ensemble.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ensemble learning and Random Forests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a complex question you aggregate the answers of thousands of people and in many cases this aggregated answer will be better than the experts answer.\n",
    "This is called wisdom of crowd (https://www.nationalgeographic.com/science/phenomena/2013/01/31/the-real-wisdom-of-the-crowds/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similary we often get better predictions with a group of predictors than with the best individual predictor, this technique is called Ensemble learning and group of predictors is called ensemble ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To support both python 2 and python 3\n",
    "from __future__ import division, print_function, unicode_literals\n",
    "\n",
    "# Common imports\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# to make this notebook's output stable across runs\n",
    "np.random.seed(42)\n",
    "\n",
    "# To plot pretty figures\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['axes.labelsize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 12\n",
    "plt.rcParams['ytick.labelsize'] = 12\n",
    "\n",
    "# Where to save the figures\n",
    "PROJECT_ROOT_DIR = \".\"\n",
    "CHAPTER_ID = \"ensembles\"\n",
    "\n",
    "def image_path(fig_id):\n",
    "    return os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id)\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True):\n",
    "    print(\"Saving figure\", fig_id)\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(image_path(fig_id) + \".png\", format='png', dpi=300)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To create an ensemble classifier we can aggregate the predictions of each classifier and predict the class which gets most votes.\n",
    "Even if each classifier is a weak learner(slightly better than random guess) the ensemble can still be a strong learner"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Biased coin (51% head / 49* tails) tossed multiple times approaches towards this ratio where head is majority and the probabiliy to get head as the majority increases as we increase the number of tosses(law of large numbers).\n",
    "With 1000 coins getting head as a majority is 75% and with 10000 coins it can be 97%\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "heads_proba = 0.51\n",
    "coin_tosses = (np.random.rand(10000, 10) < heads_proba).astype(np.int32)\n",
    "cumulative_heads_ratio = np.cumsum(coin_tosses, axis=0) / np.arange(1, 10001).reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure law_of_large_numbers_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e363a10>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,3.5))\n",
    "plt.plot(cumulative_heads_ratio)\n",
    "plt.plot([0, 10000], [0.51, 0.51], \"k--\", linewidth=2, label=\"51%\")\n",
    "plt.plot([0, 10000], [0.5, 0.5], \"k-\", label=\"50%\")\n",
    "plt.xlabel(\"Number of coin tosses\")\n",
    "plt.ylabel(\"Heads ratio\")\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.axis([0, 10000, 0.42, 0.58])\n",
    "save_fig(\"law_of_large_numbers_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly if we take 1000 classifiers , each classifier is 51% accurate and 49% inaccurate and we take majority voted class , the accuracy will be 75% but as the same dataset is shared between the classifier so they are not completely independent of each other hence it reduces the accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.datasets import make_moons\n",
    "\n",
    "X, y = make_moons(n_samples=500, noise=0.30, random_state=42)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "log_clf = LogisticRegression(solver=\"liblinear\", random_state=42)\n",
    "rnd_clf = RandomForestClassifier(n_estimators=10, random_state=42)\n",
    "svm_clf = SVC(gamma=\"auto\", random_state=42)\n",
    "\n",
    "voting_clf = VotingClassifier(\n",
    "    estimators=[('lr', log_clf), ('rf', rnd_clf), ('svc', svm_clf)],\n",
    "    voting='hard')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VotingClassifier(estimators=[(u'lr', LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=42, solver=u'liblinear',\n",
       "          tol=0.0001, verbose=0, warm_start=False)), (u'rf', Ra...f',\n",
       "  max_iter=-1, probability=False, random_state=42, shrinking=True,\n",
       "  tol=0.001, verbose=False))],\n",
       "         flatten_transform=None, n_jobs=None, voting=u'hard', weights=None)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression 0.864\n",
      "RandomForestClassifier 0.872\n",
      "SVC 0.888\n",
      "VotingClassifier 0.896\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "for clf in (log_clf, rnd_clf, svm_clf, voting_clf):\n",
    "    clf.fit(X_train, y_train)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    print(clf.__class__.__name__, accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VotingClassifier(estimators=[(u'lr', LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=42, solver=u'liblinear',\n",
       "          tol=0.0001, verbose=0, warm_start=False)), (u'rf', Ra...bf',\n",
       "  max_iter=-1, probability=True, random_state=42, shrinking=True,\n",
       "  tol=0.001, verbose=False))],\n",
       "         flatten_transform=None, n_jobs=None, voting=u'soft', weights=None)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "log_clf = LogisticRegression(solver=\"liblinear\", random_state=42)\n",
    "rnd_clf = RandomForestClassifier(n_estimators=10, random_state=42)\n",
    "svm_clf = SVC(gamma=\"auto\", probability=True, random_state=42)\n",
    "\n",
    "voting_clf = VotingClassifier(\n",
    "    estimators=[('lr', log_clf), ('rf', rnd_clf), ('svc', svm_clf)],\n",
    "    voting='soft')\n",
    "voting_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression 0.864\n",
      "RandomForestClassifier 0.872\n",
      "SVC 0.888\n",
      "VotingClassifier 0.912\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "for clf in (log_clf, rnd_clf, svm_clf, voting_clf):\n",
    "    clf.fit(X_train, y_train)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    print(clf.__class__.__name__, accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "see the improvement in the accuracy with using voting=soft ,in this class is predicted with highest class probabilty averaged over individual classifier whereas in 'Hard' it takes the majority"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bagging and Pasting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get better results with ensemble, we need diverse predictors which can be created :\n",
    "\n",
    "\n",
    "1- By training them with different algorithms\n",
    "\n",
    "\n",
    "2- By training them with same algorithms but the data using  different sampled data for training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bagging and Pasting are sampling techniques , Bagging is with replacement which allows \n",
    "\n",
    "a- same sample to be passed to different predictors\n",
    "\n",
    "b- same sample can reach same predictors several times\n",
    "\n",
    "Pasting is without replacement :\n",
    "\n",
    "a- same sample to be passed to different predictors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "bag_clf = BaggingClassifier(\n",
    "    DecisionTreeClassifier(random_state=42), n_estimators=500,\n",
    "    max_samples=100, bootstrap=True, n_jobs=-1, random_state=42)\n",
    "bag_clf.fit(X_train, y_train)\n",
    "y_pred = bag_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.904\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "print(accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.856\n"
     ]
    }
   ],
   "source": [
    "\n",
    "tree_clf = DecisionTreeClassifier(random_state=42)\n",
    "tree_clf.fit(X_train, y_train)\n",
    "y_pred_tree = tree_clf.predict(X_test)\n",
    "print(accuracy_score(y_test, y_pred_tree))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparision of normal decision tree with decision tree with bagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure decision_tree_without_and_with_bagging_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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+rFabA44sH4Iw+haxlHyVs3XukZH7cF2HYvFlXNcgFOoEBMeP3834+LdXpb1Bjs3GxHrjRz6/f9XaHHBk+RCswDh9/bTK9pWesTWe23VNSqXDFItHUNUEqprwkzc9BQLLmuTkyXuJx3ef9fYut08jwNrAYhxZS/wIhdKY5iTT0w/6eyho2mZCIVHlRyXPbDXaHHBk+bDhB7BWI4JWym7dyuBZe27TPEU2+xRChFEUHcfJ47oGQoQJheK4roGmdWIYJ0mlrlr29raC9eBID9A6WuHIWuEHeBwpFF5CSgcpJYqiY1kjQC+KoqJpndV2rZYvKuDI8mDDmxCXWyljKWjVhFF77lLpFRQlghACXd+CEBpSOtj2JK5bxnXL6PoWwuEtgd5bgGVBKxxZK/wAjyNCKITDfQihIqUDqFjWWJUfkUhfoIl4DmDDD2CtaBDCytitWx08o9GdZDKPc+rUtygUXsFxirhumUTiMtLpqxBCwXFyKEqYWOxiFEVly5ZbAjt7gGVBKxxZTX50dt5EuTzE9PTDTEz8K4XCARynVMMPz+wObpUfnZ03Bb6ocwAb3oS4nEoZraJiFhkb+xq63kM0eiHhcDcw98GQz+9nauohotGLMU1PZcA0x0inX0s43E043I0QGpY1SiTSX2dmicV2BHb2AGeMVjiy3H6dfH4/ExMPIqUkFEoTjV5AONw9r4lPSomU3v9CaLiuhRCCeHwnmtZBobAP1zWJx7fXmSEDX9T6xoYfwJZTKaMRC4X2ev6EHmw7Sy73Y+BKwuHuOQ+G2lloLLadaPRCstknsKwTSHmhv/JS2bHjk3PaFtjZAywHWuXIcvCjUtdrePgeFEXHdSWuW65yRFXDcyaXk5MPEI0OkExeBoBhjJPNPkGhsA9Nux5F0YlGdzRVugg4sr6x4U2IK6VMvVBo7+yAdBGVorml0itNTRiN5ptwuJtU6ipc1wyUtAOcFawERxbyb1UmbfH4XqQ0AFCUMIXCC01NfAFHNi42/AoMVmYWNl9YcSbzBB0dNwCg65tIpV5NsXgQ0xxF066dY8JoZr5R1QhdXTcG1WMDnDUsN0cWCruvRBWGQgqp1KsplV7BsjIIIZoORAFHNi6CAWyFMF9YMXj+gwrZdH0TiqKjadc2JdtSxU6bYS3K6QTY2Fgo7L52QNL1Tej6Jmw7g6alm/bbM+VIwI/1iw1vQlwpNIbomuYpZmYewXFKZDKP+6XPF498Wsh800op9tVUSAgQYD40C2EvlQYpl4+Ry+07axwJ+LG+EazAVgi1s0LHKZPJPIEQgnT6Ghyn4MvbFEkm9ywa+dTMfLNYcmllVjkx8SBC6MTje/ww5ED5OsDqo3HVVCoNkss9TTJ5BdHoAIoSX1GOAAE/zgEEA9gKoTasOJN5glAoRTy+pxour+udaFr6tG30C/kQYDbSUUqJELIu0jFQvg6w2mgMu7esUZLJK4jFtgMQi21fMY54GomlgB/nAIIBbAVRmRVW7P1CzFpsz5QkC/kQaomraWkcp4yiRMjnf0K5HMc0J9D1LvL5/cEsM8CqoXbVdODAB5dd1mk+jmQyT5BOXx3w4xxAMICdBcyXCCqEztDQXU3zYGqdytHoTkqlA3X7RSJ9FApHMM1RHCeLqqbQ9R7i8e0+6TUKhRcxjDEcJ4sQcRxnhnB4K0KE0LSeoApsgDWDpXAEaJpf2bhNCJ3p6UeQ0kRVU0SjF6CqYcAbyEzzFLZdoFweAlSkNIlE+gN+rCMEA9hZQLMoqVJpECEEihKus893dNzA1NRDVbt9oXCE0dF/IhLZgZSlak2jdPo6crmnfTX6JLadwTBO0NX1FgzjFDMzjxMKpdD1bixLp1Q6iqKE61QNbDsT2PoDrAm0ypHBwc8gpSQaHahuO3r001VRa9c1KBQOMjX1GEIIbDuLqiZx3RLZ7BNEIgMkk1dQKg1SLL6MokSIRPopFg8BDkKESCQuC/ixThBEIZ4FNIuSCod7iUTOm6PzdvLkvXX6b6Y5CqgUi/vqahpNTHydSGQHmpbGdfNoWton5gFAIoQAQEpQ1SiKohOJnEdb2zXzylYFCLBaaJUjljWBZU3WbSuXj1MsHgBAVVMAFIsHsaxp2tquJRSKIqVFKJQkHO6lt/ddlEovAwIhwggRQlF0wuEBQqF4wI91hGAFtsJoNAdu3foeEoldHDjwQVS1s25fVU3OKYPiOFmkNJHSrav5VS4fx7ImCIUSQGWgilcJl0pdRal0uGpejEQGsO0MmcwPsO0soVAKTfNMjgECrBYWysFqxhHHMeYcw7ImkZIqP4Tw/hrGKMXiK1UORKM7kNIgkdhFOLwN287iODlUNUU4fB6KomAY41WOKIpOPL53he9AgDNBMICdARZLgFwojHc+m3+lDIrjGJRKhzDNcWw7A6iUSodQlDCK4hWwLJcHCYe34jg5DOMkxeLLpFLXkkzuxLIytLVdUz12JvMMhjGEZXkmR8vKUC57JscAAVYKC3FksVSQ5gobYaT09A5LpUM4ThbHyQM6hnEc1zVQlDCu6wBlLGt6Dj8Aksm9dcc2jHFmZh7BtvOoahIhNGw7h2GMBMEcaxiBCfE00UoC5ELlIOYr5bBlyy2USoNMT3+PUukItl30V2AmUno5ZaY5gqJoAJjmGFLa