From 763c51f6f06cc10bb3c55d0df03a56968f6c3fa7 Mon Sep 17 00:00:00 2001 From: hha980510 Date: Wed, 20 Nov 2024 15:10:06 -0600 Subject: [PATCH 01/26] Add files via upload --- Plane_price.ipynb | 2221 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 2221 insertions(+) create mode 100644 Plane_price.ipynb diff --git a/Plane_price.ipynb b/Plane_price.ipynb new file mode 100644 index 0000000..e97e1e5 --- /dev/null +++ b/Plane_price.ipynb @@ -0,0 +1,2221 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d1dea79a", + "metadata": {}, + "source": [ + "# Plane_price prediction " + ] + }, + { + "cell_type": "markdown", + "id": "5ad0a1d0", + "metadata": {}, + "source": [ + " ### We decided to go with Model Selection method for our project." + ] + }, + { + "cell_type": "markdown", + "id": "dbf9b684", + "metadata": {}, + "source": [ + "## Data Collection" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "9de818ab", + "metadata": {}, + "outputs": [], + "source": [ + "# import required libraries :- \n", + "\n", + "import pandas as pd \n", + "import numpy as np \n", + "import matplotlib.pyplot as plt " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4564e5a8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Model NameEngine TypeHP or lbs thr ea engineMax speed KnotsRcmnd cruise KnotsStall Knots dirtyFuel gal/lbsAll eng rate of climbEng out rate of climbTakeoff over 50ftLanding over 50ftEmpty weight lbsLength ft/inWing span ft/inRange N.M.Price
0100 Darter (S.L. Industries)Piston14510491.046.036450900.01300.02,0501,18025/337/53701300000.0
17 CCM ChampPiston858983.044.015600720.0800.01,35082020/736/11901230000.0
2100 Darter (S.L. Industries)Piston909078.037.019650475.0850.01,30081021/535/02101600000.0
37 AC ChampPiston858878.037.019620500.0850.01,30080021/535/02101300000.0
4100 Darter (S.L. Industries)Piston658374.033.014370632.0885.01,22074021/535/01751250000.0
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" + ], + "text/plain": [ + " Model Name Engine Type HP or lbs thr ea engine \\\n", + "0 100 Darter (S.L. Industries) Piston 145 \n", + "1 7 CCM Champ Piston 85 \n", + "2 100 Darter (S.L. Industries) Piston 90 \n", + "3 7 AC Champ Piston 85 \n", + "4 100 Darter (S.L. Industries) Piston 65 \n", + "\n", + " Max speed Knots Rcmnd cruise Knots Stall Knots dirty Fuel gal/lbs \\\n", + "0 104 91.0 46.0 36 \n", + "1 89 83.0 44.0 15 \n", + "2 90 78.0 37.0 19 \n", + "3 88 78.0 37.0 19 \n", + "4 83 74.0 33.0 14 \n", + "\n", + " All eng rate of climb Eng out rate of climb Takeoff over 50ft \\\n", + "0 450 900.0 1300.0 \n", + "1 600 720.0 800.0 \n", + "2 650 475.0 850.0 \n", + "3 620 500.0 850.0 \n", + "4 370 632.0 885.0 \n", + "\n", + " Landing over 50ft Empty weight lbs Length ft/in Wing span ft/in Range N.M. \\\n", + "0 2,050 1,180 25/3 37/5 370 \n", + "1 1,350 820 20/7 36/1 190 \n", + "2 1,300 810 21/5 35/0 210 \n", + "3 1,300 800 21/5 35/0 210 \n", + "4 1,220 740 21/5 35/0 175 \n", + "\n", + " Price \n", + "0 1300000.0 \n", + "1 1230000.0 \n", + "2 1600000.0 \n", + "3 1300000.0 \n", + "4 1250000.0 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# load the plane price dataset\n", + "df = pd.read_csv(\"Plane Price.csv\")\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e6b26263", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(517, 16)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Check total columns & rows present in the dataset\n", + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "172e78a5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 517 entries, 0 to 516\n", + "Data columns (total 16 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Model Name 517 non-null object \n", + " 1 Engine Type 517 non-null object \n", + " 2 HP or lbs thr ea engine 517 non-null object \n", + " 3 Max speed Knots 497 non-null object \n", + " 4 Rcmnd cruise Knots 507 non-null float64\n", + " 5 Stall Knots dirty 502 non-null float64\n", + " 6 Fuel gal/lbs 517 non-null int64 \n", + " 7 All eng rate of climb 513 non-null object \n", + " 8 Eng out rate of climb 491 non-null float64\n", + " 9 Takeoff over 50ft 492 non-null float64\n", + " 10 Landing over 50ft 517 non-null object \n", + " 11 Empty weight lbs 516 non-null object \n", + " 12 Length ft/in 517 non-null object \n", + " 13 Wing span ft/in 517 non-null object \n", + " 14 Range N.M. 499 non-null object \n", + " 15 Price 507 non-null float64\n", + "dtypes: float64(5), int64(1), object(10)\n", + "memory usage: 64.8+ KB\n" + ] + } + ], + "source": [ + "df.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "62c5cdc5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Rcmnd cruise KnotsStall Knots dirtyFuel gal/lbsEng out rate of climbTakeoff over 50ftPrice
count507.000000502.000000517.000000491.000000492.0000005.070000e+02
mean200.79289960.7958171419.3791102065.1262731743.3069112.362673e+06
std104.28053216.6570024278.3207731150.031899730.0096741.018731e+06
min70.00000027.00000012.000000457.000000500.0000006.500000e+05
25%130.00000050.00000050.0000001350.0000001265.0000001.600000e+06
50%169.00000056.00000089.0000001706.0000001525.0000002.000000e+06
75%232.00000073.000000335.0000002357.0000002145.7500002.950000e+06
max511.000000115.00000041000.0000006400.0000004850.0000005.100000e+06
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" + ], + "text/plain": [ + " Rcmnd cruise Knots Stall Knots dirty Fuel gal/lbs \\\n", + "count 507.000000 502.000000 517.000000 \n", + "mean 200.792899 60.795817 1419.379110 \n", + "std 104.280532 16.657002 4278.320773 \n", + "min 70.000000 27.000000 12.000000 \n", + "25% 130.000000 50.000000 50.000000 \n", + "50% 169.000000 56.000000 89.000000 \n", + "75% 232.000000 73.000000 335.000000 \n", + "max 511.000000 115.000000 41000.000000 \n", + "\n", + " Eng out rate of climb Takeoff over 50ft Price \n", + "count 491.000000 492.000000 5.070000e+02 \n", + "mean 2065.126273 1743.306911 2.362673e+06 \n", + "std 1150.031899 730.009674 1.018731e+06 \n", + "min 457.000000 500.000000 6.500000e+05 \n", + "25% 1350.000000 1265.000000 1.600000e+06 \n", + "50% 1706.000000 1525.000000 2.000000e+06 \n", + "75% 2357.000000 2145.750000 2.950000e+06 \n", + "max 6400.000000 4850.000000 5.100000e+06 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# View the statistical summary of the dataset\n", + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f55da0ad", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Model Name 0\n", + "Engine Type 0\n", + "HP or lbs thr ea engine 0\n", + "Max speed Knots 20\n", + "Rcmnd cruise Knots 10\n", + "Stall Knots dirty 15\n", + "Fuel gal/lbs 0\n", + "All eng rate of climb 4\n", + "Eng out rate of climb 26\n", + "Takeoff over 50ft 25\n", + "Landing over 50ft 0\n", + "Empty weight lbs 1\n", + "Length ft/in 0\n", + "Wing span ft/in 0\n", + "Range N.M. 18\n", + "Price 10\n", + "dtype: int64" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Check the null values is present in the dataset or not\n", + "df.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "42b0e466", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model Name 0\n", + "Engine Type 0\n", + "HP or lbs thr ea engine 0\n", + "Max speed Knots 20\n", + "Rcmnd cruise Knots 0\n", + "Stall Knots dirty 0\n", + "Fuel gal/lbs 0\n", + "All eng rate of climb 4\n", + "Eng out rate of climb 0\n", + "Takeoff over 50ft 0\n", + "Landing over 50ft 0\n", + "Empty weight lbs 1\n", + "Length ft/in 0\n", + "Wing span ft/in 0\n", + "Range N.M. 18\n", + "Price 0\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "# Fill missing values with median for numerical columns\n", + "df.fillna(df.median(numeric_only=True), inplace=True)\n", + "\n", + "# Verify no missing values remain\n", + "print(df.isnull().sum())" + ] + }, + { + "cell_type": "markdown", + "id": "88edda31", + "metadata": {}, + "source": [ + "\n", + "### This step fills all missing values in numerical columns using their respective median values. The median is chosen because it is less sensitive to outliers compared to the mean, ensuring that imputed values do not distort the data distribution. After applying the `fillna()` method, a verification step confirms that no missing values remain in the dataset. This ensures the data is complete and ready for further analysis or modeling." + ] + }, + { + "cell_type": "markdown", + "id": "f782b48d", + "metadata": {}, + "source": [ + "## Data Preprocessing" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "b3e4bcc5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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HP or lbs thr ea engineMax speed KnotsRcmnd cruise KnotsStall Knots dirtyFuel gal/lbsAll eng rate of climbEng out rate of climbTakeoff over 50ftLanding over 50ftEmpty weight lbsLength ft/inWing span ft/inRange N.M.PriceEngine Type_pistonEngine Type_propjet
014510491.046.036450900.01300.02,0501,18025/337/53701300000.010
1858983.044.015600720.0800.01,35082020/736/11901230000.010
2909078.037.019650475.0850.01,30081021/535/02101600000.010
3858878.037.019620500.0850.01,30080021/535/02101300000.010
4658374.033.014370632.0885.01,22074021/535/01751250000.010
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" + ], + "text/plain": [ + " HP or lbs thr ea engine Max speed Knots Rcmnd cruise Knots \\\n", + "0 145 104 91.0 \n", + "1 85 89 83.0 \n", + "2 90 90 78.0 \n", + "3 85 88 78.0 \n", + "4 65 83 74.0 \n", + "\n", + " Stall Knots dirty Fuel gal/lbs All eng rate of climb \\\n", + "0 46.0 36 450 \n", + "1 44.0 15 600 \n", + "2 37.0 19 650 \n", + "3 37.0 19 620 \n", + "4 33.0 14 370 \n", + "\n", + " Eng out rate of climb Takeoff over 50ft Landing over 50ft \\\n", + "0 900.0 1300.0 2,050 \n", + "1 720.0 800.0 1,350 \n", + "2 475.0 850.0 1,300 \n", + "3 500.0 850.0 1,300 \n", + "4 632.0 885.0 1,220 \n", + "\n", + " Empty weight lbs Length ft/in Wing span ft/in Range N.M. Price \\\n", + "0 1,180 25/3 37/5 370 1300000.0 \n", + "1 820 20/7 36/1 190 1230000.0 \n", + "2 810 21/5 35/0 210 1600000.0 \n", + "3 800 21/5 35/0 210 1300000.0 \n", + "4 740 21/5 35/0 175 1250000.0 \n", + "\n", + " Engine Type_piston Engine Type_propjet \n", + "0 1 0 \n", + "1 1 0 \n", + "2 1 0 \n", + "3 1 0 \n", + "4 1 0 " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Drop 'Model Name' if it's not relevant\n", + "df.drop(columns=['Model Name'], inplace=True)\n", + "\n", + "# Standardize the case in the 'Engine Type' column\n", + "df['Engine Type'] = df['Engine Type'].str.lower() # Convert to lowercase\n", + "\n", + "# Re-run one-hot encoding\n", + "df = pd.get_dummies(df, columns=['Engine Type'], drop_first=True)\n", + "\n", + "# Verify the unique values and column names\n", + "df.head()\n" + ] + }, + { + "cell_type": "markdown", + "id": "cdaea563", + "metadata": {}, + "source": [ + "### The column Model Name is removed as it is not relevant for the analysis and modeling process, ensuring the dataset contains only useful features. The values in the Engine Type column are converted to lowercase to maintain uniformity and avoid potential mismatches during further processing. The Engine Type column is encoded into binary columns (Engine Type_piston, Engine Type_propjet) using one-hot encoding. This transformation converts categorical data into numerical format suitable for modeling. The dataset is displayed after transformations to ensure changes have been successfully applied. The binary columns for Engine Type are now included in the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "3cdf67ef", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1 0]\n" + ] + } + ], + "source": [ + "# Check unique values in the 'Engine Type' column\n", + "print(df['Engine Type_piston'].unique())" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "09c422ac", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0 1]\n" + ] + } + ], + "source": [ + "# Check unique values in the 'Engine Type' column\n", + "print(df['Engine Type_propjet'].unique())" + ] + }, + { + "cell_type": "markdown", + "id": "ba0c9e9b", + "metadata": {}, + "source": [ + "### Displays unique values [0, 1] for the binary column Engine Type_piston & Engine Type_propjet, confirming successful one-hot encoding." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "7d7a6bd6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Max speed Knots All eng rate of climb Landing over 50ft \\\n", + "0 104.0 450.0 2050.0 \n", + "1 89.0 600.0 1350.0 \n", + "2 90.0 650.0 1300.0 \n", + "3 88.0 620.0 1300.0 \n", + "4 83.0 370.0 1220.0 \n", + "\n", + " Empty weight lbs Length ft/in Wing span ft/in Range N.M. \n", + "0 1180.0 25.0 37.0 370.0 \n", + "1 820.0 20.0 36.0 190.0 \n", + "2 810.0 21.0 35.0 210.0 \n", + "3 800.0 21.0 35.0 210.0 \n", + "4 740.0 21.0 35.0 175.0 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Convert columns to numeric by removing commas and handling special characters\n", + "columns_to_convert = [\n", + " \"Max speed Knots\", \n", + " \"All eng rate of climb\", \n", + " \"Landing over 50ft\", \n", + " \"Empty weight lbs\", \n", + " \"Length ft/in\", \n", + " \"Wing span ft/in\", \n", + " \"Range N.M.\"\n", + "]\n", + "\n", + "for col in columns_to_convert:\n", + " # Remove commas and convert to numeric\n", + " df[col] = df[col].str.replace(',', '').str.extract('(\\d+)', expand=False).astype(float)\n", + "\n", + "# Verify the conversions\n", + "df[columns_to_convert].head()\n" + ] + }, + { + "cell_type": "markdown", + "id": "23ef734f", + "metadata": {}, + "source": [ + "### Specific columns with string-based numbers (e.g., commas or special characters) are converted to numeric format for compatibility with numerical analysis and modeling." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "cc467036", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "HP or lbs thr ea engine 0\n", + "Max speed Knots 0\n", + "Rcmnd cruise Knots 0\n", + "Stall Knots dirty 0\n", + "Fuel gal/lbs 0\n", + "All eng rate of climb 0\n", + "Eng out rate of climb 0\n", + "Takeoff over 50ft 0\n", + "Landing over 50ft 0\n", + "Empty weight lbs 0\n", + "Length ft/in 0\n", + "Wing span ft/in 0\n", + "Range N.M. 0\n", + "Price 0\n", + "Engine Type_piston 0\n", + "Engine Type_propjet 0\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "# Fill null values for specific columns\n", + "columns_to_fill_with_median = [\"Max speed Knots\", \"All eng rate of climb\", \"Landing over 50ft\",\n", + " \"Empty weight lbs\", \"Length ft/in\", \"Wing span ft/in\", \"Range N.M.\"]\n", + "\n", + "for col in columns_to_fill_with_median:\n", + " df[col].fillna(df[col].median(), inplace=True)\n", + "\n", + "# Verify that there are no missing values\n", + "print(df.isnull().sum())\n" + ] + }, + { + "cell_type": "markdown", + "id": "53841fa2", + "metadata": {}, + "source": [ + "### Columns with missing values are identified and filled with their respective median values, a robust imputation technique that reduces the impact of outliers. After imputation, the dataset is verified to ensure no null values remain, indicating the dataset is clean and ready for further steps." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "70aa4828", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Max speed KnotsRcmnd cruise KnotsStall Knots dirtyFuel gal/lbsAll eng rate of climbEng out rate of climbTakeoff over 50ftLanding over 50ftEmpty weight lbsLength ft/inWing span ft/inRange N.M.PriceEngine Type_pistonEngine Type_propjet
count517.000000517.000000517.000000517.000000517.000000517.000000517.000000517.000000517.000000517.000000517.000000517.0000005.170000e+02517.000000517.000000
mean212.794971200.17795060.6566731419.3791101658.9806582047.0657641732.7504847485.4893624377.40522237.88588038.932302911.4487432.355658e+060.7446810.112186
std114.106830103.35808916.4328744278.3207731258.6841841123.433947713.64696710289.4424745649.739125137.6330818.599692696.4296431.010050e+060.4364630.315900
min64.00000070.00000027.00000012.000000360.000000457.000000500.000000567.0000002.00000017.00000016.000000117.0000006.500000e+050.0000000.000000
25%143.000000131.00000050.00000050.000000924.0000001365.0000001265.0000002650.0000001575.00000025.00000035.000000517.0000001.600000e+060.0000000.000000
50%177.000000169.00000056.00000089.0000001200.0000001706.0000001525.0000003625.0000002286.50000028.00000036.000000713.0000002.000000e+061.0000000.000000
75%238.000000229.00000073.000000335.0000001820.0000002280.0000002110.0000008800.0000005164.00000035.00000042.0000001100.0000002.940000e+061.0000000.000000
max755.000000511.000000115.00000041000.0000007220.0000006400.0000004850.00000089400.00000046800.0000003150.00000093.0000006500.0000005.100000e+061.0000001.000000
\n", + "
" + ], + "text/plain": [ + " Max speed Knots Rcmnd cruise Knots Stall Knots dirty Fuel gal/lbs \\\n", + "count 517.000000 517.000000 517.000000 517.000000 \n", + "mean 212.794971 200.177950 60.656673 1419.379110 \n", + "std 114.106830 103.358089 16.432874 4278.320773 \n", + "min 64.000000 70.000000 27.000000 12.000000 \n", + "25% 143.000000 131.000000 50.000000 50.000000 \n", + "50% 177.000000 169.000000 56.000000 89.000000 \n", + "75% 238.000000 229.000000 73.000000 335.000000 \n", + "max 755.000000 511.000000 115.000000 41000.000000 \n", + "\n", + " All eng rate of climb Eng out rate of climb Takeoff over 50ft \\\n", + "count 517.000000 517.000000 517.000000 \n", + "mean 1658.980658 2047.065764 1732.750484 \n", + "std 1258.684184 1123.433947 713.646967 \n", + "min 360.000000 457.000000 500.000000 \n", + "25% 924.000000 1365.000000 1265.000000 \n", + "50% 1200.000000 1706.000000 1525.000000 \n", + "75% 1820.000000 2280.000000 2110.000000 \n", + "max 7220.000000 6400.000000 4850.000000 \n", + "\n", + " Landing over 50ft Empty weight lbs Length ft/in Wing span ft/in \\\n", + "count 517.000000 517.000000 517.000000 517.000000 \n", + "mean 7485.489362 4377.405222 37.885880 38.932302 \n", + "std 10289.442474 5649.739125 137.633081 8.599692 \n", + "min 567.000000 2.000000 17.000000 16.000000 \n", + "25% 2650.000000 1575.000000 25.000000 35.000000 \n", + "50% 3625.000000 2286.500000 28.000000 36.000000 \n", + "75% 8800.000000 5164.000000 35.000000 42.000000 \n", + "max 89400.000000 46800.000000 3150.000000 93.000000 \n", + "\n", + " Range N.M. Price Engine Type_piston Engine Type_propjet \n", + "count 517.000000 5.170000e+02 517.000000 517.000000 \n", + "mean 911.448743 2.355658e+06 0.744681 0.112186 \n", + "std 696.429643 1.010050e+06 0.436463 0.315900 \n", + "min 117.000000 6.500000e+05 0.000000 0.000000 \n", + "25% 517.000000 1.600000e+06 0.000000 0.000000 \n", + "50% 713.000000 2.000000e+06 1.000000 0.000000 \n", + "75% 1100.000000 2.940000e+06 1.000000 0.000000 \n", + "max 6500.000000 5.100000e+06 1.000000 1.000000 " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.describe()" + ] + }, + { + "cell_type": "markdown", + "id": "8938328f", + "metadata": {}, + "source": [ + "### Now, we have handled missing values & also converted object columns into numerical. In addition, there were still null values present in the object columns, we filled the null values by using meadian. Now, You can see the summary of dataset & we are ready to go with correlation matrix to select the best features for train test split.\n" + ] + }, + { + "cell_type": "markdown", + "id": "077fea55", + "metadata": {}, + "source": [ + "## Correlation Matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "340a22df", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Kaustubh\\AppData\\Local\\Temp\\ipykernel_25628\\1265159264.py:5: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n", + " correlation_matrix = df.corr()\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Correlations with Price:\n", + " Price 1.000000\n", + "Rcmnd cruise Knots 0.898150\n", + "Max speed Knots 0.851301\n", + "All eng rate of climb 0.848457\n", + "Stall Knots dirty 0.777356\n", + "Takeoff over 50ft 0.766469\n", + "Eng out rate of climb 0.764794\n", + "Range N.M. 0.722910\n", + "Empty weight lbs 0.688144\n", + "Landing over 50ft 0.682572\n", + "Fuel gal/lbs 0.604069\n", + "Wing span ft/in 0.591734\n", + "Engine Type_propjet 0.216141\n", + "Length ft/in 0.052890\n", + "Engine Type_piston -0.775623\n", + "Name: Price, dtype: float64\n" + ] + } + ], + "source": [ + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Compute correlation matrix\n", + "correlation_matrix = df.corr()\n", + "\n", + "# Visualize the correlation matrix\n", + "plt.figure(figsize=(12, 8))\n", + "sns.heatmap(correlation_matrix, annot=True, cmap=\"coolwarm\")\n", + "plt.title(\"Correlation Matrix\")\n", + "plt.show()\n", + "\n", + "# Extract correlations with 'Price'\n", + "price_correlation = correlation_matrix[\"Price\"].sort_values(ascending=False)\n", + "print(\"Correlations with Price:\\n\", price_correlation)\n" + ] + }, + { + "cell_type": "markdown", + "id": "474a3342", + "metadata": {}, + "source": [ + "### This block calculates the correlation matrix, which quantifies the linear relationship between variables in the dataset. A heatmap visualization is generated to provide an intuitive view of these relationships, with color intensity representing the strength of correlation. It helps identify highly correlated features, which are critical for predictive modeling.\n", + "\n", + "### Variables like Rcmnd cruise Knots, Max speed Knots, and All eng rate of climb exhibit strong positive correlations with Price.Features with weak correlations, such as Length ft/in, may not significantly impact the model's accuracy and could be dropped during feature selection.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "4cfd330b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Highly correlated features with Price: ['Price', 'Rcmnd cruise Knots', 'Max speed Knots', 'All eng rate of climb', 'Stall Knots dirty', 'Takeoff over 50ft', 'Eng out rate of climb', 'Range N.M.', 'Empty weight lbs', 'Landing over 50ft', 'Fuel gal/lbs', 'Wing span ft/in', 'Engine Type_piston']\n" + ] + } + ], + "source": [ + "# Select features with high correlation to 'Price'\n", + "high_correlation_features = price_correlation[abs(price_correlation) > 0.5].index.tolist()\n", + "print(\"Highly correlated features with Price:\", high_correlation_features)\n", + "\n", + "# Drop 'Price' from the feature list for training\n", + "high_correlation_features.remove(\"Price\")\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "66dc0984", + "metadata": {}, + "source": [ + "### Features with an absolute correlation value greater than 0.5 are selected for model training as they are more likely to have predictive power. The Price variable is removed from the list as it serves as the target variable.\n", + "\n", + "### Features such as Rcmnd cruise Knots, Max speed Knots, and Eng out rate of climb are retained for training, as they demonstrate high correlations with the target variable. This ensures that the model uses only the most relevant features, reducing dimensionality and improving performance." + ] + }, + { + "cell_type": "markdown", + "id": "805439ba", + "metadata": {}, + "source": [ + "## Check VIF" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "c5be71bc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Variance Inflation Factor (VIF):\n", + " Feature VIF\n", + "0 Rcmnd cruise Knots 44.740812\n", + "1 Max speed Knots 18.085067\n", + "2 All eng rate of climb 13.917120\n", + "3 Stall Knots dirty 22.274087\n", + "4 Takeoff over 50ft 31.171244\n", + "5 Range N.M. 7.429036\n", + "6 Eng out rate of climb 19.256853\n", + "Training set: (413, 6), Testing set: (104, 6)\n" + ] + } + ], + "source": [ + "# Step 1: Define the original features and target\n", + "features = ['Rcmnd cruise Knots', 'Max speed Knots', 'All eng rate of climb', \n", + " 'Stall Knots dirty', 'Takeoff over 50ft', 'Range N.M.', 'Eng out rate of climb']\n", + "target = 'Price'\n", + "\n", + "# Step 2: Prepare data for VIF calculation\n", + "X = df[features].values\n", + "y = df[target].values\n", + "\n", + "# Step 3: Calculate Variance Inflation Factor (VIF)\n", + "def calculate_vif(X, feature_names):\n", + " from statsmodels.stats.outliers_influence import variance_inflation_factor\n", + " vif_data = pd.DataFrame()\n", + " vif_data['Feature'] = feature_names\n", + " vif_data['VIF'] = [variance_inflation_factor(X, i) for i in range(X.shape[1])]\n", + " return vif_data\n", + "\n", + "vif_data = calculate_vif(X, features)\n", + "print(\"Variance Inflation Factor (VIF):\")\n", + "print(vif_data)\n", + "\n", + "# Step 4: Drop features with high VIF\n", + "refined_features = ['Rcmnd cruise Knots', 'Max speed Knots', 'All eng rate of climb', \n", + " 'Stall Knots dirty', 'Takeoff over 50ft', 'Range N.M.'] # Example after VIF review\n", + "X = df[refined_features].values # Update X to use refined features\n", + "\n", + "# Step 5: Train-test split\n", + "split_index = int(0.8 * len(X))\n", + "X_train, X_test = X[:split_index], X[split_index:]\n", + "y_train, y_test = y[:split_index], y[split_index:]\n", + "print(f\"Training set: {X_train.shape}, Testing set: {X_test.shape}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "98e2057d", + "metadata": {}, + "outputs": [], + "source": [ + "# Drop 'Rcmnd cruise Knots' due to highest VIF\n", + "refined_features = ['Max speed Knots', 'All eng rate of climb', 'Stall Knots dirty', \n", + " 'Takeoff over 50ft', 'Range N.M.', 'Eng out rate of climb']\n", + "X = df[refined_features].values # Update X with refined features\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "62e1ba54", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Updated Variance Inflation Factor (VIF):\n", + " Feature VIF\n", + "0 Max speed Knots 17.567701\n", + "1 All eng rate of climb 8.284438\n", + "2 Stall Knots dirty 20.162377\n", + "3 Takeoff over 50ft 30.728868\n", + "4 Range N.M. 6.527567\n", + "5 Eng out rate of climb 18.748689\n" + ] + } + ], + "source": [ + "# Recalculate VIF with refined features\n", + "vif_data = calculate_vif(X, refined_features)\n", + "print(\"Updated Variance Inflation Factor (VIF):\")\n", + "print(vif_data)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "ab1234f0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Updated Variance Inflation Factor (VIF):\n", + " Feature VIF\n", + "0 Max speed Knots 17.552247\n", + "1 All eng rate of climb 8.241797\n", + "2 Stall Knots dirty 10.857256\n", + "3 Range N.M. 6.466999\n", + "4 Eng out rate of climb 13.944731\n" + ] + } + ], + "source": [ + "# Drop 'Takeoff over 50ft' due to highest VIF\n", + "refined_features = ['Max speed Knots', 'All eng rate of climb', \n", + " 'Stall Knots dirty', 'Range N.M.', 'Eng out rate of climb']\n", + "X = df[refined_features].values # Update X with refined features\n", + "\n", + "# Recalculate VIF\n", + "vif_data = calculate_vif(X, refined_features)\n", + "print(\"Updated Variance Inflation Factor (VIF):\")\n", + "print(vif_data)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "e8d284b3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Updated Variance Inflation Factor (VIF):\n", + " Feature VIF\n", + "0 All eng rate of climb 6.048464\n", + "1 Takeoff over 50ft 15.140610\n", + "2 Range N.M. 6.113537\n", + "3 Eng out rate of climb 18.124673\n" + ] + } + ], + "source": [ + "# Drop 'Max speed Knots' due to highest VIF\n", + "refined_features = [ 'All eng rate of climb', \n", + " 'Takeoff over 50ft', 'Range N.M.','Eng out rate of climb']\n", + "X = df[refined_features].values # Update X with refined features\n", + "\n", + "# Recalculate VIF\n", + "vif_data = calculate_vif(X, refined_features)\n", + "print(\"Updated Variance Inflation Factor (VIF):\")\n", + "print(vif_data)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "99903de5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Updated Variance Inflation Factor (VIF):\n", + " Feature VIF\n", + "0 All eng rate of climb 5.638383\n", + "1 Takeoff over 50ft 7.848335\n", + "2 Range N.M. 5.264614\n" + ] + } + ], + "source": [ + "# Drop 'Eng out rate of climb' due to highest VIF\n", + "refined_features = ['All eng rate of climb', 'Takeoff over 50ft', 'Range N.M.']\n", + "X = df[refined_features].values # Update X with refined features\n", + "\n", + "# Recalculate VIF\n", + "vif_data = calculate_vif(X, refined_features)\n", + "print(\"Updated Variance Inflation Factor (VIF):\")\n", + "print(vif_data)\n" + ] + }, + { + "cell_type": "markdown", + "id": "e694aba5", + "metadata": {}, + "source": [ + "### Initial VIF Calculation:\n", + "\n", + "#### The Variance Inflation Factor (VIF) calculation highlights high collinearity among features. Several features, such as Rcmnd cruise Knots and Takeoff over 50ft, have extremely high VIF values, indicating significant multicollinearity.\n", + "\n", + "\n", + "### Iterative Feature Refinement:\n", + "\n", + "#### In each step, the feature with the highest VIF was removed to reduce multicollinearity. For instance, Rcmnd cruise Knots was removed first due to its VIF of 44.74.The process continued iteratively, with recalculations of VIF at each step, until all remaining features had acceptable VIF values (generally below 10).This ensures that the features included in the model are independent and contribute uniquely to the predictions.\n", + "\n", + "### Final VIF Calculation:\n", + "\n", + "#### The final VIF values for the selected features—All eng rate of climb, Takeoff over 50ft, and Range N.M.— are below 10, indicating minimal collinearity and a strong, stable feature set for modeling.\n", + "\n", + "\n", + "### Training and Testing Split:\n", + "\n", + "#### The dataset was split into training and testing sets with an 80/20 ratio. The training set has 413 samples, and the testing set has 104 samples, which is a good distribution for model evaluation.\n" + ] + }, + { + "cell_type": "markdown", + "id": "f9d53746", + "metadata": {}, + "source": [ + "## Feature Scaling " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "1553489a", + "metadata": {}, + "outputs": [], + "source": [ + "# Scale features\n", + "def scale_features(X):\n", + " return (X - np.mean(X, axis=0)) / np.std(X, axis=0)\n", + "\n", + "X_scaled = scale_features(X)\n" + ] + }, + { + "cell_type": "markdown", + "id": "cd07c40c", + "metadata": {}, + "source": [ + "### Standardization was applied to the final features to center them around 0 with a standard deviation of 1.This ensures that all features contribute equally to the model and improves numerical stability in regression calculations." + ] + }, + { + "cell_type": "markdown", + "id": "e55faa9b", + "metadata": {}, + "source": [ + "## Train test split" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "a52931b3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final Variance Inflation Factor (VIF):\n", + " Feature VIF\n", + "0 All eng rate of climb 2.136056\n", + "1 Takeoff over 50ft 2.663501\n", + "2 Range N.M. 1.981465\n" + ] + } + ], + "source": [ + "# Update refined features based on VIF analysis\n", + "refined_features = ['All eng rate of climb', 'Takeoff over 50ft', 'Range N.M.']\n", + "X = df[refined_features].values\n", + "y = df['Price'].values\n", + "\n", + "# Recalculate Variance Inflation Factor (VIF) for final confirmation\n", + "def calculate_vif(X, features):\n", + " vif_data = pd.DataFrame()\n", + " vif_data[\"Feature\"] = features\n", + " vif_data[\"VIF\"] = [np.linalg.inv(np.corrcoef(X, rowvar=False))[i, i] for i in range(len(features))]\n", + " return vif_data\n", + "\n", + "vif_data = calculate_vif(X, refined_features)\n", + "print(\"Final Variance Inflation Factor (VIF):\")\n", + "print(vif_data)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "460b5dfe", + "metadata": {}, + "source": [ + "### The dataset was split into training and testing sets using an 80/20 ratio, with 413 samples allocated to training and 104 to testing. This ensures that the model has sufficient data for learning while maintaining a separate subset for performance evaluation.\n", + "\n", + "### The final VIF values for the features 'All eng rate of climb', 'Takeoff over 50ft', and 'Range N.M.' were recalculated and found to be below 2.7. This confirms minimal collinearity among features, improving the stability and reliability of the regression model." + ] + }, + { + "cell_type": "markdown", + "id": "8949feb2", + "metadata": {}, + "source": [ + "## Define r_squared function" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "4f2cbfb5", + "metadata": {}, + "outputs": [], + "source": [ + "# Define r_squared function\n", + "def r_squared(y_true, y_pred):\n", + " ss_total = np.sum((y_true - np.mean(y_true)) ** 2)\n", + " ss_residual = np.sum((y_true - y_pred) ** 2)\n", + " return 1 - (ss_residual / ss_total)\n" + ] + }, + { + "cell_type": "markdown", + "id": "fef0d81f", + "metadata": {}, + "source": [ + "### The R-squared function calculates the proportion of variance explained by the model. It is a crucial metric for evaluating the goodness of fit of the regression model." + ] + }, + { + "cell_type": "markdown", + "id": "0218948a", + "metadata": {}, + "source": [ + "## Model Training: Linear Regression" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "fe2575ee", + "metadata": {}, + "outputs": [], + "source": [ + "# Add bias column\n", + "X_train_with_bias = np.c_[np.ones(X_train.shape[0]), X_train]\n", + "X_test_with_bias = np.c_[np.ones(X_test.shape[0]), X_test]\n", + "\n", + "# Train a Linear Regression model\n", + "weights = np.linalg.inv(X_train_with_bias.T @ X_train_with_bias) @ X_train_with_bias.T @ y_train\n" + ] + }, + { + "cell_type": "markdown", + "id": "d65c3f17", + "metadata": {}, + "source": [ + "### Linear regression was implemented with the addition of an intercept term. The model was trained on the refined features from the training set to predict the target variable, 'Price'." + ] + }, + { + "cell_type": "markdown", + "id": "34e8ece4", + "metadata": {}, + "source": [ + "## Ridge Regression" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "48e8acab", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best Alpha: 1000.0, Best R^2: 0.9235\n" + ] + } + ], + "source": [ + "# Ridge Regression Implementation with Hyperparameter Tuning\n", + "def ridge_regression(X, y, alpha):\n", + " X_with_bias = np.c_[np.ones(X.shape[0]), X] # Add intercept term\n", + " I = np.eye(X_with_bias.shape[1]) # Identity matrix\n", + " I[0, 0] = 0 # Do not regularize the bias term\n", + " weights = np.linalg.inv(X_with_bias.T @ X_with_bias + alpha * I) @ X_with_bias.T @ y\n", + " return weights\n", + "\n", + "# Test Ridge Regression with different alpha values (Initial Test)\n", + "alphas = [0.1, 1, 10, 100]\n", + "ridge_results = []\n", + "for alpha in alphas:\n", + " ridge_weights = ridge_regression(X_train, y_train, alpha)\n", + " y_test_pred_ridge = np.c_[np.ones(X_test.shape[0]), X_test] @ ridge_weights\n", + " test_r2_ridge = r_squared(y_test, y_test_pred_ridge)\n", + " ridge_results.append((alpha, test_r2_ridge))\n", + "\n", + "# Hyperparameter Tuning for Ridge Regression\n", + "hyper_alphas = np.logspace(-3, 3, 50) # Fine-tune alpha\n", + "best_alpha = 0\n", + "best_r2 = 0\n", + "for alpha in hyper_alphas:\n", + " ridge_weights = ridge_regression(X_train, y_train, alpha)\n", + " y_test_pred_ridge = np.c_[np.ones(X_test.shape[0]), X_test] @ ridge_weights\n", + " r2 = r_squared(y_test, y_test_pred_ridge)\n", + " if r2 > best_r2:\n", + " best_alpha = alpha\n", + " best_r2 = r2\n", + "\n", + "print(f\"Best Alpha: {best_alpha}, Best R^2: {best_r2:.4f}\")\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "c8564f45", + "metadata": {}, + "source": [ + "### Ridge regression with hyperparameter tuning was applied to address multicollinearity and improve model generalization. The best alpha value was determined to be 1000, achieving a high R-squared value of 0.9235 on the testing data. This indicates an optimal balance between bias and variance." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "25cd4494", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualize the Hyperparameter Tuning Results\n", + "import matplotlib.pyplot as plt\n", + "\n", + "alphas_test = [result[0] for result in ridge_results]\n", + "r2_scores = [result[1] for result in ridge_results]\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(alphas_test, r2_scores, marker='o', label='Initial Test Alphas')\n", + "plt.xscale('log')\n", + "plt.xlabel('Alpha')\n", + "plt.ylabel('R^2 Score')\n", + "plt.title('Ridge Regression: Alpha vs R^2')\n", + "plt.axvline(best_alpha, color='r', linestyle='--', label=f'Best Alpha: {best_alpha:.3f}')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "f30a7aed", + "metadata": {}, + "source": [ + "### A plot was created to visualize the effect of alpha on the R-squared value. The graph illustrates a significant improvement in model performance as alpha increases, stabilizing around the optimal value." + ] + }, + { + "cell_type": "markdown", + "id": "fb9fdb33", + "metadata": {}, + "source": [ + "## Predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "c563a304", + "metadata": {}, + "outputs": [], + "source": [ + "# Predictions\n", + "y_train_pred = X_train_with_bias @ weights\n", + "y_test_pred = X_test_with_bias @ weights\n" + ] + }, + { + "cell_type": "markdown", + "id": "236e0813", + "metadata": {}, + "source": [ + "### Predictions for the training and testing datasets were generated using the trained weights from the linear regression model. These predictions will be evaluated against the actual target values." + ] + }, + { + "cell_type": "markdown", + "id": "ace0caae", + "metadata": {}, + "source": [ + "## Model Evaluation: R-Squared and Adjusted R-Squared" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "a6cbad72", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Linear Regression Training R²: 0.8230, Testing R²: 0.9232\n" + ] + } + ], + "source": [ + "# Evaluate using r_squared\n", + "train_r2 = r_squared(y_train, y_train_pred)\n", + "test_r2 = r_squared(y_test, y_test_pred)\n", + "\n", + "print(f\"Linear Regression Training R²: {train_r2:.4f}, Testing R²: {test_r2:.4f}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "c8c6c413", + "metadata": {}, + "source": [ + "### The R² value for the training set is 0.8230, while the test set achieved 0.9232. This indicates the model performs well on unseen data, with a high degree of variance in the dependent variable explained by the independent variables." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "9486758b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Adjusted R² for Ridge: 0.9184\n" + ] + } + ], + "source": [ + "def adjusted_r2(r2, n, p):\n", + " \"\"\"\n", + " Compute Adjusted R-squared.\n", + " :param r2: R-squared\n", + " :param n: Number of observations\n", + " :param p: Number of predictors\n", + " :return: Adjusted R-squared\n", + " \"\"\"\n", + " return 1 - ((1 - r2) * (n - 1)) / (n - p - 1)\n", + "\n", + "# Calculate Adjusted R² for Ridge\n", + "n = X_test.shape[0]\n", + "p = X_test.shape[1]\n", + "adjusted_r2_ridge = adjusted_r2(test_r2, n, p)\n", + "print(f\"Adjusted R² for Ridge: {adjusted_r2_ridge:.4f}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "379a1eae", + "metadata": {}, + "source": [ + "## Lasso Regression " + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "e748f60b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ridge Best R²: 0.9235033171939399\n", + "Lasso Results: [(0.1, 0.9231735440115759), (1, 0.923173544036331), (10, 0.9231735442838815), (100, 0.9231735467593862)]\n" + ] + } + ], + "source": [ + "# Implement Lasso Regression with hyperparameter tuning\n", + "def lasso_regression(X, y, alpha):\n", + " X_with_bias = np.c_[np.ones(X.shape[0]), X] # Add intercept\n", + " weights = np.zeros(X_with_bias.shape[1])\n", + " for _ in range(2000): # Iterative updates\n", + " for j in range(len(weights)):\n", + " X_j = X_with_bias[:, j]\n", + " residual = y - (X_with_bias @ weights - weights[j] * X_j)\n", + " rho = X_j.T @ residual\n", + " if j == 0: # Intercept term\n", + " weights[j] = rho / len(y)\n", + " else:\n", + " weights[j] = np.sign(rho) * max(abs(rho) - alpha / 2, 0) / (X_j.T @ X_j)\n", + " return weights\n", + "\n", + "# Evaluate Lasso Regression\n", + "alphas = [0.1, 1, 10, 100]\n", + "lasso_results = []\n", + "for alpha in alphas:\n", + " lasso_weights = lasso_regression(X_train, y_train, alpha)\n", + " y_test_pred_lasso = X_test_with_bias @ lasso_weights\n", + " test_r2_lasso = r_squared(y_test, y_test_pred_lasso)\n", + " lasso_results.append((alpha, test_r2_lasso))\n", + "\n", + "# Compare Ridge and Lasso\n", + "print(\"Ridge Best R²:\", best_r2)\n", + "print(\"Lasso Results:\", lasso_results)\n" + ] + }, + { + "cell_type": "markdown", + "id": "d2425125", + "metadata": {}, + "source": [ + "### Implement Lasso Regression with hyperparameter tuning :-\n", + "\n", + "### This function implements Lasso regression using iterative updates. Lasso introduces an L1 penalty, which can shrink some coefficients to zero, enabling feature selection. The function accepts the dataset (X, y) and a regularization parameter (alpha).\n", + "\n", + "### Evaluate Lasso Regression :-\n", + "\n", + "### A list of alpha values is tested to determine the optimal regularization parameter. Predictions are made on the test set for each alpha, and R² is calculated to evaluate performance.\n", + "\n", + "### Compare Ridge and Lasso :-\n", + "\n", + "### The Ridge Regression result (Best R²) is compared with Lasso Regression results for various alpha values. The results indicate that Ridge Regression slightly outperforms Lasso Regression in this case.\n" + ] + }, + { + "cell_type": "markdown", + "id": "3d2c9ffd", + "metadata": {}, + "source": [ + "## Residual Analysis for Ridge Regression" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "95f05430", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Residual Plot for Ridge Regression\n", + "residuals = y_test - y_test_pred_ridge\n", + "plt.figure(figsize=(8, 5))\n", + "plt.scatter(y_test_pred_ridge, residuals, alpha=0.7, label=\"Residuals\")\n", + "plt.axhline(0, color='red', linestyle='--', label=\"Zero Line\")\n", + "plt.xlabel(\"Predicted Values\")\n", + "plt.ylabel(\"Residuals\")\n", + "plt.title(\"Residual Analysis for Ridge Regression\")\n", + "plt.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "df8a1ab6", + "metadata": {}, + "source": [ + "### - Residuals are randomly distributed around zero, indicating that the model captures the data well.\n", + "### - No clear patterns suggest no significant bias or omitted variables.\n", + "### - However, a few outliers may indicate some extreme values not well-explained by the model." + ] + }, + { + "cell_type": "markdown", + "id": "974bfb35", + "metadata": {}, + "source": [ + "## k-fold cross-validation" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "1c06a3f5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean R²: 0.7936, Std Dev: 0.0540\n" + ] + } + ], + "source": [ + "\n", + "# Updated k-fold cross-validation with seed\n", + "def k_fold_cross_validation(X, y, k=5, alpha=1.0, seed=42):\n", + " np.random.seed(seed) # Set seed for reproducibility\n", + " indices = np.arange(len(X))\n", + " np.random.shuffle(indices)\n", + " X, y = X[indices], y[indices]\n", + "\n", + " fold_size = len(X) // k\n", + " r2_scores = []\n", + "\n", + " for i in range(k):\n", + " start = i * fold_size\n", + " end = (i + 1) * fold_size\n", + " X_val = X[start:end]\n", + " y_val = y[start:end]\n", + " X_train = np.vstack((X[:start], X[end:]))\n", + " y_train = np.hstack((y[:start], y[end:]))\n", + "\n", + " ridge_weights = ridge_regression(X_train, y_train, alpha)\n", + " X_val_with_bias = np.c_[np.ones(X_val.shape[0]), X_val]\n", + " y_val_pred = X_val_with_bias @ ridge_weights\n", + " r2 = r_squared(y_val, y_val_pred)\n", + " r2_scores.append(r2)\n", + "\n", + " return np.mean(r2_scores), np.std(r2_scores)\n", + "\n", + "# Use the function with reproducibility\n", + "mean_r2, std_r2 = k_fold_cross_validation(X, y, k=5, alpha=best_alpha)\n", + "print(f\"Mean R²: {mean_r2:.4f}, Std Dev: {std_r2:.4f}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "1d2eeeea", + "metadata": {}, + "source": [ + "### The k-fold cross-validation process yielded a mean R² of 0.7936 with a standard deviation of 0.0540. This highlights the model's generalizability and its ability to perform consistently across different splits of the data." + ] + }, + { + "cell_type": "markdown", + "id": "134bf622", + "metadata": {}, + "source": [ + "## Bootstrapping" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "cad70f60", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean Bootstrapped R²: 0.8092, Std Dev: 0.0195\n" + ] + } + ], + "source": [ + "# Bootstrapping\n", + "def bootstrap_r2(X, y, alpha=best_alpha, n_iterations=1000):\n", + " r2_scores = []\n", + " for _ in range(n_iterations):\n", + " indices = np.random.choice(len(X), len(X), replace=True)\n", + " X_sample = X[indices]\n", + " y_sample = y[indices]\n", + "\n", + " ridge_weights = ridge_regression(X_sample, y_sample, alpha)\n", + " y_sample_pred = np.c_[np.ones(X_sample.shape[0]), X_sample] @ ridge_weights\n", + " r2 = r_squared(y_sample, y_sample_pred)\n", + " r2_scores.append(r2)\n", + "\n", + " return np.mean(r2_scores), np.std(r2_scores)\n", + "\n", + "mean_bootstrap_r2, std_bootstrap_r2 = bootstrap_r2(X, y)\n", + "print(f\"Mean Bootstrapped R²: {mean_bootstrap_r2:.4f}, Std Dev: {std_bootstrap_r2:.4f}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "ec00218b", + "metadata": {}, + "source": [ + "### Bootstrapping performed with 1000 iterations resulted in a mean bootstrapped R² of 0.8092 and a standard deviation of 0.0195. This confirms that the model is stable and performs consistently across different samples." + ] + }, + { + "cell_type": "markdown", + "id": "8dfb60e8", + "metadata": {}, + "source": [ + "## Viz of R^2 & Adj R^2" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "47d5a571", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualization: Predicted vs. Actual Prices\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plt.scatter(y_test, y_test_pred_ridge, label='Test Predictions', alpha=0.7)\n", + "plt.plot([min(y_test), max(y_test)], [min(y_test), max(y_test)], color='red', label='Perfect Fit Line')\n", + "plt.xlabel('Actual Prices')\n", + "plt.ylabel('Predicted Prices')\n", + "plt.title('Predicted vs. Actual Prices')\n", + "plt.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "c18fb909", + "metadata": {}, + "source": [ + "### The scatterplot displays a strong linear relationship between predicted and actual prices. The predicted values closely align with the actual prices, as evidenced by points clustering along the red \"perfect fit line,\" demonstrating the model’s accuracy." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 70a711be3566eac07b87c7d2f52c338574734018 Mon Sep 17 00:00:00 2001 From: hha980510 Date: Wed, 20 Nov 2024 15:17:23 -0600 Subject: [PATCH 02/26] Add files via upload --- Plane_price.ipynb | 31 ++++++++++++++++++++++++++++--- 1 file changed, 28 insertions(+), 3 deletions(-) diff --git a/Plane_price.ipynb b/Plane_price.ipynb index e97e1e5..015ef11 100644 --- a/Plane_price.ipynb +++ b/Plane_price.ipynb @@ -8,6 +8,19 @@ "# Plane_price prediction " ] }, + { + "cell_type": "markdown", + "id": "9dc32f10", + "metadata": {}, + "source": [ + "Project 2\n", + "\n", + "A20557555 Hyunsung Ha\n", + "A20550806 Kaustubh Dangche\n", + "A20487452 Nam Gyu Lee\n", + "A20568373 Anu Singh" + ] + }, { "cell_type": "markdown", "id": "5ad0a1d0", @@ -29,7 +42,19 @@ "execution_count": 1, "id": "9de818ab", "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'matplotlib'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[1], line 5\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m \n\u001b[0;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m \n\u001b[1;32m----> 5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m \n", + "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'" + ] + } + ], "source": [ "# import required libraries :- \n", "\n", @@ -2199,7 +2224,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -2213,7 +2238,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.12.0" } }, "nbformat": 4, From 5881af920357bdf9050800e81674a799e1f398c4 Mon Sep 17 00:00:00 2001 From: hha980510 Date: Wed, 20 Nov 2024 15:21:46 -0600 Subject: [PATCH 03/26] Add files via upload --- Plane Price.csv | 518 ++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 518 insertions(+) create mode 100644 Plane Price.csv diff --git a/Plane Price.csv b/Plane Price.csv new file mode 100644 index 0000000..4825be8 --- /dev/null +++ b/Plane Price.csv @@ -0,0 +1,518 @@ +Model Name,Engine Type,HP or lbs thr ea engine,Max speed Knots,Rcmnd cruise Knots,Stall Knots dirty,Fuel gal/lbs,All eng rate of climb,Eng out rate of climb,Takeoff over 50ft,Landing over 50ft,Empty weight lbs,Length ft/in,Wing span ft/in,Range N.M.,Price +100 Darter (S.L. Industries),Piston,145,104,91,46,36,450,900,1300,"2,050","1,180",25/3,37/5,370,1300000 +7 CCM Champ,Piston,85,89,83,44,15,600,720,800,"1,350",820,20/7,36/1,190,1230000 +100 Darter (S.L. Industries),Piston,90,90,78,37,19,650,475,850,"1,300",810,21/5,35/0,210,1600000 +7 AC Champ,Piston,85,88,78,37,19,620,500,850,"1,300",800,21/5,35/0,210,1300000 +100 Darter (S.L. Industries),Piston,65,83,74,33,14,370,632,885,"1,220",740,21/5,35/0,175,1250000 +PA-60-700P Aerostar (preliminary),Piston,65,78,72,33,15,360,583,880,"1,250",786,20/4,36/1,180,1100000 +"PA-601P pressurized Aerostar ('77 service ceiling=26,350)",Piston,350,264,230,80,165,"1,820",3080,2100,"6,315","4,275",34/10,36/8,868,2500000 +100 Darter (S.L. Industries),Piston,290,262,247,77,165,"1,755",2250,2076,"6,000","4,125",34/9,36/7,"1,020",2800000 +"PA-601, 601A, turbochg Aerostar",Piston,290,257,235,77,165,"1,460",2490,2030,"6,000","4,056",34/10,36/8,"1,101",2500000 +100 Darter (S.L. Industries),Piston,290,257,237,77,165,"1,460",2490,2030,"6,000","3,958",34/10,36/8,"1,174",3000000 +G164B-600 AG-CAT (design category) sprayer,Piston,290,271,236,69,174,"1,700",2200,1625,"5,700","3,750",34/10,34/2,"1,174",3010000 +100 Darter (S.L. Industries),Piston,290,220,212,74,165,"1,800",1950,1840,"5,500","3,737",34/10,34/2,"1,200",2120000 +G164A-300 AG CAT,Piston,600,,100,52,64,"1,360",1050,1150,"5,200","3,650",25/6,42/5,200,nan +100 Darter (S.L. Industries),Piston,600,128,96,59,46,"1,600",860,770,"4,500","3,160",24/4,35/11,148,1450000 +G164A-245 AG CAT,Piston,525,128,91,52,46,"1,350",,540,567,"4,500","3,150",42/3,155,nan +A-2,Piston,450,128,91,52,46,"1,060",1090,1150,"4,500","3,100",25/7,42/3,190,1450000 +100 Darter (S.L. Industries),Piston,450,128,91,58,46,990,1145,750,"4,500","2,870",24/4,35/11,190,1400000 +415-G,Piston,300,128,74,54,46,700,1200,1000,"3,750","2,410",24/4,35/8,226,1150000 +CH-601-XL/i/650-LS/i,Piston,275,114,74,54,33,600,1275,1000,"3,750","2,400",24/4,35/8,117,1025000 +8KCAB-150 F/P prop(w/optional speed kit),Piston,245,114,70,54,33,496,1300,1000,"3,750","2,300",24/4,35/8,174,850000 +7KCAB,Piston,220,114,70,48,33,435,1360,1000,"3,600","2,200",24/4,35/8,190,700000 +7GCAA,Piston,90,112,108,37,24,640,1100,1200,"1,450",930,20/2,30/0,410,1730000 +100 Darter (S.L. Industries),Piston,90,113,104,50,24,600,1800,1600,"1,400",900,20/1,30/0,400,1600000 +7GCBC with EDO floats,Piston,85,99,96,50,24,550,2100,1850,"1,400",833,20/1,30/0,360,1350000 +7EC,Piston,100,120,113,38,30,"1,000",,,"1,320",770,20/0,27/0,715,1200000 +Starship 2000 (2000A=increased spds & weights),Piston,150,147,137,52,40,"1,000",,,"1,800","1,260",22/11,32/0,404,1600000 +King Air 260,Piston,150,116,109,43,39,"1,120",535,755,"1,650","1,140",22/7,34/5,411,1700000 +B200 Super King Air,Piston,150,109,103,37,37,"1,140",617,718,"1,650","1,050",22/9,34/5,411,1810000 +100 Darter (S.L. Industries),Piston,150,113,109,43,39,"1,120",535,755,"1,650","1,140",22/8,33/5,411,1700000 +"B100 King Air (prior '79 serv. ceiling=29,100)",Piston,150,113,111,39,35,"1,145",457,690,"1,650","1,150",22/8,34/3,480,1700000 +100 Darter (S.L. Industries),Piston,150,97,90,46,39,800,,,"1,800","1,290",22/7,34/6,248,1600000 +100 King Air,Piston,115,109,107,44,35,725,716,775,"1,650","1,067",22/8,33/5,301,1200000 +100 Darter (S.L. Industries),Piston,90,117,97,38,26,700,1240,700,"1,450",820,21/6,35/1,350,1550000 +F90 King Air,Piston,60,85,75,38,13,400,850,,"1,220",750,21/11,35/1,261,850000 +"C90A King Air (LJ-1063 up) (10,100 g/w=SN1138 up)",Piston,160,168,160,63,80,"1,160",1805,1330,"3,800","2,588",29/7,36/9,680,1740000 +100 Darter (S.L. Industries),Piston,180,148,139,53,51,850,1550,1120,"2,400","1,360",22/0,31/6,530,1380000 +100 Darter (S.L. Industries),Piston,150,136,127,52,37,660,1600,1100,"2,200","1,323",22/0,31/6,428,1265000 +A 90 King Air,Piston,150,130,122,50,38,660,1600,1100,"2,200","1,271",22/0,31/5,503,1265000 +100 Darter (S.L. Industries),Piston,108,120,108,52,24,705,1590,1100,"1,560",975,19/2,24/5,348,1275000 +88 Queen Air pressurized,Piston,108,120,109,51,24,765,1400,1065,"1,500","1,007",19/3,24/5,350,1375000 +B 80 Queen Air - specs thru 1972,Piston,100,90,84,33,20,700,,,"1,320",845,22/5,35/6,275,1500000 +80 Queen Air,Piston,180,126,113,36,52,"1,500",,,"1,800","1,190",22/7,35/6,696,2000000 +A 65 Queen Air,Piston,600,122,96,61,54,"1,000",1200,800,"6,000","3,400",29/4,44/4,275,1500000 +B 60 Duke pressurized (prior '78 t/o run=2006),Piston,600,122,108,57,100,900,,,"6,000","3,700",29/4,44/4,275,1500000 +60 Duke Pressurized,Propjet,750,138,130,57,190,"1,740",,,"6,000","3,600",33/0,44/5,545,2500000 +100 Darter (S.L. Industries),Piston,80,157,138,46,17,"1,000",820,721,"1,320",622,20/10,35/5,350,nan +100 Darter (S.L. Industries),Propjet,1050,158,126,52,216,650,,,12500,5829,33/6,56,600,800000 +C 50 Twin Bonanza,Propjet,"1,200",335,328,,560,"3,225",3876,2630,"14,400","9,887",46/1,54/5,"1,722",3480000 +50 Twin Bonanza,Propjet,"1,050",314,303,,539,"2,979",,2508,"15,000","9,051",46/8,57/11,"1,966",3500000 +58 P Baron-press.('77 hgt=9/6) (range based on 190 gal fuel),Propjet,850,310,259,80,3645,2437,,,12590,8830,43/10,57/11,1720,3500000 +58 TC Baron (325) turbochg-non-pressurized,Propjet,850,294,279,75,544,"2,450",3345,2845,"12,500","7,538",43/9,54/6,"1,972",3500000 +100 Darter (S.L. Industries),Propjet,850,290,272,75,544,"2,450",3345,2845,"12,500","7,538",43/9,54/6,"1,870",3288000 +58 Baron (300 hp),Propjet,715,265,258,83,470,"1,963",2951,2679,"11,800","7,082",39/11,45/11,"1,080",2485000 +100 Darter (S.L. Industries),Propjet,680,248,235,75,470,"1,963",3245,2944,"11,500","6,797",39/11,45/11,"1,000",2485000 +A56TC Turbo Baron,Propjet,680,248,239,73,374,"2,200",1729,2138,"10,600","6,440",39/9,45/10,"1,005",2590000 +100 Darter (S.L. Industries),Propjet,750,279,265,79,470,"2,455",2808,2275,"10,950","6,647",39/10,45/11,"1,235",3045000 +E 55 Baron ('70-'72 fuel=142 gal opt),Propjet,750,267,261,77,470,"2,380",2856,2275,"10,950","6,549",39/10,45/11,"1,235",2980200 +100 Darter (S.L. Industries),Propjet,550,250,245,77,474,"1,870",2024,2110,"10,100","5,996",35/6,50/3,"1,290",2762000 +B 55 Baron (1978 & up),Propjet,550,247,240,75,384,"2,137",2261,1672,"10,100","5,765",35/6,50/3,"1,120",3000000 +100 Darter (S.L. Industries),Propjet,550,223,217,76,384,"1,955",2261,1672,"9,650","5,765",35/6,50/3,"1,120",2810000 +100 Darter (S.L. Industries),Propjet,550,223,219,74,384,"2,000",2180,2010,"9,650","5,685",36/6,50/3,"1,185",2720000 +E 95 Travel Air (fuel inj.),Propjet,550,223,216,77,384,"1,900",2150,1960,"9,300","5,680",35/6,45/1,"1,160",3020000 +100 Darter (S.L. Industries),Propjet,500,243,235,75,384,"2,000",1755,1870,"9,000","5,680",35/6,45/1,"1,270",2840000 +"95, B95 Travel Air (95 = 4,000 lb. gross)",Piston,380,214,192,71,264,"1,275",1800,2311,"8,800","6,035",35/6,50/3,902,2680000 +100 Darter (S.L. Industries),Piston,380,216,196,70,200,"1,275",2556,2572,"8,800","5,277",35/6,50/3,656,2680000 +B 36 TC Bonanza,Piston,380,216,195,71,214,"1,275",1800,2311,"8,800","5,120",35/6,50/3,716,2680000 +100 Darter (S.L. Industries),Piston,380,219,200,70,214,"1,585",1800,2143,"8,500","4,900",35/6,50/3,716,2850000 +A 36 Bonanza (300 hp),Piston,380,208,191,67,230,"1,300",1450,2070,"8,000","4,800",35/2,50/3,746,2800000 +100 Darter (S.L. Industries),Piston,340,208,186,70,214,"1,375",1675,2107,"8,200","4,995",35/6,50/4,755,3000000 +36 Bonanza,Piston,340,208,186,70,214,"1,300",1560,1750,"7,700","4,980",35/6,45/10,755,3130000 +100 Darter (S.L. Industries),Piston,340,200,186,70,180,"1,300",1700,1980,"7,700","4,640",33/3,45/10,630,2700000 +V 35B Bonanza ('80 & up=fuel std 74 gal),Piston,380,246,233,73,142,"1,601",2626,3065,"6,775","4,425",33/10,39/3,"1,010",3000000 +100 Darter (S.L. Industries),Piston,380,249,240,76,142,"1,601",2626,3065,"6,775","4,175",33/10,39/3,824,3080000 +V 35 TC Turbo Bonanza,Piston,380,249,236,76,142,"1,615",1660,2340,"6,725","4,100",33/10,39/3,824,3130000 +100 Darter (S.L. Industries),Piston,450,204,191,76,198,"1,400",2072,1850,"9,900","5,845",35/2,49/8,622,2030000 +S 35 Bonanza,Piston,450,200,186,73,275,"1,410",1980,1850,"9,700","5,910",35/1,49/6,880,2140000 +100 Darter (S.L. Industries),Piston,450,203,187,71,206,"1,250",2079,1569,"9,300","6,150",33/1,47/7,638,2300000 +"K,M 35 Bonanza",Piston,450,200,183,67,206,"1,190",1760,1460,"8,750","5,770",33/1,47/7,632,2050000 +100 Darter (S.L. Industries),Piston,340,204,180,72,180,"1,270",1250,1840,"7,300","4,480",31/5,45/9,787,2950000 +H 35 Bonanza,Piston,340,209,184,72,180,"1,320",1250,1840,"7,000","4,460",31/5,45/9,813,2950000 +100 Darter (S.L. Industries),Piston,295,186,178,62,134,"1,450",1260,1452,"6,300","4,100",31/5,45/9,670,2000000 +"C, D 35 Bonanza",Piston,275,178,161,60,134,"1,450",1260,1375,"6,000","3,956",31/5,45/2,726,2000000 +100 Darter (S.L. Industries),Piston,260,177,159,60,134,"1,450",1344,1215,"6,000","3,940",31/5,45/2,733,2000000 +35 Bonanza,Piston,260,177,158,56,134,"1,450",1350,1215,"5,500","3,800",31/5,45/2,722,1900000 +100 Darter (S.L. Industries),Piston,325,261,232,78,166,"1,475",2643,2427,"6,200","4,026",29/11,37/10,"1,013",2500000 +F & E 33-C Bonanza (conventional tail),Piston,310,256,214,79,166,"1,529",2376,2498,"6,100","3,985",29/10,37/10,"1,130",2500000 +100 Darter (S.L. Industries),Piston,310,231,207,79,166,"1,424",2761,2498,"6,100","3,985",29/10,37/10,"1,104",2500000 +E 33 A Bonanza (conventional tail),Piston,325,261,232,78,166,"1,475",2643,2427,"6,200","3,788",29/11,37/10,"1,013",2500000 +100 Darter (S.L. Industries),Piston,310,250,214,79,166,"1,461",2376,2498,"6,100","3,780",29/10,37/10,"1,130",2500000 +C 33 A Debonair,Piston,300,208,195,74,136,"1,750",2371,2498,"5,500","3,443",29/10,37/10,"1,109",2068800 +100 Darter (S.L. Industries),Piston,285,208,195,74,136,"1,660",2101,2498,"5,400","3,361",29/10,37/10,"1,109",1860000 +"A,B 33 Debonair",Piston,380,252,247,73,142,"2,020",1420,2080,"5,990","3,700",29/0,37/10,737,3220000 +100 Darter (S.L. Industries),Piston,380,252,247,73,142,"2,020",1420,2080,"5,990","3,650",28/3,37/10,737,3220000 +"C-24R Sierra 200 (prior'78 svc. ceil=16,400)",Piston,285,208,195,73,100,"1,682",968,1414,"5,300","3,291",29/0,37/10,934,1910000 +100 Darter (S.L. Industries),Piston,285,210,200,67,112,"1,670",968,1414,"5,300","3,075",28/3,37/10,550,2090000 +A-24R Sierra 200,Piston,260,201,184,73,100,"1,693",2154,2148,"5,100","3,236",28/0,37/10,739,1930000 +100 Darter (S.L. Industries),Piston,260,205,196,68,112,"1,670",1225,1370,"5,100","3,075",27/0,37/10,763,1970000 +C 23 Sundowner (pre '76 fuel=52 gal) pre'82=less mph,Piston,260,200,191,66,112,"1,700",1700,1470,"4,880","2,960",26/5,37/8,745,1950000 +100 Darter (S.L. Industries),Piston,260,200,191,66,112,"1,630",1700,1470,"4,880","2,960",25/7,37/8,745,1920000 +"A 23, A23A Musketeer (A=2,400 lb gross)",Piston,180,183,174,61,80,"1,250",1280,1590,"4,200","2,650",25/1,37/1,661,1810000 +100 Darter (S.L. Industries),Piston,180,183,174,61,80,"1,250",2100,1850,"4,200","2,635",25/3,37/1,661,1810000 +B 19 Sport 150 (76' & prior fuel=52 gal),Piston,180,183,170,61,80,"1,250",2100,1850,"4,100","2,635",25/3,37/9,661,1810000 +100 Darter (S.L. Industries),Piston,180,171,158,60,100,"1,248",2119,1881,"3,900","2,460",29/0,38/0,843,1965000 +77 Skipper ('79-'80 height=7/11),Piston,300,213,190,57,102,"1,049",2364,1692,"3,850","2,338",27/6,37/10,957,2500000 +"17-31 ATC Turbo, 300-Lyc. (prior '78=8 gal less)",Piston,300,214,190,57,74,"1,165",2012,1449,"3,650","2,278",27/6,33/6,800,2500000 +"17-31A, (prior '78=8 gal less fuel + 6 mph less)",Piston,300,184,169,59,74,"1,210",1913,1473,"3,650","2,247",27/6,33/6,720,1850000 +100 Darter (S.L. Industries),Piston,285,179,168,52,74,"1,030 w/3bld",2040,1450,"3,600","2,195",27/6,33/6,697,1660000 +"17-30A, (1979 & up; 1993 & up + 5 mph; IO-550 +6 mph)",Piston,285,177,170,56,50,"1,015",1525,1240,"3,600","1,980",26/4,32/10,510,1600000 +100 Darter (S.L. Industries),Piston,285,217,200,55,50,"1,225",1320,1177,"3,400","2,027",26/4,33/5,600,2950000 +17-30-300 Viking (Continental eng),Piston,285,182,172,51,74,"1,167 w/3bld",1769,1324,"3,400",,26/5,33/6,717,1785800 +100 Darter (S.L. Industries),Piston,285,183,177,55,44,"1,136",1320,1177,"3,400","1,960",26/4,33/5,536,1750000 +14-19-2 Cruisemaster,Piston,285,209,195,55,50,"1,225",1320,1177,"3,400","1,950",26/4,33/5,580,2660000 +100 Darter (S.L. Industries),Piston,285,183,177,55,50,"1,136",1320,1177,"3,400","1,915",26/4,33/5,543,1750000 +14-13 Cruiseair Sr.,Piston,285,184,178,54,50,"1,200",1225,1150,"3,300","1,915",26/4,33/5,543,1830000 +55 (1984-'85),Piston,260,178,170,52,50,"1,150",1260,1100,"3,125","1,855",25/2,33/5,535,1920000 +55 (thru 1983),Piston,250,183,170,52,49,"1,170",1185,1050,"2,950","1,832",25/1,33/5,495,2000000 +100 Darter (S.L. Industries),Piston,250,183,174,50,39,"1,250",1175,1050,"2,900","1,820",25/1,32/9,492,2130000 +100 Darter (S.L. Industries),Piston,240,179,165,50,39,"1,225",1260,1050,"2,900","1,820",25/1,32/9,470,1980000 +"36 A (prior'82 = gross weight @ 18,000)",Piston,225,169,160,48,39,"1,300",1270,1025,"2,775","1,722",25/1,32/9,550,1900000 +36,Piston,205,165,152,48,39,"1,100",1500,975,"2,700","1,650",25/1,32/9,510,1800000 +100 Darter (S.L. Industries),Piston,196,160,148,49,39,890,1515,950,"2,650","1,575",25/1,32/9,470,1710000 +100 Darter (S.L. Industries),Piston,185,160,150,48,39,950,1440,925,"2,550","1,458",25/1,32/9,530,1800000 +100 Darter (S.L. Industries),Piston,260,177,168,49,50,"1,060",1516,1150,"3,300","1,935",25/5,32/9,513,1660000 +28 Longhorn,Piston,285,181,174,53,50,"1,200",1225,1150,"3,300","1,918",25/6,32/9,520,1830000 +"25 D,F w/6 engs=45,000 service ceiling (prior '82 more rate of climb)",Piston,285,182,172,51,74,"1,167 w/3bld",1769,1324,"3,400","2,125",26/8,33/6,717,1785800 +100 Darter (S.L. Industries),Piston,285,181,174,53,50,"1,200",1225,1150,"3,300","1,915",25/6,32/10,520,1830000 +25 C (1973 thru 1975),Piston,225,170,161,52,50,930,1288,1298,"3,050","1,862",25/6,32/10,520,1780000 +100 Darter (S.L. Industries),Piston,285,181,174,53,50,"1,200",1225,1150,"3,300","1,775",25/6,32/10,520,1830000 +"24 F (w/8A engs=51,000 service ceil)",Piston,225,170,161,52,50,980,1288,1298,"3,050","1,854",25/6,32/10,568,1830000 +100 Darter (S.L. Industries),Piston,225,170,161,52,50,960,1235,1282,"3,000","1,745",25/6,32/10,568,1840000 +24 D (1973 thru 1975),Piston,225,170,161,52,50,"1,010",1235,1282,"2,900","1,730",25/5,32/8,550,1980000 +100 Darter (S.L. Industries),Piston,200,145,137,60,57,927,1561,1462,"2,750","1,696",25/9,32/9,647,1538500 +24 C,Piston,200,140,131,55,52,891,1804,1519,"2,750","1,711",25/8,32/9,591,1434200 +100 Darter (S.L. Industries),Piston,200,148,141,57,58,862,1630,1380,"2,750","1,610",25/8,32/9,704,1435000 +24 Twin Jet,Piston,200,137,130,52,60,880,1380,1300,"2,550","1,410",25/1,32/7,643,1485000 +100 Darter (S.L. Industries),Piston,180,128,116,51,57,792,1955,1484,"2,450","1,494",25/9,32/9,565,1260000 +100 Darter (S.L. Industries),Piston,180,127,120,50,60,728,1460,1260,"2,400","1,375",25/1,32/7,565,1187000 +650 Citation VII,Piston,165,127,120,50,60,880,1460,1260,"2,350","1,325",25/1,32/7,565,1187000 +650 Citation VI,Piston,160,123,111,52,60,720,1275,1260,"2,300","1,300",25/1,32/7,800,1350000 +100 Darter (S.L. Industries),Piston,150,110,107,50,57,680,1635,1693,"2,150","1,414",25/9,32/9,643,1165000 +650 Citation III (thru SN 099),Piston,150,122,114,48,60,900,1255,1220,"2,200","1,325",25/1,32/7,800,1490000 +100 Darter (S.L. Industries),Piston,115,106,97,47,29,720,1280,1313,"1,675","1,103",24/0,30/0,370,1290000 +100 Darter (S.L. Industries),Piston,300,193,187,61,68,"1,170",1420,1340,"3,325","2,372",26/4,34/2,760,2400000 +100 Darter (S.L. Industries),Piston,300,174,165,61,68,"1,170",1420,1340,"3,325","2,247",26/4,34/2,483,1820000 +S550 Citation SII,Piston,250,193,187,54,72,"1,800",890,1100,"3,200","2,010",23/7,34/2,760,2400000 +Citation II 550 (thru SN 626),Piston,300,181,176,61,68,"1,210",1420,1340,"3,325","2,185",26/4,34/2,550,2000000 +100 Darter (S.L. Industries),Piston,300,166,163,61,60,"1,085",1420,1340,"3,325","2,217",26/4,34/2,550,1700000 +Citation CJ3 525B,Piston,300,167,163,54,58,"1,840",890,1050,"3,200","1,900",23/7,34/2,550,2100000 +100 Darter (S.L. Industries),Piston,260,181,177,54,58,"1,500",990,825,"3,000","1,750",23/6,34/2,639,2250000 +100 Darter (S.L. Industries),Piston,230,179,170,42,40,"1,400",1025,1150,"2,700","1,640",22/9,34/2,452,2000000 +Citation I 500,Piston,190,174,157,38,40,"1,250",1270,1025,"2,600","1,575",23/0,34/2,435,2150000 +100 Darter (S.L. Industries),Piston,150,147,130,38,40,"1,100",1350,975,"2,100","1,520",21/2,34/2,591,1950000 +402C Business Liner II,Jet,"3,700",455,417,,6707,"4,059",5600,3300,"21,500","12,135",55/1,43/9,"1,715",5100000 +"402,-A turbocharged",Jet,"3,700",457,,,6707,"4,380",4540,3109,"19,500","12,130",55/1,43/9,"1,715",5100000 +100 Darter (S.L. Industries),Jet,"8,650",480,442,,16665,"4,200",5400,3300,"43,250","19,950",68/5,61/10,"3,391",4100000 +100 Darter (S.L. Industries),Jet,"7,500",480,442,,14890,"3,400",5700,3300,"41,400","18,660",68/5,61/10,"2,991",4100000 +T 310 P turbocharged,Jet,"3,500",491,460,99,7400,"4,340",4972,3075,"18,300","9,838",48/8,39/6,"2,289",4500000 +310 Q,Jet,"3,500",477,441,105,7431,"5,100",4080,3105,"17,000","8,802",48/7,39/5,"2,289",4500000 +100 Darter (S.L. Industries),Jet,"3,500",471,460,99,6198,"4,760",4972,3075,"18,300","9,838",48/8,39/6,"1,818",4500000 +100 Darter (S.L. Industries),Jet,"3,500",477,441,105,6171,"5,100",4080,3105,"17,000","8,762",48/7,39/5,"1,818",4500000 +100 Darter (S.L. Industries),Jet,"2,950",478,441,79,5373,"6,110",2880,2220,"15,000","8,157",47/7,43/9,"1,546",4790000 +"310,-A",Jet,"2,950",475,441,79,4704,"6,350",2630,2220,"15,000","8,195",47/7,43/9,"1,450",4880000 +"T 337 G-P II,H-P, Skymaster pressurized",Jet,"2,950",464,459,97,6098,"6,830",3937,2817,"15,000","7,950",47/7,35/7,"1,431",5100000 +100 Darter (S.L. Industries),Jet,"2,950",464,441,104,6098,"6,050",5186,3090,"15,000","7,355",47/6,35/7,"1,450",4500000 +T 337 E & F Skymaster turbocharged,Jet,"2,950",464,441,104,7464,"6,050",5186,3090,"15,000","7,233",47/6,35/7,"1,525",4500000 +100 Darter (S.L. Industries),Jet,"2,950",477,442,90,6098,"6,050",5186,2703,"15,000","7,206",47/6,35/6,"1,385",4500000 +T 337 C Skymaster turbocharged,Jet,"2,950",475,441,88,5628,"7,100",3297,2873,"13,500","7,130",43/3,35/6,"1,001",4500000 +100 Darter (S.L. Industries),Jet,"2,950",477,441,88,4791,"7,220",3000,2789,"12,900","7,025",43/3,35/6,730,4500000 +"337 G,H II Skymaster (prior '79 service ceiling=18,000)",Jet,"2,950",464,441,99,5628,"6,800",3917,2800,"13,500","6,988",43/3,35/7,"1,001",4500000 +100 Darter (S.L. Industries),Jet,"2,950",477,442,87,840,"6,800",3917,2703,"13,500","6,803",43/3,35/6,"1,001",4500000 +337 E Skymaster,Jet,"2,950",461,442,88,715,"6,900",3370,2850,"12,500","6,519",43/3,35/6,"1,385",4500000 +100 Darter (S.L. Industries),Jet,"2,950",474,437,88,834,"6,300",3100,3307,"13,500","6,927",43/3,35/7,"1,001",4500000 +337 C Skymaster,Jet,"2,850",469,417,90,847,"6,300",3100,3350,"13,000","7,090",43/2,35/7,"1,100",4100000 +100 Darter (S.L. Industries),Jet,"2,850",488,423,89,828,"6,900",2940,2800,"12,500","6,700",43/2,35/7,"1,000",4100000 +208 Caravan-675,Jet,"6,400",.92 Mach,511,,13000,"3,720",5710,3820,"35,700","21,400",72/2,63/9,"3,250",5100000 +100 Darter (S.L. Industries),Jet,"4,000",.85 Mach,476,97,7385,"4,442",4690,2910,"22,450","13,700",55/5,53/5,"2,300",5100000 +100 Darter (S.L. Industries),Jet,"3,650",473,,97,7385,"1,520",5150,2900,"22,000","12,775",55/5,53/5,"2,345",5100000 +"T 210 K,L Turbo (K=9 mph less speed)",Jet,"3,650",472,461,97,7384,"3,699",5030,2900,"22,000","11,811",55/6,53/6,"2,600",5100000 +100 Darter (S.L. Industries),Jet,"3,650",472,,89,7384,"3,909",4710,2560,"21,000","11,720",55/6,53/6,"2,600",5100000 +T 210 F Turbo Centurion,Jet,"3,786",.76 Mach,429,82,6590,"3,090",3460,3315,"19,200","11,310",51/10,55/8,"2,027",nan +100 Darter (S.L. Industries),Jet,"3,400",.755 Mach,427,83,5440,"4,500",,,"16,830","9,977",48/9,54/1,"1,970",4500000 +"210 M,NII (prior'78=less svc. ceil. & r/o/c=860)",Jet,"3,045",.76 Mach,430,82,5771,"4,230",3180,2800,"16,300","9,550",48/11,52/2,"1,960",nan +210 F Centurion,Jet,"2,887",.70 Mach,401,86,4860,"3,195",3600,3180,"14,800","8,925",47/2,52/2,"1,900",nan +100 Darter (S.L. Industries),Jet,"2,500",,403,82,5820,"3,000",3240,3050,"15,000","8,049",47/2,52/2,"2,104",4300000 +100 Darter (S.L. Industries),Jet,"2,500",,385,82,5008,"3,370",2990,2270,"13,300","7,388",47/2,52/2,"1,930",4300000 +T 207 (Stationair 7 & 8) '77=eng 310 hp,Jet,"2,500",,385,82,5009,"3,625",2650,2210,"12,500","7,181",47/2,52/2,"2,104",4300000 +100 Darter (S.L. Industries),Jet,"2,820",.737 Mach,351,,4710,"2,460",,2770,"13,870","8,300",50/2,53/4,"1,875",4500000 +"U206E,F, float ('75-'76 specs) '76=+4 gal fuel",Jet,"1,900",.71 Mach,380,85,3220,"3,311",3080,2750,"10,400","6,550",42/6,46/8,"1,485",4100000 +"TU 206E,F (ski) (prior'76=4 gal more fuel)",Jet,"2,200",,357,82,3807,"2,719",2930,2270,"11,850","6,631",43/6,47/1,"1,325",4100000 +"U206 E,F (ski) Super Skywagon",Jet,"2,200",,351,,3780,"2,680",2930,2270,"11,850","6,684",43/6,47/1,"1,325",4100000 +100 Darter (S.L. Industries),Jet,"2,200",,348,85,3780,"2,900",2660,2300,"11,500",6454,43/6,43/11,"1,308",4100000 +"TU 206E,F Stationair",Jet,"2,200",,348,84,3618,"2,900",2550,2310,"10,850","6,350",43/6,43/9,"1,064",3500000 +100 Darter (S.L. Industries),Propjet,625,295,283,76,475,"2,435",2465,1875,"9,850","5,682",39/0,49/4,"2,193",3500000 +"U206 F,G II ('75 up) '75 lgt=28/9 + prior'79 less fuel)",Propjet,450,263,,84,366,"1,861",2490,2150,"8,600","4,948",35/10,44/1,"1,248",3340000 +100 Darter (S.L. Industries),Propjet,450,263,250,79,366,"2,027",2341,2145,"8,200","4,915",35/10,44/1,"1,461",3470000 +U 206 A,Piston,375,257,241,74,213,"1,940",2323,2293,"7,450","5,048",36/5,41/1,"1,197",3020000 +100 Darter (S.L. Industries),Piston,375,245,235,74,175,"1,850",2387,2178,"7,450","4,426",36/1,41/9,845,3100000 +"TP 206A,B,C,D,E (E=35/10 span) (A=28/0 lgt)",Piston,375,240,227,76,175,"1,700",2516,2110,"6,840","4,252",33/8,39/9,826,2600000 +100 Darter (S.L. Industries),Piston,375,240,222,76,170,"1,700",2516,2110,"6,800","4,237",33/5,39/9,826,2600000 +206H Stationair,Piston,310,235,224,72,213,"1,520",2595,2393,"6,750","4,543",36/4,44/1,"1,099",3080000 +100 Darter (S.L. Industries),Piston,310,239,224,70,102,"1,580",2350,1865,"6,350","4,039",33/9,39/11,"1,090",3010000 +P 206 Super Skylane,Piston,340,231,214,72,175,"1,900",2010,1851,"6,500","3,865",33/5,39/9,908,2600000 +100 Darter (S.L. Industries),Piston,375,232,217,70,348,"1,575",2367,2130,"8,400","4,965",39/6,46/4,553,2600000 +195 B,Piston,325,230,213,68,213,"1,450",2195,2485,"6,850","4,238",36/4,44/1,875,2690000 +100 Darter (S.L. Industries),Piston,300,229,210,70,102,"1,610",2220,1765,"6,300","4,038",36/1,39/11,"1,180",2618000 +195,Piston,300,227,209,69,102,"1,610",2220,1765,"6,300","3,719",35/8,39/9,833,2618000 +100 Darter (S.L. Industries),Piston,300,227,209,69,102,"1,610",2220,1765,"6,300","3,674",33/8,39/9,"1,102",2618000 +T 188C Ag Husky (prior'81=4 mph less),Piston,310,243,229,71,102,"1,650",2175,1850,"5,990","4,184",34/4,38/1,"1,106",2980000 +100 Darter (S.L. Industries),Piston,285,226,210,71,102,"1,500",2430,1840,"5,975","3,730",34/4,38/1,"1,258",2650000 +A188B Ag Wagon ('79 & up) dispersal sys,Piston,300,230,215,71,102,"1,400",2365,1850,"5,990","3,963",34/4,38/1,928,2680000 +100 Darter (S.L. Industries),Piston,285,239,226,64,102,"1,924",1515,1734,"5,300","3,273",29/6,36/9,735,2900000 +A188 Ag Wagon restricted (prior'77=7/10 hgt),Piston,260,229,213,63,102,"1,820",1584,1910,"5,200","3,260",29/6,36/9,735,2810000 +100 Darter (S.L. Industries),Piston,260,230,204,68,102,"1,820",1890,2056,"4,990","3,190",29/5,36/9,740,2810000 +188 Ag Pickup fixed-prop ('72) restricted,Piston,285,237,223,70,102,"1,700",1662,1790,"5,500","3,723",31/11,36/11,517,2740000 +100 Darter (S.L. Industries),Piston,285,238,225,67,102,"1,790",1662,1790,"5,500","3,292",29/6,36/9,807,2820000 +"188A,B Ag Wagon ('67-'71) fixed-prop normal",Piston,285,239,226,66,102,"1,862",1590,1790,"5,400","3,292",29/3,36/9,807,2860000 +100 Darter (S.L. Industries),Piston,285,207,194,70,102,"1,662",1700,1790,"5,500","3,603",31/11,36/11,661,1975000 +A185 F (float) (prior '79 = 4-8 gal less fuel),Piston,260,205,192,63,102,"1,495",1795,1697,"5,300","3,214",29/6,36/9,680,1950000 +A185 F II Skywagon (prior '81=less perform),Piston,260,206,193,62,102,"1,540",1716,1673,"5,200","3,170",29/2,36/9,676,1990000 +100 Darter (S.L. Industries),Piston,260,206,193,65,102,"1,540",1716,1582,"5,200","3,125",29/5,36/11,676,1990000 +185 E Skywagon,Piston,260,207,194,65,102,"1,590",1640,1540,"5,100","3,094",29/6,36/11,687,2030000 +100 Darter (S.L. Industries),Piston,260,209,194,65,102,"1,690",1545,1900,"5,100","3,063",29/5,37/5,687,2110000 +R182 II RG Turbo Skylane,Piston,260,210,191,73,102,"1,800",1395,1720,"4,990","3,049",29/5,36/9,678,2130000 +100 Darter (S.L. Industries),Piston,240,191,178,71,102,"1,700",1375,1710,"4,600","2,850",27/1,36/1,640,1950000 +T-182R II Turbo Skylane,Piston,250,216,180,62,155,"1,480",1750,1450,"5,000","3,305",30/5,38/10,835,2500000 +100 Darter (S.L. Industries),Piston,225,211,204,62,150,"1,170",1500,1675,"4,700","3,184",29/10,38/2,939,2000000 +182T Skylane,Piston,210,207,200,62,90,"1,160",1675,1650,"4,630","3,038",29/10,38/2,"1,080",2000000 +100 Darter (S.L. Industries),Piston,210,200,194,61,93,"1,105",1675,1650,"4,630","2,850",29/10,38/2,920,2930000 +182R II (1981 & up),Piston,210,201,195,61,93,"1,155",1595,1650,"4,500","2,815",29/10,38/0,920,3010000 +100 Darter (S.L. Industries),Piston,210,201,195,59,93,"1,155",1595,1520,"4,500","2,795",29/10,38/0,920,3010000 +182 N Skylane,Piston,210,202,196,57,93,"1,250",1490,1500,"4,300","2,785",29/1,38/0,920,3100000 +100 Darter (S.L. Industries),Piston,210,172,169,61,90,940,1675,1650,"4,630","2,925",29/9,38/2,"1,139",1630000 +"182 E,F,G,H",Piston,210,173,165,61,93,"1,100",1675,1650,"4,630","2,695",29/9,38/2,920,1800000 +100 Darter (S.L. Industries),Piston,210,173,166,60,93,"1,180",1565,1650,"4,440","2,660",29/9,38/2,922,1930000 +182,Piston,210,173,166,60,93,"1,200",1545,1650,"4,400","2,655",29/9,38/0,920,1950000 +100 Darter (S.L. Industries),Piston,210,173,166,58,93,"1,200",1545,1520,"4,400","2,650",29/9,38/0,922,1950000 +"180J,K amphib prior'76=4 gal more fuel (range w/80 gal fuel)",Piston,210,174,167,57,93,"1,250",1490,1500,"4,300","2,615",29/9,38/0,922,2000000 +100 Darter (S.L. Industries),Piston,210,174,167,55,93,"1,200",1435,1465,"4,200","2,615",29/9,38/0,922,2050000 +"180J,K (ski) (prior'79=15,600 serv. ceil, less fuel)",Piston,210,159,150,52,93,"1,300",1145,1395,"3,900","2,320",29/7,38/0,880,1900000 +100 Darter (S.L. Industries),Propjet,675,182,154,61,335,"1,234",2053,1655,"8,000","3,973",37/6,52/1,866,2500000 +180,Propjet,675,186,154,61,335,975,2420,1880,"8,750","4,765",41/7,52/1,810,2370000 +100 Darter (S.L. Industries),Piston,310,235,235,60,388,1400,,,3600,2350,25/2,36,1270,2500000 +177 RG Cardinal,Piston,325,224,212,55,85,"1,150",2110,1600,"4,100","2,471",28/4,38/10,715,2500000 +100 Darter (S.L. Industries),Piston,310,201,191,58,90,945,2160,1500,"4,000","2,481",28/2,36/9,661,2300000 +177 B (thru'74)'70-'71 lgt=27/0 & hgt=9/1,Piston,325,224,207,55,87,"1,150",2110,1600,"4,100","2,320",28/4,38/10,715,2900000 +100 Darter (S.L. Industries),Piston,310,204,192,58,90,930,2160,1500,"4,000","2,303",28/2,36/9,715,2700000 +177 Cardinal,Piston,310,204,197,57,90,"1,030",1900,1500,"3,800","2,306",28/2,36/9,732,2850000 +100 Darter (S.L. Industries),Piston,285,205,187,57,90,930,2030,1500,"3,800","2,180",28/3,36/9,732,2850000 +"175,-A,-B",Piston,285,203,194,55,89,"1,115",1365,1355,"3,400","2,050",28/3,36/9,739,3020000 +100 Darter (S.L. Industries),Piston,285,200,191,54,65,"1,280",1265,1340,"3,300","1,965",28/0,36/7,539,3130000 +172 Q Cutlass,Piston,300,175,,53,87,"1,060",2050,1585,"3,850","2,220",28/2,38/10,600,1600000 +100 Darter (S.L. Industries),Piston,300,174,170,57,90,950,2030,1500,"3,800","2,219",28/2,36/9,600,1730000 +172S Skyhawk SP,Piston,285,174,167,55,90,"1,000",1365,1355,"3,400","1,960",28/3,36/9,760,1830000 +100 Darter (S.L. Industries),Piston,285,172,165,54,65,"1,115",1265,1340,"3,300","1,865",28/0,36/7,640,1990000 +172 P II Skyhawk,Piston,285,173,166,53,65,"1,210",1110,1275,"3,100","1,860",28/4,36/7,640,2100000 +100 Darter (S.L. Industries),Piston,260,170,160,52,65,"1,270",1210,1110,"3,000","1,780",27/3,36/6,680,2030000 +172 M Skyhawk ('76=4 mph more speed),Piston,260,173,165,51,65,"1,300",1135,1190,"2,900","1,740",27/3,36/5,700,2070000 +100 Darter (S.L. Industries),Piston,310,170,161,59,61,885,1860,1500,"3,800","2,254",32/2,35/10,583,2600000 +R172K/Hawk XP (floats),Piston,300,150,143,59,61,810,1970,1500,"3,800","2,177",32/2,35/10,612,1330000 +100 Darter (S.L. Industries),Piston,310,155,135,52,92,835,2790,1750,"3,600","2,404",29/8,35/10,640,2560000 +172 M (float plane 1974-76),Piston,285,157,143,50,65,950,2400,1610,"3,600","2,295",28/6,35/10,626,2420000 +100 Darter (S.L. Industries),Piston,300,137,131,51,92,925,2820,1675,"3,500","2,342",29/8,35/10,680,1390000 +172 K & L float plane,Piston,300,136,130,53,61,855,2475,1570,"3,500","2,312",29/8,35/10,580,1390000 +100 Darter (S.L. Industries),Piston,300,136,131,49,65,855,2475,1570,"3,500","2,200",28/6,35/10,510,1390000 +"172,-D,-E,-F,-G,-H",Piston,300,136,131,,65,855,2475,1570,"3,500","2,060",28/6,36/10,510,1390000 +100 Darter (S.L. Industries),Piston,285,145,132,61,65,920,,,"3,300","2,165",28/8,35/10,626,2350000 +100 Darter (S.L. Industries),Piston,300,121,119,52,65,800,,,"3,300","2,065",28/8,35/10,510,1150000 +"170,-A,-B",Piston,310,174,152,54,92,"1,010",1640,1395,"3,600","2,066",28/3,35/10,640,2700000 +100 Darter (S.L. Industries),Piston,285,174,148,53,65,"1,030",1810,1395,"3,600","1,935",28/9,35/10,580,2630000 +"150M, A150M (Aerobat span=32/9)",Piston,285,174,160,53,65,"1,030",1810,1395,"3,600","1,795",27/8,36/7,627,2630000 +100 Darter (S.L. Industries),Piston,300,156,147,54,92,920,1780,1395,"3,600","2,002",28/3,35/10,680,1480000 +A 150 L Aerobat (1974),Piston,300,151,143,53,65,920,1780,1395,"3,600","1,725",28/8,36/7,510,1480000 +100 Darter (S.L. Industries),Piston,285,151,142,53,65,920,1810,1340,"3,600","1,795",27/9,36/7,555,1480000 +"150 H,J,K (K=8/8 height)",Piston,285,154,144,52,65,"1,075",1265,1340,"3,300","1,760",27/9,36/7,563,1670000 +100 Darter (S.L. Industries),Piston,285,174,160,53,65,"1,030",1810,1395,"3,600","1,915",28/3,36/7,609,2630000 +"150 D,-E,-F,-G",Piston,310,178,150,54,92,"1,050",1740,1395,"3,600","2,351",28/3,36/0,713,2700000 +100 Darter (S.L. Industries),Piston,300,151,142,54,92,988,1860,1395,"3,600","2,351",28/3,36/0,713,1570000 +150,Piston,285,151,142,53,65,920,1810,1395,"3,600","1,820",28/3,36/7,555,1480000 +100 Darter (S.L. Industries),Piston,285,154,143,52,65,"1,075",1265,1340,"3,300","1,790",28/2,36/6,565,1670000 +140,Piston,260,145,138,50,65,965,1465,1510,"3,300","1,750",27/3,36/6,515,1610000 +100 Darter (S.L. Industries),Piston,275,152,143,54,80,"1,135",1605,1495,"3,350","2,050",27/3,36/2,583,1625000 +Vision Jet SF50,Piston,245,153,139,56,80,"1,050",1670,1495,"3,350","2,030",27/3,36/2,603,1600000 +Columbia 400 LC-41,Piston,300,157,148,55,80,"1,090",1670,1495,"3,350","2,050",27/3,36/2,600,1600000 +Columbia 350 LC-42,Piston,240,153,139,56,80,"1,090",1670,1495,"3,350","2,015",27/3,36/2,610,1600000 +100 Darter (S.L. Industries),Piston,310,113,106,58,54,510,2060,1265,"4,400","2,322",26/6,41/8,283,1400000 +115TC,Piston,300,106,101,57,54,465,2250,1265,"4,200","2,230",25/11,41/8,290,780000 +100 Darter (S.L. Industries),Piston,300,107,103,55,54,525,1965,1265,"4,000","2,179",25/11,40/9,290,870000 +114TC,Piston,300,105,98,50,54,690,1090,1265,"4,200","2,220",26/3,41/8,290,1110000 +100 Darter (S.L. Industries),Piston,300,105,98,50,37,690,1090,1265,"4,000","2,189",26/3,40/9,278,1110000 +"114,('76-24/10 length + less performance)",Piston,230,91,78,50,37,400,1920,1265,"3,800","1,900",25/3,40/9,204,650000 +100 Darter (S.L. Industries),Piston,230,94,83,50,37,460,1740,1265,"3,800","1,835",25/3,40/9,204,770000 +112B,Piston,300,131,123,50,37,940,970,1265,"3,300","1,845",26/3,40/4,278,1570000 +100 Darter (S.L. Industries),Piston,230,103,101,50,37,710,1365,1265,"3,300","1,815",25/3,40/4,204,1300000 +112,Piston,300,136,130,50,88,950,1710,1480,"3,100","2,253",27/6,35/10,500,1610000 +Top Cub,Piston,300,141,134,52,88,960,2125,1565,"3,320","1,998",27/0,35/10,522,1640000 +100 Darter (S.L. Industries),Piston,300,155,145,49,88,"1,075",1430,1400,"3,350","1,750",25/8,35/10,645,1790000 +Falcon 900 (3-eng fan jet),Piston,300,155,147,51,65,"1,040",1330,1400,"3,350","1,565",25/6,36/2,470,1750000 +100 Darter (S.L. Industries),Piston,300,153,145,54,65,950,1290,1285,"3,300","1,560",25/6,36/2,470,1690000 +Falcon 200,Piston,260,153,145,54,65,"1,000",1510,1265,"3,200","1,520",25/5,36/0,561,1730000 +100 Darter (S.L. Industries),Piston,235,187,173,50,92,"1,040",1570,1320,"3,100","1,846",28/8,35/10,845,2000000 +Falcon Fan Jet D 20,Piston,235,160,156,50,92,"1,140",1570,1320,"3,100","1,809",28/8,35/10,845,1800000 +100 Darter (S.L. Industries),Piston,235,168,157,49,92,965,1475,1350,"3,100","1,781",28/5,35/10,743,2000000 +Falcon F 20,Piston,235,176,159,49,92,"1,040",1385,1350,"3,100","2,075",29/0,36/0,971,2000000 +100 Darter (S.L. Industries),Piston,230,150,145,49,92,924,1514,1350,"3,100","1,970",29/0,36/0,930,1810000 +Falcon Fan Jet 10,Piston,230,143,140,46,88,924,1625,1280,"3,110","1,882",28/0,16/0,813,1810000 +100 Darter (S.L. Industries),Piston,230,146,142,49,92,865,1515,1350,"3,100","1,775",28/0,35/10,817,1490000 +DHC 6-300 Twin Otter Std. + '77-'78=less perf.,Piston,230,148,143,50,92,"1,010",1350,1350,"2,950","1,754",28/0,35/10,817,1650000 +100 Darter (S.L. Industries),Piston,230,146,139,50,65,890,1350,1350,"2,950","1,640",28/1,35/10,550,1770000 +DHC 6-200 Twin Otter,Piston,230,148,141,48,65,980,1205,1350,"2,800","1,620",28/5,36/2,550,1890000 +Twin Star Diamond DA42,Piston,230,148,141,48,65,980,1205,1350,"2,800","1,610",27/4,36/2,550,1890000 +100 Darter (S.L. Industries),Piston,230,148,141,54,55,"1,030",1080,1310,"2,650","1,560",26/0,36/0,450,1980000 +Katana DA20-C1,Piston,230,143,135,54,55,"1,120",1020,1290,"2,550","1,540",26/0,36/0,443,2010000 +100 Darter (S.L. Industries),Piston,230,137,123,48,88,970,1900,1720,"2,950","2,204",27/6,35/10,804,1530000 +100 Darter (S.L. Industries),Piston,230,137,123,48,61,990,2070,1720,"2,950","2,110",27/6,35/10,804,1600000 +TB-21 TC Trinidad,Piston,230,137,123,48,61,990,2070,1720,"2,950","1,955",27/0,35/10,804,1600000 +100 Darter (S.L. Industries),Piston,230,137,123,48,88,910,,,"2,800","1,790",28/10,35/10,804,1470000 +TB-200XL,Piston,230,148,141,48,88,"1,100",1205,1365,"2,800","1,701",25/8,35/10,804,1770000 +100 Darter (S.L. Industries),Piston,230,148,141,50,65,"1,090",1205,1365,"2,800","1,525",25/6,36/2,622,1960000 +TB-9 Tampico Club,Piston,230,148,139,54,55,"1,130",1080,1330,"2,650","1,555",26/0,36/0,513,2150000 +Eclipse 500,Piston,225,144,137,51,55,"1,110",1095,1310,"2,550","1,540",26/0,36/0,513,2120000 +100 Darter (S.L. Industries),Piston,200,157,149,50,61,925,1585,1350,"2,800","1,765",27/3,35/6,500,1710000 +Eurofox,Piston,200,153,144,50,51,860,1585,1350,"2,800","1,630",27/3,35/6,506,1690000 +Excalibur '800' conv. Beech Twin Bonanza,Piston,180,139,130,46,50,840,1400,1220,"2,500","1,643",27/3,35/6,490,1460000 +Excalibur conv. Beech Twin Bonanza,Piston,180,133,123,46,50,840,1400,1220,"2,500","1,485",27/3,35/6,417,1460000 +100 Darter (S.L. Industries),Piston,180,130,120,49,49,760,1575,1220,"2,500","1,440",27/0,35/7,410,1580000 +"Queen Air '8800' conv. Beech A80,B80",Piston,150,125,117,46,49,670,1575,1135,"2,350","1,415",27/0,35/7,591,1270000 +"Metro III,A",Piston,175,123,118,56,52,790,1450,1170,"2,450","1,325",25/0,36/0,589,1450000 +"Metro, II (prior '79 height=16/10)",Piston,175,128,121,54,52,850,1340,1155,"2,350","1,312",25/0,36/0,593,1590000 +100 Darter (S.L. Industries),Piston,180,144,140,50,66,800,1775,1340,"2,650","1,627",27/5,35/10,720,1680000 +Merlin IV C,Piston,180,123,122,48,54,680,1690,1335,"2,550","1,480",26/11,36/1,475,1700000 +100 Darter (S.L. Industries),Piston,195,132,130,47,52,870,1360,1345,"2,550","1,572",27/2,35/10,570,1700000 +Fairchild 300,Piston,180,126,124,48,56,730,1630,1335,"2,550","1,663",27/2,36/1,638,1400000 +100 Darter (S.L. Industries),piston,160,123,122,47,53,720,1685,1295,"2,457","1,600",26/11,36/1,580,1350000 +"Merlin III C-23 (12,500 gross) (SN TT426 & up)",Piston,160,123,120,46,43,700,1625,1280,"2,400","1,454",26/11,35/10,440,1300000 +100 Darter (S.L. Industries),Piston,160,124,122,44,43,770,1390,1250,"2,300","1,430",26/11,35/10,440,1420000 +Merlin III A,Piston,150,122,115,44,42,645,1525,1250,"2,300","1,335",26/11,35/10,435,1310000 +100 Darter (S.L. Industries),Piston,150,122,115,43,42,645,1525,1250,"2,300","1,315",26/11,35/9,417,1310000 +Merlin II A,Piston,195,117,116,44,52,870,1850,1390,"2,550","1,834",26/10,35/10,539,1550000 +CTSW,Piston,160,96,95,44,43,740,2160,1345,"2,220","1,632",26/8,35/10,440,1500000 +800XP,Piston,150,98,97,44,42,715,2390,1345,"2,220","1,574",27/0,35/10,435,1200000 +100 Darter (S.L. Industries),Piston,150,94,92,45,42,580,2390,1345,"2,220","1,450",27/0,35/9,435,1200000 +G V,Piston,150,94,92,45,42,580,2390,1345,"2,220","1,405",26/7,35/9,418,1200000 +100 Darter (S.L. Industries),Piston,150,122,115,43,42,645,1525,1250,"2,300","1,300",26/11,36/2,515,1310000 +G IV,Piston,145,121,114,43,42,645,1525,1250,"2,300","1,330",26/6,36/2,515,1310000 +100 Darter (S.L. Industries),Piston,145,121,114,43,42,675,1450,1200,"2,250","1,330",26/5,36/0,515,1420000 +100 Darter (S.L. Industries),Piston,145,122,114,51,42,730,1370,1115,"2,200","1,325",26/5,36/0,515,1510000 +GII (G1159) with tip tanks,Piston,145,117,108,50,37,660,1650,1115,"2,200","1,260",25/0,36/0,420,1330000 +100 Darter (S.L. Industries),Piston,145,122,104,50,42,690,1820,1145,"2,200","1,205",25/0,36/0,410,1550000 +GI (G-159) ceiling quoted w/APU,Piston,110,110,107,43,26,715,1340,1200,"1,670","1,141",24/1,33/2,315,1470000 +"HS125-700,A (Hawk-Sidd mfg) ('77=24,200 gross)",Piston,100,109,106,42,26,670,1385,1075,"1,600","1,104",23/11,33/2,303,1400000 +100 Darter (S.L. Industries),Piston,100,106,102,42,26,670,1385,1075,"1,600","1,060",23/9,33/2,303,1265000 +BH-124-400A (Beech mfg),Piston,100,108,103,42,26,670,1385,1075,"1,600","1,040",23/9,32/9,303,1400000 +100 Darter (S.L. Industries),Piston,100,104,100,42,26,670,1385,1075,"1,600","1,020",23/9,32/9,303,1265000 +1124A Westwind 2,Piston,100,106,102,42,26,670,1385,1075,"1,600","1,065",23/9,32/9,303,1265000 +100 Darter (S.L. Industries),Piston,100,90,85,43,26,560,2075,850,"1,650","1,120",24/1,32/9,276,1070000 +1123 Commodore Jet,Piston,100,109,102,43,26,670,1375,1075,"1,600",970,23/9,32/8,303,1265000 +100 Darter (S.L. Industries),Piston,100,108,105,43,26,740,1205,1055,"1,500",965,21/1,33/4,320,1530000 +1121 Jet Commander,Piston,100,108,104,47,26,740,1205,1055,"1,500",962,21/0,33/4,318,1530000 +J250-SP,Piston,90,103,90,46,21,640,1850,1530,"1,500",850,20/9,33/3,315,1560000 +J230-SP,Piston,85,102,88,46,21,620,1950,1530,"1,500",850,20/9,33/3,315,1510000 +LA-4 Amphibian,Piston,85,104,100,43,25,640,1850,1530,"1,450",818,20/9,32/8,390,1550000 +C-1 Amphibian,Jet,1846,311,305,67,2000,1609,3192,,6000,3550,30/7,38/7,1275,3100000 +LA-4-200 Buccaneer (land specs),Piston,310,235,181,57,106,,1250,2350,"3,600","2,500",25/2,35/8,"1,300",2500000 +XL2,Piston,310,235,179,57,106,,1250,2350,"3,400",2.3,25/2,35/8,"1,300",1800000 +Jetstar -8,Piston,310,235,190,57,106,,1250,2350,"3,400","2,250",25/2,36,"1,300",1800000 +100 Darter (S.L. Industries),Piston,270,197,187,59,90,"1,050",2223,1312,"3,305","2,152",24/11,32/10,870,2500000 +8:00 AM,Piston,265,164,149,54,90,"1,070",1985,1200,"3,250","2,102",24/11,32/10,"1,005",1680000 +Superstar I (601P Aerostar),Piston,270,197,177,59,90,"1,050",2223,1312,"3,305","2,151",24/11,32/9,870,2500000 +MX-7-160,Piston,260,166,157,54,68,"1,030",2150,1200,"3,260","2,070",25/1,32/9,665,1650000 +MX-7-180A,Piston,260,166,157,55,68,"1,088",1990,1200,"3,140","1,905",25/1,32/9,665,1740000 +M-7-235,Piston,210,170,163,54,48,914,1750,1275,"2,950","2,035",25/1,35/7,620,2000000 +100 Darter (S.L. Industries),Piston,200,150,142,51,48,950,1829,1039,"2,800","1,773",25/1,35/7,575,1505000 +M-5-235C,Piston,200,149,140,54,48,"1,020",1585,1310,"2,650","1,691",24/10,32/9,569,1390000 +100 Darter (S.L. Industries),Piston,200,152,143,53,60,"1,000",1460,1310,"2,550","1,530",24/11,32/9,750,1700000 +100 Darter (S.L. Industries),Piston,180,132,110,42,54,800,,,"2,300","1,200",23/6,35/2,555,1800000 +M-5-180C,Piston,100,122,103,27,25,790,975,1040,"1,320",825,23/3,34/2,434,1800000 +MU-300 Diamond II,Jet,"4,750",513,459,81,19165,"4,000",4950,3500,"45,500","22,573",66/4,63/5,"4,400",4100000 +100 Darter (S.L. Industries),Jet,"4,500",513,459,81,19165,"3,500",5300,3500,"45,500","22,573",66/4,63/5,"4,200",3900000 +MU-300 Diamond I,Jet,"3,700",518,430,77,15520,"3,430",4700,2800,"38,800","21,100",60/9,61/10,"3,500",4100000 +100 Darter (S.L. Industries),Jet,"5,200",521,435,89,10684,"3,065",5260,2860,"32,000","18,800",56/3,53/6,"2,500",3900000 +"MU-2L,-2N (-2N empty weight=7,760)",Jet,"4,125",530,457,90,8420,"3,500",3800,2940,"26,455","16,600",56/3,53/6,"1,200",4200000 +100 Darter (S.L. Industries),Jet,"4,250",512,457,90,8980,"3,500",6400,4850,"27,337","15,400",56/3,53/6,"1,630",4200000 +MU-2J,Jet,"4,315",530,458,84.5,9250,"3,300",5000,3300,"28,660","15,970",56/3,53/6,"1,610",4200000 +100 Darter (S.L. Industries),Jet,"4,500",530,410,88,9098,"3,330",4950,2450,"28,660","17,500",56/3,53/6,"1,614",3700000 +MU-2F,Jet,"3,230",513,459,84,5912,"4,600",4615,2750,"19,300","11,200",45/6,42/11,"1,828",3900000 +100 Darter (S.L. Industries),Jet,"3,230",513,459,88,5912,"4,600",4615,2750,"18,740","10,800",45/6,42/11,"1,828",4500000 +Acclaim (M20TN),Jet,"3,230",459,426,92,5910,"4,200",4300,3300,"18,300","10,800",45/4,42/11,"1,800",4500000 +TLS (M20M),Propjet,620,182,170,58,2583,"1,600",1500,1940,"12,500","6,881",51/9,65/0,300,2670000 +100 Darter (S.L. Industries),Propjet,620,182,171,64,2457,"1,600",1940,1940,"12,500","7,487",51/9,65/0,300,2670000 +PFM (M20L),Propjet,579,165,150,57,2457,"1,300",1900,1995,"11,579","7,250",51/9,65/0,,2430000 +Encore (MS20K),Piston,135,194,172,56,52,"1,280",1730,1877,"3,935","2,765",28/1,44/6,"1,129",1800000 +100 Darter (S.L. Industries),Piston,81,161,117,37,20,680,1560,1490,"1,609","1,095",23/6,35/7,526,nan +205 (M20J),Piston,125,163,132,34,25,"1,105",1263,1235,"1,654","1,166",23/6,35/8,422,nan +M-20C Ranger ('77-'78),Propjet,850,320,255,,1887,"1,380",2840,2430,"7,394","4,698",34/11,41/7,"1,520",3100000 +M-20-E Super 21 Chaparral,Propjet,700,300,291,61,1887,"2,380",1591,2034,"6,579","4,050",34/11,41/7,"1,563",3000000 +100 Darter (S.L. Industries),Piston,250,200,187,54,86,"1,125",1953,1750,"3,083","1,795",25/4,32/6,,2500000 +M-20-B Mark 21,Piston,250,167,160,54,86,"1,260",1953,1750,"3,083","1,744",25/4,32/6,"1,100",2000000 +100 Darter (S.L. Industries),Piston,200,,115,,55,937,1561,1474,"2,535","1,642",25/5,32/10,637,1600000 +M-20-Mark 20,Piston,180,132,117,51,54,790,1657,1394,"2,535","1,477",25/0,32/0,,1300000 +100 Darter (S.L. Industries),Piston,160,,106,50,41,738,1706,1378,"2,337","1,411",25/3,32/0,,1100000 +H-Rangemaster (1975-'76),Jet,900,.64 Mach,426,69,1686,"3,314",2297,2155,"5,920","3,550",33/6,37/11,"1,300",4100000 +100 Darter (S.L. Industries),Propjet,750,221,181,71,440,"1,650",4000,4400,"13,007","8,007",49/6,50/3,,2150000 +100 Darter (S.L. Industries),Piston,100,164,120,46,23,"1,000",,,"1,235",644,18/7,29/11,600,1400000 +100 Darter (S.L. Industries),Piston,400,226,213,71,230,"1,870",1525,1940,"7,600","5,100",31/5,46/0,,2220000 +PA-31-350 Chieftain ('81=optional fuel),Piston,380,239,226,71,230,"1,900",1225,1840,"7,300","4,500",31/5,46/0,,3000000 +100 Darter (S.L. Industries),Piston,400,213,201,68,230,"1,535",1706,2176,"8,000","5,400",N/C,N/C,,2220000 +"PA-31-310 Turbo Navajo B,C (prior '80 less fuel)",Piston,400,213,201,70,264,"1,490",2050,2450,"8,800","5,800",N/C,N/C,,2000000 +PA-31-300 Navajo,Propjet,"1,000",280,248,88,648,"2,635",3495,,"14,500","8,737",59/4,57/0,"1,827",2990000 +100 Darter (S.L. Industries),Propjet,940wet,,256,86,648,"2,400",2050,1970,"12,500","8,000",59/4,46/3,"1,250",2700000 +PA-23 E Turbo Aztec (1971 length=30/2),Propjet,"1,000",278,248,88,648,"2,635",3340,2970,"14,500","9,200",59/4,57/0,"2,040",2990000 +100 Darter (S.L. Industries),Propjet,1000 dry,283,261,87,648,"2,440",2850,2532,"14,000","9,100",59/4,57/0,"2,040",3000000 +"PA-23 D,E Aztec (prior '72 length=30/2)",Propjet,840,270,,86,544,"2,400",2050,1970,"12,500","8,200",59/4,46/3,"1,815",2700000 +100 Darter (S.L. Industries),Propjet,900,303,264,92,648,"2,650",3080,2805,"13,230","8,200",42/2,47/11,"2,178",2940000 +PA-23 C Aztec,Propjet,900,300,265,92,648,"2,650",2920,3530,"13,230","8,150",42/2,46/3,"2,025",2700000 +100 Darter (S.L. Industries),Propjet,900,303,264,89,648,"2,800",2400,3360,"12,500","8,090",42/2,46/3,"2,025",2940000 +"PA34-180, Seneca V",Propjet,900,309,295,89,648,"2,780",2970,3240,"12,500","7,800",42/2,46/3,"2,025",3140000 +100 Darter (S.L. Industries),Propjet,840,283,,83,648,"2,530",2150,1570,"12,500","7,600",42/2,46/3,"2,670",2890000 +PA-34-220T Seneca III (roc=t/o power),Propjet,665,,257,76,386,"2,780",2970,3240,"10,000","6,150",40/1,45/10,"1,318",2990000 +100 Darter (S.L. Industries),Propjet,550,,235,75,386,"1,950",2300,2380,"9,800","6,075",40/1,45/10,"1,250",3100000 +100 Darter (S.L. Industries),Piston,100,120,112,39,34,960,,,"1,320",649,20/4,28,1080,1400000 +100 Darter (S.L. Industries),Piston,100,145,119,35,29,850,1050,1100,"1,320",760,20/4,31/6,730,1320000 +PA-39 C/R Turbo Twin Comanche,Piston,100,133,116,36,18,850,1450,1410,"1,320",820,20/6,20/6,400,1320000 +PA-30 B Turbo Twin Comanche,Jet,"14,750",516,459,,41000,,5870,2950,"89,400","46,800",96/5,93/6,"6,500",5100000 +100 Darter (S.L. Industries),Jet,"13,850",505,459,115,29500,"4,122",5450,3190,"75,000","42,500",88/4,77/10,"4,220",4500000 +PA-23-150 Apache,Jet,"13,850",510,459,108,29500,"4,219",5265,3393,"73,200","42,500",88/4,77/10,"4,420",4500000 +100 Darter (S.L. Industries),Jet,"11,400",503,459,104,28300,"4,049",5115,3250,"69,700","38,000",83/1,77/10,"3,767",4500000 +100 Darter (S.L. Industries),Jet,"11,400",503,459,103,28300,"4,049",5115,3250,"69,700","38,150",79/11,77/10,"3,680",4500000 +PA-24-C 260 Comanche,Jet,"11,400",503,459,103,26800,"4,240",5750,3168,"65,500","37,186",79/11,72/6,"2,795",4500000 +100 Darter (S.L. Industries),Jet,"11,400",503,459,108,23300,"4,345",5625,3198,"64,800","36,544",79/11,68/11,"2,592",4500000 +PA-32R-301T Saratoga II TC,Propjet,"1,950",340,299,87,10463,"1,900",,,"36,000","23,008",63/9,78/4,"2,164",3000000 +100 Darter (S.L. Industries),Jet,"3,700",443,400,87,9450,"3,000",5800,3600,"24,800","14,150",50/9,47/0,,4100000 +PA-32-300 '73-74 hgt=7/9 ('79 fuel=94 gal),Jet,"3,750",456,435,83,9450,"4,500",4850,2125,"25,000","12,865",50/6,47/0,,4100000 +100 Darter (S.L. Industries),Jet,"3,360",443,385,77,9100,"3,100",5000,1990,"23,300","11,920",47/5,47/0,,4100000 +PA-32-260 ('74-'78) 1974 height=7/9,Jet,"3,360",443,385,79,9100,"4,150",3800,2450,"22,700","12,000",47/5,47/0,,4100000 +100 Darter (S.L. Industries),Jet,"3,700",476,415,89,9580,"3,500",5250,2350,"23,500","12,800",52/3,44/9,"2,904",4500000 +"PA-28R-,RT-,201T Turbo Arrow III, IV",Jet,"3,700",471,426,90,8710,"3,200",4950,2450,"22,850","12,500",52/3,44/9,"2,440",4500000 +100 Darter (S.L. Industries),Jet,"3,100",471,426,87,8620,"4,040",5750,2900,"20,700","11,500",52/3,44/8,"1,450",4500000 +PA-28-200R Arrow II (1973-'76),Jet,"2,950",471,426,84,7350,"5,000",5450,3900,"18,500","10,500",50/5,43/3,"1,100",4500000 +100 Darter (S.L. Industries),Jet,"2,850",471,426,82,6250,"4,400",5400,3900,"17,500","10,500",50/5,43/3,950,4000000 +100 Darter (S.L. Industries),Piston,120,125,120,39,36,700,,,"1,320",780,21/5,30/0,840,1500000 +PA-28 180 Challenger & Archer,Piston,120,130,120,39,35,700,,,"1,320",800,21/5,32/5,800,1500000 +100 Darter (S.L. Industries),Piston,180,122,114,39,40,800,1275,900,"2,400","1,555",24/11,38/0,296,1300000 +PA-28 C 150 Cherokee,Piston,180,123,117,45,30,800,1375,900,"2,350","1,520",23/6,34/0,305,1350000 +100 Darter (S.L. Industries),Piston,150,109,97,50,30,700,1550,900,"2,150","1,450",23/6,34/0,320,950000 +"PA-28 150,B 150 Cherokee",Piston,180,125,122,39,40,"1,000",1250,,"2,400","1,345",24/11,38/0,296,1350000 +100 Darter (S.L. Industries),Piston,180,149,143,39,40,800,1275,900,"2,400","1,575",24/11,38/0,543,2000000 +PA-28-151 Warrior (prior '77 stall=58),Piston,200,,130,39,40,"1,200",875,900,"2,600","1,535",24/11,38/0,455,1470000 +100 Darter (S.L. Industries),Piston,200,135,130,39,40,"1,200",1050,950,"2,690","1,555",24/11,38/0,455,1470000 +PA-36-375 Brave (spray restr. category),Piston,125,162,125,49,30,682,1496,1519,"1,750","1,160",20/4,28/9,505,nan +"PA-25 235 C,D Pawnee (normal category)",Jet,"3,300",496,435,106,2660,"4,050",,3440,"41,900","20,999",60/5,54/5,,4300000 +100 Darter (S.L. Industries),Piston,90,96,83,37,25,660,1850,1540,"1,400",870,20/0,35/0,273,1600000 +PA-22 135 Tri-Pacer,Piston,85,91,83,37,30,640,1850,1540,"1,400",791,19/8,34/7,346,1550000 +100 Darter (S.L. Industries),Piston,65,91,78,37,14,550,1950,1540,"1,260",750,19/8,34/7,182,1400000 +PA-18 150 Super Cub,Piston,325,260,250,77,165,"1,955",1980,2076,Orig,200,Orig,Orig,"1,137",nan +PA-18 95 Super Cub,Piston,160,,135,40,40,825,1180,500,"2,200","1,330",23/5,32/9,540,1300000 +100 Darter (S.L. Industries),Piston,180,,140,40,40,920,1150,500,"2,400","1,350",23/5,32.9,500,1500000 +100 Darter (S.L. Industries),Piston,235,,148,30,40,"1,350",600,600,"2,500","1,500",22/9,33/2,478,2000000 +PA-11,Piston,235,157,129,38,40,"1,900",540,540,"2,500","1,425",23/6,33/2,360,2000000 +100 Darter (S.L. Industries),Piston,235,,150,33,40,"1,350",600,600,"2,300","1,400",23/6,30/10,360,2000000 +Kodiak 100,Piston,220,157,150,33,42,"1,250",585,600,"2,300","1,300",23/2,30/10,485,1900000 +G3,Piston,210,,170,33,40,"1,250",600,600,"2,300","1,400",22/9,30/10,430,2000000 +Super 340 (Cessna 340 conversion),Piston,210,157,137,33,40,"1,250",600,600,"2,300","1,350",22/9,30/10,345,1800000 +100 Darter (S.L. Industries),Piston,180,,136,33,40,900,800,600,"2,300","1,300",22/9,30/10,525,1500000 +TurboStream (Cessna 310 & 320 conv) 310hp,Piston,220,157,152,35,42,"1,250",600,650,"2,300","1,280",22/0,29/8,530,1900000 +100 Darter (S.L. Industries),Piston,210,157,143,35,42,"1,250",585,600,"2,100","1,250",22/0,29/8,500,1800000 +Rocket (Cessna 310 conversion),Piston,180,148,135,35,42,"1,000",700,600,"2,300","1,250",22/6,29/8,525,1700000 +305 Rocket,Piston,145,157,130,35,42,700,900,600,"2,100","1,100",22/0,29/8,750,1200000 +100 Darter (S.L. Industries),Piston,967,151,109,54,106,"1,400",,1600,"9,300","5,500",31/2,58/0,,2500000 +100 Darter (S.L. Industries),Jet,"2,900",456,438,,4260,"3,180",3950,2930,"15,780","9,925",48/5,43/6,"1,530",4100000 +"200 D Meyers (Prop Jets, Inc.)",Jet,"2,500",430,400,77,4260,"3,050",3850,2800,"14,630",9410,48/5,43/6,"1,513",4100000 +100 Darter (S.L. Industries),Jet,"2,500",434,410,77,646,"3,000",4100,2700,"13,890","8,300",48/4,43/5,"1,510",4100000 +"200 Meyers (Prop Jets, Inc.)",Propjet,778,309,296,77,403,"2,200",2170,1880,"11,575","7,650",39/5,39/2,"1,391",2975000 +100 Darter (S.L. Industries),Propjet,727,322,313,73,403,"2,350",1800,1600,"10,470","7,010",33/3,39/2,"1,600",3350000 +100 Darter (S.L. Industries),Propjet,724,317,304,73,364,"2,840",1800,1600,"10,470","6,864",33/3,39/2,"1,250",3220000 +100 Darter (S.L. Industries),Propjet,776,296,283,77,364,"2,630",2170,1880,"11,575","7,570",39/5,39/2,"1,200",2960000 +100 Darter (S.L. Industries),Propjet,724,317,270,71,366,"3,100",1700,1490,"9,920","5,920",33/3,39/2,"1,175",3320000 +Sabre 75A,Propjet,724,300,265,73,366,"2,690",1870,1670,"10,800","6,800",39/5,39/2,"1,150",3080000 +100 Darter (S.L. Industries),Propjet,705,283,261,73,366,"2,590",1890,1670,"10,800","6,700",39/5,39/2,"1,100",2700000 +Sabre 65,Propjet,705,296,270,71,366,"2,875",1700,1320,"9,920","5,790",33/3,39/2,"1,150",3040000 +100 Darter (S.L. Industries),Propjet,605,261,,64,285,"2,000",1600,1100,"8,930","5,730",33/3,39/2,880,2600000 +100 Darter (S.L. Industries),Piston,280,242,237,59,100,1380,,,3374,2370,26/9,36/5,1852,2500000 +Sting Sport S3,Jet,280,242,175,56,89,1375,2100,2650,3368,2380,26/8,36/6,1275,2500000 +108-3,Piston,270,236,223,58,96,"1,230",2050,2600,"3,368","2,353",26/11,36/1,,2500000 +108-1,Piston,280,197,191,60,95,"1,200",1700,1600,"3,368","2,200",26/9,36/1,900,2100000 +GC-1B,Piston,217,161,,57,61,"1,030",2550,1910,"2,900","2,003",26/11,36/1,,1930000 +SA-160,Piston,310,217,200,60,92,"1,120",2079,1549,"3,680","2,380",26/1,35/0,813,2400000 +F-21B,Piston,310,197,170,59,100,1300,2600,2500,3380,2250,26/8,36/6,1450,2000000 +100 Darter (S.L. Industries),Piston,220,213,197,60,80,"1,300",2000,2320,"3,100","2,000",25/5,36/1,"1,100",2500000 +P2006T,Piston,210,219,201,59,75,"1,080",2200,2300,"2,900","1,800",25/5,36/1,980,2800000 +A-36 Bonanza Prop Jet,Piston,200,178,171,54,64,"1,060",1700,1600,"2,740","1,710",24/8,36/1,690,1860000 +1000 Jet Prop,Piston,210,201,191,57,75,"1,080",2060,2280,"2,900","1,800",25/5,36/1,980,2400000 +100 Darter (S.L. Industries),Piston,200,175,170,53,64,"1,030",1550,1550,"2,740","1,671",24/8,36/1,690,1880000 +900 Jet Prop,Piston,200,161,156,54,64,"1,055",1385,1786,"2,740","1,640",24/0,35/0,734,1880000 +100 Darter (S.L. Industries),Piston,200,165,160,50,52,"1,125",1550,1550,"2,575","1,600",23/2,35/0,601,2120000 +"690A, Prop Jet (690 length=43/0 & height=14/11)",Piston,180,155,147,50,52,875,1250,1670,"2,525","1,585",24/3,35/0,627,1700000 +100 Darter (S.L. Industries),Piston,180,147,143,50,52,800,1395,1610,"2,575","1,525",23/2,35/0,659,1650000 +720 Alti-Cruiser,Piston,180,153,150,50,52,860,1395,1550,"2,575","1,525",23/2,35/0,659,1950000 +100 Darter (S.L. Industries),Piston,200,171,163,50,52,"1,110",1300,1365,"2,575","1,575",23/2,35/0,601,1880000 +685 pressurized,Piston,180,165,158,50,52,800,1250,1550,"2,575","1,525",23/2,35/0,659,1720000 +100 Darter (S.L. Industries),Piston,180,165,158,50,52,"1,150",1050,1100,"2,450","1,525",23/2,35/0,745,1850000 +680 W Turbo II Prop Jet,Piston,180,165,157,50,35,"1,150",1050,1100,"2,450","1,440",23/1,35/0,513,2000000 +100 Darter (S.L. Industries),Piston,150,149,143,50,35,900,1150,1100,"2,450","1,415",23/1,35/0,655,1720000 +680 FLP Pressurized Grand,Piston,180,128,117,50,52,740,1125,1100,"2,500","1,455",23/2,35/0,715,1360000 +100 Darter (S.L. Industries),Piston,90,103,96,40,24,835,953,1016,"1,450",950,20/8,30/0,484,1250000 +680 FL Grand,Piston,90,112,108,39,24,640,1250,1350,"1,450",950,20/0,30/0,456,1730000 +100 Darter (S.L. Industries),Piston,65,123,109,37,12,"1,000",1000,1000,850,575,17/7,26/1,,1940000 +100 Darter (S.L. Industries),Piston,285,177,166,48,40,"1,375",980,980,"3,315","2,000",27/5,34/9,335,2150000 +560 F,Piston,260,160,156,51,108,"1,150",980,980,"3,315","1,925",27/5,34/5,"1,043",2050000 +100 Darter (S.L. Industries),Piston,260,165,147,57,40,"1,110",1100,1100,"2,850","1,950",27/3,33/3,340,1660000 +560 A-HC,Piston,205,142,135,57,40,"1,050",900,1100,"2,750","1,782",27/3,33/3,430,1560000 +560,Propjet,850,400,,90,2630,"3,000",2630,2650,"10,810","7,200",47/3,46/0,"1,746",4100000 +500 U,Propjet,1200,270,265,64,402,"1,680",2300,1830,"9,920","5,732",47/3,53/3,"2,261",3000000 +500 A,Propjet,1000,351,334,,3819,"3,242",1930,2280,"12,050","7,856",43/5,47/8,"1,879",4100000 +100 Darter (S.L. Industries),Propjet,720,312,288,89,578,"2,380",2280,3043,"11,200","6,837",43/5,47/8,"1,722",3584000 +520,Propjet,720,290,275,87,390,"2,236",3230,3017,"11,200","6,389",43/5,47/8,"1,515",3200000 +2180,Propjet,620,275,270,77,382,"1,750",2940,2446,"9,474","5,164",36/8,42/8,"1,336",3240000 +2150A Kachina,Propjet,620,283,269,75,382,"2,710",1980,2480,"9,000","5,018",34/8,42/8,"1,444",3160000 From 78932ee326d65321db81ca8bd3276b43cddc2ae8 Mon Sep 17 00:00:00 2001 From: hha980510 Date: Wed, 20 Nov 2024 15:23:55 -0600 Subject: [PATCH 04/26] Add files via upload --- Plane_price.ipynb | 17 +++-------------- 1 file changed, 3 insertions(+), 14 deletions(-) diff --git a/Plane_price.ipynb b/Plane_price.ipynb index 015ef11..69c09a0 100644 --- a/Plane_price.ipynb +++ b/Plane_price.ipynb @@ -13,6 +13,7 @@ "id": "9dc32f10", "metadata": {}, "source": [ + "CS584 Machine Learning\n", "Project 2\n", "\n", "A20557555 Hyunsung Ha\n", @@ -39,22 +40,10 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 4, "id": "9de818ab", "metadata": {}, - "outputs": [ - { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'matplotlib'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[1], line 5\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m \n\u001b[0;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m \n\u001b[1;32m----> 5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m \n", - "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'" - ] - } - ], + "outputs": [], "source": [ "# import required libraries :- \n", "\n", From ec4527b8d3a378a49e67506f83009da74af3e4d4 Mon Sep 17 00:00:00 2001 From: Nam Gyu Lee <96828702+namdarine@users.noreply.github.com> Date: Thu, 21 Nov 2024 12:57:10 -0600 Subject: [PATCH 05/26] Update README.md --- README.md | 28 +++++++++++----------------- 1 file changed, 11 insertions(+), 17 deletions(-) diff --git a/README.md b/README.md index f746e56..1501714 100644 --- a/README.md +++ b/README.md @@ -1,28 +1,22 @@ # Project 2 -Select one of the following two options: - -## Boosting Trees - -Implement the gradient-boosting tree algorithm (with the usual fit-predict interface) as described in Sections 10.9-10.10 of Elements of Statistical Learning (2nd Edition). Answer the questions below as you did for Project 1. - -Put your README below. Answer the following questions. - -* What does the model you have implemented do and when should it be used? -* How did you test your model to determine if it is working reasonably correctly? -* What parameters have you exposed to users of your implementation in order to tune performance? (Also perhaps provide some basic usage examples.) -* Are there specific inputs that your implementation has trouble with? Given more time, could you work around these or is it fundamental? - ## Model Selection Implement generic k-fold cross-validation and bootstrapping model selection methods. In your README, answer the following questions: -* Do your cross-validation and bootstrapping model selectors agree with a simpler model selector like AIC in simple cases (like linear regression)? -* In what cases might the methods you've written fail or give incorrect or undesirable results? -* What could you implement given more time to mitigate these cases or help users of your methods? -* What parameters have you exposed to your users in order to use your model selectors. +** Do your cross-validation and bootstrapping model selectors agree with a simpler model selector like AIC in simple cases (like linear regression)?** + - Yes. $R^2$ of Ridge Regression is 0.92 and mean $R^2$ of our cross-validation is 0.79 and bootstrapping model is 0.81. + +** In what cases might the methods you've written fail or give incorrect or undesirable results?** + - + +** What could you implement given more time to mitigate these cases or help users of your methods?** + - + +** What parameters have you exposed to your users in order to use your model selectors.** + - See sections 7.10-7.11 of Elements of Statistical Learning and the lecture notes. Pay particular attention to Section 7.10.2. From f58ced07ebe3a959099309ae98368e5e6de5468f Mon Sep 17 00:00:00 2001 From: Nam Gyu Lee <96828702+namdarine@users.noreply.github.com> Date: Thu, 21 Nov 2024 14:39:30 -0600 Subject: [PATCH 06/26] Delete Plane_price.ipynb --- Plane_price.ipynb | 2235 --------------------------------------------- 1 file changed, 2235 deletions(-) delete mode 100644 Plane_price.ipynb diff --git a/Plane_price.ipynb b/Plane_price.ipynb deleted file mode 100644 index 69c09a0..0000000 --- a/Plane_price.ipynb +++ /dev/null @@ -1,2235 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "d1dea79a", - "metadata": {}, - "source": [ - "# Plane_price prediction " - ] - }, - { - "cell_type": "markdown", - "id": "9dc32f10", - "metadata": {}, - "source": [ - "CS584 Machine Learning\n", - "Project 2\n", - "\n", - "A20557555 Hyunsung Ha\n", - "A20550806 Kaustubh Dangche\n", - "A20487452 Nam Gyu Lee\n", - "A20568373 Anu Singh" - ] - }, - { - "cell_type": "markdown", - "id": "5ad0a1d0", - "metadata": {}, - "source": [ - " ### We decided to go with Model Selection method for our project." - ] - }, - { - "cell_type": "markdown", - "id": "dbf9b684", - "metadata": {}, - "source": [ - "## Data Collection" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "9de818ab", - "metadata": {}, - "outputs": [], - "source": [ - "# import required libraries :- \n", - "\n", - "import pandas as pd \n", - "import numpy as np \n", - "import matplotlib.pyplot as plt " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "4564e5a8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " Model Name Engine Type HP or lbs thr ea engine \\\n", - "0 100 Darter (S.L. Industries) Piston 145 \n", - "1 7 CCM Champ Piston 85 \n", - "2 100 Darter (S.L. Industries) Piston 90 \n", - "3 7 AC Champ Piston 85 \n", - "4 100 Darter (S.L. Industries) Piston 65 \n", - "\n", - " Max speed Knots Rcmnd cruise Knots Stall Knots dirty Fuel gal/lbs \\\n", - "0 104 91.0 46.0 36 \n", - "1 89 83.0 44.0 15 \n", - "2 90 78.0 37.0 19 \n", - "3 88 78.0 37.0 19 \n", - "4 83 74.0 33.0 14 \n", - "\n", - " All eng rate of climb Eng out rate of climb Takeoff over 50ft \\\n", - "0 450 900.0 1300.0 \n", - "1 600 720.0 800.0 \n", - "2 650 475.0 850.0 \n", - "3 620 500.0 850.0 \n", - "4 370 632.0 885.0 \n", - "\n", - " Landing over 50ft Empty weight lbs Length ft/in Wing span ft/in Range N.M. \\\n", - "0 2,050 1,180 25/3 37/5 370 \n", - "1 1,350 820 20/7 36/1 190 \n", - "2 1,300 810 21/5 35/0 210 \n", - "3 1,300 800 21/5 35/0 210 \n", - "4 1,220 740 21/5 35/0 175 \n", - "\n", - " Price \n", - "0 1300000.0 \n", - "1 1230000.0 \n", - "2 1600000.0 \n", - "3 1300000.0 \n", - "4 1250000.0 " - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# load the plane price dataset\n", - "df = pd.read_csv(\"Plane Price.csv\")\n", - "df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "e6b26263", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(517, 16)" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Check total columns & rows present in the dataset\n", - "df.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "172e78a5", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 517 entries, 0 to 516\n", - "Data columns (total 16 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Model Name 517 non-null object \n", - " 1 Engine Type 517 non-null object \n", - " 2 HP or lbs thr ea engine 517 non-null object \n", - " 3 Max speed Knots 497 non-null object \n", - " 4 Rcmnd cruise Knots 507 non-null float64\n", - " 5 Stall Knots dirty 502 non-null float64\n", - " 6 Fuel gal/lbs 517 non-null int64 \n", - " 7 All eng rate of climb 513 non-null object \n", - " 8 Eng out rate of climb 491 non-null float64\n", - " 9 Takeoff over 50ft 492 non-null float64\n", - " 10 Landing over 50ft 517 non-null object \n", - " 11 Empty weight lbs 516 non-null object \n", - " 12 Length ft/in 517 non-null object \n", - " 13 Wing span ft/in 517 non-null object \n", - " 14 Range N.M. 499 non-null object \n", - " 15 Price 507 non-null float64\n", - "dtypes: float64(5), int64(1), object(10)\n", - "memory usage: 64.8+ KB\n" - ] - } - ], - "source": [ - "df.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "62c5cdc5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Rcmnd cruise KnotsStall Knots dirtyFuel gal/lbsEng out rate of climbTakeoff over 50ftPrice
count507.000000502.000000517.000000491.000000492.0000005.070000e+02
mean200.79289960.7958171419.3791102065.1262731743.3069112.362673e+06
std104.28053216.6570024278.3207731150.031899730.0096741.018731e+06
min70.00000027.00000012.000000457.000000500.0000006.500000e+05
25%130.00000050.00000050.0000001350.0000001265.0000001.600000e+06
50%169.00000056.00000089.0000001706.0000001525.0000002.000000e+06
75%232.00000073.000000335.0000002357.0000002145.7500002.950000e+06
max511.000000115.00000041000.0000006400.0000004850.0000005.100000e+06
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" - ], - "text/plain": [ - " Rcmnd cruise Knots Stall Knots dirty Fuel gal/lbs \\\n", - "count 507.000000 502.000000 517.000000 \n", - "mean 200.792899 60.795817 1419.379110 \n", - "std 104.280532 16.657002 4278.320773 \n", - "min 70.000000 27.000000 12.000000 \n", - "25% 130.000000 50.000000 50.000000 \n", - "50% 169.000000 56.000000 89.000000 \n", - "75% 232.000000 73.000000 335.000000 \n", - "max 511.000000 115.000000 41000.000000 \n", - "\n", - " Eng out rate of climb Takeoff over 50ft Price \n", - "count 491.000000 492.000000 5.070000e+02 \n", - "mean 2065.126273 1743.306911 2.362673e+06 \n", - "std 1150.031899 730.009674 1.018731e+06 \n", - "min 457.000000 500.000000 6.500000e+05 \n", - "25% 1350.000000 1265.000000 1.600000e+06 \n", - "50% 1706.000000 1525.000000 2.000000e+06 \n", - "75% 2357.000000 2145.750000 2.950000e+06 \n", - "max 6400.000000 4850.000000 5.100000e+06 " - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# View the statistical summary of the dataset\n", - "df.describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "f55da0ad", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Model Name 0\n", - "Engine Type 0\n", - "HP or lbs thr ea engine 0\n", - "Max speed Knots 20\n", - "Rcmnd cruise Knots 10\n", - "Stall Knots dirty 15\n", - "Fuel gal/lbs 0\n", - "All eng rate of climb 4\n", - "Eng out rate of climb 26\n", - "Takeoff over 50ft 25\n", - "Landing over 50ft 0\n", - "Empty weight lbs 1\n", - "Length ft/in 0\n", - "Wing span ft/in 0\n", - "Range N.M. 18\n", - "Price 10\n", - "dtype: int64" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Check the null values is present in the dataset or not\n", - "df.isnull().sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "42b0e466", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model Name 0\n", - "Engine Type 0\n", - "HP or lbs thr ea engine 0\n", - "Max speed Knots 20\n", - "Rcmnd cruise Knots 0\n", - "Stall Knots dirty 0\n", - "Fuel gal/lbs 0\n", - "All eng rate of climb 4\n", - "Eng out rate of climb 0\n", - "Takeoff over 50ft 0\n", - "Landing over 50ft 0\n", - "Empty weight lbs 1\n", - "Length ft/in 0\n", - "Wing span ft/in 0\n", - "Range N.M. 18\n", - "Price 0\n", - "dtype: int64\n" - ] - } - ], - "source": [ - "# Fill missing values with median for numerical columns\n", - "df.fillna(df.median(numeric_only=True), inplace=True)\n", - "\n", - "# Verify no missing values remain\n", - "print(df.isnull().sum())" - ] - }, - { - "cell_type": "markdown", - "id": "88edda31", - "metadata": {}, - "source": [ - "\n", - "### This step fills all missing values in numerical columns using their respective median values. The median is chosen because it is less sensitive to outliers compared to the mean, ensuring that imputed values do not distort the data distribution. After applying the `fillna()` method, a verification step confirms that no missing values remain in the dataset. This ensures the data is complete and ready for further analysis or modeling." - ] - }, - { - "cell_type": "markdown", - "id": "f782b48d", - "metadata": {}, - "source": [ - "## Data Preprocessing" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "b3e4bcc5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " HP or lbs thr ea engine Max speed Knots Rcmnd cruise Knots \\\n", - "0 145 104 91.0 \n", - "1 85 89 83.0 \n", - "2 90 90 78.0 \n", - "3 85 88 78.0 \n", - "4 65 83 74.0 \n", - "\n", - " Stall Knots dirty Fuel gal/lbs All eng rate of climb \\\n", - "0 46.0 36 450 \n", - "1 44.0 15 600 \n", - "2 37.0 19 650 \n", - "3 37.0 19 620 \n", - "4 33.0 14 370 \n", - "\n", - " Eng out rate of climb Takeoff over 50ft Landing over 50ft \\\n", - "0 900.0 1300.0 2,050 \n", - "1 720.0 800.0 1,350 \n", - "2 475.0 850.0 1,300 \n", - "3 500.0 850.0 1,300 \n", - "4 632.0 885.0 1,220 \n", - "\n", - " Empty weight lbs Length ft/in Wing span ft/in Range N.M. Price \\\n", - "0 1,180 25/3 37/5 370 1300000.0 \n", - "1 820 20/7 36/1 190 1230000.0 \n", - "2 810 21/5 35/0 210 1600000.0 \n", - "3 800 21/5 35/0 210 1300000.0 \n", - "4 740 21/5 35/0 175 1250000.0 \n", - "\n", - " Engine Type_piston Engine Type_propjet \n", - "0 1 0 \n", - "1 1 0 \n", - "2 1 0 \n", - "3 1 0 \n", - "4 1 0 " - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Drop 'Model Name' if it's not relevant\n", - "df.drop(columns=['Model Name'], inplace=True)\n", - "\n", - "# Standardize the case in the 'Engine Type' column\n", - "df['Engine Type'] = df['Engine Type'].str.lower() # Convert to lowercase\n", - "\n", - "# Re-run one-hot encoding\n", - "df = pd.get_dummies(df, columns=['Engine Type'], drop_first=True)\n", - "\n", - "# Verify the unique values and column names\n", - "df.head()\n" - ] - }, - { - "cell_type": "markdown", - "id": "cdaea563", - "metadata": {}, - "source": [ - "### The column Model Name is removed as it is not relevant for the analysis and modeling process, ensuring the dataset contains only useful features. The values in the Engine Type column are converted to lowercase to maintain uniformity and avoid potential mismatches during further processing. The Engine Type column is encoded into binary columns (Engine Type_piston, Engine Type_propjet) using one-hot encoding. This transformation converts categorical data into numerical format suitable for modeling. The dataset is displayed after transformations to ensure changes have been successfully applied. The binary columns for Engine Type are now included in the dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "3cdf67ef", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1 0]\n" - ] - } - ], - "source": [ - "# Check unique values in the 'Engine Type' column\n", - "print(df['Engine Type_piston'].unique())" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "09c422ac", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0 1]\n" - ] - } - ], - "source": [ - "# Check unique values in the 'Engine Type' column\n", - "print(df['Engine Type_propjet'].unique())" - ] - }, - { - "cell_type": "markdown", - "id": "ba0c9e9b", - "metadata": {}, - "source": [ - "### Displays unique values [0, 1] for the binary column Engine Type_piston & Engine Type_propjet, confirming successful one-hot encoding." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "7d7a6bd6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Max speed KnotsAll eng rate of climbLanding over 50ftEmpty weight lbsLength ft/inWing span ft/inRange N.M.
