ML+part+3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Citation- http://scikit-learn.org/stable/modules/naive_bayes.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "iris = datasets.load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "150"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.data.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sklearn.datasets.base.Bunch"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_values(['Iris Plants Database\\n\\nNotes\\n-----\\nData Set Characteristics:\\n    :Number of Instances: 150 (50 in each of three classes)\\n    :Number of Attributes: 4 numeric, predictive attributes and the class\\n    :Attribute Information:\\n        - sepal length in cm\\n        - sepal width in cm\\n        - petal length in cm\\n        - petal width in cm\\n        - class:\\n                - Iris-Setosa\\n                - Iris-Versicolour\\n                - Iris-Virginica\\n    :Summary Statistics:\\n\\n    ============== ==== ==== ======= ===== ====================\\n                    Min  Max   Mean    SD   Class Correlation\\n    ============== ==== ==== ======= ===== ====================\\n    sepal length:   4.3  7.9   5.84   0.83    0.7826\\n    sepal width:    2.0  4.4   3.05   0.43   -0.4194\\n    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\\n    petal width:    0.1  2.5   1.20  0.76     0.9565  (high!)\\n    ============== ==== ==== ======= ===== ====================\\n\\n    :Missing Attribute Values: None\\n    :Class Distribution: 33.3% for each of 3 classes.\\n    :Creator: R.A. Fisher\\n    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n    :Date: July, 1988\\n\\nThis is a copy of UCI ML iris datasets.\\nhttp://archive.ics.uci.edu/ml/datasets/Iris\\n\\nThe famous Iris database, first used by Sir R.A Fisher\\n\\nThis is perhaps the best known database to be found in the\\npattern recognition literature.  Fisher\\'s paper is a classic in the field and\\nis referenced frequently to this day.  (See Duda & Hart, for example.)  The\\ndata set contains 3 classes of 50 instances each, where each class refers to a\\ntype of iris plant.  One class is linearly separable from the other 2; the\\nlatter are NOT linearly separable from each other.\\n\\nReferences\\n----------\\n   - Fisher,R.A. \"The use of multiple measurements in taxonomic problems\"\\n     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\\n     Mathematical Statistics\" (John Wiley, NY, 1950).\\n   - Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.\\n     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\\n   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\\n     Structure and Classification Rule for Recognition in Partially Exposed\\n     Environments\".  IEEE Transactions on Pattern Analysis and Machine\\n     Intelligence, Vol. PAMI-2, No. 1, 67-71.\\n   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\\n     on Information Theory, May 1972, 431-433.\\n   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\\n     conceptual clustering system finds 3 classes in the data.\\n   - Many, many more ...\\n', ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)'], 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]), 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]]), array(['setosa', 'versicolor', 'virginica'], \n",
       "      dtype='<U10')])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(iris.target != y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 8,
     "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": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "iris = datasets.load_iris()\n",
    "digits = datasets.load_digits()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import svm\n",
    "clf = svm.SVC(gamma=0.001, C=100.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, ..., 8, 9, 8])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(digits.data[:-1], digits.target[:-1]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.predict(digits.data[-1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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": 48,
     "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": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0])"
      ]
     },
     "execution_count": 49,
     "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": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[0] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf2.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(clf2.predict(X) != y).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "clf = tree.DecisionTreeClassifier()\n",
    "clf = clf.fit(iris.data, iris.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X, y = iris.data, iris.target\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "names=['sepal length', 'sepal width', 'petal length', 'petal width','species']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(clf.predict(X)!=iris.target).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pydotplus as pydot\n",
    "\n",
    "from IPython.display import Image\n",
    "\n",
    "from sklearn.externals.six import StringIO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dot_data = StringIO() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tree.export_graphviz(clf, out_file=dot_data,feature_names=names[:-1]) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "graph = pydot.graph_from_dot_data(dot_data.getvalue()) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SduzYsWAiIZvNqq2tTdu2bVMul9Px48fV2NioVCpV1PXMNbtGwUKvlbLj6+np0cMPPyzT\nNJ1kjCSNjY1Jkurr651tXq9XkqiNAABYc4xcobF5AACgoKGhIbW2thYc4l4JhmFoamrKuWHNZrPy\n+XxOTJFIRG+88Yb6+/ud9pKc/XM/F7stX5tUKqVdu3apt7dXJ06cyNsukUgoGAzO6zscDqu7u3vR\n6ymnfNc0m2VZevXVV3XhwgX19PQoGo069Q4KHbtYn5XS2toqSTp37lxV4wAArA2MRAAAYJUKhULy\n+XxKJBKyLEter9d1w9rd3a3+/n5lMhn19fWVNRZ73v/JkycLthkaGpKkeaME7HoEi11PNdXV1cnv\n96u7u1vRaJQRBgCAdYskAgAAq9Rjjz2mQCCgYDAoj8eTN1EQi8V09OhRBQKBKkToZt9453K5eS+p\nuOuZrVLTGeY6dOiQK4mw0HcbCoVKfn4AAKqJJAIAAKtUQ0ODRkZGNDExoVAopJMnT7puvBOJhDo6\nOnT27Fk1NDRUJKZibpoL1U1Y7HrmypeMKJSgKKW6ujrXddpJhNl1EuxClLt37y75+QEAqCaSCAAA\nrFKGYciyLPn9fvX392tiYsI1nSAYDEpyF/wrFzsx0NTUVLBNNBqVJMXjcVmWJemt1Rqkxa+nVliW\npebmZufzgQMHJEk3btxwtt26dcu1DwCAtYIkAgAAq1hvb6/z1Pud73ynent7nX32E/JMJuN6+p/N\nZl1PzWff0M9uU2ibLZFIOMfH43EFAoG8T+Xt9wcPHpQ0UwPB4/HIMAz5fD7XDflC11Mu9vXPfS/N\nXOPsZSgzmYwuX76s/fv3O9vq6+sVjUY1ODgoy7JkWZYGBwcVjUYrksABAKCSSCIAALCKHTt2TOfP\nn5dhGDp//ryzMoI0U1hRmqmL4PF4FA6HFQqFdOfOHfl8Pqedx+ORJNc2+32+bbadO3fKNE15PB7V\n19crHo/nbWu/93q9SqfTCofDkmamPqTTadeN9kLXUw6GYTjXL8lJbtg2btyoxsZGGYahSCSi27dv\n562B0N7erqamJnk8Hh05ckTNzc3O6g0AAKwlLPEIAMAy1MISj9VSK0sXojgs8QgAKCVGIgAAAAAA\ngKKQRAAAAEVbqEYCAABY+0giAACAoi1UIwEAAKx9G6odAAAAWD2ogwAAwPrGSAQAAAAAAFAUkggA\nAAAAAKAoJBEAAFhlstmsEomETNOsdiiLMgzD9cLS8P0BAGoNSQQAAFaZrq4uBYNBJZPJaodStFwu\nN6+eQiqVct0gd3Z2zjsumUzKNE2Zprni612N58v3vQEAUE0kEQAAWGX6+/urHUJJXLt2zfW5qanJ\n9TmRSCgWiykejysej+vixYuKxWKcDwCAKmJ1BgAAUBWbN28u+JQ9k8koGAzq6tWrqqurkySFQiHt\n2rVLe/bskd/v53wAAFQBIxEAAKiQ8fHxgnPc+/r6nG2ZTEaWZSkWiznbIpGIstls3n7z9VdoHn02\nm3XOZZqmRkdHy3Oxi8hkMjJNU5FIROPj4/P2X7lyRZK0detWZ9uWLVskzX/Cz/kAAKgckggAAFTI\n3r17denSJUlSOBx2PaU+ceKEwuGwJiYmVF9fr1OnTqmjo0NTU1NKp9Pq6elRV1dX3n6npqbmbUun\n0/O2ZbNZtbW1adu2bcrlcjp+/LgaGxuVSqXy9js34VHotRz2OXt6evTwww/LNE1XkmRsbEySVF9f\n72zzer2StKxaBWv9fAAAVApJBAAAKmj//v0Kh8Pq6emRZVnOdvu9PYx906ZNCoVC8nq9zo3mwMBA\n3j7tm8/ZZt+c2kZHR5VMJtXS0uLEIkkXLlzI269d1G+x13IEAgFNT09rYmJC4XBYyWRSzz33nLO/\n0LVKy7vJXuvnAwCgUowcJX8BAFiyoaEhtba2LusmOpVKadeuXRoeHnZu6EdHR/Xud7973lz4TCaj\n8+fP6+TJk5LknM8eAVDoc75tC604UK7/DuSLK59YLKZkMqmRkZEFjyu2v8WstvOtJI7W1lZJ0rlz\n55Z8LAAAczESAQCACvP7/QoEAhoaGnK2vfjii/MSCLFYTEePHlUgECjJee0EQrGjCco5nWGuQ4cO\nuRIcC11zKBTifAAAVAlJBAAAquDw4cNKJpMaHx9XJpPRnj17XPsTiYQ6Ojp09uxZNTQ0lPTck5OT\nRbUr53SGuerq6lw3z/ZN9uw6AplMRpK0e/duzgcAQJWQRAAAoArsegSDg4O6cuWK9u3b59ofDAYl\n5a9tsFzRaFSSFI/HnRoM9moN1WZZlpqbm53PBw4ckCTduHHD2Xbr1i3XPs4HAEDlkUQAAKAKvF6v\nwuGwBgYGdPPmTdXV1bn220+qM5mMa+RANpt1Pb2239tPue22s5cV7OzslCQdPHhQ0syKAR6PR4Zh\nyOfzuW5uKyGRSLiWlsxkMrp8+bKTWJFmkifRaFSDg4OyLEuWZWlwcFDRaNSVWLGXqyy0wsRqPx8A\nALWGJAIAAFXy6KOPSso/P767u1vSTF0Ej8ejcDisUCikO3fuyOfzOe3s948//rgCgYB27NihZDKp\nvXv3KhAIaHh4WKdPn5Y0k7hIp9MKh8OSZhIP6XS64jetGzduVGNjowzDUCQS0e3bt/N+B+3t7Wpq\napLH49GRI0fU3Nys9vZ2V5vp6WmFQiFFIpE1eT4AAGoNqzMAALAMK1mdYT0p1eoGizFN01n5oBIq\nfT5WZwAA1ApGIgAAgFVtfHxcTzzxxJo9HwAAtWRDtQMAAABYrtHRUb3nPe8p+QoWtXI+AABqDUkE\nAABQduWa1jC7WGElVPp89vcGAECtIIkAAADKhpoRK8P3BwCoNdREAAAAAAAARSGJAAAAAAAAikIS\nAQAAAAAAFIUkAgAAAAAAKAqFFQEAWIFDhw5VO4Sy+NWvfqUNG9bPfxN+/etfK5fL6Z577ql2KCV3\n/vx5HT58uNphAADWCEYiAACwDPv371dLS0u1wyiLXC6nsbExvfzyy9UOpWK+/e1v66WXXqp2GGXR\n3Ny8Zn9WAQCVZ+RYOwgAAMzyuc99Tp/+9Kd1/fp1Pfjgg9UOpyL+/u//Xp/4xCd06dIlfeADH6h2\nOAAA1CySCAAAwPHyyy/rfe97n/7u7/5Op06dqnY4FfWxj31MExMTSqVSuv/++6sdDgAANYkkAgAA\nkCTdvXtX73//+yVJV65cWZP1ARaSzWb14IMP6qMf/aii0Wi1wwEAoCZREwEAAEiS+vr6lEql9Mwz\nz6y7BIIkeb1e9ff36+mnn9a3vvWtaocDAEBNYiQCAABwpjGcPn1af/u3f1vtcKoqGAzq29/+tn7w\ngx/one98Z7XDAQCgppBEAABgnVvv0xjmevPNN/Xggw/qz//8zzU4OFjtcAAAqClMZwAAYJ1b79MY\n5nrXu96laDSqr371q3ruueeqGks2m1UikZBpmlWNAwAAGyMRAABYx5jGUNgnP/lJffOb39RLL72k\nTZs2FX2cYRhFtSvmv2CdnZ0aGBgouj0AAOVGEgEAgHWKaQwLsyxLDz30kN7//vfrH/7hH5Z8rMfj\nkTT/5n90dFSNjY1FJwXspAT/ZQMA1AKmMwAAsE4xjWFhdXV1+tKXvqTz58/r2WefXfKxhezfv3+l\noQEAUDUkEQAAWIdefvllffrTn9bp06f14IMPVjucmvXnf/7n6ujo0F//9V/r9ddfX3F/+UYVWJal\nWCwmwzBkGIYikYiy2eyC/fT19ckwDMViMWWz2XlTKLLZrNPGNE2Njo6uOHYAACSSCAAArDt3797V\npz71Kfn9fp08ebLa4dS83t5e3X///QqFQivqJ5PJ5N1+6tQpdXR0aGpqSul0Wj09Perq6irYT19f\nn5qbm5XL5XTo0CGdOXPGtT+bzaqtrU3btm1TLpfT8ePH1djYqFQqtaL4AQCQqIkAAMC687nPfU6f\n/vSndf36dUYhFGlsbEwf+MAH9JWvfEWf+MQnijqmUIHFuf/1ikQieuONN9Tf3+86zm6X7/PU1JS8\nXq+kmaSBz+dz9icSCQWDQdd5DMNQOBxWd3d3UbEDAFAISQQAANYRVmNYvuPHj+urX/2qfvCDH+g9\n73nPou3n3vxnMhlt3769YIHETCaj8+fPO6NDCiUR7BUbhoeH9cEPfnBe/QXTNJVMJvOeg//2AQBW\niiQCAADrBKsxrMzPfvYz+f1+PfDAA7p48eKiSznmq39gGEbeG/lYLKZkMqne3l7t2LHDddzcfiYn\nJ3Xy5EknUdDb26sTJ04seF4AAEplQ7UDAAAAlWGvxnD9+nUSCMvw9re/XYODg/rTP/1TPf3002pv\nb19yH/lu7BOJhDo6OpROp1VfX79oHw0NDRoZGVEqldLAwIAzcmF2IkGaSTY0NDQsOUYAABZCYUUA\nANYBVmMojYcfflgnTpzQiRMn9Oqrr5akz2AwKElFJRCkmZEGlmXJ7/erv79fExMTrgKZ0WhUkhSP\nx2VZlqS3VmsAAGClmM4AAMACFhuyPlct/lplGkNp/fznP9f73vc+eb1eXbp0Ke/PiGVZ8ng8kqTp\n6el5dQtms2sYpNNp3blzx5nOMDU1JUny+XzOZ6/X6xRJbG9vV319vVNLwR6JYBdanKvYkQ4AACyE\nkQgAACxieHhYuVzOedlmbxseHq5ihAuzpzE888wzJBBK4Dd/8zc1ODiof/u3f9PZs2fn7TcMw0kg\nSJLH41kwGWWvmBCLxeTxeBQOhxUKhXTnzh1XMmD2+2PHjun8+fMyDMOVQJAkr9erdDqtcDgsSQqF\nQiQQAAAlw0gEAAAWkK8QXr7CdfaT51r7tcpqDOXT1dWlvr4+ff/739fv/u7vVjscAAAqgpEIAAAs\nIJ1OF9Wurq5O6XRa2WxWyWRSpmnKsix1dnYqEonIMAznZcu3TXpr/rphGDJNU6Ojo8uK/e7du/rU\npz4lv9/vmjOP0giHw2poaNAnP/lJ3b17t9rhAABQESQRAABYwFKGgNfX16utrc2Z4/6jH/1IoVBI\nb7zxhjO/fbZ8CYpsNqu2tjZt27ZNuVxOx48fV2Njo1Kp1JJj7+3tZRpDGd17770aHBzUd7/7XX3h\nC1+odjgAAFQE0xkAAFiifNMZ8u2fW1Av33FztyUSCQWDwXltwuGwM3e+GExjqJwnn3xSn/nMZ3T9\n+nX9/u//frXDAQCgrEgiAACwRMUmEYqppTB3mz2KIZ9if2WzGkNl2d93LpfTlStXtGHDhmqHBABA\n2TCdAQCAGmInEGav/DB3VYjFMI2hsu655x4NDg7qhz/8oZ566qlqhwMAQFmRRAAAoAZNTk4u67iX\nX35ZXV1dOn36tB588MESR4VCfu/3fk89PT3q7u5eVv0KAABWC5IIAADUkGg0KkmKx+OyLEvSW6s1\nLIbVGKrr+PHj+uM//mP91V/9lX7xi19UOxwAAMqCJAIAAEuQzWbzvl9omy0UCkl6a5TB+Pi4s6+z\ns1OSdPDgQUlST0+PPB6PDMOQz+dTc3PzorExjaG63va2t+nLX/6y/vu//3tJRTABAFhNSCIAAFAk\n+4be5vP5nMKIs7fZTNN07Xv88ccVCAS0Y8cOJZNJ7d27V4FAQMPDwzp9+rQkyev1Kp1OKxwOS5pJ\nPKTT6UWXmrSnMXzmM59hGkMVPfDAA/rc5z6np556St/97nerHQ4AACXH6gwAAKxyrMZQW3K5nP7f\n//t/+vGPf6zr16/rvvvuq3ZIAACUDCMRAABY5ZjGUFsMw9Azzzyj1157TZFIpNrhAABQUiQRAABY\nxZjGUJvq6+v1+c9/Xl/4whf07//+79UOBwCAkmE6AwAAqxTTGGrfhz70IU1OTmpiYkIbN26UJP3y\nl7/UZz/7WW3evFltbW1VjhAAgKUhiQAAwCr12c9+Vl1dXbp+/TqjEGrUrVu39NBDD+kv//Iv9cUv\nflEvvfSSDh8+rB/+8IeSZuonAACwmmyodgAAAGDpmMawOmzdulVnzpzRkSNH9LOf/Uxf+cpXXPtf\nffVVvfe9761KbAAALAcjEQAAWGWYxrC6/Od//qcOHDigmzdv6u7du872e+65RwMDA0xpAACsKhRW\nBABglWE1htXh17/+tT7/+c/L7/fr1q1brgSCNLOKw7/8y79UKToAAJaHkTxMX8oAACAASURBVAgA\nAKwiL7/8st73vvfpM5/5jP7mb/6m2uGggNu3b+uhhx7Sj3/84wXrHng8Hr355psyDKOC0QEAsHwk\nEQAAWCWYxrB6fO9739P73vc+GYaxaPHEiYkJ+f3+CkUGAMDKMJ0BAIBVgmkMq8fu3bv1/e9/X+99\n73u1YUPhOtb33nuvXnjhhQpGBgDAypBEAABgFWA1htVn165d+sEPfqDm5uaCbe7evat//ud/rmBU\nAACsDNMZAACocUxjWP2+/OUvq7OzU3fv3tWvfvUr17777rtPP/nJT3TvvfdWKToAAIrHSAQAAGqE\nZVn67ne/O2870xhWv09+8pP6j//4Dz3wwAPzpjfcuXNH4+PjVYoMAIClIYkAAECNOHbsmPbs2aNg\nMKg333xTEtMY1pKHHnpI3/ve99Ta2ipJzooMv/Ebv6FLly5VMzQAAIrGdAYAAGrEH/7hH+qll17S\nvffeq3e84x2KxWJ66qmnJDGNYa05d+6c2tvb9ctf/lK/+tWvtGfPHn3nO9+pdlgAACyKJAIAADXg\n9u3b2rRpk379619LkrM04L333qt//dd/dWoiYO145ZVX9PGPf1wvv/yyJOknP/mJ7r///ipHBQDA\nwgqvOQQAACrm8uXLmp3Xn/3eNE196Utf0sGDB6sRWk15/fXX9dhjj+nu3bvVDqUkduzYoenpad28\neVMf+chH9O53v7vaIWEFjhw5okAgUO0wAKCsqIkAAEANuHTpUt7q/L/85S/15ptv6iMf+YiCwaD+\n93//twrR1Y7R0VElEolqh1Ey99xzj97//verqamJBMIqd/78+TX1swkAhTASAQCAGvCtb31Lv/jF\nL/Lus0clJBIJPfDAA+rp6alkaDXp2WefrXYIgItdMBMA1jqSCAAAVFk2m9V//dd/LdjGMAy1tLTo\n1KlTFYoKAABgPqYzAABQZS+++GLBfRs2bNC9996rgYEBDQ0N6bd+67cqGBkAAIAbIxEAAKiyF198\nURs2bNAvf/lL1/Z7771X27Zt09e//nXt2rWrStEBAAC8hZEIAABU2Te/+c15CQTDMBQIBJRKpUgg\nAACAmkESAQCAKnrttdeUTqedz/b0hS9+8Yv62te+pne84x1VjA4AAMCNJAIAAFU0Ojqqt71t5tfx\nvffeq82bN+vKlSs6evRolSPDSmWzWSUSCZmmWfK+I5GIIpHIis9fzhgBAGsTSQQAAKrohRde0K9/\n/Wu97W1v01/8xV/opZde0h/90R9VOyzkYVmWDMMoun1XV5eCwaCSyWQZo1ra+edeQ7VjLJZlWRof\nH1csFis64WFfa75XIpFwtU2lUq79nZ2d5bgMAFgTKKwIAOvU66+/rscee0x3796tdijr2vnz5yVJ\nf/AHf6D77rtPHR0dVY5oZe655x594Qtf0ObNm6sdSsldvnx5Se37+/s1MDBQlli6u7uXdf6511Cu\nGFOplC5cuFBUnMXo7e2VJPX09BR9zI9+9KOC+/bv3+/6fO3aNdfnpqamJUQHAOsLIxEAYJ0aHR2d\n9zQOlbd792498sgjamhoqHYoJZFIJDQ6OlrtMErOsizFYrFqh7Ei5b4Ge2pEZ2enfvSjH+nYsWMl\n67u7u3vJCYlXX31V6XRauVzOeU1NTSkcDsvr9brabt682dUuEAiULHYAWGsYiQAA69yzzz5b7RCw\nhixluH8lZLNZfec731EsFtPIyIhisZg6OjoUCoX02GOPzUveZLNZxeNxnTx5UoFAQMePH9f+/fvV\n29vrDPm3rzGXy8myLD377LPOCJJwOKxjx47Nu0nNJ5PJaPv27c7n3t5eNTc3u7bZ5+nr69PJkycl\nSel0WleuXNHQ0JBGRkacdpZl6fnnn1cwGFQgEHCe3s/uP981zJZMJmWapkKhkE6fPl3UddijDiTp\n0UcfVUtLy6LHVML+/fvnxT86OqpHH33UtS2Tycg0TYXDYX3oQx/S3r17KxkmAKw+OQDAunTu3Lkc\nvwZQapJy586dK1v/S/25leS8rl69msvlcrnp6elcKBTKScq98sorTtupqalcIBDIDQ8P53K5XO7S\npUs5SbmJiQlXX7PZ/UxNTeXS6XROUi4UCs07fyH2MXPbBAIBp9/ZfdkxFjomFArlpqenc7lcLjc8\nPDyvXb7j5n4/r7zyyrzrmGtqaio3PDycC4VCueHhYVecc83+O1joVYyltM0n3zWNjIy44ggEAgte\nTyGHDx/OHT58eNmxAcBqwXQGAACwZuVmPWm3nzDX1dUpFApJkqug4OjoqJLJpPMk3Z43bz9lz2fT\npk0KhULyer2qr6+XpCXVGKivr1c0GpU080R/rhs3bkiSJicn1dvbK6/X6xp9YEsmk0omk3rsscdU\nV1cnSfrgBz9YdBzSW9+PPTpjoevw+Xy6efOmnnrqKbW0tCw4YiE3a5rAQq9yS6VSeuSRR+ZtDwQC\nmp6e1sTEhMLhsJLJpJ577rmyxwMAqxVJBAAAsO74/X5JcqYISNLQ0JAkuar0SwsX8+vu7lZ/f78y\nmYz6+vqWFYt9Y/vCCy9ImkkYbNu2TZL0jW98Q5L0yiuv6E/+5E8K9nHx4kVJck3PsJMJ5TA1NaVt\n27bp1KlTSiQSymazZTtXqVy4cGFeQUVbXV2d/H6/uru7FY1Ga361CgCoJpIIAAAAemtUwlKfksdi\nMR09enTZxfgaGhoUCoV08uRJWZal733vewqFQgqFQurp6ZFlWbp48aJ+53d+p2Af5VoFohCv16uW\nlhb19/dr586dOnPmjCKRSN7RFIWWWZz7Kic7yVFMjYdDhw6RRACABZBEAAAA65Y9rWG2ycnJoo9P\nJBLq6OjQ2bNnV7TChr2k4PXr1zU0NCS/3+9se/755yUVdwNcDfYT/GPHjulHP/qROjs7XaMTamE6\nQ76CioXMnu4CAJiPJAIAYNWLRCKKRCJlPwZrh50osG/UJTm1CeLxuCzLkjTzBHuhaQrBYFCSnHoI\ny7Vv3z5JUmNjozOiwd4WDAbzzuWfbaG6CpWSb3RCrRgbG3OmsCzGsiw1NzeXOSIAWL1IIgAAUEb2\nknmmaS5piPRyj0NhiURC0sxNYjweVyAQcE1BOHjwoKSZGggej0eGYcjn8zk3lHbb2YkFe1smk3GN\nYMhms646AYvVDKirq3OWZNyzZ4+zLRwOS5J2796dty/7/YEDByTNJMcymYykmafvts7OzrzXkK8v\nO4FSTNyF2KMTSml2XLPf2wolBgsVVJRmfiZmf0+ZTEaXL18uWDsBAEASAQCwBnR3dy/5hmU5xyxV\nIpFQLBZTPB5XPB7XxYsXFYvFynYcFrZz506ZpimPx6P6+nrF43HXfq/Xq3Q67dy4h0IhpdNpZ5SB\n/fNy5swZHTlyxLUtFovJ4/EoHA4rFArpzp078vl8Tt+z3xfyZ3/2ZwoEAq4n5h/60IckuQsm5uu3\nvr5e6XRa27Zt0/bt29XZ2amHHnpIgUBAw8PDOn36dN5ryNeXx+NZUtyVYBiGKy47yVOMhQoqbty4\nUY2NjTIMQ5FIRLdv3152bQsAWC+MXCXW1AEA1JyhoSG1trZWZGm19SiTyWj79u26evWqs3ReKpXS\nrl27NDExUXBo9XKPqxWGYejcuXM6fPhwWfpfzs+tfbPJzzrKqbW1VZJ07ty5KkcCAOXFSAQAQM0b\nHR2VaZoyDCPvEOxEIiHTNPN+TiaTMgxDpmk6w7zntimHK1euSJK2bt3qbNuyZYsk6dq1ayU/DgAA\noBI2VDsAAAAWYtcGuHr1qkZGRpRIJFxDrAOBgKtmQFtbm/N5fHxcgUBA6XRa27dv17Zt29Tf3+9q\nU0ixQ6ULPd0eGxuT5C64Z1fXTyaTam9vL+lxyG9uwqlWVzgAAGC1YDoDAKxTq2U6Q76h6IZhqLe3\nVydOnMjbptAxi7Upd9zFnHe5x9WKWpvOMDcZVOvfH1YvpjMAWC+YzgAAqGmF1ms/efJkhSPBapTL\n5VwvAACwMiQRAAA1zU4i2MvzpVIpSXKWwysXwzCKehWyUIX3QomRlRwHAABQCSQRAAA1ze/3a2Rk\nRDdv3nSWYRseHnamMpTL3CfYhV6F2MmA2XPy7cKOu3fvLvlxAAAAlUBhRQBATUsmk9q3b58CgUDZ\nEweldODAAUnSjRs3nGJ+t27dcu0r5XFYeyKRiCSpu7u7rMcAALAUjEQAANQ00zTl8XjmTSPo7OxU\nNpudV31/9mfLslx/5msz+30p1dfXKxqNanBwUJZlybIsDQ4OKhqNulZeiEQizo3fUo4DqsleNcU0\nzUVXOpktlUrN+3cMAFhdGIkAAKhpExMTikQi825UBgYGXH9Kci39KEkej0e5XE4ej6dgG5/PV7aC\ne+3t7Uomk/J4PAoEAjp+/Lj2799ftuOwtixnNEElRiAkEgkNDQ0pHo9Lkk6dOqXXX3+9qOVHr127\n5vrc1NRUlhgBAOVDEgEAUNPe/va36+zZsxoZGXFtn5yc1I4dO4pKAFSzKn8gEFjw/IVu+hY7DqiG\nTCajYDCoq1evqq6uTtJMwc9du3Zpz5498vv9Cx6/efNmfq4BYJVjOgMAoGYlEgk1NDTkHcbv8/k0\nPDxchaiA0hgdHZVpmjIMQ319ffOm2SQSCZmmmfdzMpmUYRgyTdMpvDm3TTlcuXJFkrR161Zn25Yt\nWyTNH2UwVyaTkWmaikQiGh8fL1uMAIDyYiQCAKBmDQ0N6ac//akOHDjgSiRMTk5qbGysqOHTQC2y\nawpcvXpVIyMjSiQSrqk2gUDANYWnra3N+Tw+Pq5AIKB0Oq3t27dr27Zt6u/vd7UpZKFlSWcrNFpg\nbGxMklz/Hu0CoMlkcsF/k/byrD09Perp6VEgENDTTz/tHA8AWB0YiQAAqFnxeFz333+/nnzySacQ\nWyQS0WuvvUYCAauaPVpg7969kqSWlhZJUm9vr3K53LzpO7M/28fYN/J2XZC5x+Sz0qVLZ9cgmWux\nBEYgEND09LQmJiYUDoeVTCb13HPPLRozAKC2kEQAANSsuro6tbS0qL+/37m56e7upsggVr1QKJR3\n+8mTJyscSWXV1dXJ7/eru7tb0Wh0SSs7AABqA0kEAACACrOTCIlEQtJbQ/17e3vLet65S6UWehUS\nCAQK7iuUGCnk0KFDJBEAYBUiiQAAAFBhfr9fIyMjunnzpjNNZ3h4WCdOnCjreVc6ncFOIswuAmkX\ndty9e/eSYqmrq1ty4gEAUH0kEQAA61olKtoDcyWTSe3bt08nTpxwaiDYdRFq2YEDByRJN27ccLbd\nunXLta9YlmWpubm5dMEBACqCJAIAYF3r6upSMBhcFcOqU6mUa8h5Z2fnvDZ21X/TNFfFNa1XpmnK\n4/HMm0bQ2dmpbDY7b7nH2Z8ty3L9ma/N7PelVF9fr2g0qsHBQVmWJcuyNDg4qGg06lqxIRKJKBKJ\nOJ8TiYRGR0edz5lMRpcvX6a+CQCsQiQRAADrWn9/f7VDKNq1a9dcn5uamlyfE4mEYrGY4vG44vG4\nLl68qFgsVskQUaSJiYm89QUGBgbU1dXlWu7R5/O5Pns8Htef+drMfl9q7e3tampqksfj0ZEjR9Tc\n3LzoaikbN25UY2OjM3Xj9u3bC9ZXAADUrg3VDgAAABRn8+bNBeerZzIZBYNBXb16VXV1dZJmCt3t\n2rVLe/bskd/vr2SoWMTb3/52nT17dt6yjJOTk9qxY8eCdQlsxbQpl0AgsOD5u7u7l9QeALB6MBIB\nAFARfX19MgxDsVhM2WzWVQHesizFYjFnSHckEnGGY8+tWZBMJp1h33ZBt0QiMW9bNpt1hvZLcvrv\n7OzU5OTkovFms1knZtM0XUOxF7uecshkMjJNU5FIROPj4/P2X7lyRZK0detWZ9uWLVskzR/BgOpK\nJBJqaGhwDf+3+Xw+DQ8PVyEqAACKQxIBAFB2fX19am5uVi6X06FDh3TmzBnX/lOnTqmjo0NTU1NK\np9Pq6elRV1eXJKmtrc2pWZBKpRQIBHT16lUNDAzoySef1Pj4uFpaWpROp51t0szNmF0XYHx8XO3t\n7ZqenpYk7dixY8FEQjabVVtbm7Zt26ZcLqfjx4+rsbHRWYZvseuZa6XL6klvLQHY09Ojhx9+WKZp\nuua9j42NSZLrxtTr9UoStRFqzNDQkGKxmJPwsk1OTurZZ59dFQUWAQDrl5FjbBkArEtDQ0NqbW2t\nyBBjwzA0NTXl3NRms1n5fD7n3JFIRG+88YZTn8C+obb3z/1c7LZ8bVKplHbt2qXe3l5nOb257RKJ\nhILB4Ly+w+Gwuru7F72ecrEsS6+++qouXLignp4eRaNRZy56vmtdaHu5GIahc+fO6fDhw2Xpv5I/\nt+ViWZaef/55jY2NaWBgQJIUDof1gQ98gEKDq1hra6sk6dy5c1WOBADKi5EIAICyC4VC8vl8SiQS\nsixLXq/XdRPY3d2t/v5+ZTIZ9fX1lTUWuzbAyZMnC7YZGhqSpHmjBHp6eiQtfj3lUldXJ7/fr+7u\nbkWjUUYYrFJ1dXVqaWlRf3+/crmccrmcuru7SSAAAFYFkggAgLJ77LHHFAgEFAwG5fF48iYKYrGY\njh49WhMV2+2bc/sGb/ZLKu56ZivFdIa5Dh065EoiLPS9hUKhJfUNAABQCEkEAEDZNTQ0aGRkRBMT\nEwqFQjp58qTrxjuRSKijo0Nnz55VQ0NDRWIq5sa6UN2Exa5nrnzJiEIJimLV1dW5rsFOIsyuk2DP\nud+9e/eS+gYAACiEJAIAoOwMw5BlWfL7/erv79fExIRrOkEwGJSkvNXqS81ODDQ1NRVsE41GJUnx\neFyWZUl6a7UGafHrqQTLstTc3Ox8PnDggCTpxo0bzrZbt2659mHtmruKCQAA5UISAQBQEb29vc6T\n8Xe+853q7e119tlP0TOZjOvpfzabdT1Zn31DP7tNoW22RCLhHB+PxxUIBPI+ubffHzx4UNJMDQSP\nxyPDMOTz+Vw37QtdT6klEgnXEpOZTEaXL192zaGvr69XNBrV4OCgLMuSZVkaHBxUNBqtSHIG1dXV\n1eWsYlLLLMvS+Pi4YrHYggkPe3lWe4WV5bYBAJQeSQQAQEUcO3ZM58+fl2EYOn/+vLMygjRTWFGa\nqYvg8XgUDocVCoV0584d+Xw+p53H45Ek1zb7fb5ttp07d8o0TXk8HtXX1ysej+dta7/3er1Kp9MK\nh8OSZqY+pNNp1834QtdTahs3blRjY6MMw1AkEtHt27fz1kBob29XU1OTPB6Pjhw5oubmZmf1Bqxt\n9somta63t1ff+MY31NHRUfDGP5FIKBaLKR6PKx6P6+LFi4rFYktuAwAoD5Z4BIB1ai0slbeYSi9v\nCJZ4rKbV9PNeKNZMJqPt27fr6tWr2rt3r6S3lmWdmJiQ3+8vqk01sMQjgPWCkQgAAABF6uvrk2EY\nisViymazrlU1LMtSLBZzVtuIRCKu6TazaxYkk0kZhqHOzk5nWkwikZi3LZvNOsP2JTn9d3Z2Fiz8\nOZtdy8MwDJmm6ZoWs9j1VMOVK1ckSVu3bnW2bdmyRZJ07dq1otsAAMqHJAIAYE1aqEYCsBx9fX1q\nbm5WLpfToUOHdObMGdf+U6dOqaOjQ1NTU0qn0+rp6VFXV5ckqa2tzalZkEqlFAgEdPXqVQ0MDOjJ\nJ5/U+Pi4WlpalE6nnW3SzBQbe87/+Pi42tvbNT09LUnasWPHgomEbDartrY2bdu2TblcTsePH1dj\nY6NSqVRR1zNXOZYqnWtsbEySu8iq1+uV9NbSq8W0AQCUD9MZAGCdWuvDwufezKzV66w1a3k6g2EY\nmpqacm5Ys9msfD6fE0skEtEbb7zh1CeYO2Q/3xD+Yrbla2MP3+/t7XXqccxtl0gkFAwG5/UdDofV\n3d296PWUU6HpDMVsX+qxlcJ0BgDrBSMRAABrUi6Xc72AlQqFQvL5fEokErIsS16v1/Wz1d3drf7+\nfmUyGWc50HKx5/0vtLTo0NCQJM0bJdDT0yNp8esBACAfkggAAABFeOyxxxQIBBQMBuXxePImCmKx\nmI4ePZp39YxKs4f2z02o2YmCYq5ntkpMZ1joewuFQkW3AQCUD0kEAACAIjQ0NGhkZEQTExMKhUI6\nefKk68Y7kUioo6NDZ8+eVUNDQ0ViKuamuVDdhMWuZ658yYhCCYrlshMEs+uY2EUmd+/eXXQbAED5\nkEQAAFTM3Ar1tayUT1dXk/V63cUwDEOWZcnv96u/v18TExOu6QTBYFCSu+BfudiJgaampoJtotGo\nJCkej8uyLElvrdYgLX491XDgwAFJ0o0bN5xtt27dcu0rpg0AoHxIIgAAKqarq8upUL9a5Hu6mkql\nXDfanZ2d846zl+WzK+uvRCX7oobEwnp7e52n3u985zvV29vr7LOfkGcyGdfT/2w263pqPvuGfnab\nQttsiUTCOT4ejysQCOR9Km+/P3jwoKSZGggej0eGYcjn86m5ubmo6ykX+/rnvpdmEjDRaFSDg4Oy\nLEuWZWlwcFDRaNRJzhTTBgBQPiQRAAAVY1etX+3mrkU/92lwIpFQLBZTPB5XPB7XxYsXFYvFlnWu\nWu1rvTp27JjOnz8vwzB0/vx5Z2UEaaawojRTF8Hj8SgcDisUCunOnTvy+XxOO4/HI0mubfb7fNts\nO3fulGma8ng8qq+vVzwez9vWfu/1epVOpxUOhyXNTH1Ip9OuG+2FrqccDMNwrl+Sk9yYrb29XU1N\nTfJ4PDpy5Iiam5vV3t6+5DYAgPJgiUcAWKeqtVRetZdhK9ZCcSaTyYLF3TKZjLZv366rV69q7969\nkt5ajm9iYsKpql+Mava13L+ntbzEY7Wsln8z6x1LPAJYLxiJAABY1Pj4eMG58n19fc62TCYjy7IU\ni8WcbZFIZN6wbFu+/grNx7fnchuGIdM0NTo6Wp6LXUQmk5FpmopEIhofH5+3/8qVK5KkrVu3Otu2\nbNkiaf4IhsXUal8AAGD9IokAAFjU3r17denSJUlSOBx2PRE9ceKEwuGwJiYmVF9fr1OnTqmjo0NT\nU1NKp9Pq6elRV1dX3n6npqbmbUun0/O2ZbNZtbW1adu2bcrlcjp+/LgaGxuVSqXy9lvOpejsc/b0\n9Ojhhx+WaZquJMnY2Jgkd3E9r9crSUuuZ1CrfaFyFqqRAABANZBEAAAUZf/+/QqHw+rp6clbGM0e\nDr9p0yaFQiF5vV7nhnVgYCBvn/ZN7Gz5CqONjo4qmUyqpaXFiUWSLly4kLffci5FFwgEND09rYmJ\nCYXDYSWTST333HPO/kLXKi39Zr1W+0LlLFQjAQCAaiCJAAAo2qOPPipJev75551t169fd7ZLM8Xl\n+vv7lclkFlxzfimGhoYkzZ/q0NPTU5L+l6qurk5+v1/d3d2KRqPchKNsSpH4AgCglEgiAACK5vf7\nFQgEnJt6SXrxxRfnFeWLxWI6evRoweKDS2XfpBc7mqCc0xnmOnTokCuJsNA1h0KhJfVdq30BAID1\niyQCAGBJDh8+rGQyqfHxcWUyGe3Zs8e1P5FIqKOjQ2fPnlVDQ0NJzz05OVlUu3JOZ5irrq7OdRNu\n36zPnr+eyWQkSbt3715S37XaFwAAWL9IIgAAlsSuRzA4OKgrV65o3759rv3BYFBS/toGyxWNRiVJ\n8XjcqcFgr9ZQbZZlqbm52fl84MABSdKNGzecbbdu3XLtK1at9rXeZbNZJRIJmaZZ7VAWVY7RN2sF\n3w0ALA9JBADAkni9XoXDYQ0MDOjmzZuqq6tz7befeGcyGdfIgWw2m7fSvP0U3247e9nEzs5OSdLB\ngwclzdRA8Hg8MgxDPp/PdfNeCYlEwrW0ZCaT0eXLl53EijSTPIlGoxocHJRlWbIsS4ODg4pGo67E\nir1cZaEVJqrVFxbX1dWlYDC4qmph5Bt9k0wmZZqmTNNc8bWkUinXDbn9b7eWz0edCQBYHpIIAIAl\nswsp5ptn393dLWmmLoLH41E4HFYoFNKdO3fyVpp//PHHFQgEtGPHDiWTSe3du1eBQEDDw8M6ffq0\npJnERTqdVjgcljSTeEin0xW/+d24caMaGxtlGIYikYhu376