1bpgxeKLRKM7mxx7lETiCkIhz+QYCtWaHAMEWH4sxpHFyqU044imdfnBGR4/DOOkX7gyh23nEULHccpIWQSUpvzI5/fPObaqhhFCRdc3AxaKEiWVuppodGBO+ZYAawfBAHaaaKVW0UJ1lOZTD+jufitCRHDdAlJavi8rCkhsO+NXXu7CdQto2mZ/tmniOEWkFNh2hpmZR5v4E7aRSl1OW9s1dHbeSFvbNUSjA4GNP8CKYTGOLFZnrBlHBgZuR9M2VfmhKFEUJQooOE7Zr07u+bSEiDblx8jIfU2PHYtdRFfXW6r8CHLC1j4CE+JpopUS6vOF8VbKQcwnkFos7kcIHXABh1AogZRRpCyh622Agm1PYVljPnklqhoHJK5rMT7+LUxznGRyT9XnNjR0V0t1zwIEWC4sxpHF+AHNOWIYR9G0Tf6gZAAWQsQRwiIc3oyqpjDNU5TLxxBCIqWBosQQIjSHH7UmzYAj6w/BAHaaWKzIXz6/H9McbRrG29Pz0XmPm8/vx7ImEUJDiDDezDKHEDFUNUEi8Sqy2SfQ9c3Y9iSWlQHwS6fbSClRVR3bzlAoHGFi4n2Ew9vQ9R4MY4RodOC0RYEDBFgKFuLI6fLDO0YJy5pGUcKADihIWUZRYnR23ohhjFMuH0cIgesaSClx3bzPKaXKj0pYvq73IKWJEOGAI+sMwQB2mlhMAXty8gEikfPQ9S1+ra9sNYy31ond6OAeGbkfIUJ+FJSGEDquWwDy6PpOCoUXkFLS1vbTmOYk09PfBbyS6Yrirdp0fYufpJkBvFwYL0HTI3TlIRJUnw2wkliII6fLDwDHyeM4eX/lFkdRIr7pUEVKl0LhBRQlTDL5GnK5JxFCIETI94VF0XUvUMp1TYrFI5jmBO3t1/sTxYAj6wnBAHaaWKyEem1NI13fBICULqY5AjSPwBoc/Ay53D5CoS7fMW3gOAWEiKAoLuHwFgzjBOn01dUyE4ZxHNMcw3XLCKH4klUh32ySQohwtXxLJHLeGWnLnU3c9YkUI8fmds/efpvb7sy2vE+A1cNCHDlx4ounxQ8pJYoSQVFigIllzRAKJdG0bkKhKKY5gpQm6fTVhMObcZwMpjmOlLYf5duHECFUNUKp9AqqmvRXX8o5x5GNwI9VH8CEEO8Dbgb2An8vpbx5gX0/BHwEiAFfBW6VlZKtq4CFivxVzCeua1ZnmLV5Jc2ERi1rApCEQjFUtR/LmsJ1iyiKTkfHDVx00R9V7fSzbbiMbPYJoN0PFz7mr9zCvgPbqCZ3rieH9MixEH0D9pztw4OhJe1zLuBc5EitedE0T1EqvVKnQTgfP6SEcHgrihLz1TeKqGqUZPLVxOPbOe+82+o4kkhc5gv1CkxTwbJO4roWkch5OE6eUCiFoqSq7TqXOLIR+LEWrmQE+CTwZrxwu6YQQrwZuB34Gf873wDu9LetOXR23sTg4GcoFo80zStp5uB2HANVjeO6Zd9ub+A4RWw7h2lOVsN/jx79NJY1Wc15gQhSFgAFRdFQlBSOk8EwxlHVMPH4Hv/468ch/cJzGi8+p83ZLlehLWsA5xxHKuZF05ykUHgJIZQ6DULbzhGP1w98lSTmeHwPhvEYrlvCcUp++Pxz1ZzGRo5I6fq5Yi6u66DrPXiBUNNIadHWdn3NOQKOrCes+gAmpfw6gBDiSmChnvMu4EtSyhf9/f8n8HesQXKCN/PU9R5McwIpTRQlRTy+F1UN+/b/5kmaFd23mZnv+74vAJ1C4XmOHv00mze/3bfpe58IAVIWSCQuJxbbXk3wNAwbx8mSTF6HrndV88x6et65LirQFvOCLX3OnO0nh9VVaM3q4lzkSMW8ePjwx/AibdvrNDoN40Rd5XKYTWIGzw9m29OVT7DtImNjXyEW2wFQx5FQKIaUJqnUa1DVRDUBWtM247pl/7hu1UeXSr2mqcj2WkPAkTUwgC0Bu4Fv1rz/CbBZCNEppZxs3FkI8R7gPQD9/VvPTgsbIKVJe/v1mOYEpdIh8vln8KIKC8RiF2AYx4lGLyYaHcBxcmhaF1JKSqXDuK7ph9ILVDWGbecpl4c5efJe4vHdJBKXVs9z6tS3MM2TxGLbCYe7CYe7fWf2/qo6fcX/ALRUCHMx8q6HQXADYl1xxFPb6CeVuqrKkUzmB7huGccpY9szTfmRzT6FbWfr+CGlWS0lBBCJnNeUI21t11a1Disc0bR0tR+nUq+pE9MO+LG2sZ4GsASQqXlf8UImgTnklFJ+EfgiwJVXXrYqq+pKyZOK6rWUCoYxhBAqmtbdpOqsN1Het+8dgIsQEVQ16kciWtj2JFKWiUR2kM+/6AdvmDhOHsuaJBa7qOoQd5wcyeSeOc7ooaG7WiqECRqTkw8zNvZV2tpeT2/vu1qWAAqwaljXHJFSYlkzgIuiqIRCPUvmx6z/SmNm5kks66T/XuA45bpzN+NIq/yoVIo4dep9RCL9dTllAT/OHtbTAJYHUjXvK7aF3Cq0pSV0dt7EqVPvA7ySEIZxElDRtM2USodpa7umadVZXe/GE0nxwn+hYip0UNUU2ewP8cwm3vPJdW2EUJiZeZx0+mpUNdI0fyWf38/ExINIKdG0NNHohej6pjmFMB3HIJd7GkWJoKrtFAr76gi4UDXo5SJoLC7JzswVionFZ5+zvf12U4d0b/9cx/UGwbrmiFe3TgCqH4lbpq3t2iXxIxLpwzDGmZr6Ho5TQFF0X4IthxAFisUj1RVdI0da5UcolMYwxikWPUX7Sk5ZhSNngx+wOEc2Aj/W0wD2InAZ8BX//WXAWDPTyFLQiiDv6ZoCKiYS287gODnAI5iixHEcb/BpFvWUTL6K6elHfOe0RAhw3TKa1k443E+x+BKOM4MQKt7gqKFpXYRCCfL5Z+nqunFO/kplVui6NrY9g2mOUii8Qip1FZFID5FIXzWwJJ9/0Q9VjgDS90W0VQk4n8JCLvfCsvkO9vyUtWgE1bkSCryMOOscOVNTWS1HDOMEqppA0zr9KMPskvlRyaV0nDxChPwBzkFRwqhqGssaRVX1OWkvS+EH4AtrR1CUcJ2vrnIvVpofsDhHNgI/Vl0LUQgREl79AxVQhRARUZlW1eOvgF8TQuwSQrQD/wO470zOvZjYaCuCvYsd33Gyfv2iFLq+pZqjVQltbxb11Nt7M7HYTnR9M0K4fi2jdgYGfh9Ni5NOXw24SOmgKCEikT5UVae9/XoSCc8k0kiMyckHcF0H1zVxXRPwopey2ccplQarVZ49JfysrwKCX4MsVX2QeBGUx5iY+A4zMz/AMMYBKJUGKZePnfa9akRl9tj4Opdmj61irXLkTPlROf4sR5KoahJVjVc5slR+JBK7kNJA0zpQlLAfQOVxJBSKEYn0s3PEaU3gAAAgAElEQVTnn87hyFL4AVQ5UuEHzE5GKxJZk5MPVjmy3PyAgCOwNlZgHwM+UfP+V4A7hRBfBvYDu6SUx6SUDwohPgt8Dy+U+GsN31syFlvqL/Z5K6UiNK0Hy5rBsjK+dpvjh7bvrosMrEUisYvzz/9o02OXSgewrAzR6AW4btlXISijqpEFQ4DL5WFM8ySa1kYolPRzzBxA1KkfHD36aUxzAtcd8WetURKJ66rl3SvXZJrTvnDwYyiKjmWNomlbcBzDF209M7PJRpg9LgFrkiPe+4VNZUvhiFcS5QSua/qaiANL5gdU/GoHgdkaYR5XQmfMj0rov2XN4DjDCKGRTnv1+yocqZXIsqwpCoXvIKVJOLxt2fgBAUdgDQxgUso7gDvm+TjRsO+fAH+yXOdeTGx0oc/Hx7/N8eN347o2mtaJ45iUSs39RJXQXU89O0UyeRlSmmja5nmlauZLAK3kz+h6xcHt5bnMR/YKIpE+stlnCIU6URRRzTfzZqmzea5CCEKhNr8StI2UYNs5FEVFUWLVawqFkuRyz/lFNWOoageqGvGTRq8MlLyXEWuZIwt9djocARswiUQuIB7fPq+ZbSERgc7Om8hmf0K5PAjIqg8sFttelaNqRCv8SCR20dFxA8eP301tzmWxeAghtCpHKhJZudxzmOYoiqLhOFrAjxXAqg9gq4nFBHnn+1yIMMeO3Q0INK0T1zUoFl8mFru4qZ+oNrTdNEe46KLPLqmdjbPYjo4bKJUO4LqFakXZhcgOFWf5AziOV43ZdQ1ct0wkMlC93oo+XSJxaTWfzCvhPsqOHZ/kxIkvVivk6vomQqE40egOwCIUSuE43oqwVDpEONy9rpJCAzTHYhxZSKz3TDiyc+efLqmdjRzZvPntzMw8Si73DABtbdfWRdI2ohV+AJRKB0inr60GcszHkVBI8fmxHSHClMuH8fzVAT+WExt6AFtIbDSf349hnGJm5j8JhTqIx3dXo/sUJYqUNqFQJ5VOCWCaJ1FVHVhcKqdVs0GzkNxS6SH6+m5dkl5bIrGL/v4Pcfz43VjWJJrWSSQygKKoRKM7GRq6i7Gxr6HrPUSjF855oDSrkOvl4niz0Gj0QrLZpxAi7JtGm5tHzzY2gh7cSmI+jqRSr2Fm5rGm/KiI9bbKkfnk1lrFQhxJJFqbLLbCD88H/AKJxE8RCqUX5cis0r6Brm/x9UrXFj9gfXNkwwxg89nim4mNwmy+Rzp9HcXii8zMPEp7++vo67uVEye+WJ1Ver51UJQwljVJe7tXsnwxqZxWc0LOxA/XiO7utxKL7ajbPxrdWZO42YNtZ+vMHLWzxMaHmaLovuTPXnR9E6nUqykU9uG6Ak1Lrwkl742gB7dcaJUjtcm+zfhREetthSMLya212neWiyOL86MXRTlINvtDUqmrqwnRC3HES3WRJJNXIoRYc/yA9c2Rtd/CZYDjlBdMLGzsRLXJjN5Ma7NfzjxdnWU5junngYAQYUxzHNfNksvt4+DBj+BVUM6Syz3naxbq6PoWQiHvwd/ovJ2PZLncC9Uw/FDIW+loWmc1InCpCZON11t7rbHYRWSzTwHCV+oOz5klKkqUTOYJACKR8wmFZqV4FEUnGt2xDhM2t/Uuvs+5jaVwZDF+AHM44rp2VUg3lzvAwYO/h5QmxeIgtj2DbU+iqgni8cuJRHqaBjfMV15lodytpXJkoWsFiMf3MjPzOIXCC+j69XOsNpOTD2DbWQzjBEIogCQWuxhd78JxcuuSH3d9IgXsGFjtdjTDhhjAbDuzpMTCxRzX0ehOJiYewLbzeGIHDrZdJJm8Ak3rZmbmcYQQRKMX4bolQEXXexBCJZt9ikjkfDKZJ+pmeWNjX6mKjxYKB8lmf8LmzW+nXD4GiKqPKZt9iljsYkKhJIcPf4xSaRiQvoZiN7res6SZZ+21VlZRxeJBTHMUTbu2OkusfRB0dNxQJe6mTW+jVDrQtKTM+oGur3YLVhtL4Ugr1chrOeK6k7huHkUJE4/vpVh8iVLJ44dljQIqkcg2hAj5viK3jh+VgapWoLdQOMjU1GNoWgohdISQVX6kUq9GUXSECJ8xRxqvVdc3kU5fTT7/LKY50tRqE4/vrvJjy5ab1z0/PPOiYa52O5phQwxgXuG7ZN22hSKAFqskOzX1ENHoxajqSSxrEtueJpm8gnT6VWQyP6jmhRQKz6MoMaR0sKxpIpFtmOYU09P/4ee6KH601P9GCJVwuJdQqA0pDcrlQY4f/wtisYur0YZebpZBPv8TotHtlErDuG4RT8Wg5JtgZnDdAvn8fgYHP4NlTeA4BsXiQXK55xkYuL2OQI3XquubUBQdTbu2zsc2n5mmVDqwLmonBVgYS+FIK9XIazlSLB5EUcKkUtfgOFMt80NVkxjGKSYmHsA0s0hZRNM2VzlSLB5E17tJpV5DLvdjFCWCEGEKhX01uqJnxpHmotsRurpubEmCKuDHymLVE5nPBoTQqwmIFSwUAdTZeZNv1sggpVt1uHZ23lR9kMdi22lru5Z0+mpfFHQfmcwPMIwxFCXsF5LMo2mendx1i1hWHssaQ0qXUKjTT24cRMoirlvENE/69b8iqGqCcnkIVY0jhIZhDFMuH/bVNyAaHUAIScVB7oXq5vGImmNk5H6KxSNICaFQCimhWDzCyMj91ev0AlXGmZz8d6anH8YwxuqutRbl8vCSJgEB1heWwpGF+AHUcSQWu8ivlAyFwk9a5kc4vBXLmiGT+SGl0kks6ySOU67jCIBhjFEuH8J1DSzrFI6TxXVNPxjpzDkSje4kk3mcU6e+xczM4xSLRwJ+rCFsiBWYFxE0A8yNNmyGVirJApjmKd9npAEWjlP2VQV0VDWKqiZQlJCfA1PAtkfxCla2YdsZwAL8mg84OE6eYvEAmrYJRYkjpcvExHcQQkFRvOM5TrF6HYoSxrZLfql0FdctImUCVU2Ryz3t71NxoEdQVUku9zRQH7nV1nYdhcKLZDKP0tb2+qY2+sVm3ZVjrjUF7o2gB7ccWApHWq1GPssPFSEEjlNEiHJL/FDVBIZxGHCQ0gK8VaK3UjpELHYBIHDdHMXiEcClMh9va3utr+TRdcYcqawkTbNibcnQ3/+hc4YfsL45siEGMFWNLEi4ZliskqzrmkxPP+wPKMLP2gdV7cQ0x9D1TuLxSykWDyCEoL39erLZHyGli6Z1YhhDfm0jhy9/+Q7Gx/trWwxIurtP8Bu/8RkgjJQ2pjmOqsbRtA6/vEQ3oOG6eRyniKrGiMcvIR7fjmEMV+shVeAJnnr/15oEPdIJCoUXyOWeZnJyU/UeVLBQygGsXYX6+cKA7/pEig/f0uG/iyWa7rSBsFSOLFaNvFA4Qi73lD9ohXDdEqoaQ1XbWuIHgON4K7wvf/n3GvgBoNHdPcS7330HUjoIoSOl6VsyJkkmL8ayMsvGEVVNAALLmuDkyXuJxXacE/yAxTny5GM6kG47u61qDRtiAIOFCbcU1Ib+2randu26JooSx3UdFEVF0zp806JJW9u1eGoAJrreha73YVmjSOlSWYGNj/fT0zNYcxYNcBgd3U4kch6WNY2Uhi/t1IZlJZicPEo8vsmfNetACSkFpdLLdHW9hWRy3I8WFAjhqQk4Tt7XUax3ThvGuO9DCPslLZrXQPJCnE+gqkmSyb11D7izpcC9XKgPHbatVW3MGsFycSQa3cno6D/5Ci5xpDTxSqSkWuaH1+dtwPX5MUR9reEQo6Pb/D7s+PyI+JPDQQYGPlxVrDlTjszyI0Io1IFpTpzz/IBZjhw7qnLsyGq3pjk2zAB2umi27K9UWq4oyofD23wZmQiJxO455R9qj+Vpv7VRLg/5xBY1LwkoqGoU1zUAiRAhIpFtAEgpmZg4wgsvJjgyuIWfvvIoe/akMc1DqGqccLgXXd/C1NRDdHTcgGF4Zg/HyaIonrZcb+/NQL3Jo6KsDaBp9TptUBtdtas6s1wsWgvWkw8gt2bLjawHNHLEMMZJJq+orsAUJYamdaJpHS3zI5t9Es8k6DLLD/A4EvL7q0DT2lHVaPUYjlMCrDozp2mOYpqvnDZHZpXnPd1RXe+iUp0Bzn1+XPMGgycfzcysdjuaIRjAFsB8y37bztLefj2WNUk2+xSKoiKETrk8jG3PEIn0MzR0F/fc89sMD0cwjGHffJEkFPoU3d2DvOMdP/TDfyvlv706SEKECIXSWNY0Qii4rleET1HClEqnyBRs9pfjTCZKfOOHF7F16wzbtu2ps73bdoZS6UBV8DSXe8FXz1aqpKs1edh2xjfBGCQSngpCsxpIMP/MsRUfQIBzD804Mj39fdrarqO9/Wd8fkQQQseyJiiVBnHdXg4c+CBf+MK7GR8f8PMc6/nxy7/8LLHYxdj2lH8mb3IH+CZGrxaYJ+3m1dtzXQPHyfmrutoV5V0kErvn5cjIyP3kck/7grtbqvtUOOLpfXbgumVct0wisfes8sNxXOpXnwEq2BBRiKeL2s4phOL7i9r8kiO5at6UqkYwjBGkLBGLXUw8vgvLyvDyy6+QSv07vb0n6e8v09t7kq6u55ic3Etv73+jvf0NfpSWBnhKHV4BPhtPLDRCLHZxVcGgVCrxo1d2MV1KE9IUZKLA5OSReaOfEolddHbeRCiUJB7fTTy+uzoIA/T13Yqmpf0HgCCVenVdRedKDaRWoqsWi0wLcG6iGUd0vYNC4cU6ftj2FIoS9fuajq73cuxYiETiX9i8+cgcfmze/Au0tf007e0/46ePKFQ4IYTii2F3E4ttR4iKrBnEYtvp7X1XXRsX68OuWySdvpqOjhtQlHC1zEllFafrXdj2FKoaqXLkbPHDtm3+9bsvQ6yEqoTQNO30fqhzFMEKbAHMt+wHQSbzeFVlW9e3YFkzxGJXEottB7yZmDdjK6EoPQBVSR3DGKaz8yZKpXtIJHYTDvehqmN+HSJvRRYObyKR2Es8vt2v9XUtIyM6h0ePoSYNwnoUu1TGMNJ1BfWgfmbXOEP83Od+ieHhOIqik0jsAa7Gsn6HdPoxPvShh5DSrXNAT04+0NLMcT7JocnJBzhx4otrKuoqwPKhGUdisd1MTz/sBzkZqGqYaHQAXd+CoujVvuRxQ/fD6dvn8KOysguHe1HVk7iuiRBezrmmtZNI7GZg4PZFI/sWWv0szI80Hj++VuWHJw+VOWv8+PrXv8mIOIVQJW//+V9AUYI1Ry2CAWwBNOv4pdIgljVNLFYfWqtp7USjA3Xfd10T285SLB5CUcKEQh2oagzHKdZ16N7eUwwP78V1C0hp+QK5cfr69gGwdet7+OM/fg2PPnqcI+OnEJqDqoSwLYfST/JccYWXt9Is+qnxATMy0s62bVM4To5Uqoti0RNRHRnpwXVNHGdkTgTaQtFVtagNApjP/FpR0i+XhymX43SmM0ws0++1FNSHDoc3vBLH6aL54FDwA4K891J6/lvTHCMen31Ae+Hxwq+9ZfgcaV+UH4oSJRzuZcuWfZw48WUikT7uvffTjI31zGlff7/D7/7u/BGCtWkxUM+Prq5LMM1TFIuvcPRomkLhRVQ1RTK5Z8X40dd3KzBb2blUOk5nV5xpI8Wei3efwS+1dKwHjgQD2AJoFhpbKnklIbwkTW+1ZdsZCoUX61ZCpnnKD9KQvn/JxjRH0LQuVNX7XqVDf+Yzs53ZccpkMk8ghCCVugrLijA8fA+HD19Cd3eO0fIEImwTUjUsw2Zi4gL6+m5lZOS+qkZhMnlF9RqaPWCkNKqyVl7uSxJQcN3inLDexXJ+5kMz34BpTnL8+N2k09ei670Ui8O86pLneXpscT/Acitm137nq391eHDJBwgAzM+RROLyKj+gOUdA+P5XrcoRL7R9No2jkR+qmqRUGiSXe5pk8gp0fQDLynDw4CCXXELVBF7B4KBa7cPNODIfP0KhVDWPzYs+7PGL0NYHaCwnPwBGRu7DdUvVge3SS8GQz/DUWB+f/6v/w0du/Z2mx1sJRfn1wJFgAFsAzTpnOLxtzkrLK4OerEsELRT2IcTP+yYPx1cjsLGsCcLh19V9v9KZP/e5X2Jw0ELKXwJACBVd76anZxTDGCYUyrG1a4xI2MByw5yaSYAX44Hrlkinr64+RCqzucYHjFcyveyv8iI1SZx6NbKqkXynE17dzLTkqSjYVbIqShLD1Lmg+2Tdfs3I+ORjOj1bHa55g1G3fT0oZp/LOBOOeKkk0vdrzeZfNaL2YX/33TcwOGjhur+Komjouqfk8fLLPfT1/RhVjWPbXk2vWOxCYHZV1owjnkXgoWqbKvxIJPZSLL5S5YhX6LV5gMZy8UNVk2QyT5BOX109V3t7H5vbj3OBdYqnhk9gmia6rs/hyJOP6cQTkmTarePIuc6Pc/vq5sFSMuKbqVM3s3knk3urUlO53AuUy8P09JxkbOx8fy9ZnWledlkSyFe/X+nMIyPt9PQ85QdyeMoDsZjG8eObse1potEhQoqDYYfQVIfe9klOFUoLRkKdd95tdQ8YRdFJpV5NNvs0ihKru9ZG53PtffIGYoGURkv+rGYz20qdpVoYlk4yOVm3rVl5hxef08hlAvv/2cJKcmRk5H4ymYfo6uphbGwnihKiwg9VTdLbOw5cWP1u7cO+wpFK4nIs5gU1PPXUNkqlIWKx7f5A5An7WtZ1gFhQy7MZP3R9E9ns0wsGaCw3PypSXo3nTCQ6SGXGAM8UC3M58uJzGqk2l+zMxuLIhhvAzjQjfqGM+8r3S6UhwuE+fuu37sVT6ShXFbK9HJj6gn2Vzgz4vgO7+j94vgLvpeG4NqguLiqu6yKZ4kMfOp+xsX5mc2UAJJs3H+PCCz8JwM/+7JW86U23kUik0XUHr+psueo4h7mCrBX1b89pPY0QcTo6rm/pnjW7T0KE0PUtdfuFNZNcOdbsEAFWCSvJEZiN+vvAB75RrZdVGTQqZVng2urxGh/2Xsh8uWo98I5poqrR6rbagJBPfOIS9u17azUKsoItW6b47d++t24ArvADWJAjK8EP254hmbxiTlAWlMiW4ove942IjTVcM2uOcByDTOaHZDJPUCweZmTkvpa+XzGZaFra92ml6zpq5fjx+B4/GZmqQvZ8YbOVEFvXNX0ndgHLmsG285RKR3GcvD/Dm51vbOrowpEKSrjMsdE+UpuGiHcer75Sm4Y4NtrHK9YMr1gzfOkff8zw8DH6+x0GB1VOndrL8eNJjh2Lc/x4Gz09o3XtGxm5j3J5EADHMQDVV/B+vppOUMkpa/U+9fd/CEVRq6HErpsjrJscGt8y73ECnH1MTj6A6zrk8y8yNfUQ+fyLuK6z4O9di4U4UrsSikYvxMtvEhSLB+cNLa8PQfeS+207i21nKZePYVnTgIum1fu/PMHgIseOqZx3nsHWraP09U1XXydOJOZEC1b40ciRLVum6tq3Evzo67uV3t53zQm3F6LAoZFtLd37jYYNtwLzlv8audzTvn07iZRlpqe/33IV2IVs3hVzh5eMvJ18/ic4Tg5F0dm6df4ien19t/qyVEWEUFFVvWa2KOjrK3DwYBfThSQoLjF1K7lplUTbCMdGLmCLZiFcB9tVCSkOquKSsyIoyRJSghly+Pa3/5077+yvHjOfl0xOfqPGTDTbvlzuGV9sNQKY