ch07 - Ensembles learning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy.misc import comb\n",
    "import math\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.base import BaseEstimator\n",
    "from sklearn.base import ClassifierMixin\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.externals import six\n",
    "from sklearn.base import clone\n",
    "from sklearn.pipeline import _name_estimators\n",
    "import operator\n",
    "from sklearn import datasets\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Comparing ensemble`s error to base clf`s error\n",
    "def ensemble_error(n_classifier, error):\n",
    "    k_start = math.ceil(n_classifier / 2.0)\n",
    "    probs = [comb(n_classifier, k) * error**k * (1-error)**(n_classifier - k)\n",
    "             for k in range(k_start, n_classifier + 1)]\n",
    "    return sum(probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.034327507019042969"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ensemble_error(n_classifier=11, error=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "error_range = np.arange(0.0, 1.01, 0.01)\n",
    "ens_errors = [ensemble_error(n_classifier=11, error=error)\n",
    "              for error in error_range]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5Dfl8CJue22SH6IS9yFfDjSUlJfHll19StGhRatWqBYCnp6dltFiakydPsmlT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\nikFFk4iIiEgxqGgSERERKYb/D5+3HcmauPfGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7d32320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the difference\n",
    "plt.plot(error_range, \n",
    "         ens_errors, \n",
    "         label='Ensemble error', \n",
    "         linewidth=2)\n",
    "\n",
    "plt.plot(error_range, \n",
    "         error_range, \n",
    "         linestyle='--',\n",
    "         label='Base error',\n",
    "         linewidth=2)\n",
    "\n",
    "plt.xlabel('Base error')\n",
    "plt.ylabel('Base/Ensemble error')\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid()\n",
    "plt.tight_layout()\n",
    "# plt.savefig('./figures/ensemble_err.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Implementing a simple majority vote classifier\n",
    "np.argmax(np.bincount([0, 0, 1], weights=[0.2, 0.2, 0.6]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.58  0.42]\n"
     ]
    }
   ],
   "source": [
    "ex = np.array([[0.9, 0.1],\n",
    "               [0.8, 0.2],\n",
    "               [0.4, 0.6]])\n",
    "\n",
    "p = np.average(ex, \n",
    "               axis=0, \n",
    "               weights=[0.2, 0.2, 0.6])\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class MajorityVoteClassifier(BaseEstimator, \n",
    "                             ClassifierMixin):\n",
    "    \"\"\" A majority vote ensemble classifier\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    classifiers : array-like, shape = [n_classifiers]\n",
    "      Different classifiers for the ensemble\n",
    "\n",
    "    vote : str, {'classlabel', 'probability'} (default='label')\n",
    "      If 'classlabel' the prediction is based on the argmax of\n",
    "        class labels. Else if 'probability', the argmax of\n",
    "        the sum of probabilities is used to predict the class label\n",
    "        (recommended for calibrated classifiers).\n",
    "\n",
    "    weights : array-like, shape = [n_classifiers], optional (default=None)\n",
    "      If a list of `int` or `float` values are provided, the classifiers\n",
    "      are weighted by importance; Uses uniform weights if `weights=None`.\n",
    "\n",
    "    \"\"\"\n",
    "    def __init__(self, classifiers, vote='classlabel', weights=None):\n",
    "\n",
    "        self.classifiers = classifiers\n",
    "        self.named_classifiers = {key: value for key, value\n",
    "                                  in _name_estimators(classifiers)}\n",
    "        self.vote = vote\n",
    "        self.weights = weights\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        \"\"\" Fit classifiers.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        X : {array-like, sparse matrix}, shape = [n_samples, n_features]\n",
    "            Matrix of training samples.\n",
    "\n",
    "        y : array-like, shape = [n_samples]\n",
    "            Vector of target class labels.\n",
    "\n",
    "        Returns\n",
    "        -------\n",
    "        self : object\n",
    "\n",
    "        \"\"\"\n",
    "        if self.vote not in ('probability', 'classlabel'):\n",
    "            raise ValueError(\"vote must be 'probability' or 'classlabel'\"\n",
    "                             \"; got (vote=%r)\"\n",
    "                             % self.vote)\n",
    "\n",
    "        if self.weights and len(self.weights) != len(self.classifiers):\n",
    "            raise ValueError('Number of classifiers and weights must be equal'\n",
    "                             '; got %d weights, %d classifiers'\n",
    "                             % (len(self.weights), len(self.classifiers)))\n",
    "\n",
    "        # Use LabelEncoder to ensure class labels start with 0, which\n",
    "        # is important for np.argmax call in self.predict\n",
    "        self.lablenc_ = LabelEncoder()\n",
    "        self.lablenc_.fit(y)\n",
    "        self.classes_ = self.lablenc_.classes_\n",
    "        self.classifiers_ = []\n",
    "        for clf in self.classifiers:\n",
    "            fitted_clf = clone(clf).fit(X, self.lablenc_.transform(y))\n",
    "            self.classifiers_.append(fitted_clf)\n",
    "        return self\n",
    "\n",
    "    def predict(self, X):\n",
    "        \"\"\" Predict class labels for X.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        X : {array-like, sparse matrix}, shape = [n_samples, n_features]\n",
    "            Matrix of training samples.\n",
    "\n",
    "        Returns\n",
    "        ----------\n",
    "        maj_vote : array-like, shape = [n_samples]\n",
    "            Predicted class labels.\n",
    "            \n",
    "        \"\"\"\n",
    "        if self.vote == 'probability':\n",
    "            maj_vote = np.argmax(self.predict_proba(X), axis=1)\n",
    "        else:  # 'classlabel' vote\n",
    "\n",
    "            #  Collect results from clf.predict calls\n",
    "            predictions = np.asarray([clf.predict(X)\n",
    "                                      for clf in self.classifiers_]).T\n",
    "\n",
    "            maj_vote = np.apply_along_axis(\n",
    "                                      lambda x:\n",
    "                                      np.argmax(np.bincount(x,\n",
    "                                                weights=self.weights)),\n",
    "                                      axis=1,\n",
    "                                      arr=predictions)\n",
    "        maj_vote = self.lablenc_.inverse_transform(maj_vote)\n",
    "        return maj_vote\n",
    "\n",
    "    def predict_proba(self, X):\n",
    "        \"\"\" Predict class probabilities for X.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        X : {array-like, sparse matrix}, shape = [n_samples, n_features]\n",
    "            Training vectors, where n_samples is the number of samples and\n",
    "            n_features is the number of features.\n",
    "\n",
    "        Returns\n",
    "        ----------\n",
    "        avg_proba : array-like, shape = [n_samples, n_classes]\n",
    "            Weighted average probability for each class per sample.\n",
    "\n",
    "        \"\"\"\n",
    "        probas = np.asarray([clf.predict_proba(X)\n",
    "                             for clf in self.classifiers_])\n",
    "        avg_proba = np.average(probas, axis=0, weights=self.weights)\n",
    "        return avg_proba\n",
    "\n",
    "    def get_params(self, deep=True):\n",
    "        \"\"\" Get classifier parameter names for GridSearch\"\"\"\n",
    "        if not deep:\n",
    "            return super(MajorityVoteClassifier, self).get_params(deep=False)\n",
    "        else:\n",
    "            out = self.named_classifiers.copy()\n",
    "            for name, step in six.iteritems(self.named_classifiers):\n",
    "                for key, value in six.iteritems(step.get_params(deep=True)):\n",
    "                    out['%s__%s' % (name, key)] = value\n",
    "            return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()\n",
    "X, y = iris.data[50:, [1, 2]], iris.target[50:]\n",
    "le = LabelEncoder()\n",
    "y = le.fit_transform(y)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier \n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "clf1 = LogisticRegression(penalty='l2', \n",
    "                          C=0.001,\n",
    "                          random_state=0)\n",
    "\n",
    "clf2 = DecisionTreeClassifier(max_depth=1,\n",
    "                              criterion='entropy',\n",
    "                              random_state=0)\n",
    "\n",
    "clf3 = KNeighborsClassifier(n_neighbors=1,\n",
    "                            p=2,\n",
    "                            metric='minkowski')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10-fold cross validation:\n",
      "\n",
      "ROC AUC: 0.92 (+/- 0.20) [Logistic Regression]\n",
      "ROC AUC: 0.92 (+/- 0.15) [Decision Tree]\n",
      "ROC AUC: 0.93 (+/- 0.10) [KNN]\n"
     ]
    }
   ],
   "source": [
    "pipe1 = Pipeline([['sc', StandardScaler()],\n",
    "                  ['clf', clf1]])\n",
    "pipe3 = Pipeline([['sc', StandardScaler()],\n",
    "                  ['clf', clf3]])\n",
    "\n",
    "clf_labels = ['Logistic Regression', 'Decision Tree', 'KNN']\n",
    "\n",
    "print('10-fold cross validation:\\n')\n",
    "for clf, label in zip([pipe1, clf2, pipe3], clf_labels):\n",
    "    scores = cross_val_score(estimator=clf,\n",
    "                             X=X_train,\n",
    "                             y=y_train,\n",
    "                             cv=10,\n",
    "                             scoring='roc_auc')\n",
    "    print(\"ROC AUC: %0.2f (+/- %0.2f) [%s]\"\n",
    "          % (scores.mean(), scores.std(), label))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC AUC: 0.92 (+/- 0.20) [Logistic Regression]\n",
      "ROC AUC: 0.92 (+/- 0.15) [Decision Tree]\n",
      "ROC AUC: 0.93 (+/- 0.10) [KNN]\n",
      "ROC AUC: 0.97 (+/- 0.10) [Majority Voting]\n"
     ]
    }
   ],
   "source": [
    "mv_clf = MajorityVoteClassifier(classifiers=[pipe1, clf2, pipe3])\n",
    "\n",
    "clf_labels += ['Majority Voting']\n",
    "all_clf = [pipe1, clf2, pipe3, mv_clf]\n",
    "\n",
    "for clf, label in zip(all_clf, clf_labels):\n",
    "    scores = cross_val_score(estimator=clf,\n",
    "                             X=X_train,\n",
    "                             y=y_train,\n",
    "                             cv=10,\n",
    "                             scoring='roc_auc')\n",
