{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import dependencies and packages neededfrom sklearn import datasets\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import metrics\n",
    "from sklearn import tree\n",
    "\n",
    "# to make this notebook's output stable across runs\n",
    "np.random.seed(42)\n",
    "\n",
    "# To plot pretty figures\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['axes.labelsize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 12\n",
    "plt.rcParams['ytick.labelsize'] = 12\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ccp_alpha': 0.0,\n",
       " 'class_weight': None,\n",
       " 'criterion': 'gini',\n",
       " 'max_depth': None,\n",
       " 'max_features': None,\n",
       " 'max_leaf_nodes': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'random_state': None,\n",
       " 'splitter': 'best'}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this step is just to demonstrate the adjustable parameters within the algorithm and not a necessary step\n",
    "# Specifically note 'criterion', max_depth', 'max_leaf_nodes'\n",
    "# Also notice 'min_samples_leaf' and 'min_samples_split'\n",
    "DecisionTreeClassifier().get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The length of the dataset is  768\n",
      "The shape of the dataset (Number of columns / features) is  (768, 10)\n"
     ]
    }
   ],
   "source": [
    "# load the dataset (stored in a variable)\n",
    "diabetes = pd.read_csv('./diabetes.csv', sep=',',header = 0)\n",
    "\n",
    "print (\"The length of the dataset is \", len(diabetes))\n",
    "print(\"The shape of the dataset (Number of columns / features) is \", diabetes.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\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>2</td>\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>3</td>\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>4</td>\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>5</td>\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": [
       "   PatientId  Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin  \\\n",
       "0          1            6      148             72             35        0   \n",
       "1          2            1       85             66             29        0   \n",
       "2          3            8      183             64              0        0   \n",
       "3          4            1       89             66             23       94   \n",
       "4          5            0      137             40             35      168   \n",
       "\n",
       "    BMI  DiabetesPedigreeFunction  Age  Outcome  \n",
       "0  33.6                     0.627   50        1  \n",
       "1  26.6                     0.351   31        0  \n",
       "2  23.3                     0.672   32        1  \n",
       "3  28.1                     0.167   21        0  \n",
       "4  43.1                     2.288   33        1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 10 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   PatientId                 768 non-null    int64  \n",
      " 1   Pregnancies               768 non-null    int64  \n",
      " 2   Glucose                   768 non-null    int64  \n",
      " 3   BloodPressure             768 non-null    int64  \n",
      " 4   SkinThickness             768 non-null    int64  \n",
      " 5   Insulin                   768 non-null    int64  \n",
      " 6   BMI                       768 non-null    float64\n",
      " 7   DiabetesPedigreeFunction  768 non-null    float64\n",
      " 8   Age                       768 non-null    int64  \n",
      " 9   Outcome                   768 non-null    int64  \n",
      "dtypes: float64(2), int64(8)\n",
      "memory usage: 60.1 KB\n"
     ]
    }
   ],
   "source": [
    "diabetes.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 576 entries, 357 to 102\n",
      "Data columns (total 10 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   PatientId                 576 non-null    int64  \n",
      " 1   Pregnancies               576 non-null    int64  \n",
      " 2   Glucose                   576 non-null    int64  \n",
      " 3   BloodPressure             576 non-null    int64  \n",
      " 4   SkinThickness             576 non-null    int64  \n",
      " 5   Insulin                   576 non-null    int64  \n",
      " 6   BMI                       576 non-null    float64\n",
      " 7   DiabetesPedigreeFunction  576 non-null    float64\n",
      " 8   Age                       576 non-null    int64  \n",
      " 9   Outcome                   576 non-null    int64  \n",
      "dtypes: float64(2), int64(8)\n",
      "memory usage: 49.5 KB\n"
     ]
    }
   ],
   "source": [
    "# split the data into train and test using the SciKitLearn package\n",
    "from sklearn.model_selection import train_test_split\n",
    "train,test=train_test_split(diabetes,test_size=0.25,random_state=42)\n",
    "# view the information\n",
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 192 entries, 668 to 615\n",
      "Data columns (total 8 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Pregnancies               192 non-null    int64  \n",
      " 1   Glucose                   192 non-null    int64  \n",
      " 2   BloodPressure             192 non-null    int64  \n",
      " 3   SkinThickness             192 non-null    int64  \n",
      " 4   Insulin                   192 non-null    int64  \n",
      " 5   BMI                       192 non-null    float64\n",
      " 6   DiabetesPedigreeFunction  192 non-null    float64\n",
      " 7   Age                       192 non-null    int64  \n",
      "dtypes: float64(2), int64(6)\n",
      "memory usage: 13.5 KB\n"
     ]
    }
   ],
   "source": [
    "# Ensure only labels are saved in y variables\n",
    "y_train=train['Outcome']\n",
    "y_test=test['Outcome']\n",
    "#drop the unwanted / irrelevant columns, features are stored in X variables\n",
    "X_train=train.drop(['PatientId','Outcome'],axis=1)\n",
    "X_test=test.drop(['PatientId','Outcome'],axis=1)\n",
    "# view the information\n",
    "X_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Pregnancies',\n",
       " 'Glucose',\n",
       " 'BloodPressure',\n",
       " 'SkinThickness',\n",
       " 'Insulin',\n",
       " 'BMI',\n",
       " 'DiabetesPedigreeFunction',\n",
       " 'Age']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#extract labelled results\n",
    "mtarget =  diabetes[\"Outcome\"].values \n",
    "mtarget = np.array(mtarget, dtype='int')  #array of class targets results win=1, lose=0\n",
    "values = np.array(diabetes.values,dtype='int') #array of attribute values for training data\n",
    "values = values[:,1:9] #slice-> cols 1,2,3 and 4 kept.  cols 0 and 9 excluded.\n",
    "targetNames=['Diabetes','No Diabetes']\n",
    "feature_names=list(diabetes)\n",
    "\n",
    "\n",
    "feature_names=feature_names[1:9]   #slice-> cols 1,2,3 and 4 kept.  cols 0 and 5 excluded.\n",
    "feature_names\n",
    "#values\n",
    "#mtarget\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=5, random_state=42)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gini_tree_clf = DecisionTreeClassifier(criterion='gini', max_depth=5, random_state=42)\n",
    "gini_tree_clf.fit(values, mtarget)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 1 1 0 1 1 1 1 1 1 0 0 0 0 0 1 0 1 0 1 0 1 1 0 0 0 0 1 1 1 1 0 1 1\n",
      " 0 0 1 0 0 1 1 0 1 1 0 0 1 0 1 1 0 0 0 1 0 0 1 1 0 0 0 0 0 0 1 0 1 1 0 0 0\n",
      " 0 0 0 0 0 0 1 0 0 0 0 1 1 1 0 0 0 0 0 1 0 0 1 0 1 0 1 0 0 0 1 0 1 1 0 1 0\n",
      " 0 0 1 0 0 1 1 0 1 0 0 1 0 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0\n",
      " 1 1 0 0 1 0 0 1 0 0 1 0 1 1 0 0 0 1 0 1 1 1 1 0 1 0 1 0 1 1 1 1 0 0 1 0 1\n",
      " 0 0 0 1 1 0 0]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "192"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_gini = gini_tree_clf.predict(X_test)\n",
    "print(y_pred_gini)\n",
    "len(y_pred_gini)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "668    0\n",
      "324    0\n",
      "624    0\n",
      "690    0\n",
      "473    0\n",
      "      ..\n",
      "554    0\n",
      "319    1\n",
      "594    0\n",
      "6      1\n",
      "615    0\n",
      "Name: Outcome, Length: 192, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[93 30]\n",
      " [14 55]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.869     0.756     0.809       123\n",
      "           1      0.647     0.797     0.714        69\n",
      "\n",
      "    accuracy                          0.771       192\n",
      "   macro avg      0.758     0.777     0.761       192\n",
      "weighted avg      0.789     0.771     0.775       192\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# display the metrics of the model's classification through confusion matrix and classification report\n",
    "# (accuracy / precision / recall / f1)\n",
    "print(metrics.confusion_matrix(y_test, y_pred_gini))\n",
    "\n",
    "print(metrics.classification_report(y_test, y_pred_gini, digits=3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Actual  Predicted\n",
      "668       0          0\n",
      "324       0          0\n",
      "624       0          0\n",
      "690       0          0\n",
      "473       0          1\n",
      "..      ...        ...\n",
      "554       0          0\n",
      "319       1          1\n",
      "594       0          1\n",
      "6         1          0\n",
      "615       0          0\n",
      "\n",
      "[192 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "# use Pandas to create a dataframe to view the ground truth and classifications side by side\n",
    "data = {'Actual': y_test,\n",
    "        'Predicted':y_pred_gini}\n",
    "df = pd.DataFrame(data)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.869     0.756     0.809       123\n",
      "           1      0.647     0.797     0.714        69\n",
      "\n",
      "    accuracy                          0.771       192\n",
      "   macro avg      0.758     0.777     0.761       192\n",
      "weighted avg      0.789     0.771     0.775       192\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Use Seaborn to create a confusion matrix heatmap and MatPlotLib to display\n",
    "import seaborn as sn\n",
    "confusion_matrix = pd.crosstab(df['Actual'], df['Predicted'], rownames=['Actual'], colnames=['Predicted'])\n",
    "sn.heatmap(confusion_matrix, annot=True)\n",
    "plt.show()\n",
    "print(metrics.classification_report(y_test, y_pred_gini, digits=3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', max_depth=3, min_samples_leaf=5,\n",
       "                       random_state=42)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entropy_tree_clf = DecisionTreeClassifier(criterion='entropy', max_depth=3, random_state=42, min_samples_leaf=5)\n",
    "entropy_tree_clf.fit(values, mtarget)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 1 0 0 0 1 1 0 1 1 0 0 0 0 0 1 1 0 0 0 0 1 1 0 0 0 0 1 1 1 1 0 1 1\n",
      " 0 0 1 0 0 0 0 0 1 0 0 0 1 0 1 1 0 0 0 1 0 0 1 1 0 0 0 0 1 0 1 0 1 1 0 0 0\n",
      " 0 0 0 0 0 0 1 0 0 1 0 1 1 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 1 1 0 0 1 0 0 0\n",
      " 1 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 1 1 1 1 1 0 0 1 0 0 1 1 0 0 0 0 1 0 0 0 0\n",
      " 0 1 0 0 1 0 0 0 0 1 1 0 1 1 0 0 0 1 0 1 1 0 1 0 0 0 1 0 0 0 0 1 0 1 0 1 1\n",
      " 0 0 0 1 0 0 0]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "192"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_entropy = entropy_tree_clf.predict(X_test)\n",
    "print(y_pred_entropy)\n",
    "len(y_pred_entropy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[100  23]\n",
      " [ 26  43]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.794     0.813     0.803       123\n",
      "           1      0.652     0.623     0.637        69\n",
      "\n",
      "    accuracy                          0.745       192\n",
      "   macro avg      0.723     0.718     0.720       192\n",
      "weighted avg      0.743     0.745     0.743       192\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# display the metrics of the model's classification through confusion matrix and classification report\n",
    "# (accuracy / precision / recall / f1)\n",
    "print(metrics.confusion_matrix(y_test, y_pred_entropy))\n",
    "\n",
    "print(metrics.classification_report(y_test, y_pred_entropy, digits=3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0.8333333333333334, 'Glucose <= 127.5\\ngini = 0.454\\nsamples = 768\\nvalue = [500, 268]\\nclass = Diabetes'),\n",
       " Text(0.25, 0.5, 'Age <= 28.5\\ngini = 0.313\\nsamples = 485\\nvalue = [391, 94]\\nclass = Diabetes'),\n",
       " Text(0.125, 0.16666666666666666, '\\n  (...)  \\n'),\n",
       " Text(0.375, 0.16666666666666666, '\\n  (...)  \\n'),\n",
       " Text(0.75, 0.5, 'BMI <= 29.5\\ngini = 0.474\\nsamples = 283\\nvalue = [109, 174]\\nclass = No Diabetes'),\n",
       " Text(0.625, 0.16666666666666666, '\\n  (...)  \\n'),\n",
       " Text(0.875, 0.16666666666666666, '\\n  (...)  \\n')]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZGtkcPXuFt19phW++3Izo3YGIyCh++uNvWtSujLurE7uPnsXN2SnZcQjxLCTpimemHvGo3zv3w1m/5yi/jX+bhet2cvnGLWpVLEnxgt5s2HuMn5dt5Oexbzx3a8aH7T56jne6tUq64WLNzsOPbC3532hj4+IZ3LUFzo4O/x5T4p9ag7uLU4oSt/8ehxBpJUlXPJMBnZowetrvrPDaTzEfb+pV8QPA3dkJO1sbvp73F6cuXqeYTx62HjjJqu0HSdCaCiUKceLC1eduzbhg9XYOnrrI/YhI6lXxo1Th/ElJ93GtJXccOUPw3TCuBYUwoHMTnB3sGfTFz3h7emBrY83Y/v+jWEFvRn6/gJ6t69O6blXe+mImzo6mJwYX8vZMdhxCPAu5kCYeK6OVjGUmciFNPI6UjAkhhAVJ0hVCCAuS6QXxWI72doFRMbF5jI4jM3Kwsw2KjI7xNjoOkfFI0hVmo0zlDMOBgUBrrfURg0N6ZkqpPMCfwGmgr9Y62uCQRBYhSVeYhVLKBvgeqAG01FpfNzik56aUcgLmATmADlrrUGMjElmBzOmK56aUcgP+AgoCdbJCwgXQWj8AOgGHgZ1KqcLGRiSyAkm64rkopQoA24BLmKYUUvZ5zMS01vFa67cxncXvUErJvb/iuUjSFc9MKVUR2IXpJ/gbWus4YyNKP1rr74B+wF9KqfZGxyMyL5nTFc9EKdUcmAMM1FovMjoeS1FKVQFWAF8Ck7X8AxJpJElXpJlSqj8wBuiotd5pdDyWppQqBKwENgFDtNbxBockMhFJuiLVlFJWwHigHdBCa33e2IiMo5TKASwGIoGXtdbhxkYkMguZ0xWpopRyBH4D/AH/7JxwAbTWd4EWwG1gi1Iqr7ERicxCkq54KqWUF7ABiAcaa61DDA4pQ9BaxwC9gaXAbqVUWYNDEpmAJF3xREqpEpgqFDYBr2itowwOKUPRJuOAEcBGpZT5HsAmsiRJuuKxlFJ1MNXgjtdaf6i1TjA6poxKaz0P6AzMU0q9ZnQ8IuOSC2nikZRSXYHJQDet9TqDw8k0lFKlMFU2LABGSUmZ+C9JuiKZxKY1H2C6EaCV1vqYwSFlOkqp3JhqeS8Ar0mzHPEwmV4QSZRStsBPmPoN+EvCfTZa61tAA8ABWK+UymlwSCIDkaQrAFBKuWP6WewN1NVa33jKW8QTaK0jMc3x7sXULKeowSGJDEKSrkApVRDYDpwD2kmhv3lorRO01u8CU4DtSil/o2MSxpOkm80ppSoDO4FZwJtZuWmNUbTWU4E+wAqlVEej4xHGkgtp2ZhSqhWmZNtfa73E6HiyusQvuBWYqkK+ksqG7EmSbjallHoDGAW011rvNjqe7EIp5QOswlT/PEh+WWQ/knSzmcSmNROBlpia1lw0OKRsJ/Gi5SIgFnhJ5tCzF5nTzUYSn/m1CKgKvCAJ1xha63uYvvRuANuUUvkNDklYkCTdbCKxYH8jplaETbTWdwwOKVvTWscCrwMLgV1KqfIGhyQsRJJuNpB4a+puYB3QXe6QyhgSm+V8DrwH/K2UamJ0TCL9SdLNopRSXZVS3kqpesAW4BOt9Wi5Yp7xaK0XAB2BuUqpvkopK6XUEIPDEulELqRlQYkXai5jqk4YjenJBhsMDUo8VWIbzVWYphy6YLrIts/YqIS5yZlu1tQDuAa8CwzAdLeZyOC01meBvsCLwH3gLWMjEulBznSzIKVUKOAIxGA6422jtb5sZEzi6RIbDq0FqgNxgCtQUGt93dDAhFnZGB2AMK/E1ozbgd+BtVrr2waHJFIpsaLhRaWUC1AbGIj8G81y5ExXCCEsSOZ0hRDCgrLsTxdHO5vAqNj4PEbHkdE52FoHRcbEeRsdR1Zl7+AYGBMdJZ9DM7GzdwiKjorM1J/XLDu9oJTSt2cNMDqMDM/r1WlorZXRcWRVSim98myE0WFkGS1LOGf6z6tMLwghhAVJ0hVCCAvKsnO6z+L+g2hqffg7U3o3oEFZH7NsMzImDke71A3zL1tOcvDiLe6ER9GwXEF61Pdjya6zrD1yBXtba8r6eNKvyb99UQKC79Nl0kpeKJkPTzdH3m9f3Swxi4zn6J6tzP5yNMXLViYk6AbdhozCt0QZ2pfLxTsTfqJO8w6cObKf97s15YfVBzm6dyvW1ja82PblJ243JjoKO3uHp+7/2sWzLP5pEgnx8cTGxvDOFz9y63oAc77+CA/PPDi5uNLznbHJ3vNBj+bk9y0OQMc+Q8hbsMizHn6WIkn3IfO3nebjLv7M33aaBmV9iI2LZ9DMTRTwdOXczVCaVvSlTbWifP7HXrTW3I+M4ZMutcjhbJ9sOw+iY1l/NIAtJ67iZG/LuJdrpWr/3ev50b2eH1prOn35Jz3q+7H+aABfdKuDm5MdXSatTJZ0AVwc7IiOi6eQl5vZxkFkTNUbNKfLG++xc90KTh3cjW+JMpSsWJ29G1dRp3kH1i+ZS7UGzZ+6nXPHDrJ9zVLu3LpJ535DKVis9FPfU6BICYZ8/gMAP3zyLjeuXODg9g00aNMF/0at+PHTYZw7dpDi5SonvcfB0RmlFNY2tuTIlfvZDzyLkaSbSGvN2sOXWTKsDVtPXufSrXucu3mX8r5eDGhagWlrjwCweOdZQsOjKOTlRmxcAscDgqld+t92qMPmbiE4LIqXa5difLc62NlYA7Dt5DWW77uQbJ+dXyhBjeJ5U8TyxbJ99G5YDoBudUvzv6/+wtpKMbB5xWTr+eRyZd3ojmit6T11HbVK5aOgpyTfrGrflrXcDbnNxVNHGfzZVACsrKwoVMKPQzs2YGtnh7PL4///796wkj9mTKZuy0607TWQnF6mIoDoqEh++uy9ZOvmLVSEjr2HpNjGuWMHiYqMoFBxP3J6efPrlE85sW87wUE3uXUjIFnSHTVtIVZWVuzesJKls76l65sfmGEUMj9JuonWH7lCdGw8o37bQUR0LDM3HKdu6fz89zJpgtbUKpWfrnVKPXI7vRuWY/ne86w6cJFLQfdoVaUI+XO5pCqGuPgExvy+k9ql8tO8cmEAvlyxn9UjO2BjbUXb8ctpXfXfJ3mbbj4z/enp6kh4ZGzaD1xkGtXqNaXLG+8REXaPyR8M4MPv5gPQpFNPBrauziczl7N8ztTHvr9M1Re4dyeYM4f3cutGADUbtqJ05Zqp3v/+LWvZt3ktb338LQCuOXIyYPRXAEwa3jfFGbOVlemSkYdnHh6E3UvTsWZlknQTzdl8kllvNSOPuxMAnb/8k/fbVWPpnvN8umQPJ6+G8HLtUrSuWpThc7dy9sYdIqJj6d+kAkW9cyRtp1T+nJRKnFs9FhDMwl1neLtVFer4FaCOX4EnxvDJot3sOx9EdGw8+y8EMapzTVpWKcKQWZuxtlJUK2Yq9xw6ewtD21Th0q17/L7jLNZWChcHW/x8cqXP4IgMYe+m1YTeDiL8fii1m7VPet3NIxc/bzyFrZ3dE9/v6u5B0849adq5J/fuBLNz3QrsHR0p6leRNz+e8sT3Xjh5hC+H9aF2s/ZM+/gd2vYciKOzK79O+QSAwqXL41O0JId3buLS6WO0f20QE4e+ipOrO3eDb9H3g/HPPwBZhNTpPsVPfx/jZmgEt+494IvudXC2tzVDdBmH1OmmL6nTNa+sUKcrZ7pP0bdROaNDEEJkIZJ0n2LNoUtYW1nRuEKhFMsOX7rFsYBgutfzS9M2LwTeZeLy/bg62lLGx5NeDcokLbt9/wHj/zD1rb4TEcV3fV4kPj6BkQt2suXEVY5M6gHArrM3+GP3OSJj4vF0dWDsSy88x1GKzGj3hpVYW1tTrX6zFMvOHTvIxVNHafq/Xmna5vXL55n/7ac4ubhRuFQ5WrzcJ8U6y+d8z/rFc/nuzz2cObKf9UvmAqbpj89/WU1+32JorZk0vC82tnZJF/2EiSTdh1y6dY9PFu2meF4PNh4LYObAJoRGRGNjZUVA8H36TF1P+xrF2H8hiM9fqU1EdCy37kWmeT/frznMBx2qU8jLjS6TVtKtbmlsrE0XHbzcnPiqVz0ARszbTmBoBEW9czCldwM6TlyRtA3/EvnwL5EPgJcm/WWGoxcZ2c2Ai8z6cjQ+RUpwYOt6PpjyK2H37mBtbUPQtSuMH9ydui07cfrIPvqP+pLIB+GEBgeleT9LZkym+5AxePv4MqZPe5p27oW1zb9p4uTB3ShlhVtOTwBKVqhKyQpVuXfnNiG3bpLftxgAy2Z/R7X6zTi0Y6N5BiALkaT7kDmbTjC4ZWUq+Hpx4mpwiuW+ud0Y0LQC87edZsfp6+ROvOj2sNSUhl0LCccnlysAuVwdCAmPSrqAB3D0ym1mbjhOeFQseT2cHxvv8n0XWLzzLNWLpSw7E1nLqgUz+F+/oRQrU4mLp4+lWO5dsDDtXxvE+iVzObp7Kx5eKXvsHNm1mW2r/0j22ottX8avin/S32/fvEru/AUB0wW6e6HBSaVl90ND2LB0Hm9+PIXdG5J/0a9aMJPmL70GwMkDu0BrSlaoJkn3EeQ24IdoDeoJU/ROiXeW2VpbERUb/8z7KZDLhashYQCEhEWRyyX5HUHlC3nxzWsNqFw4NxuPXX3sdtpWK8ovg5tz8GIQoeFRzxyPyPi01kklgo/i4Gj6cra2sSUm5tk/C155fbh1PQAwJVl3D8+kZUd2bSY+LpafPhvOjcvn2bB0HgBxsbEc2rGRqvWaAqZphls3Aljw/eecObKXkwd3P3M8WZGc6T6k14tlkqYXroWE4+rw5BKcR0lNadjAZhUZ/8deXB3taFbJFxtrK75acYC6fvlxtLPh162n0BrCo2LoVs9U+zj8l62cu3mXd+ds4a0WlTh8+Ta7z94kLj6ekvk88HB5+q2cIvNq2bVv0vTC7esBOLm4pnkbFfzrU8G//hPX6dhnCL988zFOLm7UaNgSaxsbfpv6BRX861OnRUfqtOgIwOWzJ2nY/hUAtq5aTK2mbZPqcnu9+zEAQdeu8NvUL/BLQy1wdiAlYw8Jj4zh+zWHiYiOw87GipGdsv6HRUrG0pe5SsYehIfxx8xviHoQjq2dPT2HfmSG6DIfKRnLYlwc7XhPmsaIDMjJxZVug0caHYYwA0m6ZtZx4gqWDGuTLtvWWvPmjI3YWlsx+bUGbDt5jZkbj5PTxQEPZwdGda7JhGX7OB4QTG53J5pV8qVR+ZSlbiJ7GtGzJZ/NWWn27e5a/yd7N60mMiIcn6IleWXQhxzZtZm/5v2Ia46cuOXISa93P2btojmcP36QuyG3qd2sPfVadTZ7LJlBtk26u8/eZOaG4xTI5UK9MgV4oWQ+vly+n4joWOLiNZ+9UouFO8/y99EACnq6EhoeRYl8HpwPvEvRPDkY2LwiHSasoEFZH8IiY8if04WeD9XbrjxwkZ1nbhAbl0AFXy+aVy7MO7M34+vlhm9u92S1uak1fd1RGpUvxJYTpotrW09d5+1WVajg60WHCaZyMmsrhYOdDdFx8RTO7W6ewRIWdWL/Tv76dTpe+QpQqdaLlK1WhwXff07Ug3Di4+J5feRENi6fz/4t68iTvyBhd0PxKVqSa5fOkb9wMTr2HsKIHi2oXKcRD8LD8MpbgOZdeidtf+e6FRzft43Y2FiKl61EzYat+HbUW3j7+JK3YJFH1uY+iX/j1vg3bg3AiB4tADi8azMvDRhGsTKVkl775xbk+6EhTP90mCTd7OZ6SBi5XB1oXbUIlQrnJiYuAY2pVeKuszc4ee0OAP4l8tKnUTle/2E9Dcr68EazinScuIKBzSuSoDW9GpTB1dGOThP/TJZ0v1l5iEblTaU3hy7dolap/MTGJVCvjA/+JZOXeB0PCGb2phPJXmtUviDNKhVO+vueczfRQJWiuZOSbvvqxRj88yac7G1pXtkXgLdbVcHKShF07wFvz9rM/CEtzDxyIr3dvnkVt5ye1GranhLlqxAXG4PWGkdnV47v28HlM8cBKFu1Fq279+eLt3tSuU4jOvQezIieLenYewgJWtPi5T44ubjxYa9WyZLuoulfJlUanD16gHLV6xIXG5OY4Gsni+XiqaOsWjAj2WtV6zWlZsOWKeJe/dvP+Dc2/cqr17ITkz8YgIOTMzUbtUpa5/dpEzm4/W/+1/9d8wxWJpRtk25H/xJUL56XlQcvsnzvBaoV8yaXqwP9m1TgndmbiYgydexydzL1yrWzsU7674fFxick+/MfSsG7bapiZfXvnP/3fRuy4/R1ekxZw8KhrUiLdYevEBkTx5fLD3DoUhB7zwUyZdVBFr3bmhzO9rwyeRXd6pTGxdFUcZHDyZ7o2Lg07UNkDPVbv4RfZX92rl/BttVLKF2pJu45PWnX602mjHyTqAemC3PObqZfMrZ29ri45UixnbhY02c4Pu4/nwOlePnND5KqDQDemfATx/Zs45M3XuKTmcvTHPPv0ybimsOD1t37AzDn64/4ZNYKXN09+KhfJ5p06omTiysvDRhGxz5DGPlqa6rUaZzm/WQF2Tbprth3gf0XgngQHUuN4nkp7+vJwp1nSEjQnA+8m6ptWCnFj+uPEnT3AR1qFEu2bEDTCrw1cyOero7kdneiQVkfft16CmsrRdmCnsnWLVvQky971nvivkZ1NlVSBATfZ9KKA1Qv7s3/XijJ8F+24upgh5ebIy6Odny5fD+BdyMIjYhmYLOKqR4PkXFsX7OU04f2EBX5AL8q/hQrU5GNy+aTEB/P9UvnUrUNK6VYPncqobcCU/yM7/DaICa/3w+3nJ54eOWhSu1GrF00Gysra4qUSt4kv0jp8k/tQPbnLz+wcfkCylWvzXejB/HG2Mk0bNeVqWOH4OTiRo5cuXFycWXhDxMJuRVI1INwmnTqkbZByUKkZOw5pOdFM0uRkrH0ZVSXsfS6aGa0rFAyJnekPYfMnnBF1pUVE25WIUn3IQ83lDG3F8csYt7WUwB8u+oQw+Zuofs3q9l+6joA7/2yjWFztzDk500EBN8H4NMle3hxzCJ2n7352O1eCLzLa9+v5f1ft/Hpkj1Jr2utGfjTBob8vAkw9YSoP3ohN+6Ep9chinQwomfKC1bmMqjdC6xbPAeADUvnMaZvB36b+kXS8lULZvD9mMFMHPoqN65cICY6iknD+zJ17Nt8O+otwu6FPnK7pidb9Kdn3RJJr+3fspbvRg/iu9GDeMXflwfhps94bEw0I3q2TNrvjrXL6ZPF26lmi6T7wbztnLluqkYYOnsLl2/dZ+mec3y0cBdvztjI8YDkzW3+Sb437oTz5gxTw45vVx1i5IIdvDVzIwcvpr17k4eLPa/UNd3S+1aLSkzsUY9321Zl68lr3I2IJvBuBBN71KN3o3J8v9r0PLYPO9agWSXfJ253/ZErdPYvwfhudXgQHcvhS7eAf8vL/lHHr0CKuWRhnB8+eZeA84lfwqPe4mbAJbb8tYifJ3zIpPde5+Kpo8nW/yf5BgdeZ9J7rwOw+KdJ/PjZcL5+vx9njuxPcwwu7h406dQTgIbtX6FjnyFJy+JiY9mzYSUDP/qGboNHs2TGZK5eOI1LDg/eGPs1Ves2SUrY/+Xs6s6Qz38gf+HiSa9VrdeUNz+eQsuufanRsBVOic9ymzflUxq0eSlpvVpN2yY13MmqssWFtO51SzNv22nea1+NkLBIfHO7cfTKbbTWuDrasWD7aT7tWvux7z9/M5R1R65Qp3R+HGyt2XsukMpF/u3iNHvTiRSJ+/321fF0c3zsNkf/tpODF4P4tGttcjjbU9evAB/M246Hsz3XEpvhpMZLtUryxbJ97Dp7k5uhEVwNCSM6Lj5FeZnIWJr9rxfrFs2h2+BR3LsTTN6Chblw8jBaa5xc3Pj7j194/cOJj33/tYtn2btpDRVq1sPe3pGTB3dRskLVpOWrFsxIkbi7DxmFe06vVMV3PzQY98T2jXkKFOL2jasUKV2BPRtX8+Onw0Ap4uPS3vRpxdxptO01EICtKxdT1K8C7rm8CAl6/K+5rCZbJF0/n1ycD7zLwh1naJdYZfDj+qP8NaI9e88FMn/bqWTrWyV2c4qINpXcJGgo6OnK8HbVzBbTx11eICQskiE/b+aXwc3p3bAsYJoGiE5DBzMPFwfGd6sDwMCfNlAqf05+234mRXlZ9eLeZotdPD/fkmW5dukcG5bNp17LTgCsmDOVCQvWc/LgbtYvnpts/X/KuyIjTNNDCQkJeBcoxCuDPkyX+Nw8PLl3x3Qicet6AF75fFBKJT3Rd93iOSlL0Z7i3p3b3LkdiG8JUz374V2bcXB0JDjwBiG3blKraTt8ipY074FkQNki6QK0qFyYyX8dYOdnLwNQIp8Hk/48wJ1HtET0L5mPL5buJS5BJ62bL6cL7/+6DWsrRZ3S+ZPduJDWu8vG/r6TqNh47j+I4bXEZDth2T6C70cSER3LZ6+YzrqnrzvKuiNXOB4QzIPoWF4sV5Bxi3cna8RzMzSc8UtNT5ooV9CT4nk9HlleJjIe/8Zt+H3aBKavOQRAgaIl+W3aBO6HpuzlXLZ6HX79Zhzx8aZEV7BYKXJ552fax0OxtraifM36yW5YSOtdZfu3rGXlghncvR2Ek4sbbXoMoEbDlnw/dggPwu7R9S1Tcp/28VASEuLRCQn0H2V6EvCcr8akaMAzdezbXLt4lu9GD6JT33fw9vFl9W8/0yyx5y7AoHHfAXB0z1ZOHtiVLRIuSMmYxZijvOzUtRB2n73Jqy+Wfab3vzljIyM6VCdfzn8fCS8lY+krIz+Y0hxlZZfPnuDE/p207NrXTFE9OS4pGROpli+nS1L1wrMqXSDXMyfcf6Yt7GysnysGkXV45S3w2IthqeVbooxZE+6Otctxds3aPUOybdINCL6fVE6V3hZsP01gaATHr4bw1syNzN92GjA99HL9kSuPfc+inWefuu3UlrnV8SvATwMaP/HinrCsoGtX+GbEGxbZ1/o/fqFX/VJJtxCP6NmSt8dPT6peeJR5Uz7l0ze7Mn3cu3w1rA97N60GTBfpLpw88tj3nNi/86nxPK4UrlbTtnz43fynvj8zyzZzuoGhEXy0cBde7k4UzeNOg3I+SctmbTzO5Vv3CQ6L5MOONYiMieOTxbspnNudCr5e1PUr8Nwdwv73Qkk6v2CqW3xzxkYqF8md9NDLmLj4FB3OwNSp7OzNUK4Gh/Ft7wbsuxDEX/svEp+gKZDLhfplfbhy+z4Tlu2jk38Jdp65walrITyIjqN5JV+KeudIdhztqhd7UoginYUE3eTniR/ikSs3+QsXp3LtRknLVs7/iZsBF7l/J5ge74wlOvIBs78aQ96ChSletjIV/Os/VycwgKp1m/Db1C+SnuwA8Pcfv3Ji/05iY6Jp2P4VKtV6Mdl72vV6kzJVX0Brzeg+7ShdqQaht4OIehDB3ZBbLJ8zlciIcJxcXOnx9hgA1i+Zy55Nq4gMD2PgR9+k6Grm7VOYwKuXmTflU1p168f6JXO5czuQiPv3aPFyX+JiY5J1WatUq+GzDXgGlW2S7o9/H6NPo3JUKWoq9frnBgQApRTWiU/jXbH/AhV8vXCwtaFxhUJUL+bNtZDw5+oQ9l/Vi3lz9sa/heVa88gOZzVK5GVA0wpMXXOYTSeu8dP6o1QrZroodiwgmLdaVKKQlxvD21UjIjqWnzccp1klX9yd7NlzLhBXJ7tkxyGMtWLuVFp3H0CpCqYqmKBr//7KMX0GTf8ct69ZSrEylbCzt6d6g+aUrlSTWzeuPncnML8q/hzbs41rF//9BbVh2Tw+n7ua2JhoPu7fOUXSfTi+Yn4VCbx6Oek1a2sb4mJjcHZzZ8/ff9Gh92AAqtVvTq2mbZn28VCunDuZoqtZk0498fbx5ZVBHz6y9M3DM3eyLmtZTbZJuvDoh06Ghkex8VgAcwc1Z8mus1y+HYZ/iXwUzZODzSeuMmfTCab3b/xcHcL+a8+5mwxqUYmDiTcyrD9y5ZEdzv4bbmx8AgObV8TZ3vbfY0pcS2twd7JLUdb23+MQxlIp/q9C2N07HNi6nlHTfmfzn78TePUyZavVIn/hYhzavoFVC2YwfNLs5+4EBtD97dH88Mmj2io++dqU1przJw/T4aEbKP7+41f8Kvvj37g1Zw7vTZq6SPEAzUd0NftnnceVvj3cZa3P+5+n/gAzgWyTdF9vVI6PF+3G2+MSRfO4U8cvPwBuTnbY2ljz7apDnL5+hyJ5crD91HXWHL5MQoKmfCEvTl4Nea4OYQALd57h0KVbhEXFUKd0fkrmz5mUdB/X4WzX2ZsEh0VyPSScvo3K4Wxvw9DZW/DO4YSNtekZbkW83flo4S661S1NiypFeGf2ZpztbSldIBcFPV2THYcwVpsebzBr4kh2rstH/sLFqFCzPgBOru7Y2Nmx+KdJBJw7RT7fYhzZvYU9G1eiExIoWqYil88cf65OYP/I6eWNX+Wa/PnLD4DpEezfjR5ETFQk7V8blGL9ZbO/Y/uaP4i4f4823Qfg6u6RtKxM1VosmTmZGwEXuBtyO+n1PRtXcvrwHhIS4ilU3C9FV7OOvYfgliMXsyaOokPvQSlK3+JiY5J1WctqpGQsm5OSsfSVkUvGMiMpGRNCCJEmknSFEMKCJOkKIYQFZdk5XUc7m8Co2Pg8T18ze3OwtQ6KjImTerJ0Yu/gGBgTHSWfQzOxs3cIio6KzNSf1yybdNOTUmo2EKi1fj8d96GA9cBSrfX36bUfkbUopWoBvwGltNbpdgVPKfU/YARQRWud9h6P2Zgk3TRSSlUHlgEltdapb3z7bPsqC2wESmutQ9JzXyLzU0pZAXuBr7XW89J5XwrYAvyqtf4xPfeV1UjSTYPED/VO4Aet9WwL7fM7QGut37LE/kTmpZR6FegL1NIW+IetlKoErMZ0Vn03vfeXVUjSTQOlVDdgMFBDa51goX3mAk4BL2qtj1tinyLzUUq5AaeBtlrrfRbc749AuNb6HUvtM7OTpJtKSikXTB/qzlrrXRbe95tAO6CxJc5gROajlPoCyK21ftXC+80NnADqaK1PW3LfmZUk3VRSSo0DCmutXzFg3zbAEWCE1vrZbroXWZZSqhiwGyintbb4w8aUUkOBhlrrFpbed2YkSTcVlFK+wAGggtb6mkExNAamAWW01tFGxCAyJqXUMmC31nq8Qfu3A44DQ7TWq4yIITORmyNSZyLwjVEJF0BrvR7Tz7jBRsUgMp7EL+NywGSjYtBaxwBvA5MSE7B4AjnTfQqlVH1gNqayrUiDY/nnZ2RZrXWgkbEI4z007fSh1nqZwbEoYBWwTmv9tZGxZHSSdJ9AKWUNHATGaa0XGR0PJF0w8dJav/bUlUWWltEusCqlSgNbMU2B3TI6noxKku4TKKX6AV2B+hnhQw3GlQaJjCWjlhIqpb4GnLTW/YyOJaOSpPsYSqkcmJJbc631IYPDSUYp9RrQG6idUb4MhGUl3jSjtNYDjY7lYUopD0z/bppqrQ8bHE6GJEn3MZRSkwAXrfXrRsfyXw/d7vmV1nqB0fEIy8rot4crpfoDXYAGclKQkiTdR1BKlQK2kYHnpizV2ERkLA81Qlqutf7W6Hge5aFrIZ9orRcbHU9GIyVjjzYJ+DyjJlwArfUOTF8Mw42ORVhUGyAv8IPRgTxOYtexIcCXSilHg8PJcORM9z+UUi2ArzHd3RNjdDxPopTyAQ5haq935Wnri8xNKWUPnAT6J9ZtZ2hKqcXAYa31OKNjyUgk6T4ksbD7KDBUa73S6HhSQyk1BvDTWr9kdCwifSml3gNe0Fq3NTqW1FBKFQb2A+W11teNjiejkKT7EKXU20AToEVmuQCglHLCVDrUXWu91eh4RPpQSuUFjgE1tdbnjY4ntZRSnwIFtdbdjY4lo5Ckm+ihbkl1tdanjI4nLZRSLwHvAdWki3/WpJT6GQjWWmeqOfzE7nxngE6W7s6XUUnSTaSUmg480Fq/bXQsaZV4RXsrMEdrPcPoeIR5KaWqASswPa3kvtHxpJVSqjvwFqazdIv0oc7IJOkCSqmKwFpM5VehBofzTJRSlYGVmI7hntHxCPNI/ELdAczQWv9sdDzP4qEnrkzTWs8xOh6jZfuSscQP9WRgTGZNuABa64OYku4oo2MRZvUyYIep6VKmlHh2Oxj4TCnlanQ8Rsv2Z7pKqU6YElXlzD4fqpTKg2leupbW+ozR8Yjno5RyxnRLbZfEuuxMTSk1F7imtR5hdCxGytZJN7Fw+xTwqtZ6k9HxmINS6l1MDXpaGR2LeD5KqY+BYlrrrkbHYg5KqfyYSjKraa0vGh2PUbJ70h0JVNRadzI6FnN5qIv/IK31GqPjEc9GKVUI0620FbXWV42Ox1yUUiOAqlrrDkbHYpRsm3Qf+tatqrW+ZHQ85qSUaoXpaRfltdaxRscj0k4p9TtwUmv9kdGxmJNSygHTr8veWuuNRsdjhOx8IW088ENWS7iJVgIBwBtGByLSTilVF6iJ6YszS9FaRwHvApMTn3yR7WTLM12llD+wGFPdY7jR8aQHpZQfsAXTLcK3jY5HpE5ih679wHit9e9Gx5MeEiuGNgILtdbTjI7H0rJd0k2sGdwNfKu1/sXoeNKTUuobwE5rPcDoWETqKKX6Aj0w3RmZZf9xKqUqAOsw9QS+Y3Q8lpQdk25PYACmxiFZ+u6Yh7r4N9ZaHzU6HvFkSil3TLfMtkisu87SlFLTgBitdbZ6wnW2SbpKqYnAcuB3oIPWeo/BIVmEUmoA0BmYBVhrrWcbG5H4r8TbfDsCNkAOrXUfg0OyCKWUF6ZWlc2A0Zmle9rzyk4T2TUwNX/eCFgbHItFJM4PngK8gIZAtvoZl4mUBPyAF4DOSqkcWuu7xoZkEfkwPTDgC0wXDrOF7FS9kBvT46orY+pqnx04AFOAMKAtkMvYcMRjuAOVMH1BzgO8jQ3HYpoDAzF96TgqpWwNjscislPS9cF0vBOAbNHwO/HZaVUxPVPLHdMXjsh4qgAFMJX5ldVanzY4HovQWo8HumH65WkF5DA0IAvJTnO63wKTsmhd7lMppRpjeqzPeKNjEckl9v/Io7X+3uhYjJDYY+I74PXscDNPtkm6QgiREWSn6QUhhDBchqlesLW1DYyLi8tj6f3a2NgExcbGZrkLFzKe5mXEeMpYmldGGc8MM72glNJjx461+H7Hjh2L1lpZfMfpTMbTvIwYTxlL88oo4ynTC0IIYUGSdIUQwoIk6QohhAVJ0hVCCAuSpCuEEBYkSVcIISxIkq4QQliQJF0hhLAgSbpCCGFBknSFEMKCMlXS/fvvvwkNDX3qeuvXryc8PEs+5NesZDzNS8bTfLLyWGaapBsVFUVYWBgeHh5PXbds2bLs27fPAlFlXjKe5iXjaT5ZfSwzTdINCAjAy8srVevmyZOHy5cvp29AmZyMp3nJeJpPVh/LTJN0o6OjcXBwSNW6VlaZ5rAMI+NpXjKe5pPVxzLTROzl5cWdO6aH2QYHB7NmzRoA/vzzT+7du5fstcjISJydnQ2LNTOQ8TQvGU/zyepjmWmSrre3N6GhoSQkJODp6UmzZs0AaN26Ne7u7sleO3v2LBUrVjQw2oxPxtO8ZDzNJ6uPZaZJugB16tQhLCzsqes5ODhQokQJC0SUucl4mpeMp/lk5bHMMI/rSY18+fKlar2SJUumcyRZg4ynecl4mk9WHstMdaYrhBCZnSRdIYSwIEm6QghhQZJ0hRDCgiTpCiGEBUnSFUIIC5KkK4QQFqS01kbHAICtrW1gXFxcHkvv18bGJig2Ntbb0vtNbzKe5mXEeMpYmldGGc8Mk3SFECI7kOkFIYSwIEm6QghhQZJ0hRDCgiTpCiGEBUnSFUIIC5KkK4QQFiRJVwghLEiSrhBCWJAkXSGEsCBJukIIYUGSdIUQwoIk6QohhAVJ0hVCCAuSpCuEEBYkSVcIISxIkq4QQliQJF0hhLAgSbpCCGFBknSFEMKCJOkKIYQFSdIVQggLkqQrhBAWJElXCCEsSJKuEEJYkCRdIYSwIEm6QghhQZJ0hRDCgiTpCiGEBf0fcZzbvDwD8ZYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree.plot_tree(gini_tree_clf,\n",
    "               max_depth = 1,\n",
    "           feature_names = feature_names, \n",
    "           class_names=targetNames,\n",
    "           filled = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.4505208333333333, 0.9166666666666666, 'Glucose <= 127.5\\ngini = 0.454\\nsamples = 768\\nvalue = [500, 268]\\nclass = Diabetes'),\n",
       " Text(0.203125, 0.75, 'Age <= 28.5\\ngini = 0.313\\nsamples = 485\\nvalue = [391, 94]\\nclass = Diabetes'),\n",
       " Text(0.10416666666666667, 0.5833333333333334, 'BMI <= 30.5\\ngini = 0.155\\nsamples = 271\\nvalue = [248, 23]\\nclass = Diabetes'),\n",
       " Text(0.0625, 0.4166666666666667, 'Pregnancies <= 7.5\\ngini = 0.026\\nsamples = 151\\nvalue = [149, 2]\\nclass = Diabetes'),\n",
       " Text(0.041666666666666664, 0.25, 'Pregnancies <= 2.5\\ngini = 0.013\\nsamples = 150\\nvalue = [149, 1]\\nclass = Diabetes'),\n",
       " Text(0.020833333333333332, 0.08333333333333333, 'gini = 0.0\\nsamples = 113\\nvalue = [113, 0]\\nclass = Diabetes'),\n",
       " Text(0.0625, 0.08333333333333333, 'gini = 0.053\\nsamples = 37\\nvalue = [36, 1]\\nclass = Diabetes'),\n",
       " Text(0.08333333333333333, 0.25, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]\\nclass = No Diabetes'),\n",
       " Text(0.14583333333333334, 0.4166666666666667, 'BloodPressure <= 37.0\\ngini = 0.289\\nsamples = 120\\nvalue = [99, 21]\\nclass = Diabetes'),\n",
       " Text(0.125, 0.25, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2]\\nclass = No Diabetes'),\n",
       " Text(0.16666666666666666, 0.25, 'SkinThickness <= 5.5\\ngini = 0.27\\nsamples = 118\\nvalue = [99, 19]\\nclass = Diabetes'),\n",
       " Text(0.14583333333333334, 0.08333333333333333, 'gini = 0.43\\nsamples = 16\\nvalue = [11, 5]\\nclass = Diabetes'),\n",
       " Text(0.1875, 0.08333333333333333, 'gini = 0.237\\nsamples = 102\\nvalue = [88, 14]\\nclass = Diabetes'),\n",
       " Text(0.3020833333333333, 0.5833333333333334, 'BMI <= 26.5\\ngini = 0.443\\nsamples = 214\\nvalue = [143, 71]\\nclass = Diabetes'),\n",
       " Text(0.22916666666666666, 0.4166666666666667, 'BMI <= 9.5\\ngini = 0.124\\nsamples = 45\\nvalue = [42, 3]\\nclass = Diabetes'),\n",
       " Text(0.20833333333333334, 0.25, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2]\\nclass = No Diabetes'),\n",
       " Text(0.25, 0.25, 'Age <= 29.5\\ngini = 0.045\\nsamples = 43\\nvalue = [42, 1]\\nclass = Diabetes'),\n",
       " Text(0.22916666666666666, 0.08333333333333333, 'gini = 0.5\\nsamples = 2\\nvalue = [1, 1]\\nclass = Diabetes'),\n",
       " Text(0.2708333333333333, 0.08333333333333333, 'gini = 0.0\\nsamples = 41\\nvalue = [41, 0]\\nclass = Diabetes'),\n",
       " Text(0.375, 0.4166666666666667, 'Glucose <= 99.5\\ngini = 0.481\\nsamples = 169\\nvalue = [101, 68]\\nclass = Diabetes'),\n",
       " Text(0.3333333333333333, 0.25, 'Glucose <= 28.5\\ngini = 0.306\\nsamples = 53\\nvalue = [43, 10]\\nclass = Diabetes'),\n",
       " Text(0.3125, 0.08333333333333333, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2]\\nclass = No Diabetes'),\n",
       " Text(0.3541666666666667, 0.08333333333333333, 'gini = 0.265\\nsamples = 51\\nvalue = [43, 8]\\nclass = Diabetes'),\n",
       " Text(0.4166666666666667, 0.25, 'BloodPressure <= 85.0\\ngini = 0.5\\nsamples = 116\\nvalue = [58, 58]\\nclass = Diabetes'),\n",
       " Text(0.3958333333333333, 0.08333333333333333, 'gini = 0.494\\nsamples = 92\\nvalue = [41, 51]\\nclass = No Diabetes'),\n",
       " Text(0.4375, 0.08333333333333333, 'gini = 0.413\\nsamples = 24\\nvalue = [17, 7]\\nclass = Diabetes'),\n",
       " Text(0.6979166666666666, 0.75, 'BMI <= 29.5\\ngini = 0.474\\nsamples = 283\\nvalue = [109, 174]\\nclass = No Diabetes'),\n",
       " Text(0.5625, 0.5833333333333334, 'Glucose <= 145.5\\ngini = 0.432\\nsamples = 76\\nvalue = [52, 24]\\nclass = Diabetes'),\n",
       " Text(0.5208333333333334, 0.4166666666666667, 'Insulin <= 132.5\\ngini = 0.25\\nsamples = 41\\nvalue = [35, 6]\\nclass = Diabetes'),\n",
       " Text(0.5, 0.25, 'Insulin <= 112.5\\ngini = 0.337\\nsamples = 28\\nvalue = [22, 6]\\nclass = Diabetes'),\n",
       " Text(0.4791666666666667, 0.08333333333333333, 'gini = 0.269\\nsamples = 25\\nvalue = [21, 4]\\nclass = Diabetes'),\n",
       " Text(0.5208333333333334, 0.08333333333333333, 'gini = 0.444\\nsamples = 3\\nvalue = [1, 2]\\nclass = No Diabetes'),\n",
       " Text(0.5416666666666666, 0.25, 'gini = 0.0\\nsamples = 13\\nvalue = [13, 0]\\nclass = Diabetes'),\n",
       " Text(0.6041666666666666, 0.4166666666666667, 'Age <= 25.5\\ngini = 0.5\\nsamples = 35\\nvalue = [17, 18]\\nclass = No Diabetes'),\n",
       " Text(0.5833333333333334, 0.25, 'gini = 0.0\\nsamples = 4\\nvalue = [4, 0]\\nclass = Diabetes'),\n",
       " Text(0.625, 0.25, 'Age <= 61.0\\ngini = 0.487\\nsamples = 31\\nvalue = [13, 18]\\nclass = No Diabetes'),\n",
       " Text(0.6041666666666666, 0.08333333333333333, 'gini = 0.444\\nsamples = 27\\nvalue = [9, 18]\\nclass = No Diabetes'),\n",
       " Text(0.6458333333333334, 0.08333333333333333, 'gini = 0.0\\nsamples = 4\\nvalue = [4, 0]\\nclass = Diabetes'),\n",
       " Text(0.8333333333333334, 0.5833333333333334, 'Glucose <= 157.5\\ngini = 0.399\\nsamples = 207\\nvalue = [57, 150]\\nclass = No Diabetes'),\n",
       " Text(0.75, 0.4166666666666667, 'Age <= 30.5\\ngini = 0.476\\nsamples = 115\\nvalue = [45, 70]\\nclass = No Diabetes'),\n",
       " Text(0.7083333333333334, 0.25, 'BloodPressure <= 61.0\\ngini = 0.497\\nsamples = 50\\nvalue = [27, 23]\\nclass = Diabetes'),\n",
       " Text(0.6875, 0.08333333333333333, 'gini = 0.0\\nsamples = 10\\nvalue = [0, 10]\\nclass = No Diabetes'),\n",
       " Text(0.7291666666666666, 0.08333333333333333, 'gini = 0.439\\nsamples = 40\\nvalue = [27, 13]\\nclass = Diabetes'),\n",
       " Text(0.7916666666666666, 0.25, 'Age <= 33.5\\ngini = 0.4\\nsamples = 65\\nvalue = [18, 47]\\nclass = No Diabetes'),\n",
       " Text(0.7708333333333334, 0.08333333333333333, 'gini = 0.0\\nsamples = 8\\nvalue = [0, 8]\\nclass = No Diabetes'),\n",
       " Text(0.8125, 0.08333333333333333, 'gini = 0.432\\nsamples = 57\\nvalue = [18, 39]\\nclass = No Diabetes'),\n",
       " Text(0.9166666666666666, 0.4166666666666667, 'Insulin <= 629.5\\ngini = 0.227\\nsamples = 92\\nvalue = [12, 80]\\nclass = No Diabetes'),\n",
       " Text(0.875, 0.25, 'Age <= 44.5\\ngini = 0.199\\nsamples = 89\\nvalue = [10, 79]\\nclass = No Diabetes'),\n",
       " Text(0.8541666666666666, 0.08333333333333333, 'gini = 0.14\\nsamples = 66\\nvalue = [5, 61]\\nclass = No Diabetes'),\n",
       " Text(0.8958333333333334, 0.08333333333333333, 'gini = 0.34\\nsamples = 23\\nvalue = [5, 18]\\nclass = No Diabetes'),\n",
       " Text(0.9583333333333334, 0.25, 'Age <= 45.0\\ngini = 0.444\\nsamples = 3\\nvalue = [2, 1]\\nclass = Diabetes'),\n",
       " Text(0.9375, 0.08333333333333333, 'gini = 0.0\\nsamples = 2\\nvalue = [2, 0]\\nclass = Diabetes'),\n",
       " Text(0.9791666666666666, 0.08333333333333333, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]\\nclass = No Diabetes')]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree.plot_tree(gini_tree_clf,\n",
    "           feature_names = feature_names, \n",
    "           class_names=targetNames,\n",
    "           filled = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0.875, 'Glucose <= 127.5\\nentropy = 0.933\\nsamples = 768\\nvalue = [500, 268]\\nclass = Diabetes'),\n",
       " Text(0.25, 0.625, 'Age <= 28.5\\nentropy = 0.709\\nsamples = 485\\nvalue = [391, 94]\\nclass = Diabetes'),\n",
       " Text(0.125, 0.375, 'BMI <= 30.5\\nentropy = 0.419\\nsamples = 271\\nvalue = [248, 23]\\nclass = Diabetes'),\n",
       " Text(0.0625, 0.125, 'entropy = 0.102\\nsamples = 151\\nvalue = [149, 2]\\nclass = Diabetes'),\n",
       " Text(0.1875, 0.125, 'entropy = 0.669\\nsamples = 120\\nvalue = [99, 21]\\nclass = Diabetes'),\n",
       " Text(0.375, 0.375, 'BMI <= 26.5\\nentropy = 0.917\\nsamples = 214\\nvalue = [143, 71]\\nclass = Diabetes'),\n",
       " Text(0.3125, 0.125, 'entropy = 0.353\\nsamples = 45\\nvalue = [42, 3]\\nclass = Diabetes'),\n",
       " Text(0.4375, 0.125, 'entropy = 0.972\\nsamples = 169\\nvalue = [101, 68]\\nclass = Diabetes'),\n",
       " Text(0.75, 0.625, 'BMI <= 29.5\\nentropy = 0.962\\nsamples = 283\\nvalue = [109, 174]\\nclass = No Diabetes'),\n",
       " Text(0.625, 0.375, 'Glucose <= 145.5\\nentropy = 0.9\\nsamples = 76\\nvalue = [52, 24]\\nclass = Diabetes'),\n",
       " Text(0.5625, 0.125, 'entropy = 0.601\\nsamples = 41\\nvalue = [35, 6]\\nclass = Diabetes'),\n",
       " Text(0.6875, 0.125, 'entropy = 0.999\\nsamples = 35\\nvalue = [17, 18]\\nclass = No Diabetes'),\n",
       " Text(0.875, 0.375, 'Glucose <= 157.5\\nentropy = 0.849\\nsamples = 207\\nvalue = [57, 150]\\nclass = No Diabetes'),\n",
       " Text(0.8125, 0.125, 'entropy = 0.966\\nsamples = 115\\nvalue = [45, 70]\\nclass = No Diabetes'),\n",
       " Text(0.9375, 0.125, 'entropy = 0.559\\nsamples = 92\\nvalue = [12, 80]\\nclass = No Diabetes')]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VfCW73n+NrrYWClasxdrTSXxSGmWnj9JcV8Vgvw0hBM6hQaJj4zGaozFZLAzYrPh8Xm65/wf0drVTW1YMwLrrb0Wn12M0jyxeHB2XgMFkpr6yFK/HTf6SFVhi40nPyVdBtQmifLoTwOvzszo/BYBbVuZgG3SRnxKLlJJNq3IRQqDXjXSpEOBweYgzGwCQw35d30i0eMDpweuX1LRbcXl81HbYWJQej16vo7yll8RoEwJYnp0U0mtVzC68Hi8FK9YBsP7G2xjo7yMjdwFIyZU33wFCoNONPOBHizBo96bf58Pv8wX3GRocwOf10lJXhU6nQwhBfuFKchYVMtBvpb+vm22bXyQ+KSWk1zrbUT7dSzCZ5Igthyv57vVLAHhlbyk/3LCUSL1uSu1SPt35y+X4dPd89Da3fvMhALa9/QJ3PPgo+sjIqTQviPLpjo0S3UugMtIUMw0VSJvdKPfCNLLlcOW49/X7JX/60l56B5y8uLuEradqef7zcxwsa5lGCxXzmbESJb6KkhOHeP/FX9Hf18OnbzzL4Z0fU1l8ivdf+OWYyRWKr0YF0ibIMzvPsjY/lZYeO5VtVm5ankVdZz/tfYOkxJowRumxGCKJMUVp++84i8vrY2FaHG6Pj+9ctwS318eBgJiuzksmJdbM3tIm1i1IJUIIuvsdxJmjyEywYHOoJAnF+Pnwld+weOV6utqaaKqpYM11G2lrrKOno5WE5FSiDEaM5mjM0TEA/P7lX+Nxu8jKL8DjdrHxvu/jcbs5e3QfAAUr1hKflMrKq26gtqwYERGBtacLS2wcmfmLOLZnaxivdnaiRroTZGVOMkW1HTg9PvqH3ADcujKHvJRYzIYR35gk4LYRUJiZQGFmIqXNvfj9EikJBtSGvTv2ITcNXf3Yh9xkJkYjgJRYM3WdNrw+NZJQjI8FS1dRcfYEbpeTQbuWkXbFhttIz8nHYDKP2lO78YQQ5BYsI2fRUurKSwKj1pGg2rD78YMXn8IUHY1ryEFKRjZCCJprK4lPSsXtHArxVc5ulE/3EkzUpzs6iDadKJ/u/GWqAmnTjfLpjo0a6U4xoRBchWKyhEpwFV+NEt3LYCKBskPlLZyobud0bSe/2X6a5h47mw+Ws62ojjN1nbyyt/SC/X9/rDpYeWxfaRNbDldypLKVo5VtU30ZijnGRAJkxcf2U3b6GFXnitj29gv09/WwY8urnPxiB/u3vsenbzyLa5T7YHg/x4Cdo7u38v6Lv8Lv9/Or//fneD0e3n/xV9NwRXMLFUgbJ5sPlmM2RJIUY2JfaROPbVwBjAhveryZo5Vt3HfVIpZmJXK2votu+xApsSZW52mTx7MSo7EYI/nsTD2J0cYLMs2q2qwXnC87KZr6LhsNXf04XN7g8a29M3+qkCJ0fP7BGxjNFuISkyk6uJu7H3oCGBHepNQMSk8e5oZvPEDe4uVUlZzG1ttFQnIqi5avBSAlI5vk9Cyaaitorqti7XUbKTt9lLTsfNoa6+hsaSRnkVZXZPGq9TTVVmCOjsEcHcO1m+6l6ODnLFl1BfrISBKSZ/yCFmFHjXTHSVq8hR77EP0OF7nJMdR32QAYcLqpbrfi9PhYnpOEMVJ7jvn8WqDsy7V0q9r6SIk14fX5g5lmNe1WShq7aekZoKi2AwBTlB6DXoc+QuD1+WnpHQjp9SpmB4mp6dh6uxnot5GWlUdbYy2gZZO11FXhdjnJL1xJlMEEEAyQ+b8UnG2pq6K2rJjUzByKDn6OOToOg9FEZFQU0XHxVJw9ecF+bpeTtoZasvILGBqw095UR3+fqgE9HlQg7RJMVXJEVVsfbq+fFTmTT+ctbujCYohkUXq8CqTNY6YyOaKppgKvx31ZhXGG8Xo8HN+7nevvuB9QgbSvQo10J8FEfLnDdNoclDX38PoX52nrG2DQ5fna9sZaOWJ1XgqL0uMnfG7F3GYiPtxhio/t5/cv/5qqkqKLBPfdZ3/BsT3bKD15mJb66guO6W6/MFmnrvxc8P/6yMig4Cq+GuXTnQBvHywnMyEagONV7ewqbuCxjSt4eW8JP7llJS/vLeHJ21eTFmce06d7fWEmZS29nKrpZO2CFH75ySk2LMumoaufZ3ac5aqCdPaca+TmFTnsKm6gsaufksZu7lq/gA+PVwfbVigAdn3wOslpWQCcLzrKiX2fcfdDT7Dtree5++En2fbW89z3yM9JTEkf05er0+kYsFk5c3gvXe3NLFy6mkXL15CcnsU1t97N3o8309HSQNW5U8FEi1P7dxETn0hHSwMetwuD0YQQggOf/Z6E5DQMRiNxialcfcs3wtgzMxs10p0AAoJL6vTYh8hNjqGkqZuFafHBbVmzNkIdy6d7uKKV1t4B4i0GmnrsLM1KpLylFyG0qepCQE5yDLHmKNLjLQAsD7gjRretUAAIBBE6rZCSrbebtKw8asuKycxfFNw2VGqzYsby5V5z6z2UnDwEgF4fiRDavd3d3sKxPdtIycgBuCDRInjuQFJF1oLF+Hw+DAYjWfmLyFu8guqSomm/9tmM8uleAlXwRjHTUAVvZjdqpKtQKBQhRI10L4EpSt/u9Phm3ORDY6SuY8jtTQ+3HYrQYzCa2t0u54y7J79MlMHY4XIOqXv0SyjRnSRCiFTgFPAzKeWUlloSQvwjcCuwSUrpncq2FfMDIcSvgRzgW3IKf+RCiCXAIeAuKeXJqWp3PqHcC5NACKED3gJem2rBDfBPwBDwz9PQtmKOI4T4AXAX8NhUCi6AlLIS+DmwRQiROJVtzxfUSHcSCCH+CbgeuENK6bvU/pM8RzLaSPpPpZQfTsc5FHMPIcRy4AvgdinlmWk8z38ChcB9UkpVe3QCqJHuBBFC3A08Bjw0XYILIKXsBr4LPCeEKJiu8yjmDkKIaOA94K+nU3AD/DUQB/ztNJ9nzqFGuhNACJEPHAO+LaU8FKJz/hHwB8B1UkpHKM6pmH0IbZLtW4BDSvnTEJ0zCzgBPCKl/DwU55wLKNEdJ0IIA1oA4U0p5S9DeF4BvAm4gMen2kenmBsIIf4YeBy4XkoZsqUchBC3ot2fV0kpm0N13tmMEt1xIoR4BkgCvhdq4Qu8Nh4DfimlfCGU51bMfIQQ1wIfo70N1YTh/H8L3AdslFK6Q33+2YYS3XEghPgx8PdoT/P+S+0/TTYsBQ4Ad0opVZ6lAgAhRApawPW/Syk/DpMNEcBHQI2U8s/CYcNsQonuJRBCrAL2ALdIKUvCbMv3gP8LXCml7AunLYrwE5i6uB0oklL+TZhtSUAT/7+RUr4bTltmOkp0vwYhRCxaoOCfpJRvhNseACHEU8AC4AE1VWd+I4T4X8BNaNPDwp5EI4RYD+wANkgpy8Ntz0xFie5XEAhgbQG6pJQzpuKNECIK2Ad8LKX8lzCbowgTQohvAC+gvfW0h9ueYYQQTwJ/ClwjpZz5VXnCgBLdr0AI8efAw8CNUkpXuO0ZjRAiG20E/rCUcm+47VGEFiFEHlpg9btSygPhtmc0gcHKy2i1un+sZttcjBLdMRBC3AB8gPa0rg+zOWMihLgdeBVtpNMabnsUoSEwdfEA8K6U8hfhtmcshBBm4AjwjJTyd+G2Z6ahRPdLCCHSgJPAH0opt4Xbnq9DCPEPwB3ArVJKz6X2V8x+hBC/BTKA78zkUaQQYjHavPZ7pJQnwm3PTEKlAY9CCKEH3gZememCG+D/AHZA+XbnAUKIH6I9ZH8ykwUXQEpZBfwMrTDO5FdjnYOoke4ohBD/DFwFfGM66ypMJYEb+hTwF1LK98Ntj2J6EEKsQAug3ialPBtmc8aNEOIXwHLgXjXbRkONdAMIIe4DfoQWnJoVggsgpexBK4zzTKDWqWKOIYSIAd4H/nI2CW6AvwVigL8LtyEzBTXSBYQQC9Ec/w9IKY+E257JIIT4OVqd02tVYZy5Q2A2wDuATUr5ZLjtmQxCiEy0OMmjUspd4bYn3Mx70RVCGNEc/q9JKZ8Ktz2TJfDjfA3wMw3FqxXhQQjxp8AjwA1SSme47ZksQoiNaPGSq6WUTeG1Jrwo0RXiObS6oD+Y7UIlhLCgzd98Wkr5XLjtUVweQojrgd+jvb3Uhduey0UI8dfAA8DN87kwzrwWXSHEo2g+p6uklPZw2zMVCCEKgYNowcBT4bZHMTlGrcH3cynlp+G2ZyoIvI19CDRIKf8kzOaEjXkrukKINcDnaOXoSsNtz1QihHgQ+HfgCillb7jtUUyMQCGbHcBxKeX/G257phIhRDzaw+TvpJSbw2xOWJh3ohu4of8O+DHwP6WUb4bZpGlBCPFLYBmwTUr5dLjtUYwPIcS30ObiFqKtwRf2QjZTjRBiHbATeAp4TkrZGWaTQsp8FN1lwFmgEq2ugjW8Fk0PQogrgU+BBMCk5kjODoQQJcBC4L9JKV8JsznThhBiJ1qFtD+SUr4YbntCyXycp7sJkGh1C8JSkDxEVKIFYfRATphtUYyfRWi1FeZ6IaNnAAdwb7gNCTXzbqQLmkN/ts9UGC/z6VrnAvPt+5pv1wvzVHQVCoUiXMxH94JCoVCEDX24TmyM1LW7vP60cJ3/qzDoIzqcHl96uO2YKCZDVLvT7Zlx/WmMiuwYcrlnVX/O1L6E2dWfkQZTu9ftnJH9OBp9lLHD4xoKWZ+Gzb0ghJBt/3FPWM79dWT8xVaklCLcdkwUIYTs3/d8uM24iNiNT866/hRCSNuu34TbjDGJu/2/z5r+FELI/71/5q+f+vc3JYS0T2eke+GZL2p5/UgjTb0Onnz1FA09Dv7Pp+W8d6r5on39/osfGhXtdl44UEeX3cWrhxv4tLgNgMPVPbx3qhmrY/7U+/7Nuzt5+ZP9NLb38Mg/PkNdaxf/+Oz7bN559KJ9/f6LZ5Wdrmjg2Q/20NXXz2+3fM6pci0b9cDpCjbvPEqffe4ug7XzWCmfHT3HqYoG3vn8+GW1NVbfen0+/uxXbwf//kf/8SYN7T38estuWrutAGzZc4LPjp7D6Z7d92zFkZ2UH/6M5rIizux857LaGqsvfV4vH/3izwE48v5z1BYdwOWwU3ZwGwff/jUAZ3dtofzwZ3hc4S1hETb3wqUYcGo3WUqMgc9K2inMiA7+rXfQzRcVXUTqI7i1MIUT9X34pGRxajQ5iWbyk81sPefF59c+a+/XOjneHEnvoBu700O8OTIs1xUO7INDAKQkxLL10BmWLcgK/q3HNsCek6VE6fXcdvVKjpXW4PP5KczLIDc9iXWFeZytasDn99PV148r8ONPiLXQYxugf2CIhBhLWK5rurENOti4bimHzlUD8M7nx7lxzWJKa1spb2zj7utW8fmJMoyGSPQREaQkxOD2+Cita+GRu64nMzmebYeL8fr8LMnV3rKbO/uIMRu5ZsVC9Dod161cBMCeU+WsX5KLyRBJUpwFq91BZnI86Ulx2AaGcDjdGKNm7z3rHLCx6MqN1J89DMCZne+wYO2NtNeU0lVfwdIb76Lq2G70BiMROj3RCcn4PB7aa0q58t4fE5uSSdnBbfh9PlLyFgNg7WjGaI4hd9U16PR68lZfC0BcSibOARsGcwwGSyxLb7wbgJjkDJwDNjyuISINxvB0BDN0pAuQGW+iZ9DN+rx4rshLuOBvsUY9qbEGIoTAL8Hrl/j8kmFPSXXnIJlxRjw+P+XtdiJ1EZS19RNnikQXIajpmrujs7HISk2k22rnquULuWr5wgv+FmcxkZYYR0SEwC8lXp8Pr8+HP5BL8e7nx3A43ZgMUSTHR1Pb0kVpbTPxMWb0ugiqmzvCcUkhIT7azOnKxuBoPi7axL6iCgB0EREIEYEuQhAhBMsWZHK6shGn283axTnoIrSfVkF2qta3fonP78fr8+EbNVIrrmmmpLaFfoeT+vYe9LoIEmMtVDV3UFrXQnpiHHaHMzjyna2YYuJpKT/NUL/mbjBGx1Fz6gsAhE6HEBGIQJ+mLVhGS/kZPK4hMgvXICJ0ACTnFCAiIpB+P36fD7/Pi98/Uvq6raqE9poSohNS6Gmpxef10ttSS3LOItprSolJSsM1aKe/qyX0HTAK5dP9EsqnO7Uon+7Uony6U4/y6QY433p5yWJvHG28wAf8RUUX751q5kyTlVcPN9BqHeLdE81sP9d+uabOCkpqLvaHT4RXPt0f9AO3dPby5meH+fTAaYrK63nxo33YHU62HTrD05t3TIW5s4aS2ssbNb2y7VDQX9zS1cdbO4/y6aHZtjjE5dFeU3JZx5/45NWgn9hh6+XoB89zfv+n+P1+PviXP8bW2ULR9rc5f2DrVJh72cwIn+7m4020Wp2syo6l2+4mJSaKE/V91HQNYhvyIACH20ecORJLlA6LQY/N4cHrl3zniiw6+52UBET6psXJ6HURmKN0wfZ9fonDrb2GrM2Jp7pzgHhzFKYoHWXtdu5aNStm4IybN7YformzlzWLc+m22klJiOV4aQ1VTe3YBhwIBINOF/ExZqJNRiwmA1a7A6/Px/dvv5aOHhvF1Vqd6VuuWIZer8NsNATbT4i1YDZGUVrXwr0b1lHZ2E6M2UiM2cTdN64N01VPP298doSWLitrFmfTZR0gNSGGY6W1VDV1YBscQiBwBPrVYjRo/TrgwOfz871NV9HR28+5wMNv4/pC9DodFmNUsP2EGAsmQxTn61q594Y14brMaeXUtjexdbaQuWQ1g33dRCem0FhynO7GaoYGbAghcA85MMXEE2WyEGWy4LRb8fm8rL3je9h7OmirPgfAois2otPriTKag+2LiAgGrd0Yo+OoPrGH7GXrMMUmEGU00VF7nuUbwv92PSNGuh6fZHV2LAA3FyZjHfKQn2RBSsmtS1MQAvQRI6P/YREeDob5pSasvlEzGQZcXrx+SU3XAHXdg/j8klark5quAUpb+vH5/QhgeUZMKC81JHi8PtYuyQPg1iuXY7U7WJCZgpSS269eqfWnbuShJMSwWGgBsdG+3WEGHE68Ph9VTe2aPxPByoXZVDW1c666CafLQ21LJwXZM35a5qTx+HysXaKVsbj1iqUj/QrcftXyQL+O/KSEAIfTTXy0Jgr+gE93dL/aHS68Pj/VzR3oIgRCCFYszGKu4vd6yCrUHigFV93CkN1KYuYCpJQsueY2QBDxpXvT7dREGEBKv+bL9Y0UX3M5BvD7fHQ3VeMc7CcuNQshBK5BO72t9bgdAyAE6QtXhPJSv5JZ5dN971QzD16RDcCrhxt4+JocInVT+9yYTz7dzTuP8oM7tIjvix/t45F7biRSP7UvP/PRp/vO58f5/m1XA/DiJwd45K7ridTrLnHU+JgvPt0zO99h7R3fB+D4Ry9xxT0/RqefntkbofbpzirRDQXzSXRDwXwU3elkvohuKFGBtHEwVpLEV3G0tpff7a2hf8jDjpIOfre3JhhMU2iMlSjxVRwuruTpzTtwe7z82X+8DgQm9v/bK9Nk3exkIskUpysb+avfbsE2OMTvPthLWX3bNFo2u5hIIkX92cMcfPvXOGy9nPz0dSqP7qKl4gyfPvXXuJ0zZ4HsGRFIA3hufx1rsuNosQ5R1THAhsXJ1PcM0m5zkRwThTFShyVKR4xRe8V49ota3F4/C1IsuL1+vr0+C7fXz8HqbgBWZ8WRHGPg2oWJlLbYiDVFEmPUc+fKdBamWKjuHAjn5U47v93yOesL82jq7KWioY2N65dR19pJa7eV1IRYjIZIok1GYszaJPHfbNmF2+1lUXYqLo+X7912DW6Ply+KygFYuySXlIRYrl+9hOLqJqIi9Vy7SpukvvvEedYX5ofrUkPGf32wl3VLcmnu7KWisYOb1y2hrrWbtm4bKQkxmAyRWIwGYixan/72/T24PF4WZaXg9nj57q1X4fZ42X+mEoA1BTmkJMSwbkkuZ6ubqGhoQwJDrrm9ZuPhd39H5tK12Dqa6WqoZOEVN9PbUkd/dxvRCSlEGkxEmSwYLFq85dC7/4XX7SI5exFej4s1t38Xr8dNbdF+ADKXrCE6IYX8NdfTVl1Cd2MVi664mcaSYyy59nZaK89eEGwLNzNmpLsiM4bTjVZcHj92p+Yk31iYQm6S6YKZCMPuECEES9JiWJIWTVmrHb9fIpHBgNqw0+SZfbVEG/R4fX7qugdZmGIJBtOcHt+XzZgzrCrI5mRZHS63h/5ARtqmq1aQn5FywUyE4X4SCJbmZ7A0P5PSmmb8fj9yVEBt2Av163d2EmMy4vX6OFfdRElNM3bHEPVt3fTY5vaDbOXCLE6W1eN0e0f69Mpl5GckXTALYfQ9ujQ3naW5GZTUtl7cp4Heb+nqIzslgYLsNPQREdS2doX+4kJIesFKms+fwut24RzUZh0tvvpWEjPziDKNEsfhfkSQml9ISn4h7TWlWhqwlCMBtcB+Bzf/BoM5mqScAqqO78YYHYuts4X4tOyQX+PXMWt8uqODaNPJfPHpjg6iTSfzyac7OoA2XcwHn+7oIFooUD7dryAUgjufCIXgzjemW3DnC6EU3HAwI0V3IoGyw9U9nKzvCwbM/H4ZzD5771QzLx+sZ2iUG2F4P7vTw2cl7Ww/187R2l6O1c7NlconEiQ7cLqCY6U1wSwzgD0nz7N551FOldfx1OYdF1R4Ol3RwF8+/Tb9g0PBoNrh4kqOFFdN6TXMNCYSJDtwtpLj52t59sN97D9TSW//AJ8eOntB1tnoz4arjXm8Pp7e8vl0mD+jmEigrPb0QRpLjn9lFTEYu9rY6MBa/dnD1BcfmfLrmAhhD6S9e6IZc5SOxOgovqjo4pHrtEn9w8KbHmvkWG0v967JoDA9hrNNNnoGXaREG1iVHQdAZryRzHgTpS02JASzz1weP512Fzox8uYwHFiLMUYSa4yke8BFVryRVmt4y71NBW9+dhiL0UByfDR7Tp7n8ftvBkaENyM5jsPFVTxw8xUsW5DF6YoGuq12UhNjWbM4F4DslASyUhOpbGzH5/PjGHIBsCgrjW1fSk8drkAWazEFg2pZKYm0ds38aULj5a2dRzEbDSTHRbPnVBmP37sBGBHe9KQ4jpTU8M0N61iWn8HpykZ6bAOkJMSwpkBLpMhKSaCj145tYIjE2GjyM5IvKIk5+rPhamOReh1pCbGhv+BppGj720SZzFjik6k+sYer7v8JMCK8MUkZNBQfYcXG+7WiNxVnGLR2E52QSuaS1QDEpWYx0Ns5ZhUxYMxqY6MDayl5hdjCXPAm7CPd1FgDPYNu+oc85CSaaejRpnYMunzUdA7i8vpZlhmLMVILpvllIFj2JV/0cMCsoccRzD4zREaQHB2FbcjD6UbrBft1210MeXxERMwK99i4SE+Ko9um/bhz05Ooa9ECMgNDTqqa2nG6vaxclI3RoAV9glWvfBfWJx3OMitvaMXr89PS1UtlYxupCbE0d/ZxskyrqdvS2Ut2aiJAMKg210hLjKPbNoBtMNCnbVqf2odcVDV34nJ7WbEgC6MhkB35FX2amhBDbWsXnX39vPTpQVLiY9hxTKs5MPqz4Wpjvf1zLygZk5TGoLUbp91GfHouva31gJZR1t1YjdftJH3RCiINJgBkoJKY9F8Y8B6riljFkZGaH1+uNjY6sDYTmDWBtK+iqmMAj8/P8szJd+i5ZhsWg56FKZZ5E0gbi8qGNtxeHysXTd5/fraqEYvJEEwHnk+BtLGobGzX+nSCqb0er4/PjpZw340X1mCYD4G0r6KroRKf1036opWTbqO1spgok4XknEXBz+Z1IG0ivtxhuuwuytrsvHG0kTabE4drJCd7rPbGql62KjuOhSlzqxD3RHy5w3T09lNa28wrn+6ntauPwYBr4avaG2tku2Zx7pysvzCZlSMOnK3kTFUTJ8vrae22XtCfY7U3umJZpF53keDOdiazYkTt6YMceve/OLPzHVLyllwguF+88UvKD22nvvgI3U01Fxxj67zQhTBcySxzyeoLBDcchN2nC/DO8SYy4rVXihN1vXxe1skj1+Xx6pEGHg1sn7gxn9RY45g+3esWJVLebqeooY81OfH8anc1NxYk09gzxLNf1HJVfgJ7y7u4aUkyn5d10tjroLSln2+sSuejM63BtucCb24/RGaKVvT9WEk1O46e4/H7b+bFj/bx029u5MWP9vGzb28iLSluTJ/ujWuWcL6ulZNldawrzOMXb2zlpnVLqW/r4jdbdnHNikV8fryEW69czo6j52ho66a4upF7b1zHB3tPBNueK7y54yiZKfEAHCutZcexEh6/dwMvfnKAn96nbX/2rY2kJcaO6c+9YXUBZfVtnCyrZ11hLr94awc3rVtCfVsPv31/D9csX8jnJ89zy/ql7DhWQkN7D+dqmrnn+tV8sK8o2PZspmj7W8SmZALQeO4YFUd3ctX9P+H4Ry9x9Tcf5/hHL3Hdd/6QmKS0Mf24ERE6huw2ak59ga2zhYyCVWQsXkVcaiZLb7iLMzvfxdreSEt5UTDZour4bsyxifS1N+J1uwIuC0HJvo+ISUxFbzARnZBM4XV3hrw/ZsRIVwjBcN2ankE3OYlmSlv7WZhsCW7L2uzA2D7dIzW9tFqdxJsjae4bojAthop2Owht8r8QgpxEM7GmSNID4ros4I4Y3facQAh0gc7stg6Qm57EueomFmWnBbelddooYCyf7sGzlbR09RIfY6apo4el+ZmU1bcihAApEUKQm55MrMVEelI8QNAdMbrtuYIQBFeB6LYF+rOmmUVZKcHt8DWP5c89VFxNc1dfoD97WZafQXl9G1p3SoSA3LREYi0mMgIPqxWB5ZRGtz27EUQEVn8YtPYQn55Le/U5krIXBbcdteeBsf24S2+4K7jMT4ROr5UZBGydrZQf2h5MfhidbDFyZi2xIjm3AOn3E2kwkpS9KLg6RTiY9T7dqWY++3Sng/nu051q5rNPd7qY1z5dhUKhmOuEbaRrjNS1u7z+GRdxMegjOpwe36xbSsJkiGp3uj0zrj+NUZEdQy73rOrPmdqXMLv6M9Jgave6nTOyH0ejjzJ2eFxDIevTsInuRBFCmIBmYJ2UsvES+8YCjUChlHLuLld7GQghbgf+VUq5fhz7fhP4SynljdNv2exECPE00Cel/Mdx7PsCUCWl/Nfpt2z2IYSIAKqB70kpT15iXz3QANwhpSwNhX2Xy2xyL3wbOHEpwQWQUvYDvwd+PO1WzV5+Crw4zn23AQVCiKXTaM+sRQhhBB4GXh7nIS8CjwshZoVvNgxsBOzAqUvtKKX0Aq8Cj0+zTVPGbBLdx4GXJrD/S6gbe0yEEInAN4C3xrO/lNIDvAb8ZDrtmsV8Ezgjpawf5/5HAT9ww7RZNLt5HHhJjv81/CXgx0KIqEvuOQOYFaIrhFgIrAY+msBhB9HmIatyWhfzQ2CblHIioeWXgEeEENOzUNXsZiJvDQTE5MXAcYpRCCHigXuBN8Z7jJSyGigLHDfjmRWiCzwGvCWldF1qx2ECN/ZLzKLXjhAy0bcGpJTlQC1w17RYNEsRQuQBVwAfTvDQ14FvCSHm3nLUl8dDwE4pZc8Ej5s1D7EZL7pCCB2a6I57JDGKV4EHhRDRU2rULEYIsR6IB/ZM4vBZc2OHkMeAt6WUQxM5KBDg3QvM7eKxE2dCbw2jeB+4Tggx49evn/GiC9wOdEgpiyd6oJSyDTgAfHfKrZq9/BR4WUrpv+SeF/MucJMQImOKbZqVBKLsP2FyIgHqIXYBQog1QCow4ULCUspBYAvw6FTbNdXMBtGd8Kvwl3gJdWMDwWl3P0B7A5gwUsoB4APgkam0axZzK9o0sdOTPP4zIE8IsXwKbZrNPA68IqWc7OKFsyJ4PqNFVwiRDNwBvH0ZzWxFm+5UODVWzWq+BZyUUl7O+vNqutMIk30VBmbndKfpQghhYGLT7sbiOOAEbpoSo6aJGS26aFH2T6WU1sk2EJju9DpquhNc/lsDwBG0OkLXX745s5fAtLu7GOe0u6/hZbTpTvN9Vsg3gWIpZd1kG5gtwfMZK7qBkdRljSRG8RLwaCB7ZV4ihFgArGXiUfYLUNOdgjwMbJdSXtbielLKSqCCWTLdaRqZqt/668A3hRAztr7ojBVdtGk40cAXl9uQlLIMqGN+T3d6jAlOu/sa1HSnqXlrGGbGj86mEyFELnAlWhbpZSGl7AJ2M4Nnhcxk0f0pWlbKZKLsYzFvR2eBaXeXE2W/ACllO9rD8HtT0d5sQwixDkhC+3FPBVuAG4QQmVPU3mzjMWDzRKfdfQ0z+rc+I0VXCGFGe1K9MoXNvgtsFELMigpNU8wmoEtKefaSe46f+Tw6e5zJT7u7iMB0p/eYBdOdpppR0+6m6q0BYAeQJYSY/GJq08iMFF204jbHpJRTtryslNKONt1pPhbBmSp/2Wi2AQuFEMumuN0ZTaC4zUNcXpR9LGbFdKdp4BbABhRNVYOBKWczdlbITBXd6RAJmIc3thAiCbiTy5t2dxGB6U7zsQjOA8Dpy5x2NxbHADewYYrbnelMtLjNeHkZ+NFMLIIz40RXCLEIWAF8Mg3NH0K75uumoe2Zyg+BrRMsbjNe5mMRnGkZEMyW6U5TiRAiAbgHeHOq2w4UwTkP3DfVbV8uM0500UZOb05RlP0C5tuNPWra3VT6y4JIKSvQik3fPR3tzzSEEPnAOi5z2t3X8DrwQKAI/3zgIWDHJIrbjJcZGVCbUaI7qrjNtIhEgNeA78yTIjjrgVi0wirTxYy8saeJx9CK2zino3EpZSdaIaIZO91pipkuN+Iw7wPXCiGyp/EcE2ZGiS5aym+rlPLcdJ1gnhXBmdIo+1ewBdgw14vgTFOUfSzmxUNMCLEWSGHqpt1dhJTSgTZraUbNCplpojvdT75h5vyNPaq4zSvTeZ5AEZz3mftFcDYBPZdR3Ga87AByhBArpvk84WZ4QDDZ4jbjZbhWyIzRuhljiBAiBbgN2ByC0w2v+TWXi+AMF7e55JpyU8B8mBUylRloX8l8KIIzak25V0JwupOAgxlUBGfGiC7wI+BjKaVtuk80as2vOXtjE7q3BtCK4MzZNb+msLjNeJlVa35NguE15SZd3Ga8zMRaITNCdKc7yv4VzNnpToHiNhNdU27SzINZIVNS3Ga8zLY1vyZBSN4aRvEGcN9MKYITdtENvGr8AWBkCorbjJdRa379SWC+4JxACFEA/A+mrrjNeHkN+LYQ4oEQnnPaEUJsBJ4gdG8Nw7wI/KEQYs4srCqEiBBC/JApKm4zXqSU3cAu4C9mQn2LsIsusAj4BVqN1pDVRQiIfRLwj2gVzeYK3wWeBELtr14J2IF355hv95fAYiDUqzssQ0uR/bsQn3c6iUN7mNiAglCdNHA/LkAbjNwWqvN+FTNBdNvQSjh+BLSH6qSBuZa/A2KA6cjWChcmtKXn/yXE5z0INADeaUjpDCdGNJF4J8TnfQEYQrs/5wpWIAo4gZYtFhIC9+O/Aha02ENYETPh9yGEuFFKeTBc5wYOzRWhCGQzLZjiimLjPbcOuFZKeSjU554uhBCrgOZpSqO+1LmzAYOUsibU554uwvl7E0JcAxQFAulhY0aIrkKhUMwXZoJ7QaFQKOYPUspx/TPoI9rRgl0z4p9BH9E+lp3GSN2MstMYqRvTztnQn0ZD1IyyMdinhqiLbZ1B3/vo79xoNM4Yu77uX5TB6Au3DeP5p4s0zBI7jWP+7qWU43cvCCFkyz/dOK59Q0HWPxxESnlRlFwIIbtemDHzoEl54sWvtLPt3+4Mh0ljkvFXOy6yUwghh87vCZdJX4lp+a1j2trxux+Gy6QLSPv5m0H7hBBywB322M0liY6K4LNqR7jNuCTfKDDz/fc6wm3GJXnnwbQxf/eg3AsKhUIRUqZkSfLz7YMsT7dM+vg3T7Zj1EfwnbWpeH2Sv9taw7/eX8DW0m66Bz3ctzKZ4/X9AHxjedKkz1Pa1MuKnMRJH//6/nKMkXq+e10BA043bx6s5OZlWVS2WRECrliQyv6yVmLNkdy9Ln/S5znfZmd5xuRnCr1xrAljpI4H12cy6PJyoLoXKSWtNidL06JZmxMX/OyulWmTPs+5ihpWFS6a9PEvbfkUk8HAQ/ffTnN7F/uOFhEbbSE7I5U3PtzBXzzxA06dqwDg/tsm/5ZV2tzHiuzJ57+8frAaU6SOB69ZwIDTw1uHa7hpaTrV7f102508dP0i/n7LSf794WsmfY6S4mJWrl496eNffvF5TEYTP/jhj2hpbmbfnt3ExsXhcrno6enmnnvv54u9e4iNi+O+bz4w6fPUlp9j4dJVkz5+++aXiDKa2PTAQ3S1NXPmyD4sMbH4vD6EEKy/cRM733uNtdffQv6SyU+NttaXEp8/+ZpBNbteR2cwkX/Tgwx2NtJ0dCu5NzxA87GtxOUuJSZjIR3nDhBpjiH76omVk56w6L5T1EGrzcXKzGh6BtwkR0dxsrGfmu4h+p1eBODw+Igz6rFE6bAYdFiHvPj8km+vSaXT7qa0fRCADQvj0esE5siRAbdeJ7g6V6vh7Pb6KUg24fFJchONWIfGP9PjrYOVtPYNsjo3iS67k5RYIyeqO6nusNHvcAECh9tDvNmAxRCJxajHOujG5/fz4LUFdNgclDRpWZ83L8tEr4vAbBjJGC5r6UNKcHq8lDb3siwrgThLFCaDnrLmvnGL7uYTLbRah1iVFUv3oJuUaAMnGqzUdA1iG/IgEFp/mvRYovRYDDpsDg9ev+Q76zPptLsoadUeSDcVJGl2RumC7VsMemKNeroH3GTEGbE5vRd8Nl5e+2A7ze1drF2+mK4eK6nJCRw9XUplXRM2+wBCCAaHnCTExmAxG4k2m+jrH8Dr9fHQfbfR3tVLcXk1ALdedwV6vQ6zyRRsPzEuBpPRQGlVHfffdiNnzleSlZZCr7WfPpt93Ha+fbiG1j4Hq3IT6La7SIkxcqK2i5qO/kB/gsPtJd4chcWgx2KIxOpw4fVLHrx6AZ22IUqa+7T+XJp+UX+WtVoD37sPl9dHQXosVoebaxaljtvG1199meamJtauW09XVyepqWkcPXKYysoKbDar1peDgyTEJ2CJjiY6Opq+vj58Xi/ff/iHdLS3U3z2DAC3bLoNvV6PxTwy6ElITMRkNlNaco5bNt1GXW0Ndrs9+Nl4RXfne6/R1dZMwYq1WHu6iE9OpazoKM21VQz2W0EIXEMOouPiMZotmMzRDPRb8Xm93PrNH9Db1U7t+WIA1t1wKzq9HuMoO2PiEzEYzdRVlOL1uFmwZCV1FSVIKXG7xr8wcO2etxnqaSVhwSqctm6M8Sl0V5zA3lqDe9AGQuBzOYi0xBNptKA3WnAPWvH7vOTf9CBDfZ1Y60sASFt9ExE6PXqjOdi+LsqIITYR96AVc1ImnsF+oqLj0UeZsDWUT1h0J+xe8PgkqzK1+t83FSRgG/KSn2hESsktixMQAvQRI64MAQy5NdEA8EuJ16/9G2bA7cPrl9R0D+Hy+iltH+R8+yB6XQSVnQ4iBLx+oo1ky/jrf3h9flbnaaPiW5ZnYRt0k58ai5SSTStzAnaOXL5A4HB7iTMbAJBS4vP58flG/HEDTg9en5+adht5ybHodRHUdfazJCMem8ONw6U9dJZnj3807fH5WZ0dB8DNi5OxDnnITzIhJdxamDJmfzo8PuJN2gPA75f4fNq/oJ2uQH92DdJldzHk9hERASnRUdR3O+geGPls3HZ6faxbsQSATTdcidVmZ2FOJlLCHRuuQQiBXjciTkIIHA4nCbHaveKXfrw+H17fSCW/gUEHPp+PqvomdLoIhBCsXLKQ5vYucjLS6Oju5YV3PiElMX5i/Zmr9f/GZRnYHG7yU2KQwKYVmRf3pwCH20e8OSpgp8Tr9+P1j3zvgy5t0FDT0U9ecjR6naCu006kLoKKNhvJMQZKmvsoDYj1JW30eFi3XkuC3HTbHVj7+li4aBFSSu648y6tL/Uj4yGtLweJT9BG636/H6/Xi9frDe5jH7Dj9XqpqqxEp9NpfblqNWaTGYPBwKKCguBn48Xr9VCwch0A62/cxIDNSkbuQpCSK2++AyEEEV/6zp2OQaLj4gGQfj8+nxefb8ROx6Adn89Lc10VERGanQsLV5K7aCn2/j6y8heh0+lobagdt53S5yFhoXZd6Ws34h60EZ2Wj0SSsX4TQghExKjxpRB4nQ6iouMDDfiRPi9ylJ2eoUH8Ph/2Vm2KtCE6AXtrDca4FAba67T2hCA+b+Lrsk5bIO39M518Z6329H/teBsPXZFGpG7qXMhTFUjbcqSa716nZSS+sq+MH95YSKR+6uycqkDae0WtPLheSxt/9UgTD1+dNaX9OVWBtLc/3sVD998OwPObP+ax79xNZOSUeLGCTEUg7b1jdTx4zQIAXt1fycM3FExZf05VIG3zm2/wgx/+CIAXnnuGR3/yUyIjp6c+0+UE0nZ/+DabHngIgK1vPc+d330M/TTZeTmBtPr975F/04MAVO94lYWbHiZCPz12hiWQNiy475/p5JGrM8Z1Q59tsfPa8Taa+pw8c6iZtn5X8LPpYlhwtxyp5rGNy8YluGfqu3hlXxm9A062na5n2+l6PjlVx8t7y/D5pydSPSy4ABaDbtwC4fdL/seWEpp6h3jmi3rabNOy0kyQYcF9++NdPPmD+8cluKdKKnh+88fYBx385vX3OV9Vx5bte3nmrQ8Zck5PzZ5hwQWwGCIn1J9/9vpRvD4/f/nWsWmxbZhhwQV44g9+xvvvjj8T+dTJE/zqP/6dutpanv7lf9Da0jIdJgIEBXf3h29zz8NPjktwK8+dYutbz+Pzenn6H/4YgI9e+x1nj0xfzathwQUouPNRGg+Pvwhf46EPqdr+Eo6eNpqPb6P5+LZJ2zGpIcjzh1tYkxVNi9VFVdcQNy6Ko6HXSXu/m+ToSIz6CCxROqKNWvPPHW7B7fWzIMmE2+vnW2tScXv9HKrTSueuyrCQHB3FmqwYqruGMEZGkGiOxDbkDX42GZ7ZVcLa/GRaegepbLNy09JM6rr6abc6SIk1YYzUYTFEEhN4VX9mZwkur4+FabG4vX6+c80i3F4fB8o10V+dm0RKrIm1+SlUtdlIjDaSlxyLddBFu83B4ow4uvqdpMebv86sMXnuQD1rsuNosQ5R1TnIhoIk6nsctNucJMcYMEZGYInSExPo02f31+P2+VmQbMbt9fPtdZm4vX4O1mhr/K3OiiU52sC+qm7WZMdpfWqJxDbkISPOOKn+/PVr77F+RSFNbZ1U1DZwy7XrqW1qpbWjm9SkBExGAxazkViL5rd7+tUtuNweCvKycbk9/ODeTbjdHvYdOw3A2uWLSU1K4IqVhVTWNlJaVYeUkiGXm5yMVOoaW2ls7aBwYe6EbX12dzlr8xJp7h2kqr2fDUvTqe+y024dIiXWGPju9UQbA9/97jLcXj8LU2JweX185+oFuL0+DlZoo6pVOYmkxBrZV9bGurxE9LqICflxv4rfPv0r1l1xJc2NjZSXl7Hx1k3U1dbQ2tJCaloaJqMJS3Q0sbFanOM3T/0Sl8vFooLFuF0uvvfQw7jdbr7Yq72RrFm3ntTUVBYVLGbrJx9jtlhISkqmr6+PzKysCdv3+5d/w+JV6+lqbaKxppy1191CW2MtPR2txCenYjCaMJotmKM1+z546dd43C6y8gvwuF3ccv/38bjdnD2yD4CClWuJT0plyaoraKqpRKfXs+KK6wFITs9kwG69rP6s+PRZEhetxdHdTH9zFWmrNjDQUc9QbzuGuBR0UUbNr2vW3F4VnzyD3+smOmMhfo+LvA3fwedx01miVSVIWLgKY1wK5uQsBtrr8ToHiE7Nwz0weTsnNdJdnm6hqNmOy+vH7tL8IDcXJJCbYMQcOeLjIeC6EMCSFDNLUsyUdTjw+yUSgr7dYQdHTfcQpe2DeP2SBHMkNd1Dwc+cnomPIFfmJFJU24XT46PfoQWNbl2RTV5yDOaokedN0MMioDAznsKMBEqbejU7JUHf7vB+Ne02Spp6aOqx8+oXZSTHGtHrIihvtZISOzlBW5ERw+lGm9anTq1PNy5JJjfJfEEgZ9gdJAQsSY1mSWo0ZW0DwT4d9u8O2zrg9NHY60AXIUgwR1LdNTgp+wBWFy7iRHEZLpcbm11r57YbrmJBdgYW08h1D3+jQgiWLcpj2aI8Sipr8Pv9SGTQtzt8LVX1TZwtryY3Mw29TkdNYwtmo5GoqEgWZE9u6bUV2fEU1ffg8vrpDwRgb1meSV5y9AXf/TACwZL0OJZkxHG+xRr87of9u8PXZHd6aOgeoHfANSE/7lexcvUaTh4/htPlpL9fG4TcdvudLFiw8ILg2Mj3Lli6bDlLly2n5Fyx1qdSBn28w/tVVpSTmpaGz+slMSmJqsqKSdm3cOkqKs6cwO1yMmjXArZXbLiN9JwFGE2jZiyNsi+3YCm5BUupKy8J2jfs2x22r7muitqyYtwuJ7VlxdSWnyMhOY3Whlp8o3zVEyU+fwU9VUX43C48Ds3e9LW3YEnLuyA4FhQdIYjNXkJc9hKsDeeRfj8gR3y8AXt1BjMRkQaiLHFU73oNQ1zypG2cFp/uaH/udDEVPt3R/tzpYjp8utPBVPh0R/tzp5Op9ulONVOZHDHarzudTNanO9qfGwouNzlitF93Ogm5T3e6BXeqmG7BnUqmU3CnilAI7lQxXYI71YRCcC+HUAruVBAKwb0UUyK675/pHPe+h+usnGzsvyBAtr+6j/fPdPLRuS5eOdbKkGdkWtGxehvPHGzG5fXzzMHmy7Jzy5Hqce97qLyNEzUd/P54LS/uOU9L7wCbD1Wx7XR9cJ/RgbTh4JrH6+e3Oy5/Bfn3ilrHve/hml5ONliDQbPeQTfbSzrYXjIyIhh0efmstJPtJR0crevjWN3kX4vf/njXuPfdf/wMR8+UBoNlPVYbr7y/jR37jwU/G80HO77g9zv302O18cxbH/LR5wc4eLKYQ6cur0/fO1Y37n0PVXZworaL0/U9/Hbneeq77Pzu8zLarCMjwQGnh+f2lFPeauVoVSfHqsf/GxiLzW++Me5993+xj2NHj1B06iQvPPcMdrudrZ98zFP/+YvgPl6vlz/5o58F2372d7/FbrdfsM9k2P3h2+Pe9+zR/ZwvOobf7+c//+ZntDc38P4LT9HdPhLUcwzYOfL5p7z3wq84+NmHfPrmc7hdLt574VeXZWf9/vfGvW9nySG6K07QW32G6h2v4rL3XhQsc9l7qdr+Es3HttF09FOqd7yCz+Oi/KP/mrBtEw6kvXu6A3OkjiRLJF9U9/HjqzWf27DwpsVGcby+n3tWJFGYZqG4ZYAeh4cUSyQrA/N7M+MMZMYZqO4awueXOAKvX1lxBhp6nbRYXRSkaP6Xa/LjKG0fxKCPICV6/PN0Nx+qwmzQkxRjZF9pC49tXAqMCG96vJmjVe3cd8UClmYlcLa+m+4BLYlida7mr8lK0Oyt7+pnwOm5KPFhdCBtOLgWqY8gNdZ0sUFf16cnWzBH6Ui0RPFFVQ+PXJsNjAhveqyBY3V93LsqncL0aM422+gJJFKsyooN9KkxGDRLtESRl2TG6hhJJhmdEJEVZ6R1nLMY3vhwB2aTkZTEeD4/dJInvn8fMCK8GalJHDp1jm/dcRPLFy+gqLSS7l4rqUkJrF2+GIDs9FSy01OorG2ksq6JW65dz5HTpdx50zVU1l64WPG5ihpWLF4IQFePlbiYaNYuS6W5vWvc/fnOkVrtu482sK+sjUc3aHYMC29anIlj1Z3cuz6XpZnxnG3ooWfARUqskVWBjMWsBAsWg54dxc2YDXoSLQasg24yAkHS0UkSmYlm2vrG/2r+5muvYrZYSE5JYfeunTzxByPiCJCemcnhgwd44NsPsnzFCk4XnaK7q4vU1DTWrNPmzWZn55CVnU1leTkxMTHExMZyz33fDJ5Dr9dz/fWaO9DpctLR3o7BYCAtbfyLs+z64A2MJgtxickUHfycex56EhgR3qS0DEpOHOLGb3yL/CXLqSopwtbTTXxyKgUr1gKQkpFF0YHPKVx9BQajkdjEJAb6rSSna0E9c3QM5uhYrrvtXiqLT5GzsBC7rZeE5PFnStbtewe9wYwhNon2s/souONRYER4TQlpdJUdI+e6e4nLWUpvzVlc/T0Y41JIWKhl15mTszAnZdLfWo0hJvGiYJkQEbj6u4m0xCKEICarAPeAFWPCxN/qJzzSTYuJosfhod/pJSfBSEOvNrNgwO0LJjcsSzdjDATUfIFkCN+XfMfDAbLKTgdev6TV5sIUGUGUXhBn0nO6WctCeuZQM5YoHV7f+HzPQTvjTfTYnfQ73OQmR1PfqbU34PRQ3WHD6fGxPDsRYyBI5fNryRB+/4XnMUXpMeh1LEiNDSY+7CzWhKLTNhQMpA0H15yeiQcBUmMN9Ay6R/q0R+vTQZeXmq5BXB4/yzJiMAYy94IJEV+ydTho1mV38drRJpKio/i8TBOrPod7wgkRAOkpiXT3WbH2D5CXlUZdk/YgsDscVNY34XS5WVm4EJNRSyrxBYJkX546Nxwsy8lIY9fBE8TFWIKfNbV1cqK4DIDChXlY++0MOpxkZ6QwmZV/0uK079425CYnKZr67oFA/3io7ujH5fWxPDvhS/eo/6L+rGrvJyXWiM8vSYiOoqajn13ntFFaQVpsMElioqRnZNDd3YXNaiUvP5/aWm0Cvn3ATmVlBS6nk5WrVmMKZOz5fD68Xi8+v++CdqoqKykuPoPT6aS2ppqCxYv5bNvW4N+Li89QUlyM0WAkJTWVgYGBCdmZmJKOrbeLwX4raVl5tDZqCQtDg3aa6ypxu5wsWLoSg1Gz0+/z4fN58X/JTseAnbamenQ6PbHxiTTXVnFs7/bg31sbasjKL0Cnj6Sh6jzxSRMTMlN8Gq7+HjyDNiwpOQy01wPgHRrA3lKNz+MiPm85uigt2Cv9Pi1Q9iU77a01WOtKGOxqCgbLWk9pgwuvy4EpKTOQEBJJf1MFhtjJBdNCXmWsusuB2ycnXKvB4/PzeUUvdy0PjEKnucpYVZsVt9c/4VoNHq+fncWN3LM+HwhNlbGqzgE8Pjnueg3nWvqxROlYmDLyHUxXlbGK2kbcHs9l1Wg4c76KaLOJgnztDWC6q4xVtdu0736c9RrONfViMehZmKq9dUx3lbGK8nI8bveEazV4PB62b/2U+x/41kV/m44qY001FXg87gnXavB6PBzbu40b7vjmRX+bjipj/S1V+D3uCddq8Hs9tJ7aRfY1F6cBT0sgbSJ+3GEO11kpbh3gdLOdtn4XDvfIk2as9s63j0xvitRFBAV3IkzEjzvMofI2zjZ0U1TXSVvfIIOukdf0sdorbRpZmTtSHxEU3IkwER/uMIdrenm/qJVjdX0kmCNxuEdG2WO1d75NG5Wtyoq9QHDHy0R8ucN0dPdSUlnHS1s+paWji0HHyJzrsdo7V3HxyjRrly8OCu54mIj/dphDlR28d7yO1w9WE22MJD9l5AE2Vnujp4qtykkMCu6lmIjvdpj9X+zjN0/9Mnhs4dKlQcH9j3/7F7Z9+gmHDx2kuqrqgmNami+MgVSUlY0puGMxEd/tMGeP7ueDl34dPDZnUWFQcN955hcc3b2VkpOHaamvvuCYrrYL7WysKR9TcMdiIr7bYTpLDlHxyTPBY2OzFgcFt+z3T9NycgddZcewt9VecIyj58LfVH9z5ZiCeykmVfAmI057lTzR2M/uil5+fHUGrx1v45HA9qfXZpIaEzWmP/fa/DgqOh2cbrKzJiuap75o4oaF8TT2OXnucAtX5sSyr6qPDQXx7K7opanPSWnbIHcuS+Tjku5g25fi7UOVZCZownK8uoNdxU08tnEpL+8r5yeB7ZOblpMWZx7Tn3v9kgzKWvs4VdvF2vxkfrn1LBuWZtDQbeeZnSVcVZDKnpJmbl6exa7iJhq77ZQ09XDXujw+PFEXbPuS/XmyJZiscKK+j8/Lu3nk2mxePdLEo9fl8OqRJp64MY/UGMOYvtzrFiZS3mGnqNHGmuxYfrW7lhsLkmjscfDs/nquyo9nb0U3Ny1O4vPybhp7hyht7ecbK1L56Gx7sO2v4/Xff0ZWWgoAR06X8NkXx3ji+/fx/OaPefIH9/P85o/5bz/6NukpiWP6dDdctYbzVXWcKC5j/YpC/vXZN9l4zTrqmtt4+tUtXLt2JbsOHufW66/gsy+OUd/cTnF5NfdtupH3tu8Ntn0pNh+pCfpcT9R0saukhUc3LOaV/VU8dpO2ffKWQlLjTGP6ca9fnEZ5q5Wium7W5CXxq+0l3FiYRkP3AM/sLuOqhSnsLW3l5mUZ7CppobFngNKmPu5am8OHJxuCbY/FG6+9QlaW9uA4euQwn23byhN/8DNeePZ3PPGHP+eFZ3/Hz//7n5CWnj6mDzdCp6PP2sfePbtpaW5i9eq1rF67lqysbO6+9z7eeetNGuvrKTp5IphksWvnZyQmJtHYUI/L5cJoMoEQ/P79LaSlp2MymUhJSeUbd98TtHPn+68H/a3ni45yfO927nnoST596znuffgP+PSt5/jmo/+NxJT0MX24ETodA/1WTh/eS1dbM4uWrWbR8jUkp2dx7aZ72PPRZjqaG6gsPhVMtji5fxexCYl0NDficbswGE0IITiw/QMSktMwGE3EJaVwzS13Be2s27sZU5IWT+ouP0Fr0S4K7niU6h2vUHDnY1TveIXFdz+JKSF1TB+uiNDhHrDSUbwfR08r8fkrSViwElNSJllX3kn9/vcY7Gqit/p0MMmi7fQeDDGJDHY24ve6NXeFEDQd+QRjfCq6KCPGuGQyr/j6WTwTHukKAbrAoLl30ENOgpHStgEWJpmC2/IObYQ6lj/3aL2NVpuLOJOeZquLwlQzlZ3a/lJq7WcnGIg16EiP1cR1Wbr2Qxrd9iXtRKALFDbpsTvJTY6mpLGXhamxwW1ZYLQylj/3cGUbrb0DxFuiaOoZYGlmPOWtfQi0yf8CQU5SDLGmqGAG2nChm9FtX9rOkf7sGe7PVjsLky3BbVlghDqWL/dIbS+tVifxJj3NVieFadFUdAyAEMhA+zkJJmKNkaTHauK6LOCGGN3219ooBLpAmmx3r428rDSKy6spyMsKbkurtFHBWD7dAyfO0tzeSUJsDI2tHSwryOd8TT1CiOB3npeVTly0hYxUrUjRykItmDa67Uv35ch33j3gJCcpmpLmPhamxgS3Za1Wzc4x/LiHqzpo6XMQZ46iuWeQwsw4KtpsCO1LRwjISYomxhRJekBclwdcEKPb/irbhovDdHd1kZefT/HZMyxavDi4LS05N9KHX/Lh3nPv/Rw6cACASH1k0Nfd0tLMtk8/ITtXy9obnWQRPHcgoWLJkkL8Ph8mk4mCgsUsX7GSolMnL9pXF6HZaevpIi0rj5qys2TlFwS39RWlwNg+3Gs33cO541o2l36Und3tLRzdvZXUzByAC5ItRp87t2Ap2QsW4/f5iDKYyMovIG/JcqrOFX2pQwUiYKervxtLSg599SVEZywMbm2NWpxgLB9u1lV30nX+qNaUTiteAzDU00rLyR1YkrUH5Ogki9Hnjs1eQkxmAdLvRxdlJCZjIXG5WpDuUqiVI6YZtXLE1KNWjph61MoRU4taOUKhUChmCmphyun9pxamnIY+VQtTTvk/tTDlVNs5BQtTKhQKheLyUe4FhUKhCCFKdBUKhSKEKNFVKBSKEKJEV6FQKEKIEl2FQqEIIUp0FQqFIoQo0VUoFIoQokRXoVAoQogSXYVCoQghSnQVCoUihCjRVSgUihCiRFehUChCiBJdhUKhCCFKdBUKhSKEKNFVKBSKEKJEV6FQKEKIEl2FQqEIIUp0FQqFIoQo0VUoFIoQokRXoVAoQogSXYVCoQghSnQVCoUihPz/yuglHTQq190AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree.plot_tree(entropy_tree_clf,\n",
    "           feature_names = feature_names, \n",
    "           class_names=targetNames,\n",
    "           filled = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}