1ZVhuXyMmZkf+MQSSzItAXUVbxUlyTMvXcpk2F30ngc4e8jl9lEuH6/yw3XLfkmfQsvHmI8jjb4fITRM8ySGcYJY7MKmq5ZaP5uX9Jz3j2HhOCVc1yAWcxkZ2YyizAbLua7Jtm0FslmIxS70hYWpykZJqc0ZLO+8M1/zrp4jmjYboLFS/ADm+BQt6wYms0dRUqVF7vrGw4YbwLzcj4frAhikFOh6x7y6ZEvxB1TMHY5jUC4fQdc3IWUXiiKYmnpojghoBV5nTqPr04RCqTrziKrGef/7/5bBwZd56UQGUxFcsbOH6YlnyBRjfOHP7mEqlyKmG4Q1C8PSGMunMMoR3JIOUhCa7uDN73190+vZuvU9AHU5KY5TQlU9/4JHeBvXtXDdkm9W0VEUMSc0fin+kmPHjjCZuR+6Ty36uyXTLqMn1DlO6fWgmL3e4PliRJ05rqJyMR9a5UgtPyr6grq+FUURfvHX5qj0G007TiSyrcF8WGb37hN86lN/Xo1UdJwc5fIQut7D7beHaWsziEZ3+Ca/SpDHxSQSoun5Gq+nvf2NlEoHVpwftddawUsvfQc4Ou/+4PEjO6NQyIs6jpzr/NhwA1hn502MjX0VVW0HJK5r4LplIpHtTEw8OKeTLdUfULFtF4uHa3xYBvG45wNbSLyzv99h374witJWZ6vfunUKKQ1KpTdStr5Dd8cEmekxJvIpiuNbiAhBd6zE1FQPecPzJ+lA3A3Tb3YhBLzhbbvZvv2Cptdz9OinEUIQiZxX3QZgmhOEw5vQtHZfDqtAKJSsuybbztWFxi+Hynaz8g795ztc9XrjtEOCA7QOVU1hmtO+n6ki0VRCSoMDBz445yG8FI6cCT8ANm8+xokT2+r4IaVky5bjdSsXb1DxqjArShTXzVAqHa5Tm5mZUYHMnHM0Xk+hcITR0X8imbyCaHRg1fkBcznSf74DOGcUNr8eseEGsERiF21tr6dQ2Oc7S1OoapJs9geA8GdvJqXSPVVCVHxm+fyLvqagzsjIfVx00WebHr+v71YOHHg/Uko0LU0isddfibkLinfeeWeeoaFvz4lO8hzbfTjOVn504FKu3vsMRUtSKCps7YzQ07OFLVsk27ZFSKWuqn7vW9+KMTW1ByHgK1/J8dd/fRzDyNLT8yZuu+1xQiHFN3VOIgQkEpcCXoRWInEZ+fxzCAGu66Bpm3HdYSCMZZ1CUcJ+EnQeKWdD45dDZftsEXA2FHnHwFk54TpBMrkHRYlhWaPYtlcg0rImUBSNUmmojh8Vv1bFZ+Y4WVQ1ha73zJuScbr8APjd330Cy3qwCT/SJBK3Vc83NHRXVfXDk6/yVmzF4it1uWKf+ESCY8dULGumqlnqOCW2bv0lfu/3ngTANEdR1QSWNUostn1Bfqhqyi/v4rkpLGsGUJZdhT7giIcNN4AB9Pa+qy5xeGLiXwCXcLgf1zUoFr1k5cpsbqk+s0RiF11dN56WeOfCEVwvAZCMFCnZKcBB0wocParx5JOvAkDT9KofIJcT7NhBXUJmuXyCkye3kc0+VZ2NVoJNahGNDuC6RZLJPdVVaS73MqXSAVQ1iRBeJFi5PEgkMlD33ZVS2V7uZM3ZUGTDXIbmnTOoyJzF47ur/BBCQde3zuGH5w9ams9s5fgxi1pf29BQkh/9qIeKIK6mKSiKTm+vF23Y2ztaIzocplg8wvHjfZjmKV/3MOvnsM32sWb8MIxxbDtPsfiyf6wYjjOMomgYxjjhcHe13Uvhx/j4DKheW0VjAbMarEQy81rnyIYcwGqdwhXVC03rJxSarWtomidRVf20fGbQOtEWatvczH5vAMuVY8TCFiHdIJmcYOvW41x55Y/862ivDkx/8zezs85K+2urz5ZK3mzUK/Fe3w4vb2dPXdmLgwc/QrHoPQQqiadChKoVc2u/u1QV+vv/bAdmvmPO9lryNcsPg3M/WfNsYyn8gNPzma0MP2ZR62vbvPklXvWqcT+CcZYjIyNeknMtP7z2xABR5YeqpmrC+6lecyM/8vn9vPzy+/x74ZlePcGAFKXSoeoAthR+jI2d5IHHj0B3mcEnf43bf3OuXFaFIxuRH+fulS2CiqO0oiZduwqpqL+3t187r88smbxiwVlUIrGLjo4bOHnyXgzjJOHwFrZsueWMoxwBDp/aypUDR0jGZnDdSi0kB13vRQiVXO45VDWOZV3KzMzzlMvHAHyxYOH/9QpXesTsRAjhR3iVKRZfxDSnaG9/Xd0qU0qDdPpqyuXDVUd4On0NxeIL2HamKvNTLL5cLSnTqoDv+EiEPZduLPKtZbTKD2juM5PSRVVTCx5/pfgB9b42IX7R31rPkXy+D8cpomkv+Wkqsz4zkJjmBFK66HqPHyW5E8MYm5cficQuIpF+n0c5nx9XUSwewrK8Yy2VH//3//4jZscEqqKyrfPqDTdALYYNlwfWiEikD13fUq31I6XEtrMoSqgaYquqaQzjGKXSYaR0qiUiFppF5fP7mZp6iHh8N5s2/Rfi8d1+Ts3+M27zZKGN50d2oSguimIjhOordsdxXZtyecivI6ZiWdOY5ilsu+DXH/N+cscp+7PRNOef/1EGBm7HdU0ymUeREtrarqsrLVG5V6oaIZ2+hs7OG0mnryES2UJb2+vRNE/stKIVWSkpU/v9AOsPi/Ejn9+P42Rx3SKWdQrTHEdVI8Tjl5BM7pn3uCvJD5hdqUlpIqWLEKEmHKlM6Mp+3pVAShvbnkZRouh6F6Y5Qjy+nfPP/xihUHJBfoDnP0wk9lT5EY/vJB6/BF3vOi1+TE5ZoLr0dveia2tST3dVsXGHbh8Ve3+turwQIbZt+xDgKU1Hozv8Ug4KUlp+qXS1au5oFkK8kFhnONzdckjtfJgudnBkfCubVQ3DiJDNZoEyQowDAtMsYNsWhcIIzz77WgqFJF6pFoVSKcbHP34nXV2bufnmwxw6NAiAoswAFwBxMpksoVCR7u501VQ6n9mn4swfGrqLSKR/2R3WAVYPrfBD03owzWl/ciTRtB4URa1OAJfCj0ryb6tpKwuh4mvT9U1EIrNqL14QUhRF0TGMEUDwzDNXUyi0Vcu1lEpR/viPv8aOHclqblipdICOjjc1qGTU9+9mHFEUlR07Psnk5ANL4oeUEtP29CIrE88A9djwA1itTb1Sd6tCmKGhu6okC4WSlEqvYJoTWNYoO3Z8csEwe9vO1ZWOAG/VMzPzKJ2dbzqtkNp/+IfX8Mz+vQjNIZ1o48nSGJ3RAscG+3DFEUKqQ1uqRCaXwrILqKFpTp5sZ2JiC9Fo3hdPdUmnRwhHT4+rM54AACAASURBVPLYEwOUxb7q8d943SFyhQQVnUaBYKBvmr17i3PuVTP/w3ormz4birw2S0WsBZxNfqhqklxuH6XS0GnJWFUiCmthWb/D/v1l+vr2VROYHadEJNKPZWXo6HiBkZE+JiZ6fY54Ncc2b85w4YXbGRycPV4r/XshjjSWbmn2/Vo8+uh/MsoMQpHs3nkJJ59a8PJXBGudIxt+AIPWVAN0fRO6vgnDGCOff7aa0GgY401nkoZxYo6mmVeltuO0VygTEwliqeOIsM3rrt3Lwz84hKkWkcIlV2zHdlSmcl1IKQipNq973de55d0f57N/dC89PYMgBYYdwnZVMsUEMlHAHThWPX425BLumsCwvb4qJYxM5dhR7G+a/Dyf4/x0yqa3gmb5YZXtp4NKcMhaLRWxVnC2+FGpdH66q/hjx1QGBpyGrUmefz7ByMgWHKeIqnbguu24rkV7+4+55ZaPA4LPfOZL9PQMVQWF4/GLgO11R1qsfy/GkaXwo1Qq8pd/+ziyO0siluTGN9zAd/9mwctfdn7A2ufImhjAhBAdwJeAG4AJ4KNSyr9rst/N/n61Kfs/L6V8ZCXa1djhTPMUmcwThEKp6uxwevr7tLVdV9cpm5WO8MxtU6TT19Wdo5UVSoUYodAb2No1xkw57ofTCopWBK1jlN7X/z2d8Rn29h2hv2OcqF5GFdKLFKwcSEj0kA02TBWSc85zaLyXV533CgCGrREOWYSFiqqe31Ki6ulGlgF095YZHmybs72WfBspQbMWG4cfM6hqakEF+PmQz+8nn08yMTFULaVSyfe64ooQ9967rbrfyMj9nDr1TRwnj/cItPFC7CVSWjhO1i/FUo+F+ncrydxL4Ud9aRmPwYsNUBuRH2tiAAM+D5jAZuBy4F+EED+RUr7YZN8npJSvPRuNauxwXo0vQTy+ByG85ERd76BQeJFweHP1e43lVQqFIcrlOEJcSi5XoFCYlU5y3RyKkuSVV15q2gbTPEyx+BWESGDbbySsOPSkJ3np5SdwHYdThy6nnOvgxa99gI72SZ53VIRwueCCfbz7lo8DXtg/UiCERAiJdFREPoHIx1GGZkVCp9nGcyd72DFwiFQiT36mjXjiStLpHJbV3F9RO4C1GuLcDO/6wGEuuWBNWinWAs5pftT2lcnJB5a8iq8MHq77wbpSKpV0kmefDXHLLWksa4ZiUSDEezDNt9HZuZ93v/sTeANEZZrnIqWDZU3NKY65UP+uNadCc44shR+xWIxb3nE1f/GNf6cgMjz4yEPcdueNS/2Jznms+gAmhIgDvwDskVLmgceEEN8E/jtw+2q2rdLhKvWLSqUhFCVKJvM44fBmotELicV2k8k8+v+z995hkp3lmffvPaly6DCdpqenNVkTJI2yEEEii13b2N4P47CA1hgDBhuEMLDrJRkbrZHBGGz4ZHvxR7INmGRQABshI43yaKTJeaZzqu7KVSe+3x+nqrqquzpMkmZGc1+XrlFXnao6p+rc53nPE+57Xgt6JLIV05zE80yeemqYp3auAxJcse1pTDOAZQUwDJNAwOS53dcwM/NvTffh6u2PYhhlLCtHruChxQWK1DGUUSCOY0bQdYv+vkOoqoPr+j/p6OgaPE+gKNXVnKz86wezno4xCtkVBOc0ou548C38e6HF/0PAqt4YP/rRIJ2dg7zjHZ9DVeOEQuswjHZyud2cPHl3Q7G9CtueoVweIJfbQyy29bQL8cvBuRjgPF9wIfCj3gNPSoN0+kGEMAgEOtH1ntqYxdwRjWLx6Lz3PJ27+GpDiKL4AthV+5Oq6kahIOjvd0mnd9dsWMrlNAMD62gMXv7/C6GTyTyGrrdSb4gJ8JnPXM/AwE3z9iEY3M7v//4Xcd3ckhypqtlPTd3P1NT9xGLX0NPz1nn86OzsQDg6noSZmZnl/ixNcbFy5AUPYMAGwJFSHqp77FnglgW23y6EmAKmga8Bn5ZVA606CCHeAbwDoK9v5RntoOcVCYU2YJpjlTZbX07Ktp8kHN5EMvmKWgu6prUSiWyhVDpIsSjJZHqYsdNsvuEhdp5czxOTPazrGCbWMkOuFOLZiZWkoiWIDjT97GDnKLlyCEIm6BYIyYq2bnTV4viEArqNlKBF8liOBppfAyiV4wgFPCnp7DrJ2Fg/CLAdFU+qFK0AK9fupriq8XOzuwzCK0/4f0goWIJ1K8YYGVlVU1nI5Z5C17twnDGCwb5ayuTEibuQUlZSPoP43ZAzKEq4QXrobGOxAc6LgLjnhB9wdjhS/T39xos2LGussogqV0wnJ4hEtjXwI5l8GY6T5/jxTzXoC1ZTbqd6F9+suUKIQINyBoDjZGvpSU1rRVE8/KDl0tExwNjYakCtbOMyPT3F5s2Nd2HN6myWNcnu3eHK+8eX5IhtZ3GcFKoaRUpIpx/Bskbp7/9w7Tht2+av/vZHeK3TGFqA//ZLv3bKv009FuLI/d8LXdD8OB8CWBSY+01lgflFGvhPYCtwEtgC/At+AvvTczeUUt4D3ANw7bVXLmBWvjSqq7tCYS+63o7jpJASHCeHricolQ6wceMXSaXurbXYptM7akQpFI5jihhCGly3scjh6ZuZktuZ8hv7SK7w/1sIWnCEjnAZxwvStXKGUm4zM1MGQjHoaelm1AsRbcmgaDEiuoMn/Z+0kI1QcmME1BJve9sn8KRG2YngygCu1Jgu9RLUcjw3cVvD5x2KRUkkbTzpkc5mSCRS2LY/2CmlWRtULRR2N9Q2fE3FKaQE1y3UlA08r4xtjxGJbHlB2ukvAnWCc8IPOPsc8SXMREWdReI4aTQtiq63EAisaGhB9znSqC9Yfa/Vq+88pfOkWovr6ZlhaMjPHniehaK0ks+rRKP+odUbVGpaBF0PoWlJHCfN29/+aRQlhmH4IwCKEiAUWs3GjX+15OcXi4dR1QTL5Ui5PEIg0FOnXCKw7akGfpRKRXKmDcIjlogRMALL/j5OBcWCuKD5cT7sZR6YO7KfAObp0Egpj9X9uVsI8UnggyxA0LOB6urOcbLoektlZZfC8wpo2io0LT6vRdZ1syhKDCFA00wghhAxrt28ijds/MOG91/qDsHMv4b00D0oWpzrr3oWz30Yz8mS7H0HgWg3d9weordfx7VuoZx9GqEE/FkWO0Zn13VICXZ5AM/JEpQWYBOIX82aYCeKnuDW1Y37c/LhVnr7HWzb5j8e/hkB3QaSGEY7qhqsKXA4TppQqL/htb6xH0hpoSj+9VVRArWV70KF+H0PvZnP7N1CMt7YxHGhrALPMc5rfkD9HZBLINCL48wgpX8uxOM3IqU17y6pmb5gs3OkWWs8+M4N1fmsatrxve/9dpP5xAy33+4HkLmeYEIoxONXk8vtqcihSWx7HEUJEYttX3b3rH9+9xGPX0epdHhJjvhBdDYgVTlSf+zxeIJfunUj33k6w8P/+Wre8qBHe2t7w3td4sf5EcAOAZoQYr2U8nDlsSuBZgXqufDtXM8R8vl9lMsDZLM7Kz4/DrreWlmhBTGMLmx7jIMH30e5PIDrWoTDa2ppBP9OzT9RDdVCDfbM+4yl7hAC0ctJ9r6DfOo+3PIIarCHeNebCUQvb9j+8UdWkp1ZgefkkNKmVIpw113/H51do/z2m96E5xYRSghVT+KUDoI0aev64yW/A9PWARtF8RU4gJpZX6l0opIyytZUuFU1XLvzqmriaVp80UJ8KdNO11Vl2lsbv4cLZRV4jnFe8yOVupd8fg9CHAJUFEUlGFxVUe5wKRT2IqVVOQd8fgAL6gvOPUeat8bTMJ+13OaIRx7pYWbmNhwnW+FIlD/7sy8Sj/+Ct7zlgwCVQWYX1801dU9vBk2LI6VdGyUAKjW/IjMzP0dKq1YXU9UAihJESrNWq/M8E1UNzDv2N73p1/jZL45SyqygFDtAb/+NDc9f4sd5EMCklAUhxHeBTwoh3g5sB34ZeMncbYUQtwE7pZTjQohNwP8Gvr2czzkVU8rq9vUqA2BgmqN4noWqBlHVJLnc04RC6yiVTlIuD5PP78W204RCa0mnH8K200SjFldEJrDcAKa3kun0dMPnmFaEUnm+0LNpGXXbdiISb6PamFVwoFB5rqVD5+ghg5FBQTiig9qKADpWurR3TXDi4CiuB4rhk8OTLhBCiBYKTieZ0cewM/+BZ4+i6N2Uih+mVA7iOH4wmc4lWNlWwvN8SZ7q6ral5TWMjv4jqhqtrKQzuG6m0kbcVVEj9zXxgsH+WiG+fkVdKKzjqefeSmryMnY9FuDVy7tevKjwfPEDTo0j9W3j0eh2MplHK7JNKpoWxnWLgIvjgKYlKZWGavyIx69CiCCmuRfHmaZUGgAUdD1aU/g4VSymj9jX53LihMrwsEo0GsQw/MDR0SFZseIXnDixAl1vrQQVA11PEAj01AaxG92RP8Dc7G04vB4pT9T0QKtmmqoaxrImK52RJbLZR9H19gofUlQbq1w3Rzi8phYw6znyxJO/Q2qkl2LKQ+YDvOTW+c4RL2a84AGsgncD/xeYAFLAu6SUe4UQfcA+YLOUcgB4FfCPQogoMA58Hfjzpd7cdcsLzmhAc9maeqmbqsqAXwu3CQbX47o5QqF1WNYoihIkGFyJaeoUCruRsozneZVPV3A9CTjs3fmXPH78J6QKs6myZ/e9myMj8x2JC+kV/Onn/3bpby4MxgZQ9r0bNVn3PkaZ4ZMpSuUVDI1nUZU0quIxlm2jZKlEA0/x7P0f5KpVhzAdHcvRMbR9eO7l/OzfL8d0DKRsp2AZTEaSrFs3gmWNNLQ7x2LX1DyjdD1BOLwRTYsRCKzA8wq1jqxIZE3te61fUWcyJnuPp8hkuinkIlTtLk4VzeZj9jyjg4BiXrB3l157PJbwLsSLwDnlByzMkYXchBv5kSCZvJlCYTe27Z8L1TtwvyakzeOH66YJhTZSLh9DyhJC6GjaukVdy08X1VTj7bcnGu7mLGuSycmTwJqarZDnlQmHtyCl2XS2K5F4mMOHX4qu16e7u9iwAXQ9UfuePK+LYHA1nmfVpRVjhMMb6Ol5KyMj/0gutxOAZPLmhi7Eeo4cODiNNtVOIOySy5y+nNRcjux5RqdYEKSnFR74fqj2+IXGj/MigEkpp4E3Nnl8AL+IXf37TuDOudstBcfJLKhL6HmlBufVqan3EAiswjSHiUa3V2ZZ/NRAPO5hWSOsXPkODh78QyzrCP7KsRNFERhGB46jIgQEgx04TgwhZnByWSQQ1Uv0RycYH5xNJUpTwyvp8/ZZmhrOdHje4wth7vskoykcS8N1NBRP4DgGUnFIGgWcokGmlKA/OkE5H8K0/TRnGYPX3PZlXmYHeHz/VUhXoVtN8Nd/+QE0rR2YLWgPD99DKNRfSwkBSOlVZn58l9rqrM+5btxoVge443a/lrfjwUAD8ceGVYZOaBeU1fq55gc054hlpea5CZ84cReG0UU6/QsMo4tweEONH7p+C5Y1wsaNf8WePW8ln99bSb3baFprAz8SiZvJZJ5ACL2i86fiOCkikbXPW7OPb6NSvXjXm17upbX1lU31Gt///p+g64832Kj4CFH/1R88+D5UtR1NU2ppxSo/Uql7kdKivf31zws/YD5HLhZ+LCuACSFCwGHAA9bLaoXWf+7vgduB35ZS/vM52cszhJ+Dnj/dn8k8SiJxU4WskxSLBwBRa5PPZB4lmby5dgK6bg4hDIaGvoSiGHieg6IY2PYI0IOiqOh6G6Y5ipSiIuSpEYnEKJfzBMNl1rZnKLXM1uSPB3SSofkdRmlT54qWhe0o5mLu+ySDLo4ToOgFiBoSz5N4noEeKBOkTLms0to6hGWFKRRase1I5ZUGkcQUndv3E4nk2bDhRqan7yOdfphc7mkAYrGrESIwTwqoVDqBaQ42tA0vpGNnWZOUSs9x2coB0hPrENIC5gfyM8Xc1eTQCY3PfmV6ga1PHxcjRyxrFM+bddv2PIti8RiWNYVhdOE42YaB4WoNK5/fh2kO4rp5FCWClA62PYKUbTV+BINrKZdPVowfjZpCfDR6xaI+YmcTfuZgBVK6dY0VEsuaIRTayODg52uu0aHQ+oo3WIxcbk/DbFcotPGc8COX28+KjmMMDPehK+emlPl88eNcYVkBTEpZEkJ8DPh7/HTG5wCEEJ8Gfhf4g/OVmOAXZpvprgE10pZKh3HdEpY1BVioahIhBIXCbnT9llrtR1HCaFqSSGQb5fJgxT5cxbbH0fVWgsH+SoPD8cpna+g6lSJ2iVWrHLZtm6ilY6TcsmCX1cc//j+XPLZqvjyf16lPwZVKA9xww140rZt1695AqXSkYgu/AsPoIBTqJ5/fU+kC84jHN2EYKygWj1Es5kkmr6j5ex079lGk1DCMdoSgIhfUhq7HCQZX1/L+VafepRQ7qg7RUgos2yAczlAqxDl52EStS80stgpcrHsT4ImHjYbUIfjpkb7L5jcEnA1cjByx7RS63lb7O5fbhW3PYJpDtTsXTWulWDyEohi1OmcqdS+h0CYsa7LSkRrA8xxsO0UkshkhBMXiXlQ1VBuuF0Igpcr09E8xjPaaV1Zf3/UNDRtV9PUt73esryc9/LDOrl3+OZNIeGzf7jdb6XoLihLEdf2FaySylenpn1TshySuO6vs4Tg5yuUBgsFVtazN6Og3zio/bDtNNvskY+MZylIQDmcRsp181m5IA15I/DhXOJUU4j8C7wc+IoT4O+Dt+EoAH5NSLqNY88JB0xJNdddisWtqpC2VBrDtSaQUlXkWD8cpYpoTDbWf4eF7cF2PcvkoQgRxHN9eQlWDhMObUBSV7u7bOXbs40jp4Hk2QoDjFPFlapTa6utDH0px+PBhpqcbHY2TyRk2bnyAu+5a+th+/OM309Iyg+etZWIihKoqtLa2MTnZzeDgACtXjlVa4AMUCmMcOTKIbU/hunmiUZOeHg8hFIrFQ4DG8PAOMpkEprkHgHh8kGAwg5QKpll1Y3aAHIVCB7adIxDIYppxIhGTYnEEGK3bQ4lh5Pinf/Jrgrt3v5ls9jkUxcFxFEBw2aanyE6P8Lo3/SWv/6/fRFGWzvUv1b0ZiUriSa/huWxaAc4pQf+Ri4gjQmgYRjdQvWM+VrEV0iut30VsO4WUZVpabm5QXVfVCKragmkO4Lr5iv5hqMaP48f/DFVNYFlTCOEgpV1pErKJRrfX+DE+Ps7AwDSeN7s4Ox1+ADWOAAwOGth2mBUrVFatSpNI3NiwSFUUP5Dlck9V7hIDFAq7KzWy2SBk22OV2rhEVWfTkVJaGEZXQ10sGOyb11Y/d3RASsn4+G7K5WmyZQG65MprDrKibYzRkRSf/Ur3sn7P85QfZx3LDmBSSlcI8WHg34AfALcCX5BSfvJc7dzZgqoGm7bZAjXZGt/QDoQAz3MBP+B4nt0wzChEgHT6ETQtXjF4jGLb4xUF69lmhXT6F2QyT1a6jUDTQihKlGCws6YTd/JkG6hPUVA7GvZ3criN2JB/MTnwxK9QzLUxF+FYik3X/4DJgkVBLZM1HWzLRUgPmCEQaKsE1N1Y1giGsZJ777dJdhwkWw4DZcSMCsRZvVrBssYYHEwwmQkymQsBZQDWRotonotEkjWrhJAYuk2qVOKnT11b26cbN5cIGNlaTQ0goJuYGYPHK8dTUEYZmWjBdqqrP4FQPJJtwziFYR546N+57dbXnvJvfD7gYuNIX9/7mZ7+CY6ToVg8hJQufuecVwlwAlAIhVY31ITqOaKqG7Bt31A1EOiupcvS6V+Qz+/BMJJ4nonjOKhqAMPoqekmjo31YFoPkqcDqc4GsNPhx9TwRmzTb5EHcByP4ZQgHM7zgQ/8J6bZuEhV1bZKkLqWUukIjpPB8wSBwKqGIOQ4WTxPVlQ9Zo/fdbNIaTV8LydP3r2kzuNjj+3ACI1xYrgXoXoIRSGkdjM6LOnoGgCWF8BeLDilJg4p5Y+EEM8ArwT+Gfij+ueFn0T+In431Ar8pfgXpJRfODu7e/pYqM22StrqiV11b5Vylqj1tuG+uZyobEtl1dVGInFTw8na0/O2WoOIqsaYmvoRoBIKzapcz8yUaOkuoJTKfuSsQLFstHY/xVm240S7Rubt98iB6yjbcaYn1jIxtAWrHEUIDyE8yiWLUEAwNNSKrr+MjRuvYP/+53hq4Fvc0KESiuf9LkNPcmQwyvr1G4lGr+fzf+9xxU0nCcXzKMKlJZInHC6iqQ6OqyJ0/ztSFRcPSQG9tp8Axwsr2N52CMWxZtXsNZsDAytr2219w9e5vn8vAd2qdDpKyMSIBy0yxTBPP/RTXveKVy/rLmwxxBJeZUU5i0JenPPi9MXGkXDYb6qwrDH8y4WfUZBSqQS04hzldKjniKJEMAwNVc0Ri11Ze/+enrfVOvzq+RGLXVV7l0ymQDBegHAJ5SzyQzPKeK5GsZzg8LBkYOBaXv7yD9ZeW6+yHwh0EAh0NMys2XYG1zUplY5gWRNIWcTzZjv5qmocc+e6lqPz+NVvPMLL33gfIcPC8kJcf+W1tCRacJ0Mip4Abljo5zslvFD8ONs4pQAmhPgN/CFKgJyU805dDRjDt304BlwBPCCEGJdSfutMd/ZcoEradHoHudx+wJ/IF0IFDFQ12JCjltIiHr+RUulobYA3EtmClNa8961f0RpGO7re1ahwLVxMRyMaiXHl5itqD48OBvjjd/nB8JMH1tC9an5b6/0n23nNq3Ueuj9OakJHkQoID9uGcKiAIjwmJ4t0dxc4efIepqef5oatM0zlY4w9+16GhzvI5ouoruA/fxolGNzA3iO7iazdy3+9dpxkuABiBcgY2KMENIhEoqCEQBZB72X1Fe/kluBsQHbLh3HS30WW94B0EcFtaC2/ztW3NFpTuOXDODPfZDpjceD4EMFQiZXdgqefvqxSUPfOOIA1awUeOqGdc+WCi40j9UFtdPTruK4KuJXar4IQkfq1F7A8jizFj0wmTb5URERV4pEEV2zeWnvt6fJD1QSuo5BIuFgmGOjE4mmeevZztLR8m3jcz6B86Ut/wKFDJyo1ML2S3rTYsKGfP/mT4xw//mnK5ROVGchWHCePlGUsK1WZgcsTDPY3HYRWlDCZzKOA3+wxt4HDdFyOTPZw65Yx1q25imAogetk8Jws8a43n96P2AQvFD/ONpYdwIQQrwW+CnwP/yr/P4QQn5NS1nxApJQF/OHJKnYJIX4IvBQ478hZj+7u28nl3o8QQVQ1WBnCtYhGr2nIUVdXZ8nk7BypvzrrnPee9eSvzpRUhx0/+9lbOXrkCpzjm9G0MIURXyamWkjt6fRb7cOhMLHofJsRVVWJRWPomo6qqDi2gpQarguZdAcgyGQiDA6GKZXeQEvLDVz10o+ypmWa/entrFx1gND0IFYpwtq16wkEwjy3L8K6jlHCehpFSBRFQQ10oGqbsYtHcJ0Umh7GiL2UZM9bGtRAzPx+0jPfJxhJosRvw3NzvuRVWweB6FwFkh7Mtg6K+79CLHCYfD5KPn8TqWwaNVZu2HKhYvSeZ/SmOf4XEhczR9ra3sDo6D9Vsg7+eIffuZhAVRu7ZZfLkcX48dzuZxgevoPyiW2saF/JjpHZBY2UnBY/HFvieR5Tk0k8V6BkI2SnV/Cd73yQH30vxpYtNm9+8yc5cOD/ZcuWforFWVmocHg9J08GKwH3OK7rO5wHAp1EIuspFo9UBHp1Eomb6Ol5W0Ngqp8pa219be3uqxlS+STj1ma2RrRF1XeqaMaRJx42GDiuXlAzXaeD5bbR3wB8F3gE+G2gF9/i4dM0mU+pe50OvAy4+4z39Byjo+OXmJz8IbncU7huHk2LEY3eSCDQ1SB3c7qmjXNXnGNjvk2DopcJBJVaQfVUC6mZGYX0jMAXzvDtIPw0qERVbX/+LLqbiYkr/dqUY4GXw9G3cmzKRclHuGpLG7Y9RWtymoDut7NrRit4JkZwHVpgBUZ4La41RsfG5j9lPnUfihZHreT3q//mU/c1JV4gejlW5Df59/0mykQHnS/pBuYTem4xujq3MjKo8t2vz87JhSOSrdvtWgqkmczOyWMqd9ze2mTvV83X+DpFXOwciUY309r6ajKZJ2p1Xb8etJZIpNG5+HQ4Mpcf+bxBNp8g0VYm2Qr+dIKP0aH5XYkLIZbwOHpQq/ADQAGvkrGXPl+6u48jrRWcPLmOnc+lGRk7zIYN1zYE4OqYjW1nECKAYcTwPJNgcB2BQAfh8PraDFwzNJspqz7erLRRdLppW/3fl3WMzThSyAv2PGMwNjz7XYUjktf+iu91eir8OJ/TiksGMCHEZuBefE22N1bmW44KIf4BeKcQ4mYp5SMLvPyL+KKjXz1bO3wusXr1+xvy8s2I10x3LR6/nlTq3pqN+tzhxOZW41tRtWHMcgTFC9Ty0aeah060eFimYDolUBUH29Z800opsB0D19V4ZMcrsa02LN7Fa/6fzyG9ItDS8D6meQzXVTEdAw9fVRwlgF06ghZYgefmmmo5VuGWR1CNRu8kRY3hlufXJ84EucxssH/dG2eNh5czv1Id3pwP44ycNF8sHOnpeSueV5zHj7mpsrPBkeFhHUWzMEvBebWacHT5ii0vudXkwG4dx/H54Un/vaQncF2VcjnMzidvw7UDlMshSuL3UANlHnv8OV556021Wl6xeBghImhaAl1P4Lq+r1ipdIRAoGNJ081mli9zOxBt28bxTtsUoIZcRmHdJodsWpnHkcVShAvx43zWXFx0zypSNQ8AM8BtUsr6o/9T4K3AXwA3N3ntZ4GbgFfKuQWi8xTLFQVtlvpYyEp8oedt+3JW9h4lH0jTlmzj+quvA+afZAvZiIcjjSe6IpglZ+VfgUDVbMLhHKYSJj3TTUCzEcp8hY/HHlvNiZNxvv0Pf85PY2EMtQhCpatziPd+6KmmOXgzv78mMmyVT6K5Fka4v/b8UkFvOdjzjN4wrzI8oDI5AV2bjgAAIABJREFUpp6m6NTZx4uJI6fiKHymHNmw/mk6eg/R1i246drGxoW5s1Cnwg/p+QHJq/yraQ7hcBbTCuDikZ3pwvMUMk6Ro0ePsW7dWgB27OhlfHwFH/zgb+B5v1Jp/1fp7BziQx96pukdZn1Qrhf7rmJu0PvmN79NPp5CCLjmiu1L/yAVnO8cOZdYNIBVpGpWLfDcCNBU60gI8Vf4XVavlFJOnelOPp9YTBS0HtWTc2rq/trwY1UXDmZTAwulDkqlI3S0D9IRzKNqBVzrMlSjfd7nLLRiuvtjcYZOaBTyAokEIVEVgfSqJn0QCpXR9AKO5wc0RXH9AKZ3A2XCepnWzgy53INks9uJRtOs6TsAwkBRw6iizMBgDweODTHjbqN8dDewG4CgMkKn/hCuDOESJCBM/vVrL2FkbCMeAQQuAhc9tJ7NV8ZPuzhcLAi6e2dTqpNjKuUylEuiQcOtkBfc/bHT/5zTxYuNI8vlB5wZR4Rw6YpP0xadoZT2MMLrT4sfNVT4oWgWnqdWJK08wuECrlRA8RCKh6ZKUuUISmiGQ8cP09IygpQFMpkrCIUytLc/h+eZ+Kl6l7GxXnT92LxAPjcou67FZz/7ClKpbRUjS78xJBzexNq1Ud797kPcu+MwdJTo676MbZu2zj+oBXC+c+Rc4qzfGwoh/hq/hfhWKeV8ldqLAPUnp5QSISS53FPAtQQCHQ2pgWapg3J5hHJ5kEQihSlBKCXK2acJxq9hroX5XDQr2HZ0+Sm1dZscjuwXFPIAkmi8TDanVRxyXTxXZefJ9fT0Bzl6oIhhh8kWEoxoCYrFAK2tEwD+8DUzFGyDgakkP3vGZV3HvcSCJXLlEEcmeljXMYKlWZhOobYfx06uprdnX6W+ADkzyERuhsMHXn8G3/Z8uI5AUZgziKk0bfY4H3GJI4tzJJ8/SEfnIEHdwlDLuHa6xo9mQawezfiRbPXQDcG6TQ7SLXHkgEaxoINQEGoEx9VQFRcpBVO5GEokjVtIooeKDA3FUJQ2isUIbW3HcJw8qhpBShMpbSKRzaxefSP5/L4GeSnTnGwIyuHwGlKpbXR0HKSqmBMIdBOLlRkYSKJpBkL6g/0lu1j5zk5fPupC58hycVaPRgixGngvYALH636AX0gpb1vwhRcY6leMS+XD62dKAO6++6UcP341hw5dQb5wE9WZmVgcLt86yWWbFyfo3ILt3l068aTHxJjq1wqEUlO4KBY1XC9HarqDlZ0Kk5lWUoUkH//IOKOHPs3+Q09ip1t4xQ2SP/3Tj9HefgzTBMdTcB0NqxhGFENc0zaC8FyiwqarJcuaxCSmGWJ0vAelzm5KWgYqkMkkcV0VRXXoTqQ4dnIPUq5bFiH3/vxN3HmivdZGPzasMjKoUu2qtyxRU8w6esBPmxhBSWKOqsByMCtkGo4uufFZwiWOLM4Rnx/X4Dhv4vCRKyjviaOqCuGIx+VbJwlEuxatETdToNi7S6eQVyq1tChCVXBcnyNly8ZJt4OjoqsWVrqF6zbfzw1XP8Z1V7fQ1+ffVf3RH91KR8cR/BECC0UJouvtpNMZ8vkhhoa+hOe5WNYo2exOLGuCROKlRKOzDWD+fKlJKLQGIQJIaZLNPoltv4y2tnb+22u38s0dWSbFBE8++xTXX3XdvONrFqDHhtVas4bngeM0csQISladRsdulR+FvADW9p/yGzwPOKsBTEp5knNooHe+oH7FGAr5Lq++U3Om8t9sPnxuR9bAgEJX10l6ezOk0yVcxUHXVHLZjXzsU19bsMtvIVQHEiNRjy1X2WzBb6HtWulyxfUpfvHEw1AKsW1NB48+O/s64U1iOjoK4Hl5hFAxjCiKouB5vjJILFpCeg6Xb0gCw0AAX/y8BGRYtbKX+jvGn/3EIRQMEQr6eoa5XIG87RLWM7iui6b5p1uVhKn0VRw5/h5EOcT4vgiT9uWUS0l6+51aAAuGJJ4nsCrdwJpKraOskBdEopJ8VtC7eukANrdeMjasEolKwLEXftXZxSWOLM6RwUGdzs59WLZHom0Y3QgTDhqMjvaeFj/A50ghL9hylf8z13OkrP8HpmnS4sXYuKGHsbE23vP2qzGMPXR2bqy9h5R25c7LIhxeV3lM4rpFUql78TyXYvEAihJE09qw7Rmy2R0YRludyWUaRQnVVO+rhpamOQSsIhoNIVwVkHztC+v5Z29+R+CeZ3Re/6ulhseCIYltCVwX9Iokaj1HLMvvbo4lFufIQvzoWukycMw8L2u0F9f95PMEXyrn53ie7zQbCq3FsobxPIGuJxry4XML3+ChaUl/CFRxMTQbTbXxnCyIU2+Gq8551HfhVQPEyKBBMd0BpsFjj60lUyjx9A/fw/8+uopS9qOkM1PEI9O84oYvV1QVXHQ9QjDYiueVUZRW0mmVeBw8r71GPJCUy6DrGRKJrTXRX18E2QVGEEIlHBYUsqCrjWMB1VWyNlniuWc6sAsJZNkglekARfKTH4SJJ2Xt2FwH1Eo3cCAgKRUFnvT13NZussmmFV5yq7lkt9RClhL7n8s9P/LnLxLUO5kbRjuh0Hri8esqWoKLc8R1S5gWOFJFCEFQt/Gc8mnzA6idG/VdqlWO7Nzdhm07BNU4O3duRgj4yld6yOc34XkWimLQ0zODEHZFYSNYew8pTVS1lXJ5qOYLWH3eMLopl0/UxMBLpRM4ztW4bqHiYK2iqlE0rQXXLZLNpvna955EduRoibeRG7iM3svm250MHNd44Puheb5drutzRNNA0xo5sqLLrXUjLsaRhfgB8MQvTuurP+e4FMBOEb5VxAiOk0NVozhOCdPcTzi8hv7+Dzd0XjUzygwEBnnmmXVkMgqua9c8300zxt981iWctBgfb6yD7dmlg4St2+0GFemFzOeqJ+LoxCif/uIXYbqF9P47GC0cYnJsPQd3h7CszdjmBMeLMT71qXUMDUWQ0kbXVyCEipQ64fAmVq3ajW2n0LRZvTl//mUlmhZH1xPkcrsxzcGKxYyFEEpF4FQhHraZXqS73TYj6EaJcNgjUwIUj3jSI5fxI5ZuQLlxwYkn/a6yudizS19wjuViKlyfz6h3MrftNLadwbKeIBK5nFBobUPnYX3NqK3tDaxefSeqepSnnu7FtsKoKggUQFIqRc4aP2CWI//r//xfspk8V4c3cPTo5fT3uzz4oMHMzCZsewpQefLJfkKhPPv397Nly3hdE4bOhg3tBIO9ZLM7Gzjim3j243kWhcLeBfih4roFhFjB8eOHKWslhIC3vel3+OJe/wSvHxsBPzjFk40yULrhN3LUu1G8WDhyKYCdIlKpe1HVGIoSxjSHASpmfV3LahsOBC4nny8TjU7gOCYI6be76zEmJy9DpFKs3dpYB9u7y3cX7u13ajUvYN58zHLgmGFiCZeyKUilbHSjRE/PGJrWy6c//QOktGoXFNjPyMg3mJycwLZnMIxuFEXD88oEg/1EImtYvfpOTp68m2Cwj+7uFCMjfYCKL4Ts4Hkevd3HMPMRtORsZ9WOBwNMjHdRzCgowsMuqhTLoGiN2byOLpdi3lcYqUIR4HqQzQiyaT9HP3RCA8kFN8dysaGaTrOsMTzPqnXs2fYYa9d+atHRkt7ed6FpqykVLeKJKXRNVGSqBEKNnFN+2LZJuVyiWLRJpVQSCYHnRZEyA3i87nWHGRvr5667ZhelodBGSqUfk8vtwbZn8DyHQKCzztl5U23Iuxk/fDkuQU/PPv9aUEE0HGXPLr81vtoSX0W51Cz7LJnrZu6PDYBlMcuPyqYXE0cuzL1+AZHL7aZcHkRRggSDa5HSP1kta7y2zWJT97p+E6XSNKrqIFHwEJW8eAAhdDy3uOjn14tw1p+YpzMtb3sadilMLHYr5XKCDRtmNQvrLzKJxEvJZndQLp8gGOyv2cZUB1ir9Y53vvMLgIJljWPbaTxPMpGJoOsuuZFXo2nvrKly5DIKkbiNoqqowkPXJYoNnqsxdEIjn/OtzocHVMpl/0ImhJ9C1DSJ44BuSF73xlItPdRcZeMSnk/U88M3i/T5oaqx2gJvMX6o6h9TLBeIJwSgIRQDRT13/AiFQ2TzGQ4Mltm18wiHD08zOrqN6Wn/tr+lJUgg0Eky2U46rdYEu+v5EYlsxvNscrmnkNIhGFxJMNhf48jw8D1N+aEoau0aYlm9tCXipPCDVTHvt8ZPjqkEgnV3VjP+sU2MqbUW+cyMgusKPM+X2QoGfY6YJqzsc9lylV1Ln15sHLkUwE4RvhGmaCjEep6JaY7VUiL5/B6i0e0NtgnVtuFIZIyp6QipmY1+e7sU6LqGrrsUCv2Eww7rr1zgw2kU4TyX7qn1F5loNIFhtFVqGFaDbQzMdpGBim1P4Lr5WhpSSpWSraFo8aayUoqQBAwTRQFdM3A9j3xudnRKN6Svv6eAJ31Sgp/vdxxxRvbns0XrwBkpcVzCLM6UHz09JqYZYWBgI0IGEZVmnnPFj9/9jbdy1998BrtnFDNQRA3lcDUbW/PfZ3LGoz05v39hbhCOx/3jse0xgsG+hrLBQvzwMzl6RdUkwrreE6TG1jZ8jpQOnlNASgchNBQlQTatUCrWqfMrfvehEBLHETWOwJkpzDc2dZyfHLkUwE4B+fw+LGuiMuMxXakXabhuESktbDuDYfQgxCEymUdJJm+udSBV24Y3b/4GbX0vIz+9khWtKdKpy/y2VyTZTJiZ6SAPfF8umr8/F7DtNCdP/mUtPZLL7SESmR3MNIwV6PotWNYIq1ffST6/j0OH/phcbieuW6qIHzuV9ImDEAIhTAK6ZDjTjqiTlerpc3jiYQNFlQgpcBwD6SnoeolkNE00pqGowVrh+WtfjiLw0yEr65x4pYTPfmWauz8W547bW/nJD4MNx6Qbfgpyvt2Hj2rO/ztfPXri7H2TL3YILGscyxpDUUKoagzPKy+bH3fcMcT375fkcx2s6h5hYmwllmVwrvjR3dHNr7/hV/n3Hf+BEdQIRgxUTUHRBJ7nIVWXUqkIhBo40iwIh0L9qKrBxo1/VauBnzjxfzDNMSxrBNf197fKD8cpoaoZotFtlEplYuHZmcpwRJKZtrBMgfRU/Eu1JBLJ8eo3pPjBt/pq/Hjg+yEyaQWrLLAsWeNIIS9442/5d6x33N7Knl06A8c0NM0nRJUfCzkx19fEzleOXApgy0Q1ZaCqcQIBDcfJVKzFV6MoQVS1pXYyRyJbyWYfrXUg1Wsqep4viedJjWTLeqbGNQyjjBAaQgli2wrxpH1a9a3F0N6e5ehoO44V5PihAKZpYFoBFDzuvdcjny9z992v4I47HsS2/WNTlHBT6Zt8ft8cSwkdyzoKCDQtXuloFEgpsByFohVE1slK3fmJLCMDGgH9IR79RS/FfAtBQ6VsO5RLETJplUBwNkViW75aYTVlCPCtr0RIzyi8dH0XUxMqmibJZhR0XdYGu82yIJ70GsRfm6vbn58zLhca8vl92PYMqpqopA79hY2qRjGMzmXxo9rw43oqUmnHsoxzzo+X3/AyXn7Dy7g77Z8bx58Mkp8p+cPEUkFJBPjud0tYlrmsIFy9VnieS7k8CAhc18KvfTkoigZoqGq48t20I+UhcsVIbZ+2brdpS+7giR39FPKx2uOFgsb93wuSnlZq/BgeUDEMfx5yRZfH695YYseDAYYHdL7/zXCNH8WCn2YMRyCRlDV+zBUQv5A4cimALRPVlEHVZjwQ6EZKiaL4K6l4fNaZOBDoIB6/kXz+GSxr1unVT7k9Qig+xfToRnK5KI6rorj+iRsISRA0NiZQKc/K+YXWU0kN/OZvPo7TuYt9z95GcehXiLeWmU5Pg+03SHR2Fhkb66q5RYfDmyiVDmAYbfOEjVOpe7HtVCUF4t/1KIqOEDrB4CpCoVvJ5Z7CNIsoIk1As/CcLIme32rYJyGLXLbpKbAM2hMBpvJFZma6GB+4kd41s0GnWgfIZWfTJvmcIByWdPe6FPMKgaCkmPfTimbZ3646/1Iv/trcav38nHG50JBK3Us4vKkyD5WoOROb5jDJ5Etr2y3Gj5///N8IJVeQmryMYjGG44aeF37A7B3HLVtamJqeBE9Blyq6ruI4HqAsKwhXrxX5/N5aa72i+HekhtFRcXy3UZQAjlPmiScexLKKHJ70F4tGRVvac3Jcf9PxBrPbkcEW3yg3sKLWrFLlR/W8B2qt9/X8sC2BZfkzlWZ5lh9z04wXEkcuBbBlotqo0MxmvKXl5ShKoGF7VQ3S3v76BpdmgB//+BWUsvNzWtVp+aqC9Lmqb21+xb/gHn014eQEu/Y+i5KP8LLrcgQCrQwPzxZ4Q6F+PK+Aricol4cQIoCihBgevod8fg+uW0LXO2rbK0oI1y3iulkCgQ7gWjKZ/0RKBdMxiPX8XkP9q6fPYd/O1WQyU3i2gWcaZEpl2jsGGB9sPC2NCjmrNS/wA1U03jiYqRsSxYW1m/xOxvrvsh5zZ2sgkTyT7/QSfJTLQ5U0WpRS6Qiu63tp+U0cjendZvyYmprkIx9bSckNoSgqhj5bdnm++AGw/To4MrqbcrkEtg6WzjWbJykWZ7tomwXhquL++Pi/Yhhd2PYkmubfnSlKCM8rIUQAIXJEo9dSKOxhZibD8akQRybXkHKCrOtbTzKepKfP4fi+1UjPQiiz30Nn9yjjY90N+1vlR33HYSEv0JtUrQxDNsxPVr/LuS30FwpHLgWwZWIxm/FT8T+amUkQThwmm1pNPOk1XT2dLbjlw9xw2T5ia1yCwSHa4gFSTX5yRYniO4DUvdbNEYttq9W7Zm1m2hHiELY9jBAGuu7bsvz933+E0dEWpAySzxsI4SHlr5LXiqx9+Q/4jVhj88adn8jy7LO7GNj/l1jZBP1dKzieGiEYLnHiyFsbUkRVmahqzQvgiYe7GgRMTwVzZ2toaNK/hNNFlSNVfgCVRZ5ZM29cjB/33vsAOes6Im3jhLzYOecHNDoqqMEeom23IcTN3HDVdTy68zFsxUbqNlMpl1CwccSjPgg3jgZ04TjZSuOGjq63VPjRWuk41tG0JJ5nMjw+Q++rvoGma7S0xPi937kd8Plh5tOkh+5B0eIoaqxmEvvnn/p8Q7dllR/Vmtedn8hyx+2tDQr1p4oLhSOXAtgysViQOhWbiSr0QIlsWsGywC98N5d7WciReKnBQzO/H2f6GwR0i1wujqIUuXrDEXaOrmZizraBwBpsey+eZyGlN+8CM7fjKhLZimVNYtvjqGoQKWF8vIve3hTDo2nibQ6mYzCdjzE93c2vb9qOqs43IfT0y9h5ch3rI1lUNUPZMtg/uYqtVwv61pTmbX+hzqq8WLAQR3p73wWwJD9M0w8QiqIQbxXnlB9QcRGvBAjV6MKzM6SH7sG1txAOR7n1Jbfw7N7nGJscI51pJRiwao7Ri3EkFFpPLvcUqhrDcaYQQmF8vIfVq7O47jSaFscwdHS9n2f3hVkl4FU338ptt9zWwJNA9HKSve9oCLDxrjej6smmDSxL+X1djLh0RVgmlgpSp2IzAdC5Zh+veXlPg2BmVautvjW8eT566Yt5PnUfKHFMxzenlDKMaeus6xiZF8A0rY1Q6DrGxqaa1Ozmq4UHAh20tNxCJrMDKf19NowOUqkox1J50B0U4a8Oc5Pr2HXfddzxQGPRvafP4VW/AalCkpnjG+iM9/H4wf2osfKyv8MqjLoVuuuIWtNGOCLPqM3+Ek4Ny+HIclG9QJ8rfsDCLuKOOQxsRFGUmn5nrhREjmqkUgWSydyiHKmm0Eulw5RKZXQ9gapGCQQihMPra