diff --git a/.gitignore b/.gitignore index ea6fc63..6fdbd90 100644 --- a/.gitignore +++ b/.gitignore @@ -1,8 +1,10 @@ -data/ +.venv +.ipynb_checkpoints *.org *.db -src/*/*.md -.idea/ -.ipynb_checkpoints/ -__pycache__/ -*.pyc +src/S*/*.md +uv.lock +.python-version +pyproject.toml +jupytext.toml +data/*.csv \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..778e566 --- /dev/null +++ b/README.md @@ -0,0 +1,45 @@ +# Machine Learning in Python + +This repository is associated with the "Machine Learning in Python" training. The lastest iteration of this training is February 2026, and the material for this iteration can be found in the `feb2026` branch of the repository. + + +## Introduction + +The training is structured in six sessions spread over three weeks, with two sessions per week. + +Each session is four hours long, with a total in-class time for the entire training of 24 hours. To get the most benefit from the training, we recommend that you spend as much time outside of the class working on your own, with your own datasets. + +Each session is made up of several concepts, and each concept is structured as follows: + +- A concept is introduced via slides and in a Jupyter notebook +- You will be shown some practical examples that illustrate the concept +- You will be asked to write your own code that uses the concept to solve a small problem +- I will show you how I solved the same problem + + +## Working with Jupyter + +Python is a very popular programming language for Machine Learning, and it is what we will use in this training. + +When dealing with heavier computations, the Python code is usually placed in a `.py` file which is then executed as a whole. In a prototyping or teaching situation, though, it is best to do things more interactively, which is why we will use [Jupyter](https://jupyter.org/) notebooks via `JupyterLab` to arrange our code. + +During the first session, we will cover three ways of running Jupyter, but we will only use one during the training (as much as possible): + + +### Jupyter on Habrok + +For this training, we recommend using the University of Groningen's High Performance Computing (HPC) cluster, **Habrok**. This ensures everyone has access to the same computational resources and hardware. + +**NOTE**: + +> You need to have a University of Groningen account to use Habrok. Your account also needs to be enable on Habrok, which you can request by following the [info](https://wiki.hpc.rug.nl/habrok/introduction/policies) on the wiki. + + +### Jupyter locally + +If you don't have a University account, or prefer to run things locally on your computer, you will need to install Jupyter (and Python) locally. We give a few pointers on how to do this, but it depends on the operating system you work with, so we do not go into details. + + +### Jupyter in Google + +If neither of the above options is workable for you, you can use [Google Colab](https://colab.google/), which is an online platform provided by Google to run Jupyter. Although this is easy to set up and requires minimum configuration, we do not recommend it for long-term usage. diff --git a/Syllabus.md b/Syllabus.md index faa1fb5..2051a29 100644 --- a/Syllabus.md +++ b/Syllabus.md @@ -136,87 +136,107 @@ - Kernel Trick -# Session 4: Ensemble Methods +# Session 4: Unsupervised Learning - Clustering and Dimensionality Reduction -## Introduction to Ensemble Methods +## Introduction to Unsupervised Learning -- What are Ensemble Methods? -- Why Use Ensemble Methods? -- Types of Ensemble Methods +- Supervised vs Unsupervised +- Dimensionality Reduction +- Clustering +- Anomaly Detection -## Bagging and Random Forests +## Dimensionality Reduction -- Bagging (Bootstrap Aggregating) Overview -- Random Forests -- Feature Importance in Random Forests +- Curse of Dimensionality +- Principal Component Analysis (PCA) +- Choosing the number of components +- Linear Discriminant Analysis (LDA) +- t-SNE and UMAP (Visualization techniques) -## Boosting +## Clustering -- Boosting Overview -- AdaBoost -- Gradient Boosting -- XGBoost +- k-Means Clustering +- Elbow Method and Silhouette Score +- Hierarchical Clustering (Dendrograms) +- DBSCAN (Density-based clustering) -## Ensembles of Ensembles +## Anomaly Detection -- Stacking -- Multi-level EoE +- Isolation Forests -# Session 5: Unsupervised Learning - Clustering and Dimensionality Reduction +# Session 5: Neural Networks I - Foundations and Optimization -## Introduction to Unsupervised Learning +## Introduction to Artificial Neural Networks -- Dimensionality Reduction -- Clustering -- Anomaly Detection +- Biological Inspiration +- The Perceptron Model +- Limitations of the Perceptron (XOR problem) -## Dimensionality Reduction +## The Multilayer Perceptron (MLP) -- Principal Component Analysis (PCA) -- Autoencoders -- Linear Discriminant Analysis (LDA) +- Input, Hidden, and Output Layers +- Activation Functions (Sigmoid, Tanh, ReLU, Softmax) +- Forward Propagation (Matrix notation) -## Clustering +## Training Neural Networks -- k-Means Clustering -- Hierarchical Clustering +- Loss Functions (MSE vs. Cross-Entropy) +- Gradient Descent and Stochastic Gradient Descent (SGD) +- Backpropagation intuition +- The Vanishing Gradient Problem -## Anomaly Detection +## Optimizing Neural Networks +- Weight Initialization strategies +- Optimizers (Momentum, RMSProp, Adam) +- Regularization (Dropout, Batch Normalization, Early Stopping) -# Session 6: Artificial Neural Networks and Deep Learning +## Building a Network in PyTorch/TensorFlow -## Introduction to Neural Networks +- Tensors and Operations +- Defining a simple architecture +- The Training Loop -- What are Neural Networks? -- Biological Inspiration -- Structure of a Neural Network -- Activation Functions +# Session 6: Neural Networks II - Deep Learning, Transformers, and GenAI -## Training Neural Networks -- Forward Propagation -- Backpropagation -- Loss Functions -- Gradient Descent +## Convolutional Neural Networks (CNNs) + +- Convolution and Pooling Layers +- Padding and Stride +- Common Architectures (VGG, ResNet) + + +## From RNNs to Transformers + +- Recurrent Neural Networks (RNNs) and LSTMs +- The bottleneck of sequential processing +- The Attention Mechanism +- The Transformer Architecture (Encoder-Decoder) + + +## Large Language Models (LLMs) + +- Evolution of LLMs (BERT, GPT, Llama) +- Pre-training, Fine-tuning, and Inference +- Tokenization and Context Windows +- Prompt Engineering basics -## Deep Learning +## Generative AI and Modern Applications -- What is Deep Learning? -- Deep Neural Networks -- Convolutional Neural Networks (CNNs) -- Recurrent Neural Networks (RNNs) -- Autoencoders -- Generative Adversarial Networks (GANs) +- Text Generation (Next-token prediction) +- Retrieval-Augmented Generation (RAG) +- Introduction to Diffusion Models (Image Generation) +- Ethical considerations and Bias in GenAI diff --git a/data/.placeholder b/data/.placeholder new file mode 100644 index 0000000..e69de29 diff --git a/src/S01 Introduction to Machine Learning/S01 Introduction to Machine Learning.pdf b/src/S01 Introduction to Machine Learning/S01 Introduction to Machine Learning.pdf index 0d8acbc..7e48d9f 100644 Binary files a/src/S01 Introduction to Machine Learning/S01 Introduction to Machine Learning.pdf and b/src/S01 Introduction to Machine Learning/S01 Introduction to Machine Learning.pdf differ diff --git a/src/S01 Introduction to Machine Learning/S01 Introduction to Machine Learning.tex b/src/S01 Introduction to Machine Learning/S01 Introduction to Machine Learning.tex index a83f499..5bc336f 100644 --- a/src/S01 Introduction to Machine Learning/S01 Introduction to Machine Learning.tex +++ b/src/S01 Introduction to Machine Learning/S01 Introduction to Machine Learning.tex @@ -1,4 +1,4 @@ -% Created 2025-06-26 Thu 21:20 +% Created 2026-02-23 Mon 08:41 % Intended LaTeX compiler: pdflatex \documentclass[hyperref={pdfpagelabels=false},aspectratio=169]{beamer} \usepackage[utf8]{inputenc} @@ -16,6 +16,7 @@ \usecolortheme{beaver} \useinnertheme{rounded} \useoutertheme{tree} +\author{Cristian A. Marocico} \date{\today} \title{Machine Learning in Python} \subtitle{Introduction to Machine Learning} @@ -180,17 +181,17 @@ }{% \end{block} } -\date[Jun 30\textsuperscript{th} 2025]{Monday, June 30\textsuperscript{th} 2025} +\date[Feb 3\textsuperscript{rd} 2026]{Tuesday, February 3\textsuperscript{rd} 2026} \usepackage{mathtools} \newcommand{\intsum}{\mathop{\mathrlap{\raisebox{0.1ex}{\hspace{0.2em}$\textstyle\sum$}}\int}\limits} \setbeamercovered{transparent=0} \usepackage[timeinterval=60]{tdclock} \hypersetup{ - pdfauthor={}, + pdfauthor={Cristian A. Marocico}, pdftitle={Machine Learning in Python}, pdfkeywords={}, pdfsubject={}, - pdfcreator={Emacs 30.1 (Org mode 9.7.11)}, + pdfcreator={Emacs 30.2 (Org mode 9.7.11)}, pdflang={English}} \begin{document} @@ -201,9 +202,9 @@ \end{frame} \section{Introduction to Machine Learning} -\label{sec:org2d04d73} -\begin{frame}[label={sec:org0aaa2e5}]{What is Machine Learning?} -\begin{definition}[Machine Learning]\label{sec:orgec8d6d1} +\label{sec:org7a8c6b4} +\begin{frame}[label={sec:org5fc047d}]{What is Machine Learning?} +\begin{definition}[Machine Learning]\label{sec:org4fcc272} \pause \alert{\alert{Machine Learning}} is the study of algorithms that learn patterns from data to make predictions or decisions without being explicitly programmed. \end{definition} @@ -216,26 +217,26 @@ \section{Introduction to Machine Learning} \end{alertblock} \end{frame} \section{The Machine Learning Workflow} -\label{sec:org79863f5} -\begin{frame}[label={sec:org3c14a33}]{The Machine Learning Workflow} +\label{sec:org856314d} +\begin{frame}[label={sec:orge2722b6}]{The Machine Learning Workflow} \pause A typical \alert{Machine Learning} workflow has the following structure: \pause \begin{itemize}[<+->] \item Problem Definition \item Data Collection -\item \alert{\alert{Train-Test Split}} -\item \alert{\alert{Data Exploration and Preprocessing}} -\item \alert{\alert{Model Selection}} -\item \alert{\alert{Training and Validation}} -\item \alert{\alert{Evaluation}} +\item \alert{Train-Test Split} +\item \alert{Data Exploration and Preprocessing} +\item \alert{Model Selection} +\item \alert{Training and Validation} +\item \alert{Evaluation} \item Deployment and Monitoring \end{itemize} \end{frame} \section{Train-Test Splits and Cross-Validation} -\label{sec:orgb850f45} -\begin{frame}[label={sec:org3f24e79}]{Train and Test Datasets} -\begin{definition}[Splitting the dataset]\label{sec:orga939d42} +\label{sec:orgb4e55fa} +\begin{frame}[label={sec:orgedde426}]{Train and Test Datasets} +\begin{definition}[Splitting the dataset]\label{sec:org851ed06} \pause In machine learning, we split the dataset into two parts: \pause @@ -252,12 +253,12 @@ \section{Train-Test Splits and Cross-Validation} \end{itemize} \end{alertblock} \end{frame} -\begin{frame}[label={sec:org073120d}]{Cross-Validation} +\begin{frame}[label={sec:org7fbf4af}]{Cross-Validation} The \alert{\alert{train}} dataset can be further split into \alert{\alert{training}} and \alert{\alert{validation}} sets to tune the model's hyperparameters and prevent overfitting. \end{frame} -\begin{frame}[label={sec:orgf0c6674}]{Cross-Validation} -\begin{definition}[Cross-Validation]\label{sec:org96fb9e4} +\begin{frame}[label={sec:org55f4803}]{Cross-Validation} +\begin{definition}[Cross-Validation]\label{sec:org1d85e8c} \pause Cross-validation is a technique to assess how the results of a statistical analysis will generalize to an independent dataset. \pause @@ -267,16 +268,47 @@ \section{Train-Test Splits and Cross-Validation} \end{itemize} \end{definition} \end{frame} +\begin{frame}[label={sec:orgc06a383}]{Data Leakage} +\begin{definition}[Data Leakage]\label{sec:orgbc1b546} +\pause +\alert{Data Leakage} occurs when information from outside the training set improperly influences the model, leading to overly optimistic results that fail in deployment. +\end{definition} +\begin{alertblock}{The Exam Analogy} +\pause +A student who accidentally sees the answer key scores 100\%, but learns nothing and fails the next exam. +\end{alertblock} +\end{frame} +\begin{frame}[label={sec:org22e44b1},fragile]{Common Sources of Leakage} + \begin{example}[Common Sources]\label{sec:orgff591ba} +\pause +\begin{itemize}[<+->] +\item \alert{Preprocessing before splitting}: Scaling or imputing using the full dataset leaks test statistics into training. +\item \alert{Temporal leakage}: Using future data to predict the past. +\item \alert{Target leakage}: Features that are consequences of the outcome (e.g., \texttt{treatment\_response} when predicting \texttt{needs\_treatment}). +\item \alert{Duplicate samples}: Same subject in both train and test (multiple measurements per patient). +\end{itemize} +\end{example} +\end{frame} +\begin{frame}[label={sec:org8fcdedf},fragile]{Preventing Leakage} + \begin{alertblock}{Prevention Strategies} +\pause +\begin{itemize}[<+->] +\item \alert{Always split first}: No preprocessing touches the full dataset. +\item \alert{Use Pipelines}: Scikit-learn's \texttt{Pipeline} enforces correct ordering automatically. +\item \alert{Think causally}: "Would I have this feature at prediction time?" +\end{itemize} +\end{alertblock} +\end{frame} \section{Exploratory Data Analysis (EDA)} -\label{sec:org47f21aa} -\begin{frame}[label={sec:org07f3774}]{Exploratory Data Analysis (EDA)} -\begin{definition}[Exploratory Data Analysis (EDA)]\label{sec:org89b13f7} +\label{sec:org600dc1d} +\begin{frame}[label={sec:orgfa2a55e}]{Exploratory Data Analysis (EDA)} +\begin{definition}[Exploratory Data Analysis (EDA)]\label{sec:org0fb991c} \pause \alert{\alert{Exploratory Data Analysis}} (EDA) is the process of analyzing datasets to summarize their main characteristics, often using visual methods. \end{definition} \end{frame} -\begin{frame}[label={sec:org9247409}]{Exploratory Data Analysis (EDA)} -\begin{example}[Purpose of EDA]\label{sec:org741005b} +\begin{frame}[label={sec:orgb114316}]{Exploratory Data Analysis (EDA)} +\begin{example}[Purpose of EDA]\label{sec:org31e4f88} \pause \begin{itemize}[<+->] \item To understand the data distribution and relationships between variables. @@ -284,7 +316,7 @@ \section{Exploratory Data Analysis (EDA)} \item To generate hypotheses and inform feature engineering. \end{itemize} \end{example} -\begin{example}[EDA Techniques]\label{sec:org99fb678} +\begin{example}[EDA Techniques]\label{sec:org79495c7} \pause \begin{itemize}[<+->] \item \alert{\alert{Descriptive Statistics}}: Mean, median, mode, standard deviation, etc. @@ -296,15 +328,15 @@ \section{Exploratory Data Analysis (EDA)} \end{example} \end{frame} \section{Data Preprocessing} -\label{sec:orgaf9aa64} -\begin{frame}[label={sec:org733ef7e}]{Data Preprocessing} -\begin{definition}[Data Preprocessing]\label{sec:org507807c} +\label{sec:org8ba8ffe} +\begin{frame}[label={sec:orgbc95f39}]{Data Preprocessing} +\begin{definition}[Data Preprocessing]\label{sec:org09f1eea} \pause \alert{\alert{Data Preprocessing}} is the process of transforming raw data into a format suitable for analysis and modeling. \end{definition} \end{frame} -\begin{frame}[label={sec:orgfe2bb4d}]{Data Preprocessing} -\begin{example}[Purpose of Data Preprocessing]\label{sec:org1fdb553} +\begin{frame}[label={sec:org17fe62d}]{Data Preprocessing} +\begin{example}[Purpose of Data Preprocessing]\label{sec:org7a3052a} \pause \begin{itemize}[<+->] \item To clean the data by handling missing values, outliers, and inconsistencies @@ -312,7 +344,7 @@ \section{Data Preprocessing} \item To encode categorical variables into numerical formats \end{itemize} \end{example} -\begin{example}[Common Data Preprocessing Techniques]\label{sec:org79eb6f1} +\begin{example}[Common Data Preprocessing Techniques]\label{sec:org76de8ba} \begin{itemize}[<+->] \item \alert{\alert{Handling Missing Values}}: Techniques include imputation (mean, median, mode), deletion, or using algorithms that can handle missing data. \item \alert{\alert{Outlier Detection and Treatment}}: Identifying outliers using statistical methods (e.g., Z-score, IQR) and deciding whether to remove or transform them. @@ -322,15 +354,15 @@ \section{Data Preprocessing} \end{example} \end{frame} \section{Feature Engineering} -\label{sec:orga965fd7} -\begin{frame}[label={sec:orgc951175}]{Feature Engineering} -\begin{definition}[Feature Engineering]\label{sec:org6cd6398} +\label{sec:org4a0741d} +\begin{frame}[label={sec:org19caaee}]{Feature Engineering} +\begin{definition}[Feature Engineering]\label{sec:orgba57b80} \pause \alert{\alert{Feature Engineering}} is the process of using domain knowledge to select, modify, or create features (variables) that improve the performance of machine learning models. \end{definition} \end{frame} -\begin{frame}[label={sec:org31dfe16}]{Feature Engineering} -\begin{example}[Purpose of Feature Engineering]\label{sec:org1010237} +\begin{frame}[label={sec:org5563749}]{Feature Engineering} +\begin{example}[Purpose of Feature Engineering]\label{sec:org46a3963} \pause \begin{itemize}[<+->] \item To improve model performance by providing more relevant information. @@ -338,7 +370,7 @@ \section{Feature Engineering} \item To create new features that capture important patterns in the data. \end{itemize} \end{example} -\begin{example}[Common Feature Engineering Techniques]\label{sec:org5de6103} +\begin{example}[Common Feature Engineering Techniques]\label{sec:org848e7f7} \begin{itemize}[<+->] \item \alert{\alert{Feature Selection}}: Techniques include filter methods (e.g., correlation), wrapper methods (e.g., recursive feature elimination), and embedded methods (e.g., LASSO). \item \alert{\alert{Feature Transformation}}: Techniques include logarithmic transformation, polynomial features, and interaction terms. diff --git a/src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter on Habrok.ipynb b/src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter on Habrok.ipynb deleted file mode 100644 index 5eb924e..0000000 --- a/src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter on Habrok.ipynb +++ /dev/null @@ -1,144 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "67b5b1a3", - "metadata": {}, - "source": [ - "\n", - "# Working with Jupyter Notebooks\n", - "\n", - "Throughout this workshop, we will be working in Jupyter Notebooks / JupyterLab. Jupyter Notebooks are a popular tool for data science and machine learning, allowing you to write and execute code in an interactive environment.\n", - "\n", - "In order to have a uniform environment, we will be using the Habrok High Performance Computing (HPC) cluster, through the Jupyter Python Interactive App on the Habrok web portal, located at [https://portal.hb.hpc.rug.nl](https://portal.hb.hpc.rug.nl). You will need to log in with your university account, and then you can start the Jupyter Python Interactive App.\n", - "\n", - "The app is already setup with the necessary packages for this workshop, but you will also build your own environment and Jupyter kernel." - ] - }, - { - "cell_type": "markdown", - "id": "eda28a27", - "metadata": {}, - "source": [ - "## Building your own Jupyter kernel on Habrok\n", - "\n", - "In order to build your own Jupyter kernel, you will need to create a new Python environment. This can be done on Habrok by following these steps:\n", - "\n", - "- From the Habrok web portal, navigate to the \"Clusters\" tab and select one of the available Shell sessions (e.g., \"Habrok Login1 Shell\").\n", - "- In the Shell session, first load the Python module by running:\n", - "\n", - "```shell\n", - "module load Python/3.13.1-GCCcore-14.2.0\n", - "```\n", - "\n", - "- Next, create a new Python environment using `venv`:\n", - "\n", - "```shell\n", - "python3 -m venv $HOME/venvs/ml_workshop\n", - "```\n", - "\n", - "- Activate the new environment:\n", - "\n", - "```shell\n", - "source $HOME/venvs/ml_workshop/bin/activate\n", - "```\n", - "\n", - "- Install the necessary packages for the workshop:\n", - "\n", - "```shell\n", - "pip install -r requirements.txt\n", - "```\n", - "\n", - "- Finally, install the Jupyter kernel:\n", - "\n", - "```shell\n", - "python -m ipykernel install --user --name=ml_workshop --display-name=\"ML Workshop\"\n", - "```\n", - "\n", - "Now that the kernel has been created, you can start the Jupyter Python Interactive App again from the Habrok web portal. In the app form, select the correct Python Version (3.13.1), the \"Custom virtual environment\" option for the Python Environment, and then select the path to your newly created environment, which should be `$HOME/venvs/ml_workshop`. When you start the app, the Jupyter kernel will be available, and you can select it from the \"Kernel\" menu in the Jupyter Lab interface (\"ML Workshop\")." - ] - }, - { - "cell_type": "markdown", - "id": "7b80feaa", - "metadata": {}, - "source": [ - "## Building your own Jupyter kernel on your local machine\n", - "\n", - "Similarly, you can build your own Jupyter kernel on your local machine. The steps are similar to those on Habrok, but you will need to install Python on your local machine first. You can download Python from the official website: [https://www.python.org/downloads/](https://www.python.org/downloads/).\n", - "\n", - "After following the steps above, you can start Jupyter Lab on your local machine by running:\n", - "\n", - "```shell\n", - "jupyter-lab\n", - "```\n", - "\n", - "which will open a new tab in your web browser with the Jupyter Lab interface. You can then create a new notebook and select the kernel you created from the \"Kernel\" menu." - ] - }, - { - "cell_type": "markdown", - "id": "d9ac32f2", - "metadata": {}, - "source": [ - "## Building your own Jupyter kernel on Google Colab\n", - "\n", - "If you prefer to use Google Colab, you can also build your own Jupyter kernel there. Google Colab is a free Jupyter notebook environment that runs in the cloud and is particularly useful for machine learning and data science tasks.\n", - "\n", - "To use Google Colab, you will need a Google account. Once you have an account, you can go to [Google Colab](https://colab.research.google.com/) and create a new notebook. You can then install the necessary packages by running the following code in a code cell:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "01559c58", - "metadata": {}, - "outputs": [], - "source": [ - "!pip install -r requirements.txt" - ] - }, - { - "cell_type": "markdown", - "id": "83a78148", - "metadata": {}, - "source": [ - "We do not recommend using Google Colab for your research, as it is not a suitable environment for running large-scale machine learning models. However, it can be useful for quick prototyping and testing of small models." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "47df9255", - "metadata": {}, - "outputs": [], - "source": [ - "!pip list" - ] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,md" - }, - "kernelspec": { - "display_name": "ML Workshop", - "language": "python", - "name": "ml_workshop" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter.ipynb b/src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter.ipynb new file mode 100644 index 0000000..e46e801 --- /dev/null +++ b/src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter.ipynb @@ -0,0 +1,414 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "5e23e866", + "metadata": {}, + "source": [ + "\n", + "# Working with Jupyter Notebooks\n", + "\n", + "Throughout this workshop, we will be working in **Jupyter Notebooks** and **JupyterLab**. These are industry-standard tools for data science and machine learning that allow you to combine executable code, rich text documentation, visualizations, and mathematical equations in a single, interactive document.\n", + "\n", + "While standard Python scripts (`.py` files) are excellent for production code, Jupyter Notebooks (`.ipynb` files) excel at:\n", + "\n", + "- **Exploratory Data Analysis (EDA):** Seeing results immediately after running a block of code.\n", + "- **Documentation:** Keeping notes alongside your logic.\n", + "- **Visualizing:** Rendering plots and graphs directly within the workflow.\n", + "\n", + "We will cover three ways to access this environment:\n", + "\n", + "1. **The Habrok HPC Cluster:** (Recommended for this course)\n", + "2. **Your Local Machine:** (For working offline)\n", + "3. **Google Colab:** (For quick prototyping)\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "a2bd4e99", + "metadata": {}, + "source": [ + "# Using the Habrok HPC Cluster" + ] + }, + { + "cell_type": "markdown", + "id": "bf3ebf57", + "metadata": {}, + "source": [ + "## Accessing the Portal\n", + "\n", + "1. Navigate to the Habrok web portal: [https://portal.hb.hpc.rug.nl](https://portal.hb.hpc.rug.nl).\n", + "2. Log in with your university credentials.\n", + "3. We will use the **Jupyter Python Interactive App**, but first, we need to configure a custom environment to ensure all necessary libraries are installed." + ] + }, + { + "cell_type": "markdown", + "id": "56772ca4", + "metadata": {}, + "source": [ + "## Building your own Jupyter Kernel on Habrok\n", + "\n", + "A \"Kernel\" is the computational engine that executes the code contained in a Notebook document. By creating a custom kernel, you ensure your notebook uses the specific libraries required for this workshop." + ] + }, + { + "cell_type": "markdown", + "id": "20481db1", + "metadata": {}, + "source": [ + "### Step 1: Open a Shell Session\n", + "\n", + "- From the Habrok portal, go to the **\"Clusters\"** tab.\n", + "- Select **\"Habrok Login1 Shell\"** (or similar) to open a terminal.