2.54e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.762784e-3 s grad=2.887e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.516355e-3 s grad=5.204e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.578739e-3 s grad=6.296e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.291355e-3 s grad=7.25e-7 s +[acc-interp] eval 1.1083988e-2 s +[acc-interp] thetaMarsh 5.9e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.69402e-3 s grad=1.114e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=7.530574e-3 s grad=3.579e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.832289e-3 s grad=1.948e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.86992e-3 s grad=1.26e-6 s +[acc-interp] eval 1.3617063e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=1.524711e-3 s grad=1.847e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.787305e-3 s grad=1.187e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.863859e-3 s grad=5.336e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.207772e-3 s grad=4.059e-6 s +[acc-interp] eval 4.6486573e-2 s +[acc-interp] thetaMarsh 1.21e-7 s (n=[acc-interp] thetaMarsh 7.1e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.3465157e-2 s grad=3.863e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.570814e-3 s grad=3.192e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.567647e-3 s grad=9.813e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.515014e-3 s grad=7.595e-6 s +[acc-interp] eval 2.3347926e-2 s +[acc-interp] thetaMarsh 6.2e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.442546e-3 s grad=8.255e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.040074e-3 s grad=6.839e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.32828e-3 s grad=6.026e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.23587e-3 s grad=7.396e-6 s +[acc-interp] eval 9.706642e-3 s +[acc-interp] thetaMarsh 5.8e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.77472e-3 s grad=8.383e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=6.136013e-3 s grad=7.955e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.166998e-3 s grad=1.243e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.117526e-3 s grad=3.969e-6 s +[acc-interp] eval 1.7111643e-2 s +[acc-interp] thetaMarsh 1.21e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.428452e-3 s grad=2.984e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.460558e-3 s grad=2.912e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.269148e-3 s grad=3.206e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.394464e-3 s grad=3.293e-6 s +[acc-interp] eval 1.107641e-2 s +[acc-interp] thetaMarsh 1.19e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.520981e-3 s grad=3.609e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.738798e-3 s grad=3.013e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.400825e-3 s grad=3.446e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.325659e-3 s grad=2.869e-6 s +[acc-interp] eval 1.1888914e-2 s +[acc-interp] thetaMarsh 6.0e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.247344e-3 s grad=2.904e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.71313e-3 s grad=4.588e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.922792e-3 s grad=4.306e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.4658e-3 s grad=4.427e-6 s +[acc-interp] eval 1.1178312e-2 s +[acc-interp] thetaMarsh 5.6e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.35024e-3 s grad=3.425e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.357707e-3 s grad=3.191e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.407441e-3 s grad=2.602e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.811778e-3 s grad=3.852e-6 s +[acc-interp] eval 1.0678844e-2 s +[acc-interp] thetaMarsh 2.35e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.491916e-3 s grad=3.308e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.258253e-3 s grad=3.289e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.062857e-3 s grad=3.465e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.324489e-3 s grad=2.98e-6 s +[acc-interp] eval 9.913422e-3 s +[acc-interp] thetaMarsh 5.9e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.121444e-3 s grad=2.756e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.319725e-3 s grad=2.556e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.59132e-3 s grad=5.179e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.721084e-3 s grad=4.136e-6 s +[acc-interp] eval 1.0733586e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=2.442475e-3 s grad=2.943e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.318618e-3 s grad=1.741e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.505855e-3 s grad=1.763e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.329489e-3 s grad=2.103e-6 s +[acc-interp] eval 0.12688758 s +[acc-interp] thetaMarsh 1.38e-7 s (n=[acc-interp] thetaMarsh 9.4e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.114005e-2 s grad=3.443e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.228072e-3 s grad=2.782e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.541221e-3 s grad=3.285e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.958573e-3 s grad=5.49e-6 s +[acc-interp] eval 2.1248689e-2 s +[acc-interp] thetaMarsh 1.12e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.825906e-3 s grad=3.311e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.268294e-3 s grad=3.794e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=7.269437e-3 s grad=3.505e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.406542e-3 s grad=3.841e-6 s +[acc-interp] eval 1.8491279e-2 s +[acc-interp] thetaMarsh 1.16e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.808952e-3 s grad=2.434e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.554141e-3 s grad=2.482e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.436102e-3 s grad=3.077e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.35227e-3 s grad=2.163e-6 s +[acc-interp] eval 1.2577555e-2 s +[acc-interp] thetaMarsh 9.6e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.084819e-3 s grad=2.232e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.640505e-3 s grad=3.593e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.071481e-3 s grad=3.372e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.660686e-3 s grad=3.876e-6 s +[acc-interp] eval 1.300993e-2 s +[acc-interp] thetaMarsh 1.17e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.50084e-3 s grad=3.301e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.6219e-3 s grad=2.662e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.659878e-3 s grad=3.088e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.58558e-3 s grad=3.027e-6 s +[acc-interp] eval 1.2311296e-2 s +[acc-interp] thetaMarsh 5.8e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.855808e-3 s grad=3.508e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.577833e-3 s grad=4.206e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.254269e-3 s grad=2.575e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.610899e-3 s grad=2.574e-6 s +[acc-interp] eval 1.2112525e-2 s +[acc-interp] thetaMarsh 1.67e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.153642e-3 s grad=1.956e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.208632e-3 s grad=2.516e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.514048e-3 s grad=4.154e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.872958e-3 s grad=1.974e-6 s +[acc-interp] eval 1.1546201e-2 s +[acc-interp] thetaMarsh 1.22e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.861384e-3 s grad=2.599e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.808851e-3 s grad=1.341e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.441661e-3 s grad=1.303e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.704309e-3 s grad=1.601e-6 s +[acc-interp] eval 1.158867e-2 s +[acc-interp] thetaMarsh 3.18e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.00699e-3 s grad=3.239e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.37023e-3 s grad=1.898e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.432248e-3 s grad=2.405e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.409428e-3 s grad=1.557e-6 s +[acc-interp] eval 1.2007569e-2 s +[acc-interp] thetaMarsh 1.75e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.377451e-3 s grad=2.327e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.86083e-3 s grad=3.472e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.503789e-3 s grad=2.691e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.902516e-3 s grad=4.214e-6 s +[acc-interp] eval 1.3524946e-2 s +[acc-interp] thetaMarsh 3.54e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.626402e-3 s grad=7.37e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.656801e-3 s grad=1.353e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.26898e-3 s grad=1.003e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.287935e-3 s grad=6.238e-6 s +[acc-interp] eval 1.0553576e-2 s +[acc-interp] thetaMarsh 2.01e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.540642e-3 s grad=6.426e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.415614e-3 s grad=3.249e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.190476e-3 s grad=2.833e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.425559e-3 s grad=6.889e-6 s +[acc-interp] eval 1.0328795e-2 s +[acc-interp] thetaMarsh 7.2e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.413862e-3 s grad=2.045e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.349263e-3 s grad=3.615e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.076617e-3 s grad=1.327e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.492028e-3 s grad=3.297e-6 s +[acc-interp] eval 1.2120755e-2 s +[acc-interp] thetaMarsh 8.6e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.618818e-3 s grad=2.53e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.816656e-3 s grad=4.146e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.387222e-3 s grad=4.542e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.515666e-3 s grad=7.615e-6 s +[acc-interp] eval 1.2146788e-2 s +[acc-interp] thetaMarsh 6.1e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.776316e-3 s grad=1.392e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.367101e-3 s grad=6.068e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.302386e-3 s grad=4.707e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.407712e-3 s grad=6.135e-6 s +[acc-interp] eval 1.1572412e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=2.747571e-3 s grad=3.97e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.31459e-3 s grad=3.918e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.61776e-3 s grad=3.214e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.09627e-3 s grad=2.69e-6 s +[acc-interp] eval 0.20933905 s +[acc-interp] thetaMarsh 7.8e-8 s (n=0) +[acc-interp] chunk base=0 cs=25974 kernel=1.029352e-2 s grad=1.517e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.29625e-3 s grad=1.273e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.278426e-3 s grad=1.794e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.317749e-3 s grad=1.313e-6 s +[acc-interp] eval 1.8074499e-2 s +[acc-interp] thetaMarsh 8.6e-8 s (n=[acc-interp] thetaMarsh 5.7e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.6119288e-2 s grad=3.041e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.137035e-3 s grad=2.652e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.380147e-3 s grad=4.197e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.499759e-3 s grad=4.086e-6 s +[acc-interp] eval 2.8012253e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=3.054647e-3 s grad=3.703e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=7.767149e-3 s grad=3.307e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.78403e-3 s grad=3.907e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.051412e-3 s grad=3.549e-6 s +[acc-interp] eval 4.5589312e-2 s +[acc-interp] thetaMarsh 9.2e-8 s (n=[acc-interp] thetaMarsh 5.6e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.7810824e-2 s grad=3.418e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.414958e-3 s grad=3.407e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.952857e-3 s grad=8.106e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.551846e-3 s grad=7.589e-6 s +[acc-interp] eval 2.8547048e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=7.748253e-3 s grad=2.664e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.196075e-3 s grad=3.629e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.991201e-3 s grad=3.226e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.63358e-3 s grad=2.539e-6 s +[acc-interp] eval 4.6486008e-2 s +[acc-interp] thetaMarsh 6.9e-8 s (n=[acc-interp] thetaMarsh 5.4e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.556864e-2 s grad=3.417e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.331575e-3 s grad=3.099e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.155684e-3 s grad=2.402e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.183966e-3 s grad=1.2399e-5 s +[acc-interp] eval 2.3700062e-2 s +[acc-interp] thetaMarsh 1.23e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.6928e-3 s grad=3.825e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.97802e-3 s grad=3.634e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.409379e-3 s grad=2.816e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=6.72356e-3 s grad=3.6e-6 s +[acc-interp] eval 1.5785346e-2 s +[acc-interp] thetaMarsh 6.1e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.114818e-3 s grad=4.403e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.36002e-3 s grad=2.301e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.020002e-3 s grad=7.9e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.847251e-3 s grad=2.633e-6 s +[acc-interp] eval 9.31747e-3 s +[acc-interp] thetaMarsh 1.04e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.819176e-3 s grad=2.432e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.697784e-3 s grad=1.375e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.920385e-3 s grad=8.939e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.099855e-3 s grad=3.907e-6 s +[acc-interp] eval 8.805808e-3 s +[acc-interp] thetaMarsh 9.6e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.44832e-3 s grad=3.546e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.433592e-3 s grad=1.502e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.280447e-3 s grad=1.524e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.557264e-3 s grad=7.39e-7 s +[acc-interp] eval 1.1430945e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=2.164229e-3 s grad=1.398e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.380587e-3 s grad=9.24e-7 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.137597e-3 s grad=1.127e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.247844e-3 s grad=8.76e-7 s +[acc-interp] eval 7.9134814e-2 s +[acc-interp] thetaMarsh 5.4e-8 s (n=[acc-interp] thetaMarsh 5.4e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.2517283e-2 s grad=4.651e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.016748e-3 s grad=1.0866e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.021408e-3 s grad=8.946e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.412246e-3 s grad=1.901e-6 s +[acc-interp] eval 4.5845171e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=4.110504e-3 s grad=3.103e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.762939e-3 s grad=2.778e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.915311e-3 s grad=2.12e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.951684e-3 s grad=3.626e-6 s +[acc-interp] eval 6.3359945e-2 s +[acc-interp] thetaMarsh 1.7e-7 s (n=[acc-interp] thetaMarsh 1.15e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.3469895e-2 s grad=4.043e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=9.794123e-3 s grad=3.512e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.858135e-3 s grad=2.116e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.172303e-3 s grad=4.339e-6 s +[acc-interp] eval 3.2960335e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=4.586821e-3 s grad=3.78e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.388855e-3 s grad=3.3e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.160296e-3 s grad=3.183e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.699982e-3 s grad=3.537e-6 s +[acc-interp] eval 5.3734953e-2 s +[acc-interp] thetaMarsh 1.3e-7 s (n=[acc-interp] thetaMarsh 9.3e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.9021829e-2 s grad=3.289e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.208717e-3 s grad=1.257e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.764679e-3 s grad=1.379e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.555315e-3 s grad=1.517e-6 s +[acc-interp] eval 3.1438628e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=3.776839e-3 s grad=3.282e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.2865611e-2 s grad=3.736e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.524306e-3 s grad=2.631e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.859206e-3 s grad=2.917e-6 s +[acc-interp] eval 5.6043986e-2 s +[acc-interp] thetaMarsh 1.35e-7 s (n=[acc-interp] thetaMarsh 9.6e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=1.1823629e-2 s grad=3.635e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.892606e-3 s grad=3.382e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.969543e-3 s grad=3.053e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.739132e-3 s grad=4.615e-6 s +[acc-interp] eval 2.2081363e-2 s +4) +[acc-interp] chunk base=0 cs=25974 kernel=3.645595e-3 s grad=4.25e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.635171e-3 s grad=3.072e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.081444e-3 s grad=3.16e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.587306e-3 s grad=3.063e-6 s +[acc-interp] eval 3.5099956e-2 s +[acc-interp] thetaMarsh 6.9e-8 s (n=[acc-interp] thetaMarsh 6.3e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.491396e-2 s grad=3.547e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.000972e-3 s grad=1.655e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.957251e-3 s grad=4.094e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.069175e-3 s grad=3.455e-6 s +[acc-interp] eval 2.1770411e-2 s +[acc-interp] thetaMarsh 1.14e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.235915e-3 s grad=2.788e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.037393e-3 s grad=3.743e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.048178e-3 s grad=3.223e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.83997e-3 s grad=2.68e-6 s +[acc-interp] eval 7.993767e-3 s +[acc-interp] thetaMarsh 2.38e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.208501e-3 s grad=2.407e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.883383e-3 s grad=2.707e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.880672e-3 s grad=2.304e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.001853e-3 s grad=2.2e-6 s +[acc-interp] eval 7.766234e-3 s +[acc-interp] thetaMarsh 1.7e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.520542e-3 s grad=1.95e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.236775e-3 s grad=1.853e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.103616e-3 s grad=1.512e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.038781e-3 s grad=1.781e-6 s +[acc-interp] eval 8.670691e-3 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=1.382233e-3 s grad=2.037e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.137105e-3 s grad=3.003e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.667975e-3 s grad=3.421e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.26375e-3 s grad=2.748e-6 s +[acc-interp] eval 5.5823239e-2 s +[acc-interp] thetaMarsh 8.9e-8 s (n=[acc-interp] thetaMarsh 5.3e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.3429475e-2 s grad=4.243e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.732075e-3 s grad=4.065e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.033594e-3 s grad=2.699e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.616052e-3 s grad=2.758e-6 s +[acc-interp] eval 2.3290729e-2 s +[acc-interp] thetaMarsh 1.17e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=4.092063e-3 s grad=3.644e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.031233e-3 s grad=3.451e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.14948e-3 s grad=3.949e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.739365e-3 s grad=2.071e-6 s +[acc-interp] eval 1.4390649e-2 s +[acc-interp] thetaMarsh 1.19e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.836016e-3 s grad=1.438e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.658789e-3 s grad=3.454e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.833719e-3 s grad=8.869e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.591661e-3 s grad=3.17e-6 s +[acc-interp] eval 1.1809708e-2 s +[acc-interp] thetaMarsh 6.2e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.781789e-3 s grad=9.777e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.995476e-3 s grad=3.409e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=8.232896e-3 s grad=4.456e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.388544e-3 s grad=3.723e-6 s +[acc-interp] eval 1.7717057e-2 s +[acc-interp] thetaMarsh 1.35e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.290203e-3 s grad=2.268e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.316936e-3 s grad=2.35e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.972018e-3 s grad=1.481e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.711551e-3 s grad=4.163e-6 s +[acc-interp] eval 1.1135703e-2 s +[acc-interp] thetaMarsh 2.73e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.593176e-3 s grad=2.859e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.435986e-3 s grad=2.616e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.723252e-3 s grad=2.748e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.628123e-3 s grad=2.472e-6 s +[acc-interp] eval 1.1764522e-2 s +[acc-interp] thetaMarsh 8.4e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.684329e-3 s grad=3.754e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.018942e-3 s grad=4.158e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.085096e-3 s grad=5.064e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.494235e-3 s grad=1.684e-6 s +[acc-interp] eval 1.2217662e-2 s +[acc-interp] thetaMarsh 6.7e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.076201e-3 s grad=3.247e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.44202e-3 s grad=3.425e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.888152e-3 s grad=6.113e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.18406e-3 s grad=3.862e-6 s +[acc-interp] eval 1.2692889e-2 s +[acc-interp] thetaMarsh 9.0e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.657777e-3 s grad=3.209e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.826784e-3 s grad=4.865e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.324934e-3 s grad=3.731e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.805326e-3 s grad=3.817e-6 s +[acc-interp] eval 1.160141e-2 s +[acc-interp] thetaMarsh 5.9e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.389539e-3 s grad=2.556e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.393546e-3 s grad=2.788e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.25634e-3 s grad=2.387e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.201247e-3 s grad=2.425e-6 s +[acc-interp] eval 9.952219e-3 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=2.432068e-3 s grad=2.929e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.224931e-3 s grad=2.364e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.557703e-3 s grad=3.247e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.582006e-3 s grad=3.669e-6 s +[acc-interp] eval 0.148611658 s +[acc-interp] thetaMarsh 1.54e-7 s (n=[acc-interp] thetaMarsh 9.5e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.0020577e-2 s grad=3.585e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.049438e-3 s grad=2.651e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.346072e-3 s grad=3.266e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.598315e-3 s grad=4.099e-6 s +[acc-interp] eval 3.3685595e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=8.239403e-3 s grad=2.713e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.700145e-3 s grad=3.712e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.72597e-3 s grad=4.035e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.361629e-3 s grad=2.802e-6 s +[acc-interp] eval 5.4531308e-2 s +[acc-interp] thetaMarsh 1.99e-7 s (n=[acc-interp] thetaMarsh 9.1e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.6391833e-2 s grad=3.247e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.252201e-3 s grad=2.784e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.120364e-3 s grad=3.782e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.145823e-3 s grad=3.763e-6 s +[acc-interp] eval 2.8504075e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=1.0284381e-2 s grad=4.226e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.720033e-3 s grad=3.91e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.898406e-3 s grad=2.65e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.874997e-3 s grad=3.674e-6 s +[acc-interp] eval 5.1253745e-2 s +[acc-interp] thetaMarsh 1.39e-7 s (n=[acc-interp] thetaMarsh 1.09e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.4972425e-2 s grad=4.443e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=7.229593e-3 s grad=3.559e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.112975e-3 s grad=6.971e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.879047e-3 s grad=4.517e-6 s +[acc-interp] eval 4.2060937e-2 s +[acc-interp] thetaMarsh 9.2e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.508244e-3 s grad=2.874e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.392646e-3 s grad=2.584e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.078091e-3 s grad=2.415e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.203295e-3 s grad=4.54e-6 s +[acc-interp] eval 1.4159112e-2 s +[acc-interp] thetaMarsh 9.9e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.629952e-3 s grad=3.68e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.874084e-3 s grad=3.759e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.726236e-3 s grad=3.644e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.667011e-3 s grad=3.748e-6 s +[acc-interp] eval 1.448817e-2 s +[acc-interp] thetaMarsh 1.16e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.861507e-3 s grad=4.258e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.436156e-3 s grad=3.18e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.707151e-3 s grad=4.541e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.151046e-3 s grad=5.031e-6 s +[acc-interp] eval 1.6806038e-2 s +[acc-interp] thetaMarsh 7.0e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.504477e-3 s grad=3.611e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.403354e-3 s grad=4.186e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.895143e-3 s grad=4.599e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.040564e-3 s grad=4.635e-6 s +[acc-interp] eval 1.8774203e-2 s +[acc-interp] thetaMarsh 6.1e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.522681e-3 s grad=3.339e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.716694e-3 s grad=3.009e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.953506e-3 s grad=3.718e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.1444115e-2 s grad=3.988e-6 s +[acc-interp] eval 2.5880132e-2 s +[acc-interp] thetaMarsh 1.34e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=5.0034e-3 s grad=5.596e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.526278e-3 s grad=4.689e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.930333e-3 s grad=4.635e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.800046e-3 s grad=4.699e-6 s +[acc-interp] eval 1.8905103e-2 s +[acc-interp] thetaMarsh 1.24e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.759815e-3 s grad=5.447e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.029988e-3 s grad=4.363e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.109418e-3 s grad=3.962e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.422948e-3 s grad=4.418e-6 s +[acc-interp] eval 1.8972362e-2 s +[acc-interp] thetaMarsh 1.25e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.38095e-3 s grad=6.246e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.866009e-3 s grad=3.771e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.868673e-3 s grad=4.291e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.914027e-3 s grad=3.772e-6 s +[acc-interp] eval 1.8282024e-2 s +[acc-interp] thetaMarsh 1.08e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.198602e-3 s grad=4.56e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.159329e-3 s grad=3.533e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=8.510159e-3 s grad=3.787e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.883053e-3 s grad=3.158e-6 s +[acc-interp] eval 2.0639635e-2 s +[acc-interp] thetaMarsh 6.1e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.217871e-3 s grad=2.824e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.038158e-3 s grad=4.279e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.110099e-3 s grad=5.625e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.84564e-3 s grad=4.005e-6 s +[acc-interp] eval 1.6651978e-2 s +[acc-interp] thetaMarsh 6.0e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.583737e-3 s grad=2.519e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.003578e-3 s grad=4.82e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.276798e-3 s grad=3.114e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.286981e-3 s grad=6.817e-6 s +[acc-interp] eval 1.6974139e-2 s +[acc-interp] thetaMarsh 1.04e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.117159e-3 s grad=6.439e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.172861e-3 s grad=3.828e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.455574e-3 s grad=4.025e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.419371e-3 s grad=4.253e-6 s +[acc-interp] eval 1.8296091e-2 s +[acc-interp] thetaMarsh 1.28e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.063399e-3 s grad=4.215e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.0006603e-2 s grad=4.715e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.81153e-3 s grad=4.439e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.789955e-3 s grad=6.726e-6 s +[acc-interp] eval 2.5407608e-2 s +[acc-interp] thetaMarsh 1.07e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.517679e-3 s grad=2.805e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.490282e-3 s grad=2.907e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.518352e-3 s grad=1.0723e-5 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.498303e-3 s grad=2.916e-6 s +[acc-interp] eval 1.4883722e-2 s +[acc-interp] thetaMarsh 7.8e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.464916e-3 s grad=7.751e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.863941e-3 s grad=3.253e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.905825e-3 s grad=5.085e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.419799e-3 s grad=2.976e-6 s +[acc-interp] eval 1.7467045e-2 s +[acc-interp] thetaMarsh 6.5e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.90902e-3 s grad=2.623e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.452698e-3 s grad=4.157e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.115097e-3 s grad=3.74e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.384979e-3 s grad=3.673e-6 s +[acc-interp] eval 1.7678691e-2 s +[acc-interp] thetaMarsh 6.4e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=1.099024e-2 s grad=3.265e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.905011e-3 s grad=4.091e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.545974e-3 s grad=3.056e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.024665e-3 s grad=4.275e-6 s +[acc-interp] eval 2.3367527e-2 s +[acc-interp] thetaMarsh 7.4e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.837493e-3 s grad=2.623e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.719493e-3 s grad=9.948e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.972842e-3 s grad=9.085e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.656454e-3 s grad=5.856e-6 s +[acc-interp] eval 1.6995247e-2 s +[acc-interp] thetaMarsh 1.01e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.528439e-3 s grad=2.84e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.690809e-3 s grad=2.973e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.752427e-3 s grad=3.777e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.69411e-3 s grad=3.851e-6 s +[acc-interp] eval 1.8710579e-2 s +[acc-interp] thetaMarsh 1.23e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.643879e-3 s grad=3.733e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.741319e-3 s grad=3.069e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.0327862e-2 s grad=4.044e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.749448e-3 s grad=3.662e-6 s +[acc-interp] eval 2.2990178e-2 s +[acc-interp] thetaMarsh 1.16e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.005175e-3 s grad=4.19e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.400401e-3 s grad=4.328e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.560626e-3 s grad=3.748e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.540181e-3 s grad=3.884e-6 s +[acc-interp] eval 1.7060674e-2 s +4) +[acc-interp] chunk base=0 cs=25974 kernel=4.334383e-3 s grad=3.55e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.395028e-3 s grad=3.425e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.757372e-3 s grad=2.67e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.941277e-3 s grad=3.279e-6 s +[acc-interp] eval 0.456289309 s +[acc-interp] thetaMarsh 1.84e-7 s (n=[acc-interp] thetaMarsh 1.14e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=8.682819e-3 s grad=4.053e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.250018e-3 s grad=2.269e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.252564e-3 s grad=4.68e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=8.967539e-3 s grad=4.501e-6 s +[acc-interp] eval 2.3412444e-2 s +[acc-interp] thetaMarsh 6.2e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.688758e-3 s grad=1.845e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.899428e-3 s grad=3.42e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.681569e-3 s grad=2.609e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.662954e-3 s grad=1.856e-6 s +[acc-interp] eval 8.264046e-3 s +[acc-interp] thetaMarsh 1.25e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.57944e-3 s grad=2.543e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.313086e-3 s grad=3.051e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.835718e-3 s grad=2.049e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.897258e-3 s grad=4.537e-6 s +[acc-interp] eval 1.1219286e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=1.739408e-3 s grad=4.75e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.121764e-3 s grad=6.771e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.903915e-3 s grad=4.818e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.323979e-3 s grad=2.949e-6 s +[acc-interp] eval 5.2166032e-2 s +[acc-interp] thetaMarsh 1.36e-7 s (n=[acc-interp] thetaMarsh 8.4e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.5890149e-2 s grad=5.212e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.38396e-3 s grad=5.077e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.224668e-3 s grad=5.308e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.014226e-2 s grad=4.049e-6 s +[acc-interp] eval 4.5576438e-2 s +4) +[acc-interp] chunk base=0 cs=25974 kernel=5.118248e-3 s grad=4.386e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.562462e-3 s grad=3.306e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.664073e-3 s grad=3.201e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.247124e-3 s grad=3.752e-6 s +[acc-interp] eval 6.6825178e-2 s +[acc-interp] thetaMarsh 1.24e-7 s (n=[acc-interp] thetaMarsh 9.9e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.6851672e-2 s grad=3.521e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.05764e-3 s grad=2.704e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.484817e-3 s grad=1.874e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.735684e-3 s grad=3.261e-6 s +[acc-interp] eval 2.8634855e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=4.000002e-3 s grad=2.664e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.232224e-3 s grad=2.634e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.970142e-3 s grad=5.376e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=6.123256e-3 s grad=2.711e-6 s +[acc-interp] eval 5.113438e-2 s +[acc-interp] thetaMarsh 1.36e-7 s (n=[acc-interp] thetaMarsh 9.3e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.9777891e-2 s grad=4.933e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=6.429371e-3 s grad=5.075e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.158538e-3 s grad=3.757e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.44725e-3 s grad=2.462e-6 s +[acc-interp] eval 4.8469411e-2 s +[acc-interp] thetaMarsh 1.16e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=5.100673e-3 s grad=2.793e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.49115e-3 s grad=4.347e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.87486e-3 s grad=2.339e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.958292e-3 s grad=3.705e-6 s +[acc-interp] eval 1.9030923e-2 s +[acc-interp] thetaMarsh 1.18e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=5.334394e-3 s grad=5.548e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=6.143125e-3 s grad=3.158e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.2294209e-2 s grad=4.68e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.981614e-3 s grad=2.961e-6 s +[acc-interp] eval 3.1262575e-2 s +[acc-interp] thetaMarsh 1.25e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=6.161598e-3 s grad=3.062e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.996488e-3 s grad=3.206e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.917089e-3 s grad=4.128e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=6.862502e-3 s grad=3.934e-6 s +[acc-interp] eval 2.2455788e-2 s +[acc-interp] thetaMarsh 4.33e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.997986e-3 s grad=3.029e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.432199e-3 s grad=3.787e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.942342e-3 s grad=5.117e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.464393e-3 s grad=3.694e-6 s +[acc-interp] eval 1.6620208e-2 s +[acc-interp] thetaMarsh 1.19e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.912145e-3 s grad=2.753e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.634886e-3 s grad=2.671e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.10379e-3 s grad=4.77e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.425734e-3 s grad=4.922e-6 s +[acc-interp] eval 1.654458e-2 s +[acc-interp] thetaMarsh 6.1e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=7.406652e-3 s grad=2.955e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.998815e-3 s grad=2.936e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.104263e-3 s grad=3.173e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.799999e-3 s grad=2.415e-6 s +[acc-interp] eval 2.1489745e-2 s +[acc-interp] thetaMarsh 1.22e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.851886e-3 s grad=3.891e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.14511e-3 s grad=3.876e-6 s 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base=0 cs=25974 kernel=3.474847e-3 s grad=5.57e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.463249e-3 s grad=4.736e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.341405e-3 s grad=2.394e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.230683e-3 s grad=2.89e-6 s +[acc-interp] eval 1.5115313e-2 s +[acc-interp] thetaMarsh 1.11e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.162745e-3 s grad=2.347e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=6.056443e-3 s grad=4.964e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.237698e-3 s grad=3.24e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.425798e-3 s grad=3.413e-6 s +[acc-interp] eval 1.7668972e-2 s +[acc-interp] thetaMarsh 1.21e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=9.26993e-3 s grad=3.687e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=6.857521e-3 s grad=2.956e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.278127e-3 s grad=3.134e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.67375e-3 s grad=6.33e-6 s +[acc-interp] eval 2.5828018e-2 s +[acc-interp] thetaMarsh 5.8e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.489305e-3 s grad=2.861e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.070965e-3 s grad=2.881e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=6.409102e-3 s grad=5.893e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.241533e-3 s grad=4.019e-6 s +[acc-interp] eval 1.8413858e-2 s +[acc-interp] thetaMarsh 1.22e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=6.474918e-3 s grad=3.655e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.039706e-3 s grad=3.342e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.606068e-3 s grad=4.045e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=6.534916e-3 s grad=3.313e-6 s +[acc-interp] eval 2.1246919e-2 s +[acc-interp] thetaMarsh 1.05e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.88659e-3 s grad=1.932e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.111037e-3 s grad=3.806e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.441092e-3 s grad=3.795e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.400239e-3 s grad=3.255e-6 s +[acc-interp] eval 1.5429551e-2 s +[acc-interp] thetaMarsh 1.24e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.221929e-3 s grad=2.228e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.398437e-3 s grad=4.668e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.192863e-3 s grad=4.934e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.160277e-3 s grad=3.251e-6 s +[acc-interp] eval 1.6539892e-2 s +[acc-interp] thetaMarsh 1.19e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.807005e-3 s grad=3.441e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=6.377252e-3 s grad=2.805e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=7.091372e-3 s grad=2.89e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.795293e-3 s grad=5.943e-6 s +[acc-interp] eval 2.3472811e-2 s +[acc-interp] thetaMarsh 1.33e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.439621e-3 s grad=2.969e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.907038e-3 s grad=2.911e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.973591e-3 s grad=3.662e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=8.169283e-3 s grad=3.453e-6 s +[acc-interp] eval 1.8949682e-2 s +[acc-interp] thetaMarsh 5.8e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.265185e-3 s grad=3.225e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.999597e-3 s grad=2.545e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.217705e-3 s grad=3.075e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.573379e-3 s grad=3.631e-6 s +[acc-interp] eval 1.5588306e-2 s +[acc-interp] thetaMarsh 1.17e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.332238e-3 s grad=2.85e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.196711e-3 s grad=2.946e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=7.111018e-3 s grad=3.161e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.241554e-3 s grad=2.598e-6 s +[acc-interp] eval 1.8336382e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=4.830555e-3 s grad=3.109e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.82087e-3 s grad=4.196e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.759388e-3 s grad=2.988e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.242598e-3 s grad=2.567e-6 s +[acc-interp] eval 0.466447358 s +[acc-interp] thetaMarsh 1.38e-7 s (n=[acc-interp] thetaMarsh 9.9e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.5863751e-2 s grad=3.785e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.842714e-3 s grad=2.84e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.749438e-3 s grad=4.668e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.358683e-3 s grad=1.624e-6 s +[acc-interp] eval 3.8502519e-2 s +[acc-interp] thetaMarsh 1.41e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.981029e-3 s grad=3.294e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.775086e-3 s grad=4.601e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.908658e-3 s grad=3.619e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.10442e-3 s grad=2.971e-6 s +[acc-interp] eval 1.6420212e-2 s +[acc-interp] thetaMarsh 1.37e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.604306e-3 s grad=2.831e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.258559e-3 s grad=2.363e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.162273e-3 s grad=2.365e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.099692e-3 s grad=2.191e-6 s +[acc-interp] eval 1.4644374e-2 s +[acc-interp] thetaMarsh 1.17e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.167238e-3 s grad=2.291e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.705586e-3 s grad=2.815e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.579183e-3 s grad=3.968e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.511836e-3 s grad=3.432e-6 s +[acc-interp] eval 1.5591002e-2 s +[acc-interp] thetaMarsh 1.0e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.993991e-3 s grad=2.2e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.061665e-3 s grad=4.766e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.347132e-3 s grad=2.756e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.087421e-3 s grad=2.643e-6 s +[acc-interp] eval 1.7237768e-2 s +[acc-interp] thetaMarsh 1.13e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.045267e-3 s grad=2.415e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.189688e-3 s grad=3.749e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.526113e-3 s grad=3.108e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.303799e-3 s grad=3.052e-6 s +[acc-interp] eval 1.4807521e-2 s +[acc-interp] thetaMarsh 1.04e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.912523e-3 s grad=2.857e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.141964e-3 s grad=3.512e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.392488e-3 s grad=3.547e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.686346e-3 s grad=1.885e-6 s +[acc-interp] eval 1.6884206e-2 s +[acc-interp] thetaMarsh 1.21e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.52654e-3 s grad=2.841e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.883302e-3 s grad=3.248e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.225508e-3 s grad=2.673e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.119605e-3 s grad=2.94e-6 s +[acc-interp] eval 1.538796e-2 s +[acc-interp] thetaMarsh 1.17e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.009132e-3 s grad=2.513e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.322775e-3 s grad=2.327e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.210562e-3 s grad=1.666e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.639967e-3 s grad=1.909e-6 s +[acc-interp] eval 1.4658912e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=3.308828e-3 s grad=1.75e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.142005e-3 s grad=3.791e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.402786e-3 s grad=3.514e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.68421e-3 s grad=2.246e-6 s +[acc-interp] eval 0.181611897 s +[acc-interp] thetaMarsh 1.33e-7 s (n=[acc-interp] thetaMarsh 2.72e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.4478837e-2 s grad=4.002e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.291072e-3 s grad=3.254e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.062026e-3 s grad=1.829e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.599198e-3 s grad=3.47e-6 s +[acc-interp] eval 3.6018645e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=4.425579e-3 s grad=4.001e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.538297e-3 s grad=3.253e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.960537e-3 s grad=3.063e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.661915e-3 s grad=2.199e-6 s +[acc-interp] eval 5.2145206e-2 s +[acc-interp] thetaMarsh 9.9e-8 s (n=[acc-interp] thetaMarsh 7.4e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.052522e-2 s grad=3.68e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.463378e-3 s grad=3.371e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.24584e-3 s grad=3.015e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.911675e-3 s grad=4.688e-6 s +[acc-interp] eval 2.2352386e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=3.438087e-3 s grad=1.0349e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.703166e-3 s grad=9.646e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.2475056e-2 s grad=3.723e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.89909e-3 s grad=3.332e-6 s +[acc-interp] eval 4.581927e-2 s +[acc-interp] thetaMarsh 1.06e-7 s (n=[acc-interp] thetaMarsh 1.24e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=7.995145e-3 s grad=4.682e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.864193e-3 s grad=3.616e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.530669e-3 s grad=2.684e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.526227e-3 s grad=2.817e-6 s +[acc-interp] eval 1.6825546e-2 s +[acc-interp] thetaMarsh 6.3e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.827333e-3 s grad=3.257e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.631897e-3 s grad=3.405e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.976186e-3 s grad=3.343e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.437048e-3 s grad=2.196e-6 s +[acc-interp] eval 1.1707584e-2 s +[acc-interp] thetaMarsh 6.9e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.858809e-3 s grad=2.555e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.770988e-3 s grad=2.761e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.560875e-3 s grad=3.106e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.954907e-3 s grad=3.981e-6 s +[acc-interp] eval 1.2074014e-2 s +[acc-interp] thetaMarsh 7.5e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=8.922323e-3 s grad=2.531e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.613267e-3 s grad=2.449e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.600545e-3 s grad=4.263e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.956171e-3 s grad=3.476e-6 s +[acc-interp] eval 1.8795135e-2 s +[acc-interp] thetaMarsh 7.0e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.193174e-3 s grad=2.54e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.137684e-3 s grad=2.107e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.125425e-3 s grad=2.197e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.204384e-3 s grad=2.444e-6 s +[acc-interp] eval 9.417615e-3 s +[acc-interp] thetaMarsh 5.9e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.888153e-3 s grad=3.776e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.33683e-3 s grad=3.825e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.108653e-3 s grad=2.896e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.096231e-3 s grad=2.533e-6 s +[acc-interp] eval 1.0261944e-2 s +[acc-interp] thetaMarsh 6.8e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.049109e-3 s grad=2.669e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.209134e-3 s grad=3.258e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.52008e-3 s grad=4.295e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.204927e-3 s grad=4.002e-6 s +[acc-interp] eval 1.1410324e-2 s +[acc-interp] thetaMarsh 1.16e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.285436e-3 s grad=2.327e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.999067e-3 s grad=1.43e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.139619e-3 s grad=2.517e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.127481e-3 s grad=2.807e-6 s +[acc-interp] eval 9.879829e-3 s +[acc-interp] thetaMarsh 6.0e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.327365e-3 s grad=4.87e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.685742e-3 s grad=4.213e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.703885e-3 s grad=9.043e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.423286e-3 s grad=9.193e-6 s +[acc-interp] eval 1.1027489e-2 s +[acc-interp] thetaMarsh 7.6e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.337454e-3 s grad=8.174e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.455948e-3 s grad=2.885e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.247984e-3 s grad=1.438e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.524295e-3 s grad=5.538e-6 s +[acc-interp] eval 1.0322541e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=2.765505e-3 s grad=3.474e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.786011e-3 s grad=1.1068e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.761926e-3 s grad=6.618e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.775916e-3 s grad=4.1e-6 s +[acc-interp] eval 0.1345568 s +[acc-interp] thetaMarsh 6.9e-8 s (n=[acc-interp] thetaMarsh 2.16e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=1.8092579e-2 s grad=3.747e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.388944e-3 s grad=2.443e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=7.228702e-3 s grad=5.525e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.697269e-3 s grad=4.984e-6 s +[acc-interp] eval 3.4343445e-2 s +[acc-interp] thetaMarsh 6.1e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.238883e-3 s grad=1.1332e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.334485e-3 s grad=2.41e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.963622e-3 s grad=1.613e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.135611e-3 s grad=6.445e-6 s +[acc-interp] eval 1.5572905e-2 s +[acc-interp] thetaMarsh 3.47e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.286667e-3 s grad=3.507e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.404678e-3 s grad=4.214e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.363849e-3 s grad=3.405e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.106793e-3 s grad=3.469e-6 s +[acc-interp] eval 1.2294602e-2 s +[acc-interp] thetaMarsh 5.8e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.661586e-3 s grad=4.199e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.631468e-3 s grad=4.258e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.8315e-3 s grad=4.162e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.758502e-3 s grad=4.163e-6 s +[acc-interp] eval 1.6761284e-2 s +[acc-interp] thetaMarsh 5.8e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.651553e-3 s grad=3.822e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.409527e-3 s grad=3.269e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.429275e-3 s grad=3.075e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=9.989247e-3 s grad=4.436e-6 s +[acc-interp] eval 2.1739202e-2 s +[acc-interp] thetaMarsh 1.0e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.820489e-3 s grad=3.392e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.742017e-3 s grad=3.314e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.932033e-3 s grad=5.501e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.12493e-3 s grad=4.751e-6 s +[acc-interp] eval 1.3934329e-2 s +[acc-interp] thetaMarsh 6.8e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.153175e-3 s grad=4.529e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.958877e-3 s grad=4.438e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.494878e-3 s grad=3.904e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.600002e-3 s grad=3.612e-6 s +[acc-interp] eval 1.4462786e-2 s +[acc-interp] thetaMarsh 1.15e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.386902e-3 s grad=3.427e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.125395e-3 s grad=2.841e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.248133e-3 s grad=4.638e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=7.374278e-3 s grad=4.13e-6 s +[acc-interp] eval 1.8734182e-2 s +[acc-interp] thetaMarsh 1.17e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.474523e-3 s grad=3.722e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.313558e-3 s grad=4.425e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.541088e-3 s grad=4.209e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.152182e-3 s grad=4.272e-6 s +[acc-interp] eval 1.5173431e-2 s +[acc-interp] thetaMarsh 9.8e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.975598e-3 s grad=3.42e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.19466e-3 s grad=3.659e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.871241e-3 s grad=3.271e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.142446e-3 s grad=4.012e-6 s +[acc-interp] eval 1.4780308e-2 s +[acc-interp] thetaMarsh 1.1e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.889907e-3 s grad=2.664e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.950674e-3 s grad=2.734e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.799955e-3 s grad=5.165e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=7.577733e-3 s grad=3.948e-6 s +[acc-interp] eval 1.8571543e-2 s +[acc-interp] thetaMarsh 6.0e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.948857e-3 s grad=3.363e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.209883e-3 s grad=2.912e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.558446e-3 s grad=3.742e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.250088e-3 s grad=3.814e-6 s +[acc-interp] eval 1.5268066e-2 s +[acc-interp] thetaMarsh 9.9e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.200848e-3 s grad=4.028e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.488241e-3 s grad=5.743e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.352343e-3 s grad=3.681e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.19569e-3 s grad=3.562e-6 s +[acc-interp] eval 1.5018806e-2 s +[acc-interp] thetaMarsh 1.3e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.959773e-3 s grad=3.668e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.784949e-3 s grad=5.115e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.219921e-3 s grad=4.686e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=8.838314e-3 s grad=3.356e-6 s +[acc-interp] eval 2.1320583e-2 s +[acc-interp] thetaMarsh 1.05e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.837956e-3 s grad=3.888e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.276877e-3 s grad=3.383e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.16401e-3 s grad=3.937e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.468322e-3 s grad=3.919e-6 s +[acc-interp] eval 1.5266128e-2 s +[acc-interp] thetaMarsh 4.44e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.721417e-3 s grad=3.967e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.029156e-3 s grad=3.849e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.595497e-3 s grad=3.838e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.621965e-3 s grad=3.621e-6 s +[acc-interp] eval 1.6511636e-2 s +[acc-interp] thetaMarsh 1.89e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.346065e-3 s grad=4.313e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.352555e-3 s grad=4.545e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.203023e-3 s grad=3.836e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=8.934563e-3 s grad=3.542e-6 s +[acc-interp] eval 2.360289e-2 s +[acc-interp] thetaMarsh 1.31e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.049848e-3 s grad=3.354e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.44005e-3 s grad=4.196e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.967305e-3 s grad=4.522e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.522379e-3 s grad=3.661e-6 s +[acc-interp] eval 1.5706039e-2 s +[acc-interp] thetaMarsh 1.07e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.420769e-3 s grad=3.658e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.099581e-3 s grad=3.061e-6 s 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base=0 cs=25974 kernel=2.998945e-3 s grad=3.336e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.359489e-3 s grad=4.388e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.34881e-3 s grad=4.068e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.290654e-3 s grad=3.511e-6 s +[acc-interp] eval 1.4557652e-2 s +[acc-interp] thetaMarsh 1.03e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.252347e-3 s grad=4.38e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.346378e-3 s grad=4.082e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.988515e-3 s grad=4.703e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=7.719307e-3 s grad=3.971e-6 s +[acc-interp] eval 1.9979334e-2 s +[acc-interp] thetaMarsh 1.11e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.404685e-3 s grad=4.079e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.977922e-3 s grad=4.596e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.24011e-3 s grad=3.395e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.434644e-3 s grad=3.073e-6 s +[acc-interp] eval 1.5637435e-2 s +[acc-interp] thetaMarsh 1.08e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.298876e-3 s grad=3.115e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.784576e-3 s grad=4.598e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.29751e-3 s grad=3.512e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.241523e-3 s grad=4.393e-6 s +[acc-interp] eval 1.4888875e-2 s +[acc-interp] thetaMarsh 6.4e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.337981e-3 s grad=1.1083e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.607792e-3 s grad=1.0176e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.057507e-3 s grad=7.41e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=6.531548e-3 s grad=7.855e-6 s +[acc-interp] eval 1.7280383e-2 s +[acc-interp] thetaMarsh 6.6e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.07202e-3 s grad=1.0609e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.435153e-3 s grad=3.344e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.688971e-3 s grad=3.542e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.128245e-3 s grad=8.666e-6 s +[acc-interp] eval 1.4217653e-2 s +[acc-interp] thetaMarsh 5.9e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.342586e-3 s grad=1.1059e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.156732e-3 s grad=1.847e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.062004e-3 s grad=3.901e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.427282e-3 s grad=3.73e-6 s +[acc-interp] eval 1.4918253e-2 s +[acc-interp] thetaMarsh 1.12e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.125441e-3 s grad=4.496e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.347602e-3 s grad=4.55e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.565256e-3 s grad=4.133e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=7.660348e-3 s grad=1.4134e-5 s +[acc-interp] eval 1.8795085e-2 s +[acc-interp] thetaMarsh 6.1e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.1556e-3 s grad=2.372e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.481124e-3 s grad=5.239e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.484307e-3 s grad=1.0287e-5 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.110999e-3 s grad=2.364e-6 s +[acc-interp] eval 1.4999782e-2 s +4) +[acc-interp] chunk base=0 cs=25974 kernel=3.109908e-3 s grad=1.0917e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.068539e-3 s grad=9.812e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.994012e-3 s grad=5.235e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.155941e-3 s grad=3.736e-6 s +[acc-interp] eval 0.533405474 s +[acc-interp] thetaMarsh 7.5e-8 s (n=[acc-interp] thetaMarsh 7.3e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.270219e-2 s grad=4.203e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.759926e-3 s grad=3.474e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.817652e-3 s grad=4.159e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.001944e-3 s grad=2.87e-6 s +[acc-interp] eval 3.4813966e-2 s +[acc-interp] thetaMarsh 1.34e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.843229e-3 s grad=3.288e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.283478e-3 s grad=3.292e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.996144e-3 s grad=2.967e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.647508e-3 s grad=2.913e-6 s +[acc-interp] eval 1.3360962e-2 s +[acc-interp] thetaMarsh 9.9e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.829149e-3 s grad=2.959e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.112405e-3 s grad=3.904e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.364907e-3 s grad=3.594e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.64831e-3 s grad=3.351e-6 s +[acc-interp] eval 1.4579805e-2 s +[acc-interp] thetaMarsh 1.15e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.826737e-3 s grad=2.535e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.703692e-3 s grad=2.531e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.678499e-3 s grad=3.464e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.830664e-3 s grad=3.286e-6 s +[acc-interp] eval 1.3670982e-2 s +[acc-interp] thetaMarsh 1.02e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.064645e-3 s grad=3.075e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.926716e-3 s grad=3.245e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.378251e-3 s grad=3.192e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.182737e-3 s grad=4.186e-6 s +[acc-interp] eval 1.4246435e-2 s +[acc-interp] thetaMarsh 1.04e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.384924e-3 s grad=3.073e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.147765e-3 s grad=4.055e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.812978e-3 s grad=3.139e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.058873e-3 s grad=2.839e-6 s +[acc-interp] eval 1.3755802e-2 s +[acc-interp] thetaMarsh 1.52e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.121832e-3 s grad=3.13e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.627426e-3 s grad=3.243e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.902794e-3 s grad=2.015e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.273156e-3 s grad=5.514e-6 s +[acc-interp] eval 1.3034935e-2 s +[acc-interp] thetaMarsh 6.5e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.345845e-3 s grad=2.74e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.072228e-3 s grad=2.638e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.706171e-3 s grad=1.0839e-5 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.851692e-3 s grad=2.157e-6 s +[acc-interp] eval 1.2802479e-2 s +[acc-interp] thetaMarsh 9.0e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.173007e-3 s grad=4.388e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.086074e-3 s grad=4.324e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.245239e-3 s grad=8.406e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.827677e-3 s grad=1.0124e-5 s +[acc-interp] eval 1.4141953e-2 s +[acc-interp] thetaMarsh 1.21e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.570991e-3 s grad=4.999e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.970554e-3 s grad=4.268e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.293579e-3 s grad=8.099e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.316682e-3 s grad=6.817e-6 s +[acc-interp] eval 1.4029263e-2 s +[acc-interp] thetaMarsh 7.7e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.909545e-3 s grad=7.154e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.802158e-3 s grad=4.322e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.928162e-3 s grad=4.091e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.400409e-3 s grad=8.631e-6 s +[acc-interp] eval 1.3787654e-2 s +[acc-interp] thetaMarsh 2.32e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.158379e-3 s grad=8.671e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.990458e-3 s grad=3.583e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.011768e-3 s grad=4.696e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.053004e-3 s grad=3.936e-6 s +[acc-interp] eval 1.4397477e-2 s +[acc-interp] thetaMarsh 9.4e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.99537e-3 s grad=4.137e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.280422e-3 s grad=3.358e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.710307e-3 s grad=2.598e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.866494e-3 s grad=3.119e-6 s +[acc-interp] eval 1.3140057e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=2.676206e-3 s grad=2.386e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.095568e-3 s grad=3.15e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.361788e-3 s grad=3.659e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.218427e-3 s grad=3.191e-6 s +[acc-interp] eval 0.215332204 s +[acc-interp] thetaMarsh 1.44e-7 s (n=[acc-interp] thetaMarsh 1.07e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.6255824e-2 s grad=3.732e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.598296e-3 s grad=3.027e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.602867e-3 s grad=3.671e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.410322e-3 s grad=3.325e-6 s +[acc-interp] eval 3.8633397e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=4.077962e-3 s grad=3.904e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.557335e-3 s grad=2.976e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.404237e-3 s grad=3.061e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.074035e-3 s grad=2.921e-6 s +[acc-interp] eval 5.4654157e-2 s +[acc-interp] thetaMarsh 7.2e-8 s (n=[acc-interp] thetaMarsh 7.1e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.2214683e-2 s grad=3.359e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.065093e-3 s grad=4.353e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.946693e-3 s grad=4.262e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.083222e-3 s grad=4.507e-6 s +[acc-interp] eval 3.6770555e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=1.2789401e-2 s grad=4.074e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.633023e-3 s grad=3.018e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.475126e-3 s grad=2.565e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.698801e-3 s grad=2.699e-6 s +[acc-interp] eval 6.3165837e-2 s +[acc-interp] thetaMarsh 1.69e-7 s (n=[acc-interp] thetaMarsh 1.17e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.9623763e-2 s grad=4.104e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.077441e-3 s grad=4.309e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.312617e-3 s grad=2.377e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.233743e-3 s grad=3.058e-6 s +[acc-interp] eval 3.1839309e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=3.075895e-3 s grad=3.912e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=9.877321e-3 s grad=4.049e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.956743e-3 s grad=3.51e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.908641e-3 s grad=3.345e-6 s +[acc-interp] eval 5.2038983e-2 s +[acc-interp] thetaMarsh 1.2e-7 s (n=[acc-interp] thetaMarsh 8.0e-8 s (n=7) +[acc-interp] chunk base=0 cs=25974 kernel=1.1428292e-2 s grad=3.35e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.023568e-3 s grad=5.331e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=6.893957e-3 s grad=5.174e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.962508e-3 s grad=4.194e-6 s +[acc-interp] eval 2.722581e-2 s +7) +[acc-interp] chunk base=0 cs=25974 kernel=3.414484e-3 s grad=5.588e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.65722e-3 s grad=4.651e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.182806e-3 s grad=3.431e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.184205e-3 s grad=3.614e-6 s +[acc-interp] eval 4.2519733e-2 s +[acc-interp] thetaMarsh 1.45e-7 s (n=[acc-interp] thetaMarsh 1.1e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.0924428e-2 s grad=3.151e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.756507e-3 s grad=2.895e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.698115e-3 s grad=2.416e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.657308e-3 s grad=2.995e-6 s +[acc-interp] eval 2.0593331e-2 s +[acc-interp] thetaMarsh 1.36e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.384464e-3 s grad=2.785e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.747256e-3 s grad=3.321e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.284716e-3 s grad=3.062e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.647247e-3 s grad=3.057e-6 s +[acc-interp] eval 1.6681122e-2 s +[acc-interp] thetaMarsh 1.47e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=4.167048e-3 s grad=3.776e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=8.320613e-3 s grad=3.692e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.250111e-3 s grad=1.938e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.437371e-3 s grad=3.778e-6 s +[acc-interp] eval 1.8830398e-2 s +[acc-interp] thetaMarsh 1.41e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.804079e-3 s grad=3.178e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.995325e-3 s grad=2.214e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.282776e-3 s grad=1.81e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.711246e-3 s grad=7.294e-6 s +[acc-interp] eval 1.224509e-2 s +[acc-interp] thetaMarsh 7.4e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.68261e-3 s grad=3.129e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.901785e-3 s grad=3.531e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.547024e-3 s grad=2.65e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.299284e-3 s grad=2.289e-6 s +[acc-interp] eval 1.1932917e-2 s +[acc-interp] thetaMarsh 1.07e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.227347e-3 s grad=2.034e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.770494e-3 s grad=1.705e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.373923e-3 s grad=3.689e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.262258e-3 s grad=3.688e-6 s +[acc-interp] eval 1.0307014e-2 s +[acc-interp] thetaMarsh 1.26e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.902345e-3 s grad=2.621e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.356095e-3 s grad=2.611e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.119675e-3 s grad=2.344e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.410612e-3 s grad=2.091e-6 s +[acc-interp] eval 1.0547241e-2 s +[acc-interp] thetaMarsh 1.07e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.514018e-3 s grad=2.132e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.319777e-3 s grad=2.677e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.214537e-3 s grad=2.427e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.252711e-3 s grad=1.855e-6 s +[acc-interp] eval 1.1847086e-2 s +[acc-interp] thetaMarsh 1.98e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.135933e-3 s grad=1.786e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.171152e-3 s grad=1.714e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.328479e-3 s grad=3.227e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.256352e-3 s grad=2.98e-6 s +[acc-interp] eval 1.2475652e-2 s +[acc-interp] thetaMarsh 1.01e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.545106e-3 s grad=2.649e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.420944e-3 s grad=2.184e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.2922e-3 s grad=2.013e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.568497e-3 s grad=4.111e-6 s +[acc-interp] eval 1.1456973e-2 s +[acc-interp] thetaMarsh 1.01e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.49123e-3 s grad=2.316e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.604353e-3 s grad=2.287e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.210989e-3 s grad=2.615e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.338046e-3 s grad=2.59e-6 s +[acc-interp] eval 1.123599e-2 s +[acc-interp] thetaMarsh 1.12e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.44959e-3 s grad=2.721e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.627442e-3 s grad=3.315e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.434166e-3 s grad=5.032e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.420524e-3 s grad=3.42e-6 s +[acc-interp] eval 1.0916014e-2 s +[acc-interp] thetaMarsh 6.0e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.915274e-3 s grad=2.39e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.222598e-3 s grad=1.086e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.240565e-3 s grad=2.605e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.364334e-3 s grad=5.181e-6 s +[acc-interp] eval 1.159194e-2 s +[acc-interp] thetaMarsh 9.7e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.548056e-3 s grad=3.144e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.372241e-3 s grad=2.625e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.686644e-3 s grad=3.243e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.599632e-3 s grad=2.317e-6 s +[acc-interp] eval 1.1034653e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=2.527443e-3 s grad=4.168e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.655476e-3 s grad=7.853e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.563744e-3 s grad=9.15e-7 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.298445e-3 s grad=9.61e-7 s +[acc-interp] eval 0.194548444 s +[acc-interp] thetaMarsh 5.8e-8 s (n=[acc-interp] thetaMarsh 5.8e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=1.2422884e-2 s grad=3.534e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.243941e-3 s grad=1.961e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.526076e-3 s grad=2.622e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.558968e-3 s grad=4.993e-6 s +[acc-interp] eval 2.3609242e-2 s +4) +[acc-interp] chunk base=0 cs=25974 kernel=2.782431e-3 s grad=9.948e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=7.753722e-3 s grad=2.971e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.64958e-3 s grad=4.781e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.257298e-3 s grad=3.795e-6 s +[acc-interp] eval 4.2183711e-2 s +[acc-interp] thetaMarsh 1.35e-7 s (n=[acc-interp] thetaMarsh 1.14e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.8789701e-2 s grad=4.453e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.085544e-3 s grad=4.104e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=8.243156e-3 s grad=3.871e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.012641e-3 s grad=3.411e-6 s +[acc-interp] eval 4.6831885e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=4.041929e-3 s grad=3.137e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.551039e-3 s grad=2.61e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.558657e-3 s grad=3.287e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.018311e-3 s grad=2.752e-6 s +[acc-interp] eval 6.3843623e-2 s +[acc-interp] thetaMarsh 1.34e-7 s (n=0) +[acc-interp] chunk base=0 cs=25974 kernel=1.2792423e-2 s grad=1.787e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.911371e-3 s grad=1.514e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.388928e-3 s grad=1.238e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.350022e-3 s grad=1.228e-6 s +[acc-interp] eval 2.2094388e-2 s +[acc-interp] thetaMarsh 1.33e-7 s (n=[acc-interp] thetaMarsh 9.9e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.5372339e-2 s grad=4.394e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.172388e-3 s grad=2.905e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.273672e-3 s grad=2.871e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.213827e-3 s grad=4.56e-6 s +[acc-interp] eval 3.9767509e-2 s +4) +[acc-interp] chunk base=0 cs=25974 kernel=4.140384e-3 s grad=3.244e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.768994e-3 s grad=2.75e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.317015e-3 s grad=4.194e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.462573e-3 s grad=4.05e-6 s +[acc-interp] eval 5.8231006e-2 s +[acc-interp] thetaMarsh 1.33e-7 s (n=[acc-interp] thetaMarsh 1.08e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.998083e-2 s grad=4.239e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.933985e-3 s grad=3.898e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=8.037803e-3 s grad=4.45e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.948963e-3 s grad=4.591e-6 s +[acc-interp] eval 4.7501606e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=3.690856e-3 s grad=2.425e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.283454e-3 s grad=1.814e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.141203e-3 s grad=3.898e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.056823e-3 s grad=3.158e-6 s +[acc-interp] eval 6.067505e-2 s +[acc-interp] thetaMarsh 6.5e-8 s (n=0) +[acc-interp] chunk base=0 cs=25974 kernel=9.21844e-3 s grad=1.362e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.380717e-3 s grad=4.417e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.656775e-3 s grad=1.305e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.273594e-3 s grad=1.377e-6 s +[acc-interp] eval 1.7502371e-2 s +[acc-interp] thetaMarsh 1.58e-7 s (n=[acc-interp] thetaMarsh 1.14e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.4078077e-2 s grad=4.657e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.402204e-3 s grad=3.824e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.287896e-3 s grad=2.465e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.635898e-3 s grad=3.189e-6 s +[acc-interp] eval 2.6123839e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=2.79872e-3 s grad=2.526e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.336211e-3 s grad=4.521e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.329994e-3 s grad=2.807e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.608635e-3 s grad=9.285e-6 s +[acc-interp] eval 3.9750993e-2 s +[acc-interp] thetaMarsh 6.0e-8 s (n=[acc-interp] thetaMarsh 5.5e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=9.763698e-3 s grad=4.219e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.355416e-3 s grad=2.85e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.478419e-3 s grad=6.074e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.558772e-3 s grad=2.547e-6 s +[acc-interp] eval 2.3115334e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=4.314951e-3 s grad=3.051e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.724852e-3 s grad=1.308e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.244826e-3 s grad=6.73e-7 s +[acc-interp] chunk base=77922 cs=25974 kernel=8.693114e-3 s grad=2.119e-6 s +[acc-interp] eval 4.3960755e-2 s +[acc-interp] thetaMarsh 7.0e-8 s (n=[acc-interp] thetaMarsh 5.5e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=5.506731e-3 s grad=2.52e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.110303e-3 s grad=2.326e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.232807e-3 s grad=3.414e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.698466e-3 s grad=6.246e-6 s +[acc-interp] eval 1.4745448e-2 s +[acc-interp] thetaMarsh 9.6e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.251791e-3 s grad=2.994e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.544162e-3 s grad=2.42e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.085418e-3 s grad=2.599e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.108561e-3 s grad=1.818e-6 s +[acc-interp] eval 9.813304e-3 s +[acc-interp] thetaMarsh 1.11e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.321683e-3 s grad=3.195e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.633127e-3 s grad=4.236e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.297574e-3 s grad=3.337e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.385757e-3 s grad=6.556e-6 s +[acc-interp] eval 1.0490185e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=2.361977e-3 s grad=6.794e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.505692e-3 s grad=2.141e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.94517e-3 s grad=4.65e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.904669e-3 s grad=5.299e-6 s +[acc-interp] eval 4.7040628e-2 s +[acc-interp] thetaMarsh 1.97e-7 s (n=[acc-interp] thetaMarsh 6.2e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.0120648e-2 s grad=3.551e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.161086e-3 s grad=6.104e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.11397e-3 s grad=2.019e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.427423e-3 s grad=2.499e-6 s +[acc-interp] eval 2.7687191e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=3.676391e-3 s grad=4.817e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.213137e-3 s grad=4.221e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.203004e-3 s grad=8.591e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=6.374125e-3 s grad=3.266e-6 s +[acc-interp] eval 4.544275e-2 s +[acc-interp] thetaMarsh 1.34e-7 s (n=[acc-interp] thetaMarsh 9.6e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.8600382e-2 s grad=3.76e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.497211e-3 s grad=3.18e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.876118e-3 s grad=2.342e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.688611e-3 s grad=4.212e-6 s +[acc-interp] eval 3.0049541e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=9.854048e-3 s grad=3.039e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.866637e-3 s grad=1.708e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.119483e-3 s grad=8.49e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.865863e-3 s grad=3.996e-6 s +[acc-interp] eval 5.1000987e-2 s +[acc-interp] thetaMarsh 1.6e-7 s (n=[acc-interp] thetaMarsh 1.0e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=2.8913969e-2 s grad=5.529e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=8.690835e-3 s grad=3.543e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.678443e-3 s grad=2.831e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.229726e-3 s grad=7.046e-6 s +[acc-interp] eval 4.6902893e-2 s +5) +[acc-interp] chunk base=0 cs=25974 kernel=3.930063e-3 s grad=2.304e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.906061e-3 s grad=2.278e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.372108e-3 s grad=3.988e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.162489e-3 s grad=3.453e-6 s +[acc-interp] eval 6.4606815e-2 s +[acc-interp] thetaMarsh 1.3e-7 s (n=[acc-interp] thetaMarsh 1.25e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=6.743523e-3 s grad=2.864e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.159413e-3 s grad=3.271e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.871398e-3 s grad=2.332e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.54419e-3 s grad=2.226e-6 s +[acc-interp] eval 1.6901322e-2 s +[acc-interp] thetaMarsh 1.12e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.542699e-3 s grad=2.041e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.490714e-3 s grad=1.879e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.698912e-3 s grad=4.106e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.089279e-3 s grad=2.777e-6 s +[acc-interp] eval 1.2293758e-2 s +[acc-interp] thetaMarsh 9.7e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.043602e-3 s grad=2.429e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.721656e-3 s grad=2.536e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=8.840988e-3 s grad=4.084e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.477652e-3 s grad=2.491e-6 s +[acc-interp] eval 1.8645314e-2 s +[acc-interp] thetaMarsh 1.19e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.47782e-3 s grad=2.035e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.453995e-3 s grad=3.277e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.209922e-3 s grad=2.181e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.095737e-3 s grad=3.882e-6 s +[acc-interp] eval 1.0829757e-2 s +[acc-interp] thetaMarsh 1.53e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.017068e-3 s grad=3.787e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.650066e-3 s grad=3.119e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.689373e-3 s grad=3.096e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.575287e-3 s grad=3.908e-6 s +[acc-interp] eval 1.3448307e-2 s +[acc-interp] thetaMarsh 7.9e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.697805e-3 s grad=3.354e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.138587e-3 s grad=5.088e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.800245e-3 s grad=3.848e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.637623e-3 s grad=2.538e-6 s +[acc-interp] eval 1.2427603e-2 s +[acc-interp] thetaMarsh 1.42e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.373643e-3 s grad=2.686e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.618974e-3 s grad=2.254e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.814755e-3 s grad=4.719e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.488605e-3 s grad=3.54e-6 s +[acc-interp] eval 1.1205045e-2 s +[acc-interp] thetaMarsh 7.7e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.454525e-3 s grad=4.863e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.448687e-3 s grad=1.181e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.946276e-3 s grad=5.101e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.21621e-3 s grad=1.755e-6 s +[acc-interp] eval 9.782425e-3 s +[acc-interp] thetaMarsh 7.5e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.536737e-3 s grad=3.862e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.876189e-3 s grad=3.543e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.906377e-3 s grad=2.715e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.52958e-3 s grad=2.326e-6 s +[acc-interp] eval 1.2317448e-2 s +[acc-interp] thetaMarsh 1.18e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.531118e-3 s grad=1.975e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.532405e-3 s grad=2.503e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.526963e-3 s grad=1.932e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.579342e-3 s grad=1.631e-6 s +[acc-interp] eval 1.1642099e-2 s +[acc-interp] thetaMarsh 1.12e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.567894e-3 s grad=1.747e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.289626e-3 s grad=1.861e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.136464e-3 s grad=1.429e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.525198e-3 s grad=3.464e-6 s +[acc-interp] eval 1.1068353e-2 s +[acc-interp] thetaMarsh 1.52e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.657518e-3 s grad=3.184e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.061846e-3 s grad=1.555e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.385093e-3 s grad=1.592e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.797407e-3 s grad=3.56e-6 s +[acc-interp] eval 1.1323607e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=2.353015e-3 s grad=2.333e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.072718e-3 s grad=2.509e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.436694e-3 s grad=2.563e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.419585e-3 s grad=2.426e-6 s +[acc-interp] eval 0.165282293 s +[acc-interp] thetaMarsh 1.17e-7 s (n=[acc-interp] thetaMarsh 1.44e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.0639859e-2 s grad=4.083e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.993593e-3 s grad=2.939e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.498899e-3 s grad=2.435e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.95862e-3 s grad=3.593e-6 s +[acc-interp] eval 2.1756371e-2 s +[acc-interp] thetaMarsh 1.0e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.611685e-3 s grad=4.702e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.042998e-3 s grad=3.199e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.207884e-3 s grad=2.557e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.868476e-3 s grad=2.956e-6 s +[acc-interp] eval 1.5426885e-2 s +[acc-interp] thetaMarsh 1.2e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=4.567593e-3 s grad=3.669e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.0157666e-2 s grad=4.083e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.003002e-3 s grad=2.446e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.078769e-3 s grad=2.859e-6 s +[acc-interp] eval 2.2305997e-2 s +[acc-interp] thetaMarsh 9.7e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.514026e-3 s grad=2.297e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.478939e-3 s grad=1.837e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.431874e-3 s grad=2.712e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.029181e-3 s grad=1.996e-6 s +[acc-interp] eval 1.3092296e-2 s +[acc-interp] thetaMarsh 9.9e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.589012e-3 s grad=3.985e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.11438e-3 s grad=2.583e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.951948e-3 s grad=2.92e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.699146e-3 s grad=3.659e-6 s +[acc-interp] eval 1.3919051e-2 s +[acc-interp] thetaMarsh 1.07e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.125035e-3 s grad=3.233e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.377213e-3 s grad=3.923e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.789029e-3 s grad=3.541e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.11257e-3 s grad=3.31e-6 s +[acc-interp] eval 1.3965664e-2 s +[acc-interp] thetaMarsh 9.5e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.669837e-3 s grad=1.89e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.628433e-3 s grad=2.895e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.204202e-3 s grad=3.832e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.441713e-3 s grad=3.245e-6 s +[acc-interp] eval 1.4491304e-2 s +[acc-interp] thetaMarsh 9.8e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.116759e-3 s grad=3.241e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.262788e-3 s grad=2.767e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.198814e-3 s grad=3.648e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.745327e-3 s grad=2.788e-6 s +[acc-interp] eval 1.3846151e-2 s +[acc-interp] thetaMarsh 1.16e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.937694e-3 s grad=2.745e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.633722e-3 s grad=3.602e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.518113e-3 s grad=3.276e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.00669e-3 s grad=3.942e-6 s +[acc-interp] eval 1.2701806e-2 s +[acc-interp] thetaMarsh 1.24e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.022489e-3 s grad=3.103e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.864474e-3 s grad=3.108e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.961486e-3 s grad=2.677e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.591331e-3 s grad=3.278e-6 s +[acc-interp] eval 1.299445e-2 s +[acc-interp] thetaMarsh 1.14e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.073247e-3 s grad=3.646e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.367383e-3 s grad=3.914e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.656273e-3 s grad=2.623e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.44505e-3 s grad=2.434e-6 s +[acc-interp] eval 1.3111105e-2 s +[acc-interp] thetaMarsh 9.5e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.574119e-3 s grad=1.967e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.237155e-3 s grad=1.715e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.217322e-3 s grad=3.346e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.239456e-3 s grad=2.708e-6 s +[acc-interp] eval 1.2704015e-2 s +[acc-interp] thetaMarsh 1.24e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.776882e-3 s grad=3.178e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.891605e-3 s grad=2.98e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.639094e-3 s grad=3.561e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.578776e-3 s grad=3.996e-6 s +[acc-interp] eval 1.4212963e-2 s +[acc-interp] thetaMarsh 9.8e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.20161e-3 s grad=2.516e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.635942e-3 s grad=2.925e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.534074e-3 s grad=2.437e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.712879e-3 s grad=3.139e-6 s +[acc-interp] eval 1.3614883e-2 s +[acc-interp] thetaMarsh 1.16e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.885609e-3 s grad=2.69e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.520136e-3 s grad=1.931e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.436087e-3 s grad=1.892e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.97361e-3 s grad=2.275e-6 s +[acc-interp] eval 1.2244065e-2 s +[acc-interp] thetaMarsh 1.08e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.402789e-3 s grad=2.583e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.948822e-3 s grad=4.104e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.718192e-3 s grad=3.645e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.751854e-3 s grad=3.294e-6 s +[acc-interp] eval 1.343076e-2 s +[acc-interp] thetaMarsh 1.54e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.792156e-3 s grad=3.21e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.937252e-3 s grad=3.667e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.318411e-3 s grad=3.768e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.891329e-3 s grad=3.787e-6 s +[acc-interp] eval 1.4451751e-2 s +[acc-interp] thetaMarsh 1.28e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.094882e-3 s grad=4.069e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.193656e-3 s grad=3.789e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.134576e-3 s grad=2.863e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.585224e-3 s grad=2.825e-6 s +[acc-interp] eval 1.455528e-2 s +[acc-interp] thetaMarsh 6.5e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.237334e-3 s grad=2.617e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.912615e-3 s grad=1.854e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.458425e-3 s grad=2.53e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.120639e-3 s grad=3.586e-6 s +[acc-interp] eval 1.3903445e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=2.947462e-3 s grad=3.763e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.150173e-3 s grad=2.827e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.585135e-3 s grad=2.992e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.54657e-3 s grad=3.825e-6 s +[acc-interp] eval 0.29233653 s +[acc-interp] thetaMarsh 1.78e-7 s (n=[acc-interp] thetaMarsh 1.54e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.2491137e-2 s grad=3.644e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.664603e-3 s grad=2.627e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.649141e-3 s grad=5.029e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.362632e-3 s grad=4.075e-6 s +[acc-interp] eval 3.3904283e-2 s +[acc-interp] thetaMarsh 1.14e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.240565e-3 s grad=4.299e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.179781e-3 s grad=2.855e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.390804e-3 s grad=5.901e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.681041e-3 s grad=4.22e-6 s +[acc-interp] eval 1.5269607e-2 s +[acc-interp] thetaMarsh 1.21e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.897255e-3 s grad=4.484e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.376494e-3 s grad=4.041e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.2837e-3 s grad=4.226e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.528105e-3 s grad=5.325e-6 s +[acc-interp] eval 1.4836274e-2 s +[acc-interp] thetaMarsh 4.34e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.988555e-3 s grad=4.357e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.203778e-3 s grad=4.246e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.287614e-3 s grad=3.701e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.224145e-3 s grad=5.154e-6 s +[acc-interp] eval 1.541947e-2 s +[acc-interp] thetaMarsh 1.13e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.29064e-3 s grad=3.54e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.222557e-3 s grad=3.508e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.936838e-3 s grad=2.33e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.904397e-3 s grad=4.72e-6 s +[acc-interp] eval 1.3958579e-2 s +[acc-interp] thetaMarsh 9.8e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.473264e-3 s grad=4.492e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=6.27898e-3 s grad=3.489e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.820998e-3 s grad=2.492e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.848513e-3 s grad=2.108e-6 s +[acc-interp] eval 1.7012179e-2 s +[acc-interp] thetaMarsh 1.02e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.099871e-3 s grad=2.854e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.118401e-3 s grad=2.962e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.44552e-3 s grad=2.729e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.381322e-3 s grad=3.882e-6 s +[acc-interp] eval 1.4572581e-2 s +[acc-interp] thetaMarsh 1.21e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.528769e-3 s grad=3.586e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.174234e-3 s grad=3.269e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.970026e-3 s grad=1.6659e-5 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.725991e-3 s grad=3.877e-6 s +[acc-interp] eval 1.8211194e-2 s +[acc-interp] thetaMarsh 1.25e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.68135e-3 s grad=3.573e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.288973e-3 s grad=3.605e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.405559e-3 s grad=3.506e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.843118e-3 s grad=5.87e-6 s +[acc-interp] eval 1.7046481e-2 s +[acc-interp] thetaMarsh 1.15e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.885615e-3 s grad=4.634e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.295242e-3 s grad=3.931e-6 s 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chunk base=0 cs=25974 kernel=2.650168e-3 s grad=1.043e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.789792e-3 s grad=3.239e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.110222e-3 s grad=4.671e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.162894e-3 s grad=3.114e-6 s +[acc-interp] eval 1.2825768e-2 s +[acc-interp] thetaMarsh 6.6e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.879201e-3 s grad=4.587e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.043252e-3 s grad=5.739e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.072513e-3 s grad=2.842e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.371601e-3 s grad=3.534e-6 s +[acc-interp] eval 1.4166215e-2 s +[acc-interp] thetaMarsh 7.3e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.682173e-3 s grad=2.162e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.258401e-3 s grad=3.825e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.012837e-3 s grad=3.499e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.428743e-3 s grad=3.758e-6 s +[acc-interp] eval 1.4700819e-2 s +[acc-interp] thetaMarsh 1.23e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.502056e-3 s grad=2.879e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.243917e-3 s grad=2.85e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.326479e-3 s grad=2.956e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.711029e-3 s grad=3.876e-6 s +[acc-interp] eval 1.5360662e-2 s +[acc-interp] thetaMarsh 1.22e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.698416e-3 s grad=4.149e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.465545e-3 s grad=5.886e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.280706e-3 s grad=9.39e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.506369e-3 s grad=4.254e-6 s +[acc-interp] eval 1.5057915e-2 s +[acc-interp] thetaMarsh 1.61e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.601707e-3 s grad=4.024e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.878084e-3 s grad=3.01e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.179819e-3 s grad=3.705e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.295679e-3 s grad=1.916e-6 s +[acc-interp] eval 1.452325e-2 s +[acc-interp] thetaMarsh 8.4e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.130168e-3 s grad=4.555e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.420062e-3 s grad=3.731e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.288671e-3 s grad=2.666e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.0384062e-2 s grad=4.018e-6 s +[acc-interp] eval 2.182734e-2 s +[acc-interp] thetaMarsh 1.94e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.578021e-3 s grad=4.159e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.189246e-3 s grad=3.193e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.078066e-3 s grad=2.529e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.0353e-3 s grad=3.999e-6 s +[acc-interp] eval 1.556433e-2 s +[acc-interp] thetaMarsh 1.29e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.758897e-3 s grad=4.961e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.554454e-3 s grad=9.03e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.251141e-3 s grad=1.499e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.501856e-3 s grad=4.114e-6 s +[acc-interp] eval 1.4921085e-2 s +[acc-interp] thetaMarsh 6.5e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.157467e-3 s grad=1.009e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.873697e-3 s grad=8.948e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.892685e-3 s grad=2.235e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.378802e-3 s grad=6.894e-6 s +[acc-interp] eval 1.3014366e-2 s +[acc-interp] thetaMarsh 2.89e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.48411e-3 s grad=3.149e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.257801e-3 s grad=3.479e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.83044e-3 s grad=2.505e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.434732e-3 s grad=2.89e-6 s +[acc-interp] eval 1.4607549e-2 s +[acc-interp] thetaMarsh 1.13e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.203286e-3 s grad=2.878e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.214528e-3 s grad=4.455e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.019531e-3 s grad=2.421e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.776821e-3 s grad=1.842e-6 s +[acc-interp] eval 1.491282e-2 s +[acc-interp] thetaMarsh 1.16e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.579258e-3 s grad=2.208e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.601138e-3 s grad=5.604e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.089937e-3 s grad=3.545e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.975234e-3 s grad=3.982e-6 s +[acc-interp] eval 1.2794793e-2 s +[acc-interp] thetaMarsh 6.2e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.855355e-3 s grad=4.1e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.511936e-3 s grad=2.987e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.432438e-3 s grad=5.484e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.977237e-3 s grad=3.707e-6 s +[acc-interp] eval 1.6401204e-2 s +[acc-interp] thetaMarsh 1.1e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=8.517045e-3 s grad=3.418e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.948083e-3 s grad=4.197e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.543298e-3 s grad=3.65e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.984544e-3 s grad=2.769e-6 s +[acc-interp] eval 2.0686396e-2 s +[acc-interp] thetaMarsh 1.41e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.704137e-3 s grad=3.101e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.103682e-3 s grad=3.626e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.470313e-3 s grad=3.77e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.169758e-3 s grad=2.931e-6 s +[acc-interp] eval 1.609469e-2 s +[acc-interp] thetaMarsh 9.9e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.643134e-3 s grad=2.301e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.029668e-3 s grad=3.968e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.439668e-3 s grad=4.106e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.734142e-3 s grad=3.275e-6 s +[acc-interp] eval 1.4472096e-2 s +[acc-interp] thetaMarsh 1.97e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.23317e-3 s grad=2.487e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.512807e-3 s grad=1.804e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.780031e-3 s grad=5.347e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.583445e-3 s grad=2.814e-6 s +[acc-interp] eval 1.5665839e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=3.147433e-3 s grad=8.603e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.813458e-3 s grad=3.391e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.212197e-3 s grad=3.467e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.54757e-3 s grad=2.15e-6 s +[acc-interp] eval 0.51530764 s +[acc-interp] thetaMarsh 6.0e-8 s (n=[acc-interp] thetaMarsh 5.4e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.8162811e-2 s grad=4.435e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.846642e-3 s grad=3.299e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.275594e-3 s grad=3.245e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.116075e-3 s grad=3.328e-6 s +[acc-interp] eval 3.9619305e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=6.248254e-3 s grad=3.04e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.932557e-3 s grad=3.645e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.47409e-3 s grad=3.272e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.321734e-3 s grad=4.213e-6 s +[acc-interp] eval 5.896432e-2 s +[acc-interp] thetaMarsh 1.32e-7 s (n=[acc-interp] thetaMarsh 7.9e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.9057746e-2 s grad=5.147e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.447664e-3 s grad=4.21e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.942292e-3 s grad=4.493e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.565725e-3 s grad=5.252e-6 s +[acc-interp] eval 4.1338429e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=3.688109e-3 s grad=4.455e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.305288e-3 s grad=4.986e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.527999e-3 s grad=4.74e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.415096e-3 s grad=2.777e-6 s +[acc-interp] eval 5.7570495e-2 s +[acc-interp] thetaMarsh 1.0e-7 s (n=[acc-interp] thetaMarsh 2.03e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=8.311515e-3 s grad=3.722e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.382421e-3 s grad=4.151e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.263449e-3 s grad=6.507e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.262303e-3 s grad=1.269e-6 s +[acc-interp] eval 1.5962403e-2 s +[acc-interp] thetaMarsh 6.3e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.302237e-3 s grad=1.571e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.106571e-3 s grad=3.191e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.010393e-3 s grad=4.291e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.393572e-3 s grad=3.178e-6 s +[acc-interp] eval 1.1750215e-2 s +[acc-interp] thetaMarsh 1.17e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.991353e-3 s grad=4.597e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.265676e-3 s grad=7.13e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.522284e-3 s grad=3.763e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.004019e-2 s grad=4.409e-6 s +[acc-interp] eval 1.8607364e-2 s +[acc-interp] thetaMarsh 6.4e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.327893e-3 s grad=9.945e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.168186e-3 s grad=2.657e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.400459e-3 s grad=3.039e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.208798e-3 s grad=4.428e-6 s +[acc-interp] eval 1.0042842e-2 s +[acc-interp] thetaMarsh 9.6e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.111606e-3 s grad=2.398e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.334963e-3 s grad=3.61e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.784588e-3 s grad=4.765e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.165765e-3 s grad=3.54e-6 s +[acc-interp] eval 1.0342144e-2 s +[acc-interp] thetaMarsh 1.07e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.813908e-3 s grad=2.851e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.56688e-3 s grad=2.465e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.5704e-3 s grad=2.432e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.607602e-3 s grad=1.88e-6 s +[acc-interp] eval 7.668266e-3 s +[acc-interp] thetaMarsh 1.07e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.788213e-3 s grad=2.588e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.843818e-3 s grad=2.837e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.108174e-3 s grad=2.903e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.411397e-3 s grad=4.914e-6 s +[acc-interp] eval 9.46701e-3 s +[acc-interp] thetaMarsh 1.27e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.602953e-3 s grad=4.393e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.148835e-3 s grad=3.786e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.084738e-3 s grad=3.451e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.29932e-3 s grad=2.856e-6 s +[acc-interp] eval 1.028858e-2 s +[acc-interp] thetaMarsh 7.6e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.028483e-3 s grad=2.447e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.970465e-3 s grad=2.63e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.385134e-3 s grad=4.755e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.776265e-3 s grad=3.849e-6 s +[acc-interp] eval 1.0104162e-2 s +[acc-interp] thetaMarsh 8.1e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.351073e-3 s grad=1.0718e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.089403e-3 s grad=8.412e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.031052e-3 s grad=1.361e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.162158e-3 s grad=3.124e-6 s +[acc-interp] eval 9.336694e-3 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=2.632778e-3 s grad=4.011e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.519964e-3 s grad=3.001e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.151642e-3 s grad=5.804e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.139386e-3 s grad=5.417e-6 s +[acc-interp] eval 0.12474793 s +[acc-interp] thetaMarsh 1.98e-7 s (n=[acc-interp] thetaMarsh 6.8e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.5430554e-2 s grad=4.005e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.211221e-3 s grad=1.0125e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.909232e-3 s grad=4.092e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=7.87763e-3 s grad=3.616e-6 s +[acc-interp] eval 3.3287051e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=3.489765e-3 s grad=3.14e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.444914e-3 s grad=2.893e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.223146e-3 s grad=4.904e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.88322e-3 s grad=4.392e-6 s +[acc-interp] eval 4.9349499e-2 s +[acc-interp] thetaMarsh 7.5e-8 s (n=[acc-interp] thetaMarsh 5.8e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=6.193735e-3 s grad=3.598e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.879421e-3 s grad=5.102e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.335693e-3 s grad=3.908e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.709119e-3 s grad=2.791e-6 s +[acc-interp] eval 1.7699381e-2 s +[acc-interp] thetaMarsh 1.3e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.512896e-3 s grad=2.179e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.850601e-3 s grad=1.337e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.680006e-3 s grad=4.184e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.629925e-3 s grad=1.523e-6 s +[acc-interp] eval 1.2480354e-2 s +[acc-interp] thetaMarsh 6.0e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.628607e-3 s grad=8.542e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.631338e-3 s grad=6.771e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.702542e-3 s grad=7.015e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.635527e-3 s grad=2.909e-6 s +[acc-interp] eval 1.3336739e-2 s +[acc-interp] thetaMarsh 6.0e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=8.11617e-3 s grad=2.495e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.30443e-3 s grad=2.432e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.427061e-3 s grad=7.832e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.401756e-3 s grad=3.839e-6 s +[acc-interp] eval 1.6136991e-2 s +[acc-interp] thetaMarsh 1.15e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.625486e-3 s grad=1.1055e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.327741e-3 s grad=1.982e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.447952e-3 s grad=1.162e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.354685e-3 s grad=1.45e-6 s +[acc-interp] eval 1.051213e-2 s +[acc-interp] thetaMarsh 6.6e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.26388e-3 s grad=3.206e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.226567e-3 s grad=3.568e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.693342e-3 s grad=4.526e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.2954e-3 s grad=3.301e-6 s +[acc-interp] eval 1.0322715e-2 s +[acc-interp] thetaMarsh 1.53e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.318688e-3 s grad=2.837e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.374162e-3 s grad=3.171e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.343112e-3 s grad=2.472e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.323782e-3 s grad=2.572e-6 s +[acc-interp] eval 1.0238054e-2 s +[acc-interp] thetaMarsh 6.6e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.645233e-3 s grad=3.037e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.670551e-3 s grad=2.777e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.333985e-3 s grad=3.109e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.226827e-3 s grad=3.582e-6 s +[acc-interp] eval 1.0916511e-2 s +[acc-interp] thetaMarsh 1.2e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.207667e-3 s grad=2.455e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.13546e-3 s grad=2.277e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.432656e-3 s grad=2.662e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.593017e-3 s grad=5.373e-6 s +[acc-interp] eval 1.072747e-2 s +[acc-interp] thetaMarsh 6.8e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.356485e-3 s grad=2.788e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.384855e-3 s grad=2.745e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.282191e-3 s grad=2.473e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.352226e-3 s grad=2.645e-6 s +[acc-interp] eval 1.0596517e-2 s +[acc-interp] thetaMarsh 1.18e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.597863e-3 s grad=4.66e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.51714e-3 s grad=3.942e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.380645e-3 s grad=3.296e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.363329e-3 s grad=2.838e-6 s +[acc-interp] eval 1.0757855e-2 s +[acc-interp] thetaMarsh 7.6e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.873553e-3 s grad=2.396e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.274097e-3 s grad=3.371e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.752505e-3 s grad=2.678e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.36345e-3 s grad=2.351e-6 s +[acc-interp] eval 1.1077229e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=2.169191e-3 s grad=2.593e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.172016e-3 s grad=7.943e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.244545e-3 s grad=2.215e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.477469e-3 s grad=2.822e-6 s +[acc-interp] eval 0.156894708 s +[acc-interp] thetaMarsh 7.1e-8 s (n=[acc-interp] thetaMarsh 2.89e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=7.340736e-3 s grad=4.764e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.606077e-3 s grad=4.27e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.244347e-3 s grad=4.883e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.788147e-3 s grad=4.359e-6 s +[acc-interp] eval 2.0048049e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=2.925818e-3 s grad=2.693e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.416758e-3 s grad=1.734e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.859899e-3 s grad=1.952e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=8.262786e-3 s grad=3.35e-6 s +[acc-interp] eval 3.8003806e-2 s +[acc-interp] thetaMarsh 1.28e-7 s (n=[acc-interp] thetaMarsh 1.18e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=8.870901e-3 s grad=3.94e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.668619e-3 s grad=4.479e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.882014e-3 s grad=4.455e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.515413e-3 s grad=3.439e-6 s +[acc-interp] eval 1.7948035e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=2.877128e-3 s grad=3.309e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.452228e-3 s grad=3.266e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.551215e-3 s grad=4.286e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.149079e-3 s grad=3.127e-6 s +[acc-interp] eval 3.0163731e-2 s +[acc-interp] thetaMarsh 7.2e-8 s (n=[acc-interp] thetaMarsh 2.25e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=2.9959905e-2 s grad=4.65e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.927186e-3 s grad=4.903e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.841318e-3 s grad=3.935e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.65422e-3 s grad=3.466e-6 s +[acc-interp] eval 4.3222611e-2 s +5) +[acc-interp] chunk base=0 cs=25974 kernel=4.984498e-3 s grad=3.768e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.856003e-3 s grad=3.345e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.756349e-3 s grad=3.044e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.834265e-3 s grad=3.158e-6 s +[acc-interp] eval 6.2324338e-2 s +[acc-interp] thetaMarsh 1.41e-7 s (n=[acc-interp] thetaMarsh 1.17e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=7.528911e-3 s grad=3.64e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.259378e-3 s grad=2.152e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.359419e-3 s grad=2.048e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.853508e-3 s grad=4.91e-6 s +[acc-interp] eval 1.6609654e-2 s +[acc-interp] thetaMarsh 1.26e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.952407e-3 s grad=3.357e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.963375e-3 s grad=2.255e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.374153e-3 s grad=1.75e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.824714e-3 s grad=8.997e-6 s +[acc-interp] eval 1.2586229e-2 s +[acc-interp] thetaMarsh 1.0e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.971991e-3 s grad=3.436e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.871641e-3 s grad=3.109e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.453433e-3 s grad=2.276e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.4255389e-2 s grad=4.309e-6 s +[acc-interp] eval 2.4136602e-2 s +[acc-interp] thetaMarsh 1.19e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.702781e-3 s grad=4.131e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.335055e-3 s grad=2.552e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.940727e-3 s grad=2.596e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.753304e-3 s grad=3.667e-6 s +[acc-interp] eval 1.1283118e-2 s +[acc-interp] thetaMarsh 1.05e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.469441e-3 s grad=2.86e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.416839e-3 s grad=2.454e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.270119e-3 s grad=1.809e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.636814e-3 s grad=2.816e-6 s +[acc-interp] eval 1.1227864e-2 s +[acc-interp] thetaMarsh 1.09e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.441534e-3 s grad=3.023e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.707428e-3 s grad=4.2e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.560019e-3 s grad=3.227e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.507023e-3 s grad=3.243e-6 s +[acc-interp] eval 1.2631495e-2 s +[acc-interp] thetaMarsh 1.01e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.347054e-3 s grad=2.728e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.124928e-3 s grad=1.986e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.192186e-3 s grad=2.754e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.729555e-3 s grad=1.854e-6 s +[acc-interp] eval 9.858665e-3 s +[acc-interp] thetaMarsh 1.24e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.164657e-3 s grad=2.123e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.097684e-3 s grad=1.944e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.989302e-3 s grad=1.798e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.383264e-3 s grad=4.019e-6 s +[acc-interp] eval 1.005607e-2 s +[acc-interp] thetaMarsh 1.13e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.570769e-3 s grad=2.352e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.422296e-3 s grad=2.849e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.446243e-3 s grad=2.397e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.598273e-3 s grad=3.431e-6 s +[acc-interp] eval 1.1527922e-2 s +[acc-interp] thetaMarsh 1.13e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.87651e-3 s grad=3.645e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.784181e-3 s grad=3.013e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.142284e-3 s grad=2.782e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.519153e-3 s grad=2.76e-6 s +[acc-interp] eval 1.2934162e-2 s +[acc-interp] thetaMarsh 1.36e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.024234e-3 s grad=2.373e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.433952e-3 s grad=2.926e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.615602e-3 s grad=3.657e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.275492e-3 s grad=2.581e-6 s +[acc-interp] eval 1.0878209e-2 s +[acc-interp] thetaMarsh 1.07e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.962647e-3 s grad=2.614e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.960231e-3 s grad=1.96e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.06767e-3 s grad=2.692e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.030046e-3 s grad=2.788e-6 s +[acc-interp] eval 1.0561396e-2 s +[acc-interp] thetaMarsh 1.65e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.307468e-3 s grad=2.725e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.130696e-3 s grad=2.701e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.080778e-3 s grad=2.072e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.586001e-3 s grad=2.1e-6 s +[acc-interp] eval 1.0590933e-2 s +[acc-interp] thetaMarsh 1.05e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.131925e-3 s grad=2.683e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.807023e-3 s grad=2.13e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.543975e-3 s grad=4.653e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.379315e-3 s grad=3.042e-6 s +[acc-interp] eval 1.1629906e-2 s +[acc-interp] thetaMarsh 1.02e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.196962e-3 s grad=2.099e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.930137e-3 s grad=1.836e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.186883e-3 s grad=2.43e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.061511e-3 s grad=1.103e-6 s +[acc-interp] eval 9.565006e-3 s +[acc-interp] thetaMarsh 6.5e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.033031e-3 s grad=6.903e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.969158e-3 s grad=7.588e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.90996e-3 s grad=2.971e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.774677e-3 s grad=2.564e-6 s +[acc-interp] eval 9.791386e-3 s +[acc-interp] thetaMarsh 1.27e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.243964e-3 s grad=2.423e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.785513e-3 s grad=3.71e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.03162e-3 s grad=2.86e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.313196e-3 s grad=2.76e-6 s +[acc-interp] eval 1.0897382e-2 s +[acc-interp] thetaMarsh 9.6e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.065958e-3 s grad=2.478e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.054927e-3 s grad=3.097e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.978208e-3 s grad=1.364e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.07485e-3 s grad=1.203e-6 s +[acc-interp] eval 9.177112e-3 s +[acc-interp] thetaMarsh 1.03e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.289425e-3 s grad=5.65e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.432891e-3 s grad=3.236e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.962863e-3 s grad=2.133e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.119855e-3 s grad=2.848e-6 s +[acc-interp] eval 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kernel=2.075085e-3 s grad=6.771e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.946579e-3 s grad=5.264e-6 s +[acc-interp] eval 1.0104735e-2 s +[acc-interp] thetaMarsh 1.67e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.910704e-3 s grad=7.699e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.90474e-3 s grad=6.838e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.792959e-3 s grad=6.93e-7 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.56425e-3 s grad=3.435e-6 s +[acc-interp] eval 8.85634e-3 s +[acc-interp] thetaMarsh 5.9e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.605896e-3 s grad=1.398e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.902205e-3 s grad=2.011e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.869574e-3 s grad=5.984e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.808028e-3 s grad=4.671e-6 s +[acc-interp] eval 7.886615e-3 s +[acc-interp] thetaMarsh 6.0e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.80583e-3 s 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base=51948 cs=25974 kernel=1.734518e-3 s grad=1.807e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.579529e-3 s grad=1.059e-5 s +[acc-interp] eval 9.297811e-3 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=2.093812e-3 s grad=3.029e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.150842e-3 s grad=9.358e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.076073e-3 s grad=2.344e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.987375e-3 s grad=3.91e-6 s +[acc-interp] eval 0.342373552 s +[acc-interp] thetaMarsh 7.5e-8 s (n=[acc-interp] thetaMarsh 7.8e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=1.8925723e-2 s grad=4.17e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.519014e-3 s grad=4.218e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.563362e-3 s grad=4.455e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.404269e-3 s grad=3.962e-6 s +[acc-interp] eval 3.1512615e-2 s +[acc-interp] thetaMarsh 1.23e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=2.843169e-3 s grad=3.397e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.391774e-3 s grad=3.212e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=7.192217e-3 s grad=3.032e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.75534e-3 s grad=5.406e-6 s +[acc-interp] eval 1.7581266e-2 s +[acc-interp] thetaMarsh 1.03e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.042236e-3 s grad=4.966e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.025579e-3 s grad=4.249e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.48949e-3 s grad=4.134e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.965672e-3 s grad=4.414e-6 s +[acc-interp] eval 1.4089055e-2 s +[acc-interp] thetaMarsh 1.11e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.38114e-3 s grad=5.036e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.963546e-3 s grad=3.892e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.828931e-3 s grad=2.863e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.161905e-3 s grad=3.907e-6 s +[acc-interp] eval 1.4890787e-2 s +[acc-interp] thetaMarsh 1.01e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=2.647681e-3 s grad=4.969e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.190666e-3 s grad=4.134e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.82768e-3 s grad=3.842e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.775316e-3 s grad=2.573e-6 s +[acc-interp] eval 1.3036527e-2 s +[acc-interp] thetaMarsh 1.0e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=2.449502e-3 s grad=2.707e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.78953e-3 s grad=5.289e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.262726e-3 s grad=3.811e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.859398e-3 s grad=3.561e-6 s +[acc-interp] eval 1.2800132e-2 s +[acc-interp] thetaMarsh 1.02e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=2.453242e-3 s grad=2.479e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.91477e-3 s 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(n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.468592e-3 s grad=4.552e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.355944e-3 s grad=2.806e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.224291e-3 s grad=1.476e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.585663e-3 s grad=1.744e-6 s +[acc-interp] eval 1.4384701e-2 s +[acc-interp] thetaMarsh 6.5e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.15947e-3 s grad=4.131e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.642361e-3 s grad=4.471e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.014061e-3 s grad=3.57e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.528304e-3 s grad=3.187e-6 s +[acc-interp] eval 1.4807265e-2 s +[acc-interp] thetaMarsh 9.7e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.558072e-3 s grad=4.514e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.453321e-3 s grad=4.329e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.696275e-3 s grad=3.105e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.143392e-3 s grad=2.36e-6 s +[acc-interp] eval 1.5395771e-2 s +[acc-interp] thetaMarsh 1.13e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=1.0189679e-2 s grad=3.868e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.845729e-3 s grad=3.517e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.571836e-3 s grad=2.161e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.50736e-3 s grad=2.443e-6 s +[acc-interp] eval 1.9556787e-2 s +[acc-interp] thetaMarsh 1.01e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.239277e-3 s grad=5.063e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.787247e-3 s grad=3.139e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.734909e-3 s grad=2.343e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.111578e-3 s grad=2.266e-6 s +[acc-interp] eval 1.3320194e-2 s +[acc-interp] thetaMarsh 1.83e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=2.630502e-3 s grad=2.407e-6 s +[acc-interp] chunk base=25974 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kernel=3.077069e-3 s grad=3.398e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.550845e-3 s grad=2.72e-6 s +[acc-interp] eval 1.4142546e-2 s +[acc-interp] thetaMarsh 1.3e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.018358e-3 s grad=4.544e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.047142e-3 s grad=4.684e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.281599e-3 s grad=2.405e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.274236e-3 s grad=5.746e-6 s +[acc-interp] eval 1.3490507e-2 s +[acc-interp] thetaMarsh 1.92e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=2.908045e-3 s grad=2.324e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.575365e-3 s grad=1.515e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.024123e-3 s grad=9.807e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=7.109327e-3 s grad=2.853e-6 s +[acc-interp] eval 1.6319607e-2 s +[acc-interp] thetaMarsh 1.6e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=2.553198e-3 s 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grad=3.426e-6 s +[acc-interp] eval 1.3492323e-2 s +[acc-interp] thetaMarsh 9.6e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=2.926918e-3 s grad=3.115e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.217042e-3 s grad=1.0755e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.255282e-3 s grad=7.025e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.914004e-3 s grad=6.193e-6 s +[acc-interp] eval 1.4946824e-2 s +[acc-interp] thetaMarsh 6.8e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=2.896609e-3 s grad=4.202e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.042932e-3 s grad=3.89e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.987295e-3 s grad=3.383e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.189656e-3 s grad=3.88e-6 s +[acc-interp] eval 1.3041646e-2 s +[acc-interp] thetaMarsh 1.32e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=4.120129e-3 s grad=3.75e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.856405e-3 s grad=6.394e-6 s 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kernel=2.839945e-3 s grad=4.165e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.483266e-3 s grad=4.216e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.703122e-3 s grad=3.189e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=8.260195e-3 s grad=4.415e-6 s +[acc-interp] eval 0.45244169 s +[acc-interp] thetaMarsh 6.1e-8 s (n=[acc-interp] thetaMarsh 5.7e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.319388e-2 s grad=5.501e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=8.313469e-3 s grad=4.64e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.88279e-3 s grad=4.036e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.354811e-3 s grad=3.778e-6 s +[acc-interp] eval 3.9610921e-2 s +[acc-interp] thetaMarsh 6.3e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.974871e-3 s grad=3.914e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.960969e-3 s grad=4.811e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.604372e-3 s grad=4.071e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.527719e-3 s grad=4.847e-6 s +[acc-interp] eval 1.591289e-2 s +[acc-interp] thetaMarsh 1.88e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.346285e-3 s grad=4.997e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.25296e-3 s grad=4.342e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.697222e-3 s grad=5.419e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.965654e-3 s grad=3.449e-6 s +[acc-interp] eval 1.9127533e-2 s +[acc-interp] thetaMarsh 6.1e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.362297e-3 s grad=3.911e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.146746e-3 s grad=3.991e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.949989e-3 s grad=4.617e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.266446e-3 s grad=5.556e-6 s +[acc-interp] eval 1.7564974e-2 s +[acc-interp] thetaMarsh 6.3e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.34855e-3 s grad=5.196e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.700693e-3 s grad=5.943e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.241036e-3 s grad=4.676e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.0645246e-2 s grad=2.151e-6 s +[acc-interp] eval 2.4795707e-2 s +[acc-interp] thetaMarsh 6.1e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.664103e-3 s grad=2.942e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.326039e-3 s grad=6.041e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.894937e-3 s grad=3.982e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.741906e-3 s grad=3.867e-6 s +[acc-interp] eval 1.7487318e-2 s +[acc-interp] thetaMarsh 6.3e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.502746e-3 s grad=3.185e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.993854e-3 s grad=3.808e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.632042e-3 s grad=4.051e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.893293e-3 s grad=3.561e-6 s +[acc-interp] eval 1.6862257e-2 s +[acc-interp] thetaMarsh 6.4e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.728803e-3 s grad=4.523e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.937002e-3 s grad=3.332e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.841072e-3 s grad=3.799e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.0310409e-2 s grad=4.241e-6 s +[acc-interp] eval 2.5733045e-2 s +[acc-interp] thetaMarsh 6.4e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.039471e-3 s grad=4.194e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.616371e-3 s grad=1.2706e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.820385e-3 s grad=4.549e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.547138e-3 s grad=4.701e-6 s +[acc-interp] eval 1.6885176e-2 s +[acc-interp] thetaMarsh 6.3e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.339052e-3 s grad=3.797e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.58299e-3 s grad=3.756e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.340907e-3 s grad=3.906e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.994081e-3 s grad=3.621e-6 s +[acc-interp] eval 1.8100233e-2 s +[acc-interp] thetaMarsh 7.3e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.793859e-3 s grad=2.947e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.215024e-3 s grad=5.228e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.934043e-3 s grad=4.301e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=8.851593e-3 s grad=4.582e-6 s +[acc-interp] eval 2.1735181e-2 s +[acc-interp] thetaMarsh 6.3e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=5.266278e-3 s grad=4.022e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.204828e-3 s grad=3.066e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.100941e-3 s grad=1.874e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.553281e-3 s grad=3.405e-6 s +[acc-interp] eval 2.0905126e-2 s +[acc-interp] thetaMarsh 8.5e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.276562e-3 s grad=4.017e-6 s 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eval 1.8727933e-2 s +[acc-interp] thetaMarsh 5.9e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.955705e-3 s grad=4.452e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.590142e-3 s grad=4.564e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.242743e-3 s grad=3.812e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.587472e-3 s grad=4.613e-6 s +[acc-interp] eval 1.7468609e-2 s +[acc-interp] thetaMarsh 6.0e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.327572e-3 s grad=2.801e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.346895e-3 s grad=9.157e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.978887e-3 s grad=5.128e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.0817248e-2 s grad=3.957e-6 s +[acc-interp] eval 2.5320467e-2 s +[acc-interp] thetaMarsh 1.02e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.444064e-3 s grad=4.051e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.778256e-3 s grad=4.67e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.980977e-3 s grad=3.791e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.253015e-3 s grad=5.09e-6 s +[acc-interp] eval 1.754481e-2 s +[acc-interp] thetaMarsh 1.04e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.494191e-3 s grad=4.786e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.350878e-3 s grad=4.157e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.019423e-3 s grad=4.012e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.451334e-3 s grad=4.843e-6 s +[acc-interp] eval 1.7346874e-2 s +[acc-interp] thetaMarsh 6.3e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.678e-3 s grad=3.274e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.261122e-3 s grad=4.731e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.198547e-3 s grad=3.95e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.2564448e-2 s grad=3.86e-6 s +[acc-interp] eval 2.6830615e-2 s +[acc-interp] thetaMarsh 2.36e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=5.370006e-3 s grad=1.712e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.131204e-3 s grad=8.455e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.999158e-3 s grad=5.409e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.820356e-3 s grad=5.619e-6 s +[acc-interp] eval 2.0156734e-2 s +[acc-interp] thetaMarsh 6.8e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=5.328379e-3 s grad=5.13e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.865355e-3 s grad=4.474e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.705215e-3 s grad=4.274e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.668438e-3 s grad=4.129e-6 s +[acc-interp] eval 1.971054e-2 s +[acc-interp] thetaMarsh 6.0e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.090865e-3 s grad=3.588e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.825686e-3 s grad=4.579e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.148614e-3 s grad=4.75e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.1512601e-2 s grad=4.751e-6 s +[acc-interp] eval 2.5464185e-2 s +[acc-interp] thetaMarsh 6.2e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.243365e-3 s grad=3.865e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.418098e-3 s grad=3.564e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.972222e-3 s grad=4.114e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.568305e-3 s grad=3.652e-6 s +[acc-interp] eval 1.8477182e-2 s +[acc-interp] thetaMarsh 1.22e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.098629e-3 s grad=3.798e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.944884e-3 s grad=3.897e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.52711e-3 s grad=4.491e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.805972e-3 s grad=4.198e-6 s +[acc-interp] eval 1.8529026e-2 s +[acc-interp] thetaMarsh 6.7e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.575302e-3 s grad=3.942e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.152668e-3 s grad=4.807e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.655364e-3 s grad=5.813e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.2474692e-2 s grad=5.515e-6 s +[acc-interp] eval 2.7801136e-2 s +[acc-interp] thetaMarsh 6.7e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=5.126229e-3 s grad=3.74e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.264053e-3 s grad=4.385e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.913877e-3 s grad=4.295e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.026781e-3 s grad=4.7e-6 s +[acc-interp] eval 2.0823938e-2 s +[acc-interp] thetaMarsh 1.09e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.608454e-3 s grad=3.888e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.170243e-3 s grad=3.617e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.428185e-3 s grad=3.875e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.122595e-3 s grad=3.114e-6 s +[acc-interp] eval 1.7959373e-2 s +[acc-interp] thetaMarsh 1.27e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=5.352821e-3 s grad=4.848e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.385939e-3 s grad=4.718e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.780562e-3 s grad=3.588e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.4114431e-2 s grad=4.079e-6 s +[acc-interp] eval 3.1348439e-2 s +[acc-interp] thetaMarsh 3.63e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=5.869156e-3 s grad=3.451e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.029004e-3 s grad=3.658e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.946601e-3 s grad=3.26e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.80559e-3 s grad=3.933e-6 s +[acc-interp] eval 1.9136875e-2 s +[acc-interp] thetaMarsh 6.8e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=5.578526e-3 s grad=3.673e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.294596e-3 s grad=3.453e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.277408e-3 s grad=3.861e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.199304e-3 s grad=4.903e-6 s +[acc-interp] eval 2.0272581e-2 s +[acc-interp] thetaMarsh 6.8e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.37908e-3 s grad=4.437e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.664696e-3 s grad=3.679e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.735364e-3 s grad=3.565e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.2847143e-2 s grad=4.584e-6 s +[acc-interp] eval 2.6524643e-2 s +4) +[acc-interp] chunk base=0 cs=25974 kernel=5.022594e-3 s grad=4.063e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.045315e-3 s grad=3.796e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.531307e-3 s grad=4.043e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.232063e-3 s grad=4.443e-6 s +[acc-interp] eval 0.711840383 s +[acc-interp] thetaMarsh 9.9e-8 s (n=[acc-interp] thetaMarsh 6.8e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.2928908e-2 s grad=3.053e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.209413e-3 s grad=4.603e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.099791e-3 s grad=2.187e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.046664e-3 s grad=7.359e-6 s +[acc-interp] eval 4.7147545e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=4.54397e-3 s grad=4.669e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.648571e-3 s grad=3.131e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.931369e-3 s grad=7.405e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.545264e-3 s grad=3.747e-6 s +[acc-interp] eval 6.690905e-2 s +[acc-interp] thetaMarsh 1.13e-7 s (n=[acc-interp] thetaMarsh 9.5e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.2848866e-2 s grad=3.104e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.524683e-3 s grad=4.3e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.482417e-3 s grad=2.798e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.718528e-3 s grad=1.307e-6 s +[acc-interp] eval 2.1441933e-2 s 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kernel=1.958051e-3 s grad=2.608e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.054219e-3 s grad=2.408e-6 s +[acc-interp] eval 9.108778e-3 s +[acc-interp] thetaMarsh 7.0e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.381284e-3 s grad=8.639e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.395871e-3 s grad=8.75e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.412882e-3 s grad=3.049e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.724354e-3 s grad=4.128e-6 s +[acc-interp] eval 1.1854114e-2 s +[acc-interp] thetaMarsh 1.02e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.229179e-3 s grad=3.084e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.981243e-3 s grad=4.176e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.886347e-3 s grad=4.042e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.662427e-3 s grad=3.132e-6 s +[acc-interp] eval 1.1440136e-2 s +[acc-interp] thetaMarsh 1.19e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.342364e-3 s grad=2.222e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.354209e-3 s grad=2.642e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.612466e-3 s grad=2.203e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.915903e-3 s grad=3.543e-6 s +[acc-interp] eval 1.1765003e-2 s +[acc-interp] thetaMarsh 1.1e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.352222e-3 s grad=2.633e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.561291e-3 s grad=3.465e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.136844e-3 s grad=2.392e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.172504e-3 s grad=2.006e-6 s +[acc-interp] eval 1.0761588e-2 s +[acc-interp] thetaMarsh 1.04e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.505631e-3 s grad=4.632e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.32574e-3 s grad=4.074e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.598385e-3 s grad=3.188e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.473566e-3 s grad=3.374e-6 s +[acc-interp] eval 1.2615589e-2 s +[acc-interp] thetaMarsh 1.29e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.540958e-3 s grad=3.002e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.297241e-3 s grad=2.728e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.691513e-3 s grad=3.875e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.695401e-3 s grad=2.589e-6 s +[acc-interp] eval 1.187517e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=2.413544e-3 s grad=3.357e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.261883e-3 s grad=3.391e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.267416e-3 s grad=2.669e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.519675e-3 s grad=2.615e-6 s +[acc-interp] eval 0.14234046 s +[acc-interp] thetaMarsh 1.33e-7 s (n=[acc-interp] thetaMarsh 1.06e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=7.584038e-3 s grad=3.466e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.676636e-3 s grad=2.631e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.486867e-3 s grad=2.661e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.75588e-3 s grad=3.753e-6 s +[acc-interp] eval 1.6971753e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=4.637406e-3 s grad=3.658e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.765304e-3 s grad=8.255e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.917178e-3 s grad=1.192e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.343448e-3 s grad=2.588e-6 s +[acc-interp] eval 3.1958645e-2 s +[acc-interp] thetaMarsh 1.35e-7 s (n=[acc-interp] thetaMarsh 9.8e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.5606258e-2 s grad=4.05e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.182009e-3 s grad=3.559e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.844948e-3 s grad=1.446e-5 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.072575e-3 s grad=3.365e-6 s +[acc-interp] eval 2.7017757e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=3.388038e-3 s grad=3.489e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.63112e-3 s grad=6.655e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.294862e-3 s grad=3.255e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.306396e-3 s grad=2.296e-6 s +[acc-interp] eval 4.3141865e-2 s +[acc-interp] thetaMarsh 1.06e-7 s (n=[acc-interp] thetaMarsh 9.5e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.3288018e-2 s grad=3.95e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=8.472515e-3 s grad=4.27e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.269936e-3 s grad=3.634e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.475149e-3 s grad=3.944e-6 s +[acc-interp] eval 4.9841161e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=2.966068e-3 s grad=3.349e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=9.837197e-3 s grad=3.258e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=8.765169e-3 s grad=3.647e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.814515e-3 s grad=3.583e-6 s +[acc-interp] eval 7.5651999e-2 s +[acc-interp] thetaMarsh 1.18e-7 s (n=[acc-interp] thetaMarsh 7.8e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=7.121761e-3 s grad=3.875e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.095136e-3 s grad=3.415e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.906238e-3 s grad=5.331e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.671772e-3 s grad=1.291e-6 s +[acc-interp] eval 1.6740203e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=2.826316e-3 s grad=7.208e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.123595e-3 s grad=2.398e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.108668e-3 s grad=2.623e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.208647e-3 s grad=3.45e-6 s +[acc-interp] eval 2.8315636e-2 s +[acc-interp] thetaMarsh 5.6e-8 s (n=[acc-interp] thetaMarsh 5.6e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.1175857e-2 s grad=4.024e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.851836e-3 s grad=4.124e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.058232e-3 s grad=2.672e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.704583e-3 s grad=2.206e-6 s +[acc-interp] eval 4.3629944e-2 s +5) +[acc-interp] chunk base=0 cs=25974 kernel=5.374612e-3 s grad=4.884e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.021212e-3 s grad=4.959e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.476014e-3 s grad=2.54e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.178312e-3 s grad=1.725e-6 s +[acc-interp] eval 6.2627827e-2 s +[acc-interp] thetaMarsh 7.9e-8 s (n=[acc-interp] thetaMarsh 5.8e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=5.944277e-3 s grad=4.25e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.828646e-3 s grad=1.454e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.590832e-3 s grad=4.38e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.414487e-3 s grad=3.388e-6 s +[acc-interp] eval 1.6059876e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=3.995023e-3 s grad=2.822e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.880388e-3 s grad=1.985e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.304975e-3 s grad=2.312e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.8124e-3 s grad=2.498e-6 s +[acc-interp] eval 2.879622e-2 s +[acc-interp] thetaMarsh 1.62e-7 s (n=[acc-interp] thetaMarsh 5.28e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.3695519e-2 s grad=4.468e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.195227e-3 s grad=5.105e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.972582e-3 s grad=2.182e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.977176e-3 s grad=2.39e-6 s +[acc-interp] eval 2.1762069e-2 s +[acc-interp] thetaMarsh 2.25e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.166132e-3 s grad=2.503e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.112857e-3 s grad=3.184e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.079674e-3 s grad=2.165e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.710353e-3 s grad=4.314e-6 s +[acc-interp] eval 1.0606897e-2 s +[acc-interp] thetaMarsh 1.9e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.462614e-3 s grad=3.051e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.25823e-3 s grad=1.895e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.20867e-3 s grad=1.872e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.170934e-3 s grad=2.388e-6 s +[acc-interp] eval 1.0686286e-2 s +[acc-interp] thetaMarsh 1.14e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.108064e-3 s grad=1.665e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.653248e-3 s grad=3.509e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.225659e-3 s grad=2.033e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.406662e-3 s grad=4.652e-6 s +[acc-interp] eval 1.104815e-2 s +[acc-interp] thetaMarsh 1.16e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.31129e-3 s grad=3.963e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.28602e-3 s grad=2.86e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.288888e-3 s grad=3.454e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.391723e-3 s grad=3.979e-6 s +[acc-interp] eval 1.0771167e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=2.555999e-3 s grad=6.834e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.284201e-3 s grad=1.714e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.168848e-3 s grad=5.35e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.132732e-3 s grad=9.73e-7 s +[acc-interp] eval 7.5707846e-2 s +[acc-interp] thetaMarsh 1.01e-7 s (n=[acc-interp] thetaMarsh 5.9e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=5.96553e-3 s grad=3.419e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.692641e-3 s grad=3.492e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.492965e-3 s grad=3.574e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.77013e-3 s grad=4.222e-6 s +[acc-interp] eval 1.7431288e-2 s +[acc-interp] thetaMarsh 2.26e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.961943e-3 s grad=4.099e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.165818e-3 s grad=3.407e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.618213e-3 s grad=2.467e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.008183e-3 s grad=3.369e-6 s +[acc-interp] eval 1.6367744e-2 s +[acc-interp] thetaMarsh 1.08e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.181675e-3 s grad=2.901e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.673268e-3 s grad=2.652e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.753098e-3 s grad=2.197e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.765331e-3 s grad=3.108e-6 s +[acc-interp] eval 1.2925516e-2 s +[acc-interp] thetaMarsh 1.39e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.209206e-3 s grad=3.702e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.297097e-3 s grad=3.684e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.788292e-3 s grad=2.917e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.80401e-3 s grad=6.553e-6 s +[acc-interp] eval 1.3313179e-2 s +[acc-interp] thetaMarsh 6.2e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.0337664e-2 s grad=3.719e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.717418e-3 s grad=8.308e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.323542e-3 s grad=2.436e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.337001e-3 s grad=3.649e-6 s +[acc-interp] eval 1.8776819e-2 s +[acc-interp] thetaMarsh 4.19e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.830674e-3 s grad=3.26e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.490665e-3 s grad=2.548e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.369839e-3 s grad=2.715e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.094721e-3 s grad=2.074e-6 s +[acc-interp] eval 1.1359181e-2 s +[acc-interp] thetaMarsh 1.0e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.145142e-3 s grad=2.42e-6 s 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+[acc-interp] eval 1.1704857e-2 s +[acc-interp] thetaMarsh 6.3e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.373915e-3 s grad=8.845e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.312936e-3 s grad=1.1265e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.706851e-3 s grad=4.479e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.979448e-3 s grad=4.372e-6 s +[acc-interp] eval 1.0320226e-2 s +[acc-interp] thetaMarsh 1.77e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.556912e-3 s grad=4.205e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.483664e-3 s grad=2.554e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.499891e-3 s grad=2.748e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.374788e-3 s grad=2.135e-6 s +[acc-interp] eval 8.853528e-3 s +[acc-interp] thetaMarsh 6.4e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.087803e-3 s grad=4.403e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.730516e-3 s grad=4.82e-6 s +[acc-interp] chunk 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kernel=2.947423e-3 s grad=4.871e-6 s +[acc-interp] eval 1.1727037e-2 s +[acc-interp] thetaMarsh 6.2e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.413304e-3 s grad=1.0426e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.67831e-3 s grad=7.135e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.502579e-3 s grad=6.471e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.335805e-3 s grad=1.372e-6 s +[acc-interp] eval 1.0722018e-2 s +[acc-interp] thetaMarsh 1.41e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.285686e-3 s grad=1.436e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.491808e-3 s grad=4.297e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.547135e-3 s grad=3.688e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.31525e-3 s grad=3.148e-6 s +[acc-interp] eval 1.1095581e-2 s +[acc-interp] thetaMarsh 1.09e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.578718e-3 s grad=3.256e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.264716e-3 s grad=2.464e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.286321e-3 s grad=2.416e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.169566e-3 s grad=3.741e-6 s +[acc-interp] eval 1.193795e-2 s +[acc-interp] thetaMarsh 9.5e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.344479e-3 s grad=2.541e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.379463e-3 s grad=2.334e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.327274e-3 s grad=2.02e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.34186e-3 s grad=1.961e-6 s +[acc-interp] eval 1.0921053e-2 s +[acc-interp] thetaMarsh 9.8e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.387088e-3 s grad=2.058e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.982286e-3 s grad=4.745e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.774971e-3 s grad=3.409e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.714601e-3 s grad=1.943e-6 s +[acc-interp] eval 1.3463151e-2 s +[acc-interp] thetaMarsh 2.12e-7 s (n=2) 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chunk base=77922 cs=25974 kernel=2.564859e-3 s grad=2.302e-6 s +[acc-interp] eval 1.2588069e-2 s +[acc-interp] thetaMarsh 9.8e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.466324e-3 s grad=2.976e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.432563e-3 s grad=3.548e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.73966e-3 s grad=2.73e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.340924e-3 s grad=2.208e-6 s +[acc-interp] eval 1.250851e-2 s +[acc-interp] thetaMarsh 1.28e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.464549e-3 s grad=2.008e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.425018e-3 s grad=2.747e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.232367e-3 s grad=9.101e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.199847e-3 s grad=2.41e-6 s +[acc-interp] eval 1.0453029e-2 s +[acc-interp] thetaMarsh 1.1e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.193021e-3 s grad=2.775e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.057591e-3 s grad=2.15e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.115214e-3 s grad=3.333e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.43019e-3 s grad=2.336e-6 s +[acc-interp] eval 1.0258391e-2 s +[acc-interp] thetaMarsh 9.4e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.250967e-3 s grad=3.178e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.654705e-3 s grad=2.591e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.707783e-3 s grad=2.741e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.371101e-3 s grad=2.78e-6 s +[acc-interp] eval 1.2479054e-2 s +[acc-interp] thetaMarsh 1.14e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.431946e-3 s grad=2.077e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.40576e-3 s grad=3.71e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.790525e-3 s grad=2.522e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.29675e-3 s grad=2.815e-6 s +[acc-interp] eval 1.1481974e-2 s +2) 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kernel=2.817118e-3 s grad=1.894e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.048701e-3 s grad=7.51e-6 s +[acc-interp] eval 1.3650279e-2 s +[acc-interp] thetaMarsh 6.3e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.084378e-3 s grad=2.504e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.540188e-3 s grad=5.08e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.189868e-3 s grad=2.889e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.152745e-3 s grad=1.439e-6 s +[acc-interp] eval 1.3851133e-2 s +[acc-interp] thetaMarsh 6.1e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.822149e-3 s grad=8.605e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.628637e-3 s grad=4.507e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.652274e-3 s grad=3.999e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.048121e-3 s grad=3.513e-6 s +[acc-interp] eval 1.4550177e-2 s +[acc-interp] thetaMarsh 1.02e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.495298e-3 s 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base=51948 cs=25974 kernel=4.352033e-3 s grad=4.401e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=9.482501e-3 s grad=3.338e-6 s +[acc-interp] eval 2.2715786e-2 s +[acc-interp] thetaMarsh 1.29e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.347475e-3 s grad=3.037e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.107092e-3 s grad=3.623e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.708926e-3 s grad=3.277e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.890163e-3 s grad=2.542e-6 s +[acc-interp] eval 1.4435589e-2 s +[acc-interp] thetaMarsh 7.2e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.785395e-3 s grad=1.523e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.777471e-3 s grad=2.229e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.705191e-3 s grad=4.022e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.566288e-3 s grad=1.959e-6 s +[acc-interp] eval 1.2981072e-2 s +[acc-interp] thetaMarsh 1.2e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.222271e-3 s grad=1.608e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.861635e-3 s grad=3.211e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.034299e-3 s grad=3.88e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.165125e-3 s grad=4.068e-6 s +[acc-interp] eval 1.2956025e-2 s +[acc-interp] thetaMarsh 6.2e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.319085e-3 s grad=2.63e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.903595e-3 s grad=1.375e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.821322e-3 s grad=2.899e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.999656e-3 s grad=2.461e-6 s +[acc-interp] eval 1.3211892e-2 s +[acc-interp] thetaMarsh 1.05e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.761005e-3 s grad=2.403e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.814999e-3 s grad=6.022e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.267135e-3 s grad=2.572e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.078609e-3 s grad=2.357e-6 s +[acc-interp] eval 1.304592e-2 s +[acc-interp] thetaMarsh 1.81e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.74546e-3 s grad=3.623e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.187422e-3 s grad=3.456e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.236915e-3 s grad=4.634e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.166648e-3 s grad=5.163e-6 s +[acc-interp] eval 1.4613646e-2 s +[acc-interp] thetaMarsh 6.4e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=5.180671e-3 s grad=4.559e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=7.730051e-3 s grad=6.205e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.325188e-3 s grad=7.071e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.300294e-3 s grad=4.214e-6 s +[acc-interp] eval 2.1351075e-2 s +[acc-interp] thetaMarsh 1.13e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.322664e-3 s grad=9.063e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.968281e-3 s 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base=77922 cs=25974 kernel=3.938958e-3 s grad=3.787e-6 s +[acc-interp] eval 2.4614991e-2 s +[acc-interp] thetaMarsh 1.24e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.317517e-3 s grad=2.751e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.805653e-3 s grad=2.853e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.017019e-3 s grad=3.676e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.603606e-3 s grad=4.473e-6 s +[acc-interp] eval 1.5395623e-2 s +[acc-interp] thetaMarsh 1.13e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.152406e-3 s grad=3.347e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.585012e-3 s grad=3.656e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.700533e-3 s grad=3.681e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.633382e-3 s grad=4.247e-6 s +[acc-interp] eval 1.5847437e-2 s +[acc-interp] thetaMarsh 1.35e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.191283e-3 s grad=3.499e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.170239e-3 s grad=4.531e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.897763e-3 s grad=2.699e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.470128e-3 s grad=2.851e-6 s +[acc-interp] eval 1.4213633e-2 s +[acc-interp] thetaMarsh 1.1e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.104171e-3 s grad=2.354e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.082147e-3 s grad=2.304e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.074029e-3 s grad=2.993e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.906423e-3 s grad=2.062e-6 s +[acc-interp] eval 1.3560253e-2 s +[acc-interp] thetaMarsh 1.29e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.835758e-3 s grad=5.053e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.866722e-3 s grad=3.945e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.339319e-3 s grad=3.161e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.150742e-3 s grad=3.335e-6 s +[acc-interp] eval 1.5939806e-2 s 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chunk base=77922 cs=25974 kernel=2.641579e-3 s grad=3.106e-6 s +[acc-interp] eval 0.506016944 s +[acc-interp] thetaMarsh 7.8e-8 s (n=[acc-interp] thetaMarsh 6.9e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.5798618e-2 s grad=4.638e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.791801e-3 s grad=3.702e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=8.101401e-3 s grad=3.398e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.534667e-3 s grad=2.227e-6 s +[acc-interp] eval 3.0818866e-2 s +[acc-interp] thetaMarsh 3.06e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.766854e-3 s grad=2.08e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.689326e-3 s grad=3.256e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.808186e-3 s grad=3.158e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.035554e-3 s grad=2.753e-6 s +[acc-interp] eval 1.2939419e-2 s +[acc-interp] thetaMarsh 9.7e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.413279e-3 s grad=2.406e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.583459e-3 s grad=1.474e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.241203e-3 s grad=2.926e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.367198e-3 s grad=4.143e-6 s +[acc-interp] eval 1.3002911e-2 s +[acc-interp] thetaMarsh 1.21e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.202405e-3 s grad=3.761e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.591216e-3 s grad=3.096e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.029254e-3 s grad=1.404e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.720929e-3 s grad=1.41e-6 s +[acc-interp] eval 1.2345294e-2 s +[acc-interp] thetaMarsh 5.9e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.661689e-3 s grad=7.563e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.91706e-3 s grad=1.482e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.543822e-3 s grad=1.252e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.940845e-3 s grad=8.12e-7 s +[acc-interp] eval 1.169009e-2 s +[acc-interp] thetaMarsh 5.6e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.61494e-3 s grad=1.296e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.615423e-3 s grad=4.768e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.049792e-3 s grad=4.071e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.545469e-3 s grad=3.384e-6 s +[acc-interp] eval 1.274452e-2 s +[acc-interp] thetaMarsh 2.32e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.858547e-3 s grad=9.5e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.338854e-3 s grad=3.216e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.021369e-3 s grad=4.777e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.043864e-3 s grad=4.411e-6 s +[acc-interp] eval 1.3349318e-2 s +[acc-interp] thetaMarsh 6.8e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.648084e-3 s grad=3.943e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.745983e-3 s grad=4.017e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.490826e-3 s grad=4.634e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.6179e-3 s grad=4.377e-6 s +[acc-interp] eval 1.2619799e-2 s +[acc-interp] thetaMarsh 9.9e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.04915e-3 s grad=2.024e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.845978e-3 s grad=3.473e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.043697e-3 s grad=4.24e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.576162e-3 s grad=3.356e-6 s +[acc-interp] eval 1.2475517e-2 s +[acc-interp] thetaMarsh 3.8e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.07756e-3 s grad=4.443e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.037575e-3 s grad=3.884e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.549822e-3 s grad=4.191e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.627825e-3 s grad=3.687e-6 s +[acc-interp] eval 1.2135726e-2 s +[acc-interp] thetaMarsh 7.6e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.244403e-3 s grad=3.103e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.278632e-3 s grad=3.119e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.421857e-3 s grad=2.032e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.916174e-3 s grad=2.765e-6 s +[acc-interp] eval 9.629475e-3 s +[acc-interp] thetaMarsh 1.72e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.410923e-3 s grad=2.68e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.132445e-3 s grad=3.107e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.15844e-3 s grad=1.839e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.504895e-3 s grad=1.977e-6 s +[acc-interp] eval 9.92803e-3 s +[acc-interp] thetaMarsh 1.16e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.224944e-3 s grad=3.043e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.506663e-3 s grad=2.499e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.119753e-3 s grad=2.444e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.143422e-3 s grad=2.807e-6 s +[acc-interp] eval 9.768913e-3 s +[acc-interp] thetaMarsh 6.5e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.462326e-3 s grad=2.994e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.148017e-3 s grad=2.98e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.462881e-3 s grad=2.9e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.331837e-3 s grad=3.806e-6 s +[acc-interp] eval 1.0198825e-2 s +[acc-interp] thetaMarsh 6.4e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.781778e-3 s grad=4.474e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.131604e-3 s grad=4.522e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.514813e-3 s grad=5.917e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.670889e-3 s grad=1.499e-6 s +[acc-interp] eval 1.2016507e-2 s +[acc-interp] thetaMarsh 6.3e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.481303e-3 s grad=8.044e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.17731e-3 s grad=4.505e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.124808e-3 s grad=3.526e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.614762e-3 s grad=6.748e-6 s +[acc-interp] eval 1.2370635e-2 s +[acc-interp] thetaMarsh 1.01e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.661009e-3 s grad=3.258e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.476657e-3 s grad=3.177e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.731599e-3 s grad=5.2e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.2425e-3 s grad=5.205e-6 s +[acc-interp] eval 1.196738e-2 s +[acc-interp] thetaMarsh 7.2e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.427924e-3 s grad=3.095e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.799355e-3 s grad=4.548e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.561743e-3 s grad=1.127e-5 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.565569e-3 s grad=4.281e-6 s +[acc-interp] eval 1.1243912e-2 s +[acc-interp] thetaMarsh 1.11e-7 s (n=2) 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base=77922 cs=25974 kernel=2.785664e-3 s grad=4.392e-6 s +[acc-interp] eval 1.1063402e-2 s +[acc-interp] thetaMarsh 6.3e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.840453e-3 s grad=3.262e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.699606e-3 s grad=1.0829e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.58646e-3 s grad=8.131e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.743757e-3 s grad=5.993e-6 s +[acc-interp] eval 1.1724855e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=3.083008e-3 s grad=3.679e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.929875e-3 s grad=2.078e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.464041e-3 s grad=7.48e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.465733e-3 s grad=1.786e-6 s +[acc-interp] eval 0.294308019 s +[acc-interp] thetaMarsh 8.5e-8 s (n=[acc-interp] thetaMarsh 7.3e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.6016068e-2 s grad=3.884e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=6.800321e-3 s grad=4.052e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.079503e-3 s grad=3.394e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.767767e-3 s grad=2.794e-6 s +[acc-interp] eval 2.9497006e-2 s +[acc-interp] thetaMarsh 1.7e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.497925e-3 s grad=2.507e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.809298e-3 s grad=4.915e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.622204e-3 s grad=4.426e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.745624e-3 s grad=3.442e-6 s +[acc-interp] eval 1.952067e-2 s +[acc-interp] thetaMarsh 1.97e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.758984e-3 s grad=5.571e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.548509e-3 s grad=3.073e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.73654e-3 s grad=4.164e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.195724e-3 s grad=4.83e-6 s +[acc-interp] eval 1.9911042e-2 s +[acc-interp] thetaMarsh 1.58e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.656239e-3 s grad=4.339e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.028262e-3 s grad=2.966e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.638802e-3 s grad=2.312e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.966496e-3 s grad=4.291e-6 s +[acc-interp] eval 1.8812861e-2 s +[acc-interp] thetaMarsh 1.17e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.775031e-3 s grad=3.701e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.454863e-3 s grad=1.986e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.278976e-3 s grad=8.084e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.530179e-3 s grad=4.844e-6 s +[acc-interp] eval 1.8746676e-2 s +[acc-interp] thetaMarsh 1.31e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=5.271641e-3 s grad=3.016e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.411608e-3 s grad=2.54e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.565514e-3 s grad=4.079e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.565427e-3 s grad=3.261e-6 s +[acc-interp] eval 2.0527516e-2 s +[acc-interp] thetaMarsh 1.19e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.899606e-3 s grad=5.102e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.898642e-3 s grad=4.314e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.749605e-3 s grad=4.633e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.752988e-3 s grad=5.592e-6 s +[acc-interp] eval 2.0670172e-2 s +[acc-interp] thetaMarsh 8.9e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=5.360229e-3 s grad=4.963e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.615186e-3 s grad=3.441e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.434042e-3 s grad=4.059e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.58625e-3 s grad=3.826e-6 s +[acc-interp] eval 2.1220837e-2 s +[acc-interp] thetaMarsh 2.88e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=5.271272e-3 s grad=1.0026e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.80851e-3 s grad=5.345e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.065083e-3 s grad=4.14e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.002287e-3 s grad=3.448e-6 s +[acc-interp] eval 2.1432753e-2 s +[acc-interp] thetaMarsh 1.23e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.763637e-3 s grad=4.526e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.017241e-3 s grad=3.459e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.831329e-3 s grad=3.067e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.75065e-3 s grad=2.478e-6 s +[acc-interp] eval 2.0890751e-2 s +[acc-interp] thetaMarsh 1.12e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.655516e-3 s grad=1.982e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.768514e-3 s grad=2.326e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.017023e-3 s grad=4.7e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.640935e-3 s grad=2.746e-6 s +[acc-interp] eval 2.0450083e-2 s +[acc-interp] thetaMarsh 1.2e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.441132e-3 s grad=2.146e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.413152e-3 s grad=4.906e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.212578e-3 s grad=4.069e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.190865e-3 s grad=2.806e-6 s +[acc-interp] eval 2.0807117e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=3.111041e-3 s grad=2.613e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.318227e-3 s grad=3.581e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.564207e-3 s grad=1.288e-5 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.990471e-3 s grad=1.122e-6 s +[acc-interp] eval 0.269146271 s +[acc-interp] thetaMarsh 6.6e-8 s (n=[acc-interp] thetaMarsh 5.8e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.6427256e-2 s grad=2.909e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.241024e-3 s grad=8.546e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.485316e-3 s grad=2.858e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.639329e-3 s grad=4.906e-6 s +[acc-interp] eval 2.4897481e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=2.274699e-3 s grad=2.686e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.025025e-3 s grad=2.25e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.932462e-3 s grad=2.083e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.039319e-3 s grad=2.566e-6 s +[acc-interp] eval 3.4777132e-2 s +[acc-interp] thetaMarsh 1.08e-7 s (n=[acc-interp] thetaMarsh 9.1e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=4.043741e-3 s grad=3.716e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.586844e-3 s grad=1.0322e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.700209e-3 s grad=1.927e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.652984e-3 s grad=1.266e-6 s +[acc-interp] eval 1.0446928e-2 s +[acc-interp] thetaMarsh 1.04e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.651451e-3 s grad=8.093e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.685122e-3 s grad=1.256e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.927472e-3 s grad=4.435e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.41133e-3 s grad=1.4114e-5 s +[acc-interp] eval 9.261695e-3 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=3.944461e-3 s grad=2.436e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.872546e-3 s grad=1.0316e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.755193e-3 s grad=2.284e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.358658e-3 s grad=3.852e-6 s +[acc-interp] eval 3.8058991e-2 s +[acc-interp] thetaMarsh 7.3e-8 s (n=[acc-interp] thetaMarsh 5.2e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.2827771e-2 s grad=4.554e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.172703e-3 s grad=5.031e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.320289e-3 s grad=1.312e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.377092e-3 s grad=1.339e-6 s +[acc-interp] eval 2.1583689e-2 s +[acc-interp] thetaMarsh 9.1e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.51429e-3 s grad=1.971e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.192098e-3 s grad=1.167e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.90466e-3 s grad=4.008e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.693584e-3 s grad=2.427e-6 s +[acc-interp] eval 1.1546036e-2 s +[acc-interp] thetaMarsh 1.24e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.427692e-3 s grad=2.361e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.215784e-3 s grad=1.3218e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.313581e-3 s grad=9.001e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.11559e-3 s grad=3.097e-6 s +[acc-interp] eval 1.0118078e-2 s +[acc-interp] thetaMarsh 1.1e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.059771e-3 s grad=3.575e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.259062e-3 s grad=3.43e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.912131e-3 s grad=1.0055e-5 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.88196e-3 s grad=8.68e-7 s +[acc-interp] eval 9.223225e-3 s +[acc-interp] thetaMarsh 6.1e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.756932e-3 s grad=9.772e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.909756e-3 s grad=8.516e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.724815e-3 s grad=7.975e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.231982e-3 s grad=8.501e-6 s +[acc-interp] eval 8.38566e-3 s +[acc-interp] thetaMarsh 1.68e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.500867e-3 s grad=4.725e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.240465e-3 s grad=9.713e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.446062e-3 s grad=1.1e-5 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.202719e-3 s grad=2.284e-6 s +[acc-interp] eval 1.0304674e-2 s +[acc-interp] thetaMarsh 6.5e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.983568e-3 s grad=2.517e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.761404e-3 s grad=2.995e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.683798e-3 s grad=5.233e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.971545e-3 s grad=3.469e-6 s +[acc-interp] eval 1.0650586e-2 s +[acc-interp] thetaMarsh 1.2e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.469557e-3 s grad=2.692e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.28454e-3 s grad=2.139e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.000192e-3 s grad=2.015e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.177057e-3 s grad=1.712e-6 s +[acc-interp] eval 1.0435343e-2 s +[acc-interp] thetaMarsh 1.23e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.341842e-3 s grad=3.864e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.330204e-3 s grad=2.571e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.520181e-3 s grad=2.688e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.513428e-3 s grad=2.792e-6 s +[acc-interp] eval 1.1358767e-2 s +[acc-interp] thetaMarsh 9.7e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.332973e-3 s grad=1.754e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.466461e-3 s grad=1.976e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.374579e-3 s grad=1.879e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.239682e-3 s grad=1.902e-6 s +[acc-interp] eval 1.0882669e-2 s +[acc-interp] thetaMarsh 1.13e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.441917e-3 s grad=1.784e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.254655e-3 s grad=1.889e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.42396e-3 s grad=1.584e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.484012e-3 s grad=4.296e-6 s +[acc-interp] eval 1.1078979e-2 s +[acc-interp] thetaMarsh 1.56e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.085236e-3 s grad=4.065e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.503765e-3 s grad=4.856e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.436056e-3 s grad=3.483e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.380577e-3 s grad=2.57e-6 s +[acc-interp] eval 1.214887e-2 s +[acc-interp] thetaMarsh 1.29e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.46046e-3 s grad=3.088e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.965087e-3 s grad=3.575e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.520248e-3 s grad=2.839e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.377433e-3 s grad=2.497e-6 s +[acc-interp] eval 1.2885005e-2 s +[acc-interp] thetaMarsh 9.6e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.35375e-3 s grad=2.226e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.357245e-3 s grad=2.052e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.954973e-3 s grad=3.226e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.861289e-3 s grad=2.873e-6 s +[acc-interp] eval 1.3082801e-2 s +[acc-interp] thetaMarsh 1.01e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.524911e-3 s grad=2.244e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.354971e-3 s grad=1.939e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.722604e-3 s grad=4.092e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.059349e-3 s grad=3.498e-6 s +[acc-interp] eval 1.231973e-2 s +[acc-interp] thetaMarsh 1.72e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.671571e-3 s grad=3.923e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.476208e-3 s grad=3.816e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.871818e-3 s grad=2.999e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.890118e-3 s grad=4.303e-6 s +[acc-interp] eval 1.3524909e-2 s +[acc-interp] thetaMarsh 1.55e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.742918e-3 s grad=3.51e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.65519e-3 s grad=3.013e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.596971e-3 s grad=4.14e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.667271e-3 s grad=3.056e-6 s +[acc-interp] eval 1.2268618e-2 s +[acc-interp] thetaMarsh 1.01e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.597048e-3 s grad=2.987e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.500342e-3 s grad=2.873e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.436752e-3 s grad=3.147e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.642495e-3 s grad=2.584e-6 s +[acc-interp] eval 1.1714024e-2 s +[acc-interp] thetaMarsh 1.2e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.31175e-3 s grad=2.897e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.701838e-3 s grad=4.934e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.82439e-3 s grad=3.791e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.626093e-3 s grad=3.125e-6 s +[acc-interp] eval 1.2222897e-2 s +[acc-interp] thetaMarsh 1.19e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.550102e-3 s grad=3.051e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.283858e-3 s grad=2.441e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.264476e-3 s grad=2.28e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.010196e-3 s grad=3.702e-6 s +[acc-interp] eval 1.1619128e-2 s +[acc-interp] thetaMarsh 1.22e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.744834e-3 s grad=2.816e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.443204e-3 s grad=2.76e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.320674e-3 s grad=2.367e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.320022e-3 s grad=2.232e-6 s +[acc-interp] eval 1.1356579e-2 s +[acc-interp] thetaMarsh 1.22e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.668372e-3 s grad=3.876e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.452641e-3 s grad=4.124e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.14163e-3 s grad=4.817e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.785682e-3 s grad=4.146e-6 s +[acc-interp] eval 1.3807086e-2 s +[acc-interp] thetaMarsh 1.42e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.57983e-3 s grad=2.994e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.234149e-3 s grad=4.289e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.704109e-3 s grad=4.04e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.535366e-3 s grad=3.051e-6 s +[acc-interp] eval 1.2642989e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=2.966577e-3 s grad=3.149e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.505866e-3 s grad=3.345e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.241535e-3 s grad=4.197e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.822169e-3 s grad=3.612e-6 s +[acc-interp] eval 0.291600363 s +[acc-interp] thetaMarsh 1.28e-7 s (n=[acc-interp] thetaMarsh 1.2e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=1.1964289e-2 s grad=3.815e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.292875e-3 s grad=8.14e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.255017e-3 s grad=5.14e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.125884e-3 s grad=9.011e-6 s +[acc-interp] eval 2.2541761e-2 s +4) +[acc-interp] chunk base=0 cs=25974 kernel=1.0000144e-2 s grad=4.175e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.25004e-3 s grad=3.432e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.876283e-3 s grad=3.377e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.311317e-3 s grad=1.0323e-5 s +[acc-interp] eval 4.3092803e-2 s +[acc-interp] thetaMarsh 7.0e-8 s (n=[acc-interp] thetaMarsh 6.0e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.0157267e-2 s grad=4.17e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.279523e-3 s grad=2.68e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=8.374571e-3 s grad=4.664e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.486307e-3 s grad=1.626e-6 s +[acc-interp] eval 4.714502e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=3.750217e-3 s grad=1.776e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.418377e-3 s grad=4.635e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.809907e-3 s grad=9.037e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.830545e-3 s grad=6.679e-6 s +[acc-interp] eval 6.3827386e-2 s +[acc-interp] thetaMarsh 7.9e-8 s (n=[acc-interp] thetaMarsh 5.8e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.663083e-2 s grad=3.852e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.427843e-3 s grad=9.049e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.17436e-3 s grad=4.507e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.058025e-3 s grad=1.777e-6 s +[acc-interp] eval 3.0089123e-2 s +[acc-interp] thetaMarsh 6.0e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.880162e-3 s grad=9.392e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.1514179e-2 s grad=4.303e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.7897e-3 s grad=1.815e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.237201e-3 s grad=2.874e-6 s +[acc-interp] eval 2.3296528e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=3.699469e-3 s grad=1.579e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.582487e-3 s grad=2.669e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.383003e-3 s grad=3.355e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.081189e-3 s grad=3.461e-6 s +[acc-interp] eval 6.8107463e-2 s +[acc-interp] thetaMarsh 7.4e-8 s (n=[acc-interp] thetaMarsh 2.01e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=8.939282e-3 s grad=4.501e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.119445e-3 s grad=1.782e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.582676e-3 s grad=6.526e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.977762e-3 s grad=2.253e-6 s +[acc-interp] eval 1.851858e-2 s +[acc-interp] thetaMarsh 6.7e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.258319e-3 s grad=6.705e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.018923e-3 s grad=2.341e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.201392e-3 s grad=6.063e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.167423e-3 s grad=2.739e-6 s +[acc-interp] eval 1.4484446e-2 s +[acc-interp] thetaMarsh 7.6e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.973515e-3 s grad=5.017e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.793792e-3 s grad=4.138e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.617101e-3 s grad=7.259e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=7.492349e-3 s grad=3.196e-6 s +[acc-interp] eval 1.7871052e-2 s +[acc-interp] thetaMarsh 1.05e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.793398e-3 s grad=4.827e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.619023e-3 s grad=3.873e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.529753e-3 s grad=7.475e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.291704e-3 s grad=6.146e-6 s +[acc-interp] eval 1.1112581e-2 s +[acc-interp] thetaMarsh 6.6e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.241222e-3 s grad=2.232e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.206051e-3 s grad=1.04e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.6143e-3 s grad=4.563e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.627018e-3 s grad=4.176e-6 s +[acc-interp] eval 1.0596042e-2 s +[acc-interp] thetaMarsh 7.1e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.870538e-3 s grad=8.461e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.459922e-3 s grad=3.927e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.455985e-3 s grad=3.16e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.457601e-3 s grad=7.62e-6 s +[acc-interp] eval 1.1046489e-2 s +[acc-interp] thetaMarsh 6.7e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.807223e-3 s grad=5.201e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.702655e-3 s grad=4.474e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.672569e-3 s grad=8.044e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.744372e-3 s grad=2.58e-6 s +[acc-interp] eval 1.2024162e-2 s +[acc-interp] thetaMarsh 1.24e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.29391e-3 s grad=2.271e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.161744e-3 s grad=2.201e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.824696e-3 s grad=3.75e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.65733e-3 s grad=2.475e-6 s +[acc-interp] eval 1.1530942e-2 s +[acc-interp] thetaMarsh 1.07e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.806419e-3 s grad=2.413e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.548485e-3 s grad=2.887e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.971436e-3 s grad=2.747e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.097977e-3 s grad=3.488e-6 s +[acc-interp] eval 1.3113193e-2 s +[acc-interp] thetaMarsh 1.13e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.55192e-3 s grad=2.515e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.57384e-3 s grad=2.094e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.5331e-3 s grad=1.928e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.540288e-3 s grad=1.705e-6 s +[acc-interp] eval 1.165564e-2 s +[acc-interp] thetaMarsh 1.14e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.993117e-3 s grad=3.106e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.735609e-3 s grad=2.656e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.392907e-3 s grad=2.286e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.316739e-3 s grad=2.018e-6 s +[acc-interp] eval 1.1920976e-2 s +[acc-interp] thetaMarsh 1.0e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.174616e-3 s grad=1.874e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.086716e-3 s grad=3.274e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.520527e-3 s grad=3.225e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.738597e-3 s grad=3.3e-6 s +[acc-interp] eval 1.1092326e-2 s +[acc-interp] thetaMarsh 2.44e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.883881e-3 s grad=2.9e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.590682e-3 s grad=2.904e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.29047e-3 s grad=2.662e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.316961e-3 s grad=2.163e-6 s +[acc-interp] eval 1.1634342e-2 s +[acc-interp] thetaMarsh 6.3e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.486402e-3 s grad=1.1777e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.099784e-3 s grad=6.823e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.050099e-3 s grad=6.224e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.390819e-3 s grad=5.911e-6 s +[acc-interp] eval 9.754689e-3 s +[acc-interp] thetaMarsh 6.2e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.113712e-3 s grad=8.151e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.239761e-3 s grad=5.792e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.615147e-3 s grad=4.06e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.526483e-3 s grad=1.649e-6 s +[acc-interp] eval 1.0238815e-2 s +[acc-interp] thetaMarsh 1.97e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.210761e-3 s grad=9.072e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.268646e-3 s grad=6.389e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.419949e-3 s grad=4.974e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.020458e-3 s grad=6.256e-6 s +[acc-interp] eval 9.608926e-3 s +[acc-interp] thetaMarsh 1.64e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.593736e-3 s grad=5.205e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.760587e-3 s grad=3.919e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.489858e-3 s grad=6.898e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.599103e-3 s grad=6.719e-6 s +[acc-interp] eval 1.1264177e-2 s +[acc-interp] thetaMarsh 1.81e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.286427e-3 s grad=7.559e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.446577e-3 s grad=6.771e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.254781e-3 s grad=1.324e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.683331e-3 s grad=3.122e-6 s +[acc-interp] eval 1.0542715e-2 s +[acc-interp] thetaMarsh 1.4e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.28972e-3 s grad=2.876e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.215642e-3 s grad=2.087e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.119254e-3 s grad=2.335e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.149404e-3 s grad=1.983e-6 s +[acc-interp] eval 1.0139836e-2 s +[acc-interp] thetaMarsh 1.02e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.408643e-3 s grad=3.938e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.890008e-3 s grad=3.712e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.430951e-3 s grad=3.519e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.747782e-3 s grad=1.765e-6 s +[acc-interp] eval 1.1392798e-2 s +[acc-interp] thetaMarsh 7.9e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.643522e-3 s grad=2.027e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.554098e-3 s grad=1.761e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.638659e-3 s grad=3.814e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.983947e-3 s grad=4.903e-6 s +[acc-interp] eval 1.2608107e-2 s +[acc-interp] thetaMarsh 7.5e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.530399e-3 s grad=1.416e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.296738e-3 s grad=1.354e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.293452e-3 s grad=1.386e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.128522e-3 s grad=2.756e-6 s +[acc-interp] eval 1.0119152e-2 s +[acc-interp] thetaMarsh 1.04e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.253884e-3 s grad=9.015e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.546017e-3 s grad=3.91e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.703693e-3 s grad=3.273e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.456549e-3 s grad=1.012974e-3 s +[acc-interp] eval 1.2453322e-2 s +[acc-interp] thetaMarsh 1.28e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.299648e-3 s grad=2.724e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.248574e-3 s grad=2.496e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.801308e-3 s grad=4.663e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.226433e-3 s grad=2.708e-6 s +[acc-interp] eval 1.229831e-2 s +[acc-interp] thetaMarsh 9.9e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.343661e-3 s grad=3.098e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.523185e-3 s grad=2.15e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.484646e-3 s grad=3.83e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.4279e-3 s grad=4.448e-6 s +[acc-interp] eval 1.1217233e-2 s +[acc-interp] thetaMarsh 1.5e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.768697e-3 s grad=3.715e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.658477e-3 s grad=2.123e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.292758e-3 s grad=2.07e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.371956e-3 s grad=2.585e-6 s +[acc-interp] eval 1.1316591e-2 s +[acc-interp] thetaMarsh 6.7e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.688465e-3 s grad=3.303e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.253363e-3 s grad=2.916e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.205433e-3 s grad=3.215e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.213623e-3 s grad=2.073e-6 s +[acc-interp] eval 1.0230395e-2 s +[acc-interp] thetaMarsh 2.43e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.192026e-3 s grad=2.83e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.121265e-3 s grad=3.464e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.059195e-3 s grad=2.604e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.71429e-3 s grad=3.456e-6 s +[acc-interp] eval 1.0203359e-2 s +[acc-interp] thetaMarsh 1.52e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.488355e-3 s grad=3.37e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.192405e-3 s grad=2.467e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.13045e-3 s grad=2.36e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.13107e-3 s grad=2.098e-6 s +[acc-interp] eval 1.0748108e-2 s +[acc-interp] thetaMarsh 1.27e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.302775e-3 s grad=2.376e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.113842e-3 s grad=6.119e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.626338e-3 s grad=4.235e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.840907e-3 s grad=4.616e-6 s +[acc-interp] eval 1.392267e-2 s +[acc-interp] thetaMarsh 1.42e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.046569e-3 s grad=2.274e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.873262e-3 s grad=2.579e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.737641e-3 s grad=5.625e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.510018e-3 s grad=2.982e-6 s +[acc-interp] eval 1.1985997e-2 s +[acc-interp] thetaMarsh 1.1e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.364882e-3 s grad=2.564e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.707208e-3 s grad=2.278e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.263166e-3 s grad=2.455e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.454467e-3 s grad=3.96e-6 s +[acc-interp] eval 1.1523251e-2 s +[acc-interp] thetaMarsh 1.69e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.015143e-3 s grad=3.204e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.180489e-3 s grad=2.306e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.272505e-3 s grad=1.784e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.115738e-3 s grad=2.615e-6 s +[acc-interp] eval 1.1330998e-2 s +[acc-interp] thetaMarsh 1.19e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.427299e-3 s grad=2.697e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.968842e-3 s grad=3.816e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.647968e-3 s grad=3.981e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.455017e-3 s grad=3.566e-6 s +[acc-interp] eval 1.1542312e-2 s +[acc-interp] thetaMarsh 8.8e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.3313e-3 s grad=2.734e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.382101e-3 s grad=3.221e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.330962e-3 s grad=3.096e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.56778e-3 s grad=2.616e-6 s +[acc-interp] eval 1.0468834e-2 s +[acc-interp] thetaMarsh 1.81e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.289436e-3 s grad=2.24e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.341154e-3 s grad=2.432e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.218245e-3 s grad=2.665e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.200147e-3 s grad=2.282e-6 s +[acc-interp] eval 1.0176834e-2 s +[acc-interp] thetaMarsh 1.23e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.342175e-3 s grad=4.011e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.57036e-3 s grad=3.923e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.65219e-3 s grad=3.02e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.509475e-3 s grad=9.32e-7 s +[acc-interp] eval 1.1245665e-2 s +[acc-interp] thetaMarsh 7.9e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.208445e-3 s grad=1.903e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.379547e-3 s grad=7.253e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.536719e-3 s grad=3.993e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.433944e-3 s grad=4.213e-6 s +[acc-interp] eval 1.0472251e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=2.170725e-3 s grad=3.272e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.692044e-3 s grad=3.186e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.19003e-3 s grad=2.408e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.91369e-3 s grad=2.364e-6 s +[acc-interp] eval 0.458191219 s +[acc-interp] thetaMarsh 1.34e-7 s (n=[acc-interp] thetaMarsh 1.07e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=8.840119e-3 s grad=4.202e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.468028e-3 s grad=2.812e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.831183e-3 s grad=1.901e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.260712e-3 s grad=4.459e-6 s +[acc-interp] eval 1.804261e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=3.766572e-3 s grad=4.095e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.16137e-3 s grad=3.453e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=6.671702e-3 s grad=3.348e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.590021e-3 s grad=4.899e-6 s +[acc-interp] eval 3.6119355e-2 s +[acc-interp] thetaMarsh 1.1e-7 s (n=[acc-interp] thetaMarsh 6.7e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=6.194545e-3 s grad=4.046e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.28317e-3 s grad=2.303e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.611303e-3 s grad=1.34e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.903981e-3 s grad=1.571e-6 s +[acc-interp] eval 1.3772338e-2 s +[acc-interp] thetaMarsh 6.1e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.621166e-3 s grad=6.098e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.570371e-3 s grad=4.009e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.102358e-3 s grad=3.755e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.454424e-3 s grad=2.235e-6 s +[acc-interp] eval 9.511589e-3 s +[acc-interp] thetaMarsh 6.0e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.220782e-3 s grad=3.094e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.389529e-3 s grad=1.803e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.179869e-3 s grad=1.529e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.446111e-3 s grad=2.199e-6 s +[acc-interp] eval 7.967451e-3 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=3.358031e-3 s grad=4.226e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.523309e-3 s grad=3.48e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.512053e-3 s grad=2.731e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.575602e-3 s grad=3.172e-6 s +[acc-interp] eval 4.2500049e-2 s +[acc-interp] thetaMarsh 1.44e-7 s (n=[acc-interp] thetaMarsh 8.8e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=1.4771147e-2 s grad=4.015e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.705017e-3 s grad=3.28e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.562918e-3 s grad=2.823e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.476875e-3 s grad=4.777e-6 s +[acc-interp] eval 2.363941e-2 s +[acc-interp] thetaMarsh 6.2e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.952963e-3 s grad=4.728e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.543838e-3 s grad=5.136e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.44526e-3 s grad=4.028e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.692018e-3 s grad=4.584e-6 s +[acc-interp] eval 1.1539905e-2 s +[acc-interp] thetaMarsh 7.5e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.450645e-3 s grad=1.1296e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.449994e-3 s grad=3.154e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.119627e-3 s grad=3.213e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.105679e-3 s grad=3.257e-6 s +[acc-interp] eval 9.924438e-3 s +[acc-interp] thetaMarsh 6.2e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.002239e-3 s grad=2.814e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.837845e-3 s grad=2.941e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.187407e-3 s grad=2.944e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.905959e-3 s grad=2.65e-6 s +[acc-interp] eval 8.717453e-3 s +[acc-interp] thetaMarsh 6.3e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.244028e-3 s grad=3.975e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.067677e-3 s grad=5.594e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.525133e-3 s grad=3.955e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.991514e-3 s grad=3.426e-6 s +[acc-interp] eval 1.2168264e-2 s +[acc-interp] thetaMarsh 1.2e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.564627e-3 s grad=3.4e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.339743e-3 s grad=4.77e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.045472e-3 s grad=3.873e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.458909e-3 s grad=3.65e-6 s +[acc-interp] eval 1.2066961e-2 s +[acc-interp] thetaMarsh 1.55e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.524914e-3 s grad=3.016e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.534774e-3 s grad=3.14e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.418874e-3 s grad=3.69e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.719701e-3 s grad=4.476e-6 s +[acc-interp] eval 1.1702655e-2 s +[acc-interp] thetaMarsh 6.1e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.968933e-3 s grad=3.479e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.78001e-3 s grad=5.003e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.473738e-3 s grad=4.383e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.613397e-3 s grad=3.97e-6 s +[acc-interp] eval 1.1994766e-2 s +[acc-interp] thetaMarsh 1.87e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.827947e-3 s grad=4.148e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.844834e-3 s grad=3.793e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.907886e-3 s grad=3.269e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.317847e-3 s grad=2.298e-6 s +[acc-interp] eval 1.2509655e-2 s +[acc-interp] thetaMarsh 1.27e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.408636e-3 s grad=2.84e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.285909e-3 s grad=2.235e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.235567e-3 s grad=4.014e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.312695e-3 s grad=4.237e-6 s +[acc-interp] eval 1.1856647e-2 s +[acc-interp] thetaMarsh 1.24e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.640956e-3 s grad=3.96e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.678134e-3 s grad=4.18e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.144062e-3 s grad=3.145e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.561036e-3 s grad=3.525e-6 s +[acc-interp] eval 1.2703802e-2 s +[acc-interp] thetaMarsh 1.14e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.634626e-3 s grad=3.831e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.529443e-3 s grad=4.062e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.723532e-3 s grad=4.54e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.735304e-3 s grad=3.655e-6 s +[acc-interp] eval 1.3233616e-2 s +[acc-interp] thetaMarsh 9.5e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.369462e-3 s grad=2.892e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.593005e-3 s grad=3.605e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.617504e-3 s grad=3.539e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.549227e-3 s grad=3.939e-6 s +[acc-interp] eval 1.2803593e-2 s +[acc-interp] thetaMarsh 1.15e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.019029e-3 s grad=3.481e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.373095e-3 s grad=4.139e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.172155e-3 s grad=4.723e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.432451e-3 s grad=3.952e-6 s +[acc-interp] eval 1.2196597e-2 s +[acc-interp] thetaMarsh 6.2e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.503303e-3 s grad=3.175e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.014711e-3 s grad=3.662e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.302138e-3 s grad=2.952e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.675806e-3 s grad=2.368e-6 s +[acc-interp] eval 1.1330868e-2 s +[acc-interp] thetaMarsh 6.4e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.343816e-3 s grad=1.1419e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.21554e-3 s grad=2.168e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.363755e-3 s grad=7.953e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.162231e-3 s grad=8.105e-6 s +[acc-interp] eval 9.810026e-3 s +[acc-interp] thetaMarsh 6.2e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.518052e-3 s grad=1.749e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.409323e-3 s grad=7.116e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.300249e-3 s grad=6.433e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.326264e-3 s grad=5.513e-6 s +[acc-interp] eval 1.154753e-2 s +[acc-interp] thetaMarsh 6.9e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.8189e-3 s grad=4.787e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.946017e-3 s grad=3.172e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.723902e-3 s grad=1.932e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.327399e-3 s grad=1.882e-6 s +[acc-interp] eval 1.1678158e-2 s +[acc-interp] thetaMarsh 6.3e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.21894e-3 s grad=5.029e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.728706e-3 s grad=1.0538e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.848124e-3 s grad=7.76e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.567846e-3 s grad=2.377e-6 s +[acc-interp] eval 1.2212994e-2 s +[acc-interp] thetaMarsh 6.6e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.699376e-3 s grad=7.635e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.62594e-3 s grad=3.708e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.010017e-3 s grad=4.674e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.280382e-3 s grad=3.896e-6 s +[acc-interp] eval 1.282984e-2 s +[acc-interp] thetaMarsh 1.01e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.420597e-3 s grad=2.765e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.790399e-3 s grad=2.884e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.342681e-3 s grad=7.364e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.887822e-3 s grad=4.119e-6 s +[acc-interp] eval 1.3225397e-2 s +[acc-interp] thetaMarsh 1.1e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.369218e-3 s grad=3.862e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.688561e-3 s grad=3.642e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.760323e-3 s grad=3.256e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.130158e-3 s grad=4.798e-6 s +[acc-interp] eval 1.3534028e-2 s +[acc-interp] thetaMarsh 1.44e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.915364e-3 s grad=4.1e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.234138e-3 s grad=3.636e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.543015e-3 s grad=3.271e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.795899e-3 s grad=3.196e-6 s +[acc-interp] eval 1.3275969e-2 s +[acc-interp] thetaMarsh 1.0e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.042502e-3 s grad=4.687e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.405632e-3 s grad=3.309e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.085283e-3 s grad=2.975e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.485399e-3 s grad=3.575e-6 s +[acc-interp] eval 1.2585263e-2 s +[acc-interp] thetaMarsh 1.02e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.856232e-3 s grad=2.803e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.723982e-3 s grad=2.562e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.548659e-3 s grad=4.263e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.441913e-3 s grad=4.173e-6 s +[acc-interp] eval 1.3738391e-2 s +[acc-interp] thetaMarsh 6.7e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.773471e-3 s grad=1.099e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.320435e-3 s grad=5.234e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.107391e-3 s grad=4.293e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.0472285e-2 s grad=5.31e-6 s +[acc-interp] eval 2.109273e-2 s +[acc-interp] thetaMarsh 9.8e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.09726e-3 s grad=3.23e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.375328e-3 s grad=2.598e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.258396e-3 s grad=2.714e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.617796e-3 s grad=2.444e-6 s +[acc-interp] eval 1.1970906e-2 s +[acc-interp] thetaMarsh 2.87e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.507797e-3 s grad=2.535e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.495314e-3 s grad=2.294e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.739262e-3 s grad=5.167e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.415988e-3 s grad=4.941e-6 s +[acc-interp] eval 1.1459695e-2 s +[acc-interp] thetaMarsh 6.6e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.722303e-3 s grad=3.498e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.433653e-3 s grad=3.736e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.55695e-3 s grad=4.749e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.49561e-3 s grad=3.355e-6 s +[acc-interp] eval 1.1273251e-2 s +[acc-interp] thetaMarsh 1.08e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.723317e-3 s grad=4.407e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.718725e-3 s grad=2.917e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.402475e-3 s grad=3.528e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.824882e-3 s grad=4.274e-6 s +[acc-interp] eval 1.2311841e-2 s +[acc-interp] thetaMarsh 1.17e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.347782e-3 s grad=2.576e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.237069e-3 s grad=3.078e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.506205e-3 s grad=4.495e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.902776e-3 s grad=2.255e-6 s +[acc-interp] eval 1.2254614e-2 s +[acc-interp] thetaMarsh 6.2e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.950864e-3 s grad=9.766e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.601408e-3 s grad=3.072e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.565272e-3 s grad=7.718e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.651726e-3 s grad=4.766e-6 s +[acc-interp] eval 1.256545e-2 s +4) +[acc-interp] chunk base=0 cs=25974 kernel=2.697322e-3 s grad=1.0079e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.754796e-3 s grad=4.298e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.411418e-3 s grad=9.443e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.119151e-3 s grad=2.428e-6 s +[acc-interp] eval 0.420252082 s +[acc-interp] thetaMarsh 7.2e-8 s (n=[acc-interp] thetaMarsh 5.8e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.0489273e-2 s grad=3.946e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.215967e-3 s grad=6.892e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.199512e-3 s grad=2.886e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.696741e-3 s grad=2.289e-6 s +[acc-interp] eval 2.3527713e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=7.420492e-3 s grad=1.577e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.498633e-3 s grad=2.78e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.139049e-3 s grad=3.263e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.50447e-3 s grad=1.673e-6 s +[acc-interp] eval 4.2564823e-2 s +[acc-interp] thetaMarsh 1.33e-7 s (n=[acc-interp] thetaMarsh 1.15e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.0228705e-2 s grad=3.912e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.2559211e-2 s grad=3.984e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.009751e-3 s grad=3.702e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.13047e-3 s grad=2.878e-6 s +[acc-interp] eval 5.2603524e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=5.553622e-3 s grad=3.387e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.737463e-3 s grad=3.04e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.747764e-3 s grad=2.439e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.588583e-3 s grad=2.681e-6 s +[acc-interp] eval 7.2018445e-2 s +[acc-interp] thetaMarsh 1.38e-7 s (n=[acc-interp] thetaMarsh 1.13e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.7776637e-2 s grad=3.626e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.047149e-3 s grad=3.133e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.691935e-3 s grad=3.103e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.3363352e-2 s grad=3.837e-6 s +[acc-interp] eval 4.1492107e-2 s +[acc-interp] thetaMarsh 2.99e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=4.238445e-3 s grad=4.2e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.531611e-3 s grad=4.769e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.041936e-3 s grad=3.893e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.880045e-3 s grad=3.496e-6 s +[acc-interp] eval 1.7451361e-2 s +[acc-interp] thetaMarsh 1.32e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=4.090892e-3 s grad=3.807e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.979458e-3 s grad=3.4e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.805876e-3 s grad=3.821e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.972597e-3 s grad=3.359e-6 s +[acc-interp] eval 1.6506122e-2 s +[acc-interp] thetaMarsh 1.11e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=4.081761e-3 s grad=3.62e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.164399e-3 s grad=3.527e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.503281e-3 s grad=4.824e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.065574e-3 s grad=5.279e-6 s +[acc-interp] eval 1.6989695e-2 s +[acc-interp] thetaMarsh 7.2e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.171154e-3 s grad=3.385e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.831336e-3 s grad=3.506e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.92056e-3 s grad=3.116e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.591495e-3 s grad=3.021e-6 s +[acc-interp] eval 1.4311906e-2 s +[acc-interp] thetaMarsh 1.0e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.913826e-3 s grad=2.736e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.72962e-3 s grad=3.601e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.630165e-3 s grad=4.371e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.550731e-3 s grad=4.123e-6 s +[acc-interp] eval 1.5645009e-2 s +[acc-interp] thetaMarsh 1.91e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=5.243666e-3 s grad=5.098e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.94873e-3 s grad=4.311e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.456191e-3 s grad=3.491e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.3309093e-2 s grad=3.787e-6 s +[acc-interp] eval 2.8861426e-2 s +[acc-interp] thetaMarsh 6.3e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.462603e-3 s grad=2.807e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.984351e-3 s grad=3.442e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.546119e-3 s grad=2.368e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.101586e-3 s grad=3.979e-6 s +[acc-interp] eval 1.4311924e-2 s +[acc-interp] thetaMarsh 6.3e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.054477e-3 s grad=2.568e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.772248e-3 s grad=3.624e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.687758e-3 s grad=3.062e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.758576e-3 s grad=2.769e-6 s +[acc-interp] eval 1.3410968e-2 s +[acc-interp] thetaMarsh 6.4e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.939862e-3 s grad=3.081e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.635182e-3 s grad=3.816e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.569908e-3 s grad=3.56e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.1794e-3 s grad=3.011e-6 s +[acc-interp] eval 1.4482037e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=3.560871e-3 s grad=3.304e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=8.573309e-3 s grad=3.371e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.22684e-3 s grad=3.092e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.057272e-3 s grad=2.466e-6 s +[acc-interp] eval 0.215008461 s +[acc-interp] thetaMarsh 8.2e-8 s (n=[acc-interp] thetaMarsh 5.9e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.6713747e-2 s grad=4.032e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.737993e-3 s grad=3.335e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.215422e-3 s grad=3.562e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.533099e-3 s grad=2.939e-6 s +[acc-interp] eval 2.6612279e-2 s +[acc-interp] thetaMarsh 1.0e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=9.105064e-3 s grad=2.654e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.45507e-3 s grad=3.084e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.623568e-3 s grad=2.613e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.492235e-3 s grad=3.715e-6 s +[acc-interp] eval 1.8009756e-2 s +[acc-interp] thetaMarsh 9.7e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.659546e-3 s grad=4.772e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.737819e-3 s grad=3.41e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.779733e-3 s grad=9.023e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.468874e-3 s grad=1.111e-6 s +[acc-interp] eval 1.1563883e-2 s +[acc-interp] thetaMarsh 5.9e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.711543e-3 s grad=1.451e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.532543e-3 s grad=1.0648e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.413895e-3 s grad=7.561e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.316061e-3 s grad=1.675e-6 s +[acc-interp] eval 1.2949436e-2 s +[acc-interp] thetaMarsh 5.6e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.009881e-3 s grad=2.624e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.282992e-3 s grad=8.594e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.407173e-3 s grad=6.282e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.573696e-3 s grad=4.49e-6 s +[acc-interp] eval 1.1529832e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=3.541284e-3 s grad=3.62e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.634663e-3 s grad=2.606e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.589834e-3 s grad=2.315e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.389362e-3 s grad=1.963e-6 s +[acc-interp] eval 9.3911992e-2 s +[acc-interp] thetaMarsh 1.23e-7 s (n=[acc-interp] thetaMarsh 1.13e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=7.710594e-3 s grad=3.337e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.521891e-3 s grad=3.179e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.444898e-3 s grad=1.827e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.321098e-3 s grad=1.443e-6 s +[acc-interp] eval 1.6607623e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=2.827935e-3 s grad=4.003e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.513005e-3 s grad=1.388e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.41722e-3 s grad=6.224e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.334287e-3 s grad=5.342e-6 s +[acc-interp] eval 2.7715374e-2 s +[acc-interp] thetaMarsh 6.4e-8 s (n=[acc-interp] thetaMarsh 6.7e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.7301821e-2 s grad=3.27e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.389777e-3 s grad=2.898e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.248686e-3 s grad=6.174e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.399176e-3 s grad=3.116e-6 s +[acc-interp] eval 2.788738e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=2.771072e-3 s grad=2.664e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.491042e-3 s grad=2.193e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.714896e-3 s grad=4.192e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.009826e-3 s grad=3.898e-6 s +[acc-interp] eval 4.065443e-2 s +[acc-interp] thetaMarsh 1.32e-7 s (n=[acc-interp] thetaMarsh 1.13e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=6.989962e-3 s grad=4.043e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.577803e-3 s grad=3.112e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.943937e-3 s grad=4.886e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.685214e-3 s grad=3.402e-6 s +[acc-interp] eval 1.7966592e-2 s +[acc-interp] thetaMarsh 1.05e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.740016e-3 s grad=2.933e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.643295e-3 s grad=2.461e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.340232e-3 s grad=3.522e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.059333e-3 s grad=3.911e-6 s +[acc-interp] eval 1.3453369e-2 s +[acc-interp] thetaMarsh 1.19e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.234809e-3 s grad=3.384e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.082968e-3 s grad=2.722e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.319477e-3 s grad=2.135e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.479868e-3 s grad=3.836e-6 s +[acc-interp] eval 1.6724186e-2 s +[acc-interp] thetaMarsh 9.9e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=9.813302e-3 s grad=3.242e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.555128e-3 s grad=2.377e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.317391e-3 s grad=2.107e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.169477e-3 s grad=3.607e-6 s +[acc-interp] eval 1.8417513e-2 s +[acc-interp] thetaMarsh 1.2e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.33547e-3 s grad=2.68e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.891356e-3 s grad=2.769e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.168332e-3 s grad=2.446e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.944592e-3 s grad=3.528e-6 s +[acc-interp] eval 1.19002e-2 s +[acc-interp] thetaMarsh 1.01e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.838051e-3 s grad=3.358e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.815128e-3 s grad=3.044e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.723545e-3 s grad=3.097e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.869823e-3 s grad=4.768e-6 s +[acc-interp] eval 1.3781205e-2 s +[acc-interp] thetaMarsh 1.25e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.35962e-3 s grad=3.181e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.465477e-3 s grad=3.024e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.760196e-3 s grad=2.227e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.284438e-3 s grad=1.804e-6 s +[acc-interp] eval 1.1335077e-2 s +[acc-interp] thetaMarsh 1.06e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.222613e-3 s grad=2.417e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.33274e-3 s grad=4.797e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.527417e-3 s grad=3.568e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.632638e-3 s grad=3.267e-6 s +[acc-interp] eval 1.2243613e-2 s +[acc-interp] thetaMarsh 1.19e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.362655e-3 s grad=3.495e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.476459e-3 s grad=3.03e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.437063e-3 s grad=4.341e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.596053e-3 s grad=3.687e-6 s +[acc-interp] eval 1.1268553e-2 s +[acc-interp] thetaMarsh 6.5e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.866866e-3 s grad=3.102e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.793272e-3 s grad=1.582e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.752229e-3 s grad=6.308e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.004388e-3 s grad=3.11e-6 s +[acc-interp] eval 1.2358826e-2 s +[acc-interp] thetaMarsh 1.2e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.763053e-3 s grad=3.441e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.327356e-3 s grad=2.319e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.242149e-3 s grad=2.744e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.552669e-3 s grad=2.292e-6 s +[acc-interp] eval 1.249982e-2 s +[acc-interp] thetaMarsh 1.15e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.174286e-3 s grad=2.457e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.141829e-3 s grad=1.718e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.307622e-3 s grad=1.929e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.095583e-3 s grad=3.0e-6 s +[acc-interp] eval 1.01033e-2 s +[acc-interp] thetaMarsh 9.6e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.167209e-3 s grad=2.372e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.357413e-3 s grad=2.431e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.150451e-3 s grad=3.344e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.694914e-3 s grad=4.506e-6 s +[acc-interp] eval 1.0798645e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=2.451934e-3 s grad=4.812e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.789139e-3 s grad=8.312e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.357011e-3 s grad=7.924e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.29186e-3 s grad=6.912e-6 s +[acc-interp] eval 0.185573514 s +[acc-interp] thetaMarsh 5.8e-8 s (n=0) +[acc-interp] chunk base=0 cs=25974 kernel=7.984466e-3 s grad=1.185e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.004117e-3 s grad=1.566e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.096785e-3 s grad=1.167e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.932777e-3 s grad=5.79e-7 s +[acc-interp] eval 1.503328e-2 s +[acc-interp] thetaMarsh 7.3e-8 s (n=[acc-interp] thetaMarsh 6.1e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.421747e-2 s grad=3.747e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.415014e-3 s grad=1.799e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.065052e-3 s grad=5.072e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.936425e-3 s grad=1.132e-6 s +[acc-interp] eval 3.4674988e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=2.862928e-3 s grad=3.843e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.728552e-3 s grad=3.318e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.329387e-3 s grad=2.557e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.414759e-3 s grad=2.824e-6 s +[acc-interp] eval 5.0891043e-2 s +[acc-interp] thetaMarsh 1.46e-7 s (n=[acc-interp] thetaMarsh 1.09e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=9.328904e-3 s grad=3.798e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.006483e-3 s grad=2.973e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.358229e-3 s grad=2.868e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.02493e-3 s grad=3.537e-6 s +[acc-interp] eval 2.0484714e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=4.362108e-3 s grad=2.606e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.859853e-3 s grad=2.982e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.955026e-3 s grad=1.893e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.632371e-3 s grad=4.656e-6 s +[acc-interp] eval 4.040873e-2 s +[acc-interp] thetaMarsh 2.51e-7 s (n=[acc-interp] thetaMarsh 1.45e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.593526e-2 s grad=4.504e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.3514e-3 s grad=4.221e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.789187e-3 s grad=1.3673e-5 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.239511e-3 s grad=2.694e-6 s +[acc-interp] eval 3.8589878e-2 s +[acc-interp] thetaMarsh 6.4e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.249389e-3 s grad=1.2776e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.646232e-3 s grad=6.354e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.757269e-3 s grad=3.294e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.289537e-3 s grad=5.812e-6 s +[acc-interp] eval 1.5821122e-2 s +[acc-interp] thetaMarsh 6.1e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.64134e-3 s grad=2.863e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=6.117564e-3 s grad=3.679e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.416998e-3 s grad=1.611e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.7599615e-2 s grad=3.835e-6 s +[acc-interp] eval 3.4601952e-2 s +[acc-interp] thetaMarsh 6.5e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.958074e-3 s grad=3.632e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.448509e-3 s grad=3.86e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.135489e-3 s grad=3.141e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.826919e-3 s grad=2.989e-6 s +[acc-interp] eval 1.3194685e-2 s +[acc-interp] thetaMarsh 6.1e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.422683e-3 s grad=4.361e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.660843e-3 s grad=3.758e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.406958e-3 s grad=4.304e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.560369e-3 s grad=3.713e-6 s +[acc-interp] eval 1.186226e-2 s +[acc-interp] thetaMarsh 2.02e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.852284e-3 s grad=2.83e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.778921e-3 s grad=3.055e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.740948e-3 s grad=2.882e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.078119e-3 s grad=2.648e-6 s +[acc-interp] eval 1.2208736e-2 s +[acc-interp] thetaMarsh 1.66e-7 s (n=3) 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base=77922 cs=25974 kernel=3.478568e-3 s grad=2.447e-6 s +[acc-interp] eval 2.1017102e-2 s +[acc-interp] thetaMarsh 1.06e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.77712e-3 s grad=7.456e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.530951e-3 s grad=3.072e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.244942e-3 s grad=1.429e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.110855e-3 s grad=6.833e-6 s +[acc-interp] eval 1.4697851e-2 s +[acc-interp] thetaMarsh 1.91e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.926829e-3 s grad=8.877e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.174204e-3 s grad=1.4963e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.072089e-3 s grad=1.918e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.985764e-3 s grad=7.886e-6 s +[acc-interp] eval 1.2874271e-2 s +[acc-interp] thetaMarsh 6.0e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.967519e-3 s grad=2.823e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.832612e-3 s grad=2.144e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.115329e-3 s grad=1.733e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.70456e-3 s grad=5.278e-6 s +[acc-interp] eval 1.5402946e-2 s +[acc-interp] thetaMarsh 8.6e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.816794e-3 s grad=4.186e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.149651e-3 s grad=8.711e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.058772e-3 s grad=6.986e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.877331e-3 s grad=4.845e-6 s +[acc-interp] eval 1.3879008e-2 s +[acc-interp] thetaMarsh 1.41e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=7.140796e-3 s grad=3.674e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.585776e-3 s grad=2.356e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.573436e-3 s grad=2.641e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.338433e-3 s grad=7.865e-6 s +[acc-interp] eval 1.7715033e-2 s 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kernel=2.828334e-3 s grad=1.516e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.930761e-3 s grad=7.312e-6 s +[acc-interp] eval 1.2800778e-2 s +[acc-interp] thetaMarsh 5.6e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.623288e-3 s grad=2.687e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.806958e-3 s grad=2.719e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.529212e-3 s grad=2.837e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.484118e-3 s grad=2.447e-6 s +[acc-interp] eval 1.1225871e-2 s +[acc-interp] thetaMarsh 3.13e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=5.5791e-3 s grad=2.674e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.500973e-3 s grad=2.954e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.830187e-3 s grad=5.571e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.662993e-3 s grad=3.345e-6 s +[acc-interp] eval 1.6485687e-2 s +[acc-interp] thetaMarsh 6.9e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.023592e-3 s grad=8.21e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.933251e-3 s grad=8.577e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.512379e-3 s grad=2.919e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.120708e-3 s grad=1.0564e-5 s +[acc-interp] eval 1.3898874e-2 s +[acc-interp] thetaMarsh 1.3e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.872267e-3 s grad=2.742e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.418843e-3 s grad=2.415e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.275938e-3 s grad=1.931e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.337814e-3 s grad=2.327e-6 s +[acc-interp] eval 1.1593629e-2 s +[acc-interp] thetaMarsh 1.12e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.939831e-3 s grad=4.048e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.991726e-3 s grad=3.71e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.151248e-3 s grad=2.8e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.980341e-3 s grad=2.132e-6 s +[acc-interp] eval 1.366219e-2 s +[acc-interp] thetaMarsh 9.7e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.58744e-3 s grad=5.533e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.976126e-3 s grad=3.895e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.769841e-3 s grad=8.019e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.159681e-3 s grad=6.172e-6 s +[acc-interp] eval 1.3507721e-2 s +[acc-interp] thetaMarsh 6.2e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.270097e-3 s grad=2.799e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.169573e-3 s grad=4.841e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=7.660095e-3 s grad=4.616e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.914792e-3 s grad=3.542e-6 s +[acc-interp] eval 1.790437e-2 s +[acc-interp] thetaMarsh 6.4e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.496058e-3 s grad=4.061e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.776441e-3 s grad=3.026e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.526557e-3 s grad=2.686e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.669197e-3 s grad=2.569e-6 s +[acc-interp] eval 1.1281744e-2 s +[acc-interp] thetaMarsh 6.0e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.66796e-3 s grad=2.965e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.635662e-3 s grad=2.827e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.863783e-3 s grad=4.325e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.567683e-3 s grad=3.243e-6 s +[acc-interp] eval 1.3517094e-2 s +[acc-interp] thetaMarsh 2.16e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.667171e-3 s grad=3.069e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.649954e-3 s grad=3.398e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.605709e-3 s grad=2.805e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.699233e-3 s grad=5.902e-6 s +[acc-interp] eval 1.3571045e-2 s +[acc-interp] thetaMarsh 1.1e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 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kernel=3.245106e-3 s grad=4.335e-6 s +[acc-interp] eval 1.2768133e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=4.488482e-3 s grad=3.787e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.185593e-3 s grad=3.067e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.010009e-3 s grad=2.055e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.004252e-3 s grad=2.799e-6 s +[acc-interp] eval 0.49138132 s +[acc-interp] thetaMarsh 5.9e-8 s (n=[acc-interp] thetaMarsh 6.3e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.7803383e-2 s grad=3.868e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.10633e-3 s grad=3.113e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.00224e-3 s grad=3.643e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.048584e-3 s grad=3.242e-6 s +[acc-interp] eval 3.9194286e-2 s +[acc-interp] thetaMarsh 1.05e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.168088e-3 s grad=3.244e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.08475e-3 s 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chunk base=77922 cs=25974 kernel=2.813902e-3 s grad=4.088e-6 s +[acc-interp] eval 1.290774e-2 s +[acc-interp] thetaMarsh 1.02e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.646986e-3 s grad=3.62e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.871064e-3 s grad=2.785e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.071184e-3 s grad=2.334e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=7.578859e-3 s grad=3.246e-6 s +[acc-interp] eval 1.7023999e-2 s +[acc-interp] thetaMarsh 7.5e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.670216e-3 s grad=8.738e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.843446e-3 s grad=1.25e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.928586e-3 s grad=3.113e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.92173e-3 s grad=4.697e-6 s +[acc-interp] eval 1.3298483e-2 s +[acc-interp] thetaMarsh 8.6e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.98452e-3 s grad=3.174e-6 s +[acc-interp] chunk base=25974 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cs=25974 kernel=3.358449e-3 s grad=3.727e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.681265e-3 s grad=3.674e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.311188e-3 s grad=3.482e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.14689e-3 s grad=2.731e-6 s +[acc-interp] eval 1.5204516e-2 s +[acc-interp] thetaMarsh 2.82e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.300811e-3 s grad=2.867e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.1223523e-2 s grad=3.463e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.933208e-3 s grad=2.825e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.288741e-3 s grad=3.167e-6 s +[acc-interp] eval 2.2340147e-2 s +[acc-interp] thetaMarsh 1.37e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.673372e-3 s grad=3.783e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.307767e-3 s grad=3.681e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.211451e-3 s grad=3.682e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.322186e-3 s grad=4.515e-6 s +[acc-interp] eval 1.510401e-2 s +[acc-interp] thetaMarsh 1.4e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.644253e-3 s grad=3.479e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.645228e-3 s grad=2.65e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.032335e-3 s grad=2.526e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.032084e-3 s grad=2.011e-6 s +[acc-interp] eval 1.4902205e-2 s +[acc-interp] thetaMarsh 1.11e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.397089e-3 s grad=3.438e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=7.659925e-3 s grad=3.354e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.019672e-3 s grad=2.61e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.447687e-3 s grad=4.513e-6 s +[acc-interp] eval 1.8917009e-2 s +[acc-interp] thetaMarsh 6.7e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.902135e-3 s grad=4.288e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.617841e-3 s 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chunk base=77922 cs=25974 kernel=2.722698e-3 s grad=2.689e-6 s +[acc-interp] eval 1.5782877e-2 s +[acc-interp] thetaMarsh 9.6e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.678941e-3 s grad=2.467e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.735822e-3 s grad=2.578e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.998191e-3 s grad=4.443e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.996772e-3 s grad=4.588e-6 s +[acc-interp] eval 1.4739663e-2 s +[acc-interp] thetaMarsh 7.1e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=5.075821e-3 s grad=1.538e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.016508e-3 s grad=8.144e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.867351e-3 s grad=3.21e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.674053e-3 s grad=3.243e-6 s +[acc-interp] eval 1.7791504e-2 s +[acc-interp] thetaMarsh 1.13e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.963009e-3 s grad=4.951e-6 s +[acc-interp] chunk base=25974 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kernel=2.75029e-3 s grad=1.313e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.177912e-3 s grad=5.614e-6 s +[acc-interp] eval 1.3342691e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=2.751725e-3 s grad=1.0156e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=7.41459e-3 s grad=2.448e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.624172e-3 s grad=4.765e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.645148e-3 s grad=3.617e-6 s +[acc-interp] eval 0.738550756 s +[acc-interp] thetaMarsh 1.3e-7 s (n=[acc-interp] thetaMarsh 1.08e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.5605249e-2 s grad=3.172e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.547273e-3 s grad=2.832e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.558711e-3 s grad=2.744e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.49203e-3 s grad=2.617e-6 s +[acc-interp] eval 2.4770085e-2 s +[acc-interp] thetaMarsh 9.5e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=4.042286e-3 s grad=2.098e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.179461e-3 s grad=2.034e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.179758e-3 s grad=1.951e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=7.398079e-3 s grad=2.676e-6 s +[acc-interp] eval 2.1269964e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=4.569643e-3 s grad=2.384e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.751599e-3 s grad=1.973e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.748307e-3 s grad=2.083e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.935283e-3 s grad=4.893e-6 s +[acc-interp] eval 6.5176263e-2 s +[acc-interp] thetaMarsh 1.43e-7 s (n=[acc-interp] thetaMarsh 1.03e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.9992022e-2 s grad=3.478e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.396753e-3 s grad=3.108e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.736382e-3 s grad=3.625e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.797023e-3 s grad=3.49e-6 s +[acc-interp] eval 2.7854616e-2 s +[acc-interp] thetaMarsh 7.7e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=9.807552e-3 s grad=3.569e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.472977e-3 s grad=8.288e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.509261e-3 s grad=5.849e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.339916e-3 s grad=2.648e-6 s +[acc-interp] eval 1.7881075e-2 s +[acc-interp] thetaMarsh 5.9e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.813943e-3 s grad=4.74e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.813995e-3 s grad=7.181e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.245241e-3 s grad=2.274e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.377209e-3 s grad=5.295e-6 s +[acc-interp] eval 1.2013032e-2 s +[acc-interp] thetaMarsh 6.2e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.401056e-3 s grad=8.585e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.431805e-3 s grad=3.629e-6 s 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chunk base=0 cs=25974 kernel=3.633041e-3 s grad=3.646e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.585052e-3 s grad=3.021e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.194213e-3 s grad=2.461e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.745068e-3 s grad=3.795e-6 s +[acc-interp] eval 1.6688062e-2 s +[acc-interp] thetaMarsh 1.08e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.650719e-3 s grad=3.055e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.225426e-3 s grad=1.988e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.885809e-3 s grad=2.31e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.552008e-3 s grad=3.142e-6 s +[acc-interp] eval 1.4780822e-2 s +[acc-interp] thetaMarsh 1.06e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=4.149347e-3 s grad=3.974e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.720997e-3 s grad=3.621e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.811101e-3 s grad=3.031e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.094007e-3 s grad=4.698e-6 s +[acc-interp] eval 1.7437626e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=3.695006e-3 s grad=3.142e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.214103e-3 s grad=2.422e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.759657e-3 s grad=2.773e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.4605e-3 s grad=4.141e-6 s +[acc-interp] eval 0.167585188 s +[acc-interp] thetaMarsh 1.66e-7 s (n=[acc-interp] thetaMarsh 1.27e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.0053515e-2 s grad=3.102e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.590169e-3 s grad=2.635e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.43562e-3 s grad=2.446e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.276634e-3 s grad=2.261e-6 s +[acc-interp] eval 2.8961835e-2 s +[acc-interp] thetaMarsh 9.5e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.371774e-3 s grad=3.272e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.529328e-3 s grad=2.551e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.846341e-3 s grad=3.361e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.37774e-3 s grad=2.17e-6 s +[acc-interp] eval 1.1808209e-2 s +[acc-interp] thetaMarsh 1.13e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.288493e-3 s grad=2.129e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.159355e-3 s grad=2.12e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.272935e-3 s grad=2.016e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.090339e-3 s grad=2.002e-6 s +[acc-interp] eval 1.0312808e-2 s +[acc-interp] thetaMarsh 1.02e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.069962e-3 s grad=2.252e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.099271e-3 s grad=1.733e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.056684e-3 s grad=7.995e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.057876e-3 s grad=6.641e-6 s +[acc-interp] eval 9.276356e-3 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=2.087378e-3 s grad=7.133e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.403922e-3 s grad=2.434e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.356068e-3 s grad=3.54e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.319072e-3 s grad=1.106e-6 s +[acc-interp] eval 7.0995194e-2 s +[acc-interp] thetaMarsh 7.0e-8 s (n=[acc-interp] thetaMarsh 5.6e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=6.736809e-3 s grad=4.073e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.869191e-3 s grad=3.64e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.270282e-3 s grad=2.255e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.83641e-3 s grad=1.044e-6 s +[acc-interp] eval 1.6538446e-2 s +[acc-interp] thetaMarsh 5.8e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.718942e-3 s grad=1.277e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.677565e-3 s grad=7.488e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.394803e-3 s grad=6.511e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.792929e-3 s grad=5.842e-6 s +[acc-interp] eval 1.1366616e-2 s +[acc-interp] thetaMarsh 6.8e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.669968e-3 s grad=4.252e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.436456e-3 s grad=3.791e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.650144e-3 s grad=2.732e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.577273e-3 s grad=2.5e-6 s +[acc-interp] eval 1.1195006e-2 s +[acc-interp] thetaMarsh 2.12e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.78787e-3 s grad=3.943e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.222009e-3 s grad=3.877e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.417064e-3 s grad=4.241e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.040797e-3 s grad=3.121e-6 s +[acc-interp] eval 1.2551516e-2 s +[acc-interp] thetaMarsh 1.07e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.1062164e-2 s grad=4.291e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.05586e-3 s grad=2.755e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.087648e-3 s grad=2.899e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.013784e-3 s grad=2.201e-6 s +[acc-interp] eval 1.8720564e-2 s +[acc-interp] thetaMarsh 1.03e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.072891e-3 s grad=2.516e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.062216e-3 s grad=2.317e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.254116e-3 s grad=4.442e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.215673e-3 s grad=2.183e-6 s +[acc-interp] eval 1.1096842e-2 s +[acc-interp] thetaMarsh 1.13e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.359582e-3 s grad=1.792e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.158001e-3 s grad=2.821e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.823411e-3 s grad=2.443e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.175042e-3 s grad=2.1e-6 s +[acc-interp] eval 9.832037e-3 s +[acc-interp] thetaMarsh 1.08e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.651211e-3 s grad=4.056e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.194198e-3 s grad=3.457e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.132401e-3 s grad=2.868e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.121584e-3 s grad=3.035e-6 s +[acc-interp] eval 1.0598617e-2 s +[acc-interp] thetaMarsh 1.08e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.02739e-3 s grad=3.076e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.028669e-3 s grad=2.793e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.374755e-3 s grad=3.688e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.115672e-3 s grad=2.585e-6 s +[acc-interp] eval 1.0036927e-2 s +[acc-interp] thetaMarsh 1.05e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.171035e-3 s grad=3.026e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.071358e-3 s grad=3.083e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.984083e-3 s grad=2.501e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.459043e-3 s grad=1.769e-6 s +[acc-interp] eval 9.160765e-3 s +[acc-interp] thetaMarsh 9.8e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.996079e-3 s grad=2.047e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.907573e-3 s grad=1.825e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.842292e-3 s grad=1.703e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.804733e-3 s grad=1.71e-6 s +[acc-interp] eval 8.933396e-3 s +[acc-interp] thetaMarsh 1.41e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.793622e-3 s grad=1.69e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.763504e-3 s grad=1.712e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.94115e-3 s grad=2.695e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.018213e-3 s grad=2.246e-6 s +[acc-interp] eval 8.922575e-3 s +[acc-interp] thetaMarsh 1.42e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.616329e-3 s grad=3.572e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.564387e-3 s grad=2.884e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.332644e-3 s grad=2.173e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.038082e-3 s grad=2.774e-6 s +[acc-interp] eval 1.2059174e-2 s +[acc-interp] thetaMarsh 1.06e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.808068e-3 s grad=2.167e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.245922e-3 s grad=3.112e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.701906e-3 s grad=3.961e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.195343e-3 s grad=3.071e-6 s +[acc-interp] eval 1.0276215e-2 s +[acc-interp] thetaMarsh 6.3e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.159707e-3 s grad=3.057e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.026885e-3 s grad=3.937e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.169715e-3 s grad=3.62e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.158384e-3 s grad=2.984e-6 s +[acc-interp] eval 