00fMzPTzKRW8vQP38Pk03387OsravtWDcCB6OVNHc1PBfX8sG0oFn2OXAz8uBTATgGnGqSWg2ZahmcDbnkElEaRddM2iCUydPVaHDkYpphegSiFGBw0CAYTbN68oqkl+lxFffDTJ52dv1qrYYRCEtv+CWu6ZrA9nf41r0DR27h/sJ2+NUXqLeGrx7sQelY5DJ2Yn8CvJ1pnj1sLVJom0aKScNR//F8fWtihpNrCD/UBtcnt4SWcFs42R84VP2BhF/GOzgGGTmwBICA2UMxY4AkcJc0X/66Dv/7M79HS0tbwurkc8S1jArS03Mzq1XfS0pIgHB6jWDxMNvs0qhpj13MFHO8mIskUV161DcOYPb8X5ccCJp71/Ojpc9jzjM7okNrAj3BU8tpfLi16p3YhceRSALtIoQZ7ENao/4fwME2LgGaRK4ewOj9Cd5tN4ooi6ngHX/izd9Pe3rHgey1V48vn91EsCgzDxnJ0NM1lYuRBEu0vAbGwPYxSIYinuBw4OACqCwLu+Hi6tvJdCIsFqcVQbeGvX7Xvfy6TPq03u4QLGmqwB8/O1O68wHcRf88HH6Ft9awL9Pfu28dDT/wcz/Uo2Cp/+udfYuvl/nl99dXXcNVV1y3JEdtO1xT50+ky5fIUsRYTRbmBzes31ToPl4PlpAnv/ET2tNOJFxJHLgWw5xktLRnGhlag6qJhFXW2b+OjbbdhF79MJAAFWWZ4LEXEkOw5sZa0nUcA0jboiYX5/Of7GRqaX/Dt63P5xCfyS6aGUql7EeJXSSTamM5N4giQpkNx6HGk3LbgPm5cuxFNMbBbZjiQTkJrmc6O3iWD13KwWG1k/gr2/FQZeDEiFJ+ikF5xxi3xy0G07TbSQ/cANDRJxLve3LDdG1//Szz7H29k1zMzTKYm2GnrfPeH/v7Fwll+et8wXV2Lc8Q0h1CUIFNTWSanS6AKVAMCukffyr6zfmxLYana4YXCkUsB7HnGf/kvDxEa3EswpvKZP7nrjN5r8ZPwclpWvZNt1j+xd9+DZB2daOFmEvkI7rQfrMJhl/e872o+8pGDdHWdRNPiDTn6EydmswaLpYbK5SFWrswzOtqLWw5TSDm4wkUzTCx3BgjOe82eXTr/8509pGb+gqMnjyKlX7x/fOomfvy5+R1nS6UG52Kx2sjcVNT5qjLwYkM6XeLyl32LQFjl7o+uWfoFy8BSHGnWJFFfc6p2KY4f+xWu21bmuaNZZnL+GIwEctOd3HPPt/noR9+3KEfi8QPs27eCdC4BWgQECCFIJpwF3eGa7fuD9wWxbd9luh5nkx8w/y7vfOXIpQB2HqL+xH3w/gCloj9xr2myduJ29vhdrb398z2DnnjYYGRAo6fvBu78xOX83b3/i0LW5IbkSj72sd8hl8sAoCijpNNfw/PeV5P8yWafJB6/rhbEloNgsJc//MPvoGkJpJQUCnkefexxphWTr//D14D1815TzAt6+x16+xP0r1uLaZrEo3G++rca266ePzNZr6ZxCRcfjh07zBNHx2GFzZpVGxbd9lT4Ub1QL8WRZqjvUlSUEFJm2NLnUWItUkmy79B+ikIyPbO4H+qJE0fZfMUXueLaMmbARqiCKzdtIx5VuOvjPUBzgd1mQca2fVWauSMkL1Z+XApg5xDNPMGWg/oTt6NLJ570/786eAiNRd75MxsKvf31KYDZJV4sFicWiwNw8uS/oGlJFMWoaBf6d0rF4uFTCmBz8//BoEfAsDgy0kOsNd204ByOyLqLSqj2uFkSDJ7QlrRAP5P26Us4P1DPjwd/PkBydZis2c7/+K23LPq6U+UHLIcjTfavvktRCIQSQNEgqqQIJTdw5PixZR3n33zpu6STca7pHEU4Bmv7r6SjLYznZOlb27ZgQ8bIgDYv8BYLgnKJJTnyYuHHeRHAhBCtwD8ArwWmgI9IKb+5wLbvBz4EhIHvAO+Sc6dwzxALmVGe6ns08zwKBhdullgO6tuKgZpnUCatLHnRn4u57fFAxfb91E7wZjWyg4evISWL3PzLP+F9v7dx3muqg5aNFxUQCljLGFo9k/bpCw3nGz/gzDkylx+SA1xz2RAl43KCgfkp51PBjgcDjA2r3HF7a83g8nQ40qxLUQgDzzk1A++ptEPGiHMydwOvvjpAS9RC0RPEu97Mh+4ygObdlXfc3jov8CqK3w+4FEdeLPw4X47mbwAL6ASuAn4shHhWSrm3fiMhxOuADwOvBEaA7wGfqDx2VrCY2V49QZci8EKeR/H4vsphnh5mT2h/VVZVK6ie0IMnNPJZwQPfD1HIC+64vZWnn3s7emicG37puYb3mrV5mIWUJpoWb/q9VI9XiAAgG8wv5+b/s9m/gNjJ0z7OS2jAecMPODscmcsPyw6ArdMW3dv0M08FuYxCJCobDC6X4gjMvztp1qUopYVQoxwfPE7RLABG02P1zS4PUi4PcdW24+wpRJnKXca0fA0b1t+Iopxdse4XK17wACaEiAC/DmyVUuaBh4UQPwD+O/OJ91bgH6rEFUJ8Evhmk+1OG4uZ7S3HcXmh4V/w02uGkeFMAlgVE2MKtiWYHFPJZnxiHj2gU8gLIlFZC3K9/Q4HBqfITMxvZ6+m/rq6xhgc7KzZpIfDm0inVfoqtgz1xws66fQjCCGIx29c8OL1fGC+Xpsvp3P3x+LLmpW5EHC+8QPODkfm8iMaVknZBsX8cXKFHLFIbP4HnwImxmYNUes5MnhCwyoLDIMGjsD8u5P6LsWu7mmGB6JIGWA8HWZ8egZoIRSdZuOGfMOxFgrHGBv7NrHYNYRC/bQkDnFN61F2DsI///BbZPMFXn/La87o+JaLuRypBuyTx5rXzC40frzgAQzYADhSykN1jz0L3NJk2y3AD+Zs1ymEaJNSpuo3FEK8A3gHQF/fymXvzEKBp1weqv29HAI3G/513RyWleBswLYE0bicl0qwbX/yfjmopv7uvHPhOyvY3HC8hcLe2h1aqXSUZPIlQOOxLwfNhyUhGJI1pYB6VIvyVex5Rmdk0LeRqIfET5+cK6HXFwDnhB/wwnJkLj+uu3YLP3/0MTKlIH/7tXv40Ds/sOz9aQbb8q1Cqj5ZVeQrjgbR+NIcqZdy+v0/+ApqsIdsuZ0dO75OzCiTn+xgamwtr3pVG4oye6y2PYZtWLcPAAAgAElEQVSqRrGsMcLhy1i/bgO7njvAukCeqUwr9z14Py+55gbisfmZjno044iq+qqEcznSjB/V1Gk9RyTUAvbFwJHzIYBFgblFlyzQbAkWBTJztqOybQNBpZT3APcAXHvtlcu7orNw4AkGe2t/L4fACw02ZrOb5+7qPNTfPUhokEYqFgSgoBvU8vnVlMiKLpdiUSNR8fiZqxvXDNXU31zB3voVc/3xOk4WVY0hJbhutumxL4SFCsuZGYVEi7+vq/odCnnB9S+1Fi04Fwv+KjowJ1jXW65cJIXsc8IPeGE5MpcfmmYSDtjsHl+J1IuLfv5i/JirxD6XI9UuxUhULosj9VJOZn4/qf1/4euLFqJEAhbbtu7ENPuIRmeV6qsccd0sJ0+eYNezw3gySDKag1EVqTmUzfK8ALbQ+Vrdx1xGoX+dfzxLcaRYEHT3ujUx5CouNn6cDwEsD8xdiiSAZpXSudtWGXRqVdVFsBxl+eUQeKHh33L5EZYKYIudPHfc3kpvv1MzswMabCa++/VwrRNrLkyzzMjIYNPnUql/wnVVvvzlX2ZsrNrW66LrIeADdHWN8YEPPISmxXHdcuX7iTc99oUwt7A8cFwll1FI1wUwgK6V7pIdYuGoJD0Nc4dodGMp768LrpB9XvEDzg5H5vLjmWeGeXx4NdNmjA//we8u+vlLXVybKbFXOVLVU2x2XiyFfOo+/vmr72X3Hg3bsUD690bxkMuaNYL3vOf+ynEaWNY0EGLn7kHsaJ6AbpG2dUTA5pot19LRRPmm2fk6cFxlbNgP0JE6X7ulOBKuBOiqGHIVFxs/zoc9PQRoQoj1UsrDlceuBJpVc/dWnvtW3XbjzdIjp4vlKMs3I3C5fBLP6+LgwffNK+I2FrAfOaP9q64+/S7E2dTCYitJwzDITKzmK1//Nb7+9cbnIpEZtm9/gJtvfpRCIcquXQqtrfsBEEKSTKqsXHkr+/fncZwMweBaMplHEUIQiWzBcTILWscsharO3WJBdyFsvcpGwLwuxrPtZH0e4LziB5wdjghRFZk2CQZ7efo5hZRhsnndJno6exb45OVhofT0Undbe3bptYaOue935yeyuOURxke72LZtislUhpJZxnUc/v/2zj1MrrO+75/3nDNnLju7s7qstLp4JaTalm0Z22BMbUOxuRg7CcYkbVIotHGTh4AfSBPipORJUmGShjZVSZ6WNIQGQsHmAVKMAoHaTh5MghAgXyQb2ZYvkqXVbXe1Wu3Mzn3mnLd/nDkz58xtZ3ZHOzsz7+d59pF29szMe3bPd37v+V0jeoHnntvOD3/4JPl8kOHhDGNj00zPjFMIFwiaOdbHgrwav4mbrt/FL939iy2fyy2358oGpR2ju/v6Qnmj69VIv+mj6wZMSpkSQjwMfFII8avADcDdwC11Dv8S8EUhxEPAOeAPgC8u9h7Z7ClOntzbcqqvN1Mqmz3NhQvf9T1eLWAhgqXpzEF0fX1NENd1x33xi3/C979/N1Opf4EeEHzsWP3sp1Zu7d07sWpcN4qX8dhNvEKK8KbJ0vjaChfjY8Q3TXJB2ARHZ7GMPEXTMSy6sDg3E2Z8fIhIZBeBQIw/+7M7OXfugwBIaaPrEQwjxoYNJ7jvvt9oq95NsTgroQ9oXyOL0UwjliWJx3+ElJJY7GYKhTjXXHWI7z/4u0zuv4GXv+83Iu3qo14vPxe3+3rduwxZ30i4x+qhzUhZQNd1xjeMY9s2k6ePkbYCnM9ESJg5/vGxX2ZmZoJswSQUyGPoFsFQjGuuXcOvffQhrOwhEmemia67a9ld5hWrwICVuA/4AjCD41/7sJTyOSHEBPA8cLWUclJK+YgQ4k+Ax3GqX78B7FnsxYUItJUt10qWoTdt/OTJvWiaWUkJzjtB3EJhikhkR/nxY8dmWbcuTcI4jxEUDbOfWrm1byTCO96dqSP2CCEzxPnjN5ftlxlKs/nyl7DyQdaPr+WCdTVXrX2WgCkImAaaKIJtcXZuiKmpcwQCV7Bt2/2kUjFe+9pKwDifP08i8QSnT2/x/a5GRkKcaTmqsjSGYzavHDUo5CsukmJRIKVz3n3EJdUHtKeRVtPoG2kkmXwOXXdCeNnsMWKxW8jngmjZYdZsiddc+53URzNjmE6Jsmt+OGbXzNyKrrvLydS1cwhhIiiwYV2MY9MhCAxzLPVmXjn7BobXXiCIjcTAHIpx3eUTvHr0lJOS75k7Nrr1g5fciMUvapyZrCR7FIuChx+MsHGzxbYdq3JGZVusCgMmpZwD7qnz+CROYNr72KeBT7f3DqJupmAjWsky9FIdsLasRGmYnaemRB/GspoHp9uhWRzAK8qD+02GopJsSic6Ei4HtRPzGm954zinTxj8p4/+PuAEqfc9NMr4xgJoQzz5wiyZfBgpbUSDusl0+mU0LVTq5qGVf1dbtxzhhdPLcwU1Y/NEkSOHA2TSGoZRsZThiFNCUG+H3qtcen1AOxppVx/g14hlJdC0YYSgrJF8wSSgd+4DtV19gJMY4bZoqudqC0avwoxoCO2n2MUFNGOY0bFbuWnL+rKO5p+u9Yxk5n+EENGauWPJC//vkhmwzRNFHtkXZv5irT7Gt1hMvKb3jResEgO2UrSaLddKlqGX6oC1ro9QLMYJBPwBbF2PAJ0zYlDfnXJwv8n4Fotbbs/VFHK6dTD5PL5CzpPHdbbtWMszh00OJF9HISdIZ9Nomk3+fBEhAuzZE615fzfjyouuDxOJJIDN/PBbdzB5wHEJHTkcKMUmHFfn7hucgPrGzRaPfDNcyrCsEIlK9u4Zqfth5LqJGsfBrL6pBVtJWtFIu/oAv0Z0fQTbziIlGMYIFy9eQJhpCpbe1liRVmhVHwBnJvUafYBj2H7hLWNs22Hx1E9MUsm3l+/6AyZsGLfK9Yf1sIsLCLHG95imD2Nlz/rW10gf7vXajkYGRR8DZcBazZZrJcvQS3XA2jTHyeXOEIlciZR2OUsrGNxKOu2U80wfv5pH5yoC8XYDaId67pTnDgdqCnxd8llRSqsVvkLOg/tNbn1rjslXdY7+NEDAlGiFInYxABTZssVmcrK2+NEwnA8jL5a1QDrtiHlhbpStNzrr866zuit8o5jecjKiGu3C9+4ZqQrW79y+5DfpM1rRSLv6AL9GwuGdJBI/RkpJOHwVP/jhIczhBZK5KLdu31l+jrdtmvfvdeRQoOWEhuXrA0Bj+qzu04drZ/OlvtPjW6yGd/2aMVyeXO5iWwvooc2+9TXTR6NzcY9dCq3epTqsTo0MiAGTbWXLhcNXMjv7p9h2kUBgHaa5CU3TGz63OmA9NLSD9evv8mUhjo+/l0BgtPycQi7iE0ijeFgruEJ3u3O4u7SZqSiFPMTnNcyQLBdxgrPDrFcHc8vtORbiGtGRIscnz1JIRbn11jl27LjcN17FJRK5nETiCWw7XzbWmcwJgsEU77jmKaYPzGDl16CbjQdbNjun6g+vTtSo1H4Q5Grb3w8crWukXX2AXyOWtUAsdjMgmJs7y3xe8MqFnWzedIVvFpy3bZr37+XepbRKK/q4bHuRgEk57dzVBzjxsFTSufZdfbjabdZA2MWMXI6Uk1jFeHnuWCEziWFvIhN/gsx8FjNyeVsa8XbYaNYOa6n0ikYGwoBJWSAQiNWk+tYjmXyeubnHCId3kc+fo1C4QLEYZ2LiN5s+t/4soHf5vpuYsHjhhVHSKSjmgz6BeGn31t4V1PkpneERWXZvCJzRC/msYOeuQvn9RkqFzu2mrrvn4Ddk4zz11F0Uiwl+67fejRAalpXh/GyWqdlNpBLjXJgqogcEQguVz3cxH3yjD69eqlHpJVrVyFL1AfU18tRTP+InL3wb1sS5YlfQ9/d1S0Wq9RGJ1mbawvL0AY4rEOrr4+EHI03PzbuG6rUdOXQZ0h7jE787hG2lQWhIK8PZs9uYnVnHS0czgFXWSHv6gOVugHuZgTjbUOgytm27v6VjvQHqSMQZqlcsxslkXqTaILXLAw8kGRv7Ft879RzP/P19vPPd19U9rhM7qEJekIg7rWcKBXjh2QDFoigHdFvp0lGPBx5IAlAsFvnyl/+Kqak4zz77XjZsmCORgFjsNIZRZHhE56WXryMylGV4xEKIGYySi8n1wXeC4ZhdE3BPJUVP+fFXA61qpNP6MIwASAFIfvb9P+F1r31d+WeN3Mq7ry90pA2SO9HBqw9wEh12Xtl8xlcj6mnXOQ8JOFMZMvM/Qto5nn1mHZGhPMMjeaQsljWi9NE6A2HAFsPbSTqZPEI0eoPPv99q8sdqQTcgmwWtyuMXjthk0p0pZPz61x/m7w5MYwdzzKbzpA0n5Tg0nCZXMBGBPAgImkGEyCHt9iZ6zEw5TVi9wXQA6UnNr4iwIvYjhwJEopKzk0bHXY+DjKuR6elvYJrjhMOXEww63SSWo4+rrrqWdfwDs3ach775Na7ZdQ1BM9jJpdegG5DPgW0LAgFvhp5NIS8oXGJnmV1cQNP9CVFC6G1pxNUH4NPIYvoAOHlcr9GHe3yvaWTgDVh1TYumvUQi8WNGRm4uC7TV5I9u4O064PrvQyGJVXRaz6SSAk2jFHQWFAuU6kJ0n5ti42bL119u6oxBJrEWXS8wNRVF0yrd6bPZDN/5/ss8/+LtZBbGmJvZSWLeaQabmLqCSCTBa3Y9ia4bxEYzJOJBhIig5x3jeWZSR0r8gfnDAY4cCpQzrzJpQSQiiY5IXxaVt4Fp491ub7fHWW34NTJOsZhgYeFJ4EaCwQ3L0odpBvm1e9/KH//1tyisneN7+7/HXW+9q2Nrb6SPkVEnJlzI+/VRyAsKBTj600BTfVT3X3Tfq5q9e0bKM8lcrPwtDEWd7l7R4QwLiTBSWmWNpJKCk8f1GgPj1YirD8CnkcX0Af2lkd5bcYeprmkZGrqW+fkfkkodwTRvq9vnrRNEYrMdSWH1dh3wto05dtQRjGU5bkS3oecIsHNXgXOn9fLUV8BX1HjH3Rnu+/gUv/tffg87HuG+n7udt7+98qGSz+ewpEUmsZ6rd4c5VhxhODYEgLQzLMyZTGzeyNGAyY1vfBJp5wiNvB7ddGIKDz8Y4c73+ONvbl831zV0cP84xaIgnxXlcwGnC3ej1PpWqY1TBDubu91n+N2GV5BIPAEIMpmX0fXgsvQhpeTRx36MjC4g0LjumopbvRNp3o30AY7bsFofwZAklxVEojaf/uu5cjaeVx+7ry+0fLdydtLwjDcqnbMV5uKsk/T0hn/+MlLmfRpp1DrKqxGvPrwa6YQ+oHc0MvAGrLqmxTTHiMVuJpk8RD5/tm6ft05w7du+wX/7/Td05LXci827M0ynBeGIxLbBDNZvibHctNz5qdfwfCZGfDbE/Kz76BBSjmKaLwMWQgsSjO5uOwvRcQNqNeNSwhG57CLlanH/3y8dO7GsF+xzvBoxzTFGRt5AOv0S+fwUgcCty9LH8eMv8dTkLGKswK2vf7OvD2Kn3Fn19OHSTB+wPI24d1/zc1rZ3QdghkYYGTEQIodtJdGM4bY1cin1Ab2jkYE3YPVqWnQ9xPr1d7ac+NFtmrnSvvA/ogy3MPtoKViFEEPRAlbeP5cskwlwYf7NbN6mc2H+zTDvf15kaPH17L6+4BO9SyHvxLkUK0e1RkxzDE0zCQRuXbZG5ufnwCgCgre9+bblL7YOzVxpj30rdMn04d59FYuyZiaZYYTZNGFyMVnqG+rRiNcz0gilD4eBN2CtjIboRdxdZ7EoyGWhaBXJF/IYgSLnpudJLIzw4vGTTCfma547PzvKF77+NdYOXeT4k+9g796N/OVfniIY3EogMEqhEObI8z9bPv6yqh3qudN60yyxeh2/61HI4/twic9r5HMw+apK0FhJVkoj6Wxnu9QsxuaJYlkfXlodCAvwXz+eZ/LYBWwrjaZHMIJb0AOjPjdntT7csS7L1YjShzJgLY2GAH+mon88yurEvWAff2QTuRxksk5yRD5nkE6uR9OLzM/HyTNb89yzL+7g4f/5VtYN3cgrR9/IurUgBESjc7zjHVmkHObAE60ZoVaoV7R85FCgPLQylRTkcgK7FIbI5+Gxb4UImJKdV/o/HPqhPc5qoxWNLFUfO3ZcSTD/D2RlnC987Uvs+Y3fQzRqvNlh7n8gwUOfGyKf979fPl8ZiNmII4cC/If3axzcHyESCSCEhpQ20eg8b3pHlrOT4x1bZ72i5aXqA/pLIwNvwKBREXKFVrtvXwqWOzV1bKPN3MVzDA2n0QBp6eTyQayigZ4awhC1OcN2eojt60859VzRAqOlBiLx+DDp9FHCYadWxwhkSSVjCNufmh+J1t/Buudy5FDA101hfk5jy4RVHtIHTsD6c58eZueuQjlA7WaMFS1n55nLipKwKwH2ft1pdptmGlmOPtasWcv73/16Pv/YPzInZjnw1I+59cabW15XJ/SBzGIXF5CygBABNGMYRKjp89IpwYaxnxKJXM1IrKKhRHyYfPp5wDFg7dZh1dPI7IxOOCLZMF7RyFL1Af2lEWXAWmAp3bc7RTtB5HpiTi0UiEXP8ZorDhKUEcbGhpAyz8zMTWzePMrU1Kaa18lnTSYuM9G0tZx4NQQ4PhYhdF+H/dj4q9z4hvVs27pt0bV5z6X6fB5+MMJwzObYiwZHf+rJOEwJDv2kYuhsCdigrcwGXdEiy9XHnXfeyVf3HSI5vMCrJ15ty4C1m2RRrZHUQoFQ8ALR4Rw33fxqOSNw9uKtQOO7lUhUlpr0+o2TEBp2sTIAu3oki7u2Rkaknka8I168GkmnBE/9yMSyBEKUNo0Dpg9lwFpgKd23u0E9MT97MEkyFcOydXRDJxpdh21nCQaTfPazl9V9nXvvNYlGx2ua9EppYRgj/OAHYWandqKFU+x/ZB1PhRyBRaKynGLcLgtxDYHw+fSzGclITJLPU3KVgGFAsfc8HX3NcvWhaRoaGk5zp0tLtUaePZgkOpwjuTAMQiCEU0RdzJ0BNjVNAHGa9Po72khp89QTNzB7wSQyJH2ehuXoA2o1ks1IxjbanJ92jKiuy4HThzJgLbCU7tuXkgOPB5k6U6fQsU6XbikLyKoPBiGCi84m8zbpLRbjSFnAtkcIBNaRSOgYgRzjuw5y9eVXle/A6nXQbuVcTp0wQPq7CCCcj7N8XpZa/EiskidEV1ftqqJX9FHPcDhuQ382nxCm07NwEZwmvXms4jxI2+lxKEdJZzYwFJU1vUaXqo9jLxoUCsKvkZKk52Y1bNvRh8sg6WOATnXprLZMRTfhwVvdPzOlM39B4/y0Xm5KCnBhVhCKnAHg+edv4OjRMaS0yGQi3HuvU3w8MWGVexy6mOYY4fBOIpFZEokoQkTJZIZ55ZUpFhaKBMz2GwE3OhcpwdCdoms3fi+diRZsmXBmLQ1FpZNlVUpHzpVmNvVTX7deZbn6kFIi6VwqeyN9ZNJOL9AtE359DI+cLX//xI/+GQsJk0wmzMfurTTwbRRTi0ZTzt1byYBlMmG0vM6my5bWa7TeuYCjC02raERKEJrTbSefr3TkGDR9KAPWAq1mKq4khbzwVfefn9LRDVkzwG7mXJB8Lkx8bpyLF9ezdq0FWGzdqrN9uyPk6jEpbsf5ZNJm+/Y8muYYt82bT/DRj/4Nv/M7YV4+eaFD7UZBCIkthe8OTFLx5UeGZGn4ocTwJIisj0ruuDvTV0HpXmS5+vjBD/6RZHABISSbN9XGZJdCPX1EIo47ulofC4kg6bTB2VNrmJ4aJhJJs2mr3nQKwuaJIq8+f4GtE3mEVolzbRw/yvTUJnbuvrIj5wGVhtw2lTswCeglfQRMJ6V+EPWhDFiLLJapeKmoF0ROJWvTfFNJQT4nuDArSPzEpFh0DYIkFBolEsyh6wWGhtK8+c1gGGFSKUd42azJ+fPT5df6yEec/589+0cYxsZyoPozn3kXv/3b9/LUUxMk01uQgSLpczHObAr6gtWNMsNOHq8tvEwlBYGA4793s6jA6ZCg6Y4Yd9/Qme7jikvHUvWxsJDgfz+0H7khwcjQKLfd/Ja2nt+OPiwLCgUnMcirj3xuDGlbFPPnCIfHufM9NrpZe616uf+BBDMv/nd0c9yXyPHnn34nTz8xwSuvhH3HV49IaVUjzrlUjJKrEdum/PiGcWvRurJ+ZSANWC/VdDXqsuF1j4DjWkA47gVddwK5miaxLYluFAlE50gmh3nhhTVMTsaxLIt0ehjbdgTz9NMVAzQ0dJHXv/4RXvvaaUzzJPl8qHTMuxgbexYp12OELTAsYqMjvum2e/eMsO8rEYaqUumHYzbbdlg1InO7Idi2cOJdlrOtlBJk6a6s2gWy3NRpRXNWUh8vv/wC6UAaIeDfv/ffomntTUtoRx+OLmSNPgIBx7BMTe/izOkx/v7viggtVB6AGTBhKGrXDI3UQ5uxC3F0T+zv3JkoQ0OW7y4PKiOE3Gv34H7Tp5HhmF3eBFZPKn/ucIDzUzrZbEUjUgIFp2XcIGtk4AxYN2u6LjWaBsGgZO16m7lZDV3Lkc/rbBxPgxAULQMjUCC6ZpqF7BAyF8EIZinkIugbT3L+5DUUcxGK5yeYs4L809N3sS6aYGTtFG991/9CmlkIZrECGQrpEXRpYOWHSSUFp08Y5RY41c1LYfEZYLFRm1hlYHW5oepjh6drju30aHVFhZXWh5R2OSFhdHi0+cEdwDCo0cdrdp5nIREmlRzGMGyGIs5cLncAZi7rxGC3bi9y4PEgB/ebnJ00sAr3k0+/hBABNm1J8qFf/zpSFoitDTes/XKv3ecOB3waqT6+Hl6N5LKCsSZ3XoOikf46mxboZk1Xp/COiHApFATShlxOMDerkcsJhAggbYGu6axfu77U4cCZgWRYupO+rGkIAaGIibSihIfT5DMaazfFnRc2isxfnGDdaBGhBbiQHWf8ihMEAgav3/16wqGML7uq1TZR3nMJmJLpcxrSrmRLCk0iZWc6aytap5/1YVtg2dTow49oOpdrIa6VjRlEsfIT5NMvc/pkCC0Qw4xcwa1v14HaDMT7H0gsSR9HDgdIxAXWhcr5CE2Sz+tERzqTLNKrDJwB65Warma4H+heF8HLLxhYCF8Ro205GX7nzo7Cj9+EVQxTyEe4MH0VG7eEyMzpmCaETLj95tt4dDpcHqd++823lV/nkW+Geezbb2P6TMXtkQcOLdh1CzVdTp0wyllRbkbUx+5d63Nj3P9AoizsQdgxrnb6WR+aBrb0GyzbFrx6bAOFguNKz2YDvPzSdsxQoDTY0snmazTBXDfXEzbXc+zxMP/5E2/gqZ+YHH2uvmuwmmp9PLovTCrp37QpfTRn4M5+tdWsLJXqu5KD+8dJJ7XyXKO5WY1CHmzbJhAokkg4cSxNsykWQ8xM6aRTzvTZgCl5dF+YM5M68XmNWJX7L50SS3J75LOivB6ouGHaEd2Rw4GaXevB/SaTr+pNjadiafSzPjZttTh2NEAwJB0XoiFJLUjMYB4wSadMBBIzaLAQF9g2ZEs3UgtxjUf3hZmZ8pepuCxFI9X6cJ6ntT0OpV68a1A0MnAGbLXVdHWKSFSSSkoWEs6OzrKgaIlShpQglXQ6DJhBDTPoiCpQinObppNafH5K9419qKa6r5s39uVypBRwPjPpGEjNc0u448r23R3ppKjZeT53OOBLHFF0jn7WR2JeQ+JoxMlI1JFAKhnGCBQoFnVCYQiFBQsJRx+GAblcJfXemWbemFY0cuDxIDNTOvGLFX3YtuCVowF2Xllo+9zqxbsGRSMDZ8BWY01XJ9h9faHmInZ7qL3zHsmj+8xyIXDe07+3UHBqShLzWvnxRu6S6t1c3c4CkvIO9cykXh64J+s8f6kMx2ymzuh1a3MUy2OQ9AFOH86ff78zj+zLn9UQQC7rGBRdl+RKl6xrlIpF0VAfsLhG3PjchnGLQr6iDzNkExt13I2dcAsOikYGzoBB92q6LjXesQtQ2S0eeNy5+3LnErliHBm1eeVooGxwAiZk0oKpMzqP7Auz+3pnN1jdXb7e+BNwxLH7hsoHhXeEeytZVq3iirxe9tWgpA9fSgZFH+B0ej/wuFPHuGG8kv7+wrMBrnptgVMnDJKJihfBMGSNPsCvkXrjT6ByDbp3TF59gNLIUhhIA9Zv7N0zwpFDASZfNTAMbzNcwfqNdlNXQqHUmeDUCQOBI9ChqCSVdERbb8e2ENfK/vpm3Qq8zEzp5TtC70wjhLM79o6PcJueQv3pzc163Q1K+rCidRrpI2BKwhHZVB/5rCi72E+VriGvPqBWIxV9gFcji12Drkaq5+JRanXYqDFwPWM0KBrpvRUrajg7aXDnezI1O7pXjgaIjdoesTlCHY7VGrVKQFn4jJPrv/de3M7raWVXiveODCgXkcYvVt4jk64I3p1p5B43+aqOEJQzHJsZT6hOZa7QiwJUXHoa6SMxr/lcbe51DdQdaOk1ZtWGyasR7+sMx+waj8XB/SbPHQ749AEVjXjn4rkaGR61fR0/vAa0HoOikf46G4WPDeMW77wn4ysydjlySOPcacfFWIl/Cd84dXcXd9Ob/K8bGfJ32vbekYG/15x73MMPRmq6c9c+36Wx8QRHvONbOtWJUTHIeF1tXteaPATnTuuk04JwxC5rJDpS6ypMJYVPI9X6qBhOrSZT0Xuc0kj7KAM2INz/QMIn0N03OC66g/tNhmM2Wyao6Z7RaBfndWVUMxyzeeWowUJcwyoKTh533i+fgy9/doidVxbbSuZo1CqongtEoegUXn2Mb7G45fZczR1cI1d6M32A45k49qLh0wdAsUA5HtcOg6yRruZZCiHWCiG+KYRICSFOCiHe1+TYXxZCWEKIpOfrthVcbs/jnfbqfg1Fm8cA2sUJhtuMxCRr1tlc+7o8174uz+gaiUAMRGpvJ1EaWTlWQh8AsTW1+rj2dXlC4c6/V7/T7TuwP8dp6rARuB74jhDiGSnlc4DXAKkAABOTSURBVA2O/5GU8k0NfjbwuDUoM1M6hbyT8vvwgxEiQ7KcNFG9K3NjAA4V8cQvaiQXHD+7m3zhHh+JyqYxsUakkoL8pM6XPxtlIS4o5AUSpwFxIOA0WV2z3q4ppG6HRiPgezh9WGmkQyxHH9UxMlcf56f0chcN9/il6iObEZyZdBI5TrziDLG0nAx/dF2iaWAGJddc336tmJd+0kjXDJgQYgj4BWC3lDIJ7BdC/C3wAeDj3VpXL+JekM64BovUfqfrRXUbm3qujVtuz/HIN8MgnLR7Z/ox5DICW0IoJDl1wiBUio2dmdQZXWuX37dZmxsvZkgyf1GUZxcBaDqALNXcOMXXyYRg67bmQm8mwF5LA26G0khnWK4+3DhTJBooaySXcTp1CM2ZW3fiFec1l6qPagoFZ1PnTGRwjJeuO0bOTT5p5ZzrPd5PGunmHdgVQFFK+ZLnsWeA25o85wYhxCwwB3wZ+JSUsu6VIYT4IPBBgImJLR1Z8Gql+oJsVzDplODn35/2+fiPHXUaiK5db3N+WiM22rglVL0sxeqA92XbiyQTAbZMWMxMaWQzArM0zyiXK42DAUbXOnOTqrsXNDvfPkZppAMsVx9QymS8p5LJeOxooCYT0M3iXYo+AEJhZ1q01ziZpiyPggEwg5TvwJRGumvAokD1bzkBDDc4/p+A3cBJ4Brga0AR+FS9g6WUnwM+B3Djjdd1bl75KsWboOGm6ULjZqLeYsvZGd3XC9EteNZ1Z2yDbQtynqaj1bu/eh8Qjz8SIp8VHDtamcuUTomSa8TpdGB7kqTMoDOf6aY35QdyMF8DlEY6RLv6gPrlIa5GAHTDSU5yyWXrN/6tp4/nDgeIz2s1+jj2orNGyxJO+xrAlo7rMBSSRKK20oeHSxYxFEJ8XwghG3ztB5LASNXTYsBCvdeTUh6XUr4qpbSllD8FPgn8y0u1/l7DG4B253GNjPrrvVzf/OkThifuBeGIc6xp4uuFOBSV7NxVIDJks3NXgZ27CmyZsBbNkto8UWQhLpi/6P+yLGfci1Zyu2ga5f9bved+XzZKIytHu/rwamR8i1V+jlcjsVEnGcP9akcfM+d0ps/oNfqwSwNdhdJHS1yyOzAp5W3Nfl7y7xtCiMullC+XHr4OaBScrnkLyqPwBoNmLWAa4e2Akfa4PCLRSq2KN0mjE9z/QIJ9X4mwaau/DuXY0QDzFwfqT9YUpZHO0kl9bJ4octOb8LVF6xRuO6nqzvWO275jbzMQdM2FKKVMCSEeBj4phPhV4AbgbuCWescLIe4CnpZSTgshdgF/APzNii14FdCsBcyRQ4GyW2Sm1A0eHLfEhnHnOLfC/8DjQSaPG2VRnpnUOT+lk0qKcjcMMyRJJpyAcbEoyn3aFgseN+JUKYurWAChOcM3XTTdGTgYMGVPZkJdKpRG2qOT+nATOtzneF2H4OgjV9UYu5Xkika4+sjnK0Y0X9r/ufpIp+E1V/R/cXI7dDuN/j7gC8AMcAH4sJseLISYAJ4HrpZSTgJvA74ohIgC08CDwB93ekHJ5PO+Ttzr1v1MTzQ2TadE+Y7Hu6v76dNmTXX/sRcN8jk4P+WIuJAXZDNQLDrG5dxpHcOQbL7M5prrC0RHbLbtqAjHG5yuxt0Fnz2l+9yUxSIEg07W1rr1dnkOUi4r2LmrQGJe45rrCwMTfG6DVaWRQdFHoWRI0knnsUJeMDvtdKLPpDUiURsjKlnv9uoU/uQKaHznt3fPCI99K1QaN1R6/YJA05xsQ8OQjG101litDxX/8tNVAyalnAPuafCzSZwgtvv9/cD9l3I9yeTznD79FxjGKKa5mUIhzunTf8HWrR/uCZG2SiHvCMc1Iu6/CwnBHXdnfSLZu6c6BOPgirPesEmnhY0gGKzkBRSLwmmkKiu7V3CSQhLzTqBc3X3Vspo0Mkj6cMeceDWykBB84EMpX5f3xTq7Nxo2mc2I8rwxoJTQ5JSuuMlToPSxGN2+A1tVXLjwXQxjtDyN1v33woXvrnqBugP7qvF23/biFYlLsSg4edzfwfrgfrNuzYy7y2w0SE/TJEXPj6R07/DwFSqnkoJrSl211d3X6maQ9AH+jRY4+nhkXxhkZeO2mD4aDZu0bYEZrGjE9iwjEpWMlUYcKX00RxkwD9nsaUxzs+8xXR8mmz3dpRW1TrOBffVwMwy9nDuts22H5XsdN9Dc7qyi4Zj0ZQ8UCs57epNHoMFQTMWqZJD0AZX5eS7nTus1r7NUfWiaLDcHgEod5HBM8oEPJcuPK300RxkwD6HQVgqFeHlnCWBZC4RCW7u4qgrNquvruTGAmrY24OwkoyNLb9cEcORwgHRSlIPcLjNTOhvGLd9wQHAGBLq+fEVvovTROkcOB3yjU1zcJI9Q2L+BdOvB3DsvRWsoA+Zh3bqf4fTpvwCcnaVlLVAszjM+/t4ur6w1X3s98d5xd6bG9XDk0EaEqJ0AW294ZCPSpYzF6g72bnZXNQGz4sv3rlP59XsHpY/29FE9OgUaT112s36VPtpDGTAP0ejVbN36YV+W1fj4e1eFf3+xKart+MfveHemrthPHtdrdoxu+nCjBrunThi+4udsxhkREQ77p9aOjVvKl9/jDLo+Nk8UeexvwzX6OD+l08i0zUzp5WxfcJIyAqYkmxHleXzgxOI2T9h1DaqiMcqAVRGNXr0qBNlJFtudunzs3rUkE/7BefF5jWRCYBiVneGRQwHm5zRSSUn8olZOBdZ1xzWy69oCE6+xaoYEgvNB87F717ZtyFo9B8WlpR/1Aa1fX9UF+vF5jXxWkEn775xSCxqP7guzEK+tI98yYTE2bvPYoema93b1Ue+9O7H+fkMZsAFgsd2pF3fkhEts1MYwBPe8L10WwsfuXYsQlJuaemu6xsatcgfvdt+7U+egULRLq9dXdTaj65mQ4Eu2eNPl4067qpgs6wOcUhV3Snq7792J9fcb/X12irap18ft9AnDt4s7cijA2VOOayQRF+heV+GKrFKh6A6Nshm9hmLvnhFmZ3TSSc2nD91wsg8VnUMZMEXbpFOC6Igsx77yeZDSmY80M+W4TlJJ0bAIuh1c10i9bK52R68rFCvB2UmDcMTGbUPp6kNmIRDAp49OuPf27hmp0Qc4GnFmoPUvyoD1CKttiqpbI+PORbKKkoIUCCpTavd9xZl22+7sJS+ua6TVbC7FYLLa9LFh3PbNDbOKkmKp07xXH53g7KRRNyPY0YgyYIpVQLOdWqcCuEv5EDBDkkRclMXpEh2RDEVlzdA/heJSsBL6cJ/TjkYW00ej+jRFa6jfXh+wWAC3VdG1KmZvIDs2apNMOM2AdR1fcWZiXqtbKFrvvRejOrnErZdRdTKKxWglwaHTGnGvV68+ANZvtMveC/d67sTdY7U+gIHon6gM2ADQiui8u9QjhwKkU86OMRKV7C512XZ3rNWB7Ef3hX21Ll52d6iDdnW8S7XYUXSSdjTi1QdUNOK9o/Ner159VLenavW9F6OV5Kt+RBmwPsQ7Ct3bmLeZy+Sxvw0jSpo8e0ovd+NOJWHrdmf72OiOzplj5LhFWmFQa1YUq4Ol6AMqGvHqAyoaUfpYeZQB60MW4m4xsua7U2pWE+Kdl3R+SveNWKmmWkR794yw7ysRhkquxZkpjUJeEDAhtV/UFGa2U7Pifhi4vRddIkNySQXRCsVS9AEVjXj1AbUaWUl9uM97ZF/Ypw9wNNKpTMfVijJgihpSSVFOvigUKuPUZYMNpCsQd9eY2m+SSgoKeUgltXJ6rzvlth28xdODWKipWJ3E5zWs0uXoaqRRanw9fQxFbWam9Bp9nJ10NmvtZO4uxej1C/19dgNCPZcFLH28uWU505Pd/7vpud7ebdVUt6SqTnt30FTWlWLF6bQ+AKxiZRhlRSONr+9qfWzdXuTRfeEqjTh3hEvZ6A0q6tOkD6je8TW6W1EoBhGlj/5FGbABxhssnp/TmJ9z0nCLVdp203MjUdUGRzE4VCdTuBop5AWFQmUIJTgaWc4dnWJpKAPWh7RaV+L1m++6tsBC3DFUJ48bbNvhPO5t2fTIvrAva8v7upe6YFqh6BRL0QdUNOLGh4dKG7pOayQy1JnayUFAGbA+ZClZR64ADzwe9D2+EHd6Gw7H7PKQvmqqxbZ5wvXj+wsr3R3qUtanjJ6iUyw1K++W23MceDxIKumPBS/ENQ48HuSW23MtacS9lt1YnIurj903tF87Oaj6UAZM4WMhrhGO1LoKp87oLU+krc668rJUQfVzKrCid1iIawxFJcMxu+yxAEcfp08YLWnEvZbr1XsttbvMoOpDGTBFDRvGLd55T8b3WLvpuIMqKMVg0KgzTD33YSOURpaPMmCKjqM6CSgUjVH66BzKgA0oe/eMcORQoKbmZH5OY9e1hQbPao1BLapU9Bcnj+s1+pid0Rlds7xsQ6WPzqF+YwPK2UmDO9+TqXn84QcjDQdFDmqgWDGYbNthcetb/Vpwu9I0QmlkZVEGTOGjWQqvcm8oBp3hmF1O2PDiGiilkZVFGbA+p5G//cih+v3WlpLCq1D0Ks3iUfW45facGuWzilAGrM9p5G8/uN8sj5Xw0qghqULRjzSLRx05FCg32vXSqKm1YuVRBmyAqYyV8LL8hrsqDqDoB7wjhrw0a2rdCkofnUMZsAElEpU1nQCAjvRzU3dvin4gUprfVe/x5aD00TmUARtQdl/vpMqrdF6Foj67ry8ofaxyarcXK4gQ4iNCiCeFEDkhxBdbOP43hRBTQoiEEOILQojgYs9RKHoZpRGFojHd3kqcBf4IeCfQtMBCCPFO4OPAW0vP+ybwQOkxRQOa+dvVcMmeQGnkEqL00dt09S8kpXwYQAhxI7B1kcP/HfB5KeVzped8EvgKPSbOkZEoohggHGxeENkpmvnb9+4ZaSuYrFrgrDz9rpFQKIIuDcDE0Ff+40jpo7cRchXkhAoh/gjYKqX85SbHPAP8sZTya6Xv1wGzwHop5YU6x38Q+GDp2yuBFzu97i6xHue8u8DO7ZDL1z4eNOHYiZVeTYlO/D62SSnHOrGYS4XSSFt0SSOrUh/QxxrppXvkKBD3fO9uaYaBGnFKKT8HfG4F1rWiCCGelFLe2O11rBbU78OH0gjqmqimn38flyyJQwjxfSGEbPC1fwkvmQRGPN/HSv8uLH+1CsXKozSiUCyPS3YHJqW8rcMv+RxwHfD10vfXAdP1XCMKRS+gNKJQLI9up9EbQogQoAO6ECIkhGhkVL8E/IoQ4mohxBrgD4AvrtBSVxN95/JZJn39+1AaWRJ9fU0sgb79fXQ1iUMI8QlgT9XDD0gpPyGEmACeB66WUk6Wjv8Y8B9x0om/AXxISll/9odC0QcojSgUjVkVWYgKhUKhULRLV12ICoVCoVAsFWXAFAqFQtGTKAPWIwgh1gohvimESAkhTgoh3tftNXULIURQCPH50u9hQQhxWAhxV7fXpeguSiMVBkUjvVTIPOj8OZAHNgLXA98RQjzjtg0aMAzgFPAWYBL4GeDrQohrpZQnurkwRVdRGqkwEBpRSRw9gBBiCLgI7JZSvlR67EvAWSnlqu1zt5IIIZ7Fyc77RrfXolh5lEYWpx81olyIvcEVQNEVZolngGu6tJ5VhRBiI87vaBB32goHpZEm9KtGlAHrDaJU+tq5JHB63A00QogA8BDwf6SUR7u9HkXXUBppQD9rRBmw3qC6xx04fe4GusedEEIDvowT9/hIl5ej6C5KI3Xod40oA9YbvAQYQojLPY9dR5+5A9pBCCGAz+ME7H9BSlno8pIU3UVppIpB0IhK4ugRhBBfBSTwq8ANwHeAWwY0wwohxGdxMs3eLqVMdns9iu6jNOJnEDSiDFiPIIRYC3wBeAfObKePSym/0t1VdQchxDbgBJADvONxf01K+VBXFqXoOkojFQZFI8qAKRQKhaInUTEwhUKhUPQkyoApFAqFoidRBkyhUCgUPYkyYAqFQqHoSZQBUygUCkVPogyYQqFQKHoSZcAUCoVC0ZMoA6ZQKBSKnkQZMIVCoVD0JMqA9SFCiLAQ4rQQYlIIEaz62V8JISwhxL/u1voUim6jNNIfKAPWh0gpM8Ae4DLgPvdxIcSngF8BPiql/GqXlqdQdB2lkf5A9ULsU4QQOs5E2g3ADpwO3X8K7JFSfrKba1MoVgNKI72PMmB9jBDi54BvA98Dbgc+I6X89e6uSqFYPSiN9DbKgPU5QoincWYjfRV4n6z6gwshfhH4dZy5QbNSyu0rvkiFoosojfQuKgbWxwghfglnKi3AQrUwS1wEPgP83ootTKFYJSiN9DbqDqxPEULcgeMa+TZQAP4VcK2U8oUGx98D/JnaXSoGBaWR3kfdgfUhQog3Ag8DPwT+DfD7gA18qpvrUihWC0oj/YEyYH2GEOJq4LvAS8A9UsqclPIY8Hng3UKIW7u6QIWiyyiN9A/KgPURQogJ4FEcn/1dUsqE58d/CGSAP+nG2hSK1YDSSH9hdHsBis4hpZzEKcys97OzQGRlV6RQrC6URvoLZcAGnFIxZ6D0JYQQIUBKKXPdXZlCsTpQGlm9KAOm+ADw157vM8BJYHtXVqNQrD6URlYpKo1eoVAoFD2JSuJQKBQKRU+iDJhCoVAoehJlwBQKhULRkygDplAoFIqeRBkwhUKhUPQkyoApFAqFoidRBkyhUCgUPcn/B76tIvARFSMxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a20ec0e10>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "def `1(clf, X, y, axes=[-1.5, 2.5, -1, 1.5], alpha=0.5, contour=True):\n",
    "    x1s = np.linspace(axes[0], axes[1], 100)\n",
    "    x2s = np.linspace(axes[2], axes[3], 100)\n",
    "    x1, x2 = np.meshgrid(x1s, x2s)\n",
    "    X_new = np.c_[x1.ravel(), x2.ravel()]\n",
    "    y_pred = clf.predict(X_new).reshape(x1.shape)\n",
    "    custom_cmap = ListedColormap(['#fafab0','#9898ff','#a0faa0'])\n",
    "    plt.contourf(x1, x2, y_pred, alpha=0.3, cmap=custom_cmap)\n",
    "    if contour:\n",
    "        custom_cmap2 = ListedColormap(['#7d7d58','#4c4c7f','#507d50'])\n",
    "        plt.contour(x1, x2, y_pred, cmap=custom_cmap2, alpha=0.8)\n",
    "    plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\", alpha=alpha)\n",
    "    plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\", alpha=alpha)\n",
    "    plt.axis(axes)\n",
    "    plt.xlabel(r\"$x_1$\", fontsize=18)\n",
    "    plt.ylabel(r\"$x_2$\", fontsize=18, rotation=0)\n",
    "plt.subplot(121)\n",
    "plot_decision_boundary(tree_clf, X, y)\n",
    "plt.title(\"Decision Tree\", fontsize=14)\n",
    "plt.subplot(122)\n",
    "plot_decision_boundary(bag_clf, X, y)\n",
    "plt.title(\"Decision Trees with Bagging\", fontsize=14)\n",
    "save_fig(\"decision_tree_without_and_with_bagging_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Out of Bag Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When sampling with replacement some data instances are never sampled they are oob and can be used to predict the performance of ensemble"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9013333333333333"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag_clf = BaggingClassifier(\n",
    "    DecisionTreeClassifier(random_state=42), n_estimators=500,\n",
    "    bootstrap=True, n_jobs=-1, oob_score=True, random_state=40)\n",
    "bag_clf.fit(X_train, y_train)\n",
    "bag_clf.oob_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.31746032, 0.68253968],\n",
       "       [0.34117647, 0.65882353],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.08379888, 0.91620112],\n",
       "       [0.31693989, 0.68306011],\n",
       "       [0.02923977, 0.97076023],\n",
       "       [0.97687861, 0.02312139],\n",
       "       [0.97765363, 0.02234637],\n",
       "       [0.74404762, 0.25595238],\n",
       "       [0.        , 1.        ],\n",
       "       [0.71195652, 0.28804348],\n",
       "       [0.83957219, 0.16042781],\n",
       "       [0.97777778, 0.02222222],\n",
       "       [0.0625    , 0.9375    ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.97297297, 0.02702703],\n",
       "       [0.95238095, 0.04761905],\n",
       "       [1.        , 0.        ],\n",
       "       [0.01704545, 0.98295455],\n",
       "       [0.38947368, 0.61052632],\n",
       "       [0.88700565, 0.11299435],\n",
       "       [1.        , 0.        ],\n",
       "       [0.96685083, 0.03314917],\n",
       "       [0.        , 1.        ],\n",
       "       [0.99428571, 0.00571429],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.64804469, 0.35195531],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.13402062, 0.86597938],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.36065574, 0.63934426],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.27093596, 0.72906404],\n",
       "       [0.34146341, 0.65853659],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.00531915, 0.99468085],\n",
       "       [0.98265896, 0.01734104],\n",
       "       [0.91428571, 0.08571429],\n",
       "       [0.97282609, 0.02717391],\n",
       "       [0.97029703, 0.02970297],\n",
       "       [0.        , 1.        ],\n",
       "       [0.06134969, 0.93865031],\n",
       "       [0.98019802, 0.01980198],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.97790055, 0.02209945],\n",
       "       [0.79473684, 0.20526316],\n",
       "       [0.41919192, 0.58080808],\n",
       "       [0.99473684, 0.00526316],\n",
       "       [0.        , 1.        ],\n",
       "       [0.67613636, 0.32386364],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.87356322, 0.12643678],\n",
       "       [1.        , 0.        ],\n",
       "       [0.56140351, 0.43859649],\n",
       "       [0.16304348, 0.83695652],\n",
       "       [0.67539267, 0.32460733],\n",
       "       [0.90673575, 0.09326425],\n",
       "       [0.        , 1.        ],\n",
       "       [0.16201117, 0.83798883],\n",
       "       [0.89005236, 0.10994764],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.995     , 0.005     ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.07272727, 0.92727273],\n",
       "       [0.05418719, 0.94581281],\n",
       "       [0.29533679, 0.70466321],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.81871345, 0.18128655],\n",
       "       [0.01092896, 0.98907104],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.22513089, 0.77486911],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.9368932 , 0.0631068 ],\n",
       "       [0.76536313, 0.23463687],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.17127072, 0.82872928],\n",
       "       [0.65306122, 0.34693878],\n",
       "       [0.        , 1.        ],\n",
       "       [0.03076923, 0.96923077],\n",
       "       [0.49444444, 0.50555556],\n",
       "       [1.        , 0.        ],\n",
       "       [0.02673797, 0.97326203],\n",
       "       [0.98870056, 0.01129944],\n",
       "       [0.23121387, 0.76878613],\n",
       "       [0.5       , 0.5       ],\n",
       "       [0.9947644 , 0.0052356 ],\n",
       "       [0.00555556, 0.99444444],\n",
       "       [0.98963731, 0.01036269],\n",
       "       [0.25641026, 0.74358974],\n",
       "       [0.92972973, 0.07027027],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.80681818, 0.19318182],\n",
       "       [1.        , 0.        ],\n",
       "       [0.0106383 , 0.9893617 ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.98181818, 0.01818182],\n",
       "       [1.        , 0.        ],\n",
       "       [0.01036269, 0.98963731],\n",
       "       [0.97752809, 0.02247191],\n",
       "       [0.99453552, 0.00546448],\n",
       "       [0.01960784, 0.98039216],\n",
       "       [0.18367347, 0.81632653],\n",
       "       [0.98387097, 0.01612903],\n",
       "       [0.29533679, 0.70466321],\n",
       "       [0.98295455, 0.01704545],\n",
       "       [0.        , 1.        ],\n",
       "       [0.00561798, 0.99438202],\n",
       "       [0.75138122, 0.24861878],\n",
       "       [0.38624339, 0.61375661],\n",
       "       [0.42708333, 0.57291667],\n",
       "       [0.86315789, 0.13684211],\n",
       "       [0.92964824, 0.07035176],\n",
       "       [0.05699482, 0.94300518],\n",
       "       [0.82802548, 0.17197452],\n",
       "       [0.01546392, 0.98453608],\n",
       "       [0.        , 1.        ],\n",
       "       [0.02298851, 0.97701149],\n",
       "       [0.96721311, 0.03278689],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.01041667, 0.98958333],\n",
       "       [0.        , 1.        ],\n",
       "       [0.0326087 , 0.9673913 ],\n",
       "       [0.01020408, 0.98979592],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.93785311, 0.06214689],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.99462366, 0.00537634],\n",
       "       [0.        , 1.        ],\n",
       "       [0.38860104, 0.61139896],\n",
       "       [0.32065217, 0.67934783],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.31182796, 0.68817204],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.00588235, 0.99411765],\n",
       "       [0.        , 1.        ],\n",
       "       [0.98387097, 0.01612903],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.62264151, 0.37735849],\n",
       "       [0.92344498, 0.07655502],\n",
       "       [0.        , 1.        ],\n",
       "       [0.99526066, 0.00473934],\n",
       "       [1.        , 0.        ],\n",
       "       [0.98888889, 0.01111111],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.06451613, 0.93548387],\n",
       "       [1.        , 0.        ],\n",
       "       [0.05154639, 0.94845361],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.03278689, 0.96721311],\n",
       "       [1.        , 0.        ],\n",
       "       [0.95808383, 0.04191617],\n",
       "       [0.79532164, 0.20467836],\n",
       "       [0.55665025, 0.44334975],\n",
       "       [0.        , 1.        ],\n",
       "       [0.18604651, 0.81395349],\n",
       "       [1.        , 0.        ],\n",
       "       [0.93121693, 0.06878307],\n",
       "       [0.97740113, 0.02259887],\n",
       "       [1.        , 0.        ],\n",
       "       [0.00531915, 0.99468085],\n",
       "       [0.        , 1.        ],\n",
       "       [0.44623656, 0.55376344],\n",
       "       [0.86363636, 0.13636364],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.00558659, 0.99441341],\n",
       "       [0.        , 1.        ],\n",
       "       [0.96923077, 0.03076923],\n",
       "       [0.        , 1.        ],\n",
       "       [0.21649485, 0.78350515],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.98477157, 0.01522843],\n",
       "       [0.8       , 0.2       ],\n",
       "       [0.99441341, 0.00558659],\n",
       "       [0.        , 1.        ],\n",
       "       [0.08379888, 0.91620112],\n",
       "       [0.98984772, 0.01015228],\n",
       "       [0.01142857, 0.98857143],\n",
       "       [0.        , 1.        ],\n",
       "       [0.02747253, 0.97252747],\n",
       "       [1.        , 0.        ],\n",
       "       [0.79144385, 0.20855615],\n",
       "       [0.        , 1.        ],\n",
       "       [0.90804598, 0.09195402],\n",
       "       [0.98387097, 0.01612903],\n",
       "       [0.20634921, 0.79365079],\n",
       "       [0.19767442, 0.80232558],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.20338983, 0.79661017],\n",
       "       [0.98181818, 0.01818182],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.98969072, 0.01030928],\n",
       "       [0.        , 1.        ],\n",
       "       [0.48663102, 0.51336898],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.07821229, 0.92178771],\n",
       "       [0.11176471, 0.88823529],\n",
       "       [0.99415205, 0.00584795],\n",
       "       [0.03015075, 0.96984925],\n",
       "       [1.        , 0.        ],\n",
       "       [0.40837696, 0.59162304],\n",
       "       [0.04891304, 0.95108696],\n",
       "       [0.51595745, 0.48404255],\n",
       "       [0.51898734, 0.48101266],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.59903382, 0.40096618],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.24157303, 0.75842697],\n",
       "       [0.81052632, 0.18947368],\n",
       "       [0.08717949, 0.91282051],\n",
       "       [0.99453552, 0.00546448],\n",
       "       [0.82142857, 0.17857143],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.125     , 0.875     ],\n",
       "       [0.04712042, 0.95287958],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.89150943, 0.10849057],\n",
       "       [0.1978022 , 0.8021978 ],\n",
       "       [0.95238095, 0.04761905],\n",
       "       [0.00515464, 0.99484536],\n",
       "       [0.609375  , 0.390625  ],\n",
       "       [0.07692308, 0.92307692],\n",
       "       [0.99484536, 0.00515464],\n",
       "       [0.84210526, 0.15789474],\n",
       "       [0.        , 1.        ],\n",
       "       [0.99484536, 0.00515464],\n",
       "       [0.95876289, 0.04123711],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.26903553, 0.73096447],\n",
       "       [0.98461538, 0.01538462],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.00574713, 0.99425287],\n",
       "       [0.85142857, 0.14857143],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.76506024, 0.23493976],\n",
       "       [0.8969697 , 0.1030303 ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.73333333, 0.26666667],\n",
       "       [0.47727273, 0.52272727],\n",