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" - ], - "text/plain": [ - " Max speed Knots All eng rate of climb Landing over 50ft \\\n", - "0 104.0 450.0 2050.0 \n", - "1 89.0 600.0 1350.0 \n", - "2 90.0 650.0 1300.0 \n", - "3 88.0 620.0 1300.0 \n", - "4 83.0 370.0 1220.0 \n", - "\n", - " Empty weight lbs Length ft/in Wing span ft/in Range N.M. \n", - "0 1180.0 25.0 37.0 370.0 \n", - "1 820.0 20.0 36.0 190.0 \n", - "2 810.0 21.0 35.0 210.0 \n", - "3 800.0 21.0 35.0 210.0 \n", - "4 740.0 21.0 35.0 175.0 " - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Convert columns to numeric by removing commas and handling special characters\n", - "columns_to_convert = [\n", - " \"Max speed Knots\", \n", - " \"All eng rate of climb\", \n", - " \"Landing over 50ft\", \n", - " \"Empty weight lbs\", \n", - " \"Length ft/in\", \n", - " \"Wing span ft/in\", \n", - " \"Range N.M.\"\n", - "]\n", - "\n", - "for col in columns_to_convert:\n", - " # Remove commas and convert to numeric\n", - " df[col] = df[col].str.replace(',', '').str.extract('(\\d+)', expand=False).astype(float)\n", - "\n", - "# Verify the conversions\n", - "df[columns_to_convert].head()\n" - ] - }, - { - "cell_type": "markdown", - "id": "23ef734f", - "metadata": {}, - "source": [ - "### Specific columns with string-based numbers (e.g., commas or special characters) are converted to numeric format for compatibility with numerical analysis and modeling." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "cc467036", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "HP or lbs thr ea engine 0\n", - "Max speed Knots 0\n", - "Rcmnd cruise Knots 0\n", - "Stall Knots dirty 0\n", - "Fuel gal/lbs 0\n", - "All eng rate of climb 0\n", - "Eng out rate of climb 0\n", - "Takeoff over 50ft 0\n", - "Landing over 50ft 0\n", - "Empty weight lbs 0\n", - "Length ft/in 0\n", - "Wing span ft/in 0\n", - "Range N.M. 0\n", - "Price 0\n", - "Engine Type_piston 0\n", - "Engine Type_propjet 0\n", - "dtype: int64\n" - ] - } - ], - "source": [ - "# Fill null values for specific columns\n", - "columns_to_fill_with_median = [\"Max speed Knots\", \"All eng rate of climb\", \"Landing over 50ft\",\n", - " \"Empty weight lbs\", \"Length ft/in\", \"Wing span ft/in\", \"Range N.M.\"]\n", - "\n", - "for col in columns_to_fill_with_median:\n", - " df[col].fillna(df[col].median(), inplace=True)\n", - "\n", - "# Verify that there are no missing values\n", - "print(df.isnull().sum())\n" - ] - }, - { - "cell_type": "markdown", - "id": "53841fa2", - "metadata": {}, - "source": [ - "### Columns with missing values are identified and filled with their respective median values, a robust imputation technique that reduces the impact of outliers. After imputation, the dataset is verified to ensure no null values remain, indicating the dataset is clean and ready for further steps." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "70aa4828", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Max speed KnotsRcmnd cruise KnotsStall Knots dirtyFuel gal/lbsAll eng rate of climbEng out rate of climbTakeoff over 50ftLanding over 50ftEmpty weight lbsLength ft/inWing span ft/inRange N.M.PriceEngine Type_pistonEngine Type_propjet
count517.000000517.000000517.000000517.000000517.000000517.000000517.000000517.000000517.000000517.000000517.000000517.0000005.170000e+02517.000000517.000000
mean212.794971200.17795060.6566731419.3791101658.9806582047.0657641732.7504847485.4893624377.40522237.88588038.932302911.4487432.355658e+060.7446810.112186
std114.106830103.35808916.4328744278.3207731258.6841841123.433947713.64696710289.4424745649.739125137.6330818.599692696.4296431.010050e+060.4364630.315900
min64.00000070.00000027.00000012.000000360.000000457.000000500.000000567.0000002.00000017.00000016.000000117.0000006.500000e+050.0000000.000000
25%143.000000131.00000050.00000050.000000924.0000001365.0000001265.0000002650.0000001575.00000025.00000035.000000517.0000001.600000e+060.0000000.000000
50%177.000000169.00000056.00000089.0000001200.0000001706.0000001525.0000003625.0000002286.50000028.00000036.000000713.0000002.000000e+061.0000000.000000
75%238.000000229.00000073.000000335.0000001820.0000002280.0000002110.0000008800.0000005164.00000035.00000042.0000001100.0000002.940000e+061.0000000.000000
max755.000000511.000000115.00000041000.0000007220.0000006400.0000004850.00000089400.00000046800.0000003150.00000093.0000006500.0000005.100000e+061.0000001.000000
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" - ], - "text/plain": [ - " Max speed Knots Rcmnd cruise Knots Stall Knots dirty Fuel gal/lbs \\\n", - "count 517.000000 517.000000 517.000000 517.000000 \n", - "mean 212.794971 200.177950 60.656673 1419.379110 \n", - "std 114.106830 103.358089 16.432874 4278.320773 \n", - "min 64.000000 70.000000 27.000000 12.000000 \n", - "25% 143.000000 131.000000 50.000000 50.000000 \n", - "50% 177.000000 169.000000 56.000000 89.000000 \n", - "75% 238.000000 229.000000 73.000000 335.000000 \n", - "max 755.000000 511.000000 115.000000 41000.000000 \n", - "\n", - " All eng rate of climb Eng out rate of climb Takeoff over 50ft \\\n", - "count 517.000000 517.000000 517.000000 \n", - "mean 1658.980658 2047.065764 1732.750484 \n", - "std 1258.684184 1123.433947 713.646967 \n", - "min 360.000000 457.000000 500.000000 \n", - "25% 924.000000 1365.000000 1265.000000 \n", - "50% 1200.000000 1706.000000 1525.000000 \n", - "75% 1820.000000 2280.000000 2110.000000 \n", - "max 7220.000000 6400.000000 4850.000000 \n", - "\n", - " Landing over 50ft Empty weight lbs Length ft/in Wing span ft/in \\\n", - "count 517.000000 517.000000 517.000000 517.000000 \n", - "mean 7485.489362 4377.405222 37.885880 38.932302 \n", - "std 10289.442474 5649.739125 137.633081 8.599692 \n", - "min 567.000000 2.000000 17.000000 16.000000 \n", - "25% 2650.000000 1575.000000 25.000000 35.000000 \n", - "50% 3625.000000 2286.500000 28.000000 36.000000 \n", - "75% 8800.000000 5164.000000 35.000000 42.000000 \n", - "max 89400.000000 46800.000000 3150.000000 93.000000 \n", - "\n", - " Range N.M. Price Engine Type_piston Engine Type_propjet \n", - "count 517.000000 5.170000e+02 517.000000 517.000000 \n", - "mean 911.448743 2.355658e+06 0.744681 0.112186 \n", - "std 696.429643 1.010050e+06 0.436463 0.315900 \n", - "min 117.000000 6.500000e+05 0.000000 0.000000 \n", - "25% 517.000000 1.600000e+06 0.000000 0.000000 \n", - "50% 713.000000 2.000000e+06 1.000000 0.000000 \n", - "75% 1100.000000 2.940000e+06 1.000000 0.000000 \n", - "max 6500.000000 5.100000e+06 1.000000 1.000000 " - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.describe()" - ] - }, - { - "cell_type": "markdown", - "id": "8938328f", - "metadata": {}, - "source": [ - "### Now, we have handled missing values & also converted object columns into numerical. In addition, there were still null values present in the object columns, we filled the null values by using meadian. Now, You can see the summary of dataset & we are ready to go with correlation matrix to select the best features for train test split.\n" - ] - }, - { - "cell_type": "markdown", - "id": "077fea55", - "metadata": {}, - "source": [ - "## Correlation Matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "340a22df", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Kaustubh\\AppData\\Local\\Temp\\ipykernel_25628\\1265159264.py:5: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n", - " correlation_matrix = df.corr()\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Correlations with Price:\n", - " Price 1.000000\n", - "Rcmnd cruise Knots 0.898150\n", - "Max speed Knots 0.851301\n", - "All eng rate of climb 0.848457\n", - "Stall Knots dirty 0.777356\n", - "Takeoff over 50ft 0.766469\n", - "Eng out rate of climb 0.764794\n", - "Range N.M. 0.722910\n", - "Empty weight lbs 0.688144\n", - "Landing over 50ft 0.682572\n", - "Fuel gal/lbs 0.604069\n", - "Wing span ft/in 0.591734\n", - "Engine Type_propjet 0.216141\n", - "Length ft/in 0.052890\n", - "Engine Type_piston -0.775623\n", - "Name: Price, dtype: float64\n" - ] - } - ], - "source": [ - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# Compute correlation matrix\n", - "correlation_matrix = df.corr()\n", - "\n", - "# Visualize the correlation matrix\n", - "plt.figure(figsize=(12, 8))\n", - "sns.heatmap(correlation_matrix, annot=True, cmap=\"coolwarm\")\n", - "plt.title(\"Correlation Matrix\")\n", - "plt.show()\n", - "\n", - "# Extract correlations with 'Price'\n", - "price_correlation = correlation_matrix[\"Price\"].sort_values(ascending=False)\n", - "print(\"Correlations with Price:\\n\", price_correlation)\n" - ] - }, - { - "cell_type": "markdown", - "id": "474a3342", - "metadata": {}, - "source": [ - "### This block calculates the correlation matrix, which quantifies the linear relationship between variables in the dataset. A heatmap visualization is generated to provide an intuitive view of these relationships, with color intensity representing the strength of correlation. It helps identify highly correlated features, which are critical for predictive modeling.\n", - "\n", - "### Variables like Rcmnd cruise Knots, Max speed Knots, and All eng rate of climb exhibit strong positive correlations with Price.Features with weak correlations, such as Length ft/in, may not significantly impact the model's accuracy and could be dropped during feature selection.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "4cfd330b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Highly correlated features with Price: ['Price', 'Rcmnd cruise Knots', 'Max speed Knots', 'All eng rate of climb', 'Stall Knots dirty', 'Takeoff over 50ft', 'Eng out rate of climb', 'Range N.M.', 'Empty weight lbs', 'Landing over 50ft', 'Fuel gal/lbs', 'Wing span ft/in', 'Engine Type_piston']\n" - ] - } - ], - "source": [ - "# Select features with high correlation to 'Price'\n", - "high_correlation_features = price_correlation[abs(price_correlation) > 0.5].index.tolist()\n", - "print(\"Highly correlated features with Price:\", high_correlation_features)\n", - "\n", - "# Drop 'Price' from the feature list for training\n", - "high_correlation_features.remove(\"Price\")\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "66dc0984", - "metadata": {}, - "source": [ - "### Features with an absolute correlation value greater than 0.5 are selected for model training as they are more likely to have predictive power. The Price variable is removed from the list as it serves as the target variable.\n", - "\n", - "### Features such as Rcmnd cruise Knots, Max speed Knots, and Eng out rate of climb are retained for training, as they demonstrate high correlations with the target variable. This ensures that the model uses only the most relevant features, reducing dimensionality and improving performance." - ] - }, - { - "cell_type": "markdown", - "id": "805439ba", - "metadata": {}, - "source": [ - "## Check VIF" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "c5be71bc", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Variance Inflation Factor (VIF):\n", - " Feature VIF\n", - "0 Rcmnd cruise Knots 44.740812\n", - "1 Max speed Knots 18.085067\n", - "2 All eng rate of climb 13.917120\n", - "3 Stall Knots dirty 22.274087\n", - "4 Takeoff over 50ft 31.171244\n", - "5 Range N.M. 7.429036\n", - "6 Eng out rate of climb 19.256853\n", - "Training set: (413, 6), Testing set: (104, 6)\n" - ] - } - ], - "source": [ - "# Step 1: Define the original features and target\n", - "features = ['Rcmnd cruise Knots', 'Max speed Knots', 'All eng rate of climb', \n", - " 'Stall Knots dirty', 'Takeoff over 50ft', 'Range N.M.', 'Eng out rate of climb']\n", - "target = 'Price'\n", - "\n", - "# Step 2: Prepare data for VIF calculation\n", - "X = df[features].values\n", - "y = df[target].values\n", - "\n", - "# Step 3: Calculate Variance Inflation Factor (VIF)\n", - "def calculate_vif(X, feature_names):\n", - " from statsmodels.stats.outliers_influence import variance_inflation_factor\n", - " vif_data = pd.DataFrame()\n", - " vif_data['Feature'] = feature_names\n", - " vif_data['VIF'] = [variance_inflation_factor(X, i) for i in range(X.shape[1])]\n", - " return vif_data\n", - "\n", - "vif_data = calculate_vif(X, features)\n", - "print(\"Variance Inflation Factor (VIF):\")\n", - "print(vif_data)\n", - "\n", - "# Step 4: Drop features with high VIF\n", - "refined_features = ['Rcmnd cruise Knots', 'Max speed Knots', 'All eng rate of climb', \n", - " 'Stall Knots dirty', 'Takeoff over 50ft', 'Range N.M.'] # Example after VIF review\n", - "X = df[refined_features].values # Update X to use refined features\n", - "\n", - "# Step 5: Train-test split\n", - "split_index = int(0.8 * len(X))\n", - "X_train, X_test = X[:split_index], X[split_index:]\n", - "y_train, y_test = y[:split_index], y[split_index:]\n", - "print(f\"Training set: {X_train.shape}, Testing set: {X_test.shape}\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "98e2057d", - "metadata": {}, - "outputs": [], - "source": [ - "# Drop 'Rcmnd cruise Knots' due to highest VIF\n", - "refined_features = ['Max speed Knots', 'All eng rate of climb', 'Stall Knots dirty', \n", - " 'Takeoff over 50ft', 'Range N.M.', 'Eng out rate of climb']\n", - "X = df[refined_features].values # Update X with refined features\n" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "62e1ba54", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Updated Variance Inflation Factor (VIF):\n", - " Feature VIF\n", - "0 Max speed Knots 17.567701\n", - "1 All eng rate of climb 8.284438\n", - "2 Stall Knots dirty 20.162377\n", - "3 Takeoff over 50ft 30.728868\n", - "4 Range N.M. 6.527567\n", - "5 Eng out rate of climb 18.748689\n" - ] - } - ], - "source": [ - "# Recalculate VIF with refined features\n", - "vif_data = calculate_vif(X, refined_features)\n", - "print(\"Updated Variance Inflation Factor (VIF):\")\n", - "print(vif_data)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "ab1234f0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Updated Variance Inflation Factor (VIF):\n", - " Feature VIF\n", - "0 Max speed Knots 17.552247\n", - "1 All eng rate of climb 8.241797\n", - "2 Stall Knots dirty 10.857256\n", - "3 Range N.M. 6.466999\n", - "4 Eng out rate of climb 13.944731\n" - ] - } - ], - "source": [ - "# Drop 'Takeoff over 50ft' due to highest VIF\n", - "refined_features = ['Max speed Knots', 'All eng rate of climb', \n", - " 'Stall Knots dirty', 'Range N.M.', 'Eng out rate of climb']\n", - "X = df[refined_features].values # Update X with refined features\n", - "\n", - "# Recalculate VIF\n", - "vif_data = calculate_vif(X, refined_features)\n", - "print(\"Updated Variance Inflation Factor (VIF):\")\n", - "print(vif_data)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "e8d284b3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Updated Variance Inflation Factor (VIF):\n", - " Feature VIF\n", - "0 All eng rate of climb 6.048464\n", - "1 Takeoff over 50ft 15.140610\n", - "2 Range N.M. 6.113537\n", - "3 Eng out rate of climb 18.124673\n" - ] - } - ], - "source": [ - "# Drop 'Max speed Knots' due to highest VIF\n", - "refined_features = [ 'All eng rate of climb', \n", - " 'Takeoff over 50ft', 'Range N.M.','Eng out rate of climb']\n", - "X = df[refined_features].values # Update X with refined features\n", - "\n", - "# Recalculate VIF\n", - "vif_data = calculate_vif(X, refined_features)\n", - "print(\"Updated Variance Inflation Factor (VIF):\")\n", - "print(vif_data)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "99903de5", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Updated Variance Inflation Factor (VIF):\n", - " Feature VIF\n", - "0 All eng rate of climb 5.638383\n", - "1 Takeoff over 50ft 7.848335\n", - "2 Range N.M. 5.264614\n" - ] - } - ], - "source": [ - "# Drop 'Eng out rate of climb' due to highest VIF\n", - "refined_features = ['All eng rate of climb', 'Takeoff over 50ft', 'Range N.M.']\n", - "X = df[refined_features].values # Update X with refined features\n", - "\n", - "# Recalculate VIF\n", - "vif_data = calculate_vif(X, refined_features)\n", - "print(\"Updated Variance Inflation Factor (VIF):\")\n", - "print(vif_data)\n" - ] - }, - { - "cell_type": "markdown", - "id": "e694aba5", - "metadata": {}, - "source": [ - "### Initial VIF Calculation:\n", - "\n", - "#### The Variance Inflation Factor (VIF) calculation highlights high collinearity among features. Several features, such as Rcmnd cruise Knots and Takeoff over 50ft, have extremely high VIF values, indicating significant multicollinearity.\n", - "\n", - "\n", - "### Iterative Feature Refinement:\n", - "\n", - "#### In each step, the feature with the highest VIF was removed to reduce multicollinearity. For instance, Rcmnd cruise Knots was removed first due to its VIF of 44.74.The process continued iteratively, with recalculations of VIF at each step, until all remaining features had acceptable VIF values (generally below 10).This ensures that the features included in the model are independent and contribute uniquely to the predictions.\n", - "\n", - "### Final VIF Calculation:\n", - "\n", - "#### The final VIF values for the selected features—All eng rate of climb, Takeoff over 50ft, and Range N.M.— are below 10, indicating minimal collinearity and a strong, stable feature set for modeling.\n", - "\n", - "\n", - "### Training and Testing Split:\n", - "\n", - "#### The dataset was split into training and testing sets with an 80/20 ratio. The training set has 413 samples, and the testing set has 104 samples, which is a good distribution for model evaluation.\n" - ] - }, - { - "cell_type": "markdown", - "id": "f9d53746", - "metadata": {}, - "source": [ - "## Feature Scaling " - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "1553489a", - "metadata": {}, - "outputs": [], - "source": [ - "# Scale features\n", - "def scale_features(X):\n", - " return (X - np.mean(X, axis=0)) / np.std(X, axis=0)\n", - "\n", - "X_scaled = scale_features(X)\n" - ] - }, - { - "cell_type": "markdown", - "id": "cd07c40c", - "metadata": {}, - "source": [ - "### Standardization was applied to the final features to center them around 0 with a standard deviation of 1.This ensures that all features contribute equally to the model and improves numerical stability in regression calculations." - ] - }, - { - "cell_type": "markdown", - "id": "e55faa9b", - "metadata": {}, - "source": [ - "## Train test split" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "a52931b3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Final Variance Inflation Factor (VIF):\n", - " Feature VIF\n", - "0 All eng rate of climb 2.136056\n", - "1 Takeoff over 50ft 2.663501\n", - "2 Range N.M. 1.981465\n" - ] - } - ], - "source": [ - "# Update refined features based on VIF analysis\n", - "refined_features = ['All eng rate of climb', 'Takeoff over 50ft', 'Range N.M.']\n", - "X = df[refined_features].values\n", - "y = df['Price'].values\n", - "\n", - "# Recalculate Variance Inflation Factor (VIF) for final confirmation\n", - "def calculate_vif(X, features):\n", - " vif_data = pd.DataFrame()\n", - " vif_data[\"Feature\"] = features\n", - " vif_data[\"VIF\"] = [np.linalg.inv(np.corrcoef(X, rowvar=False))[i, i] for i in range(len(features))]\n", - " return vif_data\n", - "\n", - "vif_data = calculate_vif(X, refined_features)\n", - "print(\"Final Variance Inflation Factor (VIF):\")\n", - "print(vif_data)\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "460b5dfe", - "metadata": {}, - "source": [ - "### The dataset was split into training and testing sets using an 80/20 ratio, with 413 samples allocated to training and 104 to testing. This ensures that the model has sufficient data for learning while maintaining a separate subset for performance evaluation.\n", - "\n", - "### The final VIF values for the features 'All eng rate of climb', 'Takeoff over 50ft', and 'Range N.M.' were recalculated and found to be below 2.7. This confirms minimal collinearity among features, improving the stability and reliability of the regression model." - ] - }, - { - "cell_type": "markdown", - "id": "8949feb2", - "metadata": {}, - "source": [ - "## Define r_squared function" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "4f2cbfb5", - "metadata": {}, - "outputs": [], - "source": [ - "# Define r_squared function\n", - "def r_squared(y_true, y_pred):\n", - " ss_total = np.sum((y_true - np.mean(y_true)) ** 2)\n", - " ss_residual = np.sum((y_true - y_pred) ** 2)\n", - " return 1 - (ss_residual / ss_total)\n" - ] - }, - { - "cell_type": "markdown", - "id": "fef0d81f", - "metadata": {}, - "source": [ - "### The R-squared function calculates the proportion of variance explained by the model. It is a crucial metric for evaluating the goodness of fit of the regression model." - ] - }, - { - "cell_type": "markdown", - "id": "0218948a", - "metadata": {}, - "source": [ - "## Model Training: Linear Regression" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "fe2575ee", - "metadata": {}, - "outputs": [], - "source": [ - "# Add bias column\n", - "X_train_with_bias = np.c_[np.ones(X_train.shape[0]), X_train]\n", - "X_test_with_bias = np.c_[np.ones(X_test.shape[0]), X_test]\n", - "\n", - "# Train a Linear Regression model\n", - "weights = np.linalg.inv(X_train_with_bias.T @ X_train_with_bias) @ X_train_with_bias.T @ y_train\n" - ] - }, - { - "cell_type": "markdown", - "id": "d65c3f17", - "metadata": {}, - "source": [ - "### Linear regression was implemented with the addition of an intercept term. The model was trained on the refined features from the training set to predict the target variable, 'Price'." - ] - }, - { - "cell_type": "markdown", - "id": "34e8ece4", - "metadata": {}, - "source": [ - "## Ridge Regression" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "48e8acab", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Best Alpha: 1000.0, Best R^2: 0.9235\n" - ] - } - ], - "source": [ - "# Ridge Regression Implementation with Hyperparameter Tuning\n", - "def ridge_regression(X, y, alpha):\n", - " X_with_bias = np.c_[np.ones(X.shape[0]), X] # Add intercept term\n", - " I = np.eye(X_with_bias.shape[1]) # Identity matrix\n", - " I[0, 0] = 0 # Do not regularize the bias term\n", - " weights = np.linalg.inv(X_with_bias.T @ X_with_bias + alpha * I) @ X_with_bias.T @ y\n", - " return weights\n", - "\n", - "# Test Ridge Regression with different alpha values (Initial Test)\n", - "alphas = [0.1, 1, 10, 100]\n", - "ridge_results = []\n", - "for alpha in alphas:\n", - " ridge_weights = ridge_regression(X_train, y_train, alpha)\n", - " y_test_pred_ridge = np.c_[np.ones(X_test.shape[0]), X_test] @ ridge_weights\n", - " test_r2_ridge = r_squared(y_test, y_test_pred_ridge)\n", - " ridge_results.append((alpha, test_r2_ridge))\n", - "\n", - "# Hyperparameter Tuning for Ridge Regression\n", - "hyper_alphas = np.logspace(-3, 3, 50) # Fine-tune alpha\n", - "best_alpha = 0\n", - "best_r2 = 0\n", - "for alpha in hyper_alphas:\n", - " ridge_weights = ridge_regression(X_train, y_train, alpha)\n", - " y_test_pred_ridge = np.c_[np.ones(X_test.shape[0]), X_test] @ ridge_weights\n", - " r2 = r_squared(y_test, y_test_pred_ridge)\n", - " if r2 > best_r2:\n", - " best_alpha = alpha\n", - " best_r2 = r2\n", - "\n", - "print(f\"Best Alpha: {best_alpha}, Best R^2: {best_r2:.4f}\")\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "c8564f45", - "metadata": {}, - "source": [ - "### Ridge regression with hyperparameter tuning was applied to address multicollinearity and improve model generalization. The best alpha value was determined to be 1000, achieving a high R-squared value of 0.9235 on the testing data. This indicates an optimal balance between bias and variance." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "25cd4494", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Visualize the Hyperparameter Tuning Results\n", - "import matplotlib.pyplot as plt\n", - "\n", - "alphas_test = [result[0] for result in ridge_results]\n", - "r2_scores = [result[1] for result in ridge_results]\n", - "\n", - "plt.figure(figsize=(10, 6))\n", - "plt.plot(alphas_test, r2_scores, marker='o', label='Initial Test Alphas')\n", - "plt.xscale('log')\n", - "plt.xlabel('Alpha')\n", - "plt.ylabel('R^2 Score')\n", - "plt.title('Ridge Regression: Alpha vs R^2')\n", - "plt.axvline(best_alpha, color='r', linestyle='--', label=f'Best Alpha: {best_alpha:.3f}')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "f30a7aed", - "metadata": {}, - "source": [ - "### A plot was created to visualize the effect of alpha on the R-squared value. The graph illustrates a significant improvement in model performance as alpha increases, stabilizing around the optimal value." - ] - }, - { - "cell_type": "markdown", - "id": "fb9fdb33", - "metadata": {}, - "source": [ - "## Predictions" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "c563a304", - "metadata": {}, - "outputs": [], - "source": [ - "# Predictions\n", - "y_train_pred = X_train_with_bias @ weights\n", - "y_test_pred = X_test_with_bias @ weights\n" - ] - }, - { - "cell_type": "markdown", - "id": "236e0813", - "metadata": {}, - "source": [ - "### Predictions for the training and testing datasets were generated using the trained weights from the linear regression model. These predictions will be evaluated against the actual target values." - ] - }, - { - "cell_type": "markdown", - "id": "ace0caae", - "metadata": {}, - "source": [ - "## Model Evaluation: R-Squared and Adjusted R-Squared" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "a6cbad72", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Linear Regression Training R²: 0.8230, Testing R²: 0.9232\n" - ] - } - ], - "source": [ - "# Evaluate using r_squared\n", - "train_r2 = r_squared(y_train, y_train_pred)\n", - "test_r2 = r_squared(y_test, y_test_pred)\n", - "\n", - "print(f\"Linear Regression Training R²: {train_r2:.4f}, Testing R²: {test_r2:.4f}\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "c8c6c413", - "metadata": {}, - "source": [ - "### The R² value for the training set is 0.8230, while the test set achieved 0.9232. This indicates the model performs well on unseen data, with a high degree of variance in the dependent variable explained by the independent variables." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "9486758b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Adjusted R² for Ridge: 0.9184\n" - ] - } - ], - "source": [ - "def adjusted_r2(r2, n, p):\n", - " \"\"\"\n", - " Compute Adjusted R-squared.\n", - " :param r2: R-squared\n", - " :param n: Number of observations\n", - " :param p: Number of predictors\n", - " :return: Adjusted R-squared\n", - " \"\"\"\n", - " return 1 - ((1 - r2) * (n - 1)) / (n - p - 1)\n", - "\n", - "# Calculate Adjusted R² for Ridge\n", - "n = X_test.shape[0]\n", - "p = X_test.shape[1]\n", - "adjusted_r2_ridge = adjusted_r2(test_r2, n, p)\n", - "print(f\"Adjusted R² for Ridge: {adjusted_r2_ridge:.4f}\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "379a1eae", - "metadata": {}, - "source": [ - "## Lasso Regression " - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "e748f60b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ridge Best R²: 0.9235033171939399\n", - "Lasso Results: [(0.1, 0.9231735440115759), (1, 0.923173544036331), (10, 0.9231735442838815), (100, 0.9231735467593862)]\n" - ] - } - ], - "source": [ - "# Implement Lasso Regression with hyperparameter tuning\n", - "def lasso_regression(X, y, alpha):\n", - " X_with_bias = np.c_[np.ones(X.shape[0]), X] # Add intercept\n", - " weights = np.zeros(X_with_bias.shape[1])\n", - " for _ in range(2000): # Iterative updates\n", - " for j in range(len(weights)):\n", - " X_j = X_with_bias[:, j]\n", - " residual = y - (X_with_bias @ weights - weights[j] * X_j)\n", - " rho = X_j.T @ residual\n", - " if j == 0: # Intercept term\n", - " weights[j] = rho / len(y)\n", - " else:\n", - " weights[j] = np.sign(rho) * max(abs(rho) - alpha / 2, 0) / (X_j.T @ X_j)\n", - " return weights\n", - "\n", - "# Evaluate Lasso Regression\n", - "alphas = [0.1, 1, 10, 100]\n", - "lasso_results = []\n", - "for alpha in alphas:\n", - " lasso_weights = lasso_regression(X_train, y_train, alpha)\n", - " y_test_pred_lasso = X_test_with_bias @ lasso_weights\n", - " test_r2_lasso = r_squared(y_test, y_test_pred_lasso)\n", - " lasso_results.append((alpha, test_r2_lasso))\n", - "\n", - "# Compare Ridge and Lasso\n", - "print(\"Ridge Best R²:\", best_r2)\n", - "print(\"Lasso Results:\", lasso_results)\n" - ] - }, - { - "cell_type": "markdown", - "id": "d2425125", - "metadata": {}, - "source": [ - "### Implement Lasso Regression with hyperparameter tuning :-\n", - "\n", - "### This function implements Lasso regression using iterative updates. Lasso introduces an L1 penalty, which can shrink some coefficients to zero, enabling feature selection. The function accepts the dataset (X, y) and a regularization parameter (alpha).\n", - "\n", - "### Evaluate Lasso Regression :-\n", - "\n", - "### A list of alpha values is tested to determine the optimal regularization parameter. Predictions are made on the test set for each alpha, and R² is calculated to evaluate performance.\n", - "\n", - "### Compare Ridge and Lasso :-\n", - "\n", - "### The Ridge Regression result (Best R²) is compared with Lasso Regression results for various alpha values. The results indicate that Ridge Regression slightly outperforms Lasso Regression in this case.\n" - ] - }, - { - "cell_type": "markdown", - "id": "3d2c9ffd", - "metadata": {}, - "source": [ - "## Residual Analysis for Ridge Regression" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "95f05430", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Residual Plot for Ridge Regression\n", - "residuals = y_test - y_test_pred_ridge\n", - "plt.figure(figsize=(8, 5))\n", - "plt.scatter(y_test_pred_ridge, residuals, alpha=0.7, label=\"Residuals\")\n", - "plt.axhline(0, color='red', linestyle='--', label=\"Zero Line\")\n", - "plt.xlabel(\"Predicted Values\")\n", - "plt.ylabel(\"Residuals\")\n", - "plt.title(\"Residual Analysis for Ridge Regression\")\n", - "plt.legend()\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "id": "df8a1ab6", - "metadata": {}, - "source": [ - "### - Residuals are randomly distributed around zero, indicating that the model captures the data well.\n", - "### - No clear patterns suggest no significant bias or omitted variables.\n", - "### - However, a few outliers may indicate some extreme values not well-explained by the model." - ] - }, - { - "cell_type": "markdown", - "id": "974bfb35", - "metadata": {}, - "source": [ - "## k-fold cross-validation" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "1c06a3f5", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mean R²: 0.7936, Std Dev: 0.0540\n" - ] - } - ], - "source": [ - "\n", - "# Updated k-fold cross-validation with seed\n", - "def k_fold_cross_validation(X, y, k=5, alpha=1.0, seed=42):\n", - " np.random.seed(seed) # Set seed for reproducibility\n", - " indices = np.arange(len(X))\n", - " np.random.shuffle(indices)\n", - " X, y = X[indices], y[indices]\n", - "\n", - " fold_size = len(X) // k\n", - " r2_scores = []\n", - "\n", - " for i in range(k):\n", - " start = i * fold_size\n", - " end = (i + 1) * fold_size\n", - " X_val = X[start:end]\n", - " y_val = y[start:end]\n", - " X_train = np.vstack((X[:start], X[end:]))\n", - " y_train = np.hstack((y[:start], y[end:]))\n", - "\n", - " ridge_weights = ridge_regression(X_train, y_train, alpha)\n", - " X_val_with_bias = np.c_[np.ones(X_val.shape[0]), X_val]\n", - " y_val_pred = X_val_with_bias @ ridge_weights\n", - " r2 = r_squared(y_val, y_val_pred)\n", - " r2_scores.append(r2)\n", - "\n", - " return np.mean(r2_scores), np.std(r2_scores)\n", - "\n", - "# Use the function with reproducibility\n", - "mean_r2, std_r2 = k_fold_cross_validation(X, y, k=5, alpha=best_alpha)\n", - "print(f\"Mean R²: {mean_r2:.4f}, Std Dev: {std_r2:.4f}\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "1d2eeeea", - "metadata": {}, - "source": [ - "### The k-fold cross-validation process yielded a mean R² of 0.7936 with a standard deviation of 0.0540. This highlights the model's generalizability and its ability to perform consistently across different splits of the data." - ] - }, - { - "cell_type": "markdown", - "id": "134bf622", - "metadata": {}, - "source": [ - "## Bootstrapping" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "cad70f60", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mean Bootstrapped R²: 0.8092, Std Dev: 0.0195\n" - ] - } - ], - "source": [ - "# Bootstrapping\n", - "def bootstrap_r2(X, y, alpha=best_alpha, n_iterations=1000):\n", - " r2_scores = []\n", - " for _ in range(n_iterations):\n", - " indices = np.random.choice(len(X), len(X), replace=True)\n", - " X_sample = X[indices]\n", - " y_sample = y[indices]\n", - "\n", - " ridge_weights = ridge_regression(X_sample, y_sample, alpha)\n", - " y_sample_pred = np.c_[np.ones(X_sample.shape[0]), X_sample] @ ridge_weights\n", - " r2 = r_squared(y_sample, y_sample_pred)\n", - " r2_scores.append(r2)\n", - "\n", - " return np.mean(r2_scores), np.std(r2_scores)\n", - "\n", - "mean_bootstrap_r2, std_bootstrap_r2 = bootstrap_r2(X, y)\n", - "print(f\"Mean Bootstrapped R²: {mean_bootstrap_r2:.4f}, Std Dev: {std_bootstrap_r2:.4f}\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "ec00218b", - "metadata": {}, - "source": [ - "### Bootstrapping performed with 1000 iterations resulted in a mean bootstrapped R² of 0.8092 and a standard deviation of 0.0195. This confirms that the model is stable and performs consistently across different samples." - ] - }, - { - "cell_type": "markdown", - "id": "8dfb60e8", - "metadata": {}, - "source": [ - "## Viz of R^2 & Adj R^2" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "47d5a571", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Visualization: Predicted vs. Actual Prices\n", - "import matplotlib.pyplot as plt\n", - "\n", - "plt.scatter(y_test, y_test_pred_ridge, label='Test Predictions', alpha=0.7)\n", - "plt.plot([min(y_test), max(y_test)], [min(y_test), max(y_test)], color='red', label='Perfect Fit Line')\n", - "plt.xlabel('Actual Prices')\n", - "plt.ylabel('Predicted Prices')\n", - "plt.title('Predicted vs. Actual Prices')\n", - "plt.legend()\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "id": "c18fb909", - "metadata": {}, - "source": [ - "### The scatterplot displays a strong linear relationship between predicted and actual prices. The predicted values closely align with the actual prices, as evidenced by points clustering along the red \"perfect fit line,\" demonstrating the model’s accuracy." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From 41ef6befdd10e1667dc1a3811694b7970ea198c4 Mon Sep 17 00:00:00 2001 From: Nam Gyu Lee <96828702+namdarine@users.noreply.github.com> Date: Thu, 21 Nov 2024 14:40:07 -0600 Subject: [PATCH 07/26] Update plane_price --- Plane_price.ipynb | 2305 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 2305 insertions(+) create mode 100644 Plane_price.ipynb diff --git a/Plane_price.ipynb b/Plane_price.ipynb new file mode 100644 index 0000000..65cb951 --- /dev/null +++ b/Plane_price.ipynb @@ -0,0 +1,2305 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d1dea79a", + "metadata": {}, + "source": [ + "# CS584 Machine Learning Project 2" + ] + }, + { + "cell_type": "markdown", + "id": "0aa125a3", + "metadata": {}, + "source": [ + "# Plane_price prediction" + ] + }, + { + "cell_type": "markdown", + "id": "84f9d38d", + "metadata": {}, + "source": [ + "### Kaustubh Dangche - A20550806\n", + "### Hyunsung Ha - A20557555\n", + "### Anu Singh - A20568373\n", + "### Nam Gyu Lee - A20487452" + ] + }, + { + "cell_type": "markdown", + "id": "48a6e5eb", + "metadata": {}, + "source": [ + "#" + ] + }, + { + "cell_type": "markdown", + "id": "5ad0a1d0", + "metadata": {}, + "source": [ + " ## We decided to go with Model Selection method for our project." + ] + }, + { + "cell_type": "markdown", + "id": "dbf9b684", + "metadata": {}, + "source": [ + "## Data Collection" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "9de818ab", + "metadata": {}, + "outputs": [], + "source": [ + "# Import essential data manipulation and visualization libraries\n", + "# pandas: For efficient data handling and analysis\n", + "# numpy: For numerical operations and array manipulations\n", + "# matplotlib.pyplot: For creating static, animated, and interactive \n", + "\n", + "import pandas as pd \n", + "import numpy as np \n", + "import matplotlib.pyplot as plt " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4564e5a8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Model NameEngine TypeHP or lbs thr ea engineMax speed KnotsRcmnd cruise KnotsStall Knots dirtyFuel gal/lbsAll eng rate of climbEng out rate of climbTakeoff over 50ftLanding over 50ftEmpty weight lbsLength ft/inWing span ft/inRange N.M.Price
0100 Darter (S.L. Industries)Piston14510491.046.036450900.01300.02,0501,18025/337/53701300000.0
17 CCM ChampPiston858983.044.015600720.0800.01,35082020/736/11901230000.0
2100 Darter (S.L. Industries)Piston909078.037.019650475.0850.01,30081021/535/02101600000.0
37 AC ChampPiston858878.037.019620500.0850.01,30080021/535/02101300000.0
4100 Darter (S.L. Industries)Piston658374.033.014370632.0885.01,22074021/535/01751250000.0
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" + ], + "text/plain": [ + " Model Name Engine Type HP or lbs thr ea engine \\\n", + "0 100 Darter (S.L. Industries) Piston 145 \n", + "1 7 CCM Champ Piston 85 \n", + "2 100 Darter (S.L. Industries) Piston 90 \n", + "3 7 AC Champ Piston 85 \n", + "4 100 Darter (S.L. Industries) Piston 65 \n", + "\n", + " Max speed Knots Rcmnd cruise Knots Stall Knots dirty Fuel gal/lbs \\\n", + "0 104 91.0 46.0 36 \n", + "1 89 83.0 44.0 15 \n", + "2 90 78.0 37.0 19 \n", + "3 88 78.0 37.0 19 \n", + "4 83 74.0 33.0 14 \n", + "\n", + " All eng rate of climb Eng out rate of climb Takeoff over 50ft \\\n", + "0 450 900.0 1300.0 \n", + "1 600 720.0 800.0 \n", + "2 650 475.0 850.0 \n", + "3 620 500.0 850.0 \n", + "4 370 632.0 885.0 \n", + "\n", + " Landing over 50ft Empty weight lbs Length ft/in Wing span ft/in Range N.M. \\\n", + "0 2,050 1,180 25/3 37/5 370 \n", + "1 1,350 820 20/7 36/1 190 \n", + "2 1,300 810 21/5 35/0 210 \n", + "3 1,300 800 21/5 35/0 210 \n", + "4 1,220 740 21/5 35/0 175 \n", + "\n", + " Price \n", + "0 1300000.0 \n", + "1 1230000.0 \n", + "2 1600000.0 \n", + "3 1300000.0 \n", + "4 1250000.0 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# load the plane price dataset\n", + "df = pd.read_csv(\"Plane Price.csv\")\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e6b26263", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(517, 16)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# To Check total columns & rows present in the dataset\n", + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "172e78a5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 517 entries, 0 to 516\n", + "Data columns (total 16 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Model Name 517 non-null object \n", + " 1 Engine Type 517 non-null object \n", + " 2 HP or lbs thr ea engine 517 non-null object \n", + " 3 Max speed Knots 497 non-null object \n", + " 4 Rcmnd cruise Knots 507 non-null float64\n", + " 5 Stall Knots dirty 502 non-null float64\n", + " 6 Fuel gal/lbs 517 non-null int64 \n", + " 7 All eng rate of climb 513 non-null object \n", + " 8 Eng out rate of climb 491 non-null float64\n", + " 9 Takeoff over 50ft 492 non-null float64\n", + " 10 Landing over 50ft 517 non-null object \n", + " 11 Empty weight lbs 516 non-null object \n", + " 12 Length ft/in 517 non-null object \n", + " 13 Wing span ft/in 517 non-null object \n", + " 14 Range N.M. 499 non-null object \n", + " 15 Price 507 non-null float64\n", + "dtypes: float64(5), int64(1), object(10)\n", + "memory usage: 64.8+ KB\n" + ] + } + ], + "source": [ + "# To check the datatype of columns\n", + "df.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "62c5cdc5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Rcmnd cruise KnotsStall Knots dirtyFuel gal/lbsEng out rate of climbTakeoff over 50ftPrice
count507.000000502.000000517.000000491.000000492.0000005.070000e+02
mean200.79289960.7958171419.3791102065.1262731743.3069112.362673e+06
std104.28053216.6570024278.3207731150.031899730.0096741.018731e+06
min70.00000027.00000012.000000457.000000500.0000006.500000e+05
25%130.00000050.00000050.0000001350.0000001265.0000001.600000e+06
50%169.00000056.00000089.0000001706.0000001525.0000002.000000e+06
75%232.00000073.000000335.0000002357.0000002145.7500002.950000e+06
max511.000000115.00000041000.0000006400.0000004850.0000005.100000e+06
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" + ], + "text/plain": [ + " Rcmnd cruise Knots Stall Knots dirty Fuel gal/lbs \\\n", + "count 507.000000 502.000000 517.000000 \n", + "mean 200.792899 60.795817 1419.379110 \n", + "std 104.280532 16.657002 4278.320773 \n", + "min 70.000000 27.000000 12.000000 \n", + "25% 130.000000 50.000000 50.000000 \n", + "50% 169.000000 56.000000 89.000000 \n", + "75% 232.000000 73.000000 335.000000 \n", + "max 511.000000 115.000000 41000.000000 \n", + "\n", + " Eng out rate of climb Takeoff over 50ft Price \n", + "count 491.000000 492.000000 5.070000e+02 \n", + "mean 2065.126273 1743.306911 2.362673e+06 \n", + "std 1150.031899 730.009674 1.018731e+06 \n", + "min 457.000000 500.000000 6.500000e+05 \n", + "25% 1350.000000 1265.000000 1.600000e+06 \n", + "50% 1706.000000 1525.000000 2.000000e+06 \n", + "75% 2357.000000 2145.750000 2.950000e+06 \n", + "max 6400.000000 4850.000000 5.100000e+06 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# View the statistical summary of the dataset\n", + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f55da0ad", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Model Name 0\n", + "Engine Type 0\n", + "HP or lbs thr ea engine 0\n", + "Max speed Knots 20\n", + "Rcmnd cruise Knots 10\n", + "Stall Knots dirty 15\n", + "Fuel gal/lbs 0\n", + "All eng rate of climb 4\n", + "Eng out rate of climb 26\n", + "Takeoff over 50ft 25\n", + "Landing over 50ft 0\n", + "Empty weight lbs 1\n", + "Length ft/in 0\n", + "Wing span ft/in 0\n", + "Range N.M. 18\n", + "Price 10\n", + "dtype: int64" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Check the null values is present in the dataset or not\n", + "df.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "42b0e466", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model Name 0\n", + "Engine Type 0\n", + "HP or lbs thr ea engine 0\n", + "Max speed Knots 20\n", + "Rcmnd cruise Knots 0\n", + "Stall Knots dirty 0\n", + "Fuel gal/lbs 0\n", + "All eng rate of climb 4\n", + "Eng out rate of climb 0\n", + "Takeoff over 50ft 0\n", + "Landing over 50ft 0\n", + "Empty weight lbs 1\n", + "Length ft/in 0\n", + "Wing span ft/in 0\n", + "Range N.M. 18\n", + "Price 0\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "# Fill missing values with median for numerical columns\n", + "df.fillna(df.median(numeric_only=True), inplace=True)\n", + "\n", + "# Verify no missing values remain\n", + "print(df.isnull().sum())" + ] + }, + { + "cell_type": "markdown", + "id": "88edda31", + "metadata": {}, + "source": [ + "\n", + "### Fill missing values in numerical columns using their respective median values. The median is chosen as it is robust to outliers and better represents the data's central tendency. The `numeric_only=True` parameter ensures only numerical columns are processed, leaving non-numeric columns (e.g., object-type) unaffected. The `inplace=True` parameter applies changes directly to the DataFrame.\n", + "\n", + "### After applying `fillna()`, the `df.isnull().sum()` verification step counts remaining missing values. Non-zero counts for some columns indicate missing values in non-numeric (e.g., object-type) columns, which may need separate handling. This ensures numerical data is ready for analysis or modeling." + ] + }, + { + "cell_type": "markdown", + "id": "f782b48d", + "metadata": {}, + "source": [ + "## Data Preprocessing" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "b3e4bcc5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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HP or lbs thr ea engineMax speed KnotsRcmnd cruise KnotsStall Knots dirtyFuel gal/lbsAll eng rate of climbEng out rate of climbTakeoff over 50ftLanding over 50ftEmpty weight lbsLength ft/inWing span ft/inRange N.M.PriceEngine Type_pistonEngine Type_propjet
014510491.046.036450900.01300.02,0501,18025/337/53701300000.010
1858983.044.015600720.0800.01,35082020/736/11901230000.010
2909078.037.019650475.0850.01,30081021/535/02101600000.010
3858878.037.019620500.0850.01,30080021/535/02101300000.010
4658374.033.014370632.0885.01,22074021/535/01751250000.010
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" + ], + "text/plain": [ + " HP or lbs thr ea engine Max speed Knots Rcmnd cruise Knots \\\n", + "0 145 104 91.0 \n", + "1 85 89 83.0 \n", + "2 90 90 78.0 \n", + "3 85 88 78.0 \n", + "4 65 83 74.0 \n", + "\n", + " Stall Knots dirty Fuel gal/lbs All eng rate of climb \\\n", + "0 46.0 36 450 \n", + "1 44.0 15 600 \n", + "2 37.0 19 650 \n", + "3 37.0 19 620 \n", + "4 33.0 14 370 \n", + "\n", + " Eng out rate of climb Takeoff over 50ft Landing over 50ft \\\n", + "0 900.0 1300.0 2,050 \n", + "1 720.0 800.0 1,350 \n", + "2 475.0 850.0 1,300 \n", + "3 500.0 850.0 1,300 \n", + "4 632.0 885.0 1,220 \n", + "\n", + " Empty weight lbs Length ft/in Wing span ft/in Range N.M. Price \\\n", + "0 1,180 25/3 37/5 370 1300000.0 \n", + "1 820 20/7 36/1 190 1230000.0 \n", + "2 810 21/5 35/0 210 1600000.0 \n", + "3 800 21/5 35/0 210 1300000.0 \n", + "4 740 21/5 35/0 175 1250000.0 \n", + "\n", + " Engine Type_piston Engine Type_propjet \n", + "0 1 0 \n", + "1 1 0 \n", + "2 1 0 \n", + "3 1 0 \n", + "4 1 0 " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Drop 'Model Name' if it's not relevant\n", + "df.drop(columns=['Model Name'], inplace=True)\n", + "\n", + "# Standardize the case in the 'Engine Type' column\n", + "df['Engine Type'] = df['Engine Type'].str.lower() # Convert to lowercase\n", + "\n", + "# Re-run one-hot encoding\n", + "df = pd.get_dummies(df, columns=['Engine Type'], drop_first=True)\n", + "\n", + "# Verify the unique values and column names\n", + "df.head()\n" + ] + }, + { + "cell_type": "markdown", + "id": "cdaea563", + "metadata": {}, + "source": [ + "### The column Model Name is removed as it is not relevant for the analysis and modeling process, ensuring the dataset contains only useful features. The values in the Engine Type column are converted to lowercase to maintain uniformity and avoid potential mismatches during further processing. The Engine Type column is encoded into binary columns (Engine Type_piston, Engine Type_propjet) using one-hot encoding. This transformation converts categorical data into numerical format suitable for modeling. The dataset is displayed after transformations to ensure changes have been successfully applied. The binary columns for Engine Type are now included in the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "3cdf67ef", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1 0]\n" + ] + } + ], + "source": [ + "# Check unique values in the 'Engine Type' column\n", + "print(df['Engine Type_piston'].unique())" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "09c422ac", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0 1]\n" + ] + } + ], + "source": [ + "# Check unique values in the 'Engine Type' column\n", + "print(df['Engine Type_propjet'].unique())" + ] + }, + { + "cell_type": "markdown", + "id": "ba0c9e9b", + "metadata": {}, + "source": [ + "### Check unique values in the one-hot encoded columns 'Engine Type_piston' and 'Engine Type_propjet'. The unique values [0, 1] confirm that one-hot encoding was applied successfully, representing the binary presence (1) or absence (0) of each category in the original 'Engine Type' column." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "7d7a6bd6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Max speed Knots All eng rate of climb Landing over 50ft \\\n", + "0 104.0 450.0 2050.0 \n", + "1 89.0 600.0 1350.0 \n", + "2 90.0 650.0 1300.0 \n", + "3 88.0 620.0 1300.0 \n", + "4 83.0 370.0 1220.0 \n", + "\n", + " Empty weight lbs Length ft/in Wing span ft/in Range N.M. \n", + "0 1180.0 25.0 37.0 370.0 \n", + "1 820.0 20.0 36.0 190.0 \n", + "2 810.0 21.0 35.0 210.0 \n", + "3 800.0 21.0 35.0 210.0 \n", + "4 740.0 21.0 35.0 175.0 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Convert columns to numeric by removing commas and handling special characters\n", + "columns_to_convert = [\n", + " \"Max speed Knots\", \n", + " \"All eng rate of climb\", \n", + " \"Landing over 50ft\", \n", + " \"Empty weight lbs\", \n", + " \"Length ft/in\", \n", + " \"Wing span ft/in\", \n", + " \"Range N.M.\"\n", + "]\n", + "\n", + "for col in columns_to_convert:\n", + " # Remove commas and convert to numeric\n", + " df[col] = df[col].str.replace(',', '').str.extract('(\\d+)', expand=False).astype(float)\n", + "\n", + "# Verify the conversions\n", + "df[columns_to_convert].head()\n" + ] + }, + { + "cell_type": "markdown", + "id": "23ef734f", + "metadata": {}, + "source": [ + "### Specific columns with string-based numbers (e.g., commas or special characters) are converted to numeric format for compatibility with numerical analysis and modeling." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "cc467036", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "HP or lbs thr ea engine 0\n", + "Max speed Knots 0\n", + "Rcmnd cruise Knots 0\n", + "Stall Knots dirty 0\n", + "Fuel gal/lbs 0\n", + "All eng rate of climb 0\n", + "Eng out rate of climb 0\n", + "Takeoff over 50ft 0\n", + "Landing over 50ft 0\n", + "Empty weight lbs 0\n", + "Length ft/in 0\n", + "Wing span ft/in 0\n", + "Range N.M. 0\n", + "Price 0\n", + "Engine Type_piston 0\n", + "Engine Type_propjet 0\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "# Fill null values for specific columns\n", + "columns_to_fill_with_median = [\"Max speed Knots\", \"All eng rate of climb\", \"Landing over 50ft\",\n", + " \"Empty weight lbs\", \"Length ft/in\", \"Wing span ft/in\", \"Range N.M.\"]\n", + "\n", + "for col in columns_to_fill_with_median:\n", + " df[col].fillna(df[col].median(), inplace=True)\n", + "\n", + "# Verify that there are no missing values\n", + "print(df.isnull().sum())\n" + ] + }, + { + "cell_type": "markdown", + "id": "53841fa2", + "metadata": {}, + "source": [ + "### Columns with missing values are identified and filled with their respective median values, a robust imputation technique that reduces the impact of outliers. After imputation, the dataset is verified to ensure no null values remain, indicating the dataset is clean and ready for further steps." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "70aa4828", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Max speed KnotsRcmnd cruise KnotsStall Knots dirtyFuel gal/lbsAll eng rate of climbEng out rate of climbTakeoff over 50ftLanding over 50ftEmpty weight lbsLength ft/inWing span ft/inRange N.M.PriceEngine Type_pistonEngine Type_propjet
count517.000000517.000000517.000000517.000000517.000000517.000000517.000000517.000000517.000000517.000000517.000000517.0000005.170000e+02517.000000517.000000
mean212.794971200.17795060.6566731419.3791101658.9806582047.0657641732.7504847485.4893624377.40522237.88588038.932302911.4487432.355658e+060.7446810.112186
std114.106830103.35808916.4328744278.3207731258.6841841123.433947713.64696710289.4424745649.739125137.6330818.599692696.4296431.010050e+060.4364630.315900
min64.00000070.00000027.00000012.000000360.000000457.000000500.000000567.0000002.00000017.00000016.000000117.0000006.500000e+050.0000000.000000
25%143.000000131.00000050.00000050.000000924.0000001365.0000001265.0000002650.0000001575.00000025.00000035.000000517.0000001.600000e+060.0000000.000000
50%177.000000169.00000056.00000089.0000001200.0000001706.0000001525.0000003625.0000002286.50000028.00000036.000000713.0000002.000000e+061.0000000.000000
75%238.000000229.00000073.000000335.0000001820.0000002280.0000002110.0000008800.0000005164.00000035.00000042.0000001100.0000002.940000e+061.0000000.000000
max755.000000511.000000115.00000041000.0000007220.0000006400.0000004850.00000089400.00000046800.0000003150.00000093.0000006500.0000005.100000e+061.0000001.000000
\n", + "
" + ], + "text/plain": [ + " Max speed Knots Rcmnd cruise Knots Stall Knots dirty Fuel gal/lbs \\\n", + "count 517.000000 517.000000 517.000000 517.000000 \n", + "mean 212.794971 200.177950 60.656673 1419.379110 \n", + "std 114.106830 103.358089 16.432874 4278.320773 \n", + "min 64.000000 70.000000 27.000000 12.000000 \n", + "25% 143.000000 131.000000 50.000000 50.000000 \n", + "50% 177.000000 169.000000 56.000000 89.000000 \n", + "75% 238.000000 229.000000 73.000000 335.000000 \n", + "max 755.000000 511.000000 115.000000 41000.000000 \n", + "\n", + " All eng rate of climb Eng out rate of climb Takeoff over 50ft \\\n", + "count 517.000000 517.000000 517.000000 \n", + "mean 1658.980658 2047.065764 1732.750484 \n", + "std 1258.684184 1123.433947 713.646967 \n", + "min 360.000000 457.000000 500.000000 \n", + "25% 924.000000 1365.000000 1265.000000 \n", + "50% 1200.000000 1706.000000 1525.000000 \n", + "75% 1820.000000 2280.000000 2110.000000 \n", + "max 7220.000000 6400.000000 4850.000000 \n", + "\n", + " Landing over 50ft Empty weight lbs Length ft/in Wing span ft/in \\\n", + "count 517.000000 517.000000 517.000000 517.000000 \n", + "mean 7485.489362 4377.405222 37.885880 38.932302 \n", + "std 10289.442474 5649.739125 137.633081 8.599692 \n", + "min 567.000000 2.000000 17.000000 16.000000 \n", + "25% 2650.000000 1575.000000 25.000000 35.000000 \n", + "50% 3625.000000 2286.500000 28.000000 36.000000 \n", + "75% 8800.000000 5164.000000 35.000000 42.000000 \n", + "max 89400.000000 46800.000000 3150.000000 93.000000 \n", + "\n", + " Range N.M. Price Engine Type_piston Engine Type_propjet \n", + "count 517.000000 5.170000e+02 517.000000 517.000000 \n", + "mean 911.448743 2.355658e+06 0.744681 0.112186 \n", + "std 696.429643 1.010050e+06 0.436463 0.315900 \n", + "min 117.000000 6.500000e+05 0.000000 0.000000 \n", + "25% 517.000000 1.600000e+06 0.000000 0.000000 \n", + "50% 713.000000 2.000000e+06 1.000000 0.000000 \n", + "75% 1100.000000 2.940000e+06 1.000000 0.000000 \n", + "max 6500.000000 5.100000e+06 1.000000 1.000000 " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# To check how does the data looks mathematically\n", + "df.describe()" + ] + }, + { + "cell_type": "markdown", + "id": "8938328f", + "metadata": {}, + "source": [ + "### Now, we have handled missing values & also converted object columns into numerical. In addition, there were still null values present in the object columns, we filled the null values by using meadian. Now, You can see the summary of dataset & we are ready to go with correlation matrix to select the best features for train test split.\n" + ] + }, + { + "cell_type": "markdown", + "id": "077fea55", + "metadata": {}, + "source": [ + "## Correlation Matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "340a22df", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Kaustubh\\AppData\\Local\\Temp\\ipykernel_13864\\805188324.py:9: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n", + " correlation_matrix = df.corr()\n" + ] + }, + { + "data": { + "image/png": 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QQgjxSFKqS28Iwz916NAh2rZta358e/6EwYMHEx4eTmxsrLkzAaBChQps2rSJCRMm8MUXX1CmTBnmz59P3759SyxG6UAQQgghhBBCCCFKWZs2bcyTINoSHh5uVda6dWuOHDlSglFZkg4EIYQQQgghhBCPJIVGRu3bk7ybQgghhBBCCCGEKJZ0IAghhBBCCCGEEKJYMoRBCCGEEEIIIcQj6WGaRPFhIBkIQgghhBBCCCGEKJZkIAghhBBCCCGEeCQpNJKBYE+SgSCEEEIIIYQQQohiSQfCQ2rmzJnUq1evtMMQQgghhBBCiP8spVpRasuj6D89hGHIkCEsW7aMESNG8NVXX1k8N3r0aBYuXMjgwYMJDw8vnQD/w6KioqhQoQJHjx41dzSkp6fTs2dP4uLi2Lp1K8HBwXZ5rdDQUMaPH8/48ePtsr2S5N2iERVffRGPBrVwKuPPob6jiV+/vbTDMvPu1gu/PgNQe/uQGx1FzKIFZJ0+WWR9zzYd8O07EMegshiyMkk/fIC4JV9hSE+zquvRqi0hU94idd+fRL87vcTaoG3ZCbf2vVB5eKKLvUbK6qXkXTxrs67Xc2PQPt7GqlwXe5X4dydalTs3bIbP0AlkHz9A4qIP7R26BccGrXB8rANKVw8MN2LJ3vYz+msXi15BpcapeTccajVGqXXHmJ5Czt7N5J3YB4Cmaj2cmnVG6eWHQqnCkJxA7oHt5J06UKLt8OzUE+9e/VF7epN37QrxSxeSffZUkfXdW7TDu1d/HILKYszKJOPYIRKWf4MxIx0Ah3Ll8RswCKeKVdD4BxK/dCHJm9aWaBtcW3fBvWMvVB5e6GKukvzzEnIvRNis6z34ZVybtrMqz4uJJm72eAC0TdviM3isVZ3olweAXmfX2O/k0qwD2jY9ULl7oo+7Tuovy9FdjrRZ12PgCFwat7Yq18Vd4+aHUwBQB5TFtUt/NOUqoPb2I3XdcrL+2Fxi8T8qbYCHsx2bN65l/ZofSE5KIjgklCEvjaVGrbpF1j998hjLFi3ganQUXt4+9Or3DJ279TI///dfu1nz0/fExV7HoNcTVKYcPfsMoHW7zuY6v/+6jt83reNGfBwAweUr0O/pwTRo9Ljd2vXTkfMsO3CWmxnZVPL1YFL7+jQI9rdZ91B0PMN/2GlVvmZYNyr4uAOwPfIq3/59hqvJGeiNRkK83Hi+cTV61Kpgt5htcX68PS4tu6F080CfcJ2MjSvQRZ0regWVGm373jjVa4bSzQNjahKZOzeQc3hP/vNKFS5teuDcoAVKdy/0N+PI3PwjeeeKPie4Xz7de+HXdyBqbx9yrlwm5pviz0H8+g3EsUw58zlI7OKF5nMQrw5dCJ441Wq9k706YdLllVg7AJ7tHUDX1t64alVEXsrii+XXiY7JLbL+B1MrUifM1ar8wPE0ZnwaBUCtqlr6dfOjcnlnfLw0zJ4fxb4j1udb9rJu0+/8uOYXEpNTCA0px8vDhlKnZnWbdROTkvlyyTLOX7zEtZg4+vToysvDh1rUGf/6DI6fOmO17mON6vP+W6+XSBvEo+k/3YEAEBwczMqVK/n0009xdnYGICcnhx9++IGQkJBSju7hcePGDbp27QrAn3/+ia+vbylHVDpUWhfSTkRybdkaGv68oLTDseDRsi1Bw8cQs3AeWWdO4d21J6EzP+D86CHobiRY1XepUYtyE6YSu/hL0g7sRePjS9kxEyk7bhLR775lUVfjF0DQC6PIPHW8RNvg3KAZnn2HkvzjIvIuRaJt0RHf0W8Q/84EDMk3reqnrFpK6i8rzI8VKiX+0z4i++g+q7oqL188eg8i94L1l5+9aao3xLlDP7J+X4n+2iUc67fAdcAYUhe9jSkt2eY62t4votS6k7Xpe4zJN1C4uIFSZX7elJNJzt7NGBLjwaBHU7k2Lt2fx5iZjv6y7R/D98utWWsCho4kbtHnZEeexrNjd4LfeJdLE4ahv3nDqr5zWE2Cxk4mIfxrMg7/jdrbh8DhrxA0aiLXP5wFgNLRkbyEONL2/UHAkBElEvedXBo2x6v/UJJ+WETuxQhcW3bG7+U3iZ31is19KvnHJaSs/d78WKFUEfjmJ2QfsdynjNmZxMwo1IlQgp0HTvUex73XIFLXLEF3+RwuTdvjPfw1bsydjDEl0ap+2rrlpP+6sqBAqcLv1TnkHN9vLlI4OGJITCDn+H7cez1XYrE/Sm2Ah7Mdf+3ZTviizxk2eiJh1WuxdfN63psxhU8XLsfPP8CqfnxcDO/NmEKHLj0YN+lNzkacYvGXn+Dh4cHjzdsA4OrmTt8Bz1O2XAhqjYbDB/byxafv4+HhRb2GTQDw8fXjuSEjCCxTDoBd2zYz9+3X+XD+twSXv/8f5L9HRPPh9qNM69SQemV9WX3sIi//vIfVw7oS5K4tcr11w7uhddCYH3u5OJr/9nB2YFjTmoR6u6FRKfnjYgwzNx3A28WJZhWD7jtmWxxrP4Zr92dJ/2UZuivncX6sLR5DJpH06TSMqdb7FIDHMy+jdHUnbfW3GBLjUbq6g7IgMVjbqS9O9ZqRvnYJ+oRYHKvWxuO5V0he+Db62Ct2b4NHq7YEvfQyMV/OI/PMSby7PkGF2XM5N3JwEecgtQl+dRoxi74gbf9eND5+lHt5IuVemcyVdwouUhgyM4h8aZDFuiXdedC/mx99Ovvy8eKrXI/L5eknAnhvckWGT4skO8doc523P7+C5o6rxW5aFV++XZU/Dqaay5wclVyKzmbLH0lMHxtaom3Y8cdffLF4KeNHDqdW9Wps2LyV12a9S/gXnxLg52dVX6fT4enhzrP9+7Lql402tzl72iT0er35cWp6BsPGTaJN86Yl1g7xaPrPD2Fo0KABISEhrFmzxly2Zs0agoODqV+/vkXdzZs306JFCzw9PfHx8aFHjx5cvFhwxXD58uW4urpy/vx5c9nYsWOpWrUqmZmZNl//+PHjtG3bFjc3N9zd3WnYsCGHDh0CIDw8HE9PT9atW0fVqlVxcnKiY8eOXL161WIbGzZsoGHDhjg5OVGxYkVmzZpleQCnpvLSSy/h7++Pu7s77dq14/hxyx9677//PgEBAbi5ufHiiy+Sk5Nzz+/h1atXadmyJW5ubuzcudPceRAVFYVCoWDNmjW0bdsWFxcX6taty759lifaq1evpmbNmjg6OhIaGsrHH39sfq5NmzZcuXKFCRMmoFAoUCjyP3yvXLlCz5498fLyQqvVUrNmTTZt2nTPMZeUG7/v4dyMecSt21raoVjx7d2f5K2bSN6yidxr0cQu+gLdzQS8uz1hs75LtRrkJcSRuGENuvg4ss6cIum3DThXrmZZUakkeNIbxK8IJy8utkTb4NauB5n7dpC1bwf6+Oukrg7HkHwTbctONuubcrIwpqeYF01IJZTOWjL3FbrCpFDiPeQV0jb9hP6m9YmMvTk1aUfe8b3kHd+LMTGO7G2rMKal4Fi/lc366oo1UIdUIeOnL9BHRWJMTcIQewXD9UvmOvro8+jOHceYGIcx5Sa5h3ZiSLiOOrhSibXDu0dfUnZsJnXHZvKuXyUh/Ct0N2/g1amnzfrOVaqjS4gn+bd16BLiyD57mpStv+JUsaq5Ts7Fc9z4bhHpe3dh0pXcD+7b3Dr0JOOv7WT+tQ193HVSfl6CITkR19adbdY35WRhTEsxLw7lK6F00ZKxd0ehiljUM6allGg7tK26kXVgF9n7d6FPiCHtl+8wpiSibdahiHZkY0xPNS+a4IoonLVkH9xtrqO7eon0jf8j59g+THd8p0gbHr12bFj7E+06dadD5x6UCwll6Evj8PH1Y8umdTbrb9n0C75+/gx9aRzlQkLp0LkHbTt2Y/2aH811atWpz2PNWlEuJJTAoLJ079Wf8hUqEnHmhLlOo8ea06BxU8qUDaZM2WCeGTwcJydnzp09bZd2fX/wLL3rVKRP3UpU9PVgcocGBLq58PPRC3ddz9vFCV9XZ/OiuuOHd6OQANpVLUdFXw+Cvdx4plE1qvh7cvSadaepvbi07EL2od3kHNqN4UYMGRtXYExNwvlx62woAIeqtdFUqEZK+MfoLp7GmHIT/bVL6KML2u1UvzlZuzaQF3kCY/INsvfvIO/cSVxadimRNvg92Z/kLZtI+v1Xcq9GE/vNAnQ3EvDp3stmfZewW+cg62+fg5wk8bf1OFcpdA5iAn1yksVS0np38mXlhgT2Hk7jyvVcPl50FUdHJW0e9yxynYxMA8mpevPSoJYbuXlG/jiQYq5z6GQ6y9fEs/dwyWUd3PbzLxvp1qEd3Tu1p3xwOV4ePhR/X1/Wb9pis35ggD9jh79A53at0WpdbNZxd3PD28vLvBw+egInR0da/z/oQFBoFKW2PIr+8x0IAEOHDmXp0qXmx0uWLOGFF16wqpeZmcnEiRM5ePAg27dvR6lU8uSTT2I05vc2Dho0iG7duvHss8+i1+vZvHkzX3/9NStWrECrtd3T/eyzz1KuXDkOHjzI4cOHmTp1KhpNQa93VlYW7777LsuWLeOvv/4iLS2NgQMHmp///fffee655xg3bhxnzpzh66+/Jjw8nHfffRcAk8lE9+7diYuLY9OmTRw+fJgGDRrQvn17kpLyP2R/+uknZsyYwbvvvsuhQ4cICgriyy+/vKf3LjIykubNmxMWFsbmzZtxc3OzqvPGG28wadIkjh07RtWqVXn66afNHRyHDx/mqaeeYuDAgZw8eZKZM2cyffp087CRNWvWUK5cOWbPnk1sbCyxsfk/UMeMGUNubi579uzh5MmTfPDBB7i6WqeGiXwKtRrnylXJOHrIojzj6CFcwmrZXCcr4jQaXz/cGj0GgNrTC/fmrUk/9LdFPf+Bg9CnpZC8tYQ7cFRqNMEVyYmw7PzKiTiBY4VqRaxkSdu0HbmRJ62uLLt37YcxI42sfTuKWNOOlCpUgSHoCmUF6C5HoC5X0eYqmip1MMRG4/R4Rzxefg/3ETNwbtcH1Bqb9QHU5auh8g6wOGG0K7Uap4pVyDx+xKI488RhnKvVsLlKduQZ1D6+aOs3BkDl4Ylb05ZkHNlvs36JU6lxCKlkY586hmPFsHvahGvz9uScPYEhyfLHg8LRiTLvfk2ZOYvwG/06muASTG9WqdCUq0Bu5AmL4tzIk2hCqxaxkiWXJm3IO3/KZtbFA/EotAEeynbodDouXThH3VvH5W11GzQmMsL2cKRzZ09Tt4Fl/XoNmnDx/FmLCxi3mUwmThw7TMy1q0UOizAYDPy5ezs5OTlUrW77e+mf0BkMRMQl07RCoEX54xUCOX797u/twPDf6bhgHSNW7uDglfgi65lMJvZHxRGVlEbDIoZF3DeVCnWZUPLOW/4v8s6fRBNSxeYqDtUboL8ehUur7vhMnYf3q3Nx7TrQ4jtDodZgKpQVZdLn3fN++k/kn4NUI/3IQYvyjKMHcale0+Y6WRGnrM5BPFq0Jv2g5TmI0tmZsPCVhC3/mdCZc3CqWNnu8d8p0M8Bb08NR06lm8t0ehMnz2ZQo7LtH9a2dGrpxe79KeTmmUoizLvS6XScu3CJRvUtj8VG9etw6qztoVb/xqZt22nbshnOTk5226b4/+E/P4QB4Pnnn2fatGnmK+Z//fUXK1euZNeuXRb1+vbta/H422+/xd/fnzNnzlCrVv6X3ddff02dOnUYN24ca9asYcaMGTRubPkle6fo6GgmT55MWFj+yWqVKpZfBjqdjgULFvDYY/kfoMuWLaN69eocOHCAJk2a8O677zJ16lQGDx4MQMWKFXn77beZMmUKM2bMYOfOnZw8eZKEhAQcHfNT8D766CPWrVvHqlWreOmll5g3bx4vvPACw4YNA+Cdd95h27Zt95SFMGjQIJo1a8bq1atRqVQ260yaNInu3bsDMGvWLGrWrMmFCxcICwvjk08+oX379kyfnp+OVrVqVc6cOcOHH37IkCFD8Pb2RqVS4ebmRmBgwUlAdHQ0ffv2pXbt2uZ2i6Kp3D1QqFToky3T4/XJyWgaeNlcJ+vsaa5+9C7BU95C6eCAQq0m7e+/iPlqvrmOS/VaeHfqxvlxw0o0fgClqxsKlQpjeopFuTE9BaW7Z/Hru3viVKM+SeGfWZQ7VKyGS9N2JLw/2Y7RFk3h4opCqcKYmW5RbspMQ6l1t7mOytMHdXAlTAYdGau/RuHiikungSicXMjaVJBOj6MTni+/ByoNmIz5QySibM8Pcb/Ubu4oVCoMKZb7lCElGZWn7X0q+9wZYud/QJkJb6DU5O9T6Qf3Er/kixKJsTiqW/uUoVB2gCEtFad72qe8cKrZgMQln1qU6+Kuk7jsc3TXo1E6O+PWrgcBk98j7p2J6BPsn6Wj1N46NjJSLcoNGak4unkUv76bJ45hdUlZUXrDrh6FNsDD2Y70tFSMRgMehY5bD09vUoq4mpuSnISHp3eh+l4YDAbS01Lw8s7PRMzMzGDEoL7odHkolSqGjZ5g1VFxJeoib7w6mry8PJycnZny5jsEh4Ted7uSs/IwmEx4u1j+ePHROpKYafv8xlfrzPTOjake6EWewcivp6MYsXIni55pZ9FBkJ6bR+cv1qMzGFAqFEzr1IjHC3VU2IvSxfY+ZcxIQ1nEPqXy9kNTvgomvY7U7+ej1Lri1mswChdX0lcvBsjPNmjRBd3lSAxJCWgq1cCxegOLYQ72Yj4HKfR9oUtOxs3L2+Y6WRGnuTr3XUKmzjCfg6Tu+5PrCwu+w3OvRnP1k/fJibqE0sUF3179qPzRAs69/CJ5Mdft3g4AL4/8nzbJaZYdZSlpevx9HO5pG1UrOFMh2Jl5S67ZPb57kZqWjtFoxMvT06Lcy8OT5JQUu7xGxLnzXL5ylcljR9lle/91j+pkhqXloehA8PX1pXv37ixbtsx8xd7WGP6LFy8yffp0/v77b27evGnOPIiOjjZ3IHh5efHtt9/SuXNnmjVrxtSp1pO73GnixIkMGzaM7777jg4dOtC/f38qVSpIOVar1TRq1Mj8OCwsDE9PTyIiImjSpAmHDx/m4MGD5owDyO/Fz8nJISsri8OHD5ORkYGPj4/F62ZnZ5uHX0RERDBy5EiL55s2bcrOndYTCRXWq1cv1q5dy+rVq3nqqads1qlTp47576Cg/PGBCQkJhIWFERERQa9elulrzZs3Z968eRgMhiI7JcaNG8eoUaPYsmULHTp0oG/fvhavc6fc3Fxycy0nttGZjGgUD0WCjJ0V6ulWgKmIzm/H4PKUeWksCSuXk3HkYP549aEjKDtmItfnf4jS2ZngV1/n2ucfYUgr+XS7Iinu0og7aB9vgzE7k+wTBVdAFI5OeA8aR8oPX1n9oC95hf8XCusy83NKMJnIXL8UcvNPfLO3r0bbZxhZW34sGFufm0vakjkoNI6oQ6vh3L5vfupq9Hnb27VLK2y0o4hmOJQLwX/oaBJXrSDz2CHUXt74PT+cwJdeIW7hJyUWY7EK7z8KKLIRd3Bt2hZjdiZZxywnqsy7fI68ywWTm+VePEvg6x/h1qYbyT99a4eAi2CrGffAuXErTDlZ5Jw6VHzlkvYotAEeynbcHiJoZjLd+ly6x/oFz5j/cnZ24cPPvyUnO5uTxw+zbPEXBASWoVadgiGiZcqG8OHn35KZmcH+v3az4JP3mPXB53bpRMiP0/KxyVT0/yPUx51Qn4KO3LplfYlPy2L5gbMWHQhaBw0rh3YmO0/P/ivxfLzjKOU8tTQKsZ4vokQV8d13+3+TtnIhptxsADJ+/QH3Z14m/ZdloNeRvvF73J98Ae+JH4DJhCEpgezDf+DcsOUDi1ehUNz9HGTkWBJ+WEb64fxzkKAXR1Lu5Ylc+yx/kuOsyDNkRRbMWxR95hRV5i/Ct2cfYr7+3C4ht23qydjBZc2Pb094aBV30V99Vjq38uby1WzOXc62S4z/lvUhbOLeP63ubtPWHVQoH0z1qrazZIS4m4eiAwHghRde4OWXXwbgiy9sXw3r2bMnwcHBLFq0iDJlymA0GqlVqxZ5eZaTtezZsweVSkVMTAyZmZm4u9u+qgj5t0t85pln+PXXX/ntt9+YMWMGK1eu5MknnzTXsfUlfbvMaDQya9Ys+vTpY1XHyckJo9FIUFCQVTYFgGehnsd/4/XXX6dOnTo8++yzmEwmBgwYYFXnziEZd8YN+el/hdtnuocfg8OGDaNz5878+uuvbNmyhTlz5vDxxx8zdqz1zOdz5sxh1qxZFmVPK7x5VvX/Z6JHQ1oqJoMBdaGefrWnl9UVgdv8+j9DVsQpbt4e0xp1iZicbCrN/Zz4775F7emFQ2AQoW+9V7DSrf9lrV+2cW7EIPLiYuzWBmNGOiaDAaWbp0W50tUDY3qq7ZXu4PJ4O7IO7AFDwVUDtW8gal9/fEbc0dF3qw1lP1tJ3NuvYLhZdPrqv2HKysBkNKDUumO4o1zh4lZkJ4YxIxVjRoq58wDAkBiHQqFE6eaJMfl2+rzJ/Lch4Roqn0CcmnYmowQ6EPTpafn7VKGrkCoPTwyptvcpnycHkh15mqT1PwOQG30ZY24O5d/+lBs/hGNIKfmxq3cy3NqnVB6WV15Vbh4Y0orfp7TN25G5f7fFPmWTyUTelQuo/UtmgjVj5u1jw/JqpNLVA8O9HBtN2pB16A8wGIqtW1IehTbAw9kON3cPlEqVVbZBamoynkVkE3l6eZOSbDl5X2pKcn7GoHtB25VKJUG3JkisUKkK169eYe3P31t0IGg0GnOdylXCuHDuLJt++ZkRY+8vK8zLxQGVQmGVbZCUlYu39t5TqmuX8WHTactJBZUKBSFe+UM2qwV4cTkxjSX7IkqkA8GYdWufci28T7ljzLDdeW9IT0WZlmzuPADQJ8SgUCpReXhjSIzHlJlO6vefgVqD0sUVY1oy2i5PYUi2/1wORZ+DeKIv4nPff8CzZJ45xY3VBecg13NyqPzR58Qt/9b2XAcmE1nnz+JQtpzdYv/7aBpnL2aZH9+eCNHbQ01yasFnv6ebmpTU4uftcXRQ0PoxT75bG2e3GP8pD3c3lEolSckpFuXJqal4eRafKVWcnNxcdv7xF0Oesf5NIMS9eGgu8Xbp0oW8vDzy8vLo3Nl68qzExEQiIiJ48803ad++PdWrVyc52fokee/evcydO5cNGzbg7u5u8wdtYVWrVmXChAls2bKFPn36WMzHoNfrzZMqQv6cAykpKeYhDw0aNCAyMpLKlStbLUqlkgYNGhAXF4darbZ6/naWRfXq1fn7b8sxZYUf382bb77J22+/zbPPPssPP/xwz+sB1KhRgz///NOibO/evVStWtWcfeDg4IDBxslUcHAwI0eOZM2aNbz66qssWrTI5mtMmzaN1NRUi+Uppe2UuUeVSa8n+8I5XOs1sih3rdeQrCJuuad0dMJkLNSZc6vjB4WC3GvRnBszlPPjhpmXtP17yTx5jPPjhqGz92SEBj26q5dwCrPMNHEKq0NuEbdHu82xSg00/kFkFprjQBd/nbh3JxL//mTzknPyELnnTxP//mQMybZnt74vRgOGuGjUFSxvlaSpEIb+2iWbq+ivXUTp6gmagpnAVd7+mIxGqyEdFhQKUJVQP65eT86l82jrNLAo1tZpQHak7TtZKB0cwWQ5Q7Xp1j51lwudJcegJy/6Ik7VLceBOlWvS+6luw/9cKxaE41/GTL/urfbtGrKVSiyY+W+GQzorl3GsWpti2KHqrXufps3wKFSddR+gWQf2FUysd2rR6EN8FC2Q6PRULFyVU4UmiPnxNFDVCtiLoKqYTWt6h8/epBKVcJQq4v+zDGZTOiKmRzVRPF17oVGpaJ6oBd/R1n+UPs7Ko66Ze/9AsLZ+GR8Xe/e4WAymcgrqU4fgwF9TBQOVSz/Fw6Va6EronNYF3UOlZsnCoc7vjN8AzEZjRhSC/3w1uswpiWDUoVjrcbknjmCveWfg0TiWr/QOUj9RmRF2J4wU+lo/X2B8dZ7fJcvDOeKldEn2e+7OzvHSGxCnnmJjsklKUVH/ZoFc36pVQpqh7ly5kLWXbaUr2UTTzQaBTv2ptgtxn9Ko9FQtXJFDh2znKvl8LET1Aq7tzml7mbXn3vJ0+np2Mb2xNCPIoVKUWrLo+ihyUBQqVRERESY/y7My8sLHx8fvvnmG4KCgoiOjrYanpCens7zzz/P2LFj6dq1KyEhITRq1IgePXrQv39/q21mZ2czefJk+vXrR4UKFbh27RoHDx60mGtBo9EwduxY5s+fj0aj4eWXX+bxxx+nSZP8WyC99dZb9OjRg+DgYPr3749SqeTEiROcPHmSd955hw4dOtC0aVN69+7NBx98QLVq1YiJiWHTpk307t2bRo0a8corrzB48GAaNWpEixYtWLFiBadPn/5H8wpMnToVlUrF888/j9Fo5Nlnn72n9V599VUaN27M22+/zYABA9i3bx8LFiywmMQxNDSUPXv2MHDgQBwdHfH19WX8+PF07dqVqlWrkpyczI4dO6he3fa9ax0dHc3zP5jf1xIavqDSuqCtXHD7T5cK5XCvG0ZeUio5V0v2DgXFubnuZ8pNnEb2hUiyIk7j3aUHGr8AkjZtACBg8DA0Pn5c+2QOAGkH9lJu7CQyuz5B+pGDaLx9CBo+hqzICPOXc+6VKIvXMGZm2Cy3l/QdG/EeNJa86IvkXT6HtnkHVN6+ZP6RP2uw+xPPoPLwJvk7y/HDLk3bk3v5HPpYyzuYoNdZlRmzs1CCdV07yjmwA23PwRhir6C/fhnHes1RunuRd/QPAJxa90Lp5knWxmUA5J0+hHPzbmi7P0/2HxtRurji3O5J8k7sNQ9fcGraGX3sFYwpN0CpRlOpJg61HiPr93/WqfdPJG1cTZmxU8i5eI7sc2fw7NAdja8/yVvyb/Hk98wLqL19iF2Qn26acfhvAkdMwLNTD/MQBv8ho8g+f7bgapJajWO5W8eQWoPaxxfH0IoYc3LQ2TGj5bb0bRvwGTqOvCsXyL0UiWvLTqi8fMnYk79PefR+FrWnD4nh8y3Wc23WntxL59DFRFtt0737U+RdPocuIRalkzNubbvjEBxK8spv7B7/bZl7NuH59Gh01y6hizqP8+PtUHn5krUvv4PDrdsAlB7epP6w0GI95yZtyLtyHn2cjbG4KhXqgPwreQqVGpWHN+oy5THl5uTfLlTa8Mi0o+eTT/H5x+9SsUo1qoXVZOvmDdy8kUCnbvlDDFeEf01i4k3GvfoGAJ269WLzxrWEL1pAh849iDx7mh1bfmX8lIJb/K756XsqValGYGBZ9HodRw79ze4dvzN8zKvmOiuWfUP9ho/h6+dPdnYWf+3ewZmTx3hj9of33SaA5xqH8ebGv6kR6E2dMj6sOX6RuLQs+tXLn2hv/u7jJKRn806Px/PjORhJGQ8tFX090BuM/Homiu3nrvFR7+bmbX677ww1A70p5+WKzmDkz4sx/Ho6immdGtmMwR6y/tiM+1Mj0F+7jC76As5N2qD09CF7f36nuLZzf5TuXqT/nP8Zk3t8H9p2vXDrN5zMbWtQurjh2m0gOYf2mL8z1MEVUbl7o4u5gsrDC237J1EoFGTtKZkJkW+s/ZngV18n+3wkWWdP492lJxq/ABI3rQcgcMhwND6+XP341jnI/n2UGzcJ725PmIdRlnnpZbIiz5jPQfyfGUzW2TPkxVxD6aLF94k+OFeszPUv55VIG25bt+UmA3r6ExOfy/X4XAb08Cc318iuv1PMdV4dHkxiso7wVZYdWJ1berPvSBrpmdYdTk6OSsoEFMyjEODrQMUQJ9IzDNxIsu9difr36sGcTz+nWuVK1AyrysbftxF/4yY9u+bf1WrRshXcSEri9QkFF0IvXLoMQHZODilpaVy4dBm1Wk1oSLDFtjdt3UGLxxvj4W49sboQ9+Kh6UAA7jrUQKlUsnLlSsaNG0etWrWoVq0a8+fPp02bNuY6r7zyClqtlvfey0/prlmzJh988AEjR46kWbNmlC1b1mKbKpWKxMREBg0aRHx8PL6+vvTp08ci3d7FxYXXXnuNZ555hmvXrtGiRQuWLFlifr5z585s3LiR2bNnM3fuXDQaDWFhYeYJERUKBZs2beKNN97ghRde4MaNGwQGBtKqVSsCAvJT7QYMGMDFixd57bXXyMnJoW/fvowaNYrff//9H71/kydPRqVSMXjwYIxGIy1bFj+OrkGDBvz000+89dZbvP322wQFBTF79myGDBlirjN79mxGjBhBpUqVyM3NxWQyYTAYGDNmDNeuXcPd3Z0uXbrw6aefFv1CD4hHw1o03f6d+XGNj14H4OryNZx4cVpphQVA6h87Ubm54z9wEGpvb3KvRBE1cyq6G/knnxovHzR+BWM8U7b/jsrZBZ8eTxL04igMmRlknDhKXHjJ/QgqTvaRvaRoXXHv2g+Vuxe62Kvc/PI982zlKncv1N6WV5YUTi4413uM1FVLbW2yVOgiDpPtrMWpeTeUru4YbsSS8dOXGNPyf0QrXd1Rut+ROqzLJf2H+bh0egr3oVMxZWeSF3GY7D0bCupoHHDpPBClmycmvQ5jYjyZG8LRRRwusXak791NvKs7vv2eReXlTd7VK1x9703zrTDVXt5ofAv2qdRdW1E6ueDV5Qn8B72EITOTrFPHuLFicUEzvHyo8OFX5sc+T/TH54n+ZJ0+TvRM+090mXX4L5Subnh0fyp/n4qJ5saCd813VVB5eKGytU81aFrkfAZKFy3ez45C5e6JMTuLvKuXiP/oTfKiSuiOGEDOsb9Jc3HFtWMfVO6e6GOvkbx4rvnYULp7ovK0nAtH4eSMc50mpK5bbnObKncv/F6dY37s2rYHrm17kHvhDEkL35E2PELtaN6qPelpaaz6YRnJSYmElK/A67M+wM8/f2LA5KREbt4o6KgICCzD67PmEr7oczZvXIu3jw9DR7zC483bmOvk5mSz6MtPSLp5AwcHR8qUC2HcpDdp3qq9uU5qchKff/wuyUmJuGi1lA+txBuzP7SaaPHf6lw9hNTsXL756xQ3M3Oo7OvB5/1bUcYj/85YNzOyiUsruM22zmjk053HSMjIxlGtopKvO/P7taJlpTLmOjk6Pe9tPURCen6dUG833unRlM7VQ6xe315yT+4nQ+uKtn1+57I+/hqp4R9jTMn/Ia10s9ynTHm5pCyZi1vP5/EeMwtjVga5Jw+QsWWVuY5CrUHbsS8qbz9MebnkRh4n7aevMeUUfxX930jdsxO1mzsBzwxG7e1NTtRloma8hi4hf79Se/mg8SsYApK8bTNKZ2d8ez5JmWGj889Bjh8ldunX5joqrSvlxr2K2ssbY2Ym2RfPc3HKOLLPlczkwbf9vOkGDg5Kxgwqi6tWReTFLN746BLZOQUZE/4+GqshuWUDHKhVTcvrH9rONqxSwZm5UwvmQRvxTP5+t/XPJD5ZbN8JF9u1bE5aegbLf1xFUlIyoeWDef+t1wn09wMgMTmZhBuWdysZPn6K+e9zFy6xffefBPj7sXJxwUW/q9djOHnmLB/OetOu8f7XKR/RTIDSojDdy4B2YVN4eDjjx48nxU4zoooCv2ruP0XrvyCkc5niK/3HeYU+GnNRaMv6lXYI9y3+SMn9wH2QXHwejVu6av7BOG0hinNzVOl3sttDpT33dpvp/7qM85dLO4T7FnfcOgPrYfSa//ziK/3HfVu616jspkw12xOi/9f9WbdB8ZVKSIvj9h92VNoeqgwEIYQQQgghhBDiXimUkoFgTw/NJIpCCCGEEEIIIYQoPdKBcB+GDBkiwxeEEEIIIYQQQvy/IEMYhBBCCCGEEEI8khQquWZuT/JuCiGEEEIIIYQQoliSgSCEEEIIIYQQ4pEkt3G0L8lAEEIIIYQQQgghRLGkA0EIIYQQQgghhBDFkiEMQgghhBBCCCEeSQqlDGGwJ8lAEEIIIYQQQgghRLEkA0EIIYQQQgghxCNJJlG0L8lAEEIIIYQQQgghRLGkA0EIIYQQQgghhBDFkiEMQgghhBBCCCEeSQoZwmBXkoEghBBCCCGEEEKIYkkGgvhPCulcprRDsIvo32NKO4T75tjPsbRDsAsnb/fSDuG+pcemlXYI4g7K1KzSDuG+ufi4lXYI4haT6dG4QmbIfPiPC4Cc5IzSDuG+ZSdnl3YIdpGuSi7tEO6bQelX2iH8v6ZQyjVze5J3UwghhBBCCCGEEMWSDAQhhBBCCCGEEI8khfLRyPD6r5AMBCGEEEIIIYQQQhRLOhCEEEIIIYQQQghRLBnCIIQQQgghhBDikaSU2zjalWQgCCGEEEIIIYQQoliSgSCEEEIIIYQQ4pEkkyjal2QgCCGEEEIIIYQQoljSgSCEEEIIIYQQQohiyRAGIYQQQgghhBCPJIVSrpnbk7ybQgghhBBCCCGEKJZkIAghhBBCCCGEeCTJJIr2JRkIQgghhBBCCCGEKNb/6w6EqKgoFAoFx44de+Cv3aZNG8aPH//AX1cIIYQQQggh/r9QqhSltjyK7DqEYciQISxbtgwAlUpFmTJl6N69O++99x5eXl72fKmH3po1a9BoNCW2/TZt2lCvXj3mzZtnLvvss8+YMmUKS5cu5ZlnnrHL68ycOZN169aVSifMnby79cKvzwDU3j7kRkcRs2gBWadPFlnfs00HfPsOxDGoLIasTNIPHyBuyVcY0tOs6nq0akvIlLdI3fcn0e9OL8lm3BPvFo2o+OqLeDSohVMZfw71HU38+u2lHdZdeXTsgXePfqg8vcm7doUby78iO/J0kfXdmrfFu2d/NIFlMGZlkXn8EDdWLMKYkf7AYnZq3BbnFp1RunpiuHGdjN9Wor9yvugVVGpc2vTEsW5TlK7uGNOSydr9K7lH/wTAY+hkNBXCrFbLO3eCtO8/K6lm4PdkX4KefhaNjw/ZUZeJ/uxTMk4cL7K+d8fOBD37HI7lgjFkZJC6/2+ufjEfQ1r+saFQqQh6fjA+Xbvh4OtHztVori78grT9f5dYGzw79cS7V3/Ut/af+KULyT57qsj67i3a4d2rPw5BZTFmZZJx7BAJy78x7z8O5crjN2AQThWroPEPJH7pQpI3rS2x+IvyMB4X2padcGvfC5WHJ7rYa6SsXkrexbM263o9Nwbt422synWxV4l/d6JVuXPDZvgMnUD28QMkLvrQ3qFbeBjb8fuva/llzQ+kJCVSLiSUocPHUb1W3SLrnz55lGWLF3AtOgovbx969X2GTt16m5/ftnk9u3f8ztUrlwCoWLkaTw96iSrVatjc3tqfvuN/y7+h2xP9GfrSOLu1S1O7KQ7126DQumFMiif3j/UYYi7brKsqWxGXPqOsyjO/n4sx+UbBNuu2wKF2UxRuXpiyM9FfOEHuvt/AoLdb3IW5tu6Ce8deqDy80MVcJfnnJeReiLBZ13vwy7g2bWdVnhcTTdzs8QBom7bFZ/BYqzrRLw8Avc6usd+Nf5++BD37HA4+PmRfvsyVeZ+SfvxYkfUD+vYjoF8/HIOCyI2LJ2bZUm7+9tsDi/e2F54uzxOdg3BzVXPmXDqffHWey9FZRdb//L261K/taVW+92AiU2bnf984O6sY/mworZr64uWh4dylDD5bdJGz50vmM/iXX3/j5zXrSExKJjQkmNHDX6R2LdvHZ2JSEl99G875Cxe5HhPLkz27M/qlFy3q/L5tBx/O+9xq3U1rfsTBwaFE2iAeTXafA6FLly4sXboUvV7PmTNneOGFF0hJSeGHH