From 4e9a1c11918ab0739fbc4802a73d02584960084d Mon Sep 17 00:00:00 2001 From: namshik Date: Sun, 2 Oct 2016 22:48:16 -0700 Subject: [PATCH 1/2] randomizedPCA is replaced by PCA for the updated version of scikit-learn module Added some to show how learning_curve method works. Included error bars clarify errors are decreasing by training size. --- .../05 - Model Selection and Assessment.ipynb | 3848 +++++++++-------- 1 file changed, 1980 insertions(+), 1868 deletions(-) diff --git a/rendered_notebooks/05 - Model Selection and Assessment.ipynb b/rendered_notebooks/05 - Model Selection and Assessment.ipynb index 73672ba..07c8d49 100644 --- a/rendered_notebooks/05 - Model Selection and Assessment.ipynb +++ b/rendered_notebooks/05 - Model Selection and Assessment.ipynb @@ -1,1921 +1,2033 @@ { - "metadata": { - "name": "" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "heading", - "level": 1, - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "Model Selection and Assessment" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "Outline of the session:\n", - "\n", - "- Model performance evaluation and **detection of overfitting with Cross-Validation**\n", - "- **Hyper parameter tuning** and model selection with Grid Search\n", - "- Error analysis with **learning curves** and the **Bias-Variance trade-off**\n", - "- Overfitting via Model Selection and the **Development / Evaluation set split**" - ] - }, + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Model Selection and Assessment" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "Outline of the session:\n", + "\n", + "- Model performance evaluation and **detection of overfitting with Cross-Validation**\n", + "- **Hyper parameter tuning** and model selection with Grid Search\n", + "- Error analysis with **learning curves** and the **Bias-Variance trade-off**\n", + "- Overfitting via Model Selection and the **Development / Evaluation set split**" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "# Some nice default configuration for plots\n", - "plt.rcParams['figure.figsize'] = 10, 7.5\n", - "plt.rcParams['axes.grid'] = True\n", - "plt.gray()" - ], - "language": "python", - "metadata": { - "slideshow": { - "slide_type": "slide" - } + "data": { + "text/plain": [ + "" + ] }, - "outputs": [ - { - "output_type": "display_data", - "text": [ - "" - ] - } - ], - "prompt_number": 0 - }, - { - "cell_type": "heading", - "level": 2, "metadata": {}, - "source": [ - "The Hand Written Digits Dataset" + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Some nice default configuration for plots\n", + "plt.rcParams['figure.figsize'] = 10, 7.5\n", + "plt.rcParams['axes.grid'] = True\n", + "plt.gray()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The Hand Written Digits Dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's load a simple dataset of 8x8 gray level images of handwritten digits (bundled in the sklearn source code):" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optical Recognition of Handwritten Digits Data Set\n", + "===================================================\n", + "\n", + "Notes\n", + "-----\n", + "Data Set Characteristics:\n", + " :Number of Instances: 5620\n", + " :Number of Attributes: 64\n", + " :Attribute Information: 8x8 image of integer pixels in the range 0..16.\n", + " :Missing Attribute Values: None\n", + " :Creator: E. Alpaydin (alpaydin '@' boun.edu.tr)\n", + " :Date: July; 1998\n", + "\n", + "This is a copy of the test set of the UCI ML hand-written digits datasets\n", + "http://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits\n", + "\n", + "The data set contains images of hand-written digits: 10 classes where\n", + "each class refers to a digit.\n", + "\n", + "Preprocessing programs made available by NIST were used to extract\n", + "normalized bitmaps of handwritten digits from a preprinted form. From a\n", + "total of 43 people, 30 contributed to the training set and different 13\n", + "to the test set. 32x32 bitmaps are divided into nonoverlapping blocks of\n", + "4x4 and the number of on pixels are counted in each block. This generates\n", + "an input matrix of 8x8 where each element is an integer in the range\n", + "0..16. This reduces dimensionality and gives invariance to small\n", + "distortions.\n", + "\n", + "For info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G.\n", + "T. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C.\n", + "L. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469,\n", + "1994.\n", + "\n", + "References\n", + "----------\n", + " - C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their\n", + " Applications to Handwritten Digit Recognition, MSc Thesis, Institute of\n", + " Graduate Studies in Science and Engineering, Bogazici University.\n", + " - E. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.\n", + " - Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin.\n", + " Linear dimensionalityreduction using relevance weighted LDA. School of\n", + " Electrical and Electronic Engineering Nanyang Technological University.\n", + " 2005.\n", + " - Claudio Gentile. A New Approximate Maximal Margin Classification\n", + " Algorithm. NIPS. 2000.\n", + "\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's load a simple dataset of 8x8 gray level images of handwritten digits (bundled in the sklearn source code):" + } + ], + "source": [ + "from sklearn.datasets import load_digits\n", + "digits = load_digits()\n", + "print(digits.DESCR)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "data shape: (1797, 64), target shape: (1797,)\n", + "classes: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from sklearn.datasets import load_digits\n", - "digits = load_digits()\n", - "print(digits.DESCR)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " Optical Recognition of Handwritten Digits Data Set\n", - "\n", - "Notes\n", - "-----\n", - "Data Set Characteristics:\n", - " :Number of Instances: 5620\n", - " :Number of Attributes: 64\n", - " :Attribute Information: 8x8 image of integer pixels in the range 0..16.\n", - " :Missing Attribute Values: None\n", - " :Creator: E. Alpaydin (alpaydin '@' boun.edu.tr)\n", - " :Date: July; 1998\n", - "\n", - "This is a copy of the test set of the UCI ML hand-written digits datasets\n", - "http://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits\n", - "\n", - "The data set contains images of hand-written digits: 10 classes where\n", - "each class refers to a digit.\n", - "\n", - "Preprocessing programs made available by NIST were used to extract\n", - "normalized bitmaps of handwritten digits from a preprinted form. From a\n", - "total of 43 people, 30 contributed to the training set and different 13\n", - "to the test set. 32x32 bitmaps are divided into nonoverlapping blocks of\n", - "4x4 and the number of on pixels are counted in each block. This generates\n", - "an input matrix of 8x8 where each element is an integer in the range\n", - "0..16. This reduces dimensionality and gives invariance to small\n", - "distortions.\n", - "\n", - "For info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G.\n", - "T. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C.\n", - "L. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469,\n", - "1994.\n", - "\n", - "References\n", - "----------\n", - " - C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their\n", - " Applications to Handwritten Digit Recognition, MSc Thesis, Institute of\n", - " Graduate Studies in Science and Engineering, Bogazici University.\n", - " - E. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.\n", - " - Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin.\n", - " Linear dimensionalityreduction using relevance weighted LDA. School of\n", - " Electrical and Electronic Engineering Nanyang Technological University.\n", - " 2005.\n", - " - Claudio Gentile. A New Approximate Maximal Margin Classification\n", - " Algorithm. NIPS. 2000.\n", - "\n" - ] - } - ], - "prompt_number": 1 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "X, y = digits.data, digits.target\n", - "\n", - "print(\"data shape: %r, target shape: %r\" % (X.shape, y.shape))\n", - "print(\"classes: %r\" % list(np.unique(y)))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "data shape: (1797, 64), target shape: (1797,)\n", - "classes: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n" - ] - } - ], - "prompt_number": 2 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "n_samples, n_features = X.shape\n", - "print(\"n_samples=%d\" % n_samples)\n", - "print(\"n_features=%d\" % n_features)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "n_samples=1797\n", - "n_features=64\n" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def plot_gallery(data, labels, shape, interpolation='nearest'):\n", - " for i in range(data.shape[0]):\n", - " plt.subplot(1, data.shape[0], (i + 1))\n", - " plt.imshow(data[i].reshape(shape), interpolation=interpolation)\n", - " plt.title(labels[i])\n", - " plt.xticks(()), plt.yticks(())" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 4 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "subsample = np.random.permutation(X.shape[0])[:5]\n", - "images = X[subsample]\n", - "labels = ['True class: %d' % l for l in y[subsample]]\n", - "plot_gallery(images, labels, shape=(8, 8))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's visualize the dataset on a 2D plane using a projection on the first 2 axis extracted by Principal Component Analysis:" + } + ], + "source": [ + "X, y = digits.data, digits.target\n", + "\n", + "print(\"data shape: %r, target shape: %r\" % (X.shape, y.shape))\n", + "print(\"classes: %r\" % list(np.unique(y)))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "n_samples=1797\n", + "n_features=64\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from sklearn.decomposition import RandomizedPCA\n", - "\n", - "pca = RandomizedPCA(n_components=2)\n", - "X_pca = pca.fit_transform(X)\n", - "\n", - "X_pca.shape" - ], - "language": "python", + } + ], + "source": [ + "n_samples, n_features = X.shape\n", + "print(\"n_samples=%d\" % n_samples)\n", + "print(\"n_features=%d\" % n_features)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def plot_gallery(data, labels, shape, interpolation='nearest'):\n", + " for i in range(data.shape[0]):\n", + " plt.subplot(1, data.shape[0], (i + 1))\n", + " plt.imshow(data[i].reshape(shape), interpolation=interpolation)\n", + " plt.title(labels[i])\n", + " plt.xticks(()), plt.yticks(())" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 8, - "text": [ - "(1797, 2)" - ] - } - ], - "prompt_number": 6 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from itertools import cycle\n", - "\n", - "colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']\n", - "markers = ['+', 'o', '^', 'v', '<', '>', 'D', 'h', 's']\n", - "for i, c, m in zip(np.unique(y), cycle(colors), cycle(markers)):\n", - " plt.scatter(X_pca[y == i, 0], X_pca[y == i, 1],\n", - " c=c, marker=m, label=i, alpha=0.5)\n", - " \n", - "_ = plt.legend(loc='best')" - ], - "language": "python", + "output_type": "display_data" + } + ], + "source": [ + "subsample = np.random.permutation(X.shape[0])[:5]\n", + "images = X[subsample]\n", + "labels = ['True class: %d' % l for l in y[subsample]]\n", + "plot_gallery(images, labels, shape=(8, 8))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's visualize the dataset on a 2D plane using a projection on the first 2 axis extracted by Principal Component Analysis:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(1797, 2)" + ] + }, + "execution_count": 10, "metadata": {}, - "outputs": [ - { - "output_type": "display_data", - "png": 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TL6IhHE+bRXPnnvwoOwXM4VwaKGYmB4ZS46tbrI2hmFKye/U0tBCfozUsqCgj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\nJau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- "text": [ - "" - ] - } - ], - "prompt_number": 7 - }, - { - "cell_type": "markdown", + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.decomposition import PCA\n", + "\n", + "pca = PCA(n_components=2)\n", + "X_pca = pca.fit_transform(X)\n", + "\n", + "X_pca.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Q65XqTVGRd5MSWQYGDuye8SJyPyR/i0EoYgzE81UM1KpRa1kZDA0NaCkpsVst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L2dfRQffQEH3vvEOFUklGRvyN6GLFtOt1ErT7DTy/RqMhM1N/IfjtxmBQsWjRnaxff2PUmq+4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+MzS/f98j4237OZVZetYuvTW/H5fNzyhVvYeNFG3v5/b7P/j/uJ1EbY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\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+dzrarl3v8Jvf7KC2dgoxMddTV7eHQ4eqSEhoYM6cDE6fPse77x7jyJEDTJyYxqpVc6mt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\n", + "text/plain": [ + "" + ] + }, "metadata": {}, - "source": [ - "We can observe that even in 2D, the groups of digits are quite well separated, especially the digit \"0\" that is very different from any other (the closest being \"6\" as it often share most the left hand side pixels). We can also observe that at least in 2D, there is quite a bit of overlap between the \"1\", \"2\" and \"7\" digits.\n", - "\n", - "To better understand the meaning of the \"x\" and \"y\" axes of this plot it is also visualize the values of the first two principal components that are used to compute this projection:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "labels = ['Component #%d' % i for i in range(len(pca.components_))]\n", - "plot_gallery(pca.components_, labels, shape=(8, 8))" - ], - "language": "python", + "output_type": "display_data" + } + ], + "source": [ + "from itertools import cycle\n", + "\n", + "colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']\n", + "markers = ['+', 'o', '^', 'v', '<', '>', 'D', 'h', 's']\n", + "for i, c, m in zip(np.unique(y), cycle(colors), cycle(markers)):\n", + " plt.scatter(X_pca[y == i, 0], X_pca[y == i, 1],\n", + " c=c, marker=m, label=i, alpha=0.5)\n", + " \n", + "_ = plt.legend(loc='best')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can observe that even in 2D, the groups of digits are quite well separated, especially the digit \"0\" that is very different from any other (the closest being \"6\" as it often share most the left hand side pixels). We can also observe that at least in 2D, there is quite a bit of overlap between the \"1\", \"2\" and \"7\" digits.\n", + "\n", + "To better understand the meaning of the \"x\" and \"y\" axes of this plot it is also visualize the values of the first two principal components that are used to compute this projection:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, "metadata": {}, - "outputs": [ - { - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 8 - }, - { - "cell_type": "markdown", + "output_type": "display_data" + } + ], + "source": [ + "labels = ['Component #%d' % i for i in range(len(pca.components_))]\n", + "plot_gallery(pca.components_, labels, shape=(8, 8))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Has this dataset is small, both in terms of number of samples (1797) and features (64), we can compute the full (untruncated), exact PCA and have a look at the percentage of variance explained by each component of the PCA model:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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VZC5NLwyqM1OdcA6wEPijDu1vQpxgKEmSJGkmNF1bgQeBrlHLDwZuaXfnEbEe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\nB5uvCyRg5XECAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, "metadata": {}, - "source": [ - "Has this dataset is small, both in terms of number of samples (1797) and features (64), we can compute the full (untruncated), exact PCA and have a look at the percentage of variance explained by each component of the PCA model:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from sklearn.decomposition import PCA\n", - "\n", - "pca_big = PCA().fit(X, y)\n", - "plt.title(\"Explained Variance\")\n", - "plt.ylabel(\"Percentage of explained variance\")\n", - "plt.xlabel(\"PCA Components\")\n", - "plt.plot(pca_big.explained_variance_ratio_);" - ], - "language": "python", + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.decomposition import PCA\n", + "\n", + "pca_big = PCA().fit(X, y)\n", + "plt.title(\"Explained Variance\")\n", + "plt.ylabel(\"Percentage of explained variance\")\n", + "plt.xlabel(\"PCA Components\")\n", + "plt.plot(pca_big.explained_variance_ratio_);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It might be easier to interpret by plotting the cumulated variance by previous components by using the `numpy.cumsum` function:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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o4AIz+50FfwaOAjZsuDIlKUOHDk26BFmOt98Ok/r22w/mzg3T+l57DSoqhmpi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\nRURcCVyVme9sPQ/gFuDUzDxllNvoBS7KzMVbeH1MQyn+67/gL/4Cbr8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9y7xLnxHxoNkP4HvA14HNCmsdeZkkcBnVSRQTyTmDMubXnNmVMb8y5lfG/Lq1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\nXhnzK2N+ZcyvOWfUpBGQyRXAh+d6LYK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- "text": [ - "" - ] - } - ], - "prompt_number": 9 - }, - { - "cell_type": "markdown", + "output_type": "display_data" + } + ], + "source": [ + "plt.title(\"Cumulated Explained Variance\")\n", + "plt.ylabel(\"Percentage of explained variance\")\n", + "plt.xlabel(\"PCA Components\")\n", + "plt.plot(np.cumsum(pca_big.explained_variance_ratio_));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Overfitting" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Overfitting is the problem of learning the training data by heart and being unable to generalize by making correct predictions on data samples unseen while training." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To illustrate this, let's train a Support Vector Machine naively on the digits dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0" + ] + }, + "execution_count": 15, "metadata": {}, - "source": [ - "It might be easier to interpret by plotting the cumulated variance by previous components by using the `numpy.cumsum` function:" + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.svm import SVC\n", + "SVC().fit(X, y).score(X, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Did we really learn a perfect model that can recognize the correct digit class 100% of the time? **Without new data it's impossible to tell.**\n", + "\n", + "Let's start again and split the dataset into two random, non overlapping subsets:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train data shape: (1347, 64), train target shape: (1347,)\n", + "test data shape: (450, 64), test target shape: (450,)\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "plt.title(\"Cumulated Explained Variance\")\n", - "plt.ylabel(\"Percentage of explained variance\")\n", - "plt.xlabel(\"PCA Components\")\n", - "plt.plot(np.cumsum(pca_big.explained_variance_ratio_));" - ], - "language": "python", + } + ], + "source": [ + "from sklearn.cross_validation import train_test_split\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(\n", + " X, y, test_size=0.25, random_state=0)\n", + "\n", + "print(\"train data shape: %r, train target shape: %r\"\n", + " % (X_train.shape, y_train.shape))\n", + "print(\"test data shape: %r, test target shape: %r\"\n", + " % (X_test.shape, y_test.shape))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's retrain a new model on the first subset call the **training set**:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0" + ] + }, + "execution_count": 19, "metadata": {}, - "outputs": [ - { - "output_type": "display_data", - "png": 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RsQ6wfK8L0tQiCOBTwKGZXNl2PZIkaX50s0y5G/BhYD3geuBhwEWZ+Q+9L+/+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\nfKvtWnrFtf/mzK6M+ZUxvzL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- "text": [ - "" - ] - } - ], - "prompt_number": 10 - }, - { - "cell_type": "heading", - "level": 2, + "output_type": "execute_result" + } + ], + "source": [ + "svc = SVC(kernel='rbf').fit(X_train, y_train)\n", + "train_score = svc.score(X_train, y_train) \n", + "train_score" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now compute the performance of the model on new, held out data from the **test set**:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.48666666666666669" + ] + }, + "execution_count": 20, "metadata": {}, - "source": [ - "Overfitting" - ] - }, - { - "cell_type": "markdown", + "output_type": "execute_result" + } + ], + "source": [ + "test_score = svc.score(X_test, y_test)\n", + "test_score" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This score is clearly not as good as expected! The model cannot generalize so well to new, unseen data.\n", + "\n", + "- Whenever the **test** data score is **not as good as** the **train** score the model is **overfitting**\n", + "\n", + "- Whenever the **train score is not close to 100%** accuracy the model is **underfitting**\n", + "\n", + "Ideally **we want to neither overfit nor underfit**: `test_score ~= train_score ~= 1.0`. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The previous example failed to generalized well to test data because we naively used the default parameters of the `SVC` class:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n", + " max_iter=-1, probability=False, random_state=None, shrinking=True,\n", + " tol=0.001, verbose=False)" + ] + }, + "execution_count": 21, "metadata": {}, - "source": [ - "Overfitting is the problem of learning the training data by heart and being unable to generalize by making correct predictions on data samples unseen while training." - ] - }, - { - "cell_type": "markdown", + "output_type": "execute_result" + } + ], + "source": [ + "svc" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's try again with another parameterization:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "SVC(C=100, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape=None, degree=3, gamma=0.001, kernel='rbf',\n", + " max_iter=-1, probability=False, random_state=None, shrinking=True,\n", + " tol=0.001, verbose=False)" + ] + }, + "execution_count": 22, "metadata": {}, - "source": [ - "To illustrate this, let's train a Support Vector Machine naively on the digits dataset:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from sklearn.svm import SVC\n", - "SVC().fit(X, y).score(X, y)" - ], - "language": "python", + "output_type": "execute_result" + } + ], + "source": [ + "svc_2 = SVC(kernel='rbf', C=100, gamma=0.001).fit(X_train, y_train)\n", + "svc_2" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0" + ] + }, + "execution_count": 23, "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 13, - "text": [ - "1.0" - ] - } - ], - "prompt_number": 11 - }, - { - "cell_type": "markdown", + "output_type": "execute_result" + } + ], + "source": [ + "svc_2.score(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.99333333333333329" + ] + }, + "execution_count": 24, "metadata": {}, - "source": [ - "Did we really learn a perfect model that can recognize the correct digit class 100% of the time? **Without new data it's impossible to tell.**\n", - "\n", - "Let's start again and split the dataset into two random, non overlapping subsets:" + "output_type": "execute_result" + } + ], + "source": [ + "svc_2.score(X_test, y_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this case the model is almost perfectly able to generalize, at least according to our random train, test split." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Cross Validation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Cross Validation is a procedure to repeat the train / test split several times to as to get a more accurate estimate of the real test score by averaging the values found of the individual runs.\n", + "\n", + "The `sklearn.cross_validation` package provides many strategies to compute such splits using class that implement the python iterator API:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# Cross Validation Iteration #0\n", + "train indices: [ 353 5 58 1349 1025 575 1074 1110 1745 689]...\n", + "test indices: [1081 1707 927 713 262 182 303 895 933 1266]...\n", + "train score: 0.999, test score: 0.989\n", + "\n", + "# Cross Validation Iteration #1\n", + "train indices: [1336 608 977 22 526 1587 1130 569 1481 962]...\n", + "test indices: [1014 755 1633 117 181 501 948 1076 45 659]...\n", + "train score: 0.998, test score: 0.994\n", + "\n", + "# Cross Validation Iteration #2\n", + "train indices: [ 451 409 911 1551 133 691 1306 111 852 825]...\n", + "test indices: [ 795 697 655 573 412 743 635 851 1466 1383]...\n", + "train score: 0.999, test score: 0.994\n", + "\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from sklearn.cross_validation import train_test_split\n", - "\n", - "X_train, X_test, y_train, y_test = train_test_split(\n", - " X, y, test_size=0.25, random_state=0)\n", - "\n", - "print(\"train data shape: %r, train target shape: %r\"\n", - " % (X_train.shape, y_train.shape))\n", - "print(\"test data shape: %r, test target shape: %r\"\n", - " % (X_test.shape, y_test.shape))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "train data shape: (1347, 64), train target shape: (1347,)\n", - "test data shape: (450, 64), test target shape: (450,)\n" - ] - } - ], - "prompt_number": 12 - }, - { - "cell_type": "markdown", + } + ], + "source": [ + "from sklearn.cross_validation import ShuffleSplit\n", + "\n", + "cv = ShuffleSplit(n_samples, n_iter=3, test_size=0.1,\n", + " random_state=0)\n", + "\n", + "for cv_index, (train, test) in enumerate(cv):\n", + " print(\"# Cross Validation Iteration #%d\" % cv_index)\n", + " print(\"train indices: {0}...\".format(train[:10]))\n", + " print(\"test indices: {0}...\".format(test[:10]))\n", + " \n", + " svc = SVC(kernel=\"rbf\", C=1, gamma=0.001).fit(X[train], y[train])\n", + " print(\"train score: {0:.3f}, test score: {1:.3f}\\n\".format(\n", + " svc.score(X[train], y[train]), svc.score(X[test], y[test])))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Instead of doing the above manually, `sklearn.cross_validation` provides a little utility function to compute the cross validated test scores automatically:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.98888889, 0.99444444, 0.99444444, 0.99444444, 0.99444444,\n", + " 0.99444444, 0.98888889, 0.99444444, 0.98888889, 1. ])" + ] + }, + "execution_count": 26, "metadata": {}, - "source": [ - "Let's retrain a new model on the first subset call the **training set**:" + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.cross_validation import cross_val_score\n", + "\n", + "svc = SVC(kernel=\"rbf\", C=1, gamma=0.001)\n", + "cv = ShuffleSplit(n_samples, n_iter=10, test_size=0.1,\n", + " random_state=0)\n", + "\n", + "test_scores = cross_val_score(svc, X, y, cv=cv, n_jobs=2)\n", + "test_scores" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from scipy.stats import sem\n", + "\n", + "def mean_score(scores):\n", + " \"\"\"Print the empirical mean score and standard error of the mean.\"\"\"\n", + " return (\"Mean score: {0:.3f} (+/-{1:.3f})\").format(\n", + " np.mean(scores), 2 * sem(scores))" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean score: 0.993 (+/-0.002)\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "svc = SVC(kernel='rbf').fit(X_train, y_train)\n", - "train_score = svc.score(X_train, y_train) \n", - "train_score" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 15, - "text": [ - "1.0" - ] - } - ], - "prompt_number": 13 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can now compute the performance of the model on new, held out data from the **test set**:" + } + ], + "source": [ + "print(mean_score(test_scores))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** \n", + "\n", + "- Perform 50 iterations of cross validation with randomly sampled folds of 500 training samples and 500 test samples randomly sampled from `X` and `y` (use `sklearn.cross_validation.ShuffleSplit`).\n", + "- Try with `SVC(C=1, gamma=0.01)`\n", + "- Plot distribution the test error using an histogram with 50 bins.\n", + "- Try to increase the training size\n", + "- Retry with `SVC(C=10, gamma=0.005)`, then `SVC(C=10, gamma=0.001)` with 500 samples.\n", + "\n", + "- Optional: use a smoothed kernel density estimation `scipy.stats.kde.gaussian_kde` instead of an histogram to visualize the test error distribution.\n", + "\n", + "Hints, type:\n", + "\n", + " from sklearn.cross_validation import ShuffleSplit\n", + " ShuffleSplit? # to read the docstring of the shuffle split\n", + " plt.hist? # to read the docstring of the histogram plot\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 3.51 s, sys: 20.1 ms, total: 3.53 s\n", + "Wall time: 3.55 s\n", + "Mean score: 0.905 (+/-0.008)\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "test_score = svc.score(X_test, y_test)\n", - "test_score" - ], - "language": "python", + } + ], + "source": [ + "cv = ShuffleSplit(n_samples, n_iter=50, train_size=500, test_size=500,\n", + " random_state=0)\n", + "%time scores = cross_val_score(SVC(C=10, gamma=0.005), X, y, cv=cv)\n", + "print(mean_score(scores))" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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CF9p0yH/in3tADFIBDBbt+SRHAfyllNoBIAnacU0WkQ+hTX/tDW1a724Aqur37XNov/K/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7jVc9U9pKEa5JrUSIPf0KWI2AS8TDln5Yfq/X9u26+1rcFzbSdlZGcnsB44SRnFfmZsQWtS3QR8\nQlmPk5TzoQD2Us57mgKu6jXOzO8i4h5KYf44sAQ8lJm1Np5q/ZweSZIkSfo/uW+/JEmSpE6z6JEk\nSZLUaRY9kiRJkjrNokeSJElSp1n0SJIkSeo0ix5JkiRJnWbRI0mSJKnTLHokSZIkdZpFjyRJkqRO\ns+iRJEmS1GkWPZIkSZI67R8gOEi7YuR7hQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 16, - "text": [ - "0.48666666666666669" - ] - } - ], - "prompt_number": 14 - }, - { - "cell_type": "markdown", + "output_type": "display_data" + } + ], + "source": [ + "from scipy.stats.kde import gaussian_kde\n", + "_ = plt.hist(scores, range=(0, 1), bins=30, alpha=0.2)\n", + "x = np.linspace(0, 1, 1000)\n", + "smoothed = gaussian_kde(scores).evaluate(x)\n", + "plt.plot(x, smoothed, label=\"Smoothed distribution\")\n", + "top = np.max(smoothed)\n", + "plt.vlines([np.mean(scores)], 0, top, color='r', label=\"Mean test score\")\n", + "plt.vlines([np.median(scores)], 0, top, color='b', linestyles='dashed',\n", + " label=\"Median test score\")\n", + "plt.legend(loc='best')\n", + "_ = plt.title(\"Cross Validated test scores distribution\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Selection with Grid Search" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Cross Validation makes it possible to evaluate the performance of a model class and its hyper parameters on the task at hand.