Test_python_structures.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Data analysis packages:\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "#from datetime import datetime as dt\n",
    "\n",
    "# Visualization packages:\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "filename = 'df_imputed_tot_OHE.pkl'\n",
    "df = pd.read_pickle(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age_0</th>\n",
       "      <th>age_1</th>\n",
       "      <th>age_2</th>\n",
       "      <th>age_3</th>\n",
       "      <th>age_4</th>\n",
       "      <th>sex_0</th>\n",
       "      <th>sex_1</th>\n",
       "      <th>cp_1</th>\n",
       "      <th>cp_2</th>\n",
       "      <th>cp_3</th>\n",
       "      <th>...</th>\n",
       "      <th>exang_0</th>\n",
       "      <th>exang_1</th>\n",
       "      <th>oldpeak_0</th>\n",
       "      <th>oldpeak_1</th>\n",
       "      <th>oldpeak_2</th>\n",
       "      <th>oldpeak_3</th>\n",
       "      <th>slope_1</th>\n",
       "      <th>slope_2</th>\n",
       "      <th>slope_3</th>\n",
       "      <th>heartdisease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>920.000000</td>\n",
       "      <td>920.000000</td>\n",
       "      <td>920.000000</td>\n",
       "      <td>920.000000</td>\n",
       "      <td>920.000000</td>\n",
       "      <td>920.000000</td>\n",
       "      <td>920.000000</td>\n",
       "      <td>920.000000</td>\n",
       "      <td>920.000000</td>\n",
       "      <td>920.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>920.000000</td>\n",
       "      <td>920.000000</td>\n",
       "      <td>920.000000</td>\n",
       "      <td>920.000000</td>\n",
       "      <td>920.000000</td>\n",
       "      <td>920.000000</td>\n",
       "      <td>920.000000</td>\n",
       "      <td>920.000000</td>\n",
       "      <td>920.000000</td>\n",
       "      <td>920.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.213043</td>\n",
       "      <td>0.208696</td>\n",
       "      <td>0.218478</td>\n",
       "      <td>0.191304</td>\n",
       "      <td>0.168478</td>\n",
       "      <td>0.210870</td>\n",
       "      <td>0.789130</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.189130</td>\n",
       "      <td>0.221739</td>\n",
       "      <td>...</td>\n",
       "      <td>0.633696</td>\n",
       "      <td>0.366304</td>\n",
       "      <td>0.415217</td>\n",
       "      <td>0.145652</td>\n",
       "      <td>0.142391</td>\n",
       "      <td>0.296739</td>\n",
       "      <td>0.556522</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.068478</td>\n",
       "      <td>0.553261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.409681</td>\n",
       "      <td>0.406597</td>\n",
       "      <td>0.413438</td>\n",
       "      <td>0.393542</td>\n",
       "      <td>0.374494</td>\n",
       "      <td>0.408148</td>\n",
       "      <td>0.408148</td>\n",
       "      <td>0.218063</td>\n",
       "      <td>0.391825</td>\n",
       "      <td>0.415642</td>\n",
       "      <td>...</td>\n",
       "      <td>0.482056</td>\n",
       "      <td>0.482056</td>\n",
       "      <td>0.493028</td>\n",
       "      <td>0.352949</td>\n",
       "      <td>0.349641</td>\n",
       "      <td>0.457069</td>\n",
       "      <td>0.497065</td>\n",
       "      <td>0.484386</td>\n",
       "      <td>0.252702</td>\n",
       "      <td>0.497426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            age_0       age_1       age_2       age_3       age_4       sex_0  \\\n",
       "count  920.000000  920.000000  920.000000  920.000000  920.000000  920.000000   \n",
       "mean     0.213043    0.208696    0.218478    0.191304    0.168478    0.210870   \n",
       "std      0.409681    0.406597    0.413438    0.393542    0.374494    0.408148   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "50%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "75%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "max      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000   \n",
       "\n",
       "            sex_1        cp_1        cp_2        cp_3      ...       \\\n",
       "count  920.000000  920.000000  920.000000  920.000000      ...        \n",
       "mean     0.789130    0.050000    0.189130    0.221739      ...        \n",
       "std      0.408148    0.218063    0.391825    0.415642      ...        \n",
       "min      0.000000    0.000000    0.000000    0.000000      ...        \n",
       "25%      1.000000    0.000000    0.000000    0.000000      ...        \n",
       "50%      1.000000    0.000000    0.000000    0.000000      ...        \n",
       "75%      1.000000    0.000000    0.000000    0.000000      ...        \n",
       "max      1.000000    1.000000    1.000000    1.000000      ...        \n",
       "\n",
       "          exang_0     exang_1   oldpeak_0   oldpeak_1   oldpeak_2   oldpeak_3  \\\n",
       "count  920.000000  920.000000  920.000000  920.000000  920.000000  920.000000   \n",
       "mean     0.633696    0.366304    0.415217    0.145652    0.142391    0.296739   \n",
       "std      0.482056    0.482056    0.493028    0.352949    0.349641    0.457069   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "50%      1.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "75%      1.000000    1.000000    1.000000    0.000000    0.000000    1.000000   \n",
       "max      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000   \n",
       "\n",
       "          slope_1     slope_2     slope_3  heartdisease  \n",
       "count  920.000000  920.000000  920.000000    920.000000  \n",
       "mean     0.556522    0.375000    0.068478      0.553261  \n",
       "std      0.497065    0.484386    0.252702      0.497426  \n",
       "min      0.000000    0.000000    0.000000      0.000000  \n",
       "25%      0.000000    0.000000    0.000000      0.000000  \n",
       "50%      1.000000    0.000000    0.000000      1.000000  \n",
       "75%      1.000000    1.000000    0.000000      1.000000  \n",
       "max      1.000000    1.000000    1.000000      1.000000  \n",
       "\n",
       "[8 rows x 41 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(616, 40) (616,) (304, 40) (304,)\n"
     ]
    }
   ],
   "source": [
    "#importing all the required ML packages\n",
    "from sklearn.linear_model import LogisticRegression #logistic regression\n",
    "from sklearn import svm #support vector Machine\n",
    "from sklearn.ensemble import RandomForestClassifier #Random Forest\n",
    "from sklearn.neighbors import KNeighborsClassifier #KNN\n",
    "from sklearn.naive_bayes import GaussianNB #Naive bayes\n",
    "from sklearn.tree import DecisionTreeClassifier #Decision Tree\n",
    "from sklearn.model_selection import train_test_split #training and testing data split\n",
    "from sklearn import metrics #accuracy measure\n",
    "from sklearn.metrics import confusion_matrix #for confusion matrix\n",
    "from sklearn.metrics import f1_score\n",
    "\n",
    "X = df[df.columns[:-1]]\n",
    "Y = pd.Series(df['heartdisease'])\n",
    "\n",
    "\n",
    "train_X,test_X,train_Y,test_Y =train_test_split(X,Y,test_size=0.33,shuffle = True, random_state=45)\n",
    "\n",
    "print(train_X.shape,train_Y.shape,test_X.shape, test_Y.shape)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy for linear SVM is 0.8355263157894737\n",
      "Training Accuracy for linear SVM is 0.8035714285714286\n"
     ]
    }
   ],
   "source": [
    "model=svm.SVC(kernel='linear',C=0.1,gamma=0.1,probability=True )\n",
    "model.fit(train_X,train_Y)\n",
    "prediction2=model.predict(test_X)\n",
    "pred_training = model.predict(train_X)\n",
    "print('Test Accuracy for linear SVM is',metrics.accuracy_score(test_Y, prediction2))\n",
    "print('Training Accuracy for linear SVM is',metrics.accuracy_score(train_Y, pred_training))\n",
    "\n",
    "\n",
    "\n",
    "#print('F1 micro: is',f1_score(test_Y, prediction2, average='micro'))\n",
    "#print('F1 macro: is',f1_score(test_Y, prediction2, average='macro'))\n",
    "#print('F1 weighted: is',f1_score(test_Y, prediction2, average='weighted'))\n",
    "\n",
    "\n",
    "\n",
    "model_Linear_SVM = model\n",
    "        \n",
    "    \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for rbf SVM is  0.8355263157894737\n",
      "Training Accuracy for rbf SVM is 0.8068181818181818\n"
     ]
    }
   ],
   "source": [
    "model=svm.SVC(kernel='rbf',C=1e4,gamma=1e-4, probability=True)\n",
    "model.fit(train_X,train_Y)\n",
    "prediction1=model.predict(test_X)\n",
    "print('Accuracy for rbf SVM is ',metrics.accuracy_score(prediction1,test_Y))\n",
    "\n",
    "pred_training = model.predict(train_X)\n",
    "print('Training Accuracy for rbf SVM is',metrics.accuracy_score(train_Y, pred_training))\n",
    "\n",
    "\n",
    "#print('F1 micro: is',f1_score(test_Y, prediction1, average='micro'))\n",
    "#print('F1 macro: is',f1_score(test_Y, prediction1, average='macro'))\n",
    "#print('F1 weighted: is',f1_score(test_Y, prediction1, average='weighted'))\n",
    "model_RBF_SVM = model\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the Logistic Regression is 0.8421052631578947\n",
      "Training Accuracy for Logistic Regression is 0.7808441558441559\n"
     ]
    }
   ],
   "source": [
    "model = LogisticRegression(penalty= 'l2', C = 0.01)\n",
    "\n",
    "model.fit(train_X,train_Y)\n",
    "prediction3=model.predict(test_X)\n",
    "print('The accuracy of the Logistic Regression is',metrics.accuracy_score(prediction3,test_Y))\n",
    "\n",
    "pred_training = model.predict(train_X)\n",
    "print('Training Accuracy for Logistic Regression is',metrics.accuracy_score(train_Y, pred_training))\n",
    "\n",
    "\n",
    "#print('F1 micro: is',f1_score(test_Y, prediction3, average='micro'))\n",
    "#print('F1 macro: is',f1_score(test_Y, prediction3, average='macro'))\n",
    "#print('F1 weighted: is',f1_score(test_Y, prediction3, average='weighted'))\n",
    "\n",
    "model_Logistic_Regression = model\n",
    "#model.get_params()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the Decision Tree is 0.7828947368421053\n",
      "Training Accuracy for Decision Tree is 0.7954545454545454\n"
     ]
    },
    {
     "data": {
      "image/png": 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kQALeE6AY4z0j9iABEiABEiABEggGBJKkSIn8RUt6u9PIke297cMOJEACJEACJEACJBCQ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ZcigypjR5umhCjeydMlAoUVotcaDpg71vmBDSGNev1qYPOEzqOnnJGCNjDq9fgB/00qMcZRHS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gxowZtde5aDnJCFBYGf1oSQg0+YbIo4nEmJctWya/S6wLBPQCAdKNoe9wfEbf4YEDB3J9\nqvjKiO3JhwCFmJHX0vbt20HeMFWqVOGZl6Kjo6VvocnFnLYLEwgIBAQCyYUAkfoJheCThya9vCJS\nhu57hSUPAqSFSLowRMakSZNG4f3tw4cPUbhw4eQZYCrvVZAxevYFIDc/yrBw5vRpUF74N2/fKgxx\n0bNhi+EkAQFjY2NYWGRHsWLFULWqK0hYi4R7hSU/Am3btsXu3bsV/mDR6Mil093dPfkHKkYgEIgH\nAdIVuXbtWjx7gQcPHnDh2HgLiB3JjgBlJKEMGBS2Sg88RPCTkYelg4MDbt++rVQ4WrJPRAxAICAQ\nMDgEKLkBeXknZBQCQwLllADBwsIioaJin5YQIO+ldu3a8fsBImUUGf2mLFiwAP3791e0W2zTMgKC\njNEywMo2f+bMGcyYPg3HT5yEuWlW1KjgjFIOdshtZYnMmUyUbUaU0xMEvjMx3qPnfFGmhANyWSn+\nAYpibz/fhX7E3UdPcMb3Jl6/ecdCX0qz1NajuZAjsdfCkgeB04wMrVmzpsLOTUxMuDid8GxSCI/Y\nqCcIzJw5ExMmTIhDKNKbStIkuXTpkp6MVAwjPgTIE6ZUqVIgXQZ5Tz36fSDvPEqNLUwgIBAQCOga\nARKDpZeJ8RkRMRR6Sc83JCovTPcIkEYM/YaQHEJCRvcFdevWxaFDhxIqJvZpCQFBxmgJWGWbpVSj\nf/fti4PsBKhZsSz6dWoO96rlpGEtyrYjyqV8BC5dv4MVW/Zh15HTnJRZs2at8JRJpsNKsbQFCxbE\ns2fPYo2A3h40adKEe83E2iFWBAJ6hgAJwBYvXjzOqOgh3tPTEx07doyzT2zQHwToGtS6dWvs27cv\nDhEjGaWpqSknasSDjgQR8SkQEAjoCgHyEKbQe0VGRAz9/tCLLZGQQhFCuttGgu/k7U0vYOLzjKHR\n0ItGIm8kyUV0N0LRkxDwTcbvAKWvpIvVQ/87OOIxF4fW/of61SsKIiYZj0lydl3JuSQ850yA3761\nMMYPnr503rx5yTmkVNs3PbD+/fffXGVeFgR6QBKip7KIiGV9RYDeWFKmNnnLnDkzWrZsKb9ZrOsZ\nAhSelFCoJA2XsvSRBoAwgYBAQCCgawTI20KSlEK2byJiSpYsyUOTBBEji0zyLOfJk4cfi8mTJ3O9\nmPgyLZI4PGX0E6Z7BAQZo3vMeY/kQk4ZL1rXqw6/vatRnYUlCRMIEALFCxXEcc8FmDKoO08/9xfL\nuiTRChAI6Q6Brl27SsUyJb0SGdOgQQPJqvgUCOg1AkS6yL7louW//voLpFclTH8R2LRpE6ZNmxbn\n+iM/YgpdWrFiBW7duiW/S6wLBAQCAgGtIkCew/JkDBExlLiCQpNI3FeYfiBABAyFLZPwe86cOeO8\naKRR0v3BsWPH9GPAqWwUgoxJhgNO7OSYMWMwb9xALJk8FEYZMybDKESX+owAeWYM69EOe5bNwLZt\n29ChffsE3Qv1eS4pdWyUAYBElenmgoyOScWKFUXsc0o9oKlw3BRS9/37d+nMablnz57SdbGgnwjQ\ndWbcuHE8CwmNUHINUjRaehgiLz5hAgGBgEBAlwiQZ4yslhVdp5ydnXloEoVQCtM/BCpXrswzvCp6\nqUj3BwcPHtS/QaeCEQkyRscHmdK7TZkyBcumDkffDs103LvoLqUh4O5aHvtW/It9XvswZMiQlDb8\nFD/evkzPSXKzQQ89LVq0SPFzEhNIPQhQ6uOsWbPyCROZWKZMGZQoUSL1AJBCZ2pnZ8c9Y4KDg3H4\n8GFOCtP1R/4tNE2Prk+kBUCkvTCBgEBAIKArBAICAqQvCYmIcXFxwcmTJ6W/Oboah+hHNQQodIy0\nyJYvXw5KRCFL9lOGvg8fPqjWoCitNgKCjFEbQuUbuHr1bV+6vQAAQABJREFUKvr164exfTujW0vD\nDXVISCBKebSSVlLdvpWt//37D6UG+OPHz0RdzRNryLWcE9bMGI1FixZh69atiRUX+zWIQO3atZE7\nd27eIj30NG7cWIOti6YEAtpFgB7eybuLPiU6SNrtUbSuSQTItZwEMvfs2YM3b95g7ty5PKU19SEb\nfkbHdvDgwaBUs8IEAgIBgYAuEJAkOKCH+QoVKuD48eOJprrWxbhEH8ohQJn4rl+/Dltb21hE/4kT\nJ5RrQJTSGAKCjNEYlAk39O3bN3Tp0hlVypTCuH5dEi6cQvd67DyIhj1GIFvpuijTuBtGzlyKr2ze\nujB1+1am/qfPX9B7/GzYVmsJM6c6qNK6D6YsWotv3/6EAUjm6n32Miq26AWLMvWQv0ozdBo2Fef9\nkh7X37JeDe5JRe7odFMuTDcI0MMQeceQ2dvb8z/d9Cx6EQhoBgESnCbNKdKJadOmjWYaFa3oHAEL\nCwsMGjQIlE6WXux0794dJMZMRAwZ/S7MmDFD5+MSHQoEBAKpDwH6TXn79i2feKVKlXD06FF+PUp9\nSKTsGVMSmZs3b3ItOZoJ6SLSsRSmWwREamsd4U1vtCawrAc3DqxDgTzWOupVd9147jnCiQqXUkVR\nh6Xmfhj4HLu9z6BBjUrYumAKc4NLp7XBqNu3MvXJE6Ziy164FxCEVowYKWybH/tPXMCdh0/QvXUj\nrv0jmeD2QyfRdcQ0fpzbNaqNlyHvsOfoWaRjD/YXd65AIZu4GU4kdRP6/PwlCmWadEdNN3d4rFuX\nUFGd7yN3ehJsu3PnDkJCQhAZGanzMWirQ1KYpzhaBwcHniFAW/0kZ7uZMmWCpaUlz+5GoS2FChVK\nzuHopG86riRmR2+GgoKCEBERYZBC2RQH7uXlxVO1U5iSoRh5hpibm/PvatmyZbmek6IwnpQw36Re\nP+mBiNKWBgYG4t27d5yYqVu3rng7reODnhqvnzqGWKfdJfV81Okgk7kzyuR26NAh5MiRA1WrVo3l\nWZHMQ4MhnY9EeFF6cLq3pu8l3VsTYaINe/nyJfz8/HjYEnnUCouLAL2gJT2kvHnzwsnJCdWrV+fr\ncUuqtkWQMarhlaTSdNHKnz8fujVzxz9DeyWpDX2u9CL4LfeEKVnEFt7r5jP36RjB02lL1mP6sg1Y\nNWMUOjWtq5UpqNu3svVXbNmLIdMWYVTvjpjMshxJbPi/S7B0427sWPwPGtWqwr1kitZpj3B2wXx8\naifMTbPwom9Dw2BXoxWK2dvAd88aSXWVP7ceOI4eY2bi4cOHye6lQQ95mzdvxrLlK+B3xRfpmKtq\nTpvCyGqRCxlMMpPircrz09cKz277IkeBQshkll1fh6jWuH5ER+HLx7cIDnyA71+jUbiIA3r36oke\nPXpo5IdGrcFpuDLpayxZsgR79+5FdHQ08rL0zwVt7WFqZq5XN5SanLbPxfMoWrwEIy+yabLZZG3r\nB7v+hIV9QMCjB3jPiAgiZjp06ID+/ftLQ3mSdYCJdC65fq5YthS+fleRPl1aFMplDmtTI2TOkEbq\n8ZJIM9LdX77+wIsPX/Dt5y+UzGsu3S4WtI9A1PdfePf5Ox6+/ojobz/gULgQevbuY5DXT+2jmTw9\nSM/H5cvge4U9kLLQzsJ5LGBtboLMRulhOHczmsE39FMUAl6HwaWQNX/RqJlWNdNK1PefePfpKx68\neM/Ox+8p7nykkPgdO3ZgybLl8Ll4AWnSpkPWPPZIb54LyPjHG1IzaMVu5efXKEQGXUeWAo5IZxLz\n/BK7RCpf+98v/O9rJL69f47IkKf8ucedvQAZwCRI6EVIUk2QMUlFToV6Hh4eLNShD56c3gnLbElP\n9Xbllj8mLVyL63cf8t7pwX50n04gkVdZO3DyApYz8uDOgyfIa22FauVLY+zfnWGahT0gM9t28ARW\nb/NCrcouXL9GUvfM5euYsdwTtSqV5aSDZHtin0RGECmxbdFUNKldVVr86ctgEDHhVsUF+1fNlm5X\nZYE0XC5cuw2/W/d5diH5uur2rWz9Vv3H4+Cpi8yzaT0c7ApIh3GNHQsKVxrQuSVmj+6HW/cfo0KL\nnmhRtzo2zZskLUcLzfqMgfe5ywjxPQCzrEm7yBEexdw7okWbdlw/IFYHOlyhH4oRI0fh9etXKO7a\nCI51WsOujCsyGJnocBS66+r5PT/kLVoGxIobsv1ib9qf3bmM26f24tbR7TA2yohJEydgwIABKZ6o\nePDgAQvzGMxSNx5F6TJl0bFrd7jXawjrXOwGx8DtyuVLKFehksHO8snjABw+4IWN69YgKPAJOnXq\nhNmzZ/MUnvo4abp+jhoxHK9ev0aDUrnRokxeVCmUAyYZ1fcg/fXrf+w6JR4dk+O4/2TYXwkMhdeN\nl9h17RUymmTChImTDOL6mRx46qpPfj6OHMG9zBqWtUWrioVRtVhedj7GvFjU1ThSUj9P34YjV7Ys\nMMqg/jVLW/P+ye6XfR8FY5/vY+zwCUBGYxO9Px/Ji3XIsOF49jQI2Uu7w6ICk0Uo5op0Ory3/t+v\nn/ge8R4ZzXNq69AYRLvfI8Pw8c4phF7ajjD/iyhbrjyWLFqI8uVjP5MrM1nDfrJQBgEdlNnCvAca\n1aysFhHz4Mkz1O02FG/ef8CALi0xslcH0IWmaZ/ROH7hinQW/y7fiNYDJuDLl2j0atsExQrZYOXW\nfajRvj9ev33PyzWr44qwiEiQ54rPjbt8W+TnKB5mRGRCu0Zu0vaUWQh4+pIXq1kxtgt8/tw5kZG5\nkkvII2XaojLkgnfp+h0Mnb4IdtVbwb3LEJBHiCJTt29l6wcz7LJkMkERFp4ka6WLFeIP6BS+REbl\nyMqWdOCfsv/KlorZ5v/4qexmlZaJDOjEPKy2bNmsNVfFhAZEKuuNWbpc0p7IUbwShm69jjaT18Kh\nkrvBEjGER/7iLgZPxNA807K3gQWdKqPJ0DkYsesuHBt04aSbC/uRefLkCRVJkTZnzhw4OjoihLn8\n7jl0DMcZKdrlr56pgoihA2bIRAzNz86+EAYMGQ5fRtqvXLcJp8+cZd4xRbFz507arTdG188mjRvx\n62eFXGlxeVxtrOhcFm7FrTVCxNBEBRGTfIc7HSPBKtpbYmYrJ1yb6IaOZaw46VbepWyKvn4mH6La\n7TnmfGzMz8eK+TPj6uwOWN23Duo42QgiJhHobazM9JqIoeGTNEAlhzyY3aUabs3thE6VbTGKkW7l\ny7no3flIYdKt27QFabxF5igJx+kXUKjvak7I6JKIIdzIE0cQMYREwpYhSzbkqNgCDsN2oOSEI3gS\nmZ6HS48cOVKahTXhFv7sFWTMHyy0skQhSmfPnePaKep0sIPpkERFf4XHzLEY368r9xI5uXERcmQ3\nx2avY7xp0mmZvmw995Q5vWUJJgzohrWs/O6lM0AEwOINMTemRiyV2dqZY9ib7rTowwRpSWR33NyV\neP76DRZMGAQiUVSxR0+fI5OJMbJmzhSrGhEHtvlz40P4J6W0GMjzh0R/C9VsjVodB2LTvqOoXLYU\n1s0ehxNsropM3b6VrW+bLzcimWYLecLIGpE55K1y/zfBQvMlO+N7Q7YYXyZCjUxSlq8k4V/DGpWZ\nLssb3Lp1Kwm1k17F398fTqWdccH3GnosOoAWY5bCPGfepDcoauo1AiZZzeDeeyL6e5xjLvg/UNrZ\nmWdL0OtByw2OwpBatmyJ0aNHY9T4yZyEca1eU66UWDUUBEjMtnmrNrhw9Tb/bN26NUaNGpUsxLU8\npnT9dHZyxNWLZ7CnfxUsaOeMPNli/2bK1xHrKRcBs0wZMbZhcZwYXh3fQl/AubRTirt+plz0Ex85\nPx/ZMbnmcxZeY5picY+ayGORNfGKokSKRMAssxEmtKqIM1Nb4XvYa706Hx8/fgwn5zI4eOw0ig7Z\nAvueS2CcI/aL3xQJeioadBabUpyUse3yH+YvXgrX6jVYGHWY0ggIMkZpqJJWkESXKP6vvGOxpDXw\nu9av34JNq7fvx5eoaL6VtFkendyO5f+M4OurWOjRTxYv3qdd01jx5jUrleGisTsOnZKOoXSxwhjD\nUmw/CnqBtgMngepSxh5VvWKowSfPXiG7meIfMRIrJk+XiMgv0r7lF9awORWp3RbV2vVjQrck+lsZ\nB1bPxsuL+3ioT9uGtaXaK/J11e1b2fqtG9TiXf+zZB2u34shZIiY+Xvif3w7eSmR2RfIC+fihXHa\n5zrW7ToEysAU/ikSpDlDgsZkdIzUMdLmMWGZUUh4VFdGfVVmwq4Us/r3mrOwLV1FV12LfpIZASub\nIui51Bt2FdzRoEEDrrWSzENSqnsiwt3d6+LkyZPYe/g4Bg0bmeJDrZSauCjEs3rMXrAEy9ZswLx5\n83imCG2JHioDN10/q1SuhJwZv+L4UFdUss+hTDVRxgAQKGxtigMDKsOtSHZ2/ayfYq6fBgB9vFOQ\nnI/WJr9wanJLVGbeE8JSBwKFc2fH4bFNUadkHr04H+/du4cKlSrjw/+yoNgEb5iXqJY6DoSBztKq\najsUH3MAtx4GoVKVqlxYX5mpioBIZVBSowxl6SAPkYLMs0Ido4w9lKWHUjBvP3QClVmKbNJ2IY0W\nSXYm8owh89x7BJu8jsbqLoq9IaYwpeiv37gOBO0c2bMDjpzx4Tomua0ssWjikFh1lF0hT5vXb98p\nLE7hUvS20jRL/G8AT1y8yr1yijINnDFMA6dutQpxvGwUNs42qtu3svUbsjCz/p1aYAnTxzl2/goj\nn0yZx08EOw4lUbKIHbKbm/Ih0rFeOX0UWvw9lhE1czBsxmLuOUNk2l+tGmLtjgOgeapjMR5HeXgG\nDXXaUbZuQEAA3Oq4w6qQEzrM2GzQ4UjKYpLaymUwMkar8SthnNmUu3R7e3ujZk399TAhArxFixa4\ne+8uDhw7w8VrU9sxE/MFWrfrAEoJ3bF1M5iZmWHBggU6h4Wun+51aqOUtQnW/1VOY+FIOp+I6DDJ\nCBgzXY0lHZyR1Tg9u362hrf3Ub2+fiZ5oimgYsz56AbHfObYOLCuCEdKAcdM00M0ZlpAy3vVgqlJ\nhmQ9H589e4aatd3wM3tBFBm4EemMM2t6qqK9ZEAgU14HOIzah4dzWqI2e3a6eP5cohkOBRmj5QMV\nHh7OxVqJkFDH8uWywq2DG3DozCXsPHwK567c5KTAmP9W4J8hPTG0e1t8+BjBiR8iGOStSllHvolC\namRNfl12n7LL1pbZEfD0BShjkJVFtljVQj+Gc6+ZhFKOkvBtOeY5tOvIaXQe/g/XmaleoTQas+xE\n5CVjnSP+DDbq9q1K/f/G9EfzutVx3u8WwhgR41i0EJq6uaJgtRaowsKpJFaisC2uenlgN5vPfRaa\nZJ3DghFnZfgxozIkvKyuUZYmijHVtn3+/BmNGjdBZqv8aD99kyBitA24HrdP17BGQ/7Dl/APaNGq\nFW6yt/0FChTQyxGPGTMGZ8+exX5y+2VZhISlXgRq1amLFR4b0b1TW64b1K1bN52BQdfPJo0aIm+W\ntFj3l4sgYnSGvP51RNfPf1uUQtiX72jVsgWu37ipt9dP/UNPMyPi5yPTbMpnlhGeA9wFEaMZWFNk\nK3Q+zurkig+RX5PlfPz69SuaNGuOqAxmcOi/XhAxKfJbFP+gjbLnRuEh23D/30bo2u0v7Nq5I/7C\nbI8IU0oQHvV30hvahIgIZXuIiPyM76ytZnWqYcuCKXh+YS+Orp/PRYEnLljN9WQK5svFvTBI3Jd0\nVmT/lkweioUTB3NtF0mfM1duZCE3j1CnajnuNTNk2kLJLpU+CxXMx8sHsexJsvaZaazQNpdSCYdo\nkUYNkUmXdq3EPe9NGNevCxPCDUX/yfM40VGdhS+t3rZftmnpsrp9K1ufvIoeBT1HxdIluHjyvyP6\ngsKnnr8OwUcmhly8UEE+pm8sjR6V+8bSrnZt2QCzRv2NIX+1QSkHe5AmDpE/Ei8a6SSSsEDCZPTd\n0rYNHTYMr0Leov00T6ZEH793k6bH8fHNS/gd3IjQl4EqN+1//hDPBqRyRTUqaILU/PrlEyc6EhqG\nsv38ZN8NyoykaaMbmOZjlsCEqex3ZBlrkjP0I765UVgSCfbOWbSc6dyUja+YVrYfPuiFfbtjtLlU\n6eDVyxfYtN4DgU8eq1JN7bLKfp/i64iO/zsmikw3lvEZXaeU/Z5QelltWJPmLTFw6Aj0Y+knAwNV\nv6YkdUzDhjLR/dcv4NHNBZl0mJnlVdgXbL78FEHvIlUeuved1zwbkMoV1ahAGaB0Yd9VDBGOjP6O\nD5/j/26rOma6fs5v6wQrkzTo1LGD0ueFqv2I8ooRkJyPGxgRk8kog+JCOtx6+Hogy/QToHKPr0I/\nYdNZfwS++ahyXXUqqHOefmXpppW1yOhvyhZVqxydj4u610DOLBl0fj6OHz8e9x8GwP7vtUifKcaz\nXq3JqFH564dXeHN+K6LeBKncyocbR/HeT/HzmcqNKVnhf3JOBUpWkxZTt760oUQWSPfHtucy7Nm9\nC+vWrUuwtCBjEoRHf3Y27DECLs16SAdEoSqu5Zx4SA9pkJA2SXmn4nz/YRZ6JGukWVK4Vlu0GThR\nupl0T2au2Mi1bPYu/xf1WGgQhUFJdE2kBZVYaPNbT2XD7sOxSlNbJDrcsGalWNsTWrHNn4eTHVf2\nrsHtw55chJjmRporikzdvpWtP2/tNp5ZhjyAZG2x5y4e9iXJJPWFzZcy0Aydtki2GF4Ev8Xe40zI\nmYU7pRTz8fHBqpUr0WDQLJhZ6TamOuTJPeydNRDP7vqqDNfpDXNwdMVkleslpcKV/RvgMbQ5JtXO\nhYWdK+HQ4rH48U31m3fyNpnXvhxW9quncBjK9nPz2A4s71MHU9zzYmKtnKxNF/jsWc1JWoUNJ2Ej\nkXItxq3ApUuX4OHhkYQWtFflGxMj79WrNxo0boY27Ttqr6N4Wp43cwamThgTz974N9+7cxuD+/WC\n3+XY1+74ayR9z+OARxgzfDCcHGxhnzcH2rVojLOnTyapwYVzZ6Nowdw4cjDuzdhx78OoWdkF+XOY\nwsEmF3p0bodLF87F6efTp08Y2LcHStjlQ24maOtWtQJmTJ0IOpaatDETp6KgrR369v1bk83G2xZd\nP1euWoXpzUogt7lJvOW0scP/dQSGbbsBv6APKjc//9hDTD9wT+V6Samw6VIQ2iy/CJsR+1F95glM\n3HsbX38o/9Am6bPCtGMYuu26ZDXWJxEqQ1jWP8eJR5B/uBfqzj2NmYf98e3Hr1jl5FeIhKk84wSa\nLDovv0utdSLlFrd3YtdPH727fqo1MT2vLDkfZ3aogtzZs+jFaOd5XcXUHapf8++9CMVgj9PwCwjR\n+jweh3zEmE3n4TTUE/b91qDdvIM4ey/2fXB8gwiLjEbv5cfgOGQD8vRYAbu+a9BtiTcCguMKm956\n+g4t/9vPy9j0Xo2iAzwwdN1pfIrS7O+A/FiJlFvaswYuseu1ru5nKPkGaZnlbTURxlY28kPS+fqX\nF/cRuH44Pj25qnLfLw8uwPNdM1Sul5QKb85uhv+8dvDta4ebE2vi6bbJ+PVdufvtqJAnCNoyAddG\nloPfwOK4v7Azwv01e21XNCezoiy6w60HBg0ZitDQUEVF+LZ0k5nFu1fsUBsBPz8/nDl9GkOZd4Q6\nRuE+e46eZTeo31mGojzc84JIlxnLPFm4jD0GdmkFJ5ZmeQcLYTp56SryWltx3ZW7jwLRe9wsPHnx\nGiumjeD6MqQb06jnSHxkJM2+FTN5aFFVRuwQmXL0nC86NHFHlszK3zySZ8s5v5vwOnGevY3/xb1v\nDp66iOH/LkGF0sUxe1Q/nrkpvvlTPSpP6axl/0gDhzI+kSZLoQL5OPkk34YqfZNeS9U2MTfiri5O\nvCll65sYG2HjPm8Qni6lioLe4y3fvAfzPbZj9ui/4ValHG/P2Cgjz+RxjKUbz8N0eEjPh9Jedx0R\nE361ffE/TOdG/TcylGnKKndeLqgqj4mm1jt07Ij02fKifv9pmmpS6XYotV5Wi5w8zXKWbKqJXabP\nkBH5ipVFXofSSveXlILXDm3G3tkDkcncAmXrd2D5ANPg2qFNCHnijxLVm/CQQWXb3TalB149vIHM\nrK2KzXvGqqZsP9e9t2LntL4wMskM53rtkKtQKYQ8vos7p/Yibfr0HMtYDauxQscm8gMjGDetQf/+\n/ZCeta8PtmLFCp7OePvegzA1NdP5kDJmNIKzSzmVPXIopXhO61yoXNUVOaystDbuqKgoNHGvCR9G\nijRo3BQVq7jC99JFrF+zEhUqV0G+/AWU7vv61Svo81dn/na/cbOWcCgW8zKAGti9Yxu6tm8FI6Y1\n1LVnb1jlzInDB7ywZ9d2NGzSHNmZjgsZecLUqVYRp08cg1vd+mjUtAUe3L+H/Xt24f27t3Cv35CX\n08Q/8lAtzNJdT58yEdWrV4eNjY0mmo23jY4d2iO3UTQmNykRbxlt7WA/m7AyNWZplnMgR1YjlbrJ\nmD4tnAtkg1P+2CHHKjWiROGtvs8wbPsNWGTJiLblC3BtOdpGRFIjx9zs+qlcaPc2Vmfblecomdcc\n7iVyxeqZPGHqzT+LMw/ewq1YTjR0zIMHIRE4cPM13rPwhDrFY5eXrdzH8ypuvfjIxmeEv6rayu5S\nezknOzbvPn3F2t1H0a//AL25fqo9MT1ugM7HPJl/YWpb5V8Oans6GZmWkLNdTpQuqNo1Py2718hp\nnokLD+cw1Z7HctS3H2gyYy98Hr5GgzIFUbFIbvg+Csb600x0tnAu5LOM36ODSJQmM/fhnP9Lniq8\nSTk7ToCevP0cB/yeoHXlIsj82zvpRtBbNJvlhXfhX9C8QiHULJkfQW/CcebeS5xjfx1di8VKSqLp\n45LTPDPve83OQ+x87K/187HbX93x9ocJCnaaqempJKm9NGnT8lTWZoUrIoOppUptpEmfEVkKlmZ/\njirVU7Xw2wvbELhhBDJkyQ6rKm3Y9yEtaNuXl/dhUbYhS8fNfvTisZ/fonBvdktEPLrMUoXXg1mR\nCvgU4Ic3ZzbCtFB5GFnmjaemZjZntXdB8AkPfI/+Ajc3N4WNpmHuw/RcKUxLCCxduhSTJ07Aiwt7\n1Orh+/cf6MVIlW0HT8Rqh7Iiec6ZAHubmC/TUxYW9NeoGfC5cVdarjALI5oxvI80vTaljyaPDgoH\nojTZEtu415v3Ude1Avau+FeyWalP8l5p0Xcszl+9JS1ftqQDdi+bEUdHRlrg90Jb5rFDhExCRjor\n1/YrdvNStm/K2jRgyvw481a2/hKG2YT5q7kIMo3VNEtmnn1q/viBsX4onr0KQRemfePLwpIk5sT0\nZRawMDF1s2pJ2nPvOhTFy1TAsmXLJJs0+nnt2jWULVuWp7AWmZPiQkthVOQJY21XnGG0H+nSxxBs\nJzxm4tS6WWg5dikjRNrHrahgy+W9a7lHDXmcZMluhSGb/ngDqdLPoq5V8P1bNPqtPsWFdqmriPfB\n+K+VI0xMs2Gs10MFvSd9U8T7ENZ2SXisXYvOnTsnvSEN1aSfMnv7QqhRuw5mzV+soVYNq5nxo4Zh\nxZKF2LbnAGq7x3hhvX3zBq7lnZApU2Zc93+s1ITJm6VGxTKIZuROSEgw1nhuRdMWrXhd8mhxLmaP\nTxHhuP3oGczMzfl2CmcqWSg/J23O+Fzj29asXIbRQwdiyMgxGDfpH2nfY0cMwapli+G5fTfqN2wi\n3a6Jhab1anNB+UOHDmmiOYVtSK6flMJaZE6KCxGFUVWbeRLFcpthN8MoA7FHzP47ch9zjz7AwvbO\naFMufmLw9ccozPG+j5vPwzh5Q3XbVyiAeW2daVFqHucDMXb3LQxyK4IxDYpJt4/fcxtrzj3B+u7l\nUbdkbul2ycL6C4HMS+cOC2VJx8gsY5wfU1uyS2Ofb8KjUWbqUaz1WKcX10+NTUwPG5Kcj5TCWmRO\nUv4Ajd9yASuO3sK2oQ1R2zHmfHzLCBPXcdvYuZEe1+fG/7t/6Goguiw+gv+6VEO3mn8I6VEbz2Ht\niTuYw7Z3/b2936oT2HM5AMcmtUTJAn9evjVnBA2ROWv7uaNJOXvlB56EkiEfPzPvn43sfPTQ6vn4\n4MEDFC1aFA4DNyCbo+avK0mYut5XoTCqWxNqIlPeoig2Yid7uRhzv/3Cay5e7p8Hu7/mw6py63jn\nQR40wcdXw2HwRmQrWZOX+xb+Drcm1UI6o0xwnnU53rqa2vHqyDKEei/CG3a/lDlzXKFm/XidqanZ\nGnA7lMaaNGAmDujG01FHsRh9mzy5uFcMxT1KzCZvLpzctIinm34Q+IzrkxABIKtbQ4K59CdvnZrV\nBf0lxbJmzoRjngsQ/C4Ut/wDUJqld87J9FGUsW2LpipTLN4yyvbdo01j0J+8KVu/f+eWaNvIDfeY\ndwxpLZAoMh0XeSNvmNNblnAvmqAXwdxjiTxwUpKRu6a1rYNWUliTlsnZzQtA2i7fo6NQqHxN5g3S\nC2c3zePeHBWadccL/2s44fEvqrbtD/uy1Tl0e1jYUvoMRqjeeSgOL52AZ7d92UU5Hff4aDx4NjIy\njxCyAwtGgfRXWo7VDlFFffifO8j7qNKWeX39/mGg7c5123Ey5tbJPUqRMW+C7uPwkvGo23cy/PZ7\nMi+D2K7zyvYTHRmON4H+qNiit5SIofGYWuaCbRlXBF47h58/vscaK+1Xx0wtrVHctSFWrVmj1ZsX\nZcdIgr2BgU+wsVdfZasoXe4n+84uYiE55N3xJeoLy4BQBz369MOCOTNR0rE0/urZh7c1etggREZ+\nwpKVHnydQo+MmLfMYEY2TBozAr4+l/hbt0rMA2bm3EXSH2XyMpk5bQr6DRyCajW1d4O2ddMGFCtR\nUkrE0CDJa6Wmmzt2bNmEa36+KONSno89oX+jhvTnmlWjJ0zh4VWyZR/e90dI8GuQTouEiKH95PFD\nRBmFL0UwYXtTlt3o7MmYlwut2sQmLluxLEhExlxiWQg0Tcb81asPE/Nth+DgYOTKFb9nhOycVF2m\n62eR3Nm0QsT8ZPoqS04+wpHbrxHFdBiqO+TknhuLTjxCyTxm6FrFFteffeDERt8aheBaxIoPn8J4\njJjXCxETk73uwi8wlN0XpEElO0tMb+HI3lLH/JaNY+RF5NcfjBApo+q0lS5/+HYw76NPDXspEUOV\nW5fLz8mYfddfJkjGUOhRINPDoWwoTiwrzk3mwaLIzj58yze3KBPzskpSpmXZfJyMufT4fRwy5kFw\nBMPnDiY0Lo5NPk+hLTmbnGbGqF8qN9asXqkX108JNob4SeejQ74cOiNifrL7w0UHr+Pw9SB8Yd4l\nNUvkQw+3Ulhw4BojGyzxV62SHObRjJig7/KSnrX4+mCPU+wcTYfBjcpi0raL3AslPSMqKznkxsxO\nrlJPkutP3mDm3ivoV88J1Yrn09oh23r+AYrls5ASMdSRlVkm1CyVHzsuPsS1JyEoY2etsP/Lj17z\n7c3K28fa37pSYU7GvIuIkm73exyCEgwXWSKGdrar6sDJmOuBb7ROxlgz75gGZWzZ+bhKq+cjaYdQ\nQgzzUjHHXAqClhY+v/DHS0ZafH5+F5nyFUN253rImM2aeYVsgm3nmdzT5FPgDVZmDnK594Z5MVd8\nenwVz3ZOg027qbweeaBEszAfk1yFkLtuX+Zd4i4dbdCW8fgZ/Rn2jBDRln247s36iOTjkxAx1FeO\nSq04GRN6xStBMubtxR2cyJEQMVQ3o1kOZCtRA+98duFT4HVktY1N5FMZTRqlvH7l9R927dqFLl26\nxGk67pNknCJigz4hQCmyE0uTTeQMecpIvGWSMv4jZ31w5GzibCGleJ48qLu0i1wsc1CuajEu6NKN\nOlpQt29l6ltmM0O18omHv9AxoPAq+kuJts9rP4rVYaE3WrCNY9rjoc8xHkpkx4iCpzcv4c7JvYj+\n/EkqOvv543sE+J6EY+0W0hEEB9zB5/BQTuJky1WA7WvOSZvrh7fga2QEOkzfyMs+v3sFXyI+SOtp\nY+H9iye8Wfuy1WI1b26dD+lYmNSrBzdibVe08v1rNLZN7gEbx4qo1LIPJ2PkyynbT9p06dFzyWFk\nz20TqwkiaUIe34N9uZoaJWIknRRn4VjbJ3dHWFgYsmXTbliDpM/4Pg8cOIAiLAylSNE/b8DjK6vq\ndkqNTCQCERVVq9VgYT7nsXfXDkR+igARNRK76nsZHz78iQu+y2LDQ0Pf4xAjcQrYFETzVm0Y4XEF\nWzduYIREBDZs3cmrvn/3DqeOH2VZqtpKmtL4Z+j79wj/+BHtO3WN07a9fWG+7cb1a4mSMbu2s3C4\nbVuw/+gpJt77Jk5bRMSQOZctF2efcxkXjiOFIpWrUImTNlmyZEGhIg6xyjo6OfMwv/v+92Jt18SK\nW90GLHzKCIcPH0b37n9+uzTRtqSN/fv2ok1J7RDwXdb44IT/Gx5KVKVQDlx+8p4L7n6KZqLdv5mD\n0MhvOM1Cc5o5/3lQu/cqnInRfsORO8HInz0zmjrnZaRNGA/xiWB1Pf6KIeGuPv2AMFZOmxb47hNv\n3rXwn7fgtCEf0wzKyB4+KTwoIStsbYp9A1x5ERIprjj9uMLib8KjOMlUKGfWWPtLsZAmioIi4kXW\nohm51cfTD+VtLdHD1Y6TMbL7Nb3ckIVj9fG8rBfXT03PTZ/ao/OxXTkbnQ2p44LDOH7rGSMqcqJq\n0Tw8zGfvlceIZKE7RKZK7CojIT4wXRWJ3X32HqFs/RAjcQqwECAK2bnGiBciRSK+fMOGgTHejO8/\nReHUnedowfZry0JZH+FfvqI9I0Tkzd46xtvxRtC7eMmYTtWLoVn5QjDPbByr+iUW8kTm9tvT5jvT\niKrBwpKcbWNIY9nCrz7ECJBnk2tDtowmlxu72KLn8uNaPR/37NsPU6d6sbzpNTkH2bYiHl7G/QUd\nkDajCcwZ8UDh/0GbxzEyJhcnV2zaTuLFf3wKxce7Z2BRvmnM+uePjJDxw9OtE0BkDpEemfOXwHvf\nfXi0vBdKjDuILAViCMVPT67hR2SYbLcaX44KCeRtmhetGqttI4u8oDCpyKd/IjJiFWAr3z99wM8v\nLKsxC22SN2NrW77pM6uvbTImQ5ZsMC9aBV77DwgyRv5AiPX4ESCvm2rlEicdMpmoFo8ef49ij74g\n8OrVK7x+9RL1S1fR+JDunj3AiZjKrfuiwYAZvH3yMto5rQ9uHY95ME2o048hL+DaYRDce0/iP2ZU\ndxkTX3vMPD9Usbtn9oO8UhKyzGYWqNC8h8Ii714EIAMLKzLKFPsmn4S1LfIUxLtnjzixRFog8dmR\nZRN5GFG3ubvj/WFWth/yCrIpVUHa1cUdyxEW8pxj/b9f7O15xyHSfZpcsHN25V5iV65cgbv7n7cl\nmuxD2bZIELOKa3Vliytd7qDXHk4g9Ok/CNNmzeX16Hv3d48uIGIiMXvx/BnP5DNh6gzpd7Z21fI4\nd+ZkYlVj7ScyxWPV8ljbFK00ato8ln6LpMzjgJgwNdKmkTe7QjFkDOm0JGTPngZh+KC/MWj4KFSs\nXBX79+6KU9zGNoaAPn/2NPoNGhpr/8MH/nz9gb8/J2NsbG1x4/pV/kdEjcRIZJgwfqgFMsbExARl\ny1UACXpqg4yh6+fL18Go0jz2G2HJ3NT5PHTrFSdielWzw9RmpXhTRMD033wVe669TLTpFx++oH+t\nQhjXsPjv7+L/4D7vNM4/Svi4yzd8kI3joRyRIV8mO9Na6ca8dBTZ47eRLLtUOmQxjnE3l5QhnRgb\ny8wIePuJP7SmU1I3RlJf/pPaIq8Z+isto4HzhBE49Ez8MCSGFJLUm8o8hih8aHufyvFekyVlNfFZ\npZCl3lw/NTEffWxDcj5WLVpeJ8M7ePUJJ2L6uDtiWvuYeyg6R/9moTi7fB4lOoYX75mgeYPSmNCq\novQcrT15B/cQSbSyTAHSZXnwKuGXUtlZCF733146MlX54uPgGEKU9FTkze43GfM+4ov8Lul64dzZ\npctEOl24z0JNnr0D4dO6UhE4/dbJycA8gSjFtLy9Y21TOBN5BtVxspHfrZX1qsXyavV8/Mhehjx+\n9AAOdcdoZfyyjVLGIBKsTZPeCKUmeEt1Ucj75c7UGFJPtryi5ei3T+E4lYW9W8aQ+uQ183Bpd669\nIiFjFNWT3xZ69RC+vI65/5DfJ1knLRjrml0lq7E+ySuHCKV0JllibSedGOMcBRAV8hh0n0tkk7yR\ncC9ZRrO4ZJ9Jzpjfp+8RofLVtLKemWnyXDjvobBt4RmjEBaxsai9DehPWOpD4P79GJLC2lbzHgbX\nDm/mNxhuPcdLgSUCw63HWKXImPQZjVGr22jpjTLVLVCyAl4/uo3wt6+UzvpEorZ3Tu+TjkHRgmU+\n+3jJGEq5nYnpsCgyc+v8ePv0IQ9jMska8wZJvtyDi964zLIckTcPhfvEZ0nt59iqf/D9axRv1srG\ngRNH8fWhzvZMZtlhZmEFioNObjLmPguPadGuozrTUVh3C/NiIU+3sTKaJvS9oxAdZcgYY2NjjBwX\nQx5SB1SXvEJu37zBSc/cefIq7Fd+Y+j7d5g1fYr85jjrRKzIiulKCgQ9ibkpyZbtz02yZF++AgX4\nInnOxGeUorpX145Ml6cwRrH5xGd2TLfHqXQZnDt9EhvXrUXTlq3ZjdIv7Ni2GV5MmJfsJ7txImve\nui33MJrFQrTGTJzC6xE5M37kML5fUo6vaPAf4XPvVuLea0npUnL9LJIrNlGblLbk62zzfc6+i8Bo\nGf0TIjBG1SumFBljnCEthtctKnP9TINyBS1w52U4SIdF2axP+2+8wv6br+SHF2vdzipLvGTM0/ef\nYZ4pY6zykpV82TPh0ZtP+MTCN+IrIymb2Cd5Bu1jYyUtmlH1i8IxXzauM0N6MGSyXgrH7gXDg2nF\nkIcQhRDpwrJnNmJhH5n14vqpi/kmRx+S89Ehr4VOut9y7j4/R8e2+EP+0Dk6unk5pcgYYybqO7Jp\nudjnaKFcuM28Zl4zTxFlM0HtY544XuwvISMPl/jImKC34bxqNkaqyls+y5hrWzjz1lHGiIiZsduX\nEaD/49hQffKIISJGkR29+RSD1pwCeQDNYNmvKFRKF5Y9izGssmXV2vlI90lkmfLE9TbS9PwoLOnL\nS3/krtdPSsRQH5mZ7opFucZ477M70S5zVu8sJWKocNbCMd/pqJcx80i0gd8FQv0OIPTqgQSLG1vb\nxU/GvA1C+syK77eNGFEUFRyAn1GfWJm499vRrC6Zon1Ul+wH85zRhWXK64Dnb4IRERHBEkyYxupS\nkDGx4BArAgGBAKVfo4dPEn3VtH14FcQIk7wgsVpZo/AaIloSM8qslIFlaJE1CeHxNeqz7OYEl1tN\nWImW45YlWIalR4p3P2nXkDiuIiPFdMJP3mtGUpaEb3f92w9lG3bimiuS7Yo+k9rPlBMsWwgLpXp2\n+zKOrprKvIdqYtTuuzxDlaJ+1NlGGaASStmnTtvK1iUvinCmQ2JpqVomAGXaD2I6NHny5mMCt7G/\nszYFbUFES2JmmcMqTjnz3yFdkZExbtiJtUH7KZSH3pomZhkzKn7IzchCc8jCwuK+Lf3yOebckYxL\nUR9EBPnfvY3TTHw3Q4bYHg2y5YlsWrRiDdq3bIIh/Xtj7IjB/G0jHaPO3XpggwcT0vsdSlavQWP0\n7jcQK5cuwslj3siWPTvCPnxAhUpVULxkKWTPrp2bcEvLHLHCyWTHr+6y5PqZLR6yQZ32n4Z+Rh6W\nJpvSI8taAeYBQkRLYmbJHqzoYU/WzH6P8zPTiVHWlnQsm6imDJFG8RllbApm5I8i+/KNveFkdbMa\nx/8dU1RP0Tb3krnQk3kRrT77BKfuvwEdkzD2AFne1oKJB5sie+aYc4W8YQZvuc5FgEnHRZdGHkTJ\nff3U5Xx13Zf0fGTEly4s6G0E8mTPygRuY39/bazM4px7isZjybIjGcud3+a/x076Msra8t61pVo0\n8dVJ4BRFxt9ESRjLOiZvX35fKyTjkt8vvz64URn0rFMK5CFDWjNz91/FRxYCJe8RQxmUSDSYyJiC\nDK+Vfd20qokjP05at8hqorXz8QP7bSNLn1U7v2u88d//ot895UsmjOSQt0y5i8hvUrhOYUCyJiE0\nfn6N3yNKtrxk2b7nIth1T1hThu6Z47M07H77e5ji++1fNBZWN52J4pcfaVkYE9kPFnolb5J5SOYl\nv1/T6xmyxBx3+h4IMkbT6KbQ9l4Ev8XJi36oylI82xXIo9IsDpy8gK8sxXbLejVUqqdOYbqRp5t8\ndYyyJn1jqVQtzM3ibSbyc5RKab3jbSgF74iOjmbEiBEnFDQ9jahPHxV6gpCoL3jC8IR7lCdiEi4d\n/15Kf62OUWrn9y9YDHjYO8in3v4S/oETWfGFKPnu8wCVif4cgV0z/pYOI+L9aybgC76NvHKqdxrK\nyRNl+qFMQvQne45Y5rMD/aVhb+V2zeiHh5ePo2yDjtL+NLVAJBp9Z5LTvjJBczJKpaxpC/8YButc\ncR/QSCtGmWSEFBajCeMEqRptkVAv2bOgmPhr2TGF/da5sWAkhSKjFNQL/psJIqBItFhiL58/54vr\nVq/AiWNHMHDICJ4+mkSCz/vdgtfunaDQJAqNqs6EiS+eP8vLOxQtLmkC02fP42K/JNZLRBEJIjds\n0gzF7fLyUChpQQ0uGDESTVvfWWrXiBEeCd1cJnUqHxmRYK3Aa4M8POjakZjJEzGJlY9vP5Ep6pgV\nC494wkKVKL2zfOrtsM9fOWmiboiSZHz/sHCuxk55QGK9hB+lwCbCxXHiEVRk4sVk6y8Gcj0d0t0Z\ntOWapCqCGUlD5zhts82RhYsfS3dqaIG+K9r6LmpoiCm6mZjzMb1WzkdFwJDOijVLOy1vJOqrxCkK\nExa+pwmTkClJbYuEesmevYuI00TYb50bIi7iMwrNoudryXWQ0liT2LArCwWiVNneTBdHlowhkmbE\nhrO8zqQ2FdHLzZFfR+NrX1vbtXk+Ss7ztIxc0Lb9+Bzj7ZGeaZXIG4X0KGNplXhBqlQ7vwkRZcoq\nKpPRNAfXuPke8T5O6u3vn8O414uiECVqK8Pv8KTodzH3KrLtS7RuMmSN6y0sW05Ty5LjLvkeyLYb\n+/WK7B6xbNAI3H30BH0nzsHqf0erTMbMXLERoR8jdELGeOw8iD1Hz/Ibe3tGGtWqVBb/DO3JMpSo\n9jAd+jEcLk26wzQriyE/uCHWsQ3/FImJ89dgt/dpPq9MJsZML8cJs0b9jUI2MW5ssSr8XiletwNc\nGZm1/J8Rinan2G2SH09tTCAbC+F59fAGD+GR9Rx59/wRfnyL+wZGG2OgNq8e2sTGcTPB5rOyNNM1\nu45UWMYyvz2Cbl7Eh9dPY5Ex35h3Dm2zda6qsB5tJE+SXPYlEPpbBFhS8AdLCUzZlEioOE2amIcd\nZfs5u2k+KDSpy+ztKFKxjqRJ/pmJad+QhbN03MJURyBffhvcZKEzlM45a9Y/b18CHj6AhARSvVXV\na7wJCcHcmdMSrdi+Szce7iNfUKIL85Tpvsjbvbt3+KYyLuXkd0nXyVOF7O7tW9Jtn5i7LdnzZ0+Z\nZ9JHnkmKUluTtoyFhSU6dv1LWpYWFs6dxYgZa+4BQ+vBTOw3kuFKYVv0J7GARw+52HDR4iUkm1LM\npzavnxTCQ+K29IZcVm8lgIX1fP3xS2cYbbn8FLcTEdm1MjXGUHcHhWOiECYfJjz8nHn6yJIx5J3z\nLPQLKjMtFU1YCBPwjWQEiwsLxaI/iT1meIVHfYdDrhhXcQvmnVKcZaKiDE2y9o2FUrBnaNxl4sdp\nE3hzK1tHLOsXAto8HxXNlEJwbga9xScm1pvV5M99asDrMHxlAtG6ss3n/HGLCewmZFaMNBrexEVh\nEYkuzNPf4Uqyhe69eM9XSaBYkRERk6/XShRjoWHHJ7eKVYSOB4UD3X0RKg1VIk8Y0tRxsbfG6r/r\nIK/Fn9/ZWJXFitIISHReSIg3u1Pse0IKYdKlvT2/FZHPYu4x4uuXshvlbTRE4W4KYYp4dBnR757F\nImPIs+Ur22bqUFlhPdoo0YWhuvL25eV9vilLQWf5XTpfF2SMziHXjw6LFMyPqUN6onSxwioPqE+H\nZuxNjnKxoio3LlPBc88R9Js0Fy6limJEL5aBJ/A5lmzcjaCXwdi6YApLEav8G4Q+4//jabeJjJE1\neuvVqt94nL96C+VYP33aN8O5Kzfhfc4X1+4+hO+eNbDOEZc13bjXG4HPX3MyRrY9Q1hW5m1/UueZ\nu3AplgHpKgJvXEDRyn9ExK7sj02QJbV9Zes9uXoWd894JVicvFPiI2Mca7dk2Y824BojdfIX/3Mz\nc+fUPq7VUrTKn7nJd1KpZW+WPam3/GYs6V6N1Y3GgHXnpfuU7Uei7xPgdzoOGeN3wJO3Z21fUtqu\nWFAegVKOTjzl88XzZ1C3fiNpRQq30aUR2bFx/dpEu6xUtZpCMiYX8+6pVMWVZYI6Bwq9KvhbaJe8\nXnZv38q9f0jrRZFRWNIZFp4kb96HD6Bjq2aYNG0mmraIuekm3ZmKpYvzzFGr1m+WVnn18gUO7NuD\nDp27SbctnvcfT2F9+aY/7H+LCNPOlUsW8tCuajVqScumlAVtXj/Jq4MyIF1kXh7uJXJJIdnkE5dg\nk+7UwsL5R0yIk4n4JmR2zJMkPjKmGcvktMnnKbb4sowzNn9+X0mHhtJ1y84toT4S27fkZABPYX1x\nbG3YWf15wFvFwpYorMu1sBVvgjIn0Z+8uc05hejvv3ByRE35XWI9hSCgzfNREQSlCuTgGZAuPniF\nuqULSotsOOMvXdbFwrl7L7GfifgmZES4xEfG5MqWGZWK5OZeLBQ+VDBnjEc5ab3s9gmANdvvZJND\nYfOkkeOQJztuPX0H8qLJxsgXid1hIr432fbiTAdGohkzbacPTFkI4boBdZlXUex7dEk98akaAiZ5\nWCgSE7QNv3cOLCZfWplIiXD/P/eY0h1aXAi/f4FpxhxKsAfKbBQfGWPJsjy9PbcZlGI7q92fe5RQ\nv/349S06Dtkk2xGl8TYtXAGfHvmCBImNrWz47l8/vrPsUHuR0dwamW1iXjTJ1tP1siBjdI24nvRn\nmz8PRvRsn6TRdGpaN0n1VKlEYVTD/12CSs4l4L1uPtMoiPmqEok0fdkGbD14HMqOY9VWLxw7fwXZ\nzP7cjEnGQq70RMS0b+yGtTPHSjbzPqYtWY+N+7ylOL0MeYfpS9dzkubOw4R/5KQNpcAFbb5JqtFl\nBG4c3YF9/w3B54+hyMwEYB+wNNe3T+yRurPqArI2k1aD/pJqBZ0qg/6I6MiSPSccKrnzdNaHl05g\nqaoroUz9DtKmr+xfD6+5wzixU6vbKOl2ZRaU7acw84bJyQSXfXavgkkWMxQqXwsR74K5SPGDi0eQ\nt6gzH6MyfYoysREYNno8S+W8GcMG9EXou/fIbsne9rE013t2btfpd7Yw04yJT2cj9ojjXxsyYjTa\nNm+E7p3aYujIMTAzz4ZFjBB5ykKXtu7eH2s+JeyZEPWbELz9pBrxbmZuztN/79+7G9Vq1EaDxk0R\nGPgYQ/v3AYkVT54xWzpACkdatWwxx/a/BUuQnXnTbPZcB891azBjzgJQWFRKM21eP4fWccCuqy8w\nYscNlgL3KyyYlsRx/xDsu/6SHTvdIbW8swuW4w8JrWrPlewtQX+bfZ7CKqsR3Ipbc4+fKV53UMHO\nAm3LF5A2ufFSEEbtvIlhzMtmGBMfVsUasHCkNeeeMLxuYmZLR/ZG3ghbLz9jRFAQpjV3ZOmDxYOf\nKnimxLLaPB8V4TGsSVnsvPQQw9adAaWHzp7FhGVXeoo9lwN0eo6u7FuHaa4oGqHy24YwrZe28w6i\n+1JvDG1cFmaZjLDo0HU8fReOrUMaSn8vNpy+x0OMRjR1Af2RDWzgzOodRfNZXhjRzIWTLCdvP8f2\nizHir6Obl+flPn6Oxv2XH1CqgCWWHVHsrVyZpQd3d7Lh5cU/5RAwYumrc9XugeBjK/F47WAu2hv9\nJgghJ9cp14AGSxXqtRT0l1QzLVIR9Pf23BbmGWOFbI61Qemon+34h4sK56jcRtr0tWFl8C38LSqu\neSHdlqfBQNxf2Iml5e6NPA0HIX0mM7w6spR72jgM8pR+j6UVkmFBkDHJALq2uyQtgzlrtoK0Xb5E\nf4VbZRf0Zd4ss1dthlPRQujVrgn8bt/HP0vWYXDXNqhZKYZp7DvhPx7+M7J3B4yZvRw+N+4iXbp0\nTFfGEfPHDUTmTCZ86EOnLwLpr6yeMVprU9l/4jzvY2DX1lIihjrr2NSdEyU7D59SiozxD2A3crOX\nYfqwXvDYdYgLScoO+nnwG75atayj7GbUqOAMImMi2TwlRsuPn72EGfOuKVOiCCdlJPvEp3IIUOag\n3suOYP+8Edg7exCvlLNgMXRf6MVFZo2zxLiNK9da8pWiG7zOs7bCc1Q7nN7wH/+j0RDp0f6f9UiX\n/o94H72Zo2wySok6yE1J2X5IK6bTv5uxfWovnFw3i/9Jmiru2hANB89iYxKXewkmqnxa58qFQyfO\nYeSQAVyQlupS+Mzew8fhVrUCE2KLeWOoSpvJVbZG7TpYvtYTg//uia7tW/NhmJqZ8ZTdtd3rxRrW\nLxZXTlpdSTES8O3ZpQMGsX7oj6yUU2msWr8pVqgXeeqQZsw/E8eiUpmSvFxWlmWgW4/e6N5LzScJ\n3pph/aMsPwcGuWL0zlsYvv0GnxyF2uzqVwV1551horcp4xyn65pnjwrotPoy5h97yP9oMpR+enXX\n8sjA0tlKjLRwWNSDUnobkjqSz4qM8PmnWUlMP3gPrjNP8s2EUZfKBVmmpz9eC5Ly4lMgoC4C5Nlx\naFxzjPQ8hyEeZ3hzRfNmx97RTeE2eSf3AFG3D13Vr1EyP5b3dsPgtafQdbE375Y8WKa1q4Lajn8I\nU7rHoUxJsl5ITcrZIzjsM6buuITOC49Ih2zJdGaW9qyFes4x559vQAjfR9mi6E+REdEsyBhFyCS8\nrUDLsYx4MEXw8TV4d2knz0hkWaEZJyNeHpiPdMZxX1An3GLy7KXfC4cB6/FgURe8OriA/9FIshR0\nQpG+q5BW9n6b9HBYuL+smZeohkI9FuHJ+uF4tCzmfiSdiSls2kxGtpL64fWYhp087GdOmLYQWLp0\nKSZPnIAXF/Zoq4s47TbrM4aF2VzmYTdlSjrA9+Y9hLz7gIjIz2hetzpWThuJI2d90LzvWK4Z07GJ\nO2+jcqveeB8Wjh/MDdEmrzUqlC4Bv1v3uedIk9pVsW3RVGk50ox5cHxrnL41tWHwPwuxcus+vPU7\nhKyZ/4ih0cNBttJ12TYTvLyUcJhJ9NdvqNK6DwszssCB1bNRpslf/OFCVjOGtGQKVmsJZxaudXLT\nIk4+EZlFYU2bvI7izNalKO9YLM60njx7hRL1OqJri/rJohnj3nUoipepgGXLlsUZm7obPD090aNX\nb0w5EaxuUwnWp5AcusXOYGTCBW2nNbRDy7FL4VwvaR5bCXamxZ2UHSk44DZyF3EC6cxoy5Tph86P\nsOBnePfsEceVNGfMcuTW1pB4uyt61USbhrUxe/Yfjwetdqig8aioKJ7taMsuL9Sp10BBCc1sIuE1\n+skkUd4PLOtY4fw5sWSlB9p27KyZDnTUCqWpJh0c+r6UcSnPr3ua7ppw8mdaNE+fBsLRyRl58+WP\nt4vQ9+/hf+8OHw8RNAlla4q3ERV2LJ4/B+tXL2dje6pCLeWK0vWzd8/ueDq7kXIVklgqmoXz0N0b\nCX5+YKK3xcYdZhmOnNGm3J+HpCQ2rdNqlMnozquPLO20OdOPMdZK3+RFdD84gp+7FZhoryzZo5UO\nVWjUfcEF1GnVNVmvnyoMN8UVjTkfe+Dl6l46H3v0tx+cRDRh2ZE+sHCdwv3W8gxHbas46Hws6nT4\n4+cvroNDhAvpxKRTIdchf7wAAEAASURBVJkGaec8fPUBb8K/oEAOU1A6bflsUeqMTdN1a0/dA7fm\nHbVyPu7ZswctWrRAhdUvWGKFP4Szpufwf/auAi6q7At//zUIASUUUEEMBGzFwu7uzlVX17XWWjtX\nXV1jdY3Vtdfu7sDuwgYRBASku8HY/zl3fOPMMOCAoOLO4fd47904994z9d73zvmOOn2cTUjKGuS9\nfSoiHzqjysJb6pp+1WUpUcFgzpt8xSqAeWYyIv++fYM48qhhsMagRBV6DXJlpPsnt02glOAPZzaG\nm5sb7O2VvwNyxmOUTzbBf0fBobOXBRDz8/ddsHDScLFwvuAeOOl37Drm/FFD+AYE45eBPQVJLqOR\n3Ld21yG4cNPlo30VGxw8cwmunj6KRamOzYzz46eeHVKVc8FzH18wka4iEMPl7AFQwrqw4I9h0IQ9\nd9KSyYv+RmBIOI6uX5SmGxpnVpo1ahBmLlsPm3qdhRfQDZcnCAqLEHNjHhmtZJ0FbhxYh/undqHz\n5JUwL/7hy+jJpSNiEEtb2dPxrBsx+zWxtw9v2S2ajMOfD9MixcWW3fP5r+hfv2YVdm/fSqDLBti9\nT8nMaz96WAawl6ug7FWXE+ySm7ykqlavma1T5d8PJv2ViH/TG8yUUpLXrd8wvSbaOrLAxite2HvH\nF0sJdLGz+OBFePxhgLBPuSIFcpyd2NvHPH/2fn8yQW8d24xduOc4Q2on/FVYYL3zYxGK89egxrAj\n3hRJjr7nbylnnTXk1JLez7HPTZ5qVYlcNzPCJMaZ7ZuZ8bR9ZBZ4m5II10XdYEigg01P4tjMJ/tt\nYNLbqCeXoG9dNkeaKm8Bc+J5Mc/U3P+XK7cS50ymlGRTJy0Yk02G/VJqmfSWL4J/HTVQPgW+QZvx\n8wCNwBhdnbyYNqK/HLzgvk7E2/LAzQPMmVLUQrMLmv2nLlJ2oovyOag74ExFaYEx7HlioobjhfUU\nK2KBZy9ekqdPgloeGG5z4uINrN5xSHjzWJJnTHrCWZr0dHSEVxCDTlExsowK7wg9TaQwLwaFtJI1\nFihSuiKOLZuEHdP7CQ8Ycxs7IvS9h8s7lsGhTisCaFJ7IWXNyFotWgtkzgIVKbxmyrjR6N+7G3r2\n6Qc7ewfcu3sbTD7bsnU7EbKUOc3aXloLZMwCFYrmx7QDkRj0z23ygLFGaQtDQei78txzQXorZQfK\nmFZta60FtBbIKgtUJFLbKduuiLCennXtBSBz70UwVhDXSksi9OWQJa1oLZDdFsiVV08AMIHnNuJN\nYqzgWXmTEI1QIsFNiQpEyf5/ZPcUtPozYAEtGJMBY+WEpl5+AbCyLJQKQChuVRgMtHxMCpkap2pn\nbCSLK4xPSPxYd3n9RiLD/RinDINGaQmnrg4ICVVbnZCQJMAiI4MP4UuKDQNDwzF4ynwRQsThVekJ\ne/D0Gv0r6lStQLwyP6E6hSRx1qZFxK+zbhcRWtLfshmj01OhrcuABazLVSdukx24d2I7Lm9fisTY\nKJgUtkHVtt+j9c/ziPw9bU+nDAyjbaq1QJZZoFoNJ2zbcxA7tm4mstuFiIqMBBPL9u0/EL8R30l6\n3nlZNgmtIq0FyAJVKT3z5kFO2EVZiP4iACYq4TWKmeZD75o2mN2xAoUPpP2bqjWg1gJaC2S/BTg9\n87bRrbDjipsgu42iEEKbQkboW78MfutdJ0MhPtk/W+0I37IFmDT31fHliKLsSaHXduM7HX0K7ykP\n+5FbiBA3ez1jv2W7ZsfatGBMdlj1C+qMiomFZcHUbpAc0qMJPZAmgI0my8ub9wOBqSbtVdtYmJnA\nw8cPIeGRYIBIUZjnhb1m0roJWkfZk5jThjlyfiRQRpKA4FARY89l7JUzYXBvuffOdPIcYiCGxa6E\nNeZPHIY9J89j59GzWjBGMmAW7TnzEG8cApeSEAtdyv7zpYX5WNxvnkXxCk5gnpWcKskJcdDRN8ip\n0/9q5808NLzxezYuNhZMevs1CGd1iqX5dOr6IZvA1zCv7JgDp+DObi6Z7Jh3VuvkzEO8vSNW27jk\nNzDS+7Tf2qyYH/O+OLsFoWYJU6UU0lmhW6tDa4GcZoFmlWzAm/iMJqUQaa/OZ11CUFQ8nB++RE1K\nTc0cLTlV4sh2Broff4jMduZ02lpRtgCT9xbrOg3MIvYmIYYIew0+O1dN5KNzeEueOZyeOqcKh3Yx\nx893ebIvSkILxuTUd0ca8y5W2AL3nrqLTESKfCvs7ZGc8jqNXllfvHn/Cbg8fZ6uYvOCJpgyVD3x\npW1xK0Ec7O0fqATGsHcOl9WvXjlN3WYmBVDBvqTIfKTYiNfPX9qPnnkK7hmu4xTVnDZblaSX+Ww4\n89S9J+5IoX6fCi4pzkN7LLMAh8B9DUAMzybMzxMHF4xEx4nLcxwY88r9IU6vmQX/Zy5Iio2GgXFB\nONRthZbDZkM3n4xXgkGEv36oh3dEYKYqBSys0X/RHtVi7bkaC/B79msBYnh6TETr7fUiR4Mx1crb\noXa9+li6cm0qizPQNHXCGJw/cxrBlGa7UmVHNGzaDOMo3Xhe8p78LwvffHwNQAy/Bp4hsfhl130s\n7lE5R4ExfD3QeNF5vKW9qliZ6GP7T7VEsabtVHVoz//bFhCf0c8MxLDFPQOjMHrjBSz9oWGOA2Me\n+oRizt4buO8VguiEZBQ00hOZl2b1qA3mn5HEMygKG4if56SLN2ISk1HD1hJDmldE/bJWUhPtXsEC\nDMx8CQk4uUqkkM6JYEzE/dPw3f87EgM9QOEY0DGzRrHOk2FaLevJ+bVgzJd4d2bjmBXL2OI2pa2+\nfPsBWjeUXUjwcBv2HsvGUVOrZu6VAxQClJ7YFiuaJhjTvXVjbKQ5M6ijCJQwDw3zuLRp9GFtqmMM\n69MJvKmKU+fBSExOxq0D6+VVDiVt8NzbD8cvUHap5vXl5S9fBeHu42coTaCQFoiRm0V78JVZwP/Z\nfWwY3UGEd1Vs0gX6RiZ4dP4A7hzZjIDnjzB0jbMAHmNCXyHoxVOYlyhDbZQ9zVTPv7IlaqfzDVtg\nJ4V+MZjEYIyqsCdMq8Z14fb0CTp26Qbb0vY4fvQQliyYh/DQUCxe8bdqF+251gIZskBAdKLIssRc\nO8aUsldRCiica9pOsb/2WGsBrQUyZoH73iHotOAwchPQ3LmmLYwNdHHolge2XHTFY0p7fXpGF+EB\nk0hZqvr8eVykzu7sJGt3jAiSe1PZnnFtUcu+SMYG1rbWWkDFAgzEuK8cCF3zErDuMpW8YnQQdO4f\nPF89BA76+VGgbD2VHp92qgVjPs1+X13vyeRpsvPIWYyYuRhhEVEwJQ+Pk5duYu+J83JS3s8x6U2L\npoG3zErdahVFZqN/9h0Hhyy1bOBEnjbumLTwb8Hv8n3HlnLVG/YcxcjZSzFl2PeYOqyfvFyTg2F9\nO+Ho+WsY89sy8oJ5hnbEMePm4Y2NNC6HdU0a0lcTNdo2Wgt8EQvc2L8Ob5ITMXTtORR+n4mq6aAp\nBNC0x4t7l/GUslSVb9gB4f7eYn7dpq+GZamcl7HqixhXO2i2WCDglT8WzpuN+/fu4unjR2mOsXnj\nOgHEjJkwGVNnzhHtJkydgSnjx2DtqhVo3LwFWrVpn2Z/bYXWAh+zgHdovGiysk9VlC2Sduihpu0+\nNp62XmsBrQXStsD6s4/A6cDPzOyC8sVkyUImd64hAJrLrv44evcF2lcvhbn7boI9Y3aNbYMmFYsJ\nhT81q4h6U3dhxLpzcFms3uM+7ZG1NVoLfLDAuzev4b1zOnRMi6LcpEPIYygj3TZxbAWX8dUR6Lxe\nC8Z8MJf2SJ0FOHPQ+e0rMHrOUgwjQIalrG1xnPxnCep0G4L8BvnUdfvqypjcd/+qeeg8dAp+X71V\nbDzJquXtsf3PX0VokTRpBk04DEMTThypj7SvV60Sti6eQSDPKizZsEtsXMcZlBZRavAebZpITbV7\nDS3wOjkJl7b9iftndiMmJAD5zYuipGM9tBo+h/hMZGTQrCopLhp3j22Dx53zlFHpLgrZ2MOmQk1U\nbNqVAINy8tEOUPjQ2zdv0KjfOKH3+e3zMCtaAo6t+6By8+64umslHpzdg+iQVyhsVwltRy2AmVVJ\n0d/t2kncPLhBlD04uxfP6Dwy0BdWZasRYfBcFLS2lY+j7iCRwn7OrJ0N7wfXkRAdDiYgrkZkw3ZO\nzeTNNV2vvEMWHvg+uQVOBy4BMZJqx1a9BRjDmaoYjAnzfyGqzKxKSU20exUL3LtzC3N/nY77LndF\njb1DWfwycQqaNP8A/MZER2Pb5o24cO4suL2dfRnUrFUbXbr3UkrhPHr4YLBnxy8Tp2LZ4gU4f/YM\nSpayRa/vB6Bbz95YtfxP7Nu9A6/8/VCRQm9+/2OpqOeBT504io1rV4sybnP6xDH4vvQRqajnzP+D\nPETsVGaufBodFYXfZk7F9WtXEBEehmo1ahHZ8A9o2qKVUkNN1qvUIYtOmHPnhYcHjIzyo3KVqnJ7\nq6q/dM5ZFHUl2ypKV7IfgzHXr1z+JsGYpNdvsdz5Ofbd9UVgVCKKGOuLtMy/ti9H/Akf+GFiEl9j\nx00fXHQPERmVSpsbogZxtnSuaoUyhT8AC2N3ueDN238xppkdVpDeC8+CUaKgAXrWLIYuVa2x+oIH\n9t/zQ0BkIipYFcDczhVFPdv89JNAbLrqJcr23/XDmaeB8ItIgGMxE8zqUB6laMz0JDohBfOOu+Lm\nizCEx6WgWnET9HayQZMyH9Lkarre9MbJbJ13qCxzItsjPdG0XXo6tHU50wIMDiw9dg97rz9HQEQc\nipoaom6ZIlANm4mhkJptl91w4bEv7nkFw66wCWqWtkQXp9Ioq5DOevTG83j95h1+aV8Vy4654Dy1\nL0m8Lr3qOqBbbTusOvUA+6674xWNVdGmEH7vU1fUs/VO3acHheeeiLJ9N57jNJ37hsWiakkLzOlV\nG7aWyl6vqhaPJjLh3wjIuO4egIjYRFSztRDEwk0r2sibarpeeYcsPLjjGYRyxczkQIykmrNSMRjj\nQnZlMGbnlWcoY2UqB2K4XaH8+mhUwRp7rrnj3osgOJJNvjWJ9XKB74EFiPd5KJamV9gORduOgnH5\nRkpLjX52HeF3jyH66WW8e50EQ9vqMCpdE+b1exP3iSxJRqznXbzc+xusOk1EYoAHwm4dRHK4P4wr\nNEaRNqPw7+tk+OyZjbgX95Db0JQ4XzqiaOuf5eM8/3sI9K3KIL+9EwLPbkD0s6sEWpihYK2uKNxi\n6Ee5adjzhD1N4v2eQsekMIzsa6NouzHIrffhN4Xn/ur4Xwi9sR8pkYEEjhQR7Wy6zUAuvfS/s+UT\nzcRBrMdtpIS/gk2PWXIghtXoGFvCbsQGCPLRTOhNr4vWMyY96+TQuvJ2JXFu2wokJacIgEJPV4cI\nbaPFasoTlwpLy/pOSHS9II6lf9f2rpEOlfZMbsubJGm1k+qzas+cN2e2LAVnR3ro6oHKZUvDnLxk\nVGVQ93bg7WNyY/9atU04PKkNhXQ9fu4FH+KjsSBAiwGsAkZpf9hLUjpsVfupVf4fLDy85BfcP7VL\nACWFbSsgPMCHwmY2UZiMK4auPiO3yLapfeHlcgXFytdE/T5jyXvjBW5Tu1uHN2HMtpswMrMUbQM9\nHiM6NACedy5AzzA/SlSpi8fnDsDr/lU8PLsPnncvoHTNZihgbgX3G2dE2M74vQ9FeE5UkB88bp3D\ndhrrdUoSytRtjfjIMLheOY6/BjbA8PUXUKhYafmcFA8Y3FkzvCXio8JRpUUPwb/y/PY5bJnYA61G\nzEXtbkNFc03Xq6g7K47fvnkN2+qNYeVQJZW6KJo7ixSCFO7vJeyTnBCPF3cvITYyVKzbqkxVbQYr\nstPzZ27o0LIJrG2KY+iI0dDV08OxwwfRo1Nb7Dl0HI2aNhf27NezC65cuoAaTrUxetwkeL3wAHtw\nbNqwFtddnsDSsrBo9+ThQwQE+IMBBaMCBVC3fgMc3LcHVy9fxP49O3GRwJymzVuhqFUxnD11HJ1a\nN8N9txfiPevv60vgzWnwWMlJSWjVtj1CQ0Nw4uhhNK5dDc5Xb6O0nb0YR/Ufe520blIf4WGh6N6r\nr+C3Oe98Br26tAcDOUNGjBJdNF2vqv6sOC9NqcGPnpH99ni98ET1CurXEhQYAAMDA9iqrLVipSrC\nTm6uT7NiOl+djkl7H2DPHV90rWaNckUK4GV4HLZe9xHhNMdH15fP94eNN3HVIwzVKcPSyCal4UXA\nwtYbPthy3RtXJjeBRX490fbpq2gEEKhziUCb/ET2W9u2IA7f98c1zzAcuEfvUSpvXMYcRQn0cXYN\nQteVV3FnRnMRDuAfmUDgTQh+2HATSXQD2bK8JcLiknHyUSCaLb6A0780hG0agAyP2X75ZQJhksVa\njAhIYiCo77obmNW+PAY3kAHDmq5XvvAsPPAOo5trYz3E0w33FY8QhMYmg0GtKgQ2KWam0rRdFk5N\nq+orscD4LZew+6q7AEoqEFDgHRJDYTNP4eoXjlMUNiNJv+UnccXtleAuGd3GEV7BUdhM7TZdeIrr\nv/eCpbHsQegTCrcJiIzHpad+gti3rkNRHKRQnKvUd//N57j4xA9NydujqJkhzj54KbxC7pOnB/PP\n+BPwwuANj5VMoG0rx+IIjUnEiXteaDxjD5xndUPpwuoBGQaSWs89gHBq372OPfFN5RW6elFoz5ye\ndQTfCq9F0/VK686q/es3b9GwvDWqlCiUSiUDUyzG+XQRTiASc8n0IoBGVSSy4vveod8cGJNAgInr\noq6Ct8Sy6Y/4jlJXR9w7gWdL+8JhzHYUKNdAmCPa7RpcF/cAc8QwT0tuAxNEu16G97bJSA7zFYS+\n3PBNfBRiPe/g5a5fwbpNq7bCm7hIBF/cijjvBwL8+B+F5hhXao4YAnf8DsxHHiMzmNft+X6cq4h7\n+QgBp1YRQFKLgJ4+iHp6iThW5iEp2AslBywW7dT98z+6FH6HFsGgpCMsGvZDEs0r+MJm6n8RZcbs\nQF5jGZDmtXUKQq/vJYCnC/JZl0NSiA+CL21HwqtnKD/liDrVWVKWFOIt9BhXbg4m741/+Vjs81mX\nhUmlDw9is2Sw90q0YExWWvMr0LV6x0FsP3wGa+dOhEMpG/mMDp25LI4r2ssugOQVOeCAvX0s65tm\n60yZF8axnJ3YsnWgb1z5m5RkPDi9hzxHmqPLlFXy1ZpS+upjyycjzNdTEOTGhAUKIKZe71FoMeRX\neTvzEg44vnwKfB7eQIXGneTlcREhaDpoKhqSdwxLxSadsXl8NwHIjN5yU066u3fuMAKCdiKCwAfF\nrEhJ8bEYtfmqnDDY8+5F/DO2E06tmonvF+yUj6N4cGr1LDCYM3T1WfKkqSqqGg+cjE2/dMapv2ei\nMgE0eXX1NVqvol7pmEGemwc/8BdJ5ar7cg3awby4g2oxcuXOg3ZjFqYqjyOg5SbxIn2XK7fIWsUN\nIl55IYnY9Bd1rYDXFNYkSRHyJOo6bTV5JaXvbSG1/1b3+/fuQmJiIv5evxkVKlUWyxz682iUt7XG\n7h1bBRgTSOAAAzEjx47HjDm/y03hUKYckcyOxc1rVwW3iVQREhyMKTNmYyx517B06tpDgDvXCJC5\ndu8xStnKQMDhgwdg9/atBOx4ysu4fWxMDK4Q95dEGHzpvDO6tGuJWVMnYfu+Q9wklcyePhl+vi9x\n+uI1OFarIeonTvsV3dq3wqxpkwRAY2xiAk3Wm0o5FYSHhZHXzt/qqpTK2nboBPsyZZXKMnpiU6KE\n8JphT6UqjtXk3T09ngtPSPdvEIxJphuSfeSB0oSyJS3r5ShfczFTA0w/+AgviCi3ZCFDBBHXCQMx\nIxrbYlrbcvJ2DsR9Mv3gY/JECUeHKkXl5QwyTGrlgNHNZDcwHamu99obuO4ZisuTGstJd0duvyeA\nIAYfeBxJYpPe4MLExnLC4MtVQ9B99TXMOfIEW350kpop7X87+kR40TCA5Ggje4gyvqUDenI/qmOw\nSV8nl0brVVL8/oRBHvba+Zi0rlgEzAmjTrzD4sFrqzbrNBLp5laSiuQhtKK3I0pbyPpp2k7qr91/\nGxZgwIM9YppVKoa/fmwsX1RxSlU9ZftVESrDAEAggSsMxIxsXRkzutWSt3MoYoqpO67i5vMAdKxh\nKy8PiU7AFAq/GdtOdl3RifhReiw5hmuk4xoBNxKoMHytM3aTp4dXSLS8jJXEJqbgytwe8ixNDOx0\nWXQEs3Zfx/YxreXjKB7M3nMDfgTmnJ7RWQ5UTOxUHd3+OCr6dSevHH2dPBqtV1GvdMwgCXvtfEza\nVi0B+6Kpr+fz5M6FBX1T83CExiQIot7cub4TGaqYnJjFvIAM3FIcjz2MWMKoz7cmYbcO4R09ULQd\ntFykp+b1MShzb5yjACwkMCbs9iH8j679Ks+/ToCMzEOySKvhcJlQExEPzsjBGMk+KdEhcFx0WwAt\nHF3wZF47xJEHTqF6vVCi7wLh4ZIU6ov7k5wI1LkiB2O4f3LoSxTrPhOFmw0W6qw6TCAgqDtCru6C\nOYEsBjYVpGHk+8RAT/gdWYIC5M1jP2qLnD4jigAXt8U9EXB2HWy6TSePnmSE3dwP44pNUOqHP+X9\ndQvZwGfnDCQGvYCehcy5QF75/iD87nECmNxVi5XO8xBIZdGov1KZdJIYTGAMeRAl+LnCY+1wsvv7\na2Yq4z7smcM2zkrJWm1ZOTOtrkxZoFKZ0vhl3l/oOXom+nZoIQCZO49csZhCcNo0qi08PjKlWNtJ\nawENLPDuneyC1pu8VphAtnBp2ZdxTSJPrtqmL3Ln1RVaOFxpCHnJqIYJ5dHRF/UMnigKp5Wr22uk\nvEgKY+LwJ0XQpUTlOgKMCfZxVypnLxbFzE2lqjYQIUfsVaMuvC0hJpK8bvaiiH1lORDDg+fOkxfV\n2vUXINDTS0cppKqLmNPH1iufuMJBfFQYzm2cr1Ci/pBDi9SBMepaP7t2Cvvn/0zePGFoM/J3WJSU\n3RAzZ0wKpb1u+uN0lK3XRtS7nNyBu8e3YevkXvh542Xk1Ut9caNujG+xjMMcWTZtWIPfFiyBvr6+\nSKP84Jm3/P1haGiEUxeuEmCiDFzp6cneswyeKApnXhoxZpy8qGyFiuK4boNGSqBLnboNBBjj/sxV\nqZy9WCQghjvWb9SEQo6ccPH8WTEnDuVUlMiICAp92ilCfyQghus569D3P/wovHLY26fvgIECzOC6\n9NbL9arCHjcL5s5SLU51XpKApk8FYzp16yG8iRb8NguTZ8wSmZQYmJk24Rcx3tv33zWpBs/BBVJW\nn+sEtDz2j0L5orKbi4F1S6AXhRXp5pG5mBuSlwmDHCULKXtv6r2vj0tSzpzIWV+HNfrgASjxo9Qh\nLxlF0KVWKTMBxjwPkoE+kinZi0Uxc1M9u0KoSgALe9Wo+/6MjE8RXjeVCNSQgBjWlTf3d+hTq7jw\nyjnxiG5QHWWA0cfWK81Dcc9gzKJTzxSL1B6XIFApLTDGh7yJOD345NZl0KpCYeHFs+u2L3be9EG/\n9TfhPL4R8unkhqbt1E5AW5hjLfD2/e8Ce608ehmKCu95TAY1qYDe9ct8+DySx9mp6Z1RylL2eZUW\nrEfvHRYGTxTlO/ruHtFKBvpzuRTGVLdMUSXQpY5DEQHGuL+KUCrnrEGK6bI5g1C1Uha4SKCM2s9j\nXBI4rKly8UJyIIbHzUsAyPcNygqvnGN3vcBkuCwfW69opPKPwZgFB2+rlKY+ZcBEHRiTuiWFST7w\nwaj15xFGuuf1riNCk3ZdlX3mjQ10UnWxIm8iFg6P/ObkX9k1ShB5rnD4TC4dPXxHD+QYSFF8zQs3\n+wkWjX+QAzFsB+ZAyZ0vP95QimlVKUSeLuzxwsLXFPpF7QUYY16vjzzUSLegNfJSiBCHMylKLj0j\nAQhJZXydXoRCmdiThr1k1IExQeQBQ2k9BaiheA1ToEw96BK4Ek6gE4Mx/77/7LEu9kzJV6y8GMai\n0QDwnJlQNy0Jv3OUwrSOplUtynmstMAY9uxh8VgzTHgXmTl1pvm8wasTKxHkvEHY1qq97DpENMyC\nf1owJguM+DWpqFmpLPat/A1bDpwiAGYnIqNjUdyqMAZ0bi04UHLlkl3MfU1z1s7l27EAe4o0HjAR\nZ9fPpTCg+ihIIUAcVmRXs6kIqfnu/ftPR98A1sTbwqFGj5z3I5w8N5jLJSLAR60xOGSJgRBJJFDH\nqKAslEkq5xtglrdvlH+MC1qXkprI94WK2+Pl41uIoRAoVQnzlf3opCTGY+eMAUrVSQmyH7SIV97C\nM0aT9SopeH/CtpnlnHps1ba5cn9Yt2qddB5Oc2GPomfXT8GkSHF0n7kODDhJ0mXqKuQi+1lQNiUW\n5tQpVr6GAKiu7FxBRL9HhaeP1P6/tu8/cDAO7NmFLRvXYz8BGjVr10UDAj9at+sA62I2whwcMlO1\nek1cu3JJtOUsQL4vfeDjLfvhVrWZBYUsKaZf1tWRAZGWhQsrNZU+E69TlG+gJc8Zxcb2Zcrg9s3r\nCAwgfqQiHzwfuI2nh+xJUFx8HAb27aHYDZwimsXH+4XYa7Je0VDlH4cM8dPVj4niuj/WNq36lq3b\n4afhI7Fm5XKcO3MK7NHDgFPNWnUEP4+JiWlaXXNsuX7e3BjXwh7zT7ih6R8XRAhQbQJIGhPHSkP7\nQvLQGQYIGORgz5aDLv5gglnmcnkZLiOkVTUAhywxECKJDt2EsUihTFK5FJqT8lZ24S+Vq4I+XG5H\nXiN3vCMQGJ0kNZPv2YOHJT7lLQZvUr5Bi30PFPmQV4qm65UrVjjg8Cjvhe0UStQfKq5btcUy8n7h\nG1L2KGJh7phqFPbF4VyrznuAASP24NG0nap+7XnOtgB7ikzoUA3z9t9CIwoDYk6WugSQMGlsIwqp\nyfX+esNANy+qEhhy7dkrHLjpAe/gaOJyiYEPhTSpEwsKWeL3nSQSyCqFMknlHJrEwiE8iqIK+nCd\nfRET3PYIEl46im35mMluWRikHbjytDiW/klAkQ9532i6Xqmv4p5t47fuJ8UitcfpfR6lDmy/aeRR\nxGBM8UL5sWZoU3nKaslukQTGqkoCAassBfKlfaOu2iennJs36EO8LocQcnm74Hcxsq2B/JTRx6RK\nS+iaWcmXoWdZCq/jIhBwejViie8lOcyPwoa88TYpDnkKmMvbSQeKfblMAjmkUCGp3f/+lwv/vlW+\nrtY1Ly73bJHa6ROPDUtyiI/Yq/5LDPIURSFXdyP02l6l6nfktZ0SFSR4bhhsKtpuLPwOLsSj2S3A\n62JeGebHYS8giftGScH7k1I/LkfJgR+8adS1UQSCVOs5hIsBI+OqbZTCrQxLOOLOqHJE4LsBWjBG\n1Wra81QWYD4Y3vhpb2x8AvIbKj89S9VBW6C1QBZagEOJKlAYkcvJnXC/eQa3D23ELSLRNSUAYPCK\n4zA0NUdMWJAIEwr2dhPeG1ZlHAmwaQYdAyMcJMJeVWGQR538D7KLFXV1imWGphaKp+JY0ikBO4oN\nEqIjxCkDQPz0QVE4fTSTDDOYw6LJehX7S8f8Y5CHfnA+Ve6f3o3Di38hNf9Di6GzUKvLT+SBpHwx\nwuFI6oSJiBmM4dfhvyxFilrhxv2nRJZ7FAf27iHA5aIAAGZOmYDps+fhZ/JwCQoMpDChFnhG4TFl\nypWnMKDqgtyXiWiZsFdV8uVT72mk6XvW3EIZaGT9+voynTrvgR3FMSPCw8Wpjo6O8OpRrDMhIKNL\n956wI1JiFk3Wq9hfOub3rB7x6XwumbtwCdp36iLIeiMjI1C+YmW0ad8RZUsWhRMBZt+icCgRhxjt\nJg+Nc67B2EwcMJuueQug4NDPdVHISBfBBIB0//sqnpEHCwMJVQiYaUK8L4YEIvyy634qs+jn/XDj\np1hJL6dGYk5jqoqkU0cB5JHaRLx/Ms03XrlzKQ9inC8vOpFHjJ2l7Cm2JuuV9CruxXsxjXUptkvv\nuKKVen6Nxg7mAox5Fii7mda0XXpjaetypgU4lIhDjNgjw/nRS/xz4Qk2nn+Ckub5cWRKRxEuExQV\njy4Lj+AZebAwsaxjCXMB2DAvy+iNF1ItnMFUdZLeDaJie3UhOgyksOi8945TbB9BnjFSXR4K91EU\nE0ofzSTDdgTmsGiyXsX+0rHs86h+XVIbTfZMwDt+8yW6yQdmdnfC4KYVldbERL0sL0NTA12R79dp\navj5fqM0WVNWtNExKYJKcy8h8sFZhN0+jBh38j55cgEv98yBdefJKNJymBjm1clVxMfyB4EqeWFk\n54T8ZeoKQt5AAmeSCJhRle/ee6OrlosXIFWhckFeNeCOpO9/eVL/ZnBv5qUhJEXMT1kbaL41RNG/\n7/4V+6JtRsGsenuEEGgT9fgc8dlsEdwynG667MT9yJu/kKoKcf6dBg8w1XZ8X5iXiHpZCtXu9r5E\ntsulm48Ii2uR3S8iJTqUxpdl/FJqlMmTT//kZHJgbbfstwB7CXwJIIYJd09fuonaVSvA1uYDYpv9\nK/4yIzDoJXlkpDeD16/fKGWBSq9tTq178zoFr5MSYWxhTRwvU8QWGx6MC1sWE4/JOjCJcrPB00VW\nJAYAmhNfTH3ijZGEw2yyQ9jzRgqZkvSzJ46eYQHkK5D6CbtxYRvRjAGk7jPWSl3E/t3bt0imkJ88\nunrQdL1KCt6fsF3Ob16krkqprCpljUoLTGF77f1tiAi56vErpdsjEmNViQr2h7+bC4o6VE5VL3ki\n5TPOuh8V1fFzwjmHGLGHStsOncXGn+nrVy/jx369RGaiQUOGY+kf8wUQw3wxzBsjCWc7yg5hzxuJ\nv0bS7/vyJQoYG8PUTOZWLJXz3qZ4CXFaoqQtVm/cqliFt/Se5SxGehR+xaLJetWBLsFBQVg8/zcl\n3epOevUbIMKK1NVpWsYcPTzn6jVriU3q5/HcHZwxyqHsB64UqS6n71OIJDeRyGStTPJhYqsyYguJ\nScLSM+7YSPwo6y+/wJQ2ZSnbkrsAYqa1LUu8MaXly+ZsR9khzJkihUxJ+tkTp4B+HpiqCRcoZioD\nDdnTZFXfalIXsedQLH5Cr0deQJquV0nB+xO2y5LTz9RVKZX1pPAudWDKKyInvu8biUrWxoK8WLGT\n5GFkZqgDTdsp9tcefxsWSCGPlETytrAuaAhOscxbMAEvS47eEzwm65wfY1qXmlhK5wzEzOjmRLwx\nVeSLZ8+O7BD2HJFCpiT97InDHiHqgAibgu89vwhAWj2kqdRF7DkUK44ys3FIlabrVVLw/oTtsvjw\nXXVVSmW96jmgEoVLqRO21zDiyeGQq3XDmonMVartJF4Y9uRRlad+YaLIsaS5alWOP+cQI/YGMa3a\nWmwcxhPz/CaF0gylDEvzKTRpAN4R2SwT6OYxNEXledeUMg69OrYsy23AHjeqwp44LGnxuegWLCbC\njoq0Hgn9wh9+u7gPk+X+Sx4p7BXzjrzbmatFh7x+rDuOFxvz2/A6gs5TQpBzG2HdaRJ3SyUhV3YS\nufDjVOWKBQykFG07RrFIfsxZm1j+fSvztJJX0AHz9jBQlUvXQLH4k4+VIdJPVqdVoLUAxdl5E+np\njD9w7e6jHGuOHUfOQq9MQ7wKDlW7Bg8fP+LmWQG7Jj1Q2KkdOg6ZjAs37qVqy55JP01biBL1uyA/\nsXBzevFZyzcgRSUkIVXHHFrgde8y5rSywUPnffIVsCdMvV4/i/PEWJm7rAQCVGnZU96OD9wozCY7\n5Nn1M0pqGQh5fstZpIVWqnh/Ykqpsxmk8bh1nkKelMNHLm5bItbo73YPmq5X3RiJnNr76JaPbhyC\nlJacXjuHQo2M0GvO5lRAi9QnMTYSO6b3E4CYVCbtH507IA5tKjhJRf/Jfee2LVC/RmX52hlcrVOv\ngUgHLQEZL31kr0OP3t/L2/HB6ZPZA8acoSxLisJAyLkzJ1G+gnovp+IlSwmQ5rzzaZFWW7Hv0kXz\nUbKIGVzu3hbFmqxXsb90HB0dha2bNnx08/FSH7ol6dFkv2LJIjhVLkvhV8+Vmq/5axl0dXVRv2Fj\npfJv4eSqRyjsphyn0KMPTzDZE2YYEfWyRNNNE4sEFnSvbi3OpX9nngRJh1m6d36qrJeBkPNuwZTt\nKb/acYqbGcCUPGAuUJvXKiFPy8+6izXe940gEmLN1qtuELbF9ps+H904HEqdRCW8xqB/bgugS7X+\n8P1XoohThWvaTlWH9jznW+AKpVMuOYxCV294yBfDXik/v+d74VTRLJKXRg/KUqQonHo6O+SMCsjD\nQMi5R74or5BCW3Hc4gTCmBrqiuxJqiFPDCTxGjlttKbrVdQtHTNPy9ZLrh/d1IEoko7f9t4gLpy8\n+OfnFmqBGG7HoVy17ArjhnuACAeT+vK6+HXiELBKNgWl4m9mz+S2D2c2ka+H+VnYS6NABSojAONd\nUjyFJPmLtMsmVVopATHJEa8Q7/tU3jerDhKJW0WQ3SooDKXwIxbOPKROOIMSS+RDZ6XqN5RgwmV8\ndbivHCTKOSvUnZ/LiNAsqSF7whRuIfMAehOfGoyT2kW7XaVwrh3pbmHEK5OWGLNNSTjdt6Jw+FcM\npb3WtyorACPFuk89zv2pCrT9tRb41iwQExePPzfuSnNZiUnJ6DxsKgJCKH1s6yYwLUChNZStqtOw\nKTiydiHqVqso+rInTMNeI/DUwxtdWzZE6RLWOOJ8FfNXb0NoRDT++nVsmmPk1IpiFWoQiGGG85sW\nIn+hIihsW574YLxxYfMfYkkcFsPCnh6chvr0mlmo13MkYilbEhPmPrl0RNRz9p/E2GiRyloUfOI/\nzrBkRKBQuYYdSG8UTvw1VRCEtf55rlrNHJ7U7KeZImRqz+zBqNeb0h3nM4Tr1RNiLczHwim5UxLj\nNFqvukE4pfacCyHqqjQq43UEe7kKj5+ru/5S24cJje1qNScS4mqUXnwzpbo2Qdn6bcXaH5zZI9KF\n8zmHif2Xhblh5syYgjkzp4L5VHTJ64nTUO/btUN4eBQsVAgVK1ehNNQnqM0U/Dx6HIKDg0Sa6qOH\nZICWt5en8NjIT6mss0I4w5IFhSq169gFUVGRmD5pnAg95RTV6oR5WqbPmidCpob80Je8dybA0MgI\nJ48dweIFcwUBMKfkZtFkverG4JTagZSy+HMIhyOtXbUCv/w8FIuW/gUTUzNs3/IPtvyzHvP+WCr3\nBPocc/lcY1QvbgIzg7xYTMS0lsTzwt4onNmIPWNYmhB3DEsFCq9xphCmuUefEjGvLUIoW9K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Cp1wp4xLFsOnsS2w6eVmiQmJVFWojAwV4ym61VS8P5k4/wpSt5D6tooAkHq6jUp06GMJSwR0bGp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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.externals.six import StringIO  \n",
    "from IPython.display import Image  \n",
    "from sklearn.tree import export_graphviz\n",
    "import pydotplus\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "model=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
    "            max_features=None, max_leaf_nodes=10, min_samples_leaf=1,\n",
    "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
    "            presort=False, random_state=None, splitter='random')\n",
    "\n",
    "\n",
    "model.fit(train_X,train_Y)\n",
    "prediction4=model.predict(test_X)\n",
    "print('The accuracy of the Decision Tree is',metrics.accuracy_score(prediction4,test_Y))\n",
    "\n",
    "pred_training = model.predict(train_X)\n",
    "print('Training Accuracy for Decision Tree is',metrics.accuracy_score(train_Y, pred_training))\n",
    "\n",
    "#print('F1 micro: is',f1_score(test_Y, prediction4, average='micro'))\n",
    "#print('F1 macro: is',f1_score(test_Y, prediction4, average='macro'))\n",
    "#print('F1 weighted: is',f1_score(test_Y, prediction4, average='weighted'))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "model_Decision_Tree = model\n",
    "\n",
    "dot_data = StringIO()\n",
    "\n",
    "export_graphviz(model, out_file=dot_data,  \n",
    "                filled=True, rounded=True,\n",
    "                special_characters=True, feature_names=train_X.columns)\n",
    "graph = pydotplus.graph_from_dot_data(dot_data.getvalue())  \n",
    "\n",
    "plt.show(graph)\n",
    "\n",
    "Image(graph.create_png())\n",
    "\n",
    "#graph.write_pdf(\"decision_tree_OHE.pdf\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wvy9B/r788rSBTscTEQ+kO8wi0q7Kqut5Y8U2zhvWi77dQ1r1Gb0ignn6ipP4\n9/Vj8PMx/OzVDK5/NZ1tJe4b09hdUcvzS7Zw9ok9GRnfzW2fK57jhgn9+NuM4azeVsbiTcXMnNSf\nqC6BTscSEQ+kwiwi7eqVb/LZV9/ErZMHHPdnjU+K4pNfnMq9ZyWzPLeEaU8u5okvNrllTOOxzzfi\ncsE90zvOJiXyY+cP781rPxvNT8f05frxiUc/QUQ6JRVmEWk3lbUNvPp1HqcPjmWgm+ZEA/x8uOnU\n/sy/cxLTh/Tg7/M3M+2JxXy+YfePVkFoqQ07K5i9qoBrT0mgT2Tr7oKL9xjXP4o//WQoQf56qFNE\nDk2FWUTazRsrtrG3tpHb3HB3+WA9woP4++UjeOvGsYQE+HLT6yu57tV08o5hS2TYvy3yn+ZlER7s\nz22T3J9TRES8jwqziLSL2oYmXvwqjwlJUQzrE9Fm1zm5f3fm/nwC95+dQkZ+GWc8uYS/fpbNvvrG\nFp2/cGMRX+eU8IupSYSH+LdZThER8R4qzCLSLt7J2M6eqro2ubt8MH9fH26Y0I8Fd03knBN78vTC\nLUx7fDGfrNt1xDGNxiYXf5qXTWJUKFeMiW/znCIi4h1UmEWkzTU0uXhucS4j47sxJjGy3a4bExbE\nE5cN57+3nEzXYH9mvrGKq19OY0tx1SGPfyt9OzlFVdxzZjIBfvrtUURE9tOfCCLS5j5YvYMd5TXc\nPnmAI+sZj0qI5OM7xvPAuYP5dns5059awiOfZFNd978xjcraBp76YhOjEyM5fbD713QWERHvpcIs\nIm2qyWWZtWgLg3t2ZdKgaMdy+Pn6cO0piSy4cxLnD+/Ns4u3MPXxxXy0ZifWWp5ZtIWS6nruPztF\nm5SIiMgPqDCLeIDahiYqahqcjtEmPl2/m9w91dzm0N3lg0WHBfLYJcOYPXMc3bsEcMdbq5nx/HJe\nWprHT0b05sS4tnsgUUREvJMKs4jD8vdUc8ZTSzjrb1+xt7ZjlWZrLf9cmEO/6FCmn9DD6Tg/MDK+\nG3NuH88fLziB7N2V+Bi4+4xBTscSEREP5Od0AJHObG1BOde9kk6jy1JV18hDH2fyl4uHOR3LbRZt\nLCZr117+evGJ+Po4f3f5YL4+hqvGxnPuiT0p29dAr4hgpyOJiIgH0h1mEYcs3lTMjOeXExzgy3u3\njuPmU/vxTkYBC7OLnI7mFt/dXe4dEcwFI3o7HeeIIkICSIwKdTqGiIh4KBVmEQfMXlnA9a+mk9A9\nlPdmjqN/dBd+MS2JgbFduOe9tVTs8/7RjBV5pazcWsbNE/vh76vfakRExHvpTzGRdrR/NYYc7vzv\nGsb0i+Q/N48lpmsQAIF+vjyj13uiAAAgAElEQVR+yXD2VNXz4MeZDic9fk8vzCGqSyCXpvZxOoqI\niMhxUWEWaSdNLssDczbwl083ct6wXrxy7WjCgn649fLQuHBundSf2asKmJ9V6FDS47dmezlfbd7D\nDRMSCfL3dTqOiIjIcVFhFmkHtQ1N3PHWKl5btpUbJyTy1GXDD7uT3B1TkkjuEcZv3lvntaMZTy/M\noWuQH1eM6et0FBERkeOmwizSxipqGrjm5TTmrdvN/WencN/Zg/E5wooRAX4+PHbJMEqr6/nDRxva\nMal7bCqs5PPMQq49JfFHd9BFRES8kQqzSBvaXVHLpc8uY9W2Mv42Yzg3TOjXovNO6B3OrZMH8N7q\nHXyR6V2jGc8szCEkwJfrxiU4HUVERMQtVJhF2sjmwkoufOZrdpTX8Op1ozl/+LEtrXb75AGk9OzK\nve+vo6y6vo1Sute2kn3MWbOTK8b0pVtogNNxRERE3EKFWaQNpOeXcvGzy2hwWf5z81hOGRB1zJ+x\nfzTjRMqq63nAS0YzZi3egp+PT4vvpIuIiHgDFWYRN/t0/W6ufHEF3UMDeG/mOIb0Cm/1Zw3pFc4d\nU5L48NudfLp+txtTut/uilpmryzgktQ4YpuXyhMREekIVJhF3Ojfy7dy6xsrSenZlXdnjqNPZMhx\nf+atk/szpFdX7v9gHaUePJrxwle5NFnLLRP7Ox1FRETErVSYRdzAWsvjn2/k/g/WM3lQDG/eOIZI\nN83w+vvuXzWjoqaB38/xzNGM0up63lyxjfOH9XLLXxJEREQ8SYsKszFmujFmozEmxxhzzyHe72uM\nWWiMWW2MWWuMOeuA937TfN5GY8wZ7gwv4gkam1z8evZa/rEgh8tS+/DcVSMJCfBz6zVSenbl51OS\n+GjNTj5Zt8utn+0Or3ydR01DEzMn6e6yiIh0PEctzMYYX+Bp4ExgMHC5MWbwQYfdD7xjrR0BzACe\naT53cPPrIcB04JnmzxPpEPbVN3LT6yt5J6OAn09N4pGLhuLn2zbfuLllUn+G9g7n/g/WU1JV1ybX\naI3K2gZe/Saf6UN6kBQb5nQcERERt2vJn+yjgRxrba61th54Gzj/oGMs0LX5x+HAzuYfnw+8ba2t\ns9bmATnNnyfi9Uqq6rj8hRUs2ljEwz85gf932kCMOfyGJMfru9GMytpGfveh54xmvL58K5W1jdw2\neYDTUURERNpESwpzb2D7Aa8Lmr92oAeAK40xBcA84I5jOFfE62wv3cfFzy4je9denr1yJFeMiW+X\n6w7qEcYvpiUxd90uPl678+gntLGa+iZe+iqPUwdGMzSu9auBiIiIeLKWFOZD3TKzB72+HHjVWhsH\nnAW8bozxaeG5GGNuMsZkGGMyiouLWxBJxDnrd1Rw4axvKK2u540bxnD6kB7tev2bT+3HsLhwfvvB\neoornR3N+E/6Nkqq67lNs8siItKBtaQwFwB9Dngdx/9GLr5zPfAOgLV2GRAERLXwXKy1z1trU621\nqdHR0S1PL9LOlm7ew4znlxPg68PsmSeTmhDZ7hn8mkczquua+O0H67H2R38HbRf1jS6eX5LLqIRu\njOnX3ZEMIiIi7aElhTkdSDLGJBpjAtj/EN+cg47ZBkwFMMaksL8wFzcfN8MYE2iMSQSSgDR3hRdp\nTx9+u4PrXk0jrlsws2eOY0CMcw+4JcWG8cvTBvLpht18tNaZVTM+WL2DnRW13KrZZRER6eCOWpit\ntY3A7cBnQBb7V8PYYIx50BhzXvNhdwI3GmPWAG8B19r9NrD/znMm8Clwm7W2qS1+IiJt6YUlufzi\n7W8ZGd+N/9x8Mj3Cnd/J7sYJiQzvE8HvPlxPUWVtu167yWWZtXgLQ3p1ZdJAfVdIREQ6NuPUt3MP\nJzU11WZkZDgdQwQAl8vy8LwsXlqax9kn9uSJS4cR6Oc5KyPmFFVx1t+/YuLAaJ6/amSbrtJxoI/W\n7OSOt1bzzBUncdbQnu1yTREREXczxqy01qYe7Tjt9CdyGHWNTfzff77lpaV5XDsugX/MGOFRZRlg\nQEwX7jp9IF9kFvLht+2zaoa1lqcX5tA/OpTp7fzAo4iIiBNUmEUOobK2geteSWfOmp3cc2Yyvz93\nMD4+7XP39lhdP74fJ/WN4PdzNlC0t+1HMxZkF5G9u5KZkwZ47P8nIiIi7qTCLHKQor21XPrcctLy\nSnni0mHcMrF/u406tIavj+GvlwyjtqGJe99f16arZlhr+efCHHpHBHP+8F5tdh0RERFPosIscoAt\nxVX85Jlv2FpSzcvXjuLCk+KcjtQi/aO7cPcZg/gyq4j3V+9os+sszy1l9bZybpnYD/822gJcRETE\n0+hPPJFmq7aVcfGsb6hrbOI/N53MqV62+sN1pySSGt+NB+ZsoLCNRjOeXphDVJdALkntc/SDRURE\nOggVZun0iivreH1ZPj99YTnhwf7MnjnOK7d5/m40o77JxW/ec/9oxrfby1mas4cbJyQS5O9ZDz+K\niIi0JT+nA4i0N2stmbv2siCriC+zi1izvRyAEX0jeOHqVKK6BDqcsPUSo0L51RnJPPhxJu+uLHDr\nneCnF+YQHuzPFWPj3faZIiIi3kCFWTqF2oYmlm0p4cusQhZkF7GrohZjYFhcBHeeNpCpKbGk9Azz\n6If7WuracQl8un43D36cyfikKHqGBx/3Z27cXckXmYX8YmoSXQL124aIiHQu+pNPOqzCvbUsyC5i\nflYhS3P2UNvgIjTAlwlJ0fzytBgmD4ohOsx77yYfjo+P4a+XnMj0p77intnrePW6Ucf9F4FnFuUQ\nEuDLdackuCekiIiIF1Fhlg7D5bKs31nB/KwiFmQXsW5HBQC9I4K5LLUPU1NiGdMv0uM2H2kL8d1D\n968fPWcD/80o4NJRrR/N2FpSzUdrdnLDhH5EhAS4MaWIiIh3UGEWr7avvpGvc0qY3zxqUVRZhzFw\nUt9u/Gr6IKYmxzIwtkuHGLU4VleNjeeT9bv4Y/NoRq+I1o1mPLt4C36+PtwwPtHNCUVERLyDCrN4\nnR3lNd+PWnyzpYT6RhdhgX6cOjCaqSkxTBoUQ2So7oT6+Bj+ctEwpv9tCb+evZZ//Wz0Mf/FYXdF\nLe+uLOCyUX2I6RrURklFREQ8mwqzeDyXy7KmoJz5WUXMzy4ia9deAOK7h3DlmHimpsQwKiGSAD+t\nkniwvt1D+M2Zyfz2ww28nb6dy0f3Pabzn1+Si8vCzaf2b6OEIiIink+FWTxSVV0jSzcX82VWEYs2\nFrGnqh5fH8PI+G7ce1YyU5Jj6R8d2ilHLY7VFWPi+WT9bh6em8WEpCjiuoW06LySqjreStvG+cN7\n0SeyZeeIiIh0RCrM4jG2l+5jflYh87OLWJFbSn2Ti65BfkwaFMPUlBgmDozWQ2et4ONjePSiE5n+\n1BLumb2O169v2WjGK1/nU9vYxK2TdHdZREQ6NxVmcUyTy7J6WxlfZhWxILuQTYVVAPSLDuWacfFM\nTYllZHw3/H01anG8+kSGcO/ZKdz3/nreTNvGFWOOvPnI3toGXluWz/QhPRgQE9Y+IUVERDyUCrO0\nq721DSzZVMyCrCIWbiyibF8Dfj6G0YmRXNq89FtiVKjTMTukn47uyyfrdvOnuVmcmhR9xDGL15dt\npbK2kdsmD2jHhCIiIp5JhVnaXP6e6u932EvLK6XRZYkI8Wdy86jFhKRowoP9nY7Z4RljeOSioUx/\n6it+PXst/75+DD4+Px7NqKlv4uWleUwcGM0JvcMdSCoiIuJZVJjF7RqbXGRsLWNBdhFfZhWSW1wN\nwMDYLtwwoR/TUmIY0bcbvocoa9K24rqFcN/ZKfzmvXW8sWIrV52c8KNj3k7fRkl1PbdP0d1lERER\nUGEWNynfV8/iTcXMb17VYm9tI/6+hrH9unP12HimJMfSt7tWWvAEM0b1Yd66Xfz5k2wmDoz5wa9L\nfaOL55fkMjohklEJkQ6mFBER8RwqzNIq1lq2FFd/v6rFyq1lNLks3UMDOH1ID6alxDA+KZougfpX\nzNMYs3/VjDOeXMLd767hrRvHfj+a8f7qAnZV1PLIRSc6nFJERMRzqM1Ii9U3ukjPL23eQKSQrSX7\nAEjuEcbMif2ZkhLD8LiIQ87FimfpFRHMb88ZzK9mr+X15Vu5ZlwCTS7LrEVbGNo7nFOTopyOKCIi\n4jFUmOWISqvrWZhdxILsIpZsKqayrpEAPx/G9e/ODRP6MSU5ht4RwU7HlFa4JDWOeet38cgn2Uwa\nFM2aggryS/bx7JUnaUMYERGRA6gwyw9Ya9lUWMX87ELmZxWxalsZ1kJ0WCBnn9iTKckxjE+KIiRA\n/+p4O2MMf75wKKc/uYS7/7uWvbUNDIjpwumDezgdTURExKOo9QgAOUVVvL4sn/nZRRSU1QBwQu+u\n/HxKElNTYjihV7hGLTqgnuHB/O6cwdz97loAnrh0mH6dRUREDqLCLADc+sZKtpbsY0JSFLdOGsCU\n5Bh6hAc5HUvawcUj41iQXURucTXnDuvldBwRERGPo8Is7KmqY1NhFfecmcwtE/s7HUfamTGGZ644\nifoml7YhFxEROQT96Shk5JcCaN3dTswYQ6Cfr9MxREREPJIKs5CWV0aQvw9DtQ2yiIiIyI+oMAvp\n+aUM7xNBgJ/+dRARERE5mBpSJ1dZ28CGnRWMTuzudBQRERERj6TC3Mmt2laOy8JozS+LiIiIHJIK\ncyeXnleKr49hRN8Ip6OIiIiIeCQV5k4uLb+UE3p1JTRQKwyKiIiIHIoKcydW19jEt9vLGZ2ocQwR\nERGRw1Fh7sTWFlRQ3+jS+ssiIiIiR6DC3Iml5WnDEhEREZGjUWHuxNLzS0mK6UK30ACno4iIiIh4\nLBXmTqrJZVmZX6b5ZREREZGjUGHupLJ27aWyrlGFWUREROQoVJg7qfR8zS+LiIiItIQKcyeVnl9K\n74hgekUEOx1FRERExKOpMHdC1lrS8koZo3EMERERkaNSYe6E8vZUs6eqnlEqzCIiIiJHpcLcCWl+\nWURERKTlVJg7obS8MrqHBtA/OtTpKCIiIiIeT4W5E0rLL2FUQiTGGKejiIiIiHg8FeZOZndFLdtL\nazS/LCIiItJCKsydTFrz/PJozS+LiIiItEiLCrMxZroxZqMxJscYc88h3n/SGPNt8z+bjDHlB7z3\nF2PMBmNMljHm70ZzAI5KzyslNMCXlJ5hTkcRERER8Qp+RzvAGOMLPA2cBhQA6caYOdbazO+Osdb+\n8oDj7wBGNP94HHAKcGLz20uBicAiN+WXY5SWV8rIhEj8fPXNBREREZGWaElrGg3kWGtzrbX1wNvA\n+Uc4/nLgreYfWyAICAACAX+gsPVx5XiU76tnY2EloxO6OR1FRERExGu0pDD3BrYf8Lqg+Ws/YoyJ\nBxKBBQDW2mXAQmBX8z+fWWuzjiewtF5Gfhmg9ZdFREREjkVLCvOhZo7tYY6dAbxrrW0CMMYMAFKA\nOPaX7CnGmFN/dAFjbjLGZBhjMoqLi1uWXI5Zen4pAb4+DOsT4XQUEREREa/RksJcAPQ54HUcsPMw\nx87gf+MYAD8Blltrq6y1VcAnwNiDT7LWPm+tTbXWpkZHR7csuRyzFXmlDOsTTpC/r9NRRERERLxG\nSwpzOpBkjEk0xgSwvxTPOfggY8wgoBuw7IAvbwMmGmP8jDH+7H/gTyMZDthX38j6HRUaxxARERE5\nRkctzNbaRuB24DP2l913rLUbjDEPGmPOO+DQy4G3rbUHjmu8C2wB1gFrgDXW2o/cll5a7Ntt5TS6\nrDYsERERETlGR11WDsBaOw+Yd9DXfnfQ6wcOcV4TcPNx5BM3ScsvxRgYGa8VMkRERESOhRbj7STS\n8koZ3LMrXYP8nY4iIiIi4lVUmDuBhiYXq7eVa35ZREREpBVUmDuB9TsqqGloYrTml0VERESOmQpz\nJ5CeXwpowxIRERGR1lBh7gTS8krpFxVKdFig01FEREREvI4KcwfnclnS88t0d1lERESklVSYO7jN\nRVVU1DRo/WURERGRVlJh7uDSmueXR+sOs4iIiEirqDB3cGl5pfToGkSfyGCno4iIiIh4JRXmDsxa\nS3peKaMSIzHGOB1HRERExCupMHdgBWU17N5by+gEbYctIiIi0loqzB1YWl7z+st64E9ERESk1VSY\nO7C0vFLCg/0ZGBPmdBQRERERr6XC3IGl55cyKqEbPj6aXxYRERFpLRXmDqq4so7cPdXasERERETk\nOKkwd1AZ+ZpfFhEREXEHFeYOakVeKcH+vpzQK9zpKCIiIiJeTYW5g0rPL2VE3wgC/PRLLCIiInI8\n1KY6oMraBrJ27dX8soiIiIgbqDB3QCu3luGyMFrzyyIiIiLHTYW5A0rLK8XPxzCib4TTUURERES8\nngpzB5SeX8oJvcMJCfBzOoqIiIiI11Nh7mBqG5pYs71C4xgiIiIibqLC3MGsLaigvsmlB/5ERERE\n3ESFuYNJyysBYFRCN4eTiIiIiHQMKswdTFp+GYNiw4gICXA6ioiIiEiHoMLcgTS5LKu2ljEqUXeX\nRURERNxFhbkDydq1l6q6Rs0vi4iIiLiRCnMHsiKvFNCGJSIiIiLupMLcgaTnldInMpie4cFORxER\nERHpMFSYOwhrLen5pRrHEBEREXEzFeYOIndPNSXV9YxWYRYRERFxKxXmDiJN88siIiIibUKFuYNI\nzyslqksAiVGhTkcRERER6VBUmDuItOb5ZWOM01FEREREOhQV5g5gZ3kNBWU1euBPREREpA2oMHcA\n6fmaXxYRERFpKyrMHUBaXilhgX6k9OzqdBQRERGRDkeFuQNIzy/lpPhu+PpofllERETE3VSYvVxZ\ndT2bCqs0jiEiIiLSRlSYvZzml0VERETalgqzl0vPLyXAz4cT48KdjiIiIiLSIakwe7m0/DKGx0UQ\n6OfrdBQRERGRDkmF2YtV1zWyYUcFoxK7OR1FREREpMNSYfZiq7eV0+iyjE7s7nQUERERkQ5LhdmL\npeWX4mPgpL4RTkcRERER6bBUmL1Yel4pg3t1JSzI3+koIiIiIh2WCrOXqm90sXp7GaMStJyciIiI\nSFtSYfZS63ZUUNvgYozWXxYRERFpUy0qzMaY6caYjcaYHGPMPYd4/0ljzLfN/2wyxpQf8F5fY8zn\nxpgsY0ymMSbBffE7r+82LEnVHWYRERGRNuV3tAOMMb7A08BpQAGQboyZY63N/O4Ya+0vDzj+DmDE\nAR/xL+Bha+0XxpgugMtd4Tuz9LxS+kWHEtUl0OkoIiIiIh1aS+4wjwZyrLW51tp64G3g/CMcfznw\nFoAxZjDgZ639AsBaW2Wt3XecmTs9l8uSnl/KaN1dFhEREWlzLSnMvYHtB7wuaP7ajxhj4oFEYEHz\nlwYC5caY94wxq40xf22+Yy3HYWNhJXtrGxmt+WURERGRNteSwmwO8TV7mGNnAO9aa5uaX/sBE4C7\ngFFAP+DaH13AmJuMMRnGmIzi4uIWROrcvptf1goZIiIiIm2vJYW5AOhzwOs4YOdhjp1B8zjGAeeu\nbh7naAQ+AE46+CRr7fPW2lRrbWp0dHTLkndiaXml9AwPIq5bsNNRRERERDq8lhTmdCDJGJNojAlg\nfymec/BBxphBQDdg2UHndjPGfNeCpwCZB58rLWft/vnlUQmRGHOom/8iIiIi4k5HLczNd4ZvBz4D\nsoB3rLUbjDEPGmPOO+DQy4G3rbX2gHOb2D+OMd8Ys4794x0vuPMn0NlsK91H4d46zS+LiIiItJOj\nLisHYK2dB8w76Gu/O+j1A4c59wvgxFbmk4Ok5e2fX1ZhFhEREWkf2unPy6TnlxIR4s+A6C5ORxER\nERHpFFSYvUx6fhmp8ZH4+Gh+WURERKQ9qDB7kaLKWvL2VDNG4xgiIiIi7UaF2Yuk55UBMEqFWURE\nRKTdqDB7kfT8UoL9fRnSq6vTUUREREQ6DRVmL5KWV8pJ8RH4++qXTURERKS9qHl5iYqaBrJ272V0\nQneno4iIiIh0KirMXmLV1jKshVGJ3ZyOIiIiItKpqDB7ibT8Uvx9DSP6qDCLiIiItCcVZi+RnlfK\nCb3DCQ7wdTqKiIiISKeiwuwFahuaWFNQru2wRURERBygwuwFvt1eTkOTZXSCCrOIiIhIe1Nh9gLp\neaUYA6nxKswiIiIi7U2F2Quk5ZcyKDaM8BB/p6OIiIiIdDoqzB6uscnFqq1lml8WERERcYgKs4fL\n3LWX6vomRml+WURERMQRKsweLi2vFEB3mEVEREQcosLs4dLzS+kbGUJs1yCno4iIiIh0SirMHsxa\nS3q+5pdFREREnKTC7MG2FFdRWl2v9ZdFREREHKTC7MHS8soAGKU7zCIiIiKOUWH2YOn5pUR1CSSh\ne4jTUUREREQ6LRVmD5aWV8qYxEiMMU5HEREREem0VJg91I7yGnaU1zAqoZvTUUREREQ6NRVmD5Xe\nvP6y5pdFREREnKXC7KHS8ksJC/QjuUdXp6OIiIiIdGoqzB4qLa+U1IRu+PpofllERETESSrMHqi0\nup6coiqNY4iIiIh4ABVmD5Sev39+WRuWiIiIiDhPhdkDpeeVEuDnw9C4cKejiIiIiHR6KsweKC2/\nlBF9Igj083U6ioiIiEinp8LsYarrGtmwcy+jNb8sIiIi4hFUmD3Mqm1lNLksozS/LCIiIuIRVJg9\nTHpeKT4GTorXDn8iIiIinkCF2cOsyCvlhN7hdAn0czqKiIiIiKDC7FHqGpv4dnu5xjFEREREPIgK\nswdZv6OCukaXCrOIiIiIB1Fh9iBpeWUAjErQ/LKIiIiIp1Bh9iBpeSUMiOlC9y6BTkcRERERkWYq\nzB6iyWXJ2FqmcQwRERERD6PC7CE27q6ksraR0YkaxxARERHxJCrMHiI9vxRAd5hFREREPIwKs4dY\nkVdC74hg4rqFOB1FRERERA6gwuwway3PL9nCvHW7mZIc43QcERERETmItpNzkMtleWhuFi9/ncfZ\nJ/bk/nNSnI4kIiIiIgdRYXZIXWMTd76zho/X7uK6UxL47dmD8fExTscSERERkYOoMDtgb20DN/9r\nJctyS/jNmcncdGo/jFFZFhEREfFEKsztrHBvLde8nEZOURVPXjaMn4yIczqSiIiIiByBCnM7yimq\n4pqX0yjfV8/L147i1IHRTkcSERERkaNo0SoZxpjpxpiNxpgcY8w9h3j/SWPMt83/bDLGlB/0fldj\nzA5jzD/dFdzbrNxaxsXPfkNdYxNv33SyyrKIiIiIlzjqHWZjjC/wNHAaUACkG2PmWGszvzvGWvvL\nA46/Axhx0Mf8EVjslsRe6IvMQu54axU9ugbx2s9GE9891OlIIiIiItJCLbnDPBrIsdbmWmvrgbeB\n849w/OXAW9+9MMaMBGKBz48nqLd6K20bN7+ewaDYMN6dOU5lWURERMTLtKQw9wa2H/C6oPlrP2KM\niQcSgQXNr32Ax4G7jy+m97HW8tSXm/jNe+s4dWA0b944lqgugU7HEhEREZFj1JKH/g613pk9zLEz\ngHettU3Nr28F5llrtx9p2TRjzE3ATQB9+/ZtQSTP1tjk4rcfruettO1cPDKOP184FH9fbaooIiIi\n4o1aUpgLgD4HvI4Ddh7m2BnAbQe8PhmYYIy5FegCBBhjqqy1P3hw0Fr7PPA8QGpq6uHKuFeoqW/i\njrdW82VWIbdPHsCdpw/UGssiIiIiXqwlhTkdSDLGJAI72F+Kf3rwQcaYQUA3YNl3X7PWXnHA+9cC\nqQeX5Y6krLqe619LZ/X2ch48fwhXn5zgdCQREREROU5HLczW2kZjzO3AZ4Av8LK1doMx5kEgw1o7\np/nQy4G3rbVefYe4tbaX7uOaV9IoKKvhmZ+exJlDezodSURERETcwHhav01NTbUZGRlOxzgmmTv3\ncu0radQ2NPHiNaMYnRjpdCQREREROQpjzEprberRjtNOf8fpmy17uPlfK+kS5Me7M8cxMDbM6Ugi\nIiIi4kYqzMfhozU7ufOdNSREhfDqdaPpFRHsdCQRERERcTMV5lZ6aWkef/w4k9EJkbxwdSrhIf5O\nRxIRERGRNqDCfIxcLssjn2bz/JJcpg/pwVMzhhPk7+t0LBERERFpIyrMx6C+0cWv3l3DB9/u5Kqx\n8Txw3hB8fbTGsoiIiEhHpsLcQlV1jdzy+kqW5uzh7jMGceuk/tqQRERE/n979x/rVV3Hcfz5ul5A\nfikoUigU6JRwzgDJKBETnAkxiox+TJfu1izLQpqljsZwrs2f2T9NV6iUImmKpm4pZP7oj0AB+XEJ\nRJ0oKHClZpIuUHn3x/ncdsV7zzW28znfdV+P7bvv+d4fe73P937v57zPOZ/z/ZpZD+CG+UNo2/Nv\nWhY9w6Yde7j+Kycze8KI7n/JzMzMzP4vuGHuxku73+Kbt61k9559LLxgAmeOHlp3SWZmZmaWkRvm\nEmu3vUHLomcAWHLRRMaOGFRzRWZmZmaWmxvmLjy+uY3vLV7DkIG9+W3Lpxk1pH/dJZmZmZlZDdww\nd+KeVdu4cukGxgwbyO0XnspRA/vUXZKZmZmZ1cQNcwcRwS8ff4Eblm3h9OOHcPP5pzCgj58iMzMz\ns57M3WDy3v5gwYMbuWPFy8wadwzXnnsyvZub6i7LzMzMzGrmhhnY++57zFmylkc27uQ7ZxzL5Z//\nBE3+QBIzMzMzww0zAL2amji0VxPzZ5xIy6RRdZdjZmZmZg3EDTPQ1CRu+tpYf3KfmZmZmX2AJ+km\nbpbNzMzMrDNumM3MzMzMSrhhNjMzMzMr4YbZzMzMzKyEG2YzMzMzsxJumM3MzMzMSrhhNjMzMzMr\n4YbZzMzMzKyEG2YzMzMzsxJumM3MzMzMSrhhNjMzMzMr4YbZzMzMzKyEG2YzMzMzsxKKiLpreB9J\nrwMv1xQ/BNhdU7bzne985zvf+c7vmfmNUENPzf94RBzV3Q81XMNcJ0mrImKC853vfOc73/nOd35P\nqqGn53fHUzLMzMzMzOli2mEAAAbwSURBVEq4YTYzMzMzK+GG+f1+5XznO9/5zne+851fg7pr6On5\npTyH2czMzMyshI8wm5mZmZmVcMMMSLpNUpuk1hqyR0h6XNImSRslzamhhkMlPS1pXarhqhpqOETS\ns5Iezp2d8rdK2iBpraRVNeQPknSvpM3ptfCZjNmj03q3396UdGmu/FTD3PTaa5W0RNKhmfPnpOyN\nOda9szFH0hGSlkt6Pt0Pzpw/O63/fkmVXqneRf716fW/XtL9kgZlzr86Za+VtEzS0TnzO3zvMkkh\naUjOfEkLJL3aYRyYnjM/ff0Hkp5Lr8PrcuZLurvDum+VtDZz/lhJK9q3QZJOzZz/SUl/TdvBhyQd\nVmF+p31PzjHwoEREj78Bk4HxQGsN2cOA8Wl5ILAFODFzDQIGpOVewEpgYuYafgTcBTxc02tgKzCk\njuyU/xvg22m5NzCopjoOAXZSvC9lrsxjgJeAvunxPcCFGfNPAlqBfkAz8Cfg+IozPzDmANcBV6Tl\nK4BrM+ePAUYDTwATalj/s4HmtHxtDet/WIflHwK35MxPXx8BPErxWQSVjUddrP8C4LIq/+7d5J+Z\n/vf6pMdDcz//Hb5/IzA/8/ovA6al5enAE5nznwHOSMstwNUV5nfa9+QcAw/m5iPMQEQ8Bfyjpuwd\nEbEmLe8BNlE0EDlriIj4V3rYK92yTW6XNBz4ArAwV2YjSXvyk4FbASJiX0S8UVM5U4EXIyL3hwc1\nA30lNVM0rq9lzB4DrIiItyPiXeBJYFaVgV2MOV+k2HEi3X8pZ35EbIqI56rK/BD5y9LzD7ACGJ45\n/80OD/tT4RhYss25CfhJldnd5GfRRf7FwDURsTf9TFvmfAAkCfgqsCRzfgDtR3UPp8IxsIv80cBT\naXk5cG6F+V31PdnGwIPhhrmBSBoJjKM4wps7+5B0CqoNWB4ROWv4BcVGYn/GzAMFsEzSakkXZc4+\nFngduD1NS1koqX/mGtp9nQo3FJ2JiFeBG4BXgB3APyNiWcYSWoHJko6U1I/i6M6IjPntPhIRO6DY\noABDa6ihUbQAf8wdKulnkrYB5wHzM2fPBF6NiHU5cw9wSZqWclsNp8NPAE6XtFLSk5I+lTm/3enA\nroh4PnPupcD16fV3A3Bl5vxWYGZank2mMfCAvqehx0A3zA1C0gDgPuDSA450ZBER70XEWIqjOqdK\nOilHrqQZQFtErM6RV+K0iBgPTAO+L2lyxuxmitNjN0fEOOAtitNRWUnqTTFg/j5z7mCKIwujgKOB\n/pLOz5UfEZsopgAsBx4B1gHvlv6SVUbSPIrnf3Hu7IiYFxEjUvYluXLTjto8MjfpB7gZOA4YS7Hj\nemPm/GZgMDAR+DFwTzram9s3yHzQILkYmJtef3NJZxwzaqHY9q2mmCaxr+rAuvue/5Ub5gYgqRfF\ni2ZxRCyts5Y0FeAJ4JxMkacBMyVtBX4HTJF0Z6bs/4qI19J9G3A/UNkFF53YDmzvcFT/XooGOrdp\nwJqI2JU59yzgpYh4PSLeAZYCn81ZQETcGhHjI2IyxanK3EeXAHZJGgaQ7is7Jd2oJF0AzADOizSR\nsSZ3UeEp6U4cR7HDuC6NhcOBNZI+mquAiNiVDpzsB35N3jEQinFwaZoi+DTFGcfKLnzsTJoS9mXg\n7py5yQUUYx8UBy2yPv8RsTkizo6IUyh2GF6sMq+Lvqehx0A3zDVLe9C3Apsi4uc11XBU+xXpkvpS\nNDCbc2RHxJURMTwiRlJMB/hzRGQ7ugggqb+kge3LFBcfZXvHlIjYCWyTNDp9aSrwt1z5HdR1ZOUV\nYKKkfun/YSrFnLZsJA1N9x+j2GDW8Tw8SLHRJN3/oYYaaiPpHOByYGZEvF1D/vEdHs4k0xgIEBEb\nImJoRIxMY+F2iouiduaqob1RSWaRcQxMHgCmpFpOoLj4eXfmGs4CNkfE9sy5UMxZPiMtTyHzTnuH\nMbAJ+ClwS4VZXfU9jT0G1n3VYSPcKDaOO4B3KAaqb2XMnkQxf3Y9sDbdpmde/5OBZ1MNrVR4dXA3\ndXyOGt4lg2IO8bp02wjMq6GGscCq9Dd4ABicOb8f8Hfg8Jr+9ldRNCitwB2kK+Uz5v+FYidlHTA1\nQ94HxhzgSOAxig3lY8ARmfNnpeW9wC7g0cz5LwDbOoyDVb5LRWf596XX33rgIeCYnPkHfH8r1b5L\nRmfrfwewIa3/g8CwzPm9gTvT32ANMCX38w8sAr5bVW436z8JWJ3GoJXAKZnz51C8W8UW4BrSB9tV\nlN9p35NzDDyYmz/pz8zMzMyshKdkmJmZmZmVcMNsZmZmZlbCDbOZmZmZWQk3zGZmZmZmJdwwm5mZ\nmZmVcMNsZmZmZlbCDbOZmZmZWQk3zGZmZmZmJf4DYGTtPh/bIn8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11663a6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracies for different values of n are: [0.73355263 0.74342105 0.78947368 0.79276316 0.79605263 0.80592105\n",
      " 0.78947368 0.8125     0.80592105 0.82565789 0.8125     0.81907895\n",
      " 0.80921053 0.82894737 0.82894737 0.82894737 0.82565789 0.83223684\n",
      " 0.83552632 0.83223684] with the max value as  0.8355263157894737  with index:  18\n"
     ]
    }
   ],
   "source": [
    "a_index=list(range(1,21))\n",
    "a=pd.Series()\n",
    "x=list(range(1, 21))\n",
    "for i in list(range(1,21)):\n",
    "    model=KNeighborsClassifier(n_neighbors=i) \n",
    "    model.fit(train_X,train_Y)\n",
    "    prediction=model.predict(test_X)\n",
    "    a=a.append(pd.Series(metrics.accuracy_score(prediction,test_Y)))\n",
    "plt.plot(a_index, a)\n",
    "plt.xticks(x)\n",
    "fig=plt.gcf()\n",
    "fig.set_size_inches(12,6)\n",
    "plt.show()\n",
    "print('Accuracies for different values of n are:',a.values,'with the max value as ',(a.values.max()), ' with index: ', a.values.argmax(axis=0))\n",
    "\n",
    "model_KNN = KNeighborsClassifier(n_neighbors=65)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy for RF base is  0.8256578947368421\n",
      "Test Accuracy for RF best is  0.819078947368421\n",
      "Train Accuracy for RF base is  1.0\n",
      "Train Accuracy for RF best is  0.8701298701298701\n"
     ]
    }
   ],
   "source": [
    "train_y = train_Y\n",
    "test_y = test_Y\n",
    "rfc_best = RandomForestClassifier(n_estimators= 400,\n",
    "               max_features= 'sqrt',\n",
    "               max_depth= 30,\n",
    "               min_samples_split= 2,\n",
    "               min_samples_leaf= 4,\n",
    "               bootstrap= True)\n",
    "rfc_base = RandomForestClassifier(n_estimators= 200, random_state = 42)\n",
    "\n",
    "\n",
    "\n",
    "rfc_base.fit(train_X, train_y)\n",
    "rfc_best.fit(train_X, train_y)\n",
    "prediction_base=rfc_base.predict(test_X)\n",
    "prediction_best=rfc_best.predict(test_X)\n",
    "\n",
    "\n",
    "print('Test Accuracy for RF base is ',metrics.accuracy_score(prediction_base,test_y))\n",
    "print('Test Accuracy for RF best is ',metrics.accuracy_score(prediction_best,test_y))\n",
    "\n",
    "\n",
    "prediction_base_train=rfc_base.predict(train_X)\n",
    "prediction_best_train=rfc_best.predict(train_X)\n",
    "print('Train Accuracy for RF base is ',metrics.accuracy_score(train_y,prediction_base_train))\n",
    "print('Train Accuracy for RF best is ',metrics.accuracy_score(train_y,prediction_best_train))\n",
    "\n",
    "\n",
    "model_RandomForest = rfc_best\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CV Mean</th>\n",
       "      <th>Std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Linear Svm</th>\n",
       "      <td>0.803261</td>\n",
       "      <td>0.030532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Radial Svm</th>\n",
       "      <td>0.804348</td>\n",
       "      <td>0.033325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Logistic Regression</th>\n",
       "      <td>0.796739</td>\n",
       "      <td>0.042112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNN</th>\n",
       "      <td>0.789130</td>\n",
       "      <td>0.036441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.779348</td>\n",
       "      <td>0.042669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.794565</td>\n",
       "      <td>0.039326</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      CV Mean       Std\n",
       "Linear Svm           0.803261  0.030532\n",
       "Radial Svm           0.804348  0.033325\n",
       "Logistic Regression  0.796739  0.042112\n",
       "KNN                  0.789130  0.036441\n",
       "Decision Tree        0.779348  0.042669\n",
       "Random Forest        0.794565  0.039326"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from sklearn.model_selection import KFold #for K-fold cross validation\n",
    "from sklearn.model_selection import cross_val_score #score evaluation\n",
    "from sklearn.model_selection import cross_val_predict #prediction\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "kfold = KFold(n_splits=10, random_state=22) # k=10, split the data into 10 equal parts\n",
    "xyz=[]\n",
    "accuracy=[]\n",
    "std=[]\n",
    "classifiers=['Linear Svm','Radial Svm','Logistic Regression','KNN','Decision Tree','Random Forest']\n",
    "models=[model_Linear_SVM, model_RBF_SVM, model_Logistic_Regression, model_KNN, model_Decision_Tree,  model_RandomForest ]\n",
    "for i in models:\n",
    "    model = i\n",
    "    cv_result = cross_val_score(model,X,Y, cv = kfold, scoring = \"accuracy\")\n",
    "    cv_result=cv_result\n",
    "    xyz.append(cv_result.mean())\n",
    "    std.append(cv_result.std())\n",
    "    accuracy.append(cv_result)\n",
    "new_models_dataframe2=pd.DataFrame({'CV Mean':xyz,'Std':std},index=classifiers)       \n",
    "new_models_dataframe2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0,\n",
      "  decision_function_shape='ovr', degree=3, gamma=0.1, kernel='linear',\n",
      "  max_iter=-1, probability=True, random_state=None, shrinking=True,\n",
      "  tol=0.001, verbose=False)\n",
      "Model: SVC(C=10000.0, cache_size=200, class_weight=None, coef0=0.0,\n",
      "  decision_function_shape='ovr', degree=3, gamma=0.0001, kernel='rbf',\n",
      "  max_iter=-1, probability=True, random_state=None, shrinking=True,\n",
      "  tol=0.001, verbose=False)\n",
      "Model: LogisticRegression(C=0.01, class_weight=None, dual=False, fit_intercept=True,\n",
      "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
      "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
      "          verbose=0, warm_start=False)\n",
      "Model: KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
      "           metric_params=None, n_jobs=1, n_neighbors=65, p=2,\n",
      "           weights='uniform')\n",
      "Model: DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
      "            max_features=None, max_leaf_nodes=10,\n",
      "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "            min_samples_leaf=1, min_samples_split=2,\n",
      "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
      "            splitter='random')\n",
      "Model: RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
      "            max_depth=30, max_features='sqrt', max_leaf_nodes=None,\n",
      "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "            min_samples_leaf=4, min_samples_split=2,\n",
      "            min_weight_fraction_leaf=0.0, n_estimators=400, n_jobs=1,\n",
      "            oob_score=False, random_state=None, verbose=0,\n",
      "            warm_start=False)\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Test_accuracy</th>\n",
       "      <th>Test_accuracy_std</th>\n",
       "      <th>Train_accuracy</th>\n",
       "      <th>Train_accuracy_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Linear Svm</th>\n",
       "      <td>0.803261</td>\n",
       "      <td>0.030532</td>\n",
       "      <td>0.820773</td>\n",
       "      <td>0.003976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Radial Svm</th>\n",
       "      <td>0.804348</td>\n",
       "      <td>0.033325</td>\n",
       "      <td>0.825845</td>\n",
       "      <td>0.004855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Logistic Regression</th>\n",
       "      <td>0.796739</td>\n",
       "      <td>0.042112</td>\n",
       "      <td>0.808937</td>\n",
       "      <td>0.002796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNN</th>\n",
       "      <td>0.789130</td>\n",
       "      <td>0.036441</td>\n",
       "      <td>0.808092</td>\n",
       "      <td>0.005108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.779348</td>\n",
       "      <td>0.042669</td>\n",
       "      <td>0.801329</td>\n",
       "      <td>0.007528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.793478</td>\n",
       "      <td>0.044286</td>\n",
       "      <td>0.869928</td>\n",
       "      <td>0.002649</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Test_accuracy  Test_accuracy_std  Train_accuracy  \\\n",
       "Linear Svm                0.803261           0.030532        0.820773   \n",
       "Radial Svm                0.804348           0.033325        0.825845   \n",
       "Logistic Regression       0.796739           0.042112        0.808937   \n",
       "KNN                       0.789130           0.036441        0.808092   \n",
       "Decision Tree             0.779348           0.042669        0.801329   \n",
       "Random Forest             0.793478           0.044286        0.869928   \n",
       "\n",
       "                     Train_accuracy_std  \n",
       "Linear Svm                     0.003976  \n",
       "Radial Svm                     0.004855  \n",
       "Logistic Regression            0.002796  \n",
       "KNN                            0.005108  \n",
       "Decision Tree                  0.007528  \n",
       "Random Forest                  0.002649  "
      ]
     },
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Test_F_score</th>\n",
       "      <th>Test_F_score_std</th>\n",
       "      <th>Train_F_score</th>\n",
       "      <th>Train_F_score_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Linear Svm</th>\n",
       "      <td>0.813616</td>\n",
       "      <td>0.050835</td>\n",
       "      <td>0.841219</td>\n",
       "      <td>0.007405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Radial Svm</th>\n",
       "      <td>0.811720</td>\n",
       "      <td>0.058314</td>\n",
       "      <td>0.845719</td>\n",
       "      <td>0.006887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Logistic Regression</th>\n",
       "      <td>0.807333</td>\n",
       "      <td>0.056602</td>\n",
       "      <td>0.830208</td>\n",
       "      <td>0.008832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNN</th>\n",
       "      <td>0.795344</td>\n",
       "      <td>0.052439</td>\n",
       "      <td>0.827082</td>\n",
       "      <td>0.013193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.804859</td>\n",
       "      <td>0.044853</td>\n",
       "      <td>0.831577</td>\n",
       "      <td>0.006347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.809170</td>\n",
       "      <td>0.051769</td>\n",
       "      <td>0.886182</td>\n",
       "      <td>0.005841</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Test_F_score  Test_F_score_std  Train_F_score  \\\n",
       "Linear Svm               0.813616          0.050835       0.841219   \n",
       "Radial Svm               0.811720          0.058314       0.845719   \n",
       "Logistic Regression      0.807333          0.056602       0.830208   \n",
       "KNN                      0.795344          0.052439       0.827082   \n",
       "Decision Tree            0.804859          0.044853       0.831577   \n",
       "Random Forest            0.809170          0.051769       0.886182   \n",
       "\n",
       "                     Train_F_score_std  \n",
       "Linear Svm                    0.007405  \n",
       "Radial Svm                    0.006887  \n",
       "Logistic Regression           0.008832  \n",
       "KNN                           0.013193  \n",
       "Decision Tree                 0.006347  \n",
       "Random Forest                 0.005841  "
      ]
     },
     "metadata": {},
     "output_type": "execute_result"
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    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Test_precision</th>\n",
       "      <th>Test_precision_std</th>\n",
       "      <th>Train_precision</th>\n",
       "      <th>Train_precision_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Linear Svm</th>\n",
       "      <td>0.796182</td>\n",
       "      <td>0.084611</td>\n",
       "      <td>0.823808</td>\n",
       "      <td>0.004158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Radial Svm</th>\n",
       "      <td>0.800844</td>\n",
       "      <td>0.077107</td>\n",
       "      <td>0.828415</td>\n",
       "      <td>0.006681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Logistic Regression</th>\n",
       "      <td>0.795216</td>\n",
       "      <td>0.092333</td>\n",
       "      <td>0.815254</td>\n",
       "      <td>0.004045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNN</th>\n",
       "      <td>0.806924</td>\n",
       "      <td>0.093799</td>\n",
       "      <td>0.822245</td>\n",
       "      <td>0.005789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.751582</td>\n",
       "      <td>0.078595</td>\n",
       "      <td>0.782886</td>\n",
       "      <td>0.012406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.785313</td>\n",
       "      <td>0.091389</td>\n",
       "      <td>0.857525</td>\n",
       "      <td>0.002842</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Test_precision  Test_precision_std  Train_precision  \\\n",
       "Linear Svm                 0.796182            0.084611         0.823808   \n",
       "Radial Svm                 0.800844            0.077107         0.828415   \n",
       "Logistic Regression        0.795216            0.092333         0.815254   \n",
       "KNN                        0.806924            0.093799         0.822245   \n",
       "Decision Tree              0.751582            0.078595         0.782886   \n",
       "Random Forest              0.785313            0.091389         0.857525   \n",
       "\n",
       "                     Train_precision_std  \n",
       "Linear Svm                      0.004158  \n",
       "Radial Svm                      0.006681  \n",
       "Logistic Regression             0.004045  \n",
       "KNN                             0.005789  \n",
       "Decision Tree                   0.012406  \n",
       "Random Forest                   0.002842  "
      ]
     },
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Test_Sensitivity</th>\n",
       "      <th>Test_Sensitivity_std</th>\n",
       "      <th>Train_Sensitivity</th>\n",
       "      <th>Train_Sensitivity_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Linear Svm</th>\n",
       "      <td>0.838106</td>\n",
       "      <td>0.051721</td>\n",
       "      <td>0.859452</td>\n",
       "      <td>0.012554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Radial Svm</th>\n",
       "      <td>0.829112</td>\n",
       "      <td>0.077422</td>\n",
       "      <td>0.863884</td>\n",
       "      <td>0.012457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Logistic Regression</th>\n",
       "      <td>0.828279</td>\n",
       "      <td>0.067296</td>\n",
       "      <td>0.846045</td>\n",
       "      <td>0.020396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNN</th>\n",
       "      <td>0.793581</td>\n",
       "      <td>0.068982</td>\n",
       "      <td>0.832494</td>\n",
       "      <td>0.027203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.873465</td>\n",
       "      <td>0.042217</td>\n",
       "      <td>0.887156</td>\n",
       "      <td>0.015859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.843319</td>\n",
       "      <td>0.059444</td>\n",
       "      <td>0.916888</td>\n",
       "      <td>0.011285</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Test_Sensitivity  Test_Sensitivity_std  \\\n",
       "Linear Svm                   0.838106              0.051721   \n",
       "Radial Svm                   0.829112              0.077422   \n",
       "Logistic Regression          0.828279              0.067296   \n",
       "KNN                          0.793581              0.068982   \n",
       "Decision Tree                0.873465              0.042217   \n",
       "Random Forest                0.843319              0.059444   \n",
       "\n",
       "                     Train_Sensitivity  Train_Sensitivity_std  \n",
       "Linear Svm                    0.859452               0.012554  \n",
       "Radial Svm                    0.863884               0.012457  \n",
       "Logistic Regression           0.846045               0.020396  \n",
       "KNN                           0.832494               0.027203  \n",
       "Decision Tree                 0.887156               0.015859  \n",
       "Random Forest                 0.916888               0.011285  "
      ]
     },
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fit_time</th>\n",
       "      <th>Scoring Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Linear Svm</th>\n",
       "      <td>0.087484</td>\n",
       "      <td>0.007365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Radial Svm</th>\n",
       "      <td>0.213441</td>\n",
       "      <td>0.010602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Logistic Regression</th>\n",
       "      <td>0.002006</td>\n",
       "      <td>0.001990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNN</th>\n",
       "      <td>0.001934</td>\n",
       "      <td>0.022983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.001879</td>\n",
       "      <td>0.002275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.538151</td>\n",
       "      <td>0.121311</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Fit_time  Scoring Time\n",
       "Linear Svm           0.087484      0.007365\n",
       "Radial Svm           0.213441      0.010602\n",
       "Logistic Regression  0.002006      0.001990\n",
       "KNN                  0.001934      0.022983\n",
       "Decision Tree        0.001879      0.002275\n",
       "Random Forest        0.538151      0.121311"
      ]
     },
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import KFold #for K-fold cross validation\n",
    "from sklearn.model_selection import cross_val_score, cross_validate #score evaluation\n",
    "from sklearn.model_selection import cross_val_predict #prediction\n",
    "from sklearn.metrics import recall_score, f1_score, precision_score, accuracy_score\n",
    "from sklearn.metrics.scorer import make_scorer\n",
    "\n",
    "\n",
    "scoring = {'precision': make_scorer(precision_score),\n",
    "           'recall': make_scorer(recall_score),\n",
    "           'F-score': make_scorer( f1_score),\n",
    "           'accuracy': make_scorer(accuracy_score)}\n",
    "\n",
    "kfold = KFold(n_splits=10, random_state=22) # k=10, split the data into 10 equal parts\n",
    "xyz=[]\n",
    "accuracy=[]\n",
    "std=[]\n",
    "classifiers=['Linear Svm','Radial Svm','Logistic Regression','KNN','Decision Tree','Random Forest']\n",
    "models=[model_Linear_SVM, model_RBF_SVM, model_Logistic_Regression, model_KNN, model_Decision_Tree,  model_RandomForest ]\n",
    "\n",
    "fit_time = []\n",
    "score_time = []\n",
    "\n",
    "test_accuracy = []\n",
    "test_accuracy_std = []\n",
    "train_accuracy = []\n",
    "train_accuracy_std = []\n",
    "\n",
    "\n",
    "test_F_score = []\n",
    "test_F_score_std = []\n",
    "train_F_score = []\n",
    "train_F_score_std = []\n",
    "\n",
    "\n",
    "test_precision = []\n",
    "test_precision_std = []\n",
    "train_precision = []\n",
    "train_precision_std = []\n",
    "\n",
    "\n",
    "test_recall = []\n",
    "test_recall_std = []\n",
    "train_recall = []\n",
    "train_recall_std = []\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "for i in models:\n",
    "    model = i\n",
    "    scores = cross_validate(model,X,Y, cv = kfold, scoring = scoring, return_train_score=True)\n",
    "    fit_time.append(scores['fit_time'].mean())\n",
    "    score_time.append(scores['score_time'].mean())\n",
    "    \n",
    "    test_accuracy.append(scores['test_accuracy'].mean())\n",
    "    test_accuracy_std.append(scores['test_accuracy'].std())\n",
    "    train_accuracy.append(scores['train_accuracy'].mean())\n",
    "    train_accuracy_std.append(scores['train_accuracy'].std())\n",
    "    \n",
    "    test_F_score.append(scores['test_F-score'].mean())\n",
    "    test_F_score_std.append(scores['test_F-score'].std())\n",
    "    train_F_score.append(scores['train_F-score'].mean())\n",
    "    train_F_score_std.append(scores['train_F-score'].std())\n",
    "    \n",
    "    test_precision.append(scores['test_precision'].mean())\n",
    "    test_precision_std.append(scores['test_precision'].std())\n",
    "    train_precision.append(scores['train_precision'].mean())\n",
    "    train_precision_std.append(scores['train_precision'].std())\n",
    "    \n",
    "    test_recall.append(scores['test_recall'].mean())\n",
    "    test_recall_std.append(scores['test_recall'].std())\n",
    "    train_recall.append(scores['train_recall'].mean())\n",
    "    train_recall_std.append(scores['train_recall'].std())\n",
    "    \n",
    "\n",
    "    print('Model: ' + str(i))\n",
    "\n",
    "sorted(scores.keys())     \n",
    "dct_acc = {'Test_accuracy':test_accuracy,\n",
    "               'Test_accuracy_std':test_accuracy_std,\n",
    "               'Train_accuracy':train_accuracy,\n",
    "               'Train_accuracy_std':train_accuracy_std, }\n",
    "dct_Fscore = {'Test_F_score':test_F_score,\n",
    "               'Test_F_score_std':test_F_score_std,\n",
    "               'Train_F_score': train_F_score,\n",
    "               'Train_F_score_std': train_F_score_std}\n",
    "dct_precision = {'Test_precision': test_precision,\n",
    "               'Test_precision_std': test_precision_std,\n",
    "               'Train_precision': train_precision,\n",
    "               'Train_precision_std': train_precision_std}\n",
    "dct_recall = {'Test_Sensitivity': test_recall, \n",
    "               'Test_Sensitivity_std': test_recall_std,\n",
    "               'Train_Sensitivity': train_recall,\n",
    "               'Train_Sensitivity_std': train_recall_std}\n",
    "dct_time = {'Fit_time': fit_time, 'Scoring Time':score_time}\n",
    "\n",
    "metrics_acc=pd.DataFrame(dct_acc,index=classifiers)       \n",
    "metrics_Fsc = pd.DataFrame(dct_Fscore,index=classifiers)\n",
    "metrics_precision = pd.DataFrame(dct_precision,index=classifiers) \n",
    "metrics_recall = pd.DataFrame(dct_recall,index=classifiers)\n",
    "metrics_time = pd.DataFrame(dct_time,index=classifiers)\n",
    "           \n",
    "display(metrics_acc)\n",
    "display(metrics_Fsc)\n",
    "display(metrics_precision)\n",
    "display(metrics_recall)\n",
    "display(metrics_time)\n",
    "#new_models_dataframe2=pd.DataFrame({'CV Mean':xyz,'Std':std},index=classifiers)       \n",
    "#new_models_dataframe2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1130f0d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13bb39240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(12,6))\n",
    "box=pd.DataFrame(accuracy,index=[classifiers])\n",
    "box.T.boxplot()\n",
    "plt.show()\n",
    "new_models_dataframe2['CV Mean'].plot.barh(width=0.8)\n",
    "plt.title('Average CV Mean Accuracy')\n",
    "fig=plt.gcf()\n",
    "fig.set_size_inches(8,5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Matrix for Decision Tree')"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1523c2400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "models=[model_Linear_SVM, model_RBF_SVM, model_Logistic_Regression, model_KNN, model_Decision_Tree,  model_RandomForest ]\n",
    "f,ax=plt.subplots(2,3,figsize=(12,6))\n",
    "y_pred = cross_val_predict(model_RBF_SVM,X,Y,cv=10)\n",
    "sns.heatmap(confusion_matrix(Y,y_pred),ax=ax[0,0],annot=True,fmt='2.0f')\n",
    "ax[0,0].set_title('Matrix for rbf-SVM')\n",
    "y_pred = cross_val_predict(model_Linear_SVM,X,Y,cv=10)\n",
    "sns.heatmap(confusion_matrix(Y,y_pred),ax=ax[0,1],annot=True,fmt='2.0f')\n",
    "ax[0,1].set_title('Matrix for Linear-SVM')\n",
    "y_pred = cross_val_predict(model_KNN,X,Y,cv=10)\n",
    "sns.heatmap(confusion_matrix(Y,y_pred),ax=ax[0,2],annot=True,fmt='2.0f')\n",
    "ax[0,2].set_title('Matrix for KNN')\n",
    "y_pred = cross_val_predict(model_RandomForest,X,Y,cv=10)\n",
    "sns.heatmap(confusion_matrix(Y,y_pred),ax=ax[1,0],annot=True,fmt='2.0f')\n",
    "ax[1,0].set_title('Matrix for Random-Forests')\n",
    "y_pred = cross_val_predict(model_Logistic_Regression,X,Y,cv=10)\n",
    "sns.heatmap(confusion_matrix(Y,y_pred),ax=ax[1,1],annot=True,fmt='2.0f')\n",
    "ax[1,1].set_title('Matrix for Logistic Regression')\n",
    "y_pred = cross_val_predict(model_Decision_Tree,X,Y,cv=10)\n",
    "sns.heatmap(confusion_matrix(Y,y_pred),ax=ax[1,2],annot=True,fmt='2.0f')\n",
    "ax[1,2].set_title('Matrix for Decision Tree')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f89e898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy import interp\n",
    "import matplotlib.pyplot as plt\n",
    "from itertools import cycle\n",
    "\n",
    "from sklearn import svm, datasets\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "#'aqua', 'darkorange', 'cornflowerblue'\n",
    "models = [\n",
    "    {\n",
    "        'label' : 'rbf-SVM',\n",
    "        'model': model_RBF_SVM,\n",
    "        'color': 'maroon',\n",
    "        'linestyle': 'solid', \n",
    "        'markers': 'H',\n",
    "         \n",
    "    },\n",
    "    {\n",
    "        'label' : 'Linear-SVM',\n",
    "        'model': model_Linear_SVM,\n",
    "        'color': 'navy',\n",
    "        'linestyle': 'dashed',\n",
    "        'markers': ',',\n",
    "    },\n",
    "    {\n",
    "        'label' : 'KNN',\n",
    "        'model': model_KNN, \n",
    "        'color': 'olive',\n",
    "        'linestyle': 'dashdot',\n",
    "        'markers': '',\n",
    "    },    \n",
    "    {\n",
    "        'label' : 'RandomForest',\n",
    "        'model': model_RandomForest,\n",
    "        'color': 'darkcyan',\n",
    "        'linestyle': 'dotted',\n",
    "        'markers': '>',\n",
    "       \n",
    "    },        \n",
    "    {\n",
    "        'label' : 'LogisticRegression',\n",
    "        'model': model_Logistic_Regression,\n",
    "        'color': 'fuchsia',\n",
    "        'linestyle': '-',\n",
    "        'markers': ''\n",
    " \n",
    "    },\n",
    "    {\n",
    "        'label' : 'Decision Tree',\n",
    "        'model': model_Decision_Tree, \n",
    "        'color': 'orange',\n",
    "        'linestyle': '-.',\n",
    "        'markers': ''\n",
    "    },\n",
    "\n",
    "]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# #############################################################################\n",
    "# Classification and ROC analysis\n",
    "random_state = np.random.RandomState(0)\n",
    "# Run classifier with cross-validation and plot ROC curves\n",
    "cv = StratifiedKFold(n_splits=10)\n",
    "classifier = svm.SVC(kernel='linear', probability=True,\n",
    "                     random_state=random_state)\n",
    "\n",
    "tprs = []\n",
    "aucs = []\n",
    "mean_fpr = np.linspace(0, 1, 100)\n",
    "y = Y\n",
    "\n",
    "\n",
    "\n",
    "   \n",
    "\n",
    "\n",
    "plt.figure(figsize=(12,12))\n",
    "for m in models:\n",
    "    i = 0\n",
    "    for train, test in cv.split(X, y):\n",
    "        probas_ = m['model'].fit(train_X, train_Y).predict_proba(test_X)\n",
    "        # Compute ROC curve and area the curve\n",
    "        fpr, tpr, thresholds = roc_curve(test_Y, probas_[:, 1])\n",
    "        tprs.append(interp(mean_fpr, fpr, tpr))\n",
    "        tprs[-1][0] = 0.0\n",
    "        roc_auc = auc(fpr, tpr)\n",
    "        aucs.append(roc_auc)\n",
    "        #plt.plot(fpr, tpr, lw=1, alpha=0.3,\n",
    "        #         label='ROC fold %d (AUC = %0.2f)' % (i, roc_auc))\n",
    "\n",
    "        i += 1\n",
    "    #plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',\n",
    "    #         label='Luck', alpha=.8)\n",
    "\n",
    "    mean_tpr = np.mean(tprs, axis=0)\n",
    "    mean_tpr[-1] = 1.0\n",
    "    mean_auc = auc(mean_fpr, mean_tpr)\n",
    "    std_auc = np.std(aucs)\n",
    "\n",
    "    plt.plot(mean_fpr, mean_tpr, color= m['color'], linestyle = m['linestyle'], marker= m['markers'],\n",
    "             label=r'[%s]  AUC = %0.5f $\\pm$ %0.4f ' % (m['label'] ,mean_auc, std_auc),\n",
    "             lw=2, alpha=.8)\n",
    "\n",
    "    std_tpr = np.std(tprs, axis=0)\n",
    "    tprs_upper = np.minimum(mean_tpr + std_tpr, 1)\n",
    "    tprs_lower = np.maximum(mean_tpr - std_tpr, 0)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    #plt.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2,\n",
    "                    # label=r'$\\pm$ 1 std. dev.')\n",
    "\n",
    "    plt.xlim([-0.05, 1.05])\n",
    "    plt.ylim([-0.05, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver Operating Characteristic (ROC)')\n",
    "    plt.legend(loc=\"lower right\")\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Sanchezj/scipyenv/scipy2/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy for ensembled model is: 0.8289473684210527\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Sanchezj/scipyenv/scipy2/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Sanchezj/scipyenv/scipy2/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Sanchezj/scipyenv/scipy2/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Sanchezj/scipyenv/scipy2/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Sanchezj/scipyenv/scipy2/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Sanchezj/scipyenv/scipy2/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Sanchezj/scipyenv/scipy2/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Sanchezj/scipyenv/scipy2/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Sanchezj/scipyenv/scipy2/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The cross validated score is 0.7879890212639161\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Sanchezj/scipyenv/scipy2/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import VotingClassifier\n",
    "\n",
    "models=[model_Linear_SVM, model_RBF_SVM, model_Logistic_Regression, model_KNN, model_Decision_Tree, model_GaussianNB,  model_RandomForest ]\n",
    "\n",
    "\n",
    "ensemble_lin_rbf=VotingClassifier(estimators=[('KNN',model_KNN),\n",
    "                                              ('RBF',model_RBF_SVM),\n",
    "                                              ('RFor',model_RandomForest),\n",
    "                                              ('LR',model_Logistic_Regression),\n",
    "                                              ('DT',model_Decision_Tree),\n",
    "                                              ('svm',model_Linear_SVM)\n",
    "                                             ], \n",
    "                       voting='soft').fit(train_X,train_Y)\n",
    "print('The accuracy for ensembled model is:',ensemble_lin_rbf.score(test_X,test_Y))\n",
    "cross=cross_val_score(ensemble_lin_rbf,X,Y, cv = 10,scoring = \"accuracy\")\n",
    "print('The cross validated score is',cross.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy for bagged KNN is: 0.8519736842105263\n",
      "The cross validated score for bagged KNN is: 0.792349562545916\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import BaggingClassifier\n",
    "model=BaggingClassifier(base_estimator=model_KNN,random_state=0,n_estimators=700)\n",
    "model.fit(train_X,train_Y)\n",
    "prediction=model.predict(test_X)\n",
    "print('The accuracy for bagged KNN is:',metrics.accuracy_score(prediction,test_Y))\n",
    "result=cross_val_score(model,X,Y,cv=10,scoring='accuracy')\n",
    "print('The cross validated score for bagged KNN is:',result.mean())\n",
    "\n",
    "bagged_KNN = model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy for bagged_Decision_Tree is: 0.8289473684210527\n",
      "The cross validated score for bagged_Decision_Tree is: 0.7835941874861931\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import BaggingClassifier\n",
    "bagged_Decision_Tree= BaggingClassifier(base_estimator=model_Decision_Tree,random_state=0,n_estimators=700)\n",
    "model.fit(train_X,train_Y)\n",
    "prediction=model.predict(test_X)\n",
    "print('The accuracy for bagged_Decision_Tree is:',metrics.accuracy_score(prediction,test_Y))\n",
    "result=cross_val_score(model,X,Y,cv=10,scoring='accuracy')\n",
    "print('The cross validated score for bagged_Decision_Tree is:',result.mean())\n",
    "\n",
    "bagged_Decision_Tree = model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy for bagged Logistic Regression is: 0.8421052631578947\n",
      "The cross validated score for bagged Logistic Regression is: 0.7955748552522747\n"
     ]
    }
   ],
   "source": [
    "model=BaggingClassifier(base_estimator=model_Logistic_Regression,random_state=0,n_estimators=100)\n",
    "model.fit(train_X,train_Y)\n",
    "prediction=model.predict(test_X)\n",
    "print('The accuracy for bagged Logistic Regression is:',metrics.accuracy_score(prediction,test_Y))\n",
    "result=cross_val_score(model,X,Y,cv=10,scoring='accuracy')\n",
    "print('The cross validated score for bagged Logistic Regression is:',result.mean())\n",
    "\n",
    "bagged_Logistic_Regression = model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy for bagged RBF_SVM is: 0.8322368421052632\n",
      "The cross validated score for bagged RBF_SVM is: 0.7978196651408431\n"
     ]
    }
   ],
   "source": [
    "model=BaggingClassifier(base_estimator=model_RBF_SVM,random_state=0,n_estimators=100)\n",
    "model.fit(train_X,train_Y)\n",
    "prediction=model.predict(test_X)\n",
    "print('The accuracy for bagged RBF_SVM is:',metrics.accuracy_score(prediction,test_Y))\n",
    "result=cross_val_score(model,X,Y,cv=10,scoring='accuracy')\n",
    "print('The cross validated score for bagged RBF_SVM is:',result.mean())\n",
    "\n",
    "bagged_RBF_SVM = model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy for bagged Linear_SVM is: 0.8256578947368421\n",
      "The cross validated score for bagged Linear_SVM is: 0.7988946770854205\n"
     ]
    }
   ],
   "source": [
    "model=BaggingClassifier(base_estimator=model_Linear_SVM,random_state=0,n_estimators=100)\n",
    "model.fit(train_X,train_Y)\n",
    "prediction=model.predict(test_X)\n",
    "print('The accuracy for bagged Linear_SVM is:',metrics.accuracy_score(prediction,test_Y))\n",
    "result=cross_val_score(model,X,Y,cv=10,scoring='accuracy')\n",
    "print('The cross validated score for bagged Linear_SVM is:',result.mean())\n",
    "\n",
    "bagged_Linear_SVM = model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy for bagged RandomForest is: 0.8289473684210527\n",
      "The cross validated score for bagged RandomForest is: 0.7835941874861931\n"
     ]
    }
   ],
   "source": [
    "\n",
    "model=BaggingClassifier(base_estimator=model_RandomForest,random_state=0,n_estimators=100)\n",
    "model.fit(train_X,train_Y)\n",
    "prediction=model.predict(test_X)\n",
    "print('The accuracy for bagged RandomForest is:',metrics.accuracy_score(prediction,test_Y))\n",
    "result=cross_val_score(model,X,Y,cv=10,scoring='accuracy')\n",
    "print('The cross validated score for bagged RandomForest is:',result.mean())\n",
    "bagged_RandomForest = model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Sanchezj/scipyenv/scipy2/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy for ensembled model is: 0.8388157894736842\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Sanchezj/scipyenv/scipy2/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Sanchezj/scipyenv/scipy2/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Sanchezj/scipyenv/scipy2/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Sanchezj/scipyenv/scipy2/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Sanchezj/scipyenv/scipy2/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Sanchezj/scipyenv/scipy2/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Sanchezj/scipyenv/scipy2/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Sanchezj/scipyenv/scipy2/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Sanchezj/scipyenv/scipy2/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The cross validated score is 0.7890523455039584\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Sanchezj/scipyenv/scipy2/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "ensemble_lin_rbf=VotingClassifier(estimators=[('KNN',bagged_KNN),\n",
    "                                              ('RBF',bagged_RBF_SVM),\n",
    "                                              ('RFor',bagged_RandomForest),\n",
    "                                              ('LR',bagged_Logistic_Regression),\n",
    "                                              ('DT',bagged_Decision_Tree),\n",
    "                                              ('svm',bagged_Linear_SVM)\n",
    "                                             ], \n",
    "                       voting='soft').fit(train_X,train_Y)\n",
    "print('The accuracy for ensembled model is:',ensemble_lin_rbf.score(test_X,test_Y))\n",
    "cross=cross_val_score(ensemble_lin_rbf,X,Y, cv = 10,scoring = \"accuracy\")\n",
    "print('The cross validated score is',cross.mean())\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold #for K-fold cross validation\n",
    "from sklearn.model_selection import cross_val_score #score evaluation\n",
    "from sklearn.model_selection import cross_val_predict #prediction\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "kfold = KFold(n_splits=10, random_state=22) # k=10, split the data into 10 equal parts\n",
    "xyz=[]\n",
    "accuracy=[]\n",
    "std=[]\n",
    "classifiers=['Linear Svm','Radial Svm','Logistic Regression','KNN','Decision Tree','Random Forest']\n",
    "models=[bagged_Linear_SVM, bagged_RBF_SVM, bagged_Logistic_Regression, bagged_KNN, bagged_Decision_Tree,   bagged_RandomForest ]\n",
    "for i in models:\n",
    "    model = i\n",
    "    cv_result = cross_val_score(model,X,Y, cv = kfold, scoring = \"accuracy\")\n",
    "    cv_result=cv_result\n",
    "    xyz.append(cv_result.mean())\n",
    "    std.append(cv_result.std())\n",
    "    accuracy.append(cv_result)\n",
    "    \n",
    "new_models_dataframe2=pd.DataFrame({'CV Mean':xyz,'Std':std},index=classifiers)       \n",
    "new_models_dataframe2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "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>CV Mean</th>\n",
       "      <th>Std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Linear Svm</th>\n",
       "      <td>0.805435</td>\n",
       "      <td>0.029748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Radial Svm</th>\n",
       "      <td>0.805435</td>\n",
       "      <td>0.030916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Logistic Regression</th>\n",
       "      <td>0.796739</td>\n",
       "      <td>0.041547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNN</th>\n",
       "      <td>0.794565</td>\n",
       "      <td>0.029748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.795652</td>\n",
       "      <td>0.041190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.795652</td>\n",
       "      <td>0.041190</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      CV Mean       Std\n",
       "Linear Svm           0.805435  0.029748\n",
       "Radial Svm           0.805435  0.030916\n",
       "Logistic Regression  0.796739  0.041547\n",
       "KNN                  0.794565  0.029748\n",
       "Decision Tree        0.795652  0.041190\n",
       "Random Forest        0.795652  0.041190"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifiers=['Linear Svm','Radial Svm','Logistic Regression','KNN','Decision Tree','Random Forest']\n",
    "new_models_dataframe2=pd.DataFrame({'CV Mean':xyz,'Std':std},index=classifiers)       \n",
    "new_models_dataframe2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1130f0f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f89de48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(12,6))\n",
    "box=pd.DataFrame(accuracy,index=[classifiers])\n",
    "box.T.boxplot()\n",
    "plt.show()\n",
    "new_models_dataframe2['CV Mean'].plot.barh(width=0.8)\n",
    "plt.title('Average CV Mean Accuracy')\n",
    "fig=plt.gcf()\n",
    "fig.set_size_inches(8,5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+wEt7SagfBd6x1j6d/LwKOMFaW/5pfSqhFpHD1Zzf/56P7r+feCRCJBgEwI1G8WdnE2lowPH5dqzrRqPkDBgAQP369bsta9um2unGw82X0t+7FWstjteLG4thjGFDrCffzXyS7HgN/uxs3qkexHrbjw2xnrutX+SpZ128NzdnPEU0Eud/L7mFlLIWcufWsqWxeMf6W6/sR8m21Xzl43eZVnU+J6d8xJzWEfgCATZdVIx/c/OObQZ6t3C5/3meueIKijdtom5OJkvrB+/UV9s+qpry+GHWVNxolPu+fzsxB/JiDiMqyimtX0ZKPMyMkksxrouPGL3qNnBc4we0Rj38reQbGNcl2uIjtsjS5+MyBg5azUenTsbbGsMTtUQWG0prljF46CrweJgx8CKM6+Lg0qtuIxMaZpPr1vHYoBsw1mIt9KrbSPoHQerW5LHphkF4nCh+f4TcDVWc+947pDY18dB1N+L1RfBgKaks56iKKrJbCtmQfSY9GtM4bfRfyMvfCFgM4BpnR/9gwFgMFmsMjpv4fTzv+d8Sj6fjj0cYddyvcHpvZe07N1JXOQqLoc/If9J7yL+JOn4AbNv/WHCiFtfvJPsF6xocXLa8+E22RY7CYOk94lmKS19n25JzqFp2Mq3eVAp7zGHAsY8lojSATcREPBEmGHDAsS6BSCtBfyrlC6dQtvosrGPI7zmPIUc/TNXWI1n/4bdwjYM/rZoxZ/8Er40SdfzsyDZcA9hErClO4s/W4lqHhi2j2PDON4mm+UlL3c7IKT8lEszn43/fg7EuccfLEed+D4+/BYvBGhLnEwNuMlaTOK++eIzUaJzaWA+WzLgH6zNgYdx53yfF18i8Gb8jGs0ELEOOf5Dsbivw2hgRJ/GzZixYm4jVuBbrTVw7Yy0uDouf+zVuLI2Y46X0+N+R2W0la2Z+h/rKEXjcGD1Gv0j3oa8mD9kA4HHjxI3nk1gdg8Hii0RJcV0Wfng79VXDwEDvEc9RXPoaNQtPZf3aCwHI7zGXAcdOxWB3nA+DxbrJa4TFGLCGHffT5sUXUL76dLw2RnbvxQw65k/UlY1hzYfXY40hx7+ZkvN+mTyPYE3ymkRcXJ+TiNWT6N9YF180hrt2GB99fBvGWFJTKxk55WdEgnmseeEOgv5MPDbOmPNuxeMPtd2hWOMkziM73/sYg3EtkeY8Fv77PvBYUmNhhl34E3z+ZhY//yvC4Rx+fdIrZNe8x3cnT+a0m2/+jH/7fn77m1AfspKP/dAT2NLuc1mybbeE2hhzHXAdQJ8+fb6Q4EREDqZ4NMqH999PcPt2LIYIyWTI9dJUXos3JQW/jeExiV83kbhD+bqyxMaul4ibwha3B5vcXuRTw9pTBtGrooK+8zeT59TT4KaDtdRe0g3f1lYCc5rIa6knFo1jcfnLKacQ3pZKy2wfeS27r++fG+HG+OO4UXBdS6h3KpHePpqPzsT3YSu1czJxWuJEB3hZM2AQm8b1Y+zCRTTOSSV/WDm5hQ2s7X8W8X4ZhCcF6Fe+gZ7l6wlHY2zuXUBZr3xix3joW7GZ3lvXsPr1obT0CNBSHCB4TCZDm5Zhn+vJoMqB/HjWGAaXl9C/Phd/zhaKjpmGrSvg4qcuAwzGxOj5lV9C7vF4bYzr69clzxMwHBgGxvTC1q/HWBdjobVHTyo3/hozzyUQb+J7+Q+Ca9j2nzuIG08i0Tzm91xWvwFDImHCY2BSBmZiK7Z1eWIfYTD5Fs45El8kztH/uAQcMLj0ueCHpA5dSdmfbmX8xmIMkF/qJyNQnbjWyaSKXcexks0mOcDVoymNWCwTAH8qpAZqKGz1kdqYaMt3Y6RkNJHSvo999JmemkX36kwMlnwbIzNQTZ4bJ95SABhyCj1kBGqSXZkd2+/Ub7LNSXPJNEFarUu8KRMMZBUaUjMayPGEKG7KxOLgp4mMQA3WmL3HukucxhOiIJoHUUOKbSQzUEM07qG4MRPXJF77ykirw5PSvPM53VusrktWU4Di5kzwGAwumWl1OKnNFAUzcMOJhDrX20xg11j3cE6N/WRBt2A6sWg2ANneFtIDNRS1ekhrzMQCuTb+yTndR5ykJb6sFLR6SG3KTNw7NkpmoJqQk0pxY6Itu9BD5q730z7OaZEbwTRmYg1kRhPX2XpC9GjKwMXBn5VPRqBmx/o7+k3bc58WQyjD0r0xA2PAb5vIDFQTjXvIDheTFXZwkud5t+v0KfdpNO6hR2M61mMwZJKZVoc3pZFuwXTclgCpUYd3Ro+mbvlyTuPLpyNHqF8C7rPWvp/8/CbwQ2vtpw4/a4RaRHb1Za+rbSwro2HLFn41aCit66Mwz6GssYji1CbceJzaK7vh3RzimorZPLduBD8a/DavbO7NqsCRbD0rBWdDE5kLQ1TVplOc2kSOaWDrjwdgo2E8DpRWrWNY1QZytlfx6MnXQGsExxiG1q9lYuVSes6p595rv4UbbgW/YVjVOiZtWUhKYz0PnnwdNh7HMXDctlWcXLuJYuNhXu4F9InF6B0Pk+PGMBYCoRANWek41uKNuVhjqKkaRHXNYByPJSWjhr49PyIYLGJD2bHE8VDINroNnY3HxnGNQ4zEiFpOYzNNGZk48Rgxn5fengYKsjeRlruBmmVT2P7OjbR6XaJ9FjDq3B/TVD2IeS/9GmPBY2Mcf80lQOIXddtvMactIeCTX+Btbc1VA5n/4q+IeTxk2DhHXXMxuIYPpv6NqNeDsZYjzr2N9PyNidHS5PaJJKNtJJQd/2+wuNbDO4//A2MT+xk95YekFa/imbm3MM/tSdBTgzdnIzUZMYzr0qesjDUDBpLW3IL1eIh5PGCh39bNrB8wgEBzEOv1UBbPxTUOJRvWc0W/HB7OSidYEyXkSSPq9ZJDM+PLlvPx4FJ8LSFcx0Pc48GJWwav38SK0sH4wy0YY4g7HjzW0n15BbNHjiYlGCTD20qKP05LzEePdRUsHzqE/JZqMv1RIh5vIqayLawdMIC0phas10PU4wEDg7Zs4ooB/fmlN4VIdQt1nkyiXi8BwowvW8GigcNobYS4x4PxQpGngdJN61gyeBi+UAiMQ9zxYNxErMuHDSY1FMQxhqjjJRL3UrimiuXDSshobiAvNUSr46MmmsmgTRtZVVJCv0gZ1vEQ8XixGPpv2cz6/gNIaU7EGvEm7rHhG9dyyeBS7vb6qK20xH0eYl4PxZ56SjZt5N1+R+BrCWMch4yUEGlEGLJpPUsGl5ISCmGMQywZ64DNW1hVMoBASxDjeIg4HqoimQzYvJmVg0voEd6O12eptllErI/BGzewdWhvCsI1uI6HsNeHsTB440ZWDxpMSvJaR3xePNZlxNp1XFhayq1kE2lowToOab4oGU6I3mu3MWvwGFJaWkj3Rkj1R/FYlwGbN7Ni0GBSW1rAJO4n41r6bitj7YD+ZDQ3Y71eam06QTeVQZs2sKmkH0Wt1YRMCjVkATB640rKh/UirSVIzPES8fpwrEvJmk2sLE3E6vo8RL1efG6cYevWc+6QkXzXZONpbMTrWLJTQ7gWClZWsXRYKYFgM4UpzUQ9Hhxr6VNWlrif2t/7QL+yzWwYMIC05iBxj48ymwvAkPXrqB7enbSWIJVuNlGPn8a0IKPWLOW/Jk/mVI1Q72Qr0Lvd517JNhGR/RYNhXjp+uup3FZLZSQLG4/z8lPvMuGWW5j9wANEGhvplVqHx42y4oUXGPS9X/L6vb8h0ti4o2735afe5djbbycrw8vK+24jXFdH3PFRFs7dra+2beY/+2/evPUm/G+8weBZy4iF/Dv6ev/my/B9OI+S2ctIq6+jOLoJx+ul5oTJFGTD0JFxztnWSMrKKF7XQ1VuHLINsRFH8P1h5fTeWsjY7OEUl/RmheOj5+CtFA1zyVqdSnaoN7nxfPp+XEZp3wWk+4KQYyEnC8/gDH4UeRfaEstsILsI78ACrml9l6q3rqd+y0QshoIRMyg85m9cNTebmvmXYDFk9/+Qnmf/HMe6nG3+D9h5RO6TcoBEQglQ03A528tOx+s4ZPSroceQlwluOJLi9y/FNQ6eQBqDz/3pzqNfbRlw+8f1gGtcGnwuC0vW82DgWVbmVOAJRDkiMpqBLQ08deObeMrKiMZjTIiOxbEuw6uqWVU6FE9VNTHXJer14liXYVXVrG7XXpcVYPEFT3B0WRkP//a33PjcKVBZyTvnPk6r34/HdbmqyWVTv5N262tv+xheVc1fdsQUpzDQl6bawYz2fcTcCROIbN++o/7z6KVLKcrO5vXjj25XF7piR/sbx09s1z5nR/t5z8zk1unTCZlyiDXu1NcLJ56wx75ePu2Ydu0rd7S/3W88tRXhHTGdnGyfd8ZIaisciHl2Wn/nmD7p/7hpf2LV9OmEaN0tppdPPHanutf9izW+I9aT2mI6cwS1FXE2xxyOWrWc05LtC04bwfyKwG7HtnOsy3e0H/PXaaydPp0Q5bvFlIi1frf2F0+cvMdYXz1p4k7n9dJ2MS2t8CXb5+9Y/xenjWBjRWi3fl45sf0xt4v1qWlUTp9OyFu7W0xvnXYUtRW7t//nhGP3cj9NIFzhJtsXcU67mLZWNCfbZ7WL9Yg9nr+dr88n7cc/NY2m6dMJlSfup+7tYppz5mhqKyKUxZz9up92bm8X0xlXJfcd5KhVC3a0n/jNb35Bv10+m44coT4LuJlPXkp8yFp71L761Ai1iLQ364EHmH7347zaeARb3WIKnRqwFuM4WNcFY3Ct4Ttpj+PGYvzusluIbjEE5jRR25y7Y33H6yV8RSZjyuZx1KJF/KXmEhzjJsoivlaMryy80zZnOq/x5m1f5eQmi3fDBNLL0wiUN3PcuBfwjWrlvaXn0RguJKva4Rslj1BQugoCidFO+ORJr9c4xKzbPscEoO7v06jZXkwUQ69Tf0Xm0Lco/8+PqF9zIo6x5I55jqJJf95xHtr/Te4kqiqx7fqzQNUrP6V29bEAFBzxNwqOnUbN/Iup+fBaXAspPZfQ8+LbaYpmURfPIGgy2B71Ue34sBiG5uawoiVEGhBpDRPDsL65P/1zTuCyiUfzozenc0zWfLY2ZPBR3Tg8wJl9CnCd53baxoHd+trgTWGOP4MJ/ScwvaoeTyyGGwxyWmsrZ0UiTLzgAibOnYuJRg+4vfu4cfR99NGD0tfe2jvDbAqHU0yKtevE+kXP8tHhLyUaY54GTgAKgO3AXYAPwFr7SHLavN8DZ5CYNu/qfZV7gBJqkS+rjpieLGfAAL7+4Yfkbalk/Uc9KY7W4zcxrHVZdckgMipa6Da/mnPMe6Q6UdxYjLtv+C9c18EXdRg0L0i3xTV4WqIY47Ds+lJS3VY81lK6Yi1N8/14WqJ8fP1IjGvxxh3OWN7AlIxtjO21mfQ+lRhrWP3I34nbNAyWQV/9Nt6izWx+6kGC1UMA6HHig+SNeIm49dLamk1TNJOwDWCMl5jr4trEyzmOAZ/Hh2stc+deS6wll7ALI0tfJK9oOR+Xn8S88GA2ZgSJ5Gyld59mNqR5qXFjeB2Hi0tK+OuyZRQEAsRdl+ZodKf2lEAhQddLUyRCthPhqiF9eHjpWtLTC4i5Lk3RMD6Pw9eHj+SPixbh93jwOQ7fGD6c60aPJj8tjb6PPorXcXZrB/a67LO2XzRjBkd067ZT38BBaz+YfX3aPkTk8NfhCfWhooRa5Mtnb9PBHapp32qjARbUFDHRmctvb7sVE4sxpjmNqytq6FtTgycY5K0jjsDGYjjAoLo6hgUbyTQRPtp2G90aA+SGA/Q65y78eZuo+/d3iVUNp5EAheOfIHvEKztqcE1ytgPjgvHGcAJ1iQkPkm/6V9f2YcaKC/k4LQOLS9/c9fi8EapqutHg5hH2RolmlbElK8aqbB/9ehYQx9AUiexIJP+4aBHd0tMTSW279utGj2bcX/9KYSCw27LPmvAe6qT205YpGRWRw9XhUEMtIp3EwqlTCdXW4k1NpbWhAWstNatW8dTZZ1MX9jFnmWVERpQ/1p7Ff+U/ywdLIzwz8HqiwW443p7MD5dwROpq3PIYL5Zcw7HR1XhTU3mw5kKGpGwmUs6O9fH0YlaolEKnllxPNYM9hl81VjC++2JMr1jyBTGXMc76xNRfyRgNifakHIHsAAAgAElEQVThUzOJhgPEjUurL0xKTgXbcmI0hV3itJCRXU12zva2UmFcTKJsItlXYzyFxcFBvO72YEZ2d2KeeiKly4n4/Tiuy7FlZcydMAFv2UYi8XU7tW/pN5H6SBSf4/DNkSN3JJKPLF5MXTi8W3ubPS2bv337HpPRI4uLD0r7P889d4/Xem/tn2ebT+tLRORwohFqETlgD48cSePWrcRbW4lbQ4UtYlOsB5tsb2ptDljLmf636e9JTAP3YWQsC2LDMcYk5oxtYy2NV+ZzdMVcjly4kPSWFh4Lf5VW66P2yk+mg/O2xBkV2MYvJkcZPKkcr7FYa1gb7EPUOljHiyUxdWrUurgWGm2ALd50ngmNpywQpdbfQL4nRLqJckqfEgYOLOGOWQsY6AljmmoIGYOLYUxBLovDUQo9XoL1DZTZDAwOVw4ezBOVlZ+p1vc7W7YctFFfERE59DRCLSKHRPs65lgkgnVdWpuaCDWHWOQcwSuRyRT5m7DGJTUvl1yvpbBhOQUFOWQlJ3gdWV/DmotzyN5YzqhV20gLR4BE6cjTA85leZ8xRCf1ZeC6rZz6wQZMXRMvDTqdoSWVhI/3039+X76x4lr6bF+Gxz7A2opx3JXTmw8Kc8nwp/Ct0eP446JF5KSk0BhuIupCVmo63xg+nNWLFuH3+Mh3inarAb53/lKqnVR8+Xm7lUUEjYMvP5+b2rWv2Uuye+ReEud/7uVlms8z6isiIl8eSqhFZL+1TVEXrKwkFg4TbmjAcRzyS0qIRuOsrxtEka+RXE8zQ7q18L0nb+XX2xfAjM0EFtYRjUWw8Tj9BhfRMqwPtQWphIb35LKydRwTKie9fw43pb9GkbcSn2so85zB8sC1eNJcLvpoA0ec8mOatpdQteBnWAwfe7w8Xn4Vbw12CaRl8b3hI3Yqo2iJxUjzp3N9uyR4b6US8MWURYiISOejhFpE9tvCqVNprqggGgwSC4epJZ8Mp5WSKVMYet55ZP3hOTCWvt0cCidPxE15hQ/LGrCjB/DB8F6Mjyzj1Lrt5MdG8NCKjYzLWEth3gbIbys9a8RaMK0BIjV9ydk2jJKafFxjSDEtxNdPxFvbm5g3whPjF/PUkWvIyEnnxtFjDzgJ/rRlSpBFROTTqIZaRPZLS3U1D48cSbi+nqDJYKE7muWUMiprG9su68VFl13G6bnNLJ39P4TDDQAY1+Hh9dcwYlsxE7dncEa/f1PQYzH+9GribTMku17qmwdQGezNipZClkfzqU7Lor4onRoTwsEyJL6MOeFi0p0orjWU5USweelcO2r3RFpERORgUQ21iOyXfc357MbjbF+2nBVrGlkeHsXaSA51nkIqY7n0y6kmPbuGbb2P5rGPP+YvcZcJq8/gipo0Roa6kRuNMH7+KVgLHgeKjr0PMmrYHs9iQcsgFmWW8EZ+ERX5dse0bY8vWoTf04TPCbarY740Ob2bD5/jcM3wo5RIi4jIl4YSapEurK0mevG6CBWRHKy1+B6dS7+TTyH87j/oEVlPLBxmfmsps+yRBPLziKQE8bmtnNZvDuPOeI+Am8pHGV9laDCPK54ZxpCtufhSwhRddzFeD9RuHcHq7pY1feqIZB3Huiwfxw8/iT8uXpyYRxnDN0eO+NzTwYmIiHQ0JdQiXdjCqVMpL2/m9fg4onkeckwDWMuaOR8xMLWVgswgL3tO4IQec8jKaaC4Rz7Hjb8X74q38HpyWOZdR48B4zl1Vj9ufncIvmAm5ZlB5gyvYK3pTXqPNO785ivUmrzkCPQZ3N2WOFdW6kU/ERHpFFRDLdKF/Wn8eF4vzyL/9JWk+cOkODGsBeu6+D0uqZ44GIPxeMB1MXj48QONGOMAEK0MEbvXy8Z3PqbXeT9kTtUZfH9oPq3p8R3lGt9+7TWNLIuIyGFJNdQisk+jvv4N3nzxz3h9MTLDLpk2jXhrK4GibrRUVuFJScHEHbzNqbjlUf51yS2Y2XP41qhRFCwMUPMDl2B4Cd0vvJWa4q3UDHyRrw7+H64bM3ZH8qyRZRER6eyUUIt0Yb8sLqBlfBH9Khvo++5gPK0+0goKmHnsN2Hzmwyfs4SciI9ANI/c9IG0vn8c3f6TzuzGCsY1FWFy15Jx6XepLK6gOXc0J5/4GJdk5HX0YYmIiHyhlFCLdGELK2vwuydy58yH6duaTo5rSY/mUPqbFrBjsa5LtrVkO5aUlGyy5wfxGIPHcagtWkTK5d/FV9hM7z6nknbUg+BJ6ehDEhER+cIpoRbpoioqmomGW7ll1gSyWjPZnBVleWaU/tkh1tXWEbOJqezifkt9Xi19S9bxTutCyK2msFszk3JeAxPkjWB/5tefxZ+VTIuISBelhFqkC4rFWnnggT/Su6aQk9YOI+J3ueMb73Jy9zdIYxbPBo/iX7EjyE5J4eLACq4xLxB1Xc63lhSPB4PBtZZZ8aE0lN7JfWOO6OhDEhER6TBKqEW6oPfee4xo6BF+OOtFHGt57ui1bM5pIsWGKc2AwhgMzswl5rqURfxE/F4ysrqzoD5OQyyTBpPJwJ5HcN7RN5OfntnRhyMiItKhlFCLdEELFrQy8uMLKAlbci6/jZVDhnPjqFO4bvhF5Pvg5cefJuaG8TkOY4adR5/R/01+IMCPZ8zQFHgiIiK7UEIt0sW0tsb48Pme/Hj+BRRfej+2dC7PjOpG2lETd6wzsrjPZ/pHV0RERLoyJdQiXYi1lldeWcs5a4eQWboMz6hXSSvIJG3ED3ZaT4mziIjI/nM6OgAR+WJYa/nPf25m1bQXGR/Kp/DUP1CdEcQ77EYI9Ojo8ERERA5bSqhFuoj1699gy9r5nL3sSHLHP4enYBFpJgf/wG90dGgiIiKHNSXUIl1ALNbKnDkPMWz2ufTP60HeSdOIeBrw9b0JPP6ODk9EROSwpoRapAs448lHeH/5UAbOuZjm0v9HS+oWyrdmU3DcVR0dmoiIyGFPCbVIJ9fSUs3arSHOfu4amopWEzviDSpSPFRET8UXCHR0eCIiIoc9JdQindzc2X/kh6+cQI+gj8Apv6M8I8gfW4/hpdKJVLe0dHR4IiIihz0l1CKdWFXVCpgOR28upuCcn9PQaw3lrZm8WFfKk9EoP3j33Y4OUURE5LCneahFOilrLSumP0/pzIsIjvw3zaXvU+NJ5c6K04j5Urn1qKP49ujRHR2miIjIYU8JtUgnU75gASuefZaWYC0jZ1xOYzyV3/gGkB0Zxru1A+g+bwPXtizjwilT9M+Hi4iIHARKqEU6kWgoxEvXX09LVS0neu/GF/Izq3AdU8cuo+jDkVz91mOkNjYSzs3lpRtu4OqZM/GmpnZ02CIiIoc11VCLdCILp04lWFnJKPdy+gxdSfpXf8q0DX8m//9t4cTnXyLQ0oLj85HZowehmhoWTJ3a0SGLiIgc9pRQi3QiN6xZw9Yel1HScAYpo94k3P0DhvZZS2ZVDQOdjQCkZGVhHAeP38+iadM6NmAREZFOQCUfIp1IZfe+nFB9OtsDuTy86kqG+hrpH67n7nODVC0pJDU7G+PxABCPRDjypps6OGIREZHDnxJqkU7kvCVD6dWYxdqCBn4zton08Pkc79/MQ/fdxyvnnkuwshLj8WDjcdK7dWPctdd2dMgiIiKHPSXUIp1E67ZGbqzfSsbRS/h7US65gOtL552xY7lr8WLufeQRVjz77I71Sy+8UC8kioiIHARKqEU6ieo75tJrwl9pzaoiM+9cwhU9CbXGOMvXh/+dPJn8tDS6jxvX0WGKiIh0OkqoRTqDNZDVMhMnrZEP/IW8GxlE5opW+q9N5+HnTtd80yIiIoeQZvkQOdxZCN+3FO+Yl6hLDfNej0v5YcZ4ipb6OXZsb4qK0js6QhERkU5NCbXIYW7ttFew3gfAaeVdM4B7plzPWy9tAGDKlCEdHJ2IiEjnp4Ra5DBWu30taX9fj2fAh4Rp4Cun/px1q2pZtaqa1atrOOGEfh0dooiISKenhFrkMGWty7r7XiVv1L9wTZQtoRKyS8Zzzz3vAfCLX5yI3+/p4ChFREQ6P72UKHKYCYcbmPKP/2Potmp+vC4bjt3Ktmic7JPuwHEMN9wwnr/+dTHXXXdER4cqIiLSJSihFjkMWOuybds81s5+FffVCFcvuICRDX4yLr+BZn+UZ1YXctXpOQAcf3xfJk3qgzGmg6MWERHpGpRQi3xJXfDCc/Rx6jjVqSHjnVYK5g2hdNMFWAulJ/6F9JJ3CKY2MdOTze+KTuKt3z7KE38YSHFurpJpERGRL5ASapEOdtGMGRzRrRtTuoFtXs/IkZdDBFJmehm0aAhFG/qQn1JNRiSTJm8mswfWkDN0Bb3ztrEgWMRDW8fSSipvDxnEWfdPZf49t3X0IYmIiHQpSqhFOtjcigrml5fxUHMl11QGGOCPkP6enx9tnAjWpejEB/GPeYk/l9/Iu0MLqfAEiW0twbemJ1vjWYRSMwjF/GR90MhpNbNBCbWIiMgXSgm1yJdAUUo2Nz99JBPj22nqfw/NoStYXujy2pANDBm9ljNz6in3zOT92vEMLy9nY3oWeNLweB1SF0bInNPM8c5Szr/r/I4+FBERkS5HCbVIB6mpWYPfnw4WvvZiKacPf5TAgNk0prfy0Njt/I4hpAWDdKvrxSP1XyUY8TBx4euc3tDAP08+maKNG2l8J8q62u5099UxeVg94669tqMPS0REpMtRQi3SAdaseZn33ruXnJx+XPzRtzgrtIxA/9msyorxkn88fUecSs7sjcQjEeriaYyZP5+j5s8nraWFIV//Ou/edTc3Xf0UK1lHlTX86qosTrjm+3hTUzv60ERERLocJdQiXxBrLZWVH7Ns2d9Zu/YVAAauP4Mhc7pTePlP2Z4TZFX/G7j52BvJ9fl49MmbKdqwgfHzFtDQksmatDEc12cr2+bNw+f3EnIyye7di2cemcS55w7t4KMTERHpupRQixxi4XA9a9a8zMqVL1BXtx4Aj8fHiQW/oP/0k6k/4XtEulVDOJfit2t478kbKV9fzpHLG1ldm8HfPOdTFutGsdPMkFA1Z3/nagDuvHMS+fkBCgoCHXl4IiIiXZ4SapFDZPv2JXz88dNs3Pg2rhsDIC0tj5KSsynNvpD0m7tTn/Ua2ePfo7K2jDemZbK5bDr/CR5NJf3wZ2QS80ewrsvQtK30T9lOZl7WjjrpIUMKOvLwREREJEkJtcghUFGxmH/961tY62KMQ+/exzJ06Hn07TsJJ+iFa2B75XbcK++lzlPJ0tcNNZsbmd7yNfwmiscxHDE6n5O+MpzcbbMoysoCulN64YWqkxYREfmSUUItcpC1tjby1ls/wVqXkpKzOfLIG0lPL+Li52dwypMfcOYzOWTXFOA/63+pKdzIto9DLHwvjs+B7xfPoDFrIDnUk94yl+t+/CNgSkcfkoiIiHwKJdQiB1lZ2SyCwe0UFQ1n0qSf4LE+eBm+fu9wem9Lo8ZCY493yBv0KLbM4SfPXADxei7tvx5/II1MQoTrQ4y5+vqOPhQRERHZD0qoRQ6ygQNPIzU1h0xfTzz/9MH/ARXQZ3sGNWkNPDdqPl855R5aU3J5fMEoypw+HN8zSDQYJN4axsbjpHfrpjmlRUREDhNKqEUOsotmzGCy6cW3Hy6GqmRjX/j14Ld5cehGAilBRtsMwq0eHu02if6n13DfjXey4tlnd/ShWmkREZHDhxJqkYMgGg3x2mu3MmbM1axfXc/1T4xhVUMtzX0jDPlhHnmnGjY8MAdTl0FtPI3rGi+kmwkyftlq7jrlSLqPG0f3ceM6+jBERETkc1BCLXIQLF/+D7ZunUOsMsy9T99Jj8Z0FhVVceNXXmfQlhaefvYZ7slt5sK6r0LYULi4lsvWvsWwAg8nfvPRjg5fREREDoASapGDYOTIK3Dr4wz7fxexqibMlh5N3H/lPLzWoSKSyfxGcNx0vuapJ77c4azRPlLHn67SDhERkU5ACbXIAYjFWvF6U3DCHsZOuxo2wra8ShZ9/V5aa3vSHM6gKRLnua0DOT+3Ow/87gEcr37sREREOhP9Zhf5HKy1LF78OCtXvsD5Z/6VlNuzsEstm9PrWXPpDzjdP4u+OYV87dnzGThnDf088zl33Twl0yIiIp2Q09EBiBxuIpFmXn/9dubM+T2h7XWEbq6F+VAdCPL+ld/muMAcIk2GJ/5Uwhnvv8nXzEv0yIWlf/tbR4cuIiIih4ASapHPoLZ2Lc8//3U2rp/JulnfYdK0v5O+tDfkxMm9+5dMylpEtNJy9x+OIbS8gaN9C8jr0xNfIMCiadM6OnwRERE5BPT8WWQ/uG6Mdete4733fkn2+r6cMfMB6jcPJmQMz/VfzuDr7mdkyxJaqrzc8/RXqFxfzTkpH5LVqyfetDTC9fUcedNNHX0YIiIicggooRbZRTweobZ2LdXVK3f8V1u7Fn9dBmPfvIZB688gI6OYNdlbeP7MDxhdMhVf6zoWtqbwjHsVm6u2cE7KO6QGUnBjMVqqq/UvH4qIiHRiSqhF2ovD9n8tZemrT5HWmE+goYChjeeR1phPZnN3AqmFpObnYK4uZ6H5Hl/3LcRLjCabzi8jlzOz2TLiHDjKfpW0vDxMsltNjyciItJ5KaEWaW86dPvDSFIbbsDrTcXrTWVr2OL3pZCVHcCeFMWe/3NM08ucVlmBMQ5vh0p5MjqZshqXU1av4OKsLM586CHN6CEiItJF6De+CHDRjBkcmVfM954aj9/xkXfpQOgHFC7k7yue4UPbkyU2guuL849FLzG+OMRb0RFMqxnLirpM3NkRblv7JKWDenLhH/6gZFpERKQL0W99EWBuRQWpL0dYta4vnhIvy79ewwfl27jFPs6U3m+zaNMkWkJ9afBmcm3DcfR7dzsfFY7Brmqg4KNNHBOaTYZvE6G8NDx+f0cfjoiIiHyBlFCLANFIC+fPH0/PC27hvYJU7nh7FEHSmDSwH2ubBlMddEgNt+DGYH08l1WlxaQtb2bkm0uY5J9HbmoTOX0HEGlqYsHUqRylGT1ERES6DCXUIsCIqp6M9lbj776MwtxWotWDaPTDAx/CkthE0sNBXI+HBpOFxxOn4P0qTp39MqPzqvH6faRk98Gbloa1lkXTpimhFhER6UKUUIsAU+YOJHPkVOpTwjzXMJjG+hD9FqwivsKPe3UfwoEAPo+HSavWUry4ngnRxfQ6YwwVCxaQmpOzo594JKL5pkVERLoYJdTSpcViYag2TN7kJf2Ej1gTiPJ/r4zE/2o9tcF0gllZ9EnrzuAV7zN+zhwyolFsPE56t25MefRRnjj9dIKVlRiPZ0e75psWERHpWpRQS5e2ZMmTRH7fTN9hDbR6g7yzthjv842kOjEGZNcxtGAzv7jh54TWj2fFs8/u2K70wgtJy83l7Ece2a1d802LiIh0LUqopctqbW3k4wVPcfLcuyi8+A62RRr49zNjKHJq+NaIdcSaGph4y/fIyUklZ9w4uo8bt1sf3ffSLiIiIl2H09EBiHSUJUuepGjJcHL6rsbJC7FkWzqrN2dxWvpcInU1pOXnq3xDRERE9kkj1NIlhcP1LF36NBPm/gA7eRrNGRHcMrgobxbHX3omaTk5Kt8QERGR/aKEWrqkJUueIGNTMQWOD6dgLeuJUbk1h5MvmchZf/hDR4cnIiIihxGVfEiXE49HWLnyBQbMO5Wskf8h7I+xZVkGrusw/NJLOzo8EREROcxohFq6lItmzKCfr4kBlV76bRmBPeZhKsMtvPtaMaP7ptN30qSODlFEREQOM0qopUuZW1HBB+FaLptzPaFuy7CeVpZuKaa/t4FhF1+FcfTQRkRERD4bJdTSpVgbxx9vYvLKEipqJ/LboQ34Qx8xNruWoeee29HhiYiIyGFICbV0KdFoiIE1OfSuzaU+JcLrhS4hZyyVg738V1ZWR4cnIiIihyE935Yuw3XjRKMtTFgzAE9KHbP6bsMTa+bUlSt5aMqUjg5PREREDlMaoZYuo7m5HGMMx2zpzqBvXcmZ3izSnm9l8MjRDBo2rKPDExERkcOURqily8jK6sWUvPEc47Tii/nwli3BXb9R/3S4iIiIHBAl1NKl3NVyFFXrS3jxydt5/clmwg0NLHjsMWLhcEeHJiIiIocpJdTSJZSXL+Tttxew6dHN4FoWpy0i1OIjo1s3QrW1LJg6taNDFBERkcOUaqil0ypfsIAVzz6LxbLU8w8qK31cvv5JUlu344s9SUp2Nml5eURDIRZNm8ZRN93U0SGLiIjIYUgJtXRK0VCIl66/nmBlJa4nxpbBEfoEzyV/0j/JOPLvjFsYZ9vWHgDEIxGOVDItIiIin5NKPqRTWjh1KqHaWhyPh5byOt5442QCH11C9vC5uOnN1G+PEaqro6W6mrT8fMZde21HhywiIiKHKY1QS6e0aPp0PH4/jWVlrHf7EnKKmTAoAHkVePr2ocfJJ9MDA0DphRfiTU3t4IhFRETkcKWEWjqlMVddxYe/+Q2xHhE2RnowrqGEvAFziKTEyRp6Oidd8vOODlFEREQ6CZV8SKc09tpr8aT4aR7rZdipSzjT5lHY530C+X5M0QkdHZ6IiIh0IkqopVPypaXR85yJpOdFaG3J4aSMElL6b8KXmwoFEzs6PBEREelEVPIhnVZ1bAVev48rB91I73AZJgAUHwHeQEeHJiIiIp2IRqilU4q2tlAbXw1AcP0p1BW8TmsgDkXHd3BkIiIi0tkooZZO6dTpf+Jf6cdT0dKfXstySOk1F5NloGhSR4cmIiIinYxKPqRTWlTfhM0aBY1n8bOc1aRktuLtNgTSund0aCIiItLJaIRaOp2mphpcGyUl3sqpW0qIdn+HKk+IV4O9qG5p6ejwREREpJNRQi2dzowZT2CtJR71MXlTfzL6z6IlLcrP1/v5wbvvdnR4IiIi0skooZZOZ+HC5wAYUNOL7No0lr15J38yJ3D2mLP438mTOzg6ERGR/8/encfHWdfr/399ZiazZN+XNkmbrrSlW1rassiOFJAdQRHOoRQR3I96No/6VY8ef8fjhopy0AoIKHJYLCJIQUHWQlfoviRdkjT7nsw+8/n9kYKldEmlkztNrufj0Ucz9wz1iu0fV999359bRhrtUMuI8vrrq0jEa8Gexbm7T8JiaJhRyr985CcUBAJOxxMREZERSIVaRpTnn/8dNmkp7erghp3jmZCfz9xPFeMKGKejiYiIyAillQ8ZUS6+eB5FPj9f/nMzU7J7yfjIjbhyH3c6loiIiIxgKtQyosyc+RFytp3EhPaz8Uz/K4zZCX1bnY4lIiIiI5hWPmREiMUSdHaGSU/28ufcQua4LqR3XQm5n5wJ48Y7HU9ERERGMBVqGRGee24Ld9/9I2ZXzmVCRxV5kQy8M/xwyrmg9WkRERFJIRVqOeHMnn0XVVW577pmzOuUlj6N2/smH12/hHG+BOkXpalMi4iISMqldIfaGLPYGLPNGLPTGPNvh3i/0hjzvDFmnTHmLWPMxanMIyc2ay1r1tzNN76xnczM+8jMvI9E4gXq63vo7s4kmZzD2L58ypOTyfj4dTDjG2Ct07FFRERkhEvZhNoY4wbuBC4A6oFVxpgnrLWbD/jYV4CHrbU/N8ZMB54Cxqcqk5zYamv/wpo1d2MtLFqUAKCyci5z514HQAtBNl3zXTKmr8FkhSGzG4xG1CIiIpJaqVz5WADstNbWAhhjHgIuBw4s1BbI3v91DrAvhXnkBBaJxLjjji+Tl9fLBRfcQlnZbACyssZSXJwDwH/83wqq0y7BU/1DTI6B8sucjCwiIiKjRCoL9Vig7oDX9cDCgz7zdWCFMeYzQAZwfgrzyAnsySd/g9vdRChUyKmnfg632wvANcuXM6+kl5tnzmRMZz8XhDLxFtZBQQkUneFwahERERkNnD6H+qPAvdbacuBi4H5jzHsyGWNuNcasNsasbm1tHfKQ4qxkMsFXtq/njcx5FI3/2DtlGmBVUxO/3LCBsx56iII3PFROWAE+YMLF4NI9tyIiIpJ6qSzUDUDFAa/L91870FLgYQBr7WuAHyg8+Bey1t5trZ1vrZ1fVFSUorgyXG3a9AQNJosN6TO5y53Fd1aupC0YZHNbG+3hMMF4HL/HQ6BmLGbWSnrS43QUXOB0bBERERklUlmoVwGTjTFVxhgv8BHgiYM+sxc4D8AYM42BQq0RtLwjmYzz/PM/BcCfDJDl9/LLDRs447e/5debNhFNJOiPxUiPurnAtReXN8jLNpcvral3OLmIiIiMFikr1NbaOPBp4BlgCwOneWwyxnzTGPP23WJfBD5ujHkT+C1wk7U650z+Ztu2J2hrq8NaN96MHFpDIQIeDx6Xi19u2IDbGCbl5jLjrRwKpv2FcFqc+MQP8T9nneV0dBERERklUrpkaq19ioGj8A689rUDvt4MnJ7KDHLiSiSirFr1C/r6osTTfESJ0t0bpa+nhxxr+fjkyTzQ0kJHTw8f2JpH5rnrCMX7OC3nDAoCAafji4iIyCihu7Zk2HK50jj77K+QlfV77t+dSaK7Ha8xzN+yhXPWrKE4K4u5X/oS5/y0i0sDq3AlYtRu72fto//Mkr/+FY/f7/S3ICIiIqOACrUMW8YYKitPp7LydH7y/e/jW7OG+a+/TqCvD4CWWJxPLV3DbO8HCXziAfpNC7v35RFqb2ftsmUs+NSnHP4OREREZDRw+tg8kUOKxYIkkwPr9A1vvMGF3/oWZzz7LIG+PozbjXF5OJV/ZmboI7hL6unP2sK+tiDt7Tm4vV7W33OPw9+BiIiIjBaaUMuwU/fGKzz2xxtpbyzh2eXTmVq4j90XX8zUNWuYaS2+zFyqG2+hPLYId76Pv/Y9h2tlCV0dfsCQiEY5RdNpERERGSKaUMuwEguFWP7VpXS37qWxrZ69rR42F2XRXF1N48KFJMNJ5tXcSiUVuCdsIvN3pXSM3cmejc25HNkAACAASURBVCF6mnsItrURKCigeulSp78VERERGSU0oZZhZd2yZUQ39RFZnc1L9kw8HjcXNm7G7Tmdc2++hQkbP0mO14f/hk/D5BBpp8zhQ3fdxZZHH33n15h29dW6IVFERESGjAq1DCvr772XZDxOTV8V/TaPqXk9lLqTpN1/P2ee/DkwQFUSzj4XAq2QMY6y6gBl1dVORxcREZFRSisfMqzMuekmYsEgOxPjMS4X07NbSUSjzFmyBB7Y/6FbXHDm12HeHeDRedMiIiLiLBVqGVbGXXYWoQ9FKFy4jyZXNitPL2PbzJlUL7gFdu+BvCBcChgXuL1OxxURERHRyocML7s3PE8y30dWT5jqi7x0njSF8PjxeB72wWlfg6m1EL4TArOcjioiIiICqFDLMLNn60v4PEkWTp3A569dQndRjIquLPhlLVyyCYoyIHuK0zFFRERE3qFCLcNKS/NG3MZy5lnnsmBhxcDF7wJVD0IOUHkhuHWCh4iIiAwf2qGWYSMeCXNX5qm8kHkGBbMvGLjYDfwhDlVPQQEw9jInI4qIiIi8hybUMmzUvLGCeu8Y6jxjmfXoU1w4YTP3NF5ETsFKKOyA/CrImeF0TBEREZF30YRaho3a9c9iARt3EYnHeX7nXjbc2UpfxSPE8yyMvRSMcTqmiIiIyLuoUMuw0bBnNViDibso8qTz0b3TKEyEiI5/kb3JPhh7idMRRURERN5DhVqGhWQ8Tmdv7cCEOukl05/GJa9OJGPKc1CQoGz8heArcDqmiIiIyHtoh1qGhaZNbxHz9wMQdadRvbeE6v4iihf8BXeJH8Zf5XBCERERkUPThFqGhZpVK4hhSI8EyY76+NG+cymrqMM9bhf48qDoDKcjioiIiBySCrUMC3u3vkTMusjrDjI+not/rQdOegLygLKLwKV/TBEREZHhSS1FHGeTSbpr90BOgHk5mVzSOI8Mrwem7wU3UH650xFFREREDkuFWhzXvn07nr1+ZoxZwLd+9T9wOQP/drLgZzB1D2SMczqiiIiIyGGpUIvjGteuBaBs3jzYAuwDCoE5gEtlWkRERIY37VCL4/aueYVej+XRnHI2PNBK0tcOi2v1p1NEREROCKos4iibTLKn/gWaF+7hN511tDwahKkPQeW1sOMup+OJiIiIHJUKtTiqs7aWaDhINJbBBW9mUxXOwZXtgax0KFjgdDwRERGRo9IOtTiqce1a0hsK2LL7Yi5pP5WSrHSY8Ak450ZwB5yOJyIiInJUmlCLoxrXriVhDW3hbBb0VOD3e+CDgCcdjHE6noiIiMhRqVCLY/atWcPGR3/L5s4IxYEcivJbcZ/6NEyKOB1NREREZNBUqMURsVCIJ26+mZ7sBhquSOO0nCrST3kUu/BrsP3HTscTERERGTQVanHE81/9Kh01NZgiQ3Ysyvm1xeRMf45QogPKL3M6noiIiMig6aZESanGtWvZ8uij77yuPPNMap97jlU//zkkk1AE5zQVMbXyRYy/g707e5maPdXBxCIiIiLHRoVaUiYWCvHkbbfR39KCcbmIdHez8o47KJgyhazSUsLhbuKFISa8egHpC1YQNb0w7sNOxxYRERE5Jlr5kJRZt2wZoY4OfFlZhNrbiYfDJGJRvBXZnHX/1/Gc7aMpWca4cBXu0hqCNsbEG//b6dgiIiIix0QTakmZ9ffei9vrpa+vhdicEKbEhSl00+hbxc1vzGZs9RkUbppH2pxVRLwQmHsTnvRsp2OLiIiIHBNNqCVl5tx0E4lIhMjELuz0BGaMCzyW3KLx1Jsy1voWMLthHllTXySR5SLn1M86HVlERETkmKlQS8rMXboUj9+PbUhimlykbUinaO0Mbv3CGvz+PMYFyrjYtxXjjrLClPCdDftoCwadji0iIiJyTLTyISnz0RUryLn2Wsbddx/jeidQsWghnsWL+efHXuPil6q4Yuckis7+DCFPnD96q3ly9Wp2dHXxq8WLnY4uIiIiMmgq1JIyq5qaiLjdcMMN3DZ+Lld6L8b9C0PhS71E3J2ULnwQd34dW3xpvJGczBfnz+TW2bOdji0iIiJyTFSoJWVsMok31sP8vZO44v7T2Wt7KAgEKM3P4OHSN/nA6ffT6Avxs7Vn89L3bqAgEHA6soiIiMgxU6GWlIlHwhhfnKs3LIK0ZvrOfphvuavJvTCD+mgvq1edxRhzMv/vn25UmRYREZETlgq1pEwiEqG8rZhZiR4ql3yeXSUtXDU+j/NOvZ2CQIDwZZfj9+uPoIiIiJzYdMqHpEQykSARiXLWthlEWybx4NP/j+zyG/nwKZe/M41WmRYREZGRQI1GUqJl40bGtbVz2VtV+Htc2KoSJnzw207HEhERETnuNKGWlNj+4pN86s0dVJhuut1hzvh8pdORRERERFJCE2pJiR2b/0g84x8pu+2zmGgBVTP/4HQkERERkZTQhFqOu2B7O82hvZyd3gnuOKayDOPxOR1LREREJCU0oZbjbvfLzzM2OJlxU/+E8RvKL7zZ6UgiIiIiKaMJtRx3W954nDnRk3DntOHLryBtzAecjiQiIiKSMirUclwlEwlqd22gqqQTl8sN068Coz9mIiIiMnKp6chx1bJxIzFzDp4JWwi5DJx8udORRERERFJKhVqOq43P/57zA0nA0uE9A3z5TkcSERERSSkVajmuVr+4ipljVuPCMvZDS5yOIyIiIpJyOuVDjovGtWt58/4HmGE8uNK7CUbK8I+rdjqWiIiISMqpUMv7dtXjj2Mff5zil97iG1cVALDzTQ9zIxE8fr/D6URERERSSysf8r69VlPDS+PGseO6S8kqbiMW97N24x7WLlvmdDQRERGRlFOhlvct2t9PIBLhmsZMEuFs1rTP449zZvPygw86HU1EREQk5bTyIe+bP5DJLX+Zw+lbq9nlvZYfXvwYfzk5iF20iC84HU5EREQkxVSo5X156pF7eaS2hfSt1xJ3Jblj0Wu8WtjBBTvruetHP3I6noiIiEjKqVDLoF2zfDnzSkr4+KxZFKans/yh1zhzzx14JyTZOnUB/35yB5Wljfxv1LDoc5+jJC/P6cgiIiIiKadCLYO2qqmJdS0t3LdpE//UO4+F36qia86naC+t4xcnr+I3//NVCjMynI4pIiIiMqRUqOWY5Pl9nP9GEfMfLyXhhT/tmUh/5Nd882d3qkyLiIjIqKRCLcdkbqSWL5f8nLbZH+N/mMj9i9ZyhutCvjRzptPRRERERByhY/PkmNzQtQq3N0h9UTuPVK/kg7U7+d8bb3Q6loiIiIhjVKhl0Ca79nGyezfJaDq/Cwb4zAO/5lNjxjCustLpaCIiIiKOUaGWQfsXzxYyoi4iby0kq+YHuJuaqFmxgng47HQ0EREREceoUMvg9O/hrP4NuOKGzS2Q1dJNdnk54c5OPWJcRERERjUVahmU+LZ7sX2G0OYPsDH3Wegw+LKzcXu9rL/nHqfjiYiIiDhGhVqOLtxKeMsfSBova3adx6qeMDYtHYwhEY0yZ8kSpxOKiIiIOEaFWo5uz2/x9SQJ1Z7Ga/nr2bF3Em4bJ9jWRqCggOqlS51OKCIiIuIYnUMtRxbrhZ2P4Am5SKw7n85Z/w4vz6Dy9NMomzuXaVdfjcfvdzqliIiIiGNUqOXI9j4CHUGS++awI6OF5r4AZVkeTv3iF5lw3nlOpxMRERFxnFY+5PASEezu3xJtTxJceyUbJq6gua6UUn8f+RMnOp1OREREZFhQoZbDC7fQ055NcN9E1rSOpzbvTZr2llCWGSG7osLpdCIiIiLDglY+5PAyKki8/GuCL7SwY+bT9PWl0d+Zw6TTY7jcbqfTiYiIiAwLmlDLe1yzfDnfWbmS9vog+W+kU+4fz5Vf+TCFro9SnddE4WSte4iIiIi8TRNqeY9VTU3sbdmJfWA3S7ovJve8AIXTC1k8IZPyklryJ17sdEQRERGRYUOFWg7p0sAWPjnhXvrba/lEYDbTVhYwbtcuAPImTHA4nYiIiMjwoUIth+QOpWE6S+nqLOeFeWt4/JUAUwhwizXkT5rkdDwRERGRYUM71HJIm+ovYfevH2Rdx1yiJk7lrkZaHkri8qWTWVrqdDwRERGRYUOFWg6poDGA2xgq5ubywGkXkfFKHkWJfkomj8e49MdGRERE5G1a+ZD3OKMklw97xnBSfgGes10sb+mnv8dLpb+PvIlTnI4nIiIiMqxo1Cjv8eBcw6knfwzXvDtgImzb1k4iEqHU30eenpAoIiIi8i6aUMt79LVtwAQTdDT5Wb/ldmpqZhOPRCjJ1CPHRURERA6mCbW8h23djse66OsppjG8mu3bEyQiEUr8/ZpQi4iIiBxEE2p5t2SCrP46rNuFcYXxB8pIxn1kmiD5+QHSCwudTigiIiIyrGhCLe8W3APhKMmeYrrz2xkzZjoPfHcGt0xYS97EiRhjnE4oIiIiMqyoUMu72O6tJMOQaB9PT1E9BQVT6KytJeCO6wmJIiIiIoegQi3v0tW6gd7eCJ1NA4U6L28SnTU1ALohUUREROQQVKjlXcKdW0iLu4m2jKensIHPfnYjX7mnl1DCo0eOi4iIiByCbkqUv7GWslAD1uXGhgMkMmDfPjd9HTH8+Vr5EBERETkUTajlbyJt0N+JjabTmx4CyknG4hR5e8goLMCfm+t0QhEREZFhR4Va/qZ3O0Qg2VZFT1EDfX0lxPWERBEREZEj0sqHvKOlaT1p3WFoHrghsaUld+AJib4+8iYudDqeiIiIyLCkCbW8Y2uimLYtF9G/ZzY9RfXU1GRQ2zDwhESd8CEiIiJyaCrU8o5F065i7Mr/wt+4iOQ4N7W1AcblhCn0BbXyISIiInIYWvmQd3gb3Hhxkz5tLFdcfz+FFXt4+PYv4TZWJ3yIiIiIHIYm1DIg1AhbnoWMfTAJfD4P8yZ7mJdTR1ZZGd6MDKcTioiIiAxLKtQCQOPuFXQ1foHQ9J8QregDeOcJibmaTouIiIgcllY+BIAtfS5K98wlf9dk1rj/nde/No38jjjp6JHjIiIiIkeiCbUAMGnq5WSvvBOz84O05TTx9BMdPPLwBnobGtj7yis0rl3rdEQRERGRYUmFWgCo9GVT3p1FSX45Xdmfo3VHG7ldWwh1dLD35Zd58vbbiYfDTscUERERGXZUqAWi3bB5Pbj6oRKeffxlkvEEY13NuDweMktLCbW3s3bZMqeTioiIiAw7KtRC454/07vxH4if+u9EK2Ns2tCEMVDmasXt82GMwe31sv6ee5yOKiIiIjLsqFALe/etpb8vRve+MbzU8N9UzOijkHa8JkZaIABAIhplzpIlDicVERERGX5UqEexa5Yv5zsrV1KSqCMz6SXZVkFTRg34Kin3tpOMx0kmEgTb2ggUFFC9dKnTkUVERESGHR2bN4qtampiXUsLZ/jXUBrzYFvK6V/YzHTv5eT3rCLQks9JV15JIC+PaVdfjcfvdzqyiIiIyLCjQj3KjfPHKSRIvL+EvX3jebFqKt/59AKevuxbpE2axMU//SnGpX/IEBERETkcFepRriqxk7Ski0jzVOryuvhDXxnRFSs4HyicNk1lWkREROQo1JZGuUnxbZgo9O2pZnd+J1fke1na1w9A8cknO5xOREREZPhLaaE2xiw2xmwzxuw0xvzbYT5zrTFmszFmkzHmN6nMIwezzPPsxhf34No3i9LAczTc38zqZ3cAKtQiIiIig5GyQm2McQN3AhcB04GPGmOmH/SZycC/A6dba2cAn09VHnmvi4o9zMxMUMZYAm3FdKRvp7+/mJy2jYAKtYiIiMhgpHJCvQDYaa2ttdZGgYeAyw/6zMeBO621nQDW2pYU5pGD3FWdTVlmBmbvPBIJS0OgheK8PLyRLjKKisgoLnY6ooiIiMiwl8pCPRaoO+B1/f5rB5oCTDHGvGKMWWmMWXyoX8gYc6sxZrUxZnVra2uK4o4uu7q62LvrWZJxSOycS9QVZl/SzcSiOABFmk6LiIiIDIrTNyV6gMnA2cBHgV8YY3IP/pC19m5r7Xxr7fyioqIhjjgyPbJtC60NL9HdESFWN5Om/B30BYsY6x74RwKte4iIiIgMTioLdQNQccDr8v3XDlQPPGGtjVlrdwHbGSjYkmKT8gp4IOsLuGu/Qqw7h22lr9LfX0x+73YAimfMcDihiIiIyIkhlYV6FTDZGFNljPECHwGeOOgzv2dgOo0xppCBFZDaFGaS/a6cMoUfLr6V7L9ch9uVzrqcLtLTJ5HYuwVjDEXTpx/9FxERERGR1BVqa20c+DTwDLAFeNhau8kY801jzGX7P/YM0G6M2Qw8D/yztbY9VZnkIG8AQcisLuB7v7mPb33uVJKJBHkTJpCWnu50OhEREZETQkqflGitfQp46qBrXzvgawt8Yf8PGQL90SjP1Wzioo4f4V19OpaPY84xGGOI1w+cP60bEkVEREQGz+mbEmWI/aWujj+ufoTG+tdIdLxMV9dumqbtBKBl0yZA+9MiIiIixyKlE2oZfkrS08koPY2O3nnkvwSt3lr+40e/4qVr5vKjhXqgi4iIiMix0oR6lFlQVsYPL7iEubuvwu67iJfT9rF7z+ks+/kF9DQ04PH7yZs40emYIiIiIicMFepR4Jrly/nOypW0BYMDFyz0PhFhX0M/K2IBbr/9VGYW9QJQNH06LrfbwbQiIiIiJxatfIwCq5qaWNfSwk/WreM/J/RxQesr0Hk7ne7pTPtwIUuWzGHN3XcDUKT9aREREZFjokI9SqSnpdHY10f7nqcx0b1QupN6fzlf/soHMMbQslH70yIiIiJ/D618jBLWWnK8bhal7SYz4qOzYSEbr+kiLc2NtZbWt0/4UKEWEREROSaaUI8Sfo+HmaaGwmQMV9dEMt0T+ewn/AD01NUR6ekhvbCQjOJih5OKiIiInFhUqEeJtv5uzjC7yA0ZMtrOIOeyPMgeeO/tdY+iGTMwxjiYUkREROTEo5WPUeCU0lIm93RwTfSv5EV8RDZPoX3sdgAa167l1e99j96GBtq3baNx7VqH04qIiIicWAZVqI0xXmPMpFSHkdS4//zz2dLXQ2ZGFq6Ij563xrB82S2EOjt58rbbaHjjDUIdHTSuWcOTt99OPBx2OrKIiIjICeOohdoYcwmwAXh2/+s5xpjHUx1Mjp8Xf/Ur5rr3khdxE28cT0NkMy073uIXCxbQvn07WIvL4yGzrIxQeztrly1zOrKIiIjICWMwE+pvAguBLgBr7XpA0+oTyI57HuDS3r+SHzYk9k6kNvkciUiEzpoaEpEIAJ5AAONy4fZ6WX/PPQ4nFhERETlxDKZQx6y1XQdds6kII6mR88HrGJcdwxtLJ9FaQWf5Lvz5+Yw780z8+flkV1SQXV4OQCIaZc6SJQ4nFhERETlxDKZQbzHGXAu4jDFVxpgfAitTnEuOI3vSLMZ5MgBo7YgSineQXV7OdY8/TvbYscTDYSK9vQTb2ggUFFC9dKnDiUVEREROHIM5Nu/TwNeAJPAY8Azw5VSGkuPrzrr1nJXtg5DBVE1mVvXHmHb11QTy8vjQXXex5dFH3/nstKuvxuP3O5hWRERE5MQymEJ9obX2X4F/ffuCMeYqBsq1nACst4dgRxXJsIvJn/wwky/98DvvlVVXU1Zd7WA6ERERkRPbYFY+vnKIa/9xvINI6nwz4Sdx15dJPvwATHQ6jYiIiMjIctgJtTHmQmAxMNYY84MD3spmYP1DThC9q9ZR1H8p3sIMGO90GhEREZGR5UgrHy3ARiAMbDrgei/wb6kMJcfP1vW7aK2NYYwbzwQ/pDudSERERGRkOWyhttauA9YZYx601urReSeor/56Bf/xD1vJDt1Asuv/cGvnQ0REROS4GsxNiWONMd8GpgPvHP9grZ2SslRy3LzV14zXpmM8cVwV5U7HERERERlxBnNT4r3APYABLgIeBn6XwkxynFhrib8S4tnv/4Dw/U9hJvmcjiQiIiIy4gymUKdba58BsNbWWGu/wkCxlmFu17Z9xHeHmdqeTbZ/DExwOpGIiIjIyDOYlY+IMcYF1BhjbgMagKzUxpLjYeXTq0jDUOUuxLhcUOV0IhEREZGRZzCF+p+ADOCzwLeBHODmVIaS4+OFVVv54ZeeoyL6FuHab+D3n+50JBEREZER56iF2lr7+v4ve4EbAYwxY1MZSo6PdT1d3JZncUWbSZQEnI4jIiIiMiIdcYfaGHOKMeYKY0zh/tczjDG/Bl4/0n8nzov29XGRWcMYdw8m7sUzTsfliYiIiKTCYQu1MeY7wIPAx4A/GWO+DjwPvAnoyLxhrunNN5lp95IZt9iOcrwnae1dREREJBWOtPJxOTDbWhsyxuQDdcBMa23t0EST96Nx7Vqyi4J4kn6SXeMxkwZzoIuIiIiIHKsjtaywtTYEYK3tALarTJ84fvPoNvZm5uFKeKFzAoxzOpGIiIjIyHSkCfUEY8xj+782QNUBr7HWXpXSZPJ3i4VCrGsNc9b4dBKJIDEz5oBnXIqIiIjI8XSkQn31Qa9/msogcvy0bNhAayLAVE8nJuElkVvhdCQRERGREeuwhdpa++ehDCLHT+1rq0nvilKS7McVycWML3Y6koiIiMiIpTvVRpjGtWtZ8fPfUZHXijfpJtFWgW9attOxREREREYsFeoRJBYK8Ydbb2VHTRfjSjrwJNOItYzFMynd6WgiIiIiI9agC7UxxpfKIPL+rVu2jL7mZtpMIWMvBJIBYi3lvPnCfU5HExERERmxjlqojTELjDEbgB37X882xvwk5cnkmK2/916S0Sjlmc1EQsWEm6fS053OuvuXOR1NREREZMQazIT6x8CHgHYAa+2bwDmpDCV/n5nXX0+kp4cpkV3MeKCAyC++RcdOH3OWLHE6moiIiMiINZhC7bLW7jnoWiIVYeT9ySwtxbhcYAx5PUW4olFCuR1UL13qdDQRERGREetI51C/rc4YswCwxhg38Blge2pjybGy1rL197+nr2QW3gkTado6m3nePKZ96Uo8fj3VRURERCRVBjOhvh34AlAJNAOL9l+TYWTfqlV01tayKjGH8Lw4H/qnr8KZv8R3uo7MExEREUmlwUyo49baj6Q8ibwvGx96iLZIgAZTwclZXdjuUmIdZeSelOd0NBEREZERbTCFepUxZhvwO+Axa21vijPJMerdt4+9L73Ez077IMm+DHydHyf+zKlkTbC4A16n44mIiIiMaEdd+bDWTgS+BcwDNhhjfm+M0cR6GNn08MOE4i5aSnJonxSnORIhkkgQn5jmdDQRERGREW9QD3ax1r5qrf0sUA30AA+mNJUMWiwUYtvy5bzVXQouF8VeF9N7A7SFQny39Q2+s3IlbcGg0zFFRERERqyjrnwYYzKBy4GPANOA5cBpKc4lg7Tz6acJ9fSyMXEKxhjOK9jOTdW/IJ64ir2Fp/DE6jp2dHXxq8WLnY4qIiIiMiINZod6I/AH4LvW2pdSnEeOgbWWjQ89BMCnPjmfT9PIorTNpPW76e8uY9eUbr44fz63zp7tcFIRERGRkWswhXqCtTaZ8iRyzPatXk1nbS2ZhQV89AtX8tN7vs+pnj14ooXEt53O/97hYcqEk52OKSIiIjKiHbZQG2O+b639IvCoMcYe/L619qqUJpPDaly7li2PPkrNc8/R29DApMWLcael8Yn8GsZE02HnIlxZ7VSWL3I6qoiIiMiId6QJ9e/2//zToQgigxMLhXjyttvoa2ykv6WFVxOn8PyyGqpurOfmvFqSLT66NlxAqLQNrzfD6bgiIiIiI95hC7W19o39X06z1r6rVBtjPg38OZXB5NDWLVtGX1MTsWCQuDvAVk4m3ull1R++SeaEJrIappJonkTktNecjioiIiIyKgxmh/pm3julXnqIa3Kcvb3a8baq887jpf/6L8KdnRiXi23MIOH2U+nrIJuVdPck8bx0BYm0OH3nNTuYXERERGT0ONIO9XUMHJVXZYx57IC3soCuVAcb7d5e7ehvacG4XER6enj9xz/Gl5uLtRZ/fgFbeqshZrigYhPzi8O4e8uw209lz+ynyRtf5fS3ICIiIjIqHGlC/QbQDpQDdx5wvRdYl8pQMrDaEerowJuZSW9DA4lolGQ8Tm5VFen5+Wxp9rG5p5BJ/iYWf6CFgtxi7GuX0x6KUbvwOa6YdJ/T34KIiIjIqHCkHepdwC7guaGLI29bf++9uL1e+ltaSESjuL1e0ouKiAeDXHT3Mh666QmqjOGqhQXMOb8I02UIvXkB+6aspnjeyQQCeU5/CyIiIiKjwmEfPW6M+ev+nzuNMR0H/Og0xnQMXcTRac5NNw1MpWMxALLLy3F5PMxZsoRW9xjCOZXMOHU6n/7WNDrjIWLbZxNqymXHwqc56aQrHE4vIiIiMnocaeXjnP0/Fw5FEHm3uUuX8uavf01PfT1YS7inh8zSUqqXLsXj93P//VcSjyepafoBFR2GztUX01m6hfj0IGPGzHc6voiIiMiocdgJ9QFPR6wA3NbaBHAq8AlABxynWFogwOIf/pBAXh7pRUXMuuEGPvTzn+Px+wGYPLmAadOKKJ39ryQe+wOBxvOJXRdi5qyPYcxhf1tFRERE5DgbTPP6PWCNMROBe4DJwG9SmkoAyK6sJGvsWMrmzuW8b3+bR16JsWJFzbs+M3F9LlXtE8kal8PJn7mOGTOudSitiIiIyOg0mHOok9bamDHmKuAn1tofG2N0yscQCLW3A5BeWMirr9Zx331v4vG4mDmzmLKCBHRvggdOBVxwPYP765GIiIiIHFeDqWBxY8yHgRuBJ/dfS0tdJHlbcH+hTmYW8J//+SIAievTuHfPJrprfkfLio8T8X2djngN26Y+STIZdzKuiIiIyKg0mEJ9MwM3KH7XWltrjKkCfpvaWAIQbGsD4PGNObS29jNrVgktvjC/3LCBz7/yFsH6Apo3L2LzSY+yZstdn9luAgAAIABJREFU2p0WERERccBRG5i1diPwWWC1MeYkoM5a++2UJxNC7e1s6Snk/161+P0evvGNswHI8/vZ03Mmnf/7EG17q3nm0gomzLxFhVpERETEAUdtYMaYDwA7gWXAr4DtxpjTUx1MBibUqzvHMLHCz2c+s4CKipx33rt09UR81s/rsxr5eWc7dzf4HUwqIiIiMnoN5qbEHwIXW2s3AxhjpgH3AzrsOMWCbW10x3y4PB7OOacKgDlmO6fFOzln40ISNsnyBTv54vz53Dp7tsNpRUREREanwRRq79tlGsBau8UY401hJtkv1N7OJydu48wffZzCwnRqOzu51v0ip4W7cI87jZqemXzmtBrOn7sYny/gdFwRERGRUWkwS7drjTF3GWPO2P/j54COzRsCwfZ2XAYqp4zF5TKk965nZloLud0lFNVfSX/1vXQ2PE9amsq0iIiIiFMGM6G+jYGbEv9l/+uXgJ+kLJEAYJNJQh0dAATy8wEobX6U4kAhbP0HWt37aJy6htlT/gGXazC/jSIiIiKSCkdsYsaYmcBE4HFr7XeHJpIAXPXYY9RVnoJZY8h6ehsfPcsD7StxdaTTv/58tsy+H39GLjNnXu90VBEREZFR7bCF2hjzZWApsBY4xRjzTWvtr4Ys2Si3urmZ9qllxE5K47M1LzDes5pFcUisuoS+3j52z32e88/5bwKBfKejioiIiIxqR5pQfwyYZa3tN8YUAU8xcGyeDAGbSOAKW5JZhkxa8LU9T3coj9jKc6ib8SrTT7uG8vKFTscUERERGfWOdFNixFrbD2CtbT3KZ+U4s8kkCesirc/y+fzN+HHRvepc2ntK6V68h/nzb3M6ooiIiIhw5An1BGPMY/u/NsDEA15jrb0qpclGuUQiSdIaCrxBPuBaR0a/B7vqchLjN7Pgxs/oRkQRERGRYeJIrezqg17/NJVB5N0S8QQJv4trCzdQ4nORvm4u8a4ifF8Mk5U1xul4IiIiIrLfYQu1tfbPQxlE3q20O0ZkU5jTP1gHPWF440rcZV6Krp/udDQREREROYD2ooepT+2sZ1HDbvLT+wn0p0NfJTlLxw3u5HARERERGTKqZ8NUXryJyzwNVBR/gfRnDR7/TNzXpDkdS0REREQOMuhCbYzxWWsjqQwjfxNsa8ObSDBhww14dvjhQ0Ce06lERERE5GBHXfkwxiwwxmwAdux/PdsYo0ePp1AsFGJdYyYNsXxcm30DFz/kbCYRERERObTB7FD/mIE61w5grX0TOCeVoUa7tromlu+bygvjx7Av/F26M5+GCqdTiYiIiMihDGblw2Wt3WOMOfBaIkV5BKjZ0gDA5On95Oc9SLK2HgovcjiViIiIiBzKYAp1nTFmAWCNMW7gM8D21MYa3Wq3NQOQXlNBInMWaXY6uB0OJSIiIiKHNJhCfTsDax+VQDPw3P5rkiJ7drUDMLZxOlmhW2CWw4FERERE5LCOWqittS3AR4Ygi+xXt7cbgIlZhRACSpzNIyIiIiKHd9RCbYz5BWAPvm6tvTUliYSGpiBJryGz9DW6uzPJyF+Ah2ynY4mIiIjIIQxm5eO5A772A1cCdamJIwCdXVGSxYaqKS/hPelp3GMeAxVqERERkWFpMCsfvzvwtTHmfuDllCUSvrhoDy937iPP04crCaak2OlIIiIiInIYgzmH+mBVaKs3pcId7Uy2dWS7Q7iTaVCa5XQkERERETmMwexQd/K3HWoX0AH8WypDjWY2mSTY3o5vQhhXIhPCBVBqjv4fioiIiIgjjliozcDTXGYDDfsvJa2177lBUY6f+375Or/YOYcbp/dgku6BQp3vdCoREREROZwjrnzsL89PWWsT+3+oTKfY1k2NNEcy2Dd2NnFcRG3+37eYIyIiIiJDYjBVbb0xZm7KkwgAe3Z1AFDicmMBm1bgbCAREREROaLDrnwYYzzW2jgwF1hljKkB+gHDwPC6eogyjir19b2A5cxYDWkkcWWPdTqSiIiIiBzBkXao3wCqgcuGKMuoFwzGaO8Mk5XZR7bbjTHgztKBKiIiIiLD2ZEKtQGw1tYMUZZRr76+BxuPU5zbQYbLBbggTysfIiIiIsPZkQp1kTHmC4d701r7gxTkGdX27u0mGY/jrYwS93hIYiC/0OlYIiIiInIERyrUbiCT/ZNqSb1x43I4d2IPzUUJfGmRgcO/S1SoRURERIazIxXqRmvtN4csiTB5cgHnlOxl754e/K4l4O+Cm3UItYiIiMhwdtQdahlawbY28kKQuWkproBbD3URERERGeaOdA71eUOWQgB44vFNbG/xkG6LMW4XFKO/1oiIiIgMc4ct1NbajqEMMtoFgzG+/vUXeKx5Mk2neGnP+y3Riu1OxxIRERGRo9BDrYeJ+voeEvE4JbkdZGTnkH7Bf5OsesDpWCIiIiJyFEfaoZYhVFc3cGReWr+P8a3jiId8BGbOcjqWiIiIiByFCvUw8fYZ1PneCFMaziC76yw4x+lUIiIiInI0WvkYJurqekjG4+R5Q6Qn9j8dUU8dFxERERn2VKiHibq6bmw8Tnb1DvrtVmLeOihOOh1LRERERI5ChXqYiEQS7G6xNFVkkXvmz7BXXQa5jU7HEhEREZGj0A71MHHffVdwet/vWYMbX6ALXAYK9NhxERERkeFOE+phwhiDL7mXBb3deN1RvO5s8PicjiUiIiIiR5HSQm2MWWyM2WaM2WmM+bcjfO5qY4w1xsxPZZ7hqqcngk0m6Q81kxfPBeMCo+m0iIiIyIkgZSsfxhg3cCdwAVAPrDLGPGGt3XzQ57KAzwGvpyrLcNbdHeaCC+5n6qQc5nnD5FGBMUFIU6EWERERORGkckK9ANhpra211kaBh4DLD/G5/wT+GwinMMuwtXJlPcmkJc3ESQZitHinE7MQ8eQ6HU1EREREBiGVhXosUHfA6/r9195hjKkGKqy1fzzSL2SMudUYs9oYs7q1tfX4J3XQK68M/F80Z3KAhD9KrnFjrcGdpQm1iIiIyInAsZsSjTEu4AfAF4/2WWvt3dba+dba+UVFRakPN0SSSctrr9UDMKPCkvDHmG734XW78OTrqS4iIiIiJ4JUFuoGoOKA1+X7r70tCzgZeMEYsxtYBDwxmm5M3Lq1jc7OEKWlmeS7e0gGomR6EriMS0fmiYiIiJwgUlmoVwGTjTFVxhgv8BHgibfftNZ2W2sLrbXjrbXjgZXAZdba1SnMNKy8+urAusdpp1XQ014HxuJLi2EAilWoRURERE4EKSvU1to48GngGWAL8LC1dpMx5pvGmMtS9b97Ilm5cmDd46SiEJtXPEpLtAob6CNmE5BX4HA6ERERERmMlD4p0Vr7FPDUQde+dpjPnp3KLMPRHXcs5pUXa9n+lZvoDm4nOvZiGFNHJBHG2Ew9xlJERETkBKAnJTooI8NLbs1zJLvb8DT5mPZUkujac+naMo21v37Y6XgiIiIiMggq1A5bf++9uNPSSMRijO0MkPjTjTQ8eTbr77nX6WgiIiIiMggq1A5IJi3XXfcIX/3qXzjp+n8k2t8P1pLpLgMDfaaZOUuWOB1TRERERAZBhdoB27a1UVPTwZo1jSy6bSkun4fYVSES1YXEy7dhx3RTvXSp0zFFREREZBB035sD3j4u74wzKvGmp1N6/hy6qSd7XJTcy77OgpLz8Pj9DqcUERERkcHQhNoBbz9u/LTT9j/3psmS9+ZMyqKVJNpmkD19kYPpRERERORYaEI9xHp6Imzc2ILH42LBgrFEenrorqsjPc1HVf0/YnbdBF93OqWIiIiIDJYm1ENs5cp6kknLnDmlpKensfONP2GNpWzCPEzMDDyQPd3plCIiIiIyWCrUQ+zAx40nEjFeWPV1OhbuIGvsVJLeTihJOJxQRERERI6FCvUQu/76mbS1BTn77PHs2fNXgsF2XFEPe/wnEVx8JcEzT4FIh9MxRURERGSQVKiH2JQpBeze/XkqK3PYsuUx4qEQ/qZc8l3FuANd4LHgzXU6poiIiIgMkm5KdEhv7z727n6ZB7Ov5qQxbm7JysNvPODPB6O/54iIiIicKFSoh9C2bW288kods2eXkEz+nngozD53Be3j03ly9RtcOS8J3jy8TgcVERERkUHTKHQIrVnTyM9+tornntvJ9u1/IBYKQdJFrs9HaTJMPJnkudZ+vrNyJW3BoNNxRURERGQQVKiHUHNzHwC5uTvo72/F1evGJA1Bl4fcyMB7LWnpfH/1av7lxRedjCoiIiIig6RCPYSamvr2f/UyWIurJg2A+bUV5Lp7ibmT7PV5+eL8+fzPWWc5F1REREREBk071EOoubkfr7eHcHg9PlcS774MXHPdXLdmGt6Jq0jmWf7pjMVkTdajx0VEREROFJpQD6Hm5n5KS9/C4zHkmipcMQ+XtJbwgfaxFJf2k1fqIytrjNMxRUREROQYqFAPkXg8SVtbP6Wlb+LxuAi0FLC1uJj/3H06ZRmZuE/qGPjd8BU6HVVEREREjoEK9RDp6YlQUeEhPT2O359N07ZeXqs6nZ63ioh7glBaP/BBFWoRERGRE4p2qIdIfn6Axx67GWuXEOrq4M5fXM7Nez5MjteP55r/A9MDWVMhUOZ0VBERERE5BirUQ8wYQ8e2HRT2u7mscTb55Zlw4TmQ2ALlV4IxTkcUERERkWOgQj1Erl6+nPklJXx81ixaNm5kfNfZ+NKyMQsMzKwE/j+nI4qIiIjI30GFeoj8Zfsentmwie+9vIIxwRiP9l6Kpxj4mAU0lRYRERE5UemmxCGSSFi8iSgRDFW7ZtHnK6dn8fcIJ2+E3p1OxxMRERGRv5MK9RBJJJJEIlmU+vL4hzXT8HhCNE14noZ9q8EdcDqeiIiIiPydtPIxRBJJixvDnF3FTGrLozMzwuqZP+CWiUlIH+t0PBERERH5O2lCPQTC4Tg2aYl7kix+ZRwZwRDJ9Ce5PiuLnPJznI4nIiIiIu+DCvUQaGnpJ73dxcltOzm7JovsaY+xpe3HPHn77cTDYafjiYiIiMj7oEI9BJqa+qh4qo1Z0X7MjPWkX76MC25qJ9Textply5yOJyIiIiLvgwr1EBg3LoczMl9jUi8Un3ovlhi7a/Jxe32sv+cep+OJiIiIyPugQj0ESkoyufwf5zI7HMSXU08yaNi9M5dENMqcJUucjiciIiIi74MK9RCpOP9U8k0mAH2dafS3thMoKKB66VKHk4mIiIjI+6Fj84bAM8/spKlpB3N9OQAk/SXM+tgZTLv6ajx+v8PpREREROT9UKEeAsuWrcOa1czwTSfu8lCw8Gwqr/mG07FERERE5DjQykeKWWtpaurDBEIUu3swQGDiVKdjiYiIiMhxokKdYn19UYLBGAU2SIG/CY9N4hpT6XQsERERETlOVKhTrLm5H4BS68ed0wqeJGSMcTiViIiIiBwvKtQp1tzcB1gKIwFcWe2QZsBf6nQsERERETlOVKhTrLm5H48nTNg9lYTHEiILPOlOxxIRERGR40SFOsWCwRgZGSGKrA8LRD2aTouIiIiMJDo2L8VuuGEWH75iAm+d/ADBTV8l/asq1CIiIiIjiQr1EOjatYvsvjG4e08mfcIEp+OIiIiIyHGklY8h0FlbSyBWgNvngzKn04iIiIjI8aQJdQolk5aLL36QfO+TfP8D1cQz/gpZS1CrFhERERk5NKFOoa6uMG1tQXzWEJjxV7wLfgu2z+lYIiIiInIcaUKdQk1NA+XZs2EG4Y4yzIJ2MtPHOpxKRERERI4nFeoUam7uwyYSlHZl4mk/nYJzp4LHOB1LRERERI4jrXykUHNzP/FIhLEmA7fPhylTmRYREREZaVSoU6i5uQ8b72X8zFaS8/5Ef/5bTkcSERERkeNMKx8p1Nzcj8d0UlSZRmDWQ7R7mslgltOxREREROQ4UqFOoQ9+cCI9axsp8uThAvwluiFRREREZKTRykcKnXXWOGambyXf34nHQFHVZKcjiYiIiMhxpkKdQsG2Nlz9HtxZ7eCxkK0HuoiIiIiMNCrUKdLbG+Hh+1YSiY7Bld0OaUBAhVpERERkpFGhTpFdu7r43s820euqIuH7/9m797ioqr1/4J89w3D1CogpkCAoDTNcZBCovB4NDclSTDFNTcouPpVZHc/J86RPpzLTTuqTp7QsKztD55i3o2i/o2JeKn1AMfGKlzHAVAQEhutc1u8PYhJhYIbbaH7er9e8dK+91l7ftfGFXxZr7W1AsdEZcOrs6LCIiIiIqI0xoW4nly/rYayuhr9b7bOnK+AFSHwONREREdHvDRPqdnL5sh6m6mr0k5dDARPu8gx0dEhERERE1A6YULeTy5fLYKyuhqerACBB3s3P0SERERERUTtgQt1O8s5fhUyqQRdXEwBA7hPg2ICIiIiIqF0woW4nueevwtW1CnJ3M0yQ4XLnro4OiYiIiIjaARPqdlJ4tRQ+rjIoOhfBKBMwdfFxdEhERERE1A746vF2siCxDPnrSyDtewk14b/Ax0fl6JCIiIiIqB1whrod/HL4ME5t3IAdOffDfDYQzpUj4OzCZ1ATERER/R4xoW5jhspK/Pvpp1F89iwmSUdQXVqK7D1aGKuqHB0aEREREbUDJtRt7MiaNcjOBf5legSy6LsgS1yPQtVRHF6zxtGhEREREVE7YELdxrLWrkWpuROuCU/c5V8Oj5hNkNR5yPrsM0eHRkRERETtgAl1G4ucMQP66tpXjMuyB0C/fxJ+yQEin3jCwZERERERUXtgQt3GBqSkoEreCTALeJyJgPh/D0GeZ0JUSoqjQyMiIiKidsCEuo0p3Nzg1leNLnIPdHLrCpeenTFq1VI4ubo6OjQiIiIiagd8DnU70BsV8PdwhlvcThh79IB3RKKjQyIiIiKidsKEuh2UlNYgsqsz3Ed8gDIngeKqB+DTSeHosIiIiIioHTChbgdhnkW4F+6QAOiN3dHH3d3RIRERERFRO+Ea6nZwX5cchHuXQQ4zurt1gVzG20xERET0e8VMr42ZampQXVqKzm61t9bk7uXgiIiIiIioPTGhbmOXdb/gQnk3uLkLAIC5m4+DIyIiIiKi9sSEuo0d/vEC/vFzGGQeRhggw37h4eiQiIiIiKgdMaFuY5fzCuEqFHD1uA4BoNLrLkeHRERERETtiE/5aGMFl67DR3SBS9dfAKdqxIfe7+iQiIiIiKgdcYa6jRVcKUGAsytkbmUwywS8vO9xdEhERERE1I6YULexwmvlCPU0AgBqTB6QyflCFyIiIqLfMybUbew/ajn87hEwSXJcMPWA0Wx2dEhERERE1I6YULex4u5y+PpUwwwJh03euF5V5eiQiIiIiKgdMaFuYx4KA+TZQ3H1P6/i/8lCMUirxaIff8S1igpHh0ZERERE7YAJdRsSQsDJIND1l2CUnxuNiz6BcJLJ8F5GBv64d6+jwyMiIiKidsDH5rWhmrIy9CzrBJmQ4VrXShQYK6CQyfBydDRmRUQ4OjwiIiIiagdMqNtQzrGL8C31gOew1bgi88JDAX3x34NGwMvNzdGhEREREVE74ZKPNnTx7GUMKCqHZ+RG9A7dCI1nJybTRERERL9znKFuQ1fzCtFzf3eUVj+OgphSRPr2dXRIRERERNTOmFC3oauXiuFzXYXq70dh2B97AXc5OiIiIiIiam9c8tGGrl0tQy/RDU5OcsDf0dEQERERUUfgDHUbKiqoQP8Bh6BQdEeOoRf64T5Hh0RERERE7YwJdRsSV2W4a/KHMLpVY+b+R7D1HibURERERL93XPLRhgZ4VELmVIMKgwd8PDo5OhwiIiIi6gBMqNvQ/Z4FkMMMmcEJs/t7OTocIiIiIuoATKjbiMlgQCenakAC9MIVHh49HB0SEREREXUAJtRtpOzqNbh2MgMASuSAuzsTaiIiIqI7ARPqNnLxdD7MHkYYJDn+ib4oMrs7OiQiIiIi6gBMqNvILxeuoptXAQDgBxcP9OzGt7oQERER3QmYULeRsjPFcO56DRKMGNXlCHp28XF0SERERETUAZhQtxGny1cgSUBVeWfc3ckDMpnc0SERERERUQdgQt1GPCqKAAjoq9zg7u7t6HCIiIiIqIMwoW4jXVAKyIAzwgc5Zl9Hh0NEREREHYQJdRvp5lYMIUk4KvNBodTN0eEQERERUQdxcnQAvxfOGYkoOzMUPScbEBcW6ehwiIiIiKiDMKFuA8Is4J4XApnJGRM3B0LR083RIRERERFRB+GSjzZQ87MecpMzjIoqJtNEREREdxgm1G2g8kgWpBFf4tLAvfhi3+eoqdE7OiQiIiIi6iBMqNtAzfmjcI/dBkN4JhZn/ASTqcbRIRERERFRB2nXhFqSpNGSJJ2WJOmsJEl/auT8XEmSTkiS9JMkSbskSerTnvG0l9LTAbj2nydwOi8Qw/16wtWVT/kgIiIiulO0W0ItSZIcwEoADwIIBTBZkqTQm6odARAthAgHsB7Au+0VT3uqOumH6/smIO8nFT549I+QJE78ExEREd0p2jPziwFwVghxXghRAyAVwMM3VhBCpAshKn49/BGAXzvG025kVxQAAIMPl3oQERER3WnaM6H2BZB7w3Her2XWpADY3o7xtA+TCd5BO+Ch/BGGfudw/fpFR0dERERERB3ollibIEnSVADRAJZYOT9LkqQMSZIyCgoKOja45hRcQtfBn8PzkZVYG1yBL79PdXRERERERNSB2jOhzgfgf8Ox369l9UiSNBLAfABjhRDVjV1ICLFaCBEthIju0aNHuwTbYhdrZ6RL9F5wkRnh18XTwQERERERUUdqz4T6/wD0kyQpUJIkZwDJALbcWEGSpAEAVqE2mb7ajrG0n18uQiaZIS8z4o+dd0Pp7ePoiIiIiIioA7VbQi2EMAL4LwDfAjgJ4J9CiOOSJL0hSdLYX6stAdAJwL8kScqSJGmLlcvdssxFFwABXDcIyORO8PC4xWbQiYiIiKhdObXnxYUQaQDSbip7/Ya/j2zP/juCKDsPACiSGQAA7u5MqImIiIjuJLfEpsTbmukiDJBhTadI7KruxxlqIiIiojsME+rWMFYAKIQwOuNwZ2fUSC5wcenq6KiIiIiIqAMxoW6NgouAEagp8sF9VV9jZNdSSJLk6KiIiIiIqAMxoW6N3DwIAVwv7oZukh5+XTg7TURERHSnYULdGgVlEEKgzCCDk5OMGxKJiIiI7kBMqFujQg8hBIrcXHDEIwIyF29HR0REREREHYwJdWtUl0MIoNjFAz+5KtHZw8vRERERERFRB2vX51D/7pUnoHxDZ5S5X8AEjRMC/OMcHRERERERdTAm1K0gSu+G6ZQGvj4nMXnyfCjc3R0dEhERERF1MC75aAXT9RpACAg3M5NpIiIiojsUE+pWqCz5FOZ7N+FwzxKs+vM0XMrMcHRIRERERNTBmFC3kKGyEka5Fp1HpOJcRFfsMp3A1ueeg7GqytGhEREREVEHYkLdQkfWrEFZRiyK9j+Komsm+F4zoaqwCIfXrHF0aERERETUgZhQt1DW2rUoz3wAhWnTUPRPQPmTHHJnZ2R99pmjQyMiIiKiDsSEuoUiZ8yAzPTrRkS5HgBgqqlB5BNPODAqIiIiIupoTKhbaHHvXlBozsE95Ah+9hAoL7kKNy8vRKWkODo0IiIiIupAfA51C+VczsNd4z6FscYDJdL9cK/RIPGR/4aTq6ujQyMiIiKiDsQZ6hbyMpoAADVGN5Q6d8PiXsOxtqYG1yoqHBwZEREREXUkJtQt5FVjBAAYDc7wkZVCIXfCexkZ+OPevQ6OjIiIiIg6Epd8tFA3owmQgCqTCyrgAhdJhpejozArIsLRoRERERFRB2JC3UKeJiPgBFSZXXG/PAcfPvpf6N3Nx9FhEREREVEH45KPFlK5uwMCqDEC/qZC9Oraw9EhEREREZEDcIa6haYHBqDilECpcIfZWYIkSY4OiYjojmYwGJCXl4eqqipHh0JEtxlXV1f4+flBoVC0qD0T6hYyV5ZCMgu4VV2HuksnR4dDRHTHy8vLQ+fOnREQEMBJDiKymRAChYWFyMvLQ2BgYIuuwSUfLSTKSyABEKZq9Orq5ehwiIjueFVVVfDy8mIyTUR2kSQJXl5erfrtFhPqFjJXlgAQqDILdO7s6ehwiIgIYDJNRC3S2u8dTKhbyFxVAiGTkKfoBpdO3R0dDhERERE5CBPqFjLX6CEkCWdde8PZuYujwyEiIiIiB2FC3UI1uuEo2T8JxmIF/O9SOTocIiK6Beh0Ori5uSEyMtLqebVa3ei5FStWQKlUYsqUKQ3OvfXWW1CpVAgPD0dkZCQOHjyI4cOH49tvv61Xb9myZXj22WcB1P4Ke+rUqZZzRqMRPXr0QGJiIgCgsrISkZGRcHZ2xrVr15od26ZNmyBJEk6dOtXkeBYuXIilS5cCAC5fvozk5GQEBQVBo9EgISEBZ86cabav5uzYsQMhISEIDg7GO++802id5cuXQ61WQ6VSYdmyZTa1DwgIQFhYGCIjIxEdHV2vzfvvvw+VSgW1Wo3JkyejqqoKubm5GD58OEJDQ6FSqbB8+fJWj83W8TVVz95ye/okK4QQt9VHo9GIW0Fx/AVx1f24+L9ZqxwdChERCSFOnDhhV/1LmZli12uvWT6XMjNbHcOFCxeESqVqUG42m4XJZLJ6XgghQkJCRG5uboPy77//XsTFxYmqqiohhBAFBQUiPz9frFq1SsyYMaNe3djYWPHdd98JIYTw8PAQERERoqKiQgghRFpamoiIiBBjxoyp16ZPnz6ioKCg2bFNnDhRDBo0SLz++utNjnfBggViyZIlwmw2i7i4OPHhhx9azmVlZYm9e/c221dTjEaj6Nu3rzh37pyorq4W4eHh4vjx4/XqHDt2TKhUKlFeXi4MBoMYMWKEyMnJaba9tXuRl5cnAgICLPfy0UcfFZ999pm4dOmSyPz1301paano169fg1iKNvE6AAAgAElEQVRulJ6eLqZPn97q8TVVz95ye/r8vWvsewiADGFDfsoZ6pYqq/1D3q1lzyskIiLHMVRWYuszz+Cnr77CsdRU/PTVV9j67LMwtuEzrHU6HUJCQjBt2jSo1Wrk5uYCqJ0pnjJlCpRKJSZMmICKigo888wzOH/+PB588EG8//779a7zyy+/wNvbGy4uLgAAb29v9O7dGxMmTMC2bdtQU1Nj6e/SpUsYPHiwpW1CQgK2bdsGANBqtZg8eXKLxqLX67F//36sWbMGqampNrVJT0+HQqHAM888YymLiIioF19LHDp0CMHBwejbty+cnZ2RnJyMzZs316tz8uRJxMbGwt3dHU5OThg6dCg2bNhgc/vGGI1GVFZWwmg0oqKiAr1790avXr0QFRUFAOjcuTOUSiXy8/PbfXxN1bO3vDX3hH7D51C3kKn7PhhDJBw3X0K4qQZyubOjQyIiol+tvunX9Tcrv3oV5VeuQOb023+D+l9+wf/27w8PHx+r7WZlZNgVR05ODj7//HPExcUBqE16T58+jTVr1uD+++/HzJkz8fe//x0fffQRduzYgfT0dHh7e9e7Rnx8PN544w30798fI0eOxKRJkzB06FB4enoiJiYG27dvx8MPP4zU1FRMnDix3tMKkpOT8cYbbyAxMRE//fQTZs6ciX379tk1BgDYvHkzRo8ejf79+8PLywuZmZnQaDRNtsnOzm62Tp3BgwejrKysQfnSpUsxcuTIemX5+fnw9/e3HPv5+eHgwYP16qjVasyfPx+FhYVwc3NDWlqaZQlHU+0lSUJ8fDwkScLTTz+NWbNmAQB8fX3xyiuv4O6774abmxvi4+MRHx9fr0+dTocjR44gNja2wThiY2NRXV0NvV6PoqIiy5KgxYsXY9SoUXaPr6l69pbb0ydZxxnqFnK/fxm6TnoXXzhfQFXVdUeHQ0REdqgoLIQkq/9foCSToaKwsE376dOnjyWZruPv74/7778fADB16lTs37+/yWt06tQJmZmZWL16NXr06IFJkyZh7dq1AIDJkydbZoxTU1MbzECHh4dDp9NBq9UiISGhxePQarVITk4GUJuka7VaANYfNWbvI8j27duHrKysBp+bk2lbKZVKzJs3D/Hx8Rg9ejQiIyMhl8ubbbd//34cPnwY27dvx8qVK7F3714AQHFxMTZv3owLFy7g0qVLKC8vx7p16yzt9Ho9kpKSsGzZMnTp0vBBBQcPHkRWVhY++eQTjB071jK+m5Npun1xhrolzGZUnR4Eo0s5ZD5muLh0dXRERER0g+Zmkg998AF+XLYMrt26Wcqqrl9H3EsvIWb27DaLw8PDo0HZzcnmzccrV67Exx9/DABIS0tD7969IZfLMWzYMAwbNgxhYWH4/PPPMWPGDDz88MN46aWXcPjwYVRUVDQ6Izx27Fi88sor2LNnDwpb8ANDUVERdu/ejWPHjkGSJJhMJkiShCVLlsDLywvFxcUN6gcGBsLPzw/r16+3qQ97Zqh9fX0ty2eA2jdk+vr6NmibkpKClJQUAMBrr70GPz+/ZtvX/enj44Nx48bh0KFDGDJkCHbu3InAwED06NEDADB+/Hh8//33mDp1KgwGA5KSkjBlyhSMHz/epvE2xdbxWatnb7k9fVITbFlofSt9bolNiZVCXOycKzJk58Vbc79ydDRERCTs25RYU1EhVg8cKN7v00cs69tXvN+nj1gdEyMMlZWtiuHGTXqNbdi7cOGCACC+//57IYQQKSkpYunSpUII6xviTp06Jc6cOWM5nj9/vpg9e7bleOLEiSIiIqLeZkEhajclCiFEbm6uWL58uRCidlOcvZsSV61aJWbNmlWvbMiQIZbNjxqNRuzatUsIIURhYaHo16+fOHv2rDCbzSImJkasWvXb5v2jR4+2elOiwWAQgYGB4vz585YNdNnZ2Q3qXblyRQghxMWLF0VISIgoLi5usr1erxelpaVCCCH0er249957xfbt24UQQvz4448iNDRUlJeXC7PZLKZNmyZWrFghzGazePzxx8WLL77YqjG1ZHzW6tlbbk+fv3et2ZTIGeqWKAdMJoFy1KCbZ8PZByIiurUp3NyQ+NFHOPnNN5YyZVISnFxd273vkJAQrFy5EjNnzkRoaKjlMXfW6PV6PP/887h+/TqcnJwQHByM1atXW85PnjwZ48aNs7pZ0M/PDy+88EKL49VqtZg3b169sqSkJGi1WgwZMgRffPEFZs+ejblz5wIAFixYgKCgIADAxo0bMWfOHCxevBiurq4ICAho8Ag7ezk5OeGDDz7AqFGjYDKZMHPmTKhUtY+vTUhIwCeffILevXsjKSkJhYWFUCgUWLlyJbr9+tsIa+3Pnz+PcePGAajdgPjYY49h9OjRAGrXQE+YMAFRUVFwcnLCgAEDMGvWLBw4cABffvml5VF7APD22283WF5Tt4b6Zo2tobZ1fE3Vs7e8qWuRbaTa5Pv2ER0dLTLs3BTS5i5U4eKgwzhXbsIviyox5dn45tsQEVG7OnnyJJRKpUNj0Ol0SExMRHZ2tkPjsEdAQAAyMjIabIYkutM09j1EkqRMIUTTu5zBTYktU3gcPs8/g8BZ7+Fg9VeOjoaIiG4RcrkcJSUlVl/sciupe7GLwWCATMZ0gKg1uOSjJcrLIQBUml3h4trZ0dEQEdEtwt/fv97mrluZm5sbsrKyHB0G0e8CfyRtAaEvhZMwwaOmBEMDuzs6HCIiIiJyICbULWAquw4JgDALeHXxdHQ4RERERORATKhbQJSXAABqzICLS8MHuBMRERHRnYMJdQsY9ddhlGS4IPWA5MQ11ERERER3MibULWCsKIWQJOQ5ecLDnW9JJCIiIrqTMaFuAVOVHpIww6PmCtxdmVATERER3cmYULeESQ8ZBNwNBVxDTURERHSHY0LdEuZKAEC1zMiEmoiIiOgOx4S6BYRUASFJuKJwZUJNREQWOp0Obm5uljcldurUqdF69913X0eGBQB46623oFKpEB4ejsjISBw8eBDDhw/Ht99+W6/esmXL8OyzzwIAJEnC1KlTLeeMRiN69OiBxMREAL+9bdHZ2RnXrl1rNoZNmzZBkiScOnXKUqbT6aBWq+vVW7hwIZYuXQoAuHz5MpKTkxEUFASNRoOEhAScOXOmZTfhBjt27EBISAiCg4PxzjvvWK23fPlyqNVqqFQqLFu2rNn2169fx4QJE3DPPfdAqVTihx9+AADk5uZi+PDhCA0NhUqlwvLlywEAp0+fRmRkpOXTpUuXev209/is1bO3vI7JZMKAAQMs/0buGEKI2+qj0WiEoxX993BR/k6YeOqVFGE2mxwdDhERCSFOnDjh6BDEhQsXhEqlshx7eHh0eAxms1mYTPX/b/r+++9FXFycqKqqEkIIUVBQIPLz88WqVavEjBkz6tWNjY0V3333nRCiNv6IiAhRUVEhhBAiLS1NREREiDFjxtRr06dPH1FQUNBsbBMnThSDBg0Sr7/+uqXs5nsmhBALFiwQS5YsEWazWcTFxYkPP/zQci4rK0vs3bu32b6aYjQaRd++fcW5c+dEdXW1CA8PF8ePH29Q79ixY0KlUony8nJhMBjEiBEjRE5OTpPtp02bJj7++GMhhBDV1dWiuLhYCCHEpUuXRGZmphBCiNLSUtGvX78GfRqNRtGzZ0+h0+majD89PV1Mnz691eOzVs/e8hu99957YvLkyQ3+jdwOGvseAiBD2JCfcoa6BQznY1CRcy9cJCdIEm8hERHZp27mWqfTQalU4qmnnoJKpUJ8fDwqK2uXFa5btw4xMTGIjIzE008/DZPJBAB45JFHoNFooFKpsHr1ast1QkJCMG3aNKjV6gavP//ll1/g7e0NFxcXAIC3tzd69+6NCRMmYNu2baipqbFc59KlSxg8eLClbUJCArZt2wYA0Gq1mDx5covGrNfrsX//fqxZswapqak2tUlPT4dCocAzzzxjKYuIiKgXX0scOnQIwcHB6Nu3L5ydnZGcnIzNmzc3qHfy5EnExsbC3d0dTk5OGDp0KDZs2GC1fUlJCfbu3YuUlBQAgLOzM7p16wYA6NWrF6KiogAAnTt3hlKpRH5+fr3+du3ahaCgIPTp06dDxmetnr3ldfLy8rBt2zY8+eSTrYr/duTk6ABuR4rvnoaxqBIvffWLo0MhIiIroqNXWz332muDMX68EgAQHLwC3bq5Wq2bkTGrzWO7UU5ODrRaLT7++GNMnDgR33zzDTQaDb7++mscOHAACoUCzz33HL766itMmzYNn376KTw9PVFZWYmBAwciKSnJcp3PP/8ccXFxDfqIj4/HG2+8gf79+2PkyJGYNGkShg4dCk9PT8TExGD79u14+OGHkZqaiokTJ0KSJEvb5ORkvPHGG0hMTMRPP/2EmTNnYt++fXaPc/PmzRg9ejT69+8PLy8vZGZmQqPRNNkmOzu72Tp1Bg8ejLKysgblS5cuxciRI+uV5efnw9/f33Ls5+eHgwcPNmirVqsxf/58FBYWws3NDWlpaYiOjrba/sKFC+jRoweeeOIJHD16FBqNBsuXL4eHh0e96+p0Ohw5cgSxsbH1ylNTU5v8gSU2NhbV1dXQ6/UoKiqyLC1avHgxRo0aZff4rNWzt7zOnDlz8O677zb6dfi94/SqvcyAVF172xQ93B0cDBERtda77z7g0P4DAwMtiZFGo4FOp8OuXbuQmZmJgQMHIjIyErt27cL58+cBACtWrEBERATi4uKQm5uLnJwcAECfPn0aTaaB2hnxzMxMrF69Gj169MCkSZOwdu1aAMDkyZMtM8aNJXTh4eHQ6XTQarVISEho8Ti1Wi2Sk5MB1CbpWq0WAOol7zeyVm7Nvn37kJWV1eBzczJtD6VSiXnz5iE+Ph6jR49GZGQk5HK51fpGoxGHDx/Gs88+iyNHjsDDw6PBOmO9Xo+kpCQsW7YMXbr8tg+rpqYGW7ZswaOPPmr1+gcPHkRWVhY++eQTjB071jLGG5NpR9m6dSt8fHxs/gHo94Yz1PbSG2Dqfg7VCjOMznxLIhHRrcrWmeXx45WW2WpHqFuGAQByuRyVlZUQQmD69OlYtGhRvbp79uzBzp078cMPP8Dd3R3Dhg1DVVUVANSbBV25ciU+/vhjAEBaWhp69+4NuVyOYcOGYdiwYQgLC8Pnn3+OGTNm4OGHH8ZLL72Ew4cPo6KiotGEaOzYsXjllVewZ88eFBYW2j3GoqIi7N69G8eOHYMkSTCZTJAkCUuWLIGXlxeKi4sb1A8MDISfnx/Wr19vUx/2zFD7+vrWWxaTl5cHX1/fRq+bkpJiWcLx2muvwc/Pz2p7Pz8/+Pn5WWaeJ0yYUC+hNhgMSEpKwpQpUzB+/Ph6/Wzfvh1RUVHo2bOnTeNtiq3js1bP3nIAOHDgALZs2YK0tDRUVVWhtLQUU6dOxbp161o9ntuCLQutb6WPwzcl6i6KikVh4spfRojnl/+XY2MhIiKL22lTYl35zfWXLFkiFixYII4fPy6Cg4PFlStXhBBCFBYWCp1OJzZt2iQSExOFEEKcPHlSuLi4iPT09EY39t3o1KlT4syZM5bj+fPni9mzZ1uOJ06cKCIiIuptFrwxztzcXLF8+XIhRO2GOHs3Ja5atUrMmjWrXtmQIUMsmx81Go3YtWuXZaz9+vUTZ8+eFWazWcTExIhVq1ZZ2h09erTVmxINBoMIDAwU58+ft2yuy87ObrRu3dfg4sWLIiQkRBQXFzfZftCgQeLUqVNCiNrNla+88ooQonaz6OOPPy5efPHFRvuZNGmS+PTTT1s1LnvHZ62eveU3a+zfyO2gNZsSOUNtr3Izqq/1gd7YGZ6B3R0dDRER3cIqKirg5+dnOZ47dy7mzp3bbLvQ0FC8+eabiI+Ph9lshkKhwMqVKzF69Gh89NFHUCqVCAkJsbrE42Z6vR7PP/88rl+/DicnJwQHB1s2NAK1yz7GjRtndbOgn58fXnjhBZv6aoxWq8W8efPqlSUlJUGr1WLIkCH44osvMHv2bMu9WbBgAYKCggAAGzduxJw5c7B48WK4uroiICCg1Y+Vc3JywgcffIBRo0bBZDJh5syZUKlUAGo3YX7yySfo3bu3Jc7CwkLL16Buk6G19v/7v/+LKVOmoKamBn379sVnn30GoHYG98svv0RYWJhlic/bb7+NhIQElJeX4z//+Q9WrVrVZNx1a6hvdvMaalvH11Q9e8vvdFJt8n37iI6OFhkZGY4L4CfgRNRFZOMywn/qg3tC73JcLEREZHHy5EkolY5bugHUbjZLTExEdna2Q+PoaAEBAcjIyIC3t7ejQyFqsca+h0iSlCmEiG6uLTcl2slUbIBZSKiUDPD0bvyB/UREdGeSy+UoKSmxzED+3tW92MVgMEAmY0pBdy4u+bCT4WoFBIAKWQ26drX+mCUiIrrz+Pv7N3gG9O+Zm5sbsrKyHB0GkcMxobaTuTgVff+6CsV5kfjligcC7o5tvhERERER/W7x9zN2qiwphiQ3otRZAReFs6PDISIiIiIH4wy1nZxNFZDDBE9jAVxduzo6HCIiIiJyMM5Q20lm1EOCgAv0cHFhQk1ERER0p2NCbSfJoAckoFom4OLCNyUSERER3emYUNtJX10GM2T4GZ0hl3MNNREREdGdjgm1nYyiAmbIcFrGh9cTERERERNquznLKiHBDFdR7uhQiIiIiOgWwITaTgp5FWTCjB6S3tGhEBHRLUan08HNzc3ypsROnX57o25aWhr69++PixcvAgAkScLLL79sOb906VIsXLiw2XN1byd0dnbGtWvXmo1p06ZNkCQJp06dqhenWq2uV2/hwoVYunQpAODy5ctITk5GUFAQNBoNEhIScObMGTvuRON27NiBkJAQBAcH45133rFab/ny5VCr1VCpVFi2bFmz7QMCAhAWFobIyEhERzd8S7TJZMKAAQOQmJjYbB8dMT5r9ewtP336NCIjIy2fLl26tNlYyD5MqO3kpKgEABhc+ZZEIiJqKCgoqMHbA3ft2oUXXngB27dvR58+fQAALi4u2LBhQ6NJcVPn6t5O2Lt3b5vi0Wq1GDRoELRarU31hRAYN24chg0bhnPnziEzMxOLFi3ClStXbGpvjclkwuzZs7F9+3acOHECWq0WJ06caFAvOzsbH3/8MQ4dOoSjR49i69atOHv2bLPt09PTkZWVhYyMjAbXXL58OZRKZbN9NGXPnj2YMWNGq8dnrZ695QAQEhKCrKwsZGVlITMzE+7u7hg3blyT46D2wYTaHkJApqgGIKGmi4ejoyEiotvA3r178dRTT2Hr1q0ICgqylDs5OWHWrFl4//33G7Rp6pw99Ho99u/fjzVr1iA1NdWmNunp6VAoFHjmmWcsZRERERg8eHCrYjl06BCCg4PRt29fODs7Izk5GZs3b25Q7+TJk4iNjYW7uzucnJwwdOhQbNiwweb2N8vLy8O2bdvw5JNPNttHR4zPWj17y2+2a9cuBAUFWX5go47FF7vYo6oSkAEGkzt+7uLj6GiIiKgJq1c3/NV/U7y978H48esatJ81q+GMp62qq6vxyCOPYM+ePbjnnnsanJ89ezbCw8Pxxz/+0a5zttq8eTNGjx6N/v37w8vLC5mZmdBoNE22yc7ObrbOjQYPHoyysrIG5UuXLsXIkSMtx/n5+fD397cc+/n54eDBgw3aqdVqzJ8/H4WFhXBzc0NaWhqio6ObbC9JEuLj4yFJEp5++mnMmjXLUm/OnDl4991368VorY/GxMbGorq6Gnq9HkVFRZblPIsXL8aoUaPsHp+1evaW3yw1NRWTJ09udAzU/phQ20NvQPXlYFTLJPQO4FM+iIioaQqFAvfddx/WrFmD5cuXNzjfpUsXTJs2DStWrICbm5vN52yl1Wrx4osvAgCSk5Oh1Wqh0WggSVKj9a2VN2Xfvn0tis0apVKJefPmIT4+Hh4eHoiMjIRcLm+yzf79++Hr64urV6/igQcewD333IMhQ4Zg69at8PHxgUajwZ49e1rUR13yumfPHqxduxZr165tq6G2mZqaGmzZsgWLFi1ydCh3LCbU9qjpCuOat1Elv4rZb0Q5OhoiImpCa2aW26I9AMhkMvzzn//EiBEj8Pbbb+O1115rUGfOnDmIiorCE088Yde55hQVFWH37t04duwYJEmCyWSCJElYsmQJvLy8UFxc3KB+YGAg/Pz8sH79epv7sXWG2tfXF7m5uZbjvLw8+Pr6NnrNlJQUpKSkAABee+01+Pn5Ndm+7k8fHx+MGzcOhw4dwpAhQ3DgwAFs2bIFaWlpqKqqQmlpKaZOnYp169Y12kdr2Do+a/XsLb/R9u3bERUVhZ49e7ZqDNQKQojb6qPRaISjmI4bxVX34yLHc4cwm0wOi4OIiBo6ceKEo0MQFy5cECqVynLs4eEhhBCisLBQhIaGik8++aTBOSGEePXVV4W/v79YsGBBs+fq9OnTRxQUFFiNZdWqVWLWrFn1yoYMGSK+++47IYQQGo1G7Nq1yxJfv379xNmzZ4XZbBYxMTFi1apVlnZHjx4Ve/futeUWWGUwGERgYKA4f/68qK6uFuHh4SI7O7vRuleuXBFCCHHx4kUREhIiiouLrbbX6/WitLRUCCGEXq8X9957r9i+fXuDa6anp4sxY8Y02UdHjM9aPXvLbzRp0iTx6aeftip+avx7CIAMYUN+yk2JdjBc1UNIZphdDJBkvHVERGQbT09P7NixA2+++Sa2bNnS4PzLL79s9RF4TZ1rilarbfDEh6SkJMvTPr744gv89a9/RWRkJP7whz9gwYIFCAoKgiRJ2LhxI3bu3ImgoCCoVCr8+c9/xl133WV3DDdycnLCBx98gFGjRkGpVGLixIlQqVQAgISEBFy6dKlenKGhoXjooYewcuVKdOvWzWr7K1euYNCgQYiIiEBMTAzGjBmD0aNHNxtPY300JjY2tt6j6eo+3377rc3ju3GM1urZW16nvLwc//nPfzB+/Hi7vh7UtqTa5Pv2ER0dLRp7JE5HKPnHeihy30DOZRV8//IGvL36OSQOIiJq6OTJk/UejeYIOp0OiYmJyM7Obve+AgICkJGRAW9v7ukhaguNfQ+RJClTCNHsDmdOs9pBf612vVmlJIeLMx+bR0RE9cnlcpSUlFieBNEe6l7sYjAYIONvS4luCdyUaIeyn+/DtRXxyOp5BLHvte5XX0RE9Pvj7+9fbwNZe6h7sQsR3Tr4o60dagoNkEwymIxySBJvHRERERExobZLdaGx9i9uZscGQkRERES3DC75sIO73z/g++J5dC/0B/Cwo8MhIiIiolsAZ6jtoHC9Auee51HetcbRoRARERHRLYIJtR26OlVABjM8naodHQoRERER3SKYUNvB3akKMpjRu6vk6FCIiIiI6BbBhNoOMlll7V868RnURERERFSLCbUdzPIaCEgokndydChEREREdItgQm0HoTDABDlOmNwdHQoREd2CdDod3NzcLG9KlMvliIyMhFqtxkMPPYTr16+3WV8LFy7E0qVL2+RadXHWfXQ6XZtc92bXr1/H3//+d8tx3VsfnZ2dce3aNZuusWnTJkiShFOnTgGovedqtbpenZvvzeXLl5GcnIygoCBoNBokJCTgzJkzrRrLjh07EBISguDgYLzzzjtW6y1fvhxqtRoqlQrLli2zub3JZMKAAQOQmJgIAMjNzcXw4cMRGhoKlUqF5cuXN1neFmwZo7U61sqbinfmzJnw8fFp8PW8LQghbquPRqMRDmEyiYpFYaLiHbVY/cX/OiYGIiKy6sSJEzbXTdq0Sbz9ww+ioLy8TWO4cOGCUKlUlmMPDw/L36dNmybefPPNNutrwYIFYsmSJW1yrRvjtIfZbBYmk8nm+jffnzp9+vQRBQUFNl1j4sSJYtCgQeL111+3es0b743ZbBZxcXHiww8/tJzPysoSe/futTnumxmNRtG3b19x7tw5UV1dLcLDw8Xx48cb1Dt27JhQqVSivLxcGAwGMWLECJGTk2NT+/fee09MnjxZjBkzRgghxKVLl0RmZqYQQojS0lLRr18/cfz4cavlTUlPTxfTp09v9Rit1WmqbVPxfvfddyIzM7PRfyMdobHvIQAyhA35KWeobVVRCZkwATVOUN3dy9HREBFRK/zf5cv45NgxDNJqsejHH3GtoqLd+7z33nuRn59vOX7kkUeg0WigUqmwevVqALWzrUqlEk899RRUKhXi4+NRWVlpafPWW2+hf//+GDRoEE6fPm0p/9vf/ga1Wg21Wm2ZBdXpdLjnnnswY8YM9O/fH1OmTMHOnTtx//33o1+/fjh06FCzMVu7bkhICKZNmwa1Wo3c3FysW7cOMTExiIyMxNNPPw2TyYTy8nKMGTMGERERUKvV+Prrr/GnP/0J586dQ2RkJF599VW776Fer8f+/fuxZs0apKam2tQmPT0dCoUCzzzzjKUsIiICgwcPtrv/OocOHUJwcDD69u0LZ2dnJCcnY/PmzQ3qnTx5ErGxsXB3d4eTkxOGDh2KDRs2NNs+Ly8P27Ztw5NPPmkp69WrF6KiogAAnTt3hlKpRH5+vtXy1rJljNbqNNW2qXiHDBkCT0/PVsfuCHyxi41EiR6QAJPRGT3u8nV0OERE1IzoL78EAGQ8/ril7KX0dOzLy0O1yYQe7u4wms1Yfvgw/ueHHzDEzw//GDMG3u7uKKiowIPffANvNzfsmDCh1bGYTCbs2rULKSkplrJPP/0Unp6eqKysxMCBA5GUlAQAyMnJgVarxccff4yJEyfim2++wdSpU5GZmYnU1FRkZWXBaDQiKioKGo0GmZmZ+Oyzz3Dw4EEIIRAbG4uhQ4eie/fuOHv2LP71r3/h008/xcCBA/GPf/wD+/fvx5YtW/D2229j06ZNAH5begEAgYGB2LhxY5PXzcnJweeff464uDicPHkSX3/9NQ4cOACFQoHnnnsOX331FZ/O3PgAACAASURBVDw8PNC7d29s27YNAFBSUoLY2FhkZ2cjKyurRfdx8+bNGD16NPr37w8vLy9kZmbCy8uryTbZ2dnQaDQ2XX/w4MEoKytrUL506VKMHDnScpyfnw9/f3/LsZ+fHw4ePNignVqtxvz581FYWAg3NzekpaUhOjq62fZz5szBu+++22gsQO0PNUeOHEFsbKxN5XViY2NRXV0NvV6PoqIiy9d88eLFGDVqVL26tozRWh1b709z8d5OmFDbqOZq7douo0EBT++eDo6GiIjagpNMhk4KBUqqq3EgPx9/3LsXn44e3WbXr0tU8/PzoVQq8cADD1jOrVixAhs3bgRQu640JycHd911FwIDAy2Jjkajsaxn3rdvH8aNGwd399p9PGPHjgUA7N+/H+PGjYOHR+0TqMaPH499+/Zh7NixCAwMRFhYGABApVJhxIgRkCQJYWFh9dZJu7m5NUhym7punz59EBcXBwDYtWsXMjMzMXDgQMuYfXx88Nhjj+Hll1/GvHnzkJiYiMGDB6O4uLhV91Or1eLFF18EACQnJ0Or1eL5559vtK4k2f+I23379rUqvpsplUrMmzcP8fHx8PDwQGRkJORyeZNttm7dCh8fH2g0GuzZs6fBeb1ej6SkJCxbtgxdunRptvxGdUntnj17sHbtWqxdu7bFY2stW+K9nTChtlFp3mV4SHJcF93g43b7f+GJiH7vbpyZrvP+8OEAgD6rVgEAjGYzBIA+XbpgukqFWRERAIAe7u6NtrdXXaJaUVGBUaNGYeXKlXjhhRewZ88e7Ny5Ez/88APc3d0xbNgwVFVVAQBcXFws7eVyeb0lH/a68VoymcxyLJPJYDQaW3zduiQbqN2LNX36dCxatKhBvcOHDyMtLQ1/+ctfMGLECEybNq3FfRYVFWH37t04duwYJEmCyWSCJElYsGBBg0S9qKgIgYGBAGp/kFi/fr1Nfdg6Q+3r64vc3FzLcV5eHnx9G//tdUpKiuU3E6+99hr8/PyabH/gwAFs2bIFaWlpqKqqQmlpKaZOnYp169bBYDAgKSkJU6ZMwfjx4y3trZW3hi1jtFanubbtEa+jcQ21jUquFEGYnFFlcoara1dHh0NERK1UXFWFKqMRT4aF4cBjj+HPcXHwcnNrl77c3d2xYsUKvPfeezAajSgpKUH37t3h7u6OU6dO4ccff2z2GkOGDMGmTZtQWVmJsrIy/Pvf/wZQmwRu2rQJFRUVKC8vx8aNG1u1PriOrdcdMWIE1q9fj6tXrwKoTWYvXryIS5cuwd3dHVOnTsWrr76Kw4cPo3PnzlaXMTRn/fr1ePzxx3Hx4kXodDrk5uYiMDAQR44cQa9evbB7925L/zt27MCgQYMAAH/4wx9QXV1tWacOAD/99FOjs9H79u1DVlZWg8+NyTQADBw4EDk5Obhw4QJqamqQmppq+Y3Bzeruy88//4wNGzbgsccea7L9okWLkJeXB51Oh9TUVPzhD3/AunXrIIRASkoKlEol5s6da7m+tfKmDBs2rNnZaVvGaK1OU21bEu/tgDPUNvIUUah46wuIvuche5e3jYjodjbwrrug6dkTsyIi2i2JvtmAAQMQHh4OrVaLiRMn4qOPPoJSqURISIhl+URToqKiMGnSJERERMDHx8eyxCIqKgozZsxATEwMAODJJ5/EgAEDWv3oO1uvGxoaijfffBPx8fEwm81QKBRYuXIlSkpK8Oqrr0Imk0GhUODDDz+El5cX7r//fqjVajz44INYsmSJzfFotVrMmzevXllSUhK0Wi2++OILzJ4925KgLViwAEFBQQBql35s3LgRc+bMweLFi+Hq6oqAgIB6j7Czl5OTEz744AOMGjUKJpMJM2fOhEqlspxPSEjAJ598gt69eyMpKQmFhYWW+9KtWzcAaLJ9Yw4cOIAvv/wSYWFhliVBb7/9Nrp06dJoeUJCQoNr1K2hvllja6ibGuON47NWx1q5tXEkJCRg8uTJ2LNnD65duwY/Pz/8z//8T719B7cyqfaJILeP6OhokZGR0eH9FrxzAvgrcF19Ef0OPtjh/RMRUdNOnjwJpVLp0Bh0Oh0SExORnZ3t0DhuNwEBAcjIyIC3t7ejQ6E7WGPfQyRJyhRCRDfXlks+bGQqNtT+hW8dJyIiK+RyOUpKSiwzb9S0uk2bBoMBMhlTErp9ce2CjXQVX0I5+yTOXQtCP3CGmoiIGvL396+3GYua1tjTRYhuR/xx0EZyqRgKr1xUubf/w/+JiIiI6PbBGWobFR4ch9yrj+O6+pyjQyEiIiKiWwhnqG0kFXRH9c9BcHfiWxKJiIiI6DdMqG0kVdW+2cjZs+k3HBERERHRnYVLPmzkPywVXhJw1qc/gAearU9EREREdwYm1Dbq3vcYOnUuQIW4y9GhEBEREdEthEs+bOQmr4QEgV6+Po4OhYiIiIhuIZyhtpGrogISTFCqgh0dChERERHdQjhDbQuTGZJTDSAkuPv5OzoaIiK6Rel0Ori5uVnelNipU6c2ue59991n9dz169fx97//3eb6deRyOSIjI6FWq/HQQw/h+vXrrY6zLdkyhsbUvX3R2dkZ165da7b+pk2bIEkSTp06ZSnT6XRQq9X16i1cuBBLly4FAFy+fBnJyckICgqCRqNBQkICzpw506J4b7Rjxw6EhIQgODgY77zzjtV6y5cvh1qthkqlwrJly2xqbzKZMGDAACQmJtrU5/vvvw+VSgW1Wo3Jkyejqqqqw8ZnrV5j5bm5uRg+fDhCQ0OhUqmwfPnyFvXZakKI2+qj0WhER6u6dFVUvRMqKv4aKaqryzu8fyIiat6JEyccHYK4cOGCUKlUlmMPD48O79NWN8Y2bdo08eabb7Y6FrPZLEwmU6uv0xb69OkjCgoKmq03ceJEMWjQIPH6669byhq7pwsWLBBLliwRZrNZxMXFiQ8//NByLisrS+zdu7dV8RqNRtG3b19x7tw5UV1dLcLDw8Xx48cb1Dt27JhQqVSivLxcGAwGMWLECJGTk9Ns+/fee09MnjxZjBkzptk+8/LyREBAgKioqBBCCPHoo4+Kzz77rMn409PTxfTp01s9Pmv1rJVfunRJZGZmCiGEKC0tFf369bNc19Y+6zT2PQRAhrAhP+UMtQ0unT4HE+QoN3WGQuHm6HCIiKg50e30aSN/+9vfoFaroVar680w/vWvf0VISAgGDRqEyZMnW2ZE62a6y8vLMWbMGERERECtVuPrr7/Gn/70J5w7dw6RkZF49dVX69UHgC+++ALh4eGIiIjA448/3mg89957L/Lz8y3H69atQ0xMDCIjI/H000/DZDJZjU+n0yEkJATTpk2DWq1Gbm5uo+0bi93amG4eQ2P3S6fTQalU4qmnnoJKpUJ8fDwqKyvt+jro9Xrs378fa9asQWpqqk1t0tPToVAo8Mwzz1jKIiIiMHjwYLv6vtmhQ4cQHByMvn37wtnZGcnJydi8eXODeidPnkRsbCzc3d3h5OSEoUOHYsOGDU22z8vLw7Zt2/Dkk0/a3KfRaERlZSWMRiMqKirQu3fvDhmftXrWynv16oWoqCgAQOfOnaFUKi3/lm3tsy1wDbUNrufloicAg8EZkiQ5OhwiIrqNZWZm4rPPPsPBgwchhEBsbCyGDh0Ko9GIb775BkePHoXBYEBUVBQ0Gk29tjt27EDv3r2xbds2AEBJSQliY2ORnZ2NrKysBn0dP34cb775Jr7//nt4e3ujqKioQR2TyYRdu3YhJSUFQG3C9vXXX+PAgQNQKBR47rnn8NVXX0GpVFqNLycnB59//jni4uKstvfw8GgQu7Ux2XK/unfvjpycHGi1Wnz88ceYOHEivvnmG0ydOtXmr8XmzZsxevRo9O/fH15eXsjMzGxwz2+WnZ3dbJ0bDR48GGVlZQ3Kly5dipEjR1qO8/Pz4e//27JSPz8/HDx4sEE7tVqN+fPno7CwEG5ubkhLS0N0dHST7efMmYN33323QRzW2vj6+uKVV17B3XffDTc3N8THxyM+Pr7R8cXGxqK6uhp6vR5FRUWW5U6LFy/GqFGj7B6ftXq2tNfpdDhy5AhiY2Pt6rMtMKG2gexaIeTCCFmV2dGhEBGRLTIcHYB1+/fvx7hx4+Dh4QEAGD9+PPbt2wez2YyHH34Yrq6ucHV1xUMPPdSgbVhYGF5++WXMmzcPiYmJGDx4MIqLi632tXv3bjz66KPw9vYGAHh6elrO1a01zs/Ph1KpxAMP1L5jYdeuXcjMzMTAgQMt9Xx8fFBUVGQ1vj59+iAuLq7J9o899liD2K2NyZb7NXbsWAQGBloSOI1GA51OZ+NXoZZWq8WLL74IAEhOToZWq4VGo7E6edaSSbV9+/bZ3aYpSqUS8+bNQ3x8PDw8PBAZGQm53PpL57Zu3QofHx9oNBrs2bPHpj6Ki4uxefNmXLhwAd26dcOjjz6KdevWNfrDSl2CumfPHqxduxZr165tybBaTa/XIykpCcuWLUOXLl06vH8u+bBBzfVCAIDBqHBwJEREdCfr378/Dh8+jLCwMPzlL3/BG2+80eJrubm5ISsrCxcvXoQQAitXrgRQu7dq+vTpyMrKQlZWFk6fPo2FCxc2ea26ZLep9tZib82YXFxcLH+Xy+UwGo02ty0qKsLu3bvx5JNPIiAgAEuWLME///lPCCHg5eXV4AeVoqIieHt7Q6VSITMz0+Z+Bg8ejMjIyAafnTt31qvn6+uL3Nxcy3FeXh58fX0bvWZKSgoyMzOxd+9edO/eHf3797fa/sCBA9iyZQsCAgKQnJyM3bt3WxJja2127tyJwMBA9OjRAwqFAuPHj8f3339v85gbY+v4rNVrqr3BYEBSUhKmTJmC8ePH291nm7BloXVLPwBGAzgN4CyAPzVy3gXA17+ePwggoLlrdvSmxKRNm8RnL8wVVYtCxdlnR4tdr70mLv26+J2IiG4dt8umxMzMTBEWFibKy8uFXq8XKpVKHD58WBw6dEgMGDBAVFZWirKyMtGvXz+xZMmSetfJz88XlZWVQggh/v3vf4uHH35YXLt2Tdx99931+qirn52dLfr16yeuXbsmhBCisLCw0dgOHz4s7r77bmEwGMTx48dFcHCwuHLliqWNTqezGt/NY7bWvrHYrY3pxvis3a+b+12yZIlYsGCB5bi5TYmrVq0Ss2bNqlc2ZMgQ8d133wkhhNBoNGLXrl2WMfTr10+cPXtWmM1mERMTI1atWmVpd/To0VZvSjQYDCIwMFCcP3/esoEuOzu70bp19/bixYsiJCREFBcX29Q+PT293qZEa21+/PFHERoaKsrLy4XZbBbTpk0TK1as6JDxWatnrdxsNovHH39cvPjiiy3us05rNiW225IPSZLkAFai9j3deQD+T5KkLUKIEzdUSwFQLIQIliQpGcBiAJPaK6aW+L9ffsF9MsAoc8IvCi98/803OLdzJ5747js4ubo6OjwiIrqFVVRUwM/Pz3I8d+5czJ07FzNmzEBMTAwA4Mknn8SAAQMAAGPHjkV4eDh69uyJsLAwdO3atd71jh07hldffRUymQwKhQIffvghvLy8cP/990OtVuPBBx/EkiVLLPVVKhXmz5+PoUOHQi6XY8CAAY3+Sn7AgAEIDw+HVqvF448/jjfffBPx8fEwm81QKBRYuXIl4uLimo0PAEJDQxttX1JS0iB2a2O6UVRUVKP3y97lHTfTarWYN29evbKkpCRotVoMGTIEX3zxBWbPno25c+cCABYsWICgoCAAwMaNGzFnzhwsXrwYrq6uCAgIqLe5tCWcnJzwwQcfYNSoUTCZTJg5cyZUKhUAICEhAZ988ollY2BSUhIKCwst97Zbt24AYLV9S/qcMGECoqKi4OTkhAEDBmDWrFmNXqNuDfXNbl5D3VRfN4/RWr3Gyvfv348vv/wSYWFhluU/b7/9NhISEprtsy1Jtcl3O1xYku4FsFAIMerX4z8DgBBi0Q11vv21zg+SJDkBuAygh2giqOjoaJGR0XGL43otXYq38i5iUu+92HnlXjzt4YPokyfxwtChiP+v/+qwOIiIqGknT56EUql0aAw6nQ6JiYnIzs5uUXu9Xo9OnTqhoqICQ4YMwerVqy1PMLgV3Orx3SggIAAZGRmW9eNEzWnse4gkSZlCiGaf8dOea6h9AeTecJz3a1mjdYQQRgAlALxuvpAkSbMkScqQJCmjoKCgncJtXE15OS6cvhe6T1fj8NkYyMxm7ImIwP+cONF8YyIiuqPI5XKUlJRYZsrsNWvWLERGRiIqKgpJSUm3XLJ6q8cH/LbZ0mAwQCbjVjHqGLfFUz6EEKsBrAZqZ6g7sm9nDw/8K+QEvg38GcWdALNMhmFHj+LFoUM7MgwiIroN+Pv719sEZa9//OMfbRhN27vV4wN+22xJ1JHaM6HOB3Dje7r9fi1rrE7er0s+ugIobMeY7ObcqROKupahyFiGmNP/v717j46yvvM4/v4awCCKHGFj5bIhQEhhAgkEA7Uiy6JIkZWjUYRVxBa8bG1XqvWwra64XU6rIq5WLOKtiFqw9Ror4louCiyihGsiKhTHAraCEVFuEpPv/jHDNPeZZJIZAp/XOXPOzO/yPN9nfszky29+z/N8wHeKi0k7/XSGV7s4uoiIiIicmJozoX4XyDSzDEKJ83jgX6u1KQQmAauBy4Cl9a2fToazzzqLzLQ08tet4/SsLMjKok9BgU5IFBERERGgGRNqd//GzH4EvA6kAE+4e4mZ/YLQJUgKgceBp8xsG/A5oaT7mPLc2LGhJ5WuaygiIiIiclSzrqF290XAompld1R6fhi4vDljEBERERFpTjr9VUREREQkDkqoRURERETioIRaRERERCQOSqhFREREROKghFpERKSJBINB2rZtG7lTYkpKCrm5uQQCAXJycpg1axYVFRWN3v4555wTV300paWl5Obmkpuby7e+9S26dOkSeX3kyJG4tg1/v4thmzZt+Oyzz6K2f+mllzAz3n///UhZMBgkOzu7Srs777yTe++9F4C//e1vjB8/np49e5KXl8fo0aP58MMP44598eLFZGVl0atXL+66665a2zzwwANkZ2cTCAS4//77o/Y/fPgw+fn55OTkEAgEmD59epU+5eXlDBgwgDFjxkTKPvjgg8iY5Obm0r59+xr7aq7jq69NbXU7duxg+PDh9O3bl0AgwAMPPBBpX19di+TuLeqRl5fnIiIi1b333nvJDsE/+ugjDwQCkdft2rWLPP/00099xIgRfscddyQjtAabPn26z5w5s9a6iooKLy8vb/S209PTfc+ePVHbjRs3zs8999wq71n197hyrBUVFT5kyBCfM2dOpG7Dhg3+1ltvNTpWd/dvvvnGe/To4X/+85/966+/9v79+3tJSUmVNps3b/ZAIOAHDhzwsrIyHzFihG/durXe/hUVFf7VV1+5u/uRI0c8Pz/fV69eHdnmrFmzfMKECX7RRRfVGdeZZ57pwWCwztiXLVvmkyZNivv46mtTV90nn3ziRUVF7u7+5ZdfemZmZqRPfXXJUtt3CKFLPUfNTzVDLSIix6fFgxr2+L+rau/fRNLS0njkkUeYPXs2Hr6H2dNPP01+fj65ublcf/31lJeXAzB//nz69+9PTk4OEydOjGzj1FNPBeDAgQNcdNFF5OTkkJ2dzbPPPlulHuC+++4jOzub7OzsyAxmMBikT58+XHvttQQCAUaOHMmhQ4diij8YDJKVlcXVV19NdnY2O3bsqDP++o4tVvv372flypU8/vjjLFy4MKY+y5Yto3Xr1txwww2RspycHIYOHdqgfVf3zjvv0KtXL3r06EGbNm0YP348L7/8cpU2W7ZsYfDgwZxyyim0atWKYcOG8cILL9Tb38wiY1ZWVkZZWRlmBsDOnTt59dVXmVLPnZmXLFlCz549SU9Pb/bjq69NXXVnnXUWAwcOBOC0006jT58+7NoVuml2fXUtkRJqERGRBOnRowfl5eXs3r2bLVu28Oyzz7Jq1So2bNhASkoKzzzzDCUlJcyYMYOlS5eycePGWn8KX7x4MZ07d2bjxo0UFxczatSoKvVFRUX89re/Zc2aNbz99ts8+uijrF+/HoCtW7dy4403UlJSQocOHXj++edjjn/r1q388Ic/pKSkhIMHD9YaP1DnsTXEyy+/zKhRo+jduzcdO3akqKgoap/i4mLy8vJi2v7QoUOrLJ04+vjTn/5Uo+2uXbvo1q1b5HXXrl1rJH/Z2dmsWLGC0tJSDh48yKJFi9ixY0fU/uXl5eTm5pKWlsYFF1zA4MGDAZg6dSr33HMPJ51Ud6q2cOFCJkyYUGvd4MGDyc3NZcqUKRQWFkaO7/XXX2/U8dXXJpb+wWCQ9evXR44v1rqWollv7CIiIpI0o9Ymt38US5YsoaioiLPPPhsIrS9OS0tj3759XH755XTq1AmAM844o0bffv36ccsttzBt2jTGjBlTYwZ25cqVXHLJJbRr1w6ASy+9lBUrVnDxxReTkZERWeOdl5dHMBiMOeb09HSGDBlSb/zR6mK1YMECbrrpJgDGjx/PggULyMvLi8zgVldXeV1WrFjRoPbR9OnTh2nTpjFy5EjatWtHbm4uKSkpUfulpKSwYcMGvvjiCy655BKKi4sJBoOkpaWRl5fH8uXLa+135MgRCgsL+dWvflVr/Zo1awBYvnw58+bNY968eY09tLjt37+fgoIC7r//ftq3bx9zXUuihFpERCRBtm/fTkpKCmlpabg7kyZNqpEQPfjgg1G307t3b9atW8eiRYu4/fbbGTFiBHfccUfUfgAnn3xy5HlKSkrMSz6ASIIO1Bl/tLpYfP755yxdupTNmzdjZpSXl2NmzJw5k44dO7J3794a7TMyMujatSvPPfdcTPsYOnQoX331VY3ye++9l/PPP79KWZcuXSKzzRBajtGlS5cafSdPnszkyZMB+PnPf07Xrl1j7t+hQweGDx/O4sWLKS0tpbCwkEWLFnH48GG+/PJLrrrqKp5++ulI+9dee42BAwdy5plnxnS89Yklvvra1FdXVlZGQUEBV155JZdeemmVbdZX1+LEstD6WHropEQREanNsX5S4u7du/2CCy6InGBXUlLivXr18k8//dTd3UtLSz0YDHpxcbFnZmb6Z599Fimvvr1du3b5oUOH3N39lVde8bFjx1apLyoq8n79+vmBAwd8//79HggEfN26dTXimzlzpk+fPr3WY6l+UmL1vnXFH63OPfpJiXPnzvXrrruuStl5553nb775pru75+Xl+ZIlSyLbzszM9G3btnlFRYXn5+f73LlzI/02btwY90mJZWVlnpGR4du3b4+cdFdcXFyj3dHj/fjjjz0rK8v37t1bb//du3dH2hw8eNDPPfdcf+WVV6psc9myZbWelHjFFVf4E088EddxNeT46mtTV11FRYVPnDjRb7rpphr7rK8uWeI5KVEz1CIiIs3k6GXiysrKaNWqFRMnTuTmm28GoG/fvsyYMYORI0dSUVFB69ateeihhxgyZAi33XYbw4YNIyUlhQEDBtT4uX7z5s3ceuutnHTSSbRu3Zo5c+ZUqR84cCDXXHMN+fn5AEyZMoUBAwY0aHlHNHXFn56eXm9dLBYsWMC0adOqlBUUFLBgwQLOO+885s+fz4033hh5L6dPn07Pnj0BePHFF5k6dSp33303qampdO/ePe7LyrVq1YrZs2dz4YUXUl5ezg9+8AMCgQAAo0eP5rHHHqNz584UFBRQWloaOd4OHTrU23/Tpk1MmjSJ8vJyKioqGDduXJVL5NXlwIEDvPHGG8ydO7fONoMHD+brr7+uUX733Xdz4YUXNur46mpTV/+VK1fy1FNP0a9fv8gyo1/+8peMHj2aVatW1VnXEpmHzzRuKQYNGuRr1zbvujYREWl5tmzZQp8+fZIaQzAYZMyYMRQXFyc1jmNd9+7dWbt2bWSduMixoLbvEDMrcveol/vRVT5ERESaSEpKCvv27YvMuElVlWfs67t6hUhLoyUfIiIiTaRbt25VTs6Sqtq2bcuGDRuSHYZIk9N/D0VERERE4qCEWkREREQkDkqoRUTkuNHSTrQXkWNDvN8dSqhFROS4kJqaSmlpqZJqEWkQd6e0tJTU1NRGb0MnJYqIyHGha9eu7Ny5kz179iQ7FBFpYVJTUyN3tmwMJdQiInJcaN26NRkZGckOQ0ROQFryISIiIiISByXUIiIiIiJxUEItIiIiIhIHa2lnQ5vZHuDjJO2+E/BZkvYtiaExPjFonE8MGufjn8b4xJDMcU5393+I1qjFJdTJZGZr3X1QsuOQ5qMxPjFonE8MGufjn8b4xNASxllLPkRERERE4qCEWkREREQkDkqoG+aRZAcgzU5jfGLQOJ8YNM7HP43xieGYH2etoRYRERERiYNmqEVERERE4qCEWkREREQkDkqoqzGzUWb2gZltM7P/qKX+ZDN7Nly/xsy6Jz5KiVcM43yzmb1nZpvMbImZpScjTolPtHGu1K7AzNzMjunLMklNsYyxmY0Lf55LzOx3iY5R4hfDd/Y/mtkyM1sf/t4enYw4pfHM7Akz221mxXXUm5n9OvxvYJOZDUx0jPVRQl2JmaUADwHfA/oCE8ysb7Vmk4G97t4L+B/g7sRGKfGKcZzXA4PcvT/wHHBPYqOUeMU4zpjZacBNwJrERijximWMzSwT+BnwXXcPAFMTHqjEJcbP8u3A7919ADAe+E1io5QmMA8YVU/994DM8OM6YE4CYoqZEuqq8oFt7r7d3Y8AC4Gx1dqMBZ4MP38OGGFmlsAYJX5Rx9ndl7n7wfDLt4GuCY5R4hfL5xngvwn9x/hwIoOTJhHLGF8LPOTuewHcfXeCY5T4xTLODrQPPz8d+CSB8UkTcPe3gM/raTIWmO8hbwMdzOysxEQXnRLqqroAOyq93hkuq7WNu38D7AM6JiQ6aSqxjHNlk4HXmjUiaQ5Rgs61/wAABf9JREFUxzn8k2E3d381kYFJk4nls9wb6G1mq8zsbTOrbwZMjk2xjPOdwFVmthNYBPw4MaFJAjX0b3dCtUp2ACLHMjO7ChgEDEt2LNK0zOwk4D7gmiSHIs2rFaGfiP+J0C9Nb5lZP3f/IqlRSVObAMxz91lm9h3gKTPLdveKZAcmJwbNUFe1C+hW6XXXcFmtbcysFaGflkoTEp00lVjGGTM7H7gNuNjdv05QbNJ0oo3zaUA2sNzMgsAQoFAnJrYosXyWdwKF7l7m7h8BHxJKsKXliGWcJwO/B3D31UAq0Ckh0UmixPS3O1mUUFf1LpBpZhlm1obQiQ2F1doUApPCzy8DlrrujtPSRB1nMxsAzCWUTGvNZctU7zi7+z537+Tu3d29O6G18he7+9rkhCuNEMt39kuEZqcxs06EloBsT2SQErdYxvkvwAgAM+tDKKHek9AopbkVAleHr/YxBNjn7n9NdlBHaclHJe7+jZn9CHgdSAGecPcSM/sFsNbdC4HHCf2UtI3Q4vnxyYtYGiPGcZ4JnAr8IXzO6V/c/eKkBS0NFuM4SwsW4xi/Dow0s/eAcuBWd9evii1IjON8C/Comf2E0AmK12iyq2UxswWE/vPbKbwWfjrQGsDdHya0Nn40sA04CHw/OZHWTrceFxERERGJg5Z8iIiIiIjEQQm1iIiIiEgclFCLiIiIiMRBCbWIiIiISByUUIuIiIiIxEEJtYhIA5hZuZltqPToXk/b7mZW3AT7XG5mH5jZxvAttLMasY0bzOzq8PNrzKxzpbrHzKxvE8f5rpnlxtBnqpmdEu++RUSSSQm1iEjDHHL33EqPYIL2e6W75wBPErpOeoO4+8PuPj/88hqgc6W6Ke7+XpNE+fc4f0NscU4FlFCLSIumhFpEJE7hmegVZrYu/DinljYBM3snPKu9ycwyw+VXVSqfa2YpUXb3FtAr3HeEma03s81m9oSZnRwuv8vM3gvv595w2Z1m9lMzuwwYBDwT3mfb8MzyoPAsdiQJDs9kz25knKuBLpW2NcfM1ppZiZn9V7js3wkl9svMbFm4bKSZrQ6/j38ws1Oj7EdEJOmUUIuINEzbSss9XgyX7QYucPeBwBXAr2vpdwPwgLvnEkpod4ZvkXwF8N1weTlwZZT9/wuw2cxSgXnAFe7ej9Cdb//NzDoClwABd+8PzKjc2d2fA9YSmknOdfdDlaqfD/c96gpgYSPjHEXott9H3ebug4D+wDAz6+/uvwY+AYa7+/DwrcFvB84Pv5drgZuj7EdEJOl063ERkYY5FE4qK2sNzA6vGS4HetfSbzVwm5l1BV5w961mNgLIA94N3+K+LaHkvDbPmNkhIAj8GMgCPnL3D8P1TwI3ArOBw8DjZvZH4I+xHpi77zGz7WY2BNgKfBtYFd5uQ+JsA5wKVH6fxpnZdYT+7pwF9AU2Ves7JFy+KryfNoTeNxGRY5oSahGR+P0E+BTIIfTL3+HqDdz9d2a2BrgIWGRm1wMGPOnuP4thH1e6+9qjL8zsjNoaufs3ZpYPjAAuA34E/HMDjmUhMA54H3jR3d1C2W3McQJFhNZPPwhcamYZwE+Bs919r5nNA1Jr6WvAG+4+oQHxiogknZZ8iIjE73Tgr+5eAUwEaqwvNrMewPbwMoeXCS19WAJcZmZp4TZnmFl6jPv8AOhuZr3CrycCb4bXHJ/u7osIJfo5tfT9Cjitju2+CIwFJhBKrmlonO7uwH8CQ8zs20B74ACwz8zOBL5XRyxvA989ekxm1s7MapvtFxE5piihFhGJ32+ASWa2kdAyiQO1tBkHFJvZBiAbmB++ssbtwP+a2SbgDULLIaJy98PA94E/mNlmoAJ4mFBy+sfw9lZS+xrkecDDR09KrLbdvcAWIN3d3wmXNTjO8NrsWcCt7r4RWE9o1vt3hJaRHPUIsNjMlrn7HkJXIFkQ3s9qQu+niMgxzUITCSIiIiIi0hiaoRYRERERiYMSahERERGROCihFhERERGJgxJqEREREZE4KKEWEREREYmDEmoRERERkTgooRYRERERicP/A87VFIjOWCgHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f4781d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy import interp\n",
    "import matplotlib.pyplot as plt\n",
    "from itertools import cycle\n",
    "\n",
    "from sklearn import svm, datasets\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "models=[bagged_Linear_SVM, bagged_RBF_SVM, bagged_Logistic_Regression, bagged_KNN, bagged_Decision_Tree,   bagged_RandomForest ]\n",
    "models = [\n",
    "    {\n",
    "        'label' : 'rbf-SVM',\n",
    "        'model': bagged_RBF_SVM,\n",
    "        'color': 'maroon',\n",
    "        'linestyle': 'solid', \n",
    "        'markers': 'H',\n",
    "         \n",
    "    },\n",
    "    {\n",
    "        'label' : 'Linear-SVM',\n",
    "        'model': bagged_Linear_SVM,\n",
    "        'color': 'navy',\n",
    "        'linestyle': 'dashed',\n",
    "        'markers': ',',\n",
    "    },\n",
    "    {\n",
    "        'label' : 'KNN',\n",
    "        'model': bagged_KNN, \n",
    "        'color': 'olive',\n",
    "        'linestyle': 'dashdot',\n",
    "        'markers': '',\n",
    "    },    \n",
    "    {\n",
    "        'label' : 'RandomForest',\n",
    "        'model': bagged_RandomForest,\n",
    "        'color': 'darkcyan',\n",
    "        'linestyle': 'dotted',\n",
    "        'markers': '>',\n",
    "       \n",
    "    },        \n",
    "    {\n",
    "        'label' : 'LogisticRegression',\n",
    "        'model': bagged_Logistic_Regression,\n",
    "        'color': 'fuchsia',\n",
    "        'linestyle': '-',\n",
    "        'markers': ''\n",
    " \n",
    "    },\n",
    "    {\n",
    "        'label' : 'Decision Tree',\n",
    "        'model': bagged_Decision_Tree, \n",
    "        'color': 'orange',\n",
    "        'linestyle': '-.',\n",
    "        'markers': ''\n",
    "    },\n",
    "\n",
    "]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# #############################################################################\n",
    "# Classification and ROC analysis\n",
    "random_state = np.random.RandomState(0)\n",
    "# Run classifier with cross-validation and plot ROC curves\n",
    "cv = StratifiedKFold(n_splits=10)\n",
    "classifier = svm.SVC(kernel='linear', probability=True,\n",
    "                     random_state=random_state)\n",
    "\n",
    "tprs = []\n",
    "aucs = []\n",
    "mean_fpr = np.linspace(0, 1, 100)\n",
    "y = Y\n",
    "\n",
    "\n",
    "\n",
    "   \n",
    "\n",
    "\n",
    "plt.figure(figsize=(12,12))\n",
    "for m in models:\n",
    "    i = 0\n",
    "    for train, test in cv.split(X, y):\n",
    "        probas_ = m['model'].fit(train_X, train_Y).predict_proba(test_X)\n",
    "        # Compute ROC curve and area the curve\n",
    "        fpr, tpr, thresholds = roc_curve(test_Y, probas_[:, 1])\n",
    "        tprs.append(interp(mean_fpr, fpr, tpr))\n",
    "        tprs[-1][0] = 0.0\n",
    "        roc_auc = auc(fpr, tpr)\n",
    "        aucs.append(roc_auc)\n",
    "        \n",
    "\n",
    "        i += 1\n",
    "    #plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',\n",
    "    #         label='Luck', alpha=.8)\n",
    "\n",
    "    mean_tpr = np.mean(tprs, axis=0)\n",
    "    mean_tpr[-1] = 1.0\n",
    "    mean_auc = auc(mean_fpr, mean_tpr)\n",
    "    std_auc = np.std(aucs)\n",
    "\n",
    "    plt.plot(mean_fpr, mean_tpr, color= m['color'], linestyle = m['linestyle'], marker= m['markers'],\n",
    "             label=r'[%s]  AUC = %0.5f $\\pm$ %0.4f ' % (m['label'] ,mean_auc, std_auc),\n",
    "             lw=2, alpha=.8)\n",
    "    \n",
    "    #std_tpr = np.std(tprs, axis=0)\n",
    "    #tprs_upper = np.minimum(mean_tpr + std_tpr, 1)\n",
    "    #tprs_lower = np.maximum(mean_tpr - std_tpr, 0)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    #plt.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2,\n",
    "                    # label=r'$\\pm$ 1 std. dev.')\n",
    "\n",
    "    plt.xlim([-0.05, 1.05])\n",
    "    plt.ylim([-0.05, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver Operating Characteristic (ROC)')\n",
    "    plt.legend(loc=\"lower right\")\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The cross validated score for AdaBoost is: 0.7923022979825224\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "ada=AdaBoostClassifier(n_estimators=800,random_state=0,learning_rate=0.01)\n",
    "result=cross_val_score(ada,X,Y,cv=10,scoring='accuracy')\n",
    "print('The cross validated score for AdaBoost is:',result.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "grad=GradientBoostingClassifier(n_estimators=500,random_state=0,learning_rate=0.1)\n",
    "result=cross_val_score(grad,X,Y,cv=10,scoring='accuracy')\n",
    "print('The cross validated score for Gradient Boosting is:',result.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import xgboost as xg\n",
    "xgboost=xg.XGBClassifier(n_estimators=900,learning_rate=0.1)\n",
    "result=cross_val_score(xgboost,X,Y,cv=10,scoring='accuracy')\n",
    "print('The cross validated score for XGBoost is:',result.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 120 candidates, totalling 360 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 360 out of 360 | elapsed:  6.2min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7804347826086957\n",
      "AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,\n",
      "          learning_rate=0.05, n_estimators=100, random_state=None)\n"
     ]
    }
   ],
   "source": [
    "n_estimators=list(range(100,1100,100))\n",
    "learn_rate=[0.05,0.1,0.2,0.3,0.25,0.4,0.5,0.6,0.7,0.8,0.9,1]\n",
    "hyper={'n_estimators':n_estimators,'learning_rate':learn_rate}\n",
    "gd=GridSearchCV(estimator=AdaBoostClassifier(),param_grid=hyper,verbose=True)\n",
    "gd.fit(X,Y)\n",
    "print(gd.best_score_)\n",
    "print(gd.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12efd5b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ada=AdaBoostClassifier(n_estimators=100,random_state=0,learning_rate=0.05)\n",
    "result=cross_val_predict(ada,X,Y,cv=10)\n",
    "sns.heatmap(confusion_matrix(Y,result),cmap='winter',annot=True,fmt='2.0f')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f,ax=plt.subplots(2,2,figsize=(15,12))\n",
    "model=RandomForestClassifier(n_estimators=500,random_state=0)\n",
    "model.fit(X,Y)\n",
    "pd.Series(model.feature_importances_,X.columns).sort_values(ascending=True).plot.barh(width=0.8,ax=ax[0,0])\n",
    "ax[0,0].set_title('Feature Importance in Random Forests')\n",
    "model=AdaBoostClassifier(n_estimators=200,learning_rate=0.05,random_state=0)\n",
    "model.fit(X,Y)\n",
    "pd.Series(model.feature_importances_,X.columns).sort_values(ascending=True).plot.barh(width=0.8,ax=ax[0,1],color='#ddff11')\n",
    "ax[0,1].set_title('Feature Importance in AdaBoost')\n",
    "model=GradientBoostingClassifier(n_estimators=500,learning_rate=0.1,random_state=0)\n",
    "model.fit(X,Y)\n",
    "pd.Series(model.feature_importances_,X.columns).sort_values(ascending=True).plot.barh(width=0.8,ax=ax[1,0],cmap='RdYlGn_r')\n",
    "ax[1,0].set_title('Feature Importance in Gradient Boosting')\n",
    "model=xg.XGBClassifier(n_estimators=900,learning_rate=0.1)\n",
    "model.fit(X,Y)\n",
    "pd.Series(model.feature_importances_,X.columns).sort_values(ascending=True).plot.barh(width=0.8,ax=ax[1,1],color='#FD0F00')\n",
    "ax[1,1].set_title('Feature Importance in XgBoost')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 920 entries, 0 to 919\n",
      "Data columns (total 41 columns):\n",
      "age_0           920 non-null float64\n",
      "age_1           920 non-null float64\n",
      "age_2           920 non-null float64\n",
      "age_3           920 non-null float64\n",
      "age_4           920 non-null float64\n",
      "sex_0           920 non-null float64\n",
      "sex_1           920 non-null float64\n",
      "cp_1            920 non-null float64\n",
      "cp_2            920 non-null float64\n",
      "cp_3            920 non-null float64\n",
      "cp_4            920 non-null float64\n",
      "trestbps_0      920 non-null float64\n",
      "trestbps_1      920 non-null float64\n",
      "trestbps_2      920 non-null float64\n",
      "trestbps_3      920 non-null float64\n",
      "trestbps_4      920 non-null float64\n",
      "chol_0          920 non-null float64\n",
      "chol_1          920 non-null float64\n",
      "chol_2          920 non-null float64\n",
      "chol_3          920 non-null float64\n",
      "chol_4          920 non-null float64\n",
      "fbs_0           920 non-null float64\n",
      "fbs_1           920 non-null float64\n",
      "restecg_0       920 non-null float64\n",
      "restecg_1       920 non-null float64\n",
      "restecg_2       920 non-null float64\n",
      "thalach_0       920 non-null float64\n",
      "thalach_1       920 non-null float64\n",
      "thalach_2       920 non-null float64\n",
      "thalach_3       920 non-null float64\n",
      "thalach_4       920 non-null float64\n",
      "exang_0         920 non-null float64\n",
      "exang_1         920 non-null float64\n",
      "oldpeak_0       920 non-null float64\n",
      "oldpeak_1       920 non-null float64\n",
      "oldpeak_2       920 non-null float64\n",
      "oldpeak_3       920 non-null float64\n",
      "slope_1         920 non-null float64\n",
      "slope_2         920 non-null float64\n",
      "slope_3         920 non-null float64\n",
      "heartdisease    920 non-null int64\n",
      "dtypes: float64(40), int64(1)\n",
      "memory usage: 294.8 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}