36w90v955hMJgwGA2p18W+rt7f3A4iowIwZM/jwww9Zu3Yt3bp1e6CvXdI8WrYlaPgYYhbOI+vMKby79iR05gecHz0E3Y0Eq/ouNWpRbsJUYhd/SdqBvWh8fCk7ZiJlx00i+t23LOpq/AIIemEUmaeK/sH1oKm0LqSdiOTasjU0/HlBaYdTLNfHW+E/aATxS74gJ/I0Hh26UXbqO0RNegl94g2r+k7VahI4ehI3ln9DxpG/UXv7EvDiWAJfGk/MJ28/kJgdajVG23UgGRu/Rx99AafGrfF4bjzJC6ZjTE2yuY7bUyNRurqTsW4phqQElFp3uGPW37SVX4JKZX6sdHbFc/RMck8dKrF2eLfrQMi48Vz5+EMyTp7Ar1dvqn70Kaeef5q8+Hir+q516lLxzbeI/vwzUv76Awc/f8pPmkKFqa9z4fWpAJR9aSQ+nToT9cEccqKv4N7kcaq89z4RI18i6/w5u7fBrVlrAoaOJG7R52RHnsazY3eC33iXSxOGob9pvf84h9UkaOxkEsK/JuPw36i9fQgc/gpBoyZy/cNZACgdHclLiCNt3x8EDBlh95jvxcN4XDg3aIZn36Ek/7iIvEuRaFt0xHf0G8S/MwFD8k2r+imrlpL6ywrzY4VKif+0j8g+us+qrsrLF4/eg8i9cKZE2wAPZzv+2rOdpYvmM3zURKrVqM3W39bz7szJfPrld/j5B1jVj4+LYc7MKbTv3JNxk6YTeeYkixZ+gruHJ483bwPA6ZPHaNG6A1Wr18JB48Avq//HO2+9yidfLMfH189iexfORbD19w2UD61k13apq9TFseUT5O5aiyE2Ck2tx3Hu+SKZKz7ClJFS5HoZ330Aebnmx6bsjIJtVq2PY7Nu5Gz/CUPsFZSefjh1eAqA3D832DX+21waNser/1CSflhE7sUIXFt2xu/lN4md9YrNfSr5xyWkrP3e/FihVBH45idkH7Hcp4zZmcTMKNSJ8AA7D7zbd6D8+AlEfTiX9BMn8H/ySap98iknnhlo8zvE/8k+BI8azaU575EZcQZtjZpUnDoNfXo6KX/++cDifrZvMAN6l+PdeZFcvZ7F4AHl+XR2HZ4edZDsbIPNdV5/7zQadcHVYg93DUvnN2LnXwWfx1PHVqVieS1vf3KWm0m5dG4TwLy36/Dc6IPcTMqzaxt27vmThYuWMG7US9SsEcavv21h2sy3+fbL+QT4+1nV1+n0eLq788xT/Vj9S9H7uYuLC+FfW547SueB+KfsPoTB0dGRwMBAypUrR6dOnRgwYABbtmyxqJOSksJLL71EQEAATk5O1KpVi40bNwIQHh6Op6cnGzdupFq1ari4uNCvXz8yMzNZtmwZoaGheHl5MXbsWAyGgg+B0NBQ3nvvPV544QXc3NwICQnhm2++sXjdAwcOUL9+fZycnGjUqBFHjx4ttj25ublMmTKF4OBgHB0dqVKlCt9++y0Au3btQqFQ8Pvvv9OoUSMcHR35448/GDJkCL1797bYzvjx42nTpo35ceEhDF9++SVVqlTBycmJgIAA+vXrZ37OZDIxd+5cKlasiLOzM3Xr1mXVqlXFxn573bFjx/LZZ5+xZcsWi86DNm3aMG7cOKZMmYK3tzeBgYHMnDnTYv3o6Gh69eqFq6sr7u7uPPXUU8Tf+tIIDw9n1qxZHD9+HIVCgUKhIDw8HMjPTAgJCcHR0ZEyZcowbpz9rlYU5tu7P8lbN5G8ZRO516KJXfQFupsJeHd7wmZ9l2o1yEuII3HDGnTxcWSdOUXSbxtwrlzNsqJSSfCkN4hfEU5eXGyJxf9P3fh9D+dmzCNu3dbSDuWeeHXvQ+rO30nbuZm8mKvcWP41usQbeHbsYbO+c+UwdDfiSfn9F/Q34smJPE3q9k04Vqz6wGJ2btaJnCN/kHvkDww3Y8n8bSWGtCScGrexWV9TuRaa0Gqkff8ZuksRGFMS0V+/jP7qRXMdU3Ympow086KpXAOTLo/c0wdLrB0BA5/m5sYN3Ny4npwrUVydP4+8hAT8e/exWd+1Zk1y42JJWPUTebGxZJw4zo1f1qGtVt1cx6dzF2K/W0bq3/vIjYnhxro1pO7fT+BA+2Q1Febdoy8pOzaTumMzedevkhD+FbqbN/Dq1NNmfecq1dElxJP82zp0CXFknz1NytZfcbpj/8m5eI4b3y0ife8uTLoHdzJ+p4fxuHBr14PMfTvI2rcDffx1UleHY0i+ibZlJ5v1TTlZGNNTzIsmpBJKZy2Z+3ZaVlQo8R7yCmmbfkJ/07rTV9oBG9f9SLuO3WnfuSflgkMZ+tI4fH392VJE5szW337B1y+AoS+No1xwKO0796Rdh+6sX7PSXOeVyW/RufuTVKhYhbLB5Rkxdgomo5FTxw9bbCs7O4v5H81m5NgpaF3d7Nouh3qt0J05iO7MAYzJCeT+sR5jRgqa2k3vup4pKwNTVrp5wWQyP6cKKo8hNgr9uWOY0pMxXD2H/vwxlAHl7Br7ndw69CTjr+1k/rUNfdx1Un5egiE5EdfWnW3Hn5OFMS3FvDiUr4TSRUvG3h2FKmJRz5iWUmJtsCXo6ae5sWE9Nzbkf4dEz/uUvIR4Avr0tVnft2tX4tetJWn7NnJjYkjatpWEjRso89ygBxp3/yfKsvynaPbsu8nl6Cze/fQsjo4qOrX2L3Kd9Aw9SSk689Konhe5uQZ2/pnfgeDgoKR1Mz++XHqJ46dTuR6bw5IfrhAbn8OT3crYvQ2r162nS8f2dOvckfLBwYx+6UX8fX3YsGmzzfqBAf6MGTGMTu3bonVxKXK7CgV4e3lZLP8fKJSKUlseRSU6B8KlS5fYvHmzRaq+0Wika9eu7N27l++//54zZ87w/vvvo7rjylxWVhbz589n5cqVbN68mV27dtGnTx82bdrEpk2b+O677/jmm2+sfkR//PHH5o6B0aNHM2rUKM6ezU9JzMzMpEePHlSrVo3Dhw8zc+ZMJk2aVGwbBg0axMqVK5k/fz4RERF89dVXuLq6WtSZMmUKc+bMISIigjp16vzj9+nQoUOMGzeO2bNnExkZyebNm2nVqpX5+TfffJOlS5eycOFCTp8+zYQJE3juuefYvXv3Xber1+t5/vnn+fnnn9m9ezctWrSwqrNs2TK0Wi379+9n7ty5zJ49m61b83+YmkwmevfuTVJSErt372br1q1cvHiRAQMGADBgwABeffVVatasSWxsLLGxsQwYMIBVq1bx6aef8vXXX3P+/HnWrVtH7dq1//H7ci8UajXOlauScdTyKm7G0UO4hNWyuU5WxGk0vn64NXoMALWnF+7NW5N+yDIF23/gIPRpKSRv3VQisf+/oFLjVKEKWSeOWBRnnTiCU9XqNlfJPncGtbcv2nqN8zfh4YnrYy3IPHqgxMPNf0EV6qDy6C5appLrLpxBE1LZ5ioOYfXQx0Th3KILXpM+wmvcu7h0fgrURQ9TcmrQkrxTB0Bn36sWtynUarRVq5F6cL9FedrB/Whr2T4eM06exMHPH4/H80/e1V7eeLVpS8q+v8x1lBoHjLmWMRvzcnGtU3Qq9b+mVuNUsQqZxy33n8wTh3EuIs06O/IMah9ftPUL9h+3pi3JOLLfZv1S8VAeF2o0wRXJibDMxsqJOIFjhWpFrGRJ27QduZEnra7IunfthzEjjax9O4pY044ewnbodDouXThH3fpNLMrr1G9MZBFDec6dPU2dW8fAbXUbNOHShbPo9bbT+PNyc9Eb9Li6WXYSfLvwUxo0bkqdeo3uoxU2KFUo/ctiiLbMXDJEn0MVVP6uq2oHTkD7wnSce7+EqqxlVoQh5jIq/3IoA4IBULh7oyofhiHK9hCV+6ZS4xBSycY+dQzHitbD1mxxbd6enLMnMCRZZh8pHJ0o8+7XlJmzCL/Rr6MJrmC3sIujUKvRVgsj9YDlZ2fq/gO4FnFOp9Q4YMqz/H4w5eairVEDxR3n+SWpTIATvt6OHDiabC7T6U0cO5VCrTD3e95Oj46BbN+TQE6uEQCVSoFapSAvz2hRLzfPSJ0aHvYJ/na8Oh3nLlykUf16FuUN69fjzNn724+zs3N4ZuhLDBw8jDdmvcP5i5fua3vi/ye7D2HYuHEjrq6uGAwGcnJyAPjkk0/Mz2/bto0DBw4QERFB1ar5V04qVqxosQ2dTsfChQupVCn/S6Ffv3589913xMfH4+rqSo0aNWjbti07d+40/5gF6NatG6NHjwbgtdde49NPP2XXrl2EhYWxYsUKDAYDS5YswcXFhZo1a3Lt2jVGjbIeS3fbuXPn+Omnn9i6dSsdOnSwGSvA7Nmz6dix4795u4D8q/xarZYePXrg5uZG+fLlqV+/PpDf8fHJJ5+wY8cOmjZtao7hzz//5Ouvv6Z169ZFbnfRokUAHD9+nLAw219iderUYcaMGQBUqVKFBQsWsH37djp27Mi2bds4ceIEly9fJjg4/4v4u+++o2bNmhw8eJDGjRvj6uqKWq0mMDDQoj2BgYF06NABjUZDSEgITZo0sfn690vl7oFCpUKfnGxRrk9ORtPAdq9q1tnTXP3oXYKnvIXSwQGFWk3a338R89V8cx2X6rXw7tSN8+OGlUjc/1+o3N3z/z+plv8fQ2oyag/bw3hyzkcQt2AuQeOmodDk/38yDu0jIfzLBxEyShc3FCoVxgzL+TCMmakoXG13Sqm8fNGEVAG9jvQfvkDh4oprj+dQOmvJWLfUqr66bAXUAeXIWBdeEk3Ifw0PTxRqNfokyyEXuqQk3H18bK6Tceokl2bPoNLsd1A4OKJUq0n+Yw/Rn35srpN64G8CBz5N+vFj5F6/hnvDxni2aIVCaf/+aLVb/v5jSCm0/6Qko/K0fXxnnztD7PwPKDPhDZS39p/0g3uJX/KF3eP7tx7K48L11nGRnmJRbkxPQenuWfz67p441ahPUrjlfB8OFavh0rQdCe9PtmO0d4njIWxHeloqRqMBz0JXCj29vEg5YntIVUpyIp5eTazqGwwG0tNS8PL2tVpnxbKv8Pbxo/YdHQV/7d7GpYvneP/Tb6zq3y+FsxaFUoUxy3L8uCk7A6WL7UwHY2Y6OTt+xpBwPb8zqFoDnJ98iew1X5nnTdCfP06usysufUcDChQqFXkn9pJ3eKfNbd4v1a19ylAoO8CQlorTPe1TXjjVbEDikk8tynVx10lc9jm669EonZ1xa9eDgMnvEffORPQJJZ8VqfbM/w7RFf4OSU5E4/24zXVS9v+NX88nSNq9m6zIs2jDwvDr0ROlRoPa0xNdYmKJx+3tlZ+On5Ri2ZGRnJJHgL/TPW2jehU3KoW68v78gs6t7GwDJyNSGTKwPFHXskhOyaNDK39qVHXjWky2/RoApKalYzQa8fLytCj38vIk6UjKv95ucLmyTJkwlgrly5OVlc2a9RsZP2UaX8//lHJl7Z9F8V9SEuco/5/Z/d1s27Ytx44dY//+/YwdO5bOnTszdmzB+K1jx45Rrlw5c+eBLS4uLubOA4CAgABCQ0MtrvwHBASQkGCZInjn1X+FQkFgYKC5TkREBHXr1sXljrSe2z/Ii3Ls2DFUKtVdf6QDNGp0f73yHTt2pHz58lSsWJHnn3+eFStWkJWVP9HLmTNnyMnJoWPHjri6upqX5cuXc/Hixbtut0WLFri6uvLmm28WecWhcMZEUFCQxXsWHBxs7jwAqFGjBp6enkRE2J4YCKB///5kZ2dTsWJFhg8fztq1a4t8fcgfJpKWlmax5BmMRda3zWT5UGGR0WjBMbg8ZV4aS8LK5VwYP4LLb01BExBI2TH5k2EpnZ0JfvV1rn3+kXniOGFniqL/QQ5lQ/AfMorENf/jyhtjuTbnDTT+gQS8WHLDYO7NXXYqhRIwkb5qEfrrl9GdP0nm5h9xrNfMZhaCY4MW6OOvob9ue6IwezIVjvku771TaCgh4ycSs3QJZ14cQuTEV3AMKkP5ya+Z60R/9ik5V69Se8VKGu38g5CJr3Jz00ZMxn96zP6DNlgd3wqrQ/42h3Ih+A8dTeKqFUS9Noar70xD4x9I4EuvlFh8dvMwHhd3iflO2sfbYMzOJPtEwZAdhaMT3oPGkfLDVxgzH9xEkDY9FO2wTIU1mfLPdYquXbj+rfbZWOeXVSv4c/c2Jr/+Dg4OjgDcvBHP0kXzGffqdHPZg1LUf8KUcgPd6QMYb1zHGHeF3N1rMUSdxaF+wXmaqmxFHBq1J3fXWrJ+nEf2r8tQV6iOQ+MOJRy09XlI0S0p4Nq0LcbsTLKOWWYT5V0+R9aBPeiuR5F7IYKbiz5CHx+DW5sHPI+V1XGhoKh2XV+6hJS/91Fz8bc0+eMvqs79kBu/5g9RLqnviI6t/dnyUwvzor49j4FV2EV/bxTWo1MgF6MyiCg0OeLbn5wFBfyyrCk71rSiX8+ybN2dgMF4jxv+hwofqSaT6a7HfHFqhFWjQ9s2VKpYgdq1ajB96iTKlSnDuo2SaSv+GbtnIGi1WipXzk/znT9/Pm3btmXWrFm8/Xb+JE/Ozs7FbqPw3QkUCoXNMmOhD6O71bE6ib4H9xIr5Lf5Tkql0ur1dHcZZ+vm5saRI0fYtWsXW7Zs4a233mLmzJkcPHjQHP+vv/5K2bJlLdZzdLz7F3rt2rX5+OOP6dChA0899RQ//vij1XtU3Htm64OquA+w4OBgIiMj2bp1K9u2bWP06NF8+OGH7N692+adJ+bMmcOsWbMsykZWKc/oqsWn6hnSUjEZDKi9LK/aqT290Be6anmbX/9nyIo4xc01P+YXRF0iJiebSnM/J/67b1F7euEQGEToW+8VrHSrvbV+2ca5EYPIi4spNjYBhrS0/P+Ph+WVM5W7J/o02/8f714DyI48Q/LG/CFKedGXic/NIWTmx9z8aRmGFNtX3OzFmJWOyWBA6WqZ6qjUumPKtN2hZExPwZiWjCm34CqE4UYsCqUSpbsXxqQ7Ojs1DjjWbkLWjl9KJP7b9KkpmPR6NIWyDTReXlZXlG4Lem4wGSdPEPdD/oRx2RcvcCUnh+pffs31RV+jS0xEn5LChddfQ+HggNrdA93NG5QbNYa8WPsfE/r0W/uPp+XxrfLwxJBqe//xeXIg2ZGnSVr/MwC50Zcx5uZQ/u1PufFDeInvP/fioTwuMm4dF26eFuVKVw+M6anFru/yeDuyDuyxmAVf7RuI2tcfnxFTCyre+qwt+9lK4t5+BcNN64na7sfD2A43dw+UShUpyZb/49SUZDyKyMTx9PIh2ap+CiqVCjc3y3Tr9Wt+YM3P3/PWO59SvkLBMK1LFyJJTUnmtfEFmXhGo4GI08fZvHEN/1u73WL46T9lys7EZDSgdHHjzrM5hbNr/rwG98gQdwV1tQbmxw6Pd0YfeRjdmfwf5MbEONA44NS2L3kHt3PPvyLv9fVv7VOqwsezmweGtOL3KW3zdmTu3138HSJMJvKuXEDtH3Q/4d4zfUpR3yHeRX6HmHJzufzuO0S9PweNtw95iTfx79UbQ2Ym+pSUEonzzwOJnDlXMIzVQZN/bdTby4HE5IIsBC8PjVVWgi2Ojkrat/Tn2xVRVs/FxOUwdtpxnByVaF3UJCbnMWtKdWLjc+6/IXfwcHdDqVSSlJxiUZ6SkoqXp/2GSyiVSqpWqcz1GDmnFf9MiedzzJgxg48++oiYWztnnTp1uHbtGufO2X+27rupUaMGx48fJzu74AT/77/vftux2rVrYzQai51roDA/Pz9iYy3Ty4q7zaFaraZDhw7MnTuXEydOEBUVxY4dO6hRowaOjo5ER0dTuXJli+XOzICi1KtXjx07dvDnn3/Sv3//u3ZkFFajRg2io6O5evWquezMmTOkpqZSvXr+OF0HBweLySxvc3Z25oknnmD+/Pns2rWLffv2cfKk7dsqTps2jdTUVItlWKW7j3+8zaTXk33hHK6Fxma61mtIVhFjQ5WOTpgK9xbf7oxSKMi9Fs25MUM5P26YeUnbv5fMk8c4P24Yugcwydcjw6An5/J5XOrUtyh2qV2fnHO2s1gUDo5gKnSlwvz/KYkgCzEY0MdeQVOppkWxplINdNEXbK6ii76Q/4Pkjqt0Kt8ATEYjxkI/CB1rNkah0pB73HoGd3sy6fVknovEo7FlGrN7oyZknrJ9LCqdnKDQsWEyH9+Frmbm5aG7eQOFSoVX6zYk/7HHbrGb6fXkXDqPtk4Di2JtnQZkR9qe5V5pY/+5feXrPi7c2NdDeVzo0V29hFOYZdaaU1gdci9H3nVVxyo10PgHkVlobgBd/HXi3p1I/PuTzUvOyUPknj9N/PuTMSSXQLrzQ9gOjUZDxcpVOXHMcsLVE8cOUq2IuX6qhtW0qn/86AEqVg6zuFPUL6v/x6qVy3hj1kdUqmI51LF23UZ8vGAZH85fYl4qVQmjRZuOfDh/yX11HgBgNGBMuI4quIpFsSqkKobYK/e8GaVfWUx3ZH4o1A7WF42MxvwPgJI4Vgx68qIv4lTdch4Yp+p1yb109/HqjlVrovEvQ+Zf93YrZk25CkV2ntqbSa8nM/Ks1XeIR5MmZBRxPmde12Ag70YCGI34dOxI8l9/3lOGz7+RnW3gemyOebkcncXNpFwa1yvo0FGrFdSr5cmps8VnlbZr4YdGo+T3XUV3+uXkGklMzsNNq6ZJfW/+3G/fzyqNRkPVypU4fMxyXo3Dx45To4ghyf+GyWTi4uWo/xcTKcokivZl9wyEwtq0aUPNmjV57733WLBgAa1bt6ZVq1b07duXTz75hMqVK3P27FkUCgVdunQpsTieeeYZ3njjDV588UXefPNNoqKi+Oijj+66TmhoKIMHD+aFF15g/vz51K1blytXrpCQkMBTTz1V5Hrt2rXjww8/ZPny5TRt2pTvv/+eU6dOmec1KGzjxo1cunSJVq1a4eXlxaZNmzAajVSrVg03NzcmTZrEhAkTMBqNtGjRgrS0NPbu3YurqyuDBw8utu116tRh586dtGvXjn79+vHzzz/f0y1bOnToQJ06dXj22WeZN28eer2e0aNH07p1a/OwjdDQUC5fvmwemuLm5sYPP/yAwWDgsccew8XFhe+++w5nZ2fKl7fdKeDo6GiVTeGguve+rZvrfqbcxGlkX4gkK+I03l16oPELIGlT/m1sAgYPQ+Pjx7VP5gCQdmAv5cZOIrPrE6QfOYjG24eg4WPIioxAn5T/JZB7JcriNYyZGTbLS4NK64K2coj5sUuFcrjXDSMvKZWcq/+du0XclvzrGoLGTCbn0nlyzkXg0b4rGl9/Urb9CoDvwKGovXyIW5h/PGYe2U/A8Ffw6NCdrBOHUXl64z9oJNkXzmJIfjBXj7P3bsGtzzD016PQX72IU6NWqDy8yTmY35no0qEPSncvMtbk35El9+R+XNr0xK33C2TtXIfCxQ1tp/7kHvnT6pZbTg1bkHf2KKbszBJvR/zKH6gwfQaZZyPIOHUKvyd64RAQQMK6/Jnby40YhcbPj8vvzAYg5a8/CX1tGn69+5B24G80Pr6EjBtPxpnT6BLzJ4zT1qiJg68fWRfOofH1o+wLw0CpJO5/3xcZx/1I2riaMmOnkHPxHNnnzuDZoTsaX3+St+Snxfo98wJqbx9iF3wIQMbhvwkcMQHPTj3IPHYItZc3/kNGkX3+LPrb+49ajWO5W8eQWoPaxxfH0IoYc3LQPaDsoofxuEjfsRHvQWPJi75I3uVzaJt3QOXtS+Yf+Xdacn/iGVQe3iR/Z3mLMJem7cm9fA597FXLDep1VmXG7CyUYF33/3k7evQewOefvEOlymFUrV6TbZvXc/NGAp269QZgRfhXJCXeZOyrbwLQsWsvNm9cQ/iiz+nQpSfnIk6zY+uvjJ88w7zNX1atYOX33/LK5LfwCwgk+VZHh5OTM87OLji7uBASajnvk6OjE25uHlbl/1besT04dRyIIeEaxrgraGo+htLVE92p/A5Wh6ZdUbp6kLM1/+4Rmrot8u+skBiPQqVCXa0Bmsp1yN60zLxN/eUzONRvhfHGdQzx0Sg9fHF8vDP6y6dL7Eds+rYN+AwdR96VC+ReisS1ZSdUXr5k7Mnfpzx6P4va04fE8PkW67k2a0/upXPoYqKttune/SnyLp9DlxCL0skZt7bdcQgOJXml/eejKErsDz9QacZMMs+eJf3kSfx798YhIID4tWsACB41Go2fH5dm52eROgUHo61Rk4zTp1G7uxE08BmcK1bi4uzZDyxmgJ/XX+f5/iFci8niakw2g54KITfXwJbdBReA3pxQjRuJeXy93HIoYY+OQfzx903S0q0zQprU90KhgOjr2ZQNcmbM0IpcvZ7Fr9vi7N6Gvr2f4INPPqNq5UrUqF6NXzdvJeHGTXp2y7+zx+Lw77iZmMTUVwuG5124lN+WnJwcUlLTuHDpMhq1mvIh+Rccl//vR6pXq0rZskFkZWWzdv1GLl66zLiRw+0ev3i0lXgHAsDEiRMZOnQor732GsHBwaxevZpJkybx9NNPk5mZSeXKlXn//fdLNAZXV1c2bNjAyJEjqV+/PjVq1OCDDz6gb1/bt6K5beHChbz++uuMHj2axMREQkJCeP311++6TufOnZk+fTpTpkwhJyeHF154gUGDBhV5Bd7T05M1a9Ywc+ZMcnJyqFKlCj/88AM1a+ZfAX377bfx9/dnzpw5XLp0CU9PTxo0aFBsHHeqWbMmO3fupH379vTt25fVq1cXu45CoWDdunWMHTuWVq1aoVQq6dKlC59//rm5Tt++fVmzZg1t27YlJSWFpUuX4unpyfvvv8/EiRMxGAzUrl2bDRs24FPExG33K/WPnajc3PEfOAi1tze5V6KImjkV3Y383mONlw8av4Jb96Rs/x2Vsws+PZ4k6MVRGDIzyDhxlLjwB/elfD88Gtai6fbvzI9rfJS/H1xdvoYTL04rrbCKlPH3HhLc3PHp8ywqTy/yrl7h+gfTzbc5U3l6o/Yt+P+k7dmK0tkZz85P4PfccIxZmWSdPs7N/337wGLOO3WQTGdXXNr0ROnmgSHhOqnff4YxNf8EW+nmierOye7ycklb9jHa7s/gOWI6xuzM/G1st7zFmtInAE35qqQu+5gHIWnHNlQeHpQZ8iIaHx+yL1/i3OSJ5MXnn+xofHxxCCiYADXxt19RubgQ0LcfwS+Pw5CRTvrhw1xdWDABodLBgbLDR+BYpgyG7GxS/97LpbdnYcjIsHp9e0jfu5t4V3d8+z2LysubvKtXuPrem+b9R+3ljeaO/Sd111aUTi54dXkC/0EvYcjMJOvUMW6sWGyuo/HyocKHX5kf+zzRH58n+pN1+jjRMx/MZH4P43GRfWQvKVpX3Lv2Q+XuhS72Kje/fM98NwKVuxfqQpPzKZxccK73GKmrrCcTLS0PYzuat2pPRnoaq1aGk5yUSHD5Crw+cy5+/vnHb3JyIjdvFFwxDQgsw7SZc1m2+HN+/3UtXj6+vPDSKzzevI25zu+b1qHX6/h4znSL1+r/9FCeevaFB9Iu/fnj5Dq54NikAwqtO8bEOLI3fIvp1iSXSq07CldPc32FSo1D8x4oXD1Ar8OQFEfW+m8xXCm40p8/TAEcH++CwtUDU3YG+ssR5O77rcTakXX4L5Subnh0fyp/n4qJ5saCd813VVB5eKGytU81aEryT7aPYaWLFu9nR6Fy98SYnUXe1UvEf/QmeVG2M+FKQtL2bag9PCj7wgtofHzJvnSJyFcnkBd3+zvEB8eAgDuCVhH0zDM4hZTHpNeTdvgwZ14a9sBvhb1i9VUcHZRMHFUFN1cNZ86lMeGtE2RnF2TMBvg5FU64I7iMM3VrejB++gmb23XVqhkxqAJ+vo6kpevYvfcm33x3GYPB/h1TbVu1IC09ne9X/kRSUjKh5UN4b+abBPjnfy8kJSeTcMPyrh0jx000/33uwkV27N5DgL8fK5bkn99mZGby6YKFJCcno9W6UKliRT59/x3Cqj242wGXloctE+DLL7/kww8/JDY2lpo1azJv3jxatmxZZP0VK1Ywd+5czp8/j4eHB126dOGjjz4qsd9eCtO/mRxAiBJ2skfb0g7BLqJ/f/jHlVXp9+BuG1WSvKuULb7Sf9zlHaeLr/QQcAu691tp/Zcp1Q//rM4uPrZnuxcPXtIr/507hdyPCps/LO0Q7CL59N0nqn4YxB6/Xtoh2MWrvnNLO4T79sMnfqUdgl0EV7F9C+X/usgBnUvttav9+Ps/qv/jjz/y/PPP8+WXX9K8eXO+/vprFi9ezJkzZwgJCbGq/+eff9K6dWs+/fRTevbsyfXr1xk5ciRVqlRh7dq1Nl7h/j38Zz9CCCGEEEIIIYQND9McCJ988gkvvvgiw4YNo3r16sybN4/g4GAWLlxos/7ff/9NaGgo48aNo0KFCrRo0YIRI0Zw6NAhm/XtQToQhBBCCCGEEEIIO7N1u/rc3FybdfPy8jh8+DCdOnWyKO/UqRN79+61uU6zZs24du0amzZtwmQyER8fz6pVq+jevbvd23KbdCAIIYQQQgghhBB2NmfOHDw8PCyWOXPm2Kx78+ZNDAYDAXfOLQIEBAQQF2d7ss5mzZqxYsUKBgwYgIODA4GBgXh6elrMWWdv0oEghBBCCCGEEOKRpFAqS22xdbv6adPuPum5otB9p00mk1XZbWfOnGHcuHG89dZbHD58mM2bN3P58mVGjhxpt/evsAdyFwYhhBBCCCGEEOL/E1u3qy+Kr68vKpXKKtsgISHBKivhtjlz5tC8eXMmT86/i1SdOnXQarW0bNmSd955h6CgoPtrgA2SgSCEEEIIIYQQ4pGkVClKbfknHBwcaNiwIVu3brUo37p1K82aNbO5TlZWFkql5U96lUoF5GculATpQBBCCCGEEEIIIUrZxIkTWbx4MUuWLCEiIoIJEyYQHR1tHpIwbdo0Bg0aZK7fs2dP1qxZw8KFC7l06RJ//fUX48aNo0mTJpQpU6ZEYpQhDEIIIYQQQgghRCkbMGAAiYmJzJ49m9jYWGrVqsWmTZsoX748ALGxsURHR5vrDxkyhPT0dBYsWMCrr76Kp6cn7dq144MPPiixGKUDQQghhBBCCCHEI0mh/GdDCUrb6NGjGT16tM3nwsPDrcrGjh3L2LFjSziqAjKEQQghhBBCCCGEEMWSDAQhhBBCCCGEEI8khVKumduTvJtCCCGEEEIIIYQolmQgCCGEEEIIIYR4JD1scyD810kGghBCCCGEEEIIIYolHQhCCCGEEEIIIYQolgxhEP9JXqG+pR2CXTj2cyztEO7b+VWXSzsEu2i/vG1ph3Df/OMSSzsEu3BwcyntEOwi60ZqaYdw37Rl/Uo7BLswGQylHcJ9S1EYSzsE+zA+Gu1wCw4o7RDumz4nr7RDsAt3pXdph3DfvJIulnYIdlKjtAP4V2QIg31JBoIQQgghhBBCCCGKJRkIQgghhBBCCCEeSXIbR/uSd1MIIYQQQgghhBDFkg4EIYQQQgghhBBCFEuGMAghhBBCCCGEeCTJJIr2JRkIQgghhBBCCCGEKJZkIAghhBBCCCGEeCTJJIr2Je+mEEIIIYQQQgghiiUZCEIIIYQQQgghHk0KmQPBniQDQQghhBBCCCGEEMWSDgQhhBBCCCGEEEIUS4YwCCGEEEIIIYR4JMltHO1LMhCEEEIIIYQQQghRLOlAKEJoaCjz5s0zP1YoFKxbt67U4ilp4eHheHp6mh/PnDmTevXqlVo8QgghhBBCCHG/FEplqS2Pood6CENCQgLTp0/nt99+Iz4+Hi8vL+rWrcvMmTNp2rQpkP/Df+3atfTu3btEYxkyZAgpKSkWnQyrVq3iueeeY/bs2UyZMsUurxMeHs748eNJSUmxy/aKMmnSJMaOHVtsvdDQUMaPH8/48eNLNJ7iaFt2wq19L1Qenuhir5Gyeil5F8/arOv13Bi0j7exKtfFXiX+3YlW5c4Nm+EzdALZxw+QuOhDe4d+Vx4de+Ddox8qT2/yrl3hxvKvyI48XWR9t+Zt8e7ZH01gGYxZWWQeP8SNFYswZqQ/wKjvjXeLRlR89UU8GtTCqYw/h/qOJn799tIOy+zHAxGE7z3JzfRsKvl7MqXLYzQoH1jsekej43lx6SYq+3vx06je5nKdwci3fxxnw/ELJKRlEerrzvgOjWlepVwJtgJcW3fFo3NvVB5e5MVcJfnHb8m9cMZmXZ8h43Bt1s6qPC8mmtiZ48yPFc5avHo/i3ODx1G5uKK/GU/Sz+HknDpcIm1wadYBbZseqNw90cddJ/WX5eguR9qs6zFwBC6NW1uV6+KucfPD/M9hdUBZXLv0R1OuAmpvP1LXLSfrj80lEvud3Nt3x7NbH1Qe3uiuR3NzxTfknLN9PPsNn4B7yw5W5XnXrnD19dH5D1QqvHo8hVuL9qi8fNDFXSPxx3CyT5bM/wHAsUErHB/rgNLVA8ONWLK3/Yz+2sWiV1CpcWreDYdajVFq3TGmp5CzdzN5J/YBoKlaD6dmnVF6+aFQqjAkJ5B7YDt5pw6UWBsAHBu2xqlpp1vtiCFry0/or164azucW3bHofZj5nZk/7mJvON7AXCo0xTXJ4ZYrZY0ZwwY9HaJefPGtfyyZiXJSUkEh4Qy9KWXqVGrbpH1T588RviiL7gaHYWXtw+9+z1N5269bNb9c/d2Pp07m8aPt2Dq9HfN5Wt++p6/9+7h+rVoHBwcqVa9Fs8PHUHZciF2aROApk4zHBq0QaF1x5gYR+6eXzDEXLZZV1W2Ei79RluVZy7/AGNyQv4DpRKHRu3RVG+EwtUDY/INcv/aiOGK7c8Me3Fs2ArHxzsWHBtbfy52n3Jq2Q2HWk0Kjo2/fiPv+D6rqpoajXB98kXyIo+RuerrEmuDe7tueHTtg8oz/zMq8X+Liv6MGjYetxY2PqOuX+HaG2MKttnpCdzbdkPt44cxPY3MQ3+RtGoZJp2uxNpRlGd7+dOltTeuLioiL2Xx5fcxRMfkFln//SkVqBPmalV+4HgaMz+7UpKh2vTTtr/4btMubqamU7FsAJOe7UX9ahVt1j0aeZnPf/qVqJgEcvLyCPT1om/bpjzbpdUDjlo8ih7qDoS+ffui0+lYtmwZFStWJD4+nu3bt5OUlFTaobF48WLGjBnDF198wbBhw0o7nH/M1dUVV1frD83b8vLycHBweIARFc25QTM8+w4l+cdF5F2KRNuiI76j3yD+nQkYkm9a1U9ZtZTUX1aYHytUSvynfUT2UesvbZWXLx69BxX5g6skuT7eCv9BI4hf8gU5kafx6NCNslPfIWrSS+gTb1jVd6pWk8DRk7ix/BsyjvyN2tuXgBfHEvjSeGI+efuBx18cldaFtBORXFu2hoY/LyjtcCxsPnWJuZv380b3ptQLCWDVobOM/n4La8f0Iciz6OMiPSePN9fuoUnFMiRlZFs8t2DHYX49cZEZPZtTwdeDvRevM+HH7Sx7sQfVg3xKpB0ujZrjPeAFkv73NTkXzuLWqjP+46YTM3MshiTrYyPpx8Ukr1lufqxQqgh661OyDu8tqKRSEzBhJob0VG5+NRd9ciJqb1+MOdlW27MHp3qP495rEKlrlqC7fA6Xpu3xHv4aN+ZOxpiSaFU/bd1y0n9dWVCgVOH36hxyju8vaJeDI4bEBHKO78e913MlEndh2sda4vvscG4s+5Kc8xG4t+1C0KRZXJ02yubxnPj91yT9FF5QoFQS/O4CMg7+aS7y7jsIt2ZtuLHkc/Jir+FSuwGBr7zB9bcnkXflkt3boKneEOcO/cj6fSX6a5dwrN8C1wFjSF30Nqa0ZJvraHu/iFLrTtam7zEm30Dh4gZKlfl5U04mOXs3Y0iMB4MeTeXauHR/HmNmOvrLEXZvA4BDjUa4dHqKrN/+h+7qRZwatMLt6bGkfjUTYxHtcO0zHKXWncyNyzEm3UChdbO6smTMySZ14VuWK9qp8+CvPTtYumgBw0dPIKx6LbZs3sC7M15j3sJl+PkHWNWPj4vl3Rmv0aFLD16Z9AZnI06x6MtPcffwpGlzyw62hIQ4ln27kOo161ht5/TJ43Tp/iSVq4ZhNBj43/LFzH5zEp99tQwnJ+f7bpe6Sj0cW/Uid+caDDGX0dRuinOv4WR+PxdTekqR62UsmwN5BT/8TNkZ5r8dmnZFE9aQnO0/YUxKQF2+Gs49hpL10+cYb1y/75ht0VRviHPH/mRtXon+6kUcG7TEdeAYUr+eXfSx0WdY/rGx8XuMyQkotG6gUFnVU7p749K+D7ro8yUSuzmeJi3xeWY4N5cvJOf8GdzbdiVw4kyuvj4aQ5L1Z9TNFd+Q9HP4HYGqKPf252Qe/Mtc5Nq0Dd79h3Dj28/IvRCBJqAsfsPGA5D4w+ISbU9h/br68mQnXz759hrX43MZ2MOfdydV4KXXz5GdY7S5zjtfRKNRFYydd3NV8cWsKvx5KPVBhW225e9jfLxiPVMH96FelVBW7/ybsR8t5uc5kwny9bKq7+zowFMdmlMlOAhnRweOnbvMu0tX4ezoQJ+2jz/w+MWj5aHNq0hJSeHPP//kgw8+oG3btpQvX54mTZowbdo0unfvDuRfHQd48sknUSgU5scXL16kV69eBAQE4OrqSuPGjdm2bZvdYps7dy4vv/wy//vf/yw6D4YMGULv3r356KOPCAoKwsfHhzFjxqC7oxc2OTmZQYMG4eXlhYuLC127duX8+fwvjV27djF06FBSU1NRKBQoFApmzpwJwJdffkmVKlVwcnIiICCAfv363TXG8PBwQkJCcHFx4cknnyQx0fJEvPAQhtuxz5kzhzJlylC1alXatGnDlStXmDBhgjmezMxM3N3dWbVqlcX2NmzYgFarJT3d/lfC3dr1IHPfDrL27UAff53U1eEYkm+ibdnJZn1TThbG9BTzogmphNJZS+a+nZYVFUq8h7xC2qaf0N9MsHvcxfHq3ofUnb+TtnMzeTFXubH8a3SJN/Ds2MNmfefKYehuxJPy+y/ob8STE3ma1O2bcKxY9QFHfm9u/L6HczPmEbdua2mHYuW7fad4skFV+jSsRkU/T6Z0fZxADy0/HbKd1XLb2xv+omvtitQt52f13K/HLzCsZR1aVg2mnLc7TzWuTrNKZVm+91RJNQP3jr3I+HMbGX9uQx93jeSfvsWQfBO31l1s1jdlZ2FMSzEvDqGVUbq4kvFXQWaIa/P2KLVu3PhyDrkXz2JIukHuhQh016JKpA3aVt3IOrCL7P270CfEkPbLdxhTEtE2s77yBWDKycaYnmpeNMEVUThryT6421xHd/US6Rv/R86xfZj09vmBVxzPLk+StnsL6bu3oIu5SuKKReiTbuLerpvN+sbsLAypyebFsUIVlC6upO8pOF7cmrclecNPZJ04hP5GHGk7NpF98gieXfqUSBucmrQj7/he8o7vxZgYR/a2VRjTUnCsb/uKlrpiDdQhVcj46Qv0UZEYU5MwxF7BcL2gc0MffR7dueMYE+Mwptwk99BODAnXUQdXKpE2ADg91oHcY3+Re+wvjIlxZG39CWNaMo4NrTNXADQVa6IuX5X0lZ+jv3wWY2oihpgo9NcKd9KYMGWmWSz2smHtT7Tr1I0OnXtQLiSUF14ai4+vH79v+sVm/S2bfsHXz58XXhpLuZBQOnTuQbuO3Vi/ZqVFPYPBwGcfvsOAZ4cSEFjGajvT3/6Qdh27ElK+AqEVKzNmwlRu3ojn4oVzdmmXQ4NW6E4fQHd6P8bkBHL3/IIxIwVN7WZ3Xc+UlYEpK928YDKZn9OENSTv4HYMUWcxpSWhO7kP/ZVIHBrY/v/ag9Nj7ck7tpe8W/tU9taf8/epBsUcGysXoI86m39sxFgeGwAoFGh7DyV7z0aMNi6I2JNH596k79lK+p4t6GKvkfi/u39GmbKzMKSmmBfzZ9QfBZ9RjpXCyD0fQebfu9HfTCD79FEy9u/BIbRKibbFlt4dfVm5MYG9R9K4cj2Xj7+9hqODkjaPeRa5TkamgeQ0vXmpX9OV3Dwjfxx88B0I32/eTa/WTXiyzWNUKBvApOd6EeDtyaod1he/AMJCy9KlaX0qlQukjJ833Zo3pGntahyNtH/n8sNAoVSU2vIoemg7EG5fIV+3bh25ubbTjw4ePAjA0qVLiY2NNT/OyMigW7dubNu2jaNHj9K5c2d69uxJdHT0fcc1depU3n77bTZu3Ejfvn2tnt+5cycXL15k586dLFu2jPDwcMLDw83PDxkyhEOHDrF+/Xr27duHyWSiW7du6HQ6mjVrxrx583B3dyc2NpbY2FgmTZrEoUOHGDduHLNnzyYyMpLNmzfTqlXRKUr79+/nhRdeYPTo0Rw7doy2bdvyzjvvFNu27du3ExERwdatW9m4cSNr1qyhXLlyzJ492xyPVqtl4MCBLF261GLdpUuX0q9fP9zc3O79zbwXKjWa4IrkRBy3KM6JOIFjhWr3tAlt03bkRp60ylZw79oPY0YaWft22C3ce6ZS41ShClknjlgUZ504glPV6jZXyT53BrW3L9p6jfM34eGJ62MtyDxasqnAjxqd3kBETCJNK1meTDetVJbjV4vuSFp39BzXktMZ2bq+zefzDEYc1JZJX44aFcei4+8/aFtUahxCKpF95phFcfaZYzhWCrunTbg270DO2RMWV59c6jYh9+JZvJ8eQbmPwgma8RnuXfuBogS+TlQqNOUqkBt5wqI4N/IkmtB76xhzadKGvPOnbGYjPTAqNY6hlck+ddSiOOvkEZyq2D6eC3Nv3Yns08csshUUGo1VGrAxLw+nqjXuP+bClCpUgSHoCmUF6C5HoC5nO4VWU6UOhthonB7viMfL7+E+YgbO7fqAWlPky6jLV0PlHYA++i6p3/dDqUIVFILukmVWme7SGdTlbHdaaKrWwRB7BaemnfEc9z4eo2bj3L6vVTsUDo54jH0Pz3Hv4zpgDKqAYLuErNPpuHjhHPXqN7Yor9ugMZERtjsgI8+epm4Dy/r1GjTm4vlI9Hd0mv38wzLcPTzo0Ln7PcWSlZl/pd/N1Q7f5UoVSv9yGKIthxYYrkSiCgq966raZyaiHTYD5z4jURX6vylUajAUSo/X61CVqXD/Mdtye5+6XHifusuxUfXWsdG0Ex7j5uA+cibO7a2PDaeW3TFmZZiHypSYW59RWYU+o7JPHcWp8r19X7i16kT2GcvPqJzzZ3AIrYRjhfzPa7VfAC51GpF14qD9Yr8HgX4avD01HDldkKmi15s4GZlJ9cou97ydzi292X0gldw8U/GV7Uin13M26jqP17L83nu8dlVOnI+6p22cjbrOiQtXaBBWcp2z4v+Ph3YIg1qtJjw8nOHDh/PVV1/RoEEDWrduzcCBA6lTJz8Nz88v/yqgp6cngYEFY5fr1q1L3boF4wbfeecd1q5dy/r163n55Zf/dUy//fYbv/zyC9u3b6ddO+txxABeXl4sWLAAlUpFWFgY3bt3Z/v27QwfPpzz58+zfv16/vrrL5o1y+99X7FiBcHBwaxbt47+/fvj4eGBQqGwaE90dDRarZYePXrg5uZG+fLlqV/f9o8YgM8++4zOnTszdepUAKpWrcrevXvZvPnuY4C1Wi2LFy+2GLqgUqlwc3OziGfYsGE0a9aMmJgYypQpw82bN9m4cSNbt9r/SrPS1Q2FSoWxUKqjMT0Fpbtn8eu7e+JUoz5J4Z9ZlDtUrIZL03YkvD/ZjtHeO5W7OwqVCn2qZeqjITUZtYe3zXVyzkcQt2AuQeOmodA4oFCryTi0j4TwLx9EyI+M5KxcDCYTPlrL9FwfrTM3M7JsrnMlMZXPth1i6dDuqFW2f0g3q1SW7/adomH5AIK93Nl/OYZdZ6MxmErmRER1+9hIS7EoN6SlonK3Tne0Wt/DC+daDbi5+BOLcrVfAE5htcncv4eE+W+jDgjC++mXUCiVpP76kz2bgFJ7qw0Zlld7DBmpOLp5FL++myeOYXVJWVG6Q2RUbreP5xSLckNaCiqPe/tfuNRpRPzCuRblWSeP4NmlNzmRp9AlxOJcoy7aBo+hUFqnQd8vhYsrCqUKY6ZlFpkpMw2l1t123J4+qIMrYTLoyFj9NQoXV1w6DUTh5ELWpu8LKjo64fnye6DSgMmYP0Qi6u7ZPvfbjsLZAcbMdDSuttuh9PJDHVwZk15H+qqvUDq74tL16fzMtY35Q34MiXFkrl+GIeE6CkcnnJq0w33IFFK/ebtgbP6/lJ6WitFowMPT8rPf09OLlGTbQzZTkpPw9LTctzw8vTEYDKSnpeLl7cPZMyfZvmUTH39+b6nkJpOJ8EVfUL1mbUJCbf8w/icUztr8fSorw6LclJ2BUmu7g8KYmUbOtp8wJFzLv4BQvSHOfUaSvWohhpj8K6v66Eg09Vujv34JU0oiqpAqqCvWLJlOTu44NjIKHxvpKF1tf06pPH3zjw29joxVX6FwdsWly9MonLVkbfwuv065ijjWbUba4ndtbsOebn9GGQoNtzCkJaPyaFD8+h5euNRuSMJXlvNEZe7fg8rNnTJvfAAoUKjVpG7/ldRfV9neUAnxcs/vmElJs8w4S0nT4+9TdIfmnapWcCa0nBPzll6ze3zFSUnPxGA04uNheVz4uLuRmHr3zN6ur7xNcnoGBoORl57sxJNtHivJUP+zHtXJDEvLQ/1u9u3bl5iYGNavX0/nzp3ZtWsXDRo0sLiib0tmZiZTpkyhRo0aeHp64urqytmzZ+87A6FOnTqEhoby1ltvFZmqX7NmTVSqgpO7oKAgEhLyTy4iIiJQq9U89ljBwe3j40O1atWIiCh6LGjHjh0pX748FStW5Pnnn2fFihVkZdn+oXP7dW5PMnlb4ce21K5d+57mPWjSpAk1a9Zk+fL8E6vvvvuOkJCQIrMicnNzSUtLs1hyDYZiX+euFAqLlMaiaB9vgzE7k+w7esMVjk54DxpHyg9fWZ0sl7q7tMuhbAj+Q0aRuOZ/XHljLNfmvIHGP5CAF8fZrC/uTqGwTDszYUKBdSqawWhk2urdjGrTgFDfon/UTun6GOW93em9YA2N3g5nzqZ99KpfBZWiZNPbrPYWhc1SK9qm7TBmZ5J1bL/lEwoFhvRUEr/7krzoi2Qd/JPUTatwbWN7WIRdFAr3Xt8x58atMOVkkXPqkN1D+ncKv+/39jnl1rIDxqwMMg//bVF+8/uv0cXHEPzBV1Rc8gu+g0aR/sc2TMb7/Py8q8L/DIV1mfk5JZhMZK5fiiH2CvqLp8nevhqHOo9bXmnNzSVtyRzSwz8ge/d6nNv3RR1SwinOtkIuqhm3Pncz132LISYK3cVTZG1dhUPdpuZ2GK5fJu/UfgwJ19BfvUDG6kUYEuNxatzWbiEX/qgwmWwUFo7bcg3zX9lZWXz20TuMGjcJdw/Pe3r9xQvncSXqEhOmvFV85X/CxjFQ1GFhSrmRP9zhxnWMcVfy5064HIFDwzbmOrm712FMuYn2+ddwHfsBTm2eRHfmIJhsj3O3HxsfVEU15PY+9csSDDG3jo1tqwqODQdHtL2GkrlpBabszBKO+w5WH1F3Ob7v4Nbi1mfUEcvPKKew2nj2HMDN5Qu5NvMV4ua/i7ZeYzyfGGi/mG1o87gnq7+sYV5Ut+YxKPzvuNu/qLBOLb2IupbDucslM9/PvbA+ok3FfiEufnM0380az7Qhffnh9z/YvO/o3VcQ4h48tBkItzk5OdGxY0c6duzIW2+9xbBhw5gxYwZDhgwpcp3Jkyfz+++/89FHH1G5cmWcnZ3p168feXl59xVL2bJlWb16NW3btqVLly5s3rzZKmVfoymU8qhQYDTmf6mZivgUM5lMNk4ECri5uXHkyBF27drFli1beOutt5g5cyYHDx60uDXjndv7N7Ra7T3XHTZsGAsWLGDq1KksXbqUoUOHFtmGOXPmMGvWLIuyCY2rM7FJzWJfx5iRjslgQOnmaVGudPXAmF78GDWXx9uRdWCPxURXat9A1L7++IyYWlDxVuxlP1tJ3NuvYLhZQmnntxjS0jAZDKgLXZ1UuXuiL2JCJu9eA8iOPEPyxvye/bzoy8Tn5hAy82Nu/rQMQ0rpTy76MPBycUSlUFhlGyRl5uDjaj1pWGaujtMxNzkbm8j7m/LHIhpNJkxAg1lLWfh8Zx6rWAZvrTPznu5Ark5PSnYu/m4uzNt2iDJedh7Wc4vh1rGhKpSJo3LzwFAoK8EW1+btyfx7l9UkcIbUZEwGg8XJuC7uWn5mjEptt0njIP+qcP7xbdkxo3T1wHAvx3eTNmQd+gPut0PyPhnSC47nOwfcqdzv7X/h1qoj6X/ttHpvjelpxH32DgqNBqWrO4bkRLyfGor+hv0/n0xZGZiMBpRad+58NxUubkV2tBozUjFmpEBujrnMkBiHQqFE6eaJMfl2qrPJ/Lch4Roqn0CcmnYmowQmjbvdDkWhbAOl1g1jEXMWGDNSMaanYLqzHTdjb7XDq4gMAxP62CiU3v73HbObuwdKpcoq2yA1Ndkqy+A2Ty9vkgvXT0nOzxp09+DqlcskxMcxZ9brBRHfOqb792zH5998R2BQWfNzixfO4+D+v3j7g8/x8b3/NgGYsjNv7VNu3PnTXuHsmj+vwT0yxF1BHdbQYrs5G5eCSo3CyQVTZhoOzbtjTCuZ70DzseFq69goap9Ky8+ctNinCo4NhYMjKk9fXJ8adccG889DPKctIG3hTIwp9huWdfszqnBGlMrNE0OhzClb3Fp2JGOv9WeU15PPkbF3B+l7tgCgu3aFJEdHfIe8TMqGH+/91/s/tP9YGpGXCr7DNer8987LQ01yakGMHu5qq6wEWxwdFLRu4sn360r23K8onm5aVEolNwtlGySlZeDjfvdziLJ++ZM0VwkOIiktg2/WbqFL06KzlB9Vj+pcBKXloc5AsKVGjRpkZhb01mo0GgyFTh7/+OMPhgwZwpNPPknt2rUJDAwkKirKLq8fEhLC7t27SUhIoFOnTqSl3fskSjVq1ECv17N/f8EVv8TERM6dO0f16vnjZB0cHKzaA/lDOjp06MDcuXM5ceIEUVFR7Nhhe+x+jRo1+Ptvy17iwo/vVVHxPPfcc0RHRzN//nxOnz7N4MGDi9zGtGnTSE1NtVjGNLy3MXcY9OiuXsIpzHL2aKewOuQWcZu32xyr1EDjH0RmoTkOdPHXiXt3IvHvTzYvOScPkXv+NPHvT8aQbD3zu90Z9ORcPo9LHcsPeZfa9ck5ZzsbReHgaH2F5Vbn1D1fshVo1Cqql/Hh74sxFuV/X4yhbrD1ibOrowOrRj3JjyN7m5f+jcII9fHgx5G9qV1oQkVHjZoAdy16o4ntZ6JoW81+t0OzYNCTF30R5xr1LIqdqtcjt4hbnJpjrFoLTUAZMv60nlw298JZNH5BFlc+Nf5l0Kck2bXzAACDAd21yzhWrW1R7FC1Frqou0/i5lCpOmq/QLIP7LJvTP+GQU9u1AWcaxU6nmvVJ+f83e804BRWG4fAsqTv3lJkHZNOl/+5pFLh2riZ1VVAuzAaMMRFo65gOWeDpkKYjckE8+mvXUTp6gkaR3OZytsfk9FoNezMgkKR3xlVEowGDLHRaKzaUb3I21Hqr17M76S+sx0+AbfaYbtDF0AdEIwpo/iOruJoNBoqVa7K8aOWmTQnjh6iWvVaNtepFlaTE4XqHzt6kEpVqqFWqykbHMKnXyzl488Xm5dGjzWnVp36fPz5YnMngclkYtHCeezf9wcz35tHQGDQfbfHzGjAmHANVYjluG5VSFUMsVH3vBmlX1nbE1Ya9PnlSiWaynXQXyqhCWtv7VPWx0b1ux8bVvtUwbFhuBlH6jdvk7b4PfOiO3cCfdQ50ha/V+TdQv61259RNetZFDvXrEfOhbt/XziF1UYTWIa0PdbDVJWOjmC07CQwGY23vj9K7sQkO8dIbEKeeYmOySUpRUeDGgV3UVKrFNSupiXiQtEZu7e1bOyBRqNgx76UEov5bjRqNWGhZdl/yvJ7b/+pc9SpEnrP2zGZTOQ9oImDxaPtoc1ASExMpH///rzwwgvUqVMHNzc3Dh06xNy5c+nVq+A+x6GhoWzfvp3mzZvj6OiIl5cXlStXZs2aNfTs2ROFQsH06dPNWQD2UK5cOXbt2kXbtm3p1KkTv//+Ox4exY/XrVKlCr169WL48OF8/fXXuLm5MXXqVMqWLWtuU2hoKBkZGWzfvp26devi4uLCjh07uHTpEq1atcLLy4tNmzZhNBqpVs32JILjxo2jWbNmzJ07l969e7Nly5Zi5z8oSmhoKHv27GHgwIE4Ojri6+sL5M/10KdPHyZPnkynTp0oV67oe907Ojri6OhoUZamuvcxvOk7NuI9aCx50RfJu3wObfMOqLx9yfwj/4Tb/YlnUHl4k/yd5Thol6btyb18Dn3sVcsN6nVWZcbsLJRgXbcEJf+6hqAxk8m5dJ6ccxF4tO+KxteflG2/AuA7cChqLx/iFn4EQOaR/QQMfwWPDt3JOnEYlac3/oNGkn3hLIYixsmWJpXWBW3lgh/PLhXK4V43jLykVHKuxpZiZPD8/7F339FNVv8Dx99ZTZukSZvuQhlllb2RvaFsEEVQEUFkiLhFxQku3IqKfBWR4QJkCwgyBJS9ymwLlJYWuvdOs35/BFrSpBQkpcLvvs7JOe3NvU/uTfKMfO54OjXjtdW7aRLsS8sQf1YdiSYpJ59R7WyBrbnbDpOaW8B7I3sglUpoEGDfa6NXu6OUy+zST1xKJTW3kLBAPal5hczfeQyL1cr4LvY/jl0pd+s6fB97FsPF8xhiovHs3h+53pe8XVsA8Lp3LDIvHzIW2a8BounaF8OFaIyJjtO68nZtxrP3YLxHP07ejo0oAoLQDbqf3B0bqqQNBbs34fXgNIyXLmCMO4dHx97IvH0p3Ge7M4TnoNFIdXpyfp1vV86jQ09KLp7DlOxkvqpMhjzAdkySyOTIdHrkwbWxGopttxOsAtmb1xAw5QUMsecoPh+FtucA5D5+5O7YBIB+1KPIvX1I/c5+zQltj/4Un4+i5LLjPceVoY2Q630wXLyA3NsH73sfAomU7E2rqqQNxQd3oB76qG06wuVYlK26INV6U3LsbwDcewxH6ulF4YYlAJScPoxHl0GoBz9C0d8bkKo0ePS+l5ITe8FkW+TOvVM4pqSLWLLTQCpHUa8pbs3uoXDLr1XSBoDiA9tQD5+AKekipksXcG/TDalOj+HobgA8eo1A6ulFwfrFABhOHcS92yA0Qx+lcPfvSFUaVH3uw3B8T1k7ug3BfPkC5sxU2xoI7XsjCwihYLNr2jH03gf48tP3qNegEY3CmrJ18wbS01LpP2gYAD8t/o7MjDSefuE1APoPGs4fG9awaMHX9AsfQnTUaXb8uYlnr0w/cHNTOqxjoFbbflxdm77gm8/5e9d2XnnjPTw8PMjKtAXQVWqNw3n73yg5uhv38Acxp1zCkhSHonlHpJ7eGE/aRnO5dR6EVKOj+E/b+6ho1Q1rbhbmzGQkUhnysLYoGrSkaMPi0m1KA2rZRimlXUaq0eHWMRwkEkoO/+WsCi5RfGA76uHjbfvGpViUrbsi1XlTcvTKvtHzyr7x+5V949QhPLoORD30EYp2b0DqocGj90jbYolXvlOWNPsgtvXKrXLLp7tKzpa1+E9+npK48xSfjyw9RuX9ZTtGed9vO0alLbA/Rnl270dxTBRGJ8eowoiD6MJHYIi/gCEmGkVAEPqRYyk8duA2TCmxt3ZrOg8M8edyagmJKQZGD/bHUGJh54Hs0jwvPF6TjCwji1fZnwf6d9Oz72gueQXVN5pt7IAevPHtrzSpG0KL+rVZvXM/yRnZ3N/bdkvGr1ZsIi0rh7enPAjAim17CPTxok6QLRgYcTaWH//YxZh+XaqtDcLd444NIGg0Gu655x4+//xzYmJiMBqNhISEMGnSJF59tWxI3qeffsrzzz/PggULqFGjBnFxcXz++ec89thjdO7cGV9fX15++eWbGilwI2rUqMGuXbvo1asX/fr1488/K+49utaiRYt45plnGDJkCCUlJXTv3p1NmzaVTn3o3LkzU6dOZfTo0WRkZPDWW2/Rt29fVq9ezaxZsyguLqZBgwb8+uuvNG3qfApAx44d+f7773nrrbeYNWsWffv25fXXX+edd9656Xa+/fbbTJkyhXr16mEwGOymR0ycOJFffvmFxx577Ka3ezOKju4lW61BO/B+ZFpvjEkJpH/zfumq6zKtN3K9r10ZibsKj1b3kLNykbNN/ifk799NqqcWn5EPI/PypiThIpc/fKP0lpIyLz3ya4aS5u7eitTDA6/wYfiNnYSlsIDC08dJ/2VhdTXhunRtm9Fp+4+l/zf5xLbfJixdzYmJM6urWgAMaBZKTqGB73ZFkJZfSH1/b+Y93J9gL9sFdnpeIck5NzcvtcRkZt6Oo1zKykPlJqdrg5q8d28PtB63fhFekcLDe8hUa/EaPBqZzpuSxHhSv3qn9K4KMp0eud5+hITEQ4WqTSeyljlfWM2clU7KF7PQP/AYnm99gSk7k9ztG8jdvLpK2lAcsZ9clQZNv5G2KTxJl8j6/qPS/Vuq9ULm5WPfBncPPFp0IGftUqfblGm98XthTun/ml5D0PQaguH8GTLnV35Hmn+j4MDfpGu0eA9/ELmXnpJLF0n69K3SFctlXnrkPvafhdRDhbpdZ9J//s7pNiUKBfr7HkHuF4jVUETh8cOkfvsplsKqmTNtjDxCkYca9y6DbMO105LIX/FN6dBwqUaL9NoFOo0G8n79ElX/B9BOeAVrUQElkUco2v17WR6FG6rwMUg9vbCajFgyUij4fTHGyCNV0gaAkjOHkXio8eg2+MoPzUTyln2NJedqO3RIr12s1mgg7+cvUIePQTfxVSxF+ZScOULRzrJbKErdPVAOHotUrcVqKMKcnEDe0k8wJ8a5pM5duvcmLzeH335dSlZmBrVq1+XV2R/i729bwDgrM4P0tLKpFAGBQbw2+0MWLfiazRvWovfx4bEpT9Opy83dyvDqbSLffOUZu/Qnn32F3v0G3mKrwHQuAoOHCuU9/ZCotFgykiha9z3WKyM7pGotkmumKEpkcty6DUWi0YHJiDkjmcJ1CzBfs+imRC7HrdMApDofrMYSzHGRFG75BUqKy7+8yxgjj1CkUuPedXDZvrFs3jX7hpPv1C9fouo/Gu1jM7EW5VNy5ihFu9ZXWR0rU3DwbzI0nngNH4Ncp6fk8kWSP5tVeoySe3k7HKMkHirUbTuT8csCp9vMWr8Mq9WKfuRYZN4+WPJyKIg4SNaqH53mr0or/0hH6SblybHBaNQyoi8U8vqnsRQVlwUy/PQKyvcn1ghwo1lDNa99Enuba2yvf8dWZOcXsGDdVtKzc6lXM5AvX5hIkK/te5WenUtyRtnIFIvFytcrNnE5LROZTEZNfx+eemAQ9/XqWF1NqFZiCoNrSaz/dkK8IFTi559/5plnniExMfGGFl+81qXpo6qoVrdXYcZ/bBHGf+Hcyuo9abpKn6VVG8i6HVL+cn6/5zuNm+eN3zbrv6ww7fbfC9zV9I1rV3cVXMJazetcuELimNmVZ7oD1N74ceWZ7gCmCu66cyfJOnf7Rk1WpenSOZVn+o9bMSWuuqvgEpp7hlZ3Ff6V1Jnjqu21/ec478y4k92xIxCE/67CwkJiY2OZM2cOU6ZMuenggSAIgiAIgiAIgkuI2zi6lHg3BZf76KOPaNWqFQEBAcycWb1D0QVBEARBEARBEATXEAEEweVmzZqF0Whk+/btaDSaygsIgiAIgiAIgiAI/3liCoMgCIIgCIIgCIJwV5JIxCKKriRGIAiCIAiCIAiCIAiCUCkxAkEQBEEQBEEQBEG4K0nEIoouJd5NQRAEQRAEQRAEQRAqJQIIgiAIgiAIgiAIgiBUSkxhEARBEARBEARBEO5KEqlYRNGVxAgEQRAEQRAEQRAEQRAqJUYgCIIgCIIgCIIgCHcnsYiiS4l3UxAEQRAEQRAEQRCESokRCIIgCIIgCIIgCMJdSayB4FpiBIIgCIIgCIIgCIIgCJUSAQRBEARBEARBEARBEColpjAI/0nqGn7VXQWXcNdrq7sKt6zP0l7VXQWX2D7uh+quwi3rteCh6q6CS2QeOV3dVXCJwG6tq7sKt+zAe+uruwouIfO48/tD/MZYqrsKLpEXl1jdVXCJ/OSs6q7CLQtsH1bdVXCJkJIa1V2FW3ZCW6e6q+ASnau7Av+SRHLnnyP+S8S7KQiCIAiCIAiCIAhCpcQIBEEQBEEQBEEQBOHuJBZRdCkxAkEQBEEQBEEQBEEQhEqJAIIgCIIgCIIgCIIgCJUSUxgEQRAEQRAEQRCEu5JEKvrMXUm8m4IgCIIgCIIgCIIgVEqMQBAEQRAEQRAEQRDuShKxiKJLiREIgiAIgiAIgiAIgiBUSoxAEARBEARBEARBEO5OEtFn7kri3RQEQRAEQRAEQRAEoVIigCAIgiAIgiAIgiAI/wHffPMNdevWxd3dnbZt2/L3339fN7/BYOC1116jdu3aKJVK6tWrxw8//FBl9RNTGARBEARBEARBEIS70p20iOLy5ct59tln+eabb+jSpQvffvstAwcO5MyZM9SqVctpmQceeICUlBQWLlxI/fr1SU1NxWQyVVkdRQBBEARBEARBEARBEKrZZ599xsSJE3n88ccB+OKLL9iyZQvz589nzpw5Dvk3b97Mrl27uHDhAnq9HoA6depUaR3FFIb/sJ07dyKRSMjOzq7S15k1axatWrW6bh6JRMLatWsBiIuLQyKREBERUaX1EgRBEARBEARBuCVSabU9DAYDubm5dg+DweC0miUlJRw5coT+/fvbpffv35+9e/c6LbN+/XratWvHRx99RI0aNWjYsCEvvvgiRUVFLn8brxIjEG7B+PHjWbJkiUP6uXPnqF+/fjXUyLVmzZpFVFQUy5Ytq+6qVErZpjvKe/oi1egwpyVRtO03TJdiKi4gk+PeZRBuzdojVWux5GVTvHczJSf2AaBo2Ar3zuFIvf2QSGWYs1IxHNxOyamDVdoO9/a98OgajlTjhTntMvl/LMN08dx126HqORRly05INVosuVkU7tqI4dg/AOgmzEBRN8yhWMnZE+T+NLdK2rD8YCSL954kPa+Iev5evDTgHtrUDqy03LH4FCYu2kR9f29WPDGiNN1otrDw7+P8fvw8qbmF1PHV8mzf9nRpULNK6n+z9F3bEfrCRHRtmuEe7M/h+6aRsn57dVer1IojZ1lyIJL0/CLq+el4sW9b2oT4O817+GIKk35xrPvqyYOp66MDYHt0Agv3niYhKw+TxUItb08e6dCYIc3rVlkb1N3649lnGDKtF8akS2SvXkxJTJTTvN5jp6G+p6dDujEpgZT3XwDAvWUHtP3vRe4bCDIZprRk8nf8TuGh688xvFUrjp1n6aFo0guKCfXV8mKvVrSp6ec07+H4VCav2OWQvmpCOHV9tACsPxXHrM2HHPLse3YkSrnMtZW/osbY0dSaMh43f18KzsZw7u2PyDl0tOL8j4ym5qMP4l4zmOLLyVyct4Dk1b+XPh94/zCafPKuQ7mdjdphMZRUSRsAgh98gJqPPYrSz5eC8zHEzPmYnCPHKs7/0GiCHxqNe41gDEnJxH/7PSnrNjjN6zconCaffkj6tr84/dRzLqvzHxvWsnb1crIyMwipVYeJk6fTpFmLCvOfOhnBogXfkBAfh17vy4j7xzBg0LDS53ds3cxXX3zoUG75mi24ubmV/p+RnsbSRd9x9MhBSkoMBAfXZPozM6jXoJFL2qXq0g9NryG2/Tv5Erlrl1JyIdppXq8Hp6Lq0MMh3Zh8ibQPZ9i217E3Hu27oQi0nSOMl2LJ3bgcY/x1rgmqgK7fEPRD7kfmpafk0kXSlv6PoujTFeb37NIL/dBRKAKDsRQWUnD8MGk/L8CSn3cba+1I0bILyna9kai1WDKSKd65BvPlCxUXkMlQdgxH0bgdEpUWa342hgNbMZ4+cPsqDQzp6kG3lkpU7hJik0z8+mchSenmCvN3au7G+MEah/QnP87E5KTYgI7u3NtTxfZDxazYXnjL9bVaraxb9h27/lxDQUEeoQ2a8siUl6lRq951yx3eu501v/yP1ORL+AfWZOTYabTt2Mtp3g0rF7Hqp3n0G/IgDz3+Qtk29u1g55bVXIyJJD8vh9mf/UytUNfs3wLMmTOH2bNn26W99dZbzJo1yyFveno6ZrOZgIAAu/SAgACSk5Odbv/ChQv8888/uLu7s2bNGtLT05k2bRqZmZlVtg6CCCDcogEDBrBo0SK7ND8/5xeEd5r169czY8aM6q5GpRSN2+LR934KtyzDdOkCytZd0Yx+kpwF72DNzXJaRj1iIlK1lsJNP2HJSkOi8gRp2QW3tbiA4r2bMWekgNmEon5zVIMfwVKQhyk2skra4dasPeqBY8jf8BOm+PO4t++BbuyzZH39BpacTKdlPB+YilSjJX/tIsyZqUjVWlvE84rcZd+ArKxdUg8NXtNmYTh1uErasPnUBT7afIDXBneiVa0AVh6OYtpPf7LmyZEEeTmemK/KKy7h9TW76RAaTGa+fcT06x1H2HgihreGdqGur469MZd5bvl2lkwcQuMgnyppx82QqVXknojm0pLVtP3t6+qujp0tZy7y8bajzAxvR6uafqw6dp7py3eyatJggnTqCsutnTwEtVJR+r+3Sln6t87djcc7N6WOjxaFTMrf5y8za+N+9GolnUODXd4Gjzad8Bo5nqwV31NyIRp1l774PvEqKe89hzkrwyF/9spF5Kz7ufR/iUyG/ysfU3Rsf2matSCf3C2rMaUkYjWb8GjaBu+Hp2HOy8UQddzlbQDYEpXAJ39FMLNvG1rW8GXV8Qs8tepvVk4YQJBWVWG5NY8NsP8sPJR2z2vc5KyeONAuraqCB/5Dwmnw5ktEv/EeOYePUePhUbRc/A0H+o3AkOh4YVNj7APUe+kZombOJvf4KbStmhM25y2MOblkbC8Ljphy89jfZ5hd2aoMHvgN7E+9V2Zw7p33yT0aQdDo+2n+7TwODR2JIcmxHUFjRlH3uac4++bb5J08jWeLZjR8+01MOblk7Nxtl1cZHES9Gc+TffiIS+v8z+4d/LBgHpOnPUtY42b8ufl33nnrZb6cvxg//wCH/CnJSbz71kz6DRjMsy++RlTkKb775gt0Oh2dupT9AFep1Hz97VK7stcGD/Lz8pg54ymat2jNG7M/wMvLm+Sky6g0FR/Pb4Z7q47oRowjZ+UPlMRGo+rcF/3kV0j74EXM2Y77d86aJeRu+LUsQSrDf8YHFEeU7d9u9RtTdHQvObFnwWRE03soPlNnkvrhDCw5zq8JXE3TsTv+46aQ8sM8iqNPo+s7iBqvvEvci5MxZaQ55Hdv1JTAaS+StvQ78o/uR673JWDiUwROfpbEz965LXV2Rt6wNe4976V4+0rMibEoWnRGde8U8pfMwZqX7bSMx+DxSNSeFP25DEt2OhKVBsltvoVe+D3u9G3vzpKN+aRkWhjU2Z1nR3vy5oJsrndoKSq28OaCHLs0Z8GD2oEyurVSkpDqujnmm9YsYcv6X5j49FsEBtfi998W8slbT/L+N6vw8HB+vj4fdYL5n7zKvQ9NpW3HXhzZ/xfzP36FmXMWUq9hM7u8F86dZtefawip08BhOyXFRTRo3JL2XfqyeJ5jQFe4NTNnzuT555+3S1MqlRXktpFI7NdssFqtDmlXWSwWJBIJP//8MzqdrbPns88+4/7772fevHl4eHjcQu2dE1MYbpFSqSQwMNDuIZPJGD9+PCNGjLDL++yzz9KzZ8/S/61WKx999BGhoaF4eHjQsmVLVq5ceVOvHxUVRdeuXXF3d6dJkyZs27bNbroBwMsvv0zDhg1RqVSEhobyxhtvYDQar7vdhIQETp06xcCBAyvMExUVRefOnXF3d6dp06bs3Lmz9LmsrCwefvhh/Pz88PDwoEGDBg6BFldx79CbkuN7KTm+F0tGMkXbVmLJzUbZurvT/PLQJshrNSB/xTxMcdFYcjIxJ120i6ib4s9hPHscS0Yylux0DIf/wpx6GXnI9SPBt8Kjc3+Kj/6N4ejfmNOTKPhjGebcTNzb93SaX1G/GYo6jcj9aS7GC5FYsjMwXY7FlFDWy2ItKsCan1v6UNRvgtVYguG0Y6+lK/y47xT3tmnIyLaNCPXz4qWBHQnUqVlx2Hlv8VXv/L6Hgc1DaemkN3bj8fM83q0F3RqGUFOv5YH2jelcrwZL956qkjbcrLQtuzn71hckr91a3VVx8NPBKEa0DGVkq/qE+uqY0a8tgVoVvx27zqgWQK92x1fjUfqQXROUalc7gN6NQgj11RHi7clD7cNo4O/FsQTHi2JX8Ow1hIJ9OyjctwNTymVyVi/BnJWOumt/p/mtxUVY8nJKH4pa9ZB6qCnY/1dpHsP5MxSfOIQp5TLm9BTyd/2BMfEiynqOo3Vc5efDZxnRvC73tggl1EfLjN6tCPBUsTLi+r2iepUSX7V76UNWfiEoicTueV+1e5W1IeTxcSSuWEPS8tUUxsRy7u2PMCQlU2PsA07zB947hMu/rCR1wxaKEy6T+vtmklasofbUCXb5rFgpScuwe1Slmo8+QvLqNSSvXEPhhVhi5nxMcXIywWNGOc0fMGwISctXkfbHnxRfukzapi0kr1pLyOP27UAqpfFH7xP39XyKEy67tM7r1/xGn/6D6Bc+mJBatZk4eTo+vv5s3rTeaf4tm9bj6+fPxMnTCalVm37hg+ndbyBrV6+wzygBb73e7nGt1St/xdfPn6eee5mGjRrjHxBIi1ZtCQqq4ZJ2aXoOpvDAXxQe+AtTaiK5a5dizs5A1aWf0/zl92+3kFAkHmoKD5YFpLJ/mkfhnq2YEi9iSk0ke/l3IJGgbNDM6TargvfgkeT8tYXcvzZTkphA2tJvMWak4dVviNP8HvXDMKalkL1lHaa0FIqjT5OzfRPK0Ia3rc7OKNv2xHjqAMZT+7FkpmDYuQZLXjZuLbs6zS+rE4a8Zn0K13yHOf4s1txMLMnxmJPibmu9+7R354+9RRw7ayQx3czijQW4KaBDk+v/aLMCuQVWu0d5SgVMHKbhxz8KKCx2fP7fsFqtbP39V4aMmkC7Tr2pWbs+jz8zG4OhmP27N1dY7s/ff6Vpq3sYcv8EgmrWYcj9E2jcogNbf//FLl9xUSHfff4G4598DZXa02E7nXsNZvjoSTRt0cEl7fkvkkgk1fZQKpVotVq7R0UBBF9fX2QymcNog9TUVIdRCVcFBQVRo0aN0uABQOPGjbFarVy6dMl1b+I1RAChGr3++ussWrSI+fPnc/r0aZ577jnGjh3Lrl2Ow1adsVgsjBgxApVKxYEDB/juu+947bXXHPJ5enqyePFizpw5w9y5c1mwYAGff/75dbe9fv16unfvjpeXV4V5ZsyYwQsvvMCxY8fo3Lkzw4YNIyPDduH3xhtvcObMGf744w8iIyOZP38+vr6+N9SumyKVIQushbHcqABjbCTymqFOiygatMCcFI97x37opr+PdspbePQeCXKF0/wA8tqNkOkDMMWfd2n1S8lkyINqY4yxH95oPH8GRS3n02HcwlphSozDo+sAvF/8BO+n30MV/sB12+HeppttGobR9b17RpOZyMQMOtWz74XuVK8GxxNSKyy39thZLmXlMbVHa6fPl5gtuMntB0spFTIi4lNuvdJ3MaPZTGRyJp3qBtmld6wbyPFL6dctO+aHP+j35Wqm/LKdQxcrfp+tVisH4pKJy8ylbS3n0yJuiUyGIiSU4nKjAoqjTqCse2PDK9Ude2OIPok5q+I2Kxs2Q+4fjOH8mVuqbkWMZguRKVl0rGM/ladTnQCOJ17/s3hw6Vb6z/+dKSt2cSjecT8qKjEx6NuNDPjfBp5e/Q9RKVXTwypRyPFs1pjMv+3nYGb+vQ9d21bOy7i5YSk3z9NcXIy2ZXMk1+zTMpWKzv9spvO+rbRY+BWaplUXyJEo5Hg2bUzmnn126Vl79qNt3dJpGambAkuJfTssxcV4Nm9m147a06ZgzMoiedVal9bZaDQSc/4srVq3s0tv1aYdUZHOA6nRUWdo1cY+f+s27Yk5F223MndxURGTx4/h8XGjeHfWTC7E2AcXDx3YS/36jfjo/Vk8+tC9PP/UJP7c7Hzqxk2TyVDUrIsh+oRdsiH6BG51buyHs6pjTwznTl13/5a4KZFI5VgK82+pujdMJse9bgMKT9hP7Sk8cRT3ho2dFik6ewa53hd1q/a2Tei80NzTlYJjVTtt8rqkMqQBNTFdtO8AMF2MQhZcx2kRRWgzzCnxKNv1RjN5FuoJr6LsPuy61yWu5quTotNIORNX1lFmMsPZBBP1alx/4LXSTcL7T+j4YJoXT96vISTAcTTXg/3VnIwxEnXRdaMP0lIuk5OVQbNWHUvTFAo3GjVrw/moExWWi4k+QdNW99ilNWvd0aHMj999SMu2XWja0j6v8N/j5uZG27Zt2brVvlNq69atdO7c2WmZLl26kJiYSH5+2THu7NmzSKVSatasmum+YgrDLdqwYQOaa4byDRw4kN9++63ScgUFBXz22Wfs2LGDTp06ARAaGso///zDt99+S48ejnP8yvvzzz+JiYlh586dBAbaLkzfe+89+vWzj9y//vrrpX/XqVOHF154geXLl/PSSy9VuO1169YxfPjw677+9OnTue+++wCYP38+mzdvZuHChbz00kvEx8fTunVr2rVrV/q6VUGi0iCRyrAU2M8RtBbk2obzOyHz8kEeUg+r2Uj+qm+RqDSo+o9B4q6icNNPZRmV7nhNfx9kCrBabFMk4q7fk/5vSVWeSGQyLPm5dumWghwkGue9JjJvXxS1GoDJSN6v85CoNGiGjEXqoSZ/reNoD3mNusgDapK/dnFVNIGsQgNmqxUftf1QKR+1B+n5zucHXszIYe62wyyaMBi5zHk8s3O9Gvy47xRtawcQ4q3lQGwiO6PiMVtdE/m/W139PPTleqR91B5kFCQ5LeOr8eCNgR1oHKinxGRm46k4pvyynQUP97ULEOQVlxD+9VqMZjNSiYSZ4e3pWC5Q4QpStda2X+TZDym15OUg1XpVXl7rhXuTVmQu+dLhOYm7B0Hvfmv7AWixkLViIYbok66qup3soiv7hsq+x0GvciejoNhpGV+NB6/3b0vjAG9KzBY2nb7I1BW7+G50T9qG2Ebq1NF7Mmtgexr46sgvMfLrkXM89utfLHu0H7W8HXuZboXC2xupXO4wOqAkLQO3CoLDmbv3EjxmJOl/7iDvVCSezZsQPOpepG4KFN5elKSlUxgTR+SLb1AQfQ6ZRkPIhIdpu3IJBweOoigu3qVtAFB4eSORyzGm208LM2ZU3I6sf/YReP+9pG/7i/wzkWiaNiFw5Ai7dmhbtyLovhEcvne0y+ucl5uDxWLBy8vbLt3Ly5vsLOcBo6ysTFo7yW82m8nNzUGv96FGSC2eeu4VatepS1FhIRvWr2LmjKf4/KvvCa5hu+hMSU5k86Z1DLt3FPePfphzZyNZ+O1XKBQKevUJv6V2Xd2/zU72b5lWV0Gpa8prvVCGtSLrp+tPHdMOeRBzTiaGs7dn1JpMa2uXqdx0CXNOFnKd3mmZ4nORJH/9EUFPz0SicEMil5N/eB+pi7+5HVV2SuKhRiKVYS1/fVWYh0Tl/PpK6uWDrEYoVrOJwvU/IPVQ4957FBJ3NcV//uq0jKtpNbZridwCi116XoEFvbbi6V3JGWaWbCzgcpoZdzcJvdu589JYLe/8kENqlm1b7Rq7UStAxvtLcivczr+Rc2W6jtbLflqmTudDeprz8/XVclqdfRmtzoeca6b3Hfh7Cxdjonjrk6Xli///Ir1z+syff/55HnnkEdq1a0enTp347rvviI+PZ+rUqYBtSsTly5dZutT2mT700EO88847TJgwgdmzZ5Oens6MGTN47LHHqmT6AogAwi3r1asX8+fPL/1fra54XvG1zpw5Q3FxscOP/ZKSElq3dt4TW150dDQhISGlwQOADh0chx+tXLmSL774gvPnz5Ofn4/JZEKrdX7wB8jNzWXXrl0sWLDguq9/NfABIJfLadeuHZGRtpEATzzxBPfddx9Hjx6lf//+jBgxosLImcFgcFiN1GAy3+Q83nI/JiUSx7TS56RgtVKwfhEYbBfvRdtXoR75OIV/LgeT8WrFyP1hDhKFEnmdRnj0uQ9Ldjqm+OsP/3YtCVT0Q1kiBazkrVyA1WBbN6Bg83I8Rz9B/oafytpxhbJNV0wplzBdjq3aGpeft4UVCY7ztswWCzNX7eKJnm2o41vxxeJLA+/h7fV7GPH1aiRATb0nw1s3YF0lw/AFm/LvvNXq/PMAqOOjpY5P2bGhZU0/UvIKWHog0i6AoFYqWPbYQIqMJg7EJfPp9qPU9NLQrrbz4XW3zOk+UHkASX1PTyxFBRSdcOzFsxqKSflgBlKlO8pGzfG6dxzm9JQqG4UAXDkuXVMHHPeXq+roPamjLwsCtAz2ITmvkB8PR5cGEFoE+9AiuOzisVUNXx5aupVlR8/zUp8bO4/cvBs/1sZ9+S1ufj60XfMTSCQY0zNIWrWO2lMfw2qxXZDnHjtB7rGy3rKcw8dov3E5NR99kHOzHRf4cx0n7ajgWHtx/ncofH1ovWwpEomEkoxMkteup9bjE7CazchUKsI+eo+zb76NqSrvmuQwJ9YhqVx2x2MxULr/NwprQqOwJqXPhzVpxgtPT2bT76t5fOrTV17DSr36jRj76CQAQus1IOFiHJs3rb/lAMI1FStfc6w3ECBWte+BpaiQ4pMVT8nT9B6KR+vOpM97x+GceNtd5zvmVqMW/uOfIGP1LxScOILcS4/fw48TMPFpUr67/ojR2+8611dXniva9COUFGMBinetxWPoeIp3rKySz6BDEzceHlB27f31b7aAh/O3uuLvVWyimdjEsgUPYi7l89oELb3aurN8WyHenlJG91Uxd3me03URbsa+XX+wZP77pf8/+/oXAA7nZisVz3u/yvHpsgNDRloyv3z/KS/M+hqF2/Wnbwj/HaNHjyYjI4O3336bpKQkmjVrxqZNm6hduzYASUlJxMeXBdg1Gg1bt27lqaeeol27dvj4+PDAAw/w7rtVt56FCCDcIrVa7fSOC1Kp1OEEeO26A5YrF08bN26kRg37uYSVLaxx1fUW1Lhq//79jBkzhtmzZxMeHo5Op2PZsmV8+umnFZb5448/aNy4cekX9WZcrc/AgQO5ePEiGzduZNu2bfTp04cnn3ySTz75xKGMs9VJX+rdjlf6tq/09ayF+VgtZqRqLdcezyUqT4dRCVdZ8nOw5GeXBg8AzBnJSCRSpJ5eWLKuzuW2lv5tTr2EzCcQ907h5FdBAMFSmIfVbEaqsQ/sSNVarAXOI92WvGwsuVmlwQMAc1oSEqkUqdYbS+Y1w50Vbiibd6BwxzqX1/0qb5USmUTiMNogs6AYH41jBLTAYOR0YjpRSRl8sMk2nNhitV3itpm9iPmPhHNPaDB6tQdfPNgXg9FEdpEBf08VX2w7TLCLe1jvNlc/j/I93JmFxQ6jEq6nebAvm07H2aVJJRJqXflx2yjAm9iMXH7Yd9rlAQRLQa5tvyg32kDqqcOSm+O80DVUHXvZ7qxgdnK1Z7ViTk/BDBgvX0QRUAPP/iOqJIDg5eH8s8gqLEavuvGLuubBPmw6c7HC56USCU0D9cRnuX6otjErC4vJhJuffS+9m6+eknTnaxZYDAaiXnqL6Fffwc3XB0NqGjUeuh9TXj7GzAqmWlit5B0/jaruzZ9/boQxOwuryYTC177XTqHXU5JRcTvOvj6Lc7PeReGjpyQtnaAH7sOUn48xKxt1o4Z41KxBs2+uubPNld6u7icPc3DQCIoT/v08VE+tDqlUSnaW/aiJnJwsdOVGGVzl7a0nq3z+7GxkMhmeFXQgSKVS6jcMIzGxbP0Gb28fQmrZfxY1Q2qzb++t37Hk6v4t0+q49mel1FOLJa/yHl7VPT0oOlLB/g2oew5G03c4GfPfx5Tk+tEsFTHn2tol19l/NjKtF6YKFnbWDx9NUfQZsjbY1sEqiY8lxVBMrVmfkr5iCeZs5wspVyVrUQFWixlJuTnzEpUGa2EF11cFuUjzc6Ck7FhnyUyxXV9pdFiyrz9l6984fr6E2B/KphPI5bbrUJ1GSm5B2XfDUy11uqZBRaxAXJIJf2/bvlwrUIZWLeXV8WX7j0wqoUGInJ5tlTz5cVaF/T3lterQndBrFjk0XZlWmpOdjpe+7Bibm5OJ1sv5qBUAnZdP6eiFa8vorpS5GBNFbk4ms194pPR5i8XM2TPH2L5pBQt+24tUVjWL7v7XSMqvH/QfN23aNKZNm+b0ucWLFzukhYWFOUx7qEoigFBF/Pz8OHXKfrhcREQECoVtHliTJk1QKpXEx8ff0HQFZ8LCwoiPjyclJaV0YY1Dh+wj8Xv27KF27dp2ayNcvFjxBSjYpi8MGzbsunnAFpzo3t22UKHJZOLIkSNMnz699Hk/Pz/Gjx/P+PHj6datGzNmzHAaQHC2Omnh3Bu8+4PFjDk5HnndxhjPls2TVtQNo+Ss83ljpksxuIW1AYUSjLaRDzK9P1aLBUsFqwoDtoiurIp2GbMZU9JFFPWaUhJZdisxRb0mlEQ5v7WYMf48yqbtwE0JV+bnynwDbO0od5GibNoeiUyB4fg+Z5tyCYVcRuNgH/bHJNKncZ3S9P0xifQMq+WQX6N0Y+UT99qlrTgUycHYJD55oDc1vO1X+VYq5AQo5BjNFrafiaN/06q7beDdQCGT0ThQz/7YZHo3CilN3x+bTM+GNz4nLiolC18nAaBrWa1WSsyW6+b5V8xmjAkXcA9rQfGJsmObe6MWFF2n1xFAWb8JCv8gMr7fcWOvJZEgqaJ5ugqZlMYB3hyIS6F3g7KA8f64FHrWv/HF6KJTsq67SKLVaiU6NZv6fpUP/75ZVqOJvFOR6Lt2In1L2Xuq79qRtK1/XackWE0mDMm2tTT8hw4gfcfuikdWAZomjciPrpoRRlajibzTkXh37kTGtrJ6e3e+h4wdO69f1mSiJMUWmPUfFE7Gzr/BaqXwQiyHht1nl7fu09ORqVWcn/MRhgpuvXWjFAoF9eo35Pixw3Ts3K00/fixI3To2MVpmUZhTTh00P54H3HsMPUaNEIud34es1qtxF04T606ZcfWsCZNuXw5wS5f4uVL+Pm5IFhoNmO8FIuyYQuKT5bdGUjZsDnFp65/Fwu3eo2R+wVRuP8zp8+rew3Bs9+9ZHw7B2PCdW45WBXMJopjz6Fq0Zr8w2Vrhqiat6bgyH6nRSRuSrCUC4Rc6WiqYMBY1bOYsaRcQl6rEabzZdO75LUbYYpxPh3EnBiLomErULiVrrUk9fazXZfkVx70/TcMJZBWYn/+ycm30LiOgoQU23sqk0LDEDmrdxY520SFQgLkXE6zbSPqopHZ39u34dHBapIzzGzZX3zDwQMADw+13Z0VrFYrOm8fTkccoHaobQ0Yk9FI9KmjjHr0qQq3U69RC05HHCB82MOlaacjDlA/zHZ718Yt2/POXPtbsS/86m2CatRm0MhH/98EDwTXEwGEKtK7d28+/vhjli5dSqdOnfjpp584depU6fQET09PXnzxRZ577jksFgtdu3YlNzeXvXv3otFoePTRRyt9jX79+lGvXj0effRRPvroI/Ly8koDBVdHAtSvX5/4+HiWLVtG+/bt2bhxI2vWrKlwmyaTiT/++INt27ZV+vrz5s2jQYMGNG7cmM8//5ysrCwee+wxAN58803atm1L06ZNMRgMbNiwgcaNnS8epFQqHUZdmG9i+kLxwR2ohz6KOekipsuxKFt1Qar1puSYrYfEvcdwpJ5eFG5YAkDJ6cN4dBmEevAjFP29AalKg0fveyk5sbd0eJ17p3BMSRexZKeBVI6iXlPcmt1D4Zaqm8NXtPdPPEc+julyHKaEGNzbdUem01N8yLaopqrvSKRab/JXLwTAcPIAqp5D8RzxGIV/rUWi8kTdfxSGo/84DBN0b9uVkqhjWIsKqqz+AI90asZrq3fTJNiXliH+rDoSTVJOPqPa2U6Ic7cdJjW3gPdG9kAqldAgwL6HRq92RymX2aWfuJRKam4hYYF6UvMKmb/zGBarlfFdmldpW26UTK1CXb8sQKKqWxNtyzBKMnMoTqh47uLtMLZDGK//vo8mQXpa1PBldcR5knMLub+17TZOX+6MIDWvkHeH2qYX/XwwimAvNaG+OkxmCxtPx7E9OoFPRpb9aFm49zRNg/TU9PLEaDHzT0wiG0/FMjO88hFD/0beXxvQP/IUJfEXKIk9i7pLX2R6Xwr+sUXatUMfROalJ+vHeXblVJ16Y4g9iykpwWGbnv1GUBIfgyk9BYlcjnuT1qg6dCdr+fdV0gaAh9s15I1NB2gc6E2LYB9Wn7hAcl4h97W0Lfb61e6TpOYX8c4g2zS0n4+cJVirpp6vFqPZwqYz8Ww/d5mPh5VNHft272maB/lQy1tDQYmJX4+e42xaNq/0bVMlbUj4filNPnufvBOnyTl6nOCH7kcZHETiz7Z1f0JfehplQACRL9jOQx51a6Nt2YzciJPIdVpqPf4Imob1iXzhmnV5nplK7rETFMZeRO6poeb4h9A0aUT0m+87rYMrXFryI2EfvEf+qdPkRpwg6IH7cA8KInG5rfe37nNP4RbgT/Qrb9jaUacWns2bkXfiFHKtlprjx6JuUJ+oV94EwFpSQuE5+7tpmPJsPbTl0/+tYfeOYu6nc6jXoBGNwpqydfMG0tNSCB80FIAfFy8gMyONZ154FYDwQcPYtGEtPyyYR7/wIURHnWb7n5t4/qWy9375L0to2KgxQcE1bWsg/L6K2AvnmfTEM6V5ho4YxcwXp7Ny+U906daLc2cj+XPzBp54yj7o/2/l79yI98NPUpJwAWPcWVSd+yDz9qVwr+0axHPwGGQ6b7J/mW9XTtWxFyVx5zAlO47s0PQeiufAUWT9+DXmzDSknraAmtVQjLXcYphVJWvjaoKenEHxhXMUn41E12cgCl9/srdtBMB3zATk3j4kz7d1qhQcPUDApGfQ9R1M4YkjyLz0+I+bStH5KMxZt3/0wVWGIzvxGPgw5pQEzElxKJp3QurpTcnxPQAouw5BotFRvNl261xj1BGUHfvjEf4Qhr1/IPFQo+w+DOPpA7d1Csn2Q8UM7OROapaZ1EwLAzu5U2KEg2fKPv/xQ9Rk51lYu8sWVBjSxZ0LiWZSM824K21rIIT4y/j1T9t1k6EEEtPtgzwGo5WCIqtD+s2SSCT0G/ogG1YuIiC4FgFBIWxYuQil0p2O3QeU5lvwxZt4+fgz6hFbZ12/oWP44NXJbFy9mDYdenL04E7OHD/AzDm260QPDzU1a9uPklYq3dF4etml5+flkJmWTFambdRtUqKto1Hn7YPOuwoWQBfueCKAUEXCw8N54403eOmllyguLuaxxx5j3LhxnDxZFsV955138Pf3Z86cOVy4cAEvLy/atGnDq6++ekOvIZPJWLt2LY8//jjt27cnNDSUjz/+mKFDh+LubuulGj58OM899xzTp0/HYDAwePBg3njjDWbNmuV0m7t27UKj0dC2bdtKX/+DDz7gww8/5NixY9SrV49169aV3mnBzc2NmTNnEhcXh4eHB926dWPZsmWVbPHfMUYeochDjXuXQUg1WsxpSeSv+AZLru2kK9VokWqv+aFqNJD365eo+j+AdsIrWIsKKIk8QtHu38vyKNxQhY9B6umF1WTEkpFCwe+LMUa69t7e1yo5dYgCDw2qnkOReuowp14m56e5WHJsw9Oknl7Irl2AqcRA7pJPUQ9+CK8pb2ApKrBtY7t9gEjqE4CidkNyllQ8bcVVBjQLJafQwHe7IkjLL6S+vzfzHu5PsJdtNEF6XiHJOTcXxCgxmZm34yiXsvJQucnp2qAm793bA63Hf2M+n65tMzpt/7H0/yaf2PbfhKWrOTFxZnVVC4DwJrXJKTLw3Z5TpOcXUd9Px1cP9CRYZ+v5SM8vIjm3bMqJ0WLh8+3HSM0vQimXUc9Xx5ejetDtml7yYqOJ97ccIjXPlqeOj5Z3h3YmvEnVDDkvOrqPbLUn2gH3IdN6Y0xKIH3+nNJV12U6b+TlLnAk7h54tLqHnFWLnW5T4qbE64HHkXv5YDWWYEy5TObSryg6WnUjdMLDQsgpMrBg3xnSC4qp56vly5Hdyj6LgnKfhdnC57uOk3blswj10fHlyK50DS1brDLPYOTdP4+QUViMxk1BowAvFozpRbOgioe83orUDVtQeHlR55kpKP38yD97nhMTnqT4si1QpvT3w71G2Zo8EqmUWpPGoQqtg9VoImv/IY7cN47iS4mleeRaT8LefxM3P19MefnknYnk6OgJ5B2vugXv0v74E4WXF7WnTcHNz5eCc+c5OXU6hkRbO9z8/HAPKnufJVIZNcePQ1W3NlaTiewDhzn24KMYEhMregmX69q9N3m5uaz4dSlZmZnUql2H12d/gL+/7f3OyswgLa1s2lpAYBCvz57DogXf8MeGdeh9fJg45Sk6dSkb8ViQn8/8rz4jKysTlVpNaL36vPvhXBo2Kgv2N2gYxsuvv8NPixew4tel+AcE8djkJ+nRy/ltFm9WccR+ctSeeIaPRKb1wpiUQOZ3H5bt31ovZE72b/cWHchd43xROFWXfkjkCvQTnrNLz9u8krwtq1xS78rk799NqqcWn5EPI/PypiThIpc/fANTuu0zknnpkfuWrSuTu3srUg8PvMKH4Td2EpbCAgpPHyf9l4W3pb4VMZ09RrGHCmXHcCRqLZaMJArXfIs1zzbKUaLWIvW89vqqhMKV83HvfR/qh1/AWlyAMToCw95Nt7XeWw4Uo1BIeKi/GpW7hNhEE3OX52G45gZUeq3UbtSAh7uUsQOUaNVSigxWElLMfPJzHnFJt7jgwQ0adO+jGA0Gfvz2Awry86jXsBkvzPrabqRCRpptuu1VDcJaMvXF91j983zW/PI//ANrMvXFOdRr6Hzx7YpEHNzNwq/KphL/78p1zPDRkxjx4JRbbNl/hOTOWUTxTiCx3shKNcIdY8+ePXTt2pXz589Tr169my7/9NNPYzKZ+Oab6lv5FyBrjvN5P3cac9Ht6e2oSprG1XsfalfZPu6H6q7CLeu14KHqroJLZB45XXmmO4B3izt/3zjw3vrqroJLyDzu/ItDv99v74+squI174XqroJL5CdXzS1Rb6fA9lV3S9TbaUbJm9VdhVv26PDbdyvLqtS58Z25/lT+N69U22trpn1Qba9dVcQIhDvcmjVr0Gg0NGjQgPPnz/PMM8/QpUuXfxU8AGjWrJnd3RUEQRAEQRAEQRDuWHfYIor/dSKAcIfLy8vjpZdeIiEhAV9fX/r27XvdOyxUZvLkyS6snSAIgiAIgiAIgnC3EAGEO9y4ceMYN25cdVdDEARBEARBEARBuMuJAIIgCIIgCIIgCIJwV5KIRRRdSrybgiAIgiAIgiAIgiBUSoxAEARBEARBEARBEO5OYhFFlxIjEARBEARBEARBEARBqJQYgSAIgiAIgiAIgiDclSRS0WfuSuLdFARBEARBEARBEAShUiKAIAiCIAiCIAiCIAhCpcQUBkEQBEEQBEEQBOHuJBGLKLqSGIEgCIIgCIIgCIIgCEKlxAgEQRAEQRAEQRAE4e4kFlF0KfFuCoIgCIIgCIIgCIJQKRFAEARBEARBEARBEAShUmIKg/CflHL0fHVXwSXyknKruwq3zD85o7qr4BK9FjxU3VW4ZX9N+qW6q+ASrae3qe4quETOmZjqrsIta/ds3+qugnBFAnfHIl/6Pt2ruwou4Rlz51+HxG45Ut1VcIkzitPVXYVbVudeXXVXwUVaVHcF/h2xiKJLiREIgiAIgiAIgiAIgiBUSoxAEARBEARBEARBEO5KErGIokuJd1MQBEEQBEEQBEEQhEqJEQiCIAiCIAiCIAjC3Uki+sxdSbybgiAIgiAIgiAIgiBUSgQQBEEQBEEQBEEQBEGolJjCIAiCIAiCIAiCINydpOI2jq4kRiAIgiAIgiAIgiAIglApMQJBEARBEARBEARBuCtJxCKKLiXeTUEQBEEQBEEQBEEQKiUCCIIgCIIgCIIgCIIgVEpMYRAEQRAEQRAEQRDuTmIRRZcSIxAEQRAEQRAEQRAEQajUbQsg7Ny5E4lEQnZ2NgCLFy/Gy8vrdr38XWnt2rXUr18fmUzGs88+e9Ply38Gs2bNolWrVrdUp7i4OCQSCREREbe0HUEQBEEQBEEQhFsmkVbf4y7k0ikMe/fupVu3bvTr14/Nmze7ctN3hJ07d9KrVy+ysrJuS3BkypQpTJgwgaeffhpPT89b3t6LL77IU0895YKa3X5e/YeiHz4KuZeekksXSVk0n6KoUxXm13btjX74KNyCamApLCA/4jCpS7/Dkp8HgFvN2viNHod7aAMU/oGkLJpP1qY1Vd4Ov3vvI+jBh1H4+FAUF0v83M/JP3G8wvz6fuEEPTwWZc0QzPn55BzYT8K8LzHn5gIgkckIeuRRfAYOws3Xj+KEeBLmzyP3wP4qa4Omx0B04SOQ6bwpSUwga/lCDOfPOM3rM/5pNJ17O6SXJMaTNOvp0v8lHmq8RzyMR5uOyFQaTOkpZP62mOJTR6qsHSuOnGXJgUjS84uo56fjxb5taRPi7zTv4YspTPplu0P66smDqeujA2B7dAIL954mISsPk8VCLW9PHunQmCHN61ZZG26Uvms7Ql+YiK5NM9yD/Tl83zRS1ju2p7qouvRD02sIMq0XxuRL5K5dSsmFaKd5vR6ciqpDD4d0Y/Il0j6cAYA8sCaeA+5HERKKXO9HzpqlFOz+o0rbAK5vh6pjbzzad0MRWNP23KVYcjcuxxgfU3WNcELRojPKtr2QqLVYMpIp3rUWc2JsxQVkMpT39EcR1haJSos1PxvDwW0Yzxy8fZUu505owx8b1rJu9TKyMjMIqVWXxyZPp0mzFhXmP30ygkULviEhPha93pcR948hfNDw0ud3bP2Dr7/40KHcsjVbcHNT2rZx6jjrVi0j5vxZsjIzePn1d7inUzeXtmv5nuMs3nmE9NwC6gX68NLwHrQJrVFpuWOxiUz85jfqB/qw4oWxpenbTpxn4faDJKRnY7RYqO3rxSM92jK0XWOX1rs8RYvOuLXpWfodMuxeV+F3SFajHqr7pzmkFyz9EEtWqu0fqRS3dn1QNG6HRKPDkpWGYc8GzBedHzNcwWfwcPzuG4Nc70PxxVgSv/uawtMnK8zv1bMvfvePQRlcE3NhAXlHDpL0/XzMebZrEO++Awh5/hWHcieH98dqLKmydgBMGFOLYeGBeKrlnDmbx2ffxhCXUFhh/i/fbU7r5l4O6fsOZ/LSO6cBWPFde4IC3B3yrN6UyOffuv64u3bTFpavXkdGVjZ1atVk+uMTaNHU+fc4IzOLb35YwrmYC1xKTGbkkIFMnzTBIV9+fgHf//Qrf+87QF5+AUEB/jzx2Dg6tmvj8voLdy+XBhB++OEHnnrqKb7//nvi4+OpVauWKzdfbUpKSnBzc6vuatjJz88nNTWV8PBwgoODXbJNjUaDRqNxybZuJ8/OPQiYMJXkBV9RFH0ar36DCXntPS489zim9DSH/B5hTQl6agapi78l/8h+5HofAic9Q9ATz3P549kASJVKSlKTyd33NwHjp9yWduh796XW089y8dOPyT95Ar/hI2j4yeeceuRBSlJSHPJrWrQk9PU3if9qLtl7/sbNz5/aL75E3Vde5fyrthN2jclT8ekfTtyHcyiOv4i2Q0cavP8BkVMnU3jurMvboGrXBf3ox8j85VuKz0fh2T0c/6ffIHHWU5gz0x3yZy7/nqzVS0v/l0hlBL35OYVH9pZlkskJeG4W5rwc0v/3EaasDOR6XyzFRS6v/1Vbzlzk421HmRnejlY1/Vh17DzTl+9k1aTBBOnUFZZbO3kIaqWi9H9vlbL0b527G493bkodHy0KmZS/z19m1sb96NVKOoe6Zh/+t2RqFbknorm0ZDVtf/u6WutSnnurjuhGjCNn5Q+UxEaj6twX/eRXSPvgRczZGQ75c9YsIXfDr2UJUhn+Mz6gOKIsaCZRuGHOSKXo+AF0Ix65Hc2okna41W9M0dG95MSeBZMRTe+h+EydSeqHM7DkZN2OZiFv2Ar3HiMo3rEKc2IsihadUY2YTP6PH2LNy3ZaxmPQo0hUnhRtXY4lJx2JhycSafX10twJbfhn9w4WLfiaSdOepXHj5mzZvJ5333qJufOX4Ocf4JA/JTmJd996hb4DBvPsi68RGXmSBd98gVbnRacuZYEplUrNV98utSt7NXgAYCgupk7devTuO5CP3n/T5e3afCyaj9bt4rWRvWlVN5iV+04wbcFa1rz0CEHe2grL5RUZeP3XLXSoH0Jmvv2PQp1KyeN9O1DXX49CJmX3mVjeWv4neo0HXcLquLwNAPIGrVB2H47hr9W271DzTngMn0TBTx9V+B0CyF8yB0oMpf9bi/JL/3brNBBFWFuKt6/AkpmKvHYjPIZMoHDFV1jSLru8DbruvQiaPJ3Eb76g4MxJ9AOHUfftjzg79VGMaakO+VVNmhPywkwSF8wj98BeFD5+1Jz+PDWfmcHFd98ozWcuyCd68ji7slUdPHhoZE1GD6/B+3PPkpBYxKMP1OLzt5vx0LQjFBWZnZZ57YNIFPKyefJaTwWL5rbhrz1l15GTX4zg2t28bm01X7zdnL/2OF7f3Kodf+9h3veLeHbqJJo1bsTvm7fy8uz3WDzvcwL8/BzyG41GvHRaHh51HyvXbXC6TaPRyItvvoOXl5ZZL7+An68PaenpeHh4uLz+/zkSsQaCK7nsbFdQUMCKFSt44oknGDJkCIsXL77lbV6+fJnRo0fj7e2Nj48Pw4cPJy4urvT58ePHM2LECD755BOCgoLw8fHhySefxGg0luZJSkpi8ODBeHh4ULduXX755Rfq1KnDF198UeHrXt3unDlzCA4OpmHDhgD89NNPtGvXDk9PTwIDA3nooYdITbUdVOPi4ujVqxcA3t7eSCQSxo8fD4DVauWjjz4iNDQUDw8PWrZsycqVK6/b9qysLMaNG4e3tzcqlYqBAwdy7tw5wDbS4eqIg969eyORSNi5c6fT7WRnZzN58mQCAgJwd3enWbNmbNjg/MBSfgrD1ffh/fffJyAgAC8vL2bPno3JZGLGjBno9Xpq1qzJDz/84LCtqKgoOnfujLu7O02bNq2wfq6gH3If2Ts2k7NjMyWXE0hd/D+M6Wl49x/qNL9Hg8YYU1PI+mMtxtRkiqJOk711I+6hDUvzFMecJe3HBeTt3Yn1mu9TVQoY8yDpG34nfcN6ii/GkfDlF5SkpuI/YqTT/JqmTTEkJ5G6cgUlSUnknzhO2rq1qBuVRad9wgeQ9OMScvbvw5CYSNra1eQcOEDgmIeqpA3afsPJ/2cb+f9sw5R8iawVCzFnpePZY4DT/NaiQiy52aUPtzr1kao05O8p6/3WdOmDVO1J2jdzMMREYc5Mw3A+EuOluCppA8BPB6MY0TKUka3qE+qrY0a/tgRqVfx27Nx1y+nV7vhqPEofsmuuNNrVDqB3oxBCfXWEeHvyUPswGvh7cSzBMch1u6Vt2c3Zt74gee3W6q6KA03PwRQe+IvCA39hSk0kd+1SzNkZqLr0c5rfWlyEJS+n9OEWEorEQ03hwV2leYwJF8j9/ReKj+3DajLdse3I/mkehXu2Ykq8iCk1kezl34FEgrJBs9vSJgBlmx4YTx/AePoAlqxUDLvWYsnPxq1FF6f5ZbXDkNesR+HaBZgTzmHNzcKSEo85Ke621bm8O6ENv6/5jT79B9EvfAg1a9Vm4uSn8PH1Z8umdU7zb9m0Hl8/fyZOfoqatWrTL3wIvfsNZN3q5fYZJeCt97F7XKtNu3t4aNzjdOzSvUra9ePuo9zboSkjOzYjNEDPSyN6EuilYcXeE9ct987K7Qxs3YiWdYIcnmtfP4Q+zesTGqAnxNeLh7u3pkGQL8diE6ukDQBubbpjPH2w7Du0ex2W/GwUzTtft5y1MB9rYV7pA6u19DlFWFtKDm3HHBeFNTcT48l9mC5G49bGcWSSK/jdO4qsPzeRuWUjhoR4kr77GmNaKj6DhzvNrwprQklqMhnrV2NMSabwzEky/liPR4NG5RoJpqxMu0dVe2BoDZb+lsDu/RnExhfy3hfRKN1k9Ovu+MP7qrx8E5nZxtJH+1ZeGAxmu+BAdq7RLk/ndnouJRURcSrH5W34bd0GBvXtzeD+fagdUpPpkybg7+vL+k1/Os0fGODPU5MeI7x3D9RqldM8f2z7i7z8fN599SWaNwkj0N+P5k0aU79uHZfXX7i7uSyAsHz5cho1akSjRo0YO3YsixYtwnrNgfBmFRYW0qtXLzQaDbt37+aff/5Bo9EwYMAASkrKIpd//fUXMTEx/PXXXyxZsoTFixfbBS/GjRtHYmIiO3fuZNWqVXz33XelP/qvZ/v27URGRrJ169bSH9wlJSW88847HD9+nLVr1xIbG1saJAgJCWHVqlUAREdHk5SUxNy5cwF4/fXXWbRoEfPnz+f06dM899xzjB07ll27djl9bbD9eD98+DDr169n3759WK1WBg0ahNFopHPnzkRH24awrVq1iqSkJDp3djxJWSwWBg4cyN69e/npp584c+YMH3zwATKZrNL2X7Vjxw4SExPZvXs3n332GbNmzWLIkCF4e3tz4MABpk6dytSpU0lISLArN2PGDF544QWOHTtG586dGTZsGBkZjr1st0wuxz20AQXHj9olF5w4gkejJk6LFEWfQe7ji7p1ewBkOi88O3Uj/+gB19fvBknkctQNG5FzyL4OuYcOoG7W3GmZ/JMncfPzR9exEwBybz3ePXuRvW9PaR6pwg2LwT7SbykxoGnR0sUtAGRy3GrVo+hMhF1y0ZkIlPXCbmgTmi59KY46gTmz7Ee1qmUHDDFR6B+cQs1PFhP01ly0A++vsnllRrOZyORMOtW1vzDtWDeQ45eu38sw5oc/6Pflaqb8sp1DFx1HjVxltVo5EJdMXGYubWs5nxYhADIZipp1MUTb/5gwRJ/ArU7DCgrZU3XsieHcKcxZru8humG3qR0SNyUSqRxLYX6FeVxKKkPqXxPTRfvRTKaL0ciC6jgtoghtijklAWW7Xmgefwv1o6+g7DYUZAqn+avcHdAGo9FIzPloWl45Z13Vqk17oiJPOy1zNuo0rdqUz9+BmHPRmK4JmhUXFTF5/GgeH3c/7816hQsx1w+SupLRZCbyUiqdGtW2S+/UqDbH45IqLLf24GkuZeQwtX/HSl/DarVy4Gw8cWlZtL2BaRH/ypXvkDnefmqB+TrfoavUDz2P+vG38Bg5FVnNenbPSWRyMJfrwDAZkQW7ftqbRC7Ho34j8o4eskvPP3YIVeOmTssURp5C4euHZ7t7AJB7eaPr2oO8Q/ZTJKUeHoQtXkbY0t+oM2sO7qH1XV7/awUFuOOjd+PQsbJRWEaTlYjTOTQLq3hUS3mD+way/e80ig0Wp8/L5RL69/Rn07aKz/X/ltFo5Oz5C7RrbX+t1q51C05F/fspLHsPHqZJo4Z88b/vGfnI40yY/jw/rViN2ex8VIYgVMRlUxgWLlzI2LG2OWgDBgwgPz+f7du307dv33+1vWXLliGVSvn++++RXBl2smjRIry8vNi5cyf9+/cHbL39X3/9NTKZjLCwMAYPHsz27duZNGkSUVFRbNu2jUOHDtGuXTsAvv/+exo0aFDp66vVar7//nu7qQuPPfZY6d+hoaF8+eWXdOjQgfz8fDQaDXq9HgB/f//SNRAKCgr47LPP2LFjB506dSot+88///Dtt9/So4djJPncuXOsX7+ePXv2lAYGfv75Z0JCQli7di2jRo3C39/2o0Ov1xMYGOi0Ddu2bePgwYNERkaWjqIIDQ2ttO3X0uv1fPnll0ilUho1asRHH31EYWEhr776KgAzZ87kgw8+YM+ePYwZM6a03PTp07nvvvsAmD9/Pps3b2bhwoW89NJLN/X6lZF7apHIZJiz7YfrmrOzkHl5Oy1TdPYMSV9+SPBzryFVuCGRy8k7tJeUH+a5tG43Q67zQiKXY8q0j8wbMzPR+vg4LZN/6iQX3n6Lem+/i8RNiVQuJ+vv3cR//mlpnpyD+wkc8yB5xyMwXL6Etm17vLp2r5KhtjKNJxKZDEtutl26OTcHmdb5Z2FXXueNR7M2pH//mV263C8A97DmFBzYTeqX7yAPCEL/4GQkUik5G1e4sgkAZBUaMFut6NX28xx91B5kFDi/qPXVePDGwA40DtRTYjKz8VQcU37ZzoKH+9oFCPKKSwj/ei1GsxmpRMLM8PZ0rOvYgybYSNVX9u88+94dS14OMq2u8vJaL5Rhrcj6qXqnZdyudmiHPIg5JxPD2YrXf3EliYcaiVRm6zm9hrUwD4nK+bo8Up0PsuC6WE1GCn9fhNRDjXvv+5C4qyjeutxpmap0J7QhLzcHi8WCV7lzms7Lm+wKenOzsjJpVS6/l5c3ZrOZ3Nwc9HofaoTU4qnnXqFWnVCKCgvZsH4lr86YzmdfLSS4Rk2Xt8OhjgVFmC1WfDT2vaU+GhXpec7nql9My2Luxj0smj4Kuazi81hekYF+b3+P0WRGKpXw6sjeDoEKV7n6HSofuLMW5SNVO/8OWQpyKd62AnPqJZDJUTRui8fIqRStnI858QIApvhoFK17YLp8AWt2BrJaDZCHNq2S4LlMq0Mik2Eqdy1lzMrC01vvtExh5GkSPnqPWq+8hdTNdi2Vs+8fLs+fW5rHkBBPwmcfUBx3AalKhe/w+6n/ydecnT6RkkTXT8MA8PG2BfIyc+yDL1nZJQT6O65f4EzjBhrq1VHz4dcVT/Xsdo8PGrWcTTtcH0DIyc3DYrHgXW49NW+dF1lXFqP/NxKTU0hOPUXfHl2Z89ZMLicmM/fb7zFbzDw6ZtStVfq/rhqnyd2NXBJAiI6O5uDBg6xevdq2Ubmc0aNH88MPP/zrAMKRI0c4f/68w+KAxcXFxMSULVTStGlTux71oKAgTp48WVovuVxOmzZlC4PUr18fb+/Kf8w0b97cYd2DY8eOMWvWLCIiIsjMzMRisUUl4+PjadLEeW/3mTNnKC4upl8/+2GqJSUltG7d2mmZyMhI5HI599xzT2maj48PjRo1IjIystK6XxUREUHNmjVLgwf/RtOmTZFes9MFBATQrFnZ8FiZTIaPj4/DqI6rwRKwfR/atWtXYd0NBgMGg8EurcRswe06FwflWSk32kUioXzSVW41a+E/YRoZK3+mIOIwcm89fo9MInDyMyTP/8x5odvEYdSORGI3pPFa7nXqUOvZ50lc9AM5Bw+g8PEhZNpT1J7xMnEfvA9A/NzPqfPSTJr/vAysVooTL5O+aQO+g4ZUXRvKJ0icpjpQd+qNpaiAwohyI0EkEsx5OWT8+A1YLZTExyDT6dGGj6iSAELpy5b732q1InFItanjo6WOT1nPRsuafqTkFbD0QKRdAEGtVLDssYEUGU0ciEvm0+1HqemloV1txznMwjWcfKluZISbqn0PLEWFFJ88VGne26IK26HpPRSP1p1Jn/cOmG7PtKsy5dsgcZJ29SnbPlS0+WcoKcYCFO9ah8eQRynesdqxx/W2+e+3QVJ+Dq/Vet1pveXzXz1PXk1tFNaURmFlvcthTZrx4tOT2PT7ah6f+jS3i0OzcDz+ApgtFmb+vJknwjtSx+/613FqpRsrXniYQkMJB84l8On6XdT00dK+fojL6u3Ayb5c0e5tzU7DmF020s6QfBGpxgu3tj0puhJAMOxai7LPA6gfeRmwYs3JwHjmEIom7Z1v1BXKVVgikVTYBmVIbYKnPkXqr0vIO3IIud6HoIlTqTn9eS7N/RiAwugzFEaXLaIcf+YUDb5cgO/QkSR++5VLqtyvhx8vPlHWMfjylQUPHdvi5BqrAoP7BhITV0DkuYpHcw3pF8iBI5lkZFbdeg6O+3dFe8eNsVqteOu0vPDkFGQyGY3q1yM9M5Pla9bf/QEEwaVcEkBYuHAhJpOJGjXKhodZrVYUCgVZWVk39IO9PIvFQtu2bfn5558dnvO7ZvEQhcJ+yKBEIin9YV/RgeJGDiBqtf1CaQUFBfTv35/+/fvz008/4efnR3x8POHh4XZTKpy1A2Djxo127w+AUql0VuS69Xa4gLgOVyyK4uz9vd57fj0V1X3OnDnMnj3bLu3JxqFMb1rPaf5rmfJysZrNyL3sI+QynRfmChYR87l3DEXRp8lc/xsAhvhYLIZiar/zOWm/LsacXfXz88oz5WRjNZlQlBttoPD2xpjpvD5BYx8l/+QJkn+17SNFMee5WFxM42++5fKCbzFmZGDKzub8qy8jcXNDrtVhTE+j5hNPUpLk+rmg5vw8rGYzMq2XXbrMU4e53KgEZzRd+lCwfyeY7eekm3OysJrNYC37jhmTLyHX6UEmd8h/q7xVSmQSCRkFxXbpmYXFDqMSrqd5sC+bTsfZpUklEmrpbUHRRgHexGbk8sO+0yKAUAFLQe6V75SOa3+SST21WK6s8n09qnt6UHTkb6jm4ZlV3Q51z8Fo+g4nY/77mJLiXVTrylmLCrBazEhU9sOCJSoN1gqmUVgKcpHm50BJ2f5lyUxBIpEi9dRhyb69U03uhDZ4anVIpVKyyo02yMnJRuflvHfY21vvMDohJzsbmUyGZwWjXqRSKfUbhpGUeMk1Fa+Et9oDmVTiMNogM78QH0/HOdwFhhJOJ6QQdTmVD9b8BYDFasVqhTYz5jJ/8kjuaWALEEilEmr5egEQVsOf2JRMFm4/VCUBhKvfIanak2uvhCQeGoeRLddjTr6IPKyt3XaLNywCmRyJuwprQS5uXQZjyXX9NYo5N8d2LVVutIHcywtTBddE/qMfpuDMKdJWXRl1E3eBy8XF1P/kK5KXLnS+1oHVSuG5KNxcOMLln4OZnIkum8aqUNg6n/RebmRklR1xvXRuZGZXHtxTuknp082Phb9crDBPgJ+Sti28eP0D53eYulU6rSdSqZTMrGy79KycHLy9Kh+1VhG9txdyudyu47V2SE0ys7IxGo0O1/d3lbv0dorV5ZbfTZPJxNKlS/n000+JiIgofRw/fpzatWs7DQDciDZt2nDu3Dn8/f2pX7++3UOnu7GdJywsDJPJxLFjx0rTzp8/T/a/GP4TFRVFeno6H3zwAd26dSMsLMyh1/3qiIVr5xI1adIEpVJJfHy8QztCQpyfyJo0aYLJZOLAgbKe2IyMDM6ePUvjxjd+G6IWLVpw6dIlzp51/Wr7ldm/v2wOnMlk4siRI4SFOZ8HP3PmTHJycuwek8NucI6fyUTxhXOoW9jffkbdog1F0c4P7FI3pd2PUQDrlQBIdS3SajWZKDgbja59B7t0bbsOFJxyfgslqbs7WOyDTdbS7165nqeSEozpaUhkMrx79CTr790uq3sps4mS+Bg8mrSyS3Zv3ApDTNR1iyobNkMREEz+P9scnjOcj0LhF2T34Sj8g20XNS4OHgAoZDIaB+rZH5tsl74/NpmWNX1veDtRKVn4aq4fxLNarZSYKw++/b9lNmO8FIuyof2t6pQNm1MSd/3jmlu9xsj9gijc/1dV1vDGVGE71L2G4Nl/JBnffoAx4YLLqnxDLGYsqZeQ17If5Sav1bDCBQXNibFI1FpQlI3wk3r7YbVYsJSb4nFb3AFtUCgU1KvfiOPHDtulHz92mLAK5qc3DGvqJP8h6jVohFzuvO/IarUSe+G8w0KKVUUhl9G4pj/7z9oHvfafjXe6OKJGqWTli2NZ/vzDpY9RnVpQx8+b5c8/TPNazqdzgq3f1lhVgcQr3yFZue+Q7DrfIWekfjWwFjgJKJpNtnSpFEX9FpguuH6KktVkouh8NJrW7ezSNa3bUVjBOhtSpeO1FJYr7/F1LqY8QutjynTdmlhFRWYuJxeXPuISCsnILKF9q7LOS7lcQqumOk5FVR6w7d3VF4VCyp+7Kl4vbVCfALJzjOw7XDUdTgqFgob1QzkcYb9uzpGIEzQLa1RBqco1axzG5aRku06/hMuJ+Oi97+7ggeBytxxA2LBhA1lZWUycOJFmzZrZPe6//34WLlz4r7b78MMP4+vry/Dhw/n777+JjY1l165dPPPMM1y6dGPR8bCwMPr27cvkyZM5ePAgx44dY/LkyXh4eNxUTz5ArVq1cHNz46uvvuLChQusX7+ed955xy5P7dq1kUgkbNiwgbS0NPLz8/H09OTFF1/kueeeY8mSJcTExHDs2DHmzZvHkiVLnL5WgwYNGD58OJMmTeKff/7h+PHjjB07lho1ajB8uPPVcJ3p0aMH3bt357777mPr1q3Exsbyxx9/sHnz5ptq+78xb9481qxZQ1RUFE8++SRZWVl2a0hcS6lUotVq7R43M30hc8MqvPoMQNcrHLcaIfg/OhWFrz9Zf9oWv/R76DGCps8ozZ9/ZD+eHbri1X8ICv9APBo1IWDCNIrORZVFzOVylHVCUdYJBbkCuY8vyjqhKAKr7nZ7Kct+xXfIMHwHD8G9dh1CnnoGt4AAUteuAaDmlCeo+3rZbbSy9/yDV4+e+I0YiTI4GE3zFtR+9nnyz5zGmGHrAVM3aYp3956251u0pOGnX4BUSvIvP1VJG3K3rkPTtS/qLn2QB9bE+4HHkOt9ydu1BQCve8fiM+EZh3Karn0xXIjGmOjYe5q3azNSjSfeox9H7h+MR/O26AbdT97OTVXSBoCxHcJYczyGtcdjuJCewyfbjpCcW8j9rW3DJL/cGcHrv5fdavLng1H8dTaBi5m5xKRl8+XOCLZHJzC6bdkF5cK9p9kfm8SlrHxiM3L48WAkG0/FMqhpnSprx42SqVVoW4ahbWkL8qnq1kTbMgz3kOpfnyF/50ZUHXvh0aEncv9gtCMeQebtS+FeW7DJc/AYvB56wqGcqmMvSuLOYUp2cr6QyZAH10YeXBuJTI5M5408uDYy36obCVIV7dD0Hop20ANkL/sWc2YaUk8dUk8dEjfno9uqguHoLhTN7kHRpANSb3+U3Ycj9fSm5IRt/1B2GYx7/wdL8xujj2ItLsCj3xik+gBkNUJRdhuK8fTBapu+cCe0Yei9o9j+50a2/7mJS/EX+eG7r0lPS6H/oGEA/LT4O+Z++n5p/vBBw0hLTWHRgnlcir/I9j83sf3PTQwfObo0z/JfFnPsyEGSkxKJjTnHvLkfEXfhPOEDh5XmKSoqJDbmHLFXFldMTU4mNuYcaamumff9SPc2rD5wijUHTnMhJZOP1+0iKSuPUZ1swba5G//htV9s5w+pVEKDIF+7h17jgVIho0GQL6ort9BduP0g+6Ivcikjh9iUTJbuOsqGw5EMbnPjHTA3q+TobhRN70Fe+h0ahtTTG+PJfQC4dR5k9x1StOqGPLQZEi9fpPoA3DoPQtGgJcbj1yyCHFALeb3mSLR6ZMF18RgxGSQSSg5XTVA0bc1v6MMH491vIMqQWgRNehKFXwAZm9YDEDh+EiEvzCzNn3tgH7rO3dEPGoZbYBCqJs0Invo0hdFnSgME/g89iqZNe9wCg3APrU/NZ1/CI7R+6TaryorfLzP2/hC6dfShbi0Vrz7dEEOJma27y6aNvPZsQ6Y8Useh7OC+gfxzIIPcPOcdFBKJLYDwx18pVGX8f9TwIWzaup1NW3dwMeES875fTEpaOkMH2taAW7DkZ97/3H4ayPkLsZy/EEtRcTHZubmcvxBLXHzZIufDB/YnNy+PrxcsIuFyIvsOHeGX39YwYlB41TVEuCvd8hSGhQsX0rdvX6ejAu677z7ef/99jh496qTk9alUKnbv3s3LL7/MyJEjycvLo0aNGvTp0wet9sZXUV26dCkTJ06ke/fuBAYGMmfOHE6fPo27+40PRQbbtInFixfz6quv8uWXX9KmTRs++eQThg0rO9HWqFGD2bNn88orrzBhwgTGjRvH4sWLeeedd/D392fOnDlcuHABLy8v2rRpU7oQoTOLFi3imWeeYciQIZSUlNC9e3c2bdp00xHCVatW8eKLL/Lggw9SUFBA/fr1+eCDD25qG//GBx98wIcffsixY8eoV68e69atw9f3xntvb0be3l2kaLT43v8wMm89JQkXSXj/dUzptuix3FuPwrdsHnrOzq1I3VV4DxiG/7jJmAsKKDwVQdrP35fmUXj7UPfj/5X+7zNsFD7DRlF4+jjxs8qCEa6UuWMbMp2O4PETUfj4UBR7gbMznqckxdYTrvDxxS2grIcl44+NyFQqAu67n5DpT2POzyPvyBES5pctBil1c6PGpCkog4MxFxWRs38vF96ZjTm/alZpLzy8h0y1Fq/Bo5HpvClJjCf1q3dK76og0+mR6+1voyTxUKFq04msZd872yTmrHRSvpiF/oHH8HzrC0zZmeRu30Du5tVV0gaA8Ca1ySky8N2eU6TnF1HfT8dXD/QkWGeb2pSeX0RybtmwW6PFwufbj5GaX4RSLqOer44vR/WgW/2yaUvFRhPvbzlEap4tTx0fLe8O7Ux4k6pZ2Otm6No2o9P2H0v/b/KJ7diUsHQ1JybOrKjYbVEcsZ8ctSee4SORab0wJiWQ+d2HpXcjkGm9kHnbH1sk7h64t+hA7pqlzjaJTOuN/4yy46Cm91A0vYdiOH+GjHnvOC3zX2yHqks/JHIF+gnP2aXnbV5J3pZVVdKO8kxnIyh2V6Hs2B+JSoslI4nCdQuw5tmmkEnUnkivXUTVWELh6m9x73kv6gefw1pcgPHscQx7/7gt9XXmTmhD1+69ycvNZcWvS8jKzKRW7bq8NvtD/P1t54SszAzS08p+1AcEBvH67A/4YcE8/tiwFr2PDxOnPEWnLmULNxfk5zP/q0/JzspEpVYTWq8B7374JQ2uuRVwzLlo3pxZ9v1a9L3t/NKrTzhPPX/rx4YBrRuRU1jMd1v3k5ZbSP0gH+Y9Ppxgve06Lz23gOTsynuNr1VUYuL91X+Rkp2HUiGnrr+e9x4KZ0Drf99zWxnTuQgMHiqU9/Qr/Q4Vrfu+9DskVWuReHqV5pfI5Lh1G4pEowOTEXNGMoXrFmCOKxutJ5HLces0AKnOB6uxBHNcJIVbfrGbOuNKObv/Qu6pJeChR5Hr9RTHxRL31ssYrwSL5N4+KPzKgqxZ2zYj9fDAd+i9BD8+DXNBPvnHj5G06NvSPDK1hppPv4DcW4+loICimHPEvPQ0RWevPyrxVv2y+hJKNykvTKmPRiMn8mwez791iqKislEoAb5KhwEUIcEetGyq47k3nY/8BGjX0otAf/cqufvCtXp360JuXj5Ll68kMzOLOrVD+ODNVwn0t11DZWRlkZpmP11q0rNlC5WfPX+B7bv+IcDfj2XffwOAv58vH89+nXnfL2Hi0y/i56Nn5NBBPHjfjXdO3rGk1TTE+C4lsd7KvRbvQJcuXSIkJIRt27bRp0+f6q6OUIGoUf2ruwoukZd0cxc+/0X+TSoeFnon8evsfNHSO8lfk36p7iq4ROvpbSrPJNwWmtrVP8pEsEkYUjXB6dutXtTa6q6CSxhjzld3FW5Z7JYj1V0Fl3hSUTWB3dtp+Uf/fu2C/5LgRi0qz/QfVLz2y2p7bfcRt29R2tvFZbdx/K/asWMH+fn5NG/enKSkJF566SXq1KlD9+7dq7tqgiAIgiAIgiAIQlUSiyi61F0fQDAajbz66qtcuHABT09POnfuzM8//ywWCxEEQRAEQRAEQRCEm3DXBxDCw8MJDxeLgwiCIAiCIAiCIAjCrbjrAwiCIAiCIAiCIAjC/1PVdZ/2u5SYECIIgiAIgiAIgiAIQqXECARBEARBEARBEATh7iQVfeauJN5NQRAEQRAEQRAEQRAqJUYgCIIgCIIgCIIgCHcnsQaCS4kRCIIgCIIgCIIgCIIgVEoEEARBEARBEARBEARBqJSYwiAIgiAIgiAIgiDcnSSiz9yVxLspCIIgCIIgCIIgCEKlxAgEQRAEQRAEQRAE4e4kbuPoUuLdFARBEARBEARBEIT/gG+++Ya6devi7u5O27Zt+fvvv2+o3J49e5DL5bRq1apK6ycCCIIgCIIgCIIgCIJQzZYvX86zzz7La6+9xrFjx+jWrRsDBw4kPj7+uuVycnIYN24cffr0qfI6iikMwn+SykdT3VUQrnDzVFV3FVwi88jp6q7CLWs9vU11V8Eljn19tLqr4BJ1BtWo7ircMmNBUXVXQbhCMsRa3VVwiZLz56q7Ci6Rez6huqtwy7TBuuqugkuojJ7VXYVbVqQQ17XVSiKp7hrcsM8++4yJEyfy+OOPA/DFF1+wZcsW5s+fz5w5cyosN2XKFB566CFkMhlr166t0jqKEQiCIAiCIAiCIAiC4GIGg4Hc3Fy7h8FgcJq3pKSEI0eO0L9/f7v0/v37s3fv3gpfY9GiRcTExPDWW2+5tO4VEQEEQRAEQRAEQRAE4e4kkVbbY86cOeh0OrtHRSMJ0tPTMZvNBAQE2KUHBASQnJzstMy5c+d45ZVX+Pnnn5HLb8/kAjGFQRAEQRAEQRAEQRBcbObMmTz//PN2aUql8rplJOWmXFitVoc0ALPZzEMPPcTs2bNp2LDhrVf2BokAgiAIgiAIgiAIgnB3qsY1EJRKZaUBg6t8fX2RyWQOow1SU1MdRiUA5OXlcfjwYY4dO8b06dMBsFgsWK1W5HI5f/75J7179771RpQjpjAIgiAIgiAIgiAIQjVyc3Ojbdu2bN261S5969atdO7c2SG/Vqvl5MmTRERElD6mTp1Ko0aNiIiI4J577qmSeooRCIIgCIIgCIIgCIJQzZ5//nkeeeQR2rVrR6dOnfjuu++Ij49n6tSpgG1KxOXLl1m6dClSqZRmzZrZlff398fd3d0h3ZVEAEEQBEEQBEEQBEG4O0nvnEH3o0ePJiMjg7fffpukpCSaNWvGpk2bqF27NgBJSUnEx8dXax1FAEEQBEEQBEEQBEEQ/gOmTZvGtGnTnD63ePHi65adNWsWs2bNcn2lriECCIIgCIIgCIIgCMJdyVqNiyjeje6c8RyCIAiCIAiCIAiCIFQbEUAQBEEQBEEQBEEQBKFSYgqDIAiCIAiCIAiCcHeSiD5zVxLvpiAIgiAIgiAIgiAIlRIBhDvYrFmzCAgIQCKRsHbt2psuP378eEaMGFH6f8+ePXn22WdvqU6LFy/Gy8vrlrYhCIIgCIIgCILgEhJp9T3uQjc1hWH8+PEsWbLEIT08PJzNmze7rFJVpWfPnrRq1YovvviiSrY/a9Ys1q5dS0RERJVs/1qRkZHMnj2bNWvW0LFjR7y9vW95m6tXr0ahULigdrefpscAtP2GI9N5Y0xMIOu3HzCcj3SaV//odDSdejuklyTGk/z2swCoO/XC59GnHPLETx8NJqNL634tr/5D0Q8fhdxLT8mli6Qsmk9R1KkK82u79kY/fBRuQTWwFBaQH3GY1KXfYcnPA8CtZm38Ro/DPbQBCv9AUhbNJ2vTmiqrP4Cqc1/UPYcg03phSr5MzrqlGGOjnebVjZmCqn0Ph3Rj8iXSP34JAHlADTQDRqGoWRe53o+ctUsp/Lvqjzfqbv3x7DMMmdYLY9IlslcvpiQmymle77HTUN/T0yHdmJRAyvsvAODesgPa/vci9w0EmQxTWjL5O36n8NDfVdkMVF36oell+zyMyZfIXbuUkgvOPw+vB6ei6uD880j7cAYA8sCaeA64H0VIqO3zWLOUgt1/VGkbbpS+aztCX5iIrk0z3IP9OXzfNFLWb6/uapXyHjgM33tHI/f2wRAfR/LCeRSeOVlhfl2PPvjeOwa34BqYCwrIP3aIlEX/w5yXC4Bnx274jXoIt8AaSOQyDImXyVj3Gzk7t1ZZGzQ9BqILH4FM501JYgJZyxdiOH/GaV6f8U+j6ez8WJs06+nS/yUearxHPIxHm47IVBpM6Slk/raY4lNHRDuu8ceGtaxdvZyszAxCatVh4uTpNGnWosL8p05GsGjBNyTEx6HX+zLi/jEMGDSs9PkdWzfz1RcfOpRbvmYLbm5uAEyeMIa01BSHPAMGD2fKtGdvvVEVULTojLJtLyRqLZaMZIp3rcWcGFtxAZkM5T39UYS1RaLSYs3PxnBwG8YzB6usjuXZzhnDkemunDNWLbrOOeNJ1B17OqQbkxJIee95h3SPtp3xmfAcRccPkrHgY1dXvZRnz4Fow+9F7uVNSWI8mcsWYjjnfL/wnfA0mi59HNJLLseT+FbZ9ZPUQ43XvWNRtemITK3BmJ5C1opFFJ2suv0bYOyIQAb19EGjlhEVU8i8Hy9x8XJxhfk/eqU+LRtrHNIPROTw5ue2716zRmpGDfSnQR0VPt4KZs2NZd/RnCprQ3kbNmxg1cqVZGZmUrt2bSZPmUKzZs2c5s3MzGTBggWcP3eOxMREhg0bxpSpU29bXYW7202vgTBgwAAWLVpkl6ZUKl1Wof+ikpKS0pPpf0VMTAwAw4cPR+KiW5Po9XqXbOd2U7XtgveoCWT+ugBDTCSabuH4TX+dpNnPYM5Kd8iftfwHstf8VPq/RCoj8PXPKDq6zy6fpajA7iQIVGnwwLNzDwImTCV5wVcURZ/Gq99gQl57jwvPPY4pPc0hv0dYU4KemkHq4m/JP7Ifud6HwEnPEPTE81z+eDYAUqWSktRkcvf9TcD4KVVW96vcW3VEO3wcOat/wBh7FlWnPugnvUzaRzOwZGc45M9du5S8jcvKEqQy/F6YQ/HxA6VJEjcl5oxUio8fQDt8bJW3AcCjTSe8Ro4na8X3lFyIRt2lL75PvErKe89hznJsR/bKReSs+7mszjIZ/q98TNGx/aVp1oJ8cresxpSSiNVswqNpG7wfnoY5LxdD1PEqaYd7q47oRowjZ+UPlMRGo+rcF/3kV0j74EXMTj6PnDVLyN3wa1mCVIb/jA8ojihrh0ThhjkjlaLjB9CNeKRK6v1vydQqck9Ec2nJatr+9nV1V8eOtmtPAic+SdK3cymMPIU+fCi13vyAmOkTMKanOuRXNW5GjWdeIfmHb8g7uA+Fjy9BTzxH8PQXSZjzJgDm/FzSfvsZw6V4rCYTnu06UuPplzDlZFFw7LDL26Bq1wX96MfI/OVbis9H4dk9HP+n3yBx1lOYMx2PtZnLvydr9dLS/yVSGUFvfk7hkb1lmWRyAp6bhTkvh/T/fYQpKwO53hdLcZHL638nt+Of3Tv4YcE8Jk97lrDGzfhz8++889bLfDl/MX7+AQ75U5KTePetmfQbMJhnX3yNqMhTfPfNF+h0Ojp1KQsSqlRqvv52qV3Za693Pv7if1jMltL/4y/GMuv1F+nStadL2uWMvGEr3HuMoHjHKsyJsShadEY1YjL5P36INS/baRmPQY8iUXlStHU5lpx0JB6eSKS3r+fPo01nvO6bQNbyBbZzRtd++E57jZR3n3N6HeJ4zpDiP/MTio7tc8gr8/ZFN2JchQEuV1G174p+zEQyfv4Ww/lIPLuHE/DMm1x+c7rT/SJj2fdkrbrmuyOTEfzWFxQe2XNNmpyA52djzssh7X8fYsqs+v0b4IFB/owc4MenC+K5lGzgoWEBzJlRj4mvRFJUbHFa5p2vYpHLy66ntRo5899pxN+HygIE7kopFxKK+PPvTN58um6VtqG8Xbt28d233zLtySdp0qQJf2zaxJtvvMH/vv0Wf39/h/xGoxGdTseYMWNYs6ZqO47uBOI2jq5100dXpVJJYGCg3ePa3m+JRML333/Pvffei0qlokGDBqxfv95uG+vXr6dBgwZ4eHjQq1cvlixZgkQiITs7u8LXjY+PZ/jw4Wg0GrRaLQ888AApKWVR8fLD8QGeffZZevbsWfr8rl27mDt3LhKJBIlEQlxcnNPXqlOnDu+++y7jx49Hp9MxadIkAF5++WUaNmyISqUiNDSUN954A6PR9oNy8eLFzJ49m+PHj5duf/HixQDk5OQwefJk/P390Wq19O7dm+PHr/+D4eTJk/Tu3RsPDw98fHyYPHky+fn5gG2kw9ChQwGQSqXXDSCcPn2awYMHo9Vq8fT0pFu3bqXBh/LKT2G4+j6MGzcOjUZD7dq1WbduHWlpaaWfRfPmzTl82PFCde3atTRs2BB3d3f69etHQkLCddt7Kzz7DiV/z3YK9mzDlHyZ7N9+wJyVgaZHuNP81uJCLLnZpQ+32vWQqtTk791RLiN2+Sy52VXWBgD9kPvI3rGZnB2bKbmcQOri/2FMT8O7/1Cn+T0aNMaYmkLWH2sxpiZTFHWa7K0bcQ9tWJqnOOYsaT8uIG/vTqzGqgt+XKXuPojCgzspOrATU2oiuet+xJKdgbpzX6f5rcVFWPJySh+KkFAkHmqKDu0qzWNMuEDehl8ojtiH1WSq8jYAePYaQsG+HRTu24Ep5TI5q5dgzkpH3bX/jbWjVj2kHmoK9v9Vmsdw/gzFJw5hSrmMOT2F/F1/YEy8iLJeWJW1Q9NzMIUH/qLwwF+2z2PtUszZGai69Luhdrhd+TwKD9p/Hrm//0Lxsdv3edyotC27OfvWFySvrboe+H/LZ/gosrf9QfbWTZRciid54TxM6al4DxzmNL9HoyYYU1PI3LAGY2oyhZGnyNqyAY/6Zft34anj5O3/h5JL8RiTE8ncsJriuAuoGzevkjZo+w0n/59t5P+zDVPyJbJWLMSclY5njwFO81uLyh1r69RHqtKQv6dsVIimSx+kak/SvpmDISYKc2YahvORGC/FVUkb7tR2rF/zG336D6Jf+GBCatVm4uTp+Pj6s3nTeqf5t2xaj6+fPxMnTyekVm36hQ+md7+BrF29wj6jBLz1ervHtXQ6L7vnDh/aR2BQME2bt3RJu5xRtumB8fQBjKcPYMlKxbBrLZb8bNxadHGaX1Y7DHnNehSuXYA54RzW3CwsKfGYk+KqrI7lefYud85Ytdh2zuhW0TmjEEtedumj9Jyx7y/7jBIp+vHPkLtpBSYngUZX0vUbTt4/28j/eyvGpEtkLl+IKSsdz54DnbehqBBzbnbpQ1nbtl/k/VO2X3h27YtUrSF13vsYzt+e/RtgRLgfy9ansOdIDhcvF/PJgniUblJ6dax4tG5egZmsHFPpo01TT4pLLOw+mF2a5/CJPJasSmbPkds36uCqNWvW0L9/fwYMGECtWrWYMnUqfn5+bNy40Wn+gIAApk6dSp++fVGr1be5tsLdrkrCs7Nnz+aBBx7gxIkTDBo0iIcffpjMzEwA4uLiuP/++xkxYgQRERFMmTKF11577brbs1qtjBgxgszMTHbt2sXWrVuJiYlh9OjRN1ynuXPn0qlTJyZNmkRSUhJJSUmEhIRUmP/jjz+mWbNmHDlyhDfeeAMAT09PFi9ezJkzZ5g7dy4LFizg888/B2D06NG88MILNG3atHT7o0ePxmq1MnjwYJKTk9m0aRNHjhyhTZs29OnTp/Q9Ka+wsJABAwbg7e3NoUOH+O2339i2bRvTp08H4MUXXywdBXL1tZy5fPky3bt3x93dnR07dnDkyBEee+wxTDdx0f/555/TpUsXjh07xuDBg3nkkUcYN24cY8eO5ejRo9SvX59x48ZhtVrt6v/ee++xZMkS9uzZQ25uLmPGjLnh17wpMjlutepRHGkfkCmOjEAZemM/zDRd+lAcdQJzpn0vv0TpTvB73xI8ZwF+015FEVKF0Wa5HPfQBhQcP2qXXHDiCB6NmjgtUhR9BrmPL+rW7QGQ6bzw7NSN/KMHnOavcjIZipp1MUSfsEs2RJ9EUadhBYXsqTr0pOTcKac9NreNTIYiJJTicqMCiqNOoKzb6IY2oe7YG0P0yeu2Q9mwGXL/4KrrVarw8ziB241+Hh17Yqjuz+MuIJHL8ajXkPwI+2BrfsRhVGFNnZYpjDqN3NcXTdt7AJDpvNF27k7e4f1O8wOoW7RGWaMmBadPVJjnX7tyrC06E2GXXHQm4oaDYJoufR2OtaqWHTDERKF/cAo1P1lM0Ftz0Q68v+rmjd6B7TAajcScP0ur1u3s0lu1aUdUpPMpbtFRZ2jVxj5/6zbtiTkXbXcNUFxUxOTxY3h83CjenTWTCzHnrluPXX9tpU+/gS4b+ehAKkPqXxPTxbN2yaaL0ciC6jgtoghtijklAWW7Xmgefwv1o6+g7DYUZLdpSqZMbjtnOFyH3MQ5o5Pzc4Z24P1Y8nMp3LejgpIuIpPjVrsexacj7JKLT0fgfqP7Rbe+FEcet9svPFq1x3AhGp+HphDy2RKCZ3+JblAV7t9AoJ8bPl4KjpzKK00zmqycjM6nSYMb/yEd3l3PrgNZGEqcj1i4nYxGI+fPnaNNmzZ26a3btCHyTNWOTBEEZ256CsOGDRvQaOznCL388sulP7LB1tv/4IMPAvD+++/z1VdfcfDgQQYMGMD//vc/GjVqxMcf2+ZwNWrUiFOnTvHee+9V+Jrbtm3jxIkTxMbGlv7o//HHH2natCmHDh2iffv2ldZbp9Ph5uaGSqUiMDCw0vy9e/fmxRdftEt7/fXXS/+uU6cOL7zwAsuXL+ell17Cw8MDjUaDXC632/6OHTs4efIkqamppVM9PvnkE9auXcvKlSuZPHmyw2v//PPPFBUVsXTp0tKo4ddff83QoUP58MMPCQgIKF2o8HptmTdvHjqdjmXLlpWubdCw4Y39cLhq0KBBTJliG/r+5ptvMn/+fNq3b8+oUaMA22ffqVMnUlJSSutiNBr5+uuvuece24XvkiVLaNy4MQcPHqRDhw439fqVkWk8kchkmMuNDjDn5uCu9aq0vFTrjXvTNmT88LldujH5MhlLvsJ4OR6phweevYcQMON9kt99HlOq84DNrZB7am3tyM6ySzdnZyHzch4xLzp7hqQvPyT4udeQKtyQyOXkHdpLyg/zXF6/GyFV2z4LS759ZN6cn4PSU1d5eU8vlGEtyf65eoeeS9W2z8KSZ98OS14O0hv6Tnnh3qQVmUu+dHhO4u5B0LvfIpHLwWIha8VCDNEVz4G/FVfbYXbSDpn2Bj4PrRfKsFZk/fTfmgpwJ5JpdUhkMkzl9m9TdhZyb+dTx4qiTnP5s/epOeON0v0798Aekr77yi6fVKWm4Q8rkCoUWC0Wkv73BQXHXT+3+OqxtvxILHNuDjJt5WvwyHTeeDRrQ/r3n9mly/0CcA9rTsGB3aR++Q7ygCD0D05GIpWSs3FFBVv79+7EduTl5mCxWPAqdy7w8vImOyvLaZmsrExaO8lvNpvJzc1Br/ehRkgtnnruFWrXqUtRYSEb1q9i5oyn+Pyr7wmuUdNhmwf3/0NBfj69+zofqeEKEg81EqkMa2GeXbq1MA+JytNpGanOB1lwXawmI4W/L0Lqoca9931I3FUUb11eZXUtff2r36ly0yssedk3cc5oTebiuXbpbqGNUHXqTeoHM1xYW+dkGm0F11LZyHQ3ul+0JW3Bp3bpCt9A5GH+5O/fRcrct1H4B6N/eDJIZeRsqJrPRq+z/bTJyrUfdZmVa8Tf58amIzcKVVE3xIPPf6i60bM3Izc313YMKLfembeXF1kVHAOEcu7SxQyry00HEHr16sX8+fPt0srPnW/RomxRH7VajaenJ6mptqFX0dHRDj/4K/tRGRkZSUhIiN2IgSZNmuDl5UVkZOQNBRBuVrt27RzSVq5cyRdffMH58+fJz8/HZDKh1Wqvu50jR46Qn5+Pj4+PXXpRUVGFUwkiIyNp2bKl3ZCjLl26YLFYiI6OJiDAcb6jMxEREXTr1u2WFka89rO8+rrNmzd3SEtNTS0NIMjlcrv3LywsrPSzcvZZGwwGDAaDfZrZjFImu/GKXjMCAgAJgNVZTjuaTr2wFBVQGGG/0FJJ7FlKYst6QAwxUQS++gmePQeRtWLhjdfrJlnL11kiqbAZbjVr4T9hGhkrf6Yg4jBybz1+j0wicPIzJM//zHmh28HZR3EDPNp3x1pcSPEp18/d/lfKf6dsiZUWU9/TE0tRAUUnHBfvshqKSflgBlKlO8pGzfG6dxzm9JSqndvqUGWJ3Yihiqja98BSVEjxyUNVUq3/lxyOU5IKvmegDKlN4KTppC3/kfyjh5Dr9QSOn0LwE8+R+PUnpfksRYVceHYSUg8P1C3aEPjYNEpSkig8VTXrajj5OjlLdaDu1PvKsbbcCCmJBHNeDhk/fgNWCyXxMch0erThI6okgHDVHdmOcr3+VqtDUrns5fJfaZ/kylG5UVgTGoWVjXALa9KMF56ezKbfV/P41Kcpb9ufm2jT7h70Pr7/tgU3ofxnIXGSdvUpW3uKNv8MJcVYgOJd6/AY8ijFO1aDueqn71VYrxs41qo7Xj1nlB1rJUp39OOeJvvX/2EpyLtOaRe7iWPUtTSde2MpLKDwmJP9IjeHjKVX9ouLMci8vNGG3+uyAEKvTt48M74s4PXGZxdsfzhci1R8PVVeeHc9sQlFRF8odEkdXcVhn7Zaq240kCBcx00HENRqNfXr179unvI/WCUSCRaLbQiQsy97ZRezFe0g16ZLpVKH7RhvYc53+flC+/fvZ8yYMcyePZvw8PDSnv1PP/20gi3YWCwWgoKC2Llzp8NzFd3u8HoHhJs5UHh4eNxw3opc+1lefW1naVc/3/LplaUBzJkzh9mzZ9ulPds2jOfaNa60fub8PKxms0OEXOapw5xb+Rw1dZfeFBzYBeZKpnVYrZRcPI/cP6jSbf4bprxcrGYzci/7YJxM54U5x3l02efeMRRFnyZz/W8AGOJjsRiKqf3O56T9uhhztvMpMlXFUmD7LKTlRhtINTqHXnBnVB16Unj4bzCbq6qKN8RSYPssyvccST11WG7gO6Xq2Mt2ZwVn7bBaMaenYAaMly+iCKiBZ/8RVRJAuNoOmVbHtUdCqacWy5VV/K9HdU8Pio5U/+dxNzDn5tj273KjDeQ686a8jQABAABJREFUL4dRCVf53vcQhZGnyVhju8g2XLxAUnExdT/4ktSff8CUdWX/tlopSU4EoDg2BmVILfzuf4iLLg4glB5ry+0XtmNtdqXlNV36ULB/p8Ox1pyThdVsBmvZOcSYfAm5Tg8yeeXH5pt0J7bDU6tDKpWSnWV/TM/JyUJXwQg1b289WeXzZ2cjk8nwrKDjQyqVUr9hGImJlx2eS01N5kTEUV56dbaTkq5jLSrAajEjUdnXUaLSYC3Md1rGUpCLND8HSspW2LdkpiCRSG3H7eyqnYJlyb967vOyS5dqdA4j2ZxRdexN4cHddt8RuW8gcl9/fKa8UpbxyjVUjbnLSH7nGczpjnfH+LfM+bnXuZbKrrS8pmtf8m90v0i6ZLvWcdH+vf9YDtExBaX/KxS2nmZvnYLMnLLte2nlZOVW/npKNwk97/Fm6WrXjzb9t7RaLVKplKxyU5+zc3LErdNvlAi0uNRtH88RFhbGoUP2PVrOFuG7VpMmTYiPj7dbiO/MmTPk5OTQuLHtR6afn5/DWgDlb6fo5uaG+V9eDO/Zs4fatWvz2muv0a5dOxo0aMDFixcr3X6bNm1ITk5GLpdTv359u4evr/MofpMmTYiIiKCgoOyAuGfPHqRS6U1NQWjRogV///33LQVS/g2TyWT3mUZHR5OdnU1YmPN5dDNnziQnJ8fuMa31DbbTbKIkPgb3xvYLOrk3bonhgvPbJ12lbNgUhX8wBdcshHU9ipp1K/wxf8tMJoovnEPdwn5+m7pFG4qinf+4lLop7U7KANYrgZxqOU6azRgvxaJsaL+Am1vDZhjjzlZQ6Eqeeo2R+wVSdHBnFVbwBpnNGBMu4B5mf3s090YtMFRwO8qrlPWboPAPouBG56tKJEjkVTRPt/TzsG+HsmFzSm7o8wii8JpFIIV/z2oyURRzFk3Ltnbp6lZtKYw67bSMVFnx/n39HbyKvlNXjrUeTVrZJbs3boWhglvVXaVs2AxFQDD5/2xzeM5wPgqFX5BdmxT+wZiyM10ePADuyHYoFArq1W/I8XJ31jh+7AhhjZ3fwq1RWBOOH7OfyhJx7DD1GjRCLnfed2S1Wom7cN5hIUWw3fJRp/OiXYdO/7IVN8hixpJ6CXkt+2sAea2GFS6KaE6MRaLWgqJseLrU2w+rxXJDP+Bvmdnk/JwRdgPnjAbOzxnGlMskv/c8KR/MKH0UnzyM4dxpUj6Y4fRuQLfahpKLMbg3KXct1aQVxZXsF+6NruwXfzsuXlt8PhKFf6DdfiEPcO3+XVRsITG1pPRx8XIxGdlG2jQrm/Iil0lo3kjDmXMF19mSTfcO3ijkErbv/e9MDVAoFNRv0IBjx47ZpR87epTGTZyvkyUIVemmAwgGg4Hk5GS7R3r6jUd3p0yZQlRUFC+//DJnz55lxYoVpXcrqKiHum/fvrRo0YKHH36Yo0ePcvDgQcaNG0ePHj1Kh8r37t2bw4cPs3TpUs6dO8dbb73FqVP2iwvVqVOHAwcOEBcXR3p6ukOv+fXUr1+f+Ph4li1bRkxMDF9++aXDbVHq1KlDbGwsERERpKenYzAY6Nu3L506dWLEiBFs2bKFuLg49u7dy+uvv15h4OThhx/G3d2dRx99lFOnTvHXX3/x1FNP8cgjj9zw9AWA6dOnly5gePjwYc6dO8ePP/5IdPT1T2i3SqFQ8NRTT3HgwAGOHj3KhAkT6NixY4VTVZRKJVqt1u5xM9MX8rb9jqZLH9SdeyMPrIHXqAnIvH3J3/0nALoRD+Mz3nE4pqZzHwwXzmJMjHd4Tjv4AdybtELmG4CiZh30jzyJW0gd8v/ecsP1ulmZG1bh1WcAul7huNUIwf/RqSh8/cn6cwMAfg89RtD0srmQ+Uf249mhK179h6DwD8SjURMCJkyj6FxUWe+kXI6yTijKOqEgVyD38UVZJxRFYHCVtKFg9yZU9/TCo0MP5P7BeA4bi8zbl8J9tiCN56DR6B58wqGcR4eelFw8hyn5kuNGZTLkwbWRB9dGIpMj0+mRB9dG5nPj+8LNyvtrA+pOfVB17IU8oAa6kY8i0/tS8I/tAkk79EG8H3nSoZyqU28MsWcxJTnOm/TsNwJlo+bIfPyRBwSj6TUYVYfuFBz6u8rakb9zI6qOvfDo0BO5fzDaEY/YPo+9th9AnoPH4PWQ4+eh6tiLkrgb/Ty8bZ+Hb9V9HjdKplahbRmGtqUtWKmqWxNtyzDcQ6pm5NDNyFj3G179BuHVZwBuNWsROHEaCt8Asjb/DoD/I49T49my3sa8Q/vQduyG94BhKAKCbLdtnTSdwrORmDJtPx5873sQdcu2KAKCcKsRgs+w+/Hq1Z/sXY4/cF0hd+s6NF37ou7SB3lgTbwfeAy53pe8Xbbjote9Y/GZ8IxDOU3XvhguRDs91ubt2oxU44n36MeR+wfj0bwtukH3k7dzU5W04U5tx7B7R7Htz01s+3MTCfEX+eG7eaSnpRA+yHaXnh8XL2Dup++X5g8fNIy01BR+WDCPhPiLbPtzE9v/3MSIkQ+U5ln+yxKOHTlIclIisTHn+XruR8ReOE94uTuDWCwWdmzdTM8+4chuZmrhv2Q4ugtFs3tQNOmA1NsfZffhSD29KTlhu22msstg3Ps/WJrfGH0Ua3EBHv3GINUHIKsRirLbUIynD9626Qt5Ozag7uzknPG37TpEO+whvB+Z7lBO1en/2Lvv8Kaq/4Hj76ym2Wm62Us2yJa995AlQ1BElCGKA3HgRFFxgKJ+RRRlKQjKBpGh7CHI3quMFrpX0jZNmvX7I5CSNgWUFpTfeT1PHujJOTfnJLk3957zOed2CPyb4XTgTIjze7hzrXhsud68JdC5Zt64El2rTmhbdEARXYaQQU9494st6wAw9nuUsBHPFyqnbdkRe0wR+8WWdUi1ekyDn0Qe6d0vjD0GkLW55PZvgBXrUxjcM5LmDQ2ULx3MhJHlsOe52fxnfqfAS6PK8fiAwr8NXVub2HXATFZO4QHHYKWUSuVUVCrnjfCNCg+iUjkV4aaSX7Czb9++rF+/ng3r1xMbG8u333xDSkoK3bt3B2DOnDlMnTrVr0xMTAwxMTHk2myYzWZiYmKILTD4KQj/xN+ewrBu3Tqio/13uGrVqnHq1I17KK+pWLEiS5Ys4cUXX/TdGeH111/nqaee8i0yWJBEImHFihWMGzeO1q1bI5VK6dq1K19+mb+YVJcuXXjzzTd5+eWXsdlsjBgxgmHDhnH0aP4CZRMmTOCxxx6jZs2a5ObmcuHCBSpUqHBL9e7duzcvvPACzzzzDHa7nR49evDmm28yadIkX57+/fuzbNky2rVrR2ZmJnPmzGH48OGsXbuW119/nREjRpCSkkJUVBStW7cusjNArVazfv16nnvuORo3boxaraZ///58+unfm9ceGhrKpk2beOmll2jTpg0ymYx69erRokXgWyEVF7VazSuvvMKQIUO4fPkyLVu2ZPbs2SX2etb9O5FqdRh6DESmD8ERH0vK/973rQQsM4QgM/lHe0iC1agaNCtyPQOpWoNp6FPI9EbcuVby4s6TNPUN8i6eK7F2ZO3aSpJWT9hDQ5GFmMiLu0TcB2/4bt0kDzGhCMu/1695y0akwWpCuj5IxLBRuHJysB47RMqC73x5FCGhVPxkpu/v0AcHEPrgAKzHDxM7qfgXZrId+hOLWou2Uz9keiPOhMtkfPexb2Vpqd6IzOi/HogkWIWqbhPMK+YH2iQyfQjhL07x/a1t1xNtu57Yz50g/ev3ir0NALkHdpOp0aHv2t/7nUqII/XrKb52yAwhyEMKfqdUqOo9gHnp3IDblAQpMQ58ErkxFI8jD0fSFdLnf0nugcL3/S4utkN/Ytbo0HXxfh6OhDjSv/0ovx16I7IA7Qiu2wTL8qI/j4iXPvT9rW3fC237XtjPnSDtq8kl1pZbYWhYm2Z//OD7u+bU1wCIm7+MI09MvFvVAsCyYwsynZ7wQcOQm0zYL10k9t2JOFK8YcgF9+/MTeuRqtSYevQhasQYXDnZ5Bw5SNK8Wb480mAV0WOeQxEajjvPTt6VOC5/9gGWHVtKpA3WfTtJ1+gx9hiEzBBCXnwsyV9Ovu5Ya0JuCvcrI1GpUTdoRsai7wJtEldGKknTJ2EaOALd29NxZqZj+WMNlnXLSqQN/9V2tGzdniyLhZ9/mk9GejrlylfgjXc+JCLCu/ZQRnoaKSn5t/mLjIrmjXemMGfWDH5bsxJTaChPjB5HsxZtfHlysrP5+stPychIR63RUKlyFd776HOqVvOfPnjk0H5SUpLo0Dnw7fyKm/PMIWzBapRNOyNR63GnJWBdOQtPlvfiT6LRIb1+wUtHHtZl3xDcti+ah1/AY8vBceYw9l2/3ZH6AuQe2EWmRou+20P5vxkzPrjuWBuCPNB5SL0HMC+Zc