\n", + "- Clone the GitHub repository\n", + " \n", + " ```shell\n", + " git clone https://github.com/UG-Team-Data-Science/ml-workshop.git /scratch/$USER/ml-workshop\n", + " ```\n", + "- Go to the code folder\n", + " \n", + " ```shell\n", + " cd /scratch/$USER/ml-workshop\n", + " ```\n", + "- Checkout the most current branch\n", + " \n", + " ```shell\n", + " git checkout feb2026\n", + " ```\n", + "\n", + "\n", + "### Step 2: Load Python\n", + "\n", + "We need to load a specific version of Python available on the cluster. Run the following command:\n", + "\n", + "```shell\n", + "module load Python/3.13.1-GCCcore-14.2.0\n", + "```\n", + "\n", + "> **Note:** If this specific version is unavailable in the future, you can search for available versions using `module spider Python`." + ] + }, + { + "cell_type": "markdown", + "id": "44d9ac12", + "metadata": {}, + "source": [ + "### Step 3: Create a Virtual Environment\n", + "\n", + "It is best practice to keep project dependencies isolated. We will create a virtual environment (`venv`) in your `/scratch/$USER` directory:\n", + "\n", + "```shell\n", + "python3 -m venv /scratch/$USER/venvs/ml-workshop\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "297627a9", + "metadata": {}, + "source": [ + "### Step 4: Activate the Environment\n", + "\n", + "Before installing packages, you must activate this environment:\n", + "\n", + "```shell\n", + "source /scratch/$USER/venvs/ml-workshop/bin/activate\n", + "```\n", + "\n", + "*(Your terminal prompt should now show `(ml-workshop)` at the start.)*\n", + "\n", + "**Optional**: Update the `pip` Python package manager to the latest verstion:\n", + "\n", + "```shell\n", + "pip install --upgrade pip\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "d1555713", + "metadata": {}, + "source": [ + "### Step 5: Install Dependencies\n", + "\n", + "You can install the necessary dependencies from the `requirements.txt` file, which can be found in the `src/` folder in the repository. If you've followed the above steps precisely, you should now be in the root of the repository (`/scratch/$USER/ml-workshop`), and you can install the dependencies with:\n", + "\n", + "```shell\n", + "pip install -r src/requirements.txt\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "caf1c879", + "metadata": {}, + "source": [ + "### Step 6: Install the Jupyter Kernel\n", + "\n", + "This step links your new virtual environment to the Jupyter interface so you can select it from a dropdown menu later.\n", + "\n", + "```shell\n", + "python -m ipykernel install --user --name=ml_workshop --display-name=\"ML Workshop\"\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "b013ac18", + "metadata": {}, + "source": [ + "## Starting the Jupyter App\n", + "\n", + "1. Return to the **Habrok Web Portal**.\n", + "2. From the **Interactive Apps** menu, select the **Jupyter (basic)** entry.\n", + "3. **Configuration:**\n", + " - **Node Type:** Select `Regular` for now, later on we will use GPUs as well.\n", + " - **Mode:** `JupyterLab` is probably the easiest interface.\n", + " - **Python Version:** Select `3.13.1-GCCcore-14.2.0` (matching the module you loaded).\n", + " - **Python Environment:** Select \"Custom virtual environment\".\n", + " - **Virtual Environment Path:** Enter the path to your environment (e.g., `/scratch/$USER/venvs/ml-workshop`).\n", + "4. Launch the app. Once inside the interface, you can ensure you are using the correct environment by checking the top-right corner of your notebook—it should say **\"ML Workshop\"**.\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "7de946ae", + "metadata": {}, + "source": [ + "# Building your own Kernel on a Local Machine\n", + "\n", + "Running Jupyter locally is great for offline work. However, the setup differs slightly depending on your operating system (Windows vs. macOS/Linux).\n", + "\n", + "**Prerequisite:** Ensure Python is installed ([python.org/downloads](https://www.python.org/downloads/))." + ] + }, + { + "cell_type": "markdown", + "id": "a63057e4", + "metadata": {}, + "source": [ + "## Step-by-Step Local Setup" + ] + }, + { + "cell_type": "markdown", + "id": "6b3e739e", + "metadata": {}, + "source": [ + "### Open your terminal\n", + "\n", + "- **Windows:** Command Prompt or PowerShell.\n", + "- **Mac/Linux:** Terminal." + ] + }, + { + "cell_type": "markdown", + "id": "f7d11316", + "metadata": {}, + "source": [ + "### Create the Virtual Environment\n", + "\n", + "Navigate to your project folder and run:\n", + "\n", + "```shell\n", + "# Windows, Mac, and Linux\n", + "python -m venv venv\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "9623a60e", + "metadata": {}, + "source": [ + "### Activate the Environment\n", + "\n", + "This step differs by OS:\n", + "\n", + "- **Windows:**\n", + " \n", + " ```shell\n", + " .\\venv\\Scripts\\activate\n", + " ```\n", + "- **macOS / Linux:**\n", + " \n", + " ```shell\n", + " source venv/bin/activate\n", + " ```\n", + "\n", + "\n", + "### Install Dependencies\n", + "\n", + "Install the course requirements plus the Jupyter Lab interface (which is not included in Python by default):\n", + "\n", + "```shell\n", + "pip install -r requirements.txt\n", + "pip install jupyterlab ipykernel\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "c9196874", + "metadata": {}, + "source": [ + "### Register the Kernel\n", + "\n", + "Just like on the cluster, we register this environment as a kernel:\n", + "\n", + "```shell\n", + "python -m ipykernel install --user --name=ml_workshop_local --display-name=\"ML Workshop Local\"\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "7b771a0a", + "metadata": {}, + "source": [ + "### Launch Jupyter Lab\n", + "\n", + "Start the interface by running:\n", + "\n", + "```shell\n", + "jupyter-lab\n", + "```\n", + "\n", + "This will open a tab in your default web browser. When creating a new notebook, simply select **\"ML Workshop Local\"** from the Kernel menu.\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "f189d5fc", + "metadata": {}, + "source": [ + "# Working with Google Colab\n", + "\n", + "Google Colab is a cloud-based notebook environment that requires no setup to start, though it has some limitations regarding session length and file storage. It is excellent for quick prototyping or if you are having trouble with local/HPC setups." + ] + }, + { + "cell_type": "markdown", + "id": "8ca1d6dc", + "metadata": {}, + "source": [ + "## Setup Instructions\n", + "\n", + "1. Log in to [Google Colab](https://colab.research.google.com/) with your Google Account.\n", + "2. Create a **New Notebook**." + ] + }, + { + "cell_type": "markdown", + "id": "012054e4", + "metadata": {}, + "source": [ + "## Installing Requirements\n", + "\n", + "Unlike local machines, Colab does not persist your environment after you close the tab. You must install libraries **every time you start a new session**." + ] + }, + { + "cell_type": "markdown", + "id": "334efc17", + "metadata": {}, + "source": [ + "### Option A: If you have a `requirements.txt` file\n", + "\n", + "You need to upload the file to the Colab session first.\n", + "\n", + "1. Click the **Folder icon** (Files) on the left sidebar.\n", + "2. Upload your `requirements.txt`.\n", + "3. Run the following in a code cell:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7910fbba", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "!pip install -r requirements.txt" + ] + }, + { + "cell_type": "markdown", + "id": "da060376", + "metadata": {}, + "source": [ + "### Option B: Direct Installation\n", + "\n", + "If you only need a few specific packages, you can install them directly:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "693806b0", + "metadata": {}, + "outputs": [], + "source": [ + "!pip install pandas numpy scikit-learn" + ] + }, + { + "cell_type": "markdown", + "id": "4a390f02", + "metadata": {}, + "source": [ + "> **Pro Tip:** Google Colab offers free access to GPUs. To enable this, go to **Runtime > Change runtime type** and select **T4 GPU** under Hardware accelerator. This can significantly speed up training for deep learning models.\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "4e3ebfca", + "metadata": {}, + "source": [ + "# Summary Checklist\n", + "\n", + "| Platform | Best For | Persistence | Computation Power |\n", + "| :--- | :--- | :--- | :--- |\n", + "| **Habrok HPC** | **Coursework & Research** | High (Files saved) | High (Cluster CPUs/GPUs) |\n", + "| **Local Machine** | Offline work / Small data | High (Files saved) | Dependent on your laptop |\n", + "| **Google Colab** | Quick tests / Prototyping | Low (Resets often) | Medium (Free tier limits) |" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "ML Workshop", + "language": "python", + "name": "ml_workshop" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/S01 Introduction to Machine Learning/S01C01 What is Machine Learning.ipynb b/src/S01 Introduction to Machine Learning/S01C01 What is Machine Learning.ipynb index 9b990a6..579d798 100644 --- a/src/S01 Introduction to Machine Learning/S01C01 What is Machine Learning.ipynb +++ b/src/S01 Introduction to Machine Learning/S01C01 What is Machine Learning.ipynb @@ -2,158 +2,156 @@ "cells": [ { "cell_type": "markdown", - "id": "258dca2b", + "id": "c01aa2be", "metadata": {}, "source": [ "\n", "# What is Machine Learning?\n", "\n", - "**Machine Learning** (ML) is a subfield of **artificial intelligence** (AI) that focuses on the development of algorithms and statistical models that enable computers to perform tasks without explicit instructions. Instead, ML systems learn from data, identifying patterns and making decisions based on that data." + "**Machine Learning** (ML) is a subfield of **Artificial Intelligence** (AI) that focuses on the development of algorithms and statistical models that enable computers to perform tasks without explicit instructions. Instead, ML systems learn from data, identifying patterns and making decisions based on that data.\n", + "\n", + "At its core, ML is about **generalization**: the ability to learn a concept from specific examples and apply that knowledge to new, unseen data." ] }, { "cell_type": "markdown", - "id": "16ee15a1", + "id": "601411ef", "metadata": {}, "source": [ "## ML vs. Traditional Programming\n", "\n", - "In traditional programming, a programmer writes explicit rules and instructions for the computer to follow. In contrast, machine learning allows the computer to learn from data and improve its performance over time without being explicitly programmed for every task.\n", + "In traditional programming, a human programmer analyzes a problem and writes explicit rules (code) for the computer to follow. In machine learning, the computer analyzes data to find the rules itself.\n", + "\n", + "To illustrate this, consider building a **Spam Filter**:\n", + "\n", + "- **Traditional Approach**: You write rules like \"If the email contains 'lottery', mark as spam.\" You have to manually update these rules constantly.\n", + "- **ML Approach**: You feed the algorithm thousands of emails labeled \"Spam\" or \"Not Spam.\" The algorithm learns which words (and combinations of words) are statistically correlated with spam.\n", "\n", - "One way to think about the difference is:\n", + "We can formalize this difference:\n", "\n", - "- **Traditional Programming**: input + model $\\Rightarrow$ output\n", - "- **Machine Learning**: input + output $\\Rightarrow$ model" + "- **Traditional Programming**: Data + Rules $\\Rightarrow$ Output\n", + "- **Machine Learning**: Data + Output $\\Rightarrow$ Rules (Model)" ] }, { "cell_type": "markdown", - "id": "5c3732f0", + "id": "6e4ac328", "metadata": {}, "source": [ "## Key Concepts in Machine Learning\n", "\n", - "Some key concepts in machine learning which will be useful to introduce early on in the workshop include:\n", + "Before writing code, it is essential to understand the vocabulary of the trade:\n", "\n", - "- **Data**: The foundation of machine learning. Data can be structured (e.g., tables, databases) or unstructured (e.g., text, images).\n", - "- **Observations**: Individual data points or instances in the dataset. In a tabular dataset, observations are the rows that represent different examples.\n", - "- **Features**: Individual measurable properties or characteristics of the data. In a tabular dataset, features are the columns that represent different attributes.\n", - "- **Labels**: The target variable or outcome that the model is trying to predict. This is usually one of the columns in a tabular dataset.\n", - "- **Model**: A mathematical representation of the relationship between features and labels. The model is trained on the data to learn these relationships.\n", - "- **Training**: The process of teaching a machine learning model using a dataset. During training, the model learns to identify patterns in the data.\n", - "- **Testing**: Evaluating the performance of a trained model on a separate dataset to assess its accuracy and generalization capabilities." + "- **Data**: The raw material. It can be structured (tables, Excel sheets) or unstructured (images, audio, text).\n", + "- **Observations (Samples/Instances)**: The rows in your dataset. Each row represents a distinct entity (e.g., one customer, one flower, one house).\n", + "- **Features (Attributes/Predictors)**: The columns in your dataset used to make predictions. These are the measurable properties (e.g., height, weight, color).\n", + "- **Labels (Targets/Response)**: The answer key. This is what the model tries to predict (e.g., \"Spam\" vs. \"Not Spam\", or the price of a house).\n", + "- **Model**: The mathematical engine that learns the relationship between Features and Labels.\n", + "- **Training**: The phase where the model iterates over the data to minimize errors and learn patterns.\n", + "- **Generalization**: The ability of the model to perform well on new data it has never seen before." ] }, { "cell_type": "markdown", - "id": "28256afd", + "id": "48cda2df", "metadata": {}, "source": [ "## Types of Machine Learning\n", "\n", - "Machine learning can be broadly categorized into three main types based on the nature of the learning task:\n", - "\n", - "- **Supervised Learning**: The model is trained on a labeled dataset, where each observation has a corresponding label. The goal is to learn a mapping from features to labels. Examples include **classification** and **regression** tasks.\n", - "- **Unsupervised Learning**: The model is trained on an unlabeled dataset, where the goal is to find patterns or groupings in the data. Examples include **clustering** and **dimensionality reduction** tasks.\n", - "- **Reinforcement Learning**: The model learns by interacting with an environment and receiving feedback in the form of rewards or penalties. The goal is to learn a policy that maximizes cumulative rewards over time.\n", - "\n", - "We will explore these concepts in more detail in a bit, but for now, it is important to understand that machine learning relies heavily on data and the relationships between features and labels.\n", + "Machine learning tasks are generally categorized by the nature of the available signal (feedback) used for learning:\n", "\n", - "We will also introduce more advanced concepts in later sessions, as needed." + "| Type | Description | Typical Tasks |\n", + "|-------------------------- |-------------------------------------------------------------------------------------------- |------------------------------------------------- |\n", + "| **Supervised Learning** | The data comes with \"Labels\" (answers). The model learns to map inputs to these targets. | Classification (categories), Regression (numbers) |\n", + "| **Unsupervised Learning** | The data has no labels. The model looks for hidden structures or groups on its own. | Clustering, Dimensionality Reduction |\n", + "| **Reinforcement Learning** | An agent learns by interacting with an environment and receiving \"rewards\" or \"punishments.\" | Game playing, Robotics, Navigation |" ] }, { "cell_type": "markdown", - "id": "50bf583b", + "id": "bcbb1995", "metadata": {}, "source": [ - "## Practical Demonstration\n", + "## The Machine Learning Workflow\n", "\n", - "The `scikit-learn` library, which is the most populuar machine learning library in Python, provides a wide range of tools for building and evaluating machine learning models. It includes datasets, preprocessing utilities, and various algorithms for classification, regression, clustering, and more.\n", + "Building a model is rarely a linear process, but it generally follows this cycle:\n", "\n", - "We will start by exploring some of the built-in datasets in `scikit-learn`, which are useful for learning and practicing machine learning concepts. These datasets are often used as benchmarks for testing algorithms and understanding their behavior.\n", - "\n", - "The first dataset we will explore is the **Iris dataset**, which is a classic dataset in machine learning. It contains measurements of iris flowers and their corresponding species labels. The dataset has four features (sepal length, sepal width, petal length, and petal width) and three classes (species of iris).\n", - "\n", - "Import the necessary libraries and load the Iris dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "b4f026b3", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.datasets import load_iris\n", - "data = load_iris()" + "1. *Data Collection*: Gathering raw data.\n", + "2. **Data Preparation**: Cleaning, formatting, and handling missing values.\n", + "3. **Exploratory Data Analysis (EDA)**: Visualizing data to understand relationships.\n", + "4. **Model Selection**: Choosing the right algorithm (e.g., Linear Regression, Decision Tree).\n", + "5. **Training**: Fitting the model to the data.\n", + "6. **Evaluation**: Testing the model on unseen data.\n", + "7. *Deployment*: Using the model in the real-world." ] }, { "cell_type": "markdown", - "id": "f267a0d8", + "id": "544aa712", "metadata": {}, "source": [ - "The `iris` dataset is loaded as, essentially, a dictionary:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "c264fe27", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "print(type(data))" + "## Practical Demonstration: The Iris Dataset\n", + "\n", + "The `scikit-learn` library is the industry standard for classical machine learning in Python. It includes datasets, preprocessing tools, and algorithms.\n", + "\n", + "We will use the **Iris dataset**, the \"Hello World\" of machine learning. It contains 150 samples of iris flowers with four features and a species label." ] }, { "cell_type": "markdown", - "id": "70f33251", + "id": "f7f2013a", "metadata": {}, "source": [ - "Let's print the keys of this dictionary:" + "### Loading the Data\n", + "\n", + "Import the necessary libraries and load the dataset." ] }, { "cell_type": "code", - "execution_count": 3, - "id": "2b65547f", - "metadata": {}, + "execution_count": 1, + "id": "25736e96", + "metadata": { + "lines_to_next_cell": 2 + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])\n" + "Keys: dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])\n" ] } ], "source": [ - "print(data.keys())" + "from sklearn.datasets import load_iris\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "# Load the raw object\n", + "iris_data = load_iris()\n", + "\n", + "# The dataset is essentially a dictionary\n", + "print(\"Keys:\", iris_data.keys())" ] }, { "cell_type": "markdown", - "id": "57459dc7", + "id": "18e7c777", "metadata": {}, "source": [ - "Print the description of the dataset to understand its contents." + "### Understanding the Structure\n", + "\n", + "It is crucial to read the description to understand what features and classes we are dealing with." ] }, { "cell_type": "code", - "execution_count": 4, - "id": "e52b12e4", - "metadata": {}, + "execution_count": 2, + "id": "b2e552fa", + "metadata": { + "lines_to_next_cell": 2 + }, "outputs": [ { "name": "stdout", @@ -222,205 +220,161 @@ " - See also: 1988 MLC Proceedings, 54-64. Cheeseman et al\"s AUTOCLASS II\n", " conceptual clustering system finds 3 classes in the data.\n", " - Many, many more ...\n", - "\n" + "\n", + "...\n" ] } ], "source": [ - "print(data['DESCR'])" + "print(iris_data['DESCR'] + \"\\n...\")" ] }, { "cell_type": "markdown", - "id": "ce4601da", + "id": "2f989d78", "metadata": {}, "source": [ - "Load the data and target variables into a Pandas DataFrame for easier manipulation and visualization." + "### Creating a DataFrame\n", + "\n", + "We convert the data into a Pandas DataFrame for easier manipulation. We also map the numeric target values (0, 1, 2) to their actual species names." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "230736ee", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) \\\n", + "0 5.1 3.5 1.4 0.2 \n", + "1 4.9 3.0 1.4 0.2 \n", + "2 4.7 3.2 1.3 0.2 \n", + "3 4.6 3.1 1.5 0.2 \n", + "4 5.0 3.6 1.4 0.2 \n", + "\n", + " species \n", + "0 setosa \n", + "1 setosa \n", + "2 setosa \n", + "3 setosa \n", + "4 setosa \n" + ] + } + ], + "source": [ + "df_iris = pd.DataFrame(data=iris_data['data'], \n", + " columns=iris_data['feature_names'])\n", + "\n", + "# Add the target column\n", + "df_iris['species'] = iris_data['target_names'][iris_data['target']]\n", + "\n", + "# Display the first 5 rows\n", + "print(df_iris.head())" + ] + }, + { + "cell_type": "markdown", + "id": "b3976b65", + "metadata": {}, + "source": [ + "### Exploratory Analysis\n", + "\n", + "Before modeling, always inspect your data.\n", + "\n", + "Check for missing values and data types:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f6a1b135", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 150 entries, 0 to 149\n", + "Data columns (total 5 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 sepal length (cm) 150 non-null float64\n", + " 1 sepal width (cm) 150 non-null float64\n", + " 2 petal length (cm) 150 non-null float64\n", + " 3 petal width (cm) 150 non-null float64\n", + " 4 species 150 non-null str \n", + "dtypes: float64(4), str(1)\n", + "memory usage: 6.0 KB\n" + ] + } + ], + "source": [ + "df_iris.info()" + ] + }, + { + "cell_type": "markdown", + "id": "184dd71c", + "metadata": {}, + "source": [ + "Look at statistical summaries (mean, std, min, max):" ] }, { "cell_type": "code", "execution_count": 5, - "id": "c2d48f01", + "id": "bf1d24af", "metadata": {}, "outputs": [ { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) \\\n", - "0 5.1 3.5 1.4 0.2 \n", - "1 4.9 3.0 1.4 0.2 \n", - "2 4.7 3.2 1.3 0.2 \n", - "3 4.6 3.1 1.5 0.2 \n", - "4 5.0 3.6 1.4 0.2 \n", - ".. ... ... ... ... \n", - "145 6.7 3.0 5.2 2.3 \n", - "146 6.3 2.5 5.0 1.9 \n", - "147 6.5 3.0 5.2 2.0 \n", - "148 6.2 3.4 5.4 2.3 \n", - "149 5.9 3.0 5.1 1.8 \n", - "\n", - " species \n", - "0 setosa \n", - "1 setosa \n", - "2 setosa \n", - "3 setosa \n", - "4 setosa \n", - ".. ... \n", - "145 virginica \n", - "146 virginica \n", - "147 virginica \n", - "148 virginica \n", - "149 virginica \n", - "\n", - "[150 rows x 5 columns]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + " sepal length (cm) sepal width (cm) petal length (cm) \\\n", + "count 150.000000 150.000000 150.000000 \n", + "mean 5.843333 3.057333 3.758000 \n", + "std 0.828066 0.435866 1.765298 \n", + "min 4.300000 2.000000 1.000000 \n", + "25% 5.100000 2.800000 1.600000 \n", + "50% 5.800000 3.000000 4.350000 \n", + "75% 6.400000 3.300000 5.100000 \n", + "max 7.900000 4.400000 6.900000 \n", + "\n", + " petal width (cm) \n", + "count 150.000000 \n", + "mean 1.199333 \n", + "std 0.762238 \n", + "min 0.100000 \n", + "25% 0.300000 \n", + "50% 1.300000 \n", + "75% 1.800000 \n", + "max 2.500000 \n" + ] } ], "source": [ - "import pandas as pd\n", - "df = pd.DataFrame(data=data['data'], \n", - " columns=data['feature_names'])\n", - "df['species'] = data['target_names'][data['target']]\n", - "df" + "print(df_iris.describe())" ] }, { "cell_type": "markdown", - "id": "8aae2343", + "id": "d786dac7", "metadata": {}, "source": [ - "Finally, let's save the DataFrame to a CSV file for later use." + "Finally, save the processed data for future sessions." ] }, { "cell_type": "code", "execution_count": 6, - "id": "e1581e04", + "id": "cdac3f4f", "metadata": { "lines_to_next_cell": 2 }, @@ -434,297 +388,142 @@ } ], "source": [ - "df.to_csv('../../data/iris.csv', index=False)\n", + "df_iris.to_csv('../../data/iris.csv', index=False)\n", "print(\"Iris dataset saved to 'iris.csv'\")" ] }, { "cell_type": "markdown", - "id": "9215f289", + "id": "b2d491a1", "metadata": {}, "source": [ "## Exercises\n", "\n", - "Explore the California housing dataset:\n", + "Now it is your turn. You will explore the ****California Housing dataset****, a regression problem where the goal is to predict house prices." + ] + }, + { + "cell_type": "markdown", + "id": "3d5cc07b", + "metadata": {}, + "source": [ + "### Load and Inspect\n", "\n", - "- Load the California housing dataset from `scikit-learn` and transform it into a `pandas.DataFrame`" + "- Load the California housing dataset from `scikit-learn`.\n", + "- Convert it to a DataFrame.\n", + "- Print the shape of the dataset." ] }, { "cell_type": "code", "execution_count": 7, - "id": "7a8eb4a6", - "metadata": {}, + "id": "b774d7b8", + "metadata": { + "lines_to_next_cell": 2 + }, "outputs": [ { - "data": { - "text/html": [ - "
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MedIncHouseAgeAveRoomsAveBedrmsPopulationAveOccupLatitudeLongitudeMedHouseVal
08.325241.06.9841271.023810322.02.55555637.88-122.234.526
18.301421.06.2381370.9718802401.02.10984237.86-122.223.585
27.257452.08.2881361.073446496.02.80226037.85-122.243.521
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43.846252.06.2818531.081081565.02.18146737.85-122.253.422
..............................