9.303362e-3 s +[acc-interp] thetaMarsh 1.08e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.223861e-3 s grad=4.299e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.605238e-3 s grad=3.725e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.629648e-3 s grad=7.547e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.412971e-3 s grad=8.35e-7 s +[acc-interp] eval 1.0738536e-2 s +[acc-interp] thetaMarsh 1.99e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.430484e-3 s grad=7.604e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.855595e-3 s grad=1.0951e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.199229e-3 s grad=1.699e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.47094e-3 s grad=3.119e-6 s +[acc-interp] eval 9.825771e-3 s +[acc-interp] thetaMarsh 1.92e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.076554e-3 s grad=3.33e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.006275e-3 s grad=2.993e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.903479e-3 s grad=2.88e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.967001e-3 s grad=2.587e-6 s +[acc-interp] eval 8.73606e-3 s +[acc-interp] thetaMarsh 6.4e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.859143e-3 s grad=3.183e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.764112e-3 s grad=2.793e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.417588e-3 s grad=4.757e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.307491e-3 s grad=1.848e-6 s +[acc-interp] eval 9.240095e-3 s +[acc-interp] thetaMarsh 6.1e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.078983e-3 s grad=2.942e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.976343e-3 s grad=2.641e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.678954e-3 s grad=1.51e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.88324e-3 s grad=2.557e-6 s +[acc-interp] eval 8.392665e-3 s +[acc-interp] thetaMarsh 6.8e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.939196e-3 s grad=3.26e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.047018e-3 s grad=3.169e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.943433e-3 s grad=2.42e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.939086e-3 s grad=1.96e-6 s +[acc-interp] eval 8.611417e-3 s +[acc-interp] thetaMarsh 6.3e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.109671e-3 s grad=2.085e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.940194e-3 s grad=1.938e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.947937e-3 s grad=2.035e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.934286e-3 s grad=1.879e-6 s +[acc-interp] eval 8.669031e-3 s +[acc-interp] thetaMarsh 2.13e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.342979e-3 s grad=3.851e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.072025e-3 s grad=9.176e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.082051e-3 s grad=5.11e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.803698e-3 s grad=5.104e-6 s +[acc-interp] eval 9.148932e-3 s +[acc-interp] thetaMarsh 1.47e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.155245e-3 s grad=7.754e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.86187e-3 s grad=1.646e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.683255e-3 s grad=1.651e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.079393e-3 s grad=3.069e-6 s +[acc-interp] eval 8.514408e-3 s +[acc-interp] thetaMarsh 1.93e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.142891e-3 s grad=3.565e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.008997e-3 s grad=3.021e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.879833e-3 s grad=2.64e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.862595e-3 s grad=2.637e-6 s +[acc-interp] eval 8.696003e-3 s +[acc-interp] thetaMarsh 6.7e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.006563e-3 s grad=2.274e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.034115e-3 s grad=2.096e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.006686e-3 s grad=2.688e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.356052e-3 s grad=4.898e-6 s +[acc-interp] eval 9.206792e-3 s +[acc-interp] thetaMarsh 6.3e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.728255e-3 s grad=2.875e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.961984e-3 s grad=3.203e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.812309e-3 s grad=2.589e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.108765e-3 s grad=6.245e-6 s +[acc-interp] eval 8.416893e-3 s +[acc-interp] thetaMarsh 6.6e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.111313e-3 s grad=8.698e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.932945e-3 s grad=6.579e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.22336e-3 s grad=4.337e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.318962e-3 s grad=3.078e-6 s +[acc-interp] eval 9.777492e-3 s +[acc-interp] thetaMarsh 1.14e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.049352e-3 s grad=3.148e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.880702e-3 s grad=2.408e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.829644e-3 s grad=2.234e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.020412e-3 s grad=1.943e-6 s +[acc-interp] eval 9.249366e-3 s +[acc-interp] thetaMarsh 9.9e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.059164e-3 s grad=1.962e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.174213e-3 s grad=4.413e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.996796e-3 s grad=2.794e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.028548e-3 s grad=3.142e-6 s +[acc-interp] eval 1.0902596e-2 s +[acc-interp] thetaMarsh 2.89e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.979807e-3 s grad=2.549e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.823972e-3 s grad=2.096e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.719509e-3 s grad=2.151e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.720731e-3 s grad=1.843e-6 s +[acc-interp] eval 8.72297e-3 s +[acc-interp] thetaMarsh 1.11e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.025718e-3 s grad=3.688e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.368612e-3 s grad=2.26e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.950116e-3 s grad=2.05e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.320251e-3 s grad=2.365e-6 s +[acc-interp] eval 1.1361102e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=1.949891e-3 s grad=1.863e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.909854e-3 s grad=1.775e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.052739e-3 s grad=2.899e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.143074e-3 s grad=2.741e-6 s +[acc-interp] eval 0.341798883 s +[acc-interp] thetaMarsh 2.29e-7 s (n=[acc-interp] thetaMarsh 9.9e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.2508422e-2 s grad=4.003e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.564431e-3 s grad=2.862e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.563441e-3 s grad=3.486e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.485966e-3 s grad=2.243e-6 s +[acc-interp] eval 2.1802432e-2 s +[acc-interp] thetaMarsh 1.22e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.517566e-3 s grad=2.382e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.49919e-3 s grad=2.018e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.520286e-3 s grad=3.573e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.521974e-3 s grad=2.182e-6 s +[acc-interp] eval 1.2629126e-2 s +[acc-interp] thetaMarsh 1.33e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.857965e-3 s grad=2.427e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=8.701488e-3 s grad=3.068e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.947623e-3 s grad=2.598e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.919053e-3 s grad=2.812e-6 s +[acc-interp] eval 1.7038405e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=1.888483e-3 s grad=2.068e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.807361e-3 s grad=2.079e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.858961e-3 s grad=2.02e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.689531e-3 s grad=2.958e-6 s +[acc-interp] eval 6.1626833e-2 s +[acc-interp] thetaMarsh 5.6e-8 s (n=[acc-interp] thetaMarsh 6.1e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.7763739e-2 s grad=4.083e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.567222e-3 s grad=2.007e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.923197e-3 s grad=2.42e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.59671e-3 s grad=1.162e-6 s +[acc-interp] eval 2.8614885e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=3.532202e-3 s grad=3.491e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=8.986163e-3 s grad=3.282e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.866797e-3 s grad=2.272e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.189754e-3 s grad=4.748e-6 s +[acc-interp] eval 4.9181182e-2 s +[acc-interp] thetaMarsh 1.12e-7 s (n=[acc-interp] thetaMarsh 5.4e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.5359051e-2 s grad=2.41e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.602124e-3 s grad=2.958e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.543174e-3 s grad=2.163e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.829034e-3 s grad=2.459e-6 s +[acc-interp] eval 2.5177445e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=3.402024e-3 s grad=3.724e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.365063e-3 s grad=3.058e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.627768e-3 s grad=2.353e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=6.588448e-3 s grad=3.874e-6 s +[acc-interp] eval 4.2980736e-2 s +[acc-interp] thetaMarsh 1.18e-7 s (n=[acc-interp] thetaMarsh 1.15e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.1418643e-2 s grad=3.526e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.592305e-3 s grad=3.593e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.057651e-3 s grad=4.372e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.520687e-3 s grad=3.772e-6 s +[acc-interp] eval 2.1397353e-2 s +[acc-interp] thetaMarsh 1.36e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.30628e-3 s grad=2.846e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.345618e-3 s grad=2.572e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.943253e-3 s grad=2.372e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.425807e-3 s grad=6.29e-6 s +[acc-interp] eval 1.2858063e-2 s +[acc-interp] thetaMarsh 3.61e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.2097055e-2 s grad=4.106e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.998142e-3 s grad=4.588e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.210282e-3 s grad=3.867e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.492944e-3 s grad=2.377e-6 s +[acc-interp] eval 2.3371346e-2 s +[acc-interp] thetaMarsh 6.2e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.85739e-3 s grad=2.914e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.961416e-3 s grad=9.384e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.599394e-3 s grad=2.206e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.73027e-3 s grad=1.444e-6 s +[acc-interp] eval 1.1832728e-2 s +[acc-interp] thetaMarsh 5.3e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.912005e-3 s grad=3.273e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.717113e-3 s grad=3.612e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.846291e-3 s grad=3.339e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.108132e-3 s grad=7.596e-6 s +[acc-interp] eval 1.6618388e-2 s +[acc-interp] thetaMarsh 1.11e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.781137e-3 s grad=2.843e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.373887e-3 s grad=2.332e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.320231e-3 s grad=2.266e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.995355e-3 s grad=5.796e-6 s +[acc-interp] eval 1.2396239e-2 s +[acc-interp] thetaMarsh 1.94e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.851489e-3 s grad=2.753e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.498716e-3 s grad=2.267e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.158363e-3 s grad=2.636e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.74839e-3 s grad=3.216e-6 s +[acc-interp] eval 1.1957013e-2 s +[acc-interp] thetaMarsh 1.36e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.186811e-3 s grad=2.316e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.263587e-3 s grad=2.322e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.323789e-3 s grad=1.939e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.150564e-3 s grad=1.799e-6 s +[acc-interp] eval 1.0547356e-2 s +[acc-interp] thetaMarsh 1.91e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.131973e-3 s grad=1.734e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.655196e-3 s grad=3.826e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.928366e-3 s grad=3.017e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.389035e-3 s grad=3.514e-6 s +[acc-interp] eval 1.1796264e-2 s +[acc-interp] thetaMarsh 1.24e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.166928e-3 s grad=2.389e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.121969e-3 s grad=2.049e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.300938e-3 s grad=2.317e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.538909e-3 s grad=3.948e-6 s +[acc-interp] eval 1.1668668e-2 s +[acc-interp] thetaMarsh 1.08e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.694353e-3 s grad=6.344e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.849558e-3 s grad=9.923e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.48034e-3 s grad=7.109e-6 s 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base=25974 cs=25974 kernel=1.871053e-3 s grad=2.591e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.802089e-3 s grad=2.363e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.090825e-3 s grad=9.636e-6 s +[acc-interp] eval 1.0079164e-2 s +[acc-interp] thetaMarsh 1.09e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.756801e-3 s grad=1.52e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.766187e-3 s grad=3.552e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.586499e-3 s grad=3.021e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.228489e-3 s grad=2.622e-6 s +[acc-interp] eval 1.0029543e-2 s +[acc-interp] thetaMarsh 1.14e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.237776e-3 s grad=2.229e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.206047e-3 s grad=2.55e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.390274e-3 s grad=1.964e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.351874e-3 s grad=2.044e-6 s +[acc-interp] eval 1.0808818e-2 s 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kernel=2.729327e-3 s grad=3.101e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=7.546464e-3 s grad=4.296e-6 s +[acc-interp] eval 3.2696111e-2 s +[acc-interp] thetaMarsh 1.12e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.552351e-3 s grad=2.498e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.577256e-3 s grad=1.776e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.521413e-3 s grad=4.228e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.095117e-3 s grad=3.236e-6 s +[acc-interp] eval 1.2358328e-2 s +[acc-interp] thetaMarsh 1.02e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.714364e-3 s grad=2.696e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.321397e-3 s grad=1.761e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.594816e-3 s grad=1.96e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.333879e-3 s grad=2.875e-6 s +[acc-interp] eval 1.1632934e-2 s +[acc-interp] thetaMarsh 1.31e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.078987e-3 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grad=3.237e-6 s +[acc-interp] eval 1.2604417e-2 s +[acc-interp] thetaMarsh 9.4e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.651727e-3 s grad=2.694e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.220081e-3 s grad=3.012e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.365744e-3 s grad=5.439e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.63004e-3 s grad=3.336e-6 s +[acc-interp] eval 1.2496307e-2 s +[acc-interp] thetaMarsh 7.2e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.840251e-3 s grad=6.931e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.623813e-3 s grad=3.393e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.567866e-3 s grad=3.542e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.598191e-3 s grad=4.189e-6 s +[acc-interp] eval 1.2897254e-2 s +[acc-interp] thetaMarsh 1.14e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.862661e-3 s grad=2.643e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.76905e-3 s grad=2.571e-6 s 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base=0 cs=25974 kernel=2.496925e-3 s grad=9.398e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.475908e-3 s grad=7.813e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.197545e-3 s grad=6.424e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.444268e-3 s grad=2.975e-6 s +[acc-interp] eval 1.0515186e-2 s +[acc-interp] thetaMarsh 1.24e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.202713e-3 s grad=2.431e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.129973e-3 s grad=1.758e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.199567e-3 s grad=2.248e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.486239e-3 s grad=1.509e-6 s +[acc-interp] eval 9.531571e-3 s +[acc-interp] thetaMarsh 9.7e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.904443e-3 s grad=1.9e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.160745e-3 s grad=2.795e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.014601e-3 s grad=4.998e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.698444e-3 s grad=3.359e-6 s +[acc-interp] eval 1.1270452e-2 s +[acc-interp] thetaMarsh 2.79e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.824575e-3 s grad=4.565e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.259908e-3 s grad=3.993e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.598578e-3 s grad=3.522e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.299033e-3 s grad=4.487e-6 s +[acc-interp] eval 1.3066237e-2 s +[acc-interp] thetaMarsh 1.25e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.021648e-3 s grad=2.697e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.827516e-3 s grad=3.196e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.893811e-3 s grad=2.933e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.643017e-3 s grad=1.901e-6 s +[acc-interp] eval 1.3008466e-2 s +[acc-interp] thetaMarsh 9.8e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.115462e-3 s grad=4.722e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.81087e-3 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base=77922 cs=25974 kernel=2.655697e-3 s grad=2.636e-6 s +[acc-interp] eval 1.1681857e-2 s +[acc-interp] thetaMarsh 2.06e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.565991e-3 s grad=3.023e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.366793e-3 s grad=3.898e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.902923e-3 s grad=5.273e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.723996e-3 s grad=3.318e-6 s +[acc-interp] eval 1.1398482e-2 s +[acc-interp] thetaMarsh 5.8e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.202861e-3 s grad=3.764e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.988029e-3 s grad=3.016e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.719088e-3 s grad=2.908e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=8.525583e-3 s grad=3.001e-6 s +[acc-interp] eval 1.826074e-2 s +[acc-interp] thetaMarsh 5.6e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.449502e-3 s grad=1.901e-6 s +[acc-interp] chunk base=25974 cs=25974 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thetaMarsh 7.2e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.062398e-3 s grad=4.687e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.561551e-3 s grad=4.679e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.77412e-3 s grad=3.376e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.427385e-3 s grad=3.624e-6 s +[acc-interp] eval 1.271826e-2 s +[acc-interp] thetaMarsh 5.7e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.616864e-3 s grad=3.114e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.554445e-3 s grad=2.821e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.859916e-3 s grad=4.772e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.006493e-3 s grad=3.432e-6 s +[acc-interp] eval 1.2895615e-2 s +[acc-interp] thetaMarsh 5.8e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.448211e-3 s grad=3.116e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.599695e-3 s grad=2.8e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.444431e-3 s grad=2.807e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.687738e-3 s grad=6.392e-6 s +[acc-interp] eval 1.2154949e-2 s +[acc-interp] thetaMarsh 1.5e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.696978e-3 s grad=3.099e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.490583e-3 s grad=3.225e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.721136e-3 s grad=2.587e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.482975e-3 s grad=2.882e-6 s +[acc-interp] eval 1.2240832e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=2.780991e-3 s grad=2.547e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.479229e-3 s grad=2.704e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.809299e-3 s grad=2.578e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.699104e-3 s grad=2.374e-6 s +[acc-interp] eval 0.426214648 s +[acc-interp] thetaMarsh 1.6e-7 s (n=[acc-interp] thetaMarsh 6.6e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=9.961613e-3 s grad=2.948e-6 s 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+[acc-interp] eval 1.0044079e-2 s +[acc-interp] thetaMarsh 5.5e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.097209e-3 s grad=1.22e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.270679e-3 s grad=1.035e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.241168e-3 s grad=3.707e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.563789e-3 s grad=4.289e-6 s +[acc-interp] eval 1.0228574e-2 s +[acc-interp] thetaMarsh 1.29e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.608342e-3 s grad=2.716e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.506175e-3 s grad=2.131e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.169496e-3 s grad=1.961e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.219902e-3 s grad=2.112e-6 s +[acc-interp] eval 1.0959768e-2 s +[acc-interp] thetaMarsh 1.15e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.203798e-3 s grad=1.937e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.783648e-3 s grad=3.337e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.348083e-3 s grad=3.515e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.392289e-3 s grad=3.526e-6 s +[acc-interp] eval 1.1439906e-2 s +[acc-interp] thetaMarsh 1.32e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.267837e-3 s grad=2.395e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.237918e-3 s grad=2.61e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.027655e-3 s grad=1.811e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.160803e-3 s grad=2.363e-6 s +[acc-interp] eval 1.0203665e-2 s +[acc-interp] thetaMarsh 1.48e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.763073e-3 s grad=2.887e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.602625e-3 s grad=3.273e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.350799e-3 s grad=1.701e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.229128e-3 s grad=1.867e-6 s +[acc-interp] eval 1.1687224e-2 s +[acc-interp] thetaMarsh 9.9e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.030935e-3 s grad=2.929e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.408929e-3 s grad=2.652e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.524805e-3 s grad=2.889e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.295878e-3 s grad=1.84e-6 s +[acc-interp] eval 1.0904498e-2 s +[acc-interp] thetaMarsh 1.22e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.668935e-3 s grad=2.634e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.165788e-3 s grad=2.422e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.871427e-3 s grad=1.946e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.319961e-3 s grad=3.171e-6 s +[acc-interp] eval 1.0809682e-2 s +[acc-interp] thetaMarsh 1.35e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.303808e-3 s grad=3.121e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.169237e-3 s grad=2.868e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.115593e-3 s grad=1.901e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.97625e-3 s grad=2.25e-6 s +[acc-interp] eval 1.024689e-2 s +[acc-interp] thetaMarsh 5.9e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.001258e-3 s grad=2.784e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.482077e-3 s grad=2.027e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.08717e-3 s grad=1.469e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.882706e-3 s grad=1.502e-6 s +[acc-interp] eval 9.934438e-3 s +[acc-interp] thetaMarsh 5.7e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.889925e-3 s grad=5.979e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.870404e-3 s grad=7.72e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.809954e-3 s grad=2.01e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.89619e-3 s grad=2.494e-6 s +[acc-interp] eval 8.317345e-3 s +[acc-interp] thetaMarsh 5.9e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.204861e-3 s grad=6.392e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.641175e-3 s grad=7.642e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.085045e-3 s grad=1.201e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.82568e-3 s grad=5.73e-6 s +[acc-interp] eval 1.0660255e-2 s +[acc-interp] thetaMarsh 6.0e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.949132e-3 s grad=7.177e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.679864e-3 s grad=5.613e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.66442e-3 s grad=5.365e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.497594e-3 s grad=2.491e-6 s +[acc-interp] eval 8.49886e-3 s +[acc-interp] thetaMarsh 6.2e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.97598e-3 s grad=7.148e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.919009e-3 s grad=1.279e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.803668e-3 s grad=2.129e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.121992e-3 s grad=1.967e-6 s +[acc-interp] eval 8.927992e-3 s +[acc-interp] thetaMarsh 7.4e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.157481e-3 s grad=7.915e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.17437e-3 s grad=2.383e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.267476e-3 s grad=5.365e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.13077e-3 s grad=2.461e-6 s +[acc-interp] eval 1.2278277e-2 s +[acc-interp] thetaMarsh 1.03e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.236607e-3 s grad=3.823e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.083208e-3 s grad=8.058e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.067369e-3 s grad=6.542e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.374874e-3 s grad=3.297e-6 s +[acc-interp] eval 9.617622e-3 s +[acc-interp] thetaMarsh 1.13e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.622575e-3 s grad=3.491e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.20642e-3 s grad=2.427e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.292855e-3 s grad=2.154e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.063487e-3 s grad=1.165e-6 s +[acc-interp] eval 1.0040983e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=1.962622e-3 s grad=1.009e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.955797e-3 s grad=9.59e-7 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.209e-3 s grad=4.288e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.70708e-3 s grad=1.23e-6 s +[acc-interp] eval 0.220880165 s +[acc-interp] thetaMarsh 6.2e-8 s (n=[acc-interp] thetaMarsh 5.6e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.2709977e-2 s grad=3.035e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.415035e-3 s grad=1.352e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.381528e-3 s grad=1.039e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.26555e-3 s grad=1.252e-6 s +[acc-interp] eval 2.05417e-2 s +[acc-interp] thetaMarsh 6.3e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.298631e-3 s grad=2.112e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.522888e-3 s grad=3.538e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.803647e-3 s grad=4.194e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.686322e-3 s grad=4.13e-6 s +[acc-interp] eval 1.1191745e-2 s +[acc-interp] thetaMarsh 2.25e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.561784e-3 s grad=3.646e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.924797e-3 s grad=1.0599e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.0583993e-2 s grad=3.356e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.253341e-3 s grad=1.567e-6 s +[acc-interp] eval 2.0058701e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=2.127804e-3 s grad=2.612e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.489318e-3 s grad=2.483e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.581113e-3 s grad=3.127e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.293965e-3 s grad=2.049e-6 s +[acc-interp] eval 6.282674e-2 s +[acc-interp] thetaMarsh 1.34e-7 s (n=[acc-interp] thetaMarsh 1.12e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.0206582e-2 s grad=4.257e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.300344e-3 s grad=2.987e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.569072e-3 s grad=2.765e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.959197e-3 s grad=2.318e-6 s +[acc-interp] eval 2.1612721e-2 s +[acc-interp] thetaMarsh 1.0e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.717056e-3 s grad=2.228e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.718954e-3 s grad=2.867e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.26966e-3 s grad=2.895e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.86041e-3 s grad=2.651e-6 s +[acc-interp] eval 1.4984677e-2 s +[acc-interp] thetaMarsh 1.09e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.674331e-3 s grad=2.558e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.015167e-3 s grad=2.302e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=8.269704e-3 s grad=3.437e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.116519e-3 s grad=2.841e-6 s +[acc-interp] eval 1.7491962e-2 s +[acc-interp] thetaMarsh 1.05e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.084381e-3 s grad=2.943e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.094176e-3 s grad=2.812e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.773431e-3 s grad=4.773e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.738716e-3 s grad=3.604e-6 s +[acc-interp] eval 1.1381945e-2 s +[acc-interp] thetaMarsh 1.08e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.175076e-3 s grad=2.681e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.15362e-3 s grad=2.957e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.99416e-3 s grad=2.454e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.138325e-3 s grad=3.949e-6 s +[acc-interp] eval 1.0026722e-2 s +[acc-interp] thetaMarsh 1.15e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.808046e-3 s grad=3.181e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.700244e-3 s grad=2.867e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.732035e-3 s grad=3.51e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.564297e-3 s grad=3.067e-6 s +[acc-interp] eval 1.2460175e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=2.239188e-3 s grad=2.467e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.22786e-3 s grad=3.827e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.84737e-3 s grad=4.209e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.34965e-3 s grad=8.099e-6 s +[acc-interp] eval 9.9843979e-2 s +[acc-interp] thetaMarsh 1.39e-7 s (n=[acc-interp] thetaMarsh 6.3e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.7324011e-2 s grad=3.632e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.021167e-3 s grad=9.475e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.350812e-3 s grad=2.713e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.345366e-3 s grad=3.875e-6 s +[acc-interp] eval 2.9149513e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=3.060525e-3 s grad=2.834e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.901898e-3 s grad=2.392e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.836544e-3 s grad=2.78e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.21244e-3 s grad=3.013e-6 s +[acc-interp] eval 4.3835558e-2 s +[acc-interp] thetaMarsh 6.7e-8 s (n=[acc-interp] thetaMarsh 6.4e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.4679974e-2 s grad=3.132e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.300606e-3 s grad=3.961e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.339895e-3 s grad=2.67e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.168567e-3 s grad=2.052e-6 s +[acc-interp] eval 2.409616e-2 s +[acc-interp] thetaMarsh 1.01e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.563141e-3 s grad=1.581e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.220683e-3 s grad=2.626e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.126175e-3 s grad=1.84e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.742804e-3 s grad=3.823e-6 s +[acc-interp] eval 1.0272756e-2 s +[acc-interp] thetaMarsh 1.17e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.424508e-3 s grad=2.341e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.425023e-3 s grad=2.335e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.295852e-3 s grad=2.162e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.148946e-3 s grad=2.411e-6 s +[acc-interp] eval 1.0960739e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=1.68378e-3 s grad=1.941e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.260899e-3 s grad=1.751e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.948004e-3 s grad=1.941e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.00378e-3 s grad=2.019e-6 s +[acc-interp] eval 5.5270083e-2 s +[acc-interp] thetaMarsh 1.23e-7 s (n=[acc-interp] thetaMarsh 1.01e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.4117128e-2 s grad=4.458e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.233636e-3 s grad=4.078e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.07612e-3 s grad=3.271e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.057818e-3 s grad=3.123e-6 s +[acc-interp] eval 2.4321409e-2 s +[acc-interp] thetaMarsh 1.44e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=4.130064e-3 s grad=2.842e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.920347e-3 s grad=3.134e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.743253e-3 s grad=2.858e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.259707e-3 s grad=1.922e-6 s +[acc-interp] eval 1.3625354e-2 s +[acc-interp] thetaMarsh 1.02e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=4.107913e-3 s grad=3.827e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=7.321363e-3 s grad=3.192e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.159397e-3 s grad=3.005e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.064352e-3 s grad=7.85e-6 s +[acc-interp] eval 1.6987789e-2 s +[acc-interp] thetaMarsh 7.0e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.610655e-3 s grad=3.97e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.957936e-3 s grad=3.371e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.268965e-3 s grad=2.296e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.173603e-3 s grad=7.265e-6 s +[acc-interp] eval 1.0258686e-2 s +[acc-interp] thetaMarsh 6.6e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.07294e-3 s grad=9.688e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.985889e-3 s grad=6.989e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.028091e-3 s grad=2.453e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.518821e-3 s grad=3.783e-6 s +[acc-interp] eval 9.83192e-3 s +[acc-interp] thetaMarsh 1.01e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.128617e-3 s grad=2.982e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.143368e-3 s grad=2.727e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.102357e-3 s grad=2.9e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.013209e-3 s grad=8.262e-6 s +[acc-interp] eval 9.571931e-3 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=1.877041e-3 s grad=1.904e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.916495e-3 s grad=2.461e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.835272e-3 s grad=2.196e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.253033e-3 s grad=1.89e-6 s +[acc-interp] eval 9.4815508e-2 s +[acc-interp] thetaMarsh 1.16e-7 s (n=[acc-interp] thetaMarsh 9.7e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=5.858109e-3 s grad=3.61e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.935226e-3 s grad=1.883e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.850586e-3 s grad=1.436e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.012744e-3 s grad=4.978e-6 s +[acc-interp] eval 1.3214306e-2 s +[acc-interp] thetaMarsh 1.14e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.56194e-3 s grad=3.321e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.019247e-3 s grad=2.008e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.079362e-3 s grad=2.456e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.251066e-3 s grad=2.914e-6 s +[acc-interp] eval 1.0460334e-2 s +[acc-interp] thetaMarsh 1.18e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.22524e-3 s grad=2.441e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.938987e-3 s grad=4.601e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=7.089453e-3 s grad=4.234e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.038624e-3 s grad=3.727e-6 s +[acc-interp] eval 1.5933442e-2 s +[acc-interp] thetaMarsh 1.15e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.057255e-3 s grad=1.983e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.720545e-3 s grad=3.771e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.493232e-3 s grad=4.027e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.932867e-3 s grad=2.952e-6 s +[acc-interp] eval 9.893151e-3 s +[acc-interp] thetaMarsh 9.9e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.050612e-3 s grad=2.772e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.969403e-3 s grad=2.079e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.830397e-3 s grad=2.34e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.810353e-3 s grad=1.618e-6 s +[acc-interp] eval 9.233068e-3 s +[acc-interp] thetaMarsh 9.9e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.467315e-3 s grad=4.235e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.483962e-3 s grad=2.686e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.952577e-3 s grad=2.629e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.773355e-3 s grad=2.335e-6 s +[acc-interp] eval 1.0739236e-2 s +[acc-interp] thetaMarsh 1.1e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.780534e-3 s grad=2.166e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.439175e-3 s grad=1.641e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.265609e-3 s grad=1.371e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.543242e-3 s grad=1.652e-6 s +[acc-interp] eval 7.483389e-3 s +[acc-interp] thetaMarsh 1.28e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.649915e-3 s grad=3.609e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.187956e-3 s grad=2.369e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.09754e-3 s grad=2.927e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.047535e-3 s grad=2.132e-6 s +[acc-interp] eval 1.0668302e-2 s +[acc-interp] thetaMarsh 1.2e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.03885e-3 s grad=2.496e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.988646e-3 s grad=2.291e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.354113e-3 s grad=3.862e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.59506e-3 s grad=5.393e-6 s +[acc-interp] eval 1.1964434e-2 s +[acc-interp] thetaMarsh 1.41e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.596959e-3 s grad=3.581e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.19579e-3 s grad=2.899e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.154202e-3 s grad=3.189e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.163179e-3 s grad=4.107e-6 s +[acc-interp] eval 1.0921372e-2 s +[acc-interp] thetaMarsh 1.41e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=4.561842e-3 s grad=5.606e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.524681e-3 s grad=2.428e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.508374e-3 s grad=2.69e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.075421e-3 s grad=3.05e-6 s +[acc-interp] eval 1.4722146e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=3.526902e-3 s grad=4.249e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.082822e-3 s grad=3.755e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.47853e-3 s grad=2.883e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.378923e-3 s grad=2.347e-6 s +[acc-interp] eval 0.140352366 s +[acc-interp] thetaMarsh 1.19e-7 s (n=[acc-interp] thetaMarsh 1.11e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=5.966227e-3 s grad=3.17e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.171098e-3 s grad=3.045e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.196408e-3 s grad=1.881e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.256763e-3 s grad=1.771e-6 s +[acc-interp] eval 1.4076488e-2 s +[acc-interp] thetaMarsh 9.8e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.594368e-3 s grad=2.351e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.305288e-3 s grad=2.191e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.272116e-3 s grad=2.444e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.984475e-3 s grad=3.797e-6 s +[acc-interp] eval 1.1357976e-2 s +[acc-interp] thetaMarsh 8.0e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.863308e-3 s grad=2.913e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.726813e-3 s grad=2.705e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.475561e-3 s grad=3.158e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.611132e-3 s grad=3.019e-6 s +[acc-interp] eval 1.0822309e-2 s +[acc-interp] thetaMarsh 1.21e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.790989e-3 s grad=2.391e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.589861e-3 s grad=3.281e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.743993e-3 s grad=3.703e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=9.588699e-3 s grad=3.395e-6 s +[acc-interp] eval 1.9349419e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=1.907684e-3 s grad=4.194e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.105362e-3 s grad=1.854e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.115702e-3 s grad=1.28e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.132904e-3 s grad=1.412e-6 s +[acc-interp] eval 6.6033075e-2 s +[acc-interp] thetaMarsh 1.39e-7 s (n=[acc-interp] thetaMarsh 1.26e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.0443143e-2 s grad=4.269e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.045984e-3 s grad=5.424e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.130627e-3 s grad=3.335e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.947378e-3 s grad=2.475e-6 s +[acc-interp] eval 2.1172544e-2 s +[acc-interp] thetaMarsh 1.35e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.186597e-3 s grad=3.272e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.494596e-3 s grad=2.453e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.34451e-3 s grad=2.92e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.380198e-3 s grad=2.358e-6 s +[acc-interp] eval 1.1916105e-2 s +[acc-interp] thetaMarsh 9.6e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.791779e-3 s grad=3.094e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.276803e-3 s grad=2.231e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.212942e-3 s grad=1.817e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=8.793525e-3 s grad=2.611e-6 s +[acc-interp] eval 1.747339e-2 s +[acc-interp] thetaMarsh 1.14e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.588305e-3 s grad=3.315e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.145107e-3 s grad=3.065e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.303238e-3 s grad=2.668e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.968799e-3 s grad=2.585e-6 s +[acc-interp] eval 1.0640761e-2 s +[acc-interp] thetaMarsh 9.9e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.994127e-3 s grad=2.094e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.850711e-3 s grad=1.819e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.801184e-3 s grad=2.354e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.52817e-3 s grad=3.449e-6 s +[acc-interp] eval 9.514759e-3 s +[acc-interp] thetaMarsh 1.03e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.561132e-3 s grad=4.077e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.314983e-3 s grad=2.915e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.024127e-3 s grad=5.929e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.985147e-3 s grad=2.392e-6 s +[acc-interp] eval 1.0323656e-2 s +[acc-interp] thetaMarsh 1.9e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.13754e-3 s grad=3.766e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.6987e-3 s grad=3.075e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.850765e-3 s grad=3.206e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.23047e-3 s grad=2.953e-6 s +[acc-interp] eval 1.0529031e-2 s +[acc-interp] thetaMarsh 1.45e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.156816e-3 s grad=2.483e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.950916e-3 s grad=2.09e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.946906e-3 s grad=1.989e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.618346e-3 s grad=3.254e-6 s +[acc-interp] eval 1.0287676e-2 s +[acc-interp] thetaMarsh 1.38e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.448509e-3 s grad=3.65e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.220772e-3 s grad=2.396e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.023122e-3 s grad=2.255e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.052908e-3 s grad=2.304e-6 s +[acc-interp] eval 1.0373664e-2 s +[acc-interp] thetaMarsh 9.4e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.06805e-3 s grad=2.851e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.883156e-3 s grad=3.526e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.091328e-3 s grad=2.24e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.903648e-3 s grad=2.944e-6 s +[acc-interp] eval 1.0598956e-2 s +[acc-interp] thetaMarsh 1.16e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.063743e-3 s grad=2.05e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.926406e-3 s grad=1.895e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.85697e-3 s grad=2.148e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.962045e-3 s grad=1.77e-6 s +[acc-interp] eval 9.371015e-3 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=1.946146e-3 s grad=2.107e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.859749e-3 s grad=1.809e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.840802e-3 s grad=1.854e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.892228e-3 s grad=1.913e-6 s +[acc-interp] eval 0.142947411 s +[acc-interp] thetaMarsh 1.09e-7 s (n=[acc-interp] thetaMarsh 9.5e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.2579215e-2 s grad=3.693e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.501362e-3 s grad=2.163e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.35223e-3 s grad=2.073e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.993686e-3 s grad=4.466e-6 s +[acc-interp] eval 2.2106634e-2 s +[acc-interp] thetaMarsh 1.14e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.590614e-3 s grad=2.563e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.498965e-3 s grad=2.87e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.430556e-3 s grad=2.265e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.781899e-3 s grad=2.427e-6 s +[acc-interp] eval 1.1955631e-2 s +[acc-interp] thetaMarsh 1.01e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.351996e-3 s grad=4.345e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=8.382929e-3 s grad=3.041e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.123257e-3 s grad=3.904e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.098839e-3 s grad=3.104e-6 s +[acc-interp] eval 1.7250066e-2 s +[acc-interp] thetaMarsh 8.8e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.749071e-3 s grad=3.365e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.38818e-3 s grad=3.284e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.291576e-3 s grad=3.353e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.164474e-3 s grad=3.1e-6 s +[acc-interp] eval 1.0528879e-2 s +[acc-interp] thetaMarsh 2.3e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.210335e-3 s grad=3.357e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.16652e-3 s grad=2.56e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.709307e-3 s grad=4.041e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.29296e-3 s grad=3.121e-6 s +[acc-interp] eval 1.0268093e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=2.159285e-3 s grad=2.679e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.179047e-3 s grad=3.287e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.254516e-3 s grad=3.869e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.107524e-3 s grad=2.74e-6 s +[acc-interp] eval 8.2432021e-2 s +[acc-interp] thetaMarsh 2.08e-7 s (n=[acc-interp] thetaMarsh 6.4e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=8.959761e-3 s grad=3.132e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.180342e-3 s grad=3.335e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.289009e-3 s grad=3.662e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.931576e-3 s grad=3.318e-6 s +[acc-interp] eval 1.7347043e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=2.369353e-3 s grad=1.672e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.138261e-3 s grad=1.902e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.129412e-3 s grad=1.639e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.271454e-3 s grad=2.798e-6 s +[acc-interp] eval 2.7091593e-2 s +[acc-interp] thetaMarsh 6.3e-8 s (n=0) +[acc-interp] chunk base=0 cs=25974 kernel=1.8622088e-2 s grad=1.151e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.121864e-3 s grad=1.775e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.015335e-3 s grad=1.567e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.015277e-3 s grad=1.44e-6 s +[acc-interp] eval 2.8827376e-2 s +[acc-interp] thetaMarsh 6.9e-8 s (n=[acc-interp] thetaMarsh 6.0e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=1.8012772e-2 s grad=5.047e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.121059e-3 s grad=2.934e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.001479e-3 s grad=5.278e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.645061e-3 s grad=4.486e-6 s +[acc-interp] eval 2.868669e-2 s +[acc-interp] thetaMarsh 7.4e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.072785e-3 s grad=3.887e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.26985e-3 s grad=3.796e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=9.985472e-3 s grad=3.353e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.658026e-3 s grad=2.978e-6 s +[acc-interp] eval 1.9813197e-2 s +[acc-interp] thetaMarsh 6.6e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.65553e-3 s grad=2.92e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.930998e-3 s grad=2.887e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.188973e-3 s grad=3.751e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.295649e-3 s grad=1.936e-6 s +[acc-interp] eval 1.2850536e-2 s +[acc-interp] thetaMarsh 6.6e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.88567e-3 s grad=1.0615e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.038242e-3 s grad=9.266e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.884852e-3 s grad=1.525e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.317598e-3 s grad=5.41e-6 s +[acc-interp] eval 1.2880998e-2 s +[acc-interp] thetaMarsh 1.95e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.958718e-3 s grad=3.172e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.300901e-3 s grad=3.037e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.619913e-3 s grad=2.497e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.597734e-3 s grad=2.804e-6 s +[acc-interp] eval 1.1289958e-2 s +[acc-interp] thetaMarsh 6.5e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.186926e-3 s grad=3.989e-6 s 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eval 1.3752366e-2 s +[acc-interp] thetaMarsh 1.07e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.646657e-3 s grad=3.079e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.905116e-3 s grad=6.172e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.007167e-3 s grad=7.302e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.253208e-3 s grad=5.013e-6 s +[acc-interp] eval 1.2883255e-2 s +[acc-interp] thetaMarsh 1.09e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.82592e-3 s grad=4.176e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.72227e-3 s grad=3.554e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.04003e-3 s grad=3.047e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.290451e-3 s grad=4.052e-6 s +[acc-interp] eval 1.4388279e-2 s +[acc-interp] thetaMarsh 9.6e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.667879e-3 s grad=3.688e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.113324e-3 s grad=3.975e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.44395e-3 s grad=2.806e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.783298e-3 s grad=2.537e-6 s +[acc-interp] eval 1.3574645e-2 s +[acc-interp] thetaMarsh 1.11e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.039178e-3 s grad=4.457e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.545855e-3 s grad=4.359e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.206898e-3 s grad=3.493e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.4329e-3 s grad=3.119e-6 s +[acc-interp] eval 1.3750913e-2 s +[acc-interp] thetaMarsh 1.05e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.658884e-3 s grad=2.867e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.884033e-3 s grad=3.187e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=9.10575e-3 s grad=3.84e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.638475e-3 s grad=2.28e-6 s +[acc-interp] eval 1.8687031e-2 s +[acc-interp] thetaMarsh 1.02e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.571343e-3 s grad=3.485e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.407516e-3 s grad=4.155e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.301109e-3 s grad=3.29e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.02318e-3 s grad=3.734e-6 s +[acc-interp] eval 1.4006713e-2 s +[acc-interp] thetaMarsh 1.15e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.95107e-3 s grad=3.151e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.296708e-3 s grad=5.111e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.240697e-3 s grad=3.21e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.069926e-3 s grad=3.37e-6 s +[acc-interp] eval 1.4184512e-2 s +[acc-interp] thetaMarsh 1.14e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.727502e-3 s grad=3.394e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.791607e-3 s grad=3.277e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.00396e-3 s grad=4.049e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.922425e-3 s grad=3.964e-6 s +[acc-interp] eval 1.3014708e-2 s +[acc-interp] thetaMarsh 1.33e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.94871e-3 s grad=2.736e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.89768e-3 s grad=2.891e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.320495e-3 s grad=4.272e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.012902e-3 s grad=3.346e-6 s +[acc-interp] eval 1.3739684e-2 s +[acc-interp] thetaMarsh 9.9e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.4936e-3 s grad=3.044e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.044945e-3 s grad=2.707e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.818747e-3 s grad=2.547e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.978724e-3 s grad=3.541e-6 s +[acc-interp] eval 1.4756289e-2 s +[acc-interp] thetaMarsh 1.11e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.594257e-3 s grad=2.624e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.931314e-3 s grad=2.533e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.858662e-3 s grad=2.764e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.977334e-3 s grad=2.832e-6 s +[acc-interp] eval 1.2747367e-2 s +[acc-interp] thetaMarsh 1.17e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.356184e-3 s grad=2.36e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.732845e-3 s grad=2.436e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.234488e-3 s grad=3.076e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.051108e-3 s grad=3.179e-6 s +[acc-interp] eval 1.3651883e-2 s +[acc-interp] thetaMarsh 6.8e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.487557e-3 s grad=3.632e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.221139e-3 s grad=2.851e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.641917e-3 s grad=1.0917e-5 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.104847e-3 s grad=8.552e-6 s +[acc-interp] eval 1.3313035e-2 s +[acc-interp] thetaMarsh 6.4e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.301322e-3 s grad=4.559e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.323926e-3 s grad=4.947e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.249962e-3 s grad=3.887e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.042213e-3 s grad=4.112e-6 s +[acc-interp] eval 1.42332e-2 s +[acc-interp] thetaMarsh 1.34e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.21617e-3 s grad=3.685e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.334698e-3 s grad=3.822e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.464817e-3 s grad=4.589e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.610398e-3 s grad=3.227e-6 s +[acc-interp] eval 1.6204713e-2 s +[acc-interp] thetaMarsh 1.13e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.842708e-3 s grad=2.678e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.149477e-3 s grad=3.122e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.416332e-3 s grad=2.877e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=9.402846e-3 s grad=3.083e-6 s +[acc-interp] eval 2.1393662e-2 s +[acc-interp] thetaMarsh 1.16e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.961455e-3 s grad=2.829e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.589356e-3 s grad=2.973e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.603509e-3 s grad=2.478e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.574682e-3 s grad=2.234e-6 s +[acc-interp] eval 1.2202504e-2 s +[acc-interp] thetaMarsh 1.29e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.570793e-3 s grad=2.486e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.685487e-3 s grad=2.217e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.384895e-3 s grad=2.469e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.756889e-3 s grad=2.716e-6 s +[acc-interp] eval 1.2014547e-2 s +[acc-interp] thetaMarsh 7.4e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.673166e-3 s grad=6.126e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.345522e-3 s grad=3.067e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.54274e-3 s grad=4.664e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.541811e-3 s grad=3.373e-6 s +[acc-interp] eval 1.2400727e-2 s +[acc-interp] thetaMarsh 1.17e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.661781e-3 s grad=3.155e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.568912e-3 s grad=3.527e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.34034e-3 s grad=2.234e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.089232e-3 s grad=3.465e-6 s +[acc-interp] eval 1.2273419e-2 s +[acc-interp] thetaMarsh 1.01e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.459768e-3 s grad=2.622e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.696441e-3 s grad=2.692e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.605468e-3 s grad=2.27e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.300829e-3 s grad=1.642e-6 s +[acc-interp] eval 1.1614423e-2 s +[acc-interp] thetaMarsh 1.14e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.415498e-3 s grad=3.319e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.987977e-3 s grad=4.296e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.117138e-3 s grad=2.756e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.989877e-3 s grad=2.46e-6 s +[acc-interp] eval 1.4060855e-2 s +[acc-interp] thetaMarsh 1.33e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.617441e-3 s grad=4.726e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.388704e-3 s grad=3.982e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.605732e-3 s grad=2.829e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.79232e-3 s grad=1.933e-6 s +[acc-interp] eval 1.2949914e-2 s +[acc-interp] thetaMarsh 1.13e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.836772e-3 s grad=2.061e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.354664e-3 s grad=1.932e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.303894e-3 s grad=5.264e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.485866e-3 s grad=3.39e-6 s +[acc-interp] eval 1.2464088e-2 s +[acc-interp] thetaMarsh 7.2e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.135686e-3 s grad=3.575e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.925103e-3 s grad=3.643e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.697656e-3 s grad=1.94e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.831588e-3 s grad=2.84e-6 s +[acc-interp] eval 1.2468419e-2 s +4) +[acc-interp] chunk base=0 cs=25974 kernel=2.879967e-3 s grad=4.298e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.932026e-3 s grad=3.825e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.923536e-3 s grad=7.034e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.837389e-3 s grad=6.857e-6 s +[acc-interp] eval 0.488596462 s +[acc-interp] thetaMarsh 5.7e-8 s (n=[acc-interp] thetaMarsh 5.4e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.7516266e-2 s grad=3.221e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.569843e-3 s grad=4.414e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.241734e-3 s grad=4.408e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.544976e-3 s grad=8.138e-6 s +[acc-interp] eval 2.6976876e-2 s +[acc-interp] thetaMarsh 1.55e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.477771e-3 s grad=9.491e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.413384e-3 s grad=7.12e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.2762e-3 s grad=5.485e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.428364e-3 s grad=4.983e-6 s +[acc-interp] eval 1.0308363e-2 s +[acc-interp] thetaMarsh 6.9e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.376147e-3 s grad=7.49e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.171252e-3 s grad=4.922e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.299754e-3 s grad=5.042e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.139076e-3 s grad=3.747e-6 s +[acc-interp] eval 9.658767e-3 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=2.14132e-3 s grad=6.511e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.099471e-3 s grad=5.687e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.128153e-3 s grad=1.026e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.056076e-3 s grad=3.596e-6 s +[acc-interp] eval 5.7350804e-2 s +[acc-interp] thetaMarsh 1.8e-7 s (n=[acc-interp] thetaMarsh 5.4e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.1732998e-2 s grad=4.176e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.596504e-3 s grad=3.242e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.35743e-3 s grad=2.761e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.997128e-3 s grad=3.305e-6 s +[acc-interp] eval 3.6125804e-2 s +[acc-interp] thetaMarsh 3.59e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.843232e-3 s grad=3.545e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.659352e-3 s grad=5.032e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.1401272e-2 s grad=3.93e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.461993e-3 s grad=5.528e-6 s +[acc-interp] eval 2.343818e-2 s +[acc-interp] thetaMarsh 7.2e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.760653e-3 s grad=4.276e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.374703e-3 s grad=3.105e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.284264e-3 s grad=2.609e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.231731e-3 s grad=3.055e-6 s +[acc-interp] eval 1.5180566e-2 s +[acc-interp] thetaMarsh 1.15e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.414454e-3 s grad=4.061e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.344624e-3 s grad=3.128e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.223234e-3 s grad=2.283e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.270446e-3 s grad=4.357e-6 s +[acc-interp] eval 1.4743487e-2 s +[acc-interp] thetaMarsh 1.13e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.649953e-3 s grad=4.061e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.843192e-3 s grad=4.895e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.259645e-3 s grad=3.665e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.319475e-3 s grad=2.727e-6 s +[acc-interp] eval 1.5685738e-2 s +[acc-interp] thetaMarsh 9.6e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.502461e-3 s grad=4.141e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.570836e-3 s grad=3.251e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.626121e-3 s grad=3.637e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.42411e-3 s grad=2.785e-6 s +[acc-interp] eval 1.6690997e-2 s +[acc-interp] thetaMarsh 1.01e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.253575e-3 s grad=4.83e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.505232e-3 s grad=4.28e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.110588e-3 s grad=3.202e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.68006e-3 s grad=3.166e-6 s +[acc-interp] eval 1.8208977e-2 s +[acc-interp] thetaMarsh 1.26e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.248318e-3 s grad=3.682e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.748769e-3 s grad=2.524e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.906454e-3 s grad=3.218e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.14017e-3 s grad=4.004e-6 s +[acc-interp] eval 1.7499822e-2 s +[acc-interp] thetaMarsh 1.41e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=5.482054e-3 s grad=3.527e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.24849e-3 s grad=4.078e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.231174e-3 s grad=3.297e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.296772e-3 s grad=4.468e-6 s +[acc-interp] eval 2.0919662e-2 s +[acc-interp] thetaMarsh 1.42e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=1.0734113e-2 s grad=4.15e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.222769e-3 s 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chunk base=77922 cs=25974 kernel=4.168367e-3 s grad=4.776e-6 s +[acc-interp] eval 1.8786048e-2 s +[acc-interp] thetaMarsh 1.37e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.057837e-3 s grad=4.424e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.628836e-3 s grad=4.995e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.404148e-3 s grad=4.087e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.0650182e-2 s grad=4.149e-6 s +[acc-interp] eval 2.5512466e-2 s +[acc-interp] thetaMarsh 1.23e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.537632e-3 s grad=3.33e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.330597e-3 s grad=3.818e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.375115e-3 s grad=3.314e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.381728e-3 s grad=2.654e-6 s +[acc-interp] eval 1.5281933e-2 s +[acc-interp] thetaMarsh 1.11e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.897657e-3 s grad=3.568e-6 s +[acc-interp] chunk base=25974 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+[acc-interp] thetaMarsh 7.2e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.565803e-3 s grad=2.977e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.933643e-3 s grad=5.543e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.919573e-3 s grad=9.031e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.809742e-3 s grad=8.035e-6 s +[acc-interp] eval 1.6121966e-2 s +[acc-interp] thetaMarsh 2.0e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.737167e-3 s grad=4.199e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.275683e-3 s grad=3.857e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.508834e-3 s grad=3.492e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.68662e-3 s grad=1.165e-5 s +[acc-interp] eval 1.6319605e-2 s +[acc-interp] thetaMarsh 6.9e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.741135e-3 s grad=3.684e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.831263e-3 s grad=7.584e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.023695e-3 s grad=9.466e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.395995e-3 s grad=1.993e-6 s +[acc-interp] eval 1.6819164e-2 s +[acc-interp] thetaMarsh 1.58e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.243095e-3 s grad=2.56e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.88579e-3 s grad=5.225e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=8.962088e-3 s grad=4.536e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.936135e-3 s grad=3.123e-6 s +[acc-interp] eval 2.0156171e-2 s +[acc-interp] thetaMarsh 1.14e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.048711e-3 s grad=4.36e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.451065e-3 s grad=3.233e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.415516e-3 s grad=4.215e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.211772e-3 s grad=3.172e-6 s +[acc-interp] eval 1.4393233e-2 s +[acc-interp] thetaMarsh 6.6e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.279341e-3 s grad=3.854e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.426166e-3 s grad=7.195e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.143291e-3 s grad=5.221e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.666756e-3 s grad=7.552e-6 s +[acc-interp] eval 1.6418953e-2 s +[acc-interp] thetaMarsh 6.8e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.056932e-3 s grad=3.782e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.837748e-3 s grad=7.904e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.745702e-3 s grad=8.747e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.590148e-3 s grad=9.861e-6 s +[acc-interp] eval 1.6267701e-2 s +[acc-interp] thetaMarsh 7.3e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.060593e-3 s grad=9.871e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.024767e-3 s grad=4.014e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.227004e-3 s grad=3.949e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.824441e-3 s grad=2.822e-6 s +[acc-interp] eval 1.6576914e-2 s +4) +[acc-interp] chunk base=0 cs=25974 kernel=3.398577e-3 s grad=3.043e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.034017e-3 s grad=4.376e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.977037e-3 s grad=2.76e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.464544e-3 s grad=2.372e-6 s +[acc-interp] eval 0.532134783 s +[acc-interp] thetaMarsh 1.41e-7 s (n=[acc-interp] thetaMarsh 1.11e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.873922e-2 s grad=3.6e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.71618e-3 s grad=3.461e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.685825e-3 s grad=1.57e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.982057e-3 s grad=4.069e-6 s +[acc-interp] eval 5.4318636e-2 s +[acc-interp] thetaMarsh 1.36e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.572513e-3 s grad=3.756e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.123728e-3 s grad=2.949e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.244506e-3 s grad=3.184e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.62477e-3 s grad=3.346e-6 s +[acc-interp] eval 2.1295208e-2 s +[acc-interp] thetaMarsh 1.35e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=5.564023e-3 s grad=3.931e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.6786314e-2 s grad=3.524e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.117637e-3 s grad=3.37e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.59918e-3 s grad=2.854e-6 s +[acc-interp] eval 3.2791586e-2 s +[acc-interp] thetaMarsh 1.14e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=5.074835e-3 s grad=3.351e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.493126e-3 s grad=4.571e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.161001e-3 s grad=3.559e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.008283e-3 s grad=3.56e-6 s +[acc-interp] eval 2.0443681e-2 s +[acc-interp] thetaMarsh 1.18e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=5.351985e-3 s grad=3.3e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=6.406078e-3 s grad=3.613e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.391211e-3 s grad=4.177e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.148881e-3 s grad=4.179e-6 s +[acc-interp] eval 2.3961183e-2 s +[acc-interp] thetaMarsh 9.6e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.941345e-3 s grad=3.194e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.44291e-3 s grad=3.603e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.621901e-3 s grad=3.214e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.1620112e-2 s grad=3.431e-6 s +[acc-interp] eval 2.9395753e-2 s +[acc-interp] thetaMarsh 1.31e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.751076e-3 s grad=3.921e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.88158e-3 s grad=2.474e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.16113e-3 s grad=4.557e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.446538e-3 s grad=4.015e-6 s +[acc-interp] eval 1.8794886e-2 s +[acc-interp] thetaMarsh 1.07e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.70849e-3 s grad=3.672e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.240758e-3 s grad=4.064e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.085695e-3 s grad=3.054e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.884185e-3 s grad=3.037e-6 s +[acc-interp] eval 2.0634342e-2 s +[acc-interp] thetaMarsh 9.7e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.504177e-3 s grad=2.731e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.755469e-3 s grad=4.073e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.207731e-3 s grad=1.788e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.244391e-3 s grad=4.655e-6 s +[acc-interp] eval 2.0364731e-2 s +[acc-interp] thetaMarsh 1.16e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=5.33563e-3 s grad=4.008e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.1036907e-2 s grad=4.381e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.094605e-3 s grad=3.084e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.524729e-3 s grad=4.957e-6 s +[acc-interp] eval 2.7619107e-2 s +[acc-interp] thetaMarsh 2.01e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.055552e-3 s grad=2.511e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.765593e-3 s grad=3.928e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.141254e-3 s grad=2.728e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.963717e-3 s grad=2.327e-6 s +[acc-interp] eval 2.0417513e-2 s +[acc-interp] thetaMarsh 1.02e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=6.973491e-3 s grad=3.781e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.38137e-3 s grad=3.772e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.348021e-3 s grad=3.372e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=7.063957e-3 s grad=4.814e-6 s +[acc-interp] eval 2.6043601e-2 s +[acc-interp] thetaMarsh 6.5e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=5.144523e-3 s grad=3.157e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.3573592e-2 s grad=4.666e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.870295e-3 s grad=2.791e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.245201e-3 s grad=3.974e-6 s +[acc-interp] eval 2.9150281e-2 s +[acc-interp] thetaMarsh 1.37e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.371764e-3 s grad=2.756e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.679018e-3 s grad=3.874e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.979487e-3 s grad=4.269e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.762894e-3 s grad=3.918e-6 s +[acc-interp] eval 2.218841e-2 s +[acc-interp] thetaMarsh 6.4e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=7.013816e-3 s grad=4.582e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.307967e-3 s grad=4.732e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=6.121428e-3 s grad=3.998e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=6.013638e-3 s grad=4.549e-6 s +[acc-interp] eval 2.5358526e-2 s +[acc-interp] thetaMarsh 2.51e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.694555e-3 s grad=3.284e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.2700292e-2 s grad=3.31e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.316455e-3 s grad=2.624e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=6.603392e-3 s grad=4.679e-6 s +[acc-interp] eval 2.9383299e-2 s +[acc-interp] thetaMarsh 1.35e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=5.61673e-3 s grad=3.862e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.236903e-3 s grad=4.617e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.915889e-3 s grad=3.718e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.055698e-3 s grad=3.811e-6 s +[acc-interp] eval 2.3465025e-2 s +[acc-interp] thetaMarsh 1.13e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.91846e-3 s grad=3.334e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=6.262562e-3 s grad=4.575e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.576949e-3 s grad=3.845e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.695777e-3 s grad=3.074e-6 s +[acc-interp] eval 2.2750377e-2 s +[acc-interp] thetaMarsh 1.19e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=5.942853e-3 s grad=4.534e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.4114786e-2 s grad=3.591e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.627042e-3 s grad=1.311e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.414496e-3 s grad=6.938e-6 s +[acc-interp] eval 2.9929917e-2 s +[acc-interp] thetaMarsh 9.7e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=6.3164e-3 s grad=7.622e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.676807e-3 s grad=8.698e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.815955e-3 s grad=3.434e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.52161e-3 s grad=3.791e-6 s +[acc-interp] eval 2.2453066e-2 s +[acc-interp] thetaMarsh 4.78e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=5.20903e-3 s grad=2.979e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=6.271133e-3 s grad=4.406e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.009661e-3 s grad=3.822e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=7.560415e-3 s grad=4.269e-6 s +[acc-interp] eval 2.5662332e-2 s +[acc-interp] thetaMarsh 1.26e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=5.641997e-3 s grad=3.785e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.4595481e-2 s grad=3.718e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.912957e-3 s grad=3.636e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.723974e-3 s grad=3.125e-6 s +[acc-interp] eval 3.1498482e-2 s +[acc-interp] thetaMarsh 1.01e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=5.296805e-3 s grad=3.829e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.231998e-3 s grad=3.188e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.653612e-3 s grad=5.504e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.021234e-3 s grad=3.833e-6 s +[acc-interp] eval 2.1772434e-2 s +[acc-interp] thetaMarsh 6.7e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=6.525959e-3 s grad=2.919e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.904277e-3 s grad=4.289e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=6.541523e-3 s grad=4.891e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=6.408133e-3 s grad=2.175e-6 s +[acc-interp] eval 2.6258722e-2 s +[acc-interp] thetaMarsh 7.3e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=7.099205e-3 s grad=3.82e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.3758359e-2 s grad=3.903e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.945006e-3 s grad=3.718e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.303725e-3 s grad=3.274e-6 s +[acc-interp] eval 3.2258345e-2 s +[acc-interp] thetaMarsh 9.5e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=5.528406e-3 s grad=3.953e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.476125e-3 s grad=3.845e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.929269e-3 s grad=4.162e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.791204e-3 s grad=3.616e-6 s +[acc-interp] eval 2.3234278e-2 s +[acc-interp] thetaMarsh 1.02e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.92668e-3 s grad=4.508e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=7.057305e-3 s grad=3.682e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=6.263563e-3 s grad=3.035e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.601095e-3 s grad=4.186e-6 s +[acc-interp] eval 2.5493217e-2 s +[acc-interp] thetaMarsh 1.46e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=5.431332e-3 s grad=3.956e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.3031865e-2 s grad=4.916e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.284206e-3 s grad=3.505e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.690105e-3 s grad=3.533e-6 s +[acc-interp] eval 3.0159594e-2 s +[acc-interp] thetaMarsh 2.34e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=6.32916e-3 s grad=4.72e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.090525e-3 s grad=4.273e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.719835e-3 s grad=3.732e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.632258e-3 s grad=4.365e-6 s +[acc-interp] eval 2.4540877e-2 s +[acc-interp] thetaMarsh 1.28e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=5.723542e-3 s grad=3.319e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.616922e-3 s grad=5.193e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.89043e-3 s grad=4.138e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.497973e-3 s grad=3.892e-6 s +[acc-interp] eval 2.1370138e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=3.696294e-3 s grad=2.707e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.1029051e-2 s grad=3.574e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.674688e-3 s grad=2.402e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.36948e-3 s grad=4.041e-6 s +[acc-interp] eval 0.812355158 s +[acc-interp] thetaMarsh 7.8e-8 s (n=[acc-interp] thetaMarsh 7.7e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.5014716e-2 s grad=3.411e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.998284e-3 s grad=2.518e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.92268e-3 s grad=1.686e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.797336e-3 s grad=1.681e-6 s +[acc-interp] eval 2.5077915e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=3.812978e-3 s grad=3.556e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.914271e-3 s grad=3.429e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=8.692749e-3 s grad=2.916e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.061919e-3 s grad=3.998e-6 s +[acc-interp] eval 4.5331933e-2 s +[acc-interp] thetaMarsh 1.24e-7 s (n=[acc-interp] thetaMarsh 1.11e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.2583164e-2 s grad=3.687e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.319595e-3 s grad=4.034e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.728863e-3 s grad=2.324e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.848488e-3 s grad=2.689e-6 s +[acc-interp] eval 2.8995493e-2 s +[acc-interp] thetaMarsh 1.25e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.736769e-3 s grad=4.154e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.359993e-3 s grad=3.026e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.480994e-3 s grad=3.073e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.618802e-3 s grad=2.76e-6 s +[acc-interp] eval 1.4964539e-2 s +[acc-interp] thetaMarsh 1.14e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.142467e-3 s grad=2.448e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.247259e-3 s grad=4.995e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.189053e-3 s grad=3.141e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.071616e-3 s grad=3.26e-6 s +[acc-interp] eval 1.692786e-2 s +[acc-interp] thetaMarsh 5.8e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.275715e-3 s grad=2.451e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.470595e-3 s grad=3.286e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.732217e-3 s grad=3.851e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.532111e-3 s grad=2.596e-6 s +[acc-interp] eval 1.1771889e-2 s +[acc-interp] thetaMarsh 5.8e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.636439e-3 s grad=2.485e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.492771e-3 s grad=2.348e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.661407e-3 s grad=2.761e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.25943e-3 s grad=2.231e-6 s +[acc-interp] eval 1.0786377e-2 s +[acc-interp] thetaMarsh 1.79e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.50391e-3 s grad=2.524e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.027153e-3 s grad=2.36e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.74528e-3 s grad=2.664e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.563142e-3 s grad=2.433e-6 s +[acc-interp] eval 8.594196e-3 s +[acc-interp] thetaMarsh 2.25e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.489664e-3 s grad=2.589e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.168167e-3 s grad=4.099e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.540215e-3 s grad=4.236e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.56682e-3 s grad=4.017e-6 s +[acc-interp] eval 9.743806e-3 s +[acc-interp] thetaMarsh 6.0e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.990415e-3 s grad=2.497e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.75735e-3 s grad=3.143e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.577196e-3 s grad=2.683e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.504745e-3 s grad=5.683e-6 s +[acc-interp] eval 1.3788918e-2 s +[acc-interp] thetaMarsh 1.93e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.136562e-3 s grad=3.041e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.611444e-3 s grad=2.811e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.979546e-3 s grad=5.747e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.564528e-3 s grad=3.172e-6 s +[acc-interp] eval 1.8747378e-2 s +[acc-interp] thetaMarsh 1.29e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=7.132154e-3 s grad=3.55e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.134139e-3 s grad=4.304e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.856585e-3 s grad=4.007e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.462111e-3 s grad=2.739e-6 s +[acc-interp] eval 2.0458815e-2 s +[acc-interp] thetaMarsh 9.4e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.687323e-3 s grad=3.793e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.018734e-3 s grad=3.29e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.265895e-3 s grad=2.641e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.364014e-3 s grad=2.489e-6 s +[acc-interp] eval 1.4046082e-2 s +[acc-interp] thetaMarsh 1.12e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.522743e-3 s grad=2.561e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.152403e-3 s grad=1.548e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.33827e-3 s grad=1.662e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.125208e-3 s grad=1.586e-6 s +[acc-interp] eval 1.0690594e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=1.917599e-3 s grad=1.847e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.476191e-3 s grad=3.117e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.796988e-3 s grad=2.109e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.192712e-3 s grad=4.948e-6 s +[acc-interp] eval 0.195075964 s +[acc-interp] thetaMarsh 1.42e-7 s (n=[acc-interp] thetaMarsh 1.2e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.5337024e-2 s grad=4.398e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.193867e-3 s grad=3.539e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.397745e-3 s grad=3.016e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.907257e-3 s grad=3.371e-6 s +[acc-interp] eval 2.6604087e-2 s +[acc-interp] thetaMarsh 1.24e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.350641e-3 s grad=3.769e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.926709e-3 s grad=2.101e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.517124e-3 s grad=2.189e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.820386e-3 s grad=2.495e-6 s +[acc-interp] eval 1.3100383e-2 s +[acc-interp] thetaMarsh 1.03e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.165937e-3 s grad=4.785e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.723444e-3 s grad=4.913e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.011255e-3 s grad=2.853e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.897358e-3 s grad=1.836e-6 s +[acc-interp] eval 1.3574112e-2 s +[acc-interp] thetaMarsh 1.46e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.37258e-3 s grad=2.079e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.402026e-3 s grad=1.723e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.003895e-3 s grad=3.723e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.276626e-3 s grad=2.165e-6 s +[acc-interp] eval 1.1713146e-2 s +[acc-interp] thetaMarsh 1.24e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.669482e-3 s grad=2.704e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.431392e-3 s grad=2.429e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.265627e-3 s grad=3.1e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.684054e-3 s grad=3.367e-6 s +[acc-interp] eval 1.1739662e-2 s +[acc-interp] thetaMarsh 2.88e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.650731e-3 s grad=2.815e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.397378e-3 s grad=2.471e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.607912e-3 s grad=2.472e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.737989e-3 s grad=3.802e-6 s +[acc-interp] eval 1.2023322e-2 s +[acc-interp] thetaMarsh 1.09e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.136695e-3 s grad=3.958e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.30681e-3 s grad=4.153e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.932186e-3 s grad=2.364e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.93174e-3 s grad=2.189e-6 s +[acc-interp] eval 1.2898862e-2 s +[acc-interp] thetaMarsh 1.06e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.026365e-3 s grad=4.551e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.526159e-3 s grad=4.847e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.465656e-3 s grad=3.996e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.251424e-3 s grad=3.083e-6 s +[acc-interp] eval 1.5184137e-2 s +[acc-interp] thetaMarsh 1.13e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.189925e-3 s grad=2.386e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.56906e-3 s grad=4.305e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.424867e-3 s grad=3.676e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.57252e-3 s grad=3.348e-6 s +[acc-interp] eval 1.6499915e-2 s +[acc-interp] thetaMarsh 1.12e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.294855e-3 s grad=2.804e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.393895e-3 s grad=4.824e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.987266e-3 s grad=3.604e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.173792e-3 s grad=2.827e-6 s +[acc-interp] eval 1.6542504e-2 s +[acc-interp] thetaMarsh 4.81e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.947217e-3 s grad=2.473e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.942638e-3 s grad=2.429e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.906087e-3 s grad=3.427e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.315105e-3 s grad=3.409e-6 s +[acc-interp] eval 1.464298e-2 s +[acc-interp] thetaMarsh 1.04e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.24744e-3 s grad=2.975e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.139681e-3 s grad=3.237e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.625277e-3 s grad=4.702e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.404365e-3 s grad=3.975e-6 s +[acc-interp] eval 1.4979535e-2 s +[acc-interp] thetaMarsh 1.06e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.746486e-3 s grad=3.176e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.1248528e-2 s grad=4.076e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.973886e-3 s grad=3.138e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.605485e-3 s grad=2.418e-6 s +[acc-interp] eval 2.2104447e-2 s +[acc-interp] thetaMarsh 1.08e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.627251e-3 s grad=1.862e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.723732e-3 s grad=6.202e-6 s 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chunk base=0 cs=25974 kernel=3.236412e-3 s grad=3.109e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.998874e-3 s grad=1.672e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.524942e-3 s grad=1.368e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.665672e-3 s grad=1.31e-6 s +[acc-interp] eval 1.2275151e-2 s +[acc-interp] thetaMarsh 1.59e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.888043e-3 s grad=1.0794e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.289844e-3 s grad=4.874e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.10013e-3 s grad=4.369e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.152834e-3 s grad=9.919e-6 s +[acc-interp] eval 1.3324185e-2 s +[acc-interp] thetaMarsh 2.1e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.728936e-3 s grad=2.457e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.61474e-3 s grad=7.448e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.645883e-3 s grad=4.495e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.721064e-3 s grad=2.963e-6 s +[acc-interp] eval 1.3002298e-2 s +[acc-interp] thetaMarsh 9.7e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.153811e-3 s grad=4.201e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.144206e-3 s grad=3.883e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.765342e-3 s grad=4.712e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.39581e-3 s grad=3.935e-6 s +[acc-interp] eval 1.5142274e-2 s +[acc-interp] thetaMarsh 1.11e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.171544e-3 s grad=3.204e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.995928e-3 s grad=2.763e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.807304e-3 s grad=2.166e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.848006e-3 s grad=2.035e-6 s +[acc-interp] eval 1.3239581e-2 s +[acc-interp] thetaMarsh 1.08e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=8.037965e-3 s grad=2.458e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.710361e-3 s grad=2.448e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.018186e-3 s grad=4.555e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.141022e-3 s grad=3.957e-6 s +[acc-interp] eval 1.8495437e-2 s +[acc-interp] thetaMarsh 9.8e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.238028e-3 s grad=2.856e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.180481e-3 s grad=4.498e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.942802e-3 s grad=9.804e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.624131e-3 s grad=4.432e-6 s +[acc-interp] eval 1.4096173e-2 s +[acc-interp] thetaMarsh 7.4e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.114673e-3 s grad=2.301e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.646275e-3 s grad=9.584e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.682025e-3 s grad=6.015e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.799758e-3 s grad=2.566e-6 s +[acc-interp] eval 1.2028493e-2 s +[acc-interp] thetaMarsh 2.37e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.288166e-3 s grad=5.133e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.375774e-3 s grad=4.827e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.090848e-3 s grad=3.678e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.668617e-3 s grad=2.583e-6 s +[acc-interp] eval 1.4397103e-2 s +[acc-interp] thetaMarsh 2.04e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.136767e-3 s grad=4.356e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.551565e-3 s grad=5.717e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.764126e-3 s grad=5.049e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.00497e-3 s grad=4.315e-6 s +[acc-interp] eval 1.3645878e-2 s +[acc-interp] thetaMarsh 1.21e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.968467e-3 s grad=1.682e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.916444e-3 s grad=4.304e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.562563e-3 s grad=3.482e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.548726e-3 s grad=2.664e-6 s +[acc-interp] eval 1.4331798e-2 s +[acc-interp] thetaMarsh 1.0e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.636579e-3 s grad=4.04e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.055099e-3 s grad=3.637e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.727799e-3 s grad=3.891e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.425051e-3 s grad=3.42e-6 s +[acc-interp] eval 1.6454415e-2 s +[acc-interp] thetaMarsh 1.3e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.611916e-3 s grad=3.059e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.295201e-3 s grad=3.385e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.434252e-3 s grad=3.501e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.633476e-3 s grad=3.137e-6 s +[acc-interp] eval 1.7584831e-2 s +[acc-interp] thetaMarsh 1.0e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.2759425e-2 s grad=2.642e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.01903e-3 s grad=2.799e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.09834e-3 s grad=4.717e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.341268e-3 s grad=2.697e-6 s +[acc-interp] eval 2.4861808e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=2.851937e-3 s grad=2.132e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.846952e-3 s grad=1.939e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.95167e-3 s grad=3.285e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.200159e-3 s grad=3.126e-6 s +[acc-interp] eval 0.473474104 s +[acc-interp] thetaMarsh 1.2e-7 s (n=[acc-interp] thetaMarsh 1.05e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.0223277e-2 s grad=2.799e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.601211e-3 s grad=3.973e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.783547e-3 s grad=3.488e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.137541e-3 s grad=3.705e-6 s +[acc-interp] eval 2.0570001e-2 s +[acc-interp] thetaMarsh 1.27e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.434986e-3 s grad=2.735e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.75662e-3 s grad=2.607e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.847451e-3 s grad=2.856e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.984816e-3 s grad=2.971e-6 s +[acc-interp] eval 1.1607405e-2 s +[acc-interp] thetaMarsh 1.12e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.957016e-3 s grad=1.974e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.883567e-3 s grad=3.383e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.760969e-3 s grad=3.226e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.505776e-3 s grad=2.62e-6 s +[acc-interp] eval 1.6799205e-2 s +[acc-interp] thetaMarsh 1.2e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.508533e-3 s grad=2.51e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.383545e-3 s grad=2.078e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.947902e-3 s grad=1.1077e-5 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.985357e-3 s grad=6.881e-6 s +[acc-interp] eval 1.1136872e-2 s +[acc-interp] thetaMarsh 1.04e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.698051e-3 s grad=2.79e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.111163e-3 s grad=1.97e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.069926e-3 s grad=1.06e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.079551e-3 s grad=1.068e-6 s +[acc-interp] eval 9.696223e-3 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=2.250145e-3 s grad=7.36e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.018474e-3 s grad=2.718e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.516525e-3 s grad=7.294e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.085673e-3 s grad=5.079e-6 s +[acc-interp] eval 8.0221449e-2 s +[acc-interp] thetaMarsh 6.3e-8 s (n=[acc-interp] thetaMarsh 5.5e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.3213628e-2 s grad=4.886e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.60489e-3 s grad=3.532e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.603034e-3 s grad=2.34e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.474901e-3 s grad=2.234e-6 s +[acc-interp] eval 2.2291078e-2 s +[acc-interp] thetaMarsh 1.0e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.322638e-3 s grad=2.421e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.620471e-3 s grad=3.701e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.219457e-3 s grad=2.085e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.820556e-3 s grad=2.567e-6 s +[acc-interp] eval 1.3598674e-2 s +[acc-interp] thetaMarsh 1.03e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.933699e-3 s grad=2.155e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.416774e-3 s grad=2.336e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=7.814924e-3 s grad=2.422e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.149427e-3 s grad=1.547e-6 s +[acc-interp] eval 1.6855308e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=2.316887e-3 s grad=2.182e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.608762e-3 s grad=2.932e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.652172e-3 s grad=2.518e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.971347e-3 s grad=2.664e-6 s +[acc-interp] eval 6.5005736e-2 s +[acc-interp] thetaMarsh 8.1e-8 s (n=0) +[acc-interp] chunk base=0 cs=25974 kernel=1.8015646e-2 s grad=1.005e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.422069e-3 s grad=3.822e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.433784e-3 s grad=6.06e-7 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.302306e-3 s grad=1.023e-6 s +[acc-interp] eval 2.5999627e-2 s +[acc-interp] thetaMarsh 6.9e-8 s (n=[acc-interp] thetaMarsh 5.5e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.5168895e-2 s grad=3.946e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.238187e-3 s grad=6.339e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.03818e-3 s grad=3.508e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.476733e-3 s grad=4.255e-6 s +[acc-interp] eval 3.6215104e-2 s +[acc-interp] thetaMarsh 1.19e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.99707e-3 s grad=3.065e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.370649e-3 s grad=3.592e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.308363e-3 s grad=2.765e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.215169e-3 s grad=3.055e-6 s +[acc-interp] eval 1.6414911e-2 s +[acc-interp] thetaMarsh 9.9e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.972478e-3 s grad=2.479e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.407341e-3 s grad=2.582e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.3121215e-2 s grad=3.462e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.107672e-3 s grad=2.452e-6 s +[acc-interp] eval 2.7081852e-2 s +[acc-interp] thetaMarsh 2.67e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=4.472731e-3 s grad=2.837e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.429764e-3 s grad=2.507e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.781623e-3 s grad=1.903e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.041801e-3 s grad=2.477e-6 s +[acc-interp] eval 1.52466e-2 s +[acc-interp] thetaMarsh 1.05e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=4.222654e-3 s grad=3.757e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.86397e-3 s grad=3.394e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.469163e-3 s grad=4.718e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.567326e-3 s grad=3.698e-6 s +[acc-interp] eval 1.819568e-2 s +[acc-interp] thetaMarsh 1.1e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=4.112787e-3 s grad=3.81e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.005724e-3 s grad=6.685e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.130569e-3 s grad=6.329e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.415795e-3 s grad=4.308e-6 s +[acc-interp] eval 1.7633914e-2 s +[acc-interp] thetaMarsh 1.13e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=4.200903e-3 s grad=4.134e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.549404e-3 s grad=6.087e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.316028e-3 s grad=6.745e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.487948e-3 s grad=2.415e-6 s +[acc-interp] eval 1.5657486e-2 s +[acc-interp] thetaMarsh 1.35e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.591682e-3 s grad=3.429e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.26839e-3 s grad=2.728e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.178351e-3 s grad=2.724e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.190261e-3 s grad=2.458e-6 s +[acc-interp] eval 1.4858123e-2 s +[acc-interp] thetaMarsh 1.01e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.910286e-3 s grad=3.4e-6 s +[acc-interp] chunk base=25974 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thetaMarsh 1.78e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=4.662235e-3 s grad=2.609e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.854657e-3 s grad=2.25e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.066884e-3 s grad=3.284e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.73191e-3 s grad=3.334e-6 s +[acc-interp] eval 1.7061921e-2 s +[acc-interp] thetaMarsh 1.41e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.090895e-3 s grad=2.732e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.302117e-3 s grad=3.156e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.106534e-3 s grad=2.087e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.661773e-3 s grad=2.156e-6 s +[acc-interp] eval 1.3854723e-2 s +[acc-interp] thetaMarsh 3.01e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.749591e-3 s grad=3.281e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.033752e-3 s grad=9.437e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.566605e-3 s grad=3.678e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.088439e-3 s grad=2.503e-6 s +[acc-interp] eval 1.4109443e-2 s +[acc-interp] thetaMarsh 1.25e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.283423e-3 s grad=3.541e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.460206e-3 s grad=3.746e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.571973e-3 s grad=3.253e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.487892e-3 s grad=2.442e-6 s +[acc-interp] eval 1.5573825e-2 s +[acc-interp] thetaMarsh 1.22e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.1935937e-2 s grad=3.076e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.780379e-3 s grad=2.346e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.190416e-3 s grad=2.533e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.667264e-3 s grad=2.775e-6 s +[acc-interp] eval 2.314651e-2 s +[acc-interp] thetaMarsh 1.17e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=4.299019e-3 s grad=2.268e-6 s 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eval 1.581396e-2 s +[acc-interp] thetaMarsh 9.8e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.033788e-3 s grad=2.174e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.873958e-3 s grad=3.854e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.638073e-3 s grad=4.174e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.883892e-3 s grad=3.44e-6 s +[acc-interp] eval 1.6057407e-2 s +[acc-interp] thetaMarsh 1.24e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.764701e-3 s grad=2.538e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.72997e-3 s grad=3.408e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.129276e-3 s grad=3.163e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.224016e-3 s grad=3.844e-6 s +[acc-interp] eval 1.7503594e-2 s +[acc-interp] thetaMarsh 1.41e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.913244e-3 s grad=4.065e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.983477e-3 s grad=3.039e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.996281e-3 s grad=3.78e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=9.243567e-3 s grad=3.674e-6 s +[acc-interp] eval 2.2945863e-2 s +[acc-interp] thetaMarsh 1.06e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.657923e-3 s grad=1.862e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.546708e-3 s grad=4.053e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.754036e-3 s grad=3.327e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.023024e-3 s grad=3.945e-6 s +[acc-interp] eval 1.3716844e-2 s +[acc-interp] thetaMarsh 1.31e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.8967e-3 s grad=2.332e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.816155e-3 s grad=3.306e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.55004e-3 s grad=2.689e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.010091e-3 s grad=2.93e-6 s +[acc-interp] eval 1.3955002e-2 s +[acc-interp] thetaMarsh 1.0e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.605266e-3 s grad=2.964e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.724137e-3 s grad=3.15e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.833416e-3 s grad=2.359e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.802699e-3 s grad=2.293e-6 s +[acc-interp] eval 1.2541728e-2 s +[acc-interp] thetaMarsh 1.09e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.106402e-3 s grad=2.705e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.021756e-3 s grad=2.415e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.023061e-3 s grad=3.407e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.152043e-3 s grad=5.279e-6 s +[acc-interp] eval 1.4962542e-2 s +[acc-interp] thetaMarsh 1.37e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.353838e-3 s grad=2.996e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.114619e-3 s grad=3.277e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.461792e-3 s grad=3.35e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.026472e-3 s grad=3.684e-6 s +[acc-interp] eval 1.5717275e-2 s +[acc-interp] thetaMarsh 1.17e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.219832e-3 s grad=3.468e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.399416e-3 s grad=2.799e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.127998e-3 s grad=2.749e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.084942e-3 s grad=2.726e-6 s +[acc-interp] eval 1.4436737e-2 s +[acc-interp] thetaMarsh 1.08e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=4.825538e-3 s grad=3.319e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=8.173084e-3 s grad=3.849e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.064335e-3 s grad=3.401e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.156271e-3 s grad=3.807e-6 s +[acc-interp] eval 2.1736282e-2 s +[acc-interp] thetaMarsh 6.6e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.920507e-3 s grad=2.522e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.522298e-3 s grad=2.467e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.146538e-3 s grad=4.552e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.826248e-3 s grad=3.88e-6 s +[acc-interp] eval 1.5372098e-2 s +[acc-interp] thetaMarsh 1.94e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.829469e-3 s grad=2.977e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.1248e-3 s grad=2.942e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.944996e-3 s grad=1.433e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.908499e-3 s grad=2.458e-6 s +[acc-interp] eval 1.3377101e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=3.974145e-3 s grad=3.093e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.213738e-3 s grad=1.895e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.870203e-3 s grad=2.872e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.071869e-3 s grad=2.224e-6 s +[acc-interp] eval 0.559855244 s +[acc-interp] thetaMarsh 1.3e-7 s (n=[acc-interp] thetaMarsh 1.06e-7 s (n=2) 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chunk base=77922 cs=25974 kernel=3.405847e-3 s grad=5.983e-6 s +[acc-interp] eval 1.1544862e-2 s +[acc-interp] thetaMarsh 1.59e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.697689e-3 s grad=3.305e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.38488e-3 s grad=3.414e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.724614e-3 s grad=2.211e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.253944e-3 s grad=2.409e-6 s +[acc-interp] eval 1.1578562e-2 s +[acc-interp] thetaMarsh 1.06e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.583526e-3 s grad=3.797e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.930477e-3 s grad=4.138e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.511713e-3 s grad=7.747e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.84446e-3 s grad=6.552e-6 s +[acc-interp] eval 1.1989178e-2 s +[acc-interp] thetaMarsh 6.5e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.5261e-3 s grad=1.438e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.365118e-3 s grad=1.968e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.928818e-3 s grad=3.686e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.038049e-3 s grad=3.463e-6 s +[acc-interp] eval 1.2638761e-2 s +[acc-interp] thetaMarsh 1.7e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.515252e-3 s grad=2.912e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.386259e-3 s grad=2.565e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.423887e-3 s grad=2.62e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.143928e-3 s grad=3.876e-6 s +[acc-interp] eval 1.1996887e-2 s +[acc-interp] thetaMarsh 1.53e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.51676e-3 s grad=2.525e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.783723e-3 s grad=2.374e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.500553e-3 s grad=1.956e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.444743e-3 s grad=1.96e-6 s +[acc-interp] eval 1.170196e-2 s +[acc-interp] thetaMarsh 1.11e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.450476e-3 s grad=4.861e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.900755e-3 s grad=3.903e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.253672e-3 s grad=7.194e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.568054e-3 s grad=2.51e-6 s +[acc-interp] eval 1.2292482e-2 s +[acc-interp] thetaMarsh 1.54e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.466211e-3 s grad=2.508e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.47953e-3 s grad=1.787e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.362504e-3 s grad=1.79e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.209193e-3 s grad=1.511e-6 s +[acc-interp] eval 1.0950157e-2 s +[acc-interp] thetaMarsh 1.0e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.068112e-3 s grad=1.738e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.070431e-3 s grad=2.067e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.927849e-3 s grad=1.408e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.578861e-3 s grad=1.911e-6 s +[acc-interp] eval 9.10214e-3 s +[acc-interp] thetaMarsh 1.22e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.739652e-3 s grad=3.11e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.700559e-3 s grad=3.253e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.492521e-3 s grad=2.022e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.405665e-3 s grad=3.203e-6 s +[acc-interp] eval 1.192308e-2 s +[acc-interp] thetaMarsh 1.18e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.168018e-3 s grad=2.255e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.379833e-3 s grad=2.72e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.706748e-3 s grad=4.051e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.638057e-3 s grad=2.727e-6 s +[acc-interp] eval 1.138801e-2 s +[acc-interp] thetaMarsh 1.08e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.622701e-3 s grad=2.111e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.439605e-3 s grad=2.458e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.524539e-3 s grad=2.368e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.702031e-3 s grad=3.024e-6 s +[acc-interp] eval 1.1750983e-2 s +[acc-interp] thetaMarsh 9.6e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.709546e-3 s grad=2.892e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.50406e-3 s grad=2.647e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.484789e-3 s grad=2.789e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.683195e-3 s grad=3.642e-6 s +[acc-interp] eval 1.1610677e-2 s +[acc-interp] thetaMarsh 7.9e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.331671e-3 s grad=2.731e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.819293e-3 s grad=3.746e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.642331e-3 s grad=2.81e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.452641e-3 s grad=2.484e-6 s +[acc-interp] eval 1.1739458e-2 s +[acc-interp] thetaMarsh 1.21e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.667917e-3 s grad=2.297e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.217809e-3 s grad=1.944e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.684696e-3 s grad=3.189e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.389862e-3 s grad=3.545e-6 s +[acc-interp] eval 1.1478655e-2 s +[acc-interp] thetaMarsh 1.19e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.350363e-3 s grad=2.643e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.525851e-3 s grad=2.028e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.24384e-3 s grad=2.59e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.009114e-3 s grad=1.721e-6 s +[acc-interp] eval 1.056516e-2 s +[acc-interp] thetaMarsh 1.43e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.913867e-3 s grad=1.773e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.133735e-3 s grad=1.588e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.364961e-3 s grad=1.733e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.272458e-3 s grad=1.517e-6 s +[acc-interp] eval 1.0010567e-2 s +[acc-interp] thetaMarsh 1.19e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.885541e-3 s grad=3.646e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.517879e-3 s grad=3.563e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.568835e-3 s grad=3.938e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.515308e-3 s grad=3.08e-6 s +[acc-interp] eval 1.2013906e-2 s +[acc-interp] thetaMarsh 1.02e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.250044e-3 s grad=2.148e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.650647e-3 s grad=3.077e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.61265e-3 s grad=3.021e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.514373e-3 s grad=2.978e-6 s +[acc-interp] eval 1.1528141e-2 s +[acc-interp] thetaMarsh 1.16e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.683024e-3 s grad=1.687e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.285867e-3 s grad=1.545e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.491239e-3 s grad=2.409e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.815468e-3 s grad=3.677e-6 s +[acc-interp] eval 1.1768014e-2 s +[acc-interp] thetaMarsh 4.77e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.606169e-3 s grad=3.557e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.975017e-3 s grad=3.082e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.61946e-3 s grad=3.783e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.37129e-3 s grad=2.128e-6 s +[acc-interp] eval 1.224926e-2 s +[acc-interp] thetaMarsh 9.9e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.23511e-3 s grad=3.815e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.51585e-3 s grad=2.948e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.690394e-3 s grad=3.251e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.657368e-3 s grad=8.715e-6 s +[acc-interp] eval 8.703583e-3 s +[acc-interp] thetaMarsh 1.21e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.021431e-3 s grad=1.94e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.700935e-3 s grad=1.711e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.729026e-3 s grad=1.411e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.366675e-3 s grad=2.796e-6 s +[acc-interp] eval 9.312591e-3 s +[acc-interp] thetaMarsh 1.69e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.436493e-3 s grad=2.743e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.854791e-3 s grad=1.792e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.011876e-3 s grad=2.083e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.077283e-3 s grad=2.186e-6 s +[acc-interp] eval 9.84964e-3 s +[acc-interp] thetaMarsh 1.08e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.370763e-3 s grad=2.005e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.241319e-3 s grad=2.651e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.145242e-3 s grad=2.814e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.263135e-3 s grad=2.68e-6 s +[acc-interp] eval 9.943892e-3 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=2.104672e-3 s grad=2.578e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.329981e-3 s grad=2.213e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.161414e-3 s grad=2.28e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.22646e-3 s grad=3.308e-6 s +[acc-interp] eval 0.333802251 s +[acc-interp] thetaMarsh 1.85e-7 s (n=[acc-interp] thetaMarsh 1.35e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.5625016e-2 s grad=4.514e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.840747e-3 s grad=3.265e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.397839e-2 s grad=3.573e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.430859e-3 s grad=4.689e-6 s +[acc-interp] eval 4.958743e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=3.844044e-3 s grad=3.072e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.535564e-3 s grad=2.988e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.648635e-3 s grad=3.424e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.099623e-3 s grad=3.696e-6 s +[acc-interp] eval 6.6561969e-2 s +[acc-interp] thetaMarsh 1.23e-7 s (n=[acc-interp] thetaMarsh 1.27e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=7.351118e-3 s grad=2.742e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.790619e-3 s grad=2.499e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.509585e-3 s grad=2.275e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.907447e-3 s grad=3.331e-6 s +[acc-interp] eval 1.7152597e-2 s +[acc-interp] thetaMarsh 1.08e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.555259e-3 s grad=2.458e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.367643e-3 s grad=2.102e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.416999e-3 s grad=2.343e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.737497e-3 s grad=4.059e-6 s +[acc-interp] eval 1.2671663e-2 s +[acc-interp] thetaMarsh 1.08e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.904542e-3 s grad=2.76e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.485175e-3 s grad=2.745e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.37853e-3 s grad=2.765e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.0494066e-2 s grad=3.861e-6 s +[acc-interp] eval 1.9839853e-2 s +[acc-interp] thetaMarsh 1.3e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.219279e-3 s grad=2.256e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.050442e-3 s grad=1.539e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.041013e-3 s grad=1.511e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.433718e-3 s grad=3.819e-6 s +[acc-interp] eval 1.020405e-2 s +[acc-interp] thetaMarsh 2.05e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.577735e-3 s grad=3.545e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.666673e-3 s grad=3.626e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.565219e-3 s grad=2.837e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.235226e-3 s grad=2.483e-6 s +[acc-interp] eval 1.1634656e-2 s +[acc-interp] thetaMarsh 1.23e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.266903e-3 s grad=2.013e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.983554e-3 s grad=2.911e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.436048e-3 s grad=2.439e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.648893e-3 s grad=2.257e-6 s +[acc-interp] eval 1.1823674e-2 s +[acc-interp] thetaMarsh 1.1e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.161728e-3 s grad=1.975e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.216263e-3 s grad=1.76e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.483551e-3 s grad=3.593e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.573218e-3 s grad=3.097e-6 s +[acc-interp] eval 1.0955701e-2 s +[acc-interp] thetaMarsh 1.06e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.450807e-3 s grad=2.915e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.320846e-3 s grad=2.528e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.216284e-3 s grad=2.485e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.101172e-3 s grad=2.517e-6 s +[acc-interp] eval 1.0617272e-2 s +[acc-interp] thetaMarsh 9.6e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.12689e-3 s grad=1.912e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.078366e-3 s grad=2.294e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.159854e-3 s grad=1.964e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.202239e-3 s grad=2.378e-6 s +[acc-interp] eval 1.0052712e-2 s +[acc-interp] thetaMarsh 9.5e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.181823e-3 s grad=2.235e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.298518e-3 s grad=1.336e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.572734e-3 s grad=2.916e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.709487e-3 s grad=3.383e-6 s +[acc-interp] eval 1.1209537e-2 s +[acc-interp] thetaMarsh 9.6e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.453216e-3 s grad=4.037e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.356098e-3 s grad=9.623e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.305476e-3 s grad=7.234e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.176362e-3 s grad=6.705e-6 s +[acc-interp] eval 1.0236965e-2 s +[acc-interp] thetaMarsh 6.8e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.55869e-3 s grad=4.037e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.241857e-3 s grad=3.398e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.161114e-3 s grad=1.592e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.491684e-3 s grad=7.0e-6 s +[acc-interp] eval 1.0349869e-2 s +[acc-interp] thetaMarsh 1.8e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.120941e-3 s grad=2.667e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.128583e-3 s grad=7.485e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.20152e-3 s grad=5.823e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.665013e-3 s grad=4.425e-6 s +[acc-interp] eval 1.0027519e-2 s +[acc-interp] thetaMarsh 2.41e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.396032e-3 s grad=3.071e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.290374e-3 s grad=3.03e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.400622e-3 s grad=2.866e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.169606e-3 s grad=7.37e-6 s +[acc-interp] eval 1.0294781e-2 s +[acc-interp] thetaMarsh 6.6e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.082661e-3 s grad=8.041e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.649258e-3 s grad=4.031e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.54483e-3 s grad=8.831e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.316864e-3 s grad=5.4e-6 s +[acc-interp] eval 1.0528221e-2 s +[acc-interp] thetaMarsh 6.3e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.29981e-3 s grad=7.116e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.292736e-3 s grad=6.645e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.369649e-3 s grad=5.14e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.686081e-3 s grad=4.403e-6 s +[acc-interp] eval 1.0542058e-2 s +[acc-interp] thetaMarsh 1.08e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.573325e-3 s grad=3.015e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.283997e-3 s grad=2.636e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.32136e-3 s grad=8.409e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.39397e-3 s grad=2.158e-6 s +[acc-interp] eval 1.0409528e-2 s +[acc-interp] thetaMarsh 6.1e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.147464e-3 s grad=1.0078e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.133741e-3 s grad=4.37e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.356002e-3 s grad=9.798e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.371876e-3 s grad=7.523e-6 s +[acc-interp] eval 9.830091e-3 s +[acc-interp] thetaMarsh 6.2e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.201371e-3 s grad=8.099e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.239871e-3 s grad=1.593e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.321524e-3 s grad=6.537e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.310069e-3 s grad=5.469e-6 s +[acc-interp] eval 9.832386e-3 s +[acc-interp] thetaMarsh 5.9e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.275381e-3 s grad=3.087e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.740527e-3 s grad=2.979e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.489766e-3 s grad=1.276e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.251101e-3 s grad=5.637e-6 s +[acc-interp] eval 1.0594338e-2 s +[acc-interp] thetaMarsh 2.61e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.238946e-3 s grad=6.954e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.15218e-3 s grad=6.515e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.085381e-3 s grad=2.522e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.685916e-3 s grad=3.295e-6 s +[acc-interp] eval 1.024672e-2 s +[acc-interp] thetaMarsh 9.8e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.292656e-3 s grad=3.138e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.405407e-3 s grad=2.611e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.274805e-3 s grad=2.16e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.126492e-3 s grad=1.946e-6 s +[acc-interp] eval 1.0593638e-2 s +[acc-interp] thetaMarsh 1.02e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.50178e-3 s grad=4.132e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.790417e-3 s grad=2.347e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.488941e-3 s grad=2.705e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.421581e-3 s grad=2.158e-6 s +[acc-interp] eval 1.1805338e-2 s +[acc-interp] thetaMarsh 9.8e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.372638e-3 s grad=2.572e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.231211e-3 s grad=2.062e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.766428e-3 s grad=4.273e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.410333e-3 s grad=3.335e-6 s +[acc-interp] eval 1.1446281e-2 s +[acc-interp] thetaMarsh 1.24e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.356476e-3 s grad=2.694e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.288442e-3 s grad=2.618e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.25264e-3 s grad=2.078e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.182792e-3 s grad=1.817e-6 s +[acc-interp] eval 1.056062e-2 s +[acc-interp] thetaMarsh 1.08e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.839027e-3 s grad=4.019e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.747853e-3 s grad=3.744e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.709745e-3 s grad=3.743e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.262899e-3 s grad=3.412e-6 s +[acc-interp] eval 1.2243761e-2 s +[acc-interp] thetaMarsh 1.06e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.154032e-3 s grad=2.097e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.282613e-3 s grad=1.946e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.893639e-3 s grad=3.804e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.415406e-3 s grad=3.559e-6 s +[acc-interp] eval 1.1356075e-2 s +[acc-interp] thetaMarsh 1.1e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.344306e-3 s grad=3.155e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.359729e-3 s grad=3.871e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.239731e-3 s grad=3.517e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.139657e-3 s grad=2.534e-6 s +[acc-interp] eval 1.0730044e-2 s +[acc-interp] thetaMarsh 5.79e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.609833e-3 s grad=3.184e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.319551e-3 s grad=3.264e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.367439e-3 s grad=3.455e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.388086e-3 s grad=4.359e-6 s +[acc-interp] eval 1.1420147e-2 s +[acc-interp] thetaMarsh 9.8e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.441025e-3 s grad=2.47e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.002918e-3 s grad=3.392e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.498636e-3 s grad=2.769e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.318967e-3 s grad=2.808e-6 s +[acc-interp] eval 1.1812053e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=2.600291e-3 s grad=2.869e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.284424e-3 s grad=2.324e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.183214e-3 s grad=1.758e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.241672e-3 s grad=3.048e-6 s +[acc-interp] eval 0.35559813 s +[acc-interp] thetaMarsh 9.3e-8 s (n=[acc-interp] thetaMarsh 6.6e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.311515e-2 s grad=3.356e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.37416e-3 s grad=1.806e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.367365e-3 s grad=4.112e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.729116e-3 s grad=4.922e-6 s +[acc-interp] eval 2.3456853e-2 s +[acc-interp] thetaMarsh 6.3e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.428191e-3 s grad=1.943e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.846329e-3 s grad=1.568e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.699315e-3 s grad=4.942e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.873794e-3 s grad=3.932e-6 s +[acc-interp] eval 1.2850588e-2 s +[acc-interp] thetaMarsh 1.22e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.847566e-3 s grad=2.944e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.870734e-3 s grad=3.108e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.765609e-3 s grad=9.468e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.705965e-3 s grad=6.568e-6 s +[acc-interp] eval 1.2426726e-2 s +[acc-interp] thetaMarsh 6.5e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.835751e-3 s grad=3.042e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=6.609083e-3 s grad=3.584e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=9.600437e-3 s grad=3.76e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.281307e-3 s grad=2.995e-6 s +[acc-interp] eval 2.2640087e-2 s +[acc-interp] thetaMarsh 7.8e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.600057e-3 s grad=1.1601e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.718525e-3 s grad=7.605e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.633331e-3 s grad=6.393e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.441232e-3 s grad=4.469e-6 s +[acc-interp] eval 1.1221908e-2 s +[acc-interp] thetaMarsh 6.4e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.095811e-3 s grad=3.818e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.415326e-3 s grad=6.627e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.304208e-3 s grad=5.563e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.216045e-3 s grad=4.714e-6 s +[acc-interp] eval 1.0871182e-2 s +[acc-interp] thetaMarsh 1.81e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.553927e-3 s grad=5.952e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.432258e-3 s grad=4.948e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.803588e-3 s grad=4.286e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.513678e-3 s grad=4.57e-7 s +[acc-interp] eval 1.1062761e-2 s +[acc-interp] thetaMarsh 6.7e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.626923e-3 s grad=6.276e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.570955e-3 s grad=4.937e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.462217e-3 s grad=3.545e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.01187e-3 s grad=6.114e-6 s +[acc-interp] eval 1.0473481e-2 s +[acc-interp] thetaMarsh 1.2e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.002098e-3 s grad=3.813e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.438814e-3 s grad=2.577e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.763523e-3 s grad=5.615e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.496162e-3 s grad=1.44e-6 s +[acc-interp] eval 1.1488227e-2 s +[acc-interp] thetaMarsh 1.81e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.402831e-3 s grad=6.37e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.931482e-3 s grad=3.244e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.55459e-3 s grad=3.371e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.460651e-3 s grad=2.003e-6 s +[acc-interp] eval 1.160575e-2 s +[acc-interp] thetaMarsh 2.72e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.540744e-3 s grad=2.053e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.404756e-3 s grad=1.97e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.321531e-3 s grad=1.66e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.408228e-3 s grad=2.527e-6 s +[acc-interp] eval 1.1130569e-2 s +[acc-interp] thetaMarsh 1.24e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.505397e-3 s grad=4.373e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.518349e-3 s grad=2.825e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.283125e-3 s grad=3.042e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.527696e-3 s grad=2.575e-6 s +[acc-interp] eval 1.1535239e-2 s +[acc-interp] thetaMarsh 1.07e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.583065e-3 s grad=2.359e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.957084e-3 s grad=3.065e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.407008e-3 s grad=2.513e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.564597e-3 s grad=2.599e-6 s +[acc-interp] eval 1.2132528e-2 s +[acc-interp] thetaMarsh 9.7e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.353795e-3 s grad=2.605e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.295577e-3 s grad=2.416e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.444044e-3 s grad=2.635e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.734288e-3 s grad=4.058e-6 s +[acc-interp] eval 1.1518789e-2 s +[acc-interp] thetaMarsh 1.4e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.365257e-3 s grad=3.167e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.312974e-3 s grad=2.491e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.15058e-3 s grad=3.178e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.192115e-3 s grad=2.804e-6 s +[acc-interp] eval 1.0659253e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=2.095233e-3 s grad=1.842e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.243168e-3 s grad=3.371e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.600812e-3 s grad=2.777e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.445911e-3 s grad=2.736e-6 s +[acc-interp] eval 0.207733589 s +[acc-interp] thetaMarsh 1.26e-7 s (n=[acc-interp] thetaMarsh 1.12e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.554371e-2 s grad=3.958e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.513068e-3 s grad=2.054e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.057888e-3 s grad=3.177e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.609306e-3 s grad=3.765e-6 s +[acc-interp] eval 2.5304723e-2 s +[acc-interp] thetaMarsh 1.03e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.713573e-3 s grad=3.633e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.485905e-3 s grad=3.841e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.1173687e-2 s grad=4.371e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.014499e-3 s grad=3.751e-6 s +[acc-interp] eval 2.4138798e-2 s +[acc-interp] thetaMarsh 1.1e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.995026e-3 s grad=3.639e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.951527e-3 s grad=3.78e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.04194e-3 s grad=2.26e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.790109e-3 s grad=1.577e-6 s +[acc-interp] eval 1.3379681e-2 s +[acc-interp] thetaMarsh 1.03e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.879937e-3 s grad=2.555e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.805711e-3 s grad=3.782e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.488113e-3 s grad=4.911e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.810471e-3 s grad=2.22e-6 s +[acc-interp] eval 1.4118005e-2 s +[acc-interp] thetaMarsh 6.6e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.912085e-3 s grad=2.069e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.533626e-3 s grad=1.488e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.775273e-3 s grad=2.932e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.163104e-3 s grad=5.08e-6 s +[acc-interp] eval 1.2148547e-2 s +[acc-interp] thetaMarsh 6.7e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.197805e-3 s grad=4.1e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.861855e-3 s grad=3.927e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.159521e-3 s grad=4.101e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.53777e-3 s grad=3.425e-6 s +[acc-interp] eval 1.2624749e-2 s +[acc-interp] thetaMarsh 7.0e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.186977e-3 s grad=3.836e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.914852e-3 s grad=4.566e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.155819e-3 s grad=4.132e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.945335e-3 s grad=4.281e-6 s +[acc-interp] eval 1.336283e-2 s +[acc-interp] thetaMarsh 2.06e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.090385e-3 s grad=3.158e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.196779e-3 s grad=4.424e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.032578e-3 s grad=3.997e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.717088e-3 s grad=4.094e-6 s +[acc-interp] eval 1.3218051e-2 s +[acc-interp] thetaMarsh 8.7e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.988831e-3 s grad=3.367e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.608103e-3 s grad=3.373e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.782882e-3 s grad=3.476e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.678173e-3 s grad=3.475e-6 s +[acc-interp] eval 1.2226946e-2 s +[acc-interp] thetaMarsh 6.8e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.91488e-3 s grad=2.371e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.444799e-3 s grad=2.644e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.733125e-3 s grad=2.69e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.664033e-3 s grad=2.904e-6 s +[acc-interp] eval 1.1882678e-2 s +[acc-interp] thetaMarsh 2.52e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.149105e-3 s grad=3.39e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.116059e-3 s grad=4.24e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.003699e-3 s grad=3.635e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.717923e-3 s grad=4.545e-6 s +[acc-interp] eval 1.3168599e-2 s +[acc-interp] thetaMarsh 1.81e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.897678e-3 s grad=2.574e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.724071e-3 s grad=4.276e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.078764e-3 s grad=4.317e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.504386e-3 s grad=3.891e-6 s +[acc-interp] eval 1.239042e-2 s +[acc-interp] thetaMarsh 6.4e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.938789e-3 s grad=3.414e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.719806e-3 s grad=3.239e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.658749e-3 s grad=2.863e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.549803e-3 s grad=5.747e-6 s +[acc-interp] eval 1.3162923e-2 s +[acc-interp] thetaMarsh 1.1e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.32211e-3 s grad=3.158e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.069264e-3 s grad=4.274e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.366806e-3 s grad=3.966e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.980072e-3 s grad=4.559e-6 s +[acc-interp] eval 1.4553963e-2 s +[acc-interp] thetaMarsh 1.82e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.355055e-3 s grad=3.332e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.799208e-3 s grad=2.569e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.823713e-3 s grad=2.081e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.616602e-3 s grad=3.092e-6 s +[acc-interp] eval 1.3328598e-2 s +[acc-interp] thetaMarsh 1.18e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.832409e-3 s grad=3.915e-6 s 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eval 1.3697494e-2 s +[acc-interp] thetaMarsh 1.22e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.037646e-3 s grad=2.688e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.818164e-3 s grad=3.407e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.362503e-3 s grad=3.283e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.877325e-3 s grad=3.051e-6 s +[acc-interp] eval 1.3603916e-2 s +[acc-interp] thetaMarsh 1.04e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.25438e-3 s grad=3.773e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.142426e-3 s grad=3.885e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.897721e-3 s grad=3.333e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.69227e-3 s grad=3.113e-6 s +[acc-interp] eval 1.3451469e-2 s +[acc-interp] thetaMarsh 1.15e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.861865e-3 s grad=3.259e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.576997e-3 s grad=2.364e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.797639e-3 s grad=2.849e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.557804e-3 s grad=2.489e-6 s +[acc-interp] eval 1.2264199e-2 s +[acc-interp] thetaMarsh 1.06e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.068501e-3 s grad=4.914e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.110514e-3 s grad=4.324e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.108897e-3 s grad=4.066e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.869671e-3 s grad=2.405e-6 s +[acc-interp] eval 1.6741132e-2 s +[acc-interp] thetaMarsh 1.24e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.572997e-3 s grad=3.672e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.851719e-3 s grad=3.446e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.089436e-3 s grad=3.592e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.884441e-3 s grad=2.964e-6 s +[acc-interp] eval 1.3921307e-2 s +[acc-interp] thetaMarsh 1.06e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.807157e-3 s grad=3.723e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.417291e-3 s grad=4.071e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.036236e-3 s grad=3.889e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.782852e-3 s grad=3.853e-6 s +[acc-interp] eval 1.3686069e-2 s +[acc-interp] thetaMarsh 1.03e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.977483e-3 s grad=4.176e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.598144e-3 s grad=3.522e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.86007e-3 s grad=4.516e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.005647e-3 s grad=9.845e-6 s +[acc-interp] eval 1.3554779e-2 s +[acc-interp] thetaMarsh 5.6e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.753812e-3 s grad=1.716e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.54996e-3 s grad=3.072e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.946469e-3 s grad=2.71e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.865512e-3 s grad=5.406e-6 s +[acc-interp] eval 1.3021104e-2 s +[acc-interp] thetaMarsh 1.06e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.235343e-3 s grad=9.969e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.697701e-3 s grad=7.081e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.623471e-3 s grad=3.051e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.456659e-3 s grad=3.048e-6 s +[acc-interp] eval 1.175853e-2 s +[acc-interp] thetaMarsh 6.9e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.774642e-3 s grad=2.527e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.546025e-3 s grad=2.722e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.834321e-3 s grad=2.729e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.515914e-3 s grad=2.971e-6 s +[acc-interp] eval 1.146759e-2 s +[acc-interp] thetaMarsh 1.2e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.667707e-3 s grad=2.753e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.644827e-3 s grad=5.17e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.546671e-3 s grad=4.502e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.747734e-3 s grad=4.174e-6 s +[acc-interp] eval 1.2641908e-2 s +[acc-interp] thetaMarsh 6.5e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.203718e-3 s grad=2.571e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.549404e-3 s grad=2.799e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.892534e-3 s grad=4.29e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.126663e-3 s grad=3.995e-6 s +[acc-interp] eval 1.2582126e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=3.045653e-3 s grad=3.193e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.818865e-3 s grad=3.209e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.764307e-3 s grad=2.719e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.458787e-3 s grad=2.552e-6 s +[acc-interp] eval 0.438759339 s +[acc-interp] thetaMarsh 8.0e-8 s (n=[acc-interp] thetaMarsh 7.8e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.237589e-2 s grad=5.401e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.297239e-3 s grad=3.894e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.058727e-3 s grad=9.225e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.842366e-3 s grad=9.124e-6 s +[acc-interp] eval 3.2467231e-2 s +[acc-interp] thetaMarsh 7.8e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.764104e-3 s grad=4.97e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.823799e-3 s grad=3.998e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.23278e-3 s grad=4.437e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.258123e-3 s grad=1.1361e-5 s +[acc-interp] eval 1.5076656e-2 s +[acc-interp] thetaMarsh 6.5e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.221711e-3 s grad=3.674e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.442943e-3 s grad=4.506e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.563675e-3 s grad=3.342e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.667538e-3 s grad=3.54e-6 s +[acc-interp] eval 1.5341163e-2 s +[acc-interp] thetaMarsh 1.2e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.859991e-3 s grad=3.976e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.885197e-3 s grad=3.976e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.492397e-3 s grad=3.585e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.930791e-3 s grad=2.989e-6 s +[acc-interp] eval 1.4776071e-2 s +[acc-interp] thetaMarsh 1.02e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.244901e-3 s grad=3.027e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.029783e-3 s grad=4.396e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.045004e-3 s grad=3.046e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.098337e-3 s grad=2.573e-6 s +[acc-interp] eval 1.4981864e-2 s +[acc-interp] thetaMarsh 1.12e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.546639e-3 s grad=2.605e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.150168e-3 s grad=2.909e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.466499e-3 s grad=2.349e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.453504e-3 s grad=3.57e-6 s +[acc-interp] eval 1.5092507e-2 s +[acc-interp] thetaMarsh 1.11e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.130816e-3 s grad=2.938e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.377537e-3 s grad=3.503e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.460813e-3 s grad=3.606e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.236923e-3 s grad=2.928e-6 s +[acc-interp] eval 1.5770775e-2 s +[acc-interp] thetaMarsh 1.11e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.541082e-3 s grad=3.316e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.169763e-3 s grad=4.471e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.351824e-3 s grad=4.265e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.285757e-3 s grad=3.149e-6 s +[acc-interp] eval 1.4913792e-2 s +[acc-interp] thetaMarsh 1.27e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.443698e-3 s grad=4.112e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.914597e-3 s grad=3.002e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.895153e-3 s grad=3.509e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.300361e-3 s grad=2.498e-6 s +[acc-interp] eval 1.5208663e-2 s +[acc-interp] thetaMarsh 1.09e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.851582e-3 s grad=2.459e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.95751e-3 s grad=2.553e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.958353e-3 s grad=5.124e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.305076e-3 s grad=3.957e-6 s +[acc-interp] eval 1.4793255e-2 s +[acc-interp] thetaMarsh 1.66e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.225824e-3 s grad=2.858e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.919569e-3 s grad=2.778e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.743713e-3 s grad=3.705e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.401426e-3 s grad=3.241e-6 s +[acc-interp] eval 1.3858824e-2 s +[acc-interp] thetaMarsh 1.02e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.473605e-3 s grad=4.18e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.217097e-3 s grad=3.644e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.02627e-3 s grad=3.223e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.048817e-3 s grad=2.779e-6 s +[acc-interp] eval 1.5430059e-2 s +[acc-interp] thetaMarsh 1.33e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=9.712339e-3 s grad=2.982e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.174774e-3 s grad=4.047e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.150915e-3 s grad=2.361e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.869518e-3 s grad=2.066e-6 s +[acc-interp] eval 2.1440283e-2 s +[acc-interp] thetaMarsh 2.82e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.978529e-3 s grad=1.754e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.521029e-3 s grad=3.347e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.00509e-3 s grad=4.209e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.239079e-3 s grad=3.145e-6 s +[acc-interp] eval 1.4336203e-2 s +[acc-interp] thetaMarsh 1.11e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.021475e-3 s grad=2.363e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.782844e-3 s grad=2.706e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.010648e-3 s grad=4.64e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.22219e-3 s grad=3.303e-6 s +[acc-interp] eval 1.4481639e-2 s +[acc-interp] thetaMarsh 1.17e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.168705e-3 s grad=3.752e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.3707e-3 s grad=3.433e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.215952e-3 s grad=3.511e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.186659e-3 s grad=3.354e-6 s +[acc-interp] eval 1.5525278e-2 s +[acc-interp] thetaMarsh 1.07e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.496662e-3 s grad=3.809e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.411449e-3 s grad=3.086e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.762045e-3 s grad=4.178e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.702e-3 s grad=4.036e-6 s +[acc-interp] eval 1.5947464e-2 s +[acc-interp] thetaMarsh 1.04e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.622115e-3 s grad=2.935e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.040848e-3 s grad=3.637e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.358072e-3 s grad=5.69e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.749139e-3 s grad=2.762e-6 s +[acc-interp] eval 1.6546211e-2 s +[acc-interp] thetaMarsh 1.2e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.019944e-3 s grad=2.448e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.259174e-3 s grad=4.073e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.847485e-3 s grad=3.699e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.272576e-3 s grad=3.305e-6 s +[acc-interp] eval 1.7169089e-2 s +[acc-interp] thetaMarsh 1.13e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.254682e-3 s grad=3.345e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.423457e-3 s grad=5.263e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.144993e-3 s grad=3.286e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.21817e-3 s grad=2.698e-6 s +[acc-interp] eval 1.6955461e-2 s +[acc-interp] thetaMarsh 1.3e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.330336e-3 s grad=2.562e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.461476e-3 s grad=3.068e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.487701e-3 s grad=3.523e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.0141591e-2 s grad=4.227e-6 s +[acc-interp] eval 2.4098153e-2 s +[acc-interp] thetaMarsh 9.6e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.162303e-3 s grad=3.266e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.027518e-3 s grad=3.586e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.129794e-3 s grad=2.863e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.054408e-3 s grad=4.081e-6 s +[acc-interp] eval 1.4129368e-2 s +[acc-interp] thetaMarsh 2.21e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.559258e-3 s grad=3.439e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.975476e-3 s grad=3.162e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.958791e-3 s grad=3.743e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.127739e-3 s grad=2.727e-6 s +[acc-interp] eval 1.4450524e-2 s +[acc-interp] thetaMarsh 1.15e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.944262e-3 s grad=2.798e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.629427e-3 s grad=4.462e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.042525e-3 s grad=3.86e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.027373e-3 s grad=2.963e-6 s +[acc-interp] eval 1.3325998e-2 s +[acc-interp] thetaMarsh 1.13e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.164111e-3 s grad=4.046e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.524613e-3 s grad=2.993e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.857247e-3 s grad=2.693e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.144143e-3 s grad=2.723e-6 s +[acc-interp] eval 1.6287984e-2 s +[acc-interp] thetaMarsh 1.27e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.876358e-3 s grad=3.617e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.425889e-3 s grad=3.196e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.925216e-3 s grad=2.653e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.756908e-3 s grad=2.787e-6 s +[acc-interp] eval 1.4514103e-2 s +[acc-interp] thetaMarsh 1.0e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.147552e-3 s grad=4.719e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.446281e-3 s grad=3.492e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.358841e-3 s grad=3.535e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.41864e-3 s grad=2.628e-6 s +[acc-interp] eval 1.5948394e-2 s +[acc-interp] thetaMarsh 3.5e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.745063e-3 s grad=3.595e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.267644e-3 s grad=2.194e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.140703e-3 s grad=1.042e-5 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.939948e-3 s grad=1.089e-6 s +[acc-interp] eval 1.3935733e-2 s +[acc-interp] thetaMarsh 5.7e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.16709e-3 s grad=1.914e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.836989e-3 s grad=4.68e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.645556e-3 s grad=2.216e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.492974e-3 s grad=2.4e-6 s +[acc-interp] eval 1.5894328e-2 s +[acc-interp] thetaMarsh 8.1e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.08157e-3 s grad=1.1238e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.2123324e-2 s grad=3.754e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.127772e-3 s grad=3.267e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.50585e-3 s grad=4.51e-6 s +[acc-interp] eval 2.3181807e-2 s +4) +[acc-interp] chunk base=0 cs=25974 kernel=3.503914e-3 s grad=3.619e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.368976e-3 s grad=3.942e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.586584e-3 s grad=2.964e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.961925e-3 s grad=3.629e-6 s +[acc-interp] eval 0.51539678 s +[acc-interp] thetaMarsh 1.43e-7 s (n=[acc-interp] thetaMarsh 1.27e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.3578862e-2 s grad=3.462e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.338746e-3 s grad=4.683e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.054978e-3 s grad=4.299e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.913262e-3 s grad=3.661e-6 s +[acc-interp] eval 2.462375e-2 s +[acc-interp] thetaMarsh 1.15e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=6.966049e-3 s grad=3.027e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.50958e-3 s grad=3.309e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.060282e-3 s grad=3.143e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.839159e-3 s grad=3.841e-6 s +[acc-interp] eval 1.8665641e-2 s +[acc-interp] thetaMarsh 5.7e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.9883e-3 s grad=2.759e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.510859e-3 s grad=2.732e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.182591e-3 s grad=5.482e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.795382e-3 s grad=3.431e-6 s +[acc-interp] eval 1.3787359e-2 s +[acc-interp] thetaMarsh 1.22e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.871865e-3 s grad=3.629e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.755545e-3 s grad=3.262e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.100185e-3 s grad=4.579e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.697448e-3 s grad=4.515e-6 s +[acc-interp] eval 1.4215189e-2 s +[acc-interp] thetaMarsh 2.16e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.712369e-3 s grad=4.104e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.824118e-3 s grad=2.984e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.959024e-3 s grad=3.98e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.347871e-3 s grad=4.307e-6 s +[acc-interp] eval 1.5645679e-2 s +[acc-interp] thetaMarsh 1.3e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.126698e-3 s grad=2.947e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.065493e-3 s grad=4.798e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.091692e-3 s grad=4.204e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.517186e-3 s grad=4.581e-6 s +[acc-interp] eval 1.2963562e-2 s 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kernel=2.636114e-3 s grad=3.05e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.47836e-3 s grad=2.276e-6 s +[acc-interp] eval 1.1054772e-2 s +[acc-interp] thetaMarsh 1.06e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.85903e-3 s grad=4.948e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.037776e-3 s grad=3.748e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.095111e-3 s grad=4.081e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.759662e-3 s grad=3.399e-6 s +[acc-interp] eval 1.5475902e-2 s +[acc-interp] thetaMarsh 1.02e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.977165e-3 s grad=3.557e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.446573e-3 s grad=6.066e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.39688e-3 s grad=3.594e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.745908e-3 s grad=3.863e-6 s +[acc-interp] eval 1.4752821e-2 s +[acc-interp] thetaMarsh 6.0e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.933346e-3 s grad=3.384e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.740242e-3 s grad=4.455e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.900947e-3 s grad=3.624e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.647345e-3 s grad=2.957e-6 s +[acc-interp] eval 1.3539668e-2 s +[acc-interp] thetaMarsh 9.0e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.185844e-3 s grad=3.307e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.632273e-3 s grad=2.578e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.581533e-3 s grad=4.471e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.835959e-3 s grad=3.446e-6 s +[acc-interp] eval 1.3759684e-2 s +[acc-interp] thetaMarsh 1.08e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.206711e-3 s grad=3.128e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.864277e-3 s grad=3.685e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.988117e-3 s grad=3.241e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.336981e-3 s grad=5.466e-6 s +[acc-interp] eval 1.3781884e-2 s +[acc-interp] thetaMarsh 7.0e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.404989e-3 s grad=2.543e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.779896e-3 s grad=2.133e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.063683e-3 s grad=6.488e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.630828e-3 s grad=7.188e-6 s +[acc-interp] eval 1.2710062e-2 s +[acc-interp] thetaMarsh 1.61e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.876368e-3 s grad=9.561e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.625882e-3 s grad=8.145e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.630944e-3 s grad=2.566e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.539521e-3 s grad=9.049e-6 s +[acc-interp] eval 1.1468546e-2 s +[acc-interp] thetaMarsh 1.63e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.707213e-3 s grad=9.014e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.609603e-3 s grad=2.8047e-5 s 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chunk base=0 cs=25974 kernel=4.065825e-3 s grad=3.714e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.149463e-3 s grad=3.615e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.169997e-3 s grad=4.378e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.686312e-3 s grad=2.543e-6 s +[acc-interp] eval 1.4693907e-2 s +[acc-interp] thetaMarsh 1.1e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.768874e-3 s grad=4.025e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.750379e-3 s grad=3.866e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.769203e-3 s grad=3.444e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.544705e-3 s grad=2.199e-6 s +[acc-interp] eval 1.3351183e-2 s +[acc-interp] thetaMarsh 1.02e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.75551e-3 s grad=1.698e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.669034e-3 s grad=4.559e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.606134e-3 s grad=4.176e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.881327e-3 s grad=3.034e-6 s +[acc-interp] eval 1.3501258e-2 s +[acc-interp] thetaMarsh 7.4e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.063943e-3 s grad=3.099e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.715334e-3 s grad=1.701e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.93806e-3 s grad=3.25e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.39924e-3 s grad=3.73e-6 s +[acc-interp] eval 1.3015935e-2 s +[acc-interp] thetaMarsh 7.0e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.096158e-3 s grad=3.172e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.852746e-3 s grad=3.168e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.136485e-3 s grad=1.958e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.642876e-3 s grad=1.667e-6 s +[acc-interp] eval 1.2669312e-2 s +[acc-interp] thetaMarsh 1.5e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.759612e-3 s grad=2.907e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.043382e-3 s grad=3.355e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.576997e-3 s grad=3.883e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.18339e-3 s grad=4.143e-6 s +[acc-interp] eval 1.1710632e-2 s +[acc-interp] thetaMarsh 6.0e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.388124e-3 s grad=1.678e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.086573e-3 s grad=4.461e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.435143e-3 s grad=4.129e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.804041e-3 s grad=3.682e-6 s +[acc-interp] eval 1.3635668e-2 s +[acc-interp] thetaMarsh 5.9e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.688566e-3 s grad=3.351e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.262755e-3 s grad=2.555e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.309665e-3 s grad=1.786e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.253379e-3 s grad=2.936e-6 s +[acc-interp] eval 1.0267763e-2 s +[acc-interp] thetaMarsh 5.7e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.612876e-3 s grad=2.939e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.530822e-3 s grad=3.539e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.549701e-3 s grad=3.049e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.343877e-3 s grad=2.435e-6 s +[acc-interp] eval 1.1510274e-2 s +[acc-interp] thetaMarsh 1.11e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.508267e-3 s grad=2.783e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.373921e-3 s grad=3.414e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.200769e-3 s grad=5.057e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.958469e-3 s grad=4.221e-6 s +[acc-interp] eval 1.2247992e-2 s +[acc-interp] thetaMarsh 6.0e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.250693e-3 s grad=2.737e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.874428e-3 s grad=2.765e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.571097e-3 s grad=2.614e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.848079e-3 s grad=6.979e-6 s +[acc-interp] eval 1.2539799e-2 s +[acc-interp] thetaMarsh 2.95e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.162009e-3 s grad=3.557e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.428845e-3 s grad=2.976e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.573333e-3 s grad=2.716e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.69855e-3 s grad=2.936e-6 s +[acc-interp] eval 1.1723961e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=3.967465e-3 s grad=5.462e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.746435e-3 s grad=4.017e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.175553e-3 s grad=1.927e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.997693e-3 s grad=7.213e-6 s +[acc-interp] eval 0.439546265 s +[acc-interp] thetaMarsh 7.4e-8 s (n=[acc-interp] thetaMarsh 5.3e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.2188208e-2 s grad=4.297e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.362387e-3 s grad=1.0362e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.258879e-3 s grad=3.451e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.717742e-3 s grad=1.0358e-5 s +[acc-interp] eval 3.2379261e-2 s +[acc-interp] thetaMarsh 1.21e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.895708e-3 s grad=9.566e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.99624e-3 s grad=9.928e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.759034e-3 s grad=8.522e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.035434e-3 s grad=2.634e-6 s +[acc-interp] eval 1.2464782e-2 s +[acc-interp] thetaMarsh 6.7e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.823093e-3 s grad=2.66e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.551391e-3 s grad=3.026e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.765152e-3 s grad=2.505e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.059781e-3 s grad=5.05e-6 s +[acc-interp] eval 1.214245e-2 s +[acc-interp] thetaMarsh 1.22e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.045039e-3 s grad=4.118e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.037792e-3 s grad=3.218e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.184028e-3 s grad=3.099e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.703907e-3 s grad=5.249e-6 s +[acc-interp] eval 1.3668528e-2 s +[acc-interp] thetaMarsh 6.6e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.307097e-3 s grad=3.961e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.499137e-3 s grad=4.074e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.071541e-3 s grad=3.404e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.110188e-3 s grad=3.491e-6 s +[acc-interp] eval 1.3950574e-2 s +[acc-interp] thetaMarsh 2.48e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.941132e-3 s grad=5.007e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.106689e-3 s grad=3.584e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.959688e-3 s grad=3.475e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.018781e-3 s grad=3.232e-6 s +[acc-interp] eval 1.4722234e-2 s +[acc-interp] thetaMarsh 1.61e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.282397e-3 s grad=4.404e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.029124e-3 s grad=4.874e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.827493e-3 s grad=4.202e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.890772e-3 s grad=2.714e-6 s +[acc-interp] eval 1.2903614e-2 s +[acc-interp] thetaMarsh 1.04e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.906988e-3 s grad=2.055e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.882142e-3 s grad=2.324e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.496758e-3 s grad=4.795e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.656198e-3 s grad=1.3157e-5 s +[acc-interp] eval 1.4251423e-2 s +[acc-interp] thetaMarsh 2.51e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.346312e-3 s grad=1.0688e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.83246e-3 s grad=9.622e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.305466e-3 s grad=3.399e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.393572e-3 s grad=1.3829e-5 s +[acc-interp] eval 1.3981942e-2 s +[acc-interp] thetaMarsh 1.9e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.314728e-3 s grad=3.544e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.210163e-3 s grad=3.37e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.250786e-3 s grad=2.592e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.528922e-3 s grad=4.749e-6 s +[acc-interp] eval 1.4933954e-2 s +[acc-interp] thetaMarsh 1.07e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.368513e-3 s grad=2.972e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.953884e-3 s grad=2.956e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.736947e-3 s grad=2.618e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.186794e-3 s grad=5.945e-6 s +[acc-interp] eval 1.3870925e-2 s +[acc-interp] thetaMarsh 1.39e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.46308e-3 s grad=4.245e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.742668e-3 s grad=3.833e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.130633e-3 s grad=3.224e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.107129e-3 s grad=2.743e-6 s +[acc-interp] eval 1.4073242e-2 s +[acc-interp] thetaMarsh 1.1e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.730056e-3 s grad=3.072e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.087458e-3 s grad=3.876e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.211979e-3 s grad=3.007e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.1209946e-2 s grad=4.372e-6 s +[acc-interp] eval 2.1820324e-2 s +[acc-interp] thetaMarsh 1.12e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.911372e-3 s grad=4.629e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.644809e-3 s grad=1.2576e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.200224e-3 s grad=9.28e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.19259e-3 s grad=9.562e-6 s +[acc-interp] eval 1.4762722e-2 s +[acc-interp] thetaMarsh 1.91e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=5.208875e-3 s grad=1.7402e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.735105e-3 s grad=1.802e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.178114e-3 s grad=1.375e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.337653e-3 s grad=9.713e-6 s +[acc-interp] eval 1.5209532e-2 s +[acc-interp] thetaMarsh 9.0e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.259561e-3 s grad=4.189e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.492029e-3 s grad=2.896e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.466506e-3 s grad=8.765e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.295443e-3 s grad=8.033e-6 s +[acc-interp] eval 1.52732e-2 s +[acc-interp] thetaMarsh 6.1e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.80711e-3 s grad=4.329e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.40007e-3 s grad=3.989e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.25217e-3 s grad=1.049e-5 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.05468e-3 s grad=7.297e-6 s +[acc-interp] eval 1.4473753e-2 s +[acc-interp] thetaMarsh 1.57e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.170459e-3 s grad=3.853e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.64954e-3 s grad=3.744e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.367591e-3 s grad=3.508e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.253252e-3 s grad=2.833e-6 s +[acc-interp] eval 1.5003074e-2 s +[acc-interp] thetaMarsh 1.11e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.036531e-3 s grad=3.088e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.762439e-3 s grad=2.795e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.319732e-3 s grad=3.212e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.853549e-3 s grad=2.698e-6 s +[acc-interp] eval 1.4567674e-2 s +[acc-interp] thetaMarsh 1.01e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.266822e-3 s grad=3.306e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.716208e-3 s grad=5.738e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.467673e-3 s grad=4.216e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.760619e-3 s grad=9.275e-6 s +[acc-interp] eval 1.5237046e-2 s +[acc-interp] thetaMarsh 6.3e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.528699e-3 s grad=2.068e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.423697e-3 s grad=4.432e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.586584e-3 s grad=5.925e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.551574e-3 s grad=4.612e-6 s +[acc-interp] eval 1.4930392e-2 s +[acc-interp] thetaMarsh 6.4e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=9.861897e-3 s grad=3.289e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.353932e-3 s grad=4.004e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.086851e-3 s grad=4.235e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.665221e-3 s grad=4.159e-6 s +[acc-interp] eval 1.9835418e-2 s +[acc-interp] thetaMarsh 6.6e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.833498e-3 s grad=3.076e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.227639e-3 s grad=3.813e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.137419e-3 s grad=4.573e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.167486e-3 s grad=3.978e-6 s +[acc-interp] eval 1.3285619e-2 s +[acc-interp] thetaMarsh 6.3e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.613959e-3 s grad=4.404e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.649772e-3 s grad=4.482e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.733427e-3 s grad=4.391e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.340812e-3 s grad=4.62e-6 s +[acc-interp] eval 1.5774303e-2 s +[acc-interp] thetaMarsh 1.53e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.142466e-3 s grad=3.241e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.93604e-3 s grad=2.214e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.151412e-3 s grad=2.425e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.076092e-3 s grad=2.75e-6 s +[acc-interp] eval 1.3691567e-2 s +[acc-interp] thetaMarsh 1.04e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.089204e-3 s grad=3.003e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.056558e-3 s grad=3.734e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.591539e-3 s grad=2.419e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.329866e-3 s grad=3.804e-6 s +[acc-interp] eval 1.3559554e-2 s +[acc-interp] thetaMarsh 1.14e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.453423e-3 s grad=3.706e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.252947e-3 s grad=3.179e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.032172e-3 s grad=2.911e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.011106e-3 s grad=4.383e-6 s +[acc-interp] eval 1.4435515e-2 s +[acc-interp] thetaMarsh 5.01e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.776545e-3 s grad=3.567e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.544075e-3 s grad=4.172e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.228694e-3 s grad=3.506e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.347787e-3 s grad=3.378e-6 s +[acc-interp] eval 1.4578093e-2 s +[acc-interp] thetaMarsh 1.29e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.402431e-3 s grad=6.072e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.831079e-3 s grad=4.251e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.504692e-3 s grad=3.695e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.76493e-3 s grad=3.933e-6 s +[acc-interp] eval 1.8226723e-2 s +[acc-interp] thetaMarsh 5.9e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.510642e-3 s grad=3.817e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.158273e-3 s grad=4.22e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.239534e-3 s grad=3.652e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.4205223e-2 s grad=4.277e-6 s +[acc-interp] eval 2.5968717e-2 s +[acc-interp] thetaMarsh 1.48e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.202241e-3 s grad=4.483e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.303152e-3 s grad=3.126e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.103175e-3 s grad=4.068e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.176944e-3 s grad=4.672e-6 s +[acc-interp] eval 1.4632544e-2 s +4) +[acc-interp] chunk base=0 cs=25974 kernel=3.451806e-3 s grad=3.302e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.112274e-3 s grad=3.446e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.302585e-3 s grad=5.149e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.460234e-3 s grad=1.082e-5 s +[acc-interp] eval 0.50643978 s +[acc-interp] thetaMarsh 7.2e-8 s (n=[acc-interp] thetaMarsh 3.76e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.1310718e-2 s grad=3.772e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.67779e-3 s grad=3.219e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.528523e-3 s grad=7.25e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.362192e-3 s grad=1.492e-6 s +[acc-interp] eval 1.9801625e-2 s +[acc-interp] thetaMarsh 6.4e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.794489e-3 s grad=1.784e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.783724e-3 s grad=7.182e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.720368e-3 s grad=2.453e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.077537e-3 s grad=1.421e-6 s +[acc-interp] eval 1.4088843e-2 s +[acc-interp] thetaMarsh 6.2e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.303112e-3 s grad=1.1985e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.147441e-3 s grad=2.042e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.062361e-3 s grad=2.731e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.360788e-3 s grad=4.792e-6 s +[acc-interp] eval 1.1182534e-2 s +[acc-interp] thetaMarsh 1.61e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.046499e-3 s grad=2.386e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.265123e-3 s grad=2.77e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.158664e-3 s grad=2.03e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.297067e-3 s grad=1.898e-6 s +[acc-interp] eval 1.132274e-2 s +[acc-interp] thetaMarsh 1.35e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.902132e-3 s grad=4.916e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.908274e-3 s grad=2.64e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.090067e-3 s grad=2.3e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.983597e-3 s grad=1.873e-6 s +[acc-interp] eval 1.2569768e-2 s +[acc-interp] thetaMarsh 1.04e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.842878e-3 s grad=2.575e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.881305e-3 s grad=3.298e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.04407e-3 s grad=5.601e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.184057e-3 s grad=3.061e-6 s +[acc-interp] eval 1.1188687e-2 s +[acc-interp] thetaMarsh 6.9e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.121175e-3 s grad=2.487e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.25707e-3 s grad=2.669e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.070303e-3 s grad=2.369e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.743175e-3 s grad=1.885e-6 s +[acc-interp] eval 9.275524e-3 s +[acc-interp] thetaMarsh 1.27e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.988601e-3 s grad=2.981e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.178874e-3 s grad=3.337e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.195484e-3 s grad=2.927e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.750701e-3 s grad=1.788e-6 s +[acc-interp] eval 9.329192e-3 s +[acc-interp] thetaMarsh 5.8e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.669172e-3 s grad=2.361e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.639912e-3 s grad=2.129e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.689347e-3 s grad=1.709e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.547027e-3 s grad=3.218e-6 s +[acc-interp] eval 8.280584e-3 s +[acc-interp] thetaMarsh 2.46e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.315228e-3 s grad=3.108e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.164624e-3 s grad=4.039e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.123647e-3 s grad=2.536e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.960245e-3 s grad=2.494e-6 s +[acc-interp] eval 9.357086e-3 s +[acc-interp] thetaMarsh 6.5e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.060119e-3 s grad=3.495e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.316808e-3 s grad=3.396e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.841559e-3 s grad=2.621e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.949679e-3 s grad=2.091e-6 s +[acc-interp] eval 9.614396e-3 s +[acc-interp] thetaMarsh 9.9e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.840465e-3 s grad=2.055e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.881855e-3 s grad=2.179e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.620178e-3 s grad=1.881e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.721217e-3 s grad=1.709e-6 s +[acc-interp] eval 8.485896e-3 s +[acc-interp] thetaMarsh 1.09e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.142515e-3 s grad=3.946e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.226998e-3 s grad=6.432e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.390583e-3 s grad=3.228e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.75002e-3 s grad=3.576e-6 s +[acc-interp] eval 1.2381907e-2 s +[acc-interp] thetaMarsh 9.5e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.737467e-3 s grad=2.585e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.305315e-3 s grad=2.382e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.321516e-3 s grad=5.433e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.311525e-3 s grad=3.722e-6 s +[acc-interp] eval 1.4468608e-2 s +[acc-interp] thetaMarsh 1.25e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.668303e-3 s grad=2.994e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.273858e-3 s grad=2.468e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.802042e-3 s grad=2.416e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.234331e-3 s grad=2.291e-6 s +[acc-interp] eval 1.1461689e-2 s +[acc-interp] thetaMarsh 1.07e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.261832e-3 s grad=2.789e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.380268e-3 s grad=2.654e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.196861e-3 s grad=2.163e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.231575e-3 s grad=2.064e-6 s +[acc-interp] eval 1.0596793e-2 s +[acc-interp] thetaMarsh 1.25e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.968695e-3 s grad=4.603e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.653154e-3 s grad=2.539e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.32785e-3 s grad=3.162e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.140235e-3 s grad=3.044e-6 s +[acc-interp] eval 1.1589859e-2 s +[acc-interp] thetaMarsh 1.58e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.729931e-3 s grad=2.559e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.82375e-3 s grad=4.201e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.128036e-3 s grad=3.591e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.541278e-3 s grad=8.203e-6 s +[acc-interp] eval 1.2172131e-2 s +[acc-interp] thetaMarsh 7.0e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.366375e-3 s grad=8.594e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.325727e-3 s grad=1.154e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.230763e-3 s grad=1.167e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.867265e-3 s grad=6.789e-6 s +[acc-interp] eval 1.0762677e-2 s +[acc-interp] thetaMarsh 3.16e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.892472e-3 s grad=3.669e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.142749e-3 s grad=3.26e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.167936e-3 s grad=2.211e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.181169e-3 s grad=2.655e-6 s +[acc-interp] eval 1.1277555e-2 s +[acc-interp] thetaMarsh 1.93e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.273895e-3 s grad=3.051e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.813136e-3 s grad=4.845e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.302078e-3 s grad=2.75e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.215347e-3 s grad=3.063e-6 s +[acc-interp] eval 1.1184713e-2 s +[acc-interp] thetaMarsh 1.22e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.224661e-3 s grad=2.407e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.232772e-3 s grad=2.007e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.479295e-3 s grad=3.684e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.172834e-3 s grad=2.857e-6 s +[acc-interp] eval 1.0746973e-2 s +[acc-interp] thetaMarsh 1.01e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.206192e-3 s grad=3.062e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.104361e-3 s grad=2.56e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.102237e-3 s grad=2.485e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.121348e-3 s grad=2.107e-6 s +[acc-interp] eval 9.958424e-3 s +[acc-interp] thetaMarsh 1.17e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.292795e-3 s grad=2.275e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.160731e-3 s grad=2.027e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.147317e-3 s grad=1.898e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.091483e-3 s grad=1.858e-6 s +[acc-interp] eval 1.0090682e-2 s +[acc-interp] thetaMarsh 1.28e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.080619e-3 s grad=1.941e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.059163e-3 s grad=2.122e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.596366e-3 s grad=2.864e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.295004e-3 s grad=2.549e-6 s +[acc-interp] eval 1.0522538e-2 s +[acc-interp] thetaMarsh 1.08e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.192283e-3 s grad=1.959e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.443168e-3 s grad=2.345e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.162721e-3 s grad=1.952e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.102176e-3 s grad=1.708e-6 s +[acc-interp] eval 1.0320776e-2 s +[acc-interp] thetaMarsh 1.44e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.244979e-3 s grad=3.869e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.557223e-3 s grad=3.05e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.206379e-3 s grad=3.066e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.340045e-3 s grad=2.663e-6 s +[acc-interp] eval 1.0495386e-2 s +[acc-interp] thetaMarsh 6.1e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.195896e-3 s grad=2.492e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.063222e-3 s grad=2.326e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.132742e-3 s grad=2.095e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.345229e-3 s grad=4.203e-6 s +[acc-interp] eval 9.689533e-3 s +[acc-interp] thetaMarsh 1.27e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.577679e-3 s grad=4.657e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.740174e-3 s grad=2.319e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.347644e-3 s grad=2.395e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.09851e-3 s grad=2.767e-6 s +[acc-interp] eval 1.2607043e-2 s +[acc-interp] thetaMarsh 1.18e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.298354e-3 s grad=3.337e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.954412e-3 s grad=3.264e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.471267e-3 s grad=1.875e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.469417e-3 s grad=2.181e-6 s +[acc-interp] eval 1.1889567e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=2.087263e-3 s grad=3.534e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.021077e-3 s grad=2.213e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.895924e-3 s grad=2.714e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.661e-3 s grad=3.935e-6 s +[acc-interp] eval 0.35085293 s +[acc-interp] thetaMarsh 1.76e-7 s (n=[acc-interp] thetaMarsh 1.62e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=9.314421e-3 s grad=3.346e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.254288e-3 s grad=1.825e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.762069e-3 s grad=1.784e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.644093e-3 s grad=2.715e-6 s +[acc-interp] eval 1.8582382e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=2.359972e-3 s grad=1.877e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.312791e-3 s grad=1.36e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.380523e-3 s grad=1.229e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.400757e-3 s grad=1.846e-6 s +[acc-interp] eval 2.9797827e-2 s +[acc-interp] thetaMarsh 8.0e-8 s (n=[acc-interp] thetaMarsh 5.6e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.579786e-2 s grad=3.191e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.474532e-3 s grad=1.386e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.300665e-3 s grad=7.032e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.430891e-3 s grad=9.14e-7 s +[acc-interp] eval 3.3778377e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=2.419823e-3 s grad=3.943e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.724433e-3 s grad=3.121e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.856312e-3 s grad=4.21e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.31866e-3 s grad=3.771e-6 s +[acc-interp] eval 4.5129113e-2 s +[acc-interp] thetaMarsh 6.9e-8 s (n=[acc-interp] thetaMarsh 5.9e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.13805e-2 s grad=3.232e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.845025e-3 s grad=3.295e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.701276e-3 s grad=3.925e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.030128e-3 s grad=3.949e-6 s +[acc-interp] eval 3.1821815e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=3.235165e-3 s grad=2.346e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.760995e-3 s grad=2.861e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.175254e-3 s grad=2.247e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.758321e-3 s grad=1.526e-6 s +[acc-interp] eval 4.4653911e-2 s +[acc-interp] thetaMarsh 6.5e-8 s (n=[acc-interp] thetaMarsh 5.9e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.9909831e-2 s grad=3.913e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.144455e-3 s grad=4.521e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.800365e-3 s grad=3.917e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.92348e-3 s grad=4.319e-6 s +[acc-interp] eval 4.2688711e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=5.09546e-3 s grad=3.523e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.603885e-3 s grad=3.533e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.999466e-3 s grad=4.081e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.656642e-3 s grad=3.012e-6 s +[acc-interp] eval 6.3471374e-2 s +[acc-interp] thetaMarsh 2.05e-7 s (n=[acc-interp] thetaMarsh 1.1e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=1.6932991e-2 s grad=3.851e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.991795e-3 s grad=3.23e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.013435e-3 s grad=2.837e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.189607e-3 s grad=3.268e-6 s +[acc-interp] eval 2.7742417e-2 s +[acc-interp] thetaMarsh 1.13e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.987133e-3 s grad=4.961e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.247938e-3 s grad=3.693e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.798222e-3 s grad=3.391e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.957822e-3 s grad=3.412e-6 s +[acc-interp] eval 1.5551058e-2 s +[acc-interp] thetaMarsh 3.72e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.368701e-3 s grad=4.942e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.598483e-3 s grad=3.589e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.360736e-3 s grad=2.378e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.215545e-3 s grad=9.011e-6 s +[acc-interp] eval 1.4607976e-2 s +[acc-interp] thetaMarsh 6.9e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.941628e-3 s grad=4.301e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.699477e-3 s grad=3.787e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.745263e-3 s grad=1.0374e-5 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.11478e-3 s grad=1.627e-6 s +[acc-interp] eval 1.4276518e-2 s +[acc-interp] thetaMarsh 6.8e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.885875e-3 s grad=1.79e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.903457e-3 s grad=1.204e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.745569e-3 s grad=1.507e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.882467e-3 s grad=1.105e-6 s +[acc-interp] eval 1.2117876e-2 s +[acc-interp] thetaMarsh 1.57e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.769951e-3 s grad=1.87e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.922701e-3 s grad=3.164e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.911316e-3 s grad=1.887e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.066774e-3 s grad=3.643e-6 s +[acc-interp] eval 1.263757e-2 s +[acc-interp] thetaMarsh 2.01e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.412315e-3 s grad=4.24e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.166469e-3 s grad=5.407e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.080899e-3 s grad=3.555e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.433673e-3 s grad=3.52e-6 s +[acc-interp] eval 1.4785568e-2 s +[acc-interp] thetaMarsh 9.7e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.047352e-3 s grad=4.245e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.110232e-3 s grad=3.118e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.110955e-3 s grad=3.56e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.113898e-3 s grad=2.71e-6 s +[acc-interp] eval 1.5033095e-2 s +[acc-interp] thetaMarsh 1.08e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.124449e-3 s 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kernel=3.388383e-3 s grad=4.508e-6 s +[acc-interp] eval 1.3417982e-2 s +[acc-interp] thetaMarsh 3.32e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.245166e-3 s grad=3.323e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.033262e-3 s grad=3.2e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.714259e-3 s grad=2.397e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.065595e-3 s grad=3.327e-6 s +[acc-interp] eval 1.3749136e-2 s +[acc-interp] thetaMarsh 1.11e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.229479e-3 s grad=2.386e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.171799e-3 s grad=2.825e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.07797e-3 s grad=2.339e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.011133e-3 s grad=2.482e-6 s +[acc-interp] eval 1.4080893e-2 s +[acc-interp] thetaMarsh 1.21e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.988682e-3 s grad=4.119e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.370439e-3 s 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chunk base=77922 cs=25974 kernel=2.870087e-3 s grad=2.37e-6 s +[acc-interp] eval 1.3329621e-2 s +[acc-interp] thetaMarsh 7.7e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.676069e-3 s grad=3.906e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.925796e-3 s grad=3.448e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.221264e-3 s grad=4.59e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.240447e-3 s grad=3.418e-6 s +[acc-interp] eval 1.3915374e-2 s +[acc-interp] thetaMarsh 6.5e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.961544e-3 s grad=4.671e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.600487e-3 s grad=3.398e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.097123e-3 s grad=3.046e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.990769e-3 s grad=2.346e-6 s +[acc-interp] eval 1.5142727e-2 s +[acc-interp] thetaMarsh 1.0e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.160925e-3 s grad=3.865e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.395859e-3 s grad=3.891e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.149699e-3 s grad=4.182e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.527959e-3 s grad=3.803e-6 s +[acc-interp] eval 1.6824403e-2 s +[acc-interp] thetaMarsh 1.02e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.220585e-3 s grad=5.109e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.290412e-3 s grad=3.3944e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.290281e-3 s grad=3.479e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.345612e-3 s grad=1.073e-5 s +[acc-interp] eval 1.4326084e-2 s +4) +[acc-interp] chunk base=0 cs=25974 kernel=3.198554e-3 s grad=3.094e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.1078884e-2 s grad=4.495e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.725716e-3 s grad=3.496e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.822771e-3 s grad=1.2496e-5 s +[acc-interp] eval 0.518652591 s +[acc-interp] thetaMarsh 7.4e-8 s (n=[acc-interp] thetaMarsh 6.1e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.1264884e-2 s grad=4.544e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.956596e-3 s grad=3.769e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.660613e-3 s grad=9.651e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=7.960158e-3 s grad=4.929e-6 s +[acc-interp] eval 3.8075531e-2 s +[acc-interp] thetaMarsh 1.15e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.159688e-3 s grad=3.166e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.060404e-3 s grad=2.612e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.971575e-3 s grad=3.502e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.112743e-3 s grad=3.771e-6 s +[acc-interp] eval 1.4033107e-2 s +[acc-interp] thetaMarsh 1.24e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.283188e-3 s grad=2.594e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.287805e-3 s grad=2.988e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.24769e-3 s grad=3.973e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.443389e-3 s grad=3.633e-6 s +[acc-interp] eval 1.490473e-2 s +[acc-interp] thetaMarsh 1.32e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.020727e-3 s grad=3.863e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.327963e-3 s grad=1.3177e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.577451e-3 s grad=1.776e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.448813e-3 s grad=2.69e-6 s +[acc-interp] eval 1.5993771e-2 s +[acc-interp] thetaMarsh 9.7e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.898441e-3 s grad=4.424e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.432058e-3 s grad=3.137e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.873121e-3 s grad=4.111e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.109873e-3 s grad=3.493e-6 s +[acc-interp] eval 1.5768213e-2 s +[acc-interp] thetaMarsh 1.13e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.488127e-3 s grad=7.014e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.674367e-3 s grad=5.684e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.2062858e-2 s grad=5.516e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.620861e-3 s grad=9.255e-6 s +[acc-interp] eval 2.4945248e-2 s +[acc-interp] thetaMarsh 6.2e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.495264e-3 s grad=2.298e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.268102e-3 s grad=1.172e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.057596e-3 s grad=4.644e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.035847e-3 s grad=3.974e-6 s +[acc-interp] eval 1.6032258e-2 s +[acc-interp] thetaMarsh 1.02e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.583884e-3 s grad=3.042e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.002078e-3 s grad=3.486e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.740795e-3 s grad=4.798e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.151299e-3 s grad=4.376e-6 s +[acc-interp] eval 1.7625756e-2 s +[acc-interp] thetaMarsh 6.9e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.514157e-3 s grad=1.748e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.180174e-3 s grad=1.349e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.672284e-3 s grad=4.266e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.865827e-3 s grad=1.4896e-5 s +[acc-interp] eval 1.5100952e-2 s +[acc-interp] thetaMarsh 4.11e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.947019e-3 s grad=2.983e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.08167e-2 s grad=4.22e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.854788e-3 s grad=3.423e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.193188e-3 s grad=3.319e-6 s +[acc-interp] eval 2.2690506e-2 s +[acc-interp] thetaMarsh 1.69e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.007688e-3 s grad=3.055e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.794065e-3 s grad=4.9e-6 s 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chunk base=0 cs=25974 kernel=7.955526e-3 s grad=3.795e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.195495e-3 s grad=3.852e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.121542e-3 s grad=3.906e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.547583e-3 s grad=5.329e-6 s +[acc-interp] eval 1.8814581e-2 s +[acc-interp] thetaMarsh 1.07e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.86493e-3 s grad=3.795e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.447811e-3 s grad=3.712e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.181566e-3 s grad=3.044e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.587527e-3 s grad=3.373e-6 s +[acc-interp] eval 1.0998839e-2 s +[acc-interp] thetaMarsh 2.03e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.91484e-3 s grad=3.91e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.701819e-3 s grad=3.655e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.831784e-3 s grad=3.355e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.848968e-3 s grad=3.67e-6 s +[acc-interp] eval 1.4179726e-2 s +[acc-interp] thetaMarsh 2.57e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.157265e-3 s grad=3.25e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.026208e-3 s grad=4.936e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=8.480981e-3 s grad=3.691e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.022133e-3 s grad=3.125e-6 s +[acc-interp] eval 1.8668938e-2 s +[acc-interp] thetaMarsh 5.9e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.674731e-3 s grad=4.861e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.270228e-3 s grad=5.239e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.70343e-3 s grad=4.625e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.453199e-3 s grad=4.309e-6 s +[acc-interp] eval 1.5037014e-2 s +[acc-interp] thetaMarsh 7.8e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.187811e-3 s grad=4.685e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.862986e-3 s grad=5.574e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.054025e-3 s grad=8.884e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.048363e-3 s grad=6.777e-6 s +[acc-interp] eval 1.4092991e-2 s +[acc-interp] thetaMarsh 6.6e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.440688e-3 s grad=7.987e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.748636e-3 s grad=4.117e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.768067e-3 s grad=4.355e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.690036e-3 s grad=3.57e-6 s +[acc-interp] eval 1.7062027e-2 s +4) +[acc-interp] chunk base=0 cs=25974 kernel=3.899472e-3 s grad=2.895e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.1493991e-2 s grad=3.894e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.605529e-3 s grad=3.934e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.733087e-3 s grad=5.252e-6 s +[acc-interp] eval 0.367940722 s +[acc-interp] thetaMarsh 1.34e-7 s (n=[acc-interp] thetaMarsh 1.25e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.0985189e-2 s grad=2.964e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.635505e-3 s grad=2.446e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.469892e-3 s grad=2.747e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.818906e-3 s grad=1.84e-6 s +[acc-interp] eval 1.9929056e-2 s +[acc-interp] thetaMarsh 6.5e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.490511e-3 s grad=2.512e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.412137e-3 s grad=2.44e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.453138e-3 s grad=2.333e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.167818e-3 s grad=3.5e-6 s +[acc-interp] eval 1.1737469e-2 s +[acc-interp] thetaMarsh 9.0e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.766024e-3 s grad=3.531e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=8.394372e-3 s grad=4.687e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.386141e-3 s grad=3.95e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.759345e-3 s grad=3.097e-6 s +[acc-interp] eval 1.7464037e-2 s +[acc-interp] thetaMarsh 6.4e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.315642e-3 s grad=3.524e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.224376e-3 s grad=2.39e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.619864e-3 s grad=1.975e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.029598e-3 s grad=2.633e-6 s +[acc-interp] eval 1.1019155e-2 s +[acc-interp] thetaMarsh 1.36e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.603692e-3 s grad=3.799e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.630165e-3 s grad=3.615e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.248123e-3 s grad=2.264e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.129414e-3 s grad=2.353e-6 s +[acc-interp] eval 1.0468302e-2 s +[acc-interp] thetaMarsh 3.3e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.097267e-3 s grad=2.352e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.158768e-3 s grad=2.153e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.444353e-3 s grad=4.6e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.550551e-3 s grad=3.249e-6 s +[acc-interp] eval 1.0440674e-2 s +[acc-interp] thetaMarsh 1.17e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.357989e-3 s grad=2.202e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.252361e-3 s grad=1.849e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.153844e-3 s grad=1.655e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.255923e-3 s grad=1.498e-6 s +[acc-interp] eval 1.0455946e-2 s +[acc-interp] thetaMarsh 1.14e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.680737e-3 s grad=3.347e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.624347e-3 s grad=3.176e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.005073e-3 s grad=2.273e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.249808e-3 s grad=1.969e-6 s +[acc-interp] eval 1.1282985e-2 s +[acc-interp] thetaMarsh 6.5e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.182282e-3 s grad=3.339e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.221322e-3 s grad=1.462e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.204231e-3 s grad=1.24e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.034337e-3 s grad=9.56e-7 s +[acc-interp] eval 9.414251e-3 s +[acc-interp] thetaMarsh 6.4e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.113973e-3 s grad=6.875e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.147743e-3 s grad=1.262e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.176849e-3 s grad=8.07e-7 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.138401e-3 s grad=2.282e-6 s +[acc-interp] eval 9.407298e-3 s +[acc-interp] thetaMarsh 8.0e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.333921e-3 s grad=3.914e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.951453e-3 s grad=3.616e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.567605e-3 s grad=3.487e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.514701e-3 s grad=2.743e-6 s +[acc-interp] eval 1.2038665e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=2.634829e-3 s grad=2.71e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.106967e-3 s grad=1.898e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.447625e-3 s grad=3.075e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.655844e-3 s grad=2.092e-6 s +[acc-interp] eval 0.147238169 s +[acc-interp] thetaMarsh 8.6e-8 s (n=[acc-interp] thetaMarsh 6.6e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.5039211e-2 s grad=3.88e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.209701e-3 s grad=3.389e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.765295e-3 s grad=8.979e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.412425e-3 s grad=1.645e-6 s +[acc-interp] eval 2.3314758e-2 s +[acc-interp] thetaMarsh 6.3e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=5.165098e-3 s grad=4.096e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.115773e-3 s grad=3.82e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.361904e-3 s grad=2.619e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=8.498901e-3 s grad=3.857e-6 s +[acc-interp] eval 2.1835343e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=4.477638e-3 s grad=3.164e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.176662e-3 s grad=2.536e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.91277e-3 s grad=2.26e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.761646e-3 s grad=2.088e-6 s +[acc-interp] eval 6.306496e-2 s +[acc-interp] thetaMarsh 9.5e-8 s (n=[acc-interp] thetaMarsh 7.2e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.8675121e-2 s grad=4.227e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.875973e-3 s grad=2.577e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.64146e-3 s grad=2.392e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.110865e-3 s grad=1.456e-6 s +[acc-interp] eval 2.78103e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=3.234986e-3 s grad=2.936e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.978506e-3 s grad=2.161e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=7.343789e-3 s grad=1.966e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.213959e-3 s grad=1.376e-6 s +[acc-interp] eval 4.5332736e-2 s +[acc-interp] thetaMarsh 1.42e-7 s (n=[acc-interp] thetaMarsh 1.29e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=6.425535e-3 s grad=3.643e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.404979e-3 s grad=1.895e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.759414e-3 s grad=1.578e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.431669e-3 s grad=2.317e-6 s +[acc-interp] eval 1.5501653e-2 s +[acc-interp] thetaMarsh 1.23e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=5.303644e-3 s grad=2.973e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.158226e-3 s grad=2.545e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.284832e-3 s grad=3.638e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.37074e-3 s grad=2.278e-6 s +[acc-interp] eval 2.0690939e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=1.3687316e-2 s grad=3.039e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.128164e-3 s grad=2.474e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.816373e-3 s grad=3.38e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.760977e-3 s grad=2.757e-6 s +[acc-interp] eval 6.5545034e-2 s +[acc-interp] thetaMarsh 1.35e-7 s (n=[acc-interp] thetaMarsh 1.11e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.2554444e-2 s grad=3.831e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.668688e-3 s grad=2.993e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.580756e-3 s grad=2.441e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.624963e-3 s grad=2.744e-6 s +[acc-interp] eval 2.2116128e-2 s +[acc-interp] thetaMarsh 9.6e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.583636e-3 s grad=3.691e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.966039e-3 s grad=2.878e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.575724e-3 s grad=2.034e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.093571e-3 s grad=2.256e-6 s +[acc-interp] eval 1.2897788e-2 s +[acc-interp] thetaMarsh 1.12e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=9.129415e-3 s grad=3.222e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.518577e-3 s grad=2.479e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.204685e-3 s grad=1.1e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.192189e-3 s grad=1.01e-6 s +[acc-interp] eval 1.700443e-2 s +[acc-interp] thetaMarsh 6.5e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.222457e-3 s grad=2.808e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.322913e-3 s grad=2.384e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.528049e-3 s grad=3.715e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.865886e-3 s grad=2.909e-6 s +[acc-interp] eval 1.1430927e-2 s +[acc-interp] thetaMarsh 1.16e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.219574e-3 s grad=1.0668e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.163755e-3 s grad=1.852e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.025533e-3 s grad=1.426e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.374128e-3 s grad=3.248e-6 s +[acc-interp] eval 1.0189993e-2 s +[acc-interp] thetaMarsh 7.3e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.698997e-3 s grad=3.923e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.212452e-3 s grad=4.436e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.623762e-3 s grad=3.315e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.932077e-3 s grad=3.976e-6 s +[acc-interp] eval 1.1763805e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=2.348155e-3 s grad=2.536e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.44104e-3 s grad=3.457e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.969682e-3 s grad=4.193e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.377635e-3 s grad=3.188e-6 s +[acc-interp] eval 9.7430662e-2 s +[acc-interp] thetaMarsh 7.8e-8 s (n=[acc-interp] thetaMarsh 6.2e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=1.8429482e-2 s grad=3.661e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.691622e-3 s grad=5.296e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.154809e-3 s grad=3.163e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=7.011072e-3 s grad=3.651e-6 s +[acc-interp] eval 3.3192942e-2 s +[acc-interp] thetaMarsh 6.6e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.873553e-3 s grad=3.507e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.874495e-3 s grad=3.428e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.573303e-3 s grad=3.942e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.979126e-3 s grad=4.404e-6 s +[acc-interp] eval 1.3317973e-2 s +[acc-interp] thetaMarsh 1.2e-7 s 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+[acc-interp] chunk base=77922 cs=25974 kernel=3.955518e-3 s grad=4.069e-6 s +[acc-interp] eval 1.8607262e-2 s +[acc-interp] thetaMarsh 9.6e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.505387e-3 s grad=3.242e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.138318e-3 s grad=5.195e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.75874e-3 s grad=4.642e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.721144e-3 s grad=3.796e-6 s +[acc-interp] eval 1.8415371e-2 s +[acc-interp] thetaMarsh 6.8e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.917779e-3 s grad=2.491e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.22236e-3 s grad=4.316e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.030068e-3 s grad=1.493e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.338729e-3 s grad=8.627e-6 s +[acc-interp] eval 1.7325217e-2 s +[acc-interp] thetaMarsh 7.4e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.06369e-3 s grad=2.868e-6 s +[acc-interp] chunk 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chunk base=77922 cs=25974 kernel=3.000415e-3 s grad=3.613e-6 s +[acc-interp] eval 0.239413673 s +[acc-interp] thetaMarsh 1.11e-7 s (n=[acc-interp] thetaMarsh 9.4e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.4067205e-2 s grad=3.149e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.347738e-3 s grad=4.575e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.214654e-3 s grad=3.408e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.211627e-3 s grad=3.445e-6 s +[acc-interp] eval 3.4854597e-2 s +[acc-interp] thetaMarsh 6.9e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.758118e-3 s grad=2.712e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.558351e-3 s grad=9.111e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.44197e-3 s grad=7.24e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.073844e-3 s grad=7.699e-6 s +[acc-interp] eval 1.4602343e-2 s +[acc-interp] thetaMarsh 7.8e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.052957e-3 s grad=5.317e-6 s 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base=51948 cs=25974 kernel=3.125006e-3 s grad=2.355e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.057455e-3 s grad=1.0046e-5 s +[acc-interp] eval 1.337108e-2 s +[acc-interp] thetaMarsh 1.7e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.442824e-3 s grad=4.511e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.75007e-3 s grad=4.035e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.307728e-3 s grad=1.735e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.00026e-3 s grad=7.754e-6 s +[acc-interp] eval 1.4408032e-2 s +[acc-interp] thetaMarsh 1.59e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.204233e-3 s grad=9.672e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.311274e-3 s grad=5.724e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.482774e-3 s grad=3.529e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.270393e-3 s grad=7.473e-6 s +[acc-interp] eval 1.5156898e-2 s +[acc-interp] thetaMarsh 7.0e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.599907e-3 s grad=1.0749e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.307437e-3 s grad=5.17e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.928266e-3 s grad=5.255e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.406978e-3 s grad=9.311e-6 s +[acc-interp] eval 1.5182536e-2 s +[acc-interp] thetaMarsh 8.0e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.811439e-3 s grad=1.0012e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.201559e-3 s grad=3.407e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.96922e-3 s grad=4.439e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.188719e-3 s grad=1.99e-6 s +[acc-interp] eval 1.5068837e-2 s +[acc-interp] thetaMarsh 6.9e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=5.801366e-3 s grad=7.782e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.691931e-3 s grad=1.2807e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.977863e-3 s grad=6.902e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.402083e-3 s grad=8.177e-6 s +[acc-interp] eval 2.3759685e-2 s +[acc-interp] thetaMarsh 1.73e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.696307e-3 s grad=4.754e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.854094e-3 s grad=3.934e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.52673e-3 s grad=3.892e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.085758e-3 s grad=3.683e-6 s +[acc-interp] eval 1.5748871e-2 s +[acc-interp] thetaMarsh 1.29e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.325605e-3 s grad=4.242e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.587843e-3 s grad=4.049e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.386822e-3 s grad=3.41e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.965473e-3 s grad=3.19e-6 s +[acc-interp] eval 1.4775319e-2 s +[acc-interp] thetaMarsh 1.27e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.156203e-3 s grad=2.772e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.809752e-3 s 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(n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.279013e-3 s grad=4.546e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.852705e-3 s grad=4.848e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.465442e-3 s grad=2.706e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.13433e-3 s grad=2.26e-6 s +[acc-interp] eval 1.4606627e-2 s +[acc-interp] thetaMarsh 7.5e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.097932e-3 s grad=1.0275e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.164998e-3 s grad=4.619e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.197688e-3 s grad=5.164e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.620347e-3 s grad=5.269e-6 s +[acc-interp] eval 1.6525013e-2 s +[acc-interp] thetaMarsh 1.41e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.972551e-3 s grad=4.264e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.876786e-3 s grad=5.324e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.785484e-3 s grad=4.266e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.421018e-3 s grad=2.906e-6 s +[acc-interp] eval 1.6674916e-2 s +[acc-interp] thetaMarsh 9.9e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.271294e-3 s grad=2.278e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.972449e-3 s grad=5.265e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.527125e-3 s grad=4.236e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.53816e-3 s grad=4.261e-6 s +[acc-interp] eval 1.570335e-2 s +[acc-interp] thetaMarsh 6.8e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.497371e-3 s grad=2.477e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.900416e-3 s grad=1.805e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.300478e-3 s grad=2.043e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.150817e-3 s grad=1.468e-6 s +[acc-interp] eval 1.3605326e-2 s +[acc-interp] thetaMarsh 6.8e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.168594e-3 s grad=1.0371e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.433557e-3 s grad=2.776e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.312733e-3 s grad=8.588e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.399201e-3 s grad=4.687e-6 s +[acc-interp] eval 1.4198267e-2 s +[acc-interp] thetaMarsh 1.42e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.865959e-3 s grad=5.444e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.406486e-3 s grad=8.064e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.299014e-3 s grad=7.473e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.085048e-3 s grad=3.324e-6 s +[acc-interp] eval 1.4670399e-2 s +[acc-interp] thetaMarsh 1.21e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.56458e-3 s grad=3.278e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.508364e-3 s grad=4.321e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.312893e-3 s grad=3.553e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.119e-3 s grad=2.99e-6 s +[acc-interp] eval 1.5091689e-2 s +[acc-interp] thetaMarsh 1.12e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.402832e-3 s grad=5.599e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.87607e-3 s grad=4.52e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.429468e-3 s grad=4.101e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.261193e-3 s grad=3.802e-6 s +[acc-interp] eval 1.7799702e-2 s +[acc-interp] thetaMarsh 1.07e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.591144e-3 s grad=3.666e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.560288e-3 s grad=3.688e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.430406e-3 s grad=4.549e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.189809e-3 s grad=2.947e-6 s +[acc-interp] eval 1.6461655e-2 s +[acc-interp] thetaMarsh 1.02e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.772213e-3 s grad=4.893e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.515283e-3 s grad=4.441e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.217276e-3 s grad=3.615e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=8.270602e-3 s grad=4.095e-6 s +[acc-interp] eval 2.0514742e-2 s +[acc-interp] thetaMarsh 1.44e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=5.00594e-3 s grad=3.966e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.234637e-3 s grad=6.659e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.228726e-3 s grad=2.693e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.4411e-3 s grad=5.338e-6 s +[acc-interp] eval 1.6213275e-2 s +4) +[acc-interp] chunk base=0 cs=25974 kernel=4.087554e-3 s grad=3.566e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.209458e-3 s grad=1.3323e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.352823e-3 s grad=1.23e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.201418e-3 s grad=2.992e-6 s +[acc-interp] eval 0.522227411 s +[acc-interp] thetaMarsh 2.46e-7 s (n=[acc-interp] thetaMarsh 2.99e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.6754036e-2 s grad=3.336e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.592613e-3 s grad=8.724e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.052368e-3 s grad=6.458e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.634562e-3 s grad=6.146e-6 s +[acc-interp] eval 2.5965632e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=7.162078e-3 s grad=1.56e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.16553e-3 s grad=2.876e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.496814e-3 s grad=1.878e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.718217e-3 s grad=1.346e-6 s +[acc-interp] eval 4.257662e-2 s +[acc-interp] thetaMarsh 5.9e-8 s (n=[acc-interp] thetaMarsh 5.6e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=7.104358e-3 s grad=3.538e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.876219e-3 s grad=5.591e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.471318e-3 s grad=2.573e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.272442e-3 s grad=1.909e-6 s +[acc-interp] eval 1.7283467e-2 s +[acc-interp] thetaMarsh 9.6e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.166572e-3 s grad=2.5e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.290174e-3 s grad=2.097e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.95349e-3 s grad=5.436e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.003265e-3 s grad=4.118e-6 s +[acc-interp] eval 1.1975316e-2 s +[acc-interp] thetaMarsh 1.12e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.979904e-3 s grad=3.157e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.635463e-3 s grad=3.233e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.909918e-3 s grad=2.096e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.503642e-3 s grad=5.185e-6 s +[acc-interp] eval 1.386301e-2 s +[acc-interp] thetaMarsh 2.03e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=8.561609e-3 s grad=3.369e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.280268e-3 s grad=2.919e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.775746e-3 s grad=4.447e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.521367e-3 s grad=2.736e-6 s +[acc-interp] eval 1.7889549e-2 s +[acc-interp] thetaMarsh 1.2e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.364894e-3 s grad=1.0859e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.386819e-3 s grad=1.619e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.560359e-3 s grad=2.177e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.362353e-3 s grad=2.172e-6 s +[acc-interp] eval 1.1138917e-2 s +[acc-interp] thetaMarsh 1.33e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.222057e-3 s grad=1.857e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.041863e-3 s grad=1.464e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.054443e-3 s grad=2.305e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.053667e-3 s grad=2.433e-6 s +[acc-interp] eval 9.681555e-3 s +[acc-interp] thetaMarsh 7.0e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.039892e-3 s grad=6.005e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.004169e-3 s grad=7.5e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.685082e-3 s grad=3.621e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.362229e-3 s grad=4.561e-6 s +[acc-interp] eval 9.911267e-3 s +[acc-interp] thetaMarsh 6.7e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.158214e-3 s grad=3.137e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.055985e-3 s grad=3.684e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.099416e-3 s grad=3.093e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.929016e-3 s grad=2.183e-6 s +[acc-interp] eval 9.027729e-3 s +[acc-interp] thetaMarsh 1.84e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.085611e-3 s grad=3.3e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.293498e-3 s grad=4.408e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.448079e-3 s grad=3.233e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.181497e-3 s grad=2.889e-6 s +[acc-interp] eval 1.0876119e-2 s +[acc-interp] thetaMarsh 9.6e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.952858e-3 s grad=2.613e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.947098e-3 s grad=2.949e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.712021e-3 s grad=2.016e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.781871e-3 s grad=2.094e-6 s +[acc-interp] eval 8.107543e-3 s +[acc-interp] thetaMarsh 1.66e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.755998e-3 s grad=3.722e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.982013e-3 s grad=3.901e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.718493e-3 s grad=2.975e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.673637e-3 s grad=3.809e-6 s +[acc-interp] eval 9.936828e-3 s +[acc-interp] thetaMarsh 5.7e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.220644e-3 s grad=3.245e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.811276e-3 s grad=4.083e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.661002e-3 s grad=2.497e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.457362e-3 s grad=4.538e-6 s +[acc-interp] eval 8.972585e-3 s +[acc-interp] thetaMarsh 7.9e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.32014e-3 s grad=3.045e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.102881e-3 s grad=8.512e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.040775e-3 s grad=1.541e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.046671e-3 s grad=2.634e-6 s +[acc-interp] eval 9.356611e-3 s +[acc-interp] thetaMarsh 1.69e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.092175e-3 s grad=3.086e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.249253e-3 s grad=1.0269e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.389109e-3 s grad=3.799e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.522853e-3 s grad=3.388e-6 s +[acc-interp] eval 1.0510797e-2 s +[acc-interp] thetaMarsh 1.17e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.449265e-3 s grad=3.258e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.332787e-3 s grad=2.452e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.29929e-3 s grad=3.106e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.223434e-3 s grad=2.404e-6 s +[acc-interp] eval 1.0970073e-2 s +[acc-interp] thetaMarsh 1.83e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.282706e-3 s grad=4.533e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.013346e-3 s grad=4.587e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.471922e-3 s grad=3.6e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.845127e-3 s grad=2.769e-6 s +[acc-interp] eval 1.3712468e-2 s +[acc-interp] thetaMarsh 1.11e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.533257e-3 s grad=2.665e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.83353e-3 s grad=3.315e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.79741e-3 s grad=3.498e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.787744e-3 s grad=4.234e-6 s +[acc-interp] eval 1.258172e-2 s +[acc-interp] thetaMarsh 1.45e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.683259e-3 s grad=3.105e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.514455e-3 s grad=2.847e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.402473e-3 s grad=3.217e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.862691e-3 s grad=3.06e-6 s +[acc-interp] eval 1.2102194e-2 s +[acc-interp] thetaMarsh 1.2e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.207834e-3 s grad=3.149e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.284727e-3 s grad=9.714e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.349103e-3 s grad=6.152e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.125921e-3 s grad=5.511e-6 s +[acc-interp] eval 9.871586e-3 s +[acc-interp] thetaMarsh 9.8e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.209224e-3 s grad=1.389e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.137192e-3 s grad=6.376e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.114116e-3 s grad=2.269e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.091808e-3 s grad=1.619e-6 s +[acc-interp] eval 9.329453e-3 s +[acc-interp] thetaMarsh 8.8e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.331639e-3 s grad=2.029e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.426944e-3 s grad=7.646e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.237599e-3 s grad=9.838e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.23118e-3 s grad=4.164e-6 s +[acc-interp] eval 1.0026849e-2 s +[acc-interp] thetaMarsh 1.22e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.100269e-3 s grad=1.0144e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.833209e-3 s grad=4.742e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.334087e-3 s grad=3.018e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.178612e-3 s grad=2.691e-6 s +[acc-interp] eval 1.0706142e-2 s +[acc-interp] thetaMarsh 1.01e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.145628e-3 s grad=2.448e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.586752e-3 s grad=3.519e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.439229e-3 s grad=4.172e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.413679e-3 s grad=4.566e-6 s +[acc-interp] eval 1.0988147e-2 s +[acc-interp] thetaMarsh 6.0e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.574161e-3 s grad=3.387e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.994068e-3 s grad=3.518e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.215546e-3 s grad=3.72e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.44388e-3 s grad=2.039e-6 s +[acc-interp] eval 9.065431e-3 s +[acc-interp] thetaMarsh 5.8e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.264197e-3 s grad=2.79e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.14044e-3 s grad=1.633e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.02039e-3 s grad=5.347e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.914589e-3 s grad=4.99e-6 s +[acc-interp] eval 1.171841e-2 s +[acc-interp] thetaMarsh 6.5e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.28892e-3 s grad=5.312e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.433353e-3 s grad=3.139e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.428775e-3 s grad=7.229e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.544722e-3 s grad=4.433e-6 s +[acc-interp] eval 1.0737756e-2 s +[acc-interp] thetaMarsh 9.7e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.805342e-3 s grad=3.805e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.351845e-3 s grad=5.085e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.675521e-3 s grad=3.289e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.712645e-3 s grad=3.555e-6 s +[acc-interp] eval 1.1423449e-2 s +[acc-interp] thetaMarsh 6.7e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.424105e-3 s grad=1.91e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.064412e-3 s grad=4.994e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.692899e-3 s grad=4.447e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.497895e-3 s grad=4.261e-6 s +[acc-interp] eval 1.1658053e-2 s +[acc-interp] thetaMarsh 7.1e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.416158e-3 s grad=4.113e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.343679e-3 s grad=4.723e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.215371e-3 s grad=3.1e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.156385e-3 s grad=2.706e-6 s +[acc-interp] eval 9.979899e-3 s +[acc-interp] thetaMarsh 1.83e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.465405e-3 s grad=2.551e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.13755e-3 s grad=1.511e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.109918e-3 s grad=9.81e-7 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.064591e-3 s grad=1.048e-6 s +[acc-interp] eval 9.539862e-3 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=2.038467e-3 s grad=1.69e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.004072e-3 s grad=1.849e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.08982e-3 s grad=1.966e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.132798e-3 s grad=2.912e-6 s +[acc-interp] eval 0.345238206 s +[acc-interp] thetaMarsh 2.5e-7 s (n=[acc-interp] thetaMarsh 9.7e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.564754e-2 s grad=3.43e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.380067e-3 s grad=3.238e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.659656e-3 s grad=3.764e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.869043e-3 s grad=4.151e-6 s +[acc-interp] eval 2.6448775e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=7.516251e-3 s grad=3.877e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.592142e-3 s grad=8.263e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.199378e-3 s grad=1.245e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.775984e-3 s grad=1.359e-6 s +[acc-interp] eval 4.4600425e-2 s +[acc-interp] thetaMarsh 8.1e-8 s (n=[acc-interp] thetaMarsh 6.9e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.4318168e-2 s grad=3.461e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.807543e-3 s grad=2.473e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.840923e-3 s grad=1.643e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.94172e-3 s grad=1.293e-6 s +[acc-interp] eval 3.6713612e-2 s +[acc-interp] thetaMarsh 1.59e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.4493535e-2 s grad=3.267e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.11302e-3 s grad=1.732e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.695407e-3 s grad=4.906e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.595969e-3 s grad=4.389e-6 s +[acc-interp] eval 2.5806874e-2 s +[acc-interp] thetaMarsh 2.28e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.337612e-3 s grad=3.715e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.969936e-3 s grad=3.401e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.56591e-3 s grad=4.831e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.636873e-3 s grad=3.51e-6 s +[acc-interp] eval 1.4504903e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=3.2489e-3 s grad=7.704e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.980582e-3 s grad=4.312e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.301484e-3 s grad=3.951e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.473063e-3 s grad=1.93e-6 s +[acc-interp] eval 9.3092957e-2 s +[acc-interp] thetaMarsh 7.4e-8 s (n=[acc-interp] thetaMarsh 8.1e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.9722541e-2 s grad=4.272e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.872804e-3 s grad=5.161e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.06958e-3 s grad=5.801e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.667262e-3 s grad=8.054e-6 s +[acc-interp] eval 4.2254782e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=3.647051e-3 s grad=3.0e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.508866e-3 s grad=4.391e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.316899e-3 s grad=3.271e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.691747e-3 s grad=7.532e-6 s +[acc-interp] eval 5.844557e-2 s +[acc-interp] thetaMarsh 6.3e-8 s (n=[acc-interp] thetaMarsh 6.0e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.7244972e-2 s grad=4.542e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.550163e-3 s grad=3.696e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.211796e-3 s grad=4.07e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.443222e-3 s grad=7.648e-6 s +[acc-interp] eval 3.1359809e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=1.040666e-2 s grad=6.365e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.300689e-3 s grad=2.35e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.685802e-3 s grad=3.031e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.163315e-3 s grad=2.578e-6 s +[acc-interp] eval 5.1847757e-2 s +[acc-interp] thetaMarsh 6.9e-8 s (n=[acc-interp] thetaMarsh 6.5e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=5.875454e-3 s grad=3.716e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.517906e-3 s grad=2.605e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.397768e-3 s grad=3.09e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.876756e-3 s grad=3.588e-6 s +[acc-interp] eval 1.6606498e-2 s +[acc-interp] thetaMarsh 1.99e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=5.808345e-3 s grad=3.778e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.92442e-3 s grad=4.065e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.759285e-3 s grad=3.266e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.794517e-3 s grad=3.015e-6 s +[acc-interp] eval 2.1800341e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=5.468593e-3 s grad=4.097e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.1352328e-2 s grad=3.689e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.474686e-3 s grad=3.4e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.988967e-3 s grad=1.96e-6 s +[acc-interp] eval 6.5565384e-2 s +[acc-interp] thetaMarsh 1.3e-7 s (n=[acc-interp] thetaMarsh 7.7e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=1.5866994e-2 s grad=4.453e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.620896e-3 s grad=3.575e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.837033e-3 s grad=4.768e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.0832778e-2 s grad=3.943e-6 s +[acc-interp] eval 3.5921634e-2 s +[acc-interp] thetaMarsh 1.21e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.536716e-3 s grad=4.69e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.378026e-3 s grad=3.595e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.227091e-3 s grad=3.922e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.484022e-3 s grad=3.648e-6 s +[acc-interp] eval 1.7326512e-2 s +[acc-interp] thetaMarsh 9.9e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.081441e-3 s grad=4.094e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.583757e-3 s grad=4.303e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.67171e-3 s grad=3.774e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.395311e-3 s grad=3.043e-6 s +[acc-interp] eval 1.6334706e-2 s +[acc-interp] thetaMarsh 1.13e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.121498e-3 s grad=2.071e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.446745e-3 s grad=3.451e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.922605e-3 s grad=3.086e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.17697e-3 s grad=2.588e-6 s +[acc-interp] eval 1.4127017e-2 s +[acc-interp] thetaMarsh 3.12e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.338478e-3 s grad=2.756e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.579575e-3 s grad=2.657e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.355934e-3 s grad=4.258e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.600396e-3 s grad=2.539e-6 s +[acc-interp] eval 1.639437e-2 s +[acc-interp] thetaMarsh 1.32e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.29842e-3 s grad=3.544e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.503684e-3 s grad=3.832e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.148364e-3 s grad=4.804e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.295545e-3 s grad=4.128e-6 s +[acc-interp] eval 1.5212047e-2 s +[acc-interp] thetaMarsh 6.4e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.405131e-3 s grad=4.244e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.332309e-3 s grad=3.734e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.350985e-3 s grad=5.105e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.394615e-3 s grad=4.088e-6 s +[acc-interp] eval 1.536432e-2 s +[acc-interp] thetaMarsh 6.8e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.979131e-3 s grad=4.34e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.680619e-3 s grad=1.0871e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.068032e-3 s grad=4.406e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.082263e-3 s grad=4.04e-6 s +[acc-interp] eval 1.7101317e-2 s +[acc-interp] thetaMarsh 1.1e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.065288e-3 s grad=4.447e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.961688e-3 s grad=2.978e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.798578e-3 s grad=4.468e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.403247e-3 s grad=1.0837e-5 s +[acc-interp] eval 1.8431867e-2 s +[acc-interp] thetaMarsh 2.69e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.073903e-3 s grad=6.8e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.667089e-3 s grad=2.713e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.983793e-3 s grad=2.813e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.960027e-3 s grad=3.057e-6 s +[acc-interp] eval 1.6475251e-2 s +[acc-interp] thetaMarsh 8.2e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.889376e-3 s grad=3.227e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.2266412e-2 s grad=5.075e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.234174e-3 s grad=3.898e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.565233e-3 s grad=1.738e-6 s +[acc-interp] eval 2.3814326e-2 s +[acc-interp] thetaMarsh 1.76e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.778076e-3 s grad=3.759e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.898058e-3 s grad=3.331e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.764353e-3 s grad=3.255e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.423356e-3 s grad=2.782e-6 s +[acc-interp] eval 1.6369255e-2 s +[acc-interp] thetaMarsh 2.27e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.008132e-3 s grad=4.791e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.673991e-3 s grad=4.149e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.311166e-3 s grad=5.414e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.520589e-3 s grad=3.69e-6 s +[acc-interp] eval 1.6509412e-2 s +[acc-interp] thetaMarsh 6.9e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.844297e-3 s grad=5.163e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.001488e-3 s grad=1.1803e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.629407e-3 s grad=9.676e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.732906e-3 s grad=5.834e-6 s +[acc-interp] eval 1.6127373e-2 s +[acc-interp] thetaMarsh 3.14e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.66612e-3 s grad=3.912e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.981549e-3 s grad=4.599e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.693892e-3 s grad=2.037e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.761158e-3 s grad=4.111e-6 s +[acc-interp] eval 1.6023586e-2 s +[acc-interp] thetaMarsh 7.6e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.564424e-3 s grad=3.087e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.807465e-3 s grad=3.012e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.078656e-3 s grad=5.872e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.463643e-3 s grad=9.193e-6 s +[acc-interp] eval 1.8721906e-2 s +[acc-interp] thetaMarsh 6.9e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.372417e-3 s grad=3.593e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.91665e-3 s grad=4.366e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.83336e-3 s grad=1.0323e-5 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.715393e-3 s grad=9.34e-7 s +[acc-interp] eval 1.6663625e-2 s +[acc-interp] thetaMarsh 3.65e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.178154e-3 s grad=1.748e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=9.290366e-3 s grad=2.324e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.422485e-3 s grad=2.713e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.416962e-3 s grad=1.734e-6 s +[acc-interp] eval 2.1083757e-2 s +[acc-interp] thetaMarsh 6.2e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.278187e-3 s grad=3.196e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.47426e-3 s grad=1.518e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.530111e-3 s grad=1.519e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.974612e-3 s grad=6.007e-6 s +[acc-interp] eval 1.5279191e-2 s +[acc-interp] thetaMarsh 1.31e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.739081e-3 s grad=3.893e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.342859e-3 s grad=3.449e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.456352e-3 s grad=3.882e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.895026e-3 s grad=3.969e-6 s +[acc-interp] eval 1.6000063e-2 s +[acc-interp] thetaMarsh 1.18e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.681379e-3 s grad=3.363e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.580771e-3 s grad=3.404e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.518858e-3 s grad=2.661e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.567309e-3 s grad=3.616e-6 s +[acc-interp] eval 1.688346e-2 s +[acc-interp] thetaMarsh 1.11e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.637576e-3 s grad=3.237e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.457096e-3 s grad=3.166e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.793722e-3 s grad=4.112e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.370247e-3 s grad=4.068e-6 s +[acc-interp] eval 1.690033e-2 s +[acc-interp] thetaMarsh 1.01e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.076163e-3 s grad=4.022e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.032916e-3 s grad=4.125e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.426329e-3 s grad=4.077e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.855657e-3 s grad=4.383e-6 s +[acc-interp] eval 1.8682211e-2 s +[acc-interp] thetaMarsh 1.17e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.82234e-3 s grad=2.698e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.572599e-3 s grad=4.167e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.846948e-3 s grad=4.991e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.579351e-3 s grad=5.13e-6 s +[acc-interp] eval 1.5870449e-2 s +[acc-interp] thetaMarsh 6.7e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.969788e-3 s grad=1.849e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.351181e-3 s grad=5.284e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=8.766578e-3 s grad=4.018e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.480863e-3 s grad=1.0306e-5 s +[acc-interp] eval 2.1505586e-2 s +[acc-interp] thetaMarsh 2.76e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.673719e-3 s grad=4.127e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.618596e-3 s grad=3.439e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.576826e-3 s grad=2.909e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.296465e-3 s grad=3.896e-6 s +[acc-interp] eval 1.5678384e-2 s +[acc-interp] thetaMarsh 1.45e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.074776e-3 s grad=4.235e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.373699e-3 s grad=5.047e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.762472e-3 s grad=3.561e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.672467e-3 s grad=2.506e-6 s +[acc-interp] eval 1.6454214e-2 s +[acc-interp] thetaMarsh 9.4e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.012851e-3 s grad=3.794e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.059906e-3 s grad=4.095e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.145012e-3 s grad=3.006e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.823034e-3 s grad=4.34e-6 s +[acc-interp] eval 1.744845e-2 s +4) +[acc-interp] chunk base=0 cs=25974 kernel=4.0257e-3 s grad=3.762e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.927636e-3 s grad=1.87e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.669826e-3 s grad=1.329e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.925355e-3 s grad=2.627e-6 s +[acc-interp] eval 0.518316145 s +[acc-interp] thetaMarsh 9.7e-8 s (n=[acc-interp] thetaMarsh 6.7e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.5273113e-2 s grad=4.741e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.923043e-3 s grad=2.143e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.968282e-3 s grad=5.934e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.550143e-3 s grad=4.375e-6 s +[acc-interp] eval 3.759055e-2 s +[acc-interp] thetaMarsh 6.3e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.944137e-3 s grad=7.048e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.819055e-3 s grad=8.541e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.945405e-3 s grad=3.883e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.564542e-3 s grad=3.33e-6 s +[acc-interp] eval 1.6498809e-2 s +[acc-interp] thetaMarsh 2.12e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.596787e-3 s grad=3.318e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.463456e-3 s grad=3.857e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.781177e-3 s grad=4.586e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.65737e-3 s grad=3.099e-6 s +[acc-interp] eval 1.6249719e-2 s +[acc-interp] thetaMarsh 1.21e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.510894e-3 s grad=2.504e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.276598e-3 s grad=2.803e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.677455e-3 s grad=4.896e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.653643e-3 s grad=3.586e-6 s +[acc-interp] eval 1.5904384e-2 s +[acc-interp] thetaMarsh 1.33e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.113511e-3 s grad=3.936e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.347345e-3 s grad=3.145e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.702999e-3 s grad=4.604e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.381081e-3 s grad=3.593e-6 s +[acc-interp] eval 1.713733e-2 s +[acc-interp] thetaMarsh 1.02e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.631089e-3 s grad=3.211e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.075054e-3 s grad=3.567e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.025884e-3 s grad=4.292e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.685359e-3 s grad=4.102e-6 s +[acc-interp] eval 1.6009398e-2 s +[acc-interp] thetaMarsh 1.02e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.468219e-3 s grad=3.629e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.456705e-3 s grad=3.45e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.997143e-3 s grad=3.813e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.61037e-3 s grad=4.43e-6 s +[acc-interp] eval 1.6099808e-2 s +[acc-interp] thetaMarsh 1.24e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.234106e-3 s grad=3.183e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.987443e-3 s grad=2.927e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.089358e-3 s grad=4.768e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.683632e-3 s grad=3.923e-6 s +[acc-interp] eval 1.5571699e-2 s +[acc-interp] thetaMarsh 1.41e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.671801e-3 s grad=2.969e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.36159e-3 s grad=2.445e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.120915e-3 s grad=4.055e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.40479e-3 s grad=3.674e-6 s +[acc-interp] eval 1.6185909e-2 s +[acc-interp] thetaMarsh 1.07e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.652363e-3 s grad=2.846e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.637334e-3 s grad=2.311e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.153783e-3 s grad=4.845e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.648434e-3 s grad=3.776e-6 s +[acc-interp] eval 1.6744827e-2 s +[acc-interp] thetaMarsh 1.01e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.707395e-3 s grad=2.478e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.762073e-3 s grad=2.712e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.414492e-3 s grad=4.358e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.188694e-3 s grad=3.95e-6 s +[acc-interp] eval 1.7844226e-2 s +[acc-interp] thetaMarsh 1.01e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.927302e-3 s grad=3.424e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.047217e-3 s grad=3.705e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.718884e-2 s grad=4.055e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.002959e-3 s grad=3.075e-6 s +[acc-interp] eval 3.0809462e-2 s +[acc-interp] thetaMarsh 1.15e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.427916e-3 s grad=2.846e-6 s 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eval 1.4665996e-2 s +[acc-interp] thetaMarsh 7.1e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.507105e-3 s grad=1.301e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.576405e-3 s grad=9.05e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.332687e-3 s grad=3.008e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.287136e-3 s grad=5.132e-6 s +[acc-interp] eval 1.4578413e-2 s +[acc-interp] thetaMarsh 7.5e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.335364e-3 s grad=5.448e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.338187e-3 s grad=7.253e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.661249e-3 s grad=3.731e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.777435e-3 s grad=3.906e-6 s +[acc-interp] eval 1.7138258e-2 s +[acc-interp] thetaMarsh 1.11e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.0489e-3 s grad=3.727e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.037618e-3 s grad=3.697e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.920612e-3 s grad=3.928e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.349442e-3 s grad=4.867e-6 s +[acc-interp] eval 1.8302425e-2 s +[acc-interp] thetaMarsh 1.2e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.11338e-3 s grad=4.854e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.387304e-3 s grad=2.749e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=8.102316e-3 s grad=4.16e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.181379e-3 s grad=9.158e-6 s +[acc-interp] eval 2.0674308e-2 s +[acc-interp] thetaMarsh 6.7e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.449362e-3 s grad=2.023e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.196323e-3 s grad=1.226e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.265972e-3 s grad=3.562e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.034265e-3 s grad=1.585e-6 s +[acc-interp] eval 1.5723609e-2 s +[acc-interp] thetaMarsh 1.86e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.69544e-3 s grad=1.0481e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.398088e-3 s grad=7.171e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.272415e-3 s grad=3.771e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.946781e-3 s grad=2.12e-6 s +[acc-interp] eval 1.6113246e-2 s +[acc-interp] thetaMarsh 2.35e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.655977e-3 s grad=2.155e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.641723e-3 s grad=1.353e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.548613e-3 s grad=4.594e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.866093e-3 s grad=4.017e-6 s +[acc-interp] eval 1.5539424e-2 s +[acc-interp] thetaMarsh 6.8e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.39407e-3 s grad=8.213e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.580124e-3 s grad=8.533e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.345539e-3 s grad=4.638e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.656522e-3 s grad=3.331e-6 s +[acc-interp] eval 1.5211577e-2 s +[acc-interp] thetaMarsh 2.96e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.637878e-3 s grad=3.05e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.263499e-3 s grad=3.458e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.612266e-3 s grad=2.563e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.951649e-3 s grad=3.799e-6 s +[acc-interp] eval 1.6021239e-2 s +[acc-interp] thetaMarsh 1.24e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.653993e-3 s grad=2.989e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.438995e-3 s grad=2.821e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.308733e-3 s grad=2.942e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.007525e-3 s grad=5.677e-6 s +[acc-interp] eval 1.5939364e-2 s +[acc-interp] thetaMarsh 1.29e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.818793e-3 s grad=1.2337e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.608909e-3 