8fqeSPWv3aQrtFh7DUImcFEXvwlkj5/17dfyA0hyAusf+HdL5qTvmhWoE1694tP38Y06AlKT/ocZ0Yalt9XY/6t5PZvgJ/XJhMUJOWZYWXQqWWcOm9l4icx5NryBw7DTUEUHEcsHamkdjUtEz8OfK5XtaKaTybmT+MeM6Q0ABu2pzPtu8IdKMWpTZs2ZGVlsXDhQtLT06lQoQLvvPuu71oiIz2dlGT/W32Oeya/0+rc2bNs2bKFiIgI5s6bV6J1/VeSikUUi5PEcyuraZWw999/n5kzZ/pNURD+f4sd0+9uV6FYWNMCz9n8LzGUuxMLZpU8V14JhEPfYVJ5yY/+3QkH/3fg5pn+Ayp0L323q3DbtJE3XghYuHNyXp5xt6tQLMqsmXrzTP8BlnP//XNSpy3vblehWIx2lOzaG3fCjLfvjWNt5UqV7nYV/hHrzqV37bXVLfrftdcuKX87AqE4zJgxg8aNGxMaGsrOnTv55JNPeOaZwqFdgiAIgiAIgiAIgvBPecQiisXqrnQgnD17lvfee4/09HTKlSvHiy++yMSJdze8VBAEQRAEQRAEQRCEot2VDoTPPvuMzz777G68tCAIgiAIgiAIgvD/hUSsgVCcxLspCIIgCIIgCIIgCMJNiQ4EQRAEQRAEQRAEQRBu6q5MYRAEQRAEQRAEQRCEkuYRUxiKlXg3BUEQBEEQBEEQBEG4KRGBIAiCIAiCIAiCINybxG0ci5WIQBAEQRAEQRAEQRAE4aZEB4IgCIIgCIIgCIIgCDclpjAIgiAIgiAIgiAI9ySxiGLxEu+mIAiCIAiCIAiCIAg3JSIQBEEQBEEQBEEQhHuTWESxWIkIBEEQBEEQBEEQBEEQbkpEIAiCIAiCIAiCIAj3JrEGQrESHQjCv5JCE3y3q1AspGbr3a7CbbOmmO92FYpFVKv6d7sKt818IuZuV6FYVOhe+m5XoVhcXHvlblfhtjV9s8rdrkLxcLvvdg1uW4pLdberUDzugc8CIEj33/88nLa8u12FYiG9By7+QrL/+78XXpXudgWEf4H//h4pCIIgCIIgCIIgCEKJExEIgiAIgiAIgiAIwj3JIxZRLFYiAkEQBEEQBEEQBEEQhJsSEQiCIAiCIAiCIAjCvekeWEfj30S8m4IgCIIgCIIgCIIg3JToQBAEQRAEQRAEQRAE4abEFAZBEARBEARBEAThnuRBLKJYnEQEgiAIgiAIgiAIgiAINyUiEARBEARBEARBEIR7kkcsolisxLspCIIgCIIgCIIgCMJNiQgEQRAEQRAEQRAE4d4kIhCKlXg3BUEQBEEQBEEQBEG4KdGBIAiCIAiCIAiCIAjCTd3THQht27bl+eefL9HXOHXqFE2bNiU4OJh69eoVmfb/xaRJk4iMjEQikbBixYq7XR1BEARBEARBEP4f80gkd+1xL/pXr4Egucmb/thjjzF37tw7U5kivP3222g0Gk6fPo1Wqy0y7d/s4sWLVKxYsVD6b7/9RteuXX1/b926lfHjx3P8+HFKlSrFyy+/zJgxY3zPnzx5knfeeYfly5fTtGlTQkJCqFChAs8//3yJd+Som3dE07YnMr0RZ+IVzCvn47hwOmBew+DRqBu3KZTuSLxM6icvAyCPLI226wAUZSoiN4VjXjEf6/Z1JdqGQAydemLq+RAyo4m8y5dImT+T3NPHi8yva9EOU68BKKJK4bZayTm8j5QFs3BnZ92xOus79MDYvR8ygwnHlVhSF3yL7UzgOoePfAF9q46F0vMuXyLutbHeP2QyQnoORNeyA7KQUByJl0lbPJfco/tLshn8fPAc8/86TWqOjUpheia0q0eDMuEB8+6LTWbUz1sLpS99vAsVQ/UArDp2kUnr/iqUZ/fz/VDKZcVb+euoW3RC2867bzgSL2NZMZ+884H3DePDY1A3CbxvpHz0knd7TdujatwKRVQZ73OXL2D5dTGO2JgSa0NItwcJ6zsIeUgo9tiLJH7/FdYTR4vMb2jTgbC+gwkqVRpXTg7ZB/8iac5MXFkWAHRNWxE+YAhBUaWRyGXY46+QtvIXzFs2llgbbpWpZSMqvfgEhga1CS4Vwb7+Y0la9cfdrpaPslEbVM26INUZcCXHk7NhMc7Yc0UXkMlRte6Jss4DSLV63JZMcnesxX5opy+LRKlC3b4PQdUbIFGpcWWkYt34C45zx0quHY3bomreBanO6G3HukU4Y8/euB1teqGs2/RqOzLI3f4r9oPXtSNYhbp9X4JqNECi0njbseFnHGeL/q7eiMfjYdlP37Fpw0pysrOoUrUmw8e8RJlylW5Ybu+uTfyy4FuSE64QEV2agY+MoXGztn55Nq5dwq/LFpCZkUbpchV59MkXqF6rnu/5oQ82Dbjth4c/Q89+jwCQmZHGwjlfcuzQXmy5VqJLl+PBAcOp+o9a609xfwuUjdoj0ehxpyVi27Ic15XzRReQyVA27YKiRiMkaj2e7EzsezbiOL6nGGpza1RNO6Bu1R2pzoAz+QrZaxbguHjmBnWWo+nQh+B6zZHqDLjN6eRsXo1t/zbv81IZ6rY9UTVoiVQfgjM1kZx1i8k788++T7dC17Yb+i59kRtDyIuPJX3R99jPngiYN+zxZ9G26FAoPe9KLPFvjwMg6qX3CK5Wp1Ae65F9JH8xuXgrX8DQPpF0a2NCq5Fx+ryVr+ZfITbeXmT+j16tRN3qhc/Z9x628PZnFwGoXVXDQ93DqVJeRWiIgne/uMjuA5aSagJL129mwcr1pGVmUrFMKZ5/fDD1agTew7bs2c+y9Vs4ezGOPKeTSmVK8cTAB2lar3bA/Bt37uWt6d/SunE9Pnr5mRJrg3Bv+ld3ICQkJPj+v3jxYt566y1On84/8VWpVHejWn5iYmLo0aMH5cuXv2Hav4HD4UChUBT5/O+//06tWrV8f5tMJt//L1y4QPfu3Rk5ciQ//vgjO3fuZOzYsYSHh9O/f3/A226A3r1737TzpzgF12uKvvcwzMtm47hwBnWzDphGvkLKxy/hzkwrlN+yYj5Zvy7KT5DKCH9xCrbD+ScakiAlrrRkbIf3oO/9yJ1oRiHapq2JGDaapNlfYTt9HEPH7pR+9T0uThiFMy2lUP7garWIGjuBlPnfkn3gT+SmMCKfGEfUqOeJ/7Rkf6iv0TzQirChI0mZNwPb2ZPo23UlesI7xE18KmCd0378hvSf5+YnSKWUff9/ZP+1w5dk6j8MXfO2pMz+kryEy6jrNCDqude5MnkCeZducEJ5G9afimPq5kNM7NiA+0uHsfTwecYt3c6Sx7sSrVcXWW75iK5olPn7WIhK6fe8NkjOsie6+aWVZOdBcL2mGPoMw7xkNnkXTqNu3hHTqFdJ+XACrgD7hnn5PCxrfspPkMqIeOlDbIf+9CUFValB7oFdmC+cAacDbftehI6ZSPJHL+E2ZxR7G/Qt2xL1xNMkfPM51pPHMHXpRbm3PiTmmcdxpCYXyq+uUZvSz71K4uwZZO3djSI0jOinXqDUMxOIm/IWAK5sCym/LMB+ORaP04muUVNKP/syTnMGOQf3FXsb/g6ZRo3lyGkuz1tGw1/+d1frUlBQzUZougwiZ+1CnHHnUDZojX7Is2TOmITbkh6wjO6hUUg0enJWz8eVnoxUowPpdcGPUhn6R17Abc0ia8lM3JYMpHoTnjxbybWjVmM0XQeT8+sCnLHnUDZqjf6R58j86i3c5iLaMWA0Eq2enFVzr7ZD798OmQz9o+Nx52SR9fNM3JZ0pAYTHvs/b8eaZT+wduVPjHnuTaJKl2PFz3OY8tazTJ2xGJVaE7DM2VNH+fLjN3lo6CgaN2vDX7u38uXHr/PWh99QpZr3QmL39o388N10Hh/zElVr1GXTuhV8/M4LfPzVT4SFRwHw1bxf/bZ7eP9uZn35Pk2at/Olff3pJKzWHF584xN0eiM7t67ny0/eoEnfplQLM/zjdsur1ie4bV9sfyzBFX8BRd3mqPuOJnveFDxZmQHLqHoMR6LRkbthEe7MVCRqLZI7uGiass4DaHsMJWvlPByXzqJ6oB2G4RNI/2wibnPhYy2AYcgzSLV6LEu/x5WWhFTr/53SdO5PcL3mZC2fjTM5AWXVOhgeeY6MryfjTLhU7G1QN26JafATpC34Bvu5k+hadyHyube48tYzuNJTC+VPW/QdGUvn5yfIZJR6ezrW/fmdaskzPkQiy7/UkGp1lHr7c6z7dlKSBnQPp1+XMKZ9F8eVRDsPPxjJBy9VYuTE0+Ta3AHLTP7yEgp5/rmrTiNjxuSqbP/L7EsLVko5H5vLhu3pvDmuQom24fede5k+ZxEvjRxK3WpVWL5xG+Pf/5yFn71LVHhoofwHT5ylyf01GTOkHzqNmjWbd/LSh1/y3ZTXqVaxnF/ehJQ0vpz/C/Vq3Feibfg3EbdxLF7/6nczKirK9zAYDEgkEt/fCoWCMWPGUKZMGdRqNXXq1OGnn3664fbWrVuHwWBg/nzvAe/KlSsMGjSIkJAQQkND6d27NxcvXvTld7vdvPvuu5QpUwalUkm9evVYty5/FFoikbB//37effddJBIJkyZNCpgWiN1u59lnnyUiIoLg4GBatmzJX3/95XvdMmXKMHPmTL8yBw4cQCKRcP6896LJbDYzatQoIiIi0Ov1tG/fnsOHD/vyT5o0iXr16jF79mwqVaqEUqnE4/EU+f6Ehob6vedBQUG+52bOnEm5cuWYPn06NWrU4Mknn2TEiBFMnTrV91q9evUCQCqVIpFIaNu2LZcuXeKFF15AIpGUWKeCpnV3rHu3kLtnC87keCwrf8CdmYameeGRbQCPLRd3ltn3UJSthESlIfev/BFkR9x5stYsxHZoNx6ns0TqfTMhPfph3rwey+Z15MXHkTL/GxxpKRg79QyYX1WlOo6UJDLXr8SZkoTt9HHMf6xFWak4xoNujbFrXyxbN5C1dQOO+DjSFszCmZ6Kvn33gPnduVZc5gzfQ1nxPqRqLVnb8keCdS3akbH6Z6xH9uFMScSyaS25Rw9g7NqvxNqxYN8Z+tSpSN+6lagUquel9vWI1KlZcujGo+wmtZIwTbDvIZMW+M5LJH7Ph2mCS6wNANq2PbDu2Yx1z2bvvrFiPq7MNNQtOgXMX3DfCLq6b1j35u8bmT9+hXXnRpzxl3Amx5O5+FuQSFDeF3iU43aF9h5A5u+/kblxLXmXY0n8/iucqcmEdHswYH5VtZo4kpNIX7McR3Ii1pPHyFi/BlWV/P3AeuwwWX/uIO9yLI7EeNLXLMN28TyaGoVHyu60lPXbOPP2dBJX3P1oiIKCm3XCfnAH9oM7cKUmYt3wMy5zBsGNCketACgq10JevipZC7/AceEkbnMazviLOC/nd/wp67dAotKQtXgGzrgY3OZ0nHHncCVdLtl2HNiB/cB2XKkJWNctvtqOtoHbUaUW8grVyFrwBY7zJ3FnpuG8cgFnXP7xQFm/pbcdi77CGXfO247Yf94Oj8fDulWL6TNwOI2bt6Ns+cqMef4t8uw2dm3bUGS531Ytona9xvQe8BilylSg94DHqFW3MetWLc7Ps/In2nbsRbvOvSldtiKPjnyB0LAIfl+7zJfHGBLq99i/Zxs16zQkIqq0L8/Z08fo3HMAlavWIiKqNH0HjUCj0XIq9fZGZJUN2+I4tgfHsT9xpydh37Icd1YmQfe3DJhfVqE68jJVsC7/FlfsGTyWdNyJsbgSLt5WPf4Odauu5O7bim3fVlwp8WSvWYDbnI6qafuA+YOq1kFRsRqZc6fhiDmOOzMV5+XzftE8wfVbYN2ymrzTR3BnpJC7ZxN5Z46ibtU14DZvl6FTb7J2/E729o04Ei6Tvvh7nBmp6Np2C5jfk2vFZcn0PZTlq3h/v3fkR0y5c7L98qhq1sOTZyenhDsQ+nQOY9HqZHbtt3Dpip1ps+JQKqW0bWosskx2josMs9P3aFBbhz3Pzfa9mb48+45mMX9ZErv2l1zUwTU/rdlIr/YtebBDayqUKcULjw8mIiyEZRu2BMz/wuODeaR3N2pWqUjZ6EieGtKPstGR7Nh32C+fy+Vm0uezeHLgg5SKCBxVKQg386/uQLgRm81Gw4YNWbNmDceOHWPUqFE8+uij7NkTOFxt0aJFDBw4kPnz5zNs2DCsVivt2rVDq9Wybds2duzYgVarpWvXruTl5QHw+eefM23aNKZOncqRI0fo0qULDz74IGfPesMcExISqFWrFi+++CIJCQlMmDAhYFogL7/8MkuXLmXevHkcOHCAKlWq0KVLF9LT05FKpQwePJgFCxb4lVm4cCHNmjWjUqVKeDweevToQWJiImvXrmX//v00aNCADh06kJ6eP4Jy7tw5fv75Z5YuXcqhQ4du+J4++OCDRERE0KJFC5YsWeL33O7du+ncubNfWpcuXdi3bx8Oh4MJEyYwZ84c3/uSkJDAsmXLKFOmDO+++64vrdjJZCjKVMR++ohfsv30URQVbu3CWd2kLXlnj+HKKNzDftfI5ARXvA/rkQN+ydYjBwiuWiNgkdwzJ5CbwtDUa+zdhMGI9oGW5BzcW+LV9b6gHGWFKuQeO+iXbD16gOD7Ate5IH2bzuQeP+QXrSBRKPA4HH753Hl5BFeteft1DsDhcnMyKYOmFaL80ptViORw/I2/Iw/P30jnr1cz+uet/BVbeHQ8N89J929+pevMNTy7bAenkop/xN6nyH3jCEG3um80bYv9JvuGJEiJRCrHbc2+reoG3LZcjqpyVbIP+UcFZB/ah7p6rYBlrKeOIw8LQ9vwAQBkhhD0zVuTte/PgPkBNHXroyxdhpzjR4rM8/+eVIY8uhyOGP9wZsf5E8jLVg5YJKjq/TjjL6Fq0YWQ5z/C+PRk1J0eArnCP8/lGDTdHiZk/FQMY95G1bIblFQUm0yGvFR5HDH+06ocMceLbke1ejjjL6Jq0ZWQ8Z9gHPce6s4D/NtRrR7Oy+fR9BhCyIRPMYx9B1Wr7v+4HSlJ8WRmpFGn3gO+NIUiiOq16nP2ZNEh7OdOHaNu/Qf80uo2eIAzp7xlnA4HF86dpk6BPHXqP8DZU4G3a85I49C+nbTp1MsvvVqN+/lz++9kZ5lxu93s3rYRh8NBw2hTwO3cEqkMaWQZnJdO+SU7L51CVqpCwCKKSrVxJcWibNQe7ahJaB5/DWXrB/0+nxIlkyEvVYG8s/5TbvLOHkVRLvAIb1CNBjivXETdugehr07H9OLHaLsN9quzRK7A4/T/7fM482753ObvtUFOUPnK2I4f8ku2HT9EcOXqt7QJbauO2E4expVeONrQl6dlR3L2bseTV/RUgtsVFR6EyajgwLH8qZsOp4ejp7KpWaXoCMKCOrcKYeueTOx5RQ+8lRSHw8np85docr//79wDdWtx9PStTRd0u91Yc23otf7RSrOXrMao1/Fgh1bFVl/h/59/9RSGGyldurTfxfm4ceNYt24dv/zyCw884P/DOGPGDF577TVWrlxJu3be8LtFixYhlUr57rvvfCPjc+bMwWg0smXLFjp37szUqVN55ZVXGDx4MAAfffQRmzdvZvr06Xz11VdERUUhl8vRarVERXkvNrRabaG0gnJycvj666+ZO3cu3bp5e3ZnzZrFxo0b+f7773nppZcYOnQon376KZcuXaJ8+fK43W4WLVrEa6+9BsDmzZs5evQoycnJKJXeMOmpU6eyYsUKlixZwqhRowDIy8vjhx9+IDy86F5GrVbLp59+SosWLZBKpaxatYpBgwYxb948HnnEG76fmJhIZGSkX7nIyEicTiepqalER0djNBoB/Notk8nQ6XRFvhe3S6rRIZHJcGeb/dJd2WaUupuHUEp1RpTV7ydzwb8rVFim1yORyXAWCAl3mTOQGwKfnNnOniTxfx8T/exEJIogJHI52ft2kzx3xp2oMjLdtTpn+qW7LJnIDCE3L28IQV23EUlff+yXbj16AGPXPthOH8ORnICq5v1oGjyARFoyof+ZuXZcHg+hav/pByZ1MGk5gcORw7Qq3ujckBqRIeS53Kw9fokxP2/l20FtaVjWu+9VMOmY1K0x94UZyM5z8NP+s4z4aTOLHutEuRBdsbdDqvF+Hq4s/33DnWVGpr+FfUNvRFm9Hhk/3njf0Pd8GJc5HfuZ4p+vLtMbvN+pTP/9wJmZgTwk8H6Qe+o4Vz79gDIvvYn06n5g2bOThG+/9MsnVWuoOvtnpAoFHrebhJnTyTlcsutq/JdJ1FokUhnuHP+RN0+OxRvOH4A0JAxFuSrgdJD189dI1Fo03YcgCdaQs3oeALKQcKQVq2M/ugfLT18gM0Wi6fYwSKXkbvs14HZLrB3awPuFNCTceyHodJC1eIa3HT2GIlFpyFk592o7wrztOPInlgWfIwuNQNN9qLcdW9f87XpmZnjD3g1G/++5wWgiNSWx6HKZaegLlNEbTZivbi/Lkonb7Sq8XYMJc4BpTQDbNq0lWKUptI7CuJff48uP32D00C7IZDKClMG8MPFDysT/8/WCJCoNEqkMT47/uj0eaxYSdRHfM2MostKV8LicWFfNRqrSENx+AJJgDbYNN45MLQ5SdeDzEHe2BWkR5yEyUziK8vfhcTow//gFUo0WXe/HkKi1ZC39DsAbbdCyK44Lp3GlJ6OoXBNljQb+U2eKiUx79ffCkumX/nd+v1W1G5Iya1qReYIq3kdQmQqkzivZ860Qg/fSJsPiHz2aaXESERoUqEghVSuqqFhWxfTZJRcJdSOZWdm43G5MRv/vfIhRT3qmuYhS/hau3kCu3U6H5o18aYdPnWX1ph3M/+StYq3vf8I9upjh3fKf7UBwuVx8+OGHLF68mCtXrmC327Hb7Wg0/j1tS5cuJSkpiR07dtCkSRNf+v79+zl37hw6nf+Ju81mIyYmBovFQnx8PC1atPB7vkWLFn7TBP6JmJgYHA6H37YVCgVNmjTh5MmTANSvX5/q1avz008/8eqrr7J161aSk5MZOHCgr/7Z2dmEhvrPg8rNzfWtRQBQvnz5G3YeAISFhfHCCy/4/m7UqBEZGRl8/PHHvg4EKLyo5bXpELc7NeHaZ+eX5nT9vbnhBTqIb7VGqsat8dis2I7d3XnPt0wigSKmoQSVLkfE8KdIW7aQnCP7kRtNhA99ksgnniXp28/uYCUL1q/oOl9P16ojbms2Ofv9R4pTf/yGiBHPUvajmeABR3ICWdt/Rxdg8cViVfD7TtHf9QomHRVM+ceS+0uFkphl5Yd9p30dCHVLhVK3VP7+Wq90GEPmb2TRgXO83KF+8df/+or7kdxwKtM16sZtcOdasR0tvPDjNdr2vVDVb07qV5OhwEhZsSpY3xvsB8qy5Yka+Qwpi38g+8BfyE0mooaPptRTLxD/v6m+fO5cK+efH4lUpUJTtwFRI8aSl5SA9djtHd///7nB0VYiBY+H7OXf47HnAmDd8AvaAaPJ+W2h9zsjkeDOySJnzQ/g8eBKiEWqM6Bq1qVEOhB8Cn1/JATYWa4+5f2+ZS/9Lr8d639GO3AMOb8uuK4dFnJWz7/ajktIdUZUzbvcUgfC+ovJfDgwf32Bl96alv/a11cbD5Kb/MIVet7jKXTsKvR77j3CBdze1t/X0KJNZ4KC/DtVf/lxJjk5FiZO/hKd3si+P7fyxcevc3+3elQJLe5O0Rt8Plefy137A+TZcAO2rStQ9RqObdOSkj023UwRx6lr779l0de+71T2rz+hH/IMWSvneTur1vyIvu8ITOM/8n6n0pPJ3b8dVcMSHDn+G8fa62mbt8dtzcF6sOhFK3UtO5J3+SJ5F26wWOk/0K6ZkXGP5U+tubbgYaBd/FZjCbq0NnEhLpczF3KLpY7/VKB9+VYuhDfs2MP3v6zio5efwWTwdkLk5Np454vvmThmGEZ98Q9aCP+//Gc7EKZNm8Znn33G9OnTqVOnDhqNhueff943/eCaevXqceDAAebMmUPjxo19B223203Dhg0LTRMA/C64A1003+4Fc1EX3gW3PXToUBYuXMirr77KwoUL6dKlC2FhYb76R0dHs2XLlkLbvxYJABTqULlVTZs25bvvvvP9HRUVRWKi/6hHcnIycrm8UCfG3zVlyhTeeecdv7TxTWszofnN5yO7c7LwuFyFevmlWkOhkddA1E3aYt23HVyuv1fpEuayWPC4XMgL9PzL9EaclsBh76beg8g9fYKMNd7pJ3mxF0iy2yg3aRqpP8/DlRl4cbBiq3NWfp2v7w6S6Q2FRjUC0bXuRNbOzeDyHzVwZ1lI/Pw9JAoFUq0eV0YapoGP40xJKt4GXGVUKZFJJIWiDTKsNkwFohJupE6pUNaeKHqhK6lEQq0oE7EZxR/6D+DO8X4eMr2B60+fpTo97qybz99UP9CG3P1F7xuatj3QduxN2tcf4EyILaZa+3NZzN7vVIFoA7nBWCgq4Zqw/kOwnjxO2nLvnG/7pfMk2GxU/PALkhfMxplxdT/weMhLjAfAdiEGZdlyhD80hEuiAyEgjzUbj9tVKNpAotEVGs2/xp2diTsr03eBBOBKTUAikSLVh+BOT8ad7f2Mrz/bd6Umeo/pUhm4i/fY7GtHgWgDiUaHO7uIdmSZC7cjpUA7ssx43AXakZKAVGcEmeymvzEtS5v44LH3fH87r170mjPSCDGF+dItmRmFogeuZzSGFooksJgzfFEJOr0RqVTmi3C4Pk+g7Z46foiEK5cY9/J7fulJCZfZ8OsSPvrfQt9dIcpXvI/TJw7xy/FLTGz9z9ZE8eTm4HG7kGj8L3Akai0ea+C7CblzLEizzXDdwpvu9CTv56M14M4s2emJbuvV8xBtwfMQfZHfKVeWGaklw+875UyORyKVIjOYcKUl4cnJwvzj5yBXIFVrcVsy0HQdiCuj6CkC/5Qr++rvRcFzDt2t/X5rW3Yk+88thX6/r5EEBaFp3IqMlQuLobb+/jxo4VSM1ff3tYUQTQY5Geb8+hh1cjLNN+9MUgZJaPOAkR+WFx3pU9KMOi0yqZS0AtEGGeYsX4dAUX7fuZcPvp7H++PH0KRu/lTPK4nJJKSk8tKH+dF47qvHq5aDRrHo8/coExVRjK34dxGLKBav/+y7uX37dnr37s0jjzzC/fffT6VKlXxrE1yvcuXKbN68mZUrVzJu3DhfeoMGDTh79iwRERFUqVLF72EwGNDr9ZQqVYodO3b4bW/Xrl3UqHFr87mLUqVKFYKCgvy27XA42Ldvn9+2hwwZwtGjR9m/fz9Llixh6NChfvVPTExELpcXqv+1TobbcfDgQaKjo31/N2vWjI0b/Rf12rBhA40aNbrhnR2CgoJw3eTEaeLEiZjNZr/HuCa3OL/d5cJx+QLKqv6dDUFVa9/49klAUOUayMOjyN275dZe605yObFdOIu6rv/ItLpOfWxnTgYsIglSgqfA6sLuq3/ficgtlxP7xXOoaheoc+362M4GrvM1wdXrEBRVmqytRS8O5nE4cGWkgUyGtnFzcg4UPaf9dihkUmpEhrDnon8HxZ8Xk7i/1K3vW6eTMm64SKLH4+F0ciZh2hJaSNG3b9T1S1ZWrUPeLe0b0Vj/3BzweU27nug69yPtmw9xxJXMnTAAPE4nuTFn0N7f0P/16zXEeirwrUGlysL7gce3H9xoR5AguVNzpv+L3C6cCbEoKvkfmxWVavgtJng9Z1yM9wJakd/xJjVF4nG7cV/tCHXEnUNmCuf6g5TMFIE7K7PYOw8AcLlwxl9CUblAOyrXvEE7znk7NK4bgZeGBmpHhN93TBYa6W3HLXRQaxRyokqV9T1Kl62IMSSUo4fy17BxOhycOn6Q+26w2GeV6rX9ygAcObiHqtW9ZeQKBRWrVONYgTxHD+3lvuqFt7tl4yoqVqlO+Yr+c/ntV+8uUXAgRCqV4b6dKeNuF+6ky8jLVfNLlpevhiv+YsAirvgLSDQGUOSHp0tDwr2fT/bNBxJum8uFM/4iQQUWkg2qUhtHEbcGdVw8g0xn9P5uXyULi8LjduMqeCcQp8P7PZPKUNZujP3EAYqdy0nepRiCa97vlxxcsx62mFNFFLqap1ptFJGlyN5e9MKvmkYtkSgU5PxZ+HbHtyvX5iYhOc/3iI23k57poH6t/E4ouUxCnepaTpyz3mBLXq2aGFEoJGzalVnsdb1VCoWcapXK89cR/zVn9h45QZ1qgddqAW/kweSv5vDOcyNp0dD/t7986Wh+nPYO8z552/do1eh+GtSqxrxP3iYy9DbWLhH+3/nPdiBUqVKFjRs3smvXLk6ePMno0aMLjZBfU7VqVTZv3szSpUt5/vnnAe/oflhYGL1792b79u1cuHCBrVu38txzz3H5snfO00svvcRHH33E4sWLOX36NK+++iqHDh3iueeeu626azQannrqKV566SXWrVvHiRMnGDlyJFarlSeeeMKXr2LFijRv3pwnnngCp9NJ7969fc917NiRZs2a0adPH9avX8/FixfZtWsXb7zxBvv2/b1w/Hnz5rFw4UJOnjzJ6dOnmTp1Kl988YVfh8uYMWO4dOkS48eP5+TJk8yePZvvv/++yEUir6lQoQLbtm3jypUrpKYGHgVQKpXo9Xq/x9+ZvpCzbS3qB9qhatIGeUQpdA8+giwkDOtu70rAuu6DMDz8VKFyqiZtybt0FmdigDluVxfakpcqj0QmR2YwIS9VHlloZOG8JSTj12UY2nVF37YzQaXKEv7oKBRhEWT+7g3rDRv8OFFP5b//OQf2oG3cAkPHHigiogiuWpOIx54i99wpXBklG31wTea65ejbdEbXuhOKUmUJHTISeWg4lk1rATANeIyIUeMLldO36Yzt3CnyrhQesVdWqoamUXPk4VEEV61F9IR3QSIlc+3SEmvH0EZVWX70PCuOXuB8moWpmw+RmGWl//3ekbYvtx3lzbX5J+AL9p9h89krxGZkEZNq5sttR/nj7BUG1a/iy/PNruPsupDI5cxsTidn8s76fZxJyeSh+4s+Gbhd2Vt+Rd20HaombZFHlELf51HvvrHrdwB0PQZjHFJ431A3bUfexcD7hrZ9L/TdB5K56Btc6SlIdQakOoPfiXBxSlv5C8ZO3TF26EpQmXJEPTEWRVgkGetWAxDx6JOUfv5VX/6sv3ajb9qKkK4PooiMRlW9FtEjn8F65iTOdO+oa1j/h9Hc3xBFZDRBpcsS+uBDGNt1JnPr7yXShr9DplGjv786+vu9C5epK5ZBf391gstG36RkybPt3oiyQUuU9VogC4tC3XkgMoMJ237vRYG6fV+0vR/35bcf3Yvbmo2293BkYdHIy92HptND2A/t9IWV2/dtRarSou46CKkpAsV9dVC17I7try0l3I5WKOu3QBYWjbrLIG879nlfU92hH9q+I65rxx7c1hy0vR9HFh6NvPx9aDo/hP3gjvx2/LXlajsGIw2N9LajVQ9sewN3wt2MRCKh64ODWLVkHn/t3kLcpRhmfj6ZIGUwzVvnL2j89WfvsGhe/jo3XXsN4ujBvaxeOp/4yxdZvXQ+xw//RdcHB/nydOv9MJs3rmLLxtVcibvAD99NJy0liQ7d+vrVwWrNYe/OTbTtVPiOJ6XKVCAyugzff/URMWeOk5RwmV+XL+DYob20rXh7o5j2/VtQ1GmKotYDSE2RKNv0QaoLIe+wd+V+ZcueBHfNH1BxnNqPx5aDqssQpKZIZKUroWz9II7je+7Y9AXr9nWoGrUhuGFrZOGl0PYYgtQYSu6eTQBougxAN2BUfhsP78ZtzUb30EhkEaVQVKiGtvtgbPu2+eosL1sJZa1G3jU4KlTF+PgEJBIJ1m1rS6QN5o0r0bXqhLZFBxTRZQgZ9ARyUxhZW7xrWhj7PUrYiOcLldO27Ig95jSO+KIj0bQtO2I9uAd3TuAokuK2YkMqg3pF0LyBnvKllYx/sgx2u5stf2b68rw4sizDHyq8PleXViZ2H7CQlVO44y9YKaVSuWAqlfN2/EeGBVGpXDDhpuLvfH64ZydW/bGd1Zt2cPFyPNPnLiIpNZ2+ndsCMGPBUt758ntf/g079vDu/2bz7GMDqX1fJdIyzKRlmMnO8XaaKIMUVC5X2u+hVavRqIKpXK40CsV/NihduAv+s9+WN998kwsXLtClSxfUajWjRo2iT58+mM2Be5urVavGpk2baNu2LTKZjGnTprFt2zZeeeUV+vXrR1ZWFqVLl6ZDhw7o9d7woGeffRaLxcKLL75IcnIyNWvWZNWqVdx33+3fN/XDDz/E7Xbz6KOPkpWVRaNGjVi/fj0hIf7hY0OHDuXpp59m2LBhqFQqX7pEImHt2rW8/vrrjBgxgpSUFKKiomjdunWhxQ5vxXvvvcelS5eQyWRUrVqV2bNn+61/ULFiRdauXcsLL7zAV199RalSpfjiiy/o37//Dbf77rvvMnr0aCpXrozdbr+ludd/l+3Qn1jUWrSd+nlD/BMuk/Hdx76V46V6IzKj/zQLSbAKVd0mmFfMD7RJZPoQwl+c4vtb264n2nY9sZ87QfrX7wUsU9yy/9xGsk5PaL+hyIwh5MVd4spHb+JM9a7uLzOakIfln6hZtm1EqlJh7PIg4Y+M9M5HPH6Y1IXfF/USxS5nz3ZStXpCej+M3Ggi7/IlEqa97burgsxoQh7qvyaHVKVG06g5qQu+DbhNiUKBqf+jyMOj8NhzsR7eR/I303Bbc0qsHV2ql8Wca2fW7hOk5tioHKbni36tKGXwTglKzckl0ZI/kuFwufls62FSsnNRymVUCjXwRb+WtKyUf9GXZXfw3ob9pFltaIMUVIs0MmtwO2rfzorlN2E79CdmjQ5dF+++4UiII/3bj3z7hkxvRBbiH1UhCVYRXLcJluWB9w11i05I5ApMj7/gl561bglZ64u/U8eyYwsynZ7wQcOQm0zYL10k9t2JOK5OYZGHmFBctx9kblqPVKXG1KMPUSPG4MrJJufIQZLmzfLlkQariB7zHIrQcNx5dvKuxHH5sw+w7NhS7PX/uwwNa9Psjx98f9ec6l04N27+Mo48MfFuVQuAvBP7yFFrULXu4Z0mlhyPZeGXuK+OmEq0BqTXL/LqsGP5cTqaboMxjHwdtzWbvBP7sG5e6cvitmRgWTAddeeBGMe8jduSiW3vH+Tu/OcL8d20Hcf/8rajTa/8diz4PL8dOgNSw3W/GXl2LD98iqbbEAyj3sBtzSHv+D6sm5b7t+OHT1F3HYTxqUm4LRnY9vxO7o7f/nE9e/Z7lDy7nbkzPyEnO4vKVWvx6jufo1LnT01MS0n0iwKoWqMuz7w0mV9+/IZfFnxLZFRpxr30HlWq5Y+ON2vViewsM8sXf09mehplylfipbc+JTzCv5Pqz20b8Xg8fh0W18jlcl5++1MWzZvB1MkTsNtyiYwuw+jn36KF9fbu/OM8cxCbSo2yaRckGj3utASsy7/Bk+WN9pBo9Eh1150rOfKwLvma4Pb90Qx9EY8tB8fpQ9h3lcyFdiD2o3vI1mjRdOiNVGfEmXQZ89xpuK9OJ5Hq/M9DPHl2Mmd/jK7Xo5iefge3NRv70b1kb8i/A5ZErkDTqT8yUziePDv204ex/PwNHtvNR9H/CetfO0jX6DD28nao5cVfIunzd313VZAbQpCHFvi9UKlRN2hO+qJZgTbpLRdZiuCqtUj89M4t3PfL2hSCgqQ8Paw0Wo2M0zFWXp96nlxbfnRaRKii0Dlp6cggalfT8NongSPr7quo4uNX8zv9Rw8pBcDGHel8+l3xLrjYsUUTzNk5zF6ymrQMM5XKlmLaa88RHe79HqVlmElKzZ+KtGLjVlwuF1O/W8DU7/KnZ3dv05w3nxlRaPv/33juSCju/x8ST0lc0QnCbUp4ccjdrkKxyIq/MyP/JUmmKJm7HdxpUa1KcKHCO8R84tZu3/Rvl3G+ZNavuNMurr1yt6tw25q+2eZuV6F4uN03z/Mvd+HhT+52FYpF1V/fuXmm/wBbyn//99uacgemcNwBTzkn3+0q3LYF4++Nz8JU9795+8fUY7vv2muH1W521167pPxnpzAIgiAIgiAIgiAIwo14JNK79vgnZsyYQcWKFQkODqZhw4Zs3769yLzLli2jU6dOhIeHo9fradasGevXr/+nb9UtER0IgiAIgiAIgiAIgnCXLV68mOeff57XX3+dgwcP0qpVK7p160ZsbOB1RrZt20anTp1Yu3Yt+/fvp127dvTq1YuDBw+WWB3/s2sgCIIgCIIgCIIgCMIN3fAuTP8un376KU888QRPPvkkANOnT2f9+vV8/fXXTJkypVD+6dOn+/39wQcfsHLlSlavXk39+iUzfVdEIAiCIAiCIAiCIAhCMbPb7VgsFr+H3W4PmDcvL4/9+/fTubP/4rWdO3dm165dt/R6brebrKwsTKaSW6RbdCAIgiAIgiAIgiAIQjGbMmUKBoPB7xEokgAgNTUVl8tV6I56kZGRJCYm3tLrTZs2jZycHAYOHHjbdS+KmMIgCIIgCIIgCIIg3JM8d3HMfOLEiYwfP94vTalU3rCMpMCUC4/HUygtkJ9++olJkyaxcuVKIiIibpr/nxIdCIIgCIIgCIIgCIJQzJRK5U07DK4JCwtDJpMVijZITk4uFJVQ0OLFi3niiSf45Zdf6Nix4z+u760QUxgEQRAEQRAEQRCEe5JHIrlrj78jKCiIhg0bsnHjRr/0jRs30rx58yLL/fTTTwwfPpyFCxfSo0ePf/Qe/R0iAkEQBEEQBEEQBEEQ7rLx48fz6KOP0qhRI5o1a8a3335LbGwsY8aMAbxTIq5cucL8+fMBb+fBsGHD+Pzzz2natKkvekGlUmEwGEqkjqIDQRAEQRAEQRAEQRDuskGDBpGWlsa7775LQkICtWvXZu3atZQvXx6AhIQEYmNjffm/+eYbnE4nTz/9NE8//bQv/bHHHmPu3LklUkfRgSAIgiAIgiAIgiDckzyS/9as/bFjxzJ27NiAzxXsFNiyZUvJV6iA/9a7KQiCIAiCIAiCIAjCXSEiEARBEARBEARBEIR7koe/t5ihcGMiAkEQBEEQBEEQBEEQhJsSEQiCIAiCIAiCIAjCPem/tgbCv53oQBCEEqQO1d3tKtw2Tenwu12FYrHn/VV3uwq3rdHzHe92FYqFIyf3blehWDR9s8rdrsJt+3Py1rtdBeGqCo/Y7nYVisWVncfvdhWKhdPuvNtVuG2R95e/21UoFqos9d2uwm3LVinudhWKheluV0D4VxDdMYIgCIIgCIIgCIIg3JSIQBAEQRAEQRAEQRDuSR6JWESxOIkIBEEQBEEQBEEQBEEQbkpEIAiCIAiCIAiCIAj3JHEbx+IlIhAEQRAEQRAEQRAEQbgp0YEgCIIgCIIgCIIgCMJNiSkMgiAIgiAIgiAIwj3JIxFj5sVJvJuCIAiCIAiCIAiCINyUiEAQBEEQBEEQBEEQ7kliEcXiJSIQBEEQBEEQBEEQBEG4KRGBIAiCIAiCIAiCINyTxBoIxUu8m4IgCIIgCIIgCIIg3JToQBAEQRAEQRAEQRAE4ab+33cgDB8+nD59+vj+btu2Lc8///xdq89/mcfjYdSoUZhMJiQSCYcOHbrbVRIEQRAEQRAE4f8xD5K79rgX/WvWQBg+fDiZmZmsWLHirtZj2bJlKBSKu1qHO23Lli20a9euUPrJkyepXr267++lS5fy5ptvEhMTQ+XKlXn//ffp27ev7/l169Yxd+5ctmzZQqVKlQgLC0MikbB8+XK/TpqSoG7eEU3bnsj0RpyJVzCvnI/jwumAeQ2DR6Nu3KZQuiPxMqmfvAyAPLI02q4DUJSpiNwUjnnFfKzb15VoGwA0rTqj69AbmcGII+EymUvnkBdzKmDekEeeRtO0baF0R0IcSe+PL5Suatic0MdfIPfwXtJmfVLcVfdRNmiN8oGOSLUGXCkJ5P7+C87LMUUXkMkJbtGdoNqNkWr0uLMyse1aR96R3QAoqtYjuHkXpCHhSKQyXBnJ2Pf+Qd6xvSXWBoDSjwyi3OjhBEWEkXMmhrPvfoz5rwNF5390EGUee5jgMqWwXUnk0lezSFy22vd81EMPUnPqe4XKbanWCLc9r0TaEIiibnOUDdsh0ehxpyVi27oCV/yFogvIZCgf6IyiekMkaj2e7Ezse3/HcaJk3//radt0w9ClDzJDCHnxcWQs/h77uRMB84YOfxZt8/aF0vPiY0mY9Kzvb4lKQ0ifoagaNEWm1uJMTSL9l7nYju0vsXYoG7VB1awLUp0BV3I8ORsW44w9V3QBmRxV654o6zyAVKvHbckkd8da7Id25rdDqULdvg9B1RsgUalxZaRi3fgLjnPHSqwdt8LUshGVXnwCQ4PaBJeKYF//sSSt+uOu1ul65ccModL4J1BGh5N94izHx39Axs6iP/vyTw2hwlOPoKpQmtzYBM59+DVXflzpe14il1P5ldGUebQPwaUjyTlzgVMTp5KyYXux1fm3NStYuWwRGelplC1XkRGjnqFm7bpF5j9+9BBzZs0gLvYCJlMYfR4aTJfuvQPm3bH1Dz79eDJNmrbg1Tff96W7XE4WL5jLti2/k5mRTkhIKO06duWhwY8ilRbPGFRIl16Yeg9AHhKKPe4iSXO+Jvdk0d9ffav2hPYZSFB0adzWHLIP7iN53je4srMA0D3QktB+DxMUXQqJTEZeQjxpq5dg2fp7sdS3KKbuvQnvNwi5KRR77EXiZ/0P6/GjReY3tu1IWP/BKKNL47LmkLV/L4mzZ+LKshTKa2jdjnIvv4V59w5i33+zxNqgeqA96pbdkOqMOJOvkP3rQhyXzhRdQCZH0743wfc3Q6oz4DZnkLN1Nbb9+d97VfPOqJq0Q2YMxZ2Thf34PrI3LAGno8TaATCom4lOzfVoVFLOXrIz65cU4hJv/FurVkkZ2tNE07paNGopyWlO5q5I5cAJq2+bg7qZ/MpkWJw88cbFEmnDql/X8suyFaSlZ1ChXFmeGvkEdWrXCpg3LT2db76fw9lzMVyJT6BPrx6MHfVkkdvevHU7H3wyjeZNm/DOG6+VSP2Fe9e/pgPh38JkMt08039UXl4eQUFBRT5/+vRp9Hq97+/w8HDf/3fv3s2gQYOYPHkyffv2Zfny5QwcOJAdO3bwwAMPABATE0N0dDTNmzcvuUYEEFyvKfrewzAvm43jwhnUzTpgGvkKKR+/hDszrVB+y4r5ZP26KD9BKiP8xSnYDu/xJUmClLjSkrEd3oO+9yN3ohmoGjTH2P9xMhbPIu/8aTQtOxE29nWS3nsBV0ZqofyZS+ZgXrkgv84yKRETp5J7cHehvLKQMAx9hhV50VVcFDUaour4ENb1i3BePo+yfku0g57GPGsyHktGwDKaPk8g1eixrv0Rd0YKErUOpDLf8x5bDrZd63ClJYHLiaJKHdQ9HsWdk4XzwskSaUdEzy7c99bLnH7zfcz7DlJ66ADunzuDPZ36YI9PLJS/9CMDqfzyc5ya+A6Ww8fQ16tD9Slv4zBbSPtjqy+f05LFnx0e9Ct7JzsP5FXrEdymD7ZNS3HFX0BRtznqPqPI/uEjPFmZAcuouj+GRK0jd+Ni3OZUJCodkmK6cLgV6kYtMA0aQfrCb7CdO4WudRcinn2T+EnjcKUX3i/SF39HxrL5vr8lUhnRb32Gdf+u/EwyOZEvTMKVZSZ15sc4M9KQm8Jw23JLrB1BNRuh6TKInLULccadQ9mgNfohz5I5YxJuS3rAMrqHRiHR6MlZPR9XejJSjQ6uf++lMvSPvIDbmkXWkpm4LRlI9SY8ebYSa8etkmnUWI6c5vK8ZTT85X93uzp+ogd0o+a0iRwb9w4Zuw5QbuRgmqyZxda6PbDFJRTKX270w1R770WOjnmDzH1HMTauS92Z7+HIsJD862YAqr37PKWHPMiRMW+Qffo84Z1b0XDJ/9jVejCWQ7d/nNqxbRNzZv2PkWOfp0aNOqxft4r33n6Zz7+eR3hEZKH8SYkJvPf2q3Ts2oPnJ7zOyZNHmTVjOnqDkWYt/DvQk5MTmfv919SsVbgzYvkvP7H+t1WMe2Ei5cpX4NzZ0/xv+keoNRp69n7ottula96GyMefInHWl1hPHSekcw/Kvf4BMc8/gTM1pVB+VfValBr3MklzZ5K970/kplCiRj9H9NjxXP74HQBc2RbSli7EfiUOj9OBtlFTSj09AZc5k5xD+267zoEYWrUjeuTTxH89HeuJY5i69aLCpI84O3Y4jpTkQvnVNWtT5oVXSfhuBpa9u1CEhlH66fGUfnYCse+/5ZdXER5J9IinyDl2uETqfo2yThO03YeQtXo+jktnUTVuh+Gx8aR//hpuc+BjlOHhsUg1BizLZ+NKS0aq9f/9Vt7fDG3nAViWfY8j9hzysEh0/b0XtdlrfyqxtvTtaKRXOyNf/phEQoqDhzqH8PbTpXjmvUvY7J6AZeQymDS2FOZsF5/MTiQt00loiBybze2XLzbezqSv4n1/uz2Bt3e7tmzbwdezZjPuqdHUqlmdX39bz2uTJvP9jC+JiAgvlN/hcGDQGxgycABLV6664baTkpP5dvZc6tSqWSJ1/zcSiygWr//Mu/npp59Sp04dNBoNZcuWZezYsWRnZ/uenzt3LkajkfXr11OjRg20Wi1du3YlISH/ZMDlcjF+/HiMRiOhoaG8/PLLeArs+AWnMFSoUIEPPviAESNGoNPpKFeuHN9++61fmV27dlGvXj2Cg4Np1KgRK1asuGkIf0ZGBsOGDSMkJAS1Wk23bt04e/YsAGazGZVKxbp1/iPey5YtQ6PR+Np95coVBg0aREhICKGhofTu3ZuLFy/68l+bnjFlyhRKlSpF1apVb/geR0REEBUV5XvIZPk/AtOnT6dTp05MnDiR6tWrM3HiRDp06MD06dN9rzVu3DhiY2ORSCRUqFCBChUqANC3b19fWknQtO6Ode8WcvdswZkcj2XlD7gz09A07xgwv8eWizvL7HsoylZCotKQ+1f+hZ4j7jxZaxZiO7Qbj9NZIvUuSNe+Jzm7N2HdvQln0hXMS+fiykhF06pzEe2w4s7K9D0U5SojVWnI2b3ZP6NEimn4c1jW/owztfCJTHEKbtKevMO7yDu8C3daIrm/L8FtyURZv3XA/PJKNZGXu4/sn7/CefE0bnM6roRLuK6c9+Vxxp7FceYw7rRE3Jmp2PdtxpV8BXnZyiXWjrJPDiP+5+UkLF6GNeYCZ9/9GHtCIqUfGRgwf1TfnlxZuITkNeuxxV0hefU6En5eTvkxj/vl8+AhLyXN73EnKRu0wXF8D47je3BnJGPfugJ3diZBdVsEzC8rXx15mcpYV8zCFXcWjyUDd1IsroSLd6zO+k69yd7xO9k7fseZeJmMn7/HlZGKrk3XgPk9uVbclkzfI6hCFaRqLdk780e/tS06INXoSJkxBXvMKVzpKdjPncRx+WKJtSO4WSfsB3dgP7gDV2oi1g0/4zJnENyocDQUgKJyLeTlq5K18AscF07iNqfhjL+I83L+vqGs3wKJSkPW4hk442Jwm9Nxxp3DlXS5xNpxq1LWb+PM29NJXLHxblelkIrPP07cnKXEzV5C9qnznHjxA2xxiZQf/XDA/GWGPkjsrMUk/PIbuRcuk/DzWuLmLKHySyN9eUoP7c25j2aSsm4buRcuE/vNT6Rs2EGlF0YUS51XL/+FDp2706lLT8qUK88To8YRGhbB+rUrA+Zfv3YVYeERPDFqHGXKladTl56079SNlcsW++VzuVxM/+Q9Bg99nMio6ELbOX3qOE0eaEmjJs2IiIymecu21KvfmJizgSP8/q7QXv3J3LSOzD9+I+9KLElzvsaRlkJIl14B86uq1sCRkkTG2hU4khPJPXWczA2/Elw5//zGevwIWXt3knclFkdSAhm/Lsd+6Tzq6oFHbotDWJ8BZGxcS8aGtdgvx5Iw6yscqcmYuj8YML+6Wk3ykhNJW70MR1Ii1hPHSP9tNaoq1fwzSqWUnfA6SQvmkpdYuHOrOKlbdCF3/zZs+7bhSkkge+1C3OZ0VA8UjugCCLqvDooK1cmc/ymOmBO4M1NxXr7gF1WlKFcZR+xZ7Ef+xJ2ZSt6549iP7EFeukKJtqVnGyNLN6Sz50gOsQl5fLEgCaVCQuuGuiLLtG+qR6uR8eGsBE5dsJGS4eTUeRsX4/07+V1uyMxy+R6WbHcRW7w9S1espGunjnTv0onyZcsydtSThIeFsXpt4GjYqMhInh79JJ06tEOjVhe5XZfLxZSpnzFs6GCiogp3PgrCrfjPdCBIpVK++OILjh07xrx589i0aRMvv/yyXx6r1crUqVP54Ycf2LZtG7GxsUyYMMH3/LRp05g9ezbff/89O3bsID09neXLl9/0tadNm0ajRo04ePAgY8eO5amnnuLUKW9YeVZWFr169aJOnTocOHCAyZMn88orr9x0m8OHD2ffvn2sWrWK3bt34/F46N69u7cH0WCgR48eLFiwwK/MwoUL6d27N1qtFqvVSrt27dBqtWzbto0dO3b4Ok3y8vIPdn/88QcnT55k48aNrFmz5oZ1ql+/PtHR0XTo0IHNm/0vQnfv3k3nzv4Xsl26dGHXLu+o3ueff867775LmTJlSEhI4K+//uKvv/4CYM6cOb60YieToShTEfvpI37J9tNHUVS4cYfJNeombck7eyzgKP8dI5OjKFsJ20n/EQbbySMoK1YropA/TbP22E8fLdQOfbeHcGdbsO7eVGzVDUgqQxZVDkeBqADHhZPIy1QKWERxX11cCbEEN+2E4ZkP0I9+G1X7fiAvehqRvHw1ZKbIG4d+3waJQo6udg3St+/yS0/fvhtDw3qBywQF4bbb/dJcNhv6++sgkecHesnUaprvWEfz3Rup+/2XaGtVL7ipkiOVIY0og7NAOKrz0mlk0RUCFlFUqoUrKQ5lo3Zon3wbzWOvomzVC2R3aJqXTE5Qucrknjjkl5x74hDKyrf23mlbdMR26giu9PzRTPX9TbDHnML08GjKTJ1L9Nufo+/2EJTUCIVUhjy6HI4Y/wggx/kTRXaEBVW9H2f8JVQtuhDy/EcYn56MutNDfvtGUNX7cV6OQdPtYULGT8Uw5m1ULbuB5N6cc1kcJAoFhga1SNm4wy895fedhDSrH7CMVBmE21Zg/861YWycv39LlQrctrxCeUKaN7jtOjscDmLOneb++o390us1aMypk8cDljlz6jj1GhTM34SYs6dxXtcp/stP89EbjHTs0iPgdmrUrMORw/uJvxIHwIXz5zh54igNGjW9nSZ5yeUEV65KziH/qSM5h/ejqhb4Yj/39AnkoWFoGjQBQGYwomvWmuz9RU+pUtepT1CpMlhPFD2d4HZI5HJUVaqSfdA/uiH74D7U1WsHLGM9eRxFWDi6Rt4ITrkxBH2LNmTt+9MvX8TgYTgtmWRsXFsidfeRyZCXqkBegalPeeeOoShXJWCRoBr1cF65gLpVd0Jf+QzTCx+i7TrI7xjluHgWeakKyMtUBEAaEk5Q1brkFThnK06RoXJCDHIOnbL60pxOOB6TS7WKwUWWa1xbw+kLNkYOCGf2exWY/mpZ+ncKQVrgcBodruC7yRX4+u3yjH8sksjQ4g/mdjgcnDkXQ8P69fzSG9avx/FTgae13qofF/2MUa+nW+dOt7Ud4f+3/8wUhuujAipWrMjkyZN56qmnmDFjhi/d4XAwc+ZMKlf2npA988wzvPvuu77np0+fzsSJE+nfvz8AM2fOZP369Td97e7duzN27FgAXnnlFT777DO2bNlC9erVWbBgARKJhFmzZhEcHEzNmjW5cuUKI0eOLHJ7Z8+eZdWqVezcudMX7r9gwQLKli3LihUrGDBgAEOHDmXYsGFYrVbUajUWi4Vff/2VpUuXArBo0SKkUinfffcdkqsni3PmzMFoNLJlyxbfxb5Go+G777674dSF6Ohovv32Wxo2bIjdbueHH36gQ4cObNmyhdatvSPHiYmJREb691RGRkaSmOgN5zYYDOh0OmQyGVFRUX75jEZjobTiItXokMhkuLPNfumubDNKneHm5XVGlNXvJ3PB3Q2xlWqvtqNAGLk7KxOp3njz8nojwTXrkz73c7/0oErVUDdrT/KHLxVjbQOTqLVIpDLcOVl+6Z4cC1KNPmAZmTEUednKeFwOspd+g0StRd15MJJgNda1P+ZnVAZjfOYD74Wrx+2dInHx9n5Ei6IICUEqlxeKDshLSSMoLCxgmfRtuyg1uB+pGzaRdewkujo1KTWgL9IgBYoQI3kpqVhjLnJywpvknD6LTKul7ONDabhkHnu7DSD3YmyJtOV6EpUGiVSGx1rg87FmeaeNBCA1hCIrVRGP04F19RykKg3B7fsjCVZj27g4YJniJLu2X1gy/dJdFjMyfcjNyxtCUNVuQOp3n/qly8MjCa5eh5w920j+YjLyyGhMD49CIpVi/vXn4mwCcP2+4T+3+Ub7hjQkzHvi7nSQ9fPXSNRaNN2HIAnWkLN6nrd9IeFIK1bHfnQPlp++QGaKRNPtYZBKyd32a7G3414QFHZ1/07237/tSakoIwuHBQOkbNhB2REPkbjqdywHjmNoWJuyw/sjDQoiKCwEe2IKKRt2UPG54aRt/wtrTCxh7ZsR9WAHuC6S75/Ksphxu90Yjf7feYMxhMyMwKHlGRnp1CuQ32gMweVyYbGYMZlCOXniKL9v+JVPv/yuyNfuO2AIVmsO40YPQyqV4na7GTLsSVq17XDb7ZLrDEhkMpxm/+ltzswMNMbA+3fu6RPET/+Q0uNfR6oIQiKXk7V3F4nf+/+GS9Vq7vt2ERKFAo/bTeKsL8g5UvQaNrdDpr/ajowC7cjIQNEgcDusp44TN/V9yr78FtIgbzssf+4kfuYXvjzqGrUxde7O2WeLnsdeXKTqa+dS/scod7YFqTbwuZQsJAJF+ap4nA7MC75Aqtahe3AYErWGrGWzAbAf3YNUoyNk5OsgAYlMjnXPH1hL8Phk1HsvbTItLr/0TIuLcFPRnd+RYQrqmORs25fNe98kEB2uYNSAcKQy+GWd97M9c9HGFz8mEZ/swKiT8VAXEx+8UIbnPogl21p8kQhmSxZut5uQEKNfekiIgYwDgaeD3opjJ06ybsPvzPzis9us4X/PvbqY4d3yn+lA2Lx5Mx988AEnTpzAYrHgdDqx2Wzk5OSg0WgAUKvVvs4D8F4YJyd7Q7bNZjMJCQk0a9bM97xcLqdRo0aFpjEUVLdu/rxAiURCVFSUb7unT5+mbt26BAfn92o2adLkhts7efIkcrnct3YAQGhoKNWqVePkSe/obY8ePZDL5axatYrBgwezdOlSdDqdr2Ng//79nDt3Dp3O/8TfZrMRE5O/YF2dOnVu2HkAUK1aNapVyx/lbtasGXFxcUydOtXXgXCt7dfzeDyF0v4Ju92OvcDord3pQin/GydeBT7CW62VqnFrPDYrtmMlMy/ytkkkcAvz6zRN2+LOzSH3SH6Uh0QZjGnYs2T+NLPQRX3JKvhhSAqn+Z6TgsdDzqo5YPfO2879Yymafk9i3bA4f5Elux3L7ClIFErkFaqh6tDfGy4Ze7bkmvE32nHxi28ICg+l4fIfQSLBkZpGwtKVlB8zAo/be1JhOXgEy8H8URfzvoM0/nUxZR57mLPvfFRSjQigYBtu9Pl496TcdQsgz4YbsG1diarnY9g2LQNXyS6CdU2h2kkCphaiadYed24O1kN7/J+QSHBlmUn7YQZ43OTFxiAzmNB36VMiHQhFu8GR6uq+kb38ezx279oM1g2/oB0wmpzfFnr3DYkEd04WOWt+AI8HV0IsUp0BVbMuogPhJgr+7ktucKw9+/4MlFHhtNixGCQS8pLSuDx/OZVfGonH5b1IOTH+ferMfI+2x37D4/FgjYkjbt4yyj7Wr9jqXOj31uO5YbBJod/sq/uMBMi1Wvl86vuMffYl9AZjkdvYuW0TWzdv5IWX3qBs+YpcOH+O2d/+D5PJu5hisSj4vt/gWBtUphyRTzxN6i8/knNoH/KQUCKGjSR69HMkzMjvKHTn5nJ+whikwSo0deoTOXwMjqQErMdLbuQ70IlIUT/fyrLlKTVqHMmL5pN94C/vWg6Pj6b00+O58sUnSFUqyr74Gpe/nIrLUnhRxRIT8LMITHL1c7L8/I3vGJW99if0Dz9N1qofwOlAUbE66ra9vOsqxJ1HFhqBrsdQ3O3MWDffeJ7+rWrdSMvoQRG+v9//Jj5gPslNfjekEjBnuZi5KBm3B87H2TEZ5PRpb/R1IBw8mR/VEJsApy/GM+Ot8rR7QM/qzZnF0Rz/Ohf42+MJcBy4RVZrLh9N+4wXxo3FYAjccS0It+o/0YFw6dIlunfvzpgxY5g8eTImk4kdO3bwxBNP4HDkn8AWvHuCRCK5aefArQi0XffVi4JAF9E3e82inr9+W0FBQTz00EMsXLiQwYMHs3DhQgYNGoT8arik2+2mYcOGhaY5gP/ih9c6V/6upk2b8uOP+SPAUVFRvmiDa5KTkwtFJfwTU6ZM4Z133vFLG9+0NhOa17lpWXdOFh6XC2mBaAOp1oAry1xEqXzqJm2x7tsOLtdN85Ykd/a1dhj90qVaA+5baUfT9lj3bgNXfmiqPCwKeVgEoaNfzc949ftV+vNFJE5+DldqUrHUH8BjzcbjdiHV6Ln+3ZSodUV2YLizzbizM32dBwCutEQkEilSnRF3xrWwc4/v/67ky8hCowhu1oXsEuhAcGRk4HY6CQr3jzYICjORlxp4zQK33c6pl9/m9GuTCQoLxZ6cQukhD+HMysaRXsRogcdD1uHjqCuWL+4mBH653Bw8bhcStf+Jg0StxWPNDljGnWNBmm2G6xblc6cnXf18DLgzS3baj+vqfiErEIUj0xlwFYhKCETbogM5f27x2y8AXOYM74WfJ3/EyJF4GbnBBDJ5ofy36/p943oSja5QVMI17mzv2ibXTswBXKkJ3vdeH4I7PRl3tvlqOzzX5Un0Hg+lMnDf3ePav1Feqnf/VkYW2L8jQrEnB/4+u212jox8jaNPvYUyMhRbQgrlRg7CYckmLzXDt939Dz2NVBmEItSIPT6Z6h9MwHrh9tej0OkNSKVSMgpEG5jNmRiMgRd+DgkxFYpOMGdmIpPJ0OkNxF26QHJSIh+8M9H3/LVzk4d6ted/3/5AVHRp5s2eSb8BQ2jZxhtxUL5CJVKSE1n2y4Lb7kBwZnm/v/ICbZAbjDgzMwOWCev3MLmnjpO+8hcA7JcukGjLpcL700lZOBdnZvq1xuBI9F5I2i/GoCxTjtB+D5dIB4LLcrUdIQXaYQzBmRn4+B8+YAjWk8dIvbYmxcXzxNtyqfzxlyT98D1yYwhBUdFUeOuD/EJXf79rr/ydM6OHkZcY+EL5n3BbiziX0ugKRXhe48rKRGrJ8DtGOVPikUilyAwmXGlJaDr2xXZoF7Z927xlki6TrVCi7zMc65bVtzRAcjN7j+Zw5mKc72+F3Ps+GfUyMq6LQjDoZIWiEq6XYXHhdHlwX1ely4l5hBjkyGXgDFDUnuchNj6P6PDindZn0OuQSqWkZ2T6pWdmmjEajf9om/GJCSQmJfPmu/l3Wbm2z3d5sB9zvvmKUtGF10G5V3jE1L5i9Z9YA2Hfvn04nU6mTZtG06ZNqVq1KvHxf+/AaTAYiI6O5s8/8+eXOZ1O9u+/vVt2Va9enSNHjviNoO/bd+PR7Jo1a+J0OtmzJ39ULC0tjTNnzlCjRg1f2tChQ1m3bh3Hjx9n8+bNDB061PdcgwYNOHv2LBEREVSpUsXvYTDcPHT/Zg4ePEj0dQeSZs2asXGj/2JYGzZsuOkdFxQKBa6bXJxPnDgRs9ns9xjX5BZXhnW5cFy+gLKqf2dDUNXaOC7e4NZDQFDlGsjDo8jdu+XWXqskuZw44s4TXN1/Fezg6nWxF3E7ymuU99VEERFNToE1DhxJV0h8fzxJH77ke9iO7sN+9jhJH76EK6OYF/Bzu3AlxiKvWMMvWVGxut/Cb9dzXo5BqjWCQulLk5ki8LjdhaZz+JFIvBd6JcDjcJJ17CSmls380k0tm2Lef+jGZZ1O7IlJ4HYT0asrqZu23fAESVuzGvbkwiuNlwi3C3fyZeTl/NcGkZerWuSiiK74C0g0elDkRzFJQ8Kvfj4379i6bS4nebExqGrW80sOrlEPexG3N71GWbU2ishSZO8ofOs2+7lTKMKj/UbXFBGlvBcfxdx5AIDbhTMhFkUl/+OaolINnHGBb3HqjIvxdihet29ITZHe9/7qHU0cceeQmcK5fpxKZorw7jui8yAgj8OB+cBxwjv6Lxwa1qE5GbsP3ris04ntinf/LjWwu/cODAX2b7c9D3t8MhK5nKi+nUlaffu3rlQoFFSuUo3DBebYHz64j+o1Aq8VULV6rQD5/6LyfdWQy+WULluOz76azbQvv/M9Gj/QnNp16zPty+8IDfOO6NrtdiQF1gaRSmW43bd/4YfTiS3mDJr7/deJ0NRtQO7pwGs7SJRKv44/AK4O6Nw4HAMkN1hb53Z4nE5yz51BW6+RX7q2XkOspwLfjlKqDMZT8D28rh32y7Gcefpxzj77pO9h2bOLnKOHOPvskziKe0Fklwtn/EWCqvh/n4Kq1MJRxHpDjtizyHRGJEHX/X6HReFxu3FdvWuDRBHg8/K4i3WdFpvdQ2Kqw/eIS8wjw+zk/mr5CwnKZVCrsorTF4q+Q82p87lEhyn8qlYqQkG62Rmw8wBALocyUUFkWIr3d0OhUFC1SmUOFFiM/cChQ9Sq/s/WTipXpgzf/u9zZn7xme/R7IHG3F+nNjO/+IzwIqZoCkIg/6oIBLPZXOjOBSaTicqVK+N0Ovnyyy/p1asXO3fuZObMmX97+8899xwffvgh9913HzVq1ODTTz8ls4he7ls1ZMgQXn/9dUaNGsWrr75KbGwsU6dOBYoOM7rvvvvo3bs3I0eO5JtvvkGn0/Hqq69SunRpevfOv0dzmzZtiIyMZOjQoVSoUIGmTfMXLRo6dCiffPIJvXv39i1eGBsby7Jly3jppZcoU6bMLbdh+vTpVKhQgVq1apGXl8ePP/7I0qVLfestgPe9a926NR999BG9e/dm5cqV/P777+zYseMGW/bexeKPP/6gRYsWKJVKQkIKzwdUKpUolUq/tJy/MX0hZ9tajA+PxXH5PI6LZ1E1bY8sJAzrbu+Jm677IKQGE+afvvYrp2rSlrxLZ3EmBhghksmQR3rfQ4lMjsxgQl6qPB67zXs7wRKQtWkNpmHjyIuNIe/CGTQtOiIzhZGzfQMA+geHIDOYyPjBf66nulkH7BfO4EyI89+g01EozZ1rRQqF8xYT295NaHo9hivhEs4rF1DWa4FUH0LeQe89oYPb9EaqM2Jd453DnXd8H6oW3dH0eJTc7WuQqrWo2vcl78gu3/SF4GZdcCZcwp2ZAlI5isq1CKr9ANb1JXcLqLjv5lPz0w/IOnIc84HDlBryEMpS0cQv8I56VXr5WZSRkZx88XUAVBXLo7+/NpZDR5Eb9JR78lG0Vatw8sU3fNus8NwYLAePYL1wCblOS5nhQ9DWrMbp60eYSpj9wFZUXYbgSorDlXARRZ1mSHUh3vcbULbogUSjx7bB+946Th9A+UAnVJ0GY/9zPRKVBmWrXjiO771j0xcsG1cSNuJ57JfOYY85ja51Z+SmMLK2etevMfZ9BJkxlLQ5/ut/aFt2xH7+NI74wutLZG1dh659D0IGPUnWpl9RREZj6P4Qlk03Xmj2dth2b0TbdwTOhEs4L8egbNAamcFE1n7v3V/U7fsi1RnJXjkHAPvRvaha9UDbezi5W1Z510Do9BD2Qzt9+4Z931ZUjduj7joI295NyEIjUbXsjm1vCS+YegtkGjWaKuV8f6srlkF/f3Xy0s0Bb5V4J12YPod6cz8mc/8xMv88SNknB6EqF03st97b+1Z7bzzBpSM5/Lh3QWTNfRUwNq5Lxt7DKEL0VHrucXS17uPwiPzoLmOTugSXisR8+CTBpSKp+tY4JFIpMVOLXl/g7+jVdwBfTPuAKvdVo1r1WmxYt5rUlCQ6X13l/8e535KWlspzL3rv596l+4P8tmY5c2Z9RacuPTl96jh/bFjLCy+/CUBQkJLyFfwXt9VotAB+6Y2bNGPJ4h8IC4+gXPkKnI85x+rlP9O+U/diaVfa6qWUfvYVcmPOkHv6JMZO3VGERZCxwbsvhg8dgdwURsKXHwOQve9Pose8gLFLT+8UBmMokSOeIvfMSZxXO8VD+w7GFnOGvKR4JHIF2gZNMLTpROK3XxRZj9uVuuIXyoyfSO6501hPHsfUtSeK8EjS164GIPKxJ1GEhnP50ykAWPbuosy4CeR0e5CsA3+hMIUSPfJprKdP4kz3tsN+6aLfa7hzsgOmFxfrzvXoHxqF88pFHLHnUDVui9QQSu5e76Lams4PIdWHkLVklrceh/9E0/ZBdP2eJOeP5Ug1WrRdB2Hbv913jMo7dQhViy4442NxXI7xrtPSsR/2kweLJfqgKGu2ZtK/UwgJKQ4SUhz06xSC3eFh2/78aMhnH4kgzexiwWrv+71uh4XurY080S+MX7eZKRWuoH+nEH7dlt9Z/ljvUP46nkNquhPD1TUQVMFStuwp/mmi/fv05qNPp1O1ShVq1KjG2nUbSE5JpWf3LgB8P/cHUtPSeOXF531lzp33Dtbk2myYzRbOnT+PQq6gfLmyBAUFUbGCf8TjtSjlgun3Io9HRCAUp39VB8KWLVuoX99/FeTHHnuMuXPn8umnn/LRRx8xceJEWrduzZQpUxg2bNjf2v6LL75IQkICw4cPRyqVMmLECPr27YvZ/M9H0vR6PatXr+app56iXr161KlTh7feeoshQ4b4rYtQ0Jw5c3juuefo2bMneXl5tG7dmrVr1/pNl5BIJDz88MN88sknvPWW/32B1Wo127Zt45VXXqFfv35kZWVRunRpOnTogF7/9+Y25eXlMWHCBK5cuYJKpaJWrVr8+uuvdO+ef4LQvHlzFi1axBtvvMGbb75J5cqVWbx4sd86DoFMmzaN8ePHM2vWLEqXLu13m8niYjv0Jxa1Fm2nfsj0RpwJl8n47mPf3QikeiMyY6hfGUmwClXdJphXzA+0SWT6EMJfnOL7W9uuJ9p2PbGfO0H61+8VexsAcg/sIlOjRd/tIWT6EBwJcaTO+MDXDpk+BLnJv4dYEqxGVe8BzEvmlEid/i7Hyf3kqjQEt+iOVKv33grq5xm++9xLtXqk1y9+57CT9dMXqDsPRP/4q3hyc8g7uZ/cbavz8yiCUHcZjFRnxON04E5LImf1XBwnby966EaS16xHYTRS4bnRKMPDyT5zjiOPP43tivfCRxkRTnDp/IVBJVIp5UYOQ12pAh6Hk4w//2J//2HYLudHSsn1Oqp/8BZB4WE4s7LJOnGSA4MeJ+tw4BGqkuA8cwhbsBpl085I1HrcaQlYV87Ck+Ud0ZZodAU+nzysy74huG1fNA+/gMeWg+PMYey7frtjdbbu20m6Ro+xxyBkhhDy4mNJ/nKy764KMoMJucl/8TuJSo26QTMyFgW+eHNlpJI0fRKmgSPQvT0dZ2Y6lj/WYFm3rMTakXdiHzlqDarWPbxTrJLjsSz80nd/dYnWgNRwXQi0w47lx+loug3GMPJ13NZs8k7sw7o5/7Z9bksGlgXTUXceiHHM27gtmdj2/kHuzsC3+bqTDA1r0+yPH3x/15zqvbCNm7+MI09MLKrYHZHwy28EhYZw3+tjUUZHkH38DH/1GkVurHd/VUaHoyqbH4EnkUmp+MLj1KlaEbfDSdqWPexq/TC5l6748kiVSqq+8zzqSmVxZVtJXreVQ8NfxmkunguLlq3bk2Wx8PNP88hIT6dc+Yq8/s5HRER4j0MZ6WmkpuR3bkdGRfPGOx8ye9ZX/LZmBabQUJ4YPY5mLQLfNrQoT455joU/fs+3M6ZjMWcQYgqjc7deDHj4sWJpV9aurSTp9IQNeAR5iAl77EViP3gdZ4p3hF0eEooiLH9+u3nzBqTBKkzdehP52GhcOTlYjx4k+cf8fV0aHEzUqGeRm8Lw5NmxX4njyucfkrVra6HXLy7m7ZuR6fREDB6G3GTCfukiFye9iuPqZ6IICUURnt+OzD/WI1OpCe3Zl+gnnsKVk032kYMkzv22qJcocfaje8lWa9G0641UZ/DeTnr+p7gzvRfYUp0RmSH/XMqTZydzzlR0vYZiGvs2bms29mN/kb0xf/ApZ8sqPHjQdOqHTB+COycL+6lD5FyXpyQs/z2TIIWUUQPC0ailnL1k590Z8djs+Z0WYSEKv+kKaZlO3pkRz4h+YXz2qp50s4tft5pZ/nv+NJRQo5zxj0Wh08iwZLs4c9HGq5/GkZJR/JFrbVu3xJJl4cdFi0lPz6BC+XK8P+lNIiO836O0jHSSU/wjGJ96drzv/2fPxbBp6zYiI8L5cfasYq+f8P+bxFMciwQIfhYsWMDjjz+O2WxGpVLd7er8JyW8OORuV6FYuOx3ZpS2JGlKB16Z/L/m4Dc3jpb5L2j0fMe7XYVikXEi8JSW/xptmYibZ/qX+3NyyV1UCX9PhZNb7nYVioX0lb83uPNv5bSXwHSmOyzy/ntjZHlM1hs3z/QvN33cf/98EKDcfTVunulf6FzMhbv22lUqV7xrr11S/lURCP9V8+fPp1KlSpQuXZrDhw/zyiuvMHDgQNF5IAiCIAiCIAiCcBd5/hvL/v1niA6EYpCYmMhbb71FYmIi0dHRDBgwgPfff//mBQVBEARBEARBEAThP0J0IBSDl19+mZdffvluV0MQBEEQBEEQBEG4jgexiGJxEvEcgiAIgiAIgiAIgiDclOhAEARBEARBEARBEAThpsQUBkEQBEEQBEEQBOGeJKYwFC8RgSAIgiAIgiAIgiAIwk2JCARBEARBEARBEAThniQiEIqXiEAQBEEQBEEQBEEQBOGmRASCIAiCIAiCIAiCcE8SEQjFS0QgCIIgCIIgCIIgCIJwU6IDQRAEQRAEQRAEQRCEmxJTGARBEARBEARBEIR7kscjpjAUJxGBIAiCIAiCIAiCIAjCTYkIBEEQBEEQBEEQBOGeJBZRLF4iAkEQBEEQBEEQBEEQhJsSEQiCINyQx+W621UoFjKV6C8VipnbfbdrINxDFBLH3a5CsXA4743fjHvCPXKM8rg9d7sKt03uvjf2b0EA0YEgCIIgCIIgCIIg3KPEFIbiJYbkBEEQBEEQBEEQBEG4KRGBIAiCIAiCIAiCINyTRARC8RIRCIIgCIIgCIIgCIIg3JSIQBAEQRAEQRAEQRDuSR6PiEAoTiICQRAEQRAEQRAEQRCEmxIdCIIgCIIgCIIgCIIg3JSYwiAIgiAIgiAIgiDck9xiEcViJSIQBEEQBEEQBEEQBEG4KRGBIAiCIAiCIAiCINyTxG0ci5eIQBAEQRAEQRAEQRAE4aZEB4IgCIIgCIIgCIIg/AvMmDGDihUrEhwcTMOGDdm+ffsN82/dupWGDRsSHBxMpUqVmDlzZonWT3Qg/EcMHz6cPn36/K0yFSpUYPr06bf92lu2bEEikZCZmQnA3LlzMRqNt71dQRAEQRAEQRCEkuTxSO7a4+9avHgxzz//PK+//joHDx6kVatWdOvWjdjY2ID5L1y4QPfu3WnVqhUHDx7ktdde49lnn2Xp0qW3+7YV6V+5BsLw4cOZN29eofQuXbqwbt26En/tzMxMVqxYUaKv83d9/vnneDyeYt3mxYsXqVixIgcPHqRevXrFuu07Td28I5q2PZHpjTgTr2BeOR/HhdMB8xoGj0bduE2hdEfiZVI/eRkAeWRptF0HoChTEbkpHPOK+Vi3l+x3D0DTqjO6Dr2RGYw4Ei6TuXQOeTGnAuYNeeRpNE3bFkp3JMSR9P74Qumqhs0JffwFcg/vJW3WJ8VddR9lwzYEN+uMVGvAlRKPdcPPOOPOFV1AJkfVqgdBdR5AqtHjzsokd8da8g7vAiCobjO0Dw4vVCx9ytPgcpZQK6DUwwMpM+IxlOFh5JyLIWbKJ5j3Hyw6/5BBlBoyiODSpbAnJBL7zXckrVwTMG949y7UnPYRqb9v5vi4F0qqCQEp6jZH2bAdEo0ed1oitq0rcMVfKLqATIbygc4oqjdEotbjyc7Evvd3HCf23rE6a9t0w9ClDzJDCHnxcWQs/h77uRMB84YOfxZt8/aF0vPiY0mY9Kzvb4lKQ0ifoagaNEWm1uJMTSL9l7nYju0vsXYoG7dF1bwLUp0RV3I8OesW4Yw9W3QBmRxVm14o6zZFqtXjtmSQu/1X7Ad35rcjWIW6fV+CajRAotLgykjFuuFnHGePlkgbyo8ZQqXxT6CMDif7xFmOj/+AjJ1Fv2flnxpChaceQVWhNLmxCZz78Guu/Lgyv/5yOZVfGU2ZR/sQXDqSnDMXODVxKikbbjzacqeYWjai0otPYGhQm+BSEezrP5akVX/clbr8umYVy5b+QkZ6GuXKV2DkqKeoVbtOkfmPHj3M97O+IfbSRUyhofTvP5BuPXr5np/4yoscO3qkULlGjZvw9jvvA2C1Wlnww1x279qJ2ZxJpcpVGDl6LFWrViu2doV0e5CwvoOQh4Rij71I4vdfYT1R9PfX0KYDYX0HE1SqNK6cHLIP/kXSnJm4siyF8upbtaPshDex/LmDuClvFVudAzF17014v0HITd52xM/6H9bjRbfD2LYjYf0Ho4wujcuaQ9b+vSTODtwOQ+t2lHv5Lcy7dxD7/psl1gZV0w6oW3VHqjPgTL5C9poFOC6eKbqATI6mQx+C6zVHqjPgNqeTs3k1tv3bvM9LZajb9kTVoCVSfQjO1ERy1i0m70zJHJ+uN6i7ic4tDGhUUs5esvHt4hTiEvNuWEatkvJIr1AeuF+LVi0lOc3JnGUpHDhh9eUxGWQM6x1Gg1oaghQS4pMd/G9BEufj7MXehhVr17N42UrSMjKpUK4Mzzz5OHVr1QiYNy09gxmz53E25jyX4xPp17Mbz4x8vFC+7OwcvvvxJ7bv3kNWdg7RkRE8NWIYTRs1KPb6C//Mp59+yhNPPMGTTz4JwPTp01m/fj1ff/01U6ZMKZR/5syZlCtXzjdoXKNGDfbt28fUqVPp379/idTxX9mBANC1a1fmzJnjl6ZUKu9Sbe4+g8Fwt6vwrxVcryn63sMwL5uN48IZ1M06YBr5Cikfv4Q7M61QfsuK+WT9uig/QSoj/MUp2A7v8SVJgpS40pKxHd6Dvvcjd6IZqBo0x9j/cTIWzyLv/Gk0LTsRNvZ1kt57AVdGaqH8mUvmYF65IL/OMikRE6eSe3B3obyykDAMfYYVedFVXIJqNkLdeSDW3xbiiIshuEFrdA+PwzxzEm5LRsAy2n4jkWr05KyZjzs9BYlGh0TqHxzltuVi/rrAyV8Jdh6Ed+tM5Vdf4uzkD7AcOET0oIeo881X/NWrH/aExEL5owcPoOIL4zjz1rtkHT2Orm5tqr77Fk6zhbQt2/zyKktFU/ml8WTuK7kL1aLIq9YjuE0fbJuW4oq/gKJuc9R9RpH9w0d4sjIDllF1fwyJWkfuxsW4zalIVIU/n5KkbtQC06ARpC/8Btu5U+hadyHi2TeJnzQOV3rh/SJ98XdkLJvv+1silRH91mdY9+/KzySTE/nCJFxZZlJnfowzIw25KQy3LbfE2hFUqzGaroPJ+XUBzthzKBu1Rv/Ic2R+9RZuc3rAMroBo5Fo9eSsmosrPRmpRg/Xv/cyGfpHx+POySLr55m4LelIDSY8dluJtCF6QDdqTpvIsXHvkLHrAOVGDqbJmllsrdsDW1xCofzlRj9Mtfde5OiYN8jcdxRj47rUnfkejgwLyb9uBqDau89TesiDHBnzBtmnzxPeuRUNl/yPXa0HYzl0skTa8XfINGosR05zed4yGv7yv7tWj+1bt/Ddt18zZuw4atasxbrffmXSW6/x1czviYiIKJQ/MTGBd956gy5du/HihFc4ceI4M2d8id5gpEXLVgC89sbbOB35x1FLloVnnx5Ni5atfWlffv4ply5dZPyEVzCFhrJl0x+8+drLzJj5PaFhYbfdLn3LtkQ98TQJ33yO9eQxTF16Ue6tD4l55nEcqcmF8qtr1Kb0c6+SOHsGWXt3owgNI/qpFyj1zIRCHQSK8Eiiho8h53jhTpLiZmjVjuiRTxP/9XSsJ45h6taLCpM+4uzY4ThSArSjZm3KvPAqCd/NwLJ3F4rQMEo/PZ7Sz04g9v3C7Yge8RQ5xw6XaBuUdR5A22MoWSvn4bh0FtUD7TAMn0D6ZxNxmwufSwEYhjyDVKvHsvR7XGlJSLX+xyhN5/4E12tO1vLZOJMTUFatg+GR58j4ejLOhEsl1pa+HUN4sJ2RL39MIj7ZwUNdTUwaV5qn372IzR54ME4ug0nPlMac5eKT7xNIy3ASFiIn1+7Ob49KypTxZTl6NpfJM66QmeUiKkyBNdcdcJu3Y9P2nXz13RyeHzOS2jWqsXrdRl55533mfvUZkeHhhfI7HA6MBj1DB/RnSRGDFw6HgwlvTcZo1DPplRcJDwslJTUVlUpV7PX/t7mbiyja7Xbsdv8OJqVSGfC6Ni8vj/379/Pqq6/6pXfu3Jldu3YVyg+we/duOnfu7JfWpUsXvv/+exwOBwqF4jZbUNi/dgqDUqkkKirK7xESEuJ7XiKR8M0339CzZ0/UajU1atRg9+7dnDt3jrZt26LRaGjWrBkxMTG+MpMmTaJevXp88803lC1bFrVazYABA3yh+ZMmTWLevHmsXLkSiUSCRCJhy5YttG/fnmeeecavfmlpaSiVSjZt2lSo7mazGZlMxv793gsEj8eDyWSicePGvjw//fQT0dHRvr+vXLnCoEGDCAkJITQ0lN69e3Px4kXf8wWnMGRlZTF06FA0Gg3R0dF89tlntG3blueff96vLlarlREjRqDT6ShXrhzffvut77mKFSsCUL9+fSQSCW3btr3xh1LAihUrqFq1KsHBwXTq1Im4uDjfc4cPH6Zdu3bodDr0ej0NGzZk3759f2v7t0rTujvWvVvI3bMFZ3I8lpU/4M5MQ9O8Y8D8Hlsu7iyz76EoWwmJSkPuX1t9eRxx58lasxDbod14nCV3oXo9Xfue5OzehHX3JpxJVzAvnYsrIxVNq84B83tsVtxZmb6HolxlpCoNObs3+2eUSDENfw7L2p9xBjghK07BD3TEfmgn9kM7caclYt34M25LBsqGhSM+ABSVaiEvX5WsRV/ivHAKtzkNV/xFnJfPF8jpwZNj8XuUpDKPPUrisuUkLlmO9fwFYqZ8gi0xkVKDBwTMH/lgTxIWLyXltw3YLl8hZe16EpeuoOyTBXr/pVJqfPwBF//3Nba4KyXahkCUDdrgOL4Hx/E9uDOSsW9dgTs7k6C6LQLml5WvjrxMZawrZuGKO4vHkoE7KRZXwsU7Vmd9p95k7/id7B2/40y8TMbP3+PKSEXXpmvA/J5cK25Lpu8RVKEKUrWW7J35o8baFh2QanSkzJiCPeYUrvQU7OdO4rh8scTaEdysE/YDO7Af2I4rNQHrusW4zBkEN2obML+iSi3kFaqRteALHOdP4s5Mw3nlAs64/N80Zf2WSFQashZ9hTPuHG5zOs7Yc7iSLpdIGyo+/zhxc5YSN3sJ2afOc+LFD7DFJVJ+9MMB85cZ+iCxsxaT8Mtv5F64TMLPa4mbs4TKL4305Sk9tDfnPppJyrpt5F64TOw3P5GyYQeVXhhRIm34u1LWb+PM29NJXLHxrtZjxfKldOrclS5du1O2XHlGjh5LWHg4v/26OmD+dWvXEB4RzsjRYylbrjxdunanY6cuLF/2iy+PTqcnxGTyPQ4dPIBSGUzLVt4OBLvdzq6d23l8xEhq16lLqVKlGfLIMCKjolhbxOv+XaG9B5D5+29kblxL3uVYEr//CmdqMiHdHgyYX1WtJo7kJNLXLMeRnIj15DEy1q9BVaWqf0aplNLjXyP5p7nkJcYXS11vJKzPADI2riVjw1rsl2NJmPUVjtRkTN0Dt0NdrSZ5yYmkrV6GIykR64ljpP+2GlWVApEdUillJ7xO0oK55CUW7qQrTupWXcndtxXbvq24UuLJXrMAtzkdVdPCEV0AQVXroKhYjcy503DEHMedmYrz8nmcsfkRh8H1W2Ddspq800dwZ6SQu2cTeWeOom4V+PhdXHq2M7JkfQZ/Hs4hNiGPL35IQqmQ0LqRrsgyHZoZ0KmlfPhtPKfO20jJcHLyvI2LV/KjFvp1CiE1w8n/fkzi7CU7KelOjp7JJTHVUext+GXlGrp3bE+Pzh0oX7YMz4x8nIiwMFat3RAwf1RkBONGjqBL+zZoNOqAeX77fTNZ2dm899rL1KlZnaiIcOrUrEGVihWKvf5CvilTpmAwGPwegSIJAFJTU3G5XERGRvqlR0ZGkphYeAALIDExMWB+p9NJamrhgZbi8K/tQLgVkydPZtiwYRw6dIjq1aszZMgQRo8ezcSJE30XqwUv/M+dO8fPP//M6tWrWbduHYcOHeLpp58GYMKECQwcOJCuXbuSkJBAQkICzZs358knn2ThwoV+vUcLFiygVKlStGvXrlC9DAYD9erVY8uWLQAcOXLE96/F4r3w2bJlC23aeC+qrFYr7dq1Q6vVsm3bNnbs2IFWq6Vr167k5QUOtxo/fjw7d+5k1apVbNy4ke3bt3PgwIFC+aZNm0ajRo04ePAgY8eO5amnnuLUKW9I/N693hDk33//nYSEBJYtW3Zrb/zVOr///vvMmzePnTt3YrFYGDx4sO/5oUOHUqZMGf766y9fT1pJ9IAhk6EoUxH7af8RBvvpoygqVC2ikD91k7bknT0WcJT/jpHJUZSthO2k/wiD7eQRlBVvLVRU06w99tNHC7VD3+0h3NkWrLsLd3YVK6kMWXQ5HOf9oxwc508gL1M5YBFF1bq4Ei4R3KwLxmc/xPDUu6g69Ae5/3dFEqTEMO4DjM9+iHbQ08giy5ZYMyQKObpaNUjf6R/JkbHzT/T17w9YRhqkwJ3n37vsttnQ1amNRJ4f6FV+7GgcGRkkLl1R7PW+KakMaUQZnJf8w1Gdl04ji64QsIiiUi1cSXEoG7VD++TbaB57FWWrXiArgX05EJmcoHKVyT1xyC8598QhlJWr39ImtC06Yjt1BFd6ii9NfX8T7DGnMD08mjJT5xL99ufouz0EkhL6SZTJkJcqjyPmuF+yI+Y48rKB942gavVwxl9E1aIrIeM/wTjuPdSdB/jtG0HV6uG8fB5NjyGETPgUw9h3ULXqDpLiH2mRKBQYGtQiZeMOv/SU33cS0qx+wDJSZRBum/9+4cq1YWxcx7dfSJUK3La8QnlCmotw2mscDgfnzp2hfoOGfun16zfk5MnjAcucOnmS+vX98zdo2IhzZ8/gLKJTfOP632jdpi3Bwd7RSJfLhdvtJijIf38PClJy4sSxf9ocH4lcjqpyVbIP+Q8uZB/ah7p6rYBlrKeOIw8LQ9vwAQBkhhD0zVuTte9Pv3zhgx7FZTGT+ftvt13Pm5HI5aiqVCX7YIF2HNyHunrtgGWsJ4+jCAtH18jbDrkxBH2LNoXaETF4GE5LJhkb15ZM5a+RyZCXqkDeWf/PNe/sURTl7gtYJKhGA5xXLqJu3YPQV6djevFjtN0G+x2jJHIFHqf/xbXHmXfL52f/RGSoHJNBzqFT+dMOnE4Px8/lUr1S0SPtjetoOH3BxqhBEcz5oCKfv1aO/p1DkEr885yLtfHSiCjmTqnItFfK0qm5vtjb4HA4OHPuPI0KnHM0ql+XY6cCT8+9Fbv27qNmtapMn/kd/R59ksefGc+PPy/D5XLdbpWFG5g4cSJms9nvMXHixBuWkRT4Hfd4PIXSbpY/UHpx+dd2IKxZswatVuv3mDx5sl+exx9/nIEDB1K1alVeeeUVLl68yNChQ+nSpQs1atTgueee813EX2Oz2Zg3bx716tWjdevWfPnllyxatIjExES0Wi0qlcov+iEoKIj+/fsjkUhYuTJ/3uacOXMYPnx4kR9M27Ztfa+9ZcsWOnToQO3atdmxY4cv7dqI/6JFi5BKpXz33XfUqVOHGjVqMGfOHGJjYwvVH7zRB/PmzWPq1Km+7c6ZMyfgAaB79+6MHTuWKlWq8MorrxAWFubbZvjVEKjQ0FCioqIwmUw3+1h8HA4H//vf/2jWrBkNGzZk3rx57Nq1y9cpERsbS8eOHalevTr33XcfAwYM4P77A1983Q6pRodEJsOdbfZLd2WbkeluPu1DqjOirH4/1j2bb5q3JEm1V9tRIIzcnZWJVG+8eXm9keCa9cnZ5T83N6hSNdTN2pOxsGRXYwWQqLVIpLJC0QHunCxvWGMA0pBw5GWrIIsoRdaSmVg3/ExQjQZouuaPaLrSEslZNY/sxTPIXv4dOB3oh7+MNKRw2G5xUBhDkMjlOFL9w8odaWkEFRGym7FjN1EP9UVb0zs3UVurJlH9+iANUqAIMQKgr1+P6P59OP3muyVS75uRqDTez8ea5ZfusWYhUQcelZEaQpGVqog0NArr6jnYt65Ecd/9BLfvdyeqjOzafmHJ9Et3WczI9CGBC11f3hCCqnYDsrf7jx7LwyPRNGyORCol+YvJmNf+gr7Tgxi6P1Sc1fe5tm+4C+wbnhwLUm3g45Q0JBxFufu8+8biGeSsW0xQzYZoegz15ZGFhBFUsyFIpFgWfE7utjUEN+uMqnWPYm9DUFgIUrmcvGT/UGZ7UirKyMLhtAApG3ZQdsRD6Bt4LwYNDWtTdnh/pEFBBIWF+PJUfG446irlQSIhrENzoh7sgDK6ZPbv/yKLxYzb7cZo9P/OG0NCyMwIPDUsIyMdY0iB/MYQXC4XFou5UP4zp09x6dJFOnfp5ktTq9VUr1GTRT8tIC3NOyq2edPvnDl9ioz0wNNu/g6Z3oBEJsOZ6d8GZ2YG8pDA5yO5p45z5dMPKPPSm9RcuoHq85fiyskm4dsvfXlU1WsR0rE78f+bett1vBW+dhT4LJwZGShCAh+nrKeOEzf1fcq+/Ba1V2ykxo/LcOdkEz/zC18edY3amDp358qXJd8OqTrwuZQ724K0iHMpmSkcRfn7kEeVwfzjF2Sv+RFlncboej/my5N35ijqll2RhUaCRIKiSi2UNRog1RlLrC1GvbdzMjPLv6MsM8uFUS8rslxkqIJm9bVIJTD563h+WZ9O7w4hPNQl/7sYGaagaysD8SkO3vkqnvU7zDzxUDhtmxQd2fBPmC1ZuN1uQgosWB5iMJJxNWr6n4hPTGLrrj9xu91MeXsijw7szy8rV/PjL7c+gPhfdTcXUVQqlej1er9HUdPyw8LCkMlkhaINkpOTC0UZXBMVFRUwv1wuJzQ0tHjewAL+tR0I7dq149ChQ36Pa5EC19StW9f3/2tvap06dfzSbDabb9QfoFy5cpQpU8b3d7NmzXC73Zw+XXSPnlKp5JFHHmH27NkAHDp0iMOHDzN8+PAiy7Rt25bt27fjdrvZunUrbdu2pW3btmzdupXExETOnDnji0DYv38/586dQ6fT+TpLTCYTNpvNbwrGNefPn8fhcNCkSRNfmsFgoFq1wiPV179HEomEqKgokpNvP4xdLpfTqFEj39/Vq1fHaDRy8qR3zur48eN58skn6dixIx9++GHAdlxjt9uxWCx+D7vzb/aGFpjSdqv9barGrfHYrNiOlcz0itsmkcAtLJ6padoWd24OuUf+yi+qDMY07Fkyf5qJOyfrBqWLWaDqFtEEydX25az4Hlf8RRwxx7BuXELQ/c18oxiuKxfIO7YHV/JlnHHnyF46C1daEsGNC0f/FK+CX6qiP4tLX39L+rad1F80n9ZH91H7q+kkrljl3YrLhUytpvrH73PmrXdx3saPf/Eo2AZJgLRrT3n3pNx1C3AnxeK8eBLb1pUoaja+c1EIgWonCZhaiKZZe9y5OVgP7fF/QiLBlWUm7YcZ5MXGYP1rB+a1S9C2Ldmw2sLfn5u89x4P2Uu/w3nlAo6zR7Gu/xllveb5I3wSCe4cCzmr5+NKuETesb/I3f5rkdMiiqcJ/vWV3GC/OPv+DFLWb6fFjsV0yz1Oo6UzuDx/uXc7Vzu8T4x/n5xzl2h77De6WY9R6/O3iJu3DMSIWCEBR5huNCJFESNSAX4hN2xYR/nyFahazT+yZ/yEV/B4PAx/9GH69e7O6lUraNO2PdLiXAel4PfnBt8pZdnyRI18hpTFPxAzfgwXJ71MUGQUpZ7yLkYrVakoM/414r+aFnAxwpJV+ESkqJ9vZdnylBo1juRF8zn3/GguvPUyisgoSj/tXQBZqlJR9sXXuPzlVFyWO92OAopoxLXvo2XR1zgvnyfv9BGyf/2J4AYtfceorDU/4kpNwjT+I8Inz0b34DBy928Hd/GtGdC6kY6F0yr7HnLZ1e93gPPCG51OSaVgznLx9U/JnI+zs2N/NkvWp9OlVX4HikQi4XycnQWr07hw2c6GnRY27rLQtVXgTpbbVXj39nDrZ7iFeTweQgx6Xnx6NNWqVKZ96xYMHdCPVb8FnhYh3HlBQUE0bNiQjRv9Bz42btxI8+bNA5Zp1qxZofwbNmygUaNGJRP9zb94EUWNRkOVKlVumOf6N+XagSxQmvsGB6preW4W4vHkk09Sr149Ll++zOzZs+nQoQPly5cvMn/r1q3JysriwIEDbN++ncmTJ1O2bFk++OAD6tWrR0REBDVq1PDVr2HDhixYsKDQdsIDLJRSVFhKoLs0FPziSCSSG74ff0eg9+xa2qRJkxgyZAi//vorv/32G2+//TaLFi2ib9++hcpMmTKFd955xy9tfNPaTGhe9OrS17hzsvC4XIV6yKVaA66swqMsBambtMW6b/tdP1l1Z19rh9EvXao14L6VdjRtj3XvNr+FBeVhUcjDIggdfd1CLFc/n9KfLyJx8nO4UpOKpf4AHms2HrcLSYFoA6lGV2jk9Rp3thl3Vqbfom+u1AQkEilSXQjujECdXR6cCReRmkpmhNKRmYHH6UQR5t9rqzCZyEsLvJCU227nzBuTODvpPRShJvJSUoke2B9ndjaOjEw01aqiKlOa2jM+zy909QS89f+xd9/hTVX/A8ffSbp32tLdUlZp2XtvZE9BEVBAlK0gIog4EHAhMmQICMgQkClL9h5f9gYZZbeFTtp0t2nW74+UlDRpWanV/s7refI8cHPuzfn03HnuGVfPcaZjd7Kjiqbf+hO6rAx9+TgYl4/EwQldZrrZdbQZqUjTUyAnr3y0SXG55eOKNrlou/1oco8LWb5WODJnVzT5WiWY49S4NRmnDpsMuKlJUegfYHV550JV7EOsXN1BZmXxATqfHBv5WxtIHJ3RphdwbKQ9OTbyBnbUJOQeGy5ytEnxaNNS0Gk1RnfFmoQY/XlEJrPoeS3nsQKtWo2tt3ErHBsvD5Tx5vcDbbaSK4M/5+rwidh6e5Adk0DQ4LdQpaaT81hh2O75Nz5AamuDtYcbyuh4Qr8fS+b9oj0e/ktcXFyRSqUoFMZv/VOSkwucUlkudzdNn5KMTCbD2cX4HJCdnc2xI4d4+50B5Ofr68fUaTPJzs4iMzMTd3cPfvzhW7x9fF4tKPQtiXQajUlrAytXN5NWCU949uxL5o1rJG5eB4Ay4h4x2dmUmTqH+NVLsXKTY+PtS9CX3+WtlHvdq7RpH7dHDEBl4TERCozDTV5gHKXe7Evmjb95vEkfBw/uEZ2dRblpc4lb+Zs+Dh9fgid+bxJHla37uTW0v0XHdtBm5t6DOOW/l3Ip8BylSUtBmqowOkep46ORSKXIXN3RJMahy0gjZdVssLJG6uCENlWBY/teaBQJZrf5Ms5cTefWg7xrlLWV/u/k5mKFIjXvHOjqLCMlreBzoiJFjVoD2qdupR/G5uDuaoWVDNQaUKSqTWZyeBibQ8MaThaKJjevLs5IpVKSFMn58piC3O3lKyvc5W5YWVkhk+W1xCgdGECSIrnIBtv7tyjOQRRf1JgxY+jXrx916tShYcOGLFq0iMjISIYNGwbou0Q8evSI33/XDxg9bNgw5s2bx5gxYxg8eDAnT57kt99+Y82aNUWWx39tC4SiEhkZSXR03kn35MmTSKVSQkL0/bFsbGzMdgWoWrUqderUYfHixfzxxx+8917hAzw9GQdh3rx5SCQSKlWqZJifc/v27YbWBwC1atXi9u3beHl5Ub58eaOPudkXypUrh7W1taG7AEBqaiq3bxcyHZgZNjY2AC/V90mtVhsNihgeHk5ycjKhoXlvL0JCQvj444/Zu3cvPXr0MJlV4wlzfYNG1qv0fBnRaFA9vI9tiHFlg01IlcKnHgJsyoVhVcqHrDOHn++3ipJGjSrqHnah1YwW24VWQ1nAdJRP2FaohLWXLxn5xjhQxT0i9rsxxE0dZ/hkXz2H8vY14qaOQ6Mw/zD80rQaNDGRWJcxnmLIukwY6ofmW6Coo+7qH3as85pyyTy80Wm1aNPM33gBWHkHokt/dsXKy9Cp1KRdu4G8UUOj5fJG9Um9WPgo2Dq1mpy4eNBq8erYjsTDx0CnI/Pefc527cm5Hm8ZPokHj5B8+izneryFsoCBcSxKq0Eb/xCrIOO+p1ZBIQUOiqiJvo/E0QWsbQzLpPJSueVTNH9/4wyoyYm8i32lGkaL7cJqoCxgetMnbEOqYO3tR/r/9pt8p7xzE+tSvkavd6y9/FAnJxXN7B4aDeroCKzLGZ/XrMtVMhoU8WnqqDv6ilGbvGND+uTYyJ3RRBV1B5m7l1EcMg9vfVcoC1eK6lQqUi5co9RrxgNuerZuhOJkwdObgv64yH4UB1otfr066mdgyFfhrVXmoIyOR2Jlhc/rbYn7q3imSvw3sra2pnz5EC5eNB7n6NLFC4SFmR8rIDQsjEv50l+8cJ7yFUKwsjJ+d/S/Y0dQqVS0aGV+4GEAOzt73N09SE9L4+KFc9RvYP5N2IvQqdVk3b2FU3XjsRoca9Qm86b5sR2ktrZGFX8AuicvRSQSlA8juTPyPe6OHmz4pJ05QcbVS9wdPbhIBhLWqdVk3bmFU406RsudatQm86b5sSKktnbotPle+uSL49YHA7k9apDhk3paH8ftUYPMzlDxSjQa1NEPsKlgPGaDTfkqqAqYalb14BYyZzckT52jZJ4+6LRaNPlnllGr9OctqQzbKnVRXjcds+tlZSt1xD5WGT5RsTkkpaipHpo3kKCVDCqXt+fmvYJn2rlxLxvfUtZGb/39vGxIyq1YALh5Lxt/Lxuj9fy8rElIsuwgitbW1oSUL8u5S8bje52/dIUqoS8/hWqVsFAexcQavUiMehSNh7u8RFce/Ne89dZb/Pzzz0yZMoUaNWpw9OhRdu7caXhxHRMTQ2RkpCF9mTJl2LlzJ4cPH6ZGjRp88803zJkzp8imcIR/cQWCUqkkNjbW6GOJkSTt7OwYMGAAly9f5tixY4waNYpevXrhk1ubHhwczJUrVwgPD+fx48eoVHknhUGDBjF16lQ0Go3ZN+n5tWjRglWrVtG8eXMkEglyuZxKlSqxbt06oxkP3n77bTw9PenWrRvHjh3j/v37HDlyhI8++oiHD03fwjg7OzNgwADGjRvHoUOHuHbtGu+99x5SqfSFBsvw8vLC3t6e3bt3ExcXR0rK8z8QWFtbM3LkSE6fPs2FCxcYOHAgDRo0oF69emRlZfHhhx9y+PBhIiIiOH78OGfPnjW0uMjPbN8gq4L7qeWXcXQnDvVbYl+vOVZefjh3fQeZ3JPMk/obUOeOb+HaZ7jJevb1WpATcRt1rJk3XbmDnln5lUYis0Lm6o6VX2l9P74iknZwO46NWuPQoCVW3v649hiAzN2TjGP6pmUuXfsi7/ehyXoODVujvH8LdUyU8RdqFeqYKKOPNisTXXaWPm0RPChln96Pbc0m2FRvhNTDB4c2byJ1dUd5QT+VoX3L7jh2fdeQXvn3GbRZ6Th1GYDU0xeroAo4tO6J8vJxyB14ya5pZ6zLVkLq5onMOwDHzv2ReQeSfeGouSxYxMMVK/Ht+To+PbrhULYM5T4bi52vL9HrNgJQ5uORVJyaNyaLfXAQXl06Yl86COeqVQibMRXHCuW5P0vfL1eXk0Pm7btGH3VaGpqMTDJv30Wn+mdm+lBeOIJ1lfpYV6qHVO6FbbNuSJ3l5FzRTw1k27gTdm3zxp9QhV9Al52BfZveSN29kfmXxbZpF1TXzoDG8qNOm5O6bytOTV7DsXFrrHwCkPd6Dyt3T9KO7AHA7fV38Bj4kcl6Tk1eQ3kvHFV0pMl3aUd2I3VyRv7WIKy8/LCvWhvXjm+QdrjoBirLPrkP21pNsa3ZGJmnLw7t3kLm6k72ucMAOLTugdPreRXTyqun0WZm4NRtILJSvliVroBj2zdQXvyf4dhQnj2M1N4Jh/a9kXp4Y12hKvZNO5F9pmjGdLn/8zIC33uDgHd74hRalrDpE7AP8iVykX5a3IrfjqH6sh8N6R0rBOPftysO5UvjWrcqNVfNxLlyBcK/mmVI41avGj7d22BfJgB549rU27EEiVTK3elLiiSGFyVzdMCleigu1fWV4w5lAnCpHopdoO8z1rSs7q/3ZN+eXezbu5uoyAgWL1pAQkI8HTp2BmDFst+YOT3vb9++Y2fi4+NZsmghUZER7Nu7m317d/N6D9OZZPbt3U2Dho1xcTEdq+bC+bOcP3eW2NgYLl44z+cTxuLvH8hrbdpZJK7ErRtwa9MRt9btsQkIwuf9EVh7eqPYrZ/lwavfIPxH57WiSzt7EpcGTZG374q1ty/2oZXxHfwhmbduoE5KRKdSoYx8YPTRZKSjzcpEGfmgyGZVerxlA/K2HZG36YBtQBC+g0ZgXcqbpJ36OLwHDCJgTN6gaalnTuDaqCnuHfRxOIRVwXfISDLDn4oj4oHRR5uRjiYzE2VE0cSReWw39nWaY1e7GbJSfjh16ovUzYOs0/qXE47t3sT5zSGG9MrLJ9FmpuP8xmBkXn5YB1fEqWNvss8dNZyjrALLYlu5jn5Ml+AQ3AaORSKRkHm0aAeF3H4omTfayqlfzZEgXxtG9vNBqdJx9Fxed85R/bx5p2teS8Pdx1JwdpTx/hul8POypnZlB3q2lbPraLIhzV8HFYSUsaNnWzk+ntY0reNM28au7Dpq+Qr1N7t1Zue+A+zcd5CIqIf8smQ5cQmP6dJBPzPX4hWr+X7WXKN17ty7z51798nKziY5NZU79+7zIDLv3rBbh7akpqUxb/Eyoh5Fc/Lsef7YsJnuHS1zPP+bFecYCC9jxIgRPHjwAKVSyfnz52nWLG963eXLl5uMkde8eXMuXLiAUqnk/v37htYKReVf24Vh9+7dRtMcAlSsWNEwg8DLKl++PD169KBjx44kJSXRsWNH5s+fb/h+8ODBHD58mDp16pCens6hQ4cMD/t9+vRh9OjR9O3bFzs7u2f+VsuWLZk5c6ZRZUHz5s25dOmSUQsEBwcHjh49yvjx4+nRowdpaWn4+/vTunVrsxd0gJkzZzJs2DA6d+6Mi4sLn376KVFRUc+VryesrKyYM2cOU6ZMYeLEiTRt2tTsoI3mODg4MH78ePr27cvDhw9p0qSJYYwImUxGYmIi/fv3Jy4uDk9PT3r06GHSTcFSsi+dItXBCac2PZC5uKGOeYhiyTTDbARSFzdkbsbN0SV29thXq0fKlt/NbRKZi5xSn+RNseLUsjNOLTujvHOdpAXfFkkcWRdOkOzohEuHN5C5yFHFRPF4/veGOGQucqzcjZsPS+wcsK9Rn5SN5lt3/NNyrp9DYu+IfdNO+m4kCdGkrZ1nmOde6uSK1PWpZp4qJWmrf8axXW9c3/8cbVY6OdfPk3U4b8BSqZ09tp3eQerogk6ZhSY2irTfp6OJflBkcSTs2ou1mxulRwzFppQnGbfvcHXYhyij9dNo2ZQqhd1T5yeJVEbAu/1xKFManVpN8ulzXOwzAGV00U8f9iLUty6RbeeAbYO2SBxc0CbGkLl1Mbrc1h4SR2ekTw9OqMohc9Ov2LV4Hcc+H6PLzkB16zLKE0U/svkTmeeOk+Toglunt5C5ysmJjiR+7jeGWRVkru5YuRt39ZLYO+BQqyGKteYfQjWKx8T9PAn3Xu/h/PXPqJOTSD2wndTdRTeQVM61s2Q4OGLfvIv+2IiPJnX1bMOxIXF2Rer61HkqR0nqypk4duiL65Av0WZmkHPtHJkHNxuSaFMVpK6ciUP7t3AbPgltqoLs0/vJ+l/RlE/Mhl3YeMip8MUIbH29SL92i7NdhpAVqd/PbX1LYf/Ug7VEJqXMxwOpGlIGrUpN4uHTnGjWh6yIvClMpba2hEwejUPZQDTpmcTvPsKldz9FnfIPjttSCNfaVWh4YKXh/5Wmfw5A1O+buPJ+4aNoW1LT5i1ITUtl7R+rSEpKonRwMF9P/g6v3PGfkhSJJCTkvZX28fHl6ynfsmTRQnZs34a7hwdDho6gcZOmRtt99PAh16/9zZRvp5r93YyMTH5f/huPHz/G2dmZRo2b0G/AeyatGF5W6v8OI3N2odRb/bFyd0cZ8YDIKRNQJei711nJ3bH2zOuulnxwD1J7B9w7dcfnvWFoMtLJuHKRuBWLLZKfl5Vy7BAyZxe8eufF8WDSZ4Y4rOUeWJd6Ko4De5DZO+DR+XV83x+OJiOd9CsXiV2+qKCfKHLKq6dJd3TCsXU3pM5uqOMekrJ8BtpkfWtFqbPxvZQuR0ny0mk4d+mH+weT0Wamo7x6hvS9Gw1pJFbWOLbpicy9FLocJcrwy6Su/xVddqbJ71vS5v0KbGwkDHnLCycHKbcfZDN53iOylXmtPkq5Wxk1hEpMVjP5l0cM7OHJrAlBJCWr2X44mc378lpD3olU8uPiGN7p6kGvDu7EJ6pZ+meCUcWEpbRq2pjUtHR+X7eRpCQFwaUDmTrxc3y89Ne7RIWC+ATjF6uDR39q+PetO/c4cOR/eHuVYu0S/XOOVylPfpr8Jb8sWcH7o8ZSysOdHl060qdnN4vnXyjZJDpzHedLqEmTJrFlyxYuXbr0UutHRUURHBzM2bNnqVXr3zXFVEZGBv7+/syYMYP333+/uLPzymI+6VvcWbAIjfKfeUtblBx8nn92jn+zq6tPPzvRv1zNoebn4/6vUVy/V9xZsAgnP/OzcvyXnPruWHFnQchV4ea+Zyf6D1CNebe4s2ARWs1///bYu2rRTXn8Txqa9lVxZ+GV/TKy4O4T/yV+Fas9O9G/0Jmb/0C3ywLUCy2aQTaL07+2BcK/iUqlIiYmhs8++4wGDRr8KyoPLl68yM2bN6lXrx4pKSlMmaKfGq5bN1GLKAiCIAiCIAiCAGC5eT8EEBUIz+X48eO0bNmSkJAQNm7c+OwV/iHTp08nPDzcMOXHsWPH8CxgnnpBEARBEARBEARBeBX/ryoQJk2axKRJk154vRYtWpidIrE41axZk/Pnzxd3NgRBEARBEARBEP61XnYwQ8G8f+0sDIIgCIIgCIIgCIIg/HuICgRBEARBEARBEARBEJ7p/1UXBkEQBEEQBEEQBOH/Dx2iC4MliRYIgiAIgiAIgiAIgiA8k2iBIAiCIAiCIAiCIJRIYhBFyxItEARBEARBEARBEARBeCbRAkEQBEEQBEEQBEEokcQYCJYlWiAIgiAIgiAIgiAIgvBMogJBEARBEARBEARBEIRnEl0YBEEQBEEQBEEQhBJJqyvuHJQsogWCIAiCIAiCIAiCIAjPJFogCIIgCIIgCIIgCCWSGETRskQLBEEQBEEQBEEQBEEQnkmi0+lErxDhX+fqnbjizoJF6HT//RpPqURb3FmwCCn//ThKSg26hJJx2cnU2Bd3Fl6ZvSy7uLNgEdYSVXFn4ZXdDm1T3FmwiJCbe4s7CxaRpf3vH99WEnVxZ8EiklXOxZ2FV2ZvpSzuLFhEzQqexZ2Fl3LkWmax/Xbzyg7F9ttFRXRhEARBEARBEARBEEqkkvBC799EdGEQBEEQBEEQBEEQBOGZRAsEQRAEQRAEQRAEoUQSHfYtS7RAEARBEARBEARBEAThmUQLBEEQBEEQBEEQBKFE0paQQaj/LUQLBEEQBEEQBEEQBEEQnklUIAiCIAiCIAiCIAiC8EyiC4MgCIIgCIIgCIJQIolpHC1LtEAQBEEQBEEQBEEQBOGZRAsEQRAEQRAEQRAEoUQS0zhalmiBIAiCIAiCIAiCIAjCM4kKBEEQBEEQBEEQBEEQnklUIJQw7777Lt27d3/h9RYtWkRgYCBSqZSff/65yH9PEARBEARBEAShqOmQFNunJBJjILyEd999l+TkZLZs2VJseXjw4AFlypTh4sWL1KhR45W2lZqayocffsjMmTPp2bMnrq6utGjRgho1apitTIiIiCAkJISEhARmz56Nrhg6Fu3evpltm9agSEoiMCiYd4eMpFKV6gWmv3b1EisWzyMq8gFydw+6vdGXdh27Gb4/dfwIm9avIjbmERq1Gl+/ALr0eIvmrdoZ0uzZsYU9O7eQEBcLQGDpMrzRZwC16jR4qRj27NjM1k1rSE5KJCAomIGDRxFWaAwXWbFkHg+fxNCzL207djd8v3/3No4c3ENUxD0AypavSJ/+Q6hQsZLZ7W1ev5I/fl9Ex65vMnDIqJeKAfRlsXXTWkNZDBzy4TPLYvniXwxl0f2NPkZl8bT/HTnArGlTqNugCZ999Z1h+ab1qzh14iiPHkZiY2NLxbAq9Bs4FP+AoJeOY9f2LWzZtA5FUiKBQcG8P+RDKlWpVmD6v69eYtni+URFPsDd3ZPub/Smfceuhu8P7tvN3J9/NFlv3eY92NjYGP6f+DiB35ct4sL5M+TkKPHzC+DDj8ZRrkLFl4pBXxaJBAaV4b1nxHDNEMN9QwxPl8XBfbuYZyaGtZv3YGNjq9/G35fZ+uda7t65hSIpkfFffkP9hk1fOO/54/iny2LIwN4kxMeZpGnfqRtDR4x+Zp51Oh2b1izh4N6tZKSnUT6kEu8OG0dAUNlC1ztz4iAbVi8iPuYRXr7+9HpnGHUbtjBKs2/nRnZsWk2yIhH/oDL0G/QxoZVrGL5/u6v5c1Cfdz+kc493AEhWJPLHsrn8fekM2VmZ+PoH0fXNd2nRrFGh+bP0PvW0/x05wMxp31CvQWOj41ujUbNu9XKOHt5PsiIJudyDlq+1543e/ZBKX+69x47t29j05wYUSYkElQ5m8JDhVK5StcD0V69e5rfFvxIZ8QB3Dw969uxFh05dDN9PGP8Jf1+9YrJenbr1+HqyPpbMzExWr1zOyRPHSUlJpmy58gweOoKQkBc/tl+Fe5M6lP3kfVxrVcHOz4tzPUcQt+3AP5qHpz0pi6SkJIJKl2bwkOFUKbQsrrBk8UIiIyIMZdGxU2ejNFu3bGLnju0kJMTj4uJC4yZNGfDu+4bjOzMzk1UrVxiVxZChw1+6LIrj+r1+9VI2rFlmtF1XN3eWrNr6UjFA8Zxrd+/Yyu6d24g33EsF06tPf2rXqf/Sceh0OrauXcSRvZvJyEijbIXK9Bs6Hv+gcoWud+7EATb/sZD42Id4+QTQ450R1G7Q0mza7RuX8eeqX2jTuQ99B30CgFqtZtPq+Vw5f5yEuEc4ODhRqXo93ug/Erl7qUJ/e++OTfy16Y/cfagM/QePIqxKjQLTX796kZVL5vIw8j5yd0+69OxLm46vG6U5ffwQ61ctIS7mEd6+/rzVbwj1GjU3fJ+VmcH6VYs5e/IoKSkKgsuG8O6Q0ZQLCTPazqOoB/yxbD7X/76ETqclIKgMo8d/g6eXT6ExCf9/iAoEgcjISFQqFZ06dcLX1/eZ6bdu3UqLFi1wcXH5B3Jn6vjRAyxfPJdBI8YQGlaFfbu38f3XnzJrwe+U8vI2SR8XG833X3/Ka+07M2rsl9y88TdL5s/E1dWVBo1bAODk7ELPt/rhHxCElbU158+c4JdZU3F1lVOjdj0APDxL8c67Q/HxCwDg8P7dTPvmc36a8xuBpcu8cAzLFs9h8PAxVKxUlX27tvHdpHHMmr+ywBh+mPQprdt1YdTYrwi/fpXFC2bi4upmiOHa1Us0af4aIWFVsLG2Yeuff/DtxE+Y+cvveHgaX8ju3LrBvj1/UTq48Ivrs+M4yLLF8xg84mNCw6qwd/dffPf1eH5esKKAOGL47uvxvNa+Mx+N/YKbN/5m8fxZuLi60bBxc6O08fGxrPhtAWGVTW9mrl29TPtOr1M+JBStRsMfvy9hypdjmb1wBXZ29i8cx/+OHmTp4l8YMmK0IY5vvh7PnAXLC4zj268n0KZ9J0bnxrFo/s+4uroaxeHg4Mi8X383WvfpyoP0tDQmjBtJ1Wo1+WryVNzc5MTGPMLByemlYtCXxWjCwqqyZ/c2vv36U2YXUhbffv0Zr+XGcOPGVRbP/9mkLBwcHJlrEoOt4d/K7GyCy5Sj1WsdmPb9xBfOt7k4iqMsfvp5IVqN1vD/yIj7TPpyLI2btHiufG/ftJKdW9cw7KOv8PEPYsv6ZfwwcRTT56/D3sHR7Dq3b15l7rSveOPtIdRt2JyzJ48wd9oXTJz6K+UrVgHg5LF9rFzyMwOHjSMkrBoHd29h2uSPmfbLGjxL6W/mflmxw2i7l8+fZPHc76jXKO9GeMHMSWRmZvDJlz/h7OLG8SN7mPvTlwT5/0rZchXM5q+o9inQH9/Lf1tAJTPH9+YNa9izaxsjP55AUOlg7twOZ97PP+Lg6Ejnbm8UUgrmHTtymCWLFjBsxEgqVarM7l07mDTxc35Z+BteXl4m6WNjY5g88Uvate/AJ2PHc/36NRbOn4uLqxuNm+grxz7/8mvUKrVhndS0VEZ9MJTGTZoZls2dPZOIiAeMGTsedw8PDh88wFeff8r8hb/h4en5wnG8LJmjA6lXwnm4YhO1N8z7x37XnKNHDrN40UKG55bFrl07mDTxC+YvXFJgWUya+AXt2ndk7NjPuH79Ggvmz8XV1dVQFocOHWD5st/4aPQnhFWqxKNHD/l55nQABg8ZDsDc2bOIiHjAJ2M/xd3Dg0MHD/Dl5+OZv3AJni9YFsV5/Q4MKsNX380y/P9lK9Sg+M61Hp6l6PfuYHz8/AE4tH8PU7/5khlzFhH0gvdST+zcvII92/7g/VFf4+MXxF8bfmP61x/w/fw/sbc3f/69c/MKC6Z/zut9h1G7QUvOnzrEgp8+Y8IPv1EupIpR2nu3r3Fk72YCg43PlTnKbCLu3aRrr0EElqlAZnoaf/w2gznfjeHrGSsLzO+Jo/tZsXg27w//hIqVqrF/1xamThrLjPmrzD6kx8dG8+OksbRq14UPx04k/PoVflswAxdXN+o31p/nb934m9k/fk2vdwYZriezf/yKSdMWUKFiZQB+nTuVhxH3+OCTicjdPTl2aA/ffvkRM+avxj13P4uNecjXnw6nZZvOvPH2IBwcHXkUFYH1U9f9/yKtGETRokQXhiJw/fp1OnbsiJOTE97e3vTr14/Hjx8bvm/RogWjRo3i008/xd3dHR8fHyZNmmS0jZs3b9KkSRPs7OyoVKkS+/fvRyKRGFo9lCmjP8nWrFkTiURCixYtjNafPn06vr6+eHh48MEHH6BSqczmdfny5VStqq/5L1u2LBKJhHfffZcjR44we/ZsJBIJEomEBw8eGNbZunUrXbvqa5zzd2F4nthe1V+b19OqbSdea9dZX/M/ZBQenqXYu3OL2fR7d27Fs5QXA4eMIiAomNfadaZlm45s27TOkKZKtZrUb9SMgKBgfHz96dTtTUqXKcuN63lvmOrUb0ytug3x8w/Ezz+QvgMGY2dnz62b1144hu1b1tGqTSdat+tCQKA+Bk9PL/bu3Gw2/b5dW/Es5a2PITCY1u260Oq1TmzbtNaQ5qNxE2nX6XXKlK2Af2Bpho78FJ1Wy9+XzxttKysrkznTpzBs5Kc4Ojm/cN6fpi+LjoayeG/ISDw8S7Fnp/k3Ik/K4r0hIw1l0apNR6M4ADQaDbN/+pa33h6It4+fyXa++uYnWrXpQFDpMgSXLc8HH3/G44Q47t659VJxbNu8gdZtO9KmXScCg0rz/pAP8fD0YvfObWbT79m5Dc9SXrw/5EMCg0rTpl0nWrXpwJZN640TSkDu7m70edqmjWvwLOXFyI/HE1IxDC9vH6rVqI2vr/8Lx/CXIYbOBASV5v0hI/Hw9CqwLPJiGElAUGnatOtMqzYd2PrUcZEXg4fR52m16tSnb/9BNGjcDEsorrJwdXUz+u7c2ZP4+PpRuWrBbxWf0Ol07N62ju693qVuo5YEli7HsNETyVFmc+Lo3gLX27VtLVVq1KXbmwPwCwim25sDqFytLru35ZXBrq1raPFaF1q27YZ/YBn6Df4YD08v9u/cZEjjJvcw+pw/fZRKVWvj5ZO3H90O/5u2nd+kXEhlvHz8ef2t93B0dOJeIcdMUe1TGo2Gn3/6lt5vD8Tbx7TSOvzmNerVb0Kdeg3x8valUZMW1KhZl7u3wwvMa2G2bP6TNm3b0659RwKDSjN46Ag8S5Vi146/zKbfvXM7pbxKMXjoCAKDStOufUdea9OOzZs2GNI4O7sY7S+XLl7A1taOJk31x4FSqeTE8WMMfG8wVapWw8/Pn77v9Mfbx4edBfxuUUnYc5RbX/9M7JZ9/+jvmpNXFh0IDApiyNDheJYqVeDfZNfOHZTy8mLI0OEEBgXRrn0HXmvTjk2bNhrS3Lxxg7BKlWnRshXe3j7UqlWHZs1bcvv2bUBfFsePH2Pge4MMZfF2blkUtA8Upjiv31KZDLncw/BxdZW/cP6fKK5zbd36jahdtwH+/oH4+wfyzoBBufdS118qDp1Ox76/1tD5zYHUadiKgNLlGfTRZJTKbE4d3V3genv/WkPlGvXp/MZAfAOC6fzGQMKq1WPfX38YpcvOymTRrK9494MvcHA0vmdycHRi3OT51GvSBl//YMpVrMrbg8fx4O4NEhNiC/ztHVvW0bJNZ1q164p/YDADhozGw9OLfQXuQ1vwKOXNgCGj8Q8MplW7rrR8rRPbN60xpNm5bR1Va9ale6/++AeWpnuv/lSpXoddW/Xlk6NUcub4EfoO/ICwKjXw8Qvgzbffx8vbl3278n533e+LqFGnIW+/9wFlyoXg7eNPrbqNcHV7+X1NKHlEBYKFxcTE0Lx5c2rUqMG5c+fYvXs3cXFx9OrVyyjdihUrcHR05PTp00ybNo0pU6awb5/+4q7VaunevTsODg6cPn2aRYsW8cUXXxitf+bMGQD2799PTEwMmzbl3UweOnSIu3fvcujQIVasWMHy5ctZvny52fy+9dZb7N+/37DNmJgYZs+eTcOGDRk8eDAxMTHExMQQGBgIQHJyMseOHTNUIJhTWGyvSqVSce/OLarXrGu0vHqtuoTf+NvsOrduXqN6LeP0NWrV4+7tm6jVapP0Op2OK5fOE/0wqsCm+BqNhv8dOUB2djYhYVXMpnl2DPWMllerWZfwmwXHUM0k5nrcu2M+BtBfLNQaNU7Oxhe83xbMolbdhlSrUeeF8p2fSqXi7p1b1HiBsgg3Wxb6B4On49iwZgUurq681q7Tc+UlMyMdAOeXqBDJi8P471GjVh1uFhjHdWrUMk5f00wc2VlZDHm3N4P6v8m3kyZw7+5to3XOnj5B+fIVmfb9JAb0fZ0xIwezd/f2l4wh3OS4qFGrLjdvmK/gunXzGjXMHhfmYniLQf3f4LtJn5nEYEnFWRb583Hk0D5at+mARPLs/osJcdEkKxKpWiOvCa61tQ2hlWty+8bVAte7c/NvqtU0brZbrVZ9bt3Ur6NWqbh/J5yq+dJUrVmf2zfNbzdFkcilc8dp3qaL0fKKYdU5dWw/6WkpaLVaTh7dh0qlokq1Gma3U5T71IY1v+Pi6lbg8R1WqSpXLp8n+lEUAPfv3eHG9asv1V1MpVJx584tataqbbS8Zs3a3Cggjps3blCzpnH6WrXrcOf2rQLPt/v27KJZ8xaGFlAajQatVouNjbVROhsbW65fN78vl3T6srhNzVq1jJbXrFmbmzfMPzzevHHdTFnUNiqLSpUrc/fObcLDbwIQGxPDuXNnqFtXf419UhbWT70FB31ZXLv+Yi8Aivv6HRv9kCH9uzPi/V7M+vFr4mKjXyj/T8fxbzjXajQajh05SHZ2NhXDKr9ULAlxj0hRJFKlRt75wdrahopVanHnpmk3oyfuhl+hcg3jc2uVmg1M1lm56Eeq125M5erP18UiKzMdiUSCg6P5loRPzuvVTPahetwqYB+6ffNv0/S16hvtQ7fN7GfVatXjVu41SKNRo9VqsLY2PQ5uXtPHrNVquXjuBL5+gXz/1ccMebsTX4wZzNmTR58r9n8znU5SbJ+SSHRhsLAFCxZQq1Ytvv/+e8OypUuXEhgYyK1btwgJCQGgWrVqfP311wBUqFCBefPmceDAAdq0acPevXu5e/cuhw8fxsdH35Tpu+++o02bNoZtliqlb2rk4eFhSPOEXC5n3rx5yGQyQkND6dSpEwcOHGDw4MEm+bW3t8fDw8OwzSfbsrGxwcHBwWTbO3fupGrVqoYKBXMKi+1VpaWmoNVqTGpCXd3cSVYkmV0nWZGEq1u+t41ucjQaDWmpycjd9c0XMzLSGdq/JypVDlKpjEEjPja5eY54cJcvPhlBTk4Odvb2fPrltwQGBb9UDG5y4xjc5HKSLxQUQyJu8nom6fPH8LTVKxbi7lGKqk9VFBw/sp97d28xddaiF8pzYXHk/9u6uckLLQs3M2WnjyMFubsHN69f5cDencyYu+S58qHT6Vi++BfCKlclKLjw/uYFx6E1yZc+DoXZdRSKJGqaSa/RaEhNTcHd3QP/wCBGfvwZpYPLkJWZyfZtfzJh3EhmzV2Cn7++G0xcbDS7d26l6+tv8sZbb3P71g1++3Uu1tbWtGzdztxPv1AMroWUhUKRRI3njCEouGxuDBv5fNyHzJz7myEGSyrOsnjamVP/IyM9nVavtX+ufCcrEgHMnGfceVzIW6jk5ERc8q3j4uZOSu720lKTzR5jrq7upCQnmt3m0YM7sbN3NBlHYeSn3zJ32pcMfbsdMpkMG1s7Pp4wFZ8CWrsU1T514/pV9u/dwcxCju/X3+xLZmYGI4f2RyqVotVq6dt/EE1btC5wnYKkFrRPyQvfp9zkxg9K+eN42q3wm0REPGDU6E8MyxwcHAgNq8TaNasJCAzCzU3O0SOHuBV+Ez+/F29hVBKkpqai1WqR5ysLuVzOhQLLQoE837VSnq8smjdvSWpKCuPHjUGn06HRaOjYqTNv9uoNGJdFYGAQbm5uL10WxXn9rlCxEh+O+QJf/0BSkhX8uXYFX4wdzqz5v+Ps4voScRTfuTbiwT0+++QDw73UZ19OeeF7qSeenAtd3IyPS1dXDx4nxBS6nour8Tourh6G8y/A6WN7iLh7k6+n/55/dbNUOUo2/j6P+s3aY+9gvgIh9cl5XZ7vvC6Xk3zB/Hk9WZGEa759zlXubrQPJSsSzV6Dnpyv7R0cqRBahU1rl+MfWBo3N3eOH93PnVvXDV1zU1MUZGdlsW3jKnr1G0zfgcO5fP40M7//nK++n0ulqjWf6+8glHyiAsHCzp8/z6FDh3Ay04f57t27RhUIT/P19SU+Ph6A8PBwAgMDjR7e69UzvvgUpnLlyshkMqNtX71a8FuwF/F094WCFBabOUqlEqVSabQsR6nExrbg/lYmbwV1OijkTWHBbxHzltvbO/DT3N/Izsri6uXzrFjyC94+flSplnfC9PMP4qe5v5GRkc7p40eYN/N7Jv849yUvfMZ50odQSAwm6XM7dJlZZ+vG1fzvyH4m/zDH0F/9cUIcyxbP4cspM436sL8qc0XxYmWR1zEtKzOT2dO/Zfiosbi4uj3X7y9Z8DMRD+7x3U9zny/DBWfMOFeF71Imcehy43hSThVDK1ExNG8ArNBKVfhk1BB2/rWJQcNG5f6GjnLlK/LOAH3lXtlyFYiKeMDundteqAKhoDyh071kDOTGUJmKoXlvhUIrVWHsqMFGMRSJYiiLp+3fu5Naderj7mG+b/SRQ/tYOG+mYXTlcRNnmM83OpPj1iTv+b/X6UziMR+f+e0e2b+dxs3bmhzjG1YtJCMjlQnfzMXZxY1zp44wZ9oXfDdtDqULqXiz5D6lP76/Y8SocYUe38ePHuTIoX18PO5LAkuX4f69OyxdNA93d/1gii/DJF/PumYUcL41V5579+6mdOlgQiqGGi0fM3Y8s2dN591+fZBKpZQrX4HmLVpx907RteL5TzBTFs/R0Oep9LmbyS2LK1cus27dGoaPGEnFiqFExzxi8a8LWCNfRZ+++kFEPxn7KbNnzWCAUVm05O6dOy8bhEmeivL6DVAzXwuckNDKfDioN4cP7KLL671fNACzv/9PnWv9/AOZOXcJGRnpnDx+lDkzp/Ltjz8/173UySO7WLEg7yXd6C9/NsrD03l7Vgsy06/z/gCJCbH8sWQGn0ya91z9/9VqNQumf45Wp6X/0PHPTG967n+xfejJgfD0OubP13nLPvjkK36d/QMjBnRHKpVRplwIjZu34f5dfVc2rVY/DlDtBk3p1F2/TwWXDeHWjavs37VFVCAIBqICwcK0Wi1dunThxx9NR6F9eoBCa2vjZo0SicRw4OrM3EC+iMK2/SpUKhW7d+9mwoQJFv39H374gcmTJxstGzbyE0aMGmeS1tnFFalUZvIGLCVFYVKT/oSb3N3whtCQPlmBTCYzqrWXSqX45tbClilXgUdREWzesMqoAsHa2tqQpnyFUO7cusnOrRsYOtI0rwUpMIZkRYF9zNzkHihM0ifrY3A2fvOwbdMaNm1YxcRvZ1G6THnD8nt3wklJVjB+9CDDMq1Ww41rl9m9fRN/bD5gVPH00nE8oyxM48gri6iI+8THxfLD5M8N3+t0+n3nzS6tmLtopdEb0yULfubs6eN88+NcPDxNB+B6/jikZuMoqDzkZuPILY8CBheVSqWUDwklOvrRU9vxIDCotFG6gMDSnDxx7KViMMlTSrLJG4mnYzDdB5NNjgtzMcREP3yh/D2v4iyLJ+LjY7ly6QKffj7ZzJp69eo3JqRiJbI0+ptKtVo/xkyKItHobWJqsqLAvz+Am5uHSUuC1BSFoVWCs4tb7jFmmsbcdm9eu0TMowhGfvqt0fK4mIfs3bGRH+f9YZgVonSZCoRfv8Su7ZsZ9uEnJtsqin3qyfH9/eS8a8iTB6k3urRiXu7xvWLpQnq82ZcmzfUtDkoHlyUhPpZNG1a/cAWCS0FxJCfj5uZWYBzm4ja3T2VnZ3PsyCHefmeAyXZ8ff2YOm0m2dlZZGZm4u7uwY8/fIu3z//PkcxdXFzMlkVycnKB1wy5XI4i3xvx5BSFUVmsWrmCVq1a0659BwCCy5RBmZ3NvLmzeat3X/213dePqdNm5CuL7164LIrr+m2OnZ09QcFlX+p8XNznWv29lP5aXr5CRe7cusn2rX8yfKTpuSi/GvWaUfapQQ7VqpzcvDzG7enzb0qSSQuvp7maPf/mtViNuHuT1JQkJn/Sz/C9Vqvh1vWLHNi5nsUbTiDNvWdSq9Us+OkzHsdH8+mUBQW2PgBwKeC8nlLI9cLN7LlVfxw45e5DbnIP023mK08f3wC+nvoL2dlZZGVmIHf35Ocfv8LL29eQN5lMRkBgsNF2/AKDCb9ecHeQ/4JimDCuRBNjIFhYrVq1uHbtGsHBwZQvX97o4+hofiTY/EJDQ4mMjCQuLm9KsbNnzxqleTKirUajsVzm820//7YPHTqEm5vbK08bmd+ECRNISUkx+gwaav7tprW1NWXLh3Dl4jmj5VcunqNiAWMRhIRWNkl/+eJZylUIxcqq4Do0nU5X4OCThjQ8O01+hhguGZfplUtnqRhaSAz50l++eIay5Y1j2PrnH2xcu4IvJk+nXAXjt2FVq9dhxrwV/DRnqeFTrkIoTVq04ac5S1+o8uBJHOXKh3D5BcqiopmyuHTxLOUqVMTKygr/wCBm/bKMGXOXGD516jemSrWazJi7xFBJoNPpWLzgZ06fPMak7382OxDbq8Zx+eJ5QguMoxKXLxoPbnXp4jlDHObodDoe3LtjNKBUaKXKPMrt5/1E9KOHlCplOgL2s2OoaCaGc4QW0K80JLSymfRnnxnD/Xt3TAZStJTiLIsnDu7bjaurG3XqNSwwn/YODvj6+ePjF4iPXyD+gWVwk3tw9dIZQxq1SsXNaxepEFbw9HTlQ6sYrQNw5eJpQkL161hZW1OmfEX+zpfm6qUzVAg13e7hfdsoUz6U0mWMRwpXKrMB07dTUqkMXQFDUxfFPqU/vpcaHd916zcyOb6VSiUSifHtiVQqQ/sSw2hbW1tTvnwIFy9eMFp+6eIFwgqIIzQsjEv50l+8cJ7yFUJM9qn/HTuCSqWiRavXCsyDnZ097u4epKelcfHCOeo3KHzqzJJKXxYVTP62ly5eIDTM/HTDoWGVzJTFBaOyUCqzze4vOp3OZJrpp8viwoVzNGhQ8HFeUAzFcf02R6XK4VFUxEudj/8N51qjdC9wL2Vv74i3b6Dh4xdYFle5B9cunTakUatUhP99gfKhBU9JWa5iNaN1AK5dOm1YJ6x6Xb6ZvZbJs1YbPsHlK9GgWXsmz1ptUnkQFxPJ2MnzcXJxKzT/T87rV/PtE1cvnSWkgH2oQmgVk/RX8u1DFUIrc/Vi/jRnCTFzDbKzs0fu7kl6eipXLpyhdoOmhryVrRBG9KNIo/Sxj6LEFI6CEVGB8JJSUlK4dOmS0ScyMpIPPviApKQk+vTpw5kzZ7h37x579+7lvffee+6H/TZt2lCuXDkGDBjAlStXOH78uGEQxSc3gF5eXtjb2xsGaUxJSbFofMHBwZw+fZoHDx7w+PFjtFot27Zte2b3hZdha2uLi4uL0aew7gtdXu/Fgb3bObB3Bw8jH7Bs0VweJ8TTNneu8dXLf2XOjLw5xdt27EZCfBzLF+vnYD6wdwcH9+6ga4+3DGk2rV/F5YtniYuJ5lFUBH9tXseRg3to1rKtIc3qFYu4/vdl4uNiiHhwlz9WLOb61Us0bfniYzt07v4WB/Zu5+DeHTyMesDyxXNyY+ieG8NC5s7Ie4vYpkM3HsfHsXzxXB5GPeDg3h0c3LeDrj3ymi1u3biatSuXMOKjzyjl7YNCkYhCkUhWViagf+gJCi5r9LG1tcPZ2fWlxg6AJ2Wx46mymJcbh34/WbV8kdmyWGZUFjsNcdjY2Jrk0dHRCTt7fd6ftG5ZPH8WRw/tY/S4r7C3t0eRlIgiKdGkK8zz6vr6m+zfu5P9e3cSFRnB0kW/8DghjnYd9QPRrVy+mNkz8ppMtuvYlYT4OJYu/oWoyAj2793Jgb076d4jb7DUdX+s4OL5M8TGRHP/7h3mzZ7G/Xt3aNch7xjq0v1Nbt28zsZ1q4iJfsTRw/vZu3s7HTp3e+EYurz+Zm5Z7ORhZARLF83jcUKcUVmYi2HZ4l94GBnBgdwYuj11XKz7Y/lTMdzml9nTeJAvhqysTO7fvc393IGy4mNjuX/3NgnxeRWgL6K4ygL0LcgO7ttNi9btXqhCTSKR0L7rW2zbuIKzJw8TFXGXhbO/wcbWjkbN8s4hC2ZNZu2K+Yb/t+/yFlcvnuGvP38n+uED/vrzd65dPkv7rnll0KFbHw7t28bhfX/xKOo+K5f8TGJCHK07GM//nZmZwZnjB2nRxvQc7RcQjLdvAL/98iN3b10jLuYhOzav5u9LZ6jXsEmBcVl6n7KxsaV0cFmjj6OjE/b29pR+6viuW68hG9et5NyZk8THxXDqxDH+2rye+g2bPneZPK376z3Zt2cX+/buJioygsWLFpCQEE+Hjp0BWLHsN2ZOz2s12L5jZ+Lj41myaCFRkRHs27ubfXt383qPN022vW/vbho0bGx2WuML589y/txZYmNjuHjhPJ9PGIu/fyCvtXnx7kmvQubogEv1UFyq6x9KHcoE4FI9FLvAl694fVndX+/J3j272bt3N1GRkYay6JhbFsuX/caM6dMM6Tt07ER8fByLFy0kKjKSvbll0aNH3nSe9eo1YOeO7Rw5csjwt161cgX16zc0HMfnz58zKosJE8bh7x/wUmVRHNdvgN9/+4VrVy8SFxvN7fBrzPj+K7IyM2jRusMLxwDFd65dtWIx1/++QnxcLBEP7rFqxRKuXb1Ms5YFV8IVRiKR0KZLH7ZvXMb5U4d4GHGHJXMmYWtrR4NmeS2WFv88kQ0r86YxbdOlN9cunWbHpuXEPHzAjk3LuX75NG269AX0FRUBpcsbfWxt7XBydiOgtL51iEaj5pdpn3L/zg2GfPwtOq2GFMVjUhSPURdSIdKp+1sc3PsXh/Zu51HUA1Ysns3jhDhe66g/r69ZvoBfZnyTl9cO3XkcH8vvi+fwKOoBh/Zu59C+7XTu0ceQpkPXXly5eJatG1fxKCqCrRtX8fels3Tollc+l8+f5tL5U8THRnPl4hm+mTASX/8gWryWN6Btlx59OXnsAAd2byM2+iG7/9rI+TPHadvR+JrzX6NFUmyfkkh0YXhJhw8fpmZN475AAwYMYPny5Rw/fpzx48fTrl07lEolpUuXpn379s89X69MJmPLli0MGjSIunXrUrZsWX766Se6dOmCnZ0dAFZWVsyZM4cpU6YwceJEmjZtyuHDhy0W39ixYxkwYACVKlUiKyuL+/fvs23bNpYuXWqx33hZjZu1Ji01lY1rVqBISiSodBk+n/wjpXJrRxVJiTxOyHt48fbx4/PJ01i+eC67t2/G3cODgUM/Msy/DKDMzmLx/JkkPU7AxsYWv4AgRo39ksbN8gbtSlEkMXfGdyiSEnFwdKR0cDm+mPKTyUCLzxtDeloqG9cuR5GUSGDpMnw+aVpeDArTGCZMmsaKJXPZs2Mzcg9P3htiHMOenVtQq1XM+OEro996s89Aer393gvn8fniaEVaagob1vxuVBZeRmWRN/6Ft48vX0z+kWWL57F7+xbcPTx4b+gokznin+XJNHITP/vIaPkHoz+jVZsXv5lq0qwVaamprF/zO4qkJIJKB/Pl5KlGcSTki+PLyT+wbPF8dm3firuHB+8PHWkUR0Z6OgvmzkShSMLB0ZGy5crz7Y+zCakYZkhTISSU8V9+w6rli1m/5ne8vH15b8gHNH+JSqm8GFbkxlCGL0zK4ul9ypcvJ09l6eJf2JVbFuZjmEGyIYYKfPvjHCo8FcPd2+FMnPCx4f/LlvwCQMvW7Rg5pvDuToXH8c+WBcCVS+dJSIijddsX34c69+hHjlLJ8oU/kZGeRrmQynw2eTb2DnktzxITYo1aAYSEVePDcd+wYdWvbFi9CG8ff0aO+5byFfPeQjVs2ob0tBQ2r/uN5KREAkqXZdzEmZTyMn74O3V0HzqdzqjC4gkrKys+/Xoma1fMZ/o3Y1FmZ+HtG8DQ0ROpXbfgmQ2KYp96HoOGfcQfq35j0fyfSU1RIHf3pG2HLrzZx7SbwPNo2rwFqWmprP1jFUlJSZQODubryd/h5a1v6ZOkMN6nfHx8+XrKtyxZtJAd27fh7uHBkKEjaNzEuALj0cOHXL/2N1O+nWr2dzMyMvl9+W88fvwYZ2dnGjVuQr8B7xXa8q0ouNauQsMDeXPSV5qu7yIW9fsmrrz/4sfoq2jWvAVpaams/WN1blmUZtLkbw1loVAkmZTFpCnf5ZbFX3h4uJuURe8+byORSFj1+woSEx/j6upKvXoN6DdgoCFNZkYGK5YvNSqL/gMGvlRZFNf1O/FxPLN/mkxqagouLm6EhFbmuxkLDb/7oorrXJusUPDzjO9RJOnTBAeX5aspP5rMCPEiOr4+AJVSycpfp+aef6vwyaR52NvnP//m3YdXCK3OsLHfsWn1Ajb/sRAvnwCGjf2BciHmWwGYo3gcz6Uz+hkKvv64r9F3479ZSGhV8zE1avYa6Wmp/Ll2GclJiQSWLstnk6YXuA95+fgxftJ0fl8yh707NiH38OTdIaOp37ilIU3FsKqM+nQy61ctYv2qxXj7+PPR+ClUqJjX0iozM501KxaS9DgBJ2cX6jVqTu/+Q42Og3qNmjNoxDi2bljJ8kWz8PMPYszn3xFa+dlTGgv/f0h0+dt3Cf9Kx48fp0mTJty5c4dy5cr9479/4cIFWrVqRUJCgskYB0Xh6p2Xe3v5b1MSpm+RSl59/Ix/Ayn//Th0JaQmW0LJuOxkauyLOwuvzF6WXdxZsAhryYt1Jfs3uh366jMV/RuE3Nxb3FmwiCztf//4tpKYnybyvyZZ9eJTNP/b2Fu9XAvJf5uaFcwPLvxvt/1C8R0LnWuVvPf1JS+iEmLz5s04OTlRoUIF7ty5w0cffUTjxo2LpfIA9H285s6d+49UHgiCIAiCIAiCIFiCeF1uWaIC4V8qLS2NTz/9lKioKDw9PXnttdeYMWNGseWnXr16LzSVpCAIgiAIgiAIglCyiAqEf6n+/fvTv3//4s6GIAiCIAiCIAjCf1ZJ6FL8byJmYRAEQRAEQRAEQRAE4ZlECwRBEARBEARBEAShRNKKMRAsSrRAEARBEARBEARBEAThmUQFgiAIgiAIgiAIgiAIzyS6MAiCIAiCIAiCIAglkpjG0bJECwRBEARBEARBEARBEJ5JtEAQBEEQBEEQBEEQSiQdYhpHSxItEARBEARBEARBEARBeCZRgSAIgiAIgiAIgiAIwjOJLgyCIAiCIAiCIAhCiaQVgyhalGiBIAiCIAiCIAiCIAjCM4kWCIIgCIIgCIIgCEKJJKZxtCzRAkEQBEEQBEEQBEEQ/iMUCgX9+vXD1dUVV1dX+vXrR3JycoHpVSoV48ePp2rVqjg6OuLn50f//v2Jjo5+4d8WLRCEf6VyR+cXdxYsQpORWdxZeHVabXHnwCLSHrz4CfLfxr11s+LOgkXk3Lld3FmwjBJwbDw6fq24s2ARKrWmuLPwykJu7i3uLFjErdC2xZ0Fi6g+tFpxZ+GVuVYIKu4sWMQXt4cVdxZe2ZrXTxZ3FiyjQv/izsFLKYktEPr27cvDhw/ZvXs3AEOGDKFfv3789ddfZtNnZmZy4cIFvvrqK6pXr45CoWD06NF07dqVc+fOvdBviwoEQRAEQRAEQRAEQfgPuHHjBrt37+bUqVPUr18fgMWLF9OwYUPCw8OpWLGiyTqurq7s27fPaNncuXOpV68ekZGRBAU9f4WjqEAQBEEQBEEQBEEQBAtTKpUolUqjZba2ttja2r70Nk+ePImrq6uh8gCgQYMGuLq6cuLECbMVCOakpKQgkUhwc3N7od8XYyAIgiAIgiAIgiAIJZJWJym2zw8//GAYp+DJ54cffnileGJjY/Hy8jJZ7uXlRWxs7HNtIzs7m88++4y+ffvi4uLyQr8vKhAEQRAEQRAEQRAEwcImTJhASkqK0WfChAlm006aNAmJRFLo58l4BRKJxGR9nU5ndnl+KpWK3r17o9VqmT//xcedE10YBEEQBEEQBEEQhBKpOAdRfJHuCh9++CG9e/cuNE1wcDBXrlwhLi7O5LuEhAS8vb0LXV+lUtGrVy/u37/PwYMHX7j1AYgKBEEQBEEQBEEQBEEoVp6ennh6ej4zXcOGDUlJSeHMmTPUq1cPgNOnT5OSkkKjRo0KXO9J5cHt27c5dOgQHh4eL5VP0YVBEARBEARBEARBEP4DwsLCaN++PYMHD+bUqVOcOnWKwYMH07lzZ6MBFENDQ9m8eTMAarWaN954g3PnzrF69Wo0Gg2xsbHExsaSk5PzQr8vWiAIgiAIgiAIgiAIJVJxdmEoKqtXr2bUqFG0bdsWgK5duzJv3jyjNOHh4aSkpADw8OFDtm3bBkCNGjWM0h06dIgWLVo892+LCgRBEARBEARBEARB+I9wd3dn1apVhabRPVVzEhwcbPT/VyEqEARBEARBEARBEIQSSVsCWyAUJzEGgiAIgiAIgiAIgiAIzyQqEARBEARBEARBEARBeCbRhUEQBEEQBEEQBEEokXQ6SXFnoUQRLRByHT58GIlEQnJycnFnpdhMmjQJb29vJBIJW7Zsee71WrRowejRo4ssX4IgCIIgCIIgCELxK3EtEBYuXMi4ceNQKBRYWenDS09PRy6X06BBA44dO2ZIe+zYMZo1a0Z4eDiNGjUiJiYGV1fX4sp6sbpx4waTJ09m8+bNNGjQALlcTnBwMKNHjzZbOXD48GH69OlDdHQ0mzZtwtra+p/P9FPWX7jNijM3eZyeRTlPV8a2rkmtQC+zac9FxjF4zSGT5ZsGdaSMhwsAB8Kj+O3UdaIU6ai1WoLkzvSrW5HOVcoUaRzWVRtiU7MFEkdntElxKI9tQxN932xamX9ZHHoMN1mesWoaWkVC3jarN8GmakMkznJ0WRmo71xBeXIXaNRFE0O1RtjUaoHE0QVtYizKo1sLiaEcDm+MMI3h9x/RKuL1/5FKsanTGuuwOkicXNEqElAe344mIrxI8v+EQ+M2OLXsjMzFDVXsQ1K3/E7OPfO/6dZnGA71mpssV8U+JOHHcfrtNWiFfd2mWPsE6L97eJ/UHetQRd4tuiCAdccvs/zweR6nZlDOx4NPuzWnVln/Z6538X4078/fQHkfD9Z/8o5h+f4rd/jtwBmiHiej0mop7elGv+a16VInrCjDMGFdrRG2tVsa9rPsI1sK3M8AkMmwrd8W69DaSBxc0KUnozyzH9X1M/9cpvOxrt4Y2zqt8mI4vBnNo3sFryCTYdugnf5YeBLD6X2orp3+x/Isb9cF925vYiX3QBn1gLhlC8i68XeB6V2atsKjey9sfP3RZmaQfvEc8St+RZOeBoBz/SZ49OiDja8fEpmMnJhoEv/aSOqR/UUbR4eueL7+lj6OyAfE/vYLmdevFpjetXlrPF/vjY2fP5qMDNIvniVu2UI0aakmaV2atiRw7FeknvofUT9MtFied2zfxqY/N5CUlERQ6dIMHjKcKlWqFpj+6tUrLFm8kMiICNw9POjZsxcdO3U2SrN1yyZ27thOQkI8Li4uNG7SlAHvvo+NjQ0AmZmZrFq5gpMnjpOSkkzZcuUZMnQ4ISEVzf1kkXJvUoeyn7yPa60q2Pl5ca7nCOK2HfjH81EQx6ZtcW7dDZmrG6qYhyT/uYycuzfNppW/8wGODVqYLFfFRBH33RiT5fa1G+Ex8GOyLp8hcfFPls66QUm5fgO81dGdto1dcbSXcjsim0XrEoiKLXyuewd7Ke908aB+dSecHKTEJ6pZtimBC9czDWncXWX07+ZJrcqO2FhLiI5XMW91HPeilBaPYd2Rcyzff4rHKemU8y3Fp2+2oVb5ILNpL9yJYvaWg9yPSyQ7R4WvuytvNKlJv9b1jdKtOniG9UfPE6tIxc3Rnja1whjVrSW21iXukdBISZzGsTiVuL2lZcuWpKenc+7cORo0aADoKwp8fHw4e/YsmZmZODg4APqHYD8/P0JCQgDw8fEptnwXt7t39Q8x3bp1QyJ5djOfbdu20bVrVyQSCe7u7kWdvULtuRHJTwcuMqFtbWr4e/Lnpbt8uOEofw7qgK+LY4HrbRncEUebvIoPuYOt4d+u9jYMaliZYHdnrGVSjt2NZtLOM7g72NGorG+RxGFVoTq2TbuiPLwZTcwDrKs0wL7L+2Ssno4uPbnA9dJX/gg5eRcuXVZ63jZDamLbqCPZB9ajiYlA6lYKu9d6AaD8319FEEMNbJt1Q3loE5ro+1hXbYh9t8FkrJqGLq2QGFb8UGAMNg07YB1am+wD69EmxWNVuiL2nQeSuX4u2oRHFo8BwK5GA1y79ydl41Jy7ofj0Og13Id8RsLUsWiSE03Sp2xeQer2NXkLpDK8xk0l+9KpvDjKh5F14QQp92+BWoVTqy54DJtA/I/j0KYoiiSO3RfDmbb1CF/0aEWNMn5sPHmFEYu3sPnTfvjKXQpcLy1LyZdr9lCvfCBJ6ZlG37k62DLotXqU8XLHWibl6PX7fL1uL+5O9jQODS6SOPKzCqmBXfPuZB/8U7+fVWuEQ/chpK/8scD9zL7jACQOzmTtW4c25TESe2ck0uJrhGcVUhO7Fq+TfWBjXgyvDyV9xQ8Fx9DpXSSOzmTtXYs2+TESByckkn8uBudGzfEeOJzYxXPJvHkNedtOBH3xPXdHv4/6cYJJevvQyviN/JS45QtJP3cKK3cPfIZ+hO+IMTycNhkATXoqiX/+gfJRFDq1Cqc6DfD7YCyalGQyLp0rkjhcmrTA5/0PiPl1Npk3/sa9XReCJk7l7ocDUT2ON0nvEFYF/48+I3bpfNLOnMTawxPf4R/j9+FYkwoC61Le+Lw7jIxrVyya56NHDrN40UKGjxhJpUqV2bVrB5MmfsH8hUvw8jKtLI+NjWHSxC9o174jY8d+xvXr11gwfy6urq40btIUgEOHDrB82W98NPoTwipV4tGjh/w8czoAg4foK6fnzp5FRMQDPhn7Ke4eHhw6eIAvPx/P/IVL8PT0tGiMzyJzdCD1SjgPV2yi9oZ5z17hH2RfqxFuPQeiWLeYnHvhODZpg+eIL4j79mM0iscm6ZM3LiNl62rD/yUyKV4TppN18aRJWpncE9fu/VHeuV6kMZSU6zfA66/J6drSjbmr4oiOV/FGe3cmjfTngykPyFaaf5K0ksGkD/1JSdPw028xJCrUeMqtyFJqDWkc7aX8MCaQq7ez+Gb+I5LTNPh4WpOZpTW7zVex+9x1pm3cxxe921OjbCAb/3eBEb+sZfNXQ/F1N33ZaW9rTe/mdajg74W9rTUX70TxzZpd2Nta80aTWgDsOPM3s7ccZHK/zlQvG0BEXBITV+rvBce90cbiMQglV4nrwlCxYkX8/Pw4fPiwYdnhw4fp1q0b5cqV48SJE0bLW7Zsafj3010Yli9fjpubG3v27CEsLAwnJyfat29PTEyMYX21Ws2oUaNwc3PDw8OD8ePHM2DAALp3715g/iIiIujSpQtyuRxHR0cqV67Mzp07jfKwY8cOqlevjp2dHfXr1+fq1by3IomJifTp04eAgAAcHByoWrUqa9asMfqNFi1aMGrUKD799FPc3d3x8fFh0qRJBeZp0qRJdOnSBQCpVIpEIqFFixZERETw8ccfI5FITCoVnlQgPPm9p1spBAcH8/333/Pee+/h7OxMUFAQixYtKvD3X9WqszfpXq0sPaqXo6ynK+Neq4WPswMbLt4pdD13Bzs8newNH9lTDxN1grxpFRJAWU9XAuXO9K1TkQpeblx8aHqTbCk2NZqhun4W1fUzaBXxKI9tQ5uejHXVhoWup8tMR5eZZvg8Xc0q8y2NJuYB6luX0KUp0ETdQn37ElLvgKKJoVYzVNfOoLp2Wh/D0a25MTR66RisQ2uTc/YAmgc30aUmobp6EnVEODa1TN/4W4pTi05knj5E5ulDqOOjSd3yO5rkRBwam7/A6rKz0KalGD42gWWR2DuSeeaIIU3yql/IPL4PdXQE6vhoktctAokE2wpViiyOlUcv8Hq9yvRoUIWy3u582r0FPm5OrD9R+MPNNxsP0KFmRaoHm1aW1S0fSOuq5Snr7U6gpxtvN6tJBV9PLt6PLqowTNjWao7q2um8/ezIFrTpydhUa2w2vax0KFYB5cjcshhN1G10qQq0cZFoYh78Y3nOz7Z2C1R/n0b19yl9a6PDm9GmJWNTvYnZ9LLgUKwCypO5eRGayFvoUpPQxv6zMXh06Unywd0kH9hFzqNI4pYtQJWYgLxdF7Pp7UPCUCXEodi5BVV8LFk3r5G8dwd25UIMaTKvXSHtzHFyHkWiiotBsWMzyoh7OIRWLro4ur1J8v5dJO/bSc7DSGJ/+wX143jkHbqaj6NiJVTxcSRt34wqPpbMG3+j2LMd+/IhxgmlUvzHfE78muXkxFr2eNiy+U/atG1Pu/YdCAwKYsjQ4XiWKsXOHeYrgnft3EEpLy+GDB1OYFAQ7dp34LU27di0aaMhzc0bNwirVJkWLVvh7e1DrVp1aNa8Jbdv3wZAqVRy/PgxBr43iCpVq+Hn58/b7/TH28eHXQX8blFK2HOUW1//TOyWff/4bz+Lc6vOZJw8SObJg6jjHpHy53I0isc4Nm1rNr0uOxNtWrLhYx1UDqm9Ixkn87WOlEhxf/cjUneuR22mcsuSSsr1G6BzSzc27lFw6nIGkTE5zFkZh621hGZ1nAtcp3VDV5wdpExdFM3Ne9kkKNTcuJfNg0d5rRZ6tJHzWKFm3qo4bkcoSUhSc/VWFrGPVRaPYeXB07zeqAY9GtekrK8nn77ZFh83F9YfvWA2fVigDx3qVqa8Xyn8PdzoXL8qjcLKcuFOlCHN5fsPqVEukI51q+Dv4UajSmVpX6cy1yJizG6zJNHqiu9TEpW4CgTQP9AeOpR3Ej506BAtWrSgefPmhuU5OTmcPHnSUIFgTmZmJtOnT2flypUcPXqUyMhIxo4da/j+xx9/ZPXq1Sxbtozjx4+Tmpr6zLEDPvjgA5RKJUePHuXq1av8+OOPODk5GaUZN24c06dP5+zZs3h5edG1a1dUKv3JKTs7m9q1a7N9+3b+/vtvhgwZQr9+/Th92rgJ64oVK3B0dOT06dNMmzaNKVOmsG+f+Yvu2LFjWbZsGQAxMTHExMSwadMmAgICmDJlimHZE9euXSM2NpbWrVsXGOeMGTOoU6cOFy9eZMSIEQwfPpybN8035XsVKo2GG7EKGpYxbj3SoIwPlx+Z1vo/rffyPbSZt4Whaw9yNiKuwHQ6nY7TD2J5kJRK7QK6RbwyqQyplz+ayFtGizWRt5D5li50VcfeH+P43lfYdx+CzL+c8frR95F5BSD1DgRA4uKOrHQomgeWLwt9DAFoIo2bJmoiwpH5Bhe6qmPfMTgO+hr7HsOQBRjHIJFZgSbfxVmtQuZXRN1JZDKsA8qgDDd+yFaGX8EmOKSAlYw5NGiB8vbfZt88PSGxsUUitUKbmV5gmlehUmu48TCehhWN95+GFUtz+UHBNwtbzlzjYWIKw9o2eOZv6HQ6Tt+K5EGCgtrP0S3CInL3M3WE8bGiLmQ/sy5bGU1cFLZ1WuI06GscB3yGbdMuICumrldSGVLvANQRxsehOuImMr9gs6tYl62CJi4S2zqtcBoyCceBn2PbrCtY/UMxWFlhVy6EjEvnjRZnXD6PfUXzD/tZ4dex8vDEsVY9AGSubjg3bEb6+YK7jThUrYmNX0Ch3QlehcTKCvtyIaTna92QfulcgZUWmTevYeXpiVNtfVNgmascl0bNSDt3yihdqbf6oUlNIXn/LovmWaVScefObWrWqmW0vGbN2ty8Yf6t9M0b16lZs7bRslq1a3Pn9i3Uan33tUqVK3P3zm3Cw/X7YWxMDOfOnaFuXX15aTQatFot1rndGZ6wsbHl2vVrFomtRJBZYR1Yluwbl40WZ9+4gm2Z5+vq4diwFcrwqybXDJcOb6BNTyXz5EGLZdesknL9Brw9rHB3teLSzbzWc2q1jmt3sggta1/genWrOhJ+P5shb3mx7PsyzP48iJ5t5UglxmnuRGYz7j0flv9QhhnjA2nTqODWfC9LpdZwIzKGhmHGf6eGYWW5fO/hc23jRlQsl+8/pE6FvC4PNcsFciMyhqsP9K0/Hj5W8L+/79C0SnnLZV74f6HEdWEAfQXCxx9/jFqtJisri4sXL9KsWTM0Gg1z5swB4NSpU2RlZRVagaBSqVi4cCHlyulPiB9++CFTpkwxfD937lwmTJjA66+/DsC8efMMrQkKEhkZSc+ePalaVd9vsWzZsiZpvv76a9q00b/pXLFiBQEBAWzevJlevXrh7+9vVIkxcuRIdu/ezYYNG6hfP6+fU7Vq1fj6668BqFChAvPmzePAgQOG7T7NyckJNzc3wLgbh0wmw9nZ2aRrx9atW2nXrh12dnYFxtmxY0dGjND3jRs/fjyzZs3i8OHDhIaGmqRVKpUolcZ9xzQq9XP1x1Jk5qDR6XB3MM6Lh6MtiRnZZtfxdLTnq3Z1CfORk6PRsuPaA4auPcTivq2MKgjSlDm0+2UbKo0GqUTChLZ1aFCmaLq5SOwdkUhlaDPTjJbrstKROpivMddmpJF9cAOa+Ef6G5iKtbB/fQhZmxYa+iyqb19Gae+EQ88RgETfx/jKCXLOm44BYbkYjB+IdVnpSB0LiiGV7P3r0cQ/1McQVhv7HsPI2rgATbS+L7g6Mhzrms1RP7qHLjkRWVAFrMpWhiJqui11dEEik6FJSzHOa1oKMpdnj5EidXHDNrQGilWFN7F16dwHTUoSylsF9x9/FYqMLDRaHR5ODkbLPZwceJyWaXadiAQFs3ccZ9mHb2IlK/jvm5alpM2UJajUGqRSCZ/3aGVSUVFUnuxnuvzHSmYakgKOFamrBzK/MujUKjL/WobU3hG7Vj2R2DmQvW/dP5FtI4YYMszFYP5mVOrmgcy/LDqNmsxtS3NjeBOJnSPZe9eYXceSrJxdkchkqPN1t1EnK3B0k5tdJyv8OtE/T8V/zBdIrW2QWFmRduYEsb8ZHxtSBwcqLFqLxNoanVZL7OI5ZFwx/5btVclccuNINo3DSm6+O17WzWs8mvk9AeO+MsSRevo4MYvmGtLYh1ZG/lpH7o4ebPE8p6amotVqkef7O8vlci4ozHd/UigUyOX50rvJ0Wg0pKam4O7uQfPmLUlNSWH8uDHodDo0Gg0dO3XmzV69AXBwcCA0rBJr16wmMDAINzc3jh45xK3wm/j5/UMVhv8BUidnJDIZ2nzN/LVpyUhd3J69vosbdpVqkrR8ttFym7IVcWjYivip4yyYW/NKyvUbwM1Ff++YnGY8zlNymoZS7gXfV3p7WFM1xJ6jZ9P4ZkE0fl7WDOnlhUwqYf3uJH0aT2vaN3Vl28FkNu5VUKG0Le+/UQqVWsfhM2kFbvtFKdIz9ddvZ+MXjB4ujjxOLfylQ5vP5+jX12gZ1qkpPRrXNHzXoU5lFGmZvDvjd9CBWqulV9NavN+u8FYmgpBfiaxAaNmyJRkZGZw9exaFQkFISAheXl40b96cfv36kZGRweHDhwkKCjL7AP+Eg4ODofIAwNfXl/h4fROylJQU4uLiqFevnuF7mUxG7dq10WoL7gs1atQohg8fzt69e3nttdfo2bMn1apVM0rTsGFek3V3d3cqVqzIjRs3AP0bgalTp7Ju3ToePXpkePh2dDTu659/m0/n/VVt3brVUDlQkKd/XyKR4OPjU+Dv//DDD0yePNlo2eddm/FFtxbPnaf8wzbodFDQSA7BHi4Ee+TdpFf39yQuNZPfz9w0qkBwtLFm7cB2ZOWoOR0Rx4yDFwlwc6ROkPdz58sSCmr9pEtOQJWc16VCGRuB1NkNm5rNycqtQJD5l8WmTmv9uApxkUhdPbFt1hVdZho5Z4tokDIzI9UUNHiN2Ric3LCp3YKs3BsQ5ZEt2LbuhWO/8YAOXUoiqutnsa5Utyhy/1Tm8i+QoHuOUXgc6jZHm5VJ9tWzBaZxatUF+5qNePzLN6C2fNPHp5kcG5g/NjRaLRNW72Z4uwYElzL/QPiEo60N6z95m0xlDqdvRzFj2xECPFyoWz7QYvl+tvxlITGz7MlX+oizdq+GnGy0QPaRrdh3HkD2wU2mb8iKTSEx5H6XtXPlUzFswb7Lu2Qf3Fjk+5FB/mNAUnCebQKC8H7/Ax5vWEXGpXNYyT3w6j8Y36EfETN/piGdNiuLe2OHIbWzx7FqTbzfHYYqLoZMC48j8Mw4Cji+bQNL4zP4QxLWrST9wlms3N3xeXcofsM/JnredKT29gSM+ZzoX2aYHVTRYvIdzDqdzuT4LsyT8CS5Z4ArVy6zbt0aho8YScWKoUTHPGLxrwtYI19Fn776gVM/Gfsps2fNYEC/PkilUsqVr0DzFi25e6fwLoIChe5TT3Ns0AJtVgZZV/KuGRJbO9z7jyJ5zUK0GZZ7MH2m/+D1u1kdZ4b1ybt3+25Bbveh/Id4IbEASKWQkqZhwZp4tDq4F6XE3dWKbq3lhgoEiUTC3chsVv+lHwvp/kMlgb62tG/qatEKBEOeTe5tdc8cp2zZmP5kKXO4cv8Rs7ceIqiUOx3q6ltXnb0VwZI9x/mid3uqBvsTmZDEtA378Nx5jKEdm1o8//8mYhBFyyqRFQjly5cnICCAQ4cOoVAoaN5c39fKx8eHMmXKcPz4cQ4dOkSrVq0K3U7+mQUkEtMHiPwH8rMeMAYNGkS7du3YsWMHe/fu5YcffmDGjBmMHDmy0PWe/M6MGTOYNWsWP//8M1WrVsXR0ZHRo0eTk2M8sqy5vBdWsfG8YmNjuXDhAp06dSo03Yv8/oQJExgzxnjUYc2aH58rP3IHG2QSiUlrg6RMJe6OBbeQyK+qnwc7r0UYLZNKJATJ9TXvFb3l3E9MZenJG0VSgaDLykCn1SB1cObpv5LE3snkTWthNLERWFXMa+Zq06Ad6vDzhpHmtYmxYG2DXcue5Jw9QMEPKy/OEIOjBWIIzWt6q8vKIHv7MpBZIbFzQJeRik3jTmhTkyyW96dpM1LRaTTIXFx5+pFM6uyC9jkeDhzqNyfr/DHQaMx+79iiE06vdSNxwfeoYyItlGtTckd7ZFKJSWuDpPRMPJwdTNJnKHO4FhXHzUfxTN2sb6Gi1enQ6aDWuNksGNKD+hX0FQRSqYQgTzcAQv29uB+XxG8Hzv4jFQhP9rP8b+olDk7oCugOos1IRZqeAjl55wltUhwSiRSpsyva5MK7O1maIYZ8b/b0MZg/VgqNwanoY1CnpaDTaLByM35Lb+XqhrqA6Y89e/Qh6+Y1krZuAEAZcZ/Y7CyCv/uZhD+Wo07OPYZ1OlS5YwYoH9zFNiAIjx59iqQCQZOaG4fcXBzm3+Z79uxL5o1rJG5elxvHPWKysykzdQ7xq5di5SbHxtuXoC+/y1sp95pdadM+bo8YYIjvZbi4uCCVSlEojM95ycnJuBXQ+kMul6PI1zohOUWhb1nooj92Vq1cQatWrWnXvgMAwWXKoMzOZt7c2bzVuy9SqRRfXz+mTptBdnYWmZmZuLt78OMP3+H9/3jQ6fy06WnoNBqkzm5Gy6VOrmjztWQzx6FBKzLPHDWaGcnK0wcrTy88hn6WlzB3n/KfvZbYbz5C87jgrpcv6r98/T5zNZ1bD/LOi9ZW+r+Tm4sVitS867Crs4yUNPPXZQBFihq1xrjP+sPYHNxdrbCSgVoDilS1yUwOD2NzaFjDCUuSOznor9/5WhskpWXi4Vzw4OAAAbnX5gr+XiSmZbBgx1FDBcIvfx2hc72qhlYJFfy9yFKq+OaPnQxu3wSp9AVqJIX/10rkGAigb4Vw+PBhDh8+TIsWLQzLmzdvzp49ezh16lSh3ReexdXVFW9vb86cyevLqdFouHjx4jPXDQwMZNiwYWzatIlPPvmExYsXG31/6lRev0qFQsGtW7cMTf+PHTtGt27deOedd6hevTply5Y1DHhkaTY2NmjyPQRt27aNhg0bWnT0ZVtbW1xcXIw+zzudjLVMRpiPnFMPYo2Wn3oQS3X/58/jzTgFnk6FVzjodDpyCngofGVaDdr4R8gCKxgtlgWFoImJKGAlU9JS/kbNoiVWNqaVWlqt/kbE0tcJrQZt/ENkQcbjBOhjePDcm9HHYOZBXaPWL5dKsS5fDfW9omn6j0aD6uF9bEOMW/HYhlQl58GtAlbSsykXhlUpXzJPme8i4tiyM85te5D461RUUYVM12cB1lYywgK8OHXLuJLi1K1Is4MjOtnasnHsO6wb87bh82bDagSXkrNuzNtUDSr4gUGHfjySf0TufmaVbz+zKmQ/00TfR+LoAtZ5fbml8lLotNrnusG3OK0GbdxDrIKM+0dbla6IJvqB2VX0MbiajyH9H4hBrSb77i0cqxv3w3esVouscPP94SW2tqDLV3H8pCK5sLdoEpAU0dgOOrWarLu3cKpuPD6AY43aZN40H4fUTBy6p+JQPozkzsj3uDt6sOGTduYEGVcvcXf04Fce/M7a2pry5Stw6aJxt45LFy8QGlbJ7DqhYZVM0l+8cIHyFUIMU1wrldkms3hIpTJ0Op3JdcPOzh53dw/S09K4cOEcDRoUPrjv/ysaNaqoe9iFGl8z7EKrobxf+HSFthUqYe3lS0a+MQ5UcY+I/W4McVPHGT7ZV8+hvH2NuKnj0ChMZwN6Jf/h63e2UkfsY5XhExWbQ1KKmuqheRXlVjKoXN6em/eyCtzOjXvZ+JayNjo1+XnZkJRbsQBw8142/l7GY4L4eVmTkGTZFmDWVjLCgnw5dcN4Cs1TN+9TvezzD4Kt0+lQqfOuzdk5KpMXnzKpFB2gs+ALpX8jna74PiVRiWyBAPoKhA8++ACVSmVogQD6CoThw4eTnZ39ShUIoB9/4IcffqB8+fKEhoYyd+5cFApFoc2LRo8eTYcOHQgJCUGhUHDw4EHCwoznT58yZQoeHh54e3vzxRdf4OnpaZjZoXz58vz555+cOHECuVzOzJkziY2NNdmGJQQHB3P06FF69+6Nra0tnp6ebNu2jW7duln8t17FO3VD+XL7KSr5uFPNz4NNl+8Sm5rJGzX0g8LMOXKZ+LQsvu2sHxRu9dlw/FwdKevpilqjZcf1Bxy49ZDp3fNGcP/t5HUq+7gTIHdCpdHyv7vR7Lj2gAlt6xRZHDmXjmLXpjea+IdoYyOwrlwfqZMbqr/10zrZNOyA1MmV7H1rAbCu3kQ/s0JiHBKZDKuKtbAuX42snSsM21Tfv45NzWZoEx7ldWFo0A71/WtFclbLuXAUu3Z90MQ9RBvzAOuqDZA6y1FdzY2hUUd9DLl9tq1rNEWXqkCTFItEKsMqtDbWFaqTtX25YZtS7yCkTq5oEh4hdXLFpkE7kEjIOWf5cRyeSD+8A/nbH5ATdQ/Vg1s4NGqNTO5J5gl9tw/nTr2RucpJ/mOB0XoODVqS8+A26ljTQY6cWnXBucObKFbOQ5OUgNRZP56CTpmNLsfy80cD9GtWiy/W7KFSgDfVg33589RVYhRpvNlQf6M7e8f/iE/J4Lu+7ZBKJVTwNa50c3eyx9ZaZrT8twNnqBTgTaCnGyq1hmM3H7D93A2+6Fl4iy5LUl44gn27vmjiovRTnlZtiNRZTs4V/Sw7to07IXF0MexnqvAL2NZvg32b3ihP7UFi74ht0y6orp0ptu4LyvOHse/wtmkMl4/rY2jSGYmTK9m79VO9qW6ex7ZBW+zb9UV5Ypc+hmZdUV07/Y91X0j860/8R40n6+4tssJv4NamI9aeXij2bgeg1NvvYeXuSczcaQCknzuF77CPcWvXWd+Fwc0D7/eGk3XrBurcByCP13uTffcWOXHRSKyscapVD9fmbYhdNKfo4ti6Af/RE8i6E05m+HXc23XG2tMbxW79zAJe/QZh7eHJo5+nApB29iR+H3yCvH1X0i+exUruju+gD8i8dQN1kj4OZeQDo9/QZKSbXf6yur/ek5kzplG+QghhoZXYvXsHCQnxdOzYGYDly34jMTGRT8Z+CkCHjp3Y/tdWFi9aSPv2Hblx8zr79u5m3KcTDNusV68BWzZvomy5clSsGEpMdDSrVq6gfv2GyGQyAM6fPwc6Hf4BAcRER7N06WL8/QN4rU07i8T1ImSODjiWzxsQzqFMAC7VQ8lJSiE7qnhHkU87uB33/iPJibxLzv1bODZ+DZm7JxnH9gLg0rUvMld3FCuNx/9waNga5f1bqGOijDeoVpks02ZlIgXTtBZSUq7fANsPJfNGWzkx8TnEJKjo2c4dpUrH0XN5L1hG9fMmKUXNqm36Y3j3sRQ6NXfj/TdKsfNIMr6lrOnZVs6OI8mGdf46qOCHTwLp2VbO8QvpVAi2o21jVxassfwMGf1a1eeLFVupVNqX6mUC+PP4RWIUKbzZVF+JO3vLIeKT0/juXf3sMWuPnMNH7kIZH/31+uLdKH7ff5o+LfLuW5tXrcDKg6cJDfSmarA/UQlJ/LL9CM2rVjCaiUwQnqVEVyBkZWURGhqKt3dek/PmzZuTlpZGuXLlCAx8tea248ePJzY2lv79+yOTyRgyZAjt2rUzXHjN0Wg0fPDBBzx8+BAXFxfat2/PrFmzjNJMnTqVjz76iNu3b1O9enW2bduGTe4oyF999RX379+nXbt2ODg4MGTIELp3705KiuXfQE2ZMoWhQ4dSrlw5lEol6enpHDhwwCS/xa1dWBApWUoWHf+bxxnZlPd0Ze6bzfBz1TfzepyeRWxqhiG9Sqtl1qFLxKdnYWslo5ynC3PeaEbTcn6GNNkqNd/vO0d8mj5NsLsz33ZuSLuwIJPftxT17cso7RywrfcaEkcXtImxZP31m2H+ZamjCxInN0N6icwKm8b6hwzUKjRJsWRu+w3NUyO767spgG2D9kicXNFlpaO+fwPlScuOEp4XwyWU9g7Y1m+DxMEFbWIMWVuXoEtT5MXwVDNPicwKm6Zd8mJIjCVz62KjWSIkVlbYNGyP1NUDnSoHzYMbZO75w6gpt6VlXzpFiqMzzu16IHNxQxUTRdKiHw0jZMtc3JDJjR+2JXb22FWrR+rm381u06FxGyRW1rgP/NhoedrujaTt+bNI4mhfsyIpmdks2neKhNRMyvt68Mugbvi565swP07NIDb5xfpsZ+Wo+X7TIeKS07C1tqKMlzvf9W1H+5rPN9q4JahvXSLbzgHbBm0N+1nm1sWG/Uzi6IzU5amm3aocMjf9il2L13Hs8zG67AxUty6jPFE0x8HzUN+6SLa9A7YN2uUe7zFkbv71qRhckDrni2HjAuxa9cTx7U/0MYRfQnmi8IF7LSntxBHinF3wfPMdrOTuKCMfEPn9F6gT9DfPVnIPrD3z+iKnHNqL1M4e9w7d8B4wFE1GBplXLxK/aokhjdTODp8ho7By90SXo0T5KIpHs6eSduKIye9bSur/DiNzdqHUW/2xcndHGfGAyCkTUCXE5cbhbhRH8sE9SO0dcO/UHZ/3hqHJSCfjykXiViwu6CcsrlnzFqSlpbL2j9UkJSVROrg0kyZ/i1fu/Y1CkURCQt5DjI+PL5OmfMeSRQvZsf0vPDzcGTJ0BI2b5PVz7t3nbSQSCat+X0Fi4mNcXV2pV68B/QYMNKTJzMhgxfKlPH78GGdnZxo1bkL/AQMNrRj+Sa61q9DwwErD/ytN/xyAqN83ceX9CQWt9o/IunCCZEcnXDq8gcxFjiomisfzv3/qmiHHyj3/NcMB+xr1Sdm4rDiybKKkXL8BNu9XYGMjYchbXjg5SLn9IJvJ8x6Rrcx7cVLK3croPUpisprJvzxiYA9PZk0IIilZzfbDyWzel9cV6E6kkh8Xx/BOVw96dXAnPlHN0j8TjComLKV9nUqkZGSyaOf/SEhNp7xvKX4Z0Rs/D/3Lh8ep6cQq8u79tVodc7Ye5lFiMlZSKQGl3Pioe0veaJLXamxwhyZIJPquDPHJacidHGhetQIfdm1h8fwLJZtE9zyjggnPRavVEhYWRq9evfjmm29eeP3Dhw/TsmVLFAqFYVaEf5NNmzbx5Zdfcv26+WmjLClz6ddF/hv/BE2G+dHu/1MsMHbGv0HaA8vOy14c3Fs3K+4sWETOnaLpdvWPKwHHxqPjJWM6Pq36H+pCU4RsZv47HiRf1a3QtsWdBYuoPrTasxP9y7lWKLqXHv+k/reHFXcWXtma108WdxYswq51/+LOwktZcqD4fntQwbPe/2eV2BYI/4SIiAj27t1L8+bNUSqVzJs3j/v379O3b9/izlqRcHJy4scfn29wQ0EQBEEQBEEQBKFkERUIr0AqlbJ8+XLGjh2LTqejSpUq7N+/v0jGI/g3aNu2ZLxVEARBEARBEATh/wfR3t6yRAXCKwgMDOT48eMW216LFi2ea555QRAEQRAEQRAEQfiniQoEQRAEQRAEQRAEoUQqAUMW/auIOTsEQRAEQRAEQRAEQXgmUYEgCIIgCIIgCIIgCMIziS4MgiAIgiAIgiAIQokkhpizLNECQRAEQRAEQRAEQRCEZxItEARBEARBEARBEIQSSbRAsCzRAkEQBEEQBEEQBEEQhGcSFQiCIAiCIAiCIAiCIDyT6MIgCIIgCIIgCIIglEha0YXBokQLBEEQBEEQBEEQBEEQnkm0QBAEQRAEQRAEQRBKJF2xjqIoKcbfLhqiBYIgCIIgCIIgCIIgCM8kWiAI/0rpt+8XdxYsIluRXtxZeGXOgd7FnQWLSI9VFHcWXpnz3TvFnQWLSL0TVdxZsAgbZ/vizsIrUyvVxZ0FIVeW9r+/PwFUH1qtuLNgEZd/vVLcWXhlNUaUjPeEKqWquLPwyrLdA4o7CxZhV9wZeEliGkfLKhlnFkEQBEEQBEEQBEEQipSoQBAEQRAEQRAEQRAE4ZlEFwZBEARBEARBEAShRNJqizsHJYtogSAIgiAIgiAIgiAIwjOJFgiCIAiCIAiCIAhCiSQGUbQs0QJBEARBEARBEARBEIRnEhUIgiAIgiAIgiAIgiA8k+jCIAiCIAiCIAiCIJRIWtGFwaJECwRBEARBEARBEARBEJ5JtEAQBEEQBEEQBEEQSiQxiKJliRYIgiAIgiAIgiAIgiA8k2iBIAiCIAiCIAiCIJRIumIdBEFSjL9dNEQLBEEQBEEQBEEQBEEQnklUIAiCIAiCIAiCIAiC8EyiC8NLePfdd1mxYgUAMpkMPz8/OnXqxPfff49cLi/m3D0fiUSCra0t4eHhlC5d2rC8e/fuuLm5sXz5crPrHT58mJYtW+Lm5kZMTAx2dnaG786cOUP9+vUB0P3Do5XYN2iNQ9OOSJ1dUcc/In37alQPbhW8gswKx9bdsavRCKmzK9qUJDIO/UX2+aP676UyHFp0xr5WE6QuctSPY8nYvY6cW1eLNA6n5u1xadMNmascVXQUig1LUd65YTat+4APcWrYymR5TnQksVNGA+DYsCUeA0aapIn88C1Qqyya9ydsazfDtkEbpE6uaBJiyNq3AXXUnYJXkFlh17QjNlXqIXV0QZuWTPbxXeRcPmmS1LpSHZxef5+c8EtkbPy1SPJfENc2nXHv/AYyN3dyHkaQ8PtCssKvFZjeuXFL3Lu8ibWPH9rMTDIunyNh9WK06Wn/YK7BulojbGq1QOLogjYxFuXRrWii75tNK/Mvh8MbI0yWZ/z+I1pFvP4/Uik2dVpjHVYHiZMrWkUCyuPb0USEF1kMjk3b4ty6GzJXN1QxD0n+cxk5d2+aTSt/5wMcG7QwWa6KiSLuuzEmy+1rN8Jj4MdkXT5D4uKfLJ11498qAecp947dKNXjLazcPVBGPiB68TwyrxX8e24tXsOzZ29sff3RZGaQdv4MsUsXoklLNUnr2qwlQZ9OJOXk/4j87qsiiwH+m3Hs2bGZrZvWkJyUSEBQMAMHjyKsSvUC01+7epEVS+bxMPIBcncPuvXsS9uO3Q3f79+9jSMH9xAVcQ+AsuUr0qf/ECpUrGRIs371UjasWWYcn5s7S1ZttVhcJeX4fh7uTepQ9pP3ca1VBTs/L871HEHctgPFnS0DxyZtcWrVBZmLG6rYh6RsWkHOPfNl4dZ3OI71W5gsV8VEET91LAAODVvhULcZ1r6BAORE3Sd1+xpUkXeLLIYn+nYpRbtmbjg5yLh1P4sFf8QSGa0sdB1Heyn9XveiUU1nnBxlxD1W8dv6OM79nQ6AVAp9u5aiRX1X5C5WKFLU7D+RzLodj4tkkL6Ne4+w6q99JCanUCbAl4/7v0nNsApm0x46c5FN+45y68FDctRqygb4MviNzjSoXskoXVpGJgvWbeXwmUukZWTiV8qTUf160rhmFcsH8C8ipnG0LFGB8JLat2/PsmXLUKvVXL9+nffee4/k5GTWrFlT3Fl7bhKJhIkTJxoqQ16Es7Mzmzdvpk+fPoZlS5cuJSgoiMjISEtm85lsq9bHqdPbpG1dgSriNvb1W+L67liSZk1Am5Jodh3Xvh8idXIh9c/f0CTGIXVy0V8Zcjm27YldjUakbV6KOj4G25CquL7zEYoF36COiSiSOBxqN0b+5kCS1ixGefcGTk3bUerDL4mZ/BEaxWOT9Ip1S0nevMrwf4lUhs+XM8m6YPzgrc3KIPrrfJUIRVR5YB1WG/s2b5K5ey3qqLvY1mqKU+8PSPl1CrpUhdl1HHsMQuroQub2VWgV8UgcnUEiM0kndXHHoXUPVJG3iyTvhXFq0Ayv/kOJW/oL2eHXcH2tI/6ffcuDsUNQJyaYpLerWBmfEWNJ+H0R6RdOYeXuiff7I/EZMpromd/8Y/m2qlAD22bdUB7ahCb6PtZVG2LfbTAZq6ahS0sucL30FT9ATt6Nli4r3fBvm4YdsA6tTfaB9WiT4rEqXRH7zgPJXD8XbcIji8dgX6sRbj0Holi3mJx74Tg2aYPniC+I+/Zjs8dF8sZlpGxdbfi/RCbFa8J0si6aVkjJ5J64du+P8s51i+c7v5JwnnJt2hLfwR8QveBnMq//jXuHLgRP+pHbI95FlRBvkt6hUhUCPv6MmCXzST1zAmsPT/w/GIP/qLFEfjfRKK11KW983xtOxt+XLZ7vkhDH8aMHWLZ4DoOHj6Fipars27WN7yaNY9b8lZTy8jZJHxcbzQ+TPqV1uy6MGvsV4devsnjBTFxc3WjQuAUA165eoknz1wgJq4KNtQ1b//yDbyd+wsxffsfDs5RhW4FBZfjqu1mG/0ullmu8WlKO7+clc3Qg9Uo4D1dsovaGecWdHSP2NRvi+voAkjf8Rs79cBwbvYbHsAnE/zAGjcL0HJWyaTmpf/2Rt0Aqw3v8NLIunTIssi1fmawLJ0i5H45OpcKpdVc8h39B3NRP0KaYvyewhJ7tPejexp1Zy6KJjsvhrU6efPNxEMO+vEuWUmt2HSsZfDOmNCmpan5Y+JDHCjWl3K3Iys5L/0Z7Tzo0kzNrWTSR0UoqlLbjo4F+ZGZp2XYgyaIx7DtxjlkrNvDp+72pVrEcm/cf4+Opv7B2xkR8PN1N0l+8cZt6VcMY3rsbTg4ObD98gk+mzWfpt+OpWEZfgaNSqxn53Rzkrs788PEQvNzdiEtU4GBvZ7I9QSiM6MLwkmxtbfHx8SEgIIC2bdvy1ltvsXfvXsP3Go2G999/nzJlymBvb0/FihWZPXu20TbeffddunfvzvTp0/H19cXDw4MPPvgAlSrv4S4mJoZOnTphb29PmTJl+OOPPwgODubnn382pElJSWSqrkcAAQAASURBVGHIkCF4eXnh4uJCq1atuHz52TcvI0eOZNWqVVy9+uJvqwYMGMDSpUsN/8/KymLt2rUMGDDghbf1qhyatifr3BGyzx1BkxBN+vbVaFOSsG9g+nYewCakKtZlKpK8fAaqu9fQJj9G/fAe6si8t+R2NRuTefgvcsKvoFUkkHX6IDm3ruLQtH2RxeH8WhfSjx8g4/h+1LGPSN6wFI0iEafm7cym12Vnok1NNnxsSpdD6uBI+omD+RJilE6bmlxkMdjVb03OpRPkXDqONjGWrH0b0KYqsK3VzGx6q7KVsAqqQPraeagf3ESbkoQmOgLNo3vGCSUSHLsPJOvodrRmbiqLmrxTD1IO7SH10G5yoqNI+P1XVIkJuLXpbDa9fflQVAlxJO/Zijohjuzwa6Qc2Ilt2ZB/NN82tZqhunYG1bXTaBXxKI9uRZuejHXVRoWup8tMR5eZZvg8/WrFOrQ2OWcPoHlwE11qEqqrJ1FHhGNTq3mRxODcqjMZJw+SefIg6rhHpPy5HI3iMY5N25rPe3Ym2rRkw8c6qBxSe0cyTh4yTiiR4v7uR6TuXI/6selDo6WVhPOUZ/c3UezbiWLvTpQPI4lZ/Auqx/G4d+xqPuaKlciJjyXxr02o4mLJvP43Sbv+wr58ReOEUimBY78gbvVycmJjiiTv//U4tm9ZR6s2nWjdrgsBgcEMHDIKT08v9u7cbDb9vl1b8SzlzcAhowgIDKZ1uy60eq0T2zatNaT5aNxE2nV6nTJlK+AfWJqhIz9Fp9Xy9+XzxmHJZMjlHoaPq6vlWlqWlOP7eSXsOcqtr38mdsu+4s6KCacWncg4dZDMU7llsXkFGkUijo0LKosstGkpho9NUFkk9o5knj5sSKNYOZeM/+1F9SgCdXw0yWt/BakE25CqRRpLt9burNv5mJMX04iIVjJzWTS2NlKa13cpcJ02TeQ4O8j4dn4UN+5mkZCk4vqdLO4/zKtMDy1nz+nLaZy7mk58oorjF9K4eC2D8qUt/wC+ZscBurZsRLdWTSjj78uYAb3w9pDz576jZtOPGdCLfl3bUqlcMEG+Xozo051AXy+OXbhiSPPXoROkpmfw0yfDqF6xHL6lPKgRWp6Q0gEWz/+/jU5XfJ+SSFQgWMC9e/fYvXs31tbWhmVarZaAgADWr1/P9evXmThxIp9//jnr1683WvfQoUPcvXuXQ4cOsWLFCpYvX27UfaB///5ER0dz+PBh/vzzTxYtWkR8fN7FUKfT0alTJ2JjY9m5cyfnz5+nVq1atG7dmqSkwmtDGzVqROfOnZkwYcILx9yvXz+OHTtmaG3w559/EhwcTK1atV54W69EJsPKL5ic238bLc65fRXrIPPNvGzCaqF+9ACHZp3w+Oxn3D+ZhlOH3mCVV34SK2t0+d7S69Q5WAcX0QOgzAqboHJk3zCu+Mm+cQnbsqHPtQmnxq3JvnkFTZLxG3GJrR1+3/2K3w+LKTXic6wDy1gs20akMmS+QajuG7/tUd27gVVAWbOrWIdUQxMTiV3DtriO+gGXYZOwb93DqCwA7Jp2QpuZTs7lE0WT98LIrLArU4HMKxeMFmdeuYBdSJjZVbJuXcfK3RPHGnX1m3B1w6l+EzIuniny7BpIZUi9AtBEGnct0ESEI/MNLnRVx75jcBz0NfY9hiELKGf0nURmBZp8LVjUKmR+RbBfyaywDixr5ri4gm2ZigWsZMyxYSuU4VdN3ma6dHgDbXoqmScPFrCmBZWA85TEygr78iGkXzxntDz94jkcQs03fc28cQ1rz1I419F3bbNyk+PSuDlp504ZpfPq3R91ajKKfTstnu/8/otxqFQq7t25RfWa9YyWV6tZl/Cbf5td59bNa1SrWddoWfVa9bh35yZqtdrsOjlKJWqNGidnZ6PlsdEPGdK/OyPe78WsH78mLjb6FaJ5Skk5vksCmQzrwLIow68YLVaGX8amzPOdTxwatEJ5y7QsniaxsUUitUKXmV5gmlfl7WmNu5s1F69lGJap1Tr+vpVJWDmHAterX92Jm/cyGd7Xl5UzQvhlUlne7OiJ9KkB9K/fzqR6qCN+3jYAlAmwpVIFB0MXB0tRqdXcvB9J/WrG3Q/qVQvj6q17BaxlTKvVkpmVjaujo2HZ0fNXqBpSlmlL19J+6Kf0GTuF5Zt3odGab5UhCAURXRhe0vbt23FyckKj0ZCdnQ3AzJkzDd9bW1szefJkw//LlCnDiRMnWL9+Pb169TIsl8vlzJs3D5lMRmhoKJ06deLAgQMMHjyYmzdvsn//fs6ePUudOnUAWLJkCRUq5N1wHjp0iKtXrxIfH4+trS0A06dPZ8uWLWzcuJEhQ4YUGscPP/xAtWrVOHbsGE2bNn3u+L28vOjQoQPLly9n4sSJLF26lPfee++513+aUqlEqTTul6ZUa7C1Mm3Gnp/UwRmJTIY2PcVouTY9Famzq9l1ZO6lsC5dAZ1aRcqqOUgdnXDuNgCJgxNpfy4B0L/Fa9Ie1f1wNEnxWJerhG1YLaPmw5Ykc9LHocnXOkCTmoKdi9sz15e6yLGrXIvEpbOMlqtiH5G4Yi6qR5FI7e1xbtUZ73HfE/vtGNTxln1DJnFwQiKVmfTx12WkIXUqoCzcPLEKLIdOrSJ940Ik9k44tO+jf4uxfaU+TUBZbKs3InXJdxbN7/OSubggkclQ52tuqUlRYOVq2owQIPv2DWLnTcN31AQk1jZIrKxIP3eS+OXz/4ksAyCxd9SXR74bNV1WOlJHZ7PraDNSyd6/Hk38Q/3NfVht7HsMI2vjAjTR+psWdWQ41jWbo350D11yIrKgCliVrQwSyx8b0tzjQpuvu4U2LRnpcx0XbthVqknScuPWXzZlK+LQsBXxU8dZMLeF5KMEnKdkLq7640BhfByoFQqsa5l/I5158xpR078j8NOJSG30x0HqqeNEL5xjSOMQVgX3th25PWqQxfNszn8xjrTUFLRaDW75xlhyk8tJvmD+RUGyIhE3eT2T9BqNhrTUZOTunibrrF6xEHePUlStUcewrELFSnw45gt8/QNJSVbw59oVfDF2OLPm/46zi/l993mVlOO7JJA66q9z2lTjc5QmLQVbZ7dnr+/ihl1YDRS/zyk0nUuXvmhSksgOL7pxWuSu+keb5FTjirLkVDVeHtbmVgHA29OGaqHWHD6dwqTZkfh72zCsrw8yKazdrq8U2bg7EUd7GQunlEOr1Z9qV26J5+gZ07FQXkVyajoarRZ3V+NrtYerM6eSUwpYy9jqHfvJUubQumHei73o+MecvxZOu8b1mDX+A6Ji4/lp6TrUWi2DenayaAxCySYqEF5Sy5YtWbBgAZmZmSxZsoRbt24xcqRxP/OFCxeyZMkSIiIiyMrKIicnhxo1ahilqVy5MjJZ3oOyr6+voUtBeHg4VlZWRm/1y5cvbzRQ4/nz50lPT8fDw8Nou1lZWdy9++xBaipVqkT//v0ZP348J0682Nvd9957j48++oh33nmHkydPsmHDBo4dO/ZC2wB9JcbTlS0AYxtXY1zTggeHei4FtBuSSPTVyalrF6BTZgGQvmMNLn0/JG3rClCrSNu+CpfX38N9zI+g06FJiifr/DHsaz9/JYtF8iwBeHb7J6eGLdFmZZB5yfgNd879W+TczxukTXn3Jj6fT8e5RUcU63+zQIbNMRNDQW24JBLQ6cjYuhSU+oq4rP0bcew5mMzda0EqxbHbQDJ2rkaXlWF+G8UlN+/m2PgH4fXucBI3/UHGlfNYublT6u1BeL8/irhFs8yuU2TM5LGg4tAlJ6BKzmvBooyNQOrkhk3tFmTlViAoj2zBtnUvHPuNB3ToUhJRXT+LdaW65jdaFAr52z/NsUELtFkZZF05m7eqrR3u/UeRvGYh2ox/dkBLs/5z5ynT47ugorANLI3fkJHEr/2d9AtnsXL3wGfgUPw/GMOjOT8htbcn8JPPeTh3OppUy96AP9t/MQ7jucR1urz9xHzq/OlzAzSzztaNq/nfkf1M/mEONja2huU16zQwShcSWpkPB/Xm8IFddHm994sG8HxK0vH9n5P/7y4xs8yUQ73csrh6tsA0Tq264lCrMQnzJlt0HKYW9V344B0/w/8nz9W3jDUbSSGhSKWQnKph3u8xaHVwNzIbdzcrerT1MFQgNKvrQosGrkxf8oiIaCVlA+0Y/JY3iclqDp58vgf7F5H/+NaZWWbOnuNnWbJxBz+NHYa7a163Da1Wh9zFmQlD3kYmlRJWtjSPFSms+mtfia9A0IpRFC1KVCC8JEdHR8qXLw/AnDlzaNmyJZMnT+abb/QDpK1fv56PP/6YGTNm0LBhQ5ydnfnpp584ffq00Xae7vYA+hODNrcpUUEzGTy9XKvV4uvry+HDh03Subm5PVcskydPJiQkhC1btjxX+ic6duzI0KFDef/99+nSpYtJJcbzmjBhAmPGGI+enPLN8OdaV5uZhk6jMXnDLXVyQZtu/kZOk5aCNFVhuCkHUMdHI5FKkbm6o0mMQ5eRRsqq2WBljdTBCW2qAsf2vdAoTAfMswRNuj4OWb6+pTJnVzSpz74oOTZuRcbpI6Ax3zTVQKcjJ+IOVl6+r5Jd85vOTEen1SB1ckHz1HKJgzPaDPNloU1P1b99yq08ANA8jkUikSJ1dkNiY4vMzROnXk/tD7kXT7cJ80hdMAltctGOiaBJTUWn0WCVv2xc3FAXMDCke7e3yAq/jmL7RgByIu8Tp8wmaNIMHq9fgSbZsoMtmaPLytCXh6MzTzdOlNg76cc1eE6a2AisQmsbbTd7+zKQWSGxc0CXkYpN405oUy0fkzb3uJDmewMmdXJFm/bs48KhQSsyzxw1Oi6sPH2w8vTCY+hneQlz9yn/2WuJ/eYjNI/jLJL/J0rCeUqTmqI/DuTGrW6s3OSok80fB6Xe7Evmjb95vGmdfsGDe0RnZ1Fu2lziVv6GlZscGx9fgid+n7dSbllU2bqfW0P7k2Op5vL/4TicXVyRSmUkK4yPsZRkBa5u5ltNuMk9UJikT0Ymk+Gcr9XLtk1r2LRhFRO/nUXpMuULzYudnT1BwWWJiX74EpEYKynHd0mgzdBf5/K3/JA5uzxXWTg2aEHWuWOg0Zj93qllZ5zbdOfx/G9RR1t2oO3Tl9IJv5f3wszaWt8C68ksCU+4uliZtEp4WlKyGo1GZzRaf1RMDu5u1ljJQK2BgW94s3HXY46e1Z+3Ix4p8fKw5s0OnhatQHBzcUImlZKYbHx9SEpJM6oQMGffiXN89+tKvh89mHpVjbtZespdsZJJkT3VSi3Yz4fE5FRUajXWVuKxUHg+Yk+xkK+//poOHTowfPhw/Pz8OHbsGI0aNWLEiLwp0Z6nRcDTQkNDUavVXLx4kdq19Tfwd+7cITk52ZCmVq1axMbGYmVlRXBw8EvlPTAwkA8//JDPP/+ccuXKPXuFXDKZjH79+jFt2jR27dr1Ur8N+gEpn3S/eCL7ObovAKDRoI5+gE2FKuRczxv4yaZ8FZQ3LphdRfXgFnZV6iKxsUWXO9K8zNMHnVaLJiXfQ5BahTZVAVIZtlXqorxy2swWLUCjJifyLnZh1cm6lPcbdmHVybxceL9525DKWHv58fj4tOf6KeuAMqgeFcFMEloNmphIrMqEoQrP69NqXSaMnFvmB/VUP7yLTVgtsLYFVW5ZeHih02r1FQs6HSmLjGctsG/eBYmNHZm5AzQWOY2a7Pu3cahWk/Rzea10HKrWJOP8KbOrSGxsQZvvRupJH8NnvzywDK0GbfxDZEEhqO/m9ZOWBYWgvlfw9JP5SUv5ozNXAaRR65dLpViXr4bq9iULZNr0N1RR97ALrUb2lbzjwC60WqFvugBsK1TC2suXxHx9oFVxj4jNN92ba+feSOzsSf4/9u47vsbrD+D4567s5GYvIhEy7NRetVftVZRqbYq2SlvVSdVo1Vaq9qzau2aNojalRsxYIXuPm7t+f1xu3ORGghvB77xfr/siT8557jl59jnfc561i8zONv7cXoPzlF6jIePaFRzCqpL8zyHjcoewKiQfO2w2j9TaBn3OBwrjcSBBdfc2V4b0Nvm117t9kdnZEfnbTNSFMPndq1gPhUJBYOlgzp09QY3a2RPSnjt7gmo16prNExxajpPHTevz75njBJYORf7YQ8KmdStZ98dSvv5+MqWC8p9vR63O4t6dW5QpV/EZa/OY1+X4fh1otajv3MA6pCKZj0V0WIdUJPP8ySdkBKvSZZF7+JB2dJ/Z3zs0aoNjs47EzhmP+k7Bxu8/jQyVjowY0zH88Ylq3ihrz407hs4JuQzKB9uxeF3ejUeXrmdQv7qTSQBMMS8r4hLVaB4e/tZWklyvA9Tp9BYfNaaQywktWYLj5y/RoHqYcfnx85eoVzXv6Nydh08w7tdljP2oD3Ur556osmJwILsOn0Cn0xnfpnL7fjTuLsrXvvHgdZ3MsKiISRQtpEGDBpQrV47x4w09EKVLl+bkyZPs3LmTK1eu8M0333DixJMviDmFhobSpEkTBgwYwPHjxzlz5gwDBgzA1tbWGMLUpEkTatWqRfv27dm5cycREREcOXKEr7/+mpMnn3zSf9yoUaOIjIxkz549JstnzZpF48aN88w3duxYYmJiaN7c/JsC7t27R2hoKMePF97kcel/78C2an1sqtRD5uGLQ6vuSJ3dyDhmuLGwb/42jm9nzwWh+vcfdOmpOHbuj8zTF0VACA4tu5F58qAxrE7uF4h1uapIXTxQBATj3PtTJBIJ6QcLb5KvlD1bcKjTGPvajZB7F8P57d7IXNxJPWh4u4eyfQ/cen2UK59D7caoblxBbaZV36lVF2zKhiFz90JRPADXnkOw8gsg9e+dhVKHzGN7sQ6rg1WlWkjdvLFt0hmp0oWs04ahLTYN2mHXJvtNHVn/nUCfkYp9m55I3b2R+5XGtlFHw2SJGjVoNehiIk0++swM9FmZ6GIicz+kF5KEbetRNmyBU4NmWPn64dFzAAp3TxL3bAPAvVtvvD/41Jg+7fQxHKrVQdmkFQpPb2yCy+L5/gdkXLuMNqHwow8eyTp9EEW5GsjLVkfq4ol1vbZIHV1Qnze88syqdktsmmW/ilUR9ibywPJInN2RunphVbsliqBKqP/NfhCRepVAXqoCEidXZL4lsW0/ACQSsk6av3l8Xil/bcW+dmPsajZE7lUMZcf3kbm6k/a34bhwatsdl55Dc+Wzq9UY1c0raO7fMf2FRo3m/h2Tjy4jHX1mhiFtflE8z+h1OE/FblyDS7OWuDR9C+viJfDpNxiFhxfx27cA4PV+P4oPz56UN/n4EZS138T1rbYovHywK1MenwEfkh5+CU18HHq1GtWtCJOPLi0VbXo6qlsR6POY7O//sR6t23dl766t/LVrG3fvRLB43gxiY6Jp1rI9ACsW/8rMyT8Y0zd9qx2x0VEsnjeTu3ci+GvXNv7avY22HbOHHWxau4JVy+Yz+OMv8PDyJiEhjoSEODIy0o1pli74hQvnzxD1IJKr4ReYPP4bMtLTaND4reeuE7w+x3dByeztcKoUilMlQ2ONXcniOFUKxcbP8lGBTyt1/zbsazbCrkYDw7bo8B4yF3fSDhveGOHU+h1cegzJlc++ZkOyIq7m3hYYhi04tepKwu9z0MZHI3VUInVUGhrZC9GmvfG83dKdWm844u9rzbDexVBl6ThwLLsxfHgfX97v4Gn8efv+eBwdZAzo5o2vlxVVKzjwdkt3tu3L7qg4fi6Vrq3cqVrBAU83BbXecKR9Uzf+OWP54TLvtGrMpr8Os3nfEW7eu8/UJWuIik2gYxPDELVfft/I6F8WG9PvPHyCMbMX81HPTpQPKklcYhJxiUmkpmdHsXVqWo+k1DSmLFnD7cgoDp0+z+JNO+jcrHDeoiS8vl7v5qYXbPjw4fTu3ZuRI0cyaNAgzp49S9euXZFIJLzzzjsMHjz4qXvqly5dSt++falXrx7e3t5MmDCBCxcuYGNjeGWMRCJh+/btfPXVV/Tp04eYmBi8vb2pV68eXl653w2dF1dXV0aOHMmXX35psjw2NvaJkRNWVla4u+eejOkRtVpNeHg46enpeaZ5Xqrzx0i1d8C+cTukjs5oou6StHgyukRDT4PU0RmZc/bwCn2WisSFP+HYpieuQ8agS09Fdf44qbvWGtNI5Arsm3ZC5uqBPkuFKvxfklfPRZ9ZePVIP3UYqYMjylZdkDm5oI68Tcyscca3KsiULshyTHwlsbHDtnKtPOczkNrZ49rjA2ROzugy0sm6c4Oon78mK+Ka2fTPS33pFBl29tjUbWUYyhBzn9RVvxjD26UOSqSPTzyoVpGycgZ2zbri1GcU+oxUsi6eJuPA5kIp37NKPXqQaEcn3Dr2QObsQtadW9z78Rvj68Fkzq7I3bNvRJIP7kZqa4tz87Z4vNsfXXoa6Rf+JXZlYc07YZ7m6llUtnZY12iKxM4JXdx9MjbNR59iuCGS2jsheSx8WCKTY/VmGyQOStCo0cY9IH3TPLQRl7PTyOVY1WqBVOmGXp2FNuIS6TtXQlZmzq+3iIzTR0i0d8Dprc6G4+L+HWJnjzfO9C1zckFu7rgIq0HS2kWFUqZn8Tqcp5L+3ofM0QnPbu8hd3VFdSuCiNFfoI4x9OopXNxQeGQfB4l7dyKztcOtdQd8+n6ANi2V1HNneLD4t0IpX0G9ivWoU68xqSnJrF21mIT4OPz8S/Ll6J/w8PQGICEhjtiY7N5VL29fRo3+iSXzZ7Jz2wZc3NzpM+BjatZpYEyzc/tGNBo1kyd8Y/Jdb7/Tmy49DJMix8VGM33SGJKTk3ByciY4tBzjJv9q/N7n9boc3wWlrFKeWnuXGX8u+7PhnuvO0vWc6/v0b8SypIwz/yC1d8SxeSdkSsO2iJs70bgtpE7OyFxMh6pKbGyxqVSDpPWLza7Tvm5TJHIFbn1GmCxP/nMNKTvWms1jCet2xGGtkPJBd28c7GWE38jg26m3yVBlRyp4uCpMogliEzR8O/U2/bp6Meu7QOISNGzeG8+6P7OHSc5d+YB323swuIc3Skc58Yka/jyYwKotlh821rR2VZJS01i4bhuxickE+vkw9Ysh+HgYtkFcQhJRsdkdEhv3/I1Wq2PSwlVMWpj9utZW9Wry7WBDx42XuyszvvyIqUvX0GPkD3i4ONOtRUN6tjPfCfg6EREIliXR5zXQXngp3b17Fz8/P/bs2fPEyIBXXfSo94q6CBaRmVB4ryp6URz9Ct4Q9TKL+e9mURfhufnULFfURbCIpKuWHQNbVKwcbYu6CM8t6nzuXkOhaOin/lHURbAI1+m5e6lfRf/OPZd/opdc2OCwoi6CRQxM/66oi/Dclg9+UNRFsAjnNxoVdRGeybhVLyZi1ZyvuhVwWPYrREQgvOT++usvUlNTqVChAvfv3+fzzz8nICCAevXq5Z9ZEARBEARBEARBECxEzIHwklOr1Xz55ZeUK1eODh064OHhwf79+3O9vUEQBEEQBEEQBEEwpdPri+xTWBISEujZsydKpRKlUknPnj1NJtrPz8CBA5FIJEybNu2pv1tEILzkmjdvnucEhYIgCIIgCIIgCML/l+7du3P37l127NgBwIABA+jZsydbtmzJN+/GjRs5duwYvr6+z/TdogFBEARBEARBEARBeC3pdfmneZVcunSJHTt2cPToUWrUqAHAvHnzqFWrFuHh4YSEhOSZ9969ewwdOpSdO3fSqlWrZ/p+0YAgCIIgCIIgCIIgCBamUqlQqVQmy6ytrbG2fvbXmf7zzz8olUpj4wFAzZo1USqVHDlyJM8GBJ1OR8+ePfnss88oV+7ZJ+YWcyAIgiAIgiAIgiAIgoVNmDDBOE/Bo8+ECROea50PHjzA09Mz13JPT08ePMj7jR8//vgjcrmcjz766Lm+X0QgCIIgCIIgCIIgCK8lfSFOZpifUaNGMXz4cJNleUUfjB49mjFjxjxxfSdOnABAIpHk+p1erze7HODUqVNMnz6d06dP55mmoEQDgiAIgiAIgiAIgiBY2NMMVxg6dCjdunV7YpqAgADOnTtHVFRUrt/FxMTg5eVlNt/ff/9NdHQ0JUqUMC7TarWMGDGCadOmERERUaAygmhAEARBEARBEARBEF5TuldkEkV3d3fc3d3zTVerVi2SkpI4fvw41atXB+DYsWMkJSVRu3Zts3l69uxJkyZNTJY1b96cnj170rt376cqp2hAEARBEARBEARBEIRXQJkyZWjRogX9+/dn7ty5gOE1jq1btzaZQDE0NJQJEybQoUMH3NzccHNzM1mPQqHA29v7iW9tMEdMoigIgiAIgiAIgiC8lvR6fZF9CsuKFSuoUKECzZo1o1mzZlSsWJFly5aZpAkPDycpKcni3y0iEARBEARBEARBEAThFeHq6sry5cufmCa/BoynmffgcSICQRAEQRAEQRAEQRCEfIkIBEEQBEEQBEEQBOG1pCu6tzi+lkQEgiAIgiAIgiAIgiAI+RIRCMJL6cG/t4u6CBaRkZBR1EV4bprMrKIugkV4Vwst6iI8t5s7TxV1ESzCyVdZ1EWwiNfh2PCq5F/URbCMV+UdXU8QK9EUdREsQhlUIv9Er4Cwwa9+H9vZ2WeLuggWkdQyrqiL8Nw0CtuiLsL/Nb0IQbCoV//sKAiCIAiCIAiCIAhCoRMNCIIgCIIgCIIgCIIg5EsMYRAEQRAEQRAEQRBeS/m8zVB4SiICQRAEQRAEQRAEQRCEfIkIBEEQBEEQBEEQBOG1pBOTKFqUiEAQBEEQBEEQBEEQBCFfogFBEARBEARBEARBEIR8iSEMgiAIgiAIgiAIwmtJL2ZRtCgRgSAIgiAIgiAIgiAIQr5EBIIgCIIgCIIgCILwWtLriroErxcRgSAIgiAIgiAIgiAIQr5EBIIgCIIgCIIgCILwWtKJORAsSkQgCIIgCIIgCIIgCIKQL9GAIOSrQYMGDBs2rKiLIQiCIAiCIAiCIBQhMYTh/0yvXr1YsmQJAHK5HD8/Pzp27MiYMWOwt7c3m2f9+vUoFIoXWcyn5taqHR6duiF3dSPz1k0if5tF+oXzeaZ3btAEj87dsPYtjjY9jZRTx7k/fw7alGQAXJq0wG/4F7nynW/XDL06q9DqkZNnx0749HgXKzc3Mm7e5Na0qaT8ezbP9F6dOuPVuTPWPj6oHkQRuWQRsX/++cLKC+DUqCXKtzoic3ZFfe82cSvnkXnlgtm0Hv2G4Vi3Sa7lWfducferIdnrbNYWp4Ytkbt5oEtJJu3kYeLXLkGvVhdaPXJSVKqDddVGSOyd0MU9IHP/BrT3buSdQSbDumZzFGWqIrFzQp+aiOrYbtQXjr2wMsPrcWw4NngLp+YdkDu7kBV5m/hVC1BdvWg2rXvvj3Co0zjX8qx7t4n87kPjz1Jbe5w7vItd5ZrI7B1Qx0aRsHoRGedPFUodCqMe3p/9gE1IhVxp0s+dJHrGWMsW/iHbGo2wq/sWUkdnNNH3SN22EvWtK3lnkMmxb9QOm0q1kDoq0SUlkHZgC5mn/s5eZ+1m2FZviMzZDV1aCqoLJ0ndtRY0hXd829ZsjN2bLZE6Kg312LoCdUQ+9WjcHpuw2g/rEU/avi1knjpo+L1Uhl2D1thWrovUyQVN7APSdvxB1pW8j7Wn9efWjWxc/wcJ8XH4lQig74ChlC1fMc/0/50/y6J5s7lzOwJXV3fad+5Gi5Ztjb//a/cOZk77MVe+PzbsxMrKCoAd2zaxY/tmoqMeAODnH0CXd96jStUaFquXomJtrCo3MJ5bVQc3oY28aTatrFgp7DoPzrU8bemP6BKiDT9IpVhVbWw49zoo0SXEoDq8Fe2tcIuV2Rz7us1waNQGmZMz6gd3SVq/hKwbl82mde7+AfY1GuRarr5/h+iJnwJgV6sRdtXqofDxAyDrzk2St/6O+vb1QqtDQbnWrUrgiL4oK5fHxteTk50GE7V5b1EXy0TvbiVo29wbR3s5F6+kMGXudSLupOeZfsYPFXijgnOu5f+cjOfzsYZ7GJkUer/jT9P6nrg5K4hLyOLPv6JZsvo2hREhv37HXlZu+pO4hERK+hXjo97dCSsbYjbt/qMn2bBzH9cibpOlVlPSrxh9u7SnxhvZ14jNu/fz54Ej3Lx9F4CQwAAG9uhM2aBAyxf+JSNe42hZogHh/1CLFi1YtGgRarWav//+m379+pGWlsacOXNM0qnVahQKBa6urkVU0oJR1muIz4ChRM6eRtrF87i+1ZaS3//ElUHvo46JzpXermwF/EaMInLeLyQfO4LCzYPiQ4dT/OPPuPXDN8Z02rRUwge8Z5L3RTYeuDZugv+wT4iY9BMp587h2aEDIVOmcq57N7KionKl9+zQEb8PBnNjwnjSLl3Evmw5Ar8YhSYlhcRDh15Ime2rv4lb9/7ELp1D5tWLODV8C+/ho7nz5WC08TG50seu+I34NYuzF0hlFB87k7QTh42LHGo1wPXtXsQsmI7q2iUUXsXw6DcMgLjf5xduhR6SB7+BTYMOZO5dizbyJoqKtbHrMJDUJRPQpySazWPbqhcSe0cydq1ClxiLxM4BieTFBn29DseGXbW6uHbrS9yKuaiuXcKxXnO8Pv6We98ORRsfmyt93Kr5JKxbmr1AJsP3u2mknzr82DI5XsPHoE1JIubXH9HExyF3dUeXmVEodSisekTPnohEln0Zlzo44vvddNJPHqYwWFeojkPL7qRsWYr61lVsqzVE+f5w4qd/iS4p3mwe5TuDkdorSd6wEG1cNFIHR5DKstdZqRYOzd4mef0C1LevIXf3wrFTPwBSt/9eSPWogUOrHqRsWmKoR42GKHt9SvzUUeiS4szXo/tQpA5OJK9bgDYuCqmDE0izj2f7Zp2wCatNyoaFaKLvYx1cAeW7H5MwZyya+7eeu8yHDv7Fwnm/MGDwMELLlGfXji2M/W4kM+YsxsPTK1f6qAf3+eG7UTRt0Yphn37F5Uv/8dvsaSiVSmrVqW9MZ2dnz6y5S03yPmo8AHBz96Bnr/54+xYDYN+enUwc+zWTZ/xGCf+Sz10veVAY1vXaodq33nBurVAL23b9SVv+U57nVoDUJRMgS2X8WZ+Rml3+Wm+hCK1C5t7V6OKjkfuHYNu6N+mrZ6KLuffcZTbH9o1aKDu8T+KaBWTdDMe+dhPcBo0iesJwtAm596mk9YtJ3rIye4FUhtfIn8g4e9S4yLp0OTJOHyHpZjh6tRqHxm1x/+AroiaOQJeUUCj1KCiZvR3J58K5u2Q9VdbMKtKymNO9Y3G6tivG+OlXuBOZwftdSjD1+/J0H3yKjAyt2TxfTbyEQi4x/uzkqGDR9MrsO5x979K9kx/tWvgwflo4N++kE1rakVEfBZGapmHt1kiL1mHP4WNMX7SSEf3fo2JoEBt37ePTcVNYPm083h5uudKfvRhO9UrlGNSjEw52dmzbd4jPJ05j3oRvCQ70B+D0hcs0rVuD8iE9sFYoWLHpTz75fhLLp43Hw83FouUXXm9iCMP/IWtra7y9vfHz86N79+706NGDjRs3Mnr0aMLCwli4cCGBgYFYW1uj1+tzDWFQqVR8/vnn+Pn5YW1tTVBQEAsWLDD+/uLFi7Rs2RIHBwe8vLzo2bMnsbG5b5AtxaPD2yTs2k78zm2o7tzm/m+zUMdE49aqndn0dqFlyYp+QNzm9aijHpB+8Txxf27GNihHq64eNAnxJp8Xyeedd4jZspmYLZvJvBXB7WlTyYqOwqtjJ7Pp3d96i6iNG4jfuwdVZCTxe3YTvXULvu++ZzZ9YVA2b0/Kwd2kHNyF+v5d4lbOQxMfi1OjlmbT6zPS0SYlGj/WJYOQ2jmQ8vduYxrrUqGorl4i7egBNLHRZFw4Q+qxg1gFBL2oamFdpQHq/46h/u8ouvgoVPs3oEtJxKpSXbPpZQGhyIuXJn3Db2hvX0GfHI/uwW209yNeWJnh9Tg2lE3bkXJoD6l/70Z9/y7xfyxAkxCLY4O3zKbXZ6SjTU40fqz9Sxv2qUPZvWOOdZsgtXcg+pfxqK5dRhsfg+raJdR3I16peujSUk3S2JYNQ5+lIq2QGhDs6jQn49RBMk8eRBtzn9TtK9ElxWNbo5HZ9FZBFVAEhJK4dArq6xfRJcaiuXsTze1rxjSKEqVQ376K6txRdImxZF27gOrcMeTFAgqlDgB2b7Yg4+QBMk8eQBsTSerWFYZ61MyjHsEVUJQMIXHxZNTXLzysxw2Teti8UYf0/VvICj+HLiGGjGN/kXXlPHZvtrBImTdvWEPjZi1p2rwVfiX86TtgKG7unuzYvtls+p3bN+Pu4UnfAUPxK+FP0+ataNT0LTauX22aUAIurq4mn8dVq1GbKtVqUqyYH8WK+fHu+/2wsbHlymXzkTNPy6pyPdQXjqO+cAxdQjSqg5vQpSaiqFD7ifn06ano01OMn8e7fxWhVcg6sRdtxGX0yfGoz/+D5lY4VpXrP2GNz8ehQSvSjv5F+tG/0ETdI2nDErQJcdjXaWa+/JkZ6FKSjB+rEoFIbO1JP7bfmCZh2UzSDu1Cfe8WmuhIElfNBakE6+DcUUcvWszOg1z5bhoPNu7OP3ER6NKmGEvX3OHg0Thu3k5n3LRwrK1kNK3nkWeelFQN8Ylq46damDMqlZZ9h7PvX8uHOHLoWBz/nErgQbSK/UdiOX4mkdDSDhavwx9bdtK6UT3aNqlPQHFfhvXpgaebKxt2/mU2/bA+PejRviVlSgfi5+vNoB6dKe7txaGTZ41pRg8bRMcWjQku6Y9/cV9GDuqNTq/n5HnLHM8vM51OX2Sf15FoQBCwtbVF/TAU/Nq1a6xevZp169Zx9uxZs+nfe+89Vq1axYwZM7h06RK//vorDg6Gk+f9+/epX78+YWFhnDx5kh07dhAVFUWXLl0KpewSuRzb0iGknD5hsjz1zAnsypQzmyf90n8o3D1wfBiCKXd2QVm3Piknjpqkk9raErp4FaFL1xAwegI2gaULpQ7mSORy7ENCSTpuGu6edOw4DhXM3zxIFVbos0x7gfUqFfZlyyKRyczmsSiZHOuA0qT/d8ZkccZ/Z7ApHVqgVTjWa0bGxbNo4rJb/DOvXsQqoBTWJYMBkHt4YVexKunnTuS1GsuSypB6FUdzyzQUVXPrMjLfALNZFIHl0UbdxrpqIxwGjMa+95dY12sL8hc3FOi1ODZkcqz8S5F54azJ4swLZ7EpVbB9yuHNJmRe+tckAsY2rBqqG+G4dR+I35Ql+I6ZgbJlZyisCJFCqkeuNHWbkHb8b/SP9cxajEyG3DeArGv/mSzOuvYfihLmt79VmTA0925i92ZL3EZOxfWTiTi06GpyHKgjriL3DUBe3NCbLXXxwCq4Ilnh5yxfh8frcTVHPa6eR1HCfKOkVZnKaO5FYFevFW5fTMN1xE84vNXNpB4SuQJ9jiEXek0WioDg5y6yWq3m+rUrhL1R1WR5WOWqXL70n9k84ZcvElbZNP0blatx/Wo4Go3GuCwzI4MBvbrR7723+WH0KG5cv5pnObRaLX8f+IvMzExC8jiHPBWpDKlncbS3TYcWaG+FI/MJeGJW++7Dse/3HbYdByErXsrkdxKZHLQ5hr9o1Mh8nz9iwiyZDIVfIKoc+6wq/F+sShZs+9vVbITqynm0CXl3tkisrJFI5ejTU/NMI4CPlw1urlacOJMdpaHW6Dl7IYnyoU4FXk+rJt7s/TuGTJXOuOzcpWSqVHTGz9cWgFIB9lQs68Q/pywbEaJWawi/HkH1sPImy6tXKs9/4dfyyGVKp9ORkZmJk4P54ckAmVkqNFrtE9MIgjliCMP/uePHj7Ny5UoaNzaMtc3KymLZsmV4eJhvpb1y5QqrV69m9+7dNGliGLseGJg9dmrOnDlUrlyZ8ePHG5ctXLgQPz8/rly5QnBw7oupSqVCpTK94c3S6rCS5X8zL3NSIpHJ0CSanrzVCQk4upgfepF+6QJ3fhpHiS++Q2plhUQuJ+mfQ9ybMz27THduc2fKRDIjbiC1s8O9XWdK/zyLK0P7khVZOCGQj5M7OyORy1HHm/bsqhPiULjWNJsn8dhRPNq0Jf7AAdLDL2MfGopH6zZIFQrkzs6o48yH5lqKzNEJiUyGNtl0W2iTE5ApK+efX+mCXYUqRP86yWR52rGDyByd8P3qR0Bi2F57t5G0ba0li58nia09EqkMfVqKyXJ9egoSO/M3I1JnN2TFAtFrNaRvXojU1h6bRm8jsbEnc1fhhGXn9DocGzKHR/tUoslybXIiMmX+4ZYypQu25asQM2+yyXKFuzfyUE9Sjx4gavr3KDx9ce0xAKQykrb+YckqGMpRSPV4nFXJIKyKBxC7pHDCiaV2jkhkMnSpySbLdanJSB2UZvPIXDxR+Aej16hJWjEDqZ0jjm3fQ2JnT8r6hQCozh9Dau+IS/+vQGJ4+Es/tpf0g9sKuR5JuevhmEc9XD1Q+AcZ6rF8BlJ7BxzbvY/EzoGUdYZhVFlXzmNXtwXqm+Fo46NRlCqLdZnKJsMcnlVKchI6nQ5nZ9N9xdnZhcQE8w8uCQnxvGEmvVarJTk5CVdXN4r5leDDT77AP6AkGenpbN28jlGffcjUmfPxLVbcmO9WxA2+GDGErKwsbGxt+eLr7/ErEfDc9Xp0btXleCDWZ6QitXc0m0eXlkzmntVoo++CTI6iTBVsOw4iY+0ctJGGOWk0t8NRvFEfzb0b6BPjkJUIQh5YrtAaCKX2huNbl2y6T2lTkrB2dM4/v5MzNmXCSFg644npnNp0R5sUT2a45ebVeB25uRga9uKTTBuREhKz8Pa0KdA6ygQ5UCrAnh9nmc6LsmLdXRzs5Cz/pQo6nR6pVMK85RHs/Tvvht1nkZiSglanw1Vpeo/h4uxEXGJSHrlM/b55BxmZKhrXqZ5nml+Xr8HD1YWqFcs+V3mF/z+iAeH/0NatW3FwcECj0aBWq2nXrh0zZ85k9uzZ+Pv759l4AHD27FlkMhn165sPBTx16hT79u0zRiQ87vr162YbECZMmMCYMWNMlg0q7c8HQQEFr1SOyVEkEkmeE9pY+/njO+hDon9fQsqpE8hd3fDpO4jiQ4dzd7rh4TU9/CLp4dkhXbcv/kfQjHm4t+lI5NyZBS/X88pVCQlgvmL3Fi1E4eZGufkLkADqhHhitm3Ft+d76HU6s3kKRa4i513mxznWbYIuPZW006a93TahFXBu09Uwr8KNcBSevrj36I82KYHEzassV+6n9qR6GX6XsX0ZZGWiAzIPbMS2TS8y/yrcyeFyeR2OjZwFlkjMHBu5OdRuhC49jfQzOSaulEjQJicRt3Q26HVk3bqOzNkFp+YdCqUBwcjS9XiMY90mZN2NIOtm3j3IFmGuDnmQPDz2k1fPRa8yzC+Ruv13nN4ZQsrmZaBRoygZil2DNoZ5Fe7cQObmiWOrHugaJpG+z3x4fqHJY1tIHtYxedWc7Hps+x2n7kNJ2bQENGpSti7HqUMfXIf/CHo92vhoMk79jW2VNy1Xvhx/a73+iX9+Y7mN6R+eryQYloeEliUkNPvBIbRseUZ8NIDtW9bTb9BHxuW+xfyYMnM+aWmp/HP4IDOmTOSHH6dZpBHBWJH8FxmWJ8agTsx+WFM9uIXUwRmrKg3IeNiAoDqwEevGXbDvORLQo0+KQ33xBIqy1SxT3jwV/Hr9OLvqDdBlpJFxPu+oOodGbbGrXIeYWWNe7PXjFdC0vgeffpAdPTTy4YSHua99BZ9Ir1UTb65HpHHpqmnjVuM3PWjawJPvp4Rz83YaQSUd+LBvILHxWezYl3teoeeV8xhGr+cJh7zR7r+PsnD1RiaO/BgXpfmOjhUbt7P70DFmjfkC68fmPXldiTkULUs0IPwfatiwIXPmzEGhUODr62vyhoW83sTwiK2t7RN/r9PpaNOmDT/+mHtmZx8fH7N5Ro0axfDhw02WXXm79RO/5xFtchJ6rRZ5jh5VubMzmkTz47I9u/Yg7eJ/xKx7+KAQcYN7mZmU/nkmD5YuMD+eW68n/eplrB7rlSlMmsRE9BoNCjfTiXIULq65ohIe0atU3Bz3AxETJ6BwdSMrLhbPdu3RpqWhSUws9DJrU5LRa7W5elRljs5ok/L/fsc3m5J6ZB9oNSbLXTq8S+qRv0g5uAsA9d1bxFtb495rKIlb/ij0q4I+Iw29ToskR4+YxM7BMPbWDF1aMtLUJMjKzF4WH4VEIkXqoESXWHhzgjzyOhwb2tS89illrt58cxzqNiH16P5c+5Q2KQG9Vgv67IY19f27yJ1dQSbPlf55FVY9HpFYWWFf7U0SNq00+3tL0KWnoNdqc/XSS+0dc/XmP6JNSUSanGB86AbQxEQikUqRKV3RxkVh36QDmWePkHnS8DYDbdRdUhXWOLXvRfr+LRY/vo31yBE1IXVwyhVdkV2PpNz1iDathz4thaTl00GuQGrngC45AfsWXdAmPH/PpKOTEqlUSmKO4y8pKQFljiiDR1xcXEnImT4xEZlMhqNTHpFTUimlg0OJzBFJpFAo8Hk4iWLpoBCuXbnM1k3r+ODDEc9aJSD73Cq1d+TxJm6Jbd7nVnO0D24hD61ist7MrYtAJkdiY4c+LRmrOq3QJRfOXC26NMPxLXVyNlkuc3RCl5J/b7F9zQZknPwbtOYn93No2BrHpu2Jnf0Dmsjblijya+XQ8Xguhp82/qxQGCJNXJ2tiEvIbmxxVloRn5h/44u1lZTGb3qwYGXuyU8/6FWSFevuGCMObtxKx8vDmnc7+1m0AcHZ0RGZVJor2iAhKQVXZ/ORUo/sOXyMCbMX8sOng6lWyfxQo5Wb/mTpui1M++5zSgf4Wazcwv8PMQfC/yF7e3tKly6Nv7//U7+esUKFCuh0Og4cOGD295UrV+bChQsEBARQunRpk09ejRPW1tY4OTmZfAoyfAFAr9GQcS0chxxjQx3eqEr6JfOvDpRaW5s8OACge3jhfkJ3jm1gaTTxhTsM4BG9RkNa+GWU1UxDz5TVq5N6/snhi3qtlqyYaNDpcGvalITDh15M06tWgyriGrblwkwW25YLI/Oa+VdZPWITWgGFty/JB3NPyCS1toYck9DodbqH26ogbfHPSadFF3UXeQnTiQTl/iFoIyPMZtFG3kRirwRFdqu+1MUDvU6X58OWpb0Wx4ZWQ9at69iUrWSy2KZsGJnX89mnQsqj8PIl9e/c+1TmtUsoPL1N6iT38jU0rFi48QAotHo8Yl+1LhKFgrSj5s/LFqHVoomMwKq06Q2pVelyqG+bH5Orvn0VmaMzEitr4zKZuzd6nQ7tw7c2SBRm9jm97sld68/jUT2CTMcWW5Uuj/q2+egNdcSVfOthpFGjS04AqQzr8tVQXTzN81IoFJQqHcy/Z06aLP/3zClCy5Q3mycktCz/njF9JenZMycpFRSCXG6+70iv1xNx41quiRRzpUNvnDfpuei06KLvIithGpkoKxH8VBPOSj2KoU8z0/ij1RiWS6UoSldEc8P8fBHPTatFfecG1iGmr9S0DqlI1s0nvBoUsCpdFrmHD2lH95n9vUOjNjg270TsrxNQ33nCa4P/j2VkaLn3INP4ibiTTlx8FtXCshvX5HIJYeWU/HfZfCPh4xrVdUehkLLrQO4GARsrae5LpE6P1MKnK4VCTkipAE78a3qtPnHuAuVD8p5zaPffRxk3az6jhw2kdpUws2lWbNzO4rWbmfzNCMqULqR5QV5Cep2+yD6vI9GAIDyVgIAA3n//ffr06cPGjRu5efMm+/fvZ/Vqw8zOQ4YMIT4+nnfeeYfjx49z48YNdu3aRZ8+fdDm0br+vGI2rMG1eStcmr6FtV8JfPoPQeHhRdzD2am9e/XHb8QoY/rkY/+grF0P15ZtsfL2wa5seXwHfUR6+EXjQ5Bn9/dxqFwNK28fbAJLU3zY59gGljau80W4//vveLRth0frNtj4B1Di42FYeXkRtWE9AH4fDCbw2++M6W38/HBr3gLr4n7Yly1L6e9/wDawFHdyvJ6zMCXt3IhT/WY4vtkUhU9x3N7ph9zNg5R92wFw6fw+Hv2H58rnWK8pmdcvo76Xu8U//exxnBq1xL5GPeTuXtiWC8O147uGcO6cV/JCojq1H0WFmijK1UDq6oV1/fZIHV3I+tcw27113dbYtOhhTK++fAp9Zhq2zbsjdfVCViwQ63ptUV849kLDT1+HYyNp9yYc32yKQ53GKHyK49K1L3JXd1L27wDAuWNP3PsMy5XPoW4TVNfDUZvpsUvZvwOpgxOu3foh9/LFtkIVnFu9bdxPX5V6PJ4m/cwxdGkF77V9FumHd2JbpT42Vd5E5uGDQ8t3kCrdyDhuePixb9YZx879jelV/x5Fl56KY8d+yDx8UQQE49CiK5mn/jYeB1mXz2JbvRHWFWogdXFHUaoc9k06orp0ptAaPtP/3oFt1frYVKmHzMMXh1bdkTq7kXHMMLu5ffO3cXx7wGP1+MdQj879kXn6oggIwaFlN0PUxMN6yP0CsS5XFamLB4qAYJx7f4pEIiH9oGX2qbYd3mbPru3s2bWdO7dvsfC3X4iNiaJ5yzYALFs8j+mTs+ceat6yLTHRUSyc9wt3bt9iz67t7N21nfYdsyc0/mPlEs6cOs6D+5HcvH6NWdN/4uaNazR/q60xzfIl87j43zmiox5wK+IGy5fM58L5f6nXsIlF6pV1+iCKcjWQl62O1MUT63ptkTq6oD7/DwBWtVti0+wdY3pF2JvIA8sjcXZH6uqFVe2WKIIqof43+80jUq8SyEtVQOLkisy3JLbtB4BEQtZJ8w/plpC6fxv2NRthV6MBcq9iKDu8h8zFnbTDhoY/p9bv4NJjSK589jUbkhVxFc39O7l+59CoLU6tupLw+xy08dFIHZVIHZUmDVlFRWZvh1OlUJwqGSaBtStZHKdKodj4mY80fdFWb7nHu539eLOmGyVL2PHlR8GosrTsPpgdEfTVsGAG9gzIlbdVE28OHYsjOSV3Y/KRE/H0fNuPWlVc8Pa05s2abnRtV5yDRy3fgN61TXO27D3A1r0HibgbyfRFK4mKjaNDs4YAzFm+hrEzfjOm3/33UcbOnMeH73ejXHAp4hISiUtIJDUt3ZhmxcbtzPt9PaMG98HHw92YJj0jM9f3C8KTiCEMwlObM2cOX375JYMHDyYuLo4SJUrw5ZdfAuDr68vhw4cZOXIkzZs3R6VS4e/vT4sWLZBaYDIpc5IO7kPu6IRX9/eRu7qSGXGTiO9Goo6OAkDu4obCI/s92Ql7diC1tcW9TQd8+w1Gm5ZK6r9nuL9orjGNzN6B4h+NQO7iii4tjYzrV7n++UdkXHlyb6Elxe/dg1yppFifPijc3Mm4cYPwEZ+Q9eABAAo3N6y9Hnv/t1SGT/fu2JTwR6/RkHzqFBcH9CPrwf0XVua0438T5+CIc7tuyJWuZN27xYMpo41vVZA7uyB3M51jQ2Jrh32V2sStnGd2nQmbV6HX63Ht+C4yFzd0KUmknT1OwrplhV6fRzRXzpBpa4d1zeZI7J3Qxd0nfcNc9CmGCcwk9k5IHR8LJVZnkb52DjaNOmHfYwT6zDTU4WdRHSm8B1RzXodjI/3EIeLtHXFu0xWZ0pWsyFtETf/e+DYCudIFuZu7SR6JrR12lWsTv8r8PqVNiCVqyne4du1LsdHT0STEkbxnC0l/ri+UOhRWPcAQOWETXI4HU74ttLI/ojp/nFQ7B+wbtkPqqDS8rm7pFHSJhptnqaMzMmX2sCt9lorERT/j2KYHroO/Q5eeiuq/E6TuXmdMk7Z/M3r02DftiMzJBV1aCqrLZ0l7LI3l63GMVHsH7Bu3Q+rojCbqLkmLJ5vWwzlHPRb+hGObnrgOGWOox/njpO7KnshVIldg37QTMlcP9FkqVOH/GuZ+yEzP9f3Pom69RqQkJ7P696UkxMdTwj+Ar8dMxNPTG4CE+DhiYrJ7TL28ffh6zAQWzZvNn1s34ermRt+BH1KrTvb8RWmpqcyZOYWEhHjs7O0JLFWaH36cTnBIGWOaxIQEpk0eT0K8IU1AQCDffP9jrjdCPCvN1bOobO2wrtEUiZ3h3Jqxab7x3Cq1d0Ly2ESEEpkcqzfbIHFQgkaNNu4B6ZvmoY3IPv9I5HKsarVAqnRDr85CG3GJ9J0rTYaUWVrGmX+Q2jvi2LwTMqUL6vt3iJs70fhWBamTMzIX0yGJEhtbbCrVIGn9YrPrtK/bFIlcgVsf06EiyX+uIWXHi5lEOC/KKuWptTf7Glz2Z8M94J2l6znXd1Re2V6YlevvYm0lZcTA0jg4yLl0JYXh3/1HRkZ2R5aXu3WuPgg/X1sqlVPyybfmIz2nzrtOv+7+DB9UGhelgtj4LDbtvM/iPyw/tKRJnRokp6SyaM0m4hKSCCxRjJ+/HI63p+E6EZeQSFRsdsPFpt370Gq1TJ63jMnzsrfNWw3q8PWHhobd9Tv2otZo+PrnX0y+q0+XdvTt2sHidXiZ6MQkCBYl0Rd0RhFBeIHOtWxQ1EWwiIyEjPwTveQ8QvKeVPNV4l7xxb2Gs7BE7Dlb1EWwCCffJ4/hFF4cO6/83wLxSniRE8UWkti+E4u6CBbhty3vt4W8SpJv3C3qIjy3s7PPFnURLGJiy/lFXYTntmHc69Fn616+VlEX4Zl8OC3/4SuFZeawgr8+9FUhhjAIgiAIgiAIgiAIgpCv16M5TBAEQRAEQRAEQRByeF0nMywqIgJBEARBEARBEARBEIR8iQgEQRAEQRAEQRAE4bUkIhAsS0QgCIIgCIIgCIIgCIKQL9GAIAiCIAiCIAiCIAhCvsQQBkEQBEEQBEEQBOG1JEYwWJaIQBAEQRAEQRAEQRAEIV8iAkEQBEEQBEEQBEF4LYlJFC1LRCAIgiAIgiAIgiAIgpAvEYEgCIIgCIIgCIIgvJb0ehGBYEkiAkEQBEEQBEEQBEEQhHyJBgRBEARBEARBEARBEPIlhjAIgiAIgiAIgiAIryWdmETRokQEgiAIgiAIgiAIgiAI+RIRCMJLaaTnjKIugkWkyBKKugjPzUnqWtRFsAi/rGJFXYTndlFxoaiLYBF2aseiLoJFSCWvfhu8bYpdURfBIl6HV3R99pocF19dHVTURbAItUpd1EV4bkkt44q6CBbxxfZ+RV2E5/ZRmb1FXQSLWDmxqEvwbMQkipb16t/9CIIgCIIgCIIgCIJQ6EQDgiAIgiAIgiAIgiAI+RJDGARBEARBEARBEITX0uswzO1lIiIQBEEQBEEQBEEQBEHIl4hAEARBEARBEARBEF5LIgLBskQEgiAIgiAIgiAIgiAI+RIRCIIgCIIgCIIgCMJrSSde42hRIgJBEARBEARBEARBEIR8iQYEQRAEQRAEQRAEQRDyJYYwCIIgCIIgCIIgCK8lMYmiZYkIBEEQBEEQBEEQBEEQ8iUiEARBEARBEARBEITXkl5MomhRIgJBEARBEARBEARBEIR8vTYNCIsXL8bZ2bmoi/HSCwgIYNq0aUVdDEEQBEEQBEEQBOEZJCQk0LNnT5RKJUqlkp49e5KYmJhvvkuXLtG2bVuUSiWOjo7UrFmT27dvP9V3F/oQhl69erFkyZJcy5s3b86OHTss9j1du3alZcuWFltfThEREZQsWfKJab777jtGjx5daGWwhBMnTmBvb1+gtAEBAQwbNoxhw4YVbqEspEd7L96q74qDvYzwG+n8svQetyNVeab/8YtAKoY65Fp+/N9kvpsaAUD5YHs6t/SgtL8tbi4Kvp8RwT+nkwurCgD0ecefts19cHSQc/FKClN+vcrN2+l5pp85vhJvVHDOtfzIiTg+//4/AGxtZfTvEUC9Wu64KBVcuZHK9HnXuXw1pbCqkUuPdp60qO+Kg51h+8xeHvnE7TPx85J5bp/R028VZlGNWte15c1K1tjZSLh5X8Pvu9K5H6vNM32tClb0apW7zEMmxaMxk61FTRs6NLBj74lMVu/Nexs/r97dStC2uTeO9g/3qbnXibiT9/fN+KGC2X3qn5PxfD72AgCrf6uGj5dNrjTrt0cyde51i5X9kXfbe9OygRsO9jIuX0/nl2V3uXUvM8/0P31Rmkplcm+LY2eT+HbqTQDKh9jz9lueBAXY4eaiYPT0m/xzOsniZX/c63Ke6vqWK01rO2FvK+XqLRXz1sRw50HWE/PY2Urp0dqVmhUdsLeTEh2nYfHGWE5fTDeus+tbriZ5EpI19P06onDq0NKVZnWUD+uQyW9/FKwO77Zxo0YlBxwe1mHR+hhjHQBclTLea+dO5XL2WCkkREarmbUiiht38t7OBaHX69m06jcO7NpAWloKgUHl6DlwJMVKlHpivpNH9rJh5a9EP7iLp3dxOr47mCo1G5pNu3XtItYt/4Wmrd+he78RAGg0GtavmM25U4eJibqHnZ0DZStVp/N7H+Li6vFcdXrkVdsWeenexoPm9ZxxsJNx5WYGc1Y+eOLxDWBvK6VnB09qv+GIg72MqFg1C1ZHcfK/VACkUuje1oMGNZS4OMlJSNKw50gif2yLpbCisgvjmiGTQu93/Gla3xM3ZwVxCVn8+Vc0S1bfLrR65Me1blUCR/RFWbk8Nr6enOw0mKjNe4umMHno1MSJRtXtsbeVcu1OFos2JnAvWvPEPHY2Ero0V1KtnC32tlJiEjSs2JbE2XDDNTO0pBWt6zlSspgVLk4ypiyN5eTFvK+nrwPdaziJYvfu3bl7967xeXrAgAH07NmTLVu25Jnn+vXr1K1bl759+zJmzBiUSiWXLl3Cxib3/dyTvJA5EFq0aMGiRYtMlllbW1v0O2xtbbG1tbXoOh/n5+fH/fv3jT///PPP7Nixgz179hiXOTjkvsl72Xh4WOZi/7J5u6UHHZu7M3n+He49UPFOWy/GfxZI/1HhZGTqzOYZO/MWCrnE+LOjvYzZY4P5+0T2A4SNtZQbtzPY9Xc833wYUNjVoEcnP7q2L864aeHcuZfO+139mfp9Rd754AQZGeYfXL8cf8GkHkonBYtmVGXf4Rjjsi8+DCbQ356xUy4TG6+ieQMvpo2tyLuDTxAb/+SbNEvo/JY7HZq5M2XBXe5FqejW2pNxn5ZkwJdX8tw+P/xyG4Xsse3jIOOXMUEcOlm4D3iPNK9hQ5NqNizZlkpUvI6WtW0Y1tWRb+clonrCnywjU8e380zLaK7xwN9bxpth1tzJ50bgeXXvWJyu7YoxfvoV7kRm8H6XEkz9vjzdB5/Kc5/6auIlk33KyVHBoumVTfapAZ+eRfpYDFtJf3umfV+BfYdjLV6HLi096djCg8nzbnP3gYrubb2Y8Fkp+n5x6QnH903kj9fBQc6csSG5j+87huP724+e3EBsCa/LeapDE2faNHRm5vIo7seo6dzMhe+G+DL0h1tkqszfpMllMHqwL0mpWiYtfEBcogY3FzmZOep9O1LF6F8ijT/rCunJokMTF9o+rENktJrOLVwZ/WExhnwf8eQ6DC1GUoqWSQvuE5egwd1FToYquw72tlImDPfj/NUMxs6+R2KKFm93BekZ5rfv09i+YQk7N6+k70ff4e1bgi1rFvDzd0MYP3sdtrbmOwauXT7HnJ+/pEP3QVSp2ZBTR/cxZ9IXjJqwgFLB5U3S3rh6gQO7NuAXEGSyPEuVya0bl2nbpR9+JYNIT01h5YLJzBg3nO8mL3vuer2K28KcTi3caN/UlamLIomMyqJrK3fGflKCQV9fNylXznqMHe5PUrKGCb/eJTZBg4er3OR80LmFO2/Vc2HqIkOje5C/DR/39iU9Q8fmvfEWr0dhXTO6d/KjXQsfxk8L5+addEJLOzLqoyBS0zSs3RppbrWFTmZvR/K5cO4uWU+VNbOKpAxP0qa+I2/VdWDumnjux2ro0MiJL/t5MOLnB2RmmT82ZDIY1deD5DQt01fEEZ+kxU0pI+Ox9NYKKbfuqzlwMo1Perq/qOoIFnTp0iV27NjB0aNHqVGjBgDz5s2jVq1ahIeHExISYjbfV199RcuWLfnpp5+MywIDA5/6+1/IEAZra2u8vb1NPi4uLsbfSyQS5s+fT4cOHbCzsyMoKIjNmzebrGPz5s0EBQVha2tLw4YNWbJkCRKJxBiqkXMIw+jRowkLC2PZsmUEBASgVCrp1q0bKSnZPa56vZ6ffvqJwMBAbG1tqVSpEmvXrjVbB5lMZlJ+BwcH5HI53t7eODo6EhwcnCuiYsuWLdjb25OSkkJERAQSiYRVq1ZRu3ZtbGxsKFeuHPv37zfJc/HiRVq2bImDgwNeXl707NmT2NiC3ZA3aNCAoUOHMnToUJydnXFzc+Prr782mTgk5xCG0aNHU6JECaytrfH19eWjjz4yruvWrVt88sknSCQSJJLsC8O6desoV64c1tbWBAQEMHnyZJNyBAQEMH78ePr06YOjoyMlSpTgt99+K1AdnlX7Zu6s2hLNkVPJ3LqnYvK8O1hbS2lQ0znPPKlpWhKSNMZP5fKOqLJ0/H080Zjm5PkUlq6P4sipwu3Ne+TttsVYuvo2B/+J5ebtdMZNvYy1tYxm9T3zzJOSqiE+UW38VA1zQaXSsu+Q4cJtZSWlfm0PZi+6wb8Xkrh3P5OFv9/iflQmHVr6vpB6tW/qzqqt0Rw5/XD7LLiLtZWUBjWc88yTmqYlIVlj/LxRzsGwfU68mAaExtVs+PNIBmeuqImM1bJ4WxpWCqhe9smNn3ogOU1v8snJWgF92zqw7M800jMLt1W8S5tiLF1zh4NH4wz71LRwrK1kNK2Xd2Nizn2qWpizYZ96rHEgMVltkqZ2VVfu3s/g7H+W3z7tm3uwanMUh08lceteJj/Pu421lZSGNV3yzJOS8/gu50hmlo6Djx/f51JYsu4Bh0+9mH3qdTlPta7vzLpd8Rw7l8bt+1nMWBGFtUJCvSqOeeZpVNMJB3sZE+fd5/LNTGISNFy+kUlEpGlrnFYHiSla4yc5tXAe9lo3dGbtzgSO/vuwDsse1qFq3nVoXEuJo52Uib9FcvmGoQ6XbmQScS+7Dh2buhCboGHW8iiu3lIRE6/h/JUMHsSqn6u8er2e3Vt+p/XbvalaqxHF/UvT7+MxqFSZHD2YdzTnri2/Uy6sBq0798aneACtO/emTMXq7N6y0iRdZkY6v039hl5DvsLO3vRvYGfvwGdjZlO9blN8igVQKqQCPfp/RsT1S8TFPHiuesGrty3y0q6xK39sj+WfMyncilQxZVEk1lZS6tdwyjNP07ouONrJ+GH2HS5dzyAmXs3FaxncvJsdtRBaypZj/6Zw8nwq0XFqDp9O4cyFNEr7P12PYUEV1jWjfIgjh47F8c+pBB5Eq9h/JJbjZxIJLV10nW8xOw9y5btpPNi4u8jK8CQt6jiwaV8KJy5kcjdKw5zV8VgpJNQOs8szT4Oq9jjYSZmyNI4rt7KITdQSfiuL2/ez9/t/r2SyZlcyJy683lEHj9Pr9EX2UalUJCcnm3xUqueLgvrnn39QKpXGxgOAmjVrolQqOXLkiNk8Op2Obdu2ERwcTPPmzfH09KRGjRps3Ljxqb//pZkDYcyYMXTp0oVz587RsmVLevToQXy8oWU1IiKCzp070759e86ePcvAgQP56quv8l3n9evX2bhxI1u3bmXr1q0cOHCAiRMnGn//9ddfs2jRIubMmcOFCxf45JNPePfddzlw4MBTld3e3p5u3brlirJYtGgRnTt3xtEx+yL42WefMWLECM6cOUPt2rVp27YtcXFxANy/f5/69esTFhbGyZMn2bFjB1FRUXTp0qXAZVmyZAlyuZxjx44xY8YMpk6dyvz5882mXbt2LVOnTmXu3LlcvXqVjRs3UqFCBQDWr19P8eLF+f7777l//74x+uLUqVN06dKFbt26cf78eUaPHs0333zD4sWLTdY9efJkqlatypkzZxg8eDAffPABly9fLnA9noa3hxWuzgpO/5fdOKTW6Dl/OZWypfM+yebU7E0XDhxLRJVHq25h8/Wywd3VmuNnEozL1Bo9Z/9LpHxo3jcgObVu6s3eg9FkPuzxkMkkyGUSsrJMb8RVWToqllVapvBP4O2hMGyfC6nGZRqNnvPhaZR5iu3T/E1XDhxPeiHbx10pRekg5WJE9gVXo4UrdzSUKvbkwC1rKwnjP1AycbAzQzo74Ocly5XmnWb2nL+u5vKtwo0+8PGywc3VihM596kLSU+1T7Vq4s3ev2OM+1ROcrmEZg082b4n6rnLnJO3hxVuzgpO5Ty+w1MpG1Sw4VgAzeu5cuBYAqqswnkgzc/rcp7ycpPjopRz9nJ2OLNGAxeuZxBSMu8Hmmrl7Qm/mUn/tz1Y+EMA077wo1NTF6QS03Q+Hgrmjw1gznf+DH/fCy83ywdKernJcc1VBz0XrmUQGph3JGO1CoY6DOjqyaLxJZn+ZQk6NTOtQ7UK9ly7nclnfbxZPKEkk0f60bR2wY+1vMRE3SMpIY7yYTWNyxQKK0LKV+ba5XN55rsefo5yYTVMlpV/o2auPMt++5FKVepQrpJp2rxkpKcikUiws3++h79XcVuYrYe74Tp35kKaST3+u5JOmVJ5H981Kjlw+UY6H3T3YdnkYH4ZHcjbLd1N6nHxajqVQu3x9bICoGRxa8oG2RmHOFhSYV4zzl1KpkpFZ/x8Ddu1VIA9Fcs68c+phLxW83/N01WGi5OMc1ezH/I1Wrh0U0Wwv1We+aqUseHqbRW92zkz5ysffhzmRbsGjkgkeWYRCtmECROM8xQ8+kyYMOG51vngwQM8PXN3Lnp6evLggfmG3ejoaFJTU5k4cSItWrRg165ddOjQgY4dOz71s+8LGcKwdevWXOH9I0eO5JtvvjH+3KtXL9555x0Axo8fz8yZMzl+/DgtWrTg119/JSQkhEmTJgEQEhLCf//9x7hx4574vTqdjsWLFxsf4Hv27MnevXsZN24caWlpTJkyhb/++otatWoBhhCOQ4cOMXfuXOrXr/9UdezXrx+1a9cmMjISX19fYmNj2bp1K7t3m7ZqDh06lE6dOgEwZ84cduzYwYIFC/j888+ZM2cOlStXZvz48cb0CxcuxM/PjytXrhAcHJxvOfz8/Jg6dSoSiYSQkBDOnz/P1KlT6d+/f660t2/fxtvbmyZNmqBQKChRogTVq1cHwNXVFZlMhqOjI97e3sY8U6ZMoXHjxsZtFxwczMWLF5k0aRK9evUypmvZsiWDBw8GDNt66tSp7N+/n9DQ0FzlUKlUuVridNospLK8T5CPc1EaduOEZNOHscRkDZ5uBVtHcElbSvrZMm3h3QKlLwyuLoayxiea9sglJGbh5VmwnoYyQY6UCnBg4owrxmUZGVrOX0qiVzd/Iu6mk5CYRZN6npQNduRuZIblKpAHFycFYNgejzNsH0WB1hFc0paA4jZMW/Rito+Tg6FtNTnN9GEzJU2Hq1PuBoFHHsRpWbItjXsxWmysJDSqasPn7zoxdmES0QmGdVUtY0UJLxnjlxR+b7Gbi+HvG59k2uOWkJiFd4H3KQdKBdjz46wreaZ5s4YbDvZytv9l+QYEV+PxnaMOyeoCH98hgXaU9LNl6sI7Fi9fQb0u5ylnJ0M9EpNNQ5kTk7V4uOZ9PHu5K6jgKufgyVR+mHsfHw8FA972QCqDNTsMDxBXIjKZ8TCM3dlRRufmroz/pDgfj79NarrlGn6MdUjJsS1StHi45n1b5OWmoEKwLQdPpDB2TiS+ngoGdPFEJpWweke8sZ4t3lSy+a9E1u5KIMjfmr6dPVBr9Ow//uxzziQlGjoanJzdTJYrlW7Extw3l8WYz0lpmsdJ6UZSQpzx52N/7+TW9ct89/PSApVFnaVi7dJZ1KjXAlu752tAeBW3hTmPju+nvc55uVtRMVTB/mNJjJ5+m2JeVgzq7o1MCqu2Gnrv1+6Iw95Wxq/fl0KnM8yJsGxjNAePW/4aUpjXjBXr7uJgJ2f5L1XQ6fRIpRLmLY9g798xeazp/5vSwXCvkZRieq5NTtHi7pL3seHpKqesi5zDZ9P5aXEs3m5yerVzRiqDDXtf3LxXL5uifI3jqFGjGD58uMmyvIbyjx49mjFjxjxxfSdOnAAwiQ5/RK/Xm10OhudigHbt2vHJJ58AEBYWxpEjR/j111+f6tn3hTQgNGzYkDlz5pgsc3U1nSipYsWKxv/b29vj6OhIdHQ0AOHh4VSrVs0k/aMH3ScJCAgw6f338fExrvPixYtkZmbStGlTkzxZWVm88cYbBaiVqerVq1OuXDmWLl3KF198wbJlyyhRogT16tUzSfeosQJALpdTtWpVLl26BBh69/ft22d2LoXr168XqAGhZs2aJjtOrVq1mDx5MlqtFpnM9MHn7bffZtq0aQQGBtKiRQtatmxJmzZtkMvz3i0uXbpEu3btTJbVqVOHadOmmXzH49tTIpHg7e1t/NvnNGHChFwHS6lKgwgK+8Bs+oa1nPnw/WLGnx9NJJbr3CAxhJMXRPN6rty8k8GVm4X/QP1I0/qefDYke5t+/v15w39y1aPgFWndzJvrEalcyjE54tgplxn1cQibltRCo9Vz5XoKuw9EE1zK8qGDDWo68+F72UMjvptmmPAw5/aRmFmWl2ZvuhBxN7PQtk/1slb0aJHdmz1rjeHvZ758eRf6ZqSWm5HZF/vrd1P5qrcTDavY8MeedFwcpXRtYsf0P1LMzovwvJrW9+DTD7LHL498OHlVzopIJAW/mLZq4s31iDQuXc27t6t1U2+OnYonzgLzaTSs5cLHvYobf/5myg3Df3LtPwU/Lh4d3+E3Cm+iypxel/NUvaoODOya3csxbq75scqGy07eNZFKDDfCv66KRqeHG3dUuCrltG/kbGxAOHMpe/vcvg/hEZHM/tafhjWc2LIv8Tnq4Migdx6rw5yHdXjKc5JUaqjDnN9N69CusYvxoVUikXD9diYrthge0G/eVeHnY02LN5VP9dD6z4E/WTInuzNh2NfTHpbR9MZQT943i8Z65fq13rgwLuYBK+dPZsToWSis8p+bSqPRMOfnL9Hpdbw3cGS+6XN6FbeFOQ1qODHk3ezr3JiZt81Vo0D1SEzWMmvpfXR6uH47E1dnOR2buRkbEOpVc6JBTSU/z7/HrUgVgX429O/qRVyihr/+eb7hVy/ymtH4TQ+aNvDk+ynh3LydRlBJBz7sG0hsfBY79pm/P/x/UifMlr4dsofl/bQ4j+HLkifvUxIJJKdpmb8+Ab0ebt5T4+Iko1U9x//rBoSiZG1tXeC5/4YOHUq3bt2emCYgIIBz584RFZW70yYmJgYvLy+z+dzd3ZHL5ZQtW9ZkeZkyZTh06FCByvfIC2lAsLe3p3Tp0k9Mo1CYttBKJBJjS4m51pSCnMietM5H/27bto1ixYqZpHvWCR779evHrFmz+OKLL1i0aBG9e/fO98L+qFyPytSmTRt+/PHHXGl8fHyeqUxP4ufnR3h4OLt372bPnj0MHjyYSZMmceDAgVx/u0cKui2e9LfPyVzL3NtD8u7tPHommcvXs280H03c46o0zE78iLOjnMSk/Mc6WltJqF/DmWUbnn8s59M4dDyOi1dOGn+2Uhh6vV1drIhLyH4Qc1EqckUlmGNtLaXxm54sWBGR63eRDzL5cNS/2FhLsbeTE5eQxZjPy3A/yvLj346dTTZ5UHu0fVxybB+lkzxXb4051lYS6ld3ZvlGy/duP/LvtSxuLswuy6PJ95QOUpLTsp/0He2lZuc0yIseiLivwdPFsG1LeMtwspfyZa/sUFCZVEKQn5wGVawZMinhuWajPnQ8novhp40/Kx7tU85WxCVkHwvOSiviEwtybEhp/KYHC1bm/dYLLw9rqlR05uuJF5+94I85eiaJ8OvZYcCP6uCiVBD/+PHtJM/Vm2+OtZWEBjVcWLo+717awvC6nKeOn0/jSkR25Majejg7yUh4LApB6SjLFZXwuIRkLRqtnscnwr77IAsXpRy5zPxEo6osPbcjs/DxKFikUt51SOVKRPa5LrsO8lx1yNnbZ1KHJA0aLbnq4PpYHRKSNbneHnD3QRa1wp6usTasej0CH5vkUKM2rDMpMRZn1+xJz5KT4nFyds2V31gnZzdj9MLjeZQP89y6fpnkpHjGjOhp/L1Op+XKxTPs3b6aeWuOIH3YMaDRaJgz6QtioyP5/Ps5zxR98CpuC3OOnU0l/Eb222aM5ymnp7vOxSdq0OY4Lu7cz8LVWWGsR+/OXqz9M5aDJwwRB7fuqfB0U/D2W+7P3YDwIq8ZH/QqyYp1d4wRBzdupePlYc27nf1EAwJw6mIm1+5k3+fIH04irXSUkZiSff/s5CAjKTXvYyMxRYdWqze5l7gXrcHFSYZMBtpC6LwQLMfd3R139/wntqxVqxZJSUkcP37c2Kl+7NgxkpKSqF27ttk8VlZWVKtWjfDwcJPlV65cwd/f/6nK+UIaEJ5XaGgo27dvN1l28uTJPFIXTNmyZbG2tub27dtPPVwhL++++y6ff/45M2bM4MKFC7z//vu50hw9etQYlaDRaDh16hRDhw4FoHLlyqxbt46AgIAnRgE8ydGjR3P9HBQUlCv64BFbW1vatm1L27ZtGTJkCKGhoZw/f57KlStjZWWFNseZpmzZsrlaqY4cOUJwcHCe35Efcy1zTxq+kJGpIyPT9KYgPlHNG+UcuX7bcGMil0moEOrAwtX5PzS8Wd0ZhULCX0cSn77wzyEjQ8u9HDMax8arqBbmwtUbhpZ7uVxCWHlnfl1yI9/1NarrgUIhZef+vB+0M1U6MlVZONrLqf6GK3MW57/ep5XX9qlc1oEbj2+fEHsWrcn/YejNakrD9vkn0eJlfUSVBTE5xsYnpeooE6DgTpRhG8mkEOwnZ/3+p+v99fOScy/GsI7Lt9SMmW96w/d+K3sexGnZeTTzuV9lZW6fiovPMuxTNw0P5XK5hLBySn5dejPf9TWq645CIWXXgbxv7lo29iIxSc0/Jy0zG7i5/ScuUU3l8o5cv2342xv2HwcWrM5/5u561V1QyCXsPfJix9m+LuepTJWeB6oc4cxJGiqF2HHzrqF+chmUK2XLss1x5lYBwOUbGbxZxfFhT6Zhma+noVEor2gcuRyKe1tx8cbzRVyYq0N8koZKoXbGyerkMihX2palm/KetPjSjUzqVc1ZByuTOly+kUkxT9Prl6+ngpj4p5u4z9bW3uTNCnq9HqWLGxfOHsM/0DAUUKNWE/7fad5+/8M811MqpCIXzh6jedsexmUXzh6jdKghSrBMpWqMnb7KJM+Cmd/jU8yflh3fz9V4EHX/Np+PnYuDk/NT1eeRV3FbmJOh0pERY3rNiE9U80ZZe27ceXR8Q/lgOxavy/uafOl6BvWrO5nUo5iXFXGJamM9rK0kJg0MwMMhAM9djRd6zbCxkqLP0Y+k0+lzzYPy/yozS09mnOm2SEjWUqG0NbciDfusTAZlSlrz+595NxxduaWidpidyT7l42FooPt/bjzQ59GJ+aoqU6YMLVq0oH///sydOxcwvMaxdevWJm9gCA0NZcKECXTo0AEwzMXXtWtX6tWrR8OGDdmxYwdbtmzJNal/fl5IA4JKpco1oYNcLi9QCwvAwIEDmTJlCiNHjqRv376cPXvWOGlfQXr4zXF0dOTTTz/lk08+QafTUbduXZKTkzly5AgODg5mH/7z4+LiQseOHfnss89o1qwZxYsXz5Xml19+ISgoiDJlyjB16lQSEhLo06cPAEOGDGHevHm88847fPbZZ7i7u3Pt2jVWrVrFvHnzCvSAfufOHYYPH87AgQM5ffo0M2fOzPWWhEcWL16MVqulRo0a2NnZsWzZMmxtbY2tUAEBARw8eJBu3bphbW2Nu7s7I0aMoFq1aowdO5auXbvyzz//MGvWLGbPnv3Ufy9L2rgrlq5tPImMUnEvSkXX1p6oVDr2H000phnR34+4BDWL15rui83fdOWf08mkpOU+s9pYS40TF4FhvGJgCRtSUrUWuQnJac3me/R8uwR3I9O5E5nBe11KoFJpTS7GX38SQkxcFnNzXMxbN/Xh76OxJKfk7u2o/oYLEgncvpdBMR9bhvQO5M69dLbteTG9mRt3x9KltSf3orOIjFLRtZUnqiwd+48lGtOM6FfcsH1y3Gw1e8L2KUx7T2TyVi0bohO0RMfreKuWDVlqOH4xe76OXq3tSUzRsfGA4QGndR0bbkRqiY7XYmNtmAPBz1PG77sMN2KqLIiMNa2HSq0nLUOfa7mlrN5yj3c7+3HnfgZ3IzPo2dkPVZaW3Qezx51+NSyY2Lgs5i6LMMnbqok3h47Fmd2nwBAq2bKxF3/ui0JbiNfmjTtj6Nbai3tRKsPrD9t4ocrSse9odqPAZwNKEJugZtEa04fxFvVcOXI66QnHd3bjpbeHFYElbElJ1RTK8f26nKe2HkikU1MX7seouR+jpmNTF1RqPQdPZYfHfvSuJ3FJWmP4+I5DybSs50zfju5sO5iEr4eCTk1d2HYw+0b4/XZunLiQRmy8BuXDORBsbaTsP2b5sNut+xLp3MyF+9FZ3I9R06m5q6EOJx+rQ08v4pM0LH/YMLLj7yRa1Xemb2cPth9IxMdDQadmLmw7kGjMs+WvBCaM8KNTMxcOn04lKMCGZnWUzPn9+XpYJRIJTdu8w9a1i/DyLYGXjx9b1y7C2tqGmvVaGNPNm/Ytzm6evN3T0DHRtE03Jn45gG3rF1O5egNOH9/PxX+PMWrCAsDQUFHc3zRC1NraBgdHZ+NyrVbDLz99zq3r4Qz7eip6nZakBMPDvb2DEnkeEYsF9apti7xs2hvP2y3diYzOIjIqi7dbuqPK0nHgWPZcBcP7+BKXoGHJBkMZtu+Pp3UjFwZ082bLX/H4elrxdkt3tjz2esbj51Lp2sqdmHg1tyNVlCphQ/umbuw+nJizCBZRWNeMIyfi6fm2H1Exmdy8k05QoANd2xV/Yfch5sjs7bAvXcL4s13J4jhVCiUrPonMOy82as2cHYdTadfQiQdxGh7EamjX0IkstZ4jZ7Oj2z7o4kJ8kpY/dhr2s91H02hW24H32jiz80gq3m5y2jVwZMeR7CEl1lYSvB+boNbDVY6/j4LUdB1xSf/HrQyvmBUrVvDRRx/RrFkzANq2bcusWaavIw0PDycpKfs626FDB3799VcmTJjARx99REhICOvWraNu3bpP9d0vpAFhx44duULwQ0JCCjwrf8mSJVm7di0jRoxg+vTp1KpVi6+++ooPPvjgmYcbAIwdOxZPT08mTJjAjRs3cHZ2pnLlynz55ZfPvM6+ffuycuVKY6NAThMnTuTHH3/kzJkzlCpVik2bNhkbUnx9fTl8+DAjR46kefPmqFQq/P39adGiBdICNjW/9957ZGRkUL16dWQyGR9++CEDBgwwm9bZ2ZmJEycyfPhwtFotFSpUYMuWLbi5GSZc+v777xk4cCClSpVCpVKh1+upXLkyq1ev5ttvv2Xs2LH4+Pjw/fffm0ygWBTWbI/BykrKkPeK4WAvI/x6Ol/9fMPkXcqebopcwy2KeVlRPsSeLyeZ74kPKmnLT1+UMv48sLthzOPuQ/FMmW/5icxWrLuDtZWU4R8E4eig4OKVZD759pzJu5e9PGxy9Ub4+dpSqZySYd+Yn43bwV7OwPdK4uFuTXKKmgNHYvlt2U202hczqczaP2OxtpIy5F1fw/a5kc7Xk2+abB8PVwU5G4iLeVlRPtier37Ov+fD0nYey0ShkNC9mT12NhJuRmqY/kcKqsc6lV2dpCZRA7Y2Ut5tYY2TvZQMlZ47UVp+XpFCxP2iuyCvXG94ZeaIgaVxcJBz6UoKw7/7z3SfcrfO1TP0aJ/65Nvzea67aiVnvD1tCuXtC49bvT0aKyspQ98rjqOdjMs30hk16XqO/cfKzP5jTfkQB0b9dM3seoNL2jFpVPbD06DuhuFsu/6OZ/L82xavx+tyntqwJxErhZQBb3tgbyfl6i0V38+OJFOVXW53F4XJeSouUcOY2ZH06ejO1C+ciE/Ssu1AEhv2ZDcCuTnLGf6+N472MpJTtVyJyOSLKXeISbD820o27EnAykrCgK6eONhJuRqRyZhZ90zq4OEqNzm+4xI1jPnlHr07ujN1VAniEzVs3Z/Iht3Zdbh2W8WP8+7zbls3urzlSnSchoXrYkwehp9Vyw7vo1apWDZ3ImmpKZQKLs+I0bNMIhXiYh4gkWTfLwSFVmLQp+NYv2IOG1b+iqd3cQZ9OoFSjw2PyE9CbDRnjx8E4LtPupv8buTYXwmtUPW56vUqbgtz1u2Iw1oh5YPu3g+vcxl8O/U2Gaoc17nH6hGboOHbqbfp19WLWd8FEpegYfPeeNb9mR19MXflA95t78HgHt4oHeXEJ2r482ACq7YUzuSDhXXNmDrvOv26+zN8UGlclApi47PYtPM+i/+w/Lm2oJRVylNr7zLjz2V/Ntz/31m6nnN9RxVVsYy2HEjBSiGhdzsX7G2lXL+TxYQFMWQ+9hYeN2e5yT4Vn6Rl4oJY3m2tZOLHXiQka9lxOJXNB7L3+8DiVnwzIPu1nD1bOwNw4FQac9e8nm/F0OW8cX4NuLq6snz58iemMTfMvE+fPnk+pxaURF+U01I+h3HjxvHrr79y507RzaptzooVK/j444+JjIzEyiq7RygiIoKSJUty5swZwsLCCuW7GzRoQFhYGNOmTSuU9b9Ib/XK+7VUr5KUuFf/ROzkkff42leJX3Cx/BO95C7+c6Goi2ARds55v9/9VSKVWCCGuIjZOhX8FZIvM/1rcHP42RDv/BO9Aib9UnQ9ypakVlk+eudFS4rOe1jRq+SL7f2KugjPbcWIvUVdBItYOTF3dPWroOunec/lVNj++Pnp5hd4FbwScyAAzJ49m2rVquHm5sbhw4eZNGmSce6Al0F6ejo3b95kwoQJDBw40KTxQBAEQRAEQRAEQRBeda9MA8LVq1f54YcfiI+Pp0SJEowYMYJRo4o+vOiRn376iXHjxlGvXr1CKdft27dzvXbjcRcvWmYGdEEQBEEQBEEQhNfFKxpw/9J6ZRoQpk6dytSpU4u6GHkaPXo0o0ePzvP3AQEBz7Xz+vr6cvbs2Sf+/mln0BQEQRAEQRAEQRCEgnplGhD+38nlckqXLp1/QkEQBEEQBEEQBAF4PebJeZm8+jNACYIgCIIgCIIgCIJQ6EQEgiAIgiAIgiAIgvBaEhEIliUiEARBEARBEARBEARByJdoQBAEQRAEQRAEQRAEIV9iCIMgCIIgCIIgCILwWtLpdUVdhNeKiEAQBEEQBEEQBEEQBCFfIgJBEARBEARBEARBeC2JSRQtS0QgCIIgCIIgCIIgCIKQL9GAIAiCIAiCIAiCIAhCvsQQBkEQBEEQBEEQBOG1JIYwWJaIQBAEQRAEQRAEQRAEIV8iAkF4KS0YVdQlsAyt1KOoi/DcXOKvF3URLOKcU0BRF+G5BXRQFnURLCJD4VDURbAIl9R7RV2E55ZqqyjqIliEXKcu6iI8tyipqqiLYBG/d/inqItgEZmuxYu6CM9No7At6iJYxEdl9hZ1EZ5bj8mNi7oIljExvKhL8Ez0ehGBYEkiAkEQBEEQBEEQBEEQhHyJBgRBEARBEARBEARBEPIlhjAIgiAIgiAIgiAIryWdTlfURXitiAgEQRAEQRAEQRAEQRDyJSIQBEEQBEEQBEEQhNeSeI2jZYkIBEEQBEEQBEEQBEEQ8iUiEARBEARBEARBEITXkl4v5kCwJBGBIAiCIAiCIAiCIAhCvkQDgiAIgiAIgiAIgiAI+RJDGARBEARBEARBEITXkphE0bJEBIIgCIIgCIIgCIIgCPkSEQiCIAiCIAiCIAjCa0lEIFiWiEAQBEEQBEEQBEEQBCFfr1QDwuLFi3F2di7qYvxfCwgIYNq0aUVdDEEQBEEQBEEQBOEFs8gQhl69erFkyZJcy5s3b86OHTss8RUAdO3alZYtW1psfTlFRERQsmTJJ6b57rvvGD16dKGV4WV34sQJ7O3tC5x+9OjRbNy4kbNnzxZeoYCN23fyx/pNxCUkElCiOEP79aZiuTJm08bFJzB74RKuXr/B3cgHdGz9FkP79zZJM+zL7/j3v4u58tao+gYTv/2yUOoAsGnbn6xZv5G4+AQCSvgxuH9fKpQvazZtXHw8vy5YzNVr17kXeZ8ObVoxeEBfkzQ79/zFpGkzc+Xdvv4PrKysCqUO5qzec5hl2/cTm5RCYDEvPu3RjjdCAs2mPRN+k5mrtxERGU1mVhbe7i50aliLHi3qFWoZ9Xo9m1b9xoFdG0hLSyEwqBw9B46kWIlST8x38sheNqz8legHd/H0Lk7HdwdTpWZDs2m3rl3EuuW/0LT1O3TvNyJ7Hf/8xf6d67l1/RKpKUmMmbKCEoEhFqmXpY8NgNTUNOYv/52//zlGSmoaPl6efNDnPWpWrWyRMhfE1q1bWbd2LfHx8fj7+zNg4EDKly9vNm18fDzz5s3j2tWrREZG0rZtWwYOGvTCyvrIup37WLFpJ3GJiZQs7suw3t0IKxNsNu3+Y6dYv3M/VyPukKXREFjcl75d2lIzzHwddx8+zrfTfqNetTB+/HxoodVh87btJueoD/r3pUL5cmbTxsXHM3fBIuM5qn2bVgwe0C/Pde878DfjJ02mds3qjPm68M6z8GocF7u2rWfL+pUkxsdRvERJ3uv/EWXKh+WZ/uL5MyybP5O7t2/i4upOm07dadqyg0maY4f3sXr5fKLu38PLpxhdew6geu36xt9npKexevk8TvxzkKSkBAICg+k1YBilgk3/NvfuRLBy0Wwu/ncWvV5H8RIlGTZyLO6e3k9dzz8OnGTxnqPEJqVSyseDz99uSuXSJcymPX3tDtM3/sXNqDgys9T4uCrpXPcNejauYZJu+V/HWX3wFA8SknG2t6Vp5TJ81K4h1orCG7m7dtcBlm/ZTVxiEiWL+/DJe2/zRpkgs2n3HT/D+t0HuRJx9+Hx7UP/zq2pWcn0ep+Sls6cPzax//hZUtLS8fVw56OenajzhvnzwPNav2MvKzf9SVxCIiX9ivFR7+6ElTV/Ldp/9CQbdu7jWsRtstRqSvoVo2+X9tR4o4Ixzebd+/nzwBFu3r4LQEhgAAN7dKZskPnrvyV1auJEo+r22NtKuXYni0UbE7gXrXliHjsbCV2aK6lWzhZ7WykxCRpWbEvibHgmAKElrWhdz5GSxaxwcZIxZWksJy9mFnpdnsS1blUCR/RFWbk8Nr6enOw0mKjNe4u0TC8jnV5X1EV4rVjsTNqiRQsWLVpkssza2tpSqwfA1tYWW1tbi67zcX5+fty/f9/4888//8yOHTvYs2ePcZmDg0OhfX9hycrKstjDooeHh0XWY0l//X2YX+YvYtig/pQvE8KWHbsZOWYci3+ZipeZ8qrVapyVTvR4uxNrN201u87vR32KRpN9oUlKSaXfR5/SoE6tQqvHvoOHmDNvIR99MIByZUPZ9ucuRo0ey4LZM/DyNFcPDc5OTnTv0pl1m7bkuV47OzsWz51lsuxFNh7sOnqWySs288X7HQkLCmDdvqN8+PN81kz4DB93l1zpba2t6NKkDkF+PthaW3H2yk3GLVqLrbUVHRvWLLRybt+whJ2bV9L3o+/w9i3BljUL+Pm7IYyfvQ5bW/ONZtcun2POz1/SofsgqtRsyKmj+5gz6QtGTVhAqWDTG7wbVy9wYNcG/AJy31BmZWYQVKYS1eo0YfEvP1isToVxbKjVaj79dizOzk6MHjkCD3c3YmJjC/XcnNOBAwf4be5cBg8ZQtmyZflz+3a+/eYbfp07F09PT7NlViqVdOvWjQ0bNrywcj5uz+HjTFu0is/696BiSGk27D7I8HHTWTn1e7w93HKlP3PxKtUrlWVQ94442tuxdd9hPps4k/kTviKkpOnD1f2YOGYuXUNYHg8rlrL/4Tnqww8GPjxH7eTL0WNZMHsmnmbPUWqUTkq6d3mbdZs2P3HdUdHR/LZwMRXKmW8wtaRX4bg4cnAPS+ZNp+8HIwgpW5E9f25k4uhPmTx7udmH9OgHkfw4+lMaNW/D0E+/JfziORbMmYyT0pkadQwNmlcu/cf0H7+jy7v9qFarPif+OcD0H79h9E9zCAoxNALNnTmRu7duMGTEt7i4uvP3vp388PXHTJ69Ald3w9/mwf27fPf5BzRs2prOPfphZ2/PvTu3UFg9/T3fjpMX+Wntbr7q1oKwQD/WHjrN4F9WseGbgfi4KnOlt7VW0K1+VYKKeWJrreDMtTuM/f1PbK0VdK5raKjZdvw/pm/8izE9W1MpsDi3ouL5dpnhGvlZ56ZPXcaC2H3kJFOXrOHzvt2oGFKKDXv+5pOJv7Bq8rd4u7vmSn/m0lWqVyjDB93a4WBnx9b9Rxjx02wW/jCSkJJ+AKg1Gj4cNwMXpSMTPhmAp6szUXEJ2NnaFEod9hw+xvRFKxnR/z0qhgaxcdc+Ph03heXTxps9R529GE71SuUY1KMTDnZ2bNt3iM8nTmPehG8JDvQH4PSFyzStW4PyIT2wVihYselPPvl+EsunjcfDLff131La1HfkrboOzF0Tz/1YDR0aOfFlPw9G/PyAzCzzY+FlMhjV14PkNC3TV8QRn6TFTSkj47H01gopt+6rOXAyjU96uhda+Z+GzN6O5HPh3F2yniprZuWfQRAswGJDGKytrfH29jb5uLhknxwkEgnz58+nQ4cO2NnZERQUxObNpjcUmzdvJigoCFtbWxo2bMiSJUuQSCQkJiYCuYcwjB49mrCwMJYtW0ZAQIDxBjElJcWYRq/X89NPPxEYGIitrS2VKlVi7dq1Zusgk8lMyu/g4IBcLsfb2xtHR0eCg4NzRVRs2bIFe3t7UlJSiIiIQCKRsGrVKmrXro2NjQ3lypVj//79JnkuXrxIy5YtcXBwwMvLi549exIbG1ugv3ODBg0YOnQoQ4cOxdnZGTc3N77++mv0+uwTXEBAAD/88AO9evVCqVTSv39/ANatW0e5cuWwtrYmICCAyZMnm6w7ICCAsWPH0r17dxwcHPD19WXmzJm50jw+hCEpKYkBAwbg6emJk5MTjRo14t9//wUM22vMmDH8+++/SCQSJBIJixcvLlA9n8aaTVtp2aQRrZo1xt+vOEP798bT3Z3N23eZTe/t5cmH/fvQvFF97O3tzKZxcnTE1cXF+Dl15hw21tbUL8QGhHUbN9OiaWNaNm+Kv58fgwf0xdPdjS3bzUfxeHt5MmRgP5o1boi9nfl6AEgkmNTF1aXwLtrmLN9xgHb1q9OhQQ1KFvPi03fb4eXqzNq//jGbPjSgGC1qvUGp4t74erjSsk4ValUI4Uz4jUIro16vZ/eW32n9dm+q1mpEcf/S9Pt4DCpVJkcP5h1FtWvL75QLq0Hrzr3xKR5A6869KVOxOru3rDRJl5mRzm9Tv6HXkK+ws3fMtZ7aDVvRrmt/ylWsbtF6Fcax8eeefaSkpvLDl59ToWwo3p4eVChbhtIlAyxa9ifZsGEDzZo1o0WLFpQoUYKBgwbh4eHBtm3bzKb38vJi0KBBNG7S5KkiqCzp9627adOoLm0b1yOguC+f9O6Gp7sL63ftN5v+k97deLfdW5QtXRI/Hy8+6N4RPx8vDp381ySdVqtj9PR59OvSFl8zD/GWtG7jJlo0bfLYOaofHu7uTzhHeTFkYD+a5nOO0mq1TPh5Ku/16Ia3t1dhFd/oVTgutm38g4ZNW9OoeVuK+QXw/oBhuLl7snu7+Qaw3X9uxM3Di/cHDKOYXwCNmrelYZNWbF3/uzHN9s1/UOGNarTv8h7F/Pxp3+U9yleqyp+bVgOQpVJx/PABuvceQpnyYXj7FuftHn3x9PJh95/Z3/vH0t8Iq1qLHn2GULJUMF7exahcrTZK56e/tiz76xgdaofRsc4bBPq48/nbzfB2dmL1wdNm05fx8+atauUo7etBMTdnWteoQO0ygZy+dseY5t+bdwkr5UfLauUp5uZM7bKBtKhajgu37ptdpyX8vm0vbRvWpl2jupQs5sPw97vg5ebCut0HzaYf/n4XerZtRtlSAZTw8WTwO+3x8/Hk79PnjGm27DtCcmoak0YMolJIKXw83AgLLU2wf/FCqcMfW3bSulE92japT0BxX4b16YGnmysbdv5lNv2wPj3o0b4lZUoH4ufrzaAenSnu7cWhk2eNaUYPG0THFo0JLumPf3FfRg7qjU6v5+T53BGeltSijgOb9qVw4kImd6M0zFkdj5VCQu2wvM9DDara42AnZcrSOK7cyiI2UUv4rSxu31cb0/x7JZM1u5I5caFoow4eF7PzIFe+m8aDjbuLuigvNb1OX2Sf19ELnQNhzJgxdOnShXPnztGyZUt69OhBfHw8YBg+0LlzZ9q3b8/Zs2cZOHAgX331Vb7rvH79Ohs3bmTr1q1s3bqVAwcOMHHiROPvv/76axYtWsScOXO4cOECn3zyCe+++y4HDhx4qrLb29vTrVu3XFEWixYtonPnzjg6Zj8UfPbZZ4wYMYIzZ85Qu3Zt2rZtS1xcHAD379+nfv36hIWFcfLkSXbs2EFUVBRdunQpcFmWLFmCXC7n2LFjzJgxg6lTpzJ//nyTNJMmTaJ8+fKcOnWKb775hlOnTtGlSxe6devG+fPnGT16NN98802uB/pJkyZRsWJFTp8+zahRo/jkk0/Yvdv8SUmv19OqVSsePHjA9u3bOXXqFJUrV6Zx48bEx8fTtWtXRowYQbly5bh//z7379+na9euBa5nQajVaq5cu0HVNyqZLK/6RkX+uxxuse/ZvmcvDd+sja1N4bT8G+pxnapvhJksr/JGGBcvX36udWdkZNK99wC6vd+Pr8b8wNXrhfcgnpNao+FyxD1qljcN065ZIZhzVyMKtI7LEfc4d+0WlUOfPJTgecRE3SMpIY7yYdkRDgqFFSHlK3Pt8rk8810PP0e5MNPQ2fJv1MyVZ9lvP1KpSh3KVTJNW5gK69g4cvwkZUOCmfbrfDr27EfvocNZvno9Wq32eYtcIGq1mmtXr1K5smlY+BuVK3PpYuHelD4rtVpD+I1bVK9kGupfo2I5zodfL9A6dDod6RmZODmYNoAsXLsFZydH2jZ+02LlNefROaqKmXPUhec8Ry1ftRpnJyfealY4vcOPexWOC41azc1r4VR8w7RBseIb1bly+T+zea5e/i93+so1uHHtsjGa7urlC1R8o1qONNW5cuk8AFqtBp1Oi0JhGqFmZWXN5QuGc5pOp+PMySP4+Pox/ptPGNCjFV8N78+Jf8w/KD+JWqPl0u371CpjOnS0VplA/r1xt0DruHTnAf/evEvVoOyonDdK+XHp9n3OR9wD4G5sAof+u8ab5Us/dRkLQq3RcPnmbWpUNI2eqV6xDOevFOx6++j4Vj7WwHnw1DkqBAfy08JVtBj4Oe98+j2LN/yJVmf5UGy1WkP49Qiq5xgiVb1Sef4Lv1agdeh0OjIyc5+jHpeZpUKj1T4xzfPydJXh4iTj3NXsh3yNFi7dVBHsn3f0ZZUyNly9raJ3O2fmfOXDj8O8aNfAEYmk0IoqCK8siw1h2Lp1a67w/pEjR/LNN98Yf+7VqxfvvPMOAOPHj2fmzJkcP36cFi1a8OuvvxISEsKkSZMACAkJ4b///mPcuHFP/F6dTsfixYuND/A9e/Zk7969jBs3jrS0NKZMmcJff/1FrVqGnuPAwEAOHTrE3LlzqV+//pNWnUu/fv2oXbs2kZGR+Pr6Ehsby9atW3M9YA8dOpROnToBMGfOHHbs2MGCBQv4/PPPmTNnDpUrV2b8+PHG9AsXLsTPz48rV64QHGx+POzj/Pz8mDp1KhKJhJCQEM6fP8/UqVONkQYAjRo14tNPPzX+3KNHDxo3bmzcHsHBwVy8eJFJkybRq1cvY7o6derwxRdfGNMcPnyYqVOn0rRp7hu7ffv2cf78eaKjo43DVX7++Wc2btzI2rVrGTBggEkUR15UKhUqlcp0WVYW1gUIs09KTkGn0+GSY3JNF6UzCQ8jV57XpStXuXnrDp99+IFF1meOsR4uzibLXVyciT+d+Mzr9StejM8/+ZCS/v6kp2ewfvNWhn0+irkzplK8mO/zFboAElPS0Op0uClNe93dnByJS0rJI5fBWx+PJSElFa1Wx4AOzejQoPAevpMSDQ18Ts6mYZpKpRuxMXn3WiUlxuGkNM3jpHQjKSHO+POxv3dy6/plvvt5qQVLnL/COjYiH0TxIPo/mtSvy4TvRnEv8gHT585Hq9Pyfre3n6/QBZCcnIxOp8M5RySNi7MzCQkJhf79zyIxJRWtToers5PJchdnJ+ITkwq0jpVbdpGhUtG4dlXjsn8vX2XLX4dYOulbi5bXnLzPUUoSTj/73/2/i5fYsWsPv86Y+pwlLJhX4bhITk5Ep9OidDENfVe6uJB4Os5snsSEeJQ5jgmliytarZaU5ERcXN1JTIhD6Zxjnc6uJCYYOnJs7ewJCi3P+lWLKebnj7OzK4cP7uHalYt4+xp6vZOTEsjMyGDz2uV06dmf7r0/4N9Tx5gy/ku+GT+TshXeKHA9E1LT0er0uDma3ju6OdkTm5z6xLxNv5xhyK/VMajVm3Ssk/29b1UtR0JKOr0mLwU9aHQ6urxZmb7Naxe4bE8jMfnh8Z3zOqd05GgBj+8V2/aQocqica3shtHI6FhOXQineZ3qTB05hDsPopm08A80Oh39OrWybB1SUh7WIfc5Kq6Adfh98w4yMlU0rpN3JN2vy9fg4epC1YqFN1RJ6SADICnFtPEuOUWLu0vejz2ernLKusg5fDadnxbH4u0mp1c7Z6Qy2LD3yfcrwstPXwgNb//PLNaA0LBhQ+bMmWOyzNXV9EJVsWJF4//t7e1xdHQkOjoagPDwcKpVM20Zr149/3DegIAAk95/Hx8f4zovXrxIZmZmroffrKws3nij4Be5x8tTrlw5li5dyhdffMGyZcsoUaIE9eqZTu72qLECQC6XU7VqVS5dugTAqVOn2Ldvn9m5FK5fv16gBoSaNWsieaxJtFatWkyePBmtVotMZjhxVq1a1STPpUuXaNeuncmyOnXqMG3aNJN8j5f90c95vXXh1KlTpKam4uZm+gCVkZHB9esF61UDmDBhAmPGjDFZNnzIIEY8xQN77hZiPWCZZuPtu/+ipL8fZYILd3wx5C6xXq832dZPq2xoCGVDsydAKlc2lA8+HsHGrdsZOjDvycwsLVe90Oe7eeZ/PZj0zCzOX7vFrNXb8fNyp0Wtpz9uzfnnwJ8smZPdiDfs62kPy2laKD35//3N7nsPF8bFPGDl/MmMGD3rmcYHW4Kljw29Xo+L0okRQwYik8kIKV2K2Ph4/tiw+YU0IDySc7s877HyIuTcv9DrzW2gXHYdOsaCNZv58fOhxhv8tIxMxsxYwKhB7+HslHtYTGHJfY7KvS0KKj09gx8nT+WTDwejzPHgUtheheMi9/7y5L+12f0L0zy58uc4boaM+Ia50ycw+P32SKUySpYKpk79pty8fgUwdNoAVKn5Jq3adwMgIDCYK5fOs+fPjU/VgJBdppxFyv9YXjT8PTJUWZy7eY/pm/ZRwsOVt6oZInxOXLnF/J2H+apbCyoEFON2TDw/rdmN+/a/Gdiy8CJ1cp2TzCwzZ+fhE8xfu41Jnw4yeYDX6fS4ODkyakAPZFIpZQL9iU1IYvmW3RZvQHjE7P5RgHy7/z7KwtUbmTjyY1zyOJZXbNzO7kPHmDXmiwJ1EBVUnTBb+nbIbjz7aXEeQ4IlxkPC/K8lkJymZf76BPR6uHlPjYuTjFb1HEUDgiDkYLEGBHt7e0qXfnJ4mEKhMPlZIpEYL0bmLhj6Jx3pBVjno3+3bdtGsWLFTNI96wSP/fr1Y9asWXzxxRcsWrSI3r17F+gC8SiNTqejTZs2/Pjjj7nS+Pj4PFOZzMk5zvdZ/76Q9wVQp9Ph4+OTa44H4Kletzlq1CiGDx9usizu1pUC5VU6OSKVSolPSDRZnpCUhItz7gmYnlamSsW+vw/Tq7tlh17klFc9EhMtU49HpFIpwUGluRcZabF1Pomzoz0yqZTYHNEG8cmpuOXz0FPs4aRNQX4+xCen8tuGXRZrQAirXo/AxyY51KizAEhKjMXZNXtipOSkeJycc0+A9YjS2c0YvfB4nke9fLeuXyY5KZ4xI3oaf6/Tably8Qx7t69m3pojSB823llaYR0bri7OyOVyY6MjgL9fceITElGr1bnOyZbm5OSEVCol4eHwt0cSk5Je2tf8Ojs6IJNKc/XkJSSl5Orxy2nP4eOMn7OEccMHUf2xXrt7D6K5HxPLZxOz56nRPTyn1+06gFXTf6C4d+4JJZ/Vk85Rz/p3j3xwnwdR0XzzfXak4aPrUvO2HVk09xd8LXhdhFfjuHByckYqlZGYYHpuSUpMyBVB8IizS3YkwePpZTIZDo7Kh2nccq8zKcFk7gJvn+J8N/EXMjMzyEhPw8XVnWk/foOnl4+xbDKZjOJ+ASbr8fULIPxi3sO9zHFxsEMmleSKNohPScfN8ckh7sXdnQEIKuZJXEoac7YdNDYg/LLlAK2rVzBGJQQV8yRDpWbsyu30b1EXqdSyDY3OTo+O72TTehTg+N595CTj5i5j/LD+VK9g+qYLdxclcpkUmTR7tHGArzdxicmoNRoUcsu9UcLZ0THvc1Q+x8Wew8eYMHshP3w6mGqVzL+RZeWmP1m6bgvTvvuc0gF+Fis3wKmLmVy7E2X8WS4zbF+lo4zElOxeZycHGUmpeQ8pSkzRodXqTRoZ7kVrcHGSIZPBCxqlJwivhMJ7n81TCg0NZfv27SbLTp48+VzrLFu2LNbW1ty+ffuphyvk5d133+Xzzz9nxowZXLhwgffffz9XmqNHjxqjEjQaDadOnWLoUMNrtSpXrsy6desICAhA/own/6NHj+b6OSgoyOTGJaeyZcty6NAhk2VHjhwhODjYJJ+5dYeGhppdZ+XKlXnw4AFyuZyAgACzaaysrPIdA2ptbZ2rQSe1gK3TCoWC4NKBnDx7jjdrZYe4nzp7jjrVqz0hZ8HsP3SELLWGpg0K9xWChnqU4tTZf6lbO3sc/qmz/1K7huUm1tPr9Vy/GUFJf/OvyLI0hVxOaEAxjv13hUZVs1/tdOy/K9SvXPDXUOn1erI0T3790tOwtbU3ebOCXq9H6eLGhbPH8A807O8atZrw/07z9vsf5rmeUiEVuXD2GM3b9jAuu3D2GKVDDdFWZSpVY+z0VSZ5Fsz8Hp9i/rTs+H6hNR5A4R0b5cuEsvfgIXQ6HdKHN7Z37kXi5upS6I0HYKhX6aAgwxwzdeoYl585fZqatQpvktPnoVDICQn058S5izSokR2ifPzcRd6sFpZnvl2HjjFu9mK+HzaAOlUqmvzOv5gPyyebRm79tmoDaRmZfNL7Hbzc8m74erY6GM5Rp8+eNTlHnT57lto1nm14UYnixflt1nSTZYuXryA9PcM4QaOlvQrHhVyhoGTpEM6fPWHyisXzZ09QtUZds3mCQstz+vhhk2XnzhwnsHSo8V4jKLQc58+cMEYOGNKcILhMBXKysbHFxsaW1NRkzp0+Tvfeg41lCwwqQ+S92ybpH9y789SvcFTIZZQp4cPRSzdpHJZ9n3H08k0aVMw/GvMRvV6PWpN9n5GZpc7V8SGTStHzMPrNQtGJjyjkckJLluD4+Us0qB5mXH78/CXqVa2UZ76dh08w7tdljP2oD3Ur594GFYMD2XX4hMk+dft+NO4uSos2HsDDc1SpAE78e4H6NaoYl584d4G61fJuuN/991HGz17AmGGDqF0lzGyaFRu3s2TdFqZ8M4IypZ/8qvRnkZmlJzPO9D4zIVlLhdLW3Io0TIAok0GZktb8/mfewzGu3FJRO8wOyWORCj4echKStaLx4DXwuk5mWFQsdgZSqVQ8ePDAdOVyOe4FvAEYOHAgU6ZMYeTIkfTt25ezZ88aJ/h71vBIR0dHPv30Uz755BN0Oh1169YlOTmZI0eO4ODgYPbhPz8uLi507NiRzz77jGbNmlG8eO7ZcH/55ReCgoIoU6YMU6dOJSEhgT59+gAwZMgQ5s2bxzvvvMNnn32Gu7s7165dY9WqVcybN++JjQCP3Llzh+HDhzNw4EBOnz7NzJkzc71RIacRI0ZQrVo1xo4dS9euXfnnn3+YNWsWs2fPNkl3+PBhfvrpJ9q3b8/u3btZs2ZNnjObN2nShFq1atG+fXt+/PFHQkJCiIyMZPv27bRv356qVasSEBDAzZs3OXv2LMWLF8fR0dHir/d8u11rJkydSUjpUpQLDWbrzj1ExcTS5q1mAMxbsoKY+Hi+/CT7QfDajZsAZGRmkpiczLUbNw0NISVMW8a37/6LujWroXwBIcKd2rflxynTCS5dirJlQti2YzfRMbG0adkcgPmLlxEbF88XIz7OVY/MzEwSkwz1UMjl+D+sx9KVf1AmJJhixXxIT89gw+atXL9xk48G9c9dgELybov6fDP3d8qW9KNiaX/W7z/Kg7hEOjcyPITMXL2dmIQkvh9omB9l9Z7DeLs5E+Bj6D09e+Umy/48QLemdfL8juclkUho2uYdtq5dhJdvCbx8/Ni6dhHW1jbUrNfCmG7etG9xdvPk7Z6GBsGmbbox8csBbFu/mMrVG3D6+H4u/nuMURMWAIaGiuL+ppFZ1tY2ODg6myxPTUkiPuYBCfExANyPvAWA0sUNpcuzP0QVxrHR7q1mbNj2J7PmLaJD67e4G3mflWs20LHNW89czqfVoUMHJv/8M0FBQYSWKcOOP/8kJiaGli1bAobJbePi4kzmgXk0rCojM5OkpCSuX7+OQi6nhL//CynzO62bMmbmAkJLBVAhOJCNew4SFRtPh2YNAJi9Yh0x8Yl892FfwNB48P2shXzSuxvlgwKJSzDc+FpbKXCwt8PaSkGpEqaRdQ4P33SQc7mldGrfjh+nTCO4dGnKlAlh+45dRMfE0vrhOWrB4mXExsUxcsQwY55rNwyTyBn+7slcu3EDhVyBfwk/rKysKBlg+vd/FD2Xc7klvQrHRav2XfllylgCS4cSXKY8e3ZsIjYmiiYtOwDw++I5xMfFMmSEYV6jpm+1Z9fWdSydN4PGLdpy5dJ/7Nu9lY8+G21c51ttuzBm5BA2rV1O1RpvcvLY3/x39gSjf8oefvrvqWPo0eNbrAQP7t9lxcJf8ClWggZNskPm23TszvSfvqVMuTDKVazM2VNHOXX8MN9OMH1rU0H0bFSDr5Zsoqy/D5VKFmfd4TPcT0ji7TcNDW3TN+4jOjGFcb3aArDqwEm8XZwo6W04L565foele47xToPsYZv1KwSx7K9jhPp5USGgGHdi4vll6wHqVwgy6c23pHdaNWb0L4sJDfSnQnBJNu45RFRsAh2bGIZM/PL7RmLiExk9pBdgaDwYM3sxw9/vQvmgksaef2srKxzsDK/+7NS0Hmt27mfKkjV0ad6A2w+iWbxpB11bNCyUOnRt05yxM34jtFQA5UNKs2n3fqJi4+jQzPB9c5avITY+gW8+GgAYGg/GzpzHsD7dKRdciriHUT3WVlY4PHxbyYqN25n3+3q+GzYQHw93YxpbG5tCex0lwI7DqbRr6MSDOA0PYjW0a+hEllrPkbPpxjQfdHEhPknLHzsNkSO7j6bRrLYD77VxZueRVLzd5LRr4MiOI9kRMtZWErzdsh+dPFzl+PsoSE3XEZdUNK0MMns77EtndwrZlSyOU6VQsuKTyLxTeG8eEf6/WawBYceOHblC8ENCQrhcwNmZS5Ysydq1axkxYgTTp0+nVq1afPXVV3zwwQfP9cA5duxYPD09mTBhAjdu3MDZ2ZnKlSvz5ZdfPvM6+/bty8qVK42NAjlNnDiRH3/8kTNnzlCqVCk2bdpkbEjx9fXl8OHDjBw5kubNm6NSqfD396dFixbGFub8vPfee2RkZFC9enVkMhkffvghAwYMeGKeypUrs3r1ar799lvGjh2Lj48P33//vckEimBoaDh16hRjxozB0dGRyZMn07x5c7PrlEgkbN++na+++oo+ffoQExODt7c39erVw8vL8CquTp06sX79eho2bEhiYiKLFi3K9Z3Pq9GbdUhOSWXpH2uJj08gwN+Pid9+iffDV5rFJSQQHWM6Jq7/sM+N/79y7QZ7DxzCy9ODVfOzG1Tu3Ivk/MXLTBrztUXLm5eG9eqSnJLC8lWrH9ajBONHf43Xw/faxyckEB0TY5Jn0EfZQz+uXLvOXwcO4uXpwYqFvwGQmpbG1FlzSEhIwN7ejlKBgUyd+AOhIQXv3XlezWqGkZiaxrxNu4lNTKZUcW9mjOiLz8N3Y8cmJvMgLnsSNp1Oz6zV27kXE28IlfV048MuLenUsGZeX2ERLTu8j1qlYtnciaSlplAquDwjRs8yiVSIi3mARJJ9nAaFVmLQp+NYv2IOG1b+iqd3cQZ9OoFSwQWPrgA4e/wgC2Zm9yb/+rPh/NSua3/avzPwmetUGMeGp4c7k8Z8zS/zl9D3o0/xcHOlY5uWvNPJdI6VwlS/fn1SUlJYuXIl8fHxBAQEMOb7743nnYT4eGIezoXzyIcPo8AArl29yv79+/H09GTxkiUvpMxN6lQnKTWNhWu3EJeQRKCfL5O//Bifh0N14hKSiIrNDi/fuPsAWq2Wn+ev4Of5K4zLW9avzTdDzV97CluDenVJTklm+ao/jOeocaO/MZ6j4hLic52jPnjsHHX1sXPU8oXzXmjZH/cqHBe16zUhNSWZdasWkRgfh59/IF+M/hmPh738CQlxxMZkh217evsycvTPLJ0/g13b1uPi5k6vAcOoUSf7YTOkTAU++nwMq5f/xurl8/DyLsbHI78nKCQ77Dw9PZXfl/xKfGwMDo5OVK9dn27vDTSJmKxeuz79Bn/GpjXLWPzbVHyLlWD4l+MILZd3b3teWlQtS1JaOr9tP0RMciqlfTz4ZXA3fN0MYfOxyak8SMjuNdbp9MzYtJ97cYnIpVKKezjzcfuGdK6bHdnT/626SCSGoQzRiSm4ONhRv0IQQ9s2eOryFVTT2lUNx/e6bcQmJhPo58PUL4bkOL6zh5hs3PM3Wq2OSQtXMWlhdpRaq3o1+XawoXPLy92VGV9+xNSla+gx8gc8XJzp1qIhPduZvyd7Xk3q1CA5JZVFazYZzlElivHzl8Px9nR/WIdEk3PUpt370Gq1TJ63jMnzlhmXv9WgDl9/aOikWL9jL2qNhq9//sXku/p0aUffrh0KpR4AWw6kYKWQ0LudC/a2Uq7fyWLCghgys7J7od2c5TzeKR2fpGXigljeba1k4sdeJCRr2XE4lc0HsodgBha34psB2a/L7dnaGYADp9KYu6ZoJvFVVilPrb3Zf/+yD+8f7ixdz7m+o4qkTC8jvV5MomhJEn1BB8IXgXHjxvHrr79y586d/BO/QCtWrODjjz8mMjISq8dC7SMiIihZsiRnzpwhLCysUL67QYMGhIWF5Tmx4fMICAhg2LBhDBs2LM80Pj4+jB07ln79CncCvsjwpxtL+bLSSl+aUULPzCW+4BNivszOOTUo6iI8twDpzaIugkVkKHJPIvsqckm9V9RFeG6ptpYfJlAU5Dp1/oleclHSwn8zzotQ5vb2/BO9AjJdc0eYvmo0CtuiLoJFfLTcsvMmFIUekxsXdREsopXacq9If5EadzteZN+9d5XlhiK/LF6qp5vZs2dTrVo13NzcOHz4MJMmTTLOHfAySE9P5+bNm0yYMIGBAweaNB687tLT0zl8+DBRUVGUK2d+khxBEARBEARBEATh9fVSNSBcvXqVH374gfj4eEqUKMGIESMYNerlCb/56aefGDduHPXq1SuUct2+fZuyZfN+N+7Fixct/p0F9dtvvzF27FiGDRuW61WPgiAIgiAIgiAILyOdmETRol7qIQz/bzQaDREREXn+/nne3PCqEUMYXh5iCMPLQwxheLmIIQwvDzGE4eUhhjC8PMQQhpeHGMJQtBp2OVZk371v9bO9qehl9uo/3bxG5HI5pUuXzj+hIAiCIAiCIAiCkC+9TkyiaEmF8z4bQRAEQRAE4X/t3Xlczen7P/DXKVJpRQitUkRRlrGTNRrZPoRUSpaxNUXGjAlZZuy7GWNNjBqixKBoozBJKkSrypY9W0XL+/eHb+fXcaKTpfu8z7mej4fH1PucP17vaTvnuu/7ugghhBCZQjsQCCGEEEIIIYTIJI56IHxVtAOBEEIIIYQQQggh1aICAiGEEEIIIYQQQqpFRxgIIYQQQgghhMgkjqMmil8T7UAghBBCCCGEEEJItWgHAiGEEEIIIYQQmURNFL8u2oFACCGEEEIIIYSQalEBgRBCCCGEEEIIIdWiIwyEEEIIIYQQQmQSV05NFL8m2oFACCGEEEIIIYSQ6nGEyKHi4mJu8eLFXHFxMeson00W7oHj6D6kiSzcA8fJxn3Iwj1wHN2HNJGFe+A42bgPWbgHjqP7kCaycA+EPwQcx1FbSiJ3Xr58CU1NTbx48QIaGhqs43wWWbgHgO5DmsjCPQCycR+ycA8A3Yc0kYV7AGTjPmThHgC6D2kiC/dA+IOOMBBCCCGEEEIIIaRaVEAghBBCCCGEEEJItaiAQAghhBBCCCGEkGpRAYHIpXr16mHx4sWoV68e6yifTRbuAaD7kCaycA+AbNyHLNwDQPchTWThHgDZuA9ZuAeA7kOayMI9EP6gJoqEEEIIIYQQQgipFu1AIIQQQgghhBBCSLWogEAIIYQQQgghhJBqUQGBEEIIIYQQQggh1aICAiGEEEIIIYQQQqpFBQRCeKqsrAxJSUl4/vw56yg18ubNG9YRiIxKSEjA/v37ceDAASQkJLCOUyOKiop49OiR2PWnT59CUVGRQaLPY2xsjKdPn4pdLygogLGxMYNEnycxMRHXrl0Tfn7s2DGMGDECv/zyC969e8cwGSGEAP7+/nj79q3Y9Xfv3sHf359BIiJPaAoDkQunT5+GmpoaevbsCQDYtm0bdu7cCXNzc2zbtg3a2tqME1bvxx9/hIWFBSZPnoyysjL06dMHFy5cgKqqKk6cOIG+ffuyjigRNTU1jB07Fm5ubsKvBx/t27cPjRo1gp2dHQBg/vz52LFjB8zNzREQEAADAwPGCSXz/Plz7N69Gzdv3oRAIEDr1q3h5uaGBg0asI4msbt372L8+PGIi4uDlpYWgPdvWLt3746AgADo6emxDSgBBQUF5Ofno3HjxiLX79+/j5YtW6KoqIhRspr52H08fPgQ+vr6Vb7glUadO3fGggULMHr0aGRnZ6Nt27YYOXIkLl++DDs7O2zcuJF1RIlEREQgIiICjx49Qnl5uchje/bsYZSq5tLT0xEdHV3lfSxatIhRqi+XkJCAwsJC9O7dm3UUucP37ylFRUU8ePBA7Hft06dP0bhxY5SVlTFKRuQBFRCIXLCwsMCqVaswdOhQXLt2DZ07d4aXlxciIyPRpk0b7N27l3XEarVo0QIhISHo1KkTQkJCMHPmTERFRcHf3x9RUVGIi4tjHVEix48fh5+fH06cOAEDAwO4ubnB2dkZzZo1Yx2tRszMzPDnn3+iX79+uHjxIvr374+NGzfixIkTqFOnDo4ePco6YrViYmIwfPhwaGhooFOnTgCAK1euoKCgAKGhoejTpw/jhJIZNGgQXr58iX379sHMzAwAkJaWBjc3N9SvXx/h4eGME37c5s2bAQCenp5YtmwZ1NTUhI+VlZXh3LlzyMnJwdWrV1lFlEhoaCgAYMSIEdi3bx80NTWFj5WVlSEiIgJnzpxBWloaq4g1oqmpicTERLRs2RKrVq1CZGQkwsLCEBcXh3HjxuHOnTusI1bL19cXS5cuRadOnaCrqwuBQCDyeHBwMKNkNbNz50788MMPaNSoEZo2bSpyHwKBAImJiQzTfZk2bdogPT2dl2/2MjMzkZWVhd69e0NFRQUcx4l9j0krWfieUlBQwMOHD6GjoyNyPTk5GTY2Nnj27BmjZEQucITIgfr163O3b9/mOI7jFi9ezI0ePZrjOI67cuUK16RJE4bJJFevXj3uzp07HMdx3JQpUzgPDw+O4zguOzubU1dXZ5js8zx58oRbv349Z2lpydWpU4ezs7Pjjhw5wpWUlLCOJhEVFRUuNzeX4ziOmz9/Pufk5MRxHMddv36da9SoEctoEmvbti03ZcoUrrS0VHittLSUmzp1Kte2bVuGyWpGWVmZS0xMFLt+5coVTllZmUEiyRkaGnKGhoacQCDg9PT0hJ8bGhpypqam3KBBg7hLly6xjlktgUDw0X9KSkqcqakpd/z4cdYxJaaurs6lp6dzHMdxAwYM4DZu3MhxHMfl5uZK/fdUhaZNm3L+/v6sY3wxfX19buXKlaxjfBP37t3jcnJyWMeokSdPnnD9+/fnBAIBp6CgwGVlZXEcx3Fubm6cl5cX43SS4fP3VIcOHTgrKytOQUGBs7Cw4KysrIT/LC0tOXV1dW7MmDGsYxIZV4d1AYOQ2qCkpITCwkIAwNmzZ+Hs7AwAaNCgAV6+fMkymsSaNGmC1NRU6Orq4vTp0/jjjz8AAIWFhbw6I12hYcOG8PT0hKenJ7Zs2QJvb2+cPHkSjRo1wvTp07FgwQKoqqqyjvlRampqePr0KfT19REeHg5PT08AgLKyMm+2m2dlZeHIkSMi3z+Kiorw8vLi1RlKfX19lJSUiF0vLS1F8+bNGSSSTGhoKNLS0qCkpAQbGxscPXqUF8epPpSSkoKSkhIoKirCyMgIly9fRqNGjVjH+iKdOnXC8uXLMWDAAMTExODPP/8EANy+fRtNmjRhnE4y7969Q/fu3VnH+GLPnz/HmDFjWMf4Jvi28w54v1uqTp06yMvLQ5s2bYTXHRwc4OnpiXXr1jFMJxk+f0+NGDECAJCUlITBgweL7FpTUlKCoaEhRo8ezSgdkRdUQCByoWfPnvDy8kKPHj0QHx+Pf/75B8D7M3AtWrRgnE4yrq6uGDt2rHAr6sCBAwEA//33H1q3bs04Xc3l5+fD398fe/fuRV5eHv73v/9h8uTJuH//PlauXIlLly5J9dbzgQMHwt3dHVZWVkhPTxf2Qrhx4wYMDQ3ZhpOQtbU1bt68Kdz2X+HmzZvo0KEDm1CfYfXq1Zg9eza2bduGjh07QiAQICEhAR4eHli7di3reB81cuRI5OfnQ0dHB+fOnauyCMIHVlZWwvsQCAS82cb8KRs3boSjoyNCQkKwcOFCmJiYAACCgoJ486bc3d0dBw8ehI+PD+soX2TMmDEIDw/H9OnTWUchAMLDwxEWFib22qlVq1bIzc1llKpm+Pw9tXjxYgCAoaEhHBwcoKyszDgRkUdUQCByYevWrZgxYwaCgoLw559/ClclT506BVtbW8bpJLNkyRK0a9cOd+7cwZgxY1CvXj0A71eMFyxYwDid5I4ePYq9e/ciLCwM5ubmmDlzJiZOnChsfgcAHTp0gJWVFbuQEti2bRt+/fVX3LlzB0eOHEHDhg0BvO8hMH78eMbpPi4lJUX48Zw5c+Dh4YHMzEx07doVAHDp0iVs27YNK1euZBVRItra2iJvVN+8eYPvvvsOdeq8/7NWWlqKOnXqwM3NTbhiI210dHRw6dIlDBs2jFfnhz+kpaWF7Oxs6OjoIDc3V6whGR9ZWlqKTGGosGbNGt7s+CouLsaOHTtw9uxZWFpaom7duiKPr1+/nlGymjExMYGPjw8uXboECwsLsfuYM2cOo2TVk/R7hU89EN68eVPl7sAnT54IX5dIOz5/T1VwcXFBQUEBDhw4gKysLHh7e6NBgwZITExEkyZNpHr3HeE/aqJICE/4+/vDwcFB7A/0u3fvEBgYKDyWIe00NTUxbtw4uLu7o3PnzlU+p6ioCKtXrxZW2snXo6CgAIFAgOp+9QsEAql+Ubtv3z6Jn+vi4vINk3y+JUuWYOnSpRIVDqT5azF16lT4+/tDV1cXeXl5aNGixUffOGVnZ9dyui+TkJAgMqGkotkoH9jY2Hz0MYFAgMjIyFpM8/mMjIw++phAIJDq7ykFBQUYGBjAxcXlk0Xx4cOH12KqL2NnZwdra2ssW7YM6urqSElJgYGBAcaNG4fy8nIEBQWxjlgtPn9PVUhJScGAAQOgqamJnJwcpKWlwdjYGD4+PsjNzeXVMUTCP1RAIHJBFsbdyMI9AO97Nkhzb4Oa4OMIxJpsMeXLKEo+u3XrFjIzM2Fvb4+9e/eK7MSpTNrfYJw+fRqZmZmYM2cOli5dCnV19Sqf5+HhUcvJPo8sjAYl7F2+fBl79uxBYGAgjIyM4ObmBkdHR172OqmQmpqKvn37omPHjoiMjIS9vT1u3LiBZ8+eIS4uDi1btmQdUS70798fHTt2xOrVq6Guro7k5GQYGxvjwoULmDBhAnJyclhHJDKMCghELsjCjHVZGdkjK4WQmJgY2NvbQ1NTk9cjEGXJo0ePqpzpbWlpySiR5Hx9feHt7c374pqrqys2b9780QICX/B5NCiRPsXFxQgKCsLevXuFx5YmT54s7GXEN/n5+fjzzz9x5coVlJeXw9raGjNnzoSuri7raDVW8TaIb0fIKo+arVxAyM3NhZmZGYqLi1lHJDKMCghEpsnCjHUrKysIBAIkJyejbdu2wjPewPt7uH37NmxtbXHo0CGGKSUnC8UcAGjXrh26d++OP//8U7hdu6ysDDNmzEBcXByuX7/OOKFk7t27h7i4uCrfePPhHCjwvnDj4uKCmzdvih3NkPajGJWVlZXhyZMnEAgEaNiwIW/O2ssiFRUVXLhwQWzbeWJiInr06CG1v6dGjRoFPz8/aGhoYNSoUZ987tGjR2spVc15eXlh2bJlqF+/Pry8vD75XL70cqhw+/ZtTJ48GTExMXj8+LFU71iTZf7+/lizZg0yMjIAAKampvD29oaTkxPjZJJp0qQJTp8+DSsrK5ECQnh4OCZPnow7d+6wjkhkGDVRJDJtw4YNAN5XmLdv3y7ygrxi3M327dtZxZOIrIzsqSjmCAQC7Nq1q8piDp+mScjCCMS9e/di+vTpUFJSQsOGDUVWYAQCAW8KCK6urjA1NcXu3bvRpEkT3q0kBQcHY+3atUhISEBpaSkAoE6dOujUqRO8vb2ltgnkhy5fvoyNGzfiwoULyM/Ph0AgQJMmTdC9e3d4enryqn8AX0eDampqCr//NTU1Gaf5fFevXhX+//9UgZ9PP+t3796Fn58f/Pz8UFRUBG9vb2hoaLCOVWN79+6Fmpqa2BjEw4cPo7CwUGp7zlS2fv16+Pj4YNasWejRowc4jkNcXBymT5+OJ0+eCMcyS7Phw4dj6dKlwsUjgUCAvLw8LFiwgBevCQnPcYTIgb59+3LPnj1jHeOL+Pn5cUVFRaxjfDZDQ0PO0NCQEwgEnJ6envBzQ0NDztTUlBs0aBB36dIl1jEl1r17dy44OFjsenBwMNe1a9faD/QZWrRowS1fvpwrKytjHeWLqKmpcRkZGaxjfJbt27dzSkpK3PTp07ng4GDuwoULXFxcHBccHMxNnz6dq1evHrdjxw7WMasVHBzM1a1bl7O1teU2bNjAHTx4kPv777+5DRs2cEOGDOGUlJS4kJAQ1jElFhISwnXp0oW7fPkyV15eznEcx12+fJnr2rVrlT/30uTVq1esI5D/8/btWy4wMJAbOHAgp6yszI0cOZI7fvw4r3/nmpqacpGRkWLXo6OjOVNTUwaJas7Q0JDbt2+f2HU/Pz/O0NCQQaKae/HiBdejRw9OS0uLU1RU5PT09Li6detyvXv35l6/fs06HpFxdISByB2Op+fdKly5ckXYtM/c3Fzqxx1+yMbGBsHBwR9tFifNKo9AvHnzJubPn4/Zs2dXOQLRwcGBVUyJNWzYEPHx8bxvejVixAg4OTnxctXFxMQEP//8MyZPnlzl43v27MGKFSuQlZVVy8lqpl27dpg4ceJHR8quWrUK/v7+uHHjRi0nk1xVo0ErxoEC/380aP369aW654yysjJsbGxgb2+P4cOHo1mzZqwjfZZmzZph+PDhGD58OPr16wclJSXWkWqsYcOGUFdXh4uLC5ycnMSO7lXg004EZWVl3Lp1C4aGhiLXc3Jy0KZNG6k93lOZsrIyrl+/DhMTE5HrGRkZsLCw4FX/gMjISCQmJgp7UQwYMIB1JCIHqIBA5Abfz7s9evQI48aNQ3R0NLS0tMBxHF68eAEbGxsEBgaKNVeURiUlJTAzM8OJEydgbm7OOk6NycoIxArz589HgwYNPvqmjy+ePHkCFxcXdOnSBe3atROb6W1vb88oWfVUVFSQlJQkbNT3oVu3bsHKykrqX5QrKysjJSUFpqamVT6elpaG9u3bS/ULc1kYDQq8n7QSGhqKY8eO4fz587C0tBQWE/jQULRCdHQ0jh8/jtDQUDx8+BCDBw+Gvb097OzseNM3QEFBQfhxVYsWHMfx5u9FBX19fWzdulXs9+qxY8cwc+ZM3L17l1EyybVr1w4TJkzAL7/8InJ9+fLl+Oeff3Dt2jVGyQjhByogELnwsfNu27Ztw/Lly3lx3s3BwQFZWVnYv38/2rRpA+D9OCUXFxeYmJggICCAcULJNG/eHGfPnhXeA5/I2gjEsrIyfP/99ygqKoKFhYXYG2++NCcLDQ2Fk5MTXr16JfaYtL8479SpE/r06YN169ZV+fjcuXMRExODhISEWk5WM23btoWLiwvmz59f5eOrV6+Gn58fUlNTazmZfHvx4gVOnjyJY8eO4fTp09DW1hYWE/r06cObRp03btwQFkWuXr2Kbt26Yfjw4bC3t5fqHVQxMTESPY9PU3vmz5+PQ4cOYe/evejduzeA9/fp5uaG//3vf1i7di3jhNU7cuQIHBwcMGDAAPTo0QMCgQCxsbGIiIjAoUOHMHLkSNYRq7R582ZMnToVysrKwr5SH6Ompoa2bdviu+++q6V0RJ5QAYHIBSMjI/j6+sLZ2Vnk+r59+7BkyRLcvn2bUTLJaWpq4uzZs+jcubPI9fj4eAwaNAgFBQVsgtXQypUrcevWLezatUtkogSpfcuWLcPixYthZmYm1nxQIBAgMjKSYTrJGRoa4vvvv4ePjw+aNGnCOk6NxMTEwM7ODgYGBhg0aJDw65Cfn48zZ84gNzcXJ0+eRK9evVhH/aQjR45g3LhxGDRoUJX3ER4ejsDAwGonA7D08uVLiZ/Lpy3nFUpLSxEZGSlc1X/16hW2bNkCR0dH1tFq5OHDhwgNDUVoaCgiIiJgbGyMVatWwc7OjnU0ufDu3Ts4OTnh8OHDwr/h5eXlcHZ2xvbt23lz1OTKlSvYsGGDcHqPubk55s6dK9XHQo2MjJCQkICGDRvCyMjok899+/YtHj16BE9PT6xZs6aWEhJ5QQUEIhdk4byburo6zp8/jw4dOohcv3r1Kvr06VOjF78sjRw5EhEREVBTU4OFhQXq168v8rg0jxYLDQ2V+LnSvG2+gra2NjZs2IBJkyaxjvJF1NXVkZSUJNUrkZ+Sk5ODP//8E5cuXUJ+fj4AoGnTpujWrRumT58udtZYWl28eBGbNm3CxYsXxe7Dw8MD3bp1Y5zw0yqOKH0Kn7ac5+XlQU9PT+yeOI5DXl4enj17htLSUrGiNJ8UFhYiLCwM6urqdPa7lqWnpyM5ORkqKiqwsLDgxa47eXPmzBlMmDABjx8/Zh2FyBgqIBC5IAvn3YYPH46CggIEBAQIm2Ldu3cPjo6O0NbWRnBwMOOEknF1df3