    "    print(\"ROC AUC: %0.2f (+/- %0.2f) [%s]\" % (scores.mean(), scores.std(), label))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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wkSsCGveDSq6AunPnTg4f9r0Muz95EwbJmTmSt8rRWrNp0yZefvll3nzzTQCW\nLVtGXl6etYE5iNPvNTtUKvymlOoKvADMAnoDVwHtMB6BlGvo0KG43W6PV79+/Vi8eLHHditXrsTt\ndpfZf8qUKSxYsMCjLDk5GbfbzdGjnt0/Zs6cyezZsz3K9u7di9vt9qiNzp07lzlz5jBjxgyPbbOy\nsnC73cULzGTnZTN60Wi+SfyGS5MvpU+LPh7bjxs3ztLrACp1HUUWLlzIxIkTy8RWdB1XXXUVzxfW\nVLxdx9y5cx1xHSVZ/fOYO3dutbgOCO3PY+7cudXiOiB4P48DBw7Qu3dv5s+fT25uLkOHDqVevXpE\nRERwyy1lh5Db9Tqs/nnMnTvX8utYuHBh8Wdjs2bNcLvdTJs2rcw+3lg++qPw8UcWMFprvbRE+ZtA\nXa31KC/7vAXEaK3HlijrD6wDmmutS7d6OH70R15BHmM/HMvnv3zOZzd8xsBzB1odUtAlJSXRqFEj\nzjnH2v4iQgjfypsVc/DgweXONSOcwzGjP7TWuUqpJGAgsBRAGXOzDgRe9LFbLJBTqqwA0EC1m9e1\nQBcwYfEEPv35Uz4Z+4njKhQlK64lp90tKvdWBhTdwEIIGzt16hSrV68urlDUq1cPt9st806EKbs8\n/vgXcItSarxSqjMwH6Pi8CaAUuqfSqmEEtt/CoxWSt2ulGpX2ErxArBRa30wxLEHldaaO5bdwcLv\nF/LuH99lRKcRVofkt4SEBFwuF/n5+R7lPXv25J577vEoW716NS6Xy2PqXiGEfcXFxTFkyBDAaJ24\n/fbbpUIRxmxRqdBaL8KY+OpR4H9AD+AqrXXRkpTNgNYltk8A7gOmAN8BHwApwOgQhh0wpZ+bFdFa\nc9+K+3g1+VUWuBcwtttYr9vZzapVq5g5c2bx95dccgmvvfYaLpfn7TZr1iyuv/56j7IuXbrw2muv\nVWrhMF95E75JzsyRvJWvd+/e3HLLLQwbNqx4mm3JmTlOz5vljz+KaK1fAl7y8V6ZXiVa63lAtZgd\nKSsry2v5w2se5vmNzzP3mrlM6DUhtEFV0ooV8NFH8NJLUDSHza5du/jyyy955JFHAOjYsSMdO5Yd\n9vrHP/6xTFnLli2ZPHlypc7tK2/CN8mZOZK38imlaNGihUeZ5Mwcp+fN8o6aoeK0jppPrn+Sh1Y/\nxOxBs3mgv32HGC1aBG+/DZ9+Ws5Ga/8IDfpA9/Lm7xZC2FVWVhaxsbFWhyEsVNmOmrZ4/CE8zd00\nl4dWP8Rg+pAGAAAgAElEQVTDlz1s6woFwNixFVQock9B6mcQVSdkMQkhAqNo3onnn3+eX3/91epw\nhANIpcJmXv/f69ydeDf3XXwfs66YZXU4VXdoDRTkQHOZmlsIJ8nIyCAhIYHExERyc3NZunQp2dnZ\nVoclbE4qFTZQNOnJ+9+/z81Lb+a2PrfxzJBnPIZaOlZaItRuD3U6BPzQpSe9ERWTnJkTTnkrOStm\nyVFYHTp08Ot3UjjlLJCcnjepVNjApEmTWPrTUv78nz9zU4+beGnYS46uUDzxxBM0bdoUtIbUxKC1\nUkya5Gu9OeGL5MyccMlb6dYJMOadGD9+PMOGDfNrIqtwyVmgOT1vthn9Ec6unnw1f/rwT7g7uXl9\n5Ou4lLPrekOHDjVmwTz5M2TuhhbXBOU8s2bNCspxqzPJmTnhkLfc3Fxee+01j9EHVZkVMxxyFgxO\nz5uM/rDYL8d+oef8nlzR9goWX7eY6EoutuUIO16ArQ/AmHSIrGV1NEKICmzcuJHly5fLrJiiDMdM\n0x3uvjv8HafzTvPGyDccVaHYutWYn+Khh6CWr/pCWiI0uVwqFEI4RN++fSkoKKBPnz6yZocwxdnt\n7NVIVESU1SH4ZdkymDsXYmLK2ejCl+GCp0MWkxCiapRS9OvXTyoUwjSpVNiBz4Yk+1q3Dvr3h4iI\nsu9t3LiRV155BWq3g/o9gxZD6SWERcUkZ+ZUl7yF8nF3dclZqDk9b6YqFUqpvkqp15RSa5RSLQrL\nrlNKXRzY8MJEmtUB+CcvDzZsgAEDvL+/du3akMxfn5zswNqYxSRn5lSHvKWnp/PWW2+FbLG+6pAz\nKzg9b3531FRKuTEW8PoI+BPQVWv9q1JqKjBEaz088GFWnV07an6S8gmjF43m2APHaFCzgdXhVMrJ\nk/DEE3D99dAzeA0RQogAKJp3omh58gYNGnD77bcTFeWsR67CWsHsqDkTuEtrvUApdW2J8vXAQyaO\nJxwmLg6efNLqKIQQFUlPT2fp0qUerRP5+fn8/vvvNG7c2MLIRHVlplLRGVjtpfx3oH7VwhFCCFFV\npVsnisTHxzNo0KDi5cmFCDQzlYrDQDtgd6nyfsBvVQ1ICCGEeVpr3n33XXbt2lVcVrduXUaOHCnz\nToigM9NR8w3geaVUT0ADDZVSo4FngFcDGVzYeM/458iRI0yfPt3jlwHARx99xHPPPedRlpuby/Tp\n00lKSvIoX7VqFTNnzixziocffpgvv/zSo2zLli1Mnz6dvLw8j/Jnn32WTz75xKPsl19+Yfr06Rw7\ndsyjfMGCBbzxxhtl9u/WrZuPiw0ct9sd9HNUN5Izc5yUN6WUR+UhPj6eO+64I+QVCiflzE6cnjcz\nlYrHgaXAN0Bt4FuMj8V3gOcDF1oY6Wv8k5WVRWJiIhkZGR5v//DDD3z99dceZfn5+SQmJpKamupR\n/uuvv5apPIBR2Si9dPGBAwdITEykoKDAo/zrr7/mxx9/9Cg7duwYiYmJHlP4AiQlJXlWbA6tYUqH\ndxnYv4fv6w2Qu+66K+jnqG4kZ+Y4LW/9+vWjW7duxWt2WPG4w2k5swun5830NN1KqVpAJ4yKxXda\n64wKdrGUXUd/vJ30NuOXjefQfYdoEtfE6nCq7n8zYPe7cO0BcPCiaEIIIc6q7OgPv1sqlFIvKaVq\na60ztdbJWuu1WusMpVSsUuqlqgQdjr7b/h0AJ0+etDiSyhk+HEo9GfFUtCqpVCiEECLsmHn8cRsQ\n66U8Fri1auGEn6K+B3FxcRZHUrGcHGjc2BhS6lXmPjj+Q9CWOhdCnB3ZceDAAatDEaKMSlcqlFLR\nSqkagAKiC78vetUE/gAcDVag1VVcXBykQGSk/dd2i46GN96AwYN9bJC2HJQLmvvaILAWL14ckvNU\nJ5Izc+ySt/T0dBISEkhMTGTx4sVlOlnbiV1y5jROz5s/LRVngCyMER97gNMlXqeAhcjoD3O+tzqA\nAElbDg0vhujQTFeycOHCkJynOpGcmWN13rTWbNy4kfnz5xdPZHX06FF27txpaVzlsTpnTuX0vPnz\n5/E1GK0UnwM3ACU7ZuYAu7XWMk+FGX+yOoAAKMiFg6ugy4yQnfKDDz4I2bmqC8mZOVbmzdusmPXq\n1cPtdtt63gm518xxet4qXanQWq8AUEp1AX7RWhdUsIuohKI5KU6dOuWYtT+8OvoN5J6Q/hRCBNCW\nLVtYuXJlmVkxBw8eLMuTC1vy+0G+1vonAKVUJNAKiC71/s+BCS08nD59GoACp9fR6veGyxZDA/sM\n1xXC6TIzM4srFDIrpnACvysVSqmGwCvASLz3yYioalDhpHv37vAj1ImrY3UoVRNVG1qNtDoKIaqV\nSy+9lJ9++omWLVvKmh3CEcwMKf0X0Bq4EqOT5kiMYaa/AqMCF1oYcUBn33vvBbt1Sp44caLVITiO\n5Mwcq/IWERHBpEmTLJsVsyrkXjPH6XkzM45xMPBHrfW3SqkC4Cet9TKlVDpwH8YU3sIf7a0OoHyZ\nmTBvHnTqZHUknoYMGWJ1CI4jOTPHyrw5Ybi5N3KvmeP0vJlpqYgD0gq/zgAaF36dTPEqFqKy8vLy\n4HzKrL9hJxs3Ql4eDBhgdSSerr/+eqtDcBzJmTnBylt6ejpHjhwJyrGtJveaOU7Pm5lKxc9Ah8Kv\nvwMmFfazmAQcClRg4WLLli0AHD9+3OJIfFu3DurXh65drY5EiOqh5LwTH3/8Mfn5+VaHJERAmGlX\nmwu0Lfz6MSARmAjkATcHJqzw0aFDB9gLtWrVsjoUn668Epo3B5eZKqgQwkPpeScOHTrEt99+S//+\n/S2OTIiq8/tjQmv9htb6tcKvNwLtgAFAO6312wGOr9pr2LAh7MHWY84vuwxuteGqLuvXr7c6BMeR\nnJkTiLx5mxUTjHkn4uPjq3x8u5F7zRyn563Kf3tqrY9rrTdorVOVUucHIqiw87XVAVTBujGwa4El\np37qqacsOa+TSc7MqWreitbsWL58uce8E+PHj3fkyI7KkHvNHKfnzcw8FdFAgdY6r0RZV+ARjCGl\nzuyqbKUxVgdg0pkjsO8TaDncktO///77lpzXySRn5lQ1b9u2bSvTOlHd552Qe80cp+fNn1VKWyil\n1gCZwCml1P8ppWoopV4FtgJRwMAgxVlt7du3D6IhKyvL6lD8l7YS0ND8KktOHxsba8l5nUxyZk5V\n8zZgwACaNGlS7VsnSpJ7zRyn582fVoWnMIaP/hWjReJBjAmwfgA6a61/DXx41V/RcLKSc/s7Rtpy\nqNcTaja3OhIhbC0yMpJx48ZRq1atal+ZEOHNn0rFlcBYrfXXSqn3gAPAJ1rrp4MTWnjo3bs37DSe\nrzqKLoC0FdB+stWRCOEIDRo4eMFAISrJn46azYBdAFrrNCAL+DQYQYWdlVYH4N3rrxsjP7zOy5We\nDNlHLF2VdMaM0C2zXl1IzsypKG9aa2c+wgwiudfMcXre/O1UWXKGlgIgO4CxhC+bNlK0bg2XXupj\nfoq05RAZB40vCXlcRdq0aWPZuZ1KcmZOeXkrmnciNzeXyZMn45IJXQC518xyet6U1rpyGxrrfBzG\nqEwANAWO4lnRQGvdIpABBopSqjeQlJSUZDxysIlPUj5h9KLRHHvgGA1qOqh59ItLoUYTuOwTqyMR\nwhJaazZt2sTq1auL+0QNHDiQSy+91OLIhAi85ORk+vTpA9BHa53sazt/WiruqHJU5VBKTQHux3jM\nsg24W2u9uZzto4GZwI2F+6QCj2qt3wxmnIG2ebNxiRkZGc6qVFzwDCgZPSzCU0ZGBkuWLPEYJlq3\nbl1atmxpYVRCWK/Snwpa61eCFYRSahzwLHArsAmYBqxQSnXUWh/1sduHGKNRJmL09WhOACbzCrWW\nrVrCb1CzZk2rQ/FPo4utjkCIkNNas3nzZlatWuUxYisc5p0QojLs8iE8DXhFa/2W1noHcDtGR9BJ\n3jZWSl2NMTX4UK31Gq31Xq31Rq31N6ELOTBaNG8BRyAmJsbqUBxnx44dVofgOJIzc4rytnnzZhIT\nE8NmVsyqkHvNHKfnzfJKhVIqCugDrC4q00ZHj1VAPx+7jQC2AA8qpfYrpX5SSj2tlHLmJ/MXVgfg\nTA888IDVITiO5MycorxdcMEFxno9GK0Td9xxB+3atbMyNNuSe80cp+fNDg/FGwERlF02/RDQycc+\n52K0VJwBri08xstAA8B5EycMtToAZ5o7d67VITiO5MycorxFRUUxatQocnJypDJRAbnXzHF63ixv\nqTDJhTEK5Qat9Rat9XLgPuAvSilHtUEeOnQI6sGZM2esDqXYihUwahScPm11JOVz+tArK0jOzCmZ\nt5YtW0qFohLkXjPH6XkzXalQSrmUUucopSKqGEPRsNSmpcqbAgd97JMGHNBanypRlgIooFV5Jxs6\ndChut9vj1a9fPxYvXuyx3cqVK3G73WX2nzJlCgsWeK7KmZycjNvt5uhRzz6lM2fOZPbs2R5le/fu\nxe12Fz83++233wB4Zd4rZSY9ycrKwu12l1kKd+HChUycOLFMbOPGjQvIdSxefJTkZCjqO1qZ6ygy\nZ84c21yHmZ+HXIdch9a6WlwHVI+fh1xH6K9j4cKFxZ+NzZo1w+12M23atDL7eFPpeSqKdzD6LTwL\n3Izx2KKj1vpXpdRzwD6t9b/8OqBxzG+BjVrrewq/V8Be4EVv04ArpW4BngOaaK2zCstGAh8BtbXW\nZSblknkqKq9vX+jYEd55x+pIhAidonknfvnlF2644QaZxEqIEio7T4WZ/zWPA/0xegKUbLNfizFn\nhBn/Am5RSo1XSnUG5gOxwJsASql/KqUSSmz/HnAMeEMp1UUpdRnGgmcLvFUobG99xZuEyqlTkJxs\nTM9dRmoirB0FefZ4LlK6Fi8qJjnzLj09nYSEBJYvX86uXbvYtGmTx/uSN/9Jzsxxet7MdNQcA9xY\nuLBYyWaO74HzzAShtV6klGoEPIrx2GMrcJXW+kjhJs2A1iW2z1RKDQbmAJsxKhgfAP8wc37L2WiB\nUpcL3ngDrrjCy5v7l8DxHyHSHnNqyFoL/pOcefI2KybA8ePHPbaTvPlPcmaO0/Nm5vFHFtBNa/2b\nUuok0LPw8UcPYL3Wuk4wAq0qefxRRVrDkrbQ6lqIf8HqaISosqI1O0rPijly5EjpiClEKcGYprvI\n/4CrMYZwljQB2GjieGFt27ZtgPGXka0rFSd2QNZeaGHdqqRCBMru3bt57733ZFZMIQLMTKXi78BS\npVRHjI6atymlugKDgCsCGFtYqFvXWKI0KirK4kgqkLYcImKgyRVWRyJElbVo0YJatWrx+++/S+uE\nEAHkd0dNrfUaoC/GhFM7gT9hLIHeX2stLRV+atu2LWRCbGys1aGULzURmlxum/4UQJkhWKJikjND\ndHQ0I0eOrPSsmJI3/0nOzHF63kyNmdJap2it/6y17qG1PldrPaa8ZyyiAkusDqACeZlw+Ctobq9H\nH5MmeV0aRpRDcnZW27ZtK71mh+TNf5Izc5yeN78rFUqpZUqp65RS9vmT1emusDoAQ0GBjzcOfQUF\nOdDimpDGU5FZs2ZZHYLjSM7Mkbz5T3JmjtPzZqal4gAwFziklHpbKXWVUkpmiTEpIyMDWkBOTo6l\ncRQUwDnnQEKClzcb9IaLXoe4jiGPqzx2GsXjFOGSs4yMDL744gv8Hd3mS7jkLZAkZ+Y4PW9m+lTc\nhjFvxE1AFPAJkKqUelEpdVGA46v2fvrpJwAyMzMtjSMnB+65By64wMubNZtB+4mgVMjjEsIfRfNO\nvPzyy2zYsIGkpCSrQxIirJhapVRrnQcsxRgFUhsYBUwH7jR7zHB1Qe8LYO/ZUSBWiYmB+++3NAQh\nqiQjI4MlS5Z4zDuxefNmevfuLVNuCxEiVfqfppRqgNFicRtwPsasmsIPNaJrQDLyS8+E0gvziIpV\nx5yVbJ0oWaGIj49n8uTJAfm/VR3zFmySM3OcnjczHTVrKqWuV0p9irFa6F8x1v3oobXuFegAw0Ka\n1QE4U3KyDDjyV3XL2YkTJ0hISCAxMbF4Iqt69eoxfvx4hg0bRnR0dEDOU93yFgqSM3OcnjczjyqO\nAKcxVgQdqLW20XJYDjXM6gCcad68eVaH4DjVLWfR0dFGZ+dC8fHxDB48OGCViSLVLW+hIDkzx+l5\nM1OpuB5ILOxXIaooJSUFgBMnT9h7mm4hbCgmJoYRI0bw2Wef4Xa7ZVZMISzmd6VCa/1pMAIJVxER\nEQC4ZFSuEKacd955TJkyhchI6SMuhNUq9UmmlNqglKpX+PU3hd97fQU33OqnY0dj7ofatWtbFsPA\ngcZy52V8fSPslzqksD+pUAhhD5X98/grIKfE1+W9hL/es+7Uhw7Bl19CzdLzo2bugT3vGTNp2pTb\n7bY6BMdxWs601qSkpARsEiuznJY3O5CcmeP0vFWqeq+1fqjE138NXjhhqq91p15f2M12wIBSb6Qu\nBxUBzQaGPKbKuuuuu6wOwXGclLP09HSWLl3Knj17GDlyJL16WTe4zEl5swvJmTlOz5uZIaU/Fs5P\nUbq8rlLqx8CEFT4yszLhPMjLs6bf67p10K4dtGxZ6o20RGh0CUTXsySuyhgyZIjVITiOE3KmtWbj\nxo3Mnz+/eN6JFStWkJ2dbVlMTsib3UjOzHF63sw8iOzsY78YoH3Vwgk/323/DoCTJ0/SJK5JyM9/\n110wcmSpwvwcOLgauj3kdR8hgsXbrJj16tXD7XZXajVRIYS1Kl2pUEqVrD5doZT6vcT3EcAgYG+g\nAgsX3bp1g70QFxdnyfnPO894eTj6NeSdst1S56L60lqzefNmVq1aVTyJFQRv3gkhRHD48/hjeeFL\nA++X+H45sAwYAzwQ6ACru7i4OEixWe/11OUQ0xTq23uC1MWLF1sdguPYNWfZ2dmsXbs2qLNiVoVd\n82ZnkjNznJ43fyoVNYFY4DDQpvD7ole01vocrfV/Ah9iGLDbiilpidD8KrD53BkLFy60OgTHsWvO\nYmJiGD58OGC0Ttxxxx22msjKrnmzM8mZOU7Pm7J6qFaoKKV6A0lJSUm2Wq/+k5RPGL1oNMceOGaP\nGTW1hv2LoWYLaCQr2YvQOnz4ME2ahL5vkRCifMnJyfTp0wegj9ba5wIllWpzV0rdCiRorbMLv/ZJ\na/2qX5GGuV27dgFw6tQpe1QqlILWo6yOQoQpqVAI4WyVfZD/CPAxkF34tS8akEqFH06fPg1AgS4I\n8XmhRg2QFddFqJw8eZLatWujlLI6FCFEkFTqI0Vr3VxrfazE175eLYIbbvXTvXt3AOrE1QnpeZ9/\n3pifIkyefgkLFc07MWfOHH744QerwxFCBFGV/05Vhs5KqVqBCCgsWdDZd+hQeOIJ42mHU02cONHq\nEBwn1DlLT08nISGB5cuXk5uby+eff86pU6dCGkMgyL3mP8mZOU7Pm9/jGJVSTwE/aq3fVEq5gNXA\n5cBJpdRQrfXXgQ6y2rNgyrCePY2Xkzl95jkrhCpnWms2bdrE6tWrPead6NatG1FRUSGJIZDkXvOf\n5Mwcp+fNzOQI1wGjC78eBnQBegE3Ak8CpVeREOXIy8uD86GgILR9KqqD66+/3uoQHCcUOSu5ZkeR\nolkx7TRM1B9yr/lPcmaO0/NmplLRBEgr/HoYsEhrvV0pdQq4PWCRhYktW7YAcPz4cRrVamRxNEJU\nTUFBAW+99RbHjx8vLpNZMYUIH2b6VBwGOhU++rgaWFVYHoMx+kP4oUOHDgDUqmVxl5T9S2HzFAjx\nKBRRvbhcLgYPHgzYb1ZMIUTwmalUvA18APwPo6VjZWH5hcBPAYorbDRs2BD2YP0v3T0fwNFvbT+L\nZknri9ZtF5UWipx17dqV4cOH225WzKqQe81/kjNznJ43vz9BtNZ/A+7FWP9jgNb6TOFbkcDTAYwt\nfISwa6vXIaQF+XBwBbS4JnSBBMBTTz1ldQiOE4qcKaXo06eP9RXlAJJ7zX+SM3OcnjdTf5Zqrd/R\nWv9Ta/1bibIFWuuPAhdaGBkTulO9+SZ06QI5OSUK05Mg+5jjViV9//33rQ7BcQKRs3CZ2r8kudf8\nJzkzx+l5M1WpUEpdpJT6UCn1feFrkVKqb6CDCwf79u2DaMjKygrJ+dauhZgY8PgjMi0RoupCo4tD\nEkOgxMbGWh2C41Q1ZxkZGSQkJJCSkhKgiJxB7jX/Sc7McXrezMxTMRZ4D/gMeKuwuD/wtVLqBq31\nhwGMr9o7cuQIgMdY/mBatw6uKf2UI3U5NBsMLhstvy5sRWvN5s2bWbVqFbm5uRw9epRzzjnH8b8A\nhRCBZeZTZCbwN6317JKFSqkHgVmAVCr80Lt3b9gJdevWDfq50tJg1y4YUHImkexjkL4Jzrsl6OcX\nzpSRkcGSJUs85p2IiorixIkTUqkQQngw8/jjPIzFxUr7GEvmhqwGVla8SSA0aQJJSXDVVSUK074w\nhpE2v8rnfnY1Y8YMq0NwHH9yVjQr5ssvv+xRoYiPj+eOO+6gWbNmwQjRluRe85/kzByn581MS8UB\n4DJgZ6nyywvfE/4KfiMFABER0Lt3qcIGF0Dv5yG2ZWiCCKA2bdpYHYLj+JOzRYsWsWPHjuLvnT4r\nZlXIveY/yZk5Ts+bmUrF88A8pdT5wIbCsv7ArcCDgQosrFxk4bnrdDJeDnT33XdbHYLj+JOzzp07\nF1cqwn1WTLnX/Cc5M8fpefO7UqG1flEpdQSYDhQ9iN8BTNRafxDI4MLB5s2bAeO5dYOaDSyORoiz\nevTowf79++natWtYtk4IIfxnqru/1nohsDDAsYSllq1awm9Qs2ZNq0MRwoNSimHDhlkdhhDCQfzq\nqKmUciulFiil3lZKTQhkIEqpKUqp35RSp5VS3yqlLqzkfv2VUrlKqeRAxhMqLZq3gCMQExNjdSiO\nU/J5v6gcyZk5kjf/Sc7McXreKl2pUErdDCwGBmKs87FAKfVEIIJQSo0DnsUYrnoBsA1YoZQqd9lO\npVRdIIGzi5o50xfBP8X+/T6m6HawBx54wOoQHKcoZ0UjO3buLN3fWngj95r/JGfmOD1v/rRU3AP8\nU2vdVmvdGaNj5tQAxTENeEVr/ZbWegfGEupZwKQK9psPvAt8G6A4rDE0uIc/fRrOPRdefTW45wm1\nuXPnWh2C48ydO7d4VszExESWLl3KmTNnKt4xzMm95j/JmTlOz5s/lYr2wGslvn8DqKGUal6VAJRS\nUUAfYHVRmTYWF1gF9Ctnv4lAO+CRqpzfaocOHYJ6BPUXe0QELF4M1e3xuNOHXoWa1pqDBw96zDtx\n8uRJfv75Z4sjsz+51/wnOTPH6Xnzp6NmDHCq6ButdYFSKhuoag/DRkAEcKhU+SHA61hHpVQH4P+A\nSwvjqGII1vntN2NNttOnT0P94JwjOhqGlmwN0Ro23gztb4bGPuttohrxNitmOM87IYQIDn9n1Py7\nUur/il5ANHB/qbKgUkq5MB55zNRa7yoqruz+Q4cOxe12e7z69evH4sWLPbZbuXIlbre7zP5Tpkxh\nwYIFHmXJycm43W6OHj3qUT5z5kxmz/aYzZy9e/fidruLO+NcfLGxiNeH739YZia1rKws3G4369ev\n9yhfuHAhEydOLBPbuHHjKncdx39kyj9eZ8FbnjOqV+U6isyZMyd010Hgfx7V8TrGjx/v0TqRk5PD\n559/To8ePTwqFHa/jury85DrkOuw+3UsXLiw+LOxWbNmuN1upk2bVmYfb1RllzFWSn0LVLSx1lpf\nUqkDnj1uFEb/idFa66Ulyt8E6mqtR5Xavi6QAeRxtjLhKvw6Dxiitf6vl/P0BpKSkpKM9TZs4pOU\nTxg9dTTHlh4L3TwVKc/A9odhTDpEOHfUyezZs3nwQZlvrSJJSUksW7YMgC1btvDiiy9K64Sf5F7z\nn+TMHLvmLTk5mT59+gD00Vr7HG1Z6ccfWuugrIuttc5VSiVhjCpZCqCM5xkDgRe97HIC6F6qbApw\nJTAa2B2MOIMqNAuUnpW6HJpc4egKBYRuuXin6927Nz/++CMNGjQgJydHKhQmyL3mP8mZOU7PW6Vb\nKoIahLGc+psYoz42YYwGGQN01lofUUr9E2ihtf6Lj/1nAiO11j6bIGzdUrFoNMceCFFLRe4p+Lgh\nXPAMdHL2dLCi8vLz84mIiLA6DCGEQwW8pSKYtNaLCuekeBRoCmwFrtJaHyncpBnQ2qr4gmnbtm0A\nHD9+PCiVijNnwGNerUNroCAHml8d8HMJ+5IKhRAiFMwsfR4UWuuXCufAqKm17qe13lLivYla6z+U\ns+8j5bVS2FndusYSpVFRUQE/ttbQrh288EKJwrREqN0e6nQI+PmENTIyMjhwQBYIFkJYzzaVinDV\ntm1byITY2NiAH/unn+DgQejcubBAa0hNrDatFKV7S4ebolkxX375ZT788EOys7Mr3Cfcc2aW5M1/\nkjNznJ43qVTYwZLgHHbdOnC5oF/RVBS6ALrMgHbjg3PCEJs0qaIJV6uvkrNi5ubmcvz48TJDx7wJ\n55xVheTNf5Izc5yeN1N9KpRSfTGm6W4P3Ki1TlVKXQfs1lo7e8psK1wRnMN+/TX06gV16hQWuCKg\n453BOZkFZs2aZXUIIae1ZvPmzaxatYrc3LPDhuLj4xkwYECF+4djzgJB8uY/yZk5Ts+b35UKpZQb\n+AD4CGMa7aJugE2Am4DhAYsuDGRkZEALY0KiKs9NWsorr0BaWmCPaSd2GsUTCoGYFTPcchYokjf/\nSc7McXrezLRUzATu0lovUEpdW6J8PfBQYMIKHz/99BMAmZmZUDewx65RA9q2DewxnWjv3r2Of04J\n8N133/HNN98Uf9+1a1cuvvhiMjIyjMqpEEKY1KhRo4CsO2KmUtGZEot/lfA7QVu9ovq6oPcFsPfs\nKBARWHv37qVLly6On1BGCCGCKTY2lpSUlCpXLMxUKg5jrA66u1R5P+C3KkUThmpE14BkcLmkz6y/\nFtDluJMAACAASURBVCxYwOTJk8vd5ujRo2RlZfHOO+/QpUuXEEUmhBDOkZKSwk033cTRo0ctqVS8\nATyvlBqPsRZIQ6XUBcAzwFNViiZcVeN+D8GUnJxcYaWiSJcuXRz/rFIIIezOTKXicSAK+Aajk+a3\nGAt5vai1fi6AsYWPYVYH4Ezz5s2zOgQhhBAl+N3mrrUu0Fr/A2gMxGMs5NVMaz2j/D2FNykpKQCc\nOHkiYMc8cAD27y9RsO8/8MM/A3Z8IYQQwhvTD/K11pla62St9VqttXQ9N6loTQaXClyfihdegIsv\nNibQBGDX65C2ImDHF0IIIbwxM0/F5+W9r7Ueaj6c8NOxY0fYCrVr1w7YMWfMgDFjQCkgPxsOfQnd\n/xGw4wshhBDemPnzeE+pVyrGxFeXFH4v/PVeYA/XuDH07Vv4zZH1kJ8FLa4J7ElswO12Wx1CWJo1\na1ZARytNmDCh0pN3icqzOq+bNm2iRo0a7Nu3z7IYwtlDDz3ExRdfHPLzmulTcUep181a63jgJYy5\nKoS/+la8iWmpiVCzOdTrEcSTWOOuu+6yOgRLJSQk4HK5SE5ODul5lVJ+VyrS0tJ45JFH2L59e0CO\n58vEiRNxuVzFr5iYGDp16sTMmTMrteBadRLIvJrx97//nRtvvJHWrVtbFkMopaamMnbsWOrXr0/d\nunW59tpr+e23ys+yMHfuXLp27UpMTAytWrVi+vTpZebX2b9/P4888ggXXXQRDRo0oHHjxlx55ZWs\nXl126qh7772Xbdu2sWzZsipfmz8Cece9AdwSwOOFhcysTDgP8vLygnOCtOXGqqRKBef4FhoyZIjV\nIVhOWfBz/cc//uH3ZGKpqak88sgjbN26tcx7r732Gjt27AhUeMTExPDuu+/yzjvv8Nxzz9GuXTse\ne+wxbr755oCdwwkCnVd/bN26lVWrVnH77bdbcv5Qy8zM5IorrmDdunX8/e9/59FHH+V///sfV1xx\nRaVmu33wwQeZOnUqPXr04MUXX2TMmDHMmTOH0aNHe2y3ZMkSnn76aTp06MATTzzBww8/zKlTpxg8\neDAJCQke2zZt2pSRI0fyzDPPBPRaK6S1DsgLGAekBep4gX4BvQGdlJSk7WTG6zM0s9A7D+wM/MFP\n7dX6XbTe/UHgj+0QSUlJ2o4/90B48803tcvlcsS1bd68WSuldEJCQlDPM2HCBB0XF1emvF+/fjoi\nIkIfPnw4qOf3JjMzM+TntNrUqVN127ZtrQ4jZGbPnl3m/+KOHTt0ZGSk/tvf/lbuvmlpaToqKkpP\nmDDBo3zu3Lna5XLpZcuWFZf9+OOP+tixYx7bZWdn6y5duug2bdqUOfbHH3+sIyIi9G+//VZuDJX5\nPVm0DdBbl/NZ63dLhVLqvVKvhUqp/wLvAK8HqK4TNrp16wZAXFxc4A+ethyUC5oPDvyxhWMcOXKE\nyZMn06xZM2rWrEmvXr146623ymyXnp7On//8Z+rWrUv9+vWZOHEi27dvx+VyeWzvrU/FF198wYAB\nA6hfvz5xcXF07tyZv/3tbwB89dVX9O3bF6UUEyZMwOVyERERUXxMb8/+tda88MIL9OjRg5o1a9Kk\nSROuueYa0496Lr30UrTW/Prrr2XO8/zzz9O9e3dq1qxJs2bNuP322/n999/LbDdr1ixatmxJrVq1\nGDhwICkpKbRt29ZjqeqiR1Jr167lzjvvpGnTph7N/6mpqUyaNIlmzZoRExND9+7deeONN8rEO2fO\nHLp3706tWrVo0KABF154Ie+//37x+6dOneLee++lXbt2xMTE0LRpU4YMGeLREuQtr1lZWUyfPp02\nbdoQExND586defbZZ8uc3+VyMXXqVJYsWcL5559fHOuKFZUbRbZkyRL+8Ic/lClfunQpw4cPp2XL\nlsTExHDeeefx+OOPU1BQ4LFd6bwWueKKK8ocNzs7m1mzZtGpUydq1qxJixYtGD16tF+PHqrq448/\n5sILL/SYYK9Tp04MHDiQRYsWlbvvN998Q35+PuPGjfMov+6669Bae/zcu3TpQoMGDTy2i46OZujQ\noezfv99YQ6qEQYMGobVmyZIlZi/Nb2Ymvyrd3loAbAX+pbVeWvWQwktcXBykQGSkqVXoPZw4AZGR\nEBtbWFDvfOjxOERXzyVZFi9ezLXXXlvxhmHszJkzXH755fz666/cfffdtG3blg8//JAJEyZw/Phx\n7r77bsD40Bw+fDhbtmzhzjvvpFOnTixZsoS//OUvZR6xKKU8yn788UdGjBhBr169eOyxx6hRowY7\nd+5kw4YNgPGL8NFHH+Xhhx/mtttuK16i/ZJLLvF6PIBJkyaRkJDAsGHDuOWWW8jLy2PdunV8++23\npmZGLfqAqV/f8//CrbfeyltvvcWkSZO45557+O2335gzZw5bt27l66+/Lh7y/de//pWnn36akSNH\nMmTIELZt28ZVV13ls5/GnXfeSZMmTZg5c2bxL/rDhw9z0UUXERERwdSpU2nUqBGJiYlMnjyZkydP\nMnXqVAD+/e9/c8899zB27Fjuvfdezpw5w/bt29m4cSPXXXcdALfddhuffPIJd999N126dOHYsWOs\nX7+elJQUevXq5TOvI0aM4KuvvuLmm2+mZ8+erFixghkzZpCamlqmcrFu3To++eQT7rzzTuLi4oqb\n5ffu3VsmjyWlpqayd+9erz+nN998k7i4OKZPn07t2rX58ssvefjhhzl58iSzZ88u3s7XY73S5QUF\nBQwbNow1a9Zw/fXXc++993Ly5Em++OILvv/++3I7qmZmZnLmzBmf7xeJioqiTp06Pt/XWrN9+3av\ns/v27duXL774gszMTGrVquV1/6J7qGZNz2WqYwt/kSclJVUYY1paGrGxscX7FKlTpw7t27fn66+/\n5p577qnwOAFRXjNG6RcQgdGtsK4/+9nhhU0ff3z848eabuhjWccq3rgCTz+tdd26WuflBSAwBxg7\ndmyF24T744/nn39eu1wuvXDhwuKyvLw8fckll+g6deroU6dOaa2NZlKllJ4zZ47H/gMHDtQul8vj\nscWsWbO0y+Uqc4709HSfcWzZssXn448JEybodu3aFX//5ZdfaqWUnjZtWjlX713R44+jR4/qo0eP\n6l27dulnnvn/7J15XE35/8df57S4t7SptCjtCUPCMJa0KdliUJavJQzCGJNtGFuY0WRLCFlm8LPL\nvpRCdmPJPkgjspWljWjv/fsj906nu7SobnSej8d5cN7n/fl83p9zb/e8z/vz+bw/S4hlWbK3t+fo\nnjt3jhiGoZ07d3LkUVFRxDCM+J69evWKVFRUqG/fvhy9efPmEcMwNHz4cLFs06ZNxDAMOTk5UWFh\nIUd/5MiR1KBBA0pLS+PIBw4cSDo6OpSdnU1ERL1796ZmzZrJ7ae2tjZNmDCh1HtR/L4eOHCAGIah\nwMBAjp63tzcpKSlRQkKCWMYwDAkEAk7Y/Pbt28QwDIWGhspt9+TJk8QwDB09elTimqiPxfHz86O6\ndetSbm6uWGZubs65ryKcnZ3JxcVFfP7nn38SwzAUEhIi1yZp+Pr6EsMwpR7F25PG27dviWEY+u23\n3ySurV69mliWpYcPH8osf/36dWIYhn7//XeOPDIykhiGIU1NTbntx8fHk1AolBg+EdGlSxdq2rSp\n3DoUNvxBRAUAzgHQrUS/hse7cqo5dw5o2RL49HL11bNr164qqTcpKQl37tyRkN+8eROvXr3iyN6+\nfSs1JH/v3j0856Q1Bd69e4ekpOrd6CUiIgKGhobiN1wA4jflzMxMnDlzBgAQGRkJVVVVicmM48eP\nFznlMtHW1gYA7N+/v1TdsrB3716wLIs5c+ZUqHxmZib09fWhr68Pa2trTJ06FR07dsSBAwc4euHh\n4dDW1oabmxtSUlLEh4ODA+rWrYuYmBgAwIkTJ1BQUICxY8dyyouiPCVhGAajRo2SeKvet28fevbs\niYKCAk57Hh4eSE9PF3+PtLW18fz5c1y7dk1mH7W1tXH58uVyfZ8iIiKgrKwsYffkyZNRWFiIiIgI\njtzd3R3m5ubi82bNmkFTU1NiCKkkKSkpYBhGajSjTp064v9nZmYiJSUFHTt2xMePHys0qXTfvn3Q\n19ev0EqwX375BSdOnCj1kDY8VJysrCwA3L6JEAgEHB1pODg4oG3btggKCsKmTZuQmJiIiIgI+Pn5\nQUVFRW7ZrKwseHt7Q01NDYGB0rMm6+jo4O3bt3L7UJlUJOZ+D4ApAPnfLJ4y8ejRIwBFf2D1hPVK\n0ZZNYSFw/jxQy1dZVgphYWHYsGGDhFPQqVMnBAQEYNKkSWLZgQMHMGrUKImHqbe3N7p06YJly5aJ\nZZcuXcKlS5cQEBBQpfYXJzExETY2NhLyxo0bg4iQmFiUWubp06cwMjIS/wiKsLa2LrWN/v37Y+PG\njRg1ahSmT58ONzc39OnTB/369avQ6pSEhAQYGxuLnZXyIhQKceTIERARnj9/jkWLFuH169cS4eX4\n+Hikp6ejfv36EnUwDIPXr18DKLo3gOS90NHRkTkMUPxhDBTNa0lPT8e6desQFhYmt71ffvkFJ0+e\nRJs2bWBtbQ0PDw8MGjRIPFwEAIsWLYKvry9MTU3RqlUrdOvWDUOHDpUb7k9MTISxsbFEGF60e6/o\nuyBC2lJQHR2dMq1mACDVwbx37x5mzpyJmJgYvHv339YEDMMgIyOjTPUW59GjR2jUqFGFls7a2dnB\nzs6u3OVKIvpeSRsKEw2vlPzulWTfvn3o378/Ro4cCSKCsrIyJk2ahNOnT+Phw4dSyxQWFqJ///54\n8OABIiMjYWhoKFWPiKp1lVhFnIppAJYwDDMDQCwAzswQIsqtDMNqCyIvtJAKS9GUz5MnQEYG8Gm4\nmuczGDNmjMRSLgA4e/YsjIyMOLLevXtLHTves2ePxDhsu3bt0Lz515cvRCAQ4OzZs4iJicHRo0cR\nGRmJXbt2wc3NDVFRUdW+7FVJSQkuLi7icw8PD9jZ2WHMmDGcaEVhYSEMDAywfft2qQ9AfX39CttQ\n8iEimog4ePBgDBs2TGoZ0XfDzs4OcXFxOHLkCCIjI7Fv3z6sXr0ac+fOxdy5cwEUOa2dOnXC/v37\nERUVhSVLliAoKAj79+9Hly5dKmx3cZRkhDxLi0bp6uqCiCScj4yMDHTq1Ana2tr47bffYGlpCYFA\ngNjYWEyfPp0zWVPWd6agoKBS5p8BRZFDeVEAEaqqqnLnkNSrVw916tSRGjUSyYyNjeW2YWRkhLNn\nz+LRo0dITk6GjY0N6tevjwYNGhRlXZbCDz/8gGPHjmH79u1wcnKSWXdaWhr09PTktl+ZVOTTOV7i\n35LUkuB75fDNN98A9wBNDdkTgcqCpSWQng6oqlaSYbUYIyMjCecBgHgCXHH09PSk/sE2adJEQqap\nqSl3wldVYGZmJnUoR7SRneiN2szMDKdPn0Z2djYnWhEfH1/mtlxcXODi4oIlS5YgMDAQs2bNQkxM\nDFxdXcvlWFhZWSEqKgrp6ekVjlYUx9DQEP7+/pg/fz6uXLmCNp/SzVpZWeHkyZNo37691NC1CDMz\nMwDAv//+K/4/ULRapqxv7fr6+tDQ0EBBQYHUVRElEQqF8Pb2hre3N/Lz8/H999/j999/x4wZM6D6\n6Y/cwMAAfn5+8PPzw9u3b+Hg4IDff/9dplNhZmaGkydPSkwaFH0XivftcxC9/ZdcfXH69GmkpaXh\n4MGD6NChg1guitYWR0dHR2IFDlAUTbGyshKfW1lZ4cqVKygoKJDpBMli4sSJErkdpOHs7IxTp07J\nvM4wDJo1ayZ1uOry5cuwtLSUOUmzJFZWVuL+3bt3D0lJSVJXwUydOhWbN29GSEgIfHx85Nb5+PFj\nqb9dVUVFkl91/XR0k3HwlJcDpauUhbp1a5dTMXz4cEWbUOPp1q0bkpOTOfNPCgoKsHLlSmhoaKBT\np04AgC5duiA3Nxfr168X6xERQkNDS3UIpD1Y7e3tQUTikLDoR1Xag6Ikffv2RWFhIebNm1d6B8vI\nhAkTIBQK8ccff4hlPj4+yM/Px/z58yX0CwoKxOF4Nzc3KCkpYc2aNRydlStXlrl9lmXRt29f7N27\nF//884/E9eJj3qmpqZxrysrK4uGqvLw8FBYWcoYOgCLn1tjYWG7W0G7duiE/Px+rVq3iyIODg8Gy\nLLp2rZxU/sbGxjA1NZV4yCopKYGIOBGJ3NxcrF69WqIOKysr/P3335ykgEeOHJFI+d23b1+8efNG\nok9lobLmVABAv379cPXqVc78qri4OJw6dUrioZ+QkFDqvBQiwrRp06Curo4xY8Zwri1evBhLly7F\nzJkzS51L8u7dOzx69IjjxFU1ZY5UMAwzB8ASIuK3u6xsrEpX4ZGEz6hZ9OOzceNGiUl2QFGa3tGj\nRyMsLAy+vr64du2aeEnppUuXEBISIn7Y9+7dG23atMHkyZMRHx8POzs7HDp0SOwEyHMs5s+fj7Nn\nz6J79+4wMzPDq1evsGbNGjRs2BAdO3YEUPSQ0NbWxtq1a1G3bl2oq6vju+++k/p27OzsjCFDhmDF\nihV4+PAhPD09UVhYiHPnzsHV1RXjxo0r932qV68ehg8fjjVr1iAuLg6NGjVCp06dMGbMGPzxxx+4\nefMmPDw8oKKigocPHyI8PBwrVqxAnz59UL9+fUycOBHLli1Dr1694OnpiVu3biEiIgL6+voS90bW\n8MAff/yB06dPo23bthg1ahSaNGmC1NRUxMbG4tSpU2LHwsPDA4aGhujQoQMMDAxw7949hIaGokeP\nHlBXV0dGRgZMTEzQr18/2Nvbo27duoiOjsa1a9c4c3hK0rNnT7i4uGDmzJl4/PixeEnp4cOH4e/v\nX6n7hPTq1UtiYmz79u2ho6ODoUOHipfPbt26Vep364cffkB4eDi6dOkCHx8fPHr0CFu3bpWY1zJ0\n6FBs2bIFkyZNwuXLl+Ho6IjMzEycPHkS48ePR8+ePWXaWFlzKoCiJcTr169Ht27dMGXKFCgrKyM4\nOBhGRkacOVgA4OrqCpZlOY6FaOlwixYtkJeXh23btuHatWvYsmULTExMxHr79+/HL7/8AltbWzRq\n1Ajbtm3j1O3h4cEZtouOjgZQzfskyVsaUvwAUACgfln1a9qBGrqkdNftXYQA0JvMN5VTYUE+0bWf\nidLvVU59Xzi1YUmprOPFixdERPTmzRsaOXIk1a9fnwQCAdnb29OWLVsk6ktJSaHBgweTlpYW6ejo\nkK+vL50/f54YhqHdu3eL9QICAkhJSUl8HhMTQ99//z2ZmJiQQCAgExMTGjx4MP37LzdL7OHDh+mb\nb74hVVVVzjJVX19fsrS05OgWFhbS0qVLqUmTJiQQCMjAwIC6d+9ON27ckHtPfH19ZS7BS0hIIBUV\nFYmlihs2bKBvv/2W1NXVSUtLi+zt7WnGjBmUnJzMsWfu3LlkbGxM6urq5ObmRvfv3yc9PT0aN26c\nxGci6/v25s0bmjBhApmZmVGdOnXI2NiY3N3daePGjWKd9evXk7OzM+nr65NQKCQbGxuaPn06vX//\nnoiIcnNz6ZdffiEHBwfS0tIiDQ0NcnBwoLCwMIl7UfK+fvjwgSZPnkwmJiZUp04datSoES1btkzC\nTpZl6aeffpKQW1hY0IgRI6T2rTg3btwglmXpwoULHPmlS5eoffv2pK6uTiYmJjRjxgyKjo4mlmXp\nzJkzHN3g4GAyNTUloVBInTp1ouvXr5OzszO5urpy9LKzs2n27NlkZWUlvqf9+/cvNYtkZfPixQvy\n8fEhbW1t0tTUpF69etGjR48k9MzNzSU+l02bNpGDgwNpaGiQlpYWubu7S9wPov+Wc8s6SpYZMGAA\nderUqVTbK3NJaXkeyoW8U1H5VHqa7tcXi1Jzv75YOfV94XzNTkV1sH//fmJZli5e5L9PJUlPTyeG\nYWjhwoWKNqVG4ubmRkOGDFG0GbWWpKQkEgqFdPjw4VJ1FZmm+/MXofNwEC33K+tEnlJJiizKoKlb\nlVuf8nyNlMwuWFhYiJUrV0JTU7NCWSy/JqRlXgwODgbDMHB2dq5+g74AFi5ciN27d/NbnyuIkJAQ\n2Nvbo0ePHtXabnlXfzxkGEauY0FEFU+2UAvR1dUFEiGe0V0RoqKAb78FdHRQ5FQYegDs178I5/z5\n8+Ixe57PZ8KECcjKykK7du2Qk5ODvXv34u+//0ZgYKDc1RG1gV27dmHTpk3o1q0b6tati3PnzmHn\nzp3w9PREu3btFG1ejaRNmzZlSoPNUzXISoZV1ZTXqZgLoPwZSnjkc6HiRVNSgC5dgK1bgf/1fQOk\nXAVsxpZe8Ctg0aJFvFNRibi6umLZsmU4evQosrOzYW1tjVWrVklkkqyNNG/eHCoqKli8eDHevXsH\nAwMD+Pv7Y8GCBYo2jYenRlFep2InEb2uEktqM/0qXlRXtyjxlY4OgORoAAQYVU7ym5pO8d37eD6f\ngQMHYuDAgYo2o0bi4OCAqKgoRZvBw1PjKc+cCn4+RRXw7NkzQLVoS+KKYmYGaGoCeBkJ6LQAhJKJ\nm75GSu7Ix8PDw8OjWMrjVFRvrt1awps3bwAAeXl5n1cRFRbNpzDyrASreHh4eHh4yk+ZnQoiYvmh\nj8pHNKteS0vr8yoqyAasxwANP2MshYeHh4eH5zOoSJpunsqmMoZqldUA+wVAvVaVUNmXwdSpUxVt\nAg8PDw9PMXinoibwmUGK2krDhg0VbQIPDw8PTzF4p6Im0LZixf75B8itxRvNT5gwQdEm8PDw8PAU\ng3cqFMzVq1cBSN/pUR65uUDr1kAFNuerVYh2muTh4eHhqXp4p0LBNDBpAAAQCoXlKhcbC2RnA46O\nVWHVlw8R4fLlywgPD1e0KV8tiYmJYFkWW7ZsKVc5Z2dnuLi4VJFVXxfz589HixYtFG1GraVly5YI\nCAhQtBlfFLxToWCMjYyBN4BAIChXubNnAXV1wMGhigz7Anjw4IFUeVpaGjZv3ozIyEjk5+dXs1XV\nx+bNm8GyrPgQCoVo0KABPD09sXLlSmRmZla5DfK2RJdXhmWr96fHwsKCc6+kHUpKSuV2kKqStLQ0\nBAcH49dff1W0KdXGmTNn0L59e6irq8PY2BiTJ08uc6rvrKwszJ8/H02aNIG6ujpMTU0xaNAgxMXF\nSej+/fffcHd3h6amJrS0tNC9e3fcu3dPQm/atGkIDg4udyS5NlPejJpVBsMw4wFMAWAI4BaACUR0\nVYbu9wDGAmgBoA6AfwAEENGXmfIuuvxFEhOBDh0A5RrzCVY/06ZNw6FDh8TnRISrV6/ixIkTn5/3\n4wuBYRgsWLAA5ubmyMvLQ3JyMk6fPo2ff/4Zy5Ytw6FDh9CsWbMqadvMzAxZWVlQUVEpV7no6Ap8\n4T+TkJAQjpN19OhR7Ny5E8uXLy/af+cT7du3r3bbZBEWFgYVFRX061c7lolfuXIFXbp0gYODA5Yv\nX44nT55g6dKlePLkCfbu3Vtq+X79+uHkyZPw8/ND8+bN8ezZM4SGhuL48eP4559/YGhoCKDIoXB2\ndoa1tTUWLFiA3NxchIaGolOnTrh27RrMzc3FdXp7e2PChAkICwvD9OnTq6rrXxfytjCtrgNAfwDZ\nAIYCsAMQBiAVgJ4M/WAUOSCtAFgB+B1ADgB7OW3UyK3P997bS/gZlPIxpdxlc3OJ6Ol+ooQtlW/Y\nF0BiYiLnPDc3l1asWEEBAQEUEBBAwcHBdOjQoa926/NNmzYRy7JS+xYTE0NqampkYWFB2dnZCrCu\nZrNkyRJiWVbiOySLvLw8ysvLq2KruNjZ2dHo0aOrtU1F4uLiQubm5pSVlSWWrVq1iliWpXPnzskt\nm5CQQAzD0Ny5cznyiIgIYhiG1q5dK5a5urqSgYEBvX//Xix7+vQpqamp0eDBgyXqHjVqFNnZ2VWw\nV18Gitz6vKrwBxBGRFuI6AEAPwAfAYyQpkxE/kS0hIhiiegREc0EEA+gZ/WZXDm8evUK0Ja+tXJp\nqKgAiAsBnu6pfMO+AEouKVVRUUGvXr3AMAxat26NsWPHokGDBgqyTrE4Oztj9uzZSExMxNatWznX\n4uLi0K9fP+jq6kIoFOLbb7/F4cOHJerIyMiAv78/LCwsIBAIYGpqimHDhiE1NRWA9DkVr169wvDh\nw2FqagqBQABjY2P07t0bT58+5djm6urKaevNmzcYOXIkDA0NIRQK0aJFC4mhCFF7y5Ytw/r162Ft\nbQ2BQIA2bdrg2rVrn33Pit8flmURGhqKJUuWwNLSEkKhEAkJCQCK/lZnzpwJKysrCAQCmJubY9as\nWVKjY3/++SdatmwJNTU16OnpYciQIUhOTi7VhgcPHiAuLg6dO3eWuBYYGIj27dtDV1cXampqaNu2\nLSdiV7wPu3fv5shzcnLAsiwWLVrEkT979gy+vr4wMjKCUCiEtbU1JkyYIHohq3JSUlJw9uxZ+Pr6\ncoaCR44cCVVVVYl+lOTdu3cAgPr163PkouhE8TlrFy5cgKenJ+rWrSuWmZqaon379jhw4ABySyyp\nc3d3x8OHD6UOo/BIovDgOcMwKiiKOCwUyYiIGIY5AaBMewozRQO7GiiKbnxRPH78GEDReCB0ylk4\n7x3w5jzQKqTyDftCadiwIX788UfUq1dP0aYonCFDhuDXX39FVFQURo4cCQD4559/0LFjR5iYmGDG\njBlQV1fH7t270bt3b+zbtw+9evUCAHz48AEdO3ZEXFwcRo4cCQcHB7x9+xaHDh3C8+fPZd7fPn36\n4P79+/jpp59gZmaG169fIzo6Gk+fPhU7gSXnYWRnZ8PJyQkJCQmYMGECzM3NsWfPHvj6+iIjI0Ni\n6fC2bduQmZkJPz8/MAyDoKAg9O3bFwkJCVBSUqq0+7dmzRoUFBRg3LhxUFZWhpaWFgoLC9G1a1dc\nv34dfn5+sLGxwY0bNxAUFISEhARs375dXH727NkIDAzE//73P/j5+SE5ORkhISG4cuUKbty4Pyp1\nKwAAIABJREFUIXfvmosXL4JhGHHG3eKEhISgf//+GDp0KHJycrB161b06dMHUVFREs5aWXj27Bm+\n/fZbZGVlYcyYMbC1tcXTp0+xe/du5OXlQVVVVWbZtLQ0FBYWltqGurq63Hljt27dAhGhVStu8j6B\nQIBmzZrhxo0bcuu3s7ODgYEBgoKCYGFhIR7+mDJlCho1aoS+ffsCKIrM5+XlSZ0Yr6amho8fP+LB\ngwdo3ry5WN6qVSsQES5cuIBGjRqV2tdaj7wwRnUcAIwAFAJoW0IeBOBSGeuYBuAtZAyXUE0f/gio\n2PAHPd1PtA1E7x9VvmFfCWUJ632pyBv+EKGtrU2tWrUSn7u5uVGLFi0kQvkdOnSgRo0aic/nzJlD\nLMvSwYMHZdb95MkTYhiGNm/eTERE6enpxDAMLV26VK7dzs7O5OLiIj5fvnw5sSxLO3bsEMvy8/Op\nffv2pKmpSZmZmZz29PX1KSMjQ6x76NAhYlmWjh49Krfd4sgb/njw4AExDEN6enqcdoiI1q9fTyoq\nKnTt2jWOPCQkhFiWpRs3bhARUVxcHCkpKdHy5cs5ejdu3CAlJSUKDg6Wa9/UqVOJZVkqKCiQuFZy\nOCs3N5caNWpEPXr0kOjDrl27JMoyDENBQUFimY+PD6mqqtLdu3fl2iQNQ0NDYhhG7sGyLKc9aWzd\nupVYlpW4r0REXl5eZGlpWaotFy9eJAsLC07b7du3p5QU7m+rra0tNW/enCPLzs4mY2NjYlmWjh07\nxrlWWFhILMvS5MmTS7XhS6Uyhz8UHqn4XBiGGQRgNgAvInqraHsqxHkUuUXlJSkC0LAF6lpWtkU1\nloyMDGhqaorfUH/55ZfKbyQrqeiQhZIA0Goiv46Me0X7sZREaFStu8jWrVsX79+/B1D0VhkTE4MF\nCxZI5O/w8PDAvHnzkJSUBCMjI+zbtw/29vbw8vIqc1tCoRCqqqo4ffo0RowYAW1t7TKVi4iIgKGh\nIQYMGCCWKSkp4aeffsKgQYNw5swZdOvWTXxtwIAB0NTUFJ87OjqCiMTDE5VFyXYAIDw8HPb29jA3\nN0dKSopY7urqCiJCTEwMWrRogb1794JlWfTp04ejZ2JiAnNzc8TExODnn3+W2XZKSgrq1q0rdZVM\nnTp1xP9PT09Hfn4+OnTogMjIyHL3MT8/H0eOHEG/fv3QtGnTcpcPDw9HTk5OqXo2NjZyr2dlZQHg\n9k2EQCAQX5eHjo4OWrRogSFDhqB169aIi4tDYGAgvL29ERUVJY5ijRs3DpMmTcKYMWPg7++P3Nxc\nzJs3T/w5lWyLYRhoamri7dsv8/FS3dSEORVvARQAMCghNwAgd/CRYZgBANYB8CaimLI01q1bN3h5\neXGOdu3a4cCBAxy9qKgoqT+o48ePx8aNGzmy69evw8vLS+JLN3fuXAQFBXFkT58+hZeXF3c5ZB6w\nbs06ib0sPn78CC8vL5w/f54j37FjB4YPH1601XmxXUn79++v2H4AWLlyZfn7UQJp/Th+/DgcHR0R\nGhqKy5cvi+stTz/KTHwYENlK9nHeu/Q6zntLLxsfVjGbKkhmZiY0NDQAAP/++y+ICLNnz4a+vj7n\nEK3Ff/26aM/AR48e4ZtvvilXW6qqqggKCkJERAQMDAzg5OSExYsXF80bkkNiYqLUh07jxo1BREhM\nTOTITU1NOeci56Wyl/0VXwUgIj4+HtevX5e4f82bNwfDMOL79++//yI/Px9mZmYcvfr16+Px48di\nPXmQjPkM+/fvR5s2bSAUClGvXj3Ur18ff/31V4USvb18+RJZWVkVcigAoEOHDnB1dS31KPmZlUQ0\nHCHNQcnOzi41j09qaiocHR3FznHPnj0xZcoU7NixAzExMZx5RRMnTsSUKVOwadMmNGnSBA4ODkhO\nTsakSZMAgDPXQgQRVWj59JdGTEzRY3THjh3iZ6OhoSG8vLzg7+9fpjoUHqkgojyGYWIBuAE4BIjn\nSLgBWCGrHMMwAwFsANCfiMrsoh87dkzqOGVJPDw84OHhISEPDQ2VkLVs2VJiohQAzJs3T0LWsGFD\nSV0XYPTY0agn5I5Tq6mpSejeuAHo6Q3EX8vtgaObAOP/nIpdu3Ypth+QnjpbWj8AYODAgRg4cKCE\nvGQ/UlNTkZSUhM6dOyMvLw8nT56EjY2NVLtK9qP4BMEyYzMGMJHzhq5UhpwiHffIjlRUEy9evEBG\nRgasra0BQDz2PWXKFHTp0kVqGZFuRZk4cSK8vLxw4MABHD9+HHPmzEFgYCBiYmJgb2//WXWLkDVv\nQtZDuKJIe5AVFhaiVatWCAoKktqemZmZWE9VVRURERFS9UpGQEqiq6uLDx8+oKCggNPf6Oho9O3b\nF+7u7ggLC4OhoSGUlZWxdu1aHDlyRKwn6wFYUFAgt93y8vbt2zLVqaGhIXcOiZGREYgISUmSEcKk\npCQYGxvLrX/nzp1ITU1Fz57cufru7u4QCAS4cOEChg0bJpYHBQVhxowZuHfvHrS1tdGkSRNMnjwZ\nAGBra8upo7CwEO/fv4eenl6p/fzSESWlk/bbfP36dYk5L9JQuFPxiWUANn1yLq6gaDWIGoBNAMAw\nTCAAYyIa9ul80KdrPwG4yjCMKMqRRUTvqtf0z+PWrVsAisL6JZ0Kafz5J3DmDHB7VyTA1gHqO1W1\niQqDiHDlyhWcPHmSM7O+RYsWUt8mKo3KGKIobXikGtiyZQsYhoGnZ5HjaWlZNEymoqJS6oQ+Kysr\n3L17t0LtWlhYwN/fH/7+/nj06BHs7e2xdOlSmYmlzMzMcOfOHQn5/fv3xddrClZWVkhMTCw1I6iV\nlRXy8vJgY2MDExOTcrdjZ2cHoGgid3FHb9++fdDS0kJERARnaKTkS4KOTtGs7/T0dI68ZNTH2NgY\nQqGwwp91s2bNSo1EMQyDwMBATJsme4xXFOm5du0aevToIZZnZ2fjzp07+OGHH+S2IYr8lHRwCgsL\nUVhYKDUJnra2NicvyYkTJ2BpaSkRoXry5AmICI0bN5ZrA08RNWH4A0S0G0V5J+YDuAGgOYAuRPTm\nk4ohgOLxs1EAlACEAnhZ7FheXTZXFlpaRVuUljWB0IoVwMmTADRsgCbTi7Y8/wpJTU0VZ8UUORRa\nWloYOnQounfvLnXslec/Tp06hd9++w2WlpYYNGgQAEBfXx/Ozs4ICwuTuqyx+HBR3759cevWLRw8\neLDMbWZlZUmEry0sLKChoSF33L1bt25ITk7mRKgKCgqwcuVKaGhowMmp5jjOPj4+SEhIwP/93/9J\nXPv48aN4PF6UsEpaNI2ISh2qadeuHYhIYqmskpISWJblPDzj4+Nx7Ngxjp6+vj40NDRw9uxZjjw0\nNJQTxVBWVkbPnj2xd+/eCjkW4eHhOHHihNwjOjpaakSyOHp6enBycsLmzZs5y+s3btyI3Nxc+Pj4\niGV5eXmIi4vjDCHZ2tqCiLBz504J+3Jzc0uNTm/evBl37twRRyuKExsbC4ZhalRitJpMTYlUgIhW\nA1gt49rwEudfzcYB5ubmQAzkhgaLwzCAvj4A9ARMvri0HGWCiLBr1y7Oj0br1q3RuXNnjjPx9u3b\nWhGSlAcR4dixY7h//z7y8/Px6tUrnDp1CtHR0bCwsMChQ4c4SwJDQ0Ph6OiIZs2aYdSoUbC0tMSr\nV69w6dIlvHjxQrx0b+rUqQgPD4e3tzeGDx+OVq1aISUlBYcPH0ZYWJjULJ0PHz6Em5sbfHx80KRJ\nEygrK2Pfvn14/fq13IfK6NGjERYWBl9fX3FGwz179uDSpUsICQmBurp65d+4CjJy5Ejs2bMHw4cP\nR1RUFNq1a4e8vDzcu3cPe/bswfnz59GkSRPY2dlhzpw5mD9/PuLj49GzZ0+oq6vj0aNH2L9/PyZN\nmoRx48bJbKdx48awsbHBiRMnOBNYe/TogdWrV8PT0xP9+/fHy5cvsXr1atjZ2UnkURg5ciRCQkKg\noaGBFi1a4NSpU3j8+LHEcExQUBBOnz6N9u3bY8yYMWjUqBGeP3+O3bt34+bNm3KXlHbo0KGCd1KS\nwMBAODk5wcnJCSNHjsTjx48RHBwMLy8vOBbb5CghIQGNGzeGn58fVq8uemT06dMHNjY2mDlzJv79\n9198++23uH//PtasWQMzMzMMHTpUXD46OhpLly5F586doaOjgwsXLmDLli3o3bs3/Pz8JOyKjo6G\njY2NOHrEUwryloZ8TQdq8pJS2wouKf2KSUhIEGfFTEhIkKrTs2fPUuupDUtKRYdAICBjY2Pq0qUL\nrVq1SrwUsySPHz8mX19fMjY2pjp16pCpqSl5eXnR/v37OXppaWn0008/kampKQkEAmrYsCGNGDGC\nUlNTiahoiSfLsuIlpSkpKTRhwgRq0qQJaWhokI6ODrVr14727t3LqdfZ2ZlcXV05sjdv3tDIkSOp\nfv36JBAIyN7enrZs4WaKFbW3bNkyiT6xLEvz588v870rbUkpy7IUGhoqtWxeXh4FBgZS06ZNSSAQ\nkJ6eHrVt25YCAwPpw4cPHN3du3dTx44dSUNDgzQ1Nalp06bk7+8v8ztdnMDAQNLV1aX8/HyOfN26\ndWRjY0NCoZC++eYb2r59O02fPp2EQiFH78OHDzR8+HDS0tIibW1tGjJkCCUlJRHLsrRo0SKO7pMn\nT2jIkCFUv359EgqFZGNjQ/7+/lRYWFiqnZXJ6dOnqV27dqSmpkZGRkY0adIkToZNov8+n3HjxnHk\nqampNHHiRGrUqBEJhUIyMDCgoUOH0rNnzzh6cXFx5OHhQfr6+iQUCqlp06a0bNkyqct38/PzSU9P\njwIDAyu/szWIylxSqvCHfXUdNdqpGM07FdK4c+eO3BTTZfksv2angufrJiUlhbS1tWn79u2KNqXW\nsmPHDtLS0pLIdfG18TWm6a61pKWlAcaQSA3LA3zzzTdy506UZRUPD8+XSr169eDv7y+xnJun+li0\naBH8/f35DL3loMbMqaitiMZBP3z4AGgp2JhqhIqiR7Vi7TcPT0WZM2cO5syZo2gzai3Xr19XtAlf\nHHykQsE4tHQA8N8qEFkkJgK3bgFUPfv7VClpaWnYvHkzYmNjFW0KDw8PD08lwjsVCqaOah3gOqSm\n4y3O+vWAlBxWXxRERXkn1qxZg8TERERHR0usoy8PJTNp8vDw8PAoFt6pqAnI2WZCxNmzgGPHAjB3\nFwAfX1S9TZWMKDoREREhzjshFAqLhn0qCB+a5OHh4alZ8HMqagLd5V/OyQGuXAGCZsYDd+YApt8D\nag2qx7bPhIhw9epVnDhxgpMVU1reifIiLdU4Dw8PD4/i4J0KBSNKRfzu/TuZabqfPQMaNAAcLY8C\nwgaAVsU2/1EE+/fv56Rg1tLSQq9evWBhYaFAq3h4eHh4qgLeqVAwos2CWEb2SJS1NfDoEYAjGwD9\nrkVpNb8QmjVrJnYqKiM6wcPDw8NTc+GdCgVja2sL3JS+3S6HzCfAuwdA89+qxa7KwsbGBo6OjrCw\nsOCjEzw8PDxfOfxEzZrA9jLoJB0HGCXAsHOVm1PZuLq6VolD4eUlZ3tyHh4eHp5qh3cqagJtyqCT\nFAHotQdUa1GGrFL48ccfFW0CDw8PD08xeKdCwXz4+AGwBvLz82UrFeQCyScBY8/qM6wMEBEuX77M\nmYhZnXh86Yk7eHh4eL4yeKdCwdy5XfRAfv/+vWyl/PeA2QCgQc3Z6jw1NRWbN29GZGQkjh49infv\n3inaJJ4azO7du6Grq4uPHz8q2pRaycCBA9G/f39Fm8FTC+CdCgXTtGnR8lANDQ2Ja0TA0qVAwgtd\noO16QLtZdZsnxaai6MTatWuRmJgIAMjJycHDhw8VbFntY/PmzWBZViIJ2Lt379CmTRuoqakhKioK\nADBv3jywLAsjIyNkZ2dL1GVubi4xR4VlWbAsi+Dg4DK3LY3CwkIEBARg4sSJUFNTK08Xv1gePHgA\nT09PaGhoQFdXF0OHDsXbt2/LVJaIsHbtWjg4OEBDQwOGhobo1q0bLl26JFU/ISEBgwYNgoGBAdTU\n1GBra4vZs2dzdH755Rfs3btXYVFFntoD71QoGA0NDeA+oKwsuRAnORmYPx948EABhklBlBUzMjJS\nnMhKS0sLQ4cORevWravdngMHDlR7mzWNkhuyvX//Hu7u7rh79y4OHDggMUT0+vVrrFmzptR6issX\nL14s1REp62Zwhw4dwsOHDzFq1Kgy6X/pvHjxAo6OjkhISMAff/yBqVOn4ujRo/Dw8JA/zPmJKVOm\nYNy4cbC3t0dwcDCmTJmChw8fwsnJCdeuXePo3rx5E61atcKdO3cwZcoUrFq1CoMGDcLLly85ei1a\ntEDr1q2xdOnSSu0rD09J+CWlNYG70sVGRkBqas3YROzmzZs4duxYpWfF/Bx27NiB3r17K6Ttmkhm\nZiY8PDxw+/Zt7N+/X+qckxYtWmDx4sUYN25cmT63Fi1a4ObNm1i7di1+/vnnCtm1adMmdOjQAUZG\nRhUq/6Xx+++/IysrCzdv3kSDBkWZb7/99lu4u7tj06ZN+OGHH2SWLSgowNq1a+Hj44NNmzaJ5f36\n9YOlpSW2bdsmduCJCEOGDEGTJk0QExMDVVVVuXb5+PggICAAq1evrjURI57qh49U1AS8ZV9SUgKk\nBDGqHYFAIHYotLW1MXToUHTv3l2hiax27dqlsLZrGh8+fECXLl1w8+ZN7Nu3D56ekpN6GYbBnDlz\nkJycLDVaIY0OHTrA1dUVixYtQk5OTrntysnJQWRkJDp3llwK/ddff8HNzQ0GBgYQCARo2rQp1q5d\nK6HHsizmz58vITc3N8eIESM4soyMDPj7+8PCwgICgQCmpqYYNmwYUlNTy217Rdm3bx969OghdigA\nwM3NDba2tti9e7fcsnl5ecjKykL9+vU5cn19fbAsy3EGjh8/jn/++Qdz586FqqoqsrKyUFhYKLNu\nd3d3ZGZmIjo6uoI94+EpHd6pUDCPHj0CUPSWWZOxs7NDs2bN0Lp1a4wdO5ZPZFWDyMzMhKenJ2Jj\nYxEeHo6uXbvK1HV0dCy3kxAQEFAuR6Q4sbGxyM3NRcuWLSWurV27Fubm5pg5cyaWLVuGhg0bYty4\ncWVup+Twy4cPH9CxY0eEhobC09MTK1aswNixYxEXF4fnz5/Lrevdu3dISUkp9ShtA7yXL1/i9evX\nUocD27Rpgxs3bsgtLxAI0LZtW2zatAnbt2/Hs2fPcPv2bfj6+kJXV5czhHTy5EkwDAMVFRW0bt0a\n6urqUFNTw8CBA5GWliZRd5MmTSAUCnHhwgW5NvDwfA414B24dpOVlQUAKCTZbxg1hd69e5e6RfvX\nQFJS0SFCWxuwtJSu+/Yt8PQpIOWZCQAoKABu3frv3Mio6KgsiAjDhg1DUlISwsPD0b17KbvTAZg7\ndy6cnJywdu1aTJw4sVT9Dh06wMXFBYsXL8bYsWPLFZ168OABGIaR6oSePXuWU9e4cePQtWtXLFu2\nDGPHji1zGyIWLVqEe/fuYf/+/ZxJp7/++mupZXv16oUzZ87I1WEYBsOGDcOff/4pUyfp0xdH2lCP\nkZERUlNTkZeXBxUVFZl1bNu2DT4+Phg8eLBYZmVlhfPnz8Pc3Fwsi4+PBxHBx8cHXbt2xcyZM3Hr\n1i0sXLgQz58/x7lz5zj1KikpwdTUFPfu3ZPbTx6ez4F3KhTMN998A9wDNDU0FW1KqdQGhwIAwsKA\nefP+O+/XD9izR7rugQPAqFGy5718+AC0avXf+dy5QEBApZkKoGjypUAggImJSZn0HR0d4eLigkWL\nFsHPz69MTkJAQEC5HBERKSkpAAAdHR2Ja8XbfffuHfLy8tCpUydERUXh/fv3UldEyWPfvn2wt7ev\nUKbVZcuWSX27L4mxsbHc66KXBGn3VCAQiHXkORV169ZF06ZN0b59e7i5uSE5ORl//PEHevXqhfPn\nz6NevaKNB0XRzbZt22LLli0AgO+//x5CoRC//vorTp06BVdXV07dOjo6ZV6FwsNTEXinoiZwAMA0\nKfIXRwC2DmDkXuUmpKamIj09HZayXslrIMOHD8dff/1V6fWOGQMUfy5pa8vW7d1bdpQCANTVgdjY\n/84re64iwzAICwuDv78/unTpgvPnz8PGxqbUcuV1Eko6IuWFpHhdFy5cwNy5c/H3339z8lcwDIOM\njIxyOxWPHj1Cv379ym0bADg4OFSoXEmEQiEASB1aEq2gEelIo6CgAJ07d4aLiwtCQkLEcjc3NzRt\n2hSLFy9GYGCguB6GYTBgwABOHYMGDcKMGTNw8eJFCaeCiMq8aoeHpyLwTkVNwEpStHcv8F1WKBpY\n6FSpU0FEuHLlCk6ePAllZWWMHz8e6urqVdZeZVJVGTXLM0Shp1d0yEJJSb7TURk0adIEERERcHV1\nhbu7Oy5cuMCZJCgNR0dHODs7Y9GiRRgzZkyZ2pk7dy6cnZ0RFhYGLa2ypYvX1dUFULQcufhbfkJC\nAjp37ozGjRsjODgYpqamUFVVxdGjR7F8+XK5Ew5FFBQUlMmGspCWlobc3NxS9YRCITQ1ZUcVRcMe\nScXHzz6RlJSEevXqyY1SnD17Fnfv3pXIDWJtbY3GjRtz5kOI7qeBgQFHVzTJU1rkJS0trWgTQx6e\nKqJ2xLNrMPn5+UAzcH5E09MBb29C1DlDwKjqUnMXz4opmnV++vTpKmuvshk4cKCiTagxtG7dGgcO\nHMCrV6/g7u4uHnaQh2gCZlhYWJna6NSpE5ydnREUFCQO85eGnZ0diAiPHz/myA8fPozc3FwcPnwY\no0aNgqenJ1xdXcVDBMXR0dFBeno6R5aXlyfx4LayssLduzLWZ5dCnz59YGRkJPcwNjYudVmtsbEx\n9PX1JfJJAMCVK1fQokULueVfvXoFhmGkOkx5eXmcPBetWrUCEeHFixccPVGOCn19fY68oKAAz549\nQ+PGjeXawMPzOfBOhYIR/fhkZGSIZRcvAkQMHBudA4y6VHqb0rJiAkUPJnf3qh9q4akaXF1dsWPH\nDsTHx8PT07PUFUWdOnWCk5MTgoKCpCa3kkZAQACSkpKwbt26Mum3atUKqqqqEg9ZJSUlAFxnOiMj\ng5ObQYSVlRXOnj3LkYWFhUk8ePv27Ytbt27h4MGDZbKtOMuWLcOJEyfkHtHR0Zg2Tdo4JZe+ffvi\nyJEjnIf9yZMn8fDhQ/j4+HB04+Li8OzZM/G5ra0tiAg7d+7k6F2/fh1xcXGcVTS9evVCnTp1JIYA\n169fD4ZhJP6W7927h+zsbHTo0KH0G8LDU0H44Q8FY2NjAzwFZ8jh3DnASDcNVnZagNBATunyk5qa\nikOHDnGcCW1tbXh5efHLRL9ASs5V6N27N9avX48RI0agZ8+eiIyMlDsRc+7cuXBxcSlzeyJH5MyZ\nM2Uam69Tpw48PDxw4sQJBBSboerh4QEVFRX06NEDY8aMwfv377FhwwYYGBggOTmZU8cPP/wAPz8/\n9OvXD+7u7rh16xaioqIk3sSnTp2K8PBweHt7Y/jw4WjVqhVSUlJw+PBhhIWFoVkz2WnuK2tOBVC0\n2iQ8PBzOzs6YOHEi3r9/jyVLlsDe3h6+vr4c3caNG8PZ2RmnTp0CALRs2RLu7u7YvHkzMjIy4OHh\ngZcvX2LVqlVQV1fnzH8xMDDAzJkzMXfuXHTp0gW9e/fGzZs3sWHDBgwaNAitis8QBhAVFQV1dXWp\nOUN4eCoLPlKhYHR1dYFEcLLh1VEtRN824WAayM43UFFevnwpEZ34UvNOnD9/XtEmKBxpD3ZfX18s\nWbIEZ8+ehY+Pj9z5CU5OTnBycgLDMBJ1SZMBRdEKWdekMWLECPz999+cN3dbW1vs3bsXLMti6tSp\nWLduHfz8/PDTTz9JlB81ahSmT5+Oc+fOYcqUKUhMTER0dDTU1dU5Nqirq+P8+fMYO3YsIiIiMHHi\nRKxduxaNGzcu88qYysDExARnzpyBtbU1ZsyYgSVLlqBHjx6IioqSmE8h7T4eOnQI8+fPx8OHDzF5\n8mSsXLkSjo6OOHfunMQk3FmzZmHlypV4+vQp/P39cfz4ccyePVtqxCc8PBx9+/b9YuZM8XyhEFGt\nOAC0BECxsbFUk9h7by/BFpTyMeU/4ZvLRNtA9OpcpbdXWFhIu3btouXLl1NCQkKl11+d9OzZs1Sd\n2NhYqomfe22ioKCA7OzsaPbs2Yo2pdZy48YNUlJSotu3byvaFJ4aSFl+J0U6AFqSnGctP/xREyi5\nCi4pAlDRAvS+q/SmGIZBjx49oKysXOpeATWdkuPOPDUTlmUxb948jBs3DtOnT+f3nVAAQUFB8Pb2\nljsExMNTGfBOhYJ59uwZoAp8/PgR9YRFSW0gbADYjgfYqvl4vpYf9a+lH7UBHx8fiUmKPNXHjh07\nFG0CTy2BdyoUzJs3bwCAs/snrGXvYlgaRIT09HSpGQx5eHh4eHiqEn6ipoIRLRErazIheaSlpWHz\n5s3YuHEjJ0MhDw8PDw9PdcA7FTWBqM8rTp+yYq5ZswaJiYn48OEDIiMjK8e2GszUqVMVbQIPDw8P\nTzH44Y+awKcgxZEjRTtejhtX9qJpaWk4ePCgRN6Jylx3X1Np2LChok3g4eHh4SkG71TUBNoW/XP5\nMnD1atmcCiLC1atXceLECc58DFFWzC99ZUdZmDBhgqJN4OHh4eEpBu9UKJirV68CKIo4LFhQT+YW\n2iWJj49HRESE+JzPisnDw8PDo2h4p0LBNDBpADz+bzvksu5KbGNjAxsbG8THx9eq6AQPDw8PT82F\ndyoUjG9HX9iq2cJAKAD+XQc09AFUtUstJ0pilZKSUmujEw8ePICdnZ2izeDh4eHh+QS/+kPB1FWt\ni1ULV0HpzWngyhggN73UMiI0NTVrrUMBoEw7RvLw8PDwVB+8U1EDWLVqFfAyAtC0A+qai+XyNoLi\n+XTfeKoUc3NzjBgxolLrPHPmDFiWldjO/EunoKAALMti4cKFCrPh4sWLEAgEnM3beKqyFfCHAAAg\nAElEQVSPqVOnomPHjoo2Q6HUGKeCYZjxDMM8Zhgmi2GYvxmG+bYUfWeGYWIZhslmGOYhwzDDqsvW\nyqahqSmQFAkYeQIoWtlx+fJlrF69GtnZ2Qq2ruZS25eUbt68GSzLgmVZXLx4UaqOqakpWJaFl5dX\nhdpgWbbMu5GWh5J17tixAyEhIZVW//79+8GyLP7880+ZOtHR0WBZttzO6dGjR7FgwQKp18qze2tV\nMGvWLAwdOhQNGjRQmA3VyfPnz+Ht7Q0dHR1oaWnh+++/x5MnT8pcPiQkBI0bN4ZAIICpqSmmTZuG\nrKwsjo7IWSx5KCkpYdmyZRxdf39/XLt2rVbkCZJFjZhTwTBMfwBLAYwGcAWAP4DjDMPYEtFbKfrm\nAI4AWA1gEIDOADYwDPOSiKKry+7K4mpMAuKjHTFwZlekpabi0KFD4rwTx48fR69evRRsIU9NRigU\nYvv27Wjfvj1HfubMGbx48QICgaDCdcfFxYFlK/fdw8nJCVlZWZyJxdu3b8c///yDiRMnVkob3bt3\nh5aWFrZv3y4z0rJ9+3YoKytjwIAB5ar7yJEj2LhxI2bPns2RKykpISsrS2J78+ri2rVrOH36NJYs\nWaKQ9qub9+/fw8nJCdnZ2Zg9ezZYlsXSpUvh4uKCmzdvlpqlePLkyQgODsaAAQPg7++Pu3fvIjg4\nGPfv38fhw4cl9D09PTF48GCOrFWrVpxzY2Nj9OjRA0uWLIGnp+fnd/ILpEY4FShyIsKIaAsAMAzj\nB6A7gBEAFknRHwsggYhEg+pxDMN0/FTPF+dU/LX+PU6dnQOrISk4eWotJ++EsrIyiEihbz88NZtu\n3bphz549WLFiBccB2L59O1q3bo23byX88jJTmQ/InJwcqKqqgmGYKl+ppKqqin79+mHTpk1ITk6G\noaGhhC0HDhyAh4cH9PT0ylU3yVn3rcgVWH/99RcsLS3Fqf+/dlauXInExERcv34dzZs3BwB4eHig\nefPmCA4ORkBAgMyyz58/x4oVKzBy5EisX79eLLeyssKkSZNw/PhxdOnShVPGzs4OgwYNKtUuHx8f\nDBo0CM+ePYOpqWnFOvcFo/DhD4ZhVAC0AnBSJKOiv9oTANrJKPbdp+vFOS5Hv0ZzJGYT+gw4hsjj\n/yWy0tbWxtChQ9G9e3feoZBBUFCQok1QOAzDYODAgUhJSUF09H/+dF5eHsLDwzFo0CCpD8ElS5ag\nQ4cO0NPTg5qaGlq3bo29e/dK6EmbU/H48WN4e3tDV1cX6urqaNeuHY4dO8bREc2b2LVrF2bNmgUT\nExOoq6vj/fv3EnMqXFxccPToUSQmJopDy5aWlvjw4QPq1q0Lf39/CbtevHgBZWVlud+BwYMHo6Cg\nADt37pS4duTIEWRkZOB///sfR75z5060bNkSQqEQ9evXx7Bhw5CcnCy+PmTIEKxbt44TEhc5EtLm\nVMyaNQssy+LJkycYOnQotLW1oaOjg1GjRiEnJ4fTdlZWFn788Ufo6elBU1MTffr0wbNnz8o8T+Pg\nwYNwc3OTkB84cADdu3dHgwYNIBAIYGNjg4ULF0p8L0xMTDB69GiJ8h07doSHhwdHlp2djTlz5sDW\n1hYCgQDGxsbw9vbmZPatavbu3YvvvvtO7FAAQJMmTeDs7Izdu3fLLXvp0iUUFhaif//+HPmAAQNA\nRFK/M0BRv0t+biVxd3dHYWEhDh06VMaefF0o3KkAoAdACcCrEvJXAAwl1YFPcmn6mgzD1Klc86qW\n+PtPYGZzG3U034tlrVu3xtixY2v1yo6ywG+aVoS5uTm+++47zvbWx44dw7t372SG9lesWIGWLVti\nwYIFCAwMhIqKCnx8fDgJ1QDJuQ+vX79Gu3btEB0djR9//BELFy5ETk4OvLy8cPDgQYl2FixYgIiI\nCEydOhULFy4UP4CL1ztr1iy0aNECenp62LZtG7Zu3Yrly5dDXV0d33//PXbt2iXxANy+fTsASISj\ni9OpUyeYmJiIdUuWV1dX5wwtbtiwAYMGDYJAIMCiRYvwww8/YM+ePXB0dERmZiYAYPz48XB1dQXL\nsmJbt2zZItMG0RyLvn37IicnB0FBQejXrx/+/PNP/PbbbxzdwYMHY82aNejVqxcWLVoEZWVleHl5\nleml4unTp3j58qXUKMVff/0FLS0tTJ48GSEhIXBwcMCsWbMwa9YsCVtl9aE4BQUF6Nq1K37//Xd8\n9913WL58OX7++WekpaXh3r17cu388OEDUlJSSj3ev38vt56CggLcvXsXrVu3lrjWpk0bPHz4UO58\nNJFjIMoPJEJNTQ0AEBsbK1Fmw4YNUFdXh1AoxDfffCPTcdHR0YG5uTkuXLggtw9fLUSk0AOAEYBC\nAG1LyIMAXJJRJg7ALyVkXQEUAKgjo0xLABQbG0s1iX9vP6NJE1ZQQEAABQcHU0JCgqJN+qqIjY2l\nmvi5VwabNm0ilmUpNjaWQkNDSUtLi7Kzs4mIyMfHh9zc3IiIyNzcnHr27MkpK9ITkZ+fT82aNaPO\nnTtz5Obm5jR8+HDx+c8//0wsy9LFixfFsszMTLK0tCRLS0ux7PTp08QwDFlbW1NOTg6nztOnTxPL\nsnTmzBmxrEePHmRhYSHRx6ioKGJZlo4fP86R29vbk4uLi+yb84lp06YRy7IUHx8vlr17946EQiEN\nHjxYLMvJySE9PT1q2bIl5ebmiuUHDx4khmHot99+E8v8/PxIRUVFoq38/HxiGIZ+//13sWzWrFnE\nMAyNHTuWo+vl5UVGRkbi8ytXrhDDMPTLL79w9IYMGUIsy3LqlMbx48el3iciyc+aiOiHH34gTU1N\nys/PF8tMTExo1KhRErodO3Ykd3d38fm6deuIYRgKDQ2Va5M0Bg8eTAzDlHoUb08aycnJxDAM/fHH\nHxLXVqxYQSzLyv0tFd3voKAgjvzIkSPEMAzVq1dPLCsoKCBHR0datWoVHTlyhMLCwqhZs2bEMAxt\n2LBBav1ubm5kb28vtw81ibL8Top0ALQkOc/0mhCpeIsiZ8CghNwAQLKkOvBJLk3/HRHJjU1169YN\nXl5enKNdu3Y4cOAARy8qKkrqjPnx48dj48aNHNn169fh5eUlMXY9d+5cifDs06dP4eXlhQcPHgAA\nrJqZYPb8IUhOTsbTp0850YmPHz/Cy8sL58+f59SxY8cODB8+XMK2/v37K6wfIlauXCmxe2hN6UdZ\n+Jj3EdeTrlfp8TGv8iMsPj4++PjxI44cOYLMzEwcOXJEIrRfnDp1/gvopaenIy0tDY6Ojrh+/brc\ndiIiItCmTRu0a/ffSKO6ujpGjx6NJ0+eSLyp+vr6ftY8g86dO8PIyAjbtm0Ty+7evYvbt29jyJAh\npZYfPHgwiIgTrQgPD0dOTg7n/ly5cgUpKSkYP348Zx6Jl5cXrK2tcfTo0Qr3gWEYjBkzhiNzdHTE\nq1evxG/TkZGRYBgGY8eO5ehNmDBB7hwOESkpKQCK3pJLUvyzzszMREpKCjp27IjMzEw8fPiw3P3Z\nt28fDA0NJWwtC7/++itOnDhR6rF48WK59YhWaBTvmwjRxOSSqziK8+2336J169ZYuHAhtmzZgsTE\nRBw7dgzjxo2DiooKp6xoqG78+PHo3r07Ro8ejdjYWDRu3BjTp0/nzIEToaOj81lzmRRFTEwMgKLf\nZtGz0dDQEF5eXlKHIaUiz+OorgPA3wBCip0zAJ4BmCpD/w8At0rItgM4JqeNGhmp4KlayhupiH0Z\nSwhAlR6xLyvnO1g8UkFE1LVrV+rTpw9t3ryZBAIBvXv3joikRyoOHz5M3333HQkEAs4bopKSEkev\nZKRCIBDQsGHDJGw5ePAgsSxLx44dI6L/IhVbt26V0C1PpIKoKNqgqalJWVlZREQ0ffp0UlNTE/ev\nNJo1a0Z2dnbi886dO1P9+vWpoKBALNu6dSuxLEvnzp2TKN+zZ08yNjYWn5c3UsGyLKWmpnJ0N2zY\nQCzL0suXL4mIaOTIkaSqqipRZ2pqqkSd0ti+fTuxLEuXL1+WuHbnzh3y8vIiTU1NzmfNsixdunRJ\nrFfWSIWNjU2ZokRVyedGKoiInj9/Tu3btyeWZYlhGFJRUaGZM2dSq1atSF9fv1QbQkNDZd7zvn37\nkomJSdk7pGAqM1JRU1Z/LAOwiWGYWPy3pFQNwCYAYBgmEIAxEYlyUawFMJ5hmCAAfwJwA9APQLdq\ntrtSePv2bblnoPNUzX2z07ND7GjJ8dTKbqMqGDRoEEaNGoWkpCR07doVGhoaUvXOnTuHXr16wdnZ\nGWvWrIGRkRFUVFTw559/cuZlVAYlx6wrwtChQ7F48WIcOHAAAwYMwI4dO9CzZ0+Z/SvJ4MGDMWPG\nDFy/fh0NGjTA6dOnMXbs2EpfKisPJSUlqXIq6w6CpaCrqwsiQlpaGkeelpaGTp06QVdXF4GBgTA3\nN4dAIMCVK1cwc+ZMToI9WXMqCgoKKsVGAHj37p3cCIKIOnXqQFtb9nYFenp6UFFRQVJSksS1pKQk\nMAwDIyMjuW00aNAAFy5cwL///otXr17B1tYW+vr6WL9+PWxtbUu1UbSyIzU1VeJaWlparf1NrxFO\nBRHtZhhGD8B8FA1j3ATQhYjefFIxBGBaTP8JwzDdAQQD+AnAcwAjiajkipAvghEjRtTamcKfQ1Xc\nNzUVNbQ0+jKX5H3//fcYM2YMLl++jF27dsnU27dvH4RCIY4fPw5l5f9+AkoOI0nDzMwMcXFxEvL7\n9++Lr1cEeZMRmzZtCgcHB2zbtg0NGjTA06dPERoaWua6Bw4ciBkzZmD79u1o2LAhCgsLJYaGzMzM\nQESIi4uTyIgYFxfH6VdVrMYyMzNDfn4+EhMTOW3Fx8eXqbxoD5zHjx9z5KdOnUJGRgYiIiLQtm1b\nsVzaZ6ijo4P0dMltAhITE9G0aVPxuZWVFW7duoXCwsJyO2bjx4/nDGXJonPnzoiKipJ5XUlJCU2b\nNsW1a9ckrl2+fBk2NjZlzs9ibW0Na2trAMDt27fx5s0bjBs3rtRyjx49AgDo6+tLXHv8+DG+++67\nMrX/tVEjnAoAIKLVKEpmJe2axMA7EZ1F0VLULx5566l5ZMPfNy7q6upYu3Ytnjx5gp49e8rUU1JS\nAsMwyM/PFzsVT548kbp6oyTdunVDSEgILl++LH5IffjwAevWrYOFhQWaNGlSYdszMjJkXh8yZAim\nTZsGVVVV6OnplSuxkKmpKRwdHbFz504YGxvDwsKC84AF/r+9+4+uoj7zOP7+XIpAQLD8ErYhB1G0\n2J6GalEK8qNVExAFEbSAqVlW124lUlu2/gBXrZ4iVosbPQvaIkpBULq62Vp+d6GoCEVFZV2xRQWq\nkKogFfkt4dk/ZoI3yU1CLje5c5Pndc49cGe+d+aZJzeZZ77znZngioEOHTowc+ZMCgsLj+Xlueee\nY/PmzRQWfnHD3tatW1NWVsb+/fuPXS1wovLz87nzzjuZMWNGhfFLDz/88HEVMTk5OXTt2rXKTra8\nhyS+R+LQoUPMnDmzyjJOP/101q5dS1lZ2bHPlZSUUFpaWqGoGDVqFMuWLWPmzJlMmDChTts5efLk\nhOOoKuvQoUOtbUaPHs0dd9zBG2+8QW5uLgBvvfUWq1evZsqUKRXavvfee0iq8Yq6o0ePcsstt3Dy\nySdXuLQ2UY/onj17KC4upkuXLvTu3bvCvN27d7N161YmTZpU6zY0RpEpKpqypnKzmlTzvFXtPj+e\nwYvDhg1j+vTp5OfnM27cOD788ENmzJhBz5492bhxY42fvfXWW1mwYAFDhgxh4sSJtG/fnieeeIJt\n27bx7LPPJh33ueeey8KFC5k0aRJ9+vShTZs2XHrppcfmjxs3jptvvpmSkhJuuOGGak8nVKegoIDr\nr7+e0tLSKpdSQnDTqmnTpnH99dczcOBAxo4dy44dO3jooYc444wzmDhxYoVYAYqKirjoooto3rw5\nV155ZZ3iqey8885jxIgRPPDAA3z88cf06dOHVatWHTsaPp7CYsSIEVUuCb7gggto27YtBQUF3Hjj\njRw9epS5c+dW6KEqd91111FSUsKQIUMYNWoU77zzDvPnz6dHjx4V2o0fP565c+cyceJE1q5dS//+\n/dm7dy8rVqzgxz/+MUOHDq02xl69etGrV6/jSUmtioqKmDVrFkOHDmXSpEnEYjGmT59OdnY2N910\nU4W2AwcOJCsrq8LA1KKiIsrKysjNzeXw4cPMmzeP1157jXnz5lU4dVJcXMzixYsZNmwYOTk5bN++\nndmzZ7Njxw4WLFhQ5bu4fPlyYrFYjYV9o1bTgIvG9MIHajZJTeWS0pqcdtppNnz48ArTHn/8cTvr\nrLOsVatWdvbZZ9ucOXPsrrvuslgsVqFd5YGaZmZbtmyxq666ytq3b29ZWVnWt29fW7JkSYU25YMx\nn3nmmSrxJBqouW/fPisoKLD27dtbLBZLOGhz2LBhFovFbN26dTVubyK7d++2li1bWrNmzeztt9+u\ntt1TTz1l55xzjrVq1co6depkhYWFVlpaWqFNWVmZFRUVWefOna1Zs2bHBm0eOXLEYrGYTZ069Vjb\n8oGan376aYVllA/U3L59+7Fp+/fvtwkTJliHDh2sbdu2dsUVV9imTZtMkk2fPr3WbXz55ZcTDhxc\ns2aN9e3b11q3bm3Z2dl2++2329KlSy0Wi9maNWsqtH3ggQcsOzvbsrKybNCgQfb666/bgAEDLC8v\nr0K7AwcO2JQpU6xHjx7WokUL+8pXvmJjxoyxbdu21RpnKr3//vs2evRoO+WUU6xdu3Y2cuRI27Jl\nS5V22dnZduaZZ1aYNnv2bOvdu7e1adPG2rVrZ3l5eQkH6i5dutTy8vKsa9eu1qJFC2vfvr1dcskl\n9vzzzyeMafTo0fbd7343JdvXUFI5UDPtO/uGenlR0TQ15qKiIXTr1i3hFQHpMHLkSOvZs2e6w2hQ\nL7/8skmyhQsXHlf7QYMGVSkCXcPZvn27tWjRokqRHXWN7T4VTd7xDJBzVXne6teRI0fYtWtXJEax\nl5aWsmjRIq655pp0h1JvEt0Bsri4mGbNmjFgwIDjWsbUqVOZP3++P/o8TR588EH69OnTZB8mBj6m\nIhI2bNjAtddem+4wMo7nrf4sX76cBQsWcPDgwYTPk2goW7du5cUXX2TWrFmcdNJJCZ9N0Vjce++9\nbNy4kcGDBxOLxVi0aBErVqxgwoQJVR6IVp1+/frVeHtqV79qu2lXU+BFRQTU5fI49wXPW/2ZNm0a\n7777LlOnTk1rUbF69WrGjx9P9+7d+c1vfkPnzp3TFkt969evHytXruTuu+9m37595OTkcM8993Db\nbbelOzTnjpsXFc65KlauXJnuEAAoLCyscDlnY5afn1/lcdvOZRofU+Gcc865lPCiwjnnnHMp4UVF\nBCR6+qarnefNOeeixYuKCCgqKkp3CBnJ8+acc9HiAzUjIC8vL90hZKS65K38gVfOOecqSuXfRy8q\nXKPWsWNHsrKyKCgoSHcozjkXWVlZWSm50Z0XFa5Ry8nJYdOmTezcuTPdoTjnXGR17NiRnJycE16O\nFxURUFJSwuWXX57uMDLO8eYtJycnJb8sjYF/15Ljeas7z1lyMj1vPlAzAu677750h5CRPG915zlL\njuet7jxnycn0vHlREQGdOnVKdwgZyfNWd56z5Hje6s5zlpxMz5sXFc4555xLCS8qnHPOOZcSXlQ4\n55xzLiWa0tUfLSGaN0Fav349GzZsSHcYGcfzVnees+R43urOc5acqOYtbt/ZsqZ2MrP6jyYCJI0D\nnkx3HM4551wGu9rM5lc3sykVFR2AfGArcDC90TjnnHMZpSXQHVhmZruqa9RkigrnnHPO1S8fqOmc\nc865lPCiwjnnnHMp4UWFc84551LCiwrnnHPOpYQXFQ1A0gRJWyQdkLROUp9a2g+W9Kqkg5L+Iqmw\noWKNkrrkTdJIScslfSTpU0kvScpryHijoK7ftbjP9Zf0uaToXSDfAJL4HT1J0s8lbQ1/T9+T9I8N\nFG4kJJGzqyW9LmmfpB2SHpPUvqHiTTdJAyT9TtJ2SUclDT+Oz2TcvsCLinom6XvAL4E7gW8CbwDL\nJHWspn134PfA/wC5QDEwS9LFDRFvVNQ1b8BAYDkwFDgHWAU8Jym3AcKNhCRyVv65dsAc4A/1HmQE\nJZm33wLfAcYDZwJjgT/Xc6iRkcTftf4E37FfA2cDo4HzgF81SMDR0Bp4HbgBqPWyy4zdF5iZv+rx\nBawDiuPeC/gAuLma9vcBGytNWwAsTve2RDlv1SzjTeD2dG9L1HMWfr9+RrCD2JDu7Yh63oAhwCfA\nKemOPYNyNgnYXGlaEfDXdG9LmvJ3FBheS5uM3Bd4T0U9ktQcOJeg0gTAgm/GH4BvV/OxvlQ9YlxW\nQ/tGJ8m8VV6GgJMJ/vg3esnmTNJ44DSCoqLJSTJvlwGvALdI+kDSnyXdL6nG2xc3FknmbC3QTdLQ\ncBmnAlcCi+o32oyWkfsCLyrqV0egGfBhpekfAl2q+UyXatq3ldQiteFFVjJ5q+ynBN2NC1MYV5TV\nOWeSegJTCW67e7R+w4usZL5rPYABwNeAy4EfEXTn/0c9xRg1dc6Zmb0EFABPSzoMlAK7CXorXGIZ\nuS/wosI1OuFzXv4NuNLMdqY7niiSFCN4Fs6dZvZu+eQ0hpRJYgTd1+PM7BUzWwr8BCiM8h/7dJJ0\nNsGYgLsIxjzlE/SQPZrGsFw9aEpPKU2HnUAZcGql6acCf6vmM3+rpv0eMzuU2vAiK5m8ASBpDMHg\nr9Fmtqp+woukuubsZOBbQG9J5UfYMYIzR4eBPDP7Yz3FGiXJfNdKge1mtjdu2iaCoiwbeDfhpxqP\nZHJ2K7DGzKaH79+UdAPwgqQpZlb5iNxl6L7AeyrqkZl9DrwKXFg+LTzXfyHwUjUfWxvfPpQXTm8S\nkswbksYCjwFjwqPHJiOJnO0Bvg70JhhZngs8Arwd/v9P9RxyJCT5XVsD/IOkrLhpZxH0XnxQT6FG\nRpI5ywKOVJp2lOAqCO8hSywz9wXpHina2F/AVcB+4BrgqwTdfbuATuH8e4E5ce27A58RjPw9i+Dy\no8PARenelojnbVyYp38hqObLX23TvS1RzVmCzzfVqz/q+l1rDWwDngZ6EVzO/GfgkXRvS4RzVggc\nCn8/TwP6A+uBl9K9LQ2Ys9YEBXtvgoLqpvB9t2pylpH7grQH0BRe4ZdhK3CAoMr8Vty8x4GVldoP\nJDgSOABsBr6f7m2Iet4I7ktRluA1O93bEdWcJfhskywqkskbwb0plgF7wwLjF0CLdG9HxHM2Afjf\nMGcfENy3omu6t6MB8zUoLCYS/o1qLPsCf/S5c84551LCx1Q455xzLiW8qHDOOedcSnhR4ZxzzrmU\n8KLCOeeccynhRYVzzjnnUsKLCuecc86lhBcVzjnnnEsJLyqcc845lxJeVDjnnHMuJbyocK6RkHS6\npKPhY6YzjqQLJZVVelBXonbvh0+4dM5FjBcVzkWEpMfDoqAs/Lf8/z3qsJh6u+9+XNFS/vpY0lJJ\n30jRKlYTPAtif7i+ayV9nKBdb2B2itaZkKQX47bzgKS3Jf00ieXMlbSwPmJ0Loq8qHAuWpYAXeJe\nXYEtdfh8fT9G2ggectQFGAK0AxZLanPCCzY7YmYfxU0SCYokM9tlZgdPdH21hQPMINjOMwkeGPZz\nSdfW83qdy2heVDgXLYfM7GMz+yjuZQCSLgmPoHdL2inpd5JOq25Bkr4sab6kjyTtD4+2C+Lm50j6\nbdzy/ktSt1riE/BJGNerwE8JCp8+ceucFy5zr6Tfx/e0SOou6TlJn4TzN0q6OJx3YdgzkCXpQuBX\nQIe4HpvJYbtjpz8kPS1pXqXtbi5pl6Qx4XtJmiLpvTAPGySNPI6fxf5wO983s9nA/wEXx63nS5Ie\nk7QlLr9FcfPvAa4GRsVtQ78TyL1zkedFhXOZoxVwP3AOcCHBDv6ZGtrfC5wB5ANfJXhU9S4IdrzA\ncmAn0B+4gODxyksk1eXvwqEwjpPC9/OAbwBDgX5Ac2BR3DIfIfi7cwHwdeA2YH/c8sp7Jp4HJgGf\nAKcSFC4PJlj/k8BwSS3jpg0L1/vf4fs7gDHAdUAv4CFgvqRvH+9GShoMnAUcjpvcjOCx51eEy70H\nmCbp8nD+NIKfz+/jtuFPKcy9c5HzpXQH4Jyr4DJJn8W9X2xm3wMwswoFhKR/BnZIOtPM/pJgWd2A\n18zstfD9X+PmjQMOm9kP45Y3Hvg7wemNP9YWqKQvA7cDe4BXJPUiKCb6hL0YhD0jfwUuI9jJdwPm\nmdlb4WK2Jlq2mX0uaU/wX0s0rqLcEuBzYATwdDhtLFBiZgfCYuNmYGB5TMATkgYBPwDW1rDsH0n6\nIUHB1Jyg+HkoLsZDwN1x7bdJugC4Klz/PkkHK29DmJMTyr1zUeVVsXPRspLgSD83fE0snyGpp6Sn\nwm78PcBmgiP7nGqWNQP4vqRXJU2TdH7cvFygl6TPyl8ER87NgdNriXF92H4XwRH6lWa2i6A35FDc\nzptwZ7o5bAdQDPxM0guS7pT0tdpTUj0z+xz4LcFpBsKxHZcR9JhAMB6iFbCq0raOPY7tnEPws+gP\nLAPuNrNX4htIulHSKwoGrX4G/BPV/zzKnUjunYs076lwLlr2mVl1AzMXAX8h2HGVEhxBv8EXpx4q\nMLNFknIITgdcRLBj/Xczmwy0AdYB11B1cGdNPQMQdPdvBnaZ2Z7aN6lCTL+StDiMKR+YLOlHZvZI\nXZZTyZPAirDnZDhBz8kfwnnlA0jzgQ8rfa62wZ5/D38WWyRdBbwjaZ2ZPQ/HehymATcB64HPCE7n\n5Nay3BPJvXOR5kWFcxlAUmeC8RHfN7M/hdMGU/XqiArvzWwnwRH3HElrCbrrJwMbCE4ZfGRm++oQ\nigEfVFP4bAJOkvSt8iP6MO6ewFvHFmD2AfAo8KikXxCMdUhUVBwmGLdQc0BmLyUaixUAAAHsSURB\nVEgqBb4HjASeNrOj4ew3w+XkmFlNpzpqW8dnkh4Gfkk4KJVgzMjzZvbr8naSzkiwDZXvu5Fs7p2L\nPD/94Vxm2AXsBn4gqUd4dcT9CdodO/KVdI+kyxTcX+LrwCV8sXOfC3wKlEjqH16V8R1JD0s6tYY4\nqr1k1czeBhYDj0n6tqRcgtMQ7xEMVkRSsaSLw/WdCwyOi6myrUA7SYMkdag0GLOyp4AJwHcIei7K\nY9pDMMCzWFJBmLtvhqctrq5heYk8AnxN0vDw/WbgfEkXhaemfg58M8E25IbzO0hqRvK5dy7yvKhw\nLgOYWRnBkfj5BEff9wP/mqhp3P8/J+iefwNYRdDdXxAubx8wANgOPEuwY3+UoGdgb02h1BLqNeH6\nFgEvElwdcmlcz8GXCMZ6vEVQaLxJ3LiRCisyewGYBfwn8BHwkxpieBI4G9hiZusrLec2githJofr\nXUJwj42a7v+R6P4YO8P13BVOmgH8DlhIMODzZKr2uDxKUFS9Gm7D+SeQe+ciT+El8M4555xzJ8R7\nKpxzzjmXEl5UOOeccy4lvKhwzjnnXEp4UeGcc865lPCiwjnnnHMp4UWFc84551LCiwrnnHPOpYQX\nFc4555xLCS8qnHPOOZcSXlQ455xzLiW8qHDOOedcSvw/tgqRZ0IqOVsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9f7b358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluating and tuning the ensemble classifier\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import auc\n",
