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SilvaEmerson/Analysis.ipynb

Created October 13, 2017 14:39
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Analysis.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"import os\n",
"import KNN\n",
"#from sklearn.neighbors import KNeighborsClassifier as KNN\n",
"\n",
"sns.set_style(\"whitegrid\")\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(698, 10)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'''\n",
"# Attribute Domain\n",
" -- -----------------------------------------\n",
" 1. Sample code number id number\n",
" 2. Clump Thickness 1 - 10\n",
" 3. Uniformity of Cell Size 1 - 10\n",
" 4. Uniformity of Cell Shape 1 - 10\n",
" 5. Marginal Adhesion 1 - 10\n",
" 6. Single Epithelial Cell Size 1 - 10\n",
" 7. Bare Nuclei 1 - 10\n",
" 8. Bland Chromatin 1 - 10\n",
" 9. Normal Nucleoli 1 - 10\n",
" 10. Mitoses 1 - 10\n",
" 11. Class: (2 for benign, 4 for malignant)\n",
"'''\n",
"descrip = {2:'Clump Thickness',\n",
" 3:'Uniformity of Cell Size',\n",
" 4:'Uniformity of Cell',\n",
" 5:'Marginal Adhesion',\n",
" 6:'Single Epithelial Cell Size',\n",
" 7:'Bare Nuclei',\n",
" 8:'Bland Chromatin',\n",
" 9:'Normal Nucleoli',\n",
" 10:'Mitoses',\n",
" 11:'Class' \n",
" }\n",
"\n",
"columns = ['ID']\n",
"\n",
"for i in descrip.keys():\n",
" name = descrip[i].split()\n",
" name = [j[0] for j in name]\n",
" name = ''.join(name)\n",
" columns.append(name.upper())\n",
"\n",
"df = pd.read_csv('data/data.txt')\n",
"df.columns = columns\n",
"\n",
"#drop the first columns, that is pacient ID\n",
"df.drop('ID', axis=1, inplace=True)\n",
"\n",
"#refatoring the column label\n",
"df['C'] = df['C'].apply(lambda x: 1 if x == 2 else 0)\n",
"\n",
"#shape of data\n",
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"CT int64\n",
"UOCS int64\n",
"UOC int64\n",
"MA int64\n",
"SECS int64\n",
"BN object\n",
"BC int64\n",
"NN int64\n",
"M int64\n",
"C int64\n",
"dtype: object"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#check out the type of the atributes\n",
"df.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"CT False\n",
"UOCS False\n",
"UOC False\n",
"MA False\n",
"SECS False\n",
"BN False\n",
"BC False\n",
"NN False\n",
"M False\n",
"C False\n",
"dtype: bool"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#verifing whether has some miss values\n",
"df.all().isnull()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"16"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#number of null values\n",
"df.BN.tolist().count('?')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(682, 10)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# for i in range(len(df.BN)):\n",
"# if df['BN'][i] == '?':\n",
"# df.drop(i, axis=0, inplace=True)\n",
"for i in range(len(df.BN)):\n",
" try:\n",
