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Anonymous committed dc92111

Add Intro_PCA

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Files changed (1)

ca2/Intro_PCA.ipynb

+{
+ "metadata": {
+  "name": "Intro_PCA"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import numpy as np\n",
+      "import matplotlib.pyplot as plt\n",
+      "from sklearn import datasets, neighbors, metrics, cross_validation, decomposition\n",
+      "\n",
+      "import warnings\n",
+      "warnings.filterwarnings('default')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "iris = datasets.load_iris()\n",
+      "# \u043e\u043f\u0438\u0441\u0430\u043d\u0438\u0435 \u043e\u0431\u044a\u0435\u043a\u0442\u043e\u0432\n",
+      "data = iris.data\n",
+      "# \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u0446\u0435\u043b\u0435\u0432\u043e\u0433\u043e \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u0430 (\u043d\u043e\u043c\u0435\u0440 \u043a\u043b\u0430\u0441\u0441\u0430)\n",
+      "target = iris.target\n",
+      "\n",
+      "N, n = data.shape"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "pca = decomposition.PCA()\n",
+      "#pca.n_components = 2\n",
+      "pca.fit(data)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 3,
+       "text": [
+        "PCA(copy=True, n_components=None, whiten=False)"
+       ]
+      }
+     ],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print pca.explained_variance_\n",
+      "print pca.explained_variance_ratio_"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "[ 4.19667516  0.24062861  0.07800042  0.02352514]\n",
+        "[ 0.92461621  0.05301557  0.01718514  0.00518309]\n"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plt.bar(range(4), pca.explained_variance_ratio_)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 5,
+       "text": [
+        "<Container object of 4 artists>"
+       ]
+      },
+      {
+       "output_type": "display_data",
+       "png": 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Bz2U/++STogPl8ka/OSWppaVFmzdv1owZM1RbW9sfI0qSiouLtWvXrjveVA7SXkpdzykF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\nwIj/Aa79YWRNFJz+AAAAAElFTkSuQmCC\n"
+      }
+     ],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "pca.n_components = 2"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "X = pca.fit_transform(data)\n",
+      "print X.shape"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "(150, 2)\n"
+       ]
+      }
+     ],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plt.scatter(X[:,0], X[:,1], s=36, c=target)\n",
+      "plt.xlabel(u'1-\u0430\u044f \u043a\u043e\u043c\u043f\u043e\u043d\u0435\u043d\u0442\u0430')\n",
+      "plt.ylabel(u'2-\u0430\u044f \u043a\u043e\u043c\u043f\u043e\u043d\u0435\u043d\u0442\u0430')\n",
+      "plt.title(u'\u0414\u0438\u0430\u0433\u0440\u0430\u043c\u043c\u0430 \u0440\u0430\u0441\u0441\u0435\u044f\u043d\u0438\u044f \u043f\u043e 2 \u043a\u043e\u043c\u043f\u043e\u043d\u0435\u043d\u0442\u0430\u043c')\n",
+      "plt.grid()\n",
+      "plt.minorticks_on()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "display_data",
+       "png": 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I3hFN7A/URE3wfrqqV6/O78t+Z2fHXaxz9GN55ZXcXXmP/3b/h5aWVqFogoyNktq3bo1B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\njoB4AAAAAElFTkSuQmCC\n"
+      }
+     ],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "clf = neighbors.KNeighborsClassifier(n_neighbors=3, weights='distance')\n",
+      "clf.fit(X, target)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 9,
+       "text": [
+        "KNeighborsClassifier(algorithm='auto', leaf_size=30, n_neighbors=3, p=2,\n",
+        "           warn_on_equidistant=True, weights='distance')"
+       ]
+      }
+     ],
+     "prompt_number": 9
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def plot_map2d(clf, X):\n",
+      "    x_min, x_max = X[:, 0].min()-1.0, X[:, 0].max()+1.0 # \u0432\u044b\u0447\u0438\u0441\u043b\u044f\u0435\u043c \u043c\u0438\u043d\u0438\u043c\u0430\u043b\u044c\u043d\u043e\u0435 \u0438 \u043c\u0430\u043a\u0441\u0438\u043c\u0430\u043b\u044c\u043d\u043e\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u0430 \u0432 \u0441\u0442\u043e\u043b\u0431\u0446\u0435 0\n",
+      "    y_min, y_max = X[:, 1].min()-1.0, X[:, 1].max()+1.0 # \u0432\u044b\u0447\u0438\u0441\u043b\u044f\u0435\u043c \u043c\u0438\u043d\u0438\u043c\u0430\u043b\u044c\u043d\u043e\u0435 \u0438 \u043c\u0430\u043a\u0441\u0438\u043c\u0430\u043b\u044c\u043d\u043e\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u0430 \u0432 \u0441\u0442\u043e\u043b\u0431\u0446\u0435 1\n",