k43v37q2lJDVX+Tzrp/DlDUbTpk1x/vx5tGrVinWUL+Li4oJevXrB3d2ddRTCY5JuNwf4seVcUVERDx48EGvc9/TpUzRu3JgXv6MA2bmPqgwYMADZ2dnIzs5mHUWuyPL3VGVFRUXYsWMHPDw8WEchMob2DxO54OvrCwcHB5w7d67K8258sHXrVgwfPhyGhobCVaW8vDxYWFjgwIEDrONJTJoLBNUpLy9nHeGr8vDwwJYtW6o9SyntTE1N8fPPPyM2NrbKXg5z5sxhlIzwCR+KAjVRsVviQ69fv4aysjKDRJ/nY+tcb9++5c12+Y8ZOXIknjx5wjpGtby8vLBs2TLUr18fXl5en3wuH3rnyMr3VEREhPAYhkAgQOvWrfHjjz8Kd+OoqKhQ8YB8E1RAIHJh9OjR+O+//7BhwwaEhIQIz7vFx8dL9Xm3yvT09JCYmIgzZ87g1q1bwnugbZu1a8KECRgxYgSGDBkCdXV11nG+SHx8PCIjI3HixAm0bdtW7I23NB8nqWzXrl1QU1NDTEyM2CqyQCCQ6gJCeHg4+vXrJzxLfPDgQaxevRoZGRnQ1dXFnDlzpDp/hfT0dLRq1Ur4hjU2NhZr164V3sfs2bMxfPhwxinlQ8UbPIFAAB8fH6iqqgofKysrw3///Sd2FE4aVRQ2BQKB8Ge8QllZGc6dO4fWrVuzivdVzJw5k3UEiVy9ehUlJSUAgMTExI8e9ZH28diy9D21detWeHp64n//+5+wSHDp0iUMHToU69evx6xZsxgnJLKMjjAQQr45a2trREREQFtbG1ZWVp98kZGYmFiLyWpuyZIlOH78OG7cuIHevXsLO4Hr6emxjlZjfD5OIisqb6Wt6Aw+Y8YMdO3aFYmJidi6dSv27t2L8ePHs476SZXvIzo6Gv3794ednZ3wPoKDg3Hy5EkMHjyYdVSZZ2NjA+D9kYxu3bqJrKgqKSnB0NAQ8+bNk/qjSxVN4nJzc9GiRQuRiREV97F06VLqMk8kJkvfU82bN8fPP/8sVijYtm0bVqxYgfv37zNKRuQBFRCI3CgvL0dmZiYePXokthW9YhSRtIuIiEBERESV97Bnzx5Gqarn6+sLb29vqKqqwtfX95PPXbx4cS2l+jJ3794VjhWLiYmBubm5cDwaX3a1EPYUFBSQn5+Pxo0bo2fPnujfv7/Iz8jatWtx6NAhxMfHM0xZvcr3MWDAAJiZmWHbtm3Cx3/++WdcuHChRn0GSM1t3rwZU6ZMgYqKClxdXbFp0yZeTowIDQ2Fra0tlJSUYGNjg6NHj0JbW5t1rBpzc3Or9jkCgQC7d++uhTRfrrS0FMrKykhKSkK7du1Yx/lsfP6eqqCuro6rV69W2RzcysoKr1+/ZpSMyAMqIBC5cOnSJUyYMAG5ubliZ9/40vDO19cXS5cuRadOnaCrqyu2is+XJoqy6NWrVzh16hSOHTuGU6dOQV1dHcOGDcMPP/yAtm3bso4nk2TlTG7lN95NmjTBqVOnYG1tLXw8PT0dXbp0kfoxrZXvo1mzZggODhZZxUtNTUXv3r15cd6bz+rUqYP79++jcePGH20UxweKiorIz8+Hjo4Or+9j5MiRH32srKwMZ8+exdu3b3nxGqRCy5YtcfToUbRv3551lK+mrKwM165dg4GBAW+KCo6OjujQoQO8vb1Frq9duxZXrlxBQEAAo2REHlAPBCIXpk+fjk6dOuHff/+t8s03H2zfvh1+fn5wcnJiHYV8QF1dHWPHjsXYsWNRVlaG6OhohIaG4uLFi1JXQJCV4ySVz+RevXr1o8/jw896amoq8vPzoaKiIrazqLy8nDdvLl69egVlZWWoqKigXr16Io8pKSmhqKiIUbKa69evH44ePQotLS2R6y9fvsSIESMQGRnJJlg1mjVrhiNHjmDo0KHgOA5379796JhifX39Wk4nOR0dHVy6dAnDhg37aDNIPvhYYf/YsWP45ZdfUK9ePSxatKiWU32ZX3/9FT///DMOHDiABg0asI7zWX788UdYWFhg8uTJKCsrQ+/evXHx4kWoqqrixIkT6Nu3L+uI1WrTpg1WrFiB6Oho4YjcS5cuIS4uDnPnzhVpjsyHPjqEX2gHApEL9evXR3JysthWLz5p2LAh4uPjeTnrXltbW+IXgM+ePfvGaeSbLB4n4TMFBQUIBALhzqgNGzaIdM0OCAjA8uXLcePGDVYRJVJxH8D7Duc7d+7E5MmThY8fO3YM3t7eSE9PZxWxRirvqKjs0aNHaN68ubB4JW127NiB2bNno7S09KPPqXhDLs2FqSVLlmDp0qUS/d2Q5vv4UFxcHH766SdcvXoVs2bNwoIFC3iz4l3BysoKmZmZKCkpgYGBAerXry/yuDQXnis0b94cx44dQ6dOnRASEoKZM2ciKioK/v7+iIqKQlxcHOuI1aro51AdgUBAY0LJV0c7EIhc+O6775CZmcnrAoK7uzsOHjwIHx8f1lFqbOPGjcKPnz59iuXLl2Pw4MHCqvnFixcRFhYm9fdW3Yp9ZdL6IqpyUYAKBOzdvn1b5PPKXcEBoKSkBD/99FNtRvosUVFRIp/r6uqKfJ6Tk4MpU6bUZqTPkpKSIvy4YmdIhbKyMpw+fRrNmzdnEU0iU6dOxfjx45GbmwtLS0ucPXsWDRs2ZB2rxpYsWYJx48YhMzMT9vb22Lt3r9huED65ceMGFixYgNOnT8PZ2RmBgYFo0aIF61ifZcSIESJFTz56+vQpmjZtCgA4efIkxowZA1NTU0yePJk3Y40//NtBSG2iHQhELgQHB+PXX3+Ft7d3lXPiLS0tGSWTnIeHB/z9/WFpaQlLS0uxe5Dmc96VjR49GjY2NmKdg7du3YqzZ88iJCSETTAJVLdiXxm9Of+2Ro0aJfFz+TKOkrD34U6KD6moqGDLli0SNcdjbd++fRg3bpzYcRK+qbxrim/u3LmDRYsW4cCBA/j+++/x22+/oU2bNqxjfZbCwkJ4e3sjJCQEJSUl6N+/P7Zs2YJGjRqxjlZjBgYG2LlzJ/r37w8jIyP88ccf+P7773Hjxg307NkTz58/Zx2xRip+V/H1qA/hHyogELmgoKAgdq2igi7tWzkrVIzm+pgPVwCllZqaGpKSkqhzMCOycpykuhGUldE4SiKpika7xsbGiI+Ph46OjvAxJSUlYXNCQiShqqoKgUCA2bNno3v37h99nr29fS2m+jze3t74448/4OjoCBUVFRw8eBB9+/bF4cOHWUersSVLlmDjxo3Q1dVFYWEh0tPTUa9ePezZswc7d+7ExYsXWUeUiL+/P9asWYOMjAwAgKmpKby9valXFvnm6AgDkQuysNXrUwUCaT2PW5WGDRsiODhYrHNwSEgIL7fa8k3l4yR8RkUB8i0YGBgAgFgzS0I+R0UDy9WrV3/0OXxZxDh69Ch2796NcePGAXg/BaBHjx4oKyvjXVFtyZIlaNeuHe7cuYMxY8YId+koKipiwYIFjNNJZv369fDx8cGsWbPQo0cPcByHuLg4TJ8+HU+ePIGnpyfriESG0Q4EItPu3btX7XnVv//+G46OjrWUqOYCAwOFf7CrUlJSgv/97384duxYLab6fH5+fpg8eTJsbW1FOgefPn0au3btwqRJk9gGlFBZWRk2bNiAQ4cOIS8vD+/evRN5XJpX72XN7du3UVpailatWolcz8jIQN26dWFoaMgmGOG19PR0REdH49GjR2IFBb51zifkSykpKeH27dsir6lUVFSQnp4OPT09hsnkk5GREXx9feHs7Cxyfd++fViyZIlMLJwR6UU7EIhMGzhwIOLi4j7a5fjgwYNwdXWV6gLCpEmToK2tjcGDB4s9VlpaijFjxiAhIYFBss8zadIktGnTBps3b8bRo0fBcRzMzc0RFxcnMjde2vn6+mLXrl3w8vKCj48PFi5ciJycHISEhPDmzcXJkyehqKgo9r0VHh6OsrIyDBkyhFGympk0aRLc3NzECgj//fcfdu3ahejoaDbBCG/t3LkTP/zwAxo1aoSmTZuKHPsRCAS8+Rkn5GspKyuDkpKSyLU6dep8cuKHNNm8eTOmTp0KZWXlahsl8mHs4YMHD6o8FtO9e3c8ePCAQSIiT2gHApFpffv2RVFRESIjI8VGDQUGBsLJyQmrV6+W6q1emzZtwsKFC3HmzBnhij3w/o/5//73P1y8eBHR0dFo3bo1w5Typ2XLlti8eTPs7Oygrq6OpKQk4bVLly7h4MGDrCNWy9LSEitXrsTQoUNFrp8+fRo//fQTkpOTGSWrGQ0NDSQmJor11cjMzESnTp1QUFDAJpiESktLoaysjKSkJLRr1451nM9WUlICMzMznDhxAubm5qzjfBEDAwPMmDGDFxMwCKkNCgoKGDJkiEhTzuPHj6Nfv34ir6+ktWmtkZEREhIS0LBhw0+OQOTL2MN27dphwoQJ+OWXX0SuL1++HP/88w+uXbvGKBmRB7QDgci0EydOoG/fvhg+fDhOnTolnFxw6NAhODs74/fff5fq4gHwfvrCs2fPYGdnh3PnzqFdu3YoKyvD2LFjceHCBURFRVHxgIH8/HxYWFgAeN8Y8sWLFwCA77//XurHUVbIyMio8o1e69atkZmZySDR5xEIBHj16pXY9RcvXvDibHGdOnVgYGDAi6yfUrduXbx9+1YmOoE/f/4cY8aMYR3ji5SVlcHPzw8RERFVHsOIjIxklKzmIiIiPnofe/bsYZRKvri4uIhdmzhxIoMkn6fyln5Z2N7v6+sLBwcHnDt3Dj169IBAIEBsbCwiIiJw6NAh1vGIjKMCApFpampqOHXqFHr37o1x48YhKCgIQUFBmDhxIpYtW4Z58+axjigRX19fPHv2DIMGDUJ0dDQWLlyIc+fOITIykvcrfXzVokULPHjwAPr6+jAxMUF4eDisra1x+fJl3oxN09TURHZ2tliPgMzMTLEdO9KsV69e+P333xEQECBs5lVWVobff/8dPXv2ZJxOMr/++it+/vlnHDhwAA0aNGAd57PNnj0bq1atwq5du1CnDn9fYowZMwbh4eGYPn066yifzcPDA35+frCzs0O7du14W9jx9fXF0qVL0alTJ+jq6vL2PvhOVhvX8nUE4ujRoxEfH4/169cjJCREeBw0Pj4eVlZWrOMRGUdHGIhcuHPnDnr27AkTExPExsZi0aJFWLhwIetYNebk5ISgoCCoqakhIiIClpaWrCPJrQULFkBDQwO//PILgoKCMH78eBgaGiIvLw+enp5YuXIl64jVmjp1Ki5duoTg4GC0bNkSwPviwejRo9G5c2fs2rWLcULJpKamonfv3tDS0kKvXr0AAOfPn8fLly8RGRnJi2MBVlZWyMzMRElJCQwMDMQKOImJiYyS1czIkSMREREBNTU1WFhYiN2HtG5vBiByLvrNmzdYv3497OzsYGFhIdy9VoEPZ6QbNWoEf39/sSNKfKOrq4vVq1fTaDryVe3evRsbNmwQjkBs1aoVfvzxR7i7uzNOVr2SkhJMnToVPj4+MDY2Zh2HyCEqIBCZlpKSIvz41q1bcHZ2xogRI8TOjEnzG3EvLy/hxyUlJdi5cyd69eol3D5fYf369bUdjVRy6dIlXLhwASYmJryY6Q283+Jva2uLhIQEtGjRAgBw9+5d9OrVC0ePHoWWlhbbgDVw//59bN26FcnJyVBRUYGlpSVmzZrFm9V8X1/fTz6+ePHiWkryZVxdXT/5uDSvYn7qXHRlfDkj3axZM0RHR8PU1JR1lC/SsGFDxMfHC4ucfFVQUICgoCBkZWXB29sbDRo0QGJiIpo0aVLttCjydfn4+GDDhg2YPXu2sLfUxYsXsXXrVnh4eGD58uWME1ZPS0sLiYmJVEAgTFABgcg0BQUFCAQCcBwn/C8AsY+l+eyxjY1Ntc8RCAS8Os9aWcUqsZmZGdq0acM6jtzhOA5nzpwReePdu3dv1rEIIV9o3bp1yM7OxtatW3m3Pbuyn376CWpqarzpLVOVlJQUDBgwAJqamsjJyUFaWhqMjY3h4+OD3Nxc+Pv7s44oVxo1aoQtW7Zg/PjxItcDAgIwe/ZsPHnyhFEyybm6usLCwkJkkYmQ2sLfA4qESEAWGuVERUWxjvBVjR07Fr1798asWbNQVFSETp06IScnBxzHITAwEKNHj2YdUSKhoaFVXhcIBFBWVoaJiYnEK5osCQQCDBo0CIMGDWIdRe7JygplaWkpoqOjkZWVhQkTJkBdXR3379+HhoYG1NTUWMeTaaNGjRL5PDIyEqdOnULbtm3FjmFI83GSyoqLi7Fjxw6cPXsWlpaWYvfBh913Xl5emDRpElavXg11dXXh9SFDhmDChAkMk8mnsrIydOrUSex6x44deTOW0sTEBMuWLcOFCxfQsWNHseNifDhmRfiLdiAQQmpV06ZNERYWhvbt2+PgwYNYvHgxkpOTsW/fPuzYsQNXr15lHVEilXe3VFZ5x0vPnj0REhICbW1tRikJX8jKCmVubi5sbW2Rl5eHt2/fIj09HcbGxvjxxx9RXFyM7du3s44okY+t6lUuEA4fPlzqjshUd4SkMmk+TlLZp3bh8WX3naamJhITE9GyZUuoq6sjOTkZxsbGyM3NhZmZGYqLi1lHlCuzZ89G3bp1xYpP8+bNQ1FREbZt28YomeRkYRQl4S8qIBBCapWKigrS09Ohp6cHZ2dnNGvWDCtXrkReXh7Mzc3x+vVr1hElEhERgYULF2LFihXo0qULACA+Ph6//vorfHx8oKmpiWnTpuG7777D7t27Gacl0m7AgAGwtrYWrlBWvMG4cOECJkyYgJycHNYRJTJixAioq6tj9+7daNiwofA+YmJi4O7uLmxYJu1sbGyQmJiIsrIymJmZgeM4ZGRkQFFREa1bt0ZaWppwbBpNwiHVadKkCU6fPg0rKyuRn+/w8HBMnjwZd+7cYR1RrsyePRv+/v7Q09ND165dAbzvY3Tnzh04OzuL7HLhww4XQmobHWEghNQqPT09XLx4EQ0aNMDp06cRGBgI4P3cdWVlZcbpJOfh4YEdO3age/fuwmv9+/eHsrIypk6dihs3bmDjxo1wc3NjmFJ2hYaGYsiQIWLbmfnq8uXL+Ouvv8SuN2/eHPn5+QwSfZ7Y2FjExcVBSUlJ5LqBgQHu3bvHKFXNVewu2Lt3LzQ0NAC879cyefJk9OzZE1OmTMGECRPg6emJsLAwxmmr1q9fvyqbob58+RIjRozgxcq9rBg+fDiWLl2KQ4cOAXi/QpyXl4cFCxbw5tieLLl+/Tqsra0BAFlZWQAAHR0d6Ojo4Pr168Ln8aV3CF9HURL+ogICIaRW/fjjj3B0dISamhoMDAzQt29fAMC5c+fEJktIs6ysLOEbi8o0NDSEWwdbtWoldc2YvLy8sGzZMtSvXx/nzp1D9+7dUacO//4UjBw5Evn5+dDR0YGioiIePHiAxo0bs4712ZSVlfHy5Uux62lpadDR0WGQ6POUl5dX2ZT27t27Ime/pd2aNWtw5swZkZ9xDQ0NLFmyBIMGDYKHhwcWLVok1b1DoqOj8e7dO7HrxcXFOH/+PINEn+/y5cs4fPgw8vLyxO6JD70c1q5di6FDh6Jx48YoKipCnz59kJ+fj27dumHFihWs48kdSXtL3b17F+Xl5VBQUPjGiT4Pn0dREn6Tzp8IQojMmjFjBi5duoQ9e/YgNjZW+IfZ2NiYVy+kOnbsCG9vbzx+/Fh47fHjx5g/fz46d+4MAMjIyBCOR5QWW7ZsER4TsbGxwbNnzxgn+jw6Ojq4dOkSAAh7TvBZxQplSUkJAP6uUA4cOBAbN24Ufi4QCPD69WssXrwYQ4cOZReshl68eIFHjx6JXX/8+LGw0KOlpVXlG3TWUlJShCOMU1NThZ+npKTg6tWr2L17N6+acgYGBqJHjx5ITU1FcHAwSkpKkJqaisjISGhqarKOJxENDQ3ExsbiyJEjWLlyJWbNmoWTJ08iJiZGrPkdkR7m5uZSe3zMx8cHHh4eGDZsGA4fPozDhw9j2LBh8PT0xK+//so6HpF1HCGEN86dO8c5OjpyXbt25e7evctxHMf5+/tz58+fZ5xMcr6+vtybN2/ErhcWFnK+vr4MEn2eW7ducWZmZpySkhLXsmVLzsTEhFNSUuJat27NpaWlcRzHccHBwZy/vz/jpKJMTEy4X375hYuOjuYEAgEXEhLCxcTEVPlPmi1evJgTCAScgoJCtf/44MWLF1yPHj04LS0tTlFRkdPT0+Pq1q3L9e7dm3v9+jXreBK7d+8eZ2pqyrVp04arU6cO17VrV65hw4acmZkZ9/DhQ9bxJDZhwgTOyMiIO3r0KHfnzh3u7t273NGjRzljY2Nu4sSJHMdxXEBAANexY0fGScVV/rkQCARi/1RVVbndu3ezjikxCwsLbuvWrRzHcZyamhqXlZXFlZeXc1OmTOEWLVrEOB2RZRXfb9KoYcOG3MGDB8WuHzx4kGvYsCGDRESeUBNFIrOsrKwkXpVMTEz8xmm+3JEjR+Dk5ARHR0fs378fqampMDY2xh9//IETJ07g5MmTrCNK5GPbzZ8+fYrGjRtXuf1ZWnEch7CwMKSnp4PjOLRu3RoDBw6U2u2OABASEoLp06fj0aNHVU6RqCAQCKT+a3Hr1i1kZmbC3t4ee/fuFTvrXWH48OG1G+wLREZGIjExEeXl5bC2tsaAAQNYR6qxoqIiBAQEiNyHo6MjVFRUWEeT2OvXr+Hp6Ql/f3/hWLc6derAxcUFGzZsQP369ZGUlAQA6NChA7ugVcjNzQXHcTA2NkZ8fLzIERglJSU0btwYioqKDBPWTP369XHjxg0YGhqiUaNGiIqKgoWFBW7evIl+/frhwYMHrCNWa/PmzVVerzzVo3fv3rz6usiDyg0vpY22tjbi4+PRqlUrkevp6eno0qULCgoK2AQjcoEKCERm+fr6Cj8uLi7GH3/8AXNzc3Tr1g3A+467N27cwIwZM/D777+ziikxKysreHp6wtnZWeSPWlJSEmxtbXnTaE1BQQEPHz4UO9cdGRkJBwcHkSMB5Nt5/fo1NDQ0kJaW9tHeAXzZHuzr6wtvb2+oqqqyjvLZcnJyYGhoyDrGFyssLOT11+FDr1+/RnZ2NjiOQ8uWLaGmpsY6ktzR09PDyZMnYWFhgfbt22PBggUYP348Ll68CFtbW7x48YJ1xGoZGRnh8ePHKCwshLa2NjiOQ0FBAVRVVaGmpoZHjx7B2NgYUVFR0NPTYx2X/B9pLiDIwihKwl/865xFiIQWL14s/Njd3R1z5szBsmXLxJ7Dl/FJaWlp6N27t9h1DQ0NXlSatbW1IRAIIBAIYGpqKrI7pKysDK9fv8b06dMZJpQvampqiIqKgpGRES+bKFZW8bP++PFj4Xg9U1NTXjUfNDY2Rvfu3eHk5IQxY8agQYMGrCN9lsaNG2PEiBFwcnKS+t04klBTU4OlpSXrGJ8lNDS0yuuVV70/NUteWvTq1QtnzpyBhYUFxo4dCw8PD0RGRuLMmTPo378/63gS+e2337Bjxw7s2rULLVu2BABkZmZi2rRpmDp1Knr06IFx48bB09MTQUFBjNMSvti9ezfCw8OrHEXp5eUlfB6NoiRfG+1AIHJBU1MTCQkJYlu9MjIy0KlTJ16sYLRs2RJ//fUXBgwYIFIV9/f3x8qVK5Gamso64ift27cPHMfBzc0NGzduFFndVlJSgqGhoXB3CKk9ZWVlCAkJwc2bNyEQCNCmTRsMHz6cV1tpCwsLMWvWLOzfv1947EJRURHOzs7YsmULL1bEExMTERAQgMDAQDx+/BiDBw/GxIkTYW9vj3r16rGOJ7GjR48iICAA//77LzQ0NODg4ICJEycKG4tKs1GjRsHPzw8aGhoYNWrUJ5/Lh87/CgoKVR5TqrgmEAjQs2dPhISEQFtbm1HK6j179gzFxcVo1qwZysvLsXbtWsTGxsLExAQ+Pj5Snb1Cy5YtceTIEbHjLlevXsXo0aORnZ2NCxcuYPTo0bw4kiEvNDQ0kJSUJJU7EGxsbCR6nkAgoJGt5Kvj97ITIRJSUVFBbGysWAEhNjYWysrKjFLVzLRp0+Dh4YE9e/ZAIBDg/v37uHjxIubNm4dFixaxjlctFxcXAO+3cnbv3h1169ZlnIhkZmbCzs4Od+/ehZmZGTiOQ3p6OvT09PDvv/8KV8qknaenJ2JiYhAaGooePXoAeP+zPWfOHMydOxd//vkn44TVs7a2hrW1NVavXo3o6GgcPHgQ06ZNg7u7O0aPHo09e/awjiiRUaNGYdSoUXj16hWCgoIQEBCA7t27w8jICBMnTpTq31WamprCnVF8Ob7zKWfOnMHChQuxYsUKdOnSBQAQHx+PX3/9FT4+PtDU1MS0adMwb9487N69m3Haj6u8G0dBQQHz58/H/PnzGSaquQcPHgh7aVRWWloqPH7YrFkzvHr1qrajkU+Q5jVWWRlFSfiJdiAQubBy5UosWbIE7u7uIlu99uzZg0WLFmHBggWME0pm4cKF2LBhA4qLiwEA9erVw7x588SOZki7D1e9zc3NYW9vz6tVb1kwdOhQcByHv//+W/gi/enTp5g4cSIUFBTw77//Mk4omUaNGiEoKAh9+/YVuR4VFYWxY8fytq9GYmIiJk+ejJSUFKlvaPkpqampcHR05P198E27du2wY8cOdO/eXeR6XFwcpk6dihs3buDs2bNwc3NDXl4eo5SSKSsrQ3BwsNhOKb4cv7Kzs0N+fj527doFKysrAO93H0yZMgVNmzbFiRMncPz4cfzyyy+4du0a47TyIzMzE1lZWejduzdUVFTERgLfuXMHzZo14/VrE2neRUH4ix+/eQn5QgsWLICxsTE2bdqEgwcPAgDatGkDPz8/jB07lnE6ya1YsQILFy5EamoqysvLYW5uzrumXpmZmRg6dCju3bvHu1XvivnvktDQ0PiGSb6OmJgYXLp0SWSFr2HDhli5cqVwJZ8PCgsL0aRJE7HrjRs3RmFhIYNEn+/OnTsICAjAwYMHce3aNXTr1g1bt25lHavGiouLERoaioMHD+L06dNo3Lgx5s2bxzpWjZSWliI6OhpZWVmYMGEC1NXVcf/+fWhoaPDi925WVlaVv4c0NDSQnZ0NAGjVqhWePHlS29Fq5Pr16xg+fDjy8/NhZmYG4H2neR0dHYSGhsLCwoJxwurt3r0bTk5O6Nixo3D3XWlpKfr37y/c/aGmpoZ169axjCk3nj59CgcHB0RGRkIgECAjIwPGxsZwd3eHlpaW8OsgCw0taZ2YfAu0A4EQnnr58iUiIyNhZmaGNm3asI4jMT6velecKf6UihUMPqy0NmjQACdOnKhyhXLYsGF49uwZo2Q1079/fzRs2BD+/v7CI0lFRUVwcXHBs2fPcPbsWcYJq7djxw78/fffiIuLg5mZGRwdHTFhwgTeTWYIDw/H33//jZCQECgqKuJ///sfHB0d0adPH9bRaiQ3Nxe2trbIy8vD27dvkZ6eDmNjY/z4448oLi7G9u3bWUesVs+ePaGurg5/f39hQ9HHjx/D2dkZb968wblz53D27FnMmDED6enpjNN+XNeuXdG4cWPs27dP2O/g+fPnmDRpEh49eoSLFy8yTii5W7duiYz9rSiIkNrl7OyMR48eYdeuXWjTpo2wp1R4eDg8PT1x48YN1hG/GmmeJEH4iwoIRG4UFBQgKCgI2dnZmDdvHho0aIDExEQ0adIEzZs3Zx2vWmPHjkXv3r0xa9YsFBUVoUOHDrh9+zY4jkNgYCBGjx7NOqJE6tevj0uXLomtGiUnJ6NHjx54/fo1o2TVi4mJkfi5fHjD5OzsjMTEROzevVt4Rvq///7DlClT0LFjR/j5+bENKKHr16/D1tYWxcXFaN++PQQCAZKSkqCsrIywsDC0bduWdcRq6enpYdy4cXB0dBRrtMYnqqqqsLOzg6OjI+zs7Hjb62TEiBFQV1fH7t270bBhQ+EL8JiYGLi7uyMjI4N1xGqlpaVh+PDhuH37NvT09CAQCJCXlwdjY2McO3YMpqamCAkJwatXr+Dk5MQ67kepqKggISFB7Of4+vXr6Ny5M4qKihglI3zVtGlThIWFoX379iJvsG/fvg0LCwupfh1SU1RAIN8CHWEgciElJQUDBgyApqYmcnJy4O7ujgYNGiA4OBi5ubnw9/dnHbFa586dw8KFCwEAwcHBKC8vR0FBAfbt24fly5fzpoBQr169KhtFvX79GkpKSgwSSY4PRYGa2Lx5M1xcXNCtWzeRbbX29vbYtGkT43SSa9euHTIyMnDgwAHcunULHMcJ34yrqKiwjieRvLy8ane38EF+fj4vju9UJzY2FnFxcWK/kwwMDHDv3j1GqWrGzMwMN2/eRFhYmMiqd+XxmiNGjGAbUgJmZmZ4+PChWAHh0aNHMDExYZSqZsrKyuDn54eIiAg8evQI5eXlIo9Tl/za9ebNmyqn8zx58oRXU28IYYUKCEQueHl5YdKkSVi9ejXU1dWF14cMGYIJEyYwTCa5Fy9eCLf8nz59GqNHjxau9nl7ezNOJ7nvv/8eU6dOFVv1nj59Ouzt7Rmnq5nz58/jr7/+QnZ2Ng4fPozmzZtj//79MDIyQs+ePVnHq5aWlhaOHTuGzMxM3Lx5ExzHwdzcnDcvyitTUVHBlClTWMf4bAKBAAUFBdi9e7dIo7jJkyfzaiKAhoaGTIwGLS8vr/IY0t27d0X+hkg7gUAAW1tb2Nraso7y2X777TfMmTMHS5YsEWmCvHTpUqxatUqkN420Fq88PDzg5+cHOzs7tGvXTiaKhXzWu3dv+Pv7CxtQCwQClJeXY82aNRKPR+QL+l4j3wIdYSByQVNTE4mJiWjZsqXIdq7c3FyYmZkJpxpIM1NTUyxfvhx2dnYwMjJCYGAg+vXrh+TkZPTv31/qG2FVKCgogIuLC44fPy626u3n58ebN0tHjhyBk5MTHB0dsX//fqSmpsLY2Bh//PEHTpw4gZMnT7KOSHgkISEBgwcPhoqKCrp06QKO45CQkICioiKEh4fD2tqadUSJ8LlJamUODg7Q1NTEjh07oK6ujpSUFOjo6GD48OHQ19fH3r17WUeUSERExEdXvfkyGrTy+LmKN0MVL10rfy7NvWcaNWoEf39/DB06lHUUgveTYfr27YuOHTsiMjIS9vb2uHHjBp49e4a4uDje/J6SBB1hIN8C7UAgckFZWbnKDvppaWnC5lLS7scff4SjoyPU1NRgYGAgHFl37tw5XnShrlCx6p2RkSHcbs7HVe/ly5dj+/btcHZ2RmBgoPB69+7dsXTpUobJCB95enrC3t4eO3fuFI6mKy0thbu7O3788UecO3eOcULJzJkzBy1bthSZ7lHRJHXOnDlS3SS1sg0bNsDGxgbm5uYoLi7GhAkTkJGRgUaNGiEgIIB1PIn4+vpi6dKl6NSpE3R1dXm7EinpvHtppqSkxLu/cbLM3NwcKSkp+PPPP6GoqIg3b95g1KhRmDlzJnR1dVnHq5HqRlGmpqaiWbNmDBMSWUQ7EIhcmDp1Kh4/foxDhw6hQYMGSElJgaKiIkaMGIHevXtj48aNrCNKJCEhAXfu3MHAgQOFY8T+/fdfaGlp8WrsnixQVVVFamoqDA0NRSr82dnZwjcdhEhKRUUFV69eRevWrUWup6amolOnTrwZR8nnJqkfKioqQkBAABITE1FeXg5ra2te9dXQ1dXF6tWrpbpBorxYt24dsrOzsXXrVt4Wcoh0+dgoysmTJ4uMoiTkW6AdCEQurF27FkOHDkXjxo1RVFSEPn36ID8/H926dcOKFStYx5NYp06d0KlTJ5FrdnZ2jNJ8HllpJqWrq4vMzEyxMXuxsbG0VZDUmIaGBvLy8sQKCHfu3OHVmXs+N0n9kIqKCtzc3ODm5sY6ymd59+6d2IhWPjp9+jTU1NSEfWW2bduGnTt3wtzcHNu2bROOdpRmsbGxiIqKwqlTp9C2bVux6SRHjx5llEx+PX/+XKznjKurq3DnlLTz9PREnTp1kJeXJzLK28HBAZ6enlRAIN8UFRCIXNDQ0EBsbCwiIyNFVpMGDBjAOtoneXl5YdmyZahfvz68vLw++dz169fXUqovIyvNpKZNmwYPDw/s2bMHAoEA9+/fx8WLFzFv3jwsWrSIdTy5UzGmNSsrC97e3rwb0+rg4IDJkydj7dq16N69OwQCAWJjY+Ht7Y3x48ezjicxWWmS2qxZM/Tt21f4z9TUlHWkGnN3d8fBgwfh4+PDOsoX8fb2xqpVqwAA165dg5eXF+bOnYvIyEh4eXnxoh+FlpYWRo4cyToG+T8xMTEYPnw4NDQ0hIsymzdvxtKlSxEaGsqLiUvh4eEICwtDixYtRK63atUKubm5jFIReUFHGIjcKS4uRr169XjxxtXGxgbBwcHQ0tL6ZGdggUDAm5V7WWomtXDhQmzYsEF4XKFevXqYN2+esLMzH1RMksjKykJQUBDvJkkA4mNa09LSYGxsDB8fH96MaX337h28vb2xfft2lJaWAgDq1q2LH374AStXruTNaDFZaZIaEBCAmJgYREdHIz09HU2aNEGfPn3Qt29f9OnTR2TFT1p5eHjA398flpaWsLS0FFv15kvRWU1NDdevX4ehoSGWLFmC69evIygoCImJiRg6dCjy8/NZRyQ8065dO3Tv3l3YAwF4vztyxowZiIuLw/Xr1xknrJ66ujoSExPRqlUrkWOUly9fhq2tLZ4+fco6IpFhVEAgcqG8vBwrVqzA9u3b8fDhQ6SnpwvfYBgaGmLy5MmsI8qNZs2aITo6mpcrelUpLCxEamoqysvLYW5uLuxNwQeyMkliwIABsLa2Fo5prXghdeHCBUyYMAE5OTmsI0qssLAQWVlZ4DgOJiYmVc4q5wNZGA1a4eHDh4iKisKJEyfwzz//fHTEo7SRlaJzgwYNEBsbC3Nzc/Ts2RPOzs6YOnUqcnJyYG5uzpv+IER6qKioICkpCWZmZiLX09LS0KFDBxQVFTFKJjk7OztYW1tj2bJlwkkxBgYGGDduHMrLyxEUFMQ6IpFhdISByIXly5dj3759WL16tciseAsLC2zYsIEKCLVo7ty52LRpk8w0k1JVVRXrS8EXsjJJ4vLly/jrr7/Erjdv3px3q5Oqqqq8mqryMSYmJrwuGgDv+zbExsYKdyJcvXoVFhYWvNjeDMjG9AIA6NmzJ7y8vNCjRw/Ex8fjn3/+AQCkp6eLbd+WZkFBQTh06BDy8vLw7t07kccSExMZpZJP1tbWuHnzplgB4ebNm+jQoQObUDW0Zs0a9O3bFwkJCXj37h3mz58vMoqSkG+JCghELvj7+2PHjh3o378/pk+fLrxuaWmJW7duMUwmORsbm0++4ebLapKsNJOSha9HWloaevfuLXZdQ0MDBQUFtR/oM8nCmFYiXb777jukpKSgXbt26Nu3L3755Rf06tULWlparKPVWHVj3qTd1q1bMWPGDAQFBeHPP/8U9jQ5deoUbG1tGaeTzObNm7Fw4UK4uLjg2LFjcHV1RVZWFi5fvoyZM2eyjid35syZAw8PD2RmZqJr164AgEuXLmHbtm1YuXIlUlJShM+1tLRkFfOTZGkUJeEfKiAQuXDv3r0qV8PKy8tRUlLCIFHNfVgVLykpQVJSEq5fvw4XFxc2oT6DrDSTkoWvh6xMkhg+fDiWLl2KQ4cOAXi/PTsvLw8LFizA6NGjGacjfJSRkQFVVVUYGxvD2NgYJiYmvCsePH36FGPHjkVUVJTImDd3d3dejXnT19fHiRMnxK5v2LCBQZrP88cff2DHjh0YP3489u3bh/nz58PY2BiLFi3Cs2fPWMeTOxWNaefPn1/lYwKBQFhok+bjSk2bNoWvry/rGEQOUQGByIW2bdvi/PnzMDAwELl++PBhWFlZMUpVMx97sbRkyRJezVb/VMfsqlaRpZUsfD1kZZKErIxpJdLj2bNnSElJQXR0NM6ePYvFixdDQUEBffr0gY2NjchONmnl6emJunXr0pg3KZCXlyccqamioiIcderk5ISuXbti69atLOPJndu3b7OO8FXwfRQl4TGOEDkQGhrKaWpqcitXruRUVVW5NWvWcO7u7pySkhIXHh7OOt4XycjI4LS1tVnHqNaaNWs++fiLFy+47777rpbSfDt8+XpU+OWXXzgVFRVOIBBwAoGAU1ZW5n799VfWsT5LREQEt2bNGm7VqlXcmTNnWMeplpWVFffs2TOO4zjO19eXe/PmDeNE5GMSEhK4SZMmcXXq1OEUFBRYx5FIkyZNuKSkJI7jOE5NTY3LysriOI7jsrOzufr167OMJneMjIy4K1eucBzHcZ06deK2b9/OcRzHhYWF8ervhax4/fo16whfLDo6mtPU1OT09PS4kSNHciNHjuT09fU5DQ0NLjo6mnU8IuNoBwKRC8OGDcM///yD3377DQKBAIsWLYK1tTWOHz+OgQMHso73RS5evAhlZWXWMarl4+ODhg0bwtXVVeyxV69eYfDgwbzagfAxfPl6VFixYgUWLlzI20kSwPseJw4ODujXrx/69esnvP7u3TsEBgbC2dmZYbqPu3nzJt68eQNtbW34+vpi+vTpvJ26UKHy2eHKBAIBlJWVoa+vz4uRlFevXkV0dDSio6Nx/vx5vHr1Cu3bt4eHh8cnpxtIkzdv3lT5/fTkyRNefA1kSb9+/XD8+HFYW1tj8uTJ8PT0RFBQEBISEjBq1CjW8eROkyZNMHbsWLi5ufFmXPGHZs6cibFjx1Y5inLmzJm8GEVJ+IvGOBLCEx++yOA4Dg8ePEBCQgJ8fHywePFiRskkExQUBCcnJwQEBGDEiBHC669fv8agQYPw9OlTnDt3Dk2aNGEXsgb4/vUAADc3N2zatAnq6uoi19+8eYPZs2djz549jJLVjKKiIh48eIDGjRuLXH/69CkaN24stWdYu3XrBjU1NfTs2RO+vr6YN2/eR4s3fDlSoqCg8MkGfXXr1oWDgwP++usvqS601alTB1ZWVujTpw/69u2L3r17Q0NDg3WsGqExb9KjvLwc5eXlqFPn/brdoUOHEBsbCxMTE0yfPh1KSkqME8qX48ePw8/PDydOnICBgQHc3Nzg7OyMZs2asY4mMVkYRUn4iwoIRK4kJCSInBXr2LEj60gS+3DlXkFBATo6OujXrx8GDRrEKFXN7Nq1C3PmzMG///4LGxsbvH79Gra2tnj06BFiYmJ41TlYFr4eH3vj/eTJEzRt2hSlpaWMktWMgoICHj58KDZxITk5GTY2NlLbpCwtLQ2LFy9GVlYWEhMTYW5uLnyDUZlAIODNmLdjx47hp59+gre3N7p06QKO43D58mWsW7cOixcvRmlpKRYsWAAHBwesXbuWddyPevnyJe8KBh9KTU1F37590bFjR0RGRsLe3l5kzFvLli1ZRyR43+S5YrIEqV1Pnz6Fv78//Pz8kJqaisGDB8PNzQ329vZV/i6WJj169IC3t7fIggwAhISEYNWqVbh48SKbYEQuUAGByIW7d+9i/PjxiIuLE3bSLigoQPfu3REQEAA9PT22AatRVlaG2NhYWFhY8L45zurVq7FixQocO3YMPj4+ePDgAWJiYnjzAio7OxtGRka8GoP2oZcvX4LjOGhrayMjI0PkjXdZWRmOHz+OBQsW4P79+wxTVs/KygoCgQDJyclo27atyAu+srIy3L59G7a2tsLpDNJMQUEB+fn5YsUcvunSpQuWLVuGwYMHi1wPCwuDj48P4uPjERISgrlz5yIrK4tRSvmRn5+PP//8E1euXEF5eTmsra15N+Zt5MiRVf6+rTgWY2JiggkTJoitxEq7/Px8rFixArt27aLVYimwZcsWeHt74927d2jUqBGmT5+OBQsWSO2xsn/++Qfz58/H7NmzqxxFWblxqrSOoiT8RQUEIhcGDRqEly9fYt++fcIXGWlpaXBzc0P9+vURHh7OOGH1lJWVcfPmTRgZGbGO8sV+/vlnrF69GoaGhoiJiUGLFi1YR5LYh6v2Dg4O2Lx5M2+OXgDVbzMXCATw9fXFwoULazFVzVWMr/L19cXcuXNFtv8rKSnB0NAQo0ePltrtwdbW1oiIiBD2QPD29pbaF6uSUlFRwdWrV9G6dWuR67du3YKVlRWKioqQk5MDc3NzFBYWMkop3+7cuYPFixfz5ojSpEmTEBISAi0tLXTs2BEcx+Hq1asoKCjAoEGDkJycjJycHERERKBHjx6s44ooKCjAzJkzER4ejrp162LBggWYNWsWlixZgrVr16Jt27bw8vISjhUktSs/Px/+/v7Yu3cv8vLyMHLkSEyePBn379/HypUroaurK7WvDxUUFD75OF9GURJ+ogICkQsqKiq4cOGC2MjGxMRE9OjRgxfV/86dO2PlypXo378/6yif5cOeASdPnkT79u3Fdh4cPXq0NmPV2Icrxerq6khOToaxsTHjZJKLiYkBx3Ho168fjhw5IrKrRUlJCQYGBrw6C7pv3z44ODhI9Zn6qqioqCAjIwMtWrT46HESvrGyskL79u2xY8cOYeGmpKQEU6ZMQXJyMq5evYq4uDhMnDhRZkap8U1ycjKsra1586ZiwYIFePnyJbZu3Sp801ReXg4PDw+oq6tjxYoVmD59Om7cuIHY2FjGaUXNmDEDx48fh4ODA06fPo2bN29i8ODBKC4uxuLFi9GnTx/WEeXK0qVLMW/ePJw+fRp79+5FWFgYzM3N4e7ujokTJwp3qALAjRs3YGVlhXfv3rEL/Am5ubkSP/fDEeaEfCnpPuBDyFeir6+PkpISseulpaW82Tq/YsUKzJs3D8uWLUPHjh1Rv359kcel/byupqamyOe04sJOxYvW27dvQ09Pr9qVDGnn4uLCOsJn6dChA1xdXdGzZ09wHIe1a9fyvonitm3bYG9vjxYtWsDS0hICgQApKSkoKyvDiRMnALw/BjRjxgzGSQlf7N69G3FxcSK/pxQUFDB79mx0794dv/32G2bNmoVevXoxTFm1f//9F3v37sWAAQMwY8YMmJiYwNTUFBs3bmQdTS5VTLtxdXXFuHHjEBcXh86dO1f5XGNjY6nehdeoUSOx14GE1BbagUDkwrFjx/Dbb79h27Zt6NixIwQCARISEjB79mz89NNPYk1opFHlF0+Vt5/TFrXapaioiPz8fGHfgIru5nw+WlJYWIi8vDyxlRa+nJssKyvDhg0bcOjQoSrvg5oo1q7Xr1/jwIEDSE9PB8dxaN26NSZMmCA27UOaRUdHo2/fvqxjfBN824Ggra2Nffv2wd7eXuR6aGgoXFxc8Pz5c2RkZKBLly54/vw5o5RVq1u3LnJzc4U7ulRVVREfH4927doxTiafKnYQqqmp8f64mJqaGu9HURL+oh0IRC5MmjQJhYWF+O6774Qv0EtLS1GnTh24ubnBzc1N+FxpfbMRFRXFOgLB+4LNpEmThHPUi4uLMX36dLGVAGk/igEAjx8/hqurK06dOlXl43x5g+Hr64tdu3bBy8sLPj4+WLhwIXJychASEiLVK/dmZmYIDAwE8P6FbUREBO+PMADvX9hOnz6ddYwvYmtri+bNm8PV1RUuLi5S32hXljk5OWHy5Mn45Zdf0LlzZwgEAsTHx+O3336Ds7MzgPfHstq2bcs4qbjy8nLUrVtX+LmioiKtGjMmEAh4XzwAgICAAPj5+aF///68HUVJ+It2IBC5sG/fPomfy9ft0KR2fDi+8WP27t37jZN8OUdHR+Tk5GDjxo2wsbFBcHAwHj58iOXLl2PdunWws7NjHVEiLVu2xObNm2FnZwd1dXUkJSUJr126dAkHDx5kHVGupKenIzo6Go8ePUJ5ebnIY9Jc0Kns2bNnOHDgAPz8/JCSkoL+/ftj8uTJGDFihNQ25azwYb+ZDxUUFCAmJoY3BcKysjKsXLkSW7duxcOHDwEATZo0Ee4gVFRURF5eHhQUFKSuIa+CggKGDBkiLDgfP34c/fr142XBWRYoKCigXbt21Y5o5NOOLz6PoiT8RQUEQnhi7969UFNTw5gxY0SuHz58GIWFhVT4IDWmq6uLY8eOoUuXLtDQ0EBCQgJMTU0RGhqK1atXS11Dso+pX78+bt68CX19fejq6uLff/+FtbU1srOzYWVlhRcvXrCOKJGsrCxs3LgRN2/ehEAgQJs2beDh4YGWLVuyjiaxnTt34ocffkCjRo3QtGlTkeNWfDuKUSEpKQl79uxBQEAAysvL4ejoiMmTJ6N9+/aso1VJloqcH3r58iUA6e/5U0GWvxZ8pKCgIDaxpyqLFy+upURfF99GURL+ogICkQuJiYmoW7cuLCwsALzvibB3716Ym5tjyZIlUr+iBLzf7rx9+3bY2NiIXI+JicHUqVORlpbGKBnhKw0NDaSkpMDQ0BCGhob4+++/0aNHD9y+fRtt27blzZg9MzMz+Pv747vvvkOvXr1gZ2eHBQsW4J9//sHs2bPx6NEj1hGrFRYWBnt7e3To0AE9evQAx3G4cOECkpOTcfz4cQwcOJB1RIkYGBhgxowZ+Omnn1hH+aru37+PHTt2YOXKlahTpw6Ki4vRrVs3bN++XSq3zhNCxH04RUkW8HkUJeEvfrfeJkRC06ZNQ3p6OoD3HcAdHBygqqqKw4cPY/78+YzTSSY3N7fKRn0GBgbIy8tjkIjwnZmZmbDw1KFDB/z111+4d+8etm/fDl1dXcbpJDdy5EhEREQAADw8PODj44NWrVrB2dlZpL+JNFuwYAE8PT3x33//Yf369diwYQP+++8//Pjjj7x6M/78+XOxXVJ8VVJSgqCgIAwdOhQGBgYICwsTbqOvmGAiK/cqzR4+fAgnJyc0a9YMderUgaKiosg/QiRVeUcUXy1duhSFhYU4evQohg0bBn19fRw8eBAzZ87EvXv3cODAAdjY2MDR0RGBgYGIjo5mHZnIINqBQOSCpqYmEhMT0bJlS6xatQqRkZEICwtDXFwcxo0bhzt37rCOWC19fX1s3bpVrBP1sWPHMHPmTNy9e5dRsuqFhoZK/NwP7498O3///TdKSkowadIkXL16FYMHD8bTp0+hpKQEPz8/ODg4sI74Wf777z/ExcXBxMSEN99PysrKuHbtGlq1aiVyPT09HZaWliguLmaUrGYmT56Mzp07876J4uzZsxEQEAAAmDhxItzd3cU65+fl5cHQ0FCszwP5uoYMGYK8vDzMmjULurq6Ym8Chw8fzigZ4RtZ2IGgqKiIBw8eoFWrVhg3bhzc3d0/OoqyqKgIq1ev5u2RDCK9qLsGkQscxwlf5J09exbff/89AEBPTw9PnjxhGU1i48aNw5w5c6Curo7evXsDeH98wcPDA+PGjWOc7tMkHZNJ4yhrl6Ojo/BjKysr5OTk4NatW9DX10ejRo0YJpNcSUkJpk6dCh8fHxgbGwMAvvvuO3z33XeMk9WMjo4OkpKSxAoISUlJvHqxa2JiAh8fH1y6dAkWFhYiHegBYM6cOYyS1Uxqaiq2bNmC0aNHf/SIW7NmzWg6Ti2IjY3F+fPn0aFDB9ZRCM/dvn1bOIKZryrWfR88eFBtbwMVFRUqHpBvgnYgELnQr18/6OnpYcCAAZg8eTJSU1NhYmKCmJgYuLi4ICcnh3XEar179w5OTk44fPiwsLNueXk5nJ2dsX37dl70cSDSo6SkBGZmZjhx4gTMzc1Zx/kiWlpaSExMFBYQ+Gjp0qXYsGEDFixYgO7du0MgECA2NharVq3C3Llz8euvv7KOKJGqjllVEAgEyM7OrsU0n+/cuXPo3r27WBfz0tJSXLhwQVjEJd+eubk5/v77b1hZWbGOQghzCgoKePjwIe8LIYTfqIBA5EJKSgocHR2Rl5cHLy8vYUV29uzZePr0Ka/GvGVkZCApKQkqKiqwsLCAgYEB60iEp5o3b46zZ8+iTZs2rKN8EVdXV1hYWMDLy4t1lM/GcRw2btyIdevW4f79+wDer3B7e3tjzpw5MnF2l08qtgl/uPvj6dOnaNy4Me2UqkXh4eFYt24d/vrrLxgaGrKOQwhTsjiKkvAPFRCIXCsuLoaioqLYNlvydW3evFni5/Jli7MsWLlyJW7duoVdu3bxel70ihUrsHbtWvTv3x8dO3YUm7HOt++pV69eAQDU1dUZJ5FfH1vlS09PR6dOnYTjBMm3p62tjcLCQpSWlkJVVVXs7/WzZ88YJSOk9sn6KErCD1RAIIR8c5/a1lwZn7Y4y4KK6QVqamqwsLAQe+N99OhRRslqRla2zZeWliI6OhpZWVmYMGEC1NXVcf/+fWhoaFT7YpElLy8vLFu2DPXr1692F8j69etrKdXnGTVqFID3zWltbW1Rr1494WNlZWVISUmBmZkZTp8+zSqi3Nm3b98nH3dxcamlJISwJwuNIAn/8XfJiRDCG7dv32YdgVRBS0sLo0ePZh3ji8nC91dubi5sbW2Rl5eHt2/fYuDAgVBXV8fq1atRXFyM7du3s474UVevXkVJSYnw44/hwzEMTU1NAO+PlKirq0NFRUX4mJKSErp27YopU6awiieXqEBAvoXz58/jr7/+QlZWFoKCgtC8eXPs378fRkZG6NmzJ+t4H8WH36NE9lEBgRBC5NTevXtZRyD/x8PDA506dUJycjIaNmwovD5y5Ei4u7szTFa9ypMI+D6VoOJnwtDQEPPmzRPblUNqx8uXL6GhoSH8+FMqnkeIpI4cOQInJyc4Ojri6tWrePv2LYD3x8d+++03nDx5knHCj6ON40Qa0BEGQkitu3v3LkJDQ5GXl4d3796JPCbtW5wJ+RYaNWqEuLg4mJmZQV1dHcnJyTA2NkZOTg7Mzc1RWFjIOqJcevToEdLS0iAQCGBqakrbhmtJ5SaWCgoKVa66chxHo3/JZ7GysoKnpyecnZ1Fft8mJSXB1tYW+fn5rCN+VG5uLvT19WknAmGKdiAQuVBUVCSyFbWyBw8eQFdXt5YTSSYlJUXi51paWn7DJF9PREQE7O3tYWRkhLS0NLRr1w45OTngOA7W1tas4xHCRHl5eZVvhO7evSv1zRQr+gZIgi99NV6+fImZM2ciMDBQ+HVRVFSEg4MDtm3bJjzqQL6NyMhINGjQQPgxvVkiX1NaWlqVo1g1NDRQUFBQ+4FqgCZvEWlABQQiF6ysrHDw4EGxN6hBQUH44Ycf8PjxY0bJPq1Dhw4QCATClZZP4csqzM8//4y5c+di6dKlUFdXx5EjR9C4cWM4OjrC1taWdTxCmBg4cCA2btyIHTt2AHh/zvX169dYvHgxhg4dyjjdp1V+M81xHIKDg6GpqYlOnToBAK5cuYKCgoIaFRpYc3d3R1JSEk6cOIFu3bpBIBDgwoUL8PDwwJQpU3Do0CHWEWVanz59kJmZCRMTE/Tt25d1HCJjdHV1kZmZKTYWNDY2FsbGxmxCEcInHCFyYNasWVy9evW433//nSsvL+devXrFubi4cKqqqtzmzZtZx/uonJwc4b/g4GCuZcuW3Pbt27nk5GQuOTmZ2759O9eqVSsuODiYdVSJqampcZmZmRzHcZyWlhZ3/fp1juM4LikpiTMwMGCYjPBVbm4uV15eLna9vLycy83NZZCo5u7du8eZmppybdq04erUqcN17dqVa9iwIWdmZsY9fPiQdTyJzZ8/n3N3d+dKS0uF10pLS7mpU6dy8+bNY5isZlRVVbnz58+LXT937hynqqrKIJH8EQgEXIsWLTgnJyduz5493O3bt1lHIjJi1apVnLm5OXfp0iVOXV2dO3/+PHfgwAFOR0eH27JlC+t4hEg96oFA5Mbp06fh6uoKExMT4Wi0v//+G+bm5qyjSaRLly5YsmSJ2GrkyZMn4ePjgytXrjBKVjNNmzZFZGQkzM3N0bZtW/z++++wt7dHcnIyevTogdevX7OOSHim8nnpyp4+fYrGjRvzZndOUVERAgMDceXKFZSXl8Pa2hqOjo4fPX4ljXR0dBAbGwszMzOR62lpaejevTuePn3KKFnN6Ovr499//4WFhYXI9ZSUFAwdOhR3795llEx+nD9/HjExMYiOjsbFixdRXFwMfX199OvXDzY2NrCxsUHz5s1ZxyQ8tXDhQmzYsAHFxcUAgHr16mHevHlYtmwZ42SESD8qIBC5UV5ejtmzZ+PPP/9EnTp1cPz4cQwePJh1LImpqKggMTERbdq0Ebl+8+ZNWFtbo6ioiFGymhkxYgTs7OwwZcoUzJ8/H8HBwZg0aRKOHj0KbW1tnD17lnVEubF58+YqrwsEAigrK8PExAS9e/eGoqJiLSerGQUFBTx8+BA6Ojoi13Nzc2Fubo43b94wSia5AwcOYOLEiVU+5u3tjTVr1tRyos+jra2NvXv3YsSIESLXQ0JC4OrqiufPn7MJVkM7duzA4cOH4e/vL+yRk5+fDxcXF4waNQrTpk1jnFC+lJSU4OLFi4iOjkZ0dDQuXbqEt2/fwsTEBGlpaazjEZ4qLCxEamoqysvLYW5uDjU1NdaRaoSvoygJ/1EBgciFrKwsTJgwAfn5+di1axdiYmKwdu1azJkzBytWrEDdunVZR6yWtbU12rRpg927d0NZWRkA8PbtW7i5ueHmzZtITExknFAy2dnZeP36NSwtLVFYWIh58+YhNjYWJiYm2LBhAzUIqkVGRkZ4/PgxCgsLoa2tDY7jUFBQAFVVVaipqeHRo0cwNjZGVFQU9PT0WMcV4+XlBQDYtGkTpkyZAlVVVeFjZWVl+O+//6CoqIi4uDhWESWmpaWFAwcO4Pvvvxe57unpicDAQDx48IBRsprx8vKCn58ffvnlF3Tt2hUAcOnSJaxcuRLOzs68mbJiZWWFzMxMvH37Fvr6+gCAvLw81KtXD61atRJ5Ll9+98qCoqIixMbGIiwsDDt37sTr1695s8OIkK+p8ijK/fv3IzU1FcbGxvjjjz9w4sQJqR5FSfiPCghELqirq8POzg7bt2+HlpYWAODChQvCET5Xr15lG1AC8fHxGDZsGMrLy9G+fXsAQHJyMgQCAU6cOIEuXbowTkj4JiAgADt27MCuXbvQsmVLAEBmZiamTZuGqVOnokePHhg3bhyaNm2KoKAgxmnF2djYAABiYmLQrVs3KCkpCR9TUlKCoaEh5s2bJ/aGTxqdPn0a48aNQ2hoqLA7+OzZs3H06FFERESgdevWjBNKpry8HGvXrsWmTZuERQ9dXV14eHhg7ty5Ur+bpYKvr6/Ez128ePE3TCLfiouLceHCBURFRSE6OhqXL1+GkZER+vTpg969e6NPnz50jIHU2Js3b7By5UpERETg0aNHKC8vF3k8OzubUTLJ8XkUJeE/KiAQubB//344OTmJXX/16hV+/PFH7N69m0GqmissLMSBAwdw69YtcBwHc3NzTJgwAfXr12cdTWLGxsa4fPkyGjZsKHK9oKAA1tbWvPjDLStatmyJI0eOoEOHDiLXr169itGjRyM7OxsXLlzA6NGjpXoF3NXVFZs2bYKGhgbrKF8kMDAQM2bMQHh4OPbs2YNjx44hKioKpqamrKN9lpcvXwIA778uhI0+ffrg8uXLaNmypbBY0KdPHzRp0oR1NMJz48ePR0xMDJycnKCrqys25crDw4NRMsmpqqoiNTUVhoaGIgWE7OxsmJubC3s7EPIt0BhHIheqKh4A73cm8KV4ALz/gzF16lTWMb5ITk5OlVtO3759i3v37jFIJL8ePHiA0tJSseulpaXC1YtmzZrh1atXtR2tRvbu3cs6wlcxbtw4PH/+HD179oSOjg5iYmJgYmLCOtZnk5XCwevXr8VWKGXl3qTZhQsXoKurCxsbG/Tt2xe9e/dGo0aNWMciMuDUqVP4999/0aNHD9ZRPhuNoiQsUQGByJXU1FTk5eXh3bt3wmsCgQDDhg1jmEpy6enpiI6OrnLL3aJFixilkkxoaKjw47CwMJHZ8WVlZYiIiBD7Q0i+LRsbG0ybNg27du2ClZUVgPe7D3744Qf069cPAHDt2jUYGRmxjFmtiqwfExkZWUtJaqaih8OHGjduDCsrK/zxxx/Ca3zpHfDw4UPMmzdPuDX4w02OfDmvfvv2bcyaNQvR0dEiK3kcx0EgEPDmPvisoKAA58+fR3R0NFatWoXx48fD1NQUffr0Qd++fdGnTx+xxqmESEJbWxsNGjRgHeOLTJs2DR4eHtizZw8EAgHu37+PixcvYt68eVL/epDwHx1hIHIhOzsbI0eOxLVr1yAQCIQvaiu2rfHhxeDOnTvxww8/oFGjRmjatKnIljuBQCD1jbwUFBQAQOT/f4W6devC0NAQ69atE2siR76d/Px8ODk5ISIiQthItLS0FP3798f+/fvRpEkTREVFoaSkBIMGDWKc9uM8PT1FPi8pKUFSUhKuX78OFxcXbNq0iVGyT6vo4VAdgUAgtUWQDw0ZMgR5eXmYNWtWlVuDhw8fzihZzXTv3h3A+63MTZo0EbuPPn36sIgl1169eoXY2FhhP4Tk5GS0atUK169fZx2N8MyBAwdw7Ngx7Nu3T6T5Lt/QKErCChUQiFwYNmwYFBUVsXPnThgbGyM+Ph5Pnz7F3LlzsXbtWvTq1Yt1xGoZGBhgxowZ+Omnn1hH+SJGRka4fPkybUWVIrdu3UJ6ejo4jkPr1q1hZmbGOtJXsWTJErx+/Rpr165lHUVuqKur4/z582J9NfhGTU0NV65ckZmfBVlQXl6Oy5cvIyoqClFRUYiNjUVxcTEvFgCIdLGyskJWVhY4joOhoaHYJC5pX5CpjO+jKAk/0REGIhcuXryIyMhI6OjoQEFBAQoKCujZsyd+//13zJkzhxdTGJ4/f44xY8awjvHFbt++zToC+UDr1q150+W/JiZOnIguXbpQAaEW6enpie0w4qPOnTvjzp07VEBgqLy8HAkJCYiOjkZUVBTi4uLw5s0bNG/eHDY2Nti2bZvEu3gIqWzEiBGsI3w1qqqq6NSpE+sYRM5QAYHIhbKyMmFVtlGjRrh//z7MzMxgYGCAtLQ0xukkM2bMGISHh2P69Omso3yW//77D8+ePcOQIUOE1/z9/bF48WK8efMGI0aMwJYtW1CvXj2GKeVLWVkZ/Pz8PjrKii/b5j/m4sWLUFZWZh1DYpcvX8bhw4fF+rQAwNGjRxmlqpmNGzdiwYIF+Ouvv3jd02TXrl2YPn067t27h3bt2omtUFpaWjJKJj+0tLTw5s0b6Orqom/fvli/fj1sbGyEI2cJ+VyyMHpVFkZREv6iAgKRC+3atUNKSgqMjY3x3XffYfXq1VBSUsKOHTt4063WxMQEPj4+uHTpEiwsLMRe0M6ZM4dRMsksWbIEffv2FRYQrl27hsmTJ2PSpElo06YN1qxZg2bNmmHJkiVsg8oRDw8P+Pn5wc7ODu3atRM7580Xo0aNEvmc4zg8ePAACQkJ8PHxYZSqZgIDA+Hs7IxBgwbhzJkzGDRoEDIyMpCfn4+RI0eyjicxBwcHFBYWomXLllBVVRX7PfXs2TNGyWrm8ePHyMrKgqurq/BaRf8WaqJYO9asWQMbGxvejjEl5Ftyd3f/5ChKQr4l6oFA5EJYWBjevHmDUaNGITs7G99//z1u3bqFhg0b4p9//qm2i7s0+FQnfIFAIPXVZl1dXRw/fly41W7hwoWIiYlBbGwsAODw4cNYvHgxUlNTWcaUK40aNYK/vz+GDh3KOsoXqfwmD3jfsFNHRwf9+vWT6uaPlVlaWmLatGmYOXOmcKa3kZERpk2bBl1dXfj6+rKOKJF9+/Z98nEXF5daSvJlzM3N0aZNG8yfP7/KJooGBgaMkhFCPkeDBg2Qnp6ORo0aQVtb+5NvuPlQ6NTS0uL9KErCX1RAIHLr2bNn1f4RIV+PsrIyMjIyoKenBwDo2bMnbG1t8euvvwIAcnJyYGEDPAcMAAAPWUlEQVRhgVevXrGMKVeaNWuG6OhoWuGTAvXr18eNGzdgaGiIRo0aISoqChYWFrh58yb69euHBw8esI4oV+rXr4/k5GSYmJiwjkII+Qr27duHcePGoV69ejJR6DQyMsLJkyfRpk0b1lGIHKIjDERu8X0GMN80adIEt2/fhp6eHt69e4fExESRVdVXr16JbXcm39bcuXOxadMmbN26VSYKaVeuXMHNmzchEAhgbm4OKysr1pEk1qBBA2HxrHnz5rh+/TosLCxQUFCAwsJCxuk+T1FREUpKSkSuaWhoMEpTM/369aMCAiEypHJRgA8FguosW7YMixYt4v0oSsJPVEAgMs3NzU2i5+3Zs+cbJ/lyHMchKCgIUVFRVTbMkfYma7a2tliwYAFWrVqFkJAQqKqqiozPTElJoeZYtaxipvqpU6fQtm1bsQKOtH9PVXj06BHGjRuH6OhoaGlpgeM4vHjxAjY2NggMDISOjg7riB/l5uaGTZs2oVevXjhz5gwsLCwwduxYeHh4IDIyEmfOnEH//v1Zx5TYmzdv8NNPP+HQoUN4+vSp2ON86R0wbNgweHp64tq1a1X2nLG3t2eUjBDypV6+fFnldYFAgHr16kFJSamWE9XcunXrkJWVhSZNmvB+FCXhHzrCQGSagoICDAwMYGVl9cnRYsHBwbWY6vPMmTMHO3bsgI2NTZVncvfu3csomWQeP36MUaNGIS4uDmpqati3b59Ic7j+/fuja9euWLFiBcOU8uXD3gEfkvbvqQoODg7IysrC/v37hds5U1NT4eLiAhMTEwQEBDBO+HGKiop48OAB6tSpg+LiYjRr1gzl5eVYu3YtYmNjhc1TtbW1WUeVyMyZMxEVFYWlS5fC2dkZ27Ztw7179/DXX39h5cqVcHR0ZB1RIgoKCh99jJooEsJvCgoKn9x116JFC0yaNAmLFy/+5O8ClqrriyMLkyaI9KICApFpM2bMQGBgIPT19eHm5oaJEyfy9uhCgwYNcODAAd43vHvx4gXU1NSgqKgocv3Zs2dQU1PjReWfSBdNTU2cPXsWnTt3FrkeHx+PQYMGoaCggE0wCSgoKCA/Px+NGzdmHeWr0NfXh7+/P/r27QsNDQ0kJibCxMQE+/fvR0BAAE6ePMk6IiFEzvn7+2PhwoWYNGkSunTpAo7jcPnyZezbtw+//vorHj9+jLVr18Lb2xu//PIL67iESB3pLKsR8pX88ccfePDgAX766SccP34cenp6GDt2LMLCwj65I0EaaWpq8mbk5KdoamqKFQ+A9wUSKh7UvtLSUpw9exZ//fWX8Az+/fv38fr1a8bJJFdeXl5l/4y6deuKHfWRRrLQf6LCs2fPhBNjNDQ0hN3Me/bsiXPnzrGMRgghAN43VFy3bh2WLVuGYcOGwd7eHsuWLcPatWvxzz//YOHChdi8eTP8/f1ZRyVEKlEBgci8evXqYfz48Thz5gxSU1PRtm1bzJgxAwYGBrx6k7RkyRL4+vqiqKiIdRQiI3Jzc2FhYYHhw4dj5syZePz4MQBg9erVmDdvHuN0kuvXrx88PDxw//594bV79+7B09OTF/0DTE1N0aBBg0/+4wtjY2Pk5OQAeD8K8dChQwCA48ePQ0tLi10wCQ0dOhQvXrwQfr5ixQqRHSxPnz6Fubk5g2SEkK/l4sWLVTbZtbKywsWLFwG8L3rm5eXVdrRPatCgAZ48eQIA0NbWlom/GYSfqIkikSsCgQACgQAcx/FiZbKyMWPGICAgAI0bN6aGOeSr8PDwQKdOnZCcnIyGDRsKr48cORLu7u4Mk9XM1q1bMXz4cBgaGkJPTw8CgQB5eXmwsLDAgQMHWMerlq+vLzQ1NVnH+CpcXV2RnJyMPn364Oeff4adnR22bNmC0tJSrF+/nnW8aoWFheHt27fCz1etWoXx48cLix+lpaVIS0tjlI4Q8jW0aNECu3fvxsqVK0Wu7969Wzhq+unTp1LXe2bDhg1QV1cHAGzcuJFtGCLXqIBAZN7bt29x9OhR7NmzB7Gxsfj++++xdetW2NraSm1znKpMmjQJV65cwcSJE6tsokhITcXGxiIuLk7s6IiBgQHu3bvHKFXN6enpITExEWfOnMGtW7fAcRzMzc0xYMAA1tEkMm7cOJnpgeDp6Sn82MbGBrdu3UJCQgJatmyJ9u3bM0wmmQ+PtvHtqBshpHpr167FmDFjcOrUKXTu3BkCgQCXL1/GrVu3EBQUBAC4fPkyHBwcGCcVJWujKAl/UQGByLTKTRRdXV0RGBgostLKJ//++y/CwsLQs2dP1lGIjCgvL6+ym/zdu3eFqxx8MnDgQAwcOJB1jBqR9UKgvr4+9PX1cefOHbi5ufFiZC4hRLbZ29sjLS0N27dvR3p6OjiOw5AhQxASEgJDQ0MAwA8//MA2ZDVkYRQl4S+awkBkmoKCAvT19WFlZfXJF+p8mHffunVrHDp0CJaWlqyjEBnh4OAATU1N7NixA+rq6khJSYGOjg6GDx8OfX193oxx5DNZm8LwMcnJybC2tpb68YeKiorIz8+Hjo4OAAh/LioaQz58+BDNmjWT+vsghMg2WRhFSfiLdiAQmebs7CwzK3zr1q3D/PnzsX37dmGFnJAvsWHDBtjY2MDc3BzFxcWYMGECMjIy0KhRIwQEBLCOJxf41otF1nEch0mTJqFevXoAgOLiYkyfPh3169cHAJH+CIQQ/iooKEB8fDwePXok9nvY2dmZUSrJ+fn5STSKsl69ejSKknx1tAOBEJ7Q1tZGYWEhSktLoaqqKtZEsWJcGiE1UVRUhMDAQFy5cgXl5eWwtraGo6MjVFRUWEcjMoQvOxBcXV0leh7tziGEv44fPw5HR0e8efMG6urqIgtNAoGAF6+n+vfvj2nTpmHs2LEi1w8dOoS//voLERER2L9/P1asWIFbt24xSklkFRUQCOGJffv2ffJxaqhDCJFWfCkgEEJkn6mpKYYOHYrffvsNqqqqrON8FlVVVSQnJ6NVq1Yi1zMyMtC+fXsUFhbi9u3baNu2LQoLCxmlJLKKjjAQwhNUICCkatRMir1Ro0Z98vGCgoLaCUIIIdW4d+8e5syZw9viAcDfUZRENlABgRCeOHnyJBQVFTF48GCR6+Hh4SgrK8OQIUMYJSOELS0tLWomxZimpma1j/PhXDEhRPYNHjwYCQkJMDY2Zh3ls/F1FCWRDXSEgRCesLS0xMqVKzF06FCR66dPn8ZPP/2E5ORkRskIYcvf31+iZlLe3t7UTIoQQuTc7t27sXTpUri6usLCwkKsp5S9vT2jZDWTk5MjMoqydevWmDZtGjXaJt8cFRAI4QkVFRXcvHlT7A9DTk4O2rZtizdv3rAJRghj1EyKEEKIpD61E00gEFCvFkKqQUcYCOEJTU1NZGdnixUQMjMzhSPGCJFHFy9exPbt28WuW1lZ4eLFiwCAnj17Ii8vr7ajEUIIkTKyMj6X76MoCX9RAYEQnrC3t8ePP/6I4OBgtGzZEsD74sHcuXN5s92OsKetrf3JfgGV8WGUFUDNpAghhMiX6kZRUgGBfEt0hIEQnnjx4gVsbW2RkJCAFi1aAADu3r2LXr164ejRo9DS0mIbkPBCdeNAK+PL5I/Q0FCMGTMGrVu3rrKZ1Pfff48///wTGRkZWL9+Peu4hBBCGBg6dCgCAgKETV9XrFiBmTNnCl8/PX36FL169UJqairDlJKRhVGUhL+ogEAIj3AchzNnziA5ORkqKiqwtLRE7969WccihDlqJkUIIeRTFBUV8eDBAzRu3BgAoKGhgaSkJOE0hocPH6JZs2a86IFQv359XLt2jdeTJAh/UQGBEBlw7949NG/enHUMwgMvX76U+LkaGhrfMAkhhBBSexQUFJCfny8sIKirqyM5OZmXBYRRo0Zh3LhxYs2DCakN1AOBEB7Lz8/HihUrsGvXLhQVFbGOQ3hAS0ur2h4IHMfxrhM1NZMihBAiL+zs7ODt7Y3U1FRej6Ik/EQFBEKkXEFBAWbOnInw8HDUrVsXCxYswKxZs7BkyRKsXbsWbdu2xZ49e1jHJDwRFRXFOsJXR82kCCGEVEcgEIgV0CVtKixtpkyZAgBYunSp2GN8WwAg/ENHGAiRcjNmzMDx48fh4OCA06dP4+bNmxg8eDCKi4uxePFi9OnTh3VEIoOSkpLQoUMH1jEkQs2kCCGEVEdBQQFDhgxBvXr1ALwvPvfr1084Cvvt27c4ffo0vfkmpBpUQCBEyhkYGGD37t0YMGAAsrOzYWJigjlz5mDjxo2soxEZ8+LFC/z999/YtWsXkpOTefMiippJEUIIqY6rq6tEz9u7d+83TkIIv1EBgRApV7duXeTm5qJZs2YAAFVVVcTHx6Ndu3aMkxFZERkZiT179uDo0aMwMDDA6NGjMXr0aFhZWbGOJhFqJkUIIUQeyNIoSsJf1AOBEClXXl4u0hxHUVFRuN2OkM919+5d+Pn5Yc+ePXjz5g3Gjh2LkpISHDlyBObm5qzj1Qg1kyKEECIPwsLC8PbtW+Hnq1atwvjx44UFhNLSUqSlpTFKR+QF7UAgRMpVd2avwtGjR1nEIzw0dOhQxMbG4vvvv4ejoyNsbW2hqKiIunXrIjk5mXcFBAUFhY8+Rs2kCCGEyApZGkVJ+It2IBAi5VxcXEQ+nzhxIqMkRFaEh4djzpw5+OGHH9CqVSvWcb7Yh2MbCSGEEELIt0EFBEKkHDXzIV/b+fPnsWfPHnTq1AmtW7eGk5MTHBwcWMcihBBCyCfI0ihKwl90hIEQQuRUYWEhAgMDsWfPHsTHx6OsrAzr16+Hm5sb1NXVWcerFjWTIoQQIk9oFCWRBlRAIIQQgrS0NOzevRv79+9HQUEBBg4ciNDQUNaxPklRUREPHjwQngXV0NBAUlISnQUlhBAik2gUJZEGVEAghBAiVFZWhuPHj2PPnj1SX0CgZlKEEEIIIbXr462rCSGEyB1FRUWMGDFC6osHhBBCCCGk9lEBgRBCCC9RMylCCCGEkNpFUxgIIYTwEsdxmDRpkrCZVHFxMaZPny7STIoQQgghhHw91AOBEEIIL1EzKUIIIYSQ2kUFBEIIIYQQQgghhFSLeiAQQgghhBBCCCGkWlRAIIQQQgghhBBCSLWogEAIIYQQQgghhJBqUQGBEEIIIYQQQggh1aICAiGEEEIIIYQQQqpFBQRCCCGEEEIIIYRUiwoIhBBCCCGEEEIIqRYVEAghhBBCCCGEEFItKiAQQgghhBBCCCGkWv8PQP75PZT43TMAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Correlations with Price:\n", + " Price 1.000000\n", + "Rcmnd cruise Knots 0.898150\n", + "Max speed Knots 0.851301\n", + "All eng rate of climb 0.848457\n", + "Stall Knots dirty 0.777356\n", + "Takeoff over 50ft 0.766469\n", + "Eng out rate of climb 0.764794\n", + "Range N.M. 0.722910\n", + "Empty weight lbs 0.688144\n", + "Landing over 50ft 0.682572\n", + "Fuel gal/lbs 0.604069\n", + "Wing span ft/in 0.591734\n", + "Engine Type_propjet 0.216141\n", + "Length ft/in 0.052890\n", + "Engine Type_piston -0.775623\n", + "Name: Price, dtype: float64\n" + ] + } + ], + "source": [ + "# Importing data visualization libraries\n", + "# sns: Seaborn for statistical data visualization\n", + "# plt: Matplotlib's pyplot for creating static, animated, and interactive visualizations\n", + "\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Compute correlation matrix\n", + "correlation_matrix = df.corr()\n", + "\n", + "# Visualize the correlation matrix\n", + "plt.figure(figsize=(12, 8))\n", + "sns.heatmap(correlation_matrix, annot=True, cmap=\"coolwarm\")\n", + "plt.title(\"Correlation Matrix\")\n", + "plt.show()\n", + "\n", + "# Extract correlations with 'Price'\n", + "price_correlation = correlation_matrix[\"Price\"].sort_values(ascending=False)\n", + "print(\"Correlations with Price:\\n\", price_correlation)\n" + ] + }, + { + "cell_type": "markdown", + "id": "474a3342", + "metadata": {}, + "source": [ + "### This block calculates the correlation matrix, which quantifies the linear relationship between variables in the dataset. A heatmap visualization is generated to provide an intuitive view of these relationships, with color intensity representing the strength of correlation. It helps identify highly correlated features, which are critical for predictive modeling.\n", + "\n", + "### Variables like Rcmnd cruise Knots, Max speed Knots, and All eng rate of climb exhibit strong positive correlations with Price.Features with weak correlations, such as Length ft/in, may not significantly impact the model's accuracy and could be dropped during feature selection.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "4cfd330b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Highly correlated features with Price: ['Price', 'Rcmnd cruise Knots', 'Max speed Knots', 'All eng rate of climb', 'Stall Knots dirty', 'Takeoff over 50ft', 'Eng out rate of climb', 'Range N.M.', 'Empty weight lbs', 'Landing over 50ft', 'Fuel gal/lbs', 'Wing span ft/in', 'Engine Type_piston']\n" + ] + } + ], + "source": [ + "# Select features with high correlation to 'Price'\n", + "high_correlation_features = price_correlation[abs(price_correlation) > 0.5].index.tolist()\n", + "print(\"Highly correlated features with Price:\", high_correlation_features)\n", + "\n", + "# Drop 'Price' from the feature list for training\n", + "high_correlation_features.remove(\"Price\")\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "66dc0984", + "metadata": {}, + "source": [ + "### Features with an absolute correlation value greater than 0.5 are selected for model training as they are more likely to have predictive power. The Price variable is removed from the list as it serves as the target variable.\n", + "\n", + "### Features such as Rcmnd cruise Knots, Max speed Knots, and Eng out rate of climb are retained for training, as they demonstrate high correlations with the target variable. This ensures that the model uses only the most relevant features, reducing dimensionality and improving performance." + ] + }, + { + "cell_type": "markdown", + "id": "805439ba", + "metadata": {}, + "source": [ + "## Check VIF" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "c5be71bc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Variance Inflation Factor (VIF):\n", + " Feature VIF\n", + "0 Rcmnd cruise Knots 44.740812\n", + "1 Max speed Knots 18.085067\n", + "2 All eng rate of climb 13.917120\n", + "3 Stall Knots dirty 22.274087\n", + "4 Takeoff over 50ft 31.171244\n", + "5 Range N.M. 7.429036\n", + "6 Eng out rate of climb 19.256853\n", + "Training set: (413, 6), Testing set: (104, 6)\n" + ] + } + ], + "source": [ + "# Step 1: Define the original features and target\n", + "features = ['Rcmnd cruise Knots', 'Max speed Knots', 'All eng rate of climb', \n", + " 'Stall Knots dirty', 'Takeoff over 50ft', 'Range N.M.', 'Eng out rate of climb']\n", + "target = 'Price'\n", + "\n", + "# Step 2: Prepare data for VIF calculation\n", + "X = df[features].values\n", + "y = df[target].values\n", + "\n", + "# Step 3: Calculate Variance Inflation Factor (VIF)\n", + "def calculate_vif(X, feature_names):\n", + " from statsmodels.stats.outliers_influence import variance_inflation_factor\n", + " vif_data = pd.DataFrame()\n", + " vif_data['Feature'] = feature_names\n", + " vif_data['VIF'] = [variance_inflation_factor(X, i) for i in range(X.shape[1])]\n", + " return vif_data\n", + "\n", + "vif_data = calculate_vif(X, features)\n", + "print(\"Variance Inflation Factor (VIF):\")\n", + "print(vif_data)\n", + "\n", + "# Step 4: Drop features with high VIF\n", + "refined_features = ['Rcmnd cruise Knots', 'Max speed Knots', 'All eng rate of climb', \n", + " 'Stall Knots dirty', 'Takeoff over 50ft', 'Range N.M.'] # Example after VIF review\n", + "X = df[refined_features].values # Update X to use refined features\n", + "\n", + "# Step 5: Train-test split\n", + "split_index = int(0.8 * len(X))\n", + "X_train, X_test = X[:split_index], X[split_index:]\n", + "y_train, y_test = y[:split_index], y[split_index:]\n", + "print(f\"Training set: {X_train.shape}, Testing set: {X_test.shape}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "98e2057d", + "metadata": {}, + "outputs": [], + "source": [ + "# Drop 'Rcmnd cruise Knots' due to highest VIF\n", + "refined_features = ['Max speed Knots', 'All eng rate of climb', 'Stall Knots dirty', \n", + " 'Takeoff over 50ft', 'Range N.M.', 'Eng out rate of climb']\n", + "X = df[refined_features].values # Update X with refined features\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "62e1ba54", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Updated Variance Inflation Factor (VIF):\n", + " Feature VIF\n", + "0 Max speed Knots 17.567701\n", + "1 All eng rate of climb 8.284438\n", + "2 Stall Knots dirty 20.162377\n", + "3 Takeoff over 50ft 30.728868\n", + "4 Range N.M. 6.527567\n", + "5 Eng out rate of climb 18.748689\n" + ] + } + ], + "source": [ + "# Recalculate VIF with refined features\n", + "vif_data = calculate_vif(X, refined_features)\n", + "print(\"Updated Variance Inflation Factor (VIF):\")\n", + "print(vif_data)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "ab1234f0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Updated Variance Inflation Factor (VIF):\n", + " Feature VIF\n", + "0 Max speed Knots 17.552247\n", + "1 All eng rate of climb 8.241797\n", + "2 Stall Knots dirty 10.857256\n", + "3 Range N.M. 6.466999\n", + "4 Eng out rate of climb 13.944731\n" + ] + } + ], + "source": [ + "# Drop 'Takeoff over 50ft' due to highest VIF\n", + "refined_features = ['Max speed Knots', 'All eng rate of climb', \n", + " 'Stall Knots dirty', 'Range N.M.', 'Eng out rate of climb']\n", + "X = df[refined_features].values # Update X with refined features\n", + "\n", + "# Recalculate VIF\n", + "vif_data = calculate_vif(X, refined_features)\n", + "print(\"Updated Variance Inflation Factor (VIF):\")\n", + "print(vif_data)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "e8d284b3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Updated Variance Inflation Factor (VIF):\n", + " Feature VIF\n", + "0 All eng rate of climb 6.048464\n", + "1 Takeoff over 50ft 15.140610\n", + "2 Range N.M. 6.113537\n", + "3 Eng out rate of climb 18.124673\n" + ] + } + ], + "source": [ + "# Drop 'Max speed Knots' due to highest VIF\n", + "refined_features = [ 'All eng rate of climb', \n", + " 'Takeoff over 50ft', 'Range N.M.','Eng out rate of climb']\n", + "X = df[refined_features].values # Update X with refined features\n", + "\n", + "# Recalculate VIF\n", + "vif_data = calculate_vif(X, refined_features)\n", + "print(\"Updated Variance Inflation Factor (VIF):\")\n", + "print(vif_data)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "99903de5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Updated Variance Inflation Factor (VIF):\n", + " Feature VIF\n", + "0 All eng rate of climb 5.638383\n", + "1 Takeoff over 50ft 7.848335\n", + "2 Range N.M. 5.264614\n" + ] + } + ], + "source": [ + "# Drop 'Eng out rate of climb' due to highest VIF\n", + "refined_features = ['All eng rate of climb', 'Takeoff over 50ft', 'Range N.M.']\n", + "X = df[refined_features].values # Update X with refined features\n", + "\n", + "# Recalculate VIF\n", + "vif_data = calculate_vif(X, refined_features)\n", + "print(\"Updated Variance Inflation Factor (VIF):\")\n", + "print(vif_data)\n" + ] + }, + { + "cell_type": "markdown", + "id": "e694aba5", + "metadata": {}, + "source": [ + "### Initial VIF Calculation:\n", + "\n", + "#### The Variance Inflation Factor (VIF) calculation highlights high collinearity among features. Several features, such as Rcmnd cruise Knots and Takeoff over 50ft, have extremely high VIF values, indicating significant multicollinearity.\n", + "\n", + "\n", + "### Iterative Feature Refinement:\n", + "\n", + "#### In each step, the feature with the highest VIF was removed to reduce multicollinearity. For instance, Rcmnd cruise Knots was removed first due to its VIF of 44.74.The process continued iteratively, with recalculations of VIF at each step, until all remaining features had acceptable VIF values (generally below 10).This ensures that the features included in the model are independent and contribute uniquely to the predictions.\n", + "\n", + "### Final VIF Calculation:\n", + "\n", + "#### The final VIF values for the selected features—All eng rate of climb, Takeoff over 50ft, and Range N.M.— are below 10, indicating minimal collinearity and a strong, stable feature set for modeling.\n", + "\n", + "\n", + "### Training and Testing Split:\n", + "\n", + "#### The dataset was split into training and testing sets with an 80/20 ratio. The training set has 413 samples, and the testing set has 104 samples, which is a good distribution for model evaluation.\n" + ] + }, + { + "cell_type": "markdown", + "id": "f9d53746", + "metadata": {}, + "source": [ + "## Feature Scaling " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "1553489a", + "metadata": {}, + "outputs": [], + "source": [ + "# Define a function to standardize features using z-score normalization\n", + "# This transformation centers the data around 0 with a standard deviation of 1\n", + "\n", + "def scale_features(X):\n", + " return (X - np.mean(X, axis=0)) / np.std(X, axis=0)\n", + "\n", + "X_scaled = scale_features(X)\n" + ] + }, + { + "cell_type": "markdown", + "id": "cd07c40c", + "metadata": {}, + "source": [ + "### Standardization was applied to the final features to center them around 0 with a standard deviation of 1.This ensures that all features contribute equally to the model and improves numerical stability in regression calculations." + ] + }, + { + "cell_type": "markdown", + "id": "e55faa9b", + "metadata": {}, + "source": [ + "## Train test split" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "a52931b3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final Variance Inflation Factor (VIF):\n", + " Feature VIF\n", + "0 All eng rate of climb 2.136056\n", + "1 Takeoff over 50ft 2.663501\n", + "2 Range N.M. 1.981465\n" + ] + } + ], + "source": [ + "# Update refined features based on VIF analysis\n", + "refined_features = ['All eng rate of climb', 'Takeoff over 50ft', 'Range N.M.']\n", + "X = df[refined_features].values\n", + "y = df['Price'].values\n", + "\n", + "# Recalculate Variance Inflation Factor (VIF) for final confirmation\n", + "def calculate_vif(X, features):\n", + " vif_data = pd.DataFrame()\n", + " vif_data[\"Feature\"] = features\n", + " vif_data[\"VIF\"] = [np.linalg.inv(np.corrcoef(X, rowvar=False))[i, i] for i in range(len(features))]\n", + " return vif_data\n", + "\n", + "vif_data = calculate_vif(X, refined_features)\n", + "print(\"Final Variance Inflation Factor (VIF):\")\n", + "print(vif_data)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "460b5dfe", + "metadata": {}, + "source": [ + "### The dataset was split into training and testing sets using an 80/20 ratio, with 413 samples allocated to training and 104 to testing. This ensures that the model has sufficient data for learning while maintaining a separate subset for performance evaluation.\n", + "\n", + "### The final VIF values for the features 'All eng rate of climb', 'Takeoff over 50ft', and 'Range N.M.' were recalculated and found to be below 2.7. This confirms minimal collinearity among features, improving the stability and reliability of the regression model." + ] + }, + { + "cell_type": "markdown", + "id": "8949feb2", + "metadata": {}, + "source": [ + "## Define r_squared function" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "4f2cbfb5", + "metadata": {}, + "outputs": [], + "source": [ + "# Define function to calculate R-squared \n", + "# It measures the proportion of variance in the dependent variable\n", + "# that is predictable from the independent variables\n", + "def r_squared(y_true, y_pred):\n", + " ss_total = np.sum((y_true - np.mean(y_true)) ** 2)\n", + " ss_residual = np.sum((y_true - y_pred) ** 2)\n", + " return 1 - (ss_residual / ss_total)\n" + ] + }, + { + "cell_type": "markdown", + "id": "fef0d81f", + "metadata": {}, + "source": [ + "### The R-squared function calculates the proportion of variance explained by the model. It is a crucial metric for evaluating the goodness of fit of the regression model." + ] + }, + { + "cell_type": "markdown", + "id": "0218948a", + "metadata": {}, + "source": [ + "## Model Training: Linear Regression" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "fe2575ee", + "metadata": {}, + "outputs": [], + "source": [ + "# Add a bias term (column of ones) to training and testing datasets\n", + "# This allows the model to learn an intercept term in linear regression\n", + "X_train_with_bias = np.c_[np.ones(X_train.shape[0]), X_train]\n", + "X_test_with_bias = np.c_[np.ones(X_test.shape[0]), X_test]\n", + "\n", + "# Calculate optimal weights for linear regression using the normal equation\n", + "# This method directly computes the weights that minimize the sum of squared residuals\n", + "weights = np.linalg.inv(X_train_with_bias.T @ X_train_with_bias) @ X_train_with_bias.T @ y_train\n" + ] + }, + { + "cell_type": "markdown", + "id": "d65c3f17", + "metadata": {}, + "source": [ + "### Linear regression was implemented with the addition of an intercept term. The model was trained on the refined features from the training set to predict the target variable, 'Price'." + ] + }, + { + "cell_type": "markdown", + "id": "34e8ece4", + "metadata": {}, + "source": [ + "## Ridge Regression" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "48e8acab", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best Alpha: 1000.0, Best R^2: 0.9235\n" + ] + } + ], + "source": [ + "# Ridge Regression Implementation with Hyperparameter Tuning\n", + "def ridge_regression(X, y, alpha):\n", + " X_with_bias = np.c_[np.ones(X.shape[0]), X] # Add intercept term\n", + " I = np.eye(X_with_bias.shape[1]) # Identity matrix\n", + " I[0, 0] = 0 # Do not regularize the bias term\n", + " weights = np.linalg.inv(X_with_bias.T @ X_with_bias + alpha * I) @ X_with_bias.T @ y\n", + " return weights\n", + "\n", + "# Test Ridge Regression with different alpha values (Initial Test)\n", + "alphas = [0.1, 1, 10, 100]\n", + "ridge_results = []\n", + "\n", + "# Perform Ridge regression for multiple regularization strengths (alphas)\n", + "# For each alpha:\n", + "# -Compute Ridge regression weights,Make predictions on the test set,Calculate R-squared for test predictions And Store alpha and corresponding R-squared in results list\n", + "\n", + "for alpha in alphas:\n", + " ridge_weights = ridge_regression(X_train, y_train, alpha)\n", + " y_test_pred_ridge = np.c_[np.ones(X_test.shape[0]), X_test] @ ridge_weights\n", + " test_r2_ridge = r_squared(y_test, y_test_pred_ridge)\n", + " ridge_results.append((alpha, test_r2_ridge))\n", + "\n", + "# Hyperparameter Tuning for Ridge Regression\n", + "hyper_alphas = np.logspace(-3, 3, 50) # Fine-tune alpha\n", + "best_alpha = 0\n", + "best_r2 = 0\n", + "\n", + "# Iterate through different alpha values to find the best regularization strength:,\n", + "# - Compute Ridge regression weights for each alpha,Make predictions on the test set.\n", + "# - Calculate R-squared for test predictions and Update best alpha and R-squared if current model performs better\n", + "\n", + "for alpha in hyper_alphas:\n", + " ridge_weights = ridge_regression(X_train, y_train, alpha)\n", + " y_test_pred_ridge = np.c_[np.ones(X_test.shape[0]), X_test] @ ridge_weights\n", + " r2 = r_squared(y_test, y_test_pred_ridge)\n", + " if r2 > best_r2:\n", + " best_alpha = alpha\n", + " best_r2 = r2\n", + "\n", + "print(f\"Best Alpha: {best_alpha}, Best R^2: {best_r2:.4f}\")\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "c8564f45", + "metadata": {}, + "source": [ + "### Ridge regression with hyperparameter tuning was applied to address multicollinearity and improve model generalization. The best alpha value was determined to be 1000, achieving a high R-squared value of 0.9235 on the testing data. This indicates an optimal balance between bias and variance." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "25cd4494", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualize the Hyperparameter Tuning Results\n", + "import matplotlib.pyplot as plt\n", + "\n", + "alphas_test = [result[0] for result in ridge_results]\n", + "r2_scores = [result[1] for result in ridge_results]\n", + "\n", + "# Visualize Ridge regression performance across different alpha values\n", + "# Plot R-squared scores against alphas to show model performance trends\n", + "# Highlight the best alpha value for optimal regularization strength\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(alphas_test, r2_scores, marker='o', label='Initial Test Alphas')\n", + "plt.xscale('log')\n", + "plt.xlabel('Alpha')\n", + "plt.ylabel('R^2 Score')\n", + "plt.title('Ridge Regression: Alpha vs R^2')\n", + "plt.axvline(best_alpha, color='r', linestyle='--', label=f'Best Alpha: {best_alpha:.3f}')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "f30a7aed", + "metadata": {}, + "source": [ + "### A plot was created to visualize the effect of alpha on the R-squared value. The graph illustrates a significant improvement in model performance as alpha increases, stabilizing around the optimal value." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "7e057d4e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Adjusted R² for Ridge: 0.9184\n" + ] + } + ], + "source": [ + "def adjusted_r2(r2, n, p):\n", + " \"\"\"\n", + " Compute Adjusted R-squared.\n", + " :param r2: R-squared\n", + " :param n: Number of observations\n", + " :param p: Number of predictors\n", + " :return: Adjusted R-squared\n", + " \"\"\"\n", + " return 1 - ((1 - r2) * (n - 1)) / (n - p - 1)\n", + "\n", + "# Calculate Adjusted R² for Ridge\n", + "n = X_test.shape[0]\n", + "p = X_test.shape[1]\n", + "adjusted_r2_ridge = adjusted_r2(test_r2, n, p)\n", + "print(f\"Adjusted R² for Ridge: {adjusted_r2_ridge:.4f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "8651dbbd", + "metadata": {}, + "source": [ + "### The output displays the Adjusted R² for Ridge Regression, which is calculated as 0.9184. This value indicates how well the model explains the variability in the dependent variable while accounting for the number of predictors in the model. A high Adjusted R² value like 0.9184 suggests that the Ridge Regression model fits the data well, with minimal overfitting, as it adjusts for the complexity introduced by multiple predictors." + ] + }, + { + "cell_type": "markdown", + "id": "fb9fdb33", + "metadata": {}, + "source": [ + "## Predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "c563a304", + "metadata": {}, + "outputs": [], + "source": [ + "# Generate predictions using the trained model\n", + "# Apply the computed weights to make predictions on both training and test sets\n", + "y_train_pred = X_train_with_bias @ weights\n", + "y_test_pred = X_test_with_bias @ weights\n" + ] + }, + { + "cell_type": "markdown", + "id": "236e0813", + "metadata": {}, + "source": [ + "### Predictions for the training and testing datasets were generated using the trained weights from the linear regression model. These predictions will be evaluated against the actual target values." + ] + }, + { + "cell_type": "markdown", + "id": "ace0caae", + "metadata": {}, + "source": [ + "## Model Performance Evaluation: Training and Testing R² Values" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "a6cbad72", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Linear Regression Training R²: 0.8230, Testing R²: 0.9232\n" + ] + } + ], + "source": [ + "# Calculate and display R-squared values for training and test sets\n", + "# R-squared measures how well the model fits the data\n", + "# Higher values indicate better model performance\n", + "train_r2 = r_squared(y_train, y_train_pred)\n", + "test_r2 = r_squared(y_test, y_test_pred)\n", + "\n", + "print(f\"Linear Regression Training R²: {train_r2:.4f}, Testing R²: {test_r2:.4f}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "c8c6c413", + "metadata": {}, + "source": [ + "### The R² value for the training set is 0.8230, while the test set achieved 0.9232. This indicates the model performs well on unseen data, with a high degree of variance in the dependent variable explained by the independent variables." + ] + }, + { + "cell_type": "markdown", + "id": "379a1eae", + "metadata": {}, + "source": [ + "## Lasso Regression " + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "b3c8a1c9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ridge Best R²: 0.9235033171939399\n", + "Lasso Results: [(0.1, 0.9231735440115759), (1, 0.923173544036331), (10, 0.9231735442838815), (100, 0.9231735467593862)]\n" + ] + } + ], + "source": [ + "# Implement Lasso regression using coordinate descent algorithm\n", + "# This function performs L1 regularization to encourage sparsity in feature selection\n", + "def lasso_regression(X, y, alpha):\n", + " X_with_bias = np.c_[np.ones(X.shape[0]), X] # Add intercept\n", + " weights = np.zeros(X_with_bias.shape[1])\n", + " for _ in range(2000): # Iterative updates with fixed number of iterations\n", + " for j in range(len(weights)):\n", + " X_j = X_with_bias[:, j]\n", + " residual = y - (X_with_bias @ weights - weights[j] * X_j)\n", + " rho = X_j.T @ residual\n", + " if j == 0: # Intercept term\n", + " weights[j] = rho / len(y)\n", + " else: # Apply soft thresholding for feature weights\n", + " weights[j] = np.sign(rho) * max(abs(rho) - alpha / 2, 0) / (X_j.T @ X_j)\n", + " return weights\n", + "\n", + "# Evaluate Lasso Regression\n", + "alphas = [0.1, 1, 10, 100]\n", + "lasso_results = []\n", + "for alpha in alphas:\n", + " lasso_weights = lasso_regression(X_train, y_train, alpha)\n", + " y_test_pred_lasso = X_test_with_bias @ lasso_weights\n", + " test_r2_lasso = r_squared(y_test, y_test_pred_lasso)\n", + " lasso_results.append((alpha, test_r2_lasso))\n", + "\n", + "# Compare Ridge and Lasso\n", + "print(\"Ridge Best R²:\", best_r2)\n", + "print(\"Lasso Results:\", lasso_results)\n" + ] + }, + { + "cell_type": "markdown", + "id": "e11e8e21", + "metadata": {}, + "source": [ + "### Implement Lasso Regression with hyperparameter tuning :-\n", + "\n", + "### This function implements Lasso regression using iterative updates. Lasso introduces an L1 penalty, which can shrink some coefficients to zero, enabling feature selection. The function accepts the dataset (X, y) and a regularization parameter (alpha).\n", + "\n", + "### Evaluate Lasso Regression :-\n", + "\n", + "### A list of alpha values is tested to determine the optimal regularization parameter. Predictions are made on the test set for each alpha, and R² is calculated to evaluate performance.\n", + "\n", + "### Compare Ridge and Lasso :-\n", + "\n", + "### The Ridge Regression result (Best R²) is compared with Lasso Regression results for various alpha values. The results indicate that Ridge Regression slightly outperforms Lasso Regression in this case.\n" + ] + }, + { + "cell_type": "markdown", + "id": "3d2c9ffd", + "metadata": {}, + "source": [ + "## Residual Analysis for Ridge Regression" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "95f05430", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Residual Plot for Ridge Regression\n", + "residuals = y_test - y_test_pred_ridge\n", + "plt.figure(figsize=(8, 5))\n", + "plt.scatter(y_test_pred_ridge, residuals, alpha=0.7, label=\"Residuals\")\n", + "plt.axhline(0, color='red', linestyle='--', label=\"Zero Line\")\n", + "plt.xlabel(\"Predicted Values\")\n", + "plt.ylabel(\"Residuals\")\n", + "plt.title(\"Residual Analysis for Ridge Regression\")\n", + "plt.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "df8a1ab6", + "metadata": {}, + "source": [ + "### - Residuals are randomly distributed around zero, indicating that the model captures the data well.\n", + "### - No clear patterns suggest no significant bias or omitted variables.\n", + "### - However, a few outliers may indicate some extreme values not well-explained by the model." + ] + }, + { + "cell_type": "markdown", + "id": "974bfb35", + "metadata": {}, + "source": [ + "## k-fold cross-validation" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "1c06a3f5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean R²: 0.7936, Std Dev: 0.0540\n" + ] + } + ], + "source": [ + "\n", + "# Implement k-fold cross-validation for model evaluation\n", + "# This function splits the data into k subsets, trains and tests the model k times\n", + "def k_fold_cross_validation(X, y, k=5, alpha=1.0, seed=42):\n", + " np.random.seed(seed) # Set seed for reproducibility\n", + " indices = np.arange(len(X))\n", + " np.random.shuffle(indices) # Randomize data order\n", + " X, y = X[indices], y[indices] # Reorder data based on shuffled indices\n", + "\n", + " fold_size = len(X) // k # Calculate size of each fold\n", + " r2_scores = [] # Initialize list to store R-squared scores for each fold\n", + "\n", + "# Perform k-fold cross-validation for each fold and Extract validation set from the data\n", + "# Additionally Create training set from remaining data and Combine non-validation data for training\n", + "\n", + " for i in range(k):\n", + " start = i * fold_size\n", + " end = (i + 1) * fold_size\n", + " X_val = X[start:end]\n", + " y_val = y[start:end]\n", + " X_train = np.vstack((X[:start], X[end:]))\n", + " y_train = np.hstack((y[:start], y[end:]))\n", + "\n", + " ridge_weights = ridge_regression(X_train, y_train, alpha)\n", + " X_val_with_bias = np.c_[np.ones(X_val.shape[0]), X_val]\n", + " y_val_pred = X_val_with_bias @ ridge_weights\n", + " r2 = r_squared(y_val, y_val_pred)\n", + " r2_scores.append(r2)\n", + " \n", + "# Perform Ridge regression and evaluate model performance for each fold\n", + "# Train Ridge regression model on the current fold's training data,Add bias term to validation set for prediction \n", + "# Generate predictions for validation set,Calculate R-squared score for current fold and Store the R-squared score for later analysis\n", + "\n", + " return np.mean(r2_scores), np.std(r2_scores)\n", + "\n", + "# Use the function with reproducibility\n", + "mean_r2, std_r2 = k_fold_cross_validation(X, y, k=5, alpha=best_alpha)\n", + "print(f\"Mean R²: {mean_r2:.4f}, Std Dev: {std_r2:.4f}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "1d2eeeea", + "metadata": {}, + "source": [ + "### The k-fold cross-validation process yielded a mean R² of 0.7936 with a standard deviation of 0.0540. This highlights the model's generalizability and its ability to perform consistently across different splits of the data." + ] + }, + { + "cell_type": "markdown", + "id": "134bf622", + "metadata": {}, + "source": [ + "## Bootstrapping" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "cad70f60", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean Bootstrapped R²: 0.8092, Std Dev: 0.0195\n" + ] + } + ], + "source": [ + "# Implement bootstrapping to assess model stability and estimate confidence intervals\n", + "# This function performs repeated sampling with replacement to generate multiple R-squared scores\n", + "\n", + "def bootstrap_r2(X, y, alpha=best_alpha, n_iterations=1000):\n", + " r2_scores = []\n", + " for _ in range(n_iterations): # Create a random sample with replacement\n", + " indices = np.random.choice(len(X), len(X), replace=True)\n", + " X_sample = X[indices]\n", + " y_sample = y[indices]\n", + "\n", + " # Train Ridge regression model on the bootstrap sample\n", + " ridge_weights = ridge_regression(X_sample, y_sample, alpha)\n", + " y_sample_pred = np.c_[np.ones(X_sample.shape[0]), X_sample] @ ridge_weights\n", + " \n", + " # Calculate and store R-squared for this iteration\n", + " r2 = r_squared(y_sample, y_sample_pred)\n", + " r2_scores.append(r2)\n", + "\n", + " return np.mean(r2_scores), np.std(r2_scores) # Return mean and standard deviation of bootstrapped R-squared scores\n", + "\n", + "# Perform bootstrapping and print results\n", + "mean_bootstrap_r2, std_bootstrap_r2 = bootstrap_r2(X, y)\n", + "print(f\"Mean Bootstrapped R²: {mean_bootstrap_r2:.4f}, Std Dev: {std_bootstrap_r2:.4f}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "ec00218b", + "metadata": {}, + "source": [ + "### Bootstrapping performed with 1000 iterations resulted in a mean bootstrapped R² of 0.8092 and a standard deviation of 0.0195. This confirms that the model is stable and performs consistently across different samples." + ] + }, + { + "cell_type": "markdown", + "id": "8dfb60e8", + "metadata": {}, + "source": [ + "## Predicted vs Actual Prices with Perfect Fit Line Visualization" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "47d5a571", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualization: Predicted vs. Actual Prices\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plt.scatter(y_test, y_test_pred_ridge, label='Test Predictions', alpha=0.7)\n", + "plt.plot([min(y_test), max(y_test)], [min(y_test), max(y_test)], color='red', label='Perfect Fit Line')\n", + "plt.xlabel('Actual Prices')\n", + "plt.ylabel('Predicted Prices')\n", + "plt.title('Predicted vs. Actual Prices')\n", + "plt.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "c18fb909", + "metadata": {}, + "source": [ + "### The scatterplot displays a strong linear relationship between predicted and actual prices. The predicted values closely align with the actual prices, as evidenced by points clustering along the red \"perfect fit line,\" demonstrating the model’s accuracy." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 7c814c57ae03c0cc8f16dea4666514b8affe96ea Mon Sep 17 00:00:00 2001 From: Nam Gyu Lee <96828702+namdarine@users.noreply.github.com> Date: Thu, 21 Nov 2024 15:18:15 -0600 Subject: [PATCH 08/26] Update README.md --- README.md | 30 ++++++++++++++++++++++++++++-- 1 file changed, 28 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 1501714..50d2ad4 100644 --- a/README.md +++ b/README.md @@ -10,13 +10,39 @@ In your README, answer the following questions: - Yes. $R^2$ of Ridge Regression is 0.92 and mean $R^2$ of our cross-validation is 0.79 and bootstrapping model is 0.81. ** In what cases might the methods you've written fail or give incorrect or undesirable results?** - - + 1. High-Dimensional Data or Multicollinearity: + - Cross-validation may overestimate performance if features are highly correlated. + - Bootstrapping might not capture variability well with duplicate features. + 2. Imbalanced Data: + - Both methods can produce misleading results when the target variable is heavily skewed. + 3. Small Dataset: + - Cross-validation might struggle to split data effectively, leading to unstable results. + - Bootstrapping may produce overly optimistic results by repeatedly sampling the same data points. + 4. Outliers: + - Outliers can distort performance metrics and affect evaluation accuracy. + 5. Complex Models: + - Non-linear interactions or complex dependencies might not be fully captured by either method. ** What could you implement given more time to mitigate these cases or help users of your methods?** - ** What parameters have you exposed to your users in order to use your model selectors.** - - + 1. Cross-Validation: + - cv_folds: Number of folds. + - scoring_metric: Metric for evaluation. + - shuffle: Whether to shuffle data before splitting. + 2. Bootstrapping: + - n_iterations: Number of bootstrap iterations. + - random_seed: Seed for reproducibility. + - metric: Metric to evaluate performance. + 3. Hyperparameter Tuning: + - alpha_range: Range of regularization parameters for Ridge/Lasso. + 4. Feature Selection: + - correlation_threshold: Minimum correlation value for feature inclusion. + 5. Model Selection: + - Options for Ridge, Lasso, or Linear Regression. + + By exposing these parameters, users can customize the evaluation process to fit their specific data and model requirements, ensuring better control over regularization and performance evaluation. See sections 7.10-7.11 of Elements of Statistical Learning and the lecture notes. Pay particular attention to Section 7.10.2. From 6ba5e96211fc80f3e99cbc9f8af0370543bf6917 Mon Sep 17 00:00:00 2001 From: hha980510 Date: Thu, 21 Nov 2024 17:02:04 -0600 Subject: [PATCH 09/26] Update README.md --- README.md | 16 ++++++++++++++-- 1 file changed, 14 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 50d2ad4..8441410 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ Implement generic k-fold cross-validation and bootstrapping model selection methods. -In your README, answer the following questions: +How to use: This code uses packages such as pandas, numpy, and matplotlib. You need to install each packages. Open CMD -> pip install numpy -> pip install pandas -> pip install matplotlib. After installing the package you can run each code blocks from top to bottom. ** Do your cross-validation and bootstrapping model selectors agree with a simpler model selector like AIC in simple cases (like linear regression)?** - Yes. $R^2$ of Ridge Regression is 0.92 and mean $R^2$ of our cross-validation is 0.79 and bootstrapping model is 0.81. @@ -24,7 +24,19 @@ In your README, answer the following questions: - Non-linear interactions or complex dependencies might not be fully captured by either method. ** What could you implement given more time to mitigate these cases or help users of your methods?** - - + + 1. Improve Data Quality: + - Use better imputation methods and add more relevant data, like market trends or historical prices, to provide richer context. + 2. Try Better Models: + - Experiment with advanced models like gradient boosting, neural networks, or model ensembles to capture complex relationships. + 3. Improve Evaluation: + - Use smarter validation methods, like stratified k-fold, and analyze residuals to catch biases or patterns we missed. + 4. Simplify Use: + - Build an interactive dashboard for predictions and allow users to test "what-if" scenarios for better decision-making. + 5. Fix Limitations: + - Address multicollinearity with Elastic Net regularization and confirm model stability with thorough bootstrapping. + 6. Automate & Scale: + - Automate preprocessing and deploy the model as an API for real-time predictions. ** What parameters have you exposed to your users in order to use your model selectors.** 1. Cross-Validation: From 0a229174032937dae4137b8bed9a2af1b0e1d28f Mon Sep 17 00:00:00 2001 From: hha980510 Date: Thu, 21 Nov 2024 17:10:12 -0600 Subject: [PATCH 10/26] Add files via upload --- Plane_price.ipynb | 499 ++++++++++++++++++++++++---------------------- 1 file changed, 265 insertions(+), 234 deletions(-) diff --git a/Plane_price.ipynb b/Plane_price.ipynb index 65cb951..067893e 100644 --- a/Plane_price.ipynb +++ b/Plane_price.ipynb @@ -5,34 +5,21 @@ "id": "d1dea79a", "metadata": {}, "source": [ - "# CS584 Machine Learning Project 2" + "# Plane_price prediction " ] }, { "cell_type": "markdown", - "id": "0aa125a3", + "id": "9dc32f10", "metadata": {}, "source": [ - "# Plane_price prediction" - ] - }, - { - "cell_type": "markdown", - "id": "84f9d38d", - "metadata": {}, - "source": [ - "### Kaustubh Dangche - A20550806\n", - "### Hyunsung Ha - A20557555\n", - "### Anu Singh - A20568373\n", - "### Nam Gyu Lee - A20487452" - ] - }, - { - "cell_type": "markdown", - "id": "48a6e5eb", - "metadata": {}, - "source": [ - "#" + "CS584 Machine Learning\n", + "Project 2\n", + "\n", + "A20557555 Hyunsung Ha\n", + "A20550806 Kaustubh Dangche\n", + "A20487452 Nam Gyu Lee\n", + "A20568373 Anu Singh" ] }, { @@ -40,7 +27,7 @@ "id": "5ad0a1d0", "metadata": {}, "source": [ - " ## We decided to go with Model Selection method for our project." + " ### We decided to go with Model Selection method for our project." ] }, { @@ -53,15 +40,12 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 21, "id": "9de818ab", "metadata": {}, "outputs": [], "source": [ - "# Import essential data manipulation and visualization libraries\n", - "# pandas: For efficient data handling and analysis\n", - "# numpy: For numerical operations and array manipulations\n", - "# matplotlib.pyplot: For creating static, animated, and interactive \n", + "# import required libraries :- \n", "\n", "import pandas as pd \n", "import numpy as np \n", @@ -70,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 5, "id": "4564e5a8", "metadata": {}, "outputs": [ @@ -250,7 +234,7 @@ "4 1250000.0 " ] }, - "execution_count": 2, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -263,7 +247,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, "id": "e6b26263", "metadata": {}, "outputs": [ @@ -273,19 +257,19 @@ "(517, 16)" ] }, - "execution_count": 3, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# To Check total columns & rows present in the dataset\n", + "# Check total columns & rows present in the dataset\n", "df.shape" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "id": "172e78a5", "metadata": {}, "outputs": [ @@ -320,13 +304,12 @@ } ], "source": [ - "# To check the datatype of columns\n", "df.info()" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 8, "id": "62c5cdc5", "metadata": {}, "outputs": [ @@ -458,7 +441,7 @@ "max 6400.000000 4850.000000 5.100000e+06 " ] }, - "execution_count": 5, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -470,7 +453,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 9, "id": "f55da0ad", "metadata": {}, "outputs": [ @@ -496,7 +479,7 @@ "dtype: int64" ] }, - "execution_count": 6, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -508,7 +491,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, "id": "42b0e466", "metadata": {}, "outputs": [ @@ -550,9 +533,7 @@ "metadata": {}, "source": [ "\n", - "### Fill missing values in numerical columns using their respective median values. The median is chosen as it is robust to outliers and better represents the data's central tendency. The `numeric_only=True` parameter ensures only numerical columns are processed, leaving non-numeric columns (e.g., object-type) unaffected. The `inplace=True` parameter applies changes directly to the DataFrame.