d9ztob29XU1OTPB6Pjhw5oubmZrW3\nt7vaTE9PKxQKKRKJLHjOSveFxfX391c7hBVLJBKKxWKKx+OKx+O6ePGiYrHYsvu7du2a63NTU9Oa\nOh8A4C1GjhQsAKxLQ0NDam1t5UlcAfbw5nJ/P6ZpamRkpOb6Wu71G4ahc+fO6fDhwyWJY65a+bmt\n1M/HSuWLM5PJaPv27bp69ar27t0raebJ/q5duzQxMTGvUGoxkslkweKdtX6+Uv1dtra2SpLOnTu3\non4AoNYxEgEAgCoZHx/XE088UXN9rVXj4+MF58HbU0IMw1Amk5FlWYrFYs62SCTimo4zW77+Cs21\nt2t5GIYh0zRd02Mq5cqVK5KkrVu3Otu2bNkiaf4T/mJkMhmZpqlIJOKajrRWzgcAcCOJAABAFYyO\njupd73qX86S0Vvpay/bu3atLly5JksLhsOvJ84kTJxQOhzUxMaH6+nqdOnVKHR0dmpqaUjqdVk9P\nj7q6uvL2OzU1NW9bOp2ety2bzaqtrU3btm1TLpfT8ePH1djYWLCWRbmWKh0bG5PkLn7q9XolaVm1\nCuz4e3p69PDDD8s0TVfCZbWfDwDgRhIBAIAFlKty+/79+0u2BGYp+1rrler379+vcDisnp4eZ6UP\nSc57e6j7pk2bFAqF5PV6nZvRgYGBvH3aN6iz5avXMTo6qmQyqZaWFicWSbpw4ULefsu1VGmh65CW\nd5MdCAQ0PT2tiYkJhcNhJZNJPffcc2vmfAAAN5IIAADksdIbtdVqPVy3XRj0+eefd7Zdv37d2S7N\nFAjt7+9XJpMp2VKiQ0NDkuZPdejp6SlJ/9VUV1cnv9+v7u5uRaPRst+sV/p8AIC3kEQAAADrit/v\nVyAQcG7qJenFF1+cV3AvFovp6NGjBQv4LZV9o1vsaIJyTWdY6HrspfiLGAAAIABJREFUJVdX4tCh\nQ66b+rV2PgBY70giAACAdefw4cNKJpMaHx9XJpPRnj17XPsTiYQ6Ojp09uzZkk0VsU1OThbVrlzT\nGeyb7Nl1BDKZjCRp9+7dS+5vrrq6OtfN+lo7HwCsdyQRAADAumPXIxgcHNSVK1e0b98+1/5gMCgp\nf22D5YpGo5KkeDzu1GCwV2uopAMHDkiSbty44Wy7deuWa99KWJal5ubmNXs+AFjvSCIAAIB1x+v1\nKhwOa2BgQDdv3lRdXZ1rv/00O5PJuEYOZLNZ1xNu+739JNxuO3vpwc7OTknSwYMHJc3UQPB4PDIM\nQz6fz3UDXAn19fWKRqMaHByUZVmyLEuDg4OKRqOupIm9FGWh1SOkmREbs5epzGQyunz5spOkqeXz\nAQCWhyQCAABYl+xCivnm0Hd3d0uaqYvg8XgUDocVCoV0584d+Xw+p539/vHHH1cgENCOHTuUTCa1\nd+9eBQIBDQ8P6/Tp05JmEhfpdFrhcFjSTOIhnU5X5ca2vb1dTU1N8ng8OnLkiJqbm9Xe3u5qMz09\nrVAopEgkUrCfjRs3qrGxUYZhKBKJ6Pbt23m/z1o8HwBgeYzcWi29DABY0D/+4z/qox/9aLXDwBr0\n9a9/XR/5yEfK0vfQ0JBaW1vX7MoRpWYXXlzJ92WapkZGRkoVUs2drxTfkSS1trZKks6dO7fimACg\nlm2odgAAgOr48Ic/rK997Wu6e/dutUPBGnLPPffowx/+cLXDQImMj4/riSeeWLPnAwAsHUkEAFin\nNmzYoI997GPVDgNAjRodHdV73vOekq9OUSvnAwAsD0kEAACANW45Q/ZnFyushEqfz/5OAABLQxIB\nAABgjaJ2RGF8NwCwPKzOAAAAAAAAikISAQAAAAAAFIUkwv9n797jm6rvPoB/DrQVRW3doOWiqDhu\n+mg7cIhDubSOIprI0GIvIEOhpBsoruDjWGLVwrws9QY87dOyTezaZMBUEh26kbKiQsEByXiUUQRM\nBbWZSCOi0NKe5w92jkmbtEma5OTyeb9eeUlPfuec76k935zzze/8fkRERERERETkExYRiIiIiIiI\niMgnLCIQERERERERkU84OwMRERFFjYsuuggAp+ejyDR//nylQyAiCjlB5Pw2REREFCXOnTsHk8mE\n9vZ2pUPp4uzZs3j66afxySef4LnnnsOll16qdEgx5cCBA3jyyScxffp0zJs3T+lwPJowYQKuuOIK\npcMgIgop9kQgIiKiqJGQkIBZs2YpHUYXra2tUKvV+Pzzz/HOO+/ghhtuUDqkmDR69Gjk5+fjuuuu\nwzPPPKN0OEREcYlFBCIiIqJeaG1txd13342GhgZYLBYWEEJo9uzZaGtrw7x585CUlITS0lKlQyIi\nijssIhAREREFqL29Hfn5+di+fTvefvttjBs3TumQYl5BQQHa2trwwAMPICEhASUlJUqHREQUV1hE\nICIiIgpAe3s75syZgy1btuCtt97ChAkTlA4pbvzsZz9De3s7Fi5ciMTERKxYsULpkIiI4gaLCERE\nRER+EkURCxYsgMlkgslkwq233qp0SHHngQceQFtbG37+858jKSkJy5YtUzokIqK4wCICERERkR9E\nUcSiRYtQW1uLzZs3IysrS+mQ4pZGo0FrayuWLl2KhIQELF26VOmQiIhiHosIRERERH5YunQp1q9f\nj40bN2L69OlKhxP3HnzwQbS3t+OXv/wlEhISsHjxYqVDIiKKaSwiEBEREflo2bJlWLt2LYxGI9Rq\ntdLh0H88/PDDaGtrw4MPPoikpCQUFhYqHRIRUcxiEYGIiIjIB4899hheeOEFvPLKK7jnnnuUDoc6\neeSRR9Da2gqNRoOEhATcf//9SodERBSTWEQgIiIi6kFpaSlWrlyJ3/3ud8jPz1c6HPJCq9Xi3Llz\nKCwsRN++fTFv3jylQyIiijksIhARERF1o6ysDCUlJSgvL8f8+fOVDod68Pjjj6OtrQ0PPPAAkpKS\nkJeXp3RIREQxhUUEIiIiIi9eeuklLFu2DC+88AIWLVqkdDjko1WrVuHs2bOYO3cuEhISkJOTo3RI\nREQxg0UEIiIiIg+qqqqwdOlSPPvss3jooYeUDof8pNfrce7cORQUFKBv376YNWuW0iEREcUEFhGI\niIiIOnn55ZexaNEiPP7441i+fLnS4VCAnn/+ebS1tSEvLw+bNm2CSqVSOiQioqgniKIoKh0EERER\nUaQwGo2YM2cOHn30UaxcuVLpcKiXRFFEYWEhXnnlFbz++uu4/fbblQ6JiCiqsYhARERE9B9//vOf\nce+99+Khhx5CWVmZ0uFQkIiiiAceeAAGgwGbN2/GtGnTlA6JiChqsYhAREREBOCNN97A3XffjUWL\nFuHFF1+EIAhKh0RB1N7ejvnz5+PPf/4zzGYzMjMzlQ6JiCgqsYhAREREce/tt9+GWq3Gfffdh8rK\nShYQYlR7ezsKCgpgNpuxZcsWTJo0SemQiIiiDosIREREFNe2bduGO++8Ezk5Ofj973+PPn36KB0S\nhVB7eztyc3Px1ltv4a233sLEiROVDomIKKqwiEBERERx691330V2djZUKhVqamrQt29fpUOiMGht\nbUVubi7q6urw1ltvYcKECUqHREQUNVhEICIiori0a9cuZGdnIysrC3/605+QkMCZr+NJa2srZs2a\nhXfffRdbt27FjTfeqHRIRERRgUUEIiIiijt79+5FZmYmbrnlFrz66qtISkpSOiRSQGtrK+666y7s\n2rULFosFP/zhD5UOiYgo4rGIQERERHFl//79yMzMxNixY2EymXDBBRcoHRIp6Ntvv8Vdd92Fffv2\nwWKx4IYbblA6JCKiiMYiAhEREcWNAwcOYPLkybjuuuvw5ptv4qKLLlI6JIoA33zzDe644w588MEH\n2LZtG6677jqlQyIiilgsIhAREVFc+OijjzB58mRcffXVePvtt9G/f3+lQ6II8s0332D69OlobGzE\n3//+d4wePVrpkIiIIhKLCERERBTzjh49ikmTJmHIkCH461//iuTkZKVDogh06tQpTJ8+HR9//DHq\n6+vxgx/8QOmQiIgiDosIREREFNOOHTuGyZMnIzk5GXV1dUhJSVE6JIpgTqcT06ZNw6effor6+noM\nHz5c6ZCIiCIKiwhEREQUsz777DNMnjwZ/fr1g8ViwcCBA5UOiaKA0+lEVlYWvvjiC9TX1+PKK69U\nOiQioojBIgIRERHFpH//+9+YMmUKOjo6UF9fj9TUVKVDoijy5ZdfIisrC1999RXq6+tx+eWXKx0S\nEVFE6KN0AERERETBduLECdx2221obW3F1q1bWUAgv33ve9/DX//6V/Tv3x9Tp07F8ePHlQ6JiCgi\nsCcCERERxRSpK/qJEyewfft2XHHFFUqHRFFM6tHS3t6Ov//97xg0aJDSIRERKYo9EYiIiChmfPXV\nV5g+fTqam5thsVhYQKBeGzhwICwWCwAgKysL//73vxWOiIhIWSwiEBERUVRpaWlBR0dHl+XffPMN\n7rzzTtjtdtTV1XFUfQqaQYMGwWKxoLW1tdtCwldffRXmyIiIwo9FBCIiIooan3/+OS677DJMnDgR\nra2t8vJvvvkGarUa//rXv7B161aMGDFCwSgpFg0dOhTbtm3D6dOnMW3aNHz55Zdu769YsQLJycnY\nvXu3QhESEYUHiwhEREQUNVavXg0A2L17N9RqNc6cOYPW1lbcfffdsFqt2Lp1K6699lqFo6RYdfnl\nl6Ourg4nT57EtGnT4HQ6AZwvIDz99NMAgFWrVikZIhFRyHFgRSIiIooKX3/9NQYPHoyvv/4aAJCQ\nkIBbbrkF/fv3x7vvvguLxYJx48YpHCXFgyNHjmDy5MkYMmQIJkyYgNWrV0O6pBYEAR988AHGjBmj\ncJRERKHBnghEREQUFf73f/8X3377rfzzuXPn8O6772Lfvn147bXXWECgsBk+fDjq6urw9ddfuxUQ\ngPPFLalXAhFRLGJPBCIiIop4ra2tGDZsGJqbm7u8l5iYiLFjx+Ltt99GcnKyAtFRvBFFEYsXL0ZF\nRYXHQT4TEhJw5MgRzg5CRDGJPRGIiIgo4hkMBq8j4re1tWHv3r2YMmUKTp48GebIKN6IogiNRuO1\ngACcf6ThueeeC3NkREThwZ4IREREFNFEUcSoUaPw0UcfwZfLlvb2dvTpw+9JKDQWLVqEysrKHtv1\n69cPn3zyCQYMGBCGqIiIwoefsERERBTRzGYzDh061G0BITExEQCQm5vLAgKFVFpaGoDv/ua8aW9v\nx5o1a8IREhFRWLEnAhEREUW0m266CXv37sW5c+e6vJeYmIj29nbce++9KCkpwahRoxSIkOKN3W7H\nU089hd/97ncQBAFtbW0e2yUnJ+PYsWO4+OKLwxwhEVHosFRPREREEWvHjh3YvXt3lwJCYmIi+vTp\ng3vuuQcffvghamtrWUCgsLnyyitRUVGBo0ePYuHChUhMTPTYM+H06dNYt26dAhESEYUOeyIQERFR\nxFKpVHjrrbfkIgJ7HlAkOn78OJ599llUVFRAFEW3ngmDBg2C3W5HUlKSghESEQUPeyIQERFRRPrw\nww/xxhtv4Ny5c+x5QBFt6NChePHFF2G327F48WJccMEFcs+Ezz//HDU1NQpHSEQUPOyJQERERBFp\n9OjROHjwIARBQG5uLnseUNRwOBz47W9/izVr1uDMmTMAOGsIEcUOFhGIiCgi7d69GzfddJPSYRD5\n5Ne//jVWrlypdBhx54ILLkBra6vSYRD1KCkpCWfPnlU6DKKgSFA6ACIiIk8++ugjAMCGDRsUjoSo\newUFBTh69KjSYcSl1tZWzJw5E/n5+UqHQuRVbW0tXn/9daXDIAoaFhGIiCii5eTkKB0CUbd4c6Cs\nnJwc5gmKaG1tbcwTFFP4YBYRERERERER+YRFBCIiIiIiIiLyCYsIREREREREROQTFhGIiIiIiIiI\nyCcsIhARERERERGRT1hEICIi6iWHwwGj0Qi1Wt2rNuESSbEQxQPmCCKKJSwiEBERdeJ0OiEIgs/t\nS0pKkJeXB7PZ3Ks2odL5eJSMpTeqqqp8+v/icDjktoIgwGg0emxns9nkNoIgoKioKNghU4xijohM\nvuYIADCbzVCr1RAEAWq12mOeYI4g8oxFBCIiok62b9/uV/vy8vKgtAmVzscTqlhsNht0Ol3Itl1Y\nWNhjO6fTiQULFgAARFFEc3MzamtrPca1e/dut59nzJgRnGAp5jFHBCYScgQAlJWVQa1Wo7S0FKIo\norS0FHl5eSgrK3NrxxxB5FmC0gEQERFFEqfTiaqqKqXDCJpQH4/D4UBdXR3q6+sxefJkLFmyJOj7\ncDqd2LRpk09tt2zZArPZjOrqagBAamoqSktLkZGRgalTpyIzM1NuO2jQIIiiGPR4KbYxR/gn0nIE\nACxbtgwAkJ6e7vbf+vp6FBcXy+2YI4g8Y08EIiKKCQ6HQ+6eCnzXrbWoqAiNjY0e25eVlcldWevq\n6gAAer1e7sIrdWEFvrvQlpbpdDo4HI6gxO0pjs7PJJvNZrlNU1OT2zbq6urkbrllZWVucXk7HlfS\ntouKinw+JukbxdWrV2PMmDEoLy9Hbm4uUlNTA/o9dGfdunU+33jU1tYCAJKTk+VlV111FQBg48aN\n8rKmpiao1WrodDo0NDQEL1iKWMwRzBESvV4PAPK5L/2+SktL5TbMEUTdEImIiCJQTU2N6M/HFAD5\ntXPnTlEURbGlpUXUaDQiAPHgwYNy2+bmZlGlUokGg0EURVG0WCwiANFqtbpty5W0nebmZtFut4sA\nRI1G02X/vsToSxwqlarL8Xjar8lkcmtjMBjcfhfeYuu87YMHD3bZdmfNzc2iwWAQNRqNaDAYxObm\n5h6PtadXTywWixxjIL9jb8ul35v0UqlU3R5Pd/Lz88X8/PyA1qXeASDW1NT43JY5gjlCotVq5ePz\nFGswc4S/n2dEkY5/zUREFJECuejydAFptVpFAKJer5eXSRfRndfVarVet6PVaru9IQjkBiGQOHzZ\nb+fj9WU7vhyDtN2WlpbuDjNompubxcrKSrf99/Q79nRD6G3dlpYW0Wq1yjcTrvvyB4sIyvGniCC1\nZ46I7xzhSsoXWq3WY8zByhEsIlCs4V8zERFFpGAVETwtd/0Gz9O3Xt1diNrtdlGv1wflBiGQODov\nky6Cu2sTrBsEf75lDIbOF+y+/I537twpAue/LZVuCjzdJHral0qlCihOFhGUE4wigqflzBGxmyMk\ner1eNBgMYktLi6jVakWVStVt8aM3OYJFBIo1HBOBiIjijvT8r3i+mO726k5VVRUWL14MlUqlaByu\nNBoNAMjTk9lsNgDfPfMbTKmpqcjNzUV5eTnGjBmD1atXQ6fTyft05TotWncvb8xmM7Kzs/2OccKE\nCbBYLDh+/DhSUlJQVVWFEydOAABuu+02r+vNnj076qazo9BhjghMNOQI4PzvYtmyZbj99tuRnJyM\nuXPnwmw2Y8OGDV7XYY4g+g5nZyAiorggXUi7amxsxMiRI31a32g0orCwEHa7HcOGDQtqbP7E0Vl6\nejpMJhMaGxshCAJUKhUMBgNyc3ODGqOn/aanp8sjr1dUVGDy5MnIzMxEampqr0c0lwaL80QQhG63\nn5mZ6TYLQ1lZGbRarTwCuyfJycke/0YofjBHBFck54i8vDwA3w3AmpaWBgAoLCzEwoULPa7DHEH0\nHfZEICKimCaNuu46v3dlZSUAoLq6Gk6nE8B3I6B7I110BvPmIJA4OjObzZg0aRKKi4shiiJMJlPI\nbw5cefrmMRi6++bVn5sPo9GI+vp6eUo3b5xOJ3JycgKOl6IXc0RoRWKO6NxTRComdNeDhDmCyEUo\nn5UgIiIKVG/GRJBGMnd91tVVc3Ozx2eM7Xa7KIrfPYfc3NwsP0cvLbPb7fIo5VIb1+15ewbYU5vu\n4nB9T3pOt6Wlpcs2PK2P/4wJILXpfDyeYvG07UgjxedKq9XKg8xJpMHQNBqNx3EQDAaDaLFY5J/t\ndrtoMpkCjotjIigHAY6JwBwR3zlCmuVC+juQxlKR8kKwcwTHRKBYw54IREQUc8aMGQO1Wo2UlBQM\nGzYM1dXVbu+npqbCbrdDq9UCON+N2bULsjRX+OrVqzF37ly3ZVVVVUhJSYFWq4VGo8GZM2fkrrAA\n3P7tylOb7uJwbZ+SkuL2X9dtWK1Wj9+eVVRUoKSkxOPxeIrF07ajkSAISElJwe7du6HRaFBcXNyl\nTf/+/ZGVlQVBEKDT6XDy5MmgPcNO0YE5In5zBHD+kSeLxYL6+noIgoD169fDYrHIj0ExRxB1TxDF\nXj6QREREFAK1tbUoKCjwq+u6NAhXPH20NTY2ol+/fl26UDc2NmLUqFFx9btQSkFBAQCgpqZG4Uji\njyAIqKmpQX5+vs/tAeYIaTlzRHgE8nlGFMnYE4GIiChKGY1GjBw50uMz2GlpaTAYDApERUSRgjmC\niEKBszMQEVFMcDgcbv9OTU1VMJrwqK2txalTp5Cdne12k9DY2Ij6+nqvo4wTxSPmCOYIIgoO9kQg\nIqKY4Mszx7Gmuroal1xyCZ566il5TnWdTodjx47x5oCoE+YI5ggiCg72RCAiopgQj8+aJicnIzc3\nV54+jYi8Y45gjiCi4GBPBCIiIiIiIiLyCYsIREREREREROQTFhGIiIjCQKfTQafThXwdIooOzAlE\nFK1YRCAiIopzZrMZarUaarUaZrPZp3WcTicaGhpQVVUFtVrdY3ubzSa3FQTB4/4FQYBarYbRaAzo\nOIgoOALJCYGsV1VV1SUfNDU1oaioCIIgoKioCHV1dR7Xtdls8mCRUlsiCg8OrEhERBQGpaWlYVnH\nX0ajEbW1taiurgYAPProo/j88897HLldr9cDAFauXNnjPsrKyuTp5NasWQOTyeT23rJly2C1WmEy\nmWCz2ZCRkYHjx4+juLi4F0dGFNliLSf4u57NZkNhYaHbMqfTCZvNhvLycjz99NPYsmULsrKyYDKZ\noFKp3Nru3r3b7ecZM2b4dZxEFDgWEYiIiOJUU1MT8vLysHPnTiQnJwMANBoNMjIyMH78eKSnp3td\nV7qZ6amIUFRUhAEDBqC6ulreh6tly5YBgLwv6b/19fUsIhCFWaA5wd/1nE4nNm3a1GU727dvl4sF\n0swSeXl5UKvVXWbXGDRoUFzOuEEUCfg4AxERURDU1dXJXfLLysrgcDjk9xwOB4xGo9ztv/PPZrNZ\n7srf1NTksU0o7NixAwAwZMgQedngwYMBdP2WLxDSs9ulpaUeCwjAdz0aGhoaAEA+/nB840oUSvGU\nE/xdb926dViyZEmX5Z17G0g0Go3bz01NTVCr1dDpdHLuIKIwEomIiCJQTU2NGC0fUyaTSQQg7ty5\nUxRFUTQYDCIA+aVSqeR/i6Lo9rO0jt1uFwGIGo2mSxtvXPfR3csbjUbj8X0pZl9424fVahUBiCaT\nSaysrJS3abFYurTVarXy78JgMIjNzc0+7TtS5Ofni/n5+UqHEZcAiDU1NUqH0UW85QR/1rNYLPIx\n9hRPS0uLnEdcSb9f199nJOeNaPo8I/IF/5qJiCgiRdNFl6cLYQCiXq/32sbbOj21CSZv2/dnv97a\n6vV6EYBotVpFUTx/MyDdaEg3EK6k97RardjS0uLnkSiLRQTlRGoRId5ygq/rNTc3i5WVlT5v12Kx\niCqVymNOaGlpEa1Wq1yEdN1upImmzzMiX/BxBiIiol7q3NVWIj3vH486j3WQnJws/57Wr1/v1ras\nrAyTJ09GS0sLAGDu3LlwOp1hjJYouJgTPNu8eXOPAzS6euGFF7BixQqPj0MlJycjPT0dpaWlqKys\n9GsWCSLqHRYRiIiIekm6YZCmJrTZbAC+e94/VFynN+vu5Y23548B7zdBvSEVFCoqKuRlRqMRy5Yt\nw+23347k5GTMnTsXZrMZGzZsCPr+icIl3nKCL+uZzWZkZ2f7cBTnGY1GqFQqTJgwoce2s2fPZhGB\nKIxYRCAiIuql9PR0mEwmHD9+HIIgQKfTwWAwhHx2AfH8Y4k9vryRLvxdB3yTBnEbO3Zsr2KTbhw8\n9ShwveHIy8sDAPmbxrS0NADoMvUbUTSJt5zgy3pqtRpXXnmlx2JG58KGzWbDBx984HOvBdeeTkQU\neiwiEBER9ZLZbMakSZNQXFwMURRhMpmQm5urdFg9kr4VPHLkiLzs008/dXsvUDk5OQCAjz/+WF4m\nFRTy8/PlZZ2/wZSKCd19s0kU6eItJ/iyXnfFDNd/OxwObN261W2GFpvNhqKiIq/7dzqdcs4hotBj\nEYGIiKiX1Go1UlJSunQZLioqgsPh6DK1m+vP0o216zf2ntYJhWHDhqGyshLr16+H0+mE0+nE+vXr\nUVlZiWHDhsntdDqdPF2jK9eYO/c4yMzMhFarhU6nk+PfsGEDVCqV283U0qVLAXzX7Vuark1aThSN\n4i0n+LpeTxwOBxYsWIBly5a5/d4yMjIwY8YMAOdzRV1dnbxOU1MTtm/fjszMzCD8BojIFywiEBER\n9ZLVavX4zXlFRQVKSkrkLvrA+e76rj+npKS4/ddTG9d/B9vChQsxY8YMpKSkYO7cucjJyfGpC7Eg\nCG4xSzdMrkpLS6FSqZCWlia/V11d7dYmMzMTFosF9fX1EAQB69evh8Vi4Q0BRbV4zAmBrueqpKTE\n69gGo0aNAgD0798fWVlZ8mMiJ0+eZM8lojATxO4ejCIiIlJIbW0tCgoKun1+N1I0NjaiX79+Xb5x\na2xsxKhRo6LiGChwBQUFAICamhqFI4k/giCgpqbG7RGZSMCcQK6i6fOMyBfsiUBERNQLRqMRI0eO\n9NhlNy0tDQaDQYGoiEgpzAlEFOsSlA6AiIgomtXW1uLUqVPIzs52u2lobGxEfX293915iSi6MScQ\nUaxjTwQiIqJeqK6uxiWXXIKnnnpKHgRMp9Ph2LFjvFkgikPMCUQU69gTgYiIqBeSk5ORm5uL3Nxc\nlJeXKx0OESmMOYGIYh17IhARERERERGRT1hEICIiIiIiIiKfsIhAREQUIxwOB4xGI9RqtdKhEFEE\nYo4gomBgEYGIiChGlJSUIC8vD2azWelQemSz2eRB5wRBQFFRUbftq6qqIAhCmKIjik3RlCNc9XT+\n22w2VFVVQa1WM08QhQGLCERERDEimgZx2717t9vPM2bM8NrWZrOhsLAw1CERxbxoyhGSns7/srIy\n6HQ6DBo0CGvWrIEoimGMjig+cXYGIiIiCrtBgwb5dLHvdDqxadOmMERERJGmp/O/qKgIAwYMQHV1\nNZKTk8MYGVF8Y08EIiIiP5WVlUEQBFRVVcHhcLh1n3U6nXLXW2l+eIfDAaDr88hms1nuyt/U1AQA\nMBqNXZY5HA6YzWZ5PWn7RUVFaGxs7DFeh8Mhx6xWq1FXV+fz8YRCU1MT1Go1dDodGhoaum27bt06\nLFmyJKTxEAUbc0RwdHf+63Q6AEBpaSkLCEThJhIREUWgmpoaMRI/pvR6vWi320VRFMWWlhZRq9W6\nxanRaEQAYnNzs2i320UAokajEUVRFFUqlQhABCBarVZRFEVx586dcpudO3eKoih2WU9aB4DcpqWl\nRd7XwYMH5f1L7STNzc2iSqUSDQaDKIqiaLFY3Pbf0/F05hpLd6/umEwmt7YqlUpsbm7u0s5iscjH\n68t2lZKfny/m5+crHUZcAiDW1NQoHYYb5oje5wgpDm/nv9VqFQGIJpNJrKyslPOIxWLpcbtKiNTP\nM6JA8a+ZiIgiUqRedEkX/5Lm5ma3OLVarXxhL7V3fd/TBbQvyzy1kS6k9Xq913YGg8HjtrVarU/H\nEyotLS2i1WqVb0gqKyvd3m9ubnZbxiICeRKJRQTmiN7r6fzX6/VuhQ7XgolUeIgkkfp5RhQo/jUT\nEVFEitSLLulC1WAwiC0tLV7b2e12+UI3VDcIvrRz/WbT0zeNXmPHAAAgAElEQVSBvh5PKFVWVooq\nlarLMlcsIpAnkVhEYI7ovZ7O/+4KJq4FmkgRqZ9nRIHimAhERER+ePjhh6FSqZCXl4eUlBSUlZV1\naVNVVYXFixdDpVIpEKE7aSo38fwXB24vwLfjceU6LWN3L3/Mnj3bbco5s9mM7OxsP4+UKDIwR/Qu\nRwR6/qenpwMAKioq/F6XiPzD2RmIiIj8MHLkSJhMJthsNlRUVGDZsmUAgOLiYgDnBz0rLCyE3W7H\nsGHDwhKTRqPpsU1jYyNGjhzZZXlPx9OZdGMRTMnJyW7HIA0O54kgCJzCjSIac0Tvzk9fzn+NRoOK\nigo4nc4ugypGQmGGKNaxJwIREZEfBEGA0+lEeno6ysvLYbVa5YtqAMjLywOAsNwcSKOuz5gxw2ub\nyspKAEB1dTWcTieA70ZiB3o+nnBwOp3IycmRf/b2jaj0HlEkY47oHV/OfylffPzxx/J7Uuz5+fkh\ni42IzmMRgYiIyE96vV6eWu2yyy6DXq+X35O+BWtqanKbWs3hcMjTuAFwu1h3beNtmcRoNMrrV1dX\nQ6VSyfv0tN5dd90FAFi5ciVSUlIgCALS0tLcbtq7O55gMxqNbtPHNTU1Yfv27cjMzAzZPonCjTki\ntDIzM6HVat2mx9ywYQNUKhVyc3MVjY0oHrCIQERE5KclS5Zg48aNEAQBGzdudOvWW1paCuD8M88p\nKSnQarXQaDQ4c+YM0tLS5HYpKSkA4LZM+renZZIxY8ZArVYjJSUFw4YNQ3V1tce20r9TU1Nht9uh\n1WoBnO/W3LkbdXfHE2z9+/dHVlYWBEGATqfDyZMn2f2YYg5zROiVlpZCpVIhLS1NHmPB9ViJKHQE\nkf0CiYgoAtXW1qKgoIDd1/9Dukjm7yPyFBQUAABqamoUjiT+CIKAmpoadmEHc0Qk4+cZxRr2RCAi\nIiIiIiIin7CIQEREFOG6e/6ZiIg5gojCiUUEIiKiCNfd889ERMwRRBROCUoHQERERN3jc7RE1B3m\nCCIKJ/ZEICIiIiIiIiKfsIhARERERERERD5hEYGIiCgADocDRqMRarVa6VB6JAiC2ytexOtxU2Rg\njoh88XrcRL3FIgIREVEASkpKkJeXB7PZrHQoPhNFscuz02azGWq1Gmq1OqjHUlVV1eWi3OFwyMsF\nQYDRaOzVPmw2m9sNQFFRkdv7no6XKFyYI9w5nU40NDSgqqoq6IUVT/kGYI4gChUWEYiIiAJQXl6u\ndAi9ZjQaUVVVherqalRXV+Mvf/kLqqqqer1dm82GwsJCt2VOpxMLFiwAcP7Cvbm5GbW1tdDpdAHv\nZ/fu3W4/z5gxI+BtEQUbc4Q7vV6PN998E4WFhUEtrHjKNxLmCKLQ4OwMREREcaipqQl5eXnYuXMn\nkpOTAQAajQYZGRkYP3480tPTA9qu0+nEpk2buizfsmULzGYzqqurAQCpqakoLS1FRkYGpk6diszM\nTL/3NWjQIH6LSBQiwc4RpaWlAICVK1cGLUZv+UbCHEEUGuyJQEREcaWhocHrc7BlZWXysqamJjid\nTrfu9zqdDg6Hw+N2PW3P27O2DodD3pdarUZdXV1oDrYbO3bsAAAMGTJEXjZ48GAAXb+988e6deuw\nZMmSLstra2sBQL4ZAYCrrroKALBx40a/99PU1AS1Wg2dToeGhobAgiXygDnivFDliGDylm8A5gii\nUGIRgYiI4sqECRNgsVgAAFqt1u1bquLiYmi1WlitVgwbNgyPPvooCgsL0dzcDLvdjpUrV6KkpMTj\ndpubm7sss9vtXZY5HA4sWLAAQ4cOhSiKWLp0KbKysmCz2Txut/PNjLeXv+rr6wEAw4YNk5elpqYC\nQMBdjevq6jBx4kR5O648bVMqKFRUVPi9L+n3tXLlStx8881Qq9Veb96I/MEccV4ockQwdZdvAOYI\nolBiEYGIiOJOZmYmtFotVq5cCafTKS+X/i110x0wYAA0Gg1SU1PlC2lvN7yeLmRdL74ldXV1MJvN\nyM3NlWMB4LVLrjTwV08vf3V34x7IDYLD4cDhw4cxYcIEj+9rNBoAQGNjo9/b9kSlUqGlpQVWqxVa\nrRZmsxmbN28OyraJmCOCnyOCqad8AzBHEIUSiwhERBSX7rnnHgDnn9WX7NmzR14OnH+Gt7y8HE1N\nTSgrKwvKfqVu/Z2/IQzmc8JK2Lx5MxYuXOj1/Xnz5gEAnn/+eflGTPqmUK/XB7TP5ORkpKeno7S0\nFJWVlYrf2FBsYY6IXD3lGwlzBFFosIhARERxKT09HSqVSr5gB4Bt27Z1GSysqqoKixcvhkqlCsp+\npYtYX78pDFVX5e6OR+o14Cuz2Yzs7Oxu20hdxI8fP46UlBRUVVXhxIkTAIDbbrvNr/15Mnv2bN4g\nUFAxRwQvRwSTL/nGE+YIouDh7AxERBS38vPzkZeXh4aGBgwZMgTjx493e99oNKKwsBB2u91jt+Pe\naGxsxMiRI3tsF6qRxVUqFcxmMxwOh9zNuqmpCQAwduxYv7bV3ZzvgiDIx5CZmek2C0NZWRm0Wm3A\nM0G4Sk5OVvTGhmITc0RwckQw+ZpvOmOOIAoe9kQgIqK4Jd3Qrl+/Hjt27MCkSZPc3s/LywPg+bnl\nQFVWVgIAqqur5W790kjs4SR9k3fkyBF52aeffur2nq+6+8bU2wW90WhEfX09li1b5m/oHjmdTuTk\n5ARlW0QS5ojg5IhgCiTfAMwRRMHEIgIREcWt1NRUaLVaVFRU4Pjx427TDwLfdedtampyGxDQ4XC4\njfIt/bvz4IGu04oVFRUBAO666y4A559vTklJgSAISEtLC/vF7bBhw1BZWYn169fD6XTC6XRi/fr1\nqKysdLshkqaZ8zYyvD+cTidsNhuKiopw/PhxmEymLr9zX/ZnNBrdprxramrC9u3b3Xo5EAUDc0Rw\nc4SnQSpdBSvfMEcQhRaLCEREFNekQdI8Pf9bWloK4PwzzykpKdBqtdBoNDhz5gzS0tLkdtK/f/Wr\nX0GlUmHUqFEwm82YMGECVCoVDAYDnnjiCQDnb0rsdju0Wi2A8zcVoegK7YuFCxdixowZSElJwdy5\nc5GTk9NlsLKWlhZoNBrodLpe7UsQBKSkpGD37t3QaDQoLi722M6X/fXv3x9ZWVkQBAE6nQ4nT54M\n2vPoRJ0xRwQnR0g5QCIVSALZVk+YI4hCSxBD9SAVERFRL9TW1qKgoCBkz/vGE+lCvTe/S7VaDZPJ\nFKyQwrq/YBx/dwoKCgAANTU1Idk+eScIAmpqapCfn690KFEt0nJEuPNNqHMEP88o1rAnAhEREXWr\noaEBK1asiNn9EVHvBPOc5flPFPk4OwMRERF5VVdXh8svv9ynUeKjcX9E1DvBPGd5/hNFBxYRiIiI\n4kQgXXbDPRBZMPfX+XlrIuqe0jki3PmGOYIoMCwiEBERxbh4fQ43Xo+byF/xeq7E63ET9RbHRCAi\nIiIiIiIin7CIQEREREREREQ+YRGBiIiIiIiIiHzCIgIRERERERER+YQDKxIRUUTbuHGj0iEQdWvj\nxo3IyclROoy4tXHjRiQmJiodBkUxURRDOlMDP8co1rCIQEREEefzzz/H5s2bAQCzZ89WOBqinl19\n9dVKhxCXkpKS8Prrr+P1119XOhSibiUlJSkdAlHQsIhAREQR48SJE/jtb3+L1atX47LLLkNFRQUW\nLFiAvn37Kh1azBAEATU1NcjPz1c6FKJeO3v2rNIhxJyCggIAQE1NjcKRhMfZs2fxq1/9Ci+88ALm\nzp2LtWvX4uKLL1Y6LKKIxjERiIhIcadOncKTTz6Ja665Br/73e+wcuVKHDp0CIsWLWIBgYiIQuaC\nCy7Ac889B7PZjC1btmDcuHHYt2+f0mERRTQWEYiISDHffvstysrKMHz4cDz//PMoLi7G0aNH8fDD\nD+PCCy9UOjwiIooTd9xxB/bt24chQ4bg5ptvxksvvQRRFJUOiygisYhARERh19raivLycvzgBz9A\nSUkJFixYgCNHjkCn07EbKRERKWLo0KHYunUrVqxYgV/+8peYOXMmTpw4oXRYRBGHRQQiIgqb9vZ2\nvPLKKxg9ejSWLl2Ke+65B4cPH8ZTTz2Fyy67TOnwiIgozvXt2xePPfYYtm3bhn379iEjIwPvvPOO\n0mERRRQWEYiIKOREUcSmTZtw/fXX44EHHkBWVhYOHTqEF198EWlpaUqHR0RE5ObWW2/Fvn37MG7c\nOEydOhVPPvkk2tvblQ6LKCKwiEBERCH15ptv4sYbb8S9996LjIwMfPDBB6iqqsKwYcOUDo2IiMir\n73//+3jttdfw/PPP4ze/+Q1uu+02HD9+XOmwiBTHIgIREYVEfX09brnlFtx55524/PLLYbVaUVtb\ni5EjRyodGhERkU8EQcCSJUvQ0NCAzz77DBkZGXjjjTeUDotIUSwiEBFRUL3//vuYNm0apkyZggsv\nvBC7du3C5s2bcf311ysdGhERUUAyMjLwj3/8A3fccQfUajUefvhhnD17VumwiBTBIgIREQXF/v37\nMXPmTNx00004ffo06urq8Le//Q3jx49XOjQiIqJeu/jii/Hyyy9j/fr1WLduHSZOnIhDhw4pHRZR\n2LGIQEREvXLo0CHMmTMHGRkZsNvtMJvNeO+99zB16lSlQyMiIgq6uXPnYu/evRBFEePGjUNNTY3S\nIRGFFYsIREQUkE8++QSFhYW49tprsWfPHhiNRuzduxd33HGH0qERERGF1IgRI7Bjxw7cf//9mDt3\nLubPn4+vv/5a6bCIwoJFBCIi8ovD4cDSpUsxYsQI/O1vf8O6deuwf/9+5OTkQBAEpcMjIiIKiwsu\nuAAvvPACNm/eDLPZjB/96EewWq1Kh0UUciwiEBGRT06ePIkVK1bgmmuuwYYNG/Dcc8/h4MGDmDdv\nHhISEpQOj4iISBEqlQo2mw1paWm4+eabsWbNGoiiqHRYRCHDIgIREXXr66+/xqpVqzB8+HBUVVXh\nsccew+HDh/Hzn/8cSUlJSodHRESkuKFDh8JiseDRRx/F0qVLMWvWLJw4cULpsIhCgkUEIiLy6MyZ\nM3jhhRcwfPhwPPvss1i6dCkOHz6M5cuX48ILL1Q6PCIioojSt29flJSUwGKx4P3338fYsWPxzjvv\nKB0WUdCxiEBERG7a2tpQWVmJESNGYMWKFZg/fz4OHz6MkpISXHrppUqHR0REFNEmT54Mm82G9PR0\nZGZmorS0FO3t7UqHRRQ0LCIQEREAoKOjA3/84x8xZswYLFmyBDNnzsThw4fxzDPPYMCAAUqHR0RE\nFDW+//3vY/PmzdDr9Vi1ahV+8pOf4NNPP1U6LKKgYBGBiCjOiaKI1157DTfccAPmz5+PW2+9FQcP\nHsTq1asxePBgpcMjIiKKSoIg4KGHHsKOHTtw7NgxZGRk4C9/+YvSYRH1GosIRERx7O2338b48eNx\n991347rrrsP+/fvxhz/8AVdddZXSoREREcWEsWPHYu/evZg+fTruvPNOFBcXo7W1VemwiALGIgIR\nURx69913MWnSJEyfPh2DBg3Cvn378Kc//QmjR49WOjQiIqKYc/HFF+OVV17Byy+/jMrKSkycOBEf\nffSR0mERBYRFBCKiOLJnzx7cfvvtuPXWW5GYmIj33nsPZrMZ6enpSodGREQU8+677z7s2bMH586d\nw7hx41BbW6t0SER+YxGBiCgOfPjhh7jnnnvwox/9CCdPnsTf/vY3WCwW/PjHP1Y6NCIiorgycuRI\nNDQ0YN68eZgzZw7uv/9+nD59WumwiHzGIgIRUQw7cuQI7rvvPlx//fU4dOgQNm/ejIaGBtx2221K\nh0ZERBS3LrjgArz00kt47bXXYDKZcOONN+Kf//yn0mER+YRFBCKiGHT8+HFoNBqMHj0au3btQk1N\nDfbt2weVSqV0aERERPQfd911F/bt24eBAwfipptuwv/8z/8oHRJRj1hEICKKIV988QV++ctfYsSI\nEdiyZQvKy8vxwQcfIDc3F336MOUTERFFmiuuuALbtm3DI488ggcffBCzZs3CyZMnlQ6LyCteURIR\nxYCWlhY89thjuPrqq2EwGPDMM8/g0KFDeOCBB5CQkKB0eERERNSNvn374oknnsDWrVuxe/duZGRk\n4L333lM6LCKPWEQgIopip0+fxtNPP43hw4dj7dq10Gq1+Oijj7BkyRIkJSUpHR4RERH5YcqUKbBa\nrbj++usxZcoUrFq1Ch0dHUqHReSGRQQioijU2tqKl156Cddccw1WrVqFJUuW4PDhw/jv//5v9O/f\nX+nwiIiIKEADBgyA2WzGs88+i9LSUvzkJz/BZ599pnRYRDIWEYiIosi5c+ewbt06jBgxAo8++igK\nCgpw9OhRPPHEE0hJSVE6PCIiIgoCQRDw8MMP491338Unn3yC9PR0vPXWW0qHRQSARQQioqjQ0dEB\ng8GAa6+9Fr/4xS8wY8YMHDp0CGVlZRgwYIDS4REREVEI3HjjjfjHP/6BadOmYcaMGVi+fDna2tqU\nDoviHIsIREQRzmQyISMjA3PmzMHNN9+MAwcOoLy8HEOHDlU6NCIiIgqxSy+9FH/84x/x+9//HhUV\nFZg4cSKOHDmidFgUx1hEICKKUFu3bsVNN92EmTNnYtSoUdi/fz/Wr1+P4cOHKx0aRYmOjg6cPHnS\n7QWcH5DTddmZM2cUjpSIlHLq1Cm3fNDa2orW1la3ZadOnVI6TALws5/9DO+//z7a2trwwx/+EEaj\nUemQKE4JoiiKSgdBRETf2bFjB37961/j73//O2bMmIEnn3wS48aNUzosikLLly+HXq/vsV3fvn1x\n7ty5MERERJFk9+7duOmmm3xq+8EHH+Daa68NcUTkizNnzmD58uVYu3Yt7r//frz00ku46KKLlA6L\n4gh7IhARRQir1Yo777wTEydOREdHB9555x28+eabLCBQwEaPHu1TuzFjxoQ4EiKKRP70bEtNTQ1h\nJOSPfv36YfXq1Xj11Vfx2muv4cYbb8T+/fuVDoviCIsIREQK+9e//oXZs2dj7NixcDgcePvtt1Ff\nX49bbrlF6dAoyt19991ISEjotk2fPn1QXFwcpoiIKJIMGDAA06dPR9++fb226du3L6ZPn85BfCPQ\nzJkzsW/fPnz/+9/H+PHjUV5ernRIFCdYRCAiUsjHH3+Mn/3sZ7j++utx4MABvPrqq9i1axemTZum\ndGgUI1JSUpCdnd1tIaFv376YNWtWGKMiokgyZ84cdPd0syiKmDNnThgjIn8MGzYM27Ztw7Jly7B4\n8WLcc889aGlpUTosinEsIhARBdGmTZsgCAK2b9/utc1nn32GX/ziFxg1ahTee+89/OEPf4DNZsPM\nmTMhCEIYo6V4MHfuXLS3t3t8LyEhASqVCpdeemmYoyKiSKFWq5GYmOj1/cTERKjV6jBGRP5KSEhA\naWkptm7dip07dyIjIwM7d+5UOiyKYSwiEBEFyV//+lfk5eUBAB555JEu73/xxRd45JFHcM0118Bs\nNmP16tU4cOAA5syZgz59mI4pNFQqFfr16+fxvfb2duTn54c5IiKKJJdccgnUarXHHksJCQlQq9W4\n5JJLFIiM/DV16lRYrVZcd911mDRpEp566il0dHQoHRbFIF61EhEFwXvvvQe1Wi1/WO/atQt/+ctf\nAABfffUVHn/8cVxzzTVYv349fvOb36CxsRGFhYU9Pq9O1FsXXXQRZs2a5fGbxgsvvBB33HGHAlER\nUSSZM2eOxx5L7e3tfJQhygwcOBBvvPEGnnnmGTz++OPIzs7G559/rnRYFGM4xSMRUS9ZrVbccsst\nOHPmjHwR1rdvX4wePRpz587Fb3/7W7S3t2P58uV48MEHcfHFFyscMcWbN998E3feeafbssTEROTn\n5+Pll19WJigiihitra0YMGAATp065bb8kksuwRdffIGkpCSFIqPe+Mc//oHc3FycOnUKr7zyCrKz\ns5UOiWIEeyIQEfXCwYMHMXXqVJw9e9btW5z29nZ8+OGHeOKJJ7Bo0SIcPXoUK1asYAGBFDFt2jQk\nJye7LWtra0NBQYFCERFRJElKSkJOTo5bj6XExETk5OSwgBDFbrzxRuzduxdZWVm4/fbb8cgjj6Ct\nrU3psCgGsIhARBQgu92OyZMn4+uvv8a5c+e6vC8IAgYPHownn3wSKSkpCkRIdJ7U68D1BuGyyy5D\nZmamglERUSQpKChwu8FkoTE2XHrppaitrcW6deuwdu1a3HrrrTh69KjSYVGUYxGBiCgAzc3NmDx5\nMr788kuPBQQA6OjowMcff4w//vGPYY6OqKv8/Hz5BiEpKQkFBQXdzg1PRPFlypQpGDhwoPzzwIED\nMWXKFOUCoqC6//77sWfPHnz77bf44Q9/iA0bNigdEkUxFhGIiPx08uRJTJkyBZ9++qlP3QK1Wi1a\nW1vDEBmRdz/+8Y8xaNAgAOeff+asDETkqk+fPpg7dy6SkpKQlJSEuXPncuagGDN69Gjs2rULBQUF\nuPfee1FYWIhvvvlG6bAoCjEzEBH54fTp07jttttw+PBhnwoIHR0dOHbsGIxGYxiiI/JOukEAgKFD\nh2LChAkKR0REkebee+9Fa2srWltbce+99yodDoVAv379sHbtWrz66qvYtGkTxo8fj//7v//r0q6j\nowMTJkzA3XffrUCUFOk4txhFnE8++QQNDQ1Kh0HURUdHB9auXQur1dpl3uWkpCSIoigXFvr27YvB\ngwfj2muvxdVXX43bb79diZBjwrlz52AymTxOP0b+kboqp6amYtOmTQpHExsmTJiAK664Qukw4g7z\nQujZ7XbY7Xalw4gpffv2hVqtjojpnX/6059i3LhxyMvLw/jx4/H8889j0aJF8vvPPPMMdu3aBQB4\n5ZVXcN999ykVKkUgTvFIEef+++/HH/7wB6XDIOpRnz59MHjwYIwaNQpjxozBD37wA4wcORIjRozA\nVVdd5TaIHQXu9ddfx09/+lOlwyDyaP78+fj973+vdBhxh3mBotVrr72GmTNnKh2G7Ny5cygpKcHT\nTz+NWbNmoaqqCv/6178wceJEdHR0QBAE9O/fHx9++CELpiRTvgxG1MnZs2eRn5+PmpoapUMhciMI\nAiZPnozi4mKMHDkSV199Nae+CgPpeU3WvCnSFBQU4OzZs0qHEZeYFygaCYIQcWMQJCQkYNWqVcjM\nzMScOXOQnp6Os2fPQhAEAOfPsbNnz2LevHmwWCzycopvHBOBiMgPhYWFUKlUGDVqFAsIREREFBOy\nsrLwz3/+E4mJifjyyy/dHhVqa2tDfX091qxZo2CEFElYRCAiIiIiIopzf/7zn3HkyBGPA0d3dHRg\n+fLlOHTokAKRUaRhEYGIiIiIiCiO7d+/Hw899FC3jwh1dHQgPz+fA5oSiwhERERERETx6uzZs7jh\nhht6nLq6ra0Ne/fuxTPPPBOmyChSsYhAREREREQUp06fPg3g/CCKPY331NHRgZKSEthstnCERhGK\nRQQiIiIiIqI49b3vfQ8dHR3Ys2cPfvWrX+G//uu/AJyfuaFPH8+3i7m5uZydJo6xiEDUCw6HA0aj\nEWq1Oujb1ul00Ol0vd5/KGMkInfMCUTUnWg7RyMpFgotQRAwduxYPP7449i/fz+amprw4osvYsqU\nKUhISIAgCEhMTAQAnDt3DocOHcJjjz2mcNSklASlAyCKJE6nEykpKT7PO11SUoKKiooQR+Xf/