+      "    x_range = np.linspace(x_min, x_max, 150) # \u0441\u043e\u0437\u0434\u0430\u0435\u043c \u043e\u0434\u043d\u043e\u043c\u0435\u0440\u043d\u0443\u044e \u0441\u0435\u0442\u043a\u0443 \u043f\u043e \u043e\u0441\u0438 x\n",
+      "    y_range = np.linspace(y_min, y_max, 150)  # \u0441\u043e\u0437\u0434\u0430\u0435\u043c \u043e\u0434\u043d\u043e\u043c\u0435\u0440\u043d\u0443\u044e \u0441\u0435\u0442\u043a\u0443 \u043f\u043e \u043e\u0441\u0438 y\n",
+      "    xx, yy = np.meshgrid(x_range, y_range) # \u0441\u043e\u0437\u0434\u0430\u0435\u043c \u0434\u0432\u0443\u043c\u0435\u0440\u043d\u0443\u044e \u0441\u0435\u0442\u043a\u0443 \u043f\u043e \u0434\u0432\u0443\u043c \u043e\u0434\u043d\u043e\u043c\u0435\u0440\u043d\u044b\u043c\n",
+      "    #\n",
+      "    # np.c_[C1, C2] - \u0441\u043e\u0437\u0434\u0430\u0435\u0442 \u0434\u0432\u0443\u043c\u0435\u0440\u043d\u044b\u0439 \u043c\u0430\u0441\u0441\u0438\u0432, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u043f\u043e\u043b\u0443\u0447\u0430\u0435\u0442\u0441\u044f \u0432 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u0435 \u043e\u0431\u044a\u0435\u0434\u0438\u043d\u0435\u043d\u0438\u044f \n",
+      "    # \u0434\u0432\u0443\u0445 \u0441\u0442\u043e\u043b\u0431\u0446\u043e\u0432 C1, C2\n",
+      "    # xx.ravel(), yy.ravel() - \u0441\u043e\u0437\u0434\u0430\u0435\u043c \u043e\u0434\u043d\u043e\u043c\u0435\u0440\u043d\u043e\u0435 \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043b\u0435\u043d\u0438\u0435 2-\u0445 \u043c\u0435\u0440\u043d\u044b\u0445 \u0441\u0435\u0442\u043e\u043a,\n",
+      "    # \u043a\u0430\u043a \u043a\u043e\u043d\u043a\u0430\u0442\u0435\u043d\u0430\u0446\u0438\u044e \u0441\u0442\u0440\u043e\u043a \u0441\u0435\u0442\u043a\u0438\n",
+      "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()]) # \u043f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u044b\u0432\u0430\u0435\u043c \u0441 \u043f\u043e\u043c\u043e\u0449\u044c\u044e \u043e\u0431\u0443\u0447\u0435\u043d\u043d\u043e\u0433\u043e \u043a\u043b\u0430\u0441\u0441\u0438\u0444\u0438\u043a\u0430\u0442\u043e\u0440\u0430\n",
+      "    # Put the result into a color plot\n",
+      "    Z = Z.reshape(xx.shape) # \u043e\u0434\u043d\u043e\u043c\u0435\u0440\u043d\u044b\u0439 \u043c\u0430\u0441\u0441\u0438\u0432 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 \u043f\u0440\u0435\u0432\u0440\u0430\u0449\u0430\u0435\u043c \u0432 \u0434\u0432\u0443\u043c\u0435\u0440\u043d\u044b\u0439 \u0441 \u0444\u043e\u0440\u043c\u043e\u0439 \u043a\u0430\u043a \u0443 xx\n",
+      "    plt.winter()\n",
+      "    plt.pcolormesh(xx, yy, Z) # \u0432\u044b\u0432\u043e\u0434\u0438\u043c \u0446\u0432\u0435\u0442\u043e\u0432\u0443\u044e \u043a\u0430\u0440\u0442\u0443\n",
+      "    plt.xlim(x_min, x_max)\n",
+      "    plt.ylim(y_min, y_max)\n",
+      "    plt.grid()\n",
+      "    plt.minorticks_on()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plt.figure(figsize=(8.0, 7.0))\n",
+      "plot_map2d(clf, X)\n",
+      "plt.scatter(X[:,0], X[:,1], s=36, c=target)\n",
+      "plt.xlabel(u'1-\u0430\u044f \u043a\u043e\u043c\u043f\u043e\u043d\u0435\u043d\u0442\u0430')\n",
+      "plt.ylabel(u'2-\u0430\u044f \u043a\u043e\u043c\u043f\u043e\u043d\u0435\u043d\u0442\u0430')\n",
+      "plt.grid()\n",
+      "plt.minorticks_on()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stderr",
+       "text": [
+        "d:\\Python27\\lib\\site-packages\\scikit_learn-0.12.1-py2.7-win32.egg\\sklearn\\neighbors\\classification.py:131: NeighborsWarning: kneighbors: neighbor k+1 and neighbor k have the same distance: results will be dependent on data order.\n",
+        "  neigh_dist, neigh_ind = self.kneighbors(X)\n"
+       ]
+      },
+      {
+       "output_type": "display_data",
+       "png": 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IAtiHyBK2B3AZOUL2VRyv2QpYJgUVRYL8AaCpPQaqUlrWPODziu3u9+guhIdE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\nmc1LXmbZ4koAAAAASUVORK5CYII=\n"
+      }
+     ],
+     "prompt_number": 11
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "S = metrics.precision_score(target, clf.predict(X))\n",
+      "print S"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "1.0\n"
+       ]
+      }
+     ],
+     "prompt_number": 12
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def find_best_clf(X, Y, n_iter, q):\n",
+      "    cv = cross_validation.StratifiedShuffleSplit(Y, n_iter, test_size=q)\n",