" float(df['BN'][i])\n",
" except:\n",
" df.drop(i, axis=0, inplace=True)\n",
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"df.BN = df.BN.astype('int64')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"CT int64\n",
"UOCS int64\n",
"UOC int64\n",
"MA int64\n",
"SECS int64\n",
"BN int64\n",
"BC int64\n",
"NN int64\n",
"M int64\n",
"C int64\n",
"dtype: object"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>CT</th>\n",
" <th>UOCS</th>\n",
" <th>UOC</th>\n",
" <th>MA</th>\n",
" <th>SECS</th>\n",
" <th>BN</th>\n",
" <th>BC</th>\n",
" <th>NN</th>\n",
" <th>M</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>682.000000</td>\n",
" <td>682.000000</td>\n",
" <td>682.000000</td>\n",
" <td>682.000000</td>\n",
" <td>682.000000</td>\n",
" <td>682.000000</td>\n",
" <td>682.000000</td>\n",
" <td>682.000000</td>\n",
" <td>682.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>4.441349</td>\n",
" <td>3.153959</td>\n",
" <td>3.218475</td>\n",
" <td>2.832845</td>\n",
" <td>3.236070</td>\n",
" <td>3.548387</td>\n",
" <td>3.445748</td>\n",
" <td>2.872434</td>\n",
" <td>1.604106</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>2.822751</td>\n",
" <td>3.066285</td>\n",
" <td>2.989568</td>\n",
" <td>2.865805</td>\n",
" <td>2.224214</td>\n",
" <td>3.645226</td>\n",
" <td>2.451435</td>\n",
" <td>3.054065</td>\n",
" <td>1.733792</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>2.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>2.000000</td>\n",
" <td>1.000000</td>\n",
" <td>2.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>4.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>2.000000</td>\n",
" <td>1.000000</td>\n",
" <td>3.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>6.000000</td>\n",
" <td>5.000000</td>\n",
" <td>5.000000</td>\n",
" <td>4.000000</td>\n",
" <td>4.000000</td>\n",
" <td>6.000000</td>\n",
" <td>5.000000</td>\n",
" <td>4.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>10.000000</td>\n",
" <td>10.000000</td>\n",
" <td>10.000000</td>\n",
" <td>10.000000</td>\n",
" <td>10.000000</td>\n",
" <td>10.000000</td>\n",
" <td>10.000000</td>\n",
" <td>10.000000</td>\n",
" <td>10.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" CT UOCS UOC MA SECS BN \\\n",
"count 682.000000 682.000000 682.000000 682.000000 682.000000 682.000000 \n",
"mean 4.441349 3.153959 3.218475 2.832845 3.236070 3.548387 \n",
"std 2.822751 3.066285 2.989568 2.865805 2.224214 3.645226 \n",
"min 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 \n",
"25% 2.000000 1.000000 1.000000 1.000000 2.000000 1.000000 \n",
"50% 4.000000 1.000000 1.000000 1.000000 2.000000 1.000000 \n",
"75% 6.000000 5.000000 5.000000 4.000000 4.000000 6.000000 \n",
"max 10.000000 10.000000 10.000000 10.000000 10.000000 10.000000 \n",
"\n",
" BC NN M \n",