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206392.388616.05.2547171.1622641387.02.61698139.37-121.240.894
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20640 rows × 9 columns

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" - ], - "text/plain": [ - " MedInc HouseAge AveRooms AveBedrms Population AveOccup Latitude \\\n", - "0 8.3252 41.0 6.984127 1.023810 322.0 2.555556 37.88 \n", - "1 8.3014 21.0 6.238137 0.971880 2401.0 2.109842 37.86 \n", - "2 7.2574 52.0 8.288136 1.073446 496.0 2.802260 37.85 \n", - "3 5.6431 52.0 5.817352 1.073059 558.0 2.547945 37.85 \n", - "4 3.8462 52.0 6.281853 1.081081 565.0 2.181467 37.85 \n", - "... ... ... ... ... ... ... ... \n", - "20635 1.5603 25.0 5.045455 1.133333 845.0 2.560606 39.48 \n", - "20636 2.5568 18.0 6.114035 1.315789 356.0 3.122807 39.49 \n", - "20637 1.7000 17.0 5.205543 1.120092 1007.0 2.325635 39.43 \n", - "20638 1.8672 18.0 5.329513 1.171920 741.0 2.123209 39.43 \n", - "20639 2.3886 16.0 5.254717 1.162264 1387.0 2.616981 39.37 \n", - "\n", - " Longitude MedHouseVal \n", - "0 -122.23 4.526 \n", - "1 -122.22 3.585 \n", - "2 -122.24 3.521 \n", - "3 -122.25 3.413 \n", - "4 -122.25 3.422 \n", - "... ... ... \n", - "20635 -121.09 0.781 \n", - "20636 -121.21 0.771 \n", - "20637 -121.22 0.923 \n", - "20638 -121.32 0.847 \n", - "20639 -121.24 0.894 \n", - "\n", - "[20640 rows x 9 columns]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "Dataset Shape: (20640, 9)\n", + "Features: ['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup', 'Latitude', 'Longitude']\n", + "Target: ['MedHouseVal']\n" + ] } ], "source": [ "from sklearn.datasets import fetch_california_housing\n", - "data = fetch_california_housing(as_frame=True)\n", - "df = data.frame\n", - "df" + "\n", + "# fetch_california_housing allows loading directly as a frame\n", + "housing_data = fetch_california_housing(as_frame=True)\n", + "df_housing = housing_data.frame\n", + "\n", + "print(f\"Dataset Shape: {df_housing.shape}\")\n", + "print(f\"Features: {housing_data.feature_names}\")\n", + "print(f\"Target: {housing_data.target_names}\")" ] }, { "cell_type": "markdown", - "id": "d3323aab", + "id": "37a2ebed", "metadata": {}, "source": [ - "- Print the first few rows of the dataset, the names of the features and the target variable, and the number of observations and features in the dataset." + "### Statistical Summary\n", + "\n", + "- Use `.head()` to look at the raw values.\n", + "- Use `.describe()` to check the distribution of the \"MedHouseVal\" (Median House Value)." ] }, { "cell_type": "code", "execution_count": 8, - "id": "42ab1062", - "metadata": {}, + "id": "acb9259a", + "metadata": { + "lines_to_next_cell": 2 + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " MedInc HouseAge AveRooms AveBedrms Population AveOccup Latitude \\\n", - "0 8.3252 41.0 6.984127 1.023810 322.0 2.555556 37.88 \n", - "1 8.3014 21.0 6.238137 0.971880 2401.0 2.109842 37.86 \n", - "2 7.2574 52.0 8.288136 1.073446 496.0 2.802260 37.85 \n", - "3 5.6431 52.0 5.817352 1.073059 558.0 2.547945 37.85 \n", - "4 3.8462 52.0 6.281853 1.081081 565.0 2.181467 37.85 \n", + " MedInc HouseAge ... Longitude MedHouseVal\n", + "0 8.3252 41.0 ... -122.23 4.526\n", + "1 8.3014 21.0 ... -122.22 3.585\n", + "2 7.2574 52.0 ... -122.24 3.521\n", + "3 5.6431 52.0 ... -122.25 3.413\n", + "4 3.8462 52.0 ... -122.25 3.422\n", "\n", - " Longitude MedHouseVal \n", - "0 -122.23 4.526 \n", - "1 -122.22 3.585 \n", - "2 -122.24 3.521 \n", - "3 -122.25 3.413 \n", - "4 -122.25 3.422 \n", - "Features: ['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup', 'Latitude', 'Longitude']\n", - "Target variable: MedHouseVal\n", - "Number of observations: 20640\n", - "Number of features: 8\n" + "[5 rows x 9 columns]\n", + "\n", + "Statistics for Median House Value:\n", + "count 20640.000000\n", + "mean 2.068558\n", + "std 1.153956\n", + "min 0.149990\n", + "25% 1.196000\n", + "50% 1.797000\n", + "75% 2.647250\n", + "max 5.000010\n", + "Name: MedHouseVal, dtype: float64\n" ] } ], "source": [ - "# Print the first few rows of the dataset\n", - "print(df.head())\n", - "\n", - "# Print the names of the features and the target variable\n", - "print(\"Features:\", df.columns[:-1].to_list())\n", - "print(\"Target variable:\", df.columns[-1])\n", + "pd.options.display.max_columns = 5 # limit output width\n", + "print(df_housing.head())\n", "\n", - "# Print the number of observations and features (excluding the target variable) in the dataset\n", - "print(\"Number of observations:\", df.shape[0])\n", - "print(\"Number of features:\", df.shape[1] - 1)" + "print(\"\\nStatistics for Median House Value:\")\n", + "print(df_housing['MedHouseVal'].describe())" ] }, { "cell_type": "markdown", - "id": "56859ced", + "id": "4760e224", + "metadata": {}, + "source": [ + "### Saving the Data\n", + "\n", + "- Save the dataset to a CSV file named `california_housing.csv`." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "987ab7ef", "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "California housing dataset saved to 'california_housing.csv'\n" + ] + } + ], "source": [ - "- Save the DataFrame to a CSV file for later use." + "df_housing.to_csv('../../data/california_housing.csv', index=False)\n", + "print(\"California housing dataset saved to 'california_housing.csv'\")" ] } ], @@ -747,7 +546,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.3" + "version": "3.13.11" } }, "nbformat": 4, diff --git a/src/S01 Introduction to Machine Learning/S01C02 The Machine Learning Workflow.ipynb b/src/S01 Introduction to Machine Learning/S01C02 The Machine Learning Workflow.ipynb index 7913569..8988897 100644 --- a/src/S01 Introduction to Machine Learning/S01C02 The Machine Learning Workflow.ipynb +++ b/src/S01 Introduction to Machine Learning/S01C02 The Machine Learning Workflow.ipynb @@ -2,298 +2,147 @@ "cells": [ { "cell_type": "markdown", - "id": "0eecd07c", + "id": "8de14fd2", "metadata": {}, "source": [ "\n", "# The Machine Learning Workflow\n", "\n", - "A typical machine learning workflow is a structured process that guides the development of machine learning models. It consists of several key steps, each contributing to the successful deployment of a model. The workflow can be summarized as follows:\n", + "A typical machine learning workflow is a structured process that guides the development of robust models. It is rarely a linear path; rather, it is an iterative cycle of improvement.\n", "\n", - "1. **Problem Definition**: Clearly define the problem you want to solve with machine learning. This includes understanding the context and the specific goals of the project.\n", - "2. **Data Collection**: Gather the necessary data that will be used to train and evaluate the model. This data can come from various sources, such as databases, APIs, or web scraping.\n", - "3. **Data Exploration and Preprocessing**: Analyze the collected data to understand its structure, quality, and characteristics. This step often involves cleaning the data, handling missing values, and transforming features to make them suitable for modeling.\n", - "4. **Model Selection**: Choose the appropriate machine learning algorithms based on the problem type (e.g., classification, regression) and the nature of the data. This may involve experimenting with different models to find the best fit.\n", - "5. **Model Training and Validation**: Train the selected model on the training dataset and validate its performance using a separate validation set. This step includes tuning hyperparameters to optimize the model's performance.\n", - "6. **Model Evaluation**: Assess the model's performance using metrics relevant to the problem, such as accuracy, precision, recall, or F1 score. This evaluation helps determine if the model meets the desired criteria.\n", - "7. **Model Deployment and Monitoring**: Once the model is deemed satisfactory, deploy it to a production environment where it can make predictions on new data. Continuous monitoring is essential to ensure the model remains effective over time, especially as new data becomes available." + "The workflow generally consists of seven key stages:\n", + "\n", + "1. *Problem Definition*: clearly defining the business or research problem. Is it a regression task (predicting a number) or classification (predicting a label)? What is the metric for success?\n", + "2. *Data Collection*: gathering raw data from databases, APIs, web scraping, or sensors.\n", + "3. **Data Exploration & Preprocessing**: analyzing data structure, handling missing values, cleaning outliers, and normalizing features. This is often the most time-consuming step.\n", + "4. **Model Selection**: choosing algorithms suitable for the data and problem (e.g., Linear Regression vs. Random Forest).\n", + "5. **Training & Validation**: fitting the model to the data.\n", + " - **Training Set**: used to teach the model.\n", + " - **Validation Set**: used to tune parameters.\n", + "6. **Evaluation**: assessing performance on a \"hold-out\" test set using metrics like Accuracy or Mean Squared Error (MSE).\n", + "7. *Deployment & Monitoring*: integrating the model into a production system and monitoring for \"drift\" (when data changes over time)." ] }, { "cell_type": "markdown", - "id": "7c417059", + "id": "b5a2180c", "metadata": {}, "source": [ "## Practical Demonstration\n", "\n", - "We will demonstrate a simple machine learning workflow using the California housing dataset. This dataset is commonly used for regression tasks and contains information about various features of houses in California, along with their median house values.\n", - "\n", - "We begin by loading the dataset, exploring its structure, and then proceeding through the steps of the machine learning workflow, including data preprocessing, model training, and evaluation (we will not demonstrate deployment in this notebook).\n", + "We will walk through this workflow using the **California Housing dataset**. This is a classic regression problem where the goal is to predict the median value of houses in a district based on features like average income, house age, and location." + ] + }, + { + "cell_type": "markdown", + "id": "9277026b", + "metadata": {}, + "source": [ + "### Load the Data\n", "\n", - "- **Load the California housing dataset**: This dataset is available in the `sklearn.datasets` module and can be easily loaded into a pandas `DataFrame`." + "We use the built-in loader from `scikit-learn`." ] }, { "cell_type": "code", "execution_count": 1, - "id": "71da8cfb", - "metadata": {}, + "id": "31cb41ce", + "metadata": { + "lines_to_next_cell": 2 + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "dict_keys(['data', 'target', 'frame', 'target_names', 'feature_names', 'DESCR'])\n" + "Feature names: ['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup', 'Latitude', 'Longitude']\n", + "Target variable: MedHouseVal\n", + "Shape: (20640, 9)\n" ] } ], "source": [ "from sklearn.datasets import fetch_california_housing\n", - "data = fetch_california_housing(as_frame=True)\n", - "print(data.keys())" + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# Set the visual style for plots\n", + "sns.set_theme(style=\"whitegrid\")\n", + "\n", + "# Load data as a Bunch object containing the dataframe\n", + "housing_data = fetch_california_housing(as_frame=True)\n", + "df = housing_data.frame\n", + "\n", + "# Display basic metadata\n", + "print(\"Feature names:\", housing_data.feature_names)\n", + "print(\"Target variable:\", housing_data.target.name)\n", + "print(\"Shape:\", df.shape)" ] }, { - "cell_type": "code", - "execution_count": 2, - "id": "ec67de3e", + "cell_type": "markdown", + "id": "11b4c1d7", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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MedIncHouseAgeAveRoomsAveBedrmsPopulationAveOccupLatitudeLongitudeMedHouseVal
08.325241.06.9841271.023810322.02.55555637.88-122.234.526
18.301421.06.2381370.9718802401.02.10984237.86-122.223.585
27.257452.08.2881361.073446496.02.80226037.85-122.243.521
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43.846252.06.2818531.081081565.02.18146737.85-122.253.422