\n", + "\n", + "A natural extension is thus to run CV several times for various values of the parameters so as to find the best. For instance, let's fix the SVC parameter to `C=10` and compute the cross validated test score for various values of `gamma`:" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "n_gammas = 10\n", + "n_iter = 5\n", + "cv = ShuffleSplit(n_samples, n_iter=n_iter, train_size=500, test_size=500,\n", + " random_state=0)\n", + "\n", + "train_scores = np.zeros((n_gammas, n_iter))\n", + "test_scores = np.zeros((n_gammas, n_iter))\n", + "gammas = np.logspace(-7, -1, n_gammas)\n", + "\n", + "for i, gamma in enumerate(gammas):\n", + " for j, (train, test) in enumerate(cv):\n", + " clf = SVC(C=10, gamma=gamma).fit(X[train], y[train])\n", + " train_scores[i, j] = clf.score(X[train], y[train])\n", + " test_scores[i, j] = clf.score(X[test], y[test])" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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ls/0ZmSGZSVptT2aSxDNxktmk1QYhlChxMr9AW07mEzulzqsSkZS/3XyROikl\n8Ux8Tm1bNB0lFitM20xlU8t6zl6nlypHFTOpGa5pvIa2YBsnx04yODPIaHSU4Zlh6r313NB8A4Y0\naPY3s6txF067k6GZIYYiQwWfXTwT5/zUec5PnQfA7/IXRLz8Lv+ytV+j0Vx5aKGlWVd86Utf4qMf\n/ehqN0OziixnH8hk5oqp6Wnl+Dc1pdL8olElpCYn1WsiUTjAbyqlRFY6XTjAr+nw19ioBFQgMCuq\nTDHl96uI1caNSliZNVR1dSrCdSkkM8k5Ysp8vxiHPDMilcqmSBmlI0/m+0pS10xhZBM2FRkSNmw2\n9d78226zWzfUPpcPn9OH3+W3pif/6Ul+9ud/loArgNflLRAKpQRCxsiUNbyYTkwXuAguZwTN4/Dg\ntrvnFT2VRIVKTStBKpsqe53y58fT8SWnKRYjEFQ5q6hyVFmCuMqZ9z5vfn6k8Ytf/CLv/OA7OT91\nniZvE6cmTtE73Us4GWYqMcXTF56mvqqerfVbOek9SWd1Jze23sg9m+4hlAwxGBlkMDLIUGSowIJ/\nJjXDmYkznJk4A0DQHVQRr4CKeFU5q5blvDWXjr4f0FwOtNDSrCtee+01/cO6zllsHzAMFYHKF1Oh\nkBJSk5NKSMXjKs0vGp11+UsmZ0WV6fpn1lGZ41AZhjKpcLlmU/58PiWqvF5VY2XODwahvR02bJit\nsaqrU8uXSsbIEE6GS4qpShzzsjJLPK3MIpLZpEp/y9U1ZWWWTFYJMlP82ITNeu+2u6lyVBWIo3zx\nVLC+sGO32fE4PBXdUHscnnkNC77e/XW21m21rNfDyfC8FuwrFVUpPofi+WvB7c6QRkXiabkt6l12\n14LXqJLPOp1Nk8wmCSfDJLNJkpkkyWySZ196lp/50M+wf+N+bttwGyPREQ4PHuYH539AMB4kmooy\nGZ/k9aHXafY3c2biDIeHD7MhsIHbO25nT8serm++HkMajMfGGQgPMBgZZHhmuCDiGk6GCSfDnBo/\nBUCtp9ZKNWz1t+J2XOITEc2S0fcDmsuBMC1Zr3aEEHuBw4cPH9YFkBqNZg5m3VS+mDL/NsVT/hSP\nK6GUTitBlUzORq/MFEBzMl3/nE7l2OdyKSFliii3e1Zk+f1qXKqODiWszAhVTc3S7NMNaTCTmikp\npmZSMxXtQ0pp1cNYZgM5VzmH3QESZX6QE0emg5xpWFA8Lx+bsFUknqqcVRVFYkoN/FtKPK1kVGU+\n8eSyu5Yb1IkiAAAgAElEQVTlmJdKMpOsSDwtxqJ+IfI/6/mukdfpLfisM0bGEkipbIpEJmGlYeaL\np+JX0x1yPpp9zWyq2cSmmk1Ue6rJGlmeOP8Ez/Y8y2hslHBCRbjcDjd1VXWW0Uejt5FbN9zKvZvv\nLTDEyBpZRqOjDEYGGYgMMBodnbcNDd4GK82wxd+yZvqHRrMeeO2113jLW94C8BYp5WsrcQwttDQa\nzbohlZodrDdfTIVCSigVC6loVIko82fSMNTf+ZEqM+3PNKiw2VTan8Mx++rzFQ7Ya9qn19aqCFVn\npzKpMFP/XEu414qlYyXFVDgZXlQ6WyqbQkppGQKYTm0CQVZm5wzAm1+vkk++TXm+YYTb7laRqZww\ncdldyjUuZ2OeH9nKtzbPN87IN0UwUxLNOihDGgUCr5TQy/+7lPgzqSSqYp7HWrD8zhrZisRTLB1b\n1hRHt9294DVy21XkJm2kKxJI+WJqOWvy5qPGU2OJLhs2nul5hu6pbsZiYwyGBwmnwgXnAqqf727a\nzT2b72FPy545UciMkWF4ZthKNRyLjpUV+AJBo6/RSjVs9jevWJqnRqPRQmtZ0UJLo1m/TE7CkSNq\nMF9TUJlCynyfKHpwL+WsXXo0qtIHZ2ZmUwDNAX/zBZXDoaJWpqDyemejUW1tKkrV2Tlrpe71Lu48\nUtlUSTEVSoQK6kQqwRQyAoFEWgYOqUzKcvWLpWPEMuo1nS3cv03Y8Dq9Vg2UKZpMUVVJ2lu+m9yc\nui2jcN5yYVqC57fV7VBpjGZqoikEXXZXQSpjOSFYPG+h5ZXs0xRuVjSxAvG0nCmOdmGfI548Dg8O\n4Zhtb67/mE6Q+UKpWDwtZ1rhQpiC3u1wF7zPf42kIvRO9zKVmCq5D6/Ty8bgRqLpKP3hfgxpEEqE\nGIgMEElGStr+N1Q1sL9DpSK2B9tLfgdS2ZQlvAbCA0zEJ8qeh03YaPY1W6mGTb6mNZFOqtFcLWih\ntYxooaXRrD8GB5XA6uuDkRH1WiyobDaVuud0zo14JRIq9Q8KxZTp3GezzYqqQEDVTrW1KXOKzk71\nWl+vXAIrDXhkjayqmyohpvJtpivBLuwE3AGcNmUCYFqtJzIJphPTajDedJRYKma9zxpzowemqUSx\nsPI4PNRW1VLjqcHj8JA1spYddrEoMMVCPKPeJ9KJgnGdpJSXNJZTsXgqNfivGT1bC9GnhTBT1LJG\ndtlSHAE8do9yOMwZZljjXdkKI3+GNOZEmxYr5i8Fh81RUiCVei0WVosRI6FEiN5QLz3TPQzPDJdc\nJ5lNMh4bx2lzUldVh03YSGaUeDw9cZpYOlawvs/pY0vtFvZt2MfWuq20BdrKtimRSTAUGbJSDacT\n0/NekxZ/i5Vq2OBt0MJLo7kEtNBaRrTQ0gA89NBDPPbYY6vdDM0KYhhqPKojR9T4VOEwnDunolFO\nJzz//EM8/PBjRCLKbn1yUhlbmGNWmeNSORyFNVGmtbrHo4RTWxt0dako1aZNs/VUjgoyfaSUqm6q\nhJiKpCKLOl+BIOAOUO2utuo7TCOKmdQMwzPDzKRnCoRPudQxl92Fz1koqLwuLwFXgFpPLUF3EI/D\ng91mx2FzkM6mmUnNDgS8HFGVfNFl1j6ZUSe3w43LpizY80WV0+ZECFEwwLBlylFi3if//Sf57b/5\n7TmDEpfbZjmFznKRb6EPSlQ77c5ZJ8bcZA72C7NC+3JhF/YFBZL5WRYvW2kBUer/gng6Tl+oj57p\nHvrD/QUpi4Y06A/3czF0kaA7SL23nrqqOm5ouoGkkeRg30F6Q70FDyrcdjftwXY21WxiW902uuq6\naPW3ziv0Y+mYlWY4GBkknAyXXddpc9IaaLVSDeuq6q6IhwhrBX0/oNFCaxnRQksD8MQTT3D//fev\ndjM0K0AmA6dPw9GjKs0vmVSCa3R0Nup07hz09T2Bw6H6QClBZeJwqLqqhgYlpLZvhy1bYPNmZVbh\n8Szcpng6XlJMhZKhRdfIeJ1eqt3VVHuq8Tl9GBhWBGkiNsFAeIDp5HSBmEpkEiWPYxf2OREqn9NH\ntacaj8OjBt3NiRebsJHMJokkI8TSsWURHcXjVJUzw1gpY4DF/g6YUbdKhVypeZVsk8qmiKajBeI1\nnU0r0YSqJTNvpK3Inc2F3bYMg6OVwSZsixJI+a8r2a5LZaE+kDEy9If76ZnuoXe616pFjKainJ08\nawkgv8tPZ3Un79zxTtwON09feJoT4yeYjE1aQs1hc9Dqb6U92E61u5rNtZvpqu2ixd+yoDCaSc1Y\nomsgPEA0HS27rtvutqJd7cF2ajw1i70s6wp9P6DRQmsZ0UJLo7k6SSTg2DE4cUK9Nwzo74eLF1VK\noNut/j59WokjZ4kxYW02ZVjR2qrE1M6dcM01SmAtlPaXyqYIJ8MlxdRiozsuu8sSUzWeGgKuAKBu\n+iKpCCMzI/SH+xmLjc0RVKUwI0I+pw+fy2cNuBtwBXA5XNa4UQKBIY1LSg0zbcvLucjl1/ms53Sn\nZCZpWX6bUyQVIZwMV+wCuVgEomyq3UKv2oxBCe2RmRF6pnvome4hlAwxEB6gN9Rb8CBjW9023rrl\nrVS7qrkYvsgbw28wHhtnMq5El03YaPI10R5otx50bKndwpbaLTT7miuKRoUSs2N4DUQG5nWF9Dq9\nBYMnB93BZbkeGs3VghZay4gWWhrN1UU4rKJXp08r0wpQqYLd3UpsORwqJXB4WKUN5jv51dSo+qmu\nLrj2WrjuOlVTZS/zAD5rZImkIiXFVHF9xkLYhZ2gO2iJKVNY2bARz8SZjE8yFBmiP9zPcHSYaEql\n5EXT0XmFm9vuxuVQaXR2mx2nzUnAFcBhd1jOfU6bc8njM3kcHgKuAH6Xn4A7YA3+G3AFCLgD2pY6\nhyENoqnoHBFlTpeSWllpJKlU3ZJm+ZiMT9Iz3cPx0eO81P9SQXpflaOKbfXbaPY1U++tJ5qKMhGb\nYCoxxVhsjMn4JIY0qKuqY2NwI9WeamC2rqurrosmX1PFbZmKTzEQGbDE13z9y+/yz0a8cmJPo1nP\naKG1jGihpdFcHYyNqfqrCxdmbdejUSW4ZmZU9CmTgYkJZW5h2rN7PLB7N3z84ypSVYyUkmg6WlJM\nRZKRRafMBVyBOWKq2l2N0+ZkKjHFZHyS/nA//eF+BiODhJIhy0muVGRJoqzWDWlYtThmXY7X4cVp\nd+JxevA6KhvINR+BwOv0zhFQ+aJKRzZmSWfTcwSUOc2kZpZkne62uwm6gwWTee3ddiWWdP3N2iOS\njPDD7h/yXM9zTCWmrM++LdDG5prNlqFJ2kgTTUXxOr1EUhHGomNMJabwu/xsCG6gvqre+nz9Lr8S\nXbVdNPoaK26LlJKJ+IQluoYiQ/NGqavd1QURrypn1aVdDI3mCkMLrWVECy0NwL/8y7/wrne9a7Wb\noVkCfX1KYA0Nzc5LpeDkSRXJstnUFA4ru3aXS7kHBgIqJfDuu+GDH4TH/u2b3PnAnXPEVCgRWvR4\nPVWOqpJiykzRmUpMMRGbUEX04YsMhAeYjE9atun5hfMSSSarxokyTQ7sQrnBCYRKK/RUU+OuKUjF\nczvc5ZpnYRO2sgIq4Argc/nWVTpfJb8DsXSspJCKJCOLdn8EJWb9Ln+BiMoXVTrqdHlZ7v8LQokQ\nT3Y/ycnxk0zEJphMTOK0OdlWt80a0DhrZJU5TWoGn8tHtbuaWDrGWGyMRCZBW6CNZn+zFYEG9cCm\nq66LLbVbaPA2LKpNhjQYi45Zwmt4Znje37haTy3twXbaAm20+lsr+m25ktH3AxottJYRLbQ0AB/4\nwAf4xje+sdrN0FSIYSgDiyNHVBqgSTwOZ8+qtECnU6X8JRJKWPn9KnoVj6v6qi1b4Kabs7TtOc6J\niaP86a/9Kb/4mV+suA1Om7OkmKr2KJc/KaX1hLov1Ed/uJ+ByACj0VGiqSjxTNwyU7AG3c2mrcF3\nM0YGKSV2mx233Y3P5aPWoyzTLec/p4pYlcOsuyoWUObfa2VA3bXCBz7wAR79P4/OiUpFkhEr3W8p\n7nxOm3OOgDInv8u/rsTsWmcl/i+QUnJ87DivDLxCKptiOjHNRGwCt0OZVJhRYUMajMfGuRi+CBLq\nquqodleTzCYJp8J4nV5afC1zvvNBd5Cu2i666rqoq6pbdPuyRpbR6KiVajgaHZ03+trgbbCiXa3+\n1nl/g65E9P2ARgutZUQLLY3myiGVglOn4M03VVogKNE1OQkDAyqCZberCFY2qwSW2w07dii3wfFx\n5QzYucmgtvMidBwkni3v1mUTNlU35c4Jqjwx5XXOjiocT8cZjY7SO91rRaiGo8NEUhGiqag1AG8p\nMZWVWezCbtXOBNwBajw11FbVEnAFrAhVqRQ9c/3iuijzb4+jAgvEdUgik5gjosxpPve2+fA6vSWF\nVMAV0KlXGgDCyTDP9z7PYGQQwBquYEP1BuLpeMFYWVPxKfrD/UwlpnDZXTR4Gwi6gkgkVc4qq86u\nmBpPjZVeaEbMFkvGyFiDJw9GBhmLjpVNkRYImnxNlvBq9jfrdGLNFY8WWsuIFloazdonFlPi6uRJ\nJbbMecPDagDhZFJNZnAmElHRq507lbA6c0atv2WLJOMaw97+BnVdPQXHaAu0UeOpKYhQFUcbMkaG\nocgQvaFeLoYuWql/47Fxy3I7X0RlshkMZp8Mm6l+HoeHgDtArbuWOm8dQXfQcuTLt772Or0lBZQZ\nlbraniQvF8XGE8UGFEsxnjAHeS4WUWa6n7651FSClJKT4yf5Uf+PCqKjO+p3cG3jtQxGBukN9VqD\nJM+kZrgYUr8xEhXhrvOoSFdbsA2/y080FS0phGo9tVZ64aVYuqeyKWvw5MHIIBPxibLr2oSNZl+z\nlWrY5GvSEVvNFYcWWsuIFloazdplako5CJ49qyJX2ayKSg0Pq6iVECpqZeS0jBAqZbCrCxobZ8fQ\nCgahqm6CvnAPbbvP0bIpZB2js7qTm9tvLki5MaTB8Mww3ZPdnJ08S0+oh4GwSvuLZ+IFNVPlnvS6\nbGow3aA7aEWoaj21VqTJYXPgc/rKpvX5nL41Pd7QapPOpkuKqEsxnvA4PHNElDl5nV6dZqlZNiLJ\nCM/3Ps9AZMCa53V6ubPzTjqqO4in4/SGeumZ7rHGyRoIDxTUU9mEjWp3NV21XWyr34YhDYZmhkoe\nr66qjq5aJbpMR8OlksgkGIoMWamG+ZG4Yhw2By3+FjYGN7Kzcad+IKG5ItBCaxnRQkujWXsMD8Mb\nbyijC1CugcPDylmwvl6lAw4PqyiWzaZs2e12tayxUQmuZFINSuwOhhiK9zCTmWbnvn7qWlRqWKu/\nlVvab6HJ18QzPc/wxvAbXAxfZCg8xHh8nHg6TkYuXI/jEA48Do+VVlhXVWfVVjT6Ggm4AyXtz31O\nn75xnwcppWU8UcrJb75xgsohENbnUezip40nNKvByTEV3cp3Adxev53bNtxmpQams2n6w/30hno5\nN3lOia/IAOlsoXNgq7+Vm9puoi3QxlRiyoqKFdPgbbDG6VqOMbRi6ZgV7RqMDBbY2udzffP13Lrh\n1ks+nkaz0mihtYxooaUB+PCHP8yXv/zl1W7GukZK6OlRBhejoyoaNTqqBFUioRwCg0E14HA4DF6v\nmudwqIhWY6N6D0p8jU7PMBzvUS5friy79l8kWJegwdtgCayjw0f5wqtf4MT4Cbq/3M2WD28p2z4b\nNqqcVVa9VqO3kY6aDjYGN9Loa5yT1qfrchbGHIesnIvfYt0eQRlPlBJRlRhP6N8BzWr0gZnUDM/3\nPk9/uN+a53V6uaPjDjprOgvWNaPt5ybP8crAK5wePz3H7dLj8LC9fjt7mvfgsDmYTk4zHhsveexG\nb6M1Tpff5V+W84kkIwXCy6x7bPA28J6d71mWY6wk+ndAczmElo7tatYV999//2o3Yd2SyagaqqNH\nlYAKhZS4Gh9Xwqm9XUWqLl5URhiNjcox0OlU6weDUJXTNHY71DbNcOh4H8MRdWNR5Uuz+/Y+Wht8\n3NR2gPqqeo6PHefRNx/llYFXlMMXUH2tSqcxx46qq6qj1d/KhuAG9fS3ZgsNvgYrKqWjH5WRbzxR\nbECxVOMJn9NX0go96A5ekgGI/h3QrEYf8Lv8vGPbOzg9fpqX+l8ilU0RS8d4/PzjbK3byv6N+61+\nbRM2y3jizs47GY+O81L/Sxy8eJCBsEpDTGQSHB05yomxE2rcrtrNbAxuBCCaijKZmLSOPRYbYyw2\nxssDL9Pka7IiXZciugLuADvcO9jRsAOAbx7/JtOJaWtQ5rVes6V/BzSXAx3R0mg0K0oyCcePw7Fj\nSjCZ0atYDHw+2LBBCazhYeUq2NioJptN1WXZ7cqm3aSxJcGU6xjPvRgjmyvPCdQm2HfXFLdt3kOV\ns4oTYyfonurm/OR5ekO99If7kUiCriAPbH2At7S9hWvqr6HR16hrCSrEkAYzqZmyLn7zDYxajlLG\nE/lRKf3ZaK5WoqkoL/S9QF+oz5pX5ajiQMcBNtdunnfbcxPneOrCUxwbPcZ0YtqqH7UJG02+JjYE\nN1g1ozZhsx6ClKLZ12wZaeQ7rC6Fpy88zbnJcwC8d+d7qffWX9L+NJqVRke0NBrNFUskMusgOD6u\nBhqenFTpfzU1ysiitlatNz6u5m3YMLttNqvm2XIPRZ2eFNVbj/Nm/wXOvDw7cGdzW4oPPtSEx9XK\nkZEjTCemmYpPcXbyLDOpGUZmRgi6g2yp3cJ7dr6H+7v0U8xypLKpkiIqkooQSUbKGoLMR77xRLEB\nhTae0KxXfC4fD259kDMTZzh08RCpbIp4Js4Pu3/IltotHOg4UDZqu7V+K1vrtzIVn+LHgz/m1cFX\nGYuOMZmYZHhmmOGZYRq8DZbgEggCrgA2YSOZTRbUPY5ERxiJjnDo4iFa/C101XaxuXbzkkRXfVU9\n51BCayI+oYWWRoMWWhqNZpkZH1fpgcePqyjVyIiqvRICGhpg40Y1qHAgoMbI8nrVe1DrpdPKBMOV\ny9iTZPC0nSXZ+DKvnqrm4hklshw2B3t3+dlza5jjE2+QyqbIyiw9U6qA3GV3kZVZNtVsYnv9dm5o\nuYF7N9+7SldlbZBvPFHKxe9SjCdKufgF3AGdeqnRzMP2+u1sCG7ghd4X6A31AtA91c1gZJADHQfY\nUlu+nrS2qpb7uu5j/8b9HBs9xvGx44xGR5mITTARn+CN4TeodlfTHmzHkIb1UMNld2EXdlLZFBkj\nY803Rdqhi4doDbSypXYLm2s2V1yH2uCdfQA2Hhtne/32pV4WjeaqQQstzbri4MGDHDhwYLWbcVXS\n3w+vv66iWGYaIKjUv7Y2VYNVV6fqruJxZYhhGGq5YagUQ5gVXYZhkPL14tz0MnFPmDOH2xi9GLQG\nF+66NkTVppc5NaGiLJFkhNMTp3HanVzbeC2j0VG8Ti/b67fT4G3gvi33YbfZr/o+kDEyVkSq2IBi\nqcYTLrurpBW6Wce21msxirna+4BmYdZSH/A6vTyw9QHOTZ7jxb4XrajTk91PsrlmMwc6Dswrdnwu\nH/s27OPG1hs5NX6KoyNHiaaiRFIRJmIT9Ez30DPdw4bgBpp8TQXjywkENmEjnU0jhHovkZbBxYt9\nL9IWaKOrrotNNZvmrY3Mj2BNxMqPwbVWWEt9QHP1omu0NOuKhx56iMcee2y1m3HVYBjQ3Q2HDqkU\nwZGR2YGGnU4lrtralLjasUMJrMOH1atJOq3SBD25/78NaRDKDuPoeJVA6wiZtI2TL7czOeLF7XAj\nBGzc3Ufblmlr/YHwAPFMnLaAGtizP9SPsAna/G047U7edc27rPGzroY+EE/Hy7r4xdKxJe3TNJ4o\n5eJ3KcYTa5GroQ9oLo212gfi6TgH+w5yYfqCNc9td3N7x+1srdta0T4MaXBu8hxHho8wlZgClDX7\nRHyCmeQMfpefFn/LnIHQU9kUAkHGyOC0O+fUSAoE7cF2umqV6DJt6fP52tGvEU1HcdldfOiGD63p\n1OC12gc0lw9t776MaKGlAYjFYni9l1bwq1Hi6NgxeOYZOH9eOQiaVFUpgdXVBbt2wfbtanysQ4dU\nWqFJJqPSCUFFtaSUTMTHydQfpWHbBRxOg1TCzuvPtxKPeLFhw2aXXHPLAA1tMwBIJJFkhCpHlXXT\nkDWyJLNJq8bgga4HCqyTr4Q+UGw8URyVWqrxRCkRZUaq1tOgyVdCH9CsLGu9D3RPdXOw72BBOm9n\ndSd3dN6xqPqpvlAfR4aPFAxwnMqmCCfCVDmrcDlcOG3OOduZTqGZbAaPwzNHVNmEzXJq3VSzyUoR\nfvzc41YK5Ad3f3BZxu9aKdZ6H9CsPNoMQ6NZZvSP6qURi8GLL8Kzz8LAgBJLJoEAdHTATTfBzp1K\nbMVi8PLLcO7c7HpmuqDNNmt0MRmfZMp2mubrzuCvUTmEI+NJjr3YgUy6cDlsaoys2/oJ1sdp9Dbi\ndrgZCA9Y/5ELBFtqt3Bh+oIlGva175szPs1a6QOm8UQpO/SZ1MySjCeqHFVlXfyqHFVr+uny5WSt\n9AHN6rHW+8CW2i20Bdo42HeQ7qluAHpDvQwdH2L/xv0V1z91VHfQUd3BWHSMIyNHuDB1AZfdRYNP\n1VMZ0sDv9ONz+ZhOTJPMqt9fn9OnduBUadnRdBRDGlQ5qvC5fBjSoC/UR1+oD5uwsTG4ka66rgJh\nNRGbWNNCa633Ac3VgRZaGo1mQUZH4Qc/gB/9SFm051NXpyJXt92molcejxJgr78Ob7xRKMYcDjVg\ncTarRFYoEWIg1k3dtjNs6QwhMRiZGWN8zM7wG3twSj84wONNc/2BAXZ3bGBzzWaOjx0vGPSzxlPD\nvvZ9HOw7iCGV57tpgLFaSCmJpqNl7dDNG5rFYBM2/C5/WRe/4lQgjUZz5eJxeHjblrdxYeoCB/sO\nEs/ESWVTPNvzLN1T3dzRcQc+l6+ifTX6GnnblrcRToY5OnKU0+OnycosNmFjJj3DTHrGGrcrkUnQ\nM93DTEplDgTcAWs/4WSY4ZlhpJR4nV6C7iAGBr2hXnpDvUwnpjGkQV1VHeOx8QWt6jWaqx0ttDQa\nTVnefFMJrGPHlDgysdmgpQXuuANuvlm9N7lwQQmySKRwfZdL1WYJATOpGXqme3A1d7PtpjGkI05f\naIhIKkLVzE6mjl6LU6roS22dwQfeVcvejtvoD/fzXO9zBcXcu5t2s7d1L98/+30r3aXZ18wdHXes\n6LWBQuOJUk5+puhbDKbxRCkXP5/Ld8UZT2g0mktjc+1mWgOtHLp4yBqnqi/Ux7dOfIvbNtxmDRhc\nCUF3kAMdB7ip7SaOjR7jxNgJKz3RNMBo8Dawr30fQXeQvlAfPdM9TMQnrO2D7iBSSsKpMP3hfgwM\nfE4ftZ5aPA4P5ybPUVdVZ22j0axntNDSrCs+/vGP87nPfW61m7Gmicfhuefg6adhcLBwmd2u0gLv\nuw+uu27Wgh2Uy+ChQ3O3CQRUCmEiAYlMjJ7pXuLOfrbeMozwj9IdGWQmPUNHsAPHxPV0H21FSvC7\n/Ozd3szPvbsF7Cle6HuBnukea79+l5+7N91NW6CNp7qfYiw2Zs2/v+v+sjVHi+0D8XS8rB36Uo0n\nzKhUsYtf0B0sWWCuWV7074DmSusDHoeHt25+K121XbzQ9wKxdIxUNsVzvc9xfuo8d3beid/lX9T+\nbmq7iT0tezg9fpqjI0eJpNTTsfHYOE9deIqAK8D1zdfzzmveaUW5eqZ7GIoMgYBqdzXV7molupJh\nXh18laA7yExyxtrPWuZK6wOaKxMttDTrio6OjtVuwppESujrg6eeUjVV0Wjhcq8X9u+H++9XLoL5\nJBLw4x8r18F8b51AQNVjRSKQzCTpC/Uxlhhk485h6lrP0T0zQGo8RUd1B9vqt9F3opmLpxto8DbQ\nFmjjpuuquesuuBju5fne54lnZq0Kt9dvZ//G/bjsLl4beo3zU+cBNbbWg1sfnNcKubgPGNIoa4ce\nTobJGJkyeyqPw+YoKaIC7sC6M55Yi+jfAc2V2gc6azpp8bfwUv9LnJk4A0B/uJ9vHf8Wt264lZ2N\nOxe1P4fNwa6mXexs3MmFqQscGTliCaRIKsKLF1/kx4M/ZlfTLnY37WZ3027r97w31EtfqI+MkaHa\nU02Vo4rx2DjRVJR0Nk2MGPF0vOJxuC43V2of0FxZaNdBjWYdE42qtMCnnlKGFalU4fL2dnjrW+Hu\nu9UgwvkYhhJXP/7x7BhYoERZVZWKcKUyaS6GLzIYGaSmbRzvpqOMpXsxMOgIdtAaaAVpo+dIB2Ji\nB62BVjwOD3v2wA17U/yo/yVOT5y29u1xeLij4w4r7//C1AV+2P1Da/n9XfezqWZT2fPtD/czHhsv\nEFLRVHTJxhPl7NAX4wqm0Wg0S6Ev1McLvS9YKdMA7YF27uy8s6CuarEMRgY5MnyEi+GLBfPtws6O\nhh1c33y9ZXKRNbIMRAbome7he2e/R3+4n0gqwoGNB6j2VPOObe9gQ3DDktui0awk2nVQo9EsO4ah\nolevvw6vvKJS/fLrr1wuuPZaePBBZXJRyqhuYEClCU5Nzc5zOKChAaanYXQsw0BkgIuhfvBM4911\nmGnPOUIpwcbgRtoD7dhtdoKOekInbqEz04a9VkV59u+H+s4hvn3yGasYG5S18Z2dd1pPR8dj4zzT\n84y1/Jb2W8qKrKyR5ZmeZyz3rkqwCRsBV6Cki1/AFdDGExqNZlXpqO7gp3b9FC9dnH0gNRAZ4Fsn\nctGthp1Lcho1TTEm45McGT7C+anzGNIgK7OcGDvBibETbKndwvXN19Pka7KcDd8YekOZFEllmlHt\nqWY8Nq6FlmZdo4WWRrNOCIXg9Gk1YPC5czA2pkSXSV2dMra4775Cc4t8wmFldNHTUzi/rU1FtQYH\nDYZmBukL9RFKT+JsfxNH6wlidkFboI2NwY047U46qzvZGryOIwfbEBGw21T91113Zxl3v8qhM0et\nfXhDJocAACAASURBVDttTvZv3F9Q8B1Lx3j83ONWWt+2um3sadlTss2JTILHzz3OSHRkzrJi44n8\nyef0aTt0jUazpnHZXdy16S666rp4vvd5ZlIzZIyMZQt/Z+edS7ZYr6uq457N93Bz+80cGz3GybGT\n1hh+3VPddE910+pv5YaWG9gY3EhHTQevDL6C2+FmIj7BxuqNTMS0IYZmfaOFlmZdcerUKa655prV\nbsZlI5NRLoCnTqk0v/5+ldJn4vEokXTgAOzbB9XVpfeTTiur9qNHC6Nf9fXg80HfRYPhyAgXpi4w\nFhsjEzxP9TXHcVWlaPG30FHdQcAdYEf9DnY17UImgnzve7POhC4X3HznBK/Hn2ZqejZM1upv5e5N\ndxekwWSMDE+cf8JKl2nyNXFn550l2x1KhPj+ue8TTipPeofNQVO8iX037NPGE+uY9fY7oJnL1dYH\nNgQ38L5r38fL/S9zcvwkoFIA/+nEP3FL+y3saty15AdHfpefWzfcyt7WvZwYO8Gx0WOWEdDQzBBD\n54ao9dTitDmRSJx2J6G4GsV+LRtiXG19QLM20UJLs674xCc+wWOPPbbazVhxJiaUuDp9GoaG4OJF\nmMll4dlsSiB1dCiBtXu3qqkqx9mzyiAjlmew5/UqgTY4KDnVN86ZiTMMzwwTt41Sv+MUNY0TNPma\n6KxWhdu7m3azvX47TrvTGpMrkcjty2ewYe+bvDT9qmWHbhM2bmm/heuarptzc/B87/OMRkcBdQPw\nQNcDJc0lhmeGefzc49Z4VV6nlwe3PshHPvgRfvKxn1zildVcDayX3wFNea7GPuCyu7ij8w621G7h\nud7nrOjWoYuH6J7q5q7Ou6j2lHmaVuH+97Ts4bqm6zg7eZajI0eZTkwDMJWYIpQIMRgepN5bjzQk\nUkpCyRDpbHpNplpfjX1As/bQZhiadUVfX99V6zSUSsH580pgDQ/DyIiKYFmCxqtSArdsgZtugmuu\nUXVV5RgbU3VYI3kZdzab2j4Wg2PnJjg2dpyByADxTJTaTb3Ubu6lwV/DpppN7GzYye6m3WwIbrDE\nUl8fPPnk7CDGHn8U1zVPE5ZD1jEavA3cvelu6qrq5rTp9aHXeXXwVUBFp965453Ue+vnrHdu8hzP\n9jxrCbe6qjoe3Pogfpf/qu4DmsrQfUBztfeBdDbNywMvc2LshDXPLuzc3H5zyQdYS0FKSV+ojyMj\nRxieGSadTfOdU98BIOAKcM+me6hyVvHQjodo8ZfJR19FrvY+oFkYbYah0SwzV+OP6siIElfnz6sx\nsAYH1ZROq7qn5mYlsLq64IYbYPNmJZjKEYvBq6+qaFg+GzaA3w+vHJnk8OAR+sMXSWXTeOsn6bjm\nDI11LrbW3ajSVJp2UeOpKdj+1Cl44QVlAS+lJOPrg63PkJDK6lAguLH1Rva27i05KO+FqQuWyAJ4\n6+a3lhRZ+WIMVErN27a8DZddDfp1NfYBzeLQfUBztfcBp93JgY4DbKndwvO9zxNOhsnKLD/q/xEX\npi5w16a75vxGLxYhBJ01nXTWdDIyM8K/nf03As4A4/Fx/C4/M6kZqpxVTMQm1qTQutr7gGZtoIWW\nRnMFkkiolL5Tp5TzXzyunACHh5XBhd8PnZ3Q1ASbNimB1d4+/z4NA958E157TYk0k5oaJc5+dHSM\n588oVylDGjg8SVp2naW1I87uxhu4a9Nd7GjYYQmafA4fVhOoMbUivjeo234U7CqiXu2u5p7N99Dk\nayrZtmKHwZvbbp7jMGhIgxd6Xyiwg7+m4RoOdBwoKdw0Go3maqct0Mb7rn0frwy8wrHRYwCMREf4\n9olvc1PbTVzXfN2y/D42+5upr6qntqqWoegQdmFnMj5Jo6+Ribg2xNCsX7TQ0miuEKRUYurUKeX6\nZw4G3N8P4+MqetXSoqZAYDaCVT836DOHvj546SXlTGhi2ry/2dvHZ751nIHwIABCSGo3XaR9xwi3\ndd7EvZvvZVPNppKpKIYBBw+qNgOMRkdJ1r/KhmsHLNv4XY272LdhHw5b6Z+jYofBrXVbubH1xoJ1\nUtkUPzz/QwYiA9a8W9pvKetEqNFoNOsFh83B/o37Ve1Wz3OEkiGyMsvLAy/TPdXN3Zvupraq9pKP\nU++tL0j5nowr56W1bIih0aw0+jGvZl3x2c9+drWbsGiiURVl+vrX4Xvfg+5uJayOHlVjYaVSsH27\ncg285hq47TZ4+GE10PBCImt6Gr7/fWVOkS+yurZlsDed4A//6TH+7uknLZFVVRti24E3+Zm3b+Wz\nD/w+v7D3F9hcu7mkyMpk4IknlMhKZ9OcHDtJqvV5Nu5SIsvn9PGObe/g9o7by4qsrJGd4zB4V+dd\nBevMpGb411P/aoksu7Bz7+Z7y4qsK7EPaJYX3Qc067EPtPhbeO+17+X65uuteWOxMb598tu8PvS6\nVdO6VOqq6qirqsOGjXQ2TTwTB5TgutR9rwTrsQ9oLj86oqVZV8TyrfPWMOagwqdOKcdAKdW8sTEV\nwUqlVORq61ZlclFVpdwDr70W3BU4lqdSKpXv+PHCsbSqG2dIBo/xV8/2MDAawzTLsbvSdO4a4j0H\ndvOObb+Ez+Wbd/+JhBJvo6MwGZ/g3NRZNt94gcYNys99a91Wbt94+4L26s/1PlfgMHh/1/0FDoNj\n0TEeP/+4ZTXscXi4v+v+eesBrpQ+oFk5dB/QrNc+4LA5uHXDrWyu2cxzvc8xnZjGkAavDr7KhekL\nZY2IKqG+qh6/y4/b4SaZTeLMOskaWbDBdGJ6yftdKdZrH9BcXrTroEazxhgZgR/+cNZOPZNRtVcD\nA2rMqpYWNbiwzabGvbrhBti2TaUOLoSUyuTilVdm3QgB0p4h7K1HefHNfs6fs5HNU18bt0b44ANd\n/OTO+/A4PAseIxxWkbep6QzdUxcYSw5w7a391DTGcNvdlv3wQrwx/AavDLwClHYY7Jnu4ekLT1sp\nhdXuat6+7e1LHpxTo9Fo1hMZI8PhwcMcHTmKRN0L2oSNva172dOyZ9G1W+lsmr9//e/551P/TDwd\np66qjts7bifoDnL3prvZXr99JU5Do1ky2nVQo1mHvP66ElmplBJXk5PQ0KAElSenc5qb1d+dnVCp\nS+/wsLJrH8+lyxsyy4Q8h63tCD3joxz/vx7SKQEYCCFoabLzwf/H3p0GyXWdZ57/n3vzLrlUVdaK\nwlZYCIKLuIGLKFISF1EEQUqCaGk8GtoTMUP1h54eS2472lJ0OzpsyZ6JDmnGjmlb6o6escbd8kST\nstQmKNMSKZIiRUIkRVHgDoIbiB0o1F653n0+3MpbWQS4VFVuhXp/ERWsyrqZeVJ6gcSb55zn3LGB\nO6/6+IdqsCCecXvwQTg1NcMbE28QpApcfsNRsj0OIz0j3LDpBjJG5gMf59D0oaTJArh5880LmqxX\nTr/CU0efSn4ezg2z87ydH3qcQgix2qW0FNduuJYtvVv4xaFfMFWdIoxCnjvxHO9MxbNbZ0t2fS+G\nbtBj99Br9zJTnUEpxUx1hm6rm/HyuDRaYlWSRkuIDnPsGLzxRnzo8AUXxKmBtWZq82a47LJ4VuvD\nKhbjA4fffjv+2Y3KnPBexe99FdcY55WnMxSm4hOLU1qKvlyOz39qLf/DzZeTs95/iWC9o0fhoYdC\n3p44xNHZY2S7HS69/ii5HFy34ZNcNHjRh3qcifIEP3/n58nP16y7hi29W4A4Fv7pY08n6VkQL0O8\ncdONZz20WAghxPsbyg7xhYu+wL6T+3jh1AtERExUJrjvwH1cMXzFex65cTa15MGD0wdRSjFRmWBj\nz0YmypI8KFYnabTEqjI+Ps7AwEC7h/GeZmfjpX2nTsXLA2tLBLdvjxus/CKOPfH9ODDjhRfi72eD\n05zwX6FsvU1uwwSvH9A4fSj+tNJO2eTtPJ/csYb/8Y7zGe7tWdS433gDfvJIkddOv07JK9HTX+Hi\n646ysW+Imzbf9KGX81W8Cg+9ffaEQT/0efTgoxyeOZxcv2N4B1evu3pRh292eg2I5pMaEFIDC+la\nfJjxlt4tPH7o8STAYt/JfRyaPsRNm29iIPPB/3v1Z/rpT8fvK0EYUHSLAB0Z8S41IFpBUgfFqvLl\nL3+53UN4X6dPx80WQHc3XHEF/M7vwA03LK7JOngQ/v7v4dlfh5yovsUL1T28GuwhWvdrpoNjPPXz\nPGOHB+gyu9jYs5FPXHAx/+5/vY4/+t2rF91k7dsX8Xf3H2XfiecpeSUG1hW4/JPH+PiWq/nc9s99\n6CarljBYe2Meyg5xw6YbgDji/cev/zhpsjSlceOmG7lm/TWLarKg82tANJ/UgJAaOLuBzABfuOgL\nC2axJiuT3PfafTx7/Nk43OJ99Kf76bF7SKkUTuBQ9ePNwG7gMuvMNn38iyE1IFpBZrTEqvKNb3yj\n3UN4XydOzIdgbNwIH/3o4u4/ORnvwzp0vMIp/zVOePvxKNMzNEvEKM8/txZ3Zgs9Vg892R6Gunr5\nrVs2cuM1A2iL/NgliuBnj5W4f++byRvouq1TXPMxj1u2/taiE6aeOPwEo6VRII5+33neTlJaisnK\nJA++9WDSgJm6ya1bb2V99wecwPweOr0GRPNJDQipgfemKY2r113Nlnw8uzVRmSAi4oVTL3B4+jA3\nbr7xPQ+X78/0kzWycfKg75BJZaj6VeyUzUR5oqPCiqQGRCtIoyVWlU5PnDx4MG5gALZ+cDBfolqF\n556DZ18Z57j3CmPBW4SEpLtKaN1HefOtbqonLqfHytOV76LL7OLGKzew+5Yhsh9+G1bC9+E//8MR\nnnzpKEEUf8K59ZIxPnvDRq5ae9Wi90u9cOoF3px8E4j3id227TYyRoZjs8d45OAjuIELxBHvu7bt\nWlZMcKfXgGg+qQEhNfDB+jP9/NZFv8ULp15g38l9hFHIVHWK+w/cz2VrLuOqdVedcQZizsyRNtJ0\nW92MlcYICSm6ReyUzXh5PNlv2wmkBkQrSKMlRIcIgjhQAuKzsdau/eD7hCG88mrIg88c4nDlFWbC\nUwDoZgV78DBjY4rqr3eQY4jB7jTpVJpLRzbyhdvWsGHD4pbc1UwWyvxf/99rHDwWHyKsaRE7Plbg\nrhs//r7nV72XsyUMDmQGODB+gL1H9iYHXQ5kBti1bdeHSi0UQgixfLW49835zTx+6HHGy+NERLw4\n+iKHZw5z46YbWZNbs+A+/el+eu1eThZPklIpJsoTDGQGOnKflhDNJo2WEB1ifBwK8Xm+dHVB/wek\n6r59uMo/PHGA1yf3U43iZXWR5pLqO4xHifIbH8UubiRnGFi6xZa+EW6/YQ1XXK59qDO3zubFwwf5\nv39whJmZeJ2hngrZfXua3ddcj6Ebi368dycMXr3uajbnN/Pr47/m+VPPJ7dv6tnELVtvOePTUyGE\nEM3Xl+7jzgvv5KXRl3juxHOEUch0dZr7X7+fS4cu5Zr11yR/P9cHYoRRmCwtHy+Pt238QrSLhGGI\nVeV73/teu4fwnk6fjqPY4f0brUOnJ/mLHz3Bn933X3lx4lmqUZEg8gi738Fa/xpWcTvagf8eq7yV\ndCrD1t6tfPG6q/mX/2wtV125tCbL8R3u2/cE//77h5Imqyur84f/0za++LFrl9RkvTth8Lze87h8\nzeX8/J2fL2iyLhm6JNmv1QidXAOiNaQGhNTA4mlK44rhK/jiRV9csEfr5dMv86P9P+JUMV5R0Zfu\noy/Th0LFgRhBHIhR9spUvEpbxn42UgOiFaTREqvKvn1NOfi7IUZHoRSvxmP9ejDN+d9FUcRb44f5\nywce4E/u+RHPHztAgE8YBTjWEfKbDzLSPYJ24AuUj16AjsGmnk3cfME1/LPf3sAdt+t0dS1tXMdm\nj/GfHnuAPfeHuNW42dkynOfP/sVVXLF1w5Ie890Jg4OZQa7dcC3/9OY/8fbU28l112+8nus3Xr/o\nZMH308k1IFpDakBIDSxdb7qX3Rfs5tr116Kr+JO7WWeWH7/+Y546+hR5K0+31Y2pm3Gj5VWTJeCd\ntHxQakC0gqzDEavKd7/73XYP4T0dOhTv09J1GBmJb3MDlwNjr/Poy6/y8puzuHEmBGEU4upjrFnn\nMdK7jhOvbebYyRya0tjQvY5N+Y1cfaXBjh2QWuKfcj/0eebYMzy+7xhv/GYtYahIaSk+dsEW/uf/\nbi22vfTX+uSRJxckDH5sw8d44I0HkiUmKS3FLVtuYVN+09Kf5D10cg2I1pAaEFIDy6MpjcuHL2dT\nfhO/OPSL5O/zV06/gq50LN0ia2QpeSVCM6TslcmZOcbL42zoXtoHdI0mNSBaQRotITpApRIfUgyQ\ny0G6p8Avj7zEr995nTfe8pmd27sVRSEeZYaGPc4fGWDs0BCvPj5AFGoM59Yw0jPCeZtsPv7xxZ27\n9W6jxVEeO/QYr76c4p1X1gHQa/fy6R3n85ld9pKbN4AXT73IGxNvAHFDdcXwFTx88OHkvJWMkWHX\ntl0f6nBMIYQQ7ZO38+y+YDevnH6Fp489DcDxwnF67B7ydp6p6hSapjFVmSJn5pgod86MlhCtII2W\nEB3g9On5ZYO+dZrHTz7MzMESo6fnr9EinZ5+n21bs1QLWV55YphK0WQwM8jm/Cb6ezJcdx2cd97S\nxxFGIb858RueP/kCb780xPG3e9GVzpbeLXz62nVcfz2LPm+r3uHpw/zq+K+Sn7fkt/DMsWeSiPhe\nu5fbz7+dnJlb+pMIIYRoGaUUl665lNfGX2O6Os1kZZJNPZvoS/dxcPogAFPVKTb2bJRADLHqSKMl\nRAeoD8KYcE5RPFhF6aCTIqPy6OkSG7dWyNg2B18eYuxYN312Hxet3USX1cUll8BVVy3c17VYk5VJ\nHnvnMcaKk7z+3DrGjnfRbXVxQf8F3HB9hh07lvcaJyuTCxIGu8yu5OwsgPVd67n1vFsx9WW8CCGE\nEG0xkBlgujpNGIUYmkF/Jk50cgOXqhevWJhxZvACb0kBSkKsRBKGIVaV3bt3t3sIZzU6Wmu0Imac\naZQeEIQe3WYvQ1vGufiKCuWJXp57eCvu2EYuX3M5l6y5hG0jXXzxi3DddUtvsqIo4qXRl/iH1/6B\nUzNTvLx3hIkT3WzOb+by4cu5/dblN1kVr8KDbz2IF3qEUUjJLVFwC8nvLxy4kNvPv70lTVan1oBo\nHakBITXQePXLvSMieu14RYTru5T9cvK7ycpkO4Z3BqkB0QoyoyVWla985SvtHsIZogiOHQPPA4wy\ngTnBCe9VUrriE1calGfSvPjYMKrSz0V9m+hL92Pb8LGPwfnnw3IC+QpOgccOPcap4imq5RSvPrUR\nKr1cMXwB+UwXt94KGzcu7/UFYcDDBx+m6BbxQ58ThROs716f/P6addewY+0yO7lF6MQaEK0lNSCk\nBhqvvtEKwoC0kcZO2VSDKl7g4QYupm4yXh4/45DjdpAaEK0gjZZYVXbu3NnuIZxhair+AojMAsXM\nq6hoghHrfA6+uJbZE8Nszm9iID+IUoqLLoKPfhQsa3nPe2D8AE8ffRov9CjNWLzy1EYGjc1sHt5M\nNquxaxcMDi7/9T155Mm4kfOrvDX5Ftv7t6MrHU1p3LT5Jrb1bVv+kyxCJ9aAaC2pASE10Hj1jVbJ\nK5ExMvTYPZwuxpuNZ6uzDGQHOibiXWpAtII0WkK0WX0QRjUqENpjuKPrmC5/lEu2XcH569agKY2B\nAfjEJ2Bo6P0f74OUvTJPHH6CIzNHAJgey3DwuW1c2H0hPXae7m644w7o7l7mCwNeGn2JNybeoOgW\neW3sNS4cuBBTN7F0i9u23cZwbnj5TyKEEKLtTN2k2+pm1pllojLBYGaQXruXE4UT6JrORGWCgeyA\nBGKIVUUaLSHarD4IoxxOUZ7soTq6mYtHBljbtRbTjGewLrpoecsEAd6ZeocnjzyZRKmPHeti5sBV\nXDawlZSWYnAQdu2CdHqZLwo4MnOEZ449w0R5ggPjB9g+sJ0uq4tuq5vbt91Oj92z/CcRQgjRMQYy\nA8w6s/ihj6mb9KfrAjHm3ncmK5OEUYimJCZAnPukysWqsmfPnnYP4Qy1RisioKRO4hd70TEZ7Olm\n+3b40pfg4ouX12S5gctj7zy24Lyq8XfWor2zk22920lpKUZG4HOfa0yTNVmZ5NGDj3J89jj7x/az\noXsDg5lBhnPD3HnhnW1tsjqxBkRrSQ0IqYHmqF8+qFAMZAZQKBzfoeJXgPgYkenqdLuGmJAaEK0g\njZZYVe655552D2EBz4sTB6tV0NNFXGMMv5LF0iw2DKe56ablNz7HZ4/zw1d/mESpRxFU3rmcnrFd\nSfzuhRfCzp0s6yDimqpf5cE3H+TAxAHennqbgcxAfJBy73l85vzPYKfs5T/JMnRaDYjWkxoQUgPN\n8e7kwZyZw9AN3MCl7JUJoxCgI5YPSg2IVpClg2JV+cEPftDuISwwNja/PysyC7hRkSjsIpu22bQu\ns6zH9kOfZ48/yyunX0luS2GhH70FNbkB9Pi2q66KvxohCAN++uZPefbEs4yXx8mZObYPbGfH2h1c\ns+4a1HLXPjZAp9WAaD2pASE10By1pYIQf+hmpSxyZo6SWyIIA8pumZyVY7w8zvb+7W0cqdSAaA1p\ntIRoowVBGGEBL3KALnJpm/PWLz2NYqw0xmOHHluwPGPQWo//5qeYGounyJSCT34yns1qlEcOPsLP\n3v4ZBbeAqZtcMnQJN2++mQsHGvgkQgghOlLaSJM1spS8ElPVKXqsHvJ2nqnKFLqmM1WdImflmCh3\nRvKgEM0mjZYQbbQwCGMWLwhRaPRlu1g3vPjDe8MoZN/JfTx/8nkiIgB0pXNp78c4/NzFTE3FM0qp\nFHz60zAy0rCXwt4je/nh/h9S9atoSmPH8A7uvPBONnRvaNyTCCGE6GgDmQFKMyXcwGUgM0B/up+D\nUwcJooCiE7/hdUrEuxDNJo2WEG2UNFq6S9GfIPAsUlj0deXo7//Auy8wXZ3m5+/8fMHa98HMIDvy\nn+Kpx3qShs6242TB5cbE13v2+LP8zb6/wQ99AHYM7+B3L/td+tJ9jXsSIYQQHW8gM8DhmcNAHIiR\nJA/6LtUgDmNyA5dZZ5ZuqwHniAjRwSQMQ6wqd999d7uHkCgWoVCAchlSmQJuaoKgksPUbDYOZ7A/\nZGZEFEW8PPoy/23/f0uaLIXiqrVXcW3+8zz+0HyT1dUFn/98Y5usXx//Nf/x1/8xabIuW3MZ//zq\nf96xTVYn1YBoD6kBITXQPPWBGCEhfek+dKVT9auUvXLyu3YvH5QaEK0gM1piVemkk+BPn46brCiC\n0CjgerNEoU02bTOy9sNFDRbdIo8fepwThRPJbXk7z82bb6Z4epCf/hyCIL59YABuv70x8e01vzzy\nS/72hb/FCz0APjL4Ef7g2j/ATC1+2WOrdFINiPaQGhBSA81T32j5gY+dsrFTdnKWlhd4GLrBRGWC\nLb1b2jZOqQHRCtJoiVXlrrvuavcQEqOjC4Mw/MgBbHJpiy3ruz7w/m9MvMFTR5/CDdzktkuHLuWa\n9dfw+mspfvnL+Ws3bIBbbwXDaMzYgzDgsUOPcd9r9yXncl08eDF/dP0fYegNepIm6aQaEO0hNSCk\nBpona2axUzZVv0rBLZAxMuTtPKeKp1BKMevM0p/pb3vEu9SAaAVptIRokwVBGH4Bz/fRSNGdttm0\nNvue96t4FZ488iSHpg8lt+XMHDdtvom1uXX8+tfwwgvz12/fDjfcAFqDFgpX/So/e/tnPHH4CWac\nGQA+MvQRfv+jv9/xTZYQQojmG8gMcGz2GFW/Sn+6n7yd53jhOAAz1ZmOaLSEaAVptIRogzCE8fG4\n0dKtMmW3ghekMLDozWUZHDz7eVOHpg/x5OEnqfiV5Lbt/du5fuP1pJTJ44/Dm2/OX79jB1xzTePG\nPevM8tM3f8r+sf2cKp5CVzoXD17M3TvuJmu+d3MohBBi9ag1WgCapiXLCd1gPhCj7JWpeBXSRgPX\nswvRYSQMQ6wqe/fubfcQAJiYAN+Plw7qmSJVbYKgksXUbNYPpc/YR+UGLr849At+9vbPkibLTtns\nPG8nN22+CRWaPPjgwibrE59obJM1Whxlz4E9vDP9DgenDmLqJpetuYwvXvxFhrINTNdosk6pAdE+\nUgNCaqC56vdpqUgxkI5/dnxnYSBGG2PepQZEK0ijJVaVb3/72+0eAhAvG6xU4qCKMFXA1aeJghQZ\n68wgjBOFE/xo/494feL15LZNPZv47Yt/m835zZTL8I//CMfiDw/Rddi5Ey6+uHHjPTh1kAfeeICJ\n8gQHxg+QMTJcMXwFN26+kW192xr3RC3QKTUg2kdqQEgNNNeCQIzQp8vqwtRMnMCh5JaIovicx3Yu\nH5QaEK0gSwfFqnLvvfe2ewjAwv1ZlXAWL3SADF1piy0bckAcOPHs8Wd5+fTLyf0MzeD6jddzwcAF\nAExPw09/GsfEA1gW3HYbDA83bqwvnHqBZ48/ixd47B/bT5fZxUWDF3F+3/lctfaqxj1Ri3RKDYj2\nkRoQUgPN1W11Y+ombuBS8SsYukGX1UXBLeCHPmWvTNbMtjXiXWpAtII0WmJVyWQy7R4CEDdapRIo\nLaTkFfFDnxQGGTvF1vXdjJfHeeydx5iqTiX3WZtby02bb6LLihMJR0fhwQfBceLf53Jwxx2Qzzdm\njGEUsvfIXg6MHyCMQvaP7afH7mFb3zaGskPcvOVmlDr7XrJO1ik1INpHakBIDTTfQGaAE4UTVPwK\nXWYXeTvPZGUSXdOZrk6TNbNtndGSGhCtII2WEC1WrcLMTDyjlcqUqDgBrheRIk1PNk2UHmPPgX8i\njEIAdKVzzfpruHTo0qSxOXQIHn10/oys/v74jKxGvW+4gcsjBx9JNjO/NfkWveleRnpGSKfS7Dxv\nJylN/voQQghxdrVGC+L3sb50H29PvY0XeDh+/AnhjDOTnKslxLlI/qUkRIuNjcX/LRZB6ylQwL2Y\n4gAAIABJREFU9WYInCxpzWLtYJrXZ59PmqyBzAA3b76Z3nRvcv/XXoO9e+ODjgHWr4/PyDIbdEZw\n0S3y4FsPMlmZBOI9Ynk7z1B2CF3p3LbtNnJmrjFPJoQQ4pxUv08LxXwgRuBQ9ucDMSYrk6zJrWn1\n8IRoCQnDEKvK1772tXYPgdHReLmf58VBGA7TRH6KrGUzstZmrBR3YnbK5s4L71zQZD33HDz55HyT\ntW1bPJPVqCZrvDzOngN7kiar6BbJGbkkVfDGzTeuqITBs+mEGhDtJTUgpAaar77RCsOQ/kw/GhqO\nHwdi1LRr+aDUgGgFmdESq8rIyEi7h7AwCCMo4GtlFBa5tMXQoMZUEC+pGMoOoan4s5AwjBus1+eD\nB7n8cvjoR6FR26SOzBzhkYOP4Ic+EC/1sFN2skRwx/COFZcweDadUAOivaQGhNRA8/VYPaS0FH7o\n4wYuVsoibaTj5EGvRBAG6Jretoh3qQHRCjKjJVaVr371q219/iiaD8LQDZ+iW8YPPVJY2Jaip9dL\nrq19Guh58NBDC5us66+Ha69tXJO1f2w/D731UNJk9dq9GLqRNFmb85u5et3VjXmyNmt3DYj2kxoQ\nUgPNp5SiP90PQDWoYuomeTuPG7jomp6EPbVrRktqQLSCNFpCtNDMDLhuLQijgOOA60UYyiaXMTC7\nZ5NrBzODVCrwwANw9Gh8m6bBpz8Nl1zSmPFEUcQzx55h75G9RMTrETfnN2PqJlW/CkB/up+bN6/M\nhEEhhBDtU7980NAMeu1egiggCIPkPWayMpnsSxbiXCONlhAtdPp0/N9iEZRdoOJXCKoWhrIY7s9Q\nYjS51vQHuP/++fAM04TPfAa2bm3MWPzQ5+GDD/PS6EvJbVcMX4Gt24yW4nGkU2lu23abJEIJIYRY\ntAWBGEB/Jp7hcnyHilcB4qNEpqvTLR+bEK0gjZZYVQ4cONDW5z99Ol4KWK3OBWGoKcLAIGPZbFxr\nMVaOuypvtpeHf5Jldm6CK5uF3bth7drGjKPiVXjgjQc4NH0IAIXihk03kDEyHJiI/zfSlMbO83ae\ncwmD7a4B0X5SA0JqoDXqG62IaGHyoDefPNiO5YNSA6IVpNESq8rXv/71tj5/bX8WQNEv4KsSGho5\n22RwUOEGLrOTNm89cyHVeFUFfX1w553xfxthqjLFngN7OF2Kp9cMzeD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JJ3713p0weNu2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M5vNbQK52eF3LJQ8UAkKX2FxICQGBicCjMPuh3Wz890Ns3Gjo1UeippT7XTkmjB1OYOM/1KDIj+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\n2uv1ar/f36Unbf369Xr//ffXh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3CHOlBK4XNaY5vfb3nfHiowsaZ4SEMhl8vFU089xezZ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\nFVoiIiIiIiINpkJLRERERESkwf5/ln4I9352I8sAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, "metadata": {}, - "source": [ - "This score is clearly not as good as expected! The model cannot generalize so well to new, unseen data.\n", - "\n", - "- Whenever the **test** data score is **not as good as** the **train** score the model is **overfitting**\n", - "\n", - "- Whenever the **train score is not close to 100%** accuracy the model is **underfitting**\n", - "\n", - "Ideally **we want to neither overfit nor underfit**: `test_score ~= train_score ~= 1.0`. " - ] - }, - { - "cell_type": "markdown", + "output_type": "display_data" + } + ], + "source": [ + "def plot_validation_curves(param_values, train_scores, test_scores):\n", + " for i in range(train_scores.shape[1]):\n", + " plt.semilogx(param_values, train_scores[:, i], alpha=0.4, lw=2, c='b')\n", + " plt.semilogx(param_values, test_scores[:, i], alpha=0.4, lw=2, c='g')\n", + "\n", + "plot_validation_curves(gammas, train_scores, test_scores)\n", + "plt.ylabel(\"score for SVC(C=10, gamma=gamma)\")\n", + "plt.xlabel(\"gamma\")\n", + "plt.text(1e-6, 0.5, \"Underfitting\", fontsize=16, ha='center', va='bottom')\n", + "plt.text(1e-4, 0.5, \"Good\", fontsize=16, ha='center', va='bottom')\n", + "plt.text(1e-2, 0.5, \"Overfitting\", fontsize=16, ha='center', va='bottom')\n", + "plt.title('Validation curves for the gamma parameter');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that, **for this model class, on this unscaled dataset**: when `C=10`, **there is a sweet spot region for gamma around $10^4$ to $10^3$**. Both the train and test scores are high (low errors).\n", + "\n", + "- If **gamma is too low, train score is low** (and thus test scores too as it generally cannot be better than the train score): the model is not expressive enough to represent the data: the model is in an **underfitting regime**.\n", + " \n", + "- If **gamma is too high**, train score is ok but there is a high discrepency between test and train score. The model is learning the training data and its noise by heart and fails to generalize to new unseen data: the model is in an **overfitting regime**.