"count 682.000000 682.000000 682.000000 \n",
"mean 3.445748 2.872434 1.604106 \n",
"std 2.451435 3.054065 1.733792 \n",
"min 1.000000 1.000000 1.000000 \n",
"25% 2.000000 1.000000 1.000000 \n",
"50% 3.000000 1.000000 1.000000 \n",
"75% 5.000000 4.000000 1.000000 \n",
"max 10.000000 10.000000 10.000000 "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"features = df[df.columns[:-1]]\n",
"labels = df['C']\n",
"\n",
"features.describe()"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAAEBCAYAAADbxHY7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f2007a34050>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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PKuub2XLwONdP1dot4n8jk2K4feG4Hh/nryPgxOgIZo8dxuyxwTdjZM64VK4/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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f20078a7c90>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f20079bfc90>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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28tOX9rF2Rx0fXTKFO66eSUlOerJLjDtn/YiNwaZ9J/jLBzaR5nHz8GeXMLFA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\nOQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f2007760810>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f20077afbd0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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3nF/Dk9saaWjtSXccEUkwFXccjnQG2NJwjCt8mX+0PeivL5yB2+Xi/pc01i0y1qi44/Cs\n/whAxo9vDzWlNI/3nTuNh15toKl93Fy/WWRcUHHH4Zkdh5lWloeZWDTygzPIZy+dQzgc4Udrd6c7\niogkkIp7BL39IV6uO8rlvom4XJl3tuTJVJfnc9PiaTy4qV5H3SJjiIp7BGt3NdMXDGfcolLx+swl\n0aPuH75Ql+4oIpIgKu4RrNneRGm+l6Uzy9Md5bQMHnU/tElj3SJjhYr7JAZC0bMlL5s3EW+Wc79V\nn7lkDuFIhO8+uyvdUUQkAZzbRinwelMvnYEgV89P/7UlR6O6PJ+PXDiD377awLaD7emOIyKjpOI+\nifX13eRnZ2X0olLx+vzlcynPz+YfH9tOJBJJdxwRGQUV9wmEwhFeqe9hpakk15uV7jijVpLn5QtX\nz6N2fxu/33Iw3XFEZBRU3Cewub6NtkCIq85y9jDJUDctnsaCaSV8/U876eoLpjuOiJwmFfcJrN7W\nhMcNlzhgUal4ud0uvnb9WRzp7OM7T+uDShGnUnEPIxKJsGZHEwsn51Gc6013nIRaVFPGh8+v4Wcv\n72Vd3dF0xxGR06DiHsb2Qx00tPayrKYg3VGS4ivX+phVWcDfrtrKsZ7+dMcRkVOk4h7GH7cewuN2\njdnizs/28L0PLqKlu48vPfKGZpmIOMyI1+AyxriB+4AFQB9wu7W27rjHVALrgHOstY4+PS8cjvDH\nrYdYMbeCklznzyY5kflTS/jbKw3feHInq15r4APn1aQ7kojEKZ4j7huBXGvthcAXgXuHbjTGXAU8\nBYyJ6Re19W0cag9ww8Kp6Y6SdJ9cMYt3zZnAV3+/jfW7Nd4t4hTxFPdyYDWAtXYDsOS47WHgcqA1\nsdHS4w9bDpLrdTt2UalT4Xa7uO9Di5kxoYBP/bqWnU0d6Y4kInFwjTS+aYy5H/idtfbJ2O16YJa1\nNnjc4/YB84YbKqmtrY3k5+cTCATIzc3cS38FwxE+vGo/Cyfn8aWLJyYt7592JbYgrzmjGOC08x7p\nCnLXnw7idsG3r5lKZUFqrmKf6e+H4ylvcinv2/X09LB48eJh15KO5ye0Axh6BQH38aUdD5/Ph9/v\nx+fznepTU+Z5e4SOvr3cumIePt+kpOXd3FGf0P35fNHx6dPN6wN+PbWGD/x4A19b28KvPraUaWX5\nCc04nEx/PxxPeZNLed+utrb2hNviKe51wHXAKmPMBcAbCcqVcf645RDFuR4uNpXpjnJKHtwY/UXQ\n2NQxql8Ktyyt4dcb9vHe+9bzi9uWcuaU4kRFlDFo8H2XKIv0dotbPGPcjwIBY8x64DvAXcaYu40x\n1yc3WmoFBkKs2d7ENWdPJsczdmeTnMzMigI+uWI2bpeLD/z4FdbrBB2RjDTiEbe1NgzccdzdO4d5\n3IwEZUqLNdub6O4Pcf2CKemOklaTSnJ55NPL+OsHNnHrzzby2Uvm8LnL5jp6PXKRsSY1n0I5wEOb\n6qkpz+eCWRPSHSXtXrDN3LK0hsdfb+R7z9XxyOaDvH9xNRVFOae8rw+dr/nhIommwyhgT3MXG/a0\n8sGl1bjdzrogcLLkerO4afE0bllaQ0tXP9977k1Wb2uktz+U7mgi456OuIkebXvcLm5aPC3dUTLO\n2VNLmF6ez1M7mnjpzaO8tr+NS0wV580oJ9uj3/si6TDuizswEOLh2gNcedZEqoqcM4c0lYrzvNy0\nuJplsyv407ZGnnijked2HmHpzHIumDWBkryxtYKiSKYb98W9ZnsTbT0D3LJUY7EjmVKax+3LZ7G/\npZuX647y4q5mXnqzmblVRZw7vYx5k4r0IaZICoz74n5wY/RDyXfNdv51JVNl+oQCpk8ooLW7n017\nW9nS0MZDmzrJ9bqZN6mYs6YUM7eqSEMpIkkyrou77kgXG/e2cs/VRh9Knobygmyunj+JK8+ayO7m\nLrY2HMPf2MmWhmN4s1zMqijklT0tmIlFlBdkv+P5p3PCkGapiIzz4v7Zy3vJznJz8+LqdEdxNLfL\nxdyqIuZWFREKR9jX0s2OQx3Yw53YrZ38kWjJz6ksZHZVIbMqCijIGddvPZFRGbc/PU3tAX5Xe4Cb\nl0yj8jTmJ8vwstwuZlcWMruykOuAo1197DrcSd2RLrYeOMamfdFFJCcV5zIxH+aH25k5oYB8FblI\n3MbtT8v9L+0hFInwqYtmpzvKmFZRmENFYQ7LZlcQCkc40NbDnqPd7GnuYtvhbrY2RYdKJhbnMCM2\ndj5jQj6l+e8cWhGRqHFZ3G3d/fzXxnquXzCFmgnJXwVPorLcrrc+2LzEVHHg0CFCOSXsPdrN3qPd\nbGk4xsa90SPykjwv1eX5TC/Pp7o8nykluXg0Y0UEGKfF/fP1++gdCPHplTraTqcst4tpsSJfaSAc\nidDUHmBfSzf1rT3Ut/aw7WB79LEuF5NLc7FNHcyfWsI500qZXVmgMpdxadwVd2dggF+s28tVZ01k\n7sSikZ8gKeN2uZhSmseU0jyWxX6ntvcO0NDaw4G2Hhraenm49gC/fGU/ALleN2ZiEWdOKcY3uZgz\nJhZxxglmsIiMJeOuuB94eR8dgSCfXjkn3VEkDiV5XkqmljB/agkQPSo/2tXHwbZeGtsDHGrv5feb\nD/HQpoa3nlOQ46GyMPut8fXygmwmFGZTlp9NrvftS/ZqeqE40bgq7gNtPfxwbR3XnD2JBdWl6Y4j\np8HtclFVlEtVUS6LYvdFIhE6AkEOdwQ40hHgSGcfzV19+Bs76D5uUaxcr5vSvGxK8rwU5Xpoau+l\nsjiXCQXZ0YIvyKYk30tJnve01mVP9MUFxtovlnAkQmcgSN9AiGA4QjAcIcfjpjDHQ3iEyyjKX4yr\n4v7XJ/wAfOXaM9OcRBLJ5XJFj8zzvJxx3PBXb3+I1p5+Wrv7aevu51jvAO090X8PHeuldn8bJ6oL\nb5aLXE8WOV53tMTDQQpXN5PtcePNcuPNcr31ryfLjccdve1xv/32Xx7njj3XRY4nC2+WC5dr7J74\nFRgIcaCtl/rWHg629XA09t8gGB7+O57lglmVzSyqKWVRTRnLZk9g+oSCFKd2hnFT3C+92cyT25r4\nuyvPYGppXrrjSIrkZWcxNTvvhP/NQ+EI3f1BuvuCdPeF6O4P0tsfoncgRG9/iL5giMBAmL5giK6e\nATr7BujvDjMQijAQCjMQChMMRU5Y/ifjdkGOJ4tcr5u87CzyvFnkZ3soyPFQmJNFUY6XnY0dFOV5\nKc3zkp+ddUpFf/yZqck+eh8Ihdnf0sPu5i7qjnRx6FjvW9+XisIcqopy3jqLNi87K/oLzu2iLxim\nMxCkINJL84CXp3YcZtVrBwA4a0ox154zmesXTEnJdVCdYlwUd38wzNce2870CfncvmJWuuNIBsly\nuyjO9VKcO/IKh41NjUyeNPkd90ciEUKRCMFQ9E//YKzMB8JvL/iBUISBYJi+UJj+gRB9wTCBYJhA\n7JdE70CIQ8d66e4PEhgIv+N1vFkuSvKyKS/wUl6QTXl+NhMKc5hQGB3m8bhTO8MmHIlwuCPA7uZu\n3jzcyd6j3QTDEdwuqC7P55J5VUwvz2daWT552SMPOy0q7sbn8xGJRNjX0sOz/sM8/noj/77a8q01\nlst9E/nY8pmcP7N8TP+lEo9xUdw/Xrub3c3dPPDRJe/4cEpktFwuFx6Xi0SuqRUMh+kKBOkMBGnv\nHXjrf209/bT19FPf2vO2cncBZQXZVBbmUFGYTUVRDq7+fvKK+ynO8+JOQNH19ofYfqidLQ3H+HN9\nGy/YZnpinyFUFOZw3oxy5lYVMrOigJxR/Jy5XC5mVhRw+4pZ3L5iFg2tPTy0qZ6HNtXz1I7DzJ9a\nzOcvO4PLfVXjtsDHfHG/sruF7zyzi+sWTOHSeRPTHUckLh63m9L8bErzsxluJZ1IJEJvf4iW7n6O\ndvVxtKuP5q5+jnb2sedoFwOh2CDFjg48bhel+V5Wb29iSkkuVUU5VBbnUpbvpSDbQ352Fl6Pm3A4\nQigcoXcgRFtPPy1d/RzuCLCnOXqC1P7WHkKx8ekpJbmYiUXMrixkVmVBUs90rS7P556r53HnZXN5\ndPNBfrR2N5/41WvMn1rMXZefwaXzxl+Bj+niPtIZ4HMPbWZGRQFff+/Z6Y4jkjAul4v8HA/5OR6q\ny98+9huOROjoHWBXQyNhbyFt3f2xD2j72HGog5buPuKdwJHjcTOzogAzqYhrzp7MOdNKWFhdSlVx\nbsJn0Iwk15vFLUtruHnxNB7dfJDvP1fHx3/5GufNKOOL7/axeHpZSvOk05gt7mAozJ0Pbaarb4D/\nuv18CrWIkYwTbpcreqReks3kSX+5+PXgh5PBUJjW2AybwQ9lB0Jhstwustwucjzu2NTIHIrzPBl3\nNOvJcnPzkmpuXDSV/361gf945k3e98P1XHXWRO65eh6zKwvTHTHpxmSbRSIR/u/jO9iwp5V7b16A\nmaQzJEUGebLcVBXnUlXs7Ev1ebPc3HrBdP5q0VR+9vJefrx2N8/4X+SWpdV8/rIzxvSqn2NuoYdI\nJMLXHtvOL1/Zz8eXz+R9ugCwyJhWkOPhzsvmsvaeS/jw+TX8dlMDF3/zee59ytLeO5DueEkxpoo7\nHI7w1d9v45ev7OcTK2by1Wt96Y4kIilSUZjDP98wn6fvvphL51Xx/efquOjfn+c/n6+jqy+Y7ngJ\nNWaKuyMwwJ2/3cx/baznb1bO5svX+DJubE5Ekm9mRQE/+NC5PHHnchZPL+Obayzv+sZzfOfpXbR1\n96c7XkKMiTHuTXtbueu/t9DUEeALV8/jjotnqbRFxrmzppTwwEfPY0vDMe57vo7vPvsmP31pD+89\ndyofuXDGO5ZHcBJHF/fRrj5++MJufr5uL9Xl+Tx8x4Usqhk/U4JEZGQLq0v5yUeWYJs6+elLe1j1\n2gF+s6Ge82eW877F07h6/qS4zpzNJI4s7iMdAX760h5+vWE//cEw719SzVffc6am/ImcRKrnXWca\nM6mIb928gK9c42PVaw08uKmeex5+na/+fhuXmEquOHMSF51RQVVR5s+2GbHpjDFu4D5gAdAH3G6t\nrRuy/RPAp4Ag8C/W2seTEfRIR4Cndhzmidcb2bi3BYAbF07lM5fOGRfzNkUkMcoKsvnUxbP55EWz\n2HqgnT9sOcgTrzeyZvthILqw1dKZ5ZxbU8aimlKmluZl3NBrPIeoNwK51toLjTEXAPcCNwAYYyYB\ndwJLgFzgZWPM09bavkSEe3rHYR7beojN9W0caOsFYFZlAZ+9ZA7vPXcaMyq05KOInB6Xy8XC6lIW\nVpfy99eeyY7GDtbuaubFXc08tKmen6/bB0Qv5jG3qpC5EwuZPqGAKaXR1SY7uwaY0R+KawGtRIun\nuJcDqwGstRuMMUuGbFsKrIsVdZ8xpg44B3g1EeHuf2kPDa09LKop46PLZvCuORXMm1SUcb/9RMTZ\n3G4X82NXWvrMJXMYCIWxTZ1srm9jZ1Mnbx7pYs32w7QePyvldw3kebMoy/dSmOuhMCe6LG+uN7pM\n7/I5Fbz/vOFWmxmdeIq7GGgfcjtkjPFYa4PDbOsESobbSW1t7dv+jcc9S7KBbCACtNFzqI0/H4r7\n6QlxKnnjZZI0FG+meYDm5Ow8CZQ3uZyWt6cnOT9vo3FmNpxZA9RkA+Wn+OwwcITa2iMJzxVPhXQA\nQ+fNuGOlPdy2IuDY8TtYvHixDpFFRBIknhNw1gHXAMTGuN8Ysm0TsMIYk2uMKQF8wLaEpxQRkbe4\nIiOs7zhkVsk5RNdrv41okddZax+LzSr5JNFfAv/PWvu75EYWERnfRizuRIodlf+G6Nh4NnC3tfaV\nlAWI00hTIDONMcYLPADMAHKITst8LK2hRmCMqQJqgSustTvTnWckxpgvAdcTfd/eZ639WZojDSv2\nXvgl0fdCCPhEpn5/jTHnA/9mrV1pjJkD/ILoB1rbgM9Ya995/bY0Oi7vQuD7RL/HfcBHrLWHU5Ul\n1WuV3A08a629GPgo8J8pfv14vTUFEvgi0SmQmexWoMVauwK4GvhBmvOcVKxcfgz0pjtLPIwxK4Fl\nwLuAi2HYi9JkimsAj7V2GfDPwL+mOc+wjDH3APcTnUYM8G3gq7H3sIvYlONMMUze7wKfs9auBB4B\nvpDKPKmd+n3/AAAD10lEQVQu7u8Q/YGF6AejgRS/frzeNgWS6Dz1TPY/wN/HvnYRPRkqk30L+BGQ\n4jlCp+0qop/tPAr8EUjKSWYJsgvwxP5qLAYydV3T3cB7h9xeDKyNff0kcHnKE53c8Xk/aK3dEvs6\n5V2WtHPEjTEfB+467u7brLWvxk7c+Q3wv5P1+qN0simQGcda2wVgjCkCHga+mt5EJ2aM+SjQbK1d\nExt+cIIKYDrwHmAm8JgxZp61NnXjjPHrIjpMspNo7vekNc0JWGt/Z4yZMeQu15Dv5wmnFafL8Xmt\ntY0AxphlwGeBi1KZJ2nFHRsDfMc4oDHmbOC3wN9Za9e+44mZ4WRTIDOSMaaa6BHhfdbaB9Od5yQ+\nBkSMMZcDC4FfGWOut9Y2pTnXybQAO621/YA1xgSASiDxE3RH7y5gjbX2S7H3xHPGmLOttZn61+2g\noePZw04rzjTGmA8AXwGutdamdMJ8SldlMsacSfTP+g9Ya7em8rVP0TrgOmDVMFMgM44xZiLwFPBZ\na+2z6c5zMtbat45MjDEvAHdkeGkDvAx83hjzbWAyUEC0zDNRG38ZHmkFvEDqz8k+dZuNMSuttS8A\n7waeT3OekzLG3Ep0jaaV1trWVL9+qpfT+zrRwf3vGmMA2q21GfUhRMyjwBXGmPX8ZQpkJvsyUAb8\nvTFmcKz73dZaR3z4l+mstY8bYy4iet6Cm+iMh1CaY53Id4AHjDEvEZ0B82VrbXeaM8Xjb4GfGmOy\nAT/RIb+MZIzJAr4H1AOPxLpsrbX2H1OVIaXTAUVEZPTGzKXLRETGCxW3iIjDqLhFRBxGxS0i4jAq\nbhERh9HVdWVMiq0vcoe19oND7vsG0TMKnyB62v10onOcG4gueNYUe9wK4B+IzoEuAH5urb0vpf8H\nRE5CR9wy3riILgr0iLV2ZWxRoweAx40xWcaYWUTn6N4aW0DoIuAjxpir05ZY5DgqbhlvJhA98esP\ng3dYa58huojQRcD/An41uERn7CSmq4Cn05BVZFgaKpHxpploSR9vD9GhkynAlqEbrLXtwzxeJG10\nxC1jVS/Ri0oMVUh05bwZwzx+LtFTmPdz3HrbxpgFxphFiY8ocnpU3DJW+YFFxpjJAMaYXKJDIQ8C\nk4wx1w0+MDZ+PYfoetAPArcbYypj2wqJriE/ObXxRU5Ma5XImGWMeS/Rtcl7iC649FNr7U9jl037\nD6Jra0N0Vsld1tqDseddSfTCFCGiS4zeb639Yarzi5yIiltExGE0VCIi4jAqbhERh1Fxi4g4jIpb\nRMRhVNwiIg6j4hYRcRgVt4iIw6i4RUQc5v8DNVE2am18h3UAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f20076dced0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f2007a0db90>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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GgEtPGZbqKAkryErjtounsHpnHX96fVuq44hIEqi4j+FAUxtPra7ho6cOJycjkVmTfcdF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\npDpLIowxZwELgA8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"text/plain": [
"<matplotlib.figure.Figure at 0x7f2046974710>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f200799c050>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f20079ff550>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAAEBCAYAAADbxHY7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niWnaESGy5QAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f20076b3950>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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5eR63m8w+sf7/uAenAP6fkbLqBkakJbB1XzlbCyvI2X2IJRv3HXleYoyHES2z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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f200786fd90>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f2007a90f50>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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wfdEIclOHcM9z27juifV8/ypfH7vVGl5dZC63h3uf38bWI7X87pZpXDR+YFe8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\n8soAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f2007597550>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f20078ec4d0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f2008071410>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f2007a9bb10>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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q0N/acZiG5gi3XT747w3NxheunEZXT4z/WLu3INsXEbcNq0B/6t06RpQE+ORFQzO6pa9z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\nZ5NtOgrjAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f20079f2e10>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0x7f2007fc2f90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"features.astype(float)\n",
"# features.columns.shape\n",
"for i in features.columns[:9]:\n",