+      "    info_list = []\n",
+      "    for train, test in cv:\n",
+      "        X_train = X[train]\n",
+      "        Y_train = Y[train]\n",
+      "        X_test = X[test]\n",
+      "        Y_test = Y[test]\n",
+      "        Xij_train = np.c_[X_train[:, 0], X_train[:, 1]]\n",
+      "        Xij_test = np.c_[X_test[:, 0], X_test[:, 1]]\n",
+      "        clf = neighbors.KNeighborsClassifier(n_neighbors=3, weights='distance')\n",
+      "        clf.fit(Xij_train, Y_train)\n",
+      "        S_test = metrics.precision_score(Y_test, clf.predict(Xij_test))\n",
+      "        S_train = metrics.precision_score(Y_train, clf.predict(Xij_train))\n",
+      "        info = (S_test, S_train, clf)\n",
+      "        info_list.append(info)\n",
+      "    info_list.sort()\n",
+      "    m = int(3*n_iter/4)\n",
+      "    #S_test_best, S_train_best, clf_best = info_list[-1]\n",
+      "    return info_list[m:]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 20
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def build_score_graph(X, Y, n_iter):\n",
+      "    qrange = np.linspace(0, 1, 50)[1:-1]\n",
+      "    S_test_list = []\n",
+      "    S_train_list = []\n",
+      "    for q in qrange:\n",
+      "        info_list = find_best_clf(X, Y, n_iter, q)\n",
+      "        S_tests, S_trains, _ = zip(*info_list)\n",
+      "        S_test = np.mean(S_tests)\n",
+      "        S_train = np.mean(S_trains)\n",
+      "        S_test_list.append(S_test)\n",
+      "        S_train_list.append(S_train)\n",
+      "    plt.plot(qrange, S_test_list, label='test')\n",
+      "    plt.plot(qrange, S_train_list, label='train')\n",
+      "    plt.xlabel('q')\n",
+      "    plt.ylabel('precision score')\n",
+      "    ymin, ymax = plt.ylim()\n",
+      "    plt.ylim(ymin-0.1, ymax+0.1)\n",
+      "    plt.grid()\n",
+      "    plt.minorticks_on()\n",
+      "    plt.legend()\n",
+      "    \n",
+      "\n",
+      "plt.figure(figsize=(10.0, 9.0))    \n",
+      "build_score_graph(X, target, n_iter=30)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "display_data",
+       "png": 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AKgkGgwoGg5o5c2ZYPWfWFWcWDRcAAMQwLgiA59E7YS+ysxv52YvsYhPFGQAAgIewrAkA\nAOAAljUBAACiAMUZXEPvhL3Izm7kZy+yi00UZwAAAB5CzxkAAIAD6DkDAACIAhRncA29E/YiO7uR\nn73ILjZRnAEAAHgIPWcAAAAOoOcMAAAgClCcwTX0TtiL7OxGfvYiu9hEcQYAAOAhjhZneXl5SktL\nq3T/woULdeGFFyolJUVPPPGEJKmsrEzTpk1TSkqK0tLSVFBQ4OTQEAF+vz/SQ0CYyM5u5GcvsotN\nCU698AMPPKBnnnlGLVq0OOH+w4cP6/bbb1d+fr6aNWum1NRUjRo1SqtXr1ZJSYnWrl2rvLw8ZWVl\nKTc316nhAQAAeJJjM2eJiYmaP39+pasUtm7dqsTERLVu3VqNGjXSoEGDtHLlSq1Zs0YjRoyQJCUn\nJys/P9+poSFC6J2wF9nZjfzsRXaxybGZs7Fjx6qwsLDS/cXFxWrdunXF7ZYtW2rv3r0qLi5Wq1at\nKu6Pj49XWVmZ4uJOrB8zMjLUqVMnSVKbNm3Uu3fvimnf8l9iv98v30yfVP72nb7+L7cje3u9pKc8\nNB5u1/52oUx2XhkPt8kvVm5L0goPjYfbkqTl31suSZXqj/LPq6p/6sKx4uxkWrdurX379lXc3rdv\nn9q0aaNWrVqdcH9VhZkkzZs376Svffza/OFfhPTZZw0y5Jj08cdSerr00UdS48aRHg0AAN51fP1x\n/OdPPfVU5S+uBdeLs27dumnHjh0qKipS8+bNtXLlSk2fPl0+n08LFy7UuHHjtH79evXs2bNe75OQ\nIJ1xRgMNOgadcYbUvbu0eLF0xRWRHg0AALHD8a00fD6fJCknJ0ePP/64GjVqpNmzZ2v48OFKSUlR\nZmamOnbsqDFjxqhp06ZKTU1VVlaWHnzwQaeHhhpcc4309NMN93r0TtiL7OxGfvYiu9jE8U04qb17\npbPPlt57T2rfvv6vFwwGuSzcUmRnN/KzF9nZLdy6heIM1ZowQRo8WLrxxkiPBAAAu3C2JhzR0Eub\nTgqFpKFDpfXrIz0SAADCR3GGal18sfT++9K2bfV/Lad7J9atk15/XZo4UTruwl80APpe7EZ+9iK7\n2ERxhmolJEhXXy399a+RHknNcnKkrCxpyBDpttsiPRoAAMJDzxlqtHmzNGqUtHOnVMXWc55w5Ij0\n7W9Lq1dLp58u9e4t/e530pgxkR4ZACBW0XMGx/TqJbVpI61cGemRnNzy5dJZZ0mJiVLLlmam74Yb\nzGa6AADYhOIMtdIQFwY42Tvx/PPSVVcdu52SIk2dKk2ebC4UQP3Q92I38rMX2cUmijPUytVXSy+/\nLB08GOmRVFZSIuXmSuPHn3j/3XdLRUXSww9HZlwAAISDnjPU2ogR0o9+ZAo1L/nHP6TZs6UVKyo/\ntmOHNHCgeey733V/bACA2EXPGRzn1T3PcnLMZrlV6dpV+s1vzPYaJSXujgsAgHBQnKHWRo+W8vKk\nXbvCe74TvRP790uvvir94Acn/5rrrjPHUP3yl+G/z7590j//Gf7zbUffi93Iz15kF5sozlBrzZpJ\nY8dKzz0X6ZEcs2CBlJoqdehw8q/x+aQnnjBXcNb1/3NlZdJTT0nnnSf98IfSY4/Va7gAANSInjPU\nyYoV0k03SVu2mKIn0kaOlK680vTC1eTVV6Vp06RNm6S2bWv++v/8R7rlFlOg/elPUrt20qBBZhn1\noovqP3YAQHSLmZ6zQCDANG8EDR5slvg2b470SKQvvjB7r11xRe2+fsQIU8z9+MfVf90nn5gtOK64\nwhRz69dLycmmf+35501/2/bt9R8/ACA6BYNBBQKBsJ9vZXHm9/sjPYyYFRdnZqnCuTCgoYvql16S\nLrnEbDpbWw88IG3cWPXSbGmpOVXg/PPNMuk770gZGSeeipCWJv3619Lll5viMFbwDyK7kZ+9yM5O\nfr8/toozRN6PfmSKmyNHIjuO8lmsumjWTHr2WenWW82B7uVefVXq0cOcNLB2rfTb30qtWlX9GlOm\nmOJs3Djp8OHwxw8AQFXoOUNYUlKku+6SLr00Mu//8cdS9+7mytFTTqn78++/31x9+eij5rD07dul\nBx+ULrusds8/etRcvXrmmdKcOd7ovwMAeEvM9JzBGyK959nf/mYOYw+nMJOk6dPNsU4XXigNGSK9\n+WbtCzNJio83s4dr10p/+EN4YwAAoCoUZwjLlVeamacvv6z9cxqyd6K6jWdrIz7enCzw7rvST38q\nNWlS99do1UpauFCaNUtavDj8sdiAvhe7kZ+9yC42UZwhLO3aScOGSX//u/vv/d575mPo0Pq9Tps2\n0qmn1u81OnUyFyZMmiS99Vb9Xqu2FiwwM3aceAAA0YmeM4RtwQLTOL9qlbvve9990v/+Jz3yiLvv\nW51nnzU9eHl50mmnOfc+Tz9tDnRv317atk3q08fsvZaaavoA27Vz7r0BAHUTbt1CcYawlZZK3/62\ntG6d1KWLe+/bo4cpzAYPdu89a+Ouu8zVnsuWSU2bNvzrb99uirBly6SePc1+c+vXS2vWSKtXm8Lw\nO98xX1NesHXuzMUKABApXBAA1zVuLF11lfTMM7X7+obonXjrLdPnlppa75dqcPfcI51xhtlqo6H/\nDVFSYn7WM2eawkwy+7tdfLEUCEhLl0pFReaoqe7dTS/coEHmatLf/MacclAf9L3YjfzsRXaxieIM\n9VJ+1WZNxUgoJH34oTmbctIk04wfjpwcU6TEefA3Ny7OFEdbt5qCqCHdcYfpb7vhhpN/TUKC1Lev\n2cPtb3+TPvrIzOQtXGgOht+3r2HH5KTS0kiPAAAix4N/xcEmffuaKx3Xrj3x/lBIevtt6S9/McXU\nGWdId93l1+rVpk9q2jRp7ty6vVcoFN7Gs25q1swUnn/5S8NtNbJwofTyy+bw9rosUfp85sD25cvN\nhQ/JyeEfO+XWqRxHj0r33msu1nC7lzGacaqKvcguNiVEegCwm89nZs/mzZNat5aCQXM4+sqVplDx\n+6X0dLPpa6dOx543YoQ5eumLL6T/+7/avdeGDWZ26IILGv77aEhnnin9+99myfGrr6SpU8N/rY8+\nMsukL70UfrN/kyZms93HHjNLndnZddvTzS27dkkTJ5ol2D//2RT1+flSx46RHhkAuIuZM9TbD39o\nirMrrjDnVo4aZQqpnTtNIZCRYQqz43snzj3XzIw8+aT085/XrkerfEnThgb3pCRTqN53n/TQQ+G9\nxtGj5md7000N02M3daqZ1bv+enM+aF360I7PrqzMzOaNHGmK7oMH6z+2RYvMjGpamrngYfJkM97x\n4zkiqyFU17f00UcN3yOJhkPPWWyqVXG2d+9ebdmyRfv373d6PLDQWWdJe/eaDV2ffNKcvXn22bV7\n3qpV0pIlppfq6NGTf+3Ro9ILL3h7SfObEhPNLOLDD4fXg3bvvaaP7Wc/a7gxDRwo/ec/ZtPcuvah\nHTpkZt+6dzcXIVxxhfTGG+b7fOSR8PrESkqkn/xE+vGPpRdfNNuExMebx+6+W2rRQrrzzrq/Lmpn\n1y5zpfWf/xzpkQA4Xo1babz44ou69957deTIEY0bN05xcXG666673BrfCdhKIzrt22fOqTz1VOmv\nfzVXgX7T8uXS7bebmTnb7NplNsz9wQ/MFZ21mflbtcocrP7GG6Zfr6GVlEi33GLeJzfXzGSezOef\nm+LrkUfMcVdZWdL3vnfs+3j9dTP7+e675vubMKF2F2xs325mQjt1Mv10VS3bfvGF6Wt84AHz80DD\nuu02s2fgihWmYO/XL9IjAqJL2HVLqAYDBw4MHTp0KOT3+0OHDx8OXXDBBTU9xTG1GC4sdehQKDR6\ndCg0fHgotH9/5cenTAmFZs1yf1wN5dNPQ6GePUOhrKxQqKys+q/dvTsUOuusUGjRIufH9eijodCp\np1b9Xtu2hULXXx8KtWkTCl13XSj09tvVv9Zrr4VCycnm+1y48OTfZ1lZKDRvXijUoUMo9MgjNf88\n8vPN19b0/qibjz8Ohdq2DYU++igUevHFUKhz51CoqCjSowKiS7h1S43/vo2Pj1fTr3fUTEhIUIsW\nLepeAQKqvneiaVOzrPWtb5lG+qKiY4+Vlkrz55tZFluddpqZ/Vu50vSQnazfKxSSMjPNLJsbTftV\n9aGtWmWWLAcNMuN+5x3phz8MKimp+tdKSzMbEt9zj9n6Y/DgyldcFhebZe8HHpBee80sZ9c0k9i3\nr1kW/v73JTorwlPVn73f/970NJ5xhvnZXn65dO219J95DT1nsanG4mzQoEGaMGGCPvroI11//fXq\n37+/G+NCDEpIMNtrJCebZbOPPzb3//vfUrdutetj87J27cxmsVu2SNddV3WP3Z//bJaZGnqftOoc\n34d29tmmOBw+XCosNIXW6afX/rV8PrNEvWWLKfyuuUa69FJp0yZz5WWfPlLz5uaCkR49av+6111n\nxpmZSfHQED7/3PSH3nHHsft++1vzu/eHP0RuXACMGnvOvvzyS61bt05vvvmmkpKSNHLkSLfGVonP\n59OMGTPk9/vZ+yWKhUKmGX7ePFOY3X23OTfyxz+O9MgaxoED5orW004ze6E1amTu37TJzBquW2ea\n7N1WUmL6x5KTjzXlN8RrPvaYuWr1yBHTtxZu79ihQ+aq1WuuMRcRIHw/+5k5aeMvfznx/p07Tf4L\nFkgDBkRmbEA0CAaDCgaDmjlzpjNnaw4aNEirV68Oe4ANiQsCYssjj5i/1PfvN83jTh4o7rZDh8zS\nZePGZmPdI0fM8t3dd5ulpmhz4IBZnm7btn6vs3OnKRpeesksu6Lu9uwxF4C88YY5i/WbcnPNKRMb\nN4a/tx4Aw7GDz0eNGqWhQ4fqvPPOk8/nk8/n0yWXXBL2QOuD4sxuwWCwzjOeOTnm9IE//cmZMUVS\naam5svHgQXOlany82RfOi8LJzimLF5sl0/x806OImh2f3913S598Ij3++Mm//vbbpR07TD+iF49K\niyVe+rOHugu3bqnxhIB27dpp06ZN2rRpU8V9kSrOEHsmTLBrb7O6aNzY7N2WkWF6vvLzIz0iO1x6\nqek9Gz/ebFib4JFzTl55xSzFx8Wd+OHzVb4vLs4sZ591ltlKpHNn898OHZzdZLmoyCxlbthQ/dfd\nf780ZIi5aGD6dOfGA6BqNc6cSdJbb72lt99+W127dtUFETw7h5kzRKNQyPRmfX1RNGrh6FFzNWuP\nHqaRvbZKS80ScrNmDTueYFC68kpT1DRrZq56DYXMf4//OP6+r76SPvzQXHhR/vHVVycWa+UfnTtL\nvXvXvxdw5kzp/fdrd67t+++bfe3mz2+YEyqAWOTYsuYf//hHPffccxowYIDWrl2rcePGaXqE/ilF\ncQag3J49ZtPU3/3ObAVR7uhRU/Rs326W5rZvP/b5//4ntWrVsBuubt5sLuR4/nnpoovq91rFxScW\na4WFps/uzTfN1apPPx3+zNreveZCk7pccLJokXTjjaY/rUOH8N4XiGWOFWcDBgzQ6tWrlZCQoMOH\nD2vgwIHKj9D6C8WZ3eidsJdXs8vPl0aMkCZNkgoKTAFWUGAKiXPPlbp2Nf8t/7xzZ+nVV81h8v/4\nhyl46mPnTrOf2+zZZubMKQcPmn3khg8325vUVTAY1Jo1fm3bZgq8urjjDrM1yiuv0H8WCV79s4fa\ncaznTDKbz0pSo0aN1Liqs3UAIAL69TPFxsaN5irOc881s0LVLVuOHm36/UaPNhsfDxkS3nt//rkp\nlu6809nCTDLfz8KFppjs1MlsFlsXBw+a/cu+uSlwbfz616YwnDWrYc95BXByNc6cZWVlqbCwUIMH\nD9bq1avVuXNn/bYuTR4NiJkzAA1l2TJz6kROjjRsWN2eu3+/WcK85BJTvLhl2zZTTD79tCkMa2vW\nLLP8+txz4b3v//5nCuEXXjAbREdKWZlZkn7oIXP71VeP7RMIeJFjy5qS9Morr2jr1q1KSkrSZW6c\nKXMSFGcAGtKqVaZfbd48cxVobZSWSiNHmistH3/c2asrq7J6tTRmjDltolevmr/+wAHpnHPMcVnf\n/W747/vPf5qTGnJzzYUJJSU1fzRubPrxunYN/30lUww/9ZSZ/WvVyhzY/swz5oKQBx6o32sDTnJs\nWXPBggV/GkJDAAAgAElEQVTKz8/XPffco0svvVQJCQkaXpd/sgFfo3fCXtGa3eDBZjf80aOlRx81\nZ4pWp6zMLCk2aSLNmeN+YSaZzXcfftichblunfTtb1f/9XPmSElJQX33u/56vW96unTzzdLEiebK\n4iZNav7Yt8/MLLZubU7FGD26bidQfPCB2eMwO9vM2M2da64c9fnMzGHfvubnMWpUvb41T4vWP3uo\nXo3F2YwZM7R8+XJJUk5OjtLT0ynOAESNAQPMUtlll5nZnvHjT/61P/2puYLy3/+O7P5q48ebwuXS\nS83sX+vWVX/dwYPmataGWnq9444Tz+OsjbIycyzYP/4hTZtmNsC9/HJTUF18sTlr9XihkLR+vfTg\ng2bpedIksy9b584nfl2HDuYK2dGjpby8yo8DNqtxWTM5OVl5eXkVtwcPHqxV4XSVNgCWNQE4ZcsW\nMzs0a5b0ox9Vfvx3vzMzOKtWeeNYo1DInDe7Y4cpLqvqvfrDH6QVK8xeZV6xc6e5uGHBArP58ve+\nZwq19HRpzRpTlO3ebY6Qysgwy5jVeeghs8S5ejV7BcJ7HOs5u+mmm7Rnzx4NHDhQGzZsUNu2bfXH\nP/4x7IHWB8UZACdt3WpmcwIB019V7q9/le66yxQPNS0juunIEdN/1qGDWfI7fpn1q6+kLl3MXmUR\n3Du8WkVFppdtwQLT3N+7t+knu/zy2i99hkLmnNrTTzfn8QJe4lhxFgqFlJubq+3btyspKUmjIri4\nT3FmN3on7BVL2e3YYa7e/OlPzczUq69KkydLy5dLSUmRHl1lBw4cm3365S+P3f/nP0v/+pcpfKI9\nv717Tf/Zr34Vfce9RXt20c6xCwI++ugjnXvuuerevbtmzZqls88+W7179w5rkADgdV27muOYhg6V\n3nnH9DUtWODNwkwyPVuLFpk90L7zHdOjVVJijpLy0nKmk1q3NnvWXXyxmSXs1i3SIwLqp8aZsyFD\nhmjmzJl6+OGH9YMf/ECPPvqogsGgS8M7ETNnANzy4Ydmc9lf/MIss3nd1q2S32/2Mnv3XdOAv3hx\npEflrscfN312eXmVLzQAIsGxZU2/36+lS5cqPT1dS5cu1dChQ7Vs2bKwB1ofFGcAcHIrVkjjxpmL\nA158sf7HU9kmFDIzhz6f2bsuEludnMzevdKTT5q92YYN89bY4Jxw65YaT0o7fPiw7rjjDg0ZMkTL\nly9XaWlpWANsKIFAIGIzd6gfcrMX2dnhe98z+4JdcsmJhVms5OfzSX/5i9m648knIz0ao6TEzOad\ne66Z0cvKMpsB/+UvZnPdmsRKdtEmGAwqEAiE/fwai7Ps7Gx16dJFd9xxhz7//HM99dRTYb9ZQwgE\nAjRHAsBJjB9vtvyIVc2bS3//uzkHdNOmyI2jrMwsMSclmX3xliwxx19t3myuKl2yxPQIZmVJ770X\nuXHCGX6/v17FWa2Ob/IKljUBALWRk2OuXs3PP/kmvU5ZutRs1hsfb46XOtl8QmGhKdTKTz645RZz\nZitLntHD0bM1vYLiDABQWzfeKH32mZlJO1nBEwqZUwsKCswM1nvvSc2amT3XLrhAOvXU2r/fxo2m\nKNu5U7rvPrP/Wm0KrQMHpGeflcq3EC0/JouLGuxHcQbPY78ee5Gd3WI1v5ISMyM1YYI5nuu9944V\nYeX/3blTatHCHA7fpYv57759Zkl00yZTIJUXahdcYD7v3PnEomvnTrNJ8WuvSXffLU2ZUvWJDTUJ\nhcx+en/8oznxYPRoqXnzoMaO9atHD6l9+4b72cAdju1z9sEHHygnJ0dfffVVxRv98vidDgEA8KAm\nTcysmd9vDoA/vgAbMsR83rmz1LJl1c8PhczS46ZNZlYsO9t8vm+f1KuXKdYOHzZ74d18s3mPk71W\nbfh8ZlnzootMwffqq2Yj4bvuMseLtWol9exprvjs2dN8nHee1Lhx+O9Zk88/N9uyZGRE9jzZWFOr\nszUvvvhinXXWWRX3XX/99Y4PrCrMnAEAIu3zz4/NrBUXSzfdZI6PclIoJL3/vinStmyR3nzT/Lew\n0GycnJJirtQNZ8auOtdea/bLO+cc6emnpcTEhn39aOfYsubFF1+sJUuWhD2whkRxBgDAMYcOSW+/\nbY4bS0+Xpk9vuNfesMEcC7Z1q/TUU9Kvf20+pk7looXacmyfs/PPP1/PP/+8tm3bpu3bt2v79u1h\nDRBgvx57kZ3dyM9eNWV3yinmXNFHH5VmzTInWzSEUEi69Vbp3nulNm3M5ytXmlMYLr9c+vjjhnkf\nVK3GFeSNGzdq0zc2i1m+fLljAwIAAHWTmGiWV2+7zZwOUV85OVJpqek1K5eUJK1bZw6Yv+AC6eGH\nzRWpaHi1ulpzz549KigoUOfOnXVqXa4rbmAsawIAULVDh6Tzzzd7pw0fHv7rHDhgDo9/4QXTy1aV\nvDzpRz+SkpNNr1ubNuG/XzRzbFnzb3/7mwYOHKj77rtPAwYM0F//+tewBggAAJxzyimmULrpJunr\nDRbCMmuWuZr1ZIWZZIqyjRvNFaS9ekkROnI7atVYnM2ePVtvvPGGcnNztWnTJv3hD39wY1yIQvS9\n2Ivs7EZ+9qprdpdeambPfvvb8N6v/NSCWbNq/trmzaU//1l67DFz4PxPfmJm71B/NfacxcfHq0WL\nFpKkli1b6pRTTnF8UAAAIDwPPST16SP98IdmC4y6mD7dNP9/+9u1f87w4WZbjxtvNL1ol1winXlm\n5Q9OPKi9GnvOJk6cqNNPP12DBw/WqlWrtGfPHs2bN8+l4Z2InjMAAGp2//3mlIGFC2u/7UUwaC4A\n2LrVLJGGY+lS6a23pI8+qvzRuHHlgq1rV6l/f9PjFh8f3nt6mWP7nB05ckRz5szRO++8o6SkJE2d\nOlWNGnqXu1qiOAMAoGalpaYX7P77zTFQNTl61My23XWXNG5cw48nFJKKiioXbO+8Y/ZT++QTM+vW\nv/+xj3POsX8/tQYvzjZs2KD+/fvrX//6V6U3uuSSS8IbZT1RnNktVs/3iwZkZzfys1d9snvtNbPD\n/3//W/OS4qOPmu0zli+PTEFUVCS9/rop1DZskPLzpf37pX79jhVrAwc6fxJDQ2vwszVfe+019e/f\nXzk5OfJ9I6lIFWcAAKB2LrrIFDT33ivdd9/Jv66oSJoxQ/rnPyM3U9W2rTRsmPko98knpkjbsMFs\nfpuZaZZqk5IiM0Y31Wqfs6NHjyoUCmnt2rVKTk5WkyZN3BhbJcycAQBQe7t2mQPSV682fV1Vue02\nc5XlnDnujq2uHn/cXB2al2cOtbeBYz1nt956q5KSkvT+++9r48aNOv300/XUU0+FPdD68Pl8mjFj\nhvx+P1P0AADUwkMPSYsWSUuWVJ4Z27rV7Gn29ttSBPeYr5VQSPr+900v2u9+F+nRVC8YDCoYDGrm\nzJnOFGcpKSlau3at/H6/gsGghg4dqmUR2m2OmTO70fdiL7KzG/nZqyGyO3LEnL/5859L48cfuz8U\nkkaMMAem/+Qn9RunW/bskXr3lubOlS6+ONKjqZljJwSUlZXp9ddfV+fOnVVSUqJ9+/aFNUAAAOC+\nhASzsWxWllRcfOz+V16R3n9f+vGPIze2umrfXpo3T5o8Wdq9O9KjcU6NM2ePPPKI5s2bp+zsbD3+\n+OPq0aOHMjMz3RrfCZg5AwAgPNdea87AnD3bbLXx3e+a457S0yM9srr76U+lbduk3Fxvb7fhWM+Z\nl1CcAQAQns8/NwXZsmXSv/4lrVhhNqm1UWmpuRJ16lTp+usjPZqTa/Blze9///uSpG9961vq2LFj\nxccZZ5wR/igR0zjfz15kZzfys1dDZnfqqdI995gZtPvvl37/+wZ7adc1biw9+6zZNHfr1kiPpuGd\ntDh76aWXJEmffPKJ3n33XX388cd6/fXXtWvXLtcGBwAAGs6UKVJcnCnQzj030qOpn27dzB5uV18t\nlZREejQNq8ZlzUAgoNLSUt13330aP368LrjgAt15551uje8ELGsCAFA/hw6ZfcLiarwk0Pu8vr2G\nYz1nffr00RtvvFFxu3xrjUigOAMAAMfbs8ecI5qd7b3tNRzbSiM+Pl4lX88XlpaWUhwhbPS92Ivs\n7EZ+9iK7mkXj9ho1FmfTpk1Tjx49NHbsWPXu3VvTpk1zY1wAAAC1MmyYNGGCdN11ZqnTdrXaSuPz\nzz/Xe++9py5duqhDhw5ujKtKLGsCAICqlJZKAwaYrTW8sr2GYz1nb731lm644QYVFRVp0qRJSkpK\n0uWXXx72QOuD4gwAAJzMO+9IgwdLK1dKSUmRHo2DPWe33HKL5s6dq1NPPVVXX321ZsyYEdYAAXon\n7EV2diM/e5Fd3XTrJv361/Zvr1GrC2m7du0qSTrzzDPVqlUrRwcEAAAQrqlTpW99yxyObqsai7N2\n7dppzpw5OnDggHJyctSmTRs3xoUo5Pf7Iz0EhIns7EZ+9iK7uvP5pFtvNVtr2KrG4mzu3LnauXOn\nOnTooPz8fD355JM1vmhZWZmmTZumlJQUpaWlqaCg4ITHc3Jy1KdPH6WkpOjBBx+suL9Pnz5KS0tT\nWlpaxA5XBwAAdrv4YmnXLumttyI9kvAk1PQF06ZN03PPPVenF83NzVVpaanWrl2rvLw8ZWVlKTc3\nV5K0Z88e/fznP9fGjRvVunVrpaWlye/3K+nrzr3ly5eH8W3ABsFgkH8FWors7EZ+9iK78MTHS9dc\nY2bPbDxDtMaZs5KSEm3evFlfffWVSktLVVpaWuOLrlmzRunp6ZKk5ORk5efnVzxWUFCgXr16qU2b\nNvL5fBowYIBWrlypLVu26ODBgxo+fLiGDh2qvLy8enxbAAAglmVkSM88Ix0+HOmR1F2NM2fbtm3T\nFVdcUXHb5/Ppvffeq/Y5xcXFJ1w4EB8fr7KyMsXFxalr167673//q88++0wtWrTQsmXLNHbsWDVr\n1kzTp09XZmamduzYoREjRmj79u2K+8bhXxkZGerUqZMkqU2bNurdu3fFvyrKr2rhtjdvl9/nlfFw\nu/a3/X6/p8bDbfLjNrdrc7trV78WL5Zat3bn/co/LywsVH3UahPao0ePavfu3Tr11FMrFUtVycrK\n0oABAzRu3DhJ0llnnaUPP/yw4vFFixZp1qxZat++vU4//XT169dPkyZNUllZmZo2bSrJzLjNnz9f\nZ5555rHBss8ZAACopblzpQULpK87q1zn2D5nubm56tKli4YPH65u3bpp2bJlNb5oamqqFi9eLEla\nv369evbsWfHYkSNHlJ+fr1WrVumFF17Q5s2bNXToUGVnZysrK0uStGvXLhUXF6tjx451/obgXcf/\nywJ2ITu7kZ+9yK5+xo2TVqyQPvss0iOpmxqXNQOBgNavX69vfetb+vTTTzVy5Ej95z//qfY5Y8aM\n0ZIlS5SamipJys7OVk5Ojvbv368pU6YoPj5effv2VXx8vKZNm6ZzzjlHmZmZmjx5soYMGVLxnNrM\n0gEAAFSlZUtp9GjTe3b77ZEeTe3VuKw5bNgwLV269KS33cSyJgAAqIsVK6SbbpK2bDF7oLnJsbM1\nr7rqKpWVlWno0KHasGGDduzYoVGjRsnn8+l2l8tQijMAAFAXoZCUmCi98ILUr5+77+1Yz1l6erou\nu+wyNW3aVIMHD9a1116rDh06qH379mENFLGL3gl7kZ3dyM9eZFd/Pp/ZVsOmEwNq7DnLyMhwYRgA\nAADOmDRJ6tPHbEj79aYQnlarrTS8gmVNAAAQjosvljIzpauucu89HVvWBAAAsN3kyfYsbVKcwTX0\nTtiL7OxGfvYiu4YzZoy0YYN03J74nkVxBgAAot4pp0hXXik9/XSkR1Izes4AAEBMyMuTJk6Utm93\nZ88zes4AAACqceGFUuPG0urVkR5J9SjO4Bp6J+xFdnYjP3uRXcPy+ey4MIDiDAAAxIyJE6WXX5b2\n74/0SE6OnjMAABBTRo2Sxo41Jwc4iZ4zAACAWpg8WZo7N9KjODnrirNAIMAavKXIzV5kZzfysxfZ\nOeOyy6R33pHefdeZ1w8GgwoEAmE/38rizO/3R3oYAADAUo0bSz/8oTRvnjOv7/f761Wc0XMGAABi\nzpYtZgatsFCKj3fmPeg5AwAAqKWePaXTTpOWLYv0SCqjOINr6J2wF9nZjfzsRXbOCmfPs927JacX\n8RKcfXkAAABvuvpq6a67pKIiqW3bEx8rLpb++1/z8dZbxz4OHZJ27DCzbk6h5wwAAMSsK6+Uzj1X\nOu+8E4uw3bulpCTp/POl737X/Pf886Vvf7v253KGW7dQnAEAgJi1cqV0yy2mEDu+COvcuf4XClCc\nwfOCwSDboFiK7OxGfvYiO7txtSYAAEAUYOYMAADAAcycAQAARAGKM7iG/XrsRXZ2Iz97kV1sojgD\nAADwEHrOAAAAHEDPGQAAQBSgOINr6J2wF9nZjfzsRXaxieIMAADAQ+g5AwAAcEDM9JwFAgGmeQEA\ngGcFg0EFAoGwn8/MGVzDGXH2Iju7kZ+9yM5uMTNzBgAAEM2YOQMAAHAAM2cAAABRgOIMruFCDnuR\nnd3Iz15kF5sozgAAADyEnjMAAAAH0HMGAAAQBSjO4Bp6J+xFdnYjP3uRXWyiOAMAAPAQes4AAAAc\nQM8ZAABAFKA4g2vonbAX2dmN/OxFdrGJ4gwAAMBD6DkDAABwAD1nAAAAUYDiDK6hd8JeZGc38rMX\n2cUm64qzQCDALysAAPCsYDCoQCAQ9vPpOQMAAHAAPWcAAABRgOIMrmE52l5kZzfysxfZxSaKMwAA\nAA+h5wwAAMAB9JwBAABEAYozuIbeCXuRnd3Iz15kF5sozgAAADyEnjMAAAAH0HMGAAAQBSjO4Bp6\nJ+xFdnYjP3uRXWyiOAMAAPAQes4AAAAcQM8ZAABAFKA4g2vonbAX2dmN/OxFdrGJ4gwAAMBD6DkD\nAABwAD1nAAAAUcC64iwQCLAGbylysxfZ2Y387EV2dgoGgwoEAmE/P6HhhuKO+nyzAAAATvP7/fL7\n/Zo5c2ZYz6fnDAAAwAH0nAEAAEQBijO4ht4Je5Gd3cjPXmQXmyjOAAAAPISeMwAAAAfQcwYAABAF\nKM7gGnon7EV2diM/e5FdbKI4AwAA8BB6zgAAABxAzxkAAEAUoDiDa+idsBfZ2Y387EV2sYniDAAA\nwEPoOQMAAHAAPWcAAABRwJHirKysTNOmTVNKSorS0tJUUFBwwuM5OTnq06ePUlJS9OCDD9bqObAf\nvRP2Iju7kZ+9yC42JTjxorm5uSotLdXatWuVl5enrKws5ebmSpL27Nmjn//859q4caNat26ttLQ0\n+f1+7dy5UyUlJVU+BwAAIFY4UpytWbNG6enpkqTk5GTl5+dXPFZQUKBevXqpTZs2kqQBAwZo5cqV\n+uCDDzRixIgqn4Po4Pf7Iz0EhIns7EZ+9iK72ORIcVZcXKxWrVpV3I6Pj1dZWZni4uLUtWtX/fe/\n/9Vnn32mFi1aaNmyZRozZky1zzleRkaGOnXqJElq06aNevfuXfHLWz79y21uc5vb3OY2t7nt9u3y\nzwsLC1UfjlytmZWVpQEDBmjcuHGSpLPOOksffvhhxeOLFi3SrFmz1L59e51++unq27evtm3bVu1z\nJK7WtF0wGKz4RYZdyM5u5GcvsrObp67WTE1N1eLFiyVJ69evV8+ePSseO3LkiPLz87Vq1Sq98MIL\n2rx5s4YNG1btcwAAAGKFIzNnoVBIN954o7Zs2SJJys7O1uuvv679+/drypQp+tWvfqXc3FzFx8dr\n2rRpuvbaa6t8zrnnnnviYJk5AwAAlgi3bmETWgAAAAd4alkTqMrxDZOwC9nZjfzsRXaxieIMAADA\nQ1jWBAAAcADLmgAAAFGA4gyuoXfCXmRnN/KzF9nFJoozAAAAD6HnDAAAwAH0nAEAAEQBijO4ht4J\ne5Gd3cjPXmQXmyjOAAAAPISeMwAAAAfQcwYAABAFKM7gGnon7EV2diM/e5FdbLKuOAsEAvyyAgAA\nzwoGgwoEAmE/n54zAAAAB9BzBgAAEAUozuAalqPtRXZ2Iz97kV1sojgDAADwEHrOAAAAHEDPGQAA\nQBSgOINr6J2wF9nZjfzsRXaxieIMAADAQ+g5AwAAcAA9ZwAAAFGA4gyuoXfCXmRnN/KzF9nFJooz\nAAAAD6HnDAAAwAH0nAEAAEQBijO4ht4Je5Gd3cjPXmQXmyjOAAAAPISeMwAAAAfQcwYAABAFrCvO\nAoEAa/CWIjd7kZ3dyM9eZGenYDCoQCAQ9vMTGm4o7qjPNwsAAOA0v98vv9+vmTNnhvV8es4AAAAc\nQM8ZAABAFKA4g2vonbAX2dmN/OxFdrGJ4gwAAMBD6DkDAABwAD1nAAAAUYDiDK6hd8JeZGc38rMX\n2cUmijMAAAAPoecMAADAAfScAQAARAGKM7iG3gl7kZ3dyM9eZBebKM4AAAA8hJ4zAAAAB9BzBgAA\nEAUozuAaeifsRXZ2Iz97kV1sojgDAADwEOt6zmbMmCG/3y+/3x/p4QAAAFQSDAYVDAY1c+bMsHrO\nrCvOLBouAACIYVwQAM+jd8JeZGc38rMX2cUmijMAAAAPYVkTAADAASxrAgAARAGKM7iG3gl7kZ3d\nyM9eZBebKM4AAAA8hJ4zAAAAB9BzBgAAEAUozuAaeifsRXZ2Iz97kV1sojgDAADwEHrOAAAAHEDP\nGQAAQBSgOINr6J2wF9nZjfzsRXaxieIMAADAQ+g5AwAAcAA9ZwAAAFGA4gyuoXfCXmRnN/KzF9nF\nJuuKs0AgwC8rAADwrGAwqEAgEPbz6TkDAABwAD1nAAAAUYDiDK5hOdpeZGc38rMX2cUmijMAAAAP\noecMAADAAfScAQAARAGKM7iG3gl7kZ3dyM9eZBebKM4AAAA8hJ4zAAAAB9BzBgAAEAUozuAaeifs\nRXZ2Iz97kV1sojgDAADwEHrOAAAAHEDPGQAAQBSgOINr6J2wF9nZjfzsRXaxyZHirKysTNOmTVNK\nSorS0tJUUFBwwuMvv/yy+vfvrwsvvFBz5sypuL9Pnz5KS0tTWlqaMjMznRgaAACApznSczZ//nwt\nWrRIc+fOVV5enn7zm98oNze34vHOnTtr48aNat68ubp37678/Hw1adJEKSkpeuONN04+WHrOAACA\nJTzVc7ZmzRqlp6dLkpKTk5Wfn3/C440aNdKXX36pgwcPKhQKyefzafPmzTp48KCGDx+uoUOHKi8v\nz4mhAQAAeFqCEy9aXFysVq1aVdyOj49XWVmZ4uJMLZiVlaW+ffuqefPm+v73v69WrVqpefPmmj59\nujIzM7Vjxw6NGDFC27dvr3hOuYyMDHXq1EmS1KZNG/Xu3Vt+v1/SsbV5bnvz9kMPPURelt4+vu/F\nC+PhNvnFyu3y+7wyHm5Xf7v888LCQtWHI8uaWVlZGjBggMaNGydJOuuss/Thhx9Kkj744ANddtll\nWrdunZo1a6aJEydq7NixGjVqlMrKytS0aVNJZsZt/vz5OvPMM48NlmVNqwWDwYpfZNiF7OxGfvYi\nO7t5alkzNTVVixcvliStX79ePXv2rHjsq6++Unx8vJo0aaK4uDiddtppKioqUnZ2trKysiRJu3bt\nUnFxsTp27OjE8BAh/A/GXmRnN/KzF9nFJkeWNceMGaMlS5YoNTVVkpSdna2cnBzt379fU6ZM0aRJ\nk5SSkqKmTZsqMTFRkydPliRNnjxZQ4YMqXjON5c0AQAAoh0nBMA1TM/bi+zsRn72Iju7eWpZEwAA\nAOFh5gwAAMABzJwBAABEAYozuOb4fWBgF7KzG/nZi+xiE8UZAACAh9BzBgAA4AB6zgAAAKIAxRlc\nQ++EvcjObuRnL7KLTRRnAAAAHkLPGQAAgAPoOQMAAIgCFGdwDb0T9iI7u5GfvcguNlGcAQAAeIh1\nPWczZsyQ3++X3++P9HAAAAAqCQaDCgaDmjlzZlg9Z9YVZxYNFwAAxDAuCIDn0TthL7KzG/nZi+xi\nE8UZAACAh7CsCQAA4ACWNQEAAKIAxRlcQ++EvcjObuRnL7KLTRRnAAAAHkLPGQAAgAPoOQMAAIgC\nFGdwDb0T9iI7u5GfvcguNlGcAQAAeAg9ZwAAAA6g5wwAACAKUJzBNfRO2Ivs7EZ+9iK72ERxBtds\n2rQp0kNAmMjObuRnL7KLTRRncM2XX34Z6SEgTGRnN/KzF9nFJoqzarg9nRzt7+emaP9ZRnN2UvT/\nPMnP3vcjO97PDRRn1Yj2Xxq336+wsNC194r2n2U0ZydF/8+T/Ox9P7Lj/dxg3VYaAAAAtginzEpw\nYByOsaiOBAAACAvLmgAAAB5CcQYAAOAhnizOysrKNG3aNKWkpCgtLU0FBQUnPL5w4UJdeOGFSklJ\n0RNPPBGhUeJkasovJydHAwYM0KBBg3TDDTewXO0hNWVXburUqfrZz37m8uhQk5ry27Bhg4YMGaLB\ngwfrqquuUmlpaYRGim+qKbuXX35Z/fv314UXXqg5c+ZEaJSoTl5entLS0irdH1bNEvKgl156KTR5\n8uRQKBQKrV+/PjR69OiKx0pLS0OJiYmhL7/8MlRaWhrq379/6NNPP43UUFGF6vI7ePBgqEuXLqFD\nhw6FQqFQaMKECaEFCxZEZJyorLrsys2ZMyc0cODA0M9+9jO3h4caVJdfWVlZqHfv3qGCgoJQKBQK\nPfbYY6F33nknIuNEZTX92evUqVOoqKjohL8D4R2zZs0K9ejRIzRw4MAT7g+3ZvHkzNmaNWuUnp4u\nSUpOTlZ+fn7FY1u3blViYqJat26tRo0aadCgQVq5cmWkhooqVJdf06ZNtW7dOjVt2lSSdOTIEZ1y\nyikRGScqqy47SVq7dq3+85//6Prrr2fG04Oqy2/79u1q3769Zs+eLb/fry+//FLnnXdepIaKb6jp\nz16jRo305Zdf6tChQwqFQuxe4DGJiYmaP39+pf8vhluzeLI4Ky4uVqtWrSpux8fHq6ysrOKx1q1b\nV25vPvcAAANDSURBVDzWsmVL7d271/Ux4uSqy8/n8+nUU0+VJP3pT3/SgQMHNGzYsIiME5VVl93H\nH3+se+65Rw8//DCFmUdVl9/u3bu1du1a3XzzzVq6dKmWLVum5cuXR2qo+IbqspOkrKws9e3bV+ef\nf75Gjhx5wtci8saOHauEhMobYIRbs3iyOGvVqpX27dtXcbusrExxcWaorVu3PuGxffv2qW3btq6P\nESdXXX7lt//v//5Py5Yt00svvRSJIeIkqsvuxRdf1O7du3XppZdq1qxZeu655/T0009HaqioQnX5\ntW/fXomJiTrvvPOUkJCg9PT0SrMziJzqsvvggw/08MMP6/3331dhYaE+/fRTvfjii5EaKuog3JrF\nk8VZamqqFi9eLElav369evbsWfFYt27dtGPHDhUVFam0tFQrV67UwIEDIzVUVKG6/CTp+uuvV0lJ\niV5++eWK5U14Q3XZ3XzzzcrPz9fy5ct155136uqrr9Y111wTqaGiCtXld84552j//v0VjearVq3S\n+eefH5FxorLqsvvqq68UHx+vJk2aKC4uTqeddhpnbloi3JrFk5vQjhkzRkuWLFFqaqokKTs7Wzk5\nOdq/f7+mTJmi2bNna/jw4SorK1NmZqY6duwY4RHjeNXl169fP82dO1dDhgzRRRddJEm69dZbdcUV\nV0RyyPhaTX/2jkfPi/fUlN+TTz6pq6++WqFQSKmpqRoxYkSER4xyNWU3adIkpaSkqGnTpkpMTFRG\nRkZkB4wqlf9/sb41i1XHNwEAAEQ7Ty5rAgAAxCqKMwAAAA+hOAMAAPAQijMAAAAP8eTVmgDghv37\n92vixInas2ePEhMTtX37dq1ZsybSwwIQ45g5AxCzHnnkEXXv3l2rVq3Sbbfdpk8++STSQwIAijMA\nsauwsFDJycmSpJ49e1YcLQYAkURxBiBm9erVS6tWrZIkFRQUaM+ePREeEQCwCS2AGHbkyBFNnTpV\n7777rs4++2xt2LBB27Zti/SwAMQ4ijMA+FpSUpK2bt0a6WEAiHEsawLA1zgvFIAXMHMGAADgIcyc\nAQAAeAjFGQAAgIdQnAEAAHgIxRkAAICHUJwBAAB4CMUZAACAh1CcAQAAeMj/A4yzQ0iN+BwVAAAA\nAElFTkSuQmCC\n"
+      }
+     ],
+     "prompt_number": 26
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "info_list = find_best_clf(X, target, 30, 0.3)\n",
+      "S_test, S_train, clf = info_list[-1]\n",
+      "print S_test, S_train, clf"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "1.0 1.0 KNeighborsClassifier(algorithm=auto, leaf_size=30, n_neighbors=3, p=2,\n",
+        "           warn_on_equidistant=True, weights=distance)\n"
+       ]
+      }
+     ],
+     "prompt_number": 33
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plt.figure(figsize=(8.0, 7.0))\n",
+      "plot_map2d(clf, X)\n",
+      "plt.scatter(X[:,0], X[:,1], s=36, c=target)\n",
+      "plt.xlabel(u'1-\u0430\u044f \u043a\u043e\u043c\u043f\u043e\u043d\u0435\u043d\u0442\u0430')\n",
+      "plt.ylabel(u'2-\u0430\u044f \u043a\u043e\u043c\u043f\u043e\u043d\u0435\u043d\u0442\u0430')\n",
+      "plt.grid()\n",
+      "plt.minorticks_on()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "display_data",
+       "png": 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FFeQI2RcJPGcrEDspqDgS5A8Aze0xUJXWcuYH35ds93qP7kJkmGVgBqjUBHZH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\nDQAAAABJRU5ErkJggg==\n"
+      }
+     ],
+     "prompt_number": 34
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "class VotingClassifiers(object):\n",
+      "    #\n",
+      "    def __init__(self, clf_list, weights):\n",
+      "        self.clf_list = clf_list\n",
+      "        self.weights = weights\n",
+      "    #\n",
+      "    def predict(self, X):\n",
+      "        Y_list = []\n",
+      "        for clf in self.clf_list:\n",
+      "            Y = clf.predict(X)\n",