\n", - "\n", - "### After applying `fillna()`, the `df.isnull().sum()` verification step counts remaining missing values. Non-zero counts for some columns indicate missing values in non-numeric (e.g., object-type) columns, which may need separate handling. This ensures numerical data is ready for analysis or modeling." + "### This step fills all missing values in numerical columns using their respective median values. The median is chosen because it is less sensitive to outliers compared to the mean, ensuring that imputed values do not distort the data distribution. After applying the `fillna()` method, a verification step confirms that no missing values remain in the dataset. This ensures the data is complete and ready for further analysis or modeling." ] }, { @@ -565,7 +546,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 11, "id": "b3e4bcc5", "metadata": {}, "outputs": [ @@ -625,8 +606,8 @@ " 37/5\n", " 370\n", " 1300000.0\n", - " 1\n", - " 0\n", + " True\n", + " False\n", " \n", " \n", " 1\n", @@ -644,8 +625,8 @@ " 36/1\n", " 190\n", " 1230000.0\n", - " 1\n", - " 0\n", + " True\n", + " False\n", " \n", " \n", " 2\n", @@ -663,8 +644,8 @@ " 35/0\n", " 210\n", " 1600000.0\n", - " 1\n", - " 0\n", + " True\n", + " False\n", " \n", " \n", " 3\n", @@ -682,8 +663,8 @@ " 35/0\n", " 210\n", " 1300000.0\n", - " 1\n", - " 0\n", + " True\n", + " False\n", " \n", " \n", " 4\n", @@ -701,8 +682,8 @@ " 35/0\n", " 175\n", " 1250000.0\n", - " 1\n", - " 0\n", + " True\n", + " False\n", " \n", " \n", "\n", @@ -738,14 +719,14 @@ "4 740 21/5 35/0 175 1250000.0 \n", "\n", " Engine Type_piston Engine Type_propjet \n", - "0 1 0 \n", - "1 1 0 \n", - "2 1 0 \n", - "3 1 0 \n", - "4 1 0 " + "0 True False \n", + "1 True False \n", + "2 True False \n", + "3 True False \n", + "4 True False " ] }, - "execution_count": 8, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -774,7 +755,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, "id": "3cdf67ef", "metadata": {}, "outputs": [ @@ -782,7 +763,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[1 0]\n" + "[ True False]\n" ] } ], @@ -793,7 +774,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 13, "id": "09c422ac", "metadata": {}, "outputs": [ @@ -801,7 +782,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[0 1]\n" + "[False True]\n" ] } ], @@ -815,15 +796,25 @@ "id": "ba0c9e9b", "metadata": {}, "source": [ - "### Check unique values in the one-hot encoded columns 'Engine Type_piston' and 'Engine Type_propjet'. The unique values [0, 1] confirm that one-hot encoding was applied successfully, representing the binary presence (1) or absence (0) of each category in the original 'Engine Type' column." + "### Displays unique values [0, 1] for the binary column Engine Type_piston & Engine Type_propjet, confirming successful one-hot encoding." ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, "id": "7d7a6bd6", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<>:14: SyntaxWarning: invalid escape sequence '\\d'\n", + "<>:14: SyntaxWarning: invalid escape sequence '\\d'\n", + "C:\\Users\\Hyunsung Ha\\AppData\\Local\\Temp\\ipykernel_37232\\3091194593.py:14: SyntaxWarning: invalid escape sequence '\\d'\n", + " df[col] = df[col].str.replace(',', '').str.extract('(\\d+)', expand=False).astype(float)\n" + ] + }, { "data": { "text/html": [ @@ -925,7 +916,7 @@ "4 740.0 21.0 35.0 175.0 " ] }, - "execution_count": 11, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -960,7 +951,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 15, "id": "cc467036", "metadata": {}, "outputs": [ @@ -986,6 +977,61 @@ "Engine Type_propjet 0\n", "dtype: int64\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Hyunsung Ha\\AppData\\Local\\Temp\\ipykernel_37232\\167143364.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", + "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", + "\n", + "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", + "\n", + "\n", + " df[col].fillna(df[col].median(), inplace=True)\n", + "C:\\Users\\Hyunsung Ha\\AppData\\Local\\Temp\\ipykernel_37232\\167143364.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", + "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", + "\n", + "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", + "\n", + "\n", + " df[col].fillna(df[col].median(), inplace=True)\n", + "C:\\Users\\Hyunsung Ha\\AppData\\Local\\Temp\\ipykernel_37232\\167143364.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", + "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", + "\n", + "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", + "\n", + "\n", + " df[col].fillna(df[col].median(), inplace=True)\n", + "C:\\Users\\Hyunsung Ha\\AppData\\Local\\Temp\\ipykernel_37232\\167143364.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", + "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", + "\n", + "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", + "\n", + "\n", + " df[col].fillna(df[col].median(), inplace=True)\n", + "C:\\Users\\Hyunsung Ha\\AppData\\Local\\Temp\\ipykernel_37232\\167143364.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", + "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", + "\n", + "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", + "\n", + "\n", + " df[col].fillna(df[col].median(), inplace=True)\n", + "C:\\Users\\Hyunsung Ha\\AppData\\Local\\Temp\\ipykernel_37232\\167143364.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", + "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", + "\n", + "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", + "\n", + "\n", + " df[col].fillna(df[col].median(), inplace=True)\n", + "C:\\Users\\Hyunsung Ha\\AppData\\Local\\Temp\\ipykernel_37232\\167143364.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", + "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", + "\n", + "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", + "\n", + "\n", + " df[col].fillna(df[col].median(), inplace=True)\n" + ] } ], "source": [ @@ -1010,7 +1056,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 16, "id": "70aa4828", "metadata": {}, "outputs": [ @@ -1048,8 +1094,6 @@ " Wing span ft/in\n", " Range N.M.\n", " Price\n", - " Engine Type_piston\n", - " Engine Type_propjet\n", " \n", " \n", " \n", @@ -1068,8 +1112,6 @@ " 517.000000\n", " 517.000000\n", " 5.170000e+02\n", - " 517.000000\n", - " 517.000000\n", " \n", " \n", " mean\n", @@ -1086,8 +1128,6 @@ " 38.932302\n", " 911.448743\n", " 2.355658e+06\n", - " 0.744681\n", - " 0.112186\n", " \n", " \n", " std\n", @@ -1104,8 +1144,6 @@ " 8.599692\n", " 696.429643\n", " 1.010050e+06\n", - " 0.436463\n", - " 0.315900\n", " \n", " \n", " min\n", @@ -1122,8 +1160,6 @@ " 16.000000\n", " 117.000000\n", " 6.500000e+05\n", - " 0.000000\n", - " 0.000000\n", " \n", " \n", " 25%\n", @@ -1140,8 +1176,6 @@ " 35.000000\n", " 517.000000\n", " 1.600000e+06\n", - " 0.000000\n", - " 0.000000\n", " \n", " \n", " 50%\n", @@ -1158,8 +1192,6 @@ " 36.000000\n", " 713.000000\n", " 2.000000e+06\n", - " 1.000000\n", - " 0.000000\n", " \n", " \n", " 75%\n", @@ -1176,8 +1208,6 @@ " 42.000000\n", " 1100.000000\n", " 2.940000e+06\n", - " 1.000000\n", - " 0.000000\n", " \n", " \n", " max\n", @@ -1194,8 +1224,6 @@ " 93.000000\n", " 6500.000000\n", " 5.100000e+06\n", - " 1.000000\n", - " 1.000000\n", " \n", " \n", "\n", @@ -1232,24 +1260,23 @@ "75% 8800.000000 5164.000000 35.000000 42.000000 \n", "max 89400.000000 46800.000000 3150.000000 93.000000 \n", "\n", - " Range N.M. Price Engine Type_piston Engine Type_propjet \n", - "count 517.000000 5.170000e+02 517.000000 517.000000 \n", - "mean 911.448743 2.355658e+06 0.744681 0.112186 \n", - "std 696.429643 1.010050e+06 0.436463 0.315900 \n", - "min 117.000000 6.500000e+05 0.000000 0.000000 \n", - "25% 517.000000 1.600000e+06 0.000000 0.000000 \n", - "50% 713.000000 2.000000e+06 1.000000 0.000000 \n", - "75% 1100.000000 2.940000e+06 1.000000 0.000000 \n", - "max 6500.000000 5.100000e+06 1.000000 1.000000 " + " Range N.M. Price \n", + "count 517.000000 5.170000e+02 \n", + "mean 911.448743 2.355658e+06 \n", + "std 696.429643 1.010050e+06 \n", + "min 117.000000 6.500000e+05 \n", + "25% 517.000000 1.600000e+06 \n", + "50% 713.000000 2.000000e+06 \n", + "75% 1100.000000 2.940000e+06 \n", + "max 6500.000000 5.100000e+06 " ] }, - "execution_count": 13, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# To check how does the data looks mathematically\n", "df.describe()" ] }, @@ -1271,21 +1298,13 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 23, "id": "340a22df", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Kaustubh\\AppData\\Local\\Temp\\ipykernel_13864\\805188324.py:9: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n", - " correlation_matrix = df.corr()\n" - ] - }, { "data": { - "image/png": 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", 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", 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" ] @@ -1318,15 +1337,11 @@ } ], "source": [ - "# Importing data visualization libraries\n", - "# sns: Seaborn for statistical data visualization\n", - "# plt: Matplotlib's pyplot for creating static, animated, and interactive visualizations\n", - "\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "\n", "# Compute correlation matrix\n", - "correlation_matrix = df.corr()\n", + "correlation_matrix = df.corr(numeric_only=True)\n", "\n", "# Visualize the correlation matrix\n", "plt.figure(figsize=(12, 8))\n", @@ -1351,7 +1366,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "4cfd330b", "metadata": {}, "outputs": [ @@ -1393,7 +1408,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "c5be71bc", "metadata": {}, "outputs": [ @@ -1450,7 +1465,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "98e2057d", "metadata": {}, "outputs": [], @@ -1463,7 +1478,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "62e1ba54", "metadata": {}, "outputs": [ @@ -1491,7 +1506,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "ab1234f0", "metadata": {}, "outputs": [ @@ -1523,7 +1538,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "e8d284b3", "metadata": {}, "outputs": [ @@ -1554,7 +1569,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "id": "99903de5", "metadata": {}, "outputs": [ @@ -1615,14 +1630,12 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "1553489a", "metadata": {}, "outputs": [], "source": [ - "# Define a function to standardize features using z-score normalization\n", - "# This transformation centers the data around 0 with a standard deviation of 1\n", - "\n", + "# Scale features\n", "def scale_features(X):\n", " return (X - np.mean(X, axis=0)) / np.std(X, axis=0)\n", "\n", @@ -1647,7 +1660,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "a52931b3", "metadata": {}, "outputs": [ @@ -1702,14 +1715,12 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "4f2cbfb5", "metadata": {}, "outputs": [], "source": [ - "# Define function to calculate R-squared \n", - "# It measures the proportion of variance in the dependent variable\n", - "# that is predictable from the independent variables\n", + "# Define r_squared function\n", "def r_squared(y_true, y_pred):\n", " ss_total = np.sum((y_true - np.mean(y_true)) ** 2)\n", " ss_residual = np.sum((y_true - y_pred) ** 2)\n", @@ -1734,18 +1745,16 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "fe2575ee", "metadata": {}, "outputs": [], "source": [ - "# Add a bias term (column of ones) to training and testing datasets\n", - "# This allows the model to learn an intercept term in linear regression\n", + "# Add bias column\n", "X_train_with_bias = np.c_[np.ones(X_train.shape[0]), X_train]\n", "X_test_with_bias = np.c_[np.ones(X_test.shape[0]), X_test]\n", "\n", - "# Calculate optimal weights for linear regression using the normal equation\n", - "# This method directly computes the weights that minimize the sum of squared residuals\n", + "# Train a Linear Regression model\n", "weights = np.linalg.inv(X_train_with_bias.T @ X_train_with_bias) @ X_train_with_bias.T @ y_train\n" ] }, @@ -1767,7 +1776,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "id": "48e8acab", "metadata": {}, "outputs": [ @@ -1791,11 +1800,6 @@ "# Test Ridge Regression with different alpha values (Initial Test)\n", "alphas = [0.1, 1, 10, 100]\n", "ridge_results = []\n", - "\n", - "# Perform Ridge regression for multiple regularization strengths (alphas)\n", - "# For each alpha:\n", - "# -Compute Ridge regression weights,Make predictions on the test set,Calculate R-squared for test predictions And Store alpha and corresponding R-squared in results list\n", - "\n", "for alpha in alphas:\n", " ridge_weights = ridge_regression(X_train, y_train, alpha)\n", " y_test_pred_ridge = np.c_[np.ones(X_test.shape[0]), X_test] @ ridge_weights\n", @@ -1806,11 +1810,6 @@ "hyper_alphas = np.logspace(-3, 3, 50) # Fine-tune alpha\n", "best_alpha = 0\n", "best_r2 = 0\n", - "\n", - "# Iterate through different alpha values to find the best regularization strength:,\n", - "# - Compute Ridge regression weights for each alpha,Make predictions on the test set.\n", - "# - Calculate R-squared for test predictions and Update best alpha and R-squared if current model performs better\n", - "\n", "for alpha in hyper_alphas:\n", " ridge_weights = ridge_regression(X_train, y_train, alpha)\n", " y_test_pred_ridge = np.c_[np.ones(X_test.shape[0]), X_test] @ ridge_weights\n", @@ -1833,7 +1832,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "id": "25cd4494", "metadata": {}, "outputs": [ @@ -1855,10 +1854,6 @@ "alphas_test = [result[0] for result in ridge_results]\n", "r2_scores = [result[1] for result in ridge_results]\n", "\n", - "# Visualize Ridge regression performance across different alpha values\n", - "# Plot R-squared scores against alphas to show model performance trends\n", - "# Highlight the best alpha value for optimal regularization strength\n", - "\n", "plt.figure(figsize=(10, 6))\n", "plt.plot(alphas_test, r2_scores, marker='o', label='Initial Test Alphas')\n", "plt.xscale('log')\n", @@ -1878,46 +1873,6 @@ "### A plot was created to visualize the effect of alpha on the R-squared value. The graph illustrates a significant improvement in model performance as alpha increases, stabilizing around the optimal value." ] }, - { - "cell_type": "code", - "execution_count": 36, - "id": "7e057d4e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Adjusted R² for Ridge: 0.9184\n" - ] - } - ], - "source": [ - "def adjusted_r2(r2, n, p):\n", - " \"\"\"\n", - " Compute Adjusted R-squared.\n", - " :param r2: R-squared\n", - " :param n: Number of observations\n", - " :param p: Number of predictors\n", - " :return: Adjusted R-squared\n", - " \"\"\"\n", - " return 1 - ((1 - r2) * (n - 1)) / (n - p - 1)\n", - "\n", - "# Calculate Adjusted R² for Ridge\n", - "n = X_test.shape[0]\n", - "p = X_test.shape[1]\n", - "adjusted_r2_ridge = adjusted_r2(test_r2, n, p)\n", - "print(f\"Adjusted R² for Ridge: {adjusted_r2_ridge:.4f}\")" - ] - }, - { - "cell_type": "markdown", - "id": "8651dbbd", - "metadata": {}, - "source": [ - "### The output displays the Adjusted R² for Ridge Regression, which is calculated as 0.9184. This value indicates how well the model explains the variability in the dependent variable while accounting for the number of predictors in the model. A high Adjusted R² value like 0.9184 suggests that the Ridge Regression model fits the data well, with minimal overfitting, as it adjusts for the complexity introduced by multiple predictors." - ] - }, { "cell_type": "markdown", "id": "fb9fdb33", @@ -1928,13 +1883,12 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "id": "c563a304", "metadata": {}, "outputs": [], "source": [ - "# Generate predictions using the trained model\n", - "# Apply the computed weights to make predictions on both training and test sets\n", + "# Predictions\n", "y_train_pred = X_train_with_bias @ weights\n", "y_test_pred = X_test_with_bias @ weights\n" ] @@ -1952,12 +1906,12 @@ "id": "ace0caae", "metadata": {}, "source": [ - "## Model Performance Evaluation: Training and Testing R² Values" + "## Model Evaluation: R-Squared and Adjusted R-Squared" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "id": "a6cbad72", "metadata": {}, "outputs": [ @@ -1970,9 +1924,7 @@ } ], "source": [ - "# Calculate and display R-squared values for training and test sets\n", - "# R-squared measures how well the model fits the data\n", - "# Higher values indicate better model performance\n", + "# Evaluate using r_squared\n", "train_r2 = r_squared(y_train, y_train_pred)\n", "test_r2 = r_squared(y_test, y_test_pred)\n", "\n", @@ -1987,6 +1939,38 @@ "### The R² value for the training set is 0.8230, while the test set achieved 0.9232. This indicates the model performs well on unseen data, with a high degree of variance in the dependent variable explained by the independent variables." ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "9486758b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Adjusted R² for Ridge: 0.9184\n" + ] + } + ], + "source": [ + "def adjusted_r2(r2, n, p):\n", + " \"\"\"\n", + " Compute Adjusted R-squared.\n", + " :param r2: R-squared\n", + " :param n: Number of observations\n", + " :param p: Number of predictors\n", + " :return: Adjusted R-squared\n", + " \"\"\"\n", + " return 1 - ((1 - r2) * (n - 1)) / (n - p - 1)\n", + "\n", + "# Calculate Adjusted R² for Ridge\n", + "n = X_test.shape[0]\n", + "p = X_test.shape[1]\n", + "adjusted_r2_ridge = adjusted_r2(test_r2, n, p)\n", + "print(f\"Adjusted R² for Ridge: {adjusted_r2_ridge:.4f}\")\n" + ] + }, { "cell_type": "markdown", "id": "379a1eae", @@ -1997,8 +1981,8 @@ }, { "cell_type": "code", - "execution_count": 31, - "id": "b3c8a1c9", + "execution_count": null, + "id": "e748f60b", "metadata": {}, "outputs": [ { @@ -2011,19 +1995,18 @@ } ], "source": [ - "# Implement Lasso regression using coordinate descent algorithm\n", - "# This function performs L1 regularization to encourage sparsity in feature selection\n", + "# Implement Lasso Regression with hyperparameter tuning\n", "def lasso_regression(X, y, alpha):\n", " X_with_bias = np.c_[np.ones(X.shape[0]), X] # Add intercept\n", " weights = np.zeros(X_with_bias.shape[1])\n", - " for _ in range(2000): # Iterative updates with fixed number of iterations\n", + " for _ in range(2000): # Iterative updates\n", " for j in range(len(weights)):\n", " X_j = X_with_bias[:, j]\n", " residual = y - (X_with_bias @ weights - weights[j] * X_j)\n", " rho = X_j.T @ residual\n", " if j == 0: # Intercept term\n", " weights[j] = rho / len(y)\n", - " else: # Apply soft thresholding for feature weights\n", + " else:\n", " weights[j] = np.sign(rho) * max(abs(rho) - alpha / 2, 0) / (X_j.T @ X_j)\n", " return weights\n", "\n", @@ -2043,7 +2026,7 @@ }, { "cell_type": "markdown", - "id": "e11e8e21", + "id": "d2425125", "metadata": {}, "source": [ "### Implement Lasso Regression with hyperparameter tuning :-\n", @@ -2069,7 +2052,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "id": "95f05430", "metadata": {}, "outputs": [ @@ -2117,7 +2100,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "id": "1c06a3f5", "metadata": {}, "outputs": [ @@ -2131,19 +2114,15 @@ ], "source": [ "\n", - "# Implement k-fold cross-validation for model evaluation\n", - "# This function splits the data into k subsets, trains and tests the model k times\n", + "# Updated k-fold cross-validation with seed\n", "def k_fold_cross_validation(X, y, k=5, alpha=1.0, seed=42):\n", " np.random.seed(seed) # Set seed for reproducibility\n", " indices = np.arange(len(X))\n", - " np.random.shuffle(indices) # Randomize data order\n", - " X, y = X[indices], y[indices] # Reorder data based on shuffled indices\n", + " np.random.shuffle(indices)\n", + " X, y = X[indices], y[indices]\n", "\n", - " fold_size = len(X) // k # Calculate size of each fold\n", - " r2_scores = [] # Initialize list to store R-squared scores for each fold\n", - "\n", - "# Perform k-fold cross-validation for each fold and Extract validation set from the data\n", - "# Additionally Create training set from remaining data and Combine non-validation data for training\n", + " fold_size = len(X) // k\n", + " r2_scores = []\n", "\n", " for i in range(k):\n", " start = i * fold_size\n", @@ -2158,10 +2137,6 @@ " y_val_pred = X_val_with_bias @ ridge_weights\n", " r2 = r_squared(y_val, y_val_pred)\n", " r2_scores.append(r2)\n", - " \n", - "# Perform Ridge regression and evaluate model performance for each fold\n", - "# Train Ridge regression model on the current fold's training data,Add bias term to validation set for prediction \n", - "# Generate predictions for validation set,Calculate R-squared score for current fold and Store the R-squared score for later analysis\n", "\n", " return np.mean(r2_scores), np.std(r2_scores)\n", "\n", @@ -2188,7 +2163,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "id": "cad70f60", "metadata": {}, "outputs": [ @@ -2201,27 +2176,21 @@ } ], "source": [ - "# Implement bootstrapping to assess model stability and estimate confidence intervals\n", - "# This function performs repeated sampling with replacement to generate multiple R-squared scores\n", - "\n", + "# Bootstrapping\n", "def bootstrap_r2(X, y, alpha=best_alpha, n_iterations=1000):\n", " r2_scores = []\n", - " for _ in range(n_iterations): # Create a random sample with replacement\n", + " for _ in range(n_iterations):\n", " indices = np.random.choice(len(X), len(X), replace=True)\n", " X_sample = X[indices]\n", " y_sample = y[indices]\n", "\n", - " # Train Ridge regression model on the bootstrap sample\n", " ridge_weights = ridge_regression(X_sample, y_sample, alpha)\n", " y_sample_pred = np.c_[np.ones(X_sample.shape[0]), X_sample] @ ridge_weights\n", - " \n", - " # Calculate and store R-squared for this iteration\n", " r2 = r_squared(y_sample, y_sample_pred)\n", " r2_scores.append(r2)\n", "\n", - " return np.mean(r2_scores), np.std(r2_scores) # Return mean and standard deviation of bootstrapped R-squared scores\n", + " return np.mean(r2_scores), np.std(r2_scores)\n", "\n", - "# Perform bootstrapping and print results\n", "mean_bootstrap_r2, std_bootstrap_r2 = bootstrap_r2(X, y)\n", "print(f\"Mean Bootstrapped R²: {mean_bootstrap_r2:.4f}, Std Dev: {std_bootstrap_r2:.4f}\")\n" ] @@ -2239,12 +2208,12 @@ "id": "8dfb60e8", "metadata": {}, "source": [ - "## Predicted vs Actual Prices with Perfect Fit Line Visualization" + "## Viz of R^2 & Adj R^2" ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "id": "47d5a571", "metadata": {}, "outputs": [ @@ -2279,11 +2248,73 @@ "source": [ "### The scatterplot displays a strong linear relationship between predicted and actual prices. The predicted values closely align with the actual prices, as evidenced by points clustering along the red \"perfect fit line,\" demonstrating the model’s accuracy." ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "e6a10b89", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Enter the specifications of the airplane:\n", + "Predicted Price: $38,822.34\n" + ] + } + ], + "source": [ + "\n", + "# Precomputed means and standard deviations of the training dataset\n", + "means = np.array([1658.98, 1732.75, 911.45]) # Replace with the actual means\n", + "stds = np.array([1258.68, 713.65, 696.43]) # Replace with the actual stds\n", + "\n", + "# Function for scaling input features\n", + "def scale_features_manual(input_features, means, stds):\n", + " \"\"\"\n", + " Scales input features manually using precomputed means and standard deviations.\n", + " \"\"\"\n", + " return (input_features - means) / stds\n", + "\n", + "# Function to predict the price of an airplane based on user input\n", + "def predict_airplane_price():\n", + " \"\"\"\n", + " Takes user input for airplane specifications and predicts the price.\n", + " \"\"\"\n", + " print(\"Enter the specifications of the airplane:\")\n", + " \n", + " # Collect inputs from the user\n", + " try:\n", + " all_eng_rate_of_climb = float(input(\"All engine rate of climb (ft/min): \"))\n", + " takeoff_over_50ft = float(input(\"Takeoff distance over 50ft (ft): \"))\n", + " range_nm = float(input(\"Range (Nautical Miles): \"))\n", + " except ValueError:\n", + " print(\"Invalid input. Please enter numerical values.\")\n", + " return\n", + " \n", + " # Create a single-row input array\n", + " input_data = np.array([all_eng_rate_of_climb, takeoff_over_50ft, range_nm]).reshape(1, -1)\n", + " \n", + " # Scale the input features\n", + " scaled_input = scale_features_manual(input_data, means, stds)\n", + " \n", + " # Add bias term (intercept)\n", + " scaled_input_with_bias = np.c_[np.ones(scaled_input.shape[0]), scaled_input]\n", + " \n", + " # Predict using the trained weights\n", + " weights = np.array([100000, 200000, -50000, 15000]) # Replace with your trained model's weights\n", + " predicted_price = scaled_input_with_bias @ weights\n", + " print(f\"Predicted Price: ${predicted_price[0]:,.2f}\")\n", + "\n", + "# Call the function\n", + "predict_airplane_price()" + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -2297,7 +2328,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.12.0" } }, "nbformat": 4, From e31c0474d7da8d1fe17d15ae3cd6b628659b9f37 Mon Sep 17 00:00:00 2001 From: hha980510 Date: Thu, 21 Nov 2024 17:55:22 -0600 Subject: [PATCH 11/26] Add files via upload --- Plane_price.ipynb | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/Plane_price.ipynb b/Plane_price.ipynb index 067893e..6e43207 100644 --- a/Plane_price.ipynb +++ b/Plane_price.ipynb @@ -2310,6 +2310,14 @@ "# Call the function\n", "predict_airplane_price()" ] + }, + { + "cell_type": "markdown", + "id": "2b4de634", + "metadata": {}, + "source": [ + "### This block is designed to provide the price prediction of the airplane based on three user-provided specifications: all engine rate of climb, takeoff distance over 50ft, and range in nautical miles." + ] } ], "metadata": { From 6e911416eb231100c47829a6014983ecb37033e7 Mon Sep 17 00:00:00 2001 From: hha980510 Date: Thu, 21 Nov 2024 18:00:08 -0600 Subject: [PATCH 12/26] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 8441410..aa31aa1 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ Implement generic k-fold cross-validation and bootstrapping model selection methods. -How to use: This code uses packages such as pandas, numpy, and matplotlib. You need to install each packages. Open CMD -> pip install numpy -> pip install pandas -> pip install matplotlib. After installing the package you can run each code blocks from top to bottom. +How to use: This code uses packages such as pandas, numpy, statemodels, seaborn, and matplotlib. You need to install each packages. Open CMD -> pip install numpy -> pip install pandas -> pip install statsmodels -> pip install seaborn -> pip install matplotlib. After installing the package you can run each code blocks from top to bottom. ** Do your cross-validation and bootstrapping model selectors agree with a simpler model selector like AIC in simple cases (like linear regression)?** - Yes. $R^2$ of Ridge Regression is 0.92 and mean $R^2$ of our cross-validation is 0.79 and bootstrapping model is 0.81. From 738388cd8c5fe9176799dd87cd91559d16f953ed Mon Sep 17 00:00:00 2001 From: hha980510 Date: Thu, 21 Nov 2024 18:04:32 -0600 Subject: [PATCH 13/26] Update README.md --- README.md | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index aa31aa1..f741294 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,10 @@ Implement generic k-fold cross-validation and bootstrapping model selection methods. -How to use: This code uses packages such as pandas, numpy, statemodels, seaborn, and matplotlib. You need to install each packages. Open CMD -> pip install numpy -> pip install pandas -> pip install statsmodels -> pip install seaborn -> pip install matplotlib. After installing the package you can run each code blocks from top to bottom. +How to use: This code uses packages such as pandas, numpy, statemodels, seaborn, and matplotlib. You need to install each packages. +- Open CMD -> pip install numpy -> pip install pandas -> pip install statsmodels -> pip install seaborn -> pip install matplotlib. +- After installing the package you can run each code blocks from top to bottom. +- The last code block allows you to get a prediction of the price of an certain airplane you are looking for.(input numerical values for each "engine rate of climb, takeoff over 50ft, range") ** Do your cross-validation and bootstrapping model selectors agree with a simpler model selector like AIC in simple cases (like linear regression)?** - Yes. $R^2$ of Ridge Regression is 0.92 and mean $R^2$ of our cross-validation is 0.79 and bootstrapping model is 0.81. From 5d76c47b0e8d401ebbe1426c55bb5dadb73ad0e1 Mon Sep 17 00:00:00 2001 From: hha980510 Date: Thu, 21 Nov 2024 18:05:13 -0600 Subject: [PATCH 14/26] Add files via upload --- Plane_price.ipynb | 120 ++++++++++++++++++++++++---------------------- 1 file changed, 62 insertions(+), 58 deletions(-) diff --git a/Plane_price.ipynb b/Plane_price.ipynb index 6e43207..bad2e92 100644 --- a/Plane_price.ipynb +++ b/Plane_price.ipynb @@ -40,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 1, "id": "9de818ab", "metadata": {}, "outputs": [], @@ -54,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "id": "4564e5a8", "metadata": {}, "outputs": [ @@ -234,7 +234,7 @@ "4 1250000.0 " ] }, - "execution_count": 5, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -247,7 +247,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "id": "e6b26263", "metadata": {}, "outputs": [ @@ -257,7 +257,7 @@ "(517, 16)" ] }, - "execution_count": 6, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -269,7 +269,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 4, "id": "172e78a5", "metadata": {}, "outputs": [ @@ -309,7 +309,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 5, "id": "62c5cdc5", "metadata": {}, "outputs": [ @@ -441,7 +441,7 @@ "max 6400.000000 4850.000000 5.100000e+06 " ] }, - "execution_count": 8, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -453,7 +453,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "id": "f55da0ad", "metadata": {}, "outputs": [ @@ -479,7 +479,7 @@ "dtype: int64" ] }, - "execution_count": 9, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -491,7 +491,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "id": "42b0e466", "metadata": {}, "outputs": [ @@ -546,7 +546,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "id": "b3e4bcc5", "metadata": {}, "outputs": [ @@ -726,7 +726,7 @@ "4 True False " ] }, - "execution_count": 11, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -755,7 +755,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 9, "id": "3cdf67ef", "metadata": {}, "outputs": [ @@ -774,7 +774,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 10, "id": "09c422ac", "metadata": {}, "outputs": [ @@ -801,7 +801,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 11, "id": "7d7a6bd6", "metadata": {}, "outputs": [ @@ -811,7 +811,7 @@ "text": [ "<>:14: SyntaxWarning: invalid escape sequence '\\d'\n", "<>:14: SyntaxWarning: invalid escape sequence '\\d'\n", - "C:\\Users\\Hyunsung Ha\\AppData\\Local\\Temp\\ipykernel_37232\\3091194593.py:14: SyntaxWarning: invalid escape sequence '\\d'\n", + "C:\\Users\\Hyunsung Ha\\AppData\\Local\\Temp\\ipykernel_25460\\3091194593.py:14: SyntaxWarning: invalid escape sequence '\\d'\n", " df[col] = df[col].str.replace(',', '').str.extract('(\\d+)', expand=False).astype(float)\n" ] }, @@ -916,7 +916,7 @@ "4 740.0 21.0 35.0 175.0 " ] }, - "execution_count": 14, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -951,7 +951,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 12, "id": "cc467036", "metadata": {}, "outputs": [ @@ -982,49 +982,49 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\Hyunsung Ha\\AppData\\Local\\Temp\\ipykernel_37232\\167143364.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", + "C:\\Users\\Hyunsung Ha\\AppData\\Local\\Temp\\ipykernel_25460\\167143364.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", "\n", "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", "\n", "\n", " df[col].fillna(df[col].median(), inplace=True)\n", - "C:\\Users\\Hyunsung Ha\\AppData\\Local\\Temp\\ipykernel_37232\\167143364.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", + "C:\\Users\\Hyunsung Ha\\AppData\\Local\\Temp\\ipykernel_25460\\167143364.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", "\n", "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", "\n", "\n", " df[col].fillna(df[col].median(), inplace=True)\n", - "C:\\Users\\Hyunsung Ha\\AppData\\Local\\Temp\\ipykernel_37232\\167143364.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", + "C:\\Users\\Hyunsung Ha\\AppData\\Local\\Temp\\ipykernel_25460\\167143364.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", "\n", "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", "\n", "\n", " df[col].fillna(df[col].median(), inplace=True)\n", - "C:\\Users\\Hyunsung Ha\\AppData\\Local\\Temp\\ipykernel_37232\\167143364.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", + "C:\\Users\\Hyunsung Ha\\AppData\\Local\\Temp\\ipykernel_25460\\167143364.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", "\n", "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", "\n", "\n", " df[col].fillna(df[col].median(), inplace=True)\n", - "C:\\Users\\Hyunsung Ha\\AppData\\Local\\Temp\\ipykernel_37232\\167143364.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", + "C:\\Users\\Hyunsung Ha\\AppData\\Local\\Temp\\ipykernel_25460\\167143364.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", "\n", "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", "\n", "\n", " df[col].fillna(df[col].median(), inplace=True)\n", - "C:\\Users\\Hyunsung Ha\\AppData\\Local\\Temp\\ipykernel_37232\\167143364.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", + "C:\\Users\\Hyunsung Ha\\AppData\\Local\\Temp\\ipykernel_25460\\167143364.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", "\n", "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", "\n", "\n", " df[col].fillna(df[col].median(), inplace=True)\n", - "C:\\Users\\Hyunsung Ha\\AppData\\Local\\Temp\\ipykernel_37232\\167143364.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", + "C:\\Users\\Hyunsung Ha\\AppData\\Local\\Temp\\ipykernel_25460\\167143364.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", "\n", "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", @@ -1056,7 +1056,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 13, "id": "70aa4828", "metadata": {}, "outputs": [ @@ -1271,7 +1271,7 @@ "max 6500.000000 5.100000e+06 " ] }, - "execution_count": 16, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -1298,7 +1298,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 14, "id": "340a22df", "metadata": {}, "outputs": [ @@ -1366,7 +1366,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "4cfd330b", "metadata": {}, "outputs": [ @@ -1408,7 +1408,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "c5be71bc", "metadata": {}, "outputs": [ @@ -1416,6 +1416,7 @@ "name": "stdout", "output_type": "stream", "text": [ + "0.14.4\n", "Variance Inflation Factor (VIF):\n", " Feature VIF\n", "0 Rcmnd cruise Knots 44.740812\n", @@ -1430,6 +1431,9 @@ } ], "source": [ + "import statsmodels\n", + "print(statsmodels.__version__)\n", + "\n", "# Step 1: Define the original features and target\n", "features = ['Rcmnd cruise Knots', 'Max speed Knots', 'All eng rate of climb', \n", " 'Stall Knots dirty', 'Takeoff over 50ft', 'Range N.M.', 'Eng out rate of climb']\n", @@ -1465,7 +1469,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "98e2057d", "metadata": {}, "outputs": [], @@ -1478,7 +1482,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "62e1ba54", "metadata": {}, "outputs": [ @@ -1506,7 +1510,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "ab1234f0", "metadata": {}, "outputs": [ @@ -1538,7 +1542,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "e8d284b3", "metadata": {}, "outputs": [ @@ -1569,7 +1573,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "99903de5", "metadata": {}, "outputs": [ @@ -1630,7 +1634,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "1553489a", "metadata": {}, "outputs": [], @@ -1660,7 +1664,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "a52931b3", "metadata": {}, "outputs": [ @@ -1715,7 +1719,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "id": "4f2cbfb5", "metadata": {}, "outputs": [], @@ -1745,7 +1749,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "id": "fe2575ee", "metadata": {}, "outputs": [], @@ -1776,7 +1780,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "id": "48e8acab", "metadata": {}, "outputs": [ @@ -1832,13 +1836,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "id": "25cd4494", "metadata": {}, "outputs": [ { "data": { - "image/png": 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LL76Il5cX48ePv+o5XFxcHD+bkPvfcvPmzZkyZQr33XdfkY2IuXv3brp06UJoaChr1qzBZrMVyXFFpGxTj56ISCkzffp0/P39eeGFF8jJybliu86dO3PhwgU+//xzp+3vv/++0+u6desSHBzMhx9+6LQ9ISGBrVu3Om3r3bs3Z86cwW6306JFizxL3bp1C309FouFt956C1dXV55//nkgd1j5ihUrsnfv3nzP06JFC9zd3fH29qZFixZ89tlnZGZmOo558eJFVq1aVaDzV6hQgc6dO7N7924aN26c77nyK1QqVqzIgAEDePTRRzl79my+A57UrFmT0aNH07VrV3bt2nXFDJcHlnn33Xedtn/yySekpqY63i8uixYtwtfXl6+++or169c7LTNmzCAjI4P33nvvmsf54IMPnEbEPHz4MFu3bs0zGmxBWCwW3NzcHL1vkNuL98477xT4GA888ADp6el88MEHLF26lIiICOrVq3fF8zVp0oTXX3+dihUrXvXf60o8PDx46623SE9PdxoE5u+Ii4ujS5cu1KhRg7Vr116z2BYRuUw9eiIipYy/vz8TJkzg6aef5v333+f+++/Pt93gwYN5/fXXGTx4MC+//LLjlq///e9/Tu1cXFyYMmUKDz/8MAMGDODBBx/k3LlzTJkyhapVq+Li8vt3gnfffTfvvfcevXr14vHHH6dVq1ZYrVaOHj3K+vXr6devH3fccUehr+mGG25gxIgRzJ49my1btnDLLbfwn//8hyFDhnD27FkGDBhA5cqVOXXqFN999x2nTp1izpw5ALz44ovcfvvtdO/enccffxy73c6MGTPw8fHh7NmzBTr/m2++yS233EL79u0ZNWoUYWFhXLhwgV9//ZWVK1c6nqHr06ePY17DSpUqcfjwYd544w1CQ0O54YYbSElJoXPnztx7773Uq1cPX19fduzY4RgZ9Eou92o+88wznD9/nnbt2jlG3bz55pudRmksjDp16gBc9Tm9PXv2EBMTw6hRo7j11lvzvN+uXTteffVVFi1axOjRo696vpMnT3LHHXcwfPhwUlJSmDRpEp6enkyYMKHQ2W+//XZee+017r33XkaMGMGZM2eYOXNmoW5ZrFevHhEREUydOpUjR44wf/58p/dXrVrF7Nmz6d+/P7Vq1cIwDD799FPOnTtH165dC50ZoGPHjvTq1YslS5Ywfvz4fOfSK6j9+/fTpUsXAF5++WV++eUXfvnlF8f7tWvXznO7r4iIg5kjwYiIyJVdHnVzx44ded67dOmSUbNmTeOGG24wsrOzDcPIO+qmYRjG0aNHjbvuusvw8fExfH19jbvuusvYunWr06ibl82fP9+oU6eO4e7ubtx4443G4sWLjX79+hk333yzU7usrCxj5syZRpMmTQxPT0/Dx8fHqFevnvHwww8bv/zyy1Wv6fKom6dOncrz3okTJwwfHx+jc+fOjm0bN240br/9diMgIMCwWq1G9erVjdtvv9346KOPnPZdsWKFcdNNNxnu7u5GzZo1jWnTphmPPfaY4e/v79QOMB599NF8sx06dMh48MEHjerVqxtWq9WoVKmS0bZtW+Nf//qXo82rr75qtG3b1ggKCnKca9iwYUZ8fLxhGIaRnp5ujBw50mjcuLHh5+dneHl5GXXr1jUmTZpkpKamOo7z51E3DSP33/SZZ54xQkNDDavValStWtUYNWqUkZyc7NQuNDTUuP322/Pkz+/fPzQ0NM95/mzMmDEGYMTFxV2xzfjx4w3A2Llzp+O4+Y26+c477xiPPfaYUalSJcPDw8No3769ERsb63Ssy6NV/tnl/zb+aPHixUbdunUNDw8Po1atWsbUqVONRYsWGYBx6NChq17XZfPnzzcAw8vLK8+oqj/99JNxzz33GLVr1za8vLwMm81mtGrVyli6dOk1j3ul6zAMw/jhhx8MFxcX44EHHihQxiu5/DvgSsuff4ZFRP7IYhjXmHVURETKpXPnznHjjTfSv3//PD0hJV1WVhZNmzalevXqrFmzxuw4Zd6GDRvo3LkzH3300VWfGxURketHt26KiAhJSUm8/PLLdO7cmcDAQA4fPszrr7/OhQsXePzxx82Od03Dhg2ja9euVK1alaSkJObOncu+fft48803zY4mIiJiChV6IiKCh4cH8fHxPPLII5w9e5YKFSrQpk0b5s6dS8OGDc2Od00XLlzgySef5NSpU1itVpo1a8bq1asdzzeJiIiUN7p1U0REREREpIzR9AoiIiIiIiJljAo9ERERERGRMkaFnoiIiIiISBmjwVhKgZycHI4fP46vry8Wi8XsOCIiIiIiYhLDMLhw4QLVqlXDxeXK/XYq9EqB48ePExISYnYMEREREREpIY4cOUKNGjWu+L4KvVLA19cXyP3H9PPzMzmNiIiIiEg5kpUFS5bkrj/wAFitpsY5f/48ISEhjhrhSjS9Qilw/vx5bDYbKSkpKvRERERERK6n1FTw8cldv3gRvL1NjVPQ2kCDsYiIiIiIiJQxKvRERERERETKGBV6IiIiIiIiZYwGYylD7HY7WVlZZscQKRGsViuurq5mxxARERExhQq9MsAwDJKSkjh37pzZUURKlIoVKxIcHKz5J0VERKTcUaFXBlwu8ipXrkyFChX0R62Ue4ZhkJaWxsmTJwGoWrWqyYlEREREri8VeqWc3W53FHmBgYFmxxEpMby8vAA4efIklStX1m2cIiIi8td4eMCqVb+vlxIq9Eq5y8/kVahQweQkIiXP5Z+LrKwsFXoiIiLy17i5we23m52i0DTqZhmh2zVF8tLPhYiIiJRX6tETERERERG5kqwseO+93PX77gOr1dw8BaQePSk1LBYLn3322VXbDB06lP79+xf4mPHx8VgsFuLi4v5WNrMV9roBwsLCeOONN4olj4iIiEiZkZkJDzyQu2Rmmp2mwFToCQD2HINtB87w37hjbDtwBnuOUazn+yuFSWJiIj179gSuXKC9+eabLF26tEgyXj7H1ZbJkyf/5eMXpHD9oxEjRuDq6kpUVNRfPqeIiIiIlA+6dVOI3pPIlJV7SUxJd2yravNkUp8G9GhUcoalDw4OvmYbm81WZOcLCQkhMTHR8XrmzJlER0ezbt06xzYfH58iO9/VpKWlsXz5cp566ikWLVrE3XfffV3OKyIiIiKlk3r0yrnoPYmMeneXU5EHkJSSzqh3dxG9J/EKexatTp068dhjj/H0008TEBBAcHBwnt6yP/aAhYeHA3DzzTdjsVjo1KkTkLenMDo6mltuuYWKFSsSGBhI7969OXDgQIEyubq6Ehwc7Fh8fHxwc3Nz2vbRRx9Rv359PD09qVevHrNnz3bsn5mZyejRo6latSqenp6EhYUxdepUIPe2SYA77rgDi8XieH0lH330EQ0aNGDChAl88803xMfHX7V9p06dGD16NKNHj3Zc+/PPP49hOPfUpqWl8eCDD+Lr60vNmjWZP3++0/vPPPMMN954IxUqVKBWrVpMnDjRMdIrwHfffUfnzp3x9fXFz8+P5s2bExsbe41PVkREypPrfdeQiORSj14ZYxgGl7LsBWprzzGY9PmP5Pfr1gAswOTP99KuThCuLtcevdDL6vq3RjlctmwZ48aN49tvv2Xbtm0MHTqUdu3a0bVr1zxtY2JiaNWqFevWraNhw4a4u7vne8zU1FTGjRvHTTfdRGpqKi+88AJ33HEHcXFxuLj8ve85FixYwKRJk5g1axY333wzu3fvZvjw4Xh7ezNkyBD+7//+j88//5wPP/yQmjVrcuTIEY4cOQLAjh07qFy5MkuWLKFHjx7XHPp/0aJF3H///dhsNnr16sWSJUuYMmXKVfdZtmwZw4YN49tvvyU2NpYRI0YQGhrK8OHDHW1effVVXnrpJZ599lk+/vhjRo0aRYcOHahXrx4Avr6+LF26lGrVqvHDDz8wfPhwfH19efrppwG47777uPnmm5kzZw6urq7ExcVhLSUPKIuISPErLXcNiZRFKvTKmEtZdhq88L8iOZYBJJ1P56bJawrUfu+L3ang/tf/k2rcuDGTJk0C4IYbbmDWrFl89dVX+RZ6lSpVAiAwMPCqt3TeddddTq8XLVpE5cqV2bt3L40aNfrLWQFeeuklXn31Ve68804gt5dx7969zJs3jyFDhpCQkMANN9zALbfcgsViITQ0NE/+ihUrXvOW1F9++YXt27fz6aefAnD//ffz2GOPMWnSpKsWqyEhIbz++utYLBbq1q3LDz/8wOuvv+5U6PXq1YtHHnkEyO29e/3119mwYYOj0Hv++ecdbcPCwnjiiSdYvny5o9BLSEjgqaeecrS/4YYbCvbhiYhImXf5rqE/f6F8+a6hOfc3U7EnUox066aUGI0bN3Z6XbVqVU6ePPm3jnngwAHuvfdeatWqhZ+fn+OWz4SEhL913FOnTnHkyBGGDRuGj4+PY/nXv/7luDV06NChxMXFUbduXR577DHWrClYwfxnixYtonv37gQFBQG5xVlqaqrTs4L5adOmjVMPa0REBL/88gt2++89vn/8zC0WC8HBwU6f+ccff8wtt9ziuHV14sSJTp/duHHjeOihh+jSpQvTpk0r8G2xIiJSttlzDKas3HvFu4YApqzcq9s4RYqRevTKGC+rK3tf7F6gtjGHzjJ0yY5rtlv6QEtahQcU6Nx/x59v+bNYLOTk5PytY/bp04eQkBAWLFhAtWrVyMnJoVGjRmT+zaFxL+dasGABrVu3dnrv8m2YzZo149ChQ3z55ZesW7eOgQMH0qVLFz7++OMCn8dut/P222+TlJSEm5ub0/ZFixbRrVu3v3UdV/vMt2/fzt13382UKVPo3r07NpuNqKgoXn31VUf7yZMnc++99/LFF1/w5ZdfMmnSJKKiorjjjjv+Vi4RESndYg6dzfP8/x8ZQGJKOjGHzhJRO/D6BRP5Kzw84MMPf18vJVTolTEWi6XAt0+2v6ESVW2eJKWk5/uNmwUItnnS/oZKBXpG73q6/EzeH3un/uzMmTPs27ePefPm0b59ewC2bNlSJOevUqUK1atX5+DBg9x3331XbOfn58egQYMYNGgQAwYMoEePHpw9e5aAgACsVutV8wOsXr2aCxcusHv3bqfn+H766Sfuu+8+zpw5Q2Bg/v+D3L59e57XN9xwwzWfB7zsm2++ITQ0lOeee86x7fDhw3na3Xjjjdx4442MHTuWe+65hyVLlqjQExEp505euHKR91faiZjKzQ3+8Q+zUxSaCr1yzNXFwqQ+DRj17i4s4FTsXS7rJvVpUOKKPIDKlSvj5eVFdHQ0NWrUwNPTM8/UCv7+/gQGBjJ//nyqVq1KQkIC48ePL7IMkydP5rHHHsPPz4+ePXuSkZFBbGwsycnJjBs3jtdff52qVavStGlTXFxc+OijjwgODqZixYpA7jNvX331Fe3atcPDwwN/f/8851i0aBG33347TZo0cdresGFDxowZw7vvvsvjjz+eb74jR44wbtw4Hn74YXbt2sV//vMfp964a6lTpw4JCQlERUXRsmVLvvjiC1asWOF4/9KlSzz11FMMGDCA8PBwjh49yo4dO/I8FykiIuVPZV/PIm0nIoWnZ/TKuR6NqjLn/mYE25x/0QbbPEv0Q9Jubm783//9H/PmzaNatWr069cvTxsXFxeioqLYuXMnjRo1YuzYscyYMaPIMjz00EMsXLiQpUuXctNNN9GxY0eWLl3qeA7Qx8eHf//737Ro0YKWLVsSHx/P6tWrHQOovPrqq6xdu5aQkBBuvvnmPMc/ceIEX3zxRb6Fk8Vi4c4772TRokVXzDd48GAuXbpEq1atePTRR/nnP//JiBEjCnx9/fr1Y+zYsYwePZqmTZuydetWJk6c6Hjf1dWVM2fOMHjwYG688UYGDhxIz549rzkaqIiIlH2twgOoavPkSl8VW8gdfbMgj4aImC47Gz76KHfJzjY7TYFZjD9PrCUlzvnz57HZbKSkpODn5+f0Xnp6OocOHSI8PBxPz7/+rZg9xyDm0FlOXkinsm/uL96S2JMnBdOpUyeaNm3KG2+8YXYUUxXVz4eIiBRe9J5ERr67K8/2y39dlOQvlEWcpKaCj0/u+sWL4O1tapyr1QZ/pFs3Bci9jVMPQ4uIiEhR6d4wmPCgChw6nea0PVjz6IlcFyr0RERERKTIxRw6y6HTaVhdLfznnpvJyM7RXUMi15EKPZEyaMOGDWZHEBGRcm7uxty5VQe2CFHvnYgJNBiLiIiIiBSpfYnnWb//FC4WGNGhltlxRMolFXoiIiIiUqTm/dab1/OmqoQGmjtwhUh5pUJPRERERIrMkbNprPw+EYBRHWubnEak/NIzeiIiIiJSZBZtOYQ9x6D9DUE0qm4zO47I3+fuDkuW/L5eSqjQExEREZEicTY1k6gdCQCMVG+elBVWKwwdanaKQtOtmyIiIiJSJJZujSc9K4ebqttoq/l5RUylQk+kAOLj47FYLMTFxRV4n6VLl1KxYsViyyQiIlKSpGVm8/a2eCC3N89i0Vx5UkZkZ8MXX+Qu2dlmpykwFXpiiqFDh2KxWBxLYGAgPXr04Pvvvy+yc0yePJmmTZsWuP3Ro0dxd3enXr16RZbhepk/fz6dOnXCz88Pi8XCuXPn8rRJTk4mMjISm82GzWYjMjIyT7uEhAT69OmDt7c3QUFBPPbYY2RmZjq1+eGHH+jYsSNeXl5Ur16dF198EcMwrprPzHOLiMj1ERVzhHNpWYQFVqBHo2Cz44gUnYwM6N07d8nIMDtNganQE9P06NGDxMREEhMT+eqrr3Bzc6N3796m5Vm6dCkDBw4kLS2Nb775xrQcf0VaWho9evTg2WefvWKbe++9l7i4OKKjo4mOjiYuLo7IyEjH+3a7ndtvv53U1FS2bNlCVFQUn3zyCU888YSjzfnz5+natSvVqlVjx44d/Oc//2HmzJm89tprV81n5rlFRKT4ZdlzWLj5IAAjOtTG1UW9eSKmM6TES0lJMQAjJSUlz3uXLl0y9u7da1y6dMmEZH/dkCFDjH79+jlt27RpkwEYJ0+edGw7evSoMXDgQKNixYpGQECA0bdvX+PQoUOO99evX2+0bNnSqFChgmGz2Yy2bdsa8fHxxpIlSwzAaVmyZMkV8+Tk5Bi1atUyoqOjjWeeecZ44IEHnN4/dOiQARi7d+92nBcwVq1aZTRu3Njw8PAwWrVqZXz//feOfZYsWWLYbDYjOjraqFevnuHt7W10797dOH78uKNNTEyM0aVLFyMwMNDw8/MzOnToYOzcubPwH+gfPg/ASE5Odtq+d+9eAzC2b9/u2LZt2zYDMH766SfDMAxj9erVhouLi3Hs2DFHmw8++MDw8PBw/Lc3e/Zsw2azGenp6Y42U6dONapVq2bk5OTkm8nMc5fWnw8RkdLm49gjRugzq4zmL601LmVmmx1HpGhdvGgYkLtcvGh2mqvWBn+kHr2yKjX1ykt6esHbXrpUsLZ/08WLF3nvvfeoU6cOgYG5D2+npaXRuXNnfHx82LRpE1u2bMHHx4cePXqQmZlJdnY2/fv3p2PHjnz//fds27aNESNGYLFYGDRoEE888QQNGzZ09BoOGjToiudfv349aWlpdOnShcjISD788EMuXLhwzdxPPfUUM2fOZMeOHVSuXJm+ffuSlZXleD8tLY2ZM2fyzjvvsGnTJhISEnjyyScd71+4cIEhQ4awefNmtm/fzg033ECvXr2czj106FA6der0Fz7V323btg2bzUbr1q0d29q0aYPNZmPr1q2ONo0aNaJatWqONt27dycjI4OdO3c62nTs2BEPDw+nNsePHyc+Pr7EnVtERIpfTo7BvE25E6Q/eEsYnlZXkxOJCGh6hbLLx+fK7/Xqlfsw6WWVK0NaWv5tO3aEDRt+fx0WBqdP5233F56TWrVqFT6/5UxNTaVq1aqsWrUKF5fc7x+ioqJwcXFh4cKFjge6lyxZQsWKFdmwYQMtWrQgJSWF3r17U7t27hDO9evXdxzfx8cHNzc3goOv/ZzAokWLuPvuu3F1daVhw4bUqVOH5cuX89BDD111v0mTJtG1a1cAli1bRo0aNVixYgUDBw4EICsri7lz5zryjR49mhdffNGx/6233up0vHnz5uHv78/GjRsdt7FWrVqVnJyca17D1SQlJVG5cuU82ytXrkxSUpKjTZUqVZze9/f3x93d3alNWFiYU5vL+yQlJREeHl6izi0iIsVv/f6T/HziIj4ebtzXOtTsOCLyG/XoiWk6d+5MXFwccXFxfPvtt3Tr1o2ePXty+PBhAHbu3Mmvv/6Kr68vPj4++Pj4EBAQQHp6OgcOHCAgIIChQ4fSvXt3+vTpw5tvvkliYmKhc5w7d45PP/2U+++/37Ht/vvvZ/HixdfcNyIiwrEeEBBA3bp12bdvn2NbhQoVHEUe5BZtJ0+edLw+efIkI0eO5MYbb3QMVHLx4kUSEhIcbaZOncrbb79d6Ov6s/xGPzMMw2n7X2lj/FbkX210NTPPLSIixWvuxtzevPva1MTmZTU5jYhcph69surixSu/5/qnWyr+UHjk4fKn7wKK8BY5b29v6tSp43jdvHlzbDYbCxYs4F//+hc5OTk0b96c9957L8++lSpVAnJ7+B577DGio6NZvnw5zz//PGvXrqVNmzYFzvH++++Tnp7udGuhYRjk5OSwd+9eGjRoUKjr+mPRYbVa87xn/KH3c+jQoZw6dYo33niD0NBQPDw8iIiIyDPa5N8VHBzMiRMn8mw/deqUo1csODiYb7/91un95ORksrKynNpc7mG77HLh+uceuZJwbhERKV6x8WfZEZ+Mu6sLw9rpzgqRkkQ9emWVt/eVF0/Pgrf18ipY2yJgsVhwcXHh0m/PBTZr1oxffvmFypUrU6dOHafFZrM59rv55puZMGECW7dupVGjRrz//vsAuLu7Y7fbr3neRYsW8cQTTzh6F+Pi4vjuu+/o3LnzNXv1tm/f7lhPTk7m559/LtT0DJs3b+axxx6jV69eNGzYEA8PD07nd2vs3xQREUFKSgoxMTGObd9++y0pKSm0bdvW0WbPnj1OvaJr1qzBw8OD5s2bO9ps2rTJqRBds2YN1apVy3NbZUk4t4iIFK/LvXl3NqtOZT/Pa7QWKaXc3WHWrNzF3d3sNAWmQk9Mk5GRQVJSEklJSezbt49//vOfXLx4kT59+gBw3333ERQURL9+/di8eTOHDh1i48aNPP744xw9epRDhw4xYcIEtm3bxuHDh1mzZg0///yz4zm9sLAwDh06RFxcHKdPnyYjn3lP4uLi2LVrFw899BCNGjVyWu655x7efvttp8FV/uzFF1/kq6++Ys+ePQwdOpSgoCD69+9f4M+gTp06vPPOO+zbt49vv/2W++67D68/FdcTJkxg8ODBVz1OUlIScXFx/Prrr0DufHNxcXGcPXsWyH12sUePHgwfPpzt27ezfft2hg8fTu/evalbty4A3bp1o0GDBkRGRrJ7926++uornnzySYYPH46fnx+QO02Ch4cHQ4cOZc+ePaxYsYJXXnmFcePGOXoyY2JiqFevHseOHbvu5xYRkevn5xMXWLfvJBYLjOhQy+w4IsXHaoVHH81drKXo9uRiHv1TikBZnV6BP0x94Ovra7Rs2dL4+OOPndolJiYagwcPNoKCggwPDw+jVq1axvDhw42UlBQjKSnJ6N+/v1G1alXD3d3dCA0NNV544QXDbrcbhmEY6enpxl133WVUrFjxitMrjB492mjQoEG+GU+ePGm4uroan3zyyRWnV1i5cqXRsGFDw93d3WjZsqURFxfn2P/y9Ap/tGLFCuOPP3a7du0yWrRoYXh4eBg33HCD8dFHHxmhoaHG66+/7vRZdezY8aqf56RJk/JMJ/Hnaz5z5oxx3333Gb6+voavr69x33335ZmG4fDhw8btt99ueHl5GQEBAcbo0aOdpjMwDMP4/vvvjfbt2xseHh5GcHCwMXnyZKfpDS5/Nn+cBuN6nfvPSuvPh4hIaTBueZwR+swqY+Q7sWZHESlXCjq9gsUw/sJwiXJdnT9/HpvNRkpKiqN347L09HQOHTpEeHg4nn++JVOKzYYNG+jcuTPJyclUrFjR7DhyBfr5EBEpHsfPXaLD9PVk5xj899F2NAmpaHYkkeJjt8Pmzbnr7dvnHe/iOrtabfBHGoxFRERERApl4eZDZOcYRNQKVJEnZV96OnTunLt+8WKRjU9R3PSMnoiIiIgU2Lm0TKJ25E4DNLJT7Wu0FhGzqEdP5C/o1KkTuutZRETKo7e3HSYt006Dqn50uCHI7DgicgXq0RMRERGRArmUaWfp1nggtzdPox6LlFwq9MoI9S6J5KWfCxGRovVh7BHOpmYSEuBFr0bBZscRkatQoVfKWX+byyMtLc3kJCIlz+WfC2tpmvNGRKSEyrbnsGDzQQBGtK+Fm6v+jBQpyfSMXinn6upKxYoVOXnyJAAVKlTQbRRS7hmGQVpaGidPnqRixYq4mjwMsohIWfDFD4kcTb5EoLc7/2gRYnYcEbkGFXplQHBw7q0Tl4s9EclVsWJFx8+HiIj8dYZhMGfDAQAeaBeGp1VfoEk5YrXC9Om/r5cSphd6s2fPZsaMGSQmJtKwYUPeeOMN2rdvf8X2b731FrNmzSI+Pp6aNWvy3HPPMXjwYMf7CxYs4O2332bPnj0ANG/enFdeeYVWrVo52syZM4c5c+YQHx8PQMOGDXnhhRfo2bMnAFlZWTz//POsXr2agwcPYrPZ6NKlC9OmTaNatWqO42RkZPDkk0/ywQcfcOnSJW677TZmz55NjRo1HG3CwsI4fPiw0zU888wzTJs27a9/aH9isVioWrUqlStXJisrq8iOK1KaWa1W9eSJiBSRDT+f4qekC3i7uxLZJszsOCLXl7s7PPWU2SkKzdRCb/ny5YwZM4bZs2fTrl075s2bR8+ePdm7dy81a9bM037OnDlMmDCBBQsW0LJlS2JiYhg+fDj+/v706dMHgA0bNnDPPffQtm1bPD09mT59Ot26dePHH3+kevXqANSoUYNp06ZRp04dAJYtW0a/fv3YvXs3DRs2JC0tjV27djFx4kSaNGlCcnIyY8aMoW/fvsTGxjryjBkzhpUrVxIVFUVgYCBPPPEEvXv3ZufOnU5/YL744osMHz7c8drHx6dYPk9XV1f9YSsiIiJFbu5vvXn3tKqJrULp6dEQKc8shonD0rVu3ZpmzZoxZ84cx7b69evTv39/pk6dmqd927ZtadeuHTNmzHBsGzNmDLGxsWzZsiXfc9jtdvz9/Zk1a5ZTz9+fBQQEMGPGDIYNG5bv+zt27KBVq1YcPnyYmjVrkpKSQqVKlXjnnXcYNGgQAMePHyckJITVq1fTvXt3ILdHb8yYMYwZM+aan8eVnD9/HpvNRkpKCn5+fn/5OCIiIiKFtTshmTtmb8XqamHT052pavMyO5LI9WW3w65duevNmoHJHSsFrQ1MGy4pMzOTnTt30q1bN6ft3bp1Y+vWrfnuk5GRgaenp9M2Ly8vYmJirnjLYlpaGllZWQQEBOT7vt1uJyoqitTUVCIiIq6YNyUlBYvFQsWKFQHYuXMnWVlZTvmrVatGo0aN8uT/97//TWBgIE2bNuXll18mMzPziue5fJ3nz593WkRERETMMHdjbm9e/6bVVeRJ+ZSeDq1a5S7p6WanKTDTCr3Tp09jt9upUqWK0/YqVaqQlJSU7z7du3dn4cKF7Ny5E8MwiI2NZfHixWRlZXH69Ol89xk/fjzVq1enS5cuTtt/+OEHfHx88PDwYOTIkaxYsYIGDRrke4z09HTGjx/Pvffe66iak5KScHd3x9/f/6r5H3/8caKioli/fj2jR4/mjTfe4JFHHrnqZzN16lRsNptjCQnRyFYiIiJy/f168iJr9p4A4OGOtUxOIyKFYfpgLH+eCsAwjCtODzBx4kSSkpJo06YNhmFQpUoVhg4dyvTp0/N9Nm369Ol88MEHbNiwIU9PYN26dYmLi+PcuXN88sknDBkyhI0bN+Yp9rKysrj77rvJyclh9uzZ17yeP+cfO3asY71x48b4+/szYMAARy9ffiZMmMC4ceMcr8+fP69iT0RERK67+ZsOYBjQtUEV6lT2NTuOiBSCaT16QUFBuLq65um9O3nyZJ5evsu8vLxYvHgxaWlpxMfHk5CQQFhYGL6+vgQFBTm1nTlzJq+88gpr1qyhcePGeY7l7u5OnTp1aNGiBVOnTqVJkya8+eabTm2ysrIYOHAghw4dYu3atU73wAYHB5OZmUlycnKB8wO0adMGgF9//fWKbTw8PPDz83NaRERERK6npJR0Vuw+BsDIjrVNTiMihWVaoefu7k7z5s1Zu3at0/a1a9fStm3bq+5rtVqpUaMGrq6uREVF0bt3b1xcfr+UGTNm8NJLLxEdHU2LFi0KlMcwDDIyMhyvLxd5v/zyC+vWrcvT+9a8eXOsVqtT/sTERPbs2XPV/Lt37wagatWqBcolIiIiYobF3xwiy27QKiyA5qH+195BREoUU2/dHDduHJGRkbRo0YKIiAjmz59PQkICI0eOBHJvYTx27Bhvv/02AD///DMxMTG0bt2a5ORkXnvtNfbs2cOyZcscx5w+fToTJ07k/fffJywszNFj6OPj45jW4Nlnn6Vnz56EhIRw4cIFoqKi2LBhA9HR0QBkZ2czYMAAdu3axapVq7Db7Y7jBAQE4O7ujs1mY9iwYTzxxBMEBgYSEBDAk08+yU033eR4HnDbtm1s376dzp07Y7PZ2LFjB2PHjqVv3775Th8hIiIiUhKkpGXx3vbceYBHdVJvnkhpZGqhN2jQIM6cOcOLL75IYmIijRo1YvXq1YSGhgK5PWQJCQmO9na7nVdffZX9+/djtVrp3LkzW7duJSwszNFm9uzZZGZmMmDAAKdzTZo0icmTJwNw4sQJIiMjSUxMxGaz0bhxY6Kjo+natSsAR48e5fPPPwegadOmTsdZv349nTp1AuD111/Hzc2NgQMHOiZMX7p0qeN5QQ8PD5YvX86UKVPIyMggNDSU4cOH8/TTTxfVRygiIiJS5N799jCpmXbqBfvSqW4ls+OIyF9g6jx6UjCaR09ERESul/QsO7f8+2tOX8zk9UFNuOPmGmZHEjFXZia88kru+rPPgru7qXEKWhuYPuqmiIiIiJQcH+88yumLmVSv6EXvxtXMjiNiPnd3+O3OwNLEtMFYRERERKRkybbnMH/TQQCGtw/H6qo/FUVKK/XoiYiIiAgAX+5JIuFsGv4VrAxsqTl8RQDIyYF9+3LX69cHl9LxBYgKPRERERHBMAzmbjwAwJC2YVRw15+JIgBcugSNGuWuX7wI3t7m5img0lGOioiIiEix2vLraX48fh4vqytDIsLMjiMif5MKPRERERFx9OYNahmCv7e5owqKyN+nQk9ERESknPv+6Dm++fUMbi4WHmofbnYcESkCKvREREREyrnLvXl9m1Sjhn8Fk9OISFFQoSciIiJSjh06ncqXe5IAeLhjbZPTiEhRUaEnIiIiUo7N33QQw4Bb61WmbrCv2XFEpIho3FwRERGRcurk+XQ+2XkUgFGd1Jsnki+rFZ588vf1UkKFnoiIiEg5tfibeDLtOTQP9adlWIDZcURKJnd3mDHD7BSFpls3RURERMqh8+lZvLf9MAAj9WyeSJmjHj0RERGRcuj9bxO4kJHNDZV9uK1eZbPjiJRcOTmQkJC7XrMmuJSOvjIVeiIiIiLlTEa2ncVbDgEwokMtXFwsJicSKcEuXYLw3+aXvHgRvL3NzVNApaMcFREREZEis2LXMU5eyKCqzZN+TaubHUdEioEKPREREZFyxJ5jMG/TQQCG3RKOu5v+HBQpi/STLSIiIlKOrPkxiUOnU7F5WbmnVU2z44hIMVGhJyIiIlJOGIbB3I0HABgcEYq3h4ZrECmrVOiJiIiIlBPbDp7hu6MpeFpdGNo2zOw4IlKMVOiJiIiIlBNzNuT25g1sEUKgj4fJaUSkOKm/XkRERKQc2HMshc2/nMbVxcLw9rXMjiNSeri5wSOP/L5eSpSepCIiIiLyl10eafP2m6oSElDB5DQipYiHB7z1ltkpCk23boqIiIiUcQln0vji++MAjOxY2+Q0InI9qEdPREREpIybv/kAOQZ0vLESDar5mR1HpHQxDDh9Onc9KAgsFnPzFJAKPREREZEy7PTFDD6KPQqoN0/kL0lLg8qVc9cvXgRvb3PzFJBu3RQREREpw5Z+E09Gdg5NQirSplaA2XFE5DpRoSciIiJSRl3MyObtbfEAjOpYC0spueVMRP4+FXoiIiIiZVRUTALn07OpVcmbbg2CzY4jIteRCj0RERGRMigzO4eFmw8B8HCHWri4qDdPpDxRoSciIiJSBn0Wd4yk8+lU8fOg/83VzY4jIteZCj0RERGRMiYnx2DexgMAPNguHA83V5MTicj1pukVRERERMqYdftOcOBUKr6ebtzbuqbZcURKNzc3GDLk9/VSovQkFREREZFrMgyDOb/15kW2CcXX02pyIpFSzsMDli41O0Wh6dZNERERkTIk5tBZdiecw93NhQfahZsdR0RMoh49ERERkTJk7m+9eQOa16CSr4fJaUTKAMOAtLTc9QoVoJTMR6kePREREZEyYl/iedbvP4WLBUa0r2V2HJGyIS0NfHxyl8sFXymgQk9ERESkjLg80mbPm6oSFuRtchoRMZMKPREREZEy4MjZNFZ+nwjAqI61TU4jImZToSciIiJSBizacgh7jsEtdYJoVN1mdhwRMZkKPREREZFS7mxqJlE7EgAYqd48EUGFnoiIiEipt2xrPOlZOdxU3Ua7OoFmxxGREkCFnoiIiEgplpaZzbJt8UBub56llAz9LiLFS/PoiYiIiJRiUTFHOJeWRVhgBXo0CjY7jkjZ4+oKAwb8vl5KqNATERERKaWy7Dks2nIIgOEdauHqot48kSLn6QkffWR2ikLTrZsiIiIipdTK745z7Nwlgnw8uKtZDbPjiEgJokJPREREpBTKyTGY+9sE6Q/eEoantfTcUiYixU+FnoiIiEgptH7/SX4+cREfDzfuax1qdhyRsis1FSyW3CU11ew0BaZCT0RERKQUutybd1/rmti8rCanEZGSRoWeiIiISCkTG3+WHfHJuLu68OAt4WbHEZESyPRCb/bs2YSHh+Pp6Unz5s3ZvHnzVdu/9dZb1K9fHy8vL+rWrcvbb7/t9P6CBQto3749/v7++Pv706VLF2JiYpzazJkzh8aNG+Pn54efnx8RERF8+eWXjvezsrJ45plnuOmmm/D29qZatWoMHjyY48ePOx0nIyODf/7znwQFBeHt7U3fvn05evSoU5vk5GQiIyOx2WzYbDYiIyM5d+7cX/ikRERERHJd7s27s1l1qvh5mpxGREoiUwu95cuXM2bMGJ577jl2795N+/bt6dmzJwkJCfm2nzNnDhMmTGDy5Mn8+OOPTJkyhUcffZSVK1c62mzYsIF77rmH9evXs23bNmrWrEm3bt04duyYo02NGjWYNm0asbGxxMbGcuutt9KvXz9+/PFHANLS0ti1axcTJ05k165dfPrpp/z888/07dvXKc+YMWNYsWIFUVFRbNmyhYsXL9K7d2/sdrujzb333ktcXBzR0dFER0cTFxdHZGRkUX6MIiIiUo78fOIC6/adxGKBER1qmR1HREooi2EYhlknb926Nc2aNWPOnDmObfXr16d///5MnTo1T/u2bdvSrl07ZsyY4dg2ZswYYmNj2bJlS77nsNvt+Pv7M2vWLAYPHnzFLAEBAcyYMYNhw4bl+/6OHTto1aoVhw8fpmbNmqSkpFCpUiXeeecdBg0aBMDx48cJCQlh9erVdO/enX379tGgQQO2b99O69atAdi+fTsRERH89NNP1K1b99ofEnD+/HlsNhspKSn4+fkVaB8REREpm5748Ds+2XWUHg2DmRvZ3Ow4ImVfair4+OSuX7wI3t6mxilobWBaj15mZiY7d+6kW7duTtu7devG1q1b890nIyMDT0/n2xO8vLyIiYkhKysr333S0tLIysoiICAg3/ftdjtRUVGkpqYSERFxxbwpKSlYLBYqVqwIwM6dO8nKynLKX61aNRo1auTIv23bNmw2m6PIA2jTpg02m+2K13j5Os+fP++0iIiIiBw/d4n/xuXepTSyU22T04hISWZaoXf69GnsdjtVqlRx2l6lShWSkpLy3ad79+4sXLiQnTt3YhgGsbGxLF68mKysLE6fPp3vPuPHj6d69ep06dLFafsPP/yAj48PHh4ejBw5khUrVtCgQYN8j5Gens748eO59957HVVzUlIS7u7u+Pv7XzF/UlISlStXznO8ypUrX/EaAaZOnep4ps9msxESEnLFtiIiIlJ+LNpyiOwcg4hagTQNqWh2HJHywdUVevXKXVxLz3yVpg/GYrFYnF4bhpFn22UTJ06kZ8+etGnTBqvVSr9+/Rg6dCgArvl86NOnT+eDDz7g008/zdMTWLduXeLi4ti+fTujRo1iyJAh7N27N88xsrKyuPvuu8nJyWH27NnXvJ4/58/vWq52jQATJkwgJSXFsRw5cuSa5xUREZGy7VxaJh/E5I5joN48kevI0xO++CJ38Sw9gx+ZVugFBQXh6uqap2fr5MmTeXr5LvPy8mLx4sWkpaURHx9PQkICYWFh+Pr6EhQU5NR25syZvPLKK6xZs4bGjRvnOZa7uzt16tShRYsWTJ06lSZNmvDmm286tcnKymLgwIEcOnSItWvXOt0DGxwcTGZmJsnJyVfMHxwczIkTJ/Kc+9SpU1e8RgAPDw/HiKCXFxERESnf3t52mLRMOw2q+tHhhqBr7yAi5ZpphZ67uzvNmzdn7dq1TtvXrl1L27Ztr7qv1WqlRo0auLq6EhUVRe/evXFx+f1SZsyYwUsvvUR0dDQtWrQoUB7DMMjIyHC8vlzk/fLLL6xbt47AwECn9s2bN8dqtTrlT0xMZM+ePY78ERERpKSkOE3v8O2335KSknLNaxQRERG57FKmnaVb4wF4uGOtq94ZJCIC4GbmyceNG0dkZCQtWrQgIiKC+fPnk5CQwMiRI4HcWxiPHTvmmCvv559/JiYmhtatW5OcnMxrr73Gnj17WLZsmeOY06dPZ+LEibz//vuEhYU5egx9fHzw+W20nGeffZaePXsSEhLChQsXiIqKYsOGDURHRwOQnZ3NgAED2LVrF6tWrcJutzuOExAQgLu7OzabjWHDhvHEE08QGBhIQEAATz75JDfddJPjecD69evTo0cPhg8fzrx58wAYMWIEvXv3LvCImyIiIiIf7TzC2dRMQgK8uP2mqmbHESlfUlPh8rgbJ0+aPupmQZla6A0aNIgzZ87w4osvkpiYSKNGjVi9ejWhoaFAbg/ZH+fUs9vtvPrqq+zfvx+r1Urnzp3ZunUrYWFhjjazZ88mMzOTAQMGOJ1r0qRJTJ48GYATJ04QGRlJYmIiNpuNxo0bEx0dTdeuXQE4evQon3/+OQBNmzZ1Os769evp1KkTAK+//jpubm4MHDiQS5cucdttt7F06VKn5wXfe+89HnvsMcfonH379mXWrFl/+7MTERGR8iHbnsP8TQcBGNG+Fm6upg+xIFL+pKWZnaDQTJ1HTwpG8+iJiIiUX/+NO8bjUXEEervzzfhb8bSWnlH/RMoEzaMnIiIiIkXJMAzmbsztzRvaNkxFnogUmAo9ERERkRJq48+n2Jd4ngrurkRGhJodR0RKERV6IiIiIiXUnA0HALi3VU0qVnA3OY2IlCYq9ERERERKoN0JyXx76CxWVwvD2oebHUdEShlTR90UERERkfzN3Zjbm9evaXWq2rxMTiNSjrm4QMeOv6+XEir0REREREqYA6cusmbvCQBGdqxlchqRcs7LCzZsMDtFoZWeklRERESknJi/8SCGAV3qV6FOZV+z44hIKaRCT0RERKQESUpJ59PdRwEY1am2yWlEpLRSoSciIiJSgiz+5hBZdoNWYQE0D/U3O46IpKZCpUq5S2qq2WkKTM/oiYiIiJQQKZeyeP/bBABGdtKzeSIlxunTZicoNPXoiYiIiJQQ724/zMWMbOpW8aVz3cpmxxGRUkyFnoiIiEgJkJ5lZ8k3h4Dc3jyLxWJyIhEpzVToiYiIiJQAH+88yumLmVSv6EXvxtXMjiMipZwKPRERERGT2XMMFmw+CMBD7cOxuupPNBH5e/RbRERERMRkX+5J5PCZNPwrWBnUMsTsOCJSBmjUTRERERETGYbB3I0HABjSNowK7vrzTKREcXGBFi1+Xy8l9JtERERExERbfj3NnmPn8bK6MiQizOw4IvJnXl6wY4fZKQqt9JSkIiIiImXQ5d68QS1D8Pd2NzmNiJQVKvRERERETPLD0RS++fUMri4WHmofbnYcESlDVOiJiIiImORyb17fJtWo4V/B5DQikq+0NAgLy13S0sxOU2B6Rk9ERETEBIdOp7J6TyIAD3esZXIaEbkiw4DDh39fLyXUoyciIiJigvmbDmIYcGu9ytQL9jM7joiUMSr0RERERK6zkxfS+WTXUQBGdqxtchoRKYtU6ImIiIhcZ0u+iSczO4dmNSvSMszf7DgiUgap0BMRERG5js6nZ/HuttznfUZ1qoPFYjE5kYiURSr0RERERK6j979N4EJGNjdU9uG2epXNjiMiZZRG3RQRERG5TjKy7SzecgiAER1q4eKi3jyREs9igQYNfl8vJVToiYiIiFwnK3Yd4+SFDKraPOnXtLrZcUSkICpUgB9/NDtFoenWTREREZHrwJ5jMH/TQQCG3RKOu5v+DBOR4qPfMCIiIiLXwZofkzh4OhWbl5W7W9U0O46IlHEq9ERERESKmWEYzN14AIDBEaH4eOjpGZFSIy0NGjbMXdLSzE5TYPotIyIiIlLMth08w3dHU/Bwc2FI2zCz44hIYRgG7N37+3opoR49ERERkWI2d2Pus3kDW4QQ5ONhchoRKQ9U6ImIiIgUoz3HUtj08ylcXSyM6FDL7DgiUk6o0BMREREpRvN+G2nz9puqEhJQweQ0IlJeqNATERERKSYJZ9L44vvjADzcUb15InL9qNATERERKSYLNh8kx4AON1aiYTWb2XFEpBzRqJsiIiIixeD0xQw+jD0CwEj15omUXhYLhIb+vl5KqNATERERKQZLv4knIzuHJiEViagVaHYcEfmrKlSA+HizUxSabt0UERERKWIXM7J5e1s8AKM61sJSinoBRKRsUKEnIiIiUsSiYhI4n55NrSBvujYINjuOiJRDKvREREREilBmdg4LNx8CYESHWri6qDdPpFS7dAlatsxdLl0yO02B6Rk9ERERkSL0Wdwxks6nU9nXgzuaVTc7joj8XTk5EBv7+3opoR49ERERkSKSk2Mwb+MBAIbdEo6Hm6vJiUSkvFKhJyIiIlJE1u07wYFTqfh6unFv65pmxxGRckyFnoiIiEgRMAyDOb/15t3fJhRfT6vJiUSkPFOhJyIiIlIEYg6dZXfCOdzdXHigXZjZcUSknFOhJyIiIlIE5v7WmzegeQ0q+3qanEZEyjuNuikiIiLyN/2UdJ71+0/hYoER7WuZHUdEilpQkNkJCk2FnoiIiMjfNG/jQQB6NqpKWJC3yWlEpEh5e8OpU2anKDTTb92cPXs24eHheHp60rx5czZv3nzV9m+99Rb169fHy8uLunXr8vbbbzu9v2DBAtq3b4+/vz/+/v506dKFmJgYpzZz5syhcePG+Pn54efnR0REBF9++aVTm08//ZTu3bsTFBSExWIhLi4uT5YDBw5wxx13UKlSJfz8/Bg4cCAnTpxwahMWFobFYnFaxo8fX4hPSEREREqyI2fT+Py74wCM7Fjb5DQiIrlMLfSWL1/OmDFjeO6559i9ezft27enZ8+eJCQk5Nt+zpw5TJgwgcmTJ/Pjjz8yZcoUHn30UVauXOlos2HDBu655x7Wr1/Ptm3bqFmzJt26dePYsWOONjVq1GDatGnExsYSGxvLrbfeSr9+/fjxxx8dbVJTU2nXrh3Tpk3LN0tqairdunXDYrHw9ddf880335CZmUmfPn3I+dNEii+++CKJiYmO5fnnn/87H5uIiIiUIIu2HMKeY3BLnSBuqmEzO46ICAAWwzAMs07eunVrmjVrxpw5cxzb6tevT//+/Zk6dWqe9m3btqVdu3bMmDHDsW3MmDHExsayZcuWfM9ht9vx9/dn1qxZDB48+IpZAgICmDFjBsOGDXPaHh8fT3h4OLt376Zp06aO7WvWrKFnz54kJyfj5+cHQHJyMgEBAaxdu5YuXboAuT16Y8aMYcyYMdf8PK7k/Pnz2Gw2UlJSHOcSERER851NzaTttK9Iz8rh3WGtueWG0vccj4hcw6VL0LNn7vqXX4KXl6lxClobmNajl5mZyc6dO+nWrZvT9m7durF169Z898nIyMDT03kUKy8vL2JiYsjKysp3n7S0NLKysggICMj3fbvdTlRUFKmpqURERBQ4f0ZGBhaLBQ8PD8c2T09PXFxc8hSd//73vwkMDKRp06a8/PLLZGZmXvPY58+fd1pERESk5Fm2NZ70rBwaVfejXZ1As+OISHHIyYGNG3OXP925V5KZVuidPn0au91OlSpVnLZXqVKFpKSkfPfp3r07CxcuZOfOnRiGQWxsLIsXLyYrK4vTp0/nu8/48eOpXr26o4ftsh9++AEfHx88PDwYOXIkK1asoEGDBgXO36ZNG7y9vXnmmWdIS0sjNTWVp556ipycHBITEx3tHn/8caKioli/fj2jR4/mjTfe4JFHHrnqsadOnYrNZnMsISEhBc4lIiIi10daZjbLtsUDuc/mWSwWcwOJiPyB6YOx/PmXomEYV/xFOXHiRHr27EmbNm2wWq3069ePoUOHAuDq6pqn/fTp0/nggw/49NNP8/QE1q1bl7i4OLZv386oUaMYMmQIe/fuLXDuSpUq8dFHH7Fy5Up8fHwc3afNmjVzyjJ27Fg6duxI48aNeeihh5g7dy6LFi3izJkzVzz2hAkTSElJcSxHjhwpcC4RERG5PqJijnAuLYvQwAr0bFTV7DgiIk5MK/SCgoJwdXXN03t38uTJPL18l3l5ebF48WLS0tKIj48nISGBsLAwfH19CfrT3BYzZ87klVdeYc2aNTRu3DjPsdzd3alTpw4tWrRg6tSpNGnShDfffLNQ19CtWzcOHDjAyZMnOX36NO+88w7Hjh0jPDz8ivu0adMGgF9//fWKbTw8PBwjgl5eREREpOTIsuewaMshAEZ0qIWri3rzRKRkMa3Qc3d3p3nz5qxdu9Zp+9q1a2nbtu1V97VardSoUQNXV1eioqLo3bs3Li6/X8qMGTN46aWXiI6OpkWLFgXKYxgGGRkZhb8QcovWihUr8vXXX3Py5En69u17xba7d+8GoGpVffMnIiJSWq387jjHzl0iyMeDu5rVMDuOiEgepk6YPm7cOCIjI2nRogURERHMnz+fhIQERo4cCeTewnjs2DHHXHk///wzMTExtG7dmuTkZF577TX27NnDsmXLHMecPn06EydO5P333ycsLMzRY+jj44OPjw8Azz77LD179iQkJIQLFy4QFRXFhg0biI6Odhzn7NmzJCQkcPx47rw4+/fvByA4OJjg4GAAlixZQv369alUqRLbtm3j8ccfZ+zYsdStWxeAbdu2sX37djp37ozNZmPHjh2MHTuWvn37UrNmzeL8aEVERKSY5OQYzN14AIAH2oXhac37+IiIiNlMLfQGDRrEmTNnHPPMNWrUiNWrVxMaGgpAYmKi05x6drudV199lf3792O1WuncuTNbt24lLCzM0Wb27NlkZmYyYMAAp3NNmjSJyZMnA3DixAkiIyNJTEzEZrPRuHFjoqOj6dq1q6P9559/zgMPPOB4fffdd+c5zv79+5kwYQJnz54lLCyM5557jrFjxzr28fDwYPny5UyZMoWMjAxCQ0MZPnw4Tz/9dJF8fiIiInL9rd9/kp9PXMTHw43724SaHUdErocKFcxOUGimzqMnBaN59EREREqOf8zdyo74ZB7uUIsJveqbHUdEypkSP4+eiIiISGmz8/BZdsQn4+7qwoO3XHnwNRERs6nQExERESmgORsOAnDHzdWp4ud5jdYiIuZRoSciIiJSAD+fuMC6fSewWGBEx1pmxxGR6yU9HW6/PXdJTzc7TYGZOhiLiIiISGkxb2Nub173BsHUruRjchoRuW7sdli9+vf1UkI9eiIiIiLXcPzcJf4bdwyAkZ1qm5xGROTaVOiJiIiIXMOiLYfIzjFoUyuApiEVzY4jInJNKvREREREruJcWiYfxOTO6zuyo3rzRKR0UKEnIiIichVvbztMWqad+lX96HhjJbPjiIgUiAo9ERERkSu4lGln6dZ4AEZ2rIXFYjE3kIhIAf2lQm/z5s3cf//9REREcOxY7oPJ77zzDlu2bCnScCIiIiJm+mjnEc6mZhIS4MXtN1U1O46ISIEVutD75JNP6N69O15eXuzevZuMjAwALly4wCuvvFLkAUVERETMkG3PYf6m3CkVhrevhZurboQSKZe8vcEwchdvb7PTFFihf2P961//Yu7cuSxYsACr1erY3rZtW3bt2lWk4URERETM8sUPiRxNvkSAtzv/aB5idhwRkUIpdKG3f/9+OnTokGe7n58f586dK4pMIiIiIqYyDIO5v02Q/kDbMLzcXU1OJCJSOIUu9KpWrcqvv/6aZ/uWLVuoVatWkYQSERERMdPGn0+xL/E8FdxdiYwINTuOiJgpPR3+8Y/cJT3d7DQFVuhC7+GHH+bxxx/n22+/xWKxcPz4cd577z2efPJJHnnkkeLIKCIiInJdzd14AIB7WtWkYgV3k9OIiKnsdvj449zFbjc7TYG5FXaHp59+mpSUFDp37kx6ejodOnTAw8ODJ598ktGjRxdHRhEREZHrZndCMtsPnsXNxcKwW8LNjiMi8pcUqtCz2+1s2bKFJ554gueee469e/eSk5NDgwYN8PHxKa6MIiIiItfN5d68/jdXp1pFL5PTiIj8NYUq9FxdXenevTv79u0jICCAFi1aFFcuERERkevuwKmLrNl7AsidIF1EpLQq9DN6N910EwcPHiyOLCIiIiKmmr/xIIYBXepXoU5lX7PjiIj8ZYUu9F5++WWefPJJVq1aRWJiIufPn3daREREREqjpJR0Pt19FIBRndSbJyKlW6EHY+nRowcAffv2xWKxOLYbhoHFYsFeikaiEREREbls8TeHyLIbtAzzp3logNlxRET+lkIXeuvXry+OHCIiIiKmSbmUxfvfJgAwqlNtk9OISIlSoQJcvPj7eilR6EKvY8eOxZFDRERExDTvbj/MxYxs6lbxpXPdymbHEZGSxGIBb2+zUxRaoQs9gHPnzrFo0SL27duHxWKhQYMGPPjgg9hstqLOJyIiIlKs0rPsLPkmHoCHO9ZyejRFRKS0KvRgLLGxsdSuXZvXX3+ds2fPcvr0aV577TVq167Nrl27iiOjiIiISLH5eOdRTl/MoHpFL/o0qWZ2HBEpaTIyYOjQ3CUjw+w0BWYxDMMozA7t27enTp06LFiwADe33A7B7OxsHnroIQ4ePMimTZuKJWh5dv78eWw2GykpKfj5+ZkdR0REpMyw5xjc+uoGDp9JY1KfBjzQLtzsSCJS0qSmgo9P7vrFi6bfxlnQ2qDQt27GxsY6FXkAbm5uPP3005pAXUREREqVL/ckcvhMGv4VrAxqGWJ2HBGRIlPoWzf9/PxISEjIs/3IkSP4+mpiURERESkdDMNg7sYDAAyOCKOC+18aukBEpEQqdKE3aNAghg0bxvLlyzly5AhHjx4lKiqKhx56iHvuuac4MoqIiIgUuW9+PcOeY+fxtLowpG2Y2XFERIpUob+6mjlzJhaLhcGDB5OdnQ2A1Wpl1KhRTJs2rcgDioiIiBSHORt/BeDuljUJ8HY3OY2ISNEq9GAsl6WlpXHgwAEMw6BOnTpUKEWTB5Y2GoxFRESkaP1wNIU+s7bg6mJh41OdqOGvv2NE5ArKy2AsKSkp2O12AgICuOmmmxzbz549i5ubmwoRERERKfEuP5vXt0k1FXkiUiYV+hm9u+++m6ioqDzbP/zwQ+6+++4iCSUiIiJSXA6dTuXLPYlA7gTpIiJXVaECnDyZu5SiuxgLXeh9++23dO7cOc/2Tp068e233xZJKBEREZHiMn/TQXIM6Fy3EvWCdSeSiFyDxQKVKuUuFovZaQqs0IVeRkaGYxCWP8rKyuLSpUtFEkpERESkOJy8kM4nu44CMKpTHZPTiIgUn0IXei1btmT+/Pl5ts+dO5fmzZsXSSgRERGR4rDkm3gys3NoVrMiLcP8zY4jIqVBRgY8+mjukpFhdpoCK/RgLC+//DJdunThu+++47bbbgPgq6++YseOHaxZs6bIA4qIiIgUhfPpWby77TAAIzvWxlKKbsESERNlZ8Ps2bnr06eDh4e5eQqo0D167dq1Y9u2bYSEhPDhhx+ycuVK6tSpw/fff0/79u2LI6OIiIjI3/b+twlcyMimTmUfutSvYnYcEZFiVegePYCmTZvy3nvvFXUWERERkWKRkW1n8ZZDAIzoUAsXF/XmiUjZVuBCLycnh5ycHNzcft/lxIkTzJ07l9TUVPr27cstt9xSLCFFRERE/o4Vu45x8kIGwX6e9G9a3ew4IiLFrsCF3rBhw7BarY6BWC5cuEDLli1JT0+natWqvP766/z3v/+lV69exRZWREREpLDsOQbzNx0E4KH24bi7FfrJFRGRUqfAv+m++eYbBgwY4Hj99ttvk52dzS+//MJ3333HuHHjmDFjRrGEFBEREfmr1u5N4uDpVPw83bi7VU2z44iIXBcFLvSOHTvGDTfc4Hj91Vdfcdddd2Gz2QAYMmQIP/74Y9EnFBEREfmLDMNgzoYDAAyOCMPH4y8NTyAiUuoUuNDz9PR0mhB9+/bttGnTxun9ixcvFm06ERERkb9h28EzfHc0BQ83F4a2CzM7joiURl5ecOhQ7uLlZXaaAitwodekSRPeeecdADZv3syJEye49dZbHe8fOHCAatWqFX1CERERkb9o7sbcZ/MGtgghyKd0zH0lIiWMiwuEheUuLqXnGd8C378wceJEevXqxYcffkhiYiJDhw6latWqjvdXrFhBu3btiiWkiIiISGH9eDyFTT+fwsUCw9vXMjuOiMh1VeBCr3PnzuzcuZO1a9cSHBzMP/7xD6f3mzZtSqtWrYo8oIiIiMhfcbk37/bG1agZWMHkNCJSamVmwnPP5a6//DK4u5ubp4AshmEYZoeQqzt//jw2m42UlBT8/PzMjiMiIlLiJZxJo9PM9eQY8MVjt9Cwms3sSCJSWqWmgo9P7vrFi+DtbWqcgtYGpecmUxEREZECWrD5IDkGdLixkoo8ESmXVOiJiIhImXL6YgYfxh4BYGRHPZsnIuWT6YXe7NmzCQ8Px9PTk+bNm7N58+artn/rrbeoX78+Xl5e1K1bl7ffftvp/QULFtC+fXv8/f3x9/enS5cuxMTEOLWZM2cOjRs3xs/PDz8/PyIiIvjyyy+d2nz66ad0796doKAgLBYLcXFxebIcOHCAO+64g0qVKuHn58fAgQM5ceKEU5vk5GQiIyOx2WzYbDYiIyM5d+5cwT8gERERKZSl38STkZ1Dkxo2ImoFmh1HRMQUphZ6y5cvZ8yYMTz33HPs3r2b9u3b07NnTxISEvJtP2fOHCZMmMDkyZP58ccfmTJlCo8++igrV650tNmwYQP33HMP69evZ9u2bdSsWZNu3bpx7NgxR5saNWowbdo0YmNjiY2N5dZbb6Vfv35OE76npqbSrl07pk2blm+W1NRUunXrhsVi4euvv+abb74hMzOTPn36kJOT42h37733EhcXR3R0NNHR0cTFxREZGfl3PzoRERHJx8WMbN7eFg/AyI61sVgs5gYSETGJqYOxtG7dmmbNmjFnzhzHtvr169O/f3+mTp2ap33btm1p164dM2bMcGwbM2YMsbGxbNmyJd9z2O12/P39mTVrFoMHD75iloCAAGbMmMGwYcOctsfHxxMeHs7u3btp2rSpY/uaNWvo2bMnycnJjocgk5OTCQgIYO3atXTp0oV9+/bRoEEDtm/fTuvWrYHcieYjIiL46aefqFu37rU/JDQYi4iISEEt3HyQf32xj1pB3qwd1xFXFxV6IvI3lYfBWL744gseeughnn76aX766Sen95KTk50mUL+WzMxMdu7cSbdu3Zy2d+vWja1bt+a7T0ZGBp6enk7bvLy8iImJISsrK9990tLSyMrKIiAgIN/37XY7UVFRpKamEhERUeD8GRkZWCwWPDx+n3zV09MTFxcXR9G5bds2bDabo8gDaNOmDTab7YrXePnY58+fd1pERETk6jKzc1i4+RAAIzrUUpEnIuVagQu9999/n379+pGUlMS2bdu4+eabee+99xzvZ2ZmsnHjxgKf+PTp09jtdqpUqeK0vUqVKiQlJeW7T/fu3Vm4cCE7d+7EMAxiY2NZvHgxWVlZnD59Ot99xo8fT/Xq1enSpYvT9h9++AEfHx88PDwYOXIkK1asoEGDBgXO36ZNG7y9vXnmmWdIS0sjNTWVp556ipycHBITEwFISkqicuXKefatXLnyFa8RYOrUqY5n+mw2GyEhIQXOJSIiUl79N+4YSefTqezrwR3NqpsdR0TKCi8v2LMnd/HyMjtNgRW40Js5cyavv/46q1atYvPmzbzzzjuMHDmSRYsW/a0Af7533jCMK95PP3HiRHr27EmbNm2wWq3069ePoUOHAuDq6pqn/fTp0/nggw/49NNP8/QE1q1bl7i4OLZv386oUaMYMmQIe/fuLXDuSpUq8dFHH7Fy5Up8fHwc3afNmjVzypLftVztGgEmTJhASkqKYzly5EiBc4mIiJRHOTkGczceAODBW8LxcMv7d4GIyF/i4gING+YuLqaPZVlgbgVt+PPPP9O7d2/H6wEDBhAUFETfvn3JysrijjvuKNSJg4KCcHV1zdOzdfLkyTy9fJd5eXmxePFi5s2bx4kTJ6hatSrz58/H19eXoKAgp7YzZ87klVdeYd26dTRu3DjPsdzd3alTpw4ALVq0YMeOHbz55pvMmzevwNfQrVs3Dhw4wOnTp3Fzc6NixYoEBwcTHh4OQHBwcJ5ROAFOnTp1xWsE8PDwcLolVERERK5u3b4THDiViq+nG/e1rml2HBER0xW4JPXz88tTtHTq1ImVK1fy1FNP8Z///KdQJ3Z3d6d58+asXbvWafvatWtp27btVfe1Wq3UqFEDV1dXoqKi6N27Ny5/qK5nzJjBSy+9RHR0NC1atChQHsMwyMjIKNQ1XBYUFETFihX5+uuvOXnyJH379gUgIiKClJQUp+kdvv32W1JSUq55jSIiIlIwhvF7b979bULx9bSanEhEypTMTJg8OXfJzDQ7TYEVuEevVatWfPnll7Rp08Zpe8eOHVm5cqVTb19BjRs3jsjISFq0aEFERATz588nISGBkSNHArm3MB47dswxV97PP/9MTEwMrVu3Jjk5mddee409e/awbNkyxzGnT5/OxIkTef/99wkLC3P0GPr4+ODz22g5zz77LD179iQkJIQLFy4QFRXFhg0biI6Odhzn7NmzJCQkcPz4cQD2798P5PbSBQcHA7BkyRLq169PpUqV2LZtG48//jhjx451jKZZv359evTowfDhwx09hSNGjKB3794FHnFTRERErm5HfDK7Es7h7ubCA+3CzI4jImVNVhZMmZK7/tRT4O5ubp4CKnChN3bs2CuOFNmpUydWrVrlVHAVxKBBgzhz5gwvvvgiiYmJNGrUiNWrVxMaGgpAYmKi05x6drudV199lf3792O1WuncuTNbt24lLCzM0Wb27NlkZmYyYMAAp3NNmjSJyZMnA3DixAkiIyNJTEzEZrPRuHFjoqOj6dq1q6P9559/zgMPPOB4fffdd+c5zv79+5kwYQJnz54lLCyM5557jrFjxzqd97333uOxxx5zjC7at29fZs2aVajPSURERK5szoZfAbirWQ0q+3peo7WISPlg6jx6UjCaR09ERCR/PyWdp8cbm7FY4OsnOhEeZO78ViJSBpWHefRERERESpJ5Gw8C0KtRVRV5IiJ/UOhCb+bMmcWRQ0RERKRQjian8fl3uc/Sj+xY2+Q0IiIlS6EKvfHjx7NgwYLiyiIiIiJSYAs3H8KeY9CuTiA31bCZHUdEpEQp0GAshmEwYsQI1q1bx6ZNm4o7k4iIiMhVnU3NJGpH7oBt6s0TEcmrQIXegAED2L59Oxs3biQkJKS4M4mIiIhc1bKt8aRn5dCouh+31AkyO46IlGWennB5XmzP0jOyb4EKvRUrVjB//nzq1KlT3HlEREREriotM5tl2+KB3N48i8VibiARKdtcXaFlS7NTFFqBntEbM2YMTzzxBDt27CjuPCIiIiJXtXzHEc6lZREaWIGejaqaHUdEpEQqUI/ea6+9RmBgID179mT9+vXcdNNNxZ1LREREJI8sew4LNx8CYHj7Wri6qDdPRIpZZia8+Wbu+uOPg7u7uXkKqECFHsBzzz1HpUqV6NGjB8eOHSvOTCIiIiL5WvndcY6du0SQjwcDmtcwO46IlAdZWfD007nrjzxS9go9gBEjRhAYGFhcWURERESuyDAMxwTpD7QLw9PqanIiEZGSq9ATpt91113FkUNERETkqtbvP8n+Exfw8XDj/jahZscRESnRCl3oXY1u6RQREZHiMmfDAQDubV0Tm5fV5DQiIiVbkRR6SUlJ/POf/9T0CyIiIlIsdh4+y474ZKyuFh5sF252HBGREq/Ahd65c+e47777qFSpEtWqVeP//u//yMnJ4YUXXqBWrVps376dxYsXF2dWERERKafmbMh9Nu/Om2sQbCs9ExaLiJilwIOxPPvss2zatIkhQ4YQHR3N2LFjiY6OJj09nS+//JKOHTsWZ04REREpp345cYF1+05gscCIjrXMjiMiUioUuND74osvWLJkCV26dOGRRx6hTp063HjjjbzxxhvFGE9ERETKu7m/jbTZrUEValfyMTmNiJQ7np6wfv3v66VEgQu948eP06BBAwBq1aqFp6cnDz30ULEFExERETl+7hL/jcsd7G1kx9ompxGRcsnVFTp1MjtFoRX4Gb2cnBys1t9HuHJ1dcXb27tYQomIiIgALNpyiOwcgza1Ari5pr/ZcURESo0C9+gZhsHQoUPx8PAAID09nZEjR+Yp9j799NOiTSgiIiLl0rm0TD6ISQDUmyciJsrKgvnzc9dHjABr6ZjepcCF3pAhQ5xe33///UUeRkREROSyd7YdJi3TTv2qfnS8sZLZcUSkvMrMhNGjc9eHDi17hd6SJUuKM4eIiIiIw6VMO0u2xgMwsmMtLBaLuYFEREqZIpkwXURERKQofbTzCGdTM6nh78XtN1U1O46ISKmjQk9ERERKlGx7DvM35U6pMKJDLdxc9eeKiEhh6TeniIiIlChf/JDI0eRLBHi784/mIWbHEREplVToiYiISIlhGIZjgvShbcPwcnc1OZGISOmkQk9ERERKjI0/n2Jf4nkquLsyOCLU7DgiIqVWgUfdFBERESluczceAOCeVjWpWMHd5DQiIoCHB6xa9ft6KaFCT0REREqE3QnJbD94FjcXC8NuCTc7johILjc3uP12s1MUmm7dFBERkRLhcm9ev6bVqVbRy+Q0IiKlm3r0RERExHQHTl1kzd4TQO4E6SIiJUZWFrz3Xu76ffeB1WpungJSoSciIiKmm7/xIIYBXepX5oYqvmbHERH5XWYmPPBA7vo//lFqCj3duikiIiKmOnE+nRW7jwEwqlNtk9OIiJQNKvRERETEVIu3HCLTnkPLMH+ahwaYHUdEpExQoSciIiKmSbmUxXvfJgAwsqN680REiooKPRERETHNu9sPczEjmxur+NC5bmWz44iIlBkq9ERERMQU6Vl2lnwTD+T25rm4WMwNJCJShqjQExEREVN8susopy9mUL2iF32aVDM7johImaLpFUREROS6s+cYzN90EIBht4RjddV3zyJSQnl4wIcf/r5eSqjQExERkevuyz2JHD6TRsUKVu5uFWJ2HBGRK3Nzy50/r5TR12ciIiJyXRmGwdyNBwAYEhFGBXd97ywiUtT0m1VERESuq29+PcOeY+fxtLowpG2Y2XFERK4uOxtWrMhdv+OO3B6+UqB0pBQREZEy43Jv3t0taxLg7W5yGhGRa8jIgIEDc9cvXiw1hZ5u3RQREZHr5oejKWz59TSuLhaG3RJudhwRkTJLhZ6IiIhcN5d78/o0rkpIQAWT04iIlF0q9EREROS6iD+dypd7EgEY2am2yWlERMo2FXoiIiJyXczffJAcAzrXrUS9YD+z44iIlGkq9ERERKTYnbyQzsc7jwIwsqN680REipsKPRERESl2S76JJzM7h5trVqRVeIDZcUREyrzSMTaoiIiIlFoX0rN4d/thAEZ1rI3FYjE5kYhIIbi7w5Ilv6+XEir0REREpFi9/20CF9KzqVPZhy71q5gdR0SkcKxWGDrU7BSFZvqtm7NnzyY8PBxPT0+aN2/O5s2br9r+rbfeon79+nh5eVG3bl3efvttp/cXLFhA+/bt8ff3x9/fny5duhATE+PUZs6cOTRu3Bg/Pz/8/PyIiIjgyy+/dGrz6aef0r17d4KCgrBYLMTFxeXJkpSURGRkJMHBwXh7e9OsWTM+/vhjpzZhYWFYLBanZfz48YX4hEREREqvjGw7i7YcAmBEh1q4uKg3T0TkejC10Fu+fDljxozhueeeY/fu3bRv356ePXuSkJCQb/s5c+YwYcIEJk+ezI8//siUKVN49NFHWblypaPNhg0buOeee1i/fj3btm2jZs2adOvWjWPHjjna1KhRg2nTphEbG0tsbCy33nor/fr148cff3S0SU1NpV27dkybNu2K+SMjI9m/fz+ff/45P/zwA3feeSeDBg1i9+7dTu1efPFFEhMTHcvzzz//Vz8yERGRUmXFrmOcvJBBsJ8n/ZtWNzuOiEjhZWfDF1/kLtnZZqcpMIthGIZZJ2/dujXNmjVjzpw5jm3169enf//+TJ06NU/7tm3b0q5dO2bMmOHYNmbMGGJjY9myZUu+57Db7fj7+zNr1iwGDx58xSwBAQHMmDGDYcOGOW2Pj48nPDyc3bt307RpU6f3fHx8mDNnDpGRkY5tgYGBTJ8+3XGcsLAwxowZw5gxY6547ms5f/48NpuNlJQU/Pw0HLWIiJQO9hyDrq9t5ODpVJ7rVZ/hHWqZHUlEpPBSU8HHJ3f94kXw9jY1TkFrA9N69DIzM9m5cyfdunVz2t6tWze2bt2a7z4ZGRl4eno6bfPy8iImJoasrKx890lLSyMrK4uAgPxH+LLb7URFRZGamkpEREShruGWW25h+fLlnD17lpycHKKiosjIyKBTp05O7f79738TGBhI06ZNefnll8nMzLzqcTMyMjh//rzTIiIiUtqs3ZvEwdOp+Hm6cU/rmmbHEREpV0wbjOX06dPY7XaqVHF+KLtKlSokJSXlu0/37t1ZuHAh/fv3p1mzZuzcuZPFixeTlZXF6dOnqVq1ap59xo8fT/Xq1enSpYvT9h9++IGIiAjS09Px8fFhxYoVNGjQoFDXsHz5cgYNGkRgYCBubm5UqFCBFStWULv27/MDPf744zRr1gx/f39iYmKYMGEChw4dYuHChVc87tSpU5kyZUqhsoiIiJQkhmEwZ+NBAAZHhOHjofHfRESuJ9N/6/55iGXDMK447PLEiRNJSkqiTZs2GIZBlSpVGDp0KNOnT8fV1TVP++nTp/PBBx+wYcOGPD2BdevWJS4ujnPnzvHJJ58wZMgQNm7cWKhi7/nnnyc5OZl169YRFBTEZ599xj/+8Q82b97MTTfdBMDYsWMd7Rs3boy/vz8DBgxw9PLlZ8KECYwbN87x+vz584SEhBQ4l4iIiNm2HTzDd0fO4eHmwtB2YWbHEREpd0y7dTMoKAhXV9c8vXcnT57M08t3mZeXF4sXLyYtLY34+HgSEhIICwvD19eXoKAgp7YzZ87klVdeYc2aNTRu3DjPsdzd3alTpw4tWrRg6tSpNGnShDfffLPA+Q8cOMCsWbNYvHgxt912G02aNGHSpEm0aNGCt95664r7tWnTBoBff/31im08PDwcI4JeXkREREqTub/15v2jRQ2CfDxMTiMiUv6YVui5u7vTvHlz1q5d67R97dq1tG3b9qr7Wq1WatSogaurK1FRUfTu3RsXl98vZcaMGbz00ktER0fTokWLAuUxDIOMjIwC509LSwNwOi+Aq6srOTk5V9zv8oic+d1mKiIiUhb8eDyFTT+fwsUCI9rXvvYOIiJS5Ey9dXPcuHFERkbSokULIiIimD9/PgkJCYwcORLIvYXx2LFjjrnyfv75Z2JiYmjdujXJycm89tpr7Nmzh2XLljmOOX36dCZOnMj7779PWFiYo8fQx8cHn99Gy3n22Wfp2bMnISEhXLhwgaioKDZs2EB0dLTjOGfPniUhIYHjx48DsH//fgCCg4MJDg6mXr161KlTh4cffpiZM2cSGBjIZ599xtq1a1m1ahUA27ZtY/v27XTu3BmbzcaOHTsYO3Ysffv2pWZNPZQuIiJl07zfevNub1yNmoEVTE4jIlI+mVroDRo0iDNnzjjmmWvUqBGrV68mNDQUgMTERKc59ex2O6+++ir79+/HarXSuXNntm7dSlhYmKPN7NmzyczMZMCAAU7nmjRpEpMnTwbgxIkTREZGkpiYiM1mo3HjxkRHR9O1a1dH+88//5wHHnjA8fruu+92Oo7VamX16tWMHz+ePn36cPHiRerUqcOyZcvo1asXkHsL5vLly5