" sns.boxplot(features[i])\n",
" plt.show()\n",
" plt.clf()\n",
" sns.distplot(features[i])\n",
" plt.show()\n",
" plt.clf()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The atributes 10,9,8,6 and 5 has outliers, going on, check how much of the data is belong to this group"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"59"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"threshold= features.MA.quantile(.75)-features.MA.quantile(.25)\n",
"len([i for i in features.MA if i > features.MA.quantile(.75)+(1.5*threshold)])"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f2007a27e50>"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f20079aa550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"features.boxplot()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.8535564853556485"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x163d0c59b38>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#proportion of the labels\n",
"labels.hist()\n",
"float((labels.tolist()).count(1))/float((labels.tolist()).count(0))"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#sns.pairplot(features,hue='C')"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f20077828d0>"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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gINis/+6Ljv+zD84IBAKBav6/PzgjEAgERYZ/eQ5bOGyBQCC4jZj8SSAQCIoIIsIWCASC\nIoLIYQsEAkERQRQwEAgEgiKCiLAfncJ4gAWg5dmZD+/0hDEf+e2ZawLMHFM4uh6VEgtF92aYyzPX\nLBWQ9sw1AdJuFM7H+7SlRqHoPglsNnHRUSAQCIoGIsIWCASCIoK4S0QgEAiKCCLCFggEgiKCuEtE\nIBAIiggiJSIQCARFhMdIiciyrAGWArWBLGCAoigX8rSPBd4AkoE5iqL8JstyceB7wAm4AfRVFOW+\nBTOfdsUZgUAgKDo8XsWZVwBHRVEaA+OAnFL0siwHAj2wFyJvB0yTZdkZmAR8ryhKM+A4MPhBh1c0\nI2xJwn92f1xr+GDNMnFu5HIyInKL7nq3roPvqK5IEiSfvEzYuC9z2pyrlqXBlunsqTkIa9aTnfv2\n1Nkw5i/7ilWL5zyxfVqtNmb8tJvw63HoHLRM7t6KiiVyq2R//dcxthwLx9VRz1tt6tG8RqWctu92\nnSQ+JZ3hHRurF5Ykik8Yhl72xWY0ETt5Aear9vJMetkX77Hv5HQ11PInZvgUMvbb60i693wVh+LF\nSFiovioJkoTLOyPQVq4KJiOpi+ZijboOgLZyVVwGvpvT1UEOIGX6BMzhoXgu/w5LpL1UlvHgXjI3\nb1CtW3rqUAzVK2Mzmoj66DNMV3JrQ7o0r0+JYT1Aksg8c57oKUvReLhSbt5oNK7OWG4lEzX+cywJ\nSao0nd8egUOlqthMRtIWz8UanWurc//8tqbOmIDp3Clc3hmJpmQZJJ0DaV98huW8ylJskkSxscPR\n+VXBZjKS+MmnmLNLb+mqVcFzZG69RkPNAOJGT8J0KRKvSaNAqwVJInHGfMyR11TKSgybPpTKAb6Y\njCYWjlnIjYiou/p8/M1UDm4/xO/f/YFGo2HQpIFUq+WHzqDju/lr+Ofvw+rsVcPjpUSaAlsBFEU5\nJMty/Txt/sAuRVEyAWRZPg/Uyt5mRnafLdn/L7ifwEMd9r2qwsiyPAsIA34H5gE+gBa4CoxUFCU6\nu18z7N8gOsAF+FpRlKXZbeOAttltVmBUdrGDh1KyfTAag44jHSbiEeRHtam9ONlnHgBaF0eqTe5J\nyKtTMSWk4DO0EzpvN0zxKWhdnag2pRc245OfpPyrNevZvHUHTo5PtpLKztOXyDJZWD2iK6ciopn/\ny34WDugAwPkb8Ww5Fs63I7oC0OezDQT7lUNCYuq6HZyJvEnb2lUeSde59XNIBj03er6PoVZ1vEcP\nIua9KQAYlUtE9RsNgEu7ZjjfjCdjfwiSQU/xqSNxrCmT9tfeR9LVN2oKej3Jo4fgIAfg0m8IKdPt\nhYstly+Q/JG9BJm+SUv08XGYjh1GVzuIrN07SP+i4JXD78Tt+cZIBh2Rr32AYx2ZUh8O4No7HwOg\ncXGi5Nh+XOk5DktiMl4Du6L1