    "\n",
    "colors = ['black', 'orange', 'blue', 'green']\n",
    "linestyles = [':', '--', '-.', '-']\n",
    "for clf, label, clr, ls \\\n",
    "        in zip(all_clf,\n",
    "               clf_labels, colors, linestyles):\n",
    "\n",
    "    # assuming the label of the positive class is 1\n",
    "    y_pred = clf.fit(X_train, y_train).predict_proba(X_test)[:, 1]\n",
    "    fpr, tpr, thresholds = roc_curve(y_true=y_test, y_score=y_pred)\n",
    "    roc_auc = auc(x=fpr, y=tpr)\n",
    "    plt.plot(fpr, tpr,\n",
    "             color=clr,\n",
    "             linestyle=ls,\n",
    "             label='%s (auc = %0.2f)' % (label, roc_auc))\n",
    "\n",
    "plt.legend(loc='lower right')\n",
    "plt.plot([0, 1], [0, 1],\n",
    "         linestyle='--',\n",
    "         color='gray',\n",
    "         linewidth=2)\n",
    "\n",
    "plt.xlim([-0.1, 1.1])\n",
    "plt.ylim([-0.1, 1.1])\n",
    "plt.grid()\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "\n",
    "# plt.tight_layout()\n",
    "# plt.savefig('./figures/roc.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sc = StandardScaler()\n",
    "X_train_std = sc.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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39WpagIXQ4RDcQkKgz3fXchxzHjVvUkTiEld2B8XJ2qYGsWR1ZEOOdG+Z2Syi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\nFCMlKZXGyFJbiOcSb2GO1hivKSqqDO8P7JF0RVukWnJpjY6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KErf7qxZpiXKtnqV2/y+b\nODeVbt9UUjRFwyPmdizkma/ux2Mv7criJ7fkkJZO2GR5ZceWpjN6k+VMeH1LXmdllg5vYcDEiWkX\nKTNxMssxsp7ofqRLFWLJnCvvvZdbJ03iOHqOSzuOMC7t4kcf5ePDh9espRjK7/6Kk5ao5OMXafUs\nyShSWbGwqveiUN444Tk7DeUPLSey+MktGTa/JXz5E2kAWYyTmYqRdUj3I12qEMsKz89Zxn+tCzM6\n3mQz2lMbNvGbm29ma3oGeejqMvoLcP4tt9S0Qlyuqqxc1FerZwmm/melVMZDJn4vehkmkqsIL9ht\nKA+v/HGeev7bXRXhTUq7RyJ9eWTah2wwqpOtWcRa89IbNtEIcVKru3Wn+5GuuspDbGbfM7OXzWyp\nmU02s4+lXaZGkUtG/8CTX+e3S45k2k6DaWcaHfNuqmmKqgWLFjG/vZ2j6P3D2UIYbjl/wQIWLFpU\ns7KVK5eW6AqzHknaczqBK8zYd5dd+v6mP28qHfNuop1pTNtpML8dfRYPPPl1Nnp665IrwxDe50Na\nOml/5HB+u+RIHv7yvszdbTDt8y6r6vu8IkVTa2uf9+LK1tZ+70XuPry421s9HnN3G8zDX96X3y45\nkvZHDueQlk61CktiDmnpZKOnt+aWabvW7HenmEaJk4nGyAaQZJyU8tVNC7GZHQxcABwJTAXGAQ+Y\n2Vbu/laqhWsQWZhV/dqCBTiU1GXk0f61zDxRrt133JE7+0lL9Kw7R+6wQ6/HyM+OkFSrZxpZRipO\n0ZRrHd9t8IphIotf37Lnfi+jccJSNVnI0NNIcTKJGNlIkkplJ+WrmwoxoQJ8hbtfD2BmRwGfB74N\nnJtmwRpN4azq//nyk+zwWl6u4iracMkSDEpa/ciADZfMhXlvxD9hlf94Pfr002xoxsXuPAgcAV1p\niYDZwIZmPPLMM8W7NedNZc09t+KprTbksfm74/duziEJdv/nZxl5bPSuPccXJ2jXNeCCg8dw/M33\n8WBLC0d0dnbdi5YWZnd2csHBY9hljfd6tLh1GxO85EiNCZbU1fJ3p1AjxcmKY2Sh/lrrM5RStJgk\nUtlJPHVRITazlYFRwFm5be7uZvYgUOJsIilX0ZaQ12ZU/bzrnT+Qy99awnEU7w7sBC4H1l9vIO1j\nltDOkngnmji3qq06+TOGVwHOBn4EK1IjbQL8Fljm3u+M4dfZHXtva5wPEy9nbz0DkHzHy567DeXq\nMfsz4YZ/cPxDL9Pp0GKw1yeHcsJh27PjThvxYtHzDtaYYMmcWv7uFKpVnBwy6V9V6yFMMkZC9960\nPq8lY7n2CyW12p2Upy4qxMC6QCvwesH214EMJhRtLPktIQ/U4HwfOXBLpv3u+D67jJ4DRh14Jue+\nfHjs86w+ek5VMy7kZgw/B1wBbAt8H9gQeA34PfA94LtkY8Zwfs/AuRtVsbK5Nqz7fVhn/j68OWsS\n6wzfnXW/fw9/Af7ycu8vGza/JdHWcZGk1Ox3J08t4uTqeQ0h1VjNNLEYWSTrTuFwqu7Xks3FVfLl\nUtmNOflnPDFzBrsMH8H1J5+cdrEaWr1UiGMZN24cgwYN6rZt7NixjB07NqUS1a9cwK+JfQ/nF1Pu\n4qJnJvIAYWJIrsvockKQ32GHPTnl89+GXqce9G/q01szafeL6DzySrZ//p8MnJhA2fOsOXAgLYRA\n/316/tE6gfBH65Joe+GM4Vxrx4sdQ5kzH+bMWcaw6AjLl3/ISiutnGyBI7V4r2fOfII/zJoEHMab\nM29gx2cfZ/jwfjp7eqkIV/NeZElbWxttbW3dti1cuLDi4ypOJqfh4mTUQ/jSTnP51jGP0XHpxESz\nD1UaI3vLujNsfgsjN1jaPS4U6e0cMuky1ipx0fQ0WpWfmDmTJ2bOAA7j8Zk38OSsWewas3X4w+XL\nWXmlhq7yVRwj6+XuvAV0ABsUbN+A8EWyqPHjxzNy5MhqlkuqpLPTgY15DvgRr67oQnM2jp5P7lyv\nszvwz+QOGFltwAA2HDyYNdvb+02N9O6QIV0tH8VaO17eLJoo1sns2VM47bSvcOqpf2SbbbLb7deX\nCRPOoaVlBJ2d19LS8nduuukczjzzjrKP0wj3olTFKqnTp09n1KjKxvUrTtavWsTJYfNbYKfw/zX3\n3CrRRZsqiZF95RqfvehJDj2+Z1zoNu47qhhvPLi0sq53WW0nTgKcNeEPtLRsF8XJ6Zx10x+468zy\nF3mfMns2B512Bree+ktGb7NNFUqaDZXGyLqoELv7h2Y2DfgUcCeAmVn080Vplk2SN3PmE8yY8Qhw\nK3AgzgLg1SjIDwFuZcaMg5g168n+WxVTtHTZMl5/5x1+TP+pkU5ob2fpC4+zyipzi7Z25E8eu/HG\nM1my5B1uuOHMWJXItHV/f1vp7Dw19vtZ7/dCJK5GiJNlx8hly1jl9T/1m3Wnr7iQP+77gWJZanqx\nerGJk1WsGD8xcyaTZjxDV5w8jUdnHBSrlfiMGyewaMl7nHHDhFgV6mZRFxXiyG+A66KKcS7t2kDg\nujQLJcmbMOEczNbBfQBwX94zr0T/rorZOrFbFfM9P2cZ/1n0IRt1OlsSAl1S3ln0flmrDr3y/lO8\ns/NWPVdWyxsq0PVH8DBmzLgh03/setPVOnxAtOWLtLRsV/b72Qj3QiSuWsbJV1/9kEdW7WSHjjcZ\nwr8Si5Nlx8g5l9H6+a1W9Jxt9PTWPeYVlBoXyh3e0ls60mo585pHaLGP0ul5cdKGc+Y1F3LHcXuU\nfJwnX3gzqlgfxqMzbuDxhy9kly3Wq0qZq2+/qh69birE7n6Lma0LnE4YKvE08Dl3fzPdkkmSOjo6\neO65p3BfAuzb637u8NxzU+no6KC1tTXWuYrNEN9uWHK/EsuWfkDrz+/kxc4SVh1qMf669xienndU\nn1kUkhpqkJburVq5NqGWWK3E4V5sH92Lp+vuXojEVes4WTj+ttRhBv0pN0ZOP2Q/ps7fo2t1ziJp\nF6sVI4ulI03qPhSa8dgLPDbnNUKOjbw46acz6fmDuPajG7PdJ4aVdKyTP3MxLa3b0dlxLS2t0zn5\n7/M599dfqk7Bq+yzVT5+3VSIAdz9UuDStMsh1dPa2sr11z/H0qXv9bvvaqutETvI58sPdNc+n+wM\n8Y1HP8TlU+/juM7lvadGalmJjXf+PO9NOabPLApJDjVIS8/W4ZzyWol73otT6u5eiMSVRpzMH3+b\npHJi5MNPH95n/vFaxMj8LwjV8tDP94OWEVAkTmLb8evjnmavM/of+vDGzMf5xyPPsuJ+dJzGMxMP\n4tzzh7He8I9Xo+hV9dkqr0VSVxViaQ4DB67FwIG1X1WpGjPEdzzge5w8+e4+UyM939nB2fsfw/B+\nFplIaqhBWoq3DueU10rc1Tqcfy9G1M29EKlUGnEy9RgZTSzuTS1j5CEt1UmvN3PmE/zhnxPpLU7i\np/LGjINKyszzsz+cXTROvtF2Nj9QnOyht3Hs0iCWL09+IQcp3fDhu3LU0RdwMcZwjAsJs0IvBIZj\nXIJx1NEXMHz4Ln0eJ1eZ7Ow8lfBr+yFdlciJzJr1ZNllq/VnY8KEc4B1gNyYx/uAu/L+vyoQxjz2\npetenEL3YRenxL4X0rwUI9OlGNmd4mR6VCFuYLNnT+HQQ4fx7LP9LGUpVTVmzHc48rvn8RzOjzD2\nB36E8RzOd486nzFjvtPvMXItH3AA8ASwPvAk+S0g5aj1ZyM35hHeJox53Cd67J/3/32Bt1eMeexN\nrnU43It8Xa3EIqVQjMwGxchAcTJdqhA3sPzUM5KuJ564i5aW7XHC7GBnD1paRvD443f1+9qeLR+/\nBN6J/o3XAlLrz0Zraysnn3x99NPvgH8D/0NYpHW36OcrAPjpT2/odcxj8VaPHLV+SHkUI7Oj2WMk\nKE6mTRXiBtU99UxlH3x1KVamKzh9GZgIHAZMpLPzKyW9N9270M4D/hod40HgfErtQissTxKfjXLc\ndtvFmK0DbAxMAB4jXMckoA3YBLN1uPXWi3s9Ri7VVPfuxPxHV6opkb4oRmaHYmQXxcn0qELcoLq6\nS66tqHtEXYqV63ovJgLhPYERwMR+35ueXWgnAcOjY2wLnEipXWjdyzOC8Nkovysxjq40UX1fh3vv\n19HzGPsUefR9DJEcxcjsUIwMFCfTpSwTDSjJlFRaCawyXe/FGcAvyL0ncApwEJ2dZzBjxi96fW9y\nXWinnXYQ8GNC68cZ0THOALq299WF1rM8tU3dlp8m6vnn/8bZZx/W4zpOPvlGttpqVK9potJINSWN\nSTEyOxQju1+L4mR61ELcgHoOpo83iD7NbqNG0dV1dSuhxaPrPYHtgFv77brq6kL7v16O8X/9dqHl\nl6dr4kk4Rq1aQAYOXIt11tmYu+++qujn8+67r2KddTbuM5VU7hj9PdJI2yf1QzEyOxQju1OcTI8q\nxA0myVQrSXUpNqvuXVdPA6eS/56En58uo/sr3jFyek48CceoJC1RuZQKSNKmGJkdipH9lUNxspZU\nIW4wSaVa6f4L2apfxBhyXVfbbLNrQYtDTmh52Gabj3P99c/12f1VyTFyerZ8dD9GLf6YKxWQpE0x\nMjsUI/sqh+JkralC3ECSTLWSVJdis3v55X/y7LNPFrQ45ISWh2effYK5c2dW9RjFWz66H6Paf8yV\nCkjSphiZPYqRvZVDcbLWVCFuIEmlWlF3TXKSeE+SOkbP1Y+6H6OctERxKBWQpE0xMnsUI3uWQ3Ey\nHcoy0SC6xlEtIaRaKc6dFeOoeus6yrV8dK1/ntPVAqLZ1P1L4j1J8hjQ9zGg/89GXEl+PkXiUIzM\nHsXI7hQn06UKcYNIKtVK95QzvXXX1CYFTb1L4j3JP8Yxx+zK0qXrE5LNtwCdwKdYbbU3ufTSJ/s8\nRldaot8R8lAWugf4bklpieJQKiBJm2Jk9ihG9n4t/VGcTJ4qxA1k4MC1Kk6jkuuucc911xTq6q5R\nC0j/ir0ny5d/yEorrVzWMR566A8sXfoOcDXwkbxnz2bp0oOYMuUe9tnniF6PkUtL5L4x8I8ie3St\nfjRy5KdLLls5kvh8ilRCMTJ7FCO7U5xMjyrEsoK6a6pv9uwpnHbaVzj11D+yzTajS37dTTedDXyU\nYrOOYTg33HB2r8Fe76tIMvS7VH2KkZIWVYhlBXXXVF+cVa3uvvt3LF68ALiSYl20cDqLFx/Evfde\nWTTg57+vzz33N8455zC6ugVDN2B/qx+JiGJkLShGSlpUIW5wcbqeKu2+yqq0r6Nr7OHXmDHjppLH\nGIaWjyF0zToutCowpM8WkNz7es89V0WTgb5D+ENxOC0tl3D33Vdx5pn7xb00kbqlGNldmteiGClp\nqou0a2b2UzN73MwWm9mCtMtTL2bPnsKhhw7j2WenpnqMLMjCdYSxh1sA92C2eUlpcz744AMWL24H\nFhC68vYp8tgXWMDixQv44IMPej2WUkWJdKcY2V3a16IYKWmqlxbilYFbgCeBb6dclroRp+upGsfI\ngrSvo6vlY3vgRdyHrgiwfbWArLLKKlxzzUzeeeeNfs+x9trrs8oqq/T6vFJFiXSnGNldmteiGClp\nq4sKsbufBmBm30i7LPWiK7gcxowZN8RKAZTEMbIgC9eRa/lw/wdwGHDDihaQ/gLsuutuwrrrblLR\n+ZUqSqQ7xcju0r4WxUhJW10MmZDydS0rem3s5USTOEYWJHkdy5d/WPZrcoHWfXVC68e1wAjc16xZ\nN5xWPxLpTjGyu6SuRTFS6pUqxA2o+zio1ljjn5I4RhYkeR1xx9d1jYv7BxDKEf59puRxcpXoSin0\nNn2NsXN/e0VKIZFGphjZXVLXohgp9czcPZ0Tm50NnNjHLg5s6+7P573mG8B4dx/Sz7FHAtN23313\nBg0a1O25sWPHMnbs2F5fe9ddi0oofbb97GdfYObMdjo7/05utZ6Wlh356EfXKXn8UxLHyIIkr+Nn\nP/sCM2Y8wogRe5b82pkzn+Dkk8cQWj0AusoBO0b/f4Zzzrm/qt1wS5YsKjlVlJLCp2O//fq+721t\nbbS1tXXbtnDhQh599FGAUe4+vZzzNXOcVIzsLqlrUYyUaqp2jExzDPH5hH6RvrxUyQnGjx/PyJEj\nKzlE3Sk+Dqq88U9JHCNfWml8kryOuOPrJkw4B1iL0PLRvRyhBeQgYK2qT9bQ6kf1r1gldfr06Ywa\nNaqi4zZbnMxijIT6j5OKkZK2SmNkakMm3P1td3++n8fytMpXr7rGgfU+S7YWx8hJM41PktcRZ3xd\nrhsO3gOGFy0HbAu8p244kRrJWoyExoiTipFS7+piDLGZfcTMdgCGAq1mtkP0WD3tsmVJ8RyKOaXl\nUkziGPny0/jUUpLXEXd8XWtrKyeffD2h6++MouUI2zv56U9v0OpHIlWWxRgJ9R8nFSOlEdRFhRg4\nHZhO6D9ZI/r/dKCyvsIGk8Qs2SRn2nbvQqvtZJMkr6NnC0rpLSe33XZxSeW49daLy71EESlT1mIk\nNEacVIyURlAveYi/BXwr7XJkWdcs2SWEWbLFubOi66nw23YSx8jXleT8Wlpanq5ZUvMkr6OS8XVJ\n308RiS+LMRLqP04qRkqjqIsKsfSvtbWV669/ruRZssWCShLHyOkeJFsrmmxSriSvo5KVi5Ish4hU\nJmsxEhojTipGSqNQhbiBJDFLNqmZtj2DZG2XvkziOpJYuahaM5fTmpEuUs+yFCOh/uOkYqQ0knoZ\nQyx1pPhEjXiTTdKU1ZWL0pyRLiLJaIQ4qRgpjUQVYklc0imJ0pDllYvSmpEuIsmp9zipGCmNRkMm\nJFFJdKFlQVbHtsVNfi8i2dEIcVIxUhqNKsSSqFwXmnuuC61QVxdarZc3LXdMWRZXLkprRrqIJKdR\n4qRipDQSDZmQxGS5C60RxpTFTX4vItmhOFk9ipFSCbUQS2Ky2oUG3ceU1WtrQdoz0kWkcoqT1aMY\nKZVQhVgSlcUutEYYU1ZJ8nsRyRbFyeQpRkqlNGRCGl7XbO5r62L2djH1PiNdRLKt3uOkYqRUShVi\naWiNMKaseL7SnPrKWyoi2VPvcVIxUpKgCrH0a/nyD1N9fSV6thrUX2tBVpPfi0iQRIxTnIxPMVKS\noAqx9KnSWcdpzlpuhJWgsjwjXUSSiXGKk/EpRkpSNKlO+lTprOM0Zy33nHGcUz8zj7M8I11Ekolx\nipPxKUZKUlQhll5VOus4zVnLjbASVE4WZ6SLSDIxTnGycoqRkgQNmZBeVTrrOM1ZyxpTJiLVlkSM\nU5wUyQa1EEtR3VsOWstuKaj09ZXoGlO2hDCmrDh3VowpUzeaiJQjiRinOCmSHaoQS1GVrviT5opB\nGlMmItWWRIxTnBTJjsxXiM1sKPALYC9gQ+A/wE3Ame6eXp6aBlbpij9ZWDFIY8pEpFqSiHGKkyLZ\nUg9jiLcBDDgCGA6MA44CzkyzUI2s0hV/tGKQiDSyJGKc4qRItmS+QuzuD7j7d9z9r+4+193vBs4H\nDky7bI2o0hV/tGKQiDSyJGKc4qRI9mS+QtyLtYEFaReiEVU661izlkWkkSUR4xQnRbIn82OIC5nZ\nMOBY4Edpl6XRVDrrWLOWRaSRJRHjFCdFsim1CrGZnQ2c2McuDmzr7s/nvWYTwtfnm939mv7OMW7c\nOAYNGtRt29ixYxk7dmy8Qje4Smcda9aySPW0tbXR1tbWbdvChQsrPq7iZOmSiHGKkyLVUWmMNHdP\nukylnTj0F63Tz24vufvyaP+NgYeBJ9z9W/0ceyQwbdq0aYwcObKsct1116Ky9hcRScJ++5U/23/6\n9OmMGjUKYJS7Ty/ntYqTIlJPqh0jU2shdve3gbdL2TdqGX4IeAr4djXLJSIiIiLNJfNjiKOW4YnA\ny8BPgPXNDAB3fz29komIiIhII8h8hRj4DLB59Hgl2maEMcYaXCUiIiIiFcl82jV3/727txY8Wtxd\nlWERERERqVjmK8QiIiIiItWkCrGIiIiINDVViEVERESkqalCLCIiIiJNTRViEREREWlqqhCLiIiI\nSFNThVhEREREmpoqxCIiIiLS1FQhFhEREZGmpgqxiIiIiDQ1VYhFREREpKmpQiwiIiIiTU0VYhER\nERFpaqoQi4iIiEhTU4VYRERERJqaKsQiIiIi0tRUIRYRERGRpqYKsYiIiIg0tZXSLkApzOwOYEdg\nfaAdeBA40d3nJ32u/fZbK+lDiog0FMVJEWk09dJC/BDwZWAr4EBgC+CPqZZIRERERBpCXVSI3f1/\n3X2qu7/i7pOBc4BdzKy12udua2ur9ilqppGuBRrrenQt2dRI11JNjXSfdC3ZpGvJpka6lrqoEOcz\nsyHA14DH3b2j2udrpDe7ka4FGut6dC3Z1EjXUk2NdJ90Ldmka8mmRrqWuqkQm9k5ZvYe8BbwEeCA\nlIskIiIiIg0gtUl1ZnY2cGIfuziwrbs/H/18LnAVMBQ4BbgB2Levc4wbN45BgwZ12zZ27FjGjh0b\nt9giIqloa2vr0RqzcOHCio+rOCkijaDSGJlmlonzgWv72eel3H/cfQGwAHjBzJ4FXjGznd19Sm8v\nHj9+PCNHjkyksCIiaSpWSZ0+fTqjRo2q6LiKkyLSCCqNkalViN39beDtmC/PTaYb0MvzqwLMnj07\n5uG7LFy4kOnTp1d8nCxopGuBxroeXUs2Zf1a8mLcqjFerjhZhK4lm3Qt2ZT1ayknRpq7V7c0FTKz\n0cDHgMcIOYiHAacD6wHbufuHRV5zCHBTLcspIpKir7n7hHJeoDgpIk2k3xhZDxXi7YD/BbYHVgfm\nA/cBZ/a2MIeZrQN8DpgLvF+bkoqI1NyqwGbAA1GvW8kUJ0WkCZQcIzNfIRYRERERqaa6SbsmIiIi\nIlINqhCLiIiISFNThVhEREREmpoqxCIiIiLS1FQhLoOZ3WFm/zKzpWb2qpldb2YbpV2ucpnZUDO7\nysxeMrMlZjbHzE41s5XTLlscZvZTM3vczBab2YK0y1MOM/uemb0cfaYmm9nH0i5THGa2m5ndaWb/\nMbNOM/tC2mWKw8xONrOpZrbIzF43sz+b2VZpl6teKEZmk2Jk+holRkLjxklViMvzEPBlYCvgQGAL\n4I+pliiebQADjgCGA+OAo4Az0yxUBVYGbgEuS7sg5TCzg4ELCEuR7wQ8AzxgZuumWrB4VgeeBo4h\nLLter3YDLgZ2Bj5N+Gz9xcxWS7VU9UMxMpsUI9PXKDESGjROKu1aBcxsP+DPwAB370i7PJUwsxOA\no9x9WNplicvMvgGMd/chaZelFGY2GZji7j+IfjbgFeAidz831cJVwMw6gQPc/c60y1Kp6A/vG8Du\n7v5Y2uWpN4qR2aIYmQ2NFCOhceKkWohjMrMhwNeAx+s90EfWBuqqK62eRV2vo4C/5rZ5+Hb6ILBr\nWuWSHtYmtObod6NMipFSCcXIutIQcVIV4jKZ2Tlm9h7wFvAR4ICUi1QxMxsGHAtcnnZZmsi6QCvw\nesH214ENa18cKRS1Rl0IPObus9IuT71QjJSEKEbWgUaKk01fITazs6MB7r09OgoGi58L7Ah8BugA\nbkil4EXEuBbMbBPCUtg3u/s16ZS8pzjXIpKwSwnjR7+adkHSpBipGCnSh4aJkyulXYAMOB+4tp99\nXsr9x90XELoFXjCzZ4FXzGxnd59SxTKWqqxrMbONCZNgHnP371azYDGUdS116C1CZWGDgu0bAK/V\nvjiSz8wuAfYBdnP3+WmXJ2WKkYqRaVCMzLhGi5NNXyF297eBt2O+vDX6d0BCxalIOdc/t/vqAAAN\ntElEQVQStXo8BDwFfLua5Yqjwvcl89z9QzObBnwKuBNWdD19CrgozbI1uyjI7w/s4e7z0i5P2hQj\nFSPToBiZbY0YJ5u+QlwqMxsNfAx4DGgHhgGnA3OAJ1MsWtmiVo+JwMvAT4D1Q5wBdy8cr5V5ZvYR\nYAgwFGg1sx2ip15w98XplaxfvwGui4L+VEJqp4HAdWkWKg4zW53wO2HRps2j92GBu7+SXsnKY2aX\nAmOBLwCLzSzXOrXQ3d9Pr2TZpxiZXYqR6WuUGAmNGyeVdq1EZrYd8L/A9oR8gvMJ48rOrLeugij1\nTuFYOCNM4m0t8pJMM7Nrga8XeeqT7v5orctTDjM7hvAHdwNCjsrvu/vf0i1V+cxsD+BheubX/L27\nZ651rTdROqRiQfFb7n59rctTTxQjs0sxMn2NEiOhceOkKsQiIiIi0tSaPsuEiIiIiDQ3VYhFRERE\npKmpQiwiIiIiTU0VYhERERFpaqoQi4iIiEhTU4VYRERERJqaKsQiIiIi0tRUIRYRERGRpqYKsYiI\niIg0NVWIRURERKSpqUIsIiIiIk1NFWIRERERaWqqEIuIiIhIU1OFWERERESamirEItIwzKzTzH5Z\npWOfamadSZcj2veimGX6RvT6TjPrMLMhcY6TJjOba2bX1Ohc34zu1aZ52yaa2cO1OH/eOfeIyrF7\n3rY/572X/6hleUREFWIRKZGZjTCzP0UVmKVm9m8z+4uZHZt22WrEgZIqxIXMbFczO8XM1kq4TBDK\n9QPgMODdMst1spntX4UylcNrfK7C88V+XxMoS77fAIcCz6ZQFpGmpwqxiPTLzD4OPAWMAH4HfA+4\nEugAjkuxaLV0BjAw5ms/DvwSWDu54nRzh7tPcPcPy3zdT4G0K8Rp+wzwubQL4e6T3H0C8HraZRFp\nRiulXQARqQs/A94B/tvdu7VCmtm66RSptty9E/gg5sstybJIcWY2APjA3UtudXb35VUskojUCbUQ\ni0gpNgdmFlaGAdz9rcJtZnaomf3NzJaY2dtm1mZm/69gn4lm9g8zG2lmj0f7vmRm3y3Yb2UzOz06\n3jtm9p6ZPWpme8a5EDN708zOz/vZouN+mD+kwcxOjLYNjH7uMYbYzFYxs/Fm9oaZLTKz281sk4J9\nTgHOjX6cmzfed9OC/fY3sxlm9r6Z/dPMKmq1NLNhZnarmc2Phri8Er0Pa0bPdxJavHPjajtzY3nN\nbFMzu9TMno3el7fM7BYzG1pwjtwY5o+b2W+i+/Cemd1mZusUKdPPo3IsNrO/mtnwIvsMNrPzo8/G\nu2a20MzuNbPtC/bLjcM92Mx+ZWb/BhYDuesbbmYPReV/xcx+RpG/edHn8KG8n1/Oux+Fj/wxvxub\n2TVm9lree/atIsffJPpcvGdmr5vZb4AB6EuSSKaohVhESvEvYBcz+6i7z+xrx6jicTrwB8KwivUI\nwyoeMbOd3H1RtKsDQ4B7gFuACcBXgMvMbJm7XxfttxbwbaCNMFxjTeA7wP1mNtrdy52A9Diwe97P\n20fn6AA+AdwXbf8fYLq7L8krb2HL49XAIcBNwJPAXtH15O93G7AV8FXCWN+3o+1v5u2zG3AgcClh\nHPBxwJ/MbFN3by/z+jCzlYG/ACsDFwGvAZsA+xKGbbxLGK96NTCFcF8BXoz+/RiwC+Ge/xvYDDgG\neNjMhrv7+wWnvBhYAJwa7TsOuAQYm1emMwg9DXcT7vHIvDLm2xz4AvBH4GVgA+C7wMTo3K8V7P8L\nYBlwHqGi+YGZbQBMJFSAzwKWAEcCheWGnu/pD4A1Crb9CNiB6L0zs/UJ962DcH/fAsYAV5vZmu5+\nUbTfqsBDwP8D/heYTxjrvVeR84pImtxdDz300KPPB/BpwnCBDwkVynMIYy9XKthv02ifEwu2D49e\nf1LetocJFYof5G1bGZhOqDi0RtusyHnWiva5smB7J/DLfq7l+Kgsq0c/Hwu8RKjQnpV3zgXA+Xmv\nOwXoyPt5++h8FxUc/8boun5ZcM4OYNMi5ekElgKb5W0bEW0/pp9r+Uax4xIqb53AF/t5/bvANUW2\nDyiybXR0zK8VnL8TuL9g3wuie7xm9PO6hMroHQX7/Sp6/TV521Yucu5No3v0s7xte0SvnQOsUrD/\n+Oi+jMrbtg7QXni/os/hQ33coy9H5/lp3rarCF8U1i7Yd0L0uRkQ/fyD6HwH5u2zKvB8tH33Iud7\nGPhH0r/DeuihR98PDZkQkX65+4PArsAdhIrgj4EHgP+Y2X55u36JUJn8o5mtk3sAbxAqLp8sOPRy\nulon8TAp7ApgfWBUtM09GucZDW8YDKwC/I3QyliuSYTesY9HP+8WbZsU/R9ChXTtaFtv9iG08l1c\nsP1Cyu8O/z93n5v7wd1nAIsIraVxLIz+3dvMViv3xe6+LPd/M1vJQjq3lwjjyAvvuZP3HkYmAa1A\nbojFpwlfdordq8Jzr5gYaGYt0bmXAM8VOTfAde5eOLZ7DDDZ3aflHfdtQkt+yaIhHVcDf3b3s/Ke\nOhC4C2gt+Jz/hfC5yZVzDDDf3W/LK8f79LxfIpIyVYhFpCTuPs3dDwIGE1oLzyJ0Lf/RzLaJdhtG\niCsvEIYE5B5vANsQKrr5XnX3pQXbnidUKDfLbYjGqj5DaGV8Ozre54FBMS5lOqGClav85leI/9vM\nVom2OfBYH8cZSmg5fLFg+3MxyvRKkW3thHtdtqhyfQFwOPCWmd1vZsdYiWnfzGxVC+O25xGGI7xF\nuOeDKH7PC8ufG+aRK3+uYvxCQTnfyts3d24zs3Fm9nzBuUf0cu65RbYNJXwBK1TyexONtb6NcG3f\nyNu+HqHSeyTdP+NvAtcQPje5z/lQCq653HKISG1oDLGIlCVqrZ0GTDOzOcC1hG7lMwiV4U5gb4rn\ndn2v3POZ2aHROW4jTE57g9Dd/FNitKC6+3IzmwLsbmZbABsCjxIqNCsDOxPGDz8btSrWQkcv22NP\nvHL3H5vZdYS0ap8ljHU9ycx2cfdX+3n5JYRK4HhgMqHF2YGbKd6QUqz8Rrzy58agXwX8nDAEoZMw\nBrfYuQu/UCXl94TPxsfcPf9zmyvDjdE+xWhhDZE6owqxiFTib9G/G0X/vkioBM1192ItY4U2NrPV\nClqJtyZUvl6Ofv4S8GLUOr2CmZ0ev9hMAn5C6Mp/092fj445kzDhbjdCl3hf/kWoHG1B99bIbYrs\nm8oEKg8TIGcCZ5nZLsATwFGEnMh9letLhKEIP8ltsJDSrJw8yvnH/lf075bktehaSNlX2Ar+JcKY\n3iPzN5rZ2nSfiNiXf0XnKlTsvenBzE4iTOz7orsXtjS/SRh73eruD/V4cc9yfDRuOUSkdjRkQkT6\nZb2nOPt89G9uda3bCK15p/RynMKlhVciVNByz69MyCjwJmFoAxRpfTSznQljmuOaRJjc9EO6D4t4\njJAFYCP6Hj8MIVOC0XNhkh/Ss6K5OPq3WgtzdGNma5pZa8HmmYT3ZkBBuYqVqYOefx+OI4wLjuNB\nwnjx7xdsH9fLubu1LJvZlwlZMkp1LyEryn/nHWM9QkaQPpnZpwm9Hb9y9x5fijzko74V+JKZ9ajs\nWve83PcSvvR9Ke/5gcARZVyLiNSAWohFpBQXR3/I/0yo/K5CSFH2FcJkq+sA3P0lM/s5oUXyv4Db\nCa1pmwMHECbM/SbvuK8CPzGzzQhjh79KmLR3hLvnKsJ3Awea2e2ElGabEyrNM+mZHqtUTxIqaFtF\nZcp5FDiaUKHts0Ls7s+YWRtwTNR6+QTwKUKLceFQgWnRtrPM7A+ETBx3Fhk/nZS9gEvM7I+E+7oS\n8HXCNd9aUK5Pm9k4wnvxsrtPJdzzw8xsETCL8OXjU4TxvIV6GxaxYru7v2Uh9/NJZnY3oaK4E2Fo\nTWGr793ALyzkRH6CMHb4a/Qcq92XcwlfbB4ws/8ljBk/gtA6vX0fr4OQau4N4EUz+1rBc39x9zeB\nk4A9gSlmdiXhHg0hTATdi5BVA0LawWOBG6LKeS7t2mJEJFNUIRaRUhxPGCc8hlCxWAWYRxhreqZ3\n5RbG3X9tZs8RWv9yXfOvAPcDdxYct50wVvUSwgSw14Hvufs1ece7Lsor+13CWNhZhArSV+ieTxiK\n5wruwd2XmNnfCRWY/BbiSdHr57l7sYluhcf+FqHy9DXCWN2/ElrNX8nf193/Fn1ROIqwTHAL8F+E\ne9hbmUu6ll48Q7jf+xJaVpdE2/aOKrw5PyJ8ITgDWI0wJnYqIV3YckKL6qqEe/RpQmaRwjL1VsZu\n2939Z2a2lHAP9iSMTf4sPfM2n0VYMOQQwns8jZDR45xSz+3ur0W9GhcDJxImYl5GyMd8VT9lzfVi\nXFdkv08Shti8YWajCZ/vLxK+RL1N+JK2YpiJuy81s72ichxLeB9uJLw39xcru4ikw9yVG1xEas/M\nHgbWcff+WuykF2b2DUJmg1HAKzWcBCgJM7M1CF8+bgfW0u+FSG1pDLGISP2bDrxRZIy21I8bCL0N\nlYyNF5GYNGRCRKR+3U9YMTBnYW87Sub9gq6FS8pOTygilVGFWETSpDFbFXD31wnjrqXOufs/0y6D\nSDPTGGIRERERaWoaQywiIiIiTU0VYhERERFpaqoQi4iIiEhTU4VYRERERJqaKsQiIiIi0tRUIRYR\nERGRpqYKsYiIiIg0NVWIRURERKSpqUIsIiIiIk3t/wMTJ6nkIvB6tAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa00de80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from itertools import product\n",
    "\n",
    "all_clf = [pipe1, clf2, pipe3, mv_clf]\n",
    "\n",
    "x_min = X_train_std[:, 0].min() - 1\n",
    "x_max = X_train_std[:, 0].max() + 1\n",
    "y_min = X_train_std[:, 1].min() - 1\n",
    "y_max = X_train_std[:, 1].max() + 1\n",
    "\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),\n",
    "                     np.arange(y_min, y_max, 0.1))\n",
    "\n",
    "f, axarr = plt.subplots(nrows=2, ncols=2, \n",
    "                        sharex='col', \n",
    "                        sharey='row', \n",
    "                        figsize=(7, 5))\n",
    "\n",
    "for idx, clf, tt in zip(product([0, 1], [0, 1]),\n",
    "                        all_clf, clf_labels):\n",
    "    clf.fit(X_train_std, y_train)\n",
    "    \n",
    "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z = Z.reshape(xx.shape)\n",
    "\n",
    "    axarr[idx[0], idx[1]].contourf(xx, yy, Z, alpha=0.3)\n",
    "    \n",
    "    axarr[idx[0], idx[1]].scatter(X_train_std[y_train==0, 0], \n",
    "                                  X_train_std[y_train==0, 1], \n",
    "                                  c='blue', \n",
    "                                  marker='^',\n",
    "                                  s=50)\n",
    "    \n",
    "    axarr[idx[0], idx[1]].scatter(X_train_std[y_train==1, 0], \n",
    "                                  X_train_std[y_train==1, 1], \n",
    "                                  c='red', \n",
    "                                  marker='o',\n",
    "                                  s=50)\n",
    "    \n",
    "    axarr[idx[0], idx[1]].set_title(tt)\n",
    "\n",
    "plt.text(-3.5, -4.5, \n",
    "         s='Sepal width [standardized]', \n",
    "         ha='center', va='center', fontsize=12)\n",
    "plt.text(-10.5, 4.5, \n",
    "         s='Petal length [standardized]', \n",
    "         ha='center', va='center', \n",
    "         fontsize=12, rotation=90)\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.savefig('./figures/voting_panel', bbox_inches='tight', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'decisiontreeclassifier': DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=1,\n",
       "             max_features=None, max_leaf_nodes=None,\n",
       "             min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "             min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "             presort=False, random_state=0, splitter='best'),\n",
       " 'decisiontreeclassifier__class_weight': None,\n",
       " 'decisiontreeclassifier__criterion': 'entropy',\n",
       " 'decisiontreeclassifier__max_depth': 1,\n",
       " 'decisiontreeclassifier__max_features': None,\n",
       " 'decisiontreeclassifier__max_leaf_nodes': None,\n",
       " 'decisiontreeclassifier__min_impurity_split': 1e-07,\n",
       " 'decisiontreeclassifier__min_samples_leaf': 1,\n",
       " 'decisiontreeclassifier__min_samples_split': 2,\n",
       " 'decisiontreeclassifier__min_weight_fraction_leaf': 0.0,\n",
       " 'decisiontreeclassifier__presort': False,\n",
       " 'decisiontreeclassifier__random_state': 0,\n",
       " 'decisiontreeclassifier__splitter': 'best',\n",
       " 'pipeline-1': Pipeline(steps=[['sc', StandardScaler(copy=True, with_mean=True, with_std=True)], ['clf', LogisticRegression(C=0.001, class_weight=None, dual=False, fit_intercept=True,\n",
       "           intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "           penalty='l2', random_state=0, solver='liblinear', tol=0.0001,\n",
       "           verbose=0, warm_start=False)]]),\n",
       " 'pipeline-1__clf': LogisticRegression(C=0.001, class_weight=None, dual=False, fit_intercept=True,\n",
       "           intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "           penalty='l2', random_state=0, solver='liblinear', tol=0.0001,\n",
       "           verbose=0, warm_start=False),\n",
       " 'pipeline-1__clf__C': 0.001,\n",
       " 'pipeline-1__clf__class_weight': None,\n",
       " 'pipeline-1__clf__dual': False,\n",
       " 'pipeline-1__clf__fit_intercept': True,\n",
       " 'pipeline-1__clf__intercept_scaling': 1,\n",
       " 'pipeline-1__clf__max_iter': 100,\n",
       " 'pipeline-1__clf__multi_class': 'ovr',\n",
       " 'pipeline-1__clf__n_jobs': 1,\n",
       " 'pipeline-1__clf__penalty': 'l2',\n",
       " 'pipeline-1__clf__random_state': 0,\n",
       " 'pipeline-1__clf__solver': 'liblinear',\n",
       " 'pipeline-1__clf__tol': 0.0001,\n",
       " 'pipeline-1__clf__verbose': 0,\n",
       " 'pipeline-1__clf__warm_start': False,\n",
       " 'pipeline-1__sc': StandardScaler(copy=True, with_mean=True, with_std=True),\n",
       " 'pipeline-1__sc__copy': True,\n",
       " 'pipeline-1__sc__with_mean': True,\n",
       " 'pipeline-1__sc__with_std': True,\n",
       " 'pipeline-1__steps': [['sc',\n",
       "   StandardScaler(copy=True, with_mean=True, with_std=True)],\n",
       "  ['clf',\n",
       "   LogisticRegression(C=0.001, class_weight=None, dual=False, fit_intercept=True,\n",
       "             intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "             penalty='l2', random_state=0, solver='liblinear', tol=0.0001,\n",
       "             verbose=0, warm_start=False)]],\n",
       " 'pipeline-2': Pipeline(steps=[['sc', StandardScaler(copy=True, with_mean=True, with_std=True)], ['clf', KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "            metric_params=None, n_jobs=1, n_neighbors=1, p=2,\n",
       "            weights='uniform')]]),\n",
       " 'pipeline-2__clf': KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "            metric_params=None, n_jobs=1, n_neighbors=1, p=2,\n",
       "            weights='uniform'),\n",
       " 'pipeline-2__clf__algorithm': 'auto',\n",
       " 'pipeline-2__clf__leaf_size': 30,\n",
       " 'pipeline-2__clf__metric': 'minkowski',\n",
       " 'pipeline-2__clf__metric_params': None,\n",
       " 'pipeline-2__clf__n_jobs': 1,\n",
       " 'pipeline-2__clf__n_neighbors': 1,\n",
       " 'pipeline-2__clf__p': 2,\n",
       " 'pipeline-2__clf__weights': 'uniform',\n",
       " 'pipeline-2__sc': StandardScaler(copy=True, with_mean=True, with_std=True),\n",
       " 'pipeline-2__sc__copy': True,\n",
       " 'pipeline-2__sc__with_mean': True,\n",
       " 'pipeline-2__sc__with_std': True,\n",
       " 'pipeline-2__steps': [['sc',\n",
       "   StandardScaler(copy=True, with_mean=True, with_std=True)],\n",