+      "            Y_list.append(Y)   # \u0434\u043e\u0431\u0430\u0432\u043b\u044f\u0435\u043c \u043a \u0441\u043f\u0438\u0441\u043a\u0443\n",
+      "        Ya = np.column_stack(Y_list) # \u043e\u0431\u044a\u0435\u0434\u0438\u043d\u044f\u0435\u043c \u043c\u0430\u0441\u0441\u0438\u0432\u044b \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439, \u043a\u0430\u043a \u043a\u043e\u043b\u043e\u043d\u043a\u0438 \u0432 \u043d\u043e\u0432\u0443\u044e \u0442\u0430\u0431\u043b\u0438\u0446\u0443\n",
+      "\n",
+      "        Sa = np.array(self.weights) \n",
+      "        \n",
+      "        Ya_0 = (Ya == 0) # \u0441\u0442\u0440\u043e\u0438\u043c \u043c\u0430\u0441\u0441\u0438\u0432 \u0438\u0434\u0438\u043a\u0430\u0442\u043e\u0440\u043e\u0432 \u043f\u0440\u0438\u043d\u0430\u0434\u043b\u0435\u0436\u043d\u043e\u0441\u0442\u0438 \u043a\u043b\u0430\u0441\u0441\u0443 0\n",
+      "        Ya_1 = (Ya == 1) # \u0441\u0442\u0440\u043e\u0438\u043c \u043c\u0430\u0441\u0441\u0438\u0432 \u0438\u0434\u0438\u043a\u0430\u0442\u043e\u0440\u043e\u0432 \u043f\u0440\u0438\u043d\u0430\u0434\u043b\u0435\u0436\u043d\u043e\u0441\u0442\u0438 \u043a\u043b\u0430\u0441\u0441\u0443 1\n",
+      "        Ya_2 = (Ya == 2) # \u0441\u0442\u0440\u043e\u0438\u043c \u043c\u0430\u0441\u0441\u0438\u0432 \u0438\u0434\u0438\u043a\u0430\u0442\u043e\u0440\u043e\u0432 \u043f\u0440\u0438\u043d\u0430\u0434\u043b\u0435\u0436\u043d\u043e\u0441\u0442\u0438 \u043a\u043b\u0430\u0441\u0441\u0443 2\n",
+      "        \n",
+      "        Y1 = np.inner(Ya_0, Sa) # \u043d\u0430\u0445\u043e\u0434\u0438\u043c \u043c\u0430\u0441\u0441\u0438\u0432 \u0432\u0437\u0432\u0435\u0448\u0435\u043d\u043d\u044b\u0445 \u0441\u0443\u043c\u043c \u0433\u043e\u043b\u043e\u0441\u043e\u0432 \u0437\u0430 \u043a\u043b\u0430\u0441\u0441 0\n",
+      "        Y2 = np.inner(Ya_1, Sa) # \u043d\u0430\u0445\u043e\u0434\u0438\u043c \u043c\u0430\u0441\u0441\u0438\u0432 \u0432\u0437\u0432\u0435\u0448\u0435\u043d\u043d\u044b\u0445 \u0441\u0443\u043c\u043c \u0433\u043e\u043b\u043e\u0441\u043e\u0432 \u0437\u0430 \u043a\u043b\u0430\u0441\u0441 1\n",
+      "        Y3 = np.inner(Ya_2, Sa) # \u043d\u0430\u0445\u043e\u0434\u0438\u043c \u043c\u0430\u0441\u0441\u0438\u0432 \u0432\u0437\u0432\u0435\u0448\u0435\u043d\u043d\u044b\u0445 \u0441\u0443\u043c\u043c \u0433\u043e\u043b\u043e\u0441\u043e\u0432 \u0437\u0430 \u043a\u043b\u0430\u0441\u0441 2\n",
+      "        \n",
+      "        Yz = np.column_stack([Y1, Y2, Y3]) # \u043e\u0431\u044a\u0435\u0434\u0438\u043d\u044f\u0435\u043c \u043c\u0430\u0441\u0441\u0438\u0432\u044b \u0433\u043e\u043b\u043e\u0441\u043e\u0432 \u0437\u0430 \u043a\u043b\u0430\u0441\u0441\u044b, \u043a\u0430\u043a \u043a\u043e\u043b\u043e\u043d\u043a\u0438, \u0432 \u043e\u0434\u043d\u0443 \u0442\u0430\u0431\u043b\u0438\u0446\u0443\n",
+      "        \n",
+      "        Y = np.argmax(Yz, axis=1) # \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u044f\u0435\u043c \u043c\u0430\u0441\u0441\u0438\u0432 \u043a\u043b\u0430\u0441\u0441\u043e\u0432 \u0441 \u043c\u0430\u043a\u0441\u0438\u043c\u0430\u043b\u044c\u043d\u043e\u0439 \u0441\u0443\u043c\u043c\u043e\u0439 \u0433\u043e\u043b\u043e\u0441\u043e\u0432\n",
+      "        return Y\n",
+      "\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 35
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def build_score_graph2(X, Y, n_iter):\n",
+      "    qrange = np.linspace(0, 1, 50)[1:-1]\n",
+      "    S_list = []\n",
+      "    for q in qrange:\n",
+      "        info_list = find_best_clf(X, Y, n_iter, q)\n",
+      "        S_tests, S_trains, clf_list = zip(*info_list)\n",
+      "        voting_clf = VotingClassifiers(clf_list, S_tests)\n",
+      "        S = metrics.precision_score(Y, voting_clf.predict(X))\n",
+      "        S_list.append(S)\n",
+      "        \n",
+      "    plt.plot(qrange, S_list, label='voting')\n",
+      "    plt.xlabel('q')\n",
+      "    plt.ylabel('precision score')\n",
+      "    ymin, ymax = plt.ylim()\n",
+      "    plt.ylim(ymin-0.1, ymax+0.1)\n",
+      "    plt.grid()\n",
+      "    plt.minorticks_on()\n",
+      "    plt.legend()\n",
+      "    \n",
+      "\n",
+      "plt.figure(figsize=(7.0, 6.0))    \n",
+      "build_score_graph2(X, target, n_iter=30)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "display_data",
+       "png": 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+      }
+     ],
+     "prompt_number": 42
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}