decisionstats/decision trees with plot and bagging

## decision trees with plot and bagging
{
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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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   "outputs": [],
   "source": [
    "# Bagged Decision Trees for Classification\n",
    "import pandas\n",
    "from sklearn import cross_validation\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data\"\n",
    "names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']\n",
    "dataframe = pandas.read_csv(url, names=names)\n",
    "array = dataframe.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>preg</th>\n",
       "      <th>plas</th>\n",
       "      <th>pres</th>\n",
       "      <th>skin</th>\n",
       "      <th>test</th>\n",
       "      <th>mass</th>\n",
       "      <th>pedi</th>\n",
       "      <th>age</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   preg  plas  pres  skin  test  mass   pedi  age  class\n",
       "0     6   148    72    35     0  33.6  0.627   50      1\n",
       "1     1    85    66    29     0  26.6  0.351   31      0\n",
       "2     8   183    64     0     0  23.3  0.672   32      1\n",
       "3     1    89    66    23    94  28.1  0.167   21      0\n",
       "4     0   137    40    35   168  43.1  2.288   33      1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    500\n",
       "1    268\n",
       "Name: class, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pandas.value_counts(dataframe[\"class\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   6.   ,  148.   ,   72.   , ...,    0.627,   50.   ,    1.   ],\n",
       "       [   1.   ,   85.   ,   66.   , ...,    0.351,   31.   ,    0.   ],\n",
       "       [   8.   ,  183.   ,   64.   , ...,    0.672,   32.   ,    1.   ],\n",
       "       ..., \n",
       "       [   5.   ,  121.   ,   72.   , ...,    0.245,   30.   ,    0.   ],\n",
       "       [   1.   ,  126.   ,   60.   , ...,    0.349,   47.   ,    1.   ],\n",
       "       [   1.   ,   93.   ,   70.   , ...,    0.315,   23.   ,    0.   ]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "X = array[:,0:8]\n",
    "Y = array[:,8]\n",
    "num_folds = 10\n",
    "num_instances = len(X)\n",
    "seed = 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(X)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   6.   ,  148.   ,   72.   , ...,   33.6  ,    0.627,   50.   ],\n",
       "       [   1.   ,   85.   ,   66.   , ...,   26.6  ,    0.351,   31.   ],\n",
       "       [   8.   ,  183.   ,   64.   , ...,   23.3  ,    0.672,   32.   ],\n",
       "       ..., \n",
       "       [   5.   ,  121.   ,   72.   , ...,   26.2  ,    0.245,   30.   ],\n",
       "       [   1.   ,  126.   ,   60.   , ...,   30.1  ,    0.349,   47.   ],\n",
       "       [   1.   ,   93.   ,   70.   , ...,   30.4  ,    0.315,   23.   ]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
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      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=3, max_features=None,\n",
       "           max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "           splitter='best')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtr = tree.DecisionTreeRegressor(max_depth=3)\n",
    "dtr.fit(X, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# from sklearn.metrics import roc_curve, auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#!sudo pip install pydotplus\n",
    "# http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html\n",
    "# http://machinelearningmastery.com/ensemble-machine-learning-algorithms-python-scikit-learn/\n",
    "# http://machinelearningmastery.com/compare-machine-learning-algorithms-python-scikit-learn/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#!pip freeze\n",
    "#checking if we have the right packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#!pip install --upgrade pip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#!pip install pydotplus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pydotplus as pydot\n",
    "\n",
    "from IPython.display import Image\n",
    "\n",
    "from sklearn.externals.six import StringIO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Graphviz\n",
    "#sudo add-apt-repository ppa:gviz-adm/graphviz-dev\n",
    "# sudo apt-get update\n",
    "# http://www.graphviz.org/Download_linux_ubuntu.php"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "dot_data = StringIO()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "tree.export_graphviz(dtr, out_file=dot_data,feature_names=names[:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "graph = pydot.graph_from_dot_data(dot_data.getvalue())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MScaECRMylB8+fNiYM2fOXa8fERFhjBgxwrC1tTWqV69u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87h2LFjnCgj0iNjzxu6XOdv3LiB+fPnY968ebCzswMA2NnZYezYsZg5cyaGDx9e6DEEUWnCxzCJ\nTNyNGzewYsUKrbLevXvD3t4eCQkJAIChQ4fmuMilpaVh9+7d8PDwMFisRGQcdMkbhSEimD9/PqZO\nncpHMIlKmK7X8NDQUBw4cAAuLi7o1KmTocMkIiOiy3V+y5YtyMzMRK9evbTq9ezZEykpKfD39+cY\ngkwaJ8uoVDh16hTefvttjB07FlOnToWVlRWePHkCAIiNjYWHhwemTZuGESNGoFu3bvj9998BAMnJ\nydi8eTO8vLzQpUsXREZGom3btqhfvz4iIiIQExODgQMHwt7eHs2bN8fp06cBPB/YRUZG4rPPPkOD\nBg1w9+5dDBo0CFWrVsXrr7+OnTt35htvamoqlixZgtGjR6N9+/Z47733cP78eZ3O51UPHjzApUuX\n8t1u3LiRZyzvvPMOatasmaM8PT0d3bp1y3O/Q4cOwcHBAS1atMj3XImMFfOG4fNGXvz8/DBkyBDY\n2toWel8yD+yvRe+vucntGh4YGAjg+Z2g3bt3h42NDdq1a4fQ0NBCHZvIWDBv6Pc6HxERAQBwcHDQ\nqlO3bl0AwLlz53I9NscQZDKEyICCg4OlKM3O0dFR7OzsJCsrS0REPDw8JD4+XkREmjRpIo0aNRIR\nkfT0dLG1tZVWrVqJiEhWVpZcvnxZAEjlypUlNDRUoqOjBYDUr19fli5dKg8fPpSzZ88KAOnRo4eI\niGRmZkpISIiUK1dOAMjEiRPl6NGjsmXLFrGxsREAEhERoYkPgDRr1kzz85gxY+TixYuan52dnaV6\n9ery6NGjAs/nVcuWLRMA+W5du3Yt1PsZEREh5cqVkzNnzuRZx9vbW+bMmZPn66+ec1EUtT2QeQEg\nwcHBhd6PecOweSOvnHD8+HFZsWKF5udmzZoVq98XtT2QfpTU/wf7a8n219yu4fXr1xcAsnz5crl9\n+7ZERkaKg4ODAJATJ04U6vh5Yf+kouD4wDjyxqvX+datWwsASUlJ0aqXnJwsAKRTp065HqegMURB\nOD4gI7GdrZAMqqjJz97eXgDIunXrRK1Wy7lz5+Thw4ciIrJixQoJCgoSkecXv0aNGolKpdLsq1ar\nc1ysateurRWHWq0We3t7sbW11fq9jo6OAkCePn2qKVu5cqUAkCFDhmjKXj5+VFRUnhetkJCQAs9H\n3zIyMqR79+6a9yw3ycnJYmNjI9HR0XnW4WQZGUpRB1/MGyVHl7yRW064f/++fPTRR5o//EU4WWZq\nSur/g/215OR1DS9btqzUrFlTq+z7778XADJs2LAS+d3sn1QUHB8onzdyu85369ZNAEhqaqpW3ZSU\nFAEg7dq1y3EcXcYQBeH4gIzEdj6GSaXCunXrYGNjgwkTJqBr165IS0vTPM7z6aefws3NDWvXrsXC\nhQuRlpaGzMxMzb65rY9TqVIlrZ8tLCzw2muv4dGjR1rllpbPu0jFihU1Ze7u7gCAy5cv5xrrqVOn\n0LJlS4hIjq1///4Fno++zZ07F7169YKXl1eedfbv34969eqhZcuWBomJSB+YN0qOLnkjNxMmTMDw\n4cMRGxureSwkLS0NAHDp0iV+pTxpsL+WnLyu4TVr1oS1tbVWmZOTEwAgJibGILERlSTmjZKT23W+\nefPmAICHDx9q1U1KSgIA1K5dO8dxOIYgU8LJMioVPDw88Ntvv6F3796IjIxEly5dsGnTJgDAyZMn\n8cYbb6BRo0b4/PPPYWNjo9dYsi8M2c/rv+rBgwe4du0akpOTc7yWlZUFIP/zye14JbWWSUhICCpW\nrIjZs2fnWy84OJiLclKpx7xh2LyRm3379qFnz55o0aKFZrt+/ToAoEWLFnBxcSn0Mck0sb+W3Jpl\neV3DHR0dkZCQABHRlGUvzF21alWdj09kLJg39Hudb9WqFQDg9u3bWuV37twB8Hzds1dxDEEmxaA3\nspHZK+pttZ9//rnm30FBQQJA6tSpIyLPH+nJ/rfI37dGq9VqTRleuc06t8eAdC27deuWABA/P79c\nj79t2zYBoBWziEh0dLSsWrWqwPN5VUmtSXDo0CFZt25djvJjx45p/fzkyRMpX768nD9/Pt/jvfqe\nFgVvsyZdoIiP9TBvGC5vvHo++eFjmKalpP4/2F9LZu2h/K7hAQEBAkDOnj2b41xnzZql0/ELwv5J\nRcHxgXFe5x88eCC2trayfPlyrdeWLl0q1tbWEhcXp1Wu6xiiIBwfkJHgmmVkWEVNfuXLl5fExEQR\neb5IZ+XKlaVDhw4iIlK5cmUBIIcPH5bNmzdLtWrVBIBERUVJXFyc5rn6pk2bao7XqFEjASCPHz/W\nlGUvfJuZmakpy74YZmRkaMoCAwOlXbt2kp6eLiJ/L3JZv359ERFJTU2Vhg0bCgD56KOPZPPmzeLr\n6yvOzs6aBTzzOx99CA8PFycnJ/Hz89Nsq1evlilTpoivr69W3S1btkjz5s21/ph4VfY5N2nSpFhx\n8WJIuijq4It5o3gKkzcKkxM4WWZaSur/g/21ZOR3Dc/IyJBWrVqJl5eX5nU/Pz+pUaOGJtbiYv+k\nouD4wHiv84sXLxZHR0fNe/Lo0SNp0qSJzJ07N8fxdBlD6ILjAzISnCwjwypq8gMgbdu2lS+//FK8\nvb2lX79+cvXqVRERWbNmjeZiEhkZKatWrZIqVaqIu7u7REdHyyeffCIApEyZMhIeHi4HDx4UKysr\nASCTJk2S+/fvy+rVqzWfwixZskTu3bsnIn9fDJctWyb37t2T+Ph4+fLLL+XJkyciInLlyhWZNGmS\nZt+VK1dKYmKiXLt2Tdzc3MTOzk5q1KghY8eOlYSEBJ3Op6QdO3ZMypcvn+enTleuXNGq7+7unuNT\nr5cdOXJExo4dKwBEpVLJkiVL5Ndffy1SbLwYki6KOvhi3ii6wuSNwuYETpaZlpL6/2B/LRkFXcMT\nExNl1KhRMmLECPH19ZVhw4bJzZs3S+z3s39SUXB8UPD5lDRdr/NqtVr8/f1l+PDhMnPmTPHw8JD1\n69fnOiFWUP7RFccHZCS2W4i8tHABkZ5t374dQ4YMQWlpds2bN0dMTEypibe0KW3tgZRhYWGB4OBg\nDB48WOlQdMK8oV+lrT2YutL+/8H+WrJKe3sgZZS2vweZN/SrtLUHMlk7uMA/ERERERERERHRC5ws\nI8pH9jfWPH36VOFIiKi0YN4gKj3YX4mosJg3iMwDJ8uIcvH06VPMnDkTt27dAgBMnjwZkZGRCkdF\nRMaMeYOo9GB/JaLCYt4gMi8qpQMgMkY2NjZYtGgRFi1apHQoRFRKMG8QlR7sr0RUWMwbROaFd5YR\nERERERERERG9wMkyIiIiIiIiIiKiFzhZRmYhPj4e27dvx8KFC5UOhYhKAeYMIuPF/lk8IoLY2Fil\nwyBSHHOJfl29ehVff/01li5disuXLysdDlGhcbKMTN7Fixcxb948DBkyBN9//73S4RRIRLBx40Z4\nenrC19cXY8aMQVBQUInsJyLYtGkT3NzcMGPGDDg5OWHChAlISkrK87h+fn6wsLDQKktKSsKECRMw\ne/ZsTJkyBR9++CFu375dtBMmMjLmkjMAIDo6GgMGDMBrr70Ge3t7DB06NEdf1qXOX3/9hYCAAAwe\nPBidO3fONcbC5h6i3LB/Fq7vAX9fx7M3S0tLrF69WitG9k8yN+aSS3r06KHV/1/erly5ojm2v78/\n2rRpAxsbG7Ru3RoBAQEQkTyPm9v4INvjx48xceJEODs7480338TUqVPh6OhYtBMnUpIQGVBwcLAo\n0exSU1MFgDRr1szgv7uw5s6dK/Xr15fExEQREUlMTJT69evLqlWrir3funXrBICEhYWJiMiFCxcE\ngAwYMCDXY548eVLKly+v9X+WkpIiTZs2lYULF2rKNmzYIDVq1JBbt24V6lyVag9UugCQ4OBgg/5O\nc8gZ0dHRMnDgQNm1a5ecPXtWhg8fLgCkZ8+ehaqT7caNG3m+Z4XNPflRoj1Q3tg/86fP/pktv76X\nnp4unTt3li+//FKzLVu2TOLj4zV12D9JaRwfFKwoueTChQvSunVrWbZsmXz33Xea7eOPP5Y33nhD\nU2/atGkybNgwWbNmjUyePFnKlSsnAGT16tW5Hje38UG2+Ph4adu2rTg6OkpCQkKRzpXjAzIS29kK\nyaCUTH6l4WJ4/fp1UalUsmjRIq3yBQsWSIUKFeT+/fvF2q9z584CQHPxUqvVYm9vLzY2NjmOmZiY\nKL6+vtK0aVOt/7PFixcLAImJidGUpaeni52dnYwePbpQ58uLIelCqcGXKecMEZFVq1ZJcnKy5uf0\n9HSxtbWVihUrFqrOy/J6zwqTewrCwbhxYf/Mm77758vyej8CAwNl7dq1+cbJ/klK4/ggf0XNJVu3\nbpV79+7lKB85cqTMmzdPRETi4uLE29tb6/WDBw8KAGncuHGOffMaH4g8zx2urq5iaWkpkZGRhTrH\nl3F8QEZiOx/DJDIiW7ZsQWZmJnr16qVV3rNnT6SkpMDf379Y+1WtWhUA8PPPPwMAkpOTkZiYiJ49\ne2rtJyKYP38+pk6dmuMW66NHjwIA6tWrpymztrbGW2+9hR07duR7yzYRlayi5gwA8PHxQYUKFbTK\nMjMzMXr06ELV0YWuuYfIlOi7fxZErVZjyZIlmD59OpydnTF79mxcu3YtRz32TyLjVtRcMnToUNjb\n22uVpaWlYffu3fDw8AAA3LhxAytWrNCq07t3b9jb2yMhIUGrPL/xAQCEhobiwIEDcHFxQadOnQp9\nnkTGhpNlZNR27NiBqlWrwsLCArNmzdKU/+c//4GlpSXWr18PAIiNjYWHhwemTZuGESNGoFu3bvj9\n99/zPO63336reV4feP5s/YoVK7TKACA1NRVLlizB6NGj0b59e7z33ns4f/58nsd98OABLl26lO92\n48aNPPePiIgAADg4OGiV161bFwBw7ty5Yu23cuVKNGrUCFOmTMGNGzewZs0aTJ06NceaB35+fhgy\nZAhsbW1z/K74+HgAQGJiola5vb09Hj9+jLt37+Z5fkT6xpzxXEE541VqtRqzZ8/GqlWrsGrVqiLX\nyYuuuYdMG/vnc/ron7l5/PgxXFxc8PbbbyMyMhLz589H8+bNMW/ePK167J9U2jCXPFfYXAIAhw4d\ngoODA1q0aAEAeOedd1CzZs0c9dLT09GtWzetsvzGBwAQGBgI4PkH6t27d4eNjQ3atWuH0NBQneMj\nMioK39pGZqYot9WuXr1aAMj+/fs1ZTdu3BAvLy/Nz02aNJFGjRqJyN+PKrRq1UrrOHjlNutGjRrl\niOXVsjFjxsjFixc1Pzs7O0v16tXl0aNHuca6bNkyAZDv1rVr1zzPtXXr1gJAUlJStMqTk5MFgHTq\n1KnY+yUkJEiXLl2kTp068sknn+Q41vHjx2XFihWan5s1a6b1nnh5eQkACQwM1NpvxIgRAkDi4uLy\nPL9X8TZr0gUK+VgPc0bBOeNlu3btkm7dugkAadCggWzYsEHUanWh64jk/zhLQblHV4VtD6Rf7J/K\n90+Rgh8le/jwoSxYsECsrKwEgPj7+2u9zv5JSuL4QP+5JJu3t7fMmTMn3zoRERFSrlw5OXPmjKas\noPGBiEj9+vUFgCxfvlxu374tkZGR4uDgIADkxIkTOsfI8QEZCa5ZRoZVlOSXlpYmdevWFTc3N03Z\nrFmz5OzZs5qfV6xYIUFBQSIikpWVJY0aNRKVSqV1nFcvhrkl+ZfLoqKi8ryghYSEFOocdJX9B3Fq\naqpWeUpKigCQdu3aFXu/69evS79+/aRPnz4CQP79739LVlaWiIjcv39fPvroI83PIjnfpxMnToiF\nhYXUqlVLIiIi5OHDh/LDDz9IzZo1xcrKSjIyMnQ+X14MSReFHXwxZxScM16WmJgo0dHR4ufnp1mw\n97vvvit0HZH8B+z55Z7C4GDcuLB/5s1Q/VNE93WXvvnmGwEgbdu21Spn/yQlcXyQv5LIJSLPJ9ds\nbGwkOjo6zzoZGRnSvXt3zfsmotv4QESkbNmyotJNFQAAIABJREFUUrNmTa2y77//XgDIsGHDdIpR\nhOMDMhpcs4yMX5kyZeDj44PQ0FBcuXIF6enpiImJQdu2bTV1Pv30U7i5uWHt2rVYuHAh0tLSkJmZ\nWazfe+rUKbRs2RIikmPr379/cU8rV82bNwcAPHz4UKs8++vba9euXaz9Tpw4gbfeegsffvgh9uzZ\ngy5dumD58uWYPXs2AGDChAkYPnw4YmNjNbeFp6WlAQAuXbqEK1euoGPHjggLC0OtWrXg4uKCHj16\nICUlBWq1Gk5OTlCpVCXxVhAVGXNGwTnjZXZ2dmjZsiUmTpyIb7/9FgCwadOmQtfJT0G5h8wH+2fJ\n98/CGDNmDMqVK4fY2FhNGfsnlUbMJYXLJQCwf/9+1KtXDy1btsyzzty5c9GrVy94eXlpynQZHwBA\nzZo1YW1trXU8JycnAEBMTIxOMRIZE06WUakwZswYVKxYEWvWrMGePXs0i1JmO3nyJN544w00atQI\nn3/+OWxsbIr9Ox88eIBr164hOTk5x2tZWVl57lOcNQlatWoFALh9+7ZW+Z07dwA8X1egOPvNmDED\nDx48wLvvvouyZcti27ZtAKBZ22Hfvn3o2bMnWrRoodmuX78OAGjRogVcXFwAAK6urjhz5gyePn2K\n3377Dba2tkhISMDIkSPzPDciQ2LOyD9n5GXAgAEAng9CilPnVQXlHjIv7J/6658FsbKyQtWqVdGk\nSRNNGfsnlVbMJYXLJcHBwTneo5eFhISgYsWKOSbKdR0fODo6IiEhQevLvrK/YCD7i0SIShNOllGp\nYGtrizFjxiAgIADBwcF4//33tV7/4IMPkJGRAVdXVwDPF8MFkO83M2Yv1Jn9yYharcajR480+zVv\n3lyzgOfL/vjjD6xZsybXY3733XdaF5LctmHDhuUZ04gRI2Bra4uffvpJq/zIkSOwtraGt7e3puzl\nT8Z03S89PR3A339o161bF9WrV9e8F8+ePcvxKVmzZs0078mff/6ZI+anT59i6tSp6Natm9anUERK\nYs7IP2fkJfsP7759+xarzqsKyj1kXtg/9dc/C/LXX3/h9u3b8PT01JSxf1JpxVyiey55+vQpwsLC\ntPr+yw4fPoy//voL06dP1yo/fvy4zuMDb29vpKWl4bffftPsf//+fQBAx44d8zw/IqOlx2c8iXIo\nzjPoV69eFUtLS5k/f36O1ypXriwA5PDhw7J582apVq2aAJCoqCiJi4vTLIBZv359zT4DBw4UADJr\n1iyJjY2Vr776Suzs7ASAHDhwQJ4+fSoNGzYUAPLRRx/J5s2bxdfXV5ydnfNcwLMkLF68WBwdHeXx\n48ciIvLo0SNp0qSJzJ07V1NnwYIFUqVKFbl27Vqh9lu7dq0A0KxDcP36dQEgkydPzjOe3NYkyJaW\nliaDBw+Wpk2bys2bNwt9rlyTgHSBIq6Bw5yRf85YsWKF+Pv7S1JSkoiIpKamyoABA2Tw4MGadUl0\nqZMt+z1r0qRJjhiLknvyUtT2QPrB/pk/ffbPbHn1vTlz5sikSZPkjz/+EJHn6xu5ubnJwIEDJTMz\nU1OP/ZOUxvFBwYqaS7Jt2bJFmjdvnusXhISHh4uTk5P4+flpttWrV8uUKVPE19c313hyGx9kZGRI\nq1atxMvLS/N7/Pz8pEaNGpKYmKjzuXJ8QEaCC/yTYRU3+fn4+Mj9+/dzlK9Zs0YqV64sHTp0kMjI\nSFm1apVUqVJF3N3d5eTJkzJp0iTN4psrV66UxMREiYmJkY4dO0qFChXE2dlZYmJi5J133pHhw4fL\n1q1b5dmzZ3Lt2jVxc3MTOzs7qVGjhowdO1YSEhKK8xYUSK1Wi7+/vwwfPlxmzpwpHh4esn79eq2L\n21dffSX16tXTmqDSZT+1Wi1r1qyRDh06yKeffioDBw6Uzz//PMeCoS/La7LswoUL0qFDB/H29pa7\nd+8W6Vx5MSRdFGfwxZzxXG4544svvpDGjRtLlSpV5OOPP5bJkydLeHi41n661BEROXLkiIwdO1YA\niEqlkiVLlsivv/6qFWNhc09eOBg3Luyf+dNn/xTJv+8FBARI69atpUKFCuLl5SWjRo2Sffv25TgG\n+ycpjeODghU1l2Rzd3eXzz//PEf5sWPHNF8ektt25cqVXOPJa3yQmJgoo0aNkhEjRoivr68MGzas\n0B+oc3xARmK7hUg+96ESlbDt27djyJAh+d7+TMbt+vXrCAwMhJWVFdzc3NC6desiH4vtgXRhYWGB\n4OBgDB48WOlQyAiwPRgX/n/Qy9geqCj49yC9jO2BjMQOfm0dERVKgwYN8MUXXygdBhEREREREZFe\ncIF/IiIiIiIiIiKiFzhZRkRERERERERE9AIny4iIiIiIiIiIiF7gZBkREREREREREdELnCwjIiIi\nIiIiIiJ6gZNlREREREREREREL3CyjIiIiIiIiIiI6AWV0gGQedqxY4fOddVqNSwtOa9riqKiopQO\ngUqJqKgoWFhY6O34IqLX4xOZMn33Tyo5zHVkzAozPqDSJysrC1ZWVgXW4/iAjIWFiIjSQZD5+OWX\nX9CzZ09kZmYqHQoZCQcHB9y8eVPpMMiI1a1bF7du3VI6DDISKpUKR44cQbdu3ZQOhcD+SdrYP6ko\nOD6gV3F8QEZgB2/XIYPq1q0bMjIyICIFbv/3f/+HatWqoW3btoiLi9Npn9KyBQcHA4DicRjDxgsh\nFeTmzZt6a3979uyBSqXCrFmzFO8L+tguXLgAADh+/LjisZTUlpGRwYG4EdFn/1RiW7t2LWxsbPDs\n2TPFYynpLTMzEx4eHrC1tcWZM2fYP8loFGZ8YMqbKY8PHjx4AGdnZ5QrVw7//e9/C6zP8QEZA06W\nkVFav349evXqBScnJ0RERKBu3bpKh0REJubIkSMYOnQoxo4di/nz5ysdjl60atUKjo6O2Ldvn9Kh\nEJUKYWFh6N27N8qWLat0KCXOysoKW7ZsQefOndGnTx9cvHhR6ZCIyExUrVoVBw4cgI+PD0aOHInx\n48fzTkIyepwsI6OSmZkJHx8ffPzxx/j000+xbds2VKhQQemwiMjEnDx5EgMHDsQ//vEPrFmzRulw\n9MrNzQ179+5VOgwio5eamoqff/4Z/fr1UzoUvSlTpgx27tyJZs2aoXfv3rh+/brSIRGRmbCyssLi\nxYsRFBSEzZs3o1+/fkhKSlI6LKI8cbKMjEZiYiL69OmDjRs3YufOnVi8eDEXoSWiEhcdHY2+ffui\nc+fO+O6770z+C0Tc3d1x8eJFxMbGKh0KkVELDw9Hamoq+vTpo3QoelWhQgWEhoaievXqcHZ2xp07\nd5QOiYjMiJeXF44dO4aYmBh06NBBs2QEkbEx7REClRqXL19G165dcenSJRw9ehTvv/++0iERkQmK\ni4uDq6srmjZtil27dqFMmTJKh6R377zzDuzt7fkoJlEBwsLC0L59e9SuXVvpUPTO1tYWBw8ehEql\ngouLCxITE5UOiYjMSJs2bXD69GnUq1cPnTt3xs6dO5UOiSgHTpaR4g4dOoSOHTvCzs4Op0+fxltv\nvaV0SERkghISEtC7d29UrVoVYWFhqFixotIhGYSVlRVcXV05WUaUDxHB/v37TfoRzFdVq1YN4eHh\nePLkCfr27YsnT54oHRIRmRF7e3scPnwY//rXv+Dp6Ynp06dDrVYrHRaRBifLSFHr169H//794erq\niv/973+oWbOm0iERkQl69OgRXFxcoFarcejQIdjZ2SkdkkENGDAAx48fx/3795UOhcgo/frrr7h1\n6xb69++vdCgG5eDggPDwcNy4cQMDBw7Es2fPlA6JiMyISqXC4sWL8c0332DlypVwc3PDo0ePlA6L\nCAAny0gh6enpGD16ND7++GP4+voiKCgI5cuXVzosIjJBKSkp6N+/P+7fv4/w8HDUqFFD6ZAMzsXF\nBdbW1ggLC1M6FCKjFBYWhlq1aqFdu3ZKh2JwTZo0weHDh/Hbb79hyJAhyMjIUDokIjIz48aNw08/\n/YSzZ8+iY8eO/LZeMgqcLCODe/DgAVxcXLB9+3bs3r0bc+bMUTokIjJR6enpGDRoEGJiYhAeHo76\n9esrHZIibGxs4OTkxEcxifIQFhaG/v37m+0XC73xxhvYv38/jhw5Ai8vL2RlZSkdEhGZmS5duuD0\n6dOws7NDp06d+DcLKY6TZWRQ58+fR4cOHRAXF4eoqCgMGDBA6ZCIyERlZWVhxIgROH78OA4cOIDm\nzZsrHZKi3N3dcejQIT5mRfSKhIQEnDp1yqzWK8vN22+/jb179yIsLAxjx46FiCgdEhGZmTp16uDo\n0aMYNGgQBg4ciDlz5jAXkWI4WUYGc+DAAXTr1g21a9dGZGQkWrVqpXRIRGSiRAQTJkxASEgIQkJC\n+MUheD5ZlpKSgv/9739Kh0JkVMLCwmBtbY1evXopHYrievbsieDgYHz//ff45JNPlA6HiMxQ2bJl\nERAQgG+++QaLFi2Cl5cXkpOTlQ6LzBAny8ggvv76a/Tv3x+enp44cuQIqlevrnRIRGTCpk2bhv/+\n97/YsWMHunfvrnQ4RqF27dpo3749H2sgekVYWBicnJxgY2OjdChGwd3dHf/973