\n", + "\n", + "Note: scikit-learn provides tools to compute such curves easily, we can do the same kind analysis to identify good values for C when gamma is fixed to $10^3$:" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from sklearn.learning_curve import validation_curve\n", + "\n", + "n_Cs = 10\n", + "Cs = np.logspace(-5, 5, n_Cs)\n", + "\n", + "train_scores, test_scores = validation_curve(\n", + " SVC(gamma=1e-3), X, y, 'C', Cs, cv=cv)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Xf7NcLPwP/Buev8O/GfwL/GFfiwuspa47tXhV4x98kzDeCPwf/Lkav40/zMpVbfdRM9uF\nP9frufjC6jJ8MbT4TesV+OtAvR9/vam34N+QnfL8+BbSv8Q3d3gJ/k3d2zm1499Sr2XJ17SMV+Hf\nsF+BX+kYBX4IfK9qm7fi3zS+DH9o37/jszi+yueo9i/AM/CrYl87ZcDO3Wb+QrxvwRc4efyhWN/j\nZMe5ceAf8EXVSznZ3v2PnHM3Lt7nEk4Zs3Pu62b2fHxL9Lfhz9+7DXiTO3kNrNCcc8fM7E/x12j6\nKCcL2DtY5c/OOTdlZk8F/he+890f4H9O9+KvjTaxwhgC4NVmdgt+nr4R312wD38I4VVLHHK7lH/G\nvz94Pb55w3eB/1lePay4FH89qMoK51fxXRiP1Hiti63FPlaTa2Bmv43/d+LVnDzM+D580VjdZOYN\n+Pn3DvxlA24EfuSc6zGz38D/jrwEX1ANcvK6XKsZk4hsEbaoOZeIiIhsQWZ2Hr7Yf4Nz7oP1Ho+I\nSLNqiHOwzOxpZnarmR01s8DM9q3iMc80sy4zmzaze83sDzZirCIiIiIiIstpiAILf2jIT4DXsYql\n9fI1UL4IfAt/bYsPAB81s+es3xBFRERERERqa4hzsJxzX8Ufe125YvpK/hS43zl3Tfnre8rHrV8F\nfGN9RikiIrLp1TpfTEREVqEhCqwInsSp1z/5Gv6EchEREQmpfC22WheWFhGRVWiUQwTD2oNvGVut\nF2g1s9VcG0VERERERGTNNesK1lIqhxYueWhD+Rozz8Vf3HJ6g8YkIiIiIiKNJwOcg78e5OBa7rhZ\nC6weYPei+3YBY8652WUe81z89XVEREREREQAXgl8ei132KwF1neB5y+675Ly/cs5CPDJT36S888/\nf80HdNVVV/H+96/PKWBR972ax620TSO+rvXc/2ofs1lz01zbuP1rrjXOXJuYgPFxGBk5+XlwEPr7\nYXgYRkf9/SdOwOys/5ibg2LRPz4I/IdzMDl5Ffn86l5XLAbJJKRSUChASwt0dMCuXXDGGXDOObBn\nz8LHvPvdV/HmN4fLbbWPWWm7KM+9Wo2479U8brNmFnX/mmuaaxu1/7WYa/fffxdXX/0qKNcIa6kh\nCiwzywMP5+Rhfuea2WOBIefcYTN7N3Cmc65yrat/BK40s+vwV5x/Fv6q9y+o8TTTAOeffz579+5d\n89fQ1ta2Lvs9nX2v5nErbdPf399wr2s997/ax2zW3DTXNm7/mmvrO9cuuGAvQ0O+UBoehqEhGBvz\nn3/+834+/OG9DA76Ymp6emHhNDsLpdLJoqlSQFW+XswM4uXWELFYG5nMXhIJyGQgm4VcDtrbYfdu\neMhD4Lzz4BGPgF/5Fdi2zX9/Vf1zgZtuauMlLwmX22ofs9J2f/3X/aGfe7WivK713vdqHrdZM4u6\nf801zbWN2v9azLXu7vmba37qUEMUWMCvA9/mZHvY95bv/zhwBb6pxUMrGzvnDprZbwHvA14PHAFe\n65xb3Flww1x22WUNt+/VPG6lbUqlUqTnXo31zCzq/lf7mM2am+baxu1fcy38voMA9u27jPvu84XS\n0JBfXaqsPA0OQm8vHDt2Gc9//smiqbp4mpuD0dES3/zm8gXUUkVUPO5XmyqfK4VTSwu0tvqVp4c8\nBI4fv4wXvxjOPNMXVa2tfptUag1CQ3NtI/etf9c01zZq35pr9Ztr68XcUv+TbEJmthfo6urqWte/\nZG82+/bt49Zbb633MJqOcgtPmUWzGXKbnoaBgVOLprExf3tszBdPg4MwM7NwxanyUSyePGQPThZO\nSxVQIyP7aG29lVjMF0zxuD9cL5GAdNoXT5XCqL3dF0+Fgr991ln+EL7W1pPbtLb678eatS/vKm2G\nubbRlFk0yi08ZRZed3c3nZ2dAJ3Oue6Vtg+jUVawRERkEykWT57LNDTkb1cXTZXzmsbH/TlQMzML\nP6oP15ub88XRYksVUOAPt6sctheP+9WjRMKvOOXz8MtfwsUX+6KoUPD3tbScLKIWF0+Vj0xmYzMU\nEZHmpAJLaqrX0mqzU27hKbNo6p1bEMAXvwj3339ytWly0hdNzvnVpsXFU6Vwqqw8LXcgReVwPed8\nwVRZIap8rhyyl0r5IqnSKCKfP1lMVb4uFE4WXHfe6Q8hrC6eKgVVvM6X2S0FJUZnRhmZHmH4xDAz\npZn6DqhK5yWd3HH4jnoPo6kos2iUW3jKLLx7eu5Zt33rEEGpaWBggB07dtR7GE1HuYWnzKKpd27f\n+ha8971+JWqp4qmWymF5ldWmSuFUWYECf8heS4svmLLZhcVTpaBa3CQikzm1eKp8ZLMwOFj/uVYM\nivNF1PD08PztsZkxZkozDJ8YZmRmhJm5ximwToyeINuWrfcwmooyi0a5hafMwus/0M9n3vAZ0CGC\nstGuuOIKHdMbgXILT5lFU+/cvvpVuOuuk1+b+aIplfKFTyLhP2KxhQUUnNyu0jCiunCqfCx1WF4s\n5r+31ApUa6tf2aplIzObKc744qmqiKrcHpseY2TmZGE1PjvOxMwEJ0onNmRsYd17w7088spH1nsY\nTUWZRaPcwlNm4U0OTK7bvlVgSU3XXnttvYfQlJRbeMosmnrn9otfnDwP6tGP9sVPECzsulcpoBYX\nT7UKoVRq6eKptdXv53QaSqxHZlNzU0uuSPVO9M4XUaMzo4zPjDM+O87U3BSOhUeQGEYqnqIl1UIq\nniIZSxKzxumckbs0R3uhvd7DaCrKLBrlFp4yC28kN7Ju+9YhgiIiEtmFF8Lx477gee1r/XWd8vnV\nnctUWYVa3EyitdUfGthonHNMzk2eUkQdHTtK31TfKUXUxOwEJXdq6+S4xUnFU76IiidJxpLgICAg\nZjGyySypWIp0Ik0mkcFY5UWyRERk1foO9HHz628GHSIoIiKNor/fX8TXOX8o3znnLPx+IrF08dTS\n0hgNJZYTuICxmTFGpkcWrEQdHDlI/4l+RqdH/SF9M+NMzE0wW5pdcj9xi5NNZEnH0yTjSeIWx8ww\nM3KJHJlEhlTcF1LtmXY6Mh1kkzqHQkRkI7QMtazbvlVgiYhIJD/6kW9qAb69+YUX+hWsSiGVy9V3\nfCupdOyrrEgNTA1wcOQgh0YPMXRi6GQRNTvBVHFq2f0kLDG/IpWKp4hZjHgsTi6RI5vMkklmSMVS\nZJNZ2jPttKZbVxxb3OK0Z9rJp/Jr+ZJFRKRstrD0H8fWggosqenGG2/kta99bb2H0XSUW3jKLJp6\n5tbdffLaU7t3wxOfuLCbX6OYK80taDTxzzf+M+f85jkcHT/KyMwI49PjjM+NMzU7RcASF9wqS8VO\nFlHJWJJYLEY+maeQKpBL5ea/n0/lacu0EbeVl+iSsSQd2Y75FazK7ZZUC9ZgYep3NDxlFo1yC0+Z\nhdc9tqZHBS6gAktq6u7u1i9sBMotPGUWTT1zu+uukwXWIx9Z/+Jqujg9f0jfsfFjHBg6wIOjD3J8\n/Ph8l77J2UkOfPsA55x5zpL7qDSaqJwflYqlyCQydGQ6aEm3kE6k5wus1nQryfgKLQvLKvuoFFCV\ngmq5FarJ2Ul6J3uZmlt+5WyjfeM73+CJv/PEeg+jqSizaJRbeMosvANDB9Zt32pyISIikTzlKXBP\n+TqNf/u38JrXbMzzTs1NzZ8XdWD4APcP38/h0cMMTA3MN5dY6QK9MWK+gCoXU7lEjvasL3oKqYI/\nNyqeJhFPkEks0St+GflkfskVqVr7CFzA0Ikheid66Z3spWeih4nZiVU/p4iIhHfo7kO881XvBDW5\nEBGRRhAE0NvrbyeTcMEFa7t/5xwTsxMMTg1yYPgAB4YOcHDkIMfGjzE8PczETO3zoiqqO/al42m2\nZbexPbvdN5RI+QYUqXgKINQhea3pVjoy5UKqalWqsq9aZkuz9E320TPRQ+9EL32TfcwFc6t+bhER\naWwqsEREJLQDB+BE+Xq4hYI/ByuKSse+B4Yf4N7Be/1q1Nhheid6GZsZY2J2ouZ5URXVjSbaM+3s\nyu9ie84XUplEhnQ8TTwW50Rx9RfxjVmMtnTbghWp9kw7bZk2ErHV//c5PjM+vzLVO9HL0ImhU66B\nteC1xBLsyu9id3437Zn2hjsXS0RkM/j52M/Xbd8qsEREJLQf/hCKRX+7o8N/1FIMihwdO8o9g/dw\nYPDkuVF9U31MzE6segUnGfPXjSqkC+wp7GFXbhc7cztpzbSSiftCaqo4dUohNRvMslydloglFpwX\nVSmoWtOtoS/0G7iAwalBX0yVi6qVzqPKJ/PsLuxmT2EPu/O72Z7b3lAXGBYR2YzG2sbWbd8qsKSm\nffv2ceutt9Z7GE1HuYWnzKKpV24/+cnJBhdnnulXscCfH3VX/13cO3gvD4w8wOFRvxo1eGKQ6dL0\nqvZtGMl4kkw8w878Tvbk97CnsIcduR3zhVTJlRieHp6/BlUxKDIRTDAxt/y5S6l4io5MB9dfeT3/\n8Kl/mC+oCqlC5FWimeIMvZO98+dP9U32UQyKNV/btuw2X0yVi6pCqhDpuTeafkfDU2bRKLfwlFlj\nUYElNV155ZX1HkJTUm7hKbNo6pXbL3/pLzDsKDGy82u8+t/20zfVx+jMaM3D36pVOvZ1ZDvYU9jD\nmYUz2ZPfw678LvLpPDFijM36C/4WgyJFV2ToxNCK+80msqcc1teR7SCX9Bfmyl6T5cLdF0Z63WMz\nY/RM9Mwf7jc8PVxz+2Qsye7Cbnbnd7O7sJtd+V2rOk+rEel3NDxlFo1yC0+ZNRZ1ERQRkdAe/3g4\nfBhmksfYc+nb2XbB8g2YYsRoSbewM7+Th7Q8hLNaz2J3fjc78zvJxrNMFicZPjHM6MwogVv5fKuK\nQqpwymF9HZkO0on0WrxESkGJgamBBedPrXQOVyHlD12sHO63LbtN51CJiDSg7u5uOjs7QV0ERUSk\n3sbHYbiycJMdItM+AvgL8e7I7+DMwpmc1XIWD217KLvzu9mR37HgYr/jM+OMzowyOjO64nMZRmu6\ndUG3vsqq1GqvQbVa08Vpeid658+f6p/sp+RKNce2I7djwflTy13XSkREtg4VWCIiEsqdd8KsP/UJ\nyw8Qy48wV5rjA8/7AIEL5i/2Ozk3ycHRgxwcPbjiPmMWW7LRRFu6jXgsvi6vY2R6ZH5lqmeiZ8WC\nLxVPsTu/e/78qV35XaG6CYqIyNag/xmkpltuuYUXvehF9R5G01Fu4SmzaOqR249+BKUSQACFHkbt\nAXLFGHf23rniYxOxxCnXj+rIdNCSblnXznnFoEj/ZD+9k7187vOf47yLzlvxYsSt6db5lak9hT1b\nvmW6fkfDU2bRKLfwlFljUYElNe3fv1+/sBEot/CUWTT1yO0Xv/AdBEt2gljbESw5Q3vmjAXbpOPp\nJRtN5JP5DSlSpuam5jv79Uz0MDA1MH9+15c//2X+6Al/tGD7mMXYkdux4PypbDK77uNsJvodDU+Z\nRaPcwlNmjUVNLkREJJRnP9u3aZ9N9JF+9rso/MYXeMzOx/COi9/BrvwuOjIdG1qcOOcYnh6eP9yv\nd9JfpLiWTCIz39lvT2EPO3M71+1QRBERaTxqciEiIg1hehp6evxtl5gk0XEMgD35PTx292M3ZHWq\nGBTpm+xbUFBVroe1nPZM+4Lzp9oz7es+ThER2ZpUYImIyKodPw5j5cUhlx0i1jIIwPk7z1+34mpy\ndnK+s1/PRA+DU4M1r7UVt7i/QHH5UL/dhd1kEpl1GZuIiMhiKrBERGTVfvITmJsDcFihh3hulLjF\neczOx6zJ/gMXMHRiaEG79InZiZqPySay8ytTewp72JHbsa4NM0RERGrR/0BS0+WXX17vITQl5Rae\nMotmo3P76U99gwtHEQr9xLJj5JN59rTsibS/2dIsR8aO8KNjP+JL936Jj//k43zurs/xncPf4b7h\n+5YsrrZlt3H+jvN55jnP5OUXvJxXP/bVPOe853Dh7gvZld+1YnGluRaNcgtPmUWj3MJTZo1FK1hS\n0yWXXFLvITQl5RaeMotmo3O75x7for0UO0Gi7TiWmKMjeybbsttW9fjxmfH5Q/16JnoYOjFUc/tE\nLMGu/K4Fh/ul4qnTeg2aa9Eot/CUWTTKLTxl1ljURVBERFZldhYuvhjuvhtmk8fJPved5B7/JTrP\n6ORjL/wtJUTkAAAgAElEQVQYLemWBdsHLmBwanDB+VNTc1M1nyOfzM8f6rc7v5vtue063E9ERNac\nugiKiEjdDQ3BoO9pgUtOzHcQPLPlTAqpAjPFGXone+fPn+qf6qcYFJfdn2Fsy25bcP5UIVXYiJci\nIiKyblRgiYjIqhw+DOPj5S9yg1h+GHNGOp7ms7/4LMPTwzUfn4wl2V3YPd8ufVd+F8l4cv0HLiIi\nsoF03IXUdPvtt9d7CE1JuYWnzKLZyNx++lMoFn2DCyv0EsuN4MwxF8wtWVwVUgUevu3hPPXsp/LS\n81/Kax73Gl7wiBfQeWYnZ7WeVbfiSnMtGuUWnjKLRrmFp8waiwosqen666+v9xCaknILT5lFs5G5\n/exnvoNgYCc7CKZiKVrTrQBsz27ngl0X8Oxzn80rf+2VvOLXXsHFv3Ixj975aLbntm/IRYhXQ3Mt\nGuUWnjKLRrmFp8wai5pcSE1TU1Pkcrl6D6PpKLfwlFk0G5VbsQgveQnccQcUE8PELvogbc+8ibZ0\nGy+/4OVsy27j8sdd3hSH/GmuRaPcwlNm0Si38JRZeOvZ5EIrWFKTflmjUW7hKbNoNiq3oSHo6/O3\ng8QEqe1HcM6RTWTJJXO0pFqaorgCzbWolFt4yiwa5RaeMmssKrBERGRFg4O+yAJfYMXbfbXVlm4j\nk8jQke2o4+hEREQahwosERFZ0cGDMDUFjgDLD2C5IQxjR34HwKovNCwiIrLZqcCSmq6++up6D6Ep\nKbfwlFk0G5XbyQ6Cc1ihn1h2lFgsxq7cLqC5CizNtWiUW3jKLBrlFp4yaywqsKSms88+u95DaErK\nLTxlFs1G5BYEcM89/rOLlwuszASZeIaWdAvQXAWW5lo0yi08ZRaNcgtPmTUWdREUEZGaBgfh1a+G\nH/wAiql+4k++gdanfYJtmW287DEvY3tuO1c8/gpipr/ZiYhIc1AXQRERqZvBQejt9beD+CTJ7YfB\nQTaZJZ/K05ZuU3ElIiJSpv8RRUSkpoEBGB0FCHCpMRKtfZgZrelWUvFUUx0eKCIist5UYElNd999\nd72H0JSUW3jKLJqNyO3+++HECQgoYfkhLD/sOwjmmrODoOZaNMotPGUWjXILT5k1FhVYUtM111xT\n7yE0JeUWnjKLZr1zcw7uvNN3ECQ2h+X7ieVGSMQS7MztBGi6a2BprkWj3MJTZtEot/CUWWNRgSU1\n3XDDDfUeQlNSbuEps2jWO7fRUTh6tNJBcJZYoY9Yeop0PN2UHQRBcy0q5RaeMotGuYWnzBqLCiyp\nSW0/o1Fu4SmzaNY7t4EB6OvzK1kufoLE9qMAZJIZcskciViCllTLuo5hrWmuRaPcwlNm0Si38JRZ\nY1GBJSIiyxoYgP5+AEcpNkVi22FixMgkfIG1LbsNM6v3MEVERBqGCiwREVlWf78/TNBRxDJjJFoH\nFnQQ7Mg01/lXIiIi600FltR03XXX1XsITUm5hafMolnv3H75S5iZAWIlLDcEuSEMY2feN7hotvOv\nQHMtKuUWnjKLRrmFp8waiwosqWlqaqreQ2hKyi08ZRbNeuY2Pg4HD0KpBMTLHQSzoyRiCbZntwPN\nWWBprkWj3MJTZtEot/CUWWMx51y9x7AhzGwv0NXV1cXevXvrPRwRkYb3wAPwpjfBbbdBMTkIF36K\n9ue/n/ZMOy981As5s+VMXn3hq8kms/UeqoiISCjd3d10dnYCdDrnutdy31rBEhGRJQ0MQG+v7yAY\nxKdIlhtcpONp8qk8mURGxZWIiMgiKrBERGRJAwMwOOgbXLj4NMntR4hZjFQ8RS6Ra8rDA0VERNab\nCiypaWBgoN5DaErKLTxlFs165tbX5zsIEitimXFirQPELEZ7pp1kPNm0BZbmWjTKLTxlFo1yC0+Z\nNRYVWFLTFVdcUe8hNCXlFp4yi2a9cpuagiNHYG4OX2DlBrDsCBjszDVvB0HQXItKuYWnzKJRbuEp\ns8aiAktquvbaa+s9hKak3MJTZtGsV24DA77ACgIgNgflDoLJWJKOrL/2VbMWWJpr0Si38JRZNMot\nPGXWWFRgSU3quBiNcgtPmUWzXrkNDPiLDJdKEMRmibceJ5Yoko6naU23AjTtRYY116JRbuEps2iU\nW3jKrLGowBIRkVPMdxAkIIhNk9hxmLjFScaT5JI5WlItJOPJeg9TRESk4ajAEhGRU1Q6CGJzvoPg\ntmPELU4mkSGXzM0fJigiIiILqcCSmm688cZ6D6EpKbfwlFk065Hb9DSMjMD4OBAvYpkxLD+ImdGe\naScRSzTt+VeguRaVcgtPmUWj3MJTZo1FBZbU1N29phe23jKUW3jKLJr1yG1w0B8euLiDYMxiTd9B\nEDTXolJu4SmzaJRbeMqssZhzrt5j2BBmthfo6urq0omAIiI1/PSn8OEPwy23QCk1QOm8r9DxwrfR\nks3yvIc/j4dvezi/++jfbeoiS0REtrbu7m46OzsBOp1za1qhagVLREQWqDS4KAUBgc0Sbz9CIgnJ\nWJJCqjB/sWERERE5lQosERFZYGAA+voAK+Fis6S2HyHOyQ6Cbek2Yqb/PkRERJai/yFFRGTe7CyM\njsLQEBCbI4hPk9h2jHgsTjaRJZ/M69BAERGRGlRgSU379u2r9xCaknILT5lFs9a5DQ35ImtyEogX\niWVGITsE+MYW8Vi86QsszbVolFt4yiwa5RaeMmssKrCkpiuvvLLeQ2hKyi08ZRbNWuc2MADHjkGx\nCMSKkB/AcsPEY3G257YDNP01sDTXolFu4SmzaJRbeMqssajAkpouueSSeg+hKSm38JRZNGud28AA\nHD8OQeBwNkss308qO42Z0ZHxhVWzr2BprkWj3MJTZtEot/CUWWNRgSUiIvMqDS5KQREXKxHfdphE\nIjbfQTARS9CSaqn3MEVERBqWCiwREQH8YYHDw+UOgvEijjkS248QtziJWIJ8yje4MLN6D1VERKRh\nqcCSmm655ZZ6D6EpKbfwlFk0a5nb0BA4ByMjQLxIEJ8i2XEcMyOfzJNNZucPE2xmmmvRKLfwlFk0\nyi08ZdZYVGBJTfv376/3EJqScgtPmUWzlrkNDvrugVNTQKxILDcCuXIHwdw24tb8HQRBcy0q5Rae\nMotGuYWnzBqLOefqPYYNYWZ7ga6uri727t1b7+GIiDSc//ovuPVW+MQnoJjqIzjz+7S/9K9obYnx\npIc8ic4zO/mtR/wWZ7WeVe+hioiInJbu7m46OzsBOp1z3Wu5b61giYgI4Btc9PRA4IoQK2GFXlLZ\nWbCTrdk3wwqWiIjIelKBJSIiBIE/RLCvDwIrEuA7CCbjSRKWoJAqkElkyCaz9R6qiIhIQ1OBJSIi\njIz4Iqu/HyxWJGCW5M5DxCxGOpEmn8xr9UpERGQVVGBJTZdffnm9h9CUlFt4yiyatcptYMB/Hh4G\n4kVITpFo6wWY7yC4WQoszbVolFt4yiwa5RaeMmssKrCkJl0ZPBrlFp4yi2atchsYgLExmJkBZ3NY\nbhTLjgD+vKuYxTZNgaW5Fo1yC0+ZRaPcwlNmjUVdBEVEhFtvhW9/Gz61v0QpNUBw5nfZ/ntvIZ+L\ncdFDL2LvGXt54aNeyO7C7noPVURE5LSpi6CIiKwb56o6CFIEC4i19pDOBADzFxeudBIUERGR5anA\nEhHZ4kZHoViE3l6g3EEwsf0QiViCRCxBIV2gkCqQiqfqPVQREZGGpwJLarr99tvrPYSmpNzCU2bR\nrEVulQYXg4NAbM53ENx+GDMjk8iQS+Q2zflXoLkWlXILT5lFo9zCU2aNRQWW1HT99dfXewhNSbmF\np8yiWYvcBgb8YYKjo0CsCKkJ4m39AORTm6uDIGiuRaXcwlNm0Si38JRZY1GBJTXdfPPN9R5CU1Ju\n4SmzaNYit4EB3559eibAWQnLjRDPjRIEwabrIAiaa1Ept/CUWTTKLTxl1lhUYElNuVyu3kNoSsot\nPGUWzVrkNjgIR45AwBxmDiv0ky5MYWbzjS02U4GluRaNcgtPmUWj3MJTZo1FBZaIyBY2Pu6vfdXb\nCwElAgISbcdJpvwlPDoyHcQsRnumvc4jFRERaQ6Jeg9ARETqp9LgotJB0FEivv1B4hYnZjEKqQJt\n6TZipr/HiYiIrIb+x5Sarr766noPoSkpt/CUWTSnm9viDoIlZknt8B0Ec8kcueTm6iAImmtRKbfw\nlFk0yi08ZdZYVGBJTWeffXa9h9CUlFt4yiya081tcBBKJRgbc7hYiVh6nETLCCVXopAukElkNl2B\npbkWjXILT5lFo9zCU2aNxZxz9R7DhjCzvUBXV1cXe/furfdwREQawic/CQ88AB/+h1mKyWFK2+5i\nx6VvJts2yQW7LuCZ5zyTS867hHPaz6n3UEVERNZMd3c3nZ2dAJ3Oue613LdWsEREtqipKf9x9Cg4\nK+IIiOX7SedPYNh8Y4vNtoIlIiKynlRgiYhsUdUNLgJKOByxjiMkUwb4wioRS9CSaqnjKEVERJqL\nCiyp6e677673EJqScgtPmUVzOrlVF1jOigQUSWw/SIwYyXiSfDLPtuw2zGyNRtsYNNeiUW7hKbNo\nlFt4yqyxqMCSmq655pp6D6EpKbfwlFk0p5NbpcAaGgJicwTMkt7Zg8P5DoKpHB2ZjrUZaAPRXItG\nuYWnzKJRbuEps8aiAktquuGGG+o9hKak3MJTZtGcTm4DAzA3B+OTc2COWHacZGGEYlCkkCqQiW++\nDoKguRaVcgtPmUWj3MJTZo1FBZbUpLaf0Si38JRZNFFzm56GiQno6YHAzQFguUES+TEAOjIdmNmm\nLLA016JRbuEps2iUW3jKrLEk6j0AERHZeIOD/vOxYxBYyXcQbBkglZsGjI6sPzRwMxZYIiIi60kr\nWCIiW1Dl/Ku+PghciYCAxLbDJBNxANoz7WQSGbLJbB1HKSIi0nxUYElN1113Xb2H0JSUW3jKLJqo\nuS3oIBibw1EiseMQZkYqnqKQKmza1SvNtWiUW3jKLBrlFp4yaywqsKSmqampeg+hKSm38JRZNFFz\nqxRYwyNFsIDApsnu6KEYFMmlcuSSuU1bYGmuRaPcwlNm0Si38JRZYzHnXL3HsCHMbC/Q1dXVxd69\ne+s9HBGRupmbg499DGZm4D1/e4IZxijmDrPnlf+L1PbjnN12NvsetY+nP+zp/OqOX633cEVERNZc\nd3c3nZ2dAJ3Oue613LdWsEREtphKg4ujR6FEEQDLD5HIj2EY7Zl2gE15DSwREZH1pgJLRGSLqRwe\n6Fu0F3EExFv7SOVmgZOFVaWToIiIiKyeCiypaaDyTkxCUW7hKbNoouS2sMAqEVDyHQTj/sod7Zl2\nCqkCqXhqLYfaMDTXolFu4SmzaJRbeMqssajAkpquuOKKeg+hKSm38JRZNFFym2/RPlCCWAnnSmR2\nHaYUlEgn0pu6gyBorkWl3MJTZtEot/CUWWNRgSU1XXvttfUeQlNSbuEps2jC5lYswvCwvz024Q8J\ndIkZMtsGfAfB5ObuIAiaa1Ept/CUWTTKLTxl1lgapsAys9eZ2QNmdsLMvmdmv7HC9m8ws7vNbMrM\nDpnZ+8wsvVHj3SrUcTEa5RaeMosmbG7Dw+AcTE7CiWnf4CKWGyFRGCUIAlpSLaQT6U1dYGmuRaPc\nwlNm0Si38JRZY2mIAsvMLgXeC7wVeDzwU+BrZrZjme1fAby7vP2vAlcAlwLv3JABi4g0qcrhgUeO\nQEARcMSygyTy42DQlmkD2NQFloiIyHpqiAILuAr4v865Tzjn7gb+BJjCF05LuQi43Tn3L865Q865\nbwL7gSdszHBFRJpTpcDq7YWSK+EIiLX1ks6UMIxt2W3ELDbfql1ERETCqXuBZWZJoBP4VuU+569+\n/E18IbWUO4DOymGEZnYu8ALgS+s72q3nxhtvrPcQmpJyC0+ZRRM2t0qBdbwnIKBEQEB651Ec/qLz\nHZkO2tJtxKzu/z2sG821aJRbeMosGuUWnjJrLI3wP+gOIA70Lrq/F9iz1AOcc/vxhwfebmazwC+B\nbzvnrlvPgW5F3d1remHrLUO5hafMogmTWxDA0JC/PTg67e9zRTI7j1ByJTLJzKZvcAGaa1Ept/CU\nWTTKLTxl1ljMLxbVcQBmZwBHgYucc9+vuv964KnOuScv8Zhn4g8J/CvgB8DDgQ8CH3HOvWOZ59kL\ndHV1delEQBHZkoaG4LOf9beve/8Yo1MnmEsM8dBXv5X4nnvYlt3GS89/KU9+6JPZe4b+nRQRkc2r\nu7ubzs5OgE7n3JpWqI2wgjUAlIDdi+7fxamrWhVvAz7hnPuYc+7nzrkv4IutN630ZC94wQvYt2/f\ngo+LLrqIW265ZcF2X//619m3b98pj3/d6153yjJsd3c3+/btO+Uib29961u57rqFi2qHDh1i3759\n3H333Qvu/9CHPsTVV1+94L6pqSn27dvH7bffvuD+/fv3c/nll58ytksvvVSvQ69Dr0OvY9nXMTAA\nhw5183d/t4/JqT4AYrlR4vlRev69h96v9pKKp+ZXsBr1dcDm+Hnodeh16HXodeh1bMzr2L9///z7\n/mc84xns2bOHK6+88pTt10rdV7AAzOx7wPedc39e/tqAQ8AHnXP/Z4ntfwR8wzn35qr7LgM+ChTc\nEi9KK1gistXdcQf893/7j8/8+wBzwRyc8WPO+f23U7QpHrvnsTz17Kfy8gteTmu6td7DFRERWTfr\nuYKVWMudnYb3AR83sy78IX9XATngJgAz+wRwxDn3V+Xt/x24ysx+AnwfeAR+VesLSxVXIiJyssFF\nXx+UXIDDkWrvIZEKmJtztGfaScQStKRa6jtQERGRJtYIhwjinPsM8Bf4IunHwIXAc51z/eVNHsLC\nhhdvx1836+3Az4GPAF/Bt3eXNbTUcqusTLmFp8yiWW1uzlUVWIPTOOdwlMjsOopzbr5Fe0emA38Q\nwealuRaNcgtPmUWj3MJTZo2lUVawcM79PfD3y3zv4kVfB/ji6u0bMLQtbT2PT93MlFt4yiya1eY2\nOgrFor99soNgiczOIxSDItlklnwyv+k7CILmWlTKLTxlFo1yC0+ZNZaGWMGSxnXJJZfUewhNSbmF\np8yiWW1uldUr52B0PPBfpCdJdwxRDIrkk/kt0aIdNNeiUm7hKbNolFt4yqyxqMASEdkCKgXWwADM\nzpUAiGdHiOdHASikCyTjyS1RYImIiKwnFVgiIlvA4KD/fPQolFwJR0CsMEwiNwFAe7odgI5sR72G\nKCIisimowJKaFl9nQFZHuYWnzKJZbW6VFaz+oVlKJQgIyGzvxeJ+Nasj20EmkSGXzK3XUBuG5lo0\nyi08ZRaNcgtPmTUWFVhS0/79++s9hKak3MJTZtGsJrfxcZiZ8bf7R6bAGY6AzM7DBC4gbnHaM+1b\n5vBAzbVolFt4yiwa5RaeMmssDXGh4Y2gCw2LyFb1wAPwjW/42x/8aD89vQFznODsV76LxMN+SDaR\n5cXnv5inPPQpPPmhT67vYEVERDbAel5oWCtYIiKbXOX8q2IRxsb8bUuPk24boRSUyCVz5JN5OjI6\n/0pEROR0qcASEdnk5i8w3AfFUrmDYH4Ey44AUEgVSMQSW+YQQRERkfWkAktEZJOrFFg9fXMUgwBH\nQLIwgmXGMEwdBEVERNaQCiyp6fLLL6/3EJqScgtPmUWzUm5TU/4DYGBsgqAUI6BEZmcPFnM4HB3Z\nDgqpAql4agNGXH+aa9Eot/CUWTTKLTxl1lhUYElNujJ4NMotPGUWzUq5VVavAHoHZn0HQReQ3XWE\nUlAiEUtsqQ6CoLkWlXILT5lFo9zCU2aNRV0ERUQ2se5u+NGP/O2/++gx+nrjzDHJua95N+6MH5JP\n5nnx+S/m6Q97Ok846wn1HayIiMgGURdBERGJpLKCNTsLY+MGQCw7TqowShAE8x0Et9IKloiIyHpS\ngSUisolVCqzevoBiKQAcidw4QXYQzHcQjMfiKrBERETWiAosqen222+v9xCaknILT5lFUyu36WmY\nmPC3+8fGKRVjOALSbSNY2n+jPdNOzGK0Z9o3YrgNQXMtGuUWnjKLRrmFp8waiwosqen666+v9xCa\nknILT5lFUyu3ygWGAfqGpnClGAEB2Z3HAH/+bUe2g7Z0GzHbOv8daK5Fo9zCU2bRKLfwlFlj2Tr/\no0okN998c72H0JSUW3jKLJpauVV3EDzeVwTAuRLZPUcoua3ZQRA016JSbuEps2iUW3jKrLGowJKa\ncrlcvYfQlJRbeMosmlq5VRdYQ4N+xcrFimR2HqcYFMkn8+ST+S13gWHNtWiUW3jKLBrlFp4yaywq\nsERENqlKgTU97RgvdxBM5CaI5YZxzpFJZsilcltuBUtERGQ9qcASEdmE5uZgdNTfHhyboFTyDS4S\n+XGCjK+8WlItxE0dBEVERNaSCiyp6eqrr673EJqScgtPmUWzXG7VDS56hscJijEcjkz7EJY+AfgO\ngolYgpZUy0YMtWForkWj3MJTZtEot/CUWWNRgSU1nX322fUeQlNSbuEps2iWy636/Ktj/dO4IIaj\nRG73MZw72UGwI9OBmW3EUBuG5lo0yi08ZRaNcgtPmTUWq/xHu9mZ2V6gq6uri71799Z7OCIi6+o/\n/gPuvdff/shn7+OBewoU3TQPf9lHCR7xJWIW40W/+iKedvbTeMY5z6jrWEVERDZad3c3nZ2dAJ3O\nue613LdWsERENqHKCpYZDA+W/6lPFElsO0oxKJJL5sgl1eBCRERkranAEhHZZEolGB72t+fcFJOT\ncQASuXHIDYGDTCKjAktERGQdqMCSmu6+++56D6EpKbfwlFk0S+U2NASVo7+PD48SFOM4AlKFSYL0\nIJjvIBiz2Ja7BhZorkWl3MJTZtEot/CUWWNRgSU1XXPNNfUeQlNSbuEps2iWyq26wcXxgSmCUpyA\ngOy2QSw5C/gOgpVVrK1Gcy0a5RaeMotGuYWnzBqLCiyp6YYbbqj3EJqScgtPmUWzVG7VBdbRnllc\nYEBAfvcxAhdgGB3Zji17eKDmWjTKLTxlFo1yC0+ZNRYVWFKT2n5Go9zCU2bRLJVbdYHVc9x/di4g\nt+cwxaBIKp6iI7N1CyzNtWiUW3jKLBrlFp4yaywqsERENpEg8OdgAeRbZxkeSAIQS84R6zhCKSiR\nTWbJJXN0ZLbe+VciIiLrTQWWiMgmMjLiuwgCjM4OMXMiDUAyP0mQPtlBMJvMbtkVLBERkfWkAktq\nuu666+o9hKak3MJTZtEszq368MDDveOUqjoIuszQlu8gCJprUSm38JRZNMotPGXWWFRgSU1TU1P1\nHkJTUm7hKbNoFue2oMFF7zRBKUZAifyOPogXAejIdlBIFUjFUxs51IahuRaNcgtPmUWj3MJTZo3F\nXOViKZucme0Furq6uti7d2+9hyMisi5uvRV6evztf/mPn/LT7+yhyAznPf+L2N5/phgUueTcS7j4\n3It53sOfV9/BioiI1El3dzednZ0Anc657rXct1awREQ2CedOrmDlCwG9PXEADCO968H5DoJt2Tad\nfyUiIrJOVGCJiGwSo6NQ9EcBEi8MMTbgLyIcT83iWo7OdxDMJ/MqsERERNaJCiypaaD6hA5ZNeUW\nnjKLpjq36gj7x0eYnfYt2lOFScgOYmZk4hkyicyWLrA016JRbuEps2iUW3jKrLGowJKarrjiinoP\noSkpt/CUWTTVuQ0Onrz/cM8kQSmOo0SmZYIgNQJAa7qVuMVpS7dt9FAbhuZaNMotPGUWjXILT5k1\nFhVYUtO1115b7yE0JeUWnjKLpjq36j9gHjk+U+4gGJDf1YuzEjhoz7TTnmknHotv/GAbhOZaNMot\nPGUWjXILT5k1FhVYUpM6Lkaj3MJTZtFU51YpsLJZx6EHDZwBkNtzhJIrEY/H2ZbdtmWvf1WhuRaN\ncgtPmUWj3MJTZo1FBZaIyCYwPg4zM/52rmOMgZ48AAYkd1d1EMyog6CIiMh6UoElIrIJVB8eWMoM\nMDHkOwgmszME2WM458gm1EFQRERkvanAkppuvPHGeg+hKSm38JRZNJXcqhtcHBscpTiXwOFI5aYI\nsr76SsfTW76DIGiuRaXcwlNm0Si38JRZY1GBJTV1d6/pha23DOUWnjKLppJb9QrWA4emKRXjOAKy\n7RO41BgAbZk2kvEkLamWegy1YWiuRaPcwlNm0Si38JRZYzHnXL3HsCHMbC/Q1dXVpRMBRWTT+eQn\nYWoK0mn4fPd/cvstj6JEkXOe/H2SF7+H6eI0T3rIk3j+w5/Pi89/cb2HKyIiUlfd3d10dnYCdDrn\n1rRC1QqWiEiTm5ryHwCFjimOHcoCYBjZM3yDi0QsQUe2Y8sfHigiIrLeVGCJiDS56sMDE60DDPcW\nADBzxHc8MN9BsCOjAktERGS9qcASEWly1QXWCRtgcth3EExlZyhle8BBJpEhl8ypwBIREVlnKrCk\npn379tV7CE1JuYWnzKLZt2/fggLrwWOTlIoJHAHplilcZhDMF1jpeHrLX2QYNNeiUm7hKbNolFt4\nyqyxqMCSmq688sp6D6EpKbfwlFk0V1555XyBlUzCA4dmCIoxHI5s+xhBcgKAtnQb2WSWXDJXx9E2\nBs21aJRbeMosGuUWnjJrLCqwpKZLLrmk3kNoSsotPGUWzTOecQkTvoaitWOWBx6I44IYhpHfc5TA\nBRhGe6ZdhweWaa5Fo9zCU2bRKLfwlFljUYElItLEqg8PTLYN0n+kbf7rzJ4H5jsIbsttU4ElIiKy\nARJhNjazduDFwNOAhwE5oB/4MfA159wdaz5CERFZVnWBZfkBRnvzAMTjjtj2gxSDIoVkgbZ0Gx0Z\nnX8lIiKy3la1gmVmZ5rZR4HjwF8DWeAnwLeAI8BvAt8ws1+Y2aXrNVjZeLfccku9h9CUlFt4yiya\nL3zhZG4j00OcGPfXwErlpill+jBnpBNpdRCsorkWjXILT5lFo9zCU2aNZbWHCP4YGMJf6fg859xl\nzsQwF1AAACAASURBVLm/cM79tXPuz5xzTwd2AG8H3mBmb1yvAcvG2r9/f72H0JSUW3jKLJqvfMXn\nFo/DfYemKBXjvoNg4QRBZgAMssksmURGHQTLNNeiUW7hKbNolFt4yqyxmHNu5Y3MtjvnBle905Db\nbwQz2wt0dXV1sXfv3noPR0TktM3Nwcc+5m9v21Hilp99la/dtJcgMM684JfkXvxGpuameMzOx7Dv\nUft4xa+9or4DFhERaRDd3d10dnaCX0DqXst9r+ocrLDFUqMVVyIim9Fg1b+06bYheh5sKXcQhOye\nQ5SCEnGLq4OgiIjIBgrb5GI7cCHwU+fckJntAF4LpIF/dc7dtQ5jFBGRJVQ3uIgVBuk/2jL/dXbP\nIUYrHQSz6iAoIiKyUVZdYJnZE4CvA63AiJk9B/hXoIg/l+tNZvbUtV5iExGRpVUXWKX0AGMDuwGI\nJwJof5BSUCKXzNGWblOBJSIiskHCXAfrnfiCqg14F3AL8C3n3COdcw8HbgbesvZDlHq6/PLL6z2E\npqTcwlNm4Q0MwE03XY4Z9I4OMz2RASCdn6aY7sHMSMVT5FN5FVhVNNeiUW7hKbNolFt4yqyxhCmw\nOoH3OefGgQ8AZwIfqfr+DcBvrOHYpAHoyuDRKLfwlFk4pRIMD8OjH30J7e2Ou+6dpVTy/6RnWiZx\nmSEcjlwyRzqepi3dtsIetw7NtWiUW3jKLBrlFp4yayyr6iIIYGYTwAXOuYPlr8eBxzrn7i9/fTZw\nj3Muu05jPS3qIigim0l/P3z+8/72meeO8Inb7uC2T+/FXIyzHvcLUr99DdNz0zxm12N4yfkv4fce\n83v1HbCIiEgDWc8ugmFWsA4D51Z9/XL8hYcrzgAGEBGRdbewwcUAvYdawRkAuTMOUXIl4vE4HdkO\nXf9KRERkA4UpsG4GdlW+cM59yTl3our7+4AfrNXARERkedUFFtlBBo+1+tsG6d0HKQZFEqYOgiIi\nIhtt1QWWc+5/O+durrHJOwFdxXKTuf322+s9hKak3MJTZuFUCqwDB25nOj7A+FAegEQywLUeIggC\nkvEkrelWFViLaK5Fo9zCU2bRKLfwlFljCbOCdQoze4qZpQGcc1POuZm1GZY0iuuvv77eQ2hKyi08\nZbZ6QQBDQ/72bbddz31HxpiZSmEY6cIUxXQfZkY6niaXzKnAWkRzLRrlFp4yi0a5hafMGstpFVjA\nV4Cz1mIg0phuvrnWoqUsR7mFp8xWb2TEdxEEeNPf3MgD9yUJSnHASLdOUEoPApBNZsklc7SkWpbf\n2RakuRaNcgtPmUWj3MJTZo3ldAssW5NRSMPK5XL1HkJTUm7hKbPVqz7/Kr9zit4HCwSlGIZR2NVH\niTkA2jPtdGQ6MNM/1dU016JRbuEps2iUW3jKrLGcboElIiIbrLrAstwgvQ+2newguPuQb3ARS9Ce\nadfhgSIiIhvsdAusPwZ612IgIiKyOtUFVjE9wGCPPwTQDFJ7fAfBuP3/7N15fFT1vf/x1/fMkpkk\nk50k7IsR694SF1BURMClFav0p1KrIqJdoFqt2l61dbku1VpXrNdWERSLtrVaW62gKCpaRRNbtEqV\nLUAgQPaEyTLL9/fHSYbJSs7JwMwkn+d95HFP5pw588nbaYZPzjmf45AJgkIIIUQc2G6wlFJFQBUQ\nbvtezkEZgK6//vp4l5CUJDfrJLO+0Xpvg5WeDo/e9TCN1WmAwpUSIJS+BTS4HW4yPZlyD6xuyHvN\nHsnNOsnMHsnNOskssVhusJRSuUqpN4AvgVcxbzAM8KRS6jexLE7E36hRo+JdQlKS3KyTzPqmrg6C\nQXM5I6cF3FkEWlwYGLjbJgiC2WClOmWCYHfkvWaP5GadZGaP5GadZJZYlNba2hOUehrzhsPzgC+A\no7XWG5VSpwP3a60Pj32Z/aeUmgCUlJSUMGHChHiXI4QQtmzYACtXmsujj9jOCx9+wEuPTESFPOQe\ntJXM7/6QxtYGRmeN5vzDz+eSoy+Jb8FCCCFEAiotLaW4uBigWGtdGst9O208ZwZwutZ6W6ezAr8C\nRsekKiGEEN2Kvv6K1Ep2lPkIhxw4gPT8CoI6gKEMMlMy5eiVEEIIEQd2rsFKA/zdPJ4DyI2GhRBi\nP4pusMKeKnZtzYh87x1qThB0GI7IiHYhhBBCHFh2Gqx3gehzTrRSygBuAN6KSVUiYaxbty7eJSQl\nyc06yaxv2hus1FRoDFWyc+NuAJShcRdsJBgO4jJc5Hpz5QhWD+S9Zo/kZp1kZo/kZp1klljsNFg3\nAFcqpf4BuIF7gc+Ak4GfxbA2kQBuuOGGeJeQlCQ36ySzfWtogJa28wSyc4NU1NZSsf5BDJy4PC0E\nU7cB4DScZHgypMHqgbzX7JHcrJPM7JHcrJPMEovlBktr/RkwHlgN/BXzlMG/AN/QWm+IbXki3hYu\nXBjvEpKS5GadZLZvVVV7l10Z1VRs85CeeydKtU8Q3I1CmRMEXakyor0H8l6zR3KzTjKzR3KzTjJL\nLHaGXKC1rgPujHEtIgHJ2E97JDfrJLN9i77+ykirYkeZD6VGobQiNaueoLMO3apJc6eR5cnC7XDH\nr9gEJu81eyQ36yQzeyQ36ySzxGL7RsOdKaXSlFInx2p/QgghOopusEIplVSU+dBhAwWkF+4gEDIn\nCGZ5suT0QCGEECJOYtZgAUXIkAshhNhv2huslBRoUlXs2poJgAa8QzdHBlxke7KlwRJCCCHiJJYN\nlhiA7rnnnniXkJQkN+sks975/eYXQE5umEp/FbU702mufxRlBHEO2UwoHMJhOMjx5kiD1Qt5r9kj\nuVknmdkjuVknmSWWPl+DpZSq3scmjn7WIhKQ39/dLc/Evkhu1klmvYs+PdCTVUd9fZim+lTQLbi8\nzQS85SilcConvhSfNFi9kPeaPZKbdZKZPZKbdZJZYlFa675tqNQe4DHg0x42GQ3corVOyEZLKTUB\nKCkpKWHChAnxLkcIISwpLYWPPzaXDzp2Pcs/+5Alt03BCPpIL9hJ4eU/ojZUQZ43jwsOv4Ariq/A\nYSTkr2MhhBAi7kpLSykuLgYo1lqXxnLfVqYI/gvYqrVe0t1KpdTRwC0xqUoIIUQH0Uewwh5zwEU4\nZGAAaTl1BFQDaEh3p5ObmivNlRBCCBEnVq7BegXI6mV9NfB0/8oRQgjRnfYGy+WCZqOS7W0TBAHS\nhpYT0AEchoMsT5bc/0oIIYSIoz43WFrru7TWt/WyfqvW+rLYlCUSRWX0n81Fn0lu1klmPWtpgcZG\nczk3F6qbqqjaloHCIBzaHZkg6DScZHtlguC+yHvNHsnNOsnMHsnNOsksscgUQdGruXPnxruEpCS5\nWSeZ9Sz6czM1q5GmYAu1u304lJO66vk4c8sIh8MyQbCP5L1mj+RmnWRmj+RmnWSWWGw1WEqpEUop\no/OyGHhuvfXWeJeQlCQ36ySznkU3WEZ6JfW1Bs2NHgwcZA+9ipaUvRMEM1IypMHaB3mv2SO5WSeZ\n2SO5WSeZJRa7jdHnwJhulsUAIxMX7ZHcrJPMehbdYGlvJRVb0ggFHYAiI7+IgHsXAG6nG1+KD5/b\nF59Ck4S81+yR3KyTzOyR3KyTzBKL3QZL9bAshBAixqqqzP/vcECrs4odm9MJtzVY6Xk1BGkCwOf2\nkefNQyn5tSyEEELEi5zaJ4QQCSwQgNpaczknB2qaq6jYnIXSBhAmfdg2WsOtkQmCcnqgEEIIEV/S\nYIlePfnkk/EuISlJbtZJZt1rP3oF4MtuprG1kaodPgzlJEyIyu1/NCcIKqc0WH0k7zV7JDfrJDN7\nJDfrJLPEIg2W6FVpaUxvbD1oSG7WSWbdi77+yumrIhSCusp0HDgxXAEaa75Aa41hGOR4c+QeWH0g\n7zV7JDfrJDN7JDfrJLPEorTW1p+kVANwtNZ6Y/RyzKuLIaXUBKCkpKRELgQUQiSNVavgyy/N5YNP\n+jfvl33MI1fNwNmagzOzinE/uJZK/SW+FB/fOew7fP+Y75PqSo1rzUIIIUSiKy0tpbi4GKBYax3T\nDlWOYAkhRAJrP4KlFARdVVRsTicccgCQmtFEwL0bjSbFmUK2J1uaKyGEECLOpMESQogEFQpBTY25\nnJ0NNS2V7CjzodsarPT8KlrCTSil8KX4yEvLi2O1QgghhAD7DdZdQHU3y0IIIWKkuhraz+LOzg1S\n21zLzrIMlHYSIkT60G0EwgGchpOslCyyPXL9lRBCCBFvthosrfXdWuvazsti4Jk5c2a8S0hKkpt1\nkllXHQdcmH/HqtqRgUO50CqIZ+gmNjy6AafhJNubLRME+0jea/ZIbtZJZvZIbtZJZolFThEUvVqw\nYEG8S0hKkpt1kllX0Q0WaZUEAtBQndY2QbAVnVlGzik5KKXI8eZIg9VH8l6zR3KzTjKzR3KzTjJL\nLE47T1JKHQv8P2AU4I5ep7U+LwZ1iQQxY8aMeJeQlCQ36ySzrqIbrJC7ksqdLgJNKTgx8KS10OLa\nge8wHy7Dhc/tkxHtfSTvNXskN+skM3skN+sks8Ri+QiWUupC4D3gUOBcwAUcBkwF6mJanRBCDFLh\nsHkNFkBmJtS1VlFR5iMcNFBovJl+Ai7zLsQpjhSGpA3B7XD3skchhBBCHAh2ThG8EbhGa3020Apc\njdls/RHYEsPahBBi0KqtNacIAuTkhqluqmZHmQ9CbsKEySyopDnYhIFhThBMlQmCQgghRCKw02Ad\nBLzSttwKpGnzbsUPAFfGqjCRGF566aV4l5CUJDfrJLOOok8PTMmsJaRD7CzLwKGchAmQNnwrgXCA\nxk8byUrJkuuvLJD3mj2Sm3WSmT2Sm3WSWWKx02BVA7625XLgiLblLEDucDnALFu2LN4lJCXJzTrJ\nrKPoBkulmd9U7/Rh4EKrECkFmwiFQ9R+VCsTBC2S95o9kpt1kpk9kpt1kllisdNgvQtMb1v+E/CQ\nUur3wDJgpd1ClFLzlVKblFJNSqkP2gZp9LZ9plLqUaXU9rbnrFNKnWH39UX3nn/++XiXkJQkN+sk\ns46iG6ywp4qmZvDXpGHgwJnSivZtBQVF3y8ix5sj98CyQN5r9khu1klm9khu1klmicXOFMEFgKdt\n+U4gAJwAvADcYacIpdQFwG8wTzFcA1wDLFdKjddaV3azvQt4A6gAzgO2A6MBuR+XECLpab23wUpP\nh4ZgFZXbU2htcZGiFJ70Fprd2yEALsNFZkomWZ6s+BYthBBCCMBGg6W1ro5aDgO/ikEd1wCPa62f\nBlBK/QD4JjAXuLeb7S/HPCVxota67TJwGbAhhBgY6uogGDSX8/Jgu7+S7Vt8EHKhtSY1q5EWVQ0a\nPE4PBekFOAxHfIsWQgghBNCPGw0rpfKVUkcopY6K/rKxHxdQTNTphW1DM94AJvXwtLOBfwK/VUpV\nKKU+VUr9j1JKbpwshEh6VVV7l71ZDbSGWtlZloEKuwkRwFdQSXOoGYfhwOf2kZuaG79ihRBCCNGB\nnftgFSulPgN2AGuBf0V9fWKjhjzAAezs9PhOoLCH54zDvNGxAZwJ/C/wU8wR8iKGLrvssniXkJQk\nN+sks726G3Cxc2smBk7ChEgfvoVAOIDTcPLZ7z+TARcWyXvNHsnNOsnMHsnNOskssdi5BmsR8CXm\naXo7AR3TivZSvezbaHvtK9uOdn2ilBoOXIfN68BE9+TO4PZIbtZJZntFN1h4qwg3QW1FOg5caKMF\nd8FGwuEwDqeDg489WBosi+S9Zo/kZp1kZo/kZp1klljsnFI3DrhBa/2h1nqz1ros+svG/iqBEFDQ\n6fF8uh7VarcD+LKtuWr3BVColOq1aTzrrLOYOXNmh69JkyZ1uX/AihUrmDlzZpfnz58/nyeffLLD\nY6WlpcycOZPKyo7zOG655RbuueeeDo9t2bKFmTNnsm7dug6PP/LII1x//fUdHvP7/cycOZPVq1d3\neHzZsmXd/qXiggsuiPnPMXv27AHxc8CB/e8xe/bsAfFzwIH77zF79uwB8XNA//97VFbCmjXLWLr0\nMhrDlfj3QFN9KoZyUFc5l/Kv3otse8KZJ/DJ6k8S8ueAxPzvMXv27AHxc7Q7UD9He27J/nO0OxA/\nR3tmyf5ztDtQP0d7bsn+c7Q7ED9He2bJ/nNEi+XPsWzZssi/+0855RQKCwtZsGBBl+1jRXXsUfrw\nBKVeAp7RWr8QsyKU+gD4UGt9ddv3CnNoxcNa6193s/2dwGyt9biox64Grtdaj+jhNSYAJSUlJUyY\nMCFWpQshREw1NsIf/mAujxoFlcOW8p/PNE/9chopoTxShmxjxPfns7t1K5kpmcw+YjY/OvZHmL82\nhRBCCNEXpaWlFBcXAxRrrUtjuW87pwjOA5YopY4APsMc0x6htX7Zxj7vb9tnCXvHtKcCiwGUUk8D\n27TW7ddYPQYsUEo9BCwExgP/Azxo47WFECJhRP9hMD27iS0BP9vLhqBCKYR0kLScBlqoQ6HwOD0M\n9Q2V5koIIYRIIHZOEZwEnAjcgnmj4Zeivl60U4TW+o+YQypuxxyUcRRwutZ6d9smI4gaeKG13gbM\nAI4F/o3ZWD0AdDw+Kfqt8+Fa0TeSm3WSmSm6wTLSzHGCFZszUNpJmCCZQ3fRFGzCYTjISMmg/LPy\nOFWavOS9Zo/kZp1kZo/kZp1klljsNFiPAEuBoVpro9OX7RuxaK1/q7Ueo7X2aq0naa0/jlo3VWs9\nt9P2H2qtT9Bap2qtD9Za36Otnu8o9unee7u7DZnYF8nNOsnMFN1gaa/5ze7yDAxchAmSNnwLwXAQ\np3KS5cnihSdidrb2oCHvNXskN+skM3skN+sks8Rip8HKBR7QWvc0gEIMIM8991y8S0hKkpt1kpmp\nvcFKSYFmVUU4DLW7fDiUE+UI4BqyCa01hmGQ7c3m90t+H9+Ck5C81+yR3KyTzOyR3KyTzBKLnQbr\nL8CpsS5EJKbU1NR4l5CUJDfrJDPw+80vgLw8qPRX0lCvaGlIQ2Hg8rYS8G6LbJ/jzWF47vA4VZu8\n5L1mj+RmnWRmj+RmnWSWWOwMufgSuFspNRn4lK5DLh6ORWFCCDHYRJ8emJkdoLyljoptqeiAhzBB\nvOmtNDm3QxDchpshqUNIdcmHqhBCCJFI7E4RbAROafuKpgFpsIQQwoboBsvhq4JWqCjzQchNWIfI\nyKunWTeglDlBcJhvWPyKFUIIIUS3LJ8iqLUe28vXuH3vQSSTzjeDE30juVknmXVssEg1Jwju2JyJ\noc0BFxnDKmgKNuE0nGSkZJDtyZbcbJDM7JHcrJPM7JHcrJPMEouda7DEIDJq1Kh4l5CUJDfrJDOo\nMnsqXC5oMcxuq6o8A0OZI9rTh5kTBB3KQZYnixxvjuRmg2Rmj+RmnWRmj+RmnWSWWJTVyebKvKPl\ndzAHXeTTqUnTWp8Xs+piSCk1ASgpKSlhwoQJ8S5HCCE6aGmBJUvM5cJCCB78FyrqK/n1/ClQVUSz\nsZtJ1z7IV+rvpLpTOWnUSVx/wvUUpBfEtW4hhBAiGZWWllJcXAxQrLUujeW+7RzBehB4BhiLeS1W\nXacvIYQQFkWfHpiTG6a6qZraGoPWBh+aMClprbS4y0G1bePNIdubHZ9ihRBCCNEjO0MuLgbO01q/\nGutihBBisIpusFwZNYRbw+zcmg4hDyFC+HwtNDsrIGBOEByaPhS3wx2/goUQQgjRLTtHsOqAjbEu\nRCSmdevWxbuEpCS5WTfYM2u//gpARQZc+CDoQusgmfn17AnVYygDj9PDUN9QQHKzQzKzR3KzTjKz\nR3KzTjJLLHYarFuBW5RS3hjXIhLQDTfcEO8SkpLkZt1gz6z9CJbDAa1O85sdW7IwlIsQQTKG76A5\n2IzT4STDk0GONweQ3OyQzOyR3KyTzOyR3KyTzBKLnVME/wjMBnYppTbT9UbDMkFiAFm4cGG8S0hK\nkpt1gzmzQABqa83lnByobjYbrOpyHw6cBFSQ1OGbCYVDpDhTyPZkRxqswZybXZKZPZKbdZKZPZKb\ndZJZYrHTYC0BioGlwE7MmwuLAUrGftojuVk3mDOLPj0wN1ezwV9Fayv4azJRGChnAJW5ObJNjieH\nbI854GIw52aXZGaP5GadZGaP5GadZJZY7DRY3wRO11qvjnUxQggxGEUPuPBmNRAIBKiudhDa40MR\nxJMaoMVTDi2gUOSk5pDlyYpfwUIIIYTokZ1rsLYC9bEuRAghBqvoBotU85uKzenoUAphHSItsxm/\nqgANLsPFCN8IHIYjPsUKIYQQold2GqyfAvcqpcbEthSRiO655554l5CUJDfrBnNm7Q2WUhBKMc8X\nrNiSDmFzwEVWYS3+4B4chgOvy0uhrzDy3MGcm12SmT2Sm3WSmT2Sm3WSWWKxc4rgUiAV2KCU8tN1\nyEVOLAoTicHv98e7hKQkuVk3WDMLhaCmxlzOzoaatgEXOzdn4cBJkGYyh+9gW6gZl+EiMyUzMuAC\nBm9u/SGZ2SO5WSeZ2SO5WSeZJRaltbUZFUqpS3tbr7Ve0q+K9hOl1ASgpKSkhAkTZNChECIx7N4N\nL75oLo8fD9tylrKn1c9D102mqewwmqjkxB8+y3/Tf0+aO42j8o/ippNvYkzWmLjWLYQQQiSz0tJS\niouLAYq11qWx3LflI1iJ2kAJIUQyir7+Ki3Ljz/gp7kZmmuygDCGKwCZW1AhBUCON6fDESwhhBBC\nJBY7pwhGtN1s2BX9mNZaBmAIIUQfRTdYRnoV1EH1bheh5lQghDe9lZaUcvCbEwSHpA3B5/bFrV4h\nhBBC9M7ykAulVJpSaqFSahfQCNR0+hIDSGWH8WairyQ36wZrZtE/dijF/GZHWTo66Cakg6RnNdNI\nBWBOEByZMRKlVNTzB2du/SGZ2SO5WSeZ2SO5WSeZJRY7UwTvBaYCPwRagHnALcB24JLYlSYSwdy5\nc+NdQlKS3KwbjJmFw1BdbS5nZkJ9wJwguKPMhwq7CBMka2gte1r34HQ4SXWlUphe2GEfgzG3/pLM\n7JHcrJPM7JHcrJPMEoudUwTPBi7RWq9SSj0FvKu1Xq+UKgMuAp6NaYUirm699dZ4l5CUJDfrBmNm\ntbXmFEGAvDzY7Tf/Arm7LAcDJ2GCZI7YRk2ohRRHCpmeTLK92R32MRhz6y/JzB7JzTrJzB7JzTrJ\nLLHYOYKVA2xqW65v+x5gNXByLIoSiUMmLtojuVk3GDOLPqMjM6eV+pZ6whoad+UBCowgKQWb0Fpj\nGAbZnuwuAy4GY279JZnZI7lZJ5nZI7lZJ5klFjsN1kZgTNvyOuD8tuWzgdoY1CSEEINCdIPlSDfP\nFfTvgea6DDRhnO4AOmMLtF1yleXNkgmCQgghRIKz02A9BRzdtvwrYL5SqgV4APh1rAoTQoiBLrrB\nCnvMb6p2ugk2ewgTwJveSrN7O2hzguCw9GGkulLjVK0QQggh+sJyg6W1fkBr/XDb8hvA14DZwDe0\n1g/FuD4RZ08++WS8S0hKkpt1gy0zraHKnGlBejo0hNonCPog7CasQ2TkNFEf2oVSCpfDxcjMkV32\nM9hyiwXJzB7JzTrJzB7JzTrJLLHYOYLVgda6TGv9F6312lgUJBJLaWlMb2w9aEhu1g22zOrrIRAw\nl/PyoMrfNkFwcyYq7GybIFiDP+DHaThJdaZSkFbQZT+DLbdYkMzskdysk8zskdysk8wSi9JaW3uC\nUlf1sEoDzcB64B2tdaiftcWUUmoCUFJSUiIXAgoh4m7DBli50lz+RnGIf6unCOswi/93Ejv+fSRN\nuppJF73B+hH/i8fhYVz2OG6dciuHDjk0voULIYQQA0BpaSnFxcUAxVrrmHaodsa0XwMMAVIxbyys\ngCzAj3nj4Xxgo1LqVK311lgVKoQQA0n09VcuXw3hhjChENTvbBti4TAnCKLBMAyyPDLgQgghhEgG\ndk4RvBH4CDhYa52rtc4BxgMfAlcDo4AKzKEXQgghuhHdYJFqnh7Y0AgtDelogrhSAoR8eycIZnuz\nu9wDSwghhBCJx84RrDuAWVrrDe0PtN1o+DrgBa31OKXUDcALsSpSCCEGmvYGKzUV9oTbbjC83UOo\n1UOIEF5fgCbHjsgEwVGZo3A73HGsWAghhBB9YecI1lC6b8ycQGHb8nbAZ7cokThmzpwZ7xKSkuRm\n3WDKrLERWlrM5bw8qGoyj2BVbPGhg060DpKVu4e64G4MZeByuBiVOarbfQ2m3GJFMrNHcrNOMrNH\ncrNOMkssdhqst4DHlVLfaH+gbfkx4M22h44ENvW/PBFvCxYsiHcJSUlys24wZRZ9emBOjo5MEKzc\nnA/aQYgQOSOq2BPYg9Nwku5OJz8tv9t9DabcYkUys0dys04ys0dys04ySyx2GqzLgWqgRCnV0naT\n4Y/bHru8bZtG4KexKVHE04wZMyw/p6ysDMMwcDgcbNmypddtx4wZg2EYPP3003ZL7LPbbrsNwzC4\n/fbb9/trRee2fft2Lr74YoYPH47L5cIwDObOnQvAnDlz9uvP357vvv47JAI777VkFd1gpWTWEwib\n89rrtptj2LUK4Bu+jdZQK06Hk8yUzB4HXAym3GJFMrNHcutZ++/azl9nnHFGZPnhhx+Od5mW7dmz\nh9/85jdMmTKFgoICUlJSKCgo4NRTT+XBBx/E7/fvl9fdX++1//znP5xzzjnk5+fjdDoxDIO77roL\ngMmTJ2MYBu+//37MXzcUCmEYBm73/jvNW/73mVgsX4Olta4ApiulDgEOwbwEe53W+r9R27wVuxLF\nQKaUQil1QF/vQDv33HP56KOPOPzww5k6dSoul4vJkydH6umupiVLlnDZZZcxZ84cFi1a1O1+58yZ\nw9NPP83ixYu55JJLut1GKYVh9Pt2dyLGOg64qIRG855YjZVZgEYZQZxDNqHaJlxkebLI9siACyES\nVfvv8hNPPJGioqJutznssMMOcFX9884773D++eeza9cuvF4vJ5xwAgUFBezcuZP33nuPt99+0Bhn\nEgAAIABJREFUm1//+te88MILTJw4Md7l7lNjYyNnnXUW27Zt47jjjuPMM8/E4XBw9NFHAz1/Xt58\n883cdddd3HHHHdx4443d7nvy5Mm8//77rF69mhNOOKHHGuLxbxARH3aGXADQ1lD9d58bCpFArN73\nrb/Kysr46KOPGD16NGvXru3yy/VXv/oV//M//8PQoUMt77svzembb75JIBBg+PDhlvcv9p/2Bisl\nBVqMtgmCDYqW+lTChHB7AwTTtpo3vwByvblkebLiVK0Qoq/mzZvX4x+8ksn777/PjBkzCAQCXHLJ\nJTz44INkZe39HVRbW8uCBQv4wx/+wGmnncbq1av5xje+0cse4+/DDz9k69atTJkyhTfffLPL+mXL\nluH3+xk9enSHx/vyWbuvbRwOB+vWrZMGaxCRP22LXr300kvxLiEptedWVlYGwNixY7v9xVpQUMD4\n8ePx+TrOhIlVIzh27FjGjx+Pw+GIyf72p8HyXvP7zS8wB1xU+s1ua+c2L6GAm7AOkuprxW9sR2uN\nQjE6azQOo/v/hoMlt1iSzOyR3KxLxsxaW1uZPXs2gUCACy64gMWLF3dorgCysrJYunQp5557Lk1N\nTVx44YWEw+GY1bA/cmv/PO7pCOOIESMYP348KSkpHR6P1efx+PHjOfjgg2Oyr+4k43ttIJMGS/Rq\n2bJlB/w1p0yZgmEYvPPOO/zrX//ivPPOY8iQIXg8Hg4//HDuv//+Hp/b3NzMrbfeyvjx4/F4PAwb\nNow5c+awdeu+73ldWlrKRRddxOjRo/F4POTm5nLGGWfwj3/8o9vtx4wZE7nO7OWXX2batGnk5uZi\nGAZ33303hmEwZcoUAFatWhU5Dz/62rTursEaM2YMc+fORSnF4sWLO5zPP3Xq1Mg1bkuWLEFrHdlH\n+1f0NWY9XYPVn4z9fj+/+MUvIhkPHz6cyy+/nO3bt3Prrbfavs4tHu+1eIg+PTA3d2+Dtbssl3DI\nSYgg2fl7qG2twmk4cTvcjM4c3cPeBk9usSSZ2SO5WbevzMrLy7nmmms49NBDSUtLIyMjg+OPP57f\n/va3hEKhDtteddVVGIbBo48+2mU/48ePxzAMTjrppC7rfvnLX2IYBnfeeWefal66dClbt24lJSVl\nn9eNLVy4EKfTyfr163n++ecB83qjoUOHYhgGn3zySY/PvfrqqzEMg5/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wz3/+s8s24XCY\nlStX8tFHH1n6mfqjL5+17Z/ZB6qpGTduHGeddRbBYJAf/vCHHS7HqK2t7XLPLHHgWWmwDgb+3cv6\ntW3biAFk9erVtp530kkn8dBDD0Xunl5UVMT555/Pd7/7XY477jgmT55MdXU1s2fP5mc/+5nl/Xd3\nzdXtt9/O8ccfz+eff8748eM555xzuOCCCxg3bhyrV6/mkksu6fZ53/rWt3j44Yepqalh5syZjB8/\nnrPPPpvvfe97nH766RQWFjJp0qQOI8v3xW5u7SZOnMiwYcP45JNPKC4uZs6cOVxxxRXcd999kW2+\n/e1vYxgGDz/8MDNmzODyyy/niiuu4G9/+1u/Xrtdd1ndeeedHHPMMXz22WcUFRXx7W9/mwsvvJBx\n48bx1ltvcemll6K17vZu9fvS38wSXfT1V9EDLupqnDTvcRMiiCc1SGvKNtoGCDI0fSiprt6vzxjo\nue0Pkpk9kpt1PWU2cuRI/vrXv5KRkcG1117LqFGjmD59OhdffDEzZ86kqKiIoqIi/u///q/Lc486\n6ijy8/MjfzTsrsEC84+Lhx12GIWFhZbrPvHEE1mxYgX5+fksXbqUYcOGMX36dC666CKmT5/O8OHD\nee655xg6dCgrV67sdTT6nDlzUEqxZMkSamtrOfXUU3u8LODqq6/m2muv5csvv+TEE0/kqKOO4rzz\nzuO73/0up556Krm5ucyYMYNPP/3U8s9k15lnnonX6+XPf/4zJ598MnPnzuWKK67g6aefjmwza9as\nyB8fZ86cybx587jiiitYs2bNfqvr8ccfZ8yYMbz++uuMHTuWU089lVmzZnHQQQdRVVUVuX2Mnc9j\n0X9WGiwnMKSX9UOI8dAMEX/33nuv7ecuWLCAjz/+mHnz5uF0OvnHP/7Biy++SEVFBeeeey4vv/wy\nS5cu7fZCzH1dnNn5jusAqampvPXWW/ziF7+gsLCQFStW8O677zJ9+nQ+/vhjxowZ0+3z2mv95JNP\n+P73v49hGLz55pv89a9/ZePGjUyYMIFHHnmEq666qs91RufW02v2xuVysWLFCmbOnEl5eXnkSGD7\naF+AI488kr/85S9MmjSJNWvWsGTJEhYtWsQnn3zSpxrtZJyWlsaqVau48cYbKSgoYPny5ZGMS0pK\nMAwDpVSv5+z3pD/vtWQQff1Vdk6ImqYaABrKhxEMGoR1kPSsFhrYYWaPYlx2zxd5txvoue0Pkpk9\nklvvuvud2ltmp5xyCp9//jk33XQTw4cP56OPPuLPf/4za9euZdiwYdx+++3dNlhgHpVSSpGZmclx\nxx3XYd3UqVNxOBwdhl/Yccopp7BhwwbuvfdejjvuONauXRup7/jjj+c3v/kNX331FZMmTep1PyNG\njGD69OmRyYDR977qzn333cekSZO46KKLaGxs5LXXXuPVV1+loqKC0047jUWLFvGd73