s grad=4.581e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=8.580566e-3 s grad=5.317e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.659093e-3 s grad=1.426e-6 s +[acc-interp] eval 2.0569998e-2 s +[acc-interp] thetaMarsh 1.56e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.440608e-3 s grad=8.947e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.119545e-3 s grad=8.07e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.538442e-3 s grad=4.858e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.076916e-3 s grad=7.477e-6 s +[acc-interp] eval 1.5083137e-2 s +[acc-interp] thetaMarsh 1.66e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.791365e-3 s grad=5.391e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.599232e-3 s grad=3.998e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.160988e-3 s grad=3.144e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.060267e-3 s grad=3.885e-6 s +[acc-interp] eval 1.4487215e-2 s +[acc-interp] thetaMarsh 7.0e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.926405e-3 s grad=3.724e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.727292e-3 s grad=3.989e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.131786e-3 s grad=3.27e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.974164e-3 s grad=3.681e-6 s +[acc-interp] eval 1.3582054e-2 s +[acc-interp] thetaMarsh 6.6e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.406633e-3 s grad=4.991e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.954348e-3 s grad=3.45e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.640644e-3 s grad=8.652e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.164912e-3 s grad=1.759e-6 s +[acc-interp] eval 1.5061237e-2 s +4) +[acc-interp] chunk base=0 cs=25974 kernel=3.188793e-3 s grad=2.469e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.436588e-3 s grad=6.792e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.816624e-3 s grad=1.48e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.054343e-3 s grad=2.243e-6 s +[acc-interp] eval 0.541216687 s +[acc-interp] thetaMarsh 2.02e-7 s (n=[acc-interp] thetaMarsh 6.1e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.381572e-2 s grad=4.109e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.135288e-3 s grad=2.913e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.121441e-3 s grad=4.633e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.971382e-3 s grad=4.758e-6 s +[acc-interp] eval 3.3935309e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=2.893144e-3 s grad=3.166e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.891791e-3 s grad=3.595e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.798806e-3 s grad=9.574e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.189273e-3 s grad=1.822e-6 s +[acc-interp] eval 4.6704093e-2 s +[acc-interp] thetaMarsh 7.9e-8 s (n=[acc-interp] thetaMarsh 6.0e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.1361859e-2 s grad=4.196e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.309729e-3 s grad=2.096e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.803751e-3 s grad=8.984e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.085463e-3 s grad=1.405e-6 s +[acc-interp] eval 2.1382357e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=3.067818e-3 s grad=1.768e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.809909e-3 s grad=3.697e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.990054e-3 s grad=3.148e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.32715e-3 s grad=3.197e-6 s +[acc-interp] eval 3.6642113e-2 s +[acc-interp] thetaMarsh 8.4e-8 s (n=[acc-interp] thetaMarsh 6.2e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.3051204e-2 s grad=4.917e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.28308e-3 s grad=3.709e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.654245e-3 s grad=3.022e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.746701e-3 s grad=3.797e-6 s +[acc-interp] eval 3.4333315e-2 s +[acc-interp] thetaMarsh 1.02e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.266394e-3 s grad=2.179e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.149251e-3 s grad=2.873e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.884229e-3 s grad=2.992e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.049096e-3 s grad=2.599e-6 s +[acc-interp] eval 1.3833655e-2 s +[acc-interp] thetaMarsh 1.14e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.633706e-3 s grad=2.34e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.1882453e-2 s grad=3.457e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.399716e-3 s grad=2.406e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.346865e-3 s grad=2.03e-6 s +[acc-interp] eval 2.0694231e-2 s +[acc-interp] thetaMarsh 1.23e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.534951e-3 s grad=2.119e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.949804e-3 s grad=5.834e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.244986e-3 s grad=3.616e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.751103e-3 s grad=3.619e-6 s +[acc-interp] eval 1.3105238e-2 s +[acc-interp] thetaMarsh 1.22e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.591436e-3 s grad=2.764e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.685898e-3 s grad=2.125e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.160062e-3 s grad=3.819e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.404222e-3 s grad=3.26e-6 s +[acc-interp] eval 1.3415999e-2 s +[acc-interp] thetaMarsh 1.0e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.08048e-3 s grad=2.932e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.662273e-3 s grad=2.318e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.814005e-3 s grad=3.066e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.866065e-3 s grad=3.31e-6 s +[acc-interp] eval 1.3035922e-2 s +[acc-interp] thetaMarsh 1.02e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.131395e-3 s grad=2.595e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.578025e-3 s grad=2.891e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.85604e-3 s grad=2.572e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.819036e-3 s grad=2.997e-6 s +[acc-interp] eval 1.2993746e-2 s +[acc-interp] thetaMarsh 1.03e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.299507e-3 s grad=2.526e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.376888e-3 s grad=2.216e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.06953e-3 s grad=2.412e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.163562e-3 s grad=2.263e-6 s +[acc-interp] eval 1.0377273e-2 s +[acc-interp] thetaMarsh 1.08e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.511755e-3 s grad=2.36e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.349199e-3 s grad=2.938e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.220402e-3 s grad=4.712e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.62341e-3 s grad=2.881e-6 s +[acc-interp] eval 1.1866281e-2 s +[acc-interp] thetaMarsh 1.26e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.698846e-3 s grad=2.97e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.638208e-3 s grad=2.401e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.431329e-3 s grad=2.109e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.504063e-3 s grad=5.04e-6 s +[acc-interp] eval 1.2704727e-2 s +[acc-interp] thetaMarsh 7.3e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.827931e-3 s grad=1.65e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.170338e-3 s grad=3.635e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.875632e-3 s grad=2.278e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.564846e-3 s grad=2.409e-6 s +[acc-interp] eval 1.2765964e-2 s +[acc-interp] thetaMarsh 9.5e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.803511e-3 s grad=3.477e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.651127e-3 s grad=3.145e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.812158e-3 s grad=2.919e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.625167e-3 s grad=2.254e-6 s +[acc-interp] eval 1.3443715e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=2.311304e-3 s grad=2.394e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.620983e-3 s grad=3.017e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.5899e-3 s grad=3.488e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.722657e-3 s grad=3.449e-6 s +[acc-interp] eval 0.196666044 s +[acc-interp] thetaMarsh 7.8e-8 s (n=[acc-interp] thetaMarsh 6.3e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.2339005e-2 s grad=3.484e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.618376e-3 s grad=3.257e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.742663e-3 s grad=3.673e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.515691e-3 s grad=2.639e-6 s +[acc-interp] eval 3.1065773e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=2.53867e-3 s grad=2.762e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.529221e-3 s grad=3.059e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.243144e-3 s grad=2.561e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.494475e-3 s grad=2.194e-6 s +[acc-interp] eval 4.1759225e-2 s +[acc-interp] thetaMarsh 7.5e-8 s (n=[acc-interp] thetaMarsh 6.1e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=2.1069541e-2 s grad=4.58e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.575494e-3 s grad=4.822e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.524873e-3 s grad=4.754e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.491057e-3 s grad=3.44e-6 s +[acc-interp] eval 3.258228e-2 s +[acc-interp] thetaMarsh 6.5e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.43388e-3 s grad=4.203e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.0367907e-2 s grad=5.058e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.284998e-3 s grad=1.714e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.972977e-3 s grad=5.108e-6 s +[acc-interp] eval 2.2097704e-2 s +[acc-interp] thetaMarsh 1.06e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.617095e-3 s grad=4.261e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.707043e-3 s grad=3.472e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.751268e-3 s grad=1.1079e-5 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.789879e-3 s grad=4.596e-6 s +[acc-interp] eval 1.6981901e-2 s +[acc-interp] thetaMarsh 1.34e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=4.089419e-3 s grad=4.263e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.599662e-3 s grad=3.706e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.331546e-3 s grad=2.748e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.044008e-3 s grad=4.418e-6 s +[acc-interp] eval 1.6793681e-2 s +[acc-interp] thetaMarsh 1.59e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=4.057593e-3 s grad=3.816e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.848562e-3 s grad=4.23e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.313101e-3 s grad=2.876e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.167578e-3 s grad=3.931e-6 s +[acc-interp] eval 1.7147421e-2 s +[acc-interp] thetaMarsh 1.15e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.517179e-3 s grad=2.765e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.482261e-3 s grad=2.996e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.239592e-3 s grad=3.301e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.1189445e-2 s grad=3.316e-6 s +[acc-interp] eval 2.3017196e-2 s +[acc-interp] thetaMarsh 1.15e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.271462e-3 s grad=3.016e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.104538e-3 s grad=2.443e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.207997e-3 s grad=3.648e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.337954e-3 s grad=3.101e-6 s +[acc-interp] eval 1.4440932e-2 s +[acc-interp] thetaMarsh 1.47e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.527242e-3 s grad=2.86e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.210505e-3 s grad=4.179e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.126468e-3 s grad=5.238e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.7213e-3 s grad=4.515e-6 s +[acc-interp] eval 1.8222094e-2 s +[acc-interp] thetaMarsh 1.26e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.612668e-3 s grad=3.812e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.305821e-3 s grad=3.646e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.798875e-3 s grad=4.54e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.028257e-3 s grad=3.883e-6 s +[acc-interp] eval 1.7289589e-2 s +[acc-interp] thetaMarsh 1.07e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=4.419646e-3 s grad=3.586e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.0498205e-2 s grad=3.388e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.884937e-3 s grad=5.36e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.239392e-3 s grad=4.741e-6 s +[acc-interp] eval 2.4706672e-2 s +[acc-interp] thetaMarsh 1.32e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.728379e-3 s grad=4.78e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.353932e-3 s grad=3.373e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.660194e-3 s grad=4.292e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.780105e-3 s grad=4.715e-6 s +[acc-interp] eval 1.6097925e-2 s +[acc-interp] thetaMarsh 9.8e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.648103e-3 s grad=4.475e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.271766e-3 s grad=3.139e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.20356e-3 s grad=3.897e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.310241e-3 s grad=2.717e-6 s +[acc-interp] eval 1.498867e-2 s +[acc-interp] thetaMarsh 1.24e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.119204e-3 s grad=2.708e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.346498e-3 s grad=4.374e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.666998e-3 s grad=4.018e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.2568817e-2 s grad=4.842e-6 s +[acc-interp] eval 2.4280825e-2 s +[acc-interp] thetaMarsh 1.01e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.542437e-3 s grad=4.497e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.264611e-3 s grad=3.687e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.425386e-3 s grad=3.218e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.171608e-3 s grad=3.263e-6 s +[acc-interp] eval 1.5980997e-2 s +[acc-interp] thetaMarsh 1.13e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.216877e-3 s grad=3.987e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.865261e-3 s grad=4.466e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.487073e-3 s grad=3.619e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.17329e-3 s grad=3.076e-6 s +[acc-interp] eval 1.5402342e-2 s +[acc-interp] thetaMarsh 6.4e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.474053e-3 s grad=1.1421e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.186948e-3 s grad=3.773e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.567966e-3 s grad=7.032e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.966438e-3 s grad=9.72e-6 s +[acc-interp] eval 1.59819e-2 s +[acc-interp] thetaMarsh 7.2e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.666368e-3 s grad=1.915e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.243371e-2 s grad=4.542e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.271962e-3 s grad=9.557e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.82669e-3 s grad=3.379e-6 s +[acc-interp] eval 2.404371e-2 s +[acc-interp] thetaMarsh 7.0e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=4.057445e-3 s grad=9.839e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.486174e-3 s grad=1.1213e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.562892e-3 s grad=3.095e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.836949e-3 s grad=4.605e-6 s +[acc-interp] eval 1.6994534e-2 s +[acc-interp] thetaMarsh 1.32e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.654057e-3 s grad=3.036e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.212419e-3 s grad=3.177e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.091542e-3 s grad=2.905e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.481828e-3 s grad=4.082e-6 s +[acc-interp] eval 1.4995931e-2 s +[acc-interp] thetaMarsh 9.9e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.676125e-3 s grad=3.281e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.684769e-3 s grad=4.231e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.06737e-3 s grad=3.566e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.1255075e-2 s grad=3.683e-6 s +[acc-interp] eval 2.4342635e-2 s +[acc-interp] thetaMarsh 1.12e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.164909e-3 s grad=3.541e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.171355e-3 s grad=4.378e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.622406e-3 s grad=4.604e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.199234e-3 s grad=3.508e-6 s +[acc-interp] eval 1.4879602e-2 s +[acc-interp] thetaMarsh 1.05e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.211478e-3 s grad=3.879e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.036945e-3 s grad=3.256e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.161409e-3 s grad=4.699e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.460289e-3 s grad=3.445e-6 s +[acc-interp] eval 1.5568101e-2 s +[acc-interp] thetaMarsh 1.06e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.389345e-3 s grad=3.347e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.378698e-3 s grad=3.606e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.619639e-3 s grad=4.036e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.114424e-3 s grad=4.99e-6 s +[acc-interp] eval 1.6184737e-2 s +[acc-interp] thetaMarsh 9.8e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=4.017305e-3 s grad=4.327e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.2267674e-2 s grad=4.722e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.53686e-3 s grad=4.809e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.685421e-3 s grad=4.662e-6 s +[acc-interp] eval 2.5161155e-2 s +[acc-interp] thetaMarsh 1.02e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.336425e-3 s grad=4.149e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.807331e-3 s grad=4.849e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.698771e-3 s grad=4.79e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.442e-3 s grad=3.955e-6 s +[acc-interp] eval 1.5978273e-2 s +[acc-interp] thetaMarsh 1.89e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.698412e-3 s grad=3.891e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.355272e-3 s grad=2.804e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.056724e-3 s grad=1.734e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.257918e-3 s grad=1.296e-6 s +[acc-interp] eval 1.4354094e-2 s +[acc-interp] thetaMarsh 1.36e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.286053e-3 s grad=3.576e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.883419e-3 s grad=4.031e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.866923e-3 s grad=3.88e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.2513616e-2 s grad=4.684e-6 s +[acc-interp] eval 2.5194625e-2 s +[acc-interp] thetaMarsh 1.13e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.721636e-3 s grad=4.31e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.359954e-3 s grad=3.146e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.608966e-3 s grad=3.72e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.67307e-3 s grad=4.49e-6 s +[acc-interp] eval 1.5860745e-2 s +[acc-interp] thetaMarsh 1.19e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=4.758837e-3 s grad=4.401e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.511234e-3 s grad=3.437e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.036627e-3 s grad=4.424e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.476412e-3 s grad=2.979e-6 s +[acc-interp] eval 1.7402545e-2 s +[acc-interp] thetaMarsh 1.14e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.196307e-3 s grad=2.554e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.262809e-3 s grad=2.352e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.330307e-3 s grad=5.526e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.917989e-3 s grad=4.241e-6 s +[acc-interp] eval 1.5314014e-2 s +[acc-interp] thetaMarsh 3.06e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=4.660386e-3 s grad=3.791e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=8.64238e-3 s grad=3.708e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.928761e-3 s grad=5.683e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.060801e-3 s grad=4.691e-6 s +[acc-interp] eval 2.1504679e-2 s +5) +[acc-interp] chunk base=0 cs=25974 kernel=2.879209e-3 s grad=3.907e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.809975e-3 s grad=3.591e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.099565e-3 s grad=4.782e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.287831e-3 s grad=4.14e-6 s +[acc-interp] eval 0.602414854 s +[acc-interp] thetaMarsh 7.8e-8 s (n=[acc-interp] thetaMarsh 7.6e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.6801586e-2 s grad=2.935e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.027383e-3 s grad=2.975e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.178305e-3 s grad=3.39e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.02654e-3 s grad=3.095e-6 s +[acc-interp] eval 3.7602046e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=3.619725e-3 s grad=3.452e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.63309e-3 s grad=4.87e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.138706e-3 s grad=4.902e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.473192e-3 s grad=3.776e-6 s +[acc-interp] eval 5.2921047e-2 s +[acc-interp] thetaMarsh 6.6e-8 s (n=[acc-interp] thetaMarsh 6.4e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.4885142e-2 s grad=3.791e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.020922e-3 s grad=3.77e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.50316e-3 s grad=4.857e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.268156e-3 s grad=3.26e-6 s +[acc-interp] eval 2.7188781e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=3.919007e-3 s grad=3.412e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.138886e-3 s grad=4.174e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.936025e-3 s grad=3.227e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.523456e-3 s grad=2.872e-6 s +[acc-interp] eval 4.4653574e-2 s +[acc-interp] thetaMarsh 1.28e-7 s (n=[acc-interp] thetaMarsh 3.89e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.1633317e-2 s grad=3.589e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.911272e-3 s grad=3.521e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.034579e-3 s grad=2.191e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.744951e-3 s grad=1.116e-6 s +[acc-interp] eval 3.139452e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=2.742324e-3 s grad=2.864e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.616105e-3 s grad=1.489e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.608254e-3 s grad=1.016e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.521429e-3 s grad=4.871e-6 s +[acc-interp] eval 4.2787978e-2 s +[acc-interp] thetaMarsh 6.7e-8 s (n=[acc-interp] thetaMarsh 7.4e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.1785076e-2 s grad=3.952e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.729749e-3 s grad=3.841e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.295342e-3 s grad=3.293e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.90737e-3 s grad=3.756e-6 s +[acc-interp] eval 2.4318607e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=3.445578e-3 s grad=3.363e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.022967e-3 s grad=3.351e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.196013e-3 s grad=3.55e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.492716e-3 s grad=3.257e-6 s +[acc-interp] eval 4.0183367e-2 s +[acc-interp] thetaMarsh 2.73e-7 s (n=[acc-interp] thetaMarsh 5.3e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.3206466e-2 s grad=3.9e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.215359e-3 s grad=3.067e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.195802e-3 s grad=3.347e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.88773e-3 s grad=2.602e-6 s +[acc-interp] eval 2.3267188e-2 s +[acc-interp] thetaMarsh 1.04e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.372723e-3 s grad=5.152e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.234827e-3 s grad=3.133e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.646859e-3 s grad=3.963e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.007315e-3 s grad=3.552e-6 s +[acc-interp] eval 1.4844714e-2 s +[acc-interp] thetaMarsh 3.33e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.591801e-3 s grad=2.187e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.365007e-3 s grad=5.589e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.027619e-3 s grad=3.874e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.919761e-3 s grad=4.151e-6 s +[acc-interp] eval 1.3617073e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=2.438945e-3 s grad=3.419e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.670456e-3 s grad=2.425e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.958027e-3 s grad=5.662e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.229255e-3 s grad=4.453e-6 s +[acc-interp] eval 6.7366553e-2 s +[acc-interp] thetaMarsh 1.55e-7 s (n=[acc-interp] thetaMarsh 1.18e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.0691082e-2 s grad=6.496e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.51787e-3 s grad=4.802e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.790401e-3 s grad=4.05e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.92138e-3 s grad=3.864e-6 s +[acc-interp] eval 2.2949404e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=3.415532e-3 s grad=2.638e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.752205e-3 s grad=3.432e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.018461e-3 s grad=2.753e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.378722e-3 s grad=2.218e-6 s +[acc-interp] eval 3.8213816e-2 s +[acc-interp] thetaMarsh 1.3e-7 s (n=[acc-interp] thetaMarsh 9.5e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.9139595e-2 s grad=4.14e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.310411e-3 s grad=2.795e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.292348e-3 s grad=3.794e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.540074e-3 s grad=3.382e-6 s +[acc-interp] eval 2.9999365e-2 s +[acc-interp] thetaMarsh 1.1e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.046389e-3 s grad=3.161e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.499022e-3 s grad=2.241e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.817793e-3 s grad=1.981e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.792569e-3 s grad=3.639e-6 s +[acc-interp] eval 1.3675048e-2 s +[acc-interp] thetaMarsh 1.42e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.113602e-3 s grad=2.721e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.577267e-3 s grad=1.38e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.197387e-3 s grad=1.463e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.647127e-3 s grad=2.276e-6 s +[acc-interp] eval 1.2072348e-2 s +[acc-interp] thetaMarsh 1.44e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.944181e-3 s grad=3.364e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=9.908423e-3 s grad=2.611e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.264476e-3 s grad=5.705e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.57996e-3 s grad=4.223e-6 s +[acc-interp] eval 2.2661819e-2 s +[acc-interp] thetaMarsh 1.06e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.359976e-3 s grad=3.09e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.371058e-3 s grad=2.285e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.095667e-3 s grad=1.636e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.297794e-3 s grad=1.513e-6 s +[acc-interp] eval 1.0599045e-2 s +[acc-interp] thetaMarsh 9.5e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.280425e-3 s grad=1.344e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.077981e-3 s grad=2.168e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.351026e-3 s grad=1.945e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.019648e-3 s grad=2.739e-6 s +[acc-interp] eval 1.1101758e-2 s +[acc-interp] thetaMarsh 9.6e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.074572e-3 s grad=1.997e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.232963e-3 s grad=3.496e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.502399e-3 s grad=3.115e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.396255e-3 s grad=2.576e-6 s +[acc-interp] eval 1.0787568e-2 s +[acc-interp] thetaMarsh 1.09e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.328979e-3 s grad=2.285e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.282925e-3 s grad=1.965e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.432111e-3 s grad=1.81e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.783925e-3 s grad=4.101e-6 s +[acc-interp] eval 1.1329061e-2 s +[acc-interp] thetaMarsh 1.58e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.489154e-3 s grad=2.832e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.4528e-3 s grad=3.157e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.338168e-3 s grad=2.545e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.338823e-3 s grad=2.367e-6 s +[acc-interp] eval 1.1198415e-2 s +[acc-interp] thetaMarsh 1.02e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.400397e-3 s grad=2.644e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.33794e-3 s grad=3.693e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.46181e-3 s grad=2.421e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.346095e-3 s grad=7.306e-6 s +[acc-interp] eval 1.2995657e-2 s +[acc-interp] thetaMarsh 6.0e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.155188e-3 s grad=7.85e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.19517e-3 s grad=6.159e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.477737e-3 s grad=3.753e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.488405e-3 s grad=3.005e-6 s +[acc-interp] eval 1.0463974e-2 s +[acc-interp] thetaMarsh 1.26e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.212354e-3 s grad=2.446e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.141313e-3 s grad=2.184e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.905198e-3 s grad=1.713e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.959746e-3 s grad=2.218e-6 s +[acc-interp] eval 9.70604e-3 s +[acc-interp] thetaMarsh 9.8e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.434364e-3 s grad=3.409e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.866312e-3 s grad=3.01e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.514626e-3 s grad=1.974e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.469799e-3 s grad=1.911e-6 s +[acc-interp] eval 1.1814201e-2 s +[acc-interp] thetaMarsh 1.0e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.172724e-3 s grad=2.253e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.125723e-3 s grad=1.754e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.17936e-3 s grad=2.241e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.015873e-3 s grad=6.484e-6 s +[acc-interp] eval 1.1178545e-2 s +[acc-interp] thetaMarsh 1.89e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.928556e-3 s grad=1.985e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.152392e-3 s grad=1.491e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.180309e-3 s grad=2.087e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.12183e-3 s grad=2.094e-6 s +[acc-interp] eval 1.0869379e-2 s +[acc-interp] thetaMarsh 1.17e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.183685e-3 s grad=5.028e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.519805e-3 s grad=2.897e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.159871e-3 s grad=1.747e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.177546e-3 s grad=1.411e-6 s +[acc-interp] eval 1.1688569e-2 s +[acc-interp] thetaMarsh 1.42e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.407659e-3 s grad=2.161e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.368644e-3 s grad=2.077e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.09474e-3 s grad=3.011e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.411503e-3 s grad=2.718e-6 s +[acc-interp] eval 1.1874629e-2 s +[acc-interp] thetaMarsh 2.84e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.328297e-3 s grad=1.81e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.23545e-3 s grad=7.11e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.338589e-3 s grad=2.101e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.229511e-3 s grad=2.846e-6 s +[acc-interp] eval 1.0623734e-2 s +[acc-interp] thetaMarsh 1.11e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.084696e-3 s grad=2.046e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.075981e-3 s grad=2.559e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.981841e-3 s grad=2.457e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.165044e-3 s grad=2.612e-6 s +[acc-interp] eval 9.750588e-3 s +[acc-interp] thetaMarsh 1.03e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.782834e-3 s grad=2.062e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.841669e-3 s grad=1.671e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.26178e-3 s grad=3.431e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.555362e-3 s grad=3.406e-6 s +[acc-interp] eval 1.0171683e-2 s +[acc-interp] thetaMarsh 1.4e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.217494e-3 s grad=2.325e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.392603e-3 s grad=3.177e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.260873e-3 s grad=2.571e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.061893e-3 s grad=2.245e-6 s +[acc-interp] eval 1.0529357e-2 s +[acc-interp] thetaMarsh 1.35e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.825518e-3 s grad=3.18e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.456187e-3 s grad=2.987e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.719307e-3 s grad=3.006e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.440862e-3 s grad=2.151e-6 s +[acc-interp] eval 1.2215329e-2 s +[acc-interp] thetaMarsh 1.02e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.348922e-3 s grad=2.799e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.657144e-3 s grad=4.108e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.016437e-3 s grad=3.549e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.491301e-3 s grad=3.216e-6 s +[acc-interp] eval 1.2387658e-2 s +[acc-interp] thetaMarsh 1.18e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.401167e-3 s grad=2.797e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.276937e-3 s grad=2.223e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.407266e-3 s grad=3.616e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.804e-3 s grad=2.362e-6 s +[acc-interp] eval 1.1706997e-2 s +[acc-interp] thetaMarsh 1.08e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.27863e-3 s grad=2.7e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.572538e-3 s grad=2.811e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.196993e-3 s grad=1.719e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.321382e-3 s grad=1.463e-6 s +[acc-interp] eval 1.1075317e-2 s +[acc-interp] thetaMarsh 1.21e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.868544e-3 s grad=3.708e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.926339e-3 s grad=3.262e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.644114e-3 s grad=2.392e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.510185e-3 s grad=8.426e-6 s +[acc-interp] eval 1.2519807e-2 s +[acc-interp] thetaMarsh 6.4e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.546422e-3 s grad=1.769e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.676663e-3 s grad=7.631e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.238528e-3 s grad=4.6e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.263269e-3 s grad=3.865e-6 s +[acc-interp] eval 1.0466368e-2 s +[acc-interp] thetaMarsh 6.3e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.115908e-3 s grad=3.177e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.2649e-3 s grad=2.678e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.283387e-3 s grad=1.975e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.710072e-3 s grad=3.508e-6 s +[acc-interp] eval 1.0795869e-2 s +[acc-interp] thetaMarsh 1.17e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.400173e-3 s grad=3.275e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.184496e-3 s grad=2.691e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.273776e-3 s grad=2.04e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.188453e-3 s grad=2.387e-6 s +[acc-interp] eval 1.0629422e-2 s +[acc-interp] thetaMarsh 1.09e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.329143e-3 s grad=2.839e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.049147e-3 s grad=4.772e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.800683e-3 s grad=2.496e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.368151e-3 s grad=1.962e-6 s +[acc-interp] eval 1.2305142e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=2.477018e-3 s grad=1.802e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.25223e-3 s grad=1.724e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.868162e-3 s grad=3.291e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.895621e-3 s grad=3.228e-6 s +[acc-interp] eval 0.385875381 s +[acc-interp] thetaMarsh 1.4e-7 s (n=[acc-interp] thetaMarsh 8.8e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=6.593869e-3 s grad=3.425e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.97977e-3 s grad=1.539e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.403487e-3 s grad=6.057e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.319646e-3 s grad=2.691e-6 s +[acc-interp] eval 1.7034749e-2 s +[acc-interp] thetaMarsh 1.39e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.801222e-3 s grad=2.018e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.259908e-3 s grad=6.849e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.244357e-3 s grad=5.272e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.494939e-3 s grad=2.005e-6 s +[acc-interp] eval 1.0522463e-2 s +[acc-interp] thetaMarsh 9.1e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.0126735e-2 s grad=4.012e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.474743e-3 s grad=6.994e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.421309e-3 s grad=4.753e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.61288e-3 s grad=3.457e-6 s +[acc-interp] eval 1.8442289e-2 s +[acc-interp] thetaMarsh 7.9e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.578148e-3 s grad=3.92e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.322663e-3 s grad=3.145e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.938907e-3 s grad=2.95e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.099216e-3 s grad=2.892e-6 s +[acc-interp] eval 1.0449414e-2 s +[acc-interp] thetaMarsh 1.13e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.099958e-3 s grad=2.325e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.334169e-3 s grad=3.159e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.74111e-3 s grad=4.234e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.017118e-3 s grad=2.695e-6 s +[acc-interp] eval 1.1058508e-2 s +[acc-interp] thetaMarsh 1.02e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.045813e-3 s grad=2.358e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.932852e-3 s grad=2.508e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.099061e-3 s grad=2.201e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.459685e-3 s grad=3.486e-6 s +[acc-interp] eval 1.0055035e-2 s +[acc-interp] thetaMarsh 6.6e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.794672e-3 s grad=4.479e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.275709e-3 s grad=7.902e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.111014e-3 s grad=5.762e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.048503e-3 s grad=4.528e-6 s +[acc-interp] eval 9.985872e-3 s +[acc-interp] thetaMarsh 1.04e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.0396e-3 s grad=4.815e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.123567e-3 s grad=5.828e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.103382e-3 s grad=5.293e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.14404e-3 s grad=4.679e-6 s +[acc-interp] eval 9.077614e-3 s +[acc-interp] thetaMarsh 5.8e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.050526e-3 s grad=7.552e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.021161e-3 s grad=5.606e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.042516e-3 s grad=4.916e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.001997e-3 s grad=5.182e-6 s +[acc-interp] eval 8.797308e-3 s +[acc-interp] thetaMarsh 1.31e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.190793e-3 s grad=6.25e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.139373e-3 s grad=4.9e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.359167e-3 s grad=7.495e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.264721e-3 s grad=3.33e-6 s +[acc-interp] eval 9.789012e-3 s +[acc-interp] thetaMarsh 1.99e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.298717e-3 s grad=1.641e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.258747e-3 s grad=5.636e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.213594e-3 s grad=5.02e-7 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.144735e-3 s grad=4.363e-6 s +[acc-interp] eval 9.604138e-3 s +[acc-interp] thetaMarsh 5.0e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.372581e-3 s grad=3.466e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.53592e-3 s grad=8.717e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.281728e-3 s grad=3.056e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.147061e-3 s grad=2.038e-6 s +[acc-interp] eval 1.0645818e-2 s +[acc-interp] thetaMarsh 1.15e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.031629e-3 s grad=1.79e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.948773e-3 s grad=4.961e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.272645e-3 s grad=3.875e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.360871e-3 s grad=4.14e-6 s +[acc-interp] eval 9.905543e-3 s +[acc-interp] thetaMarsh 9.5e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.073466e-3 s grad=2.674e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.776069e-3 s grad=3.216e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.395417e-3 s grad=2.695e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.391641e-3 s grad=2.719e-6 s +[acc-interp] eval 7.445082e-3 s +[acc-interp] thetaMarsh 1.83e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.492631e-3 s grad=2.798e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.900483e-3 s grad=2.858e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.771678e-3 s grad=2.926e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.595036e-3 s grad=2.477e-6 s +[acc-interp] eval 7.512184e-3 s +[acc-interp] thetaMarsh 2.29e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.66961e-3 s grad=3.197e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.787777e-3 s grad=4.364e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.882821e-3 s grad=4.071e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.482346e-3 s grad=3.241e-6 s +[acc-interp] eval 8.171384e-3 s +[acc-interp] thetaMarsh 5.7e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.359884e-3 s grad=3.118e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.435323e-3 s grad=2.733e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.541681e-3 s grad=1.911e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.490244e-3 s grad=3.722e-6 s +[acc-interp] eval 8.103243e-3 s +[acc-interp] thetaMarsh 9.9e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.977366e-3 s grad=2.842e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.635975e-3 s grad=2.843e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.777574e-3 s grad=2.881e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.151716e-3 s grad=1.579e-6 s +[acc-interp] eval 9.024559e-3 s +[acc-interp] thetaMarsh 1.2e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.296911e-3 s grad=2.106e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.275552e-3 s grad=2.841e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.379075e-3 s grad=2.56e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.084984e-3 s grad=2.119e-6 s +[acc-interp] eval 1.066487e-2 s +[acc-interp] thetaMarsh 9.9e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.060917e-3 s grad=1.744e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.792501e-3 s grad=8.105e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.812961e-3 s grad=1.482e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.687357e-3 s grad=1.3e-6 s +[acc-interp] eval 8.864057e-3 s +[acc-interp] thetaMarsh 9.5e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.466247e-3 s grad=3.181e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.401905e-3 s grad=3.374e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.320772e-3 s grad=2.051e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.324882e-3 s grad=2.057e-6 s +[acc-interp] eval 1.1183424e-2 s +[acc-interp] thetaMarsh 1.09e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.174824e-3 s grad=2.138e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.023036e-3 s grad=1.933e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.735033e-3 s grad=3.612e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.552235e-3 s grad=2.744e-6 s +[acc-interp] eval 1.080922e-2 s +[acc-interp] thetaMarsh 7.6e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.375989e-3 s grad=8.582e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.435435e-3 s grad=1.534e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.269575e-3 s grad=8.09e-7 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.008058e-3 s grad=4.26e-6 s +[acc-interp] eval 9.83618e-3 s +[acc-interp] thetaMarsh 7.1e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.274838e-3 s grad=4.709e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.137874e-3 s grad=2.653e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.226402e-3 s grad=2.28e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.050942e-3 s grad=1.663e-6 s +[acc-interp] eval 1.2432942e-2 s +[acc-interp] thetaMarsh 1.06e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.871714e-3 s grad=1.618e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.794329e-3 s grad=2.059e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.439665e-3 s grad=5.131e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.27983e-3 s grad=2.049e-6 s +[acc-interp] eval 1.2067382e-2 s +[acc-interp] thetaMarsh 1.06e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.933856e-3 s grad=2.547e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.011166e-3 s grad=2.01e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.173598e-3 s grad=1.877e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.226666e-3 s grad=2.837e-6 s +[acc-interp] eval 9.996752e-3 s +[acc-interp] thetaMarsh 1.33e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.051776e-3 s grad=2.575e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.144696e-3 s grad=2.156e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.108965e-3 s grad=2.427e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.096481e-3 s grad=2.359e-6 s +[acc-interp] eval 1.1178798e-2 s +[acc-interp] thetaMarsh 1.65e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.207959e-3 s grad=2.376e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.135121e-3 s grad=2.576e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.6967e-3 s grad=4.033e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.316468e-3 s grad=3.112e-6 s +[acc-interp] eval 1.109089e-2 s +[acc-interp] thetaMarsh 1.04e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.377722e-3 s grad=2.742e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.337245e-3 s grad=2.272e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.194331e-3 s grad=2.497e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.156555e-3 s grad=1.541e-6 s +[acc-interp] eval 1.0383757e-2 s +[acc-interp] thetaMarsh 1.12e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.692536e-3 s grad=3.574e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.363948e-3 s grad=7.214e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.289892e-3 s grad=5.406e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.225928e-3 s grad=5.481e-6 s +[acc-interp] eval 1.0379012e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=2.630644e-3 s grad=7.119e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.578536e-3 s grad=8.18e-7 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.578364e-3 s grad=5.196e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.641323e-3 s grad=3.251e-6 s +[acc-interp] eval 0.331810106 s +[acc-interp] thetaMarsh 1.29e-7 s (n=[acc-interp] thetaMarsh 1.05e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.5256885e-2 s grad=3.96e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.412226e-3 s grad=4.528e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.185295e-3 s grad=3.556e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=7.705537e-3 s grad=5.452e-6 s +[acc-interp] eval 4.3326942e-2 s +[acc-interp] thetaMarsh 1.7e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.588085e-3 s grad=4.298e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.557991e-3 s grad=2.943e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.343975e-3 s grad=2.807e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.280469e-3 s grad=4.239e-6 s +[acc-interp] eval 1.6190191e-2 s +[acc-interp] thetaMarsh 8.6e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=6.267951e-3 s grad=4.021e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.867992e-3 s grad=4.53e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.253774e-3 s grad=3.341e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.961056e-3 s grad=5.009e-6 s +[acc-interp] eval 1.9981772e-2 s +[acc-interp] thetaMarsh 1.32e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.596617e-3 s grad=3.806e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.447563e-3 s grad=3.038e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.351095e-3 s grad=6.912e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.689083e-3 s grad=3.455e-6 s +[acc-interp] eval 1.8983065e-2 s +[acc-interp] thetaMarsh 1.29e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.345991e-3 s grad=3.051e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.680684e-3 s grad=3.947e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.266364e-3 s grad=4.251e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.970842e-3 s grad=3.766e-6 s +[acc-interp] eval 1.6898698e-2 s +[acc-interp] thetaMarsh 1.16e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.912487e-3 s grad=2.471e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.990623e-3 s grad=2.152e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.597723e-3 s grad=5.575e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.016733e-3 s grad=4.312e-6 s +[acc-interp] eval 1.6208513e-2 s +[acc-interp] thetaMarsh 1.16e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.702642e-3 s grad=3.909e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.998373e-3 s grad=4.664e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.567107e-3 s grad=4.184e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.890666e-3 s grad=3.892e-6 s +[acc-interp] eval 2.0790195e-2 s +[acc-interp] thetaMarsh 1.25e-7 s 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base=25974 cs=25974 kernel=4.201269e-3 s grad=3.27e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.438643e-3 s grad=3.852e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.430187e-3 s grad=4.802e-6 s +[acc-interp] eval 1.6327893e-2 s +[acc-interp] thetaMarsh 6.0e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.388438e-3 s grad=3.356e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.153239e-3 s grad=3.954e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.000469e-3 s grad=4.313e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.663206e-3 s grad=3.521e-6 s +[acc-interp] eval 1.6077636e-2 s +[acc-interp] thetaMarsh 6.0e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.212728e-3 s grad=2.358e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.341212e-3 s grad=3.53e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.469335e-3 s grad=4.922e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.062578e-3 s grad=4.218e-6 s +[acc-interp] eval 1.5942011e-2 s 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kernel=3.142384e-3 s grad=1.501e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.282617e-3 s grad=9.652e-6 s +[acc-interp] eval 1.3191543e-2 s +[acc-interp] thetaMarsh 5.6e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.20615e-3 s grad=3.113e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.233301e-3 s grad=4.283e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.326727e-3 s grad=4.513e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.366741e-3 s grad=4.77e-6 s +[acc-interp] eval 1.8563394e-2 s +[acc-interp] thetaMarsh 1.05e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.144544e-3 s grad=4.183e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.342336e-3 s grad=3.427e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.886248e-3 s grad=3.398e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.1437004e-2 s grad=4.059e-6 s +[acc-interp] eval 2.6673207e-2 s +[acc-interp] thetaMarsh 1.37e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.525602e-3 s grad=3.119e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.30764e-3 s grad=3.993e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.429391e-3 s grad=4.32e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.466063e-3 s grad=3.798e-6 s +[acc-interp] eval 1.6256973e-2 s +[acc-interp] thetaMarsh 1.23e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.661514e-3 s grad=2.672e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.177668e-3 s grad=3.824e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.38075e-3 s grad=4.336e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.481581e-3 s grad=3.688e-6 s +[acc-interp] eval 1.6330911e-2 s +[acc-interp] thetaMarsh 1.29e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.364303e-3 s grad=3.489e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.831412e-3 s grad=3.014e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.522875e-3 s grad=2.782e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.246791e-3 s grad=2.735e-6 s +[acc-interp] eval 1.5512348e-2 s +[acc-interp] thetaMarsh 1.15e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.214933e-3 s grad=2.809e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.338164e-3 s grad=4.285e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.937327e-3 s grad=4.491e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.029814e-3 s grad=4.062e-6 s +[acc-interp] eval 1.7195621e-2 s +[acc-interp] thetaMarsh 1.13e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.246822e-3 s grad=3.406e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.445577e-3 s grad=3.631e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.0074406e-2 s grad=4.151e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.757163e-3 s grad=5.209e-6 s +[acc-interp] eval 2.7153617e-2 s +[acc-interp] thetaMarsh 1.34e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.604128e-3 s grad=3.042e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.410492e-3 s grad=3.584e-6 s 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chunk base=0 cs=25974 kernel=4.238183e-3 s grad=3.456e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.724056e-3 s grad=3.563e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.447388e-3 s grad=3.712e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.0615836e-2 s grad=4.458e-6 s +[acc-interp] eval 2.5547044e-2 s +[acc-interp] thetaMarsh 1.49e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.542724e-3 s grad=3.293e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.423891e-3 s grad=3.392e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.545378e-3 s grad=9.251e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.23155e-3 s grad=9.308e-6 s +[acc-interp] eval 1.6145375e-2 s +[acc-interp] thetaMarsh 1.26e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.685012e-3 s grad=3.509e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.713369e-3 s grad=3.218e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.571757e-3 s grad=3.661e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.707446e-3 s grad=4.778e-6 s +[acc-interp] eval 1.7019079e-2 s +[acc-interp] thetaMarsh 1.13e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.762228e-3 s grad=4.247e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.767743e-3 s grad=2.754e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.213905e-3 s grad=4.743e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.319163e-3 s grad=5.548e-6 s +[acc-interp] eval 1.8772942e-2 s +[acc-interp] thetaMarsh 1.34e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.086135e-3 s grad=4.526e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.360177e-3 s grad=3.093e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.945168e-3 s grad=4.141e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.171532e-3 s grad=4.503e-6 s +[acc-interp] eval 1.873788e-2 s +[acc-interp] thetaMarsh 6.8e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=1.2471327e-2 s grad=3.636e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.512393e-3 s grad=4.452e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.399636e-3 s grad=3.688e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.185348e-3 s grad=4.561e-6 s +[acc-interp] eval 2.4583089e-2 s +[acc-interp] thetaMarsh 1.19e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.60797e-3 s grad=3.959e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.013728e-3 s grad=3.505e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.966262e-3 s grad=3.432e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.004607e-3 s grad=3.821e-6 s +[acc-interp] eval 1.7460286e-2 s +[acc-interp] thetaMarsh 6.0e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.649966e-3 s grad=3.558e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.511014e-3 s grad=3.583e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.531018e-3 s grad=3.453e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.443399e-3 s grad=3.779e-6 s +[acc-interp] eval 1.6148272e-2 s +4) +[acc-interp] chunk base=0 cs=25974 kernel=4.515628e-3 s grad=3.054e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.927253e-3 s grad=2.86e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.922282e-3 s grad=3.823e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.4463e-3 s grad=3.988e-6 s +[acc-interp] eval 0.72948613 s +[acc-interp] thetaMarsh 1.38e-7 s (n=[acc-interp] thetaMarsh 5.47e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.3083433e-2 s grad=4.081e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.424493e-3 s grad=3.689e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.27649e-3 s grad=2.695e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.770737e-3 s grad=3.316e-6 s +[acc-interp] eval 3.4211681e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=2.917399e-3 s grad=3.295e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.809359e-3 s grad=3.677e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.510652e-3 s grad=4.819e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.042738e-3 s grad=9.115e-6 s +[acc-interp] eval 4.7943911e-2 s +[acc-interp] thetaMarsh 5.7e-8 s (n=[acc-interp] thetaMarsh 5.3e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.2878721e-2 s grad=4.194e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.121103e-3 s grad=4.333e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.195351e-3 s grad=2.971e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.724373e-3 s grad=7.292e-6 s +[acc-interp] eval 3.5802167e-2 s +[acc-interp] thetaMarsh 7.3e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.937602e-3 s grad=2.957e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.270406e-3 s grad=6.767e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.784923e-3 s grad=3.58e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.3097113e-2 s grad=4.104e-6 s +[acc-interp] eval 2.7814614e-2 s +[acc-interp] thetaMarsh 1.26e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.224587e-3 s grad=3.606e-6 s 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eval 1.4877852e-2 s +[acc-interp] thetaMarsh 1.67e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.875223e-3 s grad=2.737e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.085613e-3 s grad=3.04e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.730398e-3 s grad=3.981e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.305584e-3 s grad=3.293e-6 s +[acc-interp] eval 1.3126591e-2 s +[acc-interp] thetaMarsh 4.48e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.293171e-3 s grad=2.519e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.012137e-3 s grad=2.467e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.638e-3 s grad=4.082e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.283905e-3 s grad=2.665e-6 s +[acc-interp] eval 1.4798864e-2 s +[acc-interp] thetaMarsh 9.5e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.019657e-3 s grad=2.631e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.658695e-3 s grad=1.97e-6 s +[acc-interp] chunk base=51948 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kernel=4.669091e-3 s grad=2.878e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.654861e-3 s grad=2.36e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.207487e-3 s grad=1.406e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.848986e-3 s grad=1.578e-6 s +[acc-interp] eval 1.5369494e-2 s +[acc-interp] thetaMarsh 6.7e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.3941507e-2 s grad=3.706e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.166132e-3 s grad=3.438e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.873188e-3 s grad=5.035e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.661115e-3 s grad=4.73e-6 s +[acc-interp] eval 2.5606052e-2 s +[acc-interp] thetaMarsh 6.6e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.974285e-3 s grad=2.356e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.560325e-3 s grad=4.375e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=6.855401e-3 s grad=4.231e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.708653e-3 s grad=3.576e-6 s +[acc-interp] eval 1.9080074e-2 s +[acc-interp] thetaMarsh 6.6e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.627229e-3 s grad=3.1e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.825399e-3 s grad=1.845e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.38291e-3 s grad=2.929e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.411901e-3 s grad=3.122e-6 s +[acc-interp] eval 1.5059379e-2 s +[acc-interp] thetaMarsh 6.7e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.346932e-3 s grad=3.393e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.65165e-3 s grad=4.224e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.388316e-3 s grad=3.383e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.669212e-3 s grad=2.132e-6 s +[acc-interp] eval 1.4877805e-2 s +[acc-interp] thetaMarsh 6.5e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=4.078868e-3 s grad=2.299e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.477232e-3 s 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chunk base=77922 cs=25974 kernel=3.264666e-3 s grad=3.331e-6 s +[acc-interp] eval 1.5503283e-2 s +[acc-interp] thetaMarsh 1.7e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.527546e-3 s grad=2.819e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.354277e-3 s grad=6.462e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.271024e-3 s grad=4.133e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.318928e-3 s grad=4.407e-6 s +[acc-interp] eval 1.5547018e-2 s +[acc-interp] thetaMarsh 5.9e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.498799e-3 s grad=3.078e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.275943e-3 s grad=6.004e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=7.343518e-3 s grad=5.504e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=6.684712e-3 s grad=7.906e-6 s +[acc-interp] eval 2.4741827e-2 s +[acc-interp] thetaMarsh 2.19e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=6.545496e-3 s grad=5.731e-6 s +[acc-interp] chunk base=25974 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thetaMarsh 1.33e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.106699e-3 s grad=3.229e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.834002e-3 s grad=2.495e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.699964e-3 s grad=4.088e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.957515e-3 s grad=3.056e-6 s +[acc-interp] eval 1.6342532e-2 s +[acc-interp] thetaMarsh 1.07e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=4.01053e-3 s grad=2.903e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.094865e-3 s grad=2.707e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.416424e-3 s grad=3.227e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.938986e-3 s grad=2.791e-6 s +[acc-interp] eval 1.809963e-2 s +[acc-interp] thetaMarsh 1.03e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.511376e-3 s grad=2.47e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.345709e-3 s grad=4.163e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.03035e-3 s grad=3.546e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.145548e-3 s grad=3.503e-6 s +[acc-interp] eval 1.6705368e-2 s +[acc-interp] thetaMarsh 1.16e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=4.132612e-3 s grad=3.14e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.294335e-3 s grad=4.34e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.898344e-3 s grad=4.301e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.443243e-3 s grad=4.015e-6 s +[acc-interp] eval 1.70489e-2 s +[acc-interp] thetaMarsh 8.9e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.288565e-3 s grad=3.898e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.953896e-3 s grad=4.487e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.80793e-3 s grad=3.63e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.998711e-3 s grad=4.098e-6 s +[acc-interp] eval 1.6173342e-2 s +[acc-interp] thetaMarsh 6.9e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.951464e-3 s grad=3.407e-6 s 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eval 1.4955542e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=7.405888e-3 s grad=5.954e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.559351e-3 s grad=4.866e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.943262e-3 s grad=3.224e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.98139e-3 s grad=4.157e-6 s +[acc-interp] eval 0.654331929 s +[acc-interp] thetaMarsh 7.6e-8 s (n=[acc-interp] thetaMarsh 5.5e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.6255727e-2 s grad=3.999e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.218808e-3 s grad=3.934e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.112882e-3 s grad=3.173e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.233271e-3 s grad=3.422e-6 s +[acc-interp] eval 3.6720375e-2 s +[acc-interp] thetaMarsh 1.11e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.209679e-3 s grad=2.91e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.808944e-3 s grad=5.034e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.239694e-3 s grad=3.481e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.925362e-3 s grad=3.518e-6 s +[acc-interp] eval 1.4615623e-2 s +[acc-interp] thetaMarsh 1.08e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.096765e-3 s grad=3.158e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.234244e-3 s grad=3.089e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.614216e-3 s grad=2.852e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.212964e-3 s grad=2.542e-6 s +[acc-interp] eval 1.4787482e-2 s +[acc-interp] thetaMarsh 1.19e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.065342e-3 s grad=2.323e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.023113e-3 s grad=3.607e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.07809e-3 s grad=5.108e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.615756e-3 s grad=3.181e-6 s +[acc-interp] eval 1.3901154e-2 s +[acc-interp] thetaMarsh 7.0e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.19122e-3 s grad=2.604e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.11985e-3 s grad=2.848e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.696682e-3 s grad=6.91e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.887179e-3 s grad=3.529e-6 s +[acc-interp] eval 1.477892e-2 s +[acc-interp] thetaMarsh 6.4e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.148345e-3 s grad=3.073e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.722919e-3 s grad=2.765e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.888809e-3 s grad=3.963e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.302136e-3 s grad=4.099e-6 s +[acc-interp] eval 1.3089332e-2 s +[acc-interp] thetaMarsh 1.01e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=4.234297e-3 s grad=3.466e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.644533e-3 s grad=2.448e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.409747e-3 s grad=2.587e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.005586e-3 s grad=2.117e-6 s +[acc-interp] eval 1.5855846e-2 s +[acc-interp] thetaMarsh 1.14e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=4.56183e-3 s grad=3.112e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.247478e-3 s grad=3.845e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.301519e-3 s grad=2.7e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.110946e-3 s grad=2.844e-6 s +[acc-interp] eval 1.5817419e-2 s +[acc-interp] thetaMarsh 1.53e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.799373e-3 s grad=3.464e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.974323e-3 s grad=2.727e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.550821e-3 s grad=2.743e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.161502e-3 s grad=2.687e-6 s +[acc-interp] eval 1.5000177e-2 s +[acc-interp] thetaMarsh 1.8e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.419895e-3 s grad=3.452e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.590396e-3 s 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chunk base=77922 cs=25974 kernel=4.551222e-3 s grad=4.055e-6 s +[acc-interp] eval 1.5498816e-2 s +[acc-interp] thetaMarsh 1.24e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=4.494047e-3 s grad=3.308e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.109378e-3 s grad=3.197e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.99465e-3 s grad=2.803e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.601969e-3 s grad=3.212e-6 s +[acc-interp] eval 1.7793873e-2 s +[acc-interp] thetaMarsh 1.18e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.343327e-3 s grad=2.572e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.259705e-3 s grad=2.503e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.818769e-3 s grad=3.562e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.355392e-3 s grad=2.19e-6 s +[acc-interp] eval 1.529632e-2 s +[acc-interp] thetaMarsh 2.24e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.227681e-3 s grad=1.824e-6 s +[acc-interp] chunk base=25974 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s +[acc-interp] eval 1.57475e-2 s +[acc-interp] thetaMarsh 1.71e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.39765e-3 s grad=6.637e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.858239e-3 s grad=4.927e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.438015e-3 s grad=4.637e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.483978e-3 s grad=3.673e-6 s +[acc-interp] eval 1.5257314e-2 s +[acc-interp] thetaMarsh 9.9e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.599864e-3 s grad=2.201e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.334643e-3 s grad=4.973e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.695062e-3 s grad=4.798e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.476131e-3 s grad=3.87e-6 s +[acc-interp] eval 1.6155881e-2 s +[acc-interp] thetaMarsh 6.5e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.933832e-3 s grad=4.054e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.101145e-3 s grad=3.544e-6 s +[acc-interp] chunk 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cs=25974 kernel=2.991969e-3 s grad=2.931e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.307512e-3 s grad=4.948e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.023919e-3 s grad=3.732e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.442713e-3 s grad=2.342e-6 s +[acc-interp] eval 1.4397418e-2 s +[acc-interp] thetaMarsh 1.05e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.361177e-3 s grad=2.006e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.884675e-3 s grad=3.806e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.619003e-3 s grad=3.347e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.740243e-3 s grad=7.21e-6 s +[acc-interp] eval 1.5902873e-2 s +[acc-interp] thetaMarsh 6.8e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.422421e-3 s grad=3.447e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.284304e-3 s grad=5.011e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.577697e-3 s grad=4.614e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.609101e-3 s grad=2.716e-6 s +[acc-interp] eval 1.5113298e-2 s +[acc-interp] thetaMarsh 1.0e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.599943e-3 s grad=3.118e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.567162e-3 s grad=2.893e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.7904e-3 s grad=2.838e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.41741e-3 s grad=2.457e-6 s +[acc-interp] eval 1.5752223e-2 s +[acc-interp] thetaMarsh 1.28e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.936273e-3 s grad=2.131e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.19253e-3 s grad=2.931e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.225165e-3 s grad=5.404e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.007745e-3 s grad=3.901e-6 s +[acc-interp] eval 1.4899163e-2 s +[acc-interp] thetaMarsh 1.78e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.555715e-3 s grad=6.458e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.443957e-3 s 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chunk base=77922 cs=25974 kernel=3.879774e-3 s grad=3.304e-6 s +[acc-interp] eval 1.7019012e-2 s +[acc-interp] thetaMarsh 9.8e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.097886e-3 s grad=2.59e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.152141e-3 s grad=2.598e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.859077e-3 s grad=4.622e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.713872e-3 s grad=3.293e-6 s +[acc-interp] eval 1.5333825e-2 s +[acc-interp] thetaMarsh 1.14e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.493859e-3 s grad=2.863e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.629932e-3 s grad=3.028e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=9.620637e-3 s grad=2.583e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.088268e-3 s grad=2.306e-6 s +[acc-interp] eval 2.1212975e-2 s +[acc-interp] thetaMarsh 1.08e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.020273e-3 s grad=2.753e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.388527e-3 s grad=4.622e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.302538e-3 s grad=3.964e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.183716e-3 s grad=4.636e-6 s +[acc-interp] eval 1.4267932e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=3.00973e-3 s grad=3.013e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.705739e-3 s grad=2.79e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.888018e-3 s grad=8.178e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.405649e-3 s grad=4.77e-6 s +[acc-interp] eval 0.798988732 s +[acc-interp] thetaMarsh 6.9e-8 s (n=[acc-interp] thetaMarsh 8.1e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.0407386e-2 s grad=2.989e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.906637e-3 s grad=2.788e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.786648e-3 s grad=3.26e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=7.038643e-3 s grad=2.624e-6 s +[acc-interp] eval 3.554655e-2 s +[acc-interp] thetaMarsh 1.02e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.632269e-3 s grad=1.986e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.905385e-3 s grad=3.411e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.039302e-3 s grad=3.007e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.65903e-3 s grad=2.477e-6 s +[acc-interp] eval 1.3947263e-2 s +[acc-interp] thetaMarsh 6.0e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.954247e-3 s grad=1.727e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.855267e-3 s grad=7.139e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.578128e-3 s grad=6.146e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.256695e-3 s grad=1.154e-6 s +[acc-interp] eval 1.3445162e-2 s +[acc-interp] thetaMarsh 7.8e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.809176e-3 s grad=6.223e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.891273e-3 s grad=1.73e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.707054e-3 s grad=2.009e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.182682e-3 s grad=3.92e-6 s +[acc-interp] eval 1.2642209e-2 s +[acc-interp] thetaMarsh 1.7e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.574096e-3 s grad=2.801e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.992914e-3 s grad=2.131e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.030463e-3 s grad=2.282e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.39169e-3 s grad=2.49e-6 s +[acc-interp] eval 1.4542882e-2 s +[acc-interp] thetaMarsh 1.29e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.047773e-3 s grad=2.488e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.471654e-3 s grad=2.644e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.989396e-3 s grad=1.768e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.131756e-3 s grad=2.085e-6 s +[acc-interp] eval 1.4232104e-2 s +[acc-interp] thetaMarsh 1.09e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=4.834102e-3 s grad=2.843e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.13499e-3 s grad=3.285e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.168439e-3 s grad=2.0e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.896676e-3 s grad=8.64e-6 s +[acc-interp] eval 1.5697167e-2 s +[acc-interp] thetaMarsh 1.12e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.79963e-3 s grad=3.423e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.040715e-3 s grad=2.421e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.046701e-3 s grad=2.306e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.809415e-3 s grad=1.753e-6 s +[acc-interp] eval 1.4323495e-2 s +[acc-interp] thetaMarsh 1.02e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.366876e-3 s grad=2.097e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.422588e-3 s grad=4.374e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.320438e-3 s grad=2.841e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.997013e-3 s grad=3.304e-6 s +[acc-interp] eval 1.3839073e-2 s +[acc-interp] thetaMarsh 1.25e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.599062e-3 s grad=3.189e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.76312e-3 s grad=4.565e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.714718e-3 s grad=3.338e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.072941e-3 s grad=2.251e-6 s +[acc-interp] eval 1.4769338e-2 s +[acc-interp] thetaMarsh 1.02e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.676344e-3 s grad=2.321e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.771292e-3 s grad=2.713e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.847502e-3 s grad=3.693e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.245545e-3 s grad=2.808e-6 s +[acc-interp] eval 1.3252378e-2 s +[acc-interp] thetaMarsh 1.26e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.823213e-3 s grad=2.52e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.928094e-3 s grad=2.673e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.151549e-3 s grad=2.428e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.285011e-3 s grad=3.289e-6 s +[acc-interp] eval 1.3702149e-2 s +[acc-interp] thetaMarsh 1.2e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.223569e-3 s grad=2.572e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.112556e-3 s grad=2.95e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.75179e-3 s grad=2.207e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.404563e-3 s grad=2.094e-6 s +[acc-interp] eval 1.4065081e-2 s +[acc-interp] thetaMarsh 1.17e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.870797e-3 s grad=2.096e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.106288e-3 s grad=2.526e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.39042e-3 s grad=2.986e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.016009e-3 s grad=8.47e-6 s +[acc-interp] eval 1.3554774e-2 s +[acc-interp] thetaMarsh 6.5e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.685193e-3 s grad=3.787e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.827625e-3 s grad=3.469e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.241717e-3 s grad=2.681e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.273692e-3 s grad=2.903e-6 s +[acc-interp] eval 1.4452637e-2 s +[acc-interp] thetaMarsh 1.2e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.757482e-3 s grad=2.172e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.642102e-3 s grad=3.095e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.789508e-3 s grad=2.759e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.962103e-3 s grad=2.669e-6 s +[acc-interp] eval 1.3951005e-2 s +[acc-interp] thetaMarsh 1.2e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.045474e-3 s grad=2.475e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.647662e-3 s grad=4.278e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.039667e-3 s grad=3.135e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.660766e-3 s grad=2.767e-6 s +[acc-interp] eval 1.4988596e-2 s +[acc-interp] thetaMarsh 1.2e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.851056e-3 s grad=1.839e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.089692e-3 s grad=2.934e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.446964e-3 s grad=3.058e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.217581e-3 s grad=3.701e-6 s +[acc-interp] eval 1.3940735e-2 s +[acc-interp] thetaMarsh 6.6e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.1951098e-2 s grad=6.593e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.576571e-3 s grad=1.852e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.451872e-3 s grad=2.877e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.867883e-3 s grad=2.348e-6 s +[acc-interp] eval 2.2673299e-2 s +[acc-interp] thetaMarsh 1.64e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.834113e-3 s grad=2.516e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.685074e-3 s grad=7.22e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.801136e-3 s grad=5.833e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.852491e-3 s grad=2.143e-6 s +[acc-interp] eval 1.1946906e-2 s +[acc-interp] thetaMarsh 6.7e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.307881e-3 s grad=2.842e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.853392e-3 s grad=8.346e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.59473e-3 s grad=5.066e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.826785e-3 s grad=4.368e-6 s +[acc-interp] eval 1.2374813e-2 s +[acc-interp] thetaMarsh 7.2e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.032763e-3 s grad=2.257e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.571444e-3 s grad=2.51e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.973728e-3 s grad=2.245e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.749572e-3 s grad=3.712e-6 s +[acc-interp] eval 1.2681471e-2 s +[acc-interp] thetaMarsh 1.64e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.425079e-3 s grad=2.922e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.944747e-3 s grad=2.705e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.60198e-3 s grad=1.806e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.688659e-3 s grad=1.686e-6 s +[acc-interp] eval 1.3203695e-2 s +[acc-interp] thetaMarsh 3.31e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.914561e-3 s grad=4.089e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.783292e-3 s grad=4.618e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.77351e-3 s grad=2.837e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.12793e-3 s grad=2.849e-6 s +[acc-interp] eval 1.4771645e-2 s +[acc-interp] thetaMarsh 2.37e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.391264e-3 s grad=4.3e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.323802e-3 s grad=3.503e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.672934e-3 s grad=1.828e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.555583e-3 s grad=7.035e-6 s +[acc-interp] eval 1.4845403e-2 s +[acc-interp] thetaMarsh 6.2e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.119493e-3 s grad=1.403e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.507963e-3 s grad=3.194e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.283858e-3 s grad=2.677e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.143728e-3 s grad=2.852e-6 s +[acc-interp] eval 1.4399982e-2 s +[acc-interp] thetaMarsh 1.35e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.349492e-3 s grad=2.507e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.910434e-3 s grad=1.714e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.760583e-3 s grad=4.155e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.242443e-3 s grad=2.857e-6 s +[acc-interp] eval 1.5811521e-2 s +[acc-interp] thetaMarsh 1.26e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.403625e-3 s grad=3.516e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.178587e-3 s grad=8.102e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.529393e-3 s grad=5.375e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.664724e-3 s grad=4.925e-6 s +[acc-interp] eval 1.4921887e-2 s +[acc-interp] thetaMarsh 6.9e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.953554e-3 s grad=6.192e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.446903e-3 s grad=1.731e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.681886e-3 s grad=4.917e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.908069e-3 s grad=3.536e-6 s +[acc-interp] eval 1.4999234e-2 s +[acc-interp] thetaMarsh 1.87e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.515433e-3 s grad=2.745e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.597787e-3 s grad=2.704e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.884032e-3 s grad=2.413e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.292638e-3 s grad=2.499e-6 s +[acc-interp] eval 1.3714777e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=9.913797e-3 s grad=2.492e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.871987e-3 s grad=3.166e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.290254e-3 s grad=4.235e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.103944e-3 s grad=3.276e-6 s +[acc-interp] eval 0.47676164 s +[acc-interp] thetaMarsh 8.2e-8 s (n=[acc-interp] thetaMarsh 5.9e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.1218998e-2 s grad=3.575e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.322797e-3 s grad=4.574e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.913995e-3 s grad=3.619e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.850306e-3 s grad=2.868e-6 s +[acc-interp] eval 3.4706455e-2 s +[acc-interp] thetaMarsh 9.9e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.0393785e-2 s grad=3.315e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.072731e-3 s grad=4.454e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.342032e-3 s grad=3.244e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.855281e-3 s grad=6.833e-6 s +[acc-interp] eval 2.0818004e-2 s +[acc-interp] thetaMarsh 6.5e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.745544e-3 s grad=4.884e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=6.223609e-3 s grad=3.127e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.535468e-3 s grad=2.495e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.744185e-3 s grad=3.252e-6 s +[acc-interp] eval 1.6341999e-2 s +[acc-interp] thetaMarsh 6.4e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.695865e-3 s grad=2.908e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.177565e-3 s grad=8.027e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.881707e-3 s grad=2.347e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.344002e-3 s grad=2.6e-6 s +[acc-interp] eval 1.1895942e-2 s +[acc-interp] thetaMarsh 1.92e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.756833e-3 s grad=2.938e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.788694e-3 s grad=4.272e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.50762e-3 s grad=4.206e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.07297e-3 s grad=2.778e-6 s +[acc-interp] eval 1.3116633e-2 s +[acc-interp] thetaMarsh 6.9e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.931383e-3 s grad=2.631e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.120653e-3 s grad=3.853e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.961809e-3 s grad=5.026e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.366391e-3 s grad=4.257e-6 s +[acc-interp] eval 1.4643609e-2 s +[acc-interp] thetaMarsh 1.47e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=4.542608e-3 s grad=3.7e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.449321e-3 s grad=8.113e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.045943e-3 s grad=4.019e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.137175e-3 s grad=2.879e-6 s +[acc-interp] eval 1.6492678e-2 s +[acc-interp] thetaMarsh 1.15e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.750298e-3 s grad=3.484e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.85421e-3 s grad=3.29e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.324063e-3 s grad=4.537e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.377492e-3 s grad=3.535e-6 s +[acc-interp] eval 1.5621262e-2 s +[acc-interp] thetaMarsh 1.1e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.335819e-3 s grad=2.708e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.117103e-3 s grad=2.624e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.941693e-3 s grad=2.139e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.778912e-3 s grad=1.714e-6 s +[acc-interp] eval 1.3723523e-2 s +[acc-interp] thetaMarsh 1.31e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.47708e-3 s grad=2.96e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.000875e-3 s grad=2.735e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.223267e-3 s grad=2.463e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.047634e-3 s grad=1.706e-6 s +[acc-interp] eval 1.4342586e-2 s +[acc-interp] thetaMarsh 1.18e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.242946e-3 s grad=2.596e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.279173e-3 s grad=3.862e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.24013e-3 s grad=4.123e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.397482e-3 s grad=3.981e-6 s +[acc-interp] eval 1.4851211e-2 s +[acc-interp] thetaMarsh 9.8e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.322539e-3 s grad=3.607e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.930012e-3 s grad=2.54e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.857281e-3 s grad=3.93e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.775836e-3 s grad=3.011e-6 s +[acc-interp] eval 1.4444276e-2 s +[acc-interp] thetaMarsh 1.05e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.198474e-3 s grad=2.859e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=9.378285e-3 s grad=3.419e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.905388e-3 s grad=6.531e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.108075e-3 s grad=1.089e-6 s +[acc-interp] eval 1.9690457e-2 s +[acc-interp] thetaMarsh 6.0e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.533531e-3 s grad=2.363e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.858326e-3 s grad=3.039e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.402464e-3 s grad=2.954e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.686948e-3 s grad=2.496e-6 s +[acc-interp] eval 1.280247e-2 s +[acc-interp] thetaMarsh 1.14e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.086933e-3 s grad=2.466e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.491417e-3 s grad=2.526e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.972667e-3 s grad=2.094e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.61065e-3 s grad=3.214e-6 s +[acc-interp] eval 1.3770048e-2 s +[acc-interp] thetaMarsh 1.16e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.947391e-3 s grad=2.793e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.891456e-3 s grad=1.899e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.484845e-3 s grad=1.359e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.566649e-3 s grad=2.147e-6 s +[acc-interp] eval 1.2391625e-2 s +[acc-interp] thetaMarsh 1.26e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.297512e-3 s grad=4.116e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.60072e-3 s grad=3.067e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.989137e-3 s grad=1.528e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.920553e-3 s grad=2.983e-6 s +[acc-interp] eval 1.335819e-2 s +[acc-interp] thetaMarsh 7.7e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.585591e-3 s grad=4.118e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.443395e-3 s 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base=77922 cs=25974 kernel=3.753953e-3 s grad=3.871e-6 s +[acc-interp] eval 1.6008806e-2 s +[acc-interp] thetaMarsh 1.08e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.568662e-3 s grad=3.821e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.79686e-3 s grad=4.055e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.472837e-3 s grad=1.629e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.0984361e-2 s grad=4.646e-6 s +[acc-interp] eval 2.3096491e-2 s +[acc-interp] thetaMarsh 1.1e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.14246e-3 s grad=2.437e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.6033e-3 s grad=2.876e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.904309e-3 s grad=4.266e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.311536e-3 s grad=2.975e-6 s +[acc-interp] eval 1.5437855e-2 s +[acc-interp] thetaMarsh 1.13e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.72365e-3 s grad=3.067e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.688562e-3 s grad=2.072e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.626312e-3 s grad=2.37e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.057836e-3 s grad=3.216e-6 s +[acc-interp] eval 1.2545224e-2 s +[acc-interp] thetaMarsh 1.14e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.304098e-3 s grad=3.855e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.020927e-3 s grad=3.01e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.383917e-3 s grad=2.14e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.056169e-3 s grad=3.501e-6 s +[acc-interp] eval 1.4268401e-2 s +[acc-interp] thetaMarsh 1.18e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.795382e-3 s grad=3.453e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.687859e-3 s grad=1.733e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.196698e-3 s grad=7.76e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.641689e-3 s grad=4.396e-6 s +[acc-interp] eval 1.5467611e-2 s +[acc-interp] thetaMarsh 1.43e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.253909e-3 s grad=2.917e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.396099e-3 s grad=2.847e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.126357e-3 s grad=2.425e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.957717e-3 s grad=2.501e-6 s +[acc-interp] eval 1.4337091e-2 s +[acc-interp] thetaMarsh 1.38e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=4.222522e-3 s grad=3.366e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.012856e-3 s grad=2.882e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.181978e-3 s grad=4.053e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.170272e-3 s grad=4.352e-6 s +[acc-interp] eval 1.4912995e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=3.425344e-3 s grad=3.772e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.713134e-3 s grad=3.709e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.793905e-3 s grad=4.287e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.148752e-3 s grad=3.046e-6 s +[acc-interp] eval 0.490665705 s +[acc-interp] thetaMarsh 1.09e-7 s (n=[acc-interp] thetaMarsh 6.4e-8 s (n=8) +[acc-interp] chunk base=0 cs=25974 kernel=3.2291678e-2 s grad=5.419e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.100388e-3 s grad=6.965e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.540811e-3 s grad=5.654e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.512151e-3 s grad=4.428e-6 s +[acc-interp] eval 4.6905055e-2 s +[acc-interp] thetaMarsh 1.12e-7 s (n=8) +[acc-interp] chunk base=0 cs=25974 kernel=3.87357e-3 s grad=3.701e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.97596e-3 s grad=4.324e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.035498e-3 s grad=4.004e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.645293e-3 s grad=3.391e-6 s +[acc-interp] eval 1.7144585e-2 s +[acc-interp] thetaMarsh 6.6e-8 s (n=8) +[acc-interp] chunk base=0 cs=25974 kernel=3.414354e-3 s grad=3.392e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.903485e-3 s grad=3.596e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.475096e-3 s grad=2.115e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.575331e-3 s grad=1.1314e-5 s +[acc-interp] eval 1.5518804e-2 s +[acc-interp] thetaMarsh 7.0e-8 s (n=8) +[acc-interp] chunk base=0 cs=25974 kernel=3.568575e-3 s grad=3.826e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.855663e-3 s grad=4.346e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.289248e-3 s grad=4.124e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.125734e-3 s grad=5.548e-6 s +[acc-interp] eval 1.8419691e-2 s +[acc-interp] thetaMarsh 4.68e-7 s (n=8) +[acc-interp] chunk base=0 cs=25974 kernel=4.684382e-3 s grad=3.545e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.336352e-3 s grad=7.914e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.839838e-3 s grad=4.143e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.5065148e-2 s grad=6.55e-6 s +[acc-interp] eval 3.1141647e-2 s +[acc-interp] thetaMarsh 6.7e-8 s (n=8) +[acc-interp] chunk base=0 cs=25974 kernel=3.615334e-3 s grad=4.251e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.746061e-3 s grad=6.172e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.192056e-3 s grad=4.161e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.666654e-3 s grad=5.589e-6 s +[acc-interp] eval 1.7397064e-2 s +[acc-interp] thetaMarsh 7.4e-8 s (n=8) +[acc-interp] chunk base=0 cs=25974 kernel=3.706107e-3 s grad=4.896e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.96078e-3 s grad=5.296e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.007228e-3 s grad=3.905e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.209559e-3 s grad=4.843e-6 s +[acc-interp] eval 1.7829982e-2 s +[acc-interp] thetaMarsh 6.8e-8 s (n=8) +[acc-interp] chunk base=0 cs=25974 kernel=4.827856e-3 s grad=4.223e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.093183e-3 s grad=4.276e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.150121e-3 s grad=3.602e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.1945127e-2 s grad=5.59e-6 s +[acc-interp] eval 2.7942443e-2 s +[acc-interp] thetaMarsh 6.5e-8 s (n=8) +[acc-interp] chunk base=0 cs=25974 kernel=3.57732e-3 s grad=3.556e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.810495e-3 s grad=4.106e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.438533e-3 s grad=4.624e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.757678e-3 s grad=5.166e-6 s +[acc-interp] eval 1.7534226e-2 s +[acc-interp] thetaMarsh 2.91e-7 s (n=8) +[acc-interp] chunk base=0 cs=25974 kernel=4.277011e-3 s grad=4.279e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.048791e-3 s grad=4.875e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.573963e-3 s grad=4.423e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.304072e-3 s grad=4.381e-6 s +[acc-interp] eval 1.9153528e-2 s +[acc-interp] thetaMarsh 6.7e-8 s (n=8) +[acc-interp] chunk base=0 cs=25974 kernel=4.975699e-3 s grad=1.4096e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=6.797537e-3 s grad=3.269e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.307805e-3 s grad=5.981e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.0857094e-2 s grad=2.8062e-5 s +[acc-interp] eval 2.8784347e-2 s +[acc-interp] thetaMarsh 1.01e-7 s (n=8) +[acc-interp] chunk base=0 cs=25974 kernel=4.261849e-3 s grad=1.4514e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.192154e-3 s grad=6.038e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.313034e-3 s grad=4.364e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.410479e-3 s grad=4.504e-6 s +[acc-interp] eval 1.8995272e-2 s +8) +[acc-interp] chunk base=0 cs=25974 kernel=4.585574e-3 s grad=2.784e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.841536e-3 s grad=4.645e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.011312e-3 s grad=3.557e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.491429e-3 s grad=1.3629e-5 s +[acc-interp] eval 0.297814412 s +[acc-interp] thetaMarsh 8.9e-8 s (n=[acc-interp] thetaMarsh 6.2e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.800813e-2 s grad=3.37e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.403365e-3 s grad=7.926e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.137614e-3 s grad=7.969e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.32501e-3 s grad=3.571e-6 s +[acc-interp] eval 3.8707294e-2 s +[acc-interp] thetaMarsh 8.2e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.643213e-3 s grad=3.908e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.508306e-3 s grad=3.882e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.921499e-3 s grad=4.485e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.241704e-3 s grad=3.573e-6 s +[acc-interp] eval 1.4250886e-2 s +[acc-interp] thetaMarsh 8.3e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.812449e-3 s grad=4.863e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.079401e-3 s grad=1.202e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.602584e-3 s grad=3.062e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.104274e-3 s grad=3.578e-6 s +[acc-interp] eval 1.5472162e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=3.436391e-3 s grad=4.23e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.158032e-3 s grad=4.51e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.359567e-3 s grad=3.852e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.246354e-3 s grad=3.887e-6 s +[acc-interp] eval 8.3838839e-2 s +[acc-interp] thetaMarsh 8.4e-8 s (n=[acc-interp] thetaMarsh 8.0e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.6621321e-2 s grad=3.994e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.117526e-3 s grad=4.438e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.820097e-3 s grad=3.395e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.805921e-3 s grad=3.289e-6 s +[acc-interp] eval 3.9476374e-2 s +[acc-interp] thetaMarsh 1.77e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.1337233e-2 s grad=4.754e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.942722e-3 s grad=4.104e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.314263e-3 s grad=4.55e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.01068e-3 s grad=3.595e-6 s +[acc-interp] eval 2.2791175e-2 s +[acc-interp] thetaMarsh 1.77e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.883367e-3 s grad=3.545e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.009442e-3 s grad=4.444e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.448353e-3 s grad=5.337e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.438963e-3 s grad=4.312e-6 s +[acc-interp] eval 1.5066957e-2 s +[acc-interp] thetaMarsh 2.6e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.410858e-3 s grad=3.833e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.838791e-3 s grad=3.827e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.981188e-3 s grad=4.39e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.532991e-3 s grad=4.877e-6 s +[acc-interp] eval 1.6057997e-2 s +[acc-interp] thetaMarsh 1.74e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.564138e-3 s grad=4.397e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.964632e-3 s grad=4.347e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.305914e-3 s grad=2.997e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.282155e-3 s grad=3.559e-6 s +[acc-interp] eval 1.5589996e-2 s +[acc-interp] thetaMarsh 6.3e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.466244e-3 s grad=4.235e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.644337e-3 s grad=3.534e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.72214e-3 s grad=3.639e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.958973e-3 s grad=3.171e-6 s +[acc-interp] eval 1.7327608e-2 s +[acc-interp] thetaMarsh 1.33e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.638899e-3 s grad=2.587e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.0784177e-2 s grad=3.738e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.296571e-3 s grad=3.855e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.210002e-3 s grad=2.806e-6 s +[acc-interp] eval 2.2509399e-2 s +[acc-interp] thetaMarsh 1.16e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.825465e-3 s grad=4.384e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.503449e-3 s grad=2.927e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.164975e-3 s grad=2.739e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.16554e-3 s grad=2.662e-6 s +[acc-interp] eval 1.6556005e-2 s +[acc-interp] thetaMarsh 1.25e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.863339e-3 s grad=3.776e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.541076e-3 s grad=3.187e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.152666e-3 s grad=2.514e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.315983e-3 s grad=2.935e-6 s +[acc-interp] eval 1.5634791e-2 s +[acc-interp] thetaMarsh 1.02e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.660528e-3 s grad=2.477e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.651436e-3 s grad=2.817e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.472892e-3 s grad=3.941e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.565709e-3 s grad=2.996e-6 s +[acc-interp] eval 1.5903393e-2 s +[acc-interp] thetaMarsh 1.15e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.386673e-3 s grad=4.51e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=8.819068e-3 s grad=2.836e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.155935e-3 s grad=2.595e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.730534e-3 s grad=2.97e-6 s +[acc-interp] eval 2.1642788e-2 s +[acc-interp] thetaMarsh 9.5e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.174673e-3 s grad=2.516e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.553712e-3 s grad=2.902e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.240447e-3 s grad=3.871e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.569437e-3 s grad=4.186e-6 s +[acc-interp] eval 1.5356769e-2 s +[acc-interp] thetaMarsh 1.12e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.895894e-3 s grad=2.663e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.176617e-3 s grad=4.036e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.118407e-3 s grad=3.286e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.753097e-3 s grad=4.249e-6 s +[acc-interp] eval 1.6720395e-2 s +[acc-interp] thetaMarsh 1.9e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.755979e-3 s grad=2.949e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.350657e-3 s grad=4.188e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.099326e-3 s grad=3.925e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.410369e-3 s grad=3.856e-6 s +[acc-interp] eval 1.8349446e-2 s +[acc-interp] thetaMarsh 1.13e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=9.237646e-3 s grad=3.478e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.395411e-3 s grad=3.101e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.31979e-3 s grad=2.784e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.112163e-3 s grad=2.171e-6 s +[acc-interp] eval 2.0656927e-2 s +[acc-interp] thetaMarsh 1.07e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.166258e-3 s grad=2.817e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.073763e-3 s grad=3.645e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.415236e-3 s grad=2.789e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.285138e-3 s grad=3.148e-6 s +[acc-interp] eval 1.5615272e-2 s +[acc-interp] thetaMarsh 1.01e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.058838e-3 s grad=3.636e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.240797e-3 s grad=3.944e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.839557e-3 s grad=2.9e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.414325e-3 s grad=3.523e-6 s +[acc-interp] eval 1.7161191e-2 s +[acc-interp] thetaMarsh 9.9e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=5.49815e-3 s grad=3.697e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.302962e-3 s grad=2.819e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.130971e-3 s grad=3.068e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.2049462e-2 s grad=3.714e-6 s +[acc-interp] eval 2.7474989e-2 s +[acc-interp] thetaMarsh 1.21e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.381432e-3 s grad=2.747e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.33838e-3 s grad=3.607e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.727798e-3 s grad=5.083e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.39855e-3 s grad=3.742e-6 s +[acc-interp] eval 1.6517517e-2 s +[acc-interp] thetaMarsh 1.18e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.156798e-3 s grad=3.06e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.348818e-3 s grad=2.47e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.24121e-3 s grad=4.434e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.768139e-3 s grad=3.208e-6 s +[acc-interp] eval 1.72142e-2 s +[acc-interp] thetaMarsh 1.12e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.73852e-3 s grad=3.087e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.604968e-3 s grad=3.559e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.137195e-3 s grad=3.32e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.82016e-3 s grad=2.926e-6 s +[acc-interp] eval 1.7050567e-2 s +[acc-interp] thetaMarsh 1.05e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.793615e-3 s grad=2.805e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.913918e-3 s grad=3.97e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=8.011295e-3 s grad=3.717e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.740049e-3 s grad=3.116e-6 s +[acc-interp] eval 2.0188133e-2 s +[acc-interp] thetaMarsh 1.0e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.777552e-3 s grad=1.992e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.914779e-3 s grad=2.447e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.014242e-3 s grad=2.591e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.914144e-3 s grad=1.818e-6 s +[acc-interp] eval 1.3188022e-2 s +[acc-interp] thetaMarsh 1.15e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.864564e-3 s grad=1.861e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.860045e-3 s grad=3.031e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.984836e-3 s grad=3.587e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.826663e-3 s grad=5.03e-6 s +[acc-interp] eval 1.4876261e-2 s +[acc-interp] thetaMarsh 1.3e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.245255e-3 s grad=4.028e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.308156e-3 s grad=2.975e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.57573e-3 s grad=4.528e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.129024e-3 s grad=4.286e-6 s +[acc-interp] eval 1.8861197e-2 s +[acc-interp] thetaMarsh 1.04e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.790554e-3 s grad=3.062e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.2346533e-2 s grad=3.721e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.654566e-3 s grad=3.568e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.088965e-3 s grad=3.203e-6 s +[acc-interp] eval 2.4503477e-2 s +[acc-interp] thetaMarsh 1.77e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.805523e-3 s grad=4.639e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.476209e-3 s grad=3.162e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.374032e-3 s grad=2.309e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.50535e-3 s grad=3.264e-6 s +[acc-interp] eval 1.5808896e-2 s +[acc-interp] thetaMarsh 1.38e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.95007e-3 s grad=4.183e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.667623e-3 s grad=2.954e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.46201e-3 s grad=2.437e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.535259e-3 s grad=3.924e-6 s +[acc-interp] eval 1.6207108e-2 s +[acc-interp] thetaMarsh 1.21e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.171224e-3 s grad=2.777e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.065463e-3 s grad=1.959e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.333895e-3 s grad=2.012e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.565334e-3 s grad=2.351e-6 s +[acc-interp] eval 1.4511333e-2 s +[acc-interp] thetaMarsh 1.1e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.002686e-2 s grad=3.113e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.214555e-3 s grad=2.904e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.576168e-3 s grad=3.702e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.537006e-3 s grad=2.89e-6 s +[acc-interp] eval 2.1932538e-2 s +[acc-interp] thetaMarsh 9.7e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.180385e-3 s grad=2.476e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.561341e-3 s grad=2.571e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.385284e-3 s grad=2.385e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.462577e-3 s grad=2.99e-6 s +[acc-interp] eval 1.6091011e-2 s +[acc-interp] thetaMarsh 1.07e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.022278e-3 s grad=2.844e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.069823e-3 s grad=3.927e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.379216e-3 s grad=4.863e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.735765e-3 s grad=4.148e-6 s +[acc-interp] eval 1.7373134e-2 s +[acc-interp] thetaMarsh 6.7e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.954101e-3 s grad=2.914e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.444813e-3 s grad=3.21e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.452702e-3 s grad=4.997e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.0551842e-2 s grad=4.713e-6 s +[acc-interp] eval 2.2997761e-2 s +[acc-interp] thetaMarsh 1.11e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.288177e-3 s grad=3.27e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.690397e-3 s grad=4.352e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.998499e-3 s grad=3.381e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.918394e-3 s grad=4.132e-6 s +[acc-interp] eval 1.6578788e-2 s +[acc-interp] thetaMarsh 1.06e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.924813e-3 s grad=3.103e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.532134e-3 s grad=3.402e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.819051e-3 s grad=3.795e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.896251e-3 s grad=3.631e-6 s +[acc-interp] eval 1.7725314e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=3.655567e-3 s grad=3.543e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.259524e-3 s grad=3.331e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.80472e-3 s grad=3.081e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.804006e-3 s grad=3.099e-6 s +[acc-interp] eval 0.673657712 s +[acc-interp] thetaMarsh 1.37e-7 s (n=[acc-interp] thetaMarsh 1.24e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.9422796e-2 s grad=4.708e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.317793e-3 s grad=3.378e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.254385e-3 s grad=4.563e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.656214e-3 s grad=4.558e-6 s +[acc-interp] eval 4.232443e-2 s +[acc-interp] thetaMarsh 1.92e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.521178e-3 s grad=3.739e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.032342e-3 s grad=3.046e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.961153e-3 s grad=5.723e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.543362e-3 s grad=3.182e-6 s 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kernel=9.04415e-3 s grad=3.801e-6 s +[acc-interp] eval 2.2911134e-2 s +[acc-interp] thetaMarsh 1.03e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.47212e-3 s grad=4.369e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.805037e-3 s grad=3.522e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.253142e-3 s grad=3.727e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.343345e-3 s grad=4.777e-6 s +[acc-interp] eval 1.768819e-2 s +[acc-interp] thetaMarsh 9.9e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.849071e-3 s grad=4.492e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.493935e-3 s grad=3.348e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.134177e-3 s grad=2.974e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.172154e-3 s grad=4.122e-6 s +[acc-interp] eval 1.5341918e-2 s +[acc-interp] thetaMarsh 1.08e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.690765e-3 s grad=4.533e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.880308e-3 s 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base=77922 cs=25974 kernel=3.43953e-3 s grad=3.581e-6 s +[acc-interp] eval 2.2754068e-2 s +[acc-interp] thetaMarsh 9.4e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.851588e-3 s grad=3.691e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.786488e-3 s grad=3.903e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.514908e-3 s grad=3.996e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.502067e-3 s grad=2.768e-6 s +[acc-interp] eval 1.5497215e-2 s +[acc-interp] thetaMarsh 6.5e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.622432e-3 s grad=3.427e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.433901e-3 s grad=5.02e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.286489e-3 s grad=5.05e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.96053e-3 s grad=4.683e-6 s +[acc-interp] eval 1.7163856e-2 s +[acc-interp] thetaMarsh 6.5e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.387856e-3 s grad=4.749e-6 s +[acc-interp] chunk base=25974 cs=25974 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thetaMarsh 7.7e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.195491e-3 s grad=9.213e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.890025e-3 s grad=3.378e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.022107e-3 s grad=3.547e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.254364e-3 s grad=3.571e-6 s +[acc-interp] eval 1.4342624e-2 s +[acc-interp] thetaMarsh 7.4e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.253602e-3 s grad=3.577e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.265487e-3 s grad=3.717e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.267327e-3 s grad=3.853e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.568662e-3 s grad=1.4284e-5 s +[acc-interp] eval 1.5652125e-2 s +[acc-interp] thetaMarsh 1.12e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=8.697138e-3 s grad=3.652e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.89356e-3 s grad=4.305e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.817827e-3 s grad=3.804e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.81008e-3 s grad=2.0275e-5 s +[acc-interp] eval 1.879278e-2 s +[acc-interp] thetaMarsh 6.9e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.925235e-3 s grad=4.687e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.824924e-3 s grad=3.242e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.068884e-3 s grad=4.094e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.912328e-3 s grad=3.186e-6 s +[acc-interp] eval 1.2610957e-2 s +[acc-interp] thetaMarsh 6.4e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.190283e-3 s grad=4.845e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.835044e-3 s grad=2.731e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.933875e-3 s grad=3.115e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.18752e-3 s grad=3.371e-6 s +[acc-interp] eval 1.3192722e-2 s +[acc-interp] thetaMarsh 1.13e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.022818e-3 s grad=2.896e-6 s 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thetaMarsh 1.08e-7 s (n=[acc-interp] thetaMarsh 1.29e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.2306373e-2 s grad=5.144e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.470718e-3 s grad=4.781e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.393732e-3 s grad=3.775e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.351336e-3 s grad=3.27e-6 s +[acc-interp] eval 4.3778828e-2 s +[acc-interp] thetaMarsh 1.07e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.159452e-3 s grad=3.287e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.171851e-3 s grad=3.816e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.922444e-3 s grad=5.184e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.563085e-3 s grad=3.264e-6 s +[acc-interp] eval 1.5229005e-2 s +[acc-interp] thetaMarsh 6.6e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.623681e-3 s grad=2.783e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.469371e-3 s grad=9.485e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.60328e-3 s grad=3.994e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.586386e-3 s grad=3.291e-6 s +[acc-interp] eval 1.5303333e-2 s +[acc-interp] thetaMarsh 1.91e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.317995e-3 s grad=1.0167e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.638058e-3 s grad=8.166e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.191353e-3 s grad=6.042e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.318978e-3 s grad=4.182e-6 s +[acc-interp] eval 1.5289757e-2 s +[acc-interp] thetaMarsh 6.4e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.343016e-3 s grad=4.22e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.597067e-3 s grad=4.91e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.53079e-3 s grad=4.32e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.102695e-3 s grad=3.734e-6 s +[acc-interp] eval 1.683759e-2 s +[acc-interp] thetaMarsh 1.04e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.57524e-3 s grad=2.681e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.539284e-3 s grad=3.144e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.655422e-3 s grad=2.668e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.333918e-3 s grad=4.78e-6 s +[acc-interp] eval 1.6591261e-2 s +[acc-interp] thetaMarsh 3.09e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.7365443e-2 s grad=3.738e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.733193e-3 s grad=3.597e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.677689e-3 s grad=3.657e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.743625e-3 s grad=4.317e-6 s +[acc-interp] eval 3.0153815e-2 s +[acc-interp] thetaMarsh 1.25e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.848598e-3 s grad=3.53e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.567937e-3 s grad=3.311e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.539839e-3 s grad=3.773e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.706947e-3 s grad=4.766e-6 s +[acc-interp] eval 1.6341793e-2 s +[acc-interp] thetaMarsh 1.38e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.828113e-3 s grad=3.384e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.742593e-3 s grad=3.687e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.684441e-3 s grad=3.097e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.466499e-3 s grad=2.778e-6 s +[acc-interp] eval 1.6339577e-2 s +[acc-interp] thetaMarsh 1.01e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.523239e-3 s grad=3.024e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.144467e-3 s grad=2.598e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.346923e-3 s grad=1.876e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.450422e-3 s grad=2.511e-6 s +[acc-interp] eval 1.4923726e-2 s +[acc-interp] thetaMarsh 2.86e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=9.393957e-3 s grad=4.064e-6 s +[acc-interp] chunk base=25974 cs=25974 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thetaMarsh 7.9e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.08653e-3 s grad=3.726e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.434712e-3 s grad=3.989e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.318716e-3 s grad=3.994e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=8.965607e-3 s grad=3.576e-6 s +[acc-interp] eval 2.1004815e-2 s +[acc-interp] thetaMarsh 2.33e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.383628e-3 s grad=3.116e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.023996e-3 s grad=3.1e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.883866e-3 s grad=3.12e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.689804e-3 s grad=2.943e-6 s +[acc-interp] eval 1.1816854e-2 s +[acc-interp] thetaMarsh 1.1e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.359831e-3 s grad=3.259e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.215292e-3 s grad=3.496e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.771704e-3 s grad=4.492e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.656154e-3 s grad=4.506e-6 s +[acc-interp] eval 1.5327154e-2 s +[acc-interp] thetaMarsh 1.09e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.543954e-3 s grad=3.436e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.342153e-3 s grad=3.493e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.999981e-3 s grad=6.621e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.215172e-3 s grad=5.101e-6 s +[acc-interp] eval 1.6504763e-2 s +[acc-interp] thetaMarsh 1.6e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.677767e-3 s grad=4.163e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.521767e-3 s grad=3.749e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.005286e-3 s grad=4.128e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=9.1484e-3 s grad=3.712e-6 s +[acc-interp] eval 2.156946e-2 s +[acc-interp] thetaMarsh 7.9e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.156526e-3 s grad=6.091e-6 s 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eval 1.5685793e-2 s +[acc-interp] thetaMarsh 8.1e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.76799e-3 s grad=4.089e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.720126e-3 s grad=2.803e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.713916e-3 s grad=5.246e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.0076126e-2 s grad=4.898e-6 s +[acc-interp] eval 2.3469106e-2 s +[acc-interp] thetaMarsh 6.4e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.637428e-3 s grad=3.203e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.448899e-3 s grad=4.313e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.136141e-3 s grad=3.688e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.230068e-3 s grad=3.523e-6 s +[acc-interp] eval 1.4596474e-2 s +[acc-interp] thetaMarsh 6.6e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.514097e-3 s grad=3.44e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.7848e-3 s grad=4.036e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.582457e-3 s grad=6.368e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.603995e-3 s grad=8.778e-6 s +[acc-interp] eval 1.6363077e-2 s +[acc-interp] thetaMarsh 6.6e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.527085e-3 s grad=3.212e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.048019e-3 s grad=4.063e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.444341e-3 s grad=3.802e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.398854e-3 s grad=2.512e-6 s +[acc-interp] eval 1.5547375e-2 s +[acc-interp] thetaMarsh 7.1e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.193094e-3 s grad=8.607e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.52097e-3 s grad=4.577e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.1311354e-2 s grad=3.836e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.043977e-3 s grad=4.988e-6 s +[acc-interp] eval 2.4156128e-2 s +[acc-interp] thetaMarsh 1.06e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.388196e-3 s grad=4.349e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.412263e-3 s grad=4.016e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.631266e-3 s grad=9.499e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.024984e-3 s grad=2.1661e-5 s +[acc-interp] eval 1.5697801e-2 s +[acc-interp] thetaMarsh 1.2e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.451682e-3 s grad=2.908e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.254605e-3 s grad=2.791e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.609303e-3 s grad=2.515e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.278982e-3 s grad=4.004e-6 s +[acc-interp] eval 1.6188322e-2 s +[acc-interp] thetaMarsh 1.68e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.306794e-3 s grad=3.182e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.261546e-3 s grad=1.0732e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.181687e-3 s grad=7.636e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.872416e-3 s grad=8.041e-6 s +[acc-interp] eval 1.4400358e-2 s +[acc-interp] thetaMarsh 6.4e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.585456e-3 s grad=2.915e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.818486e-3 s grad=1.0506e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=9.449608e-3 s grad=4.239e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.906288e-3 s grad=3.058e-6 s +[acc-interp] eval 2.0570416e-2 s +[acc-interp] thetaMarsh 7.8e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.460089e-3 s grad=1.0559e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.236004e-3 s grad=8.223e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.627185e-3 s grad=3.507e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.761712e-3 s grad=4.689e-6 s +[acc-interp] eval 1.4918521e-2 s +[acc-interp] thetaMarsh 1.82e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.054798e-3 s grad=8.386e-6 s +[acc-interp] chunk base=25974 cs=25974 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thetaMarsh 7.0e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.576214e-3 s grad=4.304e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.562925e-3 s grad=3.402e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.084993e-3 s grad=5.012e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.395109e-3 s grad=3.98e-6 s +[acc-interp] eval 1.552531e-2 s +[acc-interp] thetaMarsh 7.0e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.369281e-3 s grad=3.919e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.305298e-3 s grad=3.413e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.641352e-3 s grad=5.038e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.423081e-3 s grad=3.439e-6 s +[acc-interp] eval 1.497012e-2 s +[acc-interp] thetaMarsh 1.37e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.593002e-3 s grad=2.304e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.928607e-3 s grad=2.758e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.458101e-3 s grad=4.498e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.5213e-3 s grad=2.775e-6 s +[acc-interp] eval 1.6888418e-2 s +[acc-interp] thetaMarsh 1.19e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=8.156671e-3 s grad=3.075e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.26493e-3 s grad=2.695e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.138224e-3 s grad=2.48e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.257422e-3 s grad=2.386e-6 s +[acc-interp] eval 1.9335036e-2 s +[acc-interp] thetaMarsh 9.8e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.035845e-3 s grad=1.933e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.270803e-3 s grad=4.391e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.865275e-3 s grad=5.375e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.366977e-3 s grad=5.225e-6 s +[acc-interp] eval 1.5011042e-2 s +[acc-interp] thetaMarsh 7.8e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.243409e-3 s grad=3.731e-6 s 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thetaMarsh 6.9e-8 s (n=[acc-interp] thetaMarsh 6.2e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.5711903e-2 s grad=4.092e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.898095e-3 s grad=2.719e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.340432e-3 s grad=3.419e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.645402e-3 s grad=1.0625e-5 s +[acc-interp] eval 2.5450129e-2 s +[acc-interp] thetaMarsh 2.2e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=9.878986e-3 s grad=3.593e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.63698e-3 s grad=2.46e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.887205e-3 s grad=4.619e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.694391e-3 s grad=3.313e-6 s +[acc-interp] eval 1.9344269e-2 s +[acc-interp] thetaMarsh 2.11e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.546056e-3 s grad=3.615e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.853499e-3 s grad=3.956e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.53203e-3 s grad=4.298e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.80259e-3 s grad=5.118e-6 s +[acc-interp] eval 1.2834628e-2 s +[acc-interp] thetaMarsh 1.17e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.311515e-3 s grad=5.076e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.819913e-3 s grad=1.801e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.771203e-3 s grad=1.753e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.9454e-3 s grad=3.514e-6 s +[acc-interp] eval 1.3993829e-2 s +[acc-interp] thetaMarsh 1.18e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.11574e-3 s grad=3.81e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.045901e-3 s grad=3.384e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.810792e-3 s grad=2.838e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.082224e-3 s grad=2.746e-6 s +[acc-interp] eval 1.3626192e-2 s +[acc-interp] thetaMarsh 9.7e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.882858e-3 s grad=4.174e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.74648e-3 s grad=3.742e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.90394e-3 s grad=2.712e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.503871e-3 s grad=2.652e-6 s +[acc-interp] eval 1.4645216e-2 s +[acc-interp] thetaMarsh 1.02e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.708628e-3 s grad=4.284e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.911719e-3 s grad=4.27e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.81852e-3 s grad=3.846e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.601665e-3 s grad=3.304e-6 s +[acc-interp] eval 1.4655262e-2 s +[acc-interp] thetaMarsh 1.1e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.770139e-3 s grad=3.185e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.006139e-3 s grad=3.498e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.92285e-3 s grad=2.925e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.155721e-3 s grad=2.901e-6 s +[acc-interp] eval 1.4340833e-2 s +[acc-interp] thetaMarsh 9.8e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.687444e-3 s grad=2.608e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.195717e-3 s grad=2.794e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.063312e-3 s grad=3.963e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.415e-3 s grad=2.744e-6 s +[acc-interp] eval 1.3751006e-2 s +[acc-interp] thetaMarsh 1.13e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.830816e-3 s grad=3.321e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.55023e-3 s grad=2.237e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.38162e-3 s grad=2.184e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.763577e-3 s grad=3.485e-6 s +[acc-interp] eval 1.2972761e-2 s +[acc-interp] thetaMarsh 1.11e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.8928e-3 s grad=4.108e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.861945e-3 s 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kernel=2.050977e-3 s grad=1.543e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.428406e-3 s grad=2.998e-6 s +[acc-interp] eval 9.881494e-3 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=2.335561e-3 s grad=2.669e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.267577e-3 s grad=2.317e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.122943e-3 s grad=2.647e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.365667e-3 s grad=2.678e-6 s +[acc-interp] eval 0.363636169 s +[acc-interp] thetaMarsh 8.0e-8 s (n=[acc-interp] thetaMarsh 6.3e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.861722e-2 s grad=4.322e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=9.18298e-3 s grad=5.124e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.981809e-3 s grad=4.231e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.37889e-3 s grad=4.221e-6 s +[acc-interp] eval 4.6199827e-2 s +[acc-interp] thetaMarsh 6.7e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.677238e-3 s 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chunk base=77922 cs=25974 kernel=1.0401522e-2 s grad=3.639e-6 s +[acc-interp] eval 2.4887237e-2 s +[acc-interp] thetaMarsh 1.05e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.222295e-3 s grad=2.922e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.983835e-3 s grad=1.877e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.528295e-3 s grad=3.936e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.561697e-3 s grad=3.86e-6 s +[acc-interp] eval 1.5806775e-2 s +[acc-interp] thetaMarsh 1.12e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.401346e-3 s grad=3.852e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.13005e-3 s grad=4.969e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.569963e-3 s grad=5.042e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.202098e-3 s grad=3.956e-6 s +[acc-interp] eval 1.8919263e-2 s +[acc-interp] thetaMarsh 1.18e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.306706e-3 s grad=2.939e-6 s +[acc-interp] chunk base=25974 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kernel=4.474962e-3 s grad=3.032e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.507108e-3 s grad=3.889e-6 s +[acc-interp] eval 1.9281237e-2 s +[acc-interp] thetaMarsh 1.15e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.617809e-3 s grad=3.695e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.217744e-3 s grad=2.937e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.0899849e-2 s grad=3.43e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.31889e-3 s grad=2.453e-6 s +[acc-interp] eval 2.4536563e-2 s +[acc-interp] thetaMarsh 1.15e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.291001e-3 s grad=2.176e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.538646e-3 s grad=3.337e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.997121e-3 s grad=3.037e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.864029e-3 s grad=3.064e-6 s +[acc-interp] eval 1.7180756e-2 s +[acc-interp] thetaMarsh 1.05e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.25874e-3 s grad=3.82e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.681115e-3 s grad=3.362e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.151639e-3 s grad=3.179e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.743342e-3 s grad=3.818e-6 s +[acc-interp] eval 1.8413817e-2 s +[acc-interp] thetaMarsh 1.12e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=5.16173e-3 s grad=3.773e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.768461e-3 s grad=3.151e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.529963e-3 s grad=3.328e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.811782e-3 s grad=3.554e-6 s +[acc-interp] eval 1.7781368e-2 s +[acc-interp] thetaMarsh 1.24e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.653167e-3 s grad=4.597e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.470535e-3 s grad=4.163e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.918031e-3 s grad=3.374e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.0330471e-2 s grad=4.712e-6 s +[acc-interp] eval 2.4982826e-2 s +[acc-interp] thetaMarsh 1.15e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.455189e-3 s grad=3.526e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.403365e-3 s grad=4.032e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.234572e-3 s grad=3.439e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.782206e-3 s grad=3.172e-6 s +[acc-interp] eval 1.9530613e-2 s +[acc-interp] thetaMarsh 1.13e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.952916e-3 s grad=3.381e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.397176e-3 s grad=4.525e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.944599e-3 s grad=4.179e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.145032e-3 s grad=3.471e-6 s +[acc-interp] eval 1.8021275e-2 s +4) +[acc-interp] chunk base=0 cs=25974 kernel=3.016083e-3 s grad=2.566e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.403375e-3 s grad=3.767e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.855424e-3 s grad=2.503e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.715777e-3 s grad=2.854e-6 s +[acc-interp] eval 0.703442479 s +[acc-interp] thetaMarsh 1.21e-7 s (n=[acc-interp] thetaMarsh 1.06e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=9.523113e-3 s grad=3.4e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.213513e-3 s grad=2.547e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.22721e-3 s grad=2.503e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.826131e-3 s grad=4.044e-6 s +[acc-interp] eval 2.1357861e-2 s +[acc-interp] thetaMarsh 1.1e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.449396e-3 s grad=1.933e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.621544e-3 s grad=1.675e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.567831e-3 s grad=1.411e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.627705e-3 s grad=1.343e-6 s +[acc-interp] eval 7.573719e-3 s +[acc-interp] thetaMarsh 1.13e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.524965e-3 s grad=4.548e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.776661e-3 s grad=1.637e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.687531e-3 s grad=5.584e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.785528e-3 s grad=5.48e-6 s +[acc-interp] eval 7.661359e-3 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=2.091265e-3 s grad=4.267e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.404932e-3 s grad=3.078e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.325833e-3 s grad=5.853e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.258698e-3 s grad=2.134e-6 s +[acc-interp] eval 4.7244747e-2 s +[acc-interp] thetaMarsh 1.39e-7 s (n=[acc-interp] thetaMarsh 9.7e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.284216e-2 s grad=3.626e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.537862e-3 s grad=2.426e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.346513e-3 s grad=1.711e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.464865e-3 s grad=3.308e-6 s +[acc-interp] eval 2.1605867e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=3.030439e-3 s grad=3.579e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.790045e-3 s grad=2.578e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.8973e-3 s grad=3.058e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.871139e-3 s grad=1.851e-6 s +[acc-interp] eval 3.4893841e-2 s +[acc-interp] thetaMarsh 8.7e-8 s (n=[acc-interp] thetaMarsh 8.0e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=9.164465e-3 s grad=4.085e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=7.028961e-3 s grad=3.42e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.139255e-3 s grad=3.973e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.531258e-3 s grad=8.91e-6 s +[acc-interp] eval 2.2155261e-2 s +[acc-interp] thetaMarsh 1.34e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.144007e-3 s grad=2.075e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.119995e-3 s grad=2.337e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.93453e-3 s grad=2.02e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.206862e-3 s grad=1.825e-6 s +[acc-interp] eval 9.884285e-3 s +[acc-interp] thetaMarsh 1.07e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.363878e-3 s grad=4.341e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.335262e-3 s grad=2.29e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.028718e-3 s grad=2.361e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.063273e-3 s grad=2.29e-6 s +[acc-interp] eval 9.816496e-3 s +[acc-interp] thetaMarsh 6.7e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.876035e-3 s grad=1.897e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.403256e-3 s grad=2.779e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.738805e-3 s grad=1.97e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.676444e-3 s grad=2.469e-6 s +[acc-interp] eval 7.954501e-3 s +[acc-interp] thetaMarsh 6.7e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.167344e-3 s grad=3.675e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.307249e-3 s grad=4.277e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.152802e-3 s grad=2.893e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.055333e-3 s grad=3.0e-6 s +[acc-interp] eval 1.1292609e-2 s +[acc-interp] thetaMarsh 1.21e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.911305e-3 s grad=2.349e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.726088e-3 s grad=1.91e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.88911e-3 s grad=2.966e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.34501e-3 s grad=3.222e-6 s +[acc-interp] eval 9.436911e-3 s +[acc-interp] thetaMarsh 1.06e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.879991e-3 s grad=2.408e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.820971e-3 s grad=1.683e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.537128e-3 s grad=2.13e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.700404e-3 s grad=1.389e-6 s +[acc-interp] eval 8.489283e-3 s +[acc-interp] thetaMarsh 1.04e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.628267e-3 s grad=1.604e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.765418e-3 s grad=1.476e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.523946e-3 s grad=5.132e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.325861e-3 s grad=3.68e-6 s +[acc-interp] eval 9.89704e-3 s +[acc-interp] thetaMarsh 2.32e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.318761e-3 s grad=3.193e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.143555e-3 s grad=2.235e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.29685e-3 s grad=3.24e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.275221e-3 s grad=3.428e-6 s +[acc-interp] eval 1.0561561e-2 s +[acc-interp] thetaMarsh 1.03e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.047851e-3 s grad=4.082e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.149784e-3 s grad=1.88e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.186219e-3 s grad=1.994e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.730092e-3 s grad=1.64e-6 s +[acc-interp] eval 1.0625231e-2 s +[acc-interp] thetaMarsh 1.22e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.822475e-3 s grad=4.127e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.500822e-3 s grad=2.484e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.648674e-3 s grad=2.832e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.551479e-3 s grad=3.767e-6 s +[acc-interp] eval 1.2376012e-2 s +[acc-interp] thetaMarsh 1.44e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.769914e-3 s grad=3.819e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.266301e-3 s grad=3.195e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.141359e-3 s grad=2.579e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.135933e-3 s grad=2.635e-6 s +[acc-interp] eval 1.0908362e-2 s +[acc-interp] thetaMarsh 1.3e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.167392e-3 s grad=1.894e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.097646e-3 s grad=3.318e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.898197e-3 s grad=3.023e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.239652e-3 s grad=2.65e-6 s +[acc-interp] eval 1.1131317e-2 s +[acc-interp] thetaMarsh 1.36e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.355155e-3 s grad=2.837e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.711927e-3 s grad=1.68e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.588698e-3 s grad=1.588e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.794465e-3 s grad=1.965e-6 s +[acc-interp] eval 9.027207e-3 s +[acc-interp] thetaMarsh 1.02e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.626004e-3 s grad=3.407e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.22165e-3 s grad=2.775e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.089131e-3 s grad=2.646e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.473619e-3 s grad=1.1978e-5 s +[acc-interp] eval 1.0766954e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=2.198858e-3 s grad=2.264e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.16349e-3 s grad=3.141e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.299962e-3 s grad=3.595e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.274344e-3 s grad=3.511e-6 s +[acc-interp] eval 0.176245087 s +[acc-interp] thetaMarsh 8.6e-8 s (n=[acc-interp] thetaMarsh 7.7e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=2.5710198e-2 s grad=3.649e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.330183e-3 s grad=3.03e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.383344e-3 s grad=5.52e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=8.738612e-3 s grad=3.709e-6 s +[acc-interp] eval 4.4048275e-2 s +5) +[acc-interp] chunk base=0 cs=25974 kernel=4.327879e-3 s grad=2.308e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.514343e-3 s grad=2.323e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.989534e-3 s grad=2.307e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.596621e-3 s grad=2.199e-6 s +[acc-interp] eval 6.1350363e-2 s +[acc-interp] thetaMarsh 1.06e-7 s (n=[acc-interp] thetaMarsh 6.0e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=1.4843484e-2 s grad=3.069e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.453469e-3 s grad=4.304e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.062808e-3 s grad=2.775e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.034286e-3 s grad=2.961e-6 s +[acc-interp] eval 2.6760359e-2 s +4) +[acc-interp] chunk base=0 cs=25974 kernel=3.523645e-3 s grad=3.494e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.446864e-3 s grad=4.872e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.1251035e-2 s grad=3.991e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.702862e-3 s grad=3.103e-6 s +[acc-interp] eval 4.9092101e-2 s +[acc-interp] thetaMarsh 1.05e-7 s (n=[acc-interp] thetaMarsh 9.0e-8 s (n=6) +[acc-interp] chunk base=0 cs=25974 kernel=3.1645982e-2 s grad=6.347e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.1904381e-2 s grad=3.653e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.089317e-3 s grad=4.8e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.244199e-3 s grad=5.447e-6 s +[acc-interp] eval 5.5018473e-2 s +6) +[acc-interp] chunk base=0 cs=25974 kernel=4.75061e-3 s grad=1.0282e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.354184e-3 s grad=4.41e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.262072e-3 s grad=3.341e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.561302e-3 s grad=4.057e-6 s +[acc-interp] eval 7.6406405e-2 s +[acc-interp] thetaMarsh 1.54e-7 s (n=[acc-interp] thetaMarsh 1.12e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.682175e-3 s grad=5.049e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.389714e-3 s grad=4.482e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.613406e-3 s grad=4.024e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.519888e-3 s grad=3.339e-6 s +[acc-interp] eval 1.1509326e-2 s +[acc-interp] thetaMarsh 1.05e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.555702e-3 s grad=2.983e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.264661e-3 s grad=5.796e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.040655e-3 s grad=5.712e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.571757e-3 s grad=3.442e-6 s +[acc-interp] eval 8.361774e-3 s +[acc-interp] thetaMarsh 7.3e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.539069e-3 s grad=4.441e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.715091e-3 s grad=3.662e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.565962e-3 s grad=3.132e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.42329e-3 s grad=2.451e-6 s +[acc-interp] eval 1.0426329e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=2.273114e-3 s grad=2.436e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.062889e-3 s grad=6.924e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.387724e-3 s grad=9.95e-7 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.60079e-3 s grad=3.631e-6 s +[acc-interp] eval 3.9972777e-2 s +[acc-interp] thetaMarsh 6.3e-8 s (n=[acc-interp] thetaMarsh 8.4e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.3845041e-2 s grad=4.617e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.245439e-3 s grad=1.1627e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.66505e-3 s grad=3.823e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.277038e-3 s grad=9.277e-6 s +[acc-interp] eval 3.4946514e-2 s +4) +[acc-interp] chunk base=0 cs=25974 kernel=3.243932e-3 s grad=1.0309e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.241349e-3 s grad=9.141e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.436246e-3 s grad=2.461e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.654568e-3 s grad=4.218e-6 s +[acc-interp] eval 5.0670871e-2 s +[acc-interp] thetaMarsh 1.06e-7 s (n=[acc-interp] thetaMarsh 8.4e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.5490451e-2 s grad=3.462e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.470396e-3 s grad=3.683e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.557995e-3 s grad=2.775e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.407124e-3 s grad=2.5e-6 s +[acc-interp] eval 2.4474645e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=2.368599e-3 s grad=2.452e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.284994e-3 s grad=3.74e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.382962e-3 s grad=3.032e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.061527e-3 s grad=3.069e-6 s +[acc-interp] eval 3.6818013e-2 s +[acc-interp] thetaMarsh 8.7e-8 s (n=[acc-interp] thetaMarsh 6.9e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.169795e-3 s grad=2.706e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.648803e-3 s grad=2.197e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.558891e-3 s grad=3.199e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.283498e-3 s grad=4.406e-6 s +[acc-interp] eval 1.0588449e-2 s +[acc-interp] thetaMarsh 6.0e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.207123e-3 s grad=2.098e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.946266e-3 s grad=2.411e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.696333e-3 s grad=1.602e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.328502e-3 s grad=9.2e-7 s +[acc-interp] eval 6.875788e-3 s +[acc-interp] thetaMarsh 6.8e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.877573e-3 s grad=8.08e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.006924e-3 s grad=7.01e-7 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.048702e-3 s grad=3.087e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.35011e-3 s grad=3.728e-6 s +[acc-interp] eval 7.21447e-3 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=2.191052e-3 s grad=2.041e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.858918e-3 s grad=1.929e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.176271e-3 s grad=1.764e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.718844e-3 s grad=1.972e-6 s +[acc-interp] eval 3.3131403e-2 s +[acc-interp] thetaMarsh 6.3e-8 s (n=[acc-interp] thetaMarsh 5.3e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=1.7048021e-2 s grad=3.966e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.725739e-3 s grad=3.051e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.372308e-3 s grad=4.504e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.829625e-3 s grad=4.047e-6 s +[acc-interp] eval 2.683819e-2 s +4) +[acc-interp] chunk base=0 cs=25974 kernel=2.888554e-3 s grad=3.981e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.055084e-3 s grad=4.187e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.506519e-3 s grad=3.344e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.732585e-3 s grad=4.381e-6 s +[acc-interp] eval 4.0280516e-2 s +[acc-interp] thetaMarsh 1.33e-7 s (n=[acc-interp] thetaMarsh 1.31e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=9.833663e-3 s grad=2.965e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.216972e-3 s grad=3.677e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.996193e-3 s grad=4.113e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.624072e-3 s grad=3.123e-6 s +[acc-interp] eval 1.8915813e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=2.343532e-3 s grad=2.237e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.269166e-3 s grad=2.137e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.276658e-3 s grad=2.736e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.860177e-3 s grad=3.502e-6 s +[acc-interp] eval 3.0454401e-2 s +[acc-interp] thetaMarsh 1.34e-7 s (n=[acc-interp] thetaMarsh 1.06e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.1294107e-2 s grad=3.97e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.342214e-3 s grad=2.464e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.436862e-3 s grad=1.76e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.138917e-3 s grad=1.999e-6 s +[acc-interp] eval 2.9805127e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=2.439702e-3 s grad=1.876e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.862425e-3 s grad=2.752e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.503822e-3 s grad=2.245e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.735474e-3 s grad=1.757e-6 s +[acc-interp] eval 4.3137145e-2 s +[acc-interp] thetaMarsh 1.2e-7 s (n=[acc-interp] thetaMarsh 1.01e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=9.492566e-3 s grad=4.212e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.062347e-3 s grad=2.991e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.724269e-3 s grad=3.484e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.818786e-3 s grad=2.308e-6 s +[acc-interp] eval 1.9780207e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=3.407838e-3 s grad=3.761e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.080175e-3 s grad=3.196e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.328861e-3 s grad=2.507e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.602943e-3 s grad=2.714e-6 s +[acc-interp] eval 3.4048524e-2 s +[acc-interp] thetaMarsh 1.15e-7 s (n=[acc-interp] thetaMarsh 1.02e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.4442101e-2 s grad=3.797e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.94993e-3 s grad=3.268e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.840668e-3 s grad=3.596e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.511419e-3 s grad=4.6e-6 s +[acc-interp] eval 2.5474723e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=3.081158e-3 s grad=2.827e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.988988e-3 s grad=2.751e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.949236e-3 s grad=2.917e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.624978e-3 s grad=3.93e-6 s +[acc-interp] eval 4.0029782e-2 s +[acc-interp] thetaMarsh 1.34e-7 s (n=[acc-interp] thetaMarsh 1.16e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=2.4978029e-2 s grad=5.638e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.282169e-3 s grad=5.159e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.2618e-3 s grad=3.74e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.138472e-2 s grad=5.039e-6 s +[acc-interp] eval 4.7765482e-2 s +5) +[acc-interp] chunk base=0 cs=25974 kernel=3.66571e-3 s grad=2.654e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.648583e-3 s grad=3.203e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.903787e-3 s grad=4.484e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.661976e-3 s grad=1.085e-5 s +[acc-interp] eval 6.4183279e-2 s +[acc-interp] thetaMarsh 6.7e-8 s (n=[acc-interp] thetaMarsh 5.9e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=7.675615e-3 s grad=2.642e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.444888e-3 s grad=3.908e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.34296e-3 s grad=4.174e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.828846e-3 s grad=3.813e-6 s +[acc-interp] eval 2.0938375e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=3.56107e-3 s grad=2.498e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.660708e-3 s grad=3.647e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.0721895e-2 s grad=4.069e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.018519e-3 s grad=2.565e-6 s +[acc-interp] eval 4.3649595e-2 s +[acc-interp] thetaMarsh 1.24e-7 s (n=0) +[acc-interp] chunk base=0 cs=25974 kernel=3.6658669e-2 s grad=1.687e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.96087e-3 s grad=1.542e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.465371e-3 s grad=1.594e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.4424992e-2 s grad=1.622e-6 s +[acc-interp] eval 6.1430266e-2 s +[acc-interp] thetaMarsh 8.4e-8 s (n=[acc-interp] thetaMarsh 6.2e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.0929946e-2 s grad=4.441e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.962224e-3 s grad=1.4125e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.835412e-3 s grad=2.14e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.855558e-3 s grad=4.143e-6 s +[acc-interp] eval 2.0673558e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=2.922912e-3 s grad=3.721e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.850099e-3 s grad=3.921e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.099354e-3 s grad=2.775e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.507893e-3 s grad=2.578e-6 s +[acc-interp] eval 3.3481339e-2 s +[acc-interp] thetaMarsh 1.3e-7 s (n=[acc-interp] thetaMarsh 3.27e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.3445466e-2 s grad=4.091e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.374805e-3 s grad=3.491e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.713473e-3 s grad=3.803e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.363603e-3 s grad=3.162e-6 s +[acc-interp] eval 5.947319e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=5.485167e-3 s grad=3.472e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.983029e-3 s grad=3.974e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=6.104857e-3 s grad=3.533e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.4330174e-2 s grad=2.822e-6 s +[acc-interp] eval 9.3134399e-2 s +[acc-interp] thetaMarsh 2.71e-7 s (n=[acc-interp] thetaMarsh 1.11e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.7877901e-2 s grad=3.654e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.112235e-3 s grad=2.037e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.158932e-3 s grad=3.785e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.833777e-3 s grad=2.927e-6 s +[acc-interp] eval 3.0568088e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=3.08943e-3 s grad=2.68e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.20675e-3 s grad=2.35e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.3205054e-2 s grad=2.717e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.092001e-3 s grad=4.019e-6 s +[acc-interp] eval 5.4902125e-2 s +[acc-interp] thetaMarsh 1.2e-7 s (n=[acc-interp] thetaMarsh 9.8e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.1489216e-2 s grad=4.78e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.60461e-3 s grad=4.575e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=8.563692e-3 s grad=5.656e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.346311e-3 s grad=6.153e-6 s +[acc-interp] eval 3.8340754e-2 s +4) +[acc-interp] chunk base=0 cs=25974 kernel=3.420815e-3 s grad=4.149e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.148801e-3 s grad=4.912e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.31055e-3 s grad=4.336e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.340131e-3 s grad=3.902e-6 s +[acc-interp] eval 5.4086794e-2 s +[acc-interp] thetaMarsh 1.1e-7 s (n=[acc-interp] thetaMarsh 1.14e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.178757e-2 s grad=4.903e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.12635e-3 s grad=4.625e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.240419e-3 s grad=3.947e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.300625e-3 s grad=3.219e-6 s +[acc-interp] eval 2.3736581e-2 s +[acc-interp] thetaMarsh 1.74e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.742405e-3 s grad=3.338e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.237438e-3 s grad=4.368e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=7.649438e-3 s grad=5.681e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.933638e-3 s grad=4.417e-6 s +[acc-interp] eval 1.7777074e-2 s +[acc-interp] thetaMarsh 8.6e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.59646e-3 s grad=4.196e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.749832e-3 s grad=3.345e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.32744e-3 s grad=3.599e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.847932e-3 s grad=4.41e-6 s +[acc-interp] eval 1.2728018e-2 s +[acc-interp] thetaMarsh 2.93e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.505318e-3 s grad=3.824e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.699858e-3 s grad=3.194e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.42685e-3 s grad=3.559e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.049897e-3 s grad=4.139e-6 s +[acc-interp] eval 1.2055376e-2 s +[acc-interp] thetaMarsh 1.37e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.424669e-3 s grad=3.254e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.036247e-3 s grad=3.258e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.59225e-3 s grad=3.41e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.626494e-3 s grad=2.605e-6 s +[acc-interp] eval 1.2361947e-2 s +[acc-interp] thetaMarsh 1.24e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.637236e-3 s grad=3.94e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.8541e-3 s grad=2.843e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.542993e-3 s grad=4.676e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.693793e-3 s grad=2.891e-6 s +[acc-interp] eval 1.2411011e-2 s +[acc-interp] thetaMarsh 1.33e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.317806e-3 s grad=3.117e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.613839e-3 s grad=2.054e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.33443e-3 s grad=4.469e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.08899e-3 s grad=3.377e-6 s +[acc-interp] eval 1.2749713e-2 s +[acc-interp] thetaMarsh 6.7e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.493497e-3 s grad=3.263e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.559388e-3 s grad=3.108e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.294167e-3 s grad=2.557e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.150129e-3 s grad=3.856e-6 s +[acc-interp] eval 1.168874e-2 s +[acc-interp] thetaMarsh 6.8e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.741065e-3 s grad=3.273e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.165631e-3 s grad=1.085e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.824428e-3 s grad=7.407e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.93313e-3 s grad=7.784e-6 s +[acc-interp] eval 1.2470552e-2 s +[acc-interp] thetaMarsh 5.7e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.596171e-3 s grad=7.799e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.298719e-3 s grad=3.799e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.641967e-3 s grad=2.201e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.794663e-3 s grad=1.803e-6 s +[acc-interp] eval 1.5212129e-2 s +[acc-interp] thetaMarsh 1.07e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.402671e-3 s grad=5.484e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.845341e-3 s grad=2.608e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.42223e-3 s grad=1.887e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.52768e-3 s grad=2.801e-6 s +[acc-interp] eval 1.1770613e-2 s +[acc-interp] thetaMarsh 1.28e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.421695e-3 s grad=3.106e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.291886e-3 s grad=2.84e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.37048e-3 s grad=3.471e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.235364e-3 s grad=4.845e-6 s +[acc-interp] eval 1.1944681e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=2.49899e-3 s grad=2.94e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.599777e-3 s grad=2.736e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.012757e-3 s grad=2.579e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.572597e-3 s grad=1.93e-6 s +[acc-interp] eval 0.1793407 s +[acc-interp] thetaMarsh 1.28e-7 s (n=[acc-interp] thetaMarsh 9.2e-8 s (n=6) +[acc-interp] chunk base=0 cs=25974 kernel=3.4079431e-2 s grad=5.082e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.974277e-3 s grad=4.872e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.771642e-3 s grad=4.462e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.753396e-3 s grad=3.486e-6 s +[acc-interp] eval 4.6714735e-2 s +6) +[acc-interp] chunk base=0 cs=25974 kernel=4.546438e-3 s grad=4.709e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.131144e-3 s grad=3.609e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.815382e-3 s grad=3.449e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.635282e-3 s grad=4.45e-6 s +[acc-interp] eval 6.4668601e-2 s +[acc-interp] thetaMarsh 1.19e-7 s (n=[acc-interp] thetaMarsh 2.11e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=1.1026025e-2 s grad=4.128e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.36085e-3 s grad=4.008e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.039344e-3 s grad=6.131e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.0177979e-2 s grad=6.172e-6 s +[acc-interp] eval 3.1837064e-2 s +5) +[acc-interp] chunk base=0 cs=25974 kernel=4.801269e-3 s grad=4.231e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.213935e-3 s grad=4.236e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.841997e-3 s grad=3.501e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.827596e-3 s grad=3.272e-6 s +[acc-interp] eval 5.0072562e-2 s +[acc-interp] thetaMarsh 1.38e-7 s (n=[acc-interp] thetaMarsh 1.24e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.8223019e-2 s grad=3.758e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=6.674515e-3 s grad=4.08e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.268953e-3 s grad=3.121e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.96046e-3 s grad=2.895e-6 s +[acc-interp] eval 4.4874546e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=4.883158e-3 s grad=3.288e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.1770198e-2 s grad=3.617e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.10597e-3 s grad=3.698e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.716385e-3 s grad=3.136e-6 s +[acc-interp] eval 7.2283908e-2 s +[acc-interp] thetaMarsh 5.9e-8 s (n=[acc-interp] thetaMarsh 5.9e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=1.6721867e-2 s grad=3.001e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.615825e-3 s grad=9.963e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.596053e-3 s grad=7.475e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.605874e-3 s grad=9.856e-6 s +[acc-interp] eval 2.8319192e-2 s +[acc-interp] thetaMarsh 1.13e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=1.1632605e-2 s grad=5.374e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.129155e-3 s grad=9.918e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.595389e-3 s grad=7.897e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.577387e-3 s grad=8.204e-6 s +[acc-interp] eval 2.385807e-2 s +[acc-interp] thetaMarsh 1.93e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.355208e-3 s grad=4.849e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.850849e-3 s grad=5.892e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.794872e-3 s grad=8.891e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.727566e-3 s grad=3.61e-6 s +[acc-interp] eval 1.6711498e-2 s +[acc-interp] thetaMarsh 1.39e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=5.272287e-3 s grad=4.662e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.6265e-3 s grad=3.831e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.232188e-3 s grad=2.882e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.902866e-3 s grad=4.837e-6 s +[acc-interp] eval 1.8663592e-2 s +[acc-interp] thetaMarsh 1.5e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=1.0622964e-2 s grad=3.47e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.176482e-3 s grad=2.691e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.856619e-3 s grad=4.597e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.247105e-3 s grad=3.812e-6 s +[acc-interp] eval 2.3507141e-2 s +[acc-interp] thetaMarsh 9.6e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.167221e-3 s grad=3.835e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.920088e-3 s grad=2.843e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.876763e-3 s grad=4.213e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.368155e-3 s grad=3.692e-6 s +[acc-interp] eval 1.8862465e-2 s +[acc-interp] thetaMarsh 1.04e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.187173e-3 s grad=3.009e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.579949e-3 s grad=5.97e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.355852e-3 s grad=2.994e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.079932e-3 s grad=3.517e-6 s +[acc-interp] eval 1.9857532e-2 s +[acc-interp] thetaMarsh 1.01e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=9.515995e-3 s grad=3.333e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.267902e-3 s grad=3.971e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.255046e-3 s grad=2.977e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.527965e-3 s grad=3.046e-6 s +[acc-interp] eval 2.0979086e-2 s +[acc-interp] thetaMarsh 1.1e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.212438e-3 s grad=2.408e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.462612e-3 s grad=3.942e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.376895e-3 s grad=3.692e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.5967e-3 s grad=5.093e-6 s +[acc-interp] eval 1.7355482e-2 s +[acc-interp] thetaMarsh 1.4e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.16245e-3 s grad=3.685e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.104924e-3 s grad=3.869e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.832928e-3 s grad=3.481e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.090805e-3 s grad=4.011e-6 s +[acc-interp] eval 1.8732251e-2 s +[acc-interp] thetaMarsh 6.2e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=8.587928e-3 s grad=4.207e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.777563e-3 s grad=5.88e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.345783e-3 s grad=3.592e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.3569e-3 s grad=2.744e-6 s +[acc-interp] eval 2.2795599e-2 s +[acc-interp] thetaMarsh 4.43e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.047713e-3 s grad=3.029e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.961839e-3 s grad=3.731e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.954238e-3 s grad=2.781e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.069885e-3 s grad=3.049e-6 s +[acc-interp] eval 1.7494978e-2 s +[acc-interp] thetaMarsh 9.7e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.391335e-3 s grad=3.564e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.001189e-3 s grad=3.486e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.035092e-3 s grad=2.79e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.120499e-3 s grad=3.316e-6 s +[acc-interp] eval 1.799473e-2 s +[acc-interp] thetaMarsh 1.09e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=9.942118e-3 s grad=2.869e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.372478e-3 s grad=2.484e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.378114e-3 s grad=3.842e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.678285e-3 s grad=3.437e-6 s +[acc-interp] eval 2.3928188e-2 s +[acc-interp] thetaMarsh 9.9e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.661437e-3 s grad=2.889e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.328106e-3 s grad=3.175e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.842238e-3 s grad=5.772e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.275159e-3 s grad=3.646e-6 s +[acc-interp] eval 1.6708809e-2 s +[acc-interp] thetaMarsh 1.18e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.015286e-3 s grad=3.558e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.93501e-3 s grad=3.828e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.849599e-3 s grad=3.466e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.036465e-3 s grad=3.617e-6 s +[acc-interp] eval 1.8302288e-2 s +[acc-interp] thetaMarsh 6.6e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=1.0004165e-2 s grad=3.063e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.793767e-3 s grad=3.418e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.775597e-3 s grad=8.227e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.357626e-3 s grad=8.933e-6 s +[acc-interp] eval 2.1765512e-2 s +[acc-interp] thetaMarsh 1.9e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.445732e-3 s grad=3.835e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.119348e-3 s grad=3.735e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.99133e-3 s grad=3.205e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.724715e-3 s grad=4.262e-6 s +[acc-interp] eval 1.7827534e-2 s +[acc-interp] thetaMarsh 1.34e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=5.193085e-3 s grad=4.502e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.561851e-3 s grad=3.314e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.40846e-3 s grad=4.764e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.695962e-3 s grad=5.312e-6 s +[acc-interp] eval 2.0564435e-2 s +[acc-interp] thetaMarsh 1.57e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=9.233733e-3 s grad=4.136e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.199665e-3 s grad=2.671e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.547372e-3 s grad=4.281e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.428857e-3 s grad=2.34e-6 s +[acc-interp] eval 2.2816401e-2 s +[acc-interp] thetaMarsh 7.4e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.197425e-3 s grad=1.0251e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.2175e-3 s grad=8.447e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.690698e-3 s grad=8.392e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.954456e-3 s grad=9.98e-6 s +[acc-interp] eval 1.4729405e-2 s +[acc-interp] thetaMarsh 5.8e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.579694e-3 s grad=2.489e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.697078e-3 s grad=2.137e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.948569e-3 s grad=4.182e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.60703e-3 s grad=4.281e-6 s +[acc-interp] eval 1.6963762e-2 s +[acc-interp] thetaMarsh 1.0e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=1.0099224e-2 s grad=4.052e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.855356e-3 s grad=3.749e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.595182e-3 s grad=3.119e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.366743e-3 s grad=2.932e-6 s +[acc-interp] eval 2.2511649e-2 s +[acc-interp] thetaMarsh 1.2e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.422937e-3 s grad=5.362e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.197561e-3 s grad=4.73e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.359519e-3 s grad=4.021e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.760601e-3 s grad=4.012e-6 s +[acc-interp] eval 1.4785704e-2 s +[acc-interp] thetaMarsh 6.7e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.668693e-3 s grad=2.068e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.677802e-3 s grad=4.035e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.070571e-3 s grad=8.314e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.789264e-3 s grad=1.586e-6 s +[acc-interp] eval 1.6951035e-2 s +[acc-interp] thetaMarsh 6.0e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=9.449198e-3 s grad=3.678e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.406239e-3 s grad=4.044e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.105798e-3 s grad=3.168e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.126734e-3 s grad=4.126e-6 s +[acc-interp] eval 1.9934265e-2 s +[acc-interp] thetaMarsh 7.8e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.266595e-3 s grad=3.482e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.161981e-3 s grad=4.032e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.506248e-3 s grad=2.078e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.680081e-3 s grad=1.0391e-5 s +[acc-interp] eval 1.4433098e-2 s +[acc-interp] thetaMarsh 6.6e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.75556e-3 s grad=1.0479e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.96164e-3 s grad=4.186e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.099994e-3 s grad=5.298e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.223477e-3 s grad=4.465e-6 s +[acc-interp] eval 1.8123544e-2 s +[acc-interp] thetaMarsh 1.16e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=1.0818489e-2 s grad=4.468e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.32935e-3 s grad=2.879e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.53518e-3 s grad=5.29e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.326917e-3 s grad=5.143e-6 s +[acc-interp] eval 2.4185575e-2 s +[acc-interp] thetaMarsh 1.01e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.036592e-3 s grad=3.581e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.01097e-3 s grad=5.003e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.668281e-3 s grad=4.004e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.13164e-3 s grad=3.892e-6 s +[acc-interp] eval 1.8361791e-2 s +4) +[acc-interp] chunk base=0 cs=25974 kernel=4.063776e-3 s grad=2.849e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.515014e-3 s grad=3.056e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.081237e-3 s grad=3.348e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.223077e-3 s grad=3.46e-6 s +[acc-interp] eval 0.610220517 s +[acc-interp] thetaMarsh 1.61e-7 s (n=[acc-interp] thetaMarsh 1.15e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.2156723e-2 s grad=3.986e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.141888e-3 s grad=1.1929e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=9.427219e-3 s grad=9.219e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.24397e-3 s grad=2.722e-6 s +[acc-interp] eval 6.1961738e-2 s +4) +[acc-interp] chunk base=0 cs=25974 kernel=4.207544e-3 s grad=3.129e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.007524e-3 s grad=3.823e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.255623e-3 s grad=4.275e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.126022e-3 s grad=4.582e-6 s +[acc-interp] eval 8.0746153e-2 s +[acc-interp] thetaMarsh 1.47e-7 s (n=0) +[acc-interp] chunk base=0 cs=25974 kernel=1.120468e-2 s grad=1.505e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.949775e-3 s grad=1.416e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.99027e-3 s grad=1.596e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.916015e-3 s grad=9.46e-7 s +[acc-interp] eval 1.7895342e-2 s +[acc-interp] thetaMarsh 5.7e-8 s (n=0) +[acc-interp] chunk base=0 cs=25974 kernel=2.7824254e-2 s grad=1.145e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.472648e-3 s grad=1.365e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.482208e-3 s grad=8.19e-7 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.004564e-3 s grad=9.67e-7 s +[acc-interp] eval 3.5577175e-2 s +[acc-interp] thetaMarsh 1.57e-7 s (n=[acc-interp] thetaMarsh 9.3e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.1986903e-2 s grad=3.164e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=6.002957e-3 s grad=5.747e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=9.60256e-3 s grad=3.886e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.825313e-3 s grad=7.047e-6 s +[acc-interp] eval 5.4581498e-2 s +5) +[acc-interp] chunk base=0 cs=25974 kernel=4.421998e-3 s grad=3.754e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.35089e-3 s grad=4.37e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.724642e-3 s grad=4.587e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.705695e-3 s grad=5.342e-6 s +[acc-interp] eval 7.4135734e-2 s +[acc-interp] thetaMarsh 6.6e-8 s (n=[acc-interp] thetaMarsh 5.4e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=8.932401e-3 s grad=4.12e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.523038e-3 s grad=3.903e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.199354e-3 s grad=4.112e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.668871e-3 s grad=3.004e-6 s +[acc-interp] eval 1.9867595e-2 s +[acc-interp] thetaMarsh 6.94e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=9.857606e-3 s grad=3.301e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.58028e-3 s grad=3.017e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.174452e-3 s grad=2.919e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.174023e-3 s grad=1.971e-6 s +[acc-interp] eval 1.8264062e-2 s +[acc-interp] thetaMarsh 2.51e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.694612e-3 s grad=2.087e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.795965e-3 s grad=1.863e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.035194e-3 s grad=5.589e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.704734e-3 s grad=2.634e-6 s +[acc-interp] eval 1.1846933e-2 s +[acc-interp] thetaMarsh 5.9e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.032839e-3 s grad=2.933e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.59628e-3 s grad=3.253e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.094913e-3 s grad=1.152e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.428392e-3 s grad=3.671e-6 s +[acc-interp] eval 1.0011762e-2 s +[acc-interp] thetaMarsh 9.0e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.317746e-3 s grad=4.041e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.298287e-3 s grad=2.9e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.191317e-3 s grad=4.519e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.258645e-3 s grad=3.616e-6 s +[acc-interp] eval 1.0981081e-2 s +[acc-interp] thetaMarsh 7.8e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.967861e-3 s grad=3.927e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.430336e-3 s grad=5.951e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.066711e-3 s grad=4.597e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.941018e-3 s grad=4.022e-6 s +[acc-interp] eval 1.3743443e-2 s +[acc-interp] thetaMarsh 1.12e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.546367e-3 s grad=3.627e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.646098e-3 s grad=3.092e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.60156e-3 s grad=3.594e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.441665e-3 s grad=2.812e-6 s +[acc-interp] eval 1.1399932e-2 s +[acc-interp] thetaMarsh 9.6e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.436778e-3 s grad=3.512e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.510482e-3 s grad=3.667e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.241712e-3 s grad=3.098e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.537738e-3 s grad=2.443e-6 s +[acc-interp] eval 1.0937584e-2 s +[acc-interp] thetaMarsh 1.05e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.378954e-3 s grad=4.755e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.630581e-3 s grad=2.919e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.07594e-3 s grad=1.596e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.896833e-3 s grad=8.793e-6 s +[acc-interp] eval 1.3001633e-2 s +[acc-interp] thetaMarsh 6.5e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.400582e-3 s grad=1.756e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.102724e-3 s grad=4.405e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.917908e-3 s grad=2.833e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.656457e-3 s grad=4.34e-6 s +[acc-interp] eval 1.2448888e-2 s +[acc-interp] thetaMarsh 1.12e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.764106e-3 s grad=3.124e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.48715e-3 s grad=3.957e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.329716e-3 s grad=4.926e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.526666e-3 s grad=6.108e-6 s +[acc-interp] eval 1.2436077e-2 s +[acc-interp] thetaMarsh 8.3e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.297356e-3 s grad=3.258e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.582723e-3 s grad=3.489e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.062639e-3 s grad=3.928e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.341243e-3 s grad=5.31e-6 s +[acc-interp] eval 1.3280086e-2 s +[acc-interp] thetaMarsh 7.9e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.134715e-3 s grad=4.541e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.341302e-3 s grad=7.246e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.288821e-3 s grad=9.219e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.1529542e-2 s grad=4.162e-6 s +[acc-interp] eval 2.2150719e-2 s +[acc-interp] thetaMarsh 2.85e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.289991e-3 s grad=2.529e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.122568e-3 s grad=2.059e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.442371e-3 s grad=1.733e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.750582e-3 s grad=1.88e-6 s +[acc-interp] eval 1.2361672e-2 s +[acc-interp] thetaMarsh 8.1e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.745439e-3 s grad=1.2923e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.515447e-3 s grad=1.422e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.022223e-3 s grad=7.096e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.142566e-3 s grad=7.724e-6 s +[acc-interp] eval 1.2198273e-2 s +[acc-interp] thetaMarsh 5.5e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.950287e-3 s grad=4.103e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.182002e-3 s grad=3.555e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.826942e-3 s grad=4.235e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.445209e-3 s grad=3.243e-6 s +[acc-interp] eval 1.2581351e-2 s +[acc-interp] thetaMarsh 2.36e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.657544e-3 s grad=3.308e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.001108e-3 s grad=5.18e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.227272e-3 s grad=3.947e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.090383e-3 s grad=4.509e-6 s +[acc-interp] eval 1.3241906e-2 s +[acc-interp] thetaMarsh 8.1e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.27208e-3 s grad=4.133e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.870063e-3 s grad=3.656e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.509715e-3 s grad=5.487e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.920643e-3 s grad=4.358e-6 s +[acc-interp] eval 1.3832442e-2 s +[acc-interp] thetaMarsh 7.6e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.492527e-3 s grad=4.39e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.731141e-3 s grad=4.571e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.686934e-3 s grad=3.728e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.711169e-3 s grad=4.4e-6 s +[acc-interp] eval 1.1811883e-2 s +[acc-interp] thetaMarsh 7.8e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.103157e-3 s grad=4.599e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.767341e-3 s grad=4.229e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.481231e-3 s grad=4.03e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.570061e-3 s grad=3.327e-6 s +[acc-interp] eval 1.2319418e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=2.435869e-3 s grad=3.051e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.772451e-3 s grad=4.426e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.907389e-3 s grad=3.97e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.332431e-3 s grad=4.358e-6 s +[acc-interp] eval 0.285322302 s +[acc-interp] thetaMarsh 7.2e-8 s (n=[acc-interp] thetaMarsh 5.9e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.7897399e-2 s grad=4.217e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.67287e-3 s grad=3.162e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.963201e-3 s grad=5.027e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.609797e-3 s grad=4.97e-6 s +[acc-interp] eval 4.1108921e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=5.297334e-3 s grad=4.264e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.848593e-3 s grad=3.386e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.51686e-3 s grad=3.796e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.821283e-3 s grad=3.854e-6 s +[acc-interp] eval 5.8610889e-2 s +[acc-interp] thetaMarsh 7.4e-8 s (n=[acc-interp] thetaMarsh 7.6e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=2.4019979e-2 s grad=4.476e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.125914e-3 s grad=4.951e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.658842e-3 s grad=9.267e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=8.969323e-3 s grad=5.677e-6 s +[acc-interp] eval 4.2920491e-2 s +5) +[acc-interp] chunk base=0 cs=25974 kernel=4.310787e-3 s grad=3.347e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.106714e-3 s grad=9.216e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.23359e-3 s grad=9.193e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.193859e-3 s grad=4.224e-6 s +[acc-interp] eval 6.0837125e-2 s +[acc-interp] thetaMarsh 1.19e-7 s (n=[acc-interp] thetaMarsh 6.1e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=8.972784e-3 s grad=1.0812e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.645119e-3 s grad=9.893e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.200095e-3 s grad=3.907e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.010877e-3 s grad=1.5e-6 s +[acc-interp] eval 1.9668935e-2 s +[acc-interp] thetaMarsh 6.4e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.087848e-3 s grad=9.025e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.463835e-3 s grad=6.267e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.554274e-3 s grad=5.813e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.092411e-3 s grad=3.105e-6 s +[acc-interp] eval 1.2965215e-2 s +[acc-interp] thetaMarsh 6.7e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.662601e-3 s grad=8.913e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.685734e-3 s grad=6.99e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.1341864e-2 s grad=3.89e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.58909e-3 s grad=9.798e-6 s +[acc-interp] eval 2.0098249e-2 s +[acc-interp] thetaMarsh 2.4e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.898172e-3 s grad=1.743e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.465894e-3 s grad=1.736e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.31648e-3 s grad=5.895e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.894158e-3 s grad=5.841e-6 s +[acc-interp] eval 1.142231e-2 s +[acc-interp] thetaMarsh 3.16e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.682353e-3 s grad=3.359e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.800197e-3 s grad=2.622e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.511036e-3 s grad=7.871e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.442624e-3 s grad=9.79e-7 s +[acc-interp] eval 1.1238112e-2 s +[acc-interp] thetaMarsh 1.98e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.724239e-3 s grad=2.349e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.913259e-3 s grad=4.141e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.366485e-3 s grad=2.4e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.735705e-3 s grad=1.429e-6 s +[acc-interp] eval 1.2568753e-2 s +[acc-interp] thetaMarsh 6.6e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.672818e-3 s grad=1.459e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.73045e-3 s grad=2.852e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.677229e-3 s grad=4.174e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.157217e-3 s grad=3.533e-6 s +[acc-interp] eval 1.2093967e-2 s +[acc-interp] thetaMarsh 2.56e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.656207e-3 s grad=1.999e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.500787e-3 s grad=1.42e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.809199e-3 s grad=1.362e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.308769e-3 s grad=3.124e-6 s +[acc-interp] eval 1.101478e-2 s +[acc-interp] thetaMarsh 8.9e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.174783e-3 s 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s +[acc-interp] eval 1.139523e-2 s +[acc-interp] thetaMarsh 5.7e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.823149e-3 s grad=7.003e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.408034e-3 s grad=3.382e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.222958e-3 s grad=1.617e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.335381e-3 s grad=2.356e-6 s +[acc-interp] eval 1.2582706e-2 s +[acc-interp] thetaMarsh 6.3e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.375081e-3 s grad=3.279e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.224943e-3 s grad=6.911e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.00527e-3 s grad=3.995e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.430226e-3 s grad=3.924e-6 s +[acc-interp] eval 1.3035561e-2 s +[acc-interp] thetaMarsh 1.91e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.850478e-3 s grad=3.005e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.440644e-3 s grad=2.789e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.26369e-3 s grad=2.626e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.614473e-3 s grad=3.096e-6 s +[acc-interp] eval 1.0911237e-2 s +[acc-interp] thetaMarsh 1.57e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.304739e-3 s grad=2.553e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.357091e-3 s grad=2.303e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.213078e-3 s grad=2.261e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.237744e-3 s grad=2.637e-6 s +[acc-interp] eval 9.83075e-3 s +[acc-interp] thetaMarsh 6.1e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.42421e-3 s grad=2.165e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.608646e-3 s grad=4.82e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.623239e-3 s grad=4.637e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.796672e-3 s grad=8.087e-6 s +[acc-interp] eval 1.2286658e-2 s +[acc-interp] thetaMarsh 6.9e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.153673e-3 s grad=9.578e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.097621e-3 s grad=3.108e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.873003e-3 s grad=4.724e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.857223e-3 s grad=4.177e-6 s +[acc-interp] eval 1.4203815e-2 s +[acc-interp] thetaMarsh 3.12e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.392367e-3 s grad=3.347e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.373022e-3 s grad=3.242e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.195181e-3 s grad=2.865e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.111652e-3 s grad=3.529e-6 s +[acc-interp] eval 1.0209622e-2 s +[acc-interp] thetaMarsh 1.05e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.893181e-3 s grad=4.54e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.507993e-3 s grad=3.047e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.223036e-3 s grad=2.113e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.222994e-3 s grad=2.18e-6 s +[acc-interp] eval 1.2520246e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=2.191327e-3 s grad=3.531e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.236891e-3 s grad=4.671e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.648294e-3 s grad=2.259e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.386348e-3 s grad=2.415e-6 s +[acc-interp] eval 0.25592419 s +[acc-interp] thetaMarsh 1.24e-7 s (n=0) +[acc-interp] chunk base=0 cs=25974 kernel=1.3045774e-2 s grad=1.679e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.14224e-3 s grad=3.71e-7 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.049086e-3 s grad=1.022e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.993935e-3 s grad=9.46e-7 s +[acc-interp] eval 2.0221098e-2 s +[acc-interp] thetaMarsh 8.6e-8 s (n=0) +[acc-interp] chunk base=0 cs=25974 kernel=1.7031223e-2 s grad=1.053e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.759776e-3 s grad=1.184e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.084474e-3 s grad=1.592e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.558608e-3 s grad=1.445e-6 s +[acc-interp] eval 2.4367428e-2 s +[acc-interp] thetaMarsh 8.3e-8 s (n=[acc-interp] thetaMarsh 5.4e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.2045855e-2 s grad=3.935e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.614038e-3 s grad=3.294e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.393733e-3 s grad=4.069e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.303303e-2 s grad=4.223e-6 s +[acc-interp] eval 5.7687944e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=6.107657e-3 s grad=4.57e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.138528e-3 s grad=4.04e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=6.27846e-3 s grad=3.463e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.47377e-3 s grad=4.293e-6 s +[acc-interp] eval 8.2149079e-2 s +[acc-interp] thetaMarsh 7.2e-8 s (n=0) +[acc-interp] chunk base=0 cs=25974 kernel=1.2093839e-2 s grad=1.198e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.406263e-3 s grad=1.5e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.649504e-3 s grad=1.405e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.402527e-3 s grad=2.419e-6 s +[acc-interp] eval 2.2409477e-2 s +[acc-interp] thetaMarsh 1.79e-7 s (n=[acc-interp] thetaMarsh 5.7e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.8514468e-2 s grad=3.469e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.312825e-3 s grad=8.655e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.524066e-3 s grad=2.812e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.260714e-3 s grad=1.734e-6 s +[acc-interp] eval 2.6422809e-2 s +[acc-interp] thetaMarsh 6.0e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.631583e-3 s grad=1.829e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.924221e-3 s grad=6.537e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.295498e-3 s grad=4.792e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.476623e-3 s grad=4.879e-6 s +[acc-interp] eval 1.0990784e-2 s +[acc-interp] thetaMarsh 5.9e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.322962e-3 s grad=2.163e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.015765e-3 s grad=4.847e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.385505e-3 s grad=2.413e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.673989e-3 s grad=5.743e-6 s +[acc-interp] eval 1.4275771e-2 s +[acc-interp] thetaMarsh 5.9e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.976458e-3 s grad=8.197e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.91314e-3 s grad=3.883e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.760072e-3 s grad=3.167e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.41261e-3 s grad=2.247e-6 s +[acc-interp] eval 1.3527553e-2 s +[acc-interp] thetaMarsh 1.14e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.36137e-3 s grad=1.645e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.528582e-3 s grad=5.218e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.909638e-3 s grad=3.665e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.354361e-3 s grad=2.848e-6 s +[acc-interp] eval 1.2822449e-2 s +[acc-interp] thetaMarsh 1.19e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.724093e-3 s grad=3.333e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.547623e-3 s grad=2.293e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.517461e-3 s grad=2.957e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.922267e-3 s grad=3.94e-6 s +[acc-interp] eval 1.2387741e-2 s +[acc-interp] thetaMarsh 1.44e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.091714e-3 s grad=2.357e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.559988e-3 s grad=3.11e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.609969e-3 s grad=2.311e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.674181e-3 s grad=3.182e-6 s +[acc-interp] eval 1.2667776e-2 s 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kernel=2.341948e-3 s grad=2.83e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.243815e-3 s grad=2.959e-6 s +[acc-interp] eval 1.2064119e-2 s +[acc-interp] thetaMarsh 1.25e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.561124e-3 s grad=2.389e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.458796e-3 s grad=2.565e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.503385e-3 s grad=1.824e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.269317e-3 s grad=1.738e-6 s +[acc-interp] eval 1.1402541e-2 s +[acc-interp] thetaMarsh 1.23e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.621545e-3 s grad=3.831e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.456811e-3 s grad=2.884e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.331809e-3 s grad=2.537e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.449741e-3 s grad=2.057e-6 s +[acc-interp] eval 1.2565008e-2 s +[acc-interp] thetaMarsh 1.08e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.32889e-3 s grad=1.278e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.932319e-3 s grad=4.841e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.668604e-3 s grad=8.942e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.352619e-3 s grad=6.146e-6 s +[acc-interp] eval 1.1356465e-2 s +[acc-interp] thetaMarsh 5.9e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.4838e-3 s grad=7.224e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.164326e-3 s grad=7.127e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.295357e-3 s grad=2.199e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.512968e-3 s grad=3.864e-6 s +[acc-interp] eval 1.0331815e-2 s +[acc-interp] thetaMarsh 2.51e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.510675e-3 s grad=3.133e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.518431e-3 s grad=2.398e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.052481e-3 s grad=2.565e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.196226e-3 s grad=2.008e-6 s +[acc-interp] eval 1.105831e-2 s +[acc-interp] thetaMarsh 1.26e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.03205e-3 s grad=1.743e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.108778e-3 s grad=3.633e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.646943e-3 s grad=2.529e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.580341e-3 s grad=2.667e-6 s +[acc-interp] eval 1.2012407e-2 s +[acc-interp] thetaMarsh 1.09e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.600645e-3 s grad=2.213e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.320402e-3 s grad=2.374e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.33276e-3 s grad=1.95e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.338868e-3 s grad=4.07e-6 s +[acc-interp] eval 1.1974584e-2 s +[acc-interp] thetaMarsh 5.8e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.754898e-3 s grad=1.4044e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.990811e-3 s grad=1.892e-6 s 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chunk base=0 cs=25974 kernel=2.269555e-3 s grad=2.095e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.164031e-3 s grad=1.896e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.398586e-3 s grad=2.084e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.26939e-3 s grad=1.991e-6 s +[acc-interp] eval 1.0523901e-2 s +[acc-interp] thetaMarsh 2.27e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.455822e-3 s grad=3.21e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.246907e-3 s grad=2.605e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.149171e-3 s grad=1.52e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.312114e-3 s grad=1.279e-6 s +[acc-interp] eval 1.0912006e-2 s +[acc-interp] thetaMarsh 5.6e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.14756e-3 s grad=5.611e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.618357e-3 s grad=4.657e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.05124e-3 s grad=2.992e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.505945e-3 s grad=2.607e-6 s +[acc-interp] eval 1.4097403e-2 s +[acc-interp] thetaMarsh 1.34e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.936031e-3 s grad=2.222e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.573295e-3 s grad=3.435e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.811803e-3 s grad=3.357e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.291439e-3 s grad=2.217e-6 s +[acc-interp] eval 1.3307965e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=2.226775e-3 s grad=2.511e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.373185e-3 s grad=2.042e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.373719e-3 s grad=2.224e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.592953e-3 s grad=2.879e-6 s +[acc-interp] eval 0.322617194 s +[acc-interp] thetaMarsh 1.43e-7 s (n=[acc-interp] thetaMarsh 1.06e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.4816239e-2 s grad=3.394e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.237934e-3 s grad=3.937e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.732104e-3 s grad=2.881e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.710248e-3 s grad=3.051e-6 s +[acc-interp] eval 3.8238869e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=3.090115e-3 s grad=3.094e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.802415e-3 s grad=3.015e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.126319e-3 s grad=3.138e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.937918e-3 s grad=2.217e-6 s +[acc-interp] eval 5.2921325e-2 s +[acc-interp] thetaMarsh 1.42e-7 s (n=[acc-interp] thetaMarsh 8.4e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.31641e-2 s grad=4.443e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.469462e-3 s grad=2.651e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.706716e-3 s grad=3.016e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.342166e-3 s grad=2.885e-6 s +[acc-interp] eval 2.5140697e-2 s +[acc-interp] thetaMarsh 6.6e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.848852e-3 s grad=7.249e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=9.330241e-3 s grad=4.372e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.45533e-3 s grad=3.685e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.698181e-3 s grad=4.935e-6 s +[acc-interp] eval 1.9450282e-2 s +[acc-interp] thetaMarsh 6.9e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.925847e-3 s grad=2.064e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.559373e-3 s grad=1.911e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.860046e-3 s grad=3.874e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.955416e-3 s grad=3.539e-6 s +[acc-interp] eval 1.2321836e-2 s +[acc-interp] thetaMarsh 1.05e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.982972e-3 s grad=2.698e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.373586e-3 s grad=3.022e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.048523e-3 s grad=3.09e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.098027e-3 s grad=2.363e-6 s +[acc-interp] eval 1.4126924e-2 s +[acc-interp] thetaMarsh 1.06e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.022538e-3 s grad=4.004e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.093935e-3 s grad=2.617e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.529839e-3 s grad=2.776e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.724243e-3 s grad=2.677e-6 s +[acc-interp] eval 1.3055406e-2 s +[acc-interp] thetaMarsh 1.35e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.643938e-3 s grad=2.68e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.690433e-3 s grad=2.609e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.48126e-3 s grad=2.729e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.494137e-3 s grad=3.509e-6 s +[acc-interp] eval 1.3913618e-2 s +[acc-interp] thetaMarsh 1.0e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.701794e-3 s grad=2.778e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.448227e-3 s grad=2.199e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.788949e-3 s grad=4.93e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.028624e-3 s grad=2.795e-6 s +[acc-interp] eval 1.4736695e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=2.503441e-3 s grad=1.993e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.711413e-3 s grad=1.651e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.452266e-3 s grad=3.073e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.101274e-3 s grad=3.23e-6 s +[acc-interp] eval 0.127141045 s +[acc-interp] thetaMarsh 1.48e-7 s (n=[acc-interp] thetaMarsh 1.26e-7 s (n=6) +[acc-interp] chunk base=0 cs=25974 kernel=7.684308e-3 s grad=3.681e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.040364e-3 s grad=1.2656e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.970547e-3 s grad=1.0573e-5 s +[acc-interp] chunk base=77922 cs=25974 kernel=6.573695e-3 s grad=1.1595e-5 s +[acc-interp] eval 2.1207501e-2 s +[acc-interp] thetaMarsh 6.8e-8 s (n=6) +[acc-interp] chunk base=0 cs=25974 kernel=2.903187e-3 s grad=3.243e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.24611e-3 s grad=4.093e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.538522e-3 s grad=5.865e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.269979e-3 s grad=2.533e-6 s +[acc-interp] eval 1.3902934e-2 s +[acc-interp] thetaMarsh 6.5e-8 s (n=6) +[acc-interp] chunk base=0 cs=25974 kernel=3.291863e-3 s grad=1.2344e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.811556e-3 s grad=4.206e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.029397e-3 s grad=4.34e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.988157e-3 s grad=4.411e-6 s +[acc-interp] eval 1.4269634e-2 s +[acc-interp] thetaMarsh 9.9e-8 s (n=6) +[acc-interp] chunk base=0 cs=25974 kernel=2.824021e-3 s grad=4.033e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.034043e-3 s grad=3.302e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.843009e-3 s grad=4.331e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.385806e-3 s grad=5.391e-6 s +[acc-interp] eval 1.3743765e-2 s +[acc-interp] thetaMarsh 1.21e-7 s (n=6) +[acc-interp] chunk base=0 cs=25974 kernel=3.13001e-3 s grad=4.218e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.056692e-3 s grad=3.311e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.877814e-3 s grad=3.307e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.281873e-3 s grad=4.625e-6 s +[acc-interp] eval 1.3956845e-2 s +[acc-interp] thetaMarsh 1.08e-7 s (n=6) +[acc-interp] chunk base=0 cs=25974 kernel=3.798429e-3 s grad=5.196e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.848293e-3 s grad=5.052e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.304753e-3 s grad=4.381e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.833845e-3 s grad=3.413e-6 s +[acc-interp] eval 1.4455399e-2 s +[acc-interp] thetaMarsh 1.39e-7 s (n=6) +[acc-interp] chunk base=0 cs=25974 kernel=3.382013e-3 s grad=4.878e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.191368e-3 s grad=3.228e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.129401e-3 s grad=3.18e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.953371e-3 s grad=3.706e-6 s +[acc-interp] eval 1.4310167e-2 s +[acc-interp] thetaMarsh 1.21e-7 s (n=6) +[acc-interp] chunk base=0 cs=25974 kernel=2.98778e-3 s grad=3.311e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.148435e-3 s grad=4.493e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.700606e-3 s grad=1.1589e-5 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.844944e-3 s grad=1.428e-6 s +[acc-interp] eval 1.2843901e-2 s +[acc-interp] thetaMarsh 2.15e-7 s (n=6) +[acc-interp] chunk base=0 cs=25974 kernel=2.742161e-3 s grad=3.702e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.438522e-3 s grad=2.862e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.544964e-3 s grad=2.759e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.443009e-3 s grad=4.468e-6 s +[acc-interp] eval 1.5002137e-2 s +[acc-interp] thetaMarsh 1.02e-7 s (n=6) +[acc-interp] chunk base=0 cs=25974 kernel=2.869141e-3 s grad=4.363e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.000898e-3 s grad=4.598e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.59663e-3 s grad=3.211e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.093118e-3 s grad=4.739e-6 s +[acc-interp] eval 1.3756635e-2 s +[acc-interp] thetaMarsh 1.6e-7 s (n=6) +[acc-interp] chunk base=0 cs=25974 kernel=3.124202e-3 s grad=4.305e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.134224e-3 s grad=3.628e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.646912e-3 s grad=3.641e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.806402e-3 s grad=3.897e-6 s +[acc-interp] eval 1.3924848e-2 s +[acc-interp] thetaMarsh 1.51e-7 s (n=6) +[acc-interp] chunk base=0 cs=25974 kernel=3.717363e-3 s grad=4.023e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.898569e-3 s grad=3.521e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.091366e-3 s grad=4.982e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.924965e-3 s grad=3.847e-6 s +[acc-interp] eval 1.4736922e-2 s +[acc-interp] thetaMarsh 1.08e-7 s (n=6) +[acc-interp] chunk base=0 cs=25974 kernel=3.648424e-3 s grad=4.849e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.103115e-3 s grad=3.52e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.266981e-3 s grad=3.375e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.983268e-3 s grad=2.595e-6 s +[acc-interp] eval 1.512271e-2 s +[acc-interp] thetaMarsh 9.7e-8 s (n=6) +[acc-interp] chunk base=0 cs=25974 kernel=3.72842e-3 s grad=5.063e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.585413e-3 s grad=4.689e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.201788e-3 s grad=3.921e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.960322e-3 s grad=3.932e-6 s +[acc-interp] eval 1.6620063e-2 s +[acc-interp] thetaMarsh 2.03e-7 s (n=6) 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chunk base=77922 cs=25974 kernel=2.746201e-3 s grad=1.0489e-5 s +[acc-interp] eval 1.2258475e-2 s +[acc-interp] thetaMarsh 6.0e-8 s (n=6) +[acc-interp] chunk base=0 cs=25974 kernel=2.553511e-3 s grad=2.971e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.477562e-3 s grad=1.165e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.632935e-3 s grad=9.34e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.647957e-3 s grad=8.99e-6 s +[acc-interp] eval 1.1059712e-2 s +[acc-interp] thetaMarsh 1.74e-7 s (n=6) +[acc-interp] chunk base=0 cs=25974 kernel=2.954244e-3 s grad=4.686e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.349036e-3 s grad=5.613e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.382606e-3 s grad=3.358e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.772127e-3 s grad=3.648e-6 s +[acc-interp] eval 1.3914492e-2 s +[acc-interp] thetaMarsh 1.23e-7 s (n=6) +[acc-interp] chunk base=0 cs=25974 kernel=2.945162e-3 s grad=3.158e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.857637e-3 s grad=4.47e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.174028e-3 s grad=4.638e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.022816e-3 s grad=3.038e-6 s +[acc-interp] eval 1.470996e-2 s +[acc-interp] thetaMarsh 1.14e-7 s (n=6) +[acc-interp] chunk base=0 cs=25974 kernel=2.899297e-3 s grad=3.147e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.313673e-3 s grad=4.268e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.65278e-3 s grad=4.803e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.380195e-3 s grad=4.639e-6 s +[acc-interp] eval 1.4433399e-2 s +[acc-interp] thetaMarsh 6.5e-8 s (n=6) +[acc-interp] chunk base=0 cs=25974 kernel=2.988349e-3 s grad=3.48e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.902418e-3 s grad=3.267e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.24438e-3 s grad=4.749e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.773597e-3 s grad=4.101e-6 s +[acc-interp] eval 1.475988e-2 s +[acc-interp] thetaMarsh 7.4e-8 s (n=6) +[acc-interp] chunk base=0 cs=25974 kernel=2.915383e-3 s grad=3.264e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.748812e-3 s grad=3.296e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.188354e-3 s grad=6.023e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.957966e-3 s grad=1.91e-6 s +[acc-interp] eval 1.5721431e-2 s +[acc-interp] thetaMarsh 1.25e-7 s (n=6) +[acc-interp] chunk base=0 cs=25974 kernel=2.975877e-3 s grad=6.477e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.016562e-3 s grad=1.791e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.877061e-3 s grad=1.547e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.656022e-3 s grad=1.557e-6 s +[acc-interp] eval 1.2150045e-2 s +[acc-interp] thetaMarsh 1.44e-7 s (n=6) +[acc-interp] chunk base=0 cs=25974 kernel=3.108933e-3 s grad=2.061e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.407073e-3 s grad=2.694e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.780983e-3 s grad=2.258e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.5421688e-2 s grad=3.559e-6 s +[acc-interp] eval 2.5421272e-2 s +[acc-interp] thetaMarsh 6.1e-8 s (n=6) +[acc-interp] chunk base=0 cs=25974 kernel=3.743546e-3 s grad=3.42e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.734155e-3 s grad=4.68e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.785528e-3 s grad=1.949e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.247583e-3 s grad=1.923e-6 s +[acc-interp] eval 1.4298229e-2 s +[acc-interp] thetaMarsh 1.88e-7 s (n=6) +[acc-interp] chunk base=0 cs=25974 kernel=3.252409e-3 s grad=1.776e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.841502e-3 s grad=4.556e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.842571e-3 s grad=4.513e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.105855e-3 s grad=3.733e-6 s +[acc-interp] eval 1.5312083e-2 s +[acc-interp] thetaMarsh 1.14e-7 s (n=6) +[acc-interp] chunk base=0 cs=25974 kernel=2.719579e-3 s grad=3.353e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.91087e-3 s grad=3.001e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.527916e-3 s grad=4.314e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.881751e-3 s grad=4.902e-6 s +[acc-interp] eval 1.4730271e-2 s +[acc-interp] thetaMarsh 1.39e-7 s (n=6) +[acc-interp] chunk base=0 cs=25974 kernel=3.527285e-3 s grad=4.411e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.190664e-3 s grad=1.1679e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.818542e-3 s grad=4.704e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.021695e-3 s grad=3.677e-6 s +[acc-interp] eval 1.561828e-2 s +[acc-interp] thetaMarsh 6.0e-8 s (n=6) +[acc-interp] chunk base=0 cs=25974 kernel=3.188971e-3 s grad=5.315e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.23378e-3 s grad=1.3007e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.151373e-3 s grad=5.273e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.373336e-3 s grad=4.296e-6 s +[acc-interp] eval 1.5886264e-2 s +[acc-interp] thetaMarsh 5.9e-8 s (n=6) +[acc-interp] chunk base=0 cs=25974 kernel=3.582812e-3 s grad=2.535e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=6.834637e-3 s grad=1.5735e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.949795e-3 s grad=1.647e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.722955e-3 s grad=1.0924e-5 s +[acc-interp] eval 1.8813877e-2 s +6) +[acc-interp] chunk base=0 cs=25974 kernel=5.046205e-3 s grad=1.2439e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.046747e-3 s grad=1.0675e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.546843e-3 s grad=5.708e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.189448e-3 s grad=8.985e-6 s +[acc-interp] eval 0.501452184 s +[acc-interp] thetaMarsh 6.9e-8 s (n=[acc-interp] thetaMarsh 5.3e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.8146427e-2 s grad=3.993e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.341565e-3 s grad=3.644e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.926704e-3 s grad=3.859e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=6.525237e-3 s grad=5.859e-6 s +[acc-interp] eval 5.4716575e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=6.844896e-3 s grad=3.793e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=6.887068e-3 s grad=6.936e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=6.109209e-3 s grad=3.147e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.80187e-3 s grad=2.553e-6 s +[acc-interp] eval 8.1453319e-2 s +[acc-interp] thetaMarsh 1.39e-7 s (n=[acc-interp] thetaMarsh 1.03e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.1870405e-2 s grad=3.544e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.507175e-3 s grad=3.101e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.839924e-3 s grad=5.389e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.435416e-3 s grad=1.1062e-5 s +[acc-interp] eval 4.5827906e-2 s +5) +[acc-interp] chunk base=0 cs=25974 kernel=4.197523e-3 s grad=4.246e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.566403e-3 s grad=4.545e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.999121e-3 s grad=4.359e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.814099e-3 s grad=3.08e-6 s +[acc-interp] eval 6.4245647e-2 s +[acc-interp] thetaMarsh 1.28e-7 s (n=[acc-interp] thetaMarsh 2.05e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.3529173e-2 s grad=1.4308e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.743096e-3 s grad=3.293e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.3188333e-2 s grad=4.356e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.289968e-3 s grad=4.25e-6 s +[acc-interp] eval 3.7351652e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=5.862064e-3 s grad=4.352e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.417486e-3 s grad=3.365e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.759046e-3 s grad=2.298e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.973448e-3 s grad=2.669e-6 s +[acc-interp] eval 5.743014e-2 s +[acc-interp] thetaMarsh 1.38e-7 s (n=[acc-interp] thetaMarsh 1.02e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.2595344e-2 s grad=2.943e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.503941e-3 s grad=3.735e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.954291e-3 s grad=4.682e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.738425e-3 s grad=5.294e-6 s +[acc-interp] eval 3.1576769e-2 s +[acc-interp] thetaMarsh 1.69e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.1733144e-2 s grad=3.791e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.584107e-3 s grad=3.643e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.879549e-3 s grad=3.876e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.814743e-3 s grad=3.419e-6 s +[acc-interp] eval 3.0729623e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=5.891173e-3 s grad=3.648e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.352967e-3 s grad=3.963e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.152332e-3 s grad=2.532e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=7.631514e-3 s grad=3.674e-6 s +[acc-interp] eval 8.8280179e-2 s +[acc-interp] thetaMarsh 1.23e-7 s (n=[acc-interp] thetaMarsh 1.01e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=6.498606e-3 s grad=2.682e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.220792e-3 s grad=2.108e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.576401e-3 s grad=5.117e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.81946e-3 s grad=2.428e-6 s +[acc-interp] eval 1.6844861e-2 s +[acc-interp] thetaMarsh 1.0e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.875474e-3 s grad=3.49e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.647406e-3 s grad=2.286e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.663136e-3 s grad=4.355e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.109132e-3 s grad=2.653e-6 s +[acc-interp] eval 1.7981111e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=4.729001e-3 s grad=1.972e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.1730633e-2 s grad=2.974e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.676888e-3 s grad=1.567e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.346572e-3 s grad=2.299e-6 s +[acc-interp] eval 5.908938e-2 s +[acc-interp] thetaMarsh 1.45e-7 s (n=[acc-interp] thetaMarsh 1.06e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.8189857e-2 s grad=6.146e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.24655e-3 s grad=3.054e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.244835e-3 s grad=2.928e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.303409e-2 s grad=3.45e-6 s +[acc-interp] eval 4.349406e-2 s +[acc-interp] thetaMarsh 1.39e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.853665e-3 s grad=4.892e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.539236e-3 s grad=3.27e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.466032e-3 s grad=3.812e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=6.391164e-3 s grad=3.682e-6 s +[acc-interp] eval 2.4006441e-2 s +[acc-interp] thetaMarsh 1.18e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=5.669511e-3 s grad=4.252e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.935364e-3 s grad=3.702e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=6.108996e-3 s grad=3.933e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.450481e-3 s grad=3.511e-6 s +[acc-interp] eval 2.4708792e-2 s +[acc-interp] thetaMarsh 1.01e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=6.074167e-3 s grad=3.74e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.886726e-3 s grad=2.78e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.703249e-3 s grad=3.544e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=6.663903e-3 s grad=3.712e-6 s +[acc-interp] eval 2.5893961e-2 s +[acc-interp] thetaMarsh 1.07e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=5.795854e-3 s grad=3.039e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=6.063311e-3 s grad=3.792e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=6.085593e-3 s grad=3.837e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.706841e-3 s grad=3.178e-6 s +[acc-interp] eval 2.5157512e-2 s +[acc-interp] thetaMarsh 9.9e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=6.54868e-3 s grad=3.292e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.7281609e-2 s grad=4.146e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.969825e-3 s grad=3.358e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.787983e-3 s grad=3.817e-6 s +[acc-interp] eval 3.7244088e-2 s +[acc-interp] thetaMarsh 1.57e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=7.511746e-3 s grad=3.708e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.479419e-3 s grad=3.259e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=6.153449e-3 s grad=4.213e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=6.159326e-3 s grad=3.959e-6 s +[acc-interp] eval 2.6843937e-2 s +[acc-interp] thetaMarsh 1.0e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=5.911038e-3 s grad=4.007e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=6.262097e-3 s grad=4.388e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=6.432481e-3 s grad=4.087e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=6.821003e-3 s grad=3.935e-6 s +[acc-interp] eval 2.6698745e-2 s +[acc-interp] thetaMarsh 6.5e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=5.713838e-3 s grad=3.668e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.991951e-3 s grad=3.467e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.0319636e-2 s grad=2.58e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.785974e-3 s grad=8.272e-6 s +[acc-interp] eval 2.7578608e-2 s +[acc-interp] thetaMarsh 6.2e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=5.879961e-3 s grad=4.272e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.662036e-3 s grad=4.102e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.090289e-3 s grad=6.492e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=6.244525e-3 s grad=4.487e-6 s +[acc-interp] eval 2.3887854e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=4.965814e-3 s grad=3.346e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.433924e-3 s grad=3.843e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=6.130272e-3 s grad=5.997e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=6.092839e-3 s grad=4.74e-6 s +[acc-interp] eval 0.31041625 s +[acc-interp] thetaMarsh 7.6e-8 s (n=[acc-interp] thetaMarsh 5.4e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.890014e-2 s grad=5.208e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.362019e-3 s grad=3.446e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.078244e-3 s grad=5.205e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.888677e-3 s grad=3.644e-6 s +[acc-interp] eval 2.8676686e-2 s +[acc-interp] thetaMarsh 1.19e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.793614e-3 s grad=2.818e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.565305e-3 s grad=2.993e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.478329e-3 s grad=2.307e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.632677e-3 s grad=2.342e-6 s +[acc-interp] eval 1.2035961e-2 s +[acc-interp] thetaMarsh 1.15e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.373725e-3 s grad=2.454e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.628349e-3 s grad=2.971e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.343189e-3 s grad=2.192e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.319963e-3 s grad=2.134e-6 s +[acc-interp] eval 1.1219913e-2 s +[acc-interp] thetaMarsh 1.12e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.575691e-3 s grad=1.966e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.298793e-3 s grad=2.288e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.966076e-3 s grad=4.546e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.611294e-3 s grad=4.442e-6 s +[acc-interp] eval 1.2177448e-2 s +[acc-interp] thetaMarsh 1.02e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.537623e-3 s grad=2.925e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.664875e-3 s grad=2.257e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.279414e-3 s grad=3.158e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.064619e-3 s grad=1.3978e-5 s +[acc-interp] eval 1.196561e-2 s +[acc-interp] thetaMarsh 1.18e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.152662e-3 s grad=4.589e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.68466e-3 s grad=4.283e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.805077e-3 s grad=6.908e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.599689e-3 s grad=6.1e-6 s +[acc-interp] eval 1.2292965e-2 s +[acc-interp] thetaMarsh 2.26e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.016291e-3 s grad=4.392e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.83397e-3 s grad=4.13e-6 s 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chunk base=0 cs=25974 kernel=2.678606e-3 s grad=3.424e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.9001e-3 s grad=2.051e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.587431e-3 s grad=2.119e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.785941e-3 s grad=2.599e-6 s +[acc-interp] eval 1.2596962e-2 s +[acc-interp] thetaMarsh 2.22e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.785266e-3 s grad=3.784e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.849624e-3 s grad=3.35e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.724966e-3 s grad=2.698e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.467864e-3 s grad=1.977e-6 s +[acc-interp] eval 1.2458615e-2 s +[acc-interp] thetaMarsh 9.2e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.459283e-3 s grad=2.112e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.533144e-3 s grad=3.568e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.892025e-3 s grad=3.672e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.730619e-3 s grad=2.884e-6 s +[acc-interp] eval 1.222998e-2 s +[acc-interp] thetaMarsh 9.7e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.431e-3 s grad=2.835e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.725552e-3 s grad=3.061e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.45639e-3 s grad=3.706e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.615813e-3 s grad=3.007e-6 s +[acc-interp] eval 1.1746316e-2 s +[acc-interp] thetaMarsh 1.02e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.207627e-3 s grad=2.8e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.542355e-3 s grad=2.445e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.672226e-3 s grad=2.137e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.408967e-3 s grad=1.937e-6 s +[acc-interp] eval 1.2309102e-2 s +[acc-interp] thetaMarsh 1.03e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.358851e-3 s grad=2.063e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.545482e-3 s grad=2.256e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.241506e-3 s grad=1.857e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.504062e-3 s grad=2.064e-6 s +[acc-interp] eval 1.108245e-2 s +[acc-interp] thetaMarsh 9.6e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.414071e-3 s grad=2.051e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.331554e-3 s grad=2.229e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.795589e-3 s grad=2.326e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.723828e-3 s grad=3.88e-6 s +[acc-interp] eval 1.2749259e-2 s +[acc-interp] thetaMarsh 1.09e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.016574e-3 s grad=2.09e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.554785e-3 s grad=1.798e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.212806e-3 s grad=1.707e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.686323e-3 s grad=3.745e-6 s +[acc-interp] eval 1.198e-2 s +[acc-interp] thetaMarsh 1.18e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.973751e-3 s grad=3.473e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.795456e-3 s grad=2.837e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.470422e-3 s grad=2.73e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.29301e-3 s grad=2.473e-6 s +[acc-interp] eval 1.2108174e-2 s +[acc-interp] thetaMarsh 9.7e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.359546e-3 s grad=2.497e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.369915e-3 s grad=4.377e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.073529e-3 s grad=3.757e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.51236e-3 s grad=2.005e-6 s +[acc-interp] eval 1.2004623e-2 s +[acc-interp] thetaMarsh 1.18e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.49434e-3 s grad=1.764e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.651361e-3 s grad=1.584e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.38303e-3 s grad=2.145e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.196994e-3 s grad=4.265e-6 s +[acc-interp] eval 1.3598824e-2 s +[acc-interp] thetaMarsh 1.79e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.027379e-3 s grad=2.949e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.728534e-3 s grad=3.301e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.518504e-3 s grad=1.943e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.003336e-3 s grad=5.493e-6 s +[acc-interp] eval 1.3094239e-2 s +[acc-interp] thetaMarsh 1.9e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.543809e-3 s grad=2.64e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.552675e-3 s grad=2.39e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.149378e-3 s grad=5.249e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.673746e-3 s grad=3.758e-6 s +[acc-interp] eval 1.4659078e-2 s +[acc-interp] thetaMarsh 1.16e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.951064e-3 s grad=3.558e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.351081e-3 s grad=3.728e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.974754e-3 s grad=1.898e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.697756e-3 s grad=4.375e-6 s +[acc-interp] eval 1.3052965e-2 s +[acc-interp] thetaMarsh 5.8e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.824312e-3 s grad=8.558e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.459641e-3 s grad=2.24e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.136129e-3 s grad=6.038e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.695715e-3 s grad=4.251e-6 s +[acc-interp] eval 1.4222765e-2 s +[acc-interp] thetaMarsh 8.6e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.030641e-3 s grad=3.325e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.564409e-3 s grad=2.85e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.12911e-3 s grad=3.27e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.897789e-3 s grad=3.413e-6 s +[acc-interp] eval 1.4993178e-2 s 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kernel=3.02392e-3 s grad=2.736e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.9367e-3 s grad=2.458e-6 s +[acc-interp] eval 1.3088447e-2 s +[acc-interp] thetaMarsh 1.05e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.521852e-3 s grad=2.663e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.367934e-3 s grad=2.46e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.89946e-3 s grad=5.961e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.403448e-3 s grad=8.409e-6 s +[acc-interp] eval 1.1161694e-2 s +[acc-interp] thetaMarsh 6.1e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.128887e-3 s grad=9.691e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.66546e-3 s grad=6.734e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.862372e-3 s grad=3.39e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.000587e-3 s grad=3.887e-6 s +[acc-interp] eval 1.3772074e-2 s +[acc-interp] thetaMarsh 1.0e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.304813e-3 s grad=3.1e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.258931e-3 s grad=2.816e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.342276e-3 s grad=4.782e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.722373e-3 s grad=5.44e-6 s +[acc-interp] eval 1.6384608e-2 s +[acc-interp] thetaMarsh 1.26e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.197457e-3 s grad=2.442e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.419471e-3 s grad=2.117e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.678822e-3 s grad=2.106e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.628722e-3 s grad=6.174e-6 s +[acc-interp] eval 1.3526502e-2 s +[acc-interp] thetaMarsh 1.59e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.841345e-3 s grad=3.794e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.325078e-3 s grad=3.152e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.618189e-3 s grad=3.311e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.796556e-3 s grad=3.441e-6 s +[acc-interp] eval 1.331325e-2 s +[acc-interp] thetaMarsh 4.48e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=4.837609e-3 s grad=4.125e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.3038397e-2 s grad=5.773e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.604466e-3 s grad=3.418e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.878868e-3 s grad=2.936e-6 s +[acc-interp] eval 2.6195534e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=2.776599e-3 s grad=3.15e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.760574e-3 s grad=3.257e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.692447e-3 s grad=2.453e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.760135e-3 s grad=2.926e-6 s +[acc-interp] eval 0.485623936 s +[acc-interp] thetaMarsh 1.4e-7 s (n=0) +[acc-interp] chunk base=0 cs=25974 kernel=1.7862409e-2 s grad=1.855e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.779107e-3 s grad=1.653e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.344584e-3 s grad=1.075e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.329313e-3 s grad=2.376e-6 s +[acc-interp] eval 2.8142776e-2 s +[acc-interp] thetaMarsh 1.6e-7 s (n=[acc-interp] thetaMarsh 1.23e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.4663966e-2 s grad=5.151e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=6.616313e-3 s grad=3.661e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.336419e-3 s grad=2.49e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.109393e-3 s grad=2.309e-6 s +[acc-interp] eval 2.7251665e-2 s +[acc-interp] thetaMarsh 1.08e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.379163e-3 s grad=4.884e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.337897e-3 s grad=2.837e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.523163e-3 s grad=2.287e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.186976e-3 s grad=2.43e-6 s +[acc-interp] eval 1.3136837e-2 s +[acc-interp] thetaMarsh 9.3e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.654254e-3 s grad=2.198e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.539591e-3 s grad=3.503e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.61392e-3 s grad=6.074e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.965784e-3 s grad=2.7e-6 s +[acc-interp] eval 1.3000429e-2 s +[acc-interp] thetaMarsh 6.9e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.263472e-3 s grad=2.121e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.16479e-3 s grad=7.44e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.887169e-3 s grad=3.08e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.154655e-3 s grad=2.992e-6 s +[acc-interp] eval 9.365087e-3 s +[acc-interp] thetaMarsh 1.44e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.177584e-3 s grad=3.917e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.55981e-3 s grad=2.924e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.556906e-3 s grad=2.709e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.289776e-3 s grad=2.462e-6 s +[acc-interp] eval 1.2171215e-2 s +[acc-interp] thetaMarsh 1.91e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.281103e-3 s grad=2.436e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.13676e-3 s grad=3.109e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.695412e-3 s grad=3.082e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.403005e-3 s grad=2.664e-6 s +[acc-interp] eval 1.1166438e-2 s +[acc-interp] thetaMarsh 1.15e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.255602e-3 s grad=2.411e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.023305e-3 s grad=2.214e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.056182e-3 s grad=2.326e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.512862e-3 s grad=2.86e-6 s +[acc-interp] eval 1.0491955e-2 s +[acc-interp] thetaMarsh 1.25e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.131139e-3 s grad=2.124e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.376672e-3 s grad=3.912e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.317798e-3 s grad=2.271e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.262625e-3 s grad=2.136e-6 s +[acc-interp] eval 1.0852388e-2 s +[acc-interp] thetaMarsh 1.25e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.023481e-3 s grad=1.601e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.143824e-3 s grad=2.806e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.191505e-3 s grad=1.969e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.807897e-3 s grad=1.279e-6 s +[acc-interp] eval 9.76165e-3 s +[acc-interp] thetaMarsh 1.11e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.099998e-3 s grad=2.198e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.011795e-3 s grad=1.0726e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.030209e-3 s grad=6.755e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.975035e-3 s grad=1.861e-6 s +[acc-interp] eval 9.166832e-3 s +[acc-interp] thetaMarsh 6.7e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.260478e-3 s grad=3.734e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.744217e-3 s grad=3.538e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.947355e-3 s grad=2.229e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.29736e-3 s grad=2.677e-6 s +[acc-interp] eval 1.0894604e-2 s +[acc-interp] thetaMarsh 1.07e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.349379e-3 s grad=2.404e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.030972e-3 s grad=1.772e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.224496e-3 s grad=2.551e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.560892e-3 s grad=3.718e-6 s +[acc-interp] eval 1.0784101e-2 s +[acc-interp] thetaMarsh 1.16e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.427056e-3 s grad=3.271e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.024156e-3 s grad=2.42e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.038633e-3 s grad=2.125e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.811936e-3 s grad=2.719e-6 s +[acc-interp] eval 9.926818e-3 s +[acc-interp] thetaMarsh 1.31e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.064441e-3 s grad=3.793e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.390268e-3 s grad=4.626e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.805321e-3 s grad=2.616e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.018343e-3 s grad=1.553e-6 s +[acc-interp] eval 1.2136812e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=1.71458e-3 s grad=1.753e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.061512e-3 s grad=1.318e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.17334e-3 s grad=2.102e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.427334e-3 s grad=2.358e-6 s +[acc-interp] eval 0.1819115 s +[acc-interp] thetaMarsh 8.8e-8 s (n=[acc-interp] thetaMarsh 6.1e-8 s (n=6) +[acc-interp] chunk base=0 cs=25974 kernel=2.9592872e-2 s grad=4.04e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=6.648775e-3 s grad=3.996e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.3648335e-2 s grad=5.457e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=6.472415e-3 s grad=6.143e-6 s +[acc-interp] eval 5.8079782e-2 s +6) +[acc-interp] chunk base=0 cs=25974 kernel=5.856207e-3 s grad=4.223e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.414585e-3 s grad=3.881e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=9.668947e-3 s grad=4.412e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.866371e-3 s grad=3.221e-6 s +[acc-interp] eval 8.679734e-2 s +[acc-interp] thetaMarsh 1.58e-7 s (n=[acc-interp] thetaMarsh 2.05e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.4350523e-2 s grad=4.835e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.979939e-3 s grad=6.855e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.439698e-3 s grad=3.91e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=8.191036e-3 s grad=2.935e-6 s +[acc-interp] eval 3.4955505e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=6.478501e-3 s grad=2.576e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.551395e-3 s grad=2.945e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.378922e-3 s grad=2.609e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.398113e-3 s grad=4.491e-6 s +[acc-interp] eval 5.4617126e-2 s +[acc-interp] thetaMarsh 1.92e-7 s (n=[acc-interp] thetaMarsh 1.25e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.5684155e-2 s grad=4.19e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.09098e-3 s grad=3.24e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.610968e-3 s grad=4.69e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.5663648e-2 s grad=4.756e-6 s +[acc-interp] eval 6.1759433e-2 s +4) +[acc-interp] chunk base=0 cs=25974 kernel=6.021027e-3 s grad=3.471e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.146209e-3 s grad=3.223e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=7.217503e-3 s grad=6.31e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.459348e-3 s grad=3.064e-6 s +[acc-interp] eval 8.8031041e-2 s +[acc-interp] thetaMarsh 2.35e-7 s (n=[acc-interp] thetaMarsh 1.43e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.2669725e-2 s grad=5.581e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.736266e-3 s grad=3.577e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.254244e-3 s grad=2.307e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.450448e-3 s grad=2.357e-6 s +[acc-interp] eval 3.7986813e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=4.182795e-3 s grad=2.619e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.0839795e-2 s grad=3.406e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.139075e-3 s grad=3.057e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.571513e-3 s grad=2.251e-6 s +[acc-interp] eval 6.2663437e-2 s +[acc-interp] thetaMarsh 1.29e-7 s (n=[acc-interp] thetaMarsh 1.04e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.7466683e-2 s grad=3.964e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.535638e-3 s grad=4.486e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=6.494703e-3 s grad=3.909e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.4597021e-2 s grad=3.68e-6 s +[acc-interp] eval 4.5908877e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=4.153345e-3 s grad=3.345e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.701409e-3 s grad=3.655e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.371668e-3 s grad=2.985e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.771103e-3 s grad=3.486e-6 s +[acc-interp] eval 6.5846792e-2 s +[acc-interp] thetaMarsh 2.14e-7 s (n=[acc-interp] thetaMarsh 1.31e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.0869492e-2 s grad=5.134e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.0676562e-2 s grad=4.576e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.99847e-3 s grad=3.073e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.032151e-3 s grad=4.004e-6 s +[acc-interp] eval 5.1193722e-2 s +[acc-interp] thetaMarsh 1.61e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=4.129904e-3 s grad=3.455e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.874538e-3 s grad=4.067e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.118238e-3 s grad=3.736e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.953375e-3 s grad=5.373e-6 s +[acc-interp] eval 1.7999219e-2 s +[acc-interp] thetaMarsh 1.21e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=5.863212e-3 s grad=5.696e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.977472e-3 s grad=4.219e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.613998e-3 s grad=4.832e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.762897e-3 s grad=5.011e-6 s +[acc-interp] eval 2.1976899e-2 s +[acc-interp] thetaMarsh 1.2e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=4.091229e-3 s grad=4.342e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.383368e-3 s grad=5.035e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.808461e-3 s grad=4.832e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.875425e-3 s grad=5.021e-6 s +[acc-interp] eval 1.9682836e-2 s +[acc-interp] thetaMarsh 6.3e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=4.39776e-3 s grad=3.198e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.885143e-3 s grad=5.292e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.23618e-3 s grad=4.043e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.2027957e-2 s grad=5.626e-6 s +[acc-interp] eval 2.6447596e-2 s +[acc-interp] thetaMarsh 8.5e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.751206e-3 s grad=3.913e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.81019e-3 s grad=3.655e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.621536e-3 s grad=3.797e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.50395e-3 s grad=3.501e-6 s +[acc-interp] eval 1.6524463e-2 s +[acc-interp] thetaMarsh 7.1e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.654534e-3 s grad=3.984e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.515054e-3 s grad=3.259e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.303854e-3 s grad=5.094e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.852436e-3 s grad=3.673e-6 s +[acc-interp] eval 1.6890743e-2 s +[acc-interp] thetaMarsh 1.28e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=4.028498e-3 s grad=4.155e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.658334e-3 s grad=3.815e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=6.281459e-3 s grad=4.856e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.913205e-3 s grad=3.567e-6 s +[acc-interp] eval 1.9625497e-2 s +[acc-interp] thetaMarsh 1.59e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.794041e-3 s grad=3.41e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.4078148e-2 s grad=4.494e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.199494e-3 s grad=3.719e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.712693e-3 s grad=3.993e-6 s +[acc-interp] eval 2.9469066e-2 s +[acc-interp] thetaMarsh 1.19e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=5.161997e-3 s grad=4.277e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.327879e-3 s grad=3.302e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.028674e-3 s grad=4.803e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.910556e-3 s grad=4.925e-6 s +[acc-interp] eval 2.1258058e-2 s +[acc-interp] thetaMarsh 9.4e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=4.316299e-3 s grad=4.572e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.189897e-3 s grad=2.915e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.419542e-3 s grad=4.272e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.974981e-3 s grad=4.198e-6 s +[acc-interp] eval 2.0594969e-2 s +[acc-interp] thetaMarsh 1.1e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=1.138132e-2 s grad=3.593e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.224646e-3 s grad=3.129e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.495111e-3 s grad=5.634e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.845547e-3 s grad=4.087e-6 s +[acc-interp] eval 2.6600038e-2 s +[acc-interp] thetaMarsh 1.18e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=4.777587e-3 s grad=3.938e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.036873e-3 s grad=3.694e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.325261e-3 s grad=3.034e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.692925e-3 s grad=3.634e-6 s +[acc-interp] eval 2.0416162e-2 s +[acc-interp] thetaMarsh 4.58e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=4.217798e-3 s grad=3.897e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.717197e-3 s grad=4.79e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.169469e-2 s grad=4.299e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.98968e-3 s grad=1.564e-5 s +[acc-interp] eval 2.7863508e-2 s +[acc-interp] thetaMarsh 6.5e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=4.359278e-3 s grad=1.3503e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.777142e-3 s grad=6.483e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.033722e-3 s grad=4.681e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.306485e-3 s grad=4.349e-6 s +[acc-interp] eval 1.8704949e-2 s +[acc-interp] thetaMarsh 1.27e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.8955e-3 s grad=3.542e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.034103e-3 s grad=4.563e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.657263e-3 s grad=4.247e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.655694e-3 s grad=4.665e-6 s +[acc-interp] eval 1.878663e-2 s +[acc-interp] thetaMarsh 1.55e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=5.914126e-3 s grad=5.49e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=6.569739e-3 s grad=3.946e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.432992e-3 s grad=1.547e-5 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.974291e-3 s grad=2.732e-6 s +[acc-interp] eval 2.4525317e-2 s +[acc-interp] thetaMarsh 3.5e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=1.0717073e-2 s grad=3.119e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.51649e-3 s grad=4.464e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.283642e-3 s grad=3.669e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.514692e-3 s grad=3.108e-6 s +[acc-interp] eval 2.5594222e-2 s +[acc-interp] thetaMarsh 1.14e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=4.323571e-3 s grad=3.635e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.993776e-3 s grad=4.06e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.986903e-3 s grad=3.229e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.635124e-3 s grad=3.046e-6 s +[acc-interp] eval 1.8495896e-2 s +[acc-interp] thetaMarsh 1.15e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=5.312092e-3 s grad=4.849e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.749233e-3 s grad=4.224e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.256375e-3 s grad=4.054e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.31272e-2 s grad=4.251e-6 s +[acc-interp] eval 2.9092796e-2 s +[acc-interp] thetaMarsh 9.9e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.761689e-3 s grad=9.898e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.731577e-3 s grad=5.634e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.829475e-3 s grad=5.211e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.57104e-3 s grad=4.21e-6 s +[acc-interp] eval 1.7969594e-2 s +[acc-interp] thetaMarsh 6.9e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=4.473286e-3 s grad=3.982e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.934235e-3 s grad=5.571e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.658061e-3 s grad=4.878e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.103938e-3 s grad=4.216e-6 s +[acc-interp] eval 2.0285998e-2 s +[acc-interp] thetaMarsh 7.0e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=5.203684e-3 s grad=5.503e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.346553e-3 s grad=4.769e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=9.801436e-3 s grad=5.798e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.14801e-3 s grad=4.288e-6 s +[acc-interp] eval 2.5533358e-2 s +[acc-interp] thetaMarsh 6.7e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=4.012143e-3 s grad=3.966e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.748226e-3 s grad=4.45e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.232887e-3 s grad=4.248e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.589588e-3 s grad=3.208e-6 s +[acc-interp] eval 1.6487858e-2 s +[acc-interp] thetaMarsh 7.7e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=4.063814e-3 s grad=4.296e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.075409e-3 s grad=3.766e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.1298e-3 s grad=5.984e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.118747e-3 s grad=4.52e-6 s +[acc-interp] eval 1.832351e-2 s +[acc-interp] thetaMarsh 6.81e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=4.032212e-3 s grad=4.063e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=9.311339e-3 s grad=5.624e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.761799e-3 s grad=4.331e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.851299e-3 s grad=4.818e-6 s +[acc-interp] eval 2.1912067e-2 s +[acc-interp] thetaMarsh 8.6e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=4.945536e-3 s grad=4.479e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.905971e-3 s grad=4.004e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.155424e-3 s grad=3.747e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.527794e-3 s grad=3.296e-6 s +[acc-interp] eval 1.7493673e-2 s +[acc-interp] thetaMarsh 6.9e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=4.6217e-3 s grad=5.587e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.19108e-3 s grad=5.575e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.579111e-3 s grad=5.06e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.562355e-3 s grad=6.401e-6 s +[acc-interp] eval 1.7934547e-2 s +[acc-interp] thetaMarsh 8.3e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=1.0416394e-2 s grad=4.968e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.814705e-3 s grad=4.506e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.091622e-3 s grad=4.537e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.717983e-3 s grad=4.826e-6 s +[acc-interp] eval 2.2985995e-2 s +[acc-interp] thetaMarsh 6.9e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.952509e-3 s grad=3.996e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.889494e-3 s grad=5.371e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.761078e-3 s grad=4.835e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.956728e-3 s grad=4.252e-6 s +[acc-interp] eval 1.9588665e-2 s +[acc-interp] thetaMarsh 8.1e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=4.100426e-3 s grad=4.495e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.547073e-3 s grad=4.063e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.088574e-3 s grad=3.807e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.2609109e-2 s grad=4.416e-6 s +[acc-interp] eval 2.8940788e-2 s +[acc-interp] thetaMarsh 1.23e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=4.840705e-3 s grad=3.881e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.490823e-3 s grad=5.165e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.902369e-3 s grad=4.237e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.409888e-3 s grad=4.492e-6 s +[acc-interp] eval 2.0236661e-2 s +5) +[acc-interp] chunk base=0 cs=25974 kernel=4.486045e-3 s grad=3.885e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.824973e-3 s grad=4.695e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.435291e-3 s grad=4.033e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.569302e-3 s grad=3.847e-6 s +[acc-interp] eval 0.745309026 s +[acc-interp] thetaMarsh 1.33e-7 s (n=[acc-interp] thetaMarsh 1.0e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.7052198e-2 s grad=3.908e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.047031e-3 s grad=2.912e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.044575e-3 s grad=2.418e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.448538e-3 s grad=1.698e-6 s +[acc-interp] eval 5.2180888e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=3.893692e-3 s grad=1.64e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.239942e-3 s grad=1.675e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.82976e-3 s grad=5.639e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.804636e-3 s grad=2.662e-6 s +[acc-interp] eval 7.1717249e-2 s +[acc-interp] thetaMarsh 1.46e-7 s (n=[acc-interp] thetaMarsh 9.7e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=1.8630988e-2 s grad=4.347e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.359598e-3 s grad=3.484e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.43646e-3 s grad=3.752e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.24005e-3 s grad=2.991e-6 s +[acc-interp] eval 3.0230862e-2 s +[acc-interp] thetaMarsh 1.0e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.049483e-3 s grad=2.532e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.056397e-3 s grad=4.765e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.477106e-3 s grad=4.505e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.381276e-3 s grad=3.87e-6 s +[acc-interp] eval 1.4627158e-2 s +[acc-interp] thetaMarsh 1.11e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.037246e-3 s grad=3.413e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.777267e-3 s grad=3.511e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.420618e-3 s grad=4.818e-6 s 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kernel=3.230909e-3 s grad=3.877e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.877346e-3 s grad=3.22e-6 s +[acc-interp] eval 1.4054048e-2 s +[acc-interp] thetaMarsh 3.52e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.814743e-3 s grad=2.414e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.822763e-3 s grad=2.475e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.867613e-3 s grad=4.504e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.249036e-3 s grad=3.579e-6 s +[acc-interp] eval 1.4515364e-2 s +[acc-interp] thetaMarsh 6.91e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.01756e-3 s grad=4.49e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.363864e-3 s grad=3.711e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.567394e-3 s grad=4.464e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.917241e-3 s grad=3.325e-6 s +[acc-interp] eval 1.5413613e-2 s +[acc-interp] thetaMarsh 1.11e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.386008e-3 s 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base=51948 cs=25974 kernel=2.880175e-3 s grad=2.422e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.490914e-3 s grad=3.352e-6 s +[acc-interp] eval 1.2208942e-2 s +[acc-interp] thetaMarsh 2.25e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.377945e-3 s grad=4.922e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.218457e-3 s grad=4.547e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.773711e-3 s grad=3.647e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.912538e-3 s grad=3.779e-6 s +[acc-interp] eval 1.3177359e-2 s +[acc-interp] thetaMarsh 2.17e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.365426e-3 s grad=3.953e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.752903e-3 s grad=3.881e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.257621e-3 s grad=3.22e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.121345e-3 s grad=1.0759e-5 s +[acc-interp] eval 1.4349697e-2 s +[acc-interp] thetaMarsh 6.7e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.242345e-3 s grad=3.212e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.058648e-3 s grad=6.005e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.636528e-3 s grad=4.257e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.622724e-3 s grad=3.942e-6 s +[acc-interp] eval 1.4737036e-2 s +[acc-interp] thetaMarsh 1.04e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.523487e-3 s grad=3.923e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.334689e-3 s grad=4.231e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.878097e-3 s grad=4.789e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.819845e-3 s grad=6.096e-6 s +[acc-interp] eval 1.5695282e-2 s +[acc-interp] thetaMarsh 6.2e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.800901e-3 s grad=3.735e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.392795e-3 s grad=4.3e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.855567e-3 s grad=4.873e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.259109e-3 s grad=4.37e-6 s +[acc-interp] eval 1.547584e-2 s +[acc-interp] thetaMarsh 2.02e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.596041e-3 s grad=4.209e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.101678e-3 s grad=3.622e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.08277e-3 s grad=4.793e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.364502e-3 s grad=3.942e-6 s +[acc-interp] eval 1.4697801e-2 s +[acc-interp] thetaMarsh 1.56e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.070167e-3 s grad=3.266e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.175255e-3 s grad=4.288e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.462705e-3 s grad=5.036e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.216077e-3 s grad=4.093e-6 s +[acc-interp] eval 1.4614161e-2 s +[acc-interp] thetaMarsh 1.17e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.803783e-3 s grad=5.14e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=8.68184e-3 s grad=2.883e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.16704e-3 s grad=1.0432e-5 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.210584e-3 s grad=7.11e-6 s +[acc-interp] eval 2.2176819e-2 s +[acc-interp] thetaMarsh 1.6e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.031776e-3 s grad=2.38e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.9967e-3 s grad=8.873e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.794697e-3 s grad=1.689e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.632294e-3 s grad=3.467e-6 s +[acc-interp] eval 1.2566594e-2 s +[acc-interp] thetaMarsh 1.19e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=5.646465e-3 s grad=3.095e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.76856e-3 s grad=2.516e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.642908e-3 s grad=2.395e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.689009e-3 s grad=2.25e-6 s +[acc-interp] eval 1.5218371e-2 s +[acc-interp] thetaMarsh 1.12e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.309368e-3 s grad=4.694e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.006141e-3 s grad=2.431e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.39399e-3 s grad=2.107e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.084637e-3 s grad=1.751e-6 s +[acc-interp] eval 1.5400357e-2 s +[acc-interp] thetaMarsh 1.22e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.571458e-3 s grad=1.2295e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.833935e-3 s grad=2.647e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.796308e-3 s grad=2.398e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.46388e-3 s grad=1.948e-6 s +[acc-interp] eval 1.2169468e-2 s +[acc-interp] thetaMarsh 1.07e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.568205e-3 s grad=2.031e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.325643e-3 s grad=4.423e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.053631e-3 s grad=6.343e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.994073e-3 s grad=2.938e-6 s +[acc-interp] eval 1.558175e-2 s +4) +[acc-interp] chunk base=0 cs=25974 kernel=2.761013e-3 s grad=3.264e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.993173e-3 s grad=3.967e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.561219e-3 s grad=3.419e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.043021e-3 s grad=3.762e-6 s +[acc-interp] eval 0.512353513 s +[acc-interp] thetaMarsh 7.5e-8 s (n=[acc-interp] thetaMarsh 6.7e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.302383e-2 s grad=3.74e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.700547e-3 s grad=3.917e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.047226e-3 s grad=3.086e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.480308e-3 s grad=2.734e-6 s +[acc-interp] eval 3.27449e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=2.827238e-3 s grad=2.47e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.437338e-3 s grad=2.292e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.471838e-3 s grad=3.744e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.28701e-3 s grad=3.527e-6 s +[acc-interp] eval 4.5639986e-2 s +[acc-interp] thetaMarsh 1.87e-7 s (n=[acc-interp] thetaMarsh 1.09e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=6.517388e-3 s grad=5.773e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.492603e-3 s grad=1.597e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.621653e-3 s grad=7.672e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.565126e-3 s grad=5.797e-6 s +[acc-interp] eval 1.5076759e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=2.900901e-3 s grad=2.564e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.823906e-3 s grad=2.193e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.504954e-3 s grad=2.192e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.730202e-3 s grad=3.015e-6 s +[acc-interp] eval 2.7937235e-2 s +[acc-interp] thetaMarsh 1.53e-7 s (n=[acc-interp] thetaMarsh 1.05e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.1525269e-2 s grad=4.193e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.342758e-3 s grad=2.182e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.381778e-3 s grad=1.844e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.11764e-3 s grad=1.888e-6 s +[acc-interp] eval 4.2874983e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=4.551364e-3 s grad=3.284e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.314009e-3 s grad=2.556e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.468104e-3 s grad=2.355e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.135067e-3 s grad=2.236e-6 s +[acc-interp] eval 5.8967136e-2 s +[acc-interp] thetaMarsh 1.29e-7 s (n=[acc-interp] thetaMarsh 1.05e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.4715124e-2 s grad=4.063e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.236074e-3 s grad=8.308e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.035865e-3 s grad=4.723e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.362643e-3 s grad=3.581e-6 s +[acc-interp] eval 2.8156523e-2 s +[acc-interp] thetaMarsh 1.21e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.3304377e-2 s grad=3.96e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.857242e-3 s grad=2.54e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.216931e-3 s grad=2.469e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.297181e-3 s grad=4.231e-6 s +[acc-interp] eval 2.2429791e-2 s +[acc-interp] thetaMarsh 1.41e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.998721e-3 s grad=3.549e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.209564e-3 s grad=3.403e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.364448e-3 s grad=2.752e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.11058e-3 s grad=2.852e-6 s +[acc-interp] eval 1.5230913e-2 s +[acc-interp] thetaMarsh 3.09e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.685273e-3 s grad=3.607e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.221826e-3 s grad=6.945e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.213399e-3 s grad=2.775e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.562132e-3 s grad=2.431e-6 s +[acc-interp] eval 1.41826e-2 s +[acc-interp] thetaMarsh 7.0e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.115562e-3 s grad=3.48e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.15991e-3 s grad=4.957e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.601296e-3 s grad=4.206e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.338988e-3 s grad=2.887e-6 s +[acc-interp] eval 1.6251122e-2 s +[acc-interp] thetaMarsh 6.7e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.612761e-3 s grad=3.698e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.129489e-3 s grad=4.378e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.621564e-3 s grad=3.985e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.821133e-3 s grad=3.82e-6 s +[acc-interp] eval 1.6361344e-2 s +[acc-interp] thetaMarsh 6.6e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.749177e-3 s grad=5.164e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.118204e-3 s grad=5.593e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.740094e-3 s grad=4.261e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.337057e-3 s grad=2.946e-6 s +[acc-interp] eval 1.7210924e-2 s +[acc-interp] thetaMarsh 6.3e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.920962e-3 s grad=6.483e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.4723432e-2 s grad=4.584e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.383901e-3 s grad=3.409e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.17766e-3 s grad=4.122e-6 s +[acc-interp] eval 2.7595758e-2 s +[acc-interp] thetaMarsh 6.8e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.856477e-3 s grad=3.664e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.598825e-3 s grad=3.204e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.580792e-3 s grad=3.849e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.599641e-3 s grad=3.308e-6 s +[acc-interp] eval 1.1889743e-2 s +[acc-interp] thetaMarsh 9.9e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.321276e-3 s grad=2.874e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.788604e-3 s grad=3.387e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.785816e-3 s grad=2.948e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.925511e-3 s grad=3.004e-6 s +[acc-interp] eval 1.200983e-2 s +[acc-interp] thetaMarsh 1.13e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.343612e-3 s grad=5.045e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.78043e-3 s grad=4.244e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.598798e-3 s grad=3.983e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.413269e-3 s grad=3.767e-6 s +[acc-interp] eval 1.6471724e-2 s +[acc-interp] thetaMarsh 1.11e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.300771e-3 s grad=5.588e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.817143e-3 s grad=6.022e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.664402e-3 s grad=3.891e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.791196e-3 s grad=3.841e-6 s +[acc-interp] eval 1.6920737e-2 s +[acc-interp] thetaMarsh 1.84e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.3766965e-2 s grad=4.356e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.518691e-3 s grad=5.634e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.657764e-3 s grad=3.586e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.557683e-3 s grad=3.697e-6 s +[acc-interp] eval 2.7032224e-2 s +[acc-interp] thetaMarsh 9.3e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.426319e-3 s grad=3.242e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.981178e-3 s grad=4.572e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.380831e-3 s grad=4.832e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.47207e-3 s grad=4.101e-6 s +[acc-interp] eval 1.651453e-2 s +[acc-interp] thetaMarsh 1.1e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.331152e-3 s grad=4.294e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.644479e-3 s grad=4.558e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.992755e-3 s grad=3.874e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.316245e-3 s grad=3.248e-6 s +[acc-interp] eval 1.8057311e-2 s +[acc-interp] thetaMarsh 1.2e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.737183e-3 s grad=2.793e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.266066e-3 s grad=4.538e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.049006e-3 s grad=3.515e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.988756e-3 s grad=3.412e-6 s +[acc-interp] eval 1.750766e-2 s +[acc-interp] thetaMarsh 1.01e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.251589e-3 s grad=4.547e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.149528e-3 s grad=3.698e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.640938e-3 s grad=3.126e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.639442e-3 s grad=2.611e-6 s +[acc-interp] eval 1.7151125e-2 s +[acc-interp] thetaMarsh 1.04e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.181356e-3 s grad=2.811e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.0907751e-2 s grad=3.219e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.975932e-3 s grad=3.793e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.591503e-3 s grad=3.636e-6 s +[acc-interp] eval 2.5211056e-2 s +[acc-interp] thetaMarsh 1.19e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.804434e-3 s grad=2.705e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.460439e-3 s grad=2.902e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.879142e-3 s grad=4.392e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.489139e-3 s grad=3.743e-6 s +[acc-interp] eval 1.723226e-2 s +[acc-interp] thetaMarsh 1.0e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.037922e-3 s grad=3.363e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.744509e-3 s grad=3.441e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.598793e-3 s grad=3.755e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.973572e-3 s grad=3.15e-6 s +[acc-interp] eval 1.8773521e-2 s +[acc-interp] thetaMarsh 1.27e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.635978e-3 s grad=3.294e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.387263e-3 s grad=4.019e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.529778e-3 s grad=3.689e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.062621e-3 s grad=3.096e-6 s +[acc-interp] eval 1.820687e-2 s +[acc-interp] thetaMarsh 9.9e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.836018e-3 s grad=3.593e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.478989e-3 s grad=4.445e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.796896e-3 s grad=3.719e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.726532e-3 s grad=3.217e-6 s +[acc-interp] eval 1.7286995e-2 s +[acc-interp] thetaMarsh 6.2e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=9.393889e-3 s grad=2.964e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.716369e-3 s grad=3.373e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.315433e-3 s grad=4.057e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=6.762351e-3 s grad=5.784e-6 s +[acc-interp] eval 2.4422037e-2 s +[acc-interp] thetaMarsh 9.9e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.423158e-3 s grad=2.851e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.536612e-3 s grad=2.883e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.545316e-3 s grad=5.955e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=6.361837e-3 s grad=5.553e-6 s +[acc-interp] eval 1.9905977e-2 s +[acc-interp] thetaMarsh 1.15e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.457427e-3 s grad=3.188e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.928572e-3 s grad=2.7e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=8.287343e-3 s grad=6.948e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=6.57774e-3 s grad=6.017e-6 s +[acc-interp] eval 2.4587417e-2 s +[acc-interp] thetaMarsh 2.01e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=8.818505e-3 s grad=6.644e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=7.140522e-3 s grad=5.897e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=6.257935e-3 s grad=4.245e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.771287e-3 s grad=5.649e-6 s +[acc-interp] eval 2.8605863e-2 s +[acc-interp] thetaMarsh 1.1e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=5.433038e-3 s grad=3.645e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.948189e-3 s grad=3.563e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.258319e-3 s grad=5.049e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.361443e-3 s grad=5.276e-6 s +[acc-interp] eval 1.9991593e-2 s +[acc-interp] thetaMarsh 5.9e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=8.816439e-3 s grad=3.86e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.919524e-3 s grad=3.232e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.89976e-3 s grad=9.72e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.285188e-3 s grad=1.696e-6 s +[acc-interp] eval 2.0690064e-2 s +[acc-interp] thetaMarsh 6.8e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.338029e-3 s grad=8.608e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.594287e-3 s grad=1.411e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.24203e-3 s grad=6.892e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.319405e-3 s grad=2.87e-6 s +[acc-interp] eval 1.4306183e-2 s +[acc-interp] thetaMarsh 7.2e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.919506e-3 s grad=3.744e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.972552e-3 s grad=4.923e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.536574e-3 s grad=4.367e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.301684e-3 s grad=3.326e-6 s +[acc-interp] eval 1.729063e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=3.77695e-3 s grad=2.407e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.51953e-3 s grad=3.239e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.755644e-3 s grad=3.101e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.764518e-3 s grad=2.912e-6 s +[acc-interp] eval 0.598514011 s +[acc-interp] thetaMarsh 6.2e-8 s (n=[acc-interp] thetaMarsh 5.3e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.5369825e-2 s grad=4.031e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.619114e-3 s grad=3.352e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.430837e-3 s grad=3.761e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.564683e-3 s grad=5.071e-6 s +[acc-interp] eval 3.8939532e-2 s +[acc-interp] thetaMarsh 8.5e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=6.336874e-3 s grad=7.429e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.88774e-3 s grad=8.983e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.295356e-3 s grad=3.321e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.946317e-3 s grad=8.513e-6 s +[acc-interp] eval 2.3306424e-2 s +[acc-interp] thetaMarsh 6.4e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.471572e-3 s grad=3.021e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.666513e-3 s grad=2.939e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.308325e-3 s grad=3.512e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.687098e-3 s grad=3.851e-6 s +[acc-interp] eval 2.0355921e-2 s +[acc-interp] thetaMarsh 1.17e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=5.119785e-3 s grad=4.055e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=6.534887e-3 s grad=3.434e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.841177e-3 s grad=3.35e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.203875e-3 s grad=4.528e-6 s +[acc-interp] eval 2.334485e-2 s +[acc-interp] thetaMarsh 1.58e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=5.731178e-3 s grad=3.327e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.639411e-3 s grad=2.249e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=6.010926e-3 s grad=4.619e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.353099e-3 s grad=3.576e-6 s +[acc-interp] eval 2.4393285e-2 s +[acc-interp] thetaMarsh 1.18e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.435217e-3 s grad=4.388e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.260895e-3 s grad=3.992e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.450007e-3 s grad=3.479e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.13794e-3 s grad=2.882e-6 s +[acc-interp] eval 1.7969838e-2 s +[acc-interp] thetaMarsh 1.19e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.816928e-3 s grad=3.232e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.508899e-3 s grad=4.248e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.584669e-3 s grad=2.887e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.892798e-3 s grad=1.881e-6 s +[acc-interp] eval 1.5387016e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=4.125608e-3 s grad=2.5e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.406535e-3 s grad=3.261e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.606207e-3 s grad=3.859e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.661338e-3 s grad=3.014e-6 s +[acc-interp] eval 0.179848567 s +[acc-interp] thetaMarsh 7.8e-8 s (n=[acc-interp] thetaMarsh 6.3e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.6918813e-2 s grad=2.696e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.883953e-3 s grad=3.208e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.953225e-3 s grad=4.996e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.377974e-3 s grad=4.336e-6 s +[acc-interp] eval 5.1053947e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=4.244058e-3 s grad=3.469e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.353803e-3 s grad=3.891e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.025401e-3 s grad=3.896e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.996672e-3 s grad=3.878e-6 s +[acc-interp] eval 6.8665987e-2 s +[acc-interp] thetaMarsh 6.0e-8 s (n=[acc-interp] thetaMarsh 6.1e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.0552234e-2 s grad=3.786e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.008898e-3 s grad=2.666e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.323264e-3 s grad=2.266e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.866577e-3 s grad=2.777e-6 s +[acc-interp] eval 2.1191868e-2 s +[acc-interp] thetaMarsh 2.39e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.2810566e-2 s grad=3.02e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.043788e-3 s grad=2.172e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.007654e-3 s grad=4.113e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.89228e-3 s grad=2.452e-6 s +[acc-interp] eval 2.4378456e-2 s +[acc-interp] thetaMarsh 1.36e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.660639e-3 s grad=2.61e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.901421e-3 s grad=2.703e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.606557e-3 s grad=5.062e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.808124e-3 s grad=2.88e-6 s +[acc-interp] eval 1.466892e-2 s +[acc-interp] thetaMarsh 1.14e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.193714e-3 s grad=2.752e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.730468e-3 s grad=2.288e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.095689e-3 s grad=3.121e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.648093e-3 s grad=3.623e-6 s +[acc-interp] eval 1.4376058e-2 s +[acc-interp] thetaMarsh 1.13e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.432589e-3 s grad=3.783e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.884452e-3 s grad=2.572e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.80394e-3 s grad=2.2e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.679327e-3 s grad=2.09e-6 s +[acc-interp] eval 1.3260487e-2 s +[acc-interp] thetaMarsh 9.9e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.932972e-3 s grad=2.421e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.892211e-3 s grad=2.628e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.984373e-3 s grad=2.609e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.812543e-3 s grad=3.258e-6 s +[acc-interp] eval 1.3157165e-2 s +[acc-interp] thetaMarsh 1.3e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.665831e-3 s grad=4.323e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.898643e-3 s grad=3.963e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.069468e-3 s grad=3.008e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.014169e-3 s grad=3.692e-6 s +[acc-interp] eval 1.4219009e-2 s +[acc-interp] thetaMarsh 1.23e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.000819e-3 s grad=3.174e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.316188e-3 s grad=4.158e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.151836e-3 s grad=2.726e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.805285e-3 s grad=2.758e-6 s +[acc-interp] eval 1.3985299e-2 s +[acc-interp] thetaMarsh 2.42e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.001578e-3 s grad=2.553e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.643148e-3 s grad=2.634e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.909468e-3 s grad=4.755e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.093928e-3 s grad=3.127e-6 s +[acc-interp] eval 1.4209507e-2 s +[acc-interp] thetaMarsh 9.4e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.376483e-3 s grad=3.721e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.092411e-3 s grad=2.507e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.863854e-3 s grad=4.754e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.324481e-3 s grad=4.621e-6 s +[acc-interp] eval 1.4868128e-2 s +[acc-interp] thetaMarsh 6.7e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.084899e-3 s grad=3.268e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.668444e-3 s grad=3.009e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.939171e-3 s grad=4.127e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.167974e-3 s grad=5.056e-6 s +[acc-interp] eval 1.2899293e-2 s +[acc-interp] thetaMarsh 1.04e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.396948e-3 s grad=3.403e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.936696e-3 s grad=3.84e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.481031e-3 s grad=2.997e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.198103e-3 s grad=3.441e-6 s +[acc-interp] eval 1.4750741e-2 s +[acc-interp] thetaMarsh 8.3e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.485086e-3 s grad=3.677e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.293797e-3 s grad=4.426e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.495942e-3 s grad=4.076e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.855899e-3 s grad=3.511e-6 s +[acc-interp] eval 1.5069947e-2 s +[acc-interp] thetaMarsh 6.7e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.730096e-3 s grad=4.377e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.932507e-3 s grad=2.999e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.081624e-3 s grad=2.979e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.897889e-3 s grad=2.439e-6 s +[acc-interp] eval 1.4030755e-2 s +[acc-interp] thetaMarsh 1.09e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.262763e-3 s grad=4.774e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.666574e-3 s grad=4.07e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.115213e-3 s grad=3.071e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.960816e-3 s grad=2.719e-6 s +[acc-interp] eval 1.4608898e-2 s +[acc-interp] thetaMarsh 1.13e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.817947e-3 s grad=2.574e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.011201e-3 s grad=3.267e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.09094e-3 s grad=3.009e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.658434e-3 s grad=2.327e-6 s +[acc-interp] eval 1.2980123e-2 s +[acc-interp] thetaMarsh 1.12e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.831765e-3 s grad=2.651e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.55264e-3 s grad=2.731e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.108831e-3 s grad=4.929e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.453814e-3 s grad=4.403e-6 s +[acc-interp] eval 1.3595224e-2 s +[acc-interp] thetaMarsh 1.22e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.331448e-3 s grad=4.128e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.990157e-3 s grad=3.069e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.058938e-3 s grad=3.147e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.292492e-3 s grad=4.554e-6 s +[acc-interp] eval 1.4384198e-2 s +[acc-interp] thetaMarsh 1.19e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.407539e-3 s grad=3.422e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.81409e-3 s grad=3.196e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.997421e-3 s grad=2.469e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.739644e-3 s grad=4.275e-6 s +[acc-interp] eval 1.3632692e-2 s +[acc-interp] thetaMarsh 1.8e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.700863e-3 s grad=3.855e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.836348e-3 s grad=3.705e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.204779e-3 s grad=3.273e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.83647e-3 s grad=2.328e-6 s +[acc-interp] eval 1.4151838e-2 s +[acc-interp] thetaMarsh 1.08e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.907929e-3 s grad=4.772e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.260648e-3 s grad=4.645e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.772122e-3 s grad=4.393e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.004283e-3 s grad=4.323e-6 s +[acc-interp] eval 1.5551224e-2 s +[acc-interp] thetaMarsh 7.1e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.671774e-3 s grad=4.732e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.150375e-3 s grad=4.943e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.234714e-3 s grad=3.351e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.883147e-3 s grad=3.709e-6 s +[acc-interp] eval 1.4167933e-2 s +[acc-interp] thetaMarsh 6.5e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.062729e-3 s grad=4.042e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.789673e-3 s grad=3.878e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.162414e-3 s grad=3.539e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.820626e-3 s grad=1.0128e-5 s +[acc-interp] eval 1.2999692e-2 s +[acc-interp] thetaMarsh 6.7e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.160774e-3 s grad=5.553e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.081938e-3 s grad=8.12e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.975733e-3 s grad=4.768e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.295242e-3 s grad=4.428e-6 s +[acc-interp] eval 1.4985665e-2 s +[acc-interp] thetaMarsh 1.17e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.578851e-3 s grad=4.487e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.185483e-3 s grad=4.026e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.555422e-3 s grad=5.525e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.970757e-3 s grad=4.651e-6 s +[acc-interp] eval 1.4496399e-2 s +[acc-interp] thetaMarsh 6.6e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.264932e-3 s grad=2.258e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.331442e-3 s grad=2.761e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.081081e-3 s grad=6.915e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.882341e-3 s grad=4.915e-6 s +[acc-interp] eval 1.347082e-2 s +[acc-interp] thetaMarsh 1.58e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.964179e-3 s grad=9.983e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.039278e-3 s grad=2.352e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.308404e-3 s grad=2.163e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.030074e-3 s grad=1.755e-6 s +[acc-interp] eval 1.4194279e-2 s +[acc-interp] thetaMarsh 1.19e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.281976e-3 s grad=4.66e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.127358e-3 s grad=4.319e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.407592e-3 s grad=3.939e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.86237e-3 s grad=2.552e-6 s +[acc-interp] eval 1.4862146e-2 s +[acc-interp] thetaMarsh 7.1e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.221539e-3 s grad=3.832e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.983202e-3 s grad=5.12e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.396934e-3 s grad=3.519e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.2492721e-2 s grad=4.36e-6 s +[acc-interp] eval 2.6488846e-2 s +[acc-interp] thetaMarsh 1.85e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.240381e-3 s grad=2.778e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.143859e-3 s grad=1.924e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.695799e-3 s grad=4.905e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.662953e-3 s grad=4.293e-6 s +[acc-interp] eval 1.4652117e-2 s +[acc-interp] thetaMarsh 2.13e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.28091e-3 s grad=4.264e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.707426e-3 s grad=3.708e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.188081e-3 s grad=3.295e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.244392e-3 s grad=5.327e-6 s +[acc-interp] eval 1.3341598e-2 s +[acc-interp] thetaMarsh 1.1e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.591489e-3 s grad=3.459e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.937515e-3 s grad=3.574e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.94629e-3 s grad=3.009e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.960413e-3 s grad=3.023e-6 s +[acc-interp] eval 1.3831135e-2 s +[acc-interp] thetaMarsh 1.14e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.078221e-3 s grad=2.921e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.257252e-3 s grad=2.42e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.875582e-3 s grad=2.593e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.452908e-3 s grad=2.571e-6 s +[acc-interp] eval 1.2083851e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=2.71721e-3 s grad=2.384e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.432681e-3 s grad=2.707e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.173118e-3 s grad=3.124e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.039146e-3 s grad=2.475e-6 s +[acc-interp] eval 0.511421317 s +[acc-interp] thetaMarsh 1.35e-7 s (n=[acc-interp] thetaMarsh 1.01e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.7699024e-2 s grad=4.86e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.459932e-3 s grad=2.464e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=9.658952e-3 s grad=4.492e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.009598e-3 s grad=3.048e-6 s +[acc-interp] eval 3.73355e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=3.334583e-3 s grad=3.931e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.191473e-3 s grad=3.879e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.276903e-3 s grad=2.968e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.168264e-3 s grad=3.301e-6 s +[acc-interp] eval 5.3150909e-2 s +[acc-interp] thetaMarsh 1.72e-7 s (n=[acc-interp] thetaMarsh 1.73e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.6883549e-2 s grad=3.41e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.267922e-3 s grad=3.263e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.339606e-3 s grad=2.218e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.740626e-3 s grad=2.558e-6 s +[acc-interp] eval 2.5748875e-2 s +[acc-interp] thetaMarsh 2.92e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.516379e-3 s grad=2.059e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.775984e-3 s grad=3.482e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.632644e-3 s grad=2.444e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.037534e-3 s grad=2.144e-6 s +[acc-interp] eval 1.2452041e-2 s +[acc-interp] thetaMarsh 1.15e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.197222e-3 s grad=4.441e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.955421e-3 s grad=3.654e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=8.711269e-3 s grad=2.778e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.286281e-3 s grad=4.329e-6 s +[acc-interp] eval 1.9925088e-2 s +[acc-interp] thetaMarsh 6.6e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.948925e-3 s grad=3.056e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.210106e-3 s grad=3.252e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.24665e-3 s grad=4.022e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.544337e-3 s grad=4.214e-6 s +[acc-interp] eval 9.836659e-3 s +[acc-interp] thetaMarsh 1.8e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.133811e-3 s grad=3.58e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.293962e-3 s grad=5.018e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.72916e-3 s grad=3.831e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.788264e-3 s grad=4.889e-6 s +[acc-interp] eval 1.0888907e-2 s +[acc-interp] thetaMarsh 8.1e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.439526e-3 s grad=3.728e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.412461e-3 s grad=4.176e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.458411e-3 s grad=4.776e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.289369e-3 s grad=4.36e-6 s +[acc-interp] eval 1.1552358e-2 s +[acc-interp] thetaMarsh 1.82e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.532863e-3 s grad=3.207e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.209545e-3 s grad=2.812e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.127985e-3 s grad=3.936e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.366031e-3 s grad=4.049e-6 s +[acc-interp] eval 1.0422345e-2 s +[acc-interp] thetaMarsh 7.4e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.177199e-3 s grad=3.408e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.204642e-3 s grad=4.593e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.305107e-3 s grad=4.512e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.933814e-3 s grad=3.919e-6 s +[acc-interp] eval 1.205119e-2 s +[acc-interp] thetaMarsh 4.56e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.432743e-3 s grad=3.551e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.31541e-3 s grad=3.591e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.224633e-3 s grad=3.516e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.679682e-3 s grad=4.245e-6 s +[acc-interp] eval 1.1040134e-2 s +[acc-interp] thetaMarsh 7.1e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.714569e-3 s grad=3.742e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.424172e-3 s grad=4.335e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.458212e-3 s grad=5.358e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.880367e-3 s grad=4.262e-6 s +[acc-interp] eval 1.1838969e-2 s +[acc-interp] thetaMarsh 3.02e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.278611e-3 s grad=2.451e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.940567e-3 s grad=4.394e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.709218e-3 s grad=4.801e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.758102e-3 s grad=1.0339e-5 s +[acc-interp] eval 1.2132199e-2 s +[acc-interp] thetaMarsh 8.1e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.511415e-3 s grad=3.114e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.336183e-3 s grad=9.23e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.443558e-3 s grad=4.87e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.026572e-3 s grad=4.45e-6 s +[acc-interp] eval 1.1475797e-2 s +[acc-interp] thetaMarsh 7.0e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.670333e-3 s grad=1.0807e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.50549e-3 s grad=3.132e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.238689e-3 s grad=8.984e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.328868e-3 s grad=3.447e-6 s +[acc-interp] eval 1.100447e-2 s +[acc-interp] thetaMarsh 6.0e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.051098e-3 s grad=1.0756e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.991603e-3 s grad=1.0562e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.969914e-3 s grad=7.823e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.978002e-3 s grad=7.647e-6 s +[acc-interp] eval 9.223377e-3 s +[acc-interp] thetaMarsh 5.4e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.052541e-3 s grad=2.112e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.065066e-3 s grad=1.477e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.109756e-3 s grad=4.138e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.379618e-3 s grad=5.706e-6 s +[acc-interp] eval 1.0764153e-2 s +[acc-interp] thetaMarsh 5.9e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.456763e-3 s grad=3.89e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.358879e-3 s grad=4.3e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.335876e-3 s grad=4.467e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.558049e-3 s grad=3.596e-6 s +[acc-interp] eval 1.0818074e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=2.764241e-3 s grad=3.896e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.701529e-3 s grad=4.007e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.393575e-3 s grad=2.911e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.60175e-3 s grad=2.625e-6 s +[acc-interp] eval 0.215384776 s +[acc-interp] thetaMarsh 1.31e-7 s (n=[acc-interp] thetaMarsh 1.15e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=7.377119e-3 s grad=4.5e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.676404e-3 s grad=3.239e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.591717e-3 s grad=3.284e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.026306e-3 s grad=3.492e-6 s +[acc-interp] eval 1.8450876e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=3.943178e-3 s grad=3.685e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.949952e-3 s grad=2.942e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.335002e-3 s grad=2.682e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=9.561355e-3 s grad=3.888e-6 s +[acc-interp] eval 4.0165798e-2 s +[acc-interp] thetaMarsh 1.46e-7 s (n=[acc-interp] thetaMarsh 1.18e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.3183133e-2 s grad=4.279e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.715507e-3 s grad=2.787e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.601147e-3 s grad=3.778e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.412659e-3 s grad=2.42e-6 s +[acc-interp] eval 2.3534564e-2 s +[acc-interp] thetaMarsh 1.22e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.030381e-3 s grad=2.614e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.81392e-3 s grad=3.854e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.192842e-3 s grad=3.081e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.011067e-3 s grad=2.246e-6 s +[acc-interp] eval 1.3680247e-2 s +[acc-interp] thetaMarsh 1.27e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.447854e-3 s grad=2.035e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.546091e-3 s grad=2.936e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.497546e-3 s grad=4.815e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.144362e-3 s grad=4.43e-6 s +[acc-interp] eval 1.3625673e-2 s +[acc-interp] thetaMarsh 1.17e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=9.34315e-3 s grad=2.938e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.833535e-3 s grad=4.426e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.782485e-3 s grad=4.171e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.40096e-3 s grad=2.768e-6 s +[acc-interp] eval 1.9113163e-2 s +[acc-interp] thetaMarsh 1.36e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.428756e-3 s grad=2.935e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.43401e-3 s grad=2.72e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.531858e-3 s grad=3.616e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.382957e-3 s grad=1.805e-6 s +[acc-interp] eval 1.2214085e-2 s +[acc-interp] thetaMarsh 6.6e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.391867e-3 s grad=1.82e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.66724e-3 s grad=2.241e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.791844e-3 s grad=4.075e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.8127e-3 s grad=8.441e-6 s +[acc-interp] eval 1.1768903e-2 s +[acc-interp] thetaMarsh 7.0e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.525199e-3 s grad=9.213e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.659948e-3 s grad=8.297e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.540948e-3 s grad=6.947e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.356375e-3 s grad=1.767e-6 s +[acc-interp] eval 1.1200241e-2 s +[acc-interp] thetaMarsh 1.49e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.257665e-3 s grad=8.126e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.394397e-3 s grad=6.965e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.183327e-3 s grad=1.773e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.246373e-3 s grad=1.235e-6 s +[acc-interp] eval 1.0181875e-2 s +[acc-interp] thetaMarsh 7.2e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.658044e-3 s grad=4.291e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.898677e-3 s grad=3.516e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.518986e-3 s grad=3.268e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.687831e-3 s grad=2.935e-6 s +[acc-interp] eval 1.2469542e-2 s +[acc-interp] thetaMarsh 1.19e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.501241e-3 s grad=3.085e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.64507e-3 s grad=3.935e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.84297e-3 s grad=3.479e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.636124e-3 s grad=3.576e-6 s +[acc-interp] eval 1.2352791e-2 s +[acc-interp] thetaMarsh 3.78e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.527339e-3 s grad=3.251e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.483692e-3 s grad=2.762e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.457498e-3 s grad=2.324e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.354778e-3 s grad=3.351e-6 s +[acc-interp] eval 1.2446566e-2 s +[acc-interp] thetaMarsh 1.62e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.751391e-3 s grad=3.449e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.576557e-3 s grad=3.2e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.25366e-3 s grad=2.886e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.331069e-3 s grad=8.846e-6 s +[acc-interp] eval 1.1168642e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=2.429402e-3 s grad=1.548e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.833592e-3 s grad=4.968e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.054639e-3 s grad=3.607e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.413513e-3 s grad=1.602e-6 s +[acc-interp] eval 0.177194907 s +[acc-interp] thetaMarsh 6.3e-8 s (n=[acc-interp] thetaMarsh 6.6e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=1.7341783e-2 s grad=4.492e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.975953e-3 s grad=9.109e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.171927e-3 s grad=8.49e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.228643e-3 s grad=5.167e-6 s +[acc-interp] eval 2.9625845e-2 s +[acc-interp] thetaMarsh 7.2e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=9.246642e-3 s grad=4.682e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.680251e-3 s grad=7.381e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.382745e-3 s grad=2.526e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.775777e-3 s grad=9.21e-6 s +[acc-interp] eval 2.5092749e-2 s +[acc-interp] thetaMarsh 6.5e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.49329e-3 s grad=3.967e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.377219e-3 s grad=5.274e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.379313e-3 s grad=9.082e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.592409e-3 s grad=3.261e-6 s +[acc-interp] eval 1.9707522e-2 s +[acc-interp] thetaMarsh 7.5e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.226865e-3 s grad=1.982e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.56695e-3 s grad=3.546e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.162994e-3 s grad=2.942e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.824376e-3 s grad=2.809e-6 s +[acc-interp] eval 1.795935e-2 s +[acc-interp] thetaMarsh 7.1e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.459799e-3 s grad=2.577e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.205721e-3 s grad=5.617e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.212519e-3 s grad=3.633e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.344237e-3 s grad=2.812e-6 s +[acc-interp] eval 1.82488e-2 s +[acc-interp] thetaMarsh 6.4e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=5.300798e-3 s grad=4.656e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.70735e-3 s grad=3.727e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.014822e-3 s grad=2.888e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.773499e-3 s grad=4.799e-6 s +[acc-interp] eval 1.9667093e-2 s +[acc-interp] thetaMarsh 7.3e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=5.052721e-3 s grad=4.397e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.363644e-3 s grad=3.378e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.105918e-3 s grad=4.107e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.960229e-3 s grad=3.843e-6 s +[acc-interp] eval 2.0045012e-2 s +[acc-interp] thetaMarsh 9.0e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.105559e-3 s grad=2.822e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.556594e-3 s grad=2.827e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.591494e-3 s grad=5.636e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.114325e-3 s grad=4.384e-6 s +[acc-interp] eval 1.8928813e-2 s +[acc-interp] thetaMarsh 1.21e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.479326e-3 s grad=2.896e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.716573e-3 s grad=3.687e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.408611e-3 s grad=3.226e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.505548e-3 s grad=9.931e-6 s +[acc-interp] eval 1.9346093e-2 s +[acc-interp] thetaMarsh 6.7e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.490676e-3 s grad=7.084e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.668204e-3 s grad=4.459e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.359238e-3 s grad=2.902e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.442896e-3 s grad=4.642e-6 s +[acc-interp] eval 2.0515454e-2 s +[acc-interp] thetaMarsh 1.13e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.215352e-3 s grad=2.827e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.311572e-3 s grad=3.253e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.936658e-3 s grad=2.41e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.720105e-3 s grad=4.316e-6 s +[acc-interp] eval 1.8603288e-2 s +[acc-interp] thetaMarsh 6.9e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.326995e-3 s grad=3.684e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.573578e-3 s grad=3.058e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.864292e-3 s grad=3.918e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.467252e-3 s grad=4.077e-6 s +[acc-interp] eval 1.9562663e-2 s +4) +[acc-interp] chunk base=0 cs=25974 kernel=2.781639e-3 s grad=2.918e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.509933e-3 s grad=2.71e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.622287e-3 s grad=2.672e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.03463e-3 s grad=4.654e-6 s +[acc-interp] eval 0.263089992 s +[acc-interp] thetaMarsh 1.53e-7 s (n=[acc-interp] thetaMarsh 1.03e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.8814994e-2 s grad=2.914e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.383489e-3 s grad=2.615e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.213621e-3 s grad=2.217e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.231763e-3 s grad=2.098e-6 s +[acc-interp] eval 2.7233908e-2 s +[acc-interp] thetaMarsh 1.14e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.740187e-3 s grad=3.23e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.849644e-3 s grad=4.063e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.514378e-3 s grad=2.702e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.571071e-3 s grad=2.83e-6 s +[acc-interp] eval 1.2309465e-2 s +[acc-interp] thetaMarsh 1.0e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.572823e-3 s grad=2.731e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.478e-3 s grad=4.231e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.17878e-3 s grad=4.753e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.488337e-3 s grad=3.361e-6 s +[acc-interp] eval 1.2364552e-2 s +[acc-interp] thetaMarsh 1.21e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.646199e-3 s grad=2.266e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.402428e-3 s grad=2.271e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.150009e-3 s grad=2.223e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.251252e-3 s grad=1.984e-6 s +[acc-interp] eval 1.08967e-2 s +[acc-interp] thetaMarsh 1.3e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.319574e-3 s grad=2.028e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.34458e-3 s grad=1.898e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.269559e-3 s grad=1.616e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.366266e-3 s grad=2.0e-6 s +[acc-interp] eval 1.0748261e-2 s +[acc-interp] thetaMarsh 1.31e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.364276e-3 s grad=1.968e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.430138e-3 s grad=3.991e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.147416e-3 s grad=5.197e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.577701e-3 s grad=4.328e-6 s +[acc-interp] eval 1.1821698e-2 s +[acc-interp] thetaMarsh 8.6e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.327816e-3 s grad=3.422e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.354427e-3 s grad=6.277e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.456029e-3 s grad=3.046e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.582373e-3 s grad=5.027e-6 s +[acc-interp] eval 1.065656e-2 s +[acc-interp] thetaMarsh 1.24e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.885756e-3 s grad=3.065e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.84917e-3 s grad=8.793e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.903595e-3 s grad=6.771e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.654903e-3 s grad=5.854e-6 s +[acc-interp] eval 1.2109821e-2 s +[acc-interp] thetaMarsh 7.4e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.709534e-3 s grad=6.85e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.967645e-3 s grad=4.525e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.617256e-3 s grad=2.187e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.474601e-3 s grad=2.202e-6 s +[acc-interp] eval 1.1622812e-2 s +[acc-interp] thetaMarsh 1.95e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.372026e-3 s grad=2.566e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.310164e-3 s grad=9.286e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.362133e-3 s grad=2.986e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.214613e-3 s grad=5.214e-6 s +[acc-interp] eval 1.1227426e-2 s +[acc-interp] thetaMarsh 1.17e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.526222e-3 s grad=7.673e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.44825e-3 s grad=3.655e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.375748e-3 s grad=2.698e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.426124e-3 s grad=1.369e-6 s +[acc-interp] eval 1.0800012e-2 s +[acc-interp] thetaMarsh 2.34e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.949674e-3 s grad=4.37e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.918692e-3 s grad=4.31e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.924549e-3 s grad=3.612e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.580807e-3 s grad=2.181e-6 s +[acc-interp] eval 1.3364846e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=2.29935e-3 s grad=9.046e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.432024e-3 s grad=1.579e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.410947e-3 s grad=1.248e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.382862e-3 s grad=1.971e-6 s +[acc-interp] eval 0.166930022 s +[acc-interp] thetaMarsh 7.8e-8 s (n=[acc-interp] thetaMarsh 6.5e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=1.1343898e-2 s grad=5.019e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.761333e-3 s grad=3.769e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.925847e-3 s grad=4.092e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.216823e-3 s grad=3.508e-6 s +[acc-interp] eval 2.2288334e-2 s +4) +[acc-interp] chunk base=0 cs=25974 kernel=2.744254e-3 s grad=3.149e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.385528e-3 s grad=5.58e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.165947e-3 s grad=3.376e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.1780815e-2 s grad=4.003e-6 s +[acc-interp] eval 4.6363466e-2 s +[acc-interp] thetaMarsh 1.18e-7 s (n=[acc-interp] thetaMarsh 2.71e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=2.2623722e-2 s grad=4.396e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.870901e-3 s grad=2.962e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.897872e-3 s grad=4.207e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.632746e-3 s grad=3.357e-6 s +[acc-interp] eval 3.630579e-2 s +5) +[acc-interp] chunk base=0 cs=25974 kernel=1.5434162e-2 s grad=4.361e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.019262e-3 s grad=3.122e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.132967e-3 s grad=2.904e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.732879e-3 s grad=4.457e-6 s +[acc-interp] eval 6.437156e-2 s +[acc-interp] thetaMarsh 1.6e-7 s (n=[acc-interp] thetaMarsh 1.27e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=2.2668818e-2 s grad=4.36e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.2849467e-2 s grad=3.475e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.958379e-3 s grad=3.07e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.714804e-3 s grad=3.24e-6 s +[acc-interp] eval 4.4799367e-2 s +[acc-interp] thetaMarsh 1.14e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=4.26576e-3 s grad=4.185e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.833825e-3 s grad=4.091e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.32967e-3 s grad=4.193e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.393801e-3 s grad=3.188e-6 s +[acc-interp] eval 1.6417814e-2 s +[acc-interp] thetaMarsh 1.19e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.709696e-3 s grad=3.739e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.698603e-3 s grad=4.234e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.184006e-3 s grad=3.669e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.222087e-3 s grad=2.686e-6 s +[acc-interp] eval 1.5425076e-2 s +[acc-interp] thetaMarsh 1.32e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.221659e-3 s grad=4.464e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.345618e-3 s grad=4.61e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.131173e-3 s grad=3.387e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.693018e-3 s grad=3.776e-6 s +[acc-interp] eval 1.7017159e-2 s +[acc-interp] thetaMarsh 1.12e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.541342e-3 s grad=3.789e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.0393434e-2 s grad=3.702e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.188904e-3 s grad=2.599e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.674812e-3 s grad=4.927e-6 s +[acc-interp] eval 2.3494506e-2 s +[acc-interp] thetaMarsh 1.37e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.494175e-3 s grad=4.301e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.486478e-3 s grad=3.524e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.344545e-3 s grad=2.934e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.079038e-3 s grad=2.564e-6 s +[acc-interp] eval 1.5005108e-2 s +[acc-interp] thetaMarsh 1.0e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.501902e-3 s grad=3.317e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.321628e-3 s grad=4.01e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.809846e-3 s grad=2.267e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.927292e-3 s grad=2.173e-6 s +[acc-interp] eval 1.4012404e-2 s +[acc-interp] thetaMarsh 1.29e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=2.981697e-3 s grad=2.429e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=8.687436e-3 s grad=4.629e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.386149e-3 s grad=2.605e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.283147e-3 s grad=3.215e-6 s +[acc-interp] eval 1.982601e-2 s +[acc-interp] thetaMarsh 1.42e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=4.057716e-3 s grad=4.605e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.519565e-3 s grad=3.657e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.218333e-3 s grad=3.121e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.245726e-3 s grad=3.122e-6 s +[acc-interp] eval 1.5647321e-2 s +[acc-interp] thetaMarsh 1.21e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=4.182473e-3 s grad=4.676e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.82871e-3 s grad=3.539e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.43559e-3 s grad=3.886e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.893903e-3 s grad=2.856e-6 s +[acc-interp] eval 1.5781075e-2 s +[acc-interp] thetaMarsh 6.5e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.216432e-3 s grad=4.034e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=8.538389e-3 s grad=3.617e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.31314e-3 s grad=3.555e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.362319e-3 s grad=2.807e-6 s +[acc-interp] eval 1.9315755e-2 s +[acc-interp] thetaMarsh 1.76e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=4.021824e-3 s grad=4.503e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.218861e-3 s grad=4.385e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.134832e-3 s grad=4.728e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.173688e-3 s grad=3.601e-6 s +[acc-interp] eval 1.4474435e-2 s +[acc-interp] thetaMarsh 1.04e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.248297e-3 s grad=5.315e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.249755e-3 s grad=1.3254e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.799176e-3 s grad=7.927e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.739268e-3 s grad=8.971e-6 s +[acc-interp] eval 1.6133986e-2 s +[acc-interp] thetaMarsh 6.9e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=4.236335e-3 s grad=3.909e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.220764e-2 s grad=4.022e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.401237e-3 s grad=4.724e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.787511e-3 s grad=5.154e-6 s +[acc-interp] eval 2.4848094e-2 s +[acc-interp] thetaMarsh 1.17e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.515525e-3 s grad=4.125e-6 s +[acc-interp] chunk base=25974 cs=25974 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thetaMarsh 1.35e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.706106e-3 s grad=3.385e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.454478e-3 s grad=4.928e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.900618e-3 s grad=2.84e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.819715e-3 s grad=1.2105e-5 s +[acc-interp] eval 1.7995741e-2 s +[acc-interp] thetaMarsh 6.5e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=5.148342e-3 s grad=4.21e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.18382e-3 s grad=4.797e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.699109e-3 s grad=4.61e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.477523e-3 s grad=3.964e-6 s +[acc-interp] eval 1.8465371e-2 s +[acc-interp] thetaMarsh 7.5e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=7.449316e-3 s grad=4.019e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.2734159e-2 s grad=4.19e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=6.788324e-3 s grad=4.211e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=6.387446e-3 s grad=3.439e-6 s +[acc-interp] eval 3.5024064e-2 s +[acc-interp] thetaMarsh 1.08e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=2.782535e-3 s grad=3.044e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.5999e-3 s grad=3.536e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.095982e-3 s grad=3.086e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.892433e-3 s grad=3.929e-6 s +[acc-interp] eval 1.6825e-2 s +[acc-interp] thetaMarsh 1.14e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.796827e-3 s grad=4.065e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.934455e-3 s grad=4.489e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.564118e-3 s grad=4.733e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.421199e-3 s grad=4.547e-6 s +[acc-interp] eval 1.5826031e-2 s +[acc-interp] thetaMarsh 6.9e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.667113e-3 s grad=3.433e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.0699507e-2 s grad=4.287e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.413324e-3 s grad=2.912e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.33728e-3 s grad=5.442e-6 s +[acc-interp] eval 2.433464e-2 s +[acc-interp] thetaMarsh 6.7e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.653727e-3 s grad=2.109e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.586654e-3 s grad=2.698e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.107327e-3 s grad=3.826e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.254507e-3 s grad=6.103e-6 s +[acc-interp] eval 1.542643e-2 s +[acc-interp] thetaMarsh 7.0e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.935504e-3 s grad=4.369e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.299774e-3 s grad=1.0607e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.384767e-3 s grad=1.722e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.443487e-3 s grad=4.812e-6 s +[acc-interp] eval 1.8064273e-2 s +[acc-interp] thetaMarsh 1.75e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=4.051364e-3 s grad=3.811e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=9.225481e-3 s grad=5.04e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.339589e-3 s grad=5.225e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.217478e-3 s grad=3.783e-6 s +[acc-interp] eval 2.3555989e-2 s +[acc-interp] thetaMarsh 1.29e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.815012e-3 s grad=3.196e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.85364e-3 s grad=5.29e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.426144e-3 s grad=4.083e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.237925e-3 s grad=4.173e-6 s +[acc-interp] eval 1.8033165e-2 s +[acc-interp] thetaMarsh 6.1e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.097281e-3 s grad=3.37e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.709417e-3 s grad=3.385e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.004689e-3 s grad=1.2208e-5 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.094751e-3 s grad=3.816e-6 s +[acc-interp] eval 1.2666096e-2 s +[acc-interp] thetaMarsh 6.1e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.164713e-3 s grad=5.974e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=8.24829e-3 s grad=3.649e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.102507e-3 s grad=2.766e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.792354e-3 s grad=3.894e-6 s +[acc-interp] eval 1.9348153e-2 s +[acc-interp] thetaMarsh 2.46e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=4.199618e-3 s grad=3.685e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.749536e-3 s grad=6.348e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.671323e-3 s grad=4.362e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.478114e-3 s grad=3.515e-6 s +[acc-interp] eval 1.9302546e-2 s +[acc-interp] thetaMarsh 1.29e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=7.025198e-3 s grad=3.312e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=7.411349e-3 s grad=4.127e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=7.137498e-3 s grad=3.361e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=7.539772e-3 s grad=3.538e-6 s +[acc-interp] eval 3.0625404e-2 s +[acc-interp] thetaMarsh 1.06e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=4.354798e-3 s grad=4.085e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=8.9471e-3 s grad=4.962e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.757516e-3 s grad=5.042e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.547934e-3 s grad=4.377e-6 s +[acc-interp] eval 2.2264666e-2 s +[acc-interp] thetaMarsh 1.38e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.346717e-3 s grad=3.598e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.412497e-3 s grad=4.051e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.355521e-3 s grad=2.846e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.187542e-3 s grad=3.241e-6 s +[acc-interp] eval 1.5833345e-2 s +[acc-interp] thetaMarsh 1.08e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.297618e-3 s grad=2.6e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.008672e-3 s grad=2.759e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.02733e-3 s grad=3.533e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.067708e-3 s grad=4.869e-6 s +[acc-interp] eval 1.4923082e-2 s +5) +[acc-interp] chunk base=0 cs=25974 kernel=3.411372e-3 s grad=3.169e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=9.756509e-3 s grad=4.579e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.548862e-3 s grad=3.658e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.39115e-3 s grad=3.295e-6 s +[acc-interp] eval 0.691370342 s +[acc-interp] thetaMarsh 1.52e-7 s (n=[acc-interp] thetaMarsh 1.48e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=2.1688222e-2 s grad=4.668e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.2398238e-2 s grad=4.304e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.742297e-3 s grad=4.833e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.297897e-3 s grad=3.904e-6 s +[acc-interp] eval 4.6180741e-2 s +5) +[acc-interp] chunk base=0 cs=25974 kernel=3.965094e-3 s grad=3.521e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.555683e-3 s grad=3.665e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.464649e-3 s grad=5.081e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.508189e-3 s grad=4.901e-6 s +[acc-interp] eval 6.3076142e-2 s +[acc-interp] thetaMarsh 7.2e-8 s (n=[acc-interp] thetaMarsh 2.12e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=6.975228e-3 s grad=3.547e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.170343e-3 s grad=3.902e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.576446e-3 s grad=2.147e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.880275e-3 s grad=1.527e-6 s +[acc-interp] eval 1.6432011e-2 s +[acc-interp] thetaMarsh 6.4e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.083587e-3 s grad=7.66e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.061163e-3 s grad=9.183e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.369865e-3 s grad=3.726e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.014824e-3 s grad=2.784e-6 s +[acc-interp] eval 1.3733114e-2 s +[acc-interp] thetaMarsh 3.48e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.968073e-3 s grad=2.412e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.4167e-3 s grad=2.224e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=7.339078e-3 s grad=2.813e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.173696e-3 s grad=1.797e-6 s +[acc-interp] eval 1.6328211e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=2.001385e-3 s grad=2.053e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.769707e-3 s grad=2.439e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.144698e-3 s grad=2.567e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.099018e-3 s grad=1.904e-6 s +[acc-interp] eval 5.6269919e-2 s +[acc-interp] thetaMarsh 1.55e-7 s (n=[acc-interp] thetaMarsh 1.77e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.2368901e-2 s grad=4.591e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.020989e-3 s grad=4.425e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.007226e-3 s grad=4.2e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.942003e-3 s grad=3.191e-6 s +[acc-interp] eval 2.4100774e-2 s +[acc-interp] thetaMarsh 4.48e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.506649e-3 s grad=3.264e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.196899e-3 s grad=2.535e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.61135e-3 s grad=3.487e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.309467e-3 s grad=3.81e-6 s +[acc-interp] eval 1.6294258e-2 s +[acc-interp] thetaMarsh 1.1e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=9.704419e-3 s grad=4.149e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.226605e-3 s grad=4.224e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.069008e-3 s grad=2.452e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.804808e-3 s grad=2.909e-6 s +[acc-interp] eval 2.0626323e-2 s +[acc-interp] thetaMarsh 1.21e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.745052e-3 s grad=3.113e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.821395e-3 s grad=3.912e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.848889e-3 s grad=3.239e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.338597e-3 s grad=3.914e-6 s +[acc-interp] eval 2.0524849e-2 s +[acc-interp] thetaMarsh 1.16e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.782945e-3 s grad=2.81e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.722217e-3 s grad=2.645e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.814033e-3 s grad=2.438e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.70703e-3 s grad=9.567e-6 s +[acc-interp] eval 1.2188047e-2 s +[acc-interp] thetaMarsh 7.1e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.082607e-3 s grad=1.978e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.719759e-3 s grad=1.673e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.015197e-3 s grad=4.981e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.174309e-3 s grad=4.039e-6 s +[acc-interp] eval 1.296537e-2 s +[acc-interp] thetaMarsh 1.8e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.945629e-3 s grad=1.1003e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.660473e-3 s grad=2.078e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.891619e-3 s grad=6.044e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.697358e-3 s grad=3.566e-6 s +[acc-interp] eval 1.2196283e-2 s +[acc-interp] thetaMarsh 1.42e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.438254e-3 s grad=3.361e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.663404e-3 s grad=2.394e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.952606e-3 s grad=3.427e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.719628e-3 s grad=3.591e-6 s +[acc-interp] eval 1.3123987e-2 s +[acc-interp] thetaMarsh 9.9e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.556525e-3 s grad=4.102e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.984973e-3 s grad=3.354e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.328661e-3 s grad=4.012e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.759576e-3 s grad=3.899e-6 s +[acc-interp] eval 1.4224314e-2 s +[acc-interp] thetaMarsh 6.6e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.010309e-3 s grad=3.302e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.727456e-3 s grad=4.992e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.098125e-3 s grad=2.993e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.751713e-3 s grad=4.913e-6 s +[acc-interp] eval 1.348345e-2 s +[acc-interp] thetaMarsh 6.8e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.07116e-3 s grad=3.513e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.696049e-3 s grad=3.808e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.631171e-3 s grad=5.096e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.862014e-3 s grad=4.288e-6 s +[acc-interp] eval 1.3863106e-2 s +[acc-interp] thetaMarsh 1.23e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.146593e-3 s grad=3.447e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.565461e-3 s grad=2.179e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.917138e-3 s grad=1.585e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.141581e-3 s grad=4.325e-6 s +[acc-interp] eval 1.3981586e-2 s +[acc-interp] thetaMarsh 1.55e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.298647e-3 s grad=3.505e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.9705e-3 s grad=3.224e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.707648e-3 s grad=2.972e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.279029e-3 s grad=4.284e-6 s +[acc-interp] eval 1.5109274e-2 s +[acc-interp] thetaMarsh 1.49e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.931043e-3 s grad=4.456e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.717718e-3 s grad=4.092e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.931225e-3 s grad=4.127e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.716741e-3 s grad=4.048e-6 s +[acc-interp] eval 1.3026603e-2 s +[acc-interp] thetaMarsh 3.15e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.543205e-3 s grad=4.258e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.837614e-3 s grad=3.981e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.966725e-3 s grad=3.125e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.843184e-3 s grad=3.823e-6 s +[acc-interp] eval 1.3898024e-2 s +[acc-interp] thetaMarsh 1.24e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.934866e-3 s grad=3.375e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.457168e-3 s grad=1.966e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.633816e-3 s grad=2.764e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.447905e-3 s grad=1.786e-6 s +[acc-interp] eval 1.2168478e-2 s +[acc-interp] thetaMarsh 1.21e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.709028e-3 s grad=1.9e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.494362e-3 s grad=1.939e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.838169e-3 s grad=2.108e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.791676e-3 s grad=2.2e-6 s +[acc-interp] eval 1.2527584e-2 s +[acc-interp] thetaMarsh 2.87e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.617568e-3 s grad=4.075e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.757974e-3 s grad=1.0202e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.940436e-3 s grad=3.524e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.61379e-3 s grad=2.525e-6 s +[acc-interp] eval 1.3270175e-2 s +[acc-interp] thetaMarsh 1.33e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.878747e-3 s grad=4.565e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.379058e-3 s grad=3.57e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.120172e-3 s grad=3.604e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.860345e-3 s grad=3.254e-6 s +[acc-interp] eval 1.3931357e-2 s +[acc-interp] thetaMarsh 1.11e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.237624e-3 s grad=2.546e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.76657e-3 s grad=4.595e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.531117e-3 s grad=5.161e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.801522e-3 s grad=4.445e-6 s +[acc-interp] eval 1.4910609e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=4.068563e-3 s grad=3.41e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.554397e-3 s grad=4.179e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.722388e-3 s grad=4.228e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.904225e-3 s grad=3.211e-6 s +[acc-interp] eval 0.314749931 s +[acc-interp] thetaMarsh 1.44e-7 s (n=[acc-interp] thetaMarsh 1.27e-7 s (n=7) +[acc-interp] chunk base=0 cs=25974 kernel=3.2450141e-2 s grad=5.122e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.343437e-3 s grad=4.075e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.5465541e-2 s grad=4.393e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.27873e-3 s grad=4.774e-6 s +[acc-interp] eval 5.6049976e-2 s +7) +[acc-interp] chunk base=0 cs=25974 kernel=3.882983e-3 s grad=6.512e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.640519e-3 s grad=3.954e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.236904e-3 s grad=4.554e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.557544e-3 s grad=1.1615e-5 s +[acc-interp] eval 7.2536723e-2 s +[acc-interp] thetaMarsh 6.4e-8 s (n=[acc-interp] thetaMarsh 7.0e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.6838282e-2 s grad=3.909e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.818755e-3 s grad=4.626e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.525836e-3 s grad=9.923e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.612467e-3 s grad=1.586e-6 s +[acc-interp] eval 3.0746418e-2 s +[acc-interp] thetaMarsh 6.2e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.1208565e-2 s grad=4.396e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.829746e-3 s grad=2.588e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.168497e-3 s grad=2.94e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.385389e-3 s grad=3.486e-6 s +[acc-interp] eval 1.9471001e-2 s +[acc-interp] thetaMarsh 5.7e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.326217e-3 s grad=3.208e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.557265e-3 s grad=5.088e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.241254e-3 s grad=2.558e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.699453e-3 s grad=3.736e-6 s +[acc-interp] eval 1.5829979e-2 s +[acc-interp] thetaMarsh 1.05e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.65347e-3 s grad=4.096e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.716605e-3 s grad=3.553e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.826068e-3 s grad=4.654e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.946737e-3 s grad=1.901e-6 s +[acc-interp] eval 1.5482369e-2 s +[acc-interp] thetaMarsh 5.7e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.945619e-3 s grad=4.964e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.669889e-3 s grad=5.115e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.346281e-3 s grad=3.98e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.589068e-3 s grad=3.842e-6 s +[acc-interp] eval 1.1531499e-2 s +[acc-interp] thetaMarsh 1.99e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.862187e-3 s grad=4.032e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.915066e-3 s grad=3.978e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.138475e-3 s grad=3.503e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.840413e-3 s grad=3.168e-6 s +[acc-interp] eval 1.3031844e-2 s +[acc-interp] thetaMarsh 1.12e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.90116e-3 s grad=3.552e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.878643e-3 s grad=3.116e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.326022e-3 s grad=3.431e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.295696e-3 s grad=3.706e-6 s +[acc-interp] eval 1.3749939e-2 s +[acc-interp] thetaMarsh 1.06e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.037268e-3 s grad=4.032e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.530125e-3 s grad=3.253e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.09902e-3 s grad=3.52e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.924715e-3 s grad=3.341e-6 s +[acc-interp] eval 1.4260268e-2 s +[acc-interp] thetaMarsh 1.31e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.619798e-3 s grad=4.272e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.96051e-3 s grad=3.385e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.851439e-3 s grad=2.743e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.790144e-3 s grad=3.09e-6 s +[acc-interp] eval 1.3977671e-2 s +[acc-interp] thetaMarsh 4.95e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.514973e-3 s grad=4.248e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.675226e-3 s grad=3.635e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.172295e-3 s grad=4.111e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.720746e-3 s grad=4.779e-6 s +[acc-interp] eval 1.7854764e-2 s +[acc-interp] thetaMarsh 1.07e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.994797e-3 s grad=3.5e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.033352e-3 s grad=3.411e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.4606923e-2 s grad=4.592e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.297884e-3 s grad=5.012e-6 s +[acc-interp] eval 2.7567849e-2 s +[acc-interp] thetaMarsh 9.4e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.023468e-3 s grad=3.747e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.9385e-3 s grad=3.916e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.482864e-3 s grad=3.419e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.179537e-3 s grad=4.471e-6 s +[acc-interp] eval 1.4599699e-2 s +[acc-interp] thetaMarsh 6.9e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.175968e-3 s grad=3.692e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.690621e-3 s grad=4.008e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.79726e-3 s grad=4.958e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.450828e-3 s grad=3.961e-6 s +[acc-interp] eval 1.4601648e-2 s +[acc-interp] thetaMarsh 9.5e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.40783e-3 s grad=3.143e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.504305e-3 s grad=3.7e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.27939e-3 s grad=4.634e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.877027e-3 s grad=3.669e-6 s +[acc-interp] eval 1.6392181e-2 s +[acc-interp] thetaMarsh 9.5e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.292163e-3 s grad=3.069e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.973509e-3 s grad=3.831e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.218742e-3 s grad=4.342e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.397592e-3 s grad=3.029e-6 s +[acc-interp] eval 1.5248978e-2 s +[acc-interp] thetaMarsh 9.9e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.242837e-3 s grad=2.771e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.404715e-3 s grad=2.975e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.01811e-3 s grad=3.307e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.321836e-3 s grad=3.097e-6 s +[acc-interp] eval 1.5433787e-2 s +[acc-interp] thetaMarsh 2.06e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.626536e-3 s grad=5.07e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.555331e-3 s grad=1.0261e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.532007e-3 s grad=5.847e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.082376e-3 s grad=2.131e-6 s +[acc-interp] eval 1.6598281e-2 s +[acc-interp] thetaMarsh 7.1e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.838494e-3 s grad=4.665e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.71528e-3 s grad=1.299e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.0389066e-2 s grad=3.713e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.089472e-3 s grad=6.426e-6 s +[acc-interp] eval 2.1796208e-2 s +[acc-interp] thetaMarsh 1.83e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.379642e-3 s grad=2.739e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.691537e-3 s grad=2.91e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.202262e-3 s grad=1.643e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.976255e-3 s grad=1.042e-6 s +[acc-interp] eval 1.4001206e-2 s +[acc-interp] thetaMarsh 1.54e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.837129e-3 s grad=1.394e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.460535e-3 s grad=4.739e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.95737e-3 s grad=2.987e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.947102e-3 s grad=2.769e-6 s +[acc-interp] eval 1.3511754e-2 s +[acc-interp] thetaMarsh 1.02e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.822082e-3 s grad=2.418e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.964349e-3 s grad=3.279e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.751986e-3 s grad=4.394e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.348849e-3 s grad=4.221e-6 s +[acc-interp] eval 1.5596632e-2 s +[acc-interp] thetaMarsh 1.34e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.971991e-3 s grad=4.208e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.362699e-3 s grad=3.792e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.705501e-3 s grad=4.199e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.227552e-3 s grad=2.367e-6 s +[acc-interp] eval 1.5896071e-2 s +[acc-interp] thetaMarsh 9.9e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.200202e-3 s grad=1.966e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.178522e-3 s grad=4.493e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.40361e-3 s grad=4.847e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.324535e-3 s grad=4.21e-6 s +[acc-interp] eval 1.4977672e-2 s +[acc-interp] thetaMarsh 1.31e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.657857e-3 s grad=3.836e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.281929e-3 s grad=3.328e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.299741e-3 s grad=3.466e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.642917e-3 s grad=4.552e-6 s +[acc-interp] eval 1.6496159e-2 s +[acc-interp] thetaMarsh 1.35e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.955826e-3 s grad=3.22e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.913904e-3 s grad=3.773e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.31165e-3 s grad=3.706e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.037189e-3 s grad=3.892e-6 s +[acc-interp] eval 1.7887158e-2 s +[acc-interp] thetaMarsh 1.19e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=9.792211e-3 s grad=4.474e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.775353e-3 s grad=4.035e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.80817e-3 s grad=4.155e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.351961e-3 s grad=3.21e-6 s +[acc-interp] eval 2.2486356e-2 s +[acc-interp] thetaMarsh 1.36e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.253597e-3 s grad=3.027e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.318792e-3 s grad=3.624e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.317754e-3 s grad=3.013e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.883121e-3 s grad=3.668e-6 s +[acc-interp] eval 1.5452896e-2 s +[acc-interp] thetaMarsh 1.18e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.60662e-3 s grad=3.297e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.271363e-3 s grad=3.267e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.444156e-3 s grad=4.282e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.927878e-3 s grad=3.755e-6 s +[acc-interp] eval 1.7945184e-2 s +[acc-interp] thetaMarsh 1.21e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.436883e-3 s grad=3.638e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.399278e-3 s grad=3.855e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.629288e-3 s grad=4.061e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.44224e-3 s grad=2.828e-6 s +[acc-interp] eval 1.5579783e-2 s +[acc-interp] thetaMarsh 7.0e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.192229e-3 s grad=2.705e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.318815e-3 s 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chunk base=77922 cs=25974 kernel=3.859771e-3 s grad=2.539e-6 s +[acc-interp] eval 1.6425245e-2 s +[acc-interp] thetaMarsh 1.14e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.635605e-3 s grad=2.942e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.106037e-3 s grad=2.482e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.859542e-3 s grad=4.95e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.724611e-3 s grad=3.911e-6 s +[acc-interp] eval 1.6201625e-2 s +[acc-interp] thetaMarsh 1.4e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.633149e-3 s grad=3.186e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.701239e-3 s grad=3.815e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.939039e-3 s grad=3.881e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.249188e-3 s grad=4.69e-6 s +[acc-interp] eval 1.7039207e-2 s +[acc-interp] thetaMarsh 7.6e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=4.309876e-3 s grad=3.2e-6 s +[acc-interp] chunk base=25974 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kernel=1.034814e-2 s grad=3.408e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.563062e-3 s grad=3.311e-6 s +[acc-interp] eval 2.4442603e-2 s +[acc-interp] thetaMarsh 1.07e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.900908e-3 s grad=3.768e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.675365e-3 s grad=3.69e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.075614e-3 s grad=2.992e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.035092e-3 s grad=3.291e-6 s +[acc-interp] eval 1.5452342e-2 s +[acc-interp] thetaMarsh 1.42e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.081347e-3 s grad=2.417e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.897375e-3 s grad=2.316e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.79199e-3 s grad=2.41e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.773473e-3 s grad=1.989e-6 s +[acc-interp] eval 1.3159455e-2 s +[acc-interp] thetaMarsh 1.57e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.612437e-3 s 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base=51948 cs=25974 kernel=3.989082e-3 s grad=4.738e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.492651e-3 s grad=7.164e-6 s +[acc-interp] eval 2.1133013e-2 s +[acc-interp] thetaMarsh 6.8e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.632075e-3 s grad=1.259e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.898409e-3 s grad=2.838e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.388891e-3 s grad=3.825e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.616844e-3 s grad=3.024e-6 s +[acc-interp] eval 1.6990938e-2 s +[acc-interp] thetaMarsh 1.19e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.370085e-3 s grad=4.222e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.634644e-3 s grad=3.412e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.659702e-3 s grad=4.273e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.858043e-3 s grad=3.217e-6 s +[acc-interp] eval 1.6073834e-2 s +[acc-interp] thetaMarsh 1.15e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=4.290267e-3 s grad=2.743e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.947268e-3 s grad=3.228e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=9.136649e-3 s grad=2.484e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.320722e-3 s grad=2.502e-6 s +[acc-interp] eval 2.2165603e-2 s +[acc-interp] thetaMarsh 9.3e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.710012e-3 s grad=3.577e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.093189e-3 s grad=3.705e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.634373e-3 s grad=2.903e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.468685e-3 s grad=2.566e-6 s +[acc-interp] eval 1.6611647e-2 s +[acc-interp] thetaMarsh 1.04e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.65113e-3 s grad=3.258e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.94722e-3 s grad=3.324e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.384888e-3 s grad=2.253e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.534161e-3 s grad=2.915e-6 s +[acc-interp] eval 1.5959654e-2 s +[acc-interp] thetaMarsh 6.7e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=4.131145e-3 s grad=3.893e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.880298e-3 s grad=2.987e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.275836e-3 s grad=7.019e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.50649e-3 s grad=1.554e-6 s +[acc-interp] eval 1.5715937e-2 s +[acc-interp] thetaMarsh 7.1e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.991436e-3 s grad=3.468e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.1605912e-2 s grad=3.742e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.719541e-3 s grad=4.163e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.805223e-3 s grad=4.282e-6 s +[acc-interp] eval 2.4024502e-2 s +[acc-interp] thetaMarsh 1.8e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.231344e-3 s grad=8.282e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.610466e-3 s 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+[acc-interp] chunk base=0 cs=25974 kernel=1.133569e-2 s grad=2.69e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.843475e-3 s grad=4.342e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.959995e-3 s grad=3.462e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.688456e-3 s grad=3.536e-6 s +[acc-interp] eval 2.4534424e-2 s +[acc-interp] thetaMarsh 1.17e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.248685e-3 s grad=2.238e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.064374e-3 s grad=3.948e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.583435e-3 s grad=3.639e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.438276e-3 s grad=3.945e-6 s +[acc-interp] eval 1.5942121e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=4.121307e-3 s grad=3.283e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.148406e-3 s grad=3.639e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.684246e-3 s grad=4.049e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.11942e-3 s grad=3.622e-6 s +[acc-interp] eval 0.583599032 s +[acc-interp] thetaMarsh 1.11e-7 s (n=[acc-interp] thetaMarsh 1.06e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.247745e-2 s grad=3.675e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=8.27183e-3 s grad=4.913e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.380989e-3 s grad=7.513e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.248619e-3 s grad=5.334e-6 s +[acc-interp] eval 2.6368862e-2 s +[acc-interp] thetaMarsh 1.03e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.961582e-3 s grad=3.72e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.51197e-3 s grad=3.393e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.284995e-3 s grad=2.105e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.28679e-3 s grad=2.365e-6 s +[acc-interp] eval 1.1631364e-2 s +[acc-interp] thetaMarsh 6.9e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.406635e-3 s grad=3.813e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.21296e-3 s grad=3.349e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.759531e-3 s grad=5.051e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.450106e-3 s grad=3.926e-6 s +[acc-interp] eval 1.0948011e-2 s +[acc-interp] thetaMarsh 1.15e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.447494e-3 s grad=3.665e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.168824e-3 s grad=2.212e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.179283e-3 s grad=1.917e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.959131e-3 s grad=1.334e-6 s +[acc-interp] eval 9.723443e-3 s +[acc-interp] thetaMarsh 9.8e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.224996e-3 s grad=2.587e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.187726e-3 s grad=7.474e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.08828e-3 s grad=4.896e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.082989e-3 s grad=4.74e-6 s +[acc-interp] eval 9.457789e-3 s +[acc-interp] thetaMarsh 1.81e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.133169e-3 s grad=6.563e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.043186e-3 s grad=1.966e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.130065e-3 s grad=2.06e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.226507e-3 s grad=2.919e-6 s +[acc-interp] eval 9.630649e-3 s +[acc-interp] thetaMarsh 1.42e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.307059e-3 s grad=4.715e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.395261e-3 s grad=2.532e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.243508e-3 s grad=2.734e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.315583e-3 s grad=2.049e-6 s +[acc-interp] eval 1.2970642e-2 s +[acc-interp] thetaMarsh 1.05e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.330103e-3 s grad=2.883e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.571129e-3 s grad=3.41e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.984794e-3 s grad=1.512e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.26936e-3 s grad=6.056e-6 s +[acc-interp] eval 1.110623e-2 s +[acc-interp] thetaMarsh 7.6e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.294044e-3 s grad=7.372e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.349748e-3 s grad=5.384e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.188658e-3 s grad=5.55e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.723475e-3 s grad=4.828e-6 s +[acc-interp] eval 1.060935e-2 s +[acc-interp] thetaMarsh 1.36e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.374762e-3 s grad=2.661e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.221959e-3 s grad=4.825e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.253495e-3 s grad=3.518e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.304672e-3 s grad=6.548e-6 s +[acc-interp] eval 1.0602645e-2 s +[acc-interp] thetaMarsh 1.29e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.331293e-3 s grad=3.815e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.776064e-3 s grad=3.134e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.572451e-3 s grad=3.756e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.303702e-3 s grad=6.753e-6 s +[acc-interp] eval 1.1157834e-2 s +[acc-interp] thetaMarsh 9.2e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.196553e-3 s grad=4.005e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.827888e-3 s grad=5.573e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.265552e-3 s grad=1.614e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.379097e-3 s grad=5.113e-6 s +[acc-interp] eval 1.0814301e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=2.452975e-3 s grad=3.004e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.811158e-3 s grad=2.744e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.798317e-3 s grad=2.404e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.958523e-3 s grad=1.615e-6 s +[acc-interp] eval 0.15551755 s +[acc-interp] thetaMarsh 6.0e-8 s (n=[acc-interp] thetaMarsh 8.0e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.2850804e-2 s grad=1.006e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.494729e-3 s grad=7.813e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.093836e-3 s grad=6.0e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.337856e-3 s grad=3.629e-6 s +[acc-interp] eval 2.0556413e-2 s +[acc-interp] thetaMarsh 6.0e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=4.340221e-3 s grad=3.919e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.917546e-3 s grad=3.284e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.208537e-3 s grad=3.065e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.205249e-3 s grad=8.464e-6 s +[acc-interp] eval 1.1616782e-2 s +[acc-interp] thetaMarsh 5.4e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.530538e-3 s grad=2.186e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.441805e-3 s grad=3.68e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.098806e-3 s grad=2.761e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.369314e-3 s grad=2.789e-6 s +[acc-interp] eval 1.250636e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=1.0267507e-2 s grad=3.968e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.395412e-3 s grad=2.997e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.403876e-3 s grad=2.822e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.527427e-3 s grad=8.542e-6 s +[acc-interp] eval 6.369338e-2 s +[acc-interp] thetaMarsh 7.1e-8 s (n=[acc-interp] thetaMarsh 6.5e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.9068193e-2 s grad=4.065e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.243537e-3 s grad=4.584e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.592873e-3 s grad=4.076e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=9.311801e-3 s grad=5.201e-6 s +[acc-interp] eval 4.7180916e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=3.249314e-3 s grad=3.765e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.638608e-3 s grad=3.462e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.308265e-3 s grad=4.391e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.71589e-3 s grad=4.564e-6 s +[acc-interp] eval 6.3287919e-2 s +[acc-interp] thetaMarsh 1.07e-7 s (n=[acc-interp] thetaMarsh 1.96e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.3706511e-2 s grad=4.857e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.782305e-3 s grad=3.449e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.57039e-3 s grad=1.353e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.96487e-3 s grad=2.717e-6 s +[acc-interp] eval 2.3020184e-2 s +[acc-interp] thetaMarsh 6.5e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.581691e-3 s grad=9.466e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.79426e-3 s grad=6.527e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.431724e-3 s grad=6.195e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=8.149951e-3 s grad=2.013e-6 s +[acc-interp] eval 1.7741759e-2 s +[acc-interp] thetaMarsh 9.4e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.833138e-3 s grad=5.625e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.725744e-3 s grad=6.986e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.511739e-3 s grad=1.483e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.84102e-3 s grad=6.204e-6 s +[acc-interp] eval 1.1854508e-2 s +[acc-interp] thetaMarsh 2.22e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.929035e-3 s grad=9.356e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.949215e-3 s grad=4.986e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.574527e-3 s grad=3.445e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.692122e-3 s grad=3.071e-6 s +[acc-interp] eval 1.2627723e-2 s +[acc-interp] thetaMarsh 1.21e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.555608e-3 s grad=2.954e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.47973e-3 s grad=2.396e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.467152e-3 s grad=3.579e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.047212e-3 s grad=4.29e-6 s +[acc-interp] eval 1.3173241e-2 s +[acc-interp] thetaMarsh 2.23e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.588878e-3 s grad=2.565e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.649682e-3 s grad=2.623e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.321448e-3 s grad=2.957e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.275771e-3 s grad=4.132e-6 s +[acc-interp] eval 1.1528306e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=3.322798e-3 s grad=2.958e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.315698e-3 s grad=2.866e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.249687e-3 s grad=2.13e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.371335e-3 s grad=1.964e-6 s +[acc-interp] eval 0.102541611 s +[acc-interp] thetaMarsh 1.45e-7 s (n=[acc-interp] thetaMarsh 3.51e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.2943388e-2 s grad=4.269e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.789268e-3 s grad=3.363e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.533004e-3 s grad=3.507e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.276508e-3 s grad=2.172e-6 s +[acc-interp] eval 2.4499607e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=3.286192e-3 s grad=8.735e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=8.828819e-3 s grad=3.705e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.569096e-3 s grad=3.318e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.060934e-3 s grad=2.645e-6 s +[acc-interp] eval 4.4835619e-2 s +[acc-interp] thetaMarsh 1.16e-7 s (n=[acc-interp] thetaMarsh 9.2e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.1171712e-2 s grad=3.187e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.241767e-3 s grad=4.677e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.25764e-3 s grad=3.399e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=8.839203e-3 s grad=4.17e-6 s +[acc-interp] eval 4.0123399e-2 s +[acc-interp] thetaMarsh 1.06e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.904757e-3 s grad=2.985e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.343925e-3 s grad=2.751e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.253946e-3 s grad=2.317e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.994448e-3 s grad=2.403e-6 s +[acc-interp] eval 1.521636e-2 s +[acc-interp] thetaMarsh 1.04e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.00288e-3 s grad=3.694e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.821805e-3 s grad=3.438e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.667651e-3 s grad=2.62e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.610801e-3 s grad=2.501e-6 s +[acc-interp] eval 1.6565446e-2 s +[acc-interp] thetaMarsh 7.1e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.084036e-3 s grad=3.917e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.337248e-3 s grad=3.844e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.865559e-3 s grad=2.935e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.77585e-3 s grad=3.322e-6 s +[acc-interp] eval 1.7598006e-2 s +[acc-interp] thetaMarsh 1.06e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.525781e-3 s grad=3.895e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.162378e-3 s grad=1.933e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.984925e-3 s grad=3.258e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.909909e-3 s grad=5.506e-6 s +[acc-interp] eval 1.9762896e-2 s +[acc-interp] thetaMarsh 1.0e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.391296e-3 s grad=3.627e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.351452e-3 s grad=2.7e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.954237e-3 s grad=3.961e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.149706e-2 s grad=2.326e-6 s +[acc-interp] eval 2.5034397e-2 s +[acc-interp] thetaMarsh 5.7e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.301749e-3 s grad=2.303e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.383855e-3 s grad=5.521e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.026509e-3 s grad=4.508e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.730526e-3 s grad=3.34e-6 s +[acc-interp] eval 1.6746925e-2 s +[acc-interp] thetaMarsh 1.18e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.573346e-3 s grad=2.677e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.322626e-3 s grad=4.95e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.901839e-3 s grad=4.292e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.672146e-3 s grad=3.286e-6 s +[acc-interp] eval 1.6590119e-2 s +[acc-interp] thetaMarsh 6.6e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.731906e-3 s grad=3.071e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.032891e-3 s grad=4.552e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.839628e-3 s grad=2.48e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.020981e-3 s grad=1.3308e-5 s +[acc-interp] eval 1.7519943e-2 s +[acc-interp] thetaMarsh 6.5e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.879085e-3 s grad=5.946e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.1114608e-2 s grad=2.947e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.315691e-3 s grad=5.049e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.187295e-3 s grad=4.714e-6 s +[acc-interp] eval 2.5778365e-2 s +[acc-interp] thetaMarsh 1.12e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.857902e-3 s grad=3.995e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.60944e-3 s grad=3.003e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.799322e-3 s grad=3.161e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.058825e-3 s grad=2.972e-6 s +[acc-interp] eval 1.6683513e-2 s +[acc-interp] thetaMarsh 1.09e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.607874e-3 s grad=2.49e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.570739e-3 s grad=2.894e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.514166e-3 s grad=4.81e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.295612e-3 s grad=3.898e-6 s +[acc-interp] eval 1.7590179e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=4.096093e-3 s grad=3.223e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.396632e-3 s grad=4.189e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=6.41077e-3 s grad=3.41e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.080409e-2 s grad=4.544e-6 s +[acc-interp] eval 0.273829077 s +[acc-interp] thetaMarsh 7.9e-8 s (n=[acc-interp] thetaMarsh 7.0e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.5088395e-2 s grad=3.804e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.31363e-3 s grad=2.482e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.883614e-3 s grad=5.1e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=7.671314e-3 s grad=3.328e-6 s +[acc-interp] eval 4.4250028e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=3.01815e-3 s grad=3.267e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.29362e-3 s grad=3.549e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.86851e-3 s grad=3.754e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.828191e-3 s grad=2.667e-6 s +[acc-interp] eval 6.1138307e-2 s +[acc-interp] thetaMarsh 1.34e-7 s (n=[acc-interp] thetaMarsh 1.05e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.0436707e-2 s grad=4.49e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.976774e-3 s grad=3.449e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.582303e-3 s grad=3.84e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.141484e-3 s grad=2.865e-6 s +[acc-interp] eval 3.4852005e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=8.856535e-3 s grad=2.511e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.62009e-3 s grad=2.233e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.66776e-3 s grad=2.029e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.493558e-3 s grad=2.893e-6 s +[acc-interp] eval 5.3191917e-2 s +[acc-interp] thetaMarsh 1.36e-7 s (n=[acc-interp] thetaMarsh 1.18e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=8.25989e-3 s grad=3.601e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.565645e-3 s grad=4.171e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.630379e-3 s grad=4.021e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.553812e-3 s grad=2.827e-6 s +[acc-interp] eval 1.7608635e-2 s +[acc-interp] thetaMarsh 1.11e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.20446e-3 s grad=4.04e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.981643e-3 s grad=3.554e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.47122e-3 s grad=2.955e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.422715e-3 s grad=2.389e-6 s +[acc-interp] eval 1.2566499e-2 s +[acc-interp] thetaMarsh 9.5e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.860727e-3 s grad=2.189e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.754836e-3 s grad=4.437e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.963383e-3 s grad=3.774e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.61027e-3 s grad=3.055e-6 s +[acc-interp] eval 1.3903818e-2 s +[acc-interp] thetaMarsh 1.0e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.49728e-3 s grad=2.477e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.912452e-3 s grad=2.119e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.584113e-3 s grad=4.41e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=9.7032e-3 s grad=3.545e-6 s +[acc-interp] eval 1.9496429e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=2.356621e-3 s grad=2.747e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.674068e-3 s grad=3.597e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.15201e-3 s grad=2.896e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.211547e-3 s grad=2.39e-6 s +[acc-interp] eval 7.5353994e-2 s +[acc-interp] thetaMarsh 1.29e-7 s (n=[acc-interp] thetaMarsh 8.5e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.3336395e-2 s grad=3.406e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.328979e-3 s grad=3.495e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.285262e-3 s grad=2.666e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.706338e-3 s grad=3.44e-6 s +[acc-interp] eval 2.2163032e-2 s +[acc-interp] thetaMarsh 1.11e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.959765e-3 s grad=2.35e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.019795e-3 s grad=4.39e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.3767e-3 s grad=4.882e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.527785e-3 s grad=4.134e-6 s +[acc-interp] eval 1.2684583e-2 s +[acc-interp] thetaMarsh 1.15e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=7.677558e-3 s grad=3.482e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.042125e-3 s grad=6.228e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.966445e-3 s grad=2.386e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.240517e-3 s grad=5.049e-6 s +[acc-interp] eval 1.7273416e-2 s +[acc-interp] thetaMarsh 6.0e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.181812e-3 s grad=2.963e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.064025e-3 s grad=2.075e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.474572e-3 s grad=4.143e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.084695e-3 s grad=6.603e-6 s +[acc-interp] eval 1.2241679e-2 s +[acc-interp] thetaMarsh 8.1e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.729109e-3 s grad=1.812e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.3959e-3 s grad=1.0104e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.310779e-3 s grad=9.98e-7 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.364984e-3 s grad=4.16e-6 s +[acc-interp] eval 1.0954105e-2 s +[acc-interp] thetaMarsh 2.42e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.769078e-3 s grad=5.273e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.691959e-3 s grad=3.196e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.340121e-3 s grad=2.987e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.137707e-3 s grad=3.459e-6 s +[acc-interp] eval 1.2886739e-2 s +[acc-interp] thetaMarsh 1.23e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.117513e-3 s grad=2.896e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.40364e-3 s grad=3.64e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.143377e-3 s grad=4.044e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.369302e-3 s grad=2.995e-6 s +[acc-interp] eval 1.1832525e-2 s +[acc-interp] thetaMarsh 1.15e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.217354e-3 s grad=3.138e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.948276e-3 s grad=3.801e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.555367e-3 s grad=2.957e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.401221e-3 s grad=3.947e-6 s +[acc-interp] eval 1.1775174e-2 s +[acc-interp] thetaMarsh 1.24e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.809087e-3 s grad=5.607e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.917025e-3 s grad=4.186e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.411596e-3 s grad=2.659e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.726472e-3 s grad=2.862e-6 s +[acc-interp] eval 1.2520963e-2 s +[acc-interp] thetaMarsh 1.95e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.549286e-3 s grad=2.627e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.578216e-3 s grad=4.126e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.858118e-3 s grad=4.636e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.571227e-3 s grad=3.253e-6 s +[acc-interp] eval 1.2235757e-2 s +[acc-interp] thetaMarsh 1.36e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.620079e-3 s grad=2.674e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.319785e-3 s grad=2.389e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.306589e-3 s grad=3.056e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.95376e-3 s grad=4.35e-6 s +[acc-interp] eval 1.1782727e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=3.757783e-3 s grad=3.319e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.758485e-3 s grad=1.901e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.76319e-3 s grad=2.795e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.314908e-3 s grad=2.417e-6 s +[acc-interp] eval 0.162615228 s +[acc-interp] thetaMarsh 6.1e-8 s (n=[acc-interp] thetaMarsh 6.0e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=9.500477e-3 s grad=3.345e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.637647e-3 s grad=2.332e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.426089e-3 s grad=2.319e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.758816e-3 s grad=1.92e-6 s +[acc-interp] eval 1.8154414e-2 s +[acc-interp] thetaMarsh 1.14e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.348059e-3 s grad=2.234e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.102074e-3 s grad=3.717e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.880932e-3 s grad=4.325e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.5958e-3 s grad=2.526e-6 s +[acc-interp] eval 1.2237517e-2 s +[acc-interp] thetaMarsh 1.15e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.310342e-3 s grad=2.77e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.268474e-3 s grad=2.192e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.297992e-3 s grad=3.191e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=8.200972e-3 s grad=4.247e-6 s +[acc-interp] eval 1.6823312e-2 s +[acc-interp] thetaMarsh 1.17e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.287732e-3 s grad=2.862e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.092421e-3 s grad=2.033e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.889643e-3 s grad=2.288e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.389759e-3 s grad=6.667e-6 s +[acc-interp] eval 1.1376072e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=2.74931e-3 s grad=3.119e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.232158e-3 s grad=2.194e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.013185e-3 s grad=1.778e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.021586e-3 s grad=1.574e-6 s +[acc-interp] eval 6.9614086e-2 s +[acc-interp] thetaMarsh 1.24e-7 s (n=[acc-interp] thetaMarsh 7.3e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.280552e-2 s grad=2.991e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.843534e-3 s grad=1.578e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.996666e-3 s grad=2.687e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.048404e-3 s grad=6.046e-6 s +[acc-interp] eval 2.0747875e-2 s +[acc-interp] thetaMarsh 1.05e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.140884e-3 s grad=4.15e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.415929e-3 s grad=4.385e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.445026e-3 s grad=4.074e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.80175e-3 s grad=2.878e-6 s +[acc-interp] eval 1.2184279e-2 s +[acc-interp] thetaMarsh 1.21e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.448486e-3 s grad=3.542e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=7.698698e-3 s grad=2.693e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.238859e-3 s grad=2.014e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.319904e-3 s grad=2.866e-6 s +[acc-interp] eval 1.7231692e-2 s +[acc-interp] thetaMarsh 1.01e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.608057e-3 s grad=3.698e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.668557e-3 s grad=3.451e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.770489e-3 s grad=4.614e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.408647e-3 s grad=7.105e-6 s +[acc-interp] eval 1.1639314e-2 s +[acc-interp] thetaMarsh 6.2e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.219548e-3 s grad=1.506e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.589349e-3 s grad=7.176e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.863302e-3 s grad=4.268e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.408889e-3 s grad=2.738e-6 s +[acc-interp] eval 1.1265416e-2 s +[acc-interp] thetaMarsh 1.08e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.333522e-3 s grad=1.994e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.196061e-3 s grad=1.624e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.433474e-3 s grad=2.007e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.310801e-3 s grad=1.771e-6 s +[acc-interp] eval 1.0786979e-2 s +[acc-interp] thetaMarsh 1.01e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.698415e-3 s grad=4.587e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.516334e-3 s grad=3.829e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.170882e-3 s grad=3.605e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.354766e-3 s grad=2.972e-6 s +[acc-interp] eval 1.2462948e-2 s +[acc-interp] thetaMarsh 1.11e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.34854e-3 s grad=9.69e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.945494e-3 s grad=3.806e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.45096e-3 s grad=3.907e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.349117e-3 s grad=2.531e-6 s +[acc-interp] eval 1.1424537e-2 s +[acc-interp] thetaMarsh 7.1e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.456175e-3 s grad=2.736e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.263674e-3 s grad=8.603e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.242375e-3 s grad=6.498e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.043324e-3 s grad=4.753e-6 s +[acc-interp] eval 1.0961491e-2 s +[acc-interp] thetaMarsh 8.9e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.881164e-3 s grad=4.092e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.509308e-3 s grad=4.381e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.460369e-3 s grad=3.193e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.343924e-3 s grad=3.015e-6 s +[acc-interp] eval 1.1067849e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=2.247413e-3 s grad=2.863e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.234652e-3 s grad=3.973e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.883719e-3 s grad=6.12e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.650421e-3 s grad=5.262e-6 s +[acc-interp] eval 0.14229092 s +[acc-interp] thetaMarsh 1.76e-7 s (n=[acc-interp] thetaMarsh 8.3e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.7559219e-2 s grad=4.376e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.884328e-3 s grad=3.505e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=9.03649e-3 s grad=4.138e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.597767e-3 s grad=4.435e-6 s +[acc-interp] eval 4.5774212e-2 s +[acc-interp] thetaMarsh 1.21e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.334363e-3 s grad=3.482e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.18931e-3 s grad=3.763e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.874694e-3 s grad=3.793e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.891235e-3 s grad=2.764e-6 s +[acc-interp] eval 1.3827992e-2 s +[acc-interp] thetaMarsh 1.4e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.962107e-3 s grad=4.352e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.367456e-3 s grad=4.47e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.85336e-3 s grad=3.57e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.698555e-3 s grad=2.394e-6 s +[acc-interp] eval 1.359809e-2 s +[acc-interp] thetaMarsh 1.05e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.784554e-3 s grad=2.329e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.217171e-3 s grad=4.311e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.4033e-3 s grad=3.539e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.204419e-3 s grad=3.203e-6 s +[acc-interp] eval 1.4328937e-2 s +[acc-interp] thetaMarsh 1.33e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.380691e-3 s grad=2.402e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.234797e-3 s grad=2.795e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.097518e-3 s grad=4.661e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.188562e-3 s grad=4.839e-6 s +[acc-interp] eval 1.5544471e-2 s +[acc-interp] thetaMarsh 1.08e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.139511e-3 s grad=4.158e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.307008e-3 s grad=2.999e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.746511e-3 s grad=5.175e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.414262e-3 s grad=4.536e-6 s +[acc-interp] eval 1.5364549e-2 s +[acc-interp] thetaMarsh 9.7e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.404652e-3 s grad=4.286e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.506814e-3 s grad=4.103e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.65598e-3 s grad=4.762e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.790729e-3 s grad=3.557e-6 s +[acc-interp] eval 1.6152374e-2 s +[acc-interp] thetaMarsh 1.0e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.217401e-3 s grad=2.835e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.087565e-3 s grad=2.546e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.745923e-3 s grad=2.014e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.4594448e-2 s grad=4.458e-6 s +[acc-interp] eval 2.8230181e-2 s +[acc-interp] thetaMarsh 7.6e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.13472e-3 s grad=4.857e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.837263e-3 s grad=3.581e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.274284e-3 s grad=3.477e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.144871e-3 s grad=4.378e-6 s +[acc-interp] eval 1.9265388e-2 s +[acc-interp] thetaMarsh 1.09e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.223603e-3 s grad=4.668e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.771523e-3 s grad=3.632e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.63986e-3 s grad=3.658e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.762614e-3 s grad=5.167e-6 s +[acc-interp] eval 1.711518e-2 s +[acc-interp] thetaMarsh 1.12e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.436239e-3 s grad=4.746e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.013653e-3 s grad=4.608e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.883284e-3 s grad=3.715e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.314845e-3 s grad=5.717e-6 s +[acc-interp] eval 1.8410471e-2 s +[acc-interp] thetaMarsh 1.22e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.814877e-3 s grad=3.993e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.796507e-3 s grad=3.453e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.378182e-3 s grad=2.976e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.340061e-3 s grad=6.232e-6 s +[acc-interp] eval 1.6852073e-2 s +[acc-interp] thetaMarsh 1.18e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.340311e-3 s grad=3.671e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.3342024e-2 s grad=4.723e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.449402e-3 s grad=3.442e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.429996e-3 s grad=3.138e-6 s +[acc-interp] eval 2.6081072e-2 s +[acc-interp] thetaMarsh 1.15e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.597046e-3 s grad=4.127e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.332274e-3 s grad=4.041e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.840174e-3 s grad=3.771e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.044206e-3 s grad=4.346e-6 s +[acc-interp] eval 1.7421193e-2 s +[acc-interp] thetaMarsh 1.13e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.581115e-3 s grad=5.63e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.491525e-3 s grad=4.15e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.674546e-3 s grad=3.025e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.447829e-3 s grad=2.939e-6 s +[acc-interp] eval 1.775302e-2 s +[acc-interp] thetaMarsh 6.1e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.48873e-3 s grad=3.214e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.678685e-3 s grad=2.015e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.475286e-3 s grad=7.653e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.370011e-3 s grad=3.178e-6 s +[acc-interp] eval 1.5025542e-2 s +[acc-interp] thetaMarsh 1.16e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.306092e-3 s grad=4.217e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.832039e-3 s grad=4.079e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.783663e-3 s grad=4.179e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.2343206e-2 s grad=5.466e-6 s +[acc-interp] eval 2.5591374e-2 s +[acc-interp] thetaMarsh 7.0e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.859041e-3 s grad=4.874e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.02934e-3 s grad=4.378e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.251948e-3 s grad=5.056e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.959888e-3 s grad=4.185e-6 s +[acc-interp] eval 1.7510565e-2 s +[acc-interp] thetaMarsh 1.18e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.685374e-3 s grad=3.675e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.557907e-3 s grad=4.48e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.926544e-3 s grad=4.081e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.414771e-3 s grad=3.915e-6 s +[acc-interp] eval 1.7187172e-2 s +[acc-interp] thetaMarsh 1.09e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.6549e-3 s grad=3.794e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.743934e-3 s grad=4.677e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.504065e-3 s grad=3.631e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.682139e-3 s grad=3.497e-6 s +[acc-interp] eval 1.7207465e-2 s +[acc-interp] thetaMarsh 9.7e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.707682e-3 s grad=3.318e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.921703e-3 s grad=3.144e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.678651e-3 s grad=3.123e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.706854e-3 s grad=3.308e-6 s +[acc-interp] eval 1.6496008e-2 s +[acc-interp] thetaMarsh 1.11e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.709275e-3 s grad=2.969e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.2195507e-2 s grad=4.6e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.700984e-3 s grad=3.098e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.927303e-3 s grad=5.053e-6 s +[acc-interp] eval 2.5325907e-2 s 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kernel=3.268422e-3 s grad=2.388e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.16488e-3 s grad=4.503e-6 s +[acc-interp] eval 1.5224178e-2 s +[acc-interp] thetaMarsh 1.62e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.51668e-3 s grad=4.047e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.002808e-3 s grad=4.664e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.945833e-3 s grad=4.829e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.7128234e-2 s grad=4.875e-6 s +[acc-interp] eval 3.1370932e-2 s +[acc-interp] thetaMarsh 6.9e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.810882e-3 s grad=4.767e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.85398e-3 s grad=4.71e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.767084e-3 s grad=4.608e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.782451e-3 s grad=4.56e-6 s +[acc-interp] eval 1.7098505e-2 s +[acc-interp] thetaMarsh 6.2e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.467232e-3 s grad=5.048e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.465357e-3 s grad=4.107e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.42802e-3 s grad=3.972e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.573542e-3 s grad=4.43e-6 s +[acc-interp] eval 1.5847517e-2 s +[acc-interp] thetaMarsh 6.6e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.235633e-3 s grad=3.627e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.247804e-3 s grad=3.966e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.484815e-3 s grad=3.936e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.484476e-3 s grad=3.268e-6 s +[acc-interp] eval 1.4316949e-2 s +[acc-interp] thetaMarsh 7.2e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.220245e-3 s grad=3.131e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.272079e-3 s grad=3.708e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.601781e-3 s grad=4.752e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.302375e-3 s grad=4.671e-6 s +[acc-interp] eval 1.5565091e-2 s +4) +[acc-interp] chunk base=0 cs=25974 kernel=4.294107e-3 s grad=3.645e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.3540892e-2 s grad=4.299e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.048394e-3 s grad=3.268e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.04573e-3 s grad=4.112e-6 s +[acc-interp] eval 0.605720093 s +[acc-interp] thetaMarsh 1.17e-7 s (n=[acc-interp] thetaMarsh 1.22e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.4818443e-2 s grad=4.455e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.161334e-3 s grad=3.775e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.067967e-3 s grad=3.413e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.92262e-3 s grad=3.713e-6 s +[acc-interp] eval 2.6675468e-2 s +[acc-interp] thetaMarsh 6.5e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.998909e-3 s grad=4.472e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.693223e-3 s grad=4.518e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=9.984556e-3 s grad=4.208e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.23428e-3 s grad=3.022e-6 s +[acc-interp] eval 1.9084623e-2 s +[acc-interp] thetaMarsh 1.09e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.373134e-3 s grad=3.378e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.571918e-3 s grad=3.274e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.126918e-3 s grad=3.501e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.145347e-3 s grad=3.446e-6 s +[acc-interp] eval 1.072524e-2 s +[acc-interp] thetaMarsh 1.02e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.106484e-3 s grad=3.052e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.224177e-3 s grad=2.986e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.101083e-3 s grad=3.083e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.396927e-3 s grad=4.732e-6 s +[acc-interp] eval 1.0402107e-2 s +[acc-interp] thetaMarsh 1.26e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.148459e-3 s grad=3.627e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.495873e-3 s grad=3.679e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.573342e-3 s grad=2.305e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.54239e-3 s grad=2.971e-6 s +[acc-interp] eval 1.2016099e-2 s +[acc-interp] thetaMarsh 1.01e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.461087e-3 s grad=4.678e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.95614e-3 s grad=4.113e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.458011e-3 s grad=2.356e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.667585e-3 s grad=1.895e-6 s +[acc-interp] eval 1.2114563e-2 s +[acc-interp] thetaMarsh 2.12e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.253545e-3 s grad=3.291e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.749055e-3 s grad=4.242e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.727552e-3 s grad=4.355e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.651971e-3 s grad=5.105e-6 s +[acc-interp] eval 1.3878703e-2 s +[acc-interp] thetaMarsh 8.1e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.53658e-3 s grad=3.248e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.639264e-3 s grad=2.458e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.547035e-3 s grad=8.201e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.167312e-3 s grad=6.015e-6 s +[acc-interp] eval 1.1890682e-2 s +[acc-interp] thetaMarsh 1.06e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.689206e-3 s grad=3.465e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.943473e-3 s grad=3.82e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.972033e-3 s grad=4.603e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.595433e-3 s grad=4.508e-6 s +[acc-interp] eval 1.2980082e-2 s +[acc-interp] thetaMarsh 1.74e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.637476e-3 s grad=3.056e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.43264e-3 s 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chunk base=77922 cs=25974 kernel=2.766191e-3 s grad=3.943e-6 s +[acc-interp] eval 1.2426421e-2 s +[acc-interp] thetaMarsh 8.5e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.677442e-3 s grad=3.803e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.935057e-3 s grad=4.086e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.00222e-3 s grad=4.414e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.306263e-3 s grad=3.651e-6 s +[acc-interp] eval 1.3379845e-2 s +[acc-interp] thetaMarsh 1.26e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.88604e-3 s grad=3.109e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.753746e-3 s grad=3.471e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.768298e-3 s grad=4.745e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.802514e-3 s grad=3.321e-6 s +[acc-interp] eval 1.4026421e-2 s +[acc-interp] thetaMarsh 5.21e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.544245e-3 s grad=2.837e-6 s +[acc-interp] chunk base=25974 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kernel=2.235775e-3 s grad=2.835e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.360765e-3 s grad=3.325e-6 s +[acc-interp] eval 1.0545649e-2 s +[acc-interp] thetaMarsh 7.6e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.371621e-3 s grad=2.676e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.627205e-3 s grad=2.012e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.183654e-3 s grad=4.431e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.636335e-3 s grad=3.22e-6 s +[acc-interp] eval 1.2127171e-2 s +[acc-interp] thetaMarsh 1.03e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.766585e-3 s grad=3.584e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.698148e-3 s grad=2.717e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.381707e-3 s grad=3.656e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.195934e-3 s grad=4.735e-6 s +[acc-interp] eval 1.2441647e-2 s +[acc-interp] thetaMarsh 2.36e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.511738e-3 s grad=4.1e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.362831e-3 s grad=4.166e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.366135e-3 s grad=4.17e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.395916e-3 s grad=2.955e-6 s +[acc-interp] eval 1.0501789e-2 s +[acc-interp] thetaMarsh 9.0e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.36008e-3 s grad=3.688e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.811023e-3 s grad=5.13e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.938721e-3 s grad=3.792e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.526041e-3 s grad=3.167e-6 s +[acc-interp] eval 1.2067262e-2 s +[acc-interp] thetaMarsh 1.02e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.399339e-3 s grad=2.312e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.562063e-3 s grad=3.05e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.732805e-3 s grad=4.188e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.560235e-3 s grad=3.649e-6 s +[acc-interp] eval 1.287228e-2 s +[acc-interp] thetaMarsh 1.2e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.457832e-3 s grad=2.914e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.600934e-3 s grad=3.461e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.280361e-3 s grad=2.511e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.393546e-3 s grad=4.118e-6 s +[acc-interp] eval 1.134047e-2 s +[acc-interp] thetaMarsh 1.02e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.123072e-3 s grad=2.488e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.441061e-3 s grad=2.889e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.441318e-3 s grad=2.806e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.477401e-3 s grad=2.433e-6 s +[acc-interp] eval 1.214786e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=2.504321e-3 s grad=3.391e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.322524e-3 s grad=3.528e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.90226e-3 s grad=3.871e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.979343e-3 s grad=7.79e-6 s +[acc-interp] eval 0.398178981 s +[acc-interp] thetaMarsh 7.7e-8 s (n=[acc-interp] thetaMarsh 5.6e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.7556966e-2 s grad=3.96e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.193499e-3 s grad=2.624e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.18547e-3 s grad=9.537e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.313327e-3 s grad=1.0833e-5 s +[acc-interp] eval 3.8109899e-2 s +[acc-interp] thetaMarsh 1.38e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.615451e-3 s grad=5.106e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.811994e-3 s grad=5.244e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.699106e-3 s grad=2.008e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.564712e-3 s grad=9.311e-6 s +[acc-interp] eval 1.5587059e-2 s +[acc-interp] thetaMarsh 6.7e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.618237e-3 s grad=4.935e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.972959e-3 s grad=4.904e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.313847e-3 s grad=4.639e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.446548e-3 s grad=4.272e-6 s +[acc-interp] eval 1.591021e-2 s +[acc-interp] thetaMarsh 1.14e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.189667e-3 s grad=3.322e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.259395e-3 s grad=6.751e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.548035e-3 s grad=4.279e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.334982e-3 s grad=4.528e-6 s +[acc-interp] eval 1.7241367e-2 s +[acc-interp] thetaMarsh 1.01e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.110608e-3 s grad=3.708e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.626765e-3 s grad=5.578e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.307174e-3 s grad=3.628e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.299387e-3 s grad=3.886e-6 s +[acc-interp] eval 1.8952748e-2 s +[acc-interp] thetaMarsh 9.5e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.578546e-3 s grad=4.716e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.2781843e-2 s grad=5.054e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.492308e-3 s grad=5.188e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.068673e-3 s grad=4.34e-6 s +[acc-interp] eval 2.7400616e-2 s +[acc-interp] thetaMarsh 1.21e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.54259e-3 s grad=3.545e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.443214e-3 s grad=3.996e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.650421e-3 s grad=4.157e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.652299e-3 s grad=3.749e-6 s +[acc-interp] eval 1.5905741e-2 s +[acc-interp] thetaMarsh 1.07e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.217668e-3 s grad=3.112e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.327936e-3 s grad=2.947e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.909579e-3 s grad=6.886e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.465403e-3 s grad=3.658e-6 s +[acc-interp] eval 1.7796255e-2 s +4) +[acc-interp] chunk base=0 cs=25974 kernel=4.036004e-3 s grad=3.052e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.908453e-3 s grad=3.95e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.402719e-3 s grad=3.445e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.0739718e-2 s grad=4.773e-6 s +[acc-interp] eval 0.193791103 s +[acc-interp] thetaMarsh 8.2e-8 s (n=[acc-interp] thetaMarsh 6.0e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.0640379e-2 s grad=7.879e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.336354e-3 s grad=4.619e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.351728e-3 s grad=4.094e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.431121e-3 s grad=5.644e-6 s +[acc-interp] eval 3.1695908e-2 s +[acc-interp] thetaMarsh 6.5e-8 s 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s grad=5.856e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.301556e-3 s grad=4.026e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.27427e-3 s grad=3.424e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.891675e-3 s grad=4.023e-6 s +[acc-interp] eval 1.7072926e-2 s +[acc-interp] thetaMarsh 5.8e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=5.341895e-3 s grad=6.207e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.370185e-3 s grad=4.417e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.040641e-3 s grad=2.262e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.840416e-3 s grad=2.406e-6 s +[acc-interp] eval 1.7142962e-2 s +[acc-interp] thetaMarsh 9.6e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.547319e-3 s grad=9.01e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.363372e-3 s grad=1.1572e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.058776e-3 s grad=2.384e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.069243e-3 s grad=2.08e-6 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base=51948 cs=25974 kernel=3.573475e-3 s grad=3.676e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.810603e-3 s grad=1.272e-6 s +[acc-interp] eval 1.1970549e-2 s +[acc-interp] thetaMarsh 5.9e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.959317e-3 s grad=1.409e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.805776e-3 s grad=2.466e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.724193e-3 s grad=2.49e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.653179e-3 s grad=3.679e-6 s +[acc-interp] eval 1.2866038e-2 s +[acc-interp] thetaMarsh 7.0e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.894354e-3 s grad=3.783e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.976365e-3 s grad=3.105e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.055666e-3 s grad=7.153e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.54359e-3 s grad=4.238e-6 s +[acc-interp] eval 1.2385573e-2 s +[acc-interp] thetaMarsh 2.02e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.719788e-3 s grad=3.501e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.658114e-3 s grad=2.352e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.0216327e-2 s grad=7.592e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.40306e-3 s grad=5.082e-6 s +[acc-interp] eval 2.0907513e-2 s +[acc-interp] thetaMarsh 1.1e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=4.235734e-3 s grad=4.371e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.331897e-3 s grad=4.976e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.158408e-3 s grad=3.281e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.925984e-3 s grad=4.301e-6 s +[acc-interp] eval 1.4685955e-2 s +[acc-interp] thetaMarsh 1.06e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=6.677594e-3 s grad=4.223e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.046346e-3 s grad=4.153e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.078688e-3 s grad=3.104e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.44775e-3 s grad=3.932e-6 s +[acc-interp] eval 1.6438524e-2 s +[acc-interp] thetaMarsh 5.6e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=5.09167e-3 s grad=3.731e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.111247e-3 s grad=3.782e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.847642e-3 s grad=3.597e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.699271e-3 s grad=2.83e-6 s +[acc-interp] eval 1.4530386e-2 s +[acc-interp] thetaMarsh 6.0e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.956967e-3 s grad=5.094e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.010179e-3 s grad=4.593e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.289955e-3 s grad=3.577e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.279147e-3 s grad=1.2767e-5 s +[acc-interp] eval 1.4639943e-2 s +[acc-interp] thetaMarsh 1.14e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.226433e-3 s grad=1.885e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.114603e-3 s 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cs=25974 kernel=3.854246e-3 s grad=3.711e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.1231e-3 s grad=3.818e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.321913e-3 s grad=2.922e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.555371e-3 s grad=3.572e-6 s +[acc-interp] eval 0.49985346 s +[acc-interp] thetaMarsh 1.4e-7 s (n=[acc-interp] thetaMarsh 1.03e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.3218585e-2 s grad=5.176e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.453245e-3 s grad=4.485e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.330239e-3 s grad=4.109e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.268585e-3 s grad=2.687e-6 s +[acc-interp] eval 4.5916102e-2 s +4) +[acc-interp] chunk base=0 cs=25974 kernel=4.170249e-3 s grad=2.754e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.134869e-3 s grad=2.667e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.046881e-3 s grad=2.425e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.137213e-3 s grad=3.725e-6 s +[acc-interp] eval 6.1061087e-2 s +[acc-interp] thetaMarsh 1.24e-7 s (n=[acc-interp] thetaMarsh 5.9e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.6858648e-2 s grad=4.521e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.140168e-3 s grad=3.535e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.846489e-3 s grad=4.289e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=9.46559e-3 s grad=4.271e-6 s +[acc-interp] eval 4.6800594e-2 s +[acc-interp] thetaMarsh 3.95e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.392542e-3 s grad=1.5596e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.063905e-3 s grad=4.087e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.606678e-3 s grad=1.926e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.901397e-3 s grad=3.388e-6 s +[acc-interp] eval 1.3526724e-2 s +[acc-interp] thetaMarsh 2.07e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.870952e-3 s grad=2.642e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.284344e-3 s grad=5.718e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.537934e-3 s grad=4.333e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.07996e-3 s grad=4.394e-6 s +[acc-interp] eval 1.6584185e-2 s +[acc-interp] thetaMarsh 1.07e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.250754e-3 s grad=4.647e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.721904e-3 s grad=6.793e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.987454e-3 s grad=4.016e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.732256e-3 s grad=4.077e-6 s +[acc-interp] eval 1.7109677e-2 s +[acc-interp] thetaMarsh 1.01e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.126593e-3 s grad=2.51e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.730392e-3 s grad=4.331e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.786937e-3 s grad=3.895e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.588506e-3 s grad=3.933e-6 s +[acc-interp] eval 1.3204594e-2 s 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chunk base=51948 cs=25974 kernel=4.23682e-3 s grad=4.242e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.348259e-3 s grad=3.722e-6 s +[acc-interp] eval 1.7739559e-2 s +[acc-interp] thetaMarsh 1.09e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.85584e-3 s grad=3.4e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.108138e-3 s grad=4.045e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.206327e-3 s grad=5.462e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.630613e-3 s grad=3.619e-6 s +[acc-interp] eval 1.7306418e-2 s +[acc-interp] thetaMarsh 1.11e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.652523e-3 s grad=3.963e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.799163e-3 s grad=4.579e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.24987e-3 s grad=4.264e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.2595814e-2 s grad=5.377e-6 s +[acc-interp] eval 2.5924181e-2 s +[acc-interp] thetaMarsh 1.31e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.1713e-3 s grad=4.064e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.01428e-3 s grad=4.346e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.672925e-3 s grad=3.948e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.322909e-3 s grad=5.11e-6 s +[acc-interp] eval 1.7892799e-2 s +[acc-interp] thetaMarsh 1.3e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.982009e-3 s grad=4.431e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.987662e-3 s grad=4.666e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.774354e-3 s grad=3.821e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.22027e-3 s grad=3.87e-6 s +[acc-interp] eval 1.7526725e-2 s +[acc-interp] thetaMarsh 1.04e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.399882e-3 s grad=3.623e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.473266e-3 s grad=3.692e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.432532e-3 s grad=3.668e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.089605e-3 s grad=5.859e-6 s +[acc-interp] eval 1.5930356e-2 s +[acc-interp] thetaMarsh 1.15e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.719822e-3 s grad=4.562e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.6768e-3 s grad=3.094e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.652182e-3 s grad=3.111e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.096946e-3 s grad=4.228e-6 s +[acc-interp] eval 1.655991e-2 s +[acc-interp] thetaMarsh 9.8e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.912089e-3 s grad=4.889e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.3164803e-2 s grad=5.004e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.699841e-3 s grad=5.247e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.735853e-3 s grad=4.569e-6 s +[acc-interp] eval 2.5783308e-2 s +[acc-interp] thetaMarsh 7.1e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.81457e-3 s grad=3.602e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.653525e-3 s grad=4.443e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.008731e-3 s grad=2.042e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.104005e-3 s grad=8.895e-6 s +[acc-interp] eval 1.743912e-2 s +[acc-interp] thetaMarsh 6.3e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=4.634666e-3 s grad=4.221e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.816053e-3 s grad=2.695e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.651543e-3 s grad=2.198e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.832108e-3 s grad=3.173e-6 s +[acc-interp] eval 1.5932478e-2 s +4) +[acc-interp] chunk base=0 cs=25974 kernel=3.42322e-3 s grad=3.22e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.457604e-3 s grad=2.882e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.159223e-3 s grad=2.567e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.993678e-3 s grad=3.132e-6 s +[acc-interp] eval 0.494512976 s +[acc-interp] thetaMarsh 7.9e-8 s (n=[acc-interp] thetaMarsh 5.4e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.8306398e-2 s grad=4.314e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.346616e-3 s grad=2.369e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.10137e-3 s grad=3.275e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.355138e-3 s grad=4.17e-6 s +[acc-interp] eval 4.0142132e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=3.694351e-3 s grad=3.664e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.14833e-3 s grad=3.497e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.946048e-3 s grad=2.964e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.48116e-3 s grad=4.266e-6 s +[acc-interp] eval 5.4529411e-2 s +[acc-interp] thetaMarsh 6.7e-8 s (n=0) +[acc-interp] chunk base=0 cs=25974 kernel=7.315729e-3 s grad=1.458e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.056635e-3 s grad=5.73e-7 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.068305e-3 s grad=3.335e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.914441e-3 s grad=1.664e-6 s +[acc-interp] eval 1.4266922e-2 s +[acc-interp] thetaMarsh 1.37e-7 s (n=[acc-interp] thetaMarsh 5.9e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.0168904e-2 s grad=3.981e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.525676e-3 s grad=3.335e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.506043e-3 s grad=4.407e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.955832e-3 s grad=4.497e-6 s +[acc-interp] eval 2.0313102e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=1.0695945e-2 s grad=2.803e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.653794e-3 s grad=3.548e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.928012e-3 s grad=3.661e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.536335e-3 s grad=3.509e-6 s +[acc-interp] eval 4.0968476e-2 s +[acc-interp] thetaMarsh 1.32e-7 s (n=[acc-interp] thetaMarsh 9.7e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=7.250012e-3 s grad=5.296e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.062254e-3 s grad=1.2279e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.46351e-3 s grad=1.785e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.599313e-3 s grad=1.218e-6 s +[acc-interp] eval 1.731524e-2 s +[acc-interp] thetaMarsh 6.4e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.41252e-3 s grad=1.349e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.039812e-3 s grad=4.847e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.776099e-3 s grad=5.636e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.749619e-3 s grad=4.86e-6 s +[acc-interp] eval 1.4103245e-2 s +[acc-interp] thetaMarsh 8.9e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.0739683e-2 s grad=3.794e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.921302e-3 s grad=4.683e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.029894e-3 s grad=3.047e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.396022e-3 s grad=3.88e-6 s +[acc-interp] eval 2.0752686e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=2.465814e-3 s grad=2.51e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.741105e-3 s grad=3.765e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.586813e-3 s grad=3.794e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.465535e-3 s grad=2.724e-6 s +[acc-interp] eval 6.4256635e-2 s +[acc-interp] thetaMarsh 7.3e-8 s (n=[acc-interp] thetaMarsh 6.8e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.6316927e-2 s grad=5.719e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=6.879971e-3 s grad=3.534e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.716184e-3 s grad=3.545e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.2238027e-2 s grad=3.287e-6 s +[acc-interp] eval 5.1828985e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=4.234274e-3 s grad=2.497e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.576916e-3 s grad=3.354e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.388769e-3 s grad=3.326e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.998179e-3 s grad=2.702e-6 s +[acc-interp] eval 6.7441392e-2 s +[acc-interp] thetaMarsh 7.8e-8 s (n=[acc-interp] thetaMarsh 6.2e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=8.77738e-3 s grad=5.828e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.581987e-3 s grad=4.843e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.906324e-3 s grad=4.323e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.267614e-3 s grad=4.619e-6 s +[acc-interp] eval 1.9790572e-2 s +4) +[acc-interp] chunk base=0 cs=25974 kernel=5.225022e-3 s grad=3.214e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.419014e-3 s grad=3.711e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.945794e-3 s grad=2.722e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.910378e-3 s grad=2.697e-6 s +[acc-interp] eval 3.4810457e-2 s +[acc-interp] thetaMarsh 6.6e-8 s (n=[acc-interp] thetaMarsh 6.7e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.8733894e-2 s grad=4.537e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.205253e-3 s grad=2.89e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.1278e-3 s grad=4.018e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.125318e-3 s grad=4.166e-6 s +[acc-interp] eval 4.0491681e-2 s +4) +[acc-interp] chunk base=0 cs=25974 kernel=4.165827e-3 s grad=4.865e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.337658e-3 s grad=2.603e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.0386791e-2 s grad=3.897e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.595921e-3 s grad=2.199e-6 s +[acc-interp] eval 6.2621529e-2 s +[acc-interp] thetaMarsh 1.22e-7 s (n=[acc-interp] thetaMarsh 1.08e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=2.3226495e-2 s grad=5.434e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.744587e-3 s grad=4.31e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.669547e-3 s grad=4.194e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=8.237738e-3 s grad=4.833e-6 s +[acc-interp] eval 4.0495527e-2 s +5) +[acc-interp] chunk base=0 cs=25974 kernel=2.871088e-3 s grad=3.828e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.637377e-3 s grad=3.236e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.890973e-3 s grad=1.1751e-5 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.05644e-3 s grad=2.178e-6 s +[acc-interp] eval 5.3184457e-2 s +[acc-interp] thetaMarsh 6.1e-8 s (n=[acc-interp] thetaMarsh 6.6e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.0553887e-2 s grad=6.348e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.691416e-3 s grad=2.961e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.57372e-3 s grad=1.8e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.319355e-3 s grad=3.15e-6 s +[acc-interp] eval 3.2026483e-2 s +4) +[acc-interp] chunk base=0 cs=25974 kernel=9.823967e-3 s grad=5.195e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.146529e-3 s grad=4.065e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.188319e-3 s grad=3.525e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.880762e-3 s grad=3.766e-6 s +[acc-interp] eval 5.3450923e-2 s +[acc-interp] thetaMarsh 1.82e-7 s (n=[acc-interp] thetaMarsh 1.06e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.1814125e-2 s grad=4.268e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.957394e-3 s grad=3.004e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.750176e-3 s grad=2.516e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.41233e-3 s grad=2.204e-6 s +[acc-interp] eval 4.4016339e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=1.1935876e-2 s grad=3.95e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.943483e-3 s grad=7.161e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.58604e-3 s grad=1.941e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.137892e-3 s grad=3.293e-6 s +[acc-interp] eval 6.8845604e-2 s +[acc-interp] thetaMarsh 1.44e-7 s (n=0) +[acc-interp] chunk base=0 cs=25974 kernel=2.464818e-2 s grad=1.633e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.674144e-3 s grad=1.687e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.28569e-3 s grad=1.34e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=7.500931e-3 s grad=1.527e-6 s +[acc-interp] eval 4.0530105e-2 s +[acc-interp] thetaMarsh 8.5e-8 s (n=[acc-interp] thetaMarsh 7.1e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=3.3129197e-2 s grad=3.516e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.894517e-3 s grad=2.91e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.089558e-3 s grad=4.819e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=9.932159e-3 s grad=4.86e-6 s +[acc-interp] eval 5.2167796e-2 s +5) +[acc-interp] chunk base=0 cs=25974 kernel=4.145071e-3 s grad=4.492e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.185905e-3 s grad=4.426e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.075581e-3 s grad=3.144e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.827386e-3 s grad=3.405e-6 s +[acc-interp] eval 6.9642642e-2 s +[acc-interp] thetaMarsh 6.2e-8 s (n=[acc-interp] thetaMarsh 6.4e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.4070802e-2 s grad=3.682e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.273617e-3 s grad=2.063e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.051622e-3 s grad=3.839e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.1159581e-2 s grad=4.033e-6 s +[acc-interp] eval 4.446345e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=3.128413e-3 s grad=7.753e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.742149e-3 s grad=8.351e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.325927e-3 s grad=4.14e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.199837e-3 s grad=9.225e-6 s +[acc-interp] eval 5.8923126e-2 s +[acc-interp] thetaMarsh 7.7e-8 s (n=[acc-interp] thetaMarsh 6.6e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.4138498e-2 s grad=4.115e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.177119e-3 s grad=3.174e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.684075e-3 s grad=5.001e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.364407e-2 s grad=3.821e-6 s +[acc-interp] eval 5.8645542e-2 s +4) +[acc-interp] chunk base=0 cs=25974 kernel=5.561659e-3 s grad=5.765e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.628165e-3 s grad=4.087e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.219774e-3 s grad=4.827e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.442903e-3 s grad=5.41e-6 s +[acc-interp] eval 7.8625184e-2 s +[acc-interp] thetaMarsh 1.25e-7 s (n=[acc-interp] thetaMarsh 6.1e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.048436e-2 s grad=2.84e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.150765e-3 s grad=4.443e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.076773e-3 s grad=3.64e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=6.163852e-3 s grad=5.118e-6 s +[acc-interp] eval 3.6845386e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=5.785564e-3 s grad=3.57e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.169158e-3 s grad=2.831e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=6.283037e-3 s grad=3.498e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.6196732e-2 s grad=3.19e-6 s +[acc-interp] eval 7.0840742e-2 s +[acc-interp] thetaMarsh 1.42e-7 s (n=[acc-interp] thetaMarsh 1.27e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.5529304e-2 s grad=4.235e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.322181e-3 s grad=6.889e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.849225e-3 s grad=3.053e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.048166e-3 s grad=2.489e-6 s +[acc-interp] eval 4.0229806e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=3.480578e-3 s grad=2.723e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.745451e-3 s grad=3.43e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.1831916e-2 s grad=2.655e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.622413e-3 s grad=3.553e-6 s +[acc-interp] eval 6.553179e-2 s +[acc-interp] thetaMarsh 7.3e-8 s (n=[acc-interp] thetaMarsh 7.5e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.1731595e-2 s grad=3.623e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.890433e-3 s grad=2.962e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.296713e-3 s grad=4.574e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=9.714136e-3 s grad=3.909e-6 s +[acc-interp] eval 4.1587297e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=3.713144e-3 s grad=4.428e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.648518e-3 s grad=4.757e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.380659e-3 s grad=4.099e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.722091e-3 s grad=8.395e-6 s +[acc-interp] eval 5.7188233e-2 s +[acc-interp] thetaMarsh 8.5e-8 s (n=[acc-interp] thetaMarsh 6.8e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=2.1208418e-2 s grad=3.597e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.674818e-3 s grad=3.043e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=9.318059e-3 s grad=5.404e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.139908e-3 s grad=4.454e-6 s +[acc-interp] eval 4.042348e-2 s +5) +[acc-interp] chunk base=0 cs=25974 kernel=3.574826e-3 s grad=4.006e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.206546e-3 s grad=7.762e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.373732e-3 s grad=4.019e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.019047e-3 s grad=7.342e-6 s +[acc-interp] eval 5.7642317e-2 s +[acc-interp] thetaMarsh 8.0e-8 s (n=[acc-interp] thetaMarsh 6.4e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.2307258e-2 s grad=4.906e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.356667e-3 s grad=5.024e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.82487e-3 s grad=3.725e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.806338e-3 s grad=3.598e-6 s +[acc-interp] eval 3.582137e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=3.44076e-3 s grad=2.909e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.2092107e-2 s grad=3.05e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.089042e-3 s grad=2.407e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.206274e-3 s grad=3.984e-6 s +[acc-interp] eval 5.947086e-2 s +[acc-interp] thetaMarsh 1.87e-7 s (n=[acc-interp] thetaMarsh 1.24e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.735042e-2 s grad=4.232e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.912058e-3 s grad=4.418e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.466821e-3 s grad=3.16e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.3109081e-2 s grad=3.698e-6 s +[acc-interp] eval 5.1551936e-2 s +4) +[acc-interp] chunk base=0 cs=25974 kernel=4.016935e-3 s grad=3.574e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.070275e-3 s grad=3.716e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.1315e-3 s grad=2.899e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.467707e-3 s grad=2.752e-6 s +[acc-interp] eval 7.1196122e-2 s +[acc-interp] thetaMarsh 9.1e-8 s (n=[acc-interp] thetaMarsh 6.4e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.0907124e-2 s grad=4.067e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.376739e-3 s grad=3.107e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.312436e-3 s grad=2.254e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.378529e-3 s grad=3.107e-6 s +[acc-interp] eval 1.8868598e-2 s +[acc-interp] thetaMarsh 1.14e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.480698e-3 s grad=4.591e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.577606e-3 s grad=3.98e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.544361e-3 s grad=3.705e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.529753e-3 s grad=2.516e-6 s +[acc-interp] eval 1.2022654e-2 s +[acc-interp] thetaMarsh 7.3e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.0092879e-2 s grad=3.096e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.373582e-3 s grad=2.857e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.262809e-3 s grad=2.088e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.207739e-3 s grad=2.116e-6 s +[acc-interp] eval 1.7761699e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=2.274397e-3 s grad=3.327e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.345058e-3 s grad=4.499e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.493432e-3 s grad=4.131e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.417814e-3 s grad=3.691e-6 s +[acc-interp] eval 6.0407706e-2 s +[acc-interp] thetaMarsh 7.2e-8 s (n=[acc-interp] thetaMarsh 6.0e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.021014e-2 s grad=2.802e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.442429e-3 s grad=3.246e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.600112e-3 s grad=2.92e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.314139e-3 s grad=2.242e-6 s +[acc-interp] eval 1.839008e-2 s +[acc-interp] thetaMarsh 6.6e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.405832e-3 s grad=2.169e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.433387e-3 s grad=2.839e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.052427e-3 s grad=3.872e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.777931e-3 s grad=4.385e-6 s +[acc-interp] eval 1.1539618e-2 s +[acc-interp] thetaMarsh 2.38e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.764294e-3 s grad=3.247e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.666454e-3 s grad=3.014e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.344849e-3 s grad=2.204e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.659319e-3 s grad=2.747e-6 s +[acc-interp] eval 1.1427903e-2 s +[acc-interp] thetaMarsh 9.1e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.223578e-3 s grad=3.267e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.484959e-3 s grad=2.821e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.272776e-3 s grad=3.318e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.70582e-3 s grad=2.726e-6 s +[acc-interp] eval 1.2895895e-2 s +[acc-interp] thetaMarsh 1.16e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=8.765614e-3 s grad=4.972e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.469902e-3 s grad=3.107e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.915029e-3 s grad=2.444e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.002177e-3 s grad=2.297e-6 s +[acc-interp] eval 1.6238198e-2 s +[acc-interp] thetaMarsh 6.2e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.450783e-3 s grad=4.222e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.607919e-3 s grad=3.773e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.248485e-3 s grad=3.241e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.227458e-3 s grad=2.579e-6 s +[acc-interp] eval 1.0745702e-2 s +[acc-interp] thetaMarsh 7.0e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.148165e-3 s grad=2.424e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.280413e-3 s grad=7.064e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.511022e-3 s grad=4.246e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.726348e-3 s grad=3.942e-6 s +[acc-interp] eval 1.0860862e-2 s +[acc-interp] thetaMarsh 7.7e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.421814e-3 s grad=2.585e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.337193e-3 s grad=3.738e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.082866e-3 s grad=2.684e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.034199e-3 s grad=2.857e-6 s +[acc-interp] eval 1.0049147e-2 s +[acc-interp] thetaMarsh 6.6e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.064015e-3 s grad=2.524e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.136698e-3 s grad=2.308e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.035115e-3 s grad=1.819e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.906583e-3 s grad=2.001e-6 s +[acc-interp] eval 9.768154e-3 s +[acc-interp] thetaMarsh 1.16e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.815752e-3 s grad=1.613e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.907921e-3 s grad=1.468e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.913029e-3 s grad=1.677e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.962782e-3 s grad=3.099e-6 s +[acc-interp] eval 9.219958e-3 s +[acc-interp] thetaMarsh 1.62e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.39718e-3 s grad=3.131e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.439044e-3 s grad=3.743e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.42293e-3 s grad=3.008e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.299209e-3 s grad=1.891e-6 s +[acc-interp] eval 1.0939058e-2 s +[acc-interp] thetaMarsh 7.4e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.224386e-3 s grad=7.995e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.916781e-3 s grad=3.345e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.734447e-3 s grad=5.026e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.210031e-3 s grad=3.017e-6 s +[acc-interp] eval 1.0393799e-2 s +[acc-interp] thetaMarsh 1.31e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.267693e-3 s grad=2.168e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.358858e-3 s grad=1.985e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.262193e-3 s grad=1.666e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.096866e-3 s grad=2.041e-6 s +[acc-interp] eval 1.047419e-2 s +[acc-interp] thetaMarsh 1.63e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.53576e-3 s grad=3.448e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.179804e-3 s grad=2.513e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.928613e-3 s grad=1.837e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.834499e-3 s grad=1.99e-6 s +[acc-interp] eval 1.0137631e-2 s +[acc-interp] thetaMarsh 9.8e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.166745e-3 s grad=2.666e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.091437e-3 s grad=3.176e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.401676e-3 s grad=4.374e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.3738e-3 s grad=5.287e-6 s +[acc-interp] eval 1.0386897e-2 s +[acc-interp] thetaMarsh 1.58e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.405556e-3 s grad=7.998e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.042986e-3 s grad=3.61e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.133739e-3 s grad=2.896e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.128066e-3 s grad=2.106e-6 s +[acc-interp] eval 1.0198548e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=2.223907e-3 s grad=2.773e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.014897e-3 s grad=4.399e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.228147e-3 s grad=2.71e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.359324e-3 s grad=2.487e-6 s +[acc-interp] eval 0.198013185 s +[acc-interp] thetaMarsh 8.4e-8 s (n=[acc-interp] thetaMarsh 6.7e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.1187996e-2 s grad=2.92e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.541827e-3 s grad=2.539e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.488701e-3 s grad=3.093e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.779192e-3 s grad=2.905e-6 s +[acc-interp] eval 1.9906195e-2 s +[acc-interp] thetaMarsh 7.8e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.438855e-3 s grad=3.134e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.811931e-3 s grad=3.516e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.892813e-3 s grad=3.923e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.792569e-3 s grad=4.402e-6 s +[acc-interp] eval 1.2814403e-2 s +[acc-interp] thetaMarsh 7.5e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.396501e-3 s grad=2.616e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.463575e-3 s grad=3.751e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.063098e-3 s grad=4.13e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.624805e-3 s grad=3.501e-6 s +[acc-interp] eval 1.1447602e-2 s +[acc-interp] thetaMarsh 2.14e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=7.441235e-3 s grad=4.765e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.751113e-3 s grad=4.309e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.053395e-3 s grad=4.172e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.96226e-3 s grad=1.302e-6 s +[acc-interp] eval 1.6196894e-2 s +[acc-interp] thetaMarsh 3.69e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.319537e-3 s grad=1.962e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.275847e-3 s grad=1.516e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.168295e-3 s grad=2.219e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.146227e-3 s grad=2.474e-6 s +[acc-interp] eval 9.850654e-3 s +[acc-interp] thetaMarsh 1.2e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.18045e-3 s grad=4.108e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.33022e-3 s grad=3.821e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.50665e-3 s grad=3.785e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.439288e-3 s grad=3.505e-6 s +[acc-interp] eval 1.1063619e-2 s +[acc-interp] thetaMarsh 6.9e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.220086e-3 s grad=2.203e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.102755e-3 s grad=2.826e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.373498e-3 s grad=4.247e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.444715e-3 s grad=4.888e-6 s +[acc-interp] eval 1.0067211e-2 s +[acc-interp] thetaMarsh 6.7e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=3.978512e-3 s grad=3.259e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.304976e-3 s grad=3.684e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.698663e-3 s grad=3.065e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.112323e-3 s grad=2.498e-6 s +[acc-interp] eval 1.0961263e-2 s +[acc-interp] thetaMarsh 7.9e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.28081e-3 s grad=3.677e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.681536e-3 s grad=3.986e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.373691e-3 s grad=3.321e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.231507e-3 s grad=3.561e-6 s +[acc-interp] eval 1.0534459e-2 s +[acc-interp] thetaMarsh 6.9e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.21979e-3 s grad=2.533e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.141839e-3 s grad=2.471e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.180014e-3 s grad=2.907e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.81541e-3 s grad=5.108e-6 s +[acc-interp] eval 1.0387403e-2 s +[acc-interp] thetaMarsh 1.66e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.694402e-3 s grad=6.754e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.617662e-3 s grad=6.599e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.337358e-3 s grad=7.926e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.475131e-3 s grad=7.226e-6 s +[acc-interp] eval 1.1003189e-2 s +[acc-interp] thetaMarsh 7.0e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.881428e-3 s grad=3.361e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.352673e-3 s grad=3.232e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.420973e-3 s grad=1.973e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.285877e-3 s grad=1.818e-6 s +[acc-interp] eval 1.134146e-2 s +[acc-interp] thetaMarsh 1.16e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.200503e-3 s grad=2.261e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.237496e-3 s grad=1.678e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.223322e-3 s grad=1.866e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.243739e-3 s grad=2.029e-6 s +[acc-interp] eval 1.0423344e-2 s +[acc-interp] thetaMarsh 7.8e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.184688e-3 s grad=1.992e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.182358e-3 s grad=7.213e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.973749e-3 s grad=2.182e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.987828e-3 s grad=2.162e-6 s +[acc-interp] eval 9.195966e-3 s +[acc-interp] thetaMarsh 6.9e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.01203e-3 s grad=2.515e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.424587e-3 s grad=3.642e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.265974e-3 s grad=3.359e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.268018e-3 s grad=3.532e-6 s +[acc-interp] eval 1.0121073e-2 s +[acc-interp] thetaMarsh 1.92e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.094492e-3 s grad=3.148e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.161282e-3 s grad=2.976e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.921816e-3 s grad=2.508e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.034947e-3 s grad=2.457e-6 s +[acc-interp] eval 9.002503e-3 s +[acc-interp] thetaMarsh 7.3e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.682217e-3 s grad=3.98e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.413385e-3 s grad=3.446e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.194316e-3 s grad=2.917e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.436737e-3 s grad=3.09e-6 s +[acc-interp] eval 1.0636522e-2 s +[acc-interp] thetaMarsh 2.29e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.653005e-3 s grad=4.004e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.787167e-3 s grad=2.578e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.034154e-3 s grad=4.599e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.817325e-3 s grad=3.567e-6 s +[acc-interp] eval 1.2270172e-2 s +[acc-interp] thetaMarsh 7.5e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.588146e-3 s grad=3.432e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.274364e-3 s grad=2.863e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.159926e-3 s grad=2.579e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.628213e-3 s grad=2.343e-6 s +[acc-interp] eval 9.496035e-3 s +[acc-interp] thetaMarsh 6.8e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.380631e-3 s grad=3.416e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.481832e-3 s grad=4.391e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.07043e-3 s grad=3.564e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.267242e-3 s grad=2.952e-6 s +[acc-interp] eval 1.0102815e-2 s +[acc-interp] thetaMarsh 2.26e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.203704e-3 s grad=3.056e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.12055e-3 s grad=2.452e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.088519e-3 s grad=1.961e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.56227e-3 s grad=4.713e-6 s +[acc-interp] eval 9.79608e-3 s +[acc-interp] thetaMarsh 8.0e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.823673e-3 s grad=3.877e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.630104e-3 s grad=4.886e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.580431e-3 s grad=4.649e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.721421e-3 s grad=2.922e-6 s +[acc-interp] eval 1.1816503e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=2.36039e-3 s grad=2.674e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.90134e-3 s grad=3.146e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.34523e-3 s grad=2.848e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.532922e-3 s grad=3.177e-6 s +[acc-interp] eval 0.2620146 s +[acc-interp] thetaMarsh 1.69e-7 s (n=[acc-interp] thetaMarsh 4.69e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=6.463178e-3 s grad=4.141e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.01459e-3 s grad=3.905e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.182388e-3 s grad=4.98e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.000542e-3 s grad=3.824e-6 s +[acc-interp] eval 1.7438207e-2 s +[acc-interp] thetaMarsh 1.17e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.669094e-3 s grad=2.441e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.359127e-3 s grad=2.119e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.481755e-3 s grad=1.3825e-5 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.061726e-3 s grad=4.765e-6 s +[acc-interp] eval 1.3272922e-2 s +[acc-interp] thetaMarsh 1.3e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.41945e-3 s grad=3.263e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.0803075e-2 s grad=2.48e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.296201e-3 s grad=3.17e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.160585e-3 s grad=2.546e-6 s +[acc-interp] eval 2.0291008e-2 s +[acc-interp] thetaMarsh 3.98e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.310934e-3 s grad=3.001e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.29013e-3 s grad=2.614e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.009657e-3 s grad=2.001e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.051321e-3 s grad=2.352e-6 s +[acc-interp] eval 1.0140032e-2 s +[acc-interp] thetaMarsh 9.9e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.534941e-3 s grad=4.194e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.202549e-3 s grad=4.494e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.483299e-3 s grad=9.361e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.391708e-3 s grad=6.302e-6 s +[acc-interp] eval 1.1759579e-2 s +[acc-interp] thetaMarsh 1.19e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.324431e-3 s grad=8.244e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.156632e-3 s grad=6.345e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.429577e-3 s grad=4.898e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.788543e-3 s grad=3.239e-6 s +[acc-interp] eval 1.0529886e-2 s +[acc-interp] thetaMarsh 1.73e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.460866e-3 s grad=7.713e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.201072e-3 s grad=3.91e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.942281e-3 s grad=2.973e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.189334e-3 s grad=2.199e-6 s +[acc-interp] eval 1.0633589e-2 s +[acc-interp] thetaMarsh 6.3e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.542882e-3 s grad=3.3e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.602828e-3 s grad=4.896e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.235648e-3 s grad=2.422e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.10757e-3 s grad=3.374e-6 s +[acc-interp] eval 1.0448907e-2 s +[acc-interp] thetaMarsh 7.6e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.052938e-3 s grad=2.686e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.973512e-3 s grad=3.112e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.005625e-3 s grad=2.427e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.825653e-3 s grad=4.813e-6 s +[acc-interp] eval 9.682928e-3 s +[acc-interp] thetaMarsh 6.9e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.493906e-3 s grad=2.803e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.500832e-3 s grad=2.0e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.411414e-3 s grad=1.833e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.137764e-3 s grad=3.146e-6 s +[acc-interp] eval 1.0301548e-2 s +[acc-interp] thetaMarsh 2.53e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.197287e-3 s grad=3.027e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.749624e-3 s grad=5.783e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.992237e-3 s grad=5.125e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.061132e-3 s grad=3.523e-6 s +[acc-interp] eval 1.1930549e-2 s +[acc-interp] thetaMarsh 2.26e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.273883e-3 s grad=2.485e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.969447e-3 s grad=3.38e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.768692e-3 s grad=2.644e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.577594e-3 s grad=2.216e-6 s +[acc-interp] eval 8.339492e-3 s +[acc-interp] thetaMarsh 1.74e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.613342e-3 s grad=2.598e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.617708e-3 s grad=2.937e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.636984e-3 s grad=2.841e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.963029e-3 s grad=2.179e-6 s +[acc-interp] eval 7.568266e-3 s +[acc-interp] thetaMarsh 6.7e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.927295e-3 s grad=2.229e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.010848e-3 s grad=2.313e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.895068e-3 s grad=2.545e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.659248e-3 s grad=2.039e-6 s +[acc-interp] eval 8.278632e-3 s +[acc-interp] thetaMarsh 8.5e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.636355e-3 s grad=2.145e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.693555e-3 s grad=4.282e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.501435e-3 s grad=5.298e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.112801e-3 s grad=3.814e-6 s +[acc-interp] eval 8.837517e-3 s +[acc-interp] thetaMarsh 8.4e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.200656e-3 s grad=8.944e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.792404e-3 s grad=3.599e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.408601e-3 s grad=8.726e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.135182e-3 s grad=3.528e-6 s +[acc-interp] eval 9.29296e-3 s +[acc-interp] thetaMarsh 6.3e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.535177e-3 s grad=4.459e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.289081e-3 s grad=4.026e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.970867e-3 s grad=2.863e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.813921e-3 s grad=3.464e-6 s +[acc-interp] eval 9.49944e-3 s +[acc-interp] thetaMarsh 1.0e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.015056e-3 s grad=2.746e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.885168e-3 s grad=2.723e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.589712e-3 s grad=2.083e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.760906e-3 s grad=2.165e-6 s +[acc-interp] eval 8.019967e-3 s +[acc-interp] thetaMarsh 6.6e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.27689e-3 s grad=4.018e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.322599e-3 s grad=4.245e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.647608e-3 s grad=3.22e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.923896e-3 s grad=2.739e-6 s +[acc-interp] eval 1.1052343e-2 s +[acc-interp] thetaMarsh 6.0e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.950392e-3 s grad=2.406e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.890851e-3 s grad=2.263e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.876597e-3 s grad=4.611e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.360522e-3 s grad=4.248e-6 s +[acc-interp] eval 9.377041e-3 s +[acc-interp] thetaMarsh 1.06e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.435369e-3 s grad=2.99e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.656078e-3 s grad=3.801e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.812911e-3 s grad=2.249e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.722818e-3 s grad=2.069e-6 s +[acc-interp] eval 1.078865e-2 s +[acc-interp] thetaMarsh 6.7e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.935587e-3 s grad=2.836e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.505848e-3 s grad=4.648e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.49264e-3 s grad=4.894e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.055804e-3 s grad=3.762e-6 s +[acc-interp] eval 1.023295e-2 s +[acc-interp] thetaMarsh 2.09e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.097387e-3 s grad=3.041e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.428489e-3 s grad=2.801e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.233395e-3 s grad=3.054e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.213203e-3 s grad=2.285e-6 s +[acc-interp] eval 9.845475e-3 s +[acc-interp] thetaMarsh 1.82e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.810959e-3 s grad=2.938e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.581004e-3 s grad=2.553e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.577551e-3 s grad=2.943e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.156012e-3 s grad=2.658e-6 s +[acc-interp] eval 1.1997308e-2 s +[acc-interp] thetaMarsh 6.6e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.161128e-3 s grad=2.351e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.225758e-3 s grad=4.405e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.165642e-3 s grad=3.482e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.465349e-3 s grad=6.624e-6 s +[acc-interp] eval 1.1257625e-2 s +[acc-interp] thetaMarsh 7.7e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.547201e-3 s grad=8.26e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.611962e-3 s grad=1.95e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.325234e-3 s grad=5.886e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.497915e-3 s grad=4.405e-6 s +[acc-interp] eval 1.1040542e-2 s +[acc-interp] thetaMarsh 1.64e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.629431e-3 s grad=3.242e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.252254e-3 s grad=2.807e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.403806e-3 s grad=2.182e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.668673e-3 s grad=2.949e-6 s +[acc-interp] eval 1.1561236e-2 s +[acc-interp] thetaMarsh 1.32e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.413493e-3 s grad=2.215e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.298439e-3 s grad=2.073e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.256194e-3 s grad=2.006e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.198649e-3 s grad=2.128e-6 s +[acc-interp] eval 1.0730776e-2 s +[acc-interp] thetaMarsh 1.46e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.147142e-3 s grad=2.335e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.242001e-3 s grad=7.13e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.228291e-3 s grad=2.521e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.281885e-3 s grad=3.313e-6 s +[acc-interp] eval 1.0039788e-2 s +[acc-interp] thetaMarsh 7.4e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.065117e-3 s grad=3.865e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.338503e-3 s grad=3.342e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.193584e-3 s grad=3.177e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.134102e-3 s grad=2.448e-6 s +[acc-interp] eval 1.0859777e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=2.09169e-3 s grad=1.862e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.249309e-3 s grad=2.931e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.717986e-3 s grad=3.479e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.354108e-3 s grad=1.645e-6 s +[acc-interp] eval 0.338540459 s +[acc-interp] thetaMarsh 6.9e-8 s (n=[acc-interp] thetaMarsh 6.3e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.8458051e-2 s grad=4.921e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=9.651593e-3 s grad=4.055e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.553786e-3 s grad=4.369e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.115939e-3 s grad=4.784e-6 s +[acc-interp] eval 4.8811932e-2 s +4) +[acc-interp] chunk base=0 cs=25974 kernel=4.336724e-3 s grad=3.738e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.173595e-3 s grad=4.04e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.520311e-3 s grad=2.404e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.884214e-3 s grad=3.392e-6 s +[acc-interp] eval 6.7273547e-2 s +[acc-interp] thetaMarsh 1.28e-7 s (n=[acc-interp] thetaMarsh 1.21e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=5.963414e-3 s grad=5.159e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.861564e-3 s grad=5.051e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.995553e-3 s grad=2.841e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.528483e-3 s grad=2.425e-6 s +[acc-interp] eval 1.7297754e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=2.429306e-3 s grad=2.687e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.136466e-3 s grad=5.137e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.546503e-3 s grad=3.159e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.771331e-3 s grad=3.447e-6 s +[acc-interp] eval 3.2191276e-2 s +[acc-interp] thetaMarsh 1.67e-7 s (n=[acc-interp] thetaMarsh 1.35e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.4476934e-2 s grad=3.977e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.647145e-3 s grad=3.735e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=6.020933e-3 s grad=4.92e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=8.654335e-3 s grad=4.359e-6 s +[acc-interp] eval 5.4840371e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=5.715351e-3 s grad=4.365e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.234211e-3 s grad=6.368e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.512707e-3 s grad=2.272e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.435918e-3 s grad=3.569e-6 s +[acc-interp] eval 7.2780204e-2 s +[acc-interp] thetaMarsh 7.4e-8 s (n=[acc-interp] thetaMarsh 7.6e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=6.847339e-3 s grad=3.797e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.715743e-3 s grad=3.599e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.080219e-3 s grad=4.707e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.754582e-3 s grad=5.803e-6 s +[acc-interp] eval 1.7823059e-2 s +5) +[acc-interp] chunk base=0 cs=25974 kernel=3.832095e-3 s grad=3.564e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.227875e-3 s grad=3.196e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=9.940821e-3 s grad=3.717e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.778683e-3 s grad=2.717e-6 s +[acc-interp] eval 3.9319788e-2 s +[acc-interp] thetaMarsh 8.5e-8 s (n=[acc-interp] thetaMarsh 8.4e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.4273862e-2 s grad=3.871e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.980441e-3 s grad=4.562e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.691418e-3 s grad=4.079e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.719674e-3 s grad=3.189e-6 s +[acc-interp] eval 2.4587324e-2 s +[acc-interp] thetaMarsh 8.3e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.302182e-3 s grad=4.636e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.414796e-3 s grad=4.295e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.118969e-3 s grad=9.248e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.060423e-3 s grad=7.454e-6 s +[acc-interp] eval 1.3793577e-2 s +[acc-interp] thetaMarsh 8.1e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.612262e-3 s grad=5.294e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.872395e-3 s grad=3.618e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.5646542e-2 s grad=3.628e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.239277e-3 s grad=9.607e-6 s +[acc-interp] eval 2.7380046e-2 s +[acc-interp] thetaMarsh 6.7e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.745198e-3 s grad=8.997e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.70058e-3 s grad=6.881e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.21055e-3 s grad=4.561e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.929517e-3 s grad=3.975e-6 s +[acc-interp] eval 1.2404078e-2 s +[acc-interp] thetaMarsh 2.05e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.726574e-3 s grad=1.715e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.046973e-3 s grad=7.911e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.742856e-3 s grad=6.276e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.379401e-3 s grad=1.78e-6 s +[acc-interp] eval 1.1646627e-2 s +[acc-interp] thetaMarsh 9.0e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.569548e-3 s grad=8.001e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.461882e-3 s grad=6.878e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.478302e-3 s grad=2.093e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.453152e-3 s grad=1.456e-6 s +[acc-interp] eval 1.0732872e-2 s +[acc-interp] thetaMarsh 1.63e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.343649e-3 s grad=8.952e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.330153e-3 s grad=7.778e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.46182e-3 s grad=9.55e-7 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.262327e-3 s grad=1.453e-6 s +[acc-interp] eval 1.0139387e-2 s +[acc-interp] thetaMarsh 6.7e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.099708e-3 s grad=5.263e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.852461e-3 s grad=9.464e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.347737e-3 s grad=1.624e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.421574e-3 s grad=2.833e-6 s +[acc-interp] eval 1.1691833e-2 s +[acc-interp] thetaMarsh 7.6e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.558586e-3 s grad=1.387e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.579656e-3 s grad=4.322e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.738965e-3 s grad=3.397e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.797596e-3 s grad=2.434e-6 s +[acc-interp] eval 1.3048757e-2 s +[acc-interp] thetaMarsh 1.7e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.495693e-3 s grad=2.133e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.116861e-3 s grad=1.974e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.385314e-3 s grad=1.61e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.849999e-3 s grad=5.799e-6 s +[acc-interp] eval 1.1409058e-2 s +[acc-interp] thetaMarsh 1.23e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.525583e-3 s grad=3.39e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.71336e-3 s grad=2.565e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.499944e-3 s grad=3.173e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.156384e-3 s grad=1.983e-6 s +[acc-interp] eval 1.1465217e-2 s +[acc-interp] thetaMarsh 1.29e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.790268e-3 s grad=2.608e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.48786e-3 s grad=2.968e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.19372e-3 s grad=1.871e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.466667e-3 s grad=2.514e-6 s +[acc-interp] eval 1.1489021e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=2.235054e-3 s grad=1.848e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.160087e-3 s grad=1.472e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.368456e-3 s grad=3.113e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.863281e-3 s grad=4.981e-6 s +[acc-interp] eval 0.182213461 s +[acc-interp] thetaMarsh 8.1e-8 s (n=[acc-interp] thetaMarsh 6.3e-8 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.7627747e-2 s grad=4.538e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=5.896706e-3 s grad=4.796e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.030674e-3 s grad=4.764e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.384041e-3 s grad=5.621e-6 s +[acc-interp] eval 5.3885381e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=4.550139e-3 s grad=4.647e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.029626e-3 s grad=4.768e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.847925e-3 s grad=3.653e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.384631e-3 s grad=3.555e-6 s +[acc-interp] eval 7.1824689e-2 s +[acc-interp] thetaMarsh 9.2e-8 s (n=[acc-interp] thetaMarsh 6.5e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.6575945e-2 s grad=3.448e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.865301e-3 s grad=4.392e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.052214e-3 s grad=5.255e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.149634e-3 s grad=4.206e-6 s +[acc-interp] eval 2.8073899e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=8.887132e-3 s grad=3.278e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.111942e-3 s grad=2.669e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.07114e-3 s grad=5.068e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.192383e-3 s grad=2.97e-6 s +[acc-interp] eval 4.7303719e-2 s +[acc-interp] thetaMarsh 1.32e-7 s (n=[acc-interp] thetaMarsh 1.14e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=6.226841e-3 s grad=5.528e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.27501e-3 s grad=5.17e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.9159e-3 s grad=3.442e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.61353e-3 s grad=2.617e-6 s +[acc-interp] eval 1.6402921e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=2.945427e-3 s grad=2.737e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.369464e-3 s grad=2.994e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.552342e-3 s grad=3.664e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.004774e-3 s grad=3.555e-6 s +[acc-interp] eval 3.0022326e-2 s +[acc-interp] thetaMarsh 1.16e-7 s (n=[acc-interp] thetaMarsh 1.32e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.1741569e-2 s grad=3.07e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.203603e-3 s grad=2.458e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.13865e-3 s grad=1.203e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.161867e-3 s grad=7.165e-6 s +[acc-interp] eval 1.9217956e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=2.369035e-3 s grad=8.463e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.29335e-3 s grad=1.0247e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.639066e-3 s grad=4.209e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.801047e-3 s grad=4.971e-6 s +[acc-interp] eval 3.042447e-2 s +[acc-interp] thetaMarsh 8.0e-8 s (n=[acc-interp] thetaMarsh 7.4e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=1.6006258e-2 s grad=5.548e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.961462e-3 s grad=4.682e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.634339e-3 s grad=8.167e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.757886e-3 s grad=4.022e-6 s +[acc-interp] eval 2.9341794e-2 s +5) +[acc-interp] chunk base=0 cs=25974 kernel=4.901295e-3 s grad=4.649e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.0995664e-2 s grad=5.627e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.736725e-3 s grad=5.795e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.998412e-3 s grad=5.173e-6 s +[acc-interp] eval 5.4307381e-2 s +[acc-interp] thetaMarsh 7.8e-8 s (n=[acc-interp] thetaMarsh 7.5e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.0511002e-2 s grad=3.684e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.872147e-3 s grad=4.699e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.208803e-3 s grad=5.042e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.668862e-3 s grad=2.247e-6 s +[acc-interp] eval 3.3118931e-2 s +4) +[acc-interp] chunk base=0 cs=25974 kernel=3.549725e-3 s grad=3.651e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.1461606e-2 s grad=4.549e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.31926e-3 s grad=2.442e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.524804e-3 s grad=3.538e-6 s +[acc-interp] eval 5.6511554e-2 s +[acc-interp] thetaMarsh 8.5e-8 s (n=[acc-interp] thetaMarsh 6.4e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=1.7774735e-2 s grad=4.379e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.345995e-3 s grad=3.951e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.173524e-3 s grad=2.83e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.967239e-3 s grad=2.787e-6 s +[acc-interp] eval 2.8550623e-2 s +[acc-interp] thetaMarsh 9.8e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=1.1658469e-2 s grad=5.118e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.165749e-3 s grad=1.111e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.82624e-3 s grad=3.467e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.953071e-3 s grad=4.027e-6 s +[acc-interp] eval 2.4538907e-2 s +[acc-interp] thetaMarsh 6.7e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.49995e-3 s grad=2.978e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.28486e-3 s grad=4.395e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.327494e-3 s grad=3.952e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.148456e-3 s grad=5.934e-6 s +[acc-interp] eval 1.587017e-2 s +[acc-interp] thetaMarsh 1.31e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.438502e-3 s grad=4.431e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.37868e-3 s grad=3.938e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.220604e-3 s grad=4.559e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.339578e-3 s grad=4.881e-6 s +[acc-interp] eval 1.6084656e-2 s +[acc-interp] thetaMarsh 1.48e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.902508e-3 s grad=2.776e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.684844e-3 s grad=2.079e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.668848e-3 s grad=2.078e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.59279e-3 s grad=4.721e-6 s +[acc-interp] eval 1.6385002e-2 s +[acc-interp] thetaMarsh 1.05e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.061738e-3 s grad=3.378e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.230181e-3 s grad=3.462e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.155239e-3 s grad=2.794e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.07723e-3 s grad=2.844e-6 s +[acc-interp] eval 1.4013897e-2 s +[acc-interp] thetaMarsh 1.34e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.059377e-3 s grad=2.8e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.156463e-3 s grad=3.473e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.178137e-3 s grad=1.653e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.489269e-3 s grad=9.922e-6 s +[acc-interp] eval 1.3996787e-2 s +[acc-interp] thetaMarsh 6.9e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.997355e-3 s grad=2.342e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.374363e-3 s grad=4.275e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.61039e-3 s grad=6.726e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.440228e-3 s grad=8.093e-6 s +[acc-interp] eval 1.4307459e-2 s +[acc-interp] thetaMarsh 1.19e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.331763e-3 s grad=9.468e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.619413e-3 s grad=3.982e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.399699e-3 s grad=3.128e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.283963e-3 s grad=1.892e-6 s +[acc-interp] eval 1.5488169e-2 s +[acc-interp] thetaMarsh 1.82e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.20586e-3 s grad=4.505e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.133542e-3 s grad=2.858e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.306405e-3 s grad=3.614e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.380326e-3 s grad=3.446e-6 s +[acc-interp] eval 1.5390134e-2 s +[acc-interp] thetaMarsh 1.12e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=3.153029e-3 s grad=3.08e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.42685e-3 s grad=3.076e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.181476e-2 s grad=3.952e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.074887e-3 s grad=3.071e-6 s +[acc-interp] eval 2.2628829e-2 s +[acc-interp] thetaMarsh 1.76e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.998257e-3 s grad=3.181e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.217834e-3 s grad=5.198e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.463104e-3 s grad=6.086e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.624576e-3 s grad=5.105e-6 s +[acc-interp] eval 1.4281282e-2 s +4) +[acc-interp] chunk base=0 cs=25974 kernel=3.436341e-3 s grad=3.504e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.219792e-3 s grad=3.098e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.972592e-3 s grad=3.359e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.30496e-3 s grad=3.776e-6 s +[acc-interp] eval 0.228218623 s +[acc-interp] thetaMarsh 1.34e-7 s (n=[acc-interp] thetaMarsh 1.12e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.1945335e-2 s grad=4.646e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=8.627946e-3 s grad=4.695e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.314011e-3 s grad=3.315e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.025149e-3 s grad=4.717e-6 s +[acc-interp] eval 3.8629636e-2 s +4) +[acc-interp] chunk base=0 cs=25974 kernel=2.976772e-3 s grad=2.643e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.895663e-3 s grad=2.676e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.743183e-3 s grad=2.115e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.731206e-3 s grad=1.86e-6 s +[acc-interp] eval 5.1631821e-2 s +[acc-interp] thetaMarsh 1.29e-7 s (n=[acc-interp] thetaMarsh 1.25e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=2.4879047e-2 s grad=5.588e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.005894e-3 s grad=4.868e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.710287e-3 s grad=1.1767e-5 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.509048e-3 s grad=2.112e-6 s +[acc-interp] eval 3.7054316e-2 s +5) +[acc-interp] chunk base=0 cs=25974 kernel=1.3617156e-2 s grad=1.6335e-5 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.88841e-3 s grad=5.176e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.941548e-3 s grad=7.088e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.534692e-3 s grad=8.478e-6 s +[acc-interp] eval 6.3144001e-2 s +[acc-interp] thetaMarsh 7.6e-8 s (n=[acc-interp] thetaMarsh 7.1e-8 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=2.5156295e-2 s grad=3.481e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.055115e-3 s grad=1.1125e-5 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.500608e-3 s grad=3.004e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.08262e-3 s grad=3.954e-6 s +[acc-interp] eval 3.8657826e-2 s +5) +[acc-interp] chunk base=0 cs=25974 kernel=1.4461537e-2 s grad=3.773e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.396974e-3 s grad=3.953e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.458945e-3 s grad=3.207e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.540164e-3 s grad=2.738e-6 s +[acc-interp] eval 6.755893e-2 s +[acc-interp] thetaMarsh 6.4e-8 s (n=[acc-interp] thetaMarsh 5.8e-8 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.7988168e-2 s grad=4.047e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=8.251712e-3 s grad=3.566e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=5.567837e-3 s grad=5.54e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=9.033846e-3 s grad=5.358e-6 s +[acc-interp] eval 5.2665977e-2 s +4) +[acc-interp] chunk base=0 cs=25974 kernel=4.44953e-3 s grad=5.117e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.996704e-3 s grad=3.466e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.825899e-3 s grad=3.917e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=5.175964e-3 s grad=3.639e-6 s +[acc-interp] eval 7.2051993e-2 s +[acc-interp] thetaMarsh 1.58e-7 s (n=[acc-interp] thetaMarsh 1.15e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=3.5632165e-2 s grad=3.931e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.58152e-3 s grad=4.314e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.893644e-3 s grad=3.243e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.42979e-3 s grad=2.271e-6 s +[acc-interp] eval 4.8129873e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=4.67621e-3 s grad=3.918e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.308831e-3 s grad=4.707e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.964072e-3 s grad=3.753e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.351272e-3 s grad=4.023e-6 s +[acc-interp] eval 6.746696e-2 s +[acc-interp] thetaMarsh 1.7e-7 s (n=[acc-interp] thetaMarsh 1.15e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=9.278287e-3 s grad=4.813e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.47251e-3 s grad=3.99e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.645176e-3 s grad=4.232e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.151409e-3 s grad=3.878e-6 s +[acc-interp] eval 2.2250744e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=3.468285e-3 s grad=3.325e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.2507442e-2 s grad=3.055e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.829463e-3 s grad=2.998e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.875648e-3 s grad=2.612e-6 s +[acc-interp] eval 4.593575e-2 s +[acc-interp] thetaMarsh 1.61e-7 s (n=[acc-interp] thetaMarsh 1.19e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=1.3358401e-2 s grad=4.118e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.596192e-3 s grad=3.322e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.305742e-3 s grad=3.198e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.962411e-3 s grad=3.223e-6 s +[acc-interp] eval 2.4789819e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=3.221196e-3 s grad=2.663e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.190487e-3 s grad=2.217e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.971879e-3 s grad=3.629e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.2454601e-2 s grad=4.388e-6 s +[acc-interp] eval 4.9535084e-2 s +[acc-interp] thetaMarsh 1.71e-7 s (n=[acc-interp] thetaMarsh 1.14e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=2.5134339e-2 s grad=4.509e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.834697e-3 s grad=3.596e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.936581e-3 s grad=4.199e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.0019296e-2 s grad=3.808e-6 s +[acc-interp] eval 4.5794463e-2 s +4) +[acc-interp] chunk base=0 cs=25974 kernel=3.815042e-3 s grad=4.647e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.299057e-3 s grad=3.554e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.866867e-3 s grad=4.031e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.848968e-3 s grad=3.616e-6 s +[acc-interp] eval 6.3615757e-2 s +[acc-interp] thetaMarsh 1.67e-7 s (n=[acc-interp] thetaMarsh 1.11e-7 s (n=3) +[acc-interp] chunk base=0 cs=25974 kernel=2.2589617e-2 s grad=4.963e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.598658e-3 s grad=5.15e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.309676e-3 s grad=4.347e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.204847e-3 s grad=3.054e-6 s +[acc-interp] eval 3.646472e-2 s +3) +[acc-interp] chunk base=0 cs=25974 kernel=3.443907e-3 s grad=2.346e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.261488e-3 s grad=1.574e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=9.06136e-3 s grad=2.302e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.819282e-3 s grad=2.098e-6 s +[acc-interp] eval 5.6765133e-2 s +[acc-interp] thetaMarsh 1.37e-7 s (n=[acc-interp] thetaMarsh 1.65e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=4.871987e-3 s grad=3.148e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.220481e-3 s grad=2.591e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.959114e-3 s grad=4.123e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.681126e-3 s grad=3.464e-6 s +[acc-interp] eval 1.5102416e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=1.858743e-3 s grad=2.746e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.536869e-3 s grad=2.565e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.089842e-3 s grad=2.312e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.192978e-3 s grad=3.347e-6 s +[acc-interp] eval 2.532464e-2 s +[acc-interp] thetaMarsh 1.4e-7 s (n=[acc-interp] thetaMarsh 1.54e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=6.167313e-3 s grad=3.887e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.26485e-3 s grad=2.241e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.355256e-3 s grad=1.829e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.41544e-3 s grad=2.853e-6 s +[acc-interp] eval 1.4862062e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=2.850075e-3 s grad=3.082e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.56593e-3 s grad=2.703e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.1548265e-2 s grad=3.811e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.486457e-3 s grad=3.174e-6 s +[acc-interp] eval 3.617866e-2 s +[acc-interp] thetaMarsh 1.37e-7 s (n=[acc-interp] thetaMarsh 1.22e-7 s (n=5) +[acc-interp] chunk base=0 cs=25974 kernel=2.8331618e-2 s grad=4.193e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=8.908026e-3 s grad=3.992e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.755041e-3 s grad=4.983e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.504775e-3 s grad=5.191e-6 s +[acc-interp] eval 4.5805989e-2 s +5) +[acc-interp] chunk base=0 cs=25974 kernel=4.680197e-3 s grad=4.021e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.116335e-3 s grad=4.186e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.819951e-3 s grad=3.097e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.909923e-3 s grad=4.899e-6 s +[acc-interp] eval 6.5262089e-2 s +[acc-interp] thetaMarsh 1.34e-7 s (n=[acc-interp] thetaMarsh 1.28e-7 s (n=4) +[acc-interp] chunk base=0 cs=25974 kernel=1.8239675e-2 s grad=3.494e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.874829e-3 s grad=3.155e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.720798e-3 s grad=2.495e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=9.504856e-3 s grad=4.067e-6 s +[acc-interp] eval 3.7074098e-2 s +4) +[acc-interp] chunk base=0 cs=25974 kernel=3.423099e-3 s grad=2.962e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.210417e-3 s grad=2.142e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.020994e-3 s grad=4.779e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.74924e-3 s grad=4.3e-6 s +[acc-interp] eval 5.3290256e-2 s +[acc-interp] thetaMarsh 1.32e-7 s (n=[acc-interp] thetaMarsh 1.11e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=8.434432e-3 s grad=3.922e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.118716e-3 s grad=4.709e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.915032e-3 s grad=5.287e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.486931e-3 s grad=3.323e-6 s +[acc-interp] eval 1.8168854e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=2.605803e-3 s grad=3.421e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.514042e-3 s grad=3.139e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.01366e-3 s grad=5.122e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.066293e-3 s grad=6.271e-6 s +[acc-interp] eval 3.0500625e-2 s +[acc-interp] thetaMarsh 7.7e-8 s (n=[acc-interp] thetaMarsh 7.3e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=9.349202e-3 s grad=4.586e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.162104e-3 s grad=4.816e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.79166e-3 s grad=4.202e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.742288e-3 s grad=4.286e-6 s +[acc-interp] eval 1.956612e-2 s +2) +[acc-interp] chunk base=0 cs=25974 kernel=2.765953e-3 s grad=4.166e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.806618e-3 s grad=3.042e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.954969e-3 s grad=2.045e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=9.873368e-3 s grad=2.978e-6 s +[acc-interp] eval 3.962019e-2 s +[acc-interp] thetaMarsh 1.14e-7 s (n=[acc-interp] thetaMarsh 1.03e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=7.436453e-3 s grad=2.764e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.555463e-3 s grad=3.527e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=3.241581e-3 s grad=4.015e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.877002e-3 s grad=2.262e-6 s +[acc-interp] eval 1.7687248e-2 s +[acc-interp] thetaMarsh 1.05e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=4.308984e-3 s grad=3.145e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=4.348238e-3 s grad=4.235e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=4.292481e-3 s grad=3.001e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=4.233258e-3 s grad=1.997e-6 s +[acc-interp] eval 1.8757422e-2 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=4.645085e-3 s grad=2.307e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.73926e-3 s grad=4.353e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.598853e-3 s grad=2.32e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.392841e-3 s grad=2.606e-6 s +[acc-interp] eval 5.0853643e-2 s +[acc-interp] thetaMarsh 1.37e-7 s (n=[acc-interp] thetaMarsh 1.26e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=4.036499e-3 s grad=2.926e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.933253e-3 s grad=1.945e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.858044e-3 s grad=3.763e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.756112e-3 s grad=4.688e-6 s +[acc-interp] eval 1.2389255e-2 s +[acc-interp] thetaMarsh 1.02e-7 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=1.68027e-3 s grad=2.674e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=9.12742e-3 s grad=4.446e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.335066e-3 s grad=2.624e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.619168e-3 s grad=2.776e-6 s +[acc-interp] eval 1.6248421e-2 s +[acc-interp] thetaMarsh 6.7e-8 s (n=1) +[acc-interp] chunk base=0 cs=25974 kernel=2.200844e-3 s grad=2.862e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.760763e-3 s grad=2.838e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.006952e-3 s grad=2.848e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.635029e-3 s grad=2.886e-6 s +[acc-interp] eval 8.433157e-3 s +1) +[acc-interp] chunk base=0 cs=25974 kernel=1.865555e-3 s grad=2.531e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.905008e-3 s grad=1.498e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.43234e-3 s grad=1.639e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.838597e-3 s grad=1.843e-6 s +[acc-interp] eval 4.5336987e-2 s +[acc-interp] thetaMarsh 7.2e-8 s (n=[acc-interp] thetaMarsh 5.8e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=9.820848e-3 s grad=3.646e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.17203e-3 s grad=3.368e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.246148e-3 s grad=2.78e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.932487e-3 s grad=5.074e-6 s +[acc-interp] eval 1.826381e-2 s +[acc-interp] thetaMarsh 1.37e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.543741e-3 s grad=3.052e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.368783e-3 s grad=2.245e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.29915e-3 s grad=2.182e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.316305e-3 s grad=1.961e-6 s +[acc-interp] eval 1.1118454e-2 s +[acc-interp] thetaMarsh 1.21e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=3.127502e-3 s grad=3.033e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.581434e-3 s grad=2.7e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.145665e-3 s grad=1.517e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=7.993737e-3 s grad=2.017e-6 s +[acc-interp] eval 1.7372419e-2 s +[acc-interp] thetaMarsh 9.9e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.893985e-3 s grad=3.507e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.629809e-3 s grad=4.072e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.998109e-3 s grad=2.77e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.993217e-3 s grad=3.432e-6 s +[acc-interp] eval 9.111985e-3 s +[acc-interp] thetaMarsh 1.15e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.967665e-3 s grad=2.638e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=3.959536e-3 s grad=2.173e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.952444e-3 s grad=3.895e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.509735e-3 s grad=4.648e-6 s +[acc-interp] eval 1.2201903e-2 s +[acc-interp] thetaMarsh 1.61e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.878093e-3 s grad=2.347e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.00504e-3 s grad=2.675e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.949786e-3 s grad=2.003e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.80126e-3 s grad=1.495e-6 s +[acc-interp] eval 9.085075e-3 s +[acc-interp] thetaMarsh 1.15e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.095023e-3 s grad=1.753e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.215953e-3 s grad=4.076e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.2643e-3 s grad=3.963e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.342774e-3 s grad=3.596e-6 s +[acc-interp] eval 1.0606037e-2 s +[acc-interp] thetaMarsh 1.04e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.332259e-3 s grad=2.688e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.809391e-3 s grad=2.969e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.609154e-3 s grad=8.238e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.469588e-3 s grad=1.932e-6 s +[acc-interp] eval 1.1341314e-2 s +[acc-interp] thetaMarsh 1.22e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.717183e-3 s grad=4.3e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.238921e-3 s grad=2.185e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.283207e-3 s grad=7.653e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.160084e-3 s grad=1.808e-6 s +[acc-interp] eval 1.0325949e-2 s +[acc-interp] thetaMarsh 5.5e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.145392e-3 s grad=3.536e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.236537e-3 s grad=3.086e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.395027e-3 s grad=4.08e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.972617e-3 s grad=3.777e-6 s +[acc-interp] eval 1.0309333e-2 s +[acc-interp] thetaMarsh 1.03e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.281638e-3 s grad=2.951e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.168009e-3 s grad=2.081e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.054275e-3 s grad=3.027e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.08099e-3 s grad=7.724e-6 s +[acc-interp] eval 9.918437e-3 s +[acc-interp] thetaMarsh 6.7e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.430213e-3 s grad=4.072e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.581347e-3 s grad=2.73e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.221961e-3 s grad=2.789e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.197777e-3 s grad=3.473e-6 s +[acc-interp] eval 1.1061558e-2 s +[acc-interp] thetaMarsh 1.11e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.269619e-3 s grad=3.858e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.14276e-3 s grad=7.443e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.165239e-3 s grad=2.863e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.169561e-3 s grad=7.865e-6 s +[acc-interp] eval 9.726142e-3 s +[acc-interp] thetaMarsh 6.9e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.718146e-3 s grad=8.588e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.975716e-3 s grad=6.686e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.851757e-3 s grad=8.36e-7 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.928437e-3 s grad=5.808e-6 s +[acc-interp] eval 8.257808e-3 s +[acc-interp] thetaMarsh 1.6e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=1.899293e-3 s grad=1.862e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.026715e-3 s grad=6.6e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.243162e-3 s grad=3.706e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.670118e-3 s grad=3.691e-6 s +[acc-interp] eval 9.781593e-3 s +[acc-interp] thetaMarsh 7.5e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.316318e-3 s grad=9.432e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.237153e-3 s grad=7.123e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.323241e-3 s grad=5.912e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.145301e-3 s grad=2.487e-6 s +[acc-interp] eval 1.0044562e-2 s +[acc-interp] thetaMarsh 1.05e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.414422e-3 s grad=3.003e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.97909e-3 s grad=3.394e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.224497e-3 s grad=3.412e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.096821e-3 s grad=2.241e-6 s +[acc-interp] eval 1.0395007e-2 s +[acc-interp] thetaMarsh 1.09e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.042741e-3 s grad=2.124e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.072226e-3 s grad=2.057e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.230652e-3 s grad=2.692e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.5177e-3 s grad=4.108e-6 s +[acc-interp] eval 1.0545251e-2 s +[acc-interp] thetaMarsh 1.38e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.375798e-3 s grad=3.046e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.454875e-3 s grad=2.253e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.361925e-3 s grad=1.839e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.118481e-3 s grad=1.755e-6 s +[acc-interp] eval 1.0912959e-2 s +[acc-interp] thetaMarsh 1.13e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.313947e-3 s grad=4.736e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.801255e-3 s grad=3.765e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=1.855824e-3 s grad=2.439e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=3.618748e-3 s grad=3.918e-6 s +[acc-interp] eval 1.2321637e-2 s +[acc-interp] thetaMarsh 1.05e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.193374e-3 s grad=2.523e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=1.958124e-3 s grad=1.979e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.718399e-3 s grad=4.201e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.372947e-3 s grad=2.397e-6 s +[acc-interp] eval 1.082922e-2 s +[acc-interp] thetaMarsh 9.9e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.179437e-3 s grad=2.755e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.296528e-3 s grad=3.097e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.136982e-3 s grad=1.966e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.222322e-3 s grad=2.084e-6 s +[acc-interp] eval 1.0443078e-2 s +[acc-interp] thetaMarsh 1.36e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.525722e-3 s grad=2.327e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.253581e-3 s grad=2.045e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.212901e-3 s grad=2.056e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=1.749662e-3 s grad=1.653e-6 s +[acc-interp] eval 1.0291205e-2 s +[acc-interp] thetaMarsh 1.28e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.052099e-3 s grad=2.306e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.036969e-3 s grad=1.825e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.140154e-3 s grad=4.002e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.468309e-3 s grad=3.761e-6 s +[acc-interp] eval 1.0372953e-2 s +[acc-interp] thetaMarsh 1.11e-7 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.154672e-3 s grad=2.713e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.23794e-3 s grad=3.492e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.163686e-3 s grad=6.906e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.15566e-3 s grad=7.084e-6 s +[acc-interp] eval 9.852888e-3 s +[acc-interp] thetaMarsh 7.4e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 kernel=2.171078e-3 s grad=3.609e-6 s +[acc-interp] chunk base=25974 cs=25974 kernel=2.552163e-3 s grad=2.81e-6 s +[acc-interp] chunk base=51948 cs=25974 kernel=2.408052e-3 s grad=2.451e-6 s +[acc-interp] chunk base=77922 cs=25974 kernel=2.180426e-3 s grad=2.503e-6 s +[acc-interp] eval 1.0840639e-2 s +[acc-interp] thetaMarsh 6.9e-8 s (n=2) +[acc-interp] chunk base=0 cs=25974 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file mode 100644 index 0000000..e69de29 diff --git a/st_n4_2.err b/st_n4_2.err new file mode 100644 index 0000000..e69de29 From 2a41f5b6a97d6b950beb7a1b1a2f343dcf5c3297 Mon Sep 17 00:00:00 2001 From: folivetti Date: Fri, 7 Aug 2026 13:24:41 -0300 Subject: [PATCH 126/166] multiset egraph --- src/Search.hs | 112 ++++++++++++++++++++++++++++++++++---------------- 1 file changed, 76 insertions(+), 36 deletions(-) diff --git a/src/Search.hs b/src/Search.hs index 051735d..2517523 100644 --- a/src/Search.hs +++ b/src/Search.hs @@ -16,7 +16,7 @@ import Algorithm.EqSat.DB import Algorithm.SRTree.Likelihoods import Algorithm.SRTree.ModelSelection import Control.Lens (element, makeLenses, over, (&), (+~), (-~), (.~), (^.)) -import Control.Monad (foldM, forM_, forM, when, unless, filterM, (>=>), replicateM, replicateM_) +import Control.Monad (foldM, forM_, forM, when, unless, filterM, (>=>), (<=<), replicateM, replicateM_) import Control.Monad.State.Strict import Data.HashMap.Strict (HashMap) @@ -268,38 +268,56 @@ egraphGP dataTrainVals dataTests args = do p <- canonical p' root <- getBestENode p >>= canonize case root of - Param ix -> pure . Fix $ Param ix - Const x -> pure . Fix $ Const x - Var ix -> pure . Fix $ Var ix - Uni f t' -> do t <- canonical t' - (Fix . Uni f) <$> getSubtree (pos-1) (sz+1) (Just $ Uni f) (parent:mGrandParents) cands t - Bin op l'' r'' -> + EParam ix -> pure . Fix $ Param ix + EConst x -> pure . Fix $ Const x + EVar ix -> pure . Fix $ Var ix + EUni f t' -> do t <- canonical t' + (Fix . Uni f) <$> getSubtree (pos-1) (sz+1) (Just (\eid -> EUni f eid)) (parent:mGrandParents) cands t + EBin op l'' r'' -> do l <- canonical l'' r <- canonical r'' szLft <- getSize l szRgt <- getSize r if szLft < pos then do l' <- getBestExpr l - r' <- getSubtree (pos-szLft-1) (sz+szLft+1) (Just $ Bin op l) (parent:mGrandParents) cands r + r' <- getSubtree (pos-szLft-1) (sz+szLft+1) (Just (\eid -> EBin op l eid)) (parent:mGrandParents) cands r pure . Fix $ Bin op l' r' - else do l' <- getSubtree (pos-1) (sz+szRgt+1) (Just (\t -> Bin op t r)) (parent:mGrandParents) cands l + else do l' <- getSubtree (pos-1) (sz+szRgt+1) (Just (\eid -> EBin op eid r)) (parent:mGrandParents) cands l r' <- getBestExpr r pure . Fix $ Bin op l' r' + ENAry op xs -> do + cs <- mapM canonical xs + szs <- mapM getSize cs + let totalSz = sum szs + goE [] [] _ _ = pure [] + goE (c:cs) (s:szs) acc i + | s < pos - acc = do + c' <- getBestExpr c + cs' <- goE cs szs (acc + s) (i + 1) + pure (c' : cs') + | otherwise = do + c' <- getSubtree (pos - acc - 1) (sz + 1 + (totalSz - s)) (Just (\eid -> ENAry op (replaceAt i eid xs))) (parent:mGrandParents) cands c + cs' <- mapM getBestExpr cs + pure (c' : cs') + exprs <- goE cs szs 0 0 + pure $ naryTree op exprs getAllSubClasses p' = do p <- canonical p' en <- getBestENode p case en of - Bin _ l r -> do ls <- getAllSubClasses l - rs <- getAllSubClasses r - pure (p : (ls <> rs)) - Uni _ t -> (p:) <$> getAllSubClasses t - _ -> pure [p] + EBin _ l r -> do ls <- getAllSubClasses l + rs <- getAllSubClasses r + pure (p : (ls <> rs)) + EUni _ t -> (p:) <$> getAllSubClasses t + ENAry _ xs -> do xss <- mapM getAllSubClasses xs + pure (p : concat xss) + _ -> pure [p] mutate p = do sz <- getSize p coin <- rnd $ tossBiased (_pm args) if coin - then do pos <- rnd $ randomRange (0, sz-1) + then do pos <- rnd $ randomRange (0, min sz maxSize - 1) tree <- mutAt pos maxSize Nothing p fromTree myCost (relabel tree) >>= canonical else pure p @@ -312,20 +330,21 @@ egraphGP dataTrainVals dataTests args = do peel (Fix (Const x)) = Const x mutAt :: Int -> Int -> Maybe (EClassId -> ENode) -> EClassId -> RndEGraph (Fix SRTree) - mutAt 0 sizeLeft Nothing _ = (insertRndExpr sizeLeft rndTerm rndNonTerm >>= canonical) >>= getBestExpr -- we chose to mutate the root + mutAt 0 sizeLeft Nothing _ = (insertRndExpr (max 1 sizeLeft) rndTerm rndNonTerm >>= canonical) >>= getBestExpr -- we chose to mutate the root mutAt 0 1 _ _ = rnd $ randomFrom terms -- we don't have size left mutAt 0 sizeLeft (Just parent) _ = do -- we reached the mutation place - ec <- insertRndExpr sizeLeft rndTerm rndNonTerm >>= canonical -- create a random expression with the size limit + ec <- insertRndExpr (max 1 sizeLeft) rndTerm rndNonTerm >>= canonical -- create a random expression with the size limit (Fix tree) <- getBestExpr ec -- root <- getBestENode ec exist <- canonize (parent ec) >>= doesExist if exist -- the expression `parent ec` already exists, try to fix - then do let children = childrenOf root + then do let children = eChildren root candidates <- case length children of - 0 -> filterM (checkToken parent . (replaceChildren children)) (Prelude.map peel terms) - 1 -> filterM (checkToken parent . (replaceChildren children)) uniNonTerms - 2 -> filterM (checkToken parent . (replaceChildren children)) binNonTerms + 0 -> filterM (checkToken parent <=< (toENode . replaceChildren children)) (Prelude.map peel terms) + 1 -> filterM (checkToken parent <=< (toENode . replaceChildren children)) uniNonTerms + 2 -> filterM (checkToken parent <=< (toENode . replaceChildren children)) binNonTerms + _ -> pure [] if null candidates then pure $ Fix tree -- there's no candidate, so we failed and admit defeat else do newToken <- rnd (randomFrom candidates) @@ -337,21 +356,37 @@ egraphGP dataTrainVals dataTests args = do p <- canonical p' root <- getBestENode p >>= canonize case root of - Param ix -> pure . Fix $ Param ix - Const x -> pure . Fix $ Const x - Var ix -> pure . Fix $ Var ix - Uni f t' -> canonical t' >>= \t -> (Fix . Uni f) <$> mutAt (pos-1) (sizeLeft-1) (Just $ Uni f) t - Bin op ln rn -> do l <- canonical ln - r <- canonical rn - szLft <- getSize l - szRgt <- getSize r - if szLft < pos - then do l' <- getBestExpr l - r' <- mutAt (pos-szLft-1) (sizeLeft-szLft-1) (Just $ Bin op l) r - pure . Fix $ Bin op l' r' - else do l' <- mutAt (pos-1) (sizeLeft-szRgt-1) (Just (\t -> Bin op t r)) l - r' <- getBestExpr r - pure . Fix $ Bin op l' r' + EParam ix -> pure . Fix $ Param ix + EConst x -> pure . Fix $ Const x + EVar ix -> pure . Fix $ Var ix + EUni f t' -> canonical t' >>= \t -> (Fix . Uni f) <$> mutAt (pos-1) (sizeLeft-1) (Just (\eid -> EUni f eid)) t + EBin op ln rn -> do l <- canonical ln + r <- canonical rn + szLft <- getSize l + szRgt <- getSize r + if szLft < pos + then do l' <- getBestExpr l + r' <- mutAt (pos-szLft-1) (sizeLeft-szLft-1) (Just (\eid -> EBin op l eid)) r + pure . Fix $ Bin op l' r' + else do l' <- mutAt (pos-1) (sizeLeft-szRgt-1) (Just (\eid -> EBin op eid r)) l + r' <- getBestExpr r + pure . Fix $ Bin op l' r' + ENAry op xs -> do + cs <- mapM canonical xs + szs <- mapM getSize cs + let totalSz = sum szs + goE [] [] _ _ = pure [] + goE (c:cs) (s:szs) acc i + | s < pos - acc = do + c' <- getBestExpr c + cs' <- goE cs szs (acc + s) (i + 1) + pure (c' : cs') + | otherwise = do + c' <- mutAt (pos - acc - 1) (sizeLeft - 1 - (totalSz - s)) (Just (\eid -> ENAry op (replaceAt i eid xs))) c + cs' <- mapM getBestExpr cs + pure (c' : cs') + exprs <- goE cs szs 0 0 + pure $ naryTree op exprs printExpr :: Int -> EClassId -> RndEGraph [String] @@ -425,3 +460,8 @@ egraphGP dataTrainVals dataTests args = do insertTerms = forM terms $ \t -> do fromTree myCost t >>= canonical + + replaceAt :: Int -> EClassId -> [EClassId] -> [EClassId] + replaceAt 0 e (_:cs) = e : cs + replaceAt i e (c:cs) = c : replaceAt (i-1) e cs + replaceAt _ _ [] = [] From 90bf6c6441507ec8d9060de1d200aa778461bbc1 Mon Sep 17 00:00:00 2001 From: folivetti Date: Sat, 8 Aug 2026 17:49:01 -0300 Subject: [PATCH 127/166] multiset egraph --- src/Search.hs | 14 +++++++++++++- 1 file changed, 13 insertions(+), 1 deletion(-) diff --git a/src/Search.hs b/src/Search.hs index 2517523..5d4439d 100644 --- a/src/Search.hs +++ b/src/Search.hs @@ -48,6 +48,8 @@ import Algorithm.EqSat (runEqSat,applySingleMergeOnlyEqSat) import GHC.IO (unsafePerformIO) import Control.Scheduler import Control.Monad.IO.Unlift +import Control.Exception (evaluate) +import System.Timeout (timeout) import Data.SRTree (convertProtectedOps) import Data.SRTree.Random @@ -96,6 +98,9 @@ data Args = Args csvHeader :: String csvHeader = "id,view,Expression,Numpy,Math,theta,size,loss_train,loss_val,loss_test,maxloss,R2_train,R2_val,R2_test,dl_train,dl_val,dl_test" +forceSimp :: Fix SRTree -> Fix SRTree +forceSimp t = let s = simplifyEqSatDefault t in countNodes s `seq` s + egraphGP :: [(DataSet, DataSet)] -> [DataSet] -> Args -> StateT EGraph (StateT StdGen IO) String egraphGP dataTrainVals dataTests args = do when ((not.null) (_loadFrom args)) $ (io $ BS.readFile (_loadFrom args)) >>= \eg -> put (decode eg) @@ -392,7 +397,14 @@ egraphGP dataTrainVals dataTests args = do printExpr :: Int -> EClassId -> RndEGraph [String] printExpr ix ec = do thetas' <- getTheta ec - bestExpr <- (if _simplify args then simplifyEqSatDefault else id) <$> getBestExpr ec + bestExpr0 <- getBestExpr ec + bestExpr <- if _simplify args + then do + res <- io $ timeout (10 * 1000000) (evaluate (forceSimp bestExpr0)) + case res of + Nothing -> pure bestExpr0 + Just s -> pure s + else pure bestExpr0 let best' = if shouldReparam then relabelParams bestExpr else relabelParamsOrder bestExpr nParams = countParamsUniq best' From 80eee42d9785bcaf989e28b129f9bc9af7fe7081 Mon Sep 17 00:00:00 2001 From: folivetti Date: Mon, 10 Aug 2026 08:40:17 -0300 Subject: [PATCH 128/166] pass datasets directly as double arrays, bypassing temp CSV fit/fit_mvsr previously serialized combined datasets to a temp CSV and passed the filename through loadDataset. Now the combined data is passed as a raw row-major double array over ctypes (hs_eggp_run_data / eggp_run_data / buildDataSets), reusing the same :::target:features:y_err params parsing so behavior is byte-identical to the file path. Fixes test_pyeggp.py which used stale loss strings (MSE/LOG10 are not Distribution constructor names) and a stale eggp_run argument list. --- src/EGGP.hs | 57 +++++++++++++++++++++++++++++- src/eggp/__init__.py | 83 ++++++++++++++++++++++++++++---------------- test/test_pyeggp.py | 6 ++-- 3 files changed, 112 insertions(+), 34 deletions(-) diff --git a/src/EGGP.hs b/src/EGGP.hs index dd8c65f..2e79fbd 100644 --- a/src/EGGP.hs +++ b/src/EGGP.hs @@ -54,6 +54,10 @@ import Data.SRTree.Datasets import Foreign.C (CInt (..), CDouble (..)) import Foreign.C.String (CString, newCString, withCString, peekCString, peekCAString, newCAString) +import Foreign.Marshal.Array (peekArray) +import Foreign.Ptr (Ptr) +import qualified Data.Vector.Unboxed as V +import qualified Data.ByteString.Char8 as B import Paths_eggp (version) import System.Environment (getArgs) import System.Exit (ExitCode (..)) @@ -125,6 +129,22 @@ hs_eggp_run dataset gens nPop maxSize nTournament pc pm nonterminals loss optIte out <- eggp_run dataset' (fromIntegral gens) (fromIntegral nPop) (fromIntegral maxSize) (fromIntegral nTournament) (realToFrac pc) (realToFrac pm) nonterminals' loss' (fromIntegral optIter) (fromIntegral optRepeat) (fromIntegral nParams) (fromIntegral folds) (fromIntegral maxTime) (simplify /= 0) (trace /= 0) (generational /= 0) dumpTo' loadFrom' varnames (useFracBayes /= 0) newCString out +foreign export ccall hs_eggp_run_data :: Ptr CDouble -> Ptr CInt -> CInt -> CInt -> CString -> CString -> CInt -> CInt -> CInt -> CInt -> CDouble -> CDouble -> CString -> CString -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CString -> CString -> CString -> CInt -> IO CString + +hs_eggp_run_data :: Ptr CDouble -> Ptr CInt -> CInt -> CInt -> CString -> CString -> CInt -> CInt -> CInt -> CInt -> CDouble -> CDouble -> CString -> CString -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CString -> CString -> CString -> CInt -> IO CString +hs_eggp_run_data dataPtr nrowsPtr ndatasets ncols header params gens nPop maxSize nTournament pc pm nonterminals loss optIter optRepeat nParams folds maxTime simplify trace generational dumpTo loadFrom varnames' useFracBayes = do + nonterminals' <- peekCString nonterminals + loss' <- peekCString loss + dumpTo' <- peekCString dumpTo + loadFrom' <- peekCString loadFrom + varnames <- peekCString varnames' + header' <- peekCString header + params' <- peekCString params + out <- eggp_run_data dataPtr nrowsPtr (fromIntegral ndatasets) (fromIntegral ncols) header' params' + (fromIntegral gens) (fromIntegral nPop) (fromIntegral maxSize) (fromIntegral nTournament) (realToFrac pc) (realToFrac pm) nonterminals' loss' + (fromIntegral optIter) (fromIntegral optRepeat) (fromIntegral nParams) (fromIntegral folds) (fromIntegral maxTime) (simplify /= 0) (trace /= 0) (generational /= 0) dumpTo' loadFrom' varnames (useFracBayes /= 0) + newCString out + opt :: Parser Args opt = Args <$> strOption @@ -254,13 +274,48 @@ eggp_run dataset gens nPop maxSize nTournament pc pm nonterminals loss optIter o eggp :: Args -> IO String eggp args = do - g <- getStdGen let datasets = words (_dataset args) dataTrains' <- Prelude.mapM (flip loadTrainingOnly True) datasets -- load all datasets dataTests <- if null (_testData args) then pure dataTrains' else Prelude.mapM (flip loadTrainingOnly True) $ words (_testData args) + eggpWithData args dataTrains' dataTests +eggpWithData :: Args -> [DataSet] -> [DataSet] -> IO String +eggpWithData args dataTrains' dataTests = do + g <- getStdGen let (dataTrainVals, g') = runState (Prelude.mapM (`splitData` (_folds args)) dataTrains') g alg = evalStateT (egraphGP dataTrainVals dataTests args) emptyGraph evalStateT alg g' + +eggp_run_data :: Ptr CDouble -> Ptr CInt -> Int -> Int -> String -> String -> Int -> Int -> Int -> Int -> Double -> Double -> String -> String -> Int -> Int -> Int -> Int -> Int -> Bool -> Bool -> Bool -> String -> String -> String -> Bool -> IO String +eggp_run_data dataPtr nrowsPtr ndatasets ncols header params gens nPop maxSize nTournament pc pm nonterminals loss optIter optRepeat nParams folds maxTime simplify trace generational dumpTo loadFrom varnames useFracBayes = + case readMaybe loss of + Nothing -> pure $ "Invalid loss function " <> loss + Just l -> do + dss <- buildDataSets dataPtr nrowsPtr ndatasets ncols header params + let arg = Args "" "" gens maxSize folds trace l optIter optRepeat nParams nPop nTournament pc pm nonterminals dumpTo loadFrom generational simplify maxTime varnames useFracBayes MultiThread + eggpWithData arg dss dss + +-- | Build a list of DataSets from a raw row-major double buffer. The buffer +-- contains all datasets concatenated; `nrows` gives the per-dataset row +-- count. Column selection and row ranges reuse the same `:::...` params +-- parsing as the file-based `loadDataset`, so behavior is identical. +buildDataSets :: Ptr CDouble -> Ptr CInt -> Int -> Int -> String -> String -> IO [DataSet] +buildDataSets dataPtr nrowsPtr ndatasets ncols header params = do + nrows <- map fromIntegral <$> peekArray ndatasets nrowsPtr + let totalRows = sum nrows + flat <- V.fromList . map realToFrac <$> peekArray (totalRows * ncols) dataPtr + let (_, prms) = splitFileNameParams params + headerMap = zip (map B.strip (B.split ',' (B.pack header))) [0 .. ncols-1] + (ixs, iy, iyErr) = getColumns headerMap (prms !! 2) (prms !! 3) (prms !! 4) + allCols = [ V.generate totalRows (\i -> flat V.! (i*ncols + j)) | j <- [0 .. ncols-1] ] + colAt off len j = V.slice off len (allCols !! j) + mkDs off nrows' = + let (st, end) = getRows (prms !! 0) (prms !! 1) nrows' + x = map (colAt st end) ixs + y = colAt st end iy + ye = if iyErr == -1 then Nothing else Just (colAt st end iyErr) + in (x, y, ye) + offs = init (scanl (+) 0 nrows) + pure $ zipWith mkDs offs nrows diff --git a/src/eggp/__init__.py b/src/eggp/__init__.py index ff179aa..165e6f9 100644 --- a/src/eggp/__init__.py +++ b/src/eggp/__init__.py @@ -4,10 +4,9 @@ from typing import Iterator, List import string from io import StringIO -import tempfile -import csv import os import multiprocessing +import ctypes import numpy as np import pandas as pd @@ -31,6 +30,25 @@ unsafe_hs_eggp_exit, ) +_hs_eggp_run_data = None + +def _get_hs_eggp_run_data(): + global _hs_eggp_run_data + if _hs_eggp_run_data is None: + lib = ctypes.CDLL(_binding.__file__) + lib.hs_eggp_run_data.argtypes = [ + ctypes.POINTER(ctypes.c_double), ctypes.POINTER(ctypes.c_int), + ctypes.c_int, ctypes.c_int, ctypes.c_char_p, ctypes.c_char_p, + ctypes.c_int, ctypes.c_int, ctypes.c_int, ctypes.c_int, + ctypes.c_double, ctypes.c_double, ctypes.c_char_p, ctypes.c_char_p, + ctypes.c_int, ctypes.c_int, ctypes.c_int, ctypes.c_int, + ctypes.c_int, ctypes.c_int, ctypes.c_int, ctypes.c_int, + ctypes.c_char_p, ctypes.c_char_p, ctypes.c_char_p, ctypes.c_int, + ] + lib.hs_eggp_run_data.restype = ctypes.c_char_p + _hs_eggp_run_data = lib.hs_eggp_run_data + return _hs_eggp_run_data + VERSION: str = "1.0.17" @@ -64,7 +82,7 @@ def main(args: List[str] = []) -> int: # args often come from sys.argv which includes the program name at [0]. # Strip it and prepend RTS flags so hs_init sees +RTS -N{numCores} -RTS. cli_args = args[1:] if args and not args[0].startswith('-') else args - rts_flags = ["+RTS", "-N4", "-s", "-RTS"] + rts_flags = ["+RTS", "-N4", "-RTS"] rts_args = ["eggp"] + rts_flags with hs_rts_init(rts_args + cli_args): return unsafe_hs_eggp_main() @@ -73,6 +91,30 @@ def eggp_run(dataset: str, gen: int, nPop: int, maxSize: int, nTournament: int, with hs_rts_init(): return unsafe_hs_eggp_run(dataset, gen, nPop, maxSize, nTournament, pc, pm, nonterminals, loss, optIter, optRepeat, nParams, split, max_time, simplify, trace, generational, dumpTo, loadFrom, varnames, useFracBayes) +def eggp_run_data(data: np.ndarray, nrows: List[int], header: str, params: str, gen: int, nPop: int, maxSize: int, nTournament: int, pc: float, pm: float, nonterminals: str, loss: str, optIter: int, optRepeat: int, nParams: int, split: int, max_time : int, simplify: int, trace : int, generational : int, dumpTo: str, loadFrom: str, varnames : str, useFracBayes: int) -> str: + ''' Runs eggp with the dataset passed directly as a contiguous double + array, bypassing the temp-CSV round trip. + + Parameters + ---------- + data : np.ndarray + A C-contiguous float64 matrix (row-major) with all views concatenated + vertically. + nrows : list of int + Per-view row counts; its length is the number of datasets (views). + header : str + Comma-separated column names, in column order. + params : str + The same `:::target:features:y_err` suffix used by the file-based path + (e.g. ``get_fname("", header)``). + ''' + data = np.ascontiguousarray(data, dtype=np.float64) + cdata = data.ctypes.data_as(ctypes.POINTER(ctypes.c_double)) + cnrows = (ctypes.c_int * len(nrows))(*nrows) + with hs_rts_init(): + out = _get_hs_eggp_run_data()(cdata, cnrows, len(nrows), data.shape[1], header.encode(), params.encode(), gen, nPop, maxSize, nTournament, pc, pm, nonterminals.encode(), loss.encode(), optIter, optRepeat, nParams, split, max_time, simplify, trace, generational, dumpTo.encode(), loadFrom.encode(), varnames.encode(), useFracBayes) + return out.decode("utf-8") if out is not None else "" + def make_function(expression, loss="MSE"): def func(x, t): y = eval(expression) @@ -323,18 +365,9 @@ def fit(self, X, y, Xerr = None, yerr = None): else: varnames = "" - with tempfile.NamedTemporaryFile(mode='w+', newline='', delete=False, prefix='datatemp_', suffix='.csv', dir=os.getcwd()) as temp_file: - writer = csv.writer(temp_file) - writer.writerow(header) - writer.writerows(combined) - dataset = temp_file.name - dname = self.get_fname(dataset, header) + dname = self.get_fname("", header) - try: - csv_data = eggp_run(dname, self.gen, self.nPop, self.maxSize, self.nTournament, self.pc, self.pm, self.nonterminals, self.loss, self.optIter, self.optRepeat, self.nParams, self.folds, self.max_time, self.simplify, self.trace, self.generational, self.dumpTo, self.loadFrom, varnames, self.useFracBayes) - - finally: - os.remove(dataset) + csv_data = eggp_run_data(combined, [combined.shape[0]], ",".join(header), dname, self.gen, self.nPop, self.maxSize, self.nTournament, self.pc, self.pm, self.nonterminals, self.loss, self.optIter, self.optRepeat, self.nParams, self.folds, self.max_time, self.simplify, self.trace, self.generational, self.dumpTo, self.loadFrom, varnames, self.useFracBayes) if len(csv_data) > 0: csv_io = StringIO(csv_data.strip()) @@ -359,27 +392,17 @@ def fit_mvsr(self, Xs, ys, Xerrs = None, yerrs = None): combineds = [self.combine_dataset(X, y, Xerr, yerr) for X, y, Xerr, yerr in zip(Xs, ys, Xerrs, yerrs)] header = self.get_header(Xs[0].shape[1]) - datasets = [] - datasetsNames = [] if isinstance(Xs[0], pd.DataFrame): varnames = ",".join(Xs[0].columns) else: varnames = "" - for combined in combineds: - with tempfile.NamedTemporaryFile(mode='w+', newline='', delete=False, prefix='datatemp_', suffix='.csv', dir=os.getcwd()) as temp_file: - writer = csv.writer(temp_file) - writer.writerow(header) - writer.writerows(combined) - datasetsNames.append(temp_file.name) - datasets.append(self.get_fname(temp_file.name, header)) - - try: - csv_data = eggp_run(" ".join(datasets), self.gen, self.nPop, self.maxSize, self.nTournament, self.pc, self.pm, - self.nonterminals, self.loss, self.optIter, self.optRepeat, self.nParams, self.folds, self.max_time, self.simplify, self.trace, self.generational, self.dumpTo, self.loadFrom, varnames, self.useFracBayes) - finally: - for dataset in datasetsNames: - os.remove(dataset) + data = np.vstack(combineds) + nrows = [combined.shape[0] for combined in combineds] + dname = self.get_fname("", header) + + csv_data = eggp_run_data(data, nrows, ",".join(header), dname, self.gen, self.nPop, self.maxSize, self.nTournament, self.pc, self.pm, + self.nonterminals, self.loss, self.optIter, self.optRepeat, self.nParams, self.folds, self.max_time, self.simplify, self.trace, self.generational, self.dumpTo, self.loadFrom, varnames, self.useFracBayes) if len(csv_data) > 0: csv_io = StringIO(csv_data.strip()) diff --git a/test/test_pyeggp.py b/test/test_pyeggp.py index d190927..00ea747 100644 --- a/test/test_pyeggp.py +++ b/test/test_pyeggp.py @@ -1,11 +1,11 @@ from eggp import eggp_run, EGGP import pandas as pd -output = eggp_run("test/data.csv", 100, 100, 10, 3, 0.9, 0.3, "add,sub,mul,div,log", "MSE", 50, 2, -1, 3, 0, 0, 0, "", "") +output = eggp_run("test/data.csv", 100, 100, 10, 3, 0.9, 0.3, "add,sub,mul,div,log", "Gaussian", 50, 2, -1, 3, 0, 0, 0, 0, "", "", "", 0) print(output) -output = eggp_run("test/data.csv test/data2.csv", 100, 100, 10, 3, 0.9, 0.3, "add,sub,mul,div,log", "MSE", 50, 2, -1, 3, 0, 0, 0, "", "") +output = eggp_run("test/data.csv test/data2.csv", 100, 100, 10, 3, 0.9, 0.3, "add,sub,mul,div,log", "Gaussian", 50, 2, -1, 3, 0, 0, 0, 0, "", "", "", 0) print(output) @@ -15,7 +15,7 @@ X = Z[:,:-1] y = Z[:,-1] -reg = EGGP(100, 100, 10, 3, 0.9, 0.3, "add,sub,mul,div,log", "LOG10", 50, 2, -1, 3, True, False, False, "", "") +reg = EGGP(gen=100, nPop=100, maxSize=10, nTournament=3, pc=0.9, pm=0.3, nonterminals="add,sub,mul,div,log", loss="Gaussian", optIter=50, optRepeat=2, nParams=-1, folds=3, max_time=0, simplify=False, trace=False, generational=False, dumpTo="", loadFrom="", useFracBayes=False) reg.fit(X, y) print(reg.score(X, y)) From 00087751952208c906202a449fe0e4771350b7bd Mon Sep 17 00:00:00 2001 From: folivetti Date: Mon, 10 Aug 2026 08:43:38 -0300 Subject: [PATCH 129/166] multiset egraph: expand ENAry children on demand; drop eggp diag noise Search.hs: use expandedList/imFromList so ENAry operates on demand, avoiding building child lists when unused. binding.i: remove hs_init stderr diagnostics. Also clean up stale profile/dump artifacts. --- NOTES-STATUS.md | 415 - c_1.err | 2696 ------ c_2.err | 3372 ------- c_3.err | 2950 ------- cached_n4.err | 2791 ------ dump_gaussian | 1001 --- interp_n1.err | 310 - interp_n2.err | 3199 ------- interp_n4.err | 2717 ------ interp_n4.out | 5 - mt.err | 149 - mtA_n1_1.err | 0 mtA_n1_2.err | 0 mtA_n4_1.err | 0 mtA_n4_2.err | 0 mt_n1.err | 0 mt_n4.err | 0 n1r1.err | 0 n1r2.err | 0 p2_n4.err | 3089 ------- phas2_n1.err | 3066 ------- phas2_n2.err | 3611 -------- phas2_n4.err | 3081 ------- pp_n1_1.err | 0 pp_n1_2.err | 0 pp_n2_1.err | 0 pp_n2_2.err | 0 pp_n4_1.err | 0 pp_n4_2.err | 0 pp_out_3.csv | 6 - pp_out_4.csv | 5 - pp_out_5.csv | 5 - prof_n4.err | 20515 ------------------------------------------- src/Search.hs | 14 +- src/eggp/binding.i | 6 - st_n1_1.err | 0 st_n1_2.err | 0 st_n4_1.err | 0 st_n4_2.err | 0 39 files changed, 8 insertions(+), 52995 deletions(-) delete mode 100644 NOTES-STATUS.md delete mode 100644 c_1.err delete mode 100644 c_2.err delete mode 100644 c_3.err delete mode 100644 cached_n4.err delete mode 100644 dump_gaussian delete mode 100644 interp_n1.err delete mode 100644 interp_n2.err delete mode 100644 interp_n4.err delete mode 100644 interp_n4.out delete mode 100644 mt.err delete mode 100644 mtA_n1_1.err delete mode 100644 mtA_n1_2.err delete mode 100644 mtA_n4_1.err delete mode 100644 mtA_n4_2.err delete mode 100644 mt_n1.err delete mode 100644 mt_n4.err delete mode 100644 n1r1.err delete mode 100644 n1r2.err delete mode 100644 p2_n4.err delete mode 100644 phas2_n1.err delete mode 100644 phas2_n2.err delete mode 100644 phas2_n4.err delete mode 100644 pp_n1_1.err delete mode 100644 pp_n1_2.err delete mode 100644 pp_n2_1.err delete mode 100644 pp_n2_2.err delete mode 100644 pp_n4_1.err delete mode 100644 pp_n4_2.err delete mode 100644 pp_out_3.csv delete mode 100644 pp_out_4.csv delete mode 100644 pp_out_5.csv delete mode 100644 prof_n4.err delete mode 100644 st_n1_1.err delete mode 100644 st_n1_2.err delete mode 100644 st_n4_1.err delete mode 100644 st_n4_2.err diff --git a/NOTES-STATUS.md b/NOTES-STATUS.md deleted file mode 100644 index 9f91d0e..0000000 --- a/NOTES-STATUS.md +++ /dev/null @@ -1,415 +0,0 @@ -# EGGP interpreter (Phase 2 -> fixed-shape interpreter) — status - -## Goal -Replace Phase 2's per-tree Accelerate JIT with a fixed-shape, data-driven -interpreter (one kernel compile per process) so per-eval cost approaches the -MultiThread backend. - -## Constraints -- srtree `cabal.project` forces `-fllvm -mavx2 -mfma -fexpose-all-unfoldings`. -- Benchmark config: - `EGGP -d data.tsv --non-terminals add,mul,div,exp,log -s 30 -g 1 --nPop 100 --pc 0.0 --pm 1.0 --generational --opt-retries 1 --folds 1 --backend Accelerate +RTS -N4 -qn1 -RTS` -- binary: `/home/olivetti/Projects/srtools/eggp/dist-newstyle/build/.../eggp/eggp`, - cwd `/home/olivetti/Projects/srtools/eggp`. -- `ACC_INTERP=1` gates the interpreter; stable artifacts live under `/home/olivetti/Projects/srtools/eggp/`. - -## The "deadlock" — NOW RESOLVED AS A HARNESS BUG -- Repro harness drew var indices in `[0..9]` (seed3 -> `(x6 * x1)`, uses x6) - but supplied only **4** feature columns. -- `Algorithm/SRTree/AD/Unboxed.hs:156`: `evalStatic (Var ix) = pure (xss !! ix)`. - For x6 with 4 cols -> `Prelude.!!: index too large`. -- That forced-out-of-bounds thunk is lazily evaluated **inside an accelerate - worker** during kernel execution; the scheduler (accelerate-llvm-native - `Scheduler.hs`) swallows worker exceptions into `workerException` instead of - propagating; the main thread waits forever -> `BlockedIndefinitelyOnMVar`. -- NOT a scheduler bug and NOT tree/`kDyn`-dependent. Confirmation: giving the - harness all 10 columns makes seed3 complete: `OK obj=0.999...`. -- The earlier "seed2 pass / seed3 fail" and "all `-N`" repros were this same - harness artifact (same 4-column / 9-range mismatch in everything run via the - repro harness). - -## Accomplished this session -- Built `harness/WExc` against a LOCAL patched copy of accelerate-llvm-native - (`/home/olivetti/Projects/srtools/accelerate-llvm-native-debug`) that exports - `Scheduler(workerException)`, `Target(workers)` and logs swallowed worker - exceptions to stderr -> surfaced the real error. -- Identified and confirmed root cause; fixed by supplying enough feature columns. -- Stable harness compile recipe (cabal does NOT expose inplace project libs to - standalone `ghc`; must pass `-package-id`): - `cabal exec -- ghc -O2 -threaded -rtsopts \ - -package-id srtree-2.0.1.8-inplace \ - -package-id accelerate-llvm-native-1.4.0.0-inplace \ - -o harness/WExc harness/WExc.hs` - -## Latest (MultiThread backend, improvement A applied) -- `Algorithm/SRTree/AD.hs`: hoisted the tree compile out of the per-theta - evaluator for MultiThread and SingleThread (`let cts = compileTreeMulti ...` - outside the lambda). Before, `compileTreeMulti` (theta-independent static - columns + graphs) was recomputed on EVERY objective/gradient evaluation - during an NLopt run; now computed once per (tree, data) and shared across - thetas. Safe per-tree reuse (immutable cts), no cross-tree sharing. -- Measured (canonical config, `--backend MultiThread`, 2 runs each): - N1: 12s -> ~10-11s; N4: 8s -> ~6-8s (~10-15%, noisy). -- Accelerate path unchanged (per-tree kernel reuse cache from earlier). - -## Next -- Re-run the REAL EGGP config at -N4 with ACC_INTERP=1 to confirm the - interpreter completes and measure steady per-eval cost vs Phase 2. No known - bug now blocks -N4. -- If a genuine scheduler swallow is ever hit with in-range data, decide a - containment (interpreted `multiWorker` single-worker, or serialize evals). -## Session: population-parallel fitness (fitBatch) — 2026-08-03 -Goal: parallelize the search's population fitness evals (the search loop is -serial `RndEGraph = EGraphST (StateT StdGen IO)`; each tree fitted one at a time). - -Implementation: -- `Algorithm/SRTree/AD/Unboxed.hs`: added `mtParGate :: IORef Bool` (default - False), `setMTPopParallel :: IO () -> IO ()`; while True, `compileTreeMulti` - forces `numChunks = 1` so the population batch owns the cores instead of - oversubscribing with the per-tree chunk split. Exported. -- `Algorithm/EqSat/SearchSR.hs`: new `fitBatch :: Bool -> (Fix SRTree -> RndEGraph - (Double,[Target])) -> [EClassId] -> RndEGraph ()`. Canonicalizes ecs, drops - already-fitted unless `force`, snapshots the EGraph, splits the shared StdGen - once into per-worker generators (global RNG draw order changes, accepted), - `setMTPopParallel True` -> `mapConcurrently (mapM (runJob eg fitFun)) (chunk - nCaps jobsG)` (round-robin chunks) -> `setMTPopParallel False`, then serially - `insertFitness` the results (single serial writer, correctness preserved). -- `Search.hs`: all four fitness call sites converted to fitBatch — initial - terms (`evaluateUnevaluated` replacement), initial `replicateM nPop` pop, - generation loop refits (`force=True`, drained from `_refits`) + offspring - (`force=False` = updateIfNothing semantics), run before Pareto selection so - ranking sees fresh fitness. Removed dead `refitChanged`; evolve no longer fits. - -CRITICAL BUG FOUND AND FIXED: the batch measured ~0.001s wall at N4 with 4 -chunks running and NO speedup, because `runRndEGraph` returns the fitness as a -LAZY thunk. `insertFitness` forced it later, serially, in the main thread, so -all NLopt work happened on one core regardless of -N. Fix: in `runJob`, force -results to NF inside the worker: `evaluate (DeepSeq.force f)` / -`evaluate (DeepSeq.force p)` (Target = Vector Double, both NFData). After the -fix the batch wall reflects the real work and scales. - -Results (real config, data.tsv, 100 pop, 1 gen, opt-retries 1): -- MultiThread: N1 ~10-13s, N2 ~7-9s, N4 ~6-7s (was ~12/N1, 6-8/N4 — modest - gain; MT already chunk-parallel per tree internally). -- SingleThread (clean test, no inner parallelism): N1 ~27-28s -> N4 ~11-12s, - i.e. ~2.4x scaling from the population parallelism (was flat 24s -> 23s). -- All runs rc=0, output sane (losses/R2 in normal range). - -## Session: why "per-tree" parallelism shows little gain — 2026-08-04 -User repro config (48s old MT -N1): `-d data.tsv --non-terminals add,mul,div,exp,log,power --s 50 -g 1 --nPop 300 --pc 0.0 --pm 1.0 --generational --opt-retries 1 --opt-iter 100 ---folds 1`. - -Measured (current build, with fitBatch): -- MT -N1: 56-61s, MT -N8: 28s (2x) -- ST -N1: 160s, ST -N8: 44-51s (3x) -So ST at -N8 (~44s) only matches MT at -N1 (~56-61s) — confirms user's observation. -MT's value is its vectorized per-even eval, not multi-core. Across-tree concurrency is a -weak lever because NLopt is serial WITHIN a tree (can only parallelize each eval call). - -Phase breakdown (PHASE_DEBUG instrumentation, MT -N8): popFit ~3.6s, gen1Evolve ~12s -(serial), FitRefits ~6s, FitNew ~3.3s. The serial `gen1Evolve` cap is ENTIRELY one -`runEqSat myCost rewrites 1` call = 12.6s on the first evolve (saturating the ~300-tree -egraph); subsequent invocations are 1-3ms. runEqSat is pure serial state over the shared -egraph (Algorithm/EqSat.hs:106-135); tournament/combine are negligible (~1ms). - -Strategy A/B at -N8 (env gates, since removed): round-robin chunk (current) 26-28s == -per-tree-thread flat 28-30s, and population-serial + MT inner-parallel (old model) is -WORSE 35-37s. So round-robin across trees + single-chunk-per-tree (fitBatch) is best. -Fit-batch scaling ceiling ~3x is inherent (NLopt serial iterations + memory BW). - -Conclusion: feature works and is beneficial for ST (160->44) and gives MT -N8=28s -(faster than old MT -N1). Do NOT expect larger per-tree speedup: the remaining wall is -the serial one-shot eqsat (12.6s) and the serial gen loop, neither parallelizable without -redesigning eqsat/egraph. - -## Session: fixed the one-shot eqsat serial cost (variable ordering) — 2026-08-04 (cont.) - -Root cause of the 12.6s serial `runEqSat myCost rewrites 1` call: the egraph is SHARED -across the whole population (`StateT EGraph` in `egraphGP`, all 300 initial trees -inserted at `Search.hs:109`), so the FIRST `runEqSat` call matches over the full -300-tree egraph while every later call matches a much-reduced/saturated one (~20-25 -matches, ~0s). The `>1500`-eclass throttle never engages because `runEqSat ... 1` is -called with `maxIter=1`, and `go`'s `it == 1 || not changed` branch always returns after -exactly one iteration — throttling is only reachable at `it > 1`. - -Instrumented `runEqSat`/`matchCached` (env-gated, since removed) and found the cost is -NOT in `applyMatch`/`rebuild` (~0.06s) but in match ENUMERATION -(`matchCached`/`genericJoin`, srtree/src/Algorithm/EqSat/DB.hs): the first call produces -~1750 matches, ~90% from the two simplest identity rules `x*y` and `x+y` (any mul/add -node matches). Root cause of the enumeration cost: `DB.hs:orderedVars` breaks ties (equal -`varCost`) by variable id, which puts PATTERN-VARIABLE leaves (low ids, e.g. x=0, y=1) -before the query ROOT (high id, freshly assigned, e.g. v=256). Since the root indexes the -operator trie directly, matching leaves first forces `domainX`/`intersectTries` to fold -over the ENTIRE operator trie once per candidate of the earlier variables (`O(candidates -x nodes)`), and — per `intersectTries`'s single-occurrence shortcut (DB.hs:298-304) — -the root's own domain, computed last, ignores the already-bound children entirely, -returning ALL nodes of that operator regardless of consistency. - -Fix (srtree/src/Algorithm/EqSat/DB.hs, `orderedVars`): break ties by preferring a -variable that is the ROOT of some atom (an "atom header") over pattern-variable leaves, -so `domainX` on the root does ONE fold over the trie (the true set of candidate nodes), -and children are then resolved by direct trie descent instead of repeated whole-trie -folds. This only changes the TIE-BREAK order among equal-`varCost` variables — it does -not change the frequency-based primary heuristic that already handles multi-atom -patterns (shared subterms still sort first). - -IMPORTANT CAVEATS actually verified (not assumed): -- The two orderings do NOT enumerate an identical match SET on the same call (verified - via per-rule match counts on the same seed: ~21/46 rules differ in count on the first - call, e.g. `x*y` 1738->1787 total). This means the OLD ordering was itself not a - faithful/complete enumeration (the shortcut described above lets it over/under count - depending on trie shape) — reordering is not a neutral optimization, so it was - quality-gated empirically rather than assumed safe. -- Quality gate (median best `loss_train`, same seed `-s 50`, 5 runs each, MT -N8; NB: MT - is not run-to-run deterministic due to thread scheduling / parallel fitBatch order): - old ordering: median 153.01, range [146.11, 155.72] (spread 9.6) - new ordering: median 151.80, range [149.88, 153.01] (spread 3.1) - -> not worse (marginally better median, much tighter spread) - PASSES the gate. -- Wall clock (user config, clean build, single run each): - MT -N1: 56-64s -> 49s | MT -N8: 28-31s -> 17s (was ~2x scaling, now ~2.9x) - ST -N1: 160s -> 125s | ST -N8: 44-51s -> 35s (was ~3x scaling, now ~3.6x) - Shrinking the serial one-shot-eqsat bottleneck let the existing fitBatch parallelism - scale better too (not just a flat-time win). - -Change is a single ~10-line tie-break in `orderedVars`; no callers changed, no env -gates left in the tree (all debug instrumentation used to diagnose this was added and -then fully removed again). - -## Session: unified SingleThread onto the vectorized AD kernel — 2026-08-04 (cont.) - -Investigated remaining speedup levers after the eqsat fix. Found that the `SingleThread` -ADBackEnd was not just "MultiThread minus threads" — it called a genuinely slower -algorithm: -- `compileFunAndGrad SingleThread` -> `evalGrad` (`srtree/src/Algorithm/SRTree/AD/Unboxed.hs:206-272`, - row-fused): outer loop over data ROWS, re-dispatching the node-kind `case` on - EVERY row -> scalar/branchy, no SIMD across rows. -- `compileFunAndGrad MultiThread` -> `evalGradMulti` -> always `evalGradVec` - (`Unboxed.hs:293-377`, node-outer), even at a single chunk/capability: processes rows in - 1024-row chunks, dispatching the opcode ONCE per node then running a tight straight-line - loop over the chunk (`fwdBin`/`bwdBin`/etc, lines 494-690) with a literal lambda - operator -- exactly what LLVM can auto-vectorize under the forced `-fllvm -mavx2 -mfma` - build flags. (Initially assumed this kernel allocates `O(nodes*m)`; verified from source - that it's already chunked to `stride*1024` buffers, same memory profile as `evalGrad` -- - no memory-cost tradeoff, purely a loop-shape/vectorization difference.) -- Both operate on the identical `CompiledTree` produced by `compileTree`; `evalGrad` had - exactly one caller (the `SingleThread` branch) plus one dead commented-out reference -- - very small blast radius. - -Fix (`srtree/src/Algorithm/SRTree/AD.hs`): `compileFunAndGrad SingleThread` now calls -`evalGradVec` instead of `evalGrad` (drop-in, since both consume the same `CompiledTree`). - -Caveat verified (not assumed): summation order differs slightly between row-at-a-time -(`evalGrad`) and chunk-of-1024 (`evalGradVec`) accumulation -- same left-to-right row -order, but grouped into 1024-row partial sums rather than one running total, so results -are not bit-identical (though should be equally or more numerically stable). Quality-gated -empirically: -- 5 runs each, same seed `-s 50`, `--backend SingleThread +RTS -N1`: - before: median best `loss_train` 153.01, range [152.83, 154.44] - after: median best `loss_train` 153.01 (identical median), range across 8 runs - [148.91, 161.51] -- one high outlier balanced by a new best-low; verified it's - not a correctness bug (no NaNs, converged population, just a different local - optimum -- normal GP stochastic variance from fitBatch's async scheduling). - -> PASSES the gate (median unchanged, spread comparable). -- Wall clock (user config, clean build): - ST -N1: 122-147s -> 48-54s (~61% faster) - ST -N8: 35s -> 16-18s, now equal to MT -N8 (18s) -- the ST/MT gap is fully closed, - since population-level cross-tree parallelism (`fitBatch`) already benefited both - backends equally; only the per-tree kernel differed. - MT -N1 (46-48s) and MT -N8 (17-18s) unaffected, as expected (untouched code path). - -## Session: phase-timed the post-fix generation loop — 2026-08-04 (cont.) - -With eqsat's one-shot cost fixed, re-profiled `egraphGP` (user config, MT -N8, ~18s -total) with temporary real-IO wall-clock timers (via the existing `io :: IO a -> -RndEGraph a` escape hatch -- no `unsafePerformIO`/CSE issues like the eqsat -micro-timing, since `RndEGraph` already runs in `IO`). Instrumentation was added, -measured once, and fully removed afterward (`git diff` on `Search.hs` is empty). - -Phase breakdown (single run, MT -N8): -``` -insertTerms+fitBatchUneval 2.97s -insertRndExpr+fitBatchPop 3.54s -evolveLoop (300x tournament/crossover/mutate/eqsat/cleanDB) 0.64s <- was ~12s pre-fix -fitBatchRefits 8.01s <- now the single largest phase -fitBatchNewPop 2.99s -cleanEGraph / selection / paretoFront ~0.26s (not triggered / cheap for -g 1) -``` -Confirms the eqsat fix worked (evolveLoop dropped from ~12s to 0.64s) and reveals a new -dominant cost: `fitBatchRefits` (8.0s, ~44% of total). Investigated why it costs more -than `fitBatchNewPop` (which fits the 300 new offspring): `refitIds` (eclasses flagged -dirty by eqsat merges during the evolve loop, via `_refits . _eDB`) numbered **868** -vs 300 new offspring -- each evolve call's one-shot eqsat pass merges/touches ~2.9 -*other* existing egraph classes on average (not just the offspring), so nearly 3x more -individuals get NLopt-refit than are newly created. This is a real, quantified next -optimization target (not yet acted on) -- e.g. deduplicating/capping the refit set, or -checking whether a refit's fitness actually needs recomputing before paying for NLopt. - -No code changes from this session (purely diagnostic); `Search.hs` is back to its -original committed state. - -## Session: fixed `Map.!` crash from egraph cleaning — 2026-08-05 - -**Bug:** intermittent `Map.!: given key is not an element in the map`, only with -`--generational` and only once the shared egraph crosses `maxMem = 2000000` enodes. - -**Root cause:** `Search.hs` `cleanEGraph` wipes the egraph (`put emptyGraph`) when -`totSz > max maxMem (_nPop args)` and re-inserts only the per-size pareto exprs via -`fromTrees`, but returned no ids. The `--generational` branch then ran -`Prelude.mapM canonical newPop'` on pre-wipe offspring ids against the fresh empty -`_canonicalMap` -> missing key in `canonical` (`srtree/.../Egraph.hs:242` -`m IntMap.! eclassId`). Non-generational survived because when `full` it re-derives -all ids from the fresh graph (`getTopFitEClassThat`). - -**Fix (src/Search.hs only):** -- `cleanEGraph` now returns `newIds` (the fresh re-inserted eclass ids). -- Generational branch uses them when `full`: - `maybe (Prelude.mapM canonical newPop') pure cleanedIds`. - Population after a clean = the re-inserted pareto ids (10 x maxSize); the next - generation's `evolve` regenerates the full `_nPop`. Pareto-only survival, matching - the existing non-generational clean semantics. - -**Validation:** temporarily lowered `maxMem` to 1 to force the clean path; both -`--generational` and default branches ran 3 generations with cleaning every -generation, exit 0, valid CSV, no crash. Threshold reverted to 2000000 and rebuilt. -Real config (`-s 50 -g 1 --nPop 300 --generational`, 54s @ -N1): no cleaning, exit 0. - -**Noted, not fixed:** `cleanMaps` (`srtree/.../Build.hs`) has the same latent -id-invalidation hazard but is unreachable today (only via `throttle`, which needs -`maxIter > 1`; the loop runs one-shot eqsat). - -## Session: shrink `fitBatchRefits` (refit only when the class best changed) — 2026-08-05 (cont.) - -Follow-up on the 2026-08-04 phase profile that showed `fitBatchRefits` at 8.0s of ~18s -(868 refits/gen). Goal per approved scope: reduce refit work (Tier 1: fewer fits, Tier 2: -warm-starts) without changing `joinData` semantics; every change gated with 5-run A/B -quality tests. All measurement instrumentation was env-gated (`EGGP_STATS=1`) and fully -removed afterward (`git diff src/Search.hs` is back to the `Map.!` fix only). - -**Step 0 — instrumentation + baseline (single run, MT -N8, user config).** Counters at the -three dirty sites in `srtree/.../Build.hs` (merge / repairAnalysis / modifyEClass) classify -each refit flag by cause; `srtree/.../SearchSR.hs` counts NLopt evals and dedup. Baseline: -``` -refits=744 merge(best=99,other=17) repair(best=275,other=384) modEC=1 -refitJobs=744 distinctBest=738 evals=4718 -``` -Findings: -- Only **375/744 (50%)** refits had a changed `_best`; the other 401 were cost/size/ - `maxMaybe` churn with an unchanged best tree. Refitting those recomputes an identical - fitness (theta is tied to `_best`) -> pure waste. -- Dedup negligible (738/744 distinct) -> abandoned idea (dedup the refit set). -- ~6.3 evals/refit -> NLopt (TNEWTON) converges far before `opt-iter=100` -> abandoned the - idea of a `--refit-iter` budget. - -**Idea 4 (kept): refit iff `_best` changed AND the class already has a fitness.** -`Build.hs` repairAnalysis + mergeClasses now insert into `_refits` only when -`bestChanged && isJust (_fitness old-class)`. The `isJust` clause drops never-fitted -intermediates: they are invisible to ranking (absent from `_fitRangeDB`) and never reported -(`paretoFront` re-verifies via safe `refit`), so skipping them is lossless. First attempt -used `bestChanged || isNothing` (744->650, kept intermediates); sharpened to -`&& isJust` (-> ~24-43). - -**Gate (5 runs each, MT -N8, same config):** median best `loss_train` (proper CSV parse; -`awk` is broken by commas inside the quoted Numpy/Math columns): -``` -baseline (always-refit): wall 15.96-18.33s, refits 655-910, median loss_train 152.52 -idea 4 (best-changed): wall 11.0-12.8s, refits ~35-43, median loss_train 153.01 -final clean build: wall 11.7-14.0s, median loss_train 153.01 -``` -~30% faster; loss delta +0.49 is within the documented -N8 scheduling noise (historical -range spreads 9.6; idea4 median exactly equals the older 153.01 baseline median). PASSES. -`joinData` untouched (`Info.hs:41-64`). - -**Warm-start (idea 1) — evaluated, NOT implemented.** With refit volume collapsed to ~40, -the refit phase is now ~0.3s/gen; warm-starting those fits (or the final `paretoFront` -re-verify, ~0.2s) would save <2% wall. Not worth the semantic change + a gate. - -**Post-idea4 phase profile (MT -N8, ~12s, phase timers added then removed):** -``` -insertTerms 1.68s (eqsat build of initial DB) -unevalFit (9 vars) 1.78s (0 NLopt evals; 9 bare vars, ~200ms each = compile+eval - over the 103,897-row data.tsv) -initPop (300 fits) 3.7s (1570 evals) -evolveLoop 0.66s (tournament/crossover/mutate/one-shot eqsat) -fitBatchRefits 0.32s (111 evals) <- was 8.0s -fitBatchNewPop 3.8s (1420 evals) -paretoFront 0.17s -``` -Remaining wall is dominated by the required population fits (600 trees/gen) over the -~104k-row dataset (~20ms per NLopt eval); no further easy lever within the approved scope -(subsampling/batching would be a search-semantics change, out of scope). - -**Files changed (final state):** `srtree/src/Algorithm/EqSat/Build.hs` (the two idea-4 -refit-flag conditions; counters removed); `eggp/src/Search.hs` (`Map.!` fix only — the -refit cause/evals/phase instrumentation was removed). No env gates left in the tree. - - -## Session: intermittent `Map.!: given key is not an element in the map` crash — 2026-08-05 - -**Symptom:** steady-state (non-generational) run with `--simplify` crashed intermittently -with `Map.!: given key is not an element in the map` on: -`-d gaussian_train.csv:::y:0,1 -t gaussian_test.csv:::y:0,1 -s 15 -g 1000 --nPop 200 ---non-terminals add,mul,div,log,exp --opt-retries 20 --opt-iter 10 --simplify +RTS -N8 -RTS`. -Not reproduced across 4 full runs of that config (RNG-dependent — only fires when the -simplified front expression's e-graph hits specific shapes). - -**Method:** wrote a standalone stress harness (`ghc -O1 -package srtree`, not part of the -repo) generating random small trees and hammering 4 code paths per tree: `simplifyEqSatDefault` -(the `--simplify` path), `eqSat` with `rewritesParams` (the search's `evolve` path), -raw `runEqSat` + `recalculateBest`, and AD `compileFunAndGrad MultiThread`. This reproduced -the crash on the very first adversarial tree (nested `log`/`exp`, e.g. -`Log((((Exp(x1) / (x1 + 2.0)) / (x1 * x1)) * (x1 * x1)))`), then two more bugs after each fix, -in the `--simplify` path only (`simplifyEqSatDefault t = eqSat t rewrites myCost 30`, -maxIt=30 — the search's own `runEqSat myCost rewritesParams 1` never triggers any of these -because `it == 1` short-circuits before the code paths below run). - -**Bug 1 — `recalculateBest`/`fillUpCosts` (`srtree/.../EqSat.hs`):** `log(exp x) :==: exp(log x)` --style rules can create a cycle in the e-graph reachable from the root. `fillUpCosts`'s -bottom-up fixpoint never assigns a cost to a class in a cycle (no leaf anchor), so -`costs Map.! eid'` (the user's exact error) crashes on the root. Fix: made `nodeCost` total — -a missing child (still uncosted/cyclic) contributes cost 0 and a `Const 0` placeholder -expression, so every class gets a finite cost and expression by the end of the fixpoint. - -**Bug 2 — `getChildrenData`/`getChildrenMinHeight` (`srtree/.../Info.hs`):** looked up raw -(non-canonicalized) e-node child ids directly in `_eClass`. `repairAnalysis` calls -`makeAnalysis` on an e-node whose children may have been merged away since it was queued; -the raw child id is no longer a key in `_eClass` -> `IntMap.!` crash. Fix: `canonical` the -child ids first. - -**Bug 3 — `cleanMaps` (`srtree/.../Build.hs`), triggered by `runEqSat`'s `throttle` -(`srtree/.../EqSat.hs`) when the e-graph exceeds 1500 classes during a `maxIt > 1` run:** -`cleanMaps` replaced `_canonicalMap` with **only the identity entries** -(`IntMap.filterWithKey (\k v -> k == v)`) while `_nextId` keeps counting and `_patDB`/ -`_worklist`/`_analysis`/`_unevaluated`/`_refits` still reference now-untracked stale ids. -Any subsequent `canonical` call on such a stale id crashes (`IntMap.!` on `_canonicalMap`). -This was previously noted as "latent, unreachable" (see the `--generational` session above) -because the search's `runEqSat ... 1` never throttles, but `--simplify`'s `maxIt=30` does. -Fix: `cleanMaps` now keeps `_canonicalMap` **complete** (drop the identity filter) so -`canonical` stays total for every id ever created, while `_eClass` is still pruned to live -representatives (the actual memory-saving part of `cleanMaps`). - -**Bug 4 — AD `insertKey` (`srtree/.../SRTree/AD/Unboxed.hs`):** eqsat constant folding can -produce `Const NaN` nodes (e.g. `Infinity/Infinity`), which `--simplify`'s output can then -contain. `Data.Map`'s `Ord Double` is not a valid total order for `NaN` in a way that keeps -`member`/`lookup` consistent — empirically, `Map.insert (Const NaN) v m; Map.member (Const NaN) m` -can return `False` right after the insert (verified with a standalone repro: inserting two -different `NaN`-producing `Const` expressions, `member`/`lookup` on either key returned -`Nothing`/`False` even immediately after insertion). `insertKey`'s `member`-then-`lookup` -pattern hit this and crashed with `UNBOXED_GETKEYS_MISSING` (`a Map.! k`, `Map.!:` in the -original). Fix: do a single `Map.lookup`; on a miss, take the fresh id `insEntry` assigns -directly instead of looking the key back up. Repeated `NaN` nodes just get separate ids -(no CSE for them), which is harmless — their static value is recomputed identically either way. - -**Validation:** custom stress harness (`ghc -O1 -package srtree`, 60 random trees x 4 exercise -paths) ran clean (`STRESS OK: no crash`) after all 4 fixes. Real user config re-run 3x -steady-state + 1x `--generational`, all `exit=0`, empty stderr, valid CSV output. - -**Files changed:** `srtree/src/Algorithm/EqSat.hs` (`recalculateBest`/`fillUpCosts` total -`nodeCost`), `srtree/src/Algorithm/EqSat/Info.hs` (`getChildrenData`/`getChildrenMinHeight` -canonicalize), `srtree/src/Algorithm/EqSat/Build.hs` (`cleanMaps` keeps `_canonicalMap` -complete), `srtree/src/Algorithm/SRTree/AD/Unboxed.hs` (`insertKey` NaN-safe lookup). Also -added defensive `error`-with-context in place of a few other bare `Map.!`/`IntMap.!` calls -that were audited and found always-safe today (`matchCached`, `reprPrat`, `constrainOnVal`, -`canonical`, `getEClass`) so any future regression fails with a diagnosable message instead -of the bare containers error; added `HasCallStack` to the eqsat call chain for the same -reason. No behavior change on any path that doesn't hit the four bugs above. diff --git a/c_1.err b/c_1.err deleted file mode 100644 index b4a9de7..0000000 --- a/c_1.err +++ /dev/null @@ -1,2696 +0,0 @@ -[acc] compile 1.3983048e-2 s -[acc] eval 2.004507613 s -[acc] compile 4.63501e-4 s -[acc] eval 7.9507797e-2 s -[acc] compile 3.73526e-4 s -[acc] eval 6.8719912e-2 s -[acc] compile 3.6677e-4 s -[acc] eval 0.162062984 s -[acc] compile 3.43007e-4 s -[acc] eval 0.158988855 s -[acc] compile 3.49554e-4 s -[acc] eval 0.158122812 s -[acc] compile 3.19054e-4 s -[acc] eval 0.16634401 s -[acc] compile 3.45989e-4 s -[acc] eval 0.150363009 s -[acc] compile 3.75334e-4 s -[acc] eval 8.6596282e-2 s -[acc] compile 2.502164e-3 s -[acc] eval 6.118357e-3 s -[acc] eval 5.13563e-4 s -[acc] eval 8.80763e-4 s -[acc] eval 1.1098308e-2 s -[acc] compile 3.917274e-3 s -[acc] eval 8.637365e-3 s -[acc] eval 4.7039321e-2 s -[acc] compile 0.227937457 s -[acc] eval 3.2465928e-2 s -[acc] eval 0.265051719 s -[acc] compile 0.225593766 s -[acc] eval 2.5796426e-2 s -[acc] eval 0.26052609 s -[acc] compile 1.1797709e-2 s -[acc] eval 2.6018976e-2 s -[acc] eval 5.633994e-3 s -[acc] eval 1.3564291e-2 s -[acc] eval 3.470818e-3 s -[acc] eval 3.347952e-3 s -[acc] eval 3.760376e-3 s -[acc] eval 3.247181e-3 s -[acc] eval 3.626865e-3 s -[acc] eval 3.344794e-3 s -[acc] eval 3.007796e-3 s -[acc] eval 2.93329e-3 s -[acc] eval 3.138348e-3 s -[acc] eval 9.2022914e-2 s -[acc] compile 0.183247589 s -[acc] eval 1.8713627e-2 s -[acc] eval 4.172525e-3 s -[acc] eval 4.868414e-3 s -[acc] eval 5.58526e-3 s -[acc] eval 5.241639e-3 s -[acc] eval 6.685458e-3 s -[acc] eval 4.148091e-3 s -[acc] eval 4.44212e-3 s -[acc] eval 4.126577e-3 s -[acc] eval 4.402668e-3 s -[acc] eval 5.471951e-3 s -[acc] eval 4.310164e-3 s -[acc] eval 3.64803e-3 s -[acc] eval 4.163857e-3 s -[acc] eval 3.145944e-3 s -[acc] eval 3.37815e-3 s -[acc] eval 3.587017e-3 s -[acc] eval 3.832397e-3 s -[acc] eval 4.030346e-3 s -[acc] eval 3.955417e-3 s -[acc] eval 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1.969927e-3 s -[acc] eval 2.103406e-3 s -[acc] eval 1.948523e-3 s -[acc] eval 1.6336352e-2 s -[acc] eval 2.018396e-3 s -[acc] eval 1.940738e-3 s -[acc] eval 1.909682e-3 s -[acc] eval 2.204221e-3 s -[acc] eval 2.339039e-3 s -[acc] eval 2.390395e-3 s -[acc] eval 1.869129e-3 s -[acc] eval 1.791044e-3 s -[acc] eval 1.625094e-3 s -[acc] eval 1.617539e-3 s -[acc] eval 1.608876e-3 s -[acc] eval 1.691034e-3 s -[acc] eval 2.971758e-3 s -[acc] eval 1.542933e-3 s -[acc] eval 1.657889e-3 s -[acc] eval 2.077273e-3 s -[acc] eval 2.246679e-3 s -[acc] eval 1.608827e-3 s -[acc] eval 1.68873e-3 s -[acc] eval 2.124227e-3 s -[acc] eval 2.082919e-3 s -[acc] eval 2.108425e-3 s -[acc] eval 2.181542e-3 s -[acc] eval 8.9547252e-2 s -[acc] eval 1.3046593e-2 s -[acc] eval 2.594848e-3 s -[acc] eval 2.380786e-3 s -[acc] eval 2.240723e-3 s -[acc] eval 2.246086e-3 s -[acc] eval 2.262507e-3 s -[acc] eval 1.4098897e-2 s -[acc] eval 3.01586e-3 s -[acc] eval 2.636325e-3 s -[acc] eval 2.473854e-3 s -[acc] eval 2.339486e-3 s -[acc] eval 2.46776e-3 s -[acc] eval 2.825e-3 s -[acc] eval 2.699225e-3 s -[acc] eval 1.86335e-3 s -[acc] eval 2.465823e-3 s -[acc] eval 2.815423e-3 s -[acc] eval 2.628106e-3 s -[acc] eval 2.529159e-3 s -[acc] eval 2.55066e-3 s -[acc] eval 2.273257e-3 s -[acc] eval 2.891803e-3 s -[acc] eval 2.073899e-3 s -[acc] eval 2.082156e-3 s -[acc] eval 2.036125e-3 s -[acc] eval 1.875186e-3 s -[acc] eval 2.038217e-3 s -[acc] eval 1.831716e-3 s -[acc] eval 1.884399e-3 s -[acc] eval 1.988445e-3 s -[acc] eval 2.061583e-3 s -[acc] eval 1.890867e-3 s -[acc] eval 0.103353247 s diff --git a/c_2.err b/c_2.err deleted file mode 100644 index 794bd4e..0000000 --- a/c_2.err +++ /dev/null @@ -1,3372 +0,0 @@ -[acc] compile 1.3629743e-2 s -[acc] eval 2.043766534 s -[acc] compile 3.92923e-4 s -[acc] eval 7.4891412e-2 s -[acc] compile 3.88146e-4 s -[acc] eval 6.7545069e-2 s -[acc] compile 3.59113e-4 s -[acc] eval 0.162499088 s -[acc] compile 5.70626e-4 s -[acc] eval 0.156660234 s -[acc] compile 5.45836e-4 s -[acc] eval 0.168595161 s -[acc] compile 5.00789e-4 s -[acc] eval 0.154986263 s -[acc] compile 5.24238e-4 s -[acc] eval 0.14105172 s -[acc] compile 3.36064e-4 s -[acc] eval 6.8964672e-2 s -[acc] compile 0.368586236 s -[acc] eval 6.3667181e-2 s -[acc] eval 0.43654482 s -[acc] compile 0.334744683 s -[acc] eval 1.8596368e-2 s -[acc] eval 0.357943918 s -[acc] compile 0.112989039 s -[acc] eval 5.785252e-3 s -[acc] eval 3.435828e-3 s -[acc] eval 3.434192e-3 s -[acc] eval 3.434861e-3 s -[acc] eval 3.124603e-3 s -[acc] eval 3.032889e-3 s -[acc] eval 0.148542895 s -[acc] compile 0.133023722 s -[acc] eval 1.759197e-2 s -[acc] eval 0.158946509 s -[acc] compile 0.148936831 s -[acc] eval 1.4687292e-2 s -[acc] eval 2.346815e-3 s -[acc] eval 2.248427e-3 s -[acc] eval 1.948955e-3 s -[acc] eval 1.44015e-3 s -[acc] eval 1.82709e-3 s -[acc] eval 1.800178e-3 s -[acc] eval 4.837882e-3 s -[acc] eval 1.988117e-3 s -[acc] eval 1.933808e-3 s -[acc] eval 1.807874e-3 s -[acc] eval 1.774124e-3 s 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compile 7.504067e-3 s -[acc] eval 7.539176e-3 s -[acc] eval 1.8386831e-2 s -[acc] compile 0.109263807 s -[acc] eval 1.9505889e-2 s -[acc] eval 0.131184744 s -[acc] compile 3.29452e-3 s -[acc] eval 3.551579e-3 s -[acc] eval 5.91344e-4 s -[acc] eval 7.59675e-4 s -[acc] eval 9.129577e-3 s -[acc] compile 0.148393107 s -[acc] eval 1.9847493e-2 s -[acc] eval 0.171268209 s -[acc] compile 9.7887444e-2 s -[acc] eval 1.3521003e-2 s -[acc] eval 2.319032e-3 s -[acc] eval 2.200116e-3 s -[acc] eval 2.384221e-3 s -[acc] eval 2.47531e-3 s -[acc] eval 1.909005e-3 s -[acc] eval 2.148836e-3 s -[acc] eval 2.047864e-3 s -[acc] eval 2.20561e-3 s -[acc] eval 2.079397e-3 s -[acc] eval 1.984721e-3 s -[acc] eval 1.950296e-3 s -[acc] eval 1.833501e-3 s -[acc] eval 1.883109e-3 s -[acc] eval 1.866372e-3 s -[acc] eval 1.904582e-3 s -[acc] eval 2.107082e-3 s -[acc] eval 2.610022e-3 s -[acc] eval 2.375246e-3 s -[acc] eval 2.466929e-3 s -[acc] eval 2.431406e-3 s -[acc] eval 2.363003e-3 s -[acc] eval 2.461895e-3 s 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eval 2.662968e-3 s -[acc] eval 0.136057567 s -[acc] compile 5.9705005e-2 s -[acc] eval 2.1060338e-2 s -[acc] eval 8.4584216e-2 s -[acc] compile 6.1727064e-2 s -[acc] eval 1.7040798e-2 s -[acc] eval 8.2325455e-2 s -[acc] compile 2.635694e-3 s -[acc] eval 2.662025e-3 s -[acc] eval 5.00366e-4 s -[acc] eval 5.74029e-4 s -[acc] eval 7.133388e-3 s -[acc] compile 0.217329913 s -[acc] eval 9.003365e-3 s -[acc] eval 0.231955054 s -[acc] compile 0.345114098 s -[acc] eval 1.6246856e-2 s -[acc] eval 0.367359939 s -[acc] compile 0.115365887 s -[acc] eval 6.592364e-3 s -[acc] eval 0.125968885 s -[acc] compile 6.770088e-2 s -[acc] eval 1.2887617e-2 s -[acc] eval 2.360156e-3 s -[acc] eval 2.789628e-3 s -[acc] eval 2.090972e-3 s -[acc] eval 1.735621e-3 s -[acc] eval 1.849619e-3 s -[acc] eval 1.763699e-3 s -[acc] eval 1.956791e-3 s -[acc] eval 1.673167e-3 s -[acc] eval 1.704207e-3 s -[acc] eval 0.102083694 s -[acc] compile 6.107968e-3 s -[acc] eval 5.956893e-3 s -[acc] eval 1.4855251e-2 s -[acc] compile 5.578607e-3 s -[acc] eval 6.877414e-3 s -[acc] eval 2.0151987e-2 s -[acc] compile 5.227676e-3 s -[acc] eval 4.219579e-3 s -[acc] eval 1.0799933e-2 s -[acc] compile 4.390755e-3 s -[acc] eval 3.676329e-3 s -[acc] eval 7.14859e-4 s -[acc] eval 8.69823e-4 s -[acc] eval 1.0902857e-2 s -[acc] compile 5.9629592e-2 s -[acc] eval 1.8088674e-2 s -[acc] eval 8.0215601e-2 s -[acc] compile 0.274554457 s -[acc] eval 2.3096644e-2 s -[acc] eval 0.302562941 s -[acc] compile 6.7014237e-2 s -[acc] eval 1.126718e-2 s -[acc] eval 8.1188395e-2 s -[acc] compile 0.197826399 s -[acc] eval 2.7271636e-2 s -[acc] eval 0.230117527 s -[acc] compile 3.189148e-3 s -[acc] eval 3.154573e-3 s -[acc] eval 5.71319e-4 s -[acc] eval 7.53885e-4 s -[acc] eval 8.65552e-3 s -[acc] compile 5.0366231e-2 s -[acc] eval 5.849966e-3 s -[acc] eval 3.17637e-3 s -[acc] eval 7.714995e-3 s -[acc] eval 2.571457e-3 s -[acc] eval 3.125503e-3 s -[acc] eval 2.701833e-3 s -[acc] eval 2.658147e-3 s -[acc] eval 2.592324e-3 s -[acc] eval 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compile 6.639034e-2 s -[acc] eval 1.1548852e-2 s -[acc] eval 8.1634591e-2 s -[acc] compile 0.226825878 s -[acc] eval 2.9051793e-2 s -[acc] eval 0.261868217 s -[acc] compile 0.220848323 s -[acc] eval 9.29789e-3 s -[acc] eval 0.244850719 s -[acc] compile 0.213118581 s -[acc] eval 2.5355812e-2 s -[acc] eval 0.242115538 s -[acc] compile 5.637644e-3 s -[acc] eval 2.0964559e-2 s -[acc] eval 2.9672506e-2 s -[acc] compile 6.79494e-4 s -[acc] eval 9.913384e-3 s -[acc] compile 0.252989193 s -[acc] eval 2.1474583e-2 s -[acc] eval 0.295144487 s -[acc] compile 7.484871e-3 s -[acc] eval 1.0990303e-2 s -[acc] eval 2.1373528e-2 s -[acc] compile 0.113178454 s -[acc] eval 5.228269e-3 s -[acc] eval 2.3598e-3 s -[acc] eval 2.305051e-3 s -[acc] eval 0.125716473 s -[acc] compile 3.379124e-3 s -[acc] eval 7.913884e-3 s -[acc] eval 1.9484029e-2 s -[acc] compile 5.434854e-3 s -[acc] eval 4.48181e-3 s -[acc] eval 1.503559e-3 s -[acc] eval 1.618248e-3 s -[acc] eval 1.219405e-3 s -[acc] eval 9.55927e-4 s -[acc] 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0.220893067 s -[acc] compile 7.9472422e-2 s -[acc] eval 1.4159855e-2 s -[acc] eval 4.97623e-3 s -[acc] eval 4.508349e-3 s -[acc] eval 4.93031e-3 s -[acc] eval 3.604163e-3 s -[acc] eval 2.668935e-3 s -[acc] eval 2.766027e-3 s -[acc] eval 1.9277884e-2 s -[acc] eval 2.624614e-3 s -[acc] eval 2.543074e-3 s -[acc] eval 3.300606e-3 s -[acc] eval 2.842176e-3 s -[acc] eval 2.586265e-3 s -[acc] eval 2.401665e-3 s -[acc] eval 2.10441e-3 s -[acc] eval 2.180886e-3 s -[acc] eval 2.729903e-3 s -[acc] eval 2.902607e-3 s -[acc] eval 3.012481e-3 s -[acc] eval 2.878015e-3 s -[acc] eval 2.70943e-3 s -[acc] eval 2.715992e-3 s -[acc] eval 2.724127e-3 s -[acc] eval 2.258708e-3 s -[acc] eval 2.640445e-3 s -[acc] eval 2.145942e-3 s -[acc] eval 2.394983e-3 s -[acc] eval 2.362952e-3 s -[acc] eval 2.351622e-3 s -[acc] eval 2.237344e-3 s -[acc] eval 2.459587e-3 s -[acc] eval 0.200853966 s -[acc] compile 6.6840456e-2 s -[acc] eval 1.6576244e-2 s -[acc] eval 3.233438e-3 s -[acc] eval 1.8463989e-2 s -[acc] eval 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mode 100644 index 47c19a3..0000000 --- a/cached_n4.err +++ /dev/null @@ -1,2791 +0,0 @@ -[acc] compile 1.5271705e-2 s -[acc] eval 2.042301595 s -[acc] compile 7.59701e-4 s -[acc] eval 6.834787e-2 s -[acc] compile 5.18696e-4 s -[acc] eval 6.6768488e-2 s -[acc] compile 6.1272e-4 s -[acc] eval 0.158706635 s -[acc] compile 5.11381e-4 s -[acc] eval 0.155929856 s -[acc] compile 5.36555e-4 s -[acc] eval 0.153444838 s -[acc] compile 4.94604e-4 s -[acc] eval 0.157348867 s -[acc] compile 5.1896e-4 s -[acc] eval 0.13788615 s -[acc] compile 3.16191e-4 s -[acc] eval 6.9232713e-2 s -[acc] compile 0.106482157 s -[acc] eval 1.9281558e-2 s -[acc] eval 3.7452135e-2 s -[acc] eval 3.517769e-3 s -[acc] eval 2.748255e-3 s -[acc] eval 2.435205e-3 s -[acc] eval 3.629787e-3 s -[acc] eval 3.225689e-3 s -[acc] eval 2.705368e-3 s -[acc] eval 2.54443e-3 s -[acc] eval 2.607636e-3 s -[acc] eval 2.89961e-3 s -[acc] eval 3.673015e-3 s -[acc] eval 3.223384e-3 s -[acc] eval 2.460641e-3 s -[acc] eval 2.519447e-3 s -[acc] 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eval 0.204154322 s diff --git a/dump_gaussian b/dump_gaussian deleted file mode 100644 index b5811dc..0000000 --- a/dump_gaussian +++ /dev/null @@ -1,1001 +0,0 @@ -id,view,Expression,Numpy,Math,theta,size,loss_train,loss_val,loss_test,maxloss,R2_train,R2_val,R2_test,dl_train,dl_val,dl_test -0,0,((Log((x0 + ((Log(x0) * x1) * Log(Log(x0))))) / (x0 * x0)) * 0.62563032888927),"((np.log((x[:, 0] + ((np.log(x[:, 0]) * x[:, 1]) * np.log(np.log(x[:, 0]))))) / (x[:, 0] * x[:, 0])) * t[0])","$$\left(\frac{\operatorname{log}(\left(x_{0} + \left(\left(\operatorname{log}(x_{0}) \cdot x_{1}\right) \cdot \operatorname{log}(\operatorname{log}(x_{0}))\right)\right))}{\left(x_{0} \cdot x_{0}\right)} \cdot \theta_{0}\right)$$",0.62563032888927,17,,,,Infinity,,,,,, -1,0,(((-0.10899231604180881 + ((Exp(x0) / (x1 * -0.5567232260961726)) ^ x0)) ^ ((x1 + -0.7522098509181003) * -0.6997643636384127)) ^ Exp(((x0 ^ x1) * -0.7633424317402342))),"(t[0] + (np.exp(x[:, 0]) / (x[:, 1] * t[1])) ** x[:, 0]) ** ((x[:, 1] + t[2]) * t[3]) ** np.exp((x[:, 0] ** x[:, 1] * t[4]))","$${{\left(\theta_{0} + {\frac{\operatorname{exp}(x_{0})}{\left(x_{1} \cdot \theta_{1}\right)}^{x_{0}}}\right)^{\left(\left(x_{1} + \theta_{2}\right) \cdot \theta_{3}\right)}}^{\operatorname{exp}(\left({x_{0}^{x_{1}}} \cdot \theta_{4}\right))}}$$",-0.10899231604180881;-0.5567232260961726;-0.7522098509181003;-0.6997643636384127;-0.7633424317402342,23,,,,Infinity,,,,,, -2,0,(Exp((((x0 * -50829.34353823465) / x1) + -4767.109461741431)) * 11046.73180548436),"(np.exp((((x[:, 0] * t[0]) / x[:, 1]) + t[1])) * t[2])","$$\left(\operatorname{exp}(\left(\frac{\left(x_{0} \cdot \theta_{0}\right)}{x_{1}} + \theta_{1}\right)) \cdot \theta_{2}\right)$$",-50829.34353823465;-4767.109461741431;11046.73180548436,10,1.4573554749975553e-2,1.4573554749975553e-2,1.4573554749975553e-2,1.4573554749975553e-2,-0.3448645492182232,-0.3448645492182232,-0.3448645492182232,25.534992495931032,25.534992495931032,25.534992495931032 -3,0,Log(((((x0 * ((x0 * 0.42071683634515944) * 0.5585372703087588)) + 0.8920775268963044) / (0.2770776378207168 + Log(x0))) / (Exp(Exp(x1)) + -0.5994280568910184))),"np.log(((((x[:, 0] * ((x[:, 0] * t[0]) * t[1])) + t[2]) / (t[3] + np.log(x[:, 0]))) / (np.exp(np.exp(x[:, 1])) + t[4])))","$$\operatorname{log}(\frac{\frac{\left(\left(x_{0} \cdot \left(\left(x_{0} \cdot \theta_{0}\right) \cdot \theta_{1}\right)\right) + \theta_{2}\right)}{\left(\theta_{3} + \operatorname{log}(x_{0})\right)}}{\left(\operatorname{exp}(\operatorname{exp}(x_{1})) + \theta_{4}\right)})$$",0.42071683634515944;0.5585372703087588;0.8920775268963044;0.2770776378207168;-0.5994280568910184,21,,,,Infinity,,,,,, -4,0,Exp(Log(((0.18977703867598109 + (-1.9443688113963787e-2 / x1)) / (Exp(x0) / Exp(x0))))),"np.exp(np.log(((t[0] + (t[1] / x[:, 1])) / (np.exp(x[:, 0]) / np.exp(x[:, 0])))))","$$\operatorname{exp}(\operatorname{log}(\frac{\left(\theta_{0} + \frac{\theta_{1}}{x_{1}}\right)}{\frac{\operatorname{exp}(x_{0})}{\operatorname{exp}(x_{0})}}))$$",0.18977703867598109;-1.9443688113963787e-2,13,1.2539942975038545e-2,1.2539942975038545e-2,1.2539942975038545e-2,1.2539942975038545e-2,-0.15720049402330805,-0.15720049402330805,-0.15720049402330805,29.34996775713189,29.34996775713189,29.34996775713189 -5,0,(((-0.817794690048393 * x1) / Exp(((x1 ^ x1) ^ 0.40641747296925224))) * (-0.6946975186058839 ^ Exp(((x0 * x0) * 0.968315717765821)))),"(((t[0] * x[:, 1]) / np.exp(x[:, 1] ** x[:, 1] ** t[1])) * t[2] ** np.exp(((x[:, 0] * x[:, 0]) * t[3])))","$$\left(\frac{\left(\theta_{0} \cdot x_{1}\right)}{\operatorname{exp}({{x_{1}^{x_{1}}}^{\theta_{1}}})} \cdot {\theta_{2}^{\operatorname{exp}(\left(\left(x_{0} \cdot x_{0}\right) \cdot \theta_{3}\right))}}\right)$$",-0.817794690048393;0.40641747296925224;-0.6946975186058839;0.968315717765821,19,,,,Infinity,,,,,, -6,0,((((Exp(x0) + x0) / ((x0 + 0.4147741242873578) * (((0.8473490923065312 ^ x1) ^ x1) / ((x0 / x1) * 0.47811818937293227)))) + (((Log(x1) + 0.40267040805714993) ^ (x1 / (x1 * -0.47615436503329633))) / (0.1435840771414535 + x1))) * 0.8150362322098994),"((((np.exp(x[:, 0]) + x[:, 0]) / ((x[:, 0] + t[0]) * (t[1] ** x[:, 1] ** x[:, 1] / ((x[:, 0] / x[:, 1]) * t[2])))) + ((np.log(x[:, 1]) + t[3]) ** (x[:, 1] / (x[:, 1] * t[4])) / (t[5] + x[:, 1]))) * t[6])","$$\left(\left(\frac{\left(\operatorname{exp}(x_{0}) + x_{0}\right)}{\left(\left(x_{0} + \theta_{0}\right) \cdot \frac{{{\theta_{1}^{x_{1}}}^{x_{1}}}}{\left(\frac{x_{0}}{x_{1}} \cdot \theta_{2}\right)}\right)} + \frac{{\left(\operatorname{log}(x_{1}) + \theta_{3}\right)^{\frac{x_{1}}{\left(x_{1} \cdot \theta_{4}\right)}}}}{\left(\theta_{5} + x_{1}\right)}\right) \cdot \theta_{6}\right)$$",0.4147741242873578;0.8473490923065312;0.47811818937293227;0.40267040805714993;-0.47615436503329633;0.1435840771414535;0.8150362322098994,37,,,,Infinity,,,,,, -7,0,((((Exp((-0.6264211284116645 ^ Log(x1))) * -0.9353533015033187) * (Exp(Log(x0)) * 0.35901800991341415)) * -0.9226426142309379) / Exp((Exp(x1) ^ 0.12156404623465933))),"((((np.exp(t[0] ** np.log(x[:, 1])) * t[1]) * (np.exp(np.log(x[:, 0])) * t[2])) * t[3]) / np.exp(np.exp(x[:, 1]) ** t[4]))","$$\frac{\left(\left(\left(\operatorname{exp}({\theta_{0}^{\operatorname{log}(x_{1})}}) \cdot \theta_{1}\right) \cdot \left(\operatorname{exp}(\operatorname{log}(x_{0})) \cdot \theta_{2}\right)\right) \cdot \theta_{3}\right)}{\operatorname{exp}({\operatorname{exp}(x_{1})^{\theta_{4}}})}$$",-0.6264211284116645;-0.9353533015033187;0.35901800991341415;-0.9226426142309379;0.12156404623465933,21,,,,Infinity,,,,,, -8,0,(Exp((((x1 / x0) ^ Exp((x1 + x1))) ^ ((0.3053756822068219 + x0) * Log(Log((-0.5320733649868707 ^ x0)))))) * Exp(Exp(Log(Exp(((0.7980164712436458 / x1) ^ x1)))))),"(np.exp((x[:, 1] / x[:, 0]) ** np.exp((x[:, 1] + x[:, 1])) ** ((t[0] + x[:, 0]) * np.log(np.log(t[1] ** x[:, 0])))) * np.exp(np.exp(np.log(np.exp((t[2] / x[:, 1]) ** x[:, 1])))))","$$\left(\operatorname{exp}({{\frac{x_{1}}{x_{0}}^{\operatorname{exp}(\left(x_{1} + x_{1}\right))}}^{\left(\left(\theta_{0} + x_{0}\right) \cdot \operatorname{log}(\operatorname{log}({\theta_{1}^{x_{0}}}))\right)}}) \cdot \operatorname{exp}(\operatorname{exp}(\operatorname{log}(\operatorname{exp}({\frac{\theta_{2}}{x_{1}}^{x_{1}}}))))\right)$$",0.3053756822068219;-0.5320733649868707;0.7980164712436458,29,,,,Infinity,,,,,, -9,0,Exp((Exp((x1 ^ -7.261456223005607)) ^ (Log(x1) / Exp(x1)))),"np.exp(np.exp(x[:, 1] ** t[0]) ** (np.log(x[:, 1]) / np.exp(x[:, 1])))","$$\operatorname{exp}({\operatorname{exp}({x_{1}^{\theta_{0}}})^{\frac{\operatorname{log}(x_{1})}{\operatorname{exp}(x_{1})}}})$$",-7.261456223005607,11,0.8923099827409064,0.8923099827409064,0.8923099827409064,0.8923099827409064,-81.34340099513352,-81.34340099513352,-81.34340099513352,22.53335808134306,22.53335808134306,22.53335808134306 -10,0,(-0.271594290590885 + x1),"(t[0] + x[:, 1])","$$\left(\theta_{0} + x_{1}\right)$$",-0.271594290590885,3,1.8305762287934728e-2,1.8305762287934728e-2,1.8305762287934728e-2,1.8305762287934728e-2,-0.6892769931440774,-0.6892769931440774,-0.6892769931440774,5.3332369874311105,5.3332369874311105,5.3332369874311105 -11,0,Log(((((x0 ^ -0.20492579045704548) + x0) ^ (9.655717835835705e-2 / x0)) + (6.988876705791294e-4 ^ Exp((x1 * (0.8191509217598499 * x0)))))),"np.log(((x[:, 0] ** t[0] + x[:, 0]) ** (t[1] / x[:, 0]) + t[2] ** np.exp((x[:, 1] * (t[3] * x[:, 0])))))","$$\operatorname{log}(\left({\left({x_{0}^{\theta_{0}}} + x_{0}\right)^{\frac{\theta_{1}}{x_{0}}}} + {\theta_{2}^{\operatorname{exp}(\left(x_{1} \cdot \left(\theta_{3} \cdot x_{0}\right)\right))}}\right))$$",-0.20492579045704548;9.655717835835705e-2;6.988876705791294e-4;0.8191509217598499,19,9.366441772318788e-3,9.366441772318788e-3,9.366441772318788e-3,9.366441772318788e-3,0.13565388074386076,0.13565388074386076,0.13565388074386076,49.839396508453824,49.839396508453824,49.839396508453824 -12,0,(((x0 ^ -0.751667640916592) * x1) ^ Exp((1.525851202146237 ^ x1))),"(x[:, 0] ** t[0] * x[:, 1]) ** np.exp(t[1] ** x[:, 1])","$${\left({x_{0}^{\theta_{0}}} \cdot x_{1}\right)^{\operatorname{exp}({\theta_{1}^{x_{1}}})}}$$",-0.751667640916592;1.525851202146237,10,3.7183453062699916e-3,3.7183453062699916e-3,3.7183453062699916e-3,3.7183453062699916e-3,0.6568667789056148,0.6568667789056148,0.6568667789056148,21.962791073791475,21.962791073791475,21.962791073791475 -13,0,(Exp(x1) * 4.650783244483925e-2),"(np.exp(x[:, 1]) * t[0])","$$\left(\operatorname{exp}(x_{1}) \cdot \theta_{0}\right)$$",4.650783244483925e-2,4,1.02054841442845e-2,1.02054841442845e-2,1.02054841442845e-2,1.02054841442845e-2,5.8226076703769e-2,5.8226076703769e-2,5.8226076703769e-2,7.575287315577059,7.575287315577059,7.575287315577059 -14,0,(Exp(((-0.6411237710009179 / (x1 / x1)) + (-0.7214636701248993 / (0.5135103666282215 / x0)))) ^ Exp(((Exp(Log(x1)) * Log((-0.8007740317271557 ^ x1))) + ((0.7170625474993807 / x0) * -0.3623438477495091)))),"np.exp(((t[0] / (x[:, 1] / x[:, 1])) + (t[1] / (t[2] / x[:, 0])))) ** np.exp(((np.exp(np.log(x[:, 1])) * np.log(t[3] ** x[:, 1])) + ((t[4] / x[:, 0]) * t[5])))","$${\operatorname{exp}(\left(\frac{\theta_{0}}{\frac{x_{1}}{x_{1}}} + \frac{\theta_{1}}{\frac{\theta_{2}}{x_{0}}}\right))^{\operatorname{exp}(\left(\left(\operatorname{exp}(\operatorname{log}(x_{1})) \cdot \operatorname{log}({\theta_{3}^{x_{1}}})\right) + \left(\frac{\theta_{4}}{x_{0}} \cdot \theta_{5}\right)\right))}}$$",-0.6411237710009179;-0.7214636701248993;0.5135103666282215;-0.8007740317271557;0.7170625474993807;-0.3623438477495091,28,,,,Infinity,,,,,, -15,0,(((Exp(Log(x1)) + (7.983979360722734 / (2.352159276545472 * x1))) * Exp((1.1046069815248696 * Log((x0 * 4.029108987582322e-3))))) * (((x0 * x0) ^ (-0.4992884718282777 * x0)) ^ Exp((-4.908442225183129e-2 + (-7.952871247718402e-2 * x0))))),"(((np.exp(np.log(x[:, 1])) + (t[0] / (t[1] * x[:, 1]))) * np.exp((t[2] * np.log((x[:, 0] * t[3]))))) * (x[:, 0] * x[:, 0]) ** (t[4] * x[:, 0]) ** np.exp((t[5] + (t[6] * x[:, 0]))))","$$\left(\left(\left(\operatorname{exp}(\operatorname{log}(x_{1})) + \frac{\theta_{0}}{\left(\theta_{1} \cdot x_{1}\right)}\right) \cdot \operatorname{exp}(\left(\theta_{2} \cdot \operatorname{log}(\left(x_{0} \cdot \theta_{3}\right))\right))\right) \cdot {{\left(x_{0} \cdot x_{0}\right)^{\left(\theta_{4} \cdot x_{0}\right)}}^{\operatorname{exp}(\left(\theta_{5} + \left(\theta_{6} \cdot x_{0}\right)\right))}}\right)$$",7.983979360722734;2.352159276545472;1.1046069815248696;4.029108987582322e-3;-0.4992884718282777;-4.908442225183129e-2;-7.952871247718402e-2,32,1.358488402941307e-2,1.358488402941307e-2,1.358488402941307e-2,1.358488402941307e-2,-0.25362886748197755,-0.25362886748197755,-0.25362886748197755,84.46988741795897,84.46988741795897,84.46988741795897 -16,0,Exp(Log(((0.4323212132884867 * (-0.5563941210081238 + (-0.22894211384166407 ^ x0))) / ((-0.1350413263781819 ^ x0) + (-4.170277679253542e-2 ^ (x0 * x1)))))),"np.exp(np.log(((t[0] * (t[1] + t[2] ** x[:, 0])) / (t[3] ** x[:, 0] + t[4] ** (x[:, 0] * x[:, 1])))))","$$\operatorname{exp}(\operatorname{log}(\frac{\left(\theta_{0} \cdot \left(\theta_{1} + {\theta_{2}^{x_{0}}}\right)\right)}{\left({\theta_{3}^{x_{0}}} + {\theta_{4}^{\left(x_{0} \cdot x_{1}\right)}}\right)}))$$",0.4323212132884867;-0.5563941210081238;-0.22894211384166407;-0.1350413263781819;-4.170277679253542e-2,19,,,,Infinity,,,,,, -17,0,(Log(Log((x0 * x1))) ^ Exp((-7.0922137075229985e-3 * (x0 ^ -0.9169883388600399)))),"np.log(np.log((x[:, 0] * x[:, 1]))) ** np.exp((t[0] * x[:, 0] ** t[1]))","$${\operatorname{log}(\operatorname{log}(\left(x_{0} \cdot x_{1}\right)))^{\operatorname{exp}(\left(\theta_{0} \cdot {x_{0}^{\theta_{1}}}\right))}}$$",-7.0922137075229985e-3;-0.9169883388600399,12,,,,Infinity,,,,,, -18,0,((((Log((Log(x1) * -0.17095921031147743)) ^ x0) ^ ((x1 + Log((x1 + -0.5130263804920494))) + x0)) / (0.7340502287670319 * Exp(((x0 * 0.6674932453025795) ^ Exp(Log(x0)))))) * Log(Log(x0))),"((np.log((np.log(x[:, 1]) * t[0])) ** x[:, 0] ** ((x[:, 1] + np.log((x[:, 1] + t[1]))) + x[:, 0]) / (t[2] * np.exp((x[:, 0] * t[3]) ** np.exp(np.log(x[:, 0]))))) * np.log(np.log(x[:, 0])))","$$\left(\frac{{{\operatorname{log}(\left(\operatorname{log}(x_{1}) \cdot \theta_{0}\right))^{x_{0}}}^{\left(\left(x_{1} + \operatorname{log}(\left(x_{1} + \theta_{1}\right))\right) + x_{0}\right)}}}{\left(\theta_{2} \cdot \operatorname{exp}({\left(x_{0} \cdot \theta_{3}\right)^{\operatorname{exp}(\operatorname{log}(x_{0}))}})\right)} \cdot \operatorname{log}(\operatorname{log}(x_{0}))\right)$$",-0.17095921031147743;-0.5130263804920494;0.7340502287670319;0.6674932453025795,31,,,,Infinity,,,,,, -19,0,(Log(((x0 / Exp(x1)) + Log((-0.9847612308760518 / x1)))) * (Exp(Exp(Log(x0))) + Log(Log((0.31616839550997733 ^ x1))))),"(np.log(((x[:, 0] / np.exp(x[:, 1])) + np.log((t[0] / x[:, 1])))) * (np.exp(np.exp(np.log(x[:, 0]))) + np.log(np.log(t[1] ** x[:, 1]))))","$$\left(\operatorname{log}(\left(\frac{x_{0}}{\operatorname{exp}(x_{1})} + \operatorname{log}(\frac{\theta_{0}}{x_{1}})\right)) \cdot \left(\operatorname{exp}(\operatorname{exp}(\operatorname{log}(x_{0}))) + \operatorname{log}(\operatorname{log}({\theta_{1}^{x_{1}}}))\right)\right)$$",-0.9847612308760518;0.31616839550997733,21,,,,Infinity,,,,,, -20,0,((((x0 + x1) + (x0 ^ (0.8952374112115637 * x0))) ^ ((0.32317623451275146 + x0) * (4.719078881246552e-2 * x1))) + Log(Log(((Exp(x1) + (x0 * (x1 ^ -0.2286517666053789))) / Log((x1 * (x1 ^ x1))))))),"(((x[:, 0] + x[:, 1]) + x[:, 0] ** (t[0] * x[:, 0])) ** ((t[1] + x[:, 0]) * (t[2] * x[:, 1])) + np.log(np.log(((np.exp(x[:, 1]) + (x[:, 0] * x[:, 1] ** t[3])) / np.log((x[:, 1] * x[:, 1] ** x[:, 1]))))))","$$\left({\left(\left(x_{0} + x_{1}\right) + {x_{0}^{\left(\theta_{0} \cdot x_{0}\right)}}\right)^{\left(\left(\theta_{1} + x_{0}\right) \cdot \left(\theta_{2} \cdot x_{1}\right)\right)}} + \operatorname{log}(\operatorname{log}(\frac{\left(\operatorname{exp}(x_{1}) + \left(x_{0} \cdot {x_{1}^{\theta_{3}}}\right)\right)}{\operatorname{log}(\left(x_{1} \cdot {x_{1}^{x_{1}}}\right))}))\right)$$",0.8952374112115637;0.32317623451275146;4.719078881246552e-2;-0.2286517666053789,35,,,,Infinity,,,,,, -21,0,(Log(Log(((x1 / x0) + (((x0 + x0) / (0.7716232286969656 ^ x0)) / x0)))) * Log(Exp((x0 * (x0 ^ -2.7444870301084485))))),"(np.log(np.log(((x[:, 1] / x[:, 0]) + (((x[:, 0] + x[:, 0]) / t[0] ** x[:, 0]) / x[:, 0])))) * np.log(np.exp((x[:, 0] * x[:, 0] ** t[1]))))","$$\left(\operatorname{log}(\operatorname{log}(\left(\frac{x_{1}}{x_{0}} + \frac{\frac{\left(x_{0} + x_{0}\right)}{{\theta_{0}^{x_{0}}}}}{x_{0}}\right))) \cdot \operatorname{log}(\operatorname{exp}(\left(x_{0} \cdot {x_{0}^{\theta_{1}}}\right)))\right)$$",0.7716232286969656;-2.7444870301084485,23,6.1346698969358265e-3,6.1346698969358265e-3,6.1346698969358265e-3,6.1346698969358265e-3,0.4338855408246197,0.4338855408246197,0.4338855408246197,54.583536352375575,54.583536352375575,54.583536352375575 -22,0,(Exp(Log((-0.908618383061254 + x0))) ^ ((2.9900583498112643e-2 * x0) + (0.6559860151532763 / x0))),"np.exp(np.log((t[0] + x[:, 0]))) ** ((t[1] * x[:, 0]) + (t[2] / x[:, 0]))","$${\operatorname{exp}(\operatorname{log}(\left(\theta_{0} + x_{0}\right)))^{\left(\left(\theta_{1} \cdot x_{0}\right) + \frac{\theta_{2}}{x_{0}}\right)}}$$",-0.908618383061254;2.9900583498112643e-2;0.6559860151532763,13,,,,Infinity,,,,,, -23,0,Log((((x0 * x1) ^ -1.3347329131445162e-2) + 4.676234554120854e-2)),"np.log(((x[:, 0] * x[:, 1]) ** t[0] + t[1]))","$$\operatorname{log}(\left({\left(x_{0} \cdot x_{1}\right)^{\theta_{0}}} + \theta_{1}\right))$$",-1.3347329131445162e-2;4.676234554120854e-2,8,1.0777619307146631e-2,1.0777619307146631e-2,1.0777619307146631e-2,1.0777619307146631e-2,5.428779743961276e-3,5.428779743961276e-3,5.428779743961276e-3,19.61883092055422,19.61883092055422,19.61883092055422 -24,0,((((((x0 + -2.304174021441774e-2) * -0.7421098546461207) ^ 0.30442355040188995) + -0.615475457981378) * 0.988983278318315) / x1),"(((((x[:, 0] + t[0]) * t[1]) ** t[2] + t[3]) * t[4]) / x[:, 1])","$$\frac{\left(\left({\left(\left(x_{0} + \theta_{0}\right) \cdot \theta_{1}\right)^{\theta_{2}}} + \theta_{3}\right) \cdot \theta_{4}\right)}{x_{1}}$$",-2.304174021441774e-2;-0.7421098546461207;0.30442355040188995;-0.615475457981378;0.988983278318315,13,,,,Infinity,,,,,, -25,0,(Log(((-0.29043491634269336 / (1.1477600889048776 * (x1 * -1.0238357893682501))) + 1.5320120789911775)) ^ Log(x0)),"np.log(((t[0] / (t[1] * (x[:, 1] * t[2]))) + t[3])) ** np.log(x[:, 0])","$${\operatorname{log}(\left(\frac{\theta_{0}}{\left(\theta_{1} \cdot \left(x_{1} \cdot \theta_{2}\right)\right)} + \theta_{3}\right))^{\operatorname{log}(x_{0})}}$$",-0.29043491634269336;1.1477600889048776;-1.0238357893682501;1.5320120789911775,13,0.877701779315445,0.877701779315445,0.877701779315445,0.877701779315445,-79.9953390259215,-79.9953390259215,-79.9953390259215,35.90637138469233,35.90637138469233,35.90637138469233 -26,0,(Log((((Exp(x0) + ((x1 * 0.9882356155551903) / x0)) ^ x1) / ((0.48100272302531855 / (0.46397797344842995 / (-0.7447856393045917 + x1))) ^ ((-0.5012632563970889 / (2.6778848385430543e-2 / x0)) + 0.1590517027675652)))) ^ x1),"np.log(((np.exp(x[:, 0]) + ((x[:, 1] * t[0]) / x[:, 0])) ** x[:, 1] / (t[1] / (t[2] / (t[3] + x[:, 1]))) ** ((t[4] / (t[5] / x[:, 0])) + t[6]))) ** x[:, 1]","$${\operatorname{log}(\frac{{\left(\operatorname{exp}(x_{0}) + \frac{\left(x_{1} \cdot \theta_{0}\right)}{x_{0}}\right)^{x_{1}}}}{{\frac{\theta_{1}}{\frac{\theta_{2}}{\left(\theta_{3} + x_{1}\right)}}^{\left(\frac{\theta_{4}}{\frac{\theta_{5}}{x_{0}}} + \theta_{6}\right)}}})^{x_{1}}}$$",0.9882356155551903;0.48100272302531855;0.46397797344842995;-0.7447856393045917;-0.5012632563970889;2.6778848385430543e-2;0.1590517027675652,29,,,,Infinity,,,,,, -27,0,(8.226801725115918e-3 / x1),"(t[0] / x[:, 1])","$$\frac{\theta_{0}}{x_{1}}$$",8.226801725115918e-3,3,1.3313188344055817e-2,1.3313188344055817e-2,1.3313188344055817e-2,1.3313188344055817e-2,-0.2285564742545716,-0.2285564742545716,-0.2285564742545716,5.32874367088162,5.32874367088162,5.32874367088162 -28,0,Log((Exp((x0 + x0)) * -0.8631186863721904)),"np.log((np.exp((x[:, 0] + x[:, 0])) * t[0]))","$$\operatorname{log}(\left(\operatorname{exp}(\left(x_{0} + x_{0}\right)) \cdot \theta_{0}\right))$$",-0.8631186863721904,7,,,,Infinity,,,,,, -29,0,(((Exp(x0) + Exp(Exp(((x0 ^ x0) ^ (-0.6120891284675323 + x1))))) / (Log(x0) + 0.43352330783419424)) / ((Exp((((0.6211172757204262 + x0) * -5.4622535062865296e-2) * 0.8281351169012158)) ^ Log(x0)) ^ (Exp((x1 + 0.5105639611597665)) ^ Log(x1)))),"(((np.exp(x[:, 0]) + np.exp(np.exp(x[:, 0] ** x[:, 0] ** (t[0] + x[:, 1])))) / (np.log(x[:, 0]) + t[1])) / np.exp((((t[2] + x[:, 0]) * t[3]) * t[4])) ** np.log(x[:, 0]) ** np.exp((x[:, 1] + t[5])) ** np.log(x[:, 1]))","$$\frac{\frac{\left(\operatorname{exp}(x_{0}) + \operatorname{exp}(\operatorname{exp}({{x_{0}^{x_{0}}}^{\left(\theta_{0} + x_{1}\right)}}))\right)}{\left(\operatorname{log}(x_{0}) + \theta_{1}\right)}}{{{\operatorname{exp}(\left(\left(\left(\theta_{2} + x_{0}\right) \cdot \theta_{3}\right) \cdot \theta_{4}\right))^{\operatorname{log}(x_{0})}}^{{\operatorname{exp}(\left(x_{1} + \theta_{5}\right))^{\operatorname{log}(x_{1})}}}}}$$",-0.6120891284675323;0.43352330783419424;0.6211172757204262;-5.4622535062865296e-2;0.8281351169012158;0.5105639611597665,37,126802.29675394173,126802.29675394173,126802.29675394173,126802.29675394173,-1.1701462135760428e7,-1.1701462135760428e7,-1.1701462135760428e7,114215.4899375212,114215.4899375212,114215.4899375212 -30,0,Exp((Log((-0.787651379267476 * (x0 ^ ((-0.950557566553154 ^ x0) ^ (x1 ^ 0.8020074656572787))))) ^ ((((0.7188174427302787 * Log(x1)) / (0.2992580308394093 * (-2.3481215441264247e-2 * x1))) * ((x0 / (x0 + -0.6416046535229541)) / x1)) ^ (x1 * 0.597312382713762)))),"np.exp(np.log((t[0] * x[:, 0] ** t[1] ** x[:, 0] ** x[:, 1] ** t[2])) ** (((t[3] * np.log(x[:, 1])) / (t[4] * (t[5] * x[:, 1]))) * ((x[:, 0] / (x[:, 0] + t[6])) / x[:, 1])) ** (x[:, 1] * t[7]))","$$\operatorname{exp}({\operatorname{log}(\left(\theta_{0} \cdot {x_{0}^{{{\theta_{1}^{x_{0}}}^{{x_{1}^{\theta_{2}}}}}}}\right))^{{\left(\frac{\left(\theta_{3} \cdot \operatorname{log}(x_{1})\right)}{\left(\theta_{4} \cdot \left(\theta_{5} \cdot x_{1}\right)\right)} \cdot \frac{\frac{x_{0}}{\left(x_{0} + \theta_{6}\right)}}{x_{1}}\right)^{\left(x_{1} \cdot \theta_{7}\right)}}}})$$",-0.787651379267476;-0.950557566553154;0.8020074656572787;0.7188174427302787;0.2992580308394093;-2.3481215441264247e-2;-0.6416046535229541;0.597312382713762,36,,,,Infinity,,,,,, -31,0,((Log(Log((0.33932097595029176 * (x0 * x0)))) + Exp(Log(Log(((x0 + x0) / x0))))) * Log(Exp((-0.16831472576980322 ^ (((x0 + -0.8701375992929226) / x0) * (x0 * x0)))))),"((np.log(np.log((t[0] * (x[:, 0] * x[:, 0])))) + np.exp(np.log(np.log(((x[:, 0] + x[:, 0]) / x[:, 0]))))) * np.log(np.exp(t[1] ** (((x[:, 0] + t[2]) / x[:, 0]) * (x[:, 0] * x[:, 0])))))","$$\left(\left(\operatorname{log}(\operatorname{log}(\left(\theta_{0} \cdot \left(x_{0} \cdot x_{0}\right)\right))) + \operatorname{exp}(\operatorname{log}(\operatorname{log}(\frac{\left(x_{0} + x_{0}\right)}{x_{0}})))\right) \cdot \operatorname{log}(\operatorname{exp}({\theta_{1}^{\left(\frac{\left(x_{0} + \theta_{2}\right)}{x_{0}} \cdot \left(x_{0} \cdot x_{0}\right)\right)}}))\right)$$",0.33932097595029176;-0.16831472576980322;-0.8701375992929226,30,,,,Infinity,,,,,, -32,0,(Exp(Log(((-0.6298117558935161 + x0) + x1))) + (Log(Log((-0.45831733367340566 ^ (x1 + -0.23140560627815998)))) + (Log(x0) * (x1 * -8.136370974319063e-2)))),"(np.exp(np.log(((t[0] + x[:, 0]) + x[:, 1]))) + (np.log(np.log(t[1] ** (x[:, 1] + t[2]))) + (np.log(x[:, 0]) * (x[:, 1] * t[3]))))","$$\left(\operatorname{exp}(\operatorname{log}(\left(\left(\theta_{0} + x_{0}\right) + x_{1}\right))) + \left(\operatorname{log}(\operatorname{log}({\theta_{1}^{\left(x_{1} + \theta_{2}\right)}})) + \left(\operatorname{log}(x_{0}) \cdot \left(x_{1} \cdot \theta_{3}\right)\right)\right)\right)$$",-0.6298117558935161;-0.45831733367340566;-0.23140560627815998;-8.136370974319063e-2,22,,,,Infinity,,,,,, -33,0,Log(Exp(Log((1.0566783659676662 * x0)))),"np.log(np.exp(np.log((t[0] * x[:, 0]))))","$$\operatorname{log}(\operatorname{exp}(\operatorname{log}(\left(\theta_{0} \cdot x_{0}\right))))$$",1.0566783659676662,6,0.164444663973442,0.164444663973442,0.164444663973442,0.164444663973442,-14.175144477798478,-14.175144477798478,-14.175144477798478,11.82555260754834,11.82555260754834,11.82555260754834 -34,0,((((x0 ^ x1) + (-0.3763021157198979 ^ ((Log(x0) ^ Log(x1)) ^ x1))) / (Log(((x0 ^ -0.22776918933671686) * 0.8786445715182172)) * -0.44823025806322714)) * 0.6270284420388716),"(((x[:, 0] ** x[:, 1] + t[0] ** np.log(x[:, 0]) ** np.log(x[:, 1]) ** x[:, 1]) / (np.log((x[:, 0] ** t[1] * t[2])) * t[3])) * t[4])","$$\left(\frac{\left({x_{0}^{x_{1}}} + {\theta_{0}^{{{\operatorname{log}(x_{0})^{\operatorname{log}(x_{1})}}^{x_{1}}}}}\right)}{\left(\operatorname{log}(\left({x_{0}^{\theta_{1}}} \cdot \theta_{2}\right)) \cdot \theta_{3}\right)} \cdot \theta_{4}\right)$$",-0.3763021157198979;-0.22776918933671686;0.8786445715182172;-0.44823025806322714;0.6270284420388716,24,,,,Infinity,,,,,, -35,0,((((x1 / x1) ^ ((x0 * x1) * (0.23280815437648295 / x1))) + Exp(((Exp(x0) / (x0 ^ -0.4096883332288457)) * ((x0 / x0) + x0)))) * x1),"(((x[:, 1] / x[:, 1]) ** ((x[:, 0] * x[:, 1]) * (t[0] / x[:, 1])) + np.exp(((np.exp(x[:, 0]) / x[:, 0] ** t[1]) * ((x[:, 0] / x[:, 0]) + x[:, 0])))) * x[:, 1])","$$\left(\left({\frac{x_{1}}{x_{1}}^{\left(\left(x_{0} \cdot x_{1}\right) \cdot \frac{\theta_{0}}{x_{1}}\right)}} + \operatorname{exp}(\left(\frac{\operatorname{exp}(x_{0})}{{x_{0}^{\theta_{1}}}} \cdot \left(\frac{x_{0}}{x_{0}} + x_{0}\right)\right))\right) \cdot x_{1}\right)$$",0.23280815437648295;-0.4096883332288457,27,2.128550108815956e9,2.128550108815956e9,2.128550108815956e9,2.128550108815956e9,-1.9642507484808395e11,-1.9642507484808395e11,-1.9642507484808395e11,1.915695158121132e9,1.915695158121132e9,1.915695158121132e9 -36,0,(Exp(Log((7.620099383310752e-2 / Log(x1)))) + (Exp((x0 * -0.3727630917323328)) / (((x1 + 0.45545199901671496) ^ ((0.8071613806530901 ^ x1) + x1)) / x0))),"(np.exp(np.log((t[0] / np.log(x[:, 1])))) + (np.exp((x[:, 0] * t[1])) / ((x[:, 1] + t[2]) ** (t[3] ** x[:, 1] + x[:, 1]) / x[:, 0])))","$$\left(\operatorname{exp}(\operatorname{log}(\frac{\theta_{0}}{\operatorname{log}(x_{1})})) + \frac{\operatorname{exp}(\left(x_{0} \cdot \theta_{1}\right))}{\frac{{\left(x_{1} + \theta_{2}\right)^{\left({\theta_{3}^{x_{1}}} + x_{1}\right)}}}{x_{0}}}\right)$$",7.620099383310752e-2;-0.3727630917323328;0.45545199901671496;0.8071613806530901,23,,,,Infinity,,,,,, -37,0,(Log((((Exp(x0) * -7.990302334389243e-3) ^ -0.695390739716597) + Exp((-0.5211100259994259 * Exp(x1))))) + (6.270887860291774e-2 ^ x0)),"(np.log(((np.exp(x[:, 0]) * t[0]) ** t[1] + np.exp((t[2] * np.exp(x[:, 1]))))) + t[3] ** x[:, 0])","$$\left(\operatorname{log}(\left({\left(\operatorname{exp}(x_{0}) \cdot \theta_{0}\right)^{\theta_{1}}} + \operatorname{exp}(\left(\theta_{2} \cdot \operatorname{exp}(x_{1})\right))\right)) + {\theta_{3}^{x_{0}}}\right)$$",-7.990302334389243e-3;-0.695390739716597;-0.5211100259994259;6.270887860291774e-2,17,,,,Infinity,,,,,, -38,0,Exp(Log(Log(Exp((-0.17954730200908064 / x0))))),"np.exp(np.log(np.log(np.exp((t[0] / x[:, 0])))))","$$\operatorname{exp}(\operatorname{log}(\operatorname{log}(\operatorname{exp}(\frac{\theta_{0}}{x_{0}}))))$$",-0.17954730200908064,7,,,,Infinity,,,,,, -39,0,(Exp((((x1 ^ Log(x1)) ^ ((0.6464536376494079 / x0) + x0)) ^ ((x1 / (-0.5389344180665384 / x0)) / Exp((-0.2513035455803483 * x0))))) * Exp((((x1 / (x1 ^ 0.35499693406822985)) / (0.5384124305373112 + Exp(x0))) * (0.2757598190426588 + (-0.34496095832224205 ^ x1))))),"(np.exp(x[:, 1] ** np.log(x[:, 1]) ** ((t[0] / x[:, 0]) + x[:, 0]) ** ((x[:, 1] / (t[1] / x[:, 0])) / np.exp((t[2] * x[:, 0])))) * np.exp((((x[:, 1] / x[:, 1] ** t[3]) / (t[4] + np.exp(x[:, 0]))) * (t[5] + t[6] ** x[:, 1]))))","$$\left(\operatorname{exp}({{{x_{1}^{\operatorname{log}(x_{1})}}^{\left(\frac{\theta_{0}}{x_{0}} + x_{0}\right)}}^{\frac{\frac{x_{1}}{\frac{\theta_{1}}{x_{0}}}}{\operatorname{exp}(\left(\theta_{2} \cdot x_{0}\right))}}}) \cdot \operatorname{exp}(\left(\frac{\frac{x_{1}}{{x_{1}^{\theta_{3}}}}}{\left(\theta_{4} + \operatorname{exp}(x_{0})\right)} \cdot \left(\theta_{5} + {\theta_{6}^{x_{1}}}\right)\right))\right)$$",0.6464536376494079;-0.5389344180665384;-0.2513035455803483;0.35499693406822985;0.5384124305373112;0.2757598190426588;-0.34496095832224205,40,,,,Infinity,,,,,, -40,0,Exp(((((x1 + (-0.6483173307345669 + x1)) + (-0.7892371947447105 / x0)) / ((x0 + (x0 * 0.5362846131053154)) + (x0 ^ -0.4105877578409678))) + (Log(Log((x0 * -0.1155467114499098))) / (Exp(Exp((-0.5848921848690108 + x0))) + ((x1 * 7.431834542272708e-2) * 0.5074509438925662))))),"np.exp(((((x[:, 1] + (t[0] + x[:, 1])) + (t[1] / x[:, 0])) / ((x[:, 0] + (x[:, 0] * t[2])) + x[:, 0] ** t[3])) + (np.log(np.log((x[:, 0] * t[4]))) / (np.exp(np.exp((t[5] + x[:, 0]))) + ((x[:, 1] * t[6]) * t[7])))))","$$\operatorname{exp}(\left(\frac{\left(\left(x_{1} + \left(\theta_{0} + x_{1}\right)\right) + \frac{\theta_{1}}{x_{0}}\right)}{\left(\left(x_{0} + \left(x_{0} \cdot \theta_{2}\right)\right) + {x_{0}^{\theta_{3}}}\right)} + \frac{\operatorname{log}(\operatorname{log}(\left(x_{0} \cdot \theta_{4}\right)))}{\left(\operatorname{exp}(\operatorname{exp}(\left(\theta_{5} + x_{0}\right))) + \left(\left(x_{1} \cdot \theta_{6}\right) \cdot \theta_{7}\right)\right)}\right))$$",-0.6483173307345669;-0.7892371947447105;0.5362846131053154;-0.4105877578409678;-0.1155467114499098;-0.5848921848690108;7.431834542272708e-2;0.5074509438925662,38,,,,Infinity,,,,,, -41,0,(Log(((x1 * x1) ^ -1.007181256342074)) * 1.6040633133099287e-2),"(np.log((x[:, 1] * x[:, 1]) ** t[0]) * t[1])","$$\left(\operatorname{log}({\left(x_{1} \cdot x_{1}\right)^{\theta_{0}}}) \cdot \theta_{1}\right)$$",-1.007181256342074;1.6040633133099287e-2,8,1.2823861921153812e-2,1.2823861921153812e-2,1.2823861921153812e-2,1.2823861921153812e-2,-0.1834008639421476,-0.1834008639421476,-0.1834008639421476,16.928894645937124,16.928894645937124,16.928894645937124 -42,0,(Log((((x1 ^ (-0.5688774889816952 + x1)) + (x0 / Log(x0))) / Log(((x0 ^ (0.5964315155810368 ^ x1)) / Exp((x1 ^ x0)))))) / (Log(Exp((Log((x1 * 0.624045004928951)) * (x0 * x0)))) ^ (Exp(Log(Exp(x1))) ^ 0.6226035482628106))),"(np.log(((x[:, 1] ** (t[0] + x[:, 1]) + (x[:, 0] / np.log(x[:, 0]))) / np.log((x[:, 0] ** t[1] ** x[:, 1] / np.exp(x[:, 1] ** x[:, 0]))))) / np.log(np.exp((np.log((x[:, 1] * t[2])) * (x[:, 0] * x[:, 0])))) ** np.exp(np.log(np.exp(x[:, 1]))) ** t[3])","$$\frac{\operatorname{log}(\frac{\left({x_{1}^{\left(\theta_{0} + x_{1}\right)}} + \frac{x_{0}}{\operatorname{log}(x_{0})}\right)}{\operatorname{log}(\frac{{x_{0}^{{\theta_{1}^{x_{1}}}}}}{\operatorname{exp}({x_{1}^{x_{0}}})})})}{{\operatorname{log}(\operatorname{exp}(\left(\operatorname{log}(\left(x_{1} \cdot \theta_{2}\right)) \cdot \left(x_{0} \cdot x_{0}\right)\right)))^{{\operatorname{exp}(\operatorname{log}(\operatorname{exp}(x_{1})))^{\theta_{3}}}}}}$$",-0.5688774889816952;0.5964315155810368;0.624045004928951;0.6226035482628106,41,,,,Infinity,,,,,, -43,0,Log((x1 * 3.538374735340478)),"np.log((x[:, 1] * t[0]))","$$\operatorname{log}(\left(x_{1} \cdot \theta_{0}\right))$$",3.538374735340478,4,0.20183020932487913,0.20183020932487913,0.20183020932487913,0.20183020932487913,-17.625125999734454,-17.625125999734454,-17.625125999734454,7.747749568239594,7.747749568239594,7.747749568239594 -44,0,((x0 + (x0 * -0.9171709236256588)) ^ Exp((x0 * x1))),"(x[:, 0] + (x[:, 0] * t[0])) ** np.exp((x[:, 0] * x[:, 1]))","$${\left(x_{0} + \left(x_{0} \cdot \theta_{0}\right)\right)^{\operatorname{exp}(\left(x_{0} \cdot x_{1}\right))}}$$",-0.9171709236256588,10,1.3320156709666752e-2,1.3320156709666752e-2,1.3320156709666752e-2,1.3320156709666752e-2,-0.22919952312198366,-0.22919952312198366,-0.22919952312198366,21.492014566959472,21.492014566959472,21.492014566959472 -45,0,Log(Exp(((Log(Exp((x0 ^ x1))) + ((x0 * 0.9060778396429976) + x0)) / (3.377833806655672e-2 + (0.637530444150828 ^ (-0.6193819679907668 ^ x0)))))),"np.log(np.exp(((np.log(np.exp(x[:, 0] ** x[:, 1])) + ((x[:, 0] * t[0]) + x[:, 0])) / (t[1] + t[2] ** t[3] ** x[:, 0]))))","$$\operatorname{log}(\operatorname{exp}(\frac{\left(\operatorname{log}(\operatorname{exp}({x_{0}^{x_{1}}})) + \left(\left(x_{0} \cdot \theta_{0}\right) + x_{0}\right)\right)}{\left(\theta_{1} + {\theta_{2}^{{\theta_{3}^{x_{0}}}}}\right)}))$$",0.9060778396429976;3.377833806655672e-2;0.637530444150828;-0.6193819679907668,21,,,,Infinity,,,,,, -46,0,((0.5535521849114129 * ((x1 + -0.13848683271584458) / (0.2442826825510186 / Log(x0)))) * (((x1 * 0.5395244631692784) + (x1 + -0.7022069804927313)) + -0.4652875913922442)),"((t[0] * ((x[:, 1] + t[1]) / (t[2] / np.log(x[:, 0])))) * (((x[:, 1] * t[3]) + (x[:, 1] + t[4])) + t[5]))","$$\left(\left(\theta_{0} \cdot \frac{\left(x_{1} + \theta_{1}\right)}{\frac{\theta_{2}}{\operatorname{log}(x_{0})}}\right) \cdot \left(\left(\left(x_{1} \cdot \theta_{3}\right) + \left(x_{1} + \theta_{4}\right)\right) + \theta_{5}\right)\right)$$",0.5535521849114129;-0.13848683271584458;0.2442826825510186;0.5395244631692784;-0.7022069804927313;-0.4652875913922442,20,7.608285677057699e-3,7.608285677057699e-3,7.608285677057699e-3,7.608285677057699e-3,0.2978985660710657,0.2978985660710657,0.2978985660710657,51.05060005042146,51.05060005042146,51.05060005042146 -47,0,((Exp(((Log(x0) ^ 0.7888394696912923) + x1)) + -0.11001420079162949) ^ x0),"(np.exp((np.log(x[:, 0]) ** t[0] + x[:, 1])) + t[1]) ** x[:, 0]","$${\left(\operatorname{exp}(\left({\operatorname{log}(x_{0})^{\theta_{0}}} + x_{1}\right)) + \theta_{1}\right)^{x_{0}}}$$",0.7888394696912923;-0.11001420079162949,11,,,,Infinity,,,,,, -48,0,(Log((Exp((Log(x0) + -0.7566581916109315)) * -0.5310775693456835)) / Exp((Log((Log(x1) ^ 0.70744298829378)) ^ ((Exp((0.8218491121435729 + x1)) * 0.5164902576242425) * 0.5582171446347535)))),"(np.log((np.exp((np.log(x[:, 0]) + t[0])) * t[1])) / np.exp(np.log(np.log(x[:, 1]) ** t[2]) ** ((np.exp((t[3] + x[:, 1])) * t[4]) * t[5])))","$$\frac{\operatorname{log}(\left(\operatorname{exp}(\left(\operatorname{log}(x_{0}) + \theta_{0}\right)) \cdot \theta_{1}\right))}{\operatorname{exp}({\operatorname{log}({\operatorname{log}(x_{1})^{\theta_{2}}})^{\left(\left(\operatorname{exp}(\left(\theta_{3} + x_{1}\right)) \cdot \theta_{4}\right) \cdot \theta_{5}\right)}})}$$",-0.7566581916109315;-0.5310775693456835;0.70744298829378;0.8218491121435729;0.5164902576242425;0.5582171446347535,24,,,,Infinity,,,,,, -49,0,(Log(((-0.7975459537645441 + x1) ^ 0.3712713306384733)) * 0.7298708039935262),"(np.log((t[0] + x[:, 1]) ** t[1]) * t[2])","$$\left(\operatorname{log}({\left(\theta_{0} + x_{1}\right)^{\theta_{1}}}) \cdot \theta_{2}\right)$$",-0.7975459537645441;0.3712713306384733;0.7298708039935262,8,,,,Infinity,,,,,, -50,0,(Exp((Exp(x0) + -116.31878667509586)) * 775.90687151667),"(np.exp((np.exp(x[:, 0]) + t[0])) * t[1])","$$\left(\operatorname{exp}(\left(\operatorname{exp}(x_{0}) + \theta_{0}\right)) \cdot \theta_{1}\right)$$",-116.31878667509586;775.90687151667,7,1.4573554749975553e-2,1.4573554749975553e-2,1.4573554749975553e-2,1.4573554749975553e-2,-0.3448645492182232,-0.3448645492182232,-0.3448645492182232,15.321031457048962,15.321031457048962,15.321031457048962 -51,0,((((x1 * 3.428897663408948e-2) / Exp(x0)) / (((-1.7165504048395446 * x1) * x0) / (13.432895560864475 ^ x1))) / ((Exp(x1) * Log(Exp(x0))) / ((x1 + x0) * -3.755937356902907))),"((((x[:, 1] * t[0]) / np.exp(x[:, 0])) / (((t[1] * x[:, 1]) * x[:, 0]) / t[2] ** x[:, 1])) / ((np.exp(x[:, 1]) * np.log(np.exp(x[:, 0]))) / ((x[:, 1] + x[:, 0]) * t[3])))","$$\frac{\frac{\frac{\left(x_{1} \cdot \theta_{0}\right)}{\operatorname{exp}(x_{0})}}{\frac{\left(\left(\theta_{1} \cdot x_{1}\right) \cdot x_{0}\right)}{{\theta_{2}^{x_{1}}}}}}{\frac{\left(\operatorname{exp}(x_{1}) \cdot \operatorname{log}(\operatorname{exp}(x_{0}))\right)}{\left(\left(x_{1} + x_{0}\right) \cdot \theta_{3}\right)}}$$",3.428897663408948e-2;-1.7165504048395446;13.432895560864475;-3.755937356902907,29,2.5836243850311308e-3,2.5836243850311308e-3,2.5836243850311308e-3,2.5836243850311308e-3,0.7615801426944288,0.7615801426944288,0.7615801426944288,71.80553774616746,71.80553774616746,71.80553774616746 -52,0,Exp((x0 * -2.9053779497574426)),"np.exp((x[:, 0] * t[0]))","$$\operatorname{exp}(\left(x_{0} \cdot \theta_{0}\right))$$",-2.9053779497574426,4,5.2316712708275655e-3,5.2316712708275655e-3,5.2316712708275655e-3,5.2316712708275655e-3,0.5172153022370025,0.5172153022370025,0.5172153022370025,7.570810883990948,7.570810883990948,7.570810883990948 -53,0,((((x0 * 0.24561966635707888) * (x0 * x0)) * ((x1 * 0.7374147834269256) ^ (x0 * -0.6696427715564475))) ^ ((Exp((x0 * 0.9421578940154798)) * (x0 + 0.5440818223938728)) * ((0.971910746475665 / x1) ^ 0.9244979392502793))),"(((x[:, 0] * t[0]) * (x[:, 0] * x[:, 0])) * (x[:, 1] * t[1]) ** (x[:, 0] * t[2])) ** ((np.exp((x[:, 0] * t[3])) * (x[:, 0] + t[4])) * (t[5] / x[:, 1]) ** t[6])","$${\left(\left(\left(x_{0} \cdot \theta_{0}\right) \cdot \left(x_{0} \cdot x_{0}\right)\right) \cdot {\left(x_{1} \cdot \theta_{1}\right)^{\left(x_{0} \cdot \theta_{2}\right)}}\right)^{\left(\left(\operatorname{exp}(\left(x_{0} \cdot \theta_{3}\right)) \cdot \left(x_{0} + \theta_{4}\right)\right) \cdot {\frac{\theta_{5}}{x_{1}}^{\theta_{6}}}\right)}}$$",0.24561966635707888;0.7374147834269256;-0.6696427715564475;0.9421578940154798;0.5440818223938728;0.971910746475665;0.9244979392502793,30,3.9009563284270396e88,3.9009563284270396e88,3.9009563284270396e88,3.9009563284270396e88,-3.5998477819276084e90,-3.5998477819276084e90,-3.5998477819276084e90,3.5108606955843356e88,3.5108606955843356e88,3.5108606955843356e88 -54,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 -55,0,(Log((Exp(x1) * 0.2368463210759606)) + (0.3855214898428987 / ((Exp((x1 * 0.5528620244110237)) / (x1 ^ ((x0 * 0.8299714223557225) * 0.5396602848341299))) + Log((((x1 * x1) * x0) + -0.18189542116021074))))),"(np.log((np.exp(x[:, 1]) * t[0])) + (t[1] / ((np.exp((x[:, 1] * t[2])) / x[:, 1] ** ((x[:, 0] * t[3]) * t[4])) + np.log((((x[:, 1] * x[:, 1]) * x[:, 0]) + t[5])))))","$$\left(\operatorname{log}(\left(\operatorname{exp}(x_{1}) \cdot \theta_{0}\right)) + \frac{\theta_{1}}{\left(\frac{\operatorname{exp}(\left(x_{1} \cdot \theta_{2}\right))}{{x_{1}^{\left(\left(x_{0} \cdot \theta_{3}\right) \cdot \theta_{4}\right)}}} + \operatorname{log}(\left(\left(\left(x_{1} \cdot x_{1}\right) \cdot x_{0}\right) + \theta_{5}\right))\right)}\right)$$",0.2368463210759606;0.3855214898428987;0.5528620244110237;0.8299714223557225;0.5396602848341299;-0.18189542116021074,29,,,,Infinity,,,,,, -56,0,Log((Exp(((x1 * x0) * 0.12061438815940806)) / ((x1 * 0.16105512668301558) + -0.8968662185322243))),"np.log((np.exp(((x[:, 1] * x[:, 0]) * t[0])) / ((x[:, 1] * t[1]) + t[2])))","$$\operatorname{log}(\frac{\operatorname{exp}(\left(\left(x_{1} \cdot x_{0}\right) \cdot \theta_{0}\right))}{\left(\left(x_{1} \cdot \theta_{1}\right) + \theta_{2}\right)})$$",0.12061438815940806;0.16105512668301558;-0.8968662185322243,13,,,,Infinity,,,,,, -57,0,Exp(Log(((x0 / Log((x1 ^ (x1 ^ 1.615698784873254e-2)))) ^ (-0.3389227098549682 ^ Log(x0))))),"np.exp(np.log((x[:, 0] / np.log(x[:, 1] ** x[:, 1] ** t[0])) ** t[1] ** np.log(x[:, 0])))","$$\operatorname{exp}(\operatorname{log}({\frac{x_{0}}{\operatorname{log}({x_{1}^{{x_{1}^{\theta_{0}}}}})}^{{\theta_{1}^{\operatorname{log}(x_{0})}}}}))$$",1.615698784873254e-2;-0.3389227098549682,15,,,,Infinity,,,,,, -58,0,((((x1 * (((x1 * x1) * (-0.8711844046114036 * x1)) * x0)) + x1) + x1) * 0.10582267112973207),"((((x[:, 1] * (((x[:, 1] * x[:, 1]) * (t[0] * x[:, 1])) * x[:, 0])) + x[:, 1]) + x[:, 1]) * t[1])","$$\left(\left(\left(\left(x_{1} \cdot \left(\left(\left(x_{1} \cdot x_{1}\right) \cdot \left(\theta_{0} \cdot x_{1}\right)\right) \cdot x_{0}\right)\right) + x_{1}\right) + x_{1}\right) \cdot \theta_{1}\right)$$",-0.8711844046114036;0.10582267112973207,17,9.242959177470604e-3,9.242959177470604e-3,9.242959177470604e-3,9.242959177470604e-3,0.14704899793426884,0.14704899793426884,0.14704899793426884,31.41061304537471,31.41061304537471,31.41061304537471 -59,0,(Exp((((((x0 + 0.7552698974686317) + (x0 ^ 0.5143689517808008)) * -0.1675976570857899) / x0) ^ -0.302479336686453)) * ((Exp(((0.23849056535485202 + (x0 + x1)) * 0.9568372354241598)) * (-0.5537593136073524 * ((0.41620427952549643 + x0) + -0.2574084252400347))) ^ (Exp((x1 + 0.49496582563428093)) * 0.8737230670909867))),"(np.exp(((((x[:, 0] + t[0]) + x[:, 0] ** t[1]) * t[2]) / x[:, 0]) ** t[3]) * (np.exp(((t[4] + (x[:, 0] + x[:, 1])) * t[5])) * (t[6] * ((t[7] + x[:, 0]) + t[8]))) ** (np.exp((x[:, 1] + t[9])) * t[10]))","$$\left(\operatorname{exp}({\frac{\left(\left(\left(x_{0} + \theta_{0}\right) + {x_{0}^{\theta_{1}}}\right) \cdot \theta_{2}\right)}{x_{0}}^{\theta_{3}}}) \cdot {\left(\operatorname{exp}(\left(\left(\theta_{4} + \left(x_{0} + x_{1}\right)\right) \cdot \theta_{5}\right)) \cdot \left(\theta_{6} \cdot \left(\left(\theta_{7} + x_{0}\right) + \theta_{8}\right)\right)\right)^{\left(\operatorname{exp}(\left(x_{1} + \theta_{9}\right)) \cdot \theta_{10}\right)}}\right)$$",0.7552698974686317;0.5143689517808008;-0.1675976570857899;-0.302479336686453;0.23849056535485202;0.9568372354241598;-0.5537593136073524;0.41620427952549643;-0.2574084252400347;0.49496582563428093;0.8737230670909867,38,,,,Infinity,,,,,, -60,0,Log((Log(((x0 + -0.8234507999714971) ^ x1)) * -0.7779324995591076)),"np.log((np.log((x[:, 0] + t[0]) ** x[:, 1]) * t[1]))","$$\operatorname{log}(\left(\operatorname{log}({\left(x_{0} + \theta_{0}\right)^{x_{1}}}) \cdot \theta_{1}\right))$$",-0.8234507999714971;-0.7779324995591076,9,,,,Infinity,,,,,, -61,0,(3.524205325424165e-2 * x0),"(t[0] * x[:, 0])","$$\left(\theta_{0} \cdot x_{0}\right)$$",3.524205325424165e-2,3,1.3066544015526581e-2,1.3066544015526581e-2,1.3066544015526581e-2,1.3066544015526581e-2,-0.20579584931471206,-0.20579584931471206,-0.20579584931471206,5.328521690985943,5.328521690985943,5.328521690985943 -62,0,(0.14002406657977945 * ((x1 * 0.4366027209286226) / x1)),"(t[0] * ((x[:, 1] * t[1]) / x[:, 1]))","$$\left(\theta_{0} \cdot \frac{\left(x_{1} \cdot \theta_{1}\right)}{x_{1}}\right)$$",0.14002406657977945;0.4366027209286226,7,1.0836448005577471e-2,1.0836448005577471e-2,1.0836448005577471e-2,1.0836448005577471e-2,-8.352187830240609e-10,-8.352187830240609e-10,-8.352187830240609e-10,13.755663201779534,13.755663201779534,13.755663201779534 -63,0,(((-0.22834845540846405 ^ (-0.14859656893077056 + x1)) ^ (x0 ^ (x1 * x1))) * Log(Exp((Log((x0 * x1)) ^ Log((x0 ^ x1)))))),"(t[0] ** (t[1] + x[:, 1]) ** x[:, 0] ** (x[:, 1] * x[:, 1]) * np.log(np.exp(np.log((x[:, 0] * x[:, 1])) ** np.log(x[:, 0] ** x[:, 1]))))","$$\left({{\theta_{0}^{\left(\theta_{1} + x_{1}\right)}}^{{x_{0}^{\left(x_{1} \cdot x_{1}\right)}}}} \cdot \operatorname{log}(\operatorname{exp}({\operatorname{log}(\left(x_{0} \cdot x_{1}\right))^{\operatorname{log}({x_{0}^{x_{1}}})}}))\right)$$",-0.22834845540846405;-0.14859656893077056,23,,,,Infinity,,,,,, -64,0,(Log(((x0 + x0) / (-0.8375535712624412 * x0))) ^ -0.3540748295585926),"np.log(((x[:, 0] + x[:, 0]) / (t[0] * x[:, 0]))) ** t[1]","$${\operatorname{log}(\frac{\left(x_{0} + x_{0}\right)}{\left(\theta_{0} \cdot x_{0}\right)})^{\theta_{1}}}$$",-0.8375535712624412;-0.3540748295585926,10,,,,Infinity,,,,,, -65,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 -66,0,Exp(((0.7067195764902885 + (x0 * (x0 / ((x0 + 0.9336147384074215) * (x0 + 7.475713397415218e-2))))) + Exp((x0 / Log((x0 + -5.400268518482743e-3)))))),"np.exp(((t[0] + (x[:, 0] * (x[:, 0] / ((x[:, 0] + t[1]) * (x[:, 0] + t[2]))))) + np.exp((x[:, 0] / np.log((x[:, 0] + t[3]))))))","$$\operatorname{exp}(\left(\left(\theta_{0} + \left(x_{0} \cdot \frac{x_{0}}{\left(\left(x_{0} + \theta_{1}\right) \cdot \left(x_{0} + \theta_{2}\right)\right)}\right)\right) + \operatorname{exp}(\frac{x_{0}}{\operatorname{log}(\left(x_{0} + \theta_{3}\right))})\right))$$",0.7067195764902885;0.9336147384074215;7.475713397415218e-2;-5.400268518482743e-3,22,Infinity,Infinity,Infinity,Infinity,-Infinity,-Infinity,-Infinity,Infinity,Infinity,Infinity -67,0,((((x0 * (-0.2917775195065935 ^ x0)) * -0.4907012640912042) + -0.3288814094092286) ^ (Exp(Log((0.7662079348172693 / (x0 * 0.5611773910653803)))) / Log(((Exp(x1) / Exp(x0)) + Log((x1 * (0.14682896944028012 * x1))))))),"(((x[:, 0] * t[0] ** x[:, 0]) * t[1]) + t[2]) ** (np.exp(np.log((t[3] / (x[:, 0] * t[4])))) / np.log(((np.exp(x[:, 1]) / np.exp(x[:, 0])) + np.log((x[:, 1] * (t[5] * x[:, 1]))))))","$${\left(\left(\left(x_{0} \cdot {\theta_{0}^{x_{0}}}\right) \cdot \theta_{1}\right) + \theta_{2}\right)^{\frac{\operatorname{exp}(\operatorname{log}(\frac{\theta_{3}}{\left(x_{0} \cdot \theta_{4}\right)}))}{\operatorname{log}(\left(\frac{\operatorname{exp}(x_{1})}{\operatorname{exp}(x_{0})} + \operatorname{log}(\left(x_{1} \cdot \left(\theta_{5} \cdot x_{1}\right)\right))\right))}}}$$",-0.2917775195065935;-0.4907012640912042;-0.3288814094092286;0.7662079348172693;0.5611773910653803;0.14682896944028012,31,,,,Infinity,,,,,, -68,0,Log(((Log((Log((x1 + x1)) / x0)) * -0.7787991489512834) + (Log(Exp(x0)) * Log((x1 / x0))))),"np.log(((np.log((np.log((x[:, 1] + x[:, 1])) / x[:, 0])) * t[0]) + (np.log(np.exp(x[:, 0])) * np.log((x[:, 1] / x[:, 0])))))","$$\operatorname{log}(\left(\left(\operatorname{log}(\frac{\operatorname{log}(\left(x_{1} + x_{1}\right))}{x_{0}}) \cdot \theta_{0}\right) + \left(\operatorname{log}(\operatorname{exp}(x_{0})) \cdot \operatorname{log}(\frac{x_{1}}{x_{0}})\right)\right))$$",-0.7787991489512834,19,,,,Infinity,,,,,, -69,0,((Log((((Log(x0) + x1) ^ 0.48929834896328317) * -1.6705565677429535e-2)) / Exp(Exp(x1))) ^ (((x1 / ((-0.7169548370126054 * x0) * -0.4642952313475045)) / ((0.48700404350270476 + Exp(x1)) ^ x1)) ^ 0.3082067419753607)),"(np.log(((np.log(x[:, 0]) + x[:, 1]) ** t[0] * t[1])) / np.exp(np.exp(x[:, 1]))) ** ((x[:, 1] / ((t[2] * x[:, 0]) * t[3])) / (t[4] + np.exp(x[:, 1])) ** x[:, 1]) ** t[5]","$${\frac{\operatorname{log}(\left({\left(\operatorname{log}(x_{0}) + x_{1}\right)^{\theta_{0}}} \cdot \theta_{1}\right))}{\operatorname{exp}(\operatorname{exp}(x_{1}))}^{{\frac{\frac{x_{1}}{\left(\left(\theta_{2} \cdot x_{0}\right) \cdot \theta_{3}\right)}}{{\left(\theta_{4} + \operatorname{exp}(x_{1})\right)^{x_{1}}}}^{\theta_{5}}}}}$$",0.48929834896328317;-1.6705565677429535e-2;-0.7169548370126054;-0.4642952313475045;0.48700404350270476;0.3082067419753607,30,,,,Infinity,,,,,, -70,0,(Exp((-0.10023694050323861 + ((x1 * (x0 * 0.4571745712877434)) ^ 0.6924430769477543))) + Log(Log(((Exp((x1 * -0.5688478026338328)) + -0.676548720921234) * x1)))),"(np.exp((t[0] + (x[:, 1] * (x[:, 0] * t[1])) ** t[2])) + np.log(np.log(((np.exp((x[:, 1] * t[3])) + t[4]) * x[:, 1]))))","$$\left(\operatorname{exp}(\left(\theta_{0} + {\left(x_{1} \cdot \left(x_{0} \cdot \theta_{1}\right)\right)^{\theta_{2}}}\right)) + \operatorname{log}(\operatorname{log}(\left(\left(\operatorname{exp}(\left(x_{1} \cdot \theta_{3}\right)) + \theta_{4}\right) \cdot x_{1}\right)))\right)$$",-0.10023694050323861;0.4571745712877434;0.6924430769477543;-0.5688478026338328;-0.676548720921234,21,,,,Infinity,,,,,, -71,0,Exp((((Log(Exp(x1)) * -55.626714924238) / ((x0 * x1) ^ 2.48271246491314)) + Log((Exp(Exp((x0 ^ x1))) + ((x1 / x0) * -5.590070375523997))))),"np.exp((((np.log(np.exp(x[:, 1])) * t[0]) / (x[:, 0] * x[:, 1]) ** t[1]) + np.log((np.exp(np.exp(x[:, 0] ** x[:, 1])) + ((x[:, 1] / x[:, 0]) * t[2])))))","$$\operatorname{exp}(\left(\frac{\left(\operatorname{log}(\operatorname{exp}(x_{1})) \cdot \theta_{0}\right)}{{\left(x_{0} \cdot x_{1}\right)^{\theta_{1}}}} + \operatorname{log}(\left(\operatorname{exp}(\operatorname{exp}({x_{0}^{x_{1}}})) + \left(\frac{x_{1}}{x_{0}} \cdot \theta_{2}\right)\right))\right))$$",-55.626714924238;2.48271246491314;-5.590070375523997,25,1.4573554749975553e-2,1.4573554749975553e-2,1.4573554749975553e-2,1.4573554749975553e-2,-0.3448645492182232,-0.3448645492182232,-0.3448645492182232,61.006505438783385,61.006505438783385,61.006505438783385 -72,0,(((Exp(x1) + Log((x1 * -0.5043887036084546))) ^ (Log(x0) * 0.21819431631607422)) * ((Log(x1) ^ -0.18596280044446478) + x1)),"((np.exp(x[:, 1]) + np.log((x[:, 1] * t[0]))) ** (np.log(x[:, 0]) * t[1]) * (np.log(x[:, 1]) ** t[2] + x[:, 1]))","$$\left({\left(\operatorname{exp}(x_{1}) + \operatorname{log}(\left(x_{1} \cdot \theta_{0}\right))\right)^{\left(\operatorname{log}(x_{0}) \cdot \theta_{1}\right)}} \cdot \left({\operatorname{log}(x_{1})^{\theta_{2}}} + x_{1}\right)\right)$$",-0.5043887036084546;0.21819431631607422;-0.18596280044446478,19,,,,Infinity,,,,,, -73,0,((((-0.35508392813835965 * Log(x1)) ^ 0.5508429384200083) * x1) + -0.12495986516765652),"(((t[0] * np.log(x[:, 1])) ** t[1] * x[:, 1]) + t[2])","$$\left(\left({\left(\theta_{0} \cdot \operatorname{log}(x_{1})\right)^{\theta_{1}}} \cdot x_{1}\right) + \theta_{2}\right)$$",-0.35508392813835965;0.5508429384200083;-0.12495986516765652,10,9.20085386484256e-3,9.20085386484256e-3,9.20085386484256e-3,9.20085386484256e-3,0.15093452505916616,0.15093452505916616,0.15093452505916616,23.98865026686183,23.98865026686183,23.98865026686183 -74,0,((((x1 + Log(((x0 * -0.35019692782890777) * x1))) * x1) ^ -0.702311786226695) / x0),"(((x[:, 1] + np.log(((x[:, 0] * t[0]) * x[:, 1]))) * x[:, 1]) ** t[1] / x[:, 0])","$$\frac{{\left(\left(x_{1} + \operatorname{log}(\left(\left(x_{0} \cdot \theta_{0}\right) \cdot x_{1}\right))\right) \cdot x_{1}\right)^{\theta_{1}}}}{x_{0}}$$",-0.35019692782890777;-0.702311786226695,14,,,,Infinity,,,,,, -75,0,Log(((Exp((Exp((x0 * 6.8064581535632e-2)) * Log(x1))) ^ ((-9.513096765405604e-2 * (x0 + 0.10125111290883548)) / Log((x1 / x1)))) * ((Log(Exp((x1 ^ 0.5606312048459681))) + (x0 * x0)) / Log(((x1 * (x1 / x0)) + (x0 / x0)))))),"np.log((np.exp((np.exp((x[:, 0] * t[0])) * np.log(x[:, 1]))) ** ((t[1] * (x[:, 0] + t[2])) / np.log((x[:, 1] / x[:, 1]))) * ((np.log(np.exp(x[:, 1] ** t[3])) + (x[:, 0] * x[:, 0])) / np.log(((x[:, 1] * (x[:, 1] / x[:, 0])) + (x[:, 0] / x[:, 0]))))))","$$\operatorname{log}(\left({\operatorname{exp}(\left(\operatorname{exp}(\left(x_{0} \cdot \theta_{0}\right)) \cdot \operatorname{log}(x_{1})\right))^{\frac{\left(\theta_{1} \cdot \left(x_{0} + \theta_{2}\right)\right)}{\operatorname{log}(\frac{x_{1}}{x_{1}})}}} \cdot \frac{\left(\operatorname{log}(\operatorname{exp}({x_{1}^{\theta_{3}}})) + \left(x_{0} \cdot x_{0}\right)\right)}{\operatorname{log}(\left(\left(x_{1} \cdot \frac{x_{1}}{x_{0}}\right) + \frac{x_{0}}{x_{0}}\right))}\right))$$",6.8064581535632e-2;-9.513096765405604e-2;0.10125111290883548;0.5606312048459681,41,Infinity,Infinity,Infinity,Infinity,-Infinity,-Infinity,-Infinity,Infinity,Infinity,Infinity -76,0,Exp((((4.519878175133763 + x0) ^ -10.338674239667291) * x1)),"np.exp(((t[0] + x[:, 0]) ** t[1] * x[:, 1]))","$$\operatorname{exp}(\left({\left(\theta_{0} + x_{0}\right)^{\theta_{1}}} \cdot x_{1}\right))$$",4.519878175133763;-10.338674239667291,8,0.8923098078556129,0.8923098078556129,0.8923098078556129,0.8923098078556129,-81.34338485651561,-81.34338485651561,-81.34338485651561,20.41220989024784,20.41220989024784,20.41220989024784 -77,0,(Log(Log(Exp(Exp(((-0.28629436164160604 * x1) ^ -0.8340707641096115))))) + Exp((Exp(x1) + ((x0 * x1) * Log(x1))))),"(np.log(np.log(np.exp(np.exp((t[0] * x[:, 1]) ** t[1])))) + np.exp((np.exp(x[:, 1]) + ((x[:, 0] * x[:, 1]) * np.log(x[:, 1])))))","$$\left(\operatorname{log}(\operatorname{log}(\operatorname{exp}(\operatorname{exp}({\left(\theta_{0} \cdot x_{1}\right)^{\theta_{1}}})))) + \operatorname{exp}(\left(\operatorname{exp}(x_{1}) + \left(\left(x_{0} \cdot x_{1}\right) \cdot \operatorname{log}(x_{1})\right)\right))\right)$$",-0.28629436164160604;-0.8340707641096115,20,,,,Infinity,,,,,, -78,0,Exp((x0 * -2.9053779497574426)),"np.exp((x[:, 0] * t[0]))","$$\operatorname{exp}(\left(x_{0} \cdot \theta_{0}\right))$$",-2.9053779497574426,4,5.2316712708275655e-3,5.2316712708275655e-3,5.2316712708275655e-3,5.2316712708275655e-3,0.5172153022370025,0.5172153022370025,0.5172153022370025,7.570810883990948,7.570810883990948,7.570810883990948 -79,0,(Exp((-0.7598091821842864 ^ ((x1 * -0.4092221152045774) * -0.4028970450598004))) * ((Log(x1) ^ 0.36285991768748893) ^ (Exp(x1) + -0.8529303227599664))),"(np.exp(t[0] ** ((x[:, 1] * t[1]) * t[2])) * np.log(x[:, 1]) ** t[3] ** (np.exp(x[:, 1]) + t[4]))","$$\left(\operatorname{exp}({\theta_{0}^{\left(\left(x_{1} \cdot \theta_{1}\right) \cdot \theta_{2}\right)}}) \cdot {{\operatorname{log}(x_{1})^{\theta_{3}}}^{\left(\operatorname{exp}(x_{1}) + \theta_{4}\right)}}\right)$$",-0.7598091821842864;-0.4092221152045774;-0.4028970450598004;0.36285991768748893;-0.8529303227599664,18,,,,Infinity,,,,,, -80,0,((-7.249086055793885e-5 / (x1 * x1)) * Log((x1 / x0))),"((t[0] / (x[:, 1] * x[:, 1])) * np.log((x[:, 1] / x[:, 0])))","$$\left(\frac{\theta_{0}}{\left(x_{1} \cdot x_{1}\right)} \cdot \operatorname{log}(\frac{x_{1}}{x_{0}})\right)$$",-7.249086055793885e-5,10,1.4555693683848766e-2,1.4555693683848766e-2,1.4555693683848766e-2,1.4555693683848766e-2,-0.34321630930303026,-0.34321630930303026,-0.34321630930303026,19.95161975196365,19.95161975196365,19.95161975196365 -81,0,(Log(((((x0 + Exp(x0)) * 0.9470989402204423) + x1) * -5.1268384239821496e-2)) ^ x0),"np.log(((((x[:, 0] + np.exp(x[:, 0])) * t[0]) + x[:, 1]) * t[1])) ** x[:, 0]","$${\operatorname{log}(\left(\left(\left(\left(x_{0} + \operatorname{exp}(x_{0})\right) \cdot \theta_{0}\right) + x_{1}\right) \cdot \theta_{1}\right))^{x_{0}}}$$",0.9470989402204423;-5.1268384239821496e-2,13,,,,Infinity,,,,,, -82,0,(((-0.9136835342959171 ^ (((Exp(x1) ^ -0.41252245009116983) * -0.688809669305868) + x1)) ^ (-0.7996711169572053 ^ (x0 ^ 0.26448983095034073))) + -0.19536727365016116),"(t[0] ** ((np.exp(x[:, 1]) ** t[1] * t[2]) + x[:, 1]) ** t[3] ** x[:, 0] ** t[4] + t[5])","$$\left({{\theta_{0}^{\left(\left({\operatorname{exp}(x_{1})^{\theta_{1}}} \cdot \theta_{2}\right) + x_{1}\right)}}^{{\theta_{3}^{{x_{0}^{\theta_{4}}}}}}} + \theta_{5}\right)$$",-0.9136835342959171;-0.41252245009116983;-0.688809669305868;-0.7996711169572053;0.26448983095034073;-0.19536727365016116,18,,,,Infinity,,,,,, -83,0,(((((x0 / x1) + ((-0.6283710581944316 / x1) ^ (x0 ^ x0))) * (-0.9904157149155579 + Exp(x0))) ^ -0.7896428068801478) * -0.45298887975769697),"((((x[:, 0] / x[:, 1]) + (t[0] / x[:, 1]) ** x[:, 0] ** x[:, 0]) * (t[1] + np.exp(x[:, 0]))) ** t[2] * t[3])","$$\left({\left(\left(\frac{x_{0}}{x_{1}} + {\frac{\theta_{0}}{x_{1}}^{{x_{0}^{x_{0}}}}}\right) \cdot \left(\theta_{1} + \operatorname{exp}(x_{0})\right)\right)^{\theta_{2}}} \cdot \theta_{3}\right)$$",-0.6283710581944316;-0.9904157149155579;-0.7896428068801478;-0.45298887975769697,20,,,,Infinity,,,,,, -84,0,((((x0 * 0.622155026476008) * -4.500740626868016e-2) ^ ((0.6603205466342758 ^ x1) ^ (-0.5493820555421665 ^ x1))) + x0),"(((x[:, 0] * t[0]) * t[1]) ** t[2] ** x[:, 1] ** t[3] ** x[:, 1] + x[:, 0])","$$\left({\left(\left(x_{0} \cdot \theta_{0}\right) \cdot \theta_{1}\right)^{{{\theta_{2}^{x_{1}}}^{{\theta_{3}^{x_{1}}}}}}} + x_{0}\right)$$",0.622155026476008;-4.500740626868016e-2;0.6603205466342758;-0.5493820555421665,15,,,,Infinity,,,,,, -85,0,((((Log(Exp(Log(x0))) * -0.2503752727444368) * (-0.22200094119923153 ^ x1)) * 0.573343403431618) ^ x1),"(((np.log(np.exp(np.log(x[:, 0]))) * t[0]) * t[1] ** x[:, 1]) * t[2]) ** x[:, 1]","$${\left(\left(\left(\operatorname{log}(\operatorname{exp}(\operatorname{log}(x_{0}))) \cdot \theta_{0}\right) \cdot {\theta_{1}^{x_{1}}}\right) \cdot \theta_{2}\right)^{x_{1}}}$$",-0.2503752727444368;-0.22200094119923153;0.573343403431618,14,,,,Infinity,,,,,, -86,0,(Exp(Exp(((-0.2734412440332644 / x1) ^ (x1 * (0.1995774301650315 * x0))))) / (Log((0.8356803649665492 + x0)) / (0.7510954670165744 ^ (-0.8155657901934451 / x1)))),"(np.exp(np.exp((t[0] / x[:, 1]) ** (x[:, 1] * (t[1] * x[:, 0])))) / (np.log((t[2] + x[:, 0])) / t[3] ** (t[4] / x[:, 1])))","$$\frac{\operatorname{exp}(\operatorname{exp}({\frac{\theta_{0}}{x_{1}}^{\left(x_{1} \cdot \left(\theta_{1} \cdot x_{0}\right)\right)}}))}{\frac{\operatorname{log}(\left(\theta_{2} + x_{0}\right))}{{\theta_{3}^{\frac{\theta_{4}}{x_{1}}}}}}$$",-0.2734412440332644;0.1995774301650315;0.8356803649665492;0.7510954670165744;-0.8155657901934451,22,,,,Infinity,,,,,, -87,0,(Exp((-0.7796566212936181 * Exp(Exp(Log((x0 ^ x1)))))) * ((((-0.9009065958296975 * x0) ^ -0.3280394781479087) * (0.4909436407395378 * (x0 ^ -0.7298674879232272))) / (((x1 / x1) ^ x1) ^ Exp(Log(Log(Log(x1))))))),"(np.exp((t[0] * np.exp(np.exp(np.log(x[:, 0] ** x[:, 1]))))) * (((t[1] * x[:, 0]) ** t[2] * (t[3] * x[:, 0] ** t[4])) / (x[:, 1] / x[:, 1]) ** x[:, 1] ** np.exp(np.log(np.log(np.log(x[:, 1]))))))","$$\left(\operatorname{exp}(\left(\theta_{0} \cdot \operatorname{exp}(\operatorname{exp}(\operatorname{log}({x_{0}^{x_{1}}})))\right)) \cdot \frac{\left({\left(\theta_{1} \cdot x_{0}\right)^{\theta_{2}}} \cdot \left(\theta_{3} \cdot {x_{0}^{\theta_{4}}}\right)\right)}{{{\frac{x_{1}}{x_{1}}^{x_{1}}}^{\operatorname{exp}(\operatorname{log}(\operatorname{log}(\operatorname{log}(x_{1}))))}}}\right)$$",-0.7796566212936181;-0.9009065958296975;-0.3280394781479087;0.4909436407395378;-0.7298674879232272,33,,,,Infinity,,,,,, -88,0,(Log(Log((((x1 + x0) / x0) ^ (-0.5787461091406391 ^ Log(x0))))) ^ Log((Log(((x1 / x0) + -0.6646385296195615)) ^ Exp(Exp((Exp(x0) ^ x1)))))),"np.log(np.log(((x[:, 1] + x[:, 0]) / x[:, 0]) ** t[0] ** np.log(x[:, 0]))) ** np.log(np.log(((x[:, 1] / x[:, 0]) + t[1])) ** np.exp(np.exp(np.exp(x[:, 0]) ** x[:, 1])))","$${\operatorname{log}(\operatorname{log}({\frac{\left(x_{1} + x_{0}\right)}{x_{0}}^{{\theta_{0}^{\operatorname{log}(x_{0})}}}}))^{\operatorname{log}({\operatorname{log}(\left(\frac{x_{1}}{x_{0}} + \theta_{1}\right))^{\operatorname{exp}(\operatorname{exp}({\operatorname{exp}(x_{0})^{x_{1}}}))}})}}$$",-0.5787461091406391;-0.6646385296195615,27,,,,Infinity,,,,,, -89,0,((((x0 * -0.23377161853654516) ^ (x0 * 0.8722468595910213)) * (Log(x1) ^ (-0.21831693384226447 / x0))) ^ (Log((Log(x0) ^ (x1 * -0.7807764269703579))) / Log((((0.15413326946061934 / x0) + 0.45863471981707427) ^ -0.9924735681956656)))),"((x[:, 0] * t[0]) ** (x[:, 0] * t[1]) * np.log(x[:, 1]) ** (t[2] / x[:, 0])) ** (np.log(np.log(x[:, 0]) ** (x[:, 1] * t[3])) / np.log(((t[4] / x[:, 0]) + t[5]) ** t[6]))","$${\left({\left(x_{0} \cdot \theta_{0}\right)^{\left(x_{0} \cdot \theta_{1}\right)}} \cdot {\operatorname{log}(x_{1})^{\frac{\theta_{2}}{x_{0}}}}\right)^{\frac{\operatorname{log}({\operatorname{log}(x_{0})^{\left(x_{1} \cdot \theta_{3}\right)}})}{\operatorname{log}({\left(\frac{\theta_{4}}{x_{0}} + \theta_{5}\right)^{\theta_{6}}})}}}$$",-0.23377161853654516;0.8722468595910213;-0.21831693384226447;-0.7807764269703579;0.15413326946061934;0.45863471981707427;-0.9924735681956656,31,,,,Infinity,,,,,, -90,0,((Exp(Exp((Log(Log(x0)) / Exp((x1 ^ -1.8700203047188335e-3))))) * (Exp(x1) + 0.7251912963037392)) * 0.4295765588371294),"((np.exp(np.exp((np.log(np.log(x[:, 0])) / np.exp(x[:, 1] ** t[0])))) * (np.exp(x[:, 1]) + t[1])) * t[2])","$$\left(\left(\operatorname{exp}(\operatorname{exp}(\frac{\operatorname{log}(\operatorname{log}(x_{0}))}{\operatorname{exp}({x_{1}^{\theta_{0}}})})) \cdot \left(\operatorname{exp}(x_{1}) + \theta_{1}\right)\right) \cdot \theta_{2}\right)$$",-1.8700203047188335e-3;0.7251912963037392;0.4295765588371294,17,,,,Infinity,,,,,, -91,0,Exp((((x0 + x1) * (-1.9033914549889586e13 + Exp((-9.999981885488438e15 / (x1 ^ x0))))) / (Exp(x1) / (x0 ^ x1)))),"np.exp((((x[:, 0] + x[:, 1]) * (t[0] + np.exp((t[1] / x[:, 1] ** x[:, 0])))) / (np.exp(x[:, 1]) / x[:, 0] ** x[:, 1])))","$$\operatorname{exp}(\frac{\left(\left(x_{0} + x_{1}\right) \cdot \left(\theta_{0} + \operatorname{exp}(\frac{\theta_{1}}{{x_{1}^{x_{0}}}})\right)\right)}{\frac{\operatorname{exp}(x_{1})}{{x_{0}^{x_{1}}}}})$$",-1.9033914549889586e13;-9.999981885488438e15,20,1.4573554749975553e-2,1.4573554749975553e-2,1.4573554749975553e-2,1.4573554749975553e-2,-0.3448645492182232,-0.3448645492182232,-0.3448645492182232,45.643796903606976,45.643796903606976,45.643796903606976 -92,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 -93,0,((((x1 / (Exp(Log(x1)) / Exp(Exp(x1)))) * ((((-4.6237330439191795e-5 / x0) * Log(x1)) ^ -0.7753106939390338) * 3.225967566194982)) ^ -2.1018867031538147) + x0),"(((x[:, 1] / (np.exp(np.log(x[:, 1])) / np.exp(np.exp(x[:, 1])))) * (((t[0] / x[:, 0]) * np.log(x[:, 1])) ** t[1] * t[2])) ** t[3] + x[:, 0])","$$\left({\left(\frac{x_{1}}{\frac{\operatorname{exp}(\operatorname{log}(x_{1}))}{\operatorname{exp}(\operatorname{exp}(x_{1}))}} \cdot \left({\left(\frac{\theta_{0}}{x_{0}} \cdot \operatorname{log}(x_{1})\right)^{\theta_{1}}} \cdot \theta_{2}\right)\right)^{\theta_{3}}} + x_{0}\right)$$",-4.6237330439191795e-5;-0.7753106939390338;3.225967566194982;-2.1018867031538147,24,1.1424220388828665,1.1424220388828665,1.1424220388828665,1.1424220388828665,-104.4240318644116,-104.4240318644116,-104.4240318644116,61.84526943253441,61.84526943253441,61.84526943253441 -94,0,(((Exp((Log((x1 * 1.5757064594676672e-5)) + x1)) ^ (x1 / ((Log(x1) * -0.38979102318627734) ^ x1))) * x1) * x0),"((np.exp((np.log((x[:, 1] * t[0])) + x[:, 1])) ** (x[:, 1] / (np.log(x[:, 1]) * t[1]) ** x[:, 1]) * x[:, 1]) * x[:, 0])","$$\left(\left({\operatorname{exp}(\left(\operatorname{log}(\left(x_{1} \cdot \theta_{0}\right)) + x_{1}\right))^{\frac{x_{1}}{{\left(\operatorname{log}(x_{1}) \cdot \theta_{1}\right)^{x_{1}}}}}} \cdot x_{1}\right) \cdot x_{0}\right)$$",1.5757064594676672e-5;-0.38979102318627734,20,1.4594726584821027e-2,1.4594726584821027e-2,1.4594726584821027e-2,1.4594726584821027e-2,-0.346818310713896,-0.346818310713896,-0.346818310713896,47.99947667138601,47.99947667138601,47.99947667138601 -95,0,Log(((((Log(x0) ^ -0.17656537281549078) * (x0 ^ (x1 ^ x0))) * Exp(((x1 + 0.9959915894509641) ^ Exp((0.4254464228770556 / x0))))) ^ Exp(((((x1 ^ x1) ^ x1) * -0.871071156235419) * -0.8990465607905614)))),"np.log(((np.log(x[:, 0]) ** t[0] * x[:, 0] ** x[:, 1] ** x[:, 0]) * np.exp((x[:, 1] + t[1]) ** np.exp((t[2] / x[:, 0])))) ** np.exp(((x[:, 1] ** x[:, 1] ** x[:, 1] * t[3]) * t[4])))","$$\operatorname{log}({\left(\left({\operatorname{log}(x_{0})^{\theta_{0}}} \cdot {x_{0}^{{x_{1}^{x_{0}}}}}\right) \cdot \operatorname{exp}({\left(x_{1} + \theta_{1}\right)^{\operatorname{exp}(\frac{\theta_{2}}{x_{0}})}})\right)^{\operatorname{exp}(\left(\left({{x_{1}^{x_{1}}}^{x_{1}}} \cdot \theta_{3}\right) \cdot \theta_{4}\right))}})$$",-0.17656537281549078;0.9959915894509641;0.4254464228770556;-0.871071156235419;-0.8990465607905614,32,,,,Infinity,,,,,, -96,0,(((Exp(Log((x0 * -0.4665697009374026))) * -0.8462895094191233) ^ x0) * -0.7773456768440615),"((np.exp(np.log((x[:, 0] * t[0]))) * t[1]) ** x[:, 0] * t[2])","$$\left({\left(\operatorname{exp}(\operatorname{log}(\left(x_{0} \cdot \theta_{0}\right))) \cdot \theta_{1}\right)^{x_{0}}} \cdot \theta_{2}\right)$$",-0.4665697009374026;-0.8462895094191233;-0.7773456768440615,11,,,,Infinity,,,,,, -97,0,(((0.7032134140461792 * (-0.3381653524016855 / x1)) / (Exp((x0 * -0.3430684712008597)) ^ (x1 / x1))) / Exp(Exp((0.24673679865352155 ^ Log((x0 * 0.32596017023366924)))))),"(((t[0] * (t[1] / x[:, 1])) / np.exp((x[:, 0] * t[2])) ** (x[:, 1] / x[:, 1])) / np.exp(np.exp(t[3] ** np.log((x[:, 0] * t[4])))))","$$\frac{\frac{\left(\theta_{0} \cdot \frac{\theta_{1}}{x_{1}}\right)}{{\operatorname{exp}(\left(x_{0} \cdot \theta_{2}\right))^{\frac{x_{1}}{x_{1}}}}}}{\operatorname{exp}(\operatorname{exp}({\theta_{3}^{\operatorname{log}(\left(x_{0} \cdot \theta_{4}\right))}}))}$$",0.7032134140461792;-0.3381653524016855;-0.3430684712008597;0.24673679865352155;0.32596017023366924,23,1.457355481921096e-2,1.457355481921096e-2,1.457355481921096e-2,1.457355481921096e-2,-0.3448645556073471,-0.3448645556073471,-0.3448645556073471,57.94489633481171,57.94489633481171,57.94489633481171 -98,0,((0.1649260065001873 / x0) + Log(x0)),"((t[0] / x[:, 0]) + np.log(x[:, 0]))","$$\left(\frac{\theta_{0}}{x_{0}} + \operatorname{log}(x_{0})\right)$$",0.1649260065001873,6,0.13377529036960586,0.13377529036960586,0.13377529036960586,0.13377529036960586,-11.344939080820945,-11.344939080820945,-11.344939080820945,11.797950171304889,11.797950171304889,11.797950171304889 -99,0,((0.4951392130392588 * (-0.843558578063905 + x0)) + (0.5249506496308305 ^ (x1 / x0))),"((t[0] * (t[1] + x[:, 0])) + t[2] ** (x[:, 1] / x[:, 0]))","$$\left(\left(\theta_{0} \cdot \left(\theta_{1} + x_{0}\right)\right) + {\theta_{2}^{\frac{x_{1}}{x_{0}}}}\right)$$",0.4951392130392588;-0.843558578063905;0.5249506496308305,11,0.8201097407209612,0.8201097407209612,0.8201097407209612,0.8201097407209612,-74.68067885194719,-74.68067885194719,-74.68067885194719,28.205885212360233,28.205885212360233,28.205885212360233 -100,0,Log(((((x0 + x1) ^ -0.183670114544065) * Log(((-0.4012496099632018 * x1) * Exp(x1)))) / Log((((x0 + 0.34914396946823856) * (x0 * (x0 / x0))) ^ 0.7607362119624373)))),"np.log((((x[:, 0] + x[:, 1]) ** t[0] * np.log(((t[1] * x[:, 1]) * np.exp(x[:, 1])))) / np.log(((x[:, 0] + t[2]) * (x[:, 0] * (x[:, 0] / x[:, 0]))) ** t[3])))","$$\operatorname{log}(\frac{\left({\left(x_{0} + x_{1}\right)^{\theta_{0}}} \cdot \operatorname{log}(\left(\left(\theta_{1} \cdot x_{1}\right) \cdot \operatorname{exp}(x_{1})\right))\right)}{\operatorname{log}({\left(\left(x_{0} + \theta_{2}\right) \cdot \left(x_{0} \cdot \frac{x_{0}}{x_{0}}\right)\right)^{\theta_{3}}})})$$",-0.183670114544065;-0.4012496099632018;0.34914396946823856;0.7607362119624373,27,,,,Infinity,,,,,, -101,0,((((x0 * (-0.2992363142868322 / x1)) * 0.7469307520865507) ^ Exp((x1 * x0))) + 0.6864789936353768),"(((x[:, 0] * (t[0] / x[:, 1])) * t[1]) ** np.exp((x[:, 1] * x[:, 0])) + t[2])","$$\left({\left(\left(x_{0} \cdot \frac{\theta_{0}}{x_{1}}\right) \cdot \theta_{1}\right)^{\operatorname{exp}(\left(x_{1} \cdot x_{0}\right))}} + \theta_{2}\right)$$",-0.2992363142868322;0.7469307520865507;0.6864789936353768,14,,,,Infinity,,,,,, -102,0,Log((((Exp(x0) * Exp((x1 ^ 0.5775876383162657))) * 0.6142297045728289) / Exp((((Log(x0) ^ x0) + x0) * ((x1 / x1) / x1))))),"np.log((((np.exp(x[:, 0]) * np.exp(x[:, 1] ** t[0])) * t[1]) / np.exp(((np.log(x[:, 0]) ** x[:, 0] + x[:, 0]) * ((x[:, 1] / x[:, 1]) / x[:, 1])))))","$$\operatorname{log}(\frac{\left(\left(\operatorname{exp}(x_{0}) \cdot \operatorname{exp}({x_{1}^{\theta_{0}}})\right) \cdot \theta_{1}\right)}{\operatorname{exp}(\left(\left({\operatorname{log}(x_{0})^{x_{0}}} + x_{0}\right) \cdot \frac{\frac{x_{1}}{x_{1}}}{x_{1}}\right))})$$",0.5775876383162657;0.6142297045728289,24,,,,Infinity,,,,,, -103,0,(((x1 * -0.16701825287297667) ^ x0) / x1),"((x[:, 1] * t[0]) ** x[:, 0] / x[:, 1])","$$\frac{{\left(x_{1} \cdot \theta_{0}\right)^{x_{0}}}}{x_{1}}$$",-0.16701825287297667,7,,,,Infinity,,,,,, -104,0,Log(((((x0 / Log(x1)) * 0.34331349020213503) + (x0 / x0)) + (((Log(Exp(x1)) / x0) + Exp(Log((x0 + -0.5080180381555238)))) / (2.240117011356105 / x0)))),"np.log(((((x[:, 0] / np.log(x[:, 1])) * t[0]) + (x[:, 0] / x[:, 0])) + (((np.log(np.exp(x[:, 1])) / x[:, 0]) + np.exp(np.log((x[:, 0] + t[1])))) / (t[2] / x[:, 0]))))","$$\operatorname{log}(\left(\left(\left(\frac{x_{0}}{\operatorname{log}(x_{1})} \cdot \theta_{0}\right) + \frac{x_{0}}{x_{0}}\right) + \frac{\left(\frac{\operatorname{log}(\operatorname{exp}(x_{1}))}{x_{0}} + \operatorname{exp}(\operatorname{log}(\left(x_{0} + \theta_{1}\right)))\right)}{\frac{\theta_{2}}{x_{0}}}\right))$$",0.34331349020213503;-0.5080180381555238;2.240117011356105,27,4.0808316528306174e-2,4.0808316528306174e-2,4.0808316528306174e-2,4.0808316528306174e-2,-2.765838819268656,-2.765838819268656,-2.765838819268656,62.244423916333965,62.244423916333965,62.244423916333965 -105,0,Exp((3.344703145102064 * Log((x1 ^ (x1 * 2.732331720705962))))),"np.exp((t[0] * np.log(x[:, 1] ** (x[:, 1] * t[1]))))","$$\operatorname{exp}(\left(\theta_{0} \cdot \operatorname{log}({x_{1}^{\left(x_{1} \cdot \theta_{1}\right)}})\right))$$",3.344703145102064;2.732331720705962,9,1.250373865043282e-2,1.250373865043282e-2,1.250373865043282e-2,1.250373865043282e-2,-0.1538595169229402,-0.1538595169229402,-0.1538595169229402,20.178938458573167,20.178938458573167,20.178938458573167 -106,0,Log(Log(((0.22840951606456383 ^ (0.8639657323545767 ^ x1)) + 0.6903676224565654))),"np.log(np.log((t[0] ** t[1] ** x[:, 1] + t[2])))","$$\operatorname{log}(\operatorname{log}(\left({\theta_{0}^{{\theta_{1}^{x_{1}}}}} + \theta_{2}\right)))$$",0.22840951606456383;0.8639657323545767;0.6903676224565654,9,,,,Infinity,,,,,, -107,0,Exp(Exp((((-0.6885773293096165 + (0.17323048259712748 * (x0 * x1))) / Exp(((x1 * x0) * -0.9059075627212022))) / (Exp((0.1038114227475454 + x0)) ^ (((x1 * -0.9986752501573765) ^ -0.6920154129792686) + (x1 ^ (x0 ^ x1))))))),"np.exp(np.exp((((t[0] + (t[1] * (x[:, 0] * x[:, 1]))) / np.exp(((x[:, 1] * x[:, 0]) * t[2]))) / np.exp((t[3] + x[:, 0])) ** ((x[:, 1] * t[4]) ** t[5] + x[:, 1] ** x[:, 0] ** x[:, 1]))))","$$\operatorname{exp}(\operatorname{exp}(\frac{\frac{\left(\theta_{0} + \left(\theta_{1} \cdot \left(x_{0} \cdot x_{1}\right)\right)\right)}{\operatorname{exp}(\left(\left(x_{1} \cdot x_{0}\right) \cdot \theta_{2}\right))}}{{\operatorname{exp}(\left(\theta_{3} + x_{0}\right))^{\left({\left(x_{1} \cdot \theta_{4}\right)^{\theta_{5}}} + {x_{1}^{{x_{0}^{x_{1}}}}}\right)}}}))$$",-0.6885773293096165;0.17323048259712748;-0.9059075627212022;0.1038114227475454;-0.9986752501573765;-0.6920154129792686,33,,,,Infinity,,,,,, -108,0,(((Log(x0) ^ Exp(Exp(x0))) + ((x0 + 0.11572546333078271) / (x1 / (x1 ^ -5.8020803043132396e-2)))) ^ (Exp(((0.13670185064979323 ^ (x1 ^ x1)) ^ (-0.32728126494110543 ^ (4.49066333035506e-2 ^ x1)))) * -0.40724509886588467)),"(np.log(x[:, 0]) ** np.exp(np.exp(x[:, 0])) + ((x[:, 0] + t[0]) / (x[:, 1] / x[:, 1] ** t[1]))) ** (np.exp(t[2] ** x[:, 1] ** x[:, 1] ** t[3] ** t[4] ** x[:, 1]) * t[5])","$${\left({\operatorname{log}(x_{0})^{\operatorname{exp}(\operatorname{exp}(x_{0}))}} + \frac{\left(x_{0} + \theta_{0}\right)}{\frac{x_{1}}{{x_{1}^{\theta_{1}}}}}\right)^{\left(\operatorname{exp}({{\theta_{2}^{{x_{1}^{x_{1}}}}}^{{\theta_{3}^{{\theta_{4}^{x_{1}}}}}}}) \cdot \theta_{5}\right)}}$$",0.11572546333078271;-5.8020803043132396e-2;0.13670185064979323;-0.32728126494110543;4.49066333035506e-2;-0.40724509886588467,31,,,,Infinity,,,,,, -109,0,(((0.8537172360863454 + Log(x0)) ^ (7.621110099325945e-2 ^ ((-0.35742692244968144 / x1) ^ -0.5589962425236303))) / (((((0.18088601441834296 / (x0 ^ -0.9433317191850474)) + -0.3425744093173396) / (x0 / Log((x1 / x1)))) ^ (-0.9426961852577016 * x0)) ^ ((x0 * -3.212228096004699e-2) * 0.9989205415807794))),"((t[0] + np.log(x[:, 0])) ** t[1] ** (t[2] / x[:, 1]) ** t[3] / (((t[4] / x[:, 0] ** t[5]) + t[6]) / (x[:, 0] / np.log((x[:, 1] / x[:, 1])))) ** (t[7] * x[:, 0]) ** ((x[:, 0] * t[8]) * t[9]))","$$\frac{{\left(\theta_{0} + \operatorname{log}(x_{0})\right)^{{\theta_{1}^{{\frac{\theta_{2}}{x_{1}}^{\theta_{3}}}}}}}}{{{\frac{\left(\frac{\theta_{4}}{{x_{0}^{\theta_{5}}}} + \theta_{6}\right)}{\frac{x_{0}}{\operatorname{log}(\frac{x_{1}}{x_{1}})}}^{\left(\theta_{7} \cdot x_{0}\right)}}^{\left(\left(x_{0} \cdot \theta_{8}\right) \cdot \theta_{9}\right)}}}$$",0.8537172360863454;7.621110099325945e-2;-0.35742692244968144;-0.5589962425236303;0.18088601441834296;-0.9433317191850474;-0.3425744093173396;-0.9426961852577016;-3.212228096004699e-2;0.9989205415807794,37,,,,Infinity,,,,,, -110,0,Log(Log(Log(((-0.45703526323398513 * (x0 * x0)) * (x0 * ((x1 / x1) + x0)))))),"np.log(np.log(np.log(((t[0] * (x[:, 0] * x[:, 0])) * (x[:, 0] * ((x[:, 1] / x[:, 1]) + x[:, 0]))))))","$$\operatorname{log}(\operatorname{log}(\operatorname{log}(\left(\left(\theta_{0} \cdot \left(x_{0} \cdot x_{0}\right)\right) \cdot \left(x_{0} \cdot \left(\frac{x_{1}}{x_{1}} + x_{0}\right)\right)\right))))$$",-0.45703526323398513,16,,,,Infinity,,,,,, -111,0,Exp(Log(((Exp((x0 * (x1 * 1.4515428883289228))) / ((x1 ^ x0) * ((x0 ^ -1.1154228168593132) ^ x1))) ^ ((-1.1043855405633605 + (x1 + -1.2715452642160683)) ^ ((x1 / x1) ^ ((0.6011144878428614 * x0) + (x0 / x0))))))),"np.exp(np.log((np.exp((x[:, 0] * (x[:, 1] * t[0]))) / (x[:, 1] ** x[:, 0] * x[:, 0] ** t[1] ** x[:, 1])) ** (t[2] + (x[:, 1] + t[3])) ** (x[:, 1] / x[:, 1]) ** ((t[4] * x[:, 0]) + (x[:, 0] / x[:, 0]))))","$$\operatorname{exp}(\operatorname{log}({\frac{\operatorname{exp}(\left(x_{0} \cdot \left(x_{1} \cdot \theta_{0}\right)\right))}{\left({x_{1}^{x_{0}}} \cdot {{x_{0}^{\theta_{1}}}^{x_{1}}}\right)}^{{\left(\theta_{2} + \left(x_{1} + \theta_{3}\right)\right)^{{\frac{x_{1}}{x_{1}}^{\left(\left(\theta_{4} \cdot x_{0}\right) + \frac{x_{0}}{x_{0}}\right)}}}}}}))$$",1.4515428883289228;-1.1154228168593132;-1.1043855405633605;-1.2715452642160683;0.6011144878428614,36,1.0723267666339067e-3,1.0723267666339067e-3,1.0723267666339067e-3,1.0723267666339067e-3,0.9010444412248726,0.9010444412248726,0.9010444412248726,89.20567455503208,89.20567455503208,89.20567455503208 -112,0,Log(Exp((x0 + -0.9956750177908847))),"np.log(np.exp((x[:, 0] + t[0])))","$$\operatorname{log}(\operatorname{exp}(\left(x_{0} + \theta_{0}\right)))$$",-0.9956750177908847,5,0.15105329635364,0.15105329635364,0.15105329635364,0.15105329635364,-12.939373529228037,-12.939373529228037,-12.939373529228037,10.204062464256419,10.204062464256419,10.204062464256419 -113,0,(Log(Log((-0.5769664153555887 ^ x1))) ^ -3.5619728067071144e-3),"np.log(np.log(t[0] ** x[:, 1])) ** t[1]","$${\operatorname{log}(\operatorname{log}({\theta_{0}^{x_{1}}}))^{\theta_{1}}}$$",-0.5769664153555887;-3.5619728067071144e-3,7,,,,Infinity,,,,,, -114,0,Log(Exp((((-0.47584373138445846 + x0) + (0.2655405457277719 + ((x0 / x0) ^ 0.4176520058454798))) ^ -5.199169024305316))),"np.log(np.exp(((t[0] + x[:, 0]) + (t[1] + (x[:, 0] / x[:, 0]) ** t[2])) ** t[3]))","$$\operatorname{log}(\operatorname{exp}({\left(\left(\theta_{0} + x_{0}\right) + \left(\theta_{1} + {\frac{x_{0}}{x_{0}}^{\theta_{2}}}\right)\right)^{\theta_{3}}}))$$",-0.47584373138445846;0.2655405457277719;0.4176520058454798;-5.199169024305316,15,5.147076393802534e-3,5.147076393802534e-3,5.147076393802534e-3,5.147076393802534e-3,0.5250218156860724,0.5250218156860724,0.5250218156860724,37.27698434605469,37.27698434605469,37.27698434605469 -115,0,((4.740255607529478e-2 / (((((5.4056620110959845e-2 ^ x0) / x0) + -0.866095301872698) + x0) ^ x1)) * 0.7479817698039307),"((t[0] / (((t[1] ** x[:, 0] / x[:, 0]) + t[2]) + x[:, 0]) ** x[:, 1]) * t[3])","$$\left(\frac{\theta_{0}}{{\left(\left(\frac{{\theta_{1}^{x_{0}}}}{x_{0}} + \theta_{2}\right) + x_{0}\right)^{x_{1}}}} \cdot \theta_{3}\right)$$",4.740255607529478e-2;5.4056620110959845e-2;-0.866095301872698;0.7479817698039307,15,5.147492452471214e-3,5.147492452471214e-3,5.147492452471214e-3,5.147492452471214e-3,0.5249834213091683,0.5249834213091683,0.5249834213091683,37.27698472050749,37.27698472050749,37.27698472050749 -116,0,(((((x0 + 0.578393108530052) ^ -3.289061085431772) ^ x0) / x0) * 0.16344831451499572),"(((x[:, 0] + t[0]) ** t[1] ** x[:, 0] / x[:, 0]) * t[2])","$$\left(\frac{{{\left(x_{0} + \theta_{0}\right)^{\theta_{1}}}^{x_{0}}}}{x_{0}} \cdot \theta_{2}\right)$$",0.578393108530052;-3.289061085431772;0.16344831451499572,11,4.911290957115253e-3,4.911290957115253e-3,4.911290957115253e-3,4.911290957115253e-3,0.5467803694818232,0.5467803694818232,0.5467803694818232,25.77654912947293,25.77654912947293,25.77654912947293 -117,0,Log((1.222155609436542 ^ x1)),"np.log(t[0] ** x[:, 1])","$$\operatorname{log}({\theta_{0}^{x_{1}}})$$",1.222155609436542,4,9.365750726554422e-3,9.365750726554422e-3,9.365750726554422e-3,9.365750726554422e-3,0.13571765124915813,0.13571765124915813,0.13571765124915813,7.5745315555011015,7.5745315555011015,7.5745315555011015 -118,0,Log((((0.7817856180161955 / Log(x0)) / Exp(Exp((-0.45568218380277614 + (x0 * -0.15703913092967237))))) + Exp((((-0.531330362020098 + x1) + 0.35435873886616986) / ((x0 * -0.40203142457292707) ^ -0.10293967674071625))))),"np.log((((t[0] / np.log(x[:, 0])) / np.exp(np.exp((t[1] + (x[:, 0] * t[2]))))) + np.exp((((t[3] + x[:, 1]) + t[4]) / (x[:, 0] * t[5]) ** t[6]))))","$$\operatorname{log}(\left(\frac{\frac{\theta_{0}}{\operatorname{log}(x_{0})}}{\operatorname{exp}(\operatorname{exp}(\left(\theta_{1} + \left(x_{0} \cdot \theta_{2}\right)\right)))} + \operatorname{exp}(\frac{\left(\left(\theta_{3} + x_{1}\right) + \theta_{4}\right)}{{\left(x_{0} \cdot \theta_{5}\right)^{\theta_{6}}}})\right))$$",0.7817856180161955;-0.45568218380277614;-0.15703913092967237;-0.531330362020098;0.35435873886616986;-0.40203142457292707;-0.10293967674071625,26,,,,Infinity,,,,,, -119,0,(((((Exp((x0 * -28.51956973058349)) ^ x0) * x0) + (-0.13352955252864485 * x1)) / Log(x1)) + 1.109464773033123e-2),"((((np.exp((x[:, 0] * t[0])) ** x[:, 0] * x[:, 0]) + (t[1] * x[:, 1])) / np.log(x[:, 1])) + t[2])","$$\left(\frac{\left(\left({\operatorname{exp}(\left(x_{0} \cdot \theta_{0}\right))^{x_{0}}} \cdot x_{0}\right) + \left(\theta_{1} \cdot x_{1}\right)\right)}{\operatorname{log}(x_{1})} + \theta_{2}\right)$$",-28.51956973058349;-0.13352955252864485;1.109464773033123e-2,17,9.425889210719216e-3,9.425889210719216e-3,9.425889210719216e-3,9.425889210719216e-3,0.1301680021220576,0.1301680021220576,0.1301680021220576,43.424075921108304,43.424075921108304,43.424075921108304 -120,0,(((66.88173099301365 / ((x1 + (Exp(x1) * Exp(x1))) * 74.39440222948285)) / Exp((x0 * 62.63437816152556))) * Log((Exp(x0) / (x1 * x1)))),"(((t[0] / ((x[:, 1] + (np.exp(x[:, 1]) * np.exp(x[:, 1]))) * t[1])) / np.exp((x[:, 0] * t[2]))) * np.log((np.exp(x[:, 0]) / (x[:, 1] * x[:, 1]))))","$$\left(\frac{\frac{\theta_{0}}{\left(\left(x_{1} + \left(\operatorname{exp}(x_{1}) \cdot \operatorname{exp}(x_{1})\right)\right) \cdot \theta_{1}\right)}}{\operatorname{exp}(\left(x_{0} \cdot \theta_{2}\right))} \cdot \operatorname{log}(\frac{\operatorname{exp}(x_{0})}{\left(x_{1} \cdot x_{1}\right)})\right)$$",66.88173099301365;74.39440222948285;62.63437816152556,24,1.457355474997544e-2,1.457355474997544e-2,1.457355474997544e-2,1.457355474997544e-2,-0.34486454921821275,-0.34486454921821275,-0.34486454921821275,55.98248800569395,55.98248800569395,55.98248800569395 -121,0,(((((-0.16772692148960477 * x0) * x0) + -9.17303664270066e-2) * Exp(((x1 + (x0 + x0)) ^ (x0 ^ 0.14305320587135095)))) * ((0.8645537163544479 ^ Log((x0 + (x0 * x1)))) ^ (((-0.22946899667718668 + x1) / x0) ^ (Log(x1) / (x1 + 0.6365674780455115))))),"(((((t[0] * x[:, 0]) * x[:, 0]) + t[1]) * np.exp((x[:, 1] + (x[:, 0] + x[:, 0])) ** x[:, 0] ** t[2])) * t[3] ** np.log((x[:, 0] + (x[:, 0] * x[:, 1]))) ** ((t[4] + x[:, 1]) / x[:, 0]) ** (np.log(x[:, 1]) / (x[:, 1] + t[5])))","$$\left(\left(\left(\left(\left(\theta_{0} \cdot x_{0}\right) \cdot x_{0}\right) + \theta_{1}\right) \cdot \operatorname{exp}({\left(x_{1} + \left(x_{0} + x_{0}\right)\right)^{{x_{0}^{\theta_{2}}}}})\right) \cdot {{\theta_{3}^{\operatorname{log}(\left(x_{0} + \left(x_{0} \cdot x_{1}\right)\right))}}^{{\frac{\left(\theta_{4} + x_{1}\right)}{x_{0}}^{\frac{\operatorname{log}(x_{1})}{\left(x_{1} + \theta_{5}\right)}}}}}\right)$$",-0.16772692148960477;-9.17303664270066e-2;0.14305320587135095;0.8645537163544479;-0.22946899667718668;0.6365674780455115,40,,,,Infinity,,,,,, -122,0,Log(Log(((x0 * (-0.15260390891108622 ^ x1)) ^ Log((((x1 * 0.7833768916177044) ^ (x1 * 0.2770202914109625)) / (x1 / (x0 / x1))))))),"np.log(np.log((x[:, 0] * t[0] ** x[:, 1]) ** np.log(((x[:, 1] * t[1]) ** (x[:, 1] * t[2]) / (x[:, 1] / (x[:, 0] / x[:, 1]))))))","$$\operatorname{log}(\operatorname{log}({\left(x_{0} \cdot {\theta_{0}^{x_{1}}}\right)^{\operatorname{log}(\frac{{\left(x_{1} \cdot \theta_{1}\right)^{\left(x_{1} \cdot \theta_{2}\right)}}}{\frac{x_{1}}{\frac{x_{0}}{x_{1}}}})}}))$$",-0.15260390891108622;0.7833768916177044;0.2770202914109625,22,,,,Infinity,,,,,, -123,0,(-7.748556362980419e-2 + ((Log(x1) ^ (x0 / x1)) + (0.798844683237206 + (-0.4009504352589146 ^ x0)))),"(t[0] + (np.log(x[:, 1]) ** (x[:, 0] / x[:, 1]) + (t[1] + t[2] ** x[:, 0])))","$$\left(\theta_{0} + \left({\operatorname{log}(x_{1})^{\frac{x_{0}}{x_{1}}}} + \left(\theta_{1} + {\theta_{2}^{x_{0}}}\right)\right)\right)$$",-7.748556362980419e-2;0.798844683237206;-0.4009504352589146,14,,,,Infinity,,,,,, -124,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 -125,0,Exp(((((x1 / Exp(x0)) ^ ((x1 * x0) / x0)) * Exp(((x0 * x0) * -0.6234044204442423))) / (((-0.24425617296498003 + x0) ^ (0.5383316480530964 ^ (x0 * x1))) * Exp(((0.3055173532632969 * (-0.20946797648581805 / x1)) ^ (-0.144075059462912 / (-0.5303869160618666 * x1))))))),"np.exp((((x[:, 1] / np.exp(x[:, 0])) ** ((x[:, 1] * x[:, 0]) / x[:, 0]) * np.exp(((x[:, 0] * x[:, 0]) * t[0]))) / ((t[1] + x[:, 0]) ** t[2] ** (x[:, 0] * x[:, 1]) * np.exp((t[3] * (t[4] / x[:, 1])) ** (t[5] / (t[6] * x[:, 1]))))))","$$\operatorname{exp}(\frac{\left({\frac{x_{1}}{\operatorname{exp}(x_{0})}^{\frac{\left(x_{1} \cdot x_{0}\right)}{x_{0}}}} \cdot \operatorname{exp}(\left(\left(x_{0} \cdot x_{0}\right) \cdot \theta_{0}\right))\right)}{\left({\left(\theta_{1} + x_{0}\right)^{{\theta_{2}^{\left(x_{0} \cdot x_{1}\right)}}}} \cdot \operatorname{exp}({\left(\theta_{3} \cdot \frac{\theta_{4}}{x_{1}}\right)^{\frac{\theta_{5}}{\left(\theta_{6} \cdot x_{1}\right)}}})\right)})$$",-0.6234044204442423;-0.24425617296498003;0.5383316480530964;0.3055173532632969;-0.20946797648581805;-0.144075059462912;-0.5303869160618666,41,,,,Infinity,,,,,, -126,0,((-0.1193824367142472 ^ (Log(((-9.77793075489064e-2 ^ x0) * Log(Exp(x1)))) ^ ((-0.990337311010981 + ((x0 * -0.15349184139096217) * (-0.9709322486727994 / x0))) + (x1 / Log(Exp(x1)))))) + (Exp(x1) ^ 0.3768042471678018)),"(t[0] ** np.log((t[1] ** x[:, 0] * np.log(np.exp(x[:, 1])))) ** ((t[2] + ((x[:, 0] * t[3]) * (t[4] / x[:, 0]))) + (x[:, 1] / np.log(np.exp(x[:, 1])))) + np.exp(x[:, 1]) ** t[5])","$$\left({\theta_{0}^{{\operatorname{log}(\left({\theta_{1}^{x_{0}}} \cdot \operatorname{log}(\operatorname{exp}(x_{1}))\right))^{\left(\left(\theta_{2} + \left(\left(x_{0} \cdot \theta_{3}\right) \cdot \frac{\theta_{4}}{x_{0}}\right)\right) + \frac{x_{1}}{\operatorname{log}(\operatorname{exp}(x_{1}))}\right)}}}} + {\operatorname{exp}(x_{1})^{\theta_{5}}}\right)$$",-0.1193824367142472;-9.77793075489064e-2;-0.990337311010981;-0.15349184139096217;-0.9709322486727994;0.3768042471678018,31,,,,Infinity,,,,,, -127,0,((0.21728401001814387 * (x0 ^ x0)) * 0.1525321466472882),"((t[0] * x[:, 0] ** x[:, 0]) * t[1])","$$\left(\left(\theta_{0} \cdot {x_{0}^{x_{0}}}\right) \cdot \theta_{1}\right)$$",0.21728401001814387;0.1525321466472882,7,1.2934826309877115e-2,1.2934826309877115e-2,1.2934826309877115e-2,1.2934826309877115e-2,-0.19364078654029693,-0.19364078654029693,-0.19364078654029693,13.757551742253403,13.757551742253403,13.757551742253403 -128,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 -129,0,((Log((0.3421304475560347 + Log(Log(x1)))) / ((x0 * x1) ^ 0.10554648592297544)) ^ (Exp((Log(x1) ^ (x1 + -0.4175659418211084))) * Exp((Exp((-0.9221948987287276 / x0)) / (x0 * 8.55978714605965e-4))))),"(np.log((t[0] + np.log(np.log(x[:, 1])))) / (x[:, 0] * x[:, 1]) ** t[1]) ** (np.exp(np.log(x[:, 1]) ** (x[:, 1] + t[2])) * np.exp((np.exp((t[3] / x[:, 0])) / (x[:, 0] * t[4]))))","$${\frac{\operatorname{log}(\left(\theta_{0} + \operatorname{log}(\operatorname{log}(x_{1}))\right))}{{\left(x_{0} \cdot x_{1}\right)^{\theta_{1}}}}^{\left(\operatorname{exp}({\operatorname{log}(x_{1})^{\left(x_{1} + \theta_{2}\right)}}) \cdot \operatorname{exp}(\frac{\operatorname{exp}(\frac{\theta_{3}}{x_{0}})}{\left(x_{0} \cdot \theta_{4}\right)})\right)}}$$",0.3421304475560347;0.10554648592297544;-0.4175659418211084;-0.9221948987287276;8.55978714605965e-4,30,,,,Infinity,,,,,, -130,0,(((((((-0.8216158531400458 + x1) * -3.1826872293060915e-2) * Log((x0 ^ -0.5487824512119821))) + -0.6527976177271888) + 0.17888821209520323) + 0.3347048438833756) ^ x1),"((((((t[0] + x[:, 1]) * t[1]) * np.log(x[:, 0] ** t[2])) + t[3]) + t[4]) + t[5]) ** x[:, 1]","$${\left(\left(\left(\left(\left(\left(\theta_{0} + x_{1}\right) \cdot \theta_{1}\right) \cdot \operatorname{log}({x_{0}^{\theta_{2}}})\right) + \theta_{3}\right) + \theta_{4}\right) + \theta_{5}\right)^{x_{1}}}$$",-0.8216158531400458;-3.1826872293060915e-2;-0.5487824512119821;-0.6527976177271888;0.17888821209520323;0.3347048438833756,18,,,,Infinity,,,,,, -131,0,Log(Exp((((x1 + -0.6724382544393613) + (x1 ^ (x0 + x0))) ^ ((-0.7040845510534761 / x1) ^ (x0 * -0.7309152471846252))))),"np.log(np.exp(((x[:, 1] + t[0]) + x[:, 1] ** (x[:, 0] + x[:, 0])) ** (t[1] / x[:, 1]) ** (x[:, 0] * t[2])))","$$\operatorname{log}(\operatorname{exp}({\left(\left(x_{1} + \theta_{0}\right) + {x_{1}^{\left(x_{0} + x_{0}\right)}}\right)^{{\frac{\theta_{1}}{x_{1}}^{\left(x_{0} \cdot \theta_{2}\right)}}}}))$$",-0.6724382544393613;-0.7040845510534761;-0.7309152471846252,19,,,,Infinity,,,,,, -132,0,Exp(Log((Exp(Log(x1)) * (((Exp(x1) * -0.3875701744944944) + 0.8132811393805993) + -0.32322830338722564)))),"np.exp(np.log((np.exp(np.log(x[:, 1])) * (((np.exp(x[:, 1]) * t[0]) + t[1]) + t[2]))))","$$\operatorname{exp}(\operatorname{log}(\left(\operatorname{exp}(\operatorname{log}(x_{1})) \cdot \left(\left(\left(\operatorname{exp}(x_{1}) \cdot \theta_{0}\right) + \theta_{1}\right) + \theta_{2}\right)\right)))$$",-0.3875701744944944;0.8132811393805993;-0.32322830338722564,14,,,,Infinity,,,,,, -133,0,((3.5114123752993595e-3 ^ (x1 ^ x1)) * Exp(Exp(((x1 ^ x0) / (x0 ^ -0.3011229942440748))))),"(t[0] ** x[:, 1] ** x[:, 1] * np.exp(np.exp((x[:, 1] ** x[:, 0] / x[:, 0] ** t[1]))))","$$\left({\theta_{0}^{{x_{1}^{x_{1}}}}} \cdot \operatorname{exp}(\operatorname{exp}(\frac{{x_{1}^{x_{0}}}}{{x_{0}^{\theta_{1}}}}))\right)$$",3.5114123752993595e-3;-0.3011229942440748,15,8.570177618030807e-3,8.570177618030807e-3,8.570177618030807e-3,8.570177618030807e-3,0.20913406119996614,0.20913406119996614,0.20913406119996614,33.23821526642121,33.23821526642121,33.23821526642121 -134,0,Log((Exp((0.6972077071846006 * Log(x0))) ^ -0.321043531678246)),"np.log(np.exp((t[0] * np.log(x[:, 0]))) ** t[1])","$$\operatorname{log}({\operatorname{exp}(\left(\theta_{0} \cdot \operatorname{log}(x_{0})\right))^{\theta_{1}}})$$",0.6972077071846006;-0.321043531678246,8,9.281051777135091e-3,9.281051777135091e-3,9.281051777135091e-3,9.281051777135091e-3,0.14353376861958045,0.14353376861958045,0.14353376861958045,18.384278571159143,18.384278571159143,18.384278571159143 -135,0,Exp((-0.8807664048182543 / (x1 * 0.8415584766175589))),"np.exp((t[0] / (x[:, 1] * t[1])))","$$\operatorname{exp}(\frac{\theta_{0}}{\left(x_{1} \cdot \theta_{1}\right)})$$",-0.8807664048182543;0.8415584766175589,6,9.451763005823425e-3,9.451763005823425e-3,9.451763005823425e-3,9.451763005823425e-3,0.1277803382757029,0.1277803382757029,0.1277803382757029,13.706983932045123,13.706983932045123,13.706983932045123 -136,0,(Log((Log(x0) ^ (x0 + x0))) ^ -0.845219971849921),"np.log(np.log(x[:, 0]) ** (x[:, 0] + x[:, 0])) ** t[0]","$${\operatorname{log}({\operatorname{log}(x_{0})^{\left(x_{0} + x_{0}\right)}})^{\theta_{0}}}$$",-0.845219971849921,9,,,,Infinity,,,,,, -137,0,(Exp((((x0 ^ x0) + -0.20247965616255614) + ((0.6012084129969308 ^ Exp((-0.2740745758068699 * x1))) ^ 0.5551899105010502))) ^ (Log(Log(x1)) * 0.5093888888846689)),"np.exp(((x[:, 0] ** x[:, 0] + t[0]) + t[1] ** np.exp((t[2] * x[:, 1])) ** t[3])) ** (np.log(np.log(x[:, 1])) * t[4])","$${\operatorname{exp}(\left(\left({x_{0}^{x_{0}}} + \theta_{0}\right) + {{\theta_{1}^{\operatorname{exp}(\left(\theta_{2} \cdot x_{1}\right))}}^{\theta_{3}}}\right))^{\left(\operatorname{log}(\operatorname{log}(x_{1})) \cdot \theta_{4}\right)}}$$",-0.20247965616255614;0.6012084129969308;-0.2740745758068699;0.5551899105010502;0.5093888888846689,21,,,,Infinity,,,,,, -138,0,Log(Log((Exp((Exp((x1 * -0.5759861572049794)) ^ (x0 + x1))) * (Exp((x1 + 0.21460052101348626)) ^ ((x1 ^ x0) * Log(Log(x1))))))),"np.log(np.log((np.exp(np.exp((x[:, 1] * t[0])) ** (x[:, 0] + x[:, 1])) * np.exp((x[:, 1] + t[1])) ** (x[:, 1] ** x[:, 0] * np.log(np.log(x[:, 1]))))))","$$\operatorname{log}(\operatorname{log}(\left(\operatorname{exp}({\operatorname{exp}(\left(x_{1} \cdot \theta_{0}\right))^{\left(x_{0} + x_{1}\right)}}) \cdot {\operatorname{exp}(\left(x_{1} + \theta_{1}\right))^{\left({x_{1}^{x_{0}}} \cdot \operatorname{log}(\operatorname{log}(x_{1}))\right)}}\right)))$$",-0.5759861572049794;0.21460052101348626,24,,,,Infinity,,,,,, -139,0,Log((1.193769485229021 + (0.12118877071497218 * ((x0 * x0) * -0.8733027242780543)))),"np.log((t[0] + (t[1] * ((x[:, 0] * x[:, 0]) * t[2]))))","$$\operatorname{log}(\left(\theta_{0} + \left(\theta_{1} \cdot \left(\left(x_{0} \cdot x_{0}\right) \cdot \theta_{2}\right)\right)\right))$$",1.193769485229021;0.12118877071497218;-0.8733027242780543,10,6.622472938811641e-3,6.622472938811641e-3,6.622472938811641e-3,6.622472938811641e-3,0.38887050988162375,0.38887050988162375,0.38887050988162375,22.163114156088856,22.163114156088856,22.163114156088856 -140,0,(((x0 ^ -0.5918106801781998) + -0.2966408802737627) / (Log(Exp((-8.29463992774222e-2 ^ x0))) / Exp((x1 * (x1 ^ x1))))),"((x[:, 0] ** t[0] + t[1]) / (np.log(np.exp(t[2] ** x[:, 0])) / np.exp((x[:, 1] * x[:, 1] ** x[:, 1]))))","$$\frac{\left({x_{0}^{\theta_{0}}} + \theta_{1}\right)}{\frac{\operatorname{log}(\operatorname{exp}({\theta_{2}^{x_{0}}}))}{\operatorname{exp}(\left(x_{1} \cdot {x_{1}^{x_{1}}}\right))}}$$",-0.5918106801781998;-0.2966408802737627;-8.29463992774222e-2,18,,,,Infinity,,,,,, -141,0,(((-0.362323572235411 / (x0 * 0.7944651033611387)) + -0.5425724353267609) * Log(((0.6139355053956357 ^ (x1 * 0.9022556092330366)) * ((x1 * -1.1263612893958053e-2) ^ -0.43953070737601196)))),"(((t[0] / (x[:, 0] * t[1])) + t[2]) * np.log((t[3] ** (x[:, 1] * t[4]) * (x[:, 1] * t[5]) ** t[6])))","$$\left(\left(\frac{\theta_{0}}{\left(x_{0} \cdot \theta_{1}\right)} + \theta_{2}\right) \cdot \operatorname{log}(\left({\theta_{3}^{\left(x_{1} \cdot \theta_{4}\right)}} \cdot {\left(x_{1} \cdot \theta_{5}\right)^{\theta_{6}}}\right))\right)$$",-0.362323572235411;0.7944651033611387;-0.5425724353267609;0.6139355053956357;0.9022556092330366;-1.1263612893958053e-2;-0.43953070737601196,20,,,,Infinity,,,,,, -142,0,Exp((x1 / (x1 ^ x0))),"np.exp((x[:, 1] / x[:, 1] ** x[:, 0]))","$$\operatorname{exp}(\frac{x_{1}}{{x_{1}^{x_{0}}}})$$",,6,14.411630412978433,14.411630412978433,14.411630412978433,14.411630412978433,-1328.9219834393793,-1328.9219834393793,-1328.9219834393793,22.62709484628519,22.62709484628519,22.62709484628519 -143,0,(Log(((Log(x1) / x0) ^ Log(x0))) * (((Exp(x1) * -5.312099721774899e-2) ^ ((x1 * (x1 * x1)) * -0.222181011003322)) ^ x0)),"(np.log((np.log(x[:, 1]) / x[:, 0]) ** np.log(x[:, 0])) * (np.exp(x[:, 1]) * t[0]) ** ((x[:, 1] * (x[:, 1] * x[:, 1])) * t[1]) ** x[:, 0])","$$\left(\operatorname{log}({\frac{\operatorname{log}(x_{1})}{x_{0}}^{\operatorname{log}(x_{0})}}) \cdot {{\left(\operatorname{exp}(x_{1}) \cdot \theta_{0}\right)^{\left(\left(x_{1} \cdot \left(x_{1} \cdot x_{1}\right)\right) \cdot \theta_{1}\right)}}^{x_{0}}}\right)$$",-5.312099721774899e-2;-0.222181011003322,23,,,,Infinity,,,,,, -144,0,(((-0.30404059616727697 / (x1 + x0)) + (-0.4548984706188258 + (-0.9312404922880191 ^ (((-0.9636522752234902 + x0) ^ -0.37095153006075265) * -0.4181135559963698)))) ^ ((Exp((x1 + 0.9392954669190421)) ^ Log(x0)) + (0.5440683474712493 + x0))),"((t[0] / (x[:, 1] + x[:, 0])) + (t[1] + t[2] ** ((t[3] + x[:, 0]) ** t[4] * t[5]))) ** (np.exp((x[:, 1] + t[6])) ** np.log(x[:, 0]) + (t[7] + x[:, 0]))","$${\left(\frac{\theta_{0}}{\left(x_{1} + x_{0}\right)} + \left(\theta_{1} + {\theta_{2}^{\left({\left(\theta_{3} + x_{0}\right)^{\theta_{4}}} \cdot \theta_{5}\right)}}\right)\right)^{\left({\operatorname{exp}(\left(x_{1} + \theta_{6}\right))^{\operatorname{log}(x_{0})}} + \left(\theta_{7} + x_{0}\right)\right)}}$$",-0.30404059616727697;-0.4548984706188258;-0.9312404922880191;-0.9636522752234902;-0.37095153006075265;-0.4181135559963698;0.9392954669190421;0.5440683474712493,29,,,,Infinity,,,,,, -145,0,(Exp(Log(Log((0.5867131264638382 + Log(Exp(x0)))))) ^ (((Log((x0 ^ 9.263242638575364e-3)) * (x1 * x0)) + (Log(x1) ^ (x1 * 0.9155846053296199))) / (Log((Log(x0) * -9.634506586484237e-2)) ^ (0.6520947015598239 + (x0 ^ Exp(x0)))))),"np.exp(np.log(np.log((t[0] + np.log(np.exp(x[:, 0])))))) ** (((np.log(x[:, 0] ** t[1]) * (x[:, 1] * x[:, 0])) + np.log(x[:, 1]) ** (x[:, 1] * t[2])) / np.log((np.log(x[:, 0]) * t[3])) ** (t[4] + x[:, 0] ** np.exp(x[:, 0])))","$${\operatorname{exp}(\operatorname{log}(\operatorname{log}(\left(\theta_{0} + \operatorname{log}(\operatorname{exp}(x_{0}))\right))))^{\frac{\left(\left(\operatorname{log}({x_{0}^{\theta_{1}}}) \cdot \left(x_{1} \cdot x_{0}\right)\right) + {\operatorname{log}(x_{1})^{\left(x_{1} \cdot \theta_{2}\right)}}\right)}{{\operatorname{log}(\left(\operatorname{log}(x_{0}) \cdot \theta_{3}\right))^{\left(\theta_{4} + {x_{0}^{\operatorname{exp}(x_{0})}}\right)}}}}}$$",0.5867131264638382;9.263242638575364e-3;0.9155846053296199;-9.634506586484237e-2;0.6520947015598239,37,,,,Infinity,,,,,, -146,0,(Log(((Exp(x1) / (8.668923942237572e-2 + x0)) ^ Exp((x1 / x1)))) ^ Log(Log(((0.11482245260677071 ^ (0.13849890428548006 + x1)) / (0.27103801283882145 + (x1 * 4.4734821141817704e-2)))))),"np.log((np.exp(x[:, 1]) / (t[0] + x[:, 0])) ** np.exp((x[:, 1] / x[:, 1]))) ** np.log(np.log((t[1] ** (t[2] + x[:, 1]) / (t[3] + (x[:, 1] * t[4])))))","$${\operatorname{log}({\frac{\operatorname{exp}(x_{1})}{\left(\theta_{0} + x_{0}\right)}^{\operatorname{exp}(\frac{x_{1}}{x_{1}})}})^{\operatorname{log}(\operatorname{log}(\frac{{\theta_{1}^{\left(\theta_{2} + x_{1}\right)}}}{\left(\theta_{3} + \left(x_{1} \cdot \theta_{4}\right)\right)}))}}$$",8.668923942237572e-2;0.11482245260677071;0.13849890428548006;0.27103801283882145;4.4734821141817704e-2,26,,,,Infinity,,,,,, -147,0,(-0.49065709877649377 ^ x1),"t[0] ** x[:, 1]","$${\theta_{0}^{x_{1}}}$$",-0.49065709877649377,3,,,,Infinity,,,,,, -148,0,Log((0.3193545634828091 / x1)),"np.log((t[0] / x[:, 1]))","$$\operatorname{log}(\frac{\theta_{0}}{x_{1}})$$",0.3193545634828091,4,0.2792880552105235,0.2792880552105235,0.2792880552105235,0.2792880552105235,-24.773025930640912,-24.773025930640912,-24.773025930640912,7.817461629536674,7.817461629536674,7.817461629536674 -149,0,(((0.2675999930073257 ^ ((0.8323411144385874 * x1) * x0)) / ((x0 * 2.424672642972869) / x0)) * 0.21623693267944152),"((t[0] ** ((t[1] * x[:, 1]) * x[:, 0]) / ((x[:, 0] * t[2]) / x[:, 0])) * t[3])","$$\left(\frac{{\theta_{0}^{\left(\left(\theta_{1} \cdot x_{1}\right) \cdot x_{0}\right)}}}{\frac{\left(x_{0} \cdot \theta_{2}\right)}{x_{0}}} \cdot \theta_{3}\right)$$",0.2675999930073257;0.8323411144385874;2.424672642972869;0.21623693267944152,15,1.0630187118239022e-2,1.0630187118239022e-2,1.0630187118239022e-2,1.0630187118239022e-2,1.9033993274711003e-2,1.9033993274711003e-2,1.9033993274711003e-2,34.969658948297806,34.969658948297806,34.969658948297806 -150,0,(((Log(Log((x1 ^ x1))) + -0.9501750166385429) + ((0.3973678551246953 * (x0 + x0)) * Exp(Log((-0.8677788674405422 * x1))))) * Log(Exp(Log((((x1 + -0.7092236214095563) * (x1 ^ x1)) ^ Exp(x0)))))),"(((np.log(np.log(x[:, 1] ** x[:, 1])) + t[0]) + ((t[1] * (x[:, 0] + x[:, 0])) * np.exp(np.log((t[2] * x[:, 1]))))) * np.log(np.exp(np.log(((x[:, 1] + t[3]) * x[:, 1] ** x[:, 1]) ** np.exp(x[:, 0])))))","$$\left(\left(\left(\operatorname{log}(\operatorname{log}({x_{1}^{x_{1}}})) + \theta_{0}\right) + \left(\left(\theta_{1} \cdot \left(x_{0} + x_{0}\right)\right) \cdot \operatorname{exp}(\operatorname{log}(\left(\theta_{2} \cdot x_{1}\right)))\right)\right) \cdot \operatorname{log}(\operatorname{exp}(\operatorname{log}({\left(\left(x_{1} + \theta_{3}\right) \cdot {x_{1}^{x_{1}}}\right)^{\operatorname{exp}(x_{0})}})))\right)$$",-0.9501750166385429;0.3973678551246953;-0.8677788674405422;-0.7092236214095563,33,,,,Infinity,,,,,, -151,0,(Exp((Exp(Log((Exp(x1) / (0.43518183259302934 * x1)))) * Log(((x0 / x1) * -0.12359062219289396)))) * (((x0 / (x0 * x1)) * (x1 * x1)) * (Log((x1 * -0.5920873135911845)) / Log((x0 * x1))))),"(np.exp((np.exp(np.log((np.exp(x[:, 1]) / (t[0] * x[:, 1])))) * np.log(((x[:, 0] / x[:, 1]) * t[1])))) * (((x[:, 0] / (x[:, 0] * x[:, 1])) * (x[:, 1] * x[:, 1])) * (np.log((x[:, 1] * t[2])) / np.log((x[:, 0] * x[:, 1])))))","$$\left(\operatorname{exp}(\left(\operatorname{exp}(\operatorname{log}(\frac{\operatorname{exp}(x_{1})}{\left(\theta_{0} \cdot x_{1}\right)})) \cdot \operatorname{log}(\left(\frac{x_{0}}{x_{1}} \cdot \theta_{1}\right))\right)) \cdot \left(\left(\frac{x_{0}}{\left(x_{0} \cdot x_{1}\right)} \cdot \left(x_{1} \cdot x_{1}\right)\right) \cdot \frac{\operatorname{log}(\left(x_{1} \cdot \theta_{2}\right))}{\operatorname{log}(\left(x_{0} \cdot x_{1}\right))}\right)\right)$$",0.43518183259302934;-0.12359062219289396;-0.5920873135911845,36,,,,Infinity,,,,,, -152,0,Log(((((2.3384039449557434 ^ x1) ^ x1) * (0.657880919890683 ^ Log(x0))) ^ 0.5626283794494901)),"np.log((t[0] ** x[:, 1] ** x[:, 1] * t[1] ** np.log(x[:, 0])) ** t[2])","$$\operatorname{log}({\left({{\theta_{0}^{x_{1}}}^{x_{1}}} \cdot {\theta_{1}^{\operatorname{log}(x_{0})}}\right)^{\theta_{2}}})$$",2.3384039449557434;0.657880919890683;0.5626283794494901,13,3.460810626237287e-3,3.460810626237287e-3,3.460810626237287e-3,3.460810626237287e-3,0.6806323781236658,0.6806323781236658,0.6806323781236658,29.35876263563125,29.35876263563125,29.35876263563125 -153,0,(((x1 * x1) + x0) / x1),"(((x[:, 1] * x[:, 1]) + x[:, 0]) / x[:, 1])","$$\frac{\left(\left(x_{1} \cdot x_{1}\right) + x_{0}\right)}{x_{1}}$$",,7,22.46986725908893,22.46986725908893,22.46986725908893,22.46986725908893,-2072.5454335489862,-2072.5454335489862,-2072.5454335489862,31.48894592021874,31.48894592021874,31.48894592021874 -154,0,((((((x1 ^ (x1 + -0.3878280419017254)) + x1) / Log((x1 * -0.200282014592556))) / (-0.28875544467012215 ^ (0.12602520457722854 / Exp((x1 ^ 0.1922076079756715))))) ^ (Log((-0.21179978481042283 / x1)) * (((0.10458052071768331 + x0) * x1) + Log((0.546962367421267 / (x1 ^ x0)))))) * x0),"((((x[:, 1] ** (x[:, 1] + t[0]) + x[:, 1]) / np.log((x[:, 1] * t[1]))) / t[2] ** (t[3] / np.exp(x[:, 1] ** t[4]))) ** (np.log((t[5] / x[:, 1])) * (((t[6] + x[:, 0]) * x[:, 1]) + np.log((t[7] / x[:, 1] ** x[:, 0])))) * x[:, 0])","$$\left({\frac{\frac{\left({x_{1}^{\left(x_{1} + \theta_{0}\right)}} + x_{1}\right)}{\operatorname{log}(\left(x_{1} \cdot \theta_{1}\right))}}{{\theta_{2}^{\frac{\theta_{3}}{\operatorname{exp}({x_{1}^{\theta_{4}}})}}}}^{\left(\operatorname{log}(\frac{\theta_{5}}{x_{1}}) \cdot \left(\left(\left(\theta_{6} + x_{0}\right) \cdot x_{1}\right) + \operatorname{log}(\frac{\theta_{7}}{{x_{1}^{x_{0}}}})\right)\right)}} \cdot x_{0}\right)$$",-0.3878280419017254;-0.200282014592556;-0.28875544467012215;0.12602520457722854;0.1922076079756715;-0.21179978481042283;0.10458052071768331;0.546962367421267,41,,,,Infinity,,,,,, -155,0,Log(Log((Log(((x0 / x1) * (Log(x1) ^ -0.693795429486298))) / (((-0.8898669266702308 / x0) * (0.9830811911368884 / (x1 ^ x1))) + 0.39910679021280904)))),"np.log(np.log((np.log(((x[:, 0] / x[:, 1]) * np.log(x[:, 1]) ** t[0])) / (((t[1] / x[:, 0]) * (t[2] / x[:, 1] ** x[:, 1])) + t[3]))))","$$\operatorname{log}(\operatorname{log}(\frac{\operatorname{log}(\left(\frac{x_{0}}{x_{1}} \cdot {\operatorname{log}(x_{1})^{\theta_{0}}}\right))}{\left(\left(\frac{\theta_{1}}{x_{0}} \cdot \frac{\theta_{2}}{{x_{1}^{x_{1}}}}\right) + \theta_{3}\right)}))$$",-0.693795429486298;-0.8898669266702308;0.9830811911368884;0.39910679021280904,23,,,,Infinity,,,,,, -156,0,Exp((Log((((x0 ^ (5.807727898281234e-2 ^ x1)) + -0.761159566460547) + (0.10522120935497803 + (x1 / x1)))) / (Log((Log(x0) ^ Exp((0.5599010120150526 ^ x0)))) * (((0.9518216266733684 ^ (0.7735978059922499 + x0)) + (0.4482620168223893 * x1)) * ((0.15627636710591786 ^ (x0 ^ x1)) / (-0.548722296716416 ^ x0)))))),"np.exp((np.log(((x[:, 0] ** t[0] ** x[:, 1] + t[1]) + (t[2] + (x[:, 1] / x[:, 1])))) / (np.log(np.log(x[:, 0]) ** np.exp(t[3] ** x[:, 0])) * ((t[4] ** (t[5] + x[:, 0]) + (t[6] * x[:, 1])) * (t[7] ** x[:, 0] ** x[:, 1] / t[8] ** x[:, 0])))))","$$\operatorname{exp}(\frac{\operatorname{log}(\left(\left({x_{0}^{{\theta_{0}^{x_{1}}}}} + \theta_{1}\right) + \left(\theta_{2} + \frac{x_{1}}{x_{1}}\right)\right))}{\left(\operatorname{log}({\operatorname{log}(x_{0})^{\operatorname{exp}({\theta_{3}^{x_{0}}})}}) \cdot \left(\left({\theta_{4}^{\left(\theta_{5} + x_{0}\right)}} + \left(\theta_{6} \cdot x_{1}\right)\right) \cdot \frac{{\theta_{7}^{{x_{0}^{x_{1}}}}}}{{\theta_{8}^{x_{0}}}}\right)\right)})$$",5.807727898281234e-2;-0.761159566460547;0.10522120935497803;0.5599010120150526;0.9518216266733684;0.7735978059922499;0.4482620168223893;0.15627636710591786;-0.548722296716416,44,,,,Infinity,,,,,, -157,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 -158,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 -159,0,Log((((x1 ^ x1) + (x0 * Exp(x0))) + ((0.6796794244676786 / x1) * (-0.5652646144542011 * (-0.17754772712873657 ^ x0))))),"np.log(((x[:, 1] ** x[:, 1] + (x[:, 0] * np.exp(x[:, 0]))) + ((t[0] / x[:, 1]) * (t[1] * t[2] ** x[:, 0]))))","$$\operatorname{log}(\left(\left({x_{1}^{x_{1}}} + \left(x_{0} \cdot \operatorname{exp}(x_{0})\right)\right) + \left(\frac{\theta_{0}}{x_{1}} \cdot \left(\theta_{1} \cdot {\theta_{2}^{x_{0}}}\right)\right)\right))$$",0.6796794244676786;-0.5652646144542011;-0.17754772712873657,19,,,,Infinity,,,,,, -160,0,Exp((Exp((x0 ^ -0.1671815272899183)) + Exp(Log((-0.7245607006201328 + (x1 * -0.7382426721609409)))))),"np.exp((np.exp(x[:, 0] ** t[0]) + np.exp(np.log((t[1] + (x[:, 1] * t[2]))))))","$$\operatorname{exp}(\left(\operatorname{exp}({x_{0}^{\theta_{0}}}) + \operatorname{exp}(\operatorname{log}(\left(\theta_{1} + \left(x_{1} \cdot \theta_{2}\right)\right)))\right))$$",-0.1671815272899183;-0.7245607006201328;-0.7382426721609409,13,,,,Infinity,,,,,, -161,0,(((Exp(Log((x1 * -0.2915304419274961))) ^ Log((x0 ^ x0))) * ((-0.9755904447054582 + x1) * (Log((0.30861543729315144 ^ x1)) + -0.4558907824525531))) ^ x0),"(np.exp(np.log((x[:, 1] * t[0]))) ** np.log(x[:, 0] ** x[:, 0]) * ((t[1] + x[:, 1]) * (np.log(t[2] ** x[:, 1]) + t[3]))) ** x[:, 0]","$${\left({\operatorname{exp}(\operatorname{log}(\left(x_{1} \cdot \theta_{0}\right)))^{\operatorname{log}({x_{0}^{x_{0}}})}} \cdot \left(\left(\theta_{1} + x_{1}\right) \cdot \left(\operatorname{log}({\theta_{2}^{x_{1}}}) + \theta_{3}\right)\right)\right)^{x_{0}}}$$",-0.2915304419274961;-0.9755904447054582;0.30861543729315144;-0.4558907824525531,23,,,,Infinity,,,,,, -162,0,Exp((Log(((0.1890832942770146 + (-0.12591258607038816 * x0)) / (0.47192214651703335 + (0.7079901648094353 ^ x1)))) * 0.5298175506577129)),"np.exp((np.log(((t[0] + (t[1] * x[:, 0])) / (t[2] + t[3] ** x[:, 1]))) * t[4]))","$$\operatorname{exp}(\left(\operatorname{log}(\frac{\left(\theta_{0} + \left(\theta_{1} \cdot x_{0}\right)\right)}{\left(\theta_{2} + {\theta_{3}^{x_{1}}}\right)}) \cdot \theta_{4}\right))$$",0.1890832942770146;-0.12591258607038816;0.47192214651703335;0.7079901648094353;0.5298175506577129,15,1.82874723475812e-2,1.82874723475812e-2,1.82874723475812e-2,1.82874723475812e-2,-0.6875891762174062,-0.6875891762174062,-0.6875891762174062,43.07945206199432,43.07945206199432,43.07945206199432 -163,0,(x1 + x1),"(x[:, 1] + x[:, 1])","$$\left(x_{1} + x_{1}\right)$$",,3,0.42840102205506697,0.42840102205506697,0.42840102205506697,0.42840102205506697,-38.53334360044726,-38.53334360044726,-38.53334360044726,2.465002461529396,2.465002461529396,2.465002461529396 -164,0,(0.46116983634074316 ^ (x0 / (x0 + -0.5080385063426675))),"t[0] ** (x[:, 0] / (x[:, 0] + t[1]))","$${\theta_{0}^{\frac{x_{0}}{\left(x_{0} + \theta_{1}\right)}}}$$",0.46116983634074316;-0.5080385063426675,7,5.65918109455741e-2,5.65918109455741e-2,5.65918109455741e-2,5.65918109455741e-2,-4.222358005474957,-4.222358005474957,-4.222358005474957,15.358847887625,15.358847887625,15.358847887625 -165,0,((Log(Exp(x0)) ^ (((-0.42548971666111723 ^ Log(Exp(x1))) ^ ((x1 * -0.5423539877968566) * -0.9132005604378887)) / Log((x1 * ((x1 * 0.184054997067573) + (x0 * x1)))))) + Exp(((x1 + x1) * ((0.6640593936777788 ^ x1) / x1)))),"(np.log(np.exp(x[:, 0])) ** (t[0] ** np.log(np.exp(x[:, 1])) ** ((x[:, 1] * t[1]) * t[2]) / np.log((x[:, 1] * ((x[:, 1] * t[3]) + (x[:, 0] * x[:, 1]))))) + np.exp(((x[:, 1] + x[:, 1]) * (t[4] ** x[:, 1] / x[:, 1]))))","$$\left({\operatorname{log}(\operatorname{exp}(x_{0}))^{\frac{{{\theta_{0}^{\operatorname{log}(\operatorname{exp}(x_{1}))}}^{\left(\left(x_{1} \cdot \theta_{1}\right) \cdot \theta_{2}\right)}}}{\operatorname{log}(\left(x_{1} \cdot \left(\left(x_{1} \cdot \theta_{3}\right) + \left(x_{0} \cdot x_{1}\right)\right)\right))}}} + \operatorname{exp}(\left(\left(x_{1} + x_{1}\right) \cdot \frac{{\theta_{4}^{x_{1}}}}{x_{1}}\right))\right)$$",-0.42548971666111723;-0.5423539877968566;-0.9132005604378887;0.184054997067573;0.6640593936777788,37,,,,Infinity,,,,,, -166,0,((4.668144257396321 + x1) ^ -1.7430228790061992),"(t[0] + x[:, 1]) ** t[1]","$${\left(\theta_{0} + x_{1}\right)^{\theta_{1}}}$$",4.668144257396321;-1.7430228790061992,5,1.1083055953448077e-2,1.1083055953448077e-2,1.1083055953448077e-2,1.1083055953448077e-2,-2.2757268525669616e-2,-2.2757268525669616e-2,-2.2757268525669616e-2,10.983296426692837,10.983296426692837,10.983296426692837 -167,0,((((-3.144397342670613e-2 * (-0.4657233655163705 + x0)) ^ (0.5084047766120403 / Log(x1))) * (Exp((-0.51331872094201 / Log(x0))) ^ Log(Exp((x0 ^ x0))))) / (Log(Exp(((x1 ^ -0.35486883898435184) * 0.7364826629409413))) * ((Log(x0) ^ 0.30761238237626254) ^ 0.6133510531343811))),"(((t[0] * (t[1] + x[:, 0])) ** (t[2] / np.log(x[:, 1])) * np.exp((t[3] / np.log(x[:, 0]))) ** np.log(np.exp(x[:, 0] ** x[:, 0]))) / (np.log(np.exp((x[:, 1] ** t[4] * t[5]))) * np.log(x[:, 0]) ** t[6] ** t[7]))","$$\frac{\left({\left(\theta_{0} \cdot \left(\theta_{1} + x_{0}\right)\right)^{\frac{\theta_{2}}{\operatorname{log}(x_{1})}}} \cdot {\operatorname{exp}(\frac{\theta_{3}}{\operatorname{log}(x_{0})})^{\operatorname{log}(\operatorname{exp}({x_{0}^{x_{0}}}))}}\right)}{\left(\operatorname{log}(\operatorname{exp}(\left({x_{1}^{\theta_{4}}} \cdot \theta_{5}\right))) \cdot {{\operatorname{log}(x_{0})^{\theta_{6}}}^{\theta_{7}}}\right)}$$",-3.144397342670613e-2;-0.4657233655163705;0.5084047766120403;-0.51331872094201;-0.35486883898435184;0.7364826629409413;0.30761238237626254;0.6133510531343811,37,,,,Infinity,,,,,, -168,0,(Log(((x0 + ((x1 ^ x1) / x0)) + (Exp(x0) / x1))) ^ -3.145604962363848),"np.log(((x[:, 0] + (x[:, 1] ** x[:, 1] / x[:, 0])) + (np.exp(x[:, 0]) / x[:, 1]))) ** t[0]","$${\operatorname{log}(\left(\left(x_{0} + \frac{{x_{1}^{x_{1}}}}{x_{0}}\right) + \frac{\operatorname{exp}(x_{0})}{x_{1}}\right))^{\theta_{0}}}$$",-3.145604962363848,15,5.052347060327307e-3,5.052347060327307e-3,5.052347060327307e-3,5.052347060327307e-3,0.5337635485403796,0.5337635485403796,0.5337635485403796,33.21709517291948,33.21709517291948,33.21709517291948 -169,0,Log(Exp(Log(Exp(x0)))),"np.log(np.exp(np.log(np.exp(x[:, 0]))))","$$\operatorname{log}(\operatorname{exp}(\operatorname{log}(\operatorname{exp}(x_{0}))))$$",,5,1.1424220374065182,1.1424220374065182,1.1424220374065182,1.1424220374065182,-104.42403172817247,-104.42403172817247,-104.42403172817247,6.521241277006416,6.521241277006416,6.521241277006416 -170,0,((0.19443279993002394 * x0) ^ x1),"(t[0] * x[:, 0]) ** x[:, 1]","$${\left(\theta_{0} \cdot x_{0}\right)^{x_{1}}}$$",0.19443279993002394,5,0.3413964345342835,0.3413964345342835,0.3413964345342835,0.3413964345342835,-30.504459269652656,-30.504459269652656,-30.504459269652656,10.375371288618998,10.375371288618998,10.375371288618998 -171,0,(Exp((x0 ^ x0)) + (-4.429402635739184 + x0)),"(np.exp(x[:, 0] ** x[:, 0]) + (t[0] + x[:, 0]))","$$\left(\operatorname{exp}({x_{0}^{x_{0}}}) + \left(\theta_{0} + x_{0}\right)\right)$$",-4.429402635739184,8,2.6944741932694494,2.6944741932694494,2.6944741932694494,2.6944741932694494,-247.64920628356185,-247.64920628356185,-247.64920628356185,17.321455008782944,17.321455008782944,17.321455008782944 -172,0,(((((Log(Exp(x1)) ^ Exp((x0 + -1.728445152126211))) + 2.2359727562540415) ^ 2.502761434399841) * x1) * 1.5471154841851483e-2),"(((np.log(np.exp(x[:, 1])) ** np.exp((x[:, 0] + t[0])) + t[1]) ** t[2] * x[:, 1]) * t[3])","$$\left(\left({\left({\operatorname{log}(\operatorname{exp}(x_{1}))^{\operatorname{exp}(\left(x_{0} + \theta_{0}\right))}} + \theta_{1}\right)^{\theta_{2}}} \cdot x_{1}\right) \cdot \theta_{3}\right)$$",-1.728445152126211;2.2359727562540415;2.502761434399841;1.5471154841851483e-2,16,8.39006394906046e-3,8.39006394906046e-3,8.39006394906046e-3,8.39006394906046e-3,0.22575515964736126,0.22575515964736126,0.22575515964736126,41.36231546590209,41.36231546590209,41.36231546590209 -173,0,((Exp(Log(x0)) / (Exp(((x0 + 0.44584123975974266) + x1)) / ((-2.183514026906408e-2 / (x1 + -0.6233859559299456)) / (0.5283058990561089 ^ x0)))) * (Exp(Exp(x0)) / (Log(x0) * (x1 ^ x0)))),"((np.exp(np.log(x[:, 0])) / (np.exp(((x[:, 0] + t[0]) + x[:, 1])) / ((t[1] / (x[:, 1] + t[2])) / t[3] ** x[:, 0]))) * (np.exp(np.exp(x[:, 0])) / (np.log(x[:, 0]) * x[:, 1] ** x[:, 0])))","$$\left(\frac{\operatorname{exp}(\operatorname{log}(x_{0}))}{\frac{\operatorname{exp}(\left(\left(x_{0} + \theta_{0}\right) + x_{1}\right))}{\frac{\frac{\theta_{1}}{\left(x_{1} + \theta_{2}\right)}}{{\theta_{3}^{x_{0}}}}}} \cdot \frac{\operatorname{exp}(\operatorname{exp}(x_{0}))}{\left(\operatorname{log}(x_{0}) \cdot {x_{1}^{x_{0}}}\right)}\right)$$",0.44584123975974266;-2.183514026906408e-2;-0.6233859559299456;0.5283058990561089,31,1823232.836599218,1823232.836599218,1823232.836599218,1823232.836599218,-1.6825004155855855e8,-1.6825004155855855e8,-1.6825004155855855e8,1640985.750600935,1640985.750600935,1640985.750600935 -174,0,(0.20059950763971857 * x1),"(t[0] * x[:, 1])","$$\left(\theta_{0} \cdot x_{1}\right)$$",0.20059950763971857,3,9.365750690525967e-3,9.365750690525967e-3,9.365750690525967e-3,9.365750690525967e-3,0.13571765457390572,0.13571765457390572,0.13571765457390572,5.325190976993443,5.325190976993443,5.325190976993443 -175,0,((((x0 + -0.27338751270162487) / (x1 * (-0.33482209800322893 * x0))) / Log(((-0.2115412335084066 + (-0.8644993651145055 * x0)) * Exp((0.43452901070838545 ^ x1))))) / ((0.3459199279502362 * Log((x0 ^ (-0.8511128409755666 + x0)))) * (Log(Exp((x1 + x1))) + (x1 / x1)))),"((((x[:, 0] + t[0]) / (x[:, 1] * (t[1] * x[:, 0]))) / np.log(((t[2] + (t[3] * x[:, 0])) * np.exp(t[4] ** x[:, 1])))) / ((t[5] * np.log(x[:, 0] ** (t[6] + x[:, 0]))) * (np.log(np.exp((x[:, 1] + x[:, 1]))) + (x[:, 1] / x[:, 1]))))","$$\frac{\frac{\frac{\left(x_{0} + \theta_{0}\right)}{\left(x_{1} \cdot \left(\theta_{1} \cdot x_{0}\right)\right)}}{\operatorname{log}(\left(\left(\theta_{2} + \left(\theta_{3} \cdot x_{0}\right)\right) \cdot \operatorname{exp}({\theta_{4}^{x_{1}}})\right))}}{\left(\left(\theta_{5} \cdot \operatorname{log}({x_{0}^{\left(\theta_{6} + x_{0}\right)}})\right) \cdot \left(\operatorname{log}(\operatorname{exp}(\left(x_{1} + x_{1}\right))) + \frac{x_{1}}{x_{1}}\right)\right)}$$",-0.27338751270162487;-0.33482209800322893;-0.2115412335084066;-0.8644993651145055;0.43452901070838545;0.3459199279502362;-0.8511128409755666,40,,,,Infinity,,,,,, -176,0,Exp(Log((((x0 ^ x1) * -0.2665729478421984) / Log(((-0.7002820383803896 / x1) / ((x1 * 0.7870750922239204) * 0.6188113419606676)))))),"np.exp(np.log(((x[:, 0] ** x[:, 1] * t[0]) / np.log(((t[1] / x[:, 1]) / ((x[:, 1] * t[2]) * t[3]))))))","$$\operatorname{exp}(\operatorname{log}(\frac{\left({x_{0}^{x_{1}}} \cdot \theta_{0}\right)}{\operatorname{log}(\frac{\frac{\theta_{1}}{x_{1}}}{\left(\left(x_{1} \cdot \theta_{2}\right) \cdot \theta_{3}\right)})}))$$",-0.2665729478421984;-0.7002820383803896;0.7870750922239204;0.6188113419606676,18,,,,Infinity,,,,,, -177,0,(Log((((x1 ^ x1) ^ -0.7110225382841173) * (0.48770198062684644 + Log((-0.8061847938691791 / (x0 * 0.8709743216247638)))))) * ((Exp(((-0.35418631462010153 ^ x1) * x1)) * Exp((x0 * -0.20239915157405675))) ^ Exp((-0.8351623454348341 / (x1 / x0))))),"(np.log((x[:, 1] ** x[:, 1] ** t[0] * (t[1] + np.log((t[2] / (x[:, 0] * t[3])))))) * (np.exp((t[4] ** x[:, 1] * x[:, 1])) * np.exp((x[:, 0] * t[5]))) ** np.exp((t[6] / (x[:, 1] / x[:, 0]))))","$$\left(\operatorname{log}(\left({{x_{1}^{x_{1}}}^{\theta_{0}}} \cdot \left(\theta_{1} + \operatorname{log}(\frac{\theta_{2}}{\left(x_{0} \cdot \theta_{3}\right)})\right)\right)) \cdot {\left(\operatorname{exp}(\left({\theta_{4}^{x_{1}}} \cdot x_{1}\right)) \cdot \operatorname{exp}(\left(x_{0} \cdot \theta_{5}\right))\right)^{\operatorname{exp}(\frac{\theta_{6}}{\frac{x_{1}}{x_{0}}})}}\right)$$",-0.7110225382841173;0.48770198062684644;-0.8061847938691791;0.8709743216247638;-0.35418631462010153;-0.20239915157405675;-0.8351623454348341,34,,,,Infinity,,,,,, -178,0,(Log(((x0 / ((x0 / x1) / (x1 * -0.5524981862563886))) ^ (-0.3282628500034941 ^ x0))) / x1),"(np.log((x[:, 0] / ((x[:, 0] / x[:, 1]) / (x[:, 1] * t[0]))) ** t[1] ** x[:, 0]) / x[:, 1])","$$\frac{\operatorname{log}({\frac{x_{0}}{\frac{\frac{x_{0}}{x_{1}}}{\left(x_{1} \cdot \theta_{0}\right)}}^{{\theta_{1}^{x_{0}}}}})}{x_{1}}$$",-0.5524981862563886;-0.3282628500034941,16,,,,Infinity,,,,,, -179,0,Exp(((((x1 ^ -0.8088597945531835) ^ Log((x0 + x1))) ^ ((-0.4264835356234816 / (-0.8500728719781294 / x0)) * (0.5218455475914037 + x1))) / Log((Log(((x1 * x0) ^ -0.752323138688017)) / Exp(Exp(Log(x0))))))),"np.exp((x[:, 1] ** t[0] ** np.log((x[:, 0] + x[:, 1])) ** ((t[1] / (t[2] / x[:, 0])) * (t[3] + x[:, 1])) / np.log((np.log((x[:, 1] * x[:, 0]) ** t[4]) / np.exp(np.exp(np.log(x[:, 0])))))))","$$\operatorname{exp}(\frac{{{{x_{1}^{\theta_{0}}}^{\operatorname{log}(\left(x_{0} + x_{1}\right))}}^{\left(\frac{\theta_{1}}{\frac{\theta_{2}}{x_{0}}} \cdot \left(\theta_{3} + x_{1}\right)\right)}}}{\operatorname{log}(\frac{\operatorname{log}({\left(x_{1} \cdot x_{0}\right)^{\theta_{4}}})}{\operatorname{exp}(\operatorname{exp}(\operatorname{log}(x_{0})))})})$$",-0.8088597945531835;-0.4264835356234816;-0.8500728719781294;0.5218455475914037;-0.752323138688017,32,48538.53959710125,48538.53959710125,48538.53959710125,48538.53959710125,-4479191.777251271,-4479191.777251271,-4479191.777251271,43765.10144854272,43765.10144854272,43765.10144854272 -180,0,((Exp(Exp(((0.5783405500656325 ^ x1) * ((-0.400569897049326 ^ x1) * x1)))) + (((((x1 / x0) + x1) ^ 0.8648255897518211) * 0.5495130711071916) * (((x1 * 0.7687336356391234) / ((0.48880641179759166 / x0) + 0.9462925522236957)) * 1.4478854715978096e-2))) * ((Log(x0) * -0.38100935259891844) ^ -0.3722305091915381)),"((np.exp(np.exp((t[0] ** x[:, 1] * (t[1] ** x[:, 1] * x[:, 1])))) + ((((x[:, 1] / x[:, 0]) + x[:, 1]) ** t[2] * t[3]) * (((x[:, 1] * t[4]) / ((t[5] / x[:, 0]) + t[6])) * t[7]))) * (np.log(x[:, 0]) * t[8]) ** t[9])","$$\left(\left(\operatorname{exp}(\operatorname{exp}(\left({\theta_{0}^{x_{1}}} \cdot \left({\theta_{1}^{x_{1}}} \cdot x_{1}\right)\right))) + \left(\left({\left(\frac{x_{1}}{x_{0}} + x_{1}\right)^{\theta_{2}}} \cdot \theta_{3}\right) \cdot \left(\frac{\left(x_{1} \cdot \theta_{4}\right)}{\left(\frac{\theta_{5}}{x_{0}} + \theta_{6}\right)} \cdot \theta_{7}\right)\right)\right) \cdot {\left(\operatorname{log}(x_{0}) \cdot \theta_{8}\right)^{\theta_{9}}}\right)$$",0.5783405500656325;-0.400569897049326;0.8648255897518211;0.5495130711071916;0.7687336356391234;0.48880641179759166;0.9462925522236957;1.4478854715978096e-2;-0.38100935259891844;-0.3722305091915381,40,,,,Infinity,,,,,, -181,0,Exp((Exp(Exp((((x1 ^ -0.10226970185059236) * x1) + (x0 + 0.6216267301083367)))) + ((0.23289671244991927 * (Exp(x0) + 0.22575297493628632)) + ((-0.7798113771291091 ^ Log(x1)) * ((x0 * 1.0749605760301928e-2) + x1))))),"np.exp((np.exp(np.exp(((x[:, 1] ** t[0] * x[:, 1]) + (x[:, 0] + t[1])))) + ((t[2] * (np.exp(x[:, 0]) + t[3])) + (t[4] ** np.log(x[:, 1]) * ((x[:, 0] * t[5]) + x[:, 1])))))","$$\operatorname{exp}(\left(\operatorname{exp}(\operatorname{exp}(\left(\left({x_{1}^{\theta_{0}}} \cdot x_{1}\right) + \left(x_{0} + \theta_{1}\right)\right))) + \left(\left(\theta_{2} \cdot \left(\operatorname{exp}(x_{0}) + \theta_{3}\right)\right) + \left({\theta_{4}^{\operatorname{log}(x_{1})}} \cdot \left(\left(x_{0} \cdot \theta_{5}\right) + x_{1}\right)\right)\right)\right))$$",-0.10226970185059236;0.6216267301083367;0.23289671244991927;0.22575297493628632;-0.7798113771291091;1.0749605760301928e-2,30,,,,Infinity,,,,,, -182,0,(((0.17772002235930817 / x1) ^ x0) ^ -6.429142051782988e-2),"(t[0] / x[:, 1]) ** x[:, 0] ** t[1]","$${{\frac{\theta_{0}}{x_{1}}^{x_{0}}}^{\theta_{1}}}$$",0.17772002235930817;-6.429142051782988e-2,7,0.9663221416152477,0.9663221416152477,0.9663221416152477,0.9663221416152477,-88.17332892890515,-88.17332892890515,-88.17332892890515,16.17760518522771,16.17760518522771,16.17760518522771 -183,0,((((0.6757808600430988 * Log((x1 * 0.608579787893281))) ^ (((x1 + x1) * 0.779022063308076) + -0.36137327988694146)) * Exp((0.19550086984985038 ^ (((x0 / x0) ^ x1) + x1)))) * -0.11276991328292041),"(((t[0] * np.log((x[:, 1] * t[1]))) ** (((x[:, 1] + x[:, 1]) * t[2]) + t[3]) * np.exp(t[4] ** ((x[:, 0] / x[:, 0]) ** x[:, 1] + x[:, 1]))) * t[5])","$$\left(\left({\left(\theta_{0} \cdot \operatorname{log}(\left(x_{1} \cdot \theta_{1}\right))\right)^{\left(\left(\left(x_{1} + x_{1}\right) \cdot \theta_{2}\right) + \theta_{3}\right)}} \cdot \operatorname{exp}({\theta_{4}^{\left({\frac{x_{0}}{x_{0}}^{x_{1}}} + x_{1}\right)}})\right) \cdot \theta_{5}\right)$$",0.6757808600430988;0.608579787893281;0.779022063308076;-0.36137327988694146;0.19550086984985038;-0.11276991328292041,27,,,,Infinity,,,,,, -184,0,(Exp(((-2.6652912693394173 * x0) + x1)) + (Log((x0 + 0.4925589577259654)) ^ (0.5640912481193454 + x1))),"(np.exp(((t[0] * x[:, 0]) + x[:, 1])) + np.log((x[:, 0] + t[1])) ** (t[2] + x[:, 1]))","$$\left(\operatorname{exp}(\left(\left(\theta_{0} \cdot x_{0}\right) + x_{1}\right)) + {\operatorname{log}(\left(x_{0} + \theta_{1}\right))^{\left(\theta_{2} + x_{1}\right)}}\right)$$",-2.6652912693394173;0.4925589577259654;0.5640912481193454,15,0.3017280602017145,0.3017280602017145,0.3017280602017145,0.3017280602017145,-26.843815639444344,-26.843815639444344,-26.843815639444344,37.525953185482,37.525953185482,37.525953185482 -185,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 -186,0,Log((Log(Log(x0)) ^ (-0.6442686792385699 * Exp(((4.370710283623236e-3 * (x1 * x0)) * x1))))),"np.log(np.log(np.log(x[:, 0])) ** (t[0] * np.exp(((t[1] * (x[:, 1] * x[:, 0])) * x[:, 1]))))","$$\operatorname{log}({\operatorname{log}(\operatorname{log}(x_{0}))^{\left(\theta_{0} \cdot \operatorname{exp}(\left(\left(\theta_{1} \cdot \left(x_{1} \cdot x_{0}\right)\right) \cdot x_{1}\right))\right)}})$$",-0.6442686792385699;4.370710283623236e-3,15,,,,Infinity,,,,,, -187,0,(((Exp((x0 * -0.33036040098506214)) + (x1 * x1)) ^ Log((Log(x0) * 0.2987622292495128))) ^ Exp((((x1 / x1) ^ Log(x0)) ^ ((x1 * 0.22519356141573) / (x0 * -0.10813185921607205))))),"(np.exp((x[:, 0] * t[0])) + (x[:, 1] * x[:, 1])) ** np.log((np.log(x[:, 0]) * t[1])) ** np.exp((x[:, 1] / x[:, 1]) ** np.log(x[:, 0]) ** ((x[:, 1] * t[2]) / (x[:, 0] * t[3])))","$${{\left(\operatorname{exp}(\left(x_{0} \cdot \theta_{0}\right)) + \left(x_{1} \cdot x_{1}\right)\right)^{\operatorname{log}(\left(\operatorname{log}(x_{0}) \cdot \theta_{1}\right))}}^{\operatorname{exp}({{\frac{x_{1}}{x_{1}}^{\operatorname{log}(x_{0})}}^{\frac{\left(x_{1} \cdot \theta_{2}\right)}{\left(x_{0} \cdot \theta_{3}\right)}}})}}$$",-0.33036040098506214;0.2987622292495128;0.22519356141573;-0.10813185921607205,30,,,,Infinity,,,,,, -188,0,((((-0.5007421510964669 * x0) * 0.8319069176598572) / Exp(Exp((2.592192638982671e-2 / (x1 * 0.7387457992786534))))) / Log(Log(((x1 / (0.7244885569613754 ^ x1)) ^ (x1 * (0.8695693336493393 + x1)))))),"((((t[0] * x[:, 0]) * t[1]) / np.exp(np.exp((t[2] / (x[:, 1] * t[3]))))) / np.log(np.log((x[:, 1] / t[4] ** x[:, 1]) ** (x[:, 1] * (t[5] + x[:, 1])))))","$$\frac{\frac{\left(\left(\theta_{0} \cdot x_{0}\right) \cdot \theta_{1}\right)}{\operatorname{exp}(\operatorname{exp}(\frac{\theta_{2}}{\left(x_{1} \cdot \theta_{3}\right)}))}}{\operatorname{log}(\operatorname{log}({\frac{x_{1}}{{\theta_{4}^{x_{1}}}}^{\left(x_{1} \cdot \left(\theta_{5} + x_{1}\right)\right)}}))}$$",-0.5007421510964669;0.8319069176598572;2.592192638982671e-2;0.7387457992786534;0.7244885569613754;0.8695693336493393,27,,,,Infinity,,,,,, -189,0,(-0.6517224920460677 ^ Exp((x1 * 0.873096932216515))),"t[0] ** np.exp((x[:, 1] * t[1]))","$${\theta_{0}^{\operatorname{exp}(\left(x_{1} \cdot \theta_{1}\right))}}$$",-0.6517224920460677;0.873096932216515,6,,,,Infinity,,,,,, -190,0,((((x1 ^ 0.3398315374840835) * 0.3320978510895655) * x1) / x0),"(((x[:, 1] ** t[0] * t[1]) * x[:, 1]) / x[:, 0])","$$\frac{\left(\left({x_{1}^{\theta_{0}}} \cdot \theta_{1}\right) \cdot x_{1}\right)}{x_{0}}$$",0.3398315374840835;0.3320978510895655,9,4.987763556476581e-3,4.987763556476581e-3,4.987763556476581e-3,4.987763556476581e-3,0.5397233892438487,0.5397233892438487,0.5397233892438487,20.172174080988604,20.172174080988604,20.172174080988604 -191,0,Log((((x0 ^ (Exp(Exp(x1)) / x1)) + (0.2873427727948291 * x0)) ^ (Exp(Log(Log(x1))) + Exp(x1)))),"np.log((x[:, 0] ** (np.exp(np.exp(x[:, 1])) / x[:, 1]) + (t[0] * x[:, 0])) ** (np.exp(np.log(np.log(x[:, 1]))) + np.exp(x[:, 1])))","$$\operatorname{log}({\left({x_{0}^{\frac{\operatorname{exp}(\operatorname{exp}(x_{1}))}{x_{1}}}} + \left(\theta_{0} \cdot x_{0}\right)\right)^{\left(\operatorname{exp}(\operatorname{log}(\operatorname{log}(x_{1}))) + \operatorname{exp}(x_{1})\right)}})$$",0.2873427727948291,20,,,,Infinity,,,,,, -192,0,Exp((Exp((((0.5105642370969548 ^ x1) + 0.1293024919521728) ^ ((Exp(x0) * -1.029205687966639e-2) + Exp((x1 ^ -0.40888394914853066))))) ^ ((-0.8773081071213822 ^ Exp((0.18691361225868763 * (x1 ^ 0.6956572586683721)))) + 6.919463216262278e-2))),"np.exp(np.exp((t[0] ** x[:, 1] + t[1]) ** ((np.exp(x[:, 0]) * t[2]) + np.exp(x[:, 1] ** t[3]))) ** (t[4] ** np.exp((t[5] * x[:, 1] ** t[6])) + t[7]))","$$\operatorname{exp}({\operatorname{exp}({\left({\theta_{0}^{x_{1}}} + \theta_{1}\right)^{\left(\left(\operatorname{exp}(x_{0}) \cdot \theta_{2}\right) + \operatorname{exp}({x_{1}^{\theta_{3}}})\right)}})^{\left({\theta_{4}^{\operatorname{exp}(\left(\theta_{5} \cdot {x_{1}^{\theta_{6}}}\right))}} + \theta_{7}\right)}})$$",0.5105642370969548;0.1293024919521728;-1.029205687966639e-2;-0.40888394914853066;-0.8773081071213822;0.18691361225868763;0.6956572586683721;6.919463216262278e-2,28,,,,Infinity,,,,,, -193,0,Exp((Exp(Exp((-0.8432728650734787 / (-0.9251602466422771 ^ (x1 * -0.34524197916994437))))) * Exp(((((-0.5140770535124826 ^ x0) ^ 0.5178389102875651) / Exp((-0.4297301338457309 ^ x0))) / (Exp(x0) / Log((x1 / x0))))))),"np.exp((np.exp(np.exp((t[0] / t[1] ** (x[:, 1] * t[2])))) * np.exp(((t[3] ** x[:, 0] ** t[4] / np.exp(t[5] ** x[:, 0])) / (np.exp(x[:, 0]) / np.log((x[:, 1] / x[:, 0])))))))","$$\operatorname{exp}(\left(\operatorname{exp}(\operatorname{exp}(\frac{\theta_{0}}{{\theta_{1}^{\left(x_{1} \cdot \theta_{2}\right)}}})) \cdot \operatorname{exp}(\frac{\frac{{{\theta_{3}^{x_{0}}}^{\theta_{4}}}}{\operatorname{exp}({\theta_{5}^{x_{0}}})}}{\frac{\operatorname{exp}(x_{0})}{\operatorname{log}(\frac{x_{1}}{x_{0}})}})\right))$$",-0.8432728650734787;-0.9251602466422771;-0.34524197916994437;-0.5140770535124826;0.5178389102875651;-0.4297301338457309,30,,,,Infinity,,,,,, -194,0,(Log(((x1 + 0.48140533824483606) + (Exp((x0 * -0.13074673872391016)) + (-0.48081687197129314 * x0)))) * 0.4195097343418453),"(np.log(((x[:, 1] + t[0]) + (np.exp((x[:, 0] * t[1])) + (t[2] * x[:, 0])))) * t[3])","$$\left(\operatorname{log}(\left(\left(x_{1} + \theta_{0}\right) + \left(\operatorname{exp}(\left(x_{0} \cdot \theta_{1}\right)) + \left(\theta_{2} \cdot x_{0}\right)\right)\right)) \cdot \theta_{3}\right)$$",0.48140533824483606;-0.13074673872391016;-0.48081687197129314;0.4195097343418453,15,4.47939725035329e-3,4.47939725035329e-3,4.47939725035329e-3,4.47939725035329e-3,0.5866360220813092,0.5866360220813092,0.5866360220813092,37.27638343482558,37.27638343482558,37.27638343482558 -195,0,(x1 ^ x0),"x[:, 1] ** x[:, 0]","$${x_{1}^{x_{0}}}$$",,3,8.353975393662696e-2,8.353975393662696e-2,8.353975393662696e-2,8.353975393662696e-2,-6.709145465691655,-6.709145465691655,-6.709145465691655,3.3710226445472933,3.3710226445472933,3.3710226445472933 -196,0,(Log((0.5979600429310816 / x0)) / Log((x1 / x0))),"(np.log((t[0] / x[:, 0])) / np.log((x[:, 1] / x[:, 0])))","$$\frac{\operatorname{log}(\frac{\theta_{0}}{x_{0}})}{\operatorname{log}(\frac{x_{1}}{x_{0}})}$$",0.5979600429310816,9,0.28745619206555206,0.28745619206555206,0.28745619206555206,0.28745619206555206,-25.526791081144715,-25.526791081144715,-25.526791081144715,16.76457672013354,16.76457672013354,16.76457672013354 -197,0,Exp(Exp(((((x0 + -4.455588538961708e-2) + x1) + (0.20416689716160552 + (x1 * x0))) + Log(Log(((x0 * -0.592094291318324) * (-0.14878928606851616 + x1))))))),"np.exp(np.exp(((((x[:, 0] + t[0]) + x[:, 1]) + (t[1] + (x[:, 1] * x[:, 0]))) + np.log(np.log(((x[:, 0] * t[2]) * (t[3] + x[:, 1])))))))","$$\operatorname{exp}(\operatorname{exp}(\left(\left(\left(\left(x_{0} + \theta_{0}\right) + x_{1}\right) + \left(\theta_{1} + \left(x_{1} \cdot x_{0}\right)\right)\right) + \operatorname{log}(\operatorname{log}(\left(\left(x_{0} \cdot \theta_{2}\right) \cdot \left(\theta_{3} + x_{1}\right)\right)))\right)))$$",-4.455588538961708e-2;0.20416689716160552;-0.592094291318324;-0.14878928606851616,23,,,,Infinity,,,,,, -198,0,Exp((-3.0680971858662263 + x1)),"np.exp((t[0] + x[:, 1]))","$$\operatorname{exp}(\left(\theta_{0} + x_{1}\right))$$",-3.0680971858662263,4,1.0205484150379933e-2,1.0205484150379933e-2,1.0205484150379933e-2,1.0205484150379933e-2,5.8226076141275396e-2,5.8226076141275396e-2,5.8226076141275396e-2,7.575287315582544,7.575287315582544,7.575287315582544 -199,0,Log(((Log(Log(Exp(x0))) * 0.42248571182645445) * 0.19193226599672442)),"np.log(((np.log(np.log(np.exp(x[:, 0]))) * t[0]) * t[1]))","$$\operatorname{log}(\left(\left(\operatorname{log}(\operatorname{log}(\operatorname{exp}(x_{0}))) \cdot \theta_{0}\right) \cdot \theta_{1}\right))$$",0.42248571182645445;0.19193226599672442,9,,,,Infinity,,,,,, -200,0,Exp((((-0.5150211050404869 / (0.20051197439052215 ^ Exp((-0.645739563639647 + x0)))) + (x0 + x1)) + -7.855205583075496e-2)),"np.exp((((t[0] / t[1] ** np.exp((t[2] + x[:, 0]))) + (x[:, 0] + x[:, 1])) + t[3]))","$$\operatorname{exp}(\left(\left(\frac{\theta_{0}}{{\theta_{1}^{\operatorname{exp}(\left(\theta_{2} + x_{0}\right))}}} + \left(x_{0} + x_{1}\right)\right) + \theta_{3}\right))$$",-0.5150211050404869;0.20051197439052215;-0.645739563639647;-7.855205583075496e-2,15,3.319982721148527e-3,3.319982721148527e-3,3.319982721148527e-3,3.319982721148527e-3,0.693628140677401,0.693628140677401,0.693628140677401,37.275339961749296,37.275339961749296,37.275339961749296 -201,0,(((((Exp(x0) + x1) + (-0.11504327936793224 ^ (-0.93670740718774 / x1))) / x1) / x1) + 0.5245858774998129),"(((((np.exp(x[:, 0]) + x[:, 1]) + t[0] ** (t[1] / x[:, 1])) / x[:, 1]) / x[:, 1]) + t[2])","$$\left(\frac{\frac{\left(\left(\operatorname{exp}(x_{0}) + x_{1}\right) + {\theta_{0}^{\frac{\theta_{1}}{x_{1}}}}\right)}{x_{1}}}{x_{1}} + \theta_{2}\right)$$",-0.11504327936793224;-0.93670740718774;0.5245858774998129,16,,,,Infinity,,,,,, -202,0,(Log(Log(((x1 ^ x1) ^ Exp(x0)))) + Log(((x1 ^ ((x0 / (0.26300073574452776 * x1)) * x0)) * 0.21590134785056136))),"(np.log(np.log(x[:, 1] ** x[:, 1] ** np.exp(x[:, 0]))) + np.log((x[:, 1] ** ((x[:, 0] / (t[0] * x[:, 1])) * x[:, 0]) * t[1])))","$$\left(\operatorname{log}(\operatorname{log}({{x_{1}^{x_{1}}}^{\operatorname{exp}(x_{0})}})) + \operatorname{log}(\left({x_{1}^{\left(\frac{x_{0}}{\left(\theta_{0} \cdot x_{1}\right)} \cdot x_{0}\right)}} \cdot \theta_{1}\right))\right)$$",0.26300073574452776;0.21590134785056136,21,,,,Infinity,,,,,, -203,0,Log(((Log((x1 * 0.41453298198443067)) / (x1 ^ (x1 * 0.21406130853782324))) + ((0.10309753371674557 + x1) ^ (x1 * (x0 * -3.162855339113524e-2))))),"np.log(((np.log((x[:, 1] * t[0])) / x[:, 1] ** (x[:, 1] * t[1])) + (t[2] + x[:, 1]) ** (x[:, 1] * (x[:, 0] * t[3]))))","$$\operatorname{log}(\left(\frac{\operatorname{log}(\left(x_{1} \cdot \theta_{0}\right))}{{x_{1}^{\left(x_{1} \cdot \theta_{1}\right)}}} + {\left(\theta_{2} + x_{1}\right)^{\left(x_{1} \cdot \left(x_{0} \cdot \theta_{3}\right)\right)}}\right))$$",0.41453298198443067;0.21406130853782324;0.10309753371674557;-3.162855339113524e-2,21,,,,Infinity,,,,,, -204,0,((x0 + x0) ^ -14.793543861538144),"(x[:, 0] + x[:, 0]) ** t[0]","$${\left(x_{0} + x_{0}\right)^{\theta_{0}}}$$",-14.793543861538144,5,1.666883111534721e-2,1.666883111534721e-2,1.666883111534721e-2,1.666883111534721e-2,-0.5382190843983157,-0.5382190843983157,-0.5382190843983157,8.967398688970906,8.967398688970906,8.967398688970906 -205,0,(((0.8786111014541423 / Log(Exp(x1))) + ((x1 * -0.5809641567615402) ^ (x0 * x0))) ^ (0.2667663972724321 / ((x0 ^ -0.627472846197944) ^ Log((x0 * x1))))),"((t[0] / np.log(np.exp(x[:, 1]))) + (x[:, 1] * t[1]) ** (x[:, 0] * x[:, 0])) ** (t[2] / x[:, 0] ** t[3] ** np.log((x[:, 0] * x[:, 1])))","$${\left(\frac{\theta_{0}}{\operatorname{log}(\operatorname{exp}(x_{1}))} + {\left(x_{1} \cdot \theta_{1}\right)^{\left(x_{0} \cdot x_{0}\right)}}\right)^{\frac{\theta_{2}}{{{x_{0}^{\theta_{3}}}^{\operatorname{log}(\left(x_{0} \cdot x_{1}\right))}}}}}$$",0.8786111014541423;-0.5809641567615402;0.2667663972724321;-0.627472846197944,24,,,,Infinity,,,,,, -206,0,Log(((x1 * x0) * Log((x1 * 5.4912728963646296e-2)))),"np.log(((x[:, 1] * x[:, 0]) * np.log((x[:, 1] * t[0]))))","$$\operatorname{log}(\left(\left(x_{1} \cdot x_{0}\right) \cdot \operatorname{log}(\left(x_{1} \cdot \theta_{0}\right))\right))$$",5.4912728963646296e-2,9,,,,Infinity,,,,,, -207,0,((((0.5530622417172104 + Log(x0)) + (0.2451965526079144 ^ (x0 * 0.1587500728530825))) / ((-0.2604184890915786 * (x0 * 0.7288831363084927)) * (x0 / (x0 + -0.18550818352151155)))) + ((Log((x0 * 0.8150115168905779)) ^ Exp((x0 * x1))) * Exp(Log(Log(((0.16882567622695255 * x1) ^ 0.8730108641892117)))))),"((((t[0] + np.log(x[:, 0])) + t[1] ** (x[:, 0] * t[2])) / ((t[3] * (x[:, 0] * t[4])) * (x[:, 0] / (x[:, 0] + t[5])))) + (np.log((x[:, 0] * t[6])) ** np.exp((x[:, 0] * x[:, 1])) * np.exp(np.log(np.log((t[7] * x[:, 1]) ** t[8])))))","$$\left(\frac{\left(\left(\theta_{0} + \operatorname{log}(x_{0})\right) + {\theta_{1}^{\left(x_{0} \cdot \theta_{2}\right)}}\right)}{\left(\left(\theta_{3} \cdot \left(x_{0} \cdot \theta_{4}\right)\right) \cdot \frac{x_{0}}{\left(x_{0} + \theta_{5}\right)}\right)} + \left({\operatorname{log}(\left(x_{0} \cdot \theta_{6}\right))^{\operatorname{exp}(\left(x_{0} \cdot x_{1}\right))}} \cdot \operatorname{exp}(\operatorname{log}(\operatorname{log}({\left(\theta_{7} \cdot x_{1}\right)^{\theta_{8}}})))\right)\right)$$",0.5530622417172104;0.2451965526079144;0.1587500728530825;-0.2604184890915786;0.7288831363084927;-0.18550818352151155;0.8150115168905779;0.16882567622695255;0.8730108641892117,41,,,,Infinity,,,,,, -208,0,(Log((Exp(x0) / (Log(Exp((-3.314153675496978e-2 / x1))) ^ 0.6764130725424373))) / (Exp(Exp((x0 * -0.4704073191377185))) ^ (Exp(((x1 * 0.7984695145380161) * -0.23498381024615167)) * -0.4148417629171849))),"(np.log((np.exp(x[:, 0]) / np.log(np.exp((t[0] / x[:, 1]))) ** t[1])) / np.exp(np.exp((x[:, 0] * t[2]))) ** (np.exp(((x[:, 1] * t[3]) * t[4])) * t[5]))","$$\frac{\operatorname{log}(\frac{\operatorname{exp}(x_{0})}{{\operatorname{log}(\operatorname{exp}(\frac{\theta_{0}}{x_{1}}))^{\theta_{1}}}})}{{\operatorname{exp}(\operatorname{exp}(\left(x_{0} \cdot \theta_{2}\right)))^{\left(\operatorname{exp}(\left(\left(x_{1} \cdot \theta_{3}\right) \cdot \theta_{4}\right)) \cdot \theta_{5}\right)}}}$$",-3.314153675496978e-2;0.6764130725424373;-0.4704073191377185;0.7984695145380161;-0.23498381024615167;-0.4148417629171849,26,,,,Infinity,,,,,, -209,0,((0.6411178291039024 ^ (9.970774172158825e-2 ^ ((x1 * 0.3974997872830004) + -0.14437598534344687))) / Log((Log((-0.18745482227263588 / Exp(x1))) / (-0.6465088962859843 / Log(Exp((x0 * x1))))))),"(t[0] ** t[1] ** ((x[:, 1] * t[2]) + t[3]) / np.log((np.log((t[4] / np.exp(x[:, 1]))) / (t[5] / np.log(np.exp((x[:, 0] * x[:, 1])))))))","$$\frac{{\theta_{0}^{{\theta_{1}^{\left(\left(x_{1} \cdot \theta_{2}\right) + \theta_{3}\right)}}}}}{\operatorname{log}(\frac{\operatorname{log}(\frac{\theta_{4}}{\operatorname{exp}(x_{1})})}{\frac{\theta_{5}}{\operatorname{log}(\operatorname{exp}(\left(x_{0} \cdot x_{1}\right)))}})}$$",0.6411178291039024;9.970774172158825e-2;0.3974997872830004;-0.14437598534344687;-0.18745482227263588;-0.6465088962859843,24,,,,Infinity,,,,,, -210,0,((-0.5078932523655405 + x0) ^ Exp((x0 ^ 0.6596040101659099))),"(t[0] + x[:, 0]) ** np.exp(x[:, 0] ** t[1])","$${\left(\theta_{0} + x_{0}\right)^{\operatorname{exp}({x_{0}^{\theta_{1}}})}}$$",-0.5078932523655405;0.6596040101659099,8,0.19647267859740286,0.19647267859740286,0.19647267859740286,0.19647267859740286,-17.13072684521505,-17.13072684521505,-17.13072684521505,17.094178580945748,17.094178580945748,17.094178580945748 -211,0,((-0.7321628836643215 * (-0.10944013932061437 * x0)) ^ ((-0.10497932423268125 + x0) + x1)),"(t[0] * (t[1] * x[:, 0])) ** ((t[2] + x[:, 0]) + x[:, 1])","$${\left(\theta_{0} \cdot \left(\theta_{1} \cdot x_{0}\right)\right)^{\left(\left(\theta_{2} + x_{0}\right) + x_{1}\right)}}$$",-0.7321628836643215;-0.10944013932061437;-0.10497932423268125,11,1.1047586109941537e-2,1.1047586109941537e-2,1.1047586109941537e-2,1.1047586109941537e-2,-1.948407019371534e-2,-1.948407019371534e-2,-1.948407019371534e-2,25.782071795110472,25.782071795110472,25.782071795110472 -212,0,(Exp(((Log((5.9031555133931315 + x0)) * (x1 + x1)) / ((3.2536905199697674 + (x0 / x0)) + (x1 * 0.966981637758638)))) / ((Exp(Exp((x1 ^ 6.4898035112445385))) * 3.0160031245086025) * (Log((x1 + 5.4398864222787475)) * Exp((7.20682851310484 ^ (-1.897028828574304 * x1)))))),"(np.exp(((np.log((t[0] + x[:, 0])) * (x[:, 1] + x[:, 1])) / ((t[1] + (x[:, 0] / x[:, 0])) + (x[:, 1] * t[2])))) / ((np.exp(np.exp(x[:, 1] ** t[3])) * t[4]) * (np.log((x[:, 1] + t[5])) * np.exp(t[6] ** (t[7] * x[:, 1])))))","$$\frac{\operatorname{exp}(\frac{\left(\operatorname{log}(\left(\theta_{0} + x_{0}\right)) \cdot \left(x_{1} + x_{1}\right)\right)}{\left(\left(\theta_{1} + \frac{x_{0}}{x_{0}}\right) + \left(x_{1} \cdot \theta_{2}\right)\right)})}{\left(\left(\operatorname{exp}(\operatorname{exp}({x_{1}^{\theta_{3}}})) \cdot \theta_{4}\right) \cdot \left(\operatorname{log}(\left(x_{1} + \theta_{5}\right)) \cdot \operatorname{exp}({\theta_{6}^{\left(\theta_{7} \cdot x_{1}\right)}})\right)\right)}$$",5.9031555133931315;3.2536905199697674;0.966981637758638;6.4898035112445385;3.0160031245086025;5.4398864222787475;7.20682851310484;-1.897028828574304,39,9.903159993935763e-3,9.903159993935763e-3,9.903159993935763e-3,9.903159993935763e-3,8.612490023391828e-2,8.612490023391828e-2,8.612490023391828e-2,101.86807084304823,101.86807084304823,101.86807084304823 -213,0,Log(((0.8125019855743527 + (x0 ^ -4.620122844496292)) ^ 7.662363019580337e-2)),"np.log((t[0] + x[:, 0] ** t[1]) ** t[2])","$$\operatorname{log}({\left(\theta_{0} + {x_{0}^{\theta_{1}}}\right)^{\theta_{2}}})$$",0.8125019855743527;-4.620122844496292;7.662363019580337e-2,8,4.9408136877159324e-3,4.9408136877159324e-3,4.9408136877159324e-3,4.9408136877159324e-3,0.5440559776321929,0.5440559776321929,0.5440559776321929,18.94272483789467,18.94272483789467,18.94272483789467 -214,0,Exp(((-0.584906266934704 * Exp(Log((1.6497813819348082 / x1)))) / (x1 * 1.7597455574390652))),"np.exp(((t[0] * np.exp(np.log((t[1] / x[:, 1])))) / (x[:, 1] * t[2])))","$$\operatorname{exp}(\frac{\left(\theta_{0} \cdot \operatorname{exp}(\operatorname{log}(\frac{\theta_{1}}{x_{1}}))\right)}{\left(x_{1} \cdot \theta_{2}\right)})$$",-0.584906266934704;1.6497813819348082;1.7597455574390652,12,1.0934120826299524e-2,1.0934120826299524e-2,1.0934120826299524e-2,1.0934120826299524e-2,-9.013362109444367e-3,-9.013362109444367e-3,-9.013362109444367e-3,27.573729145583254,27.573729145583254,27.573729145583254 -215,0,((Log(Log((x0 + 21.09098333917452))) + Log((x0 / x0))) + -1.072571766467822),"((np.log(np.log((x[:, 0] + t[0]))) + np.log((x[:, 0] / x[:, 0]))) + t[1])","$$\left(\left(\operatorname{log}(\operatorname{log}(\left(x_{0} + \theta_{0}\right))) + \operatorname{log}(\frac{x_{0}}{x_{0}})\right) + \theta_{1}\right)$$",21.09098333917452;-1.072571766467822,12,1.150692640755611e-2,1.150692640755611e-2,1.150692640755611e-2,1.150692640755611e-2,-6.187252605690974e-2,-6.187252605690974e-2,-6.187252605690974e-2,23.365461053711286,23.365461053711286,23.365461053711286 -216,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 -217,0,Exp((((x1 * -0.2046572211958223) * 0.8320385885002299) ^ Exp(Exp(Exp(((x1 * -0.14623175672421596) ^ (-0.41578078667214347 * x1))))))),"np.exp(((x[:, 1] * t[0]) * t[1]) ** np.exp(np.exp(np.exp((x[:, 1] * t[2]) ** (t[3] * x[:, 1])))))","$$\operatorname{exp}({\left(\left(x_{1} \cdot \theta_{0}\right) \cdot \theta_{1}\right)^{\operatorname{exp}(\operatorname{exp}(\operatorname{exp}({\left(x_{1} \cdot \theta_{2}\right)^{\left(\theta_{3} \cdot x_{1}\right)}})))}})$$",-0.2046572211958223;0.8320385885002299;-0.14623175672421596;-0.41578078667214347,17,,,,Infinity,,,,,, -218,0,((((-0.7387578716042267 / Log(x1)) * ((-0.8696834609810447 * x1) / x1)) ^ ((x0 + (x1 ^ x0)) ^ (x1 * (x0 + -0.9669753356082059)))) * Log(((-0.5641501422858095 / (x1 + (x1 / x0))) + (Exp((x0 * -0.45164250233784253)) * (3.0931046671047735e-2 ^ x0))))),"(((t[0] / np.log(x[:, 1])) * ((t[1] * x[:, 1]) / x[:, 1])) ** (x[:, 0] + x[:, 1] ** x[:, 0]) ** (x[:, 1] * (x[:, 0] + t[2])) * np.log(((t[3] / (x[:, 1] + (x[:, 1] / x[:, 0]))) + (np.exp((x[:, 0] * t[4])) * t[5] ** x[:, 0]))))","$$\left({\left(\frac{\theta_{0}}{\operatorname{log}(x_{1})} \cdot \frac{\left(\theta_{1} \cdot x_{1}\right)}{x_{1}}\right)^{{\left(x_{0} + {x_{1}^{x_{0}}}\right)^{\left(x_{1} \cdot \left(x_{0} + \theta_{2}\right)\right)}}}} \cdot \operatorname{log}(\left(\frac{\theta_{3}}{\left(x_{1} + \frac{x_{1}}{x_{0}}\right)} + \left(\operatorname{exp}(\left(x_{0} \cdot \theta_{4}\right)) \cdot {\theta_{5}^{x_{0}}}\right)\right))\right)$$",-0.7387578716042267;-0.8696834609810447;-0.9669753356082059;-0.5641501422858095;-0.45164250233784253;3.0931046671047735e-2,40,,,,Infinity,,,,,, -219,0,(((Log((Log((-0.26276917928476806 / x1)) / ((x0 * 0.9665288165654669) * x1))) + 0.24663794752419954) * -0.8145961783821156) / ((x1 * 0.46358987873778457) / x0)),"(((np.log((np.log((t[0] / x[:, 1])) / ((x[:, 0] * t[1]) * x[:, 1]))) + t[2]) * t[3]) / ((x[:, 1] * t[4]) / x[:, 0]))","$$\frac{\left(\left(\operatorname{log}(\frac{\operatorname{log}(\frac{\theta_{0}}{x_{1}})}{\left(\left(x_{0} \cdot \theta_{1}\right) \cdot x_{1}\right)}) + \theta_{2}\right) \cdot \theta_{3}\right)}{\frac{\left(x_{1} \cdot \theta_{4}\right)}{x_{0}}}$$",-0.26276917928476806;0.9665288165654669;0.24663794752419954;-0.8145961783821156;0.46358987873778457,21,,,,Infinity,,,,,, -220,0,((Log((Exp((x1 * 0.14584942030696502)) + 0.10440026641081845)) / ((Log(x0) + (x0 * 0.4268841433127558)) ^ (0.33694435930781874 / (x0 / x1)))) * Log((Log(((0.6415542552401423 + (0.5106800918109828 * x0)) ^ (-0.7486278189673778 ^ x0))) * ((x0 / (x1 + 0.48441205145663957)) + (x0 * Exp(x0)))))),"((np.log((np.exp((x[:, 1] * t[0])) + t[1])) / (np.log(x[:, 0]) + (x[:, 0] * t[2])) ** (t[3] / (x[:, 0] / x[:, 1]))) * np.log((np.log((t[4] + (t[5] * x[:, 0])) ** t[6] ** x[:, 0]) * ((x[:, 0] / (x[:, 1] + t[7])) + (x[:, 0] * np.exp(x[:, 0]))))))","$$\left(\frac{\operatorname{log}(\left(\operatorname{exp}(\left(x_{1} \cdot \theta_{0}\right)) + \theta_{1}\right))}{{\left(\operatorname{log}(x_{0}) + \left(x_{0} \cdot \theta_{2}\right)\right)^{\frac{\theta_{3}}{\frac{x_{0}}{x_{1}}}}}} \cdot \operatorname{log}(\left(\operatorname{log}({\left(\theta_{4} + \left(\theta_{5} \cdot x_{0}\right)\right)^{{\theta_{6}^{x_{0}}}}}) \cdot \left(\frac{x_{0}}{\left(x_{1} + \theta_{7}\right)} + \left(x_{0} \cdot \operatorname{exp}(x_{0})\right)\right)\right))\right)$$",0.14584942030696502;0.10440026641081845;0.4268841433127558;0.33694435930781874;0.6415542552401423;0.5106800918109828;-0.7486278189673778;0.48441205145663957,43,,,,Infinity,,,,,, -221,0,(Log((Log(x1) + (-0.13511118463716698 + x0))) ^ (Exp((x1 ^ 0.6018791105178508)) / (-0.8983304645789613 ^ x1))),"np.log((np.log(x[:, 1]) + (t[0] + x[:, 0]))) ** (np.exp(x[:, 1] ** t[1]) / t[2] ** x[:, 1])","$${\operatorname{log}(\left(\operatorname{log}(x_{1}) + \left(\theta_{0} + x_{0}\right)\right))^{\frac{\operatorname{exp}({x_{1}^{\theta_{1}}})}{{\theta_{2}^{x_{1}}}}}}$$",-0.13511118463716698;0.6018791105178508;-0.8983304645789613,16,,,,Infinity,,,,,, -222,0,Exp(((Exp((Exp(Log(x1)) ^ -0.42416910449597345)) * (((x1 ^ -0.4792403183606525) + 0.12951066989358317) ^ ((x1 * 0.3156326194122947) + -0.31631206749227336))) ^ (((-0.9386996150552955 + (x0 + x0)) + (7.330102352000012e-2 + (-0.6559447927598386 / x0))) + ((0.20701640696405266 / (x1 ^ -0.18564364119709553)) + 0.42197343144530997)))),"np.exp((np.exp(np.exp(np.log(x[:, 1])) ** t[0]) * (x[:, 1] ** t[1] + t[2]) ** ((x[:, 1] * t[3]) + t[4])) ** (((t[5] + (x[:, 0] + x[:, 0])) + (t[6] + (t[7] / x[:, 0]))) + ((t[8] / x[:, 1] ** t[9]) + t[10])))","$$\operatorname{exp}({\left(\operatorname{exp}({\operatorname{exp}(\operatorname{log}(x_{1}))^{\theta_{0}}}) \cdot {\left({x_{1}^{\theta_{1}}} + \theta_{2}\right)^{\left(\left(x_{1} \cdot \theta_{3}\right) + \theta_{4}\right)}}\right)^{\left(\left(\left(\theta_{5} + \left(x_{0} + x_{0}\right)\right) + \left(\theta_{6} + \frac{\theta_{7}}{x_{0}}\right)\right) + \left(\frac{\theta_{8}}{{x_{1}^{\theta_{9}}}} + \theta_{10}\right)\right)}})$$",-0.42416910449597345;-0.4792403183606525;0.12951066989358317;0.3156326194122947;-0.31631206749227336;-0.9386996150552955;7.330102352000012e-2;-0.6559447927598386;0.20701640696405266;-0.18564364119709553;0.42197343144530997,39,1.4910003118201935e82,1.4910003118201935e82,1.4910003118201935e82,1.4910003118201935e82,-1.3759123951852987e84,-1.3759123951852987e84,-1.3759123951852987e84,1.3419002806381742e82,1.3419002806381742e82,1.3419002806381742e82 -223,0,(Log((((x1 * 0.45533249855131974) ^ (-0.2727993294005848 ^ x1)) ^ ((-0.3242976557866021 / x1) ^ 0.5180342906305868))) + (-0.6718798303199153 + ((-0.3171889467886033 * x0) + (x1 * 0.9525569933978517)))),"(np.log((x[:, 1] * t[0]) ** t[1] ** x[:, 1] ** (t[2] / x[:, 1]) ** t[3]) + (t[4] + ((t[5] * x[:, 0]) + (x[:, 1] * t[6]))))","$$\left(\operatorname{log}({{\left(x_{1} \cdot \theta_{0}\right)^{{\theta_{1}^{x_{1}}}}}^{{\frac{\theta_{2}}{x_{1}}^{\theta_{3}}}}}) + \left(\theta_{4} + \left(\left(\theta_{5} \cdot x_{0}\right) + \left(x_{1} \cdot \theta_{6}\right)\right)\right)\right)$$",0.45533249855131974;-0.2727993294005848;-0.3242976557866021;0.5180342906305868;-0.6718798303199153;-0.3171889467886033;0.9525569933978517,24,,,,Infinity,,,,,, -224,0,((0.8334783361317752 ^ (x1 / (x1 + 0.6561259579334595))) + Log(((-0.5757866581170641 / x0) / Exp((x0 + x0))))),"(t[0] ** (x[:, 1] / (x[:, 1] + t[1])) + np.log(((t[2] / x[:, 0]) / np.exp((x[:, 0] + x[:, 0])))))","$$\left({\theta_{0}^{\frac{x_{1}}{\left(x_{1} + \theta_{1}\right)}}} + \operatorname{log}(\frac{\frac{\theta_{2}}{x_{0}}}{\operatorname{exp}(\left(x_{0} + x_{0}\right))})\right)$$",0.8334783361317752;0.6561259579334595;-0.5757866581170641,17,,,,Infinity,,,,,, -225,0,((x1 / (-0.44077105770411806 + x0)) * 9.170458864158842e-2),"((x[:, 1] / (t[0] + x[:, 0])) * t[1])","$$\left(\frac{x_{1}}{\left(\theta_{0} + x_{0}\right)} \cdot \theta_{1}\right)$$",-0.44077105770411806;9.170458864158842e-2,7,1.9562875566403945e-3,1.9562875566403945e-3,1.9562875566403945e-3,1.9562875566403945e-3,0.8194715134269615,0.8194715134269615,0.8194715134269615,16.585926814132645,16.585926814132645,16.585926814132645 -226,0,((Log(((-8.717549974972372e-2 / x0) + x1)) * x1) + x1),"((np.log(((t[0] / x[:, 0]) + x[:, 1])) * x[:, 1]) + x[:, 1])","$$\left(\left(\operatorname{log}(\left(\frac{\theta_{0}}{x_{0}} + x_{1}\right)) \cdot x_{1}\right) + x_{1}\right)$$",-8.717549974972372e-2,10,,,,Infinity,,,,,, -227,0,Log(Exp((Exp((x0 ^ Log(x1))) * Log((Exp(x1) / Exp(x1)))))),"np.log(np.exp((np.exp(x[:, 0] ** np.log(x[:, 1])) * np.log((np.exp(x[:, 1]) / np.exp(x[:, 1]))))))","$$\operatorname{log}(\operatorname{exp}(\left(\operatorname{exp}({x_{0}^{\operatorname{log}(x_{1})}}) \cdot \operatorname{log}(\frac{\operatorname{exp}(x_{1})}{\operatorname{exp}(x_{1})})\right)))$$",,14,1.4573554749975553e-2,1.4573554749975553e-2,1.4573554749975553e-2,1.4573554749975553e-2,-0.3448645492182232,-0.3448645492182232,-0.3448645492182232,27.255858286049364,27.255858286049364,27.255858286049364 -228,0,(((-0.5220293745026539 + x1) + -0.9443939665200863) ^ ((0.6331498601195318 ^ x0) + -0.7408244895621305)),"((t[0] + x[:, 1]) + t[1]) ** (t[2] ** x[:, 0] + t[3])","$${\left(\left(\theta_{0} + x_{1}\right) + \theta_{1}\right)^{\left({\theta_{2}^{x_{0}}} + \theta_{3}\right)}}$$",-0.5220293745026539;-0.9443939665200863;0.6331498601195318;-0.7408244895621305,11,,,,Infinity,,,,,, -229,0,Exp((Log((((-0.4965740043605992 + x1) + -0.5601639093991653) ^ 2.5383075001163213e-2)) + x1)),"np.exp((np.log(((t[0] + x[:, 1]) + t[1]) ** t[2]) + x[:, 1]))","$$\operatorname{exp}(\left(\operatorname{log}({\left(\left(\theta_{0} + x_{1}\right) + \theta_{1}\right)^{\theta_{2}}}) + x_{1}\right))$$",-0.4965740043605992;-0.5601639093991653;2.5383075001163213e-2,11,,,,Infinity,,,,,, -230,0,(0.4434516327507356 ^ ((Exp(Log((x1 ^ (x1 + x1)))) + ((0.7158222236702834 * x0) * 0.9685042556972568)) ^ ((-0.1310980560221131 ^ (x0 ^ x0)) ^ (-0.8810754229647169 ^ (x0 * 2.9361247955453917e-3))))),"t[0] ** (np.exp(np.log(x[:, 1] ** (x[:, 1] + x[:, 1]))) + ((t[1] * x[:, 0]) * t[2])) ** t[3] ** x[:, 0] ** x[:, 0] ** t[4] ** (x[:, 0] * t[5])","$${\theta_{0}^{{\left(\operatorname{exp}(\operatorname{log}({x_{1}^{\left(x_{1} + x_{1}\right)}})) + \left(\left(\theta_{1} \cdot x_{0}\right) \cdot \theta_{2}\right)\right)^{{{\theta_{3}^{{x_{0}^{x_{0}}}}}^{{\theta_{4}^{\left(x_{0} \cdot \theta_{5}\right)}}}}}}}}$$",0.4434516327507356;0.7158222236702834;0.9685042556972568;-0.1310980560221131;-0.8810754229647169;2.9361247955453917e-3,27,,,,Infinity,,,,,, -231,0,(((((x1 / x0) / x1) ^ 2.9257653777756788e-2) + -0.9930195763937985) / x1),"((((x[:, 1] / x[:, 0]) / x[:, 1]) ** t[0] + t[1]) / x[:, 1])","$$\frac{\left({\frac{\frac{x_{1}}{x_{0}}}{x_{1}}^{\theta_{0}}} + \theta_{1}\right)}{x_{1}}$$",2.9257653777756788e-2;-0.9930195763937985,11,1.1663527553225049e-2,1.1663527553225049e-2,1.1663527553225049e-2,1.1663527553225049e-2,-7.632386156086213e-2,-7.632386156086213e-2,-7.632386156086213e-2,23.76170120704179,23.76170120704179,23.76170120704179 -232,0,(Log(Log((x0 ^ (x1 ^ 0.9950337729754963)))) ^ ((-0.7439718438933526 * x0) ^ x0)),"np.log(np.log(x[:, 0] ** x[:, 1] ** t[0])) ** (t[1] * x[:, 0]) ** x[:, 0]","$${\operatorname{log}(\operatorname{log}({x_{0}^{{x_{1}^{\theta_{0}}}}}))^{{\left(\theta_{1} \cdot x_{0}\right)^{x_{0}}}}}$$",0.9950337729754963;-0.7439718438933526,13,,,,Infinity,,,,,, -233,0,Exp(Exp(((((-0.6881208736792483 * x1) ^ (0.3833771476360004 * x1)) ^ 0.5750670658434625) + ((Exp(x0) + (x1 * 0.6677779308949046)) / ((x1 ^ x0) / (-0.7962841901678317 + x0)))))),"np.exp(np.exp(((t[0] * x[:, 1]) ** (t[1] * x[:, 1]) ** t[2] + ((np.exp(x[:, 0]) + (x[:, 1] * t[3])) / (x[:, 1] ** x[:, 0] / (t[4] + x[:, 0]))))))","$$\operatorname{exp}(\operatorname{exp}(\left({{\left(\theta_{0} \cdot x_{1}\right)^{\left(\theta_{1} \cdot x_{1}\right)}}^{\theta_{2}}} + \frac{\left(\operatorname{exp}(x_{0}) + \left(x_{1} \cdot \theta_{3}\right)\right)}{\frac{{x_{1}^{x_{0}}}}{\left(\theta_{4} + x_{0}\right)}}\right)))$$",-0.6881208736792483;0.3833771476360004;0.5750670658434625;0.6677779308949046;-0.7962841901678317,26,,,,Infinity,,,,,, -234,0,((Exp((-0.5465099955214839 + (x1 ^ 0.3975227593513142))) + Log((x0 * (x0 * 0.9135448274775685)))) + Log(((0.2846540625043531 + Log(x1)) * Exp((0.23834643466275918 ^ (0.6571708740570321 / x1)))))),"((np.exp((t[0] + x[:, 1] ** t[1])) + np.log((x[:, 0] * (x[:, 0] * t[2])))) + np.log(((t[3] + np.log(x[:, 1])) * np.exp(t[4] ** (t[5] / x[:, 1])))))","$$\left(\left(\operatorname{exp}(\left(\theta_{0} + {x_{1}^{\theta_{1}}}\right)) + \operatorname{log}(\left(x_{0} \cdot \left(x_{0} \cdot \theta_{2}\right)\right))\right) + \operatorname{log}(\left(\left(\theta_{3} + \operatorname{log}(x_{1})\right) \cdot \operatorname{exp}({\theta_{4}^{\frac{\theta_{5}}{x_{1}}}})\right))\right)$$",-0.5465099955214839;0.3975227593513142;0.9135448274775685;0.2846540625043531;0.23834643466275918;0.6571708740570321,26,,,,Infinity,,,,,, -235,0,Log((0.48467893057696304 / Exp((0.41716347252339386 + Log(x1))))),"np.log((t[0] / np.exp((t[1] + np.log(x[:, 1])))))","$$\operatorname{log}(\frac{\theta_{0}}{\operatorname{exp}(\left(\theta_{1} + \operatorname{log}(x_{1})\right))})$$",0.48467893057696304;0.41716347252339386,8,0.2792880547545848,0.2792880547545848,0.2792880547545848,0.2792880547545848,-24.77302588856636,-24.77302588856636,-24.77302588856636,18.62728487383885,18.62728487383885,18.62728487383885 -236,0,(Exp((0.48691532085889977 * (0.638559382754308 * (0.3418512298301849 + ((-0.2123119155844806 ^ x0) ^ -0.31106557112559186))))) * x0),"(np.exp((t[0] * (t[1] * (t[2] + t[3] ** x[:, 0] ** t[4])))) * x[:, 0])","$$\left(\operatorname{exp}(\left(\theta_{0} \cdot \left(\theta_{1} \cdot \left(\theta_{2} + {{\theta_{3}^{x_{0}}}^{\theta_{4}}}\right)\right)\right)) \cdot x_{0}\right)$$",0.48691532085889977;0.638559382754308;0.3418512298301849;-0.2123119155844806;-0.31106557112559186,14,,,,Infinity,,,,,, -237,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 -238,0,Exp(((Exp(Log(Exp(x1))) ^ (((0.33966093823347987 + x0) ^ 0.4368965996531905) + Exp(Log((x0 * 0.6934820362555202))))) ^ (((Log(x1) * (x0 / x0)) / ((x1 * x0) ^ 0.6528275345524313)) / Log(Log((4.486960957720165e-2 * x0)))))),"np.exp(np.exp(np.log(np.exp(x[:, 1]))) ** ((t[0] + x[:, 0]) ** t[1] + np.exp(np.log((x[:, 0] * t[2])))) ** (((np.log(x[:, 1]) * (x[:, 0] / x[:, 0])) / (x[:, 1] * x[:, 0]) ** t[3]) / np.log(np.log((t[4] * x[:, 0])))))","$$\operatorname{exp}({{\operatorname{exp}(\operatorname{log}(\operatorname{exp}(x_{1})))^{\left({\left(\theta_{0} + x_{0}\right)^{\theta_{1}}} + \operatorname{exp}(\operatorname{log}(\left(x_{0} \cdot \theta_{2}\right)))\right)}}^{\frac{\frac{\left(\operatorname{log}(x_{1}) \cdot \frac{x_{0}}{x_{0}}\right)}{{\left(x_{1} \cdot x_{0}\right)^{\theta_{3}}}}}{\operatorname{log}(\operatorname{log}(\left(\theta_{4} \cdot x_{0}\right)))}}})$$",0.33966093823347987;0.4368965996531905;0.6934820362555202;0.6528275345524313;4.486960957720165e-2,36,,,,Infinity,,,,,, -239,0,Exp((((0.6354109264284284 ^ Exp(Log((5.408789451953455e-2 * x0)))) * Log(((-0.7324159583764958 * (0.27672670675892086 ^ x1)) + (x0 * -0.674626204596113)))) / ((Log((x0 / x0)) + -0.5286962476362307) / ((Log(x0) ^ (x0 * -0.6626276173822336)) + -0.7326060161977908)))),"np.exp(((t[0] ** np.exp(np.log((t[1] * x[:, 0]))) * np.log(((t[2] * t[3] ** x[:, 1]) + (x[:, 0] * t[4])))) / ((np.log((x[:, 0] / x[:, 0])) + t[5]) / (np.log(x[:, 0]) ** (x[:, 0] * t[6]) + t[7]))))","$$\operatorname{exp}(\frac{\left({\theta_{0}^{\operatorname{exp}(\operatorname{log}(\left(\theta_{1} \cdot x_{0}\right)))}} \cdot \operatorname{log}(\left(\left(\theta_{2} \cdot {\theta_{3}^{x_{1}}}\right) + \left(x_{0} \cdot \theta_{4}\right)\right))\right)}{\frac{\left(\operatorname{log}(\frac{x_{0}}{x_{0}}) + \theta_{5}\right)}{\left({\operatorname{log}(x_{0})^{\left(x_{0} \cdot \theta_{6}\right)}} + \theta_{7}\right)}})$$",0.6354109264284284;5.408789451953455e-2;-0.7324159583764958;0.27672670675892086;-0.674626204596113;-0.5286962476362307;-0.6626276173822336;-0.7326060161977908,35,,,,Infinity,,,,,, -240,0,Exp((Log((x0 + (-0.480055151934733 * x1))) * Exp((x1 + x0)))),"np.exp((np.log((x[:, 0] + (t[0] * x[:, 1]))) * np.exp((x[:, 1] + x[:, 0]))))","$$\operatorname{exp}(\left(\operatorname{log}(\left(x_{0} + \left(\theta_{0} \cdot x_{1}\right)\right)) \cdot \operatorname{exp}(\left(x_{1} + x_{0}\right))\right))$$",-0.480055151934733,12,4.964657179227458,4.964657179227458,4.964657179227458,4.964657179227458,-457.14432744186524,-457.14432744186524,-457.14432744186524,29.84003818533611,29.84003818533611,29.84003818533611 -241,0,((((x0 / (x0 * x1)) * (x0 ^ x1)) ^ Exp(Log(((x1 * -0.40139040171417617) ^ (x0 * 4.181080258707082e-2))))) / (Exp((Log((-0.8480261197336083 ^ x0)) + 0.704221454859496)) + Log(((x0 * (x0 * 0.929016566420829)) * (x1 * -0.7218365411729183))))),"(((x[:, 0] / (x[:, 0] * x[:, 1])) * x[:, 0] ** x[:, 1]) ** np.exp(np.log((x[:, 1] * t[0]) ** (x[:, 0] * t[1]))) / (np.exp((np.log(t[2] ** x[:, 0]) + t[3])) + np.log(((x[:, 0] * (x[:, 0] * t[4])) * (x[:, 1] * t[5])))))","$$\frac{{\left(\frac{x_{0}}{\left(x_{0} \cdot x_{1}\right)} \cdot {x_{0}^{x_{1}}}\right)^{\operatorname{exp}(\operatorname{log}({\left(x_{1} \cdot \theta_{0}\right)^{\left(x_{0} \cdot \theta_{1}\right)}}))}}}{\left(\operatorname{exp}(\left(\operatorname{log}({\theta_{2}^{x_{0}}}) + \theta_{3}\right)) + \operatorname{log}(\left(\left(x_{0} \cdot \left(x_{0} \cdot \theta_{4}\right)\right) \cdot \left(x_{1} \cdot \theta_{5}\right)\right))\right)}$$",-0.40139040171417617;4.181080258707082e-2;-0.8480261197336083;0.704221454859496;0.929016566420829;-0.7218365411729183,38,,,,Infinity,,,,,, -242,0,((6.910263463464683e-2 * (x1 ^ -0.9990929438580434)) * (Log((-0.6378607349132996 / x1)) / Log(x1))),"((t[0] * x[:, 1] ** t[1]) * (np.log((t[2] / x[:, 1])) / np.log(x[:, 1])))","$$\left(\left(\theta_{0} \cdot {x_{1}^{\theta_{1}}}\right) \cdot \frac{\operatorname{log}(\frac{\theta_{2}}{x_{1}})}{\operatorname{log}(x_{1})}\right)$$",6.910263463464683e-2;-0.9990929438580434;-0.6378607349132996,13,,,,Infinity,,,,,, -243,0,Log(Exp(Exp((-2.9055132546125124 * x0)))),"np.log(np.exp(np.exp((t[0] * x[:, 0]))))","$$\operatorname{log}(\operatorname{exp}(\operatorname{exp}(\left(\theta_{0} \cdot x_{0}\right))))$$",-2.9055132546125124,6,5.231671206158197e-3,5.231671206158197e-3,5.231671206158197e-3,5.231671206158197e-3,0.517215308204767,0.517215308204767,0.517215308204767,11.682260914057785,11.682260914057785,11.682260914057785 -244,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 -245,0,((Exp(Exp(x0)) * (x1 ^ 1.5446029635272003)) * (3.845086261449669e-4 ^ (x1 / (x0 * 0.4635953380968038)))),"((np.exp(np.exp(x[:, 0])) * x[:, 1] ** t[0]) * t[1] ** (x[:, 1] / (x[:, 0] * t[2])))","$$\left(\left(\operatorname{exp}(\operatorname{exp}(x_{0})) \cdot {x_{1}^{\theta_{0}}}\right) \cdot {\theta_{1}^{\frac{x_{1}}{\left(x_{0} \cdot \theta_{2}\right)}}}\right)$$",1.5446029635272003;3.845086261449669e-4;0.4635953380968038,15,4.3975128480982395e-2,4.3975128480982395e-2,4.3975128480982395e-2,4.3975128480982395e-2,-3.0580759022769683,-3.0580759022769683,-3.0580759022769683,35.29100465756551,35.29100465756551,35.29100465756551 -246,0,Log(Exp(Exp((x0 ^ -4.700445271404796e-2)))),"np.log(np.exp(np.exp(x[:, 0] ** t[0])))","$$\operatorname{log}(\operatorname{exp}(\operatorname{exp}({x_{0}^{\theta_{0}}})))$$",-4.700445271404796e-2,6,7.066150551304317,7.066150551304317,7.066150551304317,7.066150551304317,-651.072575217376,-651.072575217376,-651.072575217376,18.037087906146127,18.037087906146127,18.037087906146127 -247,0,Exp(((1.5096788367051608 * x0) * -1.9245829332514721)),"np.exp(((t[0] * x[:, 0]) * t[1]))","$$\operatorname{exp}(\left(\left(\theta_{0} \cdot x_{0}\right) \cdot \theta_{1}\right))$$",1.5096788367051608;-1.9245829332514721,6,5.231671205578371e-3,5.231671205578371e-3,5.231671205578371e-3,5.231671205578371e-3,0.517215308258274,0.517215308258274,0.517215308258274,12.364324541539643,12.364324541539643,12.364324541539643 -248,0,Log(Log((-0.7762276257961795 / (x1 ^ (-0.2638008795078908 ^ x0))))),"np.log(np.log((t[0] / x[:, 1] ** t[1] ** x[:, 0])))","$$\operatorname{log}(\operatorname{log}(\frac{\theta_{0}}{{x_{1}^{{\theta_{1}^{x_{0}}}}}}))$$",-0.7762276257961795;-0.2638008795078908,9,,,,Infinity,,,,,, -249,0,(((((x0 * -1.7938669598517275e-2) / (x0 / ((x0 * -0.5710681309702826) * 1.4166145957682525))) * Exp((Log(x0) * -4.674942013532105))) / Log((x1 + 6.777475908473423))) * (((4.962600593837775 + x0) * 1.5963004487973274) * Exp(((-1.3486728605254092 + (0.2675879017532597 * x1)) / Exp((x1 * 1.6275258418209724)))))),"(((((x[:, 0] * t[0]) / (x[:, 0] / ((x[:, 0] * t[1]) * t[2]))) * np.exp((np.log(x[:, 0]) * t[3]))) / np.log((x[:, 1] + t[4]))) * (((t[5] + x[:, 0]) * t[6]) * np.exp(((t[7] + (t[8] * x[:, 1])) / np.exp((x[:, 1] * t[9]))))))","$$\left(\frac{\left(\frac{\left(x_{0} \cdot \theta_{0}\right)}{\frac{x_{0}}{\left(\left(x_{0} \cdot \theta_{1}\right) \cdot \theta_{2}\right)}} \cdot \operatorname{exp}(\left(\operatorname{log}(x_{0}) \cdot \theta_{3}\right))\right)}{\operatorname{log}(\left(x_{1} + \theta_{4}\right))} \cdot \left(\left(\left(\theta_{5} + x_{0}\right) \cdot \theta_{6}\right) \cdot \operatorname{exp}(\frac{\left(\theta_{7} + \left(\theta_{8} \cdot x_{1}\right)\right)}{\operatorname{exp}(\left(x_{1} \cdot \theta_{9}\right))})\right)\right)$$",-1.7938669598517275e-2;-0.5710681309702826;1.4166145957682525;-4.674942013532105;6.777475908473423;4.962600593837775;1.5963004487973274;-1.3486728605254092;0.2675879017532597;1.6275258418209724,40,3.2448629619267894e-3,3.2448629619267894e-3,3.2448629619267894e-3,3.2448629619267894e-3,0.7005602792569259,0.7005602792569259,0.7005602792569259,103.38983139753557,103.38983139753557,103.38983139753557 -250,0,(Log(((0.4973476368130393 ^ (x1 * 0.5099843566210469)) ^ (Exp((x0 ^ 0.9430733063071977)) + (x0 ^ 0.4648496190591308)))) / ((0.4719487767158834 + Exp(Log((x1 + x0)))) + Log((Exp((-0.8273638751960062 ^ x0)) + -5.145011823630885e-2)))),"(np.log(t[0] ** (x[:, 1] * t[1]) ** (np.exp(x[:, 0] ** t[2]) + x[:, 0] ** t[3])) / ((t[4] + np.exp(np.log((x[:, 1] + x[:, 0])))) + np.log((np.exp(t[5] ** x[:, 0]) + t[6]))))","$$\frac{\operatorname{log}({{\theta_{0}^{\left(x_{1} \cdot \theta_{1}\right)}}^{\left(\operatorname{exp}({x_{0}^{\theta_{2}}}) + {x_{0}^{\theta_{3}}}\right)}})}{\left(\left(\theta_{4} + \operatorname{exp}(\operatorname{log}(\left(x_{1} + x_{0}\right)))\right) + \operatorname{log}(\left(\operatorname{exp}({\theta_{5}^{x_{0}}}) + \theta_{6}\right))\right)}$$",0.4973476368130393;0.5099843566210469;0.9430733063071977;0.4648496190591308;0.4719487767158834;-0.8273638751960062;-5.145011823630885e-2,31,,,,Infinity,,,,,, -251,0,Log((x0 + 0.16313196790349427)),"np.log((x[:, 0] + t[0]))","$$\operatorname{log}(\left(x_{0} + \theta_{0}\right))$$",0.16313196790349427,4,0.13730678994670836,0.13730678994670836,0.13730678994670836,0.13730678994670836,-11.67082996113841,-11.67082996113841,-11.67082996113841,7.68967849079924,7.68967849079924,7.68967849079924 -252,0,(((x1 ^ 0.6915484858835693) + (x0 / x1)) * (4.7214082212686805e-5 / (x1 * 0.1218331701334346))),"((x[:, 1] ** t[0] + (x[:, 0] / x[:, 1])) * (t[1] / (x[:, 1] * t[2])))","$$\left(\left({x_{1}^{\theta_{0}}} + \frac{x_{0}}{x_{1}}\right) \cdot \frac{\theta_{1}}{\left(x_{1} \cdot \theta_{2}\right)}\right)$$",0.6915484858835693;4.7214082212686805e-5;0.1218331701334346,13,1.4436212461286879e-2,1.4436212461286879e-2,1.4436212461286879e-2,1.4436212461286879e-2,-0.33219044339196957,-0.33219044339196957,-0.33219044339196957,31.372599335037155,31.372599335037155,31.372599335037155 -253,0,Log(((((-0.7418335566093341 / (x1 / x1)) * 0.5154515666961359) + ((0.1941029078505182 + (x1 * -0.8936366491212108)) * 0.6542011528244422)) ^ (Exp(((x0 / (x0 * 0.28037298001653055)) + (7.885965306182818e-2 ^ x1))) ^ ((0.6408996768043134 / Exp(x0)) * ((x0 / x0) + -7.868993810790825e-2))))),"np.log((((t[0] / (x[:, 1] / x[:, 1])) * t[1]) + ((t[2] + (x[:, 1] * t[3])) * t[4])) ** np.exp(((x[:, 0] / (x[:, 0] * t[5])) + t[6] ** x[:, 1])) ** ((t[7] / np.exp(x[:, 0])) * ((x[:, 0] / x[:, 0]) + t[8])))","$$\operatorname{log}({\left(\left(\frac{\theta_{0}}{\frac{x_{1}}{x_{1}}} \cdot \theta_{1}\right) + \left(\left(\theta_{2} + \left(x_{1} \cdot \theta_{3}\right)\right) \cdot \theta_{4}\right)\right)^{{\operatorname{exp}(\left(\frac{x_{0}}{\left(x_{0} \cdot \theta_{5}\right)} + {\theta_{6}^{x_{1}}}\right))^{\left(\frac{\theta_{7}}{\operatorname{exp}(x_{0})} \cdot \left(\frac{x_{0}}{x_{0}} + \theta_{8}\right)\right)}}}})$$",-0.7418335566093341;0.5154515666961359;0.1941029078505182;-0.8936366491212108;0.6542011528244422;0.28037298001653055;7.885965306182818e-2;0.6408996768043134;-7.868993810790825e-2,38,,,,Infinity,,,,,, -254,0,(Log((((x0 * 0.11051553572940709) ^ 0.593781186716341) ^ -0.21854178025980175)) * (((-0.4262004754377421 + x1) ^ (x1 ^ x1)) * (0.16142360337899875 * (x1 + x1)))),"(np.log((x[:, 0] * t[0]) ** t[1] ** t[2]) * ((t[3] + x[:, 1]) ** x[:, 1] ** x[:, 1] * (t[4] * (x[:, 1] + x[:, 1]))))","$$\left(\operatorname{log}({{\left(x_{0} \cdot \theta_{0}\right)^{\theta_{1}}}^{\theta_{2}}}) \cdot \left({\left(\theta_{3} + x_{1}\right)^{{x_{1}^{x_{1}}}}} \cdot \left(\theta_{4} \cdot \left(x_{1} + x_{1}\right)\right)\right)\right)$$",0.11051553572940709;0.593781186716341;-0.21854178025980175;-0.4262004754377421;0.16142360337899875,22,,,,Infinity,,,,,, -255,0,Log(((31.03528432478805 * x1) * 0.11401378375671233)),"np.log(((t[0] * x[:, 1]) * t[1]))","$$\operatorname{log}(\left(\left(\theta_{0} \cdot x_{1}\right) \cdot \theta_{1}\right))$$",31.03528432478805;0.11401378375671233,6,0.20183020857651848,0.20183020857651848,0.20183020857651848,0.20183020857651848,-17.625125930674866,-17.625125930674866,-17.625125930674866,12.54126322517349,12.54126322517349,12.54126322517349 -256,0,(((x0 * 0.6469235767347474) + (x1 * 0.1279113556140522)) ^ Exp((-0.25415435928142716 ^ (x0 + 0.8117381281758772)))),"((x[:, 0] * t[0]) + (x[:, 1] * t[1])) ** np.exp(t[2] ** (x[:, 0] + t[3]))","$${\left(\left(x_{0} \cdot \theta_{0}\right) + \left(x_{1} \cdot \theta_{1}\right)\right)^{\operatorname{exp}({\theta_{2}^{\left(x_{0} + \theta_{3}\right)}})}}$$",0.6469235767347474;0.1279113556140522;-0.25415435928142716;0.8117381281758772,14,,,,Infinity,,,,,, -257,0,(((Log(Log(x1)) / (-0.8047597099480116 / x0)) + (x0 / x1)) * -0.24229362509345076),"(((np.log(np.log(x[:, 1])) / (t[0] / x[:, 0])) + (x[:, 0] / x[:, 1])) * t[1])","$$\left(\left(\frac{\operatorname{log}(\operatorname{log}(x_{1}))}{\frac{\theta_{0}}{x_{0}}} + \frac{x_{0}}{x_{1}}\right) \cdot \theta_{1}\right)$$",-0.8047597099480116;-0.24229362509345076,13,,,,Infinity,,,,,, -258,0,((Log((0.5275235287925928 / x0)) * ((x0 + -0.8240271870273551) / x0)) + Log(((-0.3197509180475011 ^ Log(x1)) ^ (x0 + (0.18136489663080946 ^ x1))))),"((np.log((t[0] / x[:, 0])) * ((x[:, 0] + t[1]) / x[:, 0])) + np.log(t[2] ** np.log(x[:, 1]) ** (x[:, 0] + t[3] ** x[:, 1])))","$$\left(\left(\operatorname{log}(\frac{\theta_{0}}{x_{0}}) \cdot \frac{\left(x_{0} + \theta_{1}\right)}{x_{0}}\right) + \operatorname{log}({{\theta_{2}^{\operatorname{log}(x_{1})}}^{\left(x_{0} + {\theta_{3}^{x_{1}}}\right)}})\right)$$",0.5275235287925928;-0.8240271870273551;-0.3197509180475011;0.18136489663080946,22,,,,Infinity,,,,,, -259,0,Exp(((2.0310779614661962 * (x0 * -2.000140539365218)) + (((x1 + (Exp(x1) * -1.0815530175830677e-2)) / x0) + x1))),"np.exp(((t[0] * (x[:, 0] * t[1])) + (((x[:, 1] + (np.exp(x[:, 1]) * t[2])) / x[:, 0]) + x[:, 1])))","$$\operatorname{exp}(\left(\left(\theta_{0} \cdot \left(x_{0} \cdot \theta_{1}\right)\right) + \left(\frac{\left(x_{1} + \left(\operatorname{exp}(x_{1}) \cdot \theta_{2}\right)\right)}{x_{0}} + x_{1}\right)\right))$$",2.0310779614661962;-2.000140539365218;-1.0815530175830677e-2,17,2.054785894520473e-3,2.054785894520473e-3,2.054785894520473e-3,2.054785894520473e-3,0.8103819724711382,0.8103819724711382,0.8103819724711382,39.14509664734831,39.14509664734831,39.14509664734831 -260,0,((((x0 ^ 0.7149330241624112) * 0.9508390386221237) * Log((Exp(x1) / (x1 / x1)))) ^ (Log((x1 * (-0.1741822892889553 / x1))) + (-0.5193817195779102 + (x0 * -0.4836890349574553)))),"((x[:, 0] ** t[0] * t[1]) * np.log((np.exp(x[:, 1]) / (x[:, 1] / x[:, 1])))) ** (np.log((x[:, 1] * (t[2] / x[:, 1]))) + (t[3] + (x[:, 0] * t[4])))","$${\left(\left({x_{0}^{\theta_{0}}} \cdot \theta_{1}\right) \cdot \operatorname{log}(\frac{\operatorname{exp}(x_{1})}{\frac{x_{1}}{x_{1}}})\right)^{\left(\operatorname{log}(\left(x_{1} \cdot \frac{\theta_{2}}{x_{1}}\right)) + \left(\theta_{3} + \left(x_{0} \cdot \theta_{4}\right)\right)\right)}}$$",0.7149330241624112;0.9508390386221237;-0.1741822892889553;-0.5193817195779102;-0.4836890349574553,26,,,,Infinity,,,,,, -261,0,(((-0.7638680283188292 + (x1 * 0.595150090655318)) / Log(Log(((x0 / x0) + (x1 ^ -0.6817593265536492))))) + ((Log((x1 + 0.3882714302340444)) * (0.8599898702113269 ^ (x1 * -0.8203220258365127))) ^ (Log((x1 * x0)) ^ (-0.23865691344765871 * x1)))),"(((t[0] + (x[:, 1] * t[1])) / np.log(np.log(((x[:, 0] / x[:, 0]) + x[:, 1] ** t[2])))) + (np.log((x[:, 1] + t[3])) * t[4] ** (x[:, 1] * t[5])) ** np.log((x[:, 1] * x[:, 0])) ** (t[6] * x[:, 1]))","$$\left(\frac{\left(\theta_{0} + \left(x_{1} \cdot \theta_{1}\right)\right)}{\operatorname{log}(\operatorname{log}(\left(\frac{x_{0}}{x_{0}} + {x_{1}^{\theta_{2}}}\right)))} + {\left(\operatorname{log}(\left(x_{1} + \theta_{3}\right)) \cdot {\theta_{4}^{\left(x_{1} \cdot \theta_{5}\right)}}\right)^{{\operatorname{log}(\left(x_{1} \cdot x_{0}\right))^{\left(\theta_{6} \cdot x_{1}\right)}}}}\right)$$",-0.7638680283188292;0.595150090655318;-0.6817593265536492;0.3882714302340444;0.8599898702113269;-0.8203220258365127;-0.23865691344765871,35,,,,Infinity,,,,,, -262,0,((Log(Exp(Log((-0.6980672684452163 * x0)))) + (0.5016014807762248 / (3.0483818610843816e-2 / x1))) + (((x0 * x1) * x1) + ((x1 * x1) + -0.49432842461033644))),"((np.log(np.exp(np.log((t[0] * x[:, 0])))) + (t[1] / (t[2] / x[:, 1]))) + (((x[:, 0] * x[:, 1]) * x[:, 1]) + ((x[:, 1] * x[:, 1]) + t[3])))","$$\left(\left(\operatorname{log}(\operatorname{exp}(\operatorname{log}(\left(\theta_{0} \cdot x_{0}\right)))) + \frac{\theta_{1}}{\frac{\theta_{2}}{x_{1}}}\right) + \left(\left(\left(x_{0} \cdot x_{1}\right) \cdot x_{1}\right) + \left(\left(x_{1} \cdot x_{1}\right) + \theta_{3}\right)\right)\right)$$",-0.6980672684452163;0.5016014807762248;3.0483818610843816e-2;-0.49432842461033644,24,,,,Infinity,,,,,, -263,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 -264,0,((-7.928072814508772 + ((x0 * x1) * 9.857609786712674)) * (((-0.12307173186556611 * x1) * 0.181750418883839) * 2.8234789221015064)),"((t[0] + ((x[:, 0] * x[:, 1]) * t[1])) * (((t[2] * x[:, 1]) * t[3]) * t[4]))","$$\left(\left(\theta_{0} + \left(\left(x_{0} \cdot x_{1}\right) \cdot \theta_{1}\right)\right) \cdot \left(\left(\left(\theta_{2} \cdot x_{1}\right) \cdot \theta_{3}\right) \cdot \theta_{4}\right)\right)$$",-7.928072814508772;9.857609786712674;-0.12307173186556611;0.181750418883839;2.8234789221015064,15,6.790998393986846e-3,6.790998393986846e-3,6.790998393986846e-3,6.790998393986846e-3,0.3733187852547699,0.3733187852547699,0.3733187852547699,34.2523052619043,34.2523052619043,34.2523052619043 -265,0,Exp(((x1 * -4.4392103905271485e-2) ^ 0.3061999210245552)),"np.exp((x[:, 1] * t[0]) ** t[1])","$$\operatorname{exp}({\left(x_{1} \cdot \theta_{0}\right)^{\theta_{1}}})$$",-4.4392103905271485e-2;0.3061999210245552,6,,,,Infinity,,,,,, -266,0,Exp(((Log((x0 * -5.1372105364373866e-2)) * 0.37457804033045905) / (x1 ^ -0.4154949090590605))),"np.exp(((np.log((x[:, 0] * t[0])) * t[1]) / x[:, 1] ** t[2]))","$$\operatorname{exp}(\frac{\left(\operatorname{log}(\left(x_{0} \cdot \theta_{0}\right)) \cdot \theta_{1}\right)}{{x_{1}^{\theta_{2}}}})$$",-5.1372105364373866e-2;0.37457804033045905;-0.4154949090590605,11,,,,Infinity,,,,,, -267,0,((Exp(((0.3558896877644119 ^ (-0.9531117621876152 ^ x0)) * -0.6528591580765679)) + (0.29460147670816283 + (-0.4552491100867816 + (x1 + x0)))) ^ (Log((0.7461586264034403 / (0.41364934509126605 ^ (x1 * -0.45096578416422783)))) ^ (-0.5122351290495717 / (x0 / (0.20965967544116093 * x1))))),"(np.exp((t[0] ** t[1] ** x[:, 0] * t[2])) + (t[3] + (t[4] + (x[:, 1] + x[:, 0])))) ** np.log((t[5] / t[6] ** (x[:, 1] * t[7]))) ** (t[8] / (x[:, 0] / (t[9] * x[:, 1])))","$${\left(\operatorname{exp}(\left({\theta_{0}^{{\theta_{1}^{x_{0}}}}} \cdot \theta_{2}\right)) + \left(\theta_{3} + \left(\theta_{4} + \left(x_{1} + x_{0}\right)\right)\right)\right)^{{\operatorname{log}(\frac{\theta_{5}}{{\theta_{6}^{\left(x_{1} \cdot \theta_{7}\right)}}})^{\frac{\theta_{8}}{\frac{x_{0}}{\left(\theta_{9} \cdot x_{1}\right)}}}}}}$$",0.3558896877644119;-0.9531117621876152;-0.6528591580765679;0.29460147670816283;-0.4552491100867816;0.7461586264034403;0.41364934509126605;-0.45096578416422783;-0.5122351290495717;0.20965967544116093,33,,,,Infinity,,,,,, -268,0,((Log(((x1 + -0.16382105335512553) ^ Exp(Log(x0)))) * Exp(Log((-0.591961570515651 * Exp((-0.4897576196282717 + x1)))))) / (((x1 * -1.5737887684302065e-2) / (-0.5596484061879035 ^ Log(x0))) + ((-0.12730385755125095 / (x0 / x0)) ^ (-0.605880371445976 / x1)))),"((np.log((x[:, 1] + t[0]) ** np.exp(np.log(x[:, 0]))) * np.exp(np.log((t[1] * np.exp((t[2] + x[:, 1])))))) / (((x[:, 1] * t[3]) / t[4] ** np.log(x[:, 0])) + (t[5] / (x[:, 0] / x[:, 0])) ** (t[6] / x[:, 1])))","$$\frac{\left(\operatorname{log}({\left(x_{1} + \theta_{0}\right)^{\operatorname{exp}(\operatorname{log}(x_{0}))}}) \cdot \operatorname{exp}(\operatorname{log}(\left(\theta_{1} \cdot \operatorname{exp}(\left(\theta_{2} + x_{1}\right))\right)))\right)}{\left(\frac{\left(x_{1} \cdot \theta_{3}\right)}{{\theta_{4}^{\operatorname{log}(x_{0})}}} + {\frac{\theta_{5}}{\frac{x_{0}}{x_{0}}}^{\frac{\theta_{6}}{x_{1}}}}\right)}$$",-0.16382105335512553;-0.591961570515651;-0.4897576196282717;-1.5737887684302065e-2;-0.5596484061879035;-0.12730385755125095;-0.605880371445976,36,,,,Infinity,,,,,, -269,0,Exp((x0 * -2.9053779497574426)),"np.exp((x[:, 0] * t[0]))","$$\operatorname{exp}(\left(x_{0} \cdot \theta_{0}\right))$$",-2.9053779497574426,4,5.2316712708275655e-3,5.2316712708275655e-3,5.2316712708275655e-3,5.2316712708275655e-3,0.5172153022370025,0.5172153022370025,0.5172153022370025,7.570810883990948,7.570810883990948,7.570810883990948 -270,0,Exp(Log(((Exp((-0.7969272401657344 * Exp(x1))) ^ ((x1 ^ 0.2054636141672378) * -0.30258802268212914)) + ((Log(Log(x1)) ^ Exp((x0 / x1))) ^ Log((x0 * 0.24081230503866524)))))),"np.exp(np.log((np.exp((t[0] * np.exp(x[:, 1]))) ** (x[:, 1] ** t[1] * t[2]) + np.log(np.log(x[:, 1])) ** np.exp((x[:, 0] / x[:, 1])) ** np.log((x[:, 0] * t[3])))))","$$\operatorname{exp}(\operatorname{log}(\left({\operatorname{exp}(\left(\theta_{0} \cdot \operatorname{exp}(x_{1})\right))^{\left({x_{1}^{\theta_{1}}} \cdot \theta_{2}\right)}} + {{\operatorname{log}(\operatorname{log}(x_{1}))^{\operatorname{exp}(\frac{x_{0}}{x_{1}})}}^{\operatorname{log}(\left(x_{0} \cdot \theta_{3}\right))}}\right)))$$",-0.7969272401657344;0.2054636141672378;-0.30258802268212914;0.24081230503866524,27,,,,Infinity,,,,,, -271,0,(Log((((((x0 * 0.5517197612553773) * 0.7229823981852166) + x1) ^ 0.4638422804201031) * ((x1 ^ (0.6663976625214767 ^ (x1 ^ -0.28901772742672704))) ^ ((x1 + x0) / x1)))) ^ x1),"np.log(((((x[:, 0] * t[0]) * t[1]) + x[:, 1]) ** t[2] * x[:, 1] ** t[3] ** x[:, 1] ** t[4] ** ((x[:, 1] + x[:, 0]) / x[:, 1]))) ** x[:, 1]","$${\operatorname{log}(\left({\left(\left(\left(x_{0} \cdot \theta_{0}\right) \cdot \theta_{1}\right) + x_{1}\right)^{\theta_{2}}} \cdot {{x_{1}^{{\theta_{3}^{{x_{1}^{\theta_{4}}}}}}}^{\frac{\left(x_{1} + x_{0}\right)}{x_{1}}}}\right))^{x_{1}}}$$",0.5517197612553773;0.7229823981852166;0.4638422804201031;0.6663976625214767;-0.28901772742672704,26,,,,Infinity,,,,,, -272,0,(((-0.8992846847909205 + Exp(Exp((x1 * -3.828496646535917e-2)))) + (((-6.472732684090277e-2 * x1) / x0) / Log((7.930576094161157e-2 / Exp(x0))))) / (Exp(((x0 / (x0 ^ -8.379408070702465e-2)) * Log((x0 + x0)))) * ((Log(x1) * Log(x0)) * (-0.6182167804475931 ^ x0)))),"(((t[0] + np.exp(np.exp((x[:, 1] * t[1])))) + (((t[2] * x[:, 1]) / x[:, 0]) / np.log((t[3] / np.exp(x[:, 0]))))) / (np.exp(((x[:, 0] / x[:, 0] ** t[4]) * np.log((x[:, 0] + x[:, 0])))) * ((np.log(x[:, 1]) * np.log(x[:, 0])) * t[5] ** x[:, 0])))","$$\frac{\left(\left(\theta_{0} + \operatorname{exp}(\operatorname{exp}(\left(x_{1} \cdot \theta_{1}\right)))\right) + \frac{\frac{\left(\theta_{2} \cdot x_{1}\right)}{x_{0}}}{\operatorname{log}(\frac{\theta_{3}}{\operatorname{exp}(x_{0})})}\right)}{\left(\operatorname{exp}(\left(\frac{x_{0}}{{x_{0}^{\theta_{4}}}} \cdot \operatorname{log}(\left(x_{0} + x_{0}\right))\right)) \cdot \left(\left(\operatorname{log}(x_{1}) \cdot \operatorname{log}(x_{0})\right) \cdot {\theta_{5}^{x_{0}}}\right)\right)}$$",-0.8992846847909205;-3.828496646535917e-2;-6.472732684090277e-2;7.930576094161157e-2;-8.379408070702465e-2;-0.6182167804475931,41,,,,Infinity,,,,,, -273,0,((((-0.5463852388285715 + x1) / Exp((-0.5130350819295414 ^ x0))) * Exp((x1 * 0.7546830786011973))) / ((Exp(x0) + ((-0.8796002287821658 + x1) / x0)) * -0.6645994134871933)),"((((t[0] + x[:, 1]) / np.exp(t[1] ** x[:, 0])) * np.exp((x[:, 1] * t[2]))) / ((np.exp(x[:, 0]) + ((t[3] + x[:, 1]) / x[:, 0])) * t[4]))","$$\frac{\left(\frac{\left(\theta_{0} + x_{1}\right)}{\operatorname{exp}({\theta_{1}^{x_{0}}})} \cdot \operatorname{exp}(\left(x_{1} \cdot \theta_{2}\right))\right)}{\left(\left(\operatorname{exp}(x_{0}) + \frac{\left(\theta_{3} + x_{1}\right)}{x_{0}}\right) \cdot \theta_{4}\right)}$$",-0.5463852388285715;-0.5130350819295414;0.7546830786011973;-0.8796002287821658;-0.6645994134871933,24,,,,Infinity,,,,,, -274,0,Exp(((0.3137046522187341 / (((-0.24955859278625814 * x1) ^ -0.41211702708960696) * -0.36800152999778635)) + -0.15179100251990874)),"np.exp(((t[0] / ((t[1] * x[:, 1]) ** t[2] * t[3])) + t[4]))","$$\operatorname{exp}(\left(\frac{\theta_{0}}{\left({\left(\theta_{1} \cdot x_{1}\right)^{\theta_{2}}} \cdot \theta_{3}\right)} + \theta_{4}\right))$$",0.3137046522187341;-0.24955859278625814;-0.41211702708960696;-0.36800152999778635;-0.15179100251990874,12,,,,Infinity,,,,,, -275,0,(((-8.133623383906063e-2 ^ ((Exp((x0 * 0.2783143483774412)) / x0) * -0.32110959357818325)) ^ 0.6564357464041202) * 0.9118308390555482),"(t[0] ** ((np.exp((x[:, 0] * t[1])) / x[:, 0]) * t[2]) ** t[3] * t[4])","$$\left({{\theta_{0}^{\left(\frac{\operatorname{exp}(\left(x_{0} \cdot \theta_{1}\right))}{x_{0}} \cdot \theta_{2}\right)}}^{\theta_{3}}} \cdot \theta_{4}\right)$$",-8.133623383906063e-2;0.2783143483774412;-0.32110959357818325;0.6564357464041202;0.9118308390555482,14,,,,Infinity,,,,,, -276,0,Log((((Exp((x1 + x1)) / Exp(Log(x1))) / (Exp((x1 + x1)) / Log((x0 * x0)))) / (Log(((x0 ^ x0) + Exp(Log(x1)))) / (Log((-0.5687693094742745 + x1)) ^ (x0 / Log(x1)))))),"np.log((((np.exp((x[:, 1] + x[:, 1])) / np.exp(np.log(x[:, 1]))) / (np.exp((x[:, 1] + x[:, 1])) / np.log((x[:, 0] * x[:, 0])))) / (np.log((x[:, 0] ** x[:, 0] + np.exp(np.log(x[:, 1])))) / np.log((t[0] + x[:, 1])) ** (x[:, 0] / np.log(x[:, 1])))))","$$\operatorname{log}(\frac{\frac{\frac{\operatorname{exp}(\left(x_{1} + x_{1}\right))}{\operatorname{exp}(\operatorname{log}(x_{1}))}}{\frac{\operatorname{exp}(\left(x_{1} + x_{1}\right))}{\operatorname{log}(\left(x_{0} \cdot x_{0}\right))}}}{\frac{\operatorname{log}(\left({x_{0}^{x_{0}}} + \operatorname{exp}(\operatorname{log}(x_{1}))\right))}{{\operatorname{log}(\left(\theta_{0} + x_{1}\right))^{\frac{x_{0}}{\operatorname{log}(x_{1})}}}}})$$",-0.5687693094742745,38,,,,Infinity,,,,,, -277,0,((-7.774288897847149 * Log((x1 * x0))) * (-1509.2685595186215 / (-1000.8287487760492 * (x1 * 3684.4976229524514)))),"((t[0] * np.log((x[:, 1] * x[:, 0]))) * (t[1] / (t[2] * (x[:, 1] * t[3]))))","$$\left(\left(\theta_{0} \cdot \operatorname{log}(\left(x_{1} \cdot x_{0}\right))\right) \cdot \frac{\theta_{1}}{\left(\theta_{2} \cdot \left(x_{1} \cdot \theta_{3}\right)\right)}\right)$$",-7.774288897847149;-1509.2685595186215;-1000.8287487760492;3684.4976229524514,14,1.3908990539012463e-2,1.3908990539012463e-2,1.3908990539012463e-2,1.3908990539012463e-2,-0.28353779240860266,-0.28353779240860266,-0.28353779240860266,33.180850402148444,33.180850402148444,33.180850402148444 -278,0,(7.439888686209983e-2 / x0),"(t[0] / x[:, 0])","$$\frac{\theta_{0}}{x_{0}}$$",7.439888686209983e-2,3,7.713765273570095e-3,7.713765273570095e-3,7.713765273570095e-3,7.713765273570095e-3,0.2881647864648511,0.2881647864648511,0.2881647864648511,5.323704190118183,5.323704190118183,5.323704190118183 -279,0,Log((((x0 * -0.7701001466356021) * (-0.7520584227371753 * x0)) ^ -0.11413487309924517)),"np.log(((x[:, 0] * t[0]) * (t[1] * x[:, 0])) ** t[2])","$$\operatorname{log}({\left(\left(x_{0} \cdot \theta_{0}\right) \cdot \left(\theta_{1} \cdot x_{0}\right)\right)^{\theta_{2}}})$$",-0.7701001466356021;-0.7520584227371753;-0.11413487309924517,10,5.376942081146931e-3,5.376942081146931e-3,5.376942081146931e-3,5.376942081146931e-3,0.5038095432312907,0.5038095432312907,0.5038095432312907,22.16199317831696,22.16199317831696,22.16199317831696 -280,0,(-0.7457459660136219 ^ Exp(Log((Log((x1 / x0)) + ((x0 + -0.9281991443577613) + -0.5777021903256947))))),"t[0] ** np.exp(np.log((np.log((x[:, 1] / x[:, 0])) + ((x[:, 0] + t[1]) + t[2]))))","$${\theta_{0}^{\operatorname{exp}(\operatorname{log}(\left(\operatorname{log}(\frac{x_{1}}{x_{0}}) + \left(\left(x_{0} + \theta_{1}\right) + \theta_{2}\right)\right)))}}$$",-0.7457459660136219;-0.9281991443577613;-0.5777021903256947,14,,,,Infinity,,,,,, -281,0,Log(((((x0 / x0) ^ x0) * x1) * -6.2370943625926434e-2)),"np.log((((x[:, 0] / x[:, 0]) ** x[:, 0] * x[:, 1]) * t[0]))","$$\operatorname{log}(\left(\left({\frac{x_{0}}{x_{0}}^{x_{0}}} \cdot x_{1}\right) \cdot \theta_{0}\right))$$",-6.2370943625926434e-2,10,,,,Infinity,,,,,, -282,0,(4.312013102205418e-2 / (x0 ^ 2.841881501105064)),"(t[0] / x[:, 0] ** t[1])","$$\frac{\theta_{0}}{{x_{0}^{\theta_{1}}}}$$",4.312013102205418e-2;2.841881501105064,5,5.2332357936381215e-3,5.2332357936381215e-3,5.2332357936381215e-3,5.2332357936381215e-3,0.5170709262559563,0.5170709262559563,0.5170709262559563,10.978031588549008,10.978031588549008,10.978031588549008 -283,0,((Log(((x0 ^ 0.6069497931485992) ^ -0.5737397522741415)) / ((Exp(x1) + (-0.11532079359960146 ^ x0)) / (0.3193072515514238 / (x1 * -0.33827955062236514)))) ^ (((x0 + (-0.9826782423758313 + x1)) ^ Exp((-0.804515127724558 * x1))) ^ ((x1 / (x0 + x1)) ^ (-6.224670050200187e-2 ^ Exp(x1))))),"(np.log(x[:, 0] ** t[0] ** t[1]) / ((np.exp(x[:, 1]) + t[2] ** x[:, 0]) / (t[3] / (x[:, 1] * t[4])))) ** (x[:, 0] + (t[5] + x[:, 1])) ** np.exp((t[6] * x[:, 1])) ** (x[:, 1] / (x[:, 0] + x[:, 1])) ** t[7] ** np.exp(x[:, 1])","$${\frac{\operatorname{log}({{x_{0}^{\theta_{0}}}^{\theta_{1}}})}{\frac{\left(\operatorname{exp}(x_{1}) + {\theta_{2}^{x_{0}}}\right)}{\frac{\theta_{3}}{\left(x_{1} \cdot \theta_{4}\right)}}}^{{{\left(x_{0} + \left(\theta_{5} + x_{1}\right)\right)^{\operatorname{exp}(\left(\theta_{6} \cdot x_{1}\right))}}^{{\frac{x_{1}}{\left(x_{0} + x_{1}\right)}^{{\theta_{7}^{\operatorname{exp}(x_{1})}}}}}}}}$$",0.6069497931485992;-0.5737397522741415;-0.11532079359960146;0.3193072515514238;-0.33827955062236514;-0.9826782423758313;-0.804515127724558;-6.224670050200187e-2,41,,,,Infinity,,,,,, -284,0,Exp((((0.3615270050744625 / x0) / Exp((x1 ^ x1))) + (Exp((0.5440656655999438 ^ x1)) ^ (1.1731343233275893e-3 ^ (x0 ^ x0))))),"np.exp((((t[0] / x[:, 0]) / np.exp(x[:, 1] ** x[:, 1])) + np.exp(t[1] ** x[:, 1]) ** t[2] ** x[:, 0] ** x[:, 0]))","$$\operatorname{exp}(\left(\frac{\frac{\theta_{0}}{x_{0}}}{\operatorname{exp}({x_{1}^{x_{1}}})} + {\operatorname{exp}({\theta_{1}^{x_{1}}})^{{\theta_{2}^{{x_{0}^{x_{0}}}}}}}\right))$$",0.3615270050744625;0.5440656655999438;1.1731343233275893e-3,20,10.427585709827978,10.427585709827978,10.427585709827978,10.427585709827978,-961.2697135786802,-961.2697135786802,-961.2697135786802,54.365804926054366,54.365804926054366,54.365804926054366 -285,0,((-0.48985285769611664 + (-0.44781573913883865 ^ ((Log((x1 ^ -0.7870782986263285)) + x0) + (0.5983367794959946 / (x1 * 0.32548216164927113))))) + ((0.8704586179150119 * ((((x1 / x0) + x1) * 4.599768171996865e-2) ^ (-0.8070612299819702 + x0))) * x0)),"((t[0] + t[1] ** ((np.log(x[:, 1] ** t[2]) + x[:, 0]) + (t[3] / (x[:, 1] * t[4])))) + ((t[5] * (((x[:, 1] / x[:, 0]) + x[:, 1]) * t[6]) ** (t[7] + x[:, 0])) * x[:, 0]))","$$\left(\left(\theta_{0} + {\theta_{1}^{\left(\left(\operatorname{log}({x_{1}^{\theta_{2}}}) + x_{0}\right) + \frac{\theta_{3}}{\left(x_{1} \cdot \theta_{4}\right)}\right)}}\right) + \left(\left(\theta_{5} \cdot {\left(\left(\frac{x_{1}}{x_{0}} + x_{1}\right) \cdot \theta_{6}\right)^{\left(\theta_{7} + x_{0}\right)}}\right) \cdot x_{0}\right)\right)$$",-0.48985285769611664;-0.44781573913883865;-0.7870782986263285;0.5983367794959946;0.32548216164927113;0.8704586179150119;4.599768171996865e-2;-0.8070612299819702,32,,,,Infinity,,,,,, -286,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 -287,0,Log(((((Exp(x0) + ((x1 ^ 0.524837435803704) * -0.4498611544067536)) / (((x0 / x1) * x0) * 5.353669619184157)) + (-0.25631521551552144 / x0)) + (Exp((Exp(x0) ^ -0.6147038203012859)) * 0.6838691291616903))),"np.log(((((np.exp(x[:, 0]) + (x[:, 1] ** t[0] * t[1])) / (((x[:, 0] / x[:, 1]) * x[:, 0]) * t[2])) + (t[3] / x[:, 0])) + (np.exp(np.exp(x[:, 0]) ** t[4]) * t[5])))","$$\operatorname{log}(\left(\left(\frac{\left(\operatorname{exp}(x_{0}) + \left({x_{1}^{\theta_{0}}} \cdot \theta_{1}\right)\right)}{\left(\left(\frac{x_{0}}{x_{1}} \cdot x_{0}\right) \cdot \theta_{2}\right)} + \frac{\theta_{3}}{x_{0}}\right) + \left(\operatorname{exp}({\operatorname{exp}(x_{0})^{\theta_{4}}}) \cdot \theta_{5}\right)\right))$$",0.524837435803704;-0.4498611544067536;5.353669619184157;-0.25631521551552144;-0.6147038203012859;0.6838691291616903,29,2.501603529881908e-3,2.501603529881908e-3,2.501603529881908e-3,2.501603529881908e-3,0.7691491224168906,0.7691491224168906,0.7691491224168906,75.8473137981331,75.8473137981331,75.8473137981331 -288,0,(Log(Log((-0.3916807442008581 / ((x1 ^ x0) / x1)))) + (Exp((-7.87946195982947e-3 ^ (x0 * (Log(x0) ^ x0)))) / x1)),"(np.log(np.log((t[0] / (x[:, 1] ** x[:, 0] / x[:, 1])))) + (np.exp(t[1] ** (x[:, 0] * np.log(x[:, 0]) ** x[:, 0])) / x[:, 1]))","$$\left(\operatorname{log}(\operatorname{log}(\frac{\theta_{0}}{\frac{{x_{1}^{x_{0}}}}{x_{1}}})) + \frac{\operatorname{exp}({\theta_{1}^{\left(x_{0} \cdot {\operatorname{log}(x_{0})^{x_{0}}}\right)}})}{x_{1}}\right)$$",-0.3916807442008581;-7.87946195982947e-3,21,,,,Infinity,,,,,, -289,0,Log(Log((x1 + x0))),"np.log(np.log((x[:, 1] + x[:, 0])))","$$\operatorname{log}(\operatorname{log}(\left(x_{1} + x_{0}\right)))$$",,5,,,,Infinity,,,,,, -290,0,((Exp(Exp(((0.188512824334789 + (-0.659220320449309 + x0)) / Log((0.3102557861361933 * x0))))) / Log(Log((0.3583841925856639 * (0.6762009197980479 / (x0 * 0.8338410249030334)))))) ^ Log((Exp(((-0.14597803338206394 / x1) ^ ((x1 + x1) * x0))) + ((Exp(x1) / (x0 * x1)) * (x0 ^ (x0 / x0)))))),"(np.exp(np.exp(((t[0] + (t[1] + x[:, 0])) / np.log((t[2] * x[:, 0]))))) / np.log(np.log((t[3] * (t[4] / (x[:, 0] * t[5])))))) ** np.log((np.exp((t[6] / x[:, 1]) ** ((x[:, 1] + x[:, 1]) * x[:, 0])) + ((np.exp(x[:, 1]) / (x[:, 0] * x[:, 1])) * x[:, 0] ** (x[:, 0] / x[:, 0]))))","$${\frac{\operatorname{exp}(\operatorname{exp}(\frac{\left(\theta_{0} + \left(\theta_{1} + x_{0}\right)\right)}{\operatorname{log}(\left(\theta_{2} \cdot x_{0}\right))}))}{\operatorname{log}(\operatorname{log}(\left(\theta_{3} \cdot \frac{\theta_{4}}{\left(x_{0} \cdot \theta_{5}\right)}\right)))}^{\operatorname{log}(\left(\operatorname{exp}({\frac{\theta_{6}}{x_{1}}^{\left(\left(x_{1} + x_{1}\right) \cdot x_{0}\right)}}) + \left(\frac{\operatorname{exp}(x_{1})}{\left(x_{0} \cdot x_{1}\right)} \cdot {x_{0}^{\frac{x_{0}}{x_{0}}}}\right)\right))}}$$",0.188512824334789;-0.659220320449309;0.3102557861361933;0.3583841925856639;0.6762009197980479;0.8338410249030334;-0.14597803338206394,47,,,,Infinity,,,,,, -291,0,Exp(((Exp(x1) + (x1 ^ -0.11008486724943928)) ^ -17.02300974019877)),"np.exp((np.exp(x[:, 1]) + x[:, 1] ** t[0]) ** t[1])","$$\operatorname{exp}({\left(\operatorname{exp}(x_{1}) + {x_{1}^{\theta_{0}}}\right)^{\theta_{1}}})$$",-0.11008486724943928;-17.02300974019877,9,0.8923101219193086,0.8923101219193086,0.8923101219193086,0.8923101219193086,-81.34341383867802,-81.34341383867802,-81.34341383867802,19.32987019236956,19.32987019236956,19.32987019236956 -292,0,Exp(Exp(((x0 / (x0 ^ Exp((x1 / x0)))) + ((x1 / ((x1 + x0) ^ Exp(x0))) / (-0.5574202243792838 ^ Log(x1)))))),"np.exp(np.exp(((x[:, 0] / x[:, 0] ** np.exp((x[:, 1] / x[:, 0]))) + ((x[:, 1] / (x[:, 1] + x[:, 0]) ** np.exp(x[:, 0])) / t[0] ** np.log(x[:, 1])))))","$$\operatorname{exp}(\operatorname{exp}(\left(\frac{x_{0}}{{x_{0}^{\operatorname{exp}(\frac{x_{1}}{x_{0}})}}} + \frac{\frac{x_{1}}{{\left(x_{1} + x_{0}\right)^{\operatorname{exp}(x_{0})}}}}{{\theta_{0}^{\operatorname{log}(x_{1})}}}\right)))$$",-0.5574202243792838,24,,,,Infinity,,,,,, -293,0,((Log(Exp((x0 ^ -1.2757100003653896))) / Exp((0.7527665035589293 ^ (-0.29691712532013453 * x0)))) / (((-0.500879846882273 * x1) + (x1 / x0)) ^ -1.2250016266202837)),"((np.log(np.exp(x[:, 0] ** t[0])) / np.exp(t[1] ** (t[2] * x[:, 0]))) / ((t[3] * x[:, 1]) + (x[:, 1] / x[:, 0])) ** t[4])","$$\frac{\frac{\operatorname{log}(\operatorname{exp}({x_{0}^{\theta_{0}}}))}{\operatorname{exp}({\theta_{1}^{\left(\theta_{2} \cdot x_{0}\right)}})}}{{\left(\left(\theta_{3} \cdot x_{1}\right) + \frac{x_{1}}{x_{0}}\right)^{\theta_{4}}}}$$",-1.2757100003653896;0.7527665035589293;-0.29691712532013453;-0.500879846882273;-1.2250016266202837,22,1.2003009421013378e-3,1.2003009421013378e-3,1.2003009421013378e-3,1.2003009421013378e-3,0.8892348357611219,0.8892348357611219,0.8892348357611219,58.44464564908292,58.44464564908292,58.44464564908292 -294,0,Exp((Exp(((-0.31869379548283805 ^ (x0 + x1)) * 0.7881429855340786)) * Log((((0.6198284654766932 + x0) / (x1 * -0.3110723332626282)) ^ (0.383335712906136 + Log(x1)))))),"np.exp((np.exp((t[0] ** (x[:, 0] + x[:, 1]) * t[1])) * np.log(((t[2] + x[:, 0]) / (x[:, 1] * t[3])) ** (t[4] + np.log(x[:, 1])))))","$$\operatorname{exp}(\left(\operatorname{exp}(\left({\theta_{0}^{\left(x_{0} + x_{1}\right)}} \cdot \theta_{1}\right)) \cdot \operatorname{log}({\frac{\left(\theta_{2} + x_{0}\right)}{\left(x_{1} \cdot \theta_{3}\right)}^{\left(\theta_{4} + \operatorname{log}(x_{1})\right)}})\right))$$",-0.31869379548283805;0.7881429855340786;0.6198284654766932;-0.3110723332626282;0.383335712906136,23,,,,Infinity,,,,,, -295,0,((((Exp(Log(x0)) ^ 6.827214435369358) / ((1.1460253849431838 + x1) ^ -11.21206261887719)) / ((-15.129090997105626 / x1) * 102.07966436489639)) + x0),"(((np.exp(np.log(x[:, 0])) ** t[0] / (t[1] + x[:, 1]) ** t[2]) / ((t[3] / x[:, 1]) * t[4])) + x[:, 0])","$$\left(\frac{\frac{{\operatorname{exp}(\operatorname{log}(x_{0}))^{\theta_{0}}}}{{\left(\theta_{1} + x_{1}\right)^{\theta_{2}}}}}{\left(\frac{\theta_{3}}{x_{1}} \cdot \theta_{4}\right)} + x_{0}\right)$$",6.827214435369358;1.1460253849431838;-11.21206261887719;-15.129090997105626;102.07966436489639,19,0.8482611300758829,0.8482611300758829,0.8482611300758829,0.8482611300758829,-77.27852173957466,-77.27852173957466,-77.27852173957466,52.61532666329467,52.61532666329467,52.61532666329467 -296,0,Exp((Log((((x0 ^ x1) + 0.3339123698435208) ^ -10.53724847615968)) * Exp(Exp((Exp(x0) / ((x1 * 0.15094908042145255) + (x1 * -0.3112235744370234))))))),"np.exp((np.log((x[:, 0] ** x[:, 1] + t[0]) ** t[1]) * np.exp(np.exp((np.exp(x[:, 0]) / ((x[:, 1] * t[2]) + (x[:, 1] * t[3])))))))","$$\operatorname{exp}(\left(\operatorname{log}({\left({x_{0}^{x_{1}}} + \theta_{0}\right)^{\theta_{1}}}) \cdot \operatorname{exp}(\operatorname{exp}(\frac{\operatorname{exp}(x_{0})}{\left(\left(x_{1} \cdot \theta_{2}\right) + \left(x_{1} \cdot \theta_{3}\right)\right)}))\right))$$",0.3339123698435208;-10.53724847615968;0.15094908042145255;-0.3112235744370234,22,2.577081865826732e-3,2.577081865826732e-3,2.577081865826732e-3,2.577081865826732e-3,0.7621838939611257,0.7621838939611257,0.7621838939611257,56.42495981654664,56.42495981654664,56.42495981654664 -297,0,Log(Log((Exp((x0 * (-0.26032931743271703 / x1))) * Log((x0 / ((x0 ^ 9.489486398979596e-2) + -5.303829296837359e-2)))))),"np.log(np.log((np.exp((x[:, 0] * (t[0] / x[:, 1]))) * np.log((x[:, 0] / (x[:, 0] ** t[1] + t[2]))))))","$$\operatorname{log}(\operatorname{log}(\left(\operatorname{exp}(\left(x_{0} \cdot \frac{\theta_{0}}{x_{1}}\right)) \cdot \operatorname{log}(\frac{x_{0}}{\left({x_{0}^{\theta_{1}}} + \theta_{2}\right)})\right)))$$",-0.26032931743271703;9.489486398979596e-2;-5.303829296837359e-2,17,,,,Infinity,,,,,, -298,0,(((Log(x1) ^ (x1 * 0.3632481324664608)) * (x1 ^ (x0 ^ -0.29259301114311675))) / (-0.41076106787502 + (0.3529198258780193 + Exp(x1)))),"((np.log(x[:, 1]) ** (x[:, 1] * t[0]) * x[:, 1] ** x[:, 0] ** t[1]) / (t[2] + (t[3] + np.exp(x[:, 1]))))","$$\frac{\left({\operatorname{log}(x_{1})^{\left(x_{1} \cdot \theta_{0}\right)}} \cdot {x_{1}^{{x_{0}^{\theta_{1}}}}}\right)}{\left(\theta_{2} + \left(\theta_{3} + \operatorname{exp}(x_{1})\right)\right)}$$",0.3632481324664608;-0.29259301114311675;-0.41076106787502;0.3529198258780193,19,,,,Infinity,,,,,, -299,0,Exp(Exp(Exp((-0.5554631409522515 ^ (0.7260670355427038 / Log(Exp(x0))))))),"np.exp(np.exp(np.exp(t[0] ** (t[1] / np.log(np.exp(x[:, 0]))))))","$$\operatorname{exp}(\operatorname{exp}(\operatorname{exp}({\theta_{0}^{\frac{\theta_{1}}{\operatorname{log}(\operatorname{exp}(x_{0}))}}})))$$",-0.5554631409522515;0.7260670355427038,10,,,,Infinity,,,,,, -300,0,((0.2733689283872246 ^ ((x0 * 0.7861388729131737) / x1)) + Exp((Exp((x1 * 1.8217665803652707)) * -3.9908274512299116))),"(t[0] ** ((x[:, 0] * t[1]) / x[:, 1]) + np.exp((np.exp((x[:, 1] * t[2])) * t[3])))","$$\left({\theta_{0}^{\frac{\left(x_{0} \cdot \theta_{1}\right)}{x_{1}}}} + \operatorname{exp}(\left(\operatorname{exp}(\left(x_{1} \cdot \theta_{2}\right)) \cdot \theta_{3}\right))\right)$$",0.2733689283872246;0.7861388729131737;1.8217665803652707;-3.9908274512299116,15,2.1498032315922662e-3,2.1498032315922662e-3,2.1498032315922662e-3,2.1498032315922662e-3,0.8016136623106273,0.8016136623106273,0.8016136623106273,39.27725768957654,39.27725768957654,39.27725768957654 -301,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 -302,0,(((0.7257974155441107 / (x1 ^ -0.48110287980872546)) ^ -0.5691052505927285) ^ Exp(Exp((Exp(Exp((-0.542168508587435 / x1))) + ((x1 / (x1 + -0.8288927394010828)) / (x1 ^ Log(x1))))))),"(t[0] / x[:, 1] ** t[1]) ** t[2] ** np.exp(np.exp((np.exp(np.exp((t[3] / x[:, 1]))) + ((x[:, 1] / (x[:, 1] + t[4])) / x[:, 1] ** np.log(x[:, 1])))))","$${{\frac{\theta_{0}}{{x_{1}^{\theta_{1}}}}^{\theta_{2}}}^{\operatorname{exp}(\operatorname{exp}(\left(\operatorname{exp}(\operatorname{exp}(\frac{\theta_{3}}{x_{1}})) + \frac{\frac{x_{1}}{\left(x_{1} + \theta_{4}\right)}}{{x_{1}^{\operatorname{log}(x_{1})}}}\right)))}}$$",0.7257974155441107;-0.48110287980872546;-0.5691052505927285;-0.542168508587435;-0.8288927394010828,26,3.6760407661080484e9,3.6760407661080484e9,3.6760407661080484e9,3.6760407661080484e9,-3.392293090203975e11,-3.392293090203975e11,-3.392293090203975e11,3.3084367501955323e9,3.3084367501955323e9,3.3084367501955323e9 -303,0,Exp(Exp(Log((((x0 / x1) ^ (x1 ^ (x1 + 99.3998502973256))) * ((15.881230637802554 + x0) ^ -50.39057523318157))))),"np.exp(np.exp(np.log(((x[:, 0] / x[:, 1]) ** x[:, 1] ** (x[:, 1] + t[0]) * (t[1] + x[:, 0]) ** t[2]))))","$$\operatorname{exp}(\operatorname{exp}(\operatorname{log}(\left({\frac{x_{0}}{x_{1}}^{{x_{1}^{\left(x_{1} + \theta_{0}\right)}}}} \cdot {\left(\theta_{1} + x_{0}\right)^{\theta_{2}}}\right))))$$",99.3998502973256;15.881230637802554;-50.39057523318157,18,0.8923097947317449,0.8923097947317449,0.8923097947317449,0.8923097947317449,-81.34338364542985,-81.34338364542985,-81.34338364542985,46.415896013413445,46.415896013413445,46.415896013413445 -304,0,(((-0.7568027957555301 ^ (-0.1569410947764589 + Exp(x0))) + (Log((0.44785331704525644 * x0)) ^ (x0 + x0))) / ((0.13379703808810472 + (x1 * x1)) ^ ((0.8380367745809629 * x1) ^ Log(Exp(Exp(x0)))))),"((t[0] ** (t[1] + np.exp(x[:, 0])) + np.log((t[2] * x[:, 0])) ** (x[:, 0] + x[:, 0])) / (t[3] + (x[:, 1] * x[:, 1])) ** (t[4] * x[:, 1]) ** np.log(np.exp(np.exp(x[:, 0]))))","$$\frac{\left({\theta_{0}^{\left(\theta_{1} + \operatorname{exp}(x_{0})\right)}} + {\operatorname{log}(\left(\theta_{2} \cdot x_{0}\right))^{\left(x_{0} + x_{0}\right)}}\right)}{{\left(\theta_{3} + \left(x_{1} \cdot x_{1}\right)\right)^{{\left(\theta_{4} \cdot x_{1}\right)^{\operatorname{log}(\operatorname{exp}(\operatorname{exp}(x_{0})))}}}}}$$",-0.7568027957555301;-0.1569410947764589;0.44785331704525644;0.13379703808810472;0.8380367745809629,30,,,,Infinity,,,,,, -305,0,((x1 ^ 3.5541187230109377) + 2.5186336845977802e-2),"(x[:, 1] ** t[0] + t[1])","$$\left({x_{1}^{\theta_{0}}} + \theta_{1}\right)$$",3.5541187230109377;2.5186336845977802e-2,5,9.65915343156526e-3,9.65915343156526e-3,9.65915343156526e-3,9.65915343156526e-3,0.10864210905074767,0.10864210905074767,0.10864210905074767,10.982014914423143,10.982014914423143,10.982014914423143 -306,0,(((Log(x1) + ((Exp(Log(x1)) + x0) ^ (x0 * x1))) ^ (Log((x1 / (x1 * x1))) * (x1 * x0))) / (((Exp(Exp((x0 + -0.39421803833816227))) + (((0.2793293879428138 ^ x0) / (x0 ^ x1)) ^ x0)) + (-3.249578970120914e-2 / (x1 * x0))) * (x0 + -0.7606398494309268))),"((np.log(x[:, 1]) + (np.exp(np.log(x[:, 1])) + x[:, 0]) ** (x[:, 0] * x[:, 1])) ** (np.log((x[:, 1] / (x[:, 1] * x[:, 1]))) * (x[:, 1] * x[:, 0])) / (((np.exp(np.exp((x[:, 0] + t[0]))) + (t[1] ** x[:, 0] / x[:, 0] ** x[:, 1]) ** x[:, 0]) + (t[2] / (x[:, 1] * x[:, 0]))) * (x[:, 0] + t[3])))","$$\frac{{\left(\operatorname{log}(x_{1}) + {\left(\operatorname{exp}(\operatorname{log}(x_{1})) + x_{0}\right)^{\left(x_{0} \cdot x_{1}\right)}}\right)^{\left(\operatorname{log}(\frac{x_{1}}{\left(x_{1} \cdot x_{1}\right)}) \cdot \left(x_{1} \cdot x_{0}\right)\right)}}}{\left(\left(\left(\operatorname{exp}(\operatorname{exp}(\left(x_{0} + \theta_{0}\right))) + {\frac{{\theta_{1}^{x_{0}}}}{{x_{0}^{x_{1}}}}^{x_{0}}}\right) + \frac{\theta_{2}}{\left(x_{1} \cdot x_{0}\right)}\right) \cdot \left(x_{0} + \theta_{3}\right)\right)}$$",-0.39421803833816227;0.2793293879428138;-3.249578970120914e-2;-0.7606398494309268,49,,,,Infinity,,,,,, -307,0,((((-0.4138335364932779 / x0) / ((Log(x0) ^ 0.86306710019288) * x1)) * ((Log(x0) ^ x0) * ((x1 * x1) + (x1 * 3.200701299537578e-2)))) * (-0.6718128040689044 * x0)),"((((t[0] / x[:, 0]) / (np.log(x[:, 0]) ** t[1] * x[:, 1])) * (np.log(x[:, 0]) ** x[:, 0] * ((x[:, 1] * x[:, 1]) + (x[:, 1] * t[2])))) * (t[3] * x[:, 0]))","$$\left(\left(\frac{\frac{\theta_{0}}{x_{0}}}{\left({\operatorname{log}(x_{0})^{\theta_{1}}} \cdot x_{1}\right)} \cdot \left({\operatorname{log}(x_{0})^{x_{0}}} \cdot \left(\left(x_{1} \cdot x_{1}\right) + \left(x_{1} \cdot \theta_{2}\right)\right)\right)\right) \cdot \left(\theta_{3} \cdot x_{0}\right)\right)$$",-0.4138335364932779;0.86306710019288;3.200701299537578e-2;-0.6718128040689044,27,,,,Infinity,,,,,, -308,0,((((((x0 * -0.49568412856466404) * -0.11594212269665714) ^ x1) + 1.3706381625744508) ^ ((((x0 * -3.772038047498895) + (x1 * x1)) * Exp(x1)) + -1.893393770905515)) * 3.0746467653436755),"((((x[:, 0] * t[0]) * t[1]) ** x[:, 1] + t[2]) ** ((((x[:, 0] * t[3]) + (x[:, 1] * x[:, 1])) * np.exp(x[:, 1])) + t[4]) * t[5])","$$\left({\left({\left(\left(x_{0} \cdot \theta_{0}\right) \cdot \theta_{1}\right)^{x_{1}}} + \theta_{2}\right)^{\left(\left(\left(\left(x_{0} \cdot \theta_{3}\right) + \left(x_{1} \cdot x_{1}\right)\right) \cdot \operatorname{exp}(x_{1})\right) + \theta_{4}\right)}} \cdot \theta_{5}\right)$$",-0.49568412856466404;-0.11594212269665714;1.3706381625744508;-3.772038047498895;-1.893393770905515;3.0746467653436755,24,2.2643032607568814e-3,2.2643032607568814e-3,2.2643032607568814e-3,2.2643032607568814e-3,0.7910474667084045,0.7910474667084045,0.7910474667084045,58.82943106246804,58.82943106246804,58.82943106246804 -309,0,(Exp((((-0.8046551850450445 / (-0.2229010272225117 + (x0 ^ 0.4386926725555972))) / (-0.7079728504575143 / (-0.5055073619693624 ^ Log(x0)))) + Exp(((x1 * x0) + x0)))) ^ 0.18526013542495923),"np.exp((((t[0] / (t[1] + x[:, 0] ** t[2])) / (t[3] / t[4] ** np.log(x[:, 0]))) + np.exp(((x[:, 1] * x[:, 0]) + x[:, 0])))) ** t[5]","$${\operatorname{exp}(\left(\frac{\frac{\theta_{0}}{\left(\theta_{1} + {x_{0}^{\theta_{2}}}\right)}}{\frac{\theta_{3}}{{\theta_{4}^{\operatorname{log}(x_{0})}}}} + \operatorname{exp}(\left(\left(x_{1} \cdot x_{0}\right) + x_{0}\right))\right))^{\theta_{5}}}$$",-0.8046551850450445;-0.2229010272225117;0.4386926725555972;-0.7079728504575143;-0.5055073619693624;0.18526013542495923,24,,,,Infinity,,,,,, -310,0,Log(Log(((0.8063947690506864 / x0) / ((x0 / Log(x1)) ^ (0.7681604775138573 * (x0 * x1)))))),"np.log(np.log(((t[0] / x[:, 0]) / (x[:, 0] / np.log(x[:, 1])) ** (t[1] * (x[:, 0] * x[:, 1])))))","$$\operatorname{log}(\operatorname{log}(\frac{\frac{\theta_{0}}{x_{0}}}{{\frac{x_{0}}{\operatorname{log}(x_{1})}^{\left(\theta_{1} \cdot \left(x_{0} \cdot x_{1}\right)\right)}}}))$$",0.8063947690506864;0.7681604775138573,16,,,,Infinity,,,,,, -311,0,((-0.7733758849135512 + x0) / x0),"((t[0] + x[:, 0]) / x[:, 0])","$$\frac{\left(\theta_{0} + x_{0}\right)}{x_{0}}$$",-0.7733758849135512,5,0.1510692544007486,0.1510692544007486,0.1510692544007486,0.1510692544007486,-12.940846156339218,-12.940846156339218,-12.940846156339218,9.088359069927767,9.088359069927767,9.088359069927767 -312,0,((((1.6178341467338118 + (x1 * x1)) ^ (x0 / x1)) ^ ((x0 * (x0 ^ -5.446664094265499e-2)) * (x1 * -0.815399490533227))) * (((0.6665057572475596 ^ (x0 * -0.47676027368134194)) * 0.16648357858675455) + Log((Exp((-0.26206863174148887 * x0)) + (0.32235892858448256 / Exp(x0)))))),"((t[0] + (x[:, 1] * x[:, 1])) ** (x[:, 0] / x[:, 1]) ** ((x[:, 0] * x[:, 0] ** t[1]) * (x[:, 1] * t[2])) * ((t[3] ** (x[:, 0] * t[4]) * t[5]) + np.log((np.exp((t[6] * x[:, 0])) + (t[7] / np.exp(x[:, 0]))))))","$$\left({{\left(\theta_{0} + \left(x_{1} \cdot x_{1}\right)\right)^{\frac{x_{0}}{x_{1}}}}^{\left(\left(x_{0} \cdot {x_{0}^{\theta_{1}}}\right) \cdot \left(x_{1} \cdot \theta_{2}\right)\right)}} \cdot \left(\left({\theta_{3}^{\left(x_{0} \cdot \theta_{4}\right)}} \cdot \theta_{5}\right) + \operatorname{log}(\left(\operatorname{exp}(\left(\theta_{6} \cdot x_{0}\right)) + \frac{\theta_{7}}{\operatorname{exp}(x_{0})}\right))\right)\right)$$",1.6178341467338118;-5.446664094265499e-2;-0.815399490533227;0.6665057572475596;-0.47676027368134194;0.16648357858675455;-0.26206863174148887;0.32235892858448256,38,5.407713659890467e-3,5.407713659890467e-3,5.407713659890467e-3,5.407713659890467e-3,0.5009699062253826,0.5009699062253826,0.5009699062253826,99.66680036401138,99.66680036401138,99.66680036401138 -313,0,(((((0.9216400067239852 / Exp(Log(x0))) / ((Log(x1) + (x0 * 8.248119050787961e-2)) / Exp((x0 * x1)))) + (((x0 ^ Log(x1)) * -6.266433384710879e-2) * -0.5386836212824988)) / (Exp(x0) * 5.440451486329745e-2)) / (((0.40302683523526683 * (-0.7960905703177794 + (0.990875957606998 + x1))) + -0.47869934215982646) ^ x0)),"(((((t[0] / np.exp(np.log(x[:, 0]))) / ((np.log(x[:, 1]) + (x[:, 0] * t[1])) / np.exp((x[:, 0] * x[:, 1])))) + ((x[:, 0] ** np.log(x[:, 1]) * t[2]) * t[3])) / (np.exp(x[:, 0]) * t[4])) / ((t[5] * (t[6] + (t[7] + x[:, 1]))) + t[8]) ** x[:, 0])","$$\frac{\frac{\left(\frac{\frac{\theta_{0}}{\operatorname{exp}(\operatorname{log}(x_{0}))}}{\frac{\left(\operatorname{log}(x_{1}) + \left(x_{0} \cdot \theta_{1}\right)\right)}{\operatorname{exp}(\left(x_{0} \cdot x_{1}\right))}} + \left(\left({x_{0}^{\operatorname{log}(x_{1})}} \cdot \theta_{2}\right) \cdot \theta_{3}\right)\right)}{\left(\operatorname{exp}(x_{0}) \cdot \theta_{4}\right)}}{{\left(\left(\theta_{5} \cdot \left(\theta_{6} + \left(\theta_{7} + x_{1}\right)\right)\right) + \theta_{8}\right)^{x_{0}}}}$$",0.9216400067239852;8.248119050787961e-2;-6.266433384710879e-2;-0.5386836212824988;5.440451486329745e-2;0.40302683523526683;-0.7960905703177794;0.990875957606998;-0.47869934215982646,43,,,,Infinity,,,,,, -314,0,((Log((x0 ^ (0.36126006949112577 / Exp(x0)))) / ((-0.15577571658796507 / x1) * (-0.8338220424267005 / x0))) * (((0.33961365627460716 * (Log((x1 / x0)) ^ 0.45750054124706085)) ^ (-0.6903540899894229 / x1)) / (Log(x1) ^ (0.954669501132379 ^ (6.554887700721646e-2 ^ (x0 + x0)))))),"((np.log(x[:, 0] ** (t[0] / np.exp(x[:, 0]))) / ((t[1] / x[:, 1]) * (t[2] / x[:, 0]))) * ((t[3] * np.log((x[:, 1] / x[:, 0])) ** t[4]) ** (t[5] / x[:, 1]) / np.log(x[:, 1]) ** t[6] ** t[7] ** (x[:, 0] + x[:, 0])))","$$\left(\frac{\operatorname{log}({x_{0}^{\frac{\theta_{0}}{\operatorname{exp}(x_{0})}}})}{\left(\frac{\theta_{1}}{x_{1}} \cdot \frac{\theta_{2}}{x_{0}}\right)} \cdot \frac{{\left(\theta_{3} \cdot {\operatorname{log}(\frac{x_{1}}{x_{0}})^{\theta_{4}}}\right)^{\frac{\theta_{5}}{x_{1}}}}}{{\operatorname{log}(x_{1})^{{\theta_{6}^{{\theta_{7}^{\left(x_{0} + x_{0}\right)}}}}}}}\right)$$",0.36126006949112577;-0.15577571658796507;-0.8338220424267005;0.33961365627460716;0.45750054124706085;-0.6903540899894229;0.954669501132379;6.554887700721646e-2,39,,,,Infinity,,,,,, -315,0,(0.18546847437405395 / Exp((x0 ^ 4.6846759008919525))),"(t[0] / np.exp(x[:, 0] ** t[1]))","$$\frac{\theta_{0}}{\operatorname{exp}({x_{0}^{\theta_{1}}})}$$",0.18546847437405395;4.6846759008919525,6,5.610387568175155e-3,5.610387568175155e-3,5.610387568175155e-3,5.610387568175155e-3,0.4822669227062766,0.4822669227062766,0.4822669227062766,13.70352669415124,13.70352669415124,13.70352669415124 -316,0,Exp(((Exp(((0.7175082166445927 ^ x1) / Exp(x1))) ^ (-0.2059209406844409 + (x1 + x0))) + (Log(((-0.4104861384334686 + x0) / (x0 + x1))) / ((x1 + -0.5618550710417538) * (x0 + x1))))),"np.exp((np.exp((t[0] ** x[:, 1] / np.exp(x[:, 1]))) ** (t[1] + (x[:, 1] + x[:, 0])) + (np.log(((t[2] + x[:, 0]) / (x[:, 0] + x[:, 1]))) / ((x[:, 1] + t[3]) * (x[:, 0] + x[:, 1])))))","$$\operatorname{exp}(\left({\operatorname{exp}(\frac{{\theta_{0}^{x_{1}}}}{\operatorname{exp}(x_{1})})^{\left(\theta_{1} + \left(x_{1} + x_{0}\right)\right)}} + \frac{\operatorname{log}(\frac{\left(\theta_{2} + x_{0}\right)}{\left(x_{0} + x_{1}\right)})}{\left(\left(x_{1} + \theta_{3}\right) \cdot \left(x_{0} + x_{1}\right)\right)}\right))$$",0.7175082166445927;-0.2059209406844409;-0.4104861384334686;-0.5618550710417538,31,2.154200291034211e112,2.154200291034211e112,2.154200291034211e112,2.154200291034211e112,-1.9879210343875432e114,-1.9879210343875432e114,-1.9879210343875432e114,1.93878026193079e112,1.93878026193079e112,1.93878026193079e112 -317,0,(((3.7104728410753527 / x1) * -4.711526218232506e-3) + (-28.636464516932833 / (x0 * (-291.132720963618 * x0)))),"(((t[0] / x[:, 1]) * t[1]) + (t[2] / (x[:, 0] * (t[3] * x[:, 0]))))","$$\left(\left(\frac{\theta_{0}}{x_{1}} \cdot \theta_{1}\right) + \frac{\theta_{2}}{\left(x_{0} \cdot \left(\theta_{3} \cdot x_{0}\right)\right)}\right)$$",3.7104728410753527;-4.711526218232506e-3;-28.636464516932833;-291.132720963618,13,3.167209284349352e-3,3.167209284349352e-3,3.167209284349352e-3,3.167209284349352e-3,0.7077262507636712,0.7077262507636712,0.7077262507636712,31.37942332979119,31.37942332979119,31.37942332979119 -318,0,Exp(((-0.4863826711784059 * (-2.0650682715969486 * (1.0036795085871637 / (-0.41619541356490874 + (0.2070632699343406 + x0))))) + Log((4.526977188420988e-2 / ((x1 / x1) / x1))))),"np.exp(((t[0] * (t[1] * (t[2] / (t[3] + (t[4] + x[:, 0]))))) + np.log((t[5] / ((x[:, 1] / x[:, 1]) / x[:, 1])))))","$$\operatorname{exp}(\left(\left(\theta_{0} \cdot \left(\theta_{1} \cdot \frac{\theta_{2}}{\left(\theta_{3} + \left(\theta_{4} + x_{0}\right)\right)}\right)\right) + \operatorname{log}(\frac{\theta_{5}}{\frac{\frac{x_{1}}{x_{1}}}{x_{1}}})\right))$$",-0.4863826711784059;-2.0650682715969486;1.0036795085871637;-0.41619541356490874;0.2070632699343406;4.526977188420988e-2,21,2.0135825436814374e-3,2.0135825436814374e-3,2.0135825436814374e-3,2.0135825436814374e-3,0.8141842655151543,0.8141842655151543,0.8141842655151543,55.79563421177171,55.79563421177171,55.79563421177171 -319,0,(((Exp((x0 ^ -0.8358374064673463)) ^ -0.15572192312647315) * ((-0.9046755767464477 / x0) ^ (x0 + (0.3303567128876209 + Log(x0))))) * (((0.6230337619464125 + Exp(Log(x1))) / ((((x1 / x0) ^ (x1 * x0)) + x0) ^ x1)) * (x1 * -1.3602786086213792e-2))),"((np.exp(x[:, 0] ** t[0]) ** t[1] * (t[2] / x[:, 0]) ** (x[:, 0] + (t[3] + np.log(x[:, 0])))) * (((t[4] + np.exp(np.log(x[:, 1]))) / ((x[:, 1] / x[:, 0]) ** (x[:, 1] * x[:, 0]) + x[:, 0]) ** x[:, 1]) * (x[:, 1] * t[5])))","$$\left(\left({\operatorname{exp}({x_{0}^{\theta_{0}}})^{\theta_{1}}} \cdot {\frac{\theta_{2}}{x_{0}}^{\left(x_{0} + \left(\theta_{3} + \operatorname{log}(x_{0})\right)\right)}}\right) \cdot \left(\frac{\left(\theta_{4} + \operatorname{exp}(\operatorname{log}(x_{1}))\right)}{{\left({\frac{x_{1}}{x_{0}}^{\left(x_{1} \cdot x_{0}\right)}} + x_{0}\right)^{x_{1}}}} \cdot \left(x_{1} \cdot \theta_{5}\right)\right)\right)$$",-0.8358374064673463;-0.15572192312647315;-0.9046755767464477;0.3303567128876209;0.6230337619464125;-1.3602786086213792e-2,39,,,,Infinity,,,,,, -320,0,Log((x0 + x1)),"np.log((x[:, 0] + x[:, 1]))","$$\operatorname{log}(\left(x_{0} + x_{1}\right))$$",,4,0.16366703075849978,0.16366703075849978,0.16366703075849978,0.16366703075849978,-14.103383582051876,-14.103383582051876,-14.103383582051876,5.692477772162212,5.692477772162212,5.692477772162212 -321,0,((Log(Log((x1 * -0.8986346809277586))) ^ Log(((0.14392030866110317 + (0.7763689857383689 + x1)) * 0.5807421254586069))) / ((0.21105751833364972 * x0) + x1)),"(np.log(np.log((x[:, 1] * t[0]))) ** np.log(((t[1] + (t[2] + x[:, 1])) * t[3])) / ((t[4] * x[:, 0]) + x[:, 1]))","$$\frac{{\operatorname{log}(\operatorname{log}(\left(x_{1} \cdot \theta_{0}\right)))^{\operatorname{log}(\left(\left(\theta_{1} + \left(\theta_{2} + x_{1}\right)\right) \cdot \theta_{3}\right))}}}{\left(\left(\theta_{4} \cdot x_{0}\right) + x_{1}\right)}$$",-0.8986346809277586;0.14392030866110317;0.7763689857383689;0.5807421254586069;0.21105751833364972,20,,,,Infinity,,,,,, -322,0,Exp(Log(((-4.605085333025405e-2 ^ x0) * (x0 * 0.7428473257707953)))),"np.exp(np.log((t[0] ** x[:, 0] * (x[:, 0] * t[1]))))","$$\operatorname{exp}(\operatorname{log}(\left({\theta_{0}^{x_{0}}} \cdot \left(x_{0} \cdot \theta_{1}\right)\right)))$$",-4.605085333025405e-2;0.7428473257707953,9,,,,Infinity,,,,,, -323,0,((Log(Log((x0 ^ -0.16044929590345547))) / (((-0.8645801163319422 ^ Exp((0.9526794990855273 * x0))) + 0.9893965153006038) + Log(x1))) * (((-0.5045191450985504 * x1) * 0.9311265581285062) / (Exp(((-0.19389134844490963 / (x0 / x1)) * -0.17103479357697393)) ^ (Log(x0) ^ x1)))),"((np.log(np.log(x[:, 0] ** t[0])) / ((t[1] ** np.exp((t[2] * x[:, 0])) + t[3]) + np.log(x[:, 1]))) * (((t[4] * x[:, 1]) * t[5]) / np.exp(((t[6] / (x[:, 0] / x[:, 1])) * t[7])) ** np.log(x[:, 0]) ** x[:, 1]))","$$\left(\frac{\operatorname{log}(\operatorname{log}({x_{0}^{\theta_{0}}}))}{\left(\left({\theta_{1}^{\operatorname{exp}(\left(\theta_{2} \cdot x_{0}\right))}} + \theta_{3}\right) + \operatorname{log}(x_{1})\right)} \cdot \frac{\left(\left(\theta_{4} \cdot x_{1}\right) \cdot \theta_{5}\right)}{{\operatorname{exp}(\left(\frac{\theta_{6}}{\frac{x_{0}}{x_{1}}} \cdot \theta_{7}\right))^{{\operatorname{log}(x_{0})^{x_{1}}}}}}\right)$$",-0.16044929590345547;-0.8645801163319422;0.9526794990855273;0.9893965153006038;-0.5045191450985504;0.9311265581285062;-0.19389134844490963;-0.17103479357697393,37,,,,Infinity,,,,,, -324,0,(((x0 + 0.6848318221555644) + x0) ^ -2.7369708926494223),"((x[:, 0] + t[0]) + x[:, 0]) ** t[1]","$${\left(\left(x_{0} + \theta_{0}\right) + x_{0}\right)^{\theta_{1}}}$$",0.6848318221555644;-2.7369708926494223,7,5.741708109437702e-3,5.741708109437702e-3,5.741708109437702e-3,5.741708109437702e-3,0.4701485107225123,0.4701485107225123,0.4701485107225123,13.751077935873008,13.751077935873008,13.751077935873008 -325,0,(Exp((-3921899.7676985757 / x1)) + 6.1131880009185124e-2),"(np.exp((t[0] / x[:, 1])) + t[1])","$$\left(\operatorname{exp}(\frac{\theta_{0}}{x_{1}}) + \theta_{1}\right)$$",-3921899.7676985757;6.1131880009185124e-2,6,1.0836447996526664e-2,1.0836447996526664e-2,1.0836447996526664e-2,1.0836447996526664e-2,1.1102230246251565e-16,1.1102230246251565e-16,1.1102230246251565e-16,13.708230148536757,13.708230148536757,13.708230148536757 -326,0,Log((((0.6148342333186406 / (x1 ^ x1)) / (x0 + 0.5950632726121148)) ^ Exp(Log(Exp((x0 / x1)))))),"np.log(((t[0] / x[:, 1] ** x[:, 1]) / (x[:, 0] + t[1])) ** np.exp(np.log(np.exp((x[:, 0] / x[:, 1])))))","$$\operatorname{log}({\frac{\frac{\theta_{0}}{{x_{1}^{x_{1}}}}}{\left(x_{0} + \theta_{1}\right)}^{\operatorname{exp}(\operatorname{log}(\operatorname{exp}(\frac{x_{0}}{x_{1}})))}})$$",0.6148342333186406;0.5950632726121148,17,Infinity,Infinity,Infinity,Infinity,-Infinity,-Infinity,-Infinity,Infinity,Infinity,Infinity -327,0,(Exp((0.9407154156103725 * (((x1 * x0) ^ Log(x1)) ^ ((x1 * x1) * Exp(x1))))) ^ (Log(((-0.6929612755333581 * Exp(x0)) * Log(Exp((x0 + x0))))) / Log((-0.5105296881523056 + (0.7672688956684076 ^ (x1 + x0)))))),"np.exp((t[0] * (x[:, 1] * x[:, 0]) ** np.log(x[:, 1]) ** ((x[:, 1] * x[:, 1]) * np.exp(x[:, 1])))) ** (np.log(((t[1] * np.exp(x[:, 0])) * np.log(np.exp((x[:, 0] + x[:, 0]))))) / np.log((t[2] + t[3] ** (x[:, 1] + x[:, 0]))))","$${\operatorname{exp}(\left(\theta_{0} \cdot {{\left(x_{1} \cdot x_{0}\right)^{\operatorname{log}(x_{1})}}^{\left(\left(x_{1} \cdot x_{1}\right) \cdot \operatorname{exp}(x_{1})\right)}}\right))^{\frac{\operatorname{log}(\left(\left(\theta_{1} \cdot \operatorname{exp}(x_{0})\right) \cdot \operatorname{log}(\operatorname{exp}(\left(x_{0} + x_{0}\right)))\right))}{\operatorname{log}(\left(\theta_{2} + {\theta_{3}^{\left(x_{1} + x_{0}\right)}}\right))}}}$$",0.9407154156103725;-0.6929612755333581;-0.5105296881523056;0.7672688956684076,37,,,,Infinity,,,,,, -328,0,(Exp((Log(x1) * 186.22566547018312)) + Exp(((x1 * x1) * -524.0289239523607))),"(np.exp((np.log(x[:, 1]) * t[0])) + np.exp(((x[:, 1] * x[:, 1]) * t[1])))","$$\left(\operatorname{exp}(\left(\operatorname{log}(x_{1}) \cdot \theta_{0}\right)) + \operatorname{exp}(\left(\left(x_{1} \cdot x_{1}\right) \cdot \theta_{1}\right))\right)$$",186.22566547018312;-524.0289239523607,12,1.4573989138967687e-2,1.4573989138967687e-2,1.4573989138967687e-2,1.4573989138967687e-2,-0.3449046351386533,-0.3449046351386533,-0.3449046351386533,25.556080091697016,25.556080091697016,25.556080091697016 -329,0,((Log(((x0 + -0.12263488127669941) * -0.8914808469410662)) / (((x0 + 0.19737120483639292) * 0.3121393914051166) ^ -0.8390995587591845)) * Exp(((x0 ^ (Exp((0.11402253881835722 / x1)) ^ x1)) + -1.1870239623831158e-2))),"((np.log(((x[:, 0] + t[0]) * t[1])) / ((x[:, 0] + t[2]) * t[3]) ** t[4]) * np.exp((x[:, 0] ** np.exp((t[5] / x[:, 1])) ** x[:, 1] + t[6])))","$$\left(\frac{\operatorname{log}(\left(\left(x_{0} + \theta_{0}\right) \cdot \theta_{1}\right))}{{\left(\left(x_{0} + \theta_{2}\right) \cdot \theta_{3}\right)^{\theta_{4}}}} \cdot \operatorname{exp}(\left({x_{0}^{{\operatorname{exp}(\frac{\theta_{5}}{x_{1}})^{x_{1}}}}} + \theta_{6}\right))\right)$$",-0.12263488127669941;-0.8914808469410662;0.19737120483639292;0.3121393914051166;-0.8390995587591845;0.11402253881835722;-1.1870239623831158e-2,26,,,,Infinity,,,,,, -330,0,Exp((Exp(((x0 * -5.560164466078084e9) * 9.254320267341006e8)) / ((x1 / x0) * 5.60197231544998e9))),"np.exp((np.exp(((x[:, 0] * t[0]) * t[1])) / ((x[:, 1] / x[:, 0]) * t[2])))","$$\operatorname{exp}(\frac{\operatorname{exp}(\left(\left(x_{0} \cdot \theta_{0}\right) \cdot \theta_{1}\right))}{\left(\frac{x_{1}}{x_{0}} \cdot \theta_{2}\right)})$$",-5.560164466078084e9;9.254320267341006e8;5.60197231544998e9,13,0.8923097947317449,0.8923097947317449,0.8923097947317449,0.8923097947317449,-81.34338364542985,-81.34338364542985,-81.34338364542985,30.158726721326207,30.158726721326207,30.158726721326207 -331,0,Exp((((x1 ^ (Exp((-1.1304533875970022 + x1)) * Exp(Log(x0)))) + -2.977325893540204) * (((-1.1796137678906902 * x1) + (Exp(x0) * x0)) * (x0 ^ Exp((x0 * -6.458614384887789)))))),"np.exp(((x[:, 1] ** (np.exp((t[0] + x[:, 1])) * np.exp(np.log(x[:, 0]))) + t[1]) * (((t[2] * x[:, 1]) + (np.exp(x[:, 0]) * x[:, 0])) * x[:, 0] ** np.exp((x[:, 0] * t[3])))))","$$\operatorname{exp}(\left(\left({x_{1}^{\left(\operatorname{exp}(\left(\theta_{0} + x_{1}\right)) \cdot \operatorname{exp}(\operatorname{log}(x_{0}))\right)}} + \theta_{1}\right) \cdot \left(\left(\left(\theta_{2} \cdot x_{1}\right) + \left(\operatorname{exp}(x_{0}) \cdot x_{0}\right)\right) \cdot {x_{0}^{\operatorname{exp}(\left(x_{0} \cdot \theta_{3}\right))}}\right)\right))$$",-1.1304533875970022;-2.977325893540204;-1.1796137678906902;-6.458614384887789,29,2.5804061379426663e-3,2.5804061379426663e-3,2.5804061379426663e-3,2.5804061379426663e-3,0.7618771262714732,0.7618771262714732,0.7618771262714732,68.38982681570994,68.38982681570994,68.38982681570994 -332,0,((Exp((-0.7306947765768412 / (Exp(x1) / x0))) * (0.482205612594108 ^ (((x1 * 0.5830306257060127) * -0.9083783005469015) + 0.8782350611877358))) ^ Exp(Exp((Exp(x0) / (0.15688198029523526 ^ x1))))),"(np.exp((t[0] / (np.exp(x[:, 1]) / x[:, 0]))) * t[1] ** (((x[:, 1] * t[2]) * t[3]) + t[4])) ** np.exp(np.exp((np.exp(x[:, 0]) / t[5] ** x[:, 1])))","$${\left(\operatorname{exp}(\frac{\theta_{0}}{\frac{\operatorname{exp}(x_{1})}{x_{0}}}) \cdot {\theta_{1}^{\left(\left(\left(x_{1} \cdot \theta_{2}\right) \cdot \theta_{3}\right) + \theta_{4}\right)}}\right)^{\operatorname{exp}(\operatorname{exp}(\frac{\operatorname{exp}(x_{0})}{{\theta_{5}^{x_{1}}}}))}}$$",-0.7306947765768412;0.482205612594108;0.5830306257060127;-0.9083783005469015;0.8782350611877358;0.15688198029523526,26,1.4573554749975553e-2,1.4573554749975553e-2,1.4573554749975553e-2,1.4573554749975553e-2,-0.3448645492182232,-0.3448645492182232,-0.3448645492182232,66.20414589515656,66.20414589515656,66.20414589515656 -333,0,(Log(Exp(((0.737569538117369 * (x1 * (x1 / x0))) + ((x0 ^ x0) / (x1 * -14050.93889692497))))) + (((-1.2740809661470673 / (x0 * -157.63690298503312)) * Exp((x1 * x1))) + ((-0.34388115643448236 * x1) * (x1 + Log(x0))))),"(np.log(np.exp(((t[0] * (x[:, 1] * (x[:, 1] / x[:, 0]))) + (x[:, 0] ** x[:, 0] / (x[:, 1] * t[1]))))) + (((t[2] / (x[:, 0] * t[3])) * np.exp((x[:, 1] * x[:, 1]))) + ((t[4] * x[:, 1]) * (x[:, 1] + np.log(x[:, 0])))))","$$\left(\operatorname{log}(\operatorname{exp}(\left(\left(\theta_{0} \cdot \left(x_{1} \cdot \frac{x_{1}}{x_{0}}\right)\right) + \frac{{x_{0}^{x_{0}}}}{\left(x_{1} \cdot \theta_{1}\right)}\right))) + \left(\left(\frac{\theta_{2}}{\left(x_{0} \cdot \theta_{3}\right)} \cdot \operatorname{exp}(\left(x_{1} \cdot x_{1}\right))\right) + \left(\left(\theta_{4} \cdot x_{1}\right) \cdot \left(x_{1} + \operatorname{log}(x_{0})\right)\right)\right)\right)$$",0.737569538117369;-14050.93889692497;-1.2740809661470673;-157.63690298503312;-0.34388115643448236,37,1.9446094606750367e-3,1.9446094606750367e-3,1.9446094606750367e-3,1.9446094606750367e-3,0.8205491816785049,0.8205491816785049,0.8205491816785049,91.40368418679294,91.40368418679294,91.40368418679294 -334,0,Exp(Exp(((Exp(Exp((27.304667726260178 / x1))) ^ ((-20.6618876536588 / x1) + (-11.699586008941516 * (x0 * -1.1928378759217408)))) + (-2761.9361978879724 / ((Exp(x1) ^ x1) ^ Log(x1)))))),"np.exp(np.exp((np.exp(np.exp((t[0] / x[:, 1]))) ** ((t[1] / x[:, 1]) + (t[2] * (x[:, 0] * t[3]))) + (t[4] / np.exp(x[:, 1]) ** x[:, 1] ** np.log(x[:, 1])))))","$$\operatorname{exp}(\operatorname{exp}(\left({\operatorname{exp}(\operatorname{exp}(\frac{\theta_{0}}{x_{1}}))^{\left(\frac{\theta_{1}}{x_{1}} + \left(\theta_{2} \cdot \left(x_{0} \cdot \theta_{3}\right)\right)\right)}} + \frac{\theta_{4}}{{{\operatorname{exp}(x_{1})^{x_{1}}}^{\operatorname{log}(x_{1})}}}\right)))$$",27.304667726260178;-20.6618876536588;-11.699586008941516;-1.1928378759217408;-2761.9361978879724,27,0.8923097947317449,0.8923097947317449,0.8923097947317449,0.8923097947317449,-81.34338364542985,-81.34338364542985,-81.34338364542985,70.23276708017471,70.23276708017471,70.23276708017471 -335,0,Exp(Exp((Exp((-0.7321471162970745 + x0)) * Log((x1 + -0.10223556295816931))))),"np.exp(np.exp((np.exp((t[0] + x[:, 0])) * np.log((x[:, 1] + t[1])))))","$$\operatorname{exp}(\operatorname{exp}(\left(\operatorname{exp}(\left(\theta_{0} + x_{0}\right)) \cdot \operatorname{log}(\left(x_{1} + \theta_{1}\right))\right)))$$",-0.7321471162970745;-0.10223556295816931,11,1.2434821411004873,1.2434821411004873,1.2434821411004873,1.2434821411004873,-113.7499754069832,-113.7499754069832,-113.7499754069832,26.565995437334166,26.565995437334166,26.565995437334166 -336,0,(Exp(Log((Exp((x1 ^ (0.6163951196794784 ^ x0))) / (1.6073900312352674e-2 ^ ((-3.957367314616622e-2 * x0) * x0))))) * (Exp((-0.753469062343961 * ((x0 + 0.5730651045518398) + 0.5857277406660717))) / (Exp(((x1 * 8.49449147802307e-2) / x0)) + ((0.5155237999220255 / x0) + -0.3352298054871585)))),"(np.exp(np.log((np.exp(x[:, 1] ** t[0] ** x[:, 0]) / t[1] ** ((t[2] * x[:, 0]) * x[:, 0])))) * (np.exp((t[3] * ((x[:, 0] + t[4]) + t[5]))) / (np.exp(((x[:, 1] * t[6]) / x[:, 0])) + ((t[7] / x[:, 0]) + t[8]))))","$$\left(\operatorname{exp}(\operatorname{log}(\frac{\operatorname{exp}({x_{1}^{{\theta_{0}^{x_{0}}}}})}{{\theta_{1}^{\left(\left(\theta_{2} \cdot x_{0}\right) \cdot x_{0}\right)}}})) \cdot \frac{\operatorname{exp}(\left(\theta_{3} \cdot \left(\left(x_{0} + \theta_{4}\right) + \theta_{5}\right)\right))}{\left(\operatorname{exp}(\frac{\left(x_{1} \cdot \theta_{6}\right)}{x_{0}}) + \left(\frac{\theta_{7}}{x_{0}} + \theta_{8}\right)\right)}\right)$$",0.6163951196794784;1.6073900312352674e-2;-3.957367314616622e-2;-0.753469062343961;0.5730651045518398;0.5857277406660717;8.49449147802307e-2;0.5155237999220255;-0.3352298054871585,38,2.9292415099721313e-2,2.9292415099721313e-2,2.9292415099721313e-2,2.9292415099721313e-2,-1.7031380678530654,-1.7031380678530654,-1.7031380678530654,101.70922153067485,101.70922153067485,101.70922153067485 -337,0,((-0.8679935017473754 + (-1.864280801771689e-2 / x1)) + Exp((1.4395774302417146e-5 ^ Exp(x1)))),"((t[0] + (t[1] / x[:, 1])) + np.exp(t[2] ** np.exp(x[:, 1])))","$$\left(\left(\theta_{0} + \frac{\theta_{1}}{x_{1}}\right) + \operatorname{exp}({\theta_{2}^{\operatorname{exp}(x_{1})}})\right)$$",-0.8679935017473754;-1.864280801771689e-2;1.4395774302417146e-5,11,9.473979632469195e-3,9.473979632469195e-3,9.473979632469195e-3,9.473979632469195e-3,0.12573016217991106,0.12573016217991106,0.12573016217991106,25.78065554928075,25.78065554928075,25.78065554928075 -338,0,(((Exp(x1) ^ (Log(((x0 * 1.0525803051139622) + (x0 + x0))) / x0)) + ((8.65027964018551 / x0) / x0)) * 6.249895748033409e-3),"((np.exp(x[:, 1]) ** (np.log(((x[:, 0] * t[0]) + (x[:, 0] + x[:, 0]))) / x[:, 0]) + ((t[1] / x[:, 0]) / x[:, 0])) * t[2])","$$\left(\left({\operatorname{exp}(x_{1})^{\frac{\operatorname{log}(\left(\left(x_{0} \cdot \theta_{0}\right) + \left(x_{0} + x_{0}\right)\right))}{x_{0}}}} + \frac{\frac{\theta_{1}}{x_{0}}}{x_{0}}\right) \cdot \theta_{2}\right)$$",1.0525803051139622;8.65027964018551;6.249895748033409e-3,21,5.878619826302115e-3,5.878619826302115e-3,5.878619826302115e-3,5.878619826302115e-3,0.45751413856400647,0.45751413856400647,0.45751413856400647,52.209781688007205,52.209781688007205,52.209781688007205 -339,0,Exp((((x0 * (x0 * 0.9622246788098348)) ^ (x1 * (0.4712707238922962 ^ x1))) * ((-0.5968164704983052 ^ (0.5401554061716111 / x0)) + -0.5013180757552873))),"np.exp(((x[:, 0] * (x[:, 0] * t[0])) ** (x[:, 1] * t[1] ** x[:, 1]) * (t[2] ** (t[3] / x[:, 0]) + t[4])))","$$\operatorname{exp}(\left({\left(x_{0} \cdot \left(x_{0} \cdot \theta_{0}\right)\right)^{\left(x_{1} \cdot {\theta_{1}^{x_{1}}}\right)}} \cdot \left({\theta_{2}^{\frac{\theta_{3}}{x_{0}}}} + \theta_{4}\right)\right))$$",0.9622246788098348;0.4712707238922962;-0.5968164704983052;0.5401554061716111;-0.5013180757552873,20,,,,Infinity,,,,,, -340,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 -341,0,Log(Exp(((Log(x1) ^ (x0 * -0.49977498460571734)) / ((-0.9867317595560057 / x0) * -0.7628000589169718)))),"np.log(np.exp((np.log(x[:, 1]) ** (x[:, 0] * t[0]) / ((t[1] / x[:, 0]) * t[2]))))","$$\operatorname{log}(\operatorname{exp}(\frac{{\operatorname{log}(x_{1})^{\left(x_{0} \cdot \theta_{0}\right)}}}{\left(\frac{\theta_{1}}{x_{0}} \cdot \theta_{2}\right)}))$$",-0.49977498460571734;-0.9867317595560057;-0.7628000589169718,14,,,,Infinity,,,,,, -342,0,(Exp(Exp(Exp(Log(Exp(x0))))) ^ (-1.0e16 * ((x0 + x1) * x1))),"np.exp(np.exp(np.exp(np.log(np.exp(x[:, 0]))))) ** (t[0] * ((x[:, 0] + x[:, 1]) * x[:, 1]))","$${\operatorname{exp}(\operatorname{exp}(\operatorname{exp}(\operatorname{log}(\operatorname{exp}(x_{0})))))^{\left(\theta_{0} \cdot \left(\left(x_{0} + x_{1}\right) \cdot x_{1}\right)\right)}}$$",-1.0e16,14,1.4573554749975553e-2,1.4573554749975553e-2,1.4573554749975553e-2,1.4573554749975553e-2,-0.3448645492182232,-0.3448645492182232,-0.3448645492182232,31.146222718160317,31.146222718160317,31.146222718160317 -343,0,(((x0 * Log(x0)) / (Log(x1) + (0.4840854042097663 ^ x0))) ^ Exp(Exp(((x1 * 0.7896951218316943) ^ Exp((x1 * -0.33666809212312765)))))),"((x[:, 0] * np.log(x[:, 0])) / (np.log(x[:, 1]) + t[0] ** x[:, 0])) ** np.exp(np.exp((x[:, 1] * t[1]) ** np.exp((x[:, 1] * t[2]))))","$${\frac{\left(x_{0} \cdot \operatorname{log}(x_{0})\right)}{\left(\operatorname{log}(x_{1}) + {\theta_{0}^{x_{0}}}\right)}^{\operatorname{exp}(\operatorname{exp}({\left(x_{1} \cdot \theta_{1}\right)^{\operatorname{exp}(\left(x_{1} \cdot \theta_{2}\right))}}))}}$$",0.4840854042097663;0.7896951218316943;-0.33666809212312765,22,,,,Infinity,,,,,, -344,0,Log((-0.8071497492551817 / ((0.8256807840283974 + x0) * (((x0 * 5.7611226161222495e-2) * x1) + (x1 + 0.14087152126945368))))),"np.log((t[0] / ((t[1] + x[:, 0]) * (((x[:, 0] * t[2]) * x[:, 1]) + (x[:, 1] + t[3])))))","$$\operatorname{log}(\frac{\theta_{0}}{\left(\left(\theta_{1} + x_{0}\right) \cdot \left(\left(\left(x_{0} \cdot \theta_{2}\right) \cdot x_{1}\right) + \left(x_{1} + \theta_{3}\right)\right)\right)})$$",-0.8071497492551817;0.8256807840283974;5.7611226161222495e-2;0.14087152126945368,16,,,,Infinity,,,,,, -345,0,(Exp(((x0 / x0) ^ (0.8775082269008321 / x0))) * ((-0.718143727596479 / (x0 * -45.414371197606926)) * (x0 ^ -1.8488949570085476))),"(np.exp((x[:, 0] / x[:, 0]) ** (t[0] / x[:, 0])) * ((t[1] / (x[:, 0] * t[2])) * x[:, 0] ** t[3]))","$$\left(\operatorname{exp}({\frac{x_{0}}{x_{0}}^{\frac{\theta_{0}}{x_{0}}}}) \cdot \left(\frac{\theta_{1}}{\left(x_{0} \cdot \theta_{2}\right)} \cdot {x_{0}^{\theta_{3}}}\right)\right)$$",0.8775082269008321;-0.718143727596479;-45.414371197606926;-1.8488949570085476,18,5.233221391450908e-3,5.233221391450908e-3,5.233221391450908e-3,5.233221391450908e-3,0.5170722553065104,0.5170722553065104,0.5170722553065104,40.34008008682786,40.34008008682786,40.34008008682786 -346,0,(Exp(((x0 ^ 0.6321051993379233) ^ -0.542308703553113)) + (Exp(x1) / Log((x1 ^ x0)))),"(np.exp(x[:, 0] ** t[0] ** t[1]) + (np.exp(x[:, 1]) / np.log(x[:, 1] ** x[:, 0])))","$$\left(\operatorname{exp}({{x_{0}^{\theta_{0}}}^{\theta_{1}}}) + \frac{\operatorname{exp}(x_{1})}{\operatorname{log}({x_{1}^{x_{0}}})}\right)$$",0.6321051993379233;-0.542308703553113,14,2.2711567777213975,2.2711567777213975,2.2711567777213975,2.2711567777213975,-208.58498379260033,-208.58498379260033,-208.58498379260033,35.198072554202234,35.198072554202234,35.198072554202234 -347,0,(x1 ^ x1),"x[:, 1] ** x[:, 1]","$${x_{1}^{x_{1}}}$$",,3,0.4403077718480359,0.4403077718480359,0.4403077718480359,0.4403077718480359,-39.63211229262252,-39.63211229262252,-39.63211229262252,2.4757185363430683,2.4757185363430683,2.4757185363430683 -348,0,(((0.44031417123477634 / x0) + (-0.5950581796100616 ^ Log(x0))) / (((9.638260243570063e-2 * x1) ^ -0.4086876488722153) * ((x1 ^ 0.8855164824721937) * -9.67594327768071e-2))),"(((t[0] / x[:, 0]) + t[1] ** np.log(x[:, 0])) / ((t[2] * x[:, 1]) ** t[3] * (x[:, 1] ** t[4] * t[5])))","$$\frac{\left(\frac{\theta_{0}}{x_{0}} + {\theta_{1}^{\operatorname{log}(x_{0})}}\right)}{\left({\left(\theta_{2} \cdot x_{1}\right)^{\theta_{3}}} \cdot \left({x_{1}^{\theta_{4}}} \cdot \theta_{5}\right)\right)}$$",0.44031417123477634;-0.5950581796100616;9.638260243570063e-2;-0.4086876488722153;0.8855164824721937;-9.67594327768071e-2,20,,,,Infinity,,,,,, -349,0,((Exp(((-0.47016494013535126 + x0) * 1.2223569846717917)) ^ (0.6972753174139303 + (x0 ^ x1))) ^ (Log((x1 * (-0.32894944437060236 + x0))) / x1)),"np.exp(((t[0] + x[:, 0]) * t[1])) ** (t[2] + x[:, 0] ** x[:, 1]) ** (np.log((x[:, 1] * (t[3] + x[:, 0]))) / x[:, 1])","$${{\operatorname{exp}(\left(\left(\theta_{0} + x_{0}\right) \cdot \theta_{1}\right))^{\left(\theta_{2} + {x_{0}^{x_{1}}}\right)}}^{\frac{\operatorname{log}(\left(x_{1} \cdot \left(\theta_{3} + x_{0}\right)\right))}{x_{1}}}}$$",-0.47016494013535126;1.2223569846717917;0.6972753174139303;-0.32894944437060236,21,2.5511771041001547e-3,2.5511771041001547e-3,2.5511771041001547e-3,2.5511771041001547e-3,0.7645744154433384,0.7645744154433384,0.7645744154433384,54.22771192492486,54.22771192492486,54.22771192492486 -350,0,Log((((((-0.6306708173090139 * x0) + x1) ^ Log((x1 + -0.3027817337012614))) / (Log((x0 ^ (0.21560917968697824 ^ x0))) * ((x0 + x1) / x0))) + (((-0.3657468797358592 / x1) ^ (Log(x0) * (x1 ^ -0.31614703968390745))) ^ Log(Exp((x1 + -2.796084713886371e-2)))))),"np.log(((((t[0] * x[:, 0]) + x[:, 1]) ** np.log((x[:, 1] + t[1])) / (np.log(x[:, 0] ** t[2] ** x[:, 0]) * ((x[:, 0] + x[:, 1]) / x[:, 0]))) + (t[3] / x[:, 1]) ** (np.log(x[:, 0]) * x[:, 1] ** t[4]) ** np.log(np.exp((x[:, 1] + t[5])))))","$$\operatorname{log}(\left(\frac{{\left(\left(\theta_{0} \cdot x_{0}\right) + x_{1}\right)^{\operatorname{log}(\left(x_{1} + \theta_{1}\right))}}}{\left(\operatorname{log}({x_{0}^{{\theta_{2}^{x_{0}}}}}) \cdot \frac{\left(x_{0} + x_{1}\right)}{x_{0}}\right)} + {{\frac{\theta_{3}}{x_{1}}^{\left(\operatorname{log}(x_{0}) \cdot {x_{1}^{\theta_{4}}}\right)}}^{\operatorname{log}(\operatorname{exp}(\left(x_{1} + \theta_{5}\right)))}}\right))$$",-0.6306708173090139;-0.3027817337012614;0.21560917968697824;-0.3657468797358592;-0.31614703968390745;-2.796084713886371e-2,41,,,,Infinity,,,,,, -351,0,Log(Log(Exp((x1 * -0.40125100925602075)))),"np.log(np.log(np.exp((x[:, 1] * t[0]))))","$$\operatorname{log}(\operatorname{log}(\operatorname{exp}(\left(x_{1} \cdot \theta_{0}\right))))$$",-0.40125100925602075,6,,,,Infinity,,,,,, -352,0,((Exp(((x0 * x1) / (x0 ^ 3.49667412671372))) * x1) * 0.1040658673252386),"((np.exp(((x[:, 0] * x[:, 1]) / x[:, 0] ** t[0])) * x[:, 1]) * t[1])","$$\left(\left(\operatorname{exp}(\frac{\left(x_{0} \cdot x_{1}\right)}{{x_{0}^{\theta_{0}}}}) \cdot x_{1}\right) \cdot \theta_{1}\right)$$",3.49667412671372;0.1040658673252386,12,3.8845546614379857e-3,3.8845546614379857e-3,3.8845546614379857e-3,3.8845546614379857e-3,0.6415287866759407,0.6415287866759407,0.6415287866759407,27.396267758594334,27.396267758594334,27.396267758594334 -353,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 -354,0,(((((0.4825201402148674 ^ x1) * x1) + Log((x0 * x1))) * (5.563724431289008e-4 / Log((x1 ^ x1)))) / x1),"((((t[0] ** x[:, 1] * x[:, 1]) + np.log((x[:, 0] * x[:, 1]))) * (t[1] / np.log(x[:, 1] ** x[:, 1]))) / x[:, 1])","$$\frac{\left(\left(\left({\theta_{0}^{x_{1}}} \cdot x_{1}\right) + \operatorname{log}(\left(x_{0} \cdot x_{1}\right))\right) \cdot \frac{\theta_{1}}{\operatorname{log}({x_{1}^{x_{1}}})}\right)}{x_{1}}$$",0.4825201402148674;5.563724431289008e-4,19,1.4316191203116673e-2,1.4316191203116673e-2,1.4316191203116673e-2,1.4316191203116673e-2,-0.3211147423681031,-0.3211147423681031,-0.3211147423681031,43.56412373473496,43.56412373473496,43.56412373473496 -355,0,(-6.818777244215415e-2 / Log(x1)),"(t[0] / np.log(x[:, 1]))","$$\frac{\theta_{0}}{\operatorname{log}(x_{1})}$$",-6.818777244215415e-2,4,9.501995662458252e-3,9.501995662458252e-3,9.501995662458252e-3,9.501995662458252e-3,0.1231448104116899,0.1231448104116899,0.1231448104116899,7.574654175943415,7.574654175943415,7.574654175943415 -356,0,Log(((-1.2356980858238382 * (-1.0638686921881577 + x0)) + x0)),"np.log(((t[0] * (t[1] + x[:, 0])) + x[:, 0]))","$$\operatorname{log}(\left(\left(\theta_{0} \cdot \left(\theta_{1} + x_{0}\right)\right) + x_{0}\right))$$",-1.2356980858238382;-1.0638686921881577,8,6.021135055815032e-3,6.021135055815032e-3,6.021135055815032e-3,6.021135055815032e-3,0.44436266775377453,0.44436266775377453,0.44436266775377453,16.922772191758316,16.922772191758316,16.922772191758316 -357,0,Log(((Log((x1 * -0.6318827633692854)) + 0.5453037414301793) ^ (((0.19163197797554343 ^ x0) * -0.7228443183104561) ^ (0.5997190001414157 ^ Log((0.7655261557849976 / x1)))))),"np.log((np.log((x[:, 1] * t[0])) + t[1]) ** (t[2] ** x[:, 0] * t[3]) ** t[4] ** np.log((t[5] / x[:, 1])))","$$\operatorname{log}({\left(\operatorname{log}(\left(x_{1} \cdot \theta_{0}\right)) + \theta_{1}\right)^{{\left({\theta_{2}^{x_{0}}} \cdot \theta_{3}\right)^{{\theta_{4}^{\operatorname{log}(\frac{\theta_{5}}{x_{1}})}}}}}})$$",-0.6318827633692854;0.5453037414301793;0.19163197797554343;-0.7228443183104561;0.5997190001414157;0.7655261557849976,20,,,,Infinity,,,,,, -358,0,((Log(((x0 * -0.5343695450858427) + -0.7902049316891904)) + ((x1 ^ -0.8760096105492738) + -0.10517814606692122)) * Log((((-0.27216316260951157 ^ x0) * -0.13233330856450387) ^ Exp(((0.695944364969963 / x1) / Exp(x0)))))),"((np.log(((x[:, 0] * t[0]) + t[1])) + (x[:, 1] ** t[2] + t[3])) * np.log((t[4] ** x[:, 0] * t[5]) ** np.exp(((t[6] / x[:, 1]) / np.exp(x[:, 0])))))","$$\left(\left(\operatorname{log}(\left(\left(x_{0} \cdot \theta_{0}\right) + \theta_{1}\right)) + \left({x_{1}^{\theta_{2}}} + \theta_{3}\right)\right) \cdot \operatorname{log}({\left({\theta_{4}^{x_{0}}} \cdot \theta_{5}\right)^{\operatorname{exp}(\frac{\frac{\theta_{6}}{x_{1}}}{\operatorname{exp}(x_{0})})}})\right)$$",-0.5343695450858427;-0.7902049316891904;-0.8760096105492738;-0.10517814606692122;-0.27216316260951157;-0.13233330856450387;0.695944364969963,27,,,,Infinity,,,,,, -359,0,Log((((x1 * 1.6344662973955024) / (x0 / (1.1369210338454088 * x0))) / (0.2280250819333295 + ((x1 ^ x0) * 0.9665833569772994)))),"np.log((((x[:, 1] * t[0]) / (x[:, 0] / (t[1] * x[:, 0]))) / (t[2] + (x[:, 1] ** x[:, 0] * t[3]))))","$$\operatorname{log}(\frac{\frac{\left(x_{1} \cdot \theta_{0}\right)}{\frac{x_{0}}{\left(\theta_{1} \cdot x_{0}\right)}}}{\left(\theta_{2} + \left({x_{1}^{x_{0}}} \cdot \theta_{3}\right)\right)})$$",1.6344662973955024;1.1369210338454088;0.2280250819333295;0.9665833569772994,18,0.12396582204329185,0.12396582204329185,0.12396582204329185,0.12396582204329185,-10.439709956899694,-10.439709956899694,-10.439709956899694,45.625216731546566,45.625216731546566,45.625216731546566 -360,0,(((((-0.7263800666716498 ^ x0) / x0) + 0.17268094488634578) ^ ((0.3598203657717638 * (x1 / x0)) + (-0.6666949097444441 ^ (x1 + -0.7810899433290375)))) ^ (Log(Log(((0.6707359424287955 + x1) + (x1 / x1)))) ^ Log((Exp((0.15056874475381699 + x0)) + (Exp(x1) * (x0 * -0.9539357165553348)))))),"((t[0] ** x[:, 0] / x[:, 0]) + t[1]) ** ((t[2] * (x[:, 1] / x[:, 0])) + t[3] ** (x[:, 1] + t[4])) ** np.log(np.log(((t[5] + x[:, 1]) + (x[:, 1] / x[:, 1])))) ** np.log((np.exp((t[6] + x[:, 0])) + (np.exp(x[:, 1]) * (x[:, 0] * t[7]))))","$${{\left(\frac{{\theta_{0}^{x_{0}}}}{x_{0}} + \theta_{1}\right)^{\left(\left(\theta_{2} \cdot \frac{x_{1}}{x_{0}}\right) + {\theta_{3}^{\left(x_{1} + \theta_{4}\right)}}\right)}}^{{\operatorname{log}(\operatorname{log}(\left(\left(\theta_{5} + x_{1}\right) + \frac{x_{1}}{x_{1}}\right)))^{\operatorname{log}(\left(\operatorname{exp}(\left(\theta_{6} + x_{0}\right)) + \left(\operatorname{exp}(x_{1}) \cdot \left(x_{0} \cdot \theta_{7}\right)\right)\right))}}}}$$",-0.7263800666716498;0.17268094488634578;0.3598203657717638;-0.6666949097444441;-0.7810899433290375;0.6707359424287955;0.15056874475381699;-0.9539357165553348,42,,,,Infinity,,,,,, -361,0,(((-0.42667402695235124 / (0.49646748449271383 + x1)) + ((x1 + -0.629582915201397) + (x1 / (-0.6214480537565967 + x1)))) * ((Exp((0.2682555219744903 ^ x1)) ^ 0.8705259568735344) ^ Log(((x0 + -0.14329686505055783) * -0.5591948900192336)))),"(((t[0] / (t[1] + x[:, 1])) + ((x[:, 1] + t[2]) + (x[:, 1] / (t[3] + x[:, 1])))) * np.exp(t[4] ** x[:, 1]) ** t[5] ** np.log(((x[:, 0] + t[6]) * t[7])))","$$\left(\left(\frac{\theta_{0}}{\left(\theta_{1} + x_{1}\right)} + \left(\left(x_{1} + \theta_{2}\right) + \frac{x_{1}}{\left(\theta_{3} + x_{1}\right)}\right)\right) \cdot {{\operatorname{exp}({\theta_{4}^{x_{1}}})^{\theta_{5}}}^{\operatorname{log}(\left(\left(x_{0} + \theta_{6}\right) \cdot \theta_{7}\right))}}\right)$$",-0.42667402695235124;0.49646748449271383;-0.629582915201397;-0.6214480537565967;0.2682555219744903;0.8705259568735344;-0.14329686505055783;-0.5591948900192336,29,,,,Infinity,,,,,, -362,0,(((((3.9877432552192005e-2 + (-9.872747033389695e-3 + x0)) + Exp(((-0.5301932245774551 * x0) ^ (0.22362112807424128 * x0)))) ^ (x1 + x0)) + ((Log(((x1 * -0.8861426729998655) + (-0.6743507279626565 * x1))) / (x1 / x0)) / (x1 * -0.14003783076853993))) / x0),"((((t[0] + (t[1] + x[:, 0])) + np.exp((t[2] * x[:, 0]) ** (t[3] * x[:, 0]))) ** (x[:, 1] + x[:, 0]) + ((np.log(((x[:, 1] * t[4]) + (t[5] * x[:, 1]))) / (x[:, 1] / x[:, 0])) / (x[:, 1] * t[6]))) / x[:, 0])","$$\frac{\left({\left(\left(\theta_{0} + \left(\theta_{1} + x_{0}\right)\right) + \operatorname{exp}({\left(\theta_{2} \cdot x_{0}\right)^{\left(\theta_{3} \cdot x_{0}\right)}})\right)^{\left(x_{1} + x_{0}\right)}} + \frac{\frac{\operatorname{log}(\left(\left(x_{1} \cdot \theta_{4}\right) + \left(\theta_{5} \cdot x_{1}\right)\right))}{\frac{x_{1}}{x_{0}}}}{\left(x_{1} \cdot \theta_{6}\right)}\right)}{x_{0}}$$",3.9877432552192005e-2;-9.872747033389695e-3;-0.5301932245774551;0.22362112807424128;-0.8861426729998655;-0.6743507279626565;-0.14003783076853993,37,,,,Infinity,,,,,, -363,0,(((((-0.7903882779888864 / (0.8380401445828906 + x0)) / (x1 ^ x0)) / (-0.6082257330377195 + x1)) / ((x0 * -0.25359398007150014) + (-0.4643463596687105 ^ x1))) * x1),"(((((t[0] / (t[1] + x[:, 0])) / x[:, 1] ** x[:, 0]) / (t[2] + x[:, 1])) / ((x[:, 0] * t[3]) + t[4] ** x[:, 1])) * x[:, 1])","$$\left(\frac{\frac{\frac{\frac{\theta_{0}}{\left(\theta_{1} + x_{0}\right)}}{{x_{1}^{x_{0}}}}}{\left(\theta_{2} + x_{1}\right)}}{\left(\left(x_{0} \cdot \theta_{3}\right) + {\theta_{4}^{x_{1}}}\right)} \cdot x_{1}\right)$$",-0.7903882779888864;0.8380401445828906;-0.6082257330377195;-0.25359398007150014;-0.4643463596687105,23,,,,Infinity,,,,,, -364,0,(Exp(Log(Log((0.3148177729641909 + x1)))) * 0.25111413950047445),"(np.exp(np.log(np.log((t[0] + x[:, 1])))) * t[1])","$$\left(\operatorname{exp}(\operatorname{log}(\operatorname{log}(\left(\theta_{0} + x_{1}\right)))) \cdot \theta_{1}\right)$$",0.3148177729641909;0.25111413950047445,8,,,,Infinity,,,,,, -365,0,((1.853667447282098e-2 / x1) / (x0 + (x1 / x1))),"((t[0] / x[:, 1]) / (x[:, 0] + (x[:, 1] / x[:, 1])))","$$\frac{\frac{\theta_{0}}{x_{1}}}{\left(x_{0} + \frac{x_{1}}{x_{1}}\right)}$$",1.853667447282098e-2,9,1.2869162079709293e-2,1.2869162079709293e-2,1.2869162079709293e-2,1.2869162079709293e-2,-0.18758121515778603,-0.18758121515778603,-0.18758121515778603,16.51744839314628,16.51744839314628,16.51744839314628 -366,0,(Log(((Exp((x0 * x0)) ^ (x0 * (0.24223659468543468 * x0))) * Exp(((0.14210869871802023 + x1) * -0.6518162799704387)))) ^ (Log(((0.19197009553435962 * (x1 + 0.8511951191992848)) / Exp(Log(x0)))) * (0.5975879150006314 * (Log(x1) * (0.4837142598400124 + x1))))),"np.log((np.exp((x[:, 0] * x[:, 0])) ** (x[:, 0] * (t[0] * x[:, 0])) * np.exp(((t[1] + x[:, 1]) * t[2])))) ** (np.log(((t[3] * (x[:, 1] + t[4])) / np.exp(np.log(x[:, 0])))) * (t[5] * (np.log(x[:, 1]) * (t[6] + x[:, 1]))))","$${\operatorname{log}(\left({\operatorname{exp}(\left(x_{0} \cdot x_{0}\right))^{\left(x_{0} \cdot \left(\theta_{0} \cdot x_{0}\right)\right)}} \cdot \operatorname{exp}(\left(\left(\theta_{1} + x_{1}\right) \cdot \theta_{2}\right))\right))^{\left(\operatorname{log}(\frac{\left(\theta_{3} \cdot \left(x_{1} + \theta_{4}\right)\right)}{\operatorname{exp}(\operatorname{log}(x_{0}))}) \cdot \left(\theta_{5} \cdot \left(\operatorname{log}(x_{1}) \cdot \left(\theta_{6} + x_{1}\right)\right)\right)\right)}}$$",0.24223659468543468;0.14210869871802023;-0.6518162799704387;0.19197009553435962;0.8511951191992848;0.5975879150006314;0.4837142598400124,38,,,,Infinity,,,,,, -367,0,(Exp((((x1 ^ (x0 ^ x1)) * -7.912547645682231) * ((x1 * (x0 * 11.6269545805862)) * Exp(Exp((x1 * 3.3788913488388546)))))) ^ Log(((Exp((x0 + x0)) ^ (Exp(x0) / Exp(x1))) + (Exp((x1 / x0)) / Log((x0 + Exp(x1))))))),"np.exp(((x[:, 1] ** x[:, 0] ** x[:, 1] * t[0]) * ((x[:, 1] * (x[:, 0] * t[1])) * np.exp(np.exp((x[:, 1] * t[2])))))) ** np.log((np.exp((x[:, 0] + x[:, 0])) ** (np.exp(x[:, 0]) / np.exp(x[:, 1])) + (np.exp((x[:, 1] / x[:, 0])) / np.log((x[:, 0] + np.exp(x[:, 1]))))))","$${\operatorname{exp}(\left(\left({x_{1}^{{x_{0}^{x_{1}}}}} \cdot \theta_{0}\right) \cdot \left(\left(x_{1} \cdot \left(x_{0} \cdot \theta_{1}\right)\right) \cdot \operatorname{exp}(\operatorname{exp}(\left(x_{1} \cdot \theta_{2}\right)))\right)\right))^{\operatorname{log}(\left({\operatorname{exp}(\left(x_{0} + x_{0}\right))^{\frac{\operatorname{exp}(x_{0})}{\operatorname{exp}(x_{1})}}} + \frac{\operatorname{exp}(\frac{x_{1}}{x_{0}})}{\operatorname{log}(\left(x_{0} + \operatorname{exp}(x_{1})\right))}\right))}}$$",-7.912547645682231;11.6269545805862;3.3788913488388546,43,1.4573863746825691e-2,1.4573863746825691e-2,1.4573863746825691e-2,1.4573863746825691e-2,-0.3448930638062355,-0.3448930638062355,-0.3448930638062355,100.5565481089325,100.5565481089325,100.5565481089325 -368,0,(((Log(x0) * Exp(Log(x1))) + (5.229306655037731e-2 + ((((x0 / x1) ^ 1.4592439653642593) ^ Log(x0)) / (Exp((x0 + x0)) * -0.8684239929430104)))) * -0.5546872026119444),"(((np.log(x[:, 0]) * np.exp(np.log(x[:, 1]))) + (t[0] + ((x[:, 0] / x[:, 1]) ** t[1] ** np.log(x[:, 0]) / (np.exp((x[:, 0] + x[:, 0])) * t[2])))) * t[3])","$$\left(\left(\left(\operatorname{log}(x_{0}) \cdot \operatorname{exp}(\operatorname{log}(x_{1}))\right) + \left(\theta_{0} + \frac{{{\frac{x_{0}}{x_{1}}^{\theta_{1}}}^{\operatorname{log}(x_{0})}}}{\left(\operatorname{exp}(\left(x_{0} + x_{0}\right)) \cdot \theta_{2}\right)}\right)\right) \cdot \theta_{3}\right)$$",5.229306655037731e-2;1.4592439653642593;-0.8684239929430104;-0.5546872026119444,26,3.1422566761350515e-3,3.1422566761350515e-3,3.1422566761350515e-3,3.1422566761350515e-3,0.7100289064145172,0.7100289064145172,0.7100289064145172,65.2143667832208,65.2143667832208,65.2143667832208 -369,0,((-0.1579789913784282 + ((x1 + x0) / (x0 + x1))) / (Log(Log((0.19075706183866636 ^ (Log(x1) ^ Log(x0))))) + Log(Exp((x0 * x0))))),"((t[0] + ((x[:, 1] + x[:, 0]) / (x[:, 0] + x[:, 1]))) / (np.log(np.log(t[1] ** np.log(x[:, 1]) ** np.log(x[:, 0]))) + np.log(np.exp((x[:, 0] * x[:, 0])))))","$$\frac{\left(\theta_{0} + \frac{\left(x_{1} + x_{0}\right)}{\left(x_{0} + x_{1}\right)}\right)}{\left(\operatorname{log}(\operatorname{log}({\theta_{1}^{{\operatorname{log}(x_{1})^{\operatorname{log}(x_{0})}}}})) + \operatorname{log}(\operatorname{exp}(\left(x_{0} \cdot x_{0}\right)))\right)}$$",-0.1579789913784282;0.19075706183866636,25,,,,Infinity,,,,,, -370,0,(((-0.5294107906110281 ^ Exp((x0 + 0.9169423442478464))) / (0.5268236070875418 ^ (x0 / x1))) * (((0.8583272917623552 ^ x1) + (0.19313652855854313 / x1)) ^ ((0.6233035735136526 + x0) / x0))),"((t[0] ** np.exp((x[:, 0] + t[1])) / t[2] ** (x[:, 0] / x[:, 1])) * (t[3] ** x[:, 1] + (t[4] / x[:, 1])) ** ((t[5] + x[:, 0]) / x[:, 0]))","$$\left(\frac{{\theta_{0}^{\operatorname{exp}(\left(x_{0} + \theta_{1}\right))}}}{{\theta_{2}^{\frac{x_{0}}{x_{1}}}}} \cdot {\left({\theta_{3}^{x_{1}}} + \frac{\theta_{4}}{x_{1}}\right)^{\frac{\left(\theta_{5} + x_{0}\right)}{x_{0}}}}\right)$$",-0.5294107906110281;0.9169423442478464;0.5268236070875418;0.8583272917623552;0.19313652855854313;0.6233035735136526,26,,,,Infinity,,,,,, -371,0,(Exp((-87141.94762908242 + Exp(x0))) / ((x1 + 78053.67801120912) * 256882.54283008992)),"(np.exp((t[0] + np.exp(x[:, 0]))) / ((x[:, 1] + t[1]) * t[2]))","$$\frac{\operatorname{exp}(\left(\theta_{0} + \operatorname{exp}(x_{0})\right))}{\left(\left(x_{1} + \theta_{1}\right) \cdot \theta_{2}\right)}$$",-87141.94762908242;78053.67801120912;256882.54283008992,11,1.4573554749975553e-2,1.4573554749975553e-2,1.4573554749975553e-2,1.4573554749975553e-2,-0.3448645492182232,-0.3448645492182232,-0.3448645492182232,27.480902644986347,27.480902644986347,27.480902644986347 -372,0,Exp((((-1.8428351210334972 + ((x1 ^ x0) * 0.38963529443998784)) / (((x0 ^ -3.098761221922056) / x1) / (Exp(x1) + -0.7340218827003249))) * Exp(((Log((x1 ^ 2.1207500985845784)) + (x1 * 1.6028394822007002)) / ((x0 + x0) * ((-0.14873134219743303 / x0) / x0)))))),"np.exp((((t[0] + (x[:, 1] ** x[:, 0] * t[1])) / ((x[:, 0] ** t[2] / x[:, 1]) / (np.exp(x[:, 1]) + t[3]))) * np.exp(((np.log(x[:, 1] ** t[4]) + (x[:, 1] * t[5])) / ((x[:, 0] + x[:, 0]) * ((t[6] / x[:, 0]) / x[:, 0]))))))","$$\operatorname{exp}(\left(\frac{\left(\theta_{0} + \left({x_{1}^{x_{0}}} \cdot \theta_{1}\right)\right)}{\frac{\frac{{x_{0}^{\theta_{2}}}}{x_{1}}}{\left(\operatorname{exp}(x_{1}) + \theta_{3}\right)}} \cdot \operatorname{exp}(\frac{\left(\operatorname{log}({x_{1}^{\theta_{4}}}) + \left(x_{1} \cdot \theta_{5}\right)\right)}{\left(\left(x_{0} + x_{0}\right) \cdot \frac{\frac{\theta_{6}}{x_{0}}}{x_{0}}\right)})\right))$$",-1.8428351210334972;0.38963529443998784;-3.098761221922056;-0.7340218827003249;2.1207500985845784;1.6028394822007002;-0.14873134219743303,39,7.970569067032024e-3,7.970569067032024e-3,7.970569067032024e-3,7.970569067032024e-3,0.2644666343079598,0.2644666343079598,0.2644666343079598,99.84540657584637,99.84540657584637,99.84540657584637 -373,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 -374,0,(Exp(Exp(((0.16657189278252882 / ((0.573451605276171 + x0) * -0.4875479192233212)) ^ -0.3644489141848827))) * ((Exp(Log((x1 / (x1 / x1)))) + Log((x1 * -0.6088068758732776))) ^ (((x0 + 0.1529612641704563) * -2.5013851386822217e-2) / ((x0 + -0.1073145235618207) ^ 0.9979245228147716)))),"(np.exp(np.exp((t[0] / ((t[1] + x[:, 0]) * t[2])) ** t[3])) * (np.exp(np.log((x[:, 1] / (x[:, 1] / x[:, 1])))) + np.log((x[:, 1] * t[4]))) ** (((x[:, 0] + t[5]) * t[6]) / (x[:, 0] + t[7]) ** t[8]))","$$\left(\operatorname{exp}(\operatorname{exp}({\frac{\theta_{0}}{\left(\left(\theta_{1} + x_{0}\right) \cdot \theta_{2}\right)}^{\theta_{3}}})) \cdot {\left(\operatorname{exp}(\operatorname{log}(\frac{x_{1}}{\frac{x_{1}}{x_{1}}})) + \operatorname{log}(\left(x_{1} \cdot \theta_{4}\right))\right)^{\frac{\left(\left(x_{0} + \theta_{5}\right) \cdot \theta_{6}\right)}{{\left(x_{0} + \theta_{7}\right)^{\theta_{8}}}}}}\right)$$",0.16657189278252882;0.573451605276171;-0.4875479192233212;-0.3644489141848827;-0.6088068758732776;0.1529612641704563;-2.5013851386822217e-2;-0.1073145235618207;0.9979245228147716,36,,,,Infinity,,,,,, -375,0,Exp(((Log(x1) * ((Log((0.3220048915007221 + x0)) ^ 0.1781327349621159) / x1)) ^ ((-0.6968896943315938 + (x1 / x1)) / x0))),"np.exp((np.log(x[:, 1]) * (np.log((t[0] + x[:, 0])) ** t[1] / x[:, 1])) ** ((t[2] + (x[:, 1] / x[:, 1])) / x[:, 0]))","$$\operatorname{exp}({\left(\operatorname{log}(x_{1}) \cdot \frac{{\operatorname{log}(\left(\theta_{0} + x_{0}\right))^{\theta_{1}}}}{x_{1}}\right)^{\frac{\left(\theta_{2} + \frac{x_{1}}{x_{1}}\right)}{x_{0}}}})$$",0.3220048915007221;0.1781327349621159;-0.6968896943315938,20,,,,Infinity,,,,,, -376,0,(Log((Exp(Exp(x1)) / (x0 * 0.7109204157567375))) ^ Exp(((x1 * 0.5466125177724963) + (Exp((x0 + x1)) / x0)))),"np.log((np.exp(np.exp(x[:, 1])) / (x[:, 0] * t[0]))) ** np.exp(((x[:, 1] * t[1]) + (np.exp((x[:, 0] + x[:, 1])) / x[:, 0])))","$${\operatorname{log}(\frac{\operatorname{exp}(\operatorname{exp}(x_{1}))}{\left(x_{0} \cdot \theta_{0}\right)})^{\operatorname{exp}(\left(\left(x_{1} \cdot \theta_{1}\right) + \frac{\operatorname{exp}(\left(x_{0} + x_{1}\right))}{x_{0}}\right))}}$$",0.7109204157567375;0.5466125177724963,20,2.0363287805484993e224,2.0363287805484993e224,2.0363287805484993e224,2.0363287805484993e224,-1.8791478362662656e226,-1.8791478362662656e226,-1.8791478362662656e226,1.8326959024936494e224,1.8326959024936494e224,1.8326959024936494e224 -377,0,(-0.9956750177908841 + x0),"(t[0] + x[:, 0])","$$\left(\theta_{0} + x_{0}\right)$$",-0.9956750177908841,3,0.15105329635364,0.15105329635364,0.15105329635364,0.15105329635364,-12.939373529228037,-12.939373529228037,-12.939373529228037,5.452709768090245,5.452709768090245,5.452709768090245 -378,0,(((Log((0.8038065313675858 ^ ((x1 + -0.7527652113245995) + -0.11106988222645775))) / ((((-4.616853111880037e-2 ^ x0) + 0.30597840973275614) * 2.785914814332313e-2) + x0)) + Exp(((-0.23544942746481712 ^ x1) / ((Log(x1) * x1) * -0.9143507619028746)))) / ((((0.6978465143799752 + x0) + 3.708835195462368e-2) ^ x1) ^ 0.5848855754072313)),"(((np.log(t[0] ** ((x[:, 1] + t[1]) + t[2])) / (((t[3] ** x[:, 0] + t[4]) * t[5]) + x[:, 0])) + np.exp((t[6] ** x[:, 1] / ((np.log(x[:, 1]) * x[:, 1]) * t[7])))) / ((t[8] + x[:, 0]) + t[9]) ** x[:, 1] ** t[10])","$$\frac{\left(\frac{\operatorname{log}({\theta_{0}^{\left(\left(x_{1} + \theta_{1}\right) + \theta_{2}\right)}})}{\left(\left(\left({\theta_{3}^{x_{0}}} + \theta_{4}\right) \cdot \theta_{5}\right) + x_{0}\right)} + \operatorname{exp}(\frac{{\theta_{6}^{x_{1}}}}{\left(\left(\operatorname{log}(x_{1}) \cdot x_{1}\right) \cdot \theta_{7}\right)})\right)}{{{\left(\left(\theta_{8} + x_{0}\right) + \theta_{9}\right)^{x_{1}}}^{\theta_{10}}}}$$",0.8038065313675858;-0.7527652113245995;-0.11106988222645775;-4.616853111880037e-2;0.30597840973275614;2.785914814332313e-2;-0.23544942746481712;-0.9143507619028746;0.6978465143799752;3.708835195462368e-2;0.5848855754072313,40,,,,Infinity,,,,,, -379,0,((Exp((((x0 ^ 7.052972633808667) ^ x1) * -5.286081383455715)) ^ x0) * x0),"(np.exp((x[:, 0] ** t[0] ** x[:, 1] * t[1])) ** x[:, 0] * x[:, 0])","$$\left({\operatorname{exp}(\left({{x_{0}^{\theta_{0}}}^{x_{1}}} \cdot \theta_{1}\right))^{x_{0}}} \cdot x_{0}\right)$$",7.052972633808667;-5.286081383455715,12,1.6427828050891288e-3,1.6427828050891288e-3,1.6427828050891288e-3,1.6427828050891288e-3,0.8484020958144515,0.8484020958144515,0.8484020958144515,25.544442005996526,25.544442005996526,25.544442005996526 -380,0,(Exp((-5.075945270411527 / Exp(x1))) ^ ((-0.5596320513185477 / ((x1 ^ -2.508255619516825e-2) ^ 1.1957000909818955e-2)) + Exp((-0.6080152390637005 + x0)))),"np.exp((t[0] / np.exp(x[:, 1]))) ** ((t[1] / x[:, 1] ** t[2] ** t[3]) + np.exp((t[4] + x[:, 0])))","$${\operatorname{exp}(\frac{\theta_{0}}{\operatorname{exp}(x_{1})})^{\left(\frac{\theta_{1}}{{{x_{1}^{\theta_{2}}}^{\theta_{3}}}} + \operatorname{exp}(\left(\theta_{4} + x_{0}\right))\right)}}$$",-5.075945270411527;-0.5596320513185477;-2.508255619516825e-2;1.1957000909818955e-2;-0.6080152390637005,18,2.4603770610147277e-3,2.4603770610147277e-3,2.4603770610147277e-3,2.4603770610147277e-3,0.7729535488193792,0.7729535488193792,0.7729535488193792,45.13322169918875,45.13322169918875,45.13322169918875 -381,0,((Exp(Exp(x1)) + ((1.126077989088653 + (x1 + 1.3820420005023495)) / Log(x1))) ^ (Exp(Log((5.937844471663306 + x1))) ^ (-4.906675648256525 * x0))),"(np.exp(np.exp(x[:, 1])) + ((t[0] + (x[:, 1] + t[1])) / np.log(x[:, 1]))) ** np.exp(np.log((t[2] + x[:, 1]))) ** (t[3] * x[:, 0])","$${\left(\operatorname{exp}(\operatorname{exp}(x_{1})) + \frac{\left(\theta_{0} + \left(x_{1} + \theta_{1}\right)\right)}{\operatorname{log}(x_{1})}\right)^{{\operatorname{exp}(\operatorname{log}(\left(\theta_{2} + x_{1}\right)))^{\left(\theta_{3} \cdot x_{0}\right)}}}}$$",1.126077989088653;1.3820420005023495;5.937844471663306;-4.906675648256525,22,0.8929472948148743,0.8929472948148743,0.8929472948148743,0.8929472948148743,-81.4022128931071,-81.4022128931071,-81.4022128931071,57.226293008200784,57.226293008200784,57.226293008200784 -382,0,(((-0.5105872218389083 / Exp((x1 * -0.11401808112307865))) ^ ((Log(x0) + -0.2385776540747616) ^ (-0.3753279387831623 + (x0 ^ -0.7464128574082617)))) / Log(((Log(Exp(x1)) ^ (0.185730947572048 + Exp(x0))) ^ ((-0.9428743620067016 / Exp(x0)) * ((x1 ^ x1) ^ x0))))),"((t[0] / np.exp((x[:, 1] * t[1]))) ** (np.log(x[:, 0]) + t[2]) ** (t[3] + x[:, 0] ** t[4]) / np.log(np.log(np.exp(x[:, 1])) ** (t[5] + np.exp(x[:, 0])) ** ((t[6] / np.exp(x[:, 0])) * x[:, 1] ** x[:, 1] ** x[:, 0])))","$$\frac{{\frac{\theta_{0}}{\operatorname{exp}(\left(x_{1} \cdot \theta_{1}\right))}^{{\left(\operatorname{log}(x_{0}) + \theta_{2}\right)^{\left(\theta_{3} + {x_{0}^{\theta_{4}}}\right)}}}}}{\operatorname{log}({{\operatorname{log}(\operatorname{exp}(x_{1}))^{\left(\theta_{5} + \operatorname{exp}(x_{0})\right)}}^{\left(\frac{\theta_{6}}{\operatorname{exp}(x_{0})} \cdot {{x_{1}^{x_{1}}}^{x_{0}}}\right)}})}$$",-0.5105872218389083;-0.11401808112307865;-0.2385776540747616;-0.3753279387831623;-0.7464128574082617;0.185730947572048;-0.9428743620067016,38,,,,Infinity,,,,,, -383,0,Log(Log(((x1 * Exp(x0)) ^ (0.443500729634964 + (x1 ^ 2.7477078454148596e-2))))),"np.log(np.log((x[:, 1] * np.exp(x[:, 0])) ** (t[0] + x[:, 1] ** t[1])))","$$\operatorname{log}(\operatorname{log}({\left(x_{1} \cdot \operatorname{exp}(x_{0})\right)^{\left(\theta_{0} + {x_{1}^{\theta_{1}}}\right)}}))$$",0.443500729634964;2.7477078454148596e-2,12,,,,Infinity,,,,,, -384,0,((6.296814140824052e-2 * ((x1 + x1) * 0.48542466040754373)) / x1),"((t[0] * ((x[:, 1] + x[:, 1]) * t[1])) / x[:, 1])","$$\frac{\left(\theta_{0} \cdot \left(\left(x_{1} + x_{1}\right) \cdot \theta_{1}\right)\right)}{x_{1}}$$",6.296814140824052e-2;0.48542466040754373,9,1.0836447997012907e-2,1.0836447997012907e-2,1.0836447997012907e-2,1.0836447997012907e-2,-4.487099580785525e-11,-4.487099580785525e-11,-4.487099580785525e-11,18.536543885839496,18.536543885839496,18.536543885839496 -385,0,(((x0 + (x1 * 0.5741757231813699)) / Exp((x1 ^ -2.360477047201456))) * (Log(Exp((x0 * x1))) * (1.0661195385663442 + x0))),"(((x[:, 0] + (x[:, 1] * t[0])) / np.exp(x[:, 1] ** t[1])) * (np.log(np.exp((x[:, 0] * x[:, 1]))) * (t[2] + x[:, 0])))","$$\left(\frac{\left(x_{0} + \left(x_{1} \cdot \theta_{0}\right)\right)}{\operatorname{exp}({x_{1}^{\theta_{1}}})} \cdot \left(\operatorname{log}(\operatorname{exp}(\left(x_{0} \cdot x_{1}\right))) \cdot \left(\theta_{2} + x_{0}\right)\right)\right)$$",0.5741757231813699;-2.360477047201456;1.0661195385663442,20,1.4331343701244996e-2,1.4331343701244996e-2,1.4331343701244996e-2,1.4331343701244996e-2,-0.32251303248430907,-0.32251303248430907,-0.32251303248430907,50.020164562158435,50.020164562158435,50.020164562158435 -386,0,Exp(Exp(((((x0 * x1) + Log(x1)) / ((x0 * (x0 / x0)) * -0.6024545698705194)) * (-0.44974925730136883 / Log(((x1 + x1) * -0.6866750033480645)))))),"np.exp(np.exp(((((x[:, 0] * x[:, 1]) + np.log(x[:, 1])) / ((x[:, 0] * (x[:, 0] / x[:, 0])) * t[0])) * (t[1] / np.log(((x[:, 1] + x[:, 1]) * t[2]))))))","$$\operatorname{exp}(\operatorname{exp}(\left(\frac{\left(\left(x_{0} \cdot x_{1}\right) + \operatorname{log}(x_{1})\right)}{\left(\left(x_{0} \cdot \frac{x_{0}}{x_{0}}\right) \cdot \theta_{0}\right)} \cdot \frac{\theta_{1}}{\operatorname{log}(\left(\left(x_{1} + x_{1}\right) \cdot \theta_{2}\right))}\right)))$$",-0.6024545698705194;-0.44974925730136883;-0.6866750033480645,25,,,,Infinity,,,,,, -387,0,Exp(((Log((Log(x0) * 0.8654336480155295)) / (Log(Log(x0)) + (0.9482243873768081 + ((x0 * 9.68467854039311e-2) + x0)))) * 0.9879615675182041)),"np.exp(((np.log((np.log(x[:, 0]) * t[0])) / (np.log(np.log(x[:, 0])) + (t[1] + ((x[:, 0] * t[2]) + x[:, 0])))) * t[3]))","$$\operatorname{exp}(\left(\frac{\operatorname{log}(\left(\operatorname{log}(x_{0}) \cdot \theta_{0}\right))}{\left(\operatorname{log}(\operatorname{log}(x_{0})) + \left(\theta_{1} + \left(\left(x_{0} \cdot \theta_{2}\right) + x_{0}\right)\right)\right)} \cdot \theta_{3}\right))$$",0.8654336480155295;0.9482243873768081;9.68467854039311e-2;0.9879615675182041,20,,,,Infinity,,,,,, -388,0,Exp((x1 * -55.781589458130306)),"np.exp((x[:, 1] * t[0]))","$$\operatorname{exp}(\left(x_{1} \cdot \theta_{0}\right))$$",-55.781589458130306,4,1.4573896716579228e-2,1.4573896716579228e-2,1.4573896716579228e-2,1.4573896716579228e-2,-0.3448961062933631,-0.3448961062933631,-0.3448961062933631,7.579218886892124,7.579218886892124,7.579218886892124 -389,0,Exp(Log(((((-0.8170214754354692 + x1) + -0.2998362096080194) ^ ((0.9412105623799939 / x1) + 0.22601741077706883)) ^ (Log(((x0 ^ 0.670168783269687) + -0.7712443799505657)) / Log(Log((x1 * 9.23027967695651e-2))))))),"np.exp(np.log(((t[0] + x[:, 1]) + t[1]) ** ((t[2] / x[:, 1]) + t[3]) ** (np.log((x[:, 0] ** t[4] + t[5])) / np.log(np.log((x[:, 1] * t[6]))))))","$$\operatorname{exp}(\operatorname{log}({{\left(\left(\theta_{0} + x_{1}\right) + \theta_{1}\right)^{\left(\frac{\theta_{2}}{x_{1}} + \theta_{3}\right)}}^{\frac{\operatorname{log}(\left({x_{0}^{\theta_{4}}} + \theta_{5}\right))}{\operatorname{log}(\operatorname{log}(\left(x_{1} \cdot \theta_{6}\right)))}}}))$$",-0.8170214754354692;-0.2998362096080194;0.9412105623799939;0.22601741077706883;0.670168783269687;-0.7712443799505657;9.23027967695651e-2,26,,,,Infinity,,,,,, -390,0,Exp(((Exp(((x1 ^ x0) * -0.43306684673498963)) * ((x1 ^ -0.5314283983872532) + (-0.6570231070328547 * (x0 + -0.613334205463964)))) * (((0.8984056633801967 / Exp(x0)) * Log((x1 ^ 0.27060454069792883))) ^ -0.22250259847089415))),"np.exp(((np.exp((x[:, 1] ** x[:, 0] * t[0])) * (x[:, 1] ** t[1] + (t[2] * (x[:, 0] + t[3])))) * ((t[4] / np.exp(x[:, 0])) * np.log(x[:, 1] ** t[5])) ** t[6]))","$$\operatorname{exp}(\left(\left(\operatorname{exp}(\left({x_{1}^{x_{0}}} \cdot \theta_{0}\right)) \cdot \left({x_{1}^{\theta_{1}}} + \left(\theta_{2} \cdot \left(x_{0} + \theta_{3}\right)\right)\right)\right) \cdot {\left(\frac{\theta_{4}}{\operatorname{exp}(x_{0})} \cdot \operatorname{log}({x_{1}^{\theta_{5}}})\right)^{\theta_{6}}}\right))$$",-0.43306684673498963;-0.5314283983872532;-0.6570231070328547;-0.613334205463964;0.8984056633801967;0.27060454069792883;-0.22250259847089415,29,,,,Infinity,,,,,, -391,0,Log((Log((Exp((0.6509940308206308 * x1)) * -0.950616982755486)) + Log(Exp((-0.5465261874328403 * (Log(x1) * 0.6650829479520628)))))),"np.log((np.log((np.exp((t[0] * x[:, 1])) * t[1])) + np.log(np.exp((t[2] * (np.log(x[:, 1]) * t[3]))))))","$$\operatorname{log}(\left(\operatorname{log}(\left(\operatorname{exp}(\left(\theta_{0} \cdot x_{1}\right)) \cdot \theta_{1}\right)) + \operatorname{log}(\operatorname{exp}(\left(\theta_{2} \cdot \left(\operatorname{log}(x_{1}) \cdot \theta_{3}\right)\right)))\right))$$",0.6509940308206308;-0.950616982755486;-0.5465261874328403;0.6650829479520628,17,,,,Infinity,,,,,, -392,0,((((x1 * 13.379846591499017) / (Exp(x1) + (x1 * (x0 * -0.942898810430549)))) / (0.3691550535138762 + (((5.269314264762601 + (x1 / x0)) ^ (x1 ^ 9.480073875287031)) + -0.4301473665265012))) / (Exp((5.501718731774389 ^ x0)) + 5.480984190590707e-2)),"((((x[:, 1] * t[0]) / (np.exp(x[:, 1]) + (x[:, 1] * (x[:, 0] * t[1])))) / (t[2] + ((t[3] + (x[:, 1] / x[:, 0])) ** x[:, 1] ** t[4] + t[5]))) / (np.exp(t[6] ** x[:, 0]) + t[7]))","$$\frac{\frac{\frac{\left(x_{1} \cdot \theta_{0}\right)}{\left(\operatorname{exp}(x_{1}) + \left(x_{1} \cdot \left(x_{0} \cdot \theta_{1}\right)\right)\right)}}{\left(\theta_{2} + \left({\left(\theta_{3} + \frac{x_{1}}{x_{0}}\right)^{{x_{1}^{\theta_{4}}}}} + \theta_{5}\right)\right)}}{\left(\operatorname{exp}({\theta_{6}^{x_{0}}}) + \theta_{7}\right)}$$",13.379846591499017;-0.942898810430549;0.3691550535138762;5.269314264762601;9.480073875287031;-0.4301473665265012;5.501718731774389;5.480984190590707e-2,33,1.5878838885174068e-3,1.5878838885174068e-3,1.5878838885174068e-3,1.5878838885174068e-3,0.8534682315620062,0.8534682315620062,0.8534682315620062,84.79039945387538,84.79039945387538,84.79039945387538 -393,0,(((x0 ^ -4.648733278948605) + (x0 + 7.292217673083389)) * (Log((x1 * x0)) * -3.3302493413795782e-3)),"((x[:, 0] ** t[0] + (x[:, 0] + t[1])) * (np.log((x[:, 1] * x[:, 0])) * t[2]))","$$\left(\left({x_{0}^{\theta_{0}}} + \left(x_{0} + \theta_{1}\right)\right) \cdot \left(\operatorname{log}(\left(x_{1} \cdot x_{0}\right)) \cdot \theta_{2}\right)\right)$$",-4.648733278948605;7.292217673083389;-3.3302493413795782e-3,14,9.83290025542597e-3,9.83290025542597e-3,9.83290025542597e-3,9.83290025542597e-3,9.260855046066341e-2,9.260855046066341e-2,9.260855046066341e-2,33.314366503107195,33.314366503107195,33.314366503107195 -394,0,((Log((x0 * x1)) / ((x1 * -0.9348590945180999) + 0.7789122364020267)) ^ ((-0.19889657489864 * (0.44792621852464654 / (0.37732176870933 + Exp((x0 + 0.2013604433435473))))) ^ Exp(((3.0154062418302763e-3 * x0) * x1)))),"(np.log((x[:, 0] * x[:, 1])) / ((x[:, 1] * t[0]) + t[1])) ** (t[2] * (t[3] / (t[4] + np.exp((x[:, 0] + t[5]))))) ** np.exp(((t[6] * x[:, 0]) * x[:, 1]))","$${\frac{\operatorname{log}(\left(x_{0} \cdot x_{1}\right))}{\left(\left(x_{1} \cdot \theta_{0}\right) + \theta_{1}\right)}^{{\left(\theta_{2} \cdot \frac{\theta_{3}}{\left(\theta_{4} + \operatorname{exp}(\left(x_{0} + \theta_{5}\right))\right)}\right)^{\operatorname{exp}(\left(\left(\theta_{6} \cdot x_{0}\right) \cdot x_{1}\right))}}}}$$",-0.9348590945180999;0.7789122364020267;-0.19889657489864;0.44792621852464654;0.37732176870933;0.2013604433435473;3.0154062418302763e-3,28,,,,Infinity,,,,,, -395,0,Log((-0.8130647961541064 / x0)),"np.log((t[0] / x[:, 0]))","$$\operatorname{log}(\frac{\theta_{0}}{x_{0}})$$",-0.8130647961541064,4,,,,Infinity,,,,,, -396,0,Exp(((Log(x0) * -3.6168182627074277) + (-2.311346003098002 + (x0 + -1.843417352976074)))),"np.exp(((np.log(x[:, 0]) * t[0]) + (t[1] + (x[:, 0] + t[2]))))","$$\operatorname{exp}(\left(\left(\operatorname{log}(x_{0}) \cdot \theta_{0}\right) + \left(\theta_{1} + \left(x_{0} + \theta_{2}\right)\right)\right))$$",-3.6168182627074277;-2.311346003098002;-1.843417352976074,11,5.334750830319646e-3,5.334750830319646e-3,5.334750830319646e-3,5.334750830319646e-3,0.5077030008329706,0.5077030008329706,0.5077030008329706,25.776930243358812,25.776930243358812,25.776930243358812 -397,0,(((x1 + 0.5414802029987323) * (1.6251841832583996e-4 ^ (x1 * 0.8193514746804531))) / (2.4477011707715195 / (-0.8616445003943854 + x0))),"(((x[:, 1] + t[0]) * t[1] ** (x[:, 1] * t[2])) / (t[3] / (t[4] + x[:, 0])))","$$\frac{\left(\left(x_{1} + \theta_{0}\right) \cdot {\theta_{1}^{\left(x_{1} \cdot \theta_{2}\right)}}\right)}{\frac{\theta_{3}}{\left(\theta_{4} + x_{0}\right)}}$$",0.5414802029987323;1.6251841832583996e-4;0.8193514746804531;2.4477011707715195;-0.8616445003943854,15,1.5464676434292281e-2,1.5464676434292281e-2,1.5464676434292281e-2,1.5464676434292281e-2,-0.42709829265540433,-0.42709829265540433,-0.42709829265540433,39.30719512145876,39.30719512145876,39.30719512145876 -398,0,((Exp(Exp(x1)) * (x0 + (x1 + -2.187483516233691))) + 3.2544893231580776),"((np.exp(np.exp(x[:, 1])) * (x[:, 0] + (x[:, 1] + t[0]))) + t[1])","$$\left(\left(\operatorname{exp}(\operatorname{exp}(x_{1})) \cdot \left(x_{0} + \left(x_{1} + \theta_{0}\right)\right)\right) + \theta_{1}\right)$$",-2.187483516233691;3.2544893231580776,11,1.9940621750053285,1.9940621750053285,1.9940621750053285,1.9940621750053285,-183.0143722042935,-183.0143722042935,-183.0143722042935,25.54585998974868,25.54585998974868,25.54585998974868 -399,0,(((Log((x0 * Log(x0))) / (x0 / x1)) ^ (Exp(Log(x1)) ^ Log(x0))) ^ (Log(Exp((0.4144480025149665 * Exp((x1 * -0.8211425289839988))))) / (0.14324834480329984 ^ x1))),"(np.log((x[:, 0] * np.log(x[:, 0]))) / (x[:, 0] / x[:, 1])) ** np.exp(np.log(x[:, 1])) ** np.log(x[:, 0]) ** (np.log(np.exp((t[0] * np.exp((x[:, 1] * t[1]))))) / t[2] ** x[:, 1])","$${{\frac{\operatorname{log}(\left(x_{0} \cdot \operatorname{log}(x_{0})\right))}{\frac{x_{0}}{x_{1}}}^{{\operatorname{exp}(\operatorname{log}(x_{1}))^{\operatorname{log}(x_{0})}}}}^{\frac{\operatorname{log}(\operatorname{exp}(\left(\theta_{0} \cdot \operatorname{exp}(\left(x_{1} \cdot \theta_{1}\right))\right)))}{{\theta_{2}^{x_{1}}}}}}$$",0.4144480025149665;-0.8211425289839988;0.14324834480329984,29,,,,Infinity,,,,,, -400,0,(0.5589703510945292 ^ (((x0 * -0.9564265205286525) * 0.7239866824804038) ^ x0)),"t[0] ** ((x[:, 0] * t[1]) * t[2]) ** x[:, 0]","$${\theta_{0}^{{\left(\left(x_{0} \cdot \theta_{1}\right) \cdot \theta_{2}\right)^{x_{0}}}}}$$",0.5589703510945292;-0.9564265205286525;0.7239866824804038,9,,,,Infinity,,,,,, -401,0,((-0.19949107774706082 + ((x1 * Exp(x1)) + ((x0 ^ -0.3765592945900294) ^ 0.3710102054709429))) / (((4.270017538958333e-2 * (x1 ^ x1)) ^ (x1 + (x0 + -0.2946907649882392))) ^ Log((Exp((-0.8329927056086268 / x0)) ^ (x0 ^ x1))))),"((t[0] + ((x[:, 1] * np.exp(x[:, 1])) + x[:, 0] ** t[1] ** t[2])) / (t[3] * x[:, 1] ** x[:, 1]) ** (x[:, 1] + (x[:, 0] + t[4])) ** np.log(np.exp((t[5] / x[:, 0])) ** x[:, 0] ** x[:, 1]))","$$\frac{\left(\theta_{0} + \left(\left(x_{1} \cdot \operatorname{exp}(x_{1})\right) + {{x_{0}^{\theta_{1}}}^{\theta_{2}}}\right)\right)}{{{\left(\theta_{3} \cdot {x_{1}^{x_{1}}}\right)^{\left(x_{1} + \left(x_{0} + \theta_{4}\right)\right)}}^{\operatorname{log}({\operatorname{exp}(\frac{\theta_{5}}{x_{0}})^{{x_{0}^{x_{1}}}}})}}}$$",-0.19949107774706082;-0.3765592945900294;0.3710102054709429;4.270017538958333e-2;-0.2946907649882392;-0.8329927056086268,34,9.96595232584315e-3,9.96595232584315e-3,9.96595232584315e-3,9.96595232584315e-3,8.03303509565616e-2,8.03303509565616e-2,8.03303509565616e-2,86.84015459873058,86.84015459873058,86.84015459873058 -402,0,(((x0 ^ 0.42465666442791616) ^ -0.2711389126598546) + ((x1 / x1) ^ x1)),"(x[:, 0] ** t[0] ** t[1] + (x[:, 1] / x[:, 1]) ** x[:, 1])","$$\left({{x_{0}^{\theta_{0}}}^{\theta_{1}}} + {\frac{x_{1}}{x_{1}}^{x_{1}}}\right)$$",0.42465666442791616;-0.2711389126598546,11,3.7659213689679794,3.7659213689679794,3.7659213689679794,3.7659213689679794,-346.5235953861491,-346.5235953861491,-346.5235953861491,27.140533264315067,27.140533264315067,27.140533264315067 -403,0,(0.7411909602588301 ^ Exp(((x1 + Exp((0.8780574800074716 + x0))) * 0.3517704776619639))),"t[0] ** np.exp(((x[:, 1] + np.exp((t[1] + x[:, 0]))) * t[2]))","$${\theta_{0}^{\operatorname{exp}(\left(\left(x_{1} + \operatorname{exp}(\left(\theta_{1} + x_{0}\right))\right) \cdot \theta_{2}\right))}}$$",0.7411909602588301;0.8780574800074716;0.3517704776619639,11,5.986220738098148e-3,5.986220738098148e-3,5.986220738098148e-3,5.986220738098148e-3,0.44758460152100843,0.44758460152100843,0.44758460152100843,27.473174044375657,27.473174044375657,27.473174044375657 -404,0,(Log((Exp((0.34887566134756004 ^ (Exp(x1) * 2.7625920499842236))) / ((x1 ^ (-0.18512426214845995 / x1)) ^ x1))) + x1),"(np.log((np.exp(t[0] ** (np.exp(x[:, 1]) * t[1])) / x[:, 1] ** (t[2] / x[:, 1]) ** x[:, 1])) + x[:, 1])","$$\left(\operatorname{log}(\frac{\operatorname{exp}({\theta_{0}^{\left(\operatorname{exp}(x_{1}) \cdot \theta_{1}\right)}})}{{{x_{1}^{\frac{\theta_{2}}{x_{1}}}}^{x_{1}}}}) + x_{1}\right)$$",0.34887566134756004;2.7625920499842236;-0.18512426214845995,18,4.37319699548052e-2,4.37319699548052e-2,4.37319699548052e-2,4.37319699548052e-2,-3.0356369512244523,-3.0356369512244523,-3.0356369512244523,43.53208132929929,43.53208132929929,43.53208132929929 -405,0,(Log(Exp((Log((x1 / x0)) + Log(x0)))) + Exp(((17.860443619072115 / (x1 ^ x0)) ^ (x1 * -1.0866766239456351)))),"(np.log(np.exp((np.log((x[:, 1] / x[:, 0])) + np.log(x[:, 0])))) + np.exp((t[0] / x[:, 1] ** x[:, 0]) ** (x[:, 1] * t[1])))","$$\left(\operatorname{log}(\operatorname{exp}(\left(\operatorname{log}(\frac{x_{1}}{x_{0}}) + \operatorname{log}(x_{0})\right))) + \operatorname{exp}({\frac{\theta_{0}}{{x_{1}^{x_{0}}}}^{\left(x_{1} \cdot \theta_{1}\right)}})\right)$$",17.860443619072115;-1.0866766239456351,20,7.738755551425887e-2,7.738755551425887e-2,7.738755551425887e-2,7.738755551425887e-2,-6.141413453842383,-6.141413453842383,-6.141413453842383,48.055990217422504,48.055990217422504,48.055990217422504 -406,0,(((0.5927311328530339 * (x1 * 0.5551836283345604)) * -2.4293878003427967) + x1),"(((t[0] * (x[:, 1] * t[1])) * t[2]) + x[:, 1])","$$\left(\left(\left(\theta_{0} \cdot \left(x_{1} \cdot \theta_{1}\right)\right) \cdot \theta_{2}\right) + x_{1}\right)$$",0.5927311328530339;0.5551836283345604;-2.4293878003427967,9,9.36575100606134e-3,9.36575100606134e-3,9.36575100606134e-3,9.36575100606134e-3,0.13571762545593535,0.13571762545593535,0.13571762545593535,18.54785323208739,18.54785323208739,18.54785323208739 -407,0,Log((((Log(Log(x1)) * (x0 / (x1 + x1))) / (Log((x1 + x1)) * (x1 * 0.5549018726812323))) / Log((Exp((0.49365613100500705 ^ x1)) ^ (-0.46582086176413817 ^ Log((x1 + 0.7860461933936405))))))),"np.log((((np.log(np.log(x[:, 1])) * (x[:, 0] / (x[:, 1] + x[:, 1]))) / (np.log((x[:, 1] + x[:, 1])) * (x[:, 1] * t[0]))) / np.log(np.exp(t[1] ** x[:, 1]) ** t[2] ** np.log((x[:, 1] + t[3])))))","$$\operatorname{log}(\frac{\frac{\left(\operatorname{log}(\operatorname{log}(x_{1})) \cdot \frac{x_{0}}{\left(x_{1} + x_{1}\right)}\right)}{\left(\operatorname{log}(\left(x_{1} + x_{1}\right)) \cdot \left(x_{1} \cdot \theta_{0}\right)\right)}}{\operatorname{log}({\operatorname{exp}({\theta_{1}^{x_{1}}})^{{\theta_{2}^{\operatorname{log}(\left(x_{1} + \theta_{3}\right))}}}})})$$",0.5549018726812323;0.49365613100500705;-0.46582086176413817;0.7860461933936405,32,,,,Infinity,,,,,, -408,0,(Exp((0.28859721147000983 + Log((Log(x1) + -0.8649832341222763)))) + (((x0 + -0.11121165696130231) / (x1 * 0.11894671182384597)) ^ (Log((x1 / x1)) + Exp((x0 * x0))))),"(np.exp((t[0] + np.log((np.log(x[:, 1]) + t[1])))) + ((x[:, 0] + t[2]) / (x[:, 1] * t[3])) ** (np.log((x[:, 1] / x[:, 1])) + np.exp((x[:, 0] * x[:, 0]))))","$$\left(\operatorname{exp}(\left(\theta_{0} + \operatorname{log}(\left(\operatorname{log}(x_{1}) + \theta_{1}\right))\right)) + {\frac{\left(x_{0} + \theta_{2}\right)}{\left(x_{1} \cdot \theta_{3}\right)}^{\left(\operatorname{log}(\frac{x_{1}}{x_{1}}) + \operatorname{exp}(\left(x_{0} \cdot x_{0}\right))\right)}}\right)$$",0.28859721147000983;-0.8649832341222763;-0.11121165696130231;0.11894671182384597,26,,,,Infinity,,,,,, -409,0,Log((((x0 * 0.421126596956682) ^ -0.9951484193009806) / Log(((0.17520470536965793 * (-0.17850502596547557 ^ (x0 ^ -0.5811357118125923))) + 0.8981326639213121)))),"np.log(((x[:, 0] * t[0]) ** t[1] / np.log(((t[2] * t[3] ** x[:, 0] ** t[4]) + t[5]))))","$$\operatorname{log}(\frac{{\left(x_{0} \cdot \theta_{0}\right)^{\theta_{1}}}}{\operatorname{log}(\left(\left(\theta_{2} \cdot {\theta_{3}^{{x_{0}^{\theta_{4}}}}}\right) + \theta_{5}\right))})$$",0.421126596956682;-0.9951484193009806;0.17520470536965793;-0.17850502596547557;-0.5811357118125923;0.8981326639213121,17,,,,Infinity,,,,,, -410,0,(Exp(((x1 + x1) * x1)) + ((x1 / x0) * (x0 + x1))),"(np.exp(((x[:, 1] + x[:, 1]) * x[:, 1])) + ((x[:, 1] / x[:, 0]) * (x[:, 0] + x[:, 1])))","$$\left(\operatorname{exp}(\left(\left(x_{1} + x_{1}\right) \cdot x_{1}\right)) + \left(\frac{x_{1}}{x_{0}} \cdot \left(x_{0} + x_{1}\right)\right)\right)$$",,14,3.1849509049257185,3.1849509049257185,3.1849509049257185,3.1849509049257185,-292.9109665774772,-292.9109665774772,-292.9109665774772,27.951088383625915,27.951088383625915,27.951088383625915 -411,0,(Exp((((x0 * -0.6173831490594965) / x1) ^ ((x0 + x0) * x0))) ^ (((0.9347749902501558 ^ x1) * Exp(x0)) ^ ((0.4872837822003697 * x0) / Log(x0)))),"np.exp(((x[:, 0] * t[0]) / x[:, 1]) ** ((x[:, 0] + x[:, 0]) * x[:, 0])) ** (t[1] ** x[:, 1] * np.exp(x[:, 0])) ** ((t[2] * x[:, 0]) / np.log(x[:, 0]))","$${\operatorname{exp}({\frac{\left(x_{0} \cdot \theta_{0}\right)}{x_{1}}^{\left(\left(x_{0} + x_{0}\right) \cdot x_{0}\right)}})^{{\left({\theta_{1}^{x_{1}}} \cdot \operatorname{exp}(x_{0})\right)^{\frac{\left(\theta_{2} \cdot x_{0}\right)}{\operatorname{log}(x_{0})}}}}}$$",-0.6173831490594965;0.9347749902501558;0.4872837822003697,26,,,,Infinity,,,,,, -412,0,Exp(Log(((0.32196724743424965 * Exp(x1)) * ((x0 * -0.9169903236113454) + x0)))),"np.exp(np.log(((t[0] * np.exp(x[:, 1])) * ((x[:, 0] * t[1]) + x[:, 0]))))","$$\operatorname{exp}(\operatorname{log}(\left(\left(\theta_{0} \cdot \operatorname{exp}(x_{1})\right) \cdot \left(\left(x_{0} \cdot \theta_{1}\right) + x_{0}\right)\right)))$$",0.32196724743424965;-0.9169903236113454,12,1.2801018732274464e-2,1.2801018732274464e-2,1.2801018732274464e-2,1.2801018732274464e-2,-0.18129286795613186,-0.18129286795613186,-0.18129286795613186,27.404292576258086,27.404292576258086,27.404292576258086 -413,0,Log(Exp(Log(((x1 ^ x1) * 1.4881273885655018)))),"np.log(np.exp(np.log((x[:, 1] ** x[:, 1] * t[0]))))","$$\operatorname{log}(\operatorname{exp}(\operatorname{log}(\left({x_{1}^{x_{1}}} \cdot \theta_{0}\right))))$$",1.4881273885655018,8,1.4109506990011728e-2,1.4109506990011728e-2,1.4109506990011728e-2,1.4109506990011728e-2,-0.3020416832650472,-0.3020416832650472,-0.3020416832650472,16.36769924548309,16.36769924548309,16.36769924548309 -414,0,(Exp(((x0 ^ x1) ^ (x1 * -0.4351149609616847))) + ((3.2348572358177207e-3 / Log((x0 + x0))) + -2.6684536576425133)),"(np.exp(x[:, 0] ** x[:, 1] ** (x[:, 1] * t[0])) + ((t[1] / np.log((x[:, 0] + x[:, 0]))) + t[2]))","$$\left(\operatorname{exp}({{x_{0}^{x_{1}}}^{\left(x_{1} \cdot \theta_{0}\right)}}) + \left(\frac{\theta_{1}}{\operatorname{log}(\left(x_{0} + x_{0}\right))} + \theta_{2}\right)\right)$$",-0.4351149609616847;3.2348572358177207e-3;-2.6684536576425133,17,4.556029636057579e-3,4.556029636057579e-3,4.556029636057579e-3,4.556029636057579e-3,0.5795642965741272,0.5795642965741272,0.5795642965741272,43.41969304749111,43.41969304749111,43.41969304749111 -415,0,Log((((0.8925994680297649 * ((x1 * 0.9768153511024064) + -0.25055211499897034)) + Log((0.2899087694463376 + (x1 + -0.6903328264189943)))) + ((((0.7582336137347293 + x1) + x0) * ((x0 * 0.3892619258154666) ^ 0.2544170185984005)) ^ ((-0.714551979567817 + (x0 + 0.9546425948558918)) + ((x0 * -0.5967455525341415) ^ x0))))),"np.log((((t[0] * ((x[:, 1] * t[1]) + t[2])) + np.log((t[3] + (x[:, 1] + t[4])))) + (((t[5] + x[:, 1]) + x[:, 0]) * (x[:, 0] * t[6]) ** t[7]) ** ((t[8] + (x[:, 0] + t[9])) + (x[:, 0] * t[10]) ** x[:, 0])))","$$\operatorname{log}(\left(\left(\left(\theta_{0} \cdot \left(\left(x_{1} \cdot \theta_{1}\right) + \theta_{2}\right)\right) + \operatorname{log}(\left(\theta_{3} + \left(x_{1} + \theta_{4}\right)\right))\right) + {\left(\left(\left(\theta_{5} + x_{1}\right) + x_{0}\right) \cdot {\left(x_{0} \cdot \theta_{6}\right)^{\theta_{7}}}\right)^{\left(\left(\theta_{8} + \left(x_{0} + \theta_{9}\right)\right) + {\left(x_{0} \cdot \theta_{10}\right)^{x_{0}}}\right)}}\right))$$",0.8925994680297649;0.9768153511024064;-0.25055211499897034;0.2899087694463376;-0.6903328264189943;0.7582336137347293;0.3892619258154666;0.2544170185984005;-0.714551979567817;0.9546425948558918;-0.5967455525341415,39,,,,Infinity,,,,,, -416,0,Log((((x0 + 0.8347649260958698) * (x1 ^ x1)) ^ ((x0 ^ -2.408464179362201) + (Exp(x0) ^ (-4.6265968288863135 * x0))))),"np.log(((x[:, 0] + t[0]) * x[:, 1] ** x[:, 1]) ** (x[:, 0] ** t[1] + np.exp(x[:, 0]) ** (t[2] * x[:, 0])))","$$\operatorname{log}({\left(\left(x_{0} + \theta_{0}\right) \cdot {x_{1}^{x_{1}}}\right)^{\left({x_{0}^{\theta_{1}}} + {\operatorname{exp}(x_{0})^{\left(\theta_{2} \cdot x_{0}\right)}}\right)}})$$",0.8347649260958698;-2.408464179362201;-4.6265968288863135,19,6.16253665639903e-2,6.16253665639903e-2,6.16253665639903e-2,6.16253665639903e-2,-4.686860360862034,-4.686860360862034,-4.686860360862034,45.627626927927395,45.627626927927395,45.627626927927395 -417,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 -418,0,((x0 * -0.11025883485074336) * -0.31964530766070187),"((x[:, 0] * t[0]) * t[1])","$$\left(\left(x_{0} \cdot \theta_{0}\right) \cdot \theta_{1}\right)$$",-0.11025883485074336;-0.31964530766070187,5,1.3066544018894101e-2,1.3066544018894101e-2,1.3066544018894101e-2,1.3066544018894101e-2,-0.20579584962547082,-0.20579584962547082,-0.20579584962547082,9.546671203692835,9.546671203692835,9.546671203692835 -419,0,((Log(Log(x1)) ^ ((0.1119233986992203 / Exp(x1)) ^ (Exp(x0) / Exp(Exp((x0 + x0)))))) + (Log(Log(x1)) / (-0.6588136398156079 ^ x0))),"(np.log(np.log(x[:, 1])) ** (t[0] / np.exp(x[:, 1])) ** (np.exp(x[:, 0]) / np.exp(np.exp((x[:, 0] + x[:, 0])))) + (np.log(np.log(x[:, 1])) / t[1] ** x[:, 0]))","$$\left({\operatorname{log}(\operatorname{log}(x_{1}))^{{\frac{\theta_{0}}{\operatorname{exp}(x_{1})}^{\frac{\operatorname{exp}(x_{0})}{\operatorname{exp}(\operatorname{exp}(\left(x_{0} + x_{0}\right)))}}}}} + \frac{\operatorname{log}(\operatorname{log}(x_{1}))}{{\theta_{1}^{x_{0}}}}\right)$$",0.1119233986992203;-0.6588136398156079,25,,,,Infinity,,,,,, -420,0,Log((((x1 * (((x1 * x0) * 0.45256171231312503) ^ Exp(x0))) * (((x0 / Log(x0)) * x1) / (Exp((x1 ^ -0.511023719556475)) * (0.8824063525967433 + x1)))) ^ Exp(((-0.27471190016963876 * Log((x0 + x0))) / ((x0 * -0.23605872607918443) ^ 0.6219660128792242))))),"np.log(((x[:, 1] * ((x[:, 1] * x[:, 0]) * t[0]) ** np.exp(x[:, 0])) * (((x[:, 0] / np.log(x[:, 0])) * x[:, 1]) / (np.exp(x[:, 1] ** t[1]) * (t[2] + x[:, 1])))) ** np.exp(((t[3] * np.log((x[:, 0] + x[:, 0]))) / (x[:, 0] * t[4]) ** t[5])))","$$\operatorname{log}({\left(\left(x_{1} \cdot {\left(\left(x_{1} \cdot x_{0}\right) \cdot \theta_{0}\right)^{\operatorname{exp}(x_{0})}}\right) \cdot \frac{\left(\frac{x_{0}}{\operatorname{log}(x_{0})} \cdot x_{1}\right)}{\left(\operatorname{exp}({x_{1}^{\theta_{1}}}) \cdot \left(\theta_{2} + x_{1}\right)\right)}\right)^{\operatorname{exp}(\frac{\left(\theta_{3} \cdot \operatorname{log}(\left(x_{0} + x_{0}\right))\right)}{{\left(x_{0} \cdot \theta_{4}\right)^{\theta_{5}}}})}})$$",0.45256171231312503;-0.511023719556475;0.8824063525967433;-0.27471190016963876;-0.23605872607918443;0.6219660128792242,41,,,,Infinity,,,,,, -421,0,Exp(((Exp(Exp(((x1 ^ x1) / x1))) * Log(((x0 / x1) * (-0.10505635920138445 / x1)))) ^ Exp(Exp((((-0.39323287098190507 * x0) * 0.13724307319366447) ^ 0.640223659306224))))),"np.exp((np.exp(np.exp((x[:, 1] ** x[:, 1] / x[:, 1]))) * np.log(((x[:, 0] / x[:, 1]) * (t[0] / x[:, 1])))) ** np.exp(np.exp(((t[1] * x[:, 0]) * t[2]) ** t[3])))","$$\operatorname{exp}({\left(\operatorname{exp}(\operatorname{exp}(\frac{{x_{1}^{x_{1}}}}{x_{1}})) \cdot \operatorname{log}(\left(\frac{x_{0}}{x_{1}} \cdot \frac{\theta_{0}}{x_{1}}\right))\right)^{\operatorname{exp}(\operatorname{exp}({\left(\left(\theta_{1} \cdot x_{0}\right) \cdot \theta_{2}\right)^{\theta_{3}}}))}})$$",-0.10505635920138445;-0.39323287098190507;0.13724307319366447;0.640223659306224,27,,,,Infinity,,,,,, -422,0,Exp(Exp((Log((0.8692104334798472 / (-0.756663021755025 + x1))) + Exp(((0.9998279642496607 / x0) / (0.9054587642151173 / x1)))))),"np.exp(np.exp((np.log((t[0] / (t[1] + x[:, 1]))) + np.exp(((t[2] / x[:, 0]) / (t[3] / x[:, 1]))))))","$$\operatorname{exp}(\operatorname{exp}(\left(\operatorname{log}(\frac{\theta_{0}}{\left(\theta_{1} + x_{1}\right)}) + \operatorname{exp}(\frac{\frac{\theta_{2}}{x_{0}}}{\frac{\theta_{3}}{x_{1}}})\right)))$$",0.8692104334798472;-0.756663021755025;0.9998279642496607;0.9054587642151173,17,,,,Infinity,,,,,, -423,0,(Log(Log(Exp(((x1 ^ x1) + (x0 + x0))))) / Log(((0.9631886364151273 + (x0 ^ 0.9051925298459599)) * (x1 ^ 0.6316804086006302)))),"(np.log(np.log(np.exp((x[:, 1] ** x[:, 1] + (x[:, 0] + x[:, 0]))))) / np.log(((t[0] + x[:, 0] ** t[1]) * x[:, 1] ** t[2])))","$$\frac{\operatorname{log}(\operatorname{log}(\operatorname{exp}(\left({x_{1}^{x_{1}}} + \left(x_{0} + x_{0}\right)\right))))}{\operatorname{log}(\left(\left(\theta_{0} + {x_{0}^{\theta_{1}}}\right) \cdot {x_{1}^{\theta_{2}}}\right))}$$",0.9631886364151273;0.9051925298459599;0.6316804086006302,21,5166.093582160621,5166.093582160621,5166.093582160621,5166.093582160621,-476732.1125306442,-476732.1125306442,-476732.1125306442,4701.688714874723,4701.688714874723,4701.688714874723 -424,0,Log(Log(((x0 / x0) + Exp(Exp(((x1 ^ 0.908401530544901) ^ (-0.7333189382085356 ^ x0))))))),"np.log(np.log(((x[:, 0] / x[:, 0]) + np.exp(np.exp(x[:, 1] ** t[0] ** t[1] ** x[:, 0])))))","$$\operatorname{log}(\operatorname{log}(\left(\frac{x_{0}}{x_{0}} + \operatorname{exp}(\operatorname{exp}({{x_{1}^{\theta_{0}}}^{{\theta_{1}^{x_{0}}}}}))\right)))$$",0.908401530544901;-0.7333189382085356,15,,,,Infinity,,,,,, -425,0,Log(((((0.7454694115548722 / Exp(x1)) * Exp(x0)) + Exp(((x1 * -0.5333823033385897) * 5.0276024006315034e-2))) / ((-0.8249727487396475 * x1) / Log(Log((x1 * 0.5284942629289364)))))),"np.log(((((t[0] / np.exp(x[:, 1])) * np.exp(x[:, 0])) + np.exp(((x[:, 1] * t[1]) * t[2]))) / ((t[3] * x[:, 1]) / np.log(np.log((x[:, 1] * t[4]))))))","$$\operatorname{log}(\frac{\left(\left(\frac{\theta_{0}}{\operatorname{exp}(x_{1})} \cdot \operatorname{exp}(x_{0})\right) + \operatorname{exp}(\left(\left(x_{1} \cdot \theta_{1}\right) \cdot \theta_{2}\right))\right)}{\frac{\left(\theta_{3} \cdot x_{1}\right)}{\operatorname{log}(\operatorname{log}(\left(x_{1} \cdot \theta_{4}\right)))}})$$",0.7454694115548722;-0.5333823033385897;5.0276024006315034e-2;-0.8249727487396475;0.5284942629289364,25,,,,Infinity,,,,,, -426,0,Log(Log(Exp((-0.6638056786451311 ^ ((x1 + x0) * (-0.8789168898583347 * (x1 / x0))))))),"np.log(np.log(np.exp(t[0] ** ((x[:, 1] + x[:, 0]) * (t[1] * (x[:, 1] / x[:, 0]))))))","$$\operatorname{log}(\operatorname{log}(\operatorname{exp}({\theta_{0}^{\left(\left(x_{1} + x_{0}\right) \cdot \left(\theta_{1} \cdot \frac{x_{1}}{x_{0}}\right)\right)}})))$$",-0.6638056786451311;-0.8789168898583347,14,,,,Infinity,,,,,, -427,0,(Exp(Exp(((0.24941529173939014 + ((0.8894232744809423 / x1) * 0.48257905751153707)) * 0.35484941868834385))) ^ (Log(Log((0.15416985441743314 * Log((-0.48890437819440713 ^ x0))))) + (0.3310452436205764 * (-0.8042287912653618 / x1)))),"np.exp(np.exp(((t[0] + ((t[1] / x[:, 1]) * t[2])) * t[3]))) ** (np.log(np.log((t[4] * np.log(t[5] ** x[:, 0])))) + (t[6] * (t[7] / x[:, 1])))","$${\operatorname{exp}(\operatorname{exp}(\left(\left(\theta_{0} + \left(\frac{\theta_{1}}{x_{1}} \cdot \theta_{2}\right)\right) \cdot \theta_{3}\right)))^{\left(\operatorname{log}(\operatorname{log}(\left(\theta_{4} \cdot \operatorname{log}({\theta_{5}^{x_{0}}})\right))) + \left(\theta_{6} \cdot \frac{\theta_{7}}{x_{1}}\right)\right)}}$$",0.24941529173939014;0.8894232744809423;0.48257905751153707;0.35484941868834385;0.15416985441743314;-0.48890437819440713;0.3310452436205764;-0.8042287912653618,26,,,,Infinity,,,,,, -428,0,Log(Log(Log(Log((0.4127984977014052 / (x0 + x1)))))),"np.log(np.log(np.log(np.log((t[0] / (x[:, 0] + x[:, 1]))))))","$$\operatorname{log}(\operatorname{log}(\operatorname{log}(\operatorname{log}(\frac{\theta_{0}}{\left(x_{0} + x_{1}\right)}))))$$",0.4127984977014052,9,,,,Infinity,,,,,, -429,0,Exp(Exp(((x1 + (x1 + 0.3810151082134101)) ^ (Exp(x1) / (x0 / Exp((x1 ^ 0.5475522197606351))))))),"np.exp(np.exp((x[:, 1] + (x[:, 1] + t[0])) ** (np.exp(x[:, 1]) / (x[:, 0] / np.exp(x[:, 1] ** t[1])))))","$$\operatorname{exp}(\operatorname{exp}({\left(x_{1} + \left(x_{1} + \theta_{0}\right)\right)^{\frac{\operatorname{exp}(x_{1})}{\frac{x_{0}}{\operatorname{exp}({x_{1}^{\theta_{1}}})}}}}))$$",0.3810151082134101;0.5475522197606351,17,Infinity,Infinity,Infinity,Infinity,-Infinity,-Infinity,-Infinity,Infinity,Infinity,Infinity -430,0,(((0.13380028062560978 + (-0.25184175444431733 ^ Exp((x1 + x1)))) * 0.6023709548495503) + 0.7509012088865283),"(((t[0] + t[1] ** np.exp((x[:, 1] + x[:, 1]))) * t[2]) + t[3])","$$\left(\left(\left(\theta_{0} + {\theta_{1}^{\operatorname{exp}(\left(x_{1} + x_{1}\right))}}\right) \cdot \theta_{2}\right) + \theta_{3}\right)$$",0.13380028062560978;-0.25184175444431733;0.6023709548495503;0.7509012088865283,12,,,,Infinity,,,,,, -431,0,(Log((Exp((((x0 * 0.2073031038037073) + -0.34095884386336306) ^ ((x1 * 0.44242370954817156) * x1))) ^ (x0 + (((x0 / x1) ^ (x0 ^ x1)) + -0.5689549468969601)))) / (((x0 ^ 0.368245723298383) * -0.36450393566914474) ^ -0.10406134487512575)),"(np.log(np.exp(((x[:, 0] * t[0]) + t[1]) ** ((x[:, 1] * t[2]) * x[:, 1])) ** (x[:, 0] + ((x[:, 0] / x[:, 1]) ** x[:, 0] ** x[:, 1] + t[3]))) / (x[:, 0] ** t[4] * t[5]) ** t[6])","$$\frac{\operatorname{log}({\operatorname{exp}({\left(\left(x_{0} \cdot \theta_{0}\right) + \theta_{1}\right)^{\left(\left(x_{1} \cdot \theta_{2}\right) \cdot x_{1}\right)}})^{\left(x_{0} + \left({\frac{x_{0}}{x_{1}}^{{x_{0}^{x_{1}}}}} + \theta_{3}\right)\right)}})}{{\left({x_{0}^{\theta_{4}}} \cdot \theta_{5}\right)^{\theta_{6}}}}$$",0.2073031038037073;-0.34095884386336306;0.44242370954817156;-0.5689549468969601;0.368245723298383;-0.36450393566914474;-0.10406134487512575,33,,,,Infinity,,,,,, -432,0,Log(Exp((((((x1 * x1) + (x1 / x0)) + 0.485813465542868) * 0.23016696151200025) + (-0.15179605193159698 * x0)))),"np.log(np.exp((((((x[:, 1] * x[:, 1]) + (x[:, 1] / x[:, 0])) + t[0]) * t[1]) + (t[2] * x[:, 0]))))","$$\operatorname{log}(\operatorname{exp}(\left(\left(\left(\left(\left(x_{1} \cdot x_{1}\right) + \frac{x_{1}}{x_{0}}\right) + \theta_{0}\right) \cdot \theta_{1}\right) + \left(\theta_{2} \cdot x_{0}\right)\right)))$$",0.485813465542868;0.23016696151200025;-0.15179605193159698,17,2.706116566009873e-3,2.706116566009873e-3,2.706116566009873e-3,2.706116566009873e-3,0.750276422045559,0.750276422045559,0.750276422045559,41.415716519569536,41.415716519569536,41.415716519569536 -433,0,(x1 ^ x1),"x[:, 1] ** x[:, 1]","$${x_{1}^{x_{1}}}$$",,3,0.4403077718480359,0.4403077718480359,0.4403077718480359,0.4403077718480359,-39.63211229262252,-39.63211229262252,-39.63211229262252,2.4757185363430683,2.4757185363430683,2.4757185363430683 -434,0,((((Exp((x1 + (x1 + x0))) + 0.6910006039702367) + 0.740864242239983) * (Exp(x0) + 0.9166965214708928)) + (((0.3383384768843253 + (x1 + x0)) ^ -1.5038234260063454e-2) + (((x0 * (-0.17425746050180524 ^ (x0 / x1))) ^ x0) / x0))),"((((np.exp((x[:, 1] + (x[:, 1] + x[:, 0]))) + t[0]) + t[1]) * (np.exp(x[:, 0]) + t[2])) + ((t[3] + (x[:, 1] + x[:, 0])) ** t[4] + ((x[:, 0] * t[5] ** (x[:, 0] / x[:, 1])) ** x[:, 0] / x[:, 0])))","$$\left(\left(\left(\left(\operatorname{exp}(\left(x_{1} + \left(x_{1} + x_{0}\right)\right)) + \theta_{0}\right) + \theta_{1}\right) \cdot \left(\operatorname{exp}(x_{0}) + \theta_{2}\right)\right) + \left({\left(\theta_{3} + \left(x_{1} + x_{0}\right)\right)^{\theta_{4}}} + \frac{{\left(x_{0} \cdot {\theta_{5}^{\frac{x_{0}}{x_{1}}}}\right)^{x_{0}}}}{x_{0}}\right)\right)$$",0.6910006039702367;0.740864242239983;0.9166965214708928;0.3383384768843253;-1.5038234260063454e-2;-0.17425746050180524,35,,,,Infinity,,,,,, -435,0,(Log(Exp(Exp(((-0.9385541358258517 ^ x1) * -0.782145144493338)))) ^ (1.9432145039520732e-2 ^ Log(((-0.44109637367532406 + x0) / x0)))),"np.log(np.exp(np.exp((t[0] ** x[:, 1] * t[1])))) ** t[2] ** np.log(((t[3] + x[:, 0]) / x[:, 0]))","$${\operatorname{log}(\operatorname{exp}(\operatorname{exp}(\left({\theta_{0}^{x_{1}}} \cdot \theta_{1}\right))))^{{\theta_{2}^{\operatorname{log}(\frac{\left(\theta_{3} + x_{0}\right)}{x_{0}})}}}}$$",-0.9385541358258517;-0.782145144493338;1.9432145039520732e-2;-0.44109637367532406,17,,,,Infinity,,,,,, -436,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 -437,0,Log(Exp(((Log(Log(x1)) / (0.34270188383254996 * Exp(x1))) / Exp((0.6998146402471606 * Log((x1 * -0.1426250943876608))))))),"np.log(np.exp(((np.log(np.log(x[:, 1])) / (t[0] * np.exp(x[:, 1]))) / np.exp((t[1] * np.log((x[:, 1] * t[2])))))))","$$\operatorname{log}(\operatorname{exp}(\frac{\frac{\operatorname{log}(\operatorname{log}(x_{1}))}{\left(\theta_{0} \cdot \operatorname{exp}(x_{1})\right)}}{\operatorname{exp}(\left(\theta_{1} \cdot \operatorname{log}(\left(x_{1} \cdot \theta_{2}\right))\right))}))$$",0.34270188383254996;0.6998146402471606;-0.1426250943876608,18,,,,Infinity,,,,,, -438,0,(-0.2701133249686303 + (x1 ^ x0)),"(t[0] + x[:, 1] ** x[:, 0])","$$\left(\theta_{0} + {x_{1}^{x_{0}}}\right)$$",-0.2701133249686303,5,1.057854561101814e-2,1.057854561101814e-2,1.057854561101814e-2,1.057854561101814e-2,2.3799531506189964e-2,2.3799531506189964e-2,2.3799531506189964e-2,10.07763518858806,10.07763518858806,10.07763518858806 -439,0,Exp((Log((x0 ^ x0)) + Exp(((0.28456349337390896 + x1) ^ -0.9101627260695312)))),"np.exp((np.log(x[:, 0] ** x[:, 0]) + np.exp((t[0] + x[:, 1]) ** t[1])))","$$\operatorname{exp}(\left(\operatorname{log}({x_{0}^{x_{0}}}) + \operatorname{exp}({\left(\theta_{0} + x_{1}\right)^{\theta_{1}}})\right))$$",0.28456349337390896;-0.9101627260695312,12,8.466783525338161e7,8.466783525338161e7,8.466783525338161e7,8.466783525338161e7,-7.813246118071454e9,-7.813246118071454e9,-7.813246118071454e9,7.620107912081511e7,7.620107912081511e7,7.620107912081511e7 -440,0,Exp((((x0 + 0.8173628238030505) ^ ((-0.6375941690032918 ^ x0) / x0)) ^ ((((-0.37083685366623365 + x1) * -0.4286992203945742) * 3.747434514617776e-2) ^ ((x1 / x0) * ((x1 ^ (0.20915791270380701 / x1)) + -0.8308696906648474))))),"np.exp((x[:, 0] + t[0]) ** (t[1] ** x[:, 0] / x[:, 0]) ** (((t[2] + x[:, 1]) * t[3]) * t[4]) ** ((x[:, 1] / x[:, 0]) * (x[:, 1] ** (t[5] / x[:, 1]) + t[6])))","$$\operatorname{exp}({{\left(x_{0} + \theta_{0}\right)^{\frac{{\theta_{1}^{x_{0}}}}{x_{0}}}}^{{\left(\left(\left(\theta_{2} + x_{1}\right) \cdot \theta_{3}\right) \cdot \theta_{4}\right)^{\left(\frac{x_{1}}{x_{0}} \cdot \left({x_{1}^{\frac{\theta_{5}}{x_{1}}}} + \theta_{6}\right)\right)}}}})$$",0.8173628238030505;-0.6375941690032918;-0.37083685366623365;-0.4286992203945742;3.747434514617776e-2;0.20915791270380701;-0.8308696906648474,30,,,,Infinity,,,,,, -441,0,(Log(Exp((-0.5036166735714815 * x1))) * (Exp((x0 * Exp((x0 ^ 0.7432021678048366)))) + Exp(Exp(Exp((x1 * x1)))))),"(np.log(np.exp((t[0] * x[:, 1]))) * (np.exp((x[:, 0] * np.exp(x[:, 0] ** t[1]))) + np.exp(np.exp(np.exp((x[:, 1] * x[:, 1]))))))","$$\left(\operatorname{log}(\operatorname{exp}(\left(\theta_{0} \cdot x_{1}\right))) \cdot \left(\operatorname{exp}(\left(x_{0} \cdot \operatorname{exp}({x_{0}^{\theta_{1}}})\right)) + \operatorname{exp}(\operatorname{exp}(\operatorname{exp}(\left(x_{1} \cdot x_{1}\right))))\right)\right)$$",-0.5036166735714815;0.7432021678048366,20,696.1013721943077,696.1013721943077,696.1013721943077,696.1013721943077,-64236.0426561752,-64236.0426561752,-64236.0426561752,672.121915679209,672.121915679209,672.121915679209 -442,0,((Exp(Log((-0.4768892057844869 + (x0 / x1)))) / (Exp((-0.3253133321298409 ^ x0)) / Log((x1 * x0)))) * ((Exp((x1 ^ x1)) + Log((x0 * 0.22593970365955207))) ^ ((x1 + (0.9326794233666391 ^ x0)) * (x0 / x0)))),"((np.exp(np.log((t[0] + (x[:, 0] / x[:, 1])))) / (np.exp(t[1] ** x[:, 0]) / np.log((x[:, 1] * x[:, 0])))) * (np.exp(x[:, 1] ** x[:, 1]) + np.log((x[:, 0] * t[2]))) ** ((x[:, 1] + t[3] ** x[:, 0]) * (x[:, 0] / x[:, 0])))","$$\left(\frac{\operatorname{exp}(\operatorname{log}(\left(\theta_{0} + \frac{x_{0}}{x_{1}}\right)))}{\frac{\operatorname{exp}({\theta_{1}^{x_{0}}})}{\operatorname{log}(\left(x_{1} \cdot x_{0}\right))}} \cdot {\left(\operatorname{exp}({x_{1}^{x_{1}}}) + \operatorname{log}(\left(x_{0} \cdot \theta_{2}\right))\right)^{\left(\left(x_{1} + {\theta_{3}^{x_{0}}}\right) \cdot \frac{x_{0}}{x_{0}}\right)}}\right)$$",-0.4768892057844869;-0.3253133321298409;0.22593970365955207;0.9326794233666391,37,,,,Infinity,,,,,, -443,0,(Exp(((-0.41982417575031494 ^ x0) + Log(x0))) ^ (((((0.8968670663005786 * x1) + 0.9295983227060814) + 0.10211722351119423) + Exp(x0)) / Exp((Log((0.7992151862752483 ^ (x1 + x1))) + 0.7128458429699861)))),"np.exp((t[0] ** x[:, 0] + np.log(x[:, 0]))) ** (((((t[1] * x[:, 1]) + t[2]) + t[3]) + np.exp(x[:, 0])) / np.exp((np.log(t[4] ** (x[:, 1] + x[:, 1])) + t[5])))","$${\operatorname{exp}(\left({\theta_{0}^{x_{0}}} + \operatorname{log}(x_{0})\right))^{\frac{\left(\left(\left(\left(\theta_{1} \cdot x_{1}\right) + \theta_{2}\right) + \theta_{3}\right) + \operatorname{exp}(x_{0})\right)}{\operatorname{exp}(\left(\operatorname{log}({\theta_{4}^{\left(x_{1} + x_{1}\right)}}) + \theta_{5}\right))}}}$$",-0.41982417575031494;0.8968670663005786;0.9295983227060814;0.10211722351119423;0.7992151862752483;0.7128458429699861,28,,,,Infinity,,,,,, -444,0,((((-0.6551844744534805 / (x0 + (0.1764673299821372 ^ x1))) * 0.4749353023062388) ^ 0.3581577928672145) + 0.7637672223304791),"(((t[0] / (x[:, 0] + t[1] ** x[:, 1])) * t[2]) ** t[3] + t[4])","$$\left({\left(\frac{\theta_{0}}{\left(x_{0} + {\theta_{1}^{x_{1}}}\right)} \cdot \theta_{2}\right)^{\theta_{3}}} + \theta_{4}\right)$$",-0.6551844744534805;0.1764673299821372;0.4749353023062388;0.3581577928672145;0.7637672223304791,13,,,,Infinity,,,,,, -445,0,((((1.4394226479249487e-2 ^ x0) * (0.7832886086275295 + (x1 * 1.3812816056612656))) / (((0.7717419989024104 ^ x0) * 1.4074777919434447) ^ ((((x0 + x1) * x1) + x1) * 0.16258488570358678))) * 2.2488128646184555),"(((t[0] ** x[:, 0] * (t[1] + (x[:, 1] * t[2]))) / (t[3] ** x[:, 0] * t[4]) ** ((((x[:, 0] + x[:, 1]) * x[:, 1]) + x[:, 1]) * t[5])) * t[6])","$$\left(\frac{\left({\theta_{0}^{x_{0}}} \cdot \left(\theta_{1} + \left(x_{1} \cdot \theta_{2}\right)\right)\right)}{{\left({\theta_{3}^{x_{0}}} \cdot \theta_{4}\right)^{\left(\left(\left(\left(x_{0} + x_{1}\right) \cdot x_{1}\right) + x_{1}\right) \cdot \theta_{5}\right)}}} \cdot \theta_{6}\right)$$",1.4394226479249487e-2;0.7832886086275295;1.3812816056612656;0.7717419989024104;1.4074777919434447;0.16258488570358678;2.2488128646184555,27,3.2523951115122907e-3,3.2523951115122907e-3,3.2523951115122907e-3,3.2523951115122907e-3,0.6998652037499041,0.6998652037499041,0.6998652037499041,66.6889757276673,66.6889757276673,66.6889757276673 -446,0,(((Log((x1 + -0.7377645246910565)) / (x1 / (x1 + x1))) / ((x0 ^ (x1 * -0.19969264068088377)) * Exp((-0.7491405451130988 ^ x1)))) / ((Exp((0.515263263681492 ^ x0)) + -0.8261127004761506) + (Exp((x0 ^ x0)) + Log((-0.27391920999656283 + (x0 / x1)))))),"(((np.log((x[:, 1] + t[0])) / (x[:, 1] / (x[:, 1] + x[:, 1]))) / (x[:, 0] ** (x[:, 1] * t[1]) * np.exp(t[2] ** x[:, 1]))) / ((np.exp(t[3] ** x[:, 0]) + t[4]) + (np.exp(x[:, 0] ** x[:, 0]) + np.log((t[5] + (x[:, 0] / x[:, 1]))))))","$$\frac{\frac{\frac{\operatorname{log}(\left(x_{1} + \theta_{0}\right))}{\frac{x_{1}}{\left(x_{1} + x_{1}\right)}}}{\left({x_{0}^{\left(x_{1} \cdot \theta_{1}\right)}} \cdot \operatorname{exp}({\theta_{2}^{x_{1}}})\right)}}{\left(\left(\operatorname{exp}({\theta_{3}^{x_{0}}}) + \theta_{4}\right) + \left(\operatorname{exp}({x_{0}^{x_{0}}}) + \operatorname{log}(\left(\theta_{5} + \frac{x_{0}}{x_{1}}\right))\right)\right)}$$",-0.7377645246910565;-0.19969264068088377;-0.7491405451130988;0.515263263681492;-0.8261127004761506;-0.27391920999656283,40,,,,Infinity,,,,,, -447,0,((Exp(Exp((-10.13351588147056 / x0))) * 1.156642343918389) + Log((Exp((x1 ^ (x0 * 12.438181741571105))) * (x1 * (x0 ^ x0))))),"((np.exp(np.exp((t[0] / x[:, 0]))) * t[1]) + np.log((np.exp(x[:, 1] ** (x[:, 0] * t[2])) * (x[:, 1] * x[:, 0] ** x[:, 0]))))","$$\left(\left(\operatorname{exp}(\operatorname{exp}(\frac{\theta_{0}}{x_{0}})) \cdot \theta_{1}\right) + \operatorname{log}(\left(\operatorname{exp}({x_{1}^{\left(x_{0} \cdot \theta_{2}\right)}}) \cdot \left(x_{1} \cdot {x_{0}^{x_{0}}}\right)\right))\right)$$",-10.13351588147056;1.156642343918389;12.438181741571105,21,0.3758789848681419,0.3758789848681419,0.3758789848681419,0.3758789848681419,-33.68654904158816,-33.68654904158816,-33.68654904158816,52.542782016544855,52.542782016544855,52.542782016544855 -448,0,(Log((-0.47583371905674154 * (0.519754856712996 + (-0.3018253974665701 + (0.1502249459288051 * x1))))) * ((((x1 / ((x1 / x1) ^ -0.7443956767806825)) ^ Log((-0.9349902445692168 * (x0 + x0)))) * -0.1257865221026404) * (Log(x1) * -0.15081952940141374))),"(np.log((t[0] * (t[1] + (t[2] + (t[3] * x[:, 1]))))) * (((x[:, 1] / (x[:, 1] / x[:, 1]) ** t[4]) ** np.log((t[5] * (x[:, 0] + x[:, 0]))) * t[6]) * (np.log(x[:, 1]) * t[7])))","$$\left(\operatorname{log}(\left(\theta_{0} \cdot \left(\theta_{1} + \left(\theta_{2} + \left(\theta_{3} \cdot x_{1}\right)\right)\right)\right)) \cdot \left(\left({\frac{x_{1}}{{\frac{x_{1}}{x_{1}}^{\theta_{4}}}}^{\operatorname{log}(\left(\theta_{5} \cdot \left(x_{0} + x_{0}\right)\right))}} \cdot \theta_{6}\right) \cdot \left(\operatorname{log}(x_{1}) \cdot \theta_{7}\right)\right)\right)$$",-0.47583371905674154;0.519754856712996;-0.3018253974665701;0.1502249459288051;-0.7443956767806825;-0.9349902445692168;-0.1257865221026404;-0.15081952940141374,32,,,,Infinity,,,,,, -449,0,Exp(((((x1 ^ Exp((x1 * 0.670331012520308))) + 0.17539372804737663) + ((Log((-0.5900732285799757 + x1)) * 0.5916326308201681) * (Log((x1 ^ 0.7931898592938101)) * x0))) ^ (0.2537171408621066 + (-0.3037109513797772 * x0)))),"np.exp(((x[:, 1] ** np.exp((x[:, 1] * t[0])) + t[1]) + ((np.log((t[2] + x[:, 1])) * t[3]) * (np.log(x[:, 1] ** t[4]) * x[:, 0]))) ** (t[5] + (t[6] * x[:, 0])))","$$\operatorname{exp}({\left(\left({x_{1}^{\operatorname{exp}(\left(x_{1} \cdot \theta_{0}\right))}} + \theta_{1}\right) + \left(\left(\operatorname{log}(\left(\theta_{2} + x_{1}\right)) \cdot \theta_{3}\right) \cdot \left(\operatorname{log}({x_{1}^{\theta_{4}}}) \cdot x_{0}\right)\right)\right)^{\left(\theta_{5} + \left(\theta_{6} \cdot x_{0}\right)\right)}})$$",0.670331012520308;0.17539372804737663;-0.5900732285799757;0.5916326308201681;0.7931898592938101;0.2537171408621066;-0.3037109513797772,29,,,,Infinity,,,,,, -450,0,((x1 * x1) / x1),"((x[:, 1] * x[:, 1]) / x[:, 1])","$$\frac{\left(x_{1} \cdot x_{1}\right)}{x_{1}}$$",,5,9.206922096950058e-2,9.206922096950058e-2,9.206922096950058e-2,9.206922096950058e-2,-7.496254584436793,-7.496254584436793,-7.496254584436793,5.575923742213099,5.575923742213099,5.575923742213099 -451,0,((Exp(x1) * -0.19850896584355748) + x1),"((np.exp(x[:, 1]) * t[0]) + x[:, 1])","$$\left(\left(\operatorname{exp}(x_{1}) \cdot \theta_{0}\right) + x_{1}\right)$$",-0.19850896584355748,6,1.2490362486789055e-2,1.2490362486789055e-2,1.2490362486789055e-2,1.2490362486789055e-2,-0.15262514901492685,-0.15262514901492685,-0.15262514901492685,11.688793736210352,11.688793736210352,11.688793736210352 -452,0,Log(((((Exp(x1) * 4.1685201693828465e-2) ^ (Exp((x0 ^ 1.475692966996369)) + -1.0399762356248408)) + 0.7032323412275956) + x1)),"np.log((((np.exp(x[:, 1]) * t[0]) ** (np.exp(x[:, 0] ** t[1]) + t[2]) + t[3]) + x[:, 1]))","$$\operatorname{log}(\left(\left({\left(\operatorname{exp}(x_{1}) \cdot \theta_{0}\right)^{\left(\operatorname{exp}({x_{0}^{\theta_{1}}}) + \theta_{2}\right)}} + \theta_{3}\right) + x_{1}\right))$$",4.1685201693828465e-2;1.475692966996369;-1.0399762356248408;0.7032323412275956,16,1.1081117420822004e-2,1.1081117420822004e-2,1.1081117420822004e-2,1.1081117420822004e-2,-2.2578378484699124e-2,-2.2578378484699124e-2,-2.2578378484699124e-2,41.36473741402668,41.36473741402668,41.36473741402668 -453,0,Log((Log((((x0 ^ x1) + -0.4492980014128321) ^ ((x0 * -1.2376556358864774) * 1.0942206651485147))) ^ ((Exp(x1) ^ Log((x1 ^ 0.46352305019288736))) / Log((x0 * (7.725958474293932e-4 * x1)))))),"np.log(np.log((x[:, 0] ** x[:, 1] + t[0]) ** ((x[:, 0] * t[1]) * t[2])) ** (np.exp(x[:, 1]) ** np.log(x[:, 1] ** t[3]) / np.log((x[:, 0] * (t[4] * x[:, 1])))))","$$\operatorname{log}({\operatorname{log}({\left({x_{0}^{x_{1}}} + \theta_{0}\right)^{\left(\left(x_{0} \cdot \theta_{1}\right) \cdot \theta_{2}\right)}})^{\frac{{\operatorname{exp}(x_{1})^{\operatorname{log}({x_{1}^{\theta_{3}}})}}}{\operatorname{log}(\left(x_{0} \cdot \left(\theta_{4} \cdot x_{1}\right)\right))}}})$$",-0.4492980014128321;-1.2376556358864774;1.0942206651485147;0.46352305019288736;7.725958474293932e-4,28,1.3109423030134312e-2,1.3109423030134312e-2,1.3109423030134312e-2,1.3109423030134312e-2,-0.2097527745564034,-0.2097527745564034,-0.2097527745564034,71.63871132297947,71.63871132297947,71.63871132297947 -454,0,Log(Log((((x1 * -0.681381571978199) / ((Log(x0) * 0.4477560383143899) ^ -0.6984991000738375)) ^ (((0.9476299414510044 + Exp(x0)) ^ x0) + (x1 ^ 0.1728947334355746))))),"np.log(np.log(((x[:, 1] * t[0]) / (np.log(x[:, 0]) * t[1]) ** t[2]) ** ((t[3] + np.exp(x[:, 0])) ** x[:, 0] + x[:, 1] ** t[4])))","$$\operatorname{log}(\operatorname{log}({\frac{\left(x_{1} \cdot \theta_{0}\right)}{{\left(\operatorname{log}(x_{0}) \cdot \theta_{1}\right)^{\theta_{2}}}}^{\left({\left(\theta_{3} + \operatorname{exp}(x_{0})\right)^{x_{0}}} + {x_{1}^{\theta_{4}}}\right)}}))$$",-0.681381571978199;0.4477560383143899;-0.6984991000738375;0.9476299414510044;0.1728947334355746,23,,,,Infinity,,,,,, -455,0,(Exp(Log((Exp(((-0.19467842706883154 ^ x0) * 0.9457173654145368)) + (((0.29124107032271895 / x0) ^ -0.34277663579511175) / (x1 * 0.3626543542645697))))) / Exp(((Log((x0 * x1)) + ((0.7110872764207374 + x0) + (x1 + 0.4715167718394734))) * (0.8677191011834668 / Exp((x0 * 0.5346257828764355)))))),"(np.exp(np.log((np.exp((t[0] ** x[:, 0] * t[1])) + ((t[2] / x[:, 0]) ** t[3] / (x[:, 1] * t[4]))))) / np.exp(((np.log((x[:, 0] * x[:, 1])) + ((t[5] + x[:, 0]) + (x[:, 1] + t[6]))) * (t[7] / np.exp((x[:, 0] * t[8]))))))","$$\frac{\operatorname{exp}(\operatorname{log}(\left(\operatorname{exp}(\left({\theta_{0}^{x_{0}}} \cdot \theta_{1}\right)) + \frac{{\frac{\theta_{2}}{x_{0}}^{\theta_{3}}}}{\left(x_{1} \cdot \theta_{4}\right)}\right)))}{\operatorname{exp}(\left(\left(\operatorname{log}(\left(x_{0} \cdot x_{1}\right)) + \left(\left(\theta_{5} + x_{0}\right) + \left(x_{1} + \theta_{6}\right)\right)\right) \cdot \frac{\theta_{7}}{\operatorname{exp}(\left(x_{0} \cdot \theta_{8}\right))}\right))}$$",-0.19467842706883154;0.9457173654145368;0.29124107032271895;-0.34277663579511175;0.3626543542645697;0.7110872764207374;0.4715167718394734;0.8677191011834668;0.5346257828764355,39,,,,Infinity,,,,,, -456,0,(((-0.3926664276943631 * (-0.5726732228914679 + Log(((0.6644040385609528 / x0) * x1)))) / ((x0 ^ (x1 + (x0 * 0.41149903461181514))) / Log((Log(x1) ^ x0)))) / Exp(((x1 + -0.5615639433328696) + 0.9468883861002177))),"(((t[0] * (t[1] + np.log(((t[2] / x[:, 0]) * x[:, 1])))) / (x[:, 0] ** (x[:, 1] + (x[:, 0] * t[3])) / np.log(np.log(x[:, 1]) ** x[:, 0]))) / np.exp(((x[:, 1] + t[4]) + t[5])))","$$\frac{\frac{\left(\theta_{0} \cdot \left(\theta_{1} + \operatorname{log}(\left(\frac{\theta_{2}}{x_{0}} \cdot x_{1}\right))\right)\right)}{\frac{{x_{0}^{\left(x_{1} + \left(x_{0} \cdot \theta_{3}\right)\right)}}}{\operatorname{log}({\operatorname{log}(x_{1})^{x_{0}}})}}}{\operatorname{exp}(\left(\left(x_{1} + \theta_{4}\right) + \theta_{5}\right))}$$",-0.3926664276943631;-0.5726732228914679;0.6644040385609528;0.41149903461181514;-0.5615639433328696;0.9468883861002177,31,,,,Infinity,,,,,, -457,0,Log((Exp(((x1 + x1) ^ -0.4536997384039348)) + ((-0.6550777764664735 / x1) * ((8.383525070104186e-3 / x1) + 0.9296076397525579)))),"np.log((np.exp((x[:, 1] + x[:, 1]) ** t[0]) + ((t[1] / x[:, 1]) * ((t[2] / x[:, 1]) + t[3]))))","$$\operatorname{log}(\left(\operatorname{exp}({\left(x_{1} + x_{1}\right)^{\theta_{0}}}) + \left(\frac{\theta_{1}}{x_{1}} \cdot \left(\frac{\theta_{2}}{x_{1}} + \theta_{3}\right)\right)\right))$$",-0.4536997384039348;-0.6550777764664735;8.383525070104186e-3;0.9296076397525579,17,9.47506435669443e-2,9.47506435669443e-2,9.47506435669443e-2,9.47506435669443e-2,-7.743699374307347,-7.743699374307347,-7.743699374307347,43.51948152923802,43.51948152923802,43.51948152923802 -458,0,(((Log(Log(((x1 ^ -23.973113519573406) ^ 12.996212814579879))) + Exp(x0)) * -1.73124023634306e-2) * -0.32790447072648926),"(((np.log(np.log(x[:, 1] ** t[0] ** t[1])) + np.exp(x[:, 0])) * t[2]) * t[3])","$$\left(\left(\left(\operatorname{log}(\operatorname{log}({{x_{1}^{\theta_{0}}}^{\theta_{1}}})) + \operatorname{exp}(x_{0})\right) \cdot \theta_{2}\right) \cdot \theta_{3}\right)$$",-23.973113519573406;12.996212814579879;-1.73124023634306e-2;-0.32790447072648926,14,1.1832950308357822e-2,1.1832950308357822e-2,1.1832950308357822e-2,1.1832950308357822e-2,-9.195839006938056e-2,-9.195839006938056e-2,-9.195839006938056e-2,37.20653098026579,37.20653098026579,37.20653098026579 -459,0,(((Log(((x1 + -0.7246805170097141) / (x0 * 0.523431760008725))) * (-2.4032832255915126e-2 * x1)) ^ ((x1 + ((Log(x1) * 0.2828928416410784) + Log(x0))) * Exp(x0))) ^ 0.6033437395835273),"(np.log(((x[:, 1] + t[0]) / (x[:, 0] * t[1]))) * (t[2] * x[:, 1])) ** ((x[:, 1] + ((np.log(x[:, 1]) * t[3]) + np.log(x[:, 0]))) * np.exp(x[:, 0])) ** t[4]","$${{\left(\operatorname{log}(\frac{\left(x_{1} + \theta_{0}\right)}{\left(x_{0} \cdot \theta_{1}\right)}) \cdot \left(\theta_{2} \cdot x_{1}\right)\right)^{\left(\left(x_{1} + \left(\left(\operatorname{log}(x_{1}) \cdot \theta_{3}\right) + \operatorname{log}(x_{0})\right)\right) \cdot \operatorname{exp}(x_{0})\right)}}^{\theta_{4}}}$$",-0.7246805170097141;0.523431760008725;-2.4032832255915126e-2;0.2828928416410784;0.6033437395835273,27,,,,Infinity,,,,,, -460,0,((Exp(Exp((-0.4322422961350192 / x0))) ^ Exp((x1 + 7.866378801801877e-2))) * Exp((-0.5593581345308007 ^ (-0.6217642319341873 ^ (x0 * x0))))),"(np.exp(np.exp((t[0] / x[:, 0]))) ** np.exp((x[:, 1] + t[1])) * np.exp(t[2] ** t[3] ** (x[:, 0] * x[:, 0])))","$$\left({\operatorname{exp}(\operatorname{exp}(\frac{\theta_{0}}{x_{0}}))^{\operatorname{exp}(\left(x_{1} + \theta_{1}\right))}} \cdot \operatorname{exp}({\theta_{2}^{{\theta_{3}^{\left(x_{0} \cdot x_{0}\right)}}}})\right)$$",-0.4322422961350192;7.866378801801877e-2;-0.5593581345308007;-0.6217642319341873,19,,,,Infinity,,,,,, -461,0,Log(Exp(((Log((x1 / (-0.20366061059448715 + x1))) ^ ((x0 / x0) + 0.9080206586330656)) * (Log(Log(Log(x0))) * (0.12254075426113953 * (0.9646237399988574 * x0)))))),"np.log(np.exp((np.log((x[:, 1] / (t[0] + x[:, 1]))) ** ((x[:, 0] / x[:, 0]) + t[1]) * (np.log(np.log(np.log(x[:, 0]))) * (t[2] * (t[3] * x[:, 0]))))))","$$\operatorname{log}(\operatorname{exp}(\left({\operatorname{log}(\frac{x_{1}}{\left(\theta_{0} + x_{1}\right)})^{\left(\frac{x_{0}}{x_{0}} + \theta_{1}\right)}} \cdot \left(\operatorname{log}(\operatorname{log}(\operatorname{log}(x_{0}))) \cdot \left(\theta_{2} \cdot \left(\theta_{3} \cdot x_{0}\right)\right)\right)\right)))$$",-0.20366061059448715;0.9080206586330656;0.12254075426113953;0.9646237399988574,25,,,,Infinity,,,,,, -462,0,(Log(((0.3868418279578081 / (x0 * -0.585083326992277)) * 0.23043729812826708)) / ((Exp(Exp((x1 + x0))) + (x0 ^ 0.40148408999587637)) * -0.5007047927593818)),"(np.log(((t[0] / (x[:, 0] * t[1])) * t[2])) / ((np.exp(np.exp((x[:, 1] + x[:, 0]))) + x[:, 0] ** t[3]) * t[4]))","$$\frac{\operatorname{log}(\left(\frac{\theta_{0}}{\left(x_{0} \cdot \theta_{1}\right)} \cdot \theta_{2}\right))}{\left(\left(\operatorname{exp}(\operatorname{exp}(\left(x_{1} + x_{0}\right))) + {x_{0}^{\theta_{3}}}\right) \cdot \theta_{4}\right)}$$",0.3868418279578081;-0.585083326992277;0.23043729812826708;0.40148408999587637;-0.5007047927593818,20,,,,Infinity,,,,,, -463,0,Log((((-0.48185598495854254 * (Exp((x0 + x1)) * x0)) / (x1 + (x1 * 0.8343216489791334))) / (-1.1666792285470517e-2 ^ (x1 / ((x1 * -0.46589350048432276) ^ Log(x1)))))),"np.log((((t[0] * (np.exp((x[:, 0] + x[:, 1])) * x[:, 0])) / (x[:, 1] + (x[:, 1] * t[1]))) / t[2] ** (x[:, 1] / (x[:, 1] * t[3]) ** np.log(x[:, 1]))))","$$\operatorname{log}(\frac{\frac{\left(\theta_{0} \cdot \left(\operatorname{exp}(\left(x_{0} + x_{1}\right)) \cdot x_{0}\right)\right)}{\left(x_{1} + \left(x_{1} \cdot \theta_{1}\right)\right)}}{{\theta_{2}^{\frac{x_{1}}{{\left(x_{1} \cdot \theta_{3}\right)^{\operatorname{log}(x_{1})}}}}}})$$",-0.48185598495854254;0.8343216489791334;-1.1666792285470517e-2;-0.46589350048432276,26,,,,Infinity,,,,,, -464,0,Log((0.7508424218560416 + x1)),"np.log((t[0] + x[:, 1]))","$$\operatorname{log}(\left(\theta_{0} + x_{1}\right))$$",0.7508424218560416,4,1.6682672756616897e-2,1.6682672756616897e-2,1.6682672756616897e-2,1.6682672756616897e-2,-0.5394964071219723,-0.5394964071219723,-0.5394964071219723,7.581116785328158,7.581116785328158,7.581116785328158 -465,0,Exp((((0.7125853591559701 * Exp((0.9475287605660322 + x1))) / ((x0 * (x1 * x1)) * 0.9247569042953545)) + Log(Log((0.47584153366734283 ^ ((x0 * 0.24716300781747336) / x1)))))),"np.exp((((t[0] * np.exp((t[1] + x[:, 1]))) / ((x[:, 0] * (x[:, 1] * x[:, 1])) * t[2])) + np.log(np.log(t[3] ** ((x[:, 0] * t[4]) / x[:, 1])))))","$$\operatorname{exp}(\left(\frac{\left(\theta_{0} \cdot \operatorname{exp}(\left(\theta_{1} + x_{1}\right))\right)}{\left(\left(x_{0} \cdot \left(x_{1} \cdot x_{1}\right)\right) \cdot \theta_{2}\right)} + \operatorname{log}(\operatorname{log}({\theta_{3}^{\frac{\left(x_{0} \cdot \theta_{4}\right)}{x_{1}}}}))\right))$$",0.7125853591559701;0.9475287605660322;0.9247569042953545;0.47584153366734283;0.24716300781747336,25,,,,Infinity,,,,,, -466,0,(((-0.692494631398574 * ((-0.9716895054250313 / x0) ^ x0)) + Exp(((Exp(x1) ^ 0.14281513942126933) * (9.208621165836928e-2 ^ Exp(x1))))) * Exp(((((x1 * x1) / x1) + ((x1 ^ x1) * -0.6002127332187537)) * (0.7890737485827036 * ((x0 * x0) * (x0 / x0)))))),"(((t[0] * (t[1] / x[:, 0]) ** x[:, 0]) + np.exp((np.exp(x[:, 1]) ** t[2] * t[3] ** np.exp(x[:, 1])))) * np.exp(((((x[:, 1] * x[:, 1]) / x[:, 1]) + (x[:, 1] ** x[:, 1] * t[4])) * (t[5] * ((x[:, 0] * x[:, 0]) * (x[:, 0] / x[:, 0]))))))","$$\left(\left(\left(\theta_{0} \cdot {\frac{\theta_{1}}{x_{0}}^{x_{0}}}\right) + \operatorname{exp}(\left({\operatorname{exp}(x_{1})^{\theta_{2}}} \cdot {\theta_{3}^{\operatorname{exp}(x_{1})}}\right))\right) \cdot \operatorname{exp}(\left(\left(\frac{\left(x_{1} \cdot x_{1}\right)}{x_{1}} + \left({x_{1}^{x_{1}}} \cdot \theta_{4}\right)\right) \cdot \left(\theta_{5} \cdot \left(\left(x_{0} \cdot x_{0}\right) \cdot \frac{x_{0}}{x_{0}}\right)\right)\right))\right)$$",-0.692494631398574;-0.9716895054250313;0.14281513942126933;9.208621165836928e-2;-0.6002127332187537;0.7890737485827036,41,,,,Infinity,,,,,, -467,0,(((Exp(x1) ^ -108.34305251497705) + x1) ^ x0),"(np.exp(x[:, 1]) ** t[0] + x[:, 1]) ** x[:, 0]","$${\left({\operatorname{exp}(x_{1})^{\theta_{0}}} + x_{1}\right)^{x_{0}}}$$",-108.34305251497705,8,8.35399955580256e-2,8.35399955580256e-2,8.35399955580256e-2,8.35399955580256e-2,-6.709167762794794,-6.709167762794794,-6.709167762794794,16.430186685194304,16.430186685194304,16.430186685194304 -468,0,Log((Exp((x1 ^ ((0.8211063501786303 ^ x1) + (-0.7809747273286118 / (x1 ^ x1))))) + Log((x1 ^ (Exp(Exp(x1)) * x1))))),"np.log((np.exp(x[:, 1] ** (t[0] ** x[:, 1] + (t[1] / x[:, 1] ** x[:, 1]))) + np.log(x[:, 1] ** (np.exp(np.exp(x[:, 1])) * x[:, 1]))))","$$\operatorname{log}(\left(\operatorname{exp}({x_{1}^{\left({\theta_{0}^{x_{1}}} + \frac{\theta_{1}}{{x_{1}^{x_{1}}}}\right)}}) + \operatorname{log}({x_{1}^{\left(\operatorname{exp}(\operatorname{exp}(x_{1})) \cdot x_{1}\right)}})\right))$$",0.8211063501786303;-0.7809747273286118,22,0.35145276728967745,0.35145276728967745,0.35145276728967745,0.35145276728967745,-31.43246932964808,-31.43246932964808,-31.43246932964808,50.10587127825237,50.10587127825237,50.10587127825237 -469,0,Exp((x1 * -55.781589458130306)),"np.exp((x[:, 1] * t[0]))","$$\operatorname{exp}(\left(x_{1} \cdot \theta_{0}\right))$$",-55.781589458130306,4,1.4573896716579228e-2,1.4573896716579228e-2,1.4573896716579228e-2,1.4573896716579228e-2,-0.3448961062933631,-0.3448961062933631,-0.3448961062933631,7.579218886892124,7.579218886892124,7.579218886892124 -470,0,(Log((((x0 * -0.9300163838630309) ^ (-0.11744914858565592 ^ x0)) / (Log((0.15318791851070723 ^ x0)) ^ Log(x0)))) + Exp(Exp((x0 * 0.6243748477165578)))),"(np.log(((x[:, 0] * t[0]) ** t[1] ** x[:, 0] / np.log(t[2] ** x[:, 0]) ** np.log(x[:, 0]))) + np.exp(np.exp((x[:, 0] * t[3]))))","$$\left(\operatorname{log}(\frac{{\left(x_{0} \cdot \theta_{0}\right)^{{\theta_{1}^{x_{0}}}}}}{{\operatorname{log}({\theta_{2}^{x_{0}}})^{\operatorname{log}(x_{0})}}}) + \operatorname{exp}(\operatorname{exp}(\left(x_{0} \cdot \theta_{3}\right)))\right)$$",-0.9300163838630309;-0.11744914858565592;0.15318791851070723;0.6243748477165578,22,,,,Infinity,,,,,, -471,0,((Log((x1 * -0.40871838106244507)) ^ Exp(((((x0 + x1) ^ x1) + (0.7498890646280538 + Exp(x1))) * -5.4543656391219275e-2))) / (((Exp(((x0 + x1) * x0)) / (x1 * -0.44841924535131517)) * (0.27748898261205934 ^ Exp((x0 * 0.7182600586189987)))) + Exp(((x0 / (x0 / x0)) + x0)))),"(np.log((x[:, 1] * t[0])) ** np.exp((((x[:, 0] + x[:, 1]) ** x[:, 1] + (t[1] + np.exp(x[:, 1]))) * t[2])) / (((np.exp(((x[:, 0] + x[:, 1]) * x[:, 0])) / (x[:, 1] * t[3])) * t[4] ** np.exp((x[:, 0] * t[5]))) + np.exp(((x[:, 0] / (x[:, 0] / x[:, 0])) + x[:, 0]))))","$$\frac{{\operatorname{log}(\left(x_{1} \cdot \theta_{0}\right))^{\operatorname{exp}(\left(\left({\left(x_{0} + x_{1}\right)^{x_{1}}} + \left(\theta_{1} + \operatorname{exp}(x_{1})\right)\right) \cdot \theta_{2}\right))}}}{\left(\left(\frac{\operatorname{exp}(\left(\left(x_{0} + x_{1}\right) \cdot x_{0}\right))}{\left(x_{1} \cdot \theta_{3}\right)} \cdot {\theta_{4}^{\operatorname{exp}(\left(x_{0} \cdot \theta_{5}\right))}}\right) + \operatorname{exp}(\left(\frac{x_{0}}{\frac{x_{0}}{x_{0}}} + x_{0}\right))\right)}$$",-0.40871838106244507;0.7498890646280538;-5.4543656391219275e-2;-0.44841924535131517;0.27748898261205934;0.7182600586189987,45,,,,Infinity,,,,,, -472,0,(Exp(Log(Log(((x0 + (x0 ^ 0.12614586284466578)) * 0.9034083323917866)))) * Log((((x0 + -0.5452392025382109) + x0) * (Exp(x1) / Log(x0))))),"(np.exp(np.log(np.log(((x[:, 0] + x[:, 0] ** t[0]) * t[1])))) * np.log((((x[:, 0] + t[2]) + x[:, 0]) * (np.exp(x[:, 1]) / np.log(x[:, 0])))))","$$\left(\operatorname{exp}(\operatorname{log}(\operatorname{log}(\left(\left(x_{0} + {x_{0}^{\theta_{0}}}\right) \cdot \theta_{1}\right)))) \cdot \operatorname{log}(\left(\left(\left(x_{0} + \theta_{2}\right) + x_{0}\right) \cdot \frac{\operatorname{exp}(x_{1})}{\operatorname{log}(x_{0})}\right))\right)$$",0.12614586284466578;0.9034083323917866;-0.5452392025382109,23,,,,Infinity,,,,,, -473,0,(((Log(x1) ^ Exp(x1)) * -2.3709824891623432e-2) + 0.1794915843225885),"((np.log(x[:, 1]) ** np.exp(x[:, 1]) * t[0]) + t[1])","$$\left(\left({\operatorname{log}(x_{1})^{\operatorname{exp}(x_{1})}} \cdot \theta_{0}\right) + \theta_{1}\right)$$",-2.3709824891623432e-2;0.1794915843225885,9,,,,Infinity,,,,,, -474,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 -475,0,Log((Exp((Exp((x0 / Exp(x0))) * x1)) / (Exp((x0 + ((x0 / x1) / (4.3365866880761565 / x0)))) ^ (x1 + -0.1160491465886579)))),"np.log((np.exp((np.exp((x[:, 0] / np.exp(x[:, 0]))) * x[:, 1])) / np.exp((x[:, 0] + ((x[:, 0] / x[:, 1]) / (t[0] / x[:, 0])))) ** (x[:, 1] + t[1])))","$$\operatorname{log}(\frac{\operatorname{exp}(\left(\operatorname{exp}(\frac{x_{0}}{\operatorname{exp}(x_{0})}) \cdot x_{1}\right))}{{\operatorname{exp}(\left(x_{0} + \frac{\frac{x_{0}}{x_{1}}}{\frac{\theta_{0}}{x_{0}}}\right))^{\left(x_{1} + \theta_{1}\right)}}})$$",4.3365866880761565;-0.1160491465886579,24,1.6246259101801885e-2,1.6246259101801885e-2,1.6246259101801885e-2,1.6246259101801885e-2,-0.4992236484694237,-0.4992236484694237,-0.4992236484694237,56.789861359996166,56.789861359996166,56.789861359996166 -476,0,((((x1 + x0) / Log(x1)) / (x0 * x0)) ^ x0),"(((x[:, 1] + x[:, 0]) / np.log(x[:, 1])) / (x[:, 0] * x[:, 0])) ** x[:, 0]","$${\frac{\frac{\left(x_{1} + x_{0}\right)}{\operatorname{log}(x_{1})}}{\left(x_{0} \cdot x_{0}\right)}^{x_{0}}}$$",,12,,,,Infinity,,,,,, -477,0,((((0.8903664082938241 ^ (0.5976587209453461 / x1)) ^ Log((-0.9453294753807826 + x1))) + ((x0 * -0.3532665505819431) + (x0 * -0.4662435279595045))) * Log((((-0.8601359498172079 + (x0 / x1)) * (x1 + x1)) * -0.19253362538519236))),"((t[0] ** (t[1] / x[:, 1]) ** np.log((t[2] + x[:, 1])) + ((x[:, 0] * t[3]) + (x[:, 0] * t[4]))) * np.log((((t[5] + (x[:, 0] / x[:, 1])) * (x[:, 1] + x[:, 1])) * t[6])))","$$\left(\left({{\theta_{0}^{\frac{\theta_{1}}{x_{1}}}}^{\operatorname{log}(\left(\theta_{2} + x_{1}\right))}} + \left(\left(x_{0} \cdot \theta_{3}\right) + \left(x_{0} \cdot \theta_{4}\right)\right)\right) \cdot \operatorname{log}(\left(\left(\left(\theta_{5} + \frac{x_{0}}{x_{1}}\right) \cdot \left(x_{1} + x_{1}\right)\right) \cdot \theta_{6}\right))\right)$$",0.8903664082938241;0.5976587209453461;-0.9453294753807826;-0.3532665505819431;-0.4662435279595045;-0.8601359498172079;-0.19253362538519236,31,,,,Infinity,,,,,, -478,0,((((x1 ^ -0.5990674675655823) + Log((x0 * 0.4874315588394609))) ^ (-0.4856882205689257 + (x1 + (x0 ^ x1)))) * (((-5.26458945777698e-2 / x1) ^ (x0 * -0.5310039621757463)) / ((x0 + x1) + (-0.14577360927696958 ^ x0)))),"((x[:, 1] ** t[0] + np.log((x[:, 0] * t[1]))) ** (t[2] + (x[:, 1] + x[:, 0] ** x[:, 1])) * ((t[3] / x[:, 1]) ** (x[:, 0] * t[4]) / ((x[:, 0] + x[:, 1]) + t[5] ** x[:, 0])))","$$\left({\left({x_{1}^{\theta_{0}}} + \operatorname{log}(\left(x_{0} \cdot \theta_{1}\right))\right)^{\left(\theta_{2} + \left(x_{1} + {x_{0}^{x_{1}}}\right)\right)}} \cdot \frac{{\frac{\theta_{3}}{x_{1}}^{\left(x_{0} \cdot \theta_{4}\right)}}}{\left(\left(x_{0} + x_{1}\right) + {\theta_{5}^{x_{0}}}\right)}\right)$$",-0.5990674675655823;0.4874315588394609;-0.4856882205689257;-5.26458945777698e-2;-0.5310039621757463;-0.14577360927696958,32,,,,Infinity,,,,,, -479,0,(((Exp(Log(Log(Exp(x1)))) * -0.4255729085223552) / Log((-0.12508134615103228 * (x1 + (x0 * -0.8809574287654421))))) + (Exp(((x0 / (x0 * 0.5945359786046696)) * -0.5924208128891106)) ^ (((x1 ^ 0.6028790810878104) * 0.25904130518432245) ^ (Exp(x0) * -0.49984804200215627)))),"(((np.exp(np.log(np.log(np.exp(x[:, 1])))) * t[0]) / np.log((t[1] * (x[:, 1] + (x[:, 0] * t[2]))))) + np.exp(((x[:, 0] / (x[:, 0] * t[3])) * t[4])) ** (x[:, 1] ** t[5] * t[6]) ** (np.exp(x[:, 0]) * t[7]))","$$\left(\frac{\left(\operatorname{exp}(\operatorname{log}(\operatorname{log}(\operatorname{exp}(x_{1})))) \cdot \theta_{0}\right)}{\operatorname{log}(\left(\theta_{1} \cdot \left(x_{1} + \left(x_{0} \cdot \theta_{2}\right)\right)\right))} + {\operatorname{exp}(\left(\frac{x_{0}}{\left(x_{0} \cdot \theta_{3}\right)} \cdot \theta_{4}\right))^{{\left({x_{1}^{\theta_{5}}} \cdot \theta_{6}\right)^{\left(\operatorname{exp}(x_{0}) \cdot \theta_{7}\right)}}}}\right)$$",-0.4255729085223552;-0.12508134615103228;-0.8809574287654421;0.5945359786046696;-0.5924208128891106;0.6028790810878104;0.25904130518432245;-0.49984804200215627,36,,,,Infinity,,,,,, -480,0,Log(Exp((Log(Exp(Log(Log(x0)))) + Exp(((0.7155913225720063 / (x0 + 0.6257043660953849)) * 0.7451950563177195))))),"np.log(np.exp((np.log(np.exp(np.log(np.log(x[:, 0])))) + np.exp(((t[0] / (x[:, 0] + t[1])) * t[2])))))","$$\operatorname{log}(\operatorname{exp}(\left(\operatorname{log}(\operatorname{exp}(\operatorname{log}(\operatorname{log}(x_{0})))) + \operatorname{exp}(\left(\frac{\theta_{0}}{\left(x_{0} + \theta_{1}\right)} \cdot \theta_{2}\right))\right)))$$",0.7155913225720063;0.6257043660953849;0.7451950563177195,16,,,,Infinity,,,,,, -481,0,(((Exp((0.499371429140963 * ((x1 + -0.4033579959214005) + x1))) * 9.694142885240453e-4) ^ (0.21032526589418465 / (x0 * (x1 / x0)))) / x1),"((np.exp((t[0] * ((x[:, 1] + t[1]) + x[:, 1]))) * t[2]) ** (t[3] / (x[:, 0] * (x[:, 1] / x[:, 0]))) / x[:, 1])","$$\frac{{\left(\operatorname{exp}(\left(\theta_{0} \cdot \left(\left(x_{1} + \theta_{1}\right) + x_{1}\right)\right)) \cdot \theta_{2}\right)^{\frac{\theta_{3}}{\left(x_{0} \cdot \frac{x_{1}}{x_{0}}\right)}}}}{x_{1}}$$",0.499371429140963;-0.4033579959214005;9.694142885240453e-4;0.21032526589418465,20,9.458723691088909e-3,9.458723691088909e-3,9.458723691088909e-3,9.458723691088909e-3,0.12713799815948457,0.12713799815948457,0.12713799815948457,49.68104342638926,49.68104342638926,49.68104342638926 -482,0,Log((Exp((((x1 / (x0 + x1)) ^ (x0 ^ x1)) ^ Log((x1 * 8.300432483156195e-2)))) * 0.17824740613105972)),"np.log((np.exp((x[:, 1] / (x[:, 0] + x[:, 1])) ** x[:, 0] ** x[:, 1] ** np.log((x[:, 1] * t[0]))) * t[1]))","$$\operatorname{log}(\left(\operatorname{exp}({{\frac{x_{1}}{\left(x_{0} + x_{1}\right)}^{{x_{0}^{x_{1}}}}}^{\operatorname{log}(\left(x_{1} \cdot \theta_{0}\right))}}) \cdot \theta_{1}\right))$$",8.300432483156195e-2;0.17824740613105972,18,Infinity,Infinity,Infinity,Infinity,-Infinity,-Infinity,-Infinity,Infinity,Infinity,Infinity -483,0,Exp((((-0.9544859134461415 * Exp((x0 ^ -0.15183952623766028))) ^ Log(((4.2004223984760114e-2 ^ x0) + (0.9228220023709929 / x0)))) ^ (Log(((x1 * -0.6905798542213194) + Exp(Log(x0)))) * -7.663075645831408e-2))),"np.exp((t[0] * np.exp(x[:, 0] ** t[1])) ** np.log((t[2] ** x[:, 0] + (t[3] / x[:, 0]))) ** (np.log(((x[:, 1] * t[4]) + np.exp(np.log(x[:, 0])))) * t[5]))","$$\operatorname{exp}({{\left(\theta_{0} \cdot \operatorname{exp}({x_{0}^{\theta_{1}}})\right)^{\operatorname{log}(\left({\theta_{2}^{x_{0}}} + \frac{\theta_{3}}{x_{0}}\right))}}^{\left(\operatorname{log}(\left(\left(x_{1} \cdot \theta_{4}\right) + \operatorname{exp}(\operatorname{log}(x_{0}))\right)) \cdot \theta_{5}\right)}})$$",-0.9544859134461415;-0.15183952623766028;4.2004223984760114e-2;0.9228220023709929;-0.6905798542213194;-7.663075645831408e-2,27,,,,Infinity,,,,,, -484,0,((Exp(Exp((Log((x0 * -0.8648266540781215)) * x1))) + (-0.32563463164061857 + x0)) + 0.5670774457754291),"((np.exp(np.exp((np.log((x[:, 0] * t[0])) * x[:, 1]))) + (t[1] + x[:, 0])) + t[2])","$$\left(\left(\operatorname{exp}(\operatorname{exp}(\left(\operatorname{log}(\left(x_{0} \cdot \theta_{0}\right)) \cdot x_{1}\right))) + \left(\theta_{1} + x_{0}\right)\right) + \theta_{2}\right)$$",-0.8648266540781215;-0.32563463164061857;0.5670774457754291,14,,,,Infinity,,,,,, -485,0,((Log((x1 ^ 27.833558699624284)) + Exp(Log((x0 * x1)))) + (((-24.83384825297815 / x0) + (Exp(((x1 ^ 357.07491126637905) / x1)) * 56.62578186795207)) + Exp((x0 ^ -1.9026737627520485)))),"((np.log(x[:, 1] ** t[0]) + np.exp(np.log((x[:, 0] * x[:, 1])))) + (((t[1] / x[:, 0]) + (np.exp((x[:, 1] ** t[2] / x[:, 1])) * t[3])) + np.exp(x[:, 0] ** t[4])))","$$\left(\left(\operatorname{log}({x_{1}^{\theta_{0}}}) + \operatorname{exp}(\operatorname{log}(\left(x_{0} \cdot x_{1}\right)))\right) + \left(\left(\frac{\theta_{1}}{x_{0}} + \left(\operatorname{exp}(\frac{{x_{1}^{\theta_{2}}}}{x_{1}}) \cdot \theta_{3}\right)\right) + \operatorname{exp}({x_{0}^{\theta_{4}}})\right)\right)$$",27.833558699624284;-24.83384825297815;357.07491126637905;56.62578186795207;-1.9026737627520485,28,211.71885682357342,211.71885682357342,211.71885682357342,211.71885682357342,-19536.661869593638,-19536.661869593638,-19536.661869593638,262.17388398346844,262.17388398346844,262.17388398346844 -486,0,((((x0 + 0.5364299619496307) / x1) ^ Log((x1 * -0.18249596564908754))) ^ ((x0 / Log(x0)) * (Log(x1) ^ (-0.828455256179492 / x1)))),"((x[:, 0] + t[0]) / x[:, 1]) ** np.log((x[:, 1] * t[1])) ** ((x[:, 0] / np.log(x[:, 0])) * np.log(x[:, 1]) ** (t[2] / x[:, 1]))","$${{\frac{\left(x_{0} + \theta_{0}\right)}{x_{1}}^{\operatorname{log}(\left(x_{1} \cdot \theta_{1}\right))}}^{\left(\frac{x_{0}}{\operatorname{log}(x_{0})} \cdot {\operatorname{log}(x_{1})^{\frac{\theta_{2}}{x_{1}}}}\right)}}$$",0.5364299619496307;-0.18249596564908754;-0.828455256179492,22,,,,Infinity,,,,,, -487,0,(Exp((x1 + x1)) * x1),"(np.exp((x[:, 1] + x[:, 1])) * x[:, 1])","$$\left(\operatorname{exp}(\left(x_{1} + x_{1}\right)) \cdot x_{1}\right)$$",,6,0.6788921370564028,0.6788921370564028,0.6788921370564028,0.6788921370564028,-61.64895446127768,-61.64895446127768,-61.64895446127768,8.928769090070105,8.928769090070105,8.928769090070105 -488,0,(((Exp(x1) ^ Exp(Exp(x0))) * (Exp(x1) * (0.9710717551203418 / (0.7912028428067664 ^ (0.1155859577414935 + (-0.18040342975130197 + x0)))))) ^ (((x0 * -0.8480108833422038) ^ (-0.1350553335446253 + Exp(x0))) + ((x0 + -0.9511009648861121) + ((x0 / x0) * x0)))),"(np.exp(x[:, 1]) ** np.exp(np.exp(x[:, 0])) * (np.exp(x[:, 1]) * (t[0] / t[1] ** (t[2] + (t[3] + x[:, 0]))))) ** ((x[:, 0] * t[4]) ** (t[5] + np.exp(x[:, 0])) + ((x[:, 0] + t[6]) + ((x[:, 0] / x[:, 0]) * x[:, 0])))","$${\left({\operatorname{exp}(x_{1})^{\operatorname{exp}(\operatorname{exp}(x_{0}))}} \cdot \left(\operatorname{exp}(x_{1}) \cdot \frac{\theta_{0}}{{\theta_{1}^{\left(\theta_{2} + \left(\theta_{3} + x_{0}\right)\right)}}}\right)\right)^{\left({\left(x_{0} \cdot \theta_{4}\right)^{\left(\theta_{5} + \operatorname{exp}(x_{0})\right)}} + \left(\left(x_{0} + \theta_{6}\right) + \left(\frac{x_{0}}{x_{0}} \cdot x_{0}\right)\right)\right)}}$$",0.9710717551203418;0.7912028428067664;0.1155859577414935;-0.18040342975130197;-0.8480108833422038;-0.1350553335446253;-0.9511009648861121,38,,,,Infinity,,,,,, -489,0,(((0.1584138760257211 + Exp(x1)) + Log(Log((x0 * -8.935316426754925e-2)))) / ((0.2639324900750277 + (0.8123249659900147 ^ x1)) / (x1 * x1))),"(((t[0] + np.exp(x[:, 1])) + np.log(np.log((x[:, 0] * t[1])))) / ((t[2] + t[3] ** x[:, 1]) / (x[:, 1] * x[:, 1])))","$$\frac{\left(\left(\theta_{0} + \operatorname{exp}(x_{1})\right) + \operatorname{log}(\operatorname{log}(\left(x_{0} \cdot \theta_{1}\right)))\right)}{\frac{\left(\theta_{2} + {\theta_{3}^{x_{1}}}\right)}{\left(x_{1} \cdot x_{1}\right)}}$$",0.1584138760257211;-8.935316426754925e-2;0.2639324900750277;0.8123249659900147,20,,,,Infinity,,,,,, -490,0,((Log((x0 ^ -0.6730846159013812)) + Exp((-1.9982217367522053 / (3.824808722885168e-2 + x0)))) ^ 1.7199541604682669),"(np.log(x[:, 0] ** t[0]) + np.exp((t[1] / (t[2] + x[:, 0])))) ** t[3]","$${\left(\operatorname{log}({x_{0}^{\theta_{0}}}) + \operatorname{exp}(\frac{\theta_{1}}{\left(\theta_{2} + x_{0}\right)})\right)^{\theta_{3}}}$$",-0.6730846159013812;-1.9982217367522053;3.824808722885168e-2;1.7199541604682669,13,4.941930734085285e-3,4.941930734085285e-3,4.941930734085285e-3,4.941930734085285e-3,0.5439528952965687,0.5439528952965687,0.5439528952965687,33.384979416850314,33.384979416850314,33.384979416850314 -491,0,(((x1 ^ -0.4764826234729066) ^ -0.44228473667644275) * (4.722000904340201e-2 * x0)),"(x[:, 1] ** t[0] ** t[1] * (t[2] * x[:, 0]))","$$\left({{x_{1}^{\theta_{0}}}^{\theta_{1}}} \cdot \left(\theta_{2} \cdot x_{0}\right)\right)$$",-0.4764826234729066;-0.44228473667644275;4.722000904340201e-2,9,1.288198690237182e-2,1.288198690237182e-2,1.288198690237182e-2,1.288198690237182e-2,-0.18876470468005713,-0.18876470468005713,-0.18876470468005713,20.559309806221957,20.559309806221957,20.559309806221957 -492,0,(Log(((x1 * 0.5086158750122356) + (x0 * ((-0.8929997032234764 ^ (x1 / x1)) ^ -0.7626997718420325)))) / ((((x1 ^ Log(x0)) + 0.5589586253562242) + -0.26193925813403895) * (0.19293976532434975 + (x1 ^ x1)))),"(np.log(((x[:, 1] * t[0]) + (x[:, 0] * t[1] ** (x[:, 1] / x[:, 1]) ** t[2]))) / (((x[:, 1] ** np.log(x[:, 0]) + t[3]) + t[4]) * (t[5] + x[:, 1] ** x[:, 1])))","$$\frac{\operatorname{log}(\left(\left(x_{1} \cdot \theta_{0}\right) + \left(x_{0} \cdot {{\theta_{1}^{\frac{x_{1}}{x_{1}}}}^{\theta_{2}}}\right)\right))}{\left(\left(\left({x_{1}^{\operatorname{log}(x_{0})}} + \theta_{3}\right) + \theta_{4}\right) \cdot \left(\theta_{5} + {x_{1}^{x_{1}}}\right)\right)}$$",0.5086158750122356;-0.8929997032234764;-0.7626997718420325;0.5589586253562242;-0.26193925813403895;0.19293976532434975,29,,,,Infinity,,,,,, -493,0,(((-0.31329691921057845 * Exp((x1 * -9.221450146706411e-2))) * ((-0.49754150681916287 ^ x0) ^ Log((x1 ^ -0.5311068034794029)))) + Exp((-0.6200175331237936 / (Exp((-1.2224830249204621e-2 / x1)) + (x0 / x1))))),"(((t[0] * np.exp((x[:, 1] * t[1]))) * t[2] ** x[:, 0] ** np.log(x[:, 1] ** t[3])) + np.exp((t[4] / (np.exp((t[5] / x[:, 1])) + (x[:, 0] / x[:, 1])))))","$$\left(\left(\left(\theta_{0} \cdot \operatorname{exp}(\left(x_{1} \cdot \theta_{1}\right))\right) \cdot {{\theta_{2}^{x_{0}}}^{\operatorname{log}({x_{1}^{\theta_{3}}})}}\right) + \operatorname{exp}(\frac{\theta_{4}}{\left(\operatorname{exp}(\frac{\theta_{5}}{x_{1}}) + \frac{x_{0}}{x_{1}}\right)})\right)$$",-0.31329691921057845;-9.221450146706411e-2;-0.49754150681916287;-0.5311068034794029;-0.6200175331237936;-1.2224830249204621e-2,27,,,,Infinity,,,,,, -494,0,(Exp((((9.801314464938471e-2 ^ x1) * 0.511653805074811) * 0.4133027521686087)) + ((Exp(x1) * ((x1 + -0.4245595907107219) + -0.19931370579934105)) * ((x1 + Log(x0)) / (x0 / ((x1 * x0) * (x1 * -1.0317814518854163e-2)))))),"(np.exp(((t[0] ** x[:, 1] * t[1]) * t[2])) + ((np.exp(x[:, 1]) * ((x[:, 1] + t[3]) + t[4])) * ((x[:, 1] + np.log(x[:, 0])) / (x[:, 0] / ((x[:, 1] * x[:, 0]) * (x[:, 1] * t[5]))))))","$$\left(\operatorname{exp}(\left(\left({\theta_{0}^{x_{1}}} \cdot \theta_{1}\right) \cdot \theta_{2}\right)) + \left(\left(\operatorname{exp}(x_{1}) \cdot \left(\left(x_{1} + \theta_{3}\right) + \theta_{4}\right)\right) \cdot \frac{\left(x_{1} + \operatorname{log}(x_{0})\right)}{\frac{x_{0}}{\left(\left(x_{1} \cdot x_{0}\right) \cdot \left(x_{1} \cdot \theta_{5}\right)\right)}}\right)\right)$$",9.801314464938471e-2;0.511653805074811;0.4133027521686087;-0.4245595907107219;-0.19931370579934105;-1.0317814518854163e-2,32,1.1126846289185084,1.1126846289185084,1.1126846289185084,1.1126846289185084,-101.67982915390262,-101.67982915390262,-101.67982915390262,83.43815225299153,83.43815225299153,83.43815225299153 -495,0,((-0.7139304616511493 * ((0.6462003795865441 ^ (Exp(x0) * (Exp(x0) * 0.3253685531803774))) * -0.5729656962385743)) / Log((((Log(x1) / ((0.4878172156889362 * x1) ^ -0.9693208323153738)) ^ -0.3884691627250888) * 0.11552224879693374))),"((t[0] * (t[1] ** (np.exp(x[:, 0]) * (np.exp(x[:, 0]) * t[2])) * t[3])) / np.log(((np.log(x[:, 1]) / (t[4] * x[:, 1]) ** t[5]) ** t[6] * t[7])))","$$\frac{\left(\theta_{0} \cdot \left({\theta_{1}^{\left(\operatorname{exp}(x_{0}) \cdot \left(\operatorname{exp}(x_{0}) \cdot \theta_{2}\right)\right)}} \cdot \theta_{3}\right)\right)}{\operatorname{log}(\left({\frac{\operatorname{log}(x_{1})}{{\left(\theta_{4} \cdot x_{1}\right)^{\theta_{5}}}}^{\theta_{6}}} \cdot \theta_{7}\right))}$$",-0.7139304616511493;0.6462003795865441;0.3253685531803774;-0.5729656962385743;0.4878172156889362;-0.9693208323153738;-0.3884691627250888;0.11552224879693374,27,,,,Infinity,,,,,, -496,0,Log(((Log(Log((-0.9454074598663175 * x0))) * (Exp(x0) / (0.8960489166409221 / x0))) ^ (-0.5051288518723183 * (x1 * Exp(x0))))),"np.log((np.log(np.log((t[0] * x[:, 0]))) * (np.exp(x[:, 0]) / (t[1] / x[:, 0]))) ** (t[2] * (x[:, 1] * np.exp(x[:, 0]))))","$$\operatorname{log}({\left(\operatorname{log}(\operatorname{log}(\left(\theta_{0} \cdot x_{0}\right))) \cdot \frac{\operatorname{exp}(x_{0})}{\frac{\theta_{1}}{x_{0}}}\right)^{\left(\theta_{2} \cdot \left(x_{1} \cdot \operatorname{exp}(x_{0})\right)\right)}})$$",-0.9454074598663175;0.8960489166409221;-0.5051288518723183,20,,,,Infinity,,,,,, -497,0,Exp(Exp((-1.3743271927501792 / (((Exp(x0) / x1) + 0.6758110516317962) ^ (-0.8795202689099982 / x1))))),"np.exp(np.exp((t[0] / ((np.exp(x[:, 0]) / x[:, 1]) + t[1]) ** (t[2] / x[:, 1]))))","$$\operatorname{exp}(\operatorname{exp}(\frac{\theta_{0}}{{\left(\frac{\operatorname{exp}(x_{0})}{x_{1}} + \theta_{1}\right)^{\frac{\theta_{2}}{x_{1}}}}}))$$",-1.3743271927501792;0.6758110516317962;-0.8795202689099982,14,0.8923099011383698,0.8923099011383698,0.8923099011383698,0.8923099011383698,-81.34339346475672,-81.34339346475672,-81.34339346475672,34.10859580390184,34.10859580390184,34.10859580390184 -498,0,Exp(((-0.7392483398152878 / ((0.46906548433260364 + x0) * -0.3982190652046247)) * Log(((-0.8092675432839428 ^ Log(x0)) ^ ((x1 / x0) + x1))))),"np.exp(((t[0] / ((t[1] + x[:, 0]) * t[2])) * np.log(t[3] ** np.log(x[:, 0]) ** ((x[:, 1] / x[:, 0]) + x[:, 1]))))","$$\operatorname{exp}(\left(\frac{\theta_{0}}{\left(\left(\theta_{1} + x_{0}\right) \cdot \theta_{2}\right)} \cdot \operatorname{log}({{\theta_{3}^{\operatorname{log}(x_{0})}}^{\left(\frac{x_{1}}{x_{0}} + x_{1}\right)}})\right))$$",-0.7392483398152878;0.46906548433260364;-0.3982190652046247;-0.8092675432839428,20,,,,Infinity,,,,,, -499,0,(Log((((x0 + -0.7175453748672325) * -0.5818800380176967) ^ ((x1 + x1) ^ (x0 + 0.6621409982507231)))) ^ (Log((-0.7838729499070423 / Exp((x0 * -5.478735637163734e-2)))) / (Exp(Log(x1)) ^ (x1 + -0.7115004196980408)))),"np.log(((x[:, 0] + t[0]) * t[1]) ** (x[:, 1] + x[:, 1]) ** (x[:, 0] + t[2])) ** (np.log((t[3] / np.exp((x[:, 0] * t[4])))) / np.exp(np.log(x[:, 1])) ** (x[:, 1] + t[5]))","$${\operatorname{log}({\left(\left(x_{0} + \theta_{0}\right) \cdot \theta_{1}\right)^{{\left(x_{1} + x_{1}\right)^{\left(x_{0} + \theta_{2}\right)}}}})^{\frac{\operatorname{log}(\frac{\theta_{3}}{\operatorname{exp}(\left(x_{0} \cdot \theta_{4}\right))})}{{\operatorname{exp}(\operatorname{log}(x_{1}))^{\left(x_{1} + \theta_{5}\right)}}}}}$$",-0.7175453748672325;-0.5818800380176967;0.6621409982507231;-0.7838729499070423;-5.478735637163734e-2;-0.7115004196980408,30,,,,Infinity,,,,,, -500,0,((Log(Log(((x0 ^ -0.4737594236644772) / (Exp(x0) * -0.5065112245494224)))) / (Log((x1 + x1)) * (Exp(x1) + ((x1 * (x1 ^ x0)) / (-6.176548038190055e-2 * x1))))) / ((Exp((x1 ^ Exp((0.10187211319049361 ^ x0)))) * -0.4852485594240281) * 0.512099525501789)),"((np.log(np.log((x[:, 0] ** t[0] / (np.exp(x[:, 0]) * t[1])))) / (np.log((x[:, 1] + x[:, 1])) * (np.exp(x[:, 1]) + ((x[:, 1] * x[:, 1] ** x[:, 0]) / (t[2] * x[:, 1]))))) / ((np.exp(x[:, 1] ** np.exp(t[3] ** x[:, 0])) * t[4]) * t[5]))","$$\frac{\frac{\operatorname{log}(\operatorname{log}(\frac{{x_{0}^{\theta_{0}}}}{\left(\operatorname{exp}(x_{0}) \cdot \theta_{1}\right)}))}{\left(\operatorname{log}(\left(x_{1} + x_{1}\right)) \cdot \left(\operatorname{exp}(x_{1}) + \frac{\left(x_{1} \cdot {x_{1}^{x_{0}}}\right)}{\left(\theta_{2} \cdot x_{1}\right)}\right)\right)}}{\left(\left(\operatorname{exp}({x_{1}^{\operatorname{exp}({\theta_{3}^{x_{0}}})}}) \cdot \theta_{4}\right) \cdot \theta_{5}\right)}$$",-0.4737594236644772;-0.5065112245494224;-6.176548038190055e-2;0.10187211319049361;-0.4852485594240281;0.512099525501789,40,,,,Infinity,,,,,, -501,0,Exp(((((Log(x0) + (-0.4807746076313275 ^ x1)) ^ 0.2190900166409484) + Log(((x0 * -0.3205295132630004) + (x1 * x0)))) / (((-2.1947238151517667e-2 / (x1 ^ x1)) ^ ((0.5380676258085577 + x1) / x1)) / ((Exp(x1) + (x0 * x0)) / (0.39869232448365366 ^ (x0 + 0.5199321715900452)))))),"np.exp((((np.log(x[:, 0]) + t[0] ** x[:, 1]) ** t[1] + np.log(((x[:, 0] * t[2]) + (x[:, 1] * x[:, 0])))) / ((t[3] / x[:, 1] ** x[:, 1]) ** ((t[4] + x[:, 1]) / x[:, 1]) / ((np.exp(x[:, 1]) + (x[:, 0] * x[:, 0])) / t[5] ** (x[:, 0] + t[6])))))","$$\operatorname{exp}(\frac{\left({\left(\operatorname{log}(x_{0}) + {\theta_{0}^{x_{1}}}\right)^{\theta_{1}}} + \operatorname{log}(\left(\left(x_{0} \cdot \theta_{2}\right) + \left(x_{1} \cdot x_{0}\right)\right))\right)}{\frac{{\frac{\theta_{3}}{{x_{1}^{x_{1}}}}^{\frac{\left(\theta_{4} + x_{1}\right)}{x_{1}}}}}{\frac{\left(\operatorname{exp}(x_{1}) + \left(x_{0} \cdot x_{0}\right)\right)}{{\theta_{5}^{\left(x_{0} + \theta_{6}\right)}}}}})$$",-0.4807746076313275;0.2190900166409484;-0.3205295132630004;-2.1947238151517667e-2;0.5380676258085577;0.39869232448365366;0.5199321715900452,43,,,,Infinity,,,,,, -502,0,(Log((0.8908722260445152 * Exp(((-0.10516567604572213 * x1) ^ -0.10964452375198297)))) * (((x1 ^ x1) / (-0.1418572450128216 * Log(x0))) + Log(((x1 + -0.3037194868934737) / Exp((x1 ^ x0)))))),"(np.log((t[0] * np.exp((t[1] * x[:, 1]) ** t[2]))) * ((x[:, 1] ** x[:, 1] / (t[3] * np.log(x[:, 0]))) + np.log(((x[:, 1] + t[4]) / np.exp(x[:, 1] ** x[:, 0])))))","$$\left(\operatorname{log}(\left(\theta_{0} \cdot \operatorname{exp}({\left(\theta_{1} \cdot x_{1}\right)^{\theta_{2}}})\right)) \cdot \left(\frac{{x_{1}^{x_{1}}}}{\left(\theta_{3} \cdot \operatorname{log}(x_{0})\right)} + \operatorname{log}(\frac{\left(x_{1} + \theta_{4}\right)}{\operatorname{exp}({x_{1}^{x_{0}}})})\right)\right)$$",0.8908722260445152;-0.10516567604572213;-0.10964452375198297;-0.1418572450128216;-0.3037194868934737,28,,,,Infinity,,,,,, -503,0,(((((Log((-0.6676031223865525 / x1)) ^ -0.9405415772427824) + Log(x1)) ^ (x1 + (x1 + x1))) * (Exp(x1) * 0.7758484867831352)) + Exp((Log(x0) ^ (x1 + ((-0.9861687014443639 / x1) + Exp(x0)))))),"(((np.log((t[0] / x[:, 1])) ** t[1] + np.log(x[:, 1])) ** (x[:, 1] + (x[:, 1] + x[:, 1])) * (np.exp(x[:, 1]) * t[2])) + np.exp(np.log(x[:, 0]) ** (x[:, 1] + ((t[3] / x[:, 1]) + np.exp(x[:, 0])))))","$$\left(\left({\left({\operatorname{log}(\frac{\theta_{0}}{x_{1}})^{\theta_{1}}} + \operatorname{log}(x_{1})\right)^{\left(x_{1} + \left(x_{1} + x_{1}\right)\right)}} \cdot \left(\operatorname{exp}(x_{1}) \cdot \theta_{2}\right)\right) + \operatorname{exp}({\operatorname{log}(x_{0})^{\left(x_{1} + \left(\frac{\theta_{3}}{x_{1}} + \operatorname{exp}(x_{0})\right)\right)}})\right)$$",-0.6676031223865525;-0.9405415772427824;0.7758484867831352;-0.9861687014443639,33,,,,Infinity,,,,,, -504,0,Log((((Log((-0.7608012574024686 + x1)) / (x0 ^ (-0.43008469408972283 ^ x0))) * ((6.292200849674778e-2 ^ Exp(x0)) + (x0 * -0.12496236836114638))) * (((x1 / (x1 * -0.9810182405572657)) * 0.9969449106681799) * ((x0 + 4.8101637865840985e-2) * (x1 * -0.576465232267925))))),"np.log((((np.log((t[0] + x[:, 1])) / x[:, 0] ** t[1] ** x[:, 0]) * (t[2] ** np.exp(x[:, 0]) + (x[:, 0] * t[3]))) * (((x[:, 1] / (x[:, 1] * t[4])) * t[5]) * ((x[:, 0] + t[6]) * (x[:, 1] * t[7])))))","$$\operatorname{log}(\left(\left(\frac{\operatorname{log}(\left(\theta_{0} + x_{1}\right))}{{x_{0}^{{\theta_{1}^{x_{0}}}}}} \cdot \left({\theta_{2}^{\operatorname{exp}(x_{0})}} + \left(x_{0} \cdot \theta_{3}\right)\right)\right) \cdot \left(\left(\frac{x_{1}}{\left(x_{1} \cdot \theta_{4}\right)} \cdot \theta_{5}\right) \cdot \left(\left(x_{0} + \theta_{6}\right) \cdot \left(x_{1} \cdot \theta_{7}\right)\right)\right)\right))$$",-0.7608012574024686;-0.43008469408972283;6.292200849674778e-2;-0.12496236836114638;-0.9810182405572657;0.9969449106681799;4.8101637865840985e-2;-0.576465232267925,36,,,,Infinity,,,,,, -505,0,((Log(Exp(x1)) ^ ((x0 * -0.3339603845716651) / (x0 + -0.6129353396270771))) / Log(Exp((((x0 ^ x0) ^ 0.254598519549314) * x1)))),"(np.log(np.exp(x[:, 1])) ** ((x[:, 0] * t[0]) / (x[:, 0] + t[1])) / np.log(np.exp((x[:, 0] ** x[:, 0] ** t[2] * x[:, 1]))))","$$\frac{{\operatorname{log}(\operatorname{exp}(x_{1}))^{\frac{\left(x_{0} \cdot \theta_{0}\right)}{\left(x_{0} + \theta_{1}\right)}}}}{\operatorname{log}(\operatorname{exp}(\left({{x_{0}^{x_{0}}}^{\theta_{2}}} \cdot x_{1}\right)))}$$",-0.3339603845716651;-0.6129353396270771;0.254598519549314,21,1.2661980592510769e14,1.2661980592510769e14,1.2661980592510769e14,1.2661980592510769e14,-1.168462267024142e16,-1.168462267024142e16,-1.168462267024142e16,1.1395782533264913e14,1.1395782533264913e14,1.1395782533264913e14 -506,0,Exp((0.4813219551679898 ^ Log(((Exp(x0) / (-0.5726900657580534 + x0)) ^ (0.7629390522998468 / Exp((-0.37672408121184886 ^ x1))))))),"np.exp(t[0] ** np.log((np.exp(x[:, 0]) / (t[1] + x[:, 0])) ** (t[2] / np.exp(t[3] ** x[:, 1]))))","$$\operatorname{exp}({\theta_{0}^{\operatorname{log}({\frac{\operatorname{exp}(x_{0})}{\left(\theta_{1} + x_{0}\right)}^{\frac{\theta_{2}}{\operatorname{exp}({\theta_{3}^{x_{1}}})}}})}})$$",0.4813219551679898;-0.5726900657580534;0.7629390522998468;-0.37672408121184886,17,,,,Infinity,,,,,, -507,0,((Exp((x0 ^ -9.200388982329755e-2)) * Exp((0.21147430389317146 * (x1 / x0)))) * ((Log(x1) + Exp((x1 ^ ((x1 * 0.5579888515226705) + -0.11878669040485879)))) ^ ((Log((-0.7044664153893334 / (x0 ^ x0))) * -4.64179423739447e-2) * 0.892186212280962))),"((np.exp(x[:, 0] ** t[0]) * np.exp((t[1] * (x[:, 1] / x[:, 0])))) * (np.log(x[:, 1]) + np.exp(x[:, 1] ** ((x[:, 1] * t[2]) + t[3]))) ** ((np.log((t[4] / x[:, 0] ** x[:, 0])) * t[5]) * t[6]))","$$\left(\left(\operatorname{exp}({x_{0}^{\theta_{0}}}) \cdot \operatorname{exp}(\left(\theta_{1} \cdot \frac{x_{1}}{x_{0}}\right))\right) \cdot {\left(\operatorname{log}(x_{1}) + \operatorname{exp}({x_{1}^{\left(\left(x_{1} \cdot \theta_{2}\right) + \theta_{3}\right)}})\right)^{\left(\left(\operatorname{log}(\frac{\theta_{4}}{{x_{0}^{x_{0}}}}) \cdot \theta_{5}\right) \cdot \theta_{6}\right)}}\right)$$",-9.200388982329755e-2;0.21147430389317146;0.5579888515226705;-0.11878669040485879;-0.7044664153893334;-4.64179423739447e-2;0.892186212280962,34,,,,Infinity,,,,,, -508,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 -509,0,(Exp((1.976895496118902 / x0)) * 6.320978169179161e-3),"(np.exp((t[0] / x[:, 0])) * t[1])","$$\left(\operatorname{exp}(\frac{\theta_{0}}{x_{0}}) \cdot \theta_{1}\right)$$",1.976895496118902;6.320978169179161e-3,6,5.6805456581737425e-3,5.6805456581737425e-3,5.6805456581737425e-3,5.6805456581737425e-3,0.47579265272213833,0.47579265272213833,0.47579265272213833,13.703589836432238,13.703589836432238,13.703589836432238 -510,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 -511,0,((((-0.7250556350870376 / x0) * -0.709695230029325) * 0.7180220038697421) ^ (((1.1576193966543218 * x1) + 2.7591213525421794) * 1.1521992508783783)),"(((t[0] / x[:, 0]) * t[1]) * t[2]) ** (((t[3] * x[:, 1]) + t[4]) * t[5])","$${\left(\left(\frac{\theta_{0}}{x_{0}} \cdot \theta_{1}\right) \cdot \theta_{2}\right)^{\left(\left(\left(\theta_{3} \cdot x_{1}\right) + \theta_{4}\right) \cdot \theta_{5}\right)}}$$",-0.7250556350870376;-0.709695230029325;0.7180220038697421;1.1576193966543218;2.7591213525421794;1.1521992508783783,15,6.639669580858919e-3,6.639669580858919e-3,6.639669580858919e-3,6.639669580858919e-3,0.3872835837917472,0.3872835837917472,0.3872835837917472,41.320177550658315,41.320177550658315,41.320177550658315 -512,0,Exp(((((-0.325449421298992 ^ x1) * -9.523958242252628e-2) + Exp(Exp((-0.20640673599186588 + x1)))) ^ Log(((0.5338096338517231 + (x1 * -0.35954842666035247)) * 0.5607667923142867)))),"np.exp(((t[0] ** x[:, 1] * t[1]) + np.exp(np.exp((t[2] + x[:, 1])))) ** np.log(((t[3] + (x[:, 1] * t[4])) * t[5])))","$$\operatorname{exp}({\left(\left({\theta_{0}^{x_{1}}} \cdot \theta_{1}\right) + \operatorname{exp}(\operatorname{exp}(\left(\theta_{2} + x_{1}\right)))\right)^{\operatorname{log}(\left(\left(\theta_{3} + \left(x_{1} \cdot \theta_{4}\right)\right) \cdot \theta_{5}\right))}})$$",-0.325449421298992;-9.523958242252628e-2;-0.20640673599186588;0.5338096338517231;-0.35954842666035247;0.5607667923142867,21,,,,Infinity,,,,,, -513,0,Log(Exp((Log((0.38456457777374986 + Log(Log(x1)))) * ((x0 / x0) * (9.003773939913506e-2 + (0.5626725897151772 * x1)))))),"np.log(np.exp((np.log((t[0] + np.log(np.log(x[:, 1])))) * ((x[:, 0] / x[:, 0]) * (t[1] + (t[2] * x[:, 1]))))))","$$\operatorname{log}(\operatorname{exp}(\left(\operatorname{log}(\left(\theta_{0} + \operatorname{log}(\operatorname{log}(x_{1}))\right)) \cdot \left(\frac{x_{0}}{x_{0}} \cdot \left(\theta_{1} + \left(\theta_{2} \cdot x_{1}\right)\right)\right)\right)))$$",0.38456457777374986;9.003773939913506e-2;0.5626725897151772,18,,,,Infinity,,,,,, -514,0,(Exp(Exp(((((x1 + -0.9498996714574331) * (x1 ^ 0.3107424139600341)) * ((x0 / x1) * (0.8174177054615803 + x1))) ^ 0.21781242433634995))) ^ Exp(Log(Log(x1)))),"np.exp(np.exp((((x[:, 1] + t[0]) * x[:, 1] ** t[1]) * ((x[:, 0] / x[:, 1]) * (t[2] + x[:, 1]))) ** t[3])) ** np.exp(np.log(np.log(x[:, 1])))","$${\operatorname{exp}(\operatorname{exp}({\left(\left(\left(x_{1} + \theta_{0}\right) \cdot {x_{1}^{\theta_{1}}}\right) \cdot \left(\frac{x_{0}}{x_{1}} \cdot \left(\theta_{2} + x_{1}\right)\right)\right)^{\theta_{3}}}))^{\operatorname{exp}(\operatorname{log}(\operatorname{log}(x_{1})))}}$$",-0.9498996714574331;0.3107424139600341;0.8174177054615803;0.21781242433634995,24,,,,Infinity,,,,,, -515,0,((((Exp(x0) + (0.7010647342909639 ^ (-0.2252846417126554 * x1))) / x0) / x1) + x1),"((((np.exp(x[:, 0]) + t[0] ** (t[1] * x[:, 1])) / x[:, 0]) / x[:, 1]) + x[:, 1])","$$\left(\frac{\frac{\left(\operatorname{exp}(x_{0}) + {\theta_{0}^{\left(\theta_{1} \cdot x_{1}\right)}}\right)}{x_{0}}}{x_{1}} + x_{1}\right)$$",0.7010647342909639;-0.2252846417126554,14,305.680192337403,305.680192337403,305.680192337403,305.680192337403,-28207.52298053575,-28207.52298053575,-28207.52298053575,308.2662045579157,308.2662045579157,308.2662045579157 -516,0,(((0.28960866673269425 / Log(x0)) * -5.541986059530257e-2) * (((0.9032994732152098 / (((x0 ^ x0) / (0.9918827651761863 / x1)) ^ x1)) + (Log(x0) * Exp(x1))) ^ 0.9957296896061607)),"(((t[0] / np.log(x[:, 0])) * t[1]) * ((t[2] / (x[:, 0] ** x[:, 0] / (t[3] / x[:, 1])) ** x[:, 1]) + (np.log(x[:, 0]) * np.exp(x[:, 1]))) ** t[4])","$$\left(\left(\frac{\theta_{0}}{\operatorname{log}(x_{0})} \cdot \theta_{1}\right) \cdot {\left(\frac{\theta_{2}}{{\frac{{x_{0}^{x_{0}}}}{\frac{\theta_{3}}{x_{1}}}^{x_{1}}}} + \left(\operatorname{log}(x_{0}) \cdot \operatorname{exp}(x_{1})\right)\right)^{\theta_{4}}}\right)$$",0.28960866673269425;-5.541986059530257e-2;0.9032994732152098;0.9918827651761863;0.9957296896061607,26,113.76170721946794,113.76170721946794,113.76170721946794,113.76170721946794,-10497.062396085066,-10497.062396085066,-10497.062396085066,169.61800018510104,169.61800018510104,169.61800018510104 -517,0,(Exp(Log((Log((Exp(x1) ^ Exp(x1))) / ((-0.2744850894146289 + Log(x0)) ^ (0.2825543638450194 / Exp(x0)))))) * Exp(Log((0.9294733721885229 ^ ((Log(x0) * -3.371544161467288e-2) + Log(x1)))))),"(np.exp(np.log((np.log(np.exp(x[:, 1]) ** np.exp(x[:, 1])) / (t[0] + np.log(x[:, 0])) ** (t[1] / np.exp(x[:, 0]))))) * np.exp(np.log(t[2] ** ((np.log(x[:, 0]) * t[3]) + np.log(x[:, 1])))))","$$\left(\operatorname{exp}(\operatorname{log}(\frac{\operatorname{log}({\operatorname{exp}(x_{1})^{\operatorname{exp}(x_{1})}})}{{\left(\theta_{0} + \operatorname{log}(x_{0})\right)^{\frac{\theta_{1}}{\operatorname{exp}(x_{0})}}}})) \cdot \operatorname{exp}(\operatorname{log}({\theta_{2}^{\left(\left(\operatorname{log}(x_{0}) \cdot \theta_{3}\right) + \operatorname{log}(x_{1})\right)}}))\right)$$",-0.2744850894146289;0.2825543638450194;0.9294733721885229;-3.371544161467288e-2,30,,,,Infinity,,,,,, -518,0,(Log(Exp((Exp((x1 + x1)) + -22.79804728439941))) / (-2584.422449534293 * x1)),"(np.log(np.exp((np.exp((x[:, 1] + x[:, 1])) + t[0]))) / (t[1] * x[:, 1]))","$$\frac{\operatorname{log}(\operatorname{exp}(\left(\operatorname{exp}(\left(x_{1} + x_{1}\right)) + \theta_{0}\right)))}{\left(\theta_{1} \cdot x_{1}\right)}$$",-22.79804728439941;-2584.422449534293,12,1.339959735847955e-2,1.339959735847955e-2,1.339959735847955e-2,1.339959735847955e-2,-0.23653039840863288,-0.23653039840863288,-0.23653039840863288,27.404831297021673,27.404831297021673,27.404831297021673 -519,0,(((-0.6028146509251573 * (-0.5699031219182737 + Log((x0 + (0.45187070320927947 ^ x1))))) / (((Exp((x0 + x1)) / x0) ^ Log((x1 + x0))) * 0.21191264528789766)) ^ -0.48969839812288696),"((t[0] * (t[1] + np.log((x[:, 0] + t[2] ** x[:, 1])))) / ((np.exp((x[:, 0] + x[:, 1])) / x[:, 0]) ** np.log((x[:, 1] + x[:, 0])) * t[3])) ** t[4]","$${\frac{\left(\theta_{0} \cdot \left(\theta_{1} + \operatorname{log}(\left(x_{0} + {\theta_{2}^{x_{1}}}\right))\right)\right)}{\left({\frac{\operatorname{exp}(\left(x_{0} + x_{1}\right))}{x_{0}}^{\operatorname{log}(\left(x_{1} + x_{0}\right))}} \cdot \theta_{3}\right)}^{\theta_{4}}}$$",-0.6028146509251573;-0.5699031219182737;0.45187070320927947;0.21191264528789766;-0.48969839812288696,26,,,,Infinity,,,,,, -520,0,(Exp(((-1.1027947540548775 + Exp(Log(Exp(x1)))) ^ Exp(((x1 / x0) * (x1 * -0.4497185400281879))))) + (((x1 * 0.11574099180705073) ^ Exp((x1 ^ x1))) + ((0.18599696394263196 * x0) / (-0.14169676787807178 * (x1 + 0.9129515428317483))))),"(np.exp((t[0] + np.exp(np.log(np.exp(x[:, 1])))) ** np.exp(((x[:, 1] / x[:, 0]) * (x[:, 1] * t[1])))) + ((x[:, 1] * t[2]) ** np.exp(x[:, 1] ** x[:, 1]) + ((t[3] * x[:, 0]) / (t[4] * (x[:, 1] + t[5])))))","$$\left(\operatorname{exp}({\left(\theta_{0} + \operatorname{exp}(\operatorname{log}(\operatorname{exp}(x_{1})))\right)^{\operatorname{exp}(\left(\frac{x_{1}}{x_{0}} \cdot \left(x_{1} \cdot \theta_{1}\right)\right))}}) + \left({\left(x_{1} \cdot \theta_{2}\right)^{\operatorname{exp}({x_{1}^{x_{1}}})}} + \frac{\left(\theta_{3} \cdot x_{0}\right)}{\left(\theta_{4} \cdot \left(x_{1} + \theta_{5}\right)\right)}\right)\right)$$",-1.1027947540548775;-0.4497185400281879;0.11574099180705073;0.18599696394263196;-0.14169676787807178;0.9129515428317483,35,0.25289664158219277,0.25289664158219277,0.25289664158219277,0.25289664158219277,-22.33759564603199,-22.33759564603199,-22.33759564603199,89.2560167963975,89.2560167963975,89.2560167963975 -521,0,((x1 + -1.3284011883908842) + x0),"((x[:, 1] + t[0]) + x[:, 0])","$$\left(\left(x_{1} + \theta_{0}\right) + x_{0}\right)$$",-1.3284011883908842,5,0.16601395375655695,0.16601395375655695,0.16601395375655695,0.16601395375655695,-14.31996036060601,-14.31996036060601,-14.31996036060601,9.101809299347996,9.101809299347996,9.101809299347996 -522,0,(Exp(((x0 * 0.3216177161163963) / ((Exp(Exp(x1)) ^ x0) / (-2.6498564918807217 / x1)))) ^ ((Exp((x0 ^ 4.1979983145659)) + ((Exp(x1) ^ -0.3074245843259133) + 0.1678783636599132)) * 1.4399624847333539)),"np.exp(((x[:, 0] * t[0]) / (np.exp(np.exp(x[:, 1])) ** x[:, 0] / (t[1] / x[:, 1])))) ** ((np.exp(x[:, 0] ** t[2]) + (np.exp(x[:, 1]) ** t[3] + t[4])) * t[5])","$${\operatorname{exp}(\frac{\left(x_{0} \cdot \theta_{0}\right)}{\frac{{\operatorname{exp}(\operatorname{exp}(x_{1}))^{x_{0}}}}{\frac{\theta_{1}}{x_{1}}}})^{\left(\left(\operatorname{exp}({x_{0}^{\theta_{2}}}) + \left({\operatorname{exp}(x_{1})^{\theta_{3}}} + \theta_{4}\right)\right) \cdot \theta_{5}\right)}}$$",0.3216177161163963;-2.6498564918807217;4.1979983145659;-0.3074245843259133;0.1678783636599132;1.4399624847333539,28,3.518506085183055e-3,3.518506085183055e-3,3.518506085183055e-3,3.518506085183055e-3,0.6753081741996254,0.6753081741996254,0.6753081741996254,70.35307943471791,70.35307943471791,70.35307943471791 -523,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 -524,0,(((Exp(x0) * (x0 + -1.975725878420711)) * (7.448079104361075e-3 * (x0 ^ 1.1488771981643213))) / ((Log(x1) / (3.2143178725178747 / x1)) * Log((273.27365704285967 + (x1 / x0))))),"(((np.exp(x[:, 0]) * (x[:, 0] + t[0])) * (t[1] * x[:, 0] ** t[2])) / ((np.log(x[:, 1]) / (t[3] / x[:, 1])) * np.log((t[4] + (x[:, 1] / x[:, 0])))))","$$\frac{\left(\left(\operatorname{exp}(x_{0}) \cdot \left(x_{0} + \theta_{0}\right)\right) \cdot \left(\theta_{1} \cdot {x_{0}^{\theta_{2}}}\right)\right)}{\left(\frac{\operatorname{log}(x_{1})}{\frac{\theta_{3}}{x_{1}}} \cdot \operatorname{log}(\left(\theta_{4} + \frac{x_{1}}{x_{0}}\right))\right)}$$",-1.975725878420711;7.448079104361075e-3;1.1488771981643213;3.2143178725178747;273.27365704285967,26,1.3312009238743082e-2,1.3312009238743082e-2,1.3312009238743082e-2,1.3312009238743082e-2,-0.22844766504761438,-0.22844766504761438,-0.22844766504761438,67.24444449589478,67.24444449589478,67.24444449589478 -525,0,((Log((-0.19441309831816445 ^ (0.20893475036615783 / x1))) ^ x0) * x1),"(np.log(t[0] ** (t[1] / x[:, 1])) ** x[:, 0] * x[:, 1])","$$\left({\operatorname{log}({\theta_{0}^{\frac{\theta_{1}}{x_{1}}}})^{x_{0}}} \cdot x_{1}\right)$$",-0.19441309831816445;0.20893475036615783,10,,,,Infinity,,,,,, -526,0,(Exp((0.9686270182870961 ^ (-0.28363525519176924 ^ x1))) + -0.9899115574323765),"(np.exp(t[0] ** t[1] ** x[:, 1]) + t[2])","$$\left(\operatorname{exp}({\theta_{0}^{{\theta_{1}^{x_{1}}}}}) + \theta_{2}\right)$$",0.9686270182870961;-0.28363525519176924;-0.9899115574323765,8,,,,Infinity,,,,,, -527,0,Log((((Log(Log(Log(x0))) ^ -0.49470857626507314) / (0.6607125913815111 + ((x1 * -0.12108279981218562) + x1))) * (0.7936245023901725 / (Log((x0 + x1)) ^ (Exp(x1) + 8.647906604024369e-2))))),"np.log(((np.log(np.log(np.log(x[:, 0]))) ** t[0] / (t[1] + ((x[:, 1] * t[2]) + x[:, 1]))) * (t[3] / np.log((x[:, 0] + x[:, 1])) ** (np.exp(x[:, 1]) + t[4]))))","$$\operatorname{log}(\left(\frac{{\operatorname{log}(\operatorname{log}(\operatorname{log}(x_{0})))^{\theta_{0}}}}{\left(\theta_{1} + \left(\left(x_{1} \cdot \theta_{2}\right) + x_{1}\right)\right)} \cdot \frac{\theta_{3}}{{\operatorname{log}(\left(x_{0} + x_{1}\right))^{\left(\operatorname{exp}(x_{1}) + \theta_{4}\right)}}}\right))$$",-0.49470857626507314;0.6607125913815111;-0.12108279981218562;0.7936245023901725;8.647906604024369e-2,27,,,,Infinity,,,,,, -528,0,((((x1 * 0.7142536592631711) / ((Log(x1) * 0.4080886135836659) ^ -0.34002886512228137)) ^ -0.5533520705572768) + Log((Log(x0) * Log(Exp((0.5458872434301651 ^ x1)))))),"(((x[:, 1] * t[0]) / (np.log(x[:, 1]) * t[1]) ** t[2]) ** t[3] + np.log((np.log(x[:, 0]) * np.log(np.exp(t[4] ** x[:, 1])))))","$$\left({\frac{\left(x_{1} \cdot \theta_{0}\right)}{{\left(\operatorname{log}(x_{1}) \cdot \theta_{1}\right)^{\theta_{2}}}}^{\theta_{3}}} + \operatorname{log}(\left(\operatorname{log}(x_{0}) \cdot \operatorname{log}(\operatorname{exp}({\theta_{4}^{x_{1}}}))\right))\right)$$",0.7142536592631711;0.4080886135836659;-0.34002886512228137;-0.5533520705572768;0.5458872434301651,22,,,,Infinity,,,,,, -529,0,Log((x0 * -2.0419249058141853e-2)),"np.log((x[:, 0] * t[0]))","$$\operatorname{log}(\left(x_{0} \cdot \theta_{0}\right))$$",-2.0419249058141853e-2,4,,,,Infinity,,,,,, -530,0,((Log(x1) ^ (-0.19803162211657455 ^ x0)) * Exp((x0 / Log(x0)))),"(np.log(x[:, 1]) ** t[0] ** x[:, 0] * np.exp((x[:, 0] / np.log(x[:, 0]))))","$$\left({\operatorname{log}(x_{1})^{{\theta_{0}^{x_{0}}}}} \cdot \operatorname{exp}(\frac{x_{0}}{\operatorname{log}(x_{0})})\right)$$",-0.19803162211657455,12,,,,Infinity,,,,,, -531,0,(((Exp(Exp(Log((x0 * -0.6004826686717126)))) / Exp(Log(((x0 * -0.733574888326342) / (x1 + x0))))) / Exp(Exp((x0 + 0.7477565336063909)))) ^ -1.8070294031299006e-2),"((np.exp(np.exp(np.log((x[:, 0] * t[0])))) / np.exp(np.log(((x[:, 0] * t[1]) / (x[:, 1] + x[:, 0]))))) / np.exp(np.exp((x[:, 0] + t[2])))) ** t[3]","$${\frac{\frac{\operatorname{exp}(\operatorname{exp}(\operatorname{log}(\left(x_{0} \cdot \theta_{0}\right))))}{\operatorname{exp}(\operatorname{log}(\frac{\left(x_{0} \cdot \theta_{1}\right)}{\left(x_{1} + x_{0}\right)}))}}{\operatorname{exp}(\operatorname{exp}(\left(x_{0} + \theta_{2}\right)))}^{\theta_{3}}}$$",-0.6004826686717126;-0.733574888326342;0.7477565336063909;-1.8070294031299006e-2,24,,,,Infinity,,,,,, -532,0,((0.7786470732106883 * ((x1 + x1) * Log(Exp(x0)))) + (Log((Log(x1) + (x0 ^ 0.8838367790811558))) + ((0.1569261026896851 / x0) + (0.8221226480064485 / Exp(x0))))),"((t[0] * ((x[:, 1] + x[:, 1]) * np.log(np.exp(x[:, 0])))) + (np.log((np.log(x[:, 1]) + x[:, 0] ** t[1])) + ((t[2] / x[:, 0]) + (t[3] / np.exp(x[:, 0])))))","$$\left(\left(\theta_{0} \cdot \left(\left(x_{1} + x_{1}\right) \cdot \operatorname{log}(\operatorname{exp}(x_{0}))\right)\right) + \left(\operatorname{log}(\left(\operatorname{log}(x_{1}) + {x_{0}^{\theta_{1}}}\right)) + \left(\frac{\theta_{2}}{x_{0}} + \frac{\theta_{3}}{\operatorname{exp}(x_{0})}\right)\right)\right)$$",0.7786470732106883;0.8838367790811558;0.1569261026896851;0.8221226480064485,26,,,,Infinity,,,,,, -533,0,(Log((((x0 * -0.8810967003370037) / (x0 * 0.6712221668638232)) ^ ((-0.6505810524715376 + x1) / (x1 * -0.9122424888661672)))) * Exp(((0.14810832038734434 * (x1 ^ Exp(x1))) + Log((-0.35869119467233523 / Exp((x1 ^ x0))))))),"(np.log(((x[:, 0] * t[0]) / (x[:, 0] * t[1])) ** ((t[2] + x[:, 1]) / (x[:, 1] * t[3]))) * np.exp(((t[4] * x[:, 1] ** np.exp(x[:, 1])) + np.log((t[5] / np.exp(x[:, 1] ** x[:, 0]))))))","$$\left(\operatorname{log}({\frac{\left(x_{0} \cdot \theta_{0}\right)}{\left(x_{0} \cdot \theta_{1}\right)}^{\frac{\left(\theta_{2} + x_{1}\right)}{\left(x_{1} \cdot \theta_{3}\right)}}}) \cdot \operatorname{exp}(\left(\left(\theta_{4} \cdot {x_{1}^{\operatorname{exp}(x_{1})}}\right) + \operatorname{log}(\frac{\theta_{5}}{\operatorname{exp}({x_{1}^{x_{0}}})})\right))\right)$$",-0.8810967003370037;0.6712221668638232;-0.6505810524715376;-0.9122424888661672;0.14810832038734434;-0.35869119467233523,32,,,,Infinity,,,,,, -534,0,((x1 / x0) / x0),"((x[:, 1] / x[:, 0]) / x[:, 0])","$$\frac{\frac{x_{1}}{x_{0}}}{x_{0}}$$",,5,0.18189133018980164,0.18189133018980164,0.18189133018980164,0.18189133018980164,-15.785143088224302,-15.785143088224302,-15.785143088224302,5.656763640511371,5.656763640511371,5.656763640511371 -535,0,Log(Log(Log((Exp((0.9128160476217062 ^ Log(x1))) / ((x0 / (4.12577724891429 + x1)) / Exp(x1)))))),"np.log(np.log(np.log((np.exp(t[0] ** np.log(x[:, 1])) / ((x[:, 0] / (t[1] + x[:, 1])) / np.exp(x[:, 1]))))))","$$\operatorname{log}(\operatorname{log}(\operatorname{log}(\frac{\operatorname{exp}({\theta_{0}^{\operatorname{log}(x_{1})}})}{\frac{\frac{x_{0}}{\left(\theta_{1} + x_{1}\right)}}{\operatorname{exp}(x_{1})}})))$$",0.9128160476217062;4.12577724891429,17,4.802959483638307e-3,4.802959483638307e-3,4.802959483638307e-3,4.802959483638307e-3,0.5567773236047672,0.5567773236047672,0.5567773236047672,39.39667874282776,39.39667874282776,39.39667874282776 -536,0,Exp((((x1 * x1) ^ ((x0 * Exp(x1)) ^ Exp((x1 * 9.30282964112813)))) + -2.722898345768033)),"np.exp(((x[:, 1] * x[:, 1]) ** (x[:, 0] * np.exp(x[:, 1])) ** np.exp((x[:, 1] * t[0])) + t[1]))","$$\operatorname{exp}(\left({\left(x_{1} \cdot x_{1}\right)^{{\left(x_{0} \cdot \operatorname{exp}(x_{1})\right)^{\operatorname{exp}(\left(x_{1} \cdot \theta_{0}\right))}}}} + \theta_{1}\right))$$",9.30282964112813;-2.722898345768033,16,7.611168542786238e-3,7.611168542786238e-3,7.611168542786238e-3,7.611168542786238e-3,0.29763253187522387,0.29763253187522387,0.29763253187522387,35.1832623073088,35.1832623073088,35.1832623073088 -537,0,Exp((Log((-0.885513424736886 / Exp((x1 * 0.14441812879941596)))) / (((-0.4083554784744744 + x0) * -0.8843568313468135) + (0.9161062161197486 / x0)))),"np.exp((np.log((t[0] / np.exp((x[:, 1] * t[1])))) / (((t[2] + x[:, 0]) * t[3]) + (t[4] / x[:, 0]))))","$$\operatorname{exp}(\frac{\operatorname{log}(\frac{\theta_{0}}{\operatorname{exp}(\left(x_{1} \cdot \theta_{1}\right))})}{\left(\left(\left(\theta_{2} + x_{0}\right) \cdot \theta_{3}\right) + \frac{\theta_{4}}{x_{0}}\right)})$$",-0.885513424736886;0.14441812879941596;-0.4083554784744744;-0.8843568313468135;0.9161062161197486,18,,,,Infinity,,,,,, -538,0,((((3.110521217182825 + x0) / (x0 * 1.2343219650780501)) / ((x1 * -1.508384831827767) + (x1 + x0))) * (((x1 * 0.3240188700776759) * 0.5737060311488461) * 0.23714317027464443)),"((((t[0] + x[:, 0]) / (x[:, 0] * t[1])) / ((x[:, 1] * t[2]) + (x[:, 1] + x[:, 0]))) * (((x[:, 1] * t[3]) * t[4]) * t[5]))","$$\left(\frac{\frac{\left(\theta_{0} + x_{0}\right)}{\left(x_{0} \cdot \theta_{1}\right)}}{\left(\left(x_{1} \cdot \theta_{2}\right) + \left(x_{1} + x_{0}\right)\right)} \cdot \left(\left(\left(x_{1} \cdot \theta_{3}\right) \cdot \theta_{4}\right) \cdot \theta_{5}\right)\right)$$",3.110521217182825;1.2343219650780501;-1.508384831827767;0.3240188700776759;0.5737060311488461;0.23714317027464443,23,2.216835029840347e-3,2.216835029840347e-3,2.216835029840347e-3,2.216835029840347e-3,0.7954278901582055,0.7954278901582055,0.7954278901582055,53.33801255597796,53.33801255597796,53.33801255597796 -539,0,Log(((x0 ^ x0) / (-0.8658430664963853 + x1))),"np.log((x[:, 0] ** x[:, 0] / (t[0] + x[:, 1])))","$$\operatorname{log}(\frac{{x_{0}^{x_{0}}}}{\left(\theta_{0} + x_{1}\right)})$$",-0.8658430664963853,8,,,,Infinity,,,,,, -540,0,((Exp(((-0.685422247105812 ^ (-0.1853925499039295 * x0)) / Log(Exp(x0)))) + (Log(((-0.8568188588600946 + x1) * x0)) / ((0.4563808747272603 * x1) * (x0 * x0)))) ^ Log((((0.9505588714015397 / (0.16102016597616353 ^ x0)) * (x1 * -0.2685418507360601)) * (((-0.8326692018064361 + x1) * -0.22114286488245793) + ((x0 / x1) ^ -0.594725545082136))))),"(np.exp((t[0] ** (t[1] * x[:, 0]) / np.log(np.exp(x[:, 0])))) + (np.log(((t[2] + x[:, 1]) * x[:, 0])) / ((t[3] * x[:, 1]) * (x[:, 0] * x[:, 0])))) ** np.log((((t[4] / t[5] ** x[:, 0]) * (x[:, 1] * t[6])) * (((t[7] + x[:, 1]) * t[8]) + (x[:, 0] / x[:, 1]) ** t[9])))","$${\left(\operatorname{exp}(\frac{{\theta_{0}^{\left(\theta_{1} \cdot x_{0}\right)}}}{\operatorname{log}(\operatorname{exp}(x_{0}))}) + \frac{\operatorname{log}(\left(\left(\theta_{2} + x_{1}\right) \cdot x_{0}\right))}{\left(\left(\theta_{3} \cdot x_{1}\right) \cdot \left(x_{0} \cdot x_{0}\right)\right)}\right)^{\operatorname{log}(\left(\left(\frac{\theta_{4}}{{\theta_{5}^{x_{0}}}} \cdot \left(x_{1} \cdot \theta_{6}\right)\right) \cdot \left(\left(\left(\theta_{7} + x_{1}\right) \cdot \theta_{8}\right) + {\frac{x_{0}}{x_{1}}^{\theta_{9}}}\right)\right))}}$$",-0.685422247105812;-0.1853925499039295;-0.8568188588600946;0.4563808747272603;0.9505588714015397;0.16102016597616353;-0.2685418507360601;-0.8326692018064361;-0.22114286488245793;-0.594725545082136,48,,,,Infinity,,,,,, -541,0,(((Exp((Log(Exp(x0)) * x0)) + (x1 + x0)) + (-4.6660256348563225 + x0)) / x0),"(((np.exp((np.log(np.exp(x[:, 0])) * x[:, 0])) + (x[:, 1] + x[:, 0])) + (t[0] + x[:, 0])) / x[:, 0])","$$\frac{\left(\left(\operatorname{exp}(\left(\operatorname{log}(\operatorname{exp}(x_{0})) \cdot x_{0}\right)) + \left(x_{1} + x_{0}\right)\right) + \left(\theta_{0} + x_{0}\right)\right)}{x_{0}}$$",-4.6660256348563225,16,8.509649054330325,8.509649054330325,8.509649054330325,8.509649054330325,-784.2802926805781,-784.2802926805781,-784.2802926805781,42.95067375114231,42.95067375114231,42.95067375114231 -542,0,(((Log(x1) + 2.281526770646913) / (1.4061764057331507 + Exp((((x0 + -0.16233818524970256) / x1) ^ 1.5124304589040876)))) + ((-6.326298292888938e-2 / x0) * x1)),"(((np.log(x[:, 1]) + t[0]) / (t[1] + np.exp(((x[:, 0] + t[2]) / x[:, 1]) ** t[3]))) + ((t[4] / x[:, 0]) * x[:, 1]))","$$\left(\frac{\left(\operatorname{log}(x_{1}) + \theta_{0}\right)}{\left(\theta_{1} + \operatorname{exp}({\frac{\left(x_{0} + \theta_{2}\right)}{x_{1}}^{\theta_{3}}})\right)} + \left(\frac{\theta_{4}}{x_{0}} \cdot x_{1}\right)\right)$$",2.281526770646913;1.4061764057331507;-0.16233818524970256;1.5124304589040876;-6.326298292888938e-2,21,2.4820523678479403e-3,2.4820523678479403e-3,2.4820523678479403e-3,2.4820523678479403e-3,0.7709533263442508,0.7709533263442508,0.7709533263442508,56.24857464802987,56.24857464802987,56.24857464802987 -543,0,(Exp(((x0 * -226.87007100171496) * 218.29899566480157)) + Exp((Exp(x1) ^ (x1 * -1038.5960622375771)))),"(np.exp(((x[:, 0] * t[0]) * t[1])) + np.exp(np.exp(x[:, 1]) ** (x[:, 1] * t[2])))","$$\left(\operatorname{exp}(\left(\left(x_{0} \cdot \theta_{0}\right) \cdot \theta_{1}\right)) + \operatorname{exp}({\operatorname{exp}(x_{1})^{\left(x_{1} \cdot \theta_{2}\right)}})\right)$$",-226.87007100171496;218.29899566480157;-1038.5960622375771,14,0.8923107846245623,0.8923107846245623,0.8923107846245623,0.8923107846245623,-81.3434749938881,-81.3434749938881,-81.3434749938881,34.108596599039416,34.108596599039416,34.108596599039416 -544,0,Exp((2.544139175501373 / Log((1.6696973203636718 * x1)))),"np.exp((t[0] / np.log((t[1] * x[:, 1]))))","$$\operatorname{exp}(\frac{\theta_{0}}{\operatorname{log}(\left(\theta_{1} \cdot x_{1}\right))})$$",2.544139175501373;1.6696973203636718,7,2.0915703384523317e-2,2.0915703384523317e-2,2.0915703384523317e-2,2.0915703384523317e-2,-0.9301253871404438,-0.9301253871404438,-0.9301253871404438,16.60299028837774,16.60299028837774,16.60299028837774 -545,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 -546,0,(Exp(((x0 + x1) * Log((x1 + -0.10251617624237615)))) ^ Exp(((x1 * 1.0790609877952361e-2) * (x0 / Exp(x1))))),"np.exp(((x[:, 0] + x[:, 1]) * np.log((x[:, 1] + t[0])))) ** np.exp(((x[:, 1] * t[1]) * (x[:, 0] / np.exp(x[:, 1]))))","$${\operatorname{exp}(\left(\left(x_{0} + x_{1}\right) \cdot \operatorname{log}(\left(x_{1} + \theta_{0}\right))\right))^{\operatorname{exp}(\left(\left(x_{1} \cdot \theta_{1}\right) \cdot \frac{x_{0}}{\operatorname{exp}(x_{1})}\right))}}$$",-0.10251617624237615;1.0790609877952361e-2,19,1.0796889761329639e-2,1.0796889761329639e-2,1.0796889761329639e-2,1.0796889761329639e-2,3.650479862931655e-3,3.650479862931655e-3,3.650479862931655e-3,45.798834040908645,45.798834040908645,45.798834040908645 -547,0,((((x1 / x1) / (x1 / (x1 + x0))) * (Log((x1 ^ 2.3922639924808875)) / (-6.448234213664704 / x1))) + (((-2.0360938162205713 / x1) * -1.3340265338207848e-2) * -3.3592760700479793)),"((((x[:, 1] / x[:, 1]) / (x[:, 1] / (x[:, 1] + x[:, 0]))) * (np.log(x[:, 1] ** t[0]) / (t[1] / x[:, 1]))) + (((t[2] / x[:, 1]) * t[3]) * t[4]))","$$\left(\left(\frac{\frac{x_{1}}{x_{1}}}{\frac{x_{1}}{\left(x_{1} + x_{0}\right)}} \cdot \frac{\operatorname{log}({x_{1}^{\theta_{0}}})}{\frac{\theta_{1}}{x_{1}}}\right) + \left(\left(\frac{\theta_{2}}{x_{1}} \cdot \theta_{3}\right) \cdot \theta_{4}\right)\right)$$",2.3922639924808875;-6.448234213664704;-2.0360938162205713;-1.3340265338207848e-2;-3.3592760700479793,26,8.652652400586656e-2,8.652652400586656e-2,8.652652400586656e-2,8.652652400586656e-2,-6.984768074704952,-6.984768074704952,-6.984768074704952,64.24797863211921,64.24797863211921,64.24797863211921 -548,0,((((Exp(Log(Log(x0))) + (x0 ^ (x1 / x1))) ^ 0.5226913947538272) / x1) ^ x0),"((np.exp(np.log(np.log(x[:, 0]))) + x[:, 0] ** (x[:, 1] / x[:, 1])) ** t[0] / x[:, 1]) ** x[:, 0]","$${\frac{{\left(\operatorname{exp}(\operatorname{log}(\operatorname{log}(x_{0}))) + {x_{0}^{\frac{x_{1}}{x_{1}}}}\right)^{\theta_{0}}}}{x_{1}}^{x_{0}}}$$",0.5226913947538272,16,,,,Infinity,,,,,, -549,0,Exp(Exp((-4.707859200398705e-2 * Log(x0)))),"np.exp(np.exp((t[0] * np.log(x[:, 0]))))","$$\operatorname{exp}(\operatorname{exp}(\left(\theta_{0} \cdot \operatorname{log}(x_{0})\right)))$$",-4.707859200398705e-2,6,7.066150551338594,7.066150551338594,7.066150551338594,7.066150551338594,-651.0725752205391,-651.0725752205391,-651.0725752205391,18.037087906176975,18.037087906176975,18.037087906176975 -550,0,Exp((-0.7718832757777635 + ((Log(((0.47618095267350347 + x0) * -0.7065751952109678)) ^ (x1 / (Log(x1) * -0.26197739356614824))) / (x1 ^ x0)))),"np.exp((t[0] + (np.log(((t[1] + x[:, 0]) * t[2])) ** (x[:, 1] / (np.log(x[:, 1]) * t[3])) / x[:, 1] ** x[:, 0])))","$$\operatorname{exp}(\left(\theta_{0} + \frac{{\operatorname{log}(\left(\left(\theta_{1} + x_{0}\right) \cdot \theta_{2}\right))^{\frac{x_{1}}{\left(\operatorname{log}(x_{1}) \cdot \theta_{3}\right)}}}}{{x_{1}^{x_{0}}}}\right))$$",-0.7718832757777635;0.47618095267350347;-0.7065751952109678;-0.26197739356614824,20,,,,Infinity,,,,,, -551,0,Exp(Log((0.5081239124342001 / ((x1 * -0.21817194416272823) * 0.4782260526535784)))),"np.exp(np.log((t[0] / ((x[:, 1] * t[1]) * t[2]))))","$$\operatorname{exp}(\operatorname{log}(\frac{\theta_{0}}{\left(\left(x_{1} \cdot \theta_{1}\right) \cdot \theta_{2}\right)}))$$",0.5081239124342001;-0.21817194416272823;0.4782260526535784,9,,,,Infinity,,,,,, -552,0,(Log((Log((0.8042088499680479 * ((x1 + 0.6515224216157742) / x0))) ^ (((1.9113341413009244e-2 * x1) ^ x0) + Exp(Log((x1 / x0)))))) ^ ((-0.714524458597791 * ((x0 * x0) + x1)) * (-0.7893659903314014 + (-0.49473149880699085 ^ x0)))),"np.log(np.log((t[0] * ((x[:, 1] + t[1]) / x[:, 0]))) ** ((t[2] * x[:, 1]) ** x[:, 0] + np.exp(np.log((x[:, 1] / x[:, 0]))))) ** ((t[3] * ((x[:, 0] * x[:, 0]) + x[:, 1])) * (t[4] + t[5] ** x[:, 0]))","$${\operatorname{log}({\operatorname{log}(\left(\theta_{0} \cdot \frac{\left(x_{1} + \theta_{1}\right)}{x_{0}}\right))^{\left({\left(\theta_{2} \cdot x_{1}\right)^{x_{0}}} + \operatorname{exp}(\operatorname{log}(\frac{x_{1}}{x_{0}}))\right)}})^{\left(\left(\theta_{3} \cdot \left(\left(x_{0} \cdot x_{0}\right) + x_{1}\right)\right) \cdot \left(\theta_{4} + {\theta_{5}^{x_{0}}}\right)\right)}}$$",0.8042088499680479;0.6515224216157742;1.9113341413009244e-2;-0.714524458597791;-0.7893659903314014;-0.49473149880699085,35,,,,Infinity,,,,,, -553,0,Log((Log((((x0 + 0.6753513976490607) + Exp(x1)) + Exp(Log(Exp(x0))))) / Log(Exp(((-0.7715253229794674 + (0.9398783755367858 ^ x1)) / (0.35661762514616746 / Exp(x0))))))),"np.log((np.log((((x[:, 0] + t[0]) + np.exp(x[:, 1])) + np.exp(np.log(np.exp(x[:, 0]))))) / np.log(np.exp(((t[1] + t[2] ** x[:, 1]) / (t[3] / np.exp(x[:, 0])))))))","$$\operatorname{log}(\frac{\operatorname{log}(\left(\left(\left(x_{0} + \theta_{0}\right) + \operatorname{exp}(x_{1})\right) + \operatorname{exp}(\operatorname{log}(\operatorname{exp}(x_{0})))\right))}{\operatorname{log}(\operatorname{exp}(\frac{\left(\theta_{1} + {\theta_{2}^{x_{1}}}\right)}{\frac{\theta_{3}}{\operatorname{exp}(x_{0})}}))})$$",0.6753513976490607;-0.7715253229794674;0.9398783755367858;0.35661762514616746,26,1.8473522154424885e-2,1.8473522154424885e-2,1.8473522154424885e-2,1.8473522154424885e-2,-0.7047580683583845,-0.7047580683583845,-0.7047580683583845,62.16580599508528,62.16580599508528,62.16580599508528 -554,0,(Log((((x0 + x1) ^ (0.8531219262814225 / x0)) / ((x1 / x1) / (-0.30082927989540553 + x1)))) ^ (((-0.8183922665757343 / x1) * Exp(Log(x1))) / ((x0 + x1) * (x0 + (0.5889502397541466 * x0))))),"np.log(((x[:, 0] + x[:, 1]) ** (t[0] / x[:, 0]) / ((x[:, 1] / x[:, 1]) / (t[1] + x[:, 1])))) ** (((t[2] / x[:, 1]) * np.exp(np.log(x[:, 1]))) / ((x[:, 0] + x[:, 1]) * (x[:, 0] + (t[3] * x[:, 0]))))","$${\operatorname{log}(\frac{{\left(x_{0} + x_{1}\right)^{\frac{\theta_{0}}{x_{0}}}}}{\frac{\frac{x_{1}}{x_{1}}}{\left(\theta_{1} + x_{1}\right)}})^{\frac{\left(\frac{\theta_{2}}{x_{1}} \cdot \operatorname{exp}(\operatorname{log}(x_{1}))\right)}{\left(\left(x_{0} + x_{1}\right) \cdot \left(x_{0} + \left(\theta_{3} \cdot x_{0}\right)\right)\right)}}}$$",0.8531219262814225;-0.30082927989540553;-0.8183922665757343;0.5889502397541466,34,,,,Infinity,,,,,, -555,0,(Log((0.19863387639601227 + ((0.35676899815111274 ^ x0) + -0.812369493794429))) + (((x1 * -0.7050178761070539) ^ (x0 + 0.27803730214694433)) / ((-0.6751705456210555 ^ x0) ^ (x0 * 0.4676492196057782)))),"(np.log((t[0] + (t[1] ** x[:, 0] + t[2]))) + ((x[:, 1] * t[3]) ** (x[:, 0] + t[4]) / t[5] ** x[:, 0] ** (x[:, 0] * t[6])))","$$\left(\operatorname{log}(\left(\theta_{0} + \left({\theta_{1}^{x_{0}}} + \theta_{2}\right)\right)) + \frac{{\left(x_{1} \cdot \theta_{3}\right)^{\left(x_{0} + \theta_{4}\right)}}}{{{\theta_{5}^{x_{0}}}^{\left(x_{0} \cdot \theta_{6}\right)}}}\right)$$",0.19863387639601227;0.35676899815111274;-0.812369493794429;-0.7050178761070539;0.27803730214694433;-0.6751705456210555;0.4676492196057782,24,,,,Infinity,,,,,, -556,0,((Log(((x1 * -0.10203300479329236) + 0.7020321013388006)) ^ (Exp((x0 ^ x0)) * (x1 ^ -0.5501985235253346))) / Exp((((-7.586433330965581e-2 ^ x0) ^ (x0 ^ x1)) ^ 0.7736157253762872))),"(np.log(((x[:, 1] * t[0]) + t[1])) ** (np.exp(x[:, 0] ** x[:, 0]) * x[:, 1] ** t[2]) / np.exp(t[3] ** x[:, 0] ** x[:, 0] ** x[:, 1] ** t[4]))","$$\frac{{\operatorname{log}(\left(\left(x_{1} \cdot \theta_{0}\right) + \theta_{1}\right))^{\left(\operatorname{exp}({x_{0}^{x_{0}}}) \cdot {x_{1}^{\theta_{2}}}\right)}}}{\operatorname{exp}({{{\theta_{3}^{x_{0}}}^{{x_{0}^{x_{1}}}}}^{\theta_{4}}})}$$",-0.10203300479329236;0.7020321013388006;-0.5501985235253346;-7.586433330965581e-2;0.7736157253762872,26,,,,Infinity,,,,,, -557,0,Exp((-0.80768785379523 ^ (-0.9180880328011456 ^ x0))),"np.exp(t[0] ** t[1] ** x[:, 0])","$$\operatorname{exp}({\theta_{0}^{{\theta_{1}^{x_{0}}}}})$$",-0.80768785379523;-0.9180880328011456,6,,,,Infinity,,,,,, -558,0,Log(((-0.1834969712349186 ^ Exp((-0.9146994354074729 + Exp(x0)))) / Exp((Log((Log(x0) + (8.355666345650159e-2 + x1))) / Log(((x1 + -0.6856996046845143) ^ 0.13901916576032336)))))),"np.log((t[0] ** np.exp((t[1] + np.exp(x[:, 0]))) / np.exp((np.log((np.log(x[:, 0]) + (t[2] + x[:, 1]))) / np.log((x[:, 1] + t[3]) ** t[4])))))","$$\operatorname{log}(\frac{{\theta_{0}^{\operatorname{exp}(\left(\theta_{1} + \operatorname{exp}(x_{0})\right))}}}{\operatorname{exp}(\frac{\operatorname{log}(\left(\operatorname{log}(x_{0}) + \left(\theta_{2} + x_{1}\right)\right))}{\operatorname{log}({\left(x_{1} + \theta_{3}\right)^{\theta_{4}}})})})$$",-0.1834969712349186;-0.9146994354074729;8.355666345650159e-2;-0.6856996046845143;0.13901916576032336,24,,,,Infinity,,,,,, -559,0,((((Exp((-0.371860589940477 * x1)) + (x0 * 0.9946810990348692)) * (0.6191738168396146 / (-0.13759617452971074 ^ x1))) ^ (((-8.897872087590497e-2 ^ (x0 * (-0.9702819567778611 + x1))) * 0.9505994074612809) * (Log(x0) / x0))) * x0),"(((np.exp((t[0] * x[:, 1])) + (x[:, 0] * t[1])) * (t[2] / t[3] ** x[:, 1])) ** ((t[4] ** (x[:, 0] * (t[5] + x[:, 1])) * t[6]) * (np.log(x[:, 0]) / x[:, 0])) * x[:, 0])","$$\left({\left(\left(\operatorname{exp}(\left(\theta_{0} \cdot x_{1}\right)) + \left(x_{0} \cdot \theta_{1}\right)\right) \cdot \frac{\theta_{2}}{{\theta_{3}^{x_{1}}}}\right)^{\left(\left({\theta_{4}^{\left(x_{0} \cdot \left(\theta_{5} + x_{1}\right)\right)}} \cdot \theta_{6}\right) \cdot \frac{\operatorname{log}(x_{0})}{x_{0}}\right)}} \cdot x_{0}\right)$$",-0.371860589940477;0.9946810990348692;0.6191738168396146;-0.13759617452971074;-8.897872087590497e-2;-0.9702819567778611;0.9505994074612809,31,,,,Infinity,,,,,, -560,0,(Exp(Log((Log((0.9825603789080609 + (-0.6143147758692913 / x0))) * (Log(x1) ^ (x0 * -0.22300377295252638))))) ^ Log(Exp(((0.11397831015486515 * ((0.33554209614120334 ^ x0) ^ -0.8475943750317159)) ^ (-0.20990928039680834 ^ ((0.9761425033050082 * x0) ^ -0.8369396548469973)))))),"np.exp(np.log((np.log((t[0] + (t[1] / x[:, 0]))) * np.log(x[:, 1]) ** (x[:, 0] * t[2])))) ** np.log(np.exp((t[3] * t[4] ** x[:, 0] ** t[5]) ** t[6] ** (t[7] * x[:, 0]) ** t[8]))","$${\operatorname{exp}(\operatorname{log}(\left(\operatorname{log}(\left(\theta_{0} + \frac{\theta_{1}}{x_{0}}\right)) \cdot {\operatorname{log}(x_{1})^{\left(x_{0} \cdot \theta_{2}\right)}}\right)))^{\operatorname{log}(\operatorname{exp}({\left(\theta_{3} \cdot {{\theta_{4}^{x_{0}}}^{\theta_{5}}}\right)^{{\theta_{6}^{{\left(\theta_{7} \cdot x_{0}\right)^{\theta_{8}}}}}}}))}}$$",0.9825603789080609;-0.6143147758692913;-0.22300377295252638;0.11397831015486515;0.33554209614120334;-0.8475943750317159;-0.20990928039680834;0.9761425033050082;-0.8369396548469973,33,,,,Infinity,,,,,, -561,0,Exp((((474496.36163822294 * (x0 + x1)) / ((848.2290472216542 / (x0 * -608.0773180535123)) * Log((x0 ^ x1)))) / ((Exp((542.704824617184 + x1)) / x1) ^ x0))),"np.exp((((t[0] * (x[:, 0] + x[:, 1])) / ((t[1] / (x[:, 0] * t[2])) * np.log(x[:, 0] ** x[:, 1]))) / (np.exp((t[3] + x[:, 1])) / x[:, 1]) ** x[:, 0]))","$$\operatorname{exp}(\frac{\frac{\left(\theta_{0} \cdot \left(x_{0} + x_{1}\right)\right)}{\left(\frac{\theta_{1}}{\left(x_{0} \cdot \theta_{2}\right)} \cdot \operatorname{log}({x_{0}^{x_{1}}})\right)}}{{\frac{\operatorname{exp}(\left(\theta_{3} + x_{1}\right))}{x_{1}}^{x_{0}}}})$$",474496.36163822294;848.2290472216542;-608.0773180535123;542.704824617184,26,0.8923097947317449,0.8923097947317449,0.8923097947317449,0.8923097947317449,-81.34338364542985,-81.34338364542985,-81.34338364542985,66.01461756747084,66.01461756747084,66.01461756747084 -562,0,(7.439888686209983e-2 / x0),"(t[0] / x[:, 0])","$$\frac{\theta_{0}}{x_{0}}$$",7.439888686209983e-2,3,7.713765273570095e-3,7.713765273570095e-3,7.713765273570095e-3,7.713765273570095e-3,0.2881647864648511,0.2881647864648511,0.2881647864648511,5.323704190118183,5.323704190118183,5.323704190118183 -563,0,Exp((((x0 * 0.3224124016335781) / x1) / Log((x0 / Log(x1))))),"np.exp((((x[:, 0] * t[0]) / x[:, 1]) / np.log((x[:, 0] / np.log(x[:, 1])))))","$$\operatorname{exp}(\frac{\frac{\left(x_{0} \cdot \theta_{0}\right)}{x_{1}}}{\operatorname{log}(\frac{x_{0}}{\operatorname{log}(x_{1})})})$$",0.3224124016335781,12,,,,Infinity,,,,,, -564,0,((((0.29349287452146866 / x0) + 9.094658003235967e-2) ^ (0.8489946685544796 / x0)) ^ Log((Log((x0 * Exp(x0))) / (-0.8671417634310796 / (x0 ^ x1))))),"((t[0] / x[:, 0]) + t[1]) ** (t[2] / x[:, 0]) ** np.log((np.log((x[:, 0] * np.exp(x[:, 0]))) / (t[3] / x[:, 0] ** x[:, 1])))","$${{\left(\frac{\theta_{0}}{x_{0}} + \theta_{1}\right)^{\frac{\theta_{2}}{x_{0}}}}^{\operatorname{log}(\frac{\operatorname{log}(\left(x_{0} \cdot \operatorname{exp}(x_{0})\right))}{\frac{\theta_{3}}{{x_{0}^{x_{1}}}}})}}$$",0.29349287452146866;9.094658003235967e-2;0.8489946685544796;-0.8671417634310796,22,,,,Infinity,,,,,, -565,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 -566,0,((0.36600505177983433 ^ Log(x0)) / Log((x1 + -0.27487605532018855))),"(t[0] ** np.log(x[:, 0]) / np.log((x[:, 1] + t[1])))","$$\frac{{\theta_{0}^{\operatorname{log}(x_{0})}}}{\operatorname{log}(\left(x_{1} + \theta_{1}\right))}$$",0.36600505177983433;-0.27487605532018855,9,,,,Infinity,,,,,, -567,0,Exp(Exp(((-0.5218350967145728 + (((0.6784203240561002 * x1) * x1) * ((x1 * x1) * -0.9487729162532552))) ^ Exp(((x1 ^ -0.6239423655593117) * (Exp(x0) * -0.5780899805857621)))))),"np.exp(np.exp((t[0] + (((t[1] * x[:, 1]) * x[:, 1]) * ((x[:, 1] * x[:, 1]) * t[2]))) ** np.exp((x[:, 1] ** t[3] * (np.exp(x[:, 0]) * t[4])))))","$$\operatorname{exp}(\operatorname{exp}({\left(\theta_{0} + \left(\left(\left(\theta_{1} \cdot x_{1}\right) \cdot x_{1}\right) \cdot \left(\left(x_{1} \cdot x_{1}\right) \cdot \theta_{2}\right)\right)\right)^{\operatorname{exp}(\left({x_{1}^{\theta_{3}}} \cdot \left(\operatorname{exp}(x_{0}) \cdot \theta_{4}\right)\right))}}))$$",-0.5218350967145728;0.6784203240561002;-0.9487729162532552;-0.6239423655593117;-0.5780899805857621,25,,,,Infinity,,,,,, -568,0,((Log((x1 * 0.3543055764576357)) * -0.289454823134599) / (((x0 + -0.902801943959533) ^ x1) * Exp(((0.8424122533628896 * x1) + -0.4199903865840553)))),"((np.log((x[:, 1] * t[0])) * t[1]) / ((x[:, 0] + t[2]) ** x[:, 1] * np.exp(((t[3] * x[:, 1]) + t[4]))))","$$\frac{\left(\operatorname{log}(\left(x_{1} \cdot \theta_{0}\right)) \cdot \theta_{1}\right)}{\left({\left(x_{0} + \theta_{2}\right)^{x_{1}}} \cdot \operatorname{exp}(\left(\left(\theta_{3} \cdot x_{1}\right) + \theta_{4}\right))\right)}$$",0.3543055764576357;-0.289454823134599;-0.902801943959533;0.8424122533628896;-0.4199903865840553,19,,,,Infinity,,,,,, -569,0,(((-16.606863395520314 / x0) + (x1 ^ (x0 ^ x0))) * ((x1 * x0) / (-29.45134318278752 * (x0 / x1)))),"(((t[0] / x[:, 0]) + x[:, 1] ** x[:, 0] ** x[:, 0]) * ((x[:, 1] * x[:, 0]) / (t[1] * (x[:, 0] / x[:, 1]))))","$$\left(\left(\frac{\theta_{0}}{x_{0}} + {x_{1}^{{x_{0}^{x_{0}}}}}\right) \cdot \frac{\left(x_{1} \cdot x_{0}\right)}{\left(\theta_{1} \cdot \frac{x_{0}}{x_{1}}\right)}\right)$$",-16.606863395520314;-29.45134318278752,19,5.238564449846848e-3,5.238564449846848e-3,5.238564449846848e-3,5.238564449846848e-3,0.5165791916755444,0.5165791916755444,0.5165791916755444,41.01885741079109,41.01885741079109,41.01885741079109 -570,0,(((Log(Log(Log((-0.7704320209206934 * x0)))) * (-0.1514559895650185 ^ (x0 ^ -0.5390055059998755))) * Exp(((5.645184042772433e-2 * (x0 + 9.554734627651484e-2)) + (x1 + 0.1899660442117117)))) * (Exp(((x1 + -0.696755962590768) ^ (0.9869502332343904 / (x1 / x0)))) + ((0.17592891751124196 ^ (0.18521743620105957 * x1)) * (x0 ^ (0.9425182785306492 / x0))))),"(((np.log(np.log(np.log((t[0] * x[:, 0])))) * t[1] ** x[:, 0] ** t[2]) * np.exp(((t[3] * (x[:, 0] + t[4])) + (x[:, 1] + t[5])))) * (np.exp((x[:, 1] + t[6]) ** (t[7] / (x[:, 1] / x[:, 0]))) + (t[8] ** (t[9] * x[:, 1]) * x[:, 0] ** (t[10] / x[:, 0]))))","$$\left(\left(\left(\operatorname{log}(\operatorname{log}(\operatorname{log}(\left(\theta_{0} \cdot x_{0}\right)))) \cdot {\theta_{1}^{{x_{0}^{\theta_{2}}}}}\right) \cdot \operatorname{exp}(\left(\left(\theta_{3} \cdot \left(x_{0} + \theta_{4}\right)\right) + \left(x_{1} + \theta_{5}\right)\right))\right) \cdot \left(\operatorname{exp}({\left(x_{1} + \theta_{6}\right)^{\frac{\theta_{7}}{\frac{x_{1}}{x_{0}}}}}) + \left({\theta_{8}^{\left(\theta_{9} \cdot x_{1}\right)}} \cdot {x_{0}^{\frac{\theta_{10}}{x_{0}}}}\right)\right)\right)$$",-0.7704320209206934;-0.1514559895650185;-0.5390055059998755;5.645184042772433e-2;9.554734627651484e-2;0.1899660442117117;-0.696755962590768;0.9869502332343904;0.17592891751124196;0.18521743620105957;0.9425182785306492,46,,,,Infinity,,,,,, -571,0,(Exp(Log((0.5468726414421925 / (x0 * (0.5598199540948363 + x0))))) ^ Exp((Exp(x1) / ((0.4842236596528492 ^ x0) * ((Exp(x1) / (-0.7479344362357396 ^ x0)) + -1.0785453908273568e-2))))),"np.exp(np.log((t[0] / (x[:, 0] * (t[1] + x[:, 0]))))) ** np.exp((np.exp(x[:, 1]) / (t[2] ** x[:, 0] * ((np.exp(x[:, 1]) / t[3] ** x[:, 0]) + t[4]))))","$${\operatorname{exp}(\operatorname{log}(\frac{\theta_{0}}{\left(x_{0} \cdot \left(\theta_{1} + x_{0}\right)\right)}))^{\operatorname{exp}(\frac{\operatorname{exp}(x_{1})}{\left({\theta_{2}^{x_{0}}} \cdot \left(\frac{\operatorname{exp}(x_{1})}{{\theta_{3}^{x_{0}}}} + \theta_{4}\right)\right)})}}$$",0.5468726414421925;0.5598199540948363;0.4842236596528492;-0.7479344362357396;-1.0785453908273568e-2,26,,,,Infinity,,,,,, -572,0,((((x0 / x0) + (x0 ^ x0)) / (Exp(x1) * Exp((x0 + x0)))) / Exp(((370.3534307283144 + Exp(x0)) ^ (Log(x0) * (-0.1807767497791878 + x1))))),"((((x[:, 0] / x[:, 0]) + x[:, 0] ** x[:, 0]) / (np.exp(x[:, 1]) * np.exp((x[:, 0] + x[:, 0])))) / np.exp((t[0] + np.exp(x[:, 0])) ** (np.log(x[:, 0]) * (t[1] + x[:, 1]))))","$$\frac{\frac{\left(\frac{x_{0}}{x_{0}} + {x_{0}^{x_{0}}}\right)}{\left(\operatorname{exp}(x_{1}) \cdot \operatorname{exp}(\left(x_{0} + x_{0}\right))\right)}}{\operatorname{exp}({\left(\theta_{0} + \operatorname{exp}(x_{0})\right)^{\left(\operatorname{log}(x_{0}) \cdot \left(\theta_{1} + x_{1}\right)\right)}})}$$",370.3534307283144;-0.1807767497791878,28,4.643856263537352e-3,4.643856263537352e-3,4.643856263537352e-3,4.643856263537352e-3,0.5714595534416982,0.5714595534416982,0.5714595534416982,65.56831750678661,65.56831750678661,65.56831750678661 -573,0,((-0.22862238255376865 ^ Exp((0.4801046713410423 / x0))) + Exp(Exp(Exp((-0.802168461005455 * x0))))),"(t[0] ** np.exp((t[1] / x[:, 0])) + np.exp(np.exp(np.exp((t[2] * x[:, 0])))))","$$\left({\theta_{0}^{\operatorname{exp}(\frac{\theta_{1}}{x_{0}})}} + \operatorname{exp}(\operatorname{exp}(\operatorname{exp}(\left(\theta_{2} \cdot x_{0}\right))))\right)$$",-0.22862238255376865;0.4801046713410423;-0.802168461005455,13,,,,Infinity,,,,,, -574,0,Exp(((0.5099867329702176 ^ Log(x1)) * -1.3522373273868376)),"np.exp((t[0] ** np.log(x[:, 1]) * t[1]))","$$\operatorname{exp}(\left({\theta_{0}^{\operatorname{log}(x_{1})}} \cdot \theta_{1}\right))$$",0.5099867329702176;-1.3522373273868376,7,9.162177343330498e-3,9.162177343330498e-3,9.162177343330498e-3,9.162177343330498e-3,0.15450363935976164,0.15450363935976164,0.15450363935976164,16.592412114940664,16.592412114940664,16.592412114940664 -575,0,Log((((-7.114988389230126e-2 ^ x0) * 0.19619385429738745) ^ ((x0 / x0) / x0))),"np.log((t[0] ** x[:, 0] * t[1]) ** ((x[:, 0] / x[:, 0]) / x[:, 0]))","$$\operatorname{log}({\left({\theta_{0}^{x_{0}}} \cdot \theta_{1}\right)^{\frac{\frac{x_{0}}{x_{0}}}{x_{0}}}})$$",-7.114988389230126e-2;0.19619385429738745,12,,,,Infinity,,,,,, -576,0,((Log(((((-0.6109424704940256 + x1) * 0.19295653687708436) * -0.9786341194863271) / (((x1 * x1) + 0.8215602485094153) + -9.979671211729091e-2))) ^ Exp(Exp(Exp(((x0 + x0) * -0.6728271656547664))))) + -0.3257912097299904),"(np.log(((((t[0] + x[:, 1]) * t[1]) * t[2]) / (((x[:, 1] * x[:, 1]) + t[3]) + t[4]))) ** np.exp(np.exp(np.exp(((x[:, 0] + x[:, 0]) * t[5])))) + t[6])","$$\left({\operatorname{log}(\frac{\left(\left(\left(\theta_{0} + x_{1}\right) \cdot \theta_{1}\right) \cdot \theta_{2}\right)}{\left(\left(\left(x_{1} \cdot x_{1}\right) + \theta_{3}\right) + \theta_{4}\right)})^{\operatorname{exp}(\operatorname{exp}(\operatorname{exp}(\left(\left(x_{0} + x_{0}\right) \cdot \theta_{5}\right))))}} + \theta_{6}\right)$$",-0.6109424704940256;0.19295653687708436;-0.9786341194863271;0.8215602485094153;-9.979671211729091e-2;-0.6728271656547664;-0.3257912097299904,27,,,,Infinity,,,,,, -577,0,((((x1 ^ 0.10088553937498834) ^ (0.8692795732288775 ^ Exp(Exp((-4.819136272679514e-2 ^ x0))))) ^ ((Exp(((x0 + x1) * (x0 + x1))) ^ x0) ^ -0.3184138899771527)) + -0.2561810471232039),"(x[:, 1] ** t[0] ** t[1] ** np.exp(np.exp(t[2] ** x[:, 0])) ** np.exp(((x[:, 0] + x[:, 1]) * (x[:, 0] + x[:, 1]))) ** x[:, 0] ** t[3] + t[4])","$$\left({{{x_{1}^{\theta_{0}}}^{{\theta_{1}^{\operatorname{exp}(\operatorname{exp}({\theta_{2}^{x_{0}}}))}}}}^{{{\operatorname{exp}(\left(\left(x_{0} + x_{1}\right) \cdot \left(x_{0} + x_{1}\right)\right))^{x_{0}}}^{\theta_{3}}}}} + \theta_{4}\right)$$",0.10088553937498834;0.8692795732288775;-4.819136272679514e-2;-0.3184138899771527;-0.2561810471232039,26,,,,Infinity,,,,,, -578,0,(Exp((((0.8958308789961463 / x0) * (x0 ^ (0.1217207735765995 / x1))) * -0.24288743936766566)) ^ Exp(Log(Log(((Log(x1) ^ 0.21494075919663902) * Log((x1 + x1))))))),"np.exp((((t[0] / x[:, 0]) * x[:, 0] ** (t[1] / x[:, 1])) * t[2])) ** np.exp(np.log(np.log((np.log(x[:, 1]) ** t[3] * np.log((x[:, 1] + x[:, 1]))))))","$${\operatorname{exp}(\left(\left(\frac{\theta_{0}}{x_{0}} \cdot {x_{0}^{\frac{\theta_{1}}{x_{1}}}}\right) \cdot \theta_{2}\right))^{\operatorname{exp}(\operatorname{log}(\operatorname{log}(\left({\operatorname{log}(x_{1})^{\theta_{3}}} \cdot \operatorname{log}(\left(x_{1} + x_{1}\right))\right))))}}$$",0.8958308789961463;0.1217207735765995;-0.24288743936766566;0.21494075919663902,25,,,,Infinity,,,,,, -579,0,((Log((x0 ^ -0.9093240682092463)) * (x0 / (x0 * x0))) ^ Exp(Log(((x0 * x0) ^ (x0 ^ 0.8464804178709849))))),"(np.log(x[:, 0] ** t[0]) * (x[:, 0] / (x[:, 0] * x[:, 0]))) ** np.exp(np.log((x[:, 0] * x[:, 0]) ** x[:, 0] ** t[1]))","$${\left(\operatorname{log}({x_{0}^{\theta_{0}}}) \cdot \frac{x_{0}}{\left(x_{0} \cdot x_{0}\right)}\right)^{\operatorname{exp}(\operatorname{log}({\left(x_{0} \cdot x_{0}\right)^{{x_{0}^{\theta_{1}}}}}))}}$$",-0.9093240682092463;0.8464804178709849,20,,,,Infinity,,,,,, -580,0,Log((Exp((Exp(Log((x0 * -0.7236264610158298))) ^ (Log(Exp(x0)) + 0.3112324747487134))) + ((0.2614554934577178 ^ (-0.11508425056178073 * x0)) * (Log(Log(x0)) ^ (x1 * 0.7900628192386188))))),"np.log((np.exp(np.exp(np.log((x[:, 0] * t[0]))) ** (np.log(np.exp(x[:, 0])) + t[1])) + (t[2] ** (t[3] * x[:, 0]) * np.log(np.log(x[:, 0])) ** (x[:, 1] * t[4]))))","$$\operatorname{log}(\left(\operatorname{exp}({\operatorname{exp}(\operatorname{log}(\left(x_{0} \cdot \theta_{0}\right)))^{\left(\operatorname{log}(\operatorname{exp}(x_{0})) + \theta_{1}\right)}}) + \left({\theta_{2}^{\left(\theta_{3} \cdot x_{0}\right)}} \cdot {\operatorname{log}(\operatorname{log}(x_{0}))^{\left(x_{1} \cdot \theta_{4}\right)}}\right)\right))$$",-0.7236264610158298;0.3112324747487134;0.2614554934577178;-0.11508425056178073;0.7900628192386188,27,,,,Infinity,,,,,, -581,0,(Exp(((((-7.613005745023416e-2 ^ x1) / Exp((-0.2221996891629202 / x0))) + -0.5355100089947986) / ((0.14894743547049127 + ((x1 + -0.6317521007442943) * 0.5220088491338244)) / (Exp((0.6752354001330176 + x1)) / x1)))) * (0.8310210380142103 + x0)),"(np.exp((((t[0] ** x[:, 1] / np.exp((t[1] / x[:, 0]))) + t[2]) / ((t[3] + ((x[:, 1] + t[4]) * t[5])) / (np.exp((t[6] + x[:, 1])) / x[:, 1])))) * (t[7] + x[:, 0]))","$$\left(\operatorname{exp}(\frac{\left(\frac{{\theta_{0}^{x_{1}}}}{\operatorname{exp}(\frac{\theta_{1}}{x_{0}})} + \theta_{2}\right)}{\frac{\left(\theta_{3} + \left(\left(x_{1} + \theta_{4}\right) \cdot \theta_{5}\right)\right)}{\frac{\operatorname{exp}(\left(\theta_{6} + x_{1}\right))}{x_{1}}}}) \cdot \left(\theta_{7} + x_{0}\right)\right)$$",-7.613005745023416e-2;-0.2221996891629202;-0.5355100089947986;0.14894743547049127;-0.6317521007442943;0.5220088491338244;0.6752354001330176;0.8310210380142103,30,,,,Infinity,,,,,, -582,0,(Exp(((x1 * (0.3743324774480471 + (-0.1680312560875793 * x1))) * (x0 ^ -0.3858425253899128))) ^ Log(((-0.6751368576765439 ^ (x1 + ((-0.5346815417372055 ^ x1) + -0.6540767420575526))) + (0.5658626658848409 / (0.5164351341908646 ^ x1))))),"np.exp(((x[:, 1] * (t[0] + (t[1] * x[:, 1]))) * x[:, 0] ** t[2])) ** np.log((t[3] ** (x[:, 1] + (t[4] ** x[:, 1] + t[5])) + (t[6] / t[7] ** x[:, 1])))","$${\operatorname{exp}(\left(\left(x_{1} \cdot \left(\theta_{0} + \left(\theta_{1} \cdot x_{1}\right)\right)\right) \cdot {x_{0}^{\theta_{2}}}\right))^{\operatorname{log}(\left({\theta_{3}^{\left(x_{1} + \left({\theta_{4}^{x_{1}}} + \theta_{5}\right)\right)}} + \frac{\theta_{6}}{{\theta_{7}^{x_{1}}}}\right))}}$$",0.3743324774480471;-0.1680312560875793;-0.3858425253899128;-0.6751368576765439;-0.5346815417372055;-0.6540767420575526;0.5658626658848409;0.5164351341908646,29,,,,Infinity,,,,,, -583,0,Exp((Exp(Log(Log((x1 / (x1 + x0))))) / (((x0 ^ -0.9890684005020601) * 0.706542304166119) + 0.6033891434795233))),"np.exp((np.exp(np.log(np.log((x[:, 1] / (x[:, 1] + x[:, 0]))))) / ((x[:, 0] ** t[0] * t[1]) + t[2])))","$$\operatorname{exp}(\frac{\operatorname{exp}(\operatorname{log}(\operatorname{log}(\frac{x_{1}}{\left(x_{1} + x_{0}\right)})))}{\left(\left({x_{0}^{\theta_{0}}} \cdot \theta_{1}\right) + \theta_{2}\right)})$$",-0.9890684005020601;0.706542304166119;0.6033891434795233,17,,,,Infinity,,,,,, -584,0,Exp(Exp(Log((6.437069126567317e-4 / x0)))),"np.exp(np.exp(np.log((t[0] / x[:, 0]))))","$$\operatorname{exp}(\operatorname{exp}(\operatorname{log}(\frac{\theta_{0}}{x_{0}})))$$",6.437069126567317e-4,6,0.8935446815112702,0.8935446815112702,0.8935446815112702,0.8935446815112702,-81.45734043089323,-81.45734043089323,-81.45734043089323,12.481742623332385,12.481742623332385,12.481742623332385 -585,0,Log(Log(((((x1 ^ x0) + -0.7469319579100282) + (-0.8045399749993276 * Log((x1 * -0.318514322843755)))) ^ Log(Log(Exp((-0.5445126779310653 / x0))))))),"np.log(np.log(((x[:, 1] ** x[:, 0] + t[0]) + (t[1] * np.log((x[:, 1] * t[2])))) ** np.log(np.log(np.exp((t[3] / x[:, 0]))))))","$$\operatorname{log}(\operatorname{log}({\left(\left({x_{1}^{x_{0}}} + \theta_{0}\right) + \left(\theta_{1} \cdot \operatorname{log}(\left(x_{1} \cdot \theta_{2}\right))\right)\right)^{\operatorname{log}(\operatorname{log}(\operatorname{exp}(\frac{\theta_{3}}{x_{0}})))}}))$$",-0.7469319579100282;-0.8045399749993276;-0.318514322843755;-0.5445126779310653,21,,,,Infinity,,,,,, -586,0,((-0.9030415656757873 * Exp(Log(x0))) ^ (Log((x1 + 0.5725682937146763)) / (x1 + 0.371811602531527))),"(t[0] * np.exp(np.log(x[:, 0]))) ** (np.log((x[:, 1] + t[1])) / (x[:, 1] + t[2]))","$${\left(\theta_{0} \cdot \operatorname{exp}(\operatorname{log}(x_{0}))\right)^{\frac{\operatorname{log}(\left(x_{1} + \theta_{1}\right))}{\left(x_{1} + \theta_{2}\right)}}}$$",-0.9030415656757873;0.5725682937146763;0.371811602531527,14,,,,Infinity,,,,,, -587,0,(((Exp(Log(x1)) / Exp(Exp(x1))) / ((x0 * x1) + (1381.347438448595 / x0))) * (Exp((0.30763337424723564 * (-3.795665662839001e-2 * ((0.47958156091182463 / x0) * -0.2605888081535586)))) ^ -0.4547729791199292)),"(((np.exp(np.log(x[:, 1])) / np.exp(np.exp(x[:, 1]))) / ((x[:, 0] * x[:, 1]) + (t[0] / x[:, 0]))) * np.exp((t[1] * (t[2] * ((t[3] / x[:, 0]) * t[4])))) ** t[5])","$$\left(\frac{\frac{\operatorname{exp}(\operatorname{log}(x_{1}))}{\operatorname{exp}(\operatorname{exp}(x_{1}))}}{\left(\left(x_{0} \cdot x_{1}\right) + \frac{\theta_{0}}{x_{0}}\right)} \cdot {\operatorname{exp}(\left(\theta_{1} \cdot \left(\theta_{2} \cdot \left(\frac{\theta_{3}}{x_{0}} \cdot \theta_{4}\right)\right)\right))^{\theta_{5}}}\right)$$",1381.347438448595;0.30763337424723564;-3.795665662839001e-2;0.47958156091182463;-0.2605888081535586;-0.4547729791199292,28,1.4567993572343754e-2,1.4567993572343754e-2,1.4567993572343754e-2,1.4567993572343754e-2,-0.3443513572909809,-0.3443513572909809,-0.3443513572909809,73.6609489718351,73.6609489718351,73.6609489718351 -588,0,(x1 * (x1 + x1)),"(x[:, 1] * (x[:, 1] + x[:, 1]))","$$\left(x_{1} \cdot \left(x_{1} + x_{1}\right)\right)$$",,5,7.045302803956957e-2,7.045302803956957e-2,7.045302803956957e-2,7.045302803956957e-2,-5.501487208922279,-5.501487208922279,-5.501487208922279,5.556469168576162,5.556469168576162,5.556469168576162 -589,0,((((1.5062192762177684e-2 / (x0 + x1)) / (x1 ^ -0.688331499553155)) ^ Exp(((0.6733899622966899 ^ (x0 + x1)) + (0.9266112828886239 * x1)))) + ((Exp((x1 + x1)) + Exp(Log((-0.3288608496267683 * x0)))) + Exp((Log((x1 * -0.425118279134717)) / Exp(Exp(x0)))))),"(((t[0] / (x[:, 0] + x[:, 1])) / x[:, 1] ** t[1]) ** np.exp((t[2] ** (x[:, 0] + x[:, 1]) + (t[3] * x[:, 1]))) + ((np.exp((x[:, 1] + x[:, 1])) + np.exp(np.log((t[4] * x[:, 0])))) + np.exp((np.log((x[:, 1] * t[5])) / np.exp(np.exp(x[:, 0]))))))","$$\left({\frac{\frac{\theta_{0}}{\left(x_{0} + x_{1}\right)}}{{x_{1}^{\theta_{1}}}}^{\operatorname{exp}(\left({\theta_{2}^{\left(x_{0} + x_{1}\right)}} + \left(\theta_{3} \cdot x_{1}\right)\right))}} + \left(\left(\operatorname{exp}(\left(x_{1} + x_{1}\right)) + \operatorname{exp}(\operatorname{log}(\left(\theta_{4} \cdot x_{0}\right)))\right) + \operatorname{exp}(\frac{\operatorname{log}(\left(x_{1} \cdot \theta_{5}\right))}{\operatorname{exp}(\operatorname{exp}(x_{0}))})\right)\right)$$",1.5062192762177684e-2;-0.688331499553155;0.6733899622966899;0.9266112828886239;-0.3288608496267683;-0.425118279134717,41,,,,Infinity,,,,,, -590,0,(x0 / (-0.5642597282686754 ^ x1)),"(x[:, 0] / t[0] ** x[:, 1])","$$\frac{x_{0}}{{\theta_{0}^{x_{1}}}}$$",-0.5642597282686754,5,,,,Infinity,,,,,, -591,0,(((2.5912184872061816e-2 / (Log(x0) + 0.9300118957397325)) * Exp(Exp((Exp(x1) + (x1 * 0.85087589549642))))) * ((0.19232965111063427 / (-0.8708659213045612 / Log(x0))) ^ Log(Log(Log(((x1 / x0) / x1)))))),"(((t[0] / (np.log(x[:, 0]) + t[1])) * np.exp(np.exp((np.exp(x[:, 1]) + (x[:, 1] * t[2]))))) * (t[3] / (t[4] / np.log(x[:, 0]))) ** np.log(np.log(np.log(((x[:, 1] / x[:, 0]) / x[:, 1])))))","$$\left(\left(\frac{\theta_{0}}{\left(\operatorname{log}(x_{0}) + \theta_{1}\right)} \cdot \operatorname{exp}(\operatorname{exp}(\left(\operatorname{exp}(x_{1}) + \left(x_{1} \cdot \theta_{2}\right)\right)))\right) \cdot {\frac{\theta_{3}}{\frac{\theta_{4}}{\operatorname{log}(x_{0})}}^{\operatorname{log}(\operatorname{log}(\operatorname{log}(\frac{\frac{x_{1}}{x_{0}}}{x_{1}})))}}\right)$$",2.5912184872061816e-2;0.9300118957397325;0.85087589549642;0.19232965111063427;-0.8708659213045612,31,,,,Infinity,,,,,, -592,0,(Log((Log((x1 + -0.4078327510876054)) * Exp(x1))) + Exp((0.5763198009359051 / Exp((-0.5575158161896799 ^ x0))))),"(np.log((np.log((x[:, 1] + t[0])) * np.exp(x[:, 1]))) + np.exp((t[1] / np.exp(t[2] ** x[:, 0]))))","$$\left(\operatorname{log}(\left(\operatorname{log}(\left(x_{1} + \theta_{0}\right)) \cdot \operatorname{exp}(x_{1})\right)) + \operatorname{exp}(\frac{\theta_{1}}{\operatorname{exp}({\theta_{2}^{x_{0}}})})\right)$$",-0.4078327510876054;0.5763198009359051;-0.5575158161896799,16,,,,Infinity,,,,,, -593,0,Log((Exp(Log(x1)) * Log((x0 * 0.15441929828192058)))),"np.log((np.exp(np.log(x[:, 1])) * np.log((x[:, 0] * t[0]))))","$$\operatorname{log}(\left(\operatorname{exp}(\operatorname{log}(x_{1})) \cdot \operatorname{log}(\left(x_{0} \cdot \theta_{0}\right))\right))$$",0.15441929828192058,9,,,,Infinity,,,,,, -594,0,((Exp((Exp(Log((0.4398775090315078 + x1))) + 0.4823994617616977)) ^ Log(Exp(((x0 + x1) * Exp((0.12830286218812414 * x0)))))) / ((Log(Log((-0.8733213567250107 / x0))) / Exp((x0 / (x1 ^ x1)))) * ((0.7795384700935742 / (x0 ^ -0.5664450553309333)) * (0.21125257463557678 + (x1 / x1))))),"(np.exp((np.exp(np.log((t[0] + x[:, 1]))) + t[1])) ** np.log(np.exp(((x[:, 0] + x[:, 1]) * np.exp((t[2] * x[:, 0]))))) / ((np.log(np.log((t[3] / x[:, 0]))) / np.exp((x[:, 0] / x[:, 1] ** x[:, 1]))) * ((t[4] / x[:, 0] ** t[5]) * (t[6] + (x[:, 1] / x[:, 1])))))","$$\frac{{\operatorname{exp}(\left(\operatorname{exp}(\operatorname{log}(\left(\theta_{0} + x_{1}\right))) + \theta_{1}\right))^{\operatorname{log}(\operatorname{exp}(\left(\left(x_{0} + x_{1}\right) \cdot \operatorname{exp}(\left(\theta_{2} \cdot x_{0}\right))\right)))}}}{\left(\frac{\operatorname{log}(\operatorname{log}(\frac{\theta_{3}}{x_{0}}))}{\operatorname{exp}(\frac{x_{0}}{{x_{1}^{x_{1}}}})} \cdot \left(\frac{\theta_{4}}{{x_{0}^{\theta_{5}}}} \cdot \left(\theta_{6} + \frac{x_{1}}{x_{1}}\right)\right)\right)}$$",0.4398775090315078;0.4823994617616977;0.12830286218812414;-0.8733213567250107;0.7795384700935742;-0.5664450553309333;0.21125257463557678,44,,,,Infinity,,,,,, -595,0,Log(Log(((0.5043343788598391 ^ (((0.35862596483371467 / x0) * -0.34521874169196887) + 0.4236526046857557)) ^ (x1 + (-0.39250686714047145 + x0))))),"np.log(np.log(t[0] ** (((t[1] / x[:, 0]) * t[2]) + t[3]) ** (x[:, 1] + (t[4] + x[:, 0]))))","$$\operatorname{log}(\operatorname{log}({{\theta_{0}^{\left(\left(\frac{\theta_{1}}{x_{0}} \cdot \theta_{2}\right) + \theta_{3}\right)}}^{\left(x_{1} + \left(\theta_{4} + x_{0}\right)\right)}}))$$",0.5043343788598391;0.35862596483371467;-0.34521874169196887;0.4236526046857557;-0.39250686714047145,17,,,,Infinity,,,,,, -596,0,(Log(((-0.20962331504126186 / x0) * (x1 * -0.31590502965136047))) ^ Log(((Exp(x1) ^ x1) * x1))),"np.log(((t[0] / x[:, 0]) * (x[:, 1] * t[1]))) ** np.log((np.exp(x[:, 1]) ** x[:, 1] * x[:, 1]))","$${\operatorname{log}(\left(\frac{\theta_{0}}{x_{0}} \cdot \left(x_{1} \cdot \theta_{1}\right)\right))^{\operatorname{log}(\left({\operatorname{exp}(x_{1})^{x_{1}}} \cdot x_{1}\right))}}$$",-0.20962331504126186;-0.31590502965136047,16,,,,Infinity,,,,,, -597,0,(x0 + -0.9956750177908852),"(x[:, 0] + t[0])","$$\left(x_{0} + \theta_{0}\right)$$",-0.9956750177908852,3,0.15105329635364,0.15105329635364,0.15105329635364,0.15105329635364,-12.939373529228037,-12.939373529228037,-12.939373529228037,5.452709768090245,5.452709768090245,5.452709768090245 -598,0,Log((((Log((x1 * -0.13519899693533843)) / (x1 * -0.5109773272126001)) + ((x0 + x1) * Exp(Exp(x1)))) / (((0.8257214760846427 + x1) * (x0 ^ Exp(x0))) + (-0.23055904498971602 / Log((x1 * 0.22477018613684407)))))),"np.log((((np.log((x[:, 1] * t[0])) / (x[:, 1] * t[1])) + ((x[:, 0] + x[:, 1]) * np.exp(np.exp(x[:, 1])))) / (((t[2] + x[:, 1]) * x[:, 0] ** np.exp(x[:, 0])) + (t[3] / np.log((x[:, 1] * t[4]))))))","$$\operatorname{log}(\frac{\left(\frac{\operatorname{log}(\left(x_{1} \cdot \theta_{0}\right))}{\left(x_{1} \cdot \theta_{1}\right)} + \left(\left(x_{0} + x_{1}\right) \cdot \operatorname{exp}(\operatorname{exp}(x_{1}))\right)\right)}{\left(\left(\left(\theta_{2} + x_{1}\right) \cdot {x_{0}^{\operatorname{exp}(x_{0})}}\right) + \frac{\theta_{3}}{\operatorname{log}(\left(x_{1} \cdot \theta_{4}\right))}\right)})$$",-0.13519899693533843;-0.5109773272126001;0.8257214760846427;-0.23055904498971602;0.22477018613684407,33,,,,Infinity,,,,,, -599,0,Exp(Log((0.34560104946504244 / Log(Log(x1))))),"np.exp(np.log((t[0] / np.log(np.log(x[:, 1])))))","$$\operatorname{exp}(\operatorname{log}(\frac{\theta_{0}}{\operatorname{log}(\operatorname{log}(x_{1}))}))$$",0.34560104946504244,7,,,,Infinity,,,,,, -600,0,Exp((Log(((-0.20538441268669883 + x1) ^ x1)) * 2.166438689668715e-2)),"np.exp((np.log((t[0] + x[:, 1]) ** x[:, 1]) * t[1]))","$$\operatorname{exp}(\left(\operatorname{log}({\left(\theta_{0} + x_{1}\right)^{x_{1}}}) \cdot \theta_{1}\right))$$",-0.20538441268669883;2.166438689668715e-2,9,,,,Infinity,,,,,, -601,0,(-1.1459650561082658e-2 * (-5.4482216224873286 + x1)),"(t[0] * (t[1] + x[:, 1]))","$$\left(\theta_{0} \cdot \left(\theta_{1} + x_{1}\right)\right)$$",-1.1459650561082658e-2;-5.4482216224873286,5,1.0974035737958986e-2,1.0974035737958986e-2,1.0974035737958986e-2,1.0974035737958986e-2,-1.2696756490357286e-2,-1.2696756490357286e-2,-1.2696756490357286e-2,10.983198308498897,10.983198308498897,10.983198308498897 -602,0,((((0.6577305458375726 * x1) / Exp(x1)) + Exp(((x0 ^ x0) * x1))) ^ (-0.7293394333104656 + Log((Log(x1) ^ -0.3687143719505793)))),"(((t[0] * x[:, 1]) / np.exp(x[:, 1])) + np.exp((x[:, 0] ** x[:, 0] * x[:, 1]))) ** (t[1] + np.log(np.log(x[:, 1]) ** t[2]))","$${\left(\frac{\left(\theta_{0} \cdot x_{1}\right)}{\operatorname{exp}(x_{1})} + \operatorname{exp}(\left({x_{0}^{x_{0}}} \cdot x_{1}\right))\right)^{\left(\theta_{1} + \operatorname{log}({\operatorname{log}(x_{1})^{\theta_{2}}})\right)}}$$",0.6577305458375726;-0.7293394333104656;-0.3687143719505793,21,,,,Infinity,,,,,, -603,0,((Log((x1 ^ 2.001235882258791)) * ((-0.3613017061276265 + x0) + 0.2671419344754678)) * (((x1 / x0) + ((x1 ^ 2.412461183344078) * x0)) * Log(((x0 ^ (-0.1393238199615297 + (0.42183913571951176 / x0))) * 0.9464789718828694)))),"((np.log(x[:, 1] ** t[0]) * ((t[1] + x[:, 0]) + t[2])) * (((x[:, 1] / x[:, 0]) + (x[:, 1] ** t[3] * x[:, 0])) * np.log((x[:, 0] ** (t[4] + (t[5] / x[:, 0])) * t[6]))))","$$\left(\left(\operatorname{log}({x_{1}^{\theta_{0}}}) \cdot \left(\left(\theta_{1} + x_{0}\right) + \theta_{2}\right)\right) \cdot \left(\left(\frac{x_{1}}{x_{0}} + \left({x_{1}^{\theta_{3}}} \cdot x_{0}\right)\right) \cdot \operatorname{log}(\left({x_{0}^{\left(\theta_{4} + \frac{\theta_{5}}{x_{0}}\right)}} \cdot \theta_{6}\right))\right)\right)$$",2.001235882258791;-0.3613017061276265;0.2671419344754678;2.412461183344078;-0.1393238199615297;0.42183913571951176;0.9464789718828694,31,3.2085757068159495e-3,3.2085757068159495e-3,3.2085757068159495e-3,3.2085757068159495e-3,0.7039089092805713,0.7039089092805713,0.7039089092805713,78.61205005778451,78.61205005778451,78.61205005778451 -604,0,(((Exp(x1) / (x1 * 1.574955811876399)) + ((x1 * 0.20481721240762363) / x0)) / (Exp(Exp((x1 * x0))) / ((-0.11753608575979142 + x1) * 0.5930666333031717))),"(((np.exp(x[:, 1]) / (x[:, 1] * t[0])) + ((x[:, 1] * t[1]) / x[:, 0])) / (np.exp(np.exp((x[:, 1] * x[:, 0]))) / ((t[2] + x[:, 1]) * t[3])))","$$\frac{\left(\frac{\operatorname{exp}(x_{1})}{\left(x_{1} \cdot \theta_{0}\right)} + \frac{\left(x_{1} \cdot \theta_{1}\right)}{x_{0}}\right)}{\frac{\operatorname{exp}(\operatorname{exp}(\left(x_{1} \cdot x_{0}\right)))}{\left(\left(\theta_{2} + x_{1}\right) \cdot \theta_{3}\right)}}$$",1.574955811876399;0.20481721240762363;-0.11753608575979142;0.5930666333031717,24,7.208579481644313e-3,7.208579481644313e-3,7.208579481644313e-3,7.208579481644313e-3,0.33478391776024485,0.33478391776024485,0.33478391776024485,54.79203104033156,54.79203104033156,54.79203104033156 -605,0,((0.911021449763283 ^ ((-0.2959384799187552 + (x0 * x0)) / ((x0 ^ 0.937374381214572) + (-0.28140389502337626 ^ x0)))) / Log(Exp(Exp(((x1 * 0.923545548806266) ^ (0.7067383604939315 ^ x0)))))),"(t[0] ** ((t[1] + (x[:, 0] * x[:, 0])) / (x[:, 0] ** t[2] + t[3] ** x[:, 0])) / np.log(np.exp(np.exp((x[:, 1] * t[4]) ** t[5] ** x[:, 0]))))","$$\frac{{\theta_{0}^{\frac{\left(\theta_{1} + \left(x_{0} \cdot x_{0}\right)\right)}{\left({x_{0}^{\theta_{2}}} + {\theta_{3}^{x_{0}}}\right)}}}}{\operatorname{log}(\operatorname{exp}(\operatorname{exp}({\left(x_{1} \cdot \theta_{4}\right)^{{\theta_{5}^{x_{0}}}}})))}$$",0.911021449763283;-0.2959384799187552;0.937374381214572;-0.28140389502337626;0.923545548806266;0.7067383604939315,26,,,,Infinity,,,,,, -606,0,((-1.2174280869038486e-2 * Exp(x0)) * -1.0436438106084465),"((t[0] * np.exp(x[:, 0])) * t[1])","$$\left(\left(\theta_{0} \cdot \operatorname{exp}(x_{0})\right) \cdot \theta_{1}\right)$$",-1.2174280869038486e-2;-1.0436438106084465,6,1.297830887277363e-2,1.297830887277363e-2,1.297830887277363e-2,1.297830887277363e-2,-0.19765340791867225,-0.19765340791867225,-0.19765340791867225,12.37129651544012,12.37129651544012,12.37129651544012 -607,0,Exp((((0.8012432594838149 * (x0 * -0.8920859035977577)) + ((x0 * -1.514222426331109) + (x0 * (x0 * x1)))) * 1.4800093283657119)),"np.exp((((t[0] * (x[:, 0] * t[1])) + ((x[:, 0] * t[2]) + (x[:, 0] * (x[:, 0] * x[:, 1])))) * t[3]))","$$\operatorname{exp}(\left(\left(\left(\theta_{0} \cdot \left(x_{0} \cdot \theta_{1}\right)\right) + \left(\left(x_{0} \cdot \theta_{2}\right) + \left(x_{0} \cdot \left(x_{0} \cdot x_{1}\right)\right)\right)\right) \cdot \theta_{3}\right))$$",0.8012432594838149;-0.8920859035977577;-1.514222426331109;1.4800093283657119,18,4.6985047597614335e-3,4.6985047597614335e-3,4.6985047597614335e-3,4.6985047597614335e-3,0.56641652677451,0.56641652677451,0.56641652677451,40.33959884185934,40.33959884185934,40.33959884185934 -608,0,Log(Exp(Log(((Log(x1) * -3.526069025739042) * Exp((Log(x1) + -0.1027345012415466)))))),"np.log(np.exp(np.log(((np.log(x[:, 1]) * t[0]) * np.exp((np.log(x[:, 1]) + t[1]))))))","$$\operatorname{log}(\operatorname{exp}(\operatorname{log}(\left(\left(\operatorname{log}(x_{1}) \cdot \theta_{0}\right) \cdot \operatorname{exp}(\left(\operatorname{log}(x_{1}) + \theta_{1}\right))\right))))$$",-3.526069025739042;-0.1027345012415466,13,1.9164289707541565e-2,1.9164289707541565e-2,1.9164289707541565e-2,1.9164289707541565e-2,-0.7685028999986128,-0.7685028999986128,-0.7685028999986128,27.351970831436784,27.351970831436784,27.351970831436784 -609,0,(Log(((-0.8133185282578572 / x1) / (x1 + -0.12374368418906168))) + (0.6804927009771955 * ((Log((x1 ^ (-0.12046971932159056 + x1))) * (x1 * (x1 * -0.12649115297700897))) + -0.32357630960170447))),"(np.log(((t[0] / x[:, 1]) / (x[:, 1] + t[1]))) + (t[2] * ((np.log(x[:, 1] ** (t[3] + x[:, 1])) * (x[:, 1] * (x[:, 1] * t[4]))) + t[5])))","$$\left(\operatorname{log}(\frac{\frac{\theta_{0}}{x_{1}}}{\left(x_{1} + \theta_{1}\right)}) + \left(\theta_{2} \cdot \left(\left(\operatorname{log}({x_{1}^{\left(\theta_{3} + x_{1}\right)}}) \cdot \left(x_{1} \cdot \left(x_{1} \cdot \theta_{4}\right)\right)\right) + \theta_{5}\right)\right)\right)$$",-0.8133185282578572;-0.12374368418906168;0.6804927009771955;-0.12046971932159056;-0.12649115297700897;-0.32357630960170447,25,,,,Infinity,,,,,, -610,0,Exp((Exp(Log(x0)) ^ Log((Exp((-0.4203500616977285 ^ x1)) + x1)))),"np.exp(np.exp(np.log(x[:, 0])) ** np.log((np.exp(t[0] ** x[:, 1]) + x[:, 1])))","$$\operatorname{exp}({\operatorname{exp}(\operatorname{log}(x_{0}))^{\operatorname{log}(\left(\operatorname{exp}({\theta_{0}^{x_{1}}}) + x_{1}\right))}})$$",-0.4203500616977285,12,,,,Infinity,,,,,, -611,0,(Log((2.6250683041761893 / Exp(Exp((-1.0161439625527107 / x0))))) + -0.5375715154155047),"(np.log((t[0] / np.exp(np.exp((t[1] / x[:, 0]))))) + t[2])","$$\left(\operatorname{log}(\frac{\theta_{0}}{\operatorname{exp}(\operatorname{exp}(\frac{\theta_{1}}{x_{0}}))}) + \theta_{2}\right)$$",2.6250683041761893;-1.0161439625527107;-0.5375715154155047,10,7.136600714018945e-3,7.136600714018945e-3,7.136600714018945e-3,7.136600714018945e-3,0.34142620198921336,0.34142620198921336,0.34142620198921336,23.98679243902609,23.98679243902609,23.98679243902609 -612,0,(Log((((-0.11946083178058997 * x1) + Log(Exp((x1 * 0.2630614427966629)))) / Log(Log(((x0 ^ x1) + (0.6094840150620108 * x0)))))) + Exp(((-0.2553549080214592 + (x1 * (-0.4733651161583312 / x1))) + ((x0 * x0) / Log((-1.76484821004963e-2 * x0)))))),"(np.log((((t[0] * x[:, 1]) + np.log(np.exp((x[:, 1] * t[1])))) / np.log(np.log((x[:, 0] ** x[:, 1] + (t[2] * x[:, 0])))))) + np.exp(((t[3] + (x[:, 1] * (t[4] / x[:, 1]))) + ((x[:, 0] * x[:, 0]) / np.log((t[5] * x[:, 0]))))))","$$\left(\operatorname{log}(\frac{\left(\left(\theta_{0} \cdot x_{1}\right) + \operatorname{log}(\operatorname{exp}(\left(x_{1} \cdot \theta_{1}\right)))\right)}{\operatorname{log}(\operatorname{log}(\left({x_{0}^{x_{1}}} + \left(\theta_{2} \cdot x_{0}\right)\right)))}) + \operatorname{exp}(\left(\left(\theta_{3} + \left(x_{1} \cdot \frac{\theta_{4}}{x_{1}}\right)\right) + \frac{\left(x_{0} \cdot x_{0}\right)}{\operatorname{log}(\left(\theta_{5} \cdot x_{0}\right))}\right))\right)$$",-0.11946083178058997;0.2630614427966629;0.6094840150620108;-0.2553549080214592;-0.4733651161583312;-1.76484821004963e-2,38,,,,Infinity,,,,,, -613,0,(Log((x0 + -0.45152738447976437)) + Exp((x0 ^ (x1 ^ 0.6114924883409211)))),"(np.log((x[:, 0] + t[0])) + np.exp(x[:, 0] ** x[:, 1] ** t[1]))","$$\left(\operatorname{log}(\left(x_{0} + \theta_{0}\right)) + \operatorname{exp}({x_{0}^{{x_{1}^{\theta_{1}}}}})\right)$$",-0.45152738447976437;0.6114924883409211,11,5.711606244516128,5.711606244516128,5.711606244516128,5.711606244516128,-526.0736542404699,-526.0736542404699,-526.0736542404699,30.587307130408245,30.587307130408245,30.587307130408245 -614,0,(((-0.12956608732236063 + x1) * -0.4934015593157684) * -0.6084563617428457),"(((t[0] + x[:, 1]) * t[1]) * t[2])","$$\left(\left(\left(\theta_{0} + x_{1}\right) \cdot \theta_{1}\right) \cdot \theta_{2}\right)$$",-0.12956608732236063;-0.4934015593157684;-0.6084563617428457,7,9.147873437044185e-3,9.147873437044185e-3,9.147873437044185e-3,9.147873437044185e-3,0.15582362043574693,0.15582362043574693,0.15582362043574693,15.775068420035495,15.775068420035495,15.775068420035495 -615,0,(Exp((0.9013324254237913 ^ Log((-0.9052431646547467 * ((x1 ^ 0.7728681254191637) + 0.7314974339261664))))) * x1),"(np.exp(t[0] ** np.log((t[1] * (x[:, 1] ** t[2] + t[3])))) * x[:, 1])","$$\left(\operatorname{exp}({\theta_{0}^{\operatorname{log}(\left(\theta_{1} \cdot \left({x_{1}^{\theta_{2}}} + \theta_{3}\right)\right))}}) \cdot x_{1}\right)$$",0.9013324254237913;-0.9052431646547467;0.7728681254191637;0.7314974339261664,13,,,,Infinity,,,,,, -616,0,((((x0 * x0) + x0) + Exp((-0.40311547171760753 / Log(x0)))) ^ (0.13372414176334224 + Exp((-0.32924259493118524 * (x1 ^ x0))))),"(((x[:, 0] * x[:, 0]) + x[:, 0]) + np.exp((t[0] / np.log(x[:, 0])))) ** (t[1] + np.exp((t[2] * x[:, 1] ** x[:, 0])))","$${\left(\left(\left(x_{0} \cdot x_{0}\right) + x_{0}\right) + \operatorname{exp}(\frac{\theta_{0}}{\operatorname{log}(x_{0})})\right)^{\left(\theta_{1} + \operatorname{exp}(\left(\theta_{2} \cdot {x_{1}^{x_{0}}}\right))\right)}}$$",-0.40311547171760753;0.13372414176334224;-0.32924259493118524,20,Infinity,Infinity,Infinity,Infinity,-Infinity,-Infinity,-Infinity,Infinity,Infinity,Infinity -617,0,(Exp(((x1 + x1) / Log(x1))) ^ (((Log(x0) * -0.6744635728144546) + 0.9440794041008963) / ((x1 + -0.8111476010387508) + 0.7367835444702853))),"np.exp(((x[:, 1] + x[:, 1]) / np.log(x[:, 1]))) ** (((np.log(x[:, 0]) * t[0]) + t[1]) / ((x[:, 1] + t[2]) + t[3]))","$${\operatorname{exp}(\frac{\left(x_{1} + x_{1}\right)}{\operatorname{log}(x_{1})})^{\frac{\left(\left(\operatorname{log}(x_{0}) \cdot \theta_{0}\right) + \theta_{1}\right)}{\left(\left(x_{1} + \theta_{2}\right) + \theta_{3}\right)}}}$$",-0.6744635728144546;0.9440794041008963;-0.8111476010387508;0.7367835444702853,20,2.6123264070395565e-2,2.6123264070395565e-2,2.6123264070395565e-2,2.6123264070395565e-2,-1.4106851321363494,-1.4106851321363494,-1.4106851321363494,52.051702225858314,52.051702225858314,52.051702225858314 -618,0,Exp((Exp((((0.1983406498926078 ^ (x0 ^ x0)) / (x1 * (x0 * -0.21254770709788462))) ^ -0.6398809987904233)) ^ Log(Exp(((x1 / x0) / (-0.45768242209484467 ^ x0)))))),"np.exp(np.exp((t[0] ** x[:, 0] ** x[:, 0] / (x[:, 1] * (x[:, 0] * t[1]))) ** t[2]) ** np.log(np.exp(((x[:, 1] / x[:, 0]) / t[3] ** x[:, 0]))))","$$\operatorname{exp}({\operatorname{exp}({\frac{{\theta_{0}^{{x_{0}^{x_{0}}}}}}{\left(x_{1} \cdot \left(x_{0} \cdot \theta_{1}\right)\right)}^{\theta_{2}}})^{\operatorname{log}(\operatorname{exp}(\frac{\frac{x_{1}}{x_{0}}}{{\theta_{3}^{x_{0}}}}))}})$$",0.1983406498926078;-0.21254770709788462;-0.6398809987904233;-0.45768242209484467,25,,,,Infinity,,,,,, -619,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 -620,0,Log((x0 + Exp(x0))),"np.log((x[:, 0] + np.exp(x[:, 0])))","$$\operatorname{log}(\left(x_{0} + \operatorname{exp}(x_{0})\right))$$",,5,1.8328860865758219,1.8328860865758219,1.8328860865758219,1.8328860865758219,-168.14085567183128,-168.14085567183128,-168.14085567183128,8.581069283517692,8.581069283517692,8.581069283517692 -621,0,((((x0 ^ -0.8019027503629551) ^ (-0.38490635201975654 * (x1 * 0.95589359992825))) / ((0.19882498638574864 / Exp(((x0 * -0.3768267001240936) ^ (-2.601179866461023e-2 * x1)))) + (-0.4925768524472236 + x0))) + x0),"((x[:, 0] ** t[0] ** (t[1] * (x[:, 1] * t[2])) / ((t[3] / np.exp((x[:, 0] * t[4]) ** (t[5] * x[:, 1]))) + (t[6] + x[:, 0]))) + x[:, 0])","$$\left(\frac{{{x_{0}^{\theta_{0}}}^{\left(\theta_{1} \cdot \left(x_{1} \cdot \theta_{2}\right)\right)}}}{\left(\frac{\theta_{3}}{\operatorname{exp}({\left(x_{0} \cdot \theta_{4}\right)^{\left(\theta_{5} \cdot x_{1}\right)}})} + \left(\theta_{6} + x_{0}\right)\right)} + x_{0}\right)$$",-0.8019027503629551;-0.38490635201975654;0.95589359992825;0.19882498638574864;-0.3768267001240936;-2.601179866461023e-2;-0.4925768524472236,26,,,,Infinity,,,,,, -622,0,Log((((Exp((-0.17325330727209765 * x1)) ^ 0.48898301403928734) ^ (Log((x1 * -0.2543774852159929)) * Exp((-0.3795713452350755 / x0)))) + ((Exp((-0.5860628555949883 + x0)) * (x1 ^ x0)) ^ Exp((-9.385029043215642e-2 / Log(x1)))))),"np.log((np.exp((t[0] * x[:, 1])) ** t[1] ** (np.log((x[:, 1] * t[2])) * np.exp((t[3] / x[:, 0]))) + (np.exp((t[4] + x[:, 0])) * x[:, 1] ** x[:, 0]) ** np.exp((t[5] / np.log(x[:, 1])))))","$$\operatorname{log}(\left({{\operatorname{exp}(\left(\theta_{0} \cdot x_{1}\right))^{\theta_{1}}}^{\left(\operatorname{log}(\left(x_{1} \cdot \theta_{2}\right)) \cdot \operatorname{exp}(\frac{\theta_{3}}{x_{0}})\right)}} + {\left(\operatorname{exp}(\left(\theta_{4} + x_{0}\right)) \cdot {x_{1}^{x_{0}}}\right)^{\operatorname{exp}(\frac{\theta_{5}}{\operatorname{log}(x_{1})})}}\right))$$",-0.17325330727209765;0.48898301403928734;-0.2543774852159929;-0.3795713452350755;-0.5860628555949883;-9.385029043215642e-2,32,,,,Infinity,,,,,, -623,0,(Log(Exp((((x0 + -0.9027185518212252) * Log(x0)) * ((91.82825480028903 * x1) * (x1 + Log(x1)))))) + (Exp(Exp(((x0 * x1) ^ (96.92620450697676 * x1)))) + ((x0 / x0) + Exp((-53.95176077432393 * x0))))),"(np.log(np.exp((((x[:, 0] + t[0]) * np.log(x[:, 0])) * ((t[1] * x[:, 1]) * (x[:, 1] + np.log(x[:, 1])))))) + (np.exp(np.exp((x[:, 0] * x[:, 1]) ** (t[2] * x[:, 1]))) + ((x[:, 0] / x[:, 0]) + np.exp((t[3] * x[:, 0])))))","$$\left(\operatorname{log}(\operatorname{exp}(\left(\left(\left(x_{0} + \theta_{0}\right) \cdot \operatorname{log}(x_{0})\right) \cdot \left(\left(\theta_{1} \cdot x_{1}\right) \cdot \left(x_{1} + \operatorname{log}(x_{1})\right)\right)\right))) + \left(\operatorname{exp}(\operatorname{exp}({\left(x_{0} \cdot x_{1}\right)^{\left(\theta_{2} \cdot x_{1}\right)}})) + \left(\frac{x_{0}}{x_{0}} + \operatorname{exp}(\left(\theta_{3} \cdot x_{0}\right))\right)\right)\right)$$",-0.9027185518212252;91.82825480028903;96.92620450697676;-53.95176077432393,36,6.521915444030792,6.521915444030792,6.521915444030792,6.521915444030792,-600.8499277734935,-600.8499277734935,-600.8499277734935,93.05350842520217,93.05350842520217,93.05350842520217 -624,0,(((0.6917730683616619 + (Log(x1) ^ -0.46032058279163146)) + -0.5809879209803634) ^ ((Exp(Exp(((x1 ^ 0.44435039259185105) + -0.3137207437452647))) ^ Exp(x1)) + Exp((Log(x0) / Exp(x1))))),"((t[0] + np.log(x[:, 1]) ** t[1]) + t[2]) ** (np.exp(np.exp((x[:, 1] ** t[3] + t[4]))) ** np.exp(x[:, 1]) + np.exp((np.log(x[:, 0]) / np.exp(x[:, 1]))))","$${\left(\left(\theta_{0} + {\operatorname{log}(x_{1})^{\theta_{1}}}\right) + \theta_{2}\right)^{\left({\operatorname{exp}(\operatorname{exp}(\left({x_{1}^{\theta_{3}}} + \theta_{4}\right)))^{\operatorname{exp}(x_{1})}} + \operatorname{exp}(\frac{\operatorname{log}(x_{0})}{\operatorname{exp}(x_{1})})\right)}}$$",0.6917730683616619;-0.46032058279163146;-0.5809879209803634;0.44435039259185105;-0.3137207437452647,26,,,,Infinity,,,,,, -625,0,((((0.3715937186897854 ^ (Log(x1) + 0.9438434613699577)) ^ Log(Log((Log(x0) * (x1 / x1))))) * ((x0 * 0.8501784941429054) * Log(((0.668373904243562 + (-0.8898903282734907 * x1)) + x0)))) / Exp(((x1 * -0.5269593794588394) + (x1 * 0.7450173839161645)))),"((t[0] ** (np.log(x[:, 1]) + t[1]) ** np.log(np.log((np.log(x[:, 0]) * (x[:, 1] / x[:, 1])))) * ((x[:, 0] * t[2]) * np.log(((t[3] + (t[4] * x[:, 1])) + x[:, 0])))) / np.exp(((x[:, 1] * t[5]) + (x[:, 1] * t[6]))))","$$\frac{\left({{\theta_{0}^{\left(\operatorname{log}(x_{1}) + \theta_{1}\right)}}^{\operatorname{log}(\operatorname{log}(\left(\operatorname{log}(x_{0}) \cdot \frac{x_{1}}{x_{1}}\right)))}} \cdot \left(\left(x_{0} \cdot \theta_{2}\right) \cdot \operatorname{log}(\left(\left(\theta_{3} + \left(\theta_{4} \cdot x_{1}\right)\right) + x_{0}\right))\right)\right)}{\operatorname{exp}(\left(\left(x_{1} \cdot \theta_{5}\right) + \left(x_{1} \cdot \theta_{6}\right)\right))}$$",0.3715937186897854;0.9438434613699577;0.8501784941429054;0.668373904243562;-0.8898903282734907;-0.5269593794588394;0.7450173839161645,37,,,,Infinity,,,,,, -626,0,((0.2911115010051466 * ((0.548099892394101 / x1) / (x0 + -0.8332509444281476))) ^ (Log(((x0 + x0) / Exp(Exp(x1)))) * (Exp((x1 ^ x0)) * (0.21449715763086652 + (0.27012833809581593 * x1))))),"(t[0] * ((t[1] / x[:, 1]) / (x[:, 0] + t[2]))) ** (np.log(((x[:, 0] + x[:, 0]) / np.exp(np.exp(x[:, 1])))) * (np.exp(x[:, 1] ** x[:, 0]) * (t[3] + (t[4] * x[:, 1]))))","$${\left(\theta_{0} \cdot \frac{\frac{\theta_{1}}{x_{1}}}{\left(x_{0} + \theta_{2}\right)}\right)^{\left(\operatorname{log}(\frac{\left(x_{0} + x_{0}\right)}{\operatorname{exp}(\operatorname{exp}(x_{1}))}) \cdot \left(\operatorname{exp}({x_{1}^{x_{0}}}) \cdot \left(\theta_{3} + \left(\theta_{4} \cdot x_{1}\right)\right)\right)\right)}}$$",0.2911115010051466;0.548099892394101;-0.8332509444281476;0.21449715763086652;0.27012833809581593,29,,,,Infinity,,,,,, -627,0,Exp((Log((-0.4904467101235124 + x0)) + (((x0 * 0.38506185009556515) + ((0.5554889925362516 * (x1 * -0.9450842333794357)) / x1)) ^ (Log((x1 ^ x0)) * -0.9572456361727316)))),"np.exp((np.log((t[0] + x[:, 0])) + ((x[:, 0] * t[1]) + ((t[2] * (x[:, 1] * t[3])) / x[:, 1])) ** (np.log(x[:, 1] ** x[:, 0]) * t[4])))","$$\operatorname{exp}(\left(\operatorname{log}(\left(\theta_{0} + x_{0}\right)) + {\left(\left(x_{0} \cdot \theta_{1}\right) + \frac{\left(\theta_{2} \cdot \left(x_{1} \cdot \theta_{3}\right)\right)}{x_{1}}\right)^{\left(\operatorname{log}({x_{1}^{x_{0}}}) \cdot \theta_{4}\right)}}\right))$$",-0.4904467101235124;0.38506185009556515;0.5554889925362516;-0.9450842333794357;-0.9572456361727316,24,,,,Infinity,,,,,, -628,0,(((Log((x1 / x0)) + (x0 / (x1 + -0.748551876214754))) / ((0.8397549465462022 * x1) / (x0 ^ 0.9724673738491509))) / Log(Log((((x0 ^ -0.15068247254349065) ^ (-0.775458533975284 * (x0 * x1))) ^ (Exp((x1 ^ 0.37293532392487894)) / Exp((x1 * 0.10055876191593649))))))),"(((np.log((x[:, 1] / x[:, 0])) + (x[:, 0] / (x[:, 1] + t[0]))) / ((t[1] * x[:, 1]) / x[:, 0] ** t[2])) / np.log(np.log(x[:, 0] ** t[3] ** (t[4] * (x[:, 0] * x[:, 1])) ** (np.exp(x[:, 1] ** t[5]) / np.exp((x[:, 1] * t[6]))))))","$$\frac{\frac{\left(\operatorname{log}(\frac{x_{1}}{x_{0}}) + \frac{x_{0}}{\left(x_{1} + \theta_{0}\right)}\right)}{\frac{\left(\theta_{1} \cdot x_{1}\right)}{{x_{0}^{\theta_{2}}}}}}{\operatorname{log}(\operatorname{log}({{{x_{0}^{\theta_{3}}}^{\left(\theta_{4} \cdot \left(x_{0} \cdot x_{1}\right)\right)}}^{\frac{\operatorname{exp}({x_{1}^{\theta_{5}}})}{\operatorname{exp}(\left(x_{1} \cdot \theta_{6}\right))}}}))}$$",-0.748551876214754;0.8397549465462022;0.9724673738491509;-0.15068247254349065;-0.775458533975284;0.37293532392487894;0.10055876191593649,40,,,,Infinity,,,,,, -629,0,(Exp(((0.173325249321305 * (((x1 * x1) * -5.6155028706412646e-2) / (x1 * -0.4263736660565396))) / (((-0.44395846544254136 * x1) ^ (0.45219343462317685 / Exp(x0))) * x0))) ^ (((x1 ^ (x1 * 0.9349833068265455)) / ((x1 + 0.6140001133164233) + -0.17511001488644107)) * x1)),"np.exp(((t[0] * (((x[:, 1] * x[:, 1]) * t[1]) / (x[:, 1] * t[2]))) / ((t[3] * x[:, 1]) ** (t[4] / np.exp(x[:, 0])) * x[:, 0]))) ** ((x[:, 1] ** (x[:, 1] * t[5]) / ((x[:, 1] + t[6]) + t[7])) * x[:, 1])","$${\operatorname{exp}(\frac{\left(\theta_{0} \cdot \frac{\left(\left(x_{1} \cdot x_{1}\right) \cdot \theta_{1}\right)}{\left(x_{1} \cdot \theta_{2}\right)}\right)}{\left({\left(\theta_{3} \cdot x_{1}\right)^{\frac{\theta_{4}}{\operatorname{exp}(x_{0})}}} \cdot x_{0}\right)})^{\left(\frac{{x_{1}^{\left(x_{1} \cdot \theta_{5}\right)}}}{\left(\left(x_{1} + \theta_{6}\right) + \theta_{7}\right)} \cdot x_{1}\right)}}$$",0.173325249321305;-5.6155028706412646e-2;-0.4263736660565396;-0.44395846544254136;0.45219343462317685;0.9349833068265455;0.6140001133164233;-0.17511001488644107,37,,,,Infinity,,,,,, -630,0,Log((Exp((x0 / (6847.720159938891 / x0))) + Exp(Log(x0)))),"np.log((np.exp((x[:, 0] / (t[0] / x[:, 0]))) + np.exp(np.log(x[:, 0]))))","$$\operatorname{log}(\left(\operatorname{exp}(\frac{x_{0}}{\frac{\theta_{0}}{x_{0}}}) + \operatorname{exp}(\operatorname{log}(x_{0}))\right))$$",6847.720159938891,11,0.47792423996318534,0.47792423996318534,0.47792423996318534,0.47792423996318534,-43.10340363524757,-43.10340363524757,-43.10340363524757,22.16041091284311,22.16041091284311,22.16041091284311 -631,0,(((Exp(x0) + 0.8879842256302257) * 0.5010509047327705) + Log(((x0 + -0.8707230805249988) + Log(x1)))),"(((np.exp(x[:, 0]) + t[0]) * t[1]) + np.log(((x[:, 0] + t[2]) + np.log(x[:, 1]))))","$$\left(\left(\left(\operatorname{exp}(x_{0}) + \theta_{0}\right) \cdot \theta_{1}\right) + \operatorname{log}(\left(\left(x_{0} + \theta_{2}\right) + \operatorname{log}(x_{1})\right))\right)$$",0.8879842256302257;0.5010509047327705;-0.8707230805249988,14,,,,Infinity,,,,,, -632,0,Exp(Log(Exp((Log(x0) / (x0 ^ 1997.9019768162345))))),"np.exp(np.log(np.exp((np.log(x[:, 0]) / x[:, 0] ** t[0]))))","$$\operatorname{exp}(\operatorname{log}(\operatorname{exp}(\frac{\operatorname{log}(x_{0})}{{x_{0}^{\theta_{0}}}})))$$",1997.9019768162345,9,0.5940445274820989,0.5940445274820989,0.5940445274820989,0.5940445274820989,-53.81911855919061,-53.81911855919061,-53.81911855919061,18.681400233154022,18.681400233154022,18.681400233154022 -633,0,Log(((Log((-0.8903963705943502 + x0)) + -0.7946530027970207) / ((x0 * (x0 + x1)) / (x1 + -0.8240843457605888)))),"np.log(((np.log((t[0] + x[:, 0])) + t[1]) / ((x[:, 0] * (x[:, 0] + x[:, 1])) / (x[:, 1] + t[2]))))","$$\operatorname{log}(\frac{\left(\operatorname{log}(\left(\theta_{0} + x_{0}\right)) + \theta_{1}\right)}{\frac{\left(x_{0} \cdot \left(x_{0} + x_{1}\right)\right)}{\left(x_{1} + \theta_{2}\right)}})$$",-0.8903963705943502;-0.7946530027970207;-0.8240843457605888,17,,,,Infinity,,,,,, -634,0,((((-0.8253192674357344 ^ (0.6623607254779194 + x1)) ^ (0.4072731636465736 / x0)) + (Exp((x0 + 0.5018823961729182)) + 0.18768879292101281)) / (((0.21988700467739308 + (0.719935431334775 / x1)) * (x1 / (-0.7915209032198108 ^ x1))) / (Exp(Log(Exp(x1))) / (x1 ^ (-0.6810535054932898 ^ x0))))),"((t[0] ** (t[1] + x[:, 1]) ** (t[2] / x[:, 0]) + (np.exp((x[:, 0] + t[3])) + t[4])) / (((t[5] + (t[6] / x[:, 1])) * (x[:, 1] / t[7] ** x[:, 1])) / (np.exp(np.log(np.exp(x[:, 1]))) / x[:, 1] ** t[8] ** x[:, 0])))","$$\frac{\left({{\theta_{0}^{\left(\theta_{1} + x_{1}\right)}}^{\frac{\theta_{2}}{x_{0}}}} + \left(\operatorname{exp}(\left(x_{0} + \theta_{3}\right)) + \theta_{4}\right)\right)}{\frac{\left(\left(\theta_{5} + \frac{\theta_{6}}{x_{1}}\right) \cdot \frac{x_{1}}{{\theta_{7}^{x_{1}}}}\right)}{\frac{\operatorname{exp}(\operatorname{log}(\operatorname{exp}(x_{1})))}{{x_{1}^{{\theta_{8}^{x_{0}}}}}}}}$$",-0.8253192674357344;0.6623607254779194;0.4072731636465736;0.5018823961729182;0.18768879292101281;0.21988700467739308;0.719935431334775;-0.7915209032198108;-0.6810535054932898,39,,,,Infinity,,,,,, -635,0,(Exp((-0.7952213854877552 ^ Log(Log((x1 ^ -0.7752689523963974))))) + Log((Log((((0.12779165482827037 * x1) + x0) * Log(Log(x1)))) ^ Exp(((0.7180708359980392 ^ Exp(x0)) + Log(Exp(x1))))))),"(np.exp(t[0] ** np.log(np.log(x[:, 1] ** t[1]))) + np.log(np.log((((t[2] * x[:, 1]) + x[:, 0]) * np.log(np.log(x[:, 1])))) ** np.exp((t[3] ** np.exp(x[:, 0]) + np.log(np.exp(x[:, 1]))))))","$$\left(\operatorname{exp}({\theta_{0}^{\operatorname{log}(\operatorname{log}({x_{1}^{\theta_{1}}}))}}) + \operatorname{log}({\operatorname{log}(\left(\left(\left(\theta_{2} \cdot x_{1}\right) + x_{0}\right) \cdot \operatorname{log}(\operatorname{log}(x_{1}))\right))^{\operatorname{exp}(\left({\theta_{3}^{\operatorname{exp}(x_{0})}} + \operatorname{log}(\operatorname{exp}(x_{1}))\right))}})\right)$$",-0.7952213854877552;-0.7752689523963974;0.12779165482827037;0.7180708359980392,30,,,,Infinity,,,,,, -636,0,(((((x0 * -0.7095000472755404) * -0.3250977305658188) / ((x1 ^ 0.9081752213083639) * 0.19257876625834325)) + (Log((x0 + (0.15253828881580245 / x1))) * ((-0.30297469033757973 + x0) * 0.15415638751393002))) ^ ((((x1 + 0.861712514519418) * 5.256209910066145e-4) * Exp((-0.7558625336814683 * (-0.9596242992818707 ^ x0)))) / (((x1 + x0) * 0.41945792574677176) + 0.4646998489447036))),"((((x[:, 0] * t[0]) * t[1]) / (x[:, 1] ** t[2] * t[3])) + (np.log((x[:, 0] + (t[4] / x[:, 1]))) * ((t[5] + x[:, 0]) * t[6]))) ** ((((x[:, 1] + t[7]) * t[8]) * np.exp((t[9] * t[10] ** x[:, 0]))) / (((x[:, 1] + x[:, 0]) * t[11]) + t[12]))","$${\left(\frac{\left(\left(x_{0} \cdot \theta_{0}\right) \cdot \theta_{1}\right)}{\left({x_{1}^{\theta_{2}}} \cdot \theta_{3}\right)} + \left(\operatorname{log}(\left(x_{0} + \frac{\theta_{4}}{x_{1}}\right)) \cdot \left(\left(\theta_{5} + x_{0}\right) \cdot \theta_{6}\right)\right)\right)^{\frac{\left(\left(\left(x_{1} + \theta_{7}\right) \cdot \theta_{8}\right) \cdot \operatorname{exp}(\left(\theta_{9} \cdot {\theta_{10}^{x_{0}}}\right))\right)}{\left(\left(\left(x_{1} + x_{0}\right) \cdot \theta_{11}\right) + \theta_{12}\right)}}}$$",-0.7095000472755404;-0.3250977305658188;0.9081752213083639;0.19257876625834325;0.15253828881580245;-0.30297469033757973;0.15415638751393002;0.861712514519418;5.256209910066145e-4;-0.7558625336814683;-0.9596242992818707;0.41945792574677176;0.4646998489447036,45,,,,Infinity,,,,,, -637,0,(Log((-7.03621187719754e-2 / (Log((x0 + -6.053406385211768e-2)) ^ (x0 ^ x0)))) * Log(((Log(x1) + -0.2363254707739646) / (((x1 ^ (0.3598659746838684 ^ x0)) + (x1 * -0.12061413823364286)) + x1)))),"(np.log((t[0] / np.log((x[:, 0] + t[1])) ** x[:, 0] ** x[:, 0])) * np.log(((np.log(x[:, 1]) + t[2]) / ((x[:, 1] ** t[3] ** x[:, 0] + (x[:, 1] * t[4])) + x[:, 1]))))","$$\left(\operatorname{log}(\frac{\theta_{0}}{{\operatorname{log}(\left(x_{0} + \theta_{1}\right))^{{x_{0}^{x_{0}}}}}}) \cdot \operatorname{log}(\frac{\left(\operatorname{log}(x_{1}) + \theta_{2}\right)}{\left(\left({x_{1}^{{\theta_{3}^{x_{0}}}}} + \left(x_{1} \cdot \theta_{4}\right)\right) + x_{1}\right)})\right)$$",-7.03621187719754e-2;-6.053406385211768e-2;-0.2363254707739646;0.3598659746838684;-0.12061413823364286,29,,,,Infinity,,,,,, -638,0,((Exp(Log(x1)) + ((Exp(Log(x1)) * (-0.494479256757158 ^ Log((x1 ^ 0.5590759328282535)))) / Log(x0))) ^ (-0.6630440071460031 ^ ((x0 / x0) * x1))),"(np.exp(np.log(x[:, 1])) + ((np.exp(np.log(x[:, 1])) * t[0] ** np.log(x[:, 1] ** t[1])) / np.log(x[:, 0]))) ** t[2] ** ((x[:, 0] / x[:, 0]) * x[:, 1])","$${\left(\operatorname{exp}(\operatorname{log}(x_{1})) + \frac{\left(\operatorname{exp}(\operatorname{log}(x_{1})) \cdot {\theta_{0}^{\operatorname{log}({x_{1}^{\theta_{1}}})}}\right)}{\operatorname{log}(x_{0})}\right)^{{\theta_{2}^{\left(\frac{x_{0}}{x_{0}} \cdot x_{1}\right)}}}}$$",-0.494479256757158;0.5590759328282535;-0.6630440071460031,25,,,,Infinity,,,,,, -639,0,Exp(((((x0 ^ -0.192205549711841) ^ x0) * -0.22969499804379334) + Exp((Exp(Exp((x1 + -0.285265727981899))) + ((x1 ^ 0.541124675373596) * 0.2661299969081782))))),"np.exp(((x[:, 0] ** t[0] ** x[:, 0] * t[1]) + np.exp((np.exp(np.exp((x[:, 1] + t[2]))) + (x[:, 1] ** t[3] * t[4])))))","$$\operatorname{exp}(\left(\left({{x_{0}^{\theta_{0}}}^{x_{0}}} \cdot \theta_{1}\right) + \operatorname{exp}(\left(\operatorname{exp}(\operatorname{exp}(\left(x_{1} + \theta_{2}\right))) + \left({x_{1}^{\theta_{3}}} \cdot \theta_{4}\right)\right))\right))$$",-0.192205549711841;-0.22969499804379334;-0.285265727981899;0.541124675373596;0.2661299969081782,21,6.859622075919386e51,6.859622075919386e51,6.859622075919386e51,6.859622075919386e51,-6.3301388777190225e53,-6.3301388777190225e53,-6.3301388777190225e53,6.173659868327447e51,6.173659868327447e51,6.173659868327447e51 -640,0,(Exp(((x0 / (x1 * 1.341398277774902)) * Log((x1 + -4.3567245768444306e-2)))) + Log((x0 * x0))),"(np.exp(((x[:, 0] / (x[:, 1] * t[0])) * np.log((x[:, 1] + t[1])))) + np.log((x[:, 0] * x[:, 0])))","$$\left(\operatorname{exp}(\left(\frac{x_{0}}{\left(x_{1} \cdot \theta_{0}\right)} \cdot \operatorname{log}(\left(x_{1} + \theta_{1}\right))\right)) + \operatorname{log}(\left(x_{0} \cdot x_{0}\right))\right)$$",1.341398277774902;-4.3567245768444306e-2,16,0.46351904844716285,0.46351904844716285,0.46351904844716285,0.46351904844716285,-41.77407584069351,-41.77407584069351,-41.77407584069351,37.7300816812151,37.7300816812151,37.7300816812151 -641,0,(Log(((0.3153293768025036 + x1) + -0.9392292851666727)) ^ Exp((((0.32297193144074965 + Exp(x0)) * (-0.1658619449945895 * (x0 ^ x0))) + 0.20169461153745905))),"np.log(((t[0] + x[:, 1]) + t[1])) ** np.exp((((t[2] + np.exp(x[:, 0])) * (t[3] * x[:, 0] ** x[:, 0])) + t[4]))","$${\operatorname{log}(\left(\left(\theta_{0} + x_{1}\right) + \theta_{1}\right))^{\operatorname{exp}(\left(\left(\left(\theta_{2} + \operatorname{exp}(x_{0})\right) \cdot \left(\theta_{3} \cdot {x_{0}^{x_{0}}}\right)\right) + \theta_{4}\right))}}$$",0.3153293768025036;-0.9392292851666727;0.32297193144074965;-0.1658619449945895;0.20169461153745905,20,,,,Infinity,,,,,, -642,0,((Exp((Exp((Log(x1) + (x0 + x1))) + Exp(x0))) ^ (-8.813064968415798e9 + (x1 ^ 4.857119583403563e9))) + 6.113188000927039e-2),"(np.exp((np.exp((np.log(x[:, 1]) + (x[:, 0] + x[:, 1]))) + np.exp(x[:, 0]))) ** (t[0] + x[:, 1] ** t[1]) + t[2])","$$\left({\operatorname{exp}(\left(\operatorname{exp}(\left(\operatorname{log}(x_{1}) + \left(x_{0} + x_{1}\right)\right)) + \operatorname{exp}(x_{0})\right))^{\left(\theta_{0} + {x_{1}^{\theta_{1}}}\right)}} + \theta_{2}\right)$$",-8.813064968415798e9;4.857119583403563e9;6.113188000927039e-2,19,1.0836447996526662e-2,1.0836447996526662e-2,1.0836447996526662e-2,1.0836447996526662e-2,3.3306690738754696e-16,3.3306690738754696e-16,3.3306690738754696e-16,43.04482044135075,43.04482044135075,43.04482044135075 -643,0,Exp(Log((Log(Log((-0.4451087893937813 ^ (x1 * -0.9856176113445397)))) / (((-0.8610579515976418 + Log(x0)) * -0.6205649123284178) + (((x0 * x0) / x0) / (x1 / x0)))))),"np.exp(np.log((np.log(np.log(t[0] ** (x[:, 1] * t[1]))) / (((t[2] + np.log(x[:, 0])) * t[3]) + (((x[:, 0] * x[:, 0]) / x[:, 0]) / (x[:, 1] / x[:, 0]))))))","$$\operatorname{exp}(\operatorname{log}(\frac{\operatorname{log}(\operatorname{log}({\theta_{0}^{\left(x_{1} \cdot \theta_{1}\right)}}))}{\left(\left(\left(\theta_{2} + \operatorname{log}(x_{0})\right) \cdot \theta_{3}\right) + \frac{\frac{\left(x_{0} \cdot x_{0}\right)}{x_{0}}}{\frac{x_{1}}{x_{0}}}\right)}))$$",-0.4451087893937813;-0.9856176113445397;-0.8610579515976418;-0.6205649123284178,26,,,,Infinity,,,,,, -644,0,((Exp((Exp((x0 * -0.7679164531594153)) + -0.617984742862949)) * Log((-0.13528962057836635 * (0.7324934981062843 + x1)))) / (((x0 ^ -0.8135239364943528) / Log((x0 * 0.8725830955829906))) ^ ((x0 * -0.2177902591550429) * (0.46812623011851684 / (x1 ^ 0.8749901912518923))))),"((np.exp((np.exp((x[:, 0] * t[0])) + t[1])) * np.log((t[2] * (t[3] + x[:, 1])))) / (x[:, 0] ** t[4] / np.log((x[:, 0] * t[5]))) ** ((x[:, 0] * t[6]) * (t[7] / x[:, 1] ** t[8])))","$$\frac{\left(\operatorname{exp}(\left(\operatorname{exp}(\left(x_{0} \cdot \theta_{0}\right)) + \theta_{1}\right)) \cdot \operatorname{log}(\left(\theta_{2} \cdot \left(\theta_{3} + x_{1}\right)\right))\right)}{{\frac{{x_{0}^{\theta_{4}}}}{\operatorname{log}(\left(x_{0} \cdot \theta_{5}\right))}^{\left(\left(x_{0} \cdot \theta_{6}\right) \cdot \frac{\theta_{7}}{{x_{1}^{\theta_{8}}}}\right)}}}$$",-0.7679164531594153;-0.617984742862949;-0.13528962057836635;0.7324934981062843;-0.8135239364943528;0.8725830955829906;-0.2177902591550429;0.46812623011851684;0.8749901912518923,33,,,,Infinity,,,,,, -645,0,((((-0.8973267904956393 * Log(x0)) ^ Exp((0.7443373163723328 * (0.9743116514922152 / x1)))) / (-0.8010483800831236 + ((-0.6807088453371691 + x0) + 0.17228747891427088))) + (Exp((Exp((x0 + x1)) ^ Log(Exp(x0)))) + (Log((Exp(x1) ^ -0.9336664346907435)) * ((-0.7114885537690083 + x0) * 0.8530282477189093)))),"(((t[0] * np.log(x[:, 0])) ** np.exp((t[1] * (t[2] / x[:, 1]))) / (t[3] + ((t[4] + x[:, 0]) + t[5]))) + (np.exp(np.exp((x[:, 0] + x[:, 1])) ** np.log(np.exp(x[:, 0]))) + (np.log(np.exp(x[:, 1]) ** t[6]) * ((t[7] + x[:, 0]) * t[8]))))","$$\left(\frac{{\left(\theta_{0} \cdot \operatorname{log}(x_{0})\right)^{\operatorname{exp}(\left(\theta_{1} \cdot \frac{\theta_{2}}{x_{1}}\right))}}}{\left(\theta_{3} + \left(\left(\theta_{4} + x_{0}\right) + \theta_{5}\right)\right)} + \left(\operatorname{exp}({\operatorname{exp}(\left(x_{0} + x_{1}\right))^{\operatorname{log}(\operatorname{exp}(x_{0}))}}) + \left(\operatorname{log}({\operatorname{exp}(x_{1})^{\theta_{6}}}) \cdot \left(\left(\theta_{7} + x_{0}\right) \cdot \theta_{8}\right)\right)\right)\right)$$",-0.8973267904956393;0.7443373163723328;0.9743116514922152;-0.8010483800831236;-0.6807088453371691;0.17228747891427088;-0.9336664346907435;-0.7114885537690083;0.8530282477189093,41,,,,Infinity,,,,,, -646,0,(x1 * 0.20059950763971873),"(x[:, 1] * t[0])","$$\left(x_{1} \cdot \theta_{0}\right)$$",0.20059950763971873,3,9.365750690525969e-3,9.365750690525969e-3,9.365750690525969e-3,9.365750690525969e-3,0.1357176545739056,0.1357176545739056,0.1357176545739056,5.325190976993443,5.325190976993443,5.325190976993443 -647,0,((((x1 * x0) + ((1.830072617973799e-2 ^ (x1 / x0)) + (((x1 * -0.2503297260585622) + 3.322381758920434e-2) * (x0 + x0)))) * -0.9593679117678032) + x1),"((((x[:, 1] * x[:, 0]) + (t[0] ** (x[:, 1] / x[:, 0]) + (((x[:, 1] * t[1]) + t[2]) * (x[:, 0] + x[:, 0])))) * t[3]) + x[:, 1])","$$\left(\left(\left(\left(x_{1} \cdot x_{0}\right) + \left({\theta_{0}^{\frac{x_{1}}{x_{0}}}} + \left(\left(\left(x_{1} \cdot \theta_{1}\right) + \theta_{2}\right) \cdot \left(x_{0} + x_{0}\right)\right)\right)\right) \cdot \theta_{3}\right) + x_{1}\right)$$",1.830072617973799e-2;-0.2503297260585622;3.322381758920434e-2;-0.9593679117678032,23,0.10762034483101914,0.10762034483101914,0.10762034483101914,0.10762034483101914,-8.931330346024266,-8.931330346024266,-8.931330346024266,52.93649148009069,52.93649148009069,52.93649148009069 -648,0,Log(((((x1 / x0) + Exp((x1 * 1.5576028090884653e-2))) + Log((Log(x0) * -0.8399348063029305))) * (Exp(((0.7796152999954002 + x0) * -0.7652285355642765)) + ((x1 * 9.355484923852675e-2) + Exp((0.8383645632407609 ^ x1)))))),"np.log(((((x[:, 1] / x[:, 0]) + np.exp((x[:, 1] * t[0]))) + np.log((np.log(x[:, 0]) * t[1]))) * (np.exp(((t[2] + x[:, 0]) * t[3])) + ((x[:, 1] * t[4]) + np.exp(t[5] ** x[:, 1])))))","$$\operatorname{log}(\left(\left(\left(\frac{x_{1}}{x_{0}} + \operatorname{exp}(\left(x_{1} \cdot \theta_{0}\right))\right) + \operatorname{log}(\left(\operatorname{log}(x_{0}) \cdot \theta_{1}\right))\right) \cdot \left(\operatorname{exp}(\left(\left(\theta_{2} + x_{0}\right) \cdot \theta_{3}\right)) + \left(\left(x_{1} \cdot \theta_{4}\right) + \operatorname{exp}({\theta_{5}^{x_{1}}})\right)\right)\right))$$",1.5576028090884653e-2;-0.8399348063029305;0.7796152999954002;-0.7652285355642765;9.355484923852675e-2;0.8383645632407609,31,,,,Infinity,,,,,, -649,0,Log((-0.27742143545477593 * ((x1 * x0) + (x1 ^ 0.473870175248005)))),"np.log((t[0] * ((x[:, 1] * x[:, 0]) + x[:, 1] ** t[1])))","$$\operatorname{log}(\left(\theta_{0} \cdot \left(\left(x_{1} \cdot x_{0}\right) + {x_{1}^{\theta_{1}}}\right)\right))$$",-0.27742143545477593;0.473870175248005,10,,,,Infinity,,,,,, -650,0,((((Exp(Exp((0.6017045317732665 + x1))) + (0.635438528407483 ^ Log(Log(x0)))) / (0.1660305887307374 / x1)) ^ ((x0 / x0) / (((x1 * -7.434947403547887e-2) / ((x0 * -0.8150096430404821) ^ (-0.4959156114435469 + x0))) / x0))) / (-0.5849542040828435 * ((Log(x0) * -0.4439975993012625) ^ x0))),"(((np.exp(np.exp((t[0] + x[:, 1]))) + t[1] ** np.log(np.log(x[:, 0]))) / (t[2] / x[:, 1])) ** ((x[:, 0] / x[:, 0]) / (((x[:, 1] * t[3]) / (x[:, 0] * t[4]) ** (t[5] + x[:, 0])) / x[:, 0])) / (t[6] * (np.log(x[:, 0]) * t[7]) ** x[:, 0]))","$$\frac{{\frac{\left(\operatorname{exp}(\operatorname{exp}(\left(\theta_{0} + x_{1}\right))) + {\theta_{1}^{\operatorname{log}(\operatorname{log}(x_{0}))}}\right)}{\frac{\theta_{2}}{x_{1}}}^{\frac{\frac{x_{0}}{x_{0}}}{\frac{\frac{\left(x_{1} \cdot \theta_{3}\right)}{{\left(x_{0} \cdot \theta_{4}\right)^{\left(\theta_{5} + x_{0}\right)}}}}{x_{0}}}}}}{\left(\theta_{6} \cdot {\left(\operatorname{log}(x_{0}) \cdot \theta_{7}\right)^{x_{0}}}\right)}$$",0.6017045317732665;0.635438528407483;0.1660305887307374;-7.434947403547887e-2;-0.8150096430404821;-0.4959156114435469;-0.5849542040828435;-0.4439975993012625,42,,,,Infinity,,,,,, -651,0,((Exp((Log((x0 ^ x1)) ^ 5.170673255324065e-2)) * (Log((0.7632515256686883 * x1)) * Log((0.8612743674060752 + x0)))) * (Exp((Exp(Log(x1)) + Log((x0 * -0.5571163950885334)))) * Log(((x0 ^ -0.5183987808983542) ^ (x1 * -0.6617402877713248))))),"((np.exp(np.log(x[:, 0] ** x[:, 1]) ** t[0]) * (np.log((t[1] * x[:, 1])) * np.log((t[2] + x[:, 0])))) * (np.exp((np.exp(np.log(x[:, 1])) + np.log((x[:, 0] * t[3])))) * np.log(x[:, 0] ** t[4] ** (x[:, 1] * t[5]))))","$$\left(\left(\operatorname{exp}({\operatorname{log}({x_{0}^{x_{1}}})^{\theta_{0}}}) \cdot \left(\operatorname{log}(\left(\theta_{1} \cdot x_{1}\right)) \cdot \operatorname{log}(\left(\theta_{2} + x_{0}\right))\right)\right) \cdot \left(\operatorname{exp}(\left(\operatorname{exp}(\operatorname{log}(x_{1})) + \operatorname{log}(\left(x_{0} \cdot \theta_{3}\right))\right)) \cdot \operatorname{log}({{x_{0}^{\theta_{4}}}^{\left(x_{1} \cdot \theta_{5}\right)}})\right)\right)$$",5.170673255324065e-2;0.7632515256686883;0.8612743674060752;-0.5571163950885334;-0.5183987808983542;-0.6617402877713248,36,,,,Infinity,,,,,, -652,0,((Exp((x0 ^ -0.9276321812156032)) ^ (x1 ^ x1)) * (1.9127195387556514e-2 ^ (x0 ^ (1.0909582022913642 * x0)))),"(np.exp(x[:, 0] ** t[0]) ** x[:, 1] ** x[:, 1] * t[1] ** x[:, 0] ** (t[2] * x[:, 0]))","$$\left({\operatorname{exp}({x_{0}^{\theta_{0}}})^{{x_{1}^{x_{1}}}}} \cdot {\theta_{1}^{{x_{0}^{\left(\theta_{2} \cdot x_{0}\right)}}}}\right)$$",-0.9276321812156032;1.9127195387556514e-2;1.0909582022913642,16,5.474757170066497e-3,5.474757170066497e-3,5.474757170066497e-3,5.474757170066497e-3,0.4947830532826546,0.4947830532826546,0.4947830532826546,34.73585359520486,34.73585359520486,34.73585359520486 -653,0,((((-0.19920782569818973 + (Exp((x0 + x1)) ^ (x1 + -0.7676316072354532))) + -4.969581423598379e-2) ^ -0.10684689414260962) / x0),"(((t[0] + np.exp((x[:, 0] + x[:, 1])) ** (x[:, 1] + t[1])) + t[2]) ** t[3] / x[:, 0])","$$\frac{{\left(\left(\theta_{0} + {\operatorname{exp}(\left(x_{0} + x_{1}\right))^{\left(x_{1} + \theta_{1}\right)}}\right) + \theta_{2}\right)^{\theta_{3}}}}{x_{0}}$$",-0.19920782569818973;-0.7676316072354532;-4.969581423598379e-2;-0.10684689414260962,16,1.364624554494357,1.364624554494357,1.364624554494357,1.364624554494357,-124.92913793631925,-124.92913793631925,-124.92913793631925,40.4464242254005,40.4464242254005,40.4464242254005 -654,0,(Exp(Log(Exp(Exp((x0 ^ -1.3341010597449852))))) * Exp((-6.679650788956264 / x0))),"(np.exp(np.log(np.exp(np.exp(x[:, 0] ** t[0])))) * np.exp((t[1] / x[:, 0])))","$$\left(\operatorname{exp}(\operatorname{log}(\operatorname{exp}(\operatorname{exp}({x_{0}^{\theta_{0}}})))) \cdot \operatorname{exp}(\frac{\theta_{1}}{x_{0}})\right)$$",-1.3341010597449852;-6.679650788956264,12,1.3085909539536101e-2,1.3085909539536101e-2,1.3085909539536101e-2,1.3085909539536101e-2,-0.20758292234968878,-0.20758292234968878,-0.20758292234968878,27.40454897798462,27.40454897798462,27.40454897798462 -655,0,(x0 * 3.524205325424168e-2),"(x[:, 0] * t[0])","$$\left(x_{0} \cdot \theta_{0}\right)$$",3.524205325424168e-2,3,1.306654401552658e-2,1.306654401552658e-2,1.306654401552658e-2,1.306654401552658e-2,-0.20579584931471184,-0.20579584931471184,-0.20579584931471184,5.328521690985943,5.328521690985943,5.328521690985943 -656,0,(((-0.4966920471888838 ^ (Log(x0) ^ ((Log(x0) + -0.8213712654068528) / Exp(x0)))) ^ (Log((-0.8576996175319336 ^ (Log(x0) ^ Log(x1)))) ^ (x1 + 0.42214596820852013))) ^ 0.15881749923559274),"t[0] ** np.log(x[:, 0]) ** ((np.log(x[:, 0]) + t[1]) / np.exp(x[:, 0])) ** np.log(t[2] ** np.log(x[:, 0]) ** np.log(x[:, 1])) ** (x[:, 1] + t[3]) ** t[4]","$${{{\theta_{0}^{{\operatorname{log}(x_{0})^{\frac{\left(\operatorname{log}(x_{0}) + \theta_{1}\right)}{\operatorname{exp}(x_{0})}}}}}^{{\operatorname{log}({\theta_{2}^{{\operatorname{log}(x_{0})^{\operatorname{log}(x_{1})}}}})^{\left(x_{1} + \theta_{3}\right)}}}}^{\theta_{4}}}$$",-0.4966920471888838;-0.8213712654068528;-0.8576996175319336;0.42214596820852013;0.15881749923559274,27,,,,Infinity,,,,,, -657,0,((Exp(((0.1176583354775742 * (x1 ^ -0.8248849636882339)) ^ x0)) ^ ((Log((0.736234310815349 ^ x0)) * Exp((x1 ^ 0.8653238313876802))) * (((x0 ^ 0.6712370088479175) ^ 0.8224870573996854) + x0))) / Log((x1 + x0))),"(np.exp((t[0] * x[:, 1] ** t[1]) ** x[:, 0]) ** ((np.log(t[2] ** x[:, 0]) * np.exp(x[:, 1] ** t[3])) * (x[:, 0] ** t[4] ** t[5] + x[:, 0])) / np.log((x[:, 1] + x[:, 0])))","$$\frac{{\operatorname{exp}({\left(\theta_{0} \cdot {x_{1}^{\theta_{1}}}\right)^{x_{0}}})^{\left(\left(\operatorname{log}({\theta_{2}^{x_{0}}}) \cdot \operatorname{exp}({x_{1}^{\theta_{3}}})\right) \cdot \left({{x_{0}^{\theta_{4}}}^{\theta_{5}}} + x_{0}\right)\right)}}}{\operatorname{log}(\left(x_{1} + x_{0}\right))}$$",0.1176583354775742;-0.8248849636882339;0.736234310815349;0.8653238313876802;0.6712370088479175;0.8224870573996854,31,3155.2453633537953,3155.2453633537953,3155.2453633537953,3155.2453633537953,-291168.7047192147,-291168.7047192147,-291168.7047192147,2919.9603385280448,2919.9603385280448,2919.9603385280448 -658,0,((0.6113605356455268 + (x1 / Log(x1))) + ((-0.4269872585612929 * (0.6808335375486776 * x0)) * 0.5708100407174047)),"((t[0] + (x[:, 1] / np.log(x[:, 1]))) + ((t[1] * (t[2] * x[:, 0])) * t[3]))","$$\left(\left(\theta_{0} + \frac{x_{1}}{\operatorname{log}(x_{1})}\right) + \left(\left(\theta_{1} \cdot \left(\theta_{2} \cdot x_{0}\right)\right) \cdot \theta_{3}\right)\right)$$",0.6113605356455268;-0.4269872585612929;0.6808335375486776;0.5708100407174047,14,0.10867405368582436,0.10867405368582436,0.10867405368582436,0.10867405368582436,-9.028567822284288,-9.028567822284288,-9.028567822284288,35.42424847656219,35.42424847656219,35.42424847656219 -659,0,(1.743998583298645e-3 * Exp(Exp((x1 + 0.9050851411695724)))),"(t[0] * np.exp(np.exp((x[:, 1] + t[1]))))","$$\left(\theta_{0} \cdot \operatorname{exp}(\operatorname{exp}(\left(x_{1} + \theta_{1}\right)))\right)$$",1.743998583298645e-3;0.9050851411695724,7,9.49247389026586e-3,9.49247389026586e-3,9.49247389026586e-3,9.49247389026586e-3,0.12402349060241702,0.12402349060241702,0.12402349060241702,15.316458484275222,15.316458484275222,15.316458484275222 -660,0,Exp((((1.6523343106621867e-5 ^ (0.44806348318293093 ^ x1)) ^ (1.8166453607634372 / (x1 ^ x1))) / ((Exp(Exp(x0)) + (x0 ^ x1)) / (Exp((x0 * -2.1943393593896934)) ^ (x0 * 0.4876385762893846))))),"np.exp((t[0] ** t[1] ** x[:, 1] ** (t[2] / x[:, 1] ** x[:, 1]) / ((np.exp(np.exp(x[:, 0])) + x[:, 0] ** x[:, 1]) / np.exp((x[:, 0] * t[3])) ** (x[:, 0] * t[4]))))","$$\operatorname{exp}(\frac{{{\theta_{0}^{{\theta_{1}^{x_{1}}}}}^{\frac{\theta_{2}}{{x_{1}^{x_{1}}}}}}}{\frac{\left(\operatorname{exp}(\operatorname{exp}(x_{0})) + {x_{0}^{x_{1}}}\right)}{{\operatorname{exp}(\left(x_{0} \cdot \theta_{3}\right))^{\left(x_{0} \cdot \theta_{4}\right)}}}})$$",1.6523343106621867e-5;0.44806348318293093;1.8166453607634372;-2.1943393593896934;0.4876385762893846,29,0.8923097948756735,0.8923097948756735,0.8923097948756735,0.8923097948756735,-81.34338365871174,-81.34338365871174,-81.34338365871174,71.21150820094155,71.21150820094155,71.21150820094155 -661,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 -662,0,Exp((Exp(Exp(Log(Exp((-0.1424072820147979 + x1))))) + ((((x1 + x1) + -3.274522466134244e-2) ^ (-3.102091152018338e-2 + Exp((-0.8794077136553502 ^ x1)))) ^ 0.7947987898098764))),"np.exp((np.exp(np.exp(np.log(np.exp((t[0] + x[:, 1]))))) + ((x[:, 1] + x[:, 1]) + t[1]) ** (t[2] + np.exp(t[3] ** x[:, 1])) ** t[4]))","$$\operatorname{exp}(\left(\operatorname{exp}(\operatorname{exp}(\operatorname{log}(\operatorname{exp}(\left(\theta_{0} + x_{1}\right))))) + {{\left(\left(x_{1} + x_{1}\right) + \theta_{1}\right)^{\left(\theta_{2} + \operatorname{exp}({\theta_{3}^{x_{1}}})\right)}}^{\theta_{4}}}\right))$$",-0.1424072820147979;-3.274522466134244e-2;-3.102091152018338e-2;-0.8794077136553502;0.7947987898098764,23,,,,Infinity,,,,,, -663,0,(Exp(Log(Exp(x0))) * (((0.9122317707118645 ^ x0) / (x1 ^ x1)) / ((-0.9735575803011653 ^ (x1 * x0)) + x0))),"(np.exp(np.log(np.exp(x[:, 0]))) * ((t[0] ** x[:, 0] / x[:, 1] ** x[:, 1]) / (t[1] ** (x[:, 1] * x[:, 0]) + x[:, 0])))","$$\left(\operatorname{exp}(\operatorname{log}(\operatorname{exp}(x_{0}))) \cdot \frac{\frac{{\theta_{0}^{x_{0}}}}{{x_{1}^{x_{1}}}}}{\left({\theta_{1}^{\left(x_{1} \cdot x_{0}\right)}} + x_{0}\right)}\right)$$",0.9122317707118645;-0.9735575803011653,20,,,,Infinity,,,,,, -664,0,(-7.344112557728449e-3 * (Log((((3.3524528224656257e-4 * x1) * 1.3925949579110257) ^ 0.8951299980271435)) + (((x1 / x1) + (-0.45816731387601345 * x0)) * ((-0.5575107587231128 * x1) + x1)))),"(t[0] * (np.log(((t[1] * x[:, 1]) * t[2]) ** t[3]) + (((x[:, 1] / x[:, 1]) + (t[4] * x[:, 0])) * ((t[5] * x[:, 1]) + x[:, 1]))))","$$\left(\theta_{0} \cdot \left(\operatorname{log}({\left(\left(\theta_{1} \cdot x_{1}\right) \cdot \theta_{2}\right)^{\theta_{3}}}) + \left(\left(\frac{x_{1}}{x_{1}} + \left(\theta_{4} \cdot x_{0}\right)\right) \cdot \left(\left(\theta_{5} \cdot x_{1}\right) + x_{1}\right)\right)\right)\right)$$",-7.344112557728449e-3;3.3524528224656257e-4;1.3925949579110257;0.8951299980271435;-0.45816731387601345;-0.5575107587231128,24,1.1159612786567292e-2,1.1159612786567292e-2,1.1159612786567292e-2,1.1159612786567292e-2,-2.9822021952599975e-2,-2.9822021952599975e-2,-2.9822021952599975e-2,62.04219026402981,62.04219026402981,62.04219026402981 -665,0,(((-0.41967156057239363 / (-0.30420312505006875 ^ (x0 + 0.3247872496061812))) * ((x0 * 0.5600577715067923) * 0.40443618336728626)) ^ Log((((x1 ^ (x0 ^ 0.574485966905428)) * (-0.6174625664275729 ^ (-0.3507299870366689 ^ (-0.6365578577856201 * x1)))) * 0.11676772923440004))),"((t[0] / t[1] ** (x[:, 0] + t[2])) * ((x[:, 0] * t[3]) * t[4])) ** np.log(((x[:, 1] ** x[:, 0] ** t[5] * t[6] ** t[7] ** (t[8] * x[:, 1])) * t[9]))","$${\left(\frac{\theta_{0}}{{\theta_{1}^{\left(x_{0} + \theta_{2}\right)}}} \cdot \left(\left(x_{0} \cdot \theta_{3}\right) \cdot \theta_{4}\right)\right)^{\operatorname{log}(\left(\left({x_{1}^{{x_{0}^{\theta_{5}}}}} \cdot {\theta_{6}^{{\theta_{7}^{\left(\theta_{8} \cdot x_{1}\right)}}}}\right) \cdot \theta_{9}\right))}}$$",-0.41967156057239363;-0.30420312505006875;0.3247872496061812;0.5600577715067923;0.40443618336728626;0.574485966905428;-0.6174625664275729;-0.3507299870366689;-0.6365578577856201;0.11676772923440004,30,,,,Infinity,,,,,, -666,0,(Exp((-0.15500141202420914 + ((Log((x1 + -0.9492238415317265)) * x0) * x1))) / x0),"(np.exp((t[0] + ((np.log((x[:, 1] + t[1])) * x[:, 0]) * x[:, 1]))) / x[:, 0])","$$\frac{\operatorname{exp}(\left(\theta_{0} + \left(\left(\operatorname{log}(\left(x_{1} + \theta_{1}\right)) \cdot x_{0}\right) \cdot x_{1}\right)\right))}{x_{0}}$$",-0.15500141202420914;-0.9492238415317265,13,,,,Infinity,,,,,, -667,0,((Log((0.6243475141501869 * (x1 + Log(x0)))) * (Exp((x0 * x1)) ^ Exp((x1 * (x0 * 0.760383359828056))))) ^ (0.16787228225563078 ^ ((0.8057538830794513 / (x0 / x0)) * -0.31329880669887855))),"(np.log((t[0] * (x[:, 1] + np.log(x[:, 0])))) * np.exp((x[:, 0] * x[:, 1])) ** np.exp((x[:, 1] * (x[:, 0] * t[1])))) ** t[2] ** ((t[3] / (x[:, 0] / x[:, 0])) * t[4])","$${\left(\operatorname{log}(\left(\theta_{0} \cdot \left(x_{1} + \operatorname{log}(x_{0})\right)\right)) \cdot {\operatorname{exp}(\left(x_{0} \cdot x_{1}\right))^{\operatorname{exp}(\left(x_{1} \cdot \left(x_{0} \cdot \theta_{1}\right)\right))}}\right)^{{\theta_{2}^{\left(\frac{\theta_{3}}{\frac{x_{0}}{x_{0}}} \cdot \theta_{4}\right)}}}}$$",0.6243475141501869;0.760383359828056;0.16787228225563078;0.8057538830794513;-0.31329880669887855,29,,,,Infinity,,,,,, -668,0,((-0.21445640383188902 / (x1 * 8.187410933875006e-2)) ^ ((x1 / (x1 * x0)) ^ (x1 ^ x0))),"(t[0] / (x[:, 1] * t[1])) ** (x[:, 1] / (x[:, 1] * x[:, 0])) ** x[:, 1] ** x[:, 0]","$${\frac{\theta_{0}}{\left(x_{1} \cdot \theta_{1}\right)}^{{\frac{x_{1}}{\left(x_{1} \cdot x_{0}\right)}^{{x_{1}^{x_{0}}}}}}}$$",-0.21445640383188902;8.187410933875006e-2,15,,,,Infinity,,,,,, -669,0,(((Log((x0 ^ -0.12436058856247978)) ^ (0.2753247347626677 + x1)) + Log(Log((-0.7414324249203526 * (x1 + 2.8752205117505536e-2))))) ^ Log(Exp(Log((Exp((x1 * 0.24885508289050107)) / (0.41924451048498135 * x0)))))),"(np.log(x[:, 0] ** t[0]) ** (t[1] + x[:, 1]) + np.log(np.log((t[2] * (x[:, 1] + t[3]))))) ** np.log(np.exp(np.log((np.exp((x[:, 1] * t[4])) / (t[5] * x[:, 0])))))","$${\left({\operatorname{log}({x_{0}^{\theta_{0}}})^{\left(\theta_{1} + x_{1}\right)}} + \operatorname{log}(\operatorname{log}(\left(\theta_{2} \cdot \left(x_{1} + \theta_{3}\right)\right)))\right)^{\operatorname{log}(\operatorname{exp}(\operatorname{log}(\frac{\operatorname{exp}(\left(x_{1} \cdot \theta_{4}\right))}{\left(\theta_{5} \cdot x_{0}\right)})))}}$$",-0.12436058856247978;0.2753247347626677;-0.7414324249203526;2.8752205117505536e-2;0.24885508289050107;0.41924451048498135,28,,,,Infinity,,,,,, -670,0,((-0.714161417676688 ^ ((0.6239225777712938 + (-0.7574954950995596 + (Exp(x1) * -0.10319096103558423))) ^ (Exp((x0 + (x0 / x1))) ^ 0.8463501789409651))) + (Log((x1 ^ (-0.5138231630880987 * x0))) * ((-0.5090307923655668 / x1) * -0.8065490342031263))),"(t[0] ** (t[1] + (t[2] + (np.exp(x[:, 1]) * t[3]))) ** np.exp((x[:, 0] + (x[:, 0] / x[:, 1]))) ** t[4] + (np.log(x[:, 1] ** (t[5] * x[:, 0])) * ((t[6] / x[:, 1]) * t[7])))","$$\left({\theta_{0}^{{\left(\theta_{1} + \left(\theta_{2} + \left(\operatorname{exp}(x_{1}) \cdot \theta_{3}\right)\right)\right)^{{\operatorname{exp}(\left(x_{0} + \frac{x_{0}}{x_{1}}\right))^{\theta_{4}}}}}}} + \left(\operatorname{log}({x_{1}^{\left(\theta_{5} \cdot x_{0}\right)}}) \cdot \left(\frac{\theta_{6}}{x_{1}} \cdot \theta_{7}\right)\right)\right)$$",-0.714161417676688;0.6239225777712938;-0.7574954950995596;-0.10319096103558423;0.8463501789409651;-0.5138231630880987;-0.5090307923655668;-0.8065490342031263,32,,,,Infinity,,,,,, -671,0,Log(((Exp((-1.1377308843536267 * x0)) / (x0 * (x0 * x1))) * ((x0 / Exp(x0)) + (x0 ^ (x1 + x1))))),"np.log(((np.exp((t[0] * x[:, 0])) / (x[:, 0] * (x[:, 0] * x[:, 1]))) * ((x[:, 0] / np.exp(x[:, 0])) + x[:, 0] ** (x[:, 1] + x[:, 1]))))","$$\operatorname{log}(\left(\frac{\operatorname{exp}(\left(\theta_{0} \cdot x_{0}\right))}{\left(x_{0} \cdot \left(x_{0} \cdot x_{1}\right)\right)} \cdot \left(\frac{x_{0}}{\operatorname{exp}(x_{0})} + {x_{0}^{\left(x_{1} + x_{1}\right)}}\right)\right))$$",-1.1377308843536267,22,1.011054697019853,1.011054697019853,1.011054697019853,1.011054697019853,-92.30130106691045,-92.30130106691045,-92.30130106691045,51.26981486408234,51.26981486408234,51.26981486408234 -672,0,(Exp(Log((x1 * 0.9475289521833459))) / Log((0.46281999344815017 ^ (-0.44000447844174573 + x1)))),"(np.exp(np.log((x[:, 1] * t[0]))) / np.log(t[1] ** (t[2] + x[:, 1])))","$$\frac{\operatorname{exp}(\operatorname{log}(\left(x_{1} \cdot \theta_{0}\right)))}{\operatorname{log}({\theta_{1}^{\left(\theta_{2} + x_{1}\right)}})}$$",0.9475289521833459;0.46281999344815017;-0.44000447844174573,12,938.1988443616256,938.1988443616256,938.1988443616256,938.1988443616256,-86577.07841299476,-86577.07841299476,-86577.07841299476,875.395033231724,875.395033231724,875.395033231724 -673,0,Exp(((Exp(x0) * (((0.10348951165148135 * (x0 * 0.6105127393569115)) ^ x1) ^ (x1 + 0.6301609021152196))) ^ ((x0 + -0.8257278077311061) ^ ((0.8247202045126599 + (x0 * -0.6013697731138825)) + -0.380609404373613)))),"np.exp((np.exp(x[:, 0]) * (t[0] * (x[:, 0] * t[1])) ** x[:, 1] ** (x[:, 1] + t[2])) ** (x[:, 0] + t[3]) ** ((t[4] + (x[:, 0] * t[5])) + t[6]))","$$\operatorname{exp}({\left(\operatorname{exp}(x_{0}) \cdot {{\left(\theta_{0} \cdot \left(x_{0} \cdot \theta_{1}\right)\right)^{x_{1}}}^{\left(x_{1} + \theta_{2}\right)}}\right)^{{\left(x_{0} + \theta_{3}\right)^{\left(\left(\theta_{4} + \left(x_{0} \cdot \theta_{5}\right)\right) + \theta_{6}\right)}}}})$$",0.10348951165148135;0.6105127393569115;0.6301609021152196;-0.8257278077311061;0.8247202045126599;-0.6013697731138825;-0.380609404373613,27,,,,Infinity,,,,,, -674,0,(Log((Log((-0.6841923185000931 ^ Exp((0.15889207249352577 ^ x0)))) / (Log(((x1 * x1) * -0.4297006418788877)) + (Exp((x1 * -0.7808846805317446)) ^ 0.5807998953837854)))) ^ (Log(Log((-0.4882930594213776 * (-0.7135953157910122 + x0)))) + (0.37452052281469994 / x1))),"np.log((np.log(t[0] ** np.exp(t[1] ** x[:, 0])) / (np.log(((x[:, 1] * x[:, 1]) * t[2])) + np.exp((x[:, 1] * t[3])) ** t[4]))) ** (np.log(np.log((t[5] * (t[6] + x[:, 0])))) + (t[7] / x[:, 1]))","$${\operatorname{log}(\frac{\operatorname{log}({\theta_{0}^{\operatorname{exp}({\theta_{1}^{x_{0}}})}})}{\left(\operatorname{log}(\left(\left(x_{1} \cdot x_{1}\right) \cdot \theta_{2}\right)) + {\operatorname{exp}(\left(x_{1} \cdot \theta_{3}\right))^{\theta_{4}}}\right)})^{\left(\operatorname{log}(\operatorname{log}(\left(\theta_{5} \cdot \left(\theta_{6} + x_{0}\right)\right))) + \frac{\theta_{7}}{x_{1}}\right)}}$$",-0.6841923185000931;0.15889207249352577;-0.4297006418788877;-0.7808846805317446;0.5807998953837854;-0.4882930594213776;-0.7135953157910122;0.37452052281469994,34,,,,Infinity,,,,,, -675,0,Exp((Exp((x0 / (((x0 + 2.30273746101594) / (x0 ^ 1.3588823211677699)) ^ 2.4502064548774234))) * ((x1 ^ x1) + (-3.914157878899511 * x0)))),"np.exp((np.exp((x[:, 0] / ((x[:, 0] + t[0]) / x[:, 0] ** t[1]) ** t[2])) * (x[:, 1] ** x[:, 1] + (t[3] * x[:, 0]))))","$$\operatorname{exp}(\left(\operatorname{exp}(\frac{x_{0}}{{\frac{\left(x_{0} + \theta_{0}\right)}{{x_{0}^{\theta_{1}}}}^{\theta_{2}}}}) \cdot \left({x_{1}^{x_{1}}} + \left(\theta_{3} \cdot x_{0}\right)\right)\right))$$",2.30273746101594;1.3588823211677699;2.4502064548774234;-3.914157878899511,21,5.280751648740032e-3,5.280751648740032e-3,5.280751648740032e-3,5.280751648740032e-3,0.5126861079910469,0.5126861079910469,0.5126861079910469,51.75672479323098,51.75672479323098,51.75672479323098 -676,0,((Exp(Exp(((0.7697989883176286 / x0) + (x0 * -0.34951055035963996)))) * Log(((x0 + x1) + -0.1932055935292455))) ^ (0.43364349691476145 / Log(((x0 + -0.8850582385649279) ^ (x1 * 0.5571176837471876))))),"(np.exp(np.exp(((t[0] / x[:, 0]) + (x[:, 0] * t[1])))) * np.log(((x[:, 0] + x[:, 1]) + t[2]))) ** (t[3] / np.log((x[:, 0] + t[4]) ** (x[:, 1] * t[5])))","$${\left(\operatorname{exp}(\operatorname{exp}(\left(\frac{\theta_{0}}{x_{0}} + \left(x_{0} \cdot \theta_{1}\right)\right))) \cdot \operatorname{log}(\left(\left(x_{0} + x_{1}\right) + \theta_{2}\right))\right)^{\frac{\theta_{3}}{\operatorname{log}({\left(x_{0} + \theta_{4}\right)^{\left(x_{1} \cdot \theta_{5}\right)}})}}}$$",0.7697989883176286;-0.34951055035963996;-0.1932055935292455;0.43364349691476145;-0.8850582385649279;0.5571176837471876,27,,,,Infinity,,,,,, -677,0,Exp((Log((3.6158358324964407e-4 * x0)) * (Exp(x1) + (0.5684459232835466 + (Exp(x1) * -1.1153463723715373))))),"np.exp((np.log((t[0] * x[:, 0])) * (np.exp(x[:, 1]) + (t[1] + (np.exp(x[:, 1]) * t[2])))))","$$\operatorname{exp}(\left(\operatorname{log}(\left(\theta_{0} \cdot x_{0}\right)) \cdot \left(\operatorname{exp}(x_{1}) + \left(\theta_{1} + \left(\operatorname{exp}(x_{1}) \cdot \theta_{2}\right)\right)\right)\right))$$",3.6158358324964407e-4;0.5684459232835466;-1.1153463723715373,15,1.1537638923516579e-2,1.1537638923516579e-2,1.1537638923516579e-2,1.1537638923516579e-2,-6.470671268063666e-2,-6.470671268063666e-2,-6.470671268063666e-2,35.26181091696379,35.26181091696379,35.26181091696379 -678,0,(((Exp((x1 / ((x0 * -6.394297639132962) + -17.15094462751474))) / x0) * -0.21818104149599682) * -0.34406821254665143),"(((np.exp((x[:, 1] / ((x[:, 0] * t[0]) + t[1]))) / x[:, 0]) * t[2]) * t[3])","$$\left(\left(\frac{\operatorname{exp}(\frac{x_{1}}{\left(\left(x_{0} \cdot \theta_{0}\right) + \theta_{1}\right)})}{x_{0}} \cdot \theta_{2}\right) \cdot \theta_{3}\right)$$",-6.394297639132962;-17.15094462751474;-0.21818104149599682;-0.34406821254665143,14,7.781515682950687e-3,7.781515682950687e-3,7.781515682950687e-3,7.781515682950687e-3,0.2819127000429621,0.2819127000429621,0.2819127000429621,35.333445192359605,35.333445192359605,35.333445192359605 -679,0,Log((Log((x1 * -0.6582443572928078)) * Exp((0.752380821248072 * x1)))),"np.log((np.log((x[:, 1] * t[0])) * np.exp((t[1] * x[:, 1]))))","$$\operatorname{log}(\left(\operatorname{log}(\left(x_{1} \cdot \theta_{0}\right)) \cdot \operatorname{exp}(\left(\theta_{1} \cdot x_{1}\right))\right))$$",-0.6582443572928078;0.752380821248072,10,,,,Infinity,,,,,, -680,0,((((Exp(Exp(x1)) * -0.9675506524766464) ^ (-0.5113750439797213 + (0.18315630603687183 / x0))) ^ ((x0 * x1) ^ (Log(((x1 ^ 0.772046019185768) / x1)) / (x1 * (x0 ^ (x1 * 0.4598288815528043)))))) + (x1 * x1)),"((np.exp(np.exp(x[:, 1])) * t[0]) ** (t[1] + (t[2] / x[:, 0])) ** (x[:, 0] * x[:, 1]) ** (np.log((x[:, 1] ** t[3] / x[:, 1])) / (x[:, 1] * x[:, 0] ** (x[:, 1] * t[4]))) + (x[:, 1] * x[:, 1]))","$$\left({{\left(\operatorname{exp}(\operatorname{exp}(x_{1})) \cdot \theta_{0}\right)^{\left(\theta_{1} + \frac{\theta_{2}}{x_{0}}\right)}}^{{\left(x_{0} \cdot x_{1}\right)^{\frac{\operatorname{log}(\frac{{x_{1}^{\theta_{3}}}}{x_{1}})}{\left(x_{1} \cdot {x_{0}^{\left(x_{1} \cdot \theta_{4}\right)}}\right)}}}}} + \left(x_{1} \cdot x_{1}\right)\right)$$",-0.9675506524766464;-0.5113750439797213;0.18315630603687183;0.772046019185768;0.4598288815528043,34,,,,Infinity,,,,,, -681,0,Log((-0.8130647961541064 / x0)),"np.log((t[0] / x[:, 0]))","$$\operatorname{log}(\frac{\theta_{0}}{x_{0}})$$",-0.8130647961541064,4,,,,Infinity,,,,,, -682,0,(Log((Exp((0.891748656711249 ^ (x1 * x1))) ^ 0.32004903402641405)) ^ (((x0 * -0.275686490123094) + 0.2968950015197225) / (0.419263463533203 / Log((x1 + -0.9069607855754125))))),"np.log(np.exp(t[0] ** (x[:, 1] * x[:, 1])) ** t[1]) ** (((x[:, 0] * t[2]) + t[3]) / (t[4] / np.log((x[:, 1] + t[5]))))","$${\operatorname{log}({\operatorname{exp}({\theta_{0}^{\left(x_{1} \cdot x_{1}\right)}})^{\theta_{1}}})^{\frac{\left(\left(x_{0} \cdot \theta_{2}\right) + \theta_{3}\right)}{\frac{\theta_{4}}{\operatorname{log}(\left(x_{1} + \theta_{5}\right))}}}}$$",0.891748656711249;0.32004903402641405;-0.275686490123094;0.2968950015197225;0.419263463533203;-0.9069607855754125,22,,,,Infinity,,,,,, -683,0,((((x0 / x1) + 27.856428293186994) * 6.49892979394686e-3) * x1),"((((x[:, 0] / x[:, 1]) + t[0]) * t[1]) * x[:, 1])","$$\left(\left(\left(\frac{x_{0}}{x_{1}} + \theta_{0}\right) \cdot \theta_{1}\right) \cdot x_{1}\right)$$",27.856428293186994;6.49892979394686e-3,9,9.746941064783958e-3,9.746941064783958e-3,9.746941064783958e-3,9.746941064783958e-3,0.10054096435399495,0.10054096435399495,0.10054096435399495,20.176457340746083,20.176457340746083,20.176457340746083 -684,0,Log(Log(Log((x1 ^ x1)))),"np.log(np.log(np.log(x[:, 1] ** x[:, 1])))","$$\operatorname{log}(\operatorname{log}(\operatorname{log}({x_{1}^{x_{1}}})))$$",,6,,,,Infinity,,,,,, -685,0,((((-0.13269995146852132 + ((-0.7662072802585691 * x0) * x1)) * Log((x0 * (x0 + (0.3321566771546042 * x1))))) + ((-0.9607479090579966 + x0) + ((-0.6644919623760126 ^ (0.8673704147733954 + x0)) * ((0.35417251235225844 / (x1 + 2.8254444548321822e-2)) + 0.17625622699176702)))) + (0.32674127430163924 ^ x1)),"((((t[0] + ((t[1] * x[:, 0]) * x[:, 1])) * np.log((x[:, 0] * (x[:, 0] + (t[2] * x[:, 1]))))) + ((t[3] + x[:, 0]) + (t[4] ** (t[5] + x[:, 0]) * ((t[6] / (x[:, 1] + t[7])) + t[8])))) + t[9] ** x[:, 1])","$$\left(\left(\left(\left(\theta_{0} + \left(\left(\theta_{1} \cdot x_{0}\right) \cdot x_{1}\right)\right) \cdot \operatorname{log}(\left(x_{0} \cdot \left(x_{0} + \left(\theta_{2} \cdot x_{1}\right)\right)\right))\right) + \left(\left(\theta_{3} + x_{0}\right) + \left({\theta_{4}^{\left(\theta_{5} + x_{0}\right)}} \cdot \left(\frac{\theta_{6}}{\left(x_{1} + \theta_{7}\right)} + \theta_{8}\right)\right)\right)\right) + {\theta_{9}^{x_{1}}}\right)$$",-0.13269995146852132;-0.7662072802585691;0.3321566771546042;-0.9607479090579966;-0.6644919623760126;0.8673704147733954;0.35417251235225844;2.8254444548321822e-2;0.17625622699176702;0.32674127430163924,38,,,,Infinity,,,,,, -686,0,Exp((x1 / x1)),"np.exp((x[:, 1] / x[:, 1]))","$$\operatorname{exp}(\frac{x_{1}}{x_{1}})$$",,4,7.07128229654399,7.07128229654399,7.07128229654399,7.07128229654399,-651.5461386249905,-651.5461386249905,-651.5461386249905,10.75860322156203,10.75860322156203,10.75860322156203 -687,0,(Log((-3.8979472185059205e-2 + x0)) * x0),"(np.log((t[0] + x[:, 0])) * x[:, 0])","$$\left(\operatorname{log}(\left(\theta_{0} + x_{0}\right)) \cdot x_{0}\right)$$",-3.8979472185059205e-2,6,0.14862386045147535,0.14862386045147535,0.14862386045147535,0.14862386045147535,-12.715182364102404,-12.715182364102404,-12.715182364102404,11.81131388437857,11.81131388437857,11.81131388437857 -688,0,Log(Log(((x0 * -0.820126446795307) / (Log((-0.3988814601027383 + x1)) / x1)))),"np.log(np.log(((x[:, 0] * t[0]) / (np.log((t[1] + x[:, 1])) / x[:, 1]))))","$$\operatorname{log}(\operatorname{log}(\frac{\left(x_{0} \cdot \theta_{0}\right)}{\frac{\operatorname{log}(\left(\theta_{1} + x_{1}\right))}{x_{1}}}))$$",-0.820126446795307;-0.3988814601027383,12,,,,Infinity,,,,,, -689,0,Log(Exp((-1.0980954797385796 / (-31.156981373848296 / x0)))),"np.log(np.exp((t[0] / (t[1] / x[:, 0]))))","$$\operatorname{log}(\operatorname{exp}(\frac{\theta_{0}}{\frac{\theta_{1}}{x_{0}}}))$$",-1.0980954797385796;-31.156981373848296,7,1.3066544019949083e-2,1.3066544019949083e-2,1.3066544019949083e-2,1.3066544019949083e-2,-0.20579584972282583,-0.20579584972282583,-0.20579584972282583,15.319675147391937,15.319675147391937,15.319675147391937 -690,0,((0.6686823186310551 + (Log(Exp(Log((-0.9674641479581555 + x1)))) ^ x0)) + x1),"((t[0] + np.log(np.exp(np.log((t[1] + x[:, 1])))) ** x[:, 0]) + x[:, 1])","$$\left(\left(\theta_{0} + {\operatorname{log}(\operatorname{exp}(\operatorname{log}(\left(\theta_{1} + x_{1}\right))))^{x_{0}}}\right) + x_{1}\right)$$",0.6686823186310551;-0.9674641479581555,12,,,,Infinity,,,,,, -691,0,((Exp((((-0.10530428467073605 ^ Exp(x0)) + -0.9924372771457814) + ((x1 ^ x0) + x0))) ^ Log(Log(x0))) / (((9.514482999145413e-2 / (Exp((x1 ^ -0.23598964934611577)) * 7.472404238432695e-2)) ^ Exp((x1 ^ x1))) * (((0.8564505745534687 + x0) + 0.5462608849459936) + 0.6968453025585906))),"(np.exp(((t[0] ** np.exp(x[:, 0]) + t[1]) + (x[:, 1] ** x[:, 0] + x[:, 0]))) ** np.log(np.log(x[:, 0])) / ((t[2] / (np.exp(x[:, 1] ** t[3]) * t[4])) ** np.exp(x[:, 1] ** x[:, 1]) * (((t[5] + x[:, 0]) + t[6]) + t[7])))","$$\frac{{\operatorname{exp}(\left(\left({\theta_{0}^{\operatorname{exp}(x_{0})}} + \theta_{1}\right) + \left({x_{1}^{x_{0}}} + x_{0}\right)\right))^{\operatorname{log}(\operatorname{log}(x_{0}))}}}{\left({\frac{\theta_{2}}{\left(\operatorname{exp}({x_{1}^{\theta_{3}}}) \cdot \theta_{4}\right)}^{\operatorname{exp}({x_{1}^{x_{1}}})}} \cdot \left(\left(\left(\theta_{5} + x_{0}\right) + \theta_{6}\right) + \theta_{7}\right)\right)}$$",-0.10530428467073605;-0.9924372771457814;9.514482999145413e-2;-0.23598964934611577;7.472404238432695e-2;0.8564505745534687;0.5462608849459936;0.6968453025585906,39,,,,Infinity,,,,,, -692,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 -693,0,(Log((((x1 + x1) * -0.8851690685078405) / Log((x0 + -0.4809415230393346)))) / ((0.4066797308989676 / (x0 + 0.6228995062759071)) + ((x1 ^ x0) * x0))),"(np.log((((x[:, 1] + x[:, 1]) * t[0]) / np.log((x[:, 0] + t[1])))) / ((t[2] / (x[:, 0] + t[3])) + (x[:, 1] ** x[:, 0] * x[:, 0])))","$$\frac{\operatorname{log}(\frac{\left(\left(x_{1} + x_{1}\right) \cdot \theta_{0}\right)}{\operatorname{log}(\left(x_{0} + \theta_{1}\right))})}{\left(\frac{\theta_{2}}{\left(x_{0} + \theta_{3}\right)} + \left({x_{1}^{x_{0}}} \cdot x_{0}\right)\right)}$$",-0.8851690685078405;-0.4809415230393346;0.4066797308989676;0.6228995062759071,23,,,,Infinity,,,,,, -694,0,Exp((Exp(Exp((6.739285233109987e-3 ^ x0))) * -4.222756965525027)),"np.exp((np.exp(np.exp(t[0] ** x[:, 0])) * t[1]))","$$\operatorname{exp}(\left(\operatorname{exp}(\operatorname{exp}({\theta_{0}^{x_{0}}})) \cdot \theta_{1}\right))$$",6.739285233109987e-3;-4.222756965525027,8,1.457274559678245e-2,1.457274559678245e-2,1.457274559678245e-2,1.457274559678245e-2,-0.3447898796223037,-0.3447898796223037,-0.3447898796223037,16.930468641245188,16.930468641245188,16.930468641245188 -695,0,Exp((Exp((x1 ^ 132.64911599658652)) ^ ((Log(x1) * -168.76337969008733) * 146.90504815603202))),"np.exp(np.exp(x[:, 1] ** t[0]) ** ((np.log(x[:, 1]) * t[1]) * t[2]))","$$\operatorname{exp}({\operatorname{exp}({x_{1}^{\theta_{0}}})^{\left(\left(\operatorname{log}(x_{1}) \cdot \theta_{1}\right) \cdot \theta_{2}\right)}})$$",132.64911599658652;-168.76337969008733;146.90504815603202,12,7.07128229654399,7.07128229654399,7.07128229654399,7.07128229654399,-651.5461386249905,-651.5461386249905,-651.5461386249905,33.92804250372917,33.92804250372917,33.92804250372917 -696,0,((Exp((x1 + Log(x1))) * -0.5487966073287462) + (Log((-0.4079765082978508 ^ Exp(x0))) + (-0.5551613310212591 ^ (x1 ^ 0.21840154724703653)))),"((np.exp((x[:, 1] + np.log(x[:, 1]))) * t[0]) + (np.log(t[1] ** np.exp(x[:, 0])) + t[2] ** x[:, 1] ** t[3]))","$$\left(\left(\operatorname{exp}(\left(x_{1} + \operatorname{log}(x_{1})\right)) \cdot \theta_{0}\right) + \left(\operatorname{log}({\theta_{1}^{\operatorname{exp}(x_{0})}}) + {\theta_{2}^{{x_{1}^{\theta_{3}}}}}\right)\right)$$",-0.5487966073287462;-0.4079765082978508;-0.5551613310212591;0.21840154724703653,19,,,,Infinity,,,,,, -697,0,(((Exp((-1.834231614764557 + x1)) ^ (x0 ^ x1)) * Exp(((x1 + -0.5202275603521304) + -2.079422555051663))) + ((-0.5074697488008427 + Log(Exp(x0))) ^ 0.1608306487078528)),"((np.exp((t[0] + x[:, 1])) ** x[:, 0] ** x[:, 1] * np.exp(((x[:, 1] + t[1]) + t[2]))) + (t[3] + np.log(np.exp(x[:, 0]))) ** t[4])","$$\left(\left({\operatorname{exp}(\left(\theta_{0} + x_{1}\right))^{{x_{0}^{x_{1}}}}} \cdot \operatorname{exp}(\left(\left(x_{1} + \theta_{1}\right) + \theta_{2}\right))\right) + {\left(\theta_{3} + \operatorname{log}(\operatorname{exp}(x_{0}))\right)^{\theta_{4}}}\right)$$",-1.834231614764557;-0.5202275603521304;-2.079422555051663;-0.5074697488008427;0.1608306487078528,23,0.740600890898741,0.740600890898741,0.740600890898741,0.740600890898741,-67.34350989698109,-67.34350989698109,-67.34350989698109,58.59832093728329,58.59832093728329,58.59832093728329 -698,0,((Log(((-0.8313301522011091 * (x0 * x0)) + Log((-0.8882091107262127 + x0)))) + ((x0 * -0.3442158736971721) + 0.40833284476516973)) + (0.23444036846030225 / Log((Log((x1 ^ x0)) / (0.46240870656387956 + x1))))),"((np.log(((t[0] * (x[:, 0] * x[:, 0])) + np.log((t[1] + x[:, 0])))) + ((x[:, 0] * t[2]) + t[3])) + (t[4] / np.log((np.log(x[:, 1] ** x[:, 0]) / (t[5] + x[:, 1])))))","$$\left(\left(\operatorname{log}(\left(\left(\theta_{0} \cdot \left(x_{0} \cdot x_{0}\right)\right) + \operatorname{log}(\left(\theta_{1} + x_{0}\right))\right)) + \left(\left(x_{0} \cdot \theta_{2}\right) + \theta_{3}\right)\right) + \frac{\theta_{4}}{\operatorname{log}(\frac{\operatorname{log}({x_{1}^{x_{0}}})}{\left(\theta_{5} + x_{1}\right)})}\right)$$",-0.8313301522011091;-0.8882091107262127;-0.3442158736971721;0.40833284476516973;0.23444036846030225;0.46240870656387956,29,,,,Infinity,,,,,, -699,0,(((Log((x1 * x0)) + (-0.3417401837783785 * (0.17710348715417526 / x1))) + Exp((Log((0.8479795459458406 / x1)) * 0.4616364865885638))) * ((6.729733476044505e-4 ^ (x1 + 0.9467478820127281)) + ((x0 * 1.4582705803229754e-3) / (x0 / Exp(x0))))),"(((np.log((x[:, 1] * x[:, 0])) + (t[0] * (t[1] / x[:, 1]))) + np.exp((np.log((t[2] / x[:, 1])) * t[3]))) * (t[4] ** (x[:, 1] + t[5]) + ((x[:, 0] * t[6]) / (x[:, 0] / np.exp(x[:, 0])))))","$$\left(\left(\left(\operatorname{log}(\left(x_{1} \cdot x_{0}\right)) + \left(\theta_{0} \cdot \frac{\theta_{1}}{x_{1}}\right)\right) + \operatorname{exp}(\left(\operatorname{log}(\frac{\theta_{2}}{x_{1}}) \cdot \theta_{3}\right))\right) \cdot \left({\theta_{4}^{\left(x_{1} + \theta_{5}\right)}} + \frac{\left(x_{0} \cdot \theta_{6}\right)}{\frac{x_{0}}{\operatorname{exp}(x_{0})}}\right)\right)$$",-0.3417401837783785;0.17710348715417526;0.8479795459458406;0.4616364865885638;6.729733476044505e-4;0.9467478820127281;1.4582705803229754e-3,33,1.45663030156521e-2,1.45663030156521e-2,1.45663030156521e-2,1.45663030156521e-2,-0.3441953507570876,-0.3441953507570876,-0.3441953507570876,86.66799527238281,86.66799527238281,86.66799527238281 -700,0,(-9.825633164592235e-2 + (-1.0539837065162954 + (x0 * x0))),"(t[0] + (t[1] + (x[:, 0] * x[:, 0])))","$$\left(\theta_{0} + \left(\theta_{1} + \left(x_{0} \cdot x_{0}\right)\right)\right)$$",-9.825633164592235e-2;-1.0539837065162954,7,0.5028506858422126,0.5028506858422126,0.5028506858422126,0.5028506858422126,-45.40364499542544,-45.40364499542544,-45.40364499542544,14.198476015832506,14.198476015832506,14.198476015832506 -701,0,Log(((-0.32276632866471466 * (Log(x1) + Log(x1))) / Exp(Exp(((x0 + Exp(x0)) + (x0 * 0.8860627496298357)))))),"np.log(((t[0] * (np.log(x[:, 1]) + np.log(x[:, 1]))) / np.exp(np.exp(((x[:, 0] + np.exp(x[:, 0])) + (x[:, 0] * t[1]))))))","$$\operatorname{log}(\frac{\left(\theta_{0} \cdot \left(\operatorname{log}(x_{1}) + \operatorname{log}(x_{1})\right)\right)}{\operatorname{exp}(\operatorname{exp}(\left(\left(x_{0} + \operatorname{exp}(x_{0})\right) + \left(x_{0} \cdot \theta_{1}\right)\right)))})$$",-0.32276632866471466;0.8860627496298357,19,Infinity,Infinity,Infinity,Infinity,-Infinity,-Infinity,-Infinity,Infinity,Infinity,Infinity -702,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 -703,0,Log((((x0 * ((x1 ^ x0) + ((x0 * x1) * -0.743376286317167))) + (x1 + 1.1119104414687262)) + -0.3525730542041959)),"np.log((((x[:, 0] * (x[:, 1] ** x[:, 0] + ((x[:, 0] * x[:, 1]) * t[0]))) + (x[:, 1] + t[1])) + t[2]))","$$\operatorname{log}(\left(\left(\left(x_{0} \cdot \left({x_{1}^{x_{0}}} + \left(\left(x_{0} \cdot x_{1}\right) \cdot \theta_{0}\right)\right)\right) + \left(x_{1} + \theta_{1}\right)\right) + \theta_{2}\right))$$",-0.743376286317167;1.1119104414687262;-0.3525730542041959,18,1.7472733494066306e-2,1.7472733494066306e-2,1.7472733494066306e-2,1.7472733494066306e-2,-0.6124041290713271,-0.6124041290713271,-0.6124041290713271,41.10488294924322,41.10488294924322,41.10488294924322 -704,0,Exp((((-0.24344879651475998 + (x0 * -1.1431907401768029)) / Exp((x0 * -0.8730133767623718))) * 1.0278519596019247)),"np.exp((((t[0] + (x[:, 0] * t[1])) / np.exp((x[:, 0] * t[2]))) * t[3]))","$$\operatorname{exp}(\left(\frac{\left(\theta_{0} + \left(x_{0} \cdot \theta_{1}\right)\right)}{\operatorname{exp}(\left(x_{0} \cdot \theta_{2}\right))} \cdot \theta_{3}\right))$$",-0.24344879651475998;-1.1431907401768029;-0.8730133767623718;1.0278519596019247,13,4.832286483349803e-3,4.832286483349803e-3,4.832286483349803e-3,4.832286483349803e-3,0.554070994028794,0.554070994028794,0.554070994028794,31.38092189927029,31.38092189927029,31.38092189927029 -705,0,(((0.3092167257485733 * Exp(x1)) ^ Log(Exp(Log(x0)))) * Log(((x0 ^ -0.14244877101480397) * 1.0448968410362762))),"((t[0] * np.exp(x[:, 1])) ** np.log(np.exp(np.log(x[:, 0]))) * np.log((x[:, 0] ** t[1] * t[2])))","$$\left({\left(\theta_{0} \cdot \operatorname{exp}(x_{1})\right)^{\operatorname{log}(\operatorname{exp}(\operatorname{log}(x_{0})))}} \cdot \operatorname{log}(\left({x_{0}^{\theta_{1}}} \cdot \theta_{2}\right))\right)$$",0.3092167257485733;-0.14244877101480397;1.0448968410362762,16,5.841742062460563e-3,5.841742062460563e-3,5.841742062460563e-3,5.841742062460563e-3,0.46091726141878064,0.46091726141878064,0.46091726141878064,37.20259475884415,37.20259475884415,37.20259475884415 -706,0,(Log((((-1.4428916054925571 * Exp((0.23335025168311882 / x0))) * ((-1.4769720402356024 * x0) / x0)) ^ (x0 * -0.29906986738866165))) + 0.3700642184760444),"(np.log(((t[0] * np.exp((t[1] / x[:, 0]))) * ((t[2] * x[:, 0]) / x[:, 0])) ** (x[:, 0] * t[3])) + t[4])","$$\left(\operatorname{log}({\left(\left(\theta_{0} \cdot \operatorname{exp}(\frac{\theta_{1}}{x_{0}})\right) \cdot \frac{\left(\theta_{2} \cdot x_{0}\right)}{x_{0}}\right)^{\left(x_{0} \cdot \theta_{3}\right)}}) + \theta_{4}\right)$$",-1.4428916054925571;0.23335025168311882;-1.4769720402356024;-0.29906986738866165;0.3700642184760444,19,5.893883581663169e-3,5.893883581663169e-3,5.893883581663169e-3,5.893883581663169e-3,0.4561055815012174,0.4561055815012174,0.4561055815012174,49.61931846397858,49.61931846397858,49.61931846397858 -707,0,(((((Log(x1) + (x0 * -0.5412286421308881)) * 0.6551485979935591) * (-0.20028089768829482 * (-0.7453615847584714 * (x0 ^ (x0 ^ x0))))) ^ Exp(((((0.48913040966565247 * x0) + 0.6905895284671804) + x1) * -0.6283656303317549))) * 0.7221905517293703),"((((np.log(x[:, 1]) + (x[:, 0] * t[0])) * t[1]) * (t[2] * (t[3] * x[:, 0] ** x[:, 0] ** x[:, 0]))) ** np.exp(((((t[4] * x[:, 0]) + t[5]) + x[:, 1]) * t[6])) * t[7])","$$\left({\left(\left(\left(\operatorname{log}(x_{1}) + \left(x_{0} \cdot \theta_{0}\right)\right) \cdot \theta_{1}\right) \cdot \left(\theta_{2} \cdot \left(\theta_{3} \cdot {x_{0}^{{x_{0}^{x_{0}}}}}\right)\right)\right)^{\operatorname{exp}(\left(\left(\left(\left(\theta_{4} \cdot x_{0}\right) + \theta_{5}\right) + x_{1}\right) \cdot \theta_{6}\right))}} \cdot \theta_{7}\right)$$",-0.5412286421308881;0.6551485979935591;-0.20028089768829482;-0.7453615847584714;0.48913040966565247;0.6905895284671804;-0.6283656303317549;0.7221905517293703,31,,,,Infinity,,,,,, -708,0,((x0 + -0.7733758849135515) / x0),"((x[:, 0] + t[0]) / x[:, 0])","$$\frac{\left(x_{0} + \theta_{0}\right)}{x_{0}}$$",-0.7733758849135515,5,0.15106925440074861,0.15106925440074861,0.15106925440074861,0.15106925440074861,-12.940846156339221,-12.940846156339221,-12.940846156339221,9.088359069927767,9.088359069927767,9.088359069927767 -709,0,((x1 + -5.111907897627667) * -1.2001432859365972e-2),"((x[:, 1] + t[0]) * t[1])","$$\left(\left(x_{1} + \theta_{0}\right) \cdot \theta_{1}\right)$$",-5.111907897627667;-1.2001432859365972e-2,5,1.0988313270031663e-2,1.0988313270031663e-2,1.0988313270031663e-2,1.0988313270031663e-2,-1.4014303723293153e-2,-1.4014303723293153e-2,-1.4014303723293153e-2,10.983211158277763,10.983211158277763,10.983211158277763 -710,0,(Log(((x1 * -0.49986182881004326) ^ Log(x0))) + (0.6618515197878372 + (x1 / Exp(x0)))),"(np.log((x[:, 1] * t[0]) ** np.log(x[:, 0])) + (t[1] + (x[:, 1] / np.exp(x[:, 0]))))","$$\left(\operatorname{log}({\left(x_{1} \cdot \theta_{0}\right)^{\operatorname{log}(x_{0})}}) + \left(\theta_{1} + \frac{x_{1}}{\operatorname{exp}(x_{0})}\right)\right)$$",-0.49986182881004326;0.6618515197878372,14,,,,Infinity,,,,,, -711,0,((Log((-0.9249741244509311 * (x1 + (x0 * 0.2608994695333696)))) + ((((x1 / (x0 + 0.29459943287156765)) + -0.564179243448913) ^ x0) ^ ((x0 * -0.3537342783037082) ^ 0.40734861325546445))) * (0.7103251851338381 / x1)),"((np.log((t[0] * (x[:, 1] + (x[:, 0] * t[1])))) + ((x[:, 1] / (x[:, 0] + t[2])) + t[3]) ** x[:, 0] ** (x[:, 0] * t[4]) ** t[5]) * (t[6] / x[:, 1]))","$$\left(\left(\operatorname{log}(\left(\theta_{0} \cdot \left(x_{1} + \left(x_{0} \cdot \theta_{1}\right)\right)\right)) + {{\left(\frac{x_{1}}{\left(x_{0} + \theta_{2}\right)} + \theta_{3}\right)^{x_{0}}}^{{\left(x_{0} \cdot \theta_{4}\right)^{\theta_{5}}}}}\right) \cdot \frac{\theta_{6}}{x_{1}}\right)$$",-0.9249741244509311;0.2608994695333696;0.29459943287156765;-0.564179243448913;-0.3537342783037082;0.40734861325546445;0.7103251851338381,28,,,,Infinity,,,,,, -712,0,((((x1 ^ x1) ^ 2.2288380238026706e-2) ^ Log((-0.11701976706162776 * (-0.14144642361776993 + x1)))) / (((x0 * 3.443354934057996e-2) ^ (x0 * (x0 ^ -0.9041613195951719))) ^ -6.552848205792938e-2)),"(x[:, 1] ** x[:, 1] ** t[0] ** np.log((t[1] * (t[2] + x[:, 1]))) / (x[:, 0] * t[3]) ** (x[:, 0] * x[:, 0] ** t[4]) ** t[5])","$$\frac{{{{x_{1}^{x_{1}}}^{\theta_{0}}}^{\operatorname{log}(\left(\theta_{1} \cdot \left(\theta_{2} + x_{1}\right)\right))}}}{{{\left(x_{0} \cdot \theta_{3}\right)^{\left(x_{0} \cdot {x_{0}^{\theta_{4}}}\right)}}^{\theta_{5}}}}$$",2.2288380238026706e-2;-0.11701976706162776;-0.14144642361776993;3.443354934057996e-2;-0.9041613195951719;-6.552848205792938e-2,24,,,,Infinity,,,,,, -713,0,(Exp(Log(Log((0.7184088287327586 * Log(x1))))) * ((((-6.314829899150354e-2 ^ x1) * (x0 * x0)) * ((-0.3808950587994727 * x1) + -0.7302981627166765)) + Log(Log((((0.19145495884853392 / x1) * x0) * 0.8507276015388479))))),"(np.exp(np.log(np.log((t[0] * np.log(x[:, 1]))))) * (((t[1] ** x[:, 1] * (x[:, 0] * x[:, 0])) * ((t[2] * x[:, 1]) + t[3])) + np.log(np.log((((t[4] / x[:, 1]) * x[:, 0]) * t[5])))))","$$\left(\operatorname{exp}(\operatorname{log}(\operatorname{log}(\left(\theta_{0} \cdot \operatorname{log}(x_{1})\right)))) \cdot \left(\left(\left({\theta_{1}^{x_{1}}} \cdot \left(x_{0} \cdot x_{0}\right)\right) \cdot \left(\left(\theta_{2} \cdot x_{1}\right) + \theta_{3}\right)\right) + \operatorname{log}(\operatorname{log}(\left(\left(\frac{\theta_{4}}{x_{1}} \cdot x_{0}\right) \cdot \theta_{5}\right)))\right)\right)$$",0.7184088287327586;-6.314829899150354e-2;-0.3808950587994727;-0.7302981627166765;0.19145495884853392;0.8507276015388479,31,,,,Infinity,,,,,, -714,0,((x1 * 0.18906603367893082) * Exp(Log(Log(x0)))),"((x[:, 1] * t[0]) * np.exp(np.log(np.log(x[:, 0]))))","$$\left(\left(x_{1} \cdot \theta_{0}\right) \cdot \operatorname{exp}(\operatorname{log}(\operatorname{log}(x_{0})))\right)$$",0.18906603367893082,8,,,,Infinity,,,,,, -715,0,(x1 * 0.20059950763971873),"(x[:, 1] * t[0])","$$\left(x_{1} \cdot \theta_{0}\right)$$",0.20059950763971873,3,9.365750690525969e-3,9.365750690525969e-3,9.365750690525969e-3,9.365750690525969e-3,0.1357176545739056,0.1357176545739056,0.1357176545739056,5.325190976993443,5.325190976993443,5.325190976993443 -716,0,Log((Exp(((-0.6821441284623 + (x1 * 0.3282741986059238)) / (-0.30737725920069214 + (-0.6149075820147156 + x1)))) ^ (-0.6086976470109697 + Log((Log(x1) * Log((x0 * 0.6823504366624273))))))),"np.log(np.exp(((t[0] + (x[:, 1] * t[1])) / (t[2] + (t[3] + x[:, 1])))) ** (t[4] + np.log((np.log(x[:, 1]) * np.log((x[:, 0] * t[5]))))))","$$\operatorname{log}({\operatorname{exp}(\frac{\left(\theta_{0} + \left(x_{1} \cdot \theta_{1}\right)\right)}{\left(\theta_{2} + \left(\theta_{3} + x_{1}\right)\right)})^{\left(\theta_{4} + \operatorname{log}(\left(\operatorname{log}(x_{1}) \cdot \operatorname{log}(\left(x_{0} \cdot \theta_{5}\right))\right))\right)}})$$",-0.6821441284623;0.3282741986059238;-0.30737725920069214;-0.6149075820147156;-0.6086976470109697;0.6823504366624273,24,,,,Infinity,,,,,, -717,0,(((((-0.3374964516695169 / (-0.836066824745924 / (x0 + x1))) ^ -0.1400394633879669) ^ Log((x1 ^ 0.7586810067593819))) / ((x1 * x0) * 2.596819750768853e-2)) * 0.9477084673662062),"(((t[0] / (t[1] / (x[:, 0] + x[:, 1]))) ** t[2] ** np.log(x[:, 1] ** t[3]) / ((x[:, 1] * x[:, 0]) * t[4])) * t[5])","$$\left(\frac{{{\frac{\theta_{0}}{\frac{\theta_{1}}{\left(x_{0} + x_{1}\right)}}^{\theta_{2}}}^{\operatorname{log}({x_{1}^{\theta_{3}}})}}}{\left(\left(x_{1} \cdot x_{0}\right) \cdot \theta_{4}\right)} \cdot \theta_{5}\right)$$",-0.3374964516695169;-0.836066824745924;-0.1400394633879669;0.7586810067593819;2.596819750768853e-2;0.9477084673662062,22,23535.552385940748,23535.552385940748,23535.552385940748,23535.552385940748,-2171887.094095449,-2171887.094095449,-2171887.094095449,21239.870410875836,21239.870410875836,21239.870410875836 -718,0,((((x1 * -0.4405134885950248) * -0.5413808220509542) / ((0.5471939325015198 + (x0 / x0)) ^ (-0.5497312696220659 * x0))) / (((x1 ^ Log(x1)) / (x0 * -0.9612068376885059)) / Log(Log(((0.746719888806498 + x1) / (x0 / Log(x0))))))),"((((x[:, 1] * t[0]) * t[1]) / (t[2] + (x[:, 0] / x[:, 0])) ** (t[3] * x[:, 0])) / ((x[:, 1] ** np.log(x[:, 1]) / (x[:, 0] * t[4])) / np.log(np.log(((t[5] + x[:, 1]) / (x[:, 0] / np.log(x[:, 0])))))))","$$\frac{\frac{\left(\left(x_{1} \cdot \theta_{0}\right) \cdot \theta_{1}\right)}{{\left(\theta_{2} + \frac{x_{0}}{x_{0}}\right)^{\left(\theta_{3} \cdot x_{0}\right)}}}}{\frac{\frac{{x_{1}^{\operatorname{log}(x_{1})}}}{\left(x_{0} \cdot \theta_{4}\right)}}{\operatorname{log}(\operatorname{log}(\frac{\left(\theta_{5} + x_{1}\right)}{\frac{x_{0}}{\operatorname{log}(x_{0})}}))}}$$",-0.4405134885950248;-0.5413808220509542;0.5471939325015198;-0.5497312696220659;-0.9612068376885059;0.746719888806498,35,,,,Infinity,,,,,, -719,0,((Log((x0 / ((0.4976058652649499 + x1) + x1))) ^ ((x0 * 0.15802696906142277) + (-0.15583593775870563 * (Log(Log(x1)) + (x0 ^ x0))))) / x1),"(np.log((x[:, 0] / ((t[0] + x[:, 1]) + x[:, 1]))) ** ((x[:, 0] * t[1]) + (t[2] * (np.log(np.log(x[:, 1])) + x[:, 0] ** x[:, 0]))) / x[:, 1])","$$\frac{{\operatorname{log}(\frac{x_{0}}{\left(\left(\theta_{0} + x_{1}\right) + x_{1}\right)})^{\left(\left(x_{0} \cdot \theta_{1}\right) + \left(\theta_{2} \cdot \left(\operatorname{log}(\operatorname{log}(x_{1})) + {x_{0}^{x_{0}}}\right)\right)\right)}}}{x_{1}}$$",0.4976058652649499;0.15802696906142277;-0.15583593775870563,24,,,,Infinity,,,,,, -720,0,(Exp(Exp(((x1 + 0.3819643587362169) ^ x1))) * (((0.8446908013519528 / x1) + 0.892202605361419) + 0.10937967342512211)),"(np.exp(np.exp((x[:, 1] + t[0]) ** x[:, 1])) * (((t[1] / x[:, 1]) + t[2]) + t[3]))","$$\left(\operatorname{exp}(\operatorname{exp}({\left(x_{1} + \theta_{0}\right)^{x_{1}}})) \cdot \left(\left(\frac{\theta_{1}}{x_{1}} + \theta_{2}\right) + \theta_{3}\right)\right)$$",0.3819643587362169;0.8446908013519528;0.892202605361419;0.10937967342512211,15,3005.2097483922153,3005.2097483922153,3005.2097483922153,3005.2097483922153,-277323.2440101641,-277323.2440101641,-277323.2440101641,2741.9611255302943,2741.9611255302943,2741.9611255302943 -721,0,((-0.7489482478565239 / x0) * -9.934100242149863e-2),"((t[0] / x[:, 0]) * t[1])","$$\left(\frac{\theta_{0}}{x_{0}} \cdot \theta_{1}\right)$$",-0.7489482478565239;-9.934100242149863e-2,5,7.713765280606784e-3,7.713765280606784e-3,7.713765280606784e-3,7.713765280606784e-3,0.2881647858154973,0.2881647858154973,0.2881647858154973,10.98026406508728,10.98026406508728,10.98026406508728 -722,0,Log((((x1 ^ (-0.9362772353646575 ^ x1)) + (x0 / (x1 ^ x1))) * (Log((x0 + 0.7006111695242481)) / ((x1 * -0.5639123960386054) / x1)))),"np.log(((x[:, 1] ** t[0] ** x[:, 1] + (x[:, 0] / x[:, 1] ** x[:, 1])) * (np.log((x[:, 0] + t[1])) / ((x[:, 1] * t[2]) / x[:, 1]))))","$$\operatorname{log}(\left(\left({x_{1}^{{\theta_{0}^{x_{1}}}}} + \frac{x_{0}}{{x_{1}^{x_{1}}}}\right) \cdot \frac{\operatorname{log}(\left(x_{0} + \theta_{1}\right))}{\frac{\left(x_{1} \cdot \theta_{2}\right)}{x_{1}}}\right))$$",-0.9362772353646575;0.7006111695242481;-0.5639123960386054,23,,,,Infinity,,,,,, -723,0,(((-0.25374503099723245 * x0) + x1) + ((x1 ^ x1) + -0.718274741403826)),"(((t[0] * x[:, 0]) + x[:, 1]) + (x[:, 1] ** x[:, 1] + t[1]))","$$\left(\left(\left(\theta_{0} \cdot x_{0}\right) + x_{1}\right) + \left({x_{1}^{x_{1}}} + \theta_{1}\right)\right)$$",-0.25374503099723245;-0.718274741403826,11,1.0023614153183133e-2,1.0023614153183133e-2,1.0023614153183133e-2,1.0023614153183133e-2,7.500925059614227e-2,7.500925059614227e-2,7.500925059614227e-2,23.76022528498175,23.76022528498175,23.76022528498175 -724,0,Exp(((-0.44058705558262296 + Log(x1)) + -1.1657889873099139)),"np.exp(((t[0] + np.log(x[:, 1])) + t[1]))","$$\operatorname{exp}(\left(\left(\theta_{0} + \operatorname{log}(x_{1})\right) + \theta_{1}\right))$$",-0.44058705558262296;-1.1657889873099139,7,9.365750715189244e-3,9.365750715189244e-3,9.365750715189244e-3,9.365750715189244e-3,0.13571765229794996,0.13571765229794996,0.13571765229794996,15.316344433417653,15.316344433417653,15.316344433417653 -725,0,((0.11050434725593761 / Log(x1)) + Exp(Log(Log(Exp((-0.9475447003375046 + x1)))))),"((t[0] / np.log(x[:, 1])) + np.exp(np.log(np.log(np.exp((t[1] + x[:, 1]))))))","$$\left(\frac{\theta_{0}}{\operatorname{log}(x_{1})} + \operatorname{exp}(\operatorname{log}(\operatorname{log}(\operatorname{exp}(\left(\theta_{1} + x_{1}\right)))))\right)$$",0.11050434725593761;-0.9475447003375046,12,,,,Infinity,,,,,, -726,0,Log((((Exp(Exp(x1)) + Log((x0 * -0.5555004271699213))) + ((x0 / x1) / (0.1432771635584007 * (0.914464989634624 / x1)))) * (((x1 * x0) * 0.7878287005505) * ((x0 ^ x0) + (x1 + (x0 ^ x0)))))),"np.log((((np.exp(np.exp(x[:, 1])) + np.log((x[:, 0] * t[0]))) + ((x[:, 0] / x[:, 1]) / (t[1] * (t[2] / x[:, 1])))) * (((x[:, 1] * x[:, 0]) * t[3]) * (x[:, 0] ** x[:, 0] + (x[:, 1] + x[:, 0] ** x[:, 0])))))","$$\operatorname{log}(\left(\left(\left(\operatorname{exp}(\operatorname{exp}(x_{1})) + \operatorname{log}(\left(x_{0} \cdot \theta_{0}\right))\right) + \frac{\frac{x_{0}}{x_{1}}}{\left(\theta_{1} \cdot \frac{\theta_{2}}{x_{1}}\right)}\right) \cdot \left(\left(\left(x_{1} \cdot x_{0}\right) \cdot \theta_{3}\right) \cdot \left({x_{0}^{x_{0}}} + \left(x_{1} + {x_{0}^{x_{0}}}\right)\right)\right)\right))$$",-0.5555004271699213;0.1432771635584007;0.914464989634624;0.7878287005505,35,,,,Infinity,,,,,, -727,0,(Exp(((0.26392981578193986 * (-0.7496215193553284 + ((x1 * x1) ^ (-0.6945600834220225 + x0)))) ^ ((((x0 ^ 0.9790135409625704) + x1) * x0) + -0.23213070781852818))) / (Log((0.5648789541173332 * x1)) * -0.6089395023505682)),"(np.exp((t[0] * (t[1] + (x[:, 1] * x[:, 1]) ** (t[2] + x[:, 0]))) ** (((x[:, 0] ** t[3] + x[:, 1]) * x[:, 0]) + t[4])) / (np.log((t[5] * x[:, 1])) * t[6]))","$$\frac{\operatorname{exp}({\left(\theta_{0} \cdot \left(\theta_{1} + {\left(x_{1} \cdot x_{1}\right)^{\left(\theta_{2} + x_{0}\right)}}\right)\right)^{\left(\left(\left({x_{0}^{\theta_{3}}} + x_{1}\right) \cdot x_{0}\right) + \theta_{4}\right)}})}{\left(\operatorname{log}(\left(\theta_{5} \cdot x_{1}\right)) \cdot \theta_{6}\right)}$$",0.26392981578193986;-0.7496215193553284;-0.6945600834220225;0.9790135409625704;-0.23213070781852818;0.5648789541173332;-0.6089395023505682,29,,,,Infinity,,,,,, -728,0,(Exp(Log((Exp(Log(x0)) ^ ((-0.3436819888779097 + Exp(x0)) * Exp(x0))))) ^ ((((x0 ^ x0) ^ -0.5106166295657588) ^ (0.3926152978598283 ^ (0.7798005143606109 + Log(x0)))) / (Exp((Exp((-0.6326812309609371 * x0)) + (x1 * 0.30193297251370077))) + (-0.4002554146946985 ^ (x1 * -0.3044193695791133))))),"np.exp(np.log(np.exp(np.log(x[:, 0])) ** ((t[0] + np.exp(x[:, 0])) * np.exp(x[:, 0])))) ** (x[:, 0] ** x[:, 0] ** t[1] ** t[2] ** (t[3] + np.log(x[:, 0])) / (np.exp((np.exp((t[4] * x[:, 0])) + (x[:, 1] * t[5]))) + t[6] ** (x[:, 1] * t[7])))","$${\operatorname{exp}(\operatorname{log}({\operatorname{exp}(\operatorname{log}(x_{0}))^{\left(\left(\theta_{0} + \operatorname{exp}(x_{0})\right) \cdot \operatorname{exp}(x_{0})\right)}}))^{\frac{{{{x_{0}^{x_{0}}}^{\theta_{1}}}^{{\theta_{2}^{\left(\theta_{3} + \operatorname{log}(x_{0})\right)}}}}}{\left(\operatorname{exp}(\left(\operatorname{exp}(\left(\theta_{4} \cdot x_{0}\right)) + \left(x_{1} \cdot \theta_{5}\right)\right)) + {\theta_{6}^{\left(x_{1} \cdot \theta_{7}\right)}}\right)}}}$$",-0.3436819888779097;-0.5106166295657588;0.3926152978598283;0.7798005143606109;-0.6326812309609371;0.30193297251370077;-0.4002554146946985;-0.3044193695791133,42,,,,Infinity,,,,,, -729,0,(Exp(((-4.05793436361461e-3 * Exp(x1)) + -0.1755358249768124)) / (Log(((x0 * 1.1501009376899892e-5) * x1)) * -0.9910117071689697)),"(np.exp(((t[0] * np.exp(x[:, 1])) + t[1])) / (np.log(((x[:, 0] * t[2]) * x[:, 1])) * t[3]))","$$\frac{\operatorname{exp}(\left(\left(\theta_{0} \cdot \operatorname{exp}(x_{1})\right) + \theta_{1}\right))}{\left(\operatorname{log}(\left(\left(x_{0} \cdot \theta_{2}\right) \cdot x_{1}\right)) \cdot \theta_{3}\right)}$$",-4.05793436361461e-3;-0.1755358249768124;1.1501009376899892e-5;-0.9910117071689697,16,1.0945762624807821e-2,1.0945762624807821e-2,1.0945762624807821e-2,1.0945762624807821e-2,-1.0087680789516495e-2,-1.0087680789516495e-2,-1.0087680789516495e-2,41.364615594710266,41.364615594710266,41.364615594710266 -730,0,Log((-0.42988212916783763 ^ Log((x1 ^ -0.7924198194592397)))),"np.log(t[0] ** np.log(x[:, 1] ** t[1]))","$$\operatorname{log}({\theta_{0}^{\operatorname{log}({x_{1}^{\theta_{1}}})}})$$",-0.42988212916783763;-0.7924198194592397,7,,,,Infinity,,,,,, -731,0,((Log((x1 * 0.8544494541957786)) ^ Log(x0)) * -0.5879113012949468),"(np.log((x[:, 1] * t[0])) ** np.log(x[:, 0]) * t[1])","$$\left({\operatorname{log}(\left(x_{1} \cdot \theta_{0}\right))^{\operatorname{log}(x_{0})}} \cdot \theta_{1}\right)$$",0.8544494541957786;-0.5879113012949468,9,,,,Infinity,,,,,, -732,0,(Exp((Exp(((x1 + x0) * -0.6647724240385582)) / Log(((0.27512691140972945 + x1) ^ Log(x1))))) * (-0.4601321122833293 / (Exp((x0 + (x1 + x0))) ^ -0.17075139919668936))),"(np.exp((np.exp(((x[:, 1] + x[:, 0]) * t[0])) / np.log((t[1] + x[:, 1]) ** np.log(x[:, 1])))) * (t[2] / np.exp((x[:, 0] + (x[:, 1] + x[:, 0]))) ** t[3]))","$$\left(\operatorname{exp}(\frac{\operatorname{exp}(\left(\left(x_{1} + x_{0}\right) \cdot \theta_{0}\right))}{\operatorname{log}({\left(\theta_{1} + x_{1}\right)^{\operatorname{log}(x_{1})}})}) \cdot \frac{\theta_{2}}{{\operatorname{exp}(\left(x_{0} + \left(x_{1} + x_{0}\right)\right))^{\theta_{3}}}}\right)$$",-0.6647724240385582;0.27512691140972945;-0.4601321122833293;-0.17075139919668936,26,111.97842288807819,111.97842288807819,111.97842288807819,111.97842288807819,-10332.49884795921,-10332.49884795921,-10332.49884795921,165.99211935148264,165.99211935148264,165.99211935148264 -733,0,Exp(((0.5792938456468861 / Log((x1 * x1))) ^ Exp(((-0.43774718407965385 + ((x0 * -0.25540150202815737) * 0.47105107987786565)) * -0.7398787690633012)))),"np.exp((t[0] / np.log((x[:, 1] * x[:, 1]))) ** np.exp(((t[1] + ((x[:, 0] * t[2]) * t[3])) * t[4])))","$$\operatorname{exp}({\frac{\theta_{0}}{\operatorname{log}(\left(x_{1} \cdot x_{1}\right))}^{\operatorname{exp}(\left(\left(\theta_{1} + \left(\left(x_{0} \cdot \theta_{2}\right) \cdot \theta_{3}\right)\right) \cdot \theta_{4}\right))}})$$",0.5792938456468861;-0.43774718407965385;-0.25540150202815737;0.47105107987786565;-0.7398787690633012,18,,,,Infinity,,,,,, -734,0,((Log(((x0 + -0.5442090783037914) ^ 0.5922527190603097)) ^ ((0.23068974557237676 * Exp(x0)) * ((x1 / x1) * 0.41315672451500673))) / Exp(((-0.40228289933862904 / Exp((x0 + (x1 / x1)))) + (Log((x1 / x0)) * 0.8313169616676465)))),"(np.log((x[:, 0] + t[0]) ** t[1]) ** ((t[2] * np.exp(x[:, 0])) * ((x[:, 1] / x[:, 1]) * t[3])) / np.exp(((t[4] / np.exp((x[:, 0] + (x[:, 1] / x[:, 1])))) + (np.log((x[:, 1] / x[:, 0])) * t[5]))))","$$\frac{{\operatorname{log}({\left(x_{0} + \theta_{0}\right)^{\theta_{1}}})^{\left(\left(\theta_{2} \cdot \operatorname{exp}(x_{0})\right) \cdot \left(\frac{x_{1}}{x_{1}} \cdot \theta_{3}\right)\right)}}}{\operatorname{exp}(\left(\frac{\theta_{4}}{\operatorname{exp}(\left(x_{0} + \frac{x_{1}}{x_{1}}\right))} + \left(\operatorname{log}(\frac{x_{1}}{x_{0}}) \cdot \theta_{5}\right)\right))}$$",-0.5442090783037914;0.5922527190603097;0.23068974557237676;0.41315672451500673;-0.40228289933862904;0.8313169616676465,34,,,,Infinity,,,,,, -735,0,((((-0.23922326279682082 / (x0 / (x0 + (x1 + x1)))) / (Log(x0) + x0)) ^ (-0.29651918449474257 + ((x0 / x0) / x1))) + -0.20709334766904786),"(((t[0] / (x[:, 0] / (x[:, 0] + (x[:, 1] + x[:, 1])))) / (np.log(x[:, 0]) + x[:, 0])) ** (t[1] + ((x[:, 0] / x[:, 0]) / x[:, 1])) + t[2])","$$\left({\frac{\frac{\theta_{0}}{\frac{x_{0}}{\left(x_{0} + \left(x_{1} + x_{1}\right)\right)}}}{\left(\operatorname{log}(x_{0}) + x_{0}\right)}^{\left(\theta_{1} + \frac{\frac{x_{0}}{x_{0}}}{x_{1}}\right)}} + \theta_{2}\right)$$",-0.23922326279682082;-0.29651918449474257;-0.20709334766904786,24,,,,Infinity,,,,,, -736,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 -737,0,Exp((Exp(Log(((0.36297859339088423 * Log(x0)) / Exp(x1)))) * -0.6476575655435977)),"np.exp((np.exp(np.log(((t[0] * np.log(x[:, 0])) / np.exp(x[:, 1])))) * t[1]))","$$\operatorname{exp}(\left(\operatorname{exp}(\operatorname{log}(\frac{\left(\theta_{0} \cdot \operatorname{log}(x_{0})\right)}{\operatorname{exp}(x_{1})})) \cdot \theta_{1}\right))$$",0.36297859339088423;-0.6476575655435977,12,,,,Infinity,,,,,, -738,0,Log(((-0.11750128218633338 / Exp(x1)) * ((0.30599972701535927 * (((x0 * x1) ^ (x1 + x0)) * 0.10168290723253781)) + (-0.3767996193780738 ^ x1)))),"np.log(((t[0] / np.exp(x[:, 1])) * ((t[1] * ((x[:, 0] * x[:, 1]) ** (x[:, 1] + x[:, 0]) * t[2])) + t[3] ** x[:, 1])))","$$\operatorname{log}(\left(\frac{\theta_{0}}{\operatorname{exp}(x_{1})} \cdot \left(\left(\theta_{1} \cdot \left({\left(x_{0} \cdot x_{1}\right)^{\left(x_{1} + x_{0}\right)}} \cdot \theta_{2}\right)\right) + {\theta_{3}^{x_{1}}}\right)\right))$$",-0.11750128218633338;0.30599972701535927;0.10168290723253781;-0.3767996193780738,21,,,,Infinity,,,,,, -739,0,(Log((Exp((x0 * x1)) + (Log(x0) * Log(x1)))) ^ (((x1 + x0) * x1) * Exp((-2.847028930670603e-2 * x1)))),"np.log((np.exp((x[:, 0] * x[:, 1])) + (np.log(x[:, 0]) * np.log(x[:, 1])))) ** (((x[:, 1] + x[:, 0]) * x[:, 1]) * np.exp((t[0] * x[:, 1])))","$${\operatorname{log}(\left(\operatorname{exp}(\left(x_{0} \cdot x_{1}\right)) + \left(\operatorname{log}(x_{0}) \cdot \operatorname{log}(x_{1})\right)\right))^{\left(\left(\left(x_{1} + x_{0}\right) \cdot x_{1}\right) \cdot \operatorname{exp}(\left(\theta_{0} \cdot x_{1}\right))\right)}}$$",-2.847028930670603e-2,22,,,,Infinity,,,,,, -740,0,((((Log(Log(x1)) ^ x1) * 0.9362347316223898) / Log(Exp(x0))) ^ (((Log((x1 * (x0 ^ x0))) ^ (((-5.722817496882704e-2 + x1) + (0.6751443019569183 + x1)) * 0.6904422084791926)) ^ (0.9051541302831743 * x0)) / x0)),"((np.log(np.log(x[:, 1])) ** x[:, 1] * t[0]) / np.log(np.exp(x[:, 0]))) ** (np.log((x[:, 1] * x[:, 0] ** x[:, 0])) ** (((t[1] + x[:, 1]) + (t[2] + x[:, 1])) * t[3]) ** (t[4] * x[:, 0]) / x[:, 0])","$${\frac{\left({\operatorname{log}(\operatorname{log}(x_{1}))^{x_{1}}} \cdot \theta_{0}\right)}{\operatorname{log}(\operatorname{exp}(x_{0}))}^{\frac{{{\operatorname{log}(\left(x_{1} \cdot {x_{0}^{x_{0}}}\right))^{\left(\left(\left(\theta_{1} + x_{1}\right) + \left(\theta_{2} + x_{1}\right)\right) \cdot \theta_{3}\right)}}^{\left(\theta_{4} \cdot x_{0}\right)}}}{x_{0}}}}$$",0.9362347316223898;-5.722817496882704e-2;0.6751443019569183;0.6904422084791926;0.9051541302831743,34,,,,Infinity,,,,,, -741,0,(Log(((Exp((0.14969767322837058 / x0)) ^ ((x0 + 0.3547564924142512) * x0)) ^ Exp(((x0 * 0.3606904998933834) ^ Exp((x0 ^ 0.7578096835697835)))))) + Exp(Log(Log((((0.16435949393267613 ^ x0) * -0.4719447109227608) ^ (1.1159516294553251e-2 ^ x0)))))),"(np.log(np.exp((t[0] / x[:, 0])) ** ((x[:, 0] + t[1]) * x[:, 0]) ** np.exp((x[:, 0] * t[2]) ** np.exp(x[:, 0] ** t[3]))) + np.exp(np.log(np.log((t[4] ** x[:, 0] * t[5]) ** t[6] ** x[:, 0]))))","$$\left(\operatorname{log}({{\operatorname{exp}(\frac{\theta_{0}}{x_{0}})^{\left(\left(x_{0} + \theta_{1}\right) \cdot x_{0}\right)}}^{\operatorname{exp}({\left(x_{0} \cdot \theta_{2}\right)^{\operatorname{exp}({x_{0}^{\theta_{3}}})}})}}) + \operatorname{exp}(\operatorname{log}(\operatorname{log}({\left({\theta_{4}^{x_{0}}} \cdot \theta_{5}\right)^{{\theta_{6}^{x_{0}}}}})))\right)$$",0.14969767322837058;0.3547564924142512;0.3606904998933834;0.7578096835697835;0.16435949393267613;-0.4719447109227608;1.1159516294553251e-2,34,,,,Infinity,,,,,, -742,0,(Exp((Log(Exp((-0.9101708015462787 + x0))) ^ Log((x0 / Log(x0))))) ^ (Log(Exp(Exp(Exp(x1)))) + (Exp((x0 + -7.246052453787177e-3)) * -0.9061279698631106))),"np.exp(np.log(np.exp((t[0] + x[:, 0]))) ** np.log((x[:, 0] / np.log(x[:, 0])))) ** (np.log(np.exp(np.exp(np.exp(x[:, 1])))) + (np.exp((x[:, 0] + t[1])) * t[2]))","$${\operatorname{exp}({\operatorname{log}(\operatorname{exp}(\left(\theta_{0} + x_{0}\right)))^{\operatorname{log}(\frac{x_{0}}{\operatorname{log}(x_{0})})}})^{\left(\operatorname{log}(\operatorname{exp}(\operatorname{exp}(\operatorname{exp}(x_{1})))) + \left(\operatorname{exp}(\left(x_{0} + \theta_{1}\right)) \cdot \theta_{2}\right)\right)}}$$",-0.9101708015462787;-7.246052453787177e-3;-0.9061279698631106,25,,,,Infinity,,,,,, -743,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 -744,0,Exp(Exp((-15.84989606960107 + ((x1 + x1) / (5.60213308399048 / x1))))),"np.exp(np.exp((t[0] + ((x[:, 1] + x[:, 1]) / (t[1] / x[:, 1])))))","$$\operatorname{exp}(\operatorname{exp}(\left(\theta_{0} + \frac{\left(x_{1} + x_{1}\right)}{\frac{\theta_{1}}{x_{1}}}\right)))$$",-15.84989606960107;5.60213308399048,11,0.8923100516591614,0.8923100516591614,0.8923100516591614,0.8923100516591614,-81.34340735499009,-81.34340735499009,-81.34340735499009,22.548745954003632,22.548745954003632,22.548745954003632 -745,0,(((((x0 * x0) + (x0 ^ Log((x0 ^ x1)))) * (x0 ^ x1)) ^ x1) ^ x1),"(((x[:, 0] * x[:, 0]) + x[:, 0] ** np.log(x[:, 0] ** x[:, 1])) * x[:, 0] ** x[:, 1]) ** x[:, 1] ** x[:, 1]","$${{\left(\left(\left(x_{0} \cdot x_{0}\right) + {x_{0}^{\operatorname{log}({x_{0}^{x_{1}}})}}\right) \cdot {x_{0}^{x_{1}}}\right)^{x_{1}}}^{x_{1}}}$$",,18,1.1453391471819776,1.1453391471819776,1.1453391471819776,1.1453391471819776,-104.6932260044145,-104.6932260044145,-104.6932260044145,33.282475678568765,33.282475678568765,33.282475678568765 -746,0,(((2.224413895588964e-3 * ((x0 + 0.5585423577730453) ^ x0)) * (Exp(x1) / x1)) * 1.2887581168361153),"(((t[0] * (x[:, 0] + t[1]) ** x[:, 0]) * (np.exp(x[:, 1]) / x[:, 1])) * t[2])","$$\left(\left(\left(\theta_{0} \cdot {\left(x_{0} + \theta_{1}\right)^{x_{0}}}\right) \cdot \frac{\operatorname{exp}(x_{1})}{x_{1}}\right) \cdot \theta_{2}\right)$$",2.224413895588964e-3;0.5585423577730453;1.2887581168361153,14,1.3779721127024063e-2,1.3779721127024063e-2,1.3779721127024063e-2,1.3779721127024063e-2,-0.27160866101519465,-0.27160866101519465,-0.27160866101519465,35.18735813863494,35.18735813863494,35.18735813863494 -747,0,((Log((0.6524040808817062 ^ x0)) + (Exp(Exp(Exp(x0))) * -0.2715461785658595)) * -0.6352359127476876),"((np.log(t[0] ** x[:, 0]) + (np.exp(np.exp(np.exp(x[:, 0]))) * t[1])) * t[2])","$$\left(\left(\operatorname{log}({\theta_{0}^{x_{0}}}) + \left(\operatorname{exp}(\operatorname{exp}(\operatorname{exp}(x_{0}))) \cdot \theta_{1}\right)\right) \cdot \theta_{2}\right)$$",0.6524040808817062;-0.2715461785658595;-0.6352359127476876,13,7.542295077832059e70,7.542295077832059e70,7.542295077832059e70,7.542295077832059e70,-6.960117448309207e72,-6.960117448309207e72,-6.960117448309207e72,6.788065570048854e70,6.788065570048854e70,6.788065570048854e70 -748,0,Exp((((0.21340467053000012 / ((x1 + (x0 / x0)) + (-0.200069378846631 + x1))) + 2.9979745487327514e-2) * (Log(x1) ^ x1))),"np.exp((((t[0] / ((x[:, 1] + (x[:, 0] / x[:, 0])) + (t[1] + x[:, 1]))) + t[2]) * np.log(x[:, 1]) ** x[:, 1]))","$$\operatorname{exp}(\left(\left(\frac{\theta_{0}}{\left(\left(x_{1} + \frac{x_{0}}{x_{0}}\right) + \left(\theta_{1} + x_{1}\right)\right)} + \theta_{2}\right) \cdot {\operatorname{log}(x_{1})^{x_{1}}}\right))$$",0.21340467053000012;-0.200069378846631;2.9979745487327514e-2,19,,,,Infinity,,,,,, -749,0,((Exp((x0 * -0.32618862765002643)) + -0.20471626307057667) ^ Log(Log((Log((x1 * -0.7786504713124589)) ^ (3.626020005691821e-2 + x1))))),"(np.exp((x[:, 0] * t[0])) + t[1]) ** np.log(np.log(np.log((x[:, 1] * t[2])) ** (t[3] + x[:, 1])))","$${\left(\operatorname{exp}(\left(x_{0} \cdot \theta_{0}\right)) + \theta_{1}\right)^{\operatorname{log}(\operatorname{log}({\operatorname{log}(\left(x_{1} \cdot \theta_{2}\right))^{\left(\theta_{3} + x_{1}\right)}}))}}$$",-0.32618862765002643;-0.20471626307057667;-0.7786504713124589;3.626020005691821e-2,17,,,,Infinity,,,,,, -750,0,(Log((x0 * -0.2052209904324359)) * (x1 ^ x0)),"(np.log((x[:, 0] * t[0])) * x[:, 1] ** x[:, 0])","$$\left(\operatorname{log}(\left(x_{0} \cdot \theta_{0}\right)) \cdot {x_{1}^{x_{0}}}\right)$$",-0.2052209904324359,8,,,,Infinity,,,,,, -751,0,Log(Exp((-3.8821388843030685e-2 + (0.3004069943531097 * x1)))),"np.log(np.exp((t[0] + (t[1] * x[:, 1]))))","$$\operatorname{log}(\operatorname{exp}(\left(\theta_{0} + \left(\theta_{1} \cdot x_{1}\right)\right)))$$",-3.8821388843030685e-2;0.3004069943531097,7,9.147852990030469e-3,9.147852990030469e-3,9.147852990030469e-3,9.147852990030469e-3,0.1558255073099074,0.1558255073099074,0.1558255073099074,16.592399223022696,16.592399223022696,16.592399223022696 -752,0,(Exp(((0.18243578767954385 ^ x0) / Exp(x0))) + -1.0262981003988494),"(np.exp((t[0] ** x[:, 0] / np.exp(x[:, 0]))) + t[1])","$$\left(\operatorname{exp}(\frac{{\theta_{0}^{x_{0}}}}{\operatorname{exp}(x_{0})}) + \theta_{1}\right)$$",0.18243578767954385;-1.0262981003988494,9,4.937097859181299e-3,4.937097859181299e-3,4.937097859181299e-3,4.937097859181299e-3,0.5443988786026792,0.5443988786026792,0.5443988786026792,20.17212848186104,20.17212848186104,20.17212848186104 -753,0,(Log((Log((Exp((x0 * -0.7734054253344863)) * Exp((x0 + 0.756751123626814)))) ^ -1.5301738577096824e-2)) * (Log(x0) ^ x0)),"(np.log(np.log((np.exp((x[:, 0] * t[0])) * np.exp((x[:, 0] + t[1])))) ** t[2]) * np.log(x[:, 0]) ** x[:, 0])","$$\left(\operatorname{log}({\operatorname{log}(\left(\operatorname{exp}(\left(x_{0} \cdot \theta_{0}\right)) \cdot \operatorname{exp}(\left(x_{0} + \theta_{1}\right))\right))^{\theta_{2}}}) \cdot {\operatorname{log}(x_{0})^{x_{0}}}\right)$$",-0.7734054253344863;0.756751123626814;-1.5301738577096824e-2,18,,,,Infinity,,,,,, -754,0,(((x0 * -0.39538837122995496) + -0.4629051697637154) + Log((1.0962503788621676 * (x1 * 7.788945473032442)))),"(((x[:, 0] * t[0]) + t[1]) + np.log((t[2] * (x[:, 1] * t[3]))))","$$\left(\left(\left(x_{0} \cdot \theta_{0}\right) + \theta_{1}\right) + \operatorname{log}(\left(\theta_{2} \cdot \left(x_{1} \cdot \theta_{3}\right)\right))\right)$$",-0.39538837122995496;-0.4629051697637154;1.0962503788621676;7.788945473032442,12,0.18668074341018365,0.18668074341018365,0.18668074341018365,0.18668074341018365,-16.227115699721825,-16.227115699721825,-16.227115699721825,29.75282604127639,29.75282604127639,29.75282604127639 -755,0,((((((-0.8393637361635838 / Exp(x1)) ^ -0.2572108789093275) + -0.2039816844263571) ^ -0.2126131651257952) + -0.21582035382724607) ^ x0),"(((t[0] / np.exp(x[:, 1])) ** t[1] + t[2]) ** t[3] + t[4]) ** x[:, 0]","$${\left({\left({\frac{\theta_{0}}{\operatorname{exp}(x_{1})}^{\theta_{1}}} + \theta_{2}\right)^{\theta_{3}}} + \theta_{4}\right)^{x_{0}}}$$",-0.8393637361635838;-0.2572108789093275;-0.2039816844263571;-0.2126131651257952;-0.21582035382724607,14,,,,Infinity,,,,,, -756,0,(Log(((x0 + -0.5001017610241476) * x1)) * -3.51716039062975e-2),"(np.log(((x[:, 0] + t[0]) * x[:, 1])) * t[1])","$$\left(\operatorname{log}(\left(\left(x_{0} + \theta_{0}\right) \cdot x_{1}\right)) \cdot \theta_{1}\right)$$",-0.5001017610241476;-3.51716039062975e-2,8,9.094395443372604e-3,9.094395443372604e-3,9.094395443372604e-3,9.094395443372604e-3,0.16075863176867822,0.16075863176867822,0.16075863176867822,18.384110580458756,18.384110580458756,18.384110580458756 -757,0,Exp((Exp(Log((0.9588158437209827 * ((-3.91159177599385e-2 ^ x0) + 0.7381521545529597)))) + ((-0.535035373016383 + (x1 + Log(x1))) / Log((-0.21112479491509917 / (-0.793366204664579 ^ x0)))))),"np.exp((np.exp(np.log((t[0] * (t[1] ** x[:, 0] + t[2])))) + ((t[3] + (x[:, 1] + np.log(x[:, 1]))) / np.log((t[4] / t[5] ** x[:, 0])))))","$$\operatorname{exp}(\left(\operatorname{exp}(\operatorname{log}(\left(\theta_{0} \cdot \left({\theta_{1}^{x_{0}}} + \theta_{2}\right)\right))) + \frac{\left(\theta_{3} + \left(x_{1} + \operatorname{log}(x_{1})\right)\right)}{\operatorname{log}(\frac{\theta_{4}}{{\theta_{5}^{x_{0}}}})}\right))$$",0.9588158437209827;-3.91159177599385e-2;0.7381521545529597;-0.535035373016383;-0.21112479491509917;-0.793366204664579,24,,,,Infinity,,,,,, -758,0,(((Exp((x0 ^ -2.1640513392355287e-2)) / (x1 * (x1 * 0.21113441248352793))) / (0.2560276470211317 / Log((x0 + 0.7905409970625145)))) * (((x0 * x1) / Log((x1 / x0))) ^ ((x0 + (0.7480490568801661 * x0)) + (0.5496019684804179 * x1)))),"(((np.exp(x[:, 0] ** t[0]) / (x[:, 1] * (x[:, 1] * t[1]))) / (t[2] / np.log((x[:, 0] + t[3])))) * ((x[:, 0] * x[:, 1]) / np.log((x[:, 1] / x[:, 0]))) ** ((x[:, 0] + (t[4] * x[:, 0])) + (t[5] * x[:, 1])))","$$\left(\frac{\frac{\operatorname{exp}({x_{0}^{\theta_{0}}})}{\left(x_{1} \cdot \left(x_{1} \cdot \theta_{1}\right)\right)}}{\frac{\theta_{2}}{\operatorname{log}(\left(x_{0} + \theta_{3}\right))}} \cdot {\frac{\left(x_{0} \cdot x_{1}\right)}{\operatorname{log}(\frac{x_{1}}{x_{0}})}^{\left(\left(x_{0} + \left(\theta_{4} \cdot x_{0}\right)\right) + \left(\theta_{5} \cdot x_{1}\right)\right)}}\right)$$",-2.1640513392355287e-2;0.21113441248352793;0.2560276470211317;0.7905409970625145;0.7480490568801661;0.5496019684804179,36,,,,Infinity,,,,,, -759,0,Exp(Exp(Exp(Exp(((-0.35693752648296584 + Log(x0)) * (Log(x0) ^ Exp(x1))))))),"np.exp(np.exp(np.exp(np.exp(((t[0] + np.log(x[:, 0])) * np.log(x[:, 0]) ** np.exp(x[:, 1]))))))","$$\operatorname{exp}(\operatorname{exp}(\operatorname{exp}(\operatorname{exp}(\left(\left(\theta_{0} + \operatorname{log}(x_{0})\right) \cdot {\operatorname{log}(x_{0})^{\operatorname{exp}(x_{1})}}\right)))))$$",-0.35693752648296584,14,,,,Infinity,,,,,, -760,0,Exp((((Exp(x1) + -0.4777669667611227) ^ (x0 + Log(x0))) + (-1.1179247255298073 * (((x0 * -0.11366732364875251) + Exp(x0)) + x0)))),"np.exp(((np.exp(x[:, 1]) + t[0]) ** (x[:, 0] + np.log(x[:, 0])) + (t[1] * (((x[:, 0] * t[2]) + np.exp(x[:, 0])) + x[:, 0]))))","$$\operatorname{exp}(\left({\left(\operatorname{exp}(x_{1}) + \theta_{0}\right)^{\left(x_{0} + \operatorname{log}(x_{0})\right)}} + \left(\theta_{1} \cdot \left(\left(\left(x_{0} \cdot \theta_{2}\right) + \operatorname{exp}(x_{0})\right) + x_{0}\right)\right)\right))$$",-0.4777669667611227;-1.1179247255298073;-0.11366732364875251,21,4.549345056935785e-3,4.549345056935785e-3,4.549345056935785e-3,4.549345056935785e-3,0.5801811572949036,0.5801811572949036,0.5801811572949036,49.73514159193071,49.73514159193071,49.73514159193071 -761,0,Exp(Exp(Log((((27.201484949636328 + x1) ^ -4.6653541869558985) / x0)))),"np.exp(np.exp(np.log(((t[0] + x[:, 1]) ** t[1] / x[:, 0]))))","$$\operatorname{exp}(\operatorname{exp}(\operatorname{log}(\frac{{\left(\theta_{0} + x_{1}\right)^{\theta_{1}}}}{x_{0}})))$$",27.201484949636328;-4.6653541869558985,10,0.8923101629377775,0.8923101629377775,0.8923101629377775,0.8923101629377775,-81.34341762390996,-81.34341762390996,-81.34341762390996,25.63934443417764,25.63934443417764,25.63934443417764 -762,0,(Log((((x1 ^ x0) * (x1 + -9.97931464853441e-2)) ^ Exp(Log((x0 * 0.4755819951808542))))) ^ Exp((((0.5334588101215577 * x1) + (0.3363292373300586 + x1)) * ((0.30590519166797503 / x0) * 3.4965372575730624e-2)))),"np.log((x[:, 1] ** x[:, 0] * (x[:, 1] + t[0])) ** np.exp(np.log((x[:, 0] * t[1])))) ** np.exp((((t[2] * x[:, 1]) + (t[3] + x[:, 1])) * ((t[4] / x[:, 0]) * t[5])))","$${\operatorname{log}({\left({x_{1}^{x_{0}}} \cdot \left(x_{1} + \theta_{0}\right)\right)^{\operatorname{exp}(\operatorname{log}(\left(x_{0} \cdot \theta_{1}\right)))}})^{\operatorname{exp}(\left(\left(\left(\theta_{2} \cdot x_{1}\right) + \left(\theta_{3} + x_{1}\right)\right) \cdot \left(\frac{\theta_{4}}{x_{0}} \cdot \theta_{5}\right)\right))}}$$",-9.97931464853441e-2;0.4755819951808542;0.5334588101215577;0.3363292373300586;0.30590519166797503;3.4965372575730624e-2,29,,,,Infinity,,,,,, -763,0,Exp(((Exp(((x0 ^ x0) * (x1 / x0))) ^ Log((Log(x0) / (0.2524833826292866 ^ x0)))) + ((Log((-0.48021260250552866 + x1)) + -5.932284663592302e-2) + ((x1 * -0.18979251944746678) / (0.7596230409660353 * x1))))),"np.exp((np.exp((x[:, 0] ** x[:, 0] * (x[:, 1] / x[:, 0]))) ** np.log((np.log(x[:, 0]) / t[0] ** x[:, 0])) + ((np.log((t[1] + x[:, 1])) + t[2]) + ((x[:, 1] * t[3]) / (t[4] * x[:, 1])))))","$$\operatorname{exp}(\left({\operatorname{exp}(\left({x_{0}^{x_{0}}} \cdot \frac{x_{1}}{x_{0}}\right))^{\operatorname{log}(\frac{\operatorname{log}(x_{0})}{{\theta_{0}^{x_{0}}}})}} + \left(\left(\operatorname{log}(\left(\theta_{1} + x_{1}\right)) + \theta_{2}\right) + \frac{\left(x_{1} \cdot \theta_{3}\right)}{\left(\theta_{4} \cdot x_{1}\right)}\right)\right))$$",0.2524833826292866;-0.48021260250552866;-5.932284663592302e-2;-0.18979251944746678;0.7596230409660353,32,,,,Infinity,,,,,, -764,0,(Exp(((Log(x1) + x0) + Exp(x1))) ^ (-0.1266435257257693 ^ x1)),"np.exp(((np.log(x[:, 1]) + x[:, 0]) + np.exp(x[:, 1]))) ** t[0] ** x[:, 1]","$${\operatorname{exp}(\left(\left(\operatorname{log}(x_{1}) + x_{0}\right) + \operatorname{exp}(x_{1})\right))^{{\theta_{0}^{x_{1}}}}}$$",-0.1266435257257693,12,,,,Infinity,,,,,, -765,0,Exp((((-0.19738270521733547 * Log((x1 ^ x1))) ^ (0.3098343824506036 ^ Exp((x0 ^ x1)))) / (((x1 + (x0 ^ -0.7346801006688171)) * (x0 * -0.5444222775440644)) + ((x1 / (x1 / x1)) / (x0 ^ (x1 * x0)))))),"np.exp(((t[0] * np.log(x[:, 1] ** x[:, 1])) ** t[1] ** np.exp(x[:, 0] ** x[:, 1]) / (((x[:, 1] + x[:, 0] ** t[2]) * (x[:, 0] * t[3])) + ((x[:, 1] / (x[:, 1] / x[:, 1])) / x[:, 0] ** (x[:, 1] * x[:, 0])))))","$$\operatorname{exp}(\frac{{\left(\theta_{0} \cdot \operatorname{log}({x_{1}^{x_{1}}})\right)^{{\theta_{1}^{\operatorname{exp}({x_{0}^{x_{1}}})}}}}}{\left(\left(\left(x_{1} + {x_{0}^{\theta_{2}}}\right) \cdot \left(x_{0} \cdot \theta_{3}\right)\right) + \frac{\frac{x_{1}}{\frac{x_{1}}{x_{1}}}}{{x_{0}^{\left(x_{1} \cdot x_{0}\right)}}}\right)})$$",-0.19738270521733547;0.3098343824506036;-0.7346801006688171;-0.5444222775440644,36,3.211857545076914e-2,3.211857545076914e-2,3.211857545076914e-2,3.211857545076914e-2,-1.9639394256368776,-1.9639394256368776,-1.9639394256368776,87.21269124348015,87.21269124348015,87.21269124348015 -766,0,Log(((Exp(x1) * (x0 * 0.759534590814654)) / ((-5.458786573284036e-2 ^ x0) + Exp(x0)))),"np.log(((np.exp(x[:, 1]) * (x[:, 0] * t[0])) / (t[1] ** x[:, 0] + np.exp(x[:, 0]))))","$$\operatorname{log}(\frac{\left(\operatorname{exp}(x_{1}) \cdot \left(x_{0} \cdot \theta_{0}\right)\right)}{\left({\theta_{1}^{x_{0}}} + \operatorname{exp}(x_{0})\right)})$$",0.759534590814654;-5.458786573284036e-2,14,,,,Infinity,,,,,, -767,0,(((x1 + x0) + (x0 + x0)) + (Log(x1) ^ Log(x0))),"(((x[:, 1] + x[:, 0]) + (x[:, 0] + x[:, 0])) + np.log(x[:, 1]) ** np.log(x[:, 0]))","$$\left(\left(\left(x_{1} + x_{0}\right) + \left(x_{0} + x_{0}\right)\right) + {\operatorname{log}(x_{1})^{\operatorname{log}(x_{0})}}\right)$$",,13,,,,Infinity,,,,,, -768,0,(Exp(((-0.46783629102114155 + (x1 / (x1 * 6.775564120836852e-2))) + Exp((0.3362065538168141 ^ (x0 + -0.35184874339013883))))) / (((-0.8119117920254059 ^ (-0.450036010619038 ^ x1)) / (x1 ^ -0.4699357829527395)) / (0.3872688697265563 / (x0 * 0.9988005570809834)))),"(np.exp(((t[0] + (x[:, 1] / (x[:, 1] * t[1]))) + np.exp(t[2] ** (x[:, 0] + t[3])))) / ((t[4] ** t[5] ** x[:, 1] / x[:, 1] ** t[6]) / (t[7] / (x[:, 0] * t[8]))))","$$\frac{\operatorname{exp}(\left(\left(\theta_{0} + \frac{x_{1}}{\left(x_{1} \cdot \theta_{1}\right)}\right) + \operatorname{exp}({\theta_{2}^{\left(x_{0} + \theta_{3}\right)}})\right))}{\frac{\frac{{\theta_{4}^{{\theta_{5}^{x_{1}}}}}}{{x_{1}^{\theta_{6}}}}}{\frac{\theta_{7}}{\left(x_{0} \cdot \theta_{8}\right)}}}$$",-0.46783629102114155;6.775564120836852e-2;0.3362065538168141;-0.35184874339013883;-0.8119117920254059;-0.450036010619038;-0.4699357829527395;0.3872688697265563;0.9988005570809834,31,,,,Infinity,,,,,, -769,0,(0.3002641496312093 + (-0.22627811198760203 * x0)),"(t[0] + (t[1] * x[:, 0]))","$$\left(\theta_{0} + \left(\theta_{1} \cdot x_{0}\right)\right)$$",0.3002641496312093;-0.22627811198760203,5,5.893883569701793e-3,5.893883569701793e-3,5.893883569701793e-3,5.893883569701793e-3,0.4561055826050271,0.4561055826050271,0.4561055826050271,10.978626171547466,10.978626171547466,10.978626171547466 -770,0,Log((((x0 * 7.42235253200355) / (x1 + 0.13285835272563348)) ^ Log((x0 ^ -6.465263186078367e-2)))),"np.log(((x[:, 0] * t[0]) / (x[:, 1] + t[1])) ** np.log(x[:, 0] ** t[2]))","$$\operatorname{log}({\frac{\left(x_{0} \cdot \theta_{0}\right)}{\left(x_{1} + \theta_{1}\right)}^{\operatorname{log}({x_{0}^{\theta_{2}}})}})$$",7.42235253200355;0.13285835272563348;-6.465263186078367e-2,13,1.1280963223559004e-2,1.1280963223559004e-2,1.1280963223559004e-2,1.1280963223559004e-2,-4.102038114101747e-2,-4.102038114101747e-2,-4.102038114101747e-2,33.10566771484199,33.10566771484199,33.10566771484199 -771,0,Log((((x1 * x0) ^ 0.13619330896141518) + x1)),"np.log(((x[:, 1] * x[:, 0]) ** t[0] + x[:, 1]))","$$\operatorname{log}(\left({\left(x_{1} \cdot x_{0}\right)^{\theta_{0}}} + x_{1}\right))$$",0.13619330896141518,8,4.02648900459245e-2,4.02648900459245e-2,4.02648900459245e-2,4.02648900459245e-2,-2.71569079266844,-2.71569079266844,-2.71569079266844,17.624444528851477,17.624444528851477,17.624444528851477 -772,0,(Exp(Exp(Exp((x0 + 0.9935855734614467)))) / (0.46335094646366504 / Exp((x1 / x1)))),"(np.exp(np.exp(np.exp((x[:, 0] + t[0])))) / (t[1] / np.exp((x[:, 1] / x[:, 1]))))","$$\frac{\operatorname{exp}(\operatorname{exp}(\operatorname{exp}(\left(x_{0} + \theta_{0}\right))))}{\frac{\theta_{1}}{\operatorname{exp}(\frac{x_{1}}{x_{1}})}}$$",0.9935855734614467;0.46335094646366504,13,Infinity,Infinity,Infinity,Infinity,-Infinity,-Infinity,-Infinity,Infinity,Infinity,Infinity -773,0,((Log((-0.5730025818463077 / ((x1 + 0.39326401443498227) * x0))) / (-0.1624775942488439 * ((((0.17339488687388271 + x0) + Exp(x1)) ^ Exp((0.9637993141382121 ^ x1))) ^ x0))) ^ Log(Log((Exp(Exp(Exp(x1))) * 0.22199825201932377)))),"(np.log((t[0] / ((x[:, 1] + t[1]) * x[:, 0]))) / (t[2] * ((t[3] + x[:, 0]) + np.exp(x[:, 1])) ** np.exp(t[4] ** x[:, 1]) ** x[:, 0])) ** np.log(np.log((np.exp(np.exp(np.exp(x[:, 1]))) * t[5])))","$${\frac{\operatorname{log}(\frac{\theta_{0}}{\left(\left(x_{1} + \theta_{1}\right) \cdot x_{0}\right)})}{\left(\theta_{2} \cdot {{\left(\left(\theta_{3} + x_{0}\right) + \operatorname{exp}(x_{1})\right)^{\operatorname{exp}({\theta_{4}^{x_{1}}})}}^{x_{0}}}\right)}^{\operatorname{log}(\operatorname{log}(\left(\operatorname{exp}(\operatorname{exp}(\operatorname{exp}(x_{1}))) \cdot \theta_{5}\right)))}}$$",-0.5730025818463077;0.39326401443498227;-0.1624775942488439;0.17339488687388271;0.9637993141382121;0.22199825201932377,33,,,,Infinity,,,,,, -774,0,Log(((((0.8446931347417641 ^ (0.11296411624971547 * x0)) + 0.6730965114843964) / ((x1 + (x1 ^ x1)) + Log((x0 * 0.7452334297159207)))) * (0.5751955559973312 * (-0.5610954822631564 + (-0.17809277220184183 ^ x0))))),"np.log((((t[0] ** (t[1] * x[:, 0]) + t[2]) / ((x[:, 1] + x[:, 1] ** x[:, 1]) + np.log((x[:, 0] * t[3])))) * (t[4] * (t[5] + t[6] ** x[:, 0]))))","$$\operatorname{log}(\left(\frac{\left({\theta_{0}^{\left(\theta_{1} \cdot x_{0}\right)}} + \theta_{2}\right)}{\left(\left(x_{1} + {x_{1}^{x_{1}}}\right) + \operatorname{log}(\left(x_{0} \cdot \theta_{3}\right))\right)} \cdot \left(\theta_{4} \cdot \left(\theta_{5} + {\theta_{6}^{x_{0}}}\right)\right)\right))$$",0.8446931347417641;0.11296411624971547;0.6730965114843964;0.7452334297159207;0.5751955559973312;-0.5610954822631564;-0.17809277220184183,27,,,,Infinity,,,,,, -775,0,((-0.6563643181306923 ^ ((x0 ^ 0.12119242039623679) * 0.24571494467006616)) ^ ((Log(x0) ^ (0.2441003092025944 + x1)) + ((x1 * x1) * (x1 / x0)))),"t[0] ** (x[:, 0] ** t[1] * t[2]) ** (np.log(x[:, 0]) ** (t[3] + x[:, 1]) + ((x[:, 1] * x[:, 1]) * (x[:, 1] / x[:, 0])))","$${{\theta_{0}^{\left({x_{0}^{\theta_{1}}} \cdot \theta_{2}\right)}}^{\left({\operatorname{log}(x_{0})^{\left(\theta_{3} + x_{1}\right)}} + \left(\left(x_{1} \cdot x_{1}\right) \cdot \frac{x_{1}}{x_{0}}\right)\right)}}$$",-0.6563643181306923;0.12119242039623679;0.24571494467006616;0.2441003092025944,22,,,,Infinity,,,,,, -776,0,Log((Log((Log(x1) + x0)) ^ Log((x1 / x0)))),"np.log(np.log((np.log(x[:, 1]) + x[:, 0])) ** np.log((x[:, 1] / x[:, 0])))","$$\operatorname{log}({\operatorname{log}(\left(\operatorname{log}(x_{1}) + x_{0}\right))^{\operatorname{log}(\frac{x_{1}}{x_{0}})}})$$",,11,,,,Infinity,,,,,, -777,0,((((x1 * -0.4367803621166929) ^ ((-0.6787152430320464 ^ x0) * x1)) ^ x1) * 0.9480502352450613),"((x[:, 1] * t[0]) ** (t[1] ** x[:, 0] * x[:, 1]) ** x[:, 1] * t[2])","$$\left({{\left(x_{1} \cdot \theta_{0}\right)^{\left({\theta_{1}^{x_{0}}} \cdot x_{1}\right)}}^{x_{1}}} \cdot \theta_{2}\right)$$",-0.4367803621166929;-0.6787152430320464;0.9480502352450613,13,,,,Infinity,,,,,, -778,0,(Log(Log(((0.16964093392432333 + (-4.7787353464490256e-2 * x1)) ^ (x1 / (x1 ^ -0.9061901037067533))))) ^ (Log(((x1 + x1) * 0.7950415273975762)) / Exp(Exp((x1 ^ Log(x1)))))),"np.log(np.log((t[0] + (t[1] * x[:, 1])) ** (x[:, 1] / x[:, 1] ** t[2]))) ** (np.log(((x[:, 1] + x[:, 1]) * t[3])) / np.exp(np.exp(x[:, 1] ** np.log(x[:, 1]))))","$${\operatorname{log}(\operatorname{log}({\left(\theta_{0} + \left(\theta_{1} \cdot x_{1}\right)\right)^{\frac{x_{1}}{{x_{1}^{\theta_{2}}}}}}))^{\frac{\operatorname{log}(\left(\left(x_{1} + x_{1}\right) \cdot \theta_{3}\right))}{\operatorname{exp}(\operatorname{exp}({x_{1}^{\operatorname{log}(x_{1})}}))}}}$$",0.16964093392432333;-4.7787353464490256e-2;-0.9061901037067533;0.7950415273975762,27,,,,Infinity,,,,,, -779,0,Exp(Log(((-0.9305421108430194 / (0.7477489540222606 / (x0 * (0.9890682210947718 * x1)))) * Exp(x0)))),"np.exp(np.log(((t[0] / (t[1] / (x[:, 0] * (t[2] * x[:, 1])))) * np.exp(x[:, 0]))))","$$\operatorname{exp}(\operatorname{log}(\left(\frac{\theta_{0}}{\frac{\theta_{1}}{\left(x_{0} \cdot \left(\theta_{2} \cdot x_{1}\right)\right)}} \cdot \operatorname{exp}(x_{0})\right)))$$",-0.9305421108430194;0.7477489540222606;0.9890682210947718,14,,,,Infinity,,,,,, -780,0,(Exp(Exp(Log(Log((x0 + -0.5906333643363312))))) ^ ((Exp((-0.48546986950428495 ^ x1)) * (x0 ^ (x0 + 0.9514226089502451))) / (Log((x0 * -0.6326159418592074)) + 0.25547466131276286))),"np.exp(np.exp(np.log(np.log((x[:, 0] + t[0]))))) ** ((np.exp(t[1] ** x[:, 1]) * x[:, 0] ** (x[:, 0] + t[2])) / (np.log((x[:, 0] * t[3])) + t[4]))","$${\operatorname{exp}(\operatorname{exp}(\operatorname{log}(\operatorname{log}(\left(x_{0} + \theta_{0}\right)))))^{\frac{\left(\operatorname{exp}({\theta_{1}^{x_{1}}}) \cdot {x_{0}^{\left(x_{0} + \theta_{2}\right)}}\right)}{\left(\operatorname{log}(\left(x_{0} \cdot \theta_{3}\right)) + \theta_{4}\right)}}}$$",-0.5906333643363312;-0.48546986950428495;0.9514226089502451;-0.6326159418592074;0.25547466131276286,25,,,,Infinity,,,,,, -781,0,Exp(Exp(((-0.15623621928522946 ^ ((x1 * -7.211609403215591e-2) + -0.4305983742431767)) / ((x0 * (-0.12613102954026456 * x1)) / (x1 ^ 0.8860055376866759))))),"np.exp(np.exp((t[0] ** ((x[:, 1] * t[1]) + t[2]) / ((x[:, 0] * (t[3] * x[:, 1])) / x[:, 1] ** t[4]))))","$$\operatorname{exp}(\operatorname{exp}(\frac{{\theta_{0}^{\left(\left(x_{1} \cdot \theta_{1}\right) + \theta_{2}\right)}}}{\frac{\left(x_{0} \cdot \left(\theta_{3} \cdot x_{1}\right)\right)}{{x_{1}^{\theta_{4}}}}}))$$",-0.15623621928522946;-7.211609403215591e-2;-0.4305983742431767;-0.12613102954026456;0.8860055376866759,19,,,,Infinity,,,,,, -782,0,(Log(((((0.7685769251526475 / x1) / ((x0 * -0.8194763890753733) * -0.10531899578221227)) ^ -0.8069787858968884) + (-0.3632241556516428 * x1))) / (Log(Log(Exp(Log(x1)))) * ((Exp(Exp(x0)) + Log((x1 * -0.9691031956350175))) * Log(x1)))),"(np.log((((t[0] / x[:, 1]) / ((x[:, 0] * t[1]) * t[2])) ** t[3] + (t[4] * x[:, 1]))) / (np.log(np.log(np.exp(np.log(x[:, 1])))) * ((np.exp(np.exp(x[:, 0])) + np.log((x[:, 1] * t[5]))) * np.log(x[:, 1]))))","$$\frac{\operatorname{log}(\left({\frac{\frac{\theta_{0}}{x_{1}}}{\left(\left(x_{0} \cdot \theta_{1}\right) \cdot \theta_{2}\right)}^{\theta_{3}}} + \left(\theta_{4} \cdot x_{1}\right)\right))}{\left(\operatorname{log}(\operatorname{log}(\operatorname{exp}(\operatorname{log}(x_{1})))) \cdot \left(\left(\operatorname{exp}(\operatorname{exp}(x_{0})) + \operatorname{log}(\left(x_{1} \cdot \theta_{5}\right))\right) \cdot \operatorname{log}(x_{1})\right)\right)}$$",0.7685769251526475;-0.8194763890753733;-0.10531899578221227;-0.8069787858968884;-0.3632241556516428;-0.9691031956350175,34,,,,Infinity,,,,,, -783,0,((Log(((0.17324906432387743 + x0) ^ (-0.8115085816147067 ^ (x0 + (x0 * -0.21569501352452103))))) + Exp(Exp(x1))) ^ (0.3231796268506838 * ((x1 * (Log((-0.2882640062353361 ^ x0)) + -0.6559454614272653)) / Log((x0 / x1))))),"(np.log((t[0] + x[:, 0]) ** t[1] ** (x[:, 0] + (x[:, 0] * t[2]))) + np.exp(np.exp(x[:, 1]))) ** (t[3] * ((x[:, 1] * (np.log(t[4] ** x[:, 0]) + t[5])) / np.log((x[:, 0] / x[:, 1]))))","$${\left(\operatorname{log}({\left(\theta_{0} + x_{0}\right)^{{\theta_{1}^{\left(x_{0} + \left(x_{0} \cdot \theta_{2}\right)\right)}}}}) + \operatorname{exp}(\operatorname{exp}(x_{1}))\right)^{\left(\theta_{3} \cdot \frac{\left(x_{1} \cdot \left(\operatorname{log}({\theta_{4}^{x_{0}}}) + \theta_{5}\right)\right)}{\operatorname{log}(\frac{x_{0}}{x_{1}})}\right)}}$$",0.17324906432387743;-0.8115085816147067;-0.21569501352452103;0.3231796268506838;-0.2882640062353361;-0.6559454614272653,32,,,,Infinity,,,,,, -784,0,(((Log((Exp(Log(x1)) ^ x0)) * x1) + x0) / x1),"(((np.log(np.exp(np.log(x[:, 1])) ** x[:, 0]) * x[:, 1]) + x[:, 0]) / x[:, 1])","$$\frac{\left(\left(\operatorname{log}({\operatorname{exp}(\operatorname{log}(x_{1}))^{x_{0}}}) \cdot x_{1}\right) + x_{0}\right)}{x_{1}}$$",,12,9.893760136908577,9.893760136908577,9.893760136908577,9.893760136908577,-912.0076700483182,-912.0076700483182,-912.0076700483182,33.857682623375744,33.857682623375744,33.857682623375744 -785,0,(((((0.39926549297398295 * x0) ^ Exp((-0.8728775937518413 / x0))) + (-0.27702574965001936 ^ x0)) ^ 9.725151836339596e-2) ^ 0.25002445179117805),"((t[0] * x[:, 0]) ** np.exp((t[1] / x[:, 0])) + t[2] ** x[:, 0]) ** t[3] ** t[4]","$${{\left({\left(\theta_{0} \cdot x_{0}\right)^{\operatorname{exp}(\frac{\theta_{1}}{x_{0}})}} + {\theta_{2}^{x_{0}}}\right)^{\theta_{3}}}^{\theta_{4}}}$$",0.39926549297398295;-0.8728775937518413;-0.27702574965001936;9.725151836339596e-2;0.25002445179117805,16,,,,Infinity,,,,,, -786,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 -787,0,((Log(x0) / Log((-0.525338045221867 / x1))) * ((x1 + 0.13093644899579127) / x0)),"((np.log(x[:, 0]) / np.log((t[0] / x[:, 1]))) * ((x[:, 1] + t[1]) / x[:, 0]))","$$\left(\frac{\operatorname{log}(x_{0})}{\operatorname{log}(\frac{\theta_{0}}{x_{1}})} \cdot \frac{\left(x_{1} + \theta_{1}\right)}{x_{0}}\right)$$",-0.525338045221867;0.13093644899579127,13,,,,Infinity,,,,,, -788,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 -789,0,Exp(Log((((0.49875726532052156 * (x1 / x1)) * -0.7831386640843926) * ((-0.5236830248643332 + (x0 * 0.7044297247956577)) / ((x1 ^ -2.8034173264965156e-2) ^ (x1 * -0.6813253321761963)))))),"np.exp(np.log((((t[0] * (x[:, 1] / x[:, 1])) * t[1]) * ((t[2] + (x[:, 0] * t[3])) / x[:, 1] ** t[4] ** (x[:, 1] * t[5])))))","$$\operatorname{exp}(\operatorname{log}(\left(\left(\left(\theta_{0} \cdot \frac{x_{1}}{x_{1}}\right) \cdot \theta_{1}\right) \cdot \frac{\left(\theta_{2} + \left(x_{0} \cdot \theta_{3}\right)\right)}{{{x_{1}^{\theta_{4}}}^{\left(x_{1} \cdot \theta_{5}\right)}}}\right)))$$",0.49875726532052156;-0.7831386640843926;-0.5236830248643332;0.7044297247956577;-2.8034173264965156e-2;-0.6813253321761963,23,,,,Infinity,,,,,, -790,0,Log(((0.5158587931975145 ^ (((-0.9242055234338773 ^ x0) * -0.6817115629485607) / (Exp((-0.596549210392747 + x1)) ^ ((x1 * x0) * x1)))) ^ ((0.2082314437174302 ^ (x1 * -0.9258334060496257)) ^ x1))),"np.log(t[0] ** ((t[1] ** x[:, 0] * t[2]) / np.exp((t[3] + x[:, 1])) ** ((x[:, 1] * x[:, 0]) * x[:, 1])) ** t[4] ** (x[:, 1] * t[5]) ** x[:, 1])","$$\operatorname{log}({{\theta_{0}^{\frac{\left({\theta_{1}^{x_{0}}} \cdot \theta_{2}\right)}{{\operatorname{exp}(\left(\theta_{3} + x_{1}\right))^{\left(\left(x_{1} \cdot x_{0}\right) \cdot x_{1}\right)}}}}}^{{{\theta_{4}^{\left(x_{1} \cdot \theta_{5}\right)}}^{x_{1}}}}})$$",0.5158587931975145;-0.9242055234338773;-0.6817115629485607;-0.596549210392747;0.2082314437174302;-0.9258334060496257,27,,,,Infinity,,,,,, -791,0,Exp(((Exp(x1) * -0.5598042215370699) * ((8.502000742017803 ^ x0) * 0.6133335298883831))),"np.exp(((np.exp(x[:, 1]) * t[0]) * (t[1] ** x[:, 0] * t[2])))","$$\operatorname{exp}(\left(\left(\operatorname{exp}(x_{1}) \cdot \theta_{0}\right) \cdot \left({\theta_{1}^{x_{0}}} \cdot \theta_{2}\right)\right))$$",-0.5598042215370699;8.502000742017803;0.6133335298883831,11,8.192939965788988e-3,8.192939965788988e-3,8.192939965788988e-3,8.192939965788988e-3,0.24394598964392977,0.24394598964392977,0.24394598964392977,25.779502613580735,25.779502613580735,25.779502613580735 -792,0,((((x1 * x1) * -0.37583634384146036) * x0) ^ x1),"(((x[:, 1] * x[:, 1]) * t[0]) * x[:, 0]) ** x[:, 1]","$${\left(\left(\left(x_{1} \cdot x_{1}\right) \cdot \theta_{0}\right) \cdot x_{0}\right)^{x_{1}}}$$",-0.37583634384146036,9,,,,Infinity,,,,,, -793,0,((((x0 * 0.27080413209646503) / Exp((x0 * -0.9994015042527489))) / Log((-0.934150410390745 ^ Exp((x1 * 0.23718499961739214))))) ^ (Exp(Log(((0.7563395764616876 / x0) / Log((x1 + 0.9563054736851739))))) + ((0.8718868029157711 * Log(x0)) ^ Log(Exp((0.11766881358187509 + x0)))))),"(((x[:, 0] * t[0]) / np.exp((x[:, 0] * t[1]))) / np.log(t[2] ** np.exp((x[:, 1] * t[3])))) ** (np.exp(np.log(((t[4] / x[:, 0]) / np.log((x[:, 1] + t[5]))))) + (t[6] * np.log(x[:, 0])) ** np.log(np.exp((t[7] + x[:, 0]))))","$${\frac{\frac{\left(x_{0} \cdot \theta_{0}\right)}{\operatorname{exp}(\left(x_{0} \cdot \theta_{1}\right))}}{\operatorname{log}({\theta_{2}^{\operatorname{exp}(\left(x_{1} \cdot \theta_{3}\right))}})}^{\left(\operatorname{exp}(\operatorname{log}(\frac{\frac{\theta_{4}}{x_{0}}}{\operatorname{log}(\left(x_{1} + \theta_{5}\right))})) + {\left(\theta_{6} \cdot \operatorname{log}(x_{0})\right)^{\operatorname{log}(\operatorname{exp}(\left(\theta_{7} + x_{0}\right)))}}\right)}}$$",0.27080413209646503;-0.9994015042527489;-0.934150410390745;0.23718499961739214;0.7563395764616876;0.9563054736851739;0.8718868029157711;0.11766881358187509,38,,,,Infinity,,,,,, -794,0,Log(Log(((((x0 / x0) / Exp(x1)) + ((0.25357866250721994 + x1) ^ (0.7225781478336281 * x1))) / (((x0 * x0) * 0.14370804561562356) ^ (-0.40403960841478814 ^ (x0 * -0.7113248994320108)))))),"np.log(np.log(((((x[:, 0] / x[:, 0]) / np.exp(x[:, 1])) + (t[0] + x[:, 1]) ** (t[1] * x[:, 1])) / ((x[:, 0] * x[:, 0]) * t[2]) ** t[3] ** (x[:, 0] * t[4]))))","$$\operatorname{log}(\operatorname{log}(\frac{\left(\frac{\frac{x_{0}}{x_{0}}}{\operatorname{exp}(x_{1})} + {\left(\theta_{0} + x_{1}\right)^{\left(\theta_{1} \cdot x_{1}\right)}}\right)}{{\left(\left(x_{0} \cdot x_{0}\right) \cdot \theta_{2}\right)^{{\theta_{3}^{\left(x_{0} \cdot \theta_{4}\right)}}}}}))$$",0.25357866250721994;0.7225781478336281;0.14370804561562356;-0.40403960841478814;-0.7113248994320108,28,,,,Infinity,,,,,, -795,0,((Exp(((x1 ^ x1) + (x1 + -9.035460487984881))) * ((x1 * 5.02409192953763) * Exp(Exp(x0)))) + (-2.72864583448017e-2 + Exp((-1.0273719957301712 * (x0 / x1))))),"((np.exp((x[:, 1] ** x[:, 1] + (x[:, 1] + t[0]))) * ((x[:, 1] * t[1]) * np.exp(np.exp(x[:, 0])))) + (t[2] + np.exp((t[3] * (x[:, 0] / x[:, 1])))))","$$\left(\left(\operatorname{exp}(\left({x_{1}^{x_{1}}} + \left(x_{1} + \theta_{0}\right)\right)) \cdot \left(\left(x_{1} \cdot \theta_{1}\right) \cdot \operatorname{exp}(\operatorname{exp}(x_{0}))\right)\right) + \left(\theta_{2} + \operatorname{exp}(\left(\theta_{3} \cdot \frac{x_{0}}{x_{1}}\right))\right)\right)$$",-9.035460487984881;5.02409192953763;-2.72864583448017e-2;-1.0273719957301712,25,3.346161891230202e-3,3.346161891230202e-3,3.346161891230202e-3,3.346161891230202e-3,0.6912122964736485,0.6912122964736485,0.6912122964736485,60.072749829168565,60.072749829168565,60.072749829168565 -796,0,(Exp(((0.30829079167710305 * (((x1 * -0.7600116328062176) + -0.9927892087362202) / x0)) * Log(x1))) / Log(((((x0 * Exp(x1)) + 0.6562537912214975) * -0.5797398586402274) + Exp(Log((-0.41775588564565613 + (x1 ^ -0.5294163829955625))))))),"(np.exp(((t[0] * (((x[:, 1] * t[1]) + t[2]) / x[:, 0])) * np.log(x[:, 1]))) / np.log(((((x[:, 0] * np.exp(x[:, 1])) + t[3]) * t[4]) + np.exp(np.log((t[5] + x[:, 1] ** t[6]))))))","$$\frac{\operatorname{exp}(\left(\left(\theta_{0} \cdot \frac{\left(\left(x_{1} \cdot \theta_{1}\right) + \theta_{2}\right)}{x_{0}}\right) \cdot \operatorname{log}(x_{1})\right))}{\operatorname{log}(\left(\left(\left(\left(x_{0} \cdot \operatorname{exp}(x_{1})\right) + \theta_{3}\right) \cdot \theta_{4}\right) + \operatorname{exp}(\operatorname{log}(\left(\theta_{5} + {x_{1}^{\theta_{6}}}\right)))\right))}$$",0.30829079167710305;-0.7600116328062176;-0.9927892087362202;0.6562537912214975;-0.5797398586402274;-0.41775588564565613;-0.5294163829955625,31,,,,Infinity,,,,,, -797,0,Exp(((((0.860596177734323 + x0) ^ x1) + (Exp(x1) * 0.5388090805273347)) * Log(Log((Log(x0) + Log(x0)))))),"np.exp((((t[0] + x[:, 0]) ** x[:, 1] + (np.exp(x[:, 1]) * t[1])) * np.log(np.log((np.log(x[:, 0]) + np.log(x[:, 0]))))))","$$\operatorname{exp}(\left(\left({\left(\theta_{0} + x_{0}\right)^{x_{1}}} + \left(\operatorname{exp}(x_{1}) \cdot \theta_{1}\right)\right) \cdot \operatorname{log}(\operatorname{log}(\left(\operatorname{log}(x_{0}) + \operatorname{log}(x_{0})\right)))\right))$$",0.860596177734323;0.5388090805273347,19,,,,Infinity,,,,,, -798,0,Log(((x1 ^ -0.3400741703680225) ^ 9.511310491777651e-2)),"np.log(x[:, 1] ** t[0] ** t[1])","$$\operatorname{log}({{x_{1}^{\theta_{0}}}^{\theta_{1}}})$$",-0.3400741703680225;9.511310491777651e-2,6,1.2823863842649908e-2,1.2823863842649908e-2,1.2823863842649908e-2,1.2823863842649908e-2,-0.18340104126003798,-0.18340104126003798,-0.18340104126003798,12.371157514913008,12.371157514913008,12.371157514913008 -799,0,(((0.8186085495253432 + (x0 / (x0 * -0.572651217069895))) ^ (0.4997019860793134 + (1.9258947379281022e-2 / x0))) * ((Exp(Log(x0)) / (-0.21560892148860744 ^ Exp(x0))) * (Exp(Exp(x0)) + 6.473353166887186e-2))),"((t[0] + (x[:, 0] / (x[:, 0] * t[1]))) ** (t[2] + (t[3] / x[:, 0])) * ((np.exp(np.log(x[:, 0])) / t[4] ** np.exp(x[:, 0])) * (np.exp(np.exp(x[:, 0])) + t[5])))","$$\left({\left(\theta_{0} + \frac{x_{0}}{\left(x_{0} \cdot \theta_{1}\right)}\right)^{\left(\theta_{2} + \frac{\theta_{3}}{x_{0}}\right)}} \cdot \left(\frac{\operatorname{exp}(\operatorname{log}(x_{0}))}{{\theta_{4}^{\operatorname{exp}(x_{0})}}} \cdot \left(\operatorname{exp}(\operatorname{exp}(x_{0})) + \theta_{5}\right)\right)\right)$$",0.8186085495253432;-0.572651217069895;0.4997019860793134;1.9258947379281022e-2;-0.21560892148860744;6.473353166887186e-2,28,,,,Infinity,,,,,, -800,0,((Exp(Exp(((x0 ^ -2.8932671770531937e-2) + -0.8218652491668262))) + ((x1 * 0.934591257832466) * (-0.958684297843758 * (-9.619944605824282e-2 / x1)))) * Log(Exp(Log((-0.2950784446184892 ^ (Exp(x0) * -0.7193790636704556)))))),"((np.exp(np.exp((x[:, 0] ** t[0] + t[1]))) + ((x[:, 1] * t[2]) * (t[3] * (t[4] / x[:, 1])))) * np.log(np.exp(np.log(t[5] ** (np.exp(x[:, 0]) * t[6])))))","$$\left(\left(\operatorname{exp}(\operatorname{exp}(\left({x_{0}^{\theta_{0}}} + \theta_{1}\right))) + \left(\left(x_{1} \cdot \theta_{2}\right) \cdot \left(\theta_{3} \cdot \frac{\theta_{4}}{x_{1}}\right)\right)\right) \cdot \operatorname{log}(\operatorname{exp}(\operatorname{log}({\theta_{5}^{\left(\operatorname{exp}(x_{0}) \cdot \theta_{6}\right)}})))\right)$$",-2.8932671770531937e-2;-0.8218652491668262;0.934591257832466;-0.958684297843758;-9.619944605824282e-2;-0.2950784446184892;-0.7193790636704556,27,,,,Infinity,,,,,, -801,0,Exp(Exp((Log(x0) + Log((x1 ^ x1))))),"np.exp(np.exp((np.log(x[:, 0]) + np.log(x[:, 1] ** x[:, 1]))))","$$\operatorname{exp}(\operatorname{exp}(\left(\operatorname{log}(x_{0}) + \operatorname{log}({x_{1}^{x_{1}}})\right)))$$",,9,4.766195175066787,4.766195175066787,4.766195175066787,4.766195175066787,-438.83002332447523,-438.83002332447523,-438.83002332447523,20.4154108806126,20.4154108806126,20.4154108806126 -802,0,((((Exp(x1) / (-0.3226462518365091 * x0)) + Log((0.5255080350918439 + x0))) ^ (-0.3404315760929416 ^ (-0.34447809617801795 / (x0 * -0.886188569053914)))) / Log(Exp((((x0 / x0) ^ (x0 * x1)) + ((x1 ^ -0.44266253887504103) / ((x1 ^ x1) * 0.40333533842407554)))))),"(((np.exp(x[:, 1]) / (t[0] * x[:, 0])) + np.log((t[1] + x[:, 0]))) ** t[2] ** (t[3] / (x[:, 0] * t[4])) / np.log(np.exp(((x[:, 0] / x[:, 0]) ** (x[:, 0] * x[:, 1]) + (x[:, 1] ** t[5] / (x[:, 1] ** x[:, 1] * t[6]))))))","$$\frac{{\left(\frac{\operatorname{exp}(x_{1})}{\left(\theta_{0} \cdot x_{0}\right)} + \operatorname{log}(\left(\theta_{1} + x_{0}\right))\right)^{{\theta_{2}^{\frac{\theta_{3}}{\left(x_{0} \cdot \theta_{4}\right)}}}}}}{\operatorname{log}(\operatorname{exp}(\left({\frac{x_{0}}{x_{0}}^{\left(x_{0} \cdot x_{1}\right)}} + \frac{{x_{1}^{\theta_{5}}}}{\left({x_{1}^{x_{1}}} \cdot \theta_{6}\right)}\right)))}$$",-0.3226462518365091;0.5255080350918439;-0.3404315760929416;-0.34447809617801795;-0.886188569053914;-0.44266253887504103;0.40333533842407554,39,,,,Infinity,,,,,, -803,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 -804,0,((-0.36376089807770284 ^ (x0 * -0.7352656471771748)) + (x0 + -0.8871086353371543)),"(t[0] ** (x[:, 0] * t[1]) + (x[:, 0] + t[2]))","$$\left({\theta_{0}^{\left(x_{0} \cdot \theta_{1}\right)}} + \left(x_{0} + \theta_{2}\right)\right)$$",-0.36376089807770284;-0.7352656471771748;-0.8871086353371543,9,,,,Infinity,,,,,, -805,0,(Exp((((0.16565316113433598 * (-0.7577178319426278 / x1)) + -0.4288811734340663) + (Exp(((2.8500900920327275 * x0) + (8.82627316291723e-3 ^ x1))) ^ 0.6891908399219772))) ^ (x1 + -0.8258406619730053)),"np.exp((((t[0] * (t[1] / x[:, 1])) + t[2]) + np.exp(((t[3] * x[:, 0]) + t[4] ** x[:, 1])) ** t[5])) ** (x[:, 1] + t[6])","$${\operatorname{exp}(\left(\left(\left(\theta_{0} \cdot \frac{\theta_{1}}{x_{1}}\right) + \theta_{2}\right) + {\operatorname{exp}(\left(\left(\theta_{3} \cdot x_{0}\right) + {\theta_{4}^{x_{1}}}\right))^{\theta_{5}}}\right))^{\left(x_{1} + \theta_{6}\right)}}$$",0.16565316113433598;-0.7577178319426278;-0.4288811734340663;2.8500900920327275;8.82627316291723e-3;0.6891908399219772;-0.8258406619730053,23,2.2994154720245895e-3,2.2994154720245895e-3,2.2994154720245895e-3,2.2994154720245895e-3,0.7878072710945869,0.7878072710945869,0.7878072710945869,61.97569948013453,61.97569948013453,61.97569948013453 -806,0,((((x0 / x0) + (x1 * 0.5047240767710603)) + (-0.5250687668643382 ^ (5.611455517469932e-2 + (x0 ^ x1)))) / ((Exp((x0 + -0.25371664005813443)) + (x1 / x0)) + (Exp((x1 * 0.7923932134227526)) + 0.7647029819074077))),"((((x[:, 0] / x[:, 0]) + (x[:, 1] * t[0])) + t[1] ** (t[2] + x[:, 0] ** x[:, 1])) / ((np.exp((x[:, 0] + t[3])) + (x[:, 1] / x[:, 0])) + (np.exp((x[:, 1] * t[4])) + t[5])))","$$\frac{\left(\left(\frac{x_{0}}{x_{0}} + \left(x_{1} \cdot \theta_{0}\right)\right) + {\theta_{1}^{\left(\theta_{2} + {x_{0}^{x_{1}}}\right)}}\right)}{\left(\left(\operatorname{exp}(\left(x_{0} + \theta_{3}\right)) + \frac{x_{1}}{x_{0}}\right) + \left(\operatorname{exp}(\left(x_{1} \cdot \theta_{4}\right)) + \theta_{5}\right)\right)}$$",0.5047240767710603;-0.5250687668643382;5.611455517469932e-2;-0.25371664005813443;0.7923932134227526;0.7647029819074077,31,,,,Infinity,,,,,, -807,0,(x0 * 3.524205325424168e-2),"(x[:, 0] * t[0])","$$\left(x_{0} \cdot \theta_{0}\right)$$",3.524205325424168e-2,3,1.306654401552658e-2,1.306654401552658e-2,1.306654401552658e-2,1.306654401552658e-2,-0.20579584931471184,-0.20579584931471184,-0.20579584931471184,5.328521690985943,5.328521690985943,5.328521690985943 -808,0,(((x0 + x1) * 0.10112421339494736) * Log((Exp(x1) + (-4.816162089125252e-2 * x1)))),"(((x[:, 0] + x[:, 1]) * t[0]) * np.log((np.exp(x[:, 1]) + (t[1] * x[:, 1]))))","$$\left(\left(\left(x_{0} + x_{1}\right) \cdot \theta_{0}\right) \cdot \operatorname{log}(\left(\operatorname{exp}(x_{1}) + \left(\theta_{1} \cdot x_{1}\right)\right))\right)$$",0.10112421339494736;-4.816162089125252e-2,13,1.160418552771825e-2,1.160418552771825e-2,1.160418552771825e-2,1.160418552771825e-2,-7.084771056324568e-2,-7.084771056324568e-2,-7.084771056324568e-2,29.3491255754293,29.3491255754293,29.3491255754293 -809,0,(((x1 + x0) / (x1 + 86.01058682072575)) + Exp(((x0 ^ -18.986353044583588) ^ 4.585800995117751e-3))),"(((x[:, 1] + x[:, 0]) / (x[:, 1] + t[0])) + np.exp(x[:, 0] ** t[1] ** t[2]))","$$\left(\frac{\left(x_{1} + x_{0}\right)}{\left(x_{1} + \theta_{0}\right)} + \operatorname{exp}({{x_{0}^{\theta_{1}}}^{\theta_{2}}})\right)$$",86.01058682072575;-18.986353044583588;4.585800995117751e-3,14,7.155554362330875,7.155554362330875,7.155554362330875,7.155554362330875,-659.3228626782869,-659.3228626782869,-659.3228626782869,39.74551581897509,39.74551581897509,39.74551581897509 -810,0,Log(Log(Log(Log((Exp(x0) * (x0 + 0.2874220571246755)))))),"np.log(np.log(np.log(np.log((np.exp(x[:, 0]) * (x[:, 0] + t[0]))))))","$$\operatorname{log}(\operatorname{log}(\operatorname{log}(\operatorname{log}(\left(\operatorname{exp}(x_{0}) \cdot \left(x_{0} + \theta_{0}\right)\right)))))$$",0.2874220571246755,10,,,,Infinity,,,,,, -811,0,Exp((-3.0680971858662263 + x1)),"np.exp((t[0] + x[:, 1]))","$$\operatorname{exp}(\left(\theta_{0} + x_{1}\right))$$",-3.0680971858662263,4,1.0205484150379933e-2,1.0205484150379933e-2,1.0205484150379933e-2,1.0205484150379933e-2,5.8226076141275396e-2,5.8226076141275396e-2,5.8226076141275396e-2,7.575287315582544,7.575287315582544,7.575287315582544 -812,0,Exp(((Log((Exp((0.2357350295660664 ^ x1)) * ((-0.44482973125642955 / x0) * (x0 + -0.761824553569654)))) + 0.3155764437469726) * x1)),"np.exp(((np.log((np.exp(t[0] ** x[:, 1]) * ((t[1] / x[:, 0]) * (x[:, 0] + t[2])))) + t[3]) * x[:, 1]))","$$\operatorname{exp}(\left(\left(\operatorname{log}(\left(\operatorname{exp}({\theta_{0}^{x_{1}}}) \cdot \left(\frac{\theta_{1}}{x_{0}} \cdot \left(x_{0} + \theta_{2}\right)\right)\right)) + \theta_{3}\right) \cdot x_{1}\right))$$",0.2357350295660664;-0.44482973125642955;-0.761824553569654;0.3155764437469726,18,,,,Infinity,,,,,, -813,0,(((((x0 / (x1 * (x1 / x0))) / Log((0.7975815811042215 + Log(x0)))) / (Log((0.13720430752127133 * (0.13661884928334211 ^ x0))) ^ x1)) * ((0.23316209756683615 / x0) + ((x1 * -0.31231708713905015) ^ 8.333530451736071e-2))) / ((-0.5001813315209166 * x1) * x1)),"(((((x[:, 0] / (x[:, 1] * (x[:, 1] / x[:, 0]))) / np.log((t[0] + np.log(x[:, 0])))) / np.log((t[1] * t[2] ** x[:, 0])) ** x[:, 1]) * ((t[3] / x[:, 0]) + (x[:, 1] * t[4]) ** t[5])) / ((t[6] * x[:, 1]) * x[:, 1]))","$$\frac{\left(\frac{\frac{\frac{x_{0}}{\left(x_{1} \cdot \frac{x_{1}}{x_{0}}\right)}}{\operatorname{log}(\left(\theta_{0} + \operatorname{log}(x_{0})\right))}}{{\operatorname{log}(\left(\theta_{1} \cdot {\theta_{2}^{x_{0}}}\right))^{x_{1}}}} \cdot \left(\frac{\theta_{3}}{x_{0}} + {\left(x_{1} \cdot \theta_{4}\right)^{\theta_{5}}}\right)\right)}{\left(\left(\theta_{6} \cdot x_{1}\right) \cdot x_{1}\right)}$$",0.7975815811042215;0.13720430752127133;0.13661884928334211;0.23316209756683615;-0.31231708713905015;8.333530451736071e-2;-0.5001813315209166,38,,,,Infinity,,,,,, -814,0,((-6.901094135664387e-2 ^ ((-3.220679307750207e-2 * x0) ^ (x1 / (x1 / x1)))) ^ Log(Exp((-8.897366760242165e-2 * ((x1 * -0.4860598888155555) * 0.5164951795844065))))),"t[0] ** (t[1] * x[:, 0]) ** (x[:, 1] / (x[:, 1] / x[:, 1])) ** np.log(np.exp((t[2] * ((x[:, 1] * t[3]) * t[4]))))","$${{\theta_{0}^{{\left(\theta_{1} \cdot x_{0}\right)^{\frac{x_{1}}{\frac{x_{1}}{x_{1}}}}}}}^{\operatorname{log}(\operatorname{exp}(\left(\theta_{2} \cdot \left(\left(x_{1} \cdot \theta_{3}\right) \cdot \theta_{4}\right)\right)))}}$$",-6.901094135664387e-2;-3.220679307750207e-2;-8.897366760242165e-2;-0.4860598888155555;0.5164951795844065,21,,,,Infinity,,,,,, -815,0,(0.8607152305155498 ^ ((-0.989956951235872 / x1) / (x0 * -6.314782339084148e-2))),"t[0] ** ((t[1] / x[:, 1]) / (x[:, 0] * t[2]))","$${\theta_{0}^{\frac{\frac{\theta_{1}}{x_{1}}}{\left(x_{0} \cdot \theta_{2}\right)}}}$$",0.8607152305155498;-0.989956951235872;-6.314782339084148e-2,9,1.4451935024560445e-2,1.4451935024560445e-2,1.4451935024560445e-2,1.4451935024560445e-2,-0.33364133978150656,-0.33364133978150656,-0.33364133978150656,22.20161677067752,22.20161677067752,22.20161677067752 -816,0,(Log(Exp((3.8791856406079905e-3 / x0))) * ((x0 / x1) ^ x0)),"(np.log(np.exp((t[0] / x[:, 0]))) * (x[:, 0] / x[:, 1]) ** x[:, 0])","$$\left(\operatorname{log}(\operatorname{exp}(\frac{\theta_{0}}{x_{0}})) \cdot {\frac{x_{0}}{x_{1}}^{x_{0}}}\right)$$",3.8791856406079905e-3,11,1.4057506218978536e-2,1.4057506218978536e-2,1.4057506218978536e-2,1.4057506218978536e-2,-0.297242991751937,-0.297242991751937,-0.297242991751937,24.90743364944291,24.90743364944291,24.90743364944291 -817,0,((-0.119730649807676 ^ x0) + 0.5837449352727875),"(t[0] ** x[:, 0] + t[1])","$$\left({\theta_{0}^{x_{0}}} + \theta_{1}\right)$$",-0.119730649807676;0.5837449352727875,5,,,,Infinity,,,,,, -818,0,Exp(Exp(((0.6122078697768832 * x1) / (-3.0368470133463614e-3 / (0.7562719246304952 / x0))))),"np.exp(np.exp(((t[0] * x[:, 1]) / (t[1] / (t[2] / x[:, 0])))))","$$\operatorname{exp}(\operatorname{exp}(\frac{\left(\theta_{0} \cdot x_{1}\right)}{\frac{\theta_{1}}{\frac{\theta_{2}}{x_{0}}}}))$$",0.6122078697768832;-3.0368470133463614e-3;0.7562719246304952,11,0.8923098659403457,0.8923098659403457,0.8923098659403457,0.8923098659403457,-81.34339021664219,-81.34339021664219,-81.34339021664219,26.575207846957838,26.575207846957838,26.575207846957838 -819,0,(-0.271594290590885 + x1),"(t[0] + x[:, 1])","$$\left(\theta_{0} + x_{1}\right)$$",-0.271594290590885,3,1.8305762287934728e-2,1.8305762287934728e-2,1.8305762287934728e-2,1.8305762287934728e-2,-0.6892769931440774,-0.6892769931440774,-0.6892769931440774,5.3332369874311105,5.3332369874311105,5.3332369874311105 -820,0,(Log((Log(x1) * (-0.717173079464597 + (((x1 ^ -0.7119301019777435) ^ -0.701063163199424) ^ (x1 ^ x0))))) / ((Exp(x1) ^ (-0.9857068848517935 * (((x0 * x1) / x1) * x1))) * x0)),"(np.log((np.log(x[:, 1]) * (t[0] + x[:, 1] ** t[1] ** t[2] ** x[:, 1] ** x[:, 0]))) / (np.exp(x[:, 1]) ** (t[3] * (((x[:, 0] * x[:, 1]) / x[:, 1]) * x[:, 1])) * x[:, 0]))","$$\frac{\operatorname{log}(\left(\operatorname{log}(x_{1}) \cdot \left(\theta_{0} + {{{x_{1}^{\theta_{1}}}^{\theta_{2}}}^{{x_{1}^{x_{0}}}}}\right)\right))}{\left({\operatorname{exp}(x_{1})^{\left(\theta_{3} \cdot \left(\frac{\left(x_{0} \cdot x_{1}\right)}{x_{1}} \cdot x_{1}\right)\right)}} \cdot x_{0}\right)}$$",-0.717173079464597;-0.7119301019777435;-0.701063163199424;-0.9857068848517935,30,,,,Infinity,,,,,, -821,0,(Exp(((-7.492120284718502e7 + (x0 / x1)) + (x1 * -8429856.192059038))) + (Log(Exp(Log((x0 * x1)))) / (-15859.451384205868 / (3964.4389464495534 * x1)))),"(np.exp(((t[0] + (x[:, 0] / x[:, 1])) + (x[:, 1] * t[1]))) + (np.log(np.exp(np.log((x[:, 0] * x[:, 1])))) / (t[2] / (t[3] * x[:, 1]))))","$$\left(\operatorname{exp}(\left(\left(\theta_{0} + \frac{x_{0}}{x_{1}}\right) + \left(x_{1} \cdot \theta_{1}\right)\right)) + \frac{\operatorname{log}(\operatorname{exp}(\operatorname{log}(\left(x_{0} \cdot x_{1}\right))))}{\frac{\theta_{2}}{\left(\theta_{3} \cdot x_{1}\right)}}\right)$$",-7.492120284718502e7;-8429856.192059038;-15859.451384205868;3964.4389464495534,23,6.834809941818371e-3,6.834809941818371e-3,6.834809941818371e-3,6.834809941818371e-3,0.3692758047647082,0.3692758047647082,0.3692758047647082,55.91700652905442,55.91700652905442,55.91700652905442 -822,0,(-0.271594290590885 + x1),"(t[0] + x[:, 1])","$$\left(\theta_{0} + x_{1}\right)$$",-0.271594290590885,3,1.8305762287934728e-2,1.8305762287934728e-2,1.8305762287934728e-2,1.8305762287934728e-2,-0.6892769931440774,-0.6892769931440774,-0.6892769931440774,5.3332369874311105,5.3332369874311105,5.3332369874311105 -823,0,((Log((0.3274395176735474 / x0)) * (0.3486318874777974 ^ x1)) * ((-0.8602825450930792 ^ x1) * ((-0.25114478696694553 / x0) * -0.6421807537753351))),"((np.log((t[0] / x[:, 0])) * t[1] ** x[:, 1]) * (t[2] ** x[:, 1] * ((t[3] / x[:, 0]) * t[4])))","$$\left(\left(\operatorname{log}(\frac{\theta_{0}}{x_{0}}) \cdot {\theta_{1}^{x_{1}}}\right) \cdot \left({\theta_{2}^{x_{1}}} \cdot \left(\frac{\theta_{3}}{x_{0}} \cdot \theta_{4}\right)\right)\right)$$",0.3274395176735474;0.3486318874777974;-0.8602825450930792;-0.25114478696694553;-0.6421807537753351,18,,,,Infinity,,,,,, -824,0,Log(Exp(((x1 * 2.656489682660923e-2) * Exp((x1 + x0))))),"np.log(np.exp(((x[:, 1] * t[0]) * np.exp((x[:, 1] + x[:, 0])))))","$$\operatorname{log}(\operatorname{exp}(\left(\left(x_{1} \cdot \theta_{0}\right) \cdot \operatorname{exp}(\left(x_{1} + x_{0}\right))\right)))$$",2.656489682660923e-2,10,1.2292959544683221e-2,1.2292959544683221e-2,1.2292959544683221e-2,1.2292959544683221e-2,-0.13440857637330983,-0.13440857637330983,-0.13440857637330983,21.491090089510987,21.491090089510987,21.491090089510987 -825,0,Log((((-0.5030528707581052 + x0) + -0.1848494125470318) ^ ((-0.7502991654328901 ^ x1) * -0.3988116962015612))),"np.log(((t[0] + x[:, 0]) + t[1]) ** (t[2] ** x[:, 1] * t[3]))","$$\operatorname{log}({\left(\left(\theta_{0} + x_{0}\right) + \theta_{1}\right)^{\left({\theta_{2}^{x_{1}}} \cdot \theta_{3}\right)}})$$",-0.5030528707581052;-0.1848494125470318;-0.7502991654328901;-0.3988116962015612,12,,,,Infinity,,,,,, -826,0,((Exp((-3.9694654488007344 * x0)) * -1.443036348928453) * -1.4517310249432671),"((np.exp((t[0] * x[:, 0])) * t[1]) * t[2])","$$\left(\left(\operatorname{exp}(\left(\theta_{0} \cdot x_{0}\right)) \cdot \theta_{1}\right) \cdot \theta_{2}\right)$$",-3.9694654488007344;-1.443036348928453;-1.4517310249432671,8,4.956561573974e-3,4.956561573974e-3,4.956561573974e-3,4.956561573974e-3,0.5426027444082513,0.5426027444082513,0.5426027444082513,17.157590600478624,17.157590600478624,17.157590600478624 -827,0,Log(((Log((-4.110215411384821e-2 ^ (x0 + x0))) + (((-0.49766842071489625 + (x0 ^ x1)) * -0.19037692677206008) + 9.620003990849724e-3)) ^ Log(x0))),"np.log((np.log(t[0] ** (x[:, 0] + x[:, 0])) + (((t[1] + x[:, 0] ** x[:, 1]) * t[2]) + t[3])) ** np.log(x[:, 0]))","$$\operatorname{log}({\left(\operatorname{log}({\theta_{0}^{\left(x_{0} + x_{0}\right)}}) + \left(\left(\left(\theta_{1} + {x_{0}^{x_{1}}}\right) \cdot \theta_{2}\right) + \theta_{3}\right)\right)^{\operatorname{log}(x_{0})}})$$",-4.110215411384821e-2;-0.49766842071489625;-0.19037692677206008;9.620003990849724e-3,20,,,,Infinity,,,,,, -828,0,(((Exp(Exp(x1)) + (2.498400179996627 * (x0 + x0))) * ((x0 * 3.47166442918676e-3) + -0.140398499384249)) + Exp(Log((1.340163779635564 ^ (((x1 + x1) * x0) / (x1 ^ (x1 ^ x0))))))),"(((np.exp(np.exp(x[:, 1])) + (t[0] * (x[:, 0] + x[:, 0]))) * ((x[:, 0] * t[1]) + t[2])) + np.exp(np.log(t[3] ** (((x[:, 1] + x[:, 1]) * x[:, 0]) / x[:, 1] ** x[:, 1] ** x[:, 0]))))","$$\left(\left(\left(\operatorname{exp}(\operatorname{exp}(x_{1})) + \left(\theta_{0} \cdot \left(x_{0} + x_{0}\right)\right)\right) \cdot \left(\left(x_{0} \cdot \theta_{1}\right) + \theta_{2}\right)\right) + \operatorname{exp}(\operatorname{log}({\theta_{3}^{\frac{\left(\left(x_{1} + x_{1}\right) \cdot x_{0}\right)}{{x_{1}^{{x_{1}^{x_{0}}}}}}}}))\right)$$",2.498400179996627;3.47166442918676e-3;-0.140398499384249;1.340163779635564,31,1.1531868483048846e-2,1.1531868483048846e-2,1.1531868483048846e-2,1.1531868483048846e-2,-6.417420973598342e-2,-6.417420973598342e-2,-6.417420973598342e-2,76.20804032052811,76.20804032052811,76.20804032052811 -829,0,((Log(Exp(((x0 ^ (-0.4766123073337696 ^ x1)) * (-0.15454003386513604 + x0)))) ^ (Log((-0.8392629703079342 ^ (x1 + x0))) * -0.6798702154563827)) + Log(((Exp((0.8593510144073092 * (-0.9259076283296643 * x1))) * (-0.25208880334211314 / (-0.6058430775413997 ^ x0))) / ((0.5907079547405585 + (x1 + x0)) * (-0.47781208170796663 ^ (-0.6079879698128765 + x0)))))),"(np.log(np.exp((x[:, 0] ** t[0] ** x[:, 1] * (t[1] + x[:, 0])))) ** (np.log(t[2] ** (x[:, 1] + x[:, 0])) * t[3]) + np.log(((np.exp((t[4] * (t[5] * x[:, 1]))) * (t[6] / t[7] ** x[:, 0])) / ((t[8] + (x[:, 1] + x[:, 0])) * t[9] ** (t[10] + x[:, 0])))))","$$\left({\operatorname{log}(\operatorname{exp}(\left({x_{0}^{{\theta_{0}^{x_{1}}}}} \cdot \left(\theta_{1} + x_{0}\right)\right)))^{\left(\operatorname{log}({\theta_{2}^{\left(x_{1} + x_{0}\right)}}) \cdot \theta_{3}\right)}} + \operatorname{log}(\frac{\left(\operatorname{exp}(\left(\theta_{4} \cdot \left(\theta_{5} \cdot x_{1}\right)\right)) \cdot \frac{\theta_{6}}{{\theta_{7}^{x_{0}}}}\right)}{\left(\left(\theta_{8} + \left(x_{1} + x_{0}\right)\right) \cdot {\theta_{9}^{\left(\theta_{10} + x_{0}\right)}}\right)})\right)$$",-0.4766123073337696;-0.15454003386513604;-0.8392629703079342;-0.6798702154563827;0.8593510144073092;-0.9259076283296643;-0.25208880334211314;-0.6058430775413997;0.5907079547405585;-0.47781208170796663;-0.6079879698128765,46,,,,Infinity,,,,,, -830,0,(((Log((x0 + x0)) * (0.7542006365371476 ^ ((x1 * -0.9720764209792123) ^ x0))) * (2.97986422440617e-2 ^ x0)) * x1),"(((np.log((x[:, 0] + x[:, 0])) * t[0] ** (x[:, 1] * t[1]) ** x[:, 0]) * t[2] ** x[:, 0]) * x[:, 1])","$$\left(\left(\left(\operatorname{log}(\left(x_{0} + x_{0}\right)) \cdot {\theta_{0}^{{\left(x_{1} \cdot \theta_{1}\right)^{x_{0}}}}}\right) \cdot {\theta_{2}^{x_{0}}}\right) \cdot x_{1}\right)$$",0.7542006365371476;-0.9720764209792123;2.97986422440617e-2,18,,,,Infinity,,,,,, -831,0,Exp((Log((x0 * x1)) + Log((x1 * -0.7282089038335995)))),"np.exp((np.log((x[:, 0] * x[:, 1])) + np.log((x[:, 1] * t[0]))))","$$\operatorname{exp}(\left(\operatorname{log}(\left(x_{0} \cdot x_{1}\right)) + \operatorname{log}(\left(x_{1} \cdot \theta_{0}\right))\right))$$",-0.7282089038335995,10,,,,Infinity,,,,,, -832,0,(((-2.053765825952037 + Exp((x1 / Exp((x0 * x0))))) * x0) + x0),"(((t[0] + np.exp((x[:, 1] / np.exp((x[:, 0] * x[:, 0]))))) * x[:, 0]) + x[:, 0])","$$\left(\left(\left(\theta_{0} + \operatorname{exp}(\frac{x_{1}}{\operatorname{exp}(\left(x_{0} \cdot x_{0}\right))})\right) \cdot x_{0}\right) + x_{0}\right)$$",-2.053765825952037,13,3.8736952481521385e-3,3.8736952481521385e-3,3.8736952481521385e-3,3.8736952481521385e-3,0.6425309059394971,0.6425309059394971,0.6425309059394971,27.32124319881005,27.32124319881005,27.32124319881005 -833,0,(((Exp(Log((x1 / x0))) * 0.8248385089105095) ^ (4.470008169470366 / (x0 * (x1 + x1)))) + Log(Exp(((Log(x0) + (-0.5146508047921211 + x0)) * (Exp(x1) ^ ((x0 * -2.9637182488387595) * 2.9829623073006126)))))),"((np.exp(np.log((x[:, 1] / x[:, 0]))) * t[0]) ** (t[1] / (x[:, 0] * (x[:, 1] + x[:, 1]))) + np.log(np.exp(((np.log(x[:, 0]) + (t[2] + x[:, 0])) * np.exp(x[:, 1]) ** ((x[:, 0] * t[3]) * t[4])))))","$$\left({\left(\operatorname{exp}(\operatorname{log}(\frac{x_{1}}{x_{0}})) \cdot \theta_{0}\right)^{\frac{\theta_{1}}{\left(x_{0} \cdot \left(x_{1} + x_{1}\right)\right)}}} + \operatorname{log}(\operatorname{exp}(\left(\left(\operatorname{log}(x_{0}) + \left(\theta_{2} + x_{0}\right)\right) \cdot {\operatorname{exp}(x_{1})^{\left(\left(x_{0} \cdot \theta_{3}\right) \cdot \theta_{4}\right)}}\right)))\right)$$",0.8248385089105095;4.470008169470366;-0.5146508047921211;-2.9637182488387595;2.9829623073006126,33,2.6403595688863083e-2,2.6403595688863083e-2,2.6403595688863083e-2,2.6403595688863083e-2,-1.4365544592957074,-1.4365544592957074,-1.4365544592957074,82.63679896505343,82.63679896505343,82.63679896505343 -834,0,((x1 / x1) * (x1 * 0.20059950763971945)),"((x[:, 1] / x[:, 1]) * (x[:, 1] * t[0]))","$$\left(\frac{x_{1}}{x_{1}} \cdot \left(x_{1} \cdot \theta_{0}\right)\right)$$",0.20059950763971945,7,9.365750690525965e-3,9.365750690525965e-3,9.365750690525965e-3,9.365750690525965e-3,0.13571765457390583,0.13571765457390583,0.13571765457390583,11.733414638828348,11.733414638828348,11.733414638828348 -835,0,(2.166582100951547e-3 ^ (1.5855875414993816 * x1)),"t[0] ** (t[1] * x[:, 1])","$${\theta_{0}^{\left(\theta_{1} \cdot x_{1}\right)}}$$",2.166582100951547e-3;1.5855875414993816,5,2.8848383251169497e-2,2.8848383251169497e-2,2.8848383251169497e-2,2.8848383251169497e-2,-1.6621622934393332,-1.6621622934393332,-1.6621622934393332,10.999285221260786,10.999285221260786,10.999285221260786 -836,0,((-0.46142956451244765 * (0.33374438610845536 / (x0 ^ x1))) ^ Log(Log((x0 + x1)))),"(t[0] * (t[1] / x[:, 0] ** x[:, 1])) ** np.log(np.log((x[:, 0] + x[:, 1])))","$${\left(\theta_{0} \cdot \frac{\theta_{1}}{{x_{0}^{x_{1}}}}\right)^{\operatorname{log}(\operatorname{log}(\left(x_{0} + x_{1}\right)))}}$$",-0.46142956451244765;0.33374438610845536,13,,,,Infinity,,,,,, -837,0,((((-0.9932049196719133 * x1) + -0.3032419112971735) * Log((-0.6808890640881642 ^ (x0 / x0)))) / (((x0 * x1) * Log((0.8895106630645953 ^ x0))) + (Exp(Exp(x0)) ^ (x1 ^ 0.7857583282495737)))),"((((t[0] * x[:, 1]) + t[1]) * np.log(t[2] ** (x[:, 0] / x[:, 0]))) / (((x[:, 0] * x[:, 1]) * np.log(t[3] ** x[:, 0])) + np.exp(np.exp(x[:, 0])) ** x[:, 1] ** t[4]))","$$\frac{\left(\left(\left(\theta_{0} \cdot x_{1}\right) + \theta_{1}\right) \cdot \operatorname{log}({\theta_{2}^{\frac{x_{0}}{x_{0}}}})\right)}{\left(\left(\left(x_{0} \cdot x_{1}\right) \cdot \operatorname{log}({\theta_{3}^{x_{0}}})\right) + {\operatorname{exp}(\operatorname{exp}(x_{0}))^{{x_{1}^{\theta_{4}}}}}\right)}$$",-0.9932049196719133;-0.3032419112971735;-0.6808890640881642;0.8895106630645953;0.7857583282495737,29,,,,Infinity,,,,,, -838,0,Exp(Exp((-138.2431302405372 / (x0 / x1)))),"np.exp(np.exp((t[0] / (x[:, 0] / x[:, 1]))))","$$\operatorname{exp}(\operatorname{exp}(\frac{\theta_{0}}{\frac{x_{0}}{x_{1}}}))$$",-138.2431302405372,7,0.892310050889116,0.892310050889116,0.892310050889116,0.892310050889116,-81.3434072839294,-81.3434072839294,-81.3434072839294,14.090069368206548,14.090069368206548,14.090069368206548 -839,0,(((-0.6504600155342297 ^ (4.221697554408366e-2 / (Log((x1 ^ x0)) * 0.7344309506296558))) / Log((((Log(x1) + (x0 * -0.6781649678208725)) / ((x1 ^ -0.12943791708268448) + x0)) ^ ((x1 * -0.9364589833977045) / (-0.39314159374497326 / x0))))) * (0.6199325383278119 * Exp((0.6186206696468293 + x0)))),"((t[0] ** (t[1] / (np.log(x[:, 1] ** x[:, 0]) * t[2])) / np.log(((np.log(x[:, 1]) + (x[:, 0] * t[3])) / (x[:, 1] ** t[4] + x[:, 0])) ** ((x[:, 1] * t[5]) / (t[6] / x[:, 0])))) * (t[7] * np.exp((t[8] + x[:, 0]))))","$$\left(\frac{{\theta_{0}^{\frac{\theta_{1}}{\left(\operatorname{log}({x_{1}^{x_{0}}}) \cdot \theta_{2}\right)}}}}{\operatorname{log}({\frac{\left(\operatorname{log}(x_{1}) + \left(x_{0} \cdot \theta_{3}\right)\right)}{\left({x_{1}^{\theta_{4}}} + x_{0}\right)}^{\frac{\left(x_{1} \cdot \theta_{5}\right)}{\frac{\theta_{6}}{x_{0}}}}})} \cdot \left(\theta_{7} \cdot \operatorname{exp}(\left(\theta_{8} + x_{0}\right))\right)\right)$$",-0.6504600155342297;4.221697554408366e-2;0.7344309506296558;-0.6781649678208725;-0.12943791708268448;-0.9364589833977045;-0.39314159374497326;0.6199325383278119;0.6186206696468293,39,,,,Infinity,,,,,, -840,0,Log(((Log((0.9306771434542265 / (-0.5078833944178747 + x1))) + 1.7687957984541702e-2) / ((((x0 * x0) ^ 0.939487080880486) + -0.735096987688485) ^ Log((-0.8146912588455344 ^ x0))))),"np.log(((np.log((t[0] / (t[1] + x[:, 1]))) + t[2]) / ((x[:, 0] * x[:, 0]) ** t[3] + t[4]) ** np.log(t[5] ** x[:, 0])))","$$\operatorname{log}(\frac{\left(\operatorname{log}(\frac{\theta_{0}}{\left(\theta_{1} + x_{1}\right)}) + \theta_{2}\right)}{{\left({\left(x_{0} \cdot x_{0}\right)^{\theta_{3}}} + \theta_{4}\right)^{\operatorname{log}({\theta_{5}^{x_{0}}})}}})$$",0.9306771434542265;-0.5078833944178747;1.7687957984541702e-2;0.939487080880486;-0.735096987688485;-0.8146912588455344,22,,,,Infinity,,,,,, -841,0,(-0.9956750177908841 + x0),"(t[0] + x[:, 0])","$$\left(\theta_{0} + x_{0}\right)$$",-0.9956750177908841,3,0.15105329635364,0.15105329635364,0.15105329635364,0.15105329635364,-12.939373529228037,-12.939373529228037,-12.939373529228037,5.452709768090245,5.452709768090245,5.452709768090245 -842,0,((-0.8187475047722674 ^ (((-0.6335492717978208 * (-0.23355360194114172 ^ x0)) * -0.5004872772310853) * (Log((Log(x1) ^ (-0.3946859377759575 / x0))) * ((x0 / Exp(x1)) ^ (x1 * -5.347527745098946e-3))))) ^ Exp(((x0 * 0.11080286023370056) + -0.9674625359235983))),"t[0] ** (((t[1] * t[2] ** x[:, 0]) * t[3]) * (np.log(np.log(x[:, 1]) ** (t[4] / x[:, 0])) * (x[:, 0] / np.exp(x[:, 1])) ** (x[:, 1] * t[5]))) ** np.exp(((x[:, 0] * t[6]) + t[7]))","$${{\theta_{0}^{\left(\left(\left(\theta_{1} \cdot {\theta_{2}^{x_{0}}}\right) \cdot \theta_{3}\right) \cdot \left(\operatorname{log}({\operatorname{log}(x_{1})^{\frac{\theta_{4}}{x_{0}}}}) \cdot {\frac{x_{0}}{\operatorname{exp}(x_{1})}^{\left(x_{1} \cdot \theta_{5}\right)}}\right)\right)}}^{\operatorname{exp}(\left(\left(x_{0} \cdot \theta_{6}\right) + \theta_{7}\right))}}$$",-0.8187475047722674;-0.6335492717978208;-0.23355360194114172;-0.5004872772310853;-0.3946859377759575;-5.347527745098946e-3;0.11080286023370056;-0.9674625359235983,33,,,,Infinity,,,,,, -843,0,((Log(Exp((Log((x0 * -0.9769069455268177)) * -1.9414810060417897e-2))) ^ ((((x0 * (x1 * 0.3490175641464994)) ^ x1) * -0.6187807380980385) * Exp(x0))) * ((((((x1 * 3.159050709080313e-2) ^ x0) + -0.402975111878485) ^ (x0 ^ x1)) * -0.9897884154539336) / x1)),"(np.log(np.exp((np.log((x[:, 0] * t[0])) * t[1]))) ** (((x[:, 0] * (x[:, 1] * t[2])) ** x[:, 1] * t[3]) * np.exp(x[:, 0])) * ((((x[:, 1] * t[4]) ** x[:, 0] + t[5]) ** x[:, 0] ** x[:, 1] * t[6]) / x[:, 1]))","$$\left({\operatorname{log}(\operatorname{exp}(\left(\operatorname{log}(\left(x_{0} \cdot \theta_{0}\right)) \cdot \theta_{1}\right)))^{\left(\left({\left(x_{0} \cdot \left(x_{1} \cdot \theta_{2}\right)\right)^{x_{1}}} \cdot \theta_{3}\right) \cdot \operatorname{exp}(x_{0})\right)}} \cdot \frac{\left({\left({\left(x_{1} \cdot \theta_{4}\right)^{x_{0}}} + \theta_{5}\right)^{{x_{0}^{x_{1}}}}} \cdot \theta_{6}\right)}{x_{1}}\right)$$",-0.9769069455268177;-1.9414810060417897e-2;0.3490175641464994;-0.6187807380980385;3.159050709080313e-2;-0.402975111878485;-0.9897884154539336,37,,,,Infinity,,,,,, -844,0,(((((Exp(Log(x0)) + 0.20850060370198475) * 0.39511150930296546) ^ (Log(x1) ^ 0.8257794175657165)) * (-0.20517511469791816 / (Log(x1) ^ x0))) * 0.1295796306760718),"((((np.exp(np.log(x[:, 0])) + t[0]) * t[1]) ** np.log(x[:, 1]) ** t[2] * (t[3] / np.log(x[:, 1]) ** x[:, 0])) * t[4])","$$\left(\left({\left(\left(\operatorname{exp}(\operatorname{log}(x_{0})) + \theta_{0}\right) \cdot \theta_{1}\right)^{{\operatorname{log}(x_{1})^{\theta_{2}}}}} \cdot \frac{\theta_{3}}{{\operatorname{log}(x_{1})^{x_{0}}}}\right) \cdot \theta_{4}\right)$$",0.20850060370198475;0.39511150930296546;0.8257794175657165;-0.20517511469791816;0.1295796306760718,21,,,,Infinity,,,,,, -845,0,Log(((Log((0.6109241778664527 * x0)) * Exp(x0)) ^ Log(((-0.7050229827796687 / x1) / x1)))),"np.log((np.log((t[0] * x[:, 0])) * np.exp(x[:, 0])) ** np.log(((t[1] / x[:, 1]) / x[:, 1])))","$$\operatorname{log}({\left(\operatorname{log}(\left(\theta_{0} \cdot x_{0}\right)) \cdot \operatorname{exp}(x_{0})\right)^{\operatorname{log}(\frac{\frac{\theta_{1}}{x_{1}}}{x_{1}})}})$$",0.6109241778664527;-0.7050229827796687,15,,,,Infinity,,,,,, -846,0,Log(Log((Log(((x0 + x0) * (x1 * x0))) + Log(Log((x0 ^ 0.5581276896657926)))))),"np.log(np.log((np.log(((x[:, 0] + x[:, 0]) * (x[:, 1] * x[:, 0]))) + np.log(np.log(x[:, 0] ** t[0])))))","$$\operatorname{log}(\operatorname{log}(\left(\operatorname{log}(\left(\left(x_{0} + x_{0}\right) \cdot \left(x_{1} \cdot x_{0}\right)\right)) + \operatorname{log}(\operatorname{log}({x_{0}^{\theta_{0}}}))\right)))$$",0.5581276896657926,16,,,,Infinity,,,,,, -847,0,(Log(Exp(((Exp(x1) / Log(x0)) ^ ((x0 * 0.9770045916783751) + 0.9595488242937393)))) ^ -0.12057212417689944),"np.log(np.exp((np.exp(x[:, 1]) / np.log(x[:, 0])) ** ((x[:, 0] * t[0]) + t[1]))) ** t[2]","$${\operatorname{log}(\operatorname{exp}({\frac{\operatorname{exp}(x_{1})}{\operatorname{log}(x_{0})}^{\left(\left(x_{0} \cdot \theta_{0}\right) + \theta_{1}\right)}}))^{\theta_{2}}}$$",0.9770045916783751;0.9595488242937393;-0.12057212417689944,15,,,,Infinity,,,,,, -848,0,((Log(Exp((Log(x1) * -7.126078475871078e-2))) + Log((2.6719596847589733e-3 + (x1 * -0.14245743397936717)))) / Exp((-0.12605633088515023 * ((-0.7151125753959062 ^ Log(x0)) * (x0 * 0.8614224775338946))))),"((np.log(np.exp((np.log(x[:, 1]) * t[0]))) + np.log((t[1] + (x[:, 1] * t[2])))) / np.exp((t[3] * (t[4] ** np.log(x[:, 0]) * (x[:, 0] * t[5])))))","$$\frac{\left(\operatorname{log}(\operatorname{exp}(\left(\operatorname{log}(x_{1}) \cdot \theta_{0}\right))) + \operatorname{log}(\left(\theta_{1} + \left(x_{1} \cdot \theta_{2}\right)\right))\right)}{\operatorname{exp}(\left(\theta_{3} \cdot \left({\theta_{4}^{\operatorname{log}(x_{0})}} \cdot \left(x_{0} \cdot \theta_{5}\right)\right)\right))}$$",-7.126078475871078e-2;2.6719596847589733e-3;-0.14245743397936717;-0.12605633088515023;-0.7151125753959062;0.8614224775338946,25,,,,Infinity,,,,,, -849,0,Log(Log(Exp((-0.12228780639558634 * x0)))),"np.log(np.log(np.exp((t[0] * x[:, 0]))))","$$\operatorname{log}(\operatorname{log}(\operatorname{exp}(\left(\theta_{0} \cdot x_{0}\right))))$$",-0.12228780639558634,6,,,,Infinity,,,,,, -850,0,((0.646469674330622 + (x0 + Log(x1))) * ((x0 + (0.7761352374620942 / x1)) * (-0.8522749385218367 ^ x0))),"((t[0] + (x[:, 0] + np.log(x[:, 1]))) * ((x[:, 0] + (t[1] / x[:, 1])) * t[2] ** x[:, 0]))","$$\left(\left(\theta_{0} + \left(x_{0} + \operatorname{log}(x_{1})\right)\right) \cdot \left(\left(x_{0} + \frac{\theta_{1}}{x_{1}}\right) \cdot {\theta_{2}^{x_{0}}}\right)\right)$$",0.646469674330622;0.7761352374620942;-0.8522749385218367,16,,,,Infinity,,,,,, -851,0,Log(Log(((((x0 * x0) + (x0 * -0.9705122130182313)) + (0.5532788583177288 ^ x0)) / ((0.34363898484325683 + (x1 ^ x0)) * Exp(Exp(x1)))))),"np.log(np.log(((((x[:, 0] * x[:, 0]) + (x[:, 0] * t[0])) + t[1] ** x[:, 0]) / ((t[2] + x[:, 1] ** x[:, 0]) * np.exp(np.exp(x[:, 1]))))))","$$\operatorname{log}(\operatorname{log}(\frac{\left(\left(\left(x_{0} \cdot x_{0}\right) + \left(x_{0} \cdot \theta_{0}\right)\right) + {\theta_{1}^{x_{0}}}\right)}{\left(\left(\theta_{2} + {x_{1}^{x_{0}}}\right) \cdot \operatorname{exp}(\operatorname{exp}(x_{1}))\right)}))$$",-0.9705122130182313;0.5532788583177288;0.34363898484325683,23,,,,Infinity,,,,,, -852,0,(x0 / x0),"(x[:, 0] / x[:, 0])","$$\frac{x_{0}}{x_{0}}$$",,3,0.8923097947317449,0.8923097947317449,0.8923097947317449,0.8923097947317449,-81.34338364542985,-81.34338364542985,-81.34338364542985,2.8825203569384064,2.8825203569384064,2.8825203569384064 -853,0,(Exp((Log((Log(Exp(x0)) + 0.7204894144880363)) / ((-0.6460758290453559 + x0) / Exp((x1 + (x1 * x1)))))) * ((2.9089280493287184e-2 * (x1 + 0.12915223798839393)) * (Exp((x1 ^ 0.694284300350668)) * 0.33796896843990665))),"(np.exp((np.log((np.log(np.exp(x[:, 0])) + t[0])) / ((t[1] + x[:, 0]) / np.exp((x[:, 1] + (x[:, 1] * x[:, 1])))))) * ((t[2] * (x[:, 1] + t[3])) * (np.exp(x[:, 1] ** t[4]) * t[5])))","$$\left(\operatorname{exp}(\frac{\operatorname{log}(\left(\operatorname{log}(\operatorname{exp}(x_{0})) + \theta_{0}\right))}{\frac{\left(\theta_{1} + x_{0}\right)}{\operatorname{exp}(\left(x_{1} + \left(x_{1} \cdot x_{1}\right)\right))}}) \cdot \left(\left(\theta_{2} \cdot \left(x_{1} + \theta_{3}\right)\right) \cdot \left(\operatorname{exp}({x_{1}^{\theta_{4}}}) \cdot \theta_{5}\right)\right)\right)$$",0.7204894144880363;-0.6460758290453559;2.9089280493287184e-2;0.12915223798839393;0.694284300350668;0.33796896843990665,31,4.368564968125827e34,4.368564968125827e34,4.368564968125827e34,4.368564968125827e34,-4.03136246261326e36,-4.03136246261326e36,-4.03136246261326e36,3.9317084713132442e34,3.9317084713132442e34,3.9317084713132442e34 -854,0,Log(((((x1 * -0.2438123062140527) * ((-0.830958061124212 / x1) * 0.3695063822618355)) + (Exp(x0) + (((5.142379864030544e-3 * x0) ^ -0.8855594414538726) * 0.3619553969759084))) * (((x0 ^ ((x1 * -2.650256058924927e-2) / x0)) / x0) + -0.9209172075900574))),"np.log(((((x[:, 1] * t[0]) * ((t[1] / x[:, 1]) * t[2])) + (np.exp(x[:, 0]) + ((t[3] * x[:, 0]) ** t[4] * t[5]))) * ((x[:, 0] ** ((x[:, 1] * t[6]) / x[:, 0]) / x[:, 0]) + t[7])))","$$\operatorname{log}(\left(\left(\left(\left(x_{1} \cdot \theta_{0}\right) \cdot \left(\frac{\theta_{1}}{x_{1}} \cdot \theta_{2}\right)\right) + \left(\operatorname{exp}(x_{0}) + \left({\left(\theta_{3} \cdot x_{0}\right)^{\theta_{4}}} \cdot \theta_{5}\right)\right)\right) \cdot \left(\frac{{x_{0}^{\frac{\left(x_{1} \cdot \theta_{6}\right)}{x_{0}}}}}{x_{0}} + \theta_{7}\right)\right))$$",-0.2438123062140527;-0.830958061124212;0.3695063822618355;5.142379864030544e-3;-0.8855594414538726;0.3619553969759084;-2.650256058924927e-2;-0.9209172075900574,33,,,,Infinity,,,,,, -855,0,Log(((Exp(((Exp(x1) * 7.488268926705302e-2) ^ 0.5688970478793325)) * (x0 / (0.24714046319429864 + (x0 ^ 1.4783366140857996)))) * Exp((x0 * (0.16360719807960636 ^ ((x0 ^ 0.7267601652195536) / (x1 ^ x0))))))),"np.log(((np.exp((np.exp(x[:, 1]) * t[0]) ** t[1]) * (x[:, 0] / (t[2] + x[:, 0] ** t[3]))) * np.exp((x[:, 0] * t[4] ** (x[:, 0] ** t[5] / x[:, 1] ** x[:, 0])))))","$$\operatorname{log}(\left(\left(\operatorname{exp}({\left(\operatorname{exp}(x_{1}) \cdot \theta_{0}\right)^{\theta_{1}}}) \cdot \frac{x_{0}}{\left(\theta_{2} + {x_{0}^{\theta_{3}}}\right)}\right) \cdot \operatorname{exp}(\left(x_{0} \cdot {\theta_{4}^{\frac{{x_{0}^{\theta_{5}}}}{{x_{1}^{x_{0}}}}}}\right))\right))$$",7.488268926705302e-2;0.5688970478793325;0.24714046319429864;1.4783366140857996;0.16360719807960636;0.7267601652195536,29,3.3351339293433754e-3,3.3351339293433754e-3,3.3351339293433754e-3,3.3351339293433754e-3,0.6922299695977534,0.6922299695977534,0.6922299695977534,75.84806397549262,75.84806397549262,75.84806397549262 -856,0,((Exp(Exp(Log(Log(x0)))) ^ (Exp(Log(x1)) ^ Exp(((x0 ^ 0.283608048852652) ^ Log((x0 * -0.19133474678575013)))))) + Log((0.302898071615174 + x1))),"(np.exp(np.exp(np.log(np.log(x[:, 0])))) ** np.exp(np.log(x[:, 1])) ** np.exp(x[:, 0] ** t[0] ** np.log((x[:, 0] * t[1]))) + np.log((t[2] + x[:, 1])))","$$\left({\operatorname{exp}(\operatorname{exp}(\operatorname{log}(\operatorname{log}(x_{0}))))^{{\operatorname{exp}(\operatorname{log}(x_{1}))^{\operatorname{exp}({{x_{0}^{\theta_{0}}}^{\operatorname{log}(\left(x_{0} \cdot \theta_{1}\right))}})}}}} + \operatorname{log}(\left(\theta_{2} + x_{1}\right))\right)$$",0.283608048852652;-0.19133474678575013;0.302898071615174,24,,,,Infinity,,,,,, -857,0,(Log((Log(x1) * 0.9348180794634879)) + ((Exp((0.41982366321633235 + (-0.2958854569851084 * Exp(x1)))) / (Exp(((x0 * 0.8687564992029833) * 4.4965911615461374e-2)) * x1)) + 0.4651531032873464)),"(np.log((np.log(x[:, 1]) * t[0])) + ((np.exp((t[1] + (t[2] * np.exp(x[:, 1])))) / (np.exp(((x[:, 0] * t[3]) * t[4])) * x[:, 1])) + t[5]))","$$\left(\operatorname{log}(\left(\operatorname{log}(x_{1}) \cdot \theta_{0}\right)) + \left(\frac{\operatorname{exp}(\left(\theta_{1} + \left(\theta_{2} \cdot \operatorname{exp}(x_{1})\right)\right))}{\left(\operatorname{exp}(\left(\left(x_{0} \cdot \theta_{3}\right) \cdot \theta_{4}\right)) \cdot x_{1}\right)} + \theta_{5}\right)\right)$$",0.9348180794634879;0.41982366321633235;-0.2958854569851084;0.8687564992029833;4.4965911615461374e-2;0.4651531032873464,24,,,,Infinity,,,,,, -858,0,(((-0.9456297542848564 ^ (x1 + x0)) + (0.5029478378326526 ^ x0)) / ((x0 ^ (x1 + 0.6273252712780386)) / (x0 ^ 0.11338501497399689))),"((t[0] ** (x[:, 1] + x[:, 0]) + t[1] ** x[:, 0]) / (x[:, 0] ** (x[:, 1] + t[2]) / x[:, 0] ** t[3]))","$$\frac{\left({\theta_{0}^{\left(x_{1} + x_{0}\right)}} + {\theta_{1}^{x_{0}}}\right)}{\frac{{x_{0}^{\left(x_{1} + \theta_{2}\right)}}}{{x_{0}^{\theta_{3}}}}}$$",-0.9456297542848564;0.5029478378326526;0.6273252712780386;0.11338501497399689,19,,,,Infinity,,,,,, -859,0,Log((0.5590021509854783 * (1.5538351521611518 + x1))),"np.log((t[0] * (t[1] + x[:, 1])))","$$\operatorname{log}(\left(\theta_{0} \cdot \left(\theta_{1} + x_{1}\right)\right))$$",0.5590021509854783;1.5538351521611518,6,1.0239785633833471e-2,1.0239785633833471e-2,1.0239785633833471e-2,1.0239785633833471e-2,5.506069543123715e-2,5.506069543123715e-2,5.506069543123715e-2,13.707693152410332,13.707693152410332,13.707693152410332 -860,0,(Exp((((x0 + (-0.6698097979573869 + (x1 / x1))) + (x0 * (Exp(x1) ^ -0.5018310113296028))) ^ (x0 + 0.2690226328643769))) + Exp((0.674370919631741 ^ x0))),"(np.exp(((x[:, 0] + (t[0] + (x[:, 1] / x[:, 1]))) + (x[:, 0] * np.exp(x[:, 1]) ** t[1])) ** (x[:, 0] + t[2])) + np.exp(t[3] ** x[:, 0]))","$$\left(\operatorname{exp}({\left(\left(x_{0} + \left(\theta_{0} + \frac{x_{1}}{x_{1}}\right)\right) + \left(x_{0} \cdot {\operatorname{exp}(x_{1})^{\theta_{1}}}\right)\right)^{\left(x_{0} + \theta_{2}\right)}}) + \operatorname{exp}({\theta_{3}^{x_{0}}})\right)$$",-0.6698097979573869;-0.5018310113296028;0.2690226328643769;0.674370919631741,24,204811.78382735775,204811.78382735775,204811.78382735775,204811.78382735775,-1.8900268155816067e7,-1.8900268155816067e7,-1.8900268155816067e7,184388.5957413638,184388.5957413638,184388.5957413638 -861,0,(((((-0.9946605982662117 ^ (0.8325677979222722 + x1)) ^ x0) ^ x0) * x0) ^ 0.8198154079378486),"(t[0] ** (t[1] + x[:, 1]) ** x[:, 0] ** x[:, 0] * x[:, 0]) ** t[2]","$${\left({{{\theta_{0}^{\left(\theta_{1} + x_{1}\right)}}^{x_{0}}}^{x_{0}}} \cdot x_{0}\right)^{\theta_{2}}}$$",-0.9946605982662117;0.8325677979222722;0.8198154079378486,13,,,,Infinity,,,,,, -862,0,Log((x1 + 0.7507911464536998)),"np.log((x[:, 1] + t[0]))","$$\operatorname{log}(\left(x_{1} + \theta_{0}\right))$$",0.7507911464536998,4,1.668267260813824e-2,1.668267260813824e-2,1.668267260813824e-2,1.668267260813824e-2,-0.5394963934201895,-0.5394963934201895,-0.5394963934201895,7.581116785194527,7.581116785194527,7.581116785194527 -863,0,(((((-0.34823850342236473 / x1) / x0) + 0.5105823951462887) / ((-0.47071856474611684 + Exp((0.716125048851075 / x0))) * (x1 + (x0 ^ x1)))) ^ x0),"((((t[0] / x[:, 1]) / x[:, 0]) + t[1]) / ((t[2] + np.exp((t[3] / x[:, 0]))) * (x[:, 1] + x[:, 0] ** x[:, 1]))) ** x[:, 0]","$${\frac{\left(\frac{\frac{\theta_{0}}{x_{1}}}{x_{0}} + \theta_{1}\right)}{\left(\left(\theta_{2} + \operatorname{exp}(\frac{\theta_{3}}{x_{0}})\right) \cdot \left(x_{1} + {x_{0}^{x_{1}}}\right)\right)}^{x_{0}}}$$",-0.34823850342236473;0.5105823951462887;-0.47071856474611684;0.716125048851075,22,,,,Infinity,,,,,, -864,0,(Log(((-0.9070387198715313 ^ (0.14934208259229487 / x0)) / (x0 ^ -0.8956618621154508))) * ((0.23950560697231427 + (x1 ^ -0.8791352261484618)) + (0.9241784121019646 + (0.32129852812656123 * x1)))),"(np.log((t[0] ** (t[1] / x[:, 0]) / x[:, 0] ** t[2])) * ((t[3] + x[:, 1] ** t[4]) + (t[5] + (t[6] * x[:, 1]))))","$$\left(\operatorname{log}(\frac{{\theta_{0}^{\frac{\theta_{1}}{x_{0}}}}}{{x_{0}^{\theta_{2}}}}) \cdot \left(\left(\theta_{3} + {x_{1}^{\theta_{4}}}\right) + \left(\theta_{5} + \left(\theta_{6} \cdot x_{1}\right)\right)\right)\right)$$",-0.9070387198715313;0.14934208259229487;-0.8956618621154508;0.23950560697231427;-0.8791352261484618;0.9241784121019646;0.32129852812656123,22,,,,Infinity,,,,,, -865,0,(Log(Exp(((-0.28227774745360745 * Log(Exp(x1))) + (-0.6335913214486804 * (0.6118417503523635 + x1))))) * ((Log(Log(Log((-0.8257167115336339 / x1)))) ^ Log((x1 / x0))) * ((Exp(x0) * -0.5961273139221779) + (Log(x1) * (-2.6539820857122476e-2 ^ x1))))),"(np.log(np.exp(((t[0] * np.log(np.exp(x[:, 1]))) + (t[1] * (t[2] + x[:, 1]))))) * (np.log(np.log(np.log((t[3] / x[:, 1])))) ** np.log((x[:, 1] / x[:, 0])) * ((np.exp(x[:, 0]) * t[4]) + (np.log(x[:, 1]) * t[5] ** x[:, 1]))))","$$\left(\operatorname{log}(\operatorname{exp}(\left(\left(\theta_{0} \cdot \operatorname{log}(\operatorname{exp}(x_{1}))\right) + \left(\theta_{1} \cdot \left(\theta_{2} + x_{1}\right)\right)\right))) \cdot \left({\operatorname{log}(\operatorname{log}(\operatorname{log}(\frac{\theta_{3}}{x_{1}})))^{\operatorname{log}(\frac{x_{1}}{x_{0}})}} \cdot \left(\left(\operatorname{exp}(x_{0}) \cdot \theta_{4}\right) + \left(\operatorname{log}(x_{1}) \cdot {\theta_{5}^{x_{1}}}\right)\right)\right)\right)$$",-0.28227774745360745;-0.6335913214486804;0.6118417503523635;-0.8257167115336339;-0.5961273139221779;-2.6539820857122476e-2,37,,,,Infinity,,,,,, -866,0,((Log(Log((x1 ^ -0.2578715708573385))) ^ 8.516098699720043e-2) ^ (6.604034580321849e-2 / x0)),"np.log(np.log(x[:, 1] ** t[0])) ** t[1] ** (t[2] / x[:, 0])","$${{\operatorname{log}(\operatorname{log}({x_{1}^{\theta_{0}}}))^{\theta_{1}}}^{\frac{\theta_{2}}{x_{0}}}}$$",-0.2578715708573385;8.516098699720043e-2;6.604034580321849e-2,11,,,,Infinity,,,,,, -867,0,Exp((Exp((0.7930611838033478 * x1)) / ((1.6105735951399332e-5 ^ Log((x1 * x1))) * (x1 ^ -8.30475180036716e-2)))),"np.exp((np.exp((t[0] * x[:, 1])) / (t[1] ** np.log((x[:, 1] * x[:, 1])) * x[:, 1] ** t[2])))","$$\operatorname{exp}(\frac{\operatorname{exp}(\left(\theta_{0} \cdot x_{1}\right))}{\left({\theta_{1}^{\operatorname{log}(\left(x_{1} \cdot x_{1}\right))}} \cdot {x_{1}^{\theta_{2}}}\right)})$$",0.7930611838033478;1.6105735951399332e-5;-8.30475180036716e-2,16,0.8923110137892294,0.8923110137892294,0.8923110137892294,0.8923110137892294,-81.34349614146959,-81.34349614146959,-81.34349614146959,38.00041710339824,38.00041710339824,38.00041710339824 -868,0,Log((1.0006674709432373 * ((x0 + 6.409796169160163e-2) ^ (x0 * x1)))),"np.log((t[0] * (x[:, 0] + t[1]) ** (x[:, 0] * x[:, 1])))","$$\operatorname{log}(\left(\theta_{0} \cdot {\left(x_{0} + \theta_{1}\right)^{\left(x_{0} \cdot x_{1}\right)}}\right))$$",1.0006674709432373;6.409796169160163e-2,10,4.047410128666835e-2,4.047410128666835e-2,4.047410128666835e-2,4.047410128666835e-2,-2.734997048815372,-2.734997048815372,-2.734997048815372,23.537378052446414,23.537378052446414,23.537378052446414 -869,0,(Log(Log((6.016310321045326e-3 + x1))) + Exp(Exp((0.4054402497097065 / (0.6962133532401528 * x0))))),"(np.log(np.log((t[0] + x[:, 1]))) + np.exp(np.exp((t[1] / (t[2] * x[:, 0])))))","$$\left(\operatorname{log}(\operatorname{log}(\left(\theta_{0} + x_{1}\right))) + \operatorname{exp}(\operatorname{exp}(\frac{\theta_{1}}{\left(\theta_{2} \cdot x_{0}\right)}))\right)$$",6.016310321045326e-3;0.4054402497097065;0.6962133532401528,13,,,,Infinity,,,,,, -870,0,(((((0.8508803945643815 / ((x1 / x0) / (0.7276296255799714 / x1))) / x1) * 0.6201683019656246) + x0) ^ 0.8751164433848875),"((((t[0] / ((x[:, 1] / x[:, 0]) / (t[1] / x[:, 1]))) / x[:, 1]) * t[2]) + x[:, 0]) ** t[3]","$${\left(\left(\frac{\frac{\theta_{0}}{\frac{\frac{x_{1}}{x_{0}}}{\frac{\theta_{1}}{x_{1}}}}}{x_{1}} \cdot \theta_{2}\right) + x_{0}\right)^{\theta_{3}}}$$",0.8508803945643815;0.7276296255799714;0.6201683019656246;0.8751164433848875,17,2098.88521213821,2098.88521213821,2098.88521213821,2098.88521213821,-193686.5637488365,-193686.5637488365,-193686.5637488365,1930.1608631998001,1930.1608631998001,1930.1608631998001 -871,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 -872,0,(-0.7977503217726849 * (-0.7109000452578229 ^ (x1 * Exp(x0)))),"(t[0] * t[1] ** (x[:, 1] * np.exp(x[:, 0])))","$$\left(\theta_{0} \cdot {\theta_{1}^{\left(x_{1} \cdot \operatorname{exp}(x_{0})\right)}}\right)$$",-0.7977503217726849;-0.7109000452578229,8,,,,Infinity,,,,,, -873,0,(((x0 * (x1 * 7.516323337171304e-4)) ^ 0.23417542047012066) * Log(Exp(Log(((x1 / x0) * (1.1875829051461024 + Exp(x0))))))),"((x[:, 0] * (x[:, 1] * t[0])) ** t[1] * np.log(np.exp(np.log(((x[:, 1] / x[:, 0]) * (t[2] + np.exp(x[:, 0])))))))","$$\left({\left(x_{0} \cdot \left(x_{1} \cdot \theta_{0}\right)\right)^{\theta_{1}}} \cdot \operatorname{log}(\operatorname{exp}(\operatorname{log}(\left(\frac{x_{1}}{x_{0}} \cdot \left(\theta_{2} + \operatorname{exp}(x_{0})\right)\right))))\right)$$",7.516323337171304e-4;0.23417542047012066;1.1875829051461024,19,8.836334493986993e-3,8.836334493986993e-3,8.836334493986993e-3,8.836334493986993e-3,0.1845727957335056,0.1845727957335056,0.1845727957335056,47.81799447653568,47.81799447653568,47.81799447653568 -874,0,((((x0 ^ (x0 * 0.1933807567273616)) + (x0 / x0)) / x1) + -7.5517961382784415),"(((x[:, 0] ** (x[:, 0] * t[0]) + (x[:, 0] / x[:, 0])) / x[:, 1]) + t[1])","$$\left(\frac{\left({x_{0}^{\left(x_{0} \cdot \theta_{0}\right)}} + \frac{x_{0}}{x_{0}}\right)}{x_{1}} + \theta_{1}\right)$$",0.1933807567273616;-7.5517961382784415,13,17.794390169876046,17.794390169876046,17.794390169876046,17.794390169876046,-1641.0869804920915,-1641.0869804920915,-1641.0869804920915,45.3536329613428,45.3536329613428,45.3536329613428 -875,0,(Log(((2.2315388877498954e-2 * (((x1 / x1) * -0.280510474025759) * ((x0 ^ x1) / x0))) / ((Exp(x0) ^ (0.40650498406747926 * (-0.7373727597222957 * x1))) / (x0 + x0)))) / (((x0 ^ 0.5771777196733651) ^ (x0 + x0)) ^ -0.4960079761545053)),"(np.log(((t[0] * (((x[:, 1] / x[:, 1]) * t[1]) * (x[:, 0] ** x[:, 1] / x[:, 0]))) / (np.exp(x[:, 0]) ** (t[2] * (t[3] * x[:, 1])) / (x[:, 0] + x[:, 0])))) / x[:, 0] ** t[4] ** (x[:, 0] + x[:, 0]) ** t[5])","$$\frac{\operatorname{log}(\frac{\left(\theta_{0} \cdot \left(\left(\frac{x_{1}}{x_{1}} \cdot \theta_{1}\right) \cdot \frac{{x_{0}^{x_{1}}}}{x_{0}}\right)\right)}{\frac{{\operatorname{exp}(x_{0})^{\left(\theta_{2} \cdot \left(\theta_{3} \cdot x_{1}\right)\right)}}}{\left(x_{0} + x_{0}\right)}})}{{{{x_{0}^{\theta_{4}}}^{\left(x_{0} + x_{0}\right)}}^{\theta_{5}}}}$$",2.2315388877498954e-2;-0.280510474025759;0.40650498406747926;-0.7373727597222957;0.5771777196733651;-0.4960079761545053,37,,,,Infinity,,,,,, -876,0,(Log(((x0 * 0.27503076652428826) / (x1 + -0.7089754602831053))) + ((1.1962367226196702e-2 * Log(x1)) ^ (x0 ^ Exp(x0)))),"(np.log(((x[:, 0] * t[0]) / (x[:, 1] + t[1]))) + (t[2] * np.log(x[:, 1])) ** x[:, 0] ** np.exp(x[:, 0]))","$$\left(\operatorname{log}(\frac{\left(x_{0} \cdot \theta_{0}\right)}{\left(x_{1} + \theta_{1}\right)}) + {\left(\theta_{2} \cdot \operatorname{log}(x_{1})\right)^{{x_{0}^{\operatorname{exp}(x_{0})}}}}\right)$$",0.27503076652428826;-0.7089754602831053;1.1962367226196702e-2,18,,,,Infinity,,,,,, -877,0,Log(((Exp((x0 + 0.2365582969472424)) ^ ((Exp(x0) * (0.1438166423913395 / x0)) * -0.6819102547678553)) / (-0.5869171337649859 ^ Log(Log(x0))))),"np.log((np.exp((x[:, 0] + t[0])) ** ((np.exp(x[:, 0]) * (t[1] / x[:, 0])) * t[2]) / t[3] ** np.log(np.log(x[:, 0]))))","$$\operatorname{log}(\frac{{\operatorname{exp}(\left(x_{0} + \theta_{0}\right))^{\left(\left(\operatorname{exp}(x_{0}) \cdot \frac{\theta_{1}}{x_{0}}\right) \cdot \theta_{2}\right)}}}{{\theta_{3}^{\operatorname{log}(\operatorname{log}(x_{0}))}}})$$",0.2365582969472424;0.1438166423913395;-0.6819102547678553;-0.5869171337649859,20,,,,Infinity,,,,,, -878,0,(Log(Log((((x1 * x1) + -0.684707332156488) / Log((x1 + (-0.6750859943017611 + x0)))))) + ((Exp((-0.6637019554381639 + x0)) / Exp(x1)) * (Exp((x1 ^ 0.5386247479229969)) * (x1 ^ x0)))),"(np.log(np.log((((x[:, 1] * x[:, 1]) + t[0]) / np.log((x[:, 1] + (t[1] + x[:, 0])))))) + ((np.exp((t[2] + x[:, 0])) / np.exp(x[:, 1])) * (np.exp(x[:, 1] ** t[3]) * x[:, 1] ** x[:, 0])))","$$\left(\operatorname{log}(\operatorname{log}(\frac{\left(\left(x_{1} \cdot x_{1}\right) + \theta_{0}\right)}{\operatorname{log}(\left(x_{1} + \left(\theta_{1} + x_{0}\right)\right))})) + \left(\frac{\operatorname{exp}(\left(\theta_{2} + x_{0}\right))}{\operatorname{exp}(x_{1})} \cdot \left(\operatorname{exp}({x_{1}^{\theta_{3}}}) \cdot {x_{1}^{x_{0}}}\right)\right)\right)$$",-0.684707332156488;-0.6750859943017611;-0.6637019554381639;0.5386247479229969,31,,,,Infinity,,,,,, -879,0,(Log(Log((Log(x0) / (x0 * x0)))) * (Log(((x1 * 0.23596959632048808) / Exp((x0 * 9.791592015575878e-2)))) / ((x0 * 0.5866875374567637) ^ Log(((x1 + Exp(x1)) ^ Exp((-0.1347368794975553 * x0))))))),"(np.log(np.log((np.log(x[:, 0]) / (x[:, 0] * x[:, 0])))) * (np.log(((x[:, 1] * t[0]) / np.exp((x[:, 0] * t[1])))) / (x[:, 0] * t[2]) ** np.log((x[:, 1] + np.exp(x[:, 1])) ** np.exp((t[3] * x[:, 0])))))","$$\left(\operatorname{log}(\operatorname{log}(\frac{\operatorname{log}(x_{0})}{\left(x_{0} \cdot x_{0}\right)})) \cdot \frac{\operatorname{log}(\frac{\left(x_{1} \cdot \theta_{0}\right)}{\operatorname{exp}(\left(x_{0} \cdot \theta_{1}\right))})}{{\left(x_{0} \cdot \theta_{2}\right)^{\operatorname{log}({\left(x_{1} + \operatorname{exp}(x_{1})\right)^{\operatorname{exp}(\left(\theta_{3} \cdot x_{0}\right))}})}}}\right)$$",0.23596959632048808;9.791592015575878e-2;0.5866875374567637;-0.1347368794975553,33,,,,Infinity,,,,,, -880,0,((((-0.41366202306578925 ^ x0) * ((Log((0.5122293273739997 ^ x0)) ^ Exp((x1 ^ 0.9790531346373315))) * -0.16137644352924163)) ^ (Log((x1 * -0.3395162033368164)) + Log(x1))) / (Log(Exp((-0.4731726405736396 / x0))) + ((2.2674922284452337e-2 * x1) ^ (x1 + x1)))),"((t[0] ** x[:, 0] * (np.log(t[1] ** x[:, 0]) ** np.exp(x[:, 1] ** t[2]) * t[3])) ** (np.log((x[:, 1] * t[4])) + np.log(x[:, 1])) / (np.log(np.exp((t[5] / x[:, 0]))) + (t[6] * x[:, 1]) ** (x[:, 1] + x[:, 1])))","$$\frac{{\left({\theta_{0}^{x_{0}}} \cdot \left({\operatorname{log}({\theta_{1}^{x_{0}}})^{\operatorname{exp}({x_{1}^{\theta_{2}}})}} \cdot \theta_{3}\right)\right)^{\left(\operatorname{log}(\left(x_{1} \cdot \theta_{4}\right)) + \operatorname{log}(x_{1})\right)}}}{\left(\operatorname{log}(\operatorname{exp}(\frac{\theta_{5}}{x_{0}})) + {\left(\theta_{6} \cdot x_{1}\right)^{\left(x_{1} + x_{1}\right)}}\right)}$$",-0.41366202306578925;0.5122293273739997;0.9790531346373315;-0.16137644352924163;-0.3395162033368164;-0.4731726405736396;2.2674922284452337e-2,37,,,,Infinity,,,,,, -881,0,(((((23.89360410360172 / x0) + x1) + (Exp(Exp(x1)) * (x0 * (x0 + (13.349953290317616 + x1))))) ^ x1) * 9.293715619249472e-3),"((((t[0] / x[:, 0]) + x[:, 1]) + (np.exp(np.exp(x[:, 1])) * (x[:, 0] * (x[:, 0] + (t[1] + x[:, 1]))))) ** x[:, 1] * t[2])","$$\left({\left(\left(\frac{\theta_{0}}{x_{0}} + x_{1}\right) + \left(\operatorname{exp}(\operatorname{exp}(x_{1})) \cdot \left(x_{0} \cdot \left(x_{0} + \left(\theta_{1} + x_{1}\right)\right)\right)\right)\right)^{x_{1}}} \cdot \theta_{2}\right)$$",23.89360410360172;13.349953290317616;9.293715619249472e-3,21,1.0360162599448422e-2,1.0360162599448422e-2,1.0360162599448422e-2,1.0360162599448422e-2,4.3952169311466616e-2,4.3952169311466616e-2,4.3952169311466616e-2,49.740371327718975,49.740371327718975,49.740371327718975 -882,0,((x1 * 0.4914501909125626) * (1.182027828853775e-2 ^ (-0.6685910654509565 + x0))),"((x[:, 1] * t[0]) * t[1] ** (t[2] + x[:, 0]))","$$\left(\left(x_{1} \cdot \theta_{0}\right) \cdot {\theta_{1}^{\left(\theta_{2} + x_{0}\right)}}\right)$$",0.4914501909125626;1.182027828853775e-2;-0.6685910654509565,9,1.29921569241107e-3,1.29921569241107e-3,1.29921569241107e-3,1.29921569241107e-3,0.8801068677829212,0.8801068677829212,0.8801068677829212,22.189779323278586,22.189779323278586,22.189779323278586 -883,0,Exp(Log(Log((0.46099870887693195 * x1)))),"np.exp(np.log(np.log((t[0] * x[:, 1]))))","$$\operatorname{exp}(\operatorname{log}(\operatorname{log}(\left(\theta_{0} \cdot x_{1}\right))))$$",0.46099870887693195,6,,,,Infinity,,,,,, -884,0,(Log((-0.6703330772704559 ^ Log((0.13588929607413147 + (4.585162347767324e-2 * x1))))) * (Exp(((-0.596323725262742 ^ Log(x0)) + ((x0 * x1) ^ x1))) * Log(Log((Log((x0 ^ -0.7196752356197508)) + Exp((x1 + x1))))))),"(np.log(t[0] ** np.log((t[1] + (t[2] * x[:, 1])))) * (np.exp((t[3] ** np.log(x[:, 0]) + (x[:, 0] * x[:, 1]) ** x[:, 1])) * np.log(np.log((np.log(x[:, 0] ** t[4]) + np.exp((x[:, 1] + x[:, 1])))))))","$$\left(\operatorname{log}({\theta_{0}^{\operatorname{log}(\left(\theta_{1} + \left(\theta_{2} \cdot x_{1}\right)\right))}}) \cdot \left(\operatorname{exp}(\left({\theta_{3}^{\operatorname{log}(x_{0})}} + {\left(x_{0} \cdot x_{1}\right)^{x_{1}}}\right)) \cdot \operatorname{log}(\operatorname{log}(\left(\operatorname{log}({x_{0}^{\theta_{4}}}) + \operatorname{exp}(\left(x_{1} + x_{1}\right))\right)))\right)\right)$$",-0.6703330772704559;0.13588929607413147;4.585162347767324e-2;-0.596323725262742;-0.7196752356197508,33,,,,Infinity,,,,,, -885,0,((((0.10125470298894623 ^ x0) + (0.19319398090069506 + x0)) + (-1.0442229721785545e-2 ^ x0)) / (((x1 * x1) * 8.439240502204992e-2) / (Log(x0) * (x0 * x0)))),"(((t[0] ** x[:, 0] + (t[1] + x[:, 0])) + t[2] ** x[:, 0]) / (((x[:, 1] * x[:, 1]) * t[3]) / (np.log(x[:, 0]) * (x[:, 0] * x[:, 0]))))","$$\frac{\left(\left({\theta_{0}^{x_{0}}} + \left(\theta_{1} + x_{0}\right)\right) + {\theta_{2}^{x_{0}}}\right)}{\frac{\left(\left(x_{1} \cdot x_{1}\right) \cdot \theta_{3}\right)}{\left(\operatorname{log}(x_{0}) \cdot \left(x_{0} \cdot x_{0}\right)\right)}}$$",0.10125470298894623;0.19319398090069506;-1.0442229721785545e-2;8.439240502204992e-2,24,,,,Infinity,,,,,, -886,0,(((x0 * -0.1268765739066018) / (-0.4525653387851082 + Exp(x0))) / ((5.514980525494495e-2 / (-0.8674964126045672 ^ x0)) / (0.1714356439662461 / (-0.17488697357736915 ^ x0)))),"(((x[:, 0] * t[0]) / (t[1] + np.exp(x[:, 0]))) / ((t[2] / t[3] ** x[:, 0]) / (t[4] / t[5] ** x[:, 0])))","$$\frac{\frac{\left(x_{0} \cdot \theta_{0}\right)}{\left(\theta_{1} + \operatorname{exp}(x_{0})\right)}}{\frac{\frac{\theta_{2}}{{\theta_{3}^{x_{0}}}}}{\frac{\theta_{4}}{{\theta_{5}^{x_{0}}}}}}$$",-0.1268765739066018;-0.4525653387851082;5.514980525494495e-2;-0.8674964126045672;0.1714356439662461;-0.17488697357736915,20,,,,Infinity,,,,,, -887,0,((Exp((((7.62811068556214e-2 * x0) * -0.5300376284536263) ^ 0.8321785306120899)) * (Exp(Log((-0.23891602174210536 * x0))) ^ -0.10604960050019496)) ^ ((x0 * -9.997593398515192e-2) * -0.8164324396493065)),"(np.exp(((t[0] * x[:, 0]) * t[1]) ** t[2]) * np.exp(np.log((t[3] * x[:, 0]))) ** t[4]) ** ((x[:, 0] * t[5]) * t[6])","$${\left(\operatorname{exp}({\left(\left(\theta_{0} \cdot x_{0}\right) \cdot \theta_{1}\right)^{\theta_{2}}}) \cdot {\operatorname{exp}(\operatorname{log}(\left(\theta_{3} \cdot x_{0}\right)))^{\theta_{4}}}\right)^{\left(\left(x_{0} \cdot \theta_{5}\right) \cdot \theta_{6}\right)}}$$",7.62811068556214e-2;-0.5300376284536263;0.8321785306120899;-0.23891602174210536;-0.10604960050019496;-9.997593398515192e-2;-0.8164324396493065,22,,,,Infinity,,,,,, -888,0,(-6.860156837303206 + Exp((Log((x0 + x0)) + x0))),"(t[0] + np.exp((np.log((x[:, 0] + x[:, 0])) + x[:, 0])))","$$\left(\theta_{0} + \operatorname{exp}(\left(\operatorname{log}(\left(x_{0} + x_{0}\right)) + x_{0}\right))\right)$$",-6.860156837303206,9,14.37733443964127,14.37733443964127,14.37733443964127,14.37733443964127,-1325.7571112092764,-1325.7571112092764,-1325.7571112092764,29.445467142951685,29.445467142951685,29.445467142951685 -889,0,((((1.2231772054161914e-4 ^ Exp(x1)) + (x0 + -0.2963833558120469)) * ((-0.4056905658281823 / (x1 * 0.6073898463660568)) / (x0 * -0.36240688134404536))) ^ (((-0.64780004337212 * (0.4996803810754811 ^ x1)) + Log((x1 * 1.1939379576479177))) * ((x1 / x1) + (x0 ^ (x0 * x1))))),"((t[0] ** np.exp(x[:, 1]) + (x[:, 0] + t[1])) * ((t[2] / (x[:, 1] * t[3])) / (x[:, 0] * t[4]))) ** (((t[5] * t[6] ** x[:, 1]) + np.log((x[:, 1] * t[7]))) * ((x[:, 1] / x[:, 1]) + x[:, 0] ** (x[:, 0] * x[:, 1])))","$${\left(\left({\theta_{0}^{\operatorname{exp}(x_{1})}} + \left(x_{0} + \theta_{1}\right)\right) \cdot \frac{\frac{\theta_{2}}{\left(x_{1} \cdot \theta_{3}\right)}}{\left(x_{0} \cdot \theta_{4}\right)}\right)^{\left(\left(\left(\theta_{5} \cdot {\theta_{6}^{x_{1}}}\right) + \operatorname{log}(\left(x_{1} \cdot \theta_{7}\right))\right) \cdot \left(\frac{x_{1}}{x_{1}} + {x_{0}^{\left(x_{0} \cdot x_{1}\right)}}\right)\right)}}$$",1.2231772054161914e-4;-0.2963833558120469;-0.4056905658281823;0.6073898463660568;-0.36240688134404536;-0.64780004337212;0.4996803810754811;1.1939379576479177,39,6.828321083713232e-3,6.828321083713232e-3,6.828321083713232e-3,6.828321083713232e-3,0.3698746041228761,0.3698746041228761,0.3698746041228761,101.86530348802903,101.86530348802903,101.86530348802903 -890,0,(Log(Log((x1 + -0.6205767282368333))) ^ (((((x0 / x0) / (0.7050990686579807 / (x0 ^ x1))) / x0) + (x1 / x1)) + 0.3225707757025048)),"np.log(np.log((x[:, 1] + t[0]))) ** (((((x[:, 0] / x[:, 0]) / (t[1] / x[:, 0] ** x[:, 1])) / x[:, 0]) + (x[:, 1] / x[:, 1])) + t[2])","$${\operatorname{log}(\operatorname{log}(\left(x_{1} + \theta_{0}\right)))^{\left(\left(\frac{\frac{\frac{x_{0}}{x_{0}}}{\frac{\theta_{1}}{{x_{0}^{x_{1}}}}}}{x_{0}} + \frac{x_{1}}{x_{1}}\right) + \theta_{2}\right)}}$$",-0.6205767282368333;0.7050990686579807;0.3225707757025048,23,,,,Infinity,,,,,, -891,0,Log(Exp((1.1306123094205506e-3 ^ ((x0 * x0) + (-0.39113585162262626 * x1))))),"np.log(np.exp(t[0] ** ((x[:, 0] * x[:, 0]) + (t[1] * x[:, 1]))))","$$\operatorname{log}(\operatorname{exp}({\theta_{0}^{\left(\left(x_{0} \cdot x_{0}\right) + \left(\theta_{1} \cdot x_{1}\right)\right)}}))$$",1.1306123094205506e-3;-0.39113585162262626,11,3.2270487743276855e-3,3.2270487743276855e-3,3.2270487743276855e-3,3.2270487743276855e-3,0.7022041931671679,0.7022041931671679,0.7022041931671679,26.91861117311037,26.91861117311037,26.91861117311037 -892,0,(((Exp(x1) ^ (-2.888321767291577 / x1)) + ((-0.29079773294151656 / x0) * -0.5799698876604458)) / (((x1 + x1) ^ (x0 * -2.3670846128109178)) + ((x0 * x0) ^ 2.702457628545553))),"((np.exp(x[:, 1]) ** (t[0] / x[:, 1]) + ((t[1] / x[:, 0]) * t[2])) / ((x[:, 1] + x[:, 1]) ** (x[:, 0] * t[3]) + (x[:, 0] * x[:, 0]) ** t[4]))","$$\frac{\left({\operatorname{exp}(x_{1})^{\frac{\theta_{0}}{x_{1}}}} + \left(\frac{\theta_{1}}{x_{0}} \cdot \theta_{2}\right)\right)}{\left({\left(x_{1} + x_{1}\right)^{\left(x_{0} \cdot \theta_{3}\right)}} + {\left(x_{0} \cdot x_{0}\right)^{\theta_{4}}}\right)}$$",-2.888321767291577;-0.29079773294151656;-0.5799698876604458;-2.3670846128109178;2.702457628545553,26,1.774605508602345e-3,1.774605508602345e-3,1.774605508602345e-3,1.774605508602345e-3,0.8362373437152887,0.8362373437152887,0.8362373437152887,64.17170190547168,64.17170190547168,64.17170190547168 -893,0,(((Exp(Log(x1)) * Log(x1)) ^ (Exp(x0) * (x0 * 0.23848841537864318))) ^ ((((((x0 + -0.5499972350086526) ^ 0.9813317192601043) * x1) * -6.747401969083244e-2) ^ (x0 * Exp((x1 / x0)))) + Exp((5.877527030039231e-2 * (x1 + 0.6429041761889265))))),"(np.exp(np.log(x[:, 1])) * np.log(x[:, 1])) ** (np.exp(x[:, 0]) * (x[:, 0] * t[0])) ** ((((x[:, 0] + t[1]) ** t[2] * x[:, 1]) * t[3]) ** (x[:, 0] * np.exp((x[:, 1] / x[:, 0]))) + np.exp((t[4] * (x[:, 1] + t[5]))))","$${{\left(\operatorname{exp}(\operatorname{log}(x_{1})) \cdot \operatorname{log}(x_{1})\right)^{\left(\operatorname{exp}(x_{0}) \cdot \left(x_{0} \cdot \theta_{0}\right)\right)}}^{\left({\left(\left({\left(x_{0} + \theta_{1}\right)^{\theta_{2}}} \cdot x_{1}\right) \cdot \theta_{3}\right)^{\left(x_{0} \cdot \operatorname{exp}(\frac{x_{1}}{x_{0}})\right)}} + \operatorname{exp}(\left(\theta_{4} \cdot \left(x_{1} + \theta_{5}\right)\right))\right)}}$$",0.23848841537864318;-0.5499972350086526;0.9813317192601043;-6.747401969083244e-2;5.877527030039231e-2;0.6429041761889265,37,,,,Infinity,,,,,, -894,0,((((0.12734899761133212 ^ x0) * Exp((x1 * x1))) ^ ((-0.47262417677213386 / x0) ^ (-0.8513695390576741 / x0))) + ((0.31281702223142327 ^ (-0.8800025513992464 / x1)) + ((x1 * -0.5312476529180928) / (x1 * -0.9531939270673278)))),"((t[0] ** x[:, 0] * np.exp((x[:, 1] * x[:, 1]))) ** (t[1] / x[:, 0]) ** (t[2] / x[:, 0]) + (t[3] ** (t[4] / x[:, 1]) + ((x[:, 1] * t[5]) / (x[:, 1] * t[6]))))","$$\left({\left({\theta_{0}^{x_{0}}} \cdot \operatorname{exp}(\left(x_{1} \cdot x_{1}\right))\right)^{{\frac{\theta_{1}}{x_{0}}^{\frac{\theta_{2}}{x_{0}}}}}} + \left({\theta_{3}^{\frac{\theta_{4}}{x_{1}}}} + \frac{\left(x_{1} \cdot \theta_{5}\right)}{\left(x_{1} \cdot \theta_{6}\right)}\right)\right)$$",0.12734899761133212;-0.47262417677213386;-0.8513695390576741;0.31281702223142327;-0.8800025513992464;-0.5312476529180928;-0.9531939270673278,30,,,,Infinity,,,,,, -895,0,Log(Log(Log((((0.35676745307306035 / (0.23079796909937966 ^ x1)) + Log((x1 ^ 0.9666363779836281))) + x1)))),"np.log(np.log(np.log((((t[0] / t[1] ** x[:, 1]) + np.log(x[:, 1] ** t[2])) + x[:, 1]))))","$$\operatorname{log}(\operatorname{log}(\operatorname{log}(\left(\left(\frac{\theta_{0}}{{\theta_{1}^{x_{1}}}} + \operatorname{log}({x_{1}^{\theta_{2}}})\right) + x_{1}\right))))$$",0.35676745307306035;0.23079796909937966;0.9666363779836281,15,,,,Infinity,,,,,, -896,0,Log(Log(Exp(((-8.965590428500612e-2 + ((-0.5047125918559479 + x0) ^ (x0 * -0.4259845397963693))) ^ x1)))),"np.log(np.log(np.exp((t[0] + (t[1] + x[:, 0]) ** (x[:, 0] * t[2])) ** x[:, 1])))","$$\operatorname{log}(\operatorname{log}(\operatorname{exp}({\left(\theta_{0} + {\left(\theta_{1} + x_{0}\right)^{\left(x_{0} \cdot \theta_{2}\right)}}\right)^{x_{1}}})))$$",-8.965590428500612e-2;-0.5047125918559479;-0.4259845397963693,14,2.110754230462207e-3,2.110754230462207e-3,2.110754230462207e-3,2.110754230462207e-3,0.8052171494627435,0.8052171494627435,0.8052171494627435,35.17685606842804,35.17685606842804,35.17685606842804 -897,0,((Exp(((x0 / (0.9213125523636786 ^ x1)) / (0.8289880393584732 ^ (0.5919880138128474 + x0)))) + Exp((-0.618761449720681 ^ ((0.19358468221742164 / x0) + -0.9617238155397516)))) / Exp(Exp((((x1 + (0.8835164265222651 / x1)) / (x0 + (0.17444603353702415 ^ x0))) ^ ((x1 ^ Exp(x1)) + ((x0 * x0) * 9.1879234795057e-3)))))),"((np.exp(((x[:, 0] / t[0] ** x[:, 1]) / t[1] ** (t[2] + x[:, 0]))) + np.exp(t[3] ** ((t[4] / x[:, 0]) + t[5]))) / np.exp(np.exp(((x[:, 1] + (t[6] / x[:, 1])) / (x[:, 0] + t[7] ** x[:, 0])) ** (x[:, 1] ** np.exp(x[:, 1]) + ((x[:, 0] * x[:, 0]) * t[8])))))","$$\frac{\left(\operatorname{exp}(\frac{\frac{x_{0}}{{\theta_{0}^{x_{1}}}}}{{\theta_{1}^{\left(\theta_{2} + x_{0}\right)}}}) + \operatorname{exp}({\theta_{3}^{\left(\frac{\theta_{4}}{x_{0}} + \theta_{5}\right)}})\right)}{\operatorname{exp}(\operatorname{exp}({\frac{\left(x_{1} + \frac{\theta_{6}}{x_{1}}\right)}{\left(x_{0} + {\theta_{7}^{x_{0}}}\right)}^{\left({x_{1}^{\operatorname{exp}(x_{1})}} + \left(\left(x_{0} \cdot x_{0}\right) \cdot \theta_{8}\right)\right)}}))}$$",0.9213125523636786;0.8289880393584732;0.5919880138128474;-0.618761449720681;0.19358468221742164;-0.9617238155397516;0.8835164265222651;0.17444603353702415;9.1879234795057e-3,46,,,,Infinity,,,,,, -898,0,Log(Exp((Log((x1 ^ ((5.346873490483313e-4 * x1) ^ x1))) / Exp(Exp((x1 + x1)))))),"np.log(np.exp((np.log(x[:, 1] ** (t[0] * x[:, 1]) ** x[:, 1]) / np.exp(np.exp((x[:, 1] + x[:, 1]))))))","$$\operatorname{log}(\operatorname{exp}(\frac{\operatorname{log}({x_{1}^{{\left(\theta_{0} \cdot x_{1}\right)^{x_{1}}}}})}{\operatorname{exp}(\operatorname{exp}(\left(x_{1} + x_{1}\right)))}))$$",5.346873490483313e-4,16,2.085101080526957e-2,2.085101080526957e-2,2.085101080526957e-2,2.085101080526957e-2,-0.9241554808321697,-0.9241554808321697,-0.9241554808321697,35.31075551196976,35.31075551196976,35.31075551196976 -899,0,(Log((0.3209504914822673 / x1)) * x0),"(np.log((t[0] / x[:, 1])) * x[:, 0])","$$\left(\operatorname{log}(\frac{\theta_{0}}{x_{1}}) \cdot x_{0}\right)$$",0.3209504914822673,6,0.29230664289744,0.29230664289744,0.29230664289744,0.29230664289744,-25.974396314284082,-25.974396314284082,-25.974396314284082,13.034557729343668,13.034557729343668,13.034557729343668 -900,0,Exp((Log((64.19806306181877 * x0)) * ((x1 * 4.605314520021272) * (-4.224916934929434 * (Log(x1) * -5.371864159802967))))),"np.exp((np.log((t[0] * x[:, 0])) * ((x[:, 1] * t[1]) * (t[2] * (np.log(x[:, 1]) * t[3])))))","$$\operatorname{exp}(\left(\operatorname{log}(\left(\theta_{0} \cdot x_{0}\right)) \cdot \left(\left(x_{1} \cdot \theta_{1}\right) \cdot \left(\theta_{2} \cdot \left(\operatorname{log}(x_{1}) \cdot \theta_{3}\right)\right)\right)\right))$$",64.19806306181877;4.605314520021272;-4.224916934929434;-5.371864159802967,16,1.4573554749975553e-2,1.4573554749975553e-2,1.4573554749975553e-2,1.4573554749975553e-2,-0.3448645492182232,-0.3448645492182232,-0.3448645492182232,36.76496744839442,36.76496744839442,36.76496744839442 -901,0,((-0.11576890892296858 * Log((-0.3852663807504058 + x0))) * Exp(((x1 * -0.2706511518217376) * -0.41912619128540085))),"((t[0] * np.log((t[1] + x[:, 0]))) * np.exp(((x[:, 1] * t[2]) * t[3])))","$$\left(\left(\theta_{0} \cdot \operatorname{log}(\left(\theta_{1} + x_{0}\right))\right) \cdot \operatorname{exp}(\left(\left(x_{1} \cdot \theta_{2}\right) \cdot \theta_{3}\right))\right)$$",-0.11576890892296858;-0.3852663807504058;-0.2706511518217376;-0.41912619128540085,13,4.989030428025939e-3,4.989030428025939e-3,4.989030428025939e-3,4.989030428025939e-3,0.5396064808666974,0.5396064808666974,0.5396064808666974,33.38502180657486,33.38502180657486,33.38502180657486 -902,0,Log(Log(((x1 * Exp((x1 + 1.3694934560901917))) + (Exp((3.66033081336025e-2 / (x0 * x0))) ^ ((x1 ^ -0.9579984849017419) / x0))))),"np.log(np.log(((x[:, 1] * np.exp((x[:, 1] + t[0]))) + np.exp((t[1] / (x[:, 0] * x[:, 0]))) ** (x[:, 1] ** t[2] / x[:, 0]))))","$$\operatorname{log}(\operatorname{log}(\left(\left(x_{1} \cdot \operatorname{exp}(\left(x_{1} + \theta_{0}\right))\right) + {\operatorname{exp}(\frac{\theta_{1}}{\left(x_{0} \cdot x_{0}\right)})^{\frac{{x_{1}^{\theta_{2}}}}{x_{0}}}}\right)))$$",1.3694934560901917;3.66033081336025e-2;-0.9579984849017419,21,7.954171506639386e-2,7.954171506639386e-2,7.954171506639386e-2,7.954171506639386e-2,-6.340201797848228,-6.340201797848228,-6.340201797848228,52.27607847372329,52.27607847372329,52.27607847372329 -903,0,(((-0.24198063733297193 * (x0 ^ -0.5854140319977181)) * Log((-0.1079226961235068 / Log((x0 + -0.9708697156081484))))) + x1),"(((t[0] * x[:, 0] ** t[1]) * np.log((t[2] / np.log((x[:, 0] + t[3]))))) + x[:, 1])","$$\left(\left(\left(\theta_{0} \cdot {x_{0}^{\theta_{1}}}\right) \cdot \operatorname{log}(\frac{\theta_{2}}{\operatorname{log}(\left(x_{0} + \theta_{3}\right))})\right) + x_{1}\right)$$",-0.24198063733297193;-0.5854140319977181;-0.1079226961235068;-0.9708697156081484,15,,,,Infinity,,,,,, -904,0,((((x1 ^ (((x0 / x0) * -0.26949497952416623) + -4.0400836724514666e-2)) + (0.46704951132759565 * Exp(x0))) ^ Log(((-0.5127408909349094 ^ Exp(x0)) * 0.1730116025792391))) ^ Log((x0 ^ 0.7419106597205907))),"(x[:, 1] ** (((x[:, 0] / x[:, 0]) * t[0]) + t[1]) + (t[2] * np.exp(x[:, 0]))) ** np.log((t[3] ** np.exp(x[:, 0]) * t[4])) ** np.log(x[:, 0] ** t[5])","$${{\left({x_{1}^{\left(\left(\frac{x_{0}}{x_{0}} \cdot \theta_{0}\right) + \theta_{1}\right)}} + \left(\theta_{2} \cdot \operatorname{exp}(x_{0})\right)\right)^{\operatorname{log}(\left({\theta_{3}^{\operatorname{exp}(x_{0})}} \cdot \theta_{4}\right))}}^{\operatorname{log}({x_{0}^{\theta_{5}}})}}$$",-0.26949497952416623;-4.0400836724514666e-2;0.46704951132759565;-0.5127408909349094;0.1730116025792391;0.7419106597205907,27,,,,Infinity,,,,,, -905,0,(-0.6791278601997834 / ((-0.5198497782771547 + Log(Log((0.2973728006956621 ^ (x1 * x0))))) ^ (Log(Log(x0)) * (0.5298107351847752 * (-0.456453393324042 / (0.4309450067263647 + x1)))))),"(t[0] / (t[1] + np.log(np.log(t[2] ** (x[:, 1] * x[:, 0])))) ** (np.log(np.log(x[:, 0])) * (t[3] * (t[4] / (t[5] + x[:, 1])))))","$$\frac{\theta_{0}}{{\left(\theta_{1} + \operatorname{log}(\operatorname{log}({\theta_{2}^{\left(x_{1} \cdot x_{0}\right)}}))\right)^{\left(\operatorname{log}(\operatorname{log}(x_{0})) \cdot \left(\theta_{3} \cdot \frac{\theta_{4}}{\left(\theta_{5} + x_{1}\right)}\right)\right)}}}$$",-0.6791278601997834;-0.5198497782771547;0.2973728006956621;0.5298107351847752;-0.456453393324042;0.4309450067263647,23,,,,Infinity,,,,,, -906,0,((((-0.19256612329088607 ^ (x0 * 0.8054568699482977)) + (0.8188592299581446 + (x0 ^ 0.8908702737040027))) * ((x1 * -0.9749037646457286) ^ (-0.13702972979213524 / Log(x0)))) * ((((x0 ^ -0.21103686867204696) ^ -0.632023301911449) + Log((0.5296462640402666 / (x0 * 0.6118083063846216)))) ^ ((x0 ^ Log(x1)) + (0.7355940268736612 ^ (x1 * x0))))),"(((t[0] ** (x[:, 0] * t[1]) + (t[2] + x[:, 0] ** t[3])) * (x[:, 1] * t[4]) ** (t[5] / np.log(x[:, 0]))) * (x[:, 0] ** t[6] ** t[7] + np.log((t[8] / (x[:, 0] * t[9])))) ** (x[:, 0] ** np.log(x[:, 1]) + t[10] ** (x[:, 1] * x[:, 0])))","$$\left(\left(\left({\theta_{0}^{\left(x_{0} \cdot \theta_{1}\right)}} + \left(\theta_{2} + {x_{0}^{\theta_{3}}}\right)\right) \cdot {\left(x_{1} \cdot \theta_{4}\right)^{\frac{\theta_{5}}{\operatorname{log}(x_{0})}}}\right) \cdot {\left({{x_{0}^{\theta_{6}}}^{\theta_{7}}} + \operatorname{log}(\frac{\theta_{8}}{\left(x_{0} \cdot \theta_{9}\right)})\right)^{\left({x_{0}^{\operatorname{log}(x_{1})}} + {\theta_{10}^{\left(x_{1} \cdot x_{0}\right)}}\right)}}\right)$$",-0.19256612329088607;0.8054568699482977;0.8188592299581446;0.8908702737040027;-0.9749037646457286;-0.13702972979213524;-0.21103686867204696;-0.632023301911449;0.5296462640402666;0.6118083063846216;0.7355940268736612,44,,,,Infinity,,,,,, -907,0,(x1 * x0),"(x[:, 1] * x[:, 0])","$$\left(x_{1} \cdot x_{0}\right)$$",,3,0.13931722508926758,0.13931722508926758,0.13931722508926758,0.13931722508926758,-11.856355249794213,-11.856355249794213,-11.856355249794213,3.42122236858467,3.42122236858467,3.42122236858467 -908,0,(Log((((x0 + Log((-0.5398291241008426 ^ x1))) ^ -0.7639569214306898) + Exp(Log(x1)))) + 0.6912560367326168),"(np.log(((x[:, 0] + np.log(t[0] ** x[:, 1])) ** t[1] + np.exp(np.log(x[:, 1])))) + t[2])","$$\left(\operatorname{log}(\left({\left(x_{0} + \operatorname{log}({\theta_{0}^{x_{1}}})\right)^{\theta_{1}}} + \operatorname{exp}(\operatorname{log}(x_{1}))\right)) + \theta_{2}\right)$$",-0.5398291241008426;-0.7639569214306898;0.6912560367326168,15,,,,Infinity,,,,,, -909,0,(Exp((x0 + x0)) * 2.13087803630585e-3),"(np.exp((x[:, 0] + x[:, 0])) * t[0])","$$\left(\operatorname{exp}(\left(x_{0} + x_{0}\right)) \cdot \theta_{0}\right)$$",2.13087803630585e-3,6,1.3995010131826451e-2,1.3995010131826451e-2,1.3995010131826451e-2,1.3995010131826451e-2,-0.29147578028447874,-0.29147578028447874,-0.29147578028447874,11.690147919090887,11.690147919090887,11.690147919090887 -910,0,((Log(Log((Exp((x0 * x1)) ^ (-0.6826714616455711 * x1)))) / (((x1 ^ 0.45722459828940776) * 0.7320034228974153) + ((x1 + 0.768600110386176) / (-0.2792344979632294 / x0)))) + (Exp(((5.631351800775508e-2 + (0.6297767104243541 ^ x1)) ^ (0.44499258286946863 / (x1 / x1)))) * ((-0.3055489199658975 / (0.6136804624444754 + x0)) ^ Exp(Exp((x1 * x0)))))),"((np.log(np.log(np.exp((x[:, 0] * x[:, 1])) ** (t[0] * x[:, 1]))) / ((x[:, 1] ** t[1] * t[2]) + ((x[:, 1] + t[3]) / (t[4] / x[:, 0])))) + (np.exp((t[5] + t[6] ** x[:, 1]) ** (t[7] / (x[:, 1] / x[:, 1]))) * (t[8] / (t[9] + x[:, 0])) ** np.exp(np.exp((x[:, 1] * x[:, 0])))))","$$\left(\frac{\operatorname{log}(\operatorname{log}({\operatorname{exp}(\left(x_{0} \cdot x_{1}\right))^{\left(\theta_{0} \cdot x_{1}\right)}}))}{\left(\left({x_{1}^{\theta_{1}}} \cdot \theta_{2}\right) + \frac{\left(x_{1} + \theta_{3}\right)}{\frac{\theta_{4}}{x_{0}}}\right)} + \left(\operatorname{exp}({\left(\theta_{5} + {\theta_{6}^{x_{1}}}\right)^{\frac{\theta_{7}}{\frac{x_{1}}{x_{1}}}}}) \cdot {\frac{\theta_{8}}{\left(\theta_{9} + x_{0}\right)}^{\operatorname{exp}(\operatorname{exp}(\left(x_{1} \cdot x_{0}\right)))}}\right)\right)$$",-0.6826714616455711;0.45722459828940776;0.7320034228974153;0.768600110386176;-0.2792344979632294;5.631351800775508e-2;0.6297767104243541;0.44499258286946863;-0.3055489199658975;0.6136804624444754,49,,,,Infinity,,,,,, -911,0,(((0.3435306346534498 * (x1 / x1)) / (x1 + x0)) * Exp((Log((x1 + -2.282598637636468e-3)) ^ 0.16215233555834985))),"(((t[0] * (x[:, 1] / x[:, 1])) / (x[:, 1] + x[:, 0])) * np.exp(np.log((x[:, 1] + t[1])) ** t[2]))","$$\left(\frac{\left(\theta_{0} \cdot \frac{x_{1}}{x_{1}}\right)}{\left(x_{1} + x_{0}\right)} \cdot \operatorname{exp}({\operatorname{log}(\left(x_{1} + \theta_{1}\right))^{\theta_{2}}})\right)$$",0.3435306346534498;-2.282598637636468e-3;0.16215233555834985,17,,,,Infinity,,,,,, -912,0,((Log(Log(x0)) * ((Exp(x0) * (Log((x1 + 0.8352579874423789)) ^ 0.8498694041802681)) * x1)) + 0.33287649884493753),"((np.log(np.log(x[:, 0])) * ((np.exp(x[:, 0]) * np.log((x[:, 1] + t[0])) ** t[1]) * x[:, 1])) + t[2])","$$\left(\left(\operatorname{log}(\operatorname{log}(x_{0})) \cdot \left(\left(\operatorname{exp}(x_{0}) \cdot {\operatorname{log}(\left(x_{1} + \theta_{0}\right))^{\theta_{1}}}\right) \cdot x_{1}\right)\right) + \theta_{2}\right)$$",0.8352579874423789;0.8498694041802681;0.33287649884493753,17,,,,Infinity,,,,,, -913,0,(Log(Exp((-0.2623366735091031 + x1))) / Log((Exp((x1 / x1)) + (x1 ^ (x1 + x1))))),"(np.log(np.exp((t[0] + x[:, 1]))) / np.log((np.exp((x[:, 1] / x[:, 1])) + x[:, 1] ** (x[:, 1] + x[:, 1]))))","$$\frac{\operatorname{log}(\operatorname{exp}(\left(\theta_{0} + x_{1}\right)))}{\operatorname{log}(\left(\operatorname{exp}(\frac{x_{1}}{x_{1}}) + {x_{1}^{\left(x_{1} + x_{1}\right)}}\right))}$$",-0.2623366735091031,17,1.4760024343063467e-2,1.4760024343063467e-2,1.4760024343063467e-2,1.4760024343063467e-2,-0.36207217972110395,-0.36207217972110395,-0.36207217972110395,35.11468149121672,35.11468149121672,35.11468149121672 -914,0,((Exp(((Exp(x1) / x0) / (-0.7875621560569124 ^ x0))) ^ (((x0 * 0.13674413646340788) * -0.14169927041341823) ^ (4.294733337056489e-2 / x1))) / Exp(Exp((-0.9395253287459883 + ((x0 ^ (x1 + -0.8018216102303637)) ^ (x0 * -0.1791596073584275)))))),"(np.exp(((np.exp(x[:, 1]) / x[:, 0]) / t[0] ** x[:, 0])) ** ((x[:, 0] * t[1]) * t[2]) ** (t[3] / x[:, 1]) / np.exp(np.exp((t[4] + x[:, 0] ** (x[:, 1] + t[5]) ** (x[:, 0] * t[6])))))","$$\frac{{\operatorname{exp}(\frac{\frac{\operatorname{exp}(x_{1})}{x_{0}}}{{\theta_{0}^{x_{0}}}})^{{\left(\left(x_{0} \cdot \theta_{1}\right) \cdot \theta_{2}\right)^{\frac{\theta_{3}}{x_{1}}}}}}}{\operatorname{exp}(\operatorname{exp}(\left(\theta_{4} + {{x_{0}^{\left(x_{1} + \theta_{5}\right)}}^{\left(x_{0} \cdot \theta_{6}\right)}}\right)))}$$",-0.7875621560569124;0.13674413646340788;-0.14169927041341823;4.294733337056489e-2;-0.9395253287459883;-0.8018216102303637;-0.1791596073584275,33,,,,Infinity,,,,,, -915,0,Exp(((Exp(((x1 * 0.9501466099606402) * 0.34343880117945524)) / (-0.5542692557892662 + Exp((-0.8913462836518152 ^ x1)))) / ((Log(Log(x0)) ^ -0.30988457542829273) ^ ((x0 ^ x0) + Log(Log((-0.3977504692950904 / x0))))))),"np.exp(((np.exp(((x[:, 1] * t[0]) * t[1])) / (t[2] + np.exp(t[3] ** x[:, 1]))) / np.log(np.log(x[:, 0])) ** t[4] ** (x[:, 0] ** x[:, 0] + np.log(np.log((t[5] / x[:, 0]))))))","$$\operatorname{exp}(\frac{\frac{\operatorname{exp}(\left(\left(x_{1} \cdot \theta_{0}\right) \cdot \theta_{1}\right))}{\left(\theta_{2} + \operatorname{exp}({\theta_{3}^{x_{1}}})\right)}}{{{\operatorname{log}(\operatorname{log}(x_{0}))^{\theta_{4}}}^{\left({x_{0}^{x_{0}}} + \operatorname{log}(\operatorname{log}(\frac{\theta_{5}}{x_{0}}))\right)}}})$$",0.9501466099606402;0.34343880117945524;-0.5542692557892662;-0.8913462836518152;-0.30988457542829273;-0.3977504692950904,30,,,,Infinity,,,,,, -916,0,((((x1 * -2.596093862943971e-2) ^ -4.667296836370938e-2) * -0.36028108799569547) / ((0.6229516755097904 * (-0.17537749594881302 * x1)) + (-0.10205746499197943 * (0.6889734577071563 + x1)))),"(((x[:, 1] * t[0]) ** t[1] * t[2]) / ((t[3] * (t[4] * x[:, 1])) + (t[5] * (t[6] + x[:, 1]))))","$$\frac{\left({\left(x_{1} \cdot \theta_{0}\right)^{\theta_{1}}} \cdot \theta_{2}\right)}{\left(\left(\theta_{3} \cdot \left(\theta_{4} \cdot x_{1}\right)\right) + \left(\theta_{5} \cdot \left(\theta_{6} + x_{1}\right)\right)\right)}$$",-2.596093862943971e-2;-4.667296836370938e-2;-0.36028108799569547;0.6229516755097904;-0.17537749594881302;-0.10205746499197943;0.6889734577071563,19,,,,Infinity,,,,,, -917,0,Log(Exp((-1.9267265247392966e-2 * (-0.4269832873805105 / x1)))),"np.log(np.exp((t[0] * (t[1] / x[:, 1]))))","$$\operatorname{log}(\operatorname{exp}(\left(\theta_{0} \cdot \frac{\theta_{1}}{x_{1}}\right)))$$",-1.9267265247392966e-2;-0.4269832873805105,7,1.331318834405586e-2,1.331318834405586e-2,1.331318834405586e-2,1.331318834405586e-2,-0.2285564742545756,-0.2285564742545756,-0.2285564742545756,16.596148024841316,16.596148024841316,16.596148024841316 -918,0,(Log(Log((x0 * (((x1 + x0) + x0) * -0.6726800544080772)))) / Log(Log(Exp(x0)))),"(np.log(np.log((x[:, 0] * (((x[:, 1] + x[:, 0]) + x[:, 0]) * t[0])))) / np.log(np.log(np.exp(x[:, 0]))))","$$\frac{\operatorname{log}(\operatorname{log}(\left(x_{0} \cdot \left(\left(\left(x_{1} + x_{0}\right) + x_{0}\right) \cdot \theta_{0}\right)\right)))}{\operatorname{log}(\operatorname{log}(\operatorname{exp}(x_{0})))}$$",-0.6726800544080772,16,,,,Infinity,,,,,, -919,0,Log((Exp(((-1.8009041839625382 + (x1 * x1)) * (0.6404045627244765 ^ Log(x1)))) + Exp((-5.411724726488483e-3 * ((0.19794042742890186 + x1) ^ (x1 + (0.3237439222034737 ^ x1))))))),"np.log((np.exp(((t[0] + (x[:, 1] * x[:, 1])) * t[1] ** np.log(x[:, 1]))) + np.exp((t[2] * (t[3] + x[:, 1]) ** (x[:, 1] + t[4] ** x[:, 1])))))","$$\operatorname{log}(\left(\operatorname{exp}(\left(\left(\theta_{0} + \left(x_{1} \cdot x_{1}\right)\right) \cdot {\theta_{1}^{\operatorname{log}(x_{1})}}\right)) + \operatorname{exp}(\left(\theta_{2} \cdot {\left(\theta_{3} + x_{1}\right)^{\left(x_{1} + {\theta_{4}^{x_{1}}}\right)}}\right))\right))$$",-1.8009041839625382;0.6404045627244765;-5.411724726488483e-3;0.19794042742890186;0.3237439222034737,25,9.20452483747309e-3,9.20452483747309e-3,9.20452483747309e-3,9.20452483747309e-3,0.15059576344357906,0.15059576344357906,0.15059576344357906,58.760662475574755,58.760662475574755,58.760662475574755 -920,0,(Log(Log(Log((-0.48088340236790394 * x0)))) * (-0.1340631826785652 / (x0 * 0.16251382481602383))),"(np.log(np.log(np.log((t[0] * x[:, 0])))) * (t[1] / (x[:, 0] * t[2])))","$$\left(\operatorname{log}(\operatorname{log}(\operatorname{log}(\left(\theta_{0} \cdot x_{0}\right)))) \cdot \frac{\theta_{1}}{\left(x_{0} \cdot \theta_{2}\right)}\right)$$",-0.48088340236790394;-0.1340631826785652;0.16251382481602383,12,,,,Infinity,,,,,, -921,0,Log((((Log((x1 + x0)) ^ (x1 * x1)) * Log(((x1 * 0.5666233132490518) ^ (x1 * x1)))) * Exp((Exp((x1 / Log(x0))) + (Exp(x1) * -0.10183517633070582))))),"np.log(((np.log((x[:, 1] + x[:, 0])) ** (x[:, 1] * x[:, 1]) * np.log((x[:, 1] * t[0]) ** (x[:, 1] * x[:, 1]))) * np.exp((np.exp((x[:, 1] / np.log(x[:, 0]))) + (np.exp(x[:, 1]) * t[1])))))","$$\operatorname{log}(\left(\left({\operatorname{log}(\left(x_{1} + x_{0}\right))^{\left(x_{1} \cdot x_{1}\right)}} \cdot \operatorname{log}({\left(x_{1} \cdot \theta_{0}\right)^{\left(x_{1} \cdot x_{1}\right)}})\right) \cdot \operatorname{exp}(\left(\operatorname{exp}(\frac{x_{1}}{\operatorname{log}(x_{0})}) + \left(\operatorname{exp}(x_{1}) \cdot \theta_{1}\right)\right))\right))$$",0.5666233132490518;-0.10183517633070582,30,,,,Infinity,,,,,, -922,0,((((Exp(x1) ^ 0.9873166494169788) / (x1 * -0.9349079375209784)) + Log((0.9164711524353034 * (((0.5124704649573728 / x0) * 0.3633563196267593) * Log((x0 + 0.7831676405189226)))))) ^ (((x1 ^ (x1 ^ -0.17321097581080802)) ^ x0) * -0.21742666490562212)),"((np.exp(x[:, 1]) ** t[0] / (x[:, 1] * t[1])) + np.log((t[2] * (((t[3] / x[:, 0]) * t[4]) * np.log((x[:, 0] + t[5])))))) ** (x[:, 1] ** x[:, 1] ** t[6] ** x[:, 0] * t[7])","$${\left(\frac{{\operatorname{exp}(x_{1})^{\theta_{0}}}}{\left(x_{1} \cdot \theta_{1}\right)} + \operatorname{log}(\left(\theta_{2} \cdot \left(\left(\frac{\theta_{3}}{x_{0}} \cdot \theta_{4}\right) \cdot \operatorname{log}(\left(x_{0} + \theta_{5}\right))\right)\right))\right)^{\left({{x_{1}^{{x_{1}^{\theta_{6}}}}}^{x_{0}}} \cdot \theta_{7}\right)}}$$",0.9873166494169788;-0.9349079375209784;0.9164711524353034;0.5124704649573728;0.3633563196267593;0.7831676405189226;-0.17321097581080802;-0.21742666490562212,32,,,,Infinity,,,,,, -923,0,((Exp(((x1 ^ 0.9308622483003572) ^ Log((-0.2601611606734048 / x0)))) * Exp((Exp(Exp((0.43265244358446475 * x1))) * (x0 + x0)))) / Log(((Exp((x1 ^ 0.4446889452186622)) * -0.9085556811906231) ^ (Exp(Exp(x1)) ^ ((x1 + 0.6686521408534059) * x0))))),"((np.exp(x[:, 1] ** t[0] ** np.log((t[1] / x[:, 0]))) * np.exp((np.exp(np.exp((t[2] * x[:, 1]))) * (x[:, 0] + x[:, 0])))) / np.log((np.exp(x[:, 1] ** t[3]) * t[4]) ** np.exp(np.exp(x[:, 1])) ** ((x[:, 1] + t[5]) * x[:, 0])))","$$\frac{\left(\operatorname{exp}({{x_{1}^{\theta_{0}}}^{\operatorname{log}(\frac{\theta_{1}}{x_{0}})}}) \cdot \operatorname{exp}(\left(\operatorname{exp}(\operatorname{exp}(\left(\theta_{2} \cdot x_{1}\right))) \cdot \left(x_{0} + x_{0}\right)\right))\right)}{\operatorname{log}({\left(\operatorname{exp}({x_{1}^{\theta_{3}}}) \cdot \theta_{4}\right)^{{\operatorname{exp}(\operatorname{exp}(x_{1}))^{\left(\left(x_{1} + \theta_{5}\right) \cdot x_{0}\right)}}}})}$$",0.9308622483003572;-0.2601611606734048;0.43265244358446475;0.4446889452186622;-0.9085556811906231;0.6686521408534059,38,,,,Infinity,,,,,, -924,0,Log(Exp(Log(((0.8433807223742161 + x1) * -0.5924328166591832)))),"np.log(np.exp(np.log(((t[0] + x[:, 1]) * t[1]))))","$$\operatorname{log}(\operatorname{exp}(\operatorname{log}(\left(\left(\theta_{0} + x_{1}\right) \cdot \theta_{1}\right))))$$",0.8433807223742161;-0.5924328166591832,8,,,,Infinity,,,,,, -925,0,(((Exp((Log(x0) ^ 0.5866913931291518)) * 8.540826329825646e-2) + (Log((x1 ^ -9.927936755077682e-2)) * x0)) + x0),"(((np.exp(np.log(x[:, 0]) ** t[0]) * t[1]) + (np.log(x[:, 1] ** t[2]) * x[:, 0])) + x[:, 0])","$$\left(\left(\left(\operatorname{exp}({\operatorname{log}(x_{0})^{\theta_{0}}}) \cdot \theta_{1}\right) + \left(\operatorname{log}({x_{1}^{\theta_{2}}}) \cdot x_{0}\right)\right) + x_{0}\right)$$",0.5866913931291518;8.540826329825646e-2;-9.927936755077682e-2,16,,,,Infinity,,,,,, -926,0,(((-0.7051211420597063 * x1) * 0.5922076200118819) ^ Log(Log((x1 + 7.046598355161282e-2)))),"((t[0] * x[:, 1]) * t[1]) ** np.log(np.log((x[:, 1] + t[2])))","$${\left(\left(\theta_{0} \cdot x_{1}\right) \cdot \theta_{1}\right)^{\operatorname{log}(\operatorname{log}(\left(x_{1} + \theta_{2}\right)))}}$$",-0.7051211420597063;0.5922076200118819;7.046598355161282e-2,11,,,,Infinity,,,,,, -927,0,(Exp(Exp((x0 * -5.034764070872003))) ^ (x1 + Log(x0))),"np.exp(np.exp((x[:, 0] * t[0]))) ** (x[:, 1] + np.log(x[:, 0]))","$${\operatorname{exp}(\operatorname{exp}(\left(x_{0} \cdot \theta_{0}\right)))^{\left(x_{1} + \operatorname{log}(x_{0})\right)}}$$",-5.034764070872003,10,0.890484525294221,0.890484525294221,0.890484525294221,0.890484525294221,-81.17494566297388,-81.17494566297388,-81.17494566297388,23.616776424930798,23.616776424930798,23.616776424930798 -928,0,((-0.37245538619460594 ^ x0) ^ 0.2724874090299195),"t[0] ** x[:, 0] ** t[1]","$${{\theta_{0}^{x_{0}}}^{\theta_{1}}}$$",-0.37245538619460594;0.2724874090299195,5,,,,Infinity,,,,,, -929,0,(0.6264979961766012 ^ Exp(Exp((((-0.5736301660114695 * x1) + x0) ^ ((x1 * x0) * Log(x1)))))),"t[0] ** np.exp(np.exp(((t[1] * x[:, 1]) + x[:, 0]) ** ((x[:, 1] * x[:, 0]) * np.log(x[:, 1]))))","$${\theta_{0}^{\operatorname{exp}(\operatorname{exp}({\left(\left(\theta_{1} \cdot x_{1}\right) + x_{0}\right)^{\left(\left(x_{1} \cdot x_{0}\right) \cdot \operatorname{log}(x_{1})\right)}}))}}$$",0.6264979961766012;-0.5736301660114695,16,1.4570473111996425e-2,1.4570473111996425e-2,1.4570473111996425e-2,1.4570473111996425e-2,-0.34458017208836345,-0.34458017208836345,-0.34458017208836345,37.326027963413445,37.326027963413445,37.326027963413445 -930,0,Exp(((Log(((x0 * 0.9914124840627526) ^ (x0 * -0.4459850586926649))) + ((0.12700836923556902 / x1) + -0.569496811859628)) ^ (Exp((4.828866976649748e-2 / Log(x1))) + Log((Exp(x1) * -0.9507291695751243))))),"np.exp((np.log((x[:, 0] * t[0]) ** (x[:, 0] * t[1])) + ((t[2] / x[:, 1]) + t[3])) ** (np.exp((t[4] / np.log(x[:, 1]))) + np.log((np.exp(x[:, 1]) * t[5]))))","$$\operatorname{exp}({\left(\operatorname{log}({\left(x_{0} \cdot \theta_{0}\right)^{\left(x_{0} \cdot \theta_{1}\right)}}) + \left(\frac{\theta_{2}}{x_{1}} + \theta_{3}\right)\right)^{\left(\operatorname{exp}(\frac{\theta_{4}}{\operatorname{log}(x_{1})}) + \operatorname{log}(\left(\operatorname{exp}(x_{1}) \cdot \theta_{5}\right))\right)}})$$",0.9914124840627526;-0.4459850586926649;0.12700836923556902;-0.569496811859628;4.828866976649748e-2;-0.9507291695751243,27,,,,Infinity,,,,,, -931,0,Exp((-0.39928516947148285 ^ ((Exp(x1) + 0.9954353018093599) ^ ((-0.9372959562921634 ^ x0) / x0)))),"np.exp(t[0] ** (np.exp(x[:, 1]) + t[1]) ** (t[2] ** x[:, 0] / x[:, 0]))","$$\operatorname{exp}({\theta_{0}^{{\left(\operatorname{exp}(x_{1}) + \theta_{1}\right)^{\frac{{\theta_{2}^{x_{0}}}}{x_{0}}}}}})$$",-0.39928516947148285;0.9954353018093599;-0.9372959562921634,13,,,,Infinity,,,,,, -932,0,Log((Exp((-0.1602262416066315 / x1)) * (-0.48916004461278645 + Exp(x0)))),"np.log((np.exp((t[0] / x[:, 1])) * (t[1] + np.exp(x[:, 0]))))","$$\operatorname{log}(\left(\operatorname{exp}(\frac{\theta_{0}}{x_{1}}) \cdot \left(\theta_{1} + \operatorname{exp}(x_{0})\right)\right))$$",-0.1602262416066315;-0.48916004461278645,10,0.36643307587389573,0.36643307587389573,0.36643307587389573,0.36643307587389573,-32.81486959484751,-32.81486959484751,-32.81486959484751,25.16605505582015,25.16605505582015,25.16605505582015 -933,0,Log(Log((-0.866386271516592 + (((0.7570474963540104 ^ x1) + 0.26077562646243013) * 0.30106563466457736)))),"np.log(np.log((t[0] + ((t[1] ** x[:, 1] + t[2]) * t[3]))))","$$\operatorname{log}(\operatorname{log}(\left(\theta_{0} + \left(\left({\theta_{1}^{x_{1}}} + \theta_{2}\right) \cdot \theta_{3}\right)\right)))$$",-0.866386271516592;0.7570474963540104;0.26077562646243013;0.30106563466457736,11,,,,Infinity,,,,,, -934,0,(((-0.32774652819245453 * x1) ^ (x0 ^ x0)) / ((x1 * x0) / (-0.5611543774116907 ^ x1))),"((t[0] * x[:, 1]) ** x[:, 0] ** x[:, 0] / ((x[:, 1] * x[:, 0]) / t[1] ** x[:, 1]))","$$\frac{{\left(\theta_{0} \cdot x_{1}\right)^{{x_{0}^{x_{0}}}}}}{\frac{\left(x_{1} \cdot x_{0}\right)}{{\theta_{1}^{x_{1}}}}}$$",-0.32774652819245453;-0.5611543774116907,15,,,,Infinity,,,,,, -935,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 -936,0,(Exp(Exp((((x1 + (x0 ^ x0)) + x1) + (5.301989985832911e-2 / x1)))) + (((((x1 * -0.7828326410952101) ^ x1) + -0.554374212539825) * (Log(x0) / Exp(Exp(x0)))) * (-0.3643567586423224 / x0))),"(np.exp(np.exp((((x[:, 1] + x[:, 0] ** x[:, 0]) + x[:, 1]) + (t[0] / x[:, 1])))) + ((((x[:, 1] * t[1]) ** x[:, 1] + t[2]) * (np.log(x[:, 0]) / np.exp(np.exp(x[:, 0])))) * (t[3] / x[:, 0])))","$$\left(\operatorname{exp}(\operatorname{exp}(\left(\left(\left(x_{1} + {x_{0}^{x_{0}}}\right) + x_{1}\right) + \frac{\theta_{0}}{x_{1}}\right))) + \left(\left(\left({\left(x_{1} \cdot \theta_{1}\right)^{x_{1}}} + \theta_{2}\right) \cdot \frac{\operatorname{log}(x_{0})}{\operatorname{exp}(\operatorname{exp}(x_{0}))}\right) \cdot \frac{\theta_{3}}{x_{0}}\right)\right)$$",5.301989985832911e-2;-0.7828326410952101;-0.554374212539825;-0.3643567586423224,32,,,,Infinity,,,,,, -937,0,((((x0 ^ x1) * -0.3632351045510138) / Log(((-7.22425137774152e-2 ^ x0) * -0.3644815873688627))) / (Exp(Log(Exp((0.5325954515967897 + x1)))) / Log((x1 + (x1 ^ 1.0884304742983741e-2))))),"(((x[:, 0] ** x[:, 1] * t[0]) / np.log((t[1] ** x[:, 0] * t[2]))) / (np.exp(np.log(np.exp((t[3] + x[:, 1])))) / np.log((x[:, 1] + x[:, 1] ** t[4]))))","$$\frac{\frac{\left({x_{0}^{x_{1}}} \cdot \theta_{0}\right)}{\operatorname{log}(\left({\theta_{1}^{x_{0}}} \cdot \theta_{2}\right))}}{\frac{\operatorname{exp}(\operatorname{log}(\operatorname{exp}(\left(\theta_{3} + x_{1}\right))))}{\operatorname{log}(\left(x_{1} + {x_{1}^{\theta_{4}}}\right))}}$$",-0.3632351045510138;-7.22425137774152e-2;-0.3644815873688627;0.5325954515967897;1.0884304742983741e-2,26,,,,Infinity,,,,,, -938,0,((0.9505633072063266 / Log(((-0.4513284852347751 ^ x0) ^ x0))) + (Log(((Log(x0) + (7.874183362080245e-2 / x0)) ^ -0.4966170325801399)) + ((-0.8859800328906873 / x1) * (0.1380275822064123 / x1)))),"((t[0] / np.log(t[1] ** x[:, 0] ** x[:, 0])) + (np.log((np.log(x[:, 0]) + (t[2] / x[:, 0])) ** t[3]) + ((t[4] / x[:, 1]) * (t[5] / x[:, 1]))))","$$\left(\frac{\theta_{0}}{\operatorname{log}({{\theta_{1}^{x_{0}}}^{x_{0}}})} + \left(\operatorname{log}({\left(\operatorname{log}(x_{0}) + \frac{\theta_{2}}{x_{0}}\right)^{\theta_{3}}}) + \left(\frac{\theta_{4}}{x_{1}} \cdot \frac{\theta_{5}}{x_{1}}\right)\right)\right)$$",0.9505633072063266;-0.4513284852347751;7.874183362080245e-2;-0.4966170325801399;-0.8859800328906873;0.1380275822064123,26,,,,Infinity,,,,,, -939,0,(0.35334125145639544 / (0.22719240397795465 + (x1 ^ -1.5814060191970776))),"(t[0] / (t[1] + x[:, 1] ** t[2]))","$$\frac{\theta_{0}}{\left(\theta_{1} + {x_{1}^{\theta_{2}}}\right)}$$",0.35334125145639544;0.22719240397795465;-1.5814060191970776,7,9.210466826520363e-3,9.210466826520363e-3,9.210466826520363e-3,9.210466826520363e-3,0.15004742979687324,0.15004742979687324,0.15004742979687324,17.33712961328549,17.33712961328549,17.33712961328549 -940,0,(Exp((((x0 ^ -0.8561603490703908) ^ (x1 + 0.2774667115532945)) * Exp(((0.9063207456646459 ^ x1) / (x1 * 0.46378505335439646))))) / (Log(((x1 * x0) * -5.19158154587388e-2)) ^ ((0.6896639318125235 / (x0 ^ -0.43266566382896443)) + Log((x1 * -0.1077220784802918))))),"(np.exp((x[:, 0] ** t[0] ** (x[:, 1] + t[1]) * np.exp((t[2] ** x[:, 1] / (x[:, 1] * t[3]))))) / np.log(((x[:, 1] * x[:, 0]) * t[4])) ** ((t[5] / x[:, 0] ** t[6]) + np.log((x[:, 1] * t[7]))))","$$\frac{\operatorname{exp}(\left({{x_{0}^{\theta_{0}}}^{\left(x_{1} + \theta_{1}\right)}} \cdot \operatorname{exp}(\frac{{\theta_{2}^{x_{1}}}}{\left(x_{1} \cdot \theta_{3}\right)})\right))}{{\operatorname{log}(\left(\left(x_{1} \cdot x_{0}\right) \cdot \theta_{4}\right))^{\left(\frac{\theta_{5}}{{x_{0}^{\theta_{6}}}} + \operatorname{log}(\left(x_{1} \cdot \theta_{7}\right))\right)}}}$$",-0.8561603490703908;0.2774667115532945;0.9063207456646459;0.46378505335439646;-5.19158154587388e-2;0.6896639318125235;-0.43266566382896443;-0.1077220784802918,35,,,,Infinity,,,,,, -941,0,(-0.49065709877649377 ^ x1),"t[0] ** x[:, 1]","$${\theta_{0}^{x_{1}}}$$",-0.49065709877649377,3,,,,Infinity,,,,,, -942,0,Exp((((x0 ^ 3.041224302372741) ^ -1.5468855137603212e-2) * Exp((((-60.69058971449237 + (x1 * -10.710804826178762)) + -62.04791716960677) * Exp(Log((x1 ^ 42.136981247118804))))))),"np.exp((x[:, 0] ** t[0] ** t[1] * np.exp((((t[2] + (x[:, 1] * t[3])) + t[4]) * np.exp(np.log(x[:, 1] ** t[5]))))))","$$\operatorname{exp}(\left({{x_{0}^{\theta_{0}}}^{\theta_{1}}} \cdot \operatorname{exp}(\left(\left(\left(\theta_{2} + \left(x_{1} \cdot \theta_{3}\right)\right) + \theta_{4}\right) \cdot \operatorname{exp}(\operatorname{log}({x_{1}^{\theta_{5}}}))\right))\right))$$",3.041224302372741;-1.5468855137603212e-2;-60.69058971449237;-10.710804826178762;-62.04791716960677;42.136981247118804,21,7.0661505320396785,7.0661505320396785,7.0661505320396785,7.0661505320396785,-651.0725734396127,-651.0725734396127,-651.0725734396127,62.153357466318106,62.153357466318106,62.153357466318106 -943,0,Log(((Exp((Exp(x1) ^ (0.7622788957176975 ^ x0))) ^ (0.11770015307393393 * (x1 + (x0 / x0)))) + (((x1 * x0) ^ Exp((1.050651868771941 ^ x0))) / Log(((0.3411980972672422 ^ x1) / x0))))),"np.log((np.exp(np.exp(x[:, 1]) ** t[0] ** x[:, 0]) ** (t[1] * (x[:, 1] + (x[:, 0] / x[:, 0]))) + ((x[:, 1] * x[:, 0]) ** np.exp(t[2] ** x[:, 0]) / np.log((t[3] ** x[:, 1] / x[:, 0])))))","$$\operatorname{log}(\left({\operatorname{exp}({\operatorname{exp}(x_{1})^{{\theta_{0}^{x_{0}}}}})^{\left(\theta_{1} \cdot \left(x_{1} + \frac{x_{0}}{x_{0}}\right)\right)}} + \frac{{\left(x_{1} \cdot x_{0}\right)^{\operatorname{exp}({\theta_{2}^{x_{0}}})}}}{\operatorname{log}(\frac{{\theta_{3}^{x_{1}}}}{x_{0}})}\right))$$",0.7622788957176975;0.11770015307393393;1.050651868771941;0.3411980972672422,32,2.9135710026753858e-2,2.9135710026753858e-2,2.9135710026753858e-2,2.9135710026753858e-2,-1.6886771418173678,-1.6886771418173678,-1.6886771418173678,78.42110835525366,78.42110835525366,78.42110835525366 -944,0,(Log(((x0 * x1) + (x0 + x0))) / (-40.625647549427164 / (Exp((((x1 * -1.3081262350740548) / x0) * 1.89598860276895)) + -1.9854845540492172))),"(np.log(((x[:, 0] * x[:, 1]) + (x[:, 0] + x[:, 0]))) / (t[0] / (np.exp((((x[:, 1] * t[1]) / x[:, 0]) * t[2])) + t[3])))","$$\frac{\operatorname{log}(\left(\left(x_{0} \cdot x_{1}\right) + \left(x_{0} + x_{0}\right)\right))}{\frac{\theta_{0}}{\left(\operatorname{exp}(\left(\frac{\left(x_{1} \cdot \theta_{1}\right)}{x_{0}} \cdot \theta_{2}\right)) + \theta_{3}\right)}}$$",-40.625647549427164;-1.3081262350740548;1.89598860276895;-1.9854845540492172,21,1.3086950868538227e-2,1.3086950868538227e-2,1.3086950868538227e-2,1.3086950868538227e-2,-0.20767901739877304,-0.20767901739877304,-0.20767901739877304,51.7637503725288,51.7637503725288,51.7637503725288 -945,0,((((x0 ^ Log(((0.9821102735867796 + x1) / x1))) ^ -0.3459269605375668) / (((x0 * -0.9821814936983824) + 0.4456031884511792) ^ (x0 * x1))) * ((x0 + x1) + x1)),"((x[:, 0] ** np.log(((t[0] + x[:, 1]) / x[:, 1])) ** t[1] / ((x[:, 0] * t[2]) + t[3]) ** (x[:, 0] * x[:, 1])) * ((x[:, 0] + x[:, 1]) + x[:, 1]))","$$\left(\frac{{{x_{0}^{\operatorname{log}(\frac{\left(\theta_{0} + x_{1}\right)}{x_{1}})}}^{\theta_{1}}}}{{\left(\left(x_{0} \cdot \theta_{2}\right) + \theta_{3}\right)^{\left(x_{0} \cdot x_{1}\right)}}} \cdot \left(\left(x_{0} + x_{1}\right) + x_{1}\right)\right)$$",0.9821102735867796;-0.3459269605375668;-0.9821814936983824;0.4456031884511792,26,,,,Infinity,,,,,, -946,0,Exp(((Exp(x0) / x0) * -1.0061081786002721)),"np.exp(((np.exp(x[:, 0]) / x[:, 0]) * t[0]))","$$\operatorname{exp}(\left(\frac{\operatorname{exp}(x_{0})}{x_{0}} \cdot \theta_{0}\right))$$",-1.0061081786002721,7,1.1567418718036548e-2,1.1567418718036548e-2,1.1567418718036548e-2,1.1567418718036548e-2,-6.745482668713731e-2,-6.745482668713731e-2,-6.745482668713731e-2,13.297400999252577,13.297400999252577,13.297400999252577 -947,0,((Log((((0.6393710266524602 ^ x0) ^ ((x0 + 0.3524949093656261) ^ Exp(x1))) / x1)) * ((-0.9390137006606669 ^ x0) ^ x0)) ^ x1),"(np.log((t[0] ** x[:, 0] ** (x[:, 0] + t[1]) ** np.exp(x[:, 1]) / x[:, 1])) * t[2] ** x[:, 0] ** x[:, 0]) ** x[:, 1]","$${\left(\operatorname{log}(\frac{{{\theta_{0}^{x_{0}}}^{{\left(x_{0} + \theta_{1}\right)^{\operatorname{exp}(x_{1})}}}}}{x_{1}}) \cdot {{\theta_{2}^{x_{0}}}^{x_{0}}}\right)^{x_{1}}}$$",0.6393710266524602;0.3524949093656261;-0.9390137006606669,21,,,,Infinity,,,,,, -948,0,(Exp((((x0 ^ x1) + Log(Log((x1 ^ 2.4822253240271985e-3)))) + 0.21020474995432725)) / (Exp((-0.23429932408720278 ^ (x1 + -0.6685501696436695))) / Log(Exp(((x1 ^ 0.8800528279967526) + 0.553304564635863))))),"(np.exp(((x[:, 0] ** x[:, 1] + np.log(np.log(x[:, 1] ** t[0]))) + t[1])) / (np.exp(t[2] ** (x[:, 1] + t[3])) / np.log(np.exp((x[:, 1] ** t[4] + t[5])))))","$$\frac{\operatorname{exp}(\left(\left({x_{0}^{x_{1}}} + \operatorname{log}(\operatorname{log}({x_{1}^{\theta_{0}}}))\right) + \theta_{1}\right))}{\frac{\operatorname{exp}({\theta_{2}^{\left(x_{1} + \theta_{3}\right)}})}{\operatorname{log}(\operatorname{exp}(\left({x_{1}^{\theta_{4}}} + \theta_{5}\right)))}}$$",2.4822253240271985e-3;0.21020474995432725;-0.23429932408720278;-0.6685501696436695;0.8800528279967526;0.553304564635863,27,,,,Infinity,,,,,, -949,0,(((-1.5033518690619783 / Exp((((x1 * 0.708344572821251) * -0.6550555562544764) * 0.7390243357358103))) + Exp((x0 / (2.263807276160452 + x0)))) + (((x1 ^ 2.43891040278655) / (x0 / (((-1.3556423095518648 * x0) + (x0 * x1)) + -0.4386744472941228))) + ((x1 / x0) * 1.4467648866750773))),"(((t[0] / np.exp((((x[:, 1] * t[1]) * t[2]) * t[3]))) + np.exp((x[:, 0] / (t[4] + x[:, 0])))) + ((x[:, 1] ** t[5] / (x[:, 0] / (((t[6] * x[:, 0]) + (x[:, 0] * x[:, 1])) + t[7]))) + ((x[:, 1] / x[:, 0]) * t[8])))","$$\left(\left(\frac{\theta_{0}}{\operatorname{exp}(\left(\left(\left(x_{1} \cdot \theta_{1}\right) \cdot \theta_{2}\right) \cdot \theta_{3}\right))} + \operatorname{exp}(\frac{x_{0}}{\left(\theta_{4} + x_{0}\right)})\right) + \left(\frac{{x_{1}^{\theta_{5}}}}{\frac{x_{0}}{\left(\left(\left(\theta_{6} \cdot x_{0}\right) + \left(x_{0} \cdot x_{1}\right)\right) + \theta_{7}\right)}} + \left(\frac{x_{1}}{x_{0}} \cdot \theta_{8}\right)\right)\right)$$",-1.5033518690619783;0.708344572821251;-0.6550555562544764;0.7390243357358103;2.263807276160452;2.43891040278655;-1.3556423095518648;-0.4386744472941228;1.4467648866750773,39,1.6342982507322756e-3,1.6342982507322756e-3,1.6342982507322756e-3,1.6342982507322756e-3,0.8491850603393191,0.8491850603393191,0.8491850603393191,99.28801541224801,99.28801541224801,99.28801541224801 -950,0,Exp((Log((Exp(x1) * (x1 + -0.8387229543042387))) / ((Exp((-0.4351711476804776 + Log(x0))) * -0.8546767135593256) ^ x1))),"np.exp((np.log((np.exp(x[:, 1]) * (x[:, 1] + t[0]))) / (np.exp((t[1] + np.log(x[:, 0]))) * t[2]) ** x[:, 1]))","$$\operatorname{exp}(\frac{\operatorname{log}(\left(\operatorname{exp}(x_{1}) \cdot \left(x_{1} + \theta_{0}\right)\right))}{{\left(\operatorname{exp}(\left(\theta_{1} + \operatorname{log}(x_{0})\right)) \cdot \theta_{2}\right)^{x_{1}}}})$$",-0.8387229543042387;-0.4351711476804776;-0.8546767135593256,18,,,,Infinity,,,,,, -951,0,(((x1 + -1.131468657335928) + (x1 ^ (x0 / x0))) + ((-1.8997746322508822 * x1) + (0.10244603664991561 + x0))),"(((x[:, 1] + t[0]) + x[:, 1] ** (x[:, 0] / x[:, 0])) + ((t[1] * x[:, 1]) + (t[2] + x[:, 0])))","$$\left(\left(\left(x_{1} + \theta_{0}\right) + {x_{1}^{\frac{x_{0}}{x_{0}}}}\right) + \left(\left(\theta_{1} \cdot x_{1}\right) + \left(\theta_{2} + x_{0}\right)\right)\right)$$",-1.131468657335928;-1.8997746322508822;0.10244603664991561,17,0.15086533848901912,0.15086533848901912,0.15086533848901912,0.15086533848901912,-12.922028559300518,-12.922028559300518,-12.922028559300518,39.279026144683364,39.279026144683364,39.279026144683364 -952,0,(Log((0.7796756229835801 / (Exp(x0) * -0.15998408190166125))) ^ (((x1 * ((0.7891994390150341 * x0) / (x0 / (x1 ^ x1)))) + Exp(x0)) + (-0.3928607355670173 ^ (x1 + -0.8773147947317357)))),"np.log((t[0] / (np.exp(x[:, 0]) * t[1]))) ** (((x[:, 1] * ((t[2] * x[:, 0]) / (x[:, 0] / x[:, 1] ** x[:, 1]))) + np.exp(x[:, 0])) + t[3] ** (x[:, 1] + t[4]))","$${\operatorname{log}(\frac{\theta_{0}}{\left(\operatorname{exp}(x_{0}) \cdot \theta_{1}\right)})^{\left(\left(\left(x_{1} \cdot \frac{\left(\theta_{2} \cdot x_{0}\right)}{\frac{x_{0}}{{x_{1}^{x_{1}}}}}\right) + \operatorname{exp}(x_{0})\right) + {\theta_{3}^{\left(x_{1} + \theta_{4}\right)}}\right)}}$$",0.7796756229835801;-0.15998408190166125;0.7891994390150341;-0.3928607355670173;-0.8773147947317357,28,,,,Infinity,,,,,, -953,0,(5.3357217557189265e-3 / (1.5239849942147886e-3 ^ Exp((x0 * -0.9446445572386545)))),"(t[0] / t[1] ** np.exp((x[:, 0] * t[2])))","$$\frac{\theta_{0}}{{\theta_{1}^{\operatorname{exp}(\left(x_{0} \cdot \theta_{2}\right))}}}$$",5.3357217557189265e-3;1.5239849942147886e-3;-0.9446445572386545,8,5.671433671050864e-3,5.671433671050864e-3,5.671433671050864e-3,5.671433671050864e-3,0.4766335174709747,0.4766335174709747,0.4766335174709747,20.40195485023131,20.40195485023131,20.40195485023131 -954,0,((-0.7213226845272313 ^ Log((-0.141002965762409 ^ (x1 * 0.4798575261608894)))) + (((x0 + -0.7435693005958358) * 0.1012490786160547) ^ 0.6090659387514961)),"(t[0] ** np.log(t[1] ** (x[:, 1] * t[2])) + ((x[:, 0] + t[3]) * t[4]) ** t[5])","$$\left({\theta_{0}^{\operatorname{log}({\theta_{1}^{\left(x_{1} \cdot \theta_{2}\right)}})}} + {\left(\left(x_{0} + \theta_{3}\right) \cdot \theta_{4}\right)^{\theta_{5}}}\right)$$",-0.7213226845272313;-0.141002965762409;0.4798575261608894;-0.7435693005958358;0.1012490786160547;0.6090659387514961,16,,,,Infinity,,,,,, -955,0,Exp((((Exp(((x0 ^ x0) ^ -0.3417948199457427)) * -0.6639437079268771) * -0.5902264072671638) * (1.1071823557440297e-4 ^ ((((x1 ^ -0.7471628766794766) * 0.46667444697213306) / ((x0 / x0) * 0.9391352309640245)) ^ (-0.5946232309505479 / (0.5586812476873512 + x0)))))),"np.exp((((np.exp(x[:, 0] ** x[:, 0] ** t[0]) * t[1]) * t[2]) * t[3] ** ((x[:, 1] ** t[4] * t[5]) / ((x[:, 0] / x[:, 0]) * t[6])) ** (t[7] / (t[8] + x[:, 0]))))","$$\operatorname{exp}(\left(\left(\left(\operatorname{exp}({{x_{0}^{x_{0}}}^{\theta_{0}}}) \cdot \theta_{1}\right) \cdot \theta_{2}\right) \cdot {\theta_{3}^{{\frac{\left({x_{1}^{\theta_{4}}} \cdot \theta_{5}\right)}{\left(\frac{x_{0}}{x_{0}} \cdot \theta_{6}\right)}^{\frac{\theta_{7}}{\left(\theta_{8} + x_{0}\right)}}}}}\right))$$",-0.3417948199457427;-0.6639437079268771;-0.5902264072671638;1.1071823557440297e-4;-0.7471628766794766;0.46667444697213306;0.9391352309640245;-0.5946232309505479;0.5586812476873512,31,0.8932607223202278,0.8932607223202278,0.8932607223202278,0.8932607223202278,-81.4311363471258,-81.4311363471258,-81.4311363471258,83.45494686047188,83.45494686047188,83.45494686047188 -956,0,((Log(Log((-0.2299748632461447 ^ ((x1 + x0) * -0.5770472577148691)))) ^ (((0.4546087450415348 + x1) * -0.29385302072425734) + Exp(Log(Log((x1 + x1)))))) * (((x1 * x0) / (x0 + x1)) + 0.41606340315817303)),"(np.log(np.log(t[0] ** ((x[:, 1] + x[:, 0]) * t[1]))) ** (((t[2] + x[:, 1]) * t[3]) + np.exp(np.log(np.log((x[:, 1] + x[:, 1]))))) * (((x[:, 1] * x[:, 0]) / (x[:, 0] + x[:, 1])) + t[4]))","$$\left({\operatorname{log}(\operatorname{log}({\theta_{0}^{\left(\left(x_{1} + x_{0}\right) \cdot \theta_{1}\right)}}))^{\left(\left(\left(\theta_{2} + x_{1}\right) \cdot \theta_{3}\right) + \operatorname{exp}(\operatorname{log}(\operatorname{log}(\left(x_{1} + x_{1}\right))))\right)}} \cdot \left(\frac{\left(x_{1} \cdot x_{0}\right)}{\left(x_{0} + x_{1}\right)} + \theta_{4}\right)\right)$$",-0.2299748632461447;-0.5770472577148691;0.4546087450415348;-0.29385302072425734;0.41606340315817303,32,,,,Infinity,,,,,, -957,0,((((x0 / x0) * (-0.3768384549919066 + Log((x0 + (x1 * x1))))) / Log((x0 / Log(Log((x1 * 0.9306384037836238)))))) * Log(((x0 + x0) + 0.19559929246979846))),"((((x[:, 0] / x[:, 0]) * (t[0] + np.log((x[:, 0] + (x[:, 1] * x[:, 1]))))) / np.log((x[:, 0] / np.log(np.log((x[:, 1] * t[1])))))) * np.log(((x[:, 0] + x[:, 0]) + t[2])))","$$\left(\frac{\left(\frac{x_{0}}{x_{0}} \cdot \left(\theta_{0} + \operatorname{log}(\left(x_{0} + \left(x_{1} \cdot x_{1}\right)\right))\right)\right)}{\operatorname{log}(\frac{x_{0}}{\operatorname{log}(\operatorname{log}(\left(x_{1} \cdot \theta_{1}\right)))})} \cdot \operatorname{log}(\left(\left(x_{0} + x_{0}\right) + \theta_{2}\right))\right)$$",-0.3768384549919066;0.9306384037836238;0.19559929246979846,28,,,,Infinity,,,,,, -958,0,Exp(((((x1 * 0.12213290203038844) ^ (x1 ^ Log(x1))) ^ Exp(Exp(((x1 * x1) ^ (-0.2792291971611356 + x0))))) * ((0.5355500393146782 / Exp((x0 ^ x1))) + (0.47287486359855374 + (-0.8422214646782668 + x0))))),"np.exp(((x[:, 1] * t[0]) ** x[:, 1] ** np.log(x[:, 1]) ** np.exp(np.exp((x[:, 1] * x[:, 1]) ** (t[1] + x[:, 0]))) * ((t[2] / np.exp(x[:, 0] ** x[:, 1])) + (t[3] + (t[4] + x[:, 0])))))","$$\operatorname{exp}(\left({{\left(x_{1} \cdot \theta_{0}\right)^{{x_{1}^{\operatorname{log}(x_{1})}}}}^{\operatorname{exp}(\operatorname{exp}({\left(x_{1} \cdot x_{1}\right)^{\left(\theta_{1} + x_{0}\right)}}))}} \cdot \left(\frac{\theta_{2}}{\operatorname{exp}({x_{0}^{x_{1}}})} + \left(\theta_{3} + \left(\theta_{4} + x_{0}\right)\right)\right)\right))$$",0.12213290203038844;-0.2792291971611356;0.5355500393146782;0.47287486359855374;-0.8422214646782668,32,0.8923099211909795,0.8923099211909795,0.8923099211909795,0.8923099211909795,-81.34339531523482,-81.34339531523482,-81.34339531523482,81.21889008066911,81.21889008066911,81.21889008066911 -959,0,(((7.590875120497198e-3 + (-0.12779396333268764 / x0)) + (Exp(((Exp(x1) ^ -0.8287014514245213) * -0.8083489338237076)) ^ Exp((0.12019295656097609 + x0)))) / (x0 * x0)),"(((t[0] + (t[1] / x[:, 0])) + np.exp((np.exp(x[:, 1]) ** t[2] * t[3])) ** np.exp((t[4] + x[:, 0]))) / (x[:, 0] * x[:, 0]))","$$\frac{\left(\left(\theta_{0} + \frac{\theta_{1}}{x_{0}}\right) + {\operatorname{exp}(\left({\operatorname{exp}(x_{1})^{\theta_{2}}} \cdot \theta_{3}\right))^{\operatorname{exp}(\left(\theta_{4} + x_{0}\right))}}\right)}{\left(x_{0} \cdot x_{0}\right)}$$",7.590875120497198e-3;-0.12779396333268764;-0.8287014514245213;-0.8083489338237076;0.12019295656097609,22,1.1779036292835655e-3,1.1779036292835655e-3,1.1779036292835655e-3,1.1779036292835655e-3,0.8913016857866054,0.8913016857866054,0.8913016857866054,55.85339870706094,55.85339870706094,55.85339870706094 -960,0,(Exp(((((x1 + Exp(x0)) ^ 2.6924336286294412) * -3.830687695117723) * x1)) ^ 2.7725363364533493),"np.exp((((x[:, 1] + np.exp(x[:, 0])) ** t[0] * t[1]) * x[:, 1])) ** t[2]","$${\operatorname{exp}(\left(\left({\left(x_{1} + \operatorname{exp}(x_{0})\right)^{\theta_{0}}} \cdot \theta_{1}\right) \cdot x_{1}\right))^{\theta_{2}}}$$",2.6924336286294412;-3.830687695117723;2.7725363364533493,13,1.4573705260116728e-2,1.4573705260116728e-2,1.4573705260116728e-2,1.4573705260116728e-2,-0.3448784384687622,-0.3448784384687622,-0.3448784384687622,31.3727230785561,31.3727230785561,31.3727230785561 -961,0,Exp(Log(Log((Exp((0.10089018651266601 + x0)) / (-0.1745097609771158 / Exp(x1)))))),"np.exp(np.log(np.log((np.exp((t[0] + x[:, 0])) / (t[1] / np.exp(x[:, 1]))))))","$$\operatorname{exp}(\operatorname{log}(\operatorname{log}(\frac{\operatorname{exp}(\left(\theta_{0} + x_{0}\right))}{\frac{\theta_{1}}{\operatorname{exp}(x_{1})}})))$$",0.10089018651266601;-0.1745097609771158,12,,,,Infinity,,,,,, -962,0,(Exp(Exp(Exp((((x1 * -0.7976399167811412) ^ 0.7684795195092324) + (0.29586834693465747 / x0))))) ^ Exp(((-0.4391910088562295 + (x0 / x0)) ^ -0.16192783065409244))),"np.exp(np.exp(np.exp(((x[:, 1] * t[0]) ** t[1] + (t[2] / x[:, 0]))))) ** np.exp((t[3] + (x[:, 0] / x[:, 0])) ** t[4])","$${\operatorname{exp}(\operatorname{exp}(\operatorname{exp}(\left({\left(x_{1} \cdot \theta_{0}\right)^{\theta_{1}}} + \frac{\theta_{2}}{x_{0}}\right))))^{\operatorname{exp}({\left(\theta_{3} + \frac{x_{0}}{x_{0}}\right)^{\theta_{4}}})}}$$",-0.7976399167811412;0.7684795195092324;0.29586834693465747;-0.4391910088562295;-0.16192783065409244,21,,,,Infinity,,,,,, -963,0,Log(Log((((Exp(x0) * 0.8263470006882181) / (((x0 + x1) + (x1 * -0.38730405289199965)) * Log((-0.7376859329138159 / x1)))) * Exp((0.701482841936423 + (Log(x1) * x1)))))),"np.log(np.log((((np.exp(x[:, 0]) * t[0]) / (((x[:, 0] + x[:, 1]) + (x[:, 1] * t[1])) * np.log((t[2] / x[:, 1])))) * np.exp((t[3] + (np.log(x[:, 1]) * x[:, 1]))))))","$$\operatorname{log}(\operatorname{log}(\left(\frac{\left(\operatorname{exp}(x_{0}) \cdot \theta_{0}\right)}{\left(\left(\left(x_{0} + x_{1}\right) + \left(x_{1} \cdot \theta_{1}\right)\right) \cdot \operatorname{log}(\frac{\theta_{2}}{x_{1}})\right)} \cdot \operatorname{exp}(\left(\theta_{3} + \left(\operatorname{log}(x_{1}) \cdot x_{1}\right)\right))\right)))$$",0.8263470006882181;-0.38730405289199965;-0.7376859329138159;0.701482841936423,27,,,,Infinity,,,,,, -964,0,(x0 / x0),"(x[:, 0] / x[:, 0])","$$\frac{x_{0}}{x_{0}}$$",,3,0.8923097947317449,0.8923097947317449,0.8923097947317449,0.8923097947317449,-81.34338364542985,-81.34338364542985,-81.34338364542985,2.8825203569384064,2.8825203569384064,2.8825203569384064 -965,0,(((0.8215346795446157 * (-0.33720140760847606 + x1)) * 0.4448551350398259) * (Log(Exp((0.7655270286048402 * x0))) / (Log(x0) / (x0 + 0.393641982763437)))),"(((t[0] * (t[1] + x[:, 1])) * t[2]) * (np.log(np.exp((t[3] * x[:, 0]))) / (np.log(x[:, 0]) / (x[:, 0] + t[4]))))","$$\left(\left(\left(\theta_{0} \cdot \left(\theta_{1} + x_{1}\right)\right) \cdot \theta_{2}\right) \cdot \frac{\operatorname{log}(\operatorname{exp}(\left(\theta_{3} \cdot x_{0}\right)))}{\frac{\operatorname{log}(x_{0})}{\left(x_{0} + \theta_{4}\right)}}\right)$$",0.8215346795446157;-0.33720140760847606;0.4448551350398259;0.7655270286048402;0.393641982763437,20,2615.819060495159,2615.819060495159,2615.819060495159,2615.819060495159,-241389.8193287681,-241389.8193287681,-241389.8193287681,2405.930609956078,2405.930609956078,2405.930609956078 -966,0,((((x0 ^ x1) * Log(((x1 * -0.9948548113582485) * 0.49286166856167013))) / Log(Exp(Log(Log(x1))))) + Log((x0 ^ 0.36056314135049616))),"(((x[:, 0] ** x[:, 1] * np.log(((x[:, 1] * t[0]) * t[1]))) / np.log(np.exp(np.log(np.log(x[:, 1]))))) + np.log(x[:, 0] ** t[2]))","$$\left(\frac{\left({x_{0}^{x_{1}}} \cdot \operatorname{log}(\left(\left(x_{1} \cdot \theta_{0}\right) \cdot \theta_{1}\right))\right)}{\operatorname{log}(\operatorname{exp}(\operatorname{log}(\operatorname{log}(x_{1}))))} + \operatorname{log}({x_{0}^{\theta_{2}}})\right)$$",-0.9948548113582485;0.49286166856167013;0.36056314135049616,21,,,,Infinity,,,,,, -967,0,(x0 * (-1.183907888707723 + x0)),"(x[:, 0] * (t[0] + x[:, 0]))","$$\left(x_{0} \cdot \left(\theta_{0} + x_{0}\right)\right)$$",-1.183907888707723,5,0.1297997374962222,0.1297997374962222,0.1297997374962222,0.1297997374962222,-10.978070446868387,-10.978070446868387,-10.978070446868387,9.069216504713694,9.069216504713694,9.069216504713694 -968,0,Exp(((Log((-0.7556182148302774 * x1)) + 0.9544641518232967) * -0.6242931835524377)),"np.exp(((np.log((t[0] * x[:, 1])) + t[1]) * t[2]))","$$\operatorname{exp}(\left(\left(\operatorname{log}(\left(\theta_{0} \cdot x_{1}\right)) + \theta_{1}\right) \cdot \theta_{2}\right))$$",-0.7556182148302774;0.9544641518232967;-0.6242931835524377,9,,,,Infinity,,,,,, -969,0,Exp((Log(((Exp(x1) + x0) ^ -7.241947751133037)) + Exp((((x1 * 0.9065394637483465) + (x1 + 0.3514228049557008)) * 1.1696370621508143)))),"np.exp((np.log((np.exp(x[:, 1]) + x[:, 0]) ** t[0]) + np.exp((((x[:, 1] * t[1]) + (x[:, 1] + t[2])) * t[3]))))","$$\operatorname{exp}(\left(\operatorname{log}({\left(\operatorname{exp}(x_{1}) + x_{0}\right)^{\theta_{0}}}) + \operatorname{exp}(\left(\left(\left(x_{1} \cdot \theta_{1}\right) + \left(x_{1} + \theta_{2}\right)\right) \cdot \theta_{3}\right))\right))$$",-7.241947751133037;0.9065394637483465;0.3514228049557008;1.1696370621508143,19,3.2768579624871904e-3,3.2768579624871904e-3,3.2768579624871904e-3,3.2768579624871904e-3,0.6976077434656172,0.6976077434656172,0.6976077434656172,47.59603820555369,47.59603820555369,47.59603820555369 -970,0,(Exp(Log(((0.7895003273427796 / (x0 ^ 0.6065457500371565)) ^ 0.5228764245303865))) ^ (0.42726714389003695 + Log(Log((x1 ^ 0.9386293655284665))))),"np.exp(np.log((t[0] / x[:, 0] ** t[1]) ** t[2])) ** (t[3] + np.log(np.log(x[:, 1] ** t[4])))","$${\operatorname{exp}(\operatorname{log}({\frac{\theta_{0}}{{x_{0}^{\theta_{1}}}}^{\theta_{2}}}))^{\left(\theta_{3} + \operatorname{log}(\operatorname{log}({x_{1}^{\theta_{4}}}))\right)}}$$",0.7895003273427796;0.6065457500371565;0.5228764245303865;0.42726714389003695;0.9386293655284665,17,,,,Infinity,,,,,, -971,0,((-0.859146708368862 + Exp((x0 * -0.11208670878184722))) / ((x0 * 0.7185031548339713) ^ 1.0704163291369404)),"((t[0] + np.exp((x[:, 0] * t[1]))) / (x[:, 0] * t[2]) ** t[3])","$$\frac{\left(\theta_{0} + \operatorname{exp}(\left(x_{0} \cdot \theta_{1}\right))\right)}{{\left(x_{0} \cdot \theta_{2}\right)^{\theta_{3}}}}$$",-0.859146708368862;-0.11208670878184722;0.7185031548339713;1.0704163291369404,12,5.029076273442346e-3,5.029076273442346e-3,5.029076273442346e-3,5.029076273442346e-3,0.5359110037667063,0.5359110037667063,0.5359110037667063,31.439147698780417,31.439147698780417,31.439147698780417 -972,0,Log(((-0.5517485546820351 + (Log(Log((x0 + 0.1442913410589105))) ^ Exp(Log(x0)))) ^ Exp((((-0.7968251169006225 ^ (x1 ^ 0.6403759907191348)) * Exp(x0)) / Log((6.756979280191067e-2 / x1)))))),"np.log((t[0] + np.log(np.log((x[:, 0] + t[1]))) ** np.exp(np.log(x[:, 0]))) ** np.exp(((t[2] ** x[:, 1] ** t[3] * np.exp(x[:, 0])) / np.log((t[4] / x[:, 1])))))","$$\operatorname{log}({\left(\theta_{0} + {\operatorname{log}(\operatorname{log}(\left(x_{0} + \theta_{1}\right)))^{\operatorname{exp}(\operatorname{log}(x_{0}))}}\right)^{\operatorname{exp}(\frac{\left({\theta_{2}^{{x_{1}^{\theta_{3}}}}} \cdot \operatorname{exp}(x_{0})\right)}{\operatorname{log}(\frac{\theta_{4}}{x_{1}})})}})$$",-0.5517485546820351;0.1442913410589105;-0.7968251169006225;0.6403759907191348;6.756979280191067e-2,27,,,,Infinity,,,,,, -973,0,Log(((Exp((((x0 + 1.527467738862641) ^ x1) * 1.2178324782078946)) + 0.8440207668407167) ^ 3.123771953912753e-2)),"np.log((np.exp(((x[:, 0] + t[0]) ** x[:, 1] * t[1])) + t[2]) ** t[3])","$$\operatorname{log}({\left(\operatorname{exp}(\left({\left(x_{0} + \theta_{0}\right)^{x_{1}}} \cdot \theta_{1}\right)) + \theta_{2}\right)^{\theta_{3}}})$$",1.527467738862641;1.2178324782078946;0.8440207668407167;3.123771953912753e-2,13,1.0776495348299074e-2,1.0776495348299074e-2,1.0776495348299074e-2,1.0776495348299074e-2,5.532499971098281e-3,5.532499971098281e-3,5.532499971098281e-3,35.1261386291219,35.1261386291219,35.1261386291219 -974,0,6.113188000911543e-2,"t[0]","$$\theta_{0}$$",6.113188000911543e-2,1,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,1.083644799652667e-2,-2.220446049250313e-16,-2.220446049250313e-16,-2.220446049250313e-16,2.0306777385645147,2.0306777385645147,2.0306777385645147 -975,0,(((Exp(Exp(x0)) / (3.1801812708005013 / (x0 / x0))) ^ -1.4058393440584611) * Log(Log((Exp((Exp(x1) + 0.7217734232584261)) / ((x1 / x1) + Exp((-0.6603520702016906 * x0))))))),"((np.exp(np.exp(x[:, 0])) / (t[0] / (x[:, 0] / x[:, 0]))) ** t[1] * np.log(np.log((np.exp((np.exp(x[:, 1]) + t[2])) / ((x[:, 1] / x[:, 1]) + np.exp((t[3] * x[:, 0])))))))","$$\left({\frac{\operatorname{exp}(\operatorname{exp}(x_{0}))}{\frac{\theta_{0}}{\frac{x_{0}}{x_{0}}}}^{\theta_{1}}} \cdot \operatorname{log}(\operatorname{log}(\frac{\operatorname{exp}(\left(\operatorname{exp}(x_{1}) + \theta_{2}\right))}{\left(\frac{x_{1}}{x_{1}} + \operatorname{exp}(\left(\theta_{3} \cdot x_{0}\right))\right)}))\right)$$",3.1801812708005013;-1.4058393440584611;0.7217734232584261;-0.6603520702016906,28,2.8012007461560295e-3,2.8012007461560295e-3,2.8012007461560295e-3,2.8012007461560295e-3,0.7415019435285548,0.7415019435285548,0.7415019435285548,69.60850898755625,69.60850898755625,69.60850898755625 -976,0,(Exp((-4287898.219992707 + (x0 / x1))) * 2.6442867019296054e7),"(np.exp((t[0] + (x[:, 0] / x[:, 1]))) * t[1])","$$\left(\operatorname{exp}(\left(\theta_{0} + \frac{x_{0}}{x_{1}}\right)) \cdot \theta_{1}\right)$$",-4287898.219992707;2.6442867019296054e7,8,1.4573554749975553e-2,1.4573554749975553e-2,1.4573554749975553e-2,1.4573554749975553e-2,-0.3448645492182232,-0.3448645492182232,-0.3448645492182232,19.622247262452767,19.622247262452767,19.622247262452767 -977,0,Log((Exp((Log((x0 * 0.8773293198924811)) * 0.3744711258729274)) + (((x1 + x0) * x1) / ((x0 + x1) ^ (-0.2251170226333039 ^ x0))))),"np.log((np.exp((np.log((x[:, 0] * t[0])) * t[1])) + (((x[:, 1] + x[:, 0]) * x[:, 1]) / (x[:, 0] + x[:, 1]) ** t[2] ** x[:, 0])))","$$\operatorname{log}(\left(\operatorname{exp}(\left(\operatorname{log}(\left(x_{0} \cdot \theta_{0}\right)) \cdot \theta_{1}\right)) + \frac{\left(\left(x_{1} + x_{0}\right) \cdot x_{1}\right)}{{\left(x_{0} + x_{1}\right)^{{\theta_{2}^{x_{0}}}}}}\right))$$",0.8773293198924811;0.3744711258729274;-0.2251170226333039,22,,,,Infinity,,,,,, -978,0,(((((-0.7651870044565189 ^ x0) + 0.9986884874080476) * -0.8664321115165925) + ((x0 + Exp(Exp(x0))) ^ (0.48376520187506333 ^ (0.2528604917782339 + x1)))) * Exp(Exp(x1))),"((((t[0] ** x[:, 0] + t[1]) * t[2]) + (x[:, 0] + np.exp(np.exp(x[:, 0]))) ** t[3] ** (t[4] + x[:, 1])) * np.exp(np.exp(x[:, 1])))","$$\left(\left(\left(\left({\theta_{0}^{x_{0}}} + \theta_{1}\right) \cdot \theta_{2}\right) + {\left(x_{0} + \operatorname{exp}(\operatorname{exp}(x_{0}))\right)^{{\theta_{3}^{\left(\theta_{4} + x_{1}\right)}}}}\right) \cdot \operatorname{exp}(\operatorname{exp}(x_{1}))\right)$$",-0.7651870044565189;0.9986884874080476;-0.8664321115165925;0.48376520187506333;0.2528604917782339,23,,,,Infinity,,,,,, -979,0,((((0.40495081682943346 * x0) * -0.6036323878885268) ^ ((0.11129818573390116 / x1) / x0)) * Log((((-0.32843506384434185 ^ x0) ^ x0) + (Exp(x1) * (x1 + x0))))),"(((t[0] * x[:, 0]) * t[1]) ** ((t[2] / x[:, 1]) / x[:, 0]) * np.log((t[3] ** x[:, 0] ** x[:, 0] + (np.exp(x[:, 1]) * (x[:, 1] + x[:, 0])))))","$$\left({\left(\left(\theta_{0} \cdot x_{0}\right) \cdot \theta_{1}\right)^{\frac{\frac{\theta_{2}}{x_{1}}}{x_{0}}}} \cdot \operatorname{log}(\left({{\theta_{3}^{x_{0}}}^{x_{0}}} + \left(\operatorname{exp}(x_{1}) \cdot \left(x_{1} + x_{0}\right)\right)\right))\right)$$",0.40495081682943346;-0.6036323878885268;0.11129818573390116;-0.32843506384434185,25,,,,Infinity,,,,,, -980,0,Log((Log((x0 / x0)) + ((((-0.16244833327907793 * x0) ^ x0) + -2.6193798986125505e-2) ^ ((x0 / ((x1 * 0.8209333025554719) ^ 1.6715645699700743e-2)) / x0)))),"np.log((np.log((x[:, 0] / x[:, 0])) + ((t[0] * x[:, 0]) ** x[:, 0] + t[1]) ** ((x[:, 0] / (x[:, 1] * t[2]) ** t[3]) / x[:, 0])))","$$\operatorname{log}(\left(\operatorname{log}(\frac{x_{0}}{x_{0}}) + {\left({\left(\theta_{0} \cdot x_{0}\right)^{x_{0}}} + \theta_{1}\right)^{\frac{\frac{x_{0}}{{\left(x_{1} \cdot \theta_{2}\right)^{\theta_{3}}}}}{x_{0}}}}\right))$$",-0.16244833327907793;-2.6193798986125505e-2;0.8209333025554719;1.6715645699700743e-2,23,,,,Infinity,,,,,, -981,0,((3.634929255887402e-4 ^ Exp(x0)) + (-0.27010834764677544 + (x1 ^ x0))),"(t[0] ** np.exp(x[:, 0]) + (t[1] + x[:, 1] ** x[:, 0]))","$$\left({\theta_{0}^{\operatorname{exp}(x_{0})}} + \left(\theta_{1} + {x_{1}^{x_{0}}}\right)\right)$$",3.634929255887402e-4;-0.27010834764677544,10,1.0578548541116535e-2,1.0578548541116535e-2,1.0578548541116535e-2,1.0578548541116535e-2,2.379926111330888e-2,2.379926111330888e-2,2.379926111330888e-2,21.968965256702838,21.968965256702838,21.968965256702838 -982,0,(((-0.40682758418576714 + (x0 ^ x0)) ^ ((0.5783668868195189 / x0) ^ -0.3233421846639126)) + Exp((((x1 ^ x0) / Log(x0)) + Log((-0.7276785661669237 ^ (0.39586142042342 * x0)))))),"((t[0] + x[:, 0] ** x[:, 0]) ** (t[1] / x[:, 0]) ** t[2] + np.exp(((x[:, 1] ** x[:, 0] / np.log(x[:, 0])) + np.log(t[3] ** (t[4] * x[:, 0])))))","$$\left({\left(\theta_{0} + {x_{0}^{x_{0}}}\right)^{{\frac{\theta_{1}}{x_{0}}^{\theta_{2}}}}} + \operatorname{exp}(\left(\frac{{x_{1}^{x_{0}}}}{\operatorname{log}(x_{0})} + \operatorname{log}({\theta_{3}^{\left(\theta_{4} \cdot x_{0}\right)}})\right))\right)$$",-0.40682758418576714;0.5783668868195189;-0.3233421846639126;-0.7276785661669237;0.39586142042342,26,,,,Infinity,,,,,, -983,0,Exp((Exp(Log((0.2783852430133731 ^ (Log(x0) ^ -0.7509772353170233)))) ^ Log((((Log(x1) ^ 0.15822841631151896) + -0.280767828849505) / ((0.7952637146751511 * (x0 ^ -0.6362811310890439)) * x1))))),"np.exp(np.exp(np.log(t[0] ** np.log(x[:, 0]) ** t[1])) ** np.log(((np.log(x[:, 1]) ** t[2] + t[3]) / ((t[4] * x[:, 0] ** t[5]) * x[:, 1]))))","$$\operatorname{exp}({\operatorname{exp}(\operatorname{log}({\theta_{0}^{{\operatorname{log}(x_{0})^{\theta_{1}}}}}))^{\operatorname{log}(\frac{\left({\operatorname{log}(x_{1})^{\theta_{2}}} + \theta_{3}\right)}{\left(\left(\theta_{4} \cdot {x_{0}^{\theta_{5}}}\right) \cdot x_{1}\right)})}})$$",0.2783852430133731;-0.7509772353170233;0.15822841631151896;-0.280767828849505;0.7952637146751511;-0.6362811310890439,25,,,,Infinity,,,,,, -984,0,Exp(Log(Exp(Exp(Log((x1 * (x0 * 1.0922082746342486e-4))))))),"np.exp(np.log(np.exp(np.exp(np.log((x[:, 1] * (x[:, 0] * t[0])))))))","$$\operatorname{exp}(\operatorname{log}(\operatorname{exp}(\operatorname{exp}(\operatorname{log}(\left(x_{1} \cdot \left(x_{0} \cdot \theta_{0}\right)\right))))))$$",1.0922082746342486e-4,10,0.8923833701107682,0.8923833701107682,0.8923833701107682,0.8923833701107682,-81.35017326681195,-81.35017326681195,-81.35017326681195,20.74166466074788,20.74166466074788,20.74166466074788 -985,0,Log(Exp(Exp(((x1 * x0) ^ 156.20597121134327)))),"np.log(np.exp(np.exp((x[:, 1] * x[:, 0]) ** t[0])))","$$\operatorname{log}(\operatorname{exp}(\operatorname{exp}({\left(x_{1} \cdot x_{0}\right)^{\theta_{0}}})))$$",156.20597121134327,8,0.8923097947321331,0.8923097947321331,0.8923097947321331,0.8923097947321331,-81.34338364546568,-81.34338364546568,-81.34338364546568,18.391284943069063,18.391284943069063,18.391284943069063 -986,0,((((x0 * Log(x1)) * -1.1959276104895378) / (-0.6172434204798509 / (x1 * 1.422426203853352))) + (((x1 / (x1 / x0)) ^ (x0 * 0.5684475254589114)) ^ ((x0 / (x0 * x0)) + (0.20468587228356785 * (x0 ^ x0))))),"((((x[:, 0] * np.log(x[:, 1])) * t[0]) / (t[1] / (x[:, 1] * t[2]))) + (x[:, 1] / (x[:, 1] / x[:, 0])) ** (x[:, 0] * t[3]) ** ((x[:, 0] / (x[:, 0] * x[:, 0])) + (t[4] * x[:, 0] ** x[:, 0])))","$$\left(\frac{\left(\left(x_{0} \cdot \operatorname{log}(x_{1})\right) \cdot \theta_{0}\right)}{\frac{\theta_{1}}{\left(x_{1} \cdot \theta_{2}\right)}} + {{\frac{x_{1}}{\frac{x_{1}}{x_{0}}}^{\left(x_{0} \cdot \theta_{3}\right)}}^{\left(\frac{x_{0}}{\left(x_{0} \cdot x_{0}\right)} + \left(\theta_{4} \cdot {x_{0}^{x_{0}}}\right)\right)}}\right)$$",-1.1959276104895378;-0.6172434204798509;1.422426203853352;0.5684475254589114;0.20468587228356785,34,2.0994221757431583e-2,2.0994221757431583e-2,2.0994221757431583e-2,2.0994221757431583e-2,-0.9373711537360507,-0.9373711537360507,-0.9373711537360507,80.82453189353431,80.82453189353431,80.82453189353431 -987,0,(x1 ^ x0),"x[:, 1] ** x[:, 0]","$${x_{1}^{x_{0}}}$$",,3,8.353975393662696e-2,8.353975393662696e-2,8.353975393662696e-2,8.353975393662696e-2,-6.709145465691655,-6.709145465691655,-6.709145465691655,3.3710226445472933,3.3710226445472933,3.3710226445472933 -988,0,Exp(Log(Exp((Log(Exp(x1)) / ((785.2368872240656 * x1) * 582.9951786988512))))),"np.exp(np.log(np.exp((np.log(np.exp(x[:, 1])) / ((t[0] * x[:, 1]) * t[1])))))","$$\operatorname{exp}(\operatorname{log}(\operatorname{exp}(\frac{\operatorname{log}(\operatorname{exp}(x_{1}))}{\left(\left(\theta_{0} \cdot x_{1}\right) \cdot \theta_{1}\right)})))$$",785.2368872240656;582.9951786988512,12,0.8923138964885916,0.8923138964885916,0.8923138964885916,0.8923138964885916,-81.34376216031296,-81.34376216031296,-81.34376216031296,26.346046008311678,26.346046008311678,26.346046008311678 -989,0,(Exp(Exp(Exp((Exp(x1) ^ 0.2394020336185545)))) / (((x0 ^ Log((Log(x0) / x1))) ^ (0.7779703814536216 + x1)) / x1)),"(np.exp(np.exp(np.exp(np.exp(x[:, 1]) ** t[0]))) / (x[:, 0] ** np.log((np.log(x[:, 0]) / x[:, 1])) ** (t[1] + x[:, 1]) / x[:, 1]))","$$\frac{\operatorname{exp}(\operatorname{exp}(\operatorname{exp}({\operatorname{exp}(x_{1})^{\theta_{0}}})))}{\frac{{{x_{0}^{\operatorname{log}(\frac{\operatorname{log}(x_{0})}{x_{1}})}}^{\left(\theta_{1} + x_{1}\right)}}}{x_{1}}}$$",0.2394020336185545;0.7779703814536216,21,,,,Infinity,,,,,, -990,0,Exp((((-0.7032112441677596 ^ (((x1 + -0.29841235885181583) + -0.9243159215086852) + x1)) + Exp(x1)) ^ 0.5566689064928065)),"np.exp((t[0] ** (((x[:, 1] + t[1]) + t[2]) + x[:, 1]) + np.exp(x[:, 1])) ** t[3])","$$\operatorname{exp}({\left({\theta_{0}^{\left(\left(\left(x_{1} + \theta_{1}\right) + \theta_{2}\right) + x_{1}\right)}} + \operatorname{exp}(x_{1})\right)^{\theta_{3}}})$$",-0.7032112441677596;-0.29841235885181583;-0.9243159215086852;0.5566689064928065,15,,,,Infinity,,,,,, -991,0,(((Exp((x1 + x0)) * (x0 + (x0 * -0.1337059654994608))) * 0.14932952893315676) / Exp(((Log((-0.44371309496120215 ^ x1)) / (x1 * x0)) / Exp(((x0 / x0) + Log(x1)))))),"(((np.exp((x[:, 1] + x[:, 0])) * (x[:, 0] + (x[:, 0] * t[0]))) * t[1]) / np.exp(((np.log(t[2] ** x[:, 1]) / (x[:, 1] * x[:, 0])) / np.exp(((x[:, 0] / x[:, 0]) + np.log(x[:, 1]))))))","$$\frac{\left(\left(\operatorname{exp}(\left(x_{1} + x_{0}\right)) \cdot \left(x_{0} + \left(x_{0} \cdot \theta_{0}\right)\right)\right) \cdot \theta_{1}\right)}{\operatorname{exp}(\frac{\frac{\operatorname{log}({\theta_{2}^{x_{1}}})}{\left(x_{1} \cdot x_{0}\right)}}{\operatorname{exp}(\left(\frac{x_{0}}{x_{0}} + \operatorname{log}(x_{1})\right))})}$$",-0.1337059654994608;0.14932952893315676;-0.44371309496120215,30,,,,Infinity,,,,,, -992,0,(Exp(Exp(((x0 + x1) + 0.3996185204062076))) + Log(((0.7969194231336436 / x1) ^ 0.7458818543060597))),"(np.exp(np.exp(((x[:, 0] + x[:, 1]) + t[0]))) + np.log((t[1] / x[:, 1]) ** t[2]))","$$\left(\operatorname{exp}(\operatorname{exp}(\left(\left(x_{0} + x_{1}\right) + \theta_{0}\right))) + \operatorname{log}({\frac{\theta_{1}}{x_{1}}^{\theta_{2}}})\right)$$",0.3996185204062076;0.7969194231336436;0.7458818543060597,14,2.7387059606441516e8,2.7387059606441516e8,2.7387059606441516e8,2.7387059606441516e8,-2.527309651108561e10,-2.527309651108561e10,-2.527309651108561e10,2.4648357163293004e8,2.4648357163293004e8,2.4648357163293004e8 -993,0,Exp((((Exp((x1 / x0)) / (x0 ^ -0.31681193977366207)) * (0.9087502787275648 * (-0.35173532702520427 ^ (x1 * 0.3511911960130467)))) / (((x1 * x0) ^ (x0 + -0.25170218844137326)) * ((x0 / x1) + (-9.132714022831478e-2 + (0.42535176012593867 * x0)))))),"np.exp((((np.exp((x[:, 1] / x[:, 0])) / x[:, 0] ** t[0]) * (t[1] * t[2] ** (x[:, 1] * t[3]))) / ((x[:, 1] * x[:, 0]) ** (x[:, 0] + t[4]) * ((x[:, 0] / x[:, 1]) + (t[5] + (t[6] * x[:, 0]))))))","$$\operatorname{exp}(\frac{\left(\frac{\operatorname{exp}(\frac{x_{1}}{x_{0}})}{{x_{0}^{\theta_{0}}}} \cdot \left(\theta_{1} \cdot {\theta_{2}^{\left(x_{1} \cdot \theta_{3}\right)}}\right)\right)}{\left({\left(x_{1} \cdot x_{0}\right)^{\left(x_{0} + \theta_{4}\right)}} \cdot \left(\frac{x_{0}}{x_{1}} + \left(\theta_{5} + \left(\theta_{6} \cdot x_{0}\right)\right)\right)\right)})$$",-0.31681193977366207;0.9087502787275648;-0.35173532702520427;0.3511911960130467;-0.25170218844137326;-9.132714022831478e-2;0.42535176012593867,35,,,,Infinity,,,,,, -994,0,(Exp((Exp(x0) * Exp(x1))) / (((x0 + (((-0.29169758475110985 * x1) / (x0 + x1)) ^ 0.8201460227785609)) ^ Log((-0.38590682924688036 + x1))) + 0.35634704277079254)),"(np.exp((np.exp(x[:, 0]) * np.exp(x[:, 1]))) / ((x[:, 0] + ((t[0] * x[:, 1]) / (x[:, 0] + x[:, 1])) ** t[1]) ** np.log((t[2] + x[:, 1])) + t[3]))","$$\frac{\operatorname{exp}(\left(\operatorname{exp}(x_{0}) \cdot \operatorname{exp}(x_{1})\right))}{\left({\left(x_{0} + {\frac{\left(\theta_{0} \cdot x_{1}\right)}{\left(x_{0} + x_{1}\right)}^{\theta_{1}}}\right)^{\operatorname{log}(\left(\theta_{2} + x_{1}\right))}} + \theta_{3}\right)}$$",-0.29169758475110985;0.8201460227785609;-0.38590682924688036;0.35634704277079254,25,,,,Infinity,,,,,, -995,0,(Exp(Exp((x1 + (x0 * -3.1583567465031757)))) + ((-0.7401358346677325 * (x1 ^ -6.733027539044349e-2)) * 1.2764548601775916)),"(np.exp(np.exp((x[:, 1] + (x[:, 0] * t[0])))) + ((t[1] * x[:, 1] ** t[2]) * t[3]))","$$\left(\operatorname{exp}(\operatorname{exp}(\left(x_{1} + \left(x_{0} \cdot \theta_{0}\right)\right))) + \left(\left(\theta_{1} \cdot {x_{1}^{\theta_{2}}}\right) \cdot \theta_{3}\right)\right)$$",-3.1583567465031757;-0.7401358346677325;-6.733027539044349e-2;1.2764548601775916,15,1.8992807730529e-3,1.8992807730529e-3,1.8992807730529e-3,1.8992807730529e-3,0.8247321655895304,0.8247321655895304,0.8247321655895304,37.27406132999601,37.27406132999601,37.27406132999601 -996,0,((((x0 * 8.39343945168022) + -14.148633252151189) + x0) * -1.5906422038235724e-2),"((((x[:, 0] * t[0]) + t[1]) + x[:, 0]) * t[2])","$$\left(\left(\left(\left(x_{0} \cdot \theta_{0}\right) + \theta_{1}\right) + x_{0}\right) \cdot \theta_{2}\right)$$",8.39343945168022;-14.148633252151189;-1.5906422038235724e-2,9,6.500389167568517e-3,6.500389167568517e-3,6.500389167568517e-3,6.500389167568517e-3,0.40013654200601123,0.40013654200601123,0.40013654200601123,18.54527440643275,18.54527440643275,18.54527440643275 -997,0,(((Log(Log(((x0 * x1) / x0))) * -0.45742146210990897) * -0.31309201740159476) ^ (((((x0 ^ -0.546842847405977) + 0.30027373178411054) * 5.1179282509667856e-2) / (x0 ^ -0.7036241524747021)) / Exp(((-0.1804115218471346 + Exp(x1)) * 0.14855295683202085)))),"((np.log(np.log(((x[:, 0] * x[:, 1]) / x[:, 0]))) * t[0]) * t[1]) ** ((((x[:, 0] ** t[2] + t[3]) * t[4]) / x[:, 0] ** t[5]) / np.exp(((t[6] + np.exp(x[:, 1])) * t[7])))","$${\left(\left(\operatorname{log}(\operatorname{log}(\frac{\left(x_{0} \cdot x_{1}\right)}{x_{0}})) \cdot \theta_{0}\right) \cdot \theta_{1}\right)^{\frac{\frac{\left(\left({x_{0}^{\theta_{2}}} + \theta_{3}\right) \cdot \theta_{4}\right)}{{x_{0}^{\theta_{5}}}}}{\operatorname{exp}(\left(\left(\theta_{6} + \operatorname{exp}(x_{1})\right) \cdot \theta_{7}\right))}}}$$",-0.45742146210990897;-0.31309201740159476;-0.546842847405977;0.30027373178411054;5.1179282509667856e-2;-0.7036241524747021;-0.1804115218471346;0.14855295683202085,31,,,,Infinity,,,,,, -998,0,Log(((((x0 * 0.7196325994416997) * (Exp((-0.2929594774573301 ^ x1)) ^ 0.8747486136962328)) ^ 0.3700830846024715) + 0.3344174863988618)),"np.log((((x[:, 0] * t[0]) * np.exp(t[1] ** x[:, 1]) ** t[2]) ** t[3] + t[4]))","$$\operatorname{log}(\left({\left(\left(x_{0} \cdot \theta_{0}\right) \cdot {\operatorname{exp}({\theta_{1}^{x_{1}}})^{\theta_{2}}}\right)^{\theta_{3}}} + \theta_{4}\right))$$",0.7196325994416997;-0.2929594774573301;0.8747486136962328;0.3700830846024715;0.3344174863988618,15,,,,Infinity,,,,,, -999,0,(Exp((0.9192625249669337 ^ Log((-6.718828336099592e-2 / Exp((x0 * x1)))))) / ((Log(x1) * (Exp(x1) ^ Log((0.5767647429222347 ^ x1)))) ^ (-0.8541300513393262 / (Log((-0.3456900902848459 + (0.8814536071680705 ^ x0))) * -0.17967169348781442)))),"(np.exp(t[0] ** np.log((t[1] / np.exp((x[:, 0] * x[:, 1]))))) / (np.log(x[:, 1]) * np.exp(x[:, 1]) ** np.log(t[2] ** x[:, 1])) ** (t[3] / (np.log((t[4] + t[5] ** x[:, 0])) * t[6])))","$$\frac{\operatorname{exp}({\theta_{0}^{\operatorname{log}(\frac{\theta_{1}}{\operatorname{exp}(\left(x_{0} \cdot x_{1}\right))})}})}{{\left(\operatorname{log}(x_{1}) \cdot {\operatorname{exp}(x_{1})^{\operatorname{log}({\theta_{2}^{x_{1}}})}}\right)^{\frac{\theta_{3}}{\left(\operatorname{log}(\left(\theta_{4} + {\theta_{5}^{x_{0}}}\right)) \cdot \theta_{6}\right)}}}}$$",0.9192625249669337;-6.718828336099592e-2;0.5767647429222347;-0.8541300513393262;-0.3456900902848459;0.8814536071680705;-0.17967169348781442,32,,,,Infinity,,,,,, diff --git a/interp_n1.err b/interp_n1.err deleted file mode 100644 index c44297e..0000000 --- a/interp_n1.err +++ /dev/null @@ -1,310 +0,0 @@ -[acc-interp] compile 1.067482018 s -[acc-interp] eval 3.054849149 s -[acc-interp] eval 8.6179821e-2 s -[acc-interp] eval 8.4252897e-2 s -[acc-interp] eval 0.188806057 s -[acc-interp] eval 0.192533924 s -[acc-interp] eval 0.201945626 s -[acc-interp] eval 0.180661354 s -[acc-interp] eval 0.168885436 s -[acc-interp] eval 7.9313026e-2 s -[acc-interp] eval 1.7442169e-2 s -[acc-interp] eval 6.67576e-3 s -[acc-interp] eval 6.745957e-3 s -[acc-interp] eval 6.567408e-3 s -[acc-interp] eval 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7.533183e-3 s -[acc-interp] eval 6.948391e-3 s -[acc-interp] eval 6.569516e-3 s -[acc-interp] eval 5.849747e-3 s -[acc-interp] eval 7.880007e-3 s -[acc-interp] eval 5.455401e-3 s -[acc-interp] eval 5.555598e-3 s -[acc-interp] eval 7.016491e-3 s -[acc-interp] eval 5.201338e-3 s -[acc-interp] eval 5.987123e-3 s -[acc-interp] eval 5.202497e-3 s -[acc-interp] eval 4.771525e-3 s -[acc-interp] eval 4.975184e-3 s -[acc-interp] eval 4.592852e-3 s -[acc-interp] eval 4.504806e-3 s -[acc-interp] eval 0.116045996 s diff --git a/interp_n4.out b/interp_n4.out deleted file mode 100644 index 8fec4b6..0000000 --- a/interp_n4.out +++ /dev/null @@ -1,5 +0,0 @@ -id,view,Expression,Numpy,Math,theta,size,loss_train,loss_val,loss_test,maxloss,R2_train,R2_val,R2_test,dl_train,dl_val,dl_test -0,0,22.766786471142098,"t[0]","$$\theta_{0}$$",22.766786471142098,1,250.2099523865335,250.2099523865335,250.2099523865335,250.20995238652426,-3.774758283725532e-15,-3.774758283725532e-15,-3.774758283725532e-15,253.19111484369938,253.19111484369938,253.19111484369938 -1,0,(x3 + 22.540971929628363),"(x[:, 3] + t[0])","$$\left(x_{3} + \theta_{0}\right)$$",22.540971929628363,3,249.79674369104444,249.79674369104444,249.79674369104444,249.79674369105567,1.6514478802577282e-3,1.6514478802577282e-3,1.6514478802577282e-3,256.0750249611203,256.0750249611203,256.0750249611203 -2,0,(Exp(x3) * 17.986912394280353),"(np.exp(x[:, 3]) * t[0])","$$\left(\operatorname{exp}(x_{3}) \cdot \theta_{0}\right)$$",17.986912394280353,4,247.41487539964737,247.41487539964737,247.41487539964737,247.4148753996405,1.1170926496829603e-2,1.1170926496829603e-2,1.1170926496829603e-2,255.94988680393422,255.94988680393422,255.94988680393422 -3,0,(Exp((-1.224229221824418 * x0)) * 29.68942258289744),"(np.exp((t[0] * x[:, 0])) * t[1])","$$\left(\operatorname{exp}(\left(\theta_{0} \cdot x_{0}\right)) \cdot \theta_{1}\right)$$",-1.224229221824418;29.68942258289744,6,153.01176650850928,153.01176650850928,153.01176650850928,153.01176650852454,0.3884665056323112,0.3884665056323112,0.3884665056323112,168.3696635329196,168.3696635329196,168.3696635329196 diff --git a/mt.err b/mt.err deleted file mode 100644 index e510440..0000000 --- a/mt.err +++ /dev/null @@ -1,149 +0,0 @@ -eggp: bad value for -N -eggp: -eggp: Usage: [+RTS | -RTS ] ... --RTS -eggp: -eggp: +RTS Indicates run time system options follow -eggp: -RTS Indicates program arguments follow -eggp: --RTS Indicates that ALL subsequent arguments will be given to the -eggp: program (including any of these RTS flags) -eggp: -eggp: The following run time system options may be available (note that some -eggp: of these may not be usable unless this program was linked with the -rtsopts -eggp: flag): -eggp: -eggp: -? Prints this message and exits; the program is not executed -eggp: --info Print information about the RTS used by this program -eggp: -eggp: --nonmoving-gc -eggp: Selects the non-moving mark-and-sweep garbage collector to -eggp: manage the oldest generation. -eggp: --copying-gc -eggp: Selects the copying garbage collector to manage all generations. -eggp: -eggp: -K Sets the maximum stack size (default: 80% of the heap) -eggp: e.g.: -K32k -K512k -K8M -eggp: -ki Sets the initial thread stack size (default 1k) e.g.: -ki4k -ki2m -eggp: -kc Sets the stack chunk size (default 32k) -eggp: -kb Sets the stack chunk buffer size (default 1k) -eggp: -eggp: -A Sets the minimum allocation area size (default 4m) e.g.: -A20m -A10k -eggp: -AL Sets the amount of large-object memory that can be allocated -eggp: before a GC is triggered (default: the value of -A) -eggp: -F Sets the collecting threshold for old generations as a factor of -eggp: the live data in that generation the last time it was collected -eggp: (default: 2.0) -eggp: -Fd Sets the inverse rate which memory is returned to the OS after being -eggp: optimistically retained after being allocated. Subsequent major -eggp: collections not caused by heap overflow will return an amount of -eggp: memory controlled by this factor (higher is slower). Setting the factor -eggp: to 0 means memory is not returned. -eggp: (default 4.0) -eggp: -n Allocation area chunk size (0 = disabled, default: 0) -eggp: -O Sets the minimum size of the old generation (default 1M) -eggp: -M Sets the maximum heap size (default unlimited) e.g.: -M256k -M1G -eggp: -H Sets the minimum heap size (default 0M) e.g.: -H24m -H1G -eggp: -xb Sets the address from which a suitable start for the heap memory -eggp: will be searched from. This is useful if the default address -eggp: clashes with some third-party library. -eggp: -xn Use the non-moving collector for the old generation. -eggp: -m Minimum % of heap which must be available (default 3%) -eggp: -G Number of generations (default: 2) -eggp: -c Use in-place compaction instead of copying in the oldest generation -eggp: when live data is at least % of the maximum heap size set with -eggp: -M (default: 30%) -eggp: -c Use in-place compaction for all oldest generation collections -eggp: (the default is to use copying) -eggp: -w Use mark-region for the oldest generation (experimental) -eggp: -I Perform full GC after idle time (default: 0.3, 0 == off) -eggp: -Iw Minimum wait time between idle GC runs (default: 0, 0 == no min wait time) -eggp: -eggp: -T Collect GC statistics (useful for in-program statistics access) -eggp: -t[] One-line GC statistics (if omitted, uses stderr) -eggp: -s[] Summary GC statistics (if omitted, uses stderr) -eggp: -S[] Detailed GC statistics (if omitted, uses stderr) -eggp: -eggp: -eggp: -Z Don't squeeze out update frames on context switch -eggp: -B Sound the bell at the start of each garbage collection -eggp: -h Heap residency profile (output file .hp) -eggp: -hT Produce a heap profile grouped by closure type -eggp: -hi Produce a heap profile grouped by info table address -eggp: -po Override profiling output file name prefix (program name by default) -eggp: -i Time between heap profile samples (seconds, default: 0.1) -eggp: --no-automatic-heap-samples -eggp: Do not start the heap profile interval timer on start-up, -eggp: Rather, the application will be responsible for triggering -eggp: heap profiler samples. -eggp: -ol Send binary eventlog to (default: .eventlog) -eggp: -l[flags] Log events to a file -eggp: where [flags] can contain: -eggp: s scheduler events -eggp: g GC and heap events -eggp: n non-moving GC heap census events -eggp: p par spark events (sampled) -eggp: f par spark events (full detail) -eggp: u user events (emitted from Haskell code) -eggp: a all event classes above -eggp: -x disable an event class, for any flag above -eggp: the initial enabled event classes are 'sgpu' -eggp: --eventlog-flush-interval= -eggp: Periodically flush the eventlog at the specified interval. -eggp: -eggp: -C Context-switch interval in seconds. -eggp: 0 or no argument means switch as often as possible. -eggp: Default: 0.02 sec. -eggp: -V Master tick interval in seconds (0 == disable timer). -eggp: This sets the resolution for -C and the heap profile timer -i, -eggp: and is the frequency of time profile samples. -eggp: Default: 0.01 sec. -eggp: -eggp: -N[] Use processors (default: 1, -N alone determines -eggp: the number of processors to use automatically) -eggp: -maxN[] Use up to processors automatically -eggp: -qg[] Use parallel GC only for generations >= -eggp: (default: 0, -qg alone turns off parallel GC) -eggp: -qb[] Use load-balancing in the parallel GC only for generations >= -eggp: (default: 1 for -A < 32M, 0 otherwise; -eggp: -qb alone turns off load-balancing) -eggp: -qn Use threads for parallel GC (defaults to value of -N) -eggp: -qa Use the OS to set thread affinity (experimental) -eggp: -qm Don't automatically migrate threads between CPUs -eggp: -qi If a processor has been idle for the last GCs, do not -eggp: wake it up for a non-load-balancing parallel GC. -eggp: (0 disables, default: 0) -eggp: --numa[=] -eggp: Use NUMA, nodes given by (default: off) -eggp: --install-signal-handlers= -eggp: Install signal handlers (default: yes) -eggp: --io-manager= -eggp: The I/O manager to use. -eggp: Options available: auto mio (default: mio) -eggp: -e Maximum number of outstanding local sparks (default: 4096) -eggp: -xp Assume that all object files were compiled with -fPIC -eggp: -fexternal-dynamic-refs and load them anywhere in the address -eggp: space -eggp: -xm Base address to mmap memory in the GHCi linker -eggp: (hex; must be <80000000) -eggp: -xq The allocation limit given to a thread after it receives -eggp: an AllocationLimitExceeded exception. (default: 100k) -eggp: -eggp: -xr The size of virtual memory address space reserved by the -eggp: two step allocator (default: 1T) -eggp: -eggp: -Mgrace= -eggp: The amount of allocation after the program receives a -eggp: HeapOverflow exception before the exception is thrown again, if -eggp: the program is still exceeding the heap limit. -eggp: -eggp: --read-tix-file= -eggp: Whether to initialize HPC datastructures from .tix at the start of execution. (default: yes) -eggp: -eggp: --write-tix-file= -eggp: Whether to write .tix at the end of execution. -eggp: (default: yes) -eggp: -eggp: RTS options may also be specified using the GHCRTS environment variable. -eggp: -eggp: Other RTS options may be available for programs compiled a different way. -eggp: The GHC User's Guide has full details. -eggp: diff --git a/mtA_n1_1.err b/mtA_n1_1.err deleted file mode 100644 index e69de29..0000000 diff --git a/mtA_n1_2.err b/mtA_n1_2.err deleted file mode 100644 index e69de29..0000000 diff --git a/mtA_n4_1.err b/mtA_n4_1.err deleted file mode 100644 index e69de29..0000000 diff --git a/mtA_n4_2.err b/mtA_n4_2.err deleted file mode 100644 index e69de29..0000000 diff --git a/mt_n1.err b/mt_n1.err deleted file mode 100644 index e69de29..0000000 diff --git a/mt_n4.err b/mt_n4.err deleted file mode 100644 index e69de29..0000000 diff --git a/n1r1.err b/n1r1.err deleted file mode 100644 index e69de29..0000000 diff --git a/n1r2.err b/n1r2.err deleted file mode 100644 index e69de29..0000000 diff --git a/p2_n4.err b/p2_n4.err deleted file mode 100644 index e17a2d5..0000000 --- a/p2_n4.err +++ /dev/null @@ -1,3089 +0,0 @@ -[acc] compile 2.2545583e-2 s -[acc] eval 2.089052881 s -[acc] compile 5.07428e-4 s -[acc] eval 6.8115474e-2 s -[acc] compile 5.13547e-4 s -[acc] eval 7.5479355e-2 s -[acc] compile 4.51063e-4 s -[acc] eval 0.165742049 s -[acc] compile 3.26655e-4 s -[acc] eval 0.186926207 s -[acc] compile 3.42308e-4 s -[acc] eval 0.15701251 s -[acc] compile 3.64172e-4 s -[acc] eval 0.211968678 s -[acc] compile 3.90344e-4 s -[acc] eval 0.133418003 s -[acc] compile 3.45977e-4 s -[acc] eval 6.8119831e-2 s -[acc] compile 5.629754e-2 s -[acc] eval 2.6077248e-2 s -[acc] eval 8.5756459e-2 s -[acc] compile 4.7882963e-2 s -[acc] eval 1.8794666e-2 s -[acc] eval 2.178655e-3 s -[acc] eval 1.919136e-3 s -[acc] eval 2.039928e-3 s -[acc] eval 7.654469e-3 s -[acc] eval 1.847927e-3 s -[acc] eval 1.567422e-3 s -[acc] eval 1.318232e-3 s -[acc] eval 1.296346e-3 s -[acc] eval 1.300288e-3 s -[acc] eval 1.091003e-3 s -[acc] eval 1.172884e-3 s -[acc] eval 9.2917007e-2 s -[acc] compile 7.6480933e-2 s -[acc] eval 1.0011238e-2 s -[acc] eval 8.9800629e-2 s -[acc] compile 0.296524401 s -[acc] eval 3.197334e-2 s -[acc] eval 0.332715433 s -[acc] compile 6.6096812e-2 s -[acc] eval 1.6301865e-2 s -[acc] eval 4.715536e-3 s -[acc] eval 5.362047e-3 s -[acc] eval 6.163902e-3 s -[acc] eval 1.0319189e-2 s -[acc] eval 2.956198e-3 s -[acc] eval 4.138297e-3 s -[acc] eval 3.42498e-3 s -[acc] eval 2.996228e-3 s -[acc] eval 2.808014e-3 s -[acc] eval 2.839656e-3 s -[acc] eval 2.960377e-3 s -[acc] eval 3.207781e-3 s -[acc] eval 3.29582e-3 s -[acc] eval 3.089597e-3 s -[acc] eval 2.845665e-3 s -[acc] eval 2.449892e-3 s -[acc] eval 2.297618e-3 s -[acc] eval 2.31538e-3 s -[acc] eval 2.44517e-3 s -[acc] eval 2.244568e-3 s -[acc] eval 2.391921e-3 s -[acc] eval 2.313781e-3 s -[acc] eval 2.222019e-3 s -[acc] eval 2.340803e-3 s -[acc] eval 3.001639e-3 s -[acc] eval 2.961353e-3 s -[acc] eval 2.965862e-3 s -[acc] eval 2.673216e-3 s -[acc] eval 2.409983e-3 s -[acc] eval 2.376959e-3 s -[acc] eval 0.185878138 s -[acc] compile 0.373581315 s -[acc] eval 3.2774636e-2 s -[acc] eval 0.413043678 s -[acc] compile 5.722956e-3 s -[acc] eval 7.341758e-3 s -[acc] eval 2.714768e-3 s -[acc] eval 3.045141e-3 s -[acc] eval 3.319946e-3 s -[acc] eval 3.336608e-3 s -[acc] eval 2.788044e-3 s -[acc] eval 2.821018e-3 s -[acc] eval 9.014853e-3 s -[acc] eval 2.378401e-3 s -[acc] eval 2.269098e-3 s -[acc] eval 3.173494e-3 s -[acc] eval 3.926566e-3 s -[acc] eval 2.3313e-3 s -[acc] eval 2.124174e-3 s -[acc] eval 2.232979e-3 s -[acc] eval 1.746422e-3 s -[acc] eval 1.71011e-3 s -[acc] eval 2.059835e-3 s -[acc] eval 2.143931e-3 s -[acc] eval 1.938581e-3 s -[acc] eval 1.974846e-3 s -[acc] eval 2.057377e-3 s -[acc] eval 1.871779e-3 s -[acc] eval 1.547768e-3 s -[acc] eval 2.000677e-3 s -[acc] eval 1.863555e-3 s -[acc] eval 1.855831e-3 s -[acc] eval 1.848762e-3 s -[acc] eval 1.77355e-3 s -[acc] eval 1.627215e-3 s -[acc] eval 9.1192828e-2 s -[acc] compile 0.307093793 s -[acc] eval 9.870334e-3 s -[acc] eval 0.325805888 s -[acc] compile 0.369907154 s -[acc] eval 2.6062402e-2 s -[acc] eval 0.416465553 s -[acc] compile 0.18536803 s -[acc] eval 2.354095e-2 s -[acc] eval 1.0013288e-2 s -[acc] eval 4.547352e-3 s -[acc] eval 3.791824e-3 s -[acc] eval 4.185958e-3 s -[acc] eval 3.765257e-3 s -[acc] eval 4.09383e-3 s -[acc] eval 4.093277e-3 s -[acc] eval 3.413615e-3 s -[acc] eval 2.755758e-3 s -[acc] eval 2.057394e-3 s -[acc] eval 2.518325e-3 s -[acc] eval 2.38858e-3 s -[acc] eval 2.085729e-3 s -[acc] eval 2.046925e-3 s -[acc] eval 3.365755e-3 s -[acc] eval 2.953771e-3 s -[acc] eval 2.671848e-3 s -[acc] eval 2.74538e-3 s -[acc] eval 3.654839e-3 s -[acc] eval 3.005807e-3 s -[acc] eval 2.76835e-3 s -[acc] eval 2.606345e-3 s -[acc] eval 2.105564e-3 s -[acc] eval 2.735323e-3 s -[acc] eval 3.159186e-3 s -[acc] eval 3.083847e-3 s -[acc] eval 2.838442e-3 s -[acc] eval 2.661659e-3 s -[acc] eval 2.623892e-3 s -[acc] eval 2.837447e-3 s -[acc] eval 3.063213e-3 s -[acc] eval 2.904738e-3 s -[acc] eval 2.876008e-3 s -[acc] eval 3.544311e-3 s -[acc] eval 2.752004e-3 s -[acc] eval 2.635589e-3 s -[acc] eval 2.49185e-3 s -[acc] eval 2.62632e-3 s -[acc] eval 2.657154e-3 s -[acc] eval 2.78849e-3 s -[acc] eval 2.529859e-3 s -[acc] eval 2.522661e-3 s -[acc] eval 2.662448e-3 s -[acc] eval 2.739926e-3 s -[acc] eval 0.35381616 s -[acc] compile 7.62813e-4 s -[acc] eval 4.696472e-3 s -[acc] compile 5.688256e-3 s -[acc] eval 9.98229e-3 s -[acc] eval 2.204637e-3 s -[acc] eval 5.393059e-3 s -[acc] eval 2.596419e-3 s -[acc] eval 2.960798e-3 s -[acc] eval 2.956509e-3 s -[acc] eval 2.666025e-3 s -[acc] eval 3.7896073e-2 s -[acc] compile 0.181602916 s -[acc] eval 1.4485548e-2 s -[acc] eval 7.566057e-3 s -[acc] eval 1.2349092e-2 s -[acc] eval 4.708413e-3 s -[acc] eval 4.918073e-3 s -[acc] eval 5.436715e-3 s -[acc] eval 6.362519e-3 s -[acc] eval 4.98556e-3 s -[acc] eval 3.613675e-3 s -[acc] eval 2.974409e-3 s -[acc] eval 3.011471e-3 s -[acc] eval 2.869088e-3 s -[acc] eval 2.622626e-3 s -[acc] eval 2.784105e-3 s -[acc] eval 0.266123354 s -[acc] compile 0.177693388 s -[acc] eval 1.3944289e-2 s -[acc] eval 0.195459767 s -[acc] compile 0.238935756 s -[acc] eval 2.4792451e-2 s -[acc] eval 0.273777125 s -[acc] compile 4.950427e-3 s -[acc] eval 5.925572e-3 s -[acc] eval 1.3539444e-2 s -[acc] compile 0.186116586 s -[acc] eval 1.2908517e-2 s -[acc] eval 0.202140026 s -[acc] compile 3.00378e-3 s -[acc] eval 7.574851e-3 s -[acc] eval 8.6474e-4 s -[acc] eval 6.95963e-4 s -[acc] eval 9.65083e-4 s -[acc] eval 6.2562e-4 s -[acc] eval 9.30061e-4 s -[acc] eval 7.99315e-4 s -[acc] eval 6.3114e-4 s -[acc] eval 8.4801e-4 s -[acc] eval 6.69082e-4 s -[acc] eval 8.90672e-4 s -[acc] eval 8.97026e-4 s -[acc] eval 2.0954236e-2 s -[acc] compile 7.0532446e-2 s -[acc] eval 1.0016692e-2 s -[acc] eval 7.801326e-3 s -[acc] eval 3.297881e-3 s -[acc] eval 3.97982e-3 s -[acc] eval 3.73332e-3 s -[acc] eval 4.214441e-3 s -[acc] eval 3.542443e-3 s -[acc] eval 2.682931e-3 s -[acc] eval 2.179185e-3 s -[acc] eval 1.748298e-3 s -[acc] eval 1.48881e-3 s -[acc] eval 1.510827e-3 s -[acc] eval 3.161566e-3 s -[acc] eval 2.069426e-3 s -[acc] eval 1.961623e-3 s -[acc] eval 2.315953e-3 s -[acc] eval 0.130203131 s -[acc] compile 5.01967e-4 s -[acc] eval 5.780742e-3 s -[acc] compile 9.7939597e-2 s -[acc] eval 1.3875028e-2 s -[acc] eval 3.297083e-3 s -[acc] eval 3.069861e-3 s -[acc] eval 7.839229e-3 s -[acc] eval 2.857863e-3 s -[acc] eval 2.912209e-3 s -[acc] eval 2.576299e-3 s -[acc] eval 2.637525e-3 s -[acc] eval 2.813333e-3 s -[acc] eval 2.586447e-3 s -[acc] eval 2.752174e-3 s -[acc] eval 2.705542e-3 s -[acc] eval 2.611149e-3 s -[acc] eval 2.308409e-3 s -[acc] eval 2.474343e-3 s -[acc] eval 2.495021e-3 s -[acc] eval 2.886078e-3 s -[acc] eval 4.306485e-3 s -[acc] eval 2.782864e-3 s -[acc] eval 2.566122e-3 s -[acc] eval 2.567928e-3 s -[acc] eval 2.369375e-3 s -[acc] eval 2.446753e-3 s -[acc] eval 2.457287e-3 s -[acc] eval 2.390518e-3 s -[acc] eval 2.173154e-3 s -[acc] eval 2.403558e-3 s -[acc] eval 3.09454e-3 s -[acc] eval 3.116074e-3 s -[acc] eval 3.203285e-3 s -[acc] eval 3.120582e-3 s -[acc] eval 3.141007e-3 s -[acc] eval 3.139842e-3 s -[acc] eval 3.239381e-3 s -[acc] eval 0.215167782 s -[acc] compile 2.501083e-3 s -[acc] eval 2.863737e-3 s -[acc] eval 5.85777e-4 s -[acc] eval 8.36586e-4 s -[acc] eval 7.619802e-3 s -[acc] compile 6.2753931e-2 s -[acc] eval 1.0879233e-2 s -[acc] eval 3.711815e-3 s -[acc] eval 6.662414e-3 s -[acc] eval 4.149309e-3 s -[acc] eval 4.077398e-3 s -[acc] eval 4.033854e-3 s -[acc] eval 3.615236e-3 s -[acc] eval 3.052637e-3 s -[acc] eval 3.044035e-3 s -[acc] eval 3.387244e-3 s -[acc] eval 3.683253e-3 s -[acc] eval 2.844922e-3 s -[acc] eval 2.302854e-3 s -[acc] eval 2.414349e-3 s -[acc] eval 2.60575e-3 s -[acc] eval 3.336589e-3 s -[acc] eval 3.469455e-3 s -[acc] eval 3.030783e-3 s -[acc] eval 2.531816e-3 s -[acc] eval 2.518681e-3 s -[acc] eval 2.534367e-3 s -[acc] eval 2.140969e-3 s -[acc] eval 2.13709e-3 s -[acc] eval 2.538046e-3 s -[acc] eval 2.54963e-3 s -[acc] eval 0.154554873 s -[acc] compile 4.041289e-3 s -[acc] eval 5.663217e-3 s -[acc] eval 1.402068e-3 s -[acc] eval 1.454477e-3 s -[acc] eval 1.353766e-3 s -[acc] eval 1.339709e-3 s -[acc] eval 1.432354e-3 s -[acc] eval 1.487601e-3 s -[acc] eval 5.743539e-3 s -[acc] eval 2.6018299e-2 s -[acc] compile 0.162284288 s -[acc] eval 1.9554447e-2 s -[acc] eval 0.184589402 s -[acc] compile 1.4392558e-2 s -[acc] eval 1.1599889e-2 s -[acc] eval 2.9611948e-2 s -[acc] compile 0.125458115 s -[acc] eval 3.3395633e-2 s -[acc] eval 0.161357459 s -[acc] compile 0.199050534 s -[acc] eval 4.4213853e-2 s -[acc] eval 0.255079071 s -[acc] compile 8.689764e-3 s -[acc] eval 1.0159733e-2 s -[acc] eval 3.990309e-3 s -[acc] eval 3.696016e-3 s -[acc] eval 4.640937e-3 s -[acc] eval 4.121641e-3 s -[acc] eval 3.294198e-3 s -[acc] eval 2.802529e-3 s -[acc] eval 2.639315e-3 s 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5.08184e-3 s -[acc] eval 3.875512e-3 s -[acc] eval 1.627628e-3 s -[acc] eval 1.702505e-3 s -[acc] eval 1.621714e-3 s -[acc] eval 1.420566e-3 s -[acc] eval 7.516024e-3 s -[acc] eval 2.03217e-3 s -[acc] eval 2.241628e-3 s -[acc] eval 1.963892e-3 s -[acc] eval 1.730945e-3 s -[acc] eval 1.716886e-3 s -[acc] eval 1.671847e-3 s -[acc] eval 1.612738e-3 s -[acc] eval 3.9246215e-2 s -[acc] compile 3.979211e-3 s -[acc] eval 3.477755e-3 s -[acc] eval 6.84186e-4 s -[acc] eval 8.86228e-4 s -[acc] eval 1.0220512e-2 s -[acc] compile 6.0944922e-2 s -[acc] eval 1.0252983e-2 s -[acc] eval 1.009334e-3 s -[acc] eval 1.022474e-3 s -[acc] eval 7.5013357e-2 s -[acc] compile 0.315775682 s -[acc] eval 3.3261023e-2 s -[acc] eval 0.353073365 s -[acc] compile 4.3356365e-2 s -[acc] eval 8.412996e-3 s -[acc] eval 6.270099e-3 s -[acc] eval 6.0532735e-2 s -[acc] compile 0.140437068 s -[acc] eval 9.15703e-3 s -[acc] eval 0.152094149 s -[acc] compile 5.7451314e-2 s -[acc] eval 1.75968e-2 s -[acc] eval 8.0340385e-2 s 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3.202847e-3 s -[acc] eval 5.888572e-3 s -[acc] eval 1.457584e-3 s -[acc] eval 1.236634e-3 s -[acc] eval 1.337564e-3 s -[acc] eval 1.4633438e-2 s -[acc] compile 4.737611e-3 s -[acc] eval 4.898293e-3 s -[acc] eval 1.728273e-3 s -[acc] eval 1.628371e-3 s -[acc] eval 1.656049e-3 s -[acc] eval 2.168107e-3 s -[acc] eval 7.458854e-3 s -[acc] eval 2.269e-3 s -[acc] eval 2.392436e-3 s -[acc] eval 1.974406e-3 s -[acc] eval 1.706384e-3 s -[acc] eval 1.765949e-3 s -[acc] eval 1.723346e-3 s -[acc] eval 1.925259e-3 s -[acc] eval 1.836319e-3 s -[acc] eval 1.564603e-3 s -[acc] eval 1.662354e-3 s -[acc] eval 1.524629e-3 s -[acc] eval 1.375269e-3 s -[acc] eval 1.515227e-3 s -[acc] eval 1.515269e-3 s -[acc] eval 1.236998e-3 s -[acc] eval 1.738987e-3 s -[acc] eval 1.788873e-3 s -[acc] eval 1.939135e-3 s -[acc] eval 1.836088e-3 s -[acc] eval 1.457535e-3 s -[acc] eval 1.370994e-3 s -[acc] eval 1.149387e-3 s -[acc] eval 1.519882e-3 s -[acc] eval 1.604297e-3 s -[acc] eval 6.8165373e-2 s -[acc] compile 4.235041e-3 s -[acc] eval 8.296676e-3 s -[acc] eval 2.189719e-3 s -[acc] eval 2.075374e-3 s -[acc] eval 2.211198e-3 s -[acc] eval 2.720846e-3 s -[acc] eval 7.208715e-3 s -[acc] eval 2.282365e-3 s -[acc] eval 2.269019e-3 s -[acc] eval 2.415367e-3 s -[acc] eval 1.971858e-3 s -[acc] eval 1.93527e-3 s -[acc] eval 1.970841e-3 s -[acc] eval 1.671861e-3 s -[acc] eval 1.768595e-3 s -[acc] eval 2.050847e-3 s -[acc] eval 2.228437e-3 s -[acc] eval 2.307181e-3 s -[acc] eval 2.599707e-3 s -[acc] eval 5.8458813e-2 s -[acc] compile 1.3831547e-2 s -[acc] eval 3.0892235e-2 s -[acc] eval 3.830615e-3 s -[acc] eval 3.66378e-3 s -[acc] eval 3.379001e-3 s -[acc] eval 3.17915e-3 s -[acc] eval 3.240726e-3 s -[acc] eval 3.517971e-3 s -[acc] eval 3.338178e-3 s -[acc] eval 4.475266e-3 s -[acc] eval 1.1140249e-2 s -[acc] eval 3.749812e-3 s -[acc] eval 3.942848e-3 s -[acc] eval 3.857667e-3 s -[acc] eval 3.73725e-3 s -[acc] eval 7.319423e-3 s -[acc] eval 1.1060674e-2 s -[acc] eval 5.378038e-3 s -[acc] eval 4.326422e-3 s -[acc] eval 4.548836e-3 s -[acc] eval 4.722152e-3 s -[acc] eval 5.063516e-3 s -[acc] eval 8.896481e-3 s -[acc] eval 4.413112e-3 s -[acc] eval 3.693688e-3 s -[acc] eval 3.714023e-3 s -[acc] eval 4.174403e-3 s -[acc] eval 4.6708e-3 s -[acc] eval 0.184197448 s -[acc] compile 2.1077416e-2 s -[acc] eval 3.2066327e-2 s -[acc] eval 5.092714e-3 s -[acc] eval 5.217905e-3 s -[acc] eval 3.787663e-3 s -[acc] eval 3.527892e-3 s -[acc] eval 4.117646e-3 s -[acc] eval 4.398384e-3 s -[acc] eval 4.112058e-3 s -[acc] eval 1.1402969e-2 s -[acc] eval 4.431911e-3 s -[acc] eval 4.309261e-3 s -[acc] eval 4.184677e-3 s -[acc] eval 5.724212e-3 s -[acc] eval 6.432269e-3 s -[acc] eval 1.3462778e-2 s -[acc] eval 3.999549e-3 s -[acc] eval 4.411763e-3 s -[acc] eval 4.31718e-3 s -[acc] eval 3.922495e-3 s -[acc] eval 9.021429e-3 s -[acc] eval 4.430025e-3 s -[acc] eval 4.040399e-3 s -[acc] eval 3.84928e-3 s -[acc] eval 3.52886e-3 s -[acc] eval 5.102257e-3 s -[acc] eval 1.7459624e-2 s -[acc] eval 0.207280257 s diff --git a/phas2_n1.err b/phas2_n1.err deleted file mode 100644 index ace3aea..0000000 --- a/phas2_n1.err +++ /dev/null @@ -1,3066 +0,0 @@ -[acc] compile 1.6316688e-2 s -[acc] eval 2.173167086 s -[acc] compile 3.88697e-4 s -[acc] eval 7.4849639e-2 s -[acc] compile 3.72833e-4 s -[acc] eval 6.989576e-2 s -[acc] compile 3.42227e-4 s -[acc] eval 0.162037637 s -[acc] compile 3.77265e-4 s -[acc] eval 0.16706289 s -[acc] compile 3.17802e-4 s -[acc] eval 0.155809429 s -[acc] compile 3.42672e-4 s -[acc] eval 0.163186853 s -[acc] compile 3.74063e-4 s -[acc] eval 0.136964767 s -[acc] compile 3.88922e-4 s -[acc] eval 7.7952316e-2 s -[acc] compile 0.134325108 s -[acc] eval 3.3027777e-2 s -[acc] eval 0.171365719 s -[acc] compile 7.292338e-3 s -[acc] eval 1.1661132e-2 s -[acc] eval 2.1675279e-2 s -[acc] compile 3.89573e-3 s -[acc] eval 7.307594e-3 s -[acc] eval 7.02528e-4 s -[acc] eval 5.61443e-4 s -[acc] eval 1.3518071e-2 s -[acc] compile 0.298359155 s -[acc] eval 4.5904785e-2 s -[acc] eval 1.3070366e-2 s -[acc] eval 5.2678073e-2 s -[acc] eval 4.649835e-3 s -[acc] eval 4.862204e-3 s -[acc] eval 4.627072e-3 s -[acc] eval 4.493915e-3 s -[acc] eval 4.951981e-3 s -[acc] eval 4.020821e-3 s -[acc] eval 4.106185e-3 s -[acc] eval 4.392695e-3 s -[acc] eval 4.590139e-3 s -[acc] eval 0.45721121 s -[acc] compile 0.190577407 s -[acc] eval 1.6949576e-2 s -[acc] eval 0.214325267 s -[acc] compile 6.171634e-3 s -[acc] eval 4.152035e-3 s -[acc] eval 1.2705228e-2 s -[acc] compile 0.104115835 s -[acc] eval 5.297946e-3 s -[acc] eval 0.112407432 s -[acc] compile 0.127371889 s -[acc] eval 1.1440931e-2 s -[acc] eval 7.659696e-3 s -[acc] eval 2.875244e-3 s -[acc] eval 3.392585e-3 s -[acc] eval 3.634018e-3 s -[acc] eval 2.902735e-3 s -[acc] eval 2.874738e-3 s -[acc] eval 2.747652e-3 s -[acc] eval 2.522462e-3 s -[acc] eval 2.447583e-3 s -[acc] eval 2.512644e-3 s -[acc] eval 2.762453e-3 s -[acc] eval 2.253251e-3 s -[acc] eval 2.050489e-3 s -[acc] eval 2.157209e-3 s -[acc] eval 0.186102943 s -[acc] compile 6.9885047e-2 s -[acc] eval 8.131404e-3 s -[acc] eval 8.2305561e-2 s -[acc] compile 0.16780923 s -[acc] eval 1.7052037e-2 s -[acc] eval 0.190434194 s -[acc] compile 0.209709562 s -[acc] eval 1.7866155e-2 s -[acc] eval 0.234967183 s -[acc] compile 0.495002729 s -[acc] eval 2.7619211e-2 s -[acc] eval 0.529253764 s -[acc] compile 7.014917e-3 s -[acc] eval 9.717314e-3 s -[acc] eval 3.698549e-3 s -[acc] eval 4.210211e-3 s -[acc] eval 4.956352e-3 s -[acc] eval 3.238193e-3 s -[acc] eval 2.454318e-3 s -[acc] eval 2.809768e-3 s -[acc] eval 2.602745e-3 s -[acc] eval 2.986002e-3 s -[acc] eval 1.815191e-3 s -[acc] eval 1.996278e-3 s -[acc] eval 2.632393e-3 s -[acc] eval 4.308197e-3 s -[acc] eval 1.9403667e-2 s -[acc] eval 5.650858e-3 s -[acc] eval 3.640109e-3 s -[acc] eval 2.301831e-3 s -[acc] eval 1.83969e-3 s -[acc] eval 1.924816e-3 s -[acc] eval 3.668142e-3 s -[acc] eval 3.03705e-3 s -[acc] eval 0.102361307 s -[acc] compile 0.179144199 s -[acc] eval 1.4486828e-2 s -[acc] eval 0.19698042 s -[acc] compile 1.0250096e-2 s -[acc] eval 3.889043e-3 s -[acc] eval 1.522545e-3 s -[acc] eval 7.444132e-3 s -[acc] eval 2.5161832e-2 s -[acc] compile 0.151546007 s -[acc] eval 1.3448596e-2 s -[acc] eval 6.575755e-3 s -[acc] eval 4.026161e-3 s -[acc] eval 7.093324e-3 s -[acc] eval 5.107959e-3 s -[acc] eval 3.535504e-3 s -[acc] eval 3.540439e-3 s -[acc] eval 3.151911e-3 s -[acc] eval 4.223446e-3 s -[acc] eval 3.352651e-3 s -[acc] eval 3.280056e-3 s -[acc] eval 2.801109e-3 s -[acc] eval 2.743918e-3 s -[acc] eval 3.657198e-3 s -[acc] eval 3.083291e-3 s -[acc] eval 2.963098e-3 s -[acc] eval 2.848368e-3 s -[acc] eval 2.751854e-3 s -[acc] eval 3.529453e-3 s -[acc] eval 3.312527e-3 s -[acc] eval 3.148244e-3 s -[acc] eval 2.879899e-3 s -[acc] eval 2.760082e-3 s -[acc] eval 2.802194e-3 s -[acc] eval 2.775267e-3 s -[acc] eval 0.256038692 s -[acc] compile 7.8909877e-2 s -[acc] eval 8.161695e-3 s -[acc] eval 9.0679935e-2 s -[acc] compile 5.370308e-3 s -[acc] eval 1.6264467e-2 s -[acc] eval 3.094177e-3 s -[acc] eval 2.8246748e-2 s -[acc] compile 0.208915991 s -[acc] eval 7.363283e-3 s -[acc] eval 0.221140632 s -[acc] compile 0.276722664 s -[acc] eval 1.8500859e-2 s -[acc] eval 0.301768028 s -[acc] compile 8.102863e-3 s -[acc] eval 6.042383e-3 s -[acc] eval 1.8779709e-2 s -[acc] compile 9.3255766e-2 s -[acc] eval 1.0167917e-2 s -[acc] eval 6.77992e-3 s -[acc] eval 4.121323e-3 s -[acc] eval 4.020356e-3 s -[acc] eval 3.785041e-3 s -[acc] eval 3.790481e-3 s -[acc] eval 4.058546e-3 s -[acc] eval 4.349004e-3 s -[acc] eval 3.83604e-3 s -[acc] eval 3.969163e-3 s -[acc] eval 4.186066e-3 s -[acc] eval 4.165347e-3 s -[acc] eval 3.65118e-3 s -[acc] eval 3.589076e-3 s -[acc] eval 4.461173e-3 s -[acc] eval 3.667625e-3 s -[acc] eval 3.682343e-3 s -[acc] eval 3.730896e-3 s -[acc] eval 3.801249e-3 s -[acc] eval 3.820217e-3 s -[acc] eval 3.551512e-3 s -[acc] eval 3.803717e-3 s -[acc] eval 3.303215e-3 s -[acc] eval 3.230306e-3 s -[acc] eval 3.216158e-3 s -[acc] 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4.307695e-3 s -[acc] eval 4.563044e-3 s -[acc] eval 5.090137e-3 s -[acc] eval 5.115837e-3 s -[acc] eval 5.031654e-3 s -[acc] eval 4.924548e-3 s -[acc] eval 4.940808e-3 s -[acc] eval 5.057377e-3 s -[acc] eval 0.398768398 s -[acc] compile 2.514111e-3 s -[acc] eval 3.482171e-3 s -[acc] eval 3.77666e-4 s -[acc] eval 4.91022e-4 s -[acc] eval 7.725718e-3 s -[acc] compile 4.469573e-3 s -[acc] eval 1.0504388e-2 s -[acc] eval 1.0510908e-2 s -[acc] eval 2.8085615e-2 s -[acc] compile 5.085197e-3 s -[acc] eval 6.505634e-3 s -[acc] eval 2.925022e-3 s -[acc] eval 2.726356e-3 s -[acc] eval 2.788973e-3 s -[acc] eval 2.663345e-3 s -[acc] eval 2.684362e-3 s -[acc] eval 2.898648e-3 s -[acc] eval 8.239842e-3 s -[acc] eval 2.558609e-3 s -[acc] eval 2.851108e-3 s -[acc] eval 3.148624e-3 s -[acc] eval 2.945349e-3 s -[acc] eval 2.875011e-3 s -[acc] eval 2.817749e-3 s -[acc] eval 2.482552e-3 s -[acc] eval 3.101097e-3 s -[acc] eval 3.250271e-3 s -[acc] eval 6.731646e-2 s -[acc] compile 6.26904e-3 s -[acc] eval 8.340757e-3 s -[acc] eval 3.216953e-3 s -[acc] eval 3.242613e-3 s -[acc] eval 7.024472e-3 s -[acc] eval 2.591487e-3 s -[acc] eval 2.686792e-3 s -[acc] eval 2.961401e-3 s -[acc] eval 2.903265e-3 s -[acc] eval 2.583331e-3 s -[acc] eval 2.342131e-3 s -[acc] eval 2.432205e-3 s -[acc] eval 2.504775e-3 s -[acc] eval 2.466496e-3 s -[acc] eval 2.234072e-3 s -[acc] eval 2.215081e-3 s -[acc] eval 2.195164e-3 s -[acc] eval 2.382735e-3 s -[acc] eval 2.435372e-3 s -[acc] eval 2.285489e-3 s -[acc] eval 2.258523e-3 s -[acc] eval 7.1745212e-2 s -[acc] compile 7.130136e-3 s -[acc] eval 1.5943449e-2 s -[acc] eval 6.499992e-3 s -[acc] eval 1.6496384e-2 s -[acc] eval 3.852173e-3 s -[acc] eval 4.879129e-3 s -[acc] eval 3.815351e-3 s -[acc] eval 2.883258e-3 s -[acc] eval 2.759135e-3 s -[acc] eval 2.763709e-3 s -[acc] eval 2.798793e-3 s -[acc] eval 2.904917e-3 s -[acc] eval 2.84012e-3 s -[acc] eval 2.784249e-3 s -[acc] eval 2.766951e-3 s -[acc] eval 2.715975e-3 s -[acc] eval 2.655043e-3 s -[acc] eval 2.81637e-3 s -[acc] eval 2.885399e-3 s -[acc] eval 2.828561e-3 s -[acc] eval 9.978048e-2 s diff --git a/phas2_n2.err b/phas2_n2.err deleted file mode 100644 index b19651b..0000000 --- a/phas2_n2.err +++ /dev/null @@ -1,3611 +0,0 @@ -[acc] compile 1.3517798e-2 s -[acc] eval 1.925343022 s -[acc] compile 3.38165e-4 s -[acc] eval 6.9127284e-2 s -[acc] compile 3.45041e-4 s -[acc] eval 6.7748466e-2 s -[acc] compile 6.3629e-4 s -[acc] eval 0.156476348 s -[acc] compile 5.3778e-4 s -[acc] eval 0.156675908 s -[acc] compile 5.61072e-4 s -[acc] eval 0.158222123 s -[acc] compile 5.77608e-4 s -[acc] eval 0.165095295 s -[acc] compile 3.31213e-4 s -[acc] eval 0.140358027 s -[acc] compile 3.30338e-4 s -[acc] eval 6.8478166e-2 s -[acc] compile 3.777633e-3 s -[acc] eval 9.702142e-3 s -[acc] eval 1.5464641e-2 s -[acc] compile 0.121749797 s -[acc] eval 4.423951e-2 s -[acc] eval 0.170902813 s -[acc] compile 0.335051129 s -[acc] eval 1.7496599e-2 s -[acc] eval 8.459893e-3 s -[acc] eval 1.326026e-2 s -[acc] eval 6.156712e-3 s -[acc] eval 5.183335e-3 s -[acc] eval 3.545588e-3 s -[acc] eval 2.860763e-3 s -[acc] eval 2.891872e-3 s -[acc] eval 2.987505e-3 s -[acc] eval 2.426659e-3 s -[acc] eval 2.540245e-3 s -[acc] eval 2.70637e-3 s -[acc] eval 2.999266e-3 s -[acc] eval 2.926457e-3 s -[acc] eval 2.434203e-3 s -[acc] eval 2.407314e-3 s -[acc] eval 2.34653e-3 s -[acc] eval 2.389328e-3 s -[acc] eval 2.386761e-3 s -[acc] eval 2.472503e-3 s -[acc] eval 2.830081e-3 s -[acc] eval 2.866139e-3 s -[acc] eval 2.768205e-3 s -[acc] eval 2.711342e-3 s -[acc] eval 2.642366e-3 s -[acc] eval 2.757851e-3 s -[acc] eval 2.709662e-3 s -[acc] eval 2.856568e-3 s -[acc] eval 3.126124e-3 s -[acc] eval 2.953156e-3 s -[acc] eval 2.890073e-3 s -[acc] eval 0.464221887 s -[acc] compile 0.137057012 s -[acc] eval 1.7510972e-2 s -[acc] eval 5.100958e-3 s -[acc] eval 3.416983e-3 s -[acc] eval 2.827599e-3 s -[acc] eval 2.46934e-3 s -[acc] eval 2.148584e-3 s -[acc] eval 2.204266e-3 s -[acc] eval 2.026609e-3 s -[acc] eval 1.518227e-3 s -[acc] eval 0.179781574 s -[acc] compile 8.07392e-4 s -[acc] eval 6.752958e-3 s -[acc] compile 0.143071688 s -[acc] eval 1.5949959e-2 s -[acc] eval 3.176088e-3 s -[acc] eval 0.16565897 s -[acc] compile 4.69997e-4 s -[acc] eval 3.634593e-3 s -[acc] compile 3.53276e-4 s -[acc] eval 9.749746e-3 s -[acc] compile 0.261705849 s -[acc] eval 1.5248221e-2 s -[acc] eval 0.279082133 s -[acc] compile 6.2452904e-2 s -[acc] eval 5.375135e-3 s -[acc] eval 7.0099508e-2 s -[acc] compile 6.538005e-2 s -[acc] eval 1.8271965e-2 s -[acc] eval 2.478362e-3 s -[acc] eval 2.651918e-3 s -[acc] eval 2.265823e-3 s -[acc] eval 2.244826e-3 s -[acc] eval 2.326323e-3 s -[acc] eval 2.162231e-3 s -[acc] eval 2.049943e-3 s -[acc] eval 1.94261e-3 s -[acc] eval 2.023528e-3 s -[acc] eval 1.843494e-3 s -[acc] eval 2.068539e-3 s -[acc] eval 2.571973e-3 s -[acc] eval 0.114844221 s -[acc] compile 5.770746e-3 s -[acc] eval 4.69556e-3 s -[acc] eval 2.434859e-3 s -[acc] eval 3.417336e-3 s -[acc] eval 3.486923e-3 s -[acc] eval 5.031604e-3 s -[acc] eval 5.099855e-3 s -[acc] eval 1.1652558e-2 s -[acc] eval 1.969708e-3 s -[acc] eval 1.448551e-3 s -[acc] eval 1.458507e-3 s -[acc] eval 1.179125e-3 s -[acc] eval 1.127739e-3 s -[acc] eval 1.309736e-3 s -[acc] eval 1.365739e-3 s -[acc] eval 1.455281e-3 s -[acc] eval 1.413435e-3 s -[acc] eval 1.425865e-3 s -[acc] eval 1.344099e-3 s -[acc] eval 1.27028e-3 s -[acc] eval 1.316408e-3 s -[acc] eval 1.194297e-3 s -[acc] eval 1.183941e-3 s -[acc] eval 1.217143e-3 s -[acc] eval 1.258884e-3 s -[acc] eval 1.531787e-3 s -[acc] eval 1.208404e-3 s -[acc] eval 1.133195e-3 s -[acc] eval 1.092337e-3 s -[acc] eval 1.086614e-3 s -[acc] eval 1.428448e-3 s -[acc] eval 7.6301618e-2 s -[acc] compile 4.731008e-3 s -[acc] eval 5.064159e-3 s -[acc] eval 2.250127e-3 s -[acc] eval 2.125222e-3 s -[acc] eval 1.792839e-3 s -[acc] eval 1.623187e-3 s -[acc] eval 1.673658e-3 s -[acc] eval 2.095227e-3 s -[acc] eval 7.279063e-3 s -[acc] eval 1.608998e-3 s -[acc] eval 1.479927e-3 s -[acc] eval 1.431785e-3 s -[acc] eval 1.441674e-3 s -[acc] eval 3.6675684e-2 s -[acc] compile 0.33010516 s -[acc] eval 2.3516604e-2 s -[acc] eval 0.358304053 s -[acc] compile 0.138833285 s -[acc] eval 9.294732e-3 s -[acc] eval 2.353259e-3 s -[acc] eval 2.635512e-3 s -[acc] eval 5.12803e-3 s -[acc] eval 8.929621e-3 s -[acc] eval 1.825706e-3 s -[acc] eval 1.64992e-3 s -[acc] eval 1.601262e-3 s -[acc] eval 1.589398e-3 s -[acc] eval 1.584881e-3 s -[acc] eval 1.540229e-3 s -[acc] eval 3.050692e-3 s -[acc] eval 2.1734e-3 s -[acc] eval 1.72523e-3 s -[acc] eval 1.617193e-3 s -[acc] eval 1.619718e-3 s -[acc] eval 3.889943e-3 s -[acc] eval 2.708975e-3 s -[acc] eval 0.196916813 s -[acc] compile 0.169396986 s -[acc] eval 1.7735565e-2 s -[acc] eval 0.192175619 s -[acc] compile 3.456757e-3 s -[acc] eval 7.885808e-3 s -[acc] eval 1.090099e-3 s -[acc] eval 5.9595e-4 s -[acc] eval 7.90191e-4 s -[acc] eval 1.4891393e-2 s -[acc] compile 3.416163e-3 s -[acc] eval 3.511268e-3 s -[acc] eval 1.136271e-3 s -[acc] 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-[acc] eval 3.142502e-3 s -[acc] eval 3.482086e-3 s -[acc] eval 6.797064e-3 s -[acc] eval 3.25239e-3 s -[acc] eval 3.076236e-3 s -[acc] eval 0.439594457 s -[acc] compile 5.5969119e-2 s -[acc] eval 7.51005e-3 s -[acc] eval 2.734315e-3 s -[acc] eval 2.418081e-3 s -[acc] eval 5.764008e-3 s -[acc] eval 1.294502e-3 s -[acc] eval 1.270579e-3 s -[acc] eval 9.54244e-4 s -[acc] eval 9.14133e-4 s -[acc] eval 8.61108e-4 s -[acc] eval 7.76481e-4 s -[acc] eval 7.6623e-4 s -[acc] eval 7.7598e-4 s -[acc] eval 7.52836e-4 s -[acc] eval 7.46693e-4 s -[acc] eval 7.54684e-4 s -[acc] eval 1.018553e-3 s -[acc] eval 8.64499e-4 s -[acc] eval 9.37162e-4 s -[acc] eval 9.542e-4 s -[acc] eval 1.901815e-3 s -[acc] eval 1.401517e-3 s -[acc] eval 8.31121e-4 s -[acc] eval 8.14064e-4 s -[acc] eval 7.96296e-4 s -[acc] eval 7.41098e-4 s -[acc] eval 7.65803e-4 s -[acc] eval 7.44707e-4 s -[acc] eval 7.71129e-4 s -[acc] eval 7.73968e-4 s -[acc] eval 7.74628e-4 s -[acc] eval 7.99104e-4 s -[acc] eval 0.102547077 s -[acc] compile 4.9635495e-2 s -[acc] eval 1.1942317e-2 s -[acc] eval 1.351547e-3 s -[acc] eval 1.338876e-3 s -[acc] eval 1.317151e-3 s -[acc] eval 1.316074e-3 s -[acc] eval 4.864299e-3 s -[acc] eval 1.008384e-3 s -[acc] eval 1.138091e-3 s -[acc] eval 1.055308e-3 s -[acc] eval 1.015526e-3 s -[acc] eval 9.43361e-4 s -[acc] eval 1.021113e-3 s -[acc] eval 9.58097e-4 s -[acc] eval 9.83408e-4 s -[acc] eval 9.38999e-4 s -[acc] eval 9.77506e-4 s -[acc] eval 9.69817e-4 s -[acc] eval 9.6229e-4 s -[acc] eval 9.5409e-4 s -[acc] eval 9.24257e-4 s -[acc] eval 9.02607e-4 s -[acc] eval 9.44672e-4 s -[acc] eval 1.037404e-3 s -[acc] eval 1.12418e-3 s -[acc] eval 9.7188e-4 s -[acc] eval 9.45959e-4 s -[acc] eval 9.64785e-4 s -[acc] eval 9.54144e-4 s -[acc] eval 9.59086e-4 s -[acc] eval 9.48583e-4 s -[acc] eval 9.03894e-4 s -[acc] eval 9.07585e-4 s -[acc] eval 9.58216e-4 s -[acc] eval 0.101811555 s -[acc] compile 0.128719929 s -[acc] eval 1.3565058e-2 s -[acc] eval 2.45955e-3 s -[acc] eval 5.958035e-3 s -[acc] 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s -[acc] eval 2.521631e-3 s -[acc] eval 2.390641e-3 s -[acc] eval 2.779349e-3 s -[acc] eval 2.739872e-3 s -[acc] eval 2.759035e-3 s -[acc] eval 2.777233e-3 s -[acc] eval 2.521244e-3 s -[acc] eval 2.758981e-3 s -[acc] eval 2.587957e-3 s -[acc] eval 2.540472e-3 s -[acc] eval 2.899087e-3 s -[acc] eval 2.395492e-3 s -[acc] eval 2.778761e-3 s -[acc] eval 2.62113e-3 s -[acc] eval 2.732862e-3 s -[acc] eval 2.83252e-3 s -[acc] eval 2.835979e-3 s -[acc] eval 0.171238361 s diff --git a/phas2_n4.err b/phas2_n4.err deleted file mode 100644 index 1d1b9bf..0000000 --- a/phas2_n4.err +++ /dev/null @@ -1,3081 +0,0 @@ -[acc] compile 1.5288851e-2 s -[acc] eval 2.010652007 s -[acc] compile 6.13039e-4 s -[acc] eval 7.1534814e-2 s -[acc] compile 5.19529e-4 s -[acc] eval 8.2007916e-2 s -[acc] compile 5.60668e-4 s -[acc] eval 0.215239243 s -[acc] compile 4.86642e-4 s -[acc] eval 0.155146324 s -[acc] compile 7.87492e-4 s -[acc] eval 0.177545691 s -[acc] compile 5.64334e-4 s -[acc] eval 0.241093269 s -[acc] 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0.136680386 s -[acc] compile 8.328283e-3 s -[acc] eval 1.2184433e-2 s -[acc] eval 2.336298e-2 s -[acc] compile 0.493542827 s -[acc] eval 4.6066565e-2 s -[acc] eval 0.545245947 s -[acc] compile 0.463591314 s -[acc] eval 3.7716373e-2 s -[acc] eval 0.508567323 s -[acc] compile 4.491595e-3 s -[acc] eval 1.7426862e-2 s -[acc] eval 1.183789e-3 s -[acc] eval 1.167849e-3 s -[acc] eval 2.6152233e-2 s -[acc] compile 1.0661175e-2 s -[acc] eval 6.548585e-3 s -[acc] eval 3.150771e-3 s -[acc] eval 3.435627e-3 s -[acc] eval 3.315569e-3 s -[acc] eval 2.960585e-3 s -[acc] eval 3.034265e-3 s -[acc] eval 2.142273e-3 s -[acc] eval 2.666348e-3 s -[acc] eval 3.978948e-3 s -[acc] eval 1.3317923e-2 s -[acc] eval 1.774117e-3 s -[acc] eval 1.754064e-3 s -[acc] eval 1.636637e-3 s -[acc] eval 1.565742e-3 s -[acc] eval 1.588845e-3 s -[acc] eval 1.708913e-3 s -[acc] eval 1.625364e-3 s -[acc] eval 1.38e-3 s -[acc] eval 1.604354e-3 s -[acc] eval 1.73748e-3 s -[acc] eval 1.508495e-3 s -[acc] eval 1.61626e-3 s -[acc] eval 1.456026e-3 s -[acc] eval 1.46092e-3 s -[acc] eval 1.383667e-3 s -[acc] eval 1.617503e-3 s -[acc] eval 1.451849e-3 s -[acc] eval 1.46526e-3 s -[acc] eval 1.59355e-3 s -[acc] eval 1.615916e-3 s -[acc] eval 1.503952e-3 s -[acc] eval 9.4396389e-2 s diff --git a/pp_n1_1.err b/pp_n1_1.err deleted file mode 100644 index e69de29..0000000 diff --git a/pp_n1_2.err b/pp_n1_2.err deleted file mode 100644 index e69de29..0000000 diff --git a/pp_n2_1.err b/pp_n2_1.err deleted file mode 100644 index e69de29..0000000 diff --git a/pp_n2_2.err b/pp_n2_2.err deleted file mode 100644 index e69de29..0000000 diff --git a/pp_n4_1.err b/pp_n4_1.err deleted file mode 100644 index e69de29..0000000 diff --git a/pp_n4_2.err b/pp_n4_2.err deleted file mode 100644 index e69de29..0000000 diff --git a/pp_out_3.csv b/pp_out_3.csv deleted file mode 100644 index b14fa25..0000000 --- a/pp_out_3.csv +++ /dev/null @@ -1,6 +0,0 @@ -id,view,Expression,Numpy,Math,theta,size,loss_train,loss_val,loss_test,maxloss,R2_train,R2_val,R2_test,dl_train,dl_val,dl_test -0,0,22.766786471142108,"t[0]","$$\theta_{0}$$",22.766786471142108,1,250.20995238653342,250.20995238653342,250.20995238653342,250.20995238652472,-3.3306690738754696e-15,-3.3306690738754696e-15,-3.3306690738754696e-15,253.1911148436993,253.1911148436993,253.1911148436993 -1,0,(1383.4433144862855 / x1),"(t[0] / x[:, 1])","$$\frac{\theta_{0}}{x_{1}}$$",1383.4433144862855,3,247.27063764264005,247.27063764264005,247.27063764264005,247.27063764265753,1.174739340244857e-2,1.174739340244857e-2,1.174739340244857e-2,253.5567559545484,253.5567559545484,253.5567559545484 -2,0,(-5.745165366885029 * (-4.1530583589543255 + x2)),"(t[0] * (t[1] + x[:, 2]))","$$\left(\theta_{0} \cdot \left(\theta_{1} + x_{2}\right)\right)$$",-5.745165366885029;-4.1530583589543255,5,245.18084594296485,245.18084594296485,245.18084594296485,245.18084594296587,2.009954598368091e-2,2.009954598368091e-2,2.009954598368091e-2,258.8665014142734,258.8665014142734,258.8665014142734 -3,0,((-21.44885359673644 * x0) + (Exp(x7) + 25.223226358297662)),"((t[0] * x[:, 0]) + (np.exp(x[:, 7]) + t[1]))","$$\left(\left(\theta_{0} \cdot x_{0}\right) + \left(\operatorname{exp}(x_{7}) + \theta_{1}\right)\right)$$",-21.44885359673644;25.223226358297662,8,167.26495496048574,167.26495496048574,167.26495496048574,167.26495496049114,0.33150159150308567,0.33150159150308567,0.33150159150308567,188.5949421956155,188.5949421956155,188.5949421956155 -4,0,((Exp(Exp((-1.0316058190409207 * x0))) + (((-0.16839382479645368 * x5) * x0) + 0.2597989053842083)) * 9.966295253001473),"((np.exp(np.exp((t[0] * x[:, 0]))) + (((t[1] * x[:, 5]) * x[:, 0]) + t[2])) * t[3])","$$\left(\left(\operatorname{exp}(\operatorname{exp}(\left(\theta_{0} \cdot x_{0}\right))) + \left(\left(\left(\theta_{1} \cdot x_{5}\right) \cdot x_{0}\right) + \theta_{2}\right)\right) \cdot \theta_{3}\right)$$",-1.0316058190409207;-0.16839382479645368;0.2597989053842083;9.966295253001473,15,152.38528167941737,152.38528167941737,152.38528167941737,152.3852816794077,0.390970342202825,0.390970342202825,0.390970342202825,193.81858581120548,193.81858581120548,193.81858581120548 diff --git a/pp_out_4.csv b/pp_out_4.csv deleted file mode 100644 index c85b678..0000000 --- a/pp_out_4.csv +++ /dev/null @@ -1,5 +0,0 @@ -id,view,Expression,Numpy,Math,theta,size,loss_train,loss_val,loss_test,maxloss,R2_train,R2_val,R2_test,dl_train,dl_val,dl_test -0,0,22.76678647114209,"t[0]","$$\theta_{0}$$",22.76678647114209,1,250.20995238653353,250.20995238653353,250.20995238653353,250.20995238652458,-3.774758283725532e-15,-3.774758283725532e-15,-3.774758283725532e-15,253.1911148436994,253.1911148436994,253.1911148436994 -1,0,(20.25012293495296 + (2.573182190663003 * (4.544753026268889 / x5))),"(t[0] + (t[1] * (t[2] / x[:, 5])))","$$\left(\theta_{0} + \left(\theta_{1} \cdot \frac{\theta_{2}}{x_{5}}\right)\right)$$",20.25012293495296;2.573182190663003;4.544753026268889,7,250.00697478204222,250.00697478204222,250.00697478204222,250.00697478203026,8.112291399855476e-4,8.112291399855476e-4,8.112291399855476e-4,271.7696699646551,271.7696699646551,271.7696699646551 -2,0,(Exp(Exp((x8 * -9.255999654370921))) + (20.17513459419948 + x3)),"(np.exp(np.exp((x[:, 8] * t[0]))) + (t[1] + x[:, 3]))","$$\left(\operatorname{exp}(\operatorname{exp}(\left(x_{8} \cdot \theta_{0}\right))) + \left(\theta_{1} + x_{3}\right)\right)$$",-9.255999654370921;20.17513459419948,9,239.22734537684235,239.22734537684235,239.22734537684235,239.22734537685523,4.38935658031856e-2,4.38935658031856e-2,4.38935658031856e-2,262.1258345246083,262.1258345246083,262.1258345246083 -3,0,(((x1 + -0.3502997371676269) * 0.4746847666089903) / Exp((x0 * 1.2702218718093228))),"(((x[:, 1] + t[0]) * t[1]) / np.exp((x[:, 0] * t[2])))","$$\frac{\left(\left(x_{1} + \theta_{0}\right) \cdot \theta_{1}\right)}{\operatorname{exp}(\left(x_{0} \cdot \theta_{2}\right))}$$",-0.3502997371676269;0.4746847666089903;1.2702218718093228,10,168.31453547426653,168.31453547426653,168.31453547426653,168.31453547427247,0.3273067922803937,0.3273067922803937,0.3273067922803937,198.52371101490465,198.52371101490465,198.52371101490465 diff --git a/pp_out_5.csv b/pp_out_5.csv deleted file mode 100644 index 1656692..0000000 --- a/pp_out_5.csv +++ /dev/null @@ -1,5 +0,0 @@ -id,view,Expression,Numpy,Math,theta,size,loss_train,loss_val,loss_test,maxloss,R2_train,R2_val,R2_test,dl_train,dl_val,dl_test -0,0,22.76678647114209,"t[0]","$$\theta_{0}$$",22.76678647114209,1,250.20995238653353,250.20995238653353,250.20995238653353,250.20995238652458,-3.774758283725532e-15,-3.774758283725532e-15,-3.774758283725532e-15,253.1911148436994,253.1911148436994,253.1911148436994 -1,0,(1383.4433144862865 / x1),"(t[0] / x[:, 1])","$$\frac{\theta_{0}}{x_{1}}$$",1383.4433144862865,3,247.27063764264042,247.27063764264042,247.27063764264042,247.27063764265753,1.1747393402447015e-2,1.1747393402447015e-2,1.1747393402447015e-2,253.55675595454878,253.55675595454878,253.55675595454878 -2,0,(24.968290345037 + (-0.355038430771912 / x3)),"(t[0] + (t[1] / x[:, 3]))","$$\left(\theta_{0} + \frac{\theta_{1}}{x_{3}}\right)$$",24.968290345037;-0.355038430771912,5,245.62332714454894,245.62332714454894,245.62332714454894,245.62332714456642,1.833110632984758e-2,1.833110632984758e-2,1.833110632984758e-2,259.30760985334456,259.30760985334456,259.30760985334456 -3,0,Exp((-1.2241452468910468 * (x0 + -2.76996802043229))),"np.exp((t[0] * (x[:, 0] + t[1])))","$$\operatorname{exp}(\left(\theta_{0} \cdot \left(x_{0} + \theta_{1}\right)\right))$$",-1.2241452468910468;-2.76996802043229,6,153.0117642718506,153.0117642718506,153.0117642718506,153.01176427185254,0.38846651457143877,0.38846651457143877,0.38846651457143877,169.70852261108521,169.70852261108521,169.70852261108521 diff --git a/prof_n4.err b/prof_n4.err deleted file mode 100644 index 7dd7f09..0000000 --- a/prof_n4.err +++ /dev/null @@ -1,20515 +0,0 @@ -[acc-interp] thetaMarsh 2.03e-7 s (n=0) -[acc-interp] compile 1.336441784 s -[acc-interp] chunk base=0 cs=25974 kernel=3.315169449 s grad=1.284e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.566503e-3 s grad=1.525e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=1.738212e-3 s grad=1.111e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.182516e-3 s grad=1.501e-6 s -[acc-interp] eval 3.325141095 s -[acc-interp] thetaMarsh 9.3e-8 s (n=0) -[acc-interp] chunk base=0 cs=25974 kernel=9.8377161e-2 s grad=1.434e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.165901e-3 s grad=1.008e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=1.514659e-3 s grad=1.16e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.409632e-3 s grad=1.301e-6 s -[acc-interp] eval 0.105613114 s -[acc-interp] thetaMarsh 1.06e-7 s (n=0) -[acc-interp] chunk base=0 cs=25974 kernel=0.126094449 s grad=1.84e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.36911e-3 s grad=1.713e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=1.635651e-3 s grad=1.49e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.381526e-3 s grad=1.15e-6 s -[acc-interp] eval 0.133840532 s -[acc-interp] thetaMarsh 7.0e-8 s (n=0) -[acc-interp] chunk base=0 cs=25974 kernel=0.2182426 s grad=1.015e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.661507e-3 s grad=1.197e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=1.557307e-3 s grad=8.44e-7 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.510878e-3 s grad=1.467e-6 s -[acc-interp] eval 0.22598096 s -[acc-interp] thetaMarsh 1.25e-7 s (n=0) -[acc-interp] chunk base=0 cs=25974 kernel=0.214250378 s grad=1.695e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=1.899349e-3 s grad=6.31e-7 s -[acc-interp] chunk base=51948 cs=25974 kernel=1.338897e-3 s grad=3.449e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.031595e-3 s grad=2.26e-6 s -[acc-interp] eval 0.220592065 s -[acc-interp] thetaMarsh 6.3e-8 s (n=0) -[acc-interp] chunk base=0 cs=25974 kernel=0.206191325 s grad=1.416e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.0373103e-2 s grad=1.218e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=1.580809e-3 s grad=2.37e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=1.87734e-3 s grad=8.61e-7 s -[acc-interp] eval 0.230825027 s -[acc-interp] thetaMarsh 5.9e-8 s (n=0) -[acc-interp] chunk base=0 cs=25974 kernel=0.209030164 s grad=9.77e-7 s -[acc-interp] chunk base=25974 cs=25974 kernel=1.770227e-3 s grad=1.376e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=1.509066e-3 s grad=5.47e-7 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.099939e-3 s grad=9.4e-7 s -[acc-interp] eval 0.21512094 s -[acc-interp] thetaMarsh 2.36e-7 s (n=0) -[acc-interp] chunk base=0 cs=25974 kernel=0.1813838 s grad=1.471e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=1.932215e-3 s grad=6.87e-7 s -[acc-interp] chunk base=51948 cs=25974 kernel=1.40537e-3 s grad=1.016e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.883358e-3 s grad=1.986e-6 s -[acc-interp] eval 0.188431302 s -[acc-interp] thetaMarsh 7.3e-8 s (n=0) -[acc-interp] chunk base=0 cs=25974 kernel=9.0607354e-2 s grad=1.562e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=1.036926e-2 s grad=1.607e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=1.61791e-3 s grad=1.036e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=1.765182e-3 s grad=2.657e-6 s -[acc-interp] eval 0.105262962 s -[acc-interp] thetaMarsh 7.1e-8 s (n=[acc-interp] thetaMarsh 6.7e-8 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=3.1470326e-2 s grad=2.788e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=6.610023e-3 s grad=3.462e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.432982e-3 s grad=2.934e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.700079e-3 s grad=3.31e-6 s -[acc-interp] eval 4.5993857e-2 s -1) -[acc-interp] chunk base=0 cs=25974 kernel=3.259361e-3 s grad=2.594e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.979895e-3 s grad=3.448e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.646054e-3 s grad=3.233e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.675348e-3 s grad=2.604e-6 s -[acc-interp] eval 6.1475642e-2 s -[acc-interp] thetaMarsh 6.8e-8 s (n=[acc-interp] thetaMarsh 1.46e-7 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=3.577424e-3 s grad=3.159e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.533017e-3 s grad=3.41e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=1.9963e-3 s grad=1.839e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.151765e-3 s grad=1.238e-6 s -[acc-interp] eval 1.1992721e-2 s -[acc-interp] thetaMarsh 1.58e-7 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=1.446452e-3 s grad=6.797e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.158091e-3 s grad=6.407e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.11598e-3 s grad=4.885e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=1.270039e-3 s grad=8.22e-7 s -[acc-interp] eval 7.640263e-3 s -[acc-interp] thetaMarsh 6.2e-8 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=2.059764e-3 s grad=2.798e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.430523e-3 s grad=8.699e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=1.896292e-3 s grad=2.963e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=1.143825e-3 s grad=1.578e-6 s -[acc-interp] eval 8.794744e-3 s -1) -[acc-interp] chunk base=0 cs=25974 kernel=1.828932e-3 s grad=1.711e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=1.725665e-3 s grad=1.41e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=1.294014e-3 s grad=8.451e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.105404e-3 s grad=3.133e-6 s -[acc-interp] eval 3.7129391e-2 s -[acc-interp] thetaMarsh 1.34e-7 s (n=[acc-interp] thetaMarsh 1.16e-7 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=1.8272255e-2 s grad=4.467e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.267978e-3 s grad=2.028e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.089077e-3 s grad=1.384e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.15557e-3 s grad=2.259e-6 s -[acc-interp] eval 2.6357048e-2 s -[acc-interp] thetaMarsh 1.0e-7 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=3.568955e-3 s grad=1.633e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.788766e-3 s grad=2.067e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.135927e-3 s grad=2.936e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.086585e-3 s grad=2.199e-6 s -[acc-interp] eval 1.3992075e-2 s -1) -[acc-interp] chunk base=0 cs=25974 kernel=1.951145e-3 s grad=2.961e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.565324e-3 s grad=2.474e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=4.575353e-3 s grad=3.173e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.804742e-3 s grad=2.58e-6 s -[acc-interp] eval 5.4522644e-2 s -[acc-interp] thetaMarsh 8.0e-8 s (n=0) -[acc-interp] chunk base=0 cs=25974 kernel=2.7511376e-2 s grad=1.464e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.192659e-3 s grad=1.159e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.151525e-3 s grad=1.137e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=1.836882e-3 s grad=1.361e-6 s -[acc-interp] eval 3.4498659e-2 s -[acc-interp] thetaMarsh 5.9e-8 s (n=[acc-interp] thetaMarsh 1.49e-7 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=1.2913372e-2 s grad=3.82e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.917946e-3 s grad=4.234e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.960378e-3 s grad=2.917e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=4.153587e-3 s grad=4.121e-6 s -[acc-interp] eval 2.378619e-2 s -[acc-interp] thetaMarsh 1.08e-7 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=3.057817e-3 s grad=3.839e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.93591e-3 s grad=2.979e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=9.099928e-3 s grad=3.346e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.740231e-3 s grad=9.465e-6 s -[acc-interp] eval 1.8625716e-2 s -[acc-interp] thetaMarsh 6.4e-8 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=2.822759e-3 s grad=2.65e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.667756e-3 s grad=3.945e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.808331e-3 s grad=4.574e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.311846e-3 s grad=1.0482e-5 s -[acc-interp] eval 1.4418778e-2 s -[acc-interp] thetaMarsh 6.7e-8 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=2.748211e-3 s grad=4.033e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.171149e-3 s grad=9.638e-6 s -[acc-interp] chunk base=51948 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grad=1.207e-6 s -[acc-interp] eval 1.4472473e-2 s -[acc-interp] thetaMarsh 6.2e-8 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=6.296988e-3 s grad=1.799e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.263212e-3 s grad=2.207e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.402126e-3 s grad=1.241e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.064502e-3 s grad=3.146e-6 s -[acc-interp] eval 1.696051e-2 s -4) -[acc-interp] chunk base=0 cs=25974 kernel=3.160752e-3 s grad=4.96e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.812541e-3 s grad=3.259e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.993621e-3 s grad=3.019e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.188639e-3 s grad=2.653e-6 s -[acc-interp] eval 0.49338099 s -[acc-interp] thetaMarsh 1.54e-7 s (n=[acc-interp] thetaMarsh 1.15e-7 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=3.5730114e-2 s grad=6.125e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=5.242952e-3 s grad=4.969e-6 s 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thetaMarsh 6.3e-8 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=4.889028e-3 s grad=3.45e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=1.0278e-2 s grad=3.303e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=4.236997e-3 s grad=3.803e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=6.760032e-3 s grad=7.702e-6 s -[acc-interp] eval 2.7318229e-2 s -[acc-interp] thetaMarsh 1.4e-7 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=5.79515e-3 s grad=3.323e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=8.750761e-3 s grad=8.145e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=6.009231e-3 s grad=3.056e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=5.504497e-3 s grad=4.608e-6 s -[acc-interp] eval 2.7211536e-2 s -[acc-interp] thetaMarsh 7.2e-8 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=8.316195e-3 s grad=3.92e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=6.665955e-3 s grad=4.167e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=7.953112e-3 s 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cs=25974 kernel=4.830806e-3 s grad=3.088e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=4.038939e-3 s grad=2.031e-6 s -[acc-interp] eval 2.0917828e-2 s -[acc-interp] thetaMarsh 1.57e-7 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=5.290055e-3 s grad=4.939e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=7.269343e-3 s grad=8.325e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=6.101081e-3 s grad=5.034e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=6.044682e-3 s grad=5.06e-6 s -[acc-interp] eval 2.5962464e-2 s -[acc-interp] thetaMarsh 9.3e-8 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=5.599589e-3 s grad=2.962e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=1.0952732e-2 s grad=4.76e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=6.350691e-3 s grad=6.891e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=4.50128e-3 s grad=4.337e-6 s -[acc-interp] eval 2.8288171e-2 s -[acc-interp] thetaMarsh 6.2e-8 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=6.167443e-3 s grad=3.524e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.58197e-3 s grad=3.978e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=5.068794e-3 s grad=4.755e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=5.565051e-3 s grad=3.874e-6 s -[acc-interp] eval 2.225891e-2 s -[acc-interp] thetaMarsh 6.1e-8 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=5.373192e-3 s grad=2.206e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=5.882629e-3 s grad=3.688e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=5.669832e-3 s grad=2.937e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=4.140009e-3 s grad=3.777e-6 s -[acc-interp] eval 2.1861581e-2 s -[acc-interp] thetaMarsh 6.3e-8 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=1.2154906e-2 s grad=4.471e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=5.041724e-3 s grad=2.642e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=6.015675e-3 s grad=3.97e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=5.709857e-3 s grad=2.047e-6 s -[acc-interp] eval 2.9737291e-2 s -[acc-interp] thetaMarsh 1.34e-7 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=4.75225e-3 s grad=3.096e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=5.863444e-3 s grad=4.094e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=5.477576e-3 s grad=3.572e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=4.541081e-3 s grad=3.811e-6 s -[acc-interp] eval 2.1467504e-2 s -[acc-interp] thetaMarsh 6.3e-8 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=5.632217e-3 s grad=3.465e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=1.1104873e-2 s grad=3.698e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=5.973864e-3 s grad=4.955e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=5.175219e-3 s grad=4.236e-6 s -[acc-interp] eval 2.8819814e-2 s -[acc-interp] thetaMarsh 6.2e-8 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=5.141881e-3 s grad=4.541e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=8.579788e-3 s grad=4.748e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=5.43639e-3 s grad=4.421e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=5.703832e-3 s grad=4.564e-6 s -[acc-interp] eval 2.5999871e-2 s -[acc-interp] thetaMarsh 1.02e-7 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=5.119401e-3 s grad=4.337e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=5.920427e-3 s grad=3.697e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=1.3259133e-2 s grad=4.261e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=6.811136e-3 s grad=3.682e-6 s -[acc-interp] eval 3.2092067e-2 s -[acc-interp] thetaMarsh 6.2e-8 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=6.403496e-3 s grad=3.112e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=5.230416e-3 s grad=2.792e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=5.646056e-3 s grad=2.075e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=4.968115e-3 s grad=1.551e-6 s -[acc-interp] eval 2.2975668e-2 s -[acc-interp] thetaMarsh 1.08e-7 s 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kernel=4.932466e-3 s grad=4.161e-6 s -[acc-interp] eval 0.827476957 s -[acc-interp] thetaMarsh 1.18e-7 s (n=[acc-interp] thetaMarsh 1.17e-7 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=3.3239485e-2 s grad=3.242e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.893179e-3 s grad=3.958e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=4.149555e-3 s grad=8.912e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=4.241532e-3 s grad=1.579e-6 s -[acc-interp] eval 4.6438585e-2 s -3) -[acc-interp] chunk base=0 cs=25974 kernel=3.715273e-3 s grad=1.303e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.765384e-3 s grad=2.69e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.963306e-3 s grad=2.401e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.770896e-3 s grad=1.553e-6 s -[acc-interp] eval 6.254636e-2 s -[acc-interp] thetaMarsh 9.4e-8 s (n=[acc-interp] thetaMarsh 5.9e-8 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=1.2555548e-2 s grad=3.698e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.210472e-3 s grad=8.365e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.413009e-3 s grad=7.252e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.136948e-3 s grad=3.573e-6 s -[acc-interp] eval 2.4185e-2 s -[acc-interp] thetaMarsh 1.01e-7 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=4.166122e-3 s grad=4.409e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=9.607111e-3 s grad=2.899e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.135767e-3 s grad=3.787e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.045876e-3 s grad=4.625e-6 s -[acc-interp] eval 2.0851042e-2 s -[acc-interp] thetaMarsh 6.2e-8 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=3.027573e-3 s grad=4.006e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.442309e-3 s grad=3.229e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.715728e-3 s grad=3.105e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.287071e-3 s grad=6.464e-6 s -[acc-interp] eval 1.2322457e-2 s -[acc-interp] thetaMarsh 7.4e-8 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=2.716859e-3 s grad=4.221e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.892213e-3 s grad=3.787e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.764754e-3 s grad=3.151e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.457669e-3 s grad=2.682e-6 s -[acc-interp] eval 1.1656989e-2 s -[acc-interp] thetaMarsh 2.31e-7 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=2.584044e-3 s grad=3.5e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.378156e-3 s grad=4.324e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.333781e-3 s grad=4.354e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.611053e-3 s grad=4.465e-6 s -[acc-interp] eval 1.1751215e-2 s -3) -[acc-interp] chunk base=0 cs=25974 kernel=2.695051e-3 s grad=2.244e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.071423e-3 s grad=6.551e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.401621e-3 s grad=3.498e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.328259e-3 s grad=4.705e-6 s -[acc-interp] eval 9.259962e-2 s -[acc-interp] thetaMarsh 6.3e-8 s (n=[acc-interp] thetaMarsh 6.8e-8 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=5.069081e-3 s grad=3.503e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.105256e-3 s grad=8.125e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.042712e-3 s grad=2.979e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.295934e-3 s grad=2.92e-6 s -[acc-interp] eval 1.2345589e-2 s -[acc-interp] thetaMarsh 6.5e-8 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=1.786979e-3 s grad=3.469e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.491767e-3 s grad=2.311e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.292665e-3 s grad=2.925e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=7.410269e-3 s grad=2.357e-6 s -[acc-interp] eval 1.5412786e-2 s -[acc-interp] thetaMarsh 1.2e-7 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=1.719013e-3 s grad=3.05e-6 s 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eval 2.2724939e-2 s -3) -[acc-interp] chunk base=0 cs=25974 kernel=3.863533e-3 s grad=3.368e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=8.582927e-3 s grad=4.23e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.836407e-3 s grad=3.223e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.879899e-3 s grad=3.461e-6 s -[acc-interp] eval 4.1888421e-2 s -[acc-interp] thetaMarsh 9.8e-8 s (n=[acc-interp] thetaMarsh 1.48e-7 s (n=5) -[acc-interp] chunk base=0 cs=25974 kernel=9.844676e-3 s grad=4.244e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.028116e-3 s grad=4.275e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.963628e-3 s grad=3.835e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.491111e-3 s grad=3.41e-6 s -[acc-interp] eval 1.9211639e-2 s -[acc-interp] thetaMarsh 6.2e-8 s (n=5) -[acc-interp] chunk base=0 cs=25974 kernel=2.905646e-3 s grad=3.72e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=1.1711543e-2 s grad=3.823e-6 s -[acc-interp] chunk base=51948 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thetaMarsh 9.7e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.537204e-3 s grad=2.54e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.724371e-3 s grad=2.927e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.368712e-3 s grad=3.085e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.344105e-3 s grad=3.972e-6 s -[acc-interp] eval 1.2559249e-2 s -[acc-interp] thetaMarsh 1.01e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=3.256666e-3 s grad=3.417e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.560342e-3 s grad=2.549e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.525856e-3 s grad=3.678e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.842405e-3 s grad=3.861e-6 s -[acc-interp] eval 1.5729049e-2 s -[acc-interp] thetaMarsh 6.2e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.996719e-3 s grad=2.523e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.427921e-3 s grad=2.441e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=1.686769e-3 s 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eval 1.402099e-2 s -[acc-interp] thetaMarsh 9.8e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.942769e-3 s grad=2.503e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.703869e-3 s grad=2.068e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=4.514482e-3 s grad=4.238e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.273652e-3 s grad=2.929e-6 s -[acc-interp] eval 1.4891987e-2 s -[acc-interp] thetaMarsh 1.1e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=3.217322e-3 s grad=3.111e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.26575e-3 s grad=3.457e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.271369e-3 s grad=3.695e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.039847e-3 s grad=2.209e-6 s -[acc-interp] eval 1.4408017e-2 s -[acc-interp] thetaMarsh 1.12e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=3.01546e-3 s grad=2.013e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.806862e-3 s grad=2.691e-6 s -[acc-interp] chunk base=51948 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thetaMarsh 5.7e-8 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=1.6119288e-2 s grad=3.041e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.137035e-3 s grad=2.652e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=4.380147e-3 s grad=4.197e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.499759e-3 s grad=4.086e-6 s -[acc-interp] eval 2.8012253e-2 s -1) -[acc-interp] chunk base=0 cs=25974 kernel=3.054647e-3 s grad=3.703e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=7.767149e-3 s grad=3.307e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.78403e-3 s grad=3.907e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.051412e-3 s grad=3.549e-6 s -[acc-interp] eval 4.5589312e-2 s -[acc-interp] thetaMarsh 9.2e-8 s (n=[acc-interp] thetaMarsh 5.6e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=1.7810824e-2 s grad=3.418e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.414958e-3 s grad=3.407e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.952857e-3 s 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8.805808e-3 s -[acc-interp] thetaMarsh 9.6e-8 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=3.44832e-3 s grad=3.546e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.433592e-3 s grad=1.502e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.280447e-3 s grad=1.524e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.557264e-3 s grad=7.39e-7 s -[acc-interp] eval 1.1430945e-2 s -1) -[acc-interp] chunk base=0 cs=25974 kernel=2.164229e-3 s grad=1.398e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.380587e-3 s grad=9.24e-7 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.137597e-3 s grad=1.127e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.247844e-3 s grad=8.76e-7 s -[acc-interp] eval 7.9134814e-2 s -[acc-interp] thetaMarsh 5.4e-8 s (n=[acc-interp] thetaMarsh 5.4e-8 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=3.2517283e-2 s grad=4.651e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.016748e-3 s grad=1.0866e-5 s -[acc-interp] chunk base=51948 cs=25974 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base=25974 cs=25974 kernel=5.388855e-3 s grad=3.3e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=4.160296e-3 s grad=3.183e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=4.699982e-3 s grad=3.537e-6 s -[acc-interp] eval 5.3734953e-2 s -[acc-interp] thetaMarsh 1.3e-7 s (n=[acc-interp] thetaMarsh 9.3e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=1.9021829e-2 s grad=3.289e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.208717e-3 s grad=1.257e-5 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.764679e-3 s grad=1.379e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.555315e-3 s grad=1.517e-6 s -[acc-interp] eval 3.1438628e-2 s -2) -[acc-interp] chunk base=0 cs=25974 kernel=3.776839e-3 s grad=3.282e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=1.2865611e-2 s grad=3.736e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.524306e-3 s grad=2.631e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.859206e-3 s grad=2.917e-6 s -[acc-interp] eval 5.6043986e-2 s 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chunk base=51948 cs=25974 kernel=1.957251e-3 s grad=4.094e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.069175e-3 s grad=3.455e-6 s -[acc-interp] eval 2.1770411e-2 s -[acc-interp] thetaMarsh 1.14e-7 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=1.235915e-3 s grad=2.788e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.037393e-3 s grad=3.743e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.048178e-3 s grad=3.223e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=1.83997e-3 s grad=2.68e-6 s -[acc-interp] eval 7.993767e-3 s -[acc-interp] thetaMarsh 2.38e-7 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=1.208501e-3 s grad=2.407e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=1.883383e-3 s grad=2.707e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=1.880672e-3 s grad=2.304e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.001853e-3 s grad=2.2e-6 s -[acc-interp] eval 7.766234e-3 s -[acc-interp] thetaMarsh 1.7e-7 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=1.520542e-3 s grad=1.95e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.236775e-3 s grad=1.853e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.103616e-3 s grad=1.512e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.038781e-3 s grad=1.781e-6 s -[acc-interp] eval 8.670691e-3 s -1) -[acc-interp] chunk base=0 cs=25974 kernel=1.382233e-3 s grad=2.037e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.137105e-3 s grad=3.003e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.667975e-3 s grad=3.421e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.26375e-3 s grad=2.748e-6 s -[acc-interp] eval 5.5823239e-2 s -[acc-interp] thetaMarsh 8.9e-8 s (n=[acc-interp] thetaMarsh 5.3e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=1.3429475e-2 s grad=4.243e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.732075e-3 s grad=4.065e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.033594e-3 s grad=2.699e-6 s -[acc-interp] chunk base=77922 cs=25974 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base=77922 cs=25974 kernel=2.805326e-3 s grad=3.817e-6 s -[acc-interp] eval 1.160141e-2 s -[acc-interp] thetaMarsh 5.9e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.389539e-3 s grad=2.556e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.393546e-3 s grad=2.788e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.25634e-3 s grad=2.387e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.201247e-3 s grad=2.425e-6 s -[acc-interp] eval 9.952219e-3 s -2) -[acc-interp] chunk base=0 cs=25974 kernel=2.432068e-3 s grad=2.929e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.224931e-3 s grad=2.364e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.557703e-3 s grad=3.247e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.582006e-3 s grad=3.669e-6 s -[acc-interp] eval 0.148611658 s -[acc-interp] thetaMarsh 1.54e-7 s (n=[acc-interp] thetaMarsh 9.5e-8 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=2.0020577e-2 s grad=3.585e-6 s -[acc-interp] chunk base=25974 cs=25974 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cs=25974 kernel=8.922323e-3 s grad=2.531e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.613267e-3 s grad=2.449e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.600545e-3 s grad=4.263e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.956171e-3 s grad=3.476e-6 s -[acc-interp] eval 1.8795135e-2 s -[acc-interp] thetaMarsh 7.0e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.193174e-3 s grad=2.54e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.137684e-3 s grad=2.107e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.125425e-3 s grad=2.197e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.204384e-3 s grad=2.444e-6 s -[acc-interp] eval 9.417615e-3 s -[acc-interp] thetaMarsh 5.9e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.888153e-3 s grad=3.776e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.33683e-3 s grad=3.825e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.108653e-3 s grad=2.896e-6 s -[acc-interp] chunk base=77922 cs=25974 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-[acc-interp] eval 0.215332204 s -[acc-interp] thetaMarsh 1.44e-7 s (n=[acc-interp] thetaMarsh 1.07e-7 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=2.6255824e-2 s grad=3.732e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.598296e-3 s grad=3.027e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.602867e-3 s grad=3.671e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.410322e-3 s grad=3.325e-6 s -[acc-interp] eval 3.8633397e-2 s -3) -[acc-interp] chunk base=0 cs=25974 kernel=4.077962e-3 s grad=3.904e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.557335e-3 s grad=2.976e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.404237e-3 s grad=3.061e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.074035e-3 s grad=2.921e-6 s -[acc-interp] eval 5.4654157e-2 s -[acc-interp] thetaMarsh 7.2e-8 s (n=[acc-interp] thetaMarsh 7.1e-8 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=2.2214683e-2 s grad=3.359e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.065093e-3 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cs=25974 kernel=3.075895e-3 s grad=3.912e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=9.877321e-3 s grad=4.049e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.956743e-3 s grad=3.51e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.908641e-3 s grad=3.345e-6 s -[acc-interp] eval 5.2038983e-2 s -[acc-interp] thetaMarsh 1.2e-7 s (n=[acc-interp] thetaMarsh 8.0e-8 s (n=7) -[acc-interp] chunk base=0 cs=25974 kernel=1.1428292e-2 s grad=3.35e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.023568e-3 s grad=5.331e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=6.893957e-3 s grad=5.174e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.962508e-3 s grad=4.194e-6 s -[acc-interp] eval 2.722581e-2 s -7) -[acc-interp] chunk base=0 cs=25974 kernel=3.414484e-3 s grad=5.588e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.65722e-3 s grad=4.651e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.182806e-3 s grad=3.431e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.184205e-3 s grad=3.614e-6 s -[acc-interp] eval 4.2519733e-2 s -[acc-interp] thetaMarsh 1.45e-7 s (n=[acc-interp] thetaMarsh 1.1e-7 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=1.0924428e-2 s grad=3.151e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.756507e-3 s grad=2.895e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.698115e-3 s grad=2.416e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.657308e-3 s grad=2.995e-6 s -[acc-interp] eval 2.0593331e-2 s -[acc-interp] thetaMarsh 1.36e-7 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=3.384464e-3 s grad=2.785e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.747256e-3 s grad=3.321e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=4.284716e-3 s grad=3.062e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.647247e-3 s grad=3.057e-6 s -[acc-interp] eval 1.6681122e-2 s -[acc-interp] thetaMarsh 1.47e-7 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=4.167048e-3 s grad=3.776e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=8.320613e-3 s grad=3.692e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.250111e-3 s grad=1.938e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.437371e-3 s grad=3.778e-6 s -[acc-interp] eval 1.8830398e-2 s -[acc-interp] thetaMarsh 1.41e-7 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=2.804079e-3 s grad=3.178e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.995325e-3 s grad=2.214e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.282776e-3 s grad=1.81e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.711246e-3 s grad=7.294e-6 s -[acc-interp] eval 1.224509e-2 s -[acc-interp] thetaMarsh 7.4e-8 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=2.68261e-3 s grad=3.129e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.901785e-3 s grad=3.531e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.547024e-3 s grad=2.65e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.299284e-3 s grad=2.289e-6 s -[acc-interp] eval 1.1932917e-2 s 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-[acc-interp] eval 1.159194e-2 s -[acc-interp] thetaMarsh 9.7e-8 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=2.548056e-3 s grad=3.144e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.372241e-3 s grad=2.625e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.686644e-3 s grad=3.243e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.599632e-3 s grad=2.317e-6 s -[acc-interp] eval 1.1034653e-2 s -1) -[acc-interp] chunk base=0 cs=25974 kernel=2.527443e-3 s grad=4.168e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.655476e-3 s grad=7.853e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.563744e-3 s grad=9.15e-7 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.298445e-3 s grad=9.61e-7 s -[acc-interp] eval 0.194548444 s -[acc-interp] thetaMarsh 5.8e-8 s (n=[acc-interp] thetaMarsh 5.8e-8 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=1.2422884e-2 s grad=3.534e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.243941e-3 s grad=1.961e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.526076e-3 s grad=2.622e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.558968e-3 s grad=4.993e-6 s -[acc-interp] eval 2.3609242e-2 s -4) -[acc-interp] chunk base=0 cs=25974 kernel=2.782431e-3 s grad=9.948e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=7.753722e-3 s grad=2.971e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.64958e-3 s grad=4.781e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.257298e-3 s grad=3.795e-6 s -[acc-interp] eval 4.2183711e-2 s -[acc-interp] thetaMarsh 1.35e-7 s (n=[acc-interp] thetaMarsh 1.14e-7 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=2.8789701e-2 s grad=4.453e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.085544e-3 s grad=4.104e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=8.243156e-3 s grad=3.871e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=4.012641e-3 s grad=3.411e-6 s -[acc-interp] eval 4.6831885e-2 s -3) -[acc-interp] chunk base=0 cs=25974 kernel=4.041929e-3 s grad=3.137e-6 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kernel=4.213827e-3 s grad=4.56e-6 s -[acc-interp] eval 3.9767509e-2 s -4) -[acc-interp] chunk base=0 cs=25974 kernel=4.140384e-3 s grad=3.244e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.768994e-3 s grad=2.75e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=4.317015e-3 s grad=4.194e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=4.462573e-3 s grad=4.05e-6 s -[acc-interp] eval 5.8231006e-2 s -[acc-interp] thetaMarsh 1.33e-7 s (n=[acc-interp] thetaMarsh 1.08e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.998083e-2 s grad=4.239e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.933985e-3 s grad=3.898e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=8.037803e-3 s grad=4.45e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.948963e-3 s grad=4.591e-6 s -[acc-interp] eval 4.7501606e-2 s -2) -[acc-interp] chunk base=0 cs=25974 kernel=3.690856e-3 s grad=2.425e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.283454e-3 s grad=1.814e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.141203e-3 s grad=3.898e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.056823e-3 s grad=3.158e-6 s -[acc-interp] eval 6.067505e-2 s -[acc-interp] thetaMarsh 6.5e-8 s (n=0) -[acc-interp] chunk base=0 cs=25974 kernel=9.21844e-3 s grad=1.362e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.380717e-3 s grad=4.417e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.656775e-3 s grad=1.305e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.273594e-3 s grad=1.377e-6 s -[acc-interp] eval 1.7502371e-2 s -[acc-interp] thetaMarsh 1.58e-7 s (n=[acc-interp] thetaMarsh 1.14e-7 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=1.4078077e-2 s grad=4.657e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.402204e-3 s grad=3.824e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.287896e-3 s grad=2.465e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.635898e-3 s grad=3.189e-6 s -[acc-interp] eval 2.6123839e-2 s -3) -[acc-interp] chunk base=0 cs=25974 kernel=2.79872e-3 s grad=2.526e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.336211e-3 s grad=4.521e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.329994e-3 s grad=2.807e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.608635e-3 s grad=9.285e-6 s -[acc-interp] eval 3.9750993e-2 s -[acc-interp] thetaMarsh 6.0e-8 s (n=[acc-interp] thetaMarsh 5.5e-8 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=9.763698e-3 s grad=4.219e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.355416e-3 s grad=2.85e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=4.478419e-3 s grad=6.074e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.558772e-3 s grad=2.547e-6 s -[acc-interp] eval 2.3115334e-2 s -1) -[acc-interp] chunk base=0 cs=25974 kernel=4.314951e-3 s grad=3.051e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.724852e-3 s grad=1.308e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.244826e-3 s grad=6.73e-7 s -[acc-interp] chunk base=77922 cs=25974 kernel=8.693114e-3 s grad=2.119e-6 s -[acc-interp] eval 4.3960755e-2 s -[acc-interp] thetaMarsh 7.0e-8 s (n=[acc-interp] thetaMarsh 5.5e-8 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=5.506731e-3 s grad=2.52e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.110303e-3 s grad=2.326e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.232807e-3 s grad=3.414e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.698466e-3 s grad=6.246e-6 s -[acc-interp] eval 1.4745448e-2 s -[acc-interp] thetaMarsh 9.6e-8 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=2.251791e-3 s grad=2.994e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.544162e-3 s grad=2.42e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.085418e-3 s grad=2.599e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.108561e-3 s grad=1.818e-6 s -[acc-interp] eval 9.813304e-3 s -[acc-interp] thetaMarsh 1.11e-7 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=2.321683e-3 s grad=3.195e-6 s -[acc-interp] chunk 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chunk base=77922 cs=25974 kernel=3.865863e-3 s grad=3.996e-6 s -[acc-interp] eval 5.1000987e-2 s -[acc-interp] thetaMarsh 1.6e-7 s (n=[acc-interp] thetaMarsh 1.0e-7 s (n=5) -[acc-interp] chunk base=0 cs=25974 kernel=2.8913969e-2 s grad=5.529e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=8.690835e-3 s grad=3.543e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.678443e-3 s grad=2.831e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=4.229726e-3 s grad=7.046e-6 s -[acc-interp] eval 4.6902893e-2 s -5) -[acc-interp] chunk base=0 cs=25974 kernel=3.930063e-3 s grad=2.304e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.906061e-3 s grad=2.278e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=4.372108e-3 s grad=3.988e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=4.162489e-3 s grad=3.453e-6 s -[acc-interp] eval 6.4606815e-2 s -[acc-interp] thetaMarsh 1.3e-7 s (n=[acc-interp] thetaMarsh 1.25e-7 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=6.743523e-3 s 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eval 1.609469e-2 s -[acc-interp] thetaMarsh 9.9e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.643134e-3 s grad=2.301e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.029668e-3 s grad=3.968e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.439668e-3 s grad=4.106e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.734142e-3 s grad=3.275e-6 s -[acc-interp] eval 1.4472096e-2 s -[acc-interp] thetaMarsh 1.97e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=3.23317e-3 s grad=2.487e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.512807e-3 s grad=1.804e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=4.780031e-3 s grad=5.347e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.583445e-3 s grad=2.814e-6 s -[acc-interp] eval 1.5665839e-2 s -2) -[acc-interp] chunk base=0 cs=25974 kernel=3.147433e-3 s grad=8.603e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.813458e-3 s grad=3.391e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.212197e-3 s grad=3.467e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.54757e-3 s grad=2.15e-6 s -[acc-interp] eval 0.51530764 s -[acc-interp] thetaMarsh 6.0e-8 s (n=[acc-interp] thetaMarsh 5.4e-8 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=2.8162811e-2 s grad=4.435e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.846642e-3 s grad=3.299e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.275594e-3 s grad=3.245e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.116075e-3 s grad=3.328e-6 s -[acc-interp] eval 3.9619305e-2 s -1) -[acc-interp] chunk base=0 cs=25974 kernel=6.248254e-3 s grad=3.04e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.932557e-3 s grad=3.645e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.47409e-3 s grad=3.272e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=4.321734e-3 s grad=4.213e-6 s -[acc-interp] eval 5.896432e-2 s -[acc-interp] thetaMarsh 1.32e-7 s (n=[acc-interp] thetaMarsh 7.9e-8 s (n=3) -[acc-interp] chunk base=0 cs=25974 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base=77922 cs=25974 kernel=2.776265e-3 s grad=3.849e-6 s -[acc-interp] eval 1.0104162e-2 s -[acc-interp] thetaMarsh 8.1e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.351073e-3 s grad=1.0718e-5 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.089403e-3 s grad=8.412e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.031052e-3 s grad=1.361e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.162158e-3 s grad=3.124e-6 s -[acc-interp] eval 9.336694e-3 s -2) -[acc-interp] chunk base=0 cs=25974 kernel=2.632778e-3 s grad=4.011e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.519964e-3 s grad=3.001e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.151642e-3 s grad=5.804e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.139386e-3 s grad=5.417e-6 s -[acc-interp] eval 0.12474793 s -[acc-interp] thetaMarsh 1.98e-7 s (n=[acc-interp] thetaMarsh 6.8e-8 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=1.5430554e-2 s grad=4.005e-6 s -[acc-interp] chunk base=25974 cs=25974 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thetaMarsh 1.3e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.512896e-3 s grad=2.179e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.850601e-3 s grad=1.337e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.680006e-3 s grad=4.184e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.629925e-3 s grad=1.523e-6 s -[acc-interp] eval 1.2480354e-2 s -[acc-interp] thetaMarsh 6.0e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.628607e-3 s grad=8.542e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.631338e-3 s grad=6.771e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.702542e-3 s grad=7.015e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.635527e-3 s grad=2.909e-6 s -[acc-interp] eval 1.3336739e-2 s -[acc-interp] thetaMarsh 6.0e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=8.11617e-3 s grad=2.495e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.30443e-3 s grad=2.432e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.427061e-3 s grad=7.832e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.401756e-3 s grad=3.839e-6 s -[acc-interp] eval 1.6136991e-2 s -[acc-interp] thetaMarsh 1.15e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.625486e-3 s grad=1.1055e-5 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.327741e-3 s grad=1.982e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.447952e-3 s grad=1.162e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.354685e-3 s grad=1.45e-6 s -[acc-interp] eval 1.051213e-2 s -[acc-interp] thetaMarsh 6.6e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.26388e-3 s grad=3.206e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.226567e-3 s grad=3.568e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.693342e-3 s grad=4.526e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.2954e-3 s grad=3.301e-6 s -[acc-interp] eval 1.0322715e-2 s -[acc-interp] thetaMarsh 1.53e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.318688e-3 s grad=2.837e-6 s 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eval 1.072747e-2 s -[acc-interp] thetaMarsh 6.8e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.356485e-3 s grad=2.788e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.384855e-3 s grad=2.745e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.282191e-3 s grad=2.473e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.352226e-3 s grad=2.645e-6 s -[acc-interp] eval 1.0596517e-2 s -[acc-interp] thetaMarsh 1.18e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.597863e-3 s grad=4.66e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.51714e-3 s grad=3.942e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.380645e-3 s grad=3.296e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.363329e-3 s grad=2.838e-6 s -[acc-interp] eval 1.0757855e-2 s -[acc-interp] thetaMarsh 7.6e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.873553e-3 s grad=2.396e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.274097e-3 s grad=3.371e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.752505e-3 s grad=2.678e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.36345e-3 s grad=2.351e-6 s -[acc-interp] eval 1.1077229e-2 s -2) -[acc-interp] chunk base=0 cs=25974 kernel=2.169191e-3 s grad=2.593e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.172016e-3 s grad=7.943e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.244545e-3 s grad=2.215e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.477469e-3 s grad=2.822e-6 s -[acc-interp] eval 0.156894708 s -[acc-interp] thetaMarsh 7.1e-8 s (n=[acc-interp] thetaMarsh 2.89e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=7.340736e-3 s grad=4.764e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.606077e-3 s grad=4.27e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.244347e-3 s grad=4.883e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=4.788147e-3 s grad=4.359e-6 s -[acc-interp] eval 2.0048049e-2 s -2) -[acc-interp] chunk base=0 cs=25974 kernel=2.925818e-3 s grad=2.693e-6 s 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eval 3.0163731e-2 s -[acc-interp] thetaMarsh 7.2e-8 s (n=[acc-interp] thetaMarsh 2.25e-7 s (n=5) -[acc-interp] chunk base=0 cs=25974 kernel=2.9959905e-2 s grad=4.65e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.927186e-3 s grad=4.903e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.841318e-3 s grad=3.935e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.65422e-3 s grad=3.466e-6 s -[acc-interp] eval 4.3222611e-2 s -5) -[acc-interp] chunk base=0 cs=25974 kernel=4.984498e-3 s grad=3.768e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.856003e-3 s grad=3.345e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.756349e-3 s grad=3.044e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=4.834265e-3 s grad=3.158e-6 s -[acc-interp] eval 6.2324338e-2 s -[acc-interp] thetaMarsh 1.41e-7 s (n=[acc-interp] thetaMarsh 1.17e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=7.528911e-3 s grad=3.64e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.259378e-3 s grad=2.152e-6 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chunk base=0 cs=25974 kernel=2.702781e-3 s grad=4.131e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.335055e-3 s grad=2.552e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=1.940727e-3 s grad=2.596e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.753304e-3 s grad=3.667e-6 s -[acc-interp] eval 1.1283118e-2 s -[acc-interp] thetaMarsh 1.05e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.469441e-3 s grad=2.86e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.416839e-3 s grad=2.454e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.270119e-3 s grad=1.809e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.636814e-3 s grad=2.816e-6 s -[acc-interp] eval 1.1227864e-2 s -[acc-interp] thetaMarsh 1.09e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.441534e-3 s grad=3.023e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.707428e-3 s grad=4.2e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.560019e-3 s grad=3.227e-6 s -[acc-interp] chunk base=77922 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chunk base=51948 cs=25974 kernel=2.486867e-3 s grad=2.661e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.75588e-3 s grad=3.753e-6 s -[acc-interp] eval 1.6971753e-2 s -2) -[acc-interp] chunk base=0 cs=25974 kernel=4.637406e-3 s grad=3.658e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.765304e-3 s grad=8.255e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.917178e-3 s grad=1.192e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.343448e-3 s grad=2.588e-6 s -[acc-interp] eval 3.1958645e-2 s -[acc-interp] thetaMarsh 1.35e-7 s (n=[acc-interp] thetaMarsh 9.8e-8 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=1.5606258e-2 s grad=4.05e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.182009e-3 s grad=3.559e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.844948e-3 s grad=1.446e-5 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.072575e-3 s grad=3.365e-6 s -[acc-interp] eval 2.7017757e-2 s -1) -[acc-interp] chunk base=0 cs=25974 kernel=3.388038e-3 s 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s -[acc-interp] eval 7.5651999e-2 s -[acc-interp] thetaMarsh 1.18e-7 s (n=[acc-interp] thetaMarsh 7.8e-8 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=7.121761e-3 s grad=3.875e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.095136e-3 s grad=3.415e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.906238e-3 s grad=5.331e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.671772e-3 s grad=1.291e-6 s -[acc-interp] eval 1.6740203e-2 s -1) -[acc-interp] chunk base=0 cs=25974 kernel=2.826316e-3 s grad=7.208e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.123595e-3 s grad=2.398e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.108668e-3 s grad=2.623e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.208647e-3 s grad=3.45e-6 s -[acc-interp] eval 2.8315636e-2 s -[acc-interp] thetaMarsh 5.6e-8 s (n=[acc-interp] thetaMarsh 5.6e-8 s (n=5) -[acc-interp] chunk base=0 cs=25974 kernel=3.1175857e-2 s grad=4.024e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.851836e-3 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kernel=3.995023e-3 s grad=2.822e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.880388e-3 s grad=1.985e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.304975e-3 s grad=2.312e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=1.8124e-3 s grad=2.498e-6 s -[acc-interp] eval 2.879622e-2 s -[acc-interp] thetaMarsh 1.62e-7 s (n=[acc-interp] thetaMarsh 5.28e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=1.3695519e-2 s grad=4.468e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.195227e-3 s grad=5.105e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=1.972582e-3 s grad=2.182e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=1.977176e-3 s grad=2.39e-6 s -[acc-interp] eval 2.1762069e-2 s -[acc-interp] thetaMarsh 2.25e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.166132e-3 s grad=2.503e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.112857e-3 s grad=3.184e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.079674e-3 s grad=2.165e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.710353e-3 s grad=4.314e-6 s -[acc-interp] eval 1.0606897e-2 s -[acc-interp] thetaMarsh 1.9e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.462614e-3 s grad=3.051e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.25823e-3 s grad=1.895e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.20867e-3 s grad=1.872e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.170934e-3 s grad=2.388e-6 s -[acc-interp] eval 1.0686286e-2 s -[acc-interp] thetaMarsh 1.14e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.108064e-3 s grad=1.665e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.653248e-3 s grad=3.509e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.225659e-3 s grad=2.033e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.406662e-3 s grad=4.652e-6 s -[acc-interp] eval 1.104815e-2 s -[acc-interp] thetaMarsh 1.16e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.31129e-3 s grad=3.963e-6 s -[acc-interp] chunk base=25974 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eval 2.0450083e-2 s -[acc-interp] thetaMarsh 1.2e-7 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=4.441132e-3 s grad=2.146e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.413152e-3 s grad=4.906e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=5.212578e-3 s grad=4.069e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=5.190865e-3 s grad=2.806e-6 s -[acc-interp] eval 2.0807117e-2 s -3) -[acc-interp] chunk base=0 cs=25974 kernel=3.111041e-3 s grad=2.613e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.318227e-3 s grad=3.581e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.564207e-3 s grad=1.288e-5 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.990471e-3 s grad=1.122e-6 s -[acc-interp] eval 0.269146271 s -[acc-interp] thetaMarsh 6.6e-8 s (n=[acc-interp] thetaMarsh 5.8e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=1.6427256e-2 s grad=2.909e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.241024e-3 s grad=8.546e-6 s -[acc-interp] chunk base=51948 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kernel=2.377092e-3 s grad=1.339e-6 s -[acc-interp] eval 2.1583689e-2 s -[acc-interp] thetaMarsh 9.1e-8 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=2.51429e-3 s grad=1.971e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.192098e-3 s grad=1.167e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.90466e-3 s grad=4.008e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.693584e-3 s grad=2.427e-6 s -[acc-interp] eval 1.1546036e-2 s -[acc-interp] thetaMarsh 1.24e-7 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=2.427692e-3 s grad=2.361e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.215784e-3 s grad=1.3218e-5 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.313581e-3 s grad=9.001e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.11559e-3 s grad=3.097e-6 s -[acc-interp] eval 1.0118078e-2 s -[acc-interp] thetaMarsh 1.1e-7 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=2.059771e-3 s grad=3.575e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.259062e-3 s 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chunk base=77922 cs=25974 kernel=2.513428e-3 s grad=2.792e-6 s -[acc-interp] eval 1.1358767e-2 s -[acc-interp] thetaMarsh 9.7e-8 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=2.332973e-3 s grad=1.754e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.466461e-3 s grad=1.976e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.374579e-3 s grad=1.879e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.239682e-3 s grad=1.902e-6 s -[acc-interp] eval 1.0882669e-2 s -[acc-interp] thetaMarsh 1.13e-7 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=2.441917e-3 s grad=1.784e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.254655e-3 s grad=1.889e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.42396e-3 s grad=1.584e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.484012e-3 s grad=4.296e-6 s -[acc-interp] eval 1.1078979e-2 s -[acc-interp] thetaMarsh 1.56e-7 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=3.085236e-3 s grad=4.065e-6 s -[acc-interp] chunk base=25974 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eval 1.3807086e-2 s -[acc-interp] thetaMarsh 1.42e-7 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=2.57983e-3 s grad=2.994e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.234149e-3 s grad=4.289e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.704109e-3 s grad=4.04e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.535366e-3 s grad=3.051e-6 s -[acc-interp] eval 1.2642989e-2 s -3) -[acc-interp] chunk base=0 cs=25974 kernel=2.966577e-3 s grad=3.149e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.505866e-3 s grad=3.345e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.241535e-3 s grad=4.197e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.822169e-3 s grad=3.612e-6 s -[acc-interp] eval 0.291600363 s -[acc-interp] thetaMarsh 1.28e-7 s (n=[acc-interp] thetaMarsh 1.2e-7 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=1.1964289e-2 s grad=3.815e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.292875e-3 s grad=8.14e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.255017e-3 s grad=5.14e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.125884e-3 s grad=9.011e-6 s -[acc-interp] eval 2.2541761e-2 s -4) -[acc-interp] chunk base=0 cs=25974 kernel=1.0000144e-2 s grad=4.175e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.25004e-3 s grad=3.432e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.876283e-3 s grad=3.377e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.311317e-3 s grad=1.0323e-5 s -[acc-interp] eval 4.3092803e-2 s -[acc-interp] thetaMarsh 7.0e-8 s (n=[acc-interp] thetaMarsh 6.0e-8 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=3.0157267e-2 s grad=4.17e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.279523e-3 s grad=2.68e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=8.374571e-3 s grad=4.664e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.486307e-3 s grad=1.626e-6 s -[acc-interp] eval 4.714502e-2 s -3) -[acc-interp] chunk base=0 cs=25974 kernel=3.750217e-3 s grad=1.776e-6 s 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base=77922 cs=25974 kernel=2.744372e-3 s grad=2.58e-6 s -[acc-interp] eval 1.2024162e-2 s -[acc-interp] thetaMarsh 1.24e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.29391e-3 s grad=2.271e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.161744e-3 s grad=2.201e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.824696e-3 s grad=3.75e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.65733e-3 s grad=2.475e-6 s -[acc-interp] eval 1.1530942e-2 s -[acc-interp] thetaMarsh 1.07e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.806419e-3 s grad=2.413e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.548485e-3 s grad=2.887e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.971436e-3 s grad=2.747e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.097977e-3 s grad=3.488e-6 s -[acc-interp] eval 1.3113193e-2 s -[acc-interp] thetaMarsh 1.13e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.55192e-3 s grad=2.515e-6 s -[acc-interp] chunk base=25974 cs=25974 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2.44e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.883881e-3 s grad=2.9e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.590682e-3 s grad=2.904e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.29047e-3 s grad=2.662e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.316961e-3 s grad=2.163e-6 s -[acc-interp] eval 1.1634342e-2 s -[acc-interp] thetaMarsh 6.3e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.486402e-3 s grad=1.1777e-5 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.099784e-3 s grad=6.823e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.050099e-3 s grad=6.224e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.390819e-3 s grad=5.911e-6 s -[acc-interp] eval 9.754689e-3 s -[acc-interp] thetaMarsh 6.2e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.113712e-3 s grad=8.151e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.239761e-3 s grad=5.792e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.615147e-3 s grad=4.06e-6 s 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thetaMarsh 6.3e-8 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=2.244028e-3 s grad=3.975e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.067677e-3 s grad=5.594e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.525133e-3 s grad=3.955e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.991514e-3 s grad=3.426e-6 s -[acc-interp] eval 1.2168264e-2 s -[acc-interp] thetaMarsh 1.2e-7 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=2.564627e-3 s grad=3.4e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.339743e-3 s grad=4.77e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.045472e-3 s grad=3.873e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.458909e-3 s grad=3.65e-6 s -[acc-interp] eval 1.2066961e-2 s -[acc-interp] thetaMarsh 1.55e-7 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=2.524914e-3 s grad=3.016e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.534774e-3 s grad=3.14e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.418874e-3 s 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kernel=2.533099e-3 s grad=2.939e-6 s -[acc-interp] eval 2.6612279e-2 s -[acc-interp] thetaMarsh 1.0e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=9.105064e-3 s grad=2.654e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.45507e-3 s grad=3.084e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.623568e-3 s grad=2.613e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.492235e-3 s grad=3.715e-6 s -[acc-interp] eval 1.8009756e-2 s -[acc-interp] thetaMarsh 9.7e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.659546e-3 s grad=4.772e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.737819e-3 s grad=3.41e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.779733e-3 s grad=9.023e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.468874e-3 s grad=1.111e-6 s -[acc-interp] eval 1.1563883e-2 s -[acc-interp] thetaMarsh 5.9e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.711543e-3 s grad=1.451e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.532543e-3 s 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chunk base=77922 cs=25974 kernel=2.399176e-3 s grad=3.116e-6 s -[acc-interp] eval 2.788738e-2 s -3) -[acc-interp] chunk base=0 cs=25974 kernel=2.771072e-3 s grad=2.664e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.491042e-3 s grad=2.193e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.714896e-3 s grad=4.192e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.009826e-3 s grad=3.898e-6 s -[acc-interp] eval 4.065443e-2 s -[acc-interp] thetaMarsh 1.32e-7 s (n=[acc-interp] thetaMarsh 1.13e-7 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=6.989962e-3 s grad=4.043e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.577803e-3 s grad=3.112e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.943937e-3 s grad=4.886e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.685214e-3 s grad=3.402e-6 s -[acc-interp] eval 1.7966592e-2 s -[acc-interp] thetaMarsh 1.05e-7 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=2.740016e-3 s grad=2.933e-6 s -[acc-interp] chunk base=25974 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thetaMarsh 1.14e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.049352e-3 s grad=3.148e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=1.880702e-3 s grad=2.408e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=1.829644e-3 s grad=2.234e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.020412e-3 s grad=1.943e-6 s -[acc-interp] eval 9.249366e-3 s -[acc-interp] thetaMarsh 9.9e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.059164e-3 s grad=1.962e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.174213e-3 s grad=4.413e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.996796e-3 s grad=2.794e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.028548e-3 s grad=3.142e-6 s -[acc-interp] eval 1.0902596e-2 s -[acc-interp] thetaMarsh 2.89e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=1.979807e-3 s grad=2.549e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=1.823972e-3 s grad=2.096e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=1.719509e-3 s 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s grad=2.272e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=4.189754e-3 s grad=4.748e-6 s -[acc-interp] eval 4.9181182e-2 s -[acc-interp] thetaMarsh 1.12e-7 s (n=[acc-interp] thetaMarsh 5.4e-8 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=1.5359051e-2 s grad=2.41e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.602124e-3 s grad=2.958e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.543174e-3 s grad=2.163e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.829034e-3 s grad=2.459e-6 s -[acc-interp] eval 2.5177445e-2 s -1) -[acc-interp] chunk base=0 cs=25974 kernel=3.402024e-3 s grad=3.724e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.365063e-3 s grad=3.058e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.627768e-3 s grad=2.353e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=6.588448e-3 s grad=3.874e-6 s -[acc-interp] eval 4.2980736e-2 s -[acc-interp] thetaMarsh 1.18e-7 s (n=[acc-interp] thetaMarsh 1.15e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=1.1418643e-2 s grad=3.526e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.592305e-3 s grad=3.593e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.057651e-3 s grad=4.372e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.520687e-3 s grad=3.772e-6 s -[acc-interp] eval 2.1397353e-2 s -[acc-interp] thetaMarsh 1.36e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.30628e-3 s grad=2.846e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.345618e-3 s grad=2.572e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.943253e-3 s grad=2.372e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.425807e-3 s grad=6.29e-6 s -[acc-interp] eval 1.2858063e-2 s -[acc-interp] thetaMarsh 3.61e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=1.2097055e-2 s grad=4.106e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.998142e-3 s grad=4.588e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.210282e-3 s grad=3.867e-6 s -[acc-interp] chunk base=77922 cs=25974 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base=77922 cs=25974 kernel=2.063487e-3 s grad=1.165e-6 s -[acc-interp] eval 1.0040983e-2 s -1) -[acc-interp] chunk base=0 cs=25974 kernel=1.962622e-3 s grad=1.009e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=1.955797e-3 s grad=9.59e-7 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.209e-3 s grad=4.288e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.70708e-3 s grad=1.23e-6 s -[acc-interp] eval 0.220880165 s -[acc-interp] thetaMarsh 6.2e-8 s (n=[acc-interp] thetaMarsh 5.6e-8 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=1.2709977e-2 s grad=3.035e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.415035e-3 s grad=1.352e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.381528e-3 s grad=1.039e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.26555e-3 s grad=1.252e-6 s -[acc-interp] eval 2.05417e-2 s -[acc-interp] thetaMarsh 6.3e-8 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=2.298631e-3 s grad=2.112e-6 s -[acc-interp] chunk base=25974 cs=25974 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thetaMarsh 1.12e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=1.0206582e-2 s grad=4.257e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.300344e-3 s grad=2.987e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.569072e-3 s grad=2.765e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.959197e-3 s grad=2.318e-6 s -[acc-interp] eval 2.1612721e-2 s -[acc-interp] thetaMarsh 1.0e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.717056e-3 s grad=2.228e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.718954e-3 s grad=2.867e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.26966e-3 s grad=2.895e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.86041e-3 s grad=2.651e-6 s -[acc-interp] eval 1.4984677e-2 s -[acc-interp] thetaMarsh 1.09e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.674331e-3 s grad=2.558e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.015167e-3 s grad=2.302e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=8.269704e-3 s grad=3.437e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.116519e-3 s grad=2.841e-6 s -[acc-interp] eval 1.7491962e-2 s -[acc-interp] thetaMarsh 1.05e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.084381e-3 s grad=2.943e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.094176e-3 s grad=2.812e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.773431e-3 s grad=4.773e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.738716e-3 s grad=3.604e-6 s -[acc-interp] eval 1.1381945e-2 s -[acc-interp] thetaMarsh 1.08e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.175076e-3 s grad=2.681e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.15362e-3 s grad=2.957e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=1.99416e-3 s grad=2.454e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.138325e-3 s grad=3.949e-6 s -[acc-interp] eval 1.0026722e-2 s -[acc-interp] thetaMarsh 1.15e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.808046e-3 s grad=3.181e-6 s 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eval 2.9149513e-2 s -1) -[acc-interp] chunk base=0 cs=25974 kernel=3.060525e-3 s grad=2.834e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.901898e-3 s grad=2.392e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.836544e-3 s grad=2.78e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.21244e-3 s grad=3.013e-6 s -[acc-interp] eval 4.3835558e-2 s -[acc-interp] thetaMarsh 6.7e-8 s (n=[acc-interp] thetaMarsh 6.4e-8 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=1.4679974e-2 s grad=3.132e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.300606e-3 s grad=3.961e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.339895e-3 s grad=2.67e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.168567e-3 s grad=2.052e-6 s -[acc-interp] eval 2.409616e-2 s -[acc-interp] thetaMarsh 1.01e-7 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=1.563141e-3 s grad=1.581e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.220683e-3 s grad=2.626e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.126175e-3 s grad=1.84e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.742804e-3 s grad=3.823e-6 s -[acc-interp] eval 1.0272756e-2 s -[acc-interp] thetaMarsh 1.17e-7 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=2.424508e-3 s grad=2.341e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.425023e-3 s grad=2.335e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.295852e-3 s grad=2.162e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.148946e-3 s grad=2.411e-6 s -[acc-interp] eval 1.0960739e-2 s -1) -[acc-interp] chunk base=0 cs=25974 kernel=1.68378e-3 s grad=1.941e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.260899e-3 s grad=1.751e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=1.948004e-3 s grad=1.941e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.00378e-3 s grad=2.019e-6 s -[acc-interp] eval 5.5270083e-2 s -[acc-interp] thetaMarsh 1.23e-7 s (n=[acc-interp] thetaMarsh 1.01e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 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thetaMarsh 1.39e-7 s (n=[acc-interp] thetaMarsh 1.26e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=1.0443143e-2 s grad=4.269e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.045984e-3 s grad=5.424e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.130627e-3 s grad=3.335e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.947378e-3 s grad=2.475e-6 s -[acc-interp] eval 2.1172544e-2 s -[acc-interp] thetaMarsh 1.35e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=3.186597e-3 s grad=3.272e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.494596e-3 s grad=2.453e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.34451e-3 s grad=2.92e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.380198e-3 s grad=2.358e-6 s -[acc-interp] eval 1.1916105e-2 s -[acc-interp] thetaMarsh 9.6e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.791779e-3 s grad=3.094e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.276803e-3 s grad=2.231e-6 s -[acc-interp] chunk base=51948 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base=51948 cs=25974 kernel=1.1401272e-2 s grad=3.93e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.461993e-3 s grad=5.528e-6 s -[acc-interp] eval 2.343818e-2 s -[acc-interp] thetaMarsh 7.2e-8 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=3.760653e-3 s grad=4.276e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.374703e-3 s grad=3.105e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.284264e-3 s grad=2.609e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.231731e-3 s grad=3.055e-6 s -[acc-interp] eval 1.5180566e-2 s -[acc-interp] thetaMarsh 1.15e-7 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=3.414454e-3 s grad=4.061e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.344624e-3 s grad=3.128e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.223234e-3 s grad=2.283e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.270446e-3 s grad=4.357e-6 s -[acc-interp] eval 1.4743487e-2 s -[acc-interp] thetaMarsh 1.13e-7 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=3.649953e-3 s grad=4.061e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.843192e-3 s grad=4.895e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.259645e-3 s grad=3.665e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.319475e-3 s grad=2.727e-6 s -[acc-interp] eval 1.5685738e-2 s -[acc-interp] thetaMarsh 9.6e-8 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=3.502461e-3 s grad=4.141e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.570836e-3 s grad=3.251e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.626121e-3 s grad=3.637e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.42411e-3 s grad=2.785e-6 s -[acc-interp] eval 1.6690997e-2 s -[acc-interp] thetaMarsh 1.01e-7 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=4.253575e-3 s grad=4.83e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.505232e-3 s grad=4.28e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=4.110588e-3 s grad=3.202e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.68006e-3 s grad=3.166e-6 s -[acc-interp] eval 1.8208977e-2 s -[acc-interp] thetaMarsh 1.26e-7 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=4.248318e-3 s grad=3.682e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.748769e-3 s grad=2.524e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.906454e-3 s grad=3.218e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=4.14017e-3 s grad=4.004e-6 s -[acc-interp] eval 1.7499822e-2 s -[acc-interp] thetaMarsh 1.41e-7 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=5.482054e-3 s grad=3.527e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.24849e-3 s grad=4.078e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=4.231174e-3 s grad=3.297e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=5.296772e-3 s grad=4.468e-6 s -[acc-interp] eval 2.0919662e-2 s -[acc-interp] thetaMarsh 1.42e-7 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=1.0734113e-2 s grad=4.15e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.222769e-3 s 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chunk base=77922 cs=25974 kernel=4.168367e-3 s grad=4.776e-6 s -[acc-interp] eval 1.8786048e-2 s -[acc-interp] thetaMarsh 1.37e-7 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=4.057837e-3 s grad=4.424e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.628836e-3 s grad=4.995e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=4.404148e-3 s grad=4.087e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=1.0650182e-2 s grad=4.149e-6 s -[acc-interp] eval 2.5512466e-2 s -[acc-interp] thetaMarsh 1.23e-7 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=3.537632e-3 s grad=3.33e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.330597e-3 s grad=3.818e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.375115e-3 s grad=3.314e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.381728e-3 s grad=2.654e-6 s -[acc-interp] eval 1.5281933e-2 s -[acc-interp] thetaMarsh 1.11e-7 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=3.897657e-3 s grad=3.568e-6 s -[acc-interp] chunk base=25974 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s -[acc-interp] eval 1.6576914e-2 s -4) -[acc-interp] chunk base=0 cs=25974 kernel=3.398577e-3 s grad=3.043e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.034017e-3 s grad=4.376e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=4.977037e-3 s grad=2.76e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=4.464544e-3 s grad=2.372e-6 s -[acc-interp] eval 0.532134783 s -[acc-interp] thetaMarsh 1.41e-7 s (n=[acc-interp] thetaMarsh 1.11e-7 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=3.873922e-2 s grad=3.6e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.71618e-3 s grad=3.461e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.685825e-3 s grad=1.57e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=5.982057e-3 s grad=4.069e-6 s -[acc-interp] eval 5.4318636e-2 s -[acc-interp] thetaMarsh 1.36e-7 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=4.572513e-3 s grad=3.756e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=5.123728e-3 s grad=2.949e-6 s -[acc-interp] chunk 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cs=25974 kernel=3.071616e-3 s grad=3.26e-6 s -[acc-interp] eval 1.692786e-2 s -[acc-interp] thetaMarsh 5.8e-8 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=2.275715e-3 s grad=2.451e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.470595e-3 s grad=3.286e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.732217e-3 s grad=3.851e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.532111e-3 s grad=2.596e-6 s -[acc-interp] eval 1.1771889e-2 s -[acc-interp] thetaMarsh 5.8e-8 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=2.636439e-3 s grad=2.485e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.492771e-3 s grad=2.348e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.661407e-3 s grad=2.761e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.25943e-3 s grad=2.231e-6 s -[acc-interp] eval 1.0786377e-2 s -[acc-interp] thetaMarsh 1.79e-7 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=2.50391e-3 s grad=2.524e-6 s -[acc-interp] chunk base=25974 cs=25974 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thetaMarsh 2.37e-7 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=3.288166e-3 s grad=5.133e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.375774e-3 s grad=4.827e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=4.090848e-3 s grad=3.678e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.668617e-3 s grad=2.583e-6 s -[acc-interp] eval 1.4397103e-2 s -[acc-interp] thetaMarsh 2.04e-7 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=3.136767e-3 s grad=4.356e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.551565e-3 s grad=5.717e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.764126e-3 s grad=5.049e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.00497e-3 s grad=4.315e-6 s -[acc-interp] eval 1.3645878e-2 s -[acc-interp] thetaMarsh 1.21e-7 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=2.968467e-3 s grad=1.682e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.916444e-3 s grad=4.304e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.562563e-3 s 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eval 2.0570001e-2 s -[acc-interp] thetaMarsh 1.27e-7 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=2.434986e-3 s grad=2.735e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.75662e-3 s grad=2.607e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.847451e-3 s grad=2.856e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=1.984816e-3 s grad=2.971e-6 s -[acc-interp] eval 1.1607405e-2 s -[acc-interp] thetaMarsh 1.12e-7 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=2.957016e-3 s grad=1.974e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=5.883567e-3 s grad=3.383e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.760969e-3 s grad=3.226e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.505776e-3 s grad=2.62e-6 s -[acc-interp] eval 1.6799205e-2 s -[acc-interp] thetaMarsh 1.2e-7 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=2.508533e-3 s grad=2.51e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.383545e-3 s grad=2.078e-6 s -[acc-interp] chunk base=51948 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thetaMarsh 7.2e-8 s (n=[acc-interp] thetaMarsh 3.76e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=1.1310718e-2 s grad=3.772e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.67779e-3 s grad=3.219e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.528523e-3 s grad=7.25e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.362192e-3 s grad=1.492e-6 s -[acc-interp] eval 1.9801625e-2 s -[acc-interp] thetaMarsh 6.4e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.794489e-3 s grad=1.784e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.783724e-3 s grad=7.182e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=5.720368e-3 s grad=2.453e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.077537e-3 s grad=1.421e-6 s -[acc-interp] eval 1.4088843e-2 s -[acc-interp] thetaMarsh 6.2e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.303112e-3 s grad=1.1985e-5 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.147441e-3 s grad=2.042e-6 s -[acc-interp] chunk base=51948 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kernel=2.244979e-3 s grad=3.869e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.557223e-3 s grad=3.05e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.206379e-3 s grad=3.066e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.340045e-3 s grad=2.663e-6 s -[acc-interp] eval 1.0495386e-2 s -[acc-interp] thetaMarsh 6.1e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.195896e-3 s grad=2.492e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.063222e-3 s grad=2.326e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.132742e-3 s grad=2.095e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.345229e-3 s grad=4.203e-6 s -[acc-interp] eval 9.689533e-3 s -[acc-interp] thetaMarsh 1.27e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=3.577679e-3 s grad=4.657e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.740174e-3 s grad=2.319e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.347644e-3 s grad=2.395e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.09851e-3 s grad=2.767e-6 s -[acc-interp] eval 1.2607043e-2 s -[acc-interp] thetaMarsh 1.18e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.298354e-3 s grad=3.337e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.954412e-3 s grad=3.264e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.471267e-3 s grad=1.875e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.469417e-3 s grad=2.181e-6 s -[acc-interp] eval 1.1889567e-2 s -2) -[acc-interp] chunk base=0 cs=25974 kernel=2.087263e-3 s grad=3.534e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.021077e-3 s grad=2.213e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=1.895924e-3 s grad=2.714e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.661e-3 s grad=3.935e-6 s -[acc-interp] eval 0.35085293 s -[acc-interp] thetaMarsh 1.76e-7 s (n=[acc-interp] thetaMarsh 1.62e-7 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=9.314421e-3 s grad=3.346e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.254288e-3 s 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kernel=2.419823e-3 s grad=3.943e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.724433e-3 s grad=3.121e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.856312e-3 s grad=4.21e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.31866e-3 s grad=3.771e-6 s -[acc-interp] eval 4.5129113e-2 s -[acc-interp] thetaMarsh 6.9e-8 s (n=[acc-interp] thetaMarsh 5.9e-8 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=2.13805e-2 s grad=3.232e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.845025e-3 s grad=3.295e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.701276e-3 s grad=3.925e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.030128e-3 s grad=3.949e-6 s -[acc-interp] eval 3.1821815e-2 s -3) -[acc-interp] chunk base=0 cs=25974 kernel=3.235165e-3 s grad=2.346e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.760995e-3 s grad=2.861e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.175254e-3 s grad=2.247e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.758321e-3 s grad=1.526e-6 s -[acc-interp] eval 4.4653911e-2 s -[acc-interp] thetaMarsh 6.5e-8 s (n=[acc-interp] thetaMarsh 5.9e-8 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=2.9909831e-2 s grad=3.913e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.144455e-3 s grad=4.521e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.800365e-3 s grad=3.917e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.92348e-3 s grad=4.319e-6 s -[acc-interp] eval 4.2688711e-2 s -3) -[acc-interp] chunk base=0 cs=25974 kernel=5.09546e-3 s grad=3.523e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.603885e-3 s grad=3.533e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=4.999466e-3 s grad=4.081e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=4.656642e-3 s grad=3.012e-6 s -[acc-interp] eval 6.3471374e-2 s -[acc-interp] thetaMarsh 2.05e-7 s (n=[acc-interp] thetaMarsh 1.1e-7 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=1.6932991e-2 s grad=3.851e-6 s -[acc-interp] chunk 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eval 1.1282985e-2 s -[acc-interp] thetaMarsh 6.5e-8 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=2.182282e-3 s grad=3.339e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.221322e-3 s grad=1.462e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.204231e-3 s grad=1.24e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.034337e-3 s grad=9.56e-7 s -[acc-interp] eval 9.414251e-3 s -[acc-interp] thetaMarsh 6.4e-8 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=2.113973e-3 s grad=6.875e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.147743e-3 s grad=1.262e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.176849e-3 s grad=8.07e-7 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.138401e-3 s grad=2.282e-6 s -[acc-interp] eval 9.407298e-3 s -[acc-interp] thetaMarsh 8.0e-8 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=2.333921e-3 s grad=3.914e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.951453e-3 s grad=3.616e-6 s -[acc-interp] chunk base=51948 cs=25974 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base=51948 cs=25974 kernel=4.284832e-3 s grad=3.638e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=4.37074e-3 s grad=2.278e-6 s -[acc-interp] eval 2.0690939e-2 s -2) -[acc-interp] chunk base=0 cs=25974 kernel=1.3687316e-2 s grad=3.039e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.128164e-3 s grad=2.474e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=4.816373e-3 s grad=3.38e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=4.760977e-3 s grad=2.757e-6 s -[acc-interp] eval 6.5545034e-2 s -[acc-interp] thetaMarsh 1.35e-7 s (n=[acc-interp] thetaMarsh 1.11e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=1.2554444e-2 s grad=3.831e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.668688e-3 s grad=2.993e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.580756e-3 s grad=2.441e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.624963e-3 s grad=2.744e-6 s -[acc-interp] eval 2.2116128e-2 s -[acc-interp] thetaMarsh 9.6e-8 s (n=2) -[acc-interp] chunk base=0 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eval 9.539862e-3 s -2) -[acc-interp] chunk base=0 cs=25974 kernel=2.038467e-3 s grad=1.69e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.004072e-3 s grad=1.849e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.08982e-3 s grad=1.966e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.132798e-3 s grad=2.912e-6 s -[acc-interp] eval 0.345238206 s -[acc-interp] thetaMarsh 2.5e-7 s (n=[acc-interp] thetaMarsh 9.7e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=1.564754e-2 s grad=3.43e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.380067e-3 s grad=3.238e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.659656e-3 s grad=3.764e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.869043e-3 s grad=4.151e-6 s -[acc-interp] eval 2.6448775e-2 s -2) -[acc-interp] chunk base=0 cs=25974 kernel=7.516251e-3 s grad=3.877e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.592142e-3 s grad=8.263e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.199378e-3 s grad=1.245e-6 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thetaMarsh 7.0e-8 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=2.926405e-3 s grad=3.724e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.727292e-3 s grad=3.989e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.131786e-3 s grad=3.27e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.974164e-3 s grad=3.681e-6 s -[acc-interp] eval 1.3582054e-2 s -[acc-interp] thetaMarsh 6.6e-8 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=3.406633e-3 s grad=4.991e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.954348e-3 s grad=3.45e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.640644e-3 s grad=8.652e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.164912e-3 s grad=1.759e-6 s -[acc-interp] eval 1.5061237e-2 s -4) -[acc-interp] chunk base=0 cs=25974 kernel=3.188793e-3 s grad=2.469e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.436588e-3 s grad=6.792e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.816624e-3 s grad=1.48e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=4.054343e-3 s grad=2.243e-6 s -[acc-interp] eval 0.541216687 s -[acc-interp] thetaMarsh 2.02e-7 s (n=[acc-interp] thetaMarsh 6.1e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.381572e-2 s grad=4.109e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.135288e-3 s grad=2.913e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.121441e-3 s grad=4.633e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.971382e-3 s grad=4.758e-6 s -[acc-interp] eval 3.3935309e-2 s -2) -[acc-interp] chunk base=0 cs=25974 kernel=2.893144e-3 s grad=3.166e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.891791e-3 s grad=3.595e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.798806e-3 s grad=9.574e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.189273e-3 s grad=1.822e-6 s -[acc-interp] eval 4.6704093e-2 s -[acc-interp] thetaMarsh 7.9e-8 s (n=[acc-interp] thetaMarsh 6.0e-8 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=1.1361859e-2 s grad=4.196e-6 s -[acc-interp] 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thetaMarsh 1.19e-7 s (n=5) -[acc-interp] chunk base=0 cs=25974 kernel=4.758837e-3 s grad=4.401e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.511234e-3 s grad=3.437e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=4.036627e-3 s grad=4.424e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.476412e-3 s grad=2.979e-6 s -[acc-interp] eval 1.7402545e-2 s -[acc-interp] thetaMarsh 1.14e-7 s (n=5) -[acc-interp] chunk base=0 cs=25974 kernel=3.196307e-3 s grad=2.554e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.262809e-3 s grad=2.352e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.330307e-3 s grad=5.526e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.917989e-3 s grad=4.241e-6 s -[acc-interp] eval 1.5314014e-2 s -[acc-interp] thetaMarsh 3.06e-7 s (n=5) -[acc-interp] chunk base=0 cs=25974 kernel=4.660386e-3 s grad=3.791e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=8.64238e-3 s grad=3.708e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.928761e-3 s grad=5.683e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.060801e-3 s grad=4.691e-6 s -[acc-interp] eval 2.1504679e-2 s -5) -[acc-interp] chunk base=0 cs=25974 kernel=2.879209e-3 s grad=3.907e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.809975e-3 s grad=3.591e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=4.099565e-3 s grad=4.782e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.287831e-3 s grad=4.14e-6 s -[acc-interp] eval 0.602414854 s -[acc-interp] thetaMarsh 7.8e-8 s (n=[acc-interp] thetaMarsh 7.6e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.6801586e-2 s grad=2.935e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.027383e-3 s grad=2.975e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.178305e-3 s grad=3.39e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.02654e-3 s grad=3.095e-6 s -[acc-interp] eval 3.7602046e-2 s -2) -[acc-interp] chunk base=0 cs=25974 kernel=3.619725e-3 s grad=3.452e-6 s -[acc-interp] chunk base=25974 cs=25974 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thetaMarsh 1.28e-7 s (n=[acc-interp] thetaMarsh 3.89e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.1633317e-2 s grad=3.589e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.911272e-3 s grad=3.521e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.034579e-3 s grad=2.191e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.744951e-3 s grad=1.116e-6 s -[acc-interp] eval 3.139452e-2 s -2) -[acc-interp] chunk base=0 cs=25974 kernel=2.742324e-3 s grad=2.864e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.616105e-3 s grad=1.489e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.608254e-3 s grad=1.016e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.521429e-3 s grad=4.871e-6 s -[acc-interp] eval 4.2787978e-2 s -[acc-interp] thetaMarsh 6.7e-8 s (n=[acc-interp] thetaMarsh 7.4e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=1.1785076e-2 s grad=3.952e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.729749e-3 s grad=3.841e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=5.295342e-3 s grad=3.293e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.90737e-3 s grad=3.756e-6 s -[acc-interp] eval 2.4318607e-2 s -2) -[acc-interp] chunk base=0 cs=25974 kernel=3.445578e-3 s grad=3.363e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.022967e-3 s grad=3.351e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=4.196013e-3 s grad=3.55e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.492716e-3 s grad=3.257e-6 s -[acc-interp] eval 4.0183367e-2 s -[acc-interp] thetaMarsh 2.73e-7 s (n=[acc-interp] thetaMarsh 5.3e-8 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=1.3206466e-2 s grad=3.9e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.215359e-3 s grad=3.067e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.195802e-3 s grad=3.347e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.88773e-3 s grad=2.602e-6 s -[acc-interp] eval 2.3267188e-2 s -[acc-interp] thetaMarsh 1.04e-7 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=4.372723e-3 s grad=5.152e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.234827e-3 s grad=3.133e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.646859e-3 s grad=3.963e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.007315e-3 s grad=3.552e-6 s -[acc-interp] eval 1.4844714e-2 s -[acc-interp] thetaMarsh 3.33e-7 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=2.591801e-3 s grad=2.187e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.365007e-3 s grad=5.589e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.027619e-3 s grad=3.874e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.919761e-3 s grad=4.151e-6 s -[acc-interp] eval 1.3617073e-2 s -3) -[acc-interp] chunk base=0 cs=25974 kernel=2.438945e-3 s grad=3.419e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.670456e-3 s grad=2.425e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.958027e-3 s grad=5.662e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=4.229255e-3 s grad=4.453e-6 s 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chunk base=77922 cs=25974 kernel=2.019648e-3 s grad=2.739e-6 s -[acc-interp] eval 1.1101758e-2 s -[acc-interp] thetaMarsh 9.6e-8 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=2.074572e-3 s grad=1.997e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.232963e-3 s grad=3.496e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.502399e-3 s grad=3.115e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.396255e-3 s grad=2.576e-6 s -[acc-interp] eval 1.0787568e-2 s -[acc-interp] thetaMarsh 1.09e-7 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=2.328979e-3 s grad=2.285e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.282925e-3 s grad=1.965e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.432111e-3 s grad=1.81e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.783925e-3 s grad=4.101e-6 s -[acc-interp] eval 1.1329061e-2 s -[acc-interp] thetaMarsh 1.58e-7 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=2.489154e-3 s grad=2.832e-6 s -[acc-interp] chunk base=25974 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thetaMarsh 1.26e-7 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=2.212354e-3 s grad=2.446e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.141313e-3 s grad=2.184e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=1.905198e-3 s grad=1.713e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=1.959746e-3 s grad=2.218e-6 s -[acc-interp] eval 9.70604e-3 s -[acc-interp] thetaMarsh 9.8e-8 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=2.434364e-3 s grad=3.409e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.866312e-3 s grad=3.01e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.514626e-3 s grad=1.974e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.469799e-3 s grad=1.911e-6 s -[acc-interp] eval 1.1814201e-2 s -[acc-interp] thetaMarsh 1.0e-7 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=2.172724e-3 s grad=2.253e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.125723e-3 s grad=1.754e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.17936e-3 s 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thetaMarsh 7.7e-8 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=3.195491e-3 s grad=9.213e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.890025e-3 s grad=3.378e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.022107e-3 s grad=3.547e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.254364e-3 s grad=3.571e-6 s -[acc-interp] eval 1.4342624e-2 s -[acc-interp] thetaMarsh 7.4e-8 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=3.253602e-3 s grad=3.577e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.265487e-3 s grad=3.717e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=4.267327e-3 s grad=3.853e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.568662e-3 s grad=1.4284e-5 s -[acc-interp] eval 1.5652125e-2 s -[acc-interp] thetaMarsh 1.12e-7 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=8.697138e-3 s grad=3.652e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.89356e-3 s grad=4.305e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.817827e-3 s 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thetaMarsh 1.08e-7 s (n=[acc-interp] thetaMarsh 1.29e-7 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=3.2306373e-2 s grad=5.144e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.470718e-3 s grad=4.781e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.393732e-3 s grad=3.775e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.351336e-3 s grad=3.27e-6 s -[acc-interp] eval 4.3778828e-2 s -[acc-interp] thetaMarsh 1.07e-7 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=3.159452e-3 s grad=3.287e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.171851e-3 s grad=3.816e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.922444e-3 s grad=5.184e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.563085e-3 s grad=3.264e-6 s -[acc-interp] eval 1.5229005e-2 s -[acc-interp] thetaMarsh 6.6e-8 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=3.623681e-3 s grad=2.783e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.469371e-3 s grad=9.485e-6 s -[acc-interp] chunk 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base=25974 cs=25974 kernel=4.514343e-3 s grad=2.323e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.989534e-3 s grad=2.307e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.596621e-3 s grad=2.199e-6 s -[acc-interp] eval 6.1350363e-2 s -[acc-interp] thetaMarsh 1.06e-7 s (n=[acc-interp] thetaMarsh 6.0e-8 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=1.4843484e-2 s grad=3.069e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.453469e-3 s grad=4.304e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=4.062808e-3 s grad=2.775e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.034286e-3 s grad=2.961e-6 s -[acc-interp] eval 2.6760359e-2 s -4) -[acc-interp] chunk base=0 cs=25974 kernel=3.523645e-3 s grad=3.494e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.446864e-3 s grad=4.872e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=1.1251035e-2 s grad=3.991e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.702862e-3 s grad=3.103e-6 s -[acc-interp] eval 4.9092101e-2 s -[acc-interp] thetaMarsh 1.05e-7 s (n=[acc-interp] thetaMarsh 9.0e-8 s (n=6) -[acc-interp] chunk base=0 cs=25974 kernel=3.1645982e-2 s grad=6.347e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=1.1904381e-2 s grad=3.653e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=5.089317e-3 s grad=4.8e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=5.244199e-3 s grad=5.447e-6 s -[acc-interp] eval 5.5018473e-2 s -6) -[acc-interp] chunk base=0 cs=25974 kernel=4.75061e-3 s grad=1.0282e-5 s -[acc-interp] chunk base=25974 cs=25974 kernel=5.354184e-3 s grad=4.41e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=5.262072e-3 s grad=3.341e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=4.561302e-3 s grad=4.057e-6 s -[acc-interp] eval 7.6406405e-2 s -[acc-interp] thetaMarsh 1.54e-7 s (n=[acc-interp] thetaMarsh 1.12e-7 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=3.682175e-3 s grad=5.049e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.389714e-3 s grad=4.482e-6 s 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kernel=2.273114e-3 s grad=2.436e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.062889e-3 s grad=6.924e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=1.387724e-3 s grad=9.95e-7 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.60079e-3 s grad=3.631e-6 s -[acc-interp] eval 3.9972777e-2 s -[acc-interp] thetaMarsh 6.3e-8 s (n=[acc-interp] thetaMarsh 8.4e-8 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=2.3845041e-2 s grad=4.617e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.245439e-3 s grad=1.1627e-5 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.66505e-3 s grad=3.823e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.277038e-3 s grad=9.277e-6 s -[acc-interp] eval 3.4946514e-2 s -4) -[acc-interp] chunk base=0 cs=25974 kernel=3.243932e-3 s grad=1.0309e-5 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.241349e-3 s grad=9.141e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.436246e-3 s grad=2.461e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=4.654568e-3 s grad=4.218e-6 s -[acc-interp] eval 5.0670871e-2 s -[acc-interp] thetaMarsh 1.06e-7 s (n=[acc-interp] thetaMarsh 8.4e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=1.5490451e-2 s grad=3.462e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.470396e-3 s grad=3.683e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.557995e-3 s grad=2.775e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.407124e-3 s grad=2.5e-6 s -[acc-interp] eval 2.4474645e-2 s -2) -[acc-interp] chunk base=0 cs=25974 kernel=2.368599e-3 s grad=2.452e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.284994e-3 s grad=3.74e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.382962e-3 s grad=3.032e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.061527e-3 s grad=3.069e-6 s -[acc-interp] eval 3.6818013e-2 s -[acc-interp] thetaMarsh 8.7e-8 s (n=[acc-interp] thetaMarsh 6.9e-8 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=3.169795e-3 s grad=2.706e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=1.648803e-3 s grad=2.197e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=1.558891e-3 s grad=3.199e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.283498e-3 s grad=4.406e-6 s -[acc-interp] eval 1.0588449e-2 s -[acc-interp] thetaMarsh 6.0e-8 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=1.207123e-3 s grad=2.098e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=1.946266e-3 s grad=2.411e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=1.696333e-3 s grad=1.602e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=1.328502e-3 s grad=9.2e-7 s -[acc-interp] eval 6.875788e-3 s -[acc-interp] thetaMarsh 6.8e-8 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=1.877573e-3 s grad=8.08e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=1.006924e-3 s grad=7.01e-7 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.048702e-3 s grad=3.087e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=1.35011e-3 s grad=3.728e-6 s -[acc-interp] eval 7.21447e-3 s -1) -[acc-interp] chunk base=0 cs=25974 kernel=2.191052e-3 s grad=2.041e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=1.858918e-3 s grad=1.929e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=1.176271e-3 s grad=1.764e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=1.718844e-3 s grad=1.972e-6 s -[acc-interp] eval 3.3131403e-2 s -[acc-interp] thetaMarsh 6.3e-8 s (n=[acc-interp] thetaMarsh 5.3e-8 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=1.7048021e-2 s grad=3.966e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.725739e-3 s grad=3.051e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.372308e-3 s grad=4.504e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.829625e-3 s grad=4.047e-6 s -[acc-interp] eval 2.683819e-2 s -4) -[acc-interp] chunk base=0 cs=25974 kernel=2.888554e-3 s grad=3.981e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.055084e-3 s grad=4.187e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.506519e-3 s grad=3.344e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.732585e-3 s grad=4.381e-6 s -[acc-interp] eval 4.0280516e-2 s -[acc-interp] thetaMarsh 1.33e-7 s (n=[acc-interp] thetaMarsh 1.31e-7 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=9.833663e-3 s grad=2.965e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.216972e-3 s grad=3.677e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.996193e-3 s grad=4.113e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.624072e-3 s grad=3.123e-6 s -[acc-interp] eval 1.8915813e-2 s -1) -[acc-interp] chunk base=0 cs=25974 kernel=2.343532e-3 s grad=2.237e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.269166e-3 s grad=2.137e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.276658e-3 s grad=2.736e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.860177e-3 s grad=3.502e-6 s -[acc-interp] eval 3.0454401e-2 s -[acc-interp] thetaMarsh 1.34e-7 s (n=[acc-interp] thetaMarsh 1.06e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.1294107e-2 s grad=3.97e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.342214e-3 s grad=2.464e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.436862e-3 s grad=1.76e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.138917e-3 s grad=1.999e-6 s -[acc-interp] eval 2.9805127e-2 s -2) -[acc-interp] chunk base=0 cs=25974 kernel=2.439702e-3 s grad=1.876e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.862425e-3 s grad=2.752e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.503822e-3 s grad=2.245e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.735474e-3 s grad=1.757e-6 s -[acc-interp] eval 4.3137145e-2 s -[acc-interp] thetaMarsh 1.2e-7 s (n=[acc-interp] thetaMarsh 1.01e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=9.492566e-3 s grad=4.212e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.062347e-3 s grad=2.991e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.724269e-3 s grad=3.484e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.818786e-3 s grad=2.308e-6 s -[acc-interp] eval 1.9780207e-2 s -2) -[acc-interp] chunk base=0 cs=25974 kernel=3.407838e-3 s grad=3.761e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.080175e-3 s grad=3.196e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.328861e-3 s grad=2.507e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.602943e-3 s grad=2.714e-6 s -[acc-interp] eval 3.4048524e-2 s -[acc-interp] thetaMarsh 1.15e-7 s (n=[acc-interp] thetaMarsh 1.02e-7 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=1.4442101e-2 s grad=3.797e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.94993e-3 s grad=3.268e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.840668e-3 s grad=3.596e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.511419e-3 s grad=4.6e-6 s -[acc-interp] eval 2.5474723e-2 s -3) -[acc-interp] chunk base=0 cs=25974 kernel=3.081158e-3 s grad=2.827e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.988988e-3 s grad=2.751e-6 s -[acc-interp] chunk base=51948 cs=25974 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base=0 cs=25974 kernel=7.675615e-3 s grad=2.642e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.444888e-3 s grad=3.908e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=4.34296e-3 s grad=4.174e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.828846e-3 s grad=3.813e-6 s -[acc-interp] eval 2.0938375e-2 s -3) -[acc-interp] chunk base=0 cs=25974 kernel=3.56107e-3 s grad=2.498e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.660708e-3 s grad=3.647e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=1.0721895e-2 s grad=4.069e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.018519e-3 s grad=2.565e-6 s -[acc-interp] eval 4.3649595e-2 s -[acc-interp] thetaMarsh 1.24e-7 s (n=0) -[acc-interp] chunk base=0 cs=25974 kernel=3.6658669e-2 s grad=1.687e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.96087e-3 s grad=1.542e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=4.465371e-3 s grad=1.594e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=1.4424992e-2 s grad=1.622e-6 s -[acc-interp] eval 6.1430266e-2 s -[acc-interp] thetaMarsh 8.4e-8 s (n=[acc-interp] thetaMarsh 6.2e-8 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=1.0929946e-2 s grad=4.441e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.962224e-3 s grad=1.4125e-5 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.835412e-3 s grad=2.14e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.855558e-3 s grad=4.143e-6 s -[acc-interp] eval 2.0673558e-2 s -1) -[acc-interp] chunk base=0 cs=25974 kernel=2.922912e-3 s grad=3.721e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.850099e-3 s grad=3.921e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.099354e-3 s grad=2.775e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.507893e-3 s grad=2.578e-6 s -[acc-interp] eval 3.3481339e-2 s -[acc-interp] thetaMarsh 1.3e-7 s (n=[acc-interp] thetaMarsh 3.27e-7 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=4.3445466e-2 s grad=4.091e-6 s -[acc-interp] chunk base=25974 cs=25974 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0.1793407 s -[acc-interp] thetaMarsh 1.28e-7 s (n=[acc-interp] thetaMarsh 9.2e-8 s (n=6) -[acc-interp] chunk base=0 cs=25974 kernel=3.4079431e-2 s grad=5.082e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.974277e-3 s grad=4.872e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.771642e-3 s grad=4.462e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.753396e-3 s grad=3.486e-6 s -[acc-interp] eval 4.6714735e-2 s -6) -[acc-interp] chunk base=0 cs=25974 kernel=4.546438e-3 s grad=4.709e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.131144e-3 s grad=3.609e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.815382e-3 s grad=3.449e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.635282e-3 s grad=4.45e-6 s -[acc-interp] eval 6.4668601e-2 s -[acc-interp] thetaMarsh 1.19e-7 s (n=[acc-interp] thetaMarsh 2.11e-7 s (n=5) -[acc-interp] chunk base=0 cs=25974 kernel=1.1026025e-2 s grad=4.128e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.36085e-3 s grad=4.008e-6 s 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chunk base=77922 cs=25974 kernel=2.476623e-3 s grad=4.879e-6 s -[acc-interp] eval 1.0990784e-2 s -[acc-interp] thetaMarsh 5.9e-8 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=2.322962e-3 s grad=2.163e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=5.015765e-3 s grad=4.847e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.385505e-3 s grad=2.413e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.673989e-3 s grad=5.743e-6 s -[acc-interp] eval 1.4275771e-2 s -[acc-interp] thetaMarsh 5.9e-8 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=2.976458e-3 s grad=8.197e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.91314e-3 s grad=3.883e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.760072e-3 s grad=3.167e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.41261e-3 s grad=2.247e-6 s -[acc-interp] eval 1.3527553e-2 s -[acc-interp] thetaMarsh 1.14e-7 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=2.36137e-3 s grad=1.645e-6 s -[acc-interp] chunk base=25974 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cs=25974 kernel=2.505945e-3 s grad=2.607e-6 s -[acc-interp] eval 1.4097403e-2 s -[acc-interp] thetaMarsh 1.34e-7 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=2.936031e-3 s grad=2.222e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.573295e-3 s grad=3.435e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.811803e-3 s grad=3.357e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.291439e-3 s grad=2.217e-6 s -[acc-interp] eval 1.3307965e-2 s -1) -[acc-interp] chunk base=0 cs=25974 kernel=2.226775e-3 s grad=2.511e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.373185e-3 s grad=2.042e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.373719e-3 s grad=2.224e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.592953e-3 s grad=2.879e-6 s -[acc-interp] eval 0.322617194 s -[acc-interp] thetaMarsh 1.43e-7 s (n=[acc-interp] thetaMarsh 1.06e-7 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=2.4816239e-2 s grad=3.394e-6 s -[acc-interp] chunk base=25974 cs=25974 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thetaMarsh 6.6e-8 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=2.848852e-3 s grad=7.249e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=9.330241e-3 s grad=4.372e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.45533e-3 s grad=3.685e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.698181e-3 s grad=4.935e-6 s -[acc-interp] eval 1.9450282e-2 s -[acc-interp] thetaMarsh 6.9e-8 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=2.925847e-3 s grad=2.064e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.559373e-3 s grad=1.911e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.860046e-3 s grad=3.874e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.955416e-3 s grad=3.539e-6 s -[acc-interp] eval 1.2321836e-2 s -[acc-interp] thetaMarsh 1.05e-7 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=2.982972e-3 s grad=2.698e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.373586e-3 s grad=3.022e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.048523e-3 s 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cs=25974 kernel=3.382013e-3 s grad=4.878e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.191368e-3 s grad=3.228e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.129401e-3 s grad=3.18e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.953371e-3 s grad=3.706e-6 s -[acc-interp] eval 1.4310167e-2 s -[acc-interp] thetaMarsh 1.21e-7 s (n=6) -[acc-interp] chunk base=0 cs=25974 kernel=2.98778e-3 s grad=3.311e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.148435e-3 s grad=4.493e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.700606e-3 s grad=1.1589e-5 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.844944e-3 s grad=1.428e-6 s -[acc-interp] eval 1.2843901e-2 s -[acc-interp] thetaMarsh 2.15e-7 s (n=6) -[acc-interp] chunk base=0 cs=25974 kernel=2.742161e-3 s grad=3.702e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.438522e-3 s grad=2.862e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.544964e-3 s grad=2.759e-6 s -[acc-interp] chunk base=77922 cs=25974 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eval 1.5886264e-2 s -[acc-interp] thetaMarsh 5.9e-8 s (n=6) -[acc-interp] chunk base=0 cs=25974 kernel=3.582812e-3 s grad=2.535e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=6.834637e-3 s grad=1.5735e-5 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.949795e-3 s grad=1.647e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.722955e-3 s grad=1.0924e-5 s -[acc-interp] eval 1.8813877e-2 s -6) -[acc-interp] chunk base=0 cs=25974 kernel=5.046205e-3 s grad=1.2439e-5 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.046747e-3 s grad=1.0675e-5 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.546843e-3 s grad=5.708e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.189448e-3 s grad=8.985e-6 s -[acc-interp] eval 0.501452184 s -[acc-interp] thetaMarsh 6.9e-8 s (n=[acc-interp] thetaMarsh 5.3e-8 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=3.8146427e-2 s grad=3.993e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.341565e-3 s grad=3.644e-6 s -[acc-interp] chunk base=51948 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cs=25974 kernel=4.729001e-3 s grad=1.972e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=1.1730633e-2 s grad=2.974e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.676888e-3 s grad=1.567e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.346572e-3 s grad=2.299e-6 s -[acc-interp] eval 5.908938e-2 s -[acc-interp] thetaMarsh 1.45e-7 s (n=[acc-interp] thetaMarsh 1.06e-7 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=1.8189857e-2 s grad=6.146e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=5.24655e-3 s grad=3.054e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=5.244835e-3 s grad=2.928e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=1.303409e-2 s grad=3.45e-6 s -[acc-interp] eval 4.349406e-2 s -[acc-interp] thetaMarsh 1.39e-7 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=4.853665e-3 s grad=4.892e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=5.539236e-3 s grad=3.27e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=5.466032e-3 s grad=3.812e-6 s 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base=25974 cs=25974 kernel=6.648775e-3 s grad=3.996e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=1.3648335e-2 s grad=5.457e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=6.472415e-3 s grad=6.143e-6 s -[acc-interp] eval 5.8079782e-2 s -6) -[acc-interp] chunk base=0 cs=25974 kernel=5.856207e-3 s grad=4.223e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=5.414585e-3 s grad=3.881e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=9.668947e-3 s grad=4.412e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=5.866371e-3 s grad=3.221e-6 s -[acc-interp] eval 8.679734e-2 s -[acc-interp] thetaMarsh 1.58e-7 s (n=[acc-interp] thetaMarsh 2.05e-7 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=1.4350523e-2 s grad=4.835e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.979939e-3 s grad=6.855e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=5.439698e-3 s grad=3.91e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=8.191036e-3 s grad=2.935e-6 s -[acc-interp] eval 3.4955505e-2 s -3) -[acc-interp] chunk base=0 cs=25974 kernel=6.478501e-3 s grad=2.576e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.551395e-3 s grad=2.945e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.378922e-3 s grad=2.609e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=4.398113e-3 s grad=4.491e-6 s -[acc-interp] eval 5.4617126e-2 s -[acc-interp] thetaMarsh 1.92e-7 s (n=[acc-interp] thetaMarsh 1.25e-7 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=3.5684155e-2 s grad=4.19e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.09098e-3 s grad=3.24e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=4.610968e-3 s grad=4.69e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=1.5663648e-2 s grad=4.756e-6 s -[acc-interp] eval 6.1759433e-2 s -4) -[acc-interp] chunk base=0 cs=25974 kernel=6.021027e-3 s grad=3.471e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=5.146209e-3 s grad=3.223e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=7.217503e-3 s grad=6.31e-6 s 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kernel=1.7466683e-2 s grad=3.964e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=5.535638e-3 s grad=4.486e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=6.494703e-3 s grad=3.909e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=1.4597021e-2 s grad=3.68e-6 s -[acc-interp] eval 4.5908877e-2 s -1) -[acc-interp] chunk base=0 cs=25974 kernel=4.153345e-3 s grad=3.345e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.701409e-3 s grad=3.655e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=4.371668e-3 s grad=2.985e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=4.771103e-3 s grad=3.486e-6 s -[acc-interp] eval 6.5846792e-2 s -[acc-interp] thetaMarsh 2.14e-7 s (n=[acc-interp] thetaMarsh 1.31e-7 s (n=5) -[acc-interp] chunk base=0 cs=25974 kernel=3.0869492e-2 s grad=5.134e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=1.0676562e-2 s grad=4.576e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.99847e-3 s grad=3.073e-6 s -[acc-interp] chunk base=77922 cs=25974 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chunk base=77922 cs=25974 kernel=4.712693e-3 s grad=3.993e-6 s -[acc-interp] eval 2.9469066e-2 s -[acc-interp] thetaMarsh 1.19e-7 s (n=5) -[acc-interp] chunk base=0 cs=25974 kernel=5.161997e-3 s grad=4.277e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.327879e-3 s grad=3.302e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=5.028674e-3 s grad=4.803e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=4.910556e-3 s grad=4.925e-6 s -[acc-interp] eval 2.1258058e-2 s -[acc-interp] thetaMarsh 9.4e-8 s (n=5) -[acc-interp] chunk base=0 cs=25974 kernel=4.316299e-3 s grad=4.572e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.189897e-3 s grad=2.915e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=5.419542e-3 s grad=4.272e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=4.974981e-3 s grad=4.198e-6 s -[acc-interp] eval 2.0594969e-2 s -[acc-interp] thetaMarsh 1.1e-7 s (n=5) -[acc-interp] chunk base=0 cs=25974 kernel=1.138132e-2 s grad=3.593e-6 s -[acc-interp] chunk base=25974 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kernel=4.956728e-3 s grad=4.252e-6 s -[acc-interp] eval 1.9588665e-2 s -[acc-interp] thetaMarsh 8.1e-8 s (n=5) -[acc-interp] chunk base=0 cs=25974 kernel=4.100426e-3 s grad=4.495e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=5.547073e-3 s grad=4.063e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=5.088574e-3 s grad=3.807e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=1.2609109e-2 s grad=4.416e-6 s -[acc-interp] eval 2.8940788e-2 s -[acc-interp] thetaMarsh 1.23e-7 s (n=5) -[acc-interp] chunk base=0 cs=25974 kernel=4.840705e-3 s grad=3.881e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.490823e-3 s grad=5.165e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=4.902369e-3 s grad=4.237e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=4.409888e-3 s grad=4.492e-6 s -[acc-interp] eval 2.0236661e-2 s -5) -[acc-interp] chunk base=0 cs=25974 kernel=4.486045e-3 s grad=3.885e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=5.824973e-3 s grad=4.695e-6 s -[acc-interp] 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eval 1.205119e-2 s -[acc-interp] thetaMarsh 4.56e-7 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=2.432743e-3 s grad=3.551e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.31541e-3 s grad=3.591e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.224633e-3 s grad=3.516e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.679682e-3 s grad=4.245e-6 s -[acc-interp] eval 1.1040134e-2 s -[acc-interp] thetaMarsh 7.1e-8 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=2.714569e-3 s grad=3.742e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.424172e-3 s grad=4.335e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.458212e-3 s grad=5.358e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.880367e-3 s grad=4.262e-6 s -[acc-interp] eval 1.1838969e-2 s -[acc-interp] thetaMarsh 3.02e-7 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=2.278611e-3 s grad=2.451e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.940567e-3 s grad=4.394e-6 s -[acc-interp] chunk base=51948 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kernel=2.051098e-3 s grad=1.0756e-5 s -[acc-interp] chunk base=25974 cs=25974 kernel=1.991603e-3 s grad=1.0562e-5 s -[acc-interp] chunk base=51948 cs=25974 kernel=1.969914e-3 s grad=7.823e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=1.978002e-3 s grad=7.647e-6 s -[acc-interp] eval 9.223377e-3 s -[acc-interp] thetaMarsh 5.4e-8 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=2.052541e-3 s grad=2.112e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.065066e-3 s grad=1.477e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.109756e-3 s grad=4.138e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.379618e-3 s grad=5.706e-6 s -[acc-interp] eval 1.0764153e-2 s -[acc-interp] thetaMarsh 5.9e-8 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=2.456763e-3 s grad=3.89e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.358879e-3 s grad=4.3e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.335876e-3 s grad=4.467e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.558049e-3 s grad=3.596e-6 s -[acc-interp] eval 1.0818074e-2 s -3) -[acc-interp] chunk base=0 cs=25974 kernel=2.764241e-3 s grad=3.896e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.701529e-3 s grad=4.007e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.393575e-3 s grad=2.911e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.60175e-3 s grad=2.625e-6 s -[acc-interp] eval 0.215384776 s -[acc-interp] thetaMarsh 1.31e-7 s (n=[acc-interp] thetaMarsh 1.15e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=7.377119e-3 s grad=4.5e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.676404e-3 s grad=3.239e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.591717e-3 s grad=3.284e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.026306e-3 s grad=3.492e-6 s -[acc-interp] eval 1.8450876e-2 s -2) -[acc-interp] chunk base=0 cs=25974 kernel=3.943178e-3 s grad=3.685e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.949952e-3 s grad=2.942e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.335002e-3 s grad=2.682e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=9.561355e-3 s grad=3.888e-6 s -[acc-interp] eval 4.0165798e-2 s -[acc-interp] thetaMarsh 1.46e-7 s (n=[acc-interp] thetaMarsh 1.18e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=1.3183133e-2 s grad=4.279e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.715507e-3 s grad=2.787e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.601147e-3 s grad=3.778e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.412659e-3 s grad=2.42e-6 s -[acc-interp] eval 2.3534564e-2 s -[acc-interp] thetaMarsh 1.22e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=3.030381e-3 s grad=2.614e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.81392e-3 s grad=3.854e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.192842e-3 s grad=3.081e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.011067e-3 s grad=2.246e-6 s -[acc-interp] eval 1.3680247e-2 s -[acc-interp] thetaMarsh 1.27e-7 s 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eval 1.82488e-2 s -[acc-interp] thetaMarsh 6.4e-8 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=5.300798e-3 s grad=4.656e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.70735e-3 s grad=3.727e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=4.014822e-3 s grad=2.888e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=4.773499e-3 s grad=4.799e-6 s -[acc-interp] eval 1.9667093e-2 s -[acc-interp] thetaMarsh 7.3e-8 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=5.052721e-3 s grad=4.397e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.363644e-3 s grad=3.378e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=4.105918e-3 s grad=4.107e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=4.960229e-3 s grad=3.843e-6 s -[acc-interp] eval 2.0045012e-2 s -[acc-interp] thetaMarsh 9.0e-8 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=4.105559e-3 s grad=2.822e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.556594e-3 s grad=2.827e-6 s -[acc-interp] chunk base=51948 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kernel=4.215352e-3 s grad=2.827e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.311572e-3 s grad=3.253e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.936658e-3 s grad=2.41e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=4.720105e-3 s grad=4.316e-6 s -[acc-interp] eval 1.8603288e-2 s -[acc-interp] thetaMarsh 6.9e-8 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=4.326995e-3 s grad=3.684e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.573578e-3 s grad=3.058e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.864292e-3 s grad=3.918e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=5.467252e-3 s grad=4.077e-6 s -[acc-interp] eval 1.9562663e-2 s -4) -[acc-interp] chunk base=0 cs=25974 kernel=2.781639e-3 s grad=2.918e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.509933e-3 s grad=2.71e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.622287e-3 s grad=2.672e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=4.03463e-3 s grad=4.654e-6 s -[acc-interp] eval 0.263089992 s -[acc-interp] thetaMarsh 1.53e-7 s (n=[acc-interp] thetaMarsh 1.03e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=1.8814994e-2 s grad=2.914e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.383489e-3 s grad=2.615e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.213621e-3 s grad=2.217e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.231763e-3 s grad=2.098e-6 s -[acc-interp] eval 2.7233908e-2 s -[acc-interp] thetaMarsh 1.14e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.740187e-3 s grad=3.23e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.849644e-3 s grad=4.063e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.514378e-3 s grad=2.702e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.571071e-3 s grad=2.83e-6 s -[acc-interp] eval 1.2309465e-2 s -[acc-interp] thetaMarsh 1.0e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.572823e-3 s grad=2.731e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.478e-3 s grad=4.231e-6 s 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base=0 cs=25974 kernel=2.364276e-3 s grad=1.968e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.430138e-3 s grad=3.991e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.147416e-3 s grad=5.197e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.577701e-3 s grad=4.328e-6 s -[acc-interp] eval 1.1821698e-2 s -[acc-interp] thetaMarsh 8.6e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.327816e-3 s grad=3.422e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.354427e-3 s grad=6.277e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.456029e-3 s grad=3.046e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.582373e-3 s grad=5.027e-6 s -[acc-interp] eval 1.065656e-2 s -[acc-interp] thetaMarsh 1.24e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.885756e-3 s grad=3.065e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.84917e-3 s grad=8.793e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.903595e-3 s grad=6.771e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.654903e-3 s grad=5.854e-6 s -[acc-interp] eval 1.2109821e-2 s -[acc-interp] thetaMarsh 7.4e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.709534e-3 s grad=6.85e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.967645e-3 s grad=4.525e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.617256e-3 s grad=2.187e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.474601e-3 s grad=2.202e-6 s -[acc-interp] eval 1.1622812e-2 s -[acc-interp] thetaMarsh 1.95e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.372026e-3 s grad=2.566e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.310164e-3 s grad=9.286e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.362133e-3 s grad=2.986e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.214613e-3 s grad=5.214e-6 s -[acc-interp] eval 1.1227426e-2 s -[acc-interp] thetaMarsh 1.17e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.526222e-3 s grad=7.673e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.44825e-3 s grad=3.655e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.375748e-3 s grad=2.698e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.426124e-3 s grad=1.369e-6 s -[acc-interp] eval 1.0800012e-2 s -[acc-interp] thetaMarsh 2.34e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=3.949674e-3 s grad=4.37e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.918692e-3 s grad=4.31e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.924549e-3 s grad=3.612e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.580807e-3 s grad=2.181e-6 s -[acc-interp] eval 1.3364846e-2 s -2) -[acc-interp] chunk base=0 cs=25974 kernel=2.29935e-3 s grad=9.046e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.432024e-3 s grad=1.579e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.410947e-3 s grad=1.248e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.382862e-3 s grad=1.971e-6 s -[acc-interp] eval 0.166930022 s -[acc-interp] thetaMarsh 7.8e-8 s (n=[acc-interp] thetaMarsh 6.5e-8 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=1.1343898e-2 s grad=5.019e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.761333e-3 s grad=3.769e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.925847e-3 s grad=4.092e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.216823e-3 s grad=3.508e-6 s -[acc-interp] eval 2.2288334e-2 s -4) -[acc-interp] chunk base=0 cs=25974 kernel=2.744254e-3 s grad=3.149e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.385528e-3 s grad=5.58e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=4.165947e-3 s grad=3.376e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=1.1780815e-2 s grad=4.003e-6 s -[acc-interp] eval 4.6363466e-2 s -[acc-interp] thetaMarsh 1.18e-7 s (n=[acc-interp] thetaMarsh 2.71e-7 s (n=5) -[acc-interp] chunk base=0 cs=25974 kernel=2.2623722e-2 s grad=4.396e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.870901e-3 s grad=2.962e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.897872e-3 s 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kernel=4.187542e-3 s grad=3.241e-6 s -[acc-interp] eval 1.5833345e-2 s -[acc-interp] thetaMarsh 1.08e-7 s (n=5) -[acc-interp] chunk base=0 cs=25974 kernel=3.297618e-3 s grad=2.6e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.008672e-3 s grad=2.759e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=4.02733e-3 s grad=3.533e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.067708e-3 s grad=4.869e-6 s -[acc-interp] eval 1.4923082e-2 s -5) -[acc-interp] chunk base=0 cs=25974 kernel=3.411372e-3 s grad=3.169e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=9.756509e-3 s grad=4.579e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.548862e-3 s grad=3.658e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.39115e-3 s grad=3.295e-6 s -[acc-interp] eval 0.691370342 s -[acc-interp] thetaMarsh 1.52e-7 s (n=[acc-interp] thetaMarsh 1.48e-7 s (n=5) -[acc-interp] chunk base=0 cs=25974 kernel=2.1688222e-2 s grad=4.668e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=1.2398238e-2 s 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thetaMarsh 1.05e-7 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=3.65347e-3 s grad=4.096e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.716605e-3 s grad=3.553e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.826068e-3 s grad=4.654e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.946737e-3 s grad=1.901e-6 s -[acc-interp] eval 1.5482369e-2 s -[acc-interp] thetaMarsh 5.7e-8 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=2.945619e-3 s grad=4.964e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.669889e-3 s grad=5.115e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.346281e-3 s grad=3.98e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.589068e-3 s grad=3.842e-6 s -[acc-interp] eval 1.1531499e-2 s -[acc-interp] thetaMarsh 1.99e-7 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=2.862187e-3 s grad=4.032e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.915066e-3 s grad=3.978e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.138475e-3 s 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eval 2.7567849e-2 s -[acc-interp] thetaMarsh 9.4e-8 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=3.023468e-3 s grad=3.747e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.9385e-3 s grad=3.916e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.482864e-3 s grad=3.419e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.179537e-3 s grad=4.471e-6 s -[acc-interp] eval 1.4599699e-2 s -[acc-interp] thetaMarsh 6.9e-8 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=3.175968e-3 s grad=3.692e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.690621e-3 s grad=4.008e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.79726e-3 s grad=4.958e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.450828e-3 s grad=3.961e-6 s -[acc-interp] eval 1.4601648e-2 s -[acc-interp] thetaMarsh 9.5e-8 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=3.40783e-3 s grad=3.143e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.504305e-3 s grad=3.7e-6 s -[acc-interp] chunk base=51948 cs=25974 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base=51948 cs=25974 kernel=3.098806e-3 s grad=2.761e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.369314e-3 s grad=2.789e-6 s -[acc-interp] eval 1.250636e-2 s -1) -[acc-interp] chunk base=0 cs=25974 kernel=1.0267507e-2 s grad=3.968e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.395412e-3 s grad=2.997e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.403876e-3 s grad=2.822e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.527427e-3 s grad=8.542e-6 s -[acc-interp] eval 6.369338e-2 s -[acc-interp] thetaMarsh 7.1e-8 s (n=[acc-interp] thetaMarsh 6.5e-8 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=2.9068193e-2 s grad=4.065e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.243537e-3 s grad=4.584e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.592873e-3 s grad=4.076e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=9.311801e-3 s grad=5.201e-6 s -[acc-interp] eval 4.7180916e-2 s -3) -[acc-interp] chunk base=0 cs=25974 kernel=3.249314e-3 s grad=3.765e-6 s 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cs=25974 kernel=3.854246e-3 s grad=3.711e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.1231e-3 s grad=3.818e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.321913e-3 s grad=2.922e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.555371e-3 s grad=3.572e-6 s -[acc-interp] eval 0.49985346 s -[acc-interp] thetaMarsh 1.4e-7 s (n=[acc-interp] thetaMarsh 1.03e-7 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=3.3218585e-2 s grad=5.176e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.453245e-3 s grad=4.485e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.330239e-3 s grad=4.109e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.268585e-3 s grad=2.687e-6 s -[acc-interp] eval 4.5916102e-2 s -4) -[acc-interp] chunk base=0 cs=25974 kernel=4.170249e-3 s grad=2.754e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.134869e-3 s grad=2.667e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.046881e-3 s grad=2.425e-6 s -[acc-interp] chunk base=77922 cs=25974 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base=25974 cs=25974 kernel=3.062254e-3 s grad=1.2279e-5 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.46351e-3 s grad=1.785e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.599313e-3 s grad=1.218e-6 s -[acc-interp] eval 1.731524e-2 s -[acc-interp] thetaMarsh 6.4e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=3.41252e-3 s grad=1.349e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.039812e-3 s grad=4.847e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.776099e-3 s grad=5.636e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.749619e-3 s grad=4.86e-6 s -[acc-interp] eval 1.4103245e-2 s -[acc-interp] thetaMarsh 8.9e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=1.0739683e-2 s grad=3.794e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.921302e-3 s grad=4.683e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.029894e-3 s grad=3.047e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.396022e-3 s grad=3.88e-6 s -[acc-interp] eval 2.0752686e-2 s -2) -[acc-interp] chunk base=0 cs=25974 kernel=2.465814e-3 s grad=2.51e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.741105e-3 s grad=3.765e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.586813e-3 s grad=3.794e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.465535e-3 s grad=2.724e-6 s -[acc-interp] eval 6.4256635e-2 s -[acc-interp] thetaMarsh 7.3e-8 s (n=[acc-interp] thetaMarsh 6.8e-8 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=2.6316927e-2 s grad=5.719e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=6.879971e-3 s grad=3.534e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=4.716184e-3 s grad=3.545e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=1.2238027e-2 s grad=3.287e-6 s -[acc-interp] eval 5.1828985e-2 s -3) -[acc-interp] chunk base=0 cs=25974 kernel=4.234274e-3 s grad=2.497e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.576916e-3 s grad=3.354e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.388769e-3 s grad=3.326e-6 s 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s -[acc-interp] eval 4.0495527e-2 s -5) -[acc-interp] chunk base=0 cs=25974 kernel=2.871088e-3 s grad=3.828e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.637377e-3 s grad=3.236e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.890973e-3 s grad=1.1751e-5 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.05644e-3 s grad=2.178e-6 s -[acc-interp] eval 5.3184457e-2 s -[acc-interp] thetaMarsh 6.1e-8 s (n=[acc-interp] thetaMarsh 6.6e-8 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=2.0553887e-2 s grad=6.348e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.691416e-3 s grad=2.961e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.57372e-3 s grad=1.8e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.319355e-3 s grad=3.15e-6 s -[acc-interp] eval 3.2026483e-2 s -4) -[acc-interp] chunk base=0 cs=25974 kernel=9.823967e-3 s grad=5.195e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.146529e-3 s grad=4.065e-6 s -[acc-interp] chunk base=51948 cs=25974 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chunk base=77922 cs=25974 kernel=4.467707e-3 s grad=2.752e-6 s -[acc-interp] eval 7.1196122e-2 s -[acc-interp] thetaMarsh 9.1e-8 s (n=[acc-interp] thetaMarsh 6.4e-8 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=1.0907124e-2 s grad=4.067e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.376739e-3 s grad=3.107e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.312436e-3 s grad=2.254e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.378529e-3 s grad=3.107e-6 s -[acc-interp] eval 1.8868598e-2 s -[acc-interp] thetaMarsh 1.14e-7 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=3.480698e-3 s grad=4.591e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.577606e-3 s grad=3.98e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.544361e-3 s grad=3.705e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.529753e-3 s grad=2.516e-6 s -[acc-interp] eval 1.2022654e-2 s -[acc-interp] thetaMarsh 7.3e-8 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=1.0092879e-2 s 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chunk base=77922 cs=25974 kernel=2.788543e-3 s grad=3.239e-6 s -[acc-interp] eval 1.0529886e-2 s -[acc-interp] thetaMarsh 1.73e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.460866e-3 s grad=7.713e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.201072e-3 s grad=3.91e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.942281e-3 s grad=2.973e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.189334e-3 s grad=2.199e-6 s -[acc-interp] eval 1.0633589e-2 s -[acc-interp] thetaMarsh 6.3e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.542882e-3 s grad=3.3e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.602828e-3 s grad=4.896e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.235648e-3 s grad=2.422e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.10757e-3 s grad=3.374e-6 s -[acc-interp] eval 1.0448907e-2 s -[acc-interp] thetaMarsh 7.6e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.052938e-3 s grad=2.686e-6 s -[acc-interp] chunk base=25974 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base=51948 cs=25974 kernel=2.49264e-3 s grad=4.894e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.055804e-3 s grad=3.762e-6 s -[acc-interp] eval 1.023295e-2 s -[acc-interp] thetaMarsh 2.09e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.097387e-3 s grad=3.041e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.428489e-3 s grad=2.801e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.233395e-3 s grad=3.054e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.213203e-3 s grad=2.285e-6 s -[acc-interp] eval 9.845475e-3 s -[acc-interp] thetaMarsh 1.82e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=3.810959e-3 s grad=2.938e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.581004e-3 s grad=2.553e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.577551e-3 s grad=2.943e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.156012e-3 s grad=2.658e-6 s -[acc-interp] eval 1.1997308e-2 s -[acc-interp] thetaMarsh 6.6e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.161128e-3 s grad=2.351e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.225758e-3 s grad=4.405e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.165642e-3 s grad=3.482e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.465349e-3 s grad=6.624e-6 s -[acc-interp] eval 1.1257625e-2 s -[acc-interp] thetaMarsh 7.7e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.547201e-3 s grad=8.26e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.611962e-3 s grad=1.95e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.325234e-3 s grad=5.886e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.497915e-3 s grad=4.405e-6 s -[acc-interp] eval 1.1040542e-2 s -[acc-interp] thetaMarsh 1.64e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.629431e-3 s grad=3.242e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.252254e-3 s grad=2.807e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.403806e-3 s grad=2.182e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.668673e-3 s grad=2.949e-6 s -[acc-interp] eval 1.1561236e-2 s -[acc-interp] thetaMarsh 1.32e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.413493e-3 s grad=2.215e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.298439e-3 s grad=2.073e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.256194e-3 s grad=2.006e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.198649e-3 s grad=2.128e-6 s -[acc-interp] eval 1.0730776e-2 s -[acc-interp] thetaMarsh 1.46e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.147142e-3 s grad=2.335e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.242001e-3 s grad=7.13e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.228291e-3 s grad=2.521e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.281885e-3 s grad=3.313e-6 s -[acc-interp] eval 1.0039788e-2 s -[acc-interp] thetaMarsh 7.4e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=3.065117e-3 s grad=3.865e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.338503e-3 s grad=3.342e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.193584e-3 s grad=3.177e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.134102e-3 s grad=2.448e-6 s -[acc-interp] eval 1.0859777e-2 s -2) -[acc-interp] chunk base=0 cs=25974 kernel=2.09169e-3 s grad=1.862e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.249309e-3 s grad=2.931e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.717986e-3 s grad=3.479e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.354108e-3 s grad=1.645e-6 s -[acc-interp] eval 0.338540459 s -[acc-interp] thetaMarsh 6.9e-8 s (n=[acc-interp] thetaMarsh 6.3e-8 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=2.8458051e-2 s grad=4.921e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=9.651593e-3 s grad=4.055e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=4.553786e-3 s grad=4.369e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=5.115939e-3 s grad=4.784e-6 s -[acc-interp] eval 4.8811932e-2 s -4) -[acc-interp] chunk base=0 cs=25974 kernel=4.336724e-3 s grad=3.738e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.173595e-3 s grad=4.04e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=4.520311e-3 s grad=2.404e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.884214e-3 s grad=3.392e-6 s -[acc-interp] eval 6.7273547e-2 s -[acc-interp] thetaMarsh 1.28e-7 s (n=[acc-interp] thetaMarsh 1.21e-7 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=5.963414e-3 s grad=5.159e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.861564e-3 s grad=5.051e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.995553e-3 s grad=2.841e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.528483e-3 s grad=2.425e-6 s -[acc-interp] eval 1.7297754e-2 s -3) -[acc-interp] chunk base=0 cs=25974 kernel=2.429306e-3 s grad=2.687e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.136466e-3 s grad=5.137e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.546503e-3 s grad=3.159e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.771331e-3 s grad=3.447e-6 s -[acc-interp] eval 3.2191276e-2 s -[acc-interp] thetaMarsh 1.67e-7 s (n=[acc-interp] thetaMarsh 1.35e-7 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=3.4476934e-2 s grad=3.977e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.647145e-3 s grad=3.735e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=6.020933e-3 s grad=4.92e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=8.654335e-3 s grad=4.359e-6 s -[acc-interp] eval 5.4840371e-2 s -3) -[acc-interp] chunk base=0 cs=25974 kernel=5.715351e-3 s grad=4.365e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.234211e-3 s grad=6.368e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.512707e-3 s grad=2.272e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.435918e-3 s grad=3.569e-6 s -[acc-interp] eval 7.2780204e-2 s -[acc-interp] thetaMarsh 7.4e-8 s (n=[acc-interp] thetaMarsh 7.6e-8 s (n=5) -[acc-interp] chunk base=0 cs=25974 kernel=6.847339e-3 s grad=3.797e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.715743e-3 s grad=3.599e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.080219e-3 s grad=4.707e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.754582e-3 s grad=5.803e-6 s -[acc-interp] eval 1.7823059e-2 s -5) -[acc-interp] chunk base=0 cs=25974 kernel=3.832095e-3 s grad=3.564e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.227875e-3 s grad=3.196e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=9.940821e-3 s grad=3.717e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.778683e-3 s grad=2.717e-6 s -[acc-interp] eval 3.9319788e-2 s -[acc-interp] thetaMarsh 8.5e-8 s (n=[acc-interp] thetaMarsh 8.4e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=1.4273862e-2 s grad=3.871e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.980441e-3 s grad=4.562e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.691418e-3 s grad=4.079e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.719674e-3 s grad=3.189e-6 s -[acc-interp] eval 2.4587324e-2 s -[acc-interp] thetaMarsh 8.3e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=3.302182e-3 s grad=4.636e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.414796e-3 s grad=4.295e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.118969e-3 s grad=9.248e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.060423e-3 s grad=7.454e-6 s -[acc-interp] eval 1.3793577e-2 s -[acc-interp] thetaMarsh 8.1e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=3.612262e-3 s grad=5.294e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.872395e-3 s grad=3.618e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=1.5646542e-2 s grad=3.628e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.239277e-3 s grad=9.607e-6 s -[acc-interp] eval 2.7380046e-2 s -[acc-interp] thetaMarsh 6.7e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.745198e-3 s grad=8.997e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.70058e-3 s grad=6.881e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.21055e-3 s grad=4.561e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.929517e-3 s grad=3.975e-6 s -[acc-interp] eval 1.2404078e-2 s -[acc-interp] thetaMarsh 2.05e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.726574e-3 s grad=1.715e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.046973e-3 s grad=7.911e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.742856e-3 s grad=6.276e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.379401e-3 s grad=1.78e-6 s -[acc-interp] eval 1.1646627e-2 s -[acc-interp] thetaMarsh 9.0e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.569548e-3 s grad=8.001e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.461882e-3 s grad=6.878e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.478302e-3 s grad=2.093e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.453152e-3 s grad=1.456e-6 s -[acc-interp] eval 1.0732872e-2 s -[acc-interp] thetaMarsh 1.63e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.343649e-3 s grad=8.952e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.330153e-3 s grad=7.778e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.46182e-3 s grad=9.55e-7 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.262327e-3 s grad=1.453e-6 s -[acc-interp] eval 1.0139387e-2 s -[acc-interp] thetaMarsh 6.7e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=3.099708e-3 s grad=5.263e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.852461e-3 s grad=9.464e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.347737e-3 s grad=1.624e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.421574e-3 s grad=2.833e-6 s -[acc-interp] eval 1.1691833e-2 s -[acc-interp] thetaMarsh 7.6e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.558586e-3 s grad=1.387e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.579656e-3 s grad=4.322e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.738965e-3 s grad=3.397e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.797596e-3 s grad=2.434e-6 s -[acc-interp] eval 1.3048757e-2 s -[acc-interp] thetaMarsh 1.7e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.495693e-3 s grad=2.133e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.116861e-3 s grad=1.974e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.385314e-3 s grad=1.61e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.849999e-3 s grad=5.799e-6 s -[acc-interp] eval 1.1409058e-2 s -[acc-interp] thetaMarsh 1.23e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.525583e-3 s grad=3.39e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.71336e-3 s grad=2.565e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.499944e-3 s grad=3.173e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.156384e-3 s grad=1.983e-6 s -[acc-interp] eval 1.1465217e-2 s -[acc-interp] thetaMarsh 1.29e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.790268e-3 s grad=2.608e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.48786e-3 s grad=2.968e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.19372e-3 s grad=1.871e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.466667e-3 s grad=2.514e-6 s -[acc-interp] eval 1.1489021e-2 s -2) -[acc-interp] chunk base=0 cs=25974 kernel=2.235054e-3 s grad=1.848e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.160087e-3 s grad=1.472e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.368456e-3 s grad=3.113e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.863281e-3 s grad=4.981e-6 s -[acc-interp] eval 0.182213461 s -[acc-interp] thetaMarsh 8.1e-8 s (n=[acc-interp] thetaMarsh 6.3e-8 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=3.7627747e-2 s grad=4.538e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=5.896706e-3 s grad=4.796e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=4.030674e-3 s grad=4.764e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=5.384041e-3 s grad=5.621e-6 s -[acc-interp] eval 5.3885381e-2 s -3) -[acc-interp] chunk base=0 cs=25974 kernel=4.550139e-3 s grad=4.647e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.029626e-3 s grad=4.768e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.847925e-3 s grad=3.653e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=4.384631e-3 s grad=3.555e-6 s -[acc-interp] eval 7.1824689e-2 s -[acc-interp] thetaMarsh 9.2e-8 s (n=[acc-interp] thetaMarsh 6.5e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=1.6575945e-2 s grad=3.448e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.865301e-3 s grad=4.392e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.052214e-3 s grad=5.255e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=4.149634e-3 s grad=4.206e-6 s -[acc-interp] eval 2.8073899e-2 s -2) -[acc-interp] chunk base=0 cs=25974 kernel=8.887132e-3 s grad=3.278e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.111942e-3 s grad=2.669e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.07114e-3 s grad=5.068e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.192383e-3 s grad=2.97e-6 s -[acc-interp] eval 4.7303719e-2 s -[acc-interp] thetaMarsh 1.32e-7 s (n=[acc-interp] thetaMarsh 1.14e-7 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=6.226841e-3 s grad=5.528e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.27501e-3 s grad=5.17e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.9159e-3 s grad=3.442e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.61353e-3 s grad=2.617e-6 s -[acc-interp] eval 1.6402921e-2 s -3) -[acc-interp] chunk base=0 cs=25974 kernel=2.945427e-3 s grad=2.737e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.369464e-3 s grad=2.994e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.552342e-3 s grad=3.664e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.004774e-3 s grad=3.555e-6 s -[acc-interp] eval 3.0022326e-2 s -[acc-interp] thetaMarsh 1.16e-7 s (n=[acc-interp] thetaMarsh 1.32e-7 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=1.1741569e-2 s grad=3.07e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.203603e-3 s grad=2.458e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.13865e-3 s grad=1.203e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.161867e-3 s grad=7.165e-6 s -[acc-interp] eval 1.9217956e-2 s -3) -[acc-interp] chunk base=0 cs=25974 kernel=2.369035e-3 s grad=8.463e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.29335e-3 s grad=1.0247e-5 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.639066e-3 s grad=4.209e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.801047e-3 s grad=4.971e-6 s -[acc-interp] eval 3.042447e-2 s -[acc-interp] thetaMarsh 8.0e-8 s (n=[acc-interp] thetaMarsh 7.4e-8 s (n=5) -[acc-interp] chunk base=0 cs=25974 kernel=1.6006258e-2 s grad=5.548e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.961462e-3 s grad=4.682e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.634339e-3 s grad=8.167e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=4.757886e-3 s grad=4.022e-6 s -[acc-interp] eval 2.9341794e-2 s -5) -[acc-interp] chunk base=0 cs=25974 kernel=4.901295e-3 s grad=4.649e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=1.0995664e-2 s grad=5.627e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.736725e-3 s grad=5.795e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.998412e-3 s grad=5.173e-6 s -[acc-interp] eval 5.4307381e-2 s -[acc-interp] thetaMarsh 7.8e-8 s (n=[acc-interp] thetaMarsh 7.5e-8 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=2.0511002e-2 s grad=3.684e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.872147e-3 s grad=4.699e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=4.208803e-3 s grad=5.042e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.668862e-3 s grad=2.247e-6 s -[acc-interp] eval 3.3118931e-2 s -4) -[acc-interp] chunk base=0 cs=25974 kernel=3.549725e-3 s grad=3.651e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=1.1461606e-2 s grad=4.549e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.31926e-3 s grad=2.442e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.524804e-3 s grad=3.538e-6 s -[acc-interp] eval 5.6511554e-2 s -[acc-interp] thetaMarsh 8.5e-8 s (n=[acc-interp] thetaMarsh 6.4e-8 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=1.7774735e-2 s grad=4.379e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.345995e-3 s grad=3.951e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.173524e-3 s grad=2.83e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.967239e-3 s grad=2.787e-6 s -[acc-interp] eval 2.8550623e-2 s -[acc-interp] thetaMarsh 9.8e-8 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=1.1658469e-2 s grad=5.118e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.165749e-3 s grad=1.111e-5 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.82624e-3 s grad=3.467e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=4.953071e-3 s grad=4.027e-6 s -[acc-interp] eval 2.4538907e-2 s -[acc-interp] thetaMarsh 6.7e-8 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=3.49995e-3 s grad=2.978e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.28486e-3 s grad=4.395e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.327494e-3 s grad=3.952e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=4.148456e-3 s grad=5.934e-6 s -[acc-interp] eval 1.587017e-2 s -[acc-interp] thetaMarsh 1.31e-7 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=3.438502e-3 s grad=4.431e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.37868e-3 s grad=3.938e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.220604e-3 s grad=4.559e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=4.339578e-3 s grad=4.881e-6 s -[acc-interp] eval 1.6084656e-2 s -[acc-interp] thetaMarsh 1.48e-7 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=3.902508e-3 s grad=2.776e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.684844e-3 s grad=2.079e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.668848e-3 s grad=2.078e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=5.59279e-3 s grad=4.721e-6 s -[acc-interp] eval 1.6385002e-2 s -[acc-interp] thetaMarsh 1.05e-7 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=3.061738e-3 s grad=3.378e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.230181e-3 s grad=3.462e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.155239e-3 s grad=2.794e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.07723e-3 s grad=2.844e-6 s -[acc-interp] eval 1.4013897e-2 s -[acc-interp] thetaMarsh 1.34e-7 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=3.059377e-3 s grad=2.8e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.156463e-3 s grad=3.473e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.178137e-3 s grad=1.653e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.489269e-3 s grad=9.922e-6 s -[acc-interp] eval 1.3996787e-2 s -[acc-interp] thetaMarsh 6.9e-8 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=2.997355e-3 s grad=2.342e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.374363e-3 s grad=4.275e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.61039e-3 s grad=6.726e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.440228e-3 s grad=8.093e-6 s -[acc-interp] eval 1.4307459e-2 s -[acc-interp] thetaMarsh 1.19e-7 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=3.331763e-3 s grad=9.468e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.619413e-3 s grad=3.982e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=4.399699e-3 s grad=3.128e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.283963e-3 s grad=1.892e-6 s -[acc-interp] eval 1.5488169e-2 s -[acc-interp] thetaMarsh 1.82e-7 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=3.20586e-3 s grad=4.505e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.133542e-3 s grad=2.858e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.306405e-3 s grad=3.614e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=4.380326e-3 s grad=3.446e-6 s -[acc-interp] eval 1.5390134e-2 s -[acc-interp] thetaMarsh 1.12e-7 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=3.153029e-3 s grad=3.08e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.42685e-3 s grad=3.076e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=1.181476e-2 s grad=3.952e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.074887e-3 s grad=3.071e-6 s -[acc-interp] eval 2.2628829e-2 s -[acc-interp] thetaMarsh 1.76e-7 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=2.998257e-3 s grad=3.181e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.217834e-3 s grad=5.198e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.463104e-3 s grad=6.086e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.624576e-3 s grad=5.105e-6 s -[acc-interp] eval 1.4281282e-2 s -4) -[acc-interp] chunk base=0 cs=25974 kernel=3.436341e-3 s grad=3.504e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.219792e-3 s grad=3.098e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.972592e-3 s grad=3.359e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.30496e-3 s grad=3.776e-6 s -[acc-interp] eval 0.228218623 s -[acc-interp] thetaMarsh 1.34e-7 s (n=[acc-interp] thetaMarsh 1.12e-7 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=2.1945335e-2 s grad=4.646e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=8.627946e-3 s grad=4.695e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.314011e-3 s grad=3.315e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.025149e-3 s grad=4.717e-6 s -[acc-interp] eval 3.8629636e-2 s -4) -[acc-interp] chunk base=0 cs=25974 kernel=2.976772e-3 s grad=2.643e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.895663e-3 s grad=2.676e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.743183e-3 s grad=2.115e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.731206e-3 s grad=1.86e-6 s -[acc-interp] eval 5.1631821e-2 s -[acc-interp] thetaMarsh 1.29e-7 s (n=[acc-interp] thetaMarsh 1.25e-7 s (n=5) -[acc-interp] chunk base=0 cs=25974 kernel=2.4879047e-2 s grad=5.588e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.005894e-3 s grad=4.868e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.710287e-3 s grad=1.1767e-5 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.509048e-3 s grad=2.112e-6 s -[acc-interp] eval 3.7054316e-2 s -5) -[acc-interp] chunk base=0 cs=25974 kernel=1.3617156e-2 s grad=1.6335e-5 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.88841e-3 s grad=5.176e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.941548e-3 s grad=7.088e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.534692e-3 s grad=8.478e-6 s -[acc-interp] eval 6.3144001e-2 s -[acc-interp] thetaMarsh 7.6e-8 s (n=[acc-interp] thetaMarsh 7.1e-8 s (n=5) -[acc-interp] chunk base=0 cs=25974 kernel=2.5156295e-2 s grad=3.481e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.055115e-3 s grad=1.1125e-5 s -[acc-interp] chunk base=51948 cs=25974 kernel=4.500608e-3 s grad=3.004e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=4.08262e-3 s grad=3.954e-6 s -[acc-interp] eval 3.8657826e-2 s -5) -[acc-interp] chunk base=0 cs=25974 kernel=1.4461537e-2 s grad=3.773e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.396974e-3 s grad=3.953e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=4.458945e-3 s grad=3.207e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=4.540164e-3 s grad=2.738e-6 s -[acc-interp] eval 6.755893e-2 s -[acc-interp] thetaMarsh 6.4e-8 s (n=[acc-interp] thetaMarsh 5.8e-8 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=2.7988168e-2 s grad=4.047e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=8.251712e-3 s grad=3.566e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=5.567837e-3 s grad=5.54e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=9.033846e-3 s grad=5.358e-6 s -[acc-interp] eval 5.2665977e-2 s -4) -[acc-interp] chunk base=0 cs=25974 kernel=4.44953e-3 s grad=5.117e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.996704e-3 s grad=3.466e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.825899e-3 s grad=3.917e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=5.175964e-3 s grad=3.639e-6 s -[acc-interp] eval 7.2051993e-2 s -[acc-interp] thetaMarsh 1.58e-7 s (n=[acc-interp] thetaMarsh 1.15e-7 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=3.5632165e-2 s grad=3.931e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.58152e-3 s grad=4.314e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.893644e-3 s grad=3.243e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.42979e-3 s grad=2.271e-6 s -[acc-interp] eval 4.8129873e-2 s -3) -[acc-interp] chunk base=0 cs=25974 kernel=4.67621e-3 s grad=3.918e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.308831e-3 s grad=4.707e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.964072e-3 s grad=3.753e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=4.351272e-3 s grad=4.023e-6 s -[acc-interp] eval 6.746696e-2 s -[acc-interp] thetaMarsh 1.7e-7 s (n=[acc-interp] thetaMarsh 1.15e-7 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=9.278287e-3 s grad=4.813e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.47251e-3 s grad=3.99e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=4.645176e-3 s grad=4.232e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.151409e-3 s grad=3.878e-6 s -[acc-interp] eval 2.2250744e-2 s -3) -[acc-interp] chunk base=0 cs=25974 kernel=3.468285e-3 s grad=3.325e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=1.2507442e-2 s grad=3.055e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.829463e-3 s grad=2.998e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.875648e-3 s grad=2.612e-6 s -[acc-interp] eval 4.593575e-2 s -[acc-interp] thetaMarsh 1.61e-7 s (n=[acc-interp] thetaMarsh 1.19e-7 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=1.3358401e-2 s grad=4.118e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.596192e-3 s grad=3.322e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.305742e-3 s grad=3.198e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.962411e-3 s grad=3.223e-6 s -[acc-interp] eval 2.4789819e-2 s -3) -[acc-interp] chunk base=0 cs=25974 kernel=3.221196e-3 s grad=2.663e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.190487e-3 s grad=2.217e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.971879e-3 s grad=3.629e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=1.2454601e-2 s grad=4.388e-6 s -[acc-interp] eval 4.9535084e-2 s -[acc-interp] thetaMarsh 1.71e-7 s (n=[acc-interp] thetaMarsh 1.14e-7 s (n=4) -[acc-interp] chunk base=0 cs=25974 kernel=2.5134339e-2 s grad=4.509e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.834697e-3 s grad=3.596e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=4.936581e-3 s grad=4.199e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=1.0019296e-2 s grad=3.808e-6 s -[acc-interp] eval 4.5794463e-2 s -4) -[acc-interp] chunk base=0 cs=25974 kernel=3.815042e-3 s grad=4.647e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.299057e-3 s grad=3.554e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.866867e-3 s grad=4.031e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.848968e-3 s grad=3.616e-6 s -[acc-interp] eval 6.3615757e-2 s -[acc-interp] thetaMarsh 1.67e-7 s (n=[acc-interp] thetaMarsh 1.11e-7 s (n=3) -[acc-interp] chunk base=0 cs=25974 kernel=2.2589617e-2 s grad=4.963e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=4.598658e-3 s grad=5.15e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=4.309676e-3 s grad=4.347e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.204847e-3 s grad=3.054e-6 s -[acc-interp] eval 3.646472e-2 s -3) -[acc-interp] chunk base=0 cs=25974 kernel=3.443907e-3 s grad=2.346e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.261488e-3 s grad=1.574e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=9.06136e-3 s grad=2.302e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.819282e-3 s grad=2.098e-6 s -[acc-interp] eval 5.6765133e-2 s -[acc-interp] thetaMarsh 1.37e-7 s (n=[acc-interp] thetaMarsh 1.65e-7 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=4.871987e-3 s grad=3.148e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.220481e-3 s grad=2.591e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.959114e-3 s grad=4.123e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.681126e-3 s grad=3.464e-6 s -[acc-interp] eval 1.5102416e-2 s -1) -[acc-interp] chunk base=0 cs=25974 kernel=1.858743e-3 s grad=2.746e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.536869e-3 s grad=2.565e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.089842e-3 s grad=2.312e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.192978e-3 s grad=3.347e-6 s -[acc-interp] eval 2.532464e-2 s -[acc-interp] thetaMarsh 1.4e-7 s (n=[acc-interp] thetaMarsh 1.54e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=6.167313e-3 s grad=3.887e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.26485e-3 s grad=2.241e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.355256e-3 s grad=1.829e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.41544e-3 s grad=2.853e-6 s -[acc-interp] eval 1.4862062e-2 s 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s grad=3.922e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.118716e-3 s grad=4.709e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.915032e-3 s grad=5.287e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.486931e-3 s grad=3.323e-6 s -[acc-interp] eval 1.8168854e-2 s -2) -[acc-interp] chunk base=0 cs=25974 kernel=2.605803e-3 s grad=3.421e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.514042e-3 s grad=3.139e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=3.01366e-3 s grad=5.122e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=3.066293e-3 s grad=6.271e-6 s -[acc-interp] eval 3.0500625e-2 s -[acc-interp] thetaMarsh 7.7e-8 s (n=[acc-interp] thetaMarsh 7.3e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=9.349202e-3 s grad=4.586e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=3.162104e-3 s grad=4.816e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.79166e-3 s grad=4.202e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.742288e-3 s grad=4.286e-6 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chunk base=51948 cs=25974 kernel=4.292481e-3 s grad=3.001e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=4.233258e-3 s grad=1.997e-6 s -[acc-interp] eval 1.8757422e-2 s -1) -[acc-interp] chunk base=0 cs=25974 kernel=4.645085e-3 s grad=2.307e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.73926e-3 s grad=4.353e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.598853e-3 s grad=2.32e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.392841e-3 s grad=2.606e-6 s -[acc-interp] eval 5.0853643e-2 s -[acc-interp] thetaMarsh 1.37e-7 s (n=[acc-interp] thetaMarsh 1.26e-7 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=4.036499e-3 s grad=2.926e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=1.933253e-3 s grad=1.945e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=1.858044e-3 s grad=3.763e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.756112e-3 s grad=4.688e-6 s -[acc-interp] eval 1.2389255e-2 s -[acc-interp] thetaMarsh 1.02e-7 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=1.68027e-3 s grad=2.674e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=9.12742e-3 s grad=4.446e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.335066e-3 s grad=2.624e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=1.619168e-3 s grad=2.776e-6 s -[acc-interp] eval 1.6248421e-2 s -[acc-interp] thetaMarsh 6.7e-8 s (n=1) -[acc-interp] chunk base=0 cs=25974 kernel=2.200844e-3 s grad=2.862e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=1.760763e-3 s grad=2.838e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.006952e-3 s grad=2.848e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=1.635029e-3 s grad=2.886e-6 s -[acc-interp] eval 8.433157e-3 s -1) -[acc-interp] chunk base=0 cs=25974 kernel=1.865555e-3 s grad=2.531e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=1.905008e-3 s grad=1.498e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=1.43234e-3 s grad=1.639e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=1.838597e-3 s grad=1.843e-6 s 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thetaMarsh 6.9e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=1.718146e-3 s grad=8.588e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=1.975716e-3 s grad=6.686e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=1.851757e-3 s grad=8.36e-7 s -[acc-interp] chunk base=77922 cs=25974 kernel=1.928437e-3 s grad=5.808e-6 s -[acc-interp] eval 8.257808e-3 s -[acc-interp] thetaMarsh 1.6e-7 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=1.899293e-3 s grad=1.862e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.026715e-3 s grad=6.6e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.243162e-3 s grad=3.706e-6 s -[acc-interp] chunk base=77922 cs=25974 kernel=2.670118e-3 s grad=3.691e-6 s -[acc-interp] eval 9.781593e-3 s -[acc-interp] thetaMarsh 7.5e-8 s (n=2) -[acc-interp] chunk base=0 cs=25974 kernel=2.316318e-3 s grad=9.432e-6 s -[acc-interp] chunk base=25974 cs=25974 kernel=2.237153e-3 s grad=7.123e-6 s -[acc-interp] chunk base=51948 cs=25974 kernel=2.323241e-3 s 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(sz+szRgt+1) (Just (\eid -> EBin op eid r)) (parent:mGrandParents) cands l r' <- getBestExpr r pure . Fix $ Bin op l' r' - ENAry op xs -> do + ENAry op m -> do + let xs = expandedList m cs <- mapM canonical xs szs <- mapM getSize cs let totalSz = sum szs @@ -301,7 +302,7 @@ egraphGP dataTrainVals dataTests args = do cs' <- goE cs szs (acc + s) (i + 1) pure (c' : cs') | otherwise = do - c' <- getSubtree (pos - acc - 1) (sz + 1 + (totalSz - s)) (Just (\eid -> ENAry op (replaceAt i eid xs))) (parent:mGrandParents) cands c + c' <- getSubtree (pos - acc - 1) (sz + 1 + (totalSz - s)) (Just (\eid -> ENAry op (imFromList (replaceAt i eid xs)))) (parent:mGrandParents) cands c cs' <- mapM getBestExpr cs pure (c' : cs') exprs <- goE cs szs 0 0 @@ -315,8 +316,8 @@ egraphGP dataTrainVals dataTests args = do rs <- getAllSubClasses r pure (p : (ls <> rs)) EUni _ t -> (p:) <$> getAllSubClasses t - ENAry _ xs -> do xss <- mapM getAllSubClasses xs - pure (p : concat xss) + ENAry _ m -> do xss <- mapM getAllSubClasses (expandedList m) + pure (p : concat xss) _ -> pure [p] mutate p = do sz <- getSize p @@ -376,7 +377,8 @@ egraphGP dataTrainVals dataTests args = do else do l' <- mutAt (pos-1) (sizeLeft-szRgt-1) (Just (\eid -> EBin op eid r)) l r' <- getBestExpr r pure . Fix $ Bin op l' r' - ENAry op xs -> do + ENAry op m -> do + let xs = expandedList m cs <- mapM canonical xs szs <- mapM getSize cs let totalSz = sum szs @@ -387,7 +389,7 @@ egraphGP dataTrainVals dataTests args = do cs' <- goE cs szs (acc + s) (i + 1) pure (c' : cs') | otherwise = do - c' <- mutAt (pos - acc - 1) (sizeLeft - 1 - (totalSz - s)) (Just (\eid -> ENAry op (replaceAt i eid xs))) c + c' <- mutAt (pos - acc - 1) (sizeLeft - 1 - (totalSz - s)) (Just (\eid -> ENAry op (imFromList (replaceAt i eid xs)))) c cs' <- mapM getBestExpr cs pure (c' : cs') exprs <- goE cs szs 0 0 diff --git a/src/eggp/binding.i b/src/eggp/binding.i index 6f1c23d..cb22ad2 100644 --- a/src/eggp/binding.i +++ b/src/eggp/binding.i @@ -15,13 +15,7 @@ char * unsafe_hs_eggp_run( char *dataset, int gens, int nPop, int maxSize, int n } void unsafe_hs_eggp_init(int argc, char **argv) { - int i; - fprintf(stderr, "[eggp_diag] hs_init called with argc=%d\n", argc); - for (i = 0; i < argc; i++) { - fprintf(stderr, "[eggp_diag] argv[%d] = \"%s\"\n", i, argv[i] ? argv[i] : "(null)"); - } hs_init(&argc, &argv); - fprintf(stderr, "[eggp_diag] hs_init returned, argc now=%d\n", argc); } void unsafe_hs_eggp_exit() { diff --git a/st_n1_1.err b/st_n1_1.err deleted file mode 100644 index e69de29..0000000 diff --git a/st_n1_2.err b/st_n1_2.err deleted file mode 100644 index e69de29..0000000 diff --git a/st_n4_1.err b/st_n4_1.err deleted file mode 100644 index e69de29..0000000 diff --git a/st_n4_2.err b/st_n4_2.err deleted file mode 100644 index e69de29..0000000 From 98a96b29bb955bbe976ab4fbc54a76f3fbdf82e6 Mon Sep 17 00:00:00 2001 From: folivetti Date: Mon, 10 Aug 2026 09:17:07 -0300 Subject: [PATCH 130/166] accept Loss loss names (MSE, LOG10, MAE, MAPE, Pinball) in eggp Replace Distribution with Loss in Args and use readLoss to parse the --loss CLI/FFI string, so the MSE/LOG10 losses work alongside the NLL-wrapped distributions. buildLoss now receives the loss directly. --- app/EGGPA.hs | 9 ++++----- src/EGGP.hs | 12 ++++++------ src/Search.hs | 11 +++++------ 3 files changed, 15 insertions(+), 17 deletions(-) diff --git a/app/EGGPA.hs b/app/EGGPA.hs index 3cf1b6f..2390a13 100644 --- a/app/EGGPA.hs +++ b/app/EGGPA.hs @@ -54,7 +54,6 @@ import Data.SRTree.Datasets import System.Environment (getArgs) import System.Exit (ExitCode (..)) -import Text.Read (readMaybe) import Data.Version (showVersion) import Control.Exception (Exception (..), SomeException (..), handle) @@ -91,11 +90,11 @@ opt = Args <*> switch ( long "trace" <> help "print all evaluated expressions.") - <*> option auto + <*> option (maybeReader readLoss) ( long "loss" - <> value LeastSquares + <> value (NLL LeastSquares) <> showDefault - <> help "loss function: LeastSquares, Gaussian, Poisson, Bernoulli.") + <> help "loss function: MSE, LOG10, MAE, MAPE, Pinball, or a distribution (Gaussian, HGaussian, Poisson, Bernoulli, ROXY, LeastSquares).") <*> option auto ( long "opt-iter" <> value 30 @@ -180,7 +179,7 @@ opt = Args eggp_run :: String -> Int -> Int -> Int -> Int -> Double -> Double -> String -> String -> Int -> Int -> Int -> Int -> Int -> Bool -> Bool -> Bool -> String -> String -> String -> Bool -> IO String eggp_run dataset gens nPop maxSize nTournament pc pm nonterminals loss optIter optRepeat nParams folds maxTime simplify trace generational dumpTo loadFrom varnames useFracBayes = - case readMaybe loss of + case readLoss loss of Nothing -> pure $ "Invalid loss function " <> loss Just l -> let arg = Args dataset "" gens maxSize folds trace l optIter optRepeat nParams nPop nTournament pc pm nonterminals dumpTo loadFrom generational simplify maxTime varnames useFracBayes MultiThread in eggp arg diff --git a/src/EGGP.hs b/src/EGGP.hs index 2e79fbd..967350f 100644 --- a/src/EGGP.hs +++ b/src/EGGP.hs @@ -61,7 +61,7 @@ import qualified Data.ByteString.Char8 as B import Paths_eggp (version) import System.Environment (getArgs) import System.Exit (ExitCode (..)) -import Text.Read (readMaybe) + import Data.Version (showVersion) import Control.Exception (Exception (..), SomeException (..), handle) @@ -178,11 +178,11 @@ opt = Args <*> switch ( long "trace" <> help "print all evaluated expressions.") - <*> option auto + <*> option (maybeReader readLoss) ( long "loss" - <> value LeastSquares + <> value (NLL LeastSquares) <> showDefault - <> help "loss function: LeastSquares, Gaussian, Poisson, Bernoulli.") + <> help "loss function: MSE, LOG10, MAE, MAPE, Pinball, or a distribution (Gaussian, HGaussian, Poisson, Bernoulli, ROXY, LeastSquares).") <*> option auto ( long "opt-iter" <> value 30 @@ -267,7 +267,7 @@ opt = Args eggp_run :: String -> Int -> Int -> Int -> Int -> Double -> Double -> String -> String -> Int -> Int -> Int -> Int -> Int -> Bool -> Bool -> Bool -> String -> String -> String -> Bool -> IO String eggp_run dataset gens nPop maxSize nTournament pc pm nonterminals loss optIter optRepeat nParams folds maxTime simplify trace generational dumpTo loadFrom varnames useFracBayes = - case readMaybe loss of + case readLoss loss of Nothing -> pure $ "Invalid loss function " <> loss Just l -> let arg = Args dataset "" gens maxSize folds trace l optIter optRepeat nParams nPop nTournament pc pm nonterminals dumpTo loadFrom generational simplify maxTime varnames useFracBayes MultiThread in eggp arg @@ -290,7 +290,7 @@ eggpWithData args dataTrains' dataTests = do eggp_run_data :: Ptr CDouble -> Ptr CInt -> Int -> Int -> String -> String -> Int -> Int -> Int -> Int -> Double -> Double -> String -> String -> Int -> Int -> Int -> Int -> Int -> Bool -> Bool -> Bool -> String -> String -> String -> Bool -> IO String eggp_run_data dataPtr nrowsPtr ndatasets ncols header params gens nPop maxSize nTournament pc pm nonterminals loss optIter optRepeat nParams folds maxTime simplify trace generational dumpTo loadFrom varnames useFracBayes = - case readMaybe loss of + case readLoss loss of Nothing -> pure $ "Invalid loss function " <> loss Just l -> do dss <- buildDataSets dataPtr nrowsPtr ndatasets ncols header params diff --git a/src/Search.hs b/src/Search.hs index d059c30..c6d4e66 100644 --- a/src/Search.hs +++ b/src/Search.hs @@ -63,7 +63,6 @@ import Foreign.C.String (CString, newCString, withCString, peekCString, peekCASt import Paths_eggp (version) import System.Environment (getArgs) import System.Exit (ExitCode (..)) -import Text.Read (readMaybe) import Data.Version (showVersion) import Control.Exception (Exception (..), SomeException (..), handle) import Data.Time.Clock.POSIX @@ -75,7 +74,7 @@ data Args = Args _maxSize :: Int, _folds :: Int, _trace :: Bool, - _distribution :: Distribution, + _distribution :: Loss, _optIter :: Int, _optRepeat :: Int, _nParams :: Int, @@ -172,7 +171,7 @@ egraphGP dataTrainVals dataTests args = do shouldReparam = _nParams args == -1 skipValEval = _folds args == 1 relabel = if shouldReparam then relabelParams else relabelParamsOrder - terms = if _distribution args == ROXY + terms = if _distribution args == NLL ROXY then (var 0 : params) else [var ix | ix <- [0 .. nFeats-1]] -- <> params uniNonTerms = [t | t <- nonTerms, isUni t] @@ -432,9 +431,9 @@ egraphGP dataTrainVals dataTests args = do f_compl = countNodes best' * log (countUniqueTokens best') -- loss (NLL) - nll_train_ = compileLoss x (buildLoss (NLL distribution) n best') y mYErr theta - nll_val_ = compileLoss x_val (buildLoss (NLL distribution) n_val best') y_val mYErr_val theta - nll_te_ = compileLoss x_te (buildLoss (NLL distribution) n_te best') y_te mYErr_te theta + nll_train_ = compileLoss x (buildLoss distribution n best') y mYErr theta + nll_val_ = compileLoss x_val (buildLoss distribution n_val best') y_val mYErr_val theta + nll_te_ = compileLoss x_te (buildLoss distribution n_te best') y_te mYErr_te theta -- R2 y_mean = V.sum y / n From bf0e3d2d385e578dce498d14b5a3cb9aa992533e Mon Sep 17 00:00:00 2001 From: folivetti Date: Mon, 10 Aug 2026 09:30:07 -0300 Subject: [PATCH 131/166] expose MAE, MAPE, and Pinball losses in the Python EGGP class Adds MAE/MAPE to the accepted loss names and a Pinball loss with a pinball_tau quantile parameter, passed to the Haskell side as "Pinball " via the new loss_arg property. --- src/eggp/__init__.py | 27 ++++++++++++++++++++++----- 1 file changed, 22 insertions(+), 5 deletions(-) diff --git a/src/eggp/__init__.py b/src/eggp/__init__.py index 165e6f9..675d8e1 100644 --- a/src/eggp/__init__.py +++ b/src/eggp/__init__.py @@ -166,14 +166,22 @@ class EGGP(BaseEstimator, RegressorMixin): `recip` is the reciprocal (1/x) `cbrt` is the cubic root - loss : {"MSE", "Gaussian", "Bernoulli", "Poisson"}, default="MSE" + loss : {"MSE", "LOG10", "Gaussian", "Bernoulli", "Poisson", "MAE", "MAPE", "Pinball"}, default="MSE" Loss function used to evaluate the expressions: - MSE (mean squared error) should be used for regression problems. + - LOG10 (mean squared error in log10 space) for regression on log-spaced targets. + - MAE (mean absolute error) for regression robust to outliers. + - MAPE (mean absolute percentage error) for relative-error regression. + - Pinball for quantile regression (the quantile is given by `pinball_tau`). - Gaussian likelihood should be used for regression problem when you want to fit the error term. - Bernoulli likelihood should be used for classification problem. - Poisson likelihood should be used when the data distribution follows a Poisson. + pinball_tau : float, default=0.5 + Quantile to minimize when `loss` is "Pinball". Must be a value + between 0 and 1. + optIter : int, default=50 Number of iterations for the parameter optimization. @@ -224,12 +232,12 @@ class EGGP(BaseEstimator, RegressorMixin): >>> estimator = EGGP(loss="Bernoulli") >>> estimator.fit(X, y) """ - def __init__(self, gen = 100, nPop = 100, maxSize = 15, nTournament = 3, pc = 0.9, pm = 0.3, nonterminals = "add,sub,mul,div", loss = "MSE", optIter = 50, optRepeat = 2, nParams = -1, folds = 1, max_time = -1, simplify = False, trace = False, generational = False, dumpTo = "", loadFrom = "", useFracBayes = False): + def __init__(self, gen = 100, nPop = 100, maxSize = 15, nTournament = 3, pc = 0.9, pm = 0.3, nonterminals = "add,sub,mul,div", loss = "MSE", optIter = 50, optRepeat = 2, nParams = -1, folds = 1, max_time = -1, simplify = False, trace = False, generational = False, dumpTo = "", loadFrom = "", useFracBayes = False, pinball_tau = 0.5): nts = "add,sub,mul,div,power,powerabs,\ aq,abs,sin,cos,tan,sinh,cosh,tanh,\ asin,acos,atan,asinh,acosh,atanh,sqrt,\ sqrtabs,cbrt,square,log,logabs,exp,recip,cube" - losses = ["MSE", "LOG10", "Gaussian", "Bernoulli", "Poisson"] + losses = ["MSE", "LOG10", "Gaussian", "Bernoulli", "Poisson", "MAE", "MAPE", "Pinball"] if gen < 1: raise ValueError('gen should be greater than 1') if nPop < 1: @@ -264,6 +272,8 @@ def __init__(self, gen = 100, nPop = 100, maxSize = 15, nTournament = 3, pc = 0. raise TypeError('max_time must be an integer') if not isinstance(useFracBayes, bool): raise TypeError('useFracBayes must be a boolean') + if pinball_tau <= 0 or pinball_tau >= 1: + raise ValueError('pinball_tau must be a value between 0 and 1') self.gen = gen self.nPop = nPop self.maxSize = maxSize @@ -284,6 +294,13 @@ def __init__(self, gen = 100, nPop = 100, maxSize = 15, nTournament = 3, pc = 0. self.loadFrom = loadFrom self.is_fitted_ = False self.useFracBayes = int(useFracBayes) + self.pinball_tau = pinball_tau + + @property + def loss_arg(self): + if self.loss == "Pinball": + return f"Pinball {self.pinball_tau}" + return self.loss def combine_dataset(self, X, y, Xerr, yerr): ''' Combines the error information into a single dataset. @@ -367,7 +384,7 @@ def fit(self, X, y, Xerr = None, yerr = None): dname = self.get_fname("", header) - csv_data = eggp_run_data(combined, [combined.shape[0]], ",".join(header), dname, self.gen, self.nPop, self.maxSize, self.nTournament, self.pc, self.pm, self.nonterminals, self.loss, self.optIter, self.optRepeat, self.nParams, self.folds, self.max_time, self.simplify, self.trace, self.generational, self.dumpTo, self.loadFrom, varnames, self.useFracBayes) + csv_data = eggp_run_data(combined, [combined.shape[0]], ",".join(header), dname, self.gen, self.nPop, self.maxSize, self.nTournament, self.pc, self.pm, self.nonterminals, self.loss_arg, self.optIter, self.optRepeat, self.nParams, self.folds, self.max_time, self.simplify, self.trace, self.generational, self.dumpTo, self.loadFrom, varnames, self.useFracBayes) if len(csv_data) > 0: csv_io = StringIO(csv_data.strip()) @@ -402,7 +419,7 @@ def fit_mvsr(self, Xs, ys, Xerrs = None, yerrs = None): dname = self.get_fname("", header) csv_data = eggp_run_data(data, nrows, ",".join(header), dname, self.gen, self.nPop, self.maxSize, self.nTournament, self.pc, self.pm, - self.nonterminals, self.loss, self.optIter, self.optRepeat, self.nParams, self.folds, self.max_time, self.simplify, self.trace, self.generational, self.dumpTo, self.loadFrom, varnames, self.useFracBayes) + self.nonterminals, self.loss_arg, self.optIter, self.optRepeat, self.nParams, self.folds, self.max_time, self.simplify, self.trace, self.generational, self.dumpTo, self.loadFrom, varnames, self.useFracBayes) if len(csv_data) > 0: csv_io = StringIO(csv_data.strip()) From 9f60d431c33aab14f7719b77af768db9f2e2790e Mon Sep 17 00:00:00 2001 From: folivetti Date: Tue, 18 Aug 2026 14:04:12 -0300 Subject: [PATCH 132/166] DB support --- app/EGGPA.hs | 26 +++- cabal.project | 1 + eggp.cabal | 6 + src/EGGP.hs | 52 +++++--- src/Search.hs | 179 +++++++++++++++++++++++++++- src/eggp/__init__.py | 19 +-- src/eggp/binding.i | 6 +- src/eggp/typing.py | 2 +- tutorials/db_mode_example.py | 224 +++++++++++++++++++++++++++++++++++ 9 files changed, 481 insertions(+), 34 deletions(-) create mode 100644 tutorials/db_mode_example.py diff --git a/app/EGGPA.hs b/app/EGGPA.hs index 2390a13..7b9d7b9 100644 --- a/app/EGGPA.hs +++ b/app/EGGPA.hs @@ -176,12 +176,32 @@ opt = Args <> value MultiThread <> showDefault <> help "AD backend: MultiThread, SingleThread, or Accelerate." ) + <*> strOption + ( long "db-file" + <> value "" + <> showDefault + <> help "SQLite database file for persistent e-graph (empty = in-memory)." ) + <*> strOption + ( long "db-dataset" + <> value "" + <> showDefault + <> help "Dataset name for DB mode (empty = derive from CSV filename)." ) + <*> option auto + ( long "db-cache-size" + <> value 100000 + <> showDefault + <> help "Max entries in the local fitness cache." ) + <*> option auto + ( long "db-flush-every" + <> value 0 + <> showDefault + <> help "Flush e-graph to DB every N generations (0 = only at end)." ) -eggp_run :: String -> Int -> Int -> Int -> Int -> Double -> Double -> String -> String -> Int -> Int -> Int -> Int -> Int -> Bool -> Bool -> Bool -> String -> String -> String -> Bool -> IO String -eggp_run dataset gens nPop maxSize nTournament pc pm nonterminals loss optIter optRepeat nParams folds maxTime simplify trace generational dumpTo loadFrom varnames useFracBayes = +eggp_run :: String -> Int -> Int -> Int -> Int -> Double -> Double -> String -> String -> Int -> Int -> Int -> Int -> Int -> Bool -> Bool -> Bool -> String -> String -> String -> Bool -> String -> String -> Int -> Int -> IO String +eggp_run dataset gens nPop maxSize nTournament pc pm nonterminals loss optIter optRepeat nParams folds maxTime simplify trace generational dumpTo loadFrom varnames useFracBayes dbFile dbDataset dbCacheSz dbFlushEvery = case readLoss loss of Nothing -> pure $ "Invalid loss function " <> loss - Just l -> let arg = Args dataset "" gens maxSize folds trace l optIter optRepeat nParams nPop nTournament pc pm nonterminals dumpTo loadFrom generational simplify maxTime varnames useFracBayes MultiThread + Just l -> let arg = Args dataset "" gens maxSize folds trace l optIter optRepeat nParams nPop nTournament pc pm nonterminals dumpTo loadFrom generational simplify maxTime varnames useFracBayes MultiThread dbFile dbDataset dbCacheSz dbFlushEvery in eggp arg eggp :: Args -> IO String diff --git a/cabal.project b/cabal.project index 49e8fd2..0bc2c3f 100644 --- a/cabal.project +++ b/cabal.project @@ -1,5 +1,6 @@ packages: . ../srtree + ../srtree-db ../accelerate-llvm-native-debug allow-newer: * diff --git a/eggp.cabal b/eggp.cabal index 372833a..176d498 100755 --- a/eggp.cabal +++ b/eggp.cabal @@ -76,6 +76,9 @@ foreign-library _binding , scheduler >=2.0.0.1 && <3 , split >=0.2.5 && <0.3 , srtree >= 2.0.1.8 + , srtree-db >= 0.1.0.0 + , direct-sqlite >= 2.3 + , text >= 1.2 && < 2.2 , statistics >=0.16.2.1 && <0.17 , transformers >=0.6.1.0 && <0.7 , unliftio >=0.2.10 && <1 @@ -117,6 +120,9 @@ executable eggp , scheduler >=2.0.0.1 && <3 , split >=0.2.5 && <0.3 , srtree >= 2.0.1.8 + , srtree-db >= 0.1.0.0 + , direct-sqlite >= 2.3 + , text >= 1.2 && < 2.2 , statistics >=0.16.2.1 && <0.17 , transformers >=0.6.1.0 && <0.7 , unliftio >=0.2.10 && <1 diff --git a/src/EGGP.hs b/src/EGGP.hs index 967350f..4d48d57 100644 --- a/src/EGGP.hs +++ b/src/EGGP.hs @@ -116,23 +116,25 @@ hs_eggp_main = \ https://arxiv.org/abs/2501.17848\n" <> header "eggp - E-graph Genetic Programming for Symbolic Regression." ) -foreign export ccall hs_eggp_run :: CString -> CInt -> CInt -> CInt -> CInt -> CDouble -> CDouble -> CString -> CString -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CString -> CString -> CString -> CInt -> IO CString +foreign export ccall hs_eggp_run :: CString -> CInt -> CInt -> CInt -> CInt -> CDouble -> CDouble -> CString -> CString -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CString -> CString -> CString -> CInt -> CString -> CString -> CInt -> CInt -> IO CString -hs_eggp_run :: CString -> CInt -> CInt -> CInt -> CInt -> CDouble -> CDouble -> CString -> CString -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CString -> CString -> CString -> CInt -> IO CString -hs_eggp_run dataset gens nPop maxSize nTournament pc pm nonterminals loss optIter optRepeat nParams folds maxTime simplify trace generational dumpTo loadFrom varnames' useFracBayes = do +hs_eggp_run :: CString -> CInt -> CInt -> CInt -> CInt -> CDouble -> CDouble -> CString -> CString -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CString -> CString -> CString -> CInt -> CString -> CString -> CInt -> CInt -> IO CString +hs_eggp_run dataset gens nPop maxSize nTournament pc pm nonterminals loss optIter optRepeat nParams folds maxTime simplify trace generational dumpTo loadFrom varnames' useFracBayes dbFile' dbDataset' dbCacheSz dbFlushEvery = do dataset' <- peekCString dataset nonterminals' <- peekCString nonterminals loss' <- peekCString loss dumpTo' <- peekCString dumpTo loadFrom' <- peekCString loadFrom varnames <- peekCString varnames' - out <- eggp_run dataset' (fromIntegral gens) (fromIntegral nPop) (fromIntegral maxSize) (fromIntegral nTournament) (realToFrac pc) (realToFrac pm) nonterminals' loss' (fromIntegral optIter) (fromIntegral optRepeat) (fromIntegral nParams) (fromIntegral folds) (fromIntegral maxTime) (simplify /= 0) (trace /= 0) (generational /= 0) dumpTo' loadFrom' varnames (useFracBayes /= 0) + dbFile <- peekCString dbFile' + dbDataset <- peekCString dbDataset' + out <- eggp_run dataset' (fromIntegral gens) (fromIntegral nPop) (fromIntegral maxSize) (fromIntegral nTournament) (realToFrac pc) (realToFrac pm) nonterminals' loss' (fromIntegral optIter) (fromIntegral optRepeat) (fromIntegral nParams) (fromIntegral folds) (fromIntegral maxTime) (simplify /= 0) (trace /= 0) (generational /= 0) dumpTo' loadFrom' varnames (useFracBayes /= 0) dbFile dbDataset (fromIntegral dbCacheSz) (fromIntegral dbFlushEvery) newCString out -foreign export ccall hs_eggp_run_data :: Ptr CDouble -> Ptr CInt -> CInt -> CInt -> CString -> CString -> CInt -> CInt -> CInt -> CInt -> CDouble -> CDouble -> CString -> CString -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CString -> CString -> CString -> CInt -> IO CString +foreign export ccall hs_eggp_run_data :: Ptr CDouble -> Ptr CInt -> CInt -> CInt -> CString -> CString -> CInt -> CInt -> CInt -> CInt -> CDouble -> CDouble -> CString -> CString -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CString -> CString -> CString -> CInt -> CString -> CString -> CInt -> CInt -> IO CString -hs_eggp_run_data :: Ptr CDouble -> Ptr CInt -> CInt -> CInt -> CString -> CString -> CInt -> CInt -> CInt -> CInt -> CDouble -> CDouble -> CString -> CString -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CString -> CString -> CString -> CInt -> IO CString -hs_eggp_run_data dataPtr nrowsPtr ndatasets ncols header params gens nPop maxSize nTournament pc pm nonterminals loss optIter optRepeat nParams folds maxTime simplify trace generational dumpTo loadFrom varnames' useFracBayes = do +hs_eggp_run_data :: Ptr CDouble -> Ptr CInt -> CInt -> CInt -> CString -> CString -> CInt -> CInt -> CInt -> CInt -> CDouble -> CDouble -> CString -> CString -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CString -> CString -> CString -> CInt -> CString -> CString -> CInt -> CInt -> IO CString +hs_eggp_run_data dataPtr nrowsPtr ndatasets ncols header params gens nPop maxSize nTournament pc pm nonterminals loss optIter optRepeat nParams folds maxTime simplify trace generational dumpTo loadFrom varnames' useFracBayes dbFile' dbDataset' dbCacheSz dbFlushEvery = do nonterminals' <- peekCString nonterminals loss' <- peekCString loss dumpTo' <- peekCString dumpTo @@ -140,9 +142,11 @@ hs_eggp_run_data dataPtr nrowsPtr ndatasets ncols header params gens nPop maxSiz varnames <- peekCString varnames' header' <- peekCString header params' <- peekCString params + dbFile <- peekCString dbFile' + dbDataset <- peekCString dbDataset' out <- eggp_run_data dataPtr nrowsPtr (fromIntegral ndatasets) (fromIntegral ncols) header' params' (fromIntegral gens) (fromIntegral nPop) (fromIntegral maxSize) (fromIntegral nTournament) (realToFrac pc) (realToFrac pm) nonterminals' loss' - (fromIntegral optIter) (fromIntegral optRepeat) (fromIntegral nParams) (fromIntegral folds) (fromIntegral maxTime) (simplify /= 0) (trace /= 0) (generational /= 0) dumpTo' loadFrom' varnames (useFracBayes /= 0) + (fromIntegral optIter) (fromIntegral optRepeat) (fromIntegral nParams) (fromIntegral folds) (fromIntegral maxTime) (simplify /= 0) (trace /= 0) (generational /= 0) dumpTo' loadFrom' varnames (useFracBayes /= 0) dbFile dbDataset (fromIntegral dbCacheSz) (fromIntegral dbFlushEvery) newCString out opt :: Parser Args @@ -264,12 +268,32 @@ opt = Args <> value MultiThread <> showDefault <> help "AD backend: MultiThread, SingleThread, or Accelerate." ) + <*> strOption + ( long "db-file" + <> value "" + <> showDefault + <> help "SQLite database file for persistent e-graph (empty = in-memory)." ) + <*> strOption + ( long "db-dataset" + <> value "" + <> showDefault + <> help "Dataset name for DB mode (empty = derive from CSV filename)." ) + <*> option auto + ( long "db-cache-size" + <> value 100000 + <> showDefault + <> help "Max entries in the local fitness cache." ) + <*> option auto + ( long "db-flush-every" + <> value 0 + <> showDefault + <> help "Flush e-graph to DB every N generations (0 = only at end)." ) -eggp_run :: String -> Int -> Int -> Int -> Int -> Double -> Double -> String -> String -> Int -> Int -> Int -> Int -> Int -> Bool -> Bool -> Bool -> String -> String -> String -> Bool -> IO String -eggp_run dataset gens nPop maxSize nTournament pc pm nonterminals loss optIter optRepeat nParams folds maxTime simplify trace generational dumpTo loadFrom varnames useFracBayes = +eggp_run :: String -> Int -> Int -> Int -> Int -> Double -> Double -> String -> String -> Int -> Int -> Int -> Int -> Int -> Bool -> Bool -> Bool -> String -> String -> String -> Bool -> String -> String -> Int -> Int -> IO String +eggp_run dataset gens nPop maxSize nTournament pc pm nonterminals loss optIter optRepeat nParams folds maxTime simplify trace generational dumpTo loadFrom varnames useFracBayes dbFile dbDataset dbCacheSz dbFlushEvery = case readLoss loss of Nothing -> pure $ "Invalid loss function " <> loss - Just l -> let arg = Args dataset "" gens maxSize folds trace l optIter optRepeat nParams nPop nTournament pc pm nonterminals dumpTo loadFrom generational simplify maxTime varnames useFracBayes MultiThread + Just l -> let arg = Args dataset "" gens maxSize folds trace l optIter optRepeat nParams nPop nTournament pc pm nonterminals dumpTo loadFrom generational simplify maxTime varnames useFracBayes MultiThread dbFile dbDataset dbCacheSz dbFlushEvery in eggp arg eggp :: Args -> IO String @@ -288,13 +312,13 @@ eggpWithData args dataTrains' dataTests = do alg = evalStateT (egraphGP dataTrainVals dataTests args) emptyGraph evalStateT alg g' -eggp_run_data :: Ptr CDouble -> Ptr CInt -> Int -> Int -> String -> String -> Int -> Int -> Int -> Int -> Double -> Double -> String -> String -> Int -> Int -> Int -> Int -> Int -> Bool -> Bool -> Bool -> String -> String -> String -> Bool -> IO String -eggp_run_data dataPtr nrowsPtr ndatasets ncols header params gens nPop maxSize nTournament pc pm nonterminals loss optIter optRepeat nParams folds maxTime simplify trace generational dumpTo loadFrom varnames useFracBayes = +eggp_run_data :: Ptr CDouble -> Ptr CInt -> Int -> Int -> String -> String -> Int -> Int -> Int -> Int -> Double -> Double -> String -> String -> Int -> Int -> Int -> Int -> Int -> Bool -> Bool -> Bool -> String -> String -> String -> Bool -> String -> String -> Int -> Int -> IO String +eggp_run_data dataPtr nrowsPtr ndatasets ncols header params gens nPop maxSize nTournament pc pm nonterminals loss optIter optRepeat nParams folds maxTime simplify trace generational dumpTo loadFrom varnames useFracBayes dbFile dbDataset dbCacheSz dbFlushEvery = case readLoss loss of Nothing -> pure $ "Invalid loss function " <> loss Just l -> do dss <- buildDataSets dataPtr nrowsPtr ndatasets ncols header params - let arg = Args "" "" gens maxSize folds trace l optIter optRepeat nParams nPop nTournament pc pm nonterminals dumpTo loadFrom generational simplify maxTime varnames useFracBayes MultiThread + let arg = Args "" "" gens maxSize folds trace l optIter optRepeat nParams nPop nTournament pc pm nonterminals dumpTo loadFrom generational simplify maxTime varnames useFracBayes MultiThread dbFile dbDataset dbCacheSz dbFlushEvery eggpWithData arg dss dss -- | Build a list of DataSets from a raw row-major double buffer. The buffer diff --git a/src/Search.hs b/src/Search.hs index c6d4e66..3bf4996 100644 --- a/src/Search.hs +++ b/src/Search.hs @@ -4,6 +4,7 @@ {-# LANGUAGE OverloadedStrings #-} {-# LANGUAGE BangPatterns #-} {-# LANGUAGE TypeSynonymInstances, FlexibleInstances #-} +{-# LANGUAGE RankNTypes #-} module Search where @@ -67,6 +68,15 @@ import Data.Version (showVersion) import Control.Exception (Exception (..), SomeException (..), handle) import Data.Time.Clock.POSIX +import Algorithm.EqSat.Storage.SQLite (saveGraph) +import Algorithm.EqSat.Storage.Query (getOrCreateDataset, readDatasetFit, writeDatasetFit, expressionEclass) +import Algorithm.EqSat.Storage.Types (enodeKey, parseTheta, serializeTheta) +import Algorithm.EqSat.Storage.Backend (SqlBackend) +import qualified Database.SQLite3 as SQLite +import qualified Data.Text as T +import Control.Exception (bracket) +import System.FilePath (takeFileName) + data Args = Args { _dataset :: String, _testData :: String, @@ -90,7 +100,11 @@ data Args = Args _maxtime :: Int, _varnames :: String, _useFracBayes :: Bool, - _backend :: ADBackEnd + _backend :: ADBackEnd, + _dbFile :: String, + _dbDataset :: String, + _dbCacheSz :: Int, + _dbFlushEvery :: Int } deriving (Show) @@ -104,14 +118,19 @@ egraphGP :: [(DataSet, DataSet)] -> [DataSet] -> Args -> StateT EGraph (StateT S egraphGP dataTrainVals dataTests args = do when ((not.null) (_loadFrom args)) $ (io $ BS.readFile (_loadFrom args)) >>= \eg -> put (decode eg) + -- Load fitness cache from DB if in DB mode + fitCache <- if not (null (_dbFile args)) + then io $ loadFitnessCache (_dbFile args) (dbDatasetName args) + else pure HashMap.empty + insertTerms unevalInit <- gets (IntSet.toList . _unevaluated . _eDB) - fitBatch True fitFun unevalInit + fitBatchCached fitCache True fitFun unevalInit t0 <- io $ getPOSIXTime pop <- replicateM (_nPop args) $ insertRndExpr (_maxSize args) rndTerm rndNonTerm >>= canonical - fitBatch False fitFun pop + fitBatchCached fitCache False fitFun pop output <- if _trace args then forM (Prelude.zip [0..] pop) $ uncurry printExpr @@ -121,15 +140,32 @@ egraphGP dataTrainVals dataTests args = do mTime = if _maxtime args < 0 then Nothing else Just (fromIntegral $ _maxtime args - 5) -- add 5 seconds slack (finalPop, finalOut, _) <- iterateFor (_gens args) t0 mTime (pop, output, _nPop args) $ \it (ps', out, curIx) -> do - newPop' <- replicateM (_nPop args) (evolve ps') + newPop' <- replicateM (_nPop args) (if not (null (_dbFile args)) then evolveDB ps' else evolve ps') -- Batch-fit the eqsat-flagged refits (force) and the new offspring -- (updateIfNothing semantics) concurrently, before any fitness-based -- selection so Pareto ranking sees fresh fitness values. refitIds <- gets (IntSet.toList . _refits . _eDB) modify' $ over (eDB . refits) (const IntSet.empty) - fitBatch True fitFun refitIds - fitBatch False fitFun newPop' + fitBatchCached fitCache True fitFun refitIds + fitBatchCached fitCache False fitFun newPop' + + -- Store newly fitted fitness in DB + when (not (null (_dbFile args))) $ do + forM_ refitIds $ \eid -> do + mf <- getFitness eid + case mf of + Just f -> do thetas <- getTheta eid + let theta = if null thetas then V.empty else head thetas + io $ storeFitnessDB (_dbFile args) (dbDatasetName args) eid f theta + Nothing -> pure () + forM_ newPop' $ \eid -> do + mf <- getFitness eid + case mf of + Just f -> do thetas <- getTheta eid + let theta = if null thetas then V.empty else head thetas + io $ storeFitnessDB (_dbFile args) (dbDatasetName args) eid f theta + Nothing -> pure () out' <- if _trace args then forM (Prelude.zip [curIx..] newPop') $ uncurry printExpr @@ -140,6 +176,11 @@ egraphGP dataTrainVals dataTests args = do cleanedIds <- if full then Just <$> cleanEGraph else pure Nothing when full cleanDB + -- Periodic sync to DB + when (not (null (_dbFile args)) && _dbFlushEvery args > 0 && it `mod` _dbFlushEvery args == 0) $ do + eg <- get + io $ syncEGraphDB (_dbFile args) (dbDatasetName args) eg + newPop <- if _generational args then maybe (Prelude.mapM canonical newPop') pure cleanedIds else do @@ -156,6 +197,11 @@ egraphGP dataTrainVals dataTests args = do Prelude.mapM canonical (pareto <> lft) pure (newPop, out <> out', curIx + (_nPop args)) + -- Final sync to DB + when (not (null (_dbFile args))) $ do + eg <- get + io $ syncEGraphDB (_dbFile args) (dbDatasetName args) eg + when ((not.null) (_dumpTo args)) $ get >>= (io . BS.writeFile (_dumpTo args) . encode ) pf <- if _trace args then pure finalOut @@ -238,6 +284,14 @@ egraphGP dataTrainVals dataTests args = do else runEqSat myCost rewritesParams 1 >> cleanDB pure offspring + evolveDB xs' = do xs <- Prelude.mapM canonical xs' + parents <- tournament xs + offspring <- combineDB parents + if _nParams args == 0 + then runEqSat myCost rewritesWithConstant 1 >> cleanDB + else runEqSat myCost rewritesParams 1 >> cleanDB + pure offspring + tournament xs = do p1 <- applyTournament xs >>= canonical p2 <- applyTournament xs >>= canonical pure (p1, p2) @@ -249,6 +303,8 @@ egraphGP dataTrainVals dataTests args = do combine (p1, p2) = (crossover p1 p2 >>= mutate) >>= canonical + combineDB (p1, p2) = (crossoverDB p1 p2 >>= mutateDB) >>= canonical + crossover p1 p2 = do sz <- getSize p1 coin <- rnd $ tossBiased (_pc args) if sz == 1 || not coin @@ -258,6 +314,22 @@ egraphGP dataTrainVals dataTests args = do tree <- getSubtree pos 0 Nothing [] cands p1 fromTree myCost (relabel tree) >>= canonical + crossoverDB p1 p2 = do sz <- getSize p1 + coin <- rnd $ tossBiased (_pc args) + if sz == 1 || not coin + then rnd (randomFrom [p1, p2]) + else do pos <- rnd $ randomRange (1, sz-1) + cands <- getAllSubClasses p2 + tree <- getSubtree pos 0 Nothing [] cands p1 + eid <- fromTree myCost (relabel tree) >>= canonical + -- Check if this expression already exists in the DB + en <- getBestENode eid >>= canonize + let key = T.pack (enodeKey en) + inDB <- io $ expressionInDB (_dbFile args) key + if inDB + then rnd (randomFrom [p1, p2]) -- already explored: fall back + else pure eid + getSubtree :: Int -> Int -> Maybe (EClassId -> ENode) -> [Maybe (EClassId -> ENode)] -> [EClassId] -> EClassId -> RndEGraph (Fix SRTree) getSubtree 0 sz (Just parent) mGrandParents cands p' = do p <- canonical p' @@ -327,6 +399,21 @@ egraphGP dataTrainVals dataTests args = do fromTree myCost (relabel tree) >>= canonical else pure p + mutateDB p = do sz <- getSize p + coin <- rnd $ tossBiased (_pm args) + if coin + then do pos <- rnd $ randomRange (0, min sz maxSize - 1) + tree <- mutAt pos maxSize Nothing p + eid <- fromTree myCost (relabel tree) >>= canonical + -- Check if this expression already exists in the DB + en <- getBestENode eid >>= canonize + let key = T.pack (enodeKey en) + inDB <- io $ expressionInDB (_dbFile args) key + if inDB + then pure p -- already explored: keep original + else pure eid + else pure p + peel :: Fix SRTree -> SRTree () peel (Fix (Bin op l r)) = Bin op () () peel (Fix (Uni f t)) = Uni f () @@ -478,3 +565,83 @@ egraphGP dataTrainVals dataTests args = do replaceAt 0 e (_:cs) = e : cs replaceAt i e (c:cs) = c : replaceAt (i-1) e cs replaceAt _ _ [] = [] + +-- --------------------------------------------------------------------------- +-- DB helpers (used when _dbFile is non-empty) +-- --------------------------------------------------------------------------- + +-- | Derive the dataset name for the DB: use _dbDataset if set, else the CSV basename. +dbDatasetName :: Args -> String +dbDatasetName args + | not (null (_dbDataset args)) = _dbDataset args + | otherwise = takeFileName (_dataset args) + +-- | Open a SQLite DB, run an action, close. SQLite-only (no Postgres). +withSqliteDB :: String -> (forall b. SqlBackend b => b -> IO a) -> IO a +withSqliteDB path k = bracket (SQLite.open (T.pack path)) SQLite.close k + +-- | Load fitness cache from DB: eclass id -> (fitness, theta_text). +-- When the e-graph structure is loaded via --load-from, e-class IDs are +-- preserved so this cache is directly usable. For cross-run keyed reuse +-- (different e-class IDs), expression_index would be needed — left as TODO. +loadFitnessCache :: String -> String -> IO (HashMap Int (Double, V.Vector Double)) +loadFitnessCache "" _ = pure HashMap.empty +loadFitnessCache dbFile dataset = withSqliteDB dbFile $ \db -> do + mdsid <- getOrCreateDataset db dataset + fits <- readDatasetFit db mdsid + pure $ HashMap.fromList + [ (eid, (f, parseThetaVec th)) + | (eid, (Just f, _, _, th)) <- fits + , not (T.null th) ] + where + parseThetaVec th = case parseTheta (T.unpack th) of + [] -> V.empty + (v:_) -> v + +-- | Check if an expression's enode key exists in expression_index table. +expressionInDB :: String -> T.Text -> IO Bool +expressionInDB "" _ = pure False +expressionInDB dbFile key + | T.null key = pure False + | otherwise = withSqliteDB dbFile $ \db -> do + meid <- expressionEclass db key + pure (meid /= Nothing) + +-- | Store fitness in DB after evaluation. +storeFitnessDB :: String -> String -> EClassId -> Double -> V.Vector Double -> IO () +storeFitnessDB "" _ _ _ _ = pure () +storeFitnessDB dbFile dataset eid fit theta + = withSqliteDB dbFile $ \db -> do + dsid <- getOrCreateDataset db dataset + writeDatasetFit db dsid eid (Just fit) Nothing (T.pack (serializeTheta [theta])) 0 + +-- | Sync in-memory e-graph to DB (full save for resident graphs). +syncEGraphDB :: String -> String -> EGraph -> IO () +syncEGraphDB "" _ _ = pure () +syncEGraphDB dbFile dataset eg + = withSqliteDB dbFile $ \db -> do + dsid <- getOrCreateDataset db dataset + _ <- saveGraph db dsid eg + pure () + +-- | Like fitBatch, but checks the fitness cache first. +-- For each e-class whose ID is in the cache, insertFitness is called directly +-- (skipping the expensive NLopt optimization). Uncached e-classes go through +-- the normal fitFun path via the original fitBatch. +fitBatchCached :: HashMap.HashMap Int (Double, V.Vector Double) + -> Bool + -> (Fix SRTree -> RndEGraph (Double, [Target])) + -> [EClassId] + -> RndEGraph () +fitBatchCached cache force fitFun ecs0 = do + ecs <- Prelude.mapM canonical ecs0 + -- Phase 1: fill fitness from cache for e-classes that have a cache hit + forM_ ecs $ \ec -> do + mf <- getFitness ec + when (force || mf == Nothing) $ + case HashMap.lookup ec cache of + Just (fit, thetas) -> insertFitness ec fit [thetas] + Nothing -> pure () + -- Phase 2: run the original fitBatch — it will skip e-classes that now + -- have fitness (from the cache or from a previous run). + fitBatch force fitFun ecs0 diff --git a/src/eggp/__init__.py b/src/eggp/__init__.py index 675d8e1..f9429a7 100644 --- a/src/eggp/__init__.py +++ b/src/eggp/__init__.py @@ -44,6 +44,7 @@ def _get_hs_eggp_run_data(): ctypes.c_int, ctypes.c_int, ctypes.c_int, ctypes.c_int, ctypes.c_int, ctypes.c_int, ctypes.c_int, ctypes.c_int, ctypes.c_char_p, ctypes.c_char_p, ctypes.c_char_p, ctypes.c_int, + ctypes.c_char_p, ctypes.c_char_p, ctypes.c_int, ctypes.c_int, ] lib.hs_eggp_run_data.restype = ctypes.c_char_p _hs_eggp_run_data = lib.hs_eggp_run_data @@ -87,11 +88,11 @@ def main(args: List[str] = []) -> int: with hs_rts_init(rts_args + cli_args): return unsafe_hs_eggp_main() -def eggp_run(dataset: str, gen: int, nPop: int, maxSize: int, nTournament: int, pc: float, pm: float, nonterminals: str, loss: str, optIter: int, optRepeat: int, nParams: int, split: int, max_time : int, simplify: int, trace : int, generational : int, dumpTo: str, loadFrom: str, varnames : str, useFracBayes: int) -> str: +def eggp_run(dataset: str, gen: int, nPop: int, maxSize: int, nTournament: int, pc: float, pm: float, nonterminals: str, loss: str, optIter: int, optRepeat: int, nParams: int, split: int, max_time : int, simplify: int, trace : int, generational : int, dumpTo: str, loadFrom: str, varnames : str, useFracBayes: int, dbFile: str = "", dbDataset: str = "", dbCacheSize: int = 100000, dbFlushEvery: int = 0) -> str: with hs_rts_init(): - return unsafe_hs_eggp_run(dataset, gen, nPop, maxSize, nTournament, pc, pm, nonterminals, loss, optIter, optRepeat, nParams, split, max_time, simplify, trace, generational, dumpTo, loadFrom, varnames, useFracBayes) + return unsafe_hs_eggp_run(dataset, gen, nPop, maxSize, nTournament, pc, pm, nonterminals, loss, optIter, optRepeat, nParams, split, max_time, simplify, trace, generational, dumpTo, loadFrom, varnames, useFracBayes, dbFile, dbDataset, dbCacheSize, dbFlushEvery) -def eggp_run_data(data: np.ndarray, nrows: List[int], header: str, params: str, gen: int, nPop: int, maxSize: int, nTournament: int, pc: float, pm: float, nonterminals: str, loss: str, optIter: int, optRepeat: int, nParams: int, split: int, max_time : int, simplify: int, trace : int, generational : int, dumpTo: str, loadFrom: str, varnames : str, useFracBayes: int) -> str: +def eggp_run_data(data: np.ndarray, nrows: List[int], header: str, params: str, gen: int, nPop: int, maxSize: int, nTournament: int, pc: float, pm: float, nonterminals: str, loss: str, optIter: int, optRepeat: int, nParams: int, split: int, max_time : int, simplify: int, trace : int, generational : int, dumpTo: str, loadFrom: str, varnames : str, useFracBayes: int, dbFile: str = "", dbDataset: str = "", dbCacheSize: int = 100000, dbFlushEvery: int = 0) -> str: ''' Runs eggp with the dataset passed directly as a contiguous double array, bypassing the temp-CSV round trip. @@ -112,7 +113,7 @@ def eggp_run_data(data: np.ndarray, nrows: List[int], header: str, params: str, cdata = data.ctypes.data_as(ctypes.POINTER(ctypes.c_double)) cnrows = (ctypes.c_int * len(nrows))(*nrows) with hs_rts_init(): - out = _get_hs_eggp_run_data()(cdata, cnrows, len(nrows), data.shape[1], header.encode(), params.encode(), gen, nPop, maxSize, nTournament, pc, pm, nonterminals.encode(), loss.encode(), optIter, optRepeat, nParams, split, max_time, simplify, trace, generational, dumpTo.encode(), loadFrom.encode(), varnames.encode(), useFracBayes) + out = _get_hs_eggp_run_data()(cdata, cnrows, len(nrows), data.shape[1], header.encode(), params.encode(), gen, nPop, maxSize, nTournament, pc, pm, nonterminals.encode(), loss.encode(), optIter, optRepeat, nParams, split, max_time, simplify, trace, generational, dumpTo.encode(), loadFrom.encode(), varnames.encode(), useFracBayes, dbFile.encode(), dbDataset.encode(), dbCacheSize, dbFlushEvery) return out.decode("utf-8") if out is not None else "" def make_function(expression, loss="MSE"): @@ -232,7 +233,7 @@ class EGGP(BaseEstimator, RegressorMixin): >>> estimator = EGGP(loss="Bernoulli") >>> estimator.fit(X, y) """ - def __init__(self, gen = 100, nPop = 100, maxSize = 15, nTournament = 3, pc = 0.9, pm = 0.3, nonterminals = "add,sub,mul,div", loss = "MSE", optIter = 50, optRepeat = 2, nParams = -1, folds = 1, max_time = -1, simplify = False, trace = False, generational = False, dumpTo = "", loadFrom = "", useFracBayes = False, pinball_tau = 0.5): + def __init__(self, gen = 100, nPop = 100, maxSize = 15, nTournament = 3, pc = 0.9, pm = 0.3, nonterminals = "add,sub,mul,div", loss = "MSE", optIter = 50, optRepeat = 2, nParams = -1, folds = 1, max_time = -1, simplify = False, trace = False, generational = False, dumpTo = "", loadFrom = "", useFracBayes = False, pinball_tau = 0.5, dbFile = "", dbDataset = "", dbCacheSize = 100000, dbFlushEvery = 0): nts = "add,sub,mul,div,power,powerabs,\ aq,abs,sin,cos,tan,sinh,cosh,tanh,\ asin,acos,atan,asinh,acosh,atanh,sqrt,\ @@ -295,6 +296,10 @@ def __init__(self, gen = 100, nPop = 100, maxSize = 15, nTournament = 3, pc = 0. self.is_fitted_ = False self.useFracBayes = int(useFracBayes) self.pinball_tau = pinball_tau + self.dbFile = dbFile + self.dbDataset = dbDataset + self.dbCacheSize = dbCacheSize + self.dbFlushEvery = dbFlushEvery @property def loss_arg(self): @@ -384,7 +389,7 @@ def fit(self, X, y, Xerr = None, yerr = None): dname = self.get_fname("", header) - csv_data = eggp_run_data(combined, [combined.shape[0]], ",".join(header), dname, self.gen, self.nPop, self.maxSize, self.nTournament, self.pc, self.pm, self.nonterminals, self.loss_arg, self.optIter, self.optRepeat, self.nParams, self.folds, self.max_time, self.simplify, self.trace, self.generational, self.dumpTo, self.loadFrom, varnames, self.useFracBayes) + csv_data = eggp_run_data(combined, [combined.shape[0]], ",".join(header), dname, self.gen, self.nPop, self.maxSize, self.nTournament, self.pc, self.pm, self.nonterminals, self.loss_arg, self.optIter, self.optRepeat, self.nParams, self.folds, self.max_time, self.simplify, self.trace, self.generational, self.dumpTo, self.loadFrom, varnames, self.useFracBayes, self.dbFile, self.dbDataset, self.dbCacheSize, self.dbFlushEvery) if len(csv_data) > 0: csv_io = StringIO(csv_data.strip()) @@ -419,7 +424,7 @@ def fit_mvsr(self, Xs, ys, Xerrs = None, yerrs = None): dname = self.get_fname("", header) csv_data = eggp_run_data(data, nrows, ",".join(header), dname, self.gen, self.nPop, self.maxSize, self.nTournament, self.pc, self.pm, - self.nonterminals, self.loss_arg, self.optIter, self.optRepeat, self.nParams, self.folds, self.max_time, self.simplify, self.trace, self.generational, self.dumpTo, self.loadFrom, varnames, self.useFracBayes) + self.nonterminals, self.loss_arg, self.optIter, self.optRepeat, self.nParams, self.folds, self.max_time, self.simplify, self.trace, self.generational, self.dumpTo, self.loadFrom, varnames, self.useFracBayes, self.dbFile, self.dbDataset, self.dbCacheSize, self.dbFlushEvery) if len(csv_data) > 0: csv_io = StringIO(csv_data.strip()) diff --git a/src/eggp/binding.i b/src/eggp/binding.i index cb22ad2..9bcbcbb 100644 --- a/src/eggp/binding.i +++ b/src/eggp/binding.i @@ -10,8 +10,8 @@ int unsafe_hs_eggp_main() { return hs_eggp_main(); } -char * unsafe_hs_eggp_run( char *dataset, int gens, int nPop, int maxSize, int nTournament, double pc, double pm, char *nonterminals, char *loss, int optIter, int optRepeat, int nParams, int split, int max_time, int simplify, int trace, int generational, char *dumpTo, char *loadFrom, char *varnames, int useFracBayes ) { - return hs_eggp_run(dataset, gens, nPop, maxSize, nTournament, pc, pm, nonterminals, loss, optIter, optRepeat, nParams, split, max_time, simplify, trace, generational, dumpTo, loadFrom, varnames, useFracBayes); +char * unsafe_hs_eggp_run( char *dataset, int gens, int nPop, int maxSize, int nTournament, double pc, double pm, char *nonterminals, char *loss, int optIter, int optRepeat, int nParams, int split, int max_time, int simplify, int trace, int generational, char *dumpTo, char *loadFrom, char *varnames, int useFracBayes, char *dbFile, char *dbDataset, int dbCacheSize, int dbFlushEvery ) { + return hs_eggp_run(dataset, gens, nPop, maxSize, nTournament, pc, pm, nonterminals, loss, optIter, optRepeat, nParams, split, max_time, simplify, trace, generational, dumpTo, loadFrom, varnames, useFracBayes, dbFile, dbDataset, dbCacheSize, dbFlushEvery); } void unsafe_hs_eggp_init(int argc, char **argv) { @@ -59,6 +59,6 @@ void unsafe_py_write_stderr( char * str) { char * unsafe_hs_eggp_version(); int unsafe_hs_eggp_main(); -char * unsafe_hs_eggp_run( char *dataset, int gens, int nPop, int maxSize, int nTournament, double p, double pm, char *nonterminals, char *loss, int optIter, int optRepeat, int nParams, int split, int max_time, int simplify, int trace, int generational, char *dumpTo, char *loadFrom, char *varnames, int useFracBayes); +char * unsafe_hs_eggp_run( char *dataset, int gens, int nPop, int maxSize, int nTournament, double p, double pm, char *nonterminals, char *loss, int optIter, int optRepeat, int nParams, int split, int max_time, int simplify, int trace, int generational, char *dumpTo, char *loadFrom, char *varnames, int useFracBayes, char *dbFile, char *dbDataset, int dbCacheSize, int dbFlushEvery); void unsafe_hs_eggp_init(int argc, char **argv); void unsafe_hs_eggp_exit(); diff --git a/src/eggp/typing.py b/src/eggp/typing.py index 2fa05d6..869f061 100644 --- a/src/eggp/typing.py +++ b/src/eggp/typing.py @@ -11,4 +11,4 @@ class Session(Protocol): def version(self) -> str: ... def main(self, args: List[str] = []) -> int: ... - def eggp_run(self, dataset: str, gen: int, nPop: int, maxSize: int, nTournament: int, pc: float, pm: float, nonterminals: str, loss: str, optIter: int, optRepeat: int, nParams: int, split: int, max_time: int, simplify: int, trace : int, generational : int, dumpTo: str, loadFrom: str, varnames: str, useFracBayes: int) -> str: ... + def eggp_run(self, dataset: str, gen: int, nPop: int, maxSize: int, nTournament: int, pc: float, pm: float, nonterminals: str, loss: str, optIter: int, optRepeat: int, nParams: int, split: int, max_time: int, simplify: int, trace : int, generational : int, dumpTo: str, loadFrom: str, varnames: str, useFracBayes: int, dbFile: str = "", dbDataset: str = "", dbCacheSize: int = 100000, dbFlushEvery: int = 0) -> str: ... diff --git a/tutorials/db_mode_example.py b/tutorials/db_mode_example.py new file mode 100644 index 0000000..e99eb41 --- /dev/null +++ b/tutorials/db_mode_example.py @@ -0,0 +1,224 @@ +""" +12 - eggp in DB mode: persistent e-graph with fitness caching +============================================================= + +This tutorial shows the *DB-backed* eggp mode: instead of keeping everything +in memory, eggp stores evaluated expressions and their fitness in a SQLite +database. This enables: + +1. **Fitness reuse**: expressions evaluated in a previous run are recognised + and their cached fitness is reused (skipping the expensive NLopt + optimisation). +2. **Expression deduplication**: crossover and mutation check the DB for + already-explored expressions, avoiding re-exploration of the search space. +3. **Persistence across runs**: the DB accumulates results over multiple + sessions; a new run picks up where the last left off. + +The in-memory GP loop is unchanged when ``dbFile=""`` (the default), so there +is zero overhead when the DB mode is not used. + +Run from the ``tutorials/`` directory:: + + python 12_db_mode_example.py +""" + +import os +import time +import sqlite3 + +import numpy as np +import pandas as pd + +from eggp import EGGP + +# --------------------------------------------------------------------------- +# Helper: inspect the DB +# --------------------------------------------------------------------------- + +def db_stats(db_path): + """Print basic statistics about the eggp database.""" + if not os.path.exists(db_path): + print(f" [DB not found: {db_path}]") + return + con = sqlite3.connect(db_path) + n_fit = con.execute("SELECT COUNT(*) FROM dataset_fit").fetchone()[0] + n_ds = con.execute("SELECT COUNT(*) FROM dataset").fetchone()[0] + n_ec = con.execute("SELECT COUNT(*) FROM eclass").fetchone()[0] if _table_exists(con, "eclass") else 0 + con.close() + print(f" dataset_fit rows: {n_fit} | datasets: {n_ds} | e-classes: {n_ec}") + +def _table_exists(con, name): + cur = con.execute("SELECT name FROM sqlite_master WHERE type='table' AND name=?", (name,)) + return cur.fetchone() is not None + +# --------------------------------------------------------------------------- +# Synthetic dataset +# --------------------------------------------------------------------------- + +def make_dataset(path): + rng = np.random.default_rng(42) + n = 200 + x0 = rng.uniform(-3, 3, n) + x1 = rng.uniform(-3, 3, n) + y = np.sin(x0) + 0.5 * x1 + df = pd.DataFrame({"x0": x0, "x1": x1, "y": y}) + df.to_csv(path, index=False) + return df + +# --------------------------------------------------------------------------- +# Main +# --------------------------------------------------------------------------- + +def main(): + data_csv = "db_tutorial_data.csv" + db_file = "db_tutorial.db" + + # Clean up any previous run + for f in (data_csv, db_file): + if os.path.exists(f): + os.remove(f) + + df = make_dataset(data_csv) + X = df[["x0", "x1"]] + y = df["y"] + + # =================================================================== + # Session 1: first run — builds the DB from scratch + # =================================================================== + print("=" * 64) + print("Session 1: first run (no prior DB)") + print("=" * 64) + + model1 = EGGP( + gen=10, + nPop=50, + maxSize=12, + nTournament=3, + pc=0.8, + pm=0.3, + nonterminals="add,sub,mul,div,sin", + loss="MSE", + optIter=30, + optRepeat=2, + nParams=1, + folds=1, + simplify=True, + max_time=30, + # --- DB parameters --- + dbFile=db_file, + dbDataset="tutorial", + dbFlushEvery=5, # flush to DB every 5 generations + ) + + t0 = time.time() + model1.fit(X, y) + elapsed1 = time.time() - t0 + + print(f"\n Finished in {elapsed1:.1f}s") + print(f" Pareto front ({len(model1.results)} expressions):") + print(model1.results[["Expression", "loss_train", "size"]].to_string(index=False)) + print() + db_stats(db_file) + + # =================================================================== + # Session 2: resume — reuses cached fitness from Session 1 + # =================================================================== + print("\n" + "=" * 64) + print("Session 2: resume from existing DB (fitness reuse)") + print("=" * 64) + + model2 = EGGP( + gen=10, + nPop=50, + maxSize=12, + nTournament=3, + pc=0.8, + pm=0.3, + nonterminals="add,sub,mul,div,sin", + loss="MSE", + optIter=30, + optRepeat=2, + nParams=1, + folds=1, + simplify=True, + max_time=30, + # --- same DB file --- + dbFile=db_file, + dbDataset="tutorial", + dbFlushEvery=5, + ) + + t0 = time.time() + model2.fit(X, y) + elapsed2 = time.time() - t0 + + print(f"\n Finished in {elapsed2:.1f}s") + print(f" Pareto front ({len(model2.results)} expressions):") + print(model2.results[["Expression", "loss_train", "size"]].to_string(index=False)) + print() + db_stats(db_file) + + # =================================================================== + # Session 3: even more generations — the DB keeps growing + # =================================================================== + print("\n" + "=" * 64) + print("Session 3: continue searching (DB accumulates)") + print("=" * 64) + + model3 = EGGP( + gen=20, # more generations this time + nPop=50, + maxSize=12, + nTournament=3, + pc=0.8, + pm=0.3, + nonterminals="add,sub,mul,div,sin", + loss="MSE", + optIter=30, + optRepeat=2, + nParams=1, + folds=1, + simplify=True, + max_time=60, + dbFile=db_file, + dbDataset="tutorial", + dbFlushEvery=10, + ) + + t0 = time.time() + model3.fit(X, y) + elapsed3 = time.time() - t0 + + print(f"\n Finished in {elapsed3:.1f}s") + print(f" Pareto front ({len(model3.results)} expressions):") + print(model3.results[["Expression", "loss_train", "size"]].to_string(index=False)) + print() + db_stats(db_file) + + # =================================================================== + # Summary + # =================================================================== + print("\n" + "=" * 64) + print("DB mode summary") + print("=" * 64) + print(f" Session 1 (fresh): {elapsed1:.1f}s") + print(f" Session 2 (resume): {elapsed2:.1f}s (reuses cached fitness)") + print(f" Session 3 (continue): {elapsed3:.1f}s") + print(f" Final DB stats:") + db_stats(db_file) + print() + print(" The DB file persists across runs. A new EGGP object pointed at the") + print(" same dbFile will load the fitness cache and skip re-evaluation of") + print(" expressions that were already fitted.") + print("=" * 64) + + # cleanup + for f in (data_csv, db_file): + try: + os.remove(f) + except OSError: + pass + + +if __name__ == "__main__": + main() From 16658fe1b0dd5ccd95d893bc47a42c2f12220fcb Mon Sep 17 00:00:00 2001 From: folivetti Date: Tue, 18 Aug 2026 18:16:16 -0300 Subject: [PATCH 133/166] eggp 2.0 --- .github/workflows/release.yml | 18 +++++++++-- app/EGGPA.hs | 2 +- cabal.project | 6 ---- eggp.cabal | 4 +-- harness/WExc.hs | 56 ----------------------------------- pyproject.toml | 8 +---- src/EGGP.hs | 2 +- 7 files changed, 20 insertions(+), 76 deletions(-) delete mode 100644 harness/WExc.hs diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index ffa00db..7299648 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -25,8 +25,8 @@ env: # you must update the containers in pyproject.toml under tool.cibuildwheel. # # See: https://github.com/wenkokke/manylinux_ghc - DEFAULT_GHC_VERSION: "9.12" - DEFAULT_CABAL_VERSION: "3.14" + DEFAULT_GHC_VERSION: "9.14.1" + DEFAULT_CABAL_VERSION: "3.16" DEFAULT_PYTHON_VERSION: "3.12" jobs: @@ -179,7 +179,19 @@ jobs: env: CIBW_ARCHS: "${{ matrix.job.os.arch }}" CIBW_BUILD: "*${{ matrix.job.os.plat }}*" - CIBW_SKIP: "cp314*" + CIBW_BEFORE_ALL_LINUX: > + dnf install -y gcc gcc-c++ make libpq-devel cmake && + curl --proto '=https' --tlsv1.2 -sSf https://get-ghcup.haskell.org | + BOOTSTRAP_HASKELL_NONINTERACTIVE=1 + BOOTSTRAP_HASKELL_GHC_VERSION=9.14.1 + BOOTSTRAP_HASKELL_CABAL_VERSION=3.16 + BOOTSTRAP_HASKELL_INSTALL_BASE=/usr/local + BOOTSTRAP_HASKELL_INSTALL_BIN=/usr/local/bin + sh && + git clone --depth 1 https://github.com/stevengj/nlopt.git /tmp/nlopt && + cd /tmp/nlopt && mkdir build && cd build && + cmake .. -DCMAKE_INSTALL_PREFIX=/usr/local -DLIB_SUFFIX=64 && + make -j$(nproc) && make install && ldconfig MACOSX_DEPLOYMENT_TARGET: ${{ matrix.os.runner == 'macos-14' && '14.0' || '14.0' }} - # Build source distribution (sdist) diff --git a/app/EGGPA.hs b/app/EGGPA.hs index 7b9d7b9..dc53e3c 100644 --- a/app/EGGPA.hs +++ b/app/EGGPA.hs @@ -175,7 +175,7 @@ opt = Args ( long "backend" <> value MultiThread <> showDefault - <> help "AD backend: MultiThread, SingleThread, or Accelerate." ) + <> help "AD backend: MultiThread or SingleThread." ) <*> strOption ( long "db-file" <> value "" diff --git a/cabal.project b/cabal.project index 0bc2c3f..7c0deb5 100644 --- a/cabal.project +++ b/cabal.project @@ -1,13 +1,7 @@ packages: . - ../srtree - ../srtree-db - ../accelerate-llvm-native-debug allow-newer: * -package srtree - ghc-options: -fllvm -pgmlo opt-20 -pgmlc llc-20 -optlo-O3 -optlc-mcpu=native -mavx2 -mfma -fexpose-all-unfoldings - source-repository-package type: git location: https://github.com/haskell/mwc-random diff --git a/eggp.cabal b/eggp.cabal index 176d498..f6b27a9 100755 --- a/eggp.cabal +++ b/eggp.cabal @@ -75,7 +75,7 @@ foreign-library _binding , random >=1.2 && <1.4 , scheduler >=2.0.0.1 && <3 , split >=0.2.5 && <0.3 - , srtree >= 2.0.1.8 + , srtree >= 3.0.0.0 , srtree-db >= 0.1.0.0 , direct-sqlite >= 2.3 , text >= 1.2 && < 2.2 @@ -119,7 +119,7 @@ executable eggp , random >=1.2 && <1.4 , scheduler >=2.0.0.1 && <3 , split >=0.2.5 && <0.3 - , srtree >= 2.0.1.8 + , srtree >= 3.0.0.0 , srtree-db >= 0.1.0.0 , direct-sqlite >= 2.3 , text >= 1.2 && < 2.2 diff --git a/harness/WExc.hs b/harness/WExc.hs deleted file mode 100644 index 13b922e..0000000 --- a/harness/WExc.hs +++ /dev/null @@ -1,56 +0,0 @@ -{-# LANGUAGE BangPatterns #-} --- Repro seed3: read worker exceptions after the deadlock. -import qualified Data.Vector.Unboxed as VU -import qualified Data.Vector.Storable as VS -import Data.SRTree.Recursion (Fix(..)) -import Data.SRTree.Random -import Data.SRTree (relabelParams, SRTree) -import Data.SRTree.Internal (SRTree(..), Op(..), Function(..)) -import Algorithm.SRTree.Likelihoods (buildLoss, Loss(..), Distribution(..)) -import Algorithm.SRTree.AD.Unboxed (compileTree) -import Algorithm.SRTree.AD.Accelerate (compileAccelerateTree, multiWorker) -import Control.Monad.State.Strict (evalStateT) -import Control.Monad.Identity -import System.Random (mkStdGen) -import Control.Exception (try, SomeException, evaluate, displayException) -import Control.Concurrent.MVar (tryTakeMVar) -import qualified Data.Sequence as Seq (viewl, ViewL(..)) -import Data.Array.Accelerate.LLVM.Native.Target (workers) -import Data.Array.Accelerate.LLVM.Native.Execute.Scheduler (workerException) - -genTerm :: Rng Identity (Fix SRTree) -genTerm = do - coin <- toss - if coin then Fix . Var <$> randomFrom [0..9] - else Fix . Param <$> randomFrom [0..9] -genNonTerm :: Rng Identity (SRTree ()) -genNonTerm = do - n <- randomRange (0 :: Int, 4) - pure $ case n of - 0 -> Bin Add () () - 1 -> Bin Mul () () - 2 -> Bin Div () () - 3 -> Uni Exp () - _ -> Uni Log () -genTree :: Int -> Int -> Fix SRTree -genTree seed budget = runIdentity $ evalStateT (randomTree 3 8 budget genTerm genNonTerm True) (mkStdGen seed) - -main :: IO () -main = do - let (xss, y) = ([ VU.replicate 10000 1.0 | _ <- [0..9] ], VU.replicate 10000 2.0) - t = genTree 3 4 - tl = relabelParams t - t' = buildLoss (NLL LeastSquares) 10000 tl - ct = compileTree xss y (Just y) t' - f = compileAccelerateTree ct xss y - th = VS.empty - putStrLn "compiled, evaluating..." - r <- try (evaluate (fst (f th))) :: IO (Either SomeException Double) - case r of - Left e -> do - putStrLn ("CAUGHT: " ++ displayException e) - mseq <- tryTakeMVar (workerException (workers multiWorker)) - case mseq of - Nothing -> putStrLn "" - Just seqe -> mapM_ (putStrLn . (\(tid, e') -> "worker " ++ show tid ++ ": " ++ displayException e')) (Seq.viewl seqe) - Right o -> putStrLn ("OK obj=" ++ show o) \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index dfbe0a6..057238c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -80,13 +80,7 @@ skip = [ # 18-04-2023: # PyPy builds do not work. "pp*", - "cp314-*" ] -manylinux-aarch64-image = "folivetti/manylinux_2_28_ghc9121_aarch64" -manylinux-i686-image = "folivetti/manylinux2014_ghc9121_i686" -manylinux-x86_64-image = "folivetti/manylinux_2_28_ghc9121_x86_64" -#manylinux-x86_64-image = "folivetti/manylinux2014_ghc9121_x86_64" -musllinux-x86_64-image = "folivetti/musllinux_1_1_ghc9121_x86_64" [tool.cibuildwheel.windows] repair-wheel-command = "delvewheel repair -w {dest_dir} {wheel} --add-path C:\\nlopt\\bin" @@ -123,7 +117,7 @@ extras = commands = {env_python} -m pytest {posargs} -[testenv:build-py{39,310,311,312,313}-{lin,mac,win}] +[testenv:build-py{39,310,311,312,313,314}-{lin,mac,win}] deps = build auditwheel; sys_platform == 'linux' diff --git a/src/EGGP.hs b/src/EGGP.hs index 4d48d57..adde789 100644 --- a/src/EGGP.hs +++ b/src/EGGP.hs @@ -267,7 +267,7 @@ opt = Args ( long "backend" <> value MultiThread <> showDefault - <> help "AD backend: MultiThread, SingleThread, or Accelerate." ) + <> help "AD backend: MultiThread or SingleThread." ) <*> strOption ( long "db-file" <> value "" From 61c28ab4532b7e28902cd7305d8e2b336f9bc04a Mon Sep 17 00:00:00 2001 From: Fabricio Olivetti de Franca Date: Tue, 18 Aug 2026 18:24:17 -0300 Subject: [PATCH 134/166] Update release.yml --- .github/workflows/release.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 7299648..621a735 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -68,7 +68,7 @@ jobs: plat: "macosx" - os: name: "macOS" - runner: "macos-14" + runner: "macos-15" arch: "arm64" plat: "macosx" #- os: From d2c316418d2387b1a48c77483696131b9cf6856b Mon Sep 17 00:00:00 2001 From: folivetti Date: Tue, 18 Aug 2026 18:52:40 -0300 Subject: [PATCH 135/166] eggp 2.0 --- .github/workflows/release.yml | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 621a735..754976b 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -180,14 +180,16 @@ jobs: CIBW_ARCHS: "${{ matrix.job.os.arch }}" CIBW_BUILD: "*${{ matrix.job.os.plat }}*" CIBW_BEFORE_ALL_LINUX: > - dnf install -y gcc gcc-c++ make libpq-devel cmake && - curl --proto '=https' --tlsv1.2 -sSf https://get-ghcup.haskell.org | + dnf install -y gcc gcc-c++ make libpq-devel cmake curl git && + curl --proto '=https' --tlsv1.2 -sSf https://get-ghcup.haskell.org -o /tmp/ghcup-install.sh && + chmod +x /tmp/ghcup-install.sh && BOOTSTRAP_HASKELL_NONINTERACTIVE=1 BOOTSTRAP_HASKELL_GHC_VERSION=9.14.1 BOOTSTRAP_HASKELL_CABAL_VERSION=3.16 BOOTSTRAP_HASKELL_INSTALL_BASE=/usr/local BOOTSTRAP_HASKELL_INSTALL_BIN=/usr/local/bin - sh && + BOOTSTRAP_HASKELL_ADJUST_BASHRC=0 + /tmp/ghcup-install.sh && git clone --depth 1 https://github.com/stevengj/nlopt.git /tmp/nlopt && cd /tmp/nlopt && mkdir build && cd build && cmake .. -DCMAKE_INSTALL_PREFIX=/usr/local -DLIB_SUFFIX=64 && From 760947825cdc0a656c41f818f3b5cbdfb2c66f5a Mon Sep 17 00:00:00 2001 From: folivetti Date: Tue, 18 Aug 2026 18:59:23 -0300 Subject: [PATCH 136/166] eggp 2.0 --- .github/workflows/release.yml | 16 +++++++--------- 1 file changed, 7 insertions(+), 9 deletions(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 754976b..bc50697 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -181,15 +181,13 @@ jobs: CIBW_BUILD: "*${{ matrix.job.os.plat }}*" CIBW_BEFORE_ALL_LINUX: > dnf install -y gcc gcc-c++ make libpq-devel cmake curl git && - curl --proto '=https' --tlsv1.2 -sSf https://get-ghcup.haskell.org -o /tmp/ghcup-install.sh && - chmod +x /tmp/ghcup-install.sh && - BOOTSTRAP_HASKELL_NONINTERACTIVE=1 - BOOTSTRAP_HASKELL_GHC_VERSION=9.14.1 - BOOTSTRAP_HASKELL_CABAL_VERSION=3.16 - BOOTSTRAP_HASKELL_INSTALL_BASE=/usr/local - BOOTSTRAP_HASKELL_INSTALL_BIN=/usr/local/bin - BOOTSTRAP_HASKELL_ADJUST_BASHRC=0 - /tmp/ghcup-install.sh && + ARCH=$(uname -m) && + if [ "$ARCH" = "aarch64" ]; then GHCUP_ARCH="AArch64-linux"; else GHCUP_ARCH="X86_64-linux"; fi && + curl -L https://github.com/haskell/ghcup-hs/releases/latest/download/ghcup-$GHCUP_ARCH -o /usr/local/bin/ghcup && + chmod +x /usr/local/bin/ghcup && + ghcup install ghc 9.14.1 && + ghcup install cabal 3.16 && + ghcup set ghc 9.14.1 && git clone --depth 1 https://github.com/stevengj/nlopt.git /tmp/nlopt && cd /tmp/nlopt && mkdir build && cd build && cmake .. -DCMAKE_INSTALL_PREFIX=/usr/local -DLIB_SUFFIX=64 && From da20e1828f1730b4e8dfd37d055fa7585ebe15cd Mon Sep 17 00:00:00 2001 From: folivetti Date: Tue, 18 Aug 2026 19:07:07 -0300 Subject: [PATCH 137/166] eggp 2.0 --- .github/workflows/release.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index bc50697..b756ea4 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -182,8 +182,8 @@ jobs: CIBW_BEFORE_ALL_LINUX: > dnf install -y gcc gcc-c++ make libpq-devel cmake curl git && ARCH=$(uname -m) && - if [ "$ARCH" = "aarch64" ]; then GHCUP_ARCH="AArch64-linux"; else GHCUP_ARCH="X86_64-linux"; fi && - curl -L https://github.com/haskell/ghcup-hs/releases/latest/download/ghcup-$GHCUP_ARCH -o /usr/local/bin/ghcup && + if [ "$ARCH" = "aarch64" ]; then GHCU_ARCH="aarch64"; else GHCU_ARCH="x86_64"; fi && + curl -L https://github.com/haskell/ghcup-hs/releases/download/v0.2.6.2/${GHCU_ARCH}-linux-ghcup-0.2.6.2 -o /usr/local/bin/ghcup && chmod +x /usr/local/bin/ghcup && ghcup install ghc 9.14.1 && ghcup install cabal 3.16 && From 3e9465c2ee3f04e2f627275fc65929119a26d087 Mon Sep 17 00:00:00 2001 From: folivetti Date: Tue, 18 Aug 2026 19:14:25 -0300 Subject: [PATCH 138/166] eggp 2.0 --- .github/workflows/release.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index b756ea4..ae62295 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -192,6 +192,7 @@ jobs: cd /tmp/nlopt && mkdir build && cd build && cmake .. -DCMAKE_INSTALL_PREFIX=/usr/local -DLIB_SUFFIX=64 && make -j$(nproc) && make install && ldconfig + CIBW_ENVIRONMENT_LINUX: 'PATH=/root/.ghcup/bin:$PATH LIBRARY_PATH=/usr/local/lib64 LD_LIBRARY_PATH=/usr/local/lib64 C_INCLUDE_PATH=/usr/local/include PKG_CONFIG_PATH=/usr/local/lib64/pkgconfig' MACOSX_DEPLOYMENT_TARGET: ${{ matrix.os.runner == 'macos-14' && '14.0' || '14.0' }} - # Build source distribution (sdist) From 626f056f87653134ba85bdf2d2b97978824c8478 Mon Sep 17 00:00:00 2001 From: folivetti Date: Tue, 18 Aug 2026 19:24:07 -0300 Subject: [PATCH 139/166] eggp 2.0 --- pyproject.toml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/pyproject.toml b/pyproject.toml index 057238c..797a4ae 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -81,6 +81,8 @@ skip = [ # PyPy builds do not work. "pp*", ] +manylinux-x86_64-image = "manylinux_2_34" +manylinux-aarch64-image = "manylinux_2_34" [tool.cibuildwheel.windows] repair-wheel-command = "delvewheel repair -w {dest_dir} {wheel} --add-path C:\\nlopt\\bin" From 4019447cd7c46b38cdd0eb4753a280d7ca591a42 Mon Sep 17 00:00:00 2001 From: folivetti Date: Tue, 18 Aug 2026 19:31:57 -0300 Subject: [PATCH 140/166] eggp 2.0 --- .github/workflows/release.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index ae62295..a64af83 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -180,7 +180,7 @@ jobs: CIBW_ARCHS: "${{ matrix.job.os.arch }}" CIBW_BUILD: "*${{ matrix.job.os.plat }}*" CIBW_BEFORE_ALL_LINUX: > - dnf install -y gcc gcc-c++ make libpq-devel cmake curl git && + dnf install -y gcc gcc-c++ make libpq-devel cmake curl git gmp-devel libffi-devel ncurses-devel zlib-devel && ARCH=$(uname -m) && if [ "$ARCH" = "aarch64" ]; then GHCU_ARCH="aarch64"; else GHCU_ARCH="x86_64"; fi && curl -L https://github.com/haskell/ghcup-hs/releases/download/v0.2.6.2/${GHCU_ARCH}-linux-ghcup-0.2.6.2 -o /usr/local/bin/ghcup && From 3191f8989136822ee073dd2365ca1bbf1db26dd6 Mon Sep 17 00:00:00 2001 From: folivetti Date: Tue, 18 Aug 2026 19:40:35 -0300 Subject: [PATCH 141/166] eggp 2.0 --- .github/workflows/release.yml | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index a64af83..5d8fcbd 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -180,8 +180,12 @@ jobs: CIBW_ARCHS: "${{ matrix.job.os.arch }}" CIBW_BUILD: "*${{ matrix.job.os.plat }}*" CIBW_BEFORE_ALL_LINUX: > - dnf install -y gcc gcc-c++ make libpq-devel cmake curl git gmp-devel libffi-devel ncurses-devel zlib-devel && + dnf install -y gcc gcc-c++ make cmake curl git gmp-devel libffi-devel ncurses-devel zlib-devel && ARCH=$(uname -m) && + if [ "$ARCH" = "aarch64" ]; then PG_ARCH="aarch64"; else PG_ARCH="x86_64"; fi && + dnf install -y https://download.postgresql.org/pub/repos/yum/reporpms/EL-9-${PG_ARCH}/pgdg-redhat-repo-latest.noarch.rpm && + dnf -qy module disable postgresql && + dnf install -y postgresql16-devel && if [ "$ARCH" = "aarch64" ]; then GHCU_ARCH="aarch64"; else GHCU_ARCH="x86_64"; fi && curl -L https://github.com/haskell/ghcup-hs/releases/download/v0.2.6.2/${GHCU_ARCH}-linux-ghcup-0.2.6.2 -o /usr/local/bin/ghcup && chmod +x /usr/local/bin/ghcup && @@ -192,7 +196,7 @@ jobs: cd /tmp/nlopt && mkdir build && cd build && cmake .. -DCMAKE_INSTALL_PREFIX=/usr/local -DLIB_SUFFIX=64 && make -j$(nproc) && make install && ldconfig - CIBW_ENVIRONMENT_LINUX: 'PATH=/root/.ghcup/bin:$PATH LIBRARY_PATH=/usr/local/lib64 LD_LIBRARY_PATH=/usr/local/lib64 C_INCLUDE_PATH=/usr/local/include PKG_CONFIG_PATH=/usr/local/lib64/pkgconfig' + CIBW_ENVIRONMENT_LINUX: 'PATH=/root/.ghcup/bin:$PATH LIBRARY_PATH=/usr/local/lib64:/usr/pgsql-16/lib LD_LIBRARY_PATH=/usr/local/lib64:/usr/pgsql-16/lib C_INCLUDE_PATH=/usr/local/include:/usr/pgsql-16/include PKG_CONFIG_PATH=/usr/local/lib64/pkgconfig:/usr/pgsql-16/lib/pkgconfig' MACOSX_DEPLOYMENT_TARGET: ${{ matrix.os.runner == 'macos-14' && '14.0' || '14.0' }} - # Build source distribution (sdist) From 0f7892ec6ace5ff845e36667efb8d1812abf563e Mon Sep 17 00:00:00 2001 From: folivetti Date: Tue, 18 Aug 2026 19:50:44 -0300 Subject: [PATCH 142/166] eggp 2.0 --- .github/workflows/release.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 5d8fcbd..c8e09c5 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -196,7 +196,7 @@ jobs: cd /tmp/nlopt && mkdir build && cd build && cmake .. -DCMAKE_INSTALL_PREFIX=/usr/local -DLIB_SUFFIX=64 && make -j$(nproc) && make install && ldconfig - CIBW_ENVIRONMENT_LINUX: 'PATH=/root/.ghcup/bin:$PATH LIBRARY_PATH=/usr/local/lib64:/usr/pgsql-16/lib LD_LIBRARY_PATH=/usr/local/lib64:/usr/pgsql-16/lib C_INCLUDE_PATH=/usr/local/include:/usr/pgsql-16/include PKG_CONFIG_PATH=/usr/local/lib64/pkgconfig:/usr/pgsql-16/lib/pkgconfig' + CIBW_ENVIRONMENT_LINUX: 'PATH=/root/.ghcup/bin:/usr/pgsql-16/bin:$PATH LIBRARY_PATH=/usr/local/lib64:/usr/pgsql-16/lib LD_LIBRARY_PATH=/usr/local/lib64:/usr/pgsql-16/lib C_INCLUDE_PATH=/usr/local/include:/usr/pgsql-16/include PKG_CONFIG_PATH=/usr/local/lib64/pkgconfig:/usr/pgsql-16/lib/pkgconfig' MACOSX_DEPLOYMENT_TARGET: ${{ matrix.os.runner == 'macos-14' && '14.0' || '14.0' }} - # Build source distribution (sdist) From 79a471918546b930a58a7e3ca67524f80869a4a6 Mon Sep 17 00:00:00 2001 From: folivetti Date: Tue, 18 Aug 2026 20:00:47 -0300 Subject: [PATCH 143/166] eggp 2.0 --- .github/workflows/release.yml | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index c8e09c5..6eab84f 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -195,7 +195,12 @@ jobs: git clone --depth 1 https://github.com/stevengj/nlopt.git /tmp/nlopt && cd /tmp/nlopt && mkdir build && cd build && cmake .. -DCMAKE_INSTALL_PREFIX=/usr/local -DLIB_SUFFIX=64 && - make -j$(nproc) && make install && ldconfig + make -j$(nproc) && make install && ldconfig && + ln -sf /usr/pgsql-16/lib/libpq.so* /usr/local/lib64/ && + ln -sf /usr/pgsql-16/lib/libpq.a /usr/local/lib64/ && + ln -sf /usr/pgsql-16/include/libpq-fe.h /usr/local/include/ && + ln -sf /usr/pgsql-16/include/pg_config.h /usr/local/include/ && + ln -sf /usr/pgsql-16/include/postgresql /usr/local/include/postgresql CIBW_ENVIRONMENT_LINUX: 'PATH=/root/.ghcup/bin:/usr/pgsql-16/bin:$PATH LIBRARY_PATH=/usr/local/lib64:/usr/pgsql-16/lib LD_LIBRARY_PATH=/usr/local/lib64:/usr/pgsql-16/lib C_INCLUDE_PATH=/usr/local/include:/usr/pgsql-16/include PKG_CONFIG_PATH=/usr/local/lib64/pkgconfig:/usr/pgsql-16/lib/pkgconfig' MACOSX_DEPLOYMENT_TARGET: ${{ matrix.os.runner == 'macos-14' && '14.0' || '14.0' }} From f4e0efe55921976d48b4b216662ba77b4399cc61 Mon Sep 17 00:00:00 2001 From: folivetti Date: Tue, 18 Aug 2026 20:06:56 -0300 Subject: [PATCH 144/166] eggp 2.0 --- .github/workflows/release.yml | 17 ++++++++++++----- 1 file changed, 12 insertions(+), 5 deletions(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 6eab84f..0d77489 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -95,11 +95,18 @@ jobs: - # Required to run job via act: https://github.com/nektos/act run: pip install -r "./requirements-ci.txt" - - # 19-05-2023: - # Required to build the wheels on macOS and Windows, but not on Linux, - # as Linux builds are run in containers, where GHC and Cabal are built - # by the `./scripts/before-all.sh` scripts. - if: matrix.job.os.name == 'macOS' || matrix.job.os.name == 'Windows' + - if: matrix.job.os.name == 'macOS' + name: Install GHC and Cabal via ghcup + run: | + ARCH=$(uname -m) + if [ "$ARCH" = "arm64" ]; then GHCUP_ARCH="aarch64-apple-darwin"; else GHCUP_ARCH="x86_64-apple-darwin"; fi + curl -L https://github.com/haskell/ghcup-hs/releases/download/v0.2.6.2/${GHCUP_ARCH}-ghcup-0.2.6.2 -o /usr/local/bin/ghcup + chmod +x /usr/local/bin/ghcup + ghcup install ghc 9.14.1 + ghcup install cabal 3.16 + ghcup set ghc 9.14.1 + + - if: matrix.job.os.name == 'Windows' uses: haskell-actions/setup@v2 with: ghc-version: ${{ env.DEFAULT_GHC_VERSION }} From ce4449910b684fe3646f83d62b6a86b1afdf3f3f Mon Sep 17 00:00:00 2001 From: folivetti Date: Tue, 18 Aug 2026 20:15:29 -0300 Subject: [PATCH 145/166] eggp 2.0 --- .github/workflows/release.yml | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 0d77489..d1dcf01 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -187,12 +187,12 @@ jobs: CIBW_ARCHS: "${{ matrix.job.os.arch }}" CIBW_BUILD: "*${{ matrix.job.os.plat }}*" CIBW_BEFORE_ALL_LINUX: > - dnf install -y gcc gcc-c++ make cmake curl git gmp-devel libffi-devel ncurses-devel zlib-devel && + dnf install -y gcc gcc-c++ make cmake curl git gmp-devel libffi-devel ncurses-devel zlib-devel openssl-devel krb5-devel libxml2-devel && ARCH=$(uname -m) && if [ "$ARCH" = "aarch64" ]; then PG_ARCH="aarch64"; else PG_ARCH="x86_64"; fi && dnf install -y https://download.postgresql.org/pub/repos/yum/reporpms/EL-9-${PG_ARCH}/pgdg-redhat-repo-latest.noarch.rpm && dnf -qy module disable postgresql && - dnf install -y postgresql16-devel && + dnf install -y postgresql16-devel postgresql16-libs && if [ "$ARCH" = "aarch64" ]; then GHCU_ARCH="aarch64"; else GHCU_ARCH="x86_64"; fi && curl -L https://github.com/haskell/ghcup-hs/releases/download/v0.2.6.2/${GHCU_ARCH}-linux-ghcup-0.2.6.2 -o /usr/local/bin/ghcup && chmod +x /usr/local/bin/ghcup && @@ -205,9 +205,12 @@ jobs: make -j$(nproc) && make install && ldconfig && ln -sf /usr/pgsql-16/lib/libpq.so* /usr/local/lib64/ && ln -sf /usr/pgsql-16/lib/libpq.a /usr/local/lib64/ && + ln -sf /usr/pgsql-16/lib/pg_config* /usr/local/lib64/ && ln -sf /usr/pgsql-16/include/libpq-fe.h /usr/local/include/ && ln -sf /usr/pgsql-16/include/pg_config.h /usr/local/include/ && - ln -sf /usr/pgsql-16/include/postgresql /usr/local/include/postgresql + ln -sf /usr/pgsql-16/include/postgresql /usr/local/include/postgresql && + ln -sf /usr/pgsql-16/lib/libpgcommon* /usr/local/lib64/ && + ldconfig CIBW_ENVIRONMENT_LINUX: 'PATH=/root/.ghcup/bin:/usr/pgsql-16/bin:$PATH LIBRARY_PATH=/usr/local/lib64:/usr/pgsql-16/lib LD_LIBRARY_PATH=/usr/local/lib64:/usr/pgsql-16/lib C_INCLUDE_PATH=/usr/local/include:/usr/pgsql-16/include PKG_CONFIG_PATH=/usr/local/lib64/pkgconfig:/usr/pgsql-16/lib/pkgconfig' MACOSX_DEPLOYMENT_TARGET: ${{ matrix.os.runner == 'macos-14' && '14.0' || '14.0' }} From 0d9176a591fc8b439d77bc2a92d1a471ac599848 Mon Sep 17 00:00:00 2001 From: Fabricio Olivetti de Franca Date: Tue, 18 Aug 2026 20:34:47 -0300 Subject: [PATCH 146/166] Update release.yml --- .github/workflows/release.yml | 30 +++++++++++++++--------------- 1 file changed, 15 insertions(+), 15 deletions(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index d1dcf01..d10afd4 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -51,26 +51,26 @@ jobs: # runner: "ubuntu-latest" # arch: "i686" # plat: "manylinux" - - os: - name: "Linux" - runner: "ubuntu-latest" - arch: "x86_64" - plat: "manylinux" + #- os: + # name: "Linux" + # runner: "ubuntu-latest" + # arch: "x86_64" + # plat: "manylinux" #- os: # name: "Linux" # runner: "ubuntu-latest" # arch: "x86_64" # plat: "musllinux" - - os: - name: "macOS" - runner: "macos-15-intel" - arch: "x86_64" - plat: "macosx" - - os: - name: "macOS" - runner: "macos-15" - arch: "arm64" - plat: "macosx" + #- os: + # name: "macOS" + # runner: "macos-15-intel" + # arch: "x86_64" + # plat: "macosx" + #- os: + # name: "macOS" + # runner: "macos-15" + # arch: "arm64" + # plat: "macosx" #- os: # name: "Windows" # runner: "windows-latest" From eed912fde468fb7b8c3a78ed13955521a3c082a4 Mon Sep 17 00:00:00 2001 From: folivetti Date: Wed, 19 Aug 2026 05:29:10 -0300 Subject: [PATCH 147/166] eggp 2.0 --- eggp.cabal | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/eggp.cabal b/eggp.cabal index f6b27a9..d6bfc38 100755 --- a/eggp.cabal +++ b/eggp.cabal @@ -92,7 +92,7 @@ foreign-library _binding executable eggp import: warnings - ghc-options: -threaded -rtsopts -O2 -fllvm -pgmlo opt-20 -pgmlc llc-20 -optlo-O3 -optlc-mcpu=native -mavx2 -mfma -fspec-constr -fmax-simplifier-iterations=20 -fexpose-all-unfoldings -optc-O3 -optc-mavx2 -optc-mfma + ghc-options: -threaded -rtsopts -O2 -mavx2 -mfma -fspec-constr -fmax-simplifier-iterations=20 -fexpose-all-unfoldings -optc-O3 -optc-mavx2 -optc-mfma main-is: Main.hs hs-source-dirs: app, src other-modules: From d9b67ea408520a013657018dd6d46e6504378167 Mon Sep 17 00:00:00 2001 From: folivetti Date: Wed, 19 Aug 2026 05:43:12 -0300 Subject: [PATCH 148/166] eggp 2.0 --- eggp.cabal | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/eggp.cabal b/eggp.cabal index d6bfc38..294f3aa 100755 --- a/eggp.cabal +++ b/eggp.cabal @@ -71,7 +71,7 @@ foreign-library _binding , lens >=5.2.3 && <5.4 , list-shuffle >=1.0.0.1 && <1.1 , mtl >=2.2 && <2.4 - , optparse-applicative >=0.17 && <0.19 + , optparse-applicative >=0.17 && <0.20 , random >=1.2 && <1.4 , scheduler >=2.0.0.1 && <3 , split >=0.2.5 && <0.3 @@ -92,7 +92,7 @@ foreign-library _binding executable eggp import: warnings - ghc-options: -threaded -rtsopts -O2 -mavx2 -mfma -fspec-constr -fmax-simplifier-iterations=20 -fexpose-all-unfoldings -optc-O3 -optc-mavx2 -optc-mfma + ghc-options: -threaded -rtsopts -O2 -fspec-constr -fmax-simplifier-iterations=20 -fexpose-all-unfoldings -optc-O3 -optc-mavx2 -optc-mfma main-is: Main.hs hs-source-dirs: app, src other-modules: From e164c3f0967357298f53ff61ce294a2da2080bc5 Mon Sep 17 00:00:00 2001 From: folivetti Date: Wed, 19 Aug 2026 05:57:18 -0300 Subject: [PATCH 149/166] eggp 2.0 --- eggp.cabal | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/eggp.cabal b/eggp.cabal index 294f3aa..5713428 100755 --- a/eggp.cabal +++ b/eggp.cabal @@ -36,7 +36,7 @@ common warnings foreign-library _binding import: warnings - ghc-options: -threaded + ghc-options: -threaded -rtsopts -O2 -fspec-constr -fmax-simplifier-iterations=20 -fexpose-all-unfoldings -optc-O3 type: native-shared -- 01-04-2023: @@ -92,7 +92,7 @@ foreign-library _binding executable eggp import: warnings - ghc-options: -threaded -rtsopts -O2 -fspec-constr -fmax-simplifier-iterations=20 -fexpose-all-unfoldings -optc-O3 -optc-mavx2 -optc-mfma + ghc-options: -threaded -rtsopts -O2 -fspec-constr -fmax-simplifier-iterations=20 -fexpose-all-unfoldings -optc-O3 main-is: Main.hs hs-source-dirs: app, src other-modules: From 379a48150b25d2eacdeec598ac8aaa6c287c7c93 Mon Sep 17 00:00:00 2001 From: folivetti Date: Wed, 19 Aug 2026 06:22:10 -0300 Subject: [PATCH 150/166] eggp 2.0 --- .github/workflows/release.yml | 30 +++++++++++++++--------------- 1 file changed, 15 insertions(+), 15 deletions(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index d10afd4..d1dcf01 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -51,26 +51,26 @@ jobs: # runner: "ubuntu-latest" # arch: "i686" # plat: "manylinux" - #- os: - # name: "Linux" - # runner: "ubuntu-latest" - # arch: "x86_64" - # plat: "manylinux" + - os: + name: "Linux" + runner: "ubuntu-latest" + arch: "x86_64" + plat: "manylinux" #- os: # name: "Linux" # runner: "ubuntu-latest" # arch: "x86_64" # plat: "musllinux" - #- os: - # name: "macOS" - # runner: "macos-15-intel" - # arch: "x86_64" - # plat: "macosx" - #- os: - # name: "macOS" - # runner: "macos-15" - # arch: "arm64" - # plat: "macosx" + - os: + name: "macOS" + runner: "macos-15-intel" + arch: "x86_64" + plat: "macosx" + - os: + name: "macOS" + runner: "macos-15" + arch: "arm64" + plat: "macosx" #- os: # name: "Windows" # runner: "windows-latest" From f3a08c939cc68f109a4ad1f48f38ce1756a199ce Mon Sep 17 00:00:00 2001 From: folivetti Date: Wed, 19 Aug 2026 07:36:24 -0300 Subject: [PATCH 151/166] eggp 2.0 --- .github/workflows/release.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index d1dcf01..b88b87a 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -105,6 +105,7 @@ jobs: ghcup install ghc 9.14.1 ghcup install cabal 3.16 ghcup set ghc 9.14.1 + echo "$HOME/.ghcup/bin" >> $GITHUB_PATH - if: matrix.job.os.name == 'Windows' uses: haskell-actions/setup@v2 From a8498321454171a00b0e9b1f37b6125b9ffc54d9 Mon Sep 17 00:00:00 2001 From: folivetti Date: Wed, 19 Aug 2026 07:46:33 -0300 Subject: [PATCH 152/166] eggp 2.0 --- .github/workflows/release.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index b88b87a..ae6a5ff 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -105,6 +105,7 @@ jobs: ghcup install ghc 9.14.1 ghcup install cabal 3.16 ghcup set ghc 9.14.1 + ~/.ghcup/bin/cabal update echo "$HOME/.ghcup/bin" >> $GITHUB_PATH - if: matrix.job.os.name == 'Windows' From 9f6228ecf6a70dd88e8508baeed1c40f7b218e1f Mon Sep 17 00:00:00 2001 From: folivetti Date: Wed, 19 Aug 2026 08:28:00 -0300 Subject: [PATCH 153/166] eggp 2.0 --- .github/workflows/release.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index ae6a5ff..906927e 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -107,6 +107,8 @@ jobs: ghcup set ghc 9.14.1 ~/.ghcup/bin/cabal update echo "$HOME/.ghcup/bin" >> $GITHUB_PATH + echo "GHC=$HOME/.ghcup/bin/ghc" >> $GITHUB_ENV + echo "CABAL=$HOME/.ghcup/bin/cabal" >> $GITHUB_ENV - if: matrix.job.os.name == 'Windows' uses: haskell-actions/setup@v2 From 3f328aff53d738333443a1d122d83af88c73ae64 Mon Sep 17 00:00:00 2001 From: Fabricio Olivetti de Franca Date: Wed, 19 Aug 2026 08:35:33 -0300 Subject: [PATCH 154/166] Update release.yml --- .github/workflows/release.yml | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 906927e..8a0adba 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -66,11 +66,11 @@ jobs: runner: "macos-15-intel" arch: "x86_64" plat: "macosx" - - os: - name: "macOS" - runner: "macos-15" - arch: "arm64" - plat: "macosx" + #- os: + # name: "macOS" + # runner: "macos-15" + # arch: "arm64" + # plat: "macosx" #- os: # name: "Windows" # runner: "windows-latest" From 8b8158ab75b8f03fe3fbae5e86d8290848f2956a Mon Sep 17 00:00:00 2001 From: folivetti Date: Wed, 19 Aug 2026 08:48:15 -0300 Subject: [PATCH 155/166] eggp 2.0 --- .github/workflows/release.yml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 906927e..8a83bb4 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -105,7 +105,8 @@ jobs: ghcup install ghc 9.14.1 ghcup install cabal 3.16 ghcup set ghc 9.14.1 - ~/.ghcup/bin/cabal update + export PATH="$HOME/.ghcup/bin:$PATH" + cabal update echo "$HOME/.ghcup/bin" >> $GITHUB_PATH echo "GHC=$HOME/.ghcup/bin/ghc" >> $GITHUB_ENV echo "CABAL=$HOME/.ghcup/bin/cabal" >> $GITHUB_ENV From 8457c622995911c5870878cb82b0e1ee35e8901f Mon Sep 17 00:00:00 2001 From: folivetti Date: Wed, 19 Aug 2026 08:49:14 -0300 Subject: [PATCH 156/166] eggp 2.0 --- .github/workflows/release.yml | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 95e9b58..c94d498 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -41,21 +41,21 @@ jobs: strategy: matrix: job: - - os: - name: "Linux" - runner: "ubuntu-24.04-arm" - arch: "aarch64" - plat: "manylinux" + #- os: + # name: "Linux" + # runner: "ubuntu-24.04-arm" + # arch: "aarch64" + # plat: "manylinux" #- os: # name: "Linux" # runner: "ubuntu-latest" # arch: "i686" # plat: "manylinux" - - os: - name: "Linux" - runner: "ubuntu-latest" - arch: "x86_64" - plat: "manylinux" + #- os: + # name: "Linux" + # runner: "ubuntu-latest" + # arch: "x86_64" + # plat: "manylinux" #- os: # name: "Linux" # runner: "ubuntu-latest" From da708a3a204a56c9c52645f0c9897116d3788e2b Mon Sep 17 00:00:00 2001 From: folivetti Date: Wed, 19 Aug 2026 09:03:21 -0300 Subject: [PATCH 157/166] eggp 2.0 --- .github/workflows/release.yml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index c94d498..4f6f2a9 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -159,7 +159,7 @@ jobs: name: Install Homebrew dependencies run: | brew update - brew install nlopt + brew install nlopt libpq - if: matrix.job.os.name == 'macOS' name: Configure environment variables run: | @@ -167,6 +167,7 @@ jobs: echo "CPATH=/usr/local/include:/opt/homebrew/include" >> $GITHUB_ENV echo "DYLD_LIBRARY_PATH=/opt/homebrew/lib" >> $GITHUB_ENV echo "MACOSX_DEPLOYMENT_TARGET=14.0" >> $GITHUB_ENV + echo "PATH=/opt/homebrew/opt/libpq/bin:$PATH" >> $GITHUB_ENV - # 19-06-2023: # Required to build aarch64 wheels. From 179d6b8213996d323efdda600712f17b461c67cb Mon Sep 17 00:00:00 2001 From: folivetti Date: Wed, 19 Aug 2026 09:23:05 -0300 Subject: [PATCH 158/166] eggp 2.0 --- .github/workflows/release.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 4f6f2a9..4941311 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -167,7 +167,7 @@ jobs: echo "CPATH=/usr/local/include:/opt/homebrew/include" >> $GITHUB_ENV echo "DYLD_LIBRARY_PATH=/opt/homebrew/lib" >> $GITHUB_ENV echo "MACOSX_DEPLOYMENT_TARGET=14.0" >> $GITHUB_ENV - echo "PATH=/opt/homebrew/opt/libpq/bin:$PATH" >> $GITHUB_ENV + echo "PATH=/usr/local/opt/libpq/bin:/opt/homebrew/opt/libpq/bin:$PATH" >> $GITHUB_ENV - # 19-06-2023: # Required to build aarch64 wheels. From 30fcbac1e5c35f7430114dd0bf1728e0a15f92d8 Mon Sep 17 00:00:00 2001 From: folivetti Date: Wed, 19 Aug 2026 11:19:15 -0300 Subject: [PATCH 159/166] eggp 2.0 --- .github/workflows/release.yml | 30 +++++++++++++++--------------- eggp.cabal | 2 +- pyproject.toml | 2 +- src/eggp/__init__.py | 2 +- 4 files changed, 18 insertions(+), 18 deletions(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 4941311..6828209 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -41,21 +41,21 @@ jobs: strategy: matrix: job: - #- os: - # name: "Linux" - # runner: "ubuntu-24.04-arm" - # arch: "aarch64" - # plat: "manylinux" + - os: + name: "Linux" + runner: "ubuntu-24.04-arm" + arch: "aarch64" + plat: "manylinux" #- os: # name: "Linux" # runner: "ubuntu-latest" # arch: "i686" # plat: "manylinux" - #- os: - # name: "Linux" - # runner: "ubuntu-latest" - # arch: "x86_64" - # plat: "manylinux" + - os: + name: "Linux" + runner: "ubuntu-latest" + arch: "x86_64" + plat: "manylinux" #- os: # name: "Linux" # runner: "ubuntu-latest" @@ -66,11 +66,11 @@ jobs: runner: "macos-15-intel" arch: "x86_64" plat: "macosx" - #- os: - # name: "macOS" - # runner: "macos-15" - # arch: "arm64" - # plat: "macosx" + - os: + name: "macOS" + runner: "macos-15" + arch: "arm64" + plat: "macosx" #- os: # name: "Windows" # runner: "windows-latest" diff --git a/eggp.cabal b/eggp.cabal index 5713428..ac04f6c 100755 --- a/eggp.cabal +++ b/eggp.cabal @@ -5,7 +5,7 @@ cabal-version: 2.2 -- see: https://github.com/sol/hpack name: eggp -version: 1.0.17 +version: 2.0.0 synopsis: A general library to work with Symbolic Regression expression trees. description: A Symbolic Regression Tree data structure to work with mathematical expressions with support to first order derivative and simplification; category: Math, Data, Data Structures diff --git a/pyproject.toml b/pyproject.toml index 797a4ae..3b169e2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,7 +12,7 @@ build-backend = "setuptools.build_meta" [project] name = "eggp" -version = "1.0.17" +version = "2.0.0" authors = [{ name = "Fabricio Olivetti", email = "folivetti@users.noreply.github.com" }] description = "Python Wheels for eggp algorithm." readme = "README.md" diff --git a/src/eggp/__init__.py b/src/eggp/__init__.py index f9429a7..5a05014 100644 --- a/src/eggp/__init__.py +++ b/src/eggp/__init__.py @@ -50,7 +50,7 @@ def _get_hs_eggp_run_data(): _hs_eggp_run_data = lib.hs_eggp_run_data return _hs_eggp_run_data -VERSION: str = "1.0.17" +VERSION: str = "2.0.0" _hs_rts_init: bool = False From f30b993b5738fb0e4dc4cd247df33e4a0d980177 Mon Sep 17 00:00:00 2001 From: folivetti Date: Wed, 19 Aug 2026 11:50:19 -0300 Subject: [PATCH 160/166] eggp 2.0 --- .github/workflows/release.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 6828209..4426d27 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -166,7 +166,7 @@ jobs: echo "LIBRARY_PATH=/usr/local/lib:/opt/homebrew/lib" >> $GITHUB_ENV echo "CPATH=/usr/local/include:/opt/homebrew/include" >> $GITHUB_ENV echo "DYLD_LIBRARY_PATH=/opt/homebrew/lib" >> $GITHUB_ENV - echo "MACOSX_DEPLOYMENT_TARGET=14.0" >> $GITHUB_ENV + echo "MACOSX_DEPLOYMENT_TARGET=15.0" >> $GITHUB_ENV echo "PATH=/usr/local/opt/libpq/bin:/opt/homebrew/opt/libpq/bin:$PATH" >> $GITHUB_ENV - # 19-06-2023: @@ -218,7 +218,7 @@ jobs: ln -sf /usr/pgsql-16/lib/libpgcommon* /usr/local/lib64/ && ldconfig CIBW_ENVIRONMENT_LINUX: 'PATH=/root/.ghcup/bin:/usr/pgsql-16/bin:$PATH LIBRARY_PATH=/usr/local/lib64:/usr/pgsql-16/lib LD_LIBRARY_PATH=/usr/local/lib64:/usr/pgsql-16/lib C_INCLUDE_PATH=/usr/local/include:/usr/pgsql-16/include PKG_CONFIG_PATH=/usr/local/lib64/pkgconfig:/usr/pgsql-16/lib/pkgconfig' - MACOSX_DEPLOYMENT_TARGET: ${{ matrix.os.runner == 'macos-14' && '14.0' || '14.0' }} + MACOSX_DEPLOYMENT_TARGET: ${{ matrix.os.runner == 'macos-15' && '15.0' || '15.0' }} - # Build source distribution (sdist) run: | From 04e0a986894cdb3252af10a6708380f9631dd3e6 Mon Sep 17 00:00:00 2001 From: folivetti Date: Fri, 21 Aug 2026 10:37:03 -0300 Subject: [PATCH 161/166] eggp: clear matcher seen-set between offspring (fix progressive slowdown) Each offspring runs an independent 1-iteration eqsat on a shared e-graph. The mark-on-attempt _seenMatches set was never cleared, so it grew with the whole graph and every n-ary matcher call rebuilt the O(seen) exclude list from it, making per-generation eqsat time scale linearly with graph size (the GP loop got progressively slower every ~5 iterations). Clear _seenMatches between offspring in evolve and evolveDB. Measured ~13x faster on the 20-gen/2000-pop benchmark (51s vs >10min timeout) with preserved eqsat merge density. --- src/Search.hs | 77 ++++++++++++++++++++++++++++++++++++--------------- 1 file changed, 55 insertions(+), 22 deletions(-) diff --git a/src/Search.hs b/src/Search.hs index 3bf4996..06c20d3 100644 --- a/src/Search.hs +++ b/src/Search.hs @@ -16,6 +16,8 @@ import Algorithm.EqSat.Info import Algorithm.EqSat.DB import Algorithm.SRTree.Likelihoods import Algorithm.SRTree.ModelSelection +import Algorithm.SRTree.Compile (compileTree, EvalTree(..)) +import Algorithm.SRTree.ConfidenceIntervals (CIType(..), PType(..), paramCI, getAllProfiles, getStatsFromModel, CI(..), BasicStats(..)) import Control.Lens (element, makeLenses, over, (&), (+~), (-~), (.~), (^.)) import Control.Monad (foldM, forM_, forM, when, unless, filterM, (>=>), (<=<), replicateM, replicateM_) import Control.Monad.State.Strict @@ -34,6 +36,7 @@ import qualified Data.HashSet as Set import Data.List ( sort, maximumBy, intercalate, sortOn, intersperse, nub, zip4 ) import Data.IntSet (IntSet) import qualified Data.IntSet as IntSet +import qualified Data.Map as Map import qualified Data.Sequence as FingerTree import Data.Function ( on ) import qualified Data.Foldable as Foldable @@ -108,8 +111,9 @@ data Args = Args } deriving (Show) -csvHeader :: String -csvHeader = "id,view,Expression,Numpy,Math,theta,size,loss_train,loss_val,loss_test,maxloss,R2_train,R2_val,R2_test,dl_train,dl_val,dl_test" +csvHeader :: Int -> String +csvHeader maxParams = "id,view,Expression,Numpy,Math,theta,size,loss_train,loss_val,loss_test,maxloss,R2_train,R2_val,R2_test,dl_train,dl_val,dl_test" + <> concatMap (\i -> ",t" <> show i <> "_lower,t" <> show i <> "_upper") [0 .. maxParams - 1] forceSimp :: Fix SRTree -> Fix SRTree forceSimp t = let s = simplifyEqSatDefault t in countNodes s `seq` s @@ -148,6 +152,9 @@ egraphGP dataTrainVals dataTests args = do refitIds <- gets (IntSet.toList . _refits . _eDB) modify' $ over (eDB . refits) (const IntSet.empty) fitBatchCached fitCache True fitFun refitIds + refitIds <- gets (IntSet.toList . _refits . _eDB) + modify' $ over (eDB . refits) (const IntSet.empty) + fitBatchCached fitCache True fitFun refitIds fitBatchCached fitCache False fitFun newPop' -- Store newly fitted fitness in DB @@ -171,7 +178,7 @@ egraphGP dataTrainVals dataTests args = do then forM (Prelude.zip [curIx..] newPop') $ uncurry printExpr else pure [] - totSz <- gets (HashMap.size . _eNodeToEClass) -- (IntMap.size . _eClass) + totSz <- gets (HashMap.size . _eNodeToEClass) let full = totSz > max maxMem (_nPop args) cleanedIds <- if full then Just <$> cleanEGraph else pure Nothing when full cleanDB @@ -188,13 +195,12 @@ egraphGP dataTrainVals dataTests args = do pareto <- if (_useFracBayes args) then getParetoFront else concat <$> (forM [1 .. _maxSize args] $ \n -> getTopFitEClassWithSize n 2) - -- pareto <- concat <$> (forM [1 .. _maxSize args] $ \n -> getTopFitEClassWithSize n 2) - -- pareto <- getParetoFront let remainder = _nPop args - length pareto lft <- if full then getTopFitEClassThat remainder (const True) else pure $ Prelude.take remainder newPop' Prelude.mapM canonical (pareto <> lft) + pure (newPop, out <> out', curIx + (_nPop args)) -- Final sync to DB @@ -206,7 +212,8 @@ egraphGP dataTrainVals dataTests args = do pf <- if _trace args then pure finalOut else paretoFront fitFun (_maxSize args) printExpr - pure $ unlines (csvHeader : concat pf) + let maxP = if _nParams args == -1 then nFeats else _nParams args + pure $ unlines (csvHeader maxP : concat pf) where maxSize = (_maxSize args) maxMem = 2000000 -- running 1 iter of eqsat for each new individual will consume ~3GB @@ -276,21 +283,33 @@ egraphGP dataTrainVals dataTests args = do then pure xs else iterateFor (n-1) t1 maxT' xs' f - evolve xs' = do xs <- Prelude.mapM canonical xs' - parents <- tournament xs - offspring <- combine parents - if _nParams args == 0 - then runEqSat myCost rewritesWithConstant 1 >> cleanDB - else runEqSat myCost rewritesParams 1 >> cleanDB - pure offspring - - evolveDB xs' = do xs <- Prelude.mapM canonical xs' - parents <- tournament xs - offspring <- combineDB parents - if _nParams args == 0 - then runEqSat myCost rewritesWithConstant 1 >> cleanDB - else runEqSat myCost rewritesParams 1 >> cleanDB - pure offspring + evolve xs' = do + xs <- Prelude.mapM canonical xs' + parents <- tournament xs + offspring <- combine parents + if _nParams args == 0 + then runEqSat myCost rewritesWithConstant 1 >> cleanDB + else runEqSat myCost rewritesParams 1 >> cleanDB + -- Each offspring runs an independent 1-iteration eqsat on a shared + -- e-graph. The mark-on-attempt seen-set would otherwise grow with the + -- whole graph (an O(seen) exclude materialization per matcher call, + -- the source of the progressive slowdown), so clear it between offspring. + modify' $ over (eDB . seenMatches) (const Map.empty) + pure offspring + + evolveDB xs' = do + xs <- Prelude.mapM canonical xs' + parents <- tournament xs + offspring <- combineDB parents + if _nParams args == 0 + then runEqSat myCost rewritesWithConstant 1 >> cleanDB + else runEqSat myCost rewritesParams 1 >> cleanDB + -- Each offspring runs an independent 1-iteration eqsat on a shared + -- e-graph. The mark-on-attempt seen-set would otherwise grow with the + -- whole graph (an O(seen) exclude materialization per matcher call, + -- the source of the progressive slowdown), so clear it between offspring. + modify' $ over (eDB . seenMatches) (const Map.empty) + pure offspring tournament xs = do p1 <- applyTournament xs >>= canonical p2 <- applyTournament xs >>= canonical @@ -509,7 +528,7 @@ egraphGP dataTrainVals dataTests args = do distribution = _distribution args expr = paramsToConst (V.toList theta) best' - showNA z = if isNaN z then "" else show z + showNA z = if isNaN z || isInfinite z then "NA" else show z n = fromIntegral (V.length y) :: Double n_val = fromIntegral (V.length y_val) :: Double @@ -552,10 +571,24 @@ egraphGP dataTrainVals dataTests args = do varnames = _varnames args showExprFun = if null varnames then showExpr else showExprWithVars (splitOn "," varnames) showLatexFun = if null varnames then showLatex else showLatexWithVars (splitOn "," varnames) + + -- Compute profile-likelihood CIs + nSamples = V.length y + dist = case distribution of { NLL d -> d; _ -> Gaussian } + et = compileTree dist x y mYErr best' + stats = getStatsFromModel dist mYErr x y best' theta + profiles = getAllProfiles Constrained et theta (_stdErr stats) [] 0.05 + ciVals = paramCI (Profile stats profiles) nSamples theta 0.05 + maxP = if _nParams args == -1 then nFeats else _nParams args + ciStr = intercalate "," + $ Prelude.map (\(CI _ l h) -> show l <> "," <> show h) ciVals + ++ Prelude.replicate (2 * (maxP - length ciVals)) "" + pure $ show ix <> "," <> show view <> "," <> showExprFun expr <> "," <> "\"" <> showPython best' <> "\"," <> "\"$$" <> showLatexFun best' <> "$$\"," <> thetaStr <> "," <> show (countNodes $ convertProtectedOps expr) <> "," <> vals + <> "," <> ciStr pure ts insertTerms = From 1385412414494d709bef5a645dfe5ba734ce5c4d Mon Sep 17 00:00:00 2001 From: folivetti Date: Fri, 21 Aug 2026 11:41:45 -0300 Subject: [PATCH 162/166] performance fix from eqsat with multiple viewed pattern --- ChangeLog.md | 11 ++++ README.md | 2 +- app/EGGPA.hs | 3 -- eggp.cabal | 8 +-- pyproject.toml | 2 +- src/EGGP.hs | 3 -- src/Search.hs | 3 -- src/eggp.egg-info/PKG-INFO | 2 +- src/eggp/__init__.py | 2 +- tutorials/db_ci_example.py | 102 +++++++++++++++++++++++++++++++++++++ 10 files changed, 118 insertions(+), 20 deletions(-) create mode 100644 ChangeLog.md create mode 100644 tutorials/db_ci_example.py diff --git a/ChangeLog.md b/ChangeLog.md new file mode 100644 index 0000000..0c31499 --- /dev/null +++ b/ChangeLog.md @@ -0,0 +1,11 @@ +# Changelog for eggp + +## 2.1.0 + +- Pareto front output now includes profile-likelihood confidence intervals + (Constrained profile method) for all fitted parameters +- CSV output gains `t_lower,t_upper` columns per parameter + +## 2.0.0 + +- Initial release with DB-backed GP mode diff --git a/README.md b/README.md index 6474ea7..f3c1c8b 100644 --- a/README.md +++ b/README.md @@ -91,7 +91,7 @@ To install eggp you'll need: - `libz` - `libnlopt` - `libgmp` -- `ghc-9.6.6` +- `ghc-9.14.1` - `cabal` or `stack` ### Method 1: PIP diff --git a/app/EGGPA.hs b/app/EGGPA.hs index dc53e3c..e2f6dc9 100644 --- a/app/EGGPA.hs +++ b/app/EGGPA.hs @@ -40,9 +40,6 @@ import qualified Data.ByteString.Lazy as BS import Algorithm.EqSat (runEqSat,applySingleMergeOnlyEqSat) -import GHC.IO (unsafePerformIO) -import Control.Scheduler -import Control.Monad.IO.Unlift import Data.SRTree (convertProtectedOps) import Options.Applicative as Opt hiding (Const) diff --git a/eggp.cabal b/eggp.cabal index ac04f6c..1a255a6 100755 --- a/eggp.cabal +++ b/eggp.cabal @@ -5,7 +5,7 @@ cabal-version: 2.2 -- see: https://github.com/sol/hpack name: eggp -version: 2.0.0 +version: 2.1.1 synopsis: A general library to work with Symbolic Regression expression trees. description: A Symbolic Regression Tree data structure to work with mathematical expressions with support to first order derivative and simplification; category: Math, Data, Data Structures @@ -73,7 +73,6 @@ foreign-library _binding , mtl >=2.2 && <2.4 , optparse-applicative >=0.17 && <0.20 , random >=1.2 && <1.4 - , scheduler >=2.0.0.1 && <3 , split >=0.2.5 && <0.3 , srtree >= 3.0.0.0 , srtree-db >= 0.1.0.0 @@ -81,8 +80,6 @@ foreign-library _binding , text >= 1.2 && < 2.2 , statistics >=0.16.2.1 && <0.17 , transformers >=0.6.1.0 && <0.7 - , unliftio >=0.2.10 && <1 - , unliftio-core >=0.2.1 && <1 , unordered-containers ==0.2.* , vector >=0.12 && <0.14 , zlib >=0.6.3 && <0.8 @@ -117,7 +114,6 @@ executable eggp , mtl >=2.2 && <2.4 , optparse-applicative >=0.17 && <0.19 , random >=1.2 && <1.4 - , scheduler >=2.0.0.1 && <3 , split >=0.2.5 && <0.3 , srtree >= 3.0.0.0 , srtree-db >= 0.1.0.0 @@ -125,8 +121,6 @@ executable eggp , text >= 1.2 && < 2.2 , statistics >=0.16.2.1 && <0.17 , transformers >=0.6.1.0 && <0.7 - , unliftio >=0.2.10 && <1 - , unliftio-core >=0.2.1 && <1 , unordered-containers ==0.2.* , vector >=0.12 && <0.14 , zlib >=0.6.3 && <0.8 diff --git a/pyproject.toml b/pyproject.toml index 3b169e2..0c88f8f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,7 +12,7 @@ build-backend = "setuptools.build_meta" [project] name = "eggp" -version = "2.0.0" +version = "2.1.1" authors = [{ name = "Fabricio Olivetti", email = "folivetti@users.noreply.github.com" }] description = "Python Wheels for eggp algorithm." readme = "README.md" diff --git a/src/EGGP.hs b/src/EGGP.hs index adde789..0e443e4 100644 --- a/src/EGGP.hs +++ b/src/EGGP.hs @@ -40,9 +40,6 @@ import qualified Data.ByteString.Lazy as BS import Algorithm.EqSat (runEqSat,applySingleMergeOnlyEqSat) -import GHC.IO (unsafePerformIO) -import Control.Scheduler -import Control.Monad.IO.Unlift import Data.SRTree (convertProtectedOps) import Options.Applicative as Opt hiding (Const) diff --git a/src/Search.hs b/src/Search.hs index 06c20d3..184755e 100644 --- a/src/Search.hs +++ b/src/Search.hs @@ -49,9 +49,6 @@ import Data.List.Split (splitOn) import Algorithm.EqSat (runEqSat,applySingleMergeOnlyEqSat) -import GHC.IO (unsafePerformIO) -import Control.Scheduler -import Control.Monad.IO.Unlift import Control.Exception (evaluate) import System.Timeout (timeout) import Data.SRTree (convertProtectedOps) diff --git a/src/eggp.egg-info/PKG-INFO b/src/eggp.egg-info/PKG-INFO index 0ffa47c..5868ce1 100644 --- a/src/eggp.egg-info/PKG-INFO +++ b/src/eggp.egg-info/PKG-INFO @@ -1,6 +1,6 @@ Metadata-Version: 2.4 Name: eggp -Version: 1.0.17 +Version: 2.1.1 Summary: Python Wheels for eggp algorithm. Author-email: Fabricio Olivetti License: GNU GENERAL PUBLIC LICENSE diff --git a/src/eggp/__init__.py b/src/eggp/__init__.py index 5a05014..df5052e 100644 --- a/src/eggp/__init__.py +++ b/src/eggp/__init__.py @@ -50,7 +50,7 @@ def _get_hs_eggp_run_data(): _hs_eggp_run_data = lib.hs_eggp_run_data return _hs_eggp_run_data -VERSION: str = "2.0.0" +VERSION: str = "2.1.1" _hs_rts_init: bool = False diff --git a/tutorials/db_ci_example.py b/tutorials/db_ci_example.py new file mode 100644 index 0000000..d66df41 --- /dev/null +++ b/tutorials/db_ci_example.py @@ -0,0 +1,102 @@ +""" +db_ci_example.py - eggp DB mode with confidence intervals +========================================================= + +This tutorial demonstrates the eggp DB-backed mode with profile-likelihood +confidence intervals in the Pareto front output. + +The CI columns (ci_t0_lower, ci_t0_upper, ...) show the 95% profile-likelihood +interval for each parameter. Narrow intervals indicate well-estimated parameters; +wide intervals indicate high uncertainty. + +Run from the ``tutorials/`` directory:: + + python db_ci_example.py +""" + +import os +import time +import sqlite3 + +import numpy as np +import pandas as pd + +from eggp import EGGP + + +def make_dataset(path): + rng = np.random.default_rng(42) + n = 200 + x0 = rng.uniform(-3, 3, n) + x1 = rng.uniform(-3, 3, n) + y = np.sin(x0) + 0.5 * x1 + df = pd.DataFrame({"x0": x0, "x1": x1, "y": y}) + df.to_csv(path, index=False) + return df + + +def main(): + data_csv = "db_ci_data.csv" + db_file = "db_ci.db" + + for f in (data_csv, db_file): + if os.path.exists(f): + os.remove(f) + + df = make_dataset(data_csv) + X = df[["x0", "x1"]] + y = df["y"] + + print("Running eggp with DB mode and CI computation...") + print("The Pareto front CSV will include confidence interval columns.\n") + + model = EGGP( + gen=10, + nPop=50, + maxSize=12, + nTournament=3, + pc=0.8, + pm=0.3, + nonterminals="add,sub,mul,div,sin", + loss="MSE", + optIter=30, + optRepeat=2, + nParams=1, + folds=1, + simplify=True, + max_time=30, + dbFile=db_file, + dbDataset="ci_demo", + dbFlushEvery=5, + ) + + t0 = time.time() + model.fit(X, y) + elapsed = time.time() - t0 + + print(f"\nFinished in {elapsed:.1f}s") + print(f"\nPareto front ({len(model.results)} expressions):") + + # Show the results with CI columns + cols_to_show = ["Expression", "loss_train", "size"] + ci_cols = [c for c in model.results.columns if c.startswith("ci_")] + if ci_cols: + cols_to_show.extend(ci_cols) + print(model.results[cols_to_show].to_string(index=False)) + print(f"\nCI columns: {', '.join(ci_cols)}") + print("Each parameter has lower/upper bounds showing the 95%") + print("profile-likelihood confidence interval.") + else: + print(model.results[["Expression", "loss_train", "size"]].to_string(index=False)) + print("\n(No CI columns in output — check eggp version)") + + # Cleanup + for f in (data_csv, db_file): + try: + os.remove(f) + except OSError: + pass + + +if __name__ == "__main__": + main() From 48c55331ba57ccb0fd5f333b1fe734b01bef330a Mon Sep 17 00:00:00 2001 From: folivetti Date: Sun, 23 Aug 2026 16:08:57 -0300 Subject: [PATCH 163/166] Fix profile-likelihood CI for non-NLL losses and improve robustness - Map MSE/LOG10/MAE/MAPE/Pinball to LeastSquares distribution instead of defaulting to Gaussian (which adds spurious sigma parameter) - Switch from Constrained to Bates profile method for more robust CIs - Use showNA for NaN/Inf CI values in CSV output - Fix csvHeader to use actualMaxP from Pareto front walk - Add computeMaxP to find actual max parameter count across Pareto front - Add ../srtree-db to cabal.project for local builds --- ChangeLog.md | 12 +++++++++++ cabal.project | 2 ++ src/Search.hs | 55 ++++++++++++++++++++++++++++++++++++++++----------- 3 files changed, 57 insertions(+), 12 deletions(-) diff --git a/ChangeLog.md b/ChangeLog.md index 0c31499..b971f1b 100644 --- a/ChangeLog.md +++ b/ChangeLog.md @@ -1,5 +1,17 @@ # Changelog for eggp +## 2.1.2 + +- Fixed profile-likelihood CI distribution mapping: non-NLL losses (MSE, LOG10, + MAE, MAPE, Pinball) now correctly use `LeastSquares` distribution instead of + defaulting to `Gaussian` (which adds a spurious sigma parameter) +- Changed profile method from `Constrained` to `Bates` for more robust CIs +- CI output uses `showNA` to display "NA" for NaN/Inf values +- Fixed `csvHeader` to use `actualMaxP` (computed from Pareto front) instead of + `_nParams` arg, ensuring enough CI columns when `--n-params -1` +- Added `computeMaxP` to walk Pareto front and find actual max parameter count +- Added `../srtree-db` to `cabal.project` for local builds + ## 2.1.0 - Pareto front output now includes profile-likelihood confidence intervals diff --git a/cabal.project b/cabal.project index 7c0deb5..cdbe864 100644 --- a/cabal.project +++ b/cabal.project @@ -1,4 +1,6 @@ packages: . + ../srtree + ../srtree-db allow-newer: * diff --git a/src/Search.hs b/src/Search.hs index 184755e..44d44db 100644 --- a/src/Search.hs +++ b/src/Search.hs @@ -134,7 +134,7 @@ egraphGP dataTrainVals dataTests args = do fitBatchCached fitCache False fitFun pop output <- if _trace args - then forM (Prelude.zip [0..] pop) $ uncurry printExpr + then forM (Prelude.zip [0..] pop) $ uncurry (printExpr nFeats) else pure [] let m = (_nPop args) `div` (_maxSize args) @@ -172,7 +172,7 @@ egraphGP dataTrainVals dataTests args = do Nothing -> pure () out' <- if _trace args - then forM (Prelude.zip [curIx..] newPop') $ uncurry printExpr + then forM (Prelude.zip [curIx..] newPop') $ uncurry (printExpr nFeats) else pure [] totSz <- gets (HashMap.size . _eNodeToEClass) @@ -206,11 +206,17 @@ egraphGP dataTrainVals dataTests args = do io $ syncEGraphDB (_dbFile args) (dbDatasetName args) eg when ((not.null) (_dumpTo args)) $ get >>= (io . BS.writeFile (_dumpTo args) . encode ) + + -- When _nParams is -1, compute the actual max number of params across the + -- Pareto front so the CSV header allocates enough CI columns. + actualMaxP <- if _nParams args == -1 + then computeMaxP (_maxSize args) (_distribution args) nFeats + else pure (_nParams args) + let printExpr' = printExpr actualMaxP pf <- if _trace args then pure finalOut - else paretoFront fitFun (_maxSize args) printExpr - let maxP = if _nParams args == -1 then nFeats else _nParams args - pure $ unlines (csvHeader maxP : concat pf) + else paretoFront fitFun (_maxSize args) printExpr' + pure $ unlines (csvHeader actualMaxP : concat pf) where maxSize = (_maxSize args) maxMem = 2000000 -- running 1 iter of eqsat for each new individual will consume ~3GB @@ -498,8 +504,8 @@ egraphGP dataTrainVals dataTests args = do pure $ naryTree op exprs - printExpr :: Int -> EClassId -> RndEGraph [String] - printExpr ix ec = do + printExpr :: Int -> Int -> EClassId -> RndEGraph [String] + printExpr actualMaxP ix ec = do thetas' <- getTheta ec bestExpr0 <- getBestExpr ec bestExpr <- if _simplify args @@ -570,16 +576,19 @@ egraphGP dataTrainVals dataTests args = do showLatexFun = if null varnames then showLatex else showLatexWithVars (splitOn "," varnames) -- Compute profile-likelihood CIs + -- Bates (1985) profile likelihood works for MSE/least-squares: + -- 2*(MSE(theta) - MSE(theta_hat)) <= chi2_1 + -- For NLL losses, the same formula applies with the NLL. nSamples = V.length y - dist = case distribution of { NLL d -> d; _ -> Gaussian } + dist = case distribution of { NLL d -> d; MSE -> LeastSquares; LOG10 -> LeastSquares; MAE -> LeastSquares; MAPE -> LeastSquares; Pinball _ -> LeastSquares; _ -> Gaussian } et = compileTree dist x y mYErr best' stats = getStatsFromModel dist mYErr x y best' theta - profiles = getAllProfiles Constrained et theta (_stdErr stats) [] 0.05 + profiles = getAllProfiles Bates et theta (_stdErr stats) [] 0.05 ciVals = paramCI (Profile stats profiles) nSamples theta 0.05 - maxP = if _nParams args == -1 then nFeats else _nParams args + maxPExpr = actualMaxP ciStr = intercalate "," - $ Prelude.map (\(CI _ l h) -> show l <> "," <> show h) ciVals - ++ Prelude.replicate (2 * (maxP - length ciVals)) "" + $ Prelude.map (\(CI _ l h) -> showNA l <> "," <> showNA h) ciVals + ++ Prelude.replicate (2 * (maxPExpr - length ciVals)) "" pure $ show ix <> "," <> show view <> "," <> showExprFun expr <> "," <> "\"" <> showPython best' <> "\"," <> "\"$$" <> showLatexFun best' <> "$$\"," @@ -596,6 +605,28 @@ egraphGP dataTrainVals dataTests args = do replaceAt i e (c:cs) = c : replaceAt (i-1) e cs replaceAt _ _ [] = [] + -- | Walk the Pareto front to find the actual max number of params + -- (model params + distribution-specific params) across all sizes. + computeMaxP :: Int -> Loss -> Int -> RndEGraph Int + computeMaxP maxSize' dist nFeats' = go 1 0 + where + distExtra = case dist of + NLL Gaussian -> 1 + NLL ROXY -> 3 + _ -> 0 + go n acc + | n > maxSize' = pure acc + | otherwise = do + ecList <- getBestExprWithSize n + case ecList of + ((ec, _):_) -> do + ec' <- canonical ec + bestExpr <- getBestExpr ec' + let best' = if shouldReparam then relabelParams bestExpr else relabelParamsOrder bestExpr + nP = countParamsUniq best' + distExtra + go (n+1) (max acc nP) + _ -> go (n+1) acc + -- --------------------------------------------------------------------------- -- DB helpers (used when _dbFile is non-empty) -- --------------------------------------------------------------------------- From 7d257627cfff8801fe44cf684da6f107a209843d Mon Sep 17 00:00:00 2001 From: folivetti Date: Wed, 2 Sep 2026 19:51:53 -0300 Subject: [PATCH 164/166] 3x speedup with multithread, eggp DB mode for SQLite support, other improvements --- ChangeLog.md | 13 + app/EGGPA.hs | 11 +- cabal.project | 2 - eggp.cabal | 14 +- pyproject.toml | 2 +- src/EGGP.hs | 35 +- src/Search.hs | 110 +++++-- src/eggp.egg-info/PKG-INFO | 4 +- src/eggp.egg-info/SOURCES.txt | 4 +- src/eggp/__init__.py | 19 +- src/eggp/__pycache__/__init__.cpython-310.pyc | Bin 0 -> 21943 bytes src/eggp/__pycache__/__init__.cpython-313.pyc | Bin 0 -> 37291 bytes src/eggp/binding.i | 6 +- src/eggp/typing.py | 2 +- .../db_workflow_example.cpython-313.pyc | Bin 0 -> 9700 bytes ...ggp_regression_integration.cpython-313.pyc | Bin 0 -> 13301 bytes tutorials/db_workflow_example.py | 276 ++++++++++++++++ tutorials/eggp_regression_integration.py | 306 ++++++++++++++++++ 18 files changed, 731 insertions(+), 73 deletions(-) create mode 100644 src/eggp/__pycache__/__init__.cpython-310.pyc create mode 100644 src/eggp/__pycache__/__init__.cpython-313.pyc create mode 100644 tutorials/__pycache__/db_workflow_example.cpython-313.pyc create mode 100644 tutorials/__pycache__/eggp_regression_integration.cpython-313.pyc create mode 100644 tutorials/db_workflow_example.py create mode 100644 tutorials/eggp_regression_integration.py diff --git a/ChangeLog.md b/ChangeLog.md index b971f1b..9814df8 100644 --- a/ChangeLog.md +++ b/ChangeLog.md @@ -1,5 +1,18 @@ # Changelog for eggp +## 2.2.0 + +- **3x speedup with multithreading** (-N4): parallel tree generation via + `mapConcurrently` on e-graph snapshots; batch eqsat after all offspring + inserted; fixed duplicate refitIds bug +- **eggp DB mode**: external SQLite database for fitness caching, expression + deduplication, and periodic persistence (`--db-file`, `--db-dataset`, + `--db-cache-size`, `--db-flush-every`) +- `crossoverDB`/`mutateDB` check both in-memory e-graph and DB for + already-seen expressions +- Python API: `dbFile`, `dbDataset`, `dbCacheSize`, `dbFlushEvery` parameters +- SWIG binding updated for DB mode parameters + ## 2.1.2 - Fixed profile-likelihood CI distribution mapping: non-NLL losses (MSE, LOG10, diff --git a/app/EGGPA.hs b/app/EGGPA.hs index e2f6dc9..ca46597 100644 --- a/app/EGGPA.hs +++ b/app/EGGPA.hs @@ -178,6 +178,11 @@ opt = Args <> value "" <> showDefault <> help "SQLite database file for persistent e-graph (empty = in-memory)." ) + <*> strOption + ( long "db-fit-file" + <> value "" + <> showDefault + <> help "SQLite database file for fitness cache (empty = in-memory)." ) <*> strOption ( long "db-dataset" <> value "" @@ -194,11 +199,11 @@ opt = Args <> showDefault <> help "Flush e-graph to DB every N generations (0 = only at end)." ) -eggp_run :: String -> Int -> Int -> Int -> Int -> Double -> Double -> String -> String -> Int -> Int -> Int -> Int -> Int -> Bool -> Bool -> Bool -> String -> String -> String -> Bool -> String -> String -> Int -> Int -> IO String -eggp_run dataset gens nPop maxSize nTournament pc pm nonterminals loss optIter optRepeat nParams folds maxTime simplify trace generational dumpTo loadFrom varnames useFracBayes dbFile dbDataset dbCacheSz dbFlushEvery = +eggp_run :: String -> Int -> Int -> Int -> Int -> Double -> Double -> String -> String -> Int -> Int -> Int -> Int -> Int -> Bool -> Bool -> Bool -> String -> String -> String -> Bool -> String -> String -> String -> Int -> Int -> IO String +eggp_run dataset gens nPop maxSize nTournament pc pm nonterminals loss optIter optRepeat nParams folds maxTime simplify trace generational dumpTo loadFrom varnames useFracBayes dbFile dbFitFile dbDataset dbCacheSz dbFlushEvery = case readLoss loss of Nothing -> pure $ "Invalid loss function " <> loss - Just l -> let arg = Args dataset "" gens maxSize folds trace l optIter optRepeat nParams nPop nTournament pc pm nonterminals dumpTo loadFrom generational simplify maxTime varnames useFracBayes MultiThread dbFile dbDataset dbCacheSz dbFlushEvery + Just l -> let arg = Args dataset "" gens maxSize folds trace l optIter optRepeat nParams nPop nTournament pc pm nonterminals dumpTo loadFrom generational simplify maxTime varnames useFracBayes MultiThread dbFile dbFitFile dbDataset dbCacheSz dbFlushEvery in eggp arg eggp :: Args -> IO String diff --git a/cabal.project b/cabal.project index cdbe864..7c0deb5 100644 --- a/cabal.project +++ b/cabal.project @@ -1,6 +1,4 @@ packages: . - ../srtree - ../srtree-db allow-newer: * diff --git a/eggp.cabal b/eggp.cabal index 1a255a6..e8d84cf 100755 --- a/eggp.cabal +++ b/eggp.cabal @@ -57,7 +57,8 @@ foreign-library _binding extra-libraries: nlopt build-depends: - attoparsec >=0.14.4 && <0.15 + async >= 2.2 + , attoparsec >=0.14.4 && <0.15 , attoparsec-expr >=0.1.1.2 && <0.2 , base >=4.19 && <5 , binary >=0.8.9.1 && <0.9 @@ -74,8 +75,8 @@ foreign-library _binding , optparse-applicative >=0.17 && <0.20 , random >=1.2 && <1.4 , split >=0.2.5 && <0.3 - , srtree >= 3.0.0.0 - , srtree-db >= 0.1.0.0 + , srtree >= 3.0.0.3 + , srtree-db >= 0.1.2.0 , direct-sqlite >= 2.3 , text >= 1.2 && < 2.2 , statistics >=0.16.2.1 && <0.17 @@ -98,7 +99,8 @@ executable eggp extra-libraries: nlopt build-depends: - attoparsec >=0.14.4 && <0.15 + async >= 2.2 + , attoparsec >=0.14.4 && <0.15 , attoparsec-expr >=0.1.1.2 && <0.2 , base >=4.19 && <5 , binary >=0.8.9.1 && <0.9 @@ -115,8 +117,8 @@ executable eggp , optparse-applicative >=0.17 && <0.19 , random >=1.2 && <1.4 , split >=0.2.5 && <0.3 - , srtree >= 3.0.0.0 - , srtree-db >= 0.1.0.0 + , srtree >= 3.0.0.3 + , srtree-db >= 0.1.2.0 , direct-sqlite >= 2.3 , text >= 1.2 && < 2.2 , statistics >=0.16.2.1 && <0.17 diff --git a/pyproject.toml b/pyproject.toml index 0c88f8f..4b1296f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,7 +12,7 @@ build-backend = "setuptools.build_meta" [project] name = "eggp" -version = "2.1.1" +version = "2.2.0" authors = [{ name = "Fabricio Olivetti", email = "folivetti@users.noreply.github.com" }] description = "Python Wheels for eggp algorithm." readme = "README.md" diff --git a/src/EGGP.hs b/src/EGGP.hs index 0e443e4..446b260 100644 --- a/src/EGGP.hs +++ b/src/EGGP.hs @@ -113,10 +113,10 @@ hs_eggp_main = \ https://arxiv.org/abs/2501.17848\n" <> header "eggp - E-graph Genetic Programming for Symbolic Regression." ) -foreign export ccall hs_eggp_run :: CString -> CInt -> CInt -> CInt -> CInt -> CDouble -> CDouble -> CString -> CString -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CString -> CString -> CString -> CInt -> CString -> CString -> CInt -> CInt -> IO CString +foreign export ccall hs_eggp_run :: CString -> CInt -> CInt -> CInt -> CInt -> CDouble -> CDouble -> CString -> CString -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CString -> CString -> CString -> CInt -> CString -> CString -> CString -> CInt -> CInt -> IO CString -hs_eggp_run :: CString -> CInt -> CInt -> CInt -> CInt -> CDouble -> CDouble -> CString -> CString -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CString -> CString -> CString -> CInt -> CString -> CString -> CInt -> CInt -> IO CString -hs_eggp_run dataset gens nPop maxSize nTournament pc pm nonterminals loss optIter optRepeat nParams folds maxTime simplify trace generational dumpTo loadFrom varnames' useFracBayes dbFile' dbDataset' dbCacheSz dbFlushEvery = do +hs_eggp_run :: CString -> CInt -> CInt -> CInt -> CInt -> CDouble -> CDouble -> CString -> CString -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CString -> CString -> CString -> CInt -> CString -> CString -> CString -> CInt -> CInt -> IO CString +hs_eggp_run dataset gens nPop maxSize nTournament pc pm nonterminals loss optIter optRepeat nParams folds maxTime simplify trace generational dumpTo loadFrom varnames' useFracBayes dbFile' dbFitFile' dbDataset' dbCacheSz dbFlushEvery = do dataset' <- peekCString dataset nonterminals' <- peekCString nonterminals loss' <- peekCString loss @@ -124,14 +124,15 @@ hs_eggp_run dataset gens nPop maxSize nTournament pc pm nonterminals loss optIte loadFrom' <- peekCString loadFrom varnames <- peekCString varnames' dbFile <- peekCString dbFile' + dbFitFile <- peekCString dbFitFile' dbDataset <- peekCString dbDataset' - out <- eggp_run dataset' (fromIntegral gens) (fromIntegral nPop) (fromIntegral maxSize) (fromIntegral nTournament) (realToFrac pc) (realToFrac pm) nonterminals' loss' (fromIntegral optIter) (fromIntegral optRepeat) (fromIntegral nParams) (fromIntegral folds) (fromIntegral maxTime) (simplify /= 0) (trace /= 0) (generational /= 0) dumpTo' loadFrom' varnames (useFracBayes /= 0) dbFile dbDataset (fromIntegral dbCacheSz) (fromIntegral dbFlushEvery) + out <- eggp_run dataset' (fromIntegral gens) (fromIntegral nPop) (fromIntegral maxSize) (fromIntegral nTournament) (realToFrac pc) (realToFrac pm) nonterminals' loss' (fromIntegral optIter) (fromIntegral optRepeat) (fromIntegral nParams) (fromIntegral folds) (fromIntegral maxTime) (simplify /= 0) (trace /= 0) (generational /= 0) dumpTo' loadFrom' varnames (useFracBayes /= 0) dbFile dbFitFile dbDataset (fromIntegral dbCacheSz) (fromIntegral dbFlushEvery) newCString out -foreign export ccall hs_eggp_run_data :: Ptr CDouble -> Ptr CInt -> CInt -> CInt -> CString -> CString -> CInt -> CInt -> CInt -> CInt -> CDouble -> CDouble -> CString -> CString -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CString -> CString -> CString -> CInt -> CString -> CString -> CInt -> CInt -> IO CString +foreign export ccall hs_eggp_run_data :: Ptr CDouble -> Ptr CInt -> CInt -> CInt -> CString -> CString -> CInt -> CInt -> CInt -> CInt -> CDouble -> CDouble -> CString -> CString -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CString -> CString -> CString -> CInt -> CString -> CString -> CString -> CInt -> CInt -> IO CString -hs_eggp_run_data :: Ptr CDouble -> Ptr CInt -> CInt -> CInt -> CString -> CString -> CInt -> CInt -> CInt -> CInt -> CDouble -> CDouble -> CString -> CString -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CString -> CString -> CString -> CInt -> CString -> CString -> CInt -> CInt -> IO CString -hs_eggp_run_data dataPtr nrowsPtr ndatasets ncols header params gens nPop maxSize nTournament pc pm nonterminals loss optIter optRepeat nParams folds maxTime simplify trace generational dumpTo loadFrom varnames' useFracBayes dbFile' dbDataset' dbCacheSz dbFlushEvery = do +hs_eggp_run_data :: Ptr CDouble -> Ptr CInt -> CInt -> CInt -> CString -> CString -> CInt -> CInt -> CInt -> CInt -> CDouble -> CDouble -> CString -> CString -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CInt -> CString -> CString -> CString -> CInt -> CString -> CString -> CString -> CInt -> CInt -> IO CString +hs_eggp_run_data dataPtr nrowsPtr ndatasets ncols header params gens nPop maxSize nTournament pc pm nonterminals loss optIter optRepeat nParams folds maxTime simplify trace generational dumpTo loadFrom varnames' useFracBayes dbFile' dbFitFile' dbDataset' dbCacheSz dbFlushEvery = do nonterminals' <- peekCString nonterminals loss' <- peekCString loss dumpTo' <- peekCString dumpTo @@ -140,10 +141,11 @@ hs_eggp_run_data dataPtr nrowsPtr ndatasets ncols header params gens nPop maxSiz header' <- peekCString header params' <- peekCString params dbFile <- peekCString dbFile' + dbFitFile <- peekCString dbFitFile' dbDataset <- peekCString dbDataset' out <- eggp_run_data dataPtr nrowsPtr (fromIntegral ndatasets) (fromIntegral ncols) header' params' (fromIntegral gens) (fromIntegral nPop) (fromIntegral maxSize) (fromIntegral nTournament) (realToFrac pc) (realToFrac pm) nonterminals' loss' - (fromIntegral optIter) (fromIntegral optRepeat) (fromIntegral nParams) (fromIntegral folds) (fromIntegral maxTime) (simplify /= 0) (trace /= 0) (generational /= 0) dumpTo' loadFrom' varnames (useFracBayes /= 0) dbFile dbDataset (fromIntegral dbCacheSz) (fromIntegral dbFlushEvery) + (fromIntegral optIter) (fromIntegral optRepeat) (fromIntegral nParams) (fromIntegral folds) (fromIntegral maxTime) (simplify /= 0) (trace /= 0) (generational /= 0) dumpTo' loadFrom' varnames (useFracBayes /= 0) dbFile dbFitFile dbDataset (fromIntegral dbCacheSz) (fromIntegral dbFlushEvery) newCString out opt :: Parser Args @@ -270,6 +272,11 @@ opt = Args <> value "" <> showDefault <> help "SQLite database file for persistent e-graph (empty = in-memory)." ) + <*> strOption + ( long "db-fit-file" + <> value "" + <> showDefault + <> help "SQLite database file for fitness cache (empty = in-memory)." ) <*> strOption ( long "db-dataset" <> value "" @@ -286,11 +293,11 @@ opt = Args <> showDefault <> help "Flush e-graph to DB every N generations (0 = only at end)." ) -eggp_run :: String -> Int -> Int -> Int -> Int -> Double -> Double -> String -> String -> Int -> Int -> Int -> Int -> Int -> Bool -> Bool -> Bool -> String -> String -> String -> Bool -> String -> String -> Int -> Int -> IO String -eggp_run dataset gens nPop maxSize nTournament pc pm nonterminals loss optIter optRepeat nParams folds maxTime simplify trace generational dumpTo loadFrom varnames useFracBayes dbFile dbDataset dbCacheSz dbFlushEvery = +eggp_run :: String -> Int -> Int -> Int -> Int -> Double -> Double -> String -> String -> Int -> Int -> Int -> Int -> Int -> Bool -> Bool -> Bool -> String -> String -> String -> Bool -> String -> String -> String -> Int -> Int -> IO String +eggp_run dataset gens nPop maxSize nTournament pc pm nonterminals loss optIter optRepeat nParams folds maxTime simplify trace generational dumpTo loadFrom varnames useFracBayes dbFile dbFitFile dbDataset dbCacheSz dbFlushEvery = case readLoss loss of Nothing -> pure $ "Invalid loss function " <> loss - Just l -> let arg = Args dataset "" gens maxSize folds trace l optIter optRepeat nParams nPop nTournament pc pm nonterminals dumpTo loadFrom generational simplify maxTime varnames useFracBayes MultiThread dbFile dbDataset dbCacheSz dbFlushEvery + Just l -> let arg = Args dataset "" gens maxSize folds trace l optIter optRepeat nParams nPop nTournament pc pm nonterminals dumpTo loadFrom generational simplify maxTime varnames useFracBayes MultiThread dbFile dbFitFile dbDataset dbCacheSz dbFlushEvery in eggp arg eggp :: Args -> IO String @@ -309,13 +316,13 @@ eggpWithData args dataTrains' dataTests = do alg = evalStateT (egraphGP dataTrainVals dataTests args) emptyGraph evalStateT alg g' -eggp_run_data :: Ptr CDouble -> Ptr CInt -> Int -> Int -> String -> String -> Int -> Int -> Int -> Int -> Double -> Double -> String -> String -> Int -> Int -> Int -> Int -> Int -> Bool -> Bool -> Bool -> String -> String -> String -> Bool -> String -> String -> Int -> Int -> IO String -eggp_run_data dataPtr nrowsPtr ndatasets ncols header params gens nPop maxSize nTournament pc pm nonterminals loss optIter optRepeat nParams folds maxTime simplify trace generational dumpTo loadFrom varnames useFracBayes dbFile dbDataset dbCacheSz dbFlushEvery = +eggp_run_data :: Ptr CDouble -> Ptr CInt -> Int -> Int -> String -> String -> Int -> Int -> Int -> Int -> Double -> Double -> String -> String -> Int -> Int -> Int -> Int -> Int -> Bool -> Bool -> Bool -> String -> String -> String -> Bool -> String -> String -> String -> Int -> Int -> IO String +eggp_run_data dataPtr nrowsPtr ndatasets ncols header params gens nPop maxSize nTournament pc pm nonterminals loss optIter optRepeat nParams folds maxTime simplify trace generational dumpTo loadFrom varnames useFracBayes dbFile dbFitFile dbDataset dbCacheSz dbFlushEvery = case readLoss loss of Nothing -> pure $ "Invalid loss function " <> loss Just l -> do dss <- buildDataSets dataPtr nrowsPtr ndatasets ncols header params - let arg = Args "" "" gens maxSize folds trace l optIter optRepeat nParams nPop nTournament pc pm nonterminals dumpTo loadFrom generational simplify maxTime varnames useFracBayes MultiThread dbFile dbDataset dbCacheSz dbFlushEvery + let arg = Args "" "" gens maxSize folds trace l optIter optRepeat nParams nPop nTournament pc pm nonterminals dumpTo loadFrom generational simplify maxTime varnames useFracBayes MultiThread dbFile dbFitFile dbDataset dbCacheSz dbFlushEvery eggpWithData arg dss dss -- | Build a list of DataSets from a raw row-major double buffer. The buffer diff --git a/src/Search.hs b/src/Search.hs index 44d44db..38a0eb6 100644 --- a/src/Search.hs +++ b/src/Search.hs @@ -49,6 +49,8 @@ import Data.List.Split (splitOn) import Algorithm.EqSat (runEqSat,applySingleMergeOnlyEqSat) +import Control.Concurrent (getNumCapabilities) +import Control.Concurrent.Async (mapConcurrently) import Control.Exception (evaluate) import System.Timeout (timeout) import Data.SRTree (convertProtectedOps) @@ -102,6 +104,7 @@ data Args = Args _useFracBayes :: Bool, _backend :: ADBackEnd, _dbFile :: String, + _dbFitFile :: String, _dbDataset :: String, _dbCacheSz :: Int, _dbFlushEvery :: Int @@ -120,8 +123,8 @@ egraphGP dataTrainVals dataTests args = do when ((not.null) (_loadFrom args)) $ (io $ BS.readFile (_loadFrom args)) >>= \eg -> put (decode eg) -- Load fitness cache from DB if in DB mode - fitCache <- if not (null (_dbFile args)) - then io $ loadFitnessCache (_dbFile args) (dbDatasetName args) + fitCache <- if not (null (_dbFitFile args)) + then io $ loadFitnessCache (_dbFitFile args) (dbDatasetName args) else pure HashMap.empty insertTerms @@ -141,34 +144,38 @@ egraphGP dataTrainVals dataTests args = do mTime = if _maxtime args < 0 then Nothing else Just (fromIntegral $ _maxtime args - 5) -- add 5 seconds slack (finalPop, finalOut, _) <- iterateFor (_gens args) t0 mTime (pop, output, _nPop args) $ \it (ps', out, curIx) -> do - newPop' <- replicateM (_nPop args) (if not (null (_dbFile args)) then evolveDB ps' else evolve ps') + -- Phase 1: Generate offspring trees in parallel (read-only on e-graph) + trees <- generateTrees ps' + -- Phase 2: Batch insert all trees into the e-graph (sequential) + newPop' <- Prelude.mapM (\t -> fromTree myCost (relabel t) >>= canonical) trees + + -- Single batch eqsat pass over all new classes + if _nParams args == 0 + then runEqSat myCost rewritesWithConstant 1 >> cleanDB + else runEqSat myCost rewritesParams 1 >> cleanDB + modify' $ over (eDB . seenMatches) (const Map.empty) -- Batch-fit the eqsat-flagged refits (force) and the new offspring - -- (updateIfNothing semantics) concurrently, before any fitness-based - -- selection so Pareto ranking sees fresh fitness values. - refitIds <- gets (IntSet.toList . _refits . _eDB) - modify' $ over (eDB . refits) (const IntSet.empty) - fitBatchCached fitCache True fitFun refitIds refitIds <- gets (IntSet.toList . _refits . _eDB) modify' $ over (eDB . refits) (const IntSet.empty) fitBatchCached fitCache True fitFun refitIds fitBatchCached fitCache False fitFun newPop' -- Store newly fitted fitness in DB - when (not (null (_dbFile args))) $ do + when (not (null (_dbFitFile args))) $ do forM_ refitIds $ \eid -> do mf <- getFitness eid case mf of Just f -> do thetas <- getTheta eid let theta = if null thetas then V.empty else head thetas - io $ storeFitnessDB (_dbFile args) (dbDatasetName args) eid f theta + io $ storeFitnessDB (_dbFitFile args) (dbDatasetName args) eid f theta Nothing -> pure () forM_ newPop' $ \eid -> do mf <- getFitness eid case mf of Just f -> do thetas <- getTheta eid let theta = if null thetas then V.empty else head thetas - io $ storeFitnessDB (_dbFile args) (dbDatasetName args) eid f theta + io $ storeFitnessDB (_dbFitFile args) (dbDatasetName args) eid f theta Nothing -> pure () out' <- if _trace args @@ -290,28 +297,12 @@ egraphGP dataTrainVals dataTests args = do xs <- Prelude.mapM canonical xs' parents <- tournament xs offspring <- combine parents - if _nParams args == 0 - then runEqSat myCost rewritesWithConstant 1 >> cleanDB - else runEqSat myCost rewritesParams 1 >> cleanDB - -- Each offspring runs an independent 1-iteration eqsat on a shared - -- e-graph. The mark-on-attempt seen-set would otherwise grow with the - -- whole graph (an O(seen) exclude materialization per matcher call, - -- the source of the progressive slowdown), so clear it between offspring. - modify' $ over (eDB . seenMatches) (const Map.empty) pure offspring evolveDB xs' = do xs <- Prelude.mapM canonical xs' parents <- tournament xs offspring <- combineDB parents - if _nParams args == 0 - then runEqSat myCost rewritesWithConstant 1 >> cleanDB - else runEqSat myCost rewritesParams 1 >> cleanDB - -- Each offspring runs an independent 1-iteration eqsat on a shared - -- e-graph. The mark-on-attempt seen-set would otherwise grow with the - -- whole graph (an O(seen) exclude materialization per matcher call, - -- the source of the progressive slowdown), so clear it between offspring. - modify' $ over (eDB . seenMatches) (const Map.empty) pure offspring tournament xs = do p1 <- applyTournament xs >>= canonical @@ -344,14 +335,71 @@ egraphGP dataTrainVals dataTests args = do cands <- getAllSubClasses p2 tree <- getSubtree pos 0 Nothing [] cands p1 eid <- fromTree myCost (relabel tree) >>= canonical - -- Check if this expression already exists in the DB en <- getBestENode eid >>= canonize let key = T.pack (enodeKey en) - inDB <- io $ expressionInDB (_dbFile args) key + inDB <- io $ expressionInDB (_dbFitFile args) key if inDB - then rnd (randomFrom [p1, p2]) -- already explored: fall back + then rnd (randomFrom [p1, p2]) else pure eid + -- | Like crossover but returns Fix SRTree instead of inserting into the e-graph. + -- All operations are read-only on the e-graph snapshot. + crossoverTree p1 p2 = do sz <- getSize p1 + coin <- rnd $ tossBiased (_pc args) + if sz == 1 || not coin + then do p <- rnd (randomFrom [p1, p2]) + getBestExpr p + else do pos <- rnd $ randomRange (1, sz-1) + cands <- getAllSubClasses p2 + getSubtree pos 0 Nothing [] cands p1 + + -- | Like mutate but works on Fix SRTree directly without writing to the e-graph. + mutateTree tree = do + let sz = countNodes tree + coin <- rnd $ tossBiased (_pm args) + if coin + then do pos <- rnd $ randomRange (0, min sz maxSize - 1) + mutAtTree pos maxSize tree + else pure tree + + -- | Walk a Fix SRTree to a position and replace the subtree with a random expression. + -- Unlike mutAt, this works on Fix SRTree directly and never touches the e-graph. + mutAtTree :: Int -> Int -> Fix SRTree -> RndEGraph (Fix SRTree) + mutAtTree 0 sizeLeft _ = do + t <- insertRndExpr (max 1 sizeLeft) rndTerm rndNonTerm >>= canonical >>= getBestExpr + pure t + mutAtTree _ 1 _ = rnd $ randomFrom terms + mutAtTree pos sizeLeft (Fix (Uni f t)) = + (Fix . Uni f) <$> mutAtTree (pos-1) (sizeLeft-1) t + mutAtTree pos sizeLeft (Fix (Bin op l r)) = do + let szL = countNodes l + if szL < pos + then do r' <- mutAtTree (pos-szL-1) (sizeLeft-szL-1) r + pure . Fix $ Bin op l r' + else do l' <- mutAtTree (pos-1) (sizeLeft-1) l + pure . Fix $ Bin op l' r + mutAtTree _ _ tree = pure tree -- Var, Const, Param, Y: nothing to mutate + + -- | Run offspring tree generation in parallel using mapConcurrently. + -- Each worker gets a read-only snapshot of the e-graph, so tree extraction + -- (tournament, crossover, mutation) is safe. No e-graph writes happen here. + generateTrees :: [EClassId] -> RndEGraph [Fix SRTree] + generateTrees xs = do + nCaps <- io getNumCapabilities + g0 <- rnd get + let nJobs = _nPop args + gs = [ mkStdGen (fromIntegral i * 7919 + 42) | i <- [0 .. nJobs - 1] ] + eg <- get + io $ mapConcurrently (\g -> runRndEGraph eg g $ do + xs' <- Prelude.mapM canonical xs + parents <- tournament xs' + coin <- rnd toss + if coin + then crossoverTree (fst parents) (snd parents) >>= mutateTree + else do p <- rnd (randomFrom [fst parents, snd parents]) + getBestExpr p + ) (Prelude.take nJobs gs) + getSubtree :: Int -> Int -> Maybe (EClassId -> ENode) -> [Maybe (EClassId -> ENode)] -> [EClassId] -> EClassId -> RndEGraph (Fix SRTree) getSubtree 0 sz (Just parent) mGrandParents cands p' = do p <- canonical p' @@ -430,7 +478,7 @@ egraphGP dataTrainVals dataTests args = do -- Check if this expression already exists in the DB en <- getBestENode eid >>= canonize let key = T.pack (enodeKey en) - inDB <- io $ expressionInDB (_dbFile args) key + inDB <- io $ expressionInDB (_dbFitFile args) key if inDB then pure p -- already explored: keep original else pure eid diff --git a/src/eggp.egg-info/PKG-INFO b/src/eggp.egg-info/PKG-INFO index 5868ce1..59c3600 100644 --- a/src/eggp.egg-info/PKG-INFO +++ b/src/eggp.egg-info/PKG-INFO @@ -1,6 +1,6 @@ Metadata-Version: 2.4 Name: eggp -Version: 2.1.1 +Version: 2.2.0 Summary: Python Wheels for eggp algorithm. Author-email: Fabricio Olivetti License: GNU GENERAL PUBLIC LICENSE @@ -793,7 +793,7 @@ To install eggp you'll need: - `libz` - `libnlopt` - `libgmp` -- `ghc-9.6.6` +- `ghc-9.14.1` - `cabal` or `stack` ### Method 1: PIP diff --git a/src/eggp.egg-info/SOURCES.txt b/src/eggp.egg-info/SOURCES.txt index 1773578..40b2de2 100644 --- a/src/eggp.egg-info/SOURCES.txt +++ b/src/eggp.egg-info/SOURCES.txt @@ -14,4 +14,6 @@ src/eggp.egg-info/dependency_links.txt src/eggp.egg-info/entry_points.txt src/eggp.egg-info/requires.txt src/eggp.egg-info/top_level.txt -test/test_pyeggp.py \ No newline at end of file +test/test_db_mode.py +test/test_pyeggp.py +test/test_regression_integration.py \ No newline at end of file diff --git a/src/eggp/__init__.py b/src/eggp/__init__.py index df5052e..5d0d3c7 100644 --- a/src/eggp/__init__.py +++ b/src/eggp/__init__.py @@ -44,13 +44,13 @@ def _get_hs_eggp_run_data(): ctypes.c_int, ctypes.c_int, ctypes.c_int, ctypes.c_int, ctypes.c_int, ctypes.c_int, ctypes.c_int, ctypes.c_int, ctypes.c_char_p, ctypes.c_char_p, ctypes.c_char_p, ctypes.c_int, - ctypes.c_char_p, ctypes.c_char_p, ctypes.c_int, ctypes.c_int, + ctypes.c_char_p, ctypes.c_char_p, ctypes.c_char_p, ctypes.c_int, ctypes.c_int, ] lib.hs_eggp_run_data.restype = ctypes.c_char_p _hs_eggp_run_data = lib.hs_eggp_run_data return _hs_eggp_run_data -VERSION: str = "2.1.1" +VERSION: str = "2.2.0" _hs_rts_init: bool = False @@ -88,11 +88,11 @@ def main(args: List[str] = []) -> int: with hs_rts_init(rts_args + cli_args): return unsafe_hs_eggp_main() -def eggp_run(dataset: str, gen: int, nPop: int, maxSize: int, nTournament: int, pc: float, pm: float, nonterminals: str, loss: str, optIter: int, optRepeat: int, nParams: int, split: int, max_time : int, simplify: int, trace : int, generational : int, dumpTo: str, loadFrom: str, varnames : str, useFracBayes: int, dbFile: str = "", dbDataset: str = "", dbCacheSize: int = 100000, dbFlushEvery: int = 0) -> str: +def eggp_run(dataset: str, gen: int, nPop: int, maxSize: int, nTournament: int, pc: float, pm: float, nonterminals: str, loss: str, optIter: int, optRepeat: int, nParams: int, split: int, max_time : int, simplify: int, trace : int, generational : int, dumpTo: str, loadFrom: str, varnames : str, useFracBayes: int, dbFile: str = "", dbFitFile: str = "", dbDataset: str = "", dbCacheSize: int = 100000, dbFlushEvery: int = 0) -> str: with hs_rts_init(): - return unsafe_hs_eggp_run(dataset, gen, nPop, maxSize, nTournament, pc, pm, nonterminals, loss, optIter, optRepeat, nParams, split, max_time, simplify, trace, generational, dumpTo, loadFrom, varnames, useFracBayes, dbFile, dbDataset, dbCacheSize, dbFlushEvery) + return unsafe_hs_eggp_run(dataset, gen, nPop, maxSize, nTournament, pc, pm, nonterminals, loss, optIter, optRepeat, nParams, split, max_time, simplify, trace, generational, dumpTo, loadFrom, varnames, useFracBayes, dbFile, dbFitFile, dbDataset, dbCacheSize, dbFlushEvery) -def eggp_run_data(data: np.ndarray, nrows: List[int], header: str, params: str, gen: int, nPop: int, maxSize: int, nTournament: int, pc: float, pm: float, nonterminals: str, loss: str, optIter: int, optRepeat: int, nParams: int, split: int, max_time : int, simplify: int, trace : int, generational : int, dumpTo: str, loadFrom: str, varnames : str, useFracBayes: int, dbFile: str = "", dbDataset: str = "", dbCacheSize: int = 100000, dbFlushEvery: int = 0) -> str: +def eggp_run_data(data: np.ndarray, nrows: List[int], header: str, params: str, gen: int, nPop: int, maxSize: int, nTournament: int, pc: float, pm: float, nonterminals: str, loss: str, optIter: int, optRepeat: int, nParams: int, split: int, max_time : int, simplify: int, trace : int, generational : int, dumpTo: str, loadFrom: str, varnames : str, useFracBayes: int, dbFile: str = "", dbFitFile: str = "", dbDataset: str = "", dbCacheSize: int = 100000, dbFlushEvery: int = 0) -> str: ''' Runs eggp with the dataset passed directly as a contiguous double array, bypassing the temp-CSV round trip. @@ -113,7 +113,7 @@ def eggp_run_data(data: np.ndarray, nrows: List[int], header: str, params: str, cdata = data.ctypes.data_as(ctypes.POINTER(ctypes.c_double)) cnrows = (ctypes.c_int * len(nrows))(*nrows) with hs_rts_init(): - out = _get_hs_eggp_run_data()(cdata, cnrows, len(nrows), data.shape[1], header.encode(), params.encode(), gen, nPop, maxSize, nTournament, pc, pm, nonterminals.encode(), loss.encode(), optIter, optRepeat, nParams, split, max_time, simplify, trace, generational, dumpTo.encode(), loadFrom.encode(), varnames.encode(), useFracBayes, dbFile.encode(), dbDataset.encode(), dbCacheSize, dbFlushEvery) + out = _get_hs_eggp_run_data()(cdata, cnrows, len(nrows), data.shape[1], header.encode(), params.encode(), gen, nPop, maxSize, nTournament, pc, pm, nonterminals.encode(), loss.encode(), optIter, optRepeat, nParams, split, max_time, simplify, trace, generational, dumpTo.encode(), loadFrom.encode(), varnames.encode(), useFracBayes, dbFile.encode(), dbFitFile.encode(), dbDataset.encode(), dbCacheSize, dbFlushEvery) return out.decode("utf-8") if out is not None else "" def make_function(expression, loss="MSE"): @@ -233,7 +233,7 @@ class EGGP(BaseEstimator, RegressorMixin): >>> estimator = EGGP(loss="Bernoulli") >>> estimator.fit(X, y) """ - def __init__(self, gen = 100, nPop = 100, maxSize = 15, nTournament = 3, pc = 0.9, pm = 0.3, nonterminals = "add,sub,mul,div", loss = "MSE", optIter = 50, optRepeat = 2, nParams = -1, folds = 1, max_time = -1, simplify = False, trace = False, generational = False, dumpTo = "", loadFrom = "", useFracBayes = False, pinball_tau = 0.5, dbFile = "", dbDataset = "", dbCacheSize = 100000, dbFlushEvery = 0): + def __init__(self, gen = 100, nPop = 100, maxSize = 15, nTournament = 3, pc = 0.9, pm = 0.3, nonterminals = "add,sub,mul,div", loss = "MSE", optIter = 50, optRepeat = 2, nParams = -1, folds = 1, max_time = -1, simplify = False, trace = False, generational = False, dumpTo = "", loadFrom = "", useFracBayes = False, pinball_tau = 0.5, dbFile = "", dbFitFile = "", dbDataset = "", dbCacheSize = 100000, dbFlushEvery = 0): nts = "add,sub,mul,div,power,powerabs,\ aq,abs,sin,cos,tan,sinh,cosh,tanh,\ asin,acos,atan,asinh,acosh,atanh,sqrt,\ @@ -297,6 +297,7 @@ def __init__(self, gen = 100, nPop = 100, maxSize = 15, nTournament = 3, pc = 0. self.useFracBayes = int(useFracBayes) self.pinball_tau = pinball_tau self.dbFile = dbFile + self.dbFitFile = dbFitFile self.dbDataset = dbDataset self.dbCacheSize = dbCacheSize self.dbFlushEvery = dbFlushEvery @@ -389,7 +390,7 @@ def fit(self, X, y, Xerr = None, yerr = None): dname = self.get_fname("", header) - csv_data = eggp_run_data(combined, [combined.shape[0]], ",".join(header), dname, self.gen, self.nPop, self.maxSize, self.nTournament, self.pc, self.pm, self.nonterminals, self.loss_arg, self.optIter, self.optRepeat, self.nParams, self.folds, self.max_time, self.simplify, self.trace, self.generational, self.dumpTo, self.loadFrom, varnames, self.useFracBayes, self.dbFile, self.dbDataset, self.dbCacheSize, self.dbFlushEvery) + csv_data = eggp_run_data(combined, [combined.shape[0]], ",".join(header), dname, self.gen, self.nPop, self.maxSize, self.nTournament, self.pc, self.pm, self.nonterminals, self.loss_arg, self.optIter, self.optRepeat, self.nParams, self.folds, self.max_time, self.simplify, self.trace, self.generational, self.dumpTo, self.loadFrom, varnames, self.useFracBayes, self.dbFile, self.dbFitFile, self.dbDataset, self.dbCacheSize, self.dbFlushEvery) if len(csv_data) > 0: csv_io = StringIO(csv_data.strip()) @@ -424,7 +425,7 @@ def fit_mvsr(self, Xs, ys, Xerrs = None, yerrs = None): dname = self.get_fname("", header) csv_data = eggp_run_data(data, nrows, ",".join(header), dname, self.gen, self.nPop, self.maxSize, self.nTournament, self.pc, self.pm, - self.nonterminals, self.loss_arg, self.optIter, self.optRepeat, self.nParams, self.folds, self.max_time, self.simplify, self.trace, self.generational, self.dumpTo, 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zmTY;?=*FK?`6F9Zu6n3o8`)@RT5{a;>|Pq#@a)Qtymex=HSh5)jczoNRnOa1tMPnO z|I+x5U&WdYc~39YY$U74@{Rj%8}bbYmdKngz;aeA0 z>(=Y`F4-ZTX(@i!)sc5~teJDJj-1Q)uQor<-*up1I|#3Hu0X-YQ{qIyc5(+i^s?ac ztO(0FdwDx?xxfaX1Qj?$oVc4y-*OBctI>VEKcQdm-#7t(#IP-N(EkDOQkcg8 literal 0 HcmV?d00001 diff --git a/tutorials/db_workflow_example.py b/tutorials/db_workflow_example.py new file mode 100644 index 0000000..c34096d --- /dev/null +++ b/tutorials/db_workflow_example.py @@ -0,0 +1,276 @@ +""" +db_workflow_example.py - End-to-end eggp DB mode workflow +========================================================== + +This tutorial shows the full eggp DB-backed workflow: GP search with fitness +caching, persistence across sessions, and Pareto front extraction with +confidence intervals. + +The DB mode enables: + 1. **Fitness caching**: expressions evaluated in a previous run are recognised + and their cached fitness is reused (skipping NLopt optimisation). + 2. **Persistence**: the DB accumulates results over multiple sessions; a new + run picks up where the last left off. + 3. **CI reporting**: the Pareto front includes profile-likelihood confidence + intervals for each parameter. + +Run from the ``tutorials/`` directory:: + + python db_workflow_example.py +""" + +import os +import time +import sqlite3 + +import numpy as np +import pandas as pd + +from eggp import EGGP + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- +def db_stats(db_path): + """Print DB statistics.""" + if not os.path.exists(db_path): + print(f" [DB not found: {db_path}]") + return {} + con = sqlite3.connect(db_path) + stats = {} + for table in ["dataset_fit", "eclass", "enode"]: + try: + stats[table] = con.execute(f"SELECT COUNT(*) FROM {table}").fetchone()[0] + except sqlite3.OperationalError: + stats[table] = 0 + # Count datasets + try: + stats["datasets"] = con.execute("SELECT COUNT(*) FROM dataset").fetchone()[0] + except sqlite3.OperationalError: + stats["datasets"] = 0 + con.close() + print(f" dataset_fit: {stats['dataset_fit']} rows | " + f"eclass: {stats['eclass']} | " + f"datasets: {stats['datasets']}") + return stats + + +def make_dataset(path, rng_seed=42, n=200): + """Create a synthetic dataset.""" + rng = np.random.default_rng(rng_seed) + x0 = rng.uniform(-3, 3, n) + x1 = rng.uniform(-3, 3, n) + y = np.sin(x0) + 0.5 * x1 + df = pd.DataFrame({"x0": x0, "x1": x1, "y": y}) + df.to_csv(path, index=False) + return df + + +# --------------------------------------------------------------------------- +# Main +# --------------------------------------------------------------------------- +def main(): + data_csv = "db_workflow_data.csv" + egraph_db = "db_workflow_egraph.db" + fit_db = "db_workflow_fit.db" + + for f in (data_csv, egraph_db, fit_db): + if os.path.exists(f): + os.remove(f) + + df = make_dataset(data_csv) + X = df[["x0", "x1"]] + y = df["y"] + + # =================================================================== + # Session 1: first run — builds the DB from scratch + # =================================================================== + print("=" * 64) + print("Session 1: first run (no prior DB)") + print("=" * 64) + + model1 = EGGP( + gen=10, + nPop=50, + maxSize=12, + nTournament=3, + pc=0.8, + pm=0.3, + nonterminals="add,sub,mul,div,sin", + loss="MSE", + optIter=30, + optRepeat=2, + nParams=1, + folds=1, + simplify=True, + max_time=30, + # --- DB parameters --- + dbFile=egraph_db, + dbFitFile=fit_db, + dbDataset="demo", + dbFlushEvery=5, + ) + + t0 = time.time() + model1.fit(X, y) + elapsed1 = time.time() - t0 + + print(f"\n Finished in {elapsed1:.1f}s") + print(f" Pareto front ({len(model1.results)} expressions):") + print(model1.results[["Expression", "loss_train", "size"]].to_string(index=False)) + print() + print(" DB stats:") + db_stats(fit_db) + + # =================================================================== + # Session 2: resume — reuses cached fitness from Session 1 + # =================================================================== + print("\n" + "=" * 64) + print("Session 2: resume from existing DB (fitness reuse)") + print("=" * 64) + + model2 = EGGP( + gen=10, + nPop=50, + maxSize=12, + nTournament=3, + pc=0.8, + pm=0.3, + nonterminals="add,sub,mul,div,sin", + loss="MSE", + optIter=30, + optRepeat=2, + nParams=1, + folds=1, + simplify=True, + max_time=30, + # --- same DB files --- + dbFile=egraph_db, + dbFitFile=fit_db, + dbDataset="demo", + dbFlushEvery=5, + ) + + t0 = time.time() + model2.fit(X, y) + elapsed2 = time.time() - t0 + + print(f"\n Finished in {elapsed2:.1f}s") + print(f" Pareto front ({len(model2.results)} expressions):") + print(model2.results[["Expression", "loss_train", "size"]].to_string(index=False)) + print() + print(" DB stats:") + db_stats(fit_db) + + # =================================================================== + # Session 3: continue searching — the DB keeps growing + # =================================================================== + print("\n" + "=" * 64) + print("Session 3: continue searching (more generations)") + print("=" * 64) + + model3 = EGGP( + gen=20, + nPop=50, + maxSize=12, + nTournament=3, + pc=0.8, + pm=0.3, + nonterminals="add,sub,mul,div,sin", + loss="MSE", + optIter=30, + optRepeat=2, + nParams=1, + folds=1, + simplify=True, + max_time=60, + dbFile=egraph_db, + dbFitFile=fit_db, + dbDataset="demo", + dbFlushEvery=10, + ) + + t0 = time.time() + model3.fit(X, y) + elapsed3 = time.time() - t0 + + print(f"\n Finished in {elapsed3:.1f}s") + print(f" Pareto front ({len(model3.results)} expressions):") + print(model3.results[["Expression", "loss_train", "size"]].to_string(index=False)) + print() + print(" DB stats:") + db_stats(fit_db) + + # =================================================================== + # Final: extract Pareto front with CI columns + # =================================================================== + print("\n" + "=" * 64) + print("Final: Pareto front with profile-likelihood CIs") + print("=" * 64) + + model_final = EGGP( + gen=5, + nPop=50, + maxSize=12, + nTournament=3, + pc=0.8, + pm=0.3, + nonterminals="add,sub,mul,div,sin", + loss="MSE", + optIter=30, + optRepeat=2, + nParams=1, + folds=1, + simplify=True, + max_time=15, + dbFile=egraph_db, + dbFitFile=fit_db, + dbDataset="demo", + dbFlushEvery=0, + ) + model_final.fit(X, y) + + cols = ["Expression", "loss_train", "size"] + ci_cols = [c for c in model_final.results.columns if c.startswith("ci_")] + if ci_cols: + cols.extend(ci_cols) + + print(f"\nPareto front ({len(model_final.results)} expressions):") + print(model_final.results[cols].to_string(index=False)) + + if ci_cols: + print(f"\nCI columns: {', '.join(ci_cols)}") + print("Each parameter has lower/upper bounds (95% profile-likelihood CI).") + + # Export to CSV + out_csv = "db_workflow_results.csv" + model_final.results[cols].to_csv(out_csv, index=False) + print(f"\nResults exported to {out_csv}") + + # =================================================================== + # Summary + # =================================================================== + print("\n" + "=" * 64) + print("DB mode workflow summary") + print("=" * 64) + print(f" Session 1 (fresh): {elapsed1:.1f}s") + print(f" Session 2 (resume): {elapsed2:.1f}s (reuses cached fitness)") + print(f" Session 3 (continue): {elapsed3:.1f}s (DB accumulates)") + print(f" Final DB stats:") + db_stats(fit_db) + print() + print(" The DB persists across runs. A new EGGP object pointed at the") + print(" same dbFile/dbFitFile will load the fitness cache and skip") + print(" re-evaluation of expressions that were already fitted.") + print("=" * 64) + + # Cleanup + for f in (data_csv, egraph_db, fit_db, out_csv): + try: + os.remove(f) + except OSError: + pass + + +if __name__ == "__main__": + main() diff --git a/tutorials/eggp_regression_integration.py b/tutorials/eggp_regression_integration.py new file mode 100644 index 0000000..d015238 --- /dev/null +++ b/tutorials/eggp_regression_integration.py @@ -0,0 +1,306 @@ +""" +eggp_regression_integration.py - eggp and reggression integration via shared egraph +=================================================================================== + +This tutorial demonstrates cross-dataset analysis using a shared e-graph: + + 1. Run eggp on dataset A — discovers expressions, stores in egraph + fit_a + 2. Run eggp on dataset B using the SAME egraph — adds new expressions, fit_b + 3. Query top-N from both fit databases via reggression + 4. Find the intersection: e-classes that are top-ranked on BOTH datasets + +The shared egraph means expressions discovered on dataset A are available when +searching on dataset B. The intersection reveals "universally good" expressions +that perform well across different datasets. + +Run from the ``tutorials/`` directory:: + + python eggp_regression_integration.py +""" + +import os +import time +import sqlite3 + +import numpy as np +import pandas as pd + +from eggp import EGGP +from reggression import Reggression + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- +def make_dataset_a(path): + """Dataset A: y = sin(x0) + 0.5*x1 (trigonometric).""" + rng = np.random.default_rng(42) + n = 200 + x0 = rng.uniform(-3, 3, n) + x1 = rng.uniform(-3, 3, n) + y = np.sin(x0) + 0.5 * x1 + pd.DataFrame({"x0": x0, "x1": x1, "y": y}).to_csv(path, index=False) + + +def make_dataset_b(path): + """Dataset B: y = x0*x1 + 0.3*x0 (multiplicative).""" + rng = np.random.default_rng(99) + n = 200 + x0 = rng.uniform(-3, 3, n) + x1 = rng.uniform(-3, 3, n) + y = x0 * x1 + 0.3 * x0 + pd.DataFrame({"x0": x0, "x1": x1, "y": y}).to_csv(path, index=False) + + +def db_stats(db_path, label=""): + """Print DB statistics.""" + if not os.path.exists(db_path): + print(f" [{label} not found]") + return + con = sqlite3.connect(db_path) + tables = {} + for t in ["dataset_fit", "eclass", "enode"]: + try: + tables[t] = con.execute(f"SELECT COUNT(*) FROM {t}").fetchone()[0] + except sqlite3.OperationalError: + tables[t] = 0 + con.close() + print(f" {label}: fit_rows={tables['dataset_fit']} " + f"eclasses={tables['eclass']} enodes={tables['enode']}") + + +def get_top_ids(fit_db, dataset, n): + """Get the top-N eclass IDs from a fit DB.""" + con = sqlite3.connect(fit_db) + ds_rows = con.execute( + "SELECT id FROM dataset WHERE name = ?", (dataset,) + ).fetchall() + if not ds_rows: + con.close() + return [] + dsid = ds_rows[0][0] + rows = con.execute( + "SELECT eid FROM dataset_fit " + "WHERE dataset_id = ? AND fitted = 1 AND fitness IS NOT NULL " + "ORDER BY fitness DESC LIMIT ?", + (dsid, n) + ).fetchall() + con.close() + return [row[0] for row in rows] + + +# --------------------------------------------------------------------------- +# Main +# --------------------------------------------------------------------------- +def main(): + # --- Files --- + data_a_csv = "integ_data_a.csv" + data_b_csv = "integ_data_b.csv" + egraph_db = "integ_egraph.db" + fit_a_db = "integ_fit_a.db" + fit_b_db = "integ_fit_b.db" + + for f in (data_a_csv, data_b_csv, egraph_db, fit_a_db, fit_b_db): + if os.path.exists(f): + os.remove(f) + + # --- Create datasets --- + make_dataset_a(data_a_csv) + make_dataset_b(data_b_csv) + df_a = pd.read_csv(data_a_csv) + df_b = pd.read_csv(data_b_csv) + + # =================================================================== + # Step 1: Run eggp on dataset A + # =================================================================== + print("=" * 64) + print("Step 1: Run eggp on dataset A (trigonometric)") + print(" y = sin(x0) + 0.5*x1") + print("=" * 64) + + model_a = EGGP( + gen=15, + nPop=80, + maxSize=12, + nTournament=3, + pc=0.8, + pm=0.3, + nonterminals="add,sub,mul,div,sin,cos", + loss="MSE", + optIter=30, + optRepeat=2, + nParams=1, + folds=1, + simplify=True, + max_time=45, + dbFile=egraph_db, + dbFitFile=fit_a_db, + dbDataset="A", + dbFlushEvery=5, + ) + + t0 = time.time() + model_a.fit(df_a[["x0", "x1"]], df_a["y"]) + elapsed_a = time.time() - t0 + + print(f"\n Finished in {elapsed_a:.1f}s") + print(f" Pareto front ({len(model_a.results)} expressions):") + print(model_a.results[["Expression", "loss_train", "size"]].to_string(index=False)) + print() + db_stats(egraph_db, "egraph.db (after A)") + db_stats(fit_a_db, "fit_a.db") + + # =================================================================== + # Step 2: Run eggp on dataset B using the SAME egraph + # =================================================================== + print("\n" + "=" * 64) + print("Step 2: Run eggp on dataset B (multiplicative)") + print(" y = x0*x1 + 0.3*x0") + print(" (shares egraph.db with dataset A)") + print("=" * 64) + + model_b = EGGP( + gen=15, + nPop=80, + maxSize=12, + nTournament=3, + pc=0.8, + pm=0.3, + nonterminals="add,sub,mul,div,sin,cos", + loss="MSE", + optIter=30, + optRepeat=2, + nParams=1, + folds=1, + simplify=True, + max_time=45, + dbFile=egraph_db, + dbFitFile=fit_b_db, + dbDataset="B", + dbFlushEvery=5, + ) + + t0 = time.time() + model_b.fit(df_b[["x0", "x1"]], df_b["y"]) + elapsed_b = time.time() - t0 + + print(f"\n Finished in {elapsed_b:.1f}s") + print(f" Pareto front ({len(model_b.results)} expressions):") + print(model_b.results[["Expression", "loss_train", "size"]].to_string(index=False)) + print() + db_stats(egraph_db, "egraph.db (after A+B)") + db_stats(fit_b_db, "fit_b.db") + + # =================================================================== + # Step 3: Query top-N from both datasets via reggression + # =================================================================== + print("\n" + "=" * 64) + print("Step 3: Query top-N from both fit databases via reggression") + print("=" * 64) + + N = 50 # large N to increase intersection probability + egg = Reggression(dataset=data_a_csv, loss="MSE") + + top_a = egg.dbTop(egraph_db, N, fitDb=fit_a_db) + top_b = egg.dbTop(egraph_db, N, fitDb=fit_b_db) + + print(f"\n Top {N} from dataset A:") + print(top_a[["Id", "Expression", "Fitness", "Size"]].head(10).to_string(index=False)) + print(f" ... ({len(top_a)} total)") + + print(f"\n Top {N} from dataset B:") + print(top_b[["Id", "Expression", "Fitness", "Size"]].head(10).to_string(index=False)) + print(f" ... ({len(top_b)} total)") + + # =================================================================== + # Step 4: Find intersection (e-classes in both top-N lists) + # =================================================================== + print("\n" + "=" * 64) + print("Step 4: Intersection — e-classes in both top-N lists") + print("=" * 64) + + ids_a = set(top_a["Id"].astype(int)) + ids_b = set(top_b["Id"].astype(int)) + common_ids = ids_a & ids_b + + print(f"\n Dataset A top-{N}: {len(ids_a)} unique e-classes") + print(f" Dataset B top-{N}: {len(ids_b)} unique e-classes") + print(f" Intersection: {len(common_ids)} e-classes in both") + + if common_ids: + # Build lookup for both tables + lookup_a = {int(row.Id): row for _, row in top_a.iterrows()} + lookup_b = {int(row.Id): row for _, row in top_b.iterrows()} + + print(f"\n Common e-classes (ranked by average fitness):") + common_data = [] + for eid in common_ids: + expr = lookup_a[eid]["Expression"] + fit_a_val = lookup_a[eid]["Fitness"] + fit_b_val = lookup_b[eid]["Fitness"] + size = lookup_a[eid]["Size"] + avg_fit = (fit_a_val + fit_b_val) / 2 + common_data.append((eid, expr, fit_a_val, fit_b_val, avg_fit, size)) + + # Sort by average fitness (higher = better for negative-MSE) + common_data.sort(key=lambda x: x[4], reverse=True) + + rows = [] + for eid, expr, fa, fb, avg, sz in common_data: + rows.append({ + "Id": eid, + "Expression": expr, + "Fit_A": f"{fa:.4f}", + "Fit_B": f"{fb:.4f}", + "Avg_Fit": f"{avg:.4f}", + "Size": sz, + }) + common_df = pd.DataFrame(rows) + print(common_df.to_string(index=False)) + else: + print("\n No common e-classes in top-N.") + print(" This can happen when the datasets have very different structure.") + print(" Try increasing N or using more generations.") + + # =================================================================== + # Step 5: Show the union (all unique e-classes across both) + # =================================================================== + print("\n" + "=" * 64) + print("Step 5: Union — all unique e-classes across both datasets") + print("=" * 64) + + all_ids = ids_a | ids_b + only_a = ids_a - ids_b + only_b = ids_b - ids_a + + print(f"\n Total unique e-classes: {len(all_ids)}") + print(f" Only in A's top-{N}: {len(only_a)}") + print(f" Only in B's top-{N}: {len(only_b)}") + print(f" In both: {len(common_ids)}") + + # =================================================================== + # Summary + # =================================================================== + print("\n" + "=" * 64) + print("Summary") + print("=" * 64) + print(f" Dataset A ({elapsed_a:.1f}s): {len(model_a.results)} Pareto expressions") + print(f" Dataset B ({elapsed_b:.1f}s): {len(model_b.results)} Pareto expressions") + print(f" Shared egraph: {db_stats.__name__} (see above)") + print(f" Intersection: {len(common_ids)} e-classes in both top-{N}") + print() + print(" The shared egraph lets eggp on dataset B benefit from expressions") + print(" discovered on dataset A. The intersection reveals expressions that") + print(" work well across different datasets — useful for model selection") + print(" and understanding which structure is universal vs dataset-specific.") + print("=" * 64) + + # Cleanup + for f in (data_a_csv, data_b_csv, egraph_db, fit_a_db, fit_b_db): + try: + os.remove(f) + except OSError: + pass + + +if __name__ == "__main__": + main() From 0d026777e3d8a838a53e7916b5be8dd60ba48d1f Mon Sep 17 00:00:00 2001 From: folivetti Date: Sat, 12 Sep 2026 18:40:11 -0300 Subject: [PATCH 165/166] speed up --- eggp.cabal | 10 +++++----- src/Search.hs | 4 ++-- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/eggp.cabal b/eggp.cabal index e8d84cf..5487560 100755 --- a/eggp.cabal +++ b/eggp.cabal @@ -5,7 +5,7 @@ cabal-version: 2.2 -- see: https://github.com/sol/hpack name: eggp -version: 2.1.1 +version: 2.2.1 synopsis: A general library to work with Symbolic Regression expression trees. description: A Symbolic Regression Tree data structure to work with mathematical expressions with support to first order derivative and simplification; category: Math, Data, Data Structures @@ -75,8 +75,8 @@ foreign-library _binding , optparse-applicative >=0.17 && <0.20 , random >=1.2 && <1.4 , split >=0.2.5 && <0.3 - , srtree >= 3.0.0.3 - , srtree-db >= 0.1.2.0 + , srtree >= 3.0.0.4 + , srtree-db >= 0.1.3.0 , direct-sqlite >= 2.3 , text >= 1.2 && < 2.2 , statistics >=0.16.2.1 && <0.17 @@ -117,8 +117,8 @@ executable eggp , optparse-applicative >=0.17 && <0.19 , random >=1.2 && <1.4 , split >=0.2.5 && <0.3 - , srtree >= 3.0.0.3 - , srtree-db >= 0.1.2.0 + , srtree >= 3.0.0.4 + , srtree-db >= 0.1.3.0 , direct-sqlite >= 2.3 , text >= 1.2 && < 2.2 , statistics >=0.16.2.1 && <0.17 diff --git a/src/Search.hs b/src/Search.hs index 38a0eb6..67464a7 100644 --- a/src/Search.hs +++ b/src/Search.hs @@ -631,8 +631,8 @@ egraphGP dataTrainVals dataTests args = do dist = case distribution of { NLL d -> d; MSE -> LeastSquares; LOG10 -> LeastSquares; MAE -> LeastSquares; MAPE -> LeastSquares; Pinball _ -> LeastSquares; _ -> Gaussian } et = compileTree dist x y mYErr best' stats = getStatsFromModel dist mYErr x y best' theta - profiles = getAllProfiles Bates et theta (_stdErr stats) [] 0.05 - ciVals = paramCI (Profile stats profiles) nSamples theta 0.05 + profiles <- liftIO $ getAllProfiles Bates et theta (_stdErr stats) [] 0.05 + let ciVals = paramCI (Profile stats profiles) nSamples theta 0.05 maxPExpr = actualMaxP ciStr = intercalate "," $ Prelude.map (\(CI _ l h) -> showNA l <> "," <> showNA h) ciVals From 3afec76d52d4449422603fe9dcd518add9c4e993 Mon Sep 17 00:00:00 2001 From: folivetti Date: Fri, 2 Oct 2026 11:52:28 -0300 Subject: [PATCH 166/166] 2.2.1 --- ChangeLog.md | 14 +++++++++----- eggp.cabal | 29 ++++++++++++++++------------- pyproject.toml | 2 +- src/Search.hs | 32 ++++++++++++++++++-------------- src/eggp.egg-info/PKG-INFO | 2 +- src/eggp/__init__.py | 16 +++++++++++----- 6 files changed, 56 insertions(+), 39 deletions(-) diff --git a/ChangeLog.md b/ChangeLog.md index 9814df8..f390f06 100644 --- a/ChangeLog.md +++ b/ChangeLog.md @@ -1,6 +1,6 @@ # Changelog for eggp -## 2.2.0 +## 2.2.1 - **3x speedup with multithreading** (-N4): parallel tree generation via `mapConcurrently` on e-graph snapshots; batch eqsat after all offspring @@ -12,9 +12,6 @@ already-seen expressions - Python API: `dbFile`, `dbDataset`, `dbCacheSize`, `dbFlushEvery` parameters - SWIG binding updated for DB mode parameters - -## 2.1.2 - - Fixed profile-likelihood CI distribution mapping: non-NLL losses (MSE, LOG10, MAE, MAPE, Pinball) now correctly use `LeastSquares` distribution instead of defaulting to `Gaussian` (which adds a spurious sigma parameter) @@ -23,7 +20,14 @@ - Fixed `csvHeader` to use `actualMaxP` (computed from Pareto front) instead of `_nParams` arg, ensuring enough CI columns when `--n-params -1` - Added `computeMaxP` to walk Pareto front and find actual max parameter count -- Added `../srtree-db` to `cabal.project` for local builds +- **`trace=True` no longer computes per-generation CIs**: `printExpr` now takes a + `withCI` flag; the trace loop skips profile-likelihood CI computation per + individual per generation (a severe slowdown), while the final Pareto front + still computes CIs +- **`evaluate_best_model` picks the best-fit Pareto member**: selects the row + with minimum `loss_train` (`_best_row`) instead of `results.iloc[-1]` (the + largest/most complex member), fixing predictions on well-fit models +- Updated dependency: srtree >= 3.0.0.4, srtree-db >= 0.1.3.0 ## 2.1.0 diff --git a/eggp.cabal b/eggp.cabal index 5487560..e3f89a1 100755 --- a/eggp.cabal +++ b/eggp.cabal @@ -14,6 +14,7 @@ bug-reports: https://github.com/folivetti/eggp/issues author: Fabricio Olivetti de França maintainer: fabricio.olivetti@gmail.com copyright: 2023 Fabricio Olivetti de França +license: GPL-3.0-or-later license-file: LICENSE build-type: Custom extra-source-files: @@ -26,17 +27,17 @@ source-repository head custom-setup setup-depends: - base >=4.19 - , Cabal >=3.14 - , directory >=1 - , filepath >=1.4 + base >=4.19 && <4.22 + , Cabal >=3.14 && <3.17 + , directory >=1 && <1.4 + , filepath >=1.4 && <1.6 common warnings ghc-options: -Wall foreign-library _binding import: warnings - ghc-options: -threaded -rtsopts -O2 -fspec-constr -fmax-simplifier-iterations=20 -fexpose-all-unfoldings -optc-O3 + ghc-options: -threaded -O2 -fspec-constr -fmax-simplifier-iterations=20 -fexpose-all-unfoldings -optc-O3 type: native-shared -- 01-04-2023: @@ -54,6 +55,8 @@ foreign-library _binding EGGP Search Paths_eggp + autogen-modules: + Paths_eggp extra-libraries: nlopt build-depends: @@ -75,8 +78,8 @@ foreign-library _binding , optparse-applicative >=0.17 && <0.20 , random >=1.2 && <1.4 , split >=0.2.5 && <0.3 - , srtree >= 3.0.0.4 - , srtree-db >= 0.1.3.0 + , srtree >= 3.0.0.5 + , srtree-db >= 0.1.3.2 , direct-sqlite >= 2.3 , text >= 1.2 && < 2.2 , statistics >=0.16.2.1 && <0.17 @@ -84,8 +87,8 @@ foreign-library _binding , unordered-containers ==0.2.* , vector >=0.12 && <0.14 , zlib >=0.6.3 && <0.8 - , directory - , time + , directory >=1 && <1.4 + , time >=1.9 && <1.15 default-language: Haskell2010 executable eggp @@ -117,8 +120,8 @@ executable eggp , optparse-applicative >=0.17 && <0.19 , random >=1.2 && <1.4 , split >=0.2.5 && <0.3 - , srtree >= 3.0.0.4 - , srtree-db >= 0.1.3.0 + , srtree >= 3.0.0.5 + , srtree-db >= 0.1.3.2 , direct-sqlite >= 2.3 , text >= 1.2 && < 2.2 , statistics >=0.16.2.1 && <0.17 @@ -126,6 +129,6 @@ executable eggp , unordered-containers ==0.2.* , vector >=0.12 && <0.14 , zlib >=0.6.3 && <0.8 - , directory - , time + , directory >=1 && <1.4 + , time >=1.9 && <1.15 default-language: Haskell2010 diff --git a/pyproject.toml b/pyproject.toml index 4b1296f..17ca7ed 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,7 +12,7 @@ build-backend = "setuptools.build_meta" [project] name = "eggp" -version = "2.2.0" +version = "2.2.1" authors = [{ name = "Fabricio Olivetti", email = "folivetti@users.noreply.github.com" }] description = "Python Wheels for eggp algorithm." readme = "README.md" diff --git a/src/Search.hs b/src/Search.hs index 67464a7..bb1ad4c 100644 --- a/src/Search.hs +++ b/src/Search.hs @@ -137,7 +137,7 @@ egraphGP dataTrainVals dataTests args = do fitBatchCached fitCache False fitFun pop output <- if _trace args - then forM (Prelude.zip [0..] pop) $ uncurry (printExpr nFeats) + then forM (Prelude.zip [0..] pop) $ uncurry (printExpr False nFeats) else pure [] let m = (_nPop args) `div` (_maxSize args) @@ -179,7 +179,7 @@ egraphGP dataTrainVals dataTests args = do Nothing -> pure () out' <- if _trace args - then forM (Prelude.zip [curIx..] newPop') $ uncurry (printExpr nFeats) + then forM (Prelude.zip [curIx..] newPop') $ uncurry (printExpr False nFeats) else pure [] totSz <- gets (HashMap.size . _eNodeToEClass) @@ -219,7 +219,7 @@ egraphGP dataTrainVals dataTests args = do actualMaxP <- if _nParams args == -1 then computeMaxP (_maxSize args) (_distribution args) nFeats else pure (_nParams args) - let printExpr' = printExpr actualMaxP + let printExpr' = printExpr True actualMaxP pf <- if _trace args then pure finalOut else paretoFront fitFun (_maxSize args) printExpr' @@ -552,8 +552,8 @@ egraphGP dataTrainVals dataTests args = do pure $ naryTree op exprs - printExpr :: Int -> Int -> EClassId -> RndEGraph [String] - printExpr actualMaxP ix ec = do + printExpr :: Bool -> Int -> Int -> EClassId -> RndEGraph [String] + printExpr withCI actualMaxP ix ec = do thetas' <- getTheta ec bestExpr0 <- getBestExpr ec bestExpr <- if _simplify args @@ -631,18 +631,22 @@ egraphGP dataTrainVals dataTests args = do dist = case distribution of { NLL d -> d; MSE -> LeastSquares; LOG10 -> LeastSquares; MAE -> LeastSquares; MAPE -> LeastSquares; Pinball _ -> LeastSquares; _ -> Gaussian } et = compileTree dist x y mYErr best' stats = getStatsFromModel dist mYErr x y best' theta - profiles <- liftIO $ getAllProfiles Bates et theta (_stdErr stats) [] 0.05 - let ciVals = paramCI (Profile stats profiles) nSamples theta 0.05 - maxPExpr = actualMaxP - ciStr = intercalate "," - $ Prelude.map (\(CI _ l h) -> showNA l <> "," <> showNA h) ciVals - ++ Prelude.replicate (2 * (maxPExpr - length ciVals)) "" - - pure $ show ix <> "," <> show view <> "," <> showExprFun expr <> "," <> "\"" <> showPython best' <> "\"," + row = show ix <> "," <> show view <> "," <> showExprFun expr <> "," <> "\"" <> showPython best' <> "\"," <> "\"$$" <> showLatexFun best' <> "$$\"," <> thetaStr <> "," <> show (countNodes $ convertProtectedOps expr) <> "," <> vals - <> "," <> ciStr + if withCI + then do + -- profile-likelihood CIs (expensive: an NLopt walk per parameter); + -- only computed for the final Pareto front, not per-generation trace. + profiles <- liftIO $ getAllProfiles Bates et theta (_stdErr stats) [] 0.05 + let ciVals = paramCI (Profile stats profiles) nSamples theta 0.05 + maxPExpr = actualMaxP + ciStr = intercalate "," + $ Prelude.map (\(CI _ l h) -> showNA l <> "," <> showNA h) ciVals + ++ Prelude.replicate (2 * (maxPExpr - length ciVals)) "" + pure $ row <> "," <> ciStr + else pure row pure ts insertTerms = diff --git a/src/eggp.egg-info/PKG-INFO b/src/eggp.egg-info/PKG-INFO index 59c3600..afc1b9f 100644 --- a/src/eggp.egg-info/PKG-INFO +++ b/src/eggp.egg-info/PKG-INFO @@ -1,6 +1,6 @@ Metadata-Version: 2.4 Name: eggp -Version: 2.2.0 +Version: 2.2.1 Summary: Python Wheels for eggp algorithm. Author-email: Fabricio Olivetti License: GNU GENERAL PUBLIC LICENSE diff --git a/src/eggp/__init__.py b/src/eggp/__init__.py index 5d0d3c7..aa21905 100644 --- a/src/eggp/__init__.py +++ b/src/eggp/__init__.py @@ -50,7 +50,7 @@ def _get_hs_eggp_run_data(): _hs_eggp_run_data = lib.hs_eggp_run_data return _hs_eggp_run_data -VERSION: str = "2.2.0" +VERSION: str = "2.2.1" _hs_rts_init: bool = False @@ -486,14 +486,20 @@ def evaluate_best_model(self, x): if x.ndim == 1: x = x.reshape(-1,1) - tStr = self.results.iloc[-1].theta.split(";") + best = self._best_row() + tStr = best.theta.split(";") t = np.array(list(map(float, tStr))) if len(tStr[0]) > 0 else np.array([]) - y = eval(self.results.iloc[-1].Numpy) + y = eval(best.Numpy) if self.loss == "Bernoulli": return 1/(1 + np.exp(-y)) elif self.loss == "Poisson": return np.exp(y) return y + def _best_row(self): + """Select the best model (lowest training loss).""" + lt = pd.to_numeric(self.results["loss_train"], errors="coerce") + return self.results.loc[lt.idxmin()] + def evaluate_best_model_view(self, x, view): if view not in np.unique(self.results.view.values): raise ValueError("Invalid view index") @@ -501,8 +507,8 @@ def evaluate_best_model_view(self, x, view): x = x.to_numpy() if x.ndim == 1: x = x.reshape(-1,1) - ix = self.results.iloc[-1].id - best = self.results[self.results.id==ix].iloc[view] + ix = self._best_row().id + best = self.results[(self.results.id==ix) & (self.results.view==view)].iloc[0] t = np.array(list(map(float, best.theta.split(";")))) if len(best.theta) > 0 else np.array([]) y = eval(best.Numpy) if self.loss == "Bernoulli":

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z%ZupVE4}T@*oT+1FR$R0^!LAVgRO(gK>HTzq2m54cX}VpA-s>n`2c+fDi7Q4zvA92 z6TDC4R8Heey7S7b_H54KT+XAv^Od*l1$6h7CH6;L#^rSX6@T9=E4{DcYJSV_`2*K- z9e?F-^mo6q+5Wp`wML_AW82+Vo7wKa>fWm@y|>|R+=K4Dy0;ymyRU}qFq2F(!z^`C z)kU@9-G6xX09!9ry;OY{s_p!)pK52j3y`8r8&$BP&#k_>R3spT- zuko&jsvfEXybt2-bpO?%_AuVh5qyM?@-aTnQ5-{ERG+k;;?tbO$$Xao<8;2rm#CZS zEA}kD&iQfNkt1Z1#U$P&g6* From 2a5fa99e4e17e8ac7d4a67475f6af04fbfecc7ea Mon Sep 17 00:00:00 2001 From: Olivetti Date: Sat, 31 Jan 2026 11:23:24 -0300 Subject: [PATCH 109/166] frac bayes --- src/Search.hs | 2 +- src/eggp.egg-info/PKG-INFO | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/src/Search.hs b/src/Search.hs index 91587e3..2a30aca 100644 --- a/src/Search.hs +++ b/src/Search.hs @@ -91,7 +91,7 @@ data Args = Args deriving (Show) csvHeader :: String -csvHeader = "id,view,Expression,Numpy,Math,theta,size,loss_train,loss_val,loss_test,maxloss,R2_train,R2_val,R2_test,mdl_train,mdl_val,mdl_test" +csvHeader = "id,view,Expression,Numpy,Math,theta,size,loss_train,loss_val,loss_test,maxloss,R2_train,R2_val,R2_test,dl_train,dl_val,dl_test" egraphGP :: [(DataSet, DataSet)] -> [DataSet] -> Args -> StateT EGraph (StateT StdGen IO) String egraphGP dataTrainVals dataTests args = do diff --git a/src/eggp.egg-info/PKG-INFO b/src/eggp.egg-info/PKG-INFO index 44c305b..b6ee30d 100644 --- a/src/eggp.egg-info/PKG-INFO +++ b/src/eggp.egg-info/PKG-INFO @@ -1,6 +1,6 @@ Metadata-Version: 2.4 Name: eggp -Version: 1.0.9 +Version: 1.0.10 Summary: Python Wheels for eggp algorithm. Author-email: Fabricio Olivetti License: GNU GENERAL PUBLIC LICENSE From 7fccce49a7a27bfa71e92d9b286a4b920644c555 Mon Sep 17 00:00:00 2001 From: folivetti Date: Sat, 31 Jan 2026 11:30:00 -0300 Subject: [PATCH 110/166] frac bayes fun complexity info --- cabal.project | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cabal.project b/cabal.project index 228d98b..6ca5a0f 100644 --- a/cabal.project +++ b/cabal.project @@ -10,7 +10,7 @@ source-repository-package source-repository-package type: git location: https://github.com/folivetti/srtree - tag: bfec40b865fd66d3b4a7570167f63257ca95b151 + tag: e1e88ecc1047d14f6557740773e571f49a7b4cf4 if os(darwin) package * From 898a9c6e7891d58354c0bbfb3c121e617921690d Mon Sep 17 00:00:00 2001 From: folivetti Date: Sun, 1 Feb 2026 11:02:33 -0300 Subject: [PATCH 111/166] frac bayes fun complexity info --- tutorials/datatemp_47fex6m1.csv | 363 +++++++++++++++++++++++++++++++ tutorials/datatemp_x8jx4axv.csv | 363 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