jsf\ng9Ix+srpdOLAgQPYv38/zGYzTCaT39uw2WzYvXs3zGYzzGaz2/9Hm82GjIwM+WeNRoPy8vKgxE6x\nizlBOb3JCb6c78wJpARfzj8lz9FozRdAYOe0w+HA5s2bUVhYCAAwGAzIzc0NyraVsHPnThw7dixo\n2xs4cCA0Gg3mzZsHq9WK999/H3v27MG3336L9vZ2PPvss/jmm28wadKkoO2TIs+ECRNwxRVXuC8U\niSJMfn6+mJ+fr8i+TSaT6O9pAcDvdYKp8/49HUMoYrRaraJWqw3a9rRarajVagOOVa/XiyqVSjSZ\nTKLdbu/yfmVlpbxtAKLJZPJ7HwDEmpoav9ej3qmpqVHsHGNO8F0k5QRfzvdg5AQlP6/inZJ5obd8\n+ZtWKo9Ea74QRf/P6ZaWFlGlUomVlZWiKIpic3OzqFKpPMYVjHwhiqG/jnCNkS++gvWaP39+l781\n9kQg+g+n04mqqiqlw+iVUB+Dw+FAXV0d6uvrMXnyZCxZsiRo2y4tLQUArFy50u91i4qKMGDAAFRX\nVyM5Odljm0GDBvn8bTIRwJzgi0jNCb6c78wJRO6iOV8A/p/TW7ZsgdlsRnV1NQAgNTUVpaWlyMjI\nwNSpU5GZmRnwtpVUU1OD/Px8pcOgGFFQUOBx7AuOiUBRz+FwwGw2y8/rVVVVQRAEFBUVobGx0WP7\nsrIyCIIAtVqNuro6AIBer4fZbAZw/rkwQRAAfPehKi3T6XRwOBw+xdbU1CSvJwgCysrKuiyT9iPF\nJAgCmpqaPD6D6HQ6YTQa5dg7H5+3Y3BlNpvl34+vx2Gz2aDT6bB69WqMGTMG5eXlyM3NRWpqqk/r\nh5L0jHhpaanXAkJTUxPUajV0Oh0aGhrCGR4pgDnhO/GYE3w535kT4pd0XlVVVcHhcHQ5J7zlA3/y\nSm9yRHe6i801P0jntFqtRlNTk9s26urqoFar5fzjGlc054tAzuna2loAcLt2uOqqqwAAGzdu7NW2\niWJeyPrTEAXI3+6hcOlus3PnTlEUz3dR02g0IgDx4MGDclupq5rBYBBFURQtFosIQLRarW7bciVt\np7m5WbTb7SIAUaPRdNm/N9I6nduoVCp5u67bkmL0to5GoxFbWlpEURRFg8HQpZ2n9Tr/fg4ePNjl\nODprbm4WDQaDqNFoRIPB4BZnZ+imC5Tryxf+tLVarSJwvluh1NVQpVKJFovFrZ3UPVN6qVSqbo+n\nu9j4OEP4+dttmTkhfnOCKPp2vgcrJ/BxBuUE8jiDXkC4PG8AACAASURBVK+XH3draWmRH5eRdJcP\n/Mkrvc0Rntp0F5trfpBi87Rf6e9eauOaL6R9RWu+COSc9rbdzsuDlS+kbYf6cQZep1AwefucYxGB\nIk4gF2WePgikD329Xi8vkz4wO68rPf/maTtarbbbD39fPtykG1zpxkQUv7thcP1Qdo3V24eY64VK\nS0uLXzcMPS3r/L5er5dvTsLFnxsGvV7v9nt1vaCTfq+SlpYW+RlMAPIzkP7Gxg/n8AvkZoE5IT5z\ngsSX8z0YOYFFBOUEmhdcb/6am5vdthFIPvCUV4KRIzq3CSQ2X3NBd3nGn2WethvOfOHvOe2pACSK\nno8tGPlC2jaLCBRNWESgqBGsIoKn5a7Vek8V7u4+FO12u3zT6u/FgFS1lz6oDx48KH94SRcAJpPJ\n7ca383al9j0dY7AuAPz5FiGY/Llh6O6CrrtvSCorK0WVShVQbPxwDr9gFRE8LWdO6H6Zq2jICZ74\ncr4HmhNYRFBOIHlBOmcMBoPHm9tA84G35b3JEcHIVb7kjFjLFxJfzumdO3fK1wvS34OnolAg2/aG\nRQSKNiwiUNQIZRHBl8q5p/elDwzpwt/fiwFR/O7Du6WlRTQYDKLVanVbptFounRj9mU/oboAcCVV\n37Vards3p5231dPLF8Fo29M2pG9r/cUPZ2WEsojAnNDzsXoSqTnBE1/O90BzAosIygkkLxw8eNDt\nZrzzjWKg+cDT8t7miGDkqs7LpBtk6ZEITzfM0Z4vJL6e0xaLRf6bqKys7PJIW2+27UmoryN4nULB\n5u1zjgMrUszTaDRdlnkaXM0bo9GIwsJCrFmzBiNHjgw4jhkzZgAA9uzZg9raWqSnp8vLtmzZAgAR\nMSiZJ+np6SgtLcWSJUtw4MABFBUVwWg0yoMqiecLkj2+gk36f+t0Oru8p1KpvK6XnJzs8e+C4gNz\nQu9Fak7wxJfznTkhPowcORImkwlWqxUajQbLli1DWVlZl3b+5AOJ699PsHKEJ4HEJklPT4fJZMLx\n48flAR8NBgOKi4uDGKHn/YY7X/h6TmdmZsJkMkEURSxcuBD79u2DVqtFenp6r7dNFMtYRKCYJX3Q\nShflAFBZWQkAqK6ulm88pdGOvcnLywMADBs2rFfxTJo0CQCQlZUl3+BKy/Ly8jB58uRu15dit9ls\nvYqjN1JTU5Gbm4vy8nKMGTMGq1evViwWAMjJyQEAfPzxx/Iy6f9rd9MbOZ1OeV2KH8wJwRdpOcET\nX8535oT4IAgCnE4n0tPTUV5eDqvVimXLlsnvB5IPPOWVYOUIV4HE1pnZbMakSZNQXFwMURRhMpmQ\nm5sbtBh7Es58Ecg5bTQaUV9f7/Y3Eaxtk390Op08+1Yo16HAsYhAMcVoNAI4n+Crq6uhUqncvpG+\n6667AJyfdzwlJQWCICAtLU3+MJDaun4wS8uamprcvgFwOBxu0xv1NNVRcnIy9Ho9AGD8+PHyMq1W\nCwAYO3asx21J/87OzgZwPklKUzZJ0zsBQFFRkcdj8LQt12/uA512SvpmIZhc4/LUu6DzB0RmZia0\nWq3b9FkbNmyASqWSL4yMRqPb76mpqQnbt293m/+ZYhdzQnzlBF/Od+aE+KbX6+Xz5bLLLpPPQaDn\nfCDpKa/0Nkd4atNdbK7tpfPE0zmtVqvldV1frtM1RmO+8PWc9nST6XQ6YbPZUFRUhOPHj8NkMrlN\n+ch8QZ1JU72q1Wp5StRQrhexQvDoBFGv9GZMBNepjiorKz0OnGS32+XRdTUajTzdkyh+93ygVquV\nn0XuvEwaddl1mjb4+LyeFJ8raWAfT8fTebt2u11+Zlp6Xlqa8slbvJ625W/c4dA5Jk+xSc9UdiaN\ndO/p/7vr1Ezensf0J0Y+axh+vRkTgTkhvnKCL+d7MHMCx0RQTm9mZ5AGO/Q0eF53+cDXvNLbHNHd\n+e4pNl/P6c7TQbq+pMGIozFf+HpOd84XrtcN3tYJZr6Q9skxEaKXwWAQVSqV2NLSIo9d5MtMHYGu\nFwm8fc4JohimhxKJfFRQUAAAqKmp8XkdQRAAIGzP2FJ8EgQBNTU13T4qQcFXW1uLgoICv85v5gQK\nh0A+ryg4AskLvRXteaWxsRH9+vXr8phFY2MjRo0aFbXHFU1CfR3B65TQaWpqwpVXXomdO3diwoQJ\nAM4/TpiRkQGr1ep1HI1A14sU3j7n+DgDEREREVEMMxqNGDlypMdxGtLS0mAwGBSIiuJVXV0d1Go1\nBEHw+NiM0WiEWq32+LPZbIYgCFCr1fKjSZ3bhMKOHTsAAEOGDJGXDR48GACwe/fuoK8X6RKUDoCo\ntzonnkgdzZyIwoM5gYiCLdrzSm1tLU6dOoXs7Gy3QkJjYyPq6+uxcOFCBaOjeCKNDbBz506YTCYY\njUakpaXJ76tUKrcxAxYsWCD/3NDQAJVKBbvdjiuvvBJDhw5FeXm5WxtvpJ5EPfHWI6e+vh6A+4Cp\nUh4wm81ez6FA14t07IlAUc818bj+m4jiE3MCEQVbtOeV6upqXHLJJXjqqafkARV1Oh2OHTsWtTcx\nFJ2k3gJS135pIGy9Xi/PGuLK9WdpHemGvKKioksbb8ReTiUq7cuT7goYga4X6VhEoKjn68lPRPGB\nOYGIgi3a80pycrI8vaJ0DKWlpZxlgMJOo9F4XN7T1JoUWVhEICIiIiIiopCTigjSVKk2mw0A3KZb\nDYXOU5t6e3njOo1rZ94KI71ZL9KxiEBEREREREQhl56eDpPJhOPHj8uP1RgMBhQXF4d0v719nEEq\nBriOjyIN7Dh27NigrxfpOLAiUZDodDoAQGlpaUjXIaLowJxARN1hjqB4ZDabMWnSJKhUqpAXDoIp\nOzsbAHDkyBF5YMRPP/3U7b1grhfp2BOBKA5II+Gq1Wq/BnHxd72qqiqPXcF82Y7URpq2R+rmRkTB\nF2hOcOXtfFeqDREFD3MEhYparUZKSkqXxwiKiorgcDi6zITi+rPT6XT7r6c2rv8OpmHDhqGyshLr\n16+H0+mE0+nE+vXrUVlZ6Tbzgk6nk4t9/qwXdUSiCJOfny/m5+crHUbMMBgMokqlEltaWsSWlhZR\no9GIlZWVQV/ParWKAMTOacWX7ej1ehGAaLVa3bal1+sDOOLQASDW1NQoHUbcqamp6fJ3RYELNCe4\n8na+K9VGKfy8Ug7zQugwR4ROqK8jouE6xWq1iiqVSv5/5vrSaDQel7u+RFH0qU2omP6fvbuPb6q8\n+wf+CVDER4piK+Cv3XSW6TaKjtUKTljRqcwTvIdFmsp8gNbUwUSoDjCVzVbRmaIb3GuX1oda20TB\nTZKJ3mojRWkLqEvnpisTbpuJmgxnIopowfP7o/c55rlJm+ScJJ/365UX7cl5+B565XtOrnM9WK0i\nAFEQBLGjoyPofYPBIBoMhpi3U6tw1zl2ZyBKY06nE2VlZeju7sb48eMBDA7iMn36dBQVFaGwsDAu\n23m9XmzZsmXY+5FG5JV+l/7t7OxMqaZuRGo33JzgK9znXal1iCh+mCMo0Y4//nhs2rQpaFrGvXv3\nYurUqVHNfhLNOokiCELE44frajTUdqmG3RmIomS32+Xm9vX19UFNpywWizz3beDvNptNbqYvDaYS\nuE4idHV1AQAmT54sL5s0aRIAYPfu3XHbrrm5GcuXLx/2fqQReXt6egB8PeAM+3ySmmVSTvAV7vOu\n1DpEasUcoY51SD0sFgsKCgpCNuPPzc2F2WxWICoaliS3iCAakhqbh0pNkLq7u0VRHGzqB59mU77N\nskRR9Ptd2qa/v19uqhW4TjgYornWUNtLzcJC7VcQhLhs19HRIZ9jYDyx7MdgMMj/X2azWXS5XGHj\nUwpSoJlgOlJjs+VMywmSSJ93JdZRmhqvV5lCjXnBF3MEc0Qoib6PUPt9iiAIoslkEvv7+/2W9/X1\nxdxlhpIj3HVO/Z82yjhqvCkLdXEC/PvsB64Tbpuh1omncPuP9iZkqOUul8sv6Ud7fuGWSzcvBoNB\n9Hg84U9MIWq/OKcrNX5ZyLScIIpDf96TvY4aqPF6lSnUmBd8MUcwR4SS6ZUIHo9HNJvNfmMfGAyG\nlBojINOEu86xOwNRFPR6fcjlUl/+TLV161ZUVFTEZV/19fWYPXs2PB4PAGDx4sV+o+8SqUkm5oRo\nPu/JXIdIzZgjlF+H1Gf8+PFYtGgRGhoaIA4+zEZtbS1KSkqUDo1ixEoEoihINwPStIO9vb0Avu7L\nnyiB09+Ee4UjCELY98Ld4ES7nc1mG3J+22iPb7FYUF1djSuvvBLjx4/H4sWLYbPZ8NRTT0XcP5FS\nMi0nRPN5T+Y6RGrHHKHsOkSUWKxEIIpCYWEhrFYrDhw4AI1Gg5qaGpjN5oTPHCDV0g71Cke6GfAd\nzEkaoOmCCy4Y0XZarRb5+fkhb0qkn6M9fllZGQDII0Hn5uYCACorK8PGSKSkTMsJ0Xzek7kOkdox\nRzBHEKUzTvFIFAWbzYZLLrkEgiCk1JSDUk39/v37kZOTAwB4//33/d4b7nahbkKki7f0XrTHFwQB\nNptN/l2qTIj0RIRISZmWE6L5vCdzHSK1Y45gjiBluN1u2O12tLe3B00jSfHDlghEUdBqtcjOzg5q\nDlhVVQW32x00bZPv71K/ft/+/aG2SYS8vDyYTCa0tLTA6/XC6/WipaUFJpPJb3qdmpoa1NTUxLxd\nvI6/YsUKAF83+5SmepSWE6lNpuUEIooNcwSRMtatW4eysjK/h1OpoKmpKagljdvtRk1NjZw/pPtk\nNWAlAlEUHA5HyKfijY2NWLdundz8Hhhsiu/7e3Z2tt+/odbx/TneKioqMG/ePGRnZ2Px4sUoLS2N\najCi4W43nP2UlJSgo6MDnZ2d0Gg0aGlpQUdHBwfaIdXKxJxARNFjjiBSRkNDg9IhxKy3tzeoC6/b\n7cb+/ftRW1sLURRhNptRVlaG+vp6haL0pxHZ7odUpry8HADQ1tamcCRf27t3L8aNGxf0FH7v3r2Y\nOnUqm89lCI1Gg7a2Nuh0OqVDySjt7e0oLy9X1eeMOYEAdV6vMoUa84Iv5ggKJdH3EbxPGZRK3Vu8\nXi+MRiPq6uoAfB1zT08PiouL/dZV4rzCXefYEoFoCBaLBQUFBSGb8efm5sJsNisQFREphTmBiCJh\njqBMUF9fD41Gg6amJrjdbr+m+F6vV26eLw0sKnXBcbvdsFgs0Gq1AAbHD5G6+kiDeFoslqBlbrcb\nNptN3k7af1VVFfbu3TtkvG63W45Zq9XCbrdHfT6J1NzcjOXLlwctD6xAkLo3GQyGpMQ1FA6sSDSE\n9vZ2HDp0CJdffrnfDcHevXvR2dnJJn5EGYY5gYgiYY6gdFdfX4/S0lKsWrVKfpLua/Xq1WhsbITL\n5cKRI0eQn5+PgwcPoqGhAUuXLpXHK+jt7YUgCOju7sZFF10EALj++uuxaNEizJw5E/n5+QAGuyj4\nduHp6elBRUUFFi5ciNWrV2Pq1Kno6+tDQUFByHjdbjeWLl0KnU4HURRht9sxd+5cOBwOFBYWDnk+\ngaKtYBiqxYDdbsesWbPkQUzDcTqdaGpqAgAsXrw4qmMnGlsiEA2htbUVJ598MtavX+9Xo/ree+/x\nRoAoAzEnEFEkzBGU7qqrqzFu3DgAgzNqBT5JnzhxIvR6PXJycuSKtMbGRgDwmzGhsLAQwNdP3Rsb\nG+WfA7fz/UIurTN+/Hjo9XoAiDiQot1uh81mw6JFiwBAHnNry5YtUZ1PoJFOpQoMVmzs27cvqMVB\nIKfTifz8fLm7g1oGjGRLBKIhjB8/HosWLcKiRYtScrAWIoov5gQiioQ5gtKdXq+Xu+ZceeWVyMnJ\n8fvSXFtbC2DwC/DmzZsTGotUEVFdXR12OtX29nYAwS0I6urqUFtbO+T5JMLWrVujqlTMy8uDKIro\n7e3Fli1bUF1djVNOOUXxCkm2RCAiIiIiIqKo3HbbbRAEAWVlZcjOzg45Y0BTUxOWLVsWcpaSZJOe\n3odrLRDN+fgKnLo13CtSPJdffnlM51BYWCh3ZQicyUEJbIlAREREREREUSkoKIDVakVvby8aGxtR\nXV0NAHJLAIvFgsrKSvT394ccYDQRpG4NkezduzfkuAlDnU+gkbZSkAaHDEWj0YTdf7gxH5TAlghE\nKSpwdFsiokDME0Q0FOYJipVGo4HX60VhYSEaGhrgcDjkL94AUFZWBgBJqUCQZmaYN29e2HVMJhOA\nwfFKpFkOpNkagKHPJ94ijZ8QqYJCil0NM7ywEoEoRa1btw5lZWWqGWAlWtKUPIGkaXukqXcsFosC\n0RGll1TME729vWhqapLzARElVqrkCbfbjZqaGrmpOO8TlGU0GuXpFydMmOA3o4HUhcHpdPpNv+h2\nu+WpHgH4faH3XSfcMon0t/d6vWhtbYUgCPIxQ203f/58AINjIGRnZ0Oj0SA3NxelpaVRnY8StFot\n6uvr5ZikWSMMBoM8QKSSWIlAlKJScbCm3t7ekP246uvrodVqUVtbC1EUUVtbi7KysiH7pBFRZKmW\nJ+rr61FTU4MzzjgDmzZtSvjAVkSUGnnC7XZj//798n2C2WzmfYLCli9fjs2bN0Oj0WDz5s1+Tf+l\ngRWbmpqQnZ0Ng8EAvV6PI0eO+E3VmJ2dDQB+y6SfQy2TnHvuudBqtcjOzkZeXh5aW1tDriv9nJOT\ng/7+fhgMBgCDXR8Cu1pEOh8lVFRUoLq6Gvn5+dBoNGhubsZPfvIT+f9WaRqRV2hSmfLycgBAW1ub\nwpGon/SULhU+xlINqjRFjW/Moc5Do9FAEAS/qYCUptFo0NbWBp1Op3QoGaW9vR3l5eUpUc7VKFXy\nRFVVFSZOnIjq6mqMHz9e6XCiwuuVcpgX4kvteaKnpydoKjy1xxxKou8j0v0+JRX/5qku3HWOLRGI\nhlBfXw+NRoOmpia43W6/5rVer1duni/NA+3bDMu3j6HNZoNGo0FVVZXcNMlisQQtc7vdctN+4Ovm\n/1VVVX5NwsKR+nhJ3QLsdnvU55NIzc3NYefdlZqM9fT0AID8f6GW2laioTBPjExNTQ2Awc98qlQg\nEMWKeWL4AisQpGbw0pNlIkoykUhldDqdqNPplA5DFEVRNBqNYn9/vyiKoujxeESDwSD6fmz0er0I\nQHS5XGJ/f78IQNTr9aIoiqIgCCIAEYDocDhEURTF7u5ueZ3u7m5RFMWg7aRtAMjreDwe+Vh9fX3y\n8aX1JC6XSxQEQTSbzaIoimJHR4ff8Yc6n0C+sUR6DaWjo0M+l3DbSLF0d3eLZrNZdLlcQ+432QCI\nbW1tSoeRcdra2qIqZ0phnhhZnnA4HCIA0Wq1iiaTSQQgCoIgdnR0RPPfryg1Xa8yjdrzQiDmifjc\nT0jnKR3P9xxSQaLvI9L5PsXlcsnlRI33iOkq3HUudbIvZQw13ZQFJiopgUkMBoN8sZbW930/1EUx\nmmWh1pFutI1GY9j1zGZzyH0bDIaozicRXC6XaDKZwsbsS7qxMRgMosfjSWhcw5HOF2c1U/uXBeaJ\nkTEajX5fTny/5EhffNRKTderTKP2vBCIeSI+pIoS6eV7DqmAlQjDN5wKJxq5cNc5dmcgikCv1yM3\nNxcWiwVerxc5OTl+/bBqa2vR0NAAp9OZ8MF9CgsLASDilDPt7e0AIDeHlJoWSuMQDHU+ibB161ZU\nVFQMuV59fT1mz54Nj8cDAFi8eLHcXJFIzZgnRkaKVYp9/Pjx8nzfLS0tCTsuUTIxT8RHXl4eRFGE\nw+GAwWBAdXU1mpqaEn5cUp4YZkpEUkiyajGIoqWmJzt9fX1+zQhD1XibTCZREASxr68voU8Oolkv\n3HaxnE+o4w31CsdqtcrNHSPFKD3xkFofSP+Xvi0Y1ABpXMOvZmp/4sg8MbI8Ee15qJGarleZRu15\nIRDzRPy6M/jGkAp5wlei7yN4n0Lxxu4MlDLUeFPmcDjk5rW+F0rpy6/0RTkZF/1omjsO1Ucw3PnE\nW7Q3C4G/ezweVd4Y8OKsjFT5ssA8MTzSMQK7MAGDYyOomRqvV5kiVfJCIOaJ+FLjvUIkrESgVMPu\nDETDoNFo4PV6UVhYiIaGBjgcDr/mf2VlZQDgN89sokgjKc+bNy/sOiaTCQDQ2toqdwWQRlcGhj6f\neBMDmp6JPs3PfH8WBMFvO2l09sDlRGrEPDEypaWlAIB3331XXibFla7TlFHmYZ6IPykus9mc1OPS\n1wJnDlEz3645yZqZTA0Sdd6sRCAagtFolKdLmjBhgjwdIfD1l1yn0+k3XZLb7ZanZgLgdwH2XSfc\nMonFYpG3b21thSAI8jFDbTd//nwAg30Ws7OzodFokJubK9+kD3U+SlmxYgWAr89XmupRWk6kdswT\nw1dSUgKDweA3pd1TTz0FQRCwaNGihB2XKNmYJ4ZPq9Wivr5ePp7X64XRaITBYGCeUNC6detQVlYG\nm82mdChRC3yoBUCeClWr1Y74XOK5L1/SFK2+3G43ampq5AoC6XPuK9T5xkXS2kIQRUlNzUOBwdGH\npdHDA5vqSSMcGwwG0eVyyaMrB44eLH3UYl3mcDjkPocmk8mvuW+o7UTRf+ojKZZozycZAuOVdHR0\nyE0i9Xq9Kqd3A5sJKkLtzZaZJ+JDmt4x1HmolZquV5lG7XkhEPPEyFitVr8YjUaj6mdvCSXR9xFK\n3KeEu69Tm3Bxms1mURAE0ePxyLMDDXdMrnjuy5eUH3zjd7lcfp8BqUtUuM/icP9O4a5zmv/bKZFq\nlJeXAwDa2toUjkQ5Uk0jP57qotFo0NbWxibWSdbe3o7y8nJ+HgIwTyiP1yvlMC9Eh3lCXRJ9H6HE\nfUqqlLFQcTqdTuTn56O7uxvFxcUAgN7eXkyfPh0Oh0OeySQa8dyXL6nVjTQzihR/T0+PfJxI5xjN\ne5GEu86xOwMREREREVGG6OnpCdtXvr6+Xl7mdDrh9XrlpvQajcav61mgUPsL1x9fGmNDo9FAq9XC\nbrcn5mQj6OrqAgBMnjxZXjZp0iQAwO7duxXbl6/m5mYsX748aHlgBYLU1clgMAz7WLFgJQKRykTq\n00hEBDBPENHQmCconOLiYnR0dAAY/NLp+3R61apVMBgMcDgcyMvLw+rVq1FZWQmXy4X+/n7U1dVh\n3bp1IffrcrmClvX39wctc7vdWLp0KaZMmQJRFLFixQrMnTsXvb29IfcbWOER7hWrzs5OAP4Dmubk\n5ABAzOMZxHNfErvdjlmzZsn7CcfpdMpjkixevHhYx4oVKxGIVCY3Nzfkz0REEuYJIhoK8wRFIg1q\nW1dXJz/FBr5+oi01v584cSL0ej1ycnLkL8iNjY0h9xnqy26oGUfsdjtsNps8KGZJSQkAYMuWLSH3\nK4aY7SvUK1bhzgOI/Yt/PPcFDFa07Nu3L6jFQSCpG4XU3SFZg1yyEoFIZUaaEIko/TFPENFQmCdo\nKNdccw0A4LnnnpOXvf766/JyAKitrUVDQwOcTqc8xedItbe3Awju6iB9ESZg69atqKioGHK9vLw8\niKIIh8MBg8GA6upqNDU1JTw+ViIQERERERFlmMLCQgiCIH+