/h5+eHRYsWKR0O\nEZmpcePG4ccff8RPP/2Erl274tq1a0qHRGaGk2WkV2lpaRg5ciQ+++wzLFq0CBs2bECZMmWUDouI\nTNjChQuxYsUKBAYGmv1jVa9yd3fH3r17+dXsRC9kZGTgxx9/ZK54xbBhw+Dv749Zs2bhq6++Ujoc\nIjJT3bt3x+nTp6FSqdChQwfeHU8Gxcky0pt79+7B2dkZu3fvxp49ezBt2jSlQyIiE7du3TrN4M7L\ny0vpcIyOu7s74uPjcerUKaVDITIKP//8Mx49esTJslyMGjUKX331Ff79739j48aNSodDRGaqbt26\nOHr0KHr27Ik+ffpgyZIlSodEZkKldABkms6dO4cBAwbA2toaUVFRaNGihdIhEZGJ27p1KyZOnIiF\nCxfCx8dH6XCM0ptvvomGDRti3759ePvtt5UOh0hxYWFheP3119GwYUOlQzFKU6ZMQUJCAsaPH4/K\nlSvD09NT6ZCIyAxVrFgRwcHBWLp0KWbOnInz589jw4YNKF++vNKhkQnjnWVU4n744Qd06dIFTZs2\nxcmTJzlRRkR6Fx4ejlGjRmHixImYOXOm0uEYNXd3d65bRvTC/v370b9/f6XDMGqLFi3C5MmTMXz4\ncBw4cEDpcIjITFlYWGDatGkIDQ1FWFgY3nnnHcTFxSkdFpkwTpZRiRERLFmyBEOGDMHw4cMRFhYG\nOzs7pcMiIhMXGRmJ999/H0OGDMGqVauUDsfoubu748KFC7h8+bLSoRAp6tKlS7h8+TIfwdTBihUr\nMGLECHh4eOCXX35ROhwiMmOurq44efIknj17hvbt2+Po0aNKh0QmipNlVCKePXuGDz74ALNmzcKq\nVavw7bffwtraWumwiMjE/f777+jXrx+cnZ2xceNGWFhYKB2S0evevTvs7OwQGhqqdChEigoNDUXV\nqlXRqVMnpUMxehYWFvj222/Rr18/uLm54cyZM0qHRERmzNHREVFRUejatStcXFzg7++vdEhkgjhZ\nRsV2+/ZtdO/eHfv378ehQ4cwadIkpUMiIjPw559/onfv3mjXrh22bdsGlYrLcOpCpVKhb9++fBST\nzF5YWBhcXV2ZO3RkZWWFzZs3o0uXLujTpw/++OMPpUMiIjNWqVIl7Nq1C3PnzsX48eMxfvx4pKen\nKx0WmRBOllGx/Prrr+jUqRMePXqE48ePo2fPnkqHRERm4NatW3B2dkaDBg2wZ88elC1bVumQShV3\nd3f88ssvuH//vtKhECni0aNHOHbsGB/BLKQyZcrghx9+QIsWLdC7d29cu3ZN6ZCIyIxlr2O2Z88e\nbNu2Db169UJ8fLzSYZGJ4GQZFVlwcDC6du2KVq1a4eTJk2jWrJnSIRGRGbh//z569+4NGxsb7N+/\nHzY2NkqHVOr07dsXKpWKi3WT2Tpw4ADUajV69+6tdCilToUKFRASEoIaNWrA2dkZd+7cUTokIjJz\nbm5uiIiIwO3bt9G+fXucOnVK6ZDIBHCyjApNRDBnzhx4eXlh7NixCA0Nha2trdJhEZEZePz4Mfr0\n6YP09HQcPnwYVatWVTqkUsnGxgY9evTgo5hktrK/Se21115TOpRSydbWFgcPHkSZMmXQu3dvPHjw\nQOmQiMjMvfHGGzh16hRatGiB7t27IzAwUOmQqJTjZBkVytOnTzFo0CB8+eWX2LhxI77++mtYWVkp\nHRYRmYHU1FS4u7vjzp07CA8PR61atZQOqVQbMGAADh48iGfPnikdCpFBZWVl4eDBg3wEs5iqVauG\n8PBwJCcno2/fvnjy5InSIRGRmatatSoOHDgAHx8fjBw5EuPHj0dmZqbSYVEpxcky0tmtW7fw7rvv\n4pdffsHhw4cxatQopUMiIjORkZGBwYMH49y5cwgLC0PDhg2VDqnUGzBgAJKTk/Hzzz8rHQqRQUVF\nReH+/fvo37+/0qGUenXq1EF4eDhu3rwJV1dXJCcnKx0SEZk5KysrLF68GEFBQdi8eTP69euHpKQk\npcOiUoiTZaST48ePo3379khPT8epU6fQo0cPpUMiIjMhIhg3bhyOHDmC0NBQtGnTRumQTEKdOnXQ\ntm1bPopJZid7wr1FixZKh2ISGjdujMOHD+PSpUv4xz/+gbS0NKVDIiKCl5cXIiIiEBMTgw4dOuDC\nhQtKh0SlDCfLqEBBQUHo1asX3nrrLURERKBBgwZKh0REZuTTTz9FUFAQdu3aha5duyodjklxd3fH\n3r17ISJKh0JkMKGhoXBzc1M6DJPy+uuvY//+/YiMjIS3tzeysrKUDomICG3btsXp06dRr149dO7c\nGTt37lQ6JCpFOFlGecrKysL06dMxbNgwjBs3DiEhIahcubLSYRGRGZk1axb8/PywefNmuLi4KB2O\nyRkwYABu376NM2fOKB0KkUHExcXh/PnzXK9MDzp27Ii9e/di//79GDNmDCfhicgo2Nvb4/Dhw/jX\nv/4FT09PTJ8+HWq1WumwqBRQKR0AGacnT55g+PDhOHToEAIDA/HBBx8oHRIRmRk/Pz8sWrQIGzZs\ngKenp9LhmKQ2bdqgQYMG2LdvH9q3b690OER6FxISovk2WCp5Tk5O2Lt3L9zc3FC5cmV8/fXXSodE\nRASVSoXFixejUaNGmDRpEi5cuIAtW7bA1tZW6dDIiPHOMsrh6tWr6NSpE06dOoWjR49yooyIDG7T\npk3w8fHB0qVLMXr0aKXDMWn9+/fH3r17lQ6DyCDCwsLQu3dvlC1bVulQTFbv3r0RFBSEtWvXYv78\n+UqHQ0SkMW7cOPz00084c+YMOnbsiIsXLyodEhkxTpaRloiICHTu3BllypRBVFQU3n77baVDIiIz\ns3fvXowePRqff/45/v3vfysdjslzd3fH77//jmvXrikdCpFepaam4ueff+YjmAYwaNAg+Pv744sv\nvsCKFSuUDoeISKNLly44ffo07Ozs0KlTJ37REeWJk2WksWHDBvTs2RM9evTAsWPHUK9ePaVDIiIz\nc+TIEQwdOhTjxo3D3LlzlQ7HLLz77ruoUqUKQkJClA6FSK/Cw8Px7Nkz9OnTR+lQzMLIkSOxcuVK\nTJ06Ff7+/kqHQ0SkUadOHRw9ehSDBg3CwIEDMWfOHK6zSDlwsow0C/mPHz8en376KbZt24YKFSoo\nHRYRmZmTJ09iwIABGDRoEPz8/JQOx2xYW1ujT58+/GSVTF5YWBjat2+P2rVrKx2K2fDx8YGvry8+\n/vhjBAcHKx0OEZFG2bJlERAQgG+++QaLFi2Cl5cXkpOTlQ6LjAgX+DdzSUlJGDx4MCIiIrB582Z4\ne3srHRIRmaELFy7A1dUV7777Lr777jtYWvKzHENyd3fHhx9+iKSkJNja2iIqKgr79u3DjRs3EBQU\nBAsLC6VDJCqU6dOn4/Hjx+jfvz+cnJxQrlw5zbc0kmHNnz8fqampGDFiBCpVqoS+ffsqHRIRkca4\ncePQvHlzeHp6omvXrti9ezcaNmyodFhkBCyE9xuarT///BNubm548uQJ9uzZw29C06MGDRrgxo0b\nBdb7f//v/2HJkiUGiIjI8Pbu3QuVSpVjvaCrV6+iW7duaNCgAQ4fPoyKFSsqFKH5unv3LhwcHNC9\ne3ecO3cOiYmJsLKyglqtRlZWFifLqNRp3rw5YmNjISIoW7YsOnTogIiICISFhXGyRgEignHjxmHL\nli04ePAgunfvrvV6bGwsjh49irFjxyoUIZHhcXxgXG7evIn3338f169fR3BwMHr16qV0SKSsHfzo\n3oR98803WLp0aa6vhYeHo0OHDqhSpQpOnz7NiTI9c3BwKHCwaWFhwXXiyGQ9e/YMI0eOhLu7O9av\nX68pT0hIgKurK6pVq4awsDBOlBnQgwcPsGnTJgwaNAiNGzdGVlYWfvnlFyQmJgJ4/oh+2bJlOVFG\npVKlSpU068+kpaXh+PHjsLS0RP/+/fH666/j888/x4kTJxSO0nxYWFjgm2++gZubG9zc3HD69GnN\na2fPnkWnTp0wfvx4XLhwQcEoiQyL4wPjUrduXRw9ehROTk7o06dPnhOUcXFx8PHxQVpamoEjJEPj\nZJmJunz5MiZPnoxp06Zh06ZNWq+tX78e/fr1Q58+fXDkyBHUrFlToSjNx4gRIwp8rMzS0hIeHh4G\niojIsLZs2YLHjx9DrVZj/PjxWLJkCR4+fAgXFxeICA4dOoQqVaooHabZuHLlCmrVqoWPPvoIe/fu\nRUpKCgAgMzNTq165cuWUCI+o2GxtbbV+VqvVUKvVEBFER0djyZIl6NSpE3744QeFIjQ/VlZW+P77\n7/HOO+/A1dUVf/zxByIiItC9e3c8efIEKpWK35xJZoXjA+NTsWJFbN++HQsWLMDMmTMxfPhwpKam\nal5PSUlBv379sHr1at7tZwb4GKaJ6t27N37++WdkZGTA2toaR48eRYcOHfDJJ59g7dq1mD17Nr74\n4gveMWAgSUlJqF69eo6BaDZLS0v06tULhw8fNnBkRIbRsmVLxMTEQK1WA3j+SWmTJk2QlpaGiIgI\n1K1bV+EIzUtWVhZ69OiBkydPIiMjI896NWvWxJ07dwwYGVHJGDRoEHbt2pXn61ZWVnBwcMCFCxdg\nY2NjwMgoJSUFLi4uuHHjBu7du4eMjAxkZWUBeP6FI3Fxcfwgl8wCxwfG7cCBA/D29kajRo2we/du\n1K1bF0OHDsXu3bs1Y+zo6Gg4OjoqHSrpBx/DNEW7du1CeHi4ZgCkVqvRv39/9O3bF4GBgdizZw/m\nzJnDiTIDsrOzg7OzM6ysrPKsM2LECANGRGQ44eHhuHjxomaiDHi+fs2VK1fQrl071KpVS8HozJOV\nlRWCg4NRsWLFfD/V5jcjU2lVqVKlfK+5ABAcHMyJMgVUqFABEydOxJ07d5Cenq6ZKMu2bt06hSIj\nMiyOD4ybq6srTp48iWfPnqFTp06YOHEiduzYofUh40cffQTee2S6OFlmYlJTUzF58mStwU9WVhae\nPHmCy5cv48cff4S7u7uCEZqv4cOHa00WvEylUvH/hUzWV199BWtr6xzlarUaISEh+Mc//sF1HxRQ\np04dBAUF5ftHHifLqLSysbHJcwBqaWmJRYsW4e233zZwVAQ8fyx/2LBhmkdjX5aRkYHVq1drPfZE\nZMo4PjBujo6OOH78OOrVq4d169Zp/c2UkZGBY8eOYevWrQpGSPrEyTITs2DBAsTHx+f6x8etW7ew\ncOHCPBMy6deAAQNQtmzZHOUqlQpubm451lchMgWxsbE4dOhQno/6ZWVl4cCBA+jfv79m3SwyHFdX\nV/j4+OQ5qcAvXKDSqlKlSrneQW9tbY0uXbrgs88+UyAq8vPzw4gRI5CVlZXn36OPHz/G5s2bDRwZ\nkTI4PjB+iYmJuHTpUp5PZU2aNAlJSUkGjooMgZNlJuTPP//EsmXL8nzuPTMzE6GhoZg7d66BIyPg\n+aDT3d09xx02WVlZGDZsmEJREenX6tWroVKp8q2TlZWFH3/8ETt37jRQVPSypUuXok2bNrne/cdH\n1Ki0ym2i19LSEhUrVkRwcHCBj2hSybt16xZ8fHwKrCciWLp0KR9tIrPA8YFxe/r0KVxdXZGcnJzr\nBL+I4MmTJ5gxY4YC0ZG+cbLMhIwfP77AOmq1GvPnz8fu3bsNEBG9atiwYTnusClfvjxcXV0ViohI\nfx4+fIiNGzfmu4C8lZUVateujW+//Rbe3t4GjI6yWVtbIzg4GNbW1jk+Na1UqZJCUREVT6VKlXJM\ntogIAgMDUbt2bYWiMm8ODg44dOgQ2rRpAwB5TliKCP78808cPHjQkOERKYbjA+Pl7e2Nq1ev5nkz\nCvD8Ca7169cjKirKgJGRIXCyzETs3LkTR44cKXBQamlpifLly+Phw4cGjI6yubq6onLlypqfra2t\nMWTIEJQrV07BqIj0Y/369XnmJCsrK1SvXh0rVqzA1atXMW7cON7poaDGjRvju+++05pcyL4Lh6g0\nsrGx0boLQKVS4Z///CfX/1GYs7Mzzp49i/DwcLRo0QIWFha5fsmISqXC0qVLFYiQyPA4PjBOT58+\nRWRkJDIzMwt8SsLKygqjR4/Od1KNSh9OlpmAlJQU+Pj45PmNZiqVChYWFmjfvj3WrVuH+Ph4jBo1\nysBREvD84jd48GDNrdYZGRm8m4ZMUmZmJlatWpXjW85UKhUqV66MhQsX4vr16/Dx8UGZMmUUipJe\nNnjwYIwcOVLzB6GlpSUX+KdSq1KlSpr8o1Kp0KhRIyxbtkzhqCjbe++9h99//x3BwcGoX78+LC0t\nte5szczMxM8//4zffvtNwSiJDIPjA+NkY2ODW7duYe/evRg8eDDKli0LKyurXD/czczMRExMDL7+\n+msFIiV94WSZCZg/f36ORf2zk23Dhg2FNVKaAAAgAElEQVTh6+uLK1euICoqCuPGjeMaNArz9vbW\n3G3z2muvwcnJSeGIiErerl27cPfuXc3PKpUKZcuWxWeffYa4uDhMmzYN5cuXVzBCys3atWvRsGFD\nWFtbc7KMSjUbGxvNnZIWFhbYsWMHc46RsbCwgKenJ/78809s27YNDg4OWh/8lilTBitXrlQwQiLD\n4fjAOJUtWxZubm7YsmUL4uPjERAQgPfeew9WVlY5lq/IysqCr68vrl+/rlzAVKI4WVbKXbp0CcuX\nL0dmZiasrKxgYWEBGxsbjB07FlFRUbh69SrmzJmDhg0bKh0qvdCjRw9Ur14dwPOvi+ajZ2SKli9f\nDuD5xL21tTUmTZqEmzdvYvHixfxmJyNWoUIF7Ny5ExYWFkhPT+fkApVaL6+3t2rVKrz55psKRkP5\nsbS0hKenJy5fvoxVq1bB3t4eKpUK6enp2Lp1q9YHL0SmiuMD42dra4sPPvgABw8exLVr17BgwQI0\nb94cADRPSaSnp2PSpElKhkklyEJeWf302bNn2L9/f45HZ8g4LViwAL///jssLS3RunVrODk54a23\n3sr1W82KqmbNmujWrVuJHe9V169fx6lTp/R2fGO0adMmhIaGYuHChXB0dFQ6HIOxsrJC37599bYG\nA/OXcbhy5QpmzJgBS0tLvPvuu/D09MRrr72mSCz6zF+m3N7Cw8OxYcMGDBkyBIMGDVI6nFKD7c14\nXLt2DdOmTUO7du0wbdq0HF9eYQpMtb2lpaXh4MGD2L17N1JSUuDh4YHBgwcbPA7SZqrtzZiY6/gg\nN6WpvcXFxeGXX37B0aNHNeuCz5gxA23bti2R45P+5dHedkBesXPnTgHAjZtmU6lUrzaTEjV06FDF\nz5Gb4badO3fqrS0xf3F7ddNn/mJ74/bqxvbGzZAb2xs3Q25sb9wMubG9cTPklkd7257jax2yv8FB\nXvm6bTJP27dvx5AhQ/T6O7KysuDp6Ynt27fr9feQ8iwsLPT6LTHMX/Qyfecvtjd6GdsbGRLbGxkS\n2xsZEtsbGVJ+7Y1rlhEREREREREREb3AyTIiIiIiIiIiIqIXOFlGRERERERERET0AifLiIiIiIiI\niIiIXuBkGRERERERERER0QucLCMiIiIiIiIiInpBpXQApO3q1asICQlBWloa3n//fTg6OiodElGu\nRASXL19G06ZNlQ6FShHmOCIiIiIiMna8s0wHIoKNGzfC09MTvr6+GDNmDIKCgkp0v8ePH2PixIlw\ndnbGm2++ialTp2oGkUlJSZgwYQJmz56NKVOm4MMPP8Tt27dzHCM6OhoDBgzAa6+9Bnt7ewwdOjTX\nemRcitq+AOCvv/5CQEAABg8ejM6dO+d67E2bNsHNzQ0zZsyAk5MTJkyYgKSkJE2dHj16wMLCItft\nypUrmnp+fn5ar1laWmL16tXFfwNIrwyRv7Jlt5Hc5JfjCnMcMm7FaW/+/v5o06YNbGxs0Lp1awQE\nBEBEtOoVdJ3T5XqpS16k0kHf109d2uTLcstduv4NR8ZP3/ntZUVtS8xvpkPp66kubakobZuMkz6v\np7rWKexchkHGC/KK4OBgyaXYrM2dO1fq168viYmJIiKSmJgo9evXl1WrVpXIfvHx8dK2bVtxdHSU\nhIQErddSUlKkadOmsnDhQk3Zhg0bpEaNGnLr1i1NWXR0tAwcOFB27dolZ8+eleHDhwsA6dmzZ7HO\n3RDtwdPTUzw9PfX6O4xZUdtXths3bggAadasWY7X1q1bJwAkLCxMREQuXLggAGTAgAGan1u3bi3L\nli2T7777TrN9/PHH8sYbb2iOk56eLp07d5Yvv/xSsy1btkzi4+MLda4AJDg4uFD7FAbzV076zl/Z\nTp48KeXLl8/1/c8vxxXmOIWl7/bA9pZTUdvbtGnTZNiwYbJmzRqZPHmylCtXTgDI6tWrNXUKus7p\ner0sKC8WFdub4enz+qlLm3xZbrlL1zZZFGxvhqfP/Pay4rQl5jfToeT1VES3tlTYtq0rtjfD0+f1\nVJc6hZ3LMNB4YTsnywpw/fp1UalUsmjRIq3yBQsWSIUKFeT+/fvF2k+tVourq6tYWlpKZGRkjuMs\nXrxYAEhMTIymLD09Xezs7GT06NGaslWrVklycrJWHVtbW6lYsWLhT/olnCzTr6K2r1fllXg6d+4s\nADQTFGq1Wuzt7cXGxkZERLZu3Sr37t3Lsd/IkSNl3rx5mp8DAwNl7dq1Op9XfnFyssxw9J2/siUm\nJoqvr680bdo0x/tfUI7T9ThFwT+2DKuo7S0uLk68vb21yg4ePCgApHHjxpqygq5zul4vC8qLRcX2\nZlj6vH7q2iaz5ZW7dG2TRcH2Zlj6zm/ZituWmN9Mg9LXU5GC21Jh23ZhsL0Zlr7Ho7rUKcxchgHH\nC9v5GGYBtmzZgszMzP/f3plHRXXlefyL4E6LGKK4YlAQdRJGjdFo0NaEECdBmG5EcRn3c2Ift2Ta\nyYLiRmJQiSjaJoY2h7hENJNk1CRtnMlpz2CQ2JrYBhUcBZe2BQURBRrE+s4fWBWKWt57tVEFv885\ndU7q1rv33Vt88vvdd33vFp5//nmj8vHjx6OqqgoZGRl21Tt8+DC++eYbREVFYeTIkSbtHDt2DADQ\np08fQ1nr1q0xbNgwHDhwwHCb65IlS9ChQwejunV1dZg7d67GEQuuxFa/1NKlSxcAwJ///GcAQGVl\nJcrKyjB+/HgAwJQpUxAQEGBUp6amBl988QXi4uIAADqdDikpKXjzzTcRGRmJpKQkFBYW2tUvwTU4\nO34B9bdtr127FsuWLTN7K7RSjFPbjuD+2OrblStXkJqaalT24osvIiAgACUlJYYypTynNl8qxUXB\nM3Bm/lTrJGA9dql1UnB/nB3fAMe4JPGtedDU+RRQdkmL24J74+zrUTWoXctw9fWCUxbLKisrsXv3\nbiQkJGDUqFHIycnBkCFDEBQUhOzsbOTn5yM2NhYBAQEICwvDX/7yF6P6J0+exIgRIzB//nwsW7YM\n3t7euHfvHgCguroaKSkpmDt3Lp5++mm88MILOHv2rMW+lJaW4sKFC1ZfV65csVg/OzsbANCrVy+j\n8t69ewMAzpw5Y1e9zMxMAPXJb8yYMfD19cXQoUNx+PBhAEBxcTEAoKyszKidgIAAVFRU4ObNmybn\n1ul0SEpKQlpaGtLS0iyOzVMRv9SzadMmBAcHY+nSpbhy5Qq2bt2KZcuWWX0G/ciRI+jVqxcGDhwI\noH6vqaioKIwYMQI5OTlYu3YtwsLCsGbNGrv65q6IX9rqpaenY/LkyfDz8zPbllKMU9tOc0V8A557\n7jkEBgaalNfW1iIiIsJsHXN5Tm2+tCUuNhfEN3VocdJa7LJlDtecEN9c75LEN/HNEfkUUHbJlnM1\nJ8Q352FtLcPl1wsabkNTzcOHD3nx4kUCYKdOnXj48GHm5eURAIOCgrh+/XqWl5fz9OnTBMCxY8ca\n1Q8JCaG/vz8fPnxIkoyLizPsjTRv3jyeP3/ecGxkZCS7du3Ku3fvmu3Lhg0bCMDqa/To0RbHEh4e\nTgCsqqoyKq+srCQAjhw50q56QUFBBMCNGzfyxo0bzMnJYa9evQiAubm5TEhIIABmZmYatTNjxgwC\n4NWrV43KP//8c0ZERBAA+/bty48++og6nc7i+JRwx8cwxS9TYOW215KSEo4aNYo9e/bka6+9ptjW\n1KlTuWrVKrOflZeXMzk5md7e3gTAjIwMVf1r2E93fwxT/FJf7/vvv2dqaqrh8wEDBph8/0oxTm07\ntuAJt/GLb+bJzs5mu3bteOrUKZPPLOU5LflSa1xUg/jmmb5Zy58NMeekUuzSOofTgvjmmb6RzndJ\n4pv41hBb8qkerS5ZO5cWxDfP9E1NPlU6xpqTTXC94Lw9y3Q6ncmX0aNHD6O29c8++/n5GdUNCAgg\nAG7fvp06nY5nzpxheXk5T5w4YVGAQ4cO2d1nc+j/WNXV1UblVVVVBMChQ4faVa9t27YMDAw0OmbX\nrl0EwGnTpjE3N5deXl7s3r07s7OzWV5ezs8++4yBgYH09vbmgwcPjOqWlZUxLy+P6enphk3vPv74\nY5vH746LZaT41RhrgaeoqIgvv/wyX3rpJQLg73//e0NgbkxlZSV9fX2Zl5dn9XwffPABAXDIkCGq\n+tewn+6+WEaKX2rq3b59m3PmzDFyyVzSUopxatuxBU+YbJHiW2MePHjAMWPGcO/evWY/t5TntORL\nLXFRLeKbZ/qmZnJvzkk1sUvrHE4L4ptn+uYKlyS+iW96bM2nerS4pHQuLYhvnumbIxbLLDnZRNcL\nztuzzNwzpL/61a9Mjnnsscdw9+5do/Lt27fD19cXCxYswOjRo1FTUwM/Pz+cPHkSgwYNAkmT1yuv\nvOKUcYSFhQEAysvLjcr1P5vbo0cPu+oFBgaidevWRseMGzcOAJCfn49nnnkGX331Fbp3746oqCiM\nHTsWVVVV0Ol0GDduHHx8fIzq+vv7Y9CgQVi4cCE+/PBDAMAnn3yibdAeQEv3Sy25ubkYNmwYZs6c\niS+//BKjRo3Cxo0bkZSUZPb4r7/+Gn369MGgQYOstjtv3jy0a9cOBQUFdvXPXWnpfqmpt2DBAkyf\nPh0FBQWGW7xramoAABcuXMClS5cAKMc4te00Z1q6b41ZvXo1nn/+eSQkJJj93FKeU5svtcbF5ob4\nph1zTqqJXVrncM0R8c0YZ7sk8U18a4it+RTQ7pLSuZoj4pvjseRkU10vuGWWjouLw5AhQ/C73/0O\n3377LUaNGoU//vGPKC0tRWFhISorK9GxY0ejOg8fPoS3t7dJW6Wlpbh165bV87Vv3x5BQUFmPxs8\neDAA4MaNG0bPZf/9738HUP+8tj31QkJC8L//+78gafgfTr/hun5jxQkTJmDChAmGNg4ePIiSkhLM\nmjXL6rhiYmIAAG3atLF6XEujOfillrfeegulpaX49a9/jbZt22Lfvn3o06cPduzYgeTkZJPjs7Ky\nDBv7W8Pb2xtdunTB448/blf/miPNwS819VauXIkDBw6YrT9w4ED069cP//d//6cY4w4ePKiqHcE8\nzcG3hhw6dAgdO3bEm2++qXgsYJrn1ORLrXFR+IXm5psaLDmpNnbZOocTmp9vrnBJ4pvttBTfLNE4\nn2pxSeu5hObnmzNo6GSTXS9ouA1NM2h0m525W+XMla1YscLw33v37iUA9uzZk/v27SMAo89JMi8v\nj2lpaWb7YO8zvKWlpfTz8+PGjRuNytevX8/WrVsb7RHQ8BZotfV27txJADx9+rThmOvXrxMAly9f\nbtKfe/fuMTQ0lBEREYq3VF+4cIEAuHnzZqvHWcNdH8MkW7ZfjWn8XegZPXo0AbC8vNxQ1rVrV3bt\n2tXk2Hv37rF9+/Y8e/asxfPo0TuanJyseGzjfnrCY5hky/ZLSz2l70NrjLPUji14ym38ZMv2Tc+R\nI0e4fft2k/Ljx49bPKe1PGcpX2qJi1oQ3zzLNz2W8iep3Uml2KVlDqeE+OZZvrnKJYlv4hvpmHyq\n1iVbzqWE+OZZvumxlk+1HKNHaS3Do/cs0z/jGhoaaigLDg4mAFZUVBjK9Js/19XVGcrat2/PsrIy\nkmRtbS07derE4cOHs7q6mk888QQBcM6cOdy9ezcTExMZGRlpccM7R/Dee+8xJCTE0O+7d++yf//+\nXL16teGY5ORkdu7cmYWFhZrqPXjwgIMHD2ZCQoJh87r09HR269bN8B3oqampYXx8PENDQ3nt2jWj\nz1JTU5mRkcE7d+6QJKurqxkTE8P4+Hi7JmTuulgmfv2CfvPF/v37m3y2bds2AjDsH1BUVEQAXLx4\nscmxe/bsYVhYmMkPQqxatYqLFi3iuXPnSNZ/99HR0YyNjTX6XtXgKYtl4pe6eo0xl7S0xDhr7diC\np0y2xDfy6NGjHDduHNPT0w2vLVu2cOnSpUxMTCSpLc9Zy5da4qIWxDfP8U2PtfypxsnGWItd1py0\nBfHNc3xzpUsS38Q3R+VTNS7Z4rYaxDfP8U2PtXyq5hhb1jI8drHs5s2bfO211wiAbdq04dGjR/mn\nP/3J8At6ixYt4u3bt7llyxbDamdKSgpv3bpFsv6CesiQIVy3bh2nTp3Kl19+mZcvXyZJFhYWMjo6\nmv7+/uzWrRvnz5/PkpISu/qrhE6nY0ZGBqdPn863336bcXFx3LFjh9Giwvvvv88+ffoYJS019cj6\njexmz57NGTNmMDExkdOmTTNJfj///DOHDx/OqVOn8ubNmyZ9XLlyJfv168fOnTvz1Vdf5eLFi3n0\n6FG7fgmTdM/FMvHrF7777jvOnz+fAOjj48OUlBT++OOPRm1v3bqVw4cP5+uvv87Y2FiuWLHCZANH\nkpw4caLJv2KQ9XcGhYeHs0OHDkxISODs2bN58OBBm9zyhMUy8Ut9vcZYSlpqYpyadrTiCZMt8a3+\nX5/1m7iae126dImk+jynlC+1xEUtiG+e4Zsea/lTrZONsRS7lJy0BfHNM3xztUsS38Q3R+VTJZds\ndVsN4ptn+KZH6XpUzTG2rGW4YrHMiyTRgP3792Py5MloVCw0EUVFRcjMzIS3tzeio6MRHh7u0vO7\nwof4+HjDuYTmjZeXF7Kysgx/c0cj8UtoiLN9EN/ci+aeL8U3z8OZTopvLQuJb0JLQnwTXIkVHw64\n5Qb/wi/07dsXK1eubOpuCIIgCIJbI/lScDfEScFRiEuCIAiup1VTd0AQBEEQBEEQBEEQBEEQ3AVZ\nLBMEQRAEQRAEQRAEQRCER8himSAIgiAIgiAIgiAIgiA8QhbLBEEQBEEQBEEQBEEQBOERslgmCIIg\nCIIgCIIgCIIgCI9oEYtlxcXF2L9/P955552m7opDuXz5MjZv3oz169fj4sWLTd0dQYHm6qHgesQl\nwVmIW0JTIw4KrqQ5+0YSBQUFTd2NFktzdksN4l/T09IddATNfrHs/PnzWLNmDSZPnoxdu3Y1dXcs\nQhIZGRn453/+Z/j6+iI8PBw7d+4ESZNjKyoqsHDhQkRGRuKpp57CsmXLEBISAgAYO3YsvLy8zL4u\nXbrk6mEJj/AUDxuTnp4OLy+vpu6G0IDm5tLf/vY37Ny5E/Hx8Xj22WeboGeCHk9yS8kbtbkwLy8P\nMTExeOyxxxAQEIApU6bgxo0brhyK0ABPcrAhEt88E0/yTU2s0nuof7Vq1Qpbtmxpoh63bDzJLTVx\nSs0x4p974SkO3rlzBwsWLEBSUhKWLl2KmTNnmsQ2LeskjsbH6WdoYgYOHIjU1FT84Q9/aOquWOWt\nt97C9evXMX/+fBQUFGDHjh2YO3cuKisrsWjRIsNxJSUleOmll3D//n2cOHECjz/+uOGzvLw83L17\nFxs2bEBAQIChPDc3F8ePH0e/fv1cOibhFzzFw4acPHkSb7zxRlN3Q2hEc3OpZ8+eeOGFFzB37lwM\nGDDAxT0TGuJJblnzRm0uPHfuHJYvX45Zs2Zh1apVeP/997F7927cunUL//M//+PS8Qj1eJKDeiS+\neS6e4puaWPXgwQN8+umnWLdunaGej48P/u3f/q2put2i8RS3AHVxSukY8c/98AQHq6urMXLkSMyc\nORNvv/02ACAjIwNDhw7FqVOn0LNnTwDq10mcQbNfLAOAdu3aNXUXrHLt2jVcu3YNe/bsMZT9y7/8\nC1566SVs3rzZIAFJzJo1C2fOnMHx48eNFsoA4OzZs/jv//5vo4sDADh27BgmTZrk/IEIVnF3Dxty\n584d/Nd//Rd69+4tt1C7Ic3NpT59+ri4V4IlPMktS96ozYVHjx7Fnj170KFDBwDAzp07cejQIeTm\n5jqv04IinuSgxDfPxxN8UxOrPv30U0yfPh2/+93vmqqbQiM8wS09auKUtWPEP/fE3R3csmULCgoK\nEBcXZyibOXMm/uM//gMrV65ERkaG6nUSZ9HsH8P0BK5cuYLU1FSjshdffBEBAQEoKSkxlB0+fBjf\nfPMNoqKiMHLkSJN2pkyZYnJxUFNTgy+++MJIQkGwBkmsXbsWy5Ytk0cwBbsQl4SmQG0uXLJkieHi\nU09dXR3mzp3rkn4Kno3EN8FVKMUqnU6HlJQUvPnmm4iMjERSUhIKCwuboqtCC0T8E2zl2LFjAIwX\nYlu3bo1hw4bhwIEDIKl6ncRZOGyx7OTJkxgxYgTmz5+PZcuWwdvbG/fu3QMAw4rhG2+8gRkzZiAi\nIgJ//etfAQCVlZXYvXs3EhISMGrUKOTk5GDIkCEICgpCdnY28vPzERsbi4CAAISFheEvf/kLgPpJ\nSk5ODv793/8dffv2xc2bN/Hb3/4WXbp0wT/90z/hP//zP632t7q6GikpKZg7dy6efvppvPDCCzh7\n9qyq8TSmtLQUFy5csPq6cuWKxb4899xzCAwMNCmvra1FRESE4X1mZiaAeqHGjBkDX19fDB06FIcP\nH7bY9pEjR9CrVy8MHDjQ6vfRXBAPbfdQT3p6OiZPngw/Pz9V33lzRVwSl5yFuGW/W1pRyoU6nQ5J\nSUlIS0tDWlqaw8/vboiDEt9cifjmuJhnLlZVVFQgKioKI0aMQE5ODtauXYuwsDCsWbNGdbueirjl\n+nzamJbsHyAO2uNgcXExAKCsrMyoPCAgABUVFbh586bqdRKnwUZkZWXRTLEiISEh9Pf358OHD0mS\ncXFxLC4uJkn279+fwcHBJMna2lr6+flx8ODBJMmHDx/y4sWLBMBOnTrx8OHDzMvLIwAGBQVx/fr1\nLC8v5+nTpwmAY8eOJUnW1dXx0KFDbNeuHQFw4cKFPHbsGPfs2UNfX18CYHZ2tqF/ADhgwADD+3nz\n5vH8+fOG95GRkezatSvv3r2rOJ7GbNiwgQCsvkaPHq3p+8zOzma7du146tQpQ1lQUBABcOPGjbxx\n4wZzcnLYq1cvAmBubq7ZdqZOncpVq1ZpOndDbPVBC5MmTeKkSZMc0pZ4aJ+H33//PVNTUw3vBwwY\n4NC/PwBmZWU5rL3GONJXccm1LjUejyNwdvySfNn0+VKtN9Zy4eeff86IiAgCYN++ffnRRx9Rp9Op\n7oMed/XNHOKgxDdXti++OSbmqYlV5eXlTE5Opre3NwEwIyNDVdtKuKtv4pZr86nSMY7yz119M4c4\naLuDCQkJBMDMzEyj8hkzZhAAr169araeuXUSe7Diw36HLZYFBAQQALdv306dTsczZ86wvLycJJma\nmsq9e/eSrBcjODiYPj4+hro6nc7kD9mjRw+jfuh0OgYEBNDPz8/ovCEhIQTA+/fvG8o2bdpEAJw8\nebKhrGH7J06csPgHPXTokOJ4nM2DBw84ZswYw3emp23btgwMDDQq27VrFwFw2rRpJu1UVlbS19eX\neXl5NvfF0xbLxEPbuX37NufMmWMIjmTLXiwTl2zHFpda0sWkuOU41HijlAvLysqYl5fH9PR0tm/f\nngD48ccfa+6Lu/pmDnHQdiS+aUd8cwxaYtUHH3xAABwyZIhDzu2uvolbjkNNnFIby+z1z119M4c4\naDu5ubn08vJi9+7dmZ2dzfLycn722WcMDAykt7c3Hzx4YFLH0jqJPVhbLHPYY5jbt2+Hr68vFixY\ngNGjR6OmpsZwa/rrr7+O6OhobNu2De+88w5qampQV1dnqGtur4df/epXRu+9vLzw2GOP4e7du0bl\nrVrVD6Fjx46GsokTJwIALl68aLavJ0+exKBBg0DS5PXKK68ojsfZrF69Gs8//zwSEhKMygMDA9G6\ndWujsnHjxgEA8vPzTdr5+uuv0adPHwwaNMh5nXUzxEPbWbBgAaZPn46CggLDrbM1NTUAgAsXLuDS\npUtOOa+7Ii7ZjrhkHXHLtSjlQn9/fwwaNAgLFy7Ehx9+CAD45JNPXNlFlyMO2o7EN+2Ib45BS6ya\nN28e2rVr1+x/pEncck9ain+AOGgPzzzzDL766it0794dUVFRGDt2LKqqqqDT6TBu3Dj4+Jj+FqWl\ndRJn4bDFsri4OPz000948cUXkZOTg1GjRhkC+A8//IAnn3wSwcHBWLFiBXx9fR11WrP06NEDANC7\nd2+zn5eWlqKwsBCVlZUmnz18+BCA9fGYa89Rz4wfOnQIHTt2RFJSkslnISEhKCkpAUlDmX4T4y5d\nupgcn5WV1eI29hcPbffw4MGDGD9+PAYOHGh4FRUVAaj/+eGoqCgtw/d4xCVxyVmIW67dY0VLLoyJ\niQEAtGnTxqF9cDfEQYlvrkR8c3zMU4pV3t7e6NKlC/r376+5bU9C3Gr6PcvM0VL8A8RBex2cMGEC\nTp06hfv37+Onn36Cn58fSkpKMGvWLJNjra2TOA0Nt6FZZcWKFYb/3rt3LwGwZ8+eJOtvT9f/N/nL\nbYMNn7NHo1sQzd3Srrbs+vXrBMD09HSz7e/bt48AjPpMknl5eUxLS1McT2Mc9cz4kSNHuH37dpPy\n48ePkyR37txJADx9+rTJWJcvX25U5969e2zfvj3Pnj2reF5reNpjmOKhY/fOa8mPYYpLrnWp8ffl\nCNz1Nn5xy3V7rGjNhRcuXCAAbt68WXUf9Lirb+YQByW+ubJ98c2xvpHKsUo/zuTkZM1tm8NdfRO3\n3GvPMj32+ueuvplDHHScg/fu3WNoaCgjIiKMtjoglddJ7MEle5a1b9+eZWVlJOs3sOvUqROHDx9O\nkuzUqRMB8Ntvv+Xu3bv5+OOPEwBPnDjBq1evsqqqigAYGhpqaC84OJgAWFFRYSjTb3BfV1dnKNOL\n0vCZ1szMTA4dOpS1tbUk6/crAeo3yyPJ6upqPvHEEwTAOXPmcPfu3UxMTGRkZKRhcztr43EGR48e\n5bhx45ienm54bdmyhUuXLmViYiLJ+md0Bw8ezISEBMP/ZOnp6ezWrZuhr3r27NnDsLAwmzYpboin\nLZaJh46lJS+WiUuOxZpL+vH079/foed018mWuOUY1HhjLRempqYyIyODd+7cIVk/1piYGMbHx5tM\n0tTgrr6ZQxx0LBLfrCO+2YdSrKkkPRIAABQ9SURBVFq1ahUXLVrEc+fOkSSrqqoYHR3N2NhYo+/D\nHtzVN3HLMaiJU5aOcYZ/7uqbOcRBx1BTU8P4+HiGhoby2rVrRp+pWSexB5cslgH1m/itW7eOU6dO\n5csvv8zLly+TJLdu3Wr4onNycpiWlsbOnTtz4sSJzMvL42uvvUYAbNOmDY8ePco//elPhl/RWLRo\nEW/fvs0tW7YYVihTUlJ469Ytkr+IsmHDBt66dYvFxcVct24d7927R5K8dOkSFy1aZKi7adMmlpWV\nsbCwkNHR0fT392e3bt04f/58lpSUqBqPozl+/Lhho05zr0uXLhmOLSsr4+zZszljxgwmJiZy2rRp\nJkKR5MSJE01WjW3B0xbLxEPH0pIXy8Qlx2LJpe+++47z588nAPr4+DAlJYU//vijQ87prpMtcct+\n1HpjLReuXLmS/fr1Y+fOnfnqq69y8eLFPHr0qM3/yOSuvplDHHQsEt+sI77Zh1Ks2rlzJ8PDw9mh\nQwcmJCRw9uzZPHjwoN3/YN4Qd/VN3LIfNXHK2jHO8M9dfTOHOGg/P//8M4cPH86pU6fy5s2bRp9p\nWSexFWuLZV5kgw2wAOzfvx+TJ09Go2K3JSwsDPn5+R7TX0/DFT7Ex8cbzuWpiIfq8PLyQlZWluFv\n7mg8LX6ZQ1xyHM72wdN8E7eci/imjDjoOMQ3ZcQ3xyG+GSNuORfxTZnm4GBRUREyMzPh7e2N6Oho\nhIeHN0k/rPhwwPQnBgRBEARBEARBEARBEATBCfTt2xcrV65s6m5YxWG/htlU6H/N4f79+03cE6El\nIx4KjkJcEpyFuCU0NeKg4ErEN8FZiFtCUyMOugaPXSy7f/8+3n77bVy/fh0AsHjxYuTk5DRxr4SW\nhngoOApxSXAW4pbQ1IiDgisR3wRnIW4JTY046Fo89jFMX19fvPvuu3j33XebuitCC0Y8FByFuCQ4\nC3FLaGrEQcGViG+CsxC3hKZGHHQtHntnmSAIgiAIgiAIgiAIgiA4GlksEwRBEARBEARBEARBEIRH\nuM1jmMXFxTh27BguXryIxMTEpu6O0AIRBy1DEhcvXkRoaGhTd8UtEXcEVyK+Ca5EfLOdy5cv49Ch\nQ6ipqcG//uu/IiQkpKm75Pa0VN9kntU0tETfJC41HS3RNzU4yklnuO0Wd5adP38ea9asweTJk7Fr\n166m7o4iJPHHP/4RkyZNQmJiIubNm4e9e/c6pN7YsWPh5eVl9nXp0iWz7aanp8PLy8uo7M6dO1iw\nYAGSkpKwdOlSzJw5Ezdu3LB90M2cluKgWr/0TulfrVq1wpYtW5w5JI+lpbhjSz1zsYkkPvnkE0RH\nR+Ott97CuHHjsGDBAty5c0fTMS2VluIbAOTl5SEmJgaPPfYYAgICMGXKFKM8pibPiW/20ZJ8+9vf\n/oadO3ciPj4ezz77rOLx5uKbnoqKCixcuBCRkZF46qmnsGzZMqNJu5LbLZWW5JuaeZajnBTfzNOS\nfAOsxyU1bavJlbZcx7YUWopvWuZU1pzUspahlHPtgo3IysqimWKnU11dTQAcMGCAy8+tldWrVzMo\nKIhlZWUkybKyMgYFBTEtLc2uej///DPDw8O5YcMGfvzxx4bXq6++yieffNJsmz/88APbt29v9Der\nqqpiaGgo33nnHUPZRx99xG7duvH69euaxuoKHyZNmsRJkyY59RxqaO4OqvWrtraWzz77LNetW2d4\nbdiwgcXFxXb3GwCzsrLsbscSEr+UcVb8aoy52ESS27dvJwB+9dVXJOu9BMCYmBhNx6jB2T6Ib8rY\n6lteXh5jY2P5+eef8/Tp05w+fToBcPz48STV5znxzX5agm96rly5omqsluIbSRYXF3PIkCEMCQlh\nSUmJyedKbqtFfGt6bPVNyzzLXifFN+u0BN9I5bikpm2lXGnLdawlxLemx1bf1M6prDmpZS1DyW01\nWPFhv9sslpH0CHmKioro4+PDd99916g8OTmZHTp04O3bt22u9+mnn/LWrVsmdWfNmsU1a9aYlJeV\nlTExMZGhoaFGf7P33nuPAJifn28oq62tpb+/P+fOnatpvC1psYxs3g6q9SszM5Pbtm1zbKcf0VwX\ny8jm7Y7WepZiE0k+++yzBGBIaDqdjgEBAfT19dV0jBqa62SLbN6+kWRaWhorKysN72tra+nn58eO\nHTuSVJ/nxDfH0Nx9a4jSWK3FN51OxwkTJrBVq1bMyckxW1/JbbWIb02LPb5pnWfZ46T4pkxz900p\nLqltWylXar2OtYb41rTY45uaOZWSk2rneGpyrhqsLZa5xWOYnsSePXtQV1eH559/3qh8/PjxqKqq\nQkZGhs31pkyZgoCAAKPPa2pq8MUXXyAuLs6onCTWrl2LZcuWmdxyfezYMQBAnz59DGWtW7fGsGHD\ncODAAZDUNmjBrbDVQTV+6XQ6pKSk4M0330RkZCSSkpJQWFjonIEILseZ8UuPtdgEAF26dAEA/PnP\nfwYAVFZWoqysDOPHj9d0jOD+2OobACxZsgQdOnQwKqurq8PcuXMBqM9z4lvLwR7f1KIU3w4fPoxv\nvvkGUVFRGDlypNk2lNwWPANbfXP0PEvJSfGteWBPfFOKS2rbVsqVWq5jBffGHt/UzKmUnFQ7x1OT\nc+3F7sWyAwcOoEuXLvDy8sLy5csN5X/4wx/QqlUr7NixAwBQUFCAuLg4vPHGG5gxYwYiIiLw17/+\n1WK7H374oeEZZ6D+WdTU1FSjMgCorq5GSkoK5s6di6effhovvPACzp49a7Hd0tJSXLhwwerrypUr\nFutnZ2cDAHr16mVU3rt3bwDAmTNnHFrvyJEj6NWrFwYOHGhUnp6ejsmTJ8PPz8+kTnFxMQCgrKzM\nqDwgIAAVFRW4efOm2XN5KuJgPUoumaOxXxUVFYiKisKIESOQk5ODtWvXIiwsDGvWrFHdpich7tTj\nyPhlLTYBwKZNmxAcHIylS5fiypUr2Lp1K5YtW2a0D4KaYzwR8a0erbFKp9MhKSkJaWlpSEtLA6A+\nz4lv4pstudESSvEtMzMTQP0Ef8yYMfD19cXQoUNx+PBhs8ebc9tTEd/qUfLN0fMsJScbIr61PN8A\n5biktm1bcqWl61hPQ3yrR41vajxRclLtHE9rzrUJDbehWWTLli0EwK+//tpQduXKFSYkJBje9+/f\nn8HBwSR/uQV48ODBRu2g0W2JwcHBJn1pXDZv3jyeP3/e8D4yMpJdu3bl3bt3zfZ1w4YNBGD1NXr0\naItjDQ8PJwBWVVUZlVdWVhIAR44c6dB6U6dO5apVq4zKvv/+e6amphreDxgwwOg7SUhIIABmZmYa\n1ZsxYwYB8OrVqxbH1xhPeQxTHFR2yRzm/NJTXl7O5ORkent7EwAzMjJUt2sJuOFjmOKO4+KXUmzS\nU1JSwlGjRrFnz5587bXXzJ5TzTFKuONt/OKbtlj1+eefMyIiggDYt29ffvTRR9TpdJrynPgmvmnJ\njY3HqkdNfAsKCiIAbty4kTdu3GBOTg579epFAMzNzTU61pLbahHfPN83tfMse5zUI761XN+U4pKW\ntrXmSmvXGdYQ3zzXN1LZEyUn1c7xtORcazh9z7Kamhr27t2b0dHRhrLly5fz9OnThvepqancu3cv\nSfLhw4cMDg6mj4+PUTuN5TEX9BuWnThxwqIAhw4d0jQGtegTTXV1tVF5VVUVAXDo0KEOq1dZWUlf\nX1/m5eUZym7fvs05c+bw4cOHhrLG31Nubi69vLzYvXt3Zmdns7y8nJ999hkDAwPp7e3NBw8eqB6v\npyyWiYPKDjbGnF/m+OCDDwiAQ4YMsbnPegD3WywTdxwTv9TEJj1FRUV8+eWX+dJLLxEAf//73xvV\nU3uMEu442RLftMWqsrIy5uXlMT093bB59ccff6wpz4lv4puW3GhuYUJtfGvbti0DAwONynbt2kUA\nnDZtmlG5JbfVIr41D99I5XmWPU7qEd9arm9KcUlL21pypdrrDHOIb57rG6nsiZKTaud4WnKuNZy+\nZ1mbNm2wZMkSHD58GJcuXUJtbS3y8/MxZMgQwzGvv/46oqOjsW3bNrzzzjuoqalBXV2dXec9efIk\nBg0aBJImr1deecXeYZklLCwMAFBeXm5Urv851B49ejis3tdff40+ffpg0KBBhrIFCxZg+vTpKCgo\nMNxGWVNTAwC4cOECLl26hGeeeQZfffUVunfvjqioKIwdOxZVVVXQ6XQYN24cfHx8bBm6WyMOKjvY\nGHN+mWPevHlo164dCgoKbOit+yPuOCZ+qYlNAJCbm4thw4Zh5syZ+PLLLzFq1Chs3LgRSUlJhnbV\nHOOpiG/aYpW/vz8GDRqEhQsX4sMPPwQAfPLJJ6rznPgmvmnNjeZQG98CAwPRunVro7rjxo0DAOTn\n5xuVW3LbkxHfbPPNlnmWWif1iG8t1zeluKS2ba25Uu11hqcgvqnzTY0nSk6qneNpybm24rAN/ufN\nm4eOHTti69at+PLLL0028vvhhx/w5JNPIjg4GCtWrICvr6/d5ywtLUVhYSEqKytNPnv48KHFOvY8\nwzt48GAAwI0bN4zK//73vwMAnnvuOYfVy8rKMvkeDx48iPHjx2PgwIGGV1FREQBg4MCBiIqKAgBM\nmDABp06dwv379/HTTz/Bz88PJSUlmDVrlsWxeTrioHUHG2POL3N4e3ujS5cu6N+/v6p2PRFxx/74\npTY2vfXWWygtLcWvf/1rtG3bFvv27QMAw34Pao/xZMQ3bbFKT0xMDID6CSugLs+Jb+Kbrb41RG18\nCwkJQUlJidEPKek3vNZvemyOxm57MuKbdt9smWepddIc4pt9eJpvSnFJbdtac6Xa6wxPQnxT9k2N\nJ2pypZo5nq05VxMabkNTZOnSpezUqRN/85vfsLa21uizAQMGsGfPnob3ISEhBGD0vDwa3ZYYFhZG\nAPzHP/5Bsv52xsDAQEO9ffv2EQBXrFhhdK68vDympaWZ7aO9z/CWlpbSz8+PGzduNCpfv349W7du\nbbRPSsPHQLTUI8l79+6xffv2PHv2rMW+6LF227W+rdDQUEZERLjdYyWkYx7D1CMOWnawIVr8un79\nOgEwOTlZ8VglAPd7DFOPuOO4+KXHXGwaPXo0AbC8vNxQ1rVrV3bt2lXTMWpwx9v49Yhv6mJVQy5c\nuEAA3Lx5s8lnlvKc+FaP+Kbet8ZjtYS5+LZz504CMHosR59Dly9fbrEta25bQnxrHr6RyvMse5w0\nh/jWsnxTiktq29aSK7VcZ5hDfPNc39R4ojVXWprj2ZpzG+P0Pcv0XL58ma1ateLatWtNPuvUqRMB\n8Ntvv+Xu3bv5+OOPEwBPnDjBq1evGjaMCwoKMtSJjY01DLagoIDvv/8+/f39CYDffPMN79+/zyee\neIIAOGfOHO7evZuJiYmMjIy0uOGdI3jvvfcYEhLCiooKkuTdu3fZv39/rl692nBMcnIyO3fuzMLC\nQk319OzZs4dhYWGqNt+0lhxramoYHx/P0NBQXrt2TcswSXreYpk4aN1BPZb8WrVqFRctWsRz586R\nrH82PTo6mrGxsayrq7O734D7LpaJO46LX3rMxaZt27YRgGFPh6KiIgLg4sWLNR2jBneebIlv1n1L\nTU1lRkYG79y5Q5Ksrq5mTEwM4+PjTf7Rx1qeE9/qEd/U5Ub9WPv37694LnPx7cGDBxw8eDATEhIM\n+TU9PZ3dunVjWVkZSW1uW0N880zftM6z7HVSfGvZvpHq4pKatrXkSi3XseYQ3zzXNzWeqHFSj7U5\nnpZ2rOGyxTKSXLJkCW/fvm1SvnXrVnbq1InDhw9nTk4O09LS2LlzZ06cOJE//PADFy1aZFjp3LRp\nE8vKypifn89nnnmGHTp0YGRkJPPz8/ncc89x+vTp/PTTT/mPf/yDhYWFjI6Opr+/P7t168b58+ez\npKTE5v6rQafTMSMjg9OnT+fbb7/NuLg47tixwyggvP/+++zTp4/RH1VNPT0TJ040WUG2hKXFsp9/\n/pnDhw/n1KlTefPmTRtG6nmLZaQ4qMecg3os+bVz506Gh4ezQ4cOTEhI4OzZs3nw4EGbk11j3Hmx\njBR39Ngbv/SYi006nY5bt27l8OHD+frrrzM2NpYrVqww2kRUzTFqcOfJFim+6THn28qVK9mvXz92\n7tyZr776KhcvXsyjR4+a+KaU58S3XxDf6rGUG7/77jvOnz+fAOjj48OUlBT++OOPFs9lae5VVlbG\n2bNnc8aMGUxMTOS0adNsclsJ8c0zfdMyz3KEk+Jby/ZNj1JcUtO2llyp5TrWHOKb5/qm1hMlJ0l1\naxlq2lHC2mKZF9ngIU8A+/fvx+TJk9GoWPAgioqKkJmZCW9vb0RHRyM8PNzmtlzhQ3x8vOFcQvPG\ny8sLWVlZhr+5o5H4JTTE2T6Ib02HI/OcoxDfBFcivgmuRHwTXIn41rJx9RzPig8Hmt/PIgro27cv\nVq5c2dTdEARBEASnIHlOEARBEASh+eFOczyH/RqmIAiCIAiCIAiCIAiCIHg6slgmCIIgCIIgCIIg\nCIIgCI+QxTJBEARBEARBEARBEARBeIQslgmCIAiCIAiCIAiCIAjCI2SxTBAEQRAEQRAEQRAEQRAe\nIYtlgiAIgiAIgiAIgiAIgvAIWSwTBEEQBEEQBEEQBEEQhEf4WPogPj7elf0Q3JRr16655Dw5OTni\nnOAwxCUBcF38Et8EQHwTXIv4JrgS8U1wJeKb4Eqs+ea9atWqVQ0LOnbsiOLiYpB0dr8ED8DPzw9R\nUVGIiopy6nkqKiqc2r7gHjz11FOYOXMmOnfu7JT2JX4JDXF2/BLfhIaIb4IrEd8EVyK+Ca5EfBNc\niRXfznlRLBEEQRAEQRAEQRAEQRAEADgge5YJgiAIgiAIgiAIgiAIwiNksUwQBEEQBEEQBEEQBEEQ\nHiGLZYIgCIIgCIIgCIIgCILwiP8HhlaFNYP8X+AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "Image(graph.create_png())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "kfold = cross_validation.KFold(n=num_instances, n_folds=num_folds, random_state=seed)\n",
    "cart = DecisionTreeClassifier()\n",
    "num_trees = 100\n",
    "model = BaggingClassifier(base_estimator=cart, n_estimators=num_trees, random_state=seed)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BaggingClassifier(base_estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            presort=False, random_state=None, splitter='best'),\n",
       "         bootstrap=True, bootstrap_features=False, max_features=1.0,\n",
       "         max_samples=1.0, n_estimators=100, n_jobs=1, oob_score=False,\n",
       "         random_state=7, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sklearn.cross_validation.KFold(n=768, n_folds=10, shuffle=False, random_state=7)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kfold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.770745044429\n"
     ]
    }
   ],
   "source": [
    "results = cross_validation.cross_val_score(model, X, Y, cv=kfold)\n",
    "print(results.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.67532468,  0.81818182,  0.75324675,  0.63636364,  0.81818182,\n",
       "        0.81818182,  0.85714286,  0.85714286,  0.69736842,  0.77631579])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  }
 ],
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