       "  ['clf',\n",
       "   KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "              metric_params=None, n_jobs=1, n_neighbors=1, p=2,\n",
       "              weights='uniform')]]}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mv_clf.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, error_score='raise',\n",
       "       estimator=MajorityVoteClassifier(classifiers=[Pipeline(steps=[['sc', StandardScaler(copy=True, with_mean=True, with_std=True)], ['clf', LogisticRegression(C=0.001, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=0, solv...ski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=1, p=2,\n",
       "           weights='uniform')]])],\n",
       "            vote='classlabel', weights=None),\n",
       "       fit_params={}, iid=True, n_jobs=1,\n",
       "       param_grid={'decisiontreeclassifier__max_depth': [1, 2], 'pipeline-1__clf__C': [0.001, 0.1, 100.0]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "       scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "params = {'decisiontreeclassifier__max_depth': [1, 2],\n",
    "          'pipeline-1__clf__C': [0.001, 0.1, 100.0]}\n",
    "\n",
    "grid = GridSearchCV(estimator=mv_clf,\n",
    "                    param_grid=params,\n",
    "                    cv=10,\n",
    "                    scoring='roc_auc')\n",
    "\n",
    "grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.967 +/- 0.05 {'decisiontreeclassifier__max_depth': 1, 'pipeline-1__clf__C': 0.001}\n",
      "0.967 +/- 0.05 {'decisiontreeclassifier__max_depth': 1, 'pipeline-1__clf__C': 0.1}\n",
      "1.000 +/- 0.00 {'decisiontreeclassifier__max_depth': 1, 'pipeline-1__clf__C': 100.0}\n",
      "0.967 +/- 0.05 {'decisiontreeclassifier__max_depth': 2, 'pipeline-1__clf__C': 0.001}\n",
      "0.967 +/- 0.05 {'decisiontreeclassifier__max_depth': 2, 'pipeline-1__clf__C': 0.1}\n",
      "1.000 +/- 0.00 {'decisiontreeclassifier__max_depth': 2, 'pipeline-1__clf__C': 100.0}\n"
     ]
    }
   ],
   "source": [
    "cv_keys = ('mean_test_score', 'std_test_score','params')\n",
    "\n",
    "for r, _ in enumerate(grid.cv_results_['mean_test_score']):\n",
    "    print(\"%0.3f +/- %0.2f %r\"\n",
    "        % (grid.cv_results_[cv_keys[0]][r], \n",
    "           grid.cv_results_[cv_keys[1]][r] / 2.0, \n",
    "           grid.cv_results_[cv_keys[2]][r]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters: {'decisiontreeclassifier__max_depth': 1, 'pipeline-1__clf__C': 100.0}\n",
      "Accuracy: 1.00\n"
     ]
    }
   ],
   "source": [
    "print('Best parameters: %s' % grid.best_params_)\n",
    "print('Accuracy: %.2f' % grid.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Pipeline(steps=[['sc', StandardScaler(copy=True, with_mean=True, with_std=True)], ['clf', LogisticRegression(C=100.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "           intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "           penalty='l2', random_state=0, solver='liblinear', tol=0.0001,\n",
       "           verbose=0, warm_start=False)]]),\n",
       " DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=1,\n",
       "             max_features=None, max_leaf_nodes=None,\n",
       "             min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "             min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "             presort=False, random_state=0, splitter='best'),\n",
       " Pipeline(steps=[['sc', StandardScaler(copy=True, with_mean=True, with_std=True)], ['clf', KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "            metric_params=None, n_jobs=1, n_neighbors=1, p=2,\n",
       "            weights='uniform')]])]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_estimator_.classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Bagging - building an ensemble of classifiers from bootstrap samples\n",
    "import pandas as pd\n",
    "\n",
    "df_wine = pd.read_csv('https://archive.ics.uci.edu/ml/'\n",
    "                      'machine-learning-databases/wine/wine.data',\n",
    "                      header=None)\n",
    "\n",
    "df_wine.columns = ['Class label', 'Alcohol', 'Malic acid', 'Ash',\n",
    "                   'Alcalinity of ash', 'Magnesium', 'Total phenols',\n",
    "                   'Flavanoids', 'Nonflavanoid phenols', 'Proanthocyanins',\n",
    "                   'Color intensity', 'Hue', 'OD280/OD315 of diluted wines',\n",
    "                   'Proline']\n",
    "\n",
    "# drop 1 class\n",
    "df_wine = df_wine[df_wine['Class label'] != 1]\n",
    "\n",
    "y = df_wine['Class label'].values\n",
    "X = df_wine[['Alcohol', 'Hue']].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "le = LabelEncoder()\n",
    "y = le.fit_transform(y)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.40, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "tree = DecisionTreeClassifier(criterion='entropy', \n",
    "                              max_depth=None,\n",
    "                              random_state=1)\n",
    "\n",
    "bag = BaggingClassifier(base_estimator=tree,\n",
    "                        n_estimators=500, \n",
    "                        max_samples=1.0, \n",
    "                        max_features=1.0, \n",
    "                        bootstrap=True, \n",
    "                        bootstrap_features=False, \n",
    "                        n_jobs=1, \n",
    "                        random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision tree train/test accuracies 1.000/0.833\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "tree = tree.fit(X_train, y_train)\n",
    "y_train_pred = tree.predict(X_train)\n",
    "y_test_pred = tree.predict(X_test)\n",
    "\n",
    "tree_train = accuracy_score(y_train, y_train_pred)\n",
    "tree_test = accuracy_score(y_test, y_test_pred)\n",
    "print('Decision tree train/test accuracies %.3f/%.3f'\n",
    "      % (tree_train, tree_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bagging train/test accuracies 1.000/0.896\n"
     ]
    }
   ],
   "source": [
    "bag = bag.fit(X_train, y_train)\n",
    "y_train_pred = bag.predict(X_train)\n",
    "y_test_pred = bag.predict(X_test)\n",
    "\n",
    "bag_train = accuracy_score(y_train, y_train_pred) \n",
    "bag_test = accuracy_score(y_test, y_test_pred) \n",
    "print('Bagging train/test accuracies %.3f/%.3f'\n",
    "      % (bag_train, bag_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IhKKkQkREREREQlFSISIiIiIioSipEBERERGRUJRUiIiIiIhIKEoqREREREQkFCUVIiIi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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb63fba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_min = X_train[:, 0].min() - 1\n",
    "x_max = X_train[:, 0].max() + 1\n",
    "y_min = X_train[:, 1].min() - 1\n",
    "y_max = X_train[:, 1].max() + 1\n",
    "\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),\n",
    "                     np.arange(y_min, y_max, 0.1))\n",
    "\n",
    "f, axarr = plt.subplots(nrows=1, ncols=2, \n",
    "                        sharex='col', \n",
    "                        sharey='row', \n",
    "                        figsize=(8, 3))\n",
    "\n",
    "\n",
    "for idx, clf, tt in zip([0, 1],\n",
    "                        [tree, bag],\n",
    "                        ['Decision Tree', 'Bagging']):\n",
    "    clf.fit(X_train, y_train)\n",
    "\n",
    "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z = Z.reshape(xx.shape)\n",
    "\n",
    "    axarr[idx].contourf(xx, yy, Z, alpha=0.3)\n",
    "    axarr[idx].scatter(X_train[y_train == 0, 0],\n",
    "                       X_train[y_train == 0, 1],\n",
    "                       c='blue', marker='^')\n",
    "\n",
    "    axarr[idx].scatter(X_train[y_train == 1, 0],\n",
    "                       X_train[y_train == 1, 1],\n",
    "                       c='red', marker='o')\n",
    "\n",
    "    axarr[idx].set_title(tt)\n",
    "\n",
    "axarr[0].set_ylabel('Alcohol', fontsize=12)\n",
    "plt.text(10.2, -1.2,\n",
    "         s='Hue',\n",
    "         ha='center', va='center', fontsize=12)\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.savefig('./figures/bagging_region.png',\n",
    "#            dpi=300,\n",
    "#            bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Leveraging weak learners via adaptive boosting\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "\n",
    "tree = DecisionTreeClassifier(criterion='entropy', \n",
    "                              max_depth=1,\n",
    "                              random_state=0)\n",
    "\n",
    "ada = AdaBoostClassifier(base_estimator=tree,\n",
    "                         n_estimators=500, \n",
    "                         learning_rate=0.1,\n",
    "                         random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision tree train/test accuracies 0.845/0.854\n"
     ]
    }
   ],
   "source": [
    "tree = tree.fit(X_train, y_train)\n",
    "y_train_pred = tree.predict(X_train)\n",
    "y_test_pred = tree.predict(X_test)\n",
    "\n",
    "tree_train = accuracy_score(y_train, y_train_pred)\n",
    "tree_test = accuracy_score(y_test, y_test_pred)\n",
    "print('Decision tree train/test accuracies %.3f/%.3f'\n",
    "      % (tree_train, tree_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AdaBoost train/test accuracies 1.000/0.875\n"
     ]
    }
   ],
   "source": [
    "ada = ada.fit(X_train, y_train)\n",
    "y_train_pred = ada.predict(X_train)\n",
    "y_test_pred = ada.predict(X_test)\n",
    "\n",
    "ada_train = accuracy_score(y_train, y_train_pred) \n",
    "ada_test = accuracy_score(y_test, y_test_pred) \n",
    "print('AdaBoost train/test accuracies %.3f/%.3f'\n",
    "      % (ada_train, ada_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dZ0Y9n2K0iEhxRInPDdbyi4iIiIiIZKOkQkREREREIin7MRXl4oknPip1EUREimbIkB1L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\nqZAkSZI0ik2FJEmSpFH+Bx4kCOom6mpjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa0d86d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_min, x_max = X_train[:, 0].min() - 1, X_train[:, 0].max() + 1\n",
    "y_min, y_max = X_train[:, 1].min() - 1, X_train[:, 1].max() + 1\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),\n",
    "                     np.arange(y_min, y_max, 0.1))\n",
    "\n",
    "f, axarr = plt.subplots(1, 2, sharex='col', sharey='row', figsize=(8, 3))\n",
    "\n",
    "\n",
    "for idx, clf, tt in zip([0, 1],\n",
    "                        [tree, ada],\n",
    "                        ['Decision Tree', 'AdaBoost']):\n",
    "    clf.fit(X_train, y_train)\n",
    "\n",
    "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z = Z.reshape(xx.shape)\n",
    "\n",
    "    axarr[idx].contourf(xx, yy, Z, alpha=0.3)\n",
    "    axarr[idx].scatter(X_train[y_train == 0, 0],\n",
    "                       X_train[y_train == 0, 1],\n",
    "                       c='blue', marker='^')\n",
    "    axarr[idx].scatter(X_train[y_train == 1, 0],\n",
    "                       X_train[y_train == 1, 1],\n",
    "                       c='red', marker='o')\n",
    "    axarr[idx].set_title(tt)\n",
    "\n",
    "axarr[0].set_ylabel('Alcohol', fontsize=12)\n",
    "plt.text(10.2, -1.2,\n",
    "         s='Hue',\n",
    "         ha='center', va='center', fontsize=12)\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.savefig('./figures/adaboost_region.png',\n",
    "#           dpi=300,\n",
    "#           bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}