pB4CXX345aBDApqYmLFu2TJ4WdKSk\np+XRtiZIVHeGSOej1+sV25fNZsPll18e0zaFhYVyV4bKysqYth0OViIQERERERFlIJ1OB5vNhp6e\nHjidThQVFfm9b7FYUFlZiU2bNqGgoCCux967d29U6yWqO4P0xd93zBCn0wkAuOCCCxTbl1arRX5+\nftiBKsOJ998nElYiEEXB7XbDYrFAq9UqHcqQRlorm6oy9bxJPZgnlJWO50Tph3lCWel4TiMljUfQ\n0tKCrq4uXHLJJX7vl5WVAQg9tsFwmUwmAEBra6s8BoM0W0MySU/79+/fLy97//33/d5TYl+RKkgi\nVZZI/5dmszmm4w0HKxGIorBu3TqUlZUlbbCSeAhVK2uz2aDVaqHVauN6LtLUPxKv1xu2qZnFYol5\n/16vFz09PWhqagp748U+n6Q05gl/TqcTVVVV0Gg0qKqqiuv0XYE5B2AOoNTAPOEvmut7tKLJOcwT\nwXJycmAwGNDY2IgDBw5g/Pjxfu9LT9idTqdfywG32x1yBhCp6b60bk9Pj7xOVVUVAGD+/PkABsdA\nyM7OhkajQW5uLkpLS+N9ehHl5eXBZDKhpaUFXq8XXq8XLS0tMJlMfpUm0lSU4WaPiPe+oqHValFf\nXy+3dvB6vTAajTAYDPKAlQklEqmMTqcTdTqd0mEEASCmwkcmXJxms1kUBEH0eDyix+MR9Xq9aDKZ\nRnw8h8MRdMzu7m55WeDL5XLFfAyDwSAaDIao/gaJ/DsBENva2hKybwqvra0tJT57osg8IfF4PKLV\napV/NpvNIgB52UiEyjm+kvk3UOv1KhOkUl4IxDzxtViu75HEmnOU+hsk+j5iuPuX8mpfX1/Y9wwG\ng+hyuUSDwSDq9Xqxv78/6B5PFEWxv79fFATB7/9fEATRbDb73QP29/fLf3tpf4kU6W9utVpFAKIg\nCGJHR0fQ+9I5C4Iw5HHiua9I8UvHkV5Go1Hs7u6OevtohbvOqT+DUcZR601ZKl/0pUTvm1yki4LD\n4Rj2sTweT8iLv9lsDroYSBeekWAlQmZKpS8LzBODQt24x+P/JlzOifdxoqXW61UmSKW8EIh5Irpj\nxSLWnMNKhMwTj795rF/8k7WvaMS7EoHdGSit9fT0hO3/JjUn0mg0cDqd8Hq9chNZjUaDmpoav2Za\nvsINdBKqj53Ux0uj0UCr1ca1SW+0urq6AACTJ0+Wl02aNAkAsHv37mHvt7m5GcuXLw9aXlJSEtR3\nzm63+00ZRKQWzBOD4pknwo1SHesI1YHC5RyiRGOeGJSo+4mRSlTOIZL09PRg7dq1qtuXUliJQGmt\nuLgYHR0dAACDweDXD27VqlUwGAxwOBzIy8vD6tWrUVlZCZfLhf7+ftTV1WHdunUh9+tyuYKW9ff3\nBy1zu91YunQppkyZAlEUsWLFCsydOzdsP6hEMIv3KgAAIABJREFUTWHT2dkJwH9QnJycHAAYdl9G\nu92OWbNmyfvxFWpZZ2dn0JRBRGrAPDEoEXlCIg32NG/evGHvI1LOIUo05olBicwT8RSPnEMksdvt\nOPXUU1FcXKyqfSmJlQiU9kpKSmAwGFBXVydfVICvLzDSF9uJEydCr9cjJydHvjg2NjaG3Geom9hQ\no9ba7XbYbDZ5gBNpBNwtW7aE3K+YoClswp0HMLyLvtvtxr59+6JOgL29vZg9e3bMxyFKFuaJ+OcJ\nX6+//joEQQga9TtaseYcokRgnkhsnoinkeYcSl/DqUArKSmJ2/SJ8dxXNBI1EwkrESgjSM3on3vu\nOXnZ66+/7te8vra2Fg0NDXA6nXGbYqa9vR1AcNPEurq6uOxfKVu3bkVFRUXU62/ZskW+4SFSK+aJ\nxHnooYewdu3aoFG/oxVrziFKFOaJ1DDSnEPpZ6QVaKkqUefNSgTKCIWFhRAEQb4IA8DLL78c1Ly+\nqakJy5YtC9u3LlZSrXy0tf+Jan4Y6Xxi7S9os9limu9W6gfKJsikdswT8csTviwWCwRBGHYrglhz\nDlEiMU8kJk/E00hzDhENjZUIlDF0Oh1sNht6enrgdDpRVFTk977FYkFlZSU2bdoU92ZGvvPqRpKo\n5ofSRd93YCdpXtkLLrggpn1ptVrk5+eHHQwqEAdUpFTCPBGfPCHp7e3F3//+9xG1Iog15xAlGvNE\nfPNEPMUj5xDR0FiJQBlDak7f0tKCrq6uoH5yZWVlAEL3RRwuk8kEAGhtbZX7TEqjKyeT9BRv//79\n8rL333/f771oRboJCXVDwgEVKZUwT8QnTwCD5/DSSy+htrZWXtbb24uqqqqY9hNrziFKNOaJ+OWJ\neIpXziGiobESgTJGTk4ODAYDGhsbceDAgaB+clLtutPp9Kvpd7vdfjXu0s9Ssz1p3Z6eHnkd6YI1\nf/58AIN9FrOzs6HRaJCbm4vS0tJ4n15EeXl5MJlMaGlpgdfrhdfrRUtLC0wmk99NjjR1VLjRnmM1\n1ICKsRwv1CBWRPHGPBGfPCGNJF9dXe3XgmD69Ol+o6XHO+cQJQPzRHzvJ4a6vscz5xBRfIxROgCi\nZLrmmmtQV1cXsk9fbW0tbDYbmpqasHz5chgMBhw8eBBHjhxBfn6+vF5ubi5EUcSaNWtw4MABTJ06\nFVarFYIgQBAE6HQ6+SlFTk4O+vv70dTUhLq6Ouj1eqxZsyauTyeiVVFRAZvNhuzsbAiCgBUrVgQN\ndujxeKDX61FTUwOr1TriY27ZsiXinO7RHi+wyXJ2djYAPoWkxGCeGHmeWLduXdiR2qdOnRrTvojU\niHkiPvcT0Vzf45lzMkF5eTmeeeYZpcOgNLF582bodLqg5RqRd+GkMuXl5QCAtrY2hSNJTdIFeSQf\nba1Wm9Qb+ngeLx7nH2nfbW1tIZMpJU57ezvKy8tZaRRHassTqZIDAvF6pRzmhcRjnoi/RN9HrF27\nFu+8805C9k2Za/HixUEVpmyJQER+enp6sHbt2rQ9HhGNXDw/t8wBROmJeSL57r33XqVDoAzBSgQi\nktntdpx55plxH01aLccjopGL5+eWOYAoPTFPEKU3ViIQpanhNNUL7NOYaPE8Hqd6I4qd0nmCOYBI\n/ZgniCgQKxGI0kym9g/N1PMmGo50/Lyk4zkRKSkdP1PpeE5ESuAUj0REREREREQUFVYiEBERERER\nEVFUWIlARERERERERFFhJQIRERERERERRYUDK5Iqtbe3Y2BgICnH+vzzzzF27FiMHj06KccjouFZ\nuHCh0iHExcDAALKyspQOI2mOHj2KMWPS83Zj8+bN0Ol0SoeR0dIlL1DiHT58GCeccILSYRClhdG/\n+tWvfqV0EES+xo4di6NHjyblWAcPHsSOHTswMDCA3NzcpBwzmd5//3288sorOOecc5QOJS1MmzYN\nVVVVOOmkk5QOJaOcfvrpOHDgQFqMqn3kyBG88MILOPHEE3HKKacoHU7COZ1OdHV14ayzzsKoUenX\n+PE73/kOysvLMXXqVKVDyTjplBfUxuFw4MMPP8QZZ5yhdChxc+zYMbz44ov4z3/+g0mTJimWj3gf\nQelCIzL7UoZqbGzErbfeiiuuuAKtra1peUPf3t6O8vJy3mQRqYAoirjqqqvw9ttvw+FwpGXOCfTv\nf/8b3/72t3HjjTfCaDQqHQ4RRaG8vBwA0NbWpnAk8bV9+3Zcc801+MY3voFnnnkGZ555ptIhEaWs\n9HssQDSEL774AjfffDNuueUWrFmzBs8880xG3MwTkbI2btyIF154AW1tbRmTc04//XSsX78ev/3t\nb9Hb26t0OESUwebMmYNdu3bhyJEjuPDCC7Fr1y6lQyJKWaxEoIzywQcfoKSkBBaLBX/605/wq1/9\nChqNRumwiCjNvfnmm/jlL3+JmpoaXHTRRUqHk1QVFRUoKipCVVUVvvrqK6XDIaIMdvbZZ6OrqwvT\np0/HnDlz0N7ernRIRCmJlQiUMXp6ejBjxgwcPHgQPT09mD9/vtIhEVEG+Pzzz6HT6TBjxgzceeed\nSoeTdBqNBg0NDdizZw+ampqUDoeIMtwpp5wCq9WKZcuW4brrrsOdd97JCk6iGLESgTJCc3Mz5syZ\ng/PPPx+7du3Cueeeq3RIRJQhbr/9drz33nt44oknMnYWmGnTpuG2227DmjVr4Ha7lQ6HiDLc6NGj\n8cADD+Dhhx9GfX09FixYgE8//VTpsIhSBisRKK0NDAzglltuQWVlJaqrq2G1WpGdna10WESUIf78\n5z/j97//PX7/+98jPz9f6XAUddddd+Hkk0/GqlWrlA6FiAgAcOONN6KjowNdXV24+OKL0d/fr3RI\nRCmBlQiUtlwuF0pKStDa2orNmzejrq4uLacYIyJ1+vDDD7FkyRIsXrwYZWVlSoejuJNOOgkbN25E\nW1sb7Ha70uEQEQEAZs2ahd27d0MURRQVFWHnzp1Kh0SkevxGRWlpz549mDFjBj788EN0d3djwYIF\nSodERBlEFEXccMMNOPnkk7Fp0yalw1ENrVYLQRBwyy234IsvvlA6HCIiAEB+fj527tyJmTNnYu7c\nuXjssceUDolI1ViJQGmnpaUFl1xyCb773e9i9+7d+O53v6t0SESUYX7729+io6MDbW1tOPnkk5UO\nR1U2btyIAwcO4De/+Y3SoRARyU466SQ8/fTTWLlyJW666SbcfvvtOHbsmNJhEakSKxEobRw9ehS/\n+MUvcOONN+LWW2/Fn//8Z0yYMEHpsIgow/T29mL16tX41a9+hQsvvFDpcFQnLy8Pd911F+699168\n8847SodDRCQbNWoU7r33XjzxxBPYtGkT5s+fj08++UTpsIhUh5UIlBb+/e9/49JLL8UjjzwCs9mM\n++67L2NHQSci5Rw+fBg6nQ7FxcVYvXq10uGo1m233YaCggL8/Oc/VzoUIqIgOp0O27dvxxtvvIGZ\nM2di3759SodEpCqsRKCU98Ybb2DGjBn417/+hZ07d+Laa69VOiQiylDV1dX44IMP8Pjjj7MiM4Ix\nY8agoaEBL774IiwWi9LhEBEFufDCC7F7926MGzcOxcXF2L59u9IhEakGKxEopT3xxBO4+OKLUVBQ\ngN27d6OwsFDpkIgoQ1mtVjQ2NqKxsRF5eXlKh6N6M2fOREVFBVauXAmv16t0OEREQc4880zs2LED\nP/rRj/DjH/8YJpNJ6ZCIVIGVCJSSjh49ilWrVmHx4sW45ZZb8Pzzz+O0005TOiwiylAffPABlixZ\nghtuuAELFy5UOpyUsX79ehw9ehR33nmn0qEQEYV0wgkn4Mknn8Sdd94JvV6PW2+9FUePHlU6LCJF\nsRKBUs5HH32EK664Ao2NjWhra4PRaGSzYSJSjCiK+NnPfobs7Gz87ne/UzqclHLqqaeivr4ejY2N\n2LNnj9LhEBGFpNFosG7dOjz55JNobm7GT37yE3z88cdKh0WkGFYiUErp7e3FjBkz8M477+DVV1+F\nTqdTOiQiynAbNmxAZ2cn2tvbcdJJJykdTsq57rrr8MMf/hB6vZ7TqRGRqpWWluKVV17BW2+9heLi\nYuzdu1fpkIgUwUoEShkWiwUzZ87EN7/5TezZswfnn3++0iERUYb7y1/+grVr1+Luu+/GD37wA6XD\nSUkajQYNDQ34+9//jv/+7/9WOhwiooguuOAC7N69GxMmTEBxcTFefPFFpUMiSjpWIpDqHTt2DHfc\ncQd0Oh0qKirwwgsv4PTTT1c6LCLKcNJ0jrNmzcLtt9+udDgp7dvf/jaqq6tRU1ODAwcOKB0OEVFE\nkyZNwvbt2zFv3jzMmzcPGzduVDokoqRiJQKp2n/+8x/85Cc/wcaNG/HYY4/hoYcewpgxY5QOi4gI\nK1euhNvtRktLC8dliQODwYDTTz8dt956q9KhEBENady4cWhtbcXdd9+NFStWQK/XY2BgQOmwiJKC\nlQikWm+++SaKiorw1ltv4ZVXXsHPfvYzpUMiIgIAPPPMM/jDH/4Ak8mE//f//p/S4aSFcePGYdOm\nTXj66aexbds2pcMhIhqSRqPBmjVr8Mc//hFtbW348Y9/jI8++kjpsIgSjpUIpEpbtmzBzJkzMWXK\nFLz22muYMWOG0iEREQEADhw4gKVLl2Lp0qVYsGCB0uGklSuuuALXXnstli1bhsOHDysdDhFRVObP\nn4+dO3fif//3f3HhhRfirbfeUjokooRiJQKpyldffYW1a9di4cKFuP766/HSSy8hJydH6bCIiAAM\n5qjrr78ep512Gh566CGlw0lLGzZswEcffYS6ujqlQyEiitq0adOwe/duTJo0CRdddBFbVFFaYyUC\nqYbH48FVV12FDRs2oLm5GZs2bUJWVpbSYRERyYxGI1555RW0t7fjxBNPVDqctDR58mTcc889MBqN\nfJpHRCklJycHL730EhYsWACtVov6+nqlQyJKCFYikCq89dZbKCoqwl//+ld0dnbipptuUjokIiI/\nr7/+OmpqalBbW4vvf//7SoeT1qqqqjBt2jRUVVVBFEWlwyEiitpxxx2HRx55BPfffz9++ctf4qab\nbsIXX3yhdFhEccVKBFLcM888g+LiYuTk5GDPnj248MILlQ6JiMjPZ599hvLyclx88cWorq5WOpy0\nN3r0aPzhD3/Azp078dhjjykdDhFRzFatWgWr1Yqnn34al156Kdxut9IhEcUNKxFIMV999RXWrVuH\nn/70p9DpdLDb7Zg0aZLSYRERBVmxYgUOHjyIxx9/HKNG8dKZDN///vdRVVWFO+64g6OdE1FKmjdv\nHrq7u/HBBx/ILW6J0gHvhEgRn3zyCa6++mrcf//9aGxsRGNjI8aOHat0WEREQZ5++mk0NzejubkZ\nU6ZMUTqcjHLPPfdg7NixuOOOO5QOhYhoWM477zzs2rUL3/zmNzFr1ixs3bpV6ZCIRoyVCJR0fX19\nKCoqwmuvvQa73Y7KykqlQyIiCum9995DZWUlKisrcfXVVysdTsY55ZRTsGHDBjz66KN49dVXlQ6H\niGhYTjvtNLzwwgsoLy/HT3/6U6xfv57jvVBKYyUCJZXNZkNRUREmTJiAPXv2YObMmUqHREQU0rFj\nx/Czn/0MOTk5ePDBB5UOJ2Nde+21uPzyy6HX6zEwMKB0OEREw5KVlYXGxkY89NBDuOuuu7B48WIc\nOXJE6bCIhoWVCJQUoiji7rvvxtVXX43S0lJs376dzYKJSNUeeOAB7Ny5E+3t7TjhhBOUDiejbdq0\nCfv27cOGDRuUDoWIaESWL1+Obdu2Ydu2bZgzZw4++OADpUMiihkrESjhDh06hAULFqCurg4bN25E\nc3MzjjvuOKXDIiIKa8+ePbjrrrtwzz334Pzzz1c6nIx39tlnw2Aw4O6778a7776rdDhERCNy2WWX\noaenBx9//DGKiorwxhtvKB0SUUxYiUAJ9c9//hPFxcXo6upCR0cHbrnlFqVDIiKK6NNPP0V5eTlm\nz56NVatWKR0O/Z/q6mrk5eVh+fLlSodCRDRiBQUF6OnpwXnnnYcf/vCH2Lx5s9IhEUWNlQiUMM89\n9xyKiopw4okn4rXXXsMPf/hDpUMiIhrSL37xC3z88cd4/PHHodFolA6H/s9xxx2HhoYGPPvss/jj\nH/8oL//ss8/w85//HP/1X/+lYHRERLGbMGECnn32WSxZsgTXXnstfv3rX3PARUoJrESguBNFEevX\nr8dVV12F+fPnY8eOHTjzzDOVDouIaEibN2/Go48+iocffhiTJk1SOhwKMGfOHFx33XW49dZb8emn\nn8JqteKcc87B73//ezzzzDM4dOiQ0iESEcVkzJgx+N3vfofGxkbcc889uPbaa3H48GGlwyKKiJUI\nFFeffvopFi5ciLvuugsPPvggHnvsMYwbN07psIiIhuR0OnHzzTejqqoKWq1W6XAojPr6enz22WeY\nPXs25s+fD5fLJb+3Z88eBSMjIhq+yspKvPDCC7Db7bjkkkvw3nvvKR0SUVisRKC42bdvH2bOnInO\nzk68+OKL+MUvfqF0SEREUZGmc5w0aRKMRqPS4VAYR48eRUtLCz7//HO8+eabAICvvvoKADB27Fj0\n9PQoGR4R0YjMmTMHu3btwpEjR1BUVIRdu3YpHRJRSKxEoLh44YUXUFRUhKysLOzZswdz5sxROiQi\noqjdd9996Onp4XSOKtbV1YVp06Zh9erVOHLkCAYGBvzeHxgYwM6dOxWKjogoPs4++2x0dXXh/PPP\nx5w5c9De3q50SERBWIlAIyKKIh544AHMmzcPV155JV599VXk5+crHRYRUdR27dqFX//611i/fj0K\nCwuVDodC2LlzJ2bNmoV//OMfOHbsWMh1RFFEV1dXkiMjIoq/U045BVarFcuWLcN1112HtWvXyq2u\niNSAlQg0bIcPH4ZOp8PatWvxm9/8Bk888QSOP/54pcMiIoraoUOHUF5ejpKSEqxYsULpcCiMc845\nB+eccw5GjYp82+LxePDOO+8kKSoiosQZPXo0HnjgATz88MPYsGEDFixYgE8//VTpsIgAsBKBhund\nd9/FzJkz8eKLL+L555/HypUrlQ6JiChmy5cvxyeffILHHnuM0zmqWE5ODt566y1UVVVFXG/UqFEc\nF4GI0sqNN96Ijo4OdHV1YdasWejv71c6JCJWIlDsOjo6MGPGDACDI2HPnTtX4YiIiGJnsVjw+OOP\n45FHHsEZZ5yhdDg0hDFjxmDjxo145JFHkJWVhdGjRwetM3r0aFYiEFHamTVrFnbv3g0AKCoq4vgv\npDhWIlBMNmzYgCuuuAKXXXYZdu7ciW9+85tKh0REFLP+/n5UVVXhlltuwVVXXaV0OBSDG2+8ETt2\n7MCpp56KrKwsv/cGBgawY8cOhSIjIkqc/Px87Ny5EzNnzsTcuXPx6KOPKh0SZTBWIlBUPv/8cyxe\nvBh33HEH7rnnHrS3t+PEE09UOiwioog++OADHD161G/ZsWPHcN111+HMM8/EAw88oFBkNBLFxcVw\nOBwoLCzEmDFj/N576623cPjwYYUiIyJKnJNOOglPP/00Vq5ciSVLluD2228PO9gsUSKxEoGG5HQ6\ncfHFF2Pbtm149tlncccdd7DvsEppNBq/V3l5ecjl99xzj8KREiXef/7zH0yePBnf+MY3sG/fPnn5\nvffei9deew1tbW0cDDaFTZ48Ga+++irKy8v9rknHjh3Da6+9pmBkRKnjtddeC7pHaG9vR3t7e9Dy\nf/zjH0qHSxgc++Xee+/FE088gU2bNkGr1eKTTz5ROizKMKxEIGzatAmdnZ0h3+vs7MSMGTMwMDCA\n3bt34/LLL09ydBSLs846K6r1Tj/99ARHQqS8559/HgDw4YcfYtq0aXj88cfR3d2Nu+++G/fffz+m\nTZumcIQ0Uscddxwee+wx/Pa3v8WoUaMwatQojotAFIO8vLyo1z311FMTGAnFSqfTYfv27fjLX/6C\nmTNn+lWWSw4dOoQbbrgBb7zxhgIRUjrTiKIoKh0EKeell17CZZddBmCwCei5554rv7dx40asXLkS\nV199NR599FGcdNJJSoVJUXr44Ydx8803R2zaNnr0aHz44YeYOHFiEiMjSr6ysjJs2bLFrztDfn4+\nCgoK8D//8z9sUZVmXn75Zfz0pz+Fx+PB9OnT8Ze//EXpkIhSwqWXXort27eHvXcYPXo05syZg5de\neinJkVE03nvvPVx99dV49913sWXLFsyZMwfAYKus+fPn49lnn8U3vvEN/POf/wzq/kU0XKxEyGBH\njhzBt7/9bfzrX//CqFGjcOaZZ8LhcOC4446DXq9Ha2sramtrsWbNGt5spwiPx4OcnBwMDAyEfH/0\n6NG47LLL8NxzzyU5MqLkOnbsGCZMmIBDhw75Lc/KysLEiROxefNmzJo1S6Hoks9ms6G1tVXpMBLu\ns88+w7Zt2wAApaWlCkdDI/Wtb30L9957r9JhpL3HHnsMS5YswVdffRXy/VGjRuHhhx/GDTfckNzA\nKGqHDx/GDTfcgGeeeQabNm1CZWUl1qxZg9/85jf46quvMGrUKBiNRtx2221Kh0ppgpUIGaympgb3\n3Xef/JQuKysLM2fOxGeffYZ33nkHbW1tmDdvnsJRUqzmz5+Pbdu2BQ0mBwzeCLS2tkKn0ykQGVHy\nvPrqq/jhD38Y8r3Ro0dDFEXU1NTAYDBkxJOZ8vJytLe3Z8QX62PHjmFgYADjxo1TOhQagc2bNwMA\neJuaeJ988gkmTpwY9gFEVlYWDh48iFNOOSXJkVEsRFHE3XffjV//+tdYsmQJmpub/d4/4YQT8M47\n72DSpEkKRUjphJUIGertt9/GtGnTgr5ojho1CpMnT0ZHRwcKCgoUio5G4sknn0RZWVnIG69x48bh\n4MGDnFmD0t6aNWtQX18f9qZYUlRUhF27diUpKuVIg6y2tbUpHAlRdNrb21FeXs5KhCRZsGABrFZr\n0H3hmDFjoNVq8fTTTysUGcVqw4YNWL16ddD1LysrCwsWLIDZbFYoMkonHFgxA4miiKVLl4bsovDV\nV1/hwIEDePPNNxWIjOJBEISQT+CkGwFWIFAm+NOf/hSxAkFqfcBWOUREwHXXXRdyTARpSlxKDe+9\n9x7uueeekF1TBgYG8OSTT2L79u3JD4zSDisRMtCjjz6K7u7uiDfYixcvxt/+9rckRkXxcsIJJ2DB\nggXIysryW37s2DH5aSRROnM6nejr6wv7/pgxY5CXl4fdu3fj1ltvTWJkRETqdOWVV4ac8vb444/H\nlVdeqUBEFKvDhw/jyiuvxKFDhyIOkqnX64dspUc0FFYiZJh///vfWLlyZcTmgaIo4ssvv8T3vvc9\nHDhwIInRUbzodLqgC8TJJ5+MK664QqGIiJLn2WefDTnOwahRo6DRaHDTTTfhzTffxA9+8AMFoiMi\nUp9x48ahtLTU7wFEVlYWSktLOb5IihAEAX/7298iVhAcPXoU77zzDn73u98lMTJKR6xEyDArV67E\n4cOHI64zatQouQbT6/UmIyyKs0svvRTjx4+Xf8/KysK1116LsWPHKhgVUXJYrdagitKsrCxkZ2fD\narXiD3/4A0444QSFoiMiUqfy8nK/L6ADAwNswZhCJk+eDGCwtUGkWdWOHTuGmpoaPiikEWElQgZ5\n+eWX0dbWFrKGUqPRYMyYMdBoNJg1axaam5vx8ccf47zzzlMgUhqprKws6HQ6+YnCwMAAysrKFI6K\nKPE+//xz2O12v6acGo0Gl156Kd5++21cddVVCkZHRKReJSUlOO200+TfTzvtNJSUlCgYEcWitbUV\nH330ER566CF873vfA4Cgrq2So0ePYuXKlckMj9IMKxEyxBdffIGlS5di1Cj/P7mUXL773e/i/vvv\nx3vvvYcdO3ZgyZIlyM7OViJUihPfLg2nn346Zs+erXBERIn38ssv48svvwQwmN+OO+44NDQ0YNu2\nbcjJyVE4OoqV2+2GxWKBVqsd0TrJoqZYiGI1evRo6HQ6jB07FmPHjoVOp8Po0aOVDoticOqpp2LZ\nsmXo7e3F3/72N6xcuRKnn346AP8KhYGBATz11FN4+eWXlQqVUhwrETLE+vXrsX//fhw7dkxOInl5\neVi9ejXefvtt/PWvf8XKlSvlplCU+mbNmoUzzjgDwOBAmYEVSETp6PnnnwcweDP8ne98B3/9619x\n8803KxwVDde6detQVlYGm802onUSxev1+jUbVjKW4ert7UVTUxO0Wm3EJtDAYCVJU1MTNBoNNBoN\nLBZL2H1K62g0GlRVVSUidEqAsrIyfPnll/jyyy/ZgjHFfec738F9992HDz74AM8++yyuueYajB07\nFqNHj5bvCZcsWcJBFmlYNGJAx9EPP/wQt912W9hRPSn1fPbZZ9i2bRsAYOzYscjLy0NeXp5fk7VE\nGz16NB588EH5S228sdyG1tXVhQMHDuDSSy/FhAkTlA5HVb71rW/h3nvvTci+WR6Vs3nzZgDAueee\ni/POOy9lKs8SWR4ByP2a29raEnaMRJG+2EYaEDiadRLBZrNBq9X6HTcRsfT29mLLli2ora2N2z4B\noL6+Hp2dnaioqEBhYSHy8vLCruv1erF48WIIgoCKigq43W4sXboUhYWFQXE1NTWhsrJS/t1qtUIQ\nhJhia29vR3l5eUL/pjabDa2trQnbf6qS8mhpaanCkaiP9BlIhGSUx4GBAfzrX//Cu+++i48++ggA\nMGXKFMycOTOhx6XESGR5HErQ8NV2ux0Wi4WJI41kZWVh0qRJOPvss3HGGWcM+aQhESwWCwRBSNic\n7Cy3oU2fPh0nnngiKxACSDdIifrSxvKonAsuuAAnnXQScnNzlQ4laokuj5QYXq8XTU1NCdu/2+2G\n3W5HZ2cnZs+ejeXLl8d1/1VVVZg4cSJaW1v9BuIN57nnnvP7kpOTk4Pa2lpMnz4dP/rRj/z6zp9x\nxhlJr9AZDovFgs2bNzNXB7j44otx5Mjv8+SSAAAgAElEQVQRpcNQnc2bNyMrKythX9qSUR6zsrJw\n1lln4ayzzsKnn36Kf/zjHxErD0m9El0ehyQGaGtrE0MsJhoRAGJbW1vC9s9yS7FIdHlheaRYJKO8\n6HQ6UafTxbyd0WgUAYgmk0l0uVxBcbpcLnkdQRDEjo4OebnVahUFQRBFURRNJpMIQNTr9WJfX5/f\nPjwej/w+ANFgMIgul0t+X1oeSah1IsVmNpvl2KxWq7xOf3+/3z46OjpEQRBEAKLRaPSLy2AwyMf1\nPb7vz9K+9Xq937aROBwO0WAwiAaDQXQ4HFFtEyuDwSDq9fqYtpH+H3x5PB75/CT9/f3y37G7u3vY\nMar5c0GZKdHlheWRYqF0eUmNdp5ERESUVPX19SgtLYUoili4cCE2btzo977UnH3KlCkQRRErVqzA\n3Llz0dvbi9zcXGi1WthsNvT09KCiogIejwcAMHXqVOzdu1fez+rVq1FZWQmXy4X+/n7U1dVh3bp1\nI4o9UmxLly6Vxy3o6emBIAjo7++HzWbD+vXr5X3YbDbMnTsXa9euhSiKmDJlCnJzc+V+/r5N+EVR\nDHryLu27r68PjY2NEc9JGpCxqqoKb7/9NpYvX47a2loUFhYGres71kCkVzi9vb2oq6vDvHnz5PEN\ntFot7HZ7xP/TUOM8SC0YGhsb/fYPAHV1dbjoooug1Wrhdrsj7puIiFILKxGIiIgoSHV1NcaNGwdg\n8MtiYHN6u90Om82GRYsWAYDcnH3Lli1+X6iLi4vlfej1egD+X0gnTpwIvV6PnJwcuVmt75fS4YgU\nm9VqDYot1HGlGRakdaR9GY3GqJrqS9sVFBQE7TtQbm4uDhw4gPvuuw+LFi2KOJOIVGEx1Cucl156\nST5nqXJnypQpmDt3Lnp6esJuJ/3tfCuAQhEEAR6PBw6HAwaDATabDVu3bo24DRERpRZWIhAREVEQ\nvV6P3NxcWCwWeL1e5OTk+H05bW9vB4Cgp991dXVh9yk9Wa+urpaX1dbWoqGhAU6nE/X19XGJfTix\nBZK+NAfyjT1eXC4XpkyZgtWrV8NisST0yb0Uv/S38K3caWlpCbvd9ddfDwB48MEH4fV6AXzd6sBo\nNPqtO378eHnARZPJlFKzVRAR0dBYiUBERERBbrvtNgiCgLKyMmRnZwd9wZe+GMbyFDycpqYmLFu2\nLG4DRMUjNumLtTSNYbgvzPGQk5ODRYsWoaGhAeeeey42btyImpoa+Zi+RtqdIRSpQiFSa4ni4mJ0\ndHTgwIEDyM7ORlNTkzy6+6WXXhp2u4ULF7ISgYgozQTNzkBERERUUFAAq9WK3t5eNDY2yk+wV61a\n5bfe3r175Sb70fJ9ym+xWFBZWYn+/v64jxI+nNgkhYWFsFqt2Lt3LzQaDQRBgNlslrs1JEphYSEK\nCwvl2RkaGxsxe/ZslJSUBLUGGQ69Xo/GxkZ4vd6gWRmGqsQpKSnxm4Whvr4eBoMh5NgNEt+WDkRE\nlB7SuiVCTU0NampqEr4N0XCxjJKasDySL41GA6/Xi8LCQjQ0NMDhcPg15TeZTACA1tZWuXm72+2O\n2CVB6k8/b948eVlZWRkAxLUCYTixBbLZbLjkkkuwatUqiKIIq9Wa8AoEX6FaJ8SDNH3cu+++Ky+T\n/o9imYbZYrGgs7NzyO4dXq+XUyjGGXM1qQnLY2ZK60oENbPZbNBqtfLo1YnejihWLKOkJiyPyjAa\njXA6nQCACRMm+DXlnz9/PoDBcQays7Oh0WiQm5sb9IVR6g7g9XrR2toKQRD8nnhLPzudTr9B+9xu\nt9/YAOHGCQi1TqTYfNeXvjxL//ruQ6vVytv6vqqqquR1pNilCopQsYTad6yk8QXioaSkBAaDATU1\nNXI8Tz31FARB8KskCXWT7/V60dvbi6qqKhw4cABWq9WvNYPFYvGb5cHpdGLHjh1+rRcovTFXk5qw\nPCZQ4JyPnN888aT5qT0ej+jxeES9Xi+aTKaEbacGAMS2traE7Z/lNr7SvYwmurywPMYXy+PIDWc+\naQCiy+USjUajCEA0Go1B6/T394sGg0EEIOr1erG/v99vewCiw+EQBUEQAYgmk0n0eDx++3A4HCIA\n0WAwiC6XSzQYDPK+pH1Ir3BxhlonXGyh1g+1zDfuwJderw8Ze7T7VgOTySTHFOrvYjAYRIPBIP/u\nu67D4Qi5T6vVKq9nMBjCrhcNtX4uKLx0z9WJLi8sj/HF8phYrERIMummqLu7W14m3YREutgOdzu1\nYCVC6siEMspKhNTB8hgfStxsqO1Lc6z6+vr8KkV8l6fyeaWKdP1cpKtMyNWsREgdLI+Jl9LdGex2\nO7RaLTQaTchmhBaLRZ7nOfB3m80GjUYDrVYrN9UMXCcRurq6AACTJ0+Wl02aNAkAsHv37rhvR8pi\nGWUZVROWR5ZHio7FYkFBQUHIcRpyc3NhNpsViIoyBXM1c7WasDyyPIYUWKuQKk/QpCZzUk2R2Wz2\nay7o2wRRFEW/36VtpNomqVli4Dah+B4j0iscvV4f8n0p5nhvpxbIwJYILKPRbaeETGyJwPIY3XZK\nSMcnrr5N+10uV9KOGy+CIIgmkymoJUJfX58qm7Wmo3T8XESDuTq67ZSQiS0RWB6j204JSpeXlK1E\nCFV4AP8+m4HrhNtmqHXiKdz+o/0wxbqdWmRiJQLLaHTbKSETKxFYHqPbTgnp+GUp2hs9tfJ4PKLZ\nbJZvKIHBPv4dHR1Kh5Yx0vFzEQ3m6ui2U0ImViKwPEa3nRKULi8p250h3JzDQ001RJQsLKOkJiyP\nlEzi4EMK+ZVqxo8fL0+vKJ1DbW0tZxmghGOuJjVheaRwUr4SQZo6qre3FwD8pp9KhMCpnsK9wvGd\n1ipQuA/qSLYj5bCMRrcdJQfLY3TbEREpibk6uu0oOVgeo9suE6VsJUJhYSGsVisOHDgAjUaDmpoa\nmM1mrFq1KqHHDXy6Eu4Vju+c0hJpoJELLrgg7tuRclhGWUbVhOWR5ZGI1I+5mrlaTVgeWR7DGaN0\nAMNls9lwySWXQBCEhBfkeLr88ssBAPv370dOTg4A4P333/d7L57bkXJYRllG1YTlkeUx1dXU1AAA\namtrE7oNkZKYq5mr1YTlkeUxnJRtiaDVapGdnR3UrKWqqgputzto+hHf371er9+/odbx/Tme8vLy\nYDKZ0NLSAq/XC6/Xi5aWFphMJr+ppGpqauSbn1i2I/VgGWUZVROWR5ZHShybzQatVgutVgubzTas\nfTQ1NUVsnpvsdUgZzNXM1WrC8sjyGFbgSItqHFU8FIfD4TdFiO/LdzTlcC9RHHr6kESSpkwRBCHk\naM8Gg0E0GAwxb6dWyMDZGVhG1VtGM3F2BpbHzC2Poqj8KM7pzGw2i4IgiB6PR/R4PKJer495GkiH\nwzHk5yiZ66hBpn4umKvVm6szcXYGlsfMLY9DSdnuDMcffzw2bdoEq9Xqt3zv3r2YOnVqVKNBR7NO\nogiCEPH44ZpeDrUdqQfLKKkJyyNR/DmdTpSVlaG7uxvjx48HMDj41vTp01FUVITCwsIh9+H1erFl\nyxbVrEPKYq4mNWF5pHBSsjuDxWJBQUFByGYlubm5MJvNCkRF9DWWUVITlkdKBXa7HVqtFhqNBvX1\n9UFNXi0WC7RabcjfbTYbNBoNtFqtPAhW4DqJ0NXVBQCYPHmyvGzSpEkAgN27d0e1j+bmZixfvlw1\n65BymKtJTVgeKZKUbInQ3t6OQ4cO4fLLL/cr2Hv37kVnZycqKioUjI6IZZTUheWR1E4aU6C7uxtW\nqxUWiwW5ubny+4Ig+I01sHTpUvn3np4eCIKA/v5+5OfnY8qUKWhoaPBbJ5xoxwUI90Sqs7MTAPw+\nV9JgXDabbcjPlt1ux6xZs+RtlF6HlMVcTWrC8kiRpGRLhNbWVpx88slYv369PMBHTU0N3nvvPRZo\nUgWWUVITlkdSO6m1QHFxMQBg0aJFAAbnIhdFMagpre/v0jbSTW5jY2PQOuGII5xGTDpWKENVYLjd\nbuzbt0+OX+l1SHnM1aQmLI8USUq2RBg/fjwWLVqERYsWoaGhQelwiIKwjJKasDyS2un1+pBfyKur\nq1NqWrFYbN26dcgb8WSuQ8pjriY1YXmkSFKyJQIRERGlD71eD2CwDy4A9Pb2AhhsiZBIgdOWhXuF\nIwhC2PekcwrFZrMNOed4MtchIiKKRUq2RCAiIqL0UVhYCKvVir1790Kj0UAQBJjNZrlbQ6KMdPRt\naawGt9stjzUgDex4wQUXhN0u0mCPGo0GoigmdR0iIqJYsCVCGMkY1Zko3lhuSU1YHilaNpsNl1xy\nCVatWiWPgZDoCoR4kJ7w79+/X172/vvv+70XSqRxF6Sfk7kOZTbmalITlsfUwEqEMNatW4eysrIh\nB0ZSm6amJr+ml16vN2zzTKnZKKWPVCm3Xq8XPT09aGpq4kUijaVKeXS73aipqWFuVJBWq0V2dnbQ\ndaqqqgputztoukff371er9+/odbx/Tme8vLyYDKZ0NLSAq/XC6/Xi5aWFphMJr/RzGtqalBTU5OQ\nGIhGKlVyNe8dMkOqlEen04mqqir5WmW325UOKalYiRBGKg4g0tvbi8rKSr9lb7/9dtj1S0pKEh0S\nJVmqlFuj0Yhnn30WlZWVqr9I0PClQnl0u93Yv38/amtrIYoizGYzysrKUF9fr3RoGcXhcIQcX6Cx\nsRHr1q3zm+4xNzfX7/fs7Gy/f0Ot4/tzvFVUVGDevHnIzs7G4sWLUVpaykEMKaWkQq4GeO+QKVKh\nPHq9XvT29qKhoQEejwezZ8/G3LlzM6pcshIhTXi9XmzZsiVo+bvvvov+/n6/ZowulwsGg4FzRZNi\namtrUVtbq3QYRNi/f7/ftHdSE/rq6mqlQspIxx9/PDZt2hTU7L6vrw+NjY1RTcE43Gka40EQBLkb\nRqgK+mhyXjRxJnMdIrXhvQOpxY4dO+SKb2kWCyDyeDfpRvFKhPr6emg0GjQ1NcHtdgc1xZea50tz\nk0pNEgP7y9hsNrk5iTSokcViCVrmdrths9nk7aT9V1VVYe/evUPG63a75Zi1Wm1Q05VI55NIzc3N\nWL58edDykpISvyaVAGC323HNNdckJa50xXJLasLyOHy+FQjA103iDQZDwo5J/iwWCwoKCoKuVcBg\nCwKz2axAVETxx1xNasLyOHzhZuaJNCtP2hEDtLW1iSEWJ4TRaBT7+/tFURRFj8cjGgwGv2Pr9XoR\ngOhyucT+/n4RgKjX60VRFEVBEEQAIgDR4XCIoiiK3d3d8jrd3d2iKIpB20nbAJDX8Xg88rH6+vrk\n40vrSVwulygIgmg2m0VRFMWOjg6/4w91PoF8Y4n0GkpHR4d8LtFsI/1fJBMAsa2tLWH7Z7lNvXIb\nKtZkSXR5YXlMzfLY398vH8/3HBItGeVFp9OJOp0uoccYLkEQRJPJJP/dJX19faLJZFIoKlJaun0u\nmKtT/94h0eWF5TE1y6N0TACi1WqNepuRUvq6rmglglQ4JS6Xy+/YBoPB7wtv4B801B84mmWh1nE4\nHCIA0Wg0hl3PbDaH3LfBYIjqfBLB5XL53WQNVegdDof8AUwmIH0qEVhu44eVCCPH8hgf0s2O9PI9\nh0RLty9LsfJ4PKLZbJZvJKXy0NHRoXRopKB0+1wwV8cPKxFGjuUxvjo6OkRBEESPx5O0Yyp9XVe0\nEkG6YTCbzRH/0/v7+0Wj0ZjQAhzNer41b4GvWM4nngKf0gyVWA0Gg9+HLFnSqRKB5TZ+WIkwciyP\n8eVwOOQnGMl6Cp5uX5aI4iHdPhfM1fHDSoSRY3mML0EQ5NYVyaL0dV3RSoS+vj6/QhHqyY/JZBIF\nQRD7+voUL8BDJa1ozifU8YZ6hWO1WoOaf0baxuVyyTV2yQakTyUCy+3Iym2k2JMlnSoRWB7jVx59\nY0hm2Uy3L0tE8ZBunwvm6tS/d0inSgSWx/iVR7PZrEjXO6Wv64pWIkgcDodcg+T7R5earkhflJNR\ngKNpujNUX9lw5xNvsRZ8s9ks9x1KNiB9KhEkLLcjp9SNQDpVIkhYHuMrmWUz3b4sEcVDun4umKtH\njpUI8cPyODJS60UlKH1dV7QSAYBfkxOpT4zv+7H8Hu2yUOtItWy+A2IErmcymURgsP+NFLfL5ZIL\n6VDnkwyREqsSAypK0qkSgeU2fliJMHIsj/EnDZCUrPFj0vXLUjpyuVyi2WwWBUFQOpS0l26fC+bq\n+GElwsixPI6c7/F9j5us71tKX9cVn+LRaDTKU39MmDABRqNRfk+aPsPpdPpN/eF2u+VpRoCvp+Ty\nXeY7DUngMonFYpG3b21thSAI8jFDbTd//nwAQF1dHbKzs6HRaJCbm4vS0tKozkdJvb29mD17ttJh\npA2W25GTzj/wZ4ody+PwabVa1NfXy8fzer0wGo0wGAzyvM9EknXr1qGsrAw2m03pUCJyu92oqamR\np2eTPqekLObqkeO9Q/ywPA6f2+3G0qVLUV1dLedZjUaD6dOnY968eQk7rqoE1iooMaq4NGBHqNoc\n/F+tk9SfX6/XB42iLcUb6zKHwyH3nzGZTH41WKG2E0X/KcCkWKI9n2QIjFei1ICKEqRZSwSW25EJ\njDNcuU2UdGuJwPI4fFar1S9Go9GY9MGR0u2Ja7pLdr6Klcvl8ivDUrNkJe5JRiLdPhfM1SOn9L1D\nurVEYHkcPt/ZhAJfyZoiWunrukYURRE+2tvbUV5ejoDFaUWj0QBAWp+j2mg0GrS1tUGn0yVk/yy3\nFItElxeWR4pFMspLeXk5AKCtrS1hx8gUai/7PT09KC4u9lum9phD4eciPlLxb69WiS4vLI8UC6XL\ni+LdGYiIiIh81dfXQ6PRoKmpCW63W77xBAabvzY1NcnNR2tqavyaz1osFmi1WgCAzWaDRqNBVVWV\n3MzVYrEELXO73bDZbPJ20v6rqqr8mvKG43a75Zi1Wi3sdnvU5xNvgRUIUnNjg8GQsGMSEVFmGaN0\nAMkW2M8mJydHwWiIosNyS2rC8kiJVF9fj9LSUqxatUoeo8LX6tWr0djYCJfLhSNHjiA/Px8HDx5E\nQ0MDli5dKo9X0NvbC0EQ0N3djYsuuggAcP3112PRokWYOXMm8vPzAQANDQ3Izc2V99/T04OKigos\nXLgQq1evxtSpU9HX14eCgoKQ8Up9Y3U6HURRhN1ux9y5c+FwOFBYWDjk+QSKtoIhmid5TqcTTU1N\nAIDFixdHtV9KH8zVpCYsj+kl41oi+N4o+P5MpGYst6QmLI+USNXV1Rg3bhwAYPz48Vi+fLnf+xMn\nToRer0dOTg7y8vIAAI2NjQAAq9Uqr1dYWAjg6yfzjY2N8s+B2/l+IZfWGT9+PPR6PQBEHEjRbrfD\nZrPJA3GWlJQAALZs2RLV+QQSB2fOGvI1FKfTifz8fNTV1Q15DpSemKtJTVge00vGVSLEehEmUgOW\nW1ITlkdKJL1ej9zcXFgsFni9XuTk5PiVs9raWjQ0NMDpdKK+vv7/t3f/0W3V9/3HXzc/StsDjeD0\n2CPJTNmP5BQCzuAcL7Q9pCeUhWVHCn9ghp3DaA+JJ58TVkp8uhDkGWr39JTZI1sp9ixI63m2tcEK\ntcavNjaYcmyfLClWT7OOcJZMAnJibYdjrZxTIM7u949878WyJVuyr3SvpOfjHJ04V/fH+1pvS/e+\n9flR0FisQkRLS0vWdQYHByUpbYRuSfbN+1LnUyg1NTUyTVNTU1MKhUJqaWmxWyWgMvBeDS8hH8tL\nxRURAACAd33jG9+Q3+9XQ0ODfD5fxkJBOBzW/v377SnB3GR9w5+ttUAu5zPX3GLEYo9c1dbW2l0Z\nmpqalnmWAAB8rOLGRAAAAN61adMmDQ8PKxaLqaenx24FcODAAUkXB0ZsampSPB63uyUUmtWtYTGn\nTp3KOG7CUuczXyG+ocs2ngMAAMvhiZYI80dT9rLlfhNQ6ir1vBdD3rqrHM9pJchHd5XjObnFMAyl\nUinV1taqu7tbU1NTad0JGhoaJKkoBQRrZoZdu3ZlXae3t1eS1N/fb8+EYM3WIC19PsVgxTU0NFTU\n42Ih3qvdVY7ntBLko7tK+Zw8UURoa2tTQ0NDSQ36k6k/jzU9VCAQcPRcrKmmLKlUKmvzxkgksqxj\nLBU7/ZcWIm8XcmpfqVRKk5OTCofDWT/YyMl05ONCTu0rmUyqtbV10fdZ8tFZnZ2d9vSLl19+edqM\nBlYXhkQikTb9YjKZTBv9e+4N/dx1si2zWK9vKpVSf3+//H6/fcxM2+3evVvSxTEQfD6fDMNQdXW1\n6uvrczofpwUCAXV1ddnHs2aECIVC9uCPcA/v1ely+bzPB9ez+SEfF3JqX4lEQs3NzfZ0wfOn/pVK\nPB/NeQYGBswMiwtOkivHzVe2OIeGhky/32/OzMyYMzMzZjAYNHt7e1d8vKmpqQXHnJiYsJfNf0xP\nT+d9jHxiX+7rJMkcGBjIe7tckbeLK0beOrmvUChkhkKhnH6/y3kNCp0v5OPiSikfp6enzYmJibT9\nSjI7Ozszru/FfDRN02xsbDQbGxsLegynWJ9lnZ2dGX/X1udiKBQyp6enzVAoZAaDQTMejy/4TLT2\nl8+yqakp0+/3m5LM3t5ec2ZmJi22+duZpmnG43H7PcuKJdfzcdrw8HBajJ2dnWk5XCrK+e+C9+qP\n5fN5v5RCX88WOl/Ix8WV0rXDzMyMOTw8bP9sXTtYy+bzYj4uhSJCnjLFaV24zP2Qti5ypqamln2s\nmZmZjG+sQ0NDaRcopmnaF1L5yjd2igjpyFvn97VU3MtZZz6KCO4qpXzMdPO12O/Zi/lomu5fbJSC\nUvn7qRTl/HdRKrlWzGvelf5OinE9SxHBXaV07ZCpWOD0tYPbn+sr6s4wOTmZtS9HV1eXvSyRSCiV\nStnN8g3DUGtr64ImhJZM+8vWX8Tqd2gYhgKBQMamIoU2Pj4uSVq/fr297Morr5QkHTt2bNn7ffLJ\nJzPOJ71jx44FfUFHR0d1xx135H2MQsXuZeTtRU6+9pWYR04hHy/yaj5u27Yt7f9WE/lQKJTXfgCU\nNt6rL/Lq571X4yoU8vEir147ZJs5KJdBekvG/KpCvpXfkZERu1nhfKFQyK7cBINBu0mfVekJBoP2\nuppTgZmens7YVHD+sunpadPv95tDQ0NpsWSrFlnbL/VYTKZ1rHPLtK7f7190f9mMjIzYlbBc4pr7\nu8xHvrHnEksm8lhLBPLW2bwtxN+Atf1yzm0pXmuJQD6WRj7ObbL+5ptvZlzHi/lomu5/Y+F1c/9e\nltMtEM7z4t8F79Xuft4vphjXs15riUA+lsa1g2lebF0u0Z1hAeuiam6/Qasp/tx1siVsLv/PtMzq\nXzJ/neU0689VLnEttXwp09PTaf1vltrP1NSU/Uecr3xjX+45Sd4qIpgmeevka+x0vuSzvRdv2sjH\nxZViPs7vb8+YCOUlnwtZFIdX/y54r3bv83452zsZr9eKCKZJPnr92sEyMjJij7WQz7EX4/bnuiOz\nM1jN6F988UV72YkTJ9Ka17e3t6u7u1uJRMKe9milBgcHJS1sZtPR0eHI/t3y4x//WPv27ct5/Wee\neUY7duwoYETlibyFl5CP3lZTUyPTNDU1NaVQKKSWlhaFw2G3w4JDzItfqtgPIBveq+El5GNpOHz4\nsA4dOqR169a5HYpjHCki1NbWyu/32wklSa+88opqa2vT1guHw9q/f3/WfiL5sqbdmP/hn+0CINu0\niCudo3Ox88m370s0GtXOnTtzXt/q01RVVZXXcSxOxl5qyFvnXvtKziOnkI+lkY+1tbW6++67JUlN\nTU0r2heA0sN7tTc/770aV6GRj96/dohEIvL7/QvGWCp1jhQRJKmxsVHRaFSTk5NKJBKqq6tLez4S\niaipqUmPP/64Nm3a5NRhJSltnujFZEr0XJN/MZnmj7bmZ77hhhvy2lcgENBVV12VdWCT+ZY7oKLF\nydhLEXnrzGtf6XnkFPKxNPLR6d89gNLCe7X3Pu+9GlcxkI/evXaIxWI6efJkXi3MS4VjRQSrOX1f\nX5/Gx8d18803pz3f0NAgSQtmFViJ3t5eSVJ/f789YrY1UmgxWS0HTp8+bS87e/Zs2nO5WuwPKtMf\n19jY2IJqYz6cjL0UkbfOvPaVnkdOIR9LIx+t39PQ0NCK94XsksmkIpGIAoGA26EsaaXfpnlROZ6T\nU3iv9t7nvVfjKgby0ZvXDslkUkePHlV7e7u9LBaLqbm5Oe99eZFjRYSqqiqFQiH19PTo3XffXdDn\nw6ruJBKJtKpVMplMq/hYP1vNRqx1Jycn7XWsX/7u3bslXex/4/P5ZBiGqqurVV9f79Rp5aSmpka9\nvb3q6+tTKpVSKpVSX1+fent70/5grWlQYrGYI8eNxWLavn171udzOV6usZcr8taZvC3E34D1oTT/\n53JGPnovHwOBgLq6uuxvI1KplDo7OxUKhXTXXXc5cObIpq2tTQ0NDXaz2VKQ6du0aDSqQCCgQCCw\nonNJJBJqbm6WYRhqbm5e0XRqqVRKk5OTCofDWYs0jA+RHe/Vxf2853p2ceSj964dksmk9u7dq5aW\nlrRi7NatW7Vr1y5nTt5t80daXMlouFNTU6aUeeor67lQKGROT0/bI4XOH/HaOnY8Hjf9fr8pfTwd\nhjWVyNzpl+ZOuWXtr5C0yOiZw8PDpnRxGpCRkZEFz1vnnO80IdmOaf0us8nneEvFvlQsS5G8NzuD\nhbx1Lm+d2tf83+1i8S8nJ704O4OFfPRWPlr7sB6dnZ329Lv5nls2Xh2F3guW+5lTbNniHBoaskfk\nnpmZMYPBYNrsS7mamZmx/4ZnZmbskdGzTRe2lFAoZP/NL/X7des18PrfBe/Vxfu898r1rBdnZ7CQ\nj966drCmi8z0yPQaeTEfl+JoEaESOPFhutK5Rt08XjkWESqB1/LW7Zz0chGhEpCP6bx+s+SmUi4i\nWBfoc4tO1sV8trnUs8lULHDid5TUyW8AAB6JSURBVEMRoTT/LoqF9+p0Xi4iVALyMZ3b+eJYdwbk\nZnJyUocOHSrb46E8OZlH5CRWinz0tsnJyaz96a1moIZhKJFIKJVKKRwO28taW1vTmtfOlW3A4Ux9\n9q2+uYZhKBAIrKjp/3KNj49LktavX28vu/LKKyVJx44dy2tf2UYNL+dR51H6eK+Gl5CPzqKIUESj\no6O64oorijbFR7GPh/LkZB6Rk1gp8tH7tm3bppGREUlSKBRK61d/4MABhUIhTU1NqaamRgcPHlRT\nU5Omp6cVj8fV0dGhtra2jPudnp5esCwejy9YZvVF3bBhg0zT1P33369bbrkla//VQk09NjY2Jil9\nMDNrOuaVjvNg9Rsvm761KDu8V8NLyEfnrXE7gFJlXVDMvThaijV6arE4eTxGZi4PbuctOYm5yMfy\ntWPHDoVCIXV0dKilpcUe6Mu6+bVmFfrsZz+rYDBo31xLUk9Pj7q7uxfsc+46lkwDpo2OjioajWp4\neNiORZKeeeaZjLMZ5ZN/+ejp6cn63EqLCCdOnJDf718wCjtQCLxXw0vIR2+gJUKezBXOZ1qqKvW8\ny0U5vn7leE6Vohxfu3I8p5W64447JEkvvviivezEiRP2cklqb29Xd3e3EomEY1ODDQ4OSlrY1aGj\no8OR/XvB4cOHdejQoQWjsANOKsf3tXI8p0pRjq9dKZ8TRQQAAOC42tpa+f1++6Zekl555ZUFrQHC\n4bD279+ftd9/vqxv+edfnGW7QCtUd4bFzmclYxlEIhH5/f6Kb0oLAHAPRQQAAFAQjY2Nikajmpyc\nVCKRUF1dXdrzkUhETU1Nevzxx7Vp0yZHjz13PvTFZCo25FqAWIxVRJg7UGQikZAk3XDDDXnvT5Ji\nsZhOnjypffv2LWt7AACcQBEBAAAUhNV3tK+vT+Pj4wv68Dc0NEjKPLbBcvX29kqS+vv77TEYrNka\nimnnzp2SpNOnT9vLzp49m/ZcPpLJpI4ePar29nZ7WSwWU3Nz8wojBQAgPxQRAABAQVRVVSkUCqmn\np0fvvvvugj781rf1iUQireVAMplM+wbf+tnqBmCtOzk5aa9j3Uzv3r1b0sUxEHw+nwzDUHV1terr\n650+vUXV1NSot7dXfX19SqVSSqVS6uvrU29vb1rRxJqKMtvsEdLHM060tLSkdbHYunVr2gwNuezL\nYhVY5v8MAMBSKCIAAICCsQZSzDRGgPWtejgcls/nUygUUjAY1AcffKDq6mp7PevnBx98UH6/X5s3\nb1Y0GtW2bdvk9/s1NDSkRx55RNLFwkU8HlcoFJJ0sfAQj8cdb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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(graph.create_png()) "
   ]
  },
  {
   "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": 37,
   "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": 38,
   "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": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bphkZGbp+/fpS21pa2w4ePKgPP/ywnnDCCVqrVi1t0KCBnnLKKXrffffpzp07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\nqPj/JXOeLhc97i0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x845c978>"
      ]
     },
     "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": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_classification\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "X, y = make_classification(n_samples=10000, n_features=10, n_classes=2, n_informative=5)\n",
    "Xtrain = X[:9000]\n",
    "Xtest = X[9000:]\n",
    "ytrain = y[:9000]\n",
    "ytest = y[9000:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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//5T3adWqlW644QYtWrRILpdLx48fV1xcnBITExsiMgDAy5WWliovL09Lly7V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\nu9/n7t27dezYMQ4ADHAlJSVKS0tTWVmZZs+ereTkZNlsNtOxAAAAANQ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      "text/plain": [
       "<matplotlib.figure.Figure at 0xe3e1828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (14069326)>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = LogisticRegression()\n",
    "clf.fit(Xtrain, ytrain)\n",
    "from sklearn import metrics\n",
    "import pandas as pd\n",
    "from ggplot import *\n",
    "\n",
    "preds = clf.predict_proba(Xtest)[:,1]\n",
    "fpr, tpr, _ = metrics.roc_curve(ytest, preds)\n",
    "\n",
    "df = pd.DataFrame(dict(fpr=fpr, tpr=tpr))\n",
    "ggplot(df, aes(x='fpr', y='tpr')) +\\\n",
    "    geom_line() +\\\n",
    "    geom_abline(linetype='dashed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Automatically created module for IPython interactive environment\n"
     ]
    }
   ],
   "source": [
    "print(__doc__)\n",
    "\n",
    "import numpy as np\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.cross_validation import train_test_split\n",
    "from sklearn.preprocessing import label_binarize\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from scipy import interp\n",
    "\n",
    "# Import some data to play with\n",
    "iris = datasets.load_iris()\n",
    "X = iris.data\n",
    "y = iris.target\n",
    "\n",
    "# Binarize the output\n",
    "y = label_binarize(y, classes=[0, 1, 2])\n",
    "n_classes = y.shape[1]\n",
    "\n",
    "# Add noisy features to make the problem harder\n",
    "random_state = np.random.RandomState(0)\n",
    "n_samples, n_features = X.shape\n",
    "X = np.c_[X, random_state.randn(n_samples, 200 * n_features)]\n",
    "\n",
    "# shuffle and split training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5,\n",
    "                                                    random_state=0)\n",
    "\n",
    "# Learn to predict each class against the other\n",
    "classifier = OneVsRestClassifier(svm.SVC(kernel='linear', probability=True,\n",
    "                                 random_state=random_state))\n",
    "y_score = classifier.fit(X_train, y_train).decision_function(X_test)\n",
    "\n",
    "# Compute ROC curve and ROC area for each class\n",
    "fpr = dict()\n",
    "tpr = dict()\n",
    "roc_auc = dict()\n",
    "for i in range(n_classes):\n",
    "    fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i])\n",
    "    roc_auc[i] = auc(fpr[i], tpr[i])\n",
    "\n",
    "# Compute micro-average ROC curve and ROC area\n",
    "fpr[\"micro\"], tpr[\"micro\"], _ = roc_curve(y_test.ravel(), y_score.ravel())\n",
    "roc_auc[\"micro\"] = auc(fpr[\"micro\"], tpr[\"micro\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'lw' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-67-ecc63baa3f6c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[0mcolors\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcycle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'aqua'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'darkorange'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'cornflowerblue'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_classes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 32\u001b[0;31m     plt.plot(fpr[i], tpr[i], color=color, lw=lw,\n\u001b[0m\u001b[1;32m     33\u001b[0m              \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'ROC curve of class {0} (area = {1:0.2f})'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m              ''.format(i, roc_auc[i]))\n",
      "\u001b[0;31mNameError\u001b[0m: name 'lw' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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L2LE3M3Tot9wfHkS7tOu7d4+y7RTpNrw8YFCMq7MQkSJExYLINfLy8uCdd7rR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\nzHK5WDAzM7NcLhbMzMws138B7GIS8EVJJFgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe596630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute macro-average ROC curve and ROC area\n",
    "\n",
    "# First aggregate all false positive rates\n",
    "all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))\n",
    "\n",
    "# Then interpolate all ROC curves at this points\n",
    "mean_tpr = np.zeros_like(all_fpr)\n",
    "for i in range(n_classes):\n",
    "    mean_tpr += interp(all_fpr, fpr[i], tpr[i])\n",
    "\n",
    "# Finally average it and compute AUC\n",
    "mean_tpr /= n_classes\n",
    "\n",
    "fpr[\"macro\"] = all_fpr\n",
    "tpr[\"macro\"] = mean_tpr\n",
    "roc_auc[\"macro\"] = auc(fpr[\"macro\"], tpr[\"macro\"])\n",
    "\n",
    "# Plot all ROC curves\n",
    "plt.figure()\n",
    "plt.plot(fpr[\"micro\"], tpr[\"micro\"],\n",
    "         label='micro-average ROC curve (area = {0:0.2f})'\n",
    "               ''.format(roc_auc[\"micro\"]),\n",
    "         color='deeppink', linestyle=':', linewidth=4)\n",
    "\n",
    "plt.plot(fpr[\"macro\"], tpr[\"macro\"],\n",
    "         label='macro-average ROC curve (area = {0:0.2f})'\n",
    "               ''.format(roc_auc[\"macro\"]),\n",
    "         color='navy', linestyle=':', linewidth=4)\n",
    "\n",
    "colors = cycle(['aqua', 'darkorange', 'cornflowerblue'])\n",
    "for i, color in zip(range(n_classes), colors):\n",
    "    plt.plot(fpr[i], tpr[i], color=color, lw=lw,\n",
    "             label='ROC curve of class {0} (area = {1:0.2f})'\n",
    "             ''.format(i, roc_auc[i]))\n",
    "\n",
    "plt.plot([0, 1], [0, 1], 'k--', lw=lw)\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Some extension of Receiver operating characteristic to multi-class')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], \n",
       "      dtype='<U10')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "logreg=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x,y=iris.data,iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_new=[[3,5,4,2],[5,4,3,2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 0])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg.predict(x_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(x,y,test_size=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "iris_X_train2 = iris.data[:-20]\n",
    "iris_X_test2  = iris.data[-20:]\n",
    "iris_y_train2 = iris.target[:-20]\n",
    "iris_y_test2  = iris.target[-20:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.random.seed(0)\n",
    "indices = np.random.permutation(len(iris))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "iris_X_train = iris_X[indices[:-10]]\n",
    "iris_y_train = iris_y[indices[:-10]]\n",
    "iris_X_test  = iris_X[indices[-10:]]\n",
    "iris_y_test  = iris_y[indices[-10:]]"
   ]
  }
 ],
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