yny/P68jm7\nr8/j7tYVFhbyj3/8g9NOO43//Oc/PP300yxatKhD8zxz5kwef/xxjjzySN566y2eeuopFi1axPr1\n6/tUo52BFMOHD2fNmjX88Ic/xOPx8M4771BaWsoll1zCBx98QH19PYCtz2PRf6qv09eUUh8AL2qt\n7+lh/c+Bb2utJ8awvphRSk0ASkpKSpgwYUK8y0kafr9fbh5rw2DMLRgMcsQRR/DVV19RUlJi+bq6\ngZ7ZqlXQflnBSWfs5t3dLwLw4QvH8eayr9Oi6zlk0nqapv6I1nArPrePO6bewUmjT+p1vwM9t/1B\nMrNHcrNOMrNHcrMuOrPq6moOOuggGhsbqaysJDMzM87VJabS0tL2o6/FWuvSfW1vhZUjWIuAXyil\nutwJTSl1NnBz2zZiAJFfcPYM5NxKS0u7nD64Z88e5s+fz5dffsnRRx9ta2jJQM4M9h7BUgrCKXvP\nF9y12TwxIEyInJG78Qf9OJSjTxMEYeDntj9IZvZIbtZJZvZIbn1TUlISWW7PbNeuXVx66aXU1dVx\n7rnnSnMVJ30+pU9r/Tul1MnAy0qpdcB/Ma8UOBQYD/xRa/27/VOmECJRzJo1C7/fz5FHHkl+fj67\ndu3iX//6F9XV1eTl5bF48eJ4l5hwQiGoMc8IJDsbalrMbischpodbR9+RoDUoVvQWmMYBlmeLBlw\nIYQQoluhUIhjjz2WkSNHcuihh5KTk0N5eTmffPIJe/bsYdy4cTz00EPxLnPQsnTNlNb6e0qpl4Hv\nYjZVCrPRukVrbX3WqBAi6fz0pz/lxRdf5IsvvuD999/HMAxGjRrFxRdfzHXXXRe5P4fYq7raHMcO\nHQdc+P3QWONFE8LhCqGyN0XujZXlySLbmx2nioUQQiQyh8PBzTffzMqVK/n3v/9NTU0NKSkpFBUV\nMXPmTH7yk5+QlZUV7zIHLcu3jdZa/1Fr/W2t9eFa68PalqW5GqCuv/76eJeQlAZybgsWLGDlypWU\nl5fj9/tpbGzk888/54EHHuhXczWQM4secJGbq6luqgaguSabpiYHYYJ40wI0u8vRbSMER2SMwO3Y\n9/SngZzb/iKZ2SO5WSeZ2SO59c3tt9/Oe++9x44dO/jxj39MXV0dpaWl3HrrrdJcxZnlBksMLj2N\nUhW9k9ysG8iZRTdY7ow6gmHzZpQN24cTDEJYB/FlN1MXqsChHCgURTlFfdr3QM5tf5HM7JHcrJPM\n7JHcrJPMEkufpwh2eJJSnwJnaa23Ri/HvLoYkimCQoh4efFF2L3bXD7pnPW8u+1NAD587jTe/PNB\nNOt6jjjlK+om/5BgOIjP7eOe6fcwcURCDmUVQgghkl6iTBGMNgZwdbMshBAiSjhsXoMFkJkJ9YHo\nCYLmNVaaALkjKmgKNOEyXPhSfOR6c+NRrhBCCCH6SU4RFEKI/ai21pwiCB0HXASDULPTB2gwQqQU\nbjavv1LIBEEhhBAiiUmDJXq1bt26eJeQlCQ36wZqZtHXX+XlQVWTeQQr1OKloc6FJoQzJYjOLEOh\nAMj2ZJPl6dsFygM1t/1JMrNHcrNOMrNHcrNOMkss0mCJXt1www3xLiEpSW7WDdTMohssb2YjzcFm\nAFqqC2lqgpAOkpoeoMm5d4LgqMxROAxHn/Y/UHPbnyQzeyQ36yQzeyQ36ySzxCINlujVwoUL411C\nUpLcrBuomUU3WNq79/qr2i2F5gRBQmTm+akN7sJhmBMED845uM/7H6i57U+SmT2Sm3WSmT2Sm3WS\nWWKRBkv0SsZ+2iO5WTcQM9Maqtp6qvR02BOOGnCxMR+tIUyAIcMbqG2uxWW48Dg9jMwc2efXGIi5\n7W+SmT2Sm3WSmT2Sm3WSWWKRBksIIfaT+noIBMzl6AEXABVlGQBoFSJrxE6ag804DAc+t4/cVJkg\nKIQQQiQruw3Wu0BTN8tCCCHadB5w0d5ghYMuqnZ5gDAYQdyFGyLbyQRBIYQQIrnZarC01mdprXd0\nXhYDzz333BPvEpKS5GbdQMwsusHyZbfQ2NoIgCuQS329IkwItyeI9m2NDLjITc3F5/b1+TUGYm77\nm2Rmj+RmnWRmj+RmnWSWWOQUQdErv98f7xKSkuRm3UDMLLrBMtL2Xn/l31VAczOEdZBUXyt7HOWR\ndWOzxqKU6vNrDMTc9jfJzB7JzTrJzB7JzTrJLLEorXXfNlRqKrAQmKi1ru+0LhN4H/iB1vrdmFcZ\nA0qpCUBJSUkJEyZMiHc5QohBYMkSaGmB1FQ4avpaPtj2AQDlb5/OMwtH0xxuYPRRW0j9zlVU+avw\nOD384uRf8M3x34xz5UIIIcTAVlpaSnFxMUCx1ro0lvu2cgTrJ8DvOzdXAFrrOuBx4NpYFSaEEMms\nsdFsrgByczsOuNi+MbttgmCIIcPrqW2qxWk48Tg9jMgYEaeKhRBCCBELVhqso4HXelm/AijuXzlC\nCDEwdB5wUeU3TxFUGOwoSzdXqCBZIypoCbXgMBxkpGTIBEEhhBAiyVlpsAqAQC/rg8CQ/pUjEk1l\n9L8SRZ9JbtYNtMyif5ysnCC1zbUAeMimqtIAwihHCHfhpsg1V3YmCA603A4Eycweyc06ycweyc06\nySyxWGmwyoEje1l/FCDTBAeYuXPnxruEpCS5WTfQMov+rHOkV0emBBpNedTV0TZBMEAwbQttq8hP\nzSfVlWrpdQZabgeCZGaP5GadZGaP5GadZJZYrDRYrwK3K6U8nVcopbzAbcDfY1WYSAy33nprvEtI\nSpKbdQMts/YGKyUFWoy9EwQbdhTQ2gohHSAts5VGtkeOYI3NGWv5dQZabgeCZGaP5GadZGaP5Gad\nZJZYnBa2vQM4D/hSKbUQ+C/m310PBeYDDuDOmFco4komLtojuVk3kDLz+80v6DrgomLjEEIhCBMk\nt9BPZfMuHIYDA4PxOeMtv9ZAyu1Akczskdysk8zskdysk8wSS58bLK31TqXUCcBjwN1A+41aNLAc\n+JHWemfsSxRCiOTSecBFRdPeI1jlGzIA8xTBvBF1bG6pw6lkgqAQQggxUFg5goXWugw4SymVDRRh\nNllfaa1r9kdxQgiRjKr29lPk5Ib5T4P5QLorg21bXeYKI0jmiHICoQBut5sMT4blARdCCCGESDxW\nrsGK0FrXaK0/0lqvkeZqYHvyySfjXUJSktysG0iZRR/BcvvqCOkQAJ5wHlVVoAmZEwSHbES1nQyQ\nmZJJtjfb8msNpNwOFMnMHsnNOsnMHsnNOskssfSpwVJK/Z9Sqk/nriilLlBKXdS/skSiKC2N6Y2t\nBw3JzbqBlFl7g+V0QqszqtvyD6Ghwbz+ypMapNVTHpkuOMw3DLfDbfm1BlJuB4pkZo/kZp1kZo/k\nZp1klliU1nrfGyl1B/Bj4D3gZeBjzJHszUA2cBgwGbgQ2A5cqbVeu59qtkUpNQEoKSkRXu3ZAAAg\nAElEQVQpkQsBhRD7TUsLLFliLhcWQv6ED1i70/x1aKz/Fr+6aRjNQT/Zo3ZQ9KPr2VCzHq/Ly7xv\nzOOK4iviWLkQQggxeJSWllJcXAxQrLWOaYfap2uwtNY3K6UeAeZhTgw8rNMmDcAbmI3Va7EsUAgh\nkkn09Vd5eR0nCJavzyYUghBBcgsbqW6qwmk4zQmCudYnCAohhBAi8ViaIog5hv3OtiEXowAvUAls\n0H05FCaEEANc5wmCX/nNjivVlcqmr7yAeYpg/ug6vmyuw+1043V5GeYbFo9yhRBCCBFjlqYItmsb\nbCHDLYQQopPoBsuT0UhLQwsAWe5ctm0zH1dGkMzh5QR1kBSVQkaKTBAUQgghBoo+TxFUSh2slFqm\nlMroZl2mUuoPSqlxsS1PxNvMmTPjXUJSktysGyiZtTdYDgcE3Xu7LXcwj9pa0AQxnCGM3I2Rddme\nbLI8WbZeb6DkdiBJZvZIbtZJZvZIbtZJZonFypj264GtWuv6ziu01nXA1rZtxACyYMGCeJeQlCQ3\n6wZCZoEA1Naayzk5UN28t8EK1OXR2Ghef+VNC9KSsi2ybkTGCByGw9ZrDoTcDjTJzB7JzTrJzB7J\nzTrJLLFYabBOBv7Uy/o/AlP7V45INDNmzIh3CUlJcrNuIGTWecBFlX/vA7vK8ggEIKxD+LKbqQlt\nx6HMpuqgnINsv+ZAyO1Ak8zskdysk8zskdysk8wSi5UGazSwq5f1lcDI/pUjhBDJq/OAi/YJgm6H\nm03r0gmHIUyAIcMbqWqqwumQCYJCCCHEQGOlwaoDevszaxHQ5fRBIYQYLKIbrLTMZvYE9gCQ481h\nwwYFQFiFGDKyjoaWBpyGUyYICiGEEAOMlQbrHcybDffkKuDd/pUjEs1LL70U7xKSkuRm3UDIrL3B\nUgq0Z+/pgT5HHtu3A4TBCOIbvoWQDgGQmZLZrwmCAyG3A00ys0dys04ys0dys04ySyxWGqy7gTOV\nUn9WSh3XNjkwUyl1vFLqBeD0tm3EALJs2bJ4l5CUJDfrkj2zUGjvgIvsbKht3Xs4yxUwJwiGCeFy\nhTGyyyLrcrw5+Nw+26+b7LnFg2Rmj+RmnWRmj+RmnWSWWJSV+wMrpb4FLAJyO62qAuZprV+OYW0x\npZSaAJSUlJQwYcKEeJcjhBhgdu+GF180l8ePh/DoN1lfvR6AMf5Z/HheLv6WZjy5uzjxf+7g413v\nk+ZO46yis7hlyi1xrFwIIYQYfEpLSykuLgYo1lqXxnLflm40rLX+u1JqNHAG5jVXCvgSWKG19sey\nMCGESCadB1x83jbgwlAG2zdmEwhAiACZeU1Ute7AaZi/fotyiuJRrhBCCCH2kz43WEqpBcAzbfe8\nenH/lSSEEMknusHKyglSu8M8XzDHm8OaLwy0Nk8RHDJ8D9VN1TgNc4LgIbmHxKliIYQQQuwPVq7B\nuhPYoZT6g1JK7nclhBBRohssvHsHXOSl5rFhg7msVZD8kVU0tjbiMBykulMp9BUe2EKFEEIIsV9Z\nabAKgR8AQ4HXlVIblVK/UErJva8GsMsuuyzeJSQlyc26ZM4sHIbqanM5MxPqg3sbLK/OZdcugDDK\nCJE6bFvMJghCcucWL5KZPZKbdZKZPZKbdZJZYulzg6W1btJaP621PhU4GFgKXA5sUkq9ppT6f0op\n1/4qVMSH3BncHsnNumTOrLbWnCIIHW8wDKCac/dOEHSHUFmbI+vyUvNIdaX267WTObd4kczskdys\nk8zskdysk8wSi6Upgl2erJQCpgFzgG8De7TW+bEpLbZkiqAQYn/58ktYtcpcPv542Oh+kd3+3QAc\n1HAZP7zShb+1idT8XRx/w638e3cJXpeXcw45hxtPujF+hQshhBCD1P6cImjlFMEutNmdBQGNOVFQ\njmAJIQad6OuvcnLDVDeZ5wtmpmSy/r8ugkEIEyR7iJ+qlp04DAcgEwSFEEKIgchWg6WUGqmU+qVS\naiPwOjAMuALz+iwhhBhUohssR1pt5BqrvNQ81q2jbYJgkPyRjdQ01+BUThzKwSF5MkFQCCGEGGj6\n3GAppdxKqQuVUiuATZgN1R+A8VrrqVrrZ7XWzfurUBEfq1evjncJSUlysy5ZM9MaqtpmWqSnQ2N4\nb7eV481l48a27YwgQ0ZU09jaiGEYpLpSKUgr6PfrJ2tu8SSZ2SO5WSeZ2SO5WSeZJRYrR7AqgMVA\nPXA2MFprfbPWeuP+KEwkhnvvvTfeJSQlyc26ZM2svh4CAXM5Lw+q/HsnCLqDeVRWgiaEocKkDttM\n+3WvsZggCMmbWzxJZvZIbtZJZvZIbtZJZomlzzcaBu4AntZaV+5zSzFgPPfcc/EuISlJbtYla2bR\npwfm5UF59ATBpjzq683TA92eEOHMLdB2nL8gvQC3w93v10/W3OJJMrNHcrNOMrNHcrNOMkssfW6w\ntNb3d35MKeUBLgDSgNe11l/FsDaRAFJT+zdCerCS3KxL1sw6N1hrq80jWGmuNMrLPDQ3Q1iH8Pla\naNDbMJR54sCYrDExef1kzS2eJDN7JDfrJDN7JDfrJLPE0ucGSyl1P+DSWv+47Xs38E/gcMAP/7+9\nO4+S667vvP/+VVVX7+qtWrta8r6CcXcY8EBiniQY8MPT7DGESYIMnJBYSeBkpOSBZGx4JjMjA54B\nyznJ5DgYQhBbguJJAhhMcCKWOOkGA9Ziy5Yty1pavS/VSy2/54/bm7aWfj9V17236/M6p4+rq6uq\nv/fjX1f3V/fe7+VeY8xrrbU/WJZKRUQiaHGDVb1qjJlTMwC01bWxfz8LEwTXZOmf6iOVCN52r2q9\nKoxyRUREZJm5nIN1G8HEwDnvBjYTXHS4BfgK8EelK01EJPrmGqy6OsguOoJ6boIgQIE8azaNMTg5\nSNIkNUFQRERkBXNpsDqAfYs+vw34qrX2+dnrYX0KuLmUxUn4tm/fHnYJsaTc3MUxs/FxmJ4Obre1\nwcDkwoCL5nQbzz03+0kiT6ZjkGwuSyKRoL6qviQTBCGeuYVNmflRbu6UmR/l5k6ZRYtLg1UkuJjw\nnFcCP1z0+TDBnixZQTo6OsIuIZaUm7s4Znbm+Vf9iwZcpHIZhobAkieZtNSuex7L7ATBmiaaa5pL\nUkMccwubMvOj3NwpMz/KzZ0yixaXBms/wXh2jDE3EOzR+qdFX98MnPQtxBhzlzHmsDFm0hjzQ2PM\nyy/yee80xhSNMX/r+73l/H7nd34n7BJiSbm5i2Nm52uw0sk0k8ONjIxAgQLp2jyF+ufnH7uhcQPJ\nRLIkNcQxt7ApMz/KzZ0y86Pc3CmzaHFpsO4F/rsx5lHgUeAfrbWHF339duBxnyKMMXcAnwTuJjjM\n8Angm8aYzAWetxn4OPDPPt9XRORSLG6w6psmyeayQHD+1aFDMDMD1uZpWDXNkD1C0gRNVakmCIqI\niEj0XHSDZa39GkET9RPgfxKMZ18sC/ypZx0fAv7cWvs5a+0B4AOzr3fn+Z5gjEkAnwf+C3D4fI8T\nEVkucw1WdTXMpBbOv2qrDSYIFgrBgIvWdRP0T/STSgYTBK/OXB1GuSIiIlIGLnuwsNY+aq39kLV2\np7U2e8bXPmqt/a5rAcaYKqCLYK/Y3GtZ4NvALUs89W6gz1r7GdfvKRfvwNwYNHGi3NzFLbNsNviA\nYMDF4vOvMnUZDh4MbhfJs2bjGMNTw6QSqWCCYFvpJgjGLbcoUGZ+lJs7ZeZHublTZtHi1GAtkwyQ\n5Ozzt04Ca8/1BGPMq4CtwPuWtzTZsWNH2CXEknJzF7fMBhZ2WJHJwEB24Y7GVBtHjwLY2QmC/Uzm\nJwFoSDewun51yeqIW25RoMz8KDd3ysyPcnOnzKIlCg3W+RiYHbm1+E5jGoC/At5vrR1yfdHbb7+d\n7u7u0z5uueUW9uzZc9rjHnnkEbq7u896/l133cWDDz542n29vb10d3fTv/iEDODuu+9m586dp913\n5MgRuru7z/qXhvvvv/+sEZvZbJbu7m727t172v27d+9m69atZ9V2xx13lHw7du3atSK2A8r7/2PX\nrl0rYjugfP8/du3aFavtmNuchx++m7/5m53ze7CSJskLT4/y+OPdTM/8jGTKUrM2mCB44tETHPnK\nERrTjSXbjl27dmldOW7Hrl27VsR2zCnXdszlFvftmFOO7ZjLLO7bMadc2zGXW9y3Y045tmMus7hv\nx2Kl3I7du3fP/91/6623snbtWrZt23bW40vFBEfjhWf2EMEs8DZr7cOL7n8IaLLWvuWMx98E9AIF\nFsbGzzWKBeCaM4ZvzD2vE+jp6emhs7Oz5NshIpXlW9+Cw7PvNG95W46vPRccrdxe187lM2/hne+E\n8ckpqpr6ecNHP8Vjx75OfbqeV296NZ983SdDrFxERER6e3vp6uoC6LLW9pbytZ32YJlAhzGmplQF\nWGtzQA/wS4u/z+zn3z/HU/YDLwFeBtw0+/Ew8J3Z2y+UqjYRkfOZ+we8VAry6UUDLuraePrpYIJg\ngTyNLVMM5F8glQgGXGiCoIiIyMqWcny8AQ4BNwBPl7CO+4DPGmN6CEa9fwioAx4CMMZ8Djhqrf2w\ntXYG2HdaUcYME8zG2F/CmkREzml6GsbGgtuZDAxOLjRYmboM390HxWJwkeHM+gn6s/3zDda1mWvD\nKFlERETKxHWKYJGgsWorZRHW2i8Dvw98DPgR8FLgddbaU7MP2ch5Bl7I8jrzmFq5OMrNXZwyO3PA\nxZkTBJ+e/eenosmzelMwQTCZSJI0Sa5uK+2I9jjlFhXKzI9yc6fM/Cg3d8osWlz3YAH8IfBxY8xv\nWWt/VqpCrLV/ynmuo2Wt/cULPPfsM+CkJLLZ7IUfJGdRbu7ilNni83szGfjZ7B4sg6HGtnL8OEAR\nkyiQ2XSKnxWmqUvU0ZhupL2+vaS1xCm3qFBmfpSbO2XmR7m5U2bR4jzkwhgzRHD4XgqYASYXf91a\n21qy6kpIQy5EpFS+8x04dCi4/Za3Fvm7I39J0RZprmnmVc2/Qnc39A/mKKT7ueP/+yLf7H+Q+nQ9\nlzdfzu637w63eBEREVnWIRc+e7A+WMoCRETiZm4PViIB1AxRtEUgODzw2DEYH4eCzVNdl2ci/Rxm\nduDpplWbQqpYREREysW5wbLWfnY5ChERiYNcDoaHg9ttbTA0vWiCYG0bPz0YPKZInqa2KQZmjpFK\nBm+1V7ReEUbJIiIiUkZeFxo2xiSNMW8zxvyRMeYjxpi3GGOSpS5OwnfmxeTk4ig3d3HJ7EIDLvbv\nB2uDBqt9wxgDkwOkzOwEwbbSTxCMS25Rosz8KDd3ysyPcnOnzKLFucEyxlxJcC2qzwFvBd4OfB54\n0hijf55dYe68886wS4gl5eYuLpkt/h3W1nZ6g9Va2zZ/bpadnSA4Mj1CIpEgZVJclbmq5PXEJbco\nUWZ+lJs7ZeZHublTZtHiswfr08AzwCZrbae19magAzg8+zVZQe65556wS4gl5eYuLpmd3mBZBrLB\nLq2GdAO5yRr6+sBSxCSKtHb0MZOfmf96pi5T8nrikluUKDM/ys2dMvOj3Nwps2jxGXJxK/BKa+3g\n3B3W2gFjzB8C3ytZZRIJmrjoR7m5i0tmc4cIGgNVDWPkijkgOP9qaAiGhqBIjqp0kXTbc9iTwaTW\n1fWrSSfTJa8nLrlFiTLzo9zcKTM/ys2dMosWnz1Y00DjOe5vIBjbLiKyIhUKQQMF0NICwzOnn391\n9Chks1C0BWrqc4ymniNhgrfZjqaOMEoWERGRMvNpsP4e+N/GmFeYBa8E/gx4uLTliYhEx+AgFIOJ\n7GcNuGira2P/fsjngwEXze1Z+qdOkEoEBwpc3nJ5GCWLiIhImfk0WL9LcA7WD4Cp2Y/vAYeA3ytd\naRIFDz74YNglxJJycxeHzBaff5XJMH/+FQR7sA4cCCYIFsjTvn6CgewAydkBq9e3X78sNcUht6hR\nZn6Umztl5ke5uVNm0XJRDZYxZtXcbWvtsLX2TcDVBBME3wFcY619i7V2ZHnKlLD09pb0wtYVQ7m5\ni0NmZzVYk0GDVZ2spi7VwLPPzn4xkWdtxzBjM2MLEwRbSz9BEOKRW9QoMz/KzZ0y86Pc3CmzaDHW\n2gs/yJgCsM5a22eM+Q7wVmvt8LJXV0LGmE6gp6enRycCioiXPXugry+4fcd/yvKl/Z8HYH3jev5j\n5o286U3wwtE804kBfvUj/8Q3p/4r9el6Wmta+ftf/XuSCV0uUEREJAp6e3vp6uoC6LLWlrRDvdhD\nBMeBttnbrwGqSlmEiEjUFYsLEwSbmmA0d/rhgYODMDICBQpUVRdItB6e//qahjVqrkRERCrExY5p\n/zbwT8aY/bOff80Yc86JgdbaXyxJZSIiETI8HEwRhLMHXGTqMjx/ECYnwdo89Y0zjJjn58+/2ty8\nOYySRUREJAQX22D9J+A3gCsIroP1JJBdrqJERKLmzPOvTk0u7MFqq23jG7MTBAsUaFk9Sd/kwgTB\nK1uuLHe5IiIiEpKLOkTQWjtprf0za+124DHgD6y1HzrXx/KWK+XW3d0ddgmxpNzcRT2zMxusuT1Y\nqUSK5ppm9s/u3y+So33jGIOTg6SSQYN1Xft1y1ZX1HOLImXmR7m5U2Z+lJs7ZRYtzmParbX/V9wG\nXIi/bdu2hV1CLCk3d1HPbHGD1dg8w+j0KACtta0UCoYjRwAsJAqs6RhlYmYCgKpE1bLuwYp6blGk\nzPwoN3fKzI9yc6fMosXnOlhSQW677bawS4gl5eYuyplZuzDgoqEBsnZw/mtttW0MDc1ehJgCiaSl\npeM404VpAJqqm2iqaVq22qKcW1QpMz/KzZ0y86Pc3CmzaFGDJSJyAaOjkMsFt8814KK/P5ggWLR5\n0tUFaHx+/uvrGtZhjCl3ySIiIhISNVgiIhdwvvOvANrq2jh8GKamoECe+lXTDBSfmx9w0dHcUe5y\nRUREJERqsGRJe/bsCbuEWFJu7qKc2ZkN1kA2OF7QYGitbWX//mCEuyVP29oJ+idPzTdYV7Vetay1\nRTm3qFJmfpSbO2XmR7m5U2bR4tVgGWN+3hjzeWPMD4wxG2bv+zVjzKtLW56Ebffu3WGXEEvKzV2U\nM1vcYLW0FhiaGgKguaaZVCLFgQPB14omz+qN4wxODs5fWPiG1Tcsa21Rzi2qlJkf5eZOmflRbu6U\nWbQ4N1jGmLcB3wQmgZuB6tkvNQEfLl1pEgVf+tKXwi4hlpSbuyhnNtdg1dbCdGKIoi0CwflXU1Nw\n7BhAERIFVneMMJELJgimE2mubF3ea2BFObeoUmZ+lJs7ZeZHublTZtHiswfrj4APWGvfD+QW3f89\noLMkVYmIRMT4OEwHAwHPef7V0FAwYbBIgWQCWjaemJ8g2FzbTF1VXRhli4iISEh8GqxrgH8+x/0j\nQPOllSMiEi3nO/8Kgj1YfX3BlMGCzVNdmyfX+AyJ2bfWDQ0byl2uiIiIhMynwToBnOuYl1cDz15a\nOSIi0XJWgzW50GC11bbxzDPBHq4ieRqapxnIHSWVDAZcbG7eXO5yRUREJGQ+DdZfAJ8yxrwCsMB6\nY8y7gU8Af1rK4iR8W7duDbuEWFJu7qKa2eIGq63Nzu/Bakg3UJ2qZv9+KBaDBiuzboK+bB8pEzRY\nV7ddvez1RTW3KFNmfpSbO2XmR7m5U2bRkvJ4zv8gaMweBeoIDhecBj5hrd1VwtokAnRlcD/KzV1U\nM5trsNJpsOlRcsXg1NNMXQaAp54Kvm5NgfYNoxybHCaRCP7t6vrM9cteX1RzizJl5ke5uVNmfpSb\nO2UWLcZa6/dEY9IEhwo2APusteOlLKzUjDGdQE9PTw+dnZrFISIXls3C5z8f3F6/Hq77j8/w6OFH\nAfi59T/H1Y2dvO1t8NRTRSYTp/iVD/47e6v+mKpUFelkmkd//VHSyXSIWyAiIiLn0tvbS1dXF0CX\ntba3lK/ttAfLGJMCpoCXWWt/BuwrZTEiIlEysHC61TnPvxoagsFBKJAnmbQ0bTjBzIkZqqgiU5tR\ncyUiIlKBnM7BstbmgSNAcnnKERGJjjMHXCwe0Z6py3DyJIyNQdHmqa7Lk61+hoQJ3lbXr1pf7nJF\nREQkAnyGXPwJ8N+MMa2lLkaiZ+/evWGXEEvKzV0UMztfg1WTqqE+Xc/TT8PMTDDgYlXrFKemj5JK\nBAcGbGnaUpYao5hb1CkzP8rNnTLzo9zcKbNo8WmwtgG/ABwzxhw0xvQu/ihxfRKye++9N+wSYkm5\nuYtiZnMNVioFqdoJpvJTQHB4IMC+fWAtFCjQvn6C/mw/SRPs4C/HBEGIZm5Rp8z8KDd3ysyPcnOn\nzKLFZ4rgnpJXIZH1xS9+MewSYkm5uYtaZtPTweF/EOy9Gpw6/QLD1sKhQ7N3mByZjaMcmR6ZnyD4\n0jUvLUudUcstDpSZH+XmTpn5UW7ulFm0ODdY1tqPLkchEk11dXVhlxBLys1d1DI7c8DF4vOv2ura\nGBmBvj6wFDAJy/otwxzIZUmn0tQka+ho6ihLnVHLLQ6UmR/l5k6Z+VFu7pRZtPjswQLAGNMFXEdw\nseF91toflawqEZGQnX6BYTiSPX0P1tBxGB4OJgimqoo0rH+RmRdmSKfStNe3k0xoFpCIiEglcm6w\njDGrgS8CrwGGAQM0GWP+CXintfZUSSsUEQnBmQMueo8Fd6QSKZqqm3jyGIyPg7V5auryjKWenR9w\nsaFxQxgli4iISAT4DLm4H1gF3GCtbbXWtgA3zt736VIWJ+Hbvn172CXEknJzF7XM5hqsRALqV80w\nNhOckNVW24YxhqeeglwuGHDRnJmkb/LY/F6ry1ouK1udUcstDpSZH+XmTpn5UW7ulFm0+Bwi+Hrg\nl621++fusNbuM8bcBTxSssokEjo6ynMeyUqj3NxFKbNcLjj8D4LDAwenTj//ChYmCBbJkdkwzqns\nKaoSVQBc03ZN2WqNUm5xocz8KDd3ysyPcnOnzKLFWGvdnmDMGPDz1tofn3H/zcBj1tpVJayvZIwx\nnUBPT08PnZ2dYZcjIhF24gQ8/HBw+7rroPnqn/KDoz8A4Bc2/wJXtVzLHXdAT48la07xunfv45kt\nHyFv8wB89R1fZVPTprDKFxERkQvo7e2lq6sLoMtaW9JLTfkcIvgd4FPGmPVzdxhjNgD/E3i0VIWJ\niITlzAEXp00QrG1jeDh4jKVAImFZe9kwEzMTANSl6ti4amO5SxYREZGI8L3QcCPwnDHmGWPMIeDw\n7H2/U8riRETCcOaAi4HJYIKgwdBa28rg4OwEQRtMEKxdfXR+79Xq+tUYY8IoW0RERCLAucGy1r5g\nre0E/m/gfxEMtrjdWttlrT1a6gIlXAcOHAi7hFhSbu6ilNncNbCMgabmAkOTQwC01LaQTCQ5ehSy\nWShSoLYhx0jimfkBF+XeexWl3OJCmflRbu6UmR/l5k6ZRYvPHiwArLXfstbeb639tLX226UsSqJj\nx44dYZcQS8rNXVQyKxRgKOinaGmB0dwgluBc1UxdBoCDB4NBGEVytKzOcjJ7Yn7AxWXN5ZsgCNHJ\nLU6UmR/l5k6Z+VFu7pRZtDg3WMaYTxtjfvcc928zxvyv0pQlUbFr166wS4gl5eYuKpkNDkKxGNxe\nfHggBOdfAeyfnaFapED7hnEGJgfm92Bd235tWeuNSm5xosz8KDd3ysyPcnOnzKLFZw/W24DvneP+\n7wNvv7RyJGo09tOPcnMXlczOPP9q8YCLTF2GmRl4/nmAIiTyrN40wvjM+PxjXrrmpeUrlujkFifK\nzI9yc6fM/Cg3d8osWnwarDZg5Bz3jwKZSytHRCRcAws7rM5qsNrq2hgaCh5TpEAyCWs3DzORCyYI\n1lfVs7p+dblLFhERkQjxabAOEVxs+ExvAJ69tHJERMK1eA9Wa6tlcHIQgFXVq0gn0wwMwMgIFG2e\nqnSBVPvzFIoFANbVrwujZBEREYkQnwbrPuBeY8xHjTG3zn58DPgfBNfCkhVk586dYZcQS8rNXRQy\nKxYX9mA1NUG2OEK+GIxfnzv/6sgRmJyEAnnqGnMMFZ8jlUgBsLGp/Ne/ikJucaPM/Cg3d8rMj3Jz\np8yiJeX6BGvtXxpjqoGPAH88e/dzwG9Zaz9XwtokArLZbNglxJJycxeFzIaHgymCcO7DAwEOHIB8\nPjhEsHXNBH3Zk/MN1uUtl5e95ijkFjfKzI9yc6fM/Cg3d8osWoy11v/JxrQDk9ba8Qs+OGTGmE6g\np6enh87OzrDLEZEIeuop+O53g9uveAVMtf0rT5x8AoDXX/l6Opo6+M3fhEcegSz93PKGZxl/5YcZ\nmxkD4N5fvpdbt9waUvUiIiJysXp7e+nq6gLostb2lvK1fca01xpj6gCstaeANmPMB40xt5WyMBGR\ncrvQBMFsFl58EYIJggXaO0aYmJmYf8xNa24qX7EiIiISST7nYP0d8OsAxphm4HHg94G/M8b8Vglr\nExEpq8UNVlvbwjWwalO11FXVMTQUXCerQJ5kAtZfPkg2FxyWsap6Fc21zWGULSIiIhHi02B1Av8y\ne/vtwAlgM0HTddYFiCXe+hf/xSkXTbm5CzszaxcGXDQ0QD4xzlR+Clg4/6q/H0ZHgwmC6eoCxabD\nWILDrNfWrw2l7rBziyNl5ke5uVNmfpSbO2UWLT4NVh0wNnv7NuBvrbVF4IcEjZasIHfeeWfYJcSS\ncnMXdmajo5DLBbczGRjILlwQK1MXXOLvuedgagqK5GlonmYgf4RkIglAR1M4F3kMO7c4UmZ+lJs7\nZeZHublTZtHiex2sNxtjNgGvAx6ZvX81wcWGZQW55557wi4hlpSbu7AzW+r8q7kR7fv2BVMGCxRo\nWztB33gfKRNMELyi5Yqy1jsn7NziSJn5UW7ulJkf5eZOmUWLT4P1MeATBKPZ/9Va+4PZ+28DflSi\nuiQiNHHRj3JzF3ZmZzZYc+dfQbAHy9pgyiAAJk9mwxjD08MkEsHb6HXt15Wx2jm/tHQAACAASURB\nVAVh5xZHysyPcnOnzPwoN3fKLFp8roP1VWPMXmAd8MSiLz0KfK1UhYmIlNNZe7CeCe6oSlSxqnoV\nY2Nw4gRYCphEkbWbRzg2O0HQYHjJ6peEUbaIiIhEjHODBWCtPUEw3GLxfY+XpCIRkRDMNVi1tZBM\nTzM+E1zer62uDWPM/ATBInmSSch0DDBxaoLaqlqaa5ppqG4IsXoRERGJCp9DBKWCPPjgg2GXEEvK\nzV2YmY2Pw/R0cPvMwwPnzr/q64OxMSjaAtU1efKNh+cfs65hXVnrXUxrzZ0y86Pc3CkzP8rNnTKL\nFjVYsqTe3pJe2LpiKDd3YWZ2oQsMQzBBcHo6uAZWY8s0A7mjpBLBQQBhTRAErTUfysyPcnOnzPwo\nN3fKLFqMtTbsGsrCGNMJ9PT09OhEQBE5zb//O8z9bnrta+Ew3+HQ4CEA3nrdW8nUZbj7bvjc5yBr\nB7nxlqOsetPdvDj2IsYYtr18G79206+FuAUiIiLiore3l66uLoAua21JO1TtwRKRinfWBMHZa2Al\nTILW2laKRXjmmdkHJIIJgqPToxhjALguE84EQREREYkeNVgiUvHmGqx0Gmrr8wxPDQPQUtNCwiQY\nGYFTp8CSJ2EsazYPM5ELJggmSHDj6hvDKl1EREQiRg2WiFS0bDb4gGDv1eDkIJbg0Om2umDAxeBg\n8FEgTzJlad/cTzYXPKmltoWaqppQahcREZHoUYMlS+ru7g67hFhSbu7CymxgYWDgaYcHwsKAi5Mn\nF00QrMszXXsYQ3B44LrG8CYIgtaaD2XmR7m5U2Z+lJs7ZRYtarBkSdu2bQu7hFhSbu7CyuxiJgg+\n8wzMzECRHE2ZSU5OvkgqGUwQvKzpsrLWeyatNXfKzI9yc6fM/Cg3d8osWtRgyZJuu+22sEuIJeXm\nLqzMzhpwsegaWK21rQA8+SRYC0UKZNZO0Jftmx/RfmXrlWWt90xaa+6UmR/l5k6Z+VFu7pRZtKjB\nEpGKNtdgpVLQuKo4f4jgqupVpJNp8nk4fBigCIk87RtHGZ8an3/+9e3Xl79oERERiSw1WCJSsaan\ng3OrINh7NTo9QsEWgs9nDw8cGgqasCIFEglYu2WY8VzQYKVMimvbrw2ldhEREYkmNViypD179oRd\nQiwpN3dhZLZ4wEVb27nPvxocDJqsoi2QqirStOEkk/lJAFrrWkkn02Wt+Uxaa+6UmR/l5k6Z+VFu\n7pRZtKjBkiXt3r077BJiSbm5CyOzpc6/aqsNRrQfPw4TE1AgR219nmz1YRImeOvc0LChrPWei9aa\nO2XmR7m5U2Z+lJs7ZRYtarBkSV/60pfCLiGWlJu7MDJbaoLg3DWwnnpqboJggab2SfqyJ+YHXHQ0\nd5S13nPRWnOnzPwoN3fKzI9yc6fMokUNlohUrLkGK5GAlpaFa2DVVdVRV1UHwL59wWMsedrXj9Gf\n7SeZSAJwVetVZa9ZREREok0NlohUpFwOhoeD221tkM2PM12YDj6fPTxwagpeeAGCCYIF2jeMMjY9\nNv8aN66+scxVi4iISNSpwRKRinQxAy4WTxBMJmHtZcNM5CYASCfSXNF6RVlrFhERkehTgyVL2rp1\na9glxJJyc1fuzC7m/KvBQRgZgYLNk6oq0rj+ONP56fnHhD1BELTWfCgzP8rNnTLzo9zcKbNoUYMl\nS9KVwf0oN3flzmzxHqxMZuH8K1jYg3X0aDBBsEieuoYco+Z5EongbXPjqo1lrfd8tNbcKTM/ys2d\nMvOj3Nwps2hRgyVLete73hV2CbGk3NyVO7O5PVjGQGvrwh6sdDJNY7oRgIMHIZ+HAnla1kxwYvw4\nKRNMENzStKWs9Z6P1po7ZeZHublTZn6UmztlFi1qsESk4hQKwflVEEwPzDM1f25Va20rxhgADhyY\nfYLJk1k/ztDU0PwerKvaNEFQREREzqYGS0QqzuAgFIvB7TPPv5o7PHB8HI4dA0sBkyiyetMI4zPj\nABgMN7TfUPa6RUREJPrUYMmS9u7dG3YJsaTc3JUzs4s5/2phgmCeRALWbB5kYibYy1WdrGZL85ay\n1bsUrTV3ysyPcnOnzPwoN3fKLFrUYMmS7r333rBLiCXl5q6cmZ05QXBgcqHBmrsG1sBAMEGwaAtU\npQvUrz0xf52s9vp2UslU2epditaaO2XmR7m5U2Z+lJs7ZRYtarBkSV/84hfDLiGWlJu7cma2uMFa\nfA2shEnQUtsCBBMEJyeDPVj1TTMMFY+QTCQB2NC4oWy1XojWmjtl5ke5uVNmfpSbO2UWLWqwZEl1\ndXVhlxBLys1duTIrFhcOEWxqApPMMzw1DAQDLhImeFvcvz8YhlEgT9uaLMfHj5M0QYMVlcMDQWvN\nhzLzo9zcKTM/ys2dMosWNVgiUlGGh4PGCc5//pW1Z0wQ3DDGyPTI/ATBazLXlLNkERERiRE1WCJS\nUS7m/KvRUTh5Eix5TMIGEwSngwmCCRJcn7m+rDWLiIhIfKjBkiVt37497BJiSbm5K1dmZzZYi0e0\nt9UFDdbgYHAYYYECySSs3tI/f52s6lQ1G1dtLEutF0NrzZ0y86Pc3CkzP8rNnTKLFjVYsqSOjo6w\nS4gl5eauXJmdb8AFLOzB6u+HsTGwNk+6ukC69Ti5Yg6ANfVrIjNBELTWfCgzP8rNnTLzo9zcKbNo\nMdbasGsoC2NMJ9DT09NDZ2dn2OWISAishYceglwOGhrgne8q8pkffYaCLdBU3cQdN94BwN/8DezY\nAdnCCO2bBvmlj3yafz7yz6QSKV618VXc9/r7wt0QERERuSS9vb10dXUBdFlre0v52tqDJSIVY3Q0\naK4gODxweGqYgg0mXswNuAB48slg2mCRPK3rJjgxfoJUIthrdXnL5WWvW0REROJDDZaIVIyLOf+q\nUIBDhwAsJPJk1o8xOj06/zhNEBQREZGlqMGSJR2Yn1UtLpSbu3JkdtYEwXOMaB8ehr6+YO9VIkEw\nQXAmmCCYNMnINVhaa+6UmR/l5k6Z+VFu7pRZtKjBkiXt2LEj7BJiSbm5K0dmS+7Bql2YIDg0BEUb\nTBBs39JHNpcFoC5Vx4bGDctepwutNXfKzI9yc6fM/Cg3d8osWtRgyZJ27doVdgmxpNzclSOzgdkd\nVrW1UFe3cA2suqo6aqtqgWDv1egoFMiTrsmTbDlGvpgHoL2hnWQiuex1utBac6fM/Cg3d8rMj3Jz\np8yiRQ2WLEljP/0oN3fLndn4OExNBbczGRidHmWmMBN8vmjAxXPPwfR0cJHhVW1TnJp6cb6p2tgY\nnetfzdFac6fM/Cg3d8rMj3Jzp8yiJToXcymT/fvDrkBEwjC6MKfivOdfAezbF4xzL5Ins26CUxOn\n5husK1qvKFu9IiIiEk8V2WBNTIRdhYiEKZNZODwQFs6/mpmBw4cBipAokFk/ytjM2Pzjrm27tsyV\nioiISNzoEEFZ0je+sTPsEmJJubkrV2bV1bB+/ekDLub2YA0NBYMwihRIJGHN5hHGpoMGK5VIcVXb\nVWWp0cXOnVprrpSZH+XmTpn5UW7ulFm0VNwerFtugZe8JOwq4mPfviyvf33YVcSPcnNXjsyMgfb2\noMmaa7DSyTSN1Y1AMEFwcBAKNk8qaWndfJypE1MYY6ivqmdd47rlLdBDNpsNu4TYUWZ+lJs7ZeZH\nublTZtFirLVh11AWxphOoKenp4fOzs6wyxGREE3mJvmrn/wVAOsb1/PGq98IwHe/Cx/4AIxOjVHX\nMsKv3fcQf/fU31KVrOLq1qv5q7f+VYhVi4iISKn09vbS1dUF0GWt7S3la+sQQRGpOOc6/wrgmWeC\nCYJF8jRnJjmZPUbSBAMuOpo0oUlEREQuTA2WiFSc0y4wXLfQYO3bF/zXmjxt6yboz/aTSARvk5c3\nX17WGkVERCSe1GDJkvr7+y/8IDmLcnNXzszONeAim4UXXgBLERJF2jeNMj4zDoDBcE3mmrLV50Jr\nzZ0y86Pc3CkzP8rNnTKLFjVYsqQ777wz7BJiSbm5K2dmc9fASpokzTXNwOIJgjmSCVjTMTjfYFUl\nqiJ7DSytNXfKzI9yc6fM/Cg3d8osWiLTYBlj7jLGHDbGTBpjfmiMefkSj32fMeafjTGDsx/fWurx\n4u+ee+4Ju4RYUm7uypVZrpBjZHoEgNbaVhImeBscHAyarKItkEoVad50nKn8FAD16XrWNKwpS32u\ntNbcKTM/ys2dMvOj3Nwps2iJRINljLkD+CRwN3Az8ATwTWNM5jxPuRX4AvAa4JXAC8AjxpjozVCO\nOU1c9KPc3JUrs9MGXCw6/+rEieAi5EXy1NTnydWcIF/MA7C2fu18IxY1WmvulJkf5eZOmflRbu6U\nWbRE5S+GDwF/bq39nLX2APABIAucc3+ntfbXrLV/Zq39ibX2KeB9BNvyS2WrWERiae7wQFg4/wrg\n6achl4MCeVpWZzkxcXx+guCmpk1lr1NERETiKfQGyxhTBXQBj87dZ4OLc30buOUiX6YeqAIGS16g\niKwo5xpwYS387Gezd5o8mXXjDE0NzU8QvLLtynKXKSIiIjEVeoMFZIAkcPKM+08Cay/yNXYCLxI0\nZVJCDz74YNglxJJyc1euzOYOETQYWmtbARgfh5MnwZLHJCyZRRMEEyS4tvXastTmQ2vNnTLzo9zc\nKTM/ys2dMouWKDRY52MAe8EHGfOHwK8Ab7bWzix7VRWmt7ekF7auGMrNXTkyK9oig5PBju6mmiZS\niRQQDLg4dQoKFEgkYM2WgYUJgskqtrRsWfbafGmtuVNmfpSbO2XmR7m5U2bREoUGqx8oAGeO6FrN\n2Xu1TmOM+c/ADuC11tonL+ab3X777XR3d5/2ccstt7Bnz57THvfII4/Q3d191vPvuuuus/6VoLe3\nl+7u7rOuQXD33Xezc+fO0+47cuQI3d3dHDhw4LT777//frZv337afdlslu7ubvbu3Xva/bt372br\n1q1n1XbHHXeUfDseeOCBFbEdUN7/Hw888MCK2A4o3/+PBx54YNm346+//NcUbREIDg+c247BQRgZ\nAWvzVKWLfPdvPsWL330RgMZ0I+317ZH9//HAAw9oXTluxwMPPLAitmNOubZjLre4b8eccmzHXGZx\n34455dqOudzivh1zyrEdc5nFfTsWK+V27N69e/7v/ltvvZW1a9eybdu2sx5fKiY43SlcxpgfAv9q\nrf292c8NcAT4tLX24+d5znbgw8Bt1tp/u4jv0Qn09PT0aNKKSIU62H+Qx55/DIBXbHgFN629CYB/\n+Af44AdhPDdK85ph3vbf/zffOPQNkokkN66+kQe7deiFiIjIStLb20tXVxdAl7W2pLsAU6V8sUtw\nH/BZY0wP8DjBVME64CEAY8zngKPW2g/Pfr4D+BjwLuCIMWZu79e4tXaizLWLSEwsHtG+eILggQOQ\nzwcXGW5dN8GJsRMkE8EEwc2rNpe9ThEREYmvSDRY1tovz17z6mMEhwr+GHidtfbU7EM2AvlFT/kt\ngqmBXz3jpT46+xoiImdZPEFw7hpYxSIcPAhgIVGgdc34/IWIQRMERURExE0UzsECwFr7p9baLdba\nWmvtLdbaf1/0tV+01t656PPLrLXJc3youSqxcx3PKhem3Nwtd2bW2vlrYDWkG6hJ1QDBuVcnT0KR\nAgljWbN5hLGZMQCSJsmVrdFusLTW3CkzP8rNnTLzo9zcKbNoiUyDJdG0nCcArmTKzd1yZzY2M0au\nmAOgrbZt/v7BQRgYgKLNk0hCpuMU2VwWgHQyzZbmLcta16XSWnOnzPwoN3fKzI9yc6fMoiUShwiW\n0xMnnmDqhamwy4iNhusa+P4L3w+7jNhRbu6WO7O5setw+vlXAwPBXqwCedLpIvVrjjP93DSYYE/X\n4sdG0W233RZ2CbGjzPwoN3fKzI9yc6fMoqXiGqxnhp4h15cLuwwRCdHc+VcAR4/C5GRwkeGG5mkm\nTB8FWyBpkqxvXE/CaEe/iIiIXDz95SAiFaW+qp71jevnP9+/HwoFKJKndfUEfRN98xMEo354oIiI\niERPxe3Bes2W1/DSa18adhmx8Y//5x+5/f+5PewyYke5uStXZq21raQSwVtfPg+HDgEUIVGgbf04\nY9Nj84+9siXaAy4A9uzZw5vf/Oawy4gVZeZHublTZn6UmztlFi0V12C11rayun512GXExte/9nXe\n8873hF1G7Cg3d2FkNjQEfX2zEwQTsHrzCM9NjwKQMimuaL2irPX42L17t36pOlJmfpSbO2XmR7m5\nU2bRYqy1YddQFsaYTqCnp6eHzs7OsMsRkQg4eBDe8x544fgkVI/y6x/7BnuzDzJdmKauqo6vvP0r\nZOqjPeRCRERE3PX29tLV1QXQZa3tLeVr6xwsEalY/f0wOhqcf5WuLlC3+gQzhRkAmqqbThuGISIi\nInIx1GCJSMU6cgSmpoIGq7F1ipFcP4ViAYD1jesxxoRcoYiIiMSNGiwRqVj79kGxCJYCrWuynJo4\nRSIRvC1qgqCIiIj4UIMlS9q6dWvYJcSScnNX7sympuDwYZibIJhZP8rYTDBB0GBiMUEQtNZ8KDM/\nys2dMvOj3Nwps2hRgyVL0pXB/Sg3d+XObGgoOAerQJ5kElZ3DM43WFWJKra0bClrPb601twpMz/K\nzZ0y86Pc3CmzaNEUQRGpSE8+CVu3wrG+LNSM8Z4/eZh/Gf08U4UpGtINfOUdX6G1tjXsMkVERGQZ\naIqgiEiJ9fXB2Fgw4KK6pkC6pY/pwjQQTBBsqWkJuUIRERGJIzVYIlKRnn8epqehQIGmtkmGZwYo\n2iIAGxo3aIKgiIiIeFGDJUvau3dv2CXEknJzV+7MnnwSrAVMjra14wxMDcw3VZc1X1bWWi6F1po7\nZeZHublTZn6UmztlFi1qsGRJ9957b9glxJJyc1fOzCYm4OjRYDy7SVgy68cYnx4HIEGCK1qvKFst\nl0przZ0y86Pc3CkzP8rNnTKLFg25kCVls1nq6urCLiN2lJu7cmb2wgtw551w4NA0hdQwb//gDzmw\n6gFGpkeoTlZz/xvu56a1N5WllkulteZOmflRbu6UmR/l5k6ZudOQCwmNflj9KDd35cxscDAY025t\nnmTK0rzpRWbyMwBUJ6vpaOooWy2XSmvNnTLzo9zcKTM/ys2dMosWNVgiUnH6+mB8PBhwUVOXJ9nY\nz1RhCoCWmhaaa5pDrlBERETiSg2WiFScZ54JJggWydHcnmVwcoC5w6U3rNIEQREREfGnBkuWtH37\n9rBLiCXl5q5cmVkL+/YBWDAF2tZMMDQ9BLM9VZwmCILWmg9l5ke5uVNmfpSbO2UWLWqwZEkdHfE5\nFyVKlJu7cmU2OgrHjy9MEGzfNDo/QTBpklzeenlZ6igVrTV3ysyPcnOnzPwoN3fKLFo0RVBEKsrh\nw/De98LTz05RqBrhjh3/wr6av2B4epjaVC33v+F+XrLmJWGXKSIiIstIUwRFREpkboJggTxV6SKr\n1r/IdGEagJpUTawmCIqIiEj0qMESkYpy4kRwoWFLnpr6PLZugOl80GA11TSxqnpVyBWKiIhInKnB\nkiUdOHAg7BJiSbm5K1dmTz0FuRwUKdCyOsvg5CBFigBsWrUpdhMEtdbcKTM/ys2dMvOj3Nwps2hR\ngyVL2rFjR9glxJJyc1eOzAoFePppgCIk8rStHWd0anT+65e3xGvABWit+VBmfpSbO2XmR7m5U2bR\nogZLlrRr166wS4gl5eauHJkNDwcTBIsUSCSgfdMIozNBg5UyKbY0bVn2GkpNa82dMvOj3NwpMz/K\nzZ0yixY1WLIkjf30o9zclSOzwUEYGICizZNIQFvHcSZzkwCkU2k6muP3/01rzZ0y86Pc3CkzP8rN\nnTKLFjVYIlIxBgdhZGRhgmDD2hOnTxBcpV9QIiIicmnUYIlIxTh2DLLZ4BDBulUzFKqH5huslpoW\nGqsbQ65QRERE4k4Nlixp586dYZcQS8rNXTkyO3gQ8vlgRHvr6ixDk0MUbXwnCILWmg9l5ke5uVNm\nfpSbO2UWLWqwZEnZbDbsEmJJublb7sxmZhZPECzQum6MsekxAAyGK1qvWNbvv1y01twpMz/KzZ0y\n86Pc3CmzaDHW2rBrKAtjTCfQ09PTQ2dnZ9jliEiZnTwJ73sfPPGzGXKpIW5/z4/pv/xPOTFxgnQi\nzUd+4SO8/srXh12miIiIlEFvby9dXV0AXdba3lK+dqqULxYHjzwy96/YIlJJcrlgyEXR5kkmoHXz\ncV7MB+dfpVNpDbgQERGRkqi4Bmt8PJgiJiKVxdrgZ79Inup0kbo1J5h6cQqA2mQtm5o2hVyhiIiI\nrAQV12BVVUF1ddhVxMfYWD+NjZmwy4gd5eZuuTPLZmF6GpJVBRpbp5lmmJnCDACtta2xnSDY399P\nJqO15kKZ+VFu7pSZH+XmTplFS8U1WDfdBDfcEHYV8fGBD9zJn/3Zw2GXETvKzd1yZ/bUU9DaCv2T\neZrbJxidHqVgCwCxvMDwnDvvvJOHH9Zac6HM/Cg3d8rMj3Jzp8yipeIarK9/HZ54Iuwq4uPKK+/h\nC18Iu4r4UW7uljuzkyeD618Vi0Uy68YYm1mYILilacvyfeNlds8994RdQuwoMz/KzZ0y86Pc3Cmz\naKm4But734P6+rCriJNOfvzjsGuII+XmbvkzKxTzmASsuayfYzOjAKST6Viff6WpqO6UmR/l5k6Z\n+VFu7pRZtFRcg9XaCk1NYVchImEYm54mYcdov+IFDo8GEwSrk9VsbtoccmUiIiKyUlRcg/WqV0FH\nfE+3EJFLcGjwJKl1T1LV3M/0UNBg1VbVsrFpY8iViYiIyEpRcQ3WO94B2ot68R588EHe+973hl1G\n7Cg3d+XI7O8O7OPkRB+HBsaYLgQNVqYuQ0O6YVm/73LSWnOnzPwoN3fKzI9yc6fMoiURdgESbb29\nJb2wdcVQbu7KkdnQ1BAAYzNj8xMEN66K994rrTV3ysyPcnOnzPwoN3fKLFqMtTbsGsrCGNMJ9PT0\n9OhEQJEKND4zzhd+GowpfOy5xzg0dIikSfL+zvez9eatIVcnIiIi5dTb20tXVxdAl7W2pB1qxR0i\nOJAd4OT4ybDLEJEy65voAyBXyDGRmwCCCYJx34MlIiIi0VJxDdZjzz/G4drDYZchIiHJ5rJM54Pz\nr9LJNJubNUFQRERESkfnYIlIRcnmsswUZwCoq6pjQ+OGkCsSERGRlaTi9mBd1XoV16y5JuwyYmP7\n1u18/DMfD7uM2FFu7sqVWXNNM9969lsAtNW2UZ+O95XHu7u7efjhh8MuI1aUmR/l5k6Z+VFu7pRZ\ntFRcg/WSNS+hc6OGXFysP97+x7xy4yvDLiN2lJu7cmX2ZN+T5Io5gBVxeOC2bdvCLiF2lJkf5eZO\nmflRbu6UWbRoiqCIVJSPPPoRvn3426RMivd3vZ/3vOw9YZckIiIiZbacUwR1DpaIVIyJmQlOTgRT\nRNOpNBsbNUFQRERESksNlohUjMHJQUamRgCoTlazuSX+hwiKiIhItKjBkiXt2bMn7BJiSbm5K0dm\ng5ODjEwHDVZ9un5FTBDUWnOnzPwoN3fKzI9yc6fMokUNlixp9+7dYZcQS8rNXTkyOzF+gomZ4CLD\nmdoMdVV1y/49l5vWmjtl5ke5uVNmfpSbO2UWLRpyISIV4897/py//NFfYjB0X9PNh3/+w2GXJCIi\nIiHQkAsRkUtUtEWeHXwWgKpEFZtWbQq5IhEREVmJ1GCJSEUYnR5lYHIAmJ0guEoTBEVERKT0Ku5C\nw08PPE3VyaqwyxCRMhuaGmJ4chiAdDJNR1NHyBWJiIjISlRxDdZP+37KyNGRsMuIjYc++hDvufs9\nYZcRO8rNXTkym5sg2FDVwLrGdcv6vcpl69atfOYznwm7jFhRZn6Umztl5ke5uVNm0aJDBGVJ17/i\n+rBLiCXl5m65M8sVcmRzWRImwdqGtStigiDAbbfdFnYJsaPM/Cg3d8rMj3Jzp8yipeKmCO75zh5u\nvOnGsMsRkTI7OnqU+354H7WpWm5edzN/8Ko/CLskERERCclyThGsuEMEe4/3MtA8EHYZIlJmw5PD\nNKYbAVjfsD7kakRERGSlqrgG68m+J+lb1Rd2GSJSZvlifv62BlyIiIjIcqm4c7AslqIt6uMiP449\neSz0GuL4odyil1nCJKivqmfTqk1c1XZV2G9FJbN3796wS4gdZeZHublTZn6UmztlFi0Vtwfruvbr\n2Lx+c9hlxMauj+/ijb/8xrDLiB3l5q5cmXU0dbCuYWVMEAS49957efWrXx12GbGizPwoN3fKzI9y\nc6fMoqXihlz09PTQ2dkZdjmxkc1mqatbGdPWykm5uVNmfpSbO2XmR7m5U2Z+lJs7ZeZuOYdcVNwh\nguJGP6x+lJs7ZeZHublTZn6Umztl5ke5uVNm0aIGS0REREREpETUYImIiIiIiJSIGixZ0vbt28Mu\nIZaUmztl5ke5uVNmfpSbO2XmR7m5U2bRogZLltTRoesF+VBu7pSZH+XmTpn5UW7ulJkf5eZOmUWL\npgiKiIiIiEhF0RRBERERERGRGFCDJSIiIiIiUiJqsGRJBw4cCLuEWFJu7pSZH+XmTpn5UW7ulJkf\n5eZOmUWLGixZ0o4dO8IuIZaUmztl5ke5uVNmfpSbO2XmR7m5U2bRoiEXsqQjR45oMo0H5eZOmflR\nbu6UmR/l5k6Z+VFu7pSZOw25kNDoh9WPcnOnzPwoN3fKzI9yc6fM/Cg3d8osWtRgiYiIiIiIlIga\nLBERERERkRJRgyVL2rlzZ9glxJJyc6fM/Cg3d8rMj3Jzp8z8KDd3yixa1GDJkrLZbNglxJJyc6fM\n/Cg3d8rMj3Jzp8z8KDd3yixaNEVQREREREQqiqYIioiIiIiIxIAaLBERERERkRJRgyVL6u/vD7uE\nWFJu7pSZH+XmTpn5UW7ulJkf5eZOmUWLGixZ0p133hl2CbGk3NwpMz/Ke0qKVQAAC1JJREFUzZ0y\n86Pc3CkzP8rNnTKLFjVYsqR77rkn7BJiSbm5U2Z+lJs7ZeZHublTZn6UmztlFi2aIigiIiIiIhVF\nUwRFRERERERiQA2WiIiIiIhIiajBkiU9+OCDYZcQS8rNnTLzo9zcKTM/ys2dMvOj3Nwps2hRgyVL\n6u0t6SGpFUO5uVNmfpSbO2XmR7m5U2Z+lJs7ZRYtGnIhIiIiIiIVRUMuREREREREYkANloiIiIiI\nSImowRIRERERESkRNViypO7u7rBLiCXl5k6Z+VFu7pSZH+XmTpn5UW7ulFm0qMGSJW3bti3sEmJJ\nublTZn6Umztl5ke5uVNmfpSbO2UWLZoiKCIiIiIiFUVTBEVERERERGJADZaIiIiIiEiJqMGSJe3Z\nsyfsEmJJublTZn6Umztl5ke5uVNmfpSbO2UWLZFpsIwxdxljDhtjJo0xPzTGvPwCj3+HMWb/7OOf\nMMa8oVy1VpKdO3eGXUIsKTd3ysyPcnOnzPwoN3fKzI9yc6fMoiUSDZYx5g7gk8DdwM3AE8A3jTGZ\n8zz+FuALwF8ALwP2AHuMMdeXp+LK0d7eHnYJsaTc3CkzP8rNnTLzo9zcKTM/ys2dMouWSDRYwIeA\nP7fWfs5aewD4AJAF7jzP438P+Lq19j5r7UFr7d1AL6AZlSIiIiIiEprQGyxjTBXQBTw6d58NZsd/\nG7jlPE+7Zfbri31ziccvu927d0futS/mectZd9jf2+f1L/Y5KzU3rbXyvb7WmtZauV5fa01rrVyv\nr7WmtVau14/6Wgu9wQIyQBI4ecb9J4G153nOWsfHLzv9wLrTD6wfrTV3Wmt+tNbcaa350Vpzp7Xm\nR2vNndaau1Qo3/XiGMDlKsgXenwNwP79+y+lpvMaGRmht7ek1yi75Ne+mOdd6DGPP/545LZrOV//\nYp+zUnPTWivf62utaa2V6/W11rTWyvX6Wmtaa+V6/VKstUU9QY3TN78IJjgaLzyzhwhmgbdZax9e\ndP9DQJO19i3neM7zwCettZ9edN89wJustTef5/v8KvDXpa1eRERERERi7N3W2i+U8gVD34Nlrc0Z\nY3qAXwIeBjDGmNnPP32ep/3gHF9/7ez95/NN4N3Ac8DUpVUtIiIiIiIxVgNsIegRSir0PVgAxphf\nAT4L/CbwOMFUwbcD11prTxljPgcctdZ+ePbxtwCPAX8I/APwrtnbndbafSFsgoiIiIiISPh7sACs\ntV+evebVx4A1wI+B11lrT80+ZCOQX/T4Hxhj3gX8yezH0wSHB6q5EhERERGR0ERiD5aIiIiIiMhK\nEIUx7SIiIiIiIiuCGiwREREREZESUYO1iDHmOWPMj40xPzLGPBp2PXFhjKmdze7esGuJA2NMkzHm\n34wxvcaYnxhj3hd2TVFnjNlojPknY8yTsz+jbw+7prgwxvytMWbQGPPlsGuJA2PMG40xB4wxB40x\n7w27nrjQOnOj9zQ/+v3pT3+rubuUvkDnYC1ijHkWuMFaOxl2LXFijPmvwJXAEWvtjrDribrZyxBU\nW2unjDG1wJNAl7V2KOTSIssYsxZYba39iTFmDdADXKWf1QszxtwKNAC/Ya39lbDriTJjTBLYB9wK\njBGss1daa4dDLSwGtM7c6D3Nj35/+tPfau4upS/QHqzTGZSJE2PMlcA1wD+GXUtc2MDctdhqZ/9r\nwqonDqy1J6y1P5m9fRLoB1rDrSoerLWPAeNh1xET/wH42ex6myB4X3tdyDXFgtaZG72n+dHvTz/6\nW82bd1+gZuJ0ReC7xph/Ncb8atjFxMQngP8XvcE5mT3M4cfAEeDj1trBsGuKC2NMF5Cw1r4Ydi2y\n4qwHFq+rY8CGkGqRCqH3NDf6/elFf6v58e4LYttgGWN+3hjzsDHmRWNM0RjTfY7H3GWMOWyMmTTG\n/NAY8/ILvOyrrLUvB94EfNgYc8OyFB+SUmc2+/yD1tpDc3ctV+1hWo61Zq0dsda+DLgMeLcxpn25\n6g/DMv18YoxpJbgo+fuXo+6wLVdulaBE2Z3rPWxFH0evNeeulJmt9Pe0xUqV20r//blYKTKrlL/V\nFivhz6h3XxDbBguoJ7gg8V2c4xegMeYO4JPA3cDNwBPAN01wQeO5x/y2CU5c6zXGVFtrT0Cw655g\nN2rX8m9GWZU0M4LzFN5pgmNUPwG8zxjzR8u/GWVX8rU2d//sxbR/Avz88m5C2ZU8M2NMGvga8N+s\ntf9ajo0IwbKttQpwydkR7L3auOjzDcDx5So4IkqRW6UpSWYV8p62WEnX2gr+/blYKTJ7JZXxt9pi\nJVlrl9QXWGtj/0GwC6/7jPt+CHxq0ecGOArsOM9r1AENs7cbgH8nOHEy9O2LamZnPPc3gHvD3q44\n5AasWbTWmoCfEpxEGfr2RTWz2cfsBv5L2NsTt9xmH/ca4Cthb1PUswOSwEFg3ezvgf1AS9jbE/Xc\nFn2totbZpWZWae9ppcit0n5/liKzMx5fEX+rlSI3LrEviPMerPMyxlQRdJnzIxVtkNC3gVvO87Q1\nwF5jzI+A7wMPWWt7lrvWqPDMrOJ55tYB/MvsWnuM4If8yeWuNSp8MjPGvAp4B/DmRXtnVtQhvBfi\n+zNqjPkW8CXgDcaYI8aYVyx3rVFzsdlZawvA7wPfBXqBT9gKnk7msua0zgIXm5ne007nsNYq+vfn\nYvq7zY9DbpfUF6RKU27kZAj+JfLkGfefJJiichZr7WHgZctcV5Q5Z7aYtfazy1FUDPistX8j2CVd\nqXwy+x4r9/3qYnn9jFprX7ucRcXERWdnrf174O/LVFfUueSmdRa4qMz0nnaWi82t0n9/Lubzu7RS\n/1Zb7GLX2iX1BStyD9YSDCv8hOVloMz8KDd3ysyPcvOn7PwoN3fKzI9yc6fM/JQ0t5XaYPUDBYLd\ne4ut5uyOVQLKzI9yc6fM/Cg3f8rOj3Jzp8z8KDd3ysxPWXJbkQ2WtTZHcFX0X5q7zxhjZj//flh1\nRZky86Pc3CkzP8rNn7Lzo9zcKTM/ys2dMvNTrtxie/yvMaYeuJKFef6XG2NuAgattS8A9wGfNcb0\nAI8DHyKYCPJQCOVGgjLzo9zcKTM/ys2fsvOj3NwpMz/KzZ0y8xOJ3MIen3gJYxdvJRi9WDjj4y8X\nPea3geeASeAHwM+FXbcyi9+HclNmyi36H8pOuSmzaH8oN2VWSbmZ2W8iIiIiIiIil2hFnoMlIiIi\nIiISBjVYIiIiIiIiJaIGS0REREREpETUYImIiIiIiJSIGiwREREREZESUYMlIiIiIiJSImqwRERE\nRERESkQNloiIiIiISImowRIRERERESkRNVgiIiIiIiIlogZLRERERESkRNRgiYhIRTDGrDHG3G+M\necYYM2WMed4Y87Ax5hfDrk1ERFaOVNgFiIiILDdjzGbg+8Ag8J+BnwJVwOuBXcD14VUnIiIribHW\nhl2DiIjIsjLG/CNwI3C1tXbqjK+tstaOhlOZiIisNDpEUEREVjRjTAvwOmDXmc0VgJorEREpJTVY\nIiKy0l0JGOBg2IWIiMjKpwZLRERWOjP7Xx0TLyIiy04NloiIrHRPEzRX14VdiIiIrHwaciEiIive\noiEX11hrJ8/4WpO1diScykREZKXRHiwREakEvw0kgceNMW81xlxpjLnWGPO7BOPbRURESkJ7sERE\npCIYY9YAHwHeCKwDTgE9wH3W2n8JszYREVk51GCJiIiIiIiUiA4RFBERERERKRE1WCIiIiIiIiWi\nBktERERERKRE1GCJiIiIiIiUiBosERERERGRElGDJSIiIiIiUiJqsEREREREREpEDZaIiIiIiEiJ\nqMESEREREREpETVYIiIiIiIiJaIGS0REREREpETUYImIiIiIiJTI/w/lpbxThPvzKgAAAABJRU5E\nrkJggg==\n", + "text/plain": [ + "" + ] + }, "metadata": {}, - "source": [ - "The previous example failed to generalized well to test data because we naively used the default parameters of the `SVC` class:" + "output_type": "display_data" + } + ], + "source": [ + "plot_validation_curves(Cs, train_scores, test_scores)\n", + "plt.ylabel(\"score for SVC(C=C, gamma=1e-3)\")\n", + "plt.xlabel(\"C\")\n", + "plt.text(1e-3, 0.5, \"Underfitting\", fontsize=16, ha='center', va='bottom')\n", + "plt.text(1e3, 0.5, \"Few Overfitting\", fontsize=16, ha='center', va='bottom')\n", + "plt.title('Validation curves for the C parameter');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Doing this procedure several for each parameter combination is tedious, hence it's possible to automate the procedure by computing the test score for all possible combinations of parameters using the `GridSearchCV` helper." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from sklearn.grid_search import GridSearchCV\n", + "#help(GridSearchCV)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'C': array([ 0.1, 1. , 10. , 100. ]),\n", + " 'gamma': array([ 1.00000000e-04, 1.00000000e-03, 1.00000000e-02,\n", + " 1.00000000e-01, 1.00000000e+00])}\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "svc" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 17, - "text": [ - "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,\n", - " kernel='rbf', max_iter=-1, probability=False, random_state=None,\n", - " shrinking=True, tol=0.001, verbose=False)" - ] - } - ], - "prompt_number": 15 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's try again with another parameterization:" + } + ], + "source": [ + "from pprint import pprint\n", + "svc_params = {\n", + " 'C': np.logspace(-1, 2, 4),\n", + " 'gamma': np.logspace(-4, 0, 5),\n", + "}\n", + "pprint(svc_params)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As Grid Search is a costly procedure, let's do the some experiments with a smaller dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "n_subsamples = 500\n", + "X_small_train, y_small_train = X_train[:n_subsamples], y_train[:n_subsamples]" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 189 ms, sys: 39.4 ms, total: 228 ms\n", + "Wall time: 617 ms\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "svc_2 = SVC(kernel='rbf', C=100, gamma=0.001).fit(X_train, y_train)\n", - "svc_2" - ], - "language": "python", + } + ], + "source": [ + "gs_svc = GridSearchCV(SVC(), svc_params, cv=3, n_jobs=-1)\n", + "\n", + "%time _ = gs_svc.fit(X_small_train, y_small_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "({'C': 10.0, 'gamma': 0.001}, 0.976)" + ] + }, + "execution_count": 43, "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 18, - "text": [ - "SVC(C=100, cache_size=200, class_weight=None, coef0=0.0, degree=3,\n", - " gamma=0.001, kernel='rbf', max_iter=-1, probability=False,\n", - " random_state=None, shrinking=True, tol=0.001, verbose=False)" - ] - } - ], - "prompt_number": 16 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "svc_2.score(X_train, y_train)" - ], - "language": "python", + "output_type": "execute_result" + } + ], + "source": [ + "gs_svc.best_params_, gs_svc.best_score_" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[mean: 0.16200, std: 0.02588, params: {'C': 0.10000000000000001, 'gamma': 0.0001},\n", + " mean: 0.71000, std: 0.03978, params: {'C': 0.10000000000000001, 'gamma': 0.001},\n", + " mean: 0.12800, std: 0.00161, params: {'C': 0.10000000000000001, 'gamma': 0.01},\n", + " mean: 0.12800, std: 0.00161, params: {'C': 0.10000000000000001, 'gamma': 0.10000000000000001},\n", + " mean: 0.12800, std: 0.00161, params: {'C': 0.10000000000000001, 'gamma': 1.0},\n", + " mean: 0.93800, std: 0.00586, params: {'C': 1.0, 'gamma': 0.0001},\n", + " mean: 0.96600, std: 0.00295, params: {'C': 1.0, 'gamma': 0.001},\n", + " mean: 0.26600, std: 0.02009, params: {'C': 1.0, 'gamma': 0.01},\n", + " mean: 0.12800, std: 0.00161, params: {'C': 1.0, 'gamma': 0.10000000000000001},\n", + " mean: 0.12800, std: 0.00161, params: {'C': 1.0, 'gamma': 1.0},\n", + " mean: 0.97000, std: 0.00466, params: {'C': 10.0, 'gamma': 0.0001},\n", + " mean: 0.97600, std: 0.00041, params: {'C': 10.0, 'gamma': 0.001},\n", + " mean: 0.32600, std: 0.02186, params: {'C': 10.0, 'gamma': 0.01},\n", + " mean: 0.12800, std: 0.00161, params: {'C': 10.0, 'gamma': 0.10000000000000001},\n", + " mean: 0.12800, std: 0.00161, params: {'C': 10.0, 'gamma': 1.0},\n", + " mean: 0.96000, std: 0.00730, params: {'C': 100.0, 'gamma': 0.0001},\n", + " mean: 0.97600, std: 0.00041, params: {'C': 100.0, 'gamma': 0.001},\n", + " mean: 0.32600, std: 0.02186, params: {'C': 100.0, 'gamma': 0.01},\n", + " mean: 0.12800, std: 0.00161, params: {'C': 100.0, 'gamma': 0.10000000000000001},\n", + " mean: 0.12800, std: 0.00161, params: {'C': 100.0, 'gamma': 1.0}]" + ] + }, + "execution_count": 44, "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 19, - "text": [ - "1.0" - ] - } - ], - "prompt_number": 17 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "svc_2.score(X_test, y_test)" - ], - "language": "python", + "output_type": "execute_result" + } + ], + "source": [ + "gs_svc.grid_scores_" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "mean: 0.16200, std: 0.02588, params: {'C': 0.10000000000000001, 'gamma': 0.0001}" + ] + }, + "execution_count": 45, "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 20, - "text": [ - "0.99333333333333329" - ] - } - ], - "prompt_number": 18 - }, - { - "cell_type": "markdown", + "output_type": "execute_result" + } + ], + "source": [ + "first_score = gs_svc.grid_scores_[0]\n", + "first_score" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'cv_validation_scores': array([ 0.17647059, 0.1257485 , 0.18404908]),\n", + " 'mean_validation_score': 0.162,\n", + " 'parameters': {'C': 0.10000000000000001, 'gamma': 0.0001}}" + ] + }, + "execution_count": 46, "metadata": {}, - "source": [ - "In this case the model is almost perfectly able to generalize, at least according to our random train, test split." + "output_type": "execute_result" + } + ], + "source": [ + "dict(vars(first_score))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's define a couple of helper function to help us introspect the details of the grid search outcome:" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def display_scores(params, scores, append_star=False):\n", + " \"\"\"Format the mean score +/- std error for params\"\"\"\n", + " params = \", \".join(\"{0}={1}\".format(k, v)\n", + " for k, v in params.items())\n", + " line = \"{0}:\\t{1:.3f} (+/-{2:.3f})\".format(\n", + " params, np.mean(scores), sem(scores))\n", + " if append_star:\n", + " line += \" *\"\n", + " return line\n", + "\n", + "def display_grid_scores(grid_scores, top=None):\n", + " \"\"\"Helper function to format a report on a grid of scores\"\"\"\n", + " \n", + " grid_scores = sorted(grid_scores, key=lambda x: x[1], reverse=True)\n", + " if top is not None:\n", + " grid_scores = grid_scores[:top]\n", + " \n", + " # Compute a threshold for staring models with overlapping\n", + " # stderr:\n", + " _, best_mean, best_scores = grid_scores[0]\n", + " threshold = best_mean - 2 * sem(best_scores)\n", + " \n", + " for params, mean_score, scores in grid_scores:\n", + " append_star = mean_score + 2 * sem(scores) > threshold\n", + " print(display_scores(params, scores, append_star=append_star))" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "C=10.0, gamma=0.001:\t0.976 (+/-0.000) *\n", + "C=100.0, gamma=0.001:\t0.976 (+/-0.000) *\n", + "C=10.0, gamma=0.0001:\t0.970 (+/-0.003) *\n", + "C=1.0, gamma=0.001:\t0.966 (+/-0.002)\n", + "C=100.0, gamma=0.0001:\t0.960 (+/-0.005)\n", + "C=1.0, gamma=0.0001:\t0.938 (+/-0.004)\n", + "C=0.1, gamma=0.001:\t0.711 (+/-0.028)\n", + "C=10.0, gamma=0.01:\t0.326 (+/-0.015)\n", + "C=100.0, gamma=0.01:\t0.326 (+/-0.015)\n", + "C=1.0, gamma=0.01:\t0.266 (+/-0.014)\n", + "C=0.1, gamma=0.0001:\t0.162 (+/-0.018)\n", + "C=0.1, gamma=0.01:\t0.128 (+/-0.001)\n", + "C=0.1, gamma=0.1:\t0.128 (+/-0.001)\n", + "C=0.1, gamma=1.0:\t0.128 (+/-0.001)\n", + "C=1.0, gamma=0.1:\t0.128 (+/-0.001)\n", + "C=1.0, gamma=1.0:\t0.128 (+/-0.001)\n", + "C=10.0, gamma=0.1:\t0.128 (+/-0.001)\n", + "C=10.0, gamma=1.0:\t0.128 (+/-0.001)\n", + "C=100.0, gamma=0.1:\t0.128 (+/-0.001)\n", + "C=100.0, gamma=1.0:\t0.128 (+/-0.001)\n" ] - }, - { - "cell_type": "heading", - "level": 2, + } + ], + "source": [ + "display_grid_scores(gs_svc.grid_scores_, top=20)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One can see that Support Vector Machine with RBF kernel are very sensitive wrt. the `gamma` parameter (the badwith of the kernel) and to some lesser extend to the `C` parameter as well. If those parameter are not grid searched, the predictive accurracy of the support vector machine is almost no better than random guessing!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By default, the `GridSearchCV` class refits a final model on the complete training set with the best parameters found by during the grid search:" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.98666666666666669" + ] + }, + "execution_count": 49, "metadata": {}, - "source": [ - "Cross Validation" - ] - }, - { - "cell_type": "markdown", + "output_type": "execute_result" + } + ], + "source": [ + "gs_svc.score(X_test, y_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Evaluating this final model on the real test set will often yield a better score because of the larger training set, especially when the training set is small and the number of cross validation folds is small (`cv=3` here)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise**:\n", + "\n", + "1. Find a set of parameters for an `sklearn.tree.DecisionTreeClassifier` on the `X_small_train` / `y_small_train` digits dataset to reach at least 75% accuracy on the sample dataset (500 training samples)\n", + "2. In particular you can grid search good values for `criterion`, `min_samples_split` and `max_depth`\n", + "3. Which parameter(s) seems to be the most important to tune?\n", + "4. Retry with `sklearn.ensemble.ExtraTreesClassifier(n_estimators=30)` which is a randomized ensemble of decision trees. Does the parameters that make the single trees work best also make the ensemble model work best?\n", + "\n", + "Hints:\n", + "\n", + "- If the outcome of the grid search is too instable (overlapping std errors), increase the number of CV folds with `cv` constructor parameter. The default value is `cv=3`. Increasing it to `cv=5` or `cv=10` often yield more stable results but at the price of longer evaluation times.\n", + "- Start with a small grid, e.g. 2 values `criterion` and 3 for `min_samples_split` only to avoid having to wait for too long at first.\n", + "\n", + "Type:\n", + "\n", + " from sklearn.tree.DecisionTreeClassifier\n", + " DecisionTreeClassifier? # to read the docstring and know the list of important parameters\n", + " print(DecisionTreeClassifier()) # to show the list of default values\n", + "\n", + " from sklearn.ensemble.ExtraTreesClassifier\n", + " ExtraTreesClassifier? \n", + " print(ExtraTreesClassifier())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Solution**:" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n", + " max_features=None, max_leaf_nodes=None,\n", + " min_impurity_split=1e-07, min_samples_leaf=1,\n", + " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", + " presort=False, random_state=None, splitter='best')" + ] + }, + "execution_count": 50, "metadata": {}, - "source": [ - "Cross Validation is a procedure to repeat the train / test split several times to as to get a more accurate estimate of the real test score by averaging the values found of the individual runs.\n", - "\n", - "The `sklearn.cross_validation` package provides many strategies to compute such splits using class that implement the python iterator API:" + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.tree import DecisionTreeClassifier\n", + "DecisionTreeClassifier()" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 1.51 s, sys: 259 ms, total: 1.77 s\n", + "Wall time: 1.81 s\n", + "min_samples_split=2, criterion=entropy, max_depth=7:\t0.803 (+/-0.008) *\n", + "min_samples_split=2, criterion=entropy, max_depth=None:\t0.802 (+/-0.009) *\n", + "min_samples_split=20, criterion=entropy, max_depth=None:\t0.783 (+/-0.009) *\n", + "min_samples_split=10, criterion=entropy, max_depth=7:\t0.781 (+/-0.008) *\n", + "min_samples_split=10, criterion=entropy, max_depth=None:\t0.779 (+/-0.006) *\n", + "min_samples_split=2, criterion=gini, max_depth=7:\t0.775 (+/-0.007) *\n", + "min_samples_split=20, criterion=entropy, max_depth=7:\t0.775 (+/-0.007) *\n", + "min_samples_split=20, criterion=gini, max_depth=7:\t0.768 (+/-0.008)\n", + "min_samples_split=10, criterion=entropy, max_depth=5:\t0.767 (+/-0.008)\n", + "min_samples_split=2, criterion=gini, max_depth=None:\t0.766 (+/-0.007)\n", + "min_samples_split=2, criterion=entropy, max_depth=5:\t0.766 (+/-0.008)\n", + "min_samples_split=10, criterion=gini, max_depth=7:\t0.765 (+/-0.010)\n", + "min_samples_split=10, criterion=gini, max_depth=None:\t0.764 (+/-0.008)\n", + "min_samples_split=20, criterion=gini, max_depth=None:\t0.761 (+/-0.009)\n", + "min_samples_split=20, criterion=entropy, max_depth=5:\t0.733 (+/-0.009)\n", + "min_samples_split=2, criterion=gini, max_depth=5:\t0.659 (+/-0.010)\n", + "min_samples_split=20, criterion=gini, max_depth=5:\t0.654 (+/-0.010)\n", + "min_samples_split=10, criterion=gini, max_depth=5:\t0.651 (+/-0.010)\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from sklearn.cross_validation import ShuffleSplit\n", - "\n", - "cv = ShuffleSplit(n_samples, n_iter=3, test_size=0.1,\n", - " random_state=0)\n", - "\n", - "for cv_index, (train, test) in enumerate(cv):\n", - " print(\"# Cross Validation Iteration #%d\" % cv_index)\n", - " print(\"train indices: {0}...\".format(train[:10]))\n", - " print(\"test indices: {0}...\".format(test[:10]))\n", - " \n", - " svc = SVC(kernel=\"rbf\", C=1, gamma=0.001).fit(X[train], y[train])\n", - " print(\"train score: {0:.3f}, test score: {1:.3f}\\n\".format(\n", - " svc.score(X[train], y[train]), svc.score(X[test], y[test])))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "# Cross Validation Iteration #0\n", - "train indices: [ 353 5 58 1349 1025 575 1074 1110 1745 689]...\n", - "test indices: [1081 1707 927 713 262 182 303 895 933 1266]...\n", - "train score: 0.999, test score: 0.989\n" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "# Cross Validation Iteration #1\n", - "train indices: [1336 608 977 22 526 1587 1130 569 1481 962]...\n", - "test indices: [1014 755 1633 117 181 501 948 1076 45 659]...\n", - "train score: 0.998, test score: 0.994\n" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "# Cross Validation Iteration #2\n", - "train indices: [ 451 409 911 1551 133 691 1306 111 852 825]...\n", - "test indices: [ 795 697 655 573 412 743 635 851 1466 1383]...\n", - "train score: 0.999, test score: 0.994\n" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n" - ] - } - ], - "prompt_number": 19 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Instead of doing the above manually, `sklearn.cross_validation` provides a little utility function to compute the cross validated test scores automatically:" + } + ], + "source": [ + "tree = DecisionTreeClassifier()\n", + "\n", + "tree_params = {\n", + " 'criterion': ['gini', 'entropy'],\n", + " 'min_samples_split': [2, 10, 20],\n", + " 'max_depth': [5, 7, None],\n", + "}\n", + "\n", + "cv = ShuffleSplit(n_subsamples, n_iter=50, test_size=0.1)\n", + "gs_tree = GridSearchCV(tree, tree_params, n_jobs=-1, cv=cv)\n", + "\n", + "%time gs_tree.fit(X_train[:n_samples], y_train[:n_samples])\n", + "display_grid_scores(gs_tree.grid_scores_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As the dataset is quite small and decision trees are prone to overfitting, we need cross validate many times (e.g. `n_iter=50`) to get standard error of the mean test score below `0.010`.\n", + "\n", + "At that level of precision one can observe that the `entropy` split criterion yields slightly better predictions than `gini`. One can also observe that traditional regularization strategies (limiting the depth of the tree or giving a minimum number of samples to allow for a node to split does not work well on this problem.\n", + "\n", + "Indeed, the unregularized decision tree (`max_depth=None` and `min_samples_split=2`) is among the top performers while it is clearly overfitting:" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train score: 1.000\n", + "Test score: 0.802\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from sklearn.cross_validation import cross_val_score\n", - "\n", - "svc = SVC(kernel=\"rbf\", C=1, gamma=0.001)\n", - "cv = ShuffleSplit(n_samples, n_iter=10, test_size=0.1,\n", - " random_state=0)\n", - "\n", - "test_scores = cross_val_score(svc, X, y, cv=cv, n_jobs=2)\n", - "test_scores" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 22, - "text": [ - "array([ 0.98888889, 0.99444444, 0.99444444, 0.99444444, 0.99444444,\n", - " 0.99444444, 0.98888889, 0.99444444, 0.98888889, 1. ])" - ] - } - ], - "prompt_number": 20 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from scipy.stats import sem\n", - "\n", - "def mean_score(scores):\n", - " \"\"\"Print the empirical mean score and standard error of the mean.\"\"\"\n", - " return (\"Mean score: {0:.3f} (+/-{1:.3f})\").format(\n", - " np.mean(scores), 2 * sem(scores))" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 21 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print(mean_score(test_scores))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Mean score: 0.993 (+/-0.002)\n" - ] - } - ], - "prompt_number": 22 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Exercise:** \n", - "\n", - "- Perform 50 iterations of cross validation with randomly sampled folds of 500 training samples and 500 test samples randomly sampled from `X` and `y` (use `sklearn.cross_validation.ShuffleSplit`).\n", - "- Try with `SVC(C=1, gamma=0.01)`\n", - "- Plot distribution the test error using an histogram with 50 bins.\n", - "- Try to increase the training size\n", - "- Retry with `SVC(C=10, gamma=0.005)`, then `SVC(C=10, gamma=0.001)` with 500 samples.\n", - "\n", - "- Optional: use a smoothed kernel density estimation `scipy.stats.kde.gaussian_kde` instead of an histogram to visualize the test error distribution.\n", - "\n", - "Hints, type:\n", - "\n", - " from sklearn.cross_validation import ShuffleSplit\n", - " ShuffleSplit? # to read the docstring of the shuffle split\n", - " plt.hist? # to read the docstring of the histogram plot\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "cv = ShuffleSplit(n_samples, n_iter=50, train_size=500, test_size=500,\n", - " random_state=0)\n", - "%time scores = cross_val_score(SVC(C=10, gamma=0.005), X, y, cv=cv)\n", - "print(mean_score(scores))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "CPU times: user 4.1 s, sys: 50.9 ms, total: 4.15 s\n", - "Wall time: 4.23 s\n", - "Mean score: 0.905 (+/-0.008)\n" - ] - } - ], - "prompt_number": 23 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from scipy.stats.kde import gaussian_kde\n", - "_ = plt.hist(scores, range=(0, 1), bins=30, alpha=0.2)\n", - "x = np.linspace(0, 1, 1000)\n", - "smoothed = gaussian_kde(scores).evaluate(x)\n", - "plt.plot(x, smoothed, label=\"Smoothed distribution\")\n", - "top = np.max(smoothed)\n", - "plt.vlines([np.mean(scores)], 0, top, color='r', label=\"Mean test score\")\n", - "plt.vlines([np.median(scores)], 0, top, color='b', linestyles='dashed',\n", - " label=\"Median test score\")\n", - "plt.legend(loc='best')\n", - "_ = plt.title(\"Cross Validated test scores distribution\")" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "display_data", - "png": 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EFgHPmlkt8AjRyBLuPge4CXgMWAg82uLc9MXmNxF9tUI18DRREdJyNKi1c1v2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\nW0FM72VjROqvZvaCmX0+Gx2SwlCw8/z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- "text": [ - "" - ] - } - ], - "prompt_number": 24 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Model Selection with Grid Search" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Cross Validation makes it possible to evaluate the performance of a model class and its hyper parameters on the task at hand.\n", - "\n", - "A natural extension is thus to run CV several times for various values of the parameters so as to find the best. For instance, let's fix the SVC parameter to `C=10` and compute the cross validated test score for various values of `gamma`:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "n_gammas = 10\n", - "n_iter = 5\n", - "cv = ShuffleSplit(n_samples, n_iter=n_iter, train_size=500, test_size=500,\n", - " random_state=0)\n", - "\n", - "train_scores = np.zeros((n_gammas, n_iter))\n", - "test_scores = np.zeros((n_gammas, n_iter))\n", - "gammas = np.logspace(-7, -1, n_gammas)\n", - "\n", - "for i, gamma in enumerate(gammas):\n", - " for j, (train, test) in enumerate(cv):\n", - " clf = SVC(C=10, gamma=gamma).fit(X[train], y[train])\n", - " train_scores[i, j] = clf.score(X[train], y[train])\n", - " test_scores[i, j] = clf.score(X[test], y[test])" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 25 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def plot_validation_curves(param_values, train_scores, test_scores):\n", - " for i in range(train_scores.shape[1]):\n", - " plt.semilogx(param_values, train_scores[:, i], alpha=0.4, lw=2, c='b')\n", - " plt.semilogx(param_values, test_scores[:, i], alpha=0.4, lw=2, c='g')\n", - "\n", - "plot_validation_curves(gammas, train_scores, test_scores)\n", - "plt.ylabel(\"score for SVC(C=10, gamma=gamma)\")\n", - "plt.xlabel(\"gamma\")\n", - "plt.text(1e-6, 0.5, \"Underfitting\", fontsize=16, ha='center', va='bottom')\n", - "plt.text(1e-4, 0.5, \"Good\", fontsize=16, ha='center', va='bottom')\n", - "plt.text(1e-2, 0.5, \"Overfitting\", fontsize=16, ha='center', va='bottom')\n", - "plt.title('Validation curves for the gamma parameter');" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "display_data", - "png": 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tEv3OSR/cPLHzvPqYQDMqxkr6QosQQ8ON16MFAE3ohfcQWl35JBrGvJxCdVYQ\nZo62nc1c/gXHFX87Qt+WOh75bxcy1iY0dI1x9eeepfnghN+2GRUnzn/GEPCUy7gHdRyLY3P/Q1ZK\nfJWTkD9J6TDkAFq0sGR6QZViJZ3nS4Vq21yysh4NZQZv2pOoN61kPqe/KdmC3qR1ot60FtSLNk5h\nntQwsX0evfmK5j72UBBrA95L90vodeXL1fYO23lefUygGcZJIEIS+CngTWh+WRy9AO8C7kb7KuXQ\n0ESYOzNcnOM072/irO828ro/H6B95xgHLt3E4x89ncnGHM0H+nndnz9D/dFdqBgLlWMH0HBpI9oX\n7bZqXKwlK7PNE1k/x9ujCfnFbSnKTsg3jMXCVwVfhuaTClrMs32u6kzJyjq0lc6WyOpQNd1KoZBA\nKHyPEqiHPFwrutEw6MWo1+3fZwuHGssDE2iGcQL4jv9vA65GBQqoCHkUuBMVaHG0LP98QghTxVsD\njUfq2XzvOi790hBn3NoPrObVa1p56sMd5JPQtusAr/38N6kb6EPL+htQT9MDaHj0FDQc8t2Faovh\nwzOFDvnHi7HZEvJzFLWiwBLyjWWIZGUt6k1rQz3ZTwGPzlUdLVlZhYq7Myl4lA+i+agJNKc0eNeF\nwnUlgX6PJv02r6IpDS8vxPEYSxcTaEbFWG4ucd9Y9hx0wuWLKEyEfBjYDtztHAN+uzNRYRWqMoeB\nNHVHm9j4yCbO+k6Oi2/qJTXSCUzyzM85Xn77FiaahpCpu3jzZ77Nqt1XUOjLdAgVfuejF/kx4Nsu\n4wanj3F2my9AQv5MvcFGVmpC/nI7z2uBxba5v5m5DM01EzTR/y6XcYfLeG8D+j0+l0Jl51E0bNqF\netq3oqFR8UsbWhiUQJtSPxrSGqrFYtt8JbIQumW2u2rDqHlEaAJeB7wRrdACFV3PArcBTzin8/OJ\nsNZvG2YEGASE5FAbGx/ZzKYHG7n07/tp39kGJMjH9vDor23gpXfX0332U/Sdejs3yiHg/ahoyqFe\nuacoNLfNAz8qFmc6WJCsREVXsTcsddx7CkTniSzVlmLRE/INYyngvWUPSVZ2od60VcB7JCtPo+Jp\nRo+xn/T+YcnK4+gN2AX+/dehN3LPALegN4BbULE2hXrN16FpDauL92sYpTAPmrHs8En/W9CL72Vo\nrgjoBfQh4BbnOBTZvhHtnbTNr9L2D4nRTjb852Y6nlnH+d8YY+tdacT1A3uYSvfzgy808sL7Yoyt\nGqWu927ZV9NSAAAgAElEQVQ+s7oN9c4JmiR8p8u4Xp+k/E40NHKXy7gXp41X78rfgN5ll5OQXyof\nbMkn5BvGUsN70y5Fv7cxtMfZXeW2vJGsxIDT/fvDfJwTaCPqZ1zGDfsinN9Hb9L2oVM+3bSgB2Is\nOSzEaRgRRGhFQw/XoBfNetSzdABN+t/uXKHSSoQ4Gua4BPUm54AuYpOtrH9sG51Pb2PrduGcb+dI\njfSi+SP7OHrqi/ztM1uZalgHTHDRVx7h/R89G70zdhR6LuW8R+z9aP+kp4pDG5KVkA8XppgZZoZQ\npMu4MQzDWHAkKx3oDV07BQ/7w/PJv/Tzgl6EzhACeu15GQ1//jQFL9tjwNdcxg0t1PiNpYcJNKNi\n1ErOgp8b81Q0L+RS9OKYQi+EO4HbgWecm9avCBFORRtThomSDxGbTNL59HlsePQ8Ol5o5pxvDdG2\n+4jfz7PAM9zo+oC3A+2QH+EX3reHs74fEocHUK/ZYTjWvuJ9aAjkuIpNycom4M1+vIf4HGNu0N2+\n4EYyZqRWzvPlxFK1ufemXYJWW4bv810u4w7Ocz8dqFA7lUJeaDt6fRoCHkSvBbsXaOhzj2mJ2nw5\nYzloxopFhNVo77IL0DyPdf6lLlRM3QPsDPllkfe1o3lm4S63D8n1s+7xy9n4yMU0HVrDqT8ZYPN9\nuxG3A628fNZl3IAIq4D3Ak00HRznhusmWfPS2X4/zwEPhapM30vpTag4O4pWbkXF2dloi48YWoK/\nnSFev3AWMgxjPvjctEd9btp1qEf83ZKVad/tMvbTBfzYTyV1ARrajKPXnH2oaFuDzvtpGDNiHjSj\nZhAhhYYuz0ZF2SY0oX8IrZR8DHjMOQ6UeG8azUc7F71AjgH72LL9NXQ+8zrq+9bS+cwQ227ZQ93A\nk8BPgJeOCS5hHVoBmua0HyX42Z+fpP6ooO057nIZt3fa52XltWj4dAydIHnArxc/jkv8pk8Aj6zU\nKkrDWIr43LKLUa9XDE01uNtl3P4T2Fcd8BG/9AB3oVM+mbd8GWMhTmNF4KsrzwFOQ+88N6GhycNo\nftnTwFPOcfyEyNo241xUFKWBPDK5k3O/dT5Nh95CeqCDhu5xtt26jzUvbQduBfZN83ZpOPSNpPvr\nueimFq7/VA+JcYd6vu51GTc+7TOzchaaz5IHbg4hEh9CuQ4VmXn/3hcWyEyGYSwwPkf0OvS6A5r8\n/+B8+xdKVt4B/DZaQPAosMNl3NcWcKjGEsMEmlExFjtnQYQ6tB/Z2WiYMAizMAXTQTSs+IxzlEy2\nFWEDGs7U6qr00cNc+vcbEPcuEuOriU/kOeWBg2y5+3vEJ7/vMq63xD7OA15Hx7MdXPzleq76y73E\n8uOouHrluO2nV2zeHQSYv4u+Hg3FTgA/dhm3b/pnWZ5ItTGbV59as7n3pl2EetPiqMf+7uLv7xz7\nuBL4BFoMtAO9dt1UfHNXKWrN5ssBy0Ezlh3eWxbyypLAWjSM2Y/evfaivYaed46SFzcRmtECgFMB\nWP3CJFf8TSPjrR9jqm4tAKt2HuG0O/6D1r3fdBk3XGIfAlxOcvgyNt9zOhd8fYSLb9oL7EUvzse/\nRys234KKs6cj4qwVrdRsRS/ut5YSg4ZhLD18+5rHJSuvop7xTuAdkpUXgQfK7DHY65fVaEU36E3n\nvEOmxsrBPGjGksHPjflf0AvYejQk2YMm2R9FG77ucI7Skxzr+y9Gc7/ibLm7mdf8nziTTa+hf8tm\nQEiO9rL+sW+x7bavztS2wvdRu5b2HZez8eEzOP8buznr+wfQi/HzJd+jFZvvRb11e1ER5vxcftf7\nY+r260dOyECGYSwqPof0QjRlIo5Wi9/jMm7PHO9rRz1o16BTyd2HhkqfquyIjcXCPGjGcqMTzc9a\nhVZiTqDhzCeBPcUVmVFE2AZcSWKkmTNvXst5/5okMbGRQ5ecQS4VI5c6SmPXrWy+98vuSw8e38W/\nsJ8kiZG3csqDr2fVK2u58J+fZ+s9L6DtMwZKvqdQsdmOCsk7vDg7Hc1fiaNtNu5YqLk3DcOoPj43\n9UnJym7Um7YWeJtk5SX0Bm6mkGUfWmGeQOfmTWAzChhzYALNKMki5SxsRqdE2Q+8BDzpHLPOjydC\nB/A6Wndv5oybN7B1exNtu5Lsu+pMhtcmmWjqI598gOZ9X3G3fW7WcIIIDax+4edY9+TrqO+Nc/kX\nnmDt09uBJ+eosrzCj30M7W80IVm5BLjcv/4scP9clZqWJ1J9zObVZznY3GVcn2Tle2j/xcvRfNlN\nkpV7SvU3cxmXl6zsB8bRvodNFAoPKs5ysPlKxASasZQ43f/d4RyzlqCLUA9cwcaHLuf0H21izbPN\nrH1qnK7ztvD8BxqYaOxjdM1LkP+ae/pDT871wdKyfxWnP/PrNB08m7q+Ma78mztpf+UWl3HHVYZO\ne59WbF6En2MTGJKsXIMWNzg0jPF0GcduGEYN4W+4npas7EG9aeuAt0pWXkZvyIpTKLrR5rcxvLdd\nspKYz2wFxsrCBJpRkkW629rq/+6aaQMRYqT7LuScO97Gpge20Hi4kbVPjTHR3MqzH2xkuHOQ4Y5X\nGG/9Ibve9OPiGQRK7vOyL57J1qbfID7ZTuPhAS774tdZtet+37hy5vdpflloLnsfmi/3NgrVpj9x\nGffq3Iet2B1u9TGbV5/lZnOXcf2Sle8D56He9G3ARsnKvS7joteykE+7Hp2GTlChdqTiY1xmNl8p\nmEAzlgS+8nI1mne2t+Q22247iytf+GnWPH8GidEk7a/kaOhyvPi+NnrOnGCstYuBzQ+x/4rvOEff\nnJ+ZFeHBj19P/qIPEp9M0HRwL5f849+6z++cs7LKV2xej94NP+PH/B70gjuKFgN0lW0AwzBqFu9N\ne8Z7065BZw14i2RlJ3Cfy7hRVKD1ojeioZJzNVUQaEZtYgLNKMki5Cx0onkZg2gooDCW9/7yGYy3\n/Azn9Z+L5IWmg8KaFwd59Q0xHv9YA5ONffSe8QJHzvuum0of15usFJKVZh772Ic5dPFVOs9A/4Nc\n+NW/c3+1Y84kfl+x+Vb0IrsXzZd7H5r8exQVZzMWIsy4X8sTqTpm8+qznG3uMm5AsnIz2lj7SrS5\n9gbJyn3oNE/daA5ao39LVQoFlrPNlzMm0IylwkZU8OwH+iQrcfq2nMWBS99NrPk86o4K6f4EHc93\nM7BpiPs/1cnoKkf3WS/Rdd4d5NKPOkc5/Yg0b+zRX/0Q+646jVxqgqnUN93jN/xQG33P+V4B3oh6\nyvrQppPvQb9L+4EfldkXyTCMZYj3pj0nWdmLetM2olXez6Azn+TQm7l6qlgoYNQe1gfNWBKI8FHg\njaT77uPTax7hwGVvpPf0s8klU8SnhJY9PdT3HuLZD27g6KmN9Jyxl8MXPUEufXepKZ5KfkZW6plK\nX8vDv/EmjlywjuGOI/SceZPrOaPsXkS+I/hFaDXWi2hTXUG9aHf7ppaGYRgASFbOAa5GrxP7gY+j\n14/HUA/8l20u3uWH9UEzlgUixIFTkCm48q/O5oX3XcR4UxNTdZPU9/aw8ZH9vHL9Kva+7gx6tx3g\n4KWPMVV/P/DSbL3Rpn1GVrYy3vwG7v/ExRw9vYUj5z/LoUu+5hyzNpgs2seZFCo2e9GGlQCPuox7\nbH5HbRjGSsBl3POSlXPRcGYO9by3oGHOBNCGpkYYxjRMoBklqXLOwmqghfqj4GQzo60DTDV0sXV7\nF8Nr4f7fO4Mj5/Vw4LJHmWx6Gnh4pmmeipGspIDXMdxxHvf/3nl0n5Vj9+vvY6z9u85RdhK/ZGUt\nWrEZQy+y6/3fu1zGvTzvIy71GZYnUnXM5tVnhdq8G73OCSrG1qAhTvz6igq0FWrzmscEmrEU0AKB\n+u4knU830rY3RuORx3nyI1vZe/UU+y9/ivG2A8C9zpVf8SRZ2QhcS+9pHTz4O+ew97UHOXjpixC7\nxTn657GfULFZBzSj3rNx4HaXcQfndaSGYaxEuoGz0FlFetAGtw3+tTXAgtzkGcsLE2hGSap8t7UO\naKBlXx2rdray/6pRXn7rJvZf+SojHf3AI8Bz8whnxtF+RBdw6MIm7v30Zna+5XlGOvYAtzrHaLkD\ni1Rsrka9Zq+gzSZvcRlXtsgrB7vDrT5m8+qzQm0eKtPrgMOoUGvyfyteyblCbV7zmEAzlgKnAsKa\n5+p55fpVHLh8LzvetQOtkHxwnoKqA3gD0Mau69q4M9vAnp96DmJ7gR87R9lzYfqKzTegpfJb0Cqs\nw+h0TiUnWjcMwyhBDzqzyCq0UCBM+RT6PxrGccQWewDG0kRErqvO51AHrCc27mg80shIZ4KJlr3A\n953jznLFmWQlJll5DfBeoI2nfrGeb//TBHuuOQCxF4Hb5iPOPJejvYzOQqs0dwA/qJQ4q5bNjQJm\n8+qzEm3up3PqQ39zJ4F+1EHSAtRJVpoq+fkr0ebLAfOgGYtNJ9BIY3eeWK6BXGqS+MQzzlF2bpdk\npQ31dHUA8KM/G+eB3xXySQc85hyPzndQkpUzULF3CvA08ADwiJXDG4ZxgnSjHjSHFgVsANL+tdXA\n0CKNy1iimEAzSlLFnIVQIJBC8ilyyREm62ecizOKD0GG+e8S5GNDfOXOUfZc04G2wrjXOZ6f74Ak\nK+uBX0Vz415CvWbz3s98sTyR6mM2rz4r2ObdwBlo3lkvmtoRLRTYXakPXsE2r2lMoBmLjfegddWT\nTybIJ3vpOnfnXG/yIYFr0S7dMNb6Mn+5u57x1o3oROV3ODf/C55kpR34JOqN2wN8xWVcyblBDcMw\n5kEoFEgCXWgOWghtWh6acRyWg2aUpBo5CyIIOnFwnFUv1jPWmmS8pYuRzllbafjw4wdQcTbK/svu\n4n/1tXhxNgbcfBLi7A9QcXYQ+ItqijPLE6k+ZvPqs4JtHmY8aUULBfJos9o0FZ7yaQXbvKYxgWYs\nJm3AKpKDORq7GsilHbn0npkKAyQrdZKVt6D5ZingVb73xVv50iMXo564QeB7znF4vgPxjWg/4/dz\nGPgzl3FlTSFlGIYxF36O3gE0xDmCXq/iaF5ak2QlPcvbjRWIhTiNklQpZyGENx3xqUZy6UniEy+V\n2lCysgWdeLgemADu50bXC7zdr+tGe5yNzHcQkpXTgI+hfc6COKv61CuWJ1J9zObVZ4XbvBut3HRo\nJWcLhUKBNahnbcFZ4TavWUygGYuJFgg09KQQlySXHCWfnFYg4BvFXgWc7VcdALZzo2sD3o3mc+wD\nfnQCbTSQrFzk97MFvTj+tXnODMOoEN1oX0VBCwU60Oa1oHloFRFoRm1iIU6jJFXKWVCBlu6tx8VS\n5BND9G86ViDgqyk/gIqzHNrq4mZudBuAt6HibAfqOZuXOPN9014PvBnYBuwE/sll3KJdIC1PpPqY\nzavPCrd5KBRI+MdptFktVLBQYIXbvGYxD5qxKIiQBNZBLkHb3kYmmpJMNvZw+OJ9fqqmy4EL0DvN\nbuBOl3FHRbjEvwbwBPBIuVNAHfts9cq9BTgdFX8vAne7jHtuYY7OMAyjJME73wAcojDlk1DhQgGj\n9jAPmlGSKuQsrAGaqeubor67jsmGHPnkAW6UeuCngQvRPI3HgO9wo+sT4WpUnDngPud4+ATEWSPa\ngHYL6jl7AXgK9c4tKpYnUn3M5tVnJdvcZdwo2pA2iTarHUO9aC1Am2SlIk6TlWzzWsY8aMZioeHN\nxiN54pMNjKUnSQ6/ArwTzcnoA7a7jDsiQhwNRW5FQ50/cY6ymtlGkaysQUOjDWiLjpeBI8CPXcbl\nF+KgDMMw5qAb9Zrl0EKBFHrNE7Sis2vxhmYsJcyDZpSkCjkLWsFZ35smNpUmlxql+WA3BXH2LS/O\n0qho24pOMHzzCYqzzWgxQAPae+gAWvJ+q8u48QU5opPE8kSqj9m8+pjNj4U5BfWipSkUClQkzGk2\nr03Mg2YsFmuBRur66oEk+UQva14YRC9Uh13GTYnQBLwD7Zc2BNziHPNufyFZOQ94HXpBHPZ/c6jn\nrH9hDscwDKMsooUCPeiUTzajgHEcJtCMklQyZ0GERqCZ2GSKhu4mJusT5FKDnHLfmN+kS4TVaI+z\nBrQc/RbnGJ7X5+hcnVei+WyglZpb0By2BxazYrMUlidSfczm1cdsfkygpSlUcrb4dRXxoJnNaxML\ncRqLge9/1j1FXW89E815XPwQnc/rXeQP/k+cQjjyADo7wHzFWQLNW7sQnVLlQXTy8zjwnMu4Zxfs\naAzDMMrEZdwwMIo6SA6h3vwmNBet3d9YGoYJNKM0Fc5ZWAs00XAkT2Kinlx6iuTwLqCNHW9fzWO/\n8lr0YvUK6jmbmM/OJSv1wLvQ0ME4cCtasdmANoK8fwGPZcGwPJHqYzavPmZzoOBFy6FTPiXR3NgE\nmtKxoJjNaxMTaMZiEAoE6oiPp8ilRmjZ181oW5InbthEPilo64ufOEduPjuWrKwC3uc/YwD4LnAO\nGjroxyo2DcNYfIJAy6NFUWkKUz5ZHpoBmEAzZqBSOQsixFCx1ERdfwMuliSfGGL1S/0cvqCZ0VUj\nwBHnePAEepxtQHucNaPtM76Les5ORefvvG2pVGyWwvJEqo/ZvPqYzYGCQIujObYp1MMPFRBoZvPa\nxASaUW3agSSJ4QTxsUZyqST5+DAbHxmh98wWRlYPonkZ80KyciZa8ZlCiwF+gPY6uxQtCvixy7i+\nhTsMwzCMEyZaKNDD9CmfbEYBAzCBZsxABXMWNLzZ2J2nrjfNRHMeoYfO5+ro29zCSMcQcHg+O5Ss\nXAZch57PTwJ3oELwWr/J/S7j9i3cIVQGyxOpPmbz6mM2B5dxg6hXP0xll0Cb1AoV8KCZzWsTE2hG\ntQkzCEByrJFcepLU4C7ysTYGNzQxtG6YMgWaZCUuWXkDBS/ZvS7jHkJDBddjFZuGYSxdghdtEBih\n0LC2TrLSNOO7jBWDCTSjJBXMWSjMIBCfSJFLjdGyv4fus5sYaxsjnxxwjpG5diJZSaMhzTOASXRG\ngOd8e423UmjRsSQrNktheSLVx2xefczmxwgCzaEFTRUrFDCb1yYm0Iyq4adtaoN8PemBJiSfJJ8c\nYtXOfrrObWGsbZAyvGeSlRa0GGA9OjPA91zG7fX9g65DczgGgB9ZxaZhGEuU2QoFLA/NMIFmlKZC