       "       [0.        , 1.        ],\n",
       "       [0.92473118, 0.07526882],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.87709497, 0.12290503],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.74752475, 0.25247525],\n",
       "       [0.09146341, 0.90853659],\n",
       "       [0.44329897, 0.55670103],\n",
       "       [0.22395833, 0.77604167],\n",
       "       [0.        , 1.        ],\n",
       "       [0.87046632, 0.12953368],\n",
       "       [0.78212291, 0.21787709],\n",
       "       [0.00507614, 0.99492386],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.02884615, 0.97115385],\n",
       "       [0.96571429, 0.03428571],\n",
       "       [0.93478261, 0.06521739],\n",
       "       [1.        , 0.        ],\n",
       "       [0.49756098, 0.50243902],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.01604278, 0.98395722],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.96987952, 0.03012048],\n",
       "       [0.        , 1.        ],\n",
       "       [0.05747126, 0.94252874],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.98989899, 0.01010101],\n",
       "       [0.01675978, 0.98324022],\n",
       "       [1.        , 0.        ],\n",
       "       [0.13541667, 0.86458333],\n",
       "       [0.        , 1.        ],\n",
       "       [0.00546448, 0.99453552],\n",
       "       [0.        , 1.        ],\n",
       "       [0.41836735, 0.58163265],\n",
       "       [0.11309524, 0.88690476],\n",
       "       [0.22110553, 0.77889447],\n",
       "       [1.        , 0.        ],\n",
       "       [0.97647059, 0.02352941],\n",
       "       [0.22826087, 0.77173913],\n",
       "       [0.98882682, 0.01117318],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.96428571, 0.03571429],\n",
       "       [0.33507853, 0.66492147],\n",
       "       [0.98235294, 0.01764706],\n",
       "       [1.        , 0.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.99465241, 0.00534759],\n",
       "       [0.        , 1.        ],\n",
       "       [0.06043956, 0.93956044],\n",
       "       [0.97619048, 0.02380952],\n",
       "       [1.        , 0.        ],\n",
       "       [0.03108808, 0.96891192],\n",
       "       [0.57291667, 0.42708333]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag_clf.oob_decision_function_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "They are almost equivalent to ensemble of Decision trees trained via Bagging(sometimes pasting) but are more convenient and optimized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rnd_clf = RandomForestClassifier(n_estimators=500, max_leaf_nodes=16, n_jobs=-1, random_state=42)\n",
    "rnd_clf.fit(X_train, y_train)\n",
    "\n",
    "y_pred_rf = rnd_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating same with Decision tree and Bagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "bag_clf = BaggingClassifier(\n",
    "    DecisionTreeClassifier(splitter=\"random\", max_leaf_nodes=16, random_state=42),\n",
    "    n_estimators=500, max_samples=1.0, bootstrap=True, n_jobs=-1, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "bag_clf.fit(X_train, y_train)\n",
    "y_pred = bag_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking if they are equivalent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.976"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(y_pred == y_pred_rf) / len(y_pred) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Random forest grows trees by randomly selecting the subset of features to be compared at each node , whereas Decision trees selects the best feature at each node with best threshold value.\n",
    "Instead of best threshold value we choose random threshold value it will add more randomness known as Extremely Randomized trees(Extra trees)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature importance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scikit learn measures feature's importance by looking at how much node that uses that feature reduce impurity on avg(across all trees in forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sepal length (cm) 0.11249225099876374\n",
      "sepal width (cm) 0.023119288282510326\n",
      "petal length (cm) 0.44103046436395765\n",
      "petal width (cm) 0.4233579963547681\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()\n",
    "rnd_clf = RandomForestClassifier(n_estimators=500, n_jobs=-1, random_state=42)\n",
    "rnd_clf.fit(iris[\"data\"], iris[\"target\"])\n",
    "for name, score in zip(iris[\"feature_names\"], rnd_clf.feature_importances_):\n",
    "    print(name, score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.11249225, 0.02311929, 0.44103046, 0.423358  ])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_clf.feature_importances_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AdaBoost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this we use sequential predictor where every predictor tries to improve the error of its predecessor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(\"/Users/onion8/Desktop/adaboost.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At every iteration it assigns the higher weight to wrong classified observations so that in the next iteration these observations will get the high probability for classification.\n",
    "Also, It assigns the weight to the trained classifier in each iteration according to the accuracy of the classifier. The more accurate classifier will get high weight."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdaBoostClassifier(algorithm=u'SAMME.R',\n",
       "          base_estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=1,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best'),\n",
       "          learning_rate=0.5, n_estimators=200, random_state=42)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "\n",
    "ada_clf = AdaBoostClassifier(\n",
    "    DecisionTreeClassifier(max_depth=1), n_estimators=200,\n",
    "    algorithm=\"SAMME.R\", learning_rate=0.5, random_state=42)\n",
    "ada_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a20e9b650>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plot_decision_boundary(ada_clf, X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gradient Boosting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It also uses sequential predictors but instead of changing weights of instances at every iteration it  trains the predictors in the residual error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "X = np.random.rand(100, 1) - 0.5\n",
    "y = 3*X[:, 0]**2 + 0.05 * np.random.randn(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=2, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           presort=False, random_state=42, splitter='best')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "tree_reg1 = DecisionTreeRegressor(max_depth=2, random_state=42)\n",
    "tree_reg1.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Second tree trained on the residual error of first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=2, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           presort=False, random_state=42, splitter='best')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y2 = y - tree_reg1.predict(X)\n",
    "tree_reg2 = DecisionTreeRegressor(max_depth=2, random_state=42)\n",
    "tree_reg2.fit(X, y2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "third tree "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=2, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           presort=False, random_state=42, splitter='best')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y3 = y2 - tree_reg2.predict(X)\n",
    "tree_reg3 = DecisionTreeRegressor(max_depth=2, random_state=42)\n",
    "tree_reg3.fit(X, y3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_new = np.array([[0.8]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a new instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = sum(tree.predict(X_new) for tree in (tree_reg1, tree_reg2, tree_reg3))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.75026781])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure gradient_boosting_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a21151610>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_predictions(regressors, X, y, axes, label=None, style=\"r-\", data_style=\"b.\", data_label=None):\n",
    "    x1 = np.linspace(axes[0], axes[1], 500)\n",
    "    y_pred = sum(regressor.predict(x1.reshape(-1, 1)) for regressor in regressors)\n",
    "    plt.plot(X[:, 0], y, data_style, label=data_label)\n",
    "    plt.plot(x1, y_pred, style, linewidth=2, label=label)\n",
    "    if label or data_label:\n",
    "        plt.legend(loc=\"upper center\", fontsize=16)\n",
    "    plt.axis(axes)\n",
    "\n",
    "plt.figure(figsize=(11,11))\n",
    "\n",
    "plt.subplot(321)\n",
    "plot_predictions([tree_reg1], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label=\"$h_1(x_1)$\", style=\"g-\", data_label=\"Training set\")\n",
    "plt.ylabel(\"$y$\", fontsize=16, rotation=0)\n",
    "plt.title(\"Residuals and tree predictions\", fontsize=16)\n",
    "\n",
    "plt.subplot(322)\n",
    "plot_predictions([tree_reg1], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label=\"$h(x_1) = h_1(x_1)$\", data_label=\"Training set\")\n",
    "plt.ylabel(\"$y$\", fontsize=16, rotation=0)\n",
    "plt.title(\"Ensemble predictions\", fontsize=16)\n",
    "\n",
    "plt.subplot(323)\n",
    "plot_predictions([tree_reg2], X, y2, axes=[-0.5, 0.5, -0.5, 0.5], label=\"$h_2(x_1)$\", style=\"g-\", data_style=\"k+\", data_label=\"Residuals\")\n",
    "plt.ylabel(\"$y - h_1(x_1)$\", fontsize=16)\n",
    "\n",
    "plt.subplot(324)\n",
    "plot_predictions([tree_reg1, tree_reg2], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label=\"$h(x_1) = h_1(x_1) + h_2(x_1)$\")\n",
    "plt.ylabel(\"$y$\", fontsize=16, rotation=0)\n",
    "plt.subplot(325)\n",
    "plot_predictions([tree_reg3], X, y3, axes=[-0.5, 0.5, -0.5, 0.5], label=\"$h_3(x_1)$\", style=\"g-\", data_style=\"k+\")\n",
    "plt.ylabel(\"$y - h_1(x_1) - h_2(x_1)$\", fontsize=16)\n",
    "plt.xlabel(\"$x_1$\", fontsize=16)\n",
    "\n",
    "plt.subplot(326)\n",
    "plot_predictions([tree_reg1, tree_reg2, tree_reg3], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label=\"$h(x_1) = h_1(x_1) + h_2(x_1) + h_3(x_1)$\")\n",
    "plt.xlabel(\"$x_1$\", fontsize=16)\n",
    "plt.ylabel(\"$y$\", fontsize=16, rotation=0)\n",
    "\n",
    "save_fig(\"gradient_boosting_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XGBOOST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    import xgboost\n",
    "except ImportError as ex:\n",
    "    print(\"Error: the xgboost library is not installed.\")\n",
    "    xgboost = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "TODO"
   ]
  }
 ],
 "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": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}