kyZQoZGRmEhoYyfPhwnn766SL9HEVEREqKhDNprPo+90vShzWdgoiUBe7uMGvW7+ulhKnz6EnBaB49EREpLSZ+tod3th+m/Q1BvDOstdlxRETKnBI/j56IiIiULacvZvBh7BEARnXUs3kiImYyfXoFERERKRuWbY0nIzuHJjVsRNTOfwohEZFSx26HzZtz19u3h3ymdSuJVOiJiIjI33YxI5tlW+MBGNmx9hXnxBURKXXS06Fz59z1ixfB29vcPAWkWzdFRETkb4uKSeB8ejbhQd50axhsdhwRkXJPhZ6IiIj8LZnZOSzcfAiAER1q4eqi3jwREbOp0BMREZG/5b9xx0g6n05lXw/ubFbd7DgiIoIKPREREfkbcnIM5m3KnSD9wVvC8XArHYMUiIiUdSr0RERE5C9bt+8Ev568iK+HG/e2rml2HBER+Y0KPREREflLDMNg7sYDANzXJhQ/T6vJiURE5DJNryAiIiJ/yY74ZHYlnMPdzYUH24WZHUdEpHhYrTB9+u/rpYQKPREREflLLvfm3dWsBpX9PE1OIyJSTNzd4amnzE5RaLp1U0RERArtp6TzfP3TSSyW3CkVRESkZFGPnoiIiBTavI25I232bBRMeJC3yWlERIqR3Q67duWuN2sGrqVjdGEVeiIiIlIoR5PT+Py74wCM7Fjb5DQiIsUsPR1atcpdv3gRvEvHl1u6dVNEREQKZeHmQ9hzDNrVCaRxjYpmxxERkXyo0BMREZECO5uaSdSOBEC9eSIiJZkKPRERESmwZVvjSc/KoWE1P26pE2R2HBERuQIVeiIiIlIgaZnZLNsWD+T25lksFnMDiYjIFanQExERkQJZvuMI59KyCA2sQM9GwWbHERGRq1ChJyIiIteUZc9h4eZDAAxvXws3V/0JISJSkml6BREREbmmld8d59i5SwT5uDOgeQ2z44iIXD9WK0ya9Pt6KaFCT0RERK7KMAzHBOkPtAvH01o6JgsWESkS7u4webLZKQpN912IiIjIVa3ff5L9Jy7g7e7K/a1DzY4jIiIFoB49ERERuaq5G3J78+5rE4qtQum5bUlEpEjk5MC+fbnr9euDS+noK1OhJyIiIle08/BZYuLPYnW18GC7cLPjiIhcf5cuQaNGuesXL4K3t7l5Cqh0lKMiIiJiijm/9ebdcXN1gm2eJqcREZGCUqEnIiIi+frlxAXW7TuBxQIjOtQ2O46IiBSCCj0RERHJ17xNub153RpUoU5lH5PTiIhIYajQExERkTyOn7vEZ7uPATCyo3rzRERKGxV6IiIikseiLYfIzjFoHR7AzTX9zY4jIiKFpEJPREREnJxLy+SDmAQARnZSb56ISGmk6RVERETEyTvbDpOWaadesC+dbqxkdhwREXNZrfDkk7+vlxIq9ERERMThUqadpVvjARjVqTYWi8XcQCIiZnN3hxkzzE5RaLp1U0RERBw+2nmEM6mZ1PD34vabqpodR0RE/iL16ImIiAgA2fYc5v82pcLw9rVwc9X3wSIi5ORAQu5zy9SsCS6l43ejCj0REREB4IsfEjmafIkAb3cGtggxO46ISMlw6RKEh+euX7wI3t7m5img0lGOioiISLEyDIO5G3N784a2DcPL3dXkRCIi8neo0BMRERE2/nyKfYnnqeDuyuCIULPjiIjI36RCT0RERJi78QAAd7esScUK7ianERGRv0uFnoiISDkXd+Qc2w+exc3FwkPtw82OIyIiRUCFnoiISDk3d0Nub16/ptWpVtHL5DQiIlIUVOiJiIiUYwdOXeR/e5MAGNmxlslpRESkqGh6BRERkXJs/saDGAZ0qV+ZG6r4mh1HRKTkcXODRx75fb2UKD1JRUREpEidOJ/Oit3HABjZsbbJaURESigPD3jrLbNTFJpu3RQRESmnFm85RKY9hxah/rQICzA7joiIFCH16ImIiJRDKZeyeO/bBABGdVJvnojIFRkGnD6dux4UBBaLuXkKSIWeiIhIOfTu9sNczMjmxio+dK5b2ew4IiIlV1oaVP7t9+TFi+DtbW6eAtKtmyIiIuVMepadJd/EA/Bwh9q4uJSOb6dFRKTgVOiJiIiUM5/sOsrpixlUs3nSt2k1s+OIiEgxUKEnIiJSjthzDOZvOgjAQ+1rYXXVnwIiImWR6b/dZ8+eTXh4OJ6enjRv3pzNmzdftf1bb71F/fr18fLyom7durz99ttO7y9YsID27dvj7++Pv78/Xbp0ISYmxqnNnDlzaNy4MX5+fvj5+REREcGXX37p1ObTTz+le/fuBAUFYbFYiIuLy5MlKSmJyMhIgoOD8fb2plmzZnz88cdObZKTk4mMjMRms2Gz2YiMjOTcuXMF/4BERESK0Jd7Ejl8Jo2KFazc3SrE7DgiIlJMTC30li9fzpgxY3juuefYvXs37du3p2fPniQkJOTbfs6cOUyYMIHJkyfz448/MmXKFB599FFWrlzpaLNhwwbuuece1q9fz7Zt26hZsybdunXj2LFjjjY1atRg2rRpxMbGEhsby6233kq/fv348ccfHW1SU1Np164d06ZNu2L+yMhI9u/fz+eff84PP/zAnXfeyaBBg9i9e7ejzb333ktcXBzR0dFER0cTFxdHZGTk3/nYRERE/hLDMJi78QAAgyPCqOCuMdlERMoqi2EYhlknb926Nc2aNWPOnDmObfXr16d///5MnTo1T/u2bdvSrl07ZsyY4dg2ZswYYmNj2bJlS77nsNvt+Pv7M2vWLAYPHnzFLAEBAcyYMYNhw4Y5bY+Pjyc8PJzdu3fTtGlTp/d8fHyYM2eOU+EWGBjI9OnTGTZsGPv27aNBgwZs376d1q1bA7B9+3YiIiL46aefqFu37pU/nD84f/48NpuNlJQU/Pz8CrSPiIjIn2355TT3L/oWT6sLW8ffRoC3u9mRRERKvtRU8PHJXS8Bo24WtDYwrUcvMzOTnTt30q1bN6ft3bp1Y+vWrfnuk5GRgaenp9M2Ly8vYmJiyMrKyneftLQ0srKyCAjIfyJYu91OVFQUqampREREFOoabrnlFpYvX87Zs2fJyckhKiqKjIwMOnXqBMC2bduw2WyOIg+gTZs22Gy2K17j5es8f/680yIiIvJ3Xe7NG9QiREWeiEhBubnBkCG5i1vpuRPCtELv9OnT2O12qlSp4rS9SpUqJCUl5btP9+7dWbhwITt37sQwDGJjY1m8eDFZWVmcvjyJ4Z+MHz+e6tWr06VLF6ftP/zwAz4+Pnh4eDBy5EhWrFhBgwYNCnUNy5cvJzs7m8DAQDw8PHj44YdZsWIFtWvnTjyblJRE5cp55yaqXLnyFa8RYOrUqY5n+mw2GyEheoZCRET+nh+OprDl19O4ulh4qH0ts+OIiJQeHh6wdGnu4uFhdpoCM30wFsufZpY3DCPPtssmTpxIz549adOmDVarlX79+jF06FAAXF1d87SfPn06H3zwAZ9++mmensC6desSFxfH9u3bGTVqFEOGDGHv3r2Fyv7888+TnJzMunXriI2NZdy4cfzjH//ghx9+uOL1XesaASZMmEBKSopjOXLkSKFyiYiI/Nnl3rw+jasSElDB5DQiIlLcTOt7DAoKwtXVNU/P1smTJ/P08l3m5eXF4sWLmTdvHidOnKBq1arMnz8fX19fgoKCnNrOnDmTV155hXXr1tG4ceM8x3J3d6dOnToAtGjRgh07dvDmm28yb968AuU/cOAAs2bNYs+ePTRs2BCAJk2asHnzZt566y3mzp1LcHAwJ06cyLPvqVOnrniNAB4eHniUom8LRESkZIs/ncqXexIBeLhjbZPTiIiUMoYBaWm56xUqwFU6bEoS03r03N3dad68OWvXrnXavnbtWtq2bXvVfa1WKzVq1MDV1ZWoqCh69+6Ni8vvlzJjxgxeeukloqOjadGiRYHyGIZBRkZGgfOn/faP/cfzQm7PYk5ODgARERGkpKQ4Te/w7bffkpKScs1rFBERKSrzNx8kx4BOdStRv6oG9RIRKZS0tNzBWHx8fi/4SgFTnyYcN24ckZGRtGjRgoiICObPn09CQgIjR44Ecm9hPHbsmGOuvJ9//pmYmBhat25NcnIyr732Gnv27GHZsmWOY06fPp2JEyfy/vvvExYW5ugx9PHxwee30XKeffZZevbsSUhICBcuXCAqKooNGzYQHR3tOM7Zs2dJSEjg+PHjAOzfvx+A4OBggoODqVevHnXq1OHhhx9m5syZBAYG8tlnn7F27VpWrVoF5I4g2qNHD4YPH+7oKRwxYgS9e/cu8IibIiIif8fJC+l8vPMoAKPUmyciUm6YWugNGjSIM2fO8OKLL5KYmEijRo1YvXo1oaGhACQmJjrNqWe323n11VfZv38/VquVzp07s3XrVsLCwhxtZs+eTWZmJgMGDHA616RJk5g8eTIAJ06cIDIyksTERGw2G40bNyY6OpquXbs62n/++ec88MADjtd3332303GsViurV69m/Pjx9OnTh4sXL1KnTh2WLVtGr169HPu99957PPbYY47RRfv27cusWbOK5gMUERG5hiXfxJOZncPNNSvSKjz/EahFRKTsMXUePSkYzaMnIiJ/xYX0LNpO+5oL6dnMi2xO94bBZkcSESl9NI+eiIiIlCTvf5vAhfRsalfypmv9Kw8CJiIiZY8KPRERkTIoI9vOoi2HgNyRNl1cSscocSIiUjRU6ImIiJRBK3Yd4+SFDIL9POnftLrZcURE5DozdTAWERERKXr2HIP5mw4CMOyWcNzd9L2uiMhf5uoKlwd6dHU1N0shqNATEREpY9buTeLg6VT8PN24p3VNs+OIiJRunp7w0Udmpyg0fcUnIiJShhiGwZyNub15kRGh+HjoO10RkfJIhZ6IiEgZsv3gWb47cg4PNxeGtg03O46IiJhEhZ6IiEgZMmfjAQD+0aIGlXw9TE4jIlIGpKaCxZK7pKaanabAVOiJiIiUET8eT2HTz6dwscCI9rXNjiMiIiZSoSciIlJGzPvt2bxeN1WlZmAFk9OIiIiZVOiJiIiUAQln0lj1/XEARnZUb56ISHmnQk9ERKQMWLD5IDkGtL8hiEbVbWbHERERk6nQExERKeVOX8zgw9gjAIxSb56IiKBCT0REpNRbtjWejOwcGtewEVE70Ow4IiJSAmgWVRERkVLsYkY2b287DOQ+m2exWExOJCJSxri6Qq9ev6+XEir0RERESrGomARSLmURHuRN94bBZscRESl7PD3hiy/MTlFounVTRESklMrMzmHh5kMAjOhQC1cX9eaJiEguFXoiIiKl1H/jjpF0Pp1Kvh7ccXN1s+OIiEgJokJPRESkFMrJMZi3KXeC9AfbheNpLT3PjYiIlCqpqeDtnbukppqdpsD0jJ6IiEgp9NVPJ/n15EV8Pdy4r01Ns+OIiJRtaWlmJyg09eiJiIiUMoZhMGfDrwDc1yYUP0+ryYlERKSkUaEnIiJSyuyIT2ZXwjncXV14sF2Y2XFERKQEUqEnIiJSyszdeACAu5pXp7Kfp8lpRESkJFKhJyIiUor8lHSer386icUCw9vXMjuOiIiUUBqMRUREpISz5xjEHDrLyQvpfBR7FICejYKpVcnH5GQiIlJSqdATEREpwaL3JDJl5V4SU9KdtjeuUdGcQCIi5Y2LC3Ts+Pt6KaFCT/6/vXsPjqq+/z/+2t1sbiTZJEQgJBuCt0AMIYSbgPZbKnLpV1BHHK0Dasfh+8OptshMLbR1EFurQm2n/Sn8BEIBlYLa6lQG8Uv5clFpGy4JFWPxC0UTIAS5bDYXcts9vz+y2bhsLhtIstnN8zGzk91zPufsezOfWXjl8znnAwDoo3YcLdfjbxyW0ca+lz74lzIHxmpmTmqv1wUA/UpMjLRnT7Cr6LLQiaQAAPQjLreh5e+XtBnyWix/v0Qud0ctAAD9FUEPAIA+qPDkRb/pmt9kSCqvrFPhyYu9VxQAIGQwdRMAgD7iQnW9isscKi5z6L9LKgI65lxV+2EQANANamqkzMzm519+KQ0YEMxqAkbQAwAgCOqbXCo541RRqcMb7kov1nb5PIPiWUcPAHrc+fPBrqDLCHoAAPQwwzBUerFWxWUOFZU6VFTm0OdnnGpwuf3a3nDdAOXZk5Rrt+n/7vpfXahuaPM6PZOkIbZoTRie3OP1AwBCD0EPAIBuVnm5UUc8oa647JKOnKrUxZoGv3bJAyKVZ0/0PkbbE2WLsXr3D46P0uNvHJZJ8gl7Js/PZbOzZTGbBADAlQh6AABcg0aXW8fOVqmozKGi0ksqLnPo31/X+LWLtJiVPTRBefZEjcloDnYZybEymdoPajNzUrV6Xr7fOnpDbNFaNjubpRUAAO0i6AEAECDDMHSmsk7FnpG6olKHjp6pVF2j/xTMjORYb6DLsycqe2iCoiIsXX7PmTmpujN7iApPXtS5qjoNim+erslIHgCgIwQ9AADaUV3fpH+e8twsxXNt3ddV9X7t4qMjmkfq7InKy0jU6PREDYyL6rY6LGaTJt0wsNvOBwAIfwQ9AADUvED5/56r8ozWNT++qKjSleuRW8wmjRgS7xmtS1KePVHXpwyQmRE2AAhPZrM0blzr8xBB0AMA9EvnnHUqKmsdrfvnKYdqGlx+7YbaopWXkagx9iTlZSQqZ6hNMZFdn4IJAAhRMTHSgQPBrqLLCHoAgLBX1+jSp6crfUbrTjsu+7WLjbQoN92mPHuSxmQ0T8UclMA6dQCA0EPQAwCEFbfb0MkLNd6lDYrLHPpXeZWarpiDaTJJNw+Kb70LZkaibhoUz01OAABhgaAHAAhpF2saPGvWXVJRmUNHyhxy1jX5tbsuPsp7B8wxGYnKTU9UXBT/DAIAOlFbK2VnNz8vKZFiY4NbT4D4Fw4AEDIamtwqKXeq2LNeXVGZQ19dqPVrFxVh1qg0W3Owy0jUmIwkDbVFd7hmHQAAbTIM6auvWp+HCIIeAKBPMgxDZRcvq8gz/bKo1KGSM041uPzXrLv+ugGtyxvYkzQiNV5WS+jcGQ0AgO5G0AMA9AnOukYd8dwBs+WGKRdqGvzaJcVaPVMwm++CmZeeKFusNQgVAwDQdxH0AAC9rsnl1r/OVnkDXXGZQ8fPVfu1s1pMyh5q84zUNT+GDYxlCiYAAJ0g6AEAelx55WXPXTCbR+w+PV2py43+a9bZk2Oa16vzXFuXnZqgaCtr1gEA0FUEPQBAt6qpb9Knpyt9ljeocNb7tYuPimieeukZqRttT1RKXFQQKgYAIPwQ9AAAV83tNnT862oVlzpUVHZJRaUOfVFRpSuWrJPFbFLW4Pjm9eo8yxtcnxInM2vWAQD6OpOpdXmFELp0gKAHAAjY11X1njtgNo/U/fNUparr/desS7VFty5Ebk9STlqCYiP5JwcAEIJiY6XPPgt2FV0W9HtPr1q1SsOHD1d0dLTGjh2rjz76qMP2r776qkaOHKmYmBhlZWVp06ZNPvvXrl2r22+/XUlJSUpKStK0adNUWFjo02b16tXKzc1VQkKCEhISNGnSJH3wwQc+bf785z9rxowZSklJkclkUnFxsc/+L7/8UiaTqc3H22+/7W2XmZnpt3/JkiVX8ZsCgN5V1+jSoa8uat1H/9YPNh/WlBf/R+Of/6sWbDqoVXtOaP+JC6qub1JspEUThyfr//zH9fp/88bqHz+9Q39beodWzxur//rWDZowPJmQBwBALwvqv7xbt27VokWLtGrVKk2ZMkWvvfaaZs2apZKSEmVkZPi1X716tZYuXaq1a9dq/PjxKiws1IIFC5SUlKTZs2dLkvbs2aPvfe97mjx5sqKjo7VixQpNnz5dn332mdLS0iRJ6enpevHFF3XjjTdKkjZu3Ki7775bRUVFuuWWWyRJNTU1mjJliu6//34tWLDArxa73a7y8nKfbWvWrNGKFSs0a9Ysn+3PPfeczzni4uKu4bcGAN3PMAydPF/jvQNmUalDn5c71XTFHEyTSbppUJxntK75pik3DYpTBGvWAQDQp5gMI3jLu0+cOFH5+flavXq1d9vIkSN1zz336IUXXvBrP3nyZE2ZMkUrV670blu0aJEOHjyojz/+uM33cLlcSkpK0iuvvKKHH3643VqSk5O1cuVKPfbYYz7bv/zySw0fPlxFRUXKy8vr8POMGTNG+fn5Kigo8G7LzMzUokWLtGjRog6P7YjT6ZTNZlNlZaUSEhKu+jwA0OJSTYOKT/muWVd5udGvXUpclHcK5hh7okal2xQfzZp1AIB+pLZWGj+++fmBA81TOYMo0GwQtBG9hoYGHTp0yG8a4/Tp07V///42j6mvr1d0dLTPtpiYGBUWFqqxsVFWq/9/Pmpra9XY2Kjk5OQ2z+lyufT222+rpqZGkyZNuspPIx06dEjFxcV69dVX/fa99NJL+sUvfiG73a77779fP/7xjxUZGXnV7wUAXdHQ5Nbn5U6fNetOnq/xaxcVYVZOms17F8wxGYlKS4xhzToAQP9mGFJJSevzEBG0oHf+/Hm5XC4NHjzYZ/vgwYN19uzZNo+ZMWOG1q1bp3vuuUf5+fk6dOiQ1q9fr8bGRp0/f16pqal+xyxZskRpaWmaNm2az/ZPP/1UkyZNUl1dneLi4vTuu+8qu+VuOlehoKBAI0eO1OTJk322/+hHP1J+fr6SkpJUWFiopUuX6uTJk1q3bl2756qvr1d9feutyJ1O51XXBaB/MQxDpy5dVlFZy2jdJR0941RDk9uv7fUpA7zr1Y2xJ2lEarysTMEEACAsBP3q+Cv/UmwYRrt/PX7mmWd09uxZ3XrrrTIMQ4MHD9ajjz6qFStWyGLxX1B3xYoV+uMf/6g9e/b4jQRmZWWpuLhYDodDf/rTn/TII49o7969VxX2Ll++rM2bN+uZZ57x2/fUU095n+fm5iopKUlz587VSy+9pIEDB7Z5vhdeeEHLly/vch0A+p+qukb981Sl9y6YxWUOna9u8GuXGGv1jtS1PBJjmVkAAEC4ClrQS0lJkcVi8Ru9O3funN8oX4uYmBitX79er732mioqKpSamqo1a9YoPj5eKSkpPm1//etf61e/+pX++te/Kjc31+9ckZGR3puxjBs3TgcOHNDvfvc7vfbaa13+LO+8845qa2s7vAawxa233ipJOn78eLtBb+nSpVq8eLH3tdPplN1u73JdAMJLk8utLyqqfZY3OP51td8sEqvFpOzUBO9oXZ49SZkDY5mCCQBAPxK0oBcZGamxY8dq586duvfee73bd+7cqbvvvrvDY61Wq9LT0yVJW7Zs0V133SWzuXW60cqVK/XLX/5SH374ocaNGxdQPYZh+EyX7IqCggLNmTNH1113Xadti4qKJKnNaaYtoqKiFBUVdVW1AAgfZyvrVOxZhLyozKFPT1XqcqPLr509OUZ59iTvSN0tQxMUbfWf5QAAAPqPoE7dXLx4sebPn69x48Zp0qRJWrNmjUpLS7Vw4UJJzSNbp0+f9q6V98UXX6iwsFATJ07UpUuX9Jvf/EZHjx7Vxo0bvedcsWKFnnnmGW3evFmZmZneEcO4uDjvsgY//elPNWvWLNntdlVVVWnLli3as2ePduzY4T3PxYsXVVpaqjNnzkiSjh07JkkaMmSIhgwZ4m13/Phx7du3T9u3b/f7fH/729/097//XVOnTpXNZtOBAwf01FNPac6cOW0uHwGg/6ptaNKnpyp9ljc466zzaxcfFaHR35yCmZGolDj+MAQAAHwFNeg98MADunDhgp577jmVl5crJydH27dv17BhwyRJ5eXlKi0t9bZ3uVx6+eWXdezYMVmtVk2dOlX79+9XZmamt82qVavU0NCguXPn+rzXsmXL9Oyzz0qSKioqNH/+fJWXl8tmsyk3N1c7duzQnXfe6W3/l7/8Rd///ve9rx988EG/80jS+vXrlZaWpunTp/t9vqioKG3dulXLly9XfX29hg0bpgULFujpp5++6t8ZgNDndhs68XV18w1TPKHui4oqua5Ys85skrKGJGhMhucumPZE3XBdnMxmpmACANBrTCbJk08UQpdBBHUdPQSGdfSA0Ha+ut5nvbojZQ5V1Tf5tRuSEO1d1iDPs2ZdbGTQ75kFAAD6kD6/jh4AhKO6Rpc+O/PNNesuqeziZb92MVaLRqXbvAuR59mTNMQW3cYZAQAAuo6gBwBXyTAMfXWhVkVll7wjdiXlTjW6fCdKmEzSjdfFeUbrmm+acvPgOEWwZh0AAOghBD0A/YLLbajw5EWdq6rToPhoTRieLEsXr3WrrG1U8anWpQ2OlDl0qbbRr11KXKT3ZiljMpI0Kt2mhGhrd30UAADQmy5flr71rebn+/ZJMTHBrSdABD0AYW/H0XItf79E5ZWtd7FMtUVr2exszcxpe6mTRpdb/yqv8i5vUFzm0L/P1/i1i4wwK2doQvPyBp5pmOlJMaxZBwBAuHC7pYMHW5+HCIIegLC242i5Hn/jsK6869TZyjo9/sZhrZ6Xrxm3DNFpx2XvHTCLyxw6erpS9U3+X+bDUwa0Lm1gT9TI1ARFRjAFEwAA9C0EPQBhy+U2tPz9Er+QJ8m77UdbihUXFaELNQ1+bWwxVp/16vLSE5U0ILJHawYAAOgOBD0ErDuucUL/ZRiGmtyGGl1uNbqafza5Wl63bmt0uZvbNbnV6PnZ5HarweX7vOmK41rP5Xntduv0pcs+0zXbUt/kVn1TgyLMJmUPTfAZrRueMoApmAAAICQR9BCQq7nGCT3D7TbU6G4ONE0utxraCjkuw7Pds83t9oSkK9t5AtQ3nrcboNwBBi23W41NvgGuyVNvX7Vo2k1a+B83KNpqCXYpAAAA3YKgh04Fco1TKIY9lyf0NLQxstRugHK71dDUElzaH01qK0x5g5E3MLX1nm0EKE/7Bs9zl7vvBqauslpMijCbZbWYZLWYZbWYFWExKdLzs2Vby/4Ii1mRLcdEmGU1m7zHtNXutOOy/lhY1mkdE4cPJOQBAICwQtBDhzq7xskkafn7JZpyY4rchlqn3nlHlDoJUAGOEl1d6LoiQHlGtRo9AcsIn7ykSE/IifALRibPvjbCkNmsyIiWoOV/TFeDVqRnf1uhre33N/X4tEiX29CeY1/rbGVdm33YJGmIrXkaMgAAQLtSUoJdQZcR9NChwpMXO7zGyZBUXlmnUc/+d+8V1UPMJvkEm+ZQ006AMpsUGdH888pjOgpakZ5jm0OSWdYOg1bzto4CVITZJEsvBKZQZTGbtGx2th5/47BMkk/Ya/mNLZudzbWmAACgfQMGSF9/Hewquoyghw6dq+r4RhZtiTCbvKM3bY0MtT8a5B+a/IKWJyS1Bq3Opv21PSpltZg8Qas1rPGf/fA0MydVq+fl+11jOoRrTAEAQBgj6KFDg+KjA2r3h0fHa8qNKbJaGF1C3zMzJ1V3Zg/hrrEAAKDfIOihQxOGJyvVFt3pNU7fuvk6/tOMPs1iNmnSDQODXQYAAAg1ly9Ls2Y1P//gAykmJrj1BMgc7ALQt7Vc4yS1XtPUgmucAAAAEPbcbmnv3uaH2x3sagJG0EOnWq5xGmLzncY5xBYdsksrAAAAAOGMqZsICNc4AQAAAKGDoIeAcY0TAAAAEBqYugkAAAAAYYagBwAAAABhhqmbAAAAANCR2NhgV9BlBD0AAAAAaM+AAVJNTbCr6DKmbgIAAABAmCHoAQAAAECYIegBAAAAQHvq6qT//M/mR11dsKsJGNfoAQAAAEB7XC5p+/bW5yGCET0AAAAACDMEPQAAAAAIMwQ9AAAAAAgzBD0AAAAACDMEPQAAAAAIM9x1MwQYhiFJcjqdQa4EAAAA6GdqalqfO51Bv/NmSyZoyQjtIeiFgKqqKkmS3W4PciUAAABAPzZ0aLAr8KqqqpLNZmt3v8noLAoi6Nxut86cOaP4+HiZTKY224wfP14HDhzo9FyBtOuojdPplN1uV1lZmRISEjovPkQE+vsLpffujvNe7Tm6ehz999rQf7v3HPTf3kX/7d5z0H97F/23e8/TV/uv1Lf6sGEYqqqq0tChQ2U2t38lHiN6IcBsNis9Pb3DNhaLJaBOF0i7QNokJCQEvZN3p0B/f6H03t1x3qs9R1ePo/9eG/pv956D/tu76L/dew76b++i/3bvefp6/5X6Th/uaCSvBTdjCRM/+MEPuq1doOcKJ8H8zD313t1x3qs9R1ePo/9eG/pv956D/tu76L/dew76b++i/3bveei/3Yupm+gSp9Mpm82mysrKPvHXDKAr6L8IZfRfhDL6L0JdKPZhRvTQJVFRUVq2bJmioqKCXQrQZfRfhDL6L0IZ/RehLhT7MCN6AAAAABBmGNEDAAAAgDBD0AMAAACAMEPQAwAAAIAwQ9ADAAAAgDBD0AMAAACAMEPQQ4+59957lZSUpLlz5wa7FKBT27ZtU1ZWlm666SatW7cu2OUAXcZ3LkJVWVmZvv3tbys7O1u5ubl6++23g10SELCqqiqNHz9eeXl5GjVqlNauXRvskrxYXgE9Zvfu3aqurtbGjRv1zjvvBLscoF1NTU3Kzs7W7t27lZCQoPz8fP3jH/9QcnJysEsDAsZ3LkJVeXm5KioqlJeXp3Pnzik/P1/Hjh3TgAEDgl0a0CmXy6X6+nrFxsaqtrZWOTk5OnDggAYOHBjs0hjRQ8+ZOnWq4uPjg10G0KnCwkLdcsstSktLU3x8vL773e/qww8/DHZZQJfwnYtQlZqaqry8PEnSoEGDlJycrIsXLwa3KCBAFotFsbGxkqS6ujq5XC71lXE0gl4/tW/fPs2ePVtDhw6VyWTSe++959dm1apVGj58uKKjozV27Fh99NFHvV8oEIBr7c9nzpxRWlqa93V6erpOnz7dG6UDkvhORmjrzv578OBBud1u2e32Hq4aaNYd/dfhcGj06NFKT0/X008/rZSUlF6qvmMEvX6qpqZGo0eP1iuvvNLm/q1bt2rRokX62c9+pqKiIt1+++2aNWuWSktLvW3Gjh2rnJwcv8eZM2d662MAkq69P7f1lzeTydSjNQPf1B3fyUCwdFf/vXDhgh5++GGtWbOmN8oGJHVP/01MTNSRI0d08uRJbd68WRUVFb1VfscM9HuSjHfffddn24QJE4yFCxf6bBsxYoSxZMmSLp179+7dxn333XetJQIBu5r+/Mknnxj33HOPd98Pf/hD48033+zxWoG2XMt3Mt+5CLar7b91dXXG7bffbmzatKk3ygTa1B3/J164cKHx1ltv9VSJXcKIHvw0NDTo0KFDmj59us/26dOna//+/UGqCrg6gfTnCRMm6OjRozp9+rSqqqq0fft2zZgxIxjlAn74TkYoC6T/GoahRx99VN/5znc0f/78YJQJtCmQ/ltRUSGn0ylJcjqd2rdvn7Kysnq91rZEBLsA9D3nz5+Xy+XS4MGDfbYPHjxYZ8+eDfg8M2bM0OHDh1VTU6P09HS9++67Gj9+fHeXC3QokP4cERGhl19+WVOnTpXb7dbTTz/dJ+6WBUiBfyfznYu+KJD++8knn2jr1q3Kzc31Xh/1+uuva9SoUb1dLuAjkP576tQpPfbYYzIMQ4Zh6IknnlBubm4wyvVD0EO7rrxGyTCMLl23xF0L0Zd01p/nzJmjOXPm9HZZQMA668N856Iv66j/3nbbbXK73cEoCwhIR/137NixKi4uDkJVnWPqJvykpKTIYrH4jd6dO3fO7y8aQF9Hf0aoow8jlNF/EcpCvf8S9OAnMjJSY8eO1c6dO32279y5U5MnTw5SVcDVoT8j1NGHEcrovwhlod5/mbrZT1VXV+v48ePe1ydPnlRxcbGSk5OVkZGhxYsXa/78+Ro3bpwmTZqkNWvWqLS0VAsXLgxi1UDb6M8IdfRhhDL6L0JZWPff4N3wE8G0e/duQ5Lf45FHHvG2efXVV41hw4YZkZGRRn5+vrF3797gFQx0gP6MUEcfRiij/yKUhXP/NRlGGysFAwAAAABCFtfoAQAAAECYIegBAAAAQJgh6AEAAABAmCHoAQAAAECYIegBAAAAQJgh6AEAAABAmCHoAQAAAECYIegBAAAAQJgh6AEAEGR79uyRyWSSw+EI+Jhnn31WeXl5PVYTACC0EfQAAOgl+/fvl8Vi0cyZM4NdCgAgzBH0AADoJevXr9eTTz6pjz/+WKWlpcEuBwAQxgh6AAD0gpqaGr311lt6/PHHddddd2nDhg3ttt2wYYMSExP13nvv6eabb1Z0dLTuvPNOlZWV+bV9/fXXlZmZKZvNpgcffFBVVVXefTt27NBtt92mxMREDRw4UHfddZdOnDjREx8PANDHEPQAAOgFW7duVVZWlrKysjRv3jz94Q9/kGEY7bavra3V888/r40bN+qTTz6R0+nUgw8+6NPmxIkTeu+997Rt2zZt27ZNe/fu1YsvvujdX1NTo8WLF+vAgQPatWuXzGaz7r33Xrnd7h77nACAviEi2AUAANAfFBQUaN68eZKkmTNnqrq6Wrt27dK0adPabN/Y2KhXXnlFEydOlCRt3LhRI0eOVGFhoSZMmCBJcrvd2rBhg+Lj4yVJ8+fP165du/T8889Lku677z6/GgYNGqSSkhLl5OT0yOcEAPQNjOgBANDDjh07psLCQu+IXEREhB544AGtX7++3WMiIiI0btw47+sRI0YoMTFRn3/+uXdbZmamN+RJUmpqqs6dO+d9feLECT300EO6/vrrlZCQoOHDh0sS1wcCQD/AiB4AAD2soKBATU1NSktL824zDENWq1WXLl1q9ziTydThNqvV6rfvm9MyZ8+eLbvdrrVr12ro0KFyu93KyclRQ0PDtXwcAEAIYEQPAIAe1NTUpE2bNunll19WcXGx93HkyBENGzZMb775ZrvHHTx40Pv62LFjcjgcGjFiREDve+HCBX3++ef6+c9/rjvuuEMjR47sMFQCAMILI3oAAPSgbdu26dKlS3rsscdks9l89s2dO1cFBQX67W9/63ec1WrVk08+qd///veyWq164okndOutt3qvz+tMUlKSBg4cqDVr1ig1NVWlpaVasmRJt3wmAEDfx4geAAA9qKCgQNOmTfMLeVLzzVKKi4t1+PBhv32xsbH6yU9+ooceekiTJk1STEyMtmzZEvD7ms1mbdmyRYcOHVJOTo6eeuoprVy58po+CwAgdJiMju7tDAAAet2GDRu0aNEiORyOYJcCAAhRjOgBAAAAQJgh6AEAAABAmGHqJgAAAACEGUb0AAAAACDMEPQAAAAAIMwQ9AAAAAAgzBD0AAAAACDMEPQAAAAAIMwQ9AAAAAAgzBD0AAAAACDMEPQAAAAAIMwQ9AAAAAAgzPx//K7mEHFPCCgAAAAASUVORK5CYII=", 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", "text/plain": [ "
" ] @@ -1883,7 +1887,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "id": "c563a304", "metadata": {}, "outputs": [], @@ -1911,7 +1915,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "id": "a6cbad72", "metadata": {}, "outputs": [ @@ -1941,7 +1945,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "id": "9486758b", "metadata": {}, "outputs": [ @@ -1981,7 +1985,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "id": "e748f60b", "metadata": {}, "outputs": [ @@ -1989,8 +1993,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Ridge Best R²: 0.9235033171939399\n", - "Lasso Results: [(0.1, 0.9231735440115759), (1, 0.923173544036331), (10, 0.9231735442838815), (100, 0.9231735467593862)]\n" + "Ridge Best R²: 0.9235033171939397\n", + "Lasso Results: [(0.1, 0.9231735440115761), (1, 0.923173544036331), (10, 0.9231735442838817), (100, 0.9231735467593863)]\n" ] } ], @@ -2052,13 +2056,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "id": "95f05430", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -2100,7 +2104,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "id": "1c06a3f5", "metadata": {}, "outputs": [ @@ -2163,7 +2167,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "id": "cad70f60", "metadata": {}, "outputs": [ @@ -2213,13 +2217,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "id": "47d5a571", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -2251,7 +2255,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 36, "id": "e6a10b89", "metadata": {}, "outputs": [ @@ -2260,7 +2264,7 @@ "output_type": "stream", "text": [ "Enter the specifications of the airplane:\n", - "Predicted Price: $38,822.34\n" + "Invalid input. Please enter numerical values.\n" ] } ], @@ -2316,7 +2320,7 @@ "id": "2b4de634", "metadata": {}, "source": [ - "### This block is designed to provide the price prediction of the airplane based on three user-provided specifications: all engine rate of climb, takeoff distance over 50ft, and range in nautical miles." + "### This block is designed to provide the price prediction of the airplane based on three user-provided specifications: all engine rate of climb, takeoff distance over 50ft, and range in nautical miles. (input numerical values for each \"engine rate of climb, takeoff over 50ft, range\")" ] } ], From 6fe36ecd6a40199c712abae88d0b79f213cb7f70 Mon Sep 17 00:00:00 2001 From: Kaustubh Dangche <99190858+Kaustubhdangche@users.noreply.github.com> Date: Thu, 21 Nov 2024 21:13:46 -0600 Subject: [PATCH 15/26] Update README.md --- README.md | 300 +++++++++++++++++++++++++++++++++++++++++++----------- 1 file changed, 240 insertions(+), 60 deletions(-) diff --git a/README.md b/README.md index f741294..c3d9094 100644 --- a/README.md +++ b/README.md @@ -1,63 +1,243 @@ -# Project 2 - -## Model Selection - -Implement generic k-fold cross-validation and bootstrapping model selection methods. - -How to use: This code uses packages such as pandas, numpy, statemodels, seaborn, and matplotlib. You need to install each packages. -- Open CMD -> pip install numpy -> pip install pandas -> pip install statsmodels -> pip install seaborn -> pip install matplotlib. -- After installing the package you can run each code blocks from top to bottom. -- The last code block allows you to get a prediction of the price of an certain airplane you are looking for.(input numerical values for each "engine rate of climb, takeoff over 50ft, range") - -** Do your cross-validation and bootstrapping model selectors agree with a simpler model selector like AIC in simple cases (like linear regression)?** - - Yes. $R^2$ of Ridge Regression is 0.92 and mean $R^2$ of our cross-validation is 0.79 and bootstrapping model is 0.81. - -** In what cases might the methods you've written fail or give incorrect or undesirable results?** - 1. High-Dimensional Data or Multicollinearity: - - Cross-validation may overestimate performance if features are highly correlated. - - Bootstrapping might not capture variability well with duplicate features. - 2. Imbalanced Data: - - Both methods can produce misleading results when the target variable is heavily skewed. - 3. Small Dataset: - - Cross-validation might struggle to split data effectively, leading to unstable results. - - Bootstrapping may produce overly optimistic results by repeatedly sampling the same data points. - 4. Outliers: - - Outliers can distort performance metrics and affect evaluation accuracy. - 5. Complex Models: - - Non-linear interactions or complex dependencies might not be fully captured by either method. - -** What could you implement given more time to mitigate these cases or help users of your methods?** - - 1. Improve Data Quality: - - Use better imputation methods and add more relevant data, like market trends or historical prices, to provide richer context. - 2. Try Better Models: - - Experiment with advanced models like gradient boosting, neural networks, or model ensembles to capture complex relationships. - 3. Improve Evaluation: - - Use smarter validation methods, like stratified k-fold, and analyze residuals to catch biases or patterns we missed. - 4. Simplify Use: - - Build an interactive dashboard for predictions and allow users to test "what-if" scenarios for better decision-making. - 5. Fix Limitations: - - Address multicollinearity with Elastic Net regularization and confirm model stability with thorough bootstrapping. - 6. Automate & Scale: - - Automate preprocessing and deploy the model as an API for real-time predictions. - -** What parameters have you exposed to your users in order to use your model selectors.** - 1. Cross-Validation: - - cv_folds: Number of folds. - - scoring_metric: Metric for evaluation. - - shuffle: Whether to shuffle data before splitting. - 2. Bootstrapping: - - n_iterations: Number of bootstrap iterations. - - random_seed: Seed for reproducibility. - - metric: Metric to evaluate performance. - 3. Hyperparameter Tuning: - - alpha_range: Range of regularization parameters for Ridge/Lasso. - 4. Feature Selection: - - correlation_threshold: Minimum correlation value for feature inclusion. - 5. Model Selection: - - Options for Ridge, Lasso, or Linear Regression. - - By exposing these parameters, users can customize the evaluation process to fit their specific data and model requirements, ensuring better control over regularization and performance evaluation. +# Project 2 : Model Selection + +## Implement generic k-fold cross-validation and bootstrapping model selection methods. + +### Overview: + +This project implements Ridge Regression for predicting airplane prices based on critical features such as engine rate of climb, takeoff distance, and range. The project focuses on model evaluation through k-fold cross-validation and bootstrapping methods, providing robust model selection while adhering to statistical principles. + +Our implementation excludes high-level machine learning libraries like scikit-learn, relying instead on custom-built functions to ensure transparency and deeper understanding of Ridge Regression. + + +### How to Run the Code: + +1. Install Dependencies: Ensure the following Python libraries are installed: +``` +pip install numpy pandas matplotlib seaborn +``` + +2. Load the Notebook: +Open the Plane_Price.ipynb Jupyter Notebook and execute cells sequentially. Ensure paths to datasets are hardcoded as instructed. + +3. Predict Prices: + + To predict airplane prices: + + - Use the final section titled Airplane Price Predictor. + - Provide user inputs such as "Engine rate of climb," "Takeoff distance," and "Range." + - View the predicted price based on precomputed model weights. + + + +### How to Run the Code :- + +1. Clone the repository or download the notebook and data files. +2. Ensure the dataset is saved in the same directory as the notebook. Hardcode the dataset path in the notebook if required (e.g., df = pd.read_csv('plane Price.csv')). + +3. Install the required libraries: + +''' +pip install numpy +pip install pandas +pip install statsmodels +pip install seaborn +pip install matplotlib + +''' + +4. Execute the cells step by step to preprocess data, train models, and evaluate performance. +5. The last code block allows you to get a prediction of the price of an certain airplane you are looking for.(input numerical values for each "engine rate of climb, takeoff over 50ft, range") + + +## Answers to README Questions : + +### 1. Do your cross-validation and bootstrapping model selectors agree with a simpler model selector like AIC in simple cases (like linear regression)? + +### Ans : + +Yes, the cross-validation and bootstrapping results align closely with the AIC (Akaike Information Criterion) approach in simple cases like linear regression. +The following observations were noted : + +• Both methods confirmed the same regularization strength (alpha) as optimal during Ridge Regression hyperparameter tuning. +• AIC tends to minimize the residual error while penalizing model complexity. Similarly, k-fold cross-validation minimizes prediction error, and bootstrapping + validates model stability by testing on resampled datasets. +• Thus, in simple cases, the model selectors provide consistent results and help confirm that the regularization and model complexity are well-balanced. + + +### 2. In what cases might the methods you've written fail or give incorrect or undesirable results? + +### Ans : + +The methods may fail or provide undesirable results in the following scenarios: + +• Non-linear Relationships: Ridge Regression assumes linear relationships between predictors and the target. It may not perform well if the underlying + relationship is non-linear. +• Outliers in the Dataset: Although Ridge reduces the impact of multicollinearity, it does not address outliers, which may skew predictions. +• Overfitting in Small Datasets: Bootstrapping can lead to overfitting when the dataset size is small, as samples may not represent the true data distribution. +• Multicollinearity in k-fold Splits: If the feature selection is not consistent across folds, k-fold cross-validation may produce unstable results. + + + +### 3. What could you implement given more time to mitigate these cases or help users of your methods? + +### Ans : + +With additional time, the following enhancements could be implemented: + +• Polynomial Feature Transformation: To account for non-linear relationships between features and the target variable. +• Outlier Detection: Add preprocessing steps to detect and handle outliers, such as robust scaling or removing extreme values. +• Stratified Cross-Validation: Ensure more representative splits in k-fold cross-validation, particularly for datasets with imbalanced distributions. +• Dynamic Feature Selection: Incorporate automated feature selection or dimensionality reduction techniques, such as PCA, to improve model interpretability. + + + +### 4. What parameters have you exposed to your users in order to use your model selectors? + +### Ans :- +The implementation exposes the following parameters for users to fine-tune the model: + +1. Alpha (Regularization Strength): Users can adjust alpha to control the magnitude of regularization. A higher alpha shrinks coefficients more aggressively, addressing overfitting. + +2. k (Number of Folds in k-fold Cross-Validation): Users can specify the number of folds to evaluate model performance across different data splits. + +3. Bootstrap Iterations: Allows users to configure the number of resampling iterations to estimate model stability. + +4. Weights and Bias Term: Trained weights are provided, enabling users to test manual predictions with their own input data. + + + + +### Libraries Used : + +• NumPy: For numerical computations, including matrix operations and random sampling for bootstrapping. +• Pandas: For data manipulation and preprocessing. +• Matplotlib and Seaborn: For data visualization, including plotting the correlation matrix, residual analysis, and performance evaluations. +• Statsmodels: To compute Variance Inflation Factor (VIF) for multicollinearity analysis. + + + +### Key Components of the Code: + +1. Correlation Matrix: + + • Purpose: Identifies relationships between variables and highlights features strongly correlated with Price. + • Why: Aids feature selection and avoids multicollinearity, improving model efficiency. + +2.VIF Analysis: + + • Purpose: Measures multicollinearity and removes variables with high VIF. + • Why: Ensures stable and interpretable model coefficients. + +3.Ridge Regression: + + • Purpose: Regularizes the model to handle multicollinearity and prevent overfitting. + • Why: Enhances generalizability by balancing bias and variance. + +4.Hyperparameter Tuning: + + • Purpose: Fine-tunes alpha using cross-validation for optimal regularization. + • Why: Achieves the best trade-off between bias and variance. + +5.Bootstrapping: + + • Purpose: Validates model stability by evaluating R² across multiple resampled datasets. + • Why: Ensures consistent performance under different conditions. + +6.Adjusted R²: + + • Purpose: Evaluates model fit while penalizing unnecessary complexity. + • Why: Prevents overfitting by adding irrelevant predictors. + +7.Visualization: + + • Purpose: Displays predicted vs. actual prices and residual analysis to evaluate model accuracy. + • Why: Demonstrates the goodness of fit and identifies potential deviations + + + + + +### Airplane Price Predictor: + +1. Purpose: + + • An interactive module that predicts airplane prices based on user input. + • Demonstrates the model’s real-world application. + +2. How It Works: + + • Inputs: Accepts user specifications for features like rate of climb, takeoff distance, and range. + • Scaling: Standardizes inputs using precomputed means and standard deviations. + • Prediction: Applies the trained Ridge Regression model to compute the price. + + +3. Why Include It: + + • Practical Utility: Showcases how the model can be used for decision-making in aviation pricing. + • Stakeholder Engagement: Provides an interactive way to demonstrate the model’s relevance. + + + +### Code Usage: Example of Using Code : + +The project involves multiple steps for data preprocessing, model training, and evaluation. Below is an example of how to use the code: + +#### Run the Ridge Regression Model: + +• Load the dataset and preprocess it (e.g., handle missing values, scale features, compute the correlation matrix). +• Train the Ridge Regression model with different alpha values to identify the optimal regularization strength. + +#### Hyperparameter Tuning: + +• Use k-fold cross-validation to evaluate model performance across splits. +• Perform bootstrapping to validate model stability under resampling conditions. + +#### Manual Prediction: + +In the final block titled "Airplane Price Predictor," you can input airplane specifications to get a price prediction. + +Example: + +''' +# Example Inputs +Enter engine rate of climb (ft/min): 1800 +Enter takeoff distance over 50ft (ft): 4800 +Enter range (nautical miles): 2000 + +# Output +Predicted Price: $2,750,000.00 + +''' + + +### Visualization of Results : + +The project uses data visualization extensively to interpret results and validate model performance. Below are the key visual outputs included in the project: + +1. Correlation Matrix: + + • A heatmap visualizing the relationships between features. + • Example: Features like "Takeoff Distance" and "Engine Rate of Climb" show strong correlations with the price, aiding in feature selection. + +2. Residual Analysis: + + • Residual plots evaluate the accuracy of predictions. + • Example: Residuals scatter symmetrically around zero, indicating a well-fitted model. + +3. Alpha Tuning (Ridge Regression): + + • A graph shows R² values for different regularization strengths (alpha) during hyperparameter tuning. + • Example: The curve helps identify the alpha value that provides the best trade-off between bias and variance. + +4. Predicted vs. Actual Prices: + + • A scatter plot comparing model predictions with actual prices. + • Example: Points align closely to the diagonal "Perfect Fit" line, demonstrating good model predictions. + + + + + See sections 7.10-7.11 of Elements of Statistical Learning and the lecture notes. Pay particular attention to Section 7.10.2. From 6274db9f25970b108279bd0c70ccdeb4c934354a Mon Sep 17 00:00:00 2001 From: Kaustubh Dangche <99190858+Kaustubhdangche@users.noreply.github.com> Date: Thu, 21 Nov 2024 21:17:48 -0600 Subject: [PATCH 16/26] Update README.md --- README.md | 61 +++++++++++++++++++++++++++++++------------------------ 1 file changed, 34 insertions(+), 27 deletions(-) diff --git a/README.md b/README.md index c3d9094..84876d8 100644 --- a/README.md +++ b/README.md @@ -9,26 +9,6 @@ This project implements Ridge Regression for predicting airplane prices based on Our implementation excludes high-level machine learning libraries like scikit-learn, relying instead on custom-built functions to ensure transparency and deeper understanding of Ridge Regression. -### How to Run the Code: - -1. Install Dependencies: Ensure the following Python libraries are installed: -``` -pip install numpy pandas matplotlib seaborn -``` - -2. Load the Notebook: -Open the Plane_Price.ipynb Jupyter Notebook and execute cells sequentially. Ensure paths to datasets are hardcoded as instructed. - -3. Predict Prices: - - To predict airplane prices: - - - Use the final section titled Airplane Price Predictor. - - Provide user inputs such as "Engine rate of climb," "Takeoff distance," and "Range." - - View the predicted price based on precomputed model weights. - - - ### How to Run the Code :- 1. Clone the repository or download the notebook and data files. @@ -36,7 +16,7 @@ Open the Plane_Price.ipynb Jupyter Notebook and execute cells sequentially. Ensu 3. Install the required libraries: -''' +```python pip install numpy pip install pandas pip install statsmodels @@ -59,8 +39,10 @@ Yes, the cross-validation and bootstrapping results align closely with the AIC ( The following observations were noted : • Both methods confirmed the same regularization strength (alpha) as optimal during Ridge Regression hyperparameter tuning. + • AIC tends to minimize the residual error while penalizing model complexity. Similarly, k-fold cross-validation minimizes prediction error, and bootstrapping validates model stability by testing on resampled datasets. + • Thus, in simple cases, the model selectors provide consistent results and help confirm that the regularization and model complexity are well-balanced. @@ -72,8 +54,11 @@ The methods may fail or provide undesirable results in the following scenarios: • Non-linear Relationships: Ridge Regression assumes linear relationships between predictors and the target. It may not perform well if the underlying relationship is non-linear. + • Outliers in the Dataset: Although Ridge reduces the impact of multicollinearity, it does not address outliers, which may skew predictions. + • Overfitting in Small Datasets: Bootstrapping can lead to overfitting when the dataset size is small, as samples may not represent the true data distribution. + • Multicollinearity in k-fold Splits: If the feature selection is not consistent across folds, k-fold cross-validation may produce unstable results. @@ -85,8 +70,11 @@ The methods may fail or provide undesirable results in the following scenarios: With additional time, the following enhancements could be implemented: • Polynomial Feature Transformation: To account for non-linear relationships between features and the target variable. + • Outlier Detection: Add preprocessing steps to detect and handle outliers, such as robust scaling or removing extreme values. + • Stratified Cross-Validation: Ensure more representative splits in k-fold cross-validation, particularly for datasets with imbalanced distributions. + • Dynamic Feature Selection: Incorporate automated feature selection or dimensionality reduction techniques, such as PCA, to improve model interpretability. @@ -110,8 +98,11 @@ The implementation exposes the following parameters for users to fine-tune the m ### Libraries Used : • NumPy: For numerical computations, including matrix operations and random sampling for bootstrapping. + • Pandas: For data manipulation and preprocessing. + • Matplotlib and Seaborn: For data visualization, including plotting the correlation matrix, residual analysis, and performance evaluations. + • Statsmodels: To compute Variance Inflation Factor (VIF) for multicollinearity analysis. @@ -121,36 +112,43 @@ The implementation exposes the following parameters for users to fine-tune the m 1. Correlation Matrix: • Purpose: Identifies relationships between variables and highlights features strongly correlated with Price. + • Why: Aids feature selection and avoids multicollinearity, improving model efficiency. 2.VIF Analysis: • Purpose: Measures multicollinearity and removes variables with high VIF. + • Why: Ensures stable and interpretable model coefficients. 3.Ridge Regression: • Purpose: Regularizes the model to handle multicollinearity and prevent overfitting. + • Why: Enhances generalizability by balancing bias and variance. 4.Hyperparameter Tuning: • Purpose: Fine-tunes alpha using cross-validation for optimal regularization. + • Why: Achieves the best trade-off between bias and variance. 5.Bootstrapping: • Purpose: Validates model stability by evaluating R² across multiple resampled datasets. + • Why: Ensures consistent performance under different conditions. 6.Adjusted R²: • Purpose: Evaluates model fit while penalizing unnecessary complexity. + • Why: Prevents overfitting by adding irrelevant predictors. 7.Visualization: • Purpose: Displays predicted vs. actual prices and residual analysis to evaluate model accuracy. + • Why: Demonstrates the goodness of fit and identifies potential deviations @@ -162,18 +160,22 @@ The implementation exposes the following parameters for users to fine-tune the m 1. Purpose: • An interactive module that predicts airplane prices based on user input. + • Demonstrates the model’s real-world application. -2. How It Works: +3. How It Works: • Inputs: Accepts user specifications for features like rate of climb, takeoff distance, and range. + • Scaling: Standardizes inputs using precomputed means and standard deviations. + • Prediction: Applies the trained Ridge Regression model to compute the price. -3. Why Include It: +5. Why Include It: • Practical Utility: Showcases how the model can be used for decision-making in aviation pricing. + • Stakeholder Engagement: Provides an interactive way to demonstrate the model’s relevance. @@ -185,11 +187,13 @@ The project involves multiple steps for data preprocessing, model training, and #### Run the Ridge Regression Model: • Load the dataset and preprocess it (e.g., handle missing values, scale features, compute the correlation matrix). + • Train the Ridge Regression model with different alpha values to identify the optimal regularization strength. #### Hyperparameter Tuning: • Use k-fold cross-validation to evaluate model performance across splits. + • Perform bootstrapping to validate model stability under resampling conditions. #### Manual Prediction: @@ -198,15 +202,14 @@ In the final block titled "Airplane Price Predictor," you can input airplane spe Example: -''' -# Example Inputs +```python +# Example Inputs : Enter engine rate of climb (ft/min): 1800 Enter takeoff distance over 50ft (ft): 4800 Enter range (nautical miles): 2000 -# Output +# Output : Predicted Price: $2,750,000.00 - ''' @@ -217,21 +220,25 @@ The project uses data visualization extensively to interpret results and validat 1. Correlation Matrix: • A heatmap visualizing the relationships between features. + • Example: Features like "Takeoff Distance" and "Engine Rate of Climb" show strong correlations with the price, aiding in feature selection. 2. Residual Analysis: • Residual plots evaluate the accuracy of predictions. + • Example: Residuals scatter symmetrically around zero, indicating a well-fitted model. 3. Alpha Tuning (Ridge Regression): • A graph shows R² values for different regularization strengths (alpha) during hyperparameter tuning. + • Example: The curve helps identify the alpha value that provides the best trade-off between bias and variance. 4. Predicted vs. Actual Prices: • A scatter plot comparing model predictions with actual prices. + • Example: Points align closely to the diagonal "Perfect Fit" line, demonstrating good model predictions. From c029524e71e8f2ca53506915d8677b3aedce3d75 Mon Sep 17 00:00:00 2001 From: Kaustubh Dangche <99190858+Kaustubhdangche@users.noreply.github.com> Date: Thu, 21 Nov 2024 21:18:45 -0600 Subject: [PATCH 17/26] Update README.md --- README.md | 1 - 1 file changed, 1 deletion(-) diff --git a/README.md b/README.md index 84876d8..a372c77 100644 --- a/README.md +++ b/README.md @@ -22,7 +22,6 @@ pip install pandas pip install statsmodels pip install seaborn pip install matplotlib - ''' 4. Execute the cells step by step to preprocess data, train models, and evaluate performance. From 0e46dc76b0c88ff0d9a029f21b61715d076e2946 Mon Sep 17 00:00:00 2001 From: Kaustubh Dangche <99190858+Kaustubhdangche@users.noreply.github.com> Date: Thu, 21 Nov 2024 21:19:49 -0600 Subject: [PATCH 18/26] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index a372c77..2c2df20 100644 --- a/README.md +++ b/README.md @@ -22,7 +22,7 @@ pip install pandas pip install statsmodels pip install seaborn pip install matplotlib -''' +``` 4. Execute the cells step by step to preprocess data, train models, and evaluate performance. 5. The last code block allows you to get a prediction of the price of an certain airplane you are looking for.(input numerical values for each "engine rate of climb, takeoff over 50ft, range") @@ -209,7 +209,7 @@ Enter range (nautical miles): 2000 # Output : Predicted Price: $2,750,000.00 -''' +``` ### Visualization of Results : From bd666062a5e266d7a551a148928b612a77514342 Mon Sep 17 00:00:00 2001 From: Kaustubh Dangche <99190858+Kaustubhdangche@users.noreply.github.com> Date: Thu, 21 Nov 2024 21:23:03 -0600 Subject: [PATCH 19/26] Update README.md --- README.md | 26 ++++++++++++++------------ 1 file changed, 14 insertions(+), 12 deletions(-) diff --git a/README.md b/README.md index 2c2df20..adf7ee1 100644 --- a/README.md +++ b/README.md @@ -12,6 +12,7 @@ Our implementation excludes high-level machine learning libraries like scikit-le ### How to Run the Code :- 1. Clone the repository or download the notebook and data files. + 2. Ensure the dataset is saved in the same directory as the notebook. Hardcode the dataset path in the notebook if required (e.g., df = pd.read_csv('plane Price.csv')). 3. Install the required libraries: @@ -25,6 +26,7 @@ pip install matplotlib ``` 4. Execute the cells step by step to preprocess data, train models, and evaluate performance. + 5. The last code block allows you to get a prediction of the price of an certain airplane you are looking for.(input numerical values for each "engine rate of climb, takeoff over 50ft, range") @@ -110,27 +112,27 @@ The implementation exposes the following parameters for users to fine-tune the m 1. Correlation Matrix: - • Purpose: Identifies relationships between variables and highlights features strongly correlated with Price. + • Purpose: Identifies relationships between variables and highlights features strongly correlated with Price. - • Why: Aids feature selection and avoids multicollinearity, improving model efficiency. + • Why: Aids feature selection and avoids multicollinearity, improving model efficiency. 2.VIF Analysis: - • Purpose: Measures multicollinearity and removes variables with high VIF. + • Purpose: Measures multicollinearity and removes variables with high VIF. - • Why: Ensures stable and interpretable model coefficients. + • Why: Ensures stable and interpretable model coefficients. 3.Ridge Regression: - • Purpose: Regularizes the model to handle multicollinearity and prevent overfitting. + • Purpose: Regularizes the model to handle multicollinearity and prevent overfitting. - • Why: Enhances generalizability by balancing bias and variance. + • Why: Enhances generalizability by balancing bias and variance. 4.Hyperparameter Tuning: - • Purpose: Fine-tunes alpha using cross-validation for optimal regularization. + • Purpose: Fine-tunes alpha using cross-validation for optimal regularization. - • Why: Achieves the best trade-off between bias and variance. + • Why: Achieves the best trade-off between bias and variance. 5.Bootstrapping: @@ -140,15 +142,15 @@ The implementation exposes the following parameters for users to fine-tune the m 6.Adjusted R²: - • Purpose: Evaluates model fit while penalizing unnecessary complexity. + • Purpose: Evaluates model fit while penalizing unnecessary complexity. - • Why: Prevents overfitting by adding irrelevant predictors. + • Why: Prevents overfitting by adding irrelevant predictors. 7.Visualization: - • Purpose: Displays predicted vs. actual prices and residual analysis to evaluate model accuracy. + • Purpose: Displays predicted vs. actual prices and residual analysis to evaluate model accuracy. - • Why: Demonstrates the goodness of fit and identifies potential deviations + • Why: Demonstrates the goodness of fit and identifies potential deviations From 8ffc763de62100f21ab5983e66208f7961c933cb Mon Sep 17 00:00:00 2001 From: Kaustubh Dangche <99190858+Kaustubhdangche@users.noreply.github.com> Date: Thu, 21 Nov 2024 21:24:08 -0600 Subject: [PATCH 20/26] Update README.md --- README.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index adf7ee1..8e17cdd 100644 --- a/README.md +++ b/README.md @@ -116,37 +116,37 @@ The implementation exposes the following parameters for users to fine-tune the m • Why: Aids feature selection and avoids multicollinearity, improving model efficiency. -2.VIF Analysis: +2. VIF Analysis: • Purpose: Measures multicollinearity and removes variables with high VIF. • Why: Ensures stable and interpretable model coefficients. -3.Ridge Regression: +3. Ridge Regression: • Purpose: Regularizes the model to handle multicollinearity and prevent overfitting. • Why: Enhances generalizability by balancing bias and variance. -4.Hyperparameter Tuning: +4. Hyperparameter Tuning: • Purpose: Fine-tunes alpha using cross-validation for optimal regularization. • Why: Achieves the best trade-off between bias and variance. -5.Bootstrapping: +5. Bootstrapping: • Purpose: Validates model stability by evaluating R² across multiple resampled datasets. • Why: Ensures consistent performance under different conditions. -6.Adjusted R²: +6. Adjusted R²: • Purpose: Evaluates model fit while penalizing unnecessary complexity. • Why: Prevents overfitting by adding irrelevant predictors. -7.Visualization: +7. Visualization: • Purpose: Displays predicted vs. actual prices and residual analysis to evaluate model accuracy. From 426f07b46d9e0057800fc72722b121721dd24d4a Mon Sep 17 00:00:00 2001 From: Kaustubh Dangche <99190858+Kaustubhdangche@users.noreply.github.com> Date: Thu, 21 Nov 2024 21:31:16 -0600 Subject: [PATCH 21/26] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 8e17cdd..3709702 100644 --- a/README.md +++ b/README.md @@ -27,7 +27,7 @@ pip install matplotlib 4. Execute the cells step by step to preprocess data, train models, and evaluate performance. -5. The last code block allows you to get a prediction of the price of an certain airplane you are looking for.(input numerical values for each "engine rate of climb, takeoff over 50ft, range") +5. The last code block allows you to input numerical values for 'engine rate of climb', 'takeoff over 50ft', and 'range' to get a predicted airplane price. ## Answers to README Questions : From 0c7b2195f3389f01275bb120b3c0c4f742ddc5e0 Mon Sep 17 00:00:00 2001 From: Kaustubh Dangche <99190858+Kaustubhdangche@users.noreply.github.com> Date: Thu, 21 Nov 2024 21:46:00 -0600 Subject: [PATCH 22/26] Update README.md --- README.md | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 3709702..fbda0ba 100644 --- a/README.md +++ b/README.md @@ -53,8 +53,7 @@ The following observations were noted : The methods may fail or provide undesirable results in the following scenarios: -• Non-linear Relationships: Ridge Regression assumes linear relationships between predictors and the target. It may not perform well if the underlying - relationship is non-linear. +• Non-linear Relationships: Ridge Regression assumes linear relationships between predictors and the target. It might not function effectively if the underlying. • Outliers in the Dataset: Although Ridge reduces the impact of multicollinearity, it does not address outliers, which may skew predictions. @@ -162,7 +161,7 @@ The implementation exposes the following parameters for users to fine-tune the m • An interactive module that predicts airplane prices based on user input. - • Demonstrates the model’s real-world application. + • Shows how the model is used in the real world. 3. How It Works: From cd630ff292c54e97fde69540cc50b800bf639b61 Mon Sep 17 00:00:00 2001 From: hha980510 Date: Thu, 21 Nov 2024 21:49:36 -0600 Subject: [PATCH 23/26] Add files via upload --- Plane_price.ipynb | 196 +++++++++++++++++++++++++++++++++------------- 1 file changed, 143 insertions(+), 53 deletions(-) diff --git a/Plane_price.ipynb b/Plane_price.ipynb index bad2e92..4a02ac0 100644 --- a/Plane_price.ipynb +++ b/Plane_price.ipynb @@ -5,21 +5,34 @@ "id": "d1dea79a", "metadata": {}, "source": [ - "# Plane_price prediction " + "# CS584 Machine Learning Project 2" ] }, { "cell_type": "markdown", - "id": "9dc32f10", + "id": "0aa125a3", "metadata": {}, "source": [ - "CS584 Machine Learning\n", - "Project 2\n", - "\n", - "A20557555 Hyunsung Ha\n", - "A20550806 Kaustubh Dangche\n", - "A20487452 Nam Gyu Lee\n", - "A20568373 Anu Singh" + "# Plane_price prediction" + ] + }, + { + "cell_type": "markdown", + "id": "84f9d38d", + "metadata": {}, + "source": [ + "### Kaustubh Dangche - A20550806\n", + "### Hyunsung Ha - A20557555\n", + "### Anu Singh - A20568373\n", + "### Nam Gyu Lee - A20487452" + ] + }, + { + "cell_type": "markdown", + "id": "48a6e5eb", + "metadata": {}, + "source": [ + "#" ] }, { @@ -27,7 +40,7 @@ "id": "5ad0a1d0", "metadata": {}, "source": [ - " ### We decided to go with Model Selection method for our project." + " ## We decided to go with Model Selection method for our project." ] }, { @@ -45,7 +58,10 @@ "metadata": {}, "outputs": [], "source": [ - "# import required libraries :- \n", + "# Import essential data manipulation and visualization libraries\n", + "# pandas: For efficient data handling and analysis\n", + "# numpy: For numerical operations and array manipulations\n", + "# matplotlib.pyplot: For creating static, animated, and interactive \n", "\n", "import pandas as pd \n", "import numpy as np \n", @@ -263,7 +279,7 @@ } ], "source": [ - "# Check total columns & rows present in the dataset\n", + "# To Check total columns & rows present in the dataset\n", "df.shape" ] }, @@ -304,6 +320,7 @@ } ], "source": [ + "# To check the datatype of columns\n", "df.info()" ] }, @@ -533,7 +550,9 @@ "metadata": {}, "source": [ "\n", - "### This step fills all missing values in numerical columns using their respective median values. The median is chosen because it is less sensitive to outliers compared to the mean, ensuring that imputed values do not distort the data distribution. After applying the `fillna()` method, a verification step confirms that no missing values remain in the dataset. This ensures the data is complete and ready for further analysis or modeling." + "### Fill missing values in numerical columns using their respective median values. The median is chosen as it is robust to outliers and better represents the data's central tendency. The `numeric_only=True` parameter ensures only numerical columns are processed, leaving non-numeric columns (e.g., object-type) unaffected. The `inplace=True` parameter applies changes directly to the DataFrame.\n", + "\n", + "### After applying `fillna()`, the `df.isnull().sum()` verification step counts remaining missing values. Non-zero counts for some columns indicate missing values in non-numeric (e.g., object-type) columns, which may need separate handling. This ensures numerical data is ready for analysis or modeling." ] }, { @@ -796,7 +815,7 @@ "id": "ba0c9e9b", "metadata": {}, "source": [ - "### Displays unique values [0, 1] for the binary column Engine Type_piston & Engine Type_propjet, confirming successful one-hot encoding." + "### Check unique values in the one-hot encoded columns 'Engine Type_piston' and 'Engine Type_propjet'. The unique values [0, 1] confirm that one-hot encoding was applied successfully, representing the binary presence (1) or absence (0) of each category in the original 'Engine Type' column." ] }, { @@ -811,7 +830,7 @@ "text": [ "<>:14: SyntaxWarning: invalid escape sequence '\\d'\n", "<>:14: SyntaxWarning: invalid escape sequence '\\d'\n", - "C:\\Users\\Hyunsung Ha\\AppData\\Local\\Temp\\ipykernel_25460\\3091194593.py:14: SyntaxWarning: invalid escape sequence '\\d'\n", + "C:\\Users\\Hyunsung Ha\\AppData\\Local\\Temp\\ipykernel_4344\\3091194593.py:14: SyntaxWarning: invalid escape sequence '\\d'\n", " df[col] = df[col].str.replace(',', '').str.extract('(\\d+)', expand=False).astype(float)\n" ] }, @@ -982,49 +1001,49 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\Hyunsung Ha\\AppData\\Local\\Temp\\ipykernel_25460\\167143364.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", + "C:\\Users\\Hyunsung Ha\\AppData\\Local\\Temp\\ipykernel_4344\\167143364.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", "\n", "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", "\n", "\n", " df[col].fillna(df[col].median(), inplace=True)\n", - "C:\\Users\\Hyunsung Ha\\AppData\\Local\\Temp\\ipykernel_25460\\167143364.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", + "C:\\Users\\Hyunsung Ha\\AppData\\Local\\Temp\\ipykernel_4344\\167143364.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", "\n", "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", "\n", "\n", " df[col].fillna(df[col].median(), inplace=True)\n", - "C:\\Users\\Hyunsung Ha\\AppData\\Local\\Temp\\ipykernel_25460\\167143364.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", + "C:\\Users\\Hyunsung Ha\\AppData\\Local\\Temp\\ipykernel_4344\\167143364.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", "\n", "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", "\n", "\n", " df[col].fillna(df[col].median(), inplace=True)\n", - "C:\\Users\\Hyunsung Ha\\AppData\\Local\\Temp\\ipykernel_25460\\167143364.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", + "C:\\Users\\Hyunsung Ha\\AppData\\Local\\Temp\\ipykernel_4344\\167143364.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", "\n", "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", "\n", "\n", " df[col].fillna(df[col].median(), inplace=True)\n", - "C:\\Users\\Hyunsung Ha\\AppData\\Local\\Temp\\ipykernel_25460\\167143364.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", + "C:\\Users\\Hyunsung Ha\\AppData\\Local\\Temp\\ipykernel_4344\\167143364.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", "\n", "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", "\n", "\n", " df[col].fillna(df[col].median(), inplace=True)\n", - "C:\\Users\\Hyunsung Ha\\AppData\\Local\\Temp\\ipykernel_25460\\167143364.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", + "C:\\Users\\Hyunsung Ha\\AppData\\Local\\Temp\\ipykernel_4344\\167143364.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", "\n", "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", "\n", "\n", " df[col].fillna(df[col].median(), inplace=True)\n", - "C:\\Users\\Hyunsung Ha\\AppData\\Local\\Temp\\ipykernel_25460\\167143364.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", + "C:\\Users\\Hyunsung Ha\\AppData\\Local\\Temp\\ipykernel_4344\\167143364.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", "\n", "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", @@ -1277,6 +1296,7 @@ } ], "source": [ + "# To check how does the data looks mathematically\n", "df.describe()" ] }, @@ -1337,6 +1357,10 @@ } ], "source": [ + "# Importing data visualization libraries\n", + "# sns: Seaborn for statistical data visualization\n", + "# plt: Matplotlib's pyplot for creating static, animated, and interactive visualizations\n", + "\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "\n", @@ -1416,7 +1440,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.14.4\n", "Variance Inflation Factor (VIF):\n", " Feature VIF\n", "0 Rcmnd cruise Knots 44.740812\n", @@ -1431,9 +1454,6 @@ } ], "source": [ - "import statsmodels\n", - "print(statsmodels.__version__)\n", - "\n", "# Step 1: Define the original features and target\n", "features = ['Rcmnd cruise Knots', 'Max speed Knots', 'All eng rate of climb', \n", " 'Stall Knots dirty', 'Takeoff over 50ft', 'Range N.M.', 'Eng out rate of climb']\n", @@ -1639,7 +1659,9 @@ "metadata": {}, "outputs": [], "source": [ - "# Scale features\n", + "# Define a function to standardize features using z-score normalization\n", + "# This transformation centers the data around 0 with a standard deviation of 1\n", + "\n", "def scale_features(X):\n", " return (X - np.mean(X, axis=0)) / np.std(X, axis=0)\n", "\n", @@ -1724,7 +1746,9 @@ "metadata": {}, "outputs": [], "source": [ - "# Define r_squared function\n", + "# Define function to calculate R-squared \n", + "# It measures the proportion of variance in the dependent variable\n", + "# that is predictable from the independent variables\n", "def r_squared(y_true, y_pred):\n", " ss_total = np.sum((y_true - np.mean(y_true)) ** 2)\n", " ss_residual = np.sum((y_true - y_pred) ** 2)\n", @@ -1754,11 +1778,13 @@ "metadata": {}, "outputs": [], "source": [ - "# Add bias column\n", + "# Add a bias term (column of ones) to training and testing datasets\n", + "# This allows the model to learn an intercept term in linear regression\n", "X_train_with_bias = np.c_[np.ones(X_train.shape[0]), X_train]\n", "X_test_with_bias = np.c_[np.ones(X_test.shape[0]), X_test]\n", "\n", - "# Train a Linear Regression model\n", + "# Calculate optimal weights for linear regression using the normal equation\n", + "# This method directly computes the weights that minimize the sum of squared residuals\n", "weights = np.linalg.inv(X_train_with_bias.T @ X_train_with_bias) @ X_train_with_bias.T @ y_train\n" ] }, @@ -1804,6 +1830,11 @@ "# Test Ridge Regression with different alpha values (Initial Test)\n", "alphas = [0.1, 1, 10, 100]\n", "ridge_results = []\n", + "\n", + "# Perform Ridge regression for multiple regularization strengths (alphas)\n", + "# For each alpha:\n", + "# -Compute Ridge regression weights,Make predictions on the test set,Calculate R-squared for test predictions And Store alpha and corresponding R-squared in results list\n", + "\n", "for alpha in alphas:\n", " ridge_weights = ridge_regression(X_train, y_train, alpha)\n", " y_test_pred_ridge = np.c_[np.ones(X_test.shape[0]), X_test] @ ridge_weights\n", @@ -1814,6 +1845,11 @@ "hyper_alphas = np.logspace(-3, 3, 50) # Fine-tune alpha\n", "best_alpha = 0\n", "best_r2 = 0\n", + "\n", + "# Iterate through different alpha values to find the best regularization strength:,\n", + "# - Compute Ridge regression weights for each alpha,Make predictions on the test set.\n", + "# - Calculate R-squared for test predictions and Update best alpha and R-squared if current model performs better\n", + "\n", "for alpha in hyper_alphas:\n", " ridge_weights = ridge_regression(X_train, y_train, alpha)\n", " y_test_pred_ridge = np.c_[np.ones(X_test.shape[0]), X_test] @ ridge_weights\n", @@ -1858,6 +1894,10 @@ "alphas_test = [result[0] for result in ridge_results]\n", "r2_scores = [result[1] for result in ridge_results]\n", "\n", + "# Visualize Ridge regression performance across different alpha values\n", + "# Plot R-squared scores against alphas to show model performance trends\n", + "# Highlight the best alpha value for optimal regularization strength\n", + "\n", "plt.figure(figsize=(10, 6))\n", "plt.plot(alphas_test, r2_scores, marker='o', label='Initial Test Alphas')\n", "plt.xscale('log')\n", @@ -1892,7 +1932,8 @@ "metadata": {}, "outputs": [], "source": [ - "# Predictions\n", + "# Generate predictions using the trained model\n", + "# Apply the computed weights to make predictions on both training and test sets\n", "y_train_pred = X_train_with_bias @ weights\n", "y_test_pred = X_test_with_bias @ weights\n" ] @@ -1910,7 +1951,7 @@ "id": "ace0caae", "metadata": {}, "source": [ - "## Model Evaluation: R-Squared and Adjusted R-Squared" + "## Model Performance Evaluation: Training and Testing R² Values" ] }, { @@ -1928,7 +1969,9 @@ } ], "source": [ - "# Evaluate using r_squared\n", + "# Calculate and display R-squared values for training and test sets\n", + "# R-squared measures how well the model fits the data\n", + "# Higher values indicate better model performance\n", "train_r2 = r_squared(y_train, y_train_pred)\n", "test_r2 = r_squared(y_test, y_test_pred)\n", "\n", @@ -1943,10 +1986,18 @@ "### The R² value for the training set is 0.8230, while the test set achieved 0.9232. This indicates the model performs well on unseen data, with a high degree of variance in the dependent variable explained by the independent variables." ] }, + { + "cell_type": "markdown", + "id": "3dbb06b7", + "metadata": {}, + "source": [ + "## Adjusted R² Calculation for Ridge Regression" + ] + }, { "cell_type": "code", "execution_count": 30, - "id": "9486758b", + "id": "7e057d4e", "metadata": {}, "outputs": [ { @@ -1972,7 +2023,15 @@ "n = X_test.shape[0]\n", "p = X_test.shape[1]\n", "adjusted_r2_ridge = adjusted_r2(test_r2, n, p)\n", - "print(f\"Adjusted R² for Ridge: {adjusted_r2_ridge:.4f}\")\n" + "print(f\"Adjusted R² for Ridge: {adjusted_r2_ridge:.4f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "8651dbbd", + "metadata": {}, + "source": [ + "### The output displays the Adjusted R² for Ridge Regression, which is calculated as 0.9184. This value indicates how well the model explains the variability in the dependent variable while accounting for the number of predictors in the model. A high Adjusted R² value like 0.9184 suggests that the Ridge Regression model fits the data well, with minimal overfitting, as it adjusts for the complexity introduced by multiple predictors." ] }, { @@ -1986,7 +2045,7 @@ { "cell_type": "code", "execution_count": 31, - "id": "e748f60b", + "id": "b3c8a1c9", "metadata": {}, "outputs": [ { @@ -1999,18 +2058,19 @@ } ], "source": [ - "# Implement Lasso Regression with hyperparameter tuning\n", + "# Implement Lasso regression using coordinate descent algorithm\n", + "# This function performs L1 regularization to encourage sparsity in feature selection\n", "def lasso_regression(X, y, alpha):\n", " X_with_bias = np.c_[np.ones(X.shape[0]), X] # Add intercept\n", " weights = np.zeros(X_with_bias.shape[1])\n", - " for _ in range(2000): # Iterative updates\n", + " for _ in range(2000): # Iterative updates with fixed number of iterations\n", " for j in range(len(weights)):\n", " X_j = X_with_bias[:, j]\n", " residual = y - (X_with_bias @ weights - weights[j] * X_j)\n", " rho = X_j.T @ residual\n", " if j == 0: # Intercept term\n", " weights[j] = rho / len(y)\n", - " else:\n", + " else: # Apply soft thresholding for feature weights\n", " weights[j] = np.sign(rho) * max(abs(rho) - alpha / 2, 0) / (X_j.T @ X_j)\n", " return weights\n", "\n", @@ -2030,7 +2090,7 @@ }, { "cell_type": "markdown", - "id": "d2425125", + "id": "e11e8e21", "metadata": {}, "source": [ "### Implement Lasso Regression with hyperparameter tuning :-\n", @@ -2118,15 +2178,19 @@ ], "source": [ "\n", - "# Updated k-fold cross-validation with seed\n", + "# Implement k-fold cross-validation for model evaluation\n", + "# This function splits the data into k subsets, trains and tests the model k times\n", "def k_fold_cross_validation(X, y, k=5, alpha=1.0, seed=42):\n", " np.random.seed(seed) # Set seed for reproducibility\n", " indices = np.arange(len(X))\n", - " np.random.shuffle(indices)\n", - " X, y = X[indices], y[indices]\n", + " np.random.shuffle(indices) # Randomize data order\n", + " X, y = X[indices], y[indices] # Reorder data based on shuffled indices\n", "\n", - " fold_size = len(X) // k\n", - " r2_scores = []\n", + " fold_size = len(X) // k # Calculate size of each fold\n", + " r2_scores = [] # Initialize list to store R-squared scores for each fold\n", + "\n", + "# Perform k-fold cross-validation for each fold and Extract validation set from the data\n", + "# Additionally Create training set from remaining data and Combine non-validation data for training\n", "\n", " for i in range(k):\n", " start = i * fold_size\n", @@ -2141,6 +2205,10 @@ " y_val_pred = X_val_with_bias @ ridge_weights\n", " r2 = r_squared(y_val, y_val_pred)\n", " r2_scores.append(r2)\n", + " \n", + "# Perform Ridge regression and evaluate model performance for each fold\n", + "# Train Ridge regression model on the current fold's training data,Add bias term to validation set for prediction \n", + "# Generate predictions for validation set,Calculate R-squared score for current fold and Store the R-squared score for later analysis\n", "\n", " return np.mean(r2_scores), np.std(r2_scores)\n", "\n", @@ -2180,21 +2248,27 @@ } ], "source": [ - "# Bootstrapping\n", + "# Implement bootstrapping to assess model stability and estimate confidence intervals\n", + "# This function performs repeated sampling with replacement to generate multiple R-squared scores\n", + "\n", "def bootstrap_r2(X, y, alpha=best_alpha, n_iterations=1000):\n", " r2_scores = []\n", - " for _ in range(n_iterations):\n", + " for _ in range(n_iterations): # Create a random sample with replacement\n", " indices = np.random.choice(len(X), len(X), replace=True)\n", " X_sample = X[indices]\n", " y_sample = y[indices]\n", "\n", + " # Train Ridge regression model on the bootstrap sample\n", " ridge_weights = ridge_regression(X_sample, y_sample, alpha)\n", " y_sample_pred = np.c_[np.ones(X_sample.shape[0]), X_sample] @ ridge_weights\n", + " \n", + " # Calculate and store R-squared for this iteration\n", " r2 = r_squared(y_sample, y_sample_pred)\n", " r2_scores.append(r2)\n", "\n", - " return np.mean(r2_scores), np.std(r2_scores)\n", + " return np.mean(r2_scores), np.std(r2_scores) # Return mean and standard deviation of bootstrapped R-squared scores\n", "\n", + "# Perform bootstrapping and print results\n", "mean_bootstrap_r2, std_bootstrap_r2 = bootstrap_r2(X, y)\n", "print(f\"Mean Bootstrapped R²: {mean_bootstrap_r2:.4f}, Std Dev: {std_bootstrap_r2:.4f}\")\n" ] @@ -2212,7 +2286,7 @@ "id": "8dfb60e8", "metadata": {}, "source": [ - "## Viz of R^2 & Adj R^2" + "## Predicted vs Actual Prices with Perfect Fit Line Visualization" ] }, { @@ -2253,10 +2327,18 @@ "### The scatterplot displays a strong linear relationship between predicted and actual prices. The predicted values closely align with the actual prices, as evidenced by points clustering along the red \"perfect fit line,\" demonstrating the model’s accuracy." ] }, + { + "cell_type": "markdown", + "id": "293ede31", + "metadata": {}, + "source": [ + "## Airplane Price Predictor" + ] + }, { "cell_type": "code", "execution_count": 36, - "id": "e6a10b89", + "id": "b5f65311", "metadata": {}, "outputs": [ { @@ -2264,7 +2346,7 @@ "output_type": "stream", "text": [ "Enter the specifications of the airplane:\n", - "Invalid input. Please enter numerical values.\n" + "Predicted Price: $38,822.34\n" ] } ], @@ -2317,11 +2399,19 @@ }, { "cell_type": "markdown", - "id": "2b4de634", + "id": "bb7a862e", "metadata": {}, "source": [ - "### This block is designed to provide the price prediction of the airplane based on three user-provided specifications: all engine rate of climb, takeoff distance over 50ft, and range in nautical miles. (input numerical values for each \"engine rate of climb, takeoff over 50ft, range\")" + "### This block is designed to provide the price prediction of the airplane based on three user-provided specifications: all engine rate of climb, takeoff distance over 50ft, and range in nautical miles. (input numerical values for each \"engine rate of climb, takeoff over 50ft, range\" ex) 1200 enter 1500 enter 700 enter)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "56a21def", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From 911ab2df5e04420de24274c8d13d1f5789047c41 Mon Sep 17 00:00:00 2001 From: hha980510 Date: Thu, 21 Nov 2024 22:14:49 -0600 Subject: [PATCH 24/26] Update README.md --- README.md | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) diff --git a/README.md b/README.md index fbda0ba..97ba07e 100644 --- a/README.md +++ b/README.md @@ -242,6 +242,23 @@ The project uses data visualization extensively to interpret results and validat • Example: Points align closely to the diagonal "Perfect Fit" line, demonstrating good model predictions. +### Contribution + +Kaustubh Dangche - A20550806 + Data Cleaning and Preprocessing with VIF Analysis +The data cleaning process removes inconsistencies and handles missing values to ensure quality. The Variance Inflation Factor (VIF) is calculated to identify and remove highly collinear features, improving model stability and interpretability. This step ensures the selected features are relevant for predicting airplane prices. +________________________________________ +Hyunsung Ha - A20557555 +2. Feature Scaling, Train-Test Split, and Regression Models +Feature scaling standardizes input variables, ensuring uniform contribution to the model. The dataset is split into training and testing sets to evaluate performance on unseen data. Ridge and Lasso regression models are compared, with Ridge emphasizing multicollinearity handling and Lasso favoring sparse feature selection. +________________________________________ +Anu Singh - A20568373 +3. K-Fold Cross-Validation and Bootstrapping +K-fold cross-validation evaluates model performance across multiple data splits, ensuring robustness and reliability. Bootstrapping validates the model's stability by testing it on resampled datasets, providing additional confidence in its generalizability. +________________________________________ +Nam Gyu Lee - A20487452 +4. Visualization and the Plane Price Predictor +Data visualization, including correlation matrices, residual plots, and predicted vs. actual price comparisons, highlights relationships and model accuracy. The interactive airplane price predictor allows users to input specifications and get real-time predictions, demonstrating the model's practical application in aviation pricing. From 22612adaeba076cfe6fe0b8ed1cb301e421fbb75 Mon Sep 17 00:00:00 2001 From: hha980510 Date: Thu, 21 Nov 2024 22:15:17 -0600 Subject: [PATCH 25/26] Update README.md --- README.md | 3 --- 1 file changed, 3 deletions(-) diff --git a/README.md b/README.md index 97ba07e..df10be5 100644 --- a/README.md +++ b/README.md @@ -263,6 +263,3 @@ Data visualization, including correlation matrices, residual plots, and predicte -See sections 7.10-7.11 of Elements of Statistical Learning and the lecture notes. Pay particular attention to Section 7.10.2. - -As usual, above-and-beyond efforts will be considered for bonus points. From 52b0fbe55bf57317faccdda11d7d9f6f1073de25 Mon Sep 17 00:00:00 2001 From: hha980510 Date: Thu, 21 Nov 2024 22:20:24 -0600 Subject: [PATCH 26/26] Update README.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index df10be5..9cbdfa2 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,7 @@ # Project 2 : Model Selection +# Team: Data Mavericks + ## Implement generic k-fold cross-validation and bootstrapping model selection methods. ### Overview: