ML+part+2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Citation- http://scikit-learn.org/stable/modules/naive_bayes.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "iris = datasets.load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of mislabeled points out of a total 150 points : 6\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "gnb = GaussianNB()\n",
    "y_pred = gnb.fit(iris.data, iris.target).predict(iris.data)\n",
    "print(\"Number of mislabeled points out of a total %d points : %d\"\n",
    "      % (iris.data.shape[0],(iris.target != y_pred).sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "150"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.data.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False,  True, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False,  True, False,\n",
       "       False, False, False, False, False,  True, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False,  True, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False,  True, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False,  True, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False], dtype=bool)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(iris.target != y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(iris.target != y_pred).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "http://archive.ics.uci.edu/ml/datasets/Pen-Based+Recognition+of+Handwritten+Digits\n",
    "We create a digit database by collecting 250 samples from 44 writers. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "iris = datasets.load_iris()\n",
    "digits = datasets.load_digits()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import svm\n",
    "clf = svm.SVC(gamma=0.001, C=100.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0.,   0.,   5., ...,   0.,   0.,   0.],\n",
       "       [  0.,   0.,   0., ...,  10.,   0.,   0.],\n",
       "       [  0.,   0.,   0., ...,  16.,   9.,   0.],\n",
       "       ..., \n",
       "       [  0.,   0.,   1., ...,   6.,   0.,   0.],\n",
       "       [  0.,   0.,   2., ...,  12.,   0.,   0.],\n",
       "       [  0.,   0.,  10., ...,  12.,   1.,   0.]])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, ..., 8, 9, 8])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5.1,  3.5,  1.4,  0.2],\n",
       "       [ 4.9,  3. ,  1.4,  0.2],\n",
       "       [ 4.7,  3.2,  1.3,  0.2],\n",
       "       [ 4.6,  3.1,  1.5,  0.2],\n",
       "       [ 5. ,  3.6,  1.4,  0.2],\n",
       "       [ 5.4,  3.9,  1.7,  0.4],\n",
       "       [ 4.6,  3.4,  1.4,  0.3],\n",
       "       [ 5. ,  3.4,  1.5,  0.2],\n",
       "       [ 4.4,  2.9,  1.4,  0.2],\n",
       "       [ 4.9,  3.1,  1.5,  0.1],\n",
       "       [ 5.4,  3.7,  1.5,  0.2],\n",
       "       [ 4.8,  3.4,  1.6,  0.2],\n",
       "       [ 4.8,  3. ,  1.4,  0.1],\n",
       "       [ 4.3,  3. ,  1.1,  0.1],\n",
       "       [ 5.8,  4. ,  1.2,  0.2],\n",
       "       [ 5.7,  4.4,  1.5,  0.4],\n",
       "       [ 5.4,  3.9,  1.3,  0.4],\n",
       "       [ 5.1,  3.5,  1.4,  0.3],\n",
       "       [ 5.7,  3.8,  1.7,  0.3],\n",
       "       [ 5.1,  3.8,  1.5,  0.3],\n",
       "       [ 5.4,  3.4,  1.7,  0.2],\n",
       "       [ 5.1,  3.7,  1.5,  0.4],\n",
       "       [ 4.6,  3.6,  1. ,  0.2],\n",
       "       [ 5.1,  3.3,  1.7,  0.5],\n",
       "       [ 4.8,  3.4,  1.9,  0.2],\n",
       "       [ 5. ,  3. ,  1.6,  0.2],\n",
       "       [ 5. ,  3.4,  1.6,  0.4],\n",
       "       [ 5.2,  3.5,  1.5,  0.2],\n",
       "       [ 5.2,  3.4,  1.4,  0.2],\n",
       "       [ 4.7,  3.2,  1.6,  0.2],\n",
       "       [ 4.8,  3.1,  1.6,  0.2],\n",
       "       [ 5.4,  3.4,  1.5,  0.4],\n",
       "       [ 5.2,  4.1,  1.5,  0.1],\n",
       "       [ 5.5,  4.2,  1.4,  0.2],\n",
       "       [ 4.9,  3.1,  1.5,  0.1],\n",
       "       [ 5. ,  3.2,  1.2,  0.2],\n",
       "       [ 5.5,  3.5,  1.3,  0.2],\n",
       "       [ 4.9,  3.1,  1.5,  0.1],\n",
       "       [ 4.4,  3. ,  1.3,  0.2],\n",
       "       [ 5.1,  3.4,  1.5,  0.2],\n",
       "       [ 5. ,  3.5,  1.3,  0.3],\n",
       "       [ 4.5,  2.3,  1.3,  0.3],\n",
       "       [ 4.4,  3.2,  1.3,  0.2],\n",
       "       [ 5. ,  3.5,  1.6,  0.6],\n",
       "       [ 5.1,  3.8,  1.9,  0.4],\n",
       "       [ 4.8,  3. ,  1.4,  0.3],\n",
       "       [ 5.1,  3.8,  1.6,  0.2],\n",
       "       [ 4.6,  3.2,  1.4,  0.2],\n",
       "       [ 5.3,  3.7,  1.5,  0.2],\n",
       "       [ 5. ,  3.3,  1.4,  0.2],\n",
       "       [ 7. ,  3.2,  4.7,  1.4],\n",
       "       [ 6.4,  3.2,  4.5,  1.5],\n",
       "       [ 6.9,  3.1,  4.9,  1.5],\n",
       "       [ 5.5,  2.3,  4. ,  1.3],\n",
       "       [ 6.5,  2.8,  4.6,  1.5],\n",
       "       [ 5.7,  2.8,  4.5,  1.3],\n",
       "       [ 6.3,  3.3,  4.7,  1.6],\n",
       "       [ 4.9,  2.4,  3.3,  1. ],\n",
       "       [ 6.6,  2.9,  4.6,  1.3],\n",
       "       [ 5.2,  2.7,  3.9,  1.4],\n",
       "       [ 5. ,  2. ,  3.5,  1. ],\n",
       "       [ 5.9,  3. ,  4.2,  1.5],\n",
       "       [ 6. ,  2.2,  4. ,  1. ],\n",
       "       [ 6.1,  2.9,  4.7,  1.4],\n",
       "       [ 5.6,  2.9,  3.6,  1.3],\n",
       "       [ 6.7,  3.1,  4.4,  1.4],\n",
       "       [ 5.6,  3. ,  4.5,  1.5],\n",
       "       [ 5.8,  2.7,  4.1,  1. ],\n",
       "       [ 6.2,  2.2,  4.5,  1.5],\n",
       "       [ 5.6,  2.5,  3.9,  1.1],\n",
       "       [ 5.9,  3.2,  4.8,  1.8],\n",
       "       [ 6.1,  2.8,  4. ,  1.3],\n",
       "       [ 6.3,  2.5,  4.9,  1.5],\n",
       "       [ 6.1,  2.8,  4.7,  1.2],\n",
       "       [ 6.4,  2.9,  4.3,  1.3],\n",
       "       [ 6.6,  3. ,  4.4,  1.4],\n",
       "       [ 6.8,  2.8,  4.8,  1.4],\n",
       "       [ 6.7,  3. ,  5. ,  1.7],\n",
       "       [ 6. ,  2.9,  4.5,  1.5],\n",
       "       [ 5.7,  2.6,  3.5,  1. ],\n",
       "       [ 5.5,  2.4,  3.8,  1.1],\n",
       "       [ 5.5,  2.4,  3.7,  1. ],\n",
       "       [ 5.8,  2.7,  3.9,  1.2],\n",
       "       [ 6. ,  2.7,  5.1,  1.6],\n",
       "       [ 5.4,  3. ,  4.5,  1.5],\n",
       "       [ 6. ,  3.4,  4.5,  1.6],\n",
       "       [ 6.7,  3.1,  4.7,  1.5],\n",
       "       [ 6.3,  2.3,  4.4,  1.3],\n",
       "       [ 5.6,  3. ,  4.1,  1.3],\n",
       "       [ 5.5,  2.5,  4. ,  1.3],\n",
       "       [ 5.5,  2.6,  4.4,  1.2],\n",
       "       [ 6.1,  3. ,  4.6,  1.4],\n",
       "       [ 5.8,  2.6,  4. ,  1.2],\n",
       "       [ 5. ,  2.3,  3.3,  1. ],\n",
       "       [ 5.6,  2.7,  4.2,  1.3],\n",
       "       [ 5.7,  3. ,  4.2,  1.2],\n",
       "       [ 5.7,  2.9,  4.2,  1.3],\n",
       "       [ 6.2,  2.9,  4.3,  1.3],\n",
       "       [ 5.1,  2.5,  3. ,  1.1],\n",
       "       [ 5.7,  2.8,  4.1,  1.3],\n",
       "       [ 6.3,  3.3,  6. ,  2.5],\n",
       "       [ 5.8,  2.7,  5.1,  1.9],\n",
       "       [ 7.1,  3. ,  5.9,  2.1],\n",
       "       [ 6.3,  2.9,  5.6,  1.8],\n",
       "       [ 6.5,  3. ,  5.8,  2.2],\n",
       "       [ 7.6,  3. ,  6.6,  2.1],\n",
       "       [ 4.9,  2.5,  4.5,  1.7],\n",
       "       [ 7.3,  2.9,  6.3,  1.8],\n",
       "       [ 6.7,  2.5,  5.8,  1.8],\n",
       "       [ 7.2,  3.6,  6.1,  2.5],\n",
       "       [ 6.5,  3.2,  5.1,  2. ],\n",
       "       [ 6.4,  2.7,  5.3,  1.9],\n",
       "       [ 6.8,  3. ,  5.5,  2.1],\n",
       "       [ 5.7,  2.5,  5. ,  2. ],\n",
       "       [ 5.8,  2.8,  5.1,  2.4],\n",
       "       [ 6.4,  3.2,  5.3,  2.3],\n",
       "       [ 6.5,  3. ,  5.5,  1.8],\n",
       "       [ 7.7,  3.8,  6.7,  2.2],\n",
       "       [ 7.7,  2.6,  6.9,  2.3],\n",
       "       [ 6. ,  2.2,  5. ,  1.5],\n",
       "       [ 6.9,  3.2,  5.7,  2.3],\n",
       "       [ 5.6,  2.8,  4.9,  2. ],\n",
       "       [ 7.7,  2.8,  6.7,  2. ],\n",
       "       [ 6.3,  2.7,  4.9,  1.8],\n",
       "       [ 6.7,  3.3,  5.7,  2.1],\n",
       "       [ 7.2,  3.2,  6. ,  1.8],\n",
       "       [ 6.2,  2.8,  4.8,  1.8],\n",
       "       [ 6.1,  3. ,  4.9,  1.8],\n",
       "       [ 6.4,  2.8,  5.6,  2.1],\n",
       "       [ 7.2,  3. ,  5.8,  1.6],\n",
       "       [ 7.4,  2.8,  6.1,  1.9],\n",
       "       [ 7.9,  3.8,  6.4,  2. ],\n",
       "       [ 6.4,  2.8,  5.6,  2.2],\n",
       "       [ 6.3,  2.8,  5.1,  1.5],\n",
       "       [ 6.1,  2.6,  5.6,  1.4],\n",
       "       [ 7.7,  3. ,  6.1,  2.3],\n",
       "       [ 6.3,  3.4,  5.6,  2.4],\n",
       "       [ 6.4,  3.1,  5.5,  1.8],\n",
       "       [ 6. ,  3. ,  4.8,  1.8],\n",
       "       [ 6.9,  3.1,  5.4,  2.1],\n",
       "       [ 6.7,  3.1,  5.6,  2.4],\n",
       "       [ 6.9,  3.1,  5.1,  2.3],\n",
       "       [ 5.8,  2.7,  5.1,  1.9],\n",
       "       [ 6.8,  3.2,  5.9,  2.3],\n",
       "       [ 6.7,  3.3,  5.7,  2.5],\n",
       "       [ 6.7,  3. ,  5.2,  2.3],\n",
       "       [ 6.3,  2.5,  5. ,  1.9],\n",
       "       [ 6.5,  3. ,  5.2,  2. ],\n",
       "       [ 6.2,  3.4,  5.4,  2.3],\n",
       "       [ 5.9,  3. ,  5.1,  1.8]])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=100.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma=0.001, kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(digits.data[:-1], digits.target[:-1]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.predict(digits.data[-1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = svm.SVC()\n",
    "iris = datasets.load_iris()\n",
    "X, y = iris.data, iris.target\n",
    "clf.fit(X, y) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pickle\n",
    "s = pickle.dumps(clf)\n",
    "clf2 = pickle.loads(s)\n",
    "clf2.predict(X[0:1]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[0] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf2.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(clf2.predict(X) != y).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "clf = tree.DecisionTreeClassifier()\n",
    "clf = clf.fit(iris.data, iris.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            presort=False, random_state=None, splitter='best')"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(clf.predict(X)!=iris.target).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pydotplus in c:\\users\\dell\\anaconda3\\lib\\site-packages\n",
      "Requirement already satisfied: pyparsing>=2.0.1 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from pydotplus)\n"
     ]
    }
   ],
   "source": [
    "! pip install pydotplus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pydotplus as pydot\n",
    "\n",
    "from IPython.display import Image\n",
    "\n",
    "from sklearn.externals.six import StringIO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Other examples \n",
    "https://nbviewer.jupyter.org/gist/decisionstats/8b762caa7b7deebb68e3f275daf02a9d\n",
    "\n",
    "https://nbviewer.jupyter.org/gist/decisionstats/47a2324b14ebfd22657b40ec1ae5b480"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib  inline\n",
    "from collections import OrderedDict\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Automatically created module for IPython interactive environment\n"
     ]
    }
   ],
   "source": [
    "# Author: Kian Ho <hui.kian.ho@gmail.com>\n",
    "#         Gilles Louppe <g.louppe@gmail.com>\n",
    "#         Andreas Mueller <amueller@ais.uni-bonn.de>\n",
    "#\n",
    "# License: BSD 3 Clause\n",
    "\n",
    "print(__doc__)\n",
    "\n",
    "RANDOM_STATE = 123\n",
    "\n",
    "# Generate a binary classification dataset.\n",
    "X, y = make_classification(n_samples=500, n_features=25,\n",
    "                           n_clusters_per_class=1, n_informative=15,\n",
    "                           random_state=RANDOM_STATE)\n",
    "\n",
    "# NOTE: Setting the `warm_start` construction parameter to `True` disables\n",
    "# support for parallelized ensembles but is necessary for tracking the OOB\n",
    "# error trajectory during training.\n",
    "ensemble_clfs = [\n",
    "    (\"RandomForestClassifier, max_features='sqrt'\",\n",
    "        RandomForestClassifier(warm_start=True, oob_score=True,\n",
    "                               max_features=\"sqrt\",\n",
    "                               random_state=RANDOM_STATE)),\n",
    "    (\"RandomForestClassifier, max_features='log2'\",\n",
    "        RandomForestClassifier(warm_start=True, max_features='log2',\n",
    "                               oob_score=True,\n",
    "                               random_state=RANDOM_STATE)),\n",
    "    (\"RandomForestClassifier, max_features=None\",\n",
    "        RandomForestClassifier(warm_start=True, max_features=None,\n",
    "                               oob_score=True,\n",
    "                               random_state=RANDOM_STATE))\n",
    "]\n",
    "\n",
    "# Map a classifier name to a list of (<n_estimators>, <error rate>) pairs.\n",
    "error_rate = OrderedDict((label, []) for label, _ in ensemble_clfs)\n",
    "\n",
    "# Range of `n_estimators` values to explore.\n",
    "min_estimators = 15\n",
    "max_estimators = 175\n",
    "\n",
    "for label, clf in ensemble_clfs:\n",
    "    for i in range(min_estimators, max_estimators + 1):\n",
    "        clf.set_params(n_estimators=i)\n",
    "        clf.fit(X, y)\n",
    "\n",
    "        # Record the OOB error for each `n_estimators=i` setting.\n",
    "        oob_error = 1 - clf.oob_score_\n",
    "        error_rate[label].append((i, oob_error))\n",
    "\n",
    "# Generate the \"OOB error rate\" vs. \"n_estimators\" plot.\n",
    "for label, clf_err in error_rate.items():\n",
    "    xs, ys = zip(*clf_err)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bphkZGbp+/fpS21pa2w4ePKgPP/ywnnDCCVqrVi1t0KCBnnLKKXrffffpzp07\n/eu9+eab2rVrV01LS9M2bdron//8Z505c2aJYcDRPo+ff/65nn766f7PyWOPPRZ2KHGktrpeffVV\n7d27t2ZkZGhGRobm5OTo2LFjdfXq1aqqum7dOr322mu1ffv2mpaWpg0bNtT+/fvrokWLYn6/ogn9\nebrcz1dSUlKJv60LFy7UXr16ae3atf0/15UrVwat4w63/umnn4KWh3uPVEt/H8JJ5FBi0aPQkbC6\nEJFuwDJYxr/+1Y0LL/S2n7y8PLp3786yZcv8HUaNMdVXfn4+Xbp04e2332bQoEEV3RxjykVpxzb3\ncaC7qka9eqH1OfHI+pwYY2K1ePFizjjjDAsmxsTI+px4ZH1OjDGxGjNmDGPGjKnoZsTl8OHDbN26\nNeo66enpYadeN6asLJx4ULOmVU6MMdXbd999F3ayMpeIMGHChLDDv40pKwsnHtSqZZUTY0z1lp2d\nzXvvvRd1ncDZfI1JJAsnHqSmWuXEGFO9paSkVJmJ50z1Yx1iPbDKiTHGGFN+LJx4YJUTY4wxpvxY\nOPHAKifGGGNM+ak0fU5E5DfA74Bs4EvgZlX9NMK62cBfgB5AO+AxVS0xn66IXAxMAloB3wB3qOo7\nAY9PAELn912pqjnR2pqoysmKFSvKvhNjjDGmEkjkMa1ShBMRuQQnbFwHfAKMA+aJSAdVDXfhihSg\nAPhT8brh9nkG8A/gduAt4HLgNRE5WVXzA1b9GugPuBcsKHm1tBCpqWWrnDRs2JC0tDRGjBjhfSfG\nGGNMJZOWllbiOk5eVIpwghMwZqjq8wAicgNwPjAKeCh0ZVXdULwNIhLpEppjgXdU9ZHi+/eIyLnA\nTUDgbEgHVTX6TEMhUlMh4NpkcWvRogUrVqyolFc0NcYYY7xq2LAhLVq0KPN+KjyciEgNoDtwv7tM\nVVVE3gNOL8OuT8epxgSaB/wqZFl7Efke2Ad8DPxBVaNexrJWLSgoKEPLcAJKIn6AxhhjTHVTGTrE\nNgSSgC0hy7fg9D/xKjuGfS4FRgIDgRuA1sAHIhJ1PmYbrWOMMcaUnwqvnFQkVZ0XcPdrEfkE2AAM\nB2ZG2s5G6xhjjDHlpzKEk23AIaBxyPLGwOYy7HdzvPtU1UIR+QZnBFBE778/jh9/rMuFFx5Zlpub\nS25urvfWGmOMMdXEnDlzmDNnTtCywsLCmLev8HCiqkUisgxnxMzrACIixfcfL8OuPw6zj3OLl4cl\nIuk4weTf4hI4AAAgAElEQVT5aDv+9a+n8uST3Xj99TK0zhhjjKmmwv3DnpeXR/fu3WPavsLDSbFH\ngFnFIcUdSpwGzAIQkQeA41T1KncDETkJZ/hvOpBVfP+AqroDrR8DFovIrThDiXNxOt6ODtjHw8Ab\nOKdymgL3AkVAcNwLkZoKe/fC4cPgqwy9dowxxphqpFKEE1X9PxFpiDNhWmPgC2BgwBDfbKB5yGaf\nA1r8fTfgMpyQ0aZ4nx+LyGXA5OLbauBXIXOcNMOZC6UBsBX4f0BPVf0pWntr1XK+7t0LtaN2nTXG\nGGNMvCpFOAFQ1WnAtAiPXR1mWak1C1WdC8yN8rinTiKpqc7X3bstnBhjjDGJZiclPHArJzZixxhj\njEk8CyceBFZOjDHGGJNYFk48sMqJMcYYU34snHhglRNjjDGm/Fg48cAqJ8YYY0z5sXDigVVOjDHG\nmPJj4cQDq5wYY4wx5cfCiQfJyc7NKifGGGNM4lk48ah2baucGGOMMeXBwolHaWlWOTHGGGPKg4UT\nj6xyYowxxpQPCyceWeXEGGOMKR8WTjyyyokxxhhTPiyceGSVE2OMMaZ8WDjxKC3NKifGGGNMebBw\n4lHt2lY5McYYY8qDhROPrHJijDHGlA8LJx5Z5cQYY4wpHxZOPLLKiTHGGFM+LJx4ZJUTY4wxpnxY\nOPHIKifGGGNM+bBw4pGFE2OMMaZ8WDjxKCUFDh1ybsYYY4xJHAsnHqWkOF/376/YdhhjjDHVjYUT\njyycGGOMMeXDwolHFk6MMcaY8mHhxCMLJ8YYY0z5sHDikYUTY4wxpnxYOPHIwokxxhhTPiyceGTh\nxBhjjCkfFk48snBijDHGlA8LJx5ZODHGGGPKh4UTjyycGGOMMeXDwolHFk6MMcaY8lFpwomI/EZE\n1onIXhFZKiKnRFk3W0T+LiKrROSQiDwSYb2LRWRF8T6/FJHzyvK8gSycGGOMMeWjUoQTEbkE+Asw\nATgZ+BKYJyINI2ySAhQAfwK+iLDPM4B/AH8FugL/Al4TkZwyPO+RBlg4McYYY8pFpQgnwDhghqo+\nr6orgRuAPcCocCur6gZVHaeqs4EdEfY5FnhHVR9R1VWqeg+QB9zk9XkD1azpfLVwYowxxiRWhYcT\nEakBdAfed5epqgLvAaeXYdenF+8j0Dx3n2V9XhEnoFg4McYYYxKrwsMJ0BBIAraELN8CZJdhv9ml\n7LPMz5uSYuHEGGOMSbTkim5AVTRu3Djq1q3L/v3wt7/BkiWQm5tLbm5uRTfNGGOMqXBz5sxhzpw5\nQcsKCwtj3r4yhJNtwCGgccjyxsDmMux3cyn79Py8U6dOpVu3bjRrBsOHw733lqGVxhhjTDUT7h/2\nvLw8unfvHtP2FX5aR1WLgGVAf3eZiEjx/Y/KsOuPA/dZ7Nzi5Ql5XjutY4wxxiReZaicADwCzBKR\nZcAnOKNo0oBZACLyAHCcql7lbiAiJwECpANZxfcPqOqK4lUeAxaLyK3AW0AuTgfY0bE+b2ksnBhj\njDGJVynCiar+X/HcIpNwTqt8AQxU1a3Fq2QDzUM2+xzQ4u+7AZcBG4A2xfv8WEQuAyYX31YDv1LV\n/DieNyoLJ8YYY0ziVYpwAqCq04BpER67OsyyUk9JqepcYK7X5y2NhRNjjDEm8Sq8z0lVZuHEGGOM\nSTwLJ2Vg4cQYY4xJPAsnZWDhxBhjjEk8CydlYOHEGGOMSTwLJ2Vg4cQYY4xJPAsnZWDhxBhjjEk8\nCydlYOHEGGOMSTwLJ2Vg4cQYY4xJPAsnZWDhxBhjjEk8CydlYOHEGGOMSbwyhRMRSU1UQ6oiCyfG\nGGNM4sUdTkTEJyJ3i8j3wC4RaVO8/E8ick3CW1iJWTgxxhhjEs9L5eQuYCRwG3AgYPnXwLUJaFOV\nYeHEGGOMSTwv4eRK4DpV/TtwKGD5l0DHhLSqinDDiWpFt8QYY4ypPryEk6bAtxH2VaNszalaUlKc\nr0VFFdsOY4wxpjrxEk7ygV5hlv8a+Lxszala3HBip3aMMcaYxEn2sM0k4DkRaYoTboaJyPE4p3uG\nJLJxlV1gOMnIqNi2GGOMMdVF3JUTVf0XcAFwDrAbJ6x0Ai5Q1QWJbV7lZpUTY4wxJvG8VE5Q1X8D\n5ya4LVWOhRNjjDEm8bzMc7JWRBqEWZ4pImsT06yqwcKJMcYYk3heOsS2ApLCLE/BGclzzLBwYowx\nxiRezKd1ROTCgLsDRaQw4H4S0B9Yn6B2VQkWTowxxpjEi6fPyWvFXxV4LuSxIpxgMj4BbaoyLJwY\nY4wxiRdzOFFVH4CIrANOUdVt5daqKsLCiTHGGJN4cY/WUdXW5dGQqsjCiTHGGJN4noYSi0ht4Gyg\nBVAz8DFVfTwB7aoSLJwYY4wxiRd3OBGRk4G3gTSgNvAz0BDYAxQAFk6MMcYY45mXocRTgTeAesBe\noCfQElgG/C5xTav8LJwYY4wxieclnHQF/qKqh4FDQIqqfgfcBtyfyMZVdsnJ4PNVv3CiCp98UtGt\nMMYYc6zyEk6KgMPF3xfg9DsBKASaJ6JRVUlKSvULJ19+CaedBp9+WtEtMcYYcyzyEk4+B04p/n4J\nMElELgceBb5OVMOqiuoYTr7/3vn63/9WbDuMMcYcm7yEkzuBH4u//yPwC/AUkAVcl6B2VRnVMZxs\n3ep8zc+v2HYYY4w5NsU1WkdEBOdUztcAqloADCqHdlUZFk6MMcaYxIq3ciLAt5RD3xIR+Y2IrBOR\nvSKyVEROKWX9PiKyTET2icg3InJVyOPJInKPiHxbvM/PRWRgyDoTRORwyC2uQ7KFE2OMMSax4gon\nxSN0VgMNEtkIEbkE+AswATgZ+BKYJyINI6zfCngTeB84CXgM+JuInBuw2mRgNPAboBMwA3hVRE4K\n2d3XQGMgu/h2Vjxtr87hZONG2LmzYttijDHm2OOlz8kdwMMi0iWB7RgHzFDV51V1JXADzqRuoyKs\nfyOwVlVvU9VVqvok8HLxflwjgMmqOk9V16vqdJzJ40IvTnhQVbeqakHx7ed4Gl5dw0nTps73K1dW\nbFuMMcYce7yEk+eBU4Evi0+X/Bx4i3dnIlID6I5TBQFAVRV4Dzg9wmY9ix8PNC9k/RQgNDbspWRl\npL2IfC8ia0RktojEdcqquoaTs4rfJTu1Y4wx5mjzcm2dWxLchoZAErAlZPkW4PgI22RHWL+OiKSo\n6n6csHKriPwbWAOcAwwjOJAtBUYCq4AmwETgAxHpoqq7Y2l8dQ0nfftCq1YWTowxxhx9Xq5K/Fx5\nNKQc/BZ4GliJM2ncGuBZAk4Vqeq8gPW/FpFPgA3AcGBmpB2PGzeOunXrOht9DStWwJw5ueTm5ib8\nRVSErVshKwtyciycGGOMid+cOXOYM2dO0LLCwsKYt/d0VeIE24YzDX7jkOWNgc0RttkcYf0dxVUT\nVHUbMExEagINVPVHEZkCrI3UEFUtFJFvgHbRGjx16lS6desGwNChUFQE1SSXsG8f7Np1JJy88kpF\nt8gYY0xVk5tb8h/2vLw8unfvHtP2XvqcJJSqFuFcNLC/u6x4PpX+wEcRNvs4cP1iA4qXh+7/QHEw\nqQFcBLwWqS0iko4TTH6MtE6o6nZaxx2p44aTdetgz56KbZMxxphjS4WHk2KPAKNF5EoR6QhMB9KA\nWQAi8oCIBJ5Omg60EZEHReR4ERkD/Lp4PxRvc6qI/I+ItBaRXsA7OPO0PBywzsMi0ltEWorIGcCr\nONcOCq5FRVHdw4kqrFpVsW0yxhhzbKkMp3VQ1f8rntNkEs7pmS+AgapafKgkm4CJ31R1vYicD0wF\nxgKbgGtUNXAETypwH9Aa2AW8BYxQ1R0B6zQD/oEzb8tW4P8BPVX1p1jbXp3DSf36zvfLl8PJJ1dc\nm4wxxhxb4p2+vgbOcNyuqprQi/yp6jRgWoTHrg6z7AOcIciR9vcB0LmU5yxzT5HqHE7S0qB5c+sU\na4wx5uiKd4bYImAjztBfQ/UMJ2lpzg1sxI4xxpijz0ufk8nA/SJSP9GNqYqqYzjJyjpy38KJMcaY\no81LOLkJ6A38ICKrRCQv8Jbg9lV6oeHk44/jG377xBOwYUP8z7tmDUyfHv92pQkXTtasgauvdm5/\n+lPs+3rtNViyJPFtNMYYU7156RAbcSjusSg0nDz5pNOBdNiw0rc9dAhuvtnZfnzoFX9K8fLLcNdd\ncP31IBLfttGEhpNzzoGzz4ZvvoGCApg1C26/HWrWLH1f990HjRs72xtjjDGx8jJD7L3l0ZCqKjSc\nbN0a+7wgu3Yd2SZeu3fDwYNQWAiZmfFvH8nWrdAuYAq6Vq1g4ULn+xdfhEsvdSZqiyWcbN0KP8U8\n7skYY4xxeJ7nRES6i8iI4tsxO9C0LOFkx44j28TLfQ4v20YTWjkJVKuW83Xfvtj3tX69E6SMMcaY\nWMUdTkSkkYgsBD4FHi++LROR90UkwmGt+kpJcSoYhw8797dujf1gXJZw4j5HosNJQUHkcJKa6nzd\nu7f0/ezefWS9lSsT0zZjjDHHBi+Vk/8FMoDOqlpfVesDXYA6OEHlmJKS4nzdv9+ZTbUqV04OHHBO\nEyWichLYLhvtY4wxJh5eOsQOAs5R1RXuAlXNF5HfAPMT1rIqIjCcHDx45BTPoUOQVMpsMJWtcrJt\nm/O1tMqJhRNjjDHlyUvlxIdz/ZlQRR73V6UFhpPAA3Is1ZPKVjkJnB02nHhO67j76tLFwokxxpj4\neAkTC4HHROQ4d4GINMW5zs37iWpYVREpnMTS78QNJzt2xD+RW3lUTkoLJ15O65x9toUTY4wx8fE6\nCVsdYL2IrBGRNcC64mU3J7JxVUEiKidw5JRKrKpC5SQjw7lg4Nq1sW1jjDHGgLd5Tr4TkW7AOUDH\n4sUrQq4IfMxIROUEnG2bNo39ecurclKzphMqwom3cpKV5cwwe/iwM4nbSSclrq3GGGOqLy9XJX4X\nuEFVFwALyqVVVUhZKyepqc7BPt6QUV6Vk6ysyDPOxtshtlEj6NTJuZ+fb+HEGGNMbLxclfjEcmpL\nlRQaTpKL416slZM2bZzv4w0Zu3c7z1Ue4SSSeE/rZGU5s9ced5z1OzHGGBM7L31OZgPXJLohVVVo\nOGne3Lkfa+WkSRPnoO+lctK8ubOdanzbRlJaOElKgho14jutA3ZlY2OMMfHxMs9JMjBKRM4BlgFB\nNQJVvTURDasqQsNJq1awbl3slZO6dZ2DeDzh5PBhJ5wEPld6upfWBwsMV5GkpsZXOQEnnMw/5mbA\nMcYY45WXykkXIA/YCXQATg64dU1c06qG0HDSsqVzP9bKSZ068YcTt3LhPleiTu2UVjkBp1Osl8rJ\n6tXODLTGGGNMabyM1ulbHg2pqkLDSXa2M+Il1sqJl3Di7rtVK+fr1q3QunVczQ4rlnDiduCNZv9+\n2LkzOJwcOuQElM6dy95OY4wx1VtclRMRqSEiB0WkS3k1qKoJDSdZWVC7dvlWTtx9J7JycugQ/Pxz\nbOGktNM6ofOl5OQ4X63fiTHGmFh4Ga2zESjlqjHHjpo1na/btzsVjawsSEs7OpWT0sLJ3XfDww+X\nXH7xxfDGG8HLtm1zOtYm4rROaDhp0MAZVmzhxBhjTCy89DmZDNwvIvUT3ZiqyOdzRrB8/71zv1Gj\n2ConqmWvnNSr53SojbTtiy/Cyy8HL9u1y1n25pvBy7/91vnatm305/ZSOQHndFcihz0bY4ypvryM\n1rkJaAf8ICIbKDlap1siGlaVpKTApk3O97FWTvbscUbd1KnjDNH9+WfnqsbJMfxE3H2npUUONvv2\nwZo1zjqqRyZWW7nS+RpaxcjPd4JWhw7Rn9tL5QSc1xk4I64xxhgTiZdw8lrCW1HFhYaTWCon7oG6\nTh0nQAD89BM0blz687n7rl07cjj55hsn/Oza5bTNHSLshpLly4NDS36+UzVxJ1qLJJYOsVu3Oq/J\nfV1g4cQYY0zsvIzWubc8GlKVeamcBIaTpOIePFu3xhZOYqmcBFZG8vNLhpNffoGCgiPPl59/pONq\nNLGe1gntu1KnDmzZUvr+jTHGGC99ThCRTBG5VkQecPueiEg3EYnj0nXVhxtO0tOdg3e8lRP3QB5r\nn4xYKif5+U7wqFWrZFBxp8wPXR5LOIn1tE64cGKVE2OMMbGIO5yIyInAN8DtwO+AzOKHhgEPJK5p\nVUdKijOU2D0g164dX+Uk3nCye/eRqeSjhZMuXZwL74WGkAsucLZ1l+/Y4YSr8q6cWDgxxhgTCy+V\nk0eAWaraHgj8H/ptoHdCWlXFuHOduAfktLT4Kid16zphIZ7KSe3aTn+RaOEkJyf4ujZ798LatXDi\niXD88UeWr1jhfLXKiTHGmMrASzg5BZgRZvn3QHbZmlM1hYaTeConGRlOyGjYML7KidvZNCvL6fQa\nGBgOHHBmYw0MJ6qwapXzNTS05Oc7bejYsfTnjrVDrIUTY4wxXnkJJ/uBOmGWdwCOyZksvFZOUlOP\nTOIWz1wnbuUk8DkDt/32W2dYshtCtm+HzZuPhJFOnUqGk1atgkfXRFKW0zp790JRUenPYYwx5tjm\nJZy8DtwjIjWK76uItAAeBOYmrGVViNfKSZ2AiBdPOAmtnEDwtm7ocMMJOEOHly+Hpk2d00g5Oc5o\nnW3bYu8MC6Wf1ikqckYChQsn4FxzxxhjjInGSzgZD6QDBUAtYAnwLc5Viv+YuKZVHV4rJ17DSWmV\nk/x8Z3nDhs4FAVNSnGWBISTwejfxhJPSKic//RTcLpf7Wu3UjjHGmNJ4meekEDhXRM4ETsIJKnmq\n+l6iG1dVhKuc7NkTPMlZqHDh5KuvYnu+WConbthITj7S+TU/H847z1nevr3z2Kefwvr1iauchJsd\nFpxqDVg4McYYUzpP85wAqOqHqjpNVR9KRDARkd+IyDoR2SsiS0XklFLW7yMiy0Rkn4h8IyJXhTye\nLCL3iMi3xfv8XEQGlvV5wwlXOVGNfhBPVOWkVi3n+9Bw0rnzkfudO8Pnnzt9UdzlNWs6AeWVV5z7\n8VRODhxwZp8NJ1I4scqJMcaYWHkOJ4kkIpcAfwEmACcDXwLzRKRhhPVbAW8C7+NUbx4D/iYi5was\nNhkYDfwG6IQzwuhVETnJ6/NGEq5yAtH7nYQLJz/9FPmgHyiwcuJu64aCgwedUTmBYSMnBz75xNl3\n6PKPPnK+79Sp9OeFI9PbRwpeFk6MMcaUVaUIJ8A4YIaqPq+qK4EbgD3AqAjr3wisVdXbVHWVqj4J\nvFy8H9cIYLKqzlPV9ao6HWculvFleN6wwlVOIHq/k3Dh5NAhpzNpaQIrJ+62bihYu9apbISGEFdg\nCHGXt2jhDGmORa1aztdo4aRmzZL7s3BijDEmVhUeTopH/XTHqYIAoKoKvAecHmGznsWPB5oXsn4K\nzrDnQHuBs8rwvGElqnICsZ3aCVc5KShwvl++3PkaLpxkZ0P9+iWXx3pKB45UTiJ1inWHEYf2tXEn\njbNwYowxpjRerkqcaA2BJCD0snBbgOMjbJMdYf06IpKiqvtxwsqtIvJvYA1wDs4U+24g8/K8YaWk\nHLmmDnivnIBzcC9tMrRwlZMvvnD6lSxe7ASQRo2OPN62rTMDbWgI8RJOYqmchJ7SASeYlDYRW6Rt\nTdVUUADff19yec2azmcuUmdxY4wpUzgREQH64gwp/khVYzgpcdT8FngaWAkcxgkozxLnKZtYZGc7\nAcD9Y3u0KyctW8Jzz0G3bs79/v2D//DXqAFdu0KPHsH76dDBOf0Sujya0vqcbNvmDGEOJ1o42bwZ\nmjWDf/8bTo+rbmUqqzPOgDVrwj/2xhswZMjRbY8xpuqIOZyISCZOx9NuwFKcvhtvA2cUr1IgIgNU\n9b9xtmEbcAhoHLK8MbA5wjabI6y/o7hqgqpuA4aJSE2ggar+KCJTgLVleF4Axo0bR113bCxOR9Nb\nbskFcgFvlZP69cHniy2chFZO/vhHGDrUGSEEztwmoRYsOHL6yZWa6hw8GjQo/TkDt4HIp3V27IDM\nzPCPRQsnBQVOn5vPPrNwUh0UFjqfrQcegAEDgh8bMMCp8lk4Mab6mjNnDnPmzAlaVlhYGPP28VRO\n/ozTF+M54ALgXUBw+n8o8BDOCJkL4tgnqlokIsuA/jizz7oVmf7A4xE2+xg4L2TZgOLlofs/APxY\n3MfkIuCfZXheAKZOnUo3t0wRRmmVk/37nU6rgeHE53NCQmnhRLVk5aRmTTj55OjbBWSpIPGeRint\ntM6OHc4stOFECydukAu8grKputyLSQ4a5FTtAnXubD9nY6q73NxccnNzg5bl5eXRvXv3mLaPJ5yc\nB1ymqktEZBbwHdBPVT8BEJHbKT7Ie/AIMKs4LHyCM4omDZhVvO8HgONU1Z3LZDrwGxF5EOdUTX/g\n18Bgd4cicirQFPgCaIYzXFiAh2N9Xq9Kq5wEXpE4UCxznRQVORWGwMrJ0RRL5ST0dbmihRM3yNlB\nq3pwLyZ5fJjeW4FD2I0xJpx4wklj4BsAVf1eRPbhBBTXRsBTd0ZV/b/iuUUmFT/PF8BAVXUP1dlA\n84D114vI+cBUYCywCbgmZDK4VOA+oDWwC3gLGKGqOwL2U9rzelKzJiQlRa6clCWcuPuM5SJ95aG0\nPielhZPt28M/ZpWT6iU/H9q0OVJpC5STAzNnOnPyJFeGLvnGmEonnj8NPpw+Gq5DOKdzXEoZqOo0\nYFqEx64Os+wDnKHAkfb3AdA50uOxPK9XItGvr1OWcOLus6IqJ7Gc1okWTjZuDP+Y+7q2bbNRO9VB\ntOs15eQ4pzbXrXNmKTbGmFDx/t9yrYjsCth2pIhsK74f4zRex4ZoVyaOFk5Wr46+38pSOQl3Wke1\n7Kd1wDmwnX122dppKlZ+Plx6afjHAi86aeHEGBNOPOFkI8508K7NwBVh1jFU38pJzZpOZShc5WT3\nbiegeAkne/Y4Q55VLZxUdbt2wYYNkSsn2dnOiK78fPjVr45u24wxVUPM4URVW5VjO6odr5WTrVuj\nX824oisnIk71JFzlJNLrcpVWOcnIcA5c1u+kalu50vkaKZyIOI/Zz9kYE0mFT19fXZVWOUlOPnKK\nxJWV5YzGiTaLakVXTsBpd7jKSSzhZOfO8Bc33LPHec/soFX1uT+/aDMd28/ZGBNNXOFERJJF5Pci\nkiciu4pveSLyu+J5REyx2rWjh5M6dUpWR2KZJbaiKyfgdIr1Gk7AKfuH2r3bec/soFX15edDq1aQ\nnh55nZwcZy6UWK7CbYw59sQcTkSkFrAYmAJsBf5WfNsKPAi8LyKpEXdwjElLi35aJ9wBPJZwUlkq\nJ15P6wSuFyiwcrJ5M/z8c2Laao6+aCN1XDk5zmdow4aj0yZjTNUST+XkDpy5Rk5W1YGqekvxbSDO\nlPYti9cxxFY5CRVP5ST0lNDRVNbKSbhwElg5gSMzjJqqJ9Zw4q5rjDGh4gknlwK3hrt2jqp+CfwO\nuCxRDavqvFRO3AvmlVY5SUtzpruvKKX1OcmIMKg8lspJhw7Oa7ODVtW0dy+sXVt6OGnWzDntYz9n\nY0w48RziWuJM8R7JUqBF2ZpTfZRWOQl3AE9Odi4AGEs4qUjRTuukpUWe9dMNJzt3lnzMrZykpEC7\ndnbQqqpWrXJGm5UWTmzEjjEmmnjCyQ6gUZTHs4Ewh51jk5fKCZQ+14l7EK9I0U7rRHpdEFvlBOyg\nVZW5P7dOnUpf137OxphI4gkni4A7ozx+R/E6huiVk507vYeTyl45iRZO3GpRtD4nYAetqmz5cueU\nTbTPgcv9OWuZLnxhjKmO4pkh9l7gPyKyFOdqvitxrvLbCedqvjlAz4S3sIpKVOXkwAF4+WXIzXVK\n4ZWlchLuAn6lhZOkJKftsVRONm0qfX+l+egjpx9Phw6R13n2WWd0UDTNmsGVV3pvR3X36aewYIHz\n/Ztvln5Kx5WT4wwr37QJmjcvfX1jzLEjnhli80XkXOAZ4J8cudCf4ASVAaq6PPFNrJq8jNYBJ5ws\nW3bk/jvvwOWXQ+fOcNJJladyEum0TqTOsK5Is8Tu3h0cTsAZsXPaad7bec010LUrzJkT/vEtW5x1\n6tZ1+rqEs38/FBbChRc6U66bkn7/e/jPf458pq+9NrbtAkfsWDgxxgSK68J/qroU6CwiJwPuJbu+\nUdUvEt6yKi4tzZnttajIuWZMoHgqJ8uL415+vhNOKkPlxOtpHYgcTvbsOfK6jj/eqRLl53sPJ/v3\nOxdRrFkz8jpbtjhf330Xekao+S1aBP36OT8TCyfh5efDH/4A99wT33YtWzq/J/n5MHBg+bTNGFM1\neRqQqqqfAwuBhRZMwnMPtKHVk0OHnIARazhx+164XytD5cRrh1iIrXKSlgatW5et38nq1c57vWoV\nHDwYfh33fXbnlwknlrlnjmVbtzq3WE/lBPL5nI6z1r/IGBMq3unrM0XkSRHZBmwBtojINhF5QkTs\n/8oA7oE2tN+JO4w2WjjZu/fIdqHhpDpWTlSDKydQ9k6x7rb798O6deHXsXBSdu5keV7CibudhRNj\nTKh4pq+vD/wHuAqYC4wvvr0CjAQ+FpF65dDGKilS5aS0WVQDD4aHDjl//GvVqt6VEzfoBL6uRIST\nWrWOfB/O1q3OaZ9o/WQaNDiyrikpP9+Z16ZdO2/b24gdY0w48VRO7gEOAG1V9XpVfbT4dh3QDigq\nXscQuXISTzjZsMEJAeed55ym2L+/8lROEhlOwl0vKCcH1q+PPOKpNG5/lczM6OEkK6vkBRgDxTIx\n3rEsPx/at4/etyeanBxn5FdpI6aMMceWeMLJUOB3qrol9AFV3QzcBvxPohpW1SWicuIeVH/9a6eK\nsnp15aichDuto+o9nIS70rJ7mmDlSm9tzM93RjhFq8C44aQ0pc09cyyL5To60dg1dowx4cQTTpoA\n0YYKf40zS6zBe+Uk8Po6+fnOKYdzz3WW5edXjsqJe1onsBS/f78zMilRlZOOHZ2vXg5aRUXwzTfO\ngWCrEXcAACAASURBVM/CSfkqazhp3doZxm3hxBgTKJ5wsg1oFeXx1oBd6L6Y18pJSorzmBtOcnKc\nwNKokXO/slRODh92QoCrtNflirVykpEBLVp4O2itWeO0zQ0nK1Y47Q1l4aRsfvkFfvyxbOEkKckJ\nohZOjDGB4gkn84DJIlLi7LKIpAB/At5NVMOqutIqJ+npkbd1D4aB/5Xm5MBXXzkVi4oOJ25H08B+\nJ/GGk8CqS7jKCXjvFOtu44aTvXud/juhLJyUTVlH6rhsxI4xJlS8HWKPB1aLyG0icqGI/EpE7gBW\n40xjP6E8GlkVuQfwcJWTjAxnjodIsrKgoKBkOHFnjq3o0zqpqc5Xr+Hk4MHgbcNVTqBs4aRBA+d9\njNanwcJJ2eTnO5/jaJcHiIWFE2NMqJjDiapuAk4H8oEHgNeAV4HJxcvOVNXvyqORVZHP5wSUcJWT\n0g7gWVmQl+dsGxhO3P/+K7py4oaTwE6x8YSTwPUheuVk7drwc6pE44Y6Eee6OOnpJQ9+hw7BTz/F\nF05suGuw/Hxo2/bI58GrnBzYts0CoDHmiLgmYVPVdap6HtAQ5yJ/PYEsVR2kqt+WRwOrsnDX14k1\nnPz3v873geEkcL8VqayndQLXh+iVk8OHnc6t8QisOImE/8/855+dsNGoUen7a9TIea1ehzVXV2Xt\nDOuyETvGmFBep6//RVU/Kb5ZJ9gIwl2ZONZwAk4IadHC+b5z5+D9VqTyqJwkJZWcK6NTJ+drPAet\nQ4ec4ceB71e4cBLL7LAumyU2vESFk7ZtnetPWTgxxrg8hRMTm7JUTsA5OLt9U7KyjsxWWlkrJzVq\nRL66rytS5SQtreRkaJmZcNxx8R201q1zhjUHHjTDzUJq4aRsduyA775LTDipUcPpt2LhxBjjsnBS\njspaOQn8w++ennD3W5EidYitUyf6bKsQuXISKXDF21kycKRO4D527YJNm44ss3BSNu7keIkIJ+5+\nLJwYY1wWTspRWSsnoX/43fsVXTmJdFqntNcFR65jE65yEo6XcJKZCdkB0wGG69OwdaszNX1mDJer\nDJwYzzjy850g6k6WV1YWTowxgZIrugHVWSIrJ4H3K7pyEum0TizhJCXFuRUWHllWWuXkySfhwIHg\nPinvvQeTJpUcQbNmzZGROq6WLY9cPHHgQGfZ1q1O6Cit0gPO89atG1s4efpp57Vcfnnp61Zl+fnQ\nqlXiPos5Oc71dc46q+TPpEsXeOqpxDyPMaZqsHBSjjIzYUvIlYhiOYifdBJMmAD9+gUvHz7cqVbE\nEgLKU1kqJwD16jmjZVylVU7c6woFdnL95z+dUTyDBgWv37YtDBsWvMznc/rvhFZOYjml44p1rpPH\nH3c6MR8L4SRRp3TAuUTD9deXvKDkxo0wfTo89FD0q0cbY6oXCyflKCsLvv46eFksB/EaNWDixJLL\ns7Ph9tsT1jzPovU5iUXogb60ygkcuZCfKz8fBgyAWbNie87Q0wblEU7ca/q4laXqLD/fuSBlotSr\n54SQUMuWQY8eTh+XU05J3PMZYyo363NSjkIPaLFeubeyS0pyAlSiwkm0ykmDBkeuK+RSjf8/99AR\nO+URTtxr+lT3vim7d8P69YmtnERSlgtAGmOqrkoTTkTkNyKyTkT2ishSEYn6f5KI9BGRZSKyT0S+\nEZGrwqxzi4isFJE9IrJRRB4pvg6Q+/gEETkcckvYn8GsLGfmS/eic7t3OwfHqh5OwKmeeD2tE0/l\nBEpWPX780emzEm842b7d6dcA5RNO3DZW93CyapXzOQ6sZJWX2rWdvi0WTow5tlSKcCIilwB/wbk2\nz8nAl8A8EWkYYf1WwJvA+8BJwGPA30Tk3IB1LsOZZn8C0BEYBQzHmW4/0NdAYyC7+HZWgl4WWVlO\nf4nt2537sU5UVhXUqnV0KidQMpyEGy5cmtARO+UZTvbsKTlKqzpxX2eiRuqUxkbyGHPsqRThBBgH\nzFDV51V1JXADsAcnUIRzI7BWVW9T1VWq+iTwcvF+XKcD/09VX1TVjar6HvBP4NSQfR1U1a2qWlB8\nS9iMt6HzY1SncHK0KyerVjkXDATnQJWSAq1bx97e1q2dbdxTO9u2lU84SUpyvq/O1ZP8fKfT79Hq\noGrhxJhjT4WHExGpAXTHqYIAoKoKvIcTMMLpWfx4oHkh638EdHdPD4lIG2Aw8FbIdu1F5HsRWSMi\ns0WkuecXE6I6h5OyVk62bTvS/yOWyklREXxbfPWm/Hznv3Y3CMQiORmOP97Zdvt2J+jEG0527So5\nmiRQfj506+Z8X93DydHob+LKyXFm/q3O1ShjTLAKDyc4FxFMAkIG3bIF5zRLONkR1q/j9ilR1Tk4\np3T+n4gcAFYDi1T1wYBtlgIjgYE41ZrWwAcikpBpzqpzOElNPXKgLiqKb4hzVpYTDtzTXbFUTuDI\nf89eD47uf+DxzA4b2GaIHDrca/qcfXb09aqDiggnqk71zBhzbKgM4aRciEgf4E6c0HEyMAwYIiJ3\nueuo6jxVnauqX6vqApzKSj2cvillVr++M8dGdQ0n7mmdnTudr/GEEzjyvpRWOWnUyBm1456SWb7c\n28Gxc+fyCyfuNX2qezjZt+/IRHdHi5cLQBpjqrbKMM/JNuAQTqfUQI2BzRG22Rxh/R2qur/4/iTg\nBVWdWXx/uYikAzOA+8LtVFULReQboF20Bo8bN466desGLcvNzSU3Nzdomc/nHFRDw0l1mEwq8LRO\nvKEr8EDfoUPplRP3ukJusPj5Z++Vk23bjhzkEhlO3H126wbp6dU3nKxa5Yw+O5rhpE4daNbMwokx\nVcmcOXOYM2dO0LLCwKnBS1Hh4URVi0RkGdAfeB1ARKT4/uMRNvsYOC9k2YDi5a404GDIOofd/Rf3\nawlSHF7aAc9Ha/PUqVPp5nYuKEVgR8odO5yDeo0aMW1aqQVWTsoSTg4ccE7xlDYNek4OLF3qbaRO\n4D4AlixxAk/9+rFvG0s4ycyEJk2cSk91DSfu++9WM44W6xRrTNUS7h/2vLw8unfvHtP2leW0ziPA\naBG5UkQ6AtNxwsUsABF5QESeC1h/OtBGRB4UkeNFZAzw6+L9uN4AxojIJSLSqniY8STgdTeYyP9v\n78zj7aiqfP9dSW5uEkJChpuJTGTmkBCSIIRRNEA+QisiSARbEFRksHli+xhe04JEW0ABYysv+rQF\nbIgmCIgiooAShoQhAQM5IQlmYshwAxkh893vj1XVp+65Zx6r6q7v51Ofe0/Vrtp7nTpV9au991pL\n5PsicrKIDBOR44GHgH1Aa7lXBuniJA5DOlBez0nv3ioOmptTkxzzJTNMJHROx5IlKu5G5ezbyszI\nkbrv009rj1YxE2q7dtU25hInfk6fQkPdR5FkEgYNKixhYiUxcWIY7Yu695wAOOfmejFNbkaHZ14F\npjvn/Fv8AGBIoPwaETkTuBO4Cngb+JLnLuwzE+0pmQkcCjSjPTM3BMoMBu4H+njbnwWmOufeq5Rt\ncRUnXbqUHr+lY8fUcJefGLGQnpM9e+DRR9XrplMJv9yGBh1GWrq0tDf/pibYtCnztmQSJk1KlYuz\nOKnlkI5PIqF5i/bsUZdwwzDiTSjECYBz7i7grizbLs6wbj7qgpzteL4wmZmjzPnZtlWKpqaUC2zc\nxEmpwzqQeoAX03MC8NRTbRP7FUMioeKkmPkmPtlER0sLLFuWSvbX1KSf44if06jWJBL6Pa9YARMm\n1L5+wzBqS1iGdWJLXHtO0od1RPILjCD+91Joz8nAgdCzp85PKefN3d+3kuJk7VoVWcFjx7HnZO9e\nzQ5dj54T89gxjPaFiZMq4z+o4pL0zye956RHDxUohVJsz4nvsQPhEyfpk3TjKk5WrtR4LvUQJ717\na1ZuEyeG0T4IzbBOXGlq0jfOHTv0IT58eL1bVBm6dFFhsWOHuucWK7qammDVqsJ7TkAfigsW1Fec\nPPtsKq6Lz6uvqvvw4MGpctu3R29+xO7dGlAvG4sX6996iBO/XhMn4ePAgeKi9zY06P3DiB979+p9\nL53OnYu/F5o4qTL+Q3DTpnj1nPTsCW+9lbLnqKOK27/YnhOAI4/UH/jo0cXVFWT0aD3GoYcWv++g\nQRqALNM5nDo11XPkn/PNm0urpx4sW6bf7/505/s0BgzQycz1IJHQOUfpXHyxtukHP6h9mwyYPh2e\nfDJ/OZ+GBv29jRxZXD1f/7pmI//lL/OXNWrPrl0wZAi8l8GdpLExNfeyUEycVJlgfIw4iZOvflWT\n6bW06OdiJykWO+cE4Ctf0QisnTsXV1eQxkZ48cXSBM6Xv6wX34EDbbf5njrQ+pxHRZy88ILadd99\nuV2sx4ypXZvSSSRg9mzt3QnGCvrrX0tzLTfK58ABeO45uOSSwiZK79kDF10EL79cvDj5299UnBjh\nZPlyFSbf+17rpKw7d+q9c9EivX8WiomTKhNXcdKzJ3z2s6Xv39SkN6qNXoakrl3z79O1K0ycWHqd\nPkceWdp+Bx8M556bv1y+gG1hJJnUIccLLqh3S7KTSGjPzptvpibI7typE5LTAjYbNWLtWh0OnDGj\ncC+ua68tfnjOz121Z4++0BQz+d6oDf45vfzy1tejc/DNb+r2YsSJTYitMn4XeNzESbn4D/C1a1V0\ndIjRLzGq4qRec0kKJT0BJOgDC6L1XceJUiI2lzJ3yM9dBalzboSLZFJ7itNfFILpR4ohRo+EcNLQ\nAL166fyMfftMnPj4D/A1a+L3FnTQQSq4ovTAjII4aWqCvn1b3+T8/zdv1jc0o7Ykk9qjWMzwZSkP\nqkzn3AgXue4hJk5CSlOTTqQEEyc+wZ6TQuabRI0ouRN/8IGKxLCLE2h7k/P/37fP5iPUg2DahkJJ\nJDSYXi7PsEz19OypwwImTsJJPnGybFlqjmIhmDipASZO2tK3r/6NY88JREucLF+uvQ5RECdHHNFW\nnHTvrv9H5fuOE6X0uAXnDhVbT/r5N8LBnj16PnOJk127YP36wo9p4qQGmDhpS0ODJo/butV6TupN\nvTINl0IioWLKd3lOJuGEE/T/qHzfccG50sUJFCcy/Hos1k04yReg0V+/alXhxzRxUgOCCeNMnKTw\nh3as56S++LPoDz643i3Jj58AcvVqfRNbtUrdyyE633dceOstHRIsVpxkmjuUCz93lS9OVq1KRac2\nwkG+F5zBg7WH08RJyAhGIzVxksL/XqznpL5EYTKsT/Ct2x+OOukkXReV7zsulOKp41NMD8i6danc\nVcEEkEZ4SCahf//sARp9j53Vqws/pomTGmDiJDPWcxIOoiRO+vdX77dkMvVwmzBBc+9E5fuOC8mk\nvlgMHVr8vsWIk6AIsgSQ4aSQe0gikZreUAgmTmqA/xBuaIhWrpVqE/eek/ffzx8Ovt7s3q03jKiI\nE/8NbOnS1nEVoiQG40IyqWKhlBhF6XOH8tXTvbsOPR5yiKaRMHESLgoVJzasEzL8h3CxmXvjTtx7\nTiBznokwsWKFdpNHRZxA6q176VL13gATJ/WgnB634NyhQuo5/PDUvdMmxYaLffv0PlKIONm9u/Dj\nmjipAUFxYqSIe88JhP+BGSVPHR8/ZsJrr7XOMh327zpOlOqp4+OLyqVL85cNilB/30L2M2rDm2+q\nQAmeo0wU+1sxcVIDTJxkpj2IE99LK6wkkzBwoM7jiAr+G1hwOMrESW1Zv16D3pUqToJzh3KRSQQl\nEvpA9MPZG/Wl0InRw4YVN63BxEkNMHGSmfYwrBP2B2aUJsP6pD+owMRJrSnHUwcKz7fy9tua3DH9\nnB84oLE1jPqTTKpreNDxIxMdOrTOVpwPEyc1oLFRY0iYOGlNnHtOevTQCdBhf2BGUZwcemgqJos/\nHOWLE8uvUxuSSb2vFfOwSacQcZJJBJnHTrgo5h5SzO+lU2nNMYqlqcnESTpx7jkRUfvmz4d+/Vpv\n69QJPvEJTQ7o4xw89pi+JVaDDh1g+vTWgdb27tW3z6uuqk6d1cJ/6167Vl2IQb/r3bs1KJgfzr6a\nvPUWLFhQ2r5dusCZZ0LHjpVtUy1JJmHcuPJsSCTgV7+CuXOzl3n8cb1Ohg1LrevTR4eFHnqo9Lp9\n+vWDU05pvW7bNh22Gjcu+35vvAFLluj/InDqqW2HRv/2t8oP6x53nHotBVm0KLOLbmMjnHGGviRV\ngpUr4ZVX2q5/6SU47bTCjjFiRBEVOudsKXABJgNu0aJFrljOPde5mTOL3i3W7Nnj3ODBzj39dL1b\nUh1OO805lR1tl9mzW5dduDB72Uott9zSus6lS3V9FL//a65xbsaM1Oc//1ltWbWqNvWfd1555+JP\nf6pNO6vFSSc5d/755R1jwQLnOnTI/119/ONt9/3MZyp3Xaxf3/rY11/v3KBBudt+1FGtj3Hdda23\nv/tuda7hT3+6dT0tLc4dckj28g8/XPx5ycbUqdnruffewo5x++2LHOCAyS7P89Z6TmrEvHn1bkH4\n6NxZ30DjymOP6Zt8OlOnwuuvt1732mvau7FhQ3Vi4Uyf3rbOcucN1JNbb239OTjHp5yhhkJ5912Y\nMQN+9rPi9nNOh6Vef13PSRRxTr1lTj+9vONMnQo7duSPdZKpZ3XevPJ7GVetgkmT1JYBA1LrlyzR\n87t5cypBaZD9+/Xa+f734dJL4bzz2l5bvjfRK68U2VuQgxtu0HtKkHfe0fxkc+e2/T0ddpi266yz\nyq/bOb1Hfec78C//0npbhw6F91Yed1zhdZo4MYwq0bFj5qG8TJlVk0kYOTL/pLJSGT8eFi9uW6ef\n5yTq1HoCcnMzfOQjpQ3VHn54tOdLNDdrgMFKiNpS55t16FD+MPn48foikEzCtGmp9f65WbYslRoh\nyKpVOiQ6ebK2YcIEePDB1mX8OTkTJlRu+G7iRPjJTzSvkD8k7Ld1ypS230cl48H4eZQmTSrvezdv\nHcMIMZluGtWemOrHBmlpqV2dtcQXWLUUJ6UKyagHEYtyj1uQTp1g7NjW5+KDD2DNGv0/2zlKt9/P\nGfPhh63LlDsnJ51MeYWSybZzcoLlK/U7q8c5N3FiGDUmkdDhm/ffT62rhTjZtUsnkdaqzlrS2Khv\ndLUQJ/v367krV5y4iHoWJZM6yXLkyHq3pHzSH+B+MslOnXKLk169dFKufwzndN9gmUpfW5m8lHKJ\noERCJ+4eOFB+3eXkUSoVEyeGUWP8m9ayZfp3+3btNq22OIHUjW3/fr2ZxkWcgIqFWgS981MSlCNO\ntm/XeQ1RJJmEMWMq5wVST9LFif//xz6WW5wkEqlw+umiwZ+TU+lrK1NeoVwiyA9W6PcElUM5eZRK\nxcSJYdSYMWP0IvdvMm+8oX+rKRQGD9ZJa36d/rh53MRJLXpO/DrKEScQ3aGdOPW4JRI68dU/p8mk\nuuoee2x+ceLTo4deX375Ss7JydTeoAjKJ0789pZLPc65iRPDqDGNjTBqVOqmkUzqW1iuuArlkh6R\nMy7zBoJERZwMH66xTkyc1J/0B7hvWyKhPVtbt7Yuf+CA9nim21+raytYz8aNsGVL9noGDVLhVO7v\nLJ8IqhYmTgyjDqTfzIYPr36k3PQ6e/dOjZvHgaiIk44dVYhGUZy8954+FPMleYsKo0a1nl8SFCeQ\nGnr1WbtWh0ryiZNqzclJJDQY2t69+UVQoSkC8lFuHqVSMXFiGHUg/WZWiws/OBEzfdw8DtRSnDQ0\nQM+epR8jqh47cetxa2jQYdZksnUyyfShV59s9geTEVZzTk4wr1AyqbGicsVRqcTvrF7n3MSJYdSB\nREKTmm3fXltxsnOn1hunrnmfWoqTvn3LE3aJhE6ajJrHTjKpPT+jR9e7JZXDf4CvWKGuuomEuueO\nGJFZnBx8sAbSSz+G7+ZbzWsrOAyVTKordKcc0coyhRAolkrkUSoFEyeGUQf8m8zLL+ts+lqJE9BI\nj5nGzaNOU5PGqdi1q7r1lBPjxCeR0PkCtfAuqiTJpAqTzp3r3ZLK4YsTX4j43je54hGlC9Ogx041\nxUmfPpoPqNB6Egm9JsqJxF2NmC2FYOLEMOrA2LF6g3vwQX17roVQGDZM3wgffTTzuHnUqVWU2EqJ\nE4je0E4ce9z8uEPPPAMDB6YS+BUTLLF3bw2BP3++zsmpdliApUsLc1euxO+sXuc8NOJERK4UkdUi\nsktEForIR/KUP0VEFonIbhFZISIXZSjzdRF5Q0Q+FJF1InKHiDSmlSmqXsOoBN26aTfpb3+rn/03\nr2rSoYPW44fajttDppbiJD3TdLGMHKm9D34OlqgQV3ECel0EbUskYN06zf8DOjSSz3W3FtdWIqFC\navPm/PUMGaK5iUr9nVUrZkshhEKciMgM4HbgRmAS8HfgcRHJmPVDRIYDfwCeBCYCs4Cfi8hpgTIX\nAN/zjjkOuAQ4D/huqfUaRiU54gh9Yxs6VMexa1lnjx7qahgnotRzkil0etjZulXda+MmTsaM0SGL\nDRtaeyH5//seO35+mWyeSv611bGjHrNa+PUE25iNDh3KmxS7aZPGbKmHd1YoxAlwNfBT59y9zrk3\ngMuAD1FBkYnLgVXOuWucc8udcz8BHvCO43Mc8Kxz7jfOuXXOuSeAXwPHlFGvYVSMYG6OetQZJ08d\niJY4geh57PgP6biJEz/uELS2bdw4vUYKjV/ir6/2nBy/nk6dUu3OV77U31k9vbPqLk5EpAGYgvaC\nAOCcc8ATqMDIxFRve5DH08o/D0zxh2lEZARwBvBoGfUaRsWotziJG9266VJNcdLSorE+2qM4SSb1\nTbyavQL1ItN10a2bxh8KipODDtKhkkKPUQ384xfqrhwMIVAs9cyjlMMJqWb0BToCG9PWbwTGZtln\nQJbyPUSk0Tm3xzk3xxueeVZExKtjtnPu1jLqNYyKYeKk8lTbnXjLFo0zUSlx0twMF16Y2xPi6KPh\nyivLry8Xb78NN98M+/ZlL/PKK+pe27VrddtSDxIJeOihzPFL5s3T8/TCC7nzy9Tq2mpqUq+dQutJ\nJHTezD//c/E9Oi+/nN9duVqEQZxUBRE5Bfg/6FDNi8Ao4Ecist45951yjn311VfTMy0C0/nnn8/5\n559fzmGNdsaECfCFL8D06bWr87DD4JJL4JOfrF2dtWTIkNaZlytNudFhg5x8Mpx6qgb+ysbGjfpw\nvPzy6iZde/BB+OUv4Zhjspc56CB9wMWRc87ROTV9+rRef+GFMGuWxi/p1Uuv12z07asi8pxzqttW\nEfjmN2H8+MLKH388nH56aQkAe/SAGTOK3w9gzpw5zJkzp9W6bdu2Fby/uDpHAfKGVz4EznHOPRJY\nfzfQ0zl3doZ9ngYWOee+EVj3ReBO51wv7/N8YKFz7ppAmc+jc0y6l1jvZGDRokWLmDx5cnmGG4ZR\ncb76VX3DffXV6hz/mWdUVCxbVt1cSD6PPQZnnKEPlmHDqlfPZZfBwoXV+94MA2Dx4sVMmTIFYIpz\nbnGusnWfc+Kc2wcsAqb567xhmGnovJFMLAiW9zjdW+/TDdifVqbFP36J9RqGEWISCc3yfOBAdY5f\nyZ6TQqhVPJQ4uggb0abu4sTjDuArInKhiIwDZqPi4m4AEfmeiNwTKD8bGCEit4rIWBG5AjjXO47P\n74ErRGSGiAz33IxvBh5xqe6inPUahhEtEgnNb7J6dXWO39ys80P8QF3Vxo9TUU1xUs9YFoaRjVDM\nOXHOzfUmr94M9AdeBaY75/ypbQOAIYHya0TkTOBO4CrgbeBLnruwz0y0p2QmcCjQDDwC3FBEvYZh\nRIhgT0MhbpbF0tys8xKqOf8jiB84r5ripLlZY1mYODHCRCjECYBz7i7grizbLs6wbj7qCpzteL4w\nmVlqvYZhRItBg3QSXzIJn/pU5Y9fqRgnxVBtl+O4ZRo24kFYhnUMwzDKRqS6D/N6ipNq+S7UM5aF\nYWTDxIlhGLEijuJk+3YNHV8NksnCA3oZRq0wcWIYRqxIJNTVt6Wl8seulziB6gku89QxwoiJE8Mw\nYkUiAR9+qBllK009xMnw4dCli4kTo31h4sQwjFhRrZ4G5+ojTjp21IBv1RAn772nUWhNnBhhw8SJ\nYRixolqxQbZv19wztRYnoCnrqyFO/EzDRxxR+WMbRjmYODEMI1ZUKzZIraPDBkkkNFBapT12kknt\nmRk9urLHNYxyMXFiGEbsqIbHTr3FyZYtsGlTZY+bTKowKTZbrWFUGxMnhmHEDn8YpJI9Db4wqJc4\nAe09qSQWtt4IKyZODMOIHYkE7NgB77xTuWP6PSd9+lTumIUyYoT2blS6N8g8dYywYuLEMIzYUQ2P\nneZm6N0bOtUh6UenTjB2bGXt2bpVA7uZODHCSGhy6xiGYVSK4cOha1f47Gf1b5DGRvjjHwvzUHno\nIbjySg3otmMHDB1aleYWRCIBv/gFPPhgYeU7doR77oFTT02ta26GE05Qz6P9+1PHNYywYeLEMIzY\n0aED3Hdf5p6Gm26C+fMLEyePPaZh3S+9VD8fe2xFm1kU3/oWTJhQePlZs+Avf2ktTl56CVauhOuv\nV3frXr3gyCMr31bDKBcTJ4ZhxJKzz9YlnfvvL3x4JJmEk06Cf/u3yratFBKJ4no5nn++rZ3JJHTv\nDt/9riZJNIywYnNODMNoVxTqZuxctCeMZrIzmdQYMCZMjLBj4sQwjHZFoeJk40aNLRJlcbJ6teYZ\n8omy2DLaFyZODMNoVyQSsGEDvP9+7nK+gInqwzyR0N6f5cv1c9R7goz2hYkTwzDaFf7D2c8rk41k\nUmOLjBhR/TZVg8MP17++yHrnHfU4MnFiRAETJ4ZhtCvGjFFvnnxDO8mkxhapR1yTStCjBwwenLIz\n6j1BRvvCxIlhGO2KxkYYNaowcRL1B3lwfk0yqTFfhg+va5MMoyBMnBiG0e7wc+/kIpksLBZKmAna\n6XvqdLC7vhEB7GdqGEa7I5/HTnOzLnHoOXnzTdi925L8GdHCxIlhGO2ORALeflvDuGfCnywbu6eG\nFQAADndJREFU9Yd5IqGh95cvj8cwldF+MHFiGEa7I5/HTjKpE2FHjapdm6qB77Hz1FOa6M/EiREV\