csd7YDfSj54l/r8/4vxcHUp+8BZR4wt+DLqGTZF0epLHDkFbLQDn\nfkNInZFra8oEu62651qiT4jDdPwwTt3fwhJ5mbSFM9D6+KKtXFW1w3Zq2QTJoOdm/2Hoa/rj+f7b\nxI2aBIAp/CKxb9vLcDm1aY4lNo7Mg0fwmjyW1B9/IWP3fhwb1cdj6ADix0xRpfvcC43ROeoZ8cpI\nqtetzqCJA5nSf1q+Pm+N6Y2rh1vOcpsurXHQOTCy8yi8S3vTvEMzVZqqebyLju5A3m9siyzLDoqi\nmIHTwIeyLLsBeuA54Is7tkkBPB4k8DgpEQnYCGxUFKVldkj/FfCbLMtaWZZ9gc+BnoqitASaA71l\nWX5RluUAoBPwvKIoLYAR2dsWCM+GMnE7TwKQdPQ87nmckmewTGroFapN6UX9X6ZgjL2FKd5ezTrg\n00FcmLEWS3rWY5h9byqULcPCGROe+H6PX4qiSXbV6lqVSnP2amxO26WYBIKqlsOgc8Cgc6BicU/O\n34jHaDbTMbg6A54PemRdx3o1Sd9nj5izToVhCKh2Vx/JyZFiQ3oTP2upfdmgJ/WXP0lc8f0j6zoE\n1MJ01B5BmZVzOPjJd3cyOOLUoy9pKxYBoK1aDYeq1XCf+RmuY6ciFVNfJd4pqAZpe+zxQuYJBcea\nfrltdf3JCo+g5LgB+Hw/B0tcIpaEZAxVK5K6xz5GGUfP4RQUoEpTF1AL03G7rZbwczhUvbetzj36\nkp5tq65uA2xmE25T5uL0ep+c7dVgqB1IxgF7XU7jmVB0/nfrSo6OeAx6i1vz7PVAby1cRsa+Q/ZG\nrRZbllG1bo0GNQjZZR/jsONh+NXyy9fe9KWmWK02QnaF5KwLahFEfHQ801ZN5f3Zwzn01z+qdVXx\neCmRZOxlEG+jyXbWKIoSCizGHoEvBv4B4u7Yxg249aDDexyH7Q0kKYryy+0ViqL8BVzE7px7AasV\nRYnJbssAXsBegDcJqAj0k2W5nKIoJ4AGBRV2cHPGnJybl7dZrEhauyk6bzeKNanB+Y/XcPyNmVQc\n9BLOvmXwHdWVuD+PkXou8n67fSyeb9UUB4cnn2FKyzLi6qjPWdZKEmaL/WTxK+PNsYs3SMs0cist\nk5MR0WQYTbg7O/Jc9YqPpatxccaamueRaqsVtPlPF7dXXyRt+x6st5LtXZJTyThYoB9J90VydsaW\nnqtrs1pBo83Xx7FdB4z7d2FLtgcmlmtXyFjzFckfDsd4aC8ug4er1tW6OmNJyXOtJ4+9Wi93XBrW\n4ubcr7kyYBJeb72CvlI5MkMv4da6EQCubRqhcVL568rZGVvag201PJ9ta4rdVsndA42rGylTRmM8\ncgDnvkNQi+SSXxer5a731uXl9qT/vRtrUvZ7m5QMFgsOPuXxHD6Y5JWrVes6uzqTlpyra7VY0WTr\n+sg+tHqlJavnfZtvG49i7pStVIZJb03mx2Xr+eDTEap1VWGzFvx1N/uBlwBkWW6EPaome7kE4KYo\nShPgbaACcCbvNkB74IE/TR/HYcdid853cgl7iqRs9v85KIqSpCiKRVGU69gj7CbAQVmWw4D/FFTY\nnJKOg6tjzrKkkbBlOzFTQgrJJy5ijE3Ckp7FrUOhuNX0oUzXZpTt0YqgjZPQl/Sk3rrxKs0tHFwM\netLyRDNWmw2H7JPct7QX3ZsFMmT5Zmb9tIdAn1J4ujg9EV1rWjqavPvSSGDJf5K6dmhN8oatT0Tv\nNrb0dCQn59wVknTXpPL6Fm3J2p47B4rp1DFMp48DYDy0Fwff/JFbQbCk3mmvJsdeS2IKGafPY4lL\nxJaeSfqRMxj8fYlf/iO68qXw+X4O+vIlMUXFqRO9w1bpHrYaWrQl689cW20pSRgP24vcmg7vv3dU\n/hBsaelIznlslTR3vbfOL7Yh7Zc/8h9LUB2Kz51G/ORZqvPXAOmp6Ti75upKGg3WbN22XdpQvLQ3\ns9fN4vluz9N54KvUbxlE8q0U/vnL/ivi9KHTlKtcTrWuKh4vwv4ZyJRl+QD2PPQIWZZHyrLcCXs0\n7S/L8hHgD2C0oigW4BOguyzL+4HG2KPv+1KQkDADuDN0cAUqZb/uxA97FF0W+7dIDrIs18b+JZEC\nJCuK0i97fX1giyzLOxVFSXjYAd06rFCiXRAxvx7CI8iP1NArOW3Jpy/jWr0COi83zElpeAT5ce27\nHexvlBt1NT2yiGOvT3+YzL+COr5l2H0mghfq+nEqIhq/Mt45bQmpGaRlmvhmeBdSMrJ4Z9mvVC2j\nPh1wL7KOn8W5ZSPStu3BUKs6xvMR+dolV2ckvQ5LTOy9d/CImENPo2vwHMZ9O3GQA3IuJOboOrsg\n6XRY43J1XYeNwXhgD8Z9O9HVCsJ8MVy1bsaxc7i2bkDKlr041pHJUiJy2jLPXsDg54O2mDuW5FSc\n6lTn1rqtOAfX5Na6rWQcD8XthSZkHDunStMUehp98HMY9+9EWy0A8z1sxSG/raZzp9EFNcJyMRyH\nGrWxXIlALVknz+DUvDEZf+1GX9Mf08U7dF1c7npvDUF18PxgKLHvjcMSfVO1JsC5I+do+HxD9vy2\nl+p1qxMRlqv75YzcjGjPEW+SGJtIyK6jlPUpS3DrYPZt2Y+vf2VibzzZ8+0uHuO2PkVRrNij57zk\nvcBw1x0g2RmIFwuqURCHHQrUlWW5jKIoUbIsO2JPeXQBXpJluaOiKJsBZFl+EagK7MYeXW+SZXmd\noiixsiy7AsuBaUBlYJAsy