\nOQudAKT7U+AayNXFgWHWPz5C39ZmRtvnzD+TrKxF22i0ocm133EZF+a2ew1wGprbsWTm2CwXyxOp\nPmbz6mM2P0bxlE91VGjKJ7N5bWICzagmnUA9jUeExFCayUaITQ3Q8WyMgU0tDK+dVaBJVk5DJ06v\nA/ainrNh/9rpFHLR7rCKTcMwljj9wJR/fBRtVtvqn5sHzaisQBORt4nICyKyQ0Q+XeL1NSJyq4g8\nISLPiMgNlRyPUT4VylnoAJpo7IL0SANT6UlSQ7sZXd3BWFsdw5396J3kcUhWLkKnbkoAz6Eeskn/\nWgeFis0HXMbtrcDYK47liVQfs3n1MZsrLuMc6kVz6HUvjwq0ONDk82wX5rPM5jVJxSZLF5E48Dfo\nj+p+4BER+Z5z7vnIZr8JPO6c+6yIrAFeFJGvOuemSuzSqH18gcDRNPGxFLn2Udpf6eLI+c1MNA2R\nTx4ubk7r88quBs71qx5yGfdk5PVGtCggATzvMu6ZKh2LYRjGydKNzhGcZ3qhwAga5jyweEMzFptK\netCuAF52zr3qnJsEvo4mdkc5CLT4xy1Aj4mzpcFC5yyI0ArUIZMpksMtiNMCgbZXh+g5o4Wx1uPC\nm5KVJPA2VJxNoUn/UXGWQNtphIrN+xZyzNXG8kSqj9m8+pjNpxEiBoJO+ZSiUMm5YGFOs3ltUkmB\nthHNEwrs8+uifAk4T0QOoA1Gf7uC4zEWFy0QaOhO42L1TNUJ5MdY9+QQfVtaGF09TaB5z9h7gFOA\nUeBml3G7Iq+His0OrGLTMIzapLhQIDqjgM3JucKpWIgTyppH8feBJ5xz14nI6cCPROQi59xg8YYi\n8hXgVf+0z79vu3/tOijE2e35wjwPLMz+/uv58AVo7E4w+OBaxnKNNLUN0/6SY9+r2zj6A4FLDgPI\nOnkP53EF17AH6OPzDNLHOWRUwInIdVzEmbwfgAn+liG6uIoMS8p+9nzpP3fObV9K41kJz8O6pTKe\nRX5+lHvZRpw0V3EUSPMEZzNMHVfTu5CfF1hix79snnuuA7ayQIhz85qPuvwdi7wWuNE59zb//LNA\n3jn3vyPb/BD4U+fcff75HcCnnXOPFu3LOeekIgM1qoII7wfOYusdW1m18yKm6uqpG7iD1/zdfh74\nxEU89Ys/dPnENyQrp6B5i0k0BH57cbsMX7H5JvQm4NZaLQowDMOQrLwfjQS0AB8AXga92QS+7DIu\nt0hDM06ChdAtlQxxPgqcISJbRSQF/DzwvaJtXkB/jBGRtcBZ6ETXxiJTfNd1cvsijrrrG6nvqyM+\nmiKXGtcZBM5pYaJ5GJc45HPOgjjbAfywhDiLVmw+uJzE2ULa3CgPs3n1MZsfRwhzjqLFAQ1odCuG\nzil80pjNa5OKhTidc1Mi8pvAbWjZ8D84554XkV/zr38R+J/Al0XkSfRk/JRzrrdSYzIWjTVAjMRw\njPh4C7F8Ahfvo/XVfo6eenqkQGANKs56XMbdWbyToorNF1zGPV3NgzAMw6gA0Smf+tFCgTpUrK0B\nuhZpXMYiU8kcNJxztwC3FK37YuRxN/DuSo7BODGi+SILgBYINB2qJ5+oI1cnSG6czmdH2HdVCyOr\nd6MCbbPf/rhmtSUqNu9dwPEtCRbY5kYZmM2rj9n8OKJTPh1FC6MaKLTaOGnM5rWJzSRgVINOIE59\nXx35WJqpNCQmRkj3CRPNaUZX96KFHx1++yMl9nEthYrNH1vFpmEYy4RetA9aEGjRSk6bUWAFYwLN\nKMkC5yysBZpoPBIjNVxHPpWjrvcwPWevw0meoXW7fIPaTr/9NJe+ZOU1wOnoHJu3uYwbW8CxLRks\nT6T6mM2rj9l8Or4IoA/NQRtE0zxW+ZfbfUuhk8JsXpuYQDMqiggN6ATAaer6GkiM1jGVGqflYBe9\n27RAIJ88JFmp99tNoBcrfb/Ov/kaODbH5tHFOA7DMIwKEqZ86ke9aavR5twJoG0Rx2UsIibQjJIs\nYM6CesXqu+PgmpB8DGLDtO0aoP+UFsbaBtGcsxDe7PZz1CFZWYP2lYFlVrFZCssTqT5m8+pjNi9J\nyEPLUygUSPp1J52HZjavTUygGZUmFAg0kI/XkasHyY3TtmuEkc4mRlcNojlnIbx5BECy0oBVbBqG\nsTIIAi2GRhDSQKNfZzMKrFBMoBklWcCchU5AqOtvJJdMM5WC+MQo+UQdLiaMte11jkkKHrQuyUoc\nrdhsRJvVLruKzVJYnkj1MZtXH7N5SXrQEGeCQqFAk3/tpAsFzOa1iQk0o2KIIKjwaqShO0Fssg4X\ny9N4pJejp7WTj+XoP+UVv/kxgYZWbHaiCbM2x6ZhGMsal3GTaGhzxC8poNW/bB60FYoJNKMkC5Sz\nsApIIlNCarCJ1FCaXGqC5gNd9G1tYaJpGJc4LFlpodCY8UxgGzCJTuO0LCs2S2F5ItXHbF59zOYz\n0o1e93rR3+ZOYByo8026TxizeW1iAs2oJJpX1rIviYs1+NNtlNa9AwxubGa8LcwgELxnfcClWMWm\nYRgrj5CHNoHerDZS+I22fmgrEBNoRkkWKGdBBVrjkSby8TS5FMRy46QH8kzVpxhv6XGOAQoFAjn0\nnDzkMm7PAnx+TWF5ItXHbF59zOYzEi0UGEDz0Or8upMSaGbz2sQEmlFJVHilB1p0iqeUEJ8cZTKd\nBmCisTj/LJyPx031ZBiGsczp8X+jMwqEQgHLQ1uBmEAzSnKyOQsix7php6k/miYfSyPO0Xi4n4HN\nLeRjOXq3vSxZiVG4Owx9f1akQLM8kepjNq8+ZvPSuIwbRwujxoAhtFCgxb98UgLNbF6bmEAzKoW2\n10gM50iMNpEaTpNLTtC6t4eBzU1MNA+RTx5BRVwCdem3+/euSIFmGMaKpxud8mkYFWhr0BkFmiUr\n6cUcmFF9TKAZJVmAnAUNb7a/Uu97nuWJ5cep7x5mZHUj4y2DaEuNEN4cR4Va30qq3IxieSLVx2xe\nfczmsxKmfBpAKzpXox41OAkvmtm8NjGBZlQKFWgNXc24eJpcyiG5McTFQYRcco9zTFEoEAiY98ww\njJVKd+TxIFokkPLPrZJzhWECzSjJAuQsqPBKDbWSS9SRTwmxqXGmUppnNt6y029nBQIeyxOpPmbz\n6mM2n5Ug0OIUpnw66UIBs3ltYgLNWHBEaAbqgTyp4WZyqXokB02H+xle30Q+lqP7nJclKwk07yxP\noZx8xQo0wzBWNi7jQv7ZOFookGaBCgWM2sMEmlGSk8xZ8P3PDjhik/XEJuMgOVr29tO/uYGJ5iFy\n6cOoy17QC1IjelHqO8mh1yyWJ1J9zObVx2w+J93oNXEMDW+2oXlpbX6e4nljNq9NTKAZlcAXCOxs\nwCXSuFgeyY+TGJ0gl04wle5yjiEK4c0w1+Zhl3FuMQZsGIaxROhGKzf7/fP1qDctRqHS3VgBmEAz\nSnKSOQveg9alDWrzcUdsagzEn2/y6rTt1IsGKzy8aXki1cdsXn3M5nMSnfJplEIOGpxgoYDZvDYx\ngWYsKCLECReRxGgbuUSafCpGbGqcXEr7+Iy37vCbBw/aim5QaxiGESE65VM/mofW4NdZHtoKwgSa\nUZKTyFloB+KQGySWW8NUXQOxXJ7GrkGG1jeSj+c4dNHLkpU6NPk1j16A8mhftBWL5YlUH7N59TGb\nz47LuJB/NoW22kgBzf7lE/Kgmc1rExNoxkKjYcs1L8SRfIp8TIhNCk37xxheG2eyYYDJxm4K3rMJ\n9DzsdRk3uUhjNgzDWEp0o3lno+gNbJtf3y5ZkRnfZSwrTKAZJTmJnIW1AKza1UQukQbJEctNEMvn\nAJTqjMIAACAASURBVIjl9jpHnuMb1B460bEuFyxPpPqYzauP2bwswpRPo2gKyFpUsCWA1vnuzGxe\nm5hAMxYaP4NAd6tWcMYdsdw4+bg/11xxg9pQNm75Z4ZhGErIQxvi+CmfbEaBFULZAk1E6kRsstaV\nwonkLIgQ8sqmiE2uIRdPk0/GkKkxcmmdrmSy3goEZsDyRKqP2bz6mM3LIjrl0wDa+LveP593oYDZ\nvDaZUaCJSExEflpE/l1E9gO7gN0isl9E/kNE3i9isXBjGuo9i48cxSU6mGpoRKbyNHQPMrKmnnw8\nx4HLd0hWwkwD4fwbdhk3tFiDNgzDWGIMovm5U6gXLYU28wbzoK0YZvOgbQdeA3wOOM05t945tw44\nza+7HLir4iM0FoUTzFlQgbbxkRQulmS80ZEaTdDQ45homgIOu8H1oxS8Z1P+74r3noHliSwGZvPq\nYzafG9+wO8woMML0QoF5e9DM5rVJYpbX3uKcGy9e6dc9CDxoIU+jCBVoq3Y1k0+mQKaITYLkgVie\nxPhuv11H0ftMoBmGYUwnCLRx1IO2Bi2mqpOsNPp2HMYyZkYPWrE4E5FOEdkcllLbGMuH+eYsiCAE\ngVbX10o+XoeLgeTGySX1PItNvuI3DxWcIf9sxVdwguWJLAZm8+pjNi+bHiCHijQHrENDnzDPMKfZ\nvDaZs0hARN4jIjvQHLS7gFeBWyo8LqP2aEXv8oYQt45cPI1LCrHcGLk6FWKx3Iu+h88a1HsraJiz\nZ7EGbRiGsUQJhQLjaLuNZgrT4lke2gqgnCrOPwGuAl5yzp0KvAl4qKKjMhadE8hZUK9Y076jTDR1\nMNFUT2wyT3pgjMmGBOTHqe/ejeZRJNFzbwrochmXn3m3KwfLE6k+ZvPqYzYvmz70GulQz1maQqHA\nvPLQzOa1STkCbdI51w3ERCTunLsTuKzC4zJqDxVoW+5LkUsmGG+D1GCC1CDk0uMkR/e57Tc6CuHN\nIMos/8wwDKMIXyjQw/QZBUKTWpuTcwVQjkA7KiLNwD3Av4jIX6EnjLGMOYGcBRVerbvbyCfT5JIT\nJEdzxKYS5BNTJEd2+e1CgUA490ygeSxPpPqYzauP2XxeRAsF0sAq1KvWLNnyi/TM5rVJOQLtfWiZ\n7+8AtwIvA++u5KCM2kKEBHpHlyc5sop8PI2LOSQ/Ti6l51hdf7RAQChUEJtAMwzDKE03OoPAKDrr\nygY09AnmRVv2zNZmAwDntIGoiDQC3w+rKzkoY/GZZ85CByq6esilztY5OGMxYrlR8gktEGjf8YJk\nJQ60Aw3oBafPZdzYjHtdYVieSPUxm1cfs/m8CIUCo2jj2nb/F1SgHShnJ2bz2qScKs5fE5FDwNPA\no5HFMAIa3lz7xCBjq1Yx0ZwmPuGIjU8h5EgPDLh//0Y3WnkUzrk85j0zDMOYjaPotTKHRrIa0FAn\nWCXnsqecEOcngfOdc1ucc6f65bRKD8xYXOaZs6AC7dQ7U0w2pBhZEyM9kCA1DLnUOKmh4ga1wQNr\nAi2C5YlUH7N59TGbl4+vcO9F877DjALN/uWyQ5xm89qkHIG2E3WvGsZMqEBr2ddKPp5momGC1BDE\nplLkUuPUHS0uELD8M8MwjPIonlGgHb3JbfNpI8YyZc4cNOAzwAMi8gCF2Ldzzn28csMyFptycxZE\naER784xDvpN8Ig3kiE3lyScTINCyb4ffvBO9wOR0+2PJrgaWJ7IYmM2rj9l83nSj3rMx1IO2HtiL\nVnS2A11z7cBsXpuUI9D+DvgxmoOWR5PBrUjACKj3LD7ezWTzBeTjdRCL4WKTSC5GfHyKbbft9CXh\nrWgORT9w2Pf5MQzDMGamG72pHfN/O1GP2io0D21OgWbUJuWEOOPOuU84577snLvJOfcV59xNFR+Z\nsajMI2dBBdrpt48xsrqF0fYEydEYknfEp6ZoPNLj/lfvANMnSHdYePM4LE+k+pjNq4/ZfN70oM6R\nUTTy0EJhyqey8tDM5rVJOQLtFl/JuV5E2sNS8ZEZtUIQaDHGWusZWROnrj9GchhyqTHq+vf47YJA\nCxcWE2iGYRhz4DIuh6aDjPqlDmjyL1sl5zKmHIH2ITQP7X7gPyOLsYwpJ2dBhBhBeDV0rSKfSDHe\nPE56AGK5NLnUBE2HQoPaDvR8i6F3g+aWL8LyRKqP2bz6mM1PiG60kjPkoYUpn9olKzLjuzxm89qk\nnEa1W6swDqM2WYWeQ/1MNJ1CPl6HS+SITU4BKVxsik0PvOS37UTLw4eBXpdxk4s0ZsMwjFqjh+lT\nPq1DZ/VpQsWaFVwtQ8ppVJsQkfeKyMdF5BMi8rsi8olqDM5YPMrMWVgLQH1PD2Or1pFPpiAfJ5dy\nJIcnaegZpn3nIclKI1ocUIfeAR6q2MBrGMsTqT5m8+pjNj8hulFxNo6miaynIMrmDHOazWuTckKc\n3wd+CU1GbEYVe/Os7zBWCpp/du5/TDG8tonhNXHSw3FwQnxqgsZD3S7jBo9tZw1qDcMwToQw5dMY\nhSmfcn6dzcm5TCmnzcZG59yFFR+JsaQoM2dBhdeZ34vz/M80MtyRpH4gT3LEkUuO09hVPINAuCEw\ngVYCyxOpPmbz6mM2nz8u4yYlK/3AADCJ9p5M+pfnFGhm89qkHA/a7SLy1oqPxKgpREgDbUCO1Mgq\ncqk0462T1PUJ4tLk0uOsefFlv3knUI/e+Q27jBtarHEbhmHUKKFh7Sja8DsUClgl5zKlHIF2P/Bt\nERkTkUG/DFR6YMbiUkbOQvCKdTO8div5eJqp+glik0JyJEZsaphND+32FUZr0N49g5j3bEYsT6T6\nmM2rj9n8hIlWctahN75jQJ3P850Rs3ltUo5A+wvgtUCDc67ZLy0VHpex9NHwZscz/Qx3dpBLJ4lN\nxpmqg9TQJM37B9FWGq3o3V4Sdc2bQDMMw5g/YU7OKfR6uhGt7gTzoi1LyhFoe4BnnXP5Sg/GWDqU\nkbOgAu38b+QZXNfIcIeQHtSJe+OTE7Ts7/KhzM6i91kF5wxYnkj1MZtXH7P5CdONFlqNoyJtDYX5\nsWfNQzOb1yblFAnsAu4UkVuYPln6X1RuWEYNoMLrnG8KD/5OE8Nr09T154mPxcglx2nZu8tv10Hh\nPJuicMdnGIZhlInLuHHJyhDaXiOHRidCZbx50JYh5XjQdgE/QcNUocWGtdlY5syWsyBCC5oDMYKL\nr2Wqrp6x1knSA0J8MkE+Mcamh6MzCIT8sy6XMU/sTFieSPUxm1cfs/lJ0Y1eS0MeWvgtntWDZjav\nTcqZSeDG/5+98w5z7K4O9nskTe99i9e9G3dsMAa8BgMOzQSCgSS0+AshhYSEACH5wuxACM0JfEBI\nAgabEAg4dGOaDdgQmnFsbK/b7np3tpcpO31GM6M53x/nXo92dop2R/ppynmfR490r66ujo50r849\nNYAczvLCGtTK5CH6TrqQqVQZ6Zo0iYlKyvuF8v5eygYPSocksSu7GmAPnn/mOI6zGOI8tHHMYdKE\nRSZqpEPKtF3TxRTOyS8LGmgi0gq8AzgXa5UAFuJ8TiEFc4rLAjkLVsF56g9HGVzfwHiVkEqXkCkV\nSgcnqd3Th51IGpmev5nBDbR58TyR8LjOw+M6XxSxgZbGjLP1wANYykkTsG+2F7nOlye5hDi/ADwG\nnApsAjqBewsnkrMMsPyzC/4TBtdVM9ySoGwgBZIgOZ6mfuehrAIBwRvUOo7j5INuzHsWzzJegzWv\nBZ8osOLIxUBrUtWbgHFVvVtV3wi492yFM1fOgghx2FI567YEw61VDLWWUt4HiQlhqiRN86Pbo81b\nsI7XI0CftutYEOGXKZ4nEh7XeXhc58ePtusIdj4dwKISDUCc1ztnoYDrfHmSSxVnXLl5QERejLlQ\nGwonkrPEacYM+17SNWsZr64iXQ/Vh5KUDUIqPUjj9j3Rtq1Y/pk3qHUcx8kP3cBhzECrBsqi9e5B\nW2Hk4kF7n4jUA28D/hq4CfjLgkrlFJ15chYsvFky1E3v6SeSSZWRrk2TnCilvE+o7O4CuqRDSrFR\nUDVYzoQbaAvgeSLhcZ2Hx3W+aOI8tDHMOGvA2m3UR4VZR+E6X57kUsV5W/SwD9hYUGmc5UDcoHaC\n/pNqSNcpqfEEmkhR1j9F3e5e7AQSu9sFO3m4geY4jrN4sis5q7BCgV2YodaITXBxVgALetBE5OMi\n8rHoPn78XhG5LoSATnGYJ2fBDLSLboGB9dUMt0DZQAmaSJIcT9O49aC263C0XSmWzJrGDHxnHjxP\nJDyu8/C4zhdNPDR9Ahv5tBbojZ6bNQ/Ndb48ySXEWQ5cBGwBtgIXAhuAG0TkowWUzVliiFCBhSwn\n2PDzMoZbqxhuLae0P0EyrYiM0rq5M9o8blA7BBzUdtU5dus4juPkSFQhP8r0XM64Fxp4HtqKIpci\ngQuAK1V1EkBEPgn8D/BM4KECyuYUkTlyFuK5modI165ltLGG0UahcVsJ5X1TlPf2kho/FG0TG2j7\n8fBmTnieSHhc5+FxneeFHsxrdirTOWgwh4HmOl+e5OJBq8cqRWKqgcbIYPO2CasLM9Aanuij58x1\nZEpLmKiaIDlRQnkfVB/sBrqlQyqx30kldqXnBprjOE7+iEc+jTNj5JN0iBRNKiev5GKgfQi4X0Ru\nFpFbgPuBD4tIFXBnIYVzisccOQtx/lmGw6dWM9owhWQEKKX8sFC/8xCWoNqC/bY0unnSag54nkh4\nXOfhcZ3nhW4sfSSNVXKui5ZT2BD1I3CdL08WNNBU9TPAlcA3ga8Dz1TVT6vqsKq+vdACOksDEYTY\nQLvw8wkGTqhiuFUoGyhFJUUqPUbLIweiRopx/7MhoEfbdWLuPTuO4zjHSFzJOYkVY50QrYN5GtY6\ny4tcctAAhlT1GyJSp6r9BZXIWRLMkrNQj1UMDVK/s47htiqGWsopHUpROjxJ6cgg1QfjOXAtTBto\nHt7MEc8TCY/rPDyu87wwgFVyjmGtjFqwdBKwPLRt2Ru7zpcnuYQ4Ae6ace+sPtqi+0NMlLUy3FzH\nWGOSktESyg9PUn2wB8s/i08WtViOxIFiCew4jrMSiariu7FCgSms/1k88skrOVcIuRponnS4ypgl\nZ8HCmyfdNUzvGU2MVyfJVGRIjJdQ3i/U7OvCcs1qsZyIMiyB1T1oOeJ5IuFxnYfHdZ434pFPU1jE\noiRaf1SI03W+PMnVQHMcM9Au/bRy+LQqRpsyMCUkpsooPyw0bD+EnTBagQoseXU46tnjOI7j5Je4\nUGAcuyBuw8675dIhVcUUzMkPbqA5s5KdsyBCCdZrZ4qzv5Gi/8RqhpsTlAyXIpkkZQNjND2+LyoQ\nyA5vuvfsGPA8kfC4zsPjOs8bMwsFNjBHoYDrfHniBpqTCy1YmLub0pFmBtdUMdxWRulQKWWD41Qf\nOkxiqjtrWzfQHMdxCksfdp5NYwZaGxAX8Xke2grADTRnVmbkLFh4MzXaxVSimeG2esbqS0iNlVDW\nl6HqYA/QJR2SwK7c3EA7DjxPJDyu8/C4zvNDVCjQgxlqU9i5NxM9fYQHzXW+PMnVQHv1jHtndWEG\n2nm3TtB3cjUjTcJUqZCcSFLeD3W7uzDXeiOWCwF2Vdc9++4cx3GcPNCNGWmKtUKKcQ/aCiAnA01V\nH8++zxURuVZEHhORrSLyzjm22Sgi94vIZhG561j27xSOGTkLZqBd9q9Kz5nVjDZZOXdi0goEmh/P\nniAQ9z/r0nadwskZzxMJj+s8PK7zvBKPfMpgo/VqsZy0GumQ+GLZdb5MWdBAE5EzReQrIvKoiOyI\nbttzeF0S+ARwLXAu8BoROWfGNvXAvwAvUdWnAL9zXJ/CKRgiT87UHOOEX1XRf2IVQy1CIl1OaixB\n3e4RyvsPaLuOYoZcLdZE0cObjuM4hSWu5JzAohcnYb3RwL1oy55cPGg3A/+G/QA2Ap8DvpDD6y4H\ntqlqp6pOAF8Crpuxze8CX1XVPQCq6iGxJUJWzsJ0g1poYXBdNcOt5ZQOl1I2OEHNgcNMhzLjAgGf\nIHAceJ5IeFzn4XGd55XDTA9NLwPWM30+ftJAc50vT3Ix0CpU9U5AVHWnqm4CXpTD69YDu7OW90Tr\nsjkDaBSRH4vIvSLy2lyEdoLSAkDtnl6gnsF1NaRrS0mNllDWN0nN3rhAoATLQavGDTTHcZyCo+2a\nwS6eB7FK+2bMmQI+k3PZk8sszrEoXLlNRP4M2Afk0gRPc9imBLgEeC4WRvuFiPxSVbfO3FBEbgE6\no8U+4DdxXD2+OvDl/C5HtMGHz6TyP4WhthIG1ybgW21k+uooT/bTsKOLWzidDKdwA9XAKHexjrt4\nOu0sqc/jy748c1lV71pK8qyG5XjdUpFnuS9zOyewgXIuQIFGvsnFNHM6V1qoc6b3rNjyrtTliI3A\nyeQJUZ3fjhKRy4FHsQqR92IhrA+p6i8XeN3TgU2qem20/C5gSlU/mLXNOzEP3aZo+Sbge6r6lRn7\nUlX1cVOBESEBvBFI8tdr7qP77Gu44wNnsffp62l8vIWzv5Hmmr/9bxJTnwTOBF6GGe+3a7veXUzZ\nHcdxVgPSIecBv4cZB4eB9wPnYE6SmyMvmxOYfNgtC4Y4VfUeVR1U1d2q+gZVfflCxlnEvcAZInKy\niJQCrwK+NWObbwLPFJGkiFQCTwMeOdYP4eSf6KqgCUgCfVQfbKTv5CqGmwUyZZQOJ2jaOkRi6mBU\nIOANahfJzCtdp/C4zsPjOs878USBuFDgBCzKlMDSTlzny5QFQ5wichnwt5jbLt5eVfWC+V6nqpNi\nIdHvY3/yn1HVR0Xkj6Ln/11VHxOR7wEPYo32Pq2qbqAtHVqj+0PACQyuq2K4tZyS0RJKByep3dvH\ntDHWAtRheYduoDmO44ShB7swnmTaQLsPG8/XjLVAcpYhueSgfQH4a2AzZkTljKp+F/jujHX/PmP5\nRuDGY9mvU3gsN4erATjhlwNAJX0nVjJRXUlFbwnl/ZPU7e4BuqVDKjBvWwq7cusrmuDLmOwcHScM\nrvPwuM7zi7brpHTIfqw4ay1WeT8YPd0ErvPlSi4GWpeqzgxNOqsD86Bd8c/KWG2SvpNLIFFKMi2U\n90HD9i6mG9Q+Gd6MRpA4juM4YejG5nC2YWHN+BzsvdCWMbm02egQkc+IyGtE5BXR7eUFl8wpKiLN\nz8NClpOc87USus+uZqR5CiglNQYNT0xQMjqInRi8/1ke8DyR8LjOw+M6LwjdWINawUKbMU3SIeI6\nX57k4kF7PXBWtG12iPNrBZHIWSKcH8916yKRaaXvlCqGWhJAGaVDKVoeHQYOaLuOSYfEEwR2AweK\nJbHjOM4qpRur4MxglfSt2AVzNXah7SxDcjHQngqcrQv143BWGD/eDlxKYuIQcC79G6oZaSsnkS6h\nvH+Khs4BrCcemFu9GgtxekLqceJ5IuFxnYfHdV4QerBKzrhQ4ERgO3ZebnKdL09yCXH+HJul6awu\nbMTTebeOASUcPrWETHk1qbESyvomqO/sxiYI1GAhzglgn7brxNy7dBzHcfKNtus4sB8YwZq+r8U8\nauATBZYtuRhoVwC/EZEtIvJQdHuw0II5xUMEgfc/C4Bn/JMwWSb0nlYOVFIylqH6YIaqrj7Mrd4K\n1OD9zxaN54mEx3UeHtd5wTgIDESPm7BwJ0CT63x5kkuI89qCS+EsNeogVQIMs/b+ag4+pYrhtimg\nnNSo0vxYGnOndwEX4wUCjuM4xSbOQzuJqEFtRDOwqygSOYsil0kCnVj5bi32pcc3Z+XSCm/fgjWo\nbeXwaVUMtgKUUTqYovXhYWC/tmvatqUWu3JzA20ReJ5IeFzn4XGdF4zu6CZYYUAlkAbK2cSviymY\nc3zkMkngvcAbsITD7CrOqwskk1N8rP9ZZVcPcBJ9J1Uy0lyJTCapOAxNW4eBXdIhAmzAht4f1HYd\nnHuXjuM4TgHpxiIZGaAcKxToBtZjXrTh4onmHA+55KC9CjhNVa9S1avjW6EFc4pKK3z4TC7/xASQ\noPvMBJQ0kEqnKO+boG73Ycy7FntTR4G9xRR4JeB5IuFxnYfHdV4YtF3HsPNyXCiwAavuhK/z/OJJ\n5hwvuRhoD3Nk4ztnBSNCCmgEVZ728SRTCeg+uwKoJTU6Qf2ONMmJmQ1qvUDAcRyn+OzGPGUlmNds\nDIBKaosok3Oc5FIk8I/A/SKyGYtngw1Lf2nhxHKKSDOQgHf8gop3NtF7agWD66xBbWo0Q8ujcYFA\nN3AaZqAdwA20ReO5OeFxnYfHdV5QerBZyOuw6IalJb3gyZ6VzjIiFwPtP4APcOSwdG9au3Jpje4P\nASfQc2Y1w1GBQNmg0vbAEDZBIC0dshZzpcctNxzHcZzi0Y0ZaYpFvkqw5rU10iFlUWGXs0zIJcQ5\npKofU9Ufqepd0e3ugkvmFAsz0MquPQOope+UMsYaqkCTVPQKrY+OADukQ1JYEqoCO7Rdp+bepZML\nnpsTHtd5eFznBSVutQE2RWAN0MvPOBMfnL7syMVA+6mIvF9ErhCRS+JbwSVzioUZaK2bbenQeVOQ\naCGZhqoDacr7h7GQZjNQj4U73X3uOI5TZLRdhzEP2hhQgV1E90RPu4G2zMglxHkJ5iV5+oz1Xsm5\nwhChCrvqGueNBx4CLuXQeVVALamxcZq2ZTeo9QKBPOO5OeFxnYfHdV5wdmLn6RbMg/YzrmQLbqAt\nOxY00FR1YwA5nKXBdP5ZItPKUFspAyeUAFWUjEzQujmN9dnpBs7BRjx14Qaa4zjOUiEe+dSCGWVx\n+onP5Fxm5BLiREReLCLvEJF3x7dCC+YUhWkD7ac8m+6zqxlcA1BC2UCKtfcNYgUC48CpQBLYE/Xf\ncRaJ5+aEx3UeHtd5wekGeqPHDUCCn3EGUC8dkiyeWM6xsqCBJiL/DlwP/Dk2QuJ6bNaXs/IwA+3c\n/x4hQQk9ZyaZrK4HElT0QPOWUaBTOqQcK+POYBMmHMdxnKVBbKAJlidczzhD2P+9j2lcRuTiQXuG\nqr4O6FXVDiwX7azCiuWERgTBXOJw9bvhSrZw8PwMsAaZyNDQOUYiM4kN3Y3zz3xAeh7x3JzwuM7D\n4zovLNHIvW5gHKgCNnA1P42e9jy0ZUQuBtpodD8iIuuxniprCieSUyQasZzEAVoeqwfg4AWVxAUC\njY+PMd2gtgUbxjuIVXQ6juM4S4e4UKAKOIHpPpWeh7aMyMVA+7aINAAfBv4X6AT+q5BCOUUhu0Ft\nKz8pO5vDp1QAlZSMTNL2YHaBwAagDHOj9xVF2hWI5+aEx3UeHtd5EPZiMzltdN+tXBytdw/aMmJB\nA01V36Oqh1X1q8DJwNmq+vcFl2wFICK3iMjuOZ7bKCJTIvKcPL1Xp4jcvIhdmIFWMnQIaGJ0TQUD\nTQn4ww2MnnMpt37zt/gkL2QTa9nE1/kJpwJPaLuqiLxBRN44i0wbRaRdRGTG+pOjz/66RcjrrHKi\n3oxfEpHdIpIWkX4RuUdENolIUC9/Ho4/Z5kjIs8Xke+KSLeIjIrI4yLyARGpL4I43YwyyJc4kX/k\nz3mEm7mZV/I/PCM69z47S+63ishvz9yBiLxMRP5ylvUbZ+7DKQwLttkQkVcwY7STiPQDD6nqoUIJ\ntoIINRZLF/leZqA96/2TQJLW52yDT10Hn2mk5g07edHNjzDBbcAAwudZTwrzpgK8AavonPkHtRF4\nN/DeGbLtw3IZn1iEvCsOz83JHRF5G/Ah4EfA32HFKtXAlcCbgcuBFy60nzzqfLHH36phJf7OReRv\ngX8Avg7cgEUXngq8E3iFiFytqnsCitTNHWxgC41cw70M8klOp4omhM1cwwHuz9r2rcBPItmzeRnw\nXOAjM9b/L3b+frRg0jtAbo1q/wC4AvhxtLwRuA84RUTeo6r/USDZVgqy8CbFRYRSrBw7wxUfSTFG\nkv0XZ2DLegBOubKTs27uBn4ebTcMTJB7gcAROlDVceCefMnvrC5E5Gos5eIjqvq2GU9/T0TeD/xO\neMmc1Uj0e3wvR/8efyoiX8cMmv8A8hItyUGeUjbRzyEqqGKcZ9CPTRPYC5zMm9kZFRIc8bJc96+q\ng/j5OwyqOu8N+AHQlrXcFq1rAh5e6PX5uJmYhX+fAsh9C7B7juc2Yg0EnxMtdwKfB16NXZkMAb8G\nrpzltX8RbT8abfMsYAfw2RnbnQJ8AcsrGwPuB142Y5tNJseX22H9fQgjNHE/UjEAonaLvAONvIWX\n8nJAuZJvsIkS4K7oc2Tffgy0z7J+KnrPk6Pl18/UFXAR8FPMCNwC/NEsn/+a6LOMAluxK9ZbgB3F\n/s4X+XvZWGwZlsMN+B5WnJLKcfu12B9kV3QcPAD83kydY163O7Hil6Ho8WWz7C+n489vc34fG4st\nQ54/z3ejc2zpHM+/PTrfXQ48DHx1lm0uj7a5LmvdhcC3MG/cCPA/wDNnvC4+b16BXUCPAB/NOudO\nn78v5v/wPG6M1j87en3nLOfpm6PbzPXb4+8vex/Rurui8/Y1mANnGHiIGf830bavAR6Ljp8HgZdG\nr/9xsb/LPP8udLH7yKVIYIOqZntKDkXrerAyXic/KHai/0ssZPMqLGz4bRGpizcSkRswl/MPgeuw\nA/SLmGeLrO02AL8Czsdc2C/BDpyvishLjn77N/0prPklV/EhLkn+kOTrfgqvtKG7z73wp7ySL1DG\nrdREPfDS9Gq7TgB/jBlLD2Bu76dH624CPhPt/Mqs52Z+5mxqo8/yH9hB+2vgX7OTikXkXOB2rFP2\nq4C/xf4wr55lf84KQ0RSwFXAHao6mcP2VcDdwAuAd2HHzEPA50XkD7O2uyDarg54PfA67Pd4d/Rc\nvF1Ox5+zOpjxe5zr//C26P452LnthbPkpb0W83LdHu33Eszgqgf+D/CK6Pk7Z5mFXYcV7n0BuBb7\nPV5BJfdQQZo3sJXLuJ3zuW8W2V6GXex8j+lz9Hswj+B3sIuaeP1ReWpZKHAaZhzeCLwc2A/86CB9\nRwAAIABJREFUt4icFm8kIs+L5Hwk2t+N2PF0Bn7+PopcQpw/FpHbgVsxN+grgLuiE59X8OUPwUYn\nXaiq/QAicgAzUl4I/JeIJDCP1/dU9YbodT8QkS7gSzP2twn7wV+lqoejdXdEhtt7mD5pRLzwhzS1\nf4yNZz+bA+dWcMe/JODtZlBdumsPlTzAeRxiNxsAGLDwpqo+KiKDQEJVj3B7i8je6OGvVHWKhakB\n/lhV745e/1Psj/U12BUWwP/FfncvULUJBtF2nSzzoe26AnNzCkATVkG8a+YT0Z/lk0QG3BuB0zGv\nzU+ip74vIm1YzlBcTPBu7Ir+uao6EO3vDux31Y7lER3L8efMwQr7nTcB5Uzn485G/NwJwAeA92EN\n3z8FICIlWOTky1kXHR+OXveceJ2IfB/YDPw9RxpL1ZhH+IhzulTKQRJMcjLDnMwOYIj9Rwqmqr8R\nkTTQPcv5uxsYn7l+DgTTxbNU9Yno9fdhRtr1wPuj7TqAzar68qz32QzcCzyew/usKnLxoP0p5u68\nCHO5fg74E1UdVlUfmJ5ffhEbZxGbo/sN0f0JwHrMWM7ma1h/umyuxa6ABkQkFd+w8PSFIlJ95OZ/\nfD/P/2szog5eNAHJtTBly5WHM9jJop4kduU3QM/xfcR5GY6NM3gyV20L058f7EruO7FxFm13APhZ\nAeRxlglR1eZ49k1EksCzgT1ZxlnMF7B+fudGy88Gvh0bZ/Bkrs23MA8JHNvx5zhHoVYocBfmMYu5\nFjNuPg8gIhXY7/G/o+X43J3APLczqyfHgW8f9WYTpFGmMOOpEahgigkA6ijP12fKYmtsnAGoahdR\nxA0gOh4vBb6a/SJVvQ9LEXBmkEubDVXVr6jqX0a3r2gUYHUWxCoiZyeZtU1Mb/YGqpqOHsYH09ro\n/uCM7SbhKIOpFQvVTHDkH9eHMM9a1A+npszun3qAs75dBcCey6bgG2eBTmV5nXcArQhm2A3w5B9Z\nHjk8y7pxOOJksgY76Gey7CuKvT9UTvRgeWQnzljfhVXNPRX4NNM/3EaY6TcAphssx4nbDXNsd5Dp\n8OWxHH/OHKyw33n8ezx5nm3i5+KWS58HrhSReGTiazHj5lfRciP2//BuZlx0YA6TmeHRrln/kycZ\njww0eJTTgCbS0Xm7mbqjtl88vbOsSzN9/m4GSlih5+9CkEuI0zl+DgHNIpKaJV9mXXR/LKOS4j+Q\ntuyV0dXVzA7R3Vjp9Afn31djleVEyyHiUU/7LquA7iqYzETbjgN7gAsRUoAyQiEGpOdSSbSfGZ8/\nYrZ1zgpDVSdF5CfA80WkRFUnovUZLMcSEck2tHqBM2fZVRzaHMzabu0c28V/PMdy/DmrgOj3eDf2\neyzLuqjO5qXR/Y+i+68B/wK8VkQ+juUHvy9r+z4sCf8TWM7a8ZNhAhgjQRWwnhEsQlND7aL2e3x0\nYw6D1lmea2P+MPGqJJcQp3P8/Agzgq+b5blXAPtUNY675+KV3INdhb1qln3N9NR9DwtJP6Kq981y\nixJa681rRsYmCEwloPuMenhxhkQ6NtCGsCvFM+aQKw1UzrGeOZ6bjVx08EssybYiXiEia7FChGXN\nCsvNKSQfwgyiuS4+wIx9xcJJJ4jIM2Y8/7vYxdHnouW7sd/Vk6F/EanB/jzvilYdy/HnzMEK/J3f\niEUk/nHmEyJyCtYL7W5V/TU8GTr/BvD7WDuYUuA/49eo6jBWEXkRcP9s5+8ZbzP3eVOZBIY5iwlg\nHSPRBUnlER60+c7fFbOsPy6ii6h7mdECR0QuZX4P5KrFPWgFRFXvjBKNbxGRs7HeMTVYQuhLsQav\nMQt6j1R1SkQ6gJtE5LPAl7EE6HdiVY3Z+3h39H4/EZFPYLPZGoCnAKdMJznXmIF22rcHgUoOn5og\n3dQAJCkZnYpMrAPYFd0GZj8ZPAz8iYhcjzUMHVDVLdF6gLeJyPeAjKreO89HnEsH2ev/ATvAvy8i\nN2Lu87/PktFZ4ajqj0Tkb4APRBWW/4FdfZdj3rJXYxcVilVZ/gXwNRH5O6wX1O9h7QDelBUaei/w\nYuCHIhIbfu+M9vme6H2P5fhzVgmq+kMRaQc6RORkLIR5GLgE+Jvo8WtnvOzz2EXCJuB/VLVzxvN/\nhUVAvi8in8HOb83RPhOq+q6sbef+3WXIYK03moFGMliz3PIjDLRHgGeJyIuwi5YuVd2Jnb//UETe\njPVyG1PVh+ZRxWxyzFzXjhXWfB1LRWiO1vn5exaO2YMmIp8TkX8VkacUQqAVyHVY6fHrsMrJWzB3\n7nV6ZJPfnPL6VPWzWNuM52BXYa/H/pAOZ+9DVXdj+TgPYFd2PwA+ibXy+CGACEmorgCBa99mB9Le\np40Da+C75ZQMZaJ97sKuEOvRWUObH4z2eRNmFP5btP7b0Xv+CVYy/qtZXpv9+WfTwRHrVfVR4EWY\noXtr9Nk+hp1A+md5/bJhheXmFBRV/TD2W+7BfgN3YEnVr8VaDpwR5c+OYEn+P8Aq6L6BtZ75fVW9\nKdZ59MezETO0PocZfQNYFfRDWe+b0/HnzM1K/J2r6nuB38KGk38W+D420eIW4Kl69BSBOzCjZB1R\nccCM/d0PXIb9vj8W7e+jwHmYt/fJTZn7d6eRB+0wW6gCmkhGnt4SKqVDovxj3oVVUN6Knb/bo/U3\nYdXJ/4idu785432ZsTzX+Tv7c92JXSCdg4V6344ZowdY5ufvQiDHmu8vIpdjCbqXq+o7CiLV0e+p\nqupXp3lGhFasD04vm2Q3cCHf+Viae97yB/CdDVy+9RFe+NbDmJE1hh1I+4B3aXtObTOCEIWltgG3\nqeofLrT9UkVENq7A8M+SxnUeHtd5OKRDksCNbOElnMmDWDi2Nbrdpu06W2FMUETkBKzh+D+o6vsW\n2n65kA+75ZhDnFFPlHuAryzmjZ0lQZzsPF0gsPuKSqAWXtDDui9ngAxWwRmXdu8stnEWJdb+HDMW\n12EhrDrg/xVTrsXif1rhcZ2Hx3UeDm3XjHTIds6kH6sObcW8cq1YeDGogSYi5Vhj2juxooFTgXdg\nkwduCinLcmBOAy1qnncD5mFZH63ei7n1PxNXTznLGjPKkulDWBdo6Dq7FSindKibtocy2IHT8+Tz\ndqVTbMqwcFUbVmH6K+AaVd0876scx3FWH51YHlo1lkccF6Y1FUGWDHbe/nj0/sNYrt0r9MiJRQ7z\ne9DiRMdNmGEG1qjx9VjFycxKJmf5YeXOV71nHChhYF2ayeq1QJKSL5RTu2ccSxpNYJ6qKZaAgaaq\nbyq2DIXAQz/hcZ2Hx3UenE4epZxzKMW8aHEecfDWMJFj5+ULbugA8xtol6rqzLYKu4FfiEjR/6Sd\nxSFCBTZrcIIrP1wCwJ4r0tjVzRSVhzJUdk9gBQLNWFL+ENMNPh3HcZylzyGmGMYiD03YBbcC9dIh\nSW3XzLyvdorGfFWcvSJyfTR/DgARSYjIq5i9Y7CzvIibBXaRnLDHO67OROsnOa1mH6JgbTPOxPo8\nHdB2LUSDWgfPzSkGrvPwuM6D08t5PM70vMx6rBluAvOoOUuU+Qy0V2P9pg6KyNbIa3YQa8r46hDC\nOQUlNtAOPfm489k1QA1MjbH+nri89wms1xOYseY4juMsE7RdJ7A+mJNY3nET06PJipGH5uTInAaa\nqu5Q1euxP+8rolurql6vqj7YdPljRlnt7h6sge0U3WefAJRQMtJP372twChmlJ8SvWZLMQRdLazE\n/lBLHdd5eFznReAuSrHJAHXYSLPYQPMRZUuYXIeld0c3BRCR5xVeNKdQiCDEFZwvvWEKSDDaMICW\nnAwkqd4/RkXPBJZvVkYc9rReY47jOM5yop/9WCVnBTZfNm4K6x60JczxzuL8bF6lcEJTj81/G+L0\nO2oA2PnsccxoU+p3THDpwF6sQOAkzEg7jOUtOAXCc3PC4zoPj+u8CFzHbVhLiwQWMYlTWJqkQ7wJ\n/BJlvj5ot83zOre6lzdx/tlBYk/a9mvi/IRJ1t4/GhUIdGIFAgCd2n6MYyccx3GcpUAP0xfYjVhP\ntKHovg6/+F6SzNdm45nYXLuhrHWKVYI8rZBCOQUnu0DAZqo+/qImoBIYZv29KTbTxFPYhhWFgIc3\nC473hwqP6zw8rvMisIkr2MQe4AIsxBkXClRHj91AW4LMZ6D9ChiZ7UASkceP3txZRpiBdvp3BrD+\nZhP0n3QKkCI5epj6nSl6mMD63p0Uvca/c8dxnOXLFuB5mEHWjFV2nhQtP1FEuZw5mK+K81pV/dEc\nzz2rcCI5hUSEEszFPcVvv95yDyYqeiBxKpCiZu8ItXvGuYAHsFy1eqzz9K5iybxacK9CeFzn4XGd\nhyfS+RNYZX4VNhXIKzmXOMdbJOAsX5qxMHUPVd12YHZuHCX2qjVun6D6wDjmPTsr2nZv1EvHcRzH\nWZ50YgZaCXbhPRmtdwNtiZKTgSYit0b3/11YcZwAtEX30w1qH/6duMP0BOvuHUMU7qAGiEd9ed+7\nAHh/qPC4zsPjOg9PpPMujmyvUY71RiuXDqkqkmjOPOTqQYv/qE+fdytnOZBdIGAVnI+/ZA3WSmOQ\n1s02l7OH/Uw3qPXZq47jOMsYbddRYD8wxZGFAuCdGZYkHuJcfZiBdvX/HcOMshFGW84AUiTGu2nY\nUQFkeDW3YnkKCjxWLGFXE56bEx7XeXhc5+HJ0vkTHDnyqTta72HOJYgbaKsIEaqxVhpjXPW+cgCm\nkoeAU4ESavYMU7snjbnC12MGXJ+2a2+RRHYcx3Hyx2NMj3xqwz1oSxo30FYXR4c3O68aeXJ905YJ\navZbgcAdXB9tuzOwjKsWz80Jj+s8PK7z8GTpfDtWKFCOec3iPqfuQVuCuIG2uogNtK4nH//yLyqB\nWiDNmgcmSGQAdlLDumhb74/jOI6zMtiHGWWCtVtKYiHPGumQ0mIK5hxNrgbah6P7GwsliBMEM8qq\n93dhLm1l+3PXYVdTA7RFBQKwnacTt9XwBrWB8Nyc8LjOw+M6D0+sc23XYewCPbtQIE5h8TDnEiMn\nA01Vvxjdf6Gw4jiFQoQEsRv7+ldmsCunPiarzgJKkIku6ndUYkUBB4jncnoFp+M4zkpiB3Zub+PI\nSk4Pcy4x5jTQRORGEXnzLOv/SEQ+UFixnALQiI326uPEn9VH67qwAoEUNXsHqds1iR2sJ7GZNcA+\nbdfJ2Xfn5BvPzQmP6zw8rvPwzND5o5iB1oS32ljSzOdBew7wqVnWfxp4SWHEcQpIdoNaKxB4+OVp\n4skCzVvGowKBPXiDWsdxnJXKVqxQoBJYy3SI0z1oS4z5DLQyVZ2auTJaJ4UTySkQLdH99ASBn7+j\nFpvLNkLrZkhOKjZz8zSewkE8vBkUz80Jj+s8PK7z8MzQ+Q5svnIKa7cBltpSLx2SDCyaMw/zGWgj\nInLmzJUicgYwUjiRnAJhHrRT7uzD5rBl2HfpeqACGKB1cyrabjuwIXrsBQKO4zgrCG3XASysqVih\nQD3Qh9kDjUUUzZnBfAbau4HviMgbROT86PZG4DtAexjxnHwgQhl2pTTJq34n/s670dQ5QAky2UXj\nExXYATsGVPIANcDB4ki8OvHcnPC4zsPjOg/PLDrfieWhrcGMMs9DW4LMaaCp6neBl2G5aLdEt6uB\nl6vq7SGEc/JG3P+sm/J+C3VOlHdhszZTVB/op26XYoN01wKQ5pC2qxZBVsdxHKewbMUMtObo5gba\nEiQ135Oquhl4XSBZnMKRPUHAHt/zpwANgNK0ZYraPeNYE8PTAbicO4NLucrx3JzwuM7D4zoPzyw6\nfxgYx8KbLcD/Ruu9UGAJMV+bjc+KyGXzPP80Ebm5MGI5eSY20A4SFwvc++Z64gKBlkc0KhDYiXnV\nwPPPHMdxVipPYOksZZiRlo7WN0mHeBHgEmG+HLSPAG8RkS0icpuIfEpEPh093gL8MfBPYcR0FokZ\naNe8c5h4WPrh0zdgBQKDtD0Ue1L3R9tO8s9RqNMJhufmhMd1Hh7XeXhm0XkXMBA9bgOqsRFQ2ZWd\nTpGZM8Spqg8BrxORMuBi4CQsiXwn8ICqjoUR0VkMItRhV0nDPPND1dHqLuAFQAqZ6KZpazl2BaXY\nb2I/A/j36ziOswLRdlXpkD1Yo/L1TDesrY4e9xVRPCdivhBnq4icp6ppVf2lqn5ZVW/FrOyacCI6\niyQ7/8zCmwcuGMAOygSV3X3U7UwAw0w3s+30PJHwuM7D4zoPj+s8PHPofBswgf0vNAHd0XovFFgi\nzBfi/DizJww2Af+vMOI4BWC2AgHB3NhK4xMaFQjsB06Ott0WWEbHcRwnLI9glZyN+EzOJcl8Btrp\nqnr3zJWq+hPgwsKJ5OQZ84qlRqY9aI++ogHzgo7RunmK1LhiFZwbsDDn454nEh7XeXhc5+FxnYdn\nDp0/iqW2VGFGWZyT5gbaEmE+A22+MGZJvgVx8o8IKezqSHnjVRksv2yA0aZTiCcItD0Y/wb6sO98\nGDhQDHkdx3GcYOzGpgLFEwTKMIOtXDqkqpiCOcZ8Bto2EXnRzJUi8kKsRNdZ+jRj33Ev6++1ER7j\nVd3YMPSoQGBbBTCFGW8JYJ+2a9rzRMLjOg+P6zw8rvPwzKZzbdcpLHIyieUk+0SBJcZ8jWrfCtwu\nIq/EmtgJcCnwDODFAWRzFs/R+WePvmyC2IVdcXiI+s5yzGsWH5Dbw4roOI7jFIlOrEvDGqYLBdZh\n/xG7iieWA/OPetoCnA/8BEsePwm4GzhfVb2J6fLg6ArOB16XIJ4gUNc5Sd2uSeyqaX207VbwPJFi\n4DoPj+s8PK7z8Myj88eZHvmUXSjgHrQlwJweNBH5JPBFVf3s8e5cRK4FPgokgZtU9YNzbHcZ8Avg\nelX92vG+n3MUZqCtua8Hc19P0XlVA1ALpGl7aIpUWrGrpnOw0R+dxRHVcRzHCcxmrNVGHd5qY8kx\nXw7aFuDDIrJTRD4kIhcfy45FJAl8ArgWOBd4jYicM8d2HwS+h4VRnTwgQiXWdHCcG64swXTby1TZ\nqdg0gUHWPBDreyRa10d0BeV5IuFxnYfHdR4e13l45tH5FmzkUwkWWQHzqNVKh5QGEM2Zh/lCnB9V\n1SuAq4Be4LMi8riItIvImTns+3Jgm6p2quoE8CXgulm2ewvwFay7vZM/psObJWNRg9oLB7FWGgkS\nE700bSsDMtjBKcBubddMMYR1HMdxwqLtmsb+ezPACZjnrDd62r1oRWY+DxoAkYH1AVW9GHg18NtY\n/5SFWI+V8cbsYTrPCQARWY8Zbf8av10uQjs5cXSBwOZXCZZroJQfHqF+RzxBID4Qn2xQ63ki4XGd\nh8d1Hh7XeXgW0PkuzGsWFwp4w9olwoIGmoikROSlIvJFLAz5GPDyHPadi7H1UeBvVFUxD46HOPPH\n0Qba1hfGBQJQsy9N3S7BDDTLT/P2KY7jOKuNbZiB1ooXCiwp5isSeD7mMXsRcA/wX8CbVHUox33v\nxcJpMRswL1o2lwJfEhEwa/23RGRCVb81izy3MJ3A3gf8Jo6rx1cHvhwvJzfC+54Jf7Oda94xwM+4\nBGWSg+cPAPXw7TJKv91K6Ug/0M+DXIEywYXWoHbm1VbxP48v+3JhllX1rqUkz2pYjtctFXlWy3LM\nUd/H7aQ4nXLOogFo4lOUch5ncqWFOpeK/Et9OWIj0yMTF42ozu7oEpEfA18EvqqqvbNuNN+ORVJY\nCe9zsWZ49wCvUdVZw6MicjNwm85SxSkiqqruXcsREZqAVwADbJJfAC9gtP4gHzx8DnA90Mfz37aV\nZ/xzJfAgcAGwA2jX9jl+EI7jOM6KQzqkHrgda8X0MeAW4DVYFOxmz0s+PvJht8wX4nwx8LnYOBOR\ns0Xkr0Qkl/AmqjoJ/BnwfWwo65dV9VER+SMR+aPFCO0syNH9z7ZdO/Hk+sT4AE1b4gKBsmjbHdnG\n2cyrLqfwuM7D4zoPj+s8PPPpXNu1D4tKTQFrgXqgn+kRUE6RmG+SwHeBG4CtInI61qfsP4EXicjl\nqvo3C+1cVb8b7Sd73b/Pse0bc5baWYhsA+1EALa8RLCcAqW8b4T6TgGGmC6t9vwzx3Gc1ckerBn9\neqb7odVHj73DQpGYz4PWoKpbo8evx5rWvgX4LXzU01LHDLSSoWkP2o6N8eB0qDo0Rn1nAksMLQNG\nmZEfmJ0v4oTBdR4e13l4XOfhyUHn27H/gza8UGDJMJ+Blp2L9FzgTgBVHcdcoc4SRIRS7Monw5su\nmwDKmEoMM7SuCfOWTdLycJqyIbAGhZXAAOZtcxzHcVYfj2AGWiNHThTwVhtFZD4D7SERuVFE/go4\nDfgBgIjYHEdnqdICCNBDy2N2cB24cAQ70Mxbtvb+OHFxAmtS26XtR1bnep5IeFzn4XGdh8d1Hp4c\ndP4gZqDVYRfyh6P1TdIhXqBXJOYz0P4Qc3OeBDxfVYej9ecANxZaMOe4ObpAYOuLpogb1CbGB2l+\nvAQrECiPtvX8M8dxnNXLHqwnZtzMvArLUU5hs5udIjBnkYCqjgDvn2X9z4GfF1IoZ1HEBtpB4HwA\nnnhegicnCPSN0LAdpsObk1j+wRF4nkh4XOfhcZ2Hx3UenoV0ru2akQ7Zj1VxbmA6D60a++/oL7SM\nztEsOEnAWXaYgbbm/m5io2zfU8uw3AKhsmuM+s4kFqZOYflnXqXjOI6zuulkulCgkek8NC8UKBJu\noK0gRKgBKoBR3nxJKZBgpGmIyco4r2CKpq0jlA8ksUKPUmCQWQw0zxMJj+s8PK7z8LjOw5Ojzrdg\nBloLdnHvMzmLzLwGmogkRcTzzZYPR8/f3H7NBJZPUAqMs+7e7AKPJLBX23UspJCO4zjOkuNBrHCs\nAfOgeauNIjOvgaaqGeCZIl7FsUxoi+6zDTTBroimSIwP0fxYCeY9S2Fhzm2z7cjzRMLjOg+P6zw8\nrvPw5KjzrUAau5iPJwikgQrpkKoCiebMQy4hzt8A3xSR14rIK6JbTuOenOAcXcG568oUdgUkViDQ\nqVgFZxKr2jlQBDkdx3GcJYS26zDmNZsC1uENa4tOLgZaOdALPAebIPBi4CWFFMo5dkRIEo9yet5f\n9wP1TCUy9JxRQWygVfSM0bA9/s7nLRDwPJHwuM7D4zoPj+s8PMeg891YHlpsoHnD2iIy3yxOAFT1\nDQHkcBZPE+YVO8yV/1QPwP5L0miqEqgBhIYdQ1QcrsSukBLYgNzuOfbnOI7jrC62Ac/CojFNwI5o\nvXvQisCCHjQR2SAiXxeRruj2VRE5IYRwzjGR3f8smr/53AxW1VkOTLDu3kmsEWEyut+l7To52848\nTyQ8rvPwuM7D4zoPzzHofK6RT26gFYFcQpw3A9/CXJ7rgNuidc7SIjbQup583PnsBLGxlhgfofWR\nJPadTwHjmDvbcRzHcQAexv4bqjCjbBjLWa6VDiktpmCrkVwMtBZVvVlVJ6LbLUwbA87S4egWGwcu\nLiVuVlvWP0J9J1jlprDAgHTPEwmP6zw8rvPwuM7Dcww6P4T1xoTpMGdvtOxetMDkYqD1RBWcSRFJ\nicjv43lLSwoRyrF5aRO8oykNVDDUlmFobQk2/DZFZc8oDdsS+AQBx3EcZxa0XTPAPizMuZ4jJwp4\noUBgcjHQ/gC4HmvHsB94JfDGQgrlHDPT4c3K3ji8GeeWVQFJancPU9VTivW4Absq6mUOPE8kPK7z\n8LjOw+M6D88x6nwH0yOfvNVGEZmzilNEPqiq7wQuV1Vvq7G0OTq8uetZihljlUCG9b8ew66GUtig\n9J3arnr0rhzHcZxVzGOYgdaMGWWPRevdgxaY+TxoL4omCLwrlDDOcXN0g9o9T09hB5SQGB+h5dEE\nZpyNYzkG8zao9TyR8LjOw+M6D4/rPDzHqPOHsQkCNZgXrRdLjamXDknmXzpnLubrg/Zd4DBQLSKD\nM55TVa0tnFhOroggxAZa1QEz0DIpofvsaQOtrH+Ehu2xt0yYY0C64ziOs+rpxKIszdhczhrAmp/b\nsuegB2JOD5qqvl1V64HvqGrNjJsbZ0uHeiyUOcTb11YAKQ5cNMV4DVh4s5TK3jSNTySY7oG2YIGA\n54mEx3UeHtd5eFzn4TkWnWu7jmD/DxmsUMAnChSJBYsEVPWlIQRxjpujw5s7r5qK1lUBKar3DlN9\nsAQz5KaAQ9quA6EFdRzHcZYFO7E8tDV4oUDRyKWK01naHF0gsOdpsaesDphi7f1D0WPBXNX7Ftqp\n54mEx3UeHtd5eFzn4TkOnW/BDLQW3INWNNxAW/7M0qD2ohTxgPTE+BhtmwFKgAkWaFDrOI7jrHoe\nwwoF6rBCgdiD1igdIkWTapWRk4EmIpUiclahhXGODRFKsNYZU7zgL/uABgbWpeg7WbHctCRl/SM0\nPpGJX0KODWo9TyQ8rvPwuM7D4zoPz3Ho/AmsUKCcuNgMhrALfc9BD0Quw9JfCtwPfD9avlhEvlVo\nwZyciA+cXq74aAMg7Hm6MlWi2IFVRsXhMRqeEOy7TuATBBzHcZz5OYj9VwjT/dBiL5qHOQORiwdt\nE/A0rOUGqno/cGoBZXJyJw5vHnzy8e5nxAUC1UAJNftGqdlfgeWkpYGD2q6jC+3Y80TC4zoPj+s8\nPK7z8ByrzrVdp7Bc5QlgHV4oUBRyMdAmVLVvxrqpWbd0QnN0Bee+pyaxq54GQGl7sA/RpmhdN+49\ncxzHcRZmG0eOfPJCgcDkYqA9LCK/B6RE5AwR+Tjw8wLL5eRGW3RvBQKTZULPWSks/yxFIp2m7aEM\nUIYdaH3kaKB5nkh4XOfhcZ2Hx3UenuPU+RbMg9aAOQPcgxaYXAy0PwPOw8Jj/4XFpd9aSKGchRGh\nCmtEm2aTTADVHLionKHWMaACKKNscJSG7VOY9ywuEPAKTsdxHGchngCGsf+Z9cAIZgdUSIdUFVOw\n1cK8BpqIpIDbVfVvVfWp0e3vVHUskHzO3Mwyf/NpmegrLSMuEGjcFpdExwZaTmM6PE82Vd+3AAAg\nAElEQVQkPK7z8LjOw+M6D89x6nw7ZpAlscrNRtyLFpR5DTRVnQSmRKQ+kDxO7hwZ3gTYe1lsjNUC\nJVQdGKVud020rg/o1nadCCql4ziOs+yIiskOYTnnazkyD80NtADMNyw9Zhh4SETuiB6DDUv/88KJ\n5eRAtgftfHt0QQq74jGPWuvmfkTXRNsdU4GA54mEx3UeHtd5eFzn4VmEzjuBC7GRT41M/4d4oUAA\ncjHQvhbdNFqWrMdOERAhQXyA1HeaB+3wyeX0nTSBhTcrSYxPsObBESx/YAoz0Dz/zHEcx8mVrVih\nQBPmFHg0Wu8etADkMiz9Fqw44L7o9gVV/VyB5XLmpxEzrvt56ykVQCn7L0mRrhvHBqJHBQI7ZhYI\n5OxB8zyR8LjOw+M6D4/rPDyL0Pl2YBSoAjZgs5wzQK10SGl+pHPmIpdJAhuxctt/iW5bReSqAsvl\nzE92g1oLZ+69LO5NZxMEyg+P0rSlFDuYJrAKnN7AcjqO4zjLl+3Yf0cZ1m6jiun/EfeiFZhcQpz/\nDDxfVR8HEJEzgS8BlxRSMGdeYgOt68nHBy6Oje16oJSqg2nqdtVg4egerEAg5wbDnicSHtd5eFzn\n4XGdh2cROt+PzeAUzCCLCwVasDSb/fmQz5mdXPqgpWLjDEBVt5CbYecUjiNHPKWrk/SensByzay6\ns+WRPhKZJuw77sInCDiO4zjHgLarAruxRudxJae32ghELgba/4rITSKyUUSuFpGbgHsLLZgzOyKU\nYV6ySV7ypj6giYMXVDOwfhir4GwgMZ6hdXM/No8ziRUHHJOB5nki4XGdh8d1Hh7XeXgWqfNtWJpM\n7DVzAy0QuRhof4xVbvw58Bbg4WidUxxaovtuLv10I5Bg/yWQKY9HOpVROjhG89bsSlufIOA4juMc\nD51YoUA1cCJmoCnQIB2SLKJcK55cQpVJ4KOq+k8AIpLEDAGnOBzdoHb/xXGD2mqgnPLDAzQ9XoW5\npUeAMcxIyxnPEwmP6zw8rvPwuM7Ds0id78B6oK7B/n8SWDVnPVY4kNN0GufYycWD9iNstmNMJXBn\nYcRxciD2oNmIp6kEdJ0Xf491QAlVXTMLBLqiXALHcRzHORbikU8pbEpN9kQBb1hbQHIx0MpUdShe\nUNVBzEhzikP2BIFWek+vpH9DGqu0OREQmrYMkJxswCpvjqtAwPNEwuM6D4/rPDyu8/AsRufariNM\nj3yKJwp4HloAcjHQhkXk0nhBRJ6KxaOdwIhQh/U5G2GTTAB1HDq/kuG2ESyE2UIirbQ92APUACXA\nATz/zHEcxzl+tmMpM634TM5g5JKD9lbgVhGJ+52sBV5VOJGcecj2nlmo88CFiiYV82qWUzo0Rsvj\nCaxB7RRmTB+zB83zRMLjOg+P6zw8rvPw5EHnndh/SQ02UeCeaH2TdIh4Ck1hWNBAU9Vfi8g5wFlY\nTtPjqjpRcMmc2TgyvAlw4KLYC1oNlFHeN0LzY5WYgTYADEcuasdxHMc5HnZihQKNWCpNnFZTjeWl\n9RdPtJVLLqOergfKVfUh4LeBL4uITxEoDkc2qB2tT3H4FDDXczNQSmV3mrqd2QUCxxXe9DyR8LjO\nw+M6D4/rPDx50PkOrBtAKWaQ1TOdh+aFAgUilxy0v1fVARF5JvBc4LPAvxVWLGcmIiSxeL8Sj9o4\neH4NQ2sHouVTAaFp66ILBBzHcRwni73AIGYzNOITBYKQi4GWie5fDHxaVb+NJZ87YWnGvq9eNkk5\nUEHXeWWMNqWBw8BaEmlo+00flo+WxIyz4/KgeZ5IeFzn4XGdh8d1Hp7F6jzKMduJRWvW4K02gpCL\ngbZXRD6FFQbcLiLlOb7OyS+z5J9dHD9XjhUIjNP6qGBGdQZL6vQmgo7jOM5i2YWFOeuAE3APWsHJ\nxdC6Hvg+8HxV7cM6B7+9oFI5s3FkBedUArrPir+/eqCcsv4xmrZWYNWb/UCftuv48byZ54mEx3Ue\nHtd5eFzn4cmTzndhhQJVwEnaroNYsUCFdIj3Ri0ACxpoqjqsql9V1a3R8n5V/UHhRXNmcOSIp+6z\nqxhcO4pVaq4DSqnsiQsEYBHhTcdxHMeZwQ5sdGA51l6jAi8UKCgeqlwGiFCJlTOP88pX9gPNdJ1b\ny+D6Qayi8wxAaNw2SGq8njwUCHieSHhc5+FxnYfHdR6ePOl8OxbiTHL0yCcPcxYAN9CWB3F4s4vz\nvtIApOg6N8lE1STQC6wnkRbW3j+E9baLe6C5B81xHMdZNDNGPsUTBdyDVkDcQFseHF0gcOgpEq0r\nByopHZ6g9ZGpaN0U1kSw93jf0PNEwuM6D4/rPDyu8/DkUec7gXEsF309XihQUNxAWx4cWSAw3FJC\n38kZYAI7MMoo6x+jcVsl1ietHzio7ZqZfXeO4ziOc8zsJqtQAOjDIja10iGlxRRsJeIG2hJHBCGe\nuxl70A5eUMtw62C0fDJWIDBOfWcVln/WzSIb1HqeSHhc5+FxnYfHdR6ePOp8FxadqcSK04TpSI17\n0fKMG2hLnwasMfAAm2QSaKDnzGoG1w3xZIGAJmh6fIxUOp7BeRifIOA4juPkl3jkUwk2OL0Rb1hb\nMNxAW/pkhzebAeHQecJUSTxrcwOJtLDmN8PY9zmJuaAXVSDgeSLhcZ2Hx3UeHtd5ePKo872YBy2B\nOQ985FMBcQNt6XNkgcBkmdBzRlwgUApUUzKSoeWRSez7zGAu5/7wojqO4zgrFW3XKWAPVijQhI19\ncgOtQLiBtvQ5skCg65xqhtuGsTDmBqCc8oE0jU+UYf1pBoF90ey048bzRMLjOg+P6zw8rvPw5Fnn\ne7GGtVXAKZiBpkCDdEgyj++z6nEDbQkjQgnmRp7CDoJWus+uZWht3KD2FKCUyq5x6ndWYAdJL97/\nzHEcxykMuzBHQBWwQdt1EovYxGFPJ0+4gba0aSWuytwkZUA1vWdUMNw8SnaBQPOjGUrGSrC5aIPk\noUDA80TC4zoPj+s8PK7z8ORZ553AKFAG1EuH1OATBQqCG2hLmyPDmwBd50j0tXUDJ5JIJ2jdPMJ0\n/tmiCwQcx3EcZw7ikU8prJLTJwoUCDfQljZHFgj0n1DGwAnjmKesHKindGSK1s0T2MEyAXRruw4v\n9o09TyQ8rvPwuM7D4zoPTz51ru06hDkIMphx1oZ70AqCG2hLmyMNtENPqWWkOe5/dhpQRulAmsbO\nEsxAG8X61DiO4zhOodiH/d9UM10oANAkHSJzvso5JtxAW6KIUANUAGNskkGghd7TaxhcFxcInAqU\nUbtXqdslWP+zvA1I9zyR8LjOw+M6D4/rPDwF0PkerB9aFXCitusYll5TAtTm+b1WLW6gLV2yvWd1\nQCm9p5eSrhvHDLQzbYLA1gylwyVYTsAwPkHAcRzHKSw7MQOtAmiL5nD6RIE84wba0iU20A4CrUxU\nJOg9DayVRg9wIsl0gtaHR7FKz7hAIC8GmueJhMd1Hh7XeXhc5+EpgM53YLnQJViY0ycKFIBUsQXI\nFRGqiy1DYNZE9zYQ/eD5NVH+WQ9QDzSSGlGaH5nACgZGgP3aruniiOs4juOsEvZhHjTBQpotuAct\n7ywbAw343WILUAQU84hdTs9ZNQy3ZeeflVPZk6Ghcwq7ihknjwUCIrLRr3TD4joPj+s8PK7z8ORb\n59quk9IhBzHHQA1WKPCj6Gn3oOWJ5WSgDRVbgCLQySaxUua+k2oYXLsfM9CeBpRRuwfqdk9h+Wdp\nbASH4ziO4xSafVhaTRVwkrbroHRIGqiQDqnUdh0prnjLn2VjoKnyxWLLUAykg1YgQc+ZKTLlGaYn\nCCRp2D5J2WAK854NkccCAb/CDY/rPDyu8/C4zsNTIJ3vwv53WoH1UXuNHmAdFubcVYD3XFV4kcDS\np5XeUysYWjOGuZNHgFNIppO0PDqG5QBMYSOeuufZj+M4juPki04selOG5aHV4w1r84obaEufVrrO\nqWGkaRArGGgAmigZhqbHJ4BSovCmtmsmX2/qvYrC4zoPj+s8PK7z8BRI552YgZbEKjlb8ErOvFJw\nA01ErhWRx0Rkq4i8c5bnf09EHhCRB0XkZyJyQaFlWma0cPi0WobWxAUCJwGVVO+Hhp2jmPdsCpuP\n5jiO4zgFR9u1H+jHRgxWAifjMznzSkENNBFJAp8ArgXOBV4jIufM2Gw78GxVvQB4L/CpQsq0nJAO\nKQPq6Du5iuG2EeAA8Yin2n1Qu3sc63+WwRoH5g3PEwmP6zw8rvPwuM7DU0Cd78fy0KoxA60P+z+q\njZrXOoug0B60y4FtqtqpqhPAl4DrsjdQ1V+oan+0+CvghALLtJxoJV2dpO8kRZMZLL5vBQL1O5SK\nvoIUCDiO4zhODsQjn6qBDdquU0Bv9JyHORdJoQ209cDurOU90bq5uAH4TkElWl60cPCCGkYb4wIA\nBU4lmU7StCWNxf7jGZyH8/nGnicSHtd5eFzn4XGdh6eAOt+JFa6VY4PSK/A8tLxR6DYbmuuGInI1\n8AfAlXM8fwuWlAjmRv1N7LaNf3wrbZlNlNN9di09P2yBL66DTzUAzZR+uY6R/4l1O8nPKecHXEU7\n+Xz/iyCv+/Plhb7viKUijy/7ciGWgYtEZMnIs0qWC3U+38Fm6iijnjOoApq5lVNYz5lcyZYl9PlD\nnb83YqHevCCqOdtQx75zkacDm1T12mj5XcCUqn5wxnYXAF8DrlXVbbPsR1VVCiboEkU65HX8uP1S\n7nnLNkabbsfcyB9hzb1NXHfDdtY+qNjVy39qu36/uNI6juM4q4koz+xfgUuAx4F/AbZgqUw92q5f\nLaJ4RSUfdkuhQ5z3AmeIyMkiUgq8CvhW9gYiciJmnP3+bMbZakU6pIapRDn9J1Uw2pTGKjhPA8qp\nOSDU7UlHmypewek4juMERtt1HMt/HsEmCpyChTgV/n979x4j53Xed/z78L4XksuLSIqkqJVI0dbN\nl0S2ZSSuVQdJHCeunQRF7cCXurHjpkkLpE2d/pF0NEVaJEKAAk5qx2nrS1K4dlvErdVaNlIjUiQ3\nsHyRLMm6WZRI8c4ll5flcnndp388592ZHS6XszszZ26/DzCY3Zl33vfw2QHnmXOecw7rrGxL29i8\nrtfSBM3dLwO/CXwDeBb4srs/Z2YfN7OPp8P+NbG216fN7Akze7yVbeoimxjfNci5jVPAWXcmKSYI\nrNkPg+NLifqzKWJ2Z1PVDrtJ6ynm+Snm+Snm+bU45geJLZ+KiQKXieU3lhCf7bJILd/qyd0fAh6q\neewzVT9/FPhoq9vRhW5g7I41aYLAUTMM2MnyiWWs23OeePNPAeNe8om2tlRERPrVfmKi2g5gS+o1\nO07sLLAB7XCzaF2zF6eVbXm725DZZk7euprJTWPE8OYIcAPDR5czsu8ksTggzJ4l2zRVBb2SiWKe\nn2Ken2KeX4tjvpdY7mk5sBpYTwxz7iIWrH2hhdfuaV2ToAEfaXcDsjuzfQ0TW18hErQtwFqGji1j\n3ctnid0DoDKzVUREJLdXqHQYLCd2uzmcftdSGw3opr04L/bVbXKjM3bnBJeGzhPfRm4FVjJ8FNbt\nK4Y4AX7UWFjnpjqR/BTz/BTz/BTz/Foc8zPABLEn9CoqEwUg1kbruxUYmqVretC85J9vdxtyMmMH\nYMCYO9Nm3AbTy1h9CAZPQPSgXaRFQ5wiIiLX4yV3K9sRKhMFbvaSn7eyTRIzO9cQkwZkgbqpB63f\nbE73xQSB3QyOrWTNgUlir7NpYMxLfr4VF1edSH6KeX6KeX6KeX4ZYn6A6EUbBramXrNicoA2Tl8k\nJWidayZBI76B3MDQ0RWs3TdBiycIiIiILMCrxFpoK4mlNYbRlk8NU4LWQcwwM7aY8ZPEpACIBG0T\nMMLQ8aWs33OW6EGDKM5sUVtUJ5KbYp6fYp6fYp5fhpi/THwuLSFKp3agBK1hXVOD1svM2EDsErCL\n+OZR2OvOeTNuAVYxOLaEkX1TVP5uezI3VUREpNYRYpLAJWIm562kvT+BbVa2twFPe8lPtad53UkJ\nWpuYsZpIyHYxe7Xls8BLwB73mW8gu1k6tYKhMWfo2EVgKfFtpWVbPKlOJD/FPD/FPD/FPL9WxzxN\nChgnFk8fAnZ4ySesbC8Cu4HbgdutbK8SidrBVranVyhBy8iMAeKbxS4qNWYA54lk6yXgqDte9ZqY\nIDB0dIDhwxPYzFTmY17yS4iIiLTfASIRGwa2A3jJH7ayPQHcTSRqO4AdVrYTwNPAHi/5lWucr++p\nBq3FzFhhxm4z3gV8APgJIjm7RKxh9hDwX9x5zJ0j1clZshrYxNCxFazdXz1B4NXWtlt1Irkp5vkp\n5vkp5vllivl+YqmNQWCTlW0FgJf8tJf8MeCLwHeIyQQbgPuA91vZ3mhlW5mhfV1HPWgtYMZS4pvC\nTmJV5aXpqWlgH1E7ts+dy3WcbiOwjqHjxQ4CRQK3r7mtFhERWbS9RAfCEmAF0Ys2U4aTloR6wsr2\nFPHZeDeRqL0JeGMaDn3aS6410xIlaE2ShiK3EsOXtxBvUIiE6hAxfPmKOxcWeOqbsSuDrDq5hPUv\nT5JpgoDqRPJTzPNTzPNTzPPLFPN9VNboXEJ8Dl5VJ52GNF8EXrSybSMStR3AHcAdVrZ9RKJ2KEOb\nO5oStAaZsYlIym4lunYLY0QStcedyQYucRsrTw6w8sxlhg+fTte4gmZwiohI5zhFTHIr6qRHr/eC\nNFngoJVthEjUbiNGnW62sh2nUqc2Pc9pepZq0BbBjHVm3GPG+4D3AncRidNp4HvAl935ijtPNZic\nAexm6NgAqw+fxbiYHhvzki+0J25BVCeSn2Ken2Ken2KeX46YpyTqCJGkDQM3LeC1p7zkjxJ1at8l\nZoNuBP4u8CtWtjf0Y52aetDqZMYwlbXKqhfeO0dlWYyxJl9zNXAjQ8dXsGb/GMwkaAeaeR0REZEm\nOEgkaFtJWz55yWsnvl1TqlP7vpXtB8Rn7d3AeuDNwI9Z2V4AnumXOjUlaPMwYxUxjr4LuLHqqQvE\nKv4vAYfnmHnZLDFBYODEMta9XN0Tt7dF15uhOpH8FPP8FPP8FPP8MsZ8H/H5uBwYIFYsOLLQk6Q6\ntReAF6xs24lE7SbgTmbXqR1uVsM7kRK0GmYsJ8bAdxGzUIph4MvEm+8l4IA7OdZu2caKiRFWnDPW\n7xkn3vCg+jMREek8xfaDRc3YThaRoFXzkh8ADljZ1lGpUxsFRq1sY0Sd2su9WKemGjTAjCVm3GzG\nO4APAu8gZpVArO3y18BfuPNNd/ZlSs4AdrHq5AArJi6z5uApIkG7RIYlNlQnkp9inp9inp9inl/G\nmB8j6scuEysZjDbrxF7yk17yvyHq1L6XrnMD8Xn9fivb63utTq1ve9DSshhbqMzArP7DHiF6yl52\n53wbmld4LYNjAwwdO4txDhghdhCYaGObREREruIln7SynSKSp0EqHR3NvMYU8D0r25NEb9rdxHaJ\nb2F2ndqZZl87t75L0MzYSCRlO4k9wwrjVIr9254AmTEEbGXwxArW7D8GM4vaZpkgoDqR/BTz/BTz\n/BTz/DLH/CDx+boe2Naqi6Q6tedTQlbUqW0nVlW408q2l6hTa2iItZ36IkEzYy2VGZgjVU9NUEnK\nxtvRtnlsZMmFDaw6vZSRfRNUhqP3trFNIiIi8zlArG6wFVhrZVvdylGfNEt0P7DfyraeSNSKBeNv\nsbIdI+rUXum2OrWeTdDMGCSSsp3ApqqnpqgsIHu0HW2r0xYGxzew5KKz7kdHiD05IdMEATO7T990\n81LM81PM81PM88sc89qJArcB389xYS/5OPCIle1x0oxP4vP/p4CzVrZngOe95BfnOU3H6KkEzYyV\nRFHiLiJ7t/TUJSrLYhxs4bIYzbSLlacHWT51hXWvniYW/rtAbBslIiLSifYTu91cIfahvoVMCVoh\n1al9t6ZObQS4F/hxK9vzRJ1a28uZ5tM1CZoZb7/GU0uJDHlbui+GAqeJGSUHgaPEnpg7gZ1mc5yl\n89zJwIkBho5OsmR6glgTbQw4mePi+oabn2Ken2Ken2KeX+aYnyQWq71ITL5r+kSBennJLwPPpYTs\nJiJRK/b/vMvK9gpRp9aRo2ldk6ABr6n5fR0xxXYjkaRBJGGnicTsOJHBr0m3LjN9I4MnlrN2/1Fi\ngoABh1JhpIiISMfxkl+2sh0lEqJh4DVWtg8SG6cfI5K3CWByIbsMNNgmB14FXrWybaBSp3YrcGtq\nb1Gn1jEjbN2UoD1OzAq5iZipsZIoRHyVyNj3E8WJ7VwWo3mGjt3D8skpVh8cJzaehQzrnxVUJ5Kf\nYp6fYp6fYp5fG2J+kOhUWU2slvAz6fEpYlP1k8C4le0MlYTtbO3PreiQ8JKfAB6uqlO7ndjxYDMw\nUVWndqnZ116obkrQ/iWz1yqbIpbGOE5sK3FruvWGwRMjLL28hPU/OkS8yZ1K8aWIiEin2kd8Zj0G\nPEzUhu8gRrOGiP2slxOJ2EkiaTuTXjPDynaOaydwE40kUV7yc8B3rGxPALuJXrW1wFuZXad2drHX\naJR55/TmXZOZOfhjxFDfmXS70N5Wtdi2v93Ktu/cxM/89tdYdmk1UVv3+2nbCxERkY5kZdsJlIjk\n6597ya9Y2YwoSdqebluIxWxXptsyYJJIvi4QZT3X2+3oAtfofSMSuLpH1FL7dhCJ2tb0sBNDs097\nyY/Vey6IvMXdG6p476YetD8lU4F8Wy0/u5RNPxzhtq/fy+jDl1l26TQx+eE4cKLNrRMREbmeY0S5\n0TAxe/JEqu0aS7cnrGzLgRuJov3tRF15YSmRrB0n6srPEj1uw8SI0nC6FcndxrkaYWW7TCVxmyuR\nO1fUnKX7fcA+K9tGIlErluraaWU7QtSp7c1Vp9Y1PWiNZqKdKu0dtoV4o24h3mhL0u83A88Tb5Sn\nveQPZGuX6kSyU8zzU8zzU8zzyx3z1Bv1APGZ9oCX/Ok6XjNEJVnbRuw9Xe0EUWd+EDiceuUGuDpp\nW111v+I6l52mkrTNGj5N99PEWmq3UymxOgM8A7ww3xBrv/Wg9YT0hiqSsRuJiQ/Vf8RieZAlxJtk\nfXo82wQBERGRxfKSu5XtIPE5N0r0PF3vNZPAi8CLKcErtooqhkM3pNvrgStWtsNEsnaAa8y+tLKt\n4OqkrTqRG2D+lR6cmIxY7OazNbXlZuDvpHXWnmpVnVrX9KBx/8yYcLcZJGaHbEq3tTXPXyG6cY9S\nWR7kMvA24s24m3ijfspL/u1MbRYREVk0K9svA+8Fvg38h0aGBa1sS4nEqEjYaoc0p4hk7SBwICV7\n9Z53rgSuuB9idgcKxOfxdiqf5ReJDpRn0/WjB+5+XumnHrR3t7sBdSoy8rXptqrm+StEF+npdD+Z\njrk53aoNEVl7MRYvIiLSDYpRn5uBf2hlO06lBu04cKbepC0tt1EkYI9b2VYRyVqRsA0T65rtArCy\nnaIyHHroWkOR6byn0+0qVrYlxOfwXMOnNxJLiWwmdiu4LZ3nIE2qF++mBO1wuxtwDUNERr0u3Vcv\nBXKBSK7GiQkOJ5ljKvE8TgKvpQ0TBFQnkp9inp9inp9inl+bYn4g3YaJocF1RDJVrG12sSZpG6t3\n66U0O3NPumFlW0uldm0rMTFhBLgLmE4bphcJ27EFJIbTVCYYXCUNxW4A7iFqxVen6zZlcfyuSdC8\n5A+2uw1Vf4wbqdSRrSKGJIs32RSRTB5J9+OL7dq1sv08kfCd4hoZvoiISAcaJ/a/XkplBub69PMy\not56qvpmZTtPpYetSNquW9/lJS96wX6Yer2K7R+3EzsObUm3e4jEsHo49Mxi/4Hps/048HUr2zeJ\nHrW7aFKC1jU1aO2YxZn+0DdQScg2c/WskEkiETtMzCw51cTr/waxuetDXvIvNuu8IiIirWZlK5bR\nGKFS9lN0DBUF/NVDh0XSdo5K4naK2CloJnGrt8YstWEF0atWJGy1deATVHrXDnrJG1pjNXXkjHI/\nLzeatyhBq75O2ZYRmXeRkG3i6l7GM8xOyOrqkl1EW4aATxAL533WS/5oK64jIiKSQ0peBqkka2up\nJG+riRGp2pmWy4nE7TyV5G2cSKj2pfuxtDNAPW1YTSVZ28rsOvFirbZiduixxW431Yy8pa8TtLRQ\nXvUaZJu4euXik1QSsiMLydwbbNso8BvEEOefesmfzXHdmeurTiQ7xTw/xTw/xTy/boh5GrFazdXJ\n2yZi9Kq6QL+64+QykbidBA4RvW17iaU35u1AqdrdoEjYNhNDstXnPkRlOLTuxfK1DtoCpUVhq9cg\n28jsKbTFeHJ1QtauzddvIN6M42gHARER6WGpIH/OGZWpM2UNld62LcBNRA/YeiqJ2w6iLKh43Rmi\nHvwQ8CqRuI0R20Bdqdnd4Mk0ila9u8H6dM4d6XznqAyHHvCSTzUzBlf9u3u5B83KNsjVi8JWmyb+\nMEVCdtRLfrHB5jaFle0XgL9P7CTwR41sCisiItKL0pIbxSzOHVSGLjcTw6O1poi1yqoTtxNUJuNN\nFhP7Ug5RvZzHYM25xpm9u8HlmXb10xAn97OK6O0qNlCt/bnoHt1MZcZGUQxYHHuF+EMcp7J0hc9x\nnvmu0+ix9b5mE/Am4K+85H/ecBBFRET6RBq+3ArcQiVxu5FYGqvIJwpTVLZ3OkUkbuNUevSK5G2A\n2ct5VI9CXiEWnI/lRe5nrJ+GOD88x2MDzB6vXlnzfLEIXbEw7ASRUReTADrZGqK9h9px8W6oWeg1\ninl+inl+inl+/Rjz1AtWLKcBzCRtI0RHTjF0uY0YIh1Iz90K/BgxGaF6j87J9FiRtP2AyvIhI8Q6\nb1vT7c3N+Dd0U4J2kUpCVoxFF+13KoWC41W308Qwps9xX8/PjR67mNcX9/cQqyKPNxw5ERGRPpeS\ntmLR+OdhZnLCOqImvVgzrTppG06PDxB5SLFw7VkiaZtOp79A5CQr02sa1k0JmnXweCYAAAk4SURB\nVBM9YkXyBZHNFgvCHgZONrLfVydJm6pDmyYI9Nu3rU6gmOenmOenmOenmF9bmpxwIt1egJmkbT2R\nmBWJW7FT0EC6bSASuaKDqNhUfZIYEm1YNyVob2R2D9kJIiBLqBTxYeXs69m2yuZ0rxmcIiIimaSk\nrahVB2Y2Vq9N2tZRWZqrSN42Ewlbw7opQdtDdCFCrFOyqY1tyeVsPdtctEI/1iy0m2Ken2Ken2Ke\nn2LeuLRgbbEkBzCTtG0gkrUicVvH7AkIi9ZNCdpX292ANjh+/UNEREQkt5S0HUs3YGbNtg3AZxo9\nf9css9GOvThFREREFqoZeUvttkYiIiIi0mZK0GROZnZfu9vQbxTz/BTz/BTz/BTz7qQETURERKTD\nqAZNREREpIlUgyYiIiLSg5SgyZxUs5CfYp6fYp6fYp6fYt6dlKCJiIiIdBjVoImIiIg0kWrQRERE\nRHqQEjSZk2oW8lPM81PM81PM81PMu5MSNBEREZEOoxo0ERERkSZSDZqIiIhID1KCJnNSzUJ+inl+\ninl+inl+inl3UoImIiIi0mFUgyYiIiLSRKpBExEREelBStBkTqpZyE8xz08xz08xz08x705K0ERE\nREQ6jGrQRERERJpINWgiIiIiPUgJmsxJNQv5Keb5Keb5Keb5KebdSQmaiIiISIdRDZqIiIhIE6kG\nTURERKQHKUGTOalmIT/FPD/FPD/FPD/FvDu1NEEzs3ea2fNm9iMz+51rHPPJ9PwPzOyNrWyPLMgb\n2t2APqSY56eY56eY56eYd6GWJWhmthT4E+CdwB3A+83s9ppj3gXscvfbgF8DPt2q9siCjbS7AX1I\nMc9PMc9PMc9PMe9CrexBezPwkrvvdfdLwJeA99Qc8/eALwC4+7eBETPb3MI21aXR7uCFvr6e4+c7\nZqHP1ftYTop5fop5fop5fop5fop5c7QyQdsG7K/6/UB67HrHbG9hm+p1X+bX13P8fMcs9Ll6Hhud\n55ytUHv9Vr++nuPnO2ahz9Xz2Og852yF2uu3+vX1HD/fMQt9rp7HRuc5ZyvUXr/Vr6/n+PmOWehz\n9Tw2Os85W6H2+q1+fT3Hz3fMQp+r57HRec7ZCrXXb/Xr6zl+vmMW+ly9jzWkZctsmNkvA+9094+l\n3z8AvMXd/2nVMQ8Cf+Du30q//1/gE+7+/Zpzdf5aICIiIiJJo8tsLGtWQ+ZwELip6vebiB6y+Y7Z\nnh6bRWugiYiISD9p5RDnd4HbzGzUzFYA/wD4as0xXwU+BGBm9wKn3P1oC9skIiIi0vFa1oPm7pfN\n7DeBbwBLgf/s7s+Z2cfT859x96+Z2bvM7CVgEvhIq9ojIiIi0i26YqsnERERkX6inQREREREOowS\nNBEREZEO07UJmpn9pJl92sz+o5l9q93t6QcW/m3anutD7W5PPzCz+8zs0fRef3u729MvzGzIzL5j\nZj/f7rb0AzN7bXqP/zcz+9V2t6cfmNl7zOzPzOxLZvbT7W5PPzCzW8zsP5nZf6/n+K5N0Nz9MXf/\ndeB/A59vc3P6xXuJxYUvcvWSKdIa08AEsBLFPKdPAF9udyP6hbs/n/4/fx/ws+1uTz9w9//l7r8G\n/GNilQVpMXd/xd0/Wu/xbU/QzOyzZnbUzJ6uefy6G60nvwJ8sbWt7C0NxHw38C13/23g17M0tkc0\nEPNH3f1dwL8Cylka2yMWG/PUm/AsMJarrb2ikf/PzezdwP8htgWUOjXhM/R3iX2zpU5NiHld2p6g\nAZ8jNlSfYdfYaN3MPmhm/97MtqbjdgCn3X0yd6O73GJjfgA4lV4ynbPBPWBRMffKNOtTRC+a1G+x\n7/O3A/cSX/4+ZmZaKLt+i/7/3N0fdPefAz6cu9FdblExTyUrfwg85O5P5m92V1v0+3whWrmTQF3c\n/VEzG615eGajdQAz+xLwHnf/A+Avqo77R8BnMzSzpyw25mb2l8Afm9nbgIdztbcXNBDzXySGfEaA\nP87V3l7QwP8tv5ue+zAw5lqLqG4NvM/fDvwSsAr461zt7QUNxPyfAT8FrDGzXe7+mWyN7nINxHw9\n8O+AN5jZ77j7H853nbYnaNcw1ybqb6k9yN3vz9WgPnDdmLv7FFD3+LlcVz0x/wrwlZyN6nF1/d8C\n4O5fyNKi3lfP+/wR4JGcjepx9cT8k8Anczaqx9UT83Gi5q8unTDEORd9Y81PMc9PMc9PMc9PMc9P\nMc+v6THv1AStno3WpbkU8/wU8/wU8/wU8/wU8/yaHvNOTdDq2Whdmksxz08xz08xz08xz08xz6/p\nMW97gmZm/xX4f8BuM9tvZh9x98tAsdH6s8CX3f25drazlyjm+Snm+Snm+Snm+Snm+eWKuTZLFxER\nEekwbe9BExEREZHZlKCJiIiIdBglaCIiIiIdRgmaiIiISIdRgiYiIiLSYZSgiYiIiHQYJWgiIiIi\nHUYJmoiIiEiHUYImIiIi0mGUoIlIVzKz3zOz583sUTP7opn9CzP7qJk9bmZPmtn/MLOBdOznzexT\nZva3ZrbHzO4zsy+Y2bNm9rmqc541swfM7Bkz+yszu9fMHkmveXc6ZtTM/sbMvpdub21XDESkdylB\nE5GuY2ZvAn4JeB3wc8A9gAN/6e5vdvc3AM8Bv5pe4sCIu78V+C1iE+MHgDuBu83sdem4QeCb7n4X\nMAH8G+AdwC+mnwGOAj/t7j8OvA/4ZCv/rSLSn5a1uwEiIovwE8D/dPeLwEUzexAwItn6fWAtMAx8\nveo1D6b7Z4Aj7v5DADP7ITAKPAVcdPdvpOOeBs67+xUzeyYdA7AC+BMzez1wBdjdmn+iiPQz9aCJ\nSDdyIiGr9Tngn7j764AyMFD13MV0Pw1cqHp8msqX1Us1j18EcPfqY34LOJyucQ+RsImINJUSNBHp\nRt8C3m1mK81sGPiF9Phq4IiZLQc+QCRyzbYGOJJ+/hCwtAXXEJE+pwRNRLqOu3+XqCN7CvgaMRx5\nGvg94NvAY0QN2qyXXePnax1zrdd8CviwmT0JvAY4u9D2i4hcj7m34gumiEhrmdmQu0+a2SDwCPAx\nd3+y3e0SEWkGTRIQkW71Z2Z2B7AK+LySMxHpJepBExEREekwqkETERER6TBK0EREREQ6jBI0ERER\nkQ6jBE1ERESkwyhBExEREekw/x9zNio2miqqwgAAAABJRU5ErkJggg==\n", - "text": [ - "" - ] - } - ], - "prompt_number": 26 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that, **for this model class, on this unscaled dataset**: when `C=10`, **there is a sweet spot region for gamma around $10^4$ to $10^3$**. Both the train and test scores are high (low errors).\n", - "\n", - "- If **gamma is too low, train score is low** (and thus test scores too as it generally cannot be better than the train score): the model is not expressive enough to represent the data: the model is in an **underfitting regime**.\n", - " \n", - "- If **gamma is too high**, train score is ok but there is a high discrepency between test and train score. The model is learning the training data and its noise by heart and fails to generalize to new unseen data: the model is in an **overfitting regime**.\n", - "\n", - "Note: scikit-learn provides tools to compute such curves easily, we can do the same kind analysis to identify good values for C when gamma is fixed to $10^3$:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from sklearn.learning_curve import validation_curve\n", - "\n", - "n_Cs = 10\n", - "Cs = np.logspace(-5, 5, n_Cs)\n", - "\n", - "train_scores, test_scores = validation_curve(\n", - " SVC(gamma=1e-3), X, y, 'C', Cs, cv=cv)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 27 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "plot_validation_curves(Cs, train_scores, test_scores)\n", - "plt.ylabel(\"score for SVC(C=C, gamma=1e-3)\")\n", - "plt.xlabel(\"C\")\n", - "plt.text(1e-3, 0.5, \"Underfitting\", fontsize=16, ha='center', va='bottom')\n", - "plt.text(1e3, 0.5, \"Few Overfitting\", fontsize=16, ha='center', va='bottom')\n", - "plt.title('Validation curves for the C parameter');" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "display_data", - "png": 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Jb0ge/xzw98BXJN1CKBTfZWYN/4HunDugFxBYJ8pn6LtrBHympnPOVaNu3Zq1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\n8yXO5ie4t+wyChM5X37ZNrT/W4k9NTui34tI+eotcr7I2SB+vqzq1Jy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- "text": [ - "" - ] - } - ], - "prompt_number": 28 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Doing this procedure several for each parameter combination is tedious, hence it's possible to automate the procedure by computing the test score for all possible combinations of parameters using the `GridSearchCV` helper." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from sklearn.grid_search import GridSearchCV\n", - "#help(GridSearchCV)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 29 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from pprint import pprint\n", - "svc_params = {\n", - " 'C': np.logspace(-1, 2, 4),\n", - " 'gamma': np.logspace(-4, 0, 5),\n", - "}\n", - "pprint(svc_params)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "{'C': array([ 0.1, 1. , 10. , 100. ]),\n", - " 'gamma': array([ 1.00000000e-04, 1.00000000e-03, 1.00000000e-02,\n", - " 1.00000000e-01, 1.00000000e+00])}\n" - ] - } - ], - "prompt_number": 30 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As Grid Search is a costly procedure, let's do the some experiments with a smaller dataset:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "n_subsamples = 500\n", - "X_small_train, y_small_train = X_train[:n_subsamples], y_train[:n_subsamples]" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 31 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "gs_svc = GridSearchCV(SVC(), svc_params, cv=3, n_jobs=-1)\n", - "\n", - "%time _ = gs_svc.fit(X_small_train, y_small_train)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "CPU times: user 295 ms, sys: 63.5 ms, total: 358 ms\n", - "Wall time: 1.03 s\n" - ] - } - ], - "prompt_number": 32 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "gs_svc.best_params_, gs_svc.best_score_" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 35, - "text": [ - "({'C': 10.0, 'gamma': 0.001}, 0.97599999999999998)" - ] - } - ], - "prompt_number": 33 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "gs_svc.grid_scores_" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 36, - "text": [ - "[mean: 0.16200, std: 0.02588, params: {'gamma': 0.0001, 'C': 0.10000000000000001},\n", - " mean: 0.71000, std: 0.03978, params: {'gamma': 0.001, 'C': 0.10000000000000001},\n", - " mean: 0.12800, std: 0.00161, params: {'gamma': 0.01, 'C': 0.10000000000000001},\n", - " mean: 0.12800, std: 0.00161, params: {'gamma': 0.10000000000000001, 'C': 0.10000000000000001},\n", - " mean: 0.12800, std: 0.00161, params: {'gamma': 1.0, 'C': 0.10000000000000001},\n", - " mean: 0.93800, std: 0.00586, params: {'gamma': 0.0001, 'C': 1.0},\n", - " mean: 0.96600, std: 0.00295, params: {'gamma': 0.001, 'C': 1.0},\n", - " mean: 0.26600, std: 0.02009, params: {'gamma': 0.01, 'C': 1.0},\n", - " mean: 0.12800, std: 0.00161, params: {'gamma': 0.10000000000000001, 'C': 1.0},\n", - " mean: 0.12800, std: 0.00161, params: {'gamma': 1.0, 'C': 1.0},\n", - " mean: 0.97000, std: 0.00466, params: {'gamma': 0.0001, 'C': 10.0},\n", - " mean: 0.97600, std: 0.00041, params: {'gamma': 0.001, 'C': 10.0},\n", - " mean: 0.32600, std: 0.02186, params: {'gamma': 0.01, 'C': 10.0},\n", - " mean: 0.12800, std: 0.00161, params: {'gamma': 0.10000000000000001, 'C': 10.0},\n", - " mean: 0.12800, std: 0.00161, params: {'gamma': 1.0, 'C': 10.0},\n", - " mean: 0.96000, std: 0.00730, params: {'gamma': 0.0001, 'C': 100.0},\n", - " mean: 0.97600, std: 0.00041, params: {'gamma': 0.001, 'C': 100.0},\n", - " mean: 0.32600, std: 0.02186, params: {'gamma': 0.01, 'C': 100.0},\n", - " mean: 0.12800, std: 0.00161, params: {'gamma': 0.10000000000000001, 'C': 100.0},\n", - " mean: 0.12800, std: 0.00161, params: {'gamma': 1.0, 'C': 100.0}]" - ] - } - ], - "prompt_number": 34 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "first_score = gs_svc.grid_scores_[0]\n", - "first_score" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 37, - "text": [ - "mean: 0.16200, std: 0.02588, params: {'gamma': 0.0001, 'C': 0.10000000000000001}" - ] - } - ], - "prompt_number": 35 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "dict(vars(first_score))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 38, - "text": [ - "{'cv_validation_scores': array([ 0.17647059, 0.1257485 , 0.18404908]),\n", - " 'mean_validation_score': 0.16200000000000001,\n", - " 'parameters': {'C': 0.10000000000000001, 'gamma': 0.0001}}" - ] - } - ], - "prompt_number": 36 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's define a couple of helper function to help us introspect the details of the grid search outcome:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def display_scores(params, scores, append_star=False):\n", - " \"\"\"Format the mean score +/- std error for params\"\"\"\n", - " params = \", \".join(\"{0}={1}\".format(k, v)\n", - " for k, v in params.items())\n", - " line = \"{0}:\\t{1:.3f} (+/-{2:.3f})\".format(\n", - " params, np.mean(scores), sem(scores))\n", - " if append_star:\n", - " line += \" *\"\n", - " return line\n", - "\n", - "def display_grid_scores(grid_scores, top=None):\n", - " \"\"\"Helper function to format a report on a grid of scores\"\"\"\n", - " \n", - " grid_scores = sorted(grid_scores, key=lambda x: x[1], reverse=True)\n", - " if top is not None:\n", - " grid_scores = grid_scores[:top]\n", - " \n", - " # Compute a threshold for staring models with overlapping\n", - " # stderr:\n", - " _, best_mean, best_scores = grid_scores[0]\n", - " threshold = best_mean - 2 * sem(best_scores)\n", - " \n", - " for params, mean_score, scores in grid_scores:\n", - " append_star = mean_score + 2 * sem(scores) > threshold\n", - " print(display_scores(params, scores, append_star=append_star))" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 37 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "display_grid_scores(gs_svc.grid_scores_, top=20)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "gamma=0.001, C=10.0:\t0.976 (+/-0.000) *\n", - "gamma=0.001, C=100.0:\t0.976 (+/-0.000) *\n", - "gamma=0.0001, C=10.0:\t0.970 (+/-0.003) *\n", - "gamma=0.001, C=1.0:\t0.966 (+/-0.002)\n", - "gamma=0.0001, C=100.0:\t0.960 (+/-0.005)\n", - "gamma=0.0001, C=1.0:\t0.938 (+/-0.004)\n", - "gamma=0.001, C=0.1:\t0.711 (+/-0.028)\n", - "gamma=0.01, C=10.0:\t0.326 (+/-0.015)\n", - "gamma=0.01, C=100.0:\t0.326 (+/-0.015)\n", - "gamma=0.01, C=1.0:\t0.266 (+/-0.014)\n", - "gamma=0.0001, C=0.1:\t0.162 (+/-0.018)\n", - "gamma=0.01, C=0.1:\t0.128 (+/-0.001)\n", - "gamma=0.1, C=0.1:\t0.128 (+/-0.001)\n", - "gamma=1.0, C=0.1:\t0.128 (+/-0.001)\n", - "gamma=0.1, C=1.0:\t0.128 (+/-0.001)\n", - "gamma=1.0, C=1.0:\t0.128 (+/-0.001)\n", - "gamma=0.1, C=10.0:\t0.128 (+/-0.001)\n", - "gamma=1.0, C=10.0:\t0.128 (+/-0.001)\n", - "gamma=0.1, C=100.0:\t0.128 (+/-0.001)\n", - "gamma=1.0, C=100.0:\t0.128 (+/-0.001)\n" - ] - } - ], - "prompt_number": 38 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "One can see that Support Vector Machine with RBF kernel are very sensitive wrt. the `gamma` parameter (the badwith of the kernel) and to some lesser extend to the `C` parameter as well. If those parameter are not grid searched, the predictive accurracy of the support vector machine is almost no better than random guessing!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "By default, the `GridSearchCV` class refits a final model on the complete training set with the best parameters found by during the grid search:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "gs_svc.score(X_test, y_test)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 41, - "text": [ - "0.98666666666666669" - ] - } - ], - "prompt_number": 39 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Evaluating this final model on the real test set will often yield a better score because of the larger training set, especially when the training set is small and the number of cross validation folds is small (`cv=3` here)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Exercise**:\n", - "\n", - "1. Find a set of parameters for an `sklearn.tree.DecisionTreeClassifier` on the `X_small_train` / `y_small_train` digits dataset to reach at least 75% accuracy on the sample dataset (500 training samples)\n", - "2. In particular you can grid search good values for `criterion`, `min_samples_split` and `max_depth`\n", - "3. Which parameter(s) seems to be the most important to tune?\n", - "4. Retry with `sklearn.ensemble.ExtraTreesClassifier(n_estimators=30)` which is a randomized ensemble of decision trees. Does the parameters that make the single trees work best also make the ensemble model work best?\n", - "\n", - "Hints:\n", - "\n", - "- If the outcome of the grid search is too instable (overlapping std errors), increase the number of CV folds with `cv` constructor parameter. The default value is `cv=3`. Increasing it to `cv=5` or `cv=10` often yield more stable results but at the price of longer evaluation times.\n", - "- Start with a small grid, e.g. 2 values `criterion` and 3 for `min_samples_split` only to avoid having to wait for too long at first.\n", - "\n", - "Type:\n", - "\n", - " from sklearn.tree.DecisionTreeClassifier\n", - " DecisionTreeClassifier? # to read the docstring and know the list of important parameters\n", - " print(DecisionTreeClassifier()) # to show the list of default values\n", - "\n", - " from sklearn.ensemble.ExtraTreesClassifier\n", - " ExtraTreesClassifier? \n", - " print(ExtraTreesClassifier())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Solution**:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from sklearn.tree import DecisionTreeClassifier\n", - "DecisionTreeClassifier()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 42, - "text": [ - "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n", - " max_features=None, max_leaf_nodes=None, min_samples_leaf=1,\n", - " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", - " random_state=None, splitter='best')" - ] - } - ], - "prompt_number": 40 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "tree = DecisionTreeClassifier()\n", - "\n", - "tree_params = {\n", - " 'criterion': ['gini', 'entropy'],\n", - " 'min_samples_split': [2, 10, 20],\n", - " 'max_depth': [5, 7, None],\n", - "}\n", - "\n", - "cv = ShuffleSplit(n_subsamples, n_iter=50, test_size=0.1)\n", - "gs_tree = GridSearchCV(tree, tree_params, n_jobs=-1, cv=cv)\n", - "\n", - "%time gs_tree.fit(X_train[:n_samples], y_train[:n_samples])\n", - "display_grid_scores(gs_tree.grid_scores_)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "CPU times: user 3.02 s, sys: 250 ms, total: 3.27 s\n", - "Wall time: 4.53 s\n", - "max_depth=None, criterion=entropy, min_samples_split=2:\t0.805 (+/-0.009) *\n", - "max_depth=None, criterion=entropy, min_samples_split=10:\t0.800 (+/-0.008) *\n", - "max_depth=None, criterion=entropy, min_samples_split=20:\t0.795 (+/-0.008) *\n", - "max_depth=7, criterion=entropy, min_samples_split=2:\t0.793 (+/-0.008) *\n", - "max_depth=7, criterion=entropy, min_samples_split=10:\t0.791 (+/-0.008) *\n", - "max_depth=7, criterion=entropy, min_samples_split=20:\t0.778 (+/-0.008) *\n", - "max_depth=None, criterion=gini, min_samples_split=2:\t0.774 (+/-0.008) *\n", - "max_depth=7, criterion=gini, min_samples_split=2:\t0.768 (+/-0.009)\n", - "max_depth=7, criterion=gini, min_samples_split=10:\t0.767 (+/-0.008)\n", - "max_depth=None, criterion=gini, min_samples_split=10:\t0.764 (+/-0.007)\n", - "max_depth=7, criterion=gini, min_samples_split=20:\t0.756 (+/-0.008)\n", - "max_depth=None, criterion=gini, min_samples_split=20:\t0.752 (+/-0.009)\n", - "max_depth=5, criterion=entropy, min_samples_split=10:\t0.748 (+/-0.009)\n", - "max_depth=5, criterion=entropy, min_samples_split=2:\t0.745 (+/-0.008)\n", - "max_depth=5, criterion=entropy, min_samples_split=20:\t0.744 (+/-0.010)\n", - "max_depth=5, criterion=gini, min_samples_split=10:\t0.661 (+/-0.009)\n", - "max_depth=5, criterion=gini, min_samples_split=2:\t0.657 (+/-0.010)\n", - "max_depth=5, criterion=gini, min_samples_split=20:\t0.640 (+/-0.010)\n" - ] - } - ], - "prompt_number": 41 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As the dataset is quite small and decision trees are prone to overfitting, we need cross validate many times (e.g. `n_iter=50`) to get standard error of the mean test score below `0.010`.\n", - "\n", - "At that level of precision one can observe that the `entropy` split criterion yields slightly better predictions than `gini`. One can also observe that traditional regularization strategies (limiting the depth of the tree or giving a minimum number of samples to allow for a node to split does not work well on this problem.\n", - "\n", - "Indeed, the unregularized decision tree (`max_depth=None` and `min_samples_split=2`) is among the top performers while it is clearly overfitting:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "unreg_tree = DecisionTreeClassifier(criterion='entropy', max_depth=None,\n", - " min_samples_split=2)\n", - "unreg_tree.fit(X_small_train, y_small_train)\n", - "print(\"Train score: %0.3f\" % unreg_tree.score(X_small_train, y_small_train))\n", - "print(\"Test score: %0.3f\" % unreg_tree.score(X_test, y_test))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Train score: 1.000\n", - "Test score: 0.780\n" - ] - } - ], - "prompt_number": 42 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Limiting the depth to 7 or setting the minimum number of samples to 20: this regularization add as much bias (hence training error) as it removes variance (as measured by the gap between training and test score) hence does not make it possible to solve the overfitting issue efficiently, for instance:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "reg_tree = DecisionTreeClassifier(criterion='entropy', max_depth=7,\n", - " min_samples_split=10)\n", - "reg_tree.fit(X_small_train, y_small_train)\n", - "print(\"Train score: %0.3f\" % reg_tree.score(X_small_train, y_small_train))\n", - "print(\"Test score: %0.3f\" % reg_tree.score(X_test, y_test))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Train score: 0.940\n", - "Test score: 0.776\n" - ] - } - ], - "prompt_number": 43 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From the grid scores results one can also observe that regularizing too much is clearly detrimental: the models with a depth limited to 5 are clearly inferior to those limited to 7 or not depth limited at all (on this dataset)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To combat overfitting, of decision trees, it is preferable to use an ensemble approach that randomize the learning even further and then average the predictions as we will see with the `ExtraTreesClassifier` model class:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from sklearn.ensemble import ExtraTreesClassifier\n", - "print(ExtraTreesClassifier())\n", - "#ExtraTreesClassifier?" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "ExtraTreesClassifier(bootstrap=False, class_weight=None, criterion='gini',\n", - " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", - " min_samples_leaf=1, min_samples_split=2,\n", - " min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n", - " oob_score=False, random_state=None, verbose=0, warm_start=False)\n" - ] - } - ], - "prompt_number": 44 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "trees = ExtraTreesClassifier(n_estimators=30)\n", - "\n", - "cv = ShuffleSplit(n_subsamples, n_iter=5, test_size=0.1)\n", - "gs_trees = GridSearchCV(trees, tree_params, n_jobs=-1, cv=cv)\n", - "\n", - "%time gs_trees.fit(X_small_train, y_small_train)\n", - "display_grid_scores(gs_trees.grid_scores_)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "CPU times: user 468 ms, sys: 105 ms, total: 572 ms\n", - "Wall time: 2.56 s\n", - "max_depth=7, criterion=entropy, min_samples_split=2:\t0.968 (+/-0.012) *\n", - "max_depth=7, criterion=gini, min_samples_split=10:\t0.960 (+/-0.009) *\n", - "max_depth=None, criterion=gini, min_samples_split=2:\t0.956 (+/-0.019) *\n", - "max_depth=7, criterion=gini, min_samples_split=2:\t0.952 (+/-0.008) *\n", - "max_depth=5, criterion=gini, min_samples_split=2:\t0.948 (+/-0.015) *\n", - "max_depth=None, criterion=gini, min_samples_split=10:\t0.948 (+/-0.010) *\n", - "max_depth=None, criterion=gini, min_samples_split=20:\t0.940 (+/-0.013) *\n", - "max_depth=7, criterion=entropy, min_samples_split=10:\t0.940 (+/-0.024) *\n", - "max_depth=None, criterion=entropy, min_samples_split=10:\t0.936 (+/-0.007) *\n", - "max_depth=7, criterion=entropy, min_samples_split=20:\t0.928 (+/-0.008) *\n", - "max_depth=7, criterion=gini, min_samples_split=20:\t0.924 (+/-0.015) *\n", - "max_depth=None, criterion=entropy, min_samples_split=2:\t0.924 (+/-0.017) *\n", - "max_depth=None, criterion=entropy, min_samples_split=20:\t0.924 (+/-0.013) *\n", - "max_depth=5, criterion=entropy, min_samples_split=2:\t0.920 (+/-0.014) *\n", - "max_depth=5, criterion=entropy, min_samples_split=20:\t0.920 (+/-0.000)\n", - "max_depth=5, criterion=gini, min_samples_split=10:\t0.912 (+/-0.017) *\n", - "max_depth=5, criterion=entropy, min_samples_split=10:\t0.908 (+/-0.012)\n", - "max_depth=5, criterion=gini, min_samples_split=20:\t0.896 (+/-0.010)\n" - ] - } - ], - "prompt_number": 45 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A couple of remarks:\n", - "\n", - " - `ExtraTreesClassifier` achieve a much better generalization than individual decistion trees (0.97 vs 0.80) even on such a small dataset so they are indeed able to solve the overfitting issue of individual decision trees.\n", - "\n", - " - `ExtraTreesClassifier` are much longer to train than individual trees but the fact that the predictions is averaged makes it no necessary to cross validate as many times to reach a stderr on the order of `0.010`.\n", - "\n", - " - `ExtraTreesClassifier` are very robust to the choice of the parameters: most grid search point achieve a good prediction (even when higly regularized) although too much regularization is harmful. We can also note that the split criterion is no longer relevant." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally one can also observe that despite the high level of randomization of the individual trees, an ensemble model composed of unregularized trees is not underfitting:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "unreg_trees = ExtraTreesClassifier(n_estimators=50, max_depth=None, min_samples_split=2)\n", - "unreg_trees.fit(X_small_train, y_small_train)\n", - "print(\"Train score: %0.3f\" % unreg_trees.score(X_small_train, y_small_train))\n", - "print(\"Test score: %0.3f\" % unreg_trees.score(X_test, y_test))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Train score: 1.000\n", - "Test score: 0.960\n" - ] - } - ], - "prompt_number": 46 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "More interesting, an ensemble model composed of regularized trees is not underfitting much less than the individual regularized trees:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "reg_trees = ExtraTreesClassifier(n_estimators=50, max_depth=7, min_samples_split=10)\n", - "reg_trees.fit(X_small_train, y_small_train)\n", - "print(\"Train score: %0.3f\" % reg_trees.score(X_small_train, y_small_train))\n", - "print(\"Test score: %0.3f\" % reg_trees.score(X_test, y_test))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Train score: 0.994\n", - "Test score: 0.944\n" - ] - } - ], - "prompt_number": 47 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Plotting Learning Curves for Bias-Variance analysis" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In order to better understand the behavior of model (model class + contructor parameters), is it possible to run several cross validation steps for various random sub-samples of the training set and then plot the mean training and test errors.\n", - "\n", - "These plots are called the **learning curves**.\n", - "\n", - "sklearn does not yet provide turn-key utilities to plot such learning curves but is not very complicated to compute them by leveraging the `ShuffleSplit` class. First let's define a range of data set sizes for subsampling the training set:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "train_sizes = np.logspace(2, 3, 5).astype(np.int)\n", - "train_sizes" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 50, - "text": [ - "array([ 100, 177, 316, 562, 1000])" - ] - } - ], - "prompt_number": 48 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For each training set sizes we will compute `n_iter` cross validation iterations. Let's pre-allocate the arrays to store the results:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "n_iter = 20\n", - "train_scores = np.zeros((train_sizes.shape[0], n_iter), dtype=np.float)\n", - "test_scores = np.zeros((train_sizes.shape[0], n_iter), dtype=np.float)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 49 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can now loop over training set sizes and CV iterations:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "svc = SVC(C=1, gamma=0.0005)\n", - "\n", - "for i, train_size in enumerate(train_sizes):\n", - " cv = ShuffleSplit(n_samples, n_iter=n_iter, train_size=train_size)\n", - " for j, (train, test) in enumerate(cv):\n", - " svc.fit(X[train], y[train])\n", - " train_scores[i, j] = svc.score(X[train], y[train])\n", - " test_scores[i, j] = svc.score(X[test], y[test])" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 50 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can now plot the mean scores with error bars that reflect the standard errors of the means:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "mean_train = np.mean(train_scores, axis=1)\n", - "confidence = sem(train_scores, axis=1) * 2\n", - "\n", - "plt.fill_between(train_sizes,\n", - " mean_train - confidence,\n", - " mean_train + confidence,\n", - " color = 'b', alpha = .2)\n", - "plt.plot(train_sizes, mean_train, 'o-k', c='b', label='Train score')\n", - "\n", - "mean_test = np.mean(test_scores, axis=1)\n", - "confidence = sem(test_scores, axis=1) * 2\n", - "\n", - "plt.fill_between(train_sizes,\n", - " mean_test - confidence,\n", - " mean_test + confidence,\n", - " color = 'g', alpha = .2)\n", - "plt.plot(train_sizes, mean_test, 'o-k', c='g', label='Test score')\n", - "\n", - "plt.xlabel('Training set size')\n", - "plt.ylabel('Score')\n", - "plt.xlim(0, X_train.shape[0])\n", - "plt.ylim((None, 1.01)) # The best possible score is 1.0\n", - "plt.legend(loc='best')\n", - "\n", - "plt.text(250, 0.9, \"Overfitting a lot\", fontsize=16, ha='center', va='bottom')\n", - "plt.text(800, 0.9, \"Overfitting a little\", fontsize=16, ha='center', va='bottom')\n", - "plt.title('Main train and test scores +/- 2 standard errors');" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "display_data", - "png": 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jlN5Pt/soXluwtIbWKmBxuL9+b6BMQPb8Y4RV7vMuDb56GVzOaSVhtG41SR0t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\n7+hhW8za8hmyXngJZ7CaDfn+mgGqa93Oq7iUiUzlal7PjWu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- "text": [ - "" - ] - } - ], - "prompt_number": 51 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note: learning curves can be computed with there own utility function:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from sklearn.learning_curve import learning_curve" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 52 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Interpreting Learning Curves" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- If the **training set error is high** (e.g. more than 5% misclassification) at the end of the learning curve, the model suffers from **high bias** and is said to **underfit** the training set.\n", - "\n", - "- If the **testing set error is significantly larger than the training set error**, the model suffers from **high variance** and is said to **overfit** the training set.\n", - "\n", - "Another possible source of high training and testing error is label noise: the data is too noisy and there is nothing few signal learn from it." - ] - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "What to do against overfitting?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- Try to get rid of noisy features using **feature selection** methods (or better let the model do it if the regularization is able to do so: for instance l1 penalized linear models)\n", - "- Try to tune parameters to add **more regularization**:\n", - " - Smaller values of `C` for SVM\n", - " - Larger values of `alpha` for penalized linear models\n", - " - Restrict to shallower trees (decision stumps) and lower numbers of samples per leafs for tree-based models\n", - "- Try **simpler model families** such as penalized linear models (e.g. Linear SVM, Logistic Regression, Naive Bayes)\n", - "- Try the ensemble strategies that **average several independently trained models** (e.g. bagging or blending ensembles): average the predictions of independently trained models\n", - "- Collect more **labeled samples** if the learning curves of the test score has a non-zero slope on the right hand side." - ] - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "What to do against underfitting?" + } + ], + "source": [ + "unreg_tree = DecisionTreeClassifier(criterion='entropy', max_depth=None,\n", + " min_samples_split=2)\n", + "unreg_tree.fit(X_small_train, y_small_train)\n", + "print(\"Train score: %0.3f\" % unreg_tree.score(X_small_train, y_small_train))\n", + "print(\"Test score: %0.3f\" % unreg_tree.score(X_test, y_test))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Limiting the depth to 7 or setting the minimum number of samples to 20: this regularization add as much bias (hence training error) as it removes variance (as measured by the gap between training and test score) hence does not make it possible to solve the overfitting issue efficiently, for instance:" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train score: 0.938\n", + "Test score: 0.771\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- Give **more freedom** to the model by relaxing some parameters that act as regularizers:\n", - " - Larger values of `C` for SVM\n", - " - Smaller values of `alpha` for penalized linear models\n", - " - Allow deeper trees and lower numbers of samples per leafs for tree-based models\n", - "- Try **more complex / expressive model families**:\n", - " - Non linear kernel SVMs,\n", - " - Ensemble of Decision Trees...\n", - "- **Construct new features**:\n", - " - bi-gram frequencies for text classifications\n", - " - feature cross-products (possibly using the hashing trick)\n", - " - unsupervised features extraction (e.g. triangle k-means, auto-encoders...)\n", - " - non-linear kernel approximations + linear SVM instead of simple linear SVM" + } + ], + "source": [ + "reg_tree = DecisionTreeClassifier(criterion='entropy', max_depth=7,\n", + " min_samples_split=10)\n", + "reg_tree.fit(X_small_train, y_small_train)\n", + "print(\"Train score: %0.3f\" % reg_tree.score(X_small_train, y_small_train))\n", + "print(\"Test score: %0.3f\" % reg_tree.score(X_test, y_test))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the grid scores results one can also observe that regularizing too much is clearly detrimental: the models with a depth limited to 5 are clearly inferior to those limited to 7 or not depth limited at all (on this dataset)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To combat overfitting, of decision trees, it is preferable to use an ensemble approach that randomize the learning even further and then average the predictions as we will see with the `ExtraTreesClassifier` model class:" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ExtraTreesClassifier(bootstrap=False, class_weight=None, criterion='gini',\n", + " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", + " min_impurity_split=1e-07, min_samples_leaf=1,\n", + " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", + " n_estimators=10, n_jobs=1, oob_score=False, random_state=None,\n", + " verbose=0, warm_start=False)\n" ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Final Model Assessment" + } + ], + "source": [ + "from sklearn.ensemble import ExtraTreesClassifier\n", + "print(ExtraTreesClassifier())\n", + "#ExtraTreesClassifier?" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 441 ms, sys: 82.8 ms, total: 524 ms\n", + "Wall time: 2.53 s\n", + "min_samples_split=10, criterion=gini, max_depth=None:\t0.968 (+/-0.005) *\n", + "min_samples_split=10, criterion=entropy, max_depth=7:\t0.964 (+/-0.007) *\n", + "min_samples_split=10, criterion=entropy, max_depth=None:\t0.964 (+/-0.012) *\n", + "min_samples_split=20, criterion=gini, max_depth=7:\t0.948 (+/-0.015) *\n", + "min_samples_split=2, criterion=gini, max_depth=None:\t0.948 (+/-0.005)\n", + "min_samples_split=2, criterion=entropy, max_depth=None:\t0.948 (+/-0.014) *\n", + "min_samples_split=20, criterion=gini, max_depth=None:\t0.944 (+/-0.007) *\n", + "min_samples_split=2, criterion=gini, max_depth=7:\t0.940 (+/-0.009)\n", + "min_samples_split=2, criterion=entropy, max_depth=7:\t0.936 (+/-0.021) *\n", + "min_samples_split=20, criterion=entropy, max_depth=7:\t0.932 (+/-0.012)\n", + "min_samples_split=20, criterion=entropy, max_depth=None:\t0.928 (+/-0.014)\n", + "min_samples_split=2, criterion=entropy, max_depth=5:\t0.916 (+/-0.007)\n", + "min_samples_split=2, criterion=gini, max_depth=5:\t0.912 (+/-0.021)\n", + "min_samples_split=10, criterion=gini, max_depth=5:\t0.912 (+/-0.024) *\n", + "min_samples_split=10, criterion=gini, max_depth=7:\t0.912 (+/-0.010)\n", + "min_samples_split=20, criterion=entropy, max_depth=5:\t0.908 (+/-0.010)\n", + "min_samples_split=10, criterion=entropy, max_depth=5:\t0.900 (+/-0.015)\n", + "min_samples_split=20, criterion=gini, max_depth=5:\t0.868 (+/-0.028)\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Grid Search parameters tuning can it-self be considered a (meta-)learning algorithm. Hence there is a risk of not taking into account the **overfitting of the grid search procedure** it-self.\n", - "\n", - "To quantify and mitigate this risk we can nest the train / test split concept one level up:\n", - " \n", - "Maker a top level \"Development / Evaluation\" sets split:\n", - " \n", - "- Development set used for Grid Search and training of the model with optimal parameter set\n", - "- Hold out evaluation set used **only** for estimating the predictive performance of the resulting model\n", - "\n", - "For dataset sampled over time, it is **highly recommended to use a temporal split** for the Development / Evaluation split: for instance, if you have collected data over the 2008-2013 period, you can:\n", - " \n", - "- use 2008-2011 for development (grid search optimal parameters and model class),\n", - "- 2012-2013 for evaluation (compute the test score of the best model parameters)." + } + ], + "source": [ + "trees = ExtraTreesClassifier(n_estimators=30)\n", + "\n", + "cv = ShuffleSplit(n_subsamples, n_iter=5, test_size=0.1)\n", + "gs_trees = GridSearchCV(trees, tree_params, n_jobs=-1, cv=cv)\n", + "\n", + "%time gs_trees.fit(X_small_train, y_small_train)\n", + "display_grid_scores(gs_trees.grid_scores_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A couple of remarks:\n", + "\n", + " - `ExtraTreesClassifier` achieve a much better generalization than individual decistion trees (0.97 vs 0.80) even on such a small dataset so they are indeed able to solve the overfitting issue of individual decision trees.\n", + "\n", + " - `ExtraTreesClassifier` are much longer to train than individual trees but the fact that the predictions is averaged makes it no necessary to cross validate as many times to reach a stderr on the order of `0.010`.\n", + "\n", + " - `ExtraTreesClassifier` are very robust to the choice of the parameters: most grid search point achieve a good prediction (even when higly regularized) although too much regularization is harmful. We can also note that the split criterion is no longer relevant." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally one can also observe that despite the high level of randomization of the individual trees, an ensemble model composed of unregularized trees is not underfitting:" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train score: 1.000\n", + "Test score: 0.969\n" ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "One Final Note About kernel SVM Parameters Tuning" + } + ], + "source": [ + "unreg_trees = ExtraTreesClassifier(n_estimators=50, max_depth=None, min_samples_split=2)\n", + "unreg_trees.fit(X_small_train, y_small_train)\n", + "print(\"Train score: %0.3f\" % unreg_trees.score(X_small_train, y_small_train))\n", + "print(\"Test score: %0.3f\" % unreg_trees.score(X_test, y_test))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "More interesting, an ensemble model composed of regularized trees is not underfitting much less than the individual regularized trees:" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train score: 0.996\n", + "Test score: 0.949\n" ] - }, - { - "cell_type": "markdown", + } + ], + "source": [ + "reg_trees = ExtraTreesClassifier(n_estimators=50, max_depth=7, min_samples_split=10)\n", + "reg_trees.fit(X_small_train, y_small_train)\n", + "print(\"Train score: %0.3f\" % reg_trees.score(X_small_train, y_small_train))\n", + "print(\"Test score: %0.3f\" % reg_trees.score(X_test, y_test))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plotting Learning Curves for Bias-Variance analysis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to better understand the behavior of model (model class + contructor parameters), is it possible to run several cross validation steps for various random sub-samples of the training set and then plot the mean training and test errors.\n", + "\n", + "These plots are called the **learning curves**.\n", + "\n", + "sklearn does not yet provide turn-key utilities to plot such learning curves but is not very complicated to compute them by leveraging the `ShuffleSplit` class. First let's define a range of data set sizes for subsampling the training set:" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 100, 177, 316, 562, 1000])" + ] + }, + "execution_count": 58, "metadata": {}, - "source": [ - "In this session we applied the SVC model with RBF kernel on unormalized features: this is bad! If we had used a normalizer, the default parameters for `C` and `gamma` of SVC would directly have led to close to optimal performance:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from sklearn.preprocessing import StandardScaler\n", - "\n", - "scaler = StandardScaler()\n", - "X_train_scaled = scaler.fit_transform(X_train)\n", - "X_test_scaled = scaler.transform(X_test)\n", - "\n", - "clf = SVC().fit(X_train_scaled, y_train) # Look Ma'! Default params!\n", - "print(\"Train score: {0:.3f}\".format(clf.score(X_train_scaled, y_train)))\n", - "print(\"Test score: {0:.3f}\".format(clf.score(X_test_scaled, y_test)))" - ], - "language": "python", + "output_type": "execute_result" + } + ], + "source": [ + "train_sizes = np.logspace(2, 3, 5).astype(np.int)\n", + "train_sizes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For each training set sizes we will compute `n_iter` cross validation iterations. Let's pre-allocate the arrays to store the results:" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "n_iter = 20\n", + "train_scores = np.zeros((train_sizes.shape[0], n_iter), dtype=np.float)\n", + "test_scores = np.zeros((train_sizes.shape[0], n_iter), dtype=np.float)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now loop over training set sizes and CV iterations:" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "svc = SVC(C=1, gamma=0.0005)\n", + "\n", + "for i, train_size in enumerate(train_sizes):\n", + " cv = ShuffleSplit(n_samples, n_iter=n_iter, train_size=train_size)\n", + " for j, (train, test) in enumerate(cv):\n", + " svc.fit(X[train], y[train])\n", + " train_scores[i, j] = svc.score(X[train], y[train])\n", + " test_scores[i, j] = svc.score(X[test], y[test])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now plot the mean scores with error bars that reflect the standard errors of the means:" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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XXnqJ7t27c/TRR0cJpUcffZShQ4fy6KOP8vTTT9O/f3+mTZvG1KlTG/UpUb+m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\nZHp4ALNMafkINfSaBiDm5vhEZCcihQi1j64CUAJgJ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+ "text/plain": [ + "" + ] + }, "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Train score: 0.996\n", - "Test score: 0.984\n" - ] - } - ], - "prompt_number": 53 - }, - { - "cell_type": "markdown", + "output_type": "display_data" + } + ], + "source": [ + "mean_train = np.mean(train_scores, axis=1)\n", + "confidence = sem(train_scores, axis=1) * 2\n", + "\n", + "plt.fill_between(train_sizes,\n", + " mean_train - confidence,\n", + " mean_train + confidence,\n", + " color = 'b', alpha = .2)\n", + "plt.plot(train_sizes, mean_train, 'o-k', c='b', label='Train score')\n", + "\n", + "mean_test = np.mean(test_scores, axis=1)\n", + "confidence = sem(test_scores, axis=1) * 2\n", + "\n", + "plt.fill_between(train_sizes,\n", + " mean_test - confidence,\n", + " mean_test + confidence,\n", + " color = 'g', alpha = .2)\n", + "plt.plot(train_sizes, mean_test, 'o-k', c='g', label='Test score')\n", + "\n", + "plt.xlabel('Training set size')\n", + "plt.ylabel('Score')\n", + "plt.xlim(0, X_train.shape[0])\n", + "plt.ylim((None, 1.01)) # The best possible score is 1.0\n", + "plt.legend(loc='best')\n", + "\n", + "plt.text(250, 0.9, \"Overfitting a lot\", fontsize=16, ha='center', va='bottom')\n", + "plt.text(800, 0.9, \"Overfitting a little\", fontsize=16, ha='center', va='bottom')\n", + "plt.title('Main train and test scores +/- 2 standard errors');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note: learning curves can be computed with there own utility function:\n", + "learning_curve method can replace the for-loop to find train_scores and test scores. More detail can be found at [plotting learning curve in scikit-learn](http://scikit-learn.org/stable/auto_examples/model_selection/plot_learning_curve.html)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Lzp07c/zxx4cIpaeffpoRI0bw9NNP89JLL9G3b1+mT5/OtGnTmtgUza7p06cz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\nx7zSUIi29uyoMYYdO3Y0ufvXU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vT3tz61erIYOnSo112QBFKe7lCW7lCW7lCW7lCW3lBB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\nQWNknu0wy3aYZTvMsh1mOQybUnRsStGWNWvWsGDBg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+ "text/plain": [ + "" + ] + }, "metadata": {}, - "source": [ - "This is because once normalized, the digits is very regular and fits the assumptions of the default parameters of the `SVC` class very well. This is rarely the case though and usually it's always necessary to grid search the parameters.\n", - "\n", - "Nonetheless, **scaling should be a mandatory preprocessing step when using SVC, especially with a RBF kernel**." + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.learning_curve import learning_curve\n", + "estimator = SVC(C=1, gamma=0.0005)\n", + "cv = ShuffleSplit(n_samples, n_iter=n_iter, train_size=train_size)\n", + "n_jobs=5\n", + "\n", + "train_sizes, train_scores, test_scores = learning_curve(\n", + " estimator, X, y, cv=cv, n_jobs=4, train_sizes=train_sizes)\n", + "\n", + "mean_train = np.mean(train_scores, axis=1)\n", + "confidence = sem(train_scores, axis=1) * 2\n", + "\n", + "plt.fill_between(train_sizes,\n", + " mean_train - confidence,\n", + " mean_train + confidence,\n", + " color = 'b', alpha = .2)\n", + "plt.plot(train_sizes, mean_train, 'o-k', c='b', label='Train score')\n", + "\n", + "mean_test = np.mean(test_scores, axis=1)\n", + "confidence = sem(test_scores, axis=1) * 2\n", + "\n", + "plt.fill_between(train_sizes,\n", + " mean_test - confidence,\n", + " mean_test + confidence,\n", + " color = 'g', alpha = .2)\n", + "plt.plot(train_sizes, mean_test, 'o-k', c='g', label='Test score')\n", + "\n", + "plt.xlabel('Training set size')\n", + "plt.ylabel('Score')\n", + "plt.xlim(0, X_train.shape[0])\n", + "plt.ylim((None, 1.01)) # The best possible score is 1.0\n", + "plt.legend(loc='best')\n", + "plt.errorbar(train_sizes, mean_test, yerr=confidence)\n", + "\n", + "\n", + "plt.text(250, 0.9, \"Overfitting a lot\", fontsize=16, ha='center', va='bottom')\n", + "plt.text(800, 0.9, \"Overfitting a little\", fontsize=16, ha='center', va='bottom')\n", + "plt.title('Main train and test scores +/- 2 standard errors');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Interpreting Learning Curves" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- If the **training set error is high** (e.g. more than 5% misclassification) at the end of the learning curve, the model suffers from **high bias** and is said to **underfit** the training set.\n", + "\n", + "- If the **testing set error is significantly larger than the training set error**, the model suffers from **high variance** and is said to **overfit** the training set.\n", + "\n", + "Another possible source of high training and testing error is label noise: the data is too noisy and there is nothing few signal learn from it." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### What to do against overfitting?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Try to get rid of noisy features using **feature selection** methods (or better let the model do it if the regularization is able to do so: for instance l1 penalized linear models)\n", + "- Try to tune parameters to add **more regularization**:\n", + " - Smaller values of `C` for SVM\n", + " - Larger values of `alpha` for penalized linear models\n", + " - Restrict to shallower trees (decision stumps) and lower numbers of samples per leafs for tree-based models\n", + "- Try **simpler model families** such as penalized linear models (e.g. Linear SVM, Logistic Regression, Naive Bayes)\n", + "- Try the ensemble strategies that **average several independently trained models** (e.g. bagging or blending ensembles): average the predictions of independently trained models\n", + "- Collect more **labeled samples** if the learning curves of the test score has a non-zero slope on the right hand side." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### What to do against underfitting?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Give **more freedom** to the model by relaxing some parameters that act as regularizers:\n", + " - Larger values of `C` for SVM\n", + " - Smaller values of `alpha` for penalized linear models\n", + " - Allow deeper trees and lower numbers of samples per leafs for tree-based models\n", + "- Try **more complex / expressive model families**:\n", + " - Non linear kernel SVMs,\n", + " - Ensemble of Decision Trees...\n", + "- **Construct new features**:\n", + " - bi-gram frequencies for text classifications\n", + " - feature cross-products (possibly using the hashing trick)\n", + " - unsupervised features extraction (e.g. triangle k-means, auto-encoders...)\n", + " - non-linear kernel approximations + linear SVM instead of simple linear SVM" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Final Model Assessment" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Grid Search parameters tuning can it-self be considered a (meta-)learning algorithm. Hence there is a risk of not taking into account the **overfitting of the grid search procedure** it-self.\n", + "\n", + "To quantify and mitigate this risk we can nest the train / test split concept one level up:\n", + " \n", + "Maker a top level \"Development / Evaluation\" sets split:\n", + " \n", + "- Development set used for Grid Search and training of the model with optimal parameter set\n", + "- Hold out evaluation set used **only** for estimating the predictive performance of the resulting model\n", + "\n", + "For dataset sampled over time, it is **highly recommended to use a temporal split** for the Development / Evaluation split: for instance, if you have collected data over the 2008-2013 period, you can:\n", + " \n", + "- use 2008-2011 for development (grid search optimal parameters and model class),\n", + "- 2012-2013 for evaluation (compute the test score of the best model parameters)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## One Final Note About kernel SVM Parameters Tuning" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this session we applied the SVC model with RBF kernel on unormalized features: this is bad! If we had used a normalizer, the default parameters for `C` and `gamma` of SVC would directly have led to close to optimal performance:" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train score: 0.996\n", + "Test score: 0.984\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 54 } ], - "metadata": {} + "source": [ + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "scaler = StandardScaler()\n", + "X_train_scaled = scaler.fit_transform(X_train)\n", + "X_test_scaled = scaler.transform(X_test)\n", + "\n", + "clf = SVC().fit(X_train_scaled, y_train) # Look Ma'! Default params!\n", + "print(\"Train score: {0:.3f}\".format(clf.score(X_train_scaled, y_train)))\n", + "print(\"Test score: {0:.3f}\".format(clf.score(X_test_scaled, y_test)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is because once normalized, the digits is very regular and fits the assumptions of the default parameters of the `SVC` class very well. This is rarely the case though and usually it's always necessary to grid search the parameters.\n", + "\n", + "Nonetheless, **scaling should be a mandatory preprocessing step when using SVC, especially with a RBF kernel**." + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.10" } - ] + }, + "nbformat": 4, + "nbformat_minor": 0 } From b589fa3d40dc0d40fcd82796096d7366dd6d4d6d Mon Sep 17 00:00:00 2001 From: namshik Date: Sun, 2 Oct 2016 22:59:45 -0700 Subject: [PATCH 2/2] little modification --- .../05 - Model Selection and Assessment.ipynb | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/rendered_notebooks/05 - Model Selection and Assessment.ipynb b/rendered_notebooks/05 - Model Selection and Assessment.ipynb index 07c8d49..63dedd0 100644 --- a/rendered_notebooks/05 - Model Selection and Assessment.ipynb +++ b/rendered_notebooks/05 - Model Selection and Assessment.ipynb @@ -1802,16 +1802,16 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 69, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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Lzp07c/zxx4cIpaeffpoRI0bw9NNP89JLL9G3b1+mT5/OtGnTmtgUza7p06cz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\nx7zSUIi29uyoMYYdO3Y0ufvXU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vT3tz61erIYOnSo112QBFKe7lCW7lCW7lCW7lCW3lBB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\nnmqJYpIXAMuAJcBewB8AX9vAfL48cazuv4cAD1D/Tknye8CHu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66SFC6bnnnqN///4899xzvPrqq3Tt2pUZM2Ywffr0Sm2K1q4ZM2YwcOBAHn30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\n+IdDsCtez9Y96ZjekR6tepDVJosB7QdwXPvjmp4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mhPnadWuJzYi1+1jspBgv6Bd4YdUL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2twuTpfaigGFsnTPCRL85AszUOyNA/J0hg9GkqhlPoB8CDwBPA2oICpwG+VUllaa5n+N4gFIgHq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\nukNZukNZukNZeiMjCiriBdSd1tq7AYwx3wK+CEwHF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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1830,6 +1830,8 @@ "mean_train = np.mean(train_scores, axis=1)\n", "confidence = sem(train_scores, axis=1) * 2\n", "\n", + "plt.errorbar(train_sizes, mean_train, yerr=confidence)\n", + "\n", "plt.fill_between(train_sizes,\n", " mean_train - confidence,\n", " mean_train + confidence,\n", @@ -1853,7 +1855,7 @@ "plt.errorbar(train_sizes, mean_test, yerr=confidence)\n", "\n", "\n", - "plt.text(250, 0.9, \"Overfitting a lot\", fontsize=16, ha='center', va='bottom')\n", + "plt.text(300, 0.9, \"Overfitting a lot\", fontsize=16, ha='center', va='bottom')\n", "plt.text(800, 0.9, \"Overfitting a little\", fontsize=16, ha='center', va='bottom')\n", "plt.title('Main train and test scores +/- 2 standard errors');" ] @@ -1964,7 +1966,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 70, "metadata": { "collapsed": false },