nTJwYhtHuGDtWo6RmG9pJJtWrp6Ghtu2qNL16wcCB8MAD+tnEiREVTJwYhtHu6NpV45fkEidxeZAn\nEppnp7ERDjus3q0xjMIwcWIYRrsk16TYuIkTiHbMFqP9YeLEMIx2SSKhHizpbNkC69fHT5zExR6j\nfWA62jCMdkkiAWvXwsKFOuTh89prqe1xwMSJEUVMnBiG0S6ZNEn/Hndc223dusHo0bVtT7UYP17F\n19FH17slhlE4Jk4Mw2iXTJgAb7wBH3zQdltTE3TpUvs2VYPevWHdOrXJMKKCiRPDMNotY8fWuwW1\noV+/erfAMIrDJsTWkDlz5tS7CTXB7IwXZmf8aC+2mp3RJTTiRESuFJHVIrJLRBaKyEfylD9FRBaJ\nyG4RWSEiF6Vt/6uItGRYfh8oc2OG7XnSgZVOHH9AmTA744XZGT/ai61mZ3QJhTgRkRnA7cCNwCTg\n78DjItI3S/nhwB+AJ4GJwCzg5yJyWqDY2cCAwDIeOADMTTvc60D/QLkTK2GTYRiGYRilEZY5J1cD\nP3XO3QsgIpcBZwKXALdlKH85sMo5d433ebmInOgd5y8AzrmtwR1E5ALgA+CBtGPtd841V8oQwzAM\nwzDKo+49JyLSAExBe0EAcM454Akgg5MfAFO97UEez1EeVOjMcc7tSls/WkTeEZF/iMh/i8iQogww\nDMMwDKOihKHnpC/QEdiYtn4jkG0u/YAs5XuISKNzbk9wg4gcAxwBXJy2z0Lgi8ByYCBwEzBfRMY7\n5zI4GNIFYFm2VKZ52LZtG4sXLy5p3yhhdsYLszN+tBdbzc5wEXh25nfUd87VdUFFQQtwbNr6W4EF\nWfZZDlybtu4T6JySxgzlfwq8WkBbegJbgYuzbL8AcLbYYosttthiS8nLBfmex2HoOdmMior+aev7\nAxuy7LMhS/ntGXpNugEzgBvyNcQ5t01EVgCjshR5HPg8sAbYne94hmEYhmH8D12A4eizNCd1FyfO\nuX0isgiYBjwCICLiff5Rlt0WoD0lQU731qdzHtAZuC9fW0SkOypM7s3S1veA+/MdxzAMwzCMjDxf\nSKG6T4j1uAP4iohcKCLjgNlAN+BuABH5nojcEyg/GxghIreKyFgRuQI41ztOOl8CHnbObUnfICLf\nF5GTRWSYiBwPPATsA+LnNG4YhmEYEaHuPScAzrm5XkyTm9HhmVeB6QEX3wHAkED5NSJyJnAncBXw\nNvAl51wrDx4RGQMcDwTjnwQZjPaE9AGagWeBqV4PiWEYhmEYdUC8iZ6GYRiGYRihICzDOoZhGIZh\nGICJE8MwDMMwQoaJkwoiIteLyIsisl1ENorIQ968l/RyN4vIuyLyoYj8RUSyuS5HAhG5zkuaeEfa\n+sjbKSKDRORXIrLZs+PvIjI5rUyk7RSRDiIyU0RWeTa8KSJtXO+jaKeInCQij3hRoFtE5FMZyuS0\nS0QaReQn3m9gh4g8ICL9amdFfnLZKSKdPOeBJSKy0ytzj4gMTDtGpO3MUHa2V+aqtPWxsFNEDheR\n34nIVu+8viAigwPbQ29nLkycVJaTgP8EjgVOBRqAP4tIV7+AiFwLfA24FDgGzffzuIh0rn1zy0c0\ne/SlaLLG4PrI2ykihwDPAXuA6cDhwL8CWwJlIm8ncB3wVeAKYBxwDXCNiHzNLxBhOw9CJ9hfgQZ/\nakWBdv0QzfV1DnAyMAj4bXWbXTS57OwGHAV8G02sejYafft3aeWibuf/ICJno/fhdzJsjrydIjIS\neAZIojZMAGbSOv5WFOzMTr0jxMZ5QUPztwAnBta9C1wd+NwD2AWcV+/2lmBfdzRa78eBvwJ3xMlO\n4Bbg6Txl4mDn74H/l7buAeDemNnZAnyqmPPnfd4DnB0oM9Y71jH1tqlQOzOUORoNfjk4bnYChwLr\n0JeJ1cBVaec38nai4S7uybFP5OxMX6znpLocgqre9wFE5DDULTqY5HA78AK5kxaGlZ8Av3fOPRVc\nGSM7Pwm8LCJzvWG6xSLyZX9jjOx8HpgmIqMBRGQicALwR+9zXOxsRYF2HY2GXAiWWY4+/CJrO6l7\nk5+9fQoxsFNEBA2ieZtzLlMStMjb6dl4JrBSRP7k3ZsWishZgWKRt9PESZXwfkA/BJ51ziW91QPQ\nG0KmpIUDati8shGRz6Fdxddn2BwXO0cAl6O9Q6cD/xf4kYh8wdseFztvAX4DvCEie4FFwA+dc7/2\ntsfFznQKsas/sNcTLdnKRAoRaUTP+f3OuZ3e6gHEw87rUDt+nGV7HOzsh/ZaX4u+QJyGBhB9UERO\n8spE3s5QBGGLKXcBCfQNNFZ4k65+CJzqnNtX7/ZUkQ7Ai865f/c+/11ExgOXAb+qX7Mqzgw0qeXn\n0DHso4BZIvKucy5OdrZ7RKQTMA8VZVfUuTkVRUSmoEE5J9W7LVXG71R42Dnnp3hZIhrl/DJ0Lkrk\nsZ6TKiAiPwbOAE5xzq0PbNoACMUlOQwjU4AmYLGI7BORfcBHgf/lvXlvJB52rgfSu4aXAUO9/+Ny\nPm8DbnHOzXPOLXXO3YdGX/Z7xeJiZzqF2LUB6CwiPXKUiQQBYTIEOD3QawLxsPNE9L70VuC+NAy4\nQ0RWeWXiYOdmYD/5702RttPESYXxhMlZwMecc+uC25xzq9EfxrRA+R7orPKCkiGFhCfQ2eFHARO9\n5WXgv4GJzrlVxMPO59BJZEHGAmshVuezGzo5MkgL3v0hRna2okC7FqEPgmCZsehDIFOi0VASECYj\ngGmuba6xONh5L3AkqXvSRHTC822otx3EwE6vt/ol2t6bxuDdm4iBnXWfkRunBR3K2YK6FPcPLF0C\nZa4B3kMnW04AHgZWAp3r3f4ybU/31om8nehkyD1oD8JIdOhjB/C5mNn5S3Si3Bnom+bZwCbgP6Ju\nJ+qSOREV0i3A173PQwq1y7uuVwOnoL2GzwHP1Nu2Qu1Eh+9/hz64JqTdmxriYmeW8q28deJiJ/Bp\n1G34y9696WvAXuC4KNmZ8zuodwPitHg/ogMZlgvTyt2EKvoPgceBUfVuewVsf4qAOImLnegDe4ln\nw1LgkgxlIm2ndyO8w7uRfeA9nL8NdIq6nehwY6br8r8KtQtoROMXbUbF6TygX71tK9ROVHCmb/M/\nnxwXO7OUX0VbcRILO4EvAiu8a3Yx8E9RszPXYon/DMMwDMMIFTbnxDAMwzCMUGHixDAMwzCMUGHi\nxDAMwzCMUGHixDAMwzCMUGHixDAMwzCMUGHixDAMwzCMUGHixDAMwzCMUGHixDAMwzCMUGHixDAM\nwzCMUGHixDCMSCEiq0Xkqnq3wzCM6mHixDCMUCIiF4lIevZc0ISMP6tB/SaCDKNOdKp3AwzDMLIg\nQJvkX8659+rQlpIRkQanae4NwygQ6zkxDCMnIvJXEZklIreKyHsisl5Ebixw354i8nMR2SQi20Tk\nCRE5MrD9SBF5SkS2e9tfEpHJIvJRNKNuTxFpEZEDIvItb59WPRre9ktF5Pci8oGIJEVkqoiM9Nq+\nU0SeE5HDAvuMEJGHRWSDiOwQkRdFZFrQZjSb751+/YFt54jI6yKy22vLN9JsXi0iN4jIPSKyDfip\niDSIyI9F5F0R2eWVubbok2EY7QQTJ4ZhFMKFwE7gGOAa4FvBh3kOHgD6ANOByWhq9ydF5BBv+33A\nW8AUb/stwD7gOeDrwHagPzAQ+EGOem4A7gYmAsuA+4HZwHe9Ywvw40D57sCjwMeAo4DHgEdEZLC3\n/TPA28C/AwO8+hGRKcBvvOOPB24EZorIhWnt+VfgVe/YM4GrgH8CzgXGAJ8H1uSwxzDaNTasYxhG\nISxxzs30/v+HiHwNmAY8mW0HETkBnR/SLzCscY2InI0+pH8ODAVuc86t9I8d2H8b4JxzzQW077+c\nc7/19rsNWAB82zn3hLduFtoTA3rQJcCSwP43ishngE8Bdznntni9JTudc5sC5a4GnnDO/Yf3+U0R\nOQL438C9gXJPOufuDNgyFFjpnHveW/VWATYZRrvFek4MwyiEJWmf1wP98uwzETgYeN8bOtkhIjuA\n4cBIr8wdwC9E5C8icq2IjCixfa8F/t/o/X09bV0XEekOICIHicgPvCGgLV67xqFiKReHo706QZ4D\nRouIBNYtSitzNzBJRJZ7Q2Sn5TfJMNovJk4MwyiE9Amdjvz3j+7Au8CRqFDxl7HA9wGcc98GEsAf\ngI8DSRE5q8z2uRzr/DbfDpwFXAec6LXrdaBzCXVn4oPgB+fcK6gouwHoAswVkbkVqsswYocN6xiG\nUS0Wo/M1Djjn1mUr5Jx7E5gFzBKR+4GLgd8Be4GOJdbdxssnjeOBu51zjwB4PSrD08pkqn8ZcELa\nuhOBFc65nHU653YC84B5IvJb4DEROcQ5tzVPWw2j3WE9J4ZhVAVvvscC4GEROU1EhonI8SLyHc8j\np4uI/KeIfFREhnpzVD4CJL1DrAG6i8jHRaSPiHQtonrJs24l8BkRmSgiE9GJuen7rAFOFpFBItLH\nW3c7MM3zxhktIhcBV+L1BGVtjMjVIvI5ERkrImOA84ANJkwMIzMmTgzDyEe+XohcnAHMRyejLke9\nXIaic0AOoJ4893jbfo160NwE4JxbgHrc/AbYhE46zdSeTO3Lt+4bwBZ0vsjvgD+hPT1BvoX2pvzD\nq98fnjkPmIHOc7kJuME596s8de9AvZxeAl5Av4MzMpQzDAOQPD2RhmEYhmEYNcV6TgzDMAzDCBUm\nTgzDKAkRuSDoIpy2vJb/CIZhGJmxYR3DMEpCRA5Co7dmYp9zzgKNGYZREiZODMMwDMMIFTasYxiG\nYRhGqDBxYhiGYRhGqDBxYhiGYRhGqDBxYhiGYRhGqDBxYhiGYRhGqDBxYhiGYRhGqDBxYhiGYRhG\nqPj/JXOeLhc97i0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb8e75f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(xs, ys, label=label)\n",
    "plt.xlim(min_estimators, max_estimators)\n",
    "plt.xlabel(\"n_estimators\")\n",
    "plt.ylabel(\"OOB error rate\")\n",
    "plt.legend(loc=\"upper right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}