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" - ], - "text/plain": [ - " MedInc HouseAge AveRooms AveBedrms Population AveOccup Latitude \\\n", - "0 8.3252 41.0 6.984127 1.023810 322.0 2.555556 37.88 \n", - "1 8.3014 21.0 6.238137 0.971880 2401.0 2.109842 37.86 \n", - "2 7.2574 52.0 8.288136 1.073446 496.0 2.802260 37.85 \n", - "3 5.6431 52.0 5.817352 1.073059 558.0 2.547945 37.85 \n", - "4 3.8462 52.0 6.281853 1.081081 565.0 2.181467 37.85 \n", - "\n", - " Longitude MedHouseVal \n", - "0 -122.23 4.526 \n", - "1 -122.22 3.585 \n", - "2 -122.24 3.521 \n", - "3 -122.25 3.413 \n", - "4 -122.25 3.422 " - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "df = data.frame\n", - "df.head()" + "### Exploration (EDA)\n", + "\n", + "Before modeling, we must understand our data. We look at the first few rows and calculate the correlation between features.\n", + "\n", + "- Peek at the data" ] }, { "cell_type": "code", - "execution_count": 3, - "id": "b5005e59", + "execution_count": 2, + "id": "f2555cf5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "California Housing Dataset\n", - ".. _california_housing_dataset:\n", - "\n", - "California Housing dataset\n", - "--------------------------\n", - "\n", - "**Data Set Characteristics:**\n", - "\n", - ":Number of Instances: 20640\n", - "\n", - ":Number of Attributes: 8 numeric, predictive attributes and the target\n", - "\n", - ":Attribute Information:\n", - " - MedInc median income in block group\n", - " - HouseAge median house age in block group\n", - " - AveRooms average number of rooms per household\n", - " - AveBedrms average number of bedrooms per household\n", - " - Population block group population\n", - " - AveOccup average number of household members\n", - " - Latitude block group latitude\n", - " - Longitude block group longitude\n", + " MedInc HouseAge AveRooms AveBedrms Population AveOccup Latitude \\\n", + "0 8.3252 41.0 6.984127 1.023810 322.0 2.555556 37.88 \n", + "1 8.3014 21.0 6.238137 0.971880 2401.0 2.109842 37.86 \n", + "2 7.2574 52.0 8.288136 1.073446 496.0 2.802260 37.85 \n", + "3 5.6431 52.0 5.817352 1.073059 558.0 2.547945 37.85 \n", + "4 3.8462 52.0 6.281853 1.081081 565.0 2.181467 37.85 \n", "\n", - ":Missing Attribute Values: None\n", - "\n", - "This dataset was obtained from the StatLib repository.\n", - "https://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html\n", - "\n", - "The target variable is the median house value for California districts,\n", - "expressed in hundreds of thousands of dollars ($100,000).\n", - "\n", - "This dataset was derived from the 1990 U.S. census, using one row per census\n", - "block group. A block group is the smallest geographical unit for which the U.S.\n", - "Census Bureau publishes sample data (a block group typically has a population\n", - "of 600 to 3,000 people).\n", - "\n", - "A household is a group of people residing within a home. Since the average\n", - "number of rooms and bedrooms in this dataset are provided per household, these\n", - "columns may take surprisingly large values for block groups with few households\n", - "and many empty houses, such as vacation resorts.\n", - "\n", - "It can be downloaded/loaded using the\n", - ":func:`sklearn.datasets.fetch_california_housing` function.\n", - "\n", - ".. rubric:: References\n", - "\n", - "- Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions,\n", - " Statistics and Probability Letters, 33:291-297, 1997.\n", - "\n", - "Feature names: ['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup', 'Latitude', 'Longitude']\n", - "Target variable: MedHouseVal\n" + " Longitude MedHouseVal \n", + "0 -122.23 4.526 \n", + "1 -122.22 3.585 \n", + "2 -122.24 3.521 \n", + "3 -122.25 3.413 \n", + "4 -122.25 3.422 \n" ] } ], "source": [ - "# Display some key information about the dataset\n", - "print(\"California Housing Dataset\")\n", - "print(data.DESCR) # Description of the dataset\n", - "print(\"Feature names:\", data.feature_names)\n", - "print(\"Target variable:\", data.target.name)" + "print(df.head())" ] }, { "cell_type": "markdown", - "id": "d14c0100", - "metadata": {}, - "source": [ - "- **Quick exploration**: We will check the shape of the dataset, the names of the columns, and identify the target variable; we will also visualize the correlation matrix to understand the relationships between features." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "1e91f7ec", + "id": "a8942c8c", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Shape: (20640, 9)\n", - "Columns: ['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup', 'Latitude', 'Longitude', 'MedHouseVal']\n", - "Target: MedHouseVal\n" - ] - } - ], "source": [ - "print(\"Shape:\", df.shape)\n", - "print(\"Columns:\", df.columns.tolist())\n", - "print(\"Target:\", data.target.name)" + "- Visualize the correlation matrix" ] }, { "cell_type": "code", - "execution_count": 5, - "id": "940fc859", - "metadata": {}, + "execution_count": 3, + "id": "d28ea4f6", + "metadata": { + "lines_to_next_cell": 2 + }, "outputs": [ { "data": { - "image/png": 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M6pOiRYvi1KlTiI2Nhb6+vqLc0dFRqd6nWCMiIhSvdWYaNWoEQ0ND7NmzB7dv30b58uXh7u6eZu4NIPVL3NKlS7Fq1Sq8ePFCab4cc3PzLD0fANjZ2WVrYs8FCxbg8OHDuH37Nnbu3IkiRYpk2ubVq1ewtbVNkzz5dDlSRkmG9GT3/Ztbr1dGstvPjI6RjN5PGdmyZQsWLlyIR48eISkpKUvP9SV9fX3UrVs3Tbmq9+CrV6/g5+eXpvzL/qa3jLYqLi4uGD58OBYtWoQdO3agWrVqaNasGTp37pzp5UpA5q/Zp9f/63OYmZlZuucWVezt7VW+Rh8/foRUKsVvv/2W7sp0Hz9+VPz9rfsqu77e7tOnTyEIAiZOnIiJEyemG6+dnR2mTZuG5s2bw9PTE8WLF0eDBg3w888/5/jyKSIiovzEhAwRfXeMjIxga2uLe/fuZaudqslTVcloGdivH/s0+mX+/PnpznOR3nwvUqkUNWrUgJGREaZNmwY3Nzfo6Ojg5s2bGDNmTIYja3JTevOYCF9N5poRiUSCVq1aYcuWLXj+/Lli8k5VZs2ahYkTJ6JHjx6YPn06zMzMIBaLMXTo0Gz1ObvL9d66dUvxpfK///5Dx44ds9U+t3h7eytiaNGiRab1c+v1yk0ZvfY5eT9t374d3bp1Q4sWLTBq1CgUKVIEGhoamD17Np49e/bN8eZUeueML5NinyxcuBDdunXD4cOHcfr0aQwePBizZ8/GP//8A3t7+wyfJzeOwW/x6X3UuXPndJPLnxIYubGvRCKRyr6pel2B9M+7I0eOTHc046fkVfXq1fHs2TPFflm/fj0WL16MNWvWoFevXlmKl4iIqKAwIUNE36UmTZrgt99+w5UrV1CpUqUM6zo5OUEul+PJkydKE60GBwdDKpXCyckpx3G4ubkBSE0SqfrlOSMXLlxAWFgYDhw4gOrVqyvKX7x4kaZuVpNJn/oSGBiY5rFHjx7BwsJCaXRMburUqRM2btwIsViMDh06pFtv3759qFWrFjZs2KBULpVKYWFhobif1T5nRWxsLLp37w4fHx9UrlwZ8+bNQ8uWLRUrOaXHyckJZ86cQXR0tNLokU+Xk+XkvVO1alWYmppi165dGD9+fKYT+2b19foWedHP7Ni3bx9cXV1x4MABpf0+efLkPHk+JyendI+RT48Dn0eqfLmCGJD+yKgSJUqgRIkSmDBhAi5fvowqVapgzZo1mDFjxjfHC6SODPlytEhYWFimI4+ywtLSEoaGhkhJScn0PJbVfZXR8Wtqaornz5+nKc/qiDNXV1cAgJaWVpbOu2ZmZujevTu6d++OmJgYVK9eHVOmTGFChoiIvnucQ4aIvkujR4+Gvr4+evXqheDg4DSPP3v2DEuXLgWQejkNACxZskSpzqJFiwAAjRs3znEcZcuWhZubGxYsWICYmJg0j3+9dO2XPn0R//KX4sTERKxatSpNXX19/SxdwmRjYwNfX19s2bJF6UvkvXv3cPr0acVrkRdq1aqF6dOnY8WKFbC2tk63noaGRppfx/fu3auY8+GTT4mjr78M58SYMWPw+vVrbNmyBYsWLYKzszO6du2aZtnzrzVq1AgpKSlYsWKFUvnixYshEonQsGHDbMeip6eHMWPG4OHDhxgzZozKkQLbt2/HtWvXAGT99foWedHP7FB1LFy9ehVXrlzJk+dr1KgRrl27prT92NhY/Pbbb3B2dlbMj/Mp4Xrx4kVFvZSUlDSX9URFRSE5OVmprESJEhCLxZm+x7KiTp060NTUxOrVq5XKv95fOaWhoYHWrVtj//79Kkcefnkey+q+0tPTA6D6+HVzc8OjR4+Utnvnzh38/fffWYq3SJEiqFmzJtauXYsPHz5kGG9YWJjSYwYGBnB3d8+V/UJERJTXOEKGiL5Lbm5u2LlzJ9q3b4+iRYuiS5cuKF68OBITE3H58mXs3bsX3bp1AwCUKlUKXbt2xW+//aa4TOjatWvYsmULWrRogVq1auU4DrFYjPXr16Nhw4YoVqwYunfvDjs7O7x79w7nz5+HkZERjhw5orJt5cqVYWpqiq5du2Lw4MEQiUTYtm2byi/oZcuWxZ49ezB8+HCUL18eBgYGaNq0qcrtzp8/Hw0bNkSlSpXQs2dPxbLXxsbGGV5K9K3EYjEmTJiQab0mTZpg2rRp6N69OypXroz//vsPO3bsUPzq/YmbmxtMTEywZs0aGBoaQl9fH35+ftmep+LcuXNYtWoVJk+erFiGe9OmTahZsyYmTpyIefPmpdu2adOmqFWrFn799Ve8fPkSpUqVwunTp3H48GEMHTpU8YU9u0aNGoX79+9j4cKFOH/+PNq0aQNra2sEBQXh0KFDuHbtGi5fvgwg66/Xt8irfmZVkyZNcODAAbRs2RKNGzfGixcvsGbNGvj4+KhMdH6rsWPHYteuXWjYsCEGDx4MMzMzbNmyBS9evMD+/fsVkygXK1YMFStWxLhx4xAeHg4zMzPs3r07TfLl3LlzGDRoENq2bQtPT08kJydj27ZtikTHt7KyssKQIUOwcOFCNGvWDA0aNMCdO3dw4sQJWFhY5Mposjlz5uD8+fPw8/ND79694ePjg/DwcNy8eRNnzpxBeHg4gKzvK11dXfj4+GDPnj3w9PSEmZkZihcvjuLFi6NHjx5YtGgR/P390bNnT3z8+BFr1qxBsWLFsjyZ+MqVK1G1alWUKFECvXv3hqurK4KDg3HlyhW8ffsWd+7cAQD4+PigZs2aKFu2LMzMzHDjxg3s27cPgwYN+ubXjIiIKM/l/8JORERZ9/jxY6F3796Cs7OzoK2tLRgaGgpVqlQRli9frrQcalJSkjB16lTBxcVF0NLSEhwcHIRx48Yp1RGE1GVpVS2R+mmZ2/SWtb1165bQqlUrwdzcXJBIJIKTk5PQrl074ezZs4o6qpbQ/fvvv4WKFSsKurq6gq2trTB69GjFEs2fltQVBEGIiYkROnXqJJiYmAgAFEvDqlrqWBAE4cyZM0KVKlUEXV1dwcjISGjatKnw4MEDpTqflkQOCQlRKk9vqd+vfbnsdXrSW/Z6xIgRgo2NjaCrqytUqVJFuHLlisqlcA8fPiz4+PgImpqaSv3MaInpL7cTFRUlODk5CWXKlBGSkpKU6g0bNkwQi8XClStXMuxDdHS0MGzYMMHW1lbQ0tISPDw8hPnz5ystU5xZTOnZt2+fUL9+fcHMzEzQ1NQUbGxshPbt2wsXLlxQ1Mnq6/Uty15np58AhIEDB6Zp/+l9c/36daXyr5eI/hTDl7HL5XJh1qxZgpOTkyCRSITSpUsLR48eTbMMcnoyeu1VvQcFQRCePXsmtGnTRjAxMRF0dHSEChUqCEePHk3T/tmzZ0LdunUFiUQiWFlZCePHjxcCAgKU+vT8+XOhR48egpubm6CjoyOYmZkJtWrVEs6cOaO0rfSWvc7Ka5acnCxMnDhRsLa2FnR1dYXatWsLDx8+FMzNzYV+/fpl+hqlt9++FBwcLAwcOFBwcHAQtLS0BGtra6FOnTrCb7/9pqiTnX11+fJloWzZsoK2tnaaJbC3b98uuLq6Ctra2oKvr69w6tSpdJe9/nrfffLs2TOhS5cugrW1taClpSXY2dkJTZo0Efbt26eoM2PGDKFChQqCiYmJoKurK3h7ewszZ84UEhMTM33NiIiICppIEPJpRjkiIiIiyjKpVApTU1PMmDEDv/76a0GHQ0RERLmMc8gQERERFbD4+Pg0ZZ/mxapZs2b+BkNERET5gnPIEBERERWwPXv2YPPmzWjUqBEMDAxw6dIl7Nq1C/Xr10eVKlUKOjwiIiLKA0zIEBERERWwkiVLQlNTE/PmzUNUVJRiot9vXVKbiIiIvl+8ZImIiIiogJUpUwZnzpxBaGgoEhMT8ebNGyxZsgQGBgYFHRoREdF35eLFi2jatClsbW0hEolw6NChTNtcuHABZcqUgUQigbu7OzZv3pymzsqVK+Hs7AwdHR34+fnh2rVruR/8V5iQISIiIiIiIqJCITY2FqVKlcLKlSuzVP/Fixdo3LgxatWqhdu3b2Po0KHo1asXTp06paizZ88eDB8+HJMnT8bNmzdRqlQp+Pv74+PHj3nVDQAAV1kiIiIiIiIiokJHJBLh4MGDaNGiRbp1xowZg2PHjuHevXuKsg4dOkAqleLkyZMAAD8/P5QvXx4rVqwAAMjlcjg4OOCXX37B2LFj8yx+jpAhIiIiIiIiogIjk8kQFRWldJPJZLmy7StXrqBu3bpKZf7+/rhy5QoAIDExEf/++69SHbFYjLp16yrq5JXvZlLfY1peBR0CqWB293pBh0Dp8P1wuKBDoHQ8cmhY0CGQCnritMsK0/dBDHlBh0DpiEoxLOgQSAVdcUJBh0DpMF09sqBDoHTYLNxZ0CHki8L6vfr6rx0xdepUpbLJkydjypQp37ztoKAgWFlZKZVZWVkhKioK8fHxiIiIQEpKiso6jx49+ubnz8h3k5AhIiIiIiIioh/PuHHjMHz4cKUyiURSQNHkHyZkiIiIiIiIiKjASCSSPEvAWFtbIzg4WKksODgYRkZG0NXVhYaGBjQ0NFTWsba2zpOYPuEcMkRERERERESklipVqoSzZ88qlQUEBKBSpUoAAG1tbZQtW1apjlwux9mzZxV18gpHyBARERERERGpAZGWqKBDyHMxMTF4+vSp4v6LFy9w+/ZtmJmZwdHREePGjcO7d++wdetWAEC/fv2wYsUKjB49Gj169MC5c+fw+++/49ixY4ptDB8+HF27dkW5cuVQoUIFLFmyBLGxsejevXue9oUJGSIiIiIiIiIqFG7cuIFatWop7n+ae6Zr167YvHkzPnz4gNevXysed3FxwbFjxzBs2DAsXboU9vb2WL9+Pfz9/RV12rdvj5CQEEyaNAlBQUHw9fXFyZMn00z0m9tEgiAIefoMWVRYZ4NWd1xl6fvFVZa+X1xl6fvEVZa+X1xl6fvFVZa+T1xl6fvFVZa+Xz/KKkvH9bwLOoQcaRSXt6sZfa84QoaIiIiIiIhIDYg11f+SJXXCSX2JiIiIiIiIiPIZEzJERERERERERPmMCRkiIiIiIiIionzGOWSIiIiIiIiI1IBIi2MuChPuLSIiIiIiIiKifMaEDBERERERERFRPmNChoiIiIiIiIgon3EOGSIiIiIiIiI1INYUFXQIlA0cIUNERERERERElM+YkCEiIiIiIiIiyme8ZImIiIiIiIhIDYi0eMlSYcIRMkRERERERERE+YwJGSIiIiIiIiKifMaEDBERERERERFRPuMcMkRERERERERqgMteFy4cIUNERERERERElM+YkCEiIiIiIiIiymdMyBARERERERER5TPOIUNERERERESkBkRanEOmMOEIGSIiIiIiIiKifMaEDBERERERERFRPuMlS0RERERERERqgMteFy4cIUNERERERERElM9ylJAZPHgwli1blqZ8xYoVGDp06LfGRERERERERESk1nKUkNm/fz+qVKmSprxy5crYt2/fNwdFRERERERERKTOcjSHTFhYGIyNjdOUGxkZITQ09JuDIiIiIiIiIqLsEWlwDpnCJEcjZNzd3XHy5Mk05SdOnICrq+s3B0VEREREREREpM5yNEJm+PDhGDRoEEJCQlC7dm0AwNmzZ7Fw4UIsWbIkN+MjIiIiIiIiIlI7OUrI9OjRAzKZDDNnzsT06dMBAM7Ozli9ejW6dOmSqwESERERERERUebEvGSpUMlRQgYA+vfvj/79+yMkJAS6urowMDDIzbiIiIiIiIiIiNRWjhMyn1haWuZGHEREREREREREP4wcTeobHByMn3/+Gba2ttDU1ISGhobSjYiIiIiIiIiI0pejETLdunXD69evMXHiRNjY2EAk4nVqRERERERERAVJJOZ388IkRwmZS5cu4a+//oKvr28uh0NEREREREREpP5ydMmSg4MDBEHI7ViIiIiIiIiIiH4IOUrILFmyBGPHjsXLly9zORwiIiIiIiIiIvWXo0uW2rdvj7i4OLi5uUFPTw9aWlpKj4eHh+dKcERERERERESUNSKNHI25oAKSo4TMkiVLcjkMIiIiIiIiIqIfR44SMl27ds3tOL5LZlXLwXVETxiXKQ4d2yK40XoAgv84m3Gb6hXgs2AsDHw8kPDmA57OXo23Ww8q1XHq3wmuw3tCYm2JqLuPcH/odERe/y8vu6K2BEHAwV1r8WfAIcTFxsDDuyS69BsLa1vHdNsE3r+J4we34dWzR5BGhOKXsfNRtmJNxePJyck4sGM17v77Nz4Gv4OengF8SlVA2y6DYGpmmQ+9Kvx2X7iBLQFXEBYVA097K4xp748SznYq65699QgbTv6N1yHhSE6Rw7GIGbrU9UMTv5KKOquP/olTNx4gKCIKWhoa8HG0xqDmtVDCRfU2KX2CIGDvjvU4d+oIYmOj4VW0JHoOGAkbO4cM2506uh9HDuxEZEQ4HF3c0b3vMLh7+SjVefzwHvZsW4ungQ8gFovh5OqB8dMWQ1siycsuqYVjRw7j4P7fERERDhcXN/TpPwieXt7p1r/015/YsW0zPgYHwdbWDl179Ea58n6KxwVBwM7tW3D65HHExsagqE8x9B84BLZ29vnRHbVy9MgfOLB/7//3jSv69h8Irwz3zUVs37YZwcHBsLW1Q7cevVC+fAUAqf9ftm3djBvXryEo6AP09fVRyrcMunXvCXNz8/zqktoQBAH7dqzD+dN/IDY2Gp5FS6LHgNGwsc34fHb62D4cPbBDcT7r2nc43D2LKR6XRoRh58YV+O/2NSTEx8HGzhEt2nVDhSq18rpLakMQBOzavglnTh1DbGwMvIsWR9+BwzI9Bx0/ehCH9u+BNCIczi5u6NVvMDy9igIAoqOjsHv7Zty+dQOhIcEwMjaBX8Uq6PhzD+jrG+RHtwo9vSr1oF+zCTQMjZH0/jWiDm5B0ptn6dYX6ejBsFE76JQoD7GeAVIiQhF1aBtkj26nPi7RgWGDtpAUL5e6zXcvEXVoK5LePM+nHhEVftkazxQVFZWlm7rQ0NdD1N1A3Bs8NUv1dZ3tUf6PtQi7cBWXyjXHi+VbUGLtDFjUq6qoY9O2IYrOH4cnM1biUoWWiL77CH7HNkDb0iyvuqHWjh/cioCje9C13zhMmrcJEh1dLJz6CxITZem2kSXEw9HFEz/3Ha3y8URZAl49f4Rm7Xpi6qJtGDR2HoLevcLSmSPyqhtq5dSN+1i4PwB9G1fDrvG94GlvhQHLdiE8KlZlfSN9HfRqWAVbR3XH3gm90bxSKUzeegSXH3z+gOBUxBxj2/tj34Q+2DSyK2zNTdB/2U6ER6veJqXvj/07cPLIPvQaOAozFq6DREcHsycNz/CYuXzxDLatX442HXtg9tKNcHJxx+xJwxEpjVDUefzwHmZPHo6SpStgxqJ1mLl4PfybtObSi1nw15/nsWHdGnTo9DMWL18DZ1dXTJ44FtIvXt8vPXxwHwvmzkS9+g2wZPka+FWqglnTJ+PVyxeKOgf27cHRPw6i/6AhmL94BSQ6Opg8cSwSExPzq1tq4eKfF7B+3Vp07NQZS5evgourKyZNHJ/hvpk3dxbq1W+AZctXo2Klypg5fQpe/n/fyGQyPHv6BB06/oSly1dh/ITJePf2DaZPnZSf3VIbR/Zvx6mje9FjwGhMX7ABOjq6mDNpaIbnsyt/ncH29cvQqmNPzFyyGY4uHpgzaRgipZ8vt1+9aBrev3uFERPnYc6K7ShfuSaWzpuAl88C86NbauHgvt04duQA+g4chrmLVkGio4NpE0dneA66dPEcNq1bjfadumLhst/g7OKGaRNHK4638LAwhIeHolvPfliyaiN+GTYGN/+9jpVL5+dXtwo1Hd+KMGrWGTGnDyB08a9Ifv8aZn3GQmxgpLqBhgbM+o6DhqklIrYsRcicEYj8fR1SIj8fK8btekPbswQid61GyPwxkAX+B7O+4yE2Ms2nXpEqYg1Robz9qLKVkDExMYGpqWm6t0+Pq4uQUxfxePISBB8+k6X6Tn06IP7FWzwcPRcxj57j1aodCNp/Ci5DuinquAztjjcbfsfbLQcQ8/AZ/hswGSlxCXDo1jqPeqG+BEHA6SO70KxdD5TxqwEHZw/0HjIVEeGhuHn1z3TblSxbBa1/6o+yFVX/0qWnb4BRU1eiQtV6sLFzhrtXCXTuMwovnz1EWEhQXnVHbWw7exWtqpRGi8q+cLOxxISOjaCjrYVDV26rrF/e0xm1fb3hamMBB0sz/FS7AjzsrHDr6RtFnUYViqNiUVfYW5rC3dYSI9rUQ0yCDE/efcynXqkHQRBw4vDvaNm+K8pVrAYnF3cMHD4REeGhuHHlr3TbHTu0B7X9m6Jmvcawd3RBr4GjoC2R4ELAUUWdreuXokHTNmje9mc4OLnC1t4JlarVgZaWdn50rVA7fHA/6jdohLr1G8DR0QkDBg2FRCLBmdMnVdY/cvgAypQtj1Zt2sPB0Qmdu3SHq5s7jh05DCB1P/9x6ADadfgJFStVgYuLK4aNGIPwsDD8c+Xv/OxaoXfo4H74N2iIevX94ejohIGDhkAikSDg9CmV9f84fAhly5ZH6zbt4ODoiJ+7dIObmzuOHvkDAKCvr48Zs+aiWvUasLd3gLd3UfQbMAhPnz7Bx488n2WHIAg4+ccetGjXDeUqVoejizv6D5sEaXgobvxzMd12xw/tQi3/ZqhZtwnsHV3Qc8BoSCQS/PnF+ezxo//g36Qt3D2LwcraDi3bd4e+vgFePGVCJisEQcDRw/vQtv3P8KtUFc4ubhgyYhzCw0Nx9cqldNv9cXAv6jVojDr1GsLB0Rn9Bg2HREcHZ0+fAAA4ObtgzK/TUN6vMmxs7FCyVBn81KUnrl+9gpSUlPzqXqGlX70R4v45j/jrfyI5+B0i92+AkCSDboUaKuvrVagJsZ4BIjYtQtLLx0iJCEXi80dI/vA6tYKmFnRKVED00Z1IfP4IKWHBiDm9HymhwdCrXDcfe0ZUuGUrIXP+/HmcO3cu3dunx39UJhV9EXruilJZSMAlmFb0BQCItLRgXKYYQs9e/lxBEBB67jJMKpbOx0jVQ0jwO0RGhMGnZAVFmZ6+Adw8i+FZ4N1cfa74uBiIRCLocUhshpKSU/Dw9Qf4ebsoysRiEfy8nXH3+btM2wuCgKuPXuBlcBjKeKi+7CwpOQX7L92Ega4EnvZWuRb7j+Bj8HtII8JQwrecokxP3wDuXj54/OieyjbJSUl48TQQJXzLK8rEYjFK+JZTtImURuBp4AMYm5hi4si+6Nu5CaaOHYhH9+/kbYfUQFJSEp4+fQxf3zKKMrFYjFK+ZfDo0QOVbR49eoBSpcsolZUpW15RPzjoAyIiwlHqi23q6xvA06soAh+q3iallbpvnsDX9/P/Z7FYDF/f0nj06KHKNo8ePYBvaeX/52XKlku3PgDExcZCJBLBwEA/dwL/QXw6nxX/4tyU+hnAB08yOZ8VL6V8PivuWx5PAj+38fQugX/+OoOY6EjI5XJcvhiApMREFC3Bz2pZ8fkcVFZRpq9vAA+vogh8dF9lm6SkJDx7+lipjVgsRknfMum2AYC4uFjo6elBQ0Mj9zqgjjQ0oGXvAtmTL44NQYDs8T1oO3mobCIpVhZJr57AuFV3FJmyGhYj50K/TnNAlDqSQaShAZGGBoTkJKV2QnIitF288qwrROomW3PI1KihOoNKqSRWFpAFhyqVyYJDoWVsCLGOBFqmxhBrakL2MeyrOmHQ93LNz1DVQqQ09XU0NlG+7t7I2ByREWGqmuRIYqIMv29ZAb9q9aGrx4RMRiJi4pAiF2BupPzFwtzIAC+D098n0fEJqD9uKZKSUiAWizC+Y0NUKqp8TFz87wnGbDiAhMQkWBgZYs3gn2BqoJcn/VBX0ojUYcbGJsqXSBqbmEEqVb1/oqKkkMtTVLZ59zb1V7KPQanJtn07N6Jzj0FwcvXAxXMnMOPXIZi/clum89P8yKKiUr/wmXw1utTExBTv3rxR2UYaEQETk6/rmyDi//s3IiJ1eH/abX6uQ5mLiopKd9+8TWffRKSzb6TpvO6JiYnYtGk9qteoCT09JmSy49P/eVXnpvQ+A0R/Op+Zpm3z/u0rxf3BY2Zg2byJ6NOpATQ0NKAt0cGw8XNgncncNJRK8b9GxbGT3rEQ/f9zoXGa48cU7968VtkmKjISe3dtQ70GTXIhavUm1jeESEMD8uhIpXJ5TCQ0i9iqbKNpXgQa7j6Iv/k3wtfPg6aFFYxbdYdIQwMxpw9AkCUg8eVjGNRtCWnwO8ijI6FbujK0nDyQEsoR5URZleWETHbmhjEySudaxP+TyWSQyZSv700S5NAScYkuSt/lP09gy+rZivvDJizO8+dMTk7GqvnjAAjo2m9snj/fj0pfIsGe8b0RJ0vEtcCXWLAvAHYWJijv6ayoU97TCXvG94Y0Jg4H/r6F0ev3Y/voHjAz4peY9Fw6fwrrVn6+tn7M5Ly5zl4uCACAOg2ao2a9xgAAFzdP3L/zLy4EHEXHbv3z5HmJCrPk5GTMmT0DEICBgwYXdDjfvUsXTmHDyrmK+6MnLciz59q74zfExUZj/IxlMDQywY1/LmLZvAmYNGc1HJ3d8+x5C6s/zwdgzYpFivu/TpmdQe3cERcXixlTxsLe0QkdfuqW58/3QxKJII+JQuTe9YAgIPntC2gYmUG/VmPEnD4AAJDuXAXj9n1hNXkVhJQUJL17iYRbl6Fl75LJxikvcf6+wiXLCRkTExOIRFnbuZldxzl79mxMnao8UW5HkRl+0rDIajjfJVlwKCRWyn2QWFkgKTIa8gQZEkMjIE9OhqSI+Vd1zCELUh5ZQ2mVrlAdbp7FFfeTk1InhouUhsHE7PPrHhUZBkcXz29+vk/JmLCQIIyZtoqjY7LA1EAPGmIRwr6awDcsKgYWRum/fmKxCI5FUn+x9HawxosPodh48rJSQkZXog3HImZwLGKGkq72aDppJQ5evo2eDarkSV/UQVm/qnD3+rxySJLimAmH6RfHTKQ0HE4uqocsGxmZQCzWUJrw8lMbk///ymxqmnpOs3dU/gBm6+CE0JDgb++IGjMyMoZYLIY0QnmSWKk0AiZmqudkMzE1TTOprFQqhalif6S2k0ZEwMzMXKmOq6tbboav1oyMjNLdN6ZmqifiN01n35h8NSLjUzLm48ePmDV7HkfHZEHZClXh7vl5ZbfkpNTLJFSez1xVfwYw/HQ+i1B1Pks9VoI/vMXpo/swb8UO2DuljtR0cvHAo/u3EXBsP3oOHJOr/VIHFfyqwPOLVfcU/2vSnIMi4OKqOqFl+P9zYWSa4ycizfETHxeHaRPHQFdXD2MnTIemZo4Wjf2hyGOjIaSkQGxorFQuNjCGPFqqsk1KlBRISQH+/6MLACR/fAcNI1NAQwNISUFK2EeEr5oOkbYEIoku5NFSmPz8C5LDOCcWUVZleUjKl/PHbNy4EUWKFMHo0aNx8OBBHDx4EKNHj4aVlRU2btyY6bbGjRuHyMhIpVs7ceFfZUj6z22Y166oVGZRpzIi/rkNABCSkhB58z4salf6XEEkgnmtSpD+cysfIy2cdHX1YWXjoLjZOrjC2NQcD+5eV9SJj4vBs8f34eZVMoMtZe5TMib4w2uMmroSBkYm3xj9j0FLUwNFHW1wLfDzai9yuYBrgS9R0jXrS1TLBQGJyckZ1hGyUOdHp6unD2tbe8XN3tEFJqbmuHf7X0WduLhYPA18AE/v4iq3oamlBRd3L9y7c0NRJpfLce/Ov4o2llY2MDWzUBryDwBB797Aooh1HvRMfWhpacHd3RN37txUlMnlcty9fQve3j4q23h7++DubeX/Gbdv/auob2VtA1NTM9y587lOXFwsHgc+hFdR1duktFL3jQfu3LmtKJPL5bhz+za8vYuqbOPt7YPbX+2bW7duKtX/lIx5//4dZs6ak+moYkqVej5zUNzs/n8+u//FuSkuLhbPHj+ARybns/t3lc9n9+/cgIdXahuZLAEAIBIrf0QWizUUowFJma6eHmxs7RQ3B0dnmJqa4e4X57W4uFg8CXwIL+9iKrehpaUFN3dP3L2tfC787/ZNpTZxcbGYMnEUNLU0MX7STGhrc+L4LElJQdLbF5B4fPH6i0SQeBRD4qsnKpskvXgMDQsrxZwxAKBhaYOUyIjURM0XhEQZ5NFSiHT1IfEqiYT7/369OSJKR5ZTyl/OHzNt2jQsWrQIHTt2VJQ1a9YMJUqUwG+//YauXbtmuC2JRAKJRKJU9j1erqShrwd9988Ti+q52MOolDcSwyOR8OYDvGYMh46dFe50T/215NVvu+E04Cd4zx6FN5v3w6JWRdi0bYjrzfoqtvFiySaU2jgX0n/vIfL6XTgP7gpNfV282XIg3/tX2IlEItRv2hFH9m6Eta0DLIrY4cDONTA1s0AZv8/v17kTU1dUqtu4HQAgIT4OwR8+X/8f+vE9Xj0PhIGhMcwtrZGcnIyV88bg1bNHGDphMeTyFEgjUkcwGRgYQ1NLK387Wsj8XMcPE7f8AR9HGxR3tsOOc1cRL0tC80qlAAATNh9GERNDDG5RGwCw4eTf8HGygYOFKRKTU3Dp/lMcu/ofxndsCACIlyVi3YlLqFnSExbGBpDGxGPPnzfwURqNemX45TI7RCIRGjZvh4N7tsDazh5FrGzx+/Z1MDWzQLlK1RT1po8fjPKVqqNB0zYAgMYt2mP14plw9fCGu6cPjh/+HbKEBNSo21ix3aatO2Hvjg1wcvGAs6sH/jx7HO/evsLQcTMKpK+FSfOWrbFk0Ty4e3jB09MLfxw+gARZAurUawAAWLxgDszMLdC1ey8AQNPmrTB+zHAcPLAX5cv74eKf5/H0yWMM/GUYgNT90axFK/y+ewdsbe1gZWWNHds2w8zcHBUrcURZdrRo2RqLF82Hh4cHPD29cfj/+6ZuPX8AwMIF82Bubo5u3XsCAJo1b4GxY0biwIF9KF++Ai7+eQFPnzzGoF+GAEhNxsyeNR3Pnj7BpCnTIU+RIyI8dbSGgaEhtPj/JctEIhEaNGuPg3s2w9rWAZZWNti7fR1MzCxQrmJ1Rb2Zvw5CuUo14N+kLQCgUYuOWLN4OlzdveHmWQwnDu9GQkICatRNnYfE1t4ZVjb22LByLjr1GARDQ2Pc+Oci7t2+hpF5eJmUOhGJRGjSvA327t4GG1s7WFnbYOe2jTAzs4BfpaqKepPGD0fFStXQqGlLAECzlm2xbNEcuHl4wsOzKI4e3oeEhM/nwri4WEydMAoymQxDR45HXFwc4uLiAABGxsac2DcTsRePw6RDPyS9eY6k18+gV70hRNo6iL+WujKpccf+kEeGI/r4ntT6VwKgV7UejFp0Qexfp6BpaQ2DOs0R99fnFQC1vUpCBCA55AM0LKxg1KQTkj++V2yTCsaPvIR0YZSjMX5XrlzBmjVr0pSXK1cOvXr1+uagvhfGZYuj0tltivs+C8YDAN5sPYC7PcdBYmMJXQcbxePxL9/ierO+8Fk4Ds6/dEHC2yD813cCQgM+L/H3Ye8JaFuawXPyYEisLRF15yGuNemFxI+5Nwntj6RRyy6QJcRj06pZiIuNgWfRUhgxaRm0tT8n/D4GvUN0lFRx/8XTh5g7sZ/i/q6NqXPRVKnVGL2HTEFE2Efcupa6ZOakYT8pPd+Y6WtQtERZUPr8yxVDREwcVh/9E6FRsfCyt8KqXzrC/P+XLH0Ij1S6/DFelohZu07gozQaEi1NOFtbYGb35vAvl/orjlgsxsvgMIz4bT+ksXEw0ddFMSdbbBzRFe62lgXSx8KsWeufIEuIx7rl8xAXGwMvn5IYO22h0jETHPQO0VGfJ/6rXL0uoiKl2Lt9PaQR4XBy9cDYaQuVhpE3at4eSYmJ2Lp+GWKjo+Do4o5fpy+BtY19vvavMKpWoxYioyKxc9tmREREwNXVDVOmzVZcehQS8lHp1/qiPsUwYvR47Ni6Cds2b4StnR3GT5wKJ+fPl4y1atMeCQkJWLl8MWJjYuBTrDimTJvDX5OzqXqNmoiMisT2bVv/v29cMW3aTKV9I/7iWv2iPsUwavQ4bNu6GVs3b4KtnS1+nTgFzv/fN2Fhobj6T+pqjIMHKc+tNGvOfJQsWSqfeqYemrbuDFlCPNavmJP6GcCnJMZOXZzh+axStbqIiozAvh3rIY0ISz2fTV2smOhXU1MTo6cswu7Nq7Bg+ijI4uNhZWOPfkMnonS5yvnex8KqZZsOSEiIx+rlCxEbG4OiPiUwcfpcpXNQ0If3iPpi31StXhtRkZHYvX0zIiLC4eLqhknT5ir+1zx/+gSPA1NXLBvQq7PS863duAtFrDgiMyMJt/9BlL4RDPzbQMPIBEnvXiF83RzIY1LnCdUwMQcEuaK+XBqO8N/mwqh5Z1iOnIOUyAjE/nUSsef+UNQR6+jCsFEHaJiYQR4Xg4S71xF9Yg8g5zLkRFklEoTsj7/08vJC8+bNMW/ePKXy0aNH4/DhwwgMDMx2IMe0uDza98jsi8uB6Pvi++FwQYdA6Xjk0LCgQyAV9MTxBR0CpUMMeeaVqEBEpRgWdAikgq44oaBDoHSYrh5Z0CFQOmwW7izoEPLF9aoVM6/0HSp/6Z+CDqFA5GiEzOLFi9G6dWucOHECfn5+AIBr167hyZMn2L9/f64GSERERERERESkbnKUkGnUqBEeP36M1atX49GjRwCApk2bol+/fnBwcMjVAImIiIiIiIgocyLOIVOo5HidOAcHB8yaNSs3YyEiIiIiIiIi+iHkeGmjv/76C507d0blypXx7t07AMC2bdtw6dKlTFoSEREREREREf3YcpSQ2b9/P/z9/aGrq4ubN29CJpMBACIjIzlqhoiIiIiIiIgoEzlKyMyYMQNr1qzBunXroKWlpSivUqUKbt68mWvBEREREREREVHWiMTiQnn7UeWo54GBgahevXqacmNjY0il0m+NiYiIiIiIiIhIreUoIWNtbY2nT5+mKb906RJcXV2/OSgiIiIiIiIiInWWo1WWevfujSFDhmDjxo0QiUR4//49rly5gpEjR2LixIm5HSMRERERERERZUIk5rLXhUmOEjJjx46FXC5HnTp1EBcXh+rVq0MikWDkyJH45ZdfcjtGIiIiIiIiIiK1kqOEjEgkwq+//opRo0bh6dOniImJgY+PDwwMDHI7PiIiIiIiIiIitZOthEyPHj2yVG/jxo05CoaIiIiIiIiI6EeQrYTM5s2b4eTkhNKlS0MQhLyKiYiIiIiIiIiySazBOWQKk2wlZPr3749du3bhxYsX6N69Ozp37gwzM7O8io2IiIiIiIiISC1la9nrlStX4sOHDxg9ejSOHDkCBwcHtGvXDqdOneKIGSIiIiIiIiKiLMpWQgYAJBIJOnbsiICAADx48ADFihXDgAED4OzsjJiYmLyIkYiIiIiIiIgyIRKLCuXtR5XthIxSY7EYIpEIgiAgJSUlt2IiIiIiIiIiIlJr2U7IyGQy7Nq1C/Xq1YOnpyf+++8/rFixAq9fv+ay10REREREREREWZCtSX0HDBiA3bt3w8HBAT169MCuXbtgYWGRV7EREREREREREamlbCVk1qxZA0dHR7i6uuLPP//En3/+qbLegQMHciU4IiIiIiIiIsoakfibZiWhfJathEyXLl0gEv24E+4QEREREREREeWGbCVkNm/enEdhEBERERERERH9ODieiYiIiIiIiIgon2VrhAwRERERERERfZ9EYk4xUphwhAwRERERERERUT5jQoaIiIiIiIiIKJ/xkiUiIiIiIiIiNSDW4CVLhQlHyBARERERERER5TMmZIiIiIiIiIiI8hkTMkRERERERERE+YxzyBARERERERGpAS57XbhwhAwRERERERERUT5jQoaIiIiIiIiIKJ8xIUNERERERERElM84hwwRERERERGRGhCJOeaiMOHeIiIiIiIiIiLKZ0zIEBERERERERHlM16yRERERERERKQGuOx14cIRMkRERERERERE+YwJGSIiIiIiIiKifMaEDBERERERERFRPuMcMkRERERERERqgHPIFC4cIUNERERERERElM+YkCEiIiIiIiIiyme8ZImIiIiIiIhIDfCSpcKFI2SIiIiIiIiIiPLZdzNCxuzu9YIOgVQIL1m+oEOgdFy7faugQ6B0GAgJBR0CqXAr2L6gQ6B0lLF6U9AhUDoMNWIKOgRSITzJuKBDoHSY9h5d0CEQUSHCETJERERERERERPnsuxkhQ0REREREREQ5JxJzzEVhwr1FRERERERERJTPmJAhIiIiIiIiIspnTMgQEREREREREeUzziFDREREREREpAbEGqKCDoGygSNkiIiIiIiIiIjyGRMyRERERERERET5jJcsEREREREREakBkZiXLBUmHCFDRERERERERJTPmJAhIiIiIiIiIspnTMgQEREREREREeUzJmSIiIiIiIiI1IBILC6Ut5xYuXIlnJ2doaOjAz8/P1y7di3dujVr1oRIJEpza9y4saJOt27d0jzeoEGDHMWWVZzUl4iIiIiIiIgKjT179mD48OFYs2YN/Pz8sGTJEvj7+yMwMBBFihRJU//AgQNITExU3A8LC0OpUqXQtm1bpXoNGjTApk2bFPclEknedQIcIUNEREREREREhciiRYvQu3dvdO/eHT4+PlizZg309PSwceNGlfXNzMxgbW2tuAUEBEBPTy9NQkYikSjVMzU1zdN+MCFDREREREREpAZEYlGhvMlkMkRFRSndZDKZyj4mJibi33//Rd26dRVlYrEYdevWxZUrV7L0Om3YsAEdOnSAvr6+UvmFCxdQpEgReHl5oX///ggLC8v5zsgCJmSIiIiIiIiIqMDMnj0bxsbGSrfZs2errBsaGoqUlBRYWVkplVtZWSEoKCjT57p27Rru3buHXr16KZU3aNAAW7duxdmzZzF37lz8+eefaNiwIVJSUnLesUxwDhkiIiIiIiIiKjDjxo3D8OHDlcryav6WDRs2oESJEqhQoYJSeYcOHRR/lyhRAiVLloSbmxsuXLiAOnXq5EksOR4hk5ycjDNnzmDt2rWIjo4GALx//x4xMTG5FhwRERERERERqTeJRAIjIyOlW3oJGQsLC2hoaCA4OFipPDg4GNbW1hk+T2xsLHbv3o2ePXtmGpOrqyssLCzw9OnTrHckm3KUkHn16hVKlCiB5s2bY+DAgQgJCQEAzJ07FyNHjszVAImIiIiIiIgocwU9F0xOb9mhra2NsmXL4uzZs4oyuVyOs2fPolKlShm23bt3L2QyGTp37pzp87x9+xZhYWGwsbHJVnzZkaOEzJAhQ1CuXDlERERAV1dXUd6yZUulF4WIiIiIiIiIKDcNHz4c69atw5YtW/Dw4UP0798fsbGx6N69OwCgS5cuGDduXJp2GzZsQIsWLWBubq5UHhMTg1GjRuGff/7By5cvcfbsWTRv3hzu7u7w9/fPs37kaA6Zv/76C5cvX4a2trZSubOzM969e5crgRERERERERERfa19+/YICQnBpEmTEBQUBF9fX5w8eVIx0e/r168hFiuPPwkMDMSlS5dw+vTpNNvT0NDA3bt3sWXLFkilUtja2qJ+/fqYPn16ns1lA+QwISOXy1XONPz27VsYGhp+c1BEREREREREROkZNGgQBg0apPKxCxcupCnz8vKCIAgq6+vq6uLUqVO5GV6W5OiSpfr162PJkiWK+yKRCDExMZg8eTIaNWqUW7ERERERERERURaJxOJCeftR5WiEzMKFC+Hv7w8fHx8kJCSgU6dOePLkCSwsLLBr167cjpGIiIiIiIiISK3kKCFjb2+PO3fuYPfu3bh79y5iYmLQs2dP/PTTT0qT/BIRERERERERUVo5SsgAgKamZpaWiiIiIiIiIiKivJfdJaSpYOUoIfPHH3+oLBeJRNDR0YG7uztcXFy+KTAiIiIiIiIiInWVo4RMixYtIBKJ0sxQ/KlMJBKhatWqOHToEExNTXMlUCIiIiIiIiIidZGj6YwDAgJQvnx5BAQEIDIyEpGRkQgICICfnx+OHj2KixcvIiwsDCNHjszteImIiIiIiIiICr0cjZAZMmQIfvvtN1SuXFlRVqdOHejo6KBPnz64f/8+lixZgh49euRaoERERERERESUvh95CenCKEd769mzZzAyMkpTbmRkhOfPnwMAPDw8EBoa+m3RERERERERERGpoRwlZMqWLYtRo0YhJCREURYSEoLRo0ejfPnyAIAnT57AwcEhd6IkIiIiIiIiIlIjObpkacOGDWjevDns7e0VSZc3b97A1dUVhw8fBgDExMRgwoQJuRcpEREREREREaVPxGWvC5McJWS8vLzw4MEDnD59Go8fP1aU1atXD+L/X7PWokWLXAuSiIiIiIiIiEid5CghAwBisRgNGjRAgwYNFGVSqRTbt2/HoEGDciU4IiIiIiIiIiJ1lCtTMJ89exadOnWCjY0NJk+enBubJCIiIiIiIiJSWzlOyLx58wbTpk2Di4sL6tevDwA4ePAggoKCci04IiIiIiIiIsoakVhUKG8/qmwlZJKSkrB37174+/vDy8sLt2/fxvz58yEWizFhwgQ0aNAAWlpaeRUrEREREREREZFayNYcMnZ2dvD29kbnzp2xe/dumJqaAgA6duyYJ8EREREREREREamjbI2QSU5OhkgkgkgkgoaGRl7FRERERERERESk1rKVkHn//j369OmDXbt2wdraGq1bt8bBgwch4lrnRERERERERAVKJBYXytuPKls919HRwU8//YRz587hv//+Q9GiRTF48GAkJydj5syZCAgIQEpKSl7FSkRERERERESkFnKcinJzc8OMGTPw6tUrHD16FDKZDE2aNEGRIkVyMz4iIiIiIiIiIrWTrUl9VRGLxWjUqBEaNWqEkJAQbNu2LTfi+q4IgoCDu9biz4BDiIuNgYd3SXTpNxbWto7ptgm8fxPHD27Dq2ePII0IxS9j56NsxZqKx5OTk3Fgx2rc/fdvfAx+Bz09A/iUqoC2XQbB1MwyH3pVuJlVLQfXET1hXKY4dGyL4EbrAQj+42zGbapXgM+CsTDw8UDCmw94Ons13m49qFTHqX8nuA7vCYm1JaLuPsL9odMRef2/vOyKWhIEAX/sXo2/Ag4iPi4abt6l8FOf8bCydUq3zeP7/+L04a149ewBIiNC0X/MIpT2q6VUZ9PySbhy/ohSWTHfyhgyaWWe9EMdCYKAfTvW4fzpPxAbGw3PoiXRY8Bo2Ng6ZNju9LF9OHpgByIjwuHo4o6ufYfD3bOY4nFpRBh2blyB/25fQ0J8HGzsHNGiXTdUqFIrg63SlwRBwPlDy/Hvn3uREBcFR48yaPLzZJhbO6fb5tq5Xbhxfhekoe8AAJZ27qjZbCA8SlZX1ElKkuHU7rm4d/UYUpKT4Fa8Cpr8PBkGxhZ53SW1cOzIYRzYvxcREeFwcXFD3/4D4enlnW79S3/9ie3btuBjcBBsbe3QrUcvlCvvp3j88t9/4cTxo3j29Amio6OxdPlquLq550dX1A73zfdJEAQc3PkbLnzxublr/zEZfm5+dP8mThzcjpdPUz83Dx43T+lzMwDcuHIe504ewMtnDxEbHYVpi7fDydUzj3ujXg4eO4Xdh44gPEIKd2cnDO7THUU9Vb/HX7x+g007f0fgsxcI/hiCgT27oG2zxkp17tx/gN0Hj+Dx0xcIi4jA9HEjUa1i+fzoCmXgR15CujDK8QgZqVSK9evXY9y4cQgPDwcAvHnzBu3bt8+14L4Xxw9uRcDRPejabxwmzdsEiY4uFk79BYmJsnTbyBLi4ejiiZ/7jlb5eKIsAa+eP0Kzdj0xddE2DBo7D0HvXmHpzBF51Q21oqGvh6i7gbg3eGqW6us626P8H2sRduEqLpVrjhfLt6DE2hmwqFdVUcembUMUnT8OT2asxKUKLRF99xH8jm2AtqVZXnVDbZ06uBnnju1C537jMW7OVkgkulg6fSCSMjpmZPGwd/ZEp97jMtx2sdKVMX9DgOLWa/js3A5frR3Zvx2nju5FjwGjMX3BBujo6GLOpKEZns+u/HUG29cvQ6uOPTFzyWY4unhgzqRhiJSGK+qsXjQN79+9woiJ8zBnxXaUr1wTS+dNwMtngfnRLbVw6fh6XA3YhqZdpqD3xN+hpa2LbYt6ISkp/X1jbGaFum1GoO/k/egzeR9cilbErmUD8fHdE0Wdk7tm4/Ht82g3YCm6j92KaOlH7F7xS350qdD7688LWL9uLTp26owly1fDxdUVkyaOg1QaobL+wwf3MX/uLNSv3wBLl69GxUpVMHP6FLx6+UJRJyEhAT7FiqNr91751Q21xH3z/Tp+YCsCju1Bt/5jMWn+Rkh0dLFgyuBMPjcnwMHZAz/3HZVBnXh4Fi2Fdl0G5UXYau/cX5exauNWdGvfGusWzYGbixNGTZmFCGmkyvoymQw2Vlbo83NHmJmaqKyTkCCDm7MThvbtkYeRE6m3HCVk7t69C09PT8ydOxcLFiyAVCoFABw4cADjxmX8ZaqwEQQBp4/sQrN2PVDGrwYcnD3Qe8hURISH4ubVP9NtV7JsFbT+qT/KVlT967CevgFGTV2JClXrwcbOGe5eJdC5zyi8fPYQYSFBedUdtRFy6iIeT16C4MNnslTfqU8HxL94i4ej5yLm0XO8WrUDQftPwWVIN0Udl6Hd8WbD73i75QBiHj7DfwMmIyUuAQ7dWudRL9STIAg4c3QnGrfpDd8KtWDv7Inug6dDGh6CW9fOp9uuRJmqaNFpIEpXrJ3h9jW1tGFsaqG46RsY5XYX1JYgCDj5xx60aNcN5SpWh6OLO/oPmwRpeChu/HMx3XbHD+1CLf9mqFm3CewdXdBzwGhIJBL8GXBUUefxo//g36Qt3D2LwcraDi3bd4e+vgFePGVCJisEQcA/AVtRvWk/eJepA2sHL7TqPRfRER/x6Gb65zkv39rwLFUD5tbOsLB2Qd3Ww6Cto4c3z+4AABLionHr4n74dxgDV5+KsHUujhY9Z+PN01t48+x2PvWu8Dp0cD/8GzRE3foN4OjohAGDhkAikSDg9CmV9f84fBBlypZHqzbt4ODohM5dusHNzR1HjxxW1Kldpx46dvoZvqXL5Fc31BL3zfdJEAScOrIbTdumfm52dPZAn6FTIA0Pxc1/0v/cXKpsZbTp3B/lKqU/qrJKrUZo0aEXipWqkBehq729h4+hcf06aFi3Fpwd7TG8fy/oSLRx/Izqz2beHu7o370z6lSvAi0tLZV1/MqWRq/OHVCtEvcJUU7lKCEzfPhwdOvWDU+ePIGOjo6ivFGjRrh4Mf0P9YVRSPA7REaEwafk5xONnr4B3DyL4Vng3Vx9rvi4GIhEIujpG+TqdgkwqeiL0HNXlMpCAi7BtKIvAECkpQXjMsUQevby5wqCgNBzl2FSsXQ+Rlr4hQa/Q5Q0FEVLfR4GrqdvCBeP4nieC8fM43s3MKJbbUwc1AI71s5ETLT0m7f5o/gY/B7SiDAU9/08nDj1fOaDJ4/uqWyTnJSEF08DUbzU5zZisRjFfcvjSeDnNp7eJfDPX2cQEx0JuVyOyxcDkJSYiKIlePxkRUTIW8REhsC1WGVFmY6eIezcSuLN09tZ2oZcnoL/rh5DoiwODm6+AID3L+8jJSVJabuWNq4wNrfN8nZ/VElJSXj69DFK+X7+ci4Wi+HrWwaBjx6obPPo0YM0X+ZLly2HR48e5mmsPxrum+9XSPB7REaEKSVN9PQN4OpZDE8DeQl4QUlKSkbgs+coW6qEokwsFqNsqRJ4EPgkg5ZElNdyNIfM9evXsXbt2jTldnZ2CApSr9EdkdIwAICxiblSuZGxOSIjwnLteRITZfh9ywr4VasPXT0mZHKbxMoCsuBQpTJZcCi0jA0h1pFAy9QYYk1NyD6GfVUnDPpervkZaqEXJU19nQ2NlS/1MjIxR9Q3HjPFSldGab/asLCyQ0jQWxzasRzLpg/C2NlbINbQ+KZt/wg+nbOMTZT3jbGJWbrns+goKeTyFBibpm3z/u0rxf3BY2Zg2byJ6NOpATQ0NKAt0cGw8XNgncncNJQqJjIEAGBgpPy/xsDIAjGRoaqaKAS/CcT6mR2RnCSDtkQPHQatQBE7d8V2NTS1oKunPJLMwMg80+3+6KKiUpOLpqamSuUmJqZ4++aNyjbSiAiYmJikqS+NCFdZn3KG++b7ld7/GaMM/s9Q3ouMioJcLoeZibFSuamJMV6/fV9AUVFe+ZGXkC6McpSQkUgkiIqKSlP++PFjWFpmPiGtTCaDTKZ8HWliogza2pKchJOrLv95AltWf56TYtiExXn+nMnJyVg1fxwAAV37jc3z5yPKTVf/PI7ta2co7g/6dVmePVeFqg0Uf9s7ecDeyQO/DmiKwPs3ULSkXwYtf0yXLpzChpVzFfdHT1qQZ8+1d8dviIuNxvgZy2BoZIIb/1zEsnkTMGnOajg6c1LMr929cgRHtkxW3P9p6Jocb8vcxgX9ph6ELD4a96+fwsH1Y9F97DZFUoaIKK9cvnASm7/43Dx8Yt5/biYiUic5Ssg0a9YM06ZNw++//w4AEIlEeP36NcaMGYPWrTOfb2P27NmYOlV5MtYeA8ai16CCn3+mdIXqcPMsrrifnJQIIHWkjInZ5xUpoiLD4Ojy7TO7f0rGhIUEYcy0VRwdk0dkwaGQWCmvKCKxskBSZDTkCTIkhkZAnpwMSRHzr+qYQxbEX5EzUqpCDbgoHTNJAIDoyHCYfLFiWJQ0DA4uXrn63JbW9jAwMsHHD2+YkFGhbIWqcPf0Udz/tG8ipeEw/eJ8FikNT3elCkMjE4jFGoj86lfkSGk4TExTj5fgD29x+ug+zFuxA/ZOqSPKnFw88Oj+bQQc24+eA8fkar/UgZdvLdi5llTcT0lO/V8TExUGQ5MiivKYqFBYOxTNcFuamtowt0pdwczWuTjev7yHfwK2olm3aTAwtkRKchLi46KURsnERIVxlaVMGBkZQywWIyJCeZJYqTQCpmamKtuYmJoq5tX7sr6JKSeHz03cN9+P0hWqwc3r84p7SYrPzeHKn5ul4bnyuZlyxtjICGKxGOFfTeAbIY1Md8JeIsofORrPtHDhQsTExKBIkSKIj49HjRo14O7uDkNDQ8ycOTPT9uPGjUNkZKTSrUuf4TkJJdfp6urDysZBcbN1cIWxqTke3L2uqBMfF4Nnj+/DzatkBlvK3KdkTPCH1xg1dSUMjEy+MXpKj/Sf2zCvXVGpzKJOZUT8cxsAICQlIfLmfVjUrvS5gkgE81qVIP3nVj5GWvjo6OqjiI2j4mbj4AojEws8vHtVUSc+LgYvntyD6zceM1+LCA1GbHQkjE35xVIVXT19WNs6KG52ji4wMTXH/Ts3FHXi4mLx7PEDeHgXV7kNTS0tuLh74f7dz23kcjnu37kBD6/UNjJZAoC0Q2TFYg3IBSG3u6UWJLoGMLdyUtwsbd1hYGyJ5w8+z3WVEB+Dd8/uwsHdN1vbFuRyRYLH1rkYNDS08OKL7YZ+eI7IsPfZ3u6PRktLC+7unrh75/P/ALlcjju3b8HL20dlG29vH9y5rfw/4/atm/D2zjipRtnDffP90NVT/txsl87n5ueP78Pdq0QGW6K8pKWlCS83V9y8+3keH7lcjn/v3oOPl0cBRkZ5QSQWFcrbjypHI2SMjY0REBCAv//+G3fu3EFMTAzKlCmDunXrZqm9RCKBRKJ8eZK2dtpLoL4HIpEI9Zt2xJG9G2Ft6wCLInY4sHMNTM0sUMavhqLe3ImpKyrVbdwOAJAQH4fgD5+vYw79+B6vngfCwNAY5pbWSE5Oxsp5Y/Dq2SMMnbAYcnkKpBGpIzEMDIyhmc5s5pRKQ18P+u6Oivt6LvYwKuWNxPBIJLz5AK8Zw6FjZ4U73VN/mX/12244DfgJ3rNH4c3m/bCoVRE2bRvierO+im28WLIJpTbOhfTfe4i8fhfOg7tCU18Xb7YcyPf+FWYikQh1m3TC8X3rUcTGERZWdji8axVMzCxRusLn1RMWTe4LX79aqN2oA4DUYyYk6Mtj5h3evAiEnoERzC1tkBAfh6O/r0WZinVgZGqBkKA32L91KSytHVCsdOU0cVBaIpEIDZq1x8E9m2Ft6wBLKxvs3b4OJmYWKFexuqLezF8HoVylGvBv0hYA0KhFR6xZPB2u7t5w8yyGE4d3IyEhATXqNgEA2No7w8rGHhtWzkWnHoNgaGiMG/9cxL3b1zAyDy+TUicikQgV63XBxSNrYG7lDFMLO5w7uAyGpkXgXebz/9bN87qhaJm68KvbGQAQsHchPEpWh7G5DRLjY3H3n6N4GXgNP49YDyB1YuDS1Vvj5O650NU3hkTXAMe3z4CDm69i4l9KX4uWrbF40Ty4e3jC09MLhw8fRIIsAXXr+QMAFi2YC3NzC3Tt3hMA0Kx5S4wbMwIHD+xFufJ++OvPC3j65DEG/TJUsc3o6CiEfPyI8PDU+TTevX0LADA1NYOpGUdrZBX3zfdJJBLBv2kH/PH7RljZOMDSyhYHdq6BiZkFylT88nPzAJSpWBP1lD43v1U8HhL8Hq+eP4aBoRHMLa0BADHRkQgLCYY0PHXOraB3qfOYGZuawYQ/zGSqbfPGmL10Fbzc3VDUww37jhxHQoIMDevWBADMWrwCFuZm6NOlE4DUiYBfvkndJ8lJyQgNi8CT5y+hq6sDe5vUfRIXn4B3Hz7PHxoU/BFPnr+EkaEBrCy5T4iyIkcJmU+qVKmCKlWqAECaYaDqpFHLLpAlxGPTqlmIi42BZ9FSGDFpmdKcNx+D3iE6Sqq4/+LpQ8yd2E9xf9fG1Gtqq9RqjN5DpiAi7CNuXUtdkWrSsJ+Unm/M9DUoWqJsHvao8DMuWxyVzm5T3PdZMB4A8GbrAdztOQ4SG0voOtgoHo9/+RbXm/WFz8JxcP6lCxLeBuG/vhMQGnBJUefD3hPQtjSD5+TBkFhbIurOQ1xr0guJHzkJXXb5t+wGmSwe29fMQFxsNNyL+mLIxJXQ+uKYCQl6g5gvjplXzx5g4aTeivt7Ny0EAFSq1RTdf5kGsViMt6+e4Mr5I4iLi4aJqSV8fCuheccB0NLSzre+FXZNW3eGLCEe61fMST2f+ZTE2KmLlc5nwUHvEB31eVhzpWp1ERUZgX071kMaEQYnVw+MnbpYMdGvpqYmRk9ZhN2bV2HB9FGQxcfDysYe/YZOROlyTJZlVdVGvZCUGI8jmychIS4Kjp5l0Xn4Omhpfd43ER9fIy7m82UasdHhOLhuDKIjQ6CjawgrBy/8PGI93IpVUdRp0HEcRCIx9qwcguSkRLgXr4rGXSbla98Kq2o1aiIySood27YgIiICrq5umDptlmIy2ZCQj0q/6hX1KYaRo8dh+9bN2Lp5E2zt7PDrxClwcnZR1Ln6zxUsXfw5UTlvburI4o6dfkanzl3yqWeFH/fN96tRqy6QJSRg8/8/N3sULYWRk5em+dwc89Xn5jkT+ivu79q4BABQtXZj9B6SOt/WrWt/Yf2yaYo6qxb8CgBo0aEXWnbsk4c9Ug+1q1WGNCoKm3b+jvAIKdxdnDFv8jiY/X+y6+DQMKWRrqHh4eg97PMlx3sOHcGeQ0dQqrgPls5M3SeBT59h2ITP+2Tlxq0AAP/aNTBuyIB86BVR4ScShOyPJ587dy6cnZ3Rvn17AEC7du2wf/9+WFtb4/jx4yhVqlS2A7ny8PscIfOjCy9ZPvNKVCAMbvNSqu+VgVZCQYdAKjwJVT23BBW8MlaqV8YhItXCk4wzr0QFwgkvCjoESoeNt29Bh5Avgsf8XNAh5IjV3G2ZV1JDOZpDZs2aNXBwSF3KNCAgAAEBAThx4gQaNmyIUaNG5WqARERERERERJS5gp4LhnPIZE+OLlkKCgpSJGSOHj2Kdu3aoX79+nB2doafH1c6ISIiIiIiIiLKSI5GyJiamuLNm9ThxSdPnlRM5isIAlJSUnIvOiIiIiIiIiIiNZSjETKtWrVCp06d4OHhgbCwMDRs2BAAcOvWLbi7u+dqgERERERERERE6iZHCZnFixfD2dkZb968wbx582BgYAAA+PDhAwYM4IzaRERERERERPlOnKOLYKiA5Cgho6WlhZEjR6YpHzZs2DcHRERERERERESk7nKUkNm6dWuGj3fp0iVHwRARERERERER/QhylJAZMmSI0v2kpCTExcVBW1sbenp6TMgQERERERER5TOR6MddQrowytEFZhEREUq3mJgYBAYGomrVqti1a1dux0hEREREREREpFZybcYfDw8PzJkzJ83oGSIiIiIiIiIiUparUzBramri/fv3ublJIiIiIiIiIiK1k6M5ZP744w+l+4Ig4MOHD1ixYgWqVKmSK4ERERERERERUdaJuOx1oZKjhEyLFi2U7otEIlhaWqJ27dpYuHBhbsRFRERERERERKS2cpSQkcvluR0HEREREREREdEP45vHMwmCAEEQciMWIiIiIiIiIqIfQo4TMlu3bkWJEiWgq6sLXV1dlCxZEtu2bcvN2IiIiIiIiIgoi0RiUaG8/ahydMnSokWLMHHiRAwaNEgxie+lS5fQr18/hIaGYtiwYbkaJBERERERERGROslRQmb58uVYvXo1unTpoihr1qwZihUrhilTpjAhQ0RERERERESUgRwlZD58+IDKlSunKa9cuTI+fPjwzUERERERERERUTZx2etCJUd7y93dHb///nua8j179sDDw+ObgyIiIiIiIiIiUmc5GiEzdepUtG/fHhcvXlTMIfP333/j7NmzKhM1RERERERERET0WY5GyLRu3RpXr16FhYUFDh06hEOHDsHCwgLXrl1Dy5YtcztGIiIiIiIiIiK1kq0RMlFRUYq/PTw8sGrVKpV1jIyMvj0yIiIiIiIiIsqyH3kJ6cIoWwkZExMTiESZ7+CUlJQcB0REREREREREpO6ylZA5f/684m9BENCoUSOsX78ednZ2uR4YEREREREREZG6ylZCpkaNGkr3NTQ0ULFiRbi6uuZqUERERERERESUPSIRl70uTLi3iIiIiIiIiIjyGRMyRERERERERET57JsTMlmZ5JeIiIiIiIiIiD7L1hwyrVq1UrqfkJCAfv36QV9fX6n8wIED3x4ZEREREREREWUdl70uVLKVkDE2Nla637lz51wNhoiIiIiIiIjoR5CthMymTZvyKg4iIiIiIiIioh8GJ/UlIiIiIiIiIspn2RohQ0RERERERETfJ5GYYy4KE+4tIiIiIiIiIqJ8xoQMEREREREREVE+4yVLRERERERERGpAxGWvCxWOkCEiIiIiIiIiymdMyBARERERERER5TMmZIiIiIiIiIiI8hnnkCEiIiIiIiJSByKOuShMuLeIiIiIiIiIiPIZEzJERERERERERPmMlywRERERERERqQEue124cIQMEREREREREVE+Y0KGiIiIiIiIiCifMSFDRERERERERJTPvps5ZHw/HC7oEEiFa7dvFXQIlI4Y39IFHQKlw6SOVUGHQCp4LjtY0CFQOuT8fei7ZZwUWtAhkApJmloFHQKlQyshvqBDoB+dmP9TCxPuLSIiIiIiIiKifMaEDBERERERERFRPmNChoiIiIiIiIgon303c8gQERERERERUc6JRKKCDoGygSNkiIiIiIiIiIjyGRMyRERERERERET5jJcsEREREREREakDLntdqHBvERERERERERHlMyZkiIiIiIiIiIjyGRMyRERERERERET5jHPIEBEREREREakBkZjLXhcmHCFDRERERERERJTPmJAhIiIiIiIiIspnvGSJiIiIiIiISB2IOOaiMOHeIiIiIiIiIiLKZ0zIEBERERERERHls2wnZOLj4xEXF6e4/+rVKyxZsgSnT5/O1cCIiIiIiIiIiNRVtueQad68OVq1aoV+/fpBKpXCz88PWlpaCA0NxaJFi9C/f/+8iJOIiIiIiIiIMsJlrwuVbI+QuXnzJqpVqwYA2LdvH6ysrPDq1Sts3boVy5Yty/UAiYiIiIiIiIjUTbYTMnFxcTA0NAQAnD59Gq1atYJYLEbFihXx6tWrXA+QiIiIiIiIiEjdZDsh4+7ujkOHDuHNmzc4deoU6tevDwD4+PEjjIyMcj1AIiIiIiIiIiJ1k+2EzKRJkzBy5Eg4OzvDz88PlSpVApA6WqZ06dK5HiARERERERERZU4kEhfK248q25P6tmnTBlWrVsWHDx9QqlQpRXmdOnXQsmXLXA2OiIiIiIiIiEgdZTshAwDW1tawtrZWKqtQoUKuBEREREREREREpO6ynZBJSEjA8uXLcf78eXz8+BFyuVzp8Zs3b+ZacERERERERESURVz2ulDJdkKmZ8+eOH36NNq0aYMKFSpAJOIOJyIiIiIiIiLKjmwnZI4ePYrjx4+jSpUqeREPEREREREREZHay/Z0xnZ2djA0NMyLWIiIiIiIiIiIfgjZTsgsXLgQY8aMwatXr/IiHiIiIiIiIiLKAZFYXChvP6ps97xcuXJISEiAq6srDA0NYWZmpnQjIiIiIiIiIspLK1euhLOzM3R0dODn54dr166lW3fz5s0QiURKNx0dHaU6giBg0qRJsLGxga6uLurWrYsnT57kaR+yPYdMx44d8e7dO8yaNQtWVlac1JeIiIiIiIiI8s2ePXswfPhwrFmzBn5+fliyZAn8/f0RGBiIIkWKqGxjZGSEwMBAxf2vcxnz5s3DsmXLsGXLFri4uGDixInw9/fHgwcP0iRvcku2EzKXL1/GlStXUKpUqbyIh4iIiIiIiIhy4gcZMLFo0SL07t0b3bt3BwCsWbMGx44dw8aNGzF27FiVbUQiEaytrVU+JggClixZggkTJqB58+YAgK1bt8LKygqHDh1Chw4d8qQf2b5kydvbG/Hx8XkRCxERERERERH9YGQyGaKiopRuMplMZd3ExET8+++/qFu3rqJMLBajbt26uHLlSrrPERMTAycnJzg4OKB58+a4f/++4rEXL14gKChIaZvGxsbw8/PLcJvfKtsJmTlz5mDEiBG4cOECwsLC0rxoRERERERERERZNXv2bBgbGyvdZs+erbJuaGgoUlJSYGVlpVRuZWWFoKAglW28vLywceNGHD58GNu3b4dcLkflypXx9u1bAFC0y842c0O2L1lq0KABAKBOnTpK5YIgQCQSISUlJXciIyIiIiIiIiK1N27cOAwfPlypTCKR5Nr2K1WqhEqVKinuV65cGUWLFsXatWsxffr0XHue7Mp2Qub8+fN5EQcRERERERERfYtCuoS0RCLJcgLGwsICGhoaCA4OVioPDg5Od46Yr2lpaaF06dJ4+vQpACjaBQcHw8bGRmmbvr6+WdpmTmQ7IVOjRo28iIOIiIiIiIiIKEPa2tooW7Yszp49ixYtWgAA5HI5zp49i0GDBmVpGykpKfjvv//QqFEjAICLiwusra1x9uxZRQImKioKV69eRf/+/fOiGwBykJABAKlUig0bNuDhw4cAgGLFiqFHjx4wNjbO1eCIiIiIiIiIiL40fPhwdO3aFeXKlUOFChWwZMkSxMbGKlZd6tKlC+zs7BTz0EybNg0VK1aEu7s7pFIp5s+fj1evXqFXr14AUldgGjp0KGbMmAEPDw/Fste2traKpE9eyHZC5saNG/D394euri4qVKgAIHXJqZkzZ+L06dMoU6ZMrgdJRERERERERAQA7du3R0hICCZNmoSgoCD4+vri5MmTikl5X79+DfEXl29FRESgd+/eCAoKgqmpKcqWLYvLly/Dx8dHUWf06NGIjY1Fnz59IJVKUbVqVZw8eRI6Ojp51g+RIAhCdhpUq1YN7u7uWLduHTQ1U/M5ycnJ6NWrF54/f46LFy/mKJD4c9ty1I7y1jWr1gUdAqUjxrd0QYdA6bCvY5V5Jcp3KcsOFnQIlA49jfiCDoHSYZwUWtAhkArhmvw/872ySnhZ0CFQOiyKV8q8khqI2zKtoEPIEb2ukwo6hAKRoxEyXyZjAEBTUxOjR49GuXLlcjU4IiIiIiIiIiJ1lO0pmI2MjPD69es05W/evIGhoWGuBEVEREREREREpM6yPUKmffv26NmzJxYsWIDKlSsDAP7++2+MGjUKHTt2zPUAiYiIiIiIiChzokK67PWPKtsJmQULFkAkEqFLly5ITk4GkLqGd//+/TFnzpxcD5CIiIiIiIiISN1kOyGjra2NpUuXYvbs2Xj27BkAwM3NDXp6erke3Pdi94Ub2BJwBWFRMfC0t8KY9v4o4Wynsu7ZW4+w4eTfeB0SjuQUORyLmKFLXT808SupqLP66J84deMBgiKioKWhAR9HawxqXgslXFRvk9InCAL+2L0afwUcRHxcNNy8S+GnPuNhZeuUbpvH9//F6cNb8erZA0RGhKL/mEUo7VdLqc6m5ZNw5fwRpbJivpUxZNLKPOmHOjGrWg6uI3rCuExx6NgWwY3WAxD8x9mM21SvAJ8FY2Hg44GENx/wdPZqvN2qPAGrU/9OcB3eExJrS0TdfYT7Q6cj8vp/edkVtWTetCWKtOkATVMzxD9/hnerliL+8cN061u0aAvzJs2hbWmF5KhIRP51AR82/QYhKREAYNW5O6w7d1dqk/DmFQJ7/5yX3VBLgiBg3471OHf6D8TGRsOraEn0GDAKNrYOGbY7fWw/jhzYgciIcDi6uKNb3+Fw90xdMSAk+AMG91I9OfuQMTNQsWrtXO+HuhEEATu3b0bAyeOIjY2Bt09x9B84BLZ29hm2O3bkEA7t/x0REeFwdnFDn/6/wNPLW/F4YmIiNq5bjUsXzyMpKQmly5RHv4GDYWJqltddUgsHj53C7kNHEB4hhbuzEwb36Y6inu4q6754/Qabdv6OwGcvEPwxBAN7dkHbZo2V6ty5/wC7Dx7B46cvEBYRgenjRqJaxfL50RW1JAgCdm/fhIBTRxEXGwPvosXRZ+DwTI+bE0cP4tD+3ZBGhMPZxR29+g2Gh1dRxeOrly/E3dv/IiI8FDo6uvAqWhw/d+8De4f0P/fRZ/tPnMHOwycQLo2Eu7MjhvXsDB8PV5V1n79+h/W7DyDw+UsEhYRhcPeOaN/EX6nOhj0HsfH3w0pljrbW2LWcP9ITZVWOxzPp6enB1NQUpqamap2MOXXjPhbuD0DfxtWwa3wveNpbYcCyXQiPilVZ30hfB70aVsHWUd2xd0JvNK9UCpO3HsHlB88UdZyKmGNse3/sm9AHm0Z2ha25Cfov24nwaNXbpPSdOrgZ547tQud+4zFuzlZIJLpYOn0gkhJl6baRyeJh7+yJTr3HZbjtYqUrY/6GAMWt1/DZuR2+WtLQ10PU3UDcGzw1S/V1ne1R/o+1CLtwFZfKNceL5VtQYu0MWNSrqqhj07Yhis4fhyczVuJShZaIvvsIfsc2QNuSX1yyw6R6bdj2Hoig7ZvxeFAvJDx/CteZC6BpbKK6fs26sOnRB8HbN+NRn5/xZvFcmNSoDZvuvZXqxb98jvsdWyhuT0cMyofeqJ8j+7fj5NG96DlgFKYvWA+Jjg7mTBqGxAzOZ1f+OoNt65ehdccemLVkE5xc3DFn0jBESsMBAOYWRbB66xGlW5tOvaCjqwffshXzq2uF2oF9u3Hsj4PoP2go5i9eAR0dHUyZOBaJiYnptvnrz/PYuG4N2nfqgkXL18DF1Q1TJo6BVBqhqLPht1W4fu0fjB43GTPnLkZ4eChmz5iSDz0q/M79dRmrNm5Ft/atsW7RHLi5OGHUlFmIkEaqrC+TyWBjZYU+P3eEmamJyjoJCTK4OTthaN8eeRj5j+Pgvl04dmQ/+g0cjjmLVkOio4vpE0dleD67dPEcNq1bhXadumHBsnVwdnHDtImjlI4bN3dPDBo2BsvWbMHE6fMhCAKmTRyFlJSU/OhWoXbm76tYvnk3erRrgY3zp8LdyQHDpy9ARGSUyvqyRBlsrSzRv3NbmJsYp7tdFwc7/LF+ieK2euavedUFIrWU7YSMXC7HtGnTYGxsDCcnJzg5OcHExATTp0+HXC7PixgL1LazV9GqSmm0qOwLNxtLTOjYCDraWjh05bbK+uU9nVHb1xuuNhZwsDTDT7UrwMPOCreevlHUaVShOCoWdYW9pSncbS0xok09xCTI8OTdx3zqlXoQBAFnju5E4za94VuhFuydPdF98HRIw0Nw69r5dNuVKFMVLToNROmKGf8yrKmlDWNTC8VN38Aot7uglkJOXcTjyUsQfPhMluo79emA+Bdv8XD0XMQ8eo5Xq3YgaP8puAzppqjjMrQ73mz4HW+3HEDMw2f4b8BkpMQlwKEbl2XPDotW7RB+8igiAk5A9voV3i5fCEGWADP/xirr6/sUR+z9e5BeOIOk4CDE3LyOiAtnoffFr5UAgJQUJEeEK24pUaq/FFH6BEHAiT9+R8t23VCuYnU4ubhjwLBJiAgPxY1/Lqbb7tih3ajt3ww16zaBvaMLeg4YDW2JBBcCjgIAxBoaMDE1V7pd/+dPVKxaGzq66vtjSm4RBAFHDh1A2w6d4VepCpxd3DB0xBiEh4XinyuX0m13+OA+1G/QCHXrN4CjozP6DxoKiUSCM6dPAgBiY2Nw5vQJ9OjdDyV9S8PdwxODh43Go4f3EfjoQX51r9Dae/gYGtevg4Z1a8HZ0R7D+/eCjkQbx8+o/t/v7eGO/t07o071KtDS0lJZx69safTq3AHVKlXIy9B/CIIg4OjhfWjT/mdUqFQVzi5uGDxiHMLDQ3Etg+PmyMG9qNegMerUawgHR2f0HTQcEh0dnDt9XFGnfsOmKFa8FIpY2cDN3ROduvREaMhHhHwMyo+uFWp7jpxC07o10Lh2Nbg42GFU366QSLRx9Kzq/zFF3V0xqGsH1K1aEVpa6V9UoaEhhrmpieJmYsRFXgqcSFw4bz+obPf8119/xYoVKzBnzhzcunULt27dwqxZs7B8+XJMnDgxL2IsMEnJKXj4+gP8vF0UZWKxCH7ezrj7/F2m7QVBwNVHL/AyOAxlPBzTfY79l27CQFcCT3urXIv9RxAa/A5R0lAULeWnKNPTN4SLR3E8D7z7zdt/fO8GRnSrjYmDWmDH2pmIiZZ+8zYpLZOKvgg9d0WpLCTgEkwr+gIARFpaMC5TDKFnL3+uIAgIPXcZJhVL52OkhZtIUxN6Hp6IvnXjc6EgIPrWv9ArWkxlm9gH96Dn4Qldz9QEjLa1DYzKV0TUtX+U6mnb2cNnxwF4b9oNx9EToWVZJM/6oa4+Br+HNCIMxX3LKcr09A3g5umDJ4/uqWyTnJSEF08DUbzU5zZisRjFfcvjSaDqNs+fPsKr509Qq17T3O2AmgoO+oCIiHCU8i2jKNPXN4CnV1EEPlSdOElKSsKzp4+V2ojFYpTyLaNItjx78gTJycko5VtWUcfewRGWlkXwKJ3tUqqkpGQEPnuOsqVKKMrEYjHKliqBB4FPCjAy+iQ46AOkEeFK7299fQN4ePmkm3BMPW4CUfKLNmKxGCV9y6bbJiEhHucCTsDKygbmFvy/k5HU4+Ylypf0UZSJxWKUK1kM9x4/y6Bl5t5+CEazXkPRtv8oTFmyBkEhYd8aLtEPJdtzyGzZsgXr169Hs2bNFGUlS5aEnZ0dBgwYgJkzZ+ZqgAUpIiYOKXIB5kb6SuXmRgZ4GZz+ySY6PgH1xy1FUlIKxGIRxndsiEpFla/PvPjfE4zZcAAJiUmwMDLEmsE/wdSAv1ZmR5Q0FABgaKx82YqRiTmiIr7tn0Gx0pVR2q82LKzsEBL0Fod2LMey6YMwdvYWiDU0vmnbpExiZQFZcKhSmSw4FFrGhhDrSKBlagyxpiZkH8O+qhMGfS/V1z1TWhpGxhBpaCL5i6HfAJAsDYfEQXXCWHrhDDSNjeG+cAVEIhFEmpoIPXoIH/dsV9SJe/QAbxbOhuzta2iZmcPqp+5wX7ACgf26Qh4fn6d9UieREamXGBmbKJ/PjE3MIP3/Y1+LipJCLk+BsWnaNu/fvlLZ5vzpI7BzcIZn0RIqHydlERGpx4uJqalSuYmJqeKxr0VFRUIul6ts8/bNm/9vNxyamlowMDBQrmNqmu7+plSRUVGQy+Uw++oSClMTY7x++76AoqIvfXoPf31uSj1uVL+/oz8dNyZp27x781qp7MTRQ9i2aQ0SEhJgZ++AyTMXpDvyiVJJo6ORouK4MTM2wut3H3K8XR8PN/w6qBccbW0QFiHFxr2HMWDCLGxbMgP6urrfGjbRDyHbCZnw8HB4e3unKff29kZ4eNY+RMhkMshkyteQyhOTINFWj5OpvkSCPeN7I06WiGuBL7FgXwDsLExQ3tNZUae8pxP2jO8NaUwcDvx9C6PX78f20T1g9lXyhz67+udxbF87Q3F/0K/L8uy5KlRtoPjb3skD9k4e+HVAUwTev4GiJf0yaEmkPvRL+qJI+854t3IR4h49hLatHez6DUZSpy74uHMrACD6xlVF/YQXzxH76CF8tv4Ok+q1EX7qWEGF/t27dOEU1q+cp7g/etKCPH/ORJkMly8GoGX7bnn+XIXVhfNnsHr5YsX9iVNnFWA0RIXDn+cDsHbFQsX9X6fk7YSu1WvVRanS5RAREYbD+/dgweypmLVgObS1JXn6vJRWpTKfFy1xd3aAj6crWvcbiXN/X0PTujUKMDKiwiPbCZlSpUphxYoVWLZM+cvwihUrUKpUqSxtY/bs2Zg6VXnCz/FdWmBC11bZDSdPmRroQUMsQthXE/iGRcXAwsggnVaplzU5FknN8Hs7WOPFh1BsPHlZKSGjK9GGYxEzOBYxQ0lXezSdtBIHL99GzwZV8qQv6qBUhRpw8SyuuJ+clAQAiI4Mh4mZpaI8ShoGBxevXH1uS2t7GBiZ4OOHN0zI5DJZcCgkVhZKZRIrCyRFRkOeIENiaATkycmQFDH/qo45ZEHKI2sofSlRkRBSkqFpovyrvaaJGZLT+cXSuktPRJw7jfCTqYmVhJfPIdbRgcPgUfi4axsgCGnayGNjIHv3Btq2XDUuI2UrVIW75+dLxZL+v2pVpDQcpmafj4dIaTicXT1UbsPIyARisYZidM2XbVSt1HP173OQyRJQvXbD3OiCWqrgVxleX8yRlPT//zPSiAiYmX0+B0mlEXBxdVO5DSMjY4jFYki/GkEjlUbA1Cx1v5iamiE5OQkxMTFKo2SkERFcZSkTxkZGEIvFCP9qAt8IaWS6E/ZS3qrgVwWeKo6byIhwFceN6pWwDD8dN1Ll85lUmvaY0Nc3gL6+AWzt7OHp5YMu7Zvi6uVLqFazTm51Se2YGBpCQ8VxEx4ZlWbUzLcw1NeHg4013gZxXswCJRYVdASUDdmeQ2bevHnYuHEjfHx80LNnT/Ts2RM+Pj7YvHkz5s+fn6VtjBs3DpGRkUq3UR2/v+vZtTQ1UNTRBtcCXyjK5HIB1wJfoqRr1r9syAUBicnJGdYRslDnR6ejq48iNo6Km42DK4xMLPDw7udf6OPjYvDiyT24epXMYEvZFxEajNjoSBibWmRembJF+s9tmNdWXu3Fok5lRPxzGwAgJCUh8uZ9WNSu9LmCSATzWpUg/edWPkZauAnJyYh78hiGX1yfD5EIBr5lEPfwvso2YokOIP8q6fJp8naR6n/2Yh1daNvYITmc15BnRFdPH9a29oqbvaMLTEzNce/O5zl+4uJi8ezxA3h4F1e5DU0tLbi4e+He3X8VZXK5HPfv3ICHV9o25wOOomyFqjAyNk3zGKXS09ODja2d4ubg6ARTUzPcvXNTUScuLhaPAx/Cq6iPym1oaWnBzd0Td+98Pj/J5XLcvX0LXt6pbdw8PKCpqYm7tz9v9+3bNwgJ+QjvdLZLqbS0NOHl5oqbd/9TlMnlcvx79x58vFQnLylv6erpwcbWXnFzcHSGiYrj5kngA8Ux8LXU48ZL6ZhIPW7+TbdNKgECBEVSm1RLPW6cceO/z/PxpB43D1DcU3VyOSfi4hPwLvgjLJgcJcqybI+QqVGjBh4/foyVK1fi0aNHAIBWrVphwIABsLW1zdI2JBIJJBLlYYXx3+nlSj/X8cPELX/Ax9EGxZ3tsOPcVcTLktC8UupooAmbD6OIiSEGt0hdsWfDyb/h42QDBwtTJCan4NL9pzh29T+M75j6i2S8LBHrTlxCzZKesDA2gDQmHnv+vIGP0mjUK8MPYdkhEolQt0knHN+3HkVsHGFhZYfDu1bBxMwSpSvUUtRbNLkvfP1qoXajDgCAhPg4hAR9XvUq9OM7vHkRCD0DI5hb2iAhPg5Hf1+LMhXrwMjUAiFBb7B/61JYWjugWOnK+d7PwkZDXw/67p/nJNFzsYdRKW8khkci4c0HeM0YDh07K9zpPgYA8Oq33XAa8BO8Z4/Cm837YVGrImzaNsT1Zn0V23ixZBNKbZwL6b/3EHn9LpwHd4Wmvi7ebDmQ7/0rzEIP/A6HkeMQ9yQQcYEPYdmyLcQ6ugj//woWDiPHIyksFEGbfgMARF29DMuW7RD/7LHikiXrLj0RdfWyIjFj02sAoq7+jcSPwdAys4D1z92BFDkiLmRtlS1KJRKJ0LBZOxzaswXWtg4oYmWLvdt/g6mZBcpVrK6oN+PXX1C+Ug34N2kDAGjcogNWL54BV3dvuHv64MThPZAlJKBG3SZK2w96/xaP7t/G6MkLQVknEonQtEUr/L57B2xs7WFlZY2d2zbBzNwCFStVVdSbOG4kKlauisZNWwAAmrdsg6WL5sLdwxMent44cng/EmQJqFvPH0DqL/x16zfExnWrYWBoCD09ffy2Zjm8ivpk8uWTAKBt88aYvXQVvNzdUNTDDfuOHEdCggwN69YEAMxavAIW5mbo06UTgNQJTV++eQsASE5KRmhYBJ48fwldXR3Y21gD+P8XyQ+fV+oJCv6IJ89fwsjQAFaW/DEmO0QiEZo0b4N9u7elHjfWNti1bQPMzCxQ4YvjZvL44fCrVBWNmqaOkG/asi2WL5oNdw8veHgWxZHD+yBLSEDteqmfoYM+vMfff52Hb+lyMDI2QVhoCA7s3QltbQnKlK+oMhb6rH1Tf8xcvg7ebi7w8XDF70dPI0EmQ+Pa1QAA05f9BgszU/Tv3BZA6nHz4m3qIiZJySkICYvA4xevoKejA3ub1IVIVmzZjSrlfGFtaY7QcCnW7zkEDbEYdatyNDlRVmU7IQMAtra2aSbvTUhIwIIFCzBy5MhcCex74V+uGCJi4rD66J8IjYqFl70VVv3SEeb/v2TpQ3gkRF/8UhwvS8SsXSfwURoNiZYmnK0tMLN7c/iXSx2aLhaL8TI4DCN+2w9pbBxM9HVRzMkWG0d0hbutpcoYKH3+LbtBJovH9jUzEBcbDfeivhgycSW0vriOOCToDWKipIr7r549wMJJvRX3925K/YJSqVZTdP9lGsRiMd6+eoIr548gLi4aJqaW8PGthOYdB0BLSzvf+lZYGZctjkpntynu+ywYDwB4s/UA7vYcB4mNJXQdbBSPx798i+vN+sJn4Tg4/9IFCW+D8F/fCQgN+Lw05oe9J6BtaQbPyYMhsbZE1J2HuNakFxI/chRGdkgvnoOGsQmsf+4BTVMzxD9/ihcTRiom+tUuYqV0GVLwzq2AIMC6ay9omVsiOVKKqKuX8WHzOkUdLQtLOI2dDA1DIyRHShF7/z88GdYPKZFc+jq7mrbuDFlCAtavmIu42Bh4+ZTE2KmLlOZFCA56h+gvzmeVqtVFVKQU+3asgzQiHE6uHhg7dVGaIf4XzhyFmXkRlCzNJX2zq1WbDkhISMCq5YsQGxODosVKYPK02dDW/vz/IOjDe0R98Z6vVqMWoqIisXPbZkREpF7eNHnaHKX90rPPAIhEIsydORVJSUkoXbYc+g0Ykq99K6xqV6sMaVQUNu38HeERUri7OGPe5HEwMzEBAASHhkEk/jwIPDQ8HL2HjVHc33PoCPYcOoJSxX2wdOZkAEDg02cYNmGaos7KjanzZPnXroFxQwbkQ6/US8s2HSFLSMCa5QsQGxuDoj4lMHH6PKXzWdCHd4iK+nzcVK1eG1GRUuzavgnSiHC4uLpj4rR5iuNGW1sbD+/fxdHD+xAbEw1jE1P4FC+F2QtWwMSEI/8yU7eKH6SR0Vi/+yDCpZHwcHHEwgkjFJcsBYeGKX2nCY2IQPeRkxX3d/1xErv+OInSxbywYto4AMDHsHBMXrwGUdExMDEyRMmiHlg7eyJMjY3yt3OkRPQDLyFdGIkEQcUkAOkICQnB1atXoa2tjTp16kBDQwNJSUlYtWoVZs+ejeTkZISG5mxOh/hz2zKvRPnumlXrgg6B0hHjyyWfv1f2dbiE/fcoZdnBgg6B0qGnwRW5vlfGSZyr63sUrsn/M98rq4SXBR0CpcOieKXMK6mBhF1zCzqEHNHpOCbzSmooyyNkLl26hCZNmiAqKgoikQjlypXDpk2b0KJFC2hqamLKlCno2rVrXsZKRERERERERKQWsjyeacKECWjUqBHu3r2L4cOH4/r162jZsiVmzZqFBw8eoF+/ftDlevNERERERERERJnKckLmv//+w4QJE1C8eHFMmzYNIpEI8+bNQ5s2bfIyPiIiIiIiIiLKCrGocN5+UFlOyERERMDCInWWeV1dXejp6aF4cdVLcRIRERERERERUfqytcrSgwcPEBSUuiSgIAgIDAxEbGysUp2SJUvmXnRERERERERERGooWwmZOnXq4MtFmZo0aQIAEIlEEAQBIpEIKSkpuRshEREREREREWWOy14XKllOyLx48SIv4yAiIiIiIiIi+mFkOSHj5OSUl3EQEREREREREf0wcjSe6a+//kLnzp1RqVIlvHv3DgCwbds2XLp0KVeDIyIiIiIiIiJSR9lOyOzfvx/+/v7Q1dXFrVu3IJPJAACRkZGYNWtWrgdIRERERERERFkgEhXO2w8q2wmZGTNmYM2aNVi3bh20tLQU5VWqVMHNmzdzNTgiIiIiIiIiInWU7YRMYGAgqlevnqbc2NgYUqk0N2IiIiIiIiIiIlJr2U7IWFtb4+nTp2nKL126BFdX11wJioiIiIiIiIhInWV5laVPevfujSFDhmDjxo0QiUR4//49rly5gpEjR2LixIl5ESMRERERERERZUaco3V7qIBkOyEzduxYyOVy1KlTB3FxcahevTokEglGjhyJX375JS9iJCIiIiIiIiJSK9lOyIhEIvz6668YNWoUnj59ipiYGPj4+MDAwCAv4iMiIiIiIiIiUjvZTshs374drVq1gp6eHnx8fPIiJiIiIiIiIiLKLhEvWSpMsr23hg0bhiJFiqBTp044fvw4UlJS8iIuIiIiIiIiIiK1le2EzIcPH7B7926IRCK0a9cONjY2GDhwIC5fvpwX8RERERERERERqZ1sJ2Q0NTXxP/buO77m64/j+PsmIpEYSewQsgixN9XqMNuqUq3Sas1qjaKo0lbtGlUURX9qFaVae9SmaFFqb7H3yr2ZEpHc3x+pyyUhN82Q6/V8PL6P9n7vOd98jpN7v/d+ckbDhg01Z84cXbt2TWPGjNGZM2f04osvyt/fPy1iBAAAAAAAsCs2ryFzP1dXV9WvX19Go1Fnz57VkSNHUisuAAAAAABgCwdDRkcAG6RoxZ+oqCjNmTNHr7zyigoVKqSxY8eqSZMmOnToUGrHBwAAAAAAYHdsTsg0b95c+fLl0yeffCI/Pz9t2rRJwcHBGjx4sO7cuZMWMQIAAAAAANgVm6csOTo6av78+apfv74cHR0VHh6u//3vf5o6dap27drFrksAAAAAAGQEtr3OVGxOyMyZM0eStHnzZk2dOlULFiyQl5eX3njjDU2YMCHVAwQAAAAAALA3NiVkrly5ohkzZmjq1KkKCwtTs2bNFBMTo8WLFysoKCitYgQAAAAAALAryR7P9NprrykwMFD79u3T2LFjdenSJY0fPz4tYwMAAAAAALBLyR4h8/vvv6tr167q2LGjihUrlpYxAQAAAAAAWxnY9jozSfYIma1btyo8PFyVKlVStWrVNGHCBN24cSMtYwMAAAAAALBLyU7IVK9eXVOmTNHly5f14Ycfat68efLy8lJ8fLzWrl2r8PDwtIwTAAAAAADAbti8J5abm5vatm2rrVu36sCBA+rZs6eGDx+ufPnyqVGjRmkRIwAAAAAAgF35T5uUBwYGauTIkbpw4YLmzp2bWjEBAAAAAABbOThkzuMplSotd3R0VOPGjbV06dLUuBwAAAAAAIBde3pTUQAAAAAAABkk2dteAwAAAACAJxjbXmcqjJABAAAAAABIZyRkAAAAAAAA0hkJGQAAAAAAgHTGGjIAAAAAANgDA2MuMhN6CwAAAAAAIJ2RkAEAAAAAAEhnTFkCAAAAAMAeODDmIjOhtwAAAAAAANIZCRkAAAAAAIB0RkIGAAAAAAAgnbGGDAAAAAAA9sBgyOgIYIMnJiFz1PvljA4Bichujs7oEJAE99r5MzoEJOHC+qsZHQIS4WUwZ3QIQKYT6+ic0SEgEU6G2IwOAUm45Oyf0SEgCXkyOgAgEUxZAgAAAAAASGckZAAAAAAAANLZEzNlCQAAAAAA/AcGxlxkJvQWAAAAAABAOiMhAwAAAAAAkM6YsgQAAAAAgD1g2+tMhREyAAAAAAAA6YyEDAAAAAAAQDojIQMAAAAAAJDOWEMGAAAAAAB74MCYi8yE3gIAAAAAAEhnJGQAAAAAAADSGQkZAAAAAACAdMYaMgAAAAAA2AGzwZDRIcAGNo+QmTlzplasWGF53Lt3b7m7u+uZZ57R2bNnUzU4AAAAAAAAe2RzQubrr79WtmzZJEnbtm3T999/r5EjRypPnjz65JNPUj1AAAAAAAAAe2PzlKXz588rICBAkrR48WI1bdpUHTp0UM2aNfXCCy+kdnwAAAAAACA5DCwTm5nY3FvZs2fXzZs3JUlr1qxR3bp1JUkuLi66detW6kYHAAAAAABgh2weIVO3bl21b99eFSpU0PHjx/XKK69Ikg4dOiQfH5/Ujg8AAAAAAMDu2DxC5vvvv1eNGjV0/fp1LViwQLlz55Yk/fPPP2rRokWqBwgAAAAAAGBvbB4h4+7urgkTJjx0fuDAgakSEAAAAAAASAHWkMlUbE7ISFJ0dLT279+va9euKT4+3nLeYDDotddeS7XgAAAAAAAA7JHNCZlVq1bpvffesyzsez+DwaC4uLhUCQwAAAAAAMBe2Tye6eOPP1azZs10+fJlxcfHWx0kYwAAAAAAyBhmgyFTHk8rmxMyV69eVY8ePZQ/f/60iAcAAAAAAMDu2ZyQefPNN7Vp06Y0CAUAAAAAAODpYPMaMhMmTNBbb72lLVu2qEyZMnJycrJ6vmvXrqkWHAAAAAAAgD2yOSEzd+5crVmzRi4uLtq0aZMM9833MhgMJGQAAAAAAMgIbHudqdickPniiy80cOBA9enTRw4OdDYAAAAAAICtbM6o3L59W2+//TbJGAAAAAAAgBSyOavSqlUr/fLLL2kRCwAAAAAAwFPB5ilLcXFxGjlypFavXq2yZcs+tKjv6NGjUy04AAAAAACQTPet8Yonn80JmQMHDqhChQqSpIMHD1o9Z6DzAQAAAAAAHsumhExcXJwGDhyoMmXKyMPDI61iAgAAAAAAsGs2JWQcHR1Vr149HTlyhIQMAAAAAABPEjbfyVRs7q3SpUvr1KlTaRELAAAAAADAU8HmhMyQIUPUq1cvLV++XJcvX1ZYWJjVAQAAAAAAgEezeVHfV155RZLUqFEjq0V8zWazDAaD4uLiUi86AAAAAAAAO2RzQmbjxo1pEQcAAAAAAPgPzOx8nKnYnJB5/vnn0yIOAAAAAACAp0ayEjL79+9P9gXLli2b4mAAAAAAAACeBslKyJQvX14Gg8GyTsyjsIYMAAAAAAAZwMC215lJsnrr9OnTOnXqlE6fPq0FCxbI19dXEydO1J49e7Rnzx5NnDhR/v7+WrBgQVrHCwAAAAAAkOkla4RM0aJFLf//1ltvady4cZbdlqSEaUre3t7q16+fGjdunOpBAgAAAAAA2BObxzMdOHBAvr6+D5339fXV4cOHUyUoAAAAAAAAe2bzLkslS5bUsGHD9OOPPypr1qySpNu3b2vYsGEqWbJkqgf4JDCbzfp1zo/asHqZIiPDFViyrNp16qWChbwfWW/18gVatvBnhRpDVMQ3QG0+/EQBgUFWZY4fOahfZv2g4GOH5eDgoKJ+xfT5oDHK6uyclk2yG2azWb/NmaKNa5YqMjJcxUuWVdtOvVXQ69F9s2bFb1q+cI6lb1p92EMBxUtZnjcZb+rnaRN0YO/fir4VpYKFiqhxs9aqWvPFtG6SXcj9WhPle7O5snh46tapk7o48TvdOn4kyfJ5Gr+l3A1fV9a8+XUnLFShWzbp8vT/yRx7W5KUv2UbFWjZxqpO9PmzOvbBe2nZDLvi+Wxl+fVsp1wVS8vFK592Ne2kq0vXP7pOraoKGtVH2YOKKfr8ZQUPm6QLPy2yKlO04zvy69FOzgXyKmz/UR3qPlihOw+kZVPsVlrca65dvayu7d5MtF73PoNV/dmXUr0d9sZsNuvn2TO0dtVKRUZGqERQaXXs3E1ehQo/st6KZYu1eMF8GY0h8vH1V4eOH6t4YAnL86t/X67NmzboZPAJ3boVpTnzlyh79uxp3Ry7smT5Sv26cJFCjCb5+/qo84cfqERg8STL/7H1T82c/bOuXL2mQl4F1b71+6pWpbLl+boNGyda74M2rdSsaZPUDt9urVi2RAsX/CqjMUS+vv76sGNnq9/9B23d8odmz5qpa1evyMurkFq3ba/KVapZnv/rzy36feVynQw+ofDwcH03fpL8/APSoyl2x2w265c5U7V+9bKE97OSZfRBp56Pvc+sWr5QSxfOlckYoqK+/mr7YXcVu+87Tf8+H+vwwb1Wdeo2eF0duvRKi2bgMcysIZOp2NxbkydP1urVq1W4cGHVqVNHderUUeHChbV69WpNnjw5LWLMcEsXzNGqZb+pfedPNeTbKXJ2cdGwr3ro9u2YJOv8tXmdZv04Xm+2aKth301TUd8ADfuqh0JNRkuZ40cOalj/HipboaqGjJ6ioWN+VP2GTWVwYO/45Fq2YLZWL/9VbTv11uBRU+Xikk3Dv+r+yL7ZtmWdZv84Tm+0aKehY2eoiG8xDf/qE4WaQixlJo0epEsXz6pnv5EaPmG2qjzzgr4b+aXOnDyWHs3K1NxrvSSvDzrryuwZOt6lvaJPBctv6ChlyeWeePkX6qhg2w66OnuGjnZ4T+fHjJD78y+pYJsPrMrdOnNKh1o0thzBPbukQ2vsh6Obq8L2H9PBrgOTVT6bT2FVWfqDbm7aoa2VX9fp8TNV5ochylP3WUuZgm+9rJLf9NWJId9ra9UmCt9/VNVWTFXWvJ5p1Qy7lhb3mjx58mnyrKVWx1vvtpNLtmwqX6l6ejUtU1v42zytWLpIHbt01zdjJsjFxUUD+vXR7du3k6yz5Y+NmjZlst5+532NHj9Zvn7+GtDvM5nu+wwQExOjCpWq6M2330mPZtidTZu36ocfp6lli+aa9N1o+fn6qO9XA2U0mRItf+jIUX098ls1qFtHk8aNVs3q1TRg6HCdPnPWUuaXWdOtjp7dPpbBYNBzNWukU6syvy1/bNKPU35Qi3daauz4SfL189NX/fpa/e7f78jhQ/pmxNeqV6+Bvhs/SdVr1NTQwQN09sxpS5no6GgFlSqtVm3ap1cz7NaSBT/r92UL1KFzLw379gc5u2TTkK96PvI+8+fm9Zr54wS91aK1Rnz3o4r6BmjoVz2tvtNIUu36r+l/sxZbjpZtO6Z1cwC7YHNCpmrVqjp16pSGDBmismXLqmzZsho6dKhOnTqlqlWrpkWMGcpsNuv3JfPV5O1Wqlz9ORX1DVDnHv1kDLmhXdu2JFlvxeJf9FL91/RC3VdVuIiv2nf+VFmdnbVp7XJLmZ9+/E4NXntTr7/1nryL+smrcFHVeK62nJyypkfTMj2z2axVS39R42atVbl6LRXxDVDHT76SKeSGdm3fnGS9lYvn6sX6jfRCnYYqXMRX7Tr1lrOzs/64r2+OHz2g+g3fUkDxUspfoJCavN1Gbm7ZdTqYhMzj5HmjmUJWLZdx7e+KOXdWF8Z/K3NMtDzrv5poebeg0oo8dFCmTesUe/WKInbvlHHTerkGPjDiLi5Od4whliMuLDQdWmM/rq/erOP9x+rqknXJKl+0Q3PdOn1BR3qPUMTRUzo7cY6uLFgt326tLWV8u7fR+anzdWHmQkUcOakDnforLipa3q2bplEr7Fda3WscHB3l7pHb6ti5bbOqP1tbLtlc06t5mZbZbNayxQv1VvOWqlajpnx8/dW952cKuXlD27dtTbLekkW/qV6DV1SnXgMVKeKjjl26y9nZWevWrLKUadS4qd5s1kKBJexzdHFaW7B4iV6uX08N6tZW0SLe6ta5o5ydnbV6beIj/xYtXaYqlSqqWdMmKurtrdbvvasAfz8tWb7SUsbTw8Pq2LZjh8qVKa2CBQqkV7MyvcWLFqh+g5f//d0vqk5dusnZ2Vlr16xOtPzSJYtUsVIVvfFmM3kXKaqW77eWv3+Ali9bYinzUu26avHOeypfoWJ6NcMumc1mrVgyX03ffl9V/r3PdOnxhYwhN7XzEfeZ5Yt/Ue36r+nFuq/Ku4ivOnTupazOLtqwdoVVOWdnF3l45LYcrq5uad0kQN9//718fHzk4uKiatWq6e+//06y7JQpU/Tcc8/Jw8NDHh4eqlOnzkPlW7duLYPBYHU0aNAgTduQovFMbm5u6tChg0aPHq3Ro0frgw8+kJubfb7orl29JJPxpsqUvzek1dUtuwICg3T86MFE69yJjdXp4GMqU76K5ZyDg4PKlK9sqRNqMir42GHlcvdQv14f6sOWDTWwT2cdPbQvbRtkR+72Ten7/p1d3bLLv3iQTjymb0qXs+6b0uWr6MSxe3WKlyij7VvWKSI8VPHx8fpr81rF3r6tkmUqpF2D7IAhSxa5Fiuu8D277p00mxW+5x+5liyVaJ3IwwflWqy4shVP+FKStUBB5axSXWF/b7cql7VQYQXNWagS0+epSO9+csqbL83aAcm9ennd2LDN6tz1tVvlUb28JMng5KRcFUvpxvq/7hUwm3Vjw19yr87rxFZpda950Kngozpz6oRerNcwdRtgp65euSyjMUTlyt/7Iujmll3FA0vq2JHE182LjY3VyeDjVnUcHBxUrnxFHTvKWnupITY2VseDT6pi+bKWcw4ODqpYvpwOH038DyeHjx6zKi9JlStW0JEkyhuNJu3Y+Y9erlcn9QK3c7GxsQpO5He//CN+948ePfxQoqVCpco6ejTpac5ImWtXL8tkDLG6z7i5ZVdAYEkdO3oo0TqxsbE6FXxcZctXspxzcHBQ2fKVdfyBOls2rVHbdxqqR6f3NWfGZMVER6dNQ4B//fLLL+rRo4f69++v3bt3q1y5cqpfv76uXbuWaPlNmzapRYsW2rhxo7Zt2yZvb2/Vq1dPFy9etCrXoEEDXb582XLMnTs3Tdth8xoykjRr1iz98MMPOnXqlLZt26aiRYtqzJgx8vPz0+uvv57aMWYokzFhGksud+sh+LncPWUy3Uy0TliYSfHxcYnWuXjhnCTp2pWEjv/t52lq2baLivoV0+YNv2vIF930zfezHjuXE1KoMeHfP7F/57vPPSj8bt94PFzn0oV7w5a7fjZE40b2U4d3GsjR0VFZnV30yefDVeAxa9M87Rxz5pLBMYvuPDCM9Y4pRM7eRRKtY9q0Tlly5VLAtxMSMtFZsujG8sW69stsS5moo4d1/tthirlwTk6euZX/3TYKGDVBxz5qpfhbt9K0TU8r5/x5FHP1htW5mKs35JQrhxxcnOXkkUsOWbIo5trNB8rclFugX3qGahfS6l7zoI1rlquQt48CS5ZJhajtn9GY8F7m7uFhdd7d3cPy3IPCwhIS+YnVuXD+fNoE+pQJDQtXfHy8PNzdrc57uOfS+QsXEq1jNJrknkj5kCSm0qxZv0Gu2bLp2WeYrpRcd3/3PWz43TcZjQ/1i7u7h+U9EanH9O9nY3f3B/vHUyZT4v/e4WGhSdxnPHTxvs/Nz75QV3nz5pdH7jw6d/qkZs+YrEsXz+vTL4amciuQLIanY/mLuwND2rRJWGdy8uTJWrFihaZNm6Y+ffo8VH7OnDlWj3/88UctWLBA69ev1/vvv2857+zsrALpODLS5hEykyZNUo8ePfTyyy/LaDQqLi5OkuTh4aGxY8cm6xoxMTEKCwuzOh41dzE9bd24Wq3erGM54u7cSZOfE282S5JqN3hdL9R9Vb7+xdXqg27yKlzEaloT7tm6abXavPWS5UirvpGkX+f8T1GR4fp8yDgNGTNdrzRuoXEjv9S5M8Fp9jOfVm5lyyvf2y118fvROt6lvU4P+kI5q9ZQvnfuvTGG79qh0C2bFH36lML/2alT/XrLMXt2uddiQVJkTul1r7nf7ZgY/fnHWr1Yl9ExSdm0cZ3efuNVyxEXl/b9gifT6nXr9dILtSwbWACZzZaNa9TyzXqW404a3mfqNmik8pWqqaiPv557sZ669PhCf2/brCuXLz6+MvCvxHIEMTGJ5whu376tf/75R3Xq3BvF6ODgoDp16mjbtm2J1nlQVFSUYmNj5elpnXDctGmT8uXLp8DAQHXs2FE3byb+h7HUYvMImfHjx2vKlClq3Lixhg8fbjlfuXJl9eqVvJW0hw0bpoEDrReW7NDlU33Utbet4aS6StWeVUDgvakVsf/u8hJqCpGHZx7L+VBTiIr6Fkv0GjlzusvBwdFqkdi7ddz/HZnh4ZFbklS4iPUW4l7eRXXj+tX/3hA7VKnqswoofm9F9zuxsZKS6Bu/xHdZyHG3b4yJ9U1Cn1y9fEFrlv+mkRPmqHDRhL/0F/UtpqOH9mrtigVq1/mzVG2XPYkLC5U57o6yPPDXlyzunrqTxF+7CrzfTsYNaxSyKmEucvSZU3JwcZF31091be4s6d/k5f3iIyMUc/G8snoVSv1GQFLCaBjn/Hmszjnnz6PY0HDFR8fo9g2j4u/ckXO+3A+Uya2YK9Yja/Cw9LrX3G/7nxsVExOtWrXTdi50Zla12jMKvG/9qth/7zMmo1Genvd+100mo3z9/BO9Rs6cueTg4CDTAyNoTCajPDxZ8Do15MqZQw4ODg8t4Gs0hT40OuMuDw93mRIp7+n+cPkDBw/p/IWL+qI3O8TY4u7v/oOjxxJ+9xPvF3cPj4f6xWQyJvoeBttUrvas1e6udz83J/THvfuMyRQinyTuMzly5kriPmO0fG5OzN0dmK5cuqACBfmshuRJLEfQv39/DRgw4KGyN27cUFxcnPLnz291Pn/+/Dp69Giyft5nn30mLy8vq6ROgwYN9MYbb8jX11cnT57U559/rpdfflnbtm2To6Oj7Y1KBptHyJw+fVoVKjy8PoCzs7MiIyOTdY2+ffsqNDTU6mj7UTdbQ0kT2VzdVMCrsOUoXMRX7h65dXDvP5YyUVGRCj52WMVLlE70GlmcnOQbEKiD++6toxEfH6+D+/6x1Mmbv6A8PPNYTZORpCsXzytPPhaPS0xC33hbjkL/9s2h+/6do6IidfL4YRV7TN8c2m/dN4f27VKxwIQ6MTEJc14NDtYvDwcHR8vIJiTOfOeOok4cV4775hrLYFD28hUVdSTx+ckOzi5S/AP/rvHxlrqJ1nHJpqwFC+lOSNpmrJ9mpu17lfsl61148tR+RsbteyVJ5thYhe4+pDwv3Tec32BQ7hdryLR9TzpGmjml173mfhvXLFelqs8qZ67EvxhBcnV1VUGvQpbDu0hReXh4av++3ZYyUVGROn7siAJLBiV6DScnJ/kHFNf+ffdeB/Hx8dq/d48CSyReB7ZxcnJS8QB/7dm333IuPj5ee/btV1CJwETrBJUI1J69+63O7d6zVyUTKf/72nUqFuAvfz/fh55D0pycnBSQyO/+vkf87pcoEaR9e63vGXv37FYJFrv+z7K5uqqgV2HLUbiIj9w9PBO5zxxRYInE1/lzcnKSX0BxHdh3r058fLwO7PtHxZOoI0lnTp2QJHl4Jp20QdoxGxwy5ZFYjqBv375p8m80fPhwzZs3T4sWLZKLi4vlfPPmzdWoUSOVKVNGjRs31vLly7Vz505t2rQpTeKQUpCQ8fX11d69ex86v2rVKpUsmbw3T2dnZ+XMmdPqyJrV2dZQ0oXBYNDLrzfTol9mateOLTp35qQmjh4sD888qlzjOUu5wZ931aplv1kev9r4bW1YvUx/rF+pi+fPaOrEUYqJjtbzdV61XPe1pu9o1bLftH3rRl25dEG/zPqfLl44y2KLyWQwGNSg0dta9MsM/bNji86dCdak0YPk7plHlavXspQb+kUXrV7+q+XxK41baOPqpdq8foUunj+jaRNHKjo6Ws/XSfh39yrso/wFC2vq9yMUfPyQrl6+oBWLftbBvX9bXReJu7FwvjxfbiiPOg3k7F1UhT/uKQeXbApZk7CThXevz1WgTQdL+bAdfyn3q6/L/fmXlDV/QWWvUFkF3m+nsB1/WRIzBdt3kluZcnLKX0CuJUvL56shUly8jJuSt2MQEra9zlmuhHKWKyFJcvUtrJzlSsjFu6AkKXBID5WbPsJS/uz/5snV11slhn0qt0A/Ff3oHRV862Wd/m6GpczpsdPl3a6ZCr3XWNlL+Kn09wOUxS2bzs9cmK5tswdpda+568qlCzp6aK9eqv9aurXJHhgMBr3W+A3NnzdHO7b/pTOnT2nsqOHyzJ1H1Wvc2wK+X99eWrFsseXx603e1JpVK7Rh3WqdP3dWk78fq+iYaNWpW99SxhgSolMng3X5UsKQ/rNnTunUyWCFh4elW/sys6aNX9fK1Wu1Zv0GnT1/XuMmTlZ0dLTq16ktSRrx7VhNnTHLUr5Jo9e0c/ce/bpwsc6dv6Cf5szV8eCTer3hK1bXjYyK0patf+nlenXTtT32onGTplq9aqXWr1uj8+fOauL346x+90ePGqGZ06dayjd6vYl2/7NTixb+qvPnz+nn2T8p+MRxNXzt3pqU4eFhOnUyWOfPJfwR8+KFCzp1MljGENaZsYXBYNCrrzfTgl9maueOrTp75qQmjB4iD8/cqnLffWbg5930+7IFlscNG7+t9auXa9P633Xh/BlNmfitYqJv6cU6Ca+dK5cv6re5M3Qy+JiuXb2snTu2asLooSpZupyK+gakezuReSWWI3B2TjxHkCdPHjk6OurqVeuZJVevXn3s+i+jRo3S8OHDtWbNGpUtW/aRZf38/JQnTx4FB6fdshU2T1nq0aOHOnfurOjoaJnNZv3999+aO3euhg0bph9//DEtYsxwjZq+q5joW5oyfqSiIiMUGFRWfQZ9a5VEunrlosLv24b3mVp1FBZq0q+zf5TJGKKifsXUZ9C3VkMwX3n9bcXevq2ffhynyPAwFfEN0BeDx6pAwcLp2r7M7LWmLRUTfUs/ThiuqMgIFQ8qqz4Dxzyyb2o8V0dhoUb9NudHmYw3E/pm4BjLQr9ZsmRR7wGjNW/GRI0a/Klibt1S/oKF9VH3fqpQ+Zl0b2NmY9q8QY653FXgvbbK4uGpW6eCdfrLXpaFfrPmy281Denqzz9JZrMKtGovp9x5dSfUpLAdf+nyjCmWMk558qpon/5yzJFTd0JNijx0QCc++UhxoWx9nVy5KpVWjfX3vpwEjfpcknT+p4Xa366vnAvmVbZ/kzOSdOvMBe1s9KGCvu0rn4/fV/SFKzrw4Ze6sfbeVr+Xf/1dWfN6qnj/rnIukFdh+47o74btdfsaI5dSIq3uNZK0ce1yeebJp7IVqqZbe+zFG282V3R0tCaOH63IiAiVLFVG/QcNs1pb5MrlSwq77/3ouedfVFhYqH6eNUNGY8L0pv6Dhlv1y6qVyzTv558sjz/v/Ykkqesnn6p2XaaVPc4LtZ6VKTRUM2fPldFolL+fr74e1F8eHu6SpGvXr8vgcG+UZamSJdT30x6aMWuOpv80W4W8vDT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", + "image/png": 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", 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" ] }, "metadata": {}, @@ -301,11 +150,7 @@ } ], "source": [ - "# Visualize the correlation matrix\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "\n", - "plt.figure(figsize=(15, 12))\n", + "plt.figure(figsize=(10, 8))\n", "sns.heatmap(df.corr(), annot=True, fmt=\".2f\", cmap='coolwarm', square=True)\n", "plt.title('Correlation Matrix of California Housing Features')\n", "plt.show()" @@ -313,56 +158,70 @@ }, { "cell_type": "markdown", - "id": "a48cb8a0", - "metadata": {}, - "source": [ - "- **Split features and target**: We will separate the features (independent variables) from the target variable (dependent variable)." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "91afe480", - "metadata": {}, - "outputs": [], - "source": [ - "X = df.drop(columns=[data.target.name])\n", - "y = df[data.target.name]" - ] - }, - { - "cell_type": "markdown", - "id": "1dbd4290", + "id": "c31103ee", "metadata": {}, "source": [ - "- **Train-test split**: We will split the dataset into training and testing sets to evaluate the model's performance on unseen data." + "### Data Splitting\n", + "\n", + "To evaluate our model fairly, we cannot test it on the same data it learned from. We must split the data into a **Training Set** and a **Test Set**." ] }, { "cell_type": "code", - "execution_count": 7, - "id": "8eeb0674", - "metadata": {}, - "outputs": [], + "execution_count": 4, + "id": "9a147acb", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training samples: 16512\n", + "Testing samples: 4128\n" + ] + } + ], "source": [ "from sklearn.model_selection import train_test_split\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)" + "\n", + "# Separate Features (X) and Target (y)\n", + "X = df.drop(columns=[housing_data.target.name])\n", + "y = df[housing_data.target.name]\n", + "\n", + "# Split: 80% for training, 20% for testing\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", + "\n", + "print(f\"Training samples: {X_train.shape[0]}\")\n", + "print(f\"Testing samples: {X_test.shape[0]}\")" ] }, { "cell_type": "markdown", - "id": "8ed604b0", + "id": "cbc9a4b5", "metadata": {}, "source": [ - "- **Model training**: We will use a simple linear regression model to fit the training data." + "### Model Training\n", + "\n", + "We will start with a **Linear Regression** model. It assumes a straight-line relationship between the input features and the house price." ] }, { "cell_type": "code", - "execution_count": 8, - "id": "f2b97f13", - "metadata": {}, + "execution_count": 5, + "id": "358a8b18", + "metadata": { + "lines_to_next_cell": 2 + }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model trained successfully.\n" + ] + }, { "data": { "text/html": [ @@ -381,20 +240,21 @@ " --sklearn-color-fitted-level-1: #d4ebff;\n", " --sklearn-color-fitted-level-2: #b3dbfd;\n", " --sklearn-color-fitted-level-3: cornflowerblue;\n", + "}\n", "\n", + "#sk-container-id-1.light {\n", " /* Specific color for light theme */\n", - " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n", - " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n", - " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n", + " --sklearn-color-text-on-default-background: black;\n", + " --sklearn-color-background: white;\n", + " --sklearn-color-border-box: black;\n", " --sklearn-color-icon: #696969;\n", + "}\n", "\n", - " @media (prefers-color-scheme: dark) {\n", - " /* Redefinition of color scheme for dark theme */\n", - " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n", - " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n", - " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n", - " --sklearn-color-icon: #878787;\n", - " }\n", + "#sk-container-id-1.dark {\n", + " --sklearn-color-text-on-default-background: white;\n", + " --sklearn-color-background: #111;\n", + " --sklearn-color-border-box: white;\n", + " --sklearn-color-icon: #878787;\n", "}\n", "\n", "#sk-container-id-1 {\n", @@ -520,8 +380,8 @@ " padding: 0.5em;\n", " box-sizing: border-box;\n", " text-align: center;\n", - " align-items: start;\n", - " justify-content: space-between;\n", + " align-items: center;\n", + " justify-content: center;\n", " gap: 0.5em;\n", "}\n", "\n", @@ -628,7 +488,6 @@ "#sk-container-id-1 div.sk-label label {\n", " font-family: monospace;\n", " font-weight: bold;\n", - " display: inline-block;\n", " line-height: 1.2em;\n", "}\n", "\n", @@ -674,7 +533,7 @@ " font-size: smaller;\n", " line-height: 1em;\n", " font-family: monospace;\n", - " background-color: var(--sklearn-color-background);\n", + " background-color: var(--sklearn-color-unfitted-level-0);\n", " border-radius: 1em;\n", " height: 1em;\n", " width: 1em;\n", @@ -682,16 +541,17 @@ " margin-left: 0.5em;\n", " text-align: center;\n", " /* unfitted */\n", - " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n", - " color: var(--sklearn-color-unfitted-level-1);\n", + " border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n", + " color: var(--sklearn-color-unfitted-level-3);\n", "}\n", "\n", ".sk-estimator-doc-link.fitted,\n", "a:link.sk-estimator-doc-link.fitted,\n", "a:visited.sk-estimator-doc-link.fitted {\n", " /* fitted */\n", - " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n", - " color: var(--sklearn-color-fitted-level-1);\n", + " background-color: var(--sklearn-color-fitted-level-0);\n", + " border: var(--sklearn-color-fitted-level-3) 1pt solid;\n", + " color: var(--sklearn-color-fitted-level-3);\n", "}\n", "\n", "/* On hover */\n", @@ -701,7 +561,8 @@ ".sk-estimator-doc-link:hover {\n", " /* unfitted */\n", " background-color: var(--sklearn-color-unfitted-level-3);\n", - " color: var(--sklearn-color-background);\n", + " border: var(--sklearn-color-fitted-level-0) 1pt solid;\n", + " color: var(--sklearn-color-unfitted-level-0);\n", " text-decoration: none;\n", "}\n", "\n", @@ -711,7 +572,8 @@ ".sk-estimator-doc-link.fitted:hover {\n", " /* fitted */\n", " background-color: var(--sklearn-color-fitted-level-3);\n", - " color: var(--sklearn-color-background);\n", + " border: var(--sklearn-color-fitted-level-0) 1pt solid;\n", + " color: var(--sklearn-color-fitted-level-0);\n", " text-decoration: none;\n", "}\n", "\n", @@ -751,7 +613,7 @@ " font-size: 1rem;\n", " line-height: 1em;\n", " font-family: monospace;\n", - " background-color: var(--sklearn-color-background);\n", + " background-color: var(--sklearn-color-unfitted-level-0);\n", " border-radius: 1rem;\n", " height: 1rem;\n", " width: 1rem;\n", @@ -763,6 +625,7 @@ "\n", "#sk-container-id-1 a.estimator_doc_link.fitted {\n", " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-0);\n", " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n", " color: var(--sklearn-color-fitted-level-1);\n", "}\n", @@ -780,12 +643,19 @@ " background-color: var(--sklearn-color-fitted-level-3);\n", "}\n", "\n", + ".estimator-table {\n", + " font-family: monospace;\n", + "}\n", + "\n", ".estimator-table summary {\n", " padding: .5rem;\n", - " font-family: monospace;\n", " cursor: pointer;\n", "}\n", "\n", + ".estimator-table summary::marker {\n", + " font-size: 0.7rem;\n", + "}\n", + "\n", ".estimator-table details[open] {\n", " padding-left: 0.1rem;\n", " padding-right: 0.1rem;\n", @@ -795,6 +665,7 @@ ".estimator-table .parameters-table {\n", " margin-left: auto !important;\n", " margin-right: auto !important;\n", + " margin-top: 0;\n", "}\n", "\n", ".estimator-table .parameters-table tr:nth-child(odd) {\n", @@ -813,19 +684,29 @@ " border: 1px solid rgba(106, 105, 104, 0.232);\n", "}\n", "\n", + "/*\n", + " `table td`is set in notebook with right text-align.\n", + " We need to overwrite it.\n", + "*/\n", + ".estimator-table table td.param {\n", + " text-align: left;\n", + " position: relative;\n", + " padding: 0;\n", + "}\n", + "\n", ".user-set td {\n", " color:rgb(255, 94, 0);\n", - " text-align: left;\n", + " text-align: left !important;\n", "}\n", "\n", - ".user-set td.value pre {\n", - " color:rgb(255, 94, 0) !important;\n", - " background-color: transparent !important;\n", + ".user-set td.value {\n", + " color:rgb(255, 94, 0);\n", + " background-color: transparent;\n", "}\n", "\n", ".default td {\n", " color: black;\n", - " text-align: left;\n", + " text-align: left !important;\n", "}\n", "\n", ".user-set td i,\n", @@ -833,6 +714,57 @@ " color: black;\n", "}\n", "\n", + "/*\n", + " Styles for parameter documentation links\n", + " We need styling for visited so jupyter doesn't overwrite it\n", + "*/\n", + "a.param-doc-link,\n", + "a.param-doc-link:link,\n", + "a.param-doc-link:visited {\n", + " text-decoration: underline dashed;\n", + " text-underline-offset: .3em;\n", + " color: inherit;\n", + " display: block;\n", + " padding: .5em;\n", + "}\n", + "\n", + "/* \"hack\" to make the entire area of the cell containing the link clickable */\n", + "a.param-doc-link::before {\n", + " position: absolute;\n", + " content: \"\";\n", + " inset: 0;\n", + "}\n", + "\n", + ".param-doc-description {\n", + " display: none;\n", + " position: absolute;\n", + " z-index: 9999;\n", + " left: 0;\n", + " padding: .5ex;\n", + " margin-left: 1.5em;\n", + " color: var(--sklearn-color-text);\n", + " box-shadow: .3em .3em .4em #999;\n", + " width: max-content;\n", + " text-align: left;\n", + " max-height: 10em;\n", + " overflow-y: auto;\n", + "\n", + " /* unfitted */\n", + " background: var(--sklearn-color-unfitted-level-0);\n", + " border: thin solid var(--sklearn-color-unfitted-level-3);\n", + "}\n", + "\n", + "/* Fitted state for parameter tooltips */\n", + ".fitted .param-doc-description {\n", + " /* fitted */\n", + " background: var(--sklearn-color-fitted-level-0);\n", + " border: thin solid var(--sklearn-color-fitted-level-3);\n", + "}\n", + "\n", + ".param-doc-link:hover .param-doc-description {\n", + " display: block;\n", + "}\n", + "\n", ".copy-paste-icon {\n", " background-image: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCA0NDggNTEyIj48IS0tIUZvbnQgQXdlc29tZSBGcmVlIDYuNy4yIGJ5IEBmb250YXdlc29tZSAtIGh0dHBzOi8vZm9udGF3ZXNvbWUuY29tIExpY2Vuc2UgLSBodHRwczovL2ZvbnRhd2Vzb21lLmNvbS9saWNlbnNlL2ZyZWUgQ29weXJpZ2h0IDIwMjUgRm9udGljb25zLCBJbmMuLS0+PHBhdGggZD0iTTIwOCAwTDMzMi4xIDBjMTIuNyAwIDI0LjkgNS4xIDMzLjkgMTQuMWw2Ny45IDY3LjljOSA5IDE0LjEgMjEuMiAxNC4xIDMzLjlMNDQ4IDMzNmMwIDI2LjUtMjEuNSA0OC00OCA0OGwtMTkyIDBjLTI2LjUgMC00OC0yMS41LTQ4LTQ4bDAtMjg4YzAtMjYuNSAyMS41LTQ4IDQ4LTQ4ek00OCAxMjhsODAgMCAwIDY0LTY0IDAgMCAyNTYgMTkyIDAgMC0zMiA2NCAwIDAgNDhjMCAyNi41LTIxLjUgNDgtNDggNDhMNDggNTEyYy0yNi41IDAtNDgtMjEuNS00OC00OEwwIDE3NmMwLTI2LjUgMjEuNS00OCA0OC00OHoiLz48L3N2Zz4=);\n", " background-repeat: no-repeat;\n", @@ -843,7 +775,7 @@ " height: 14px;\n", " cursor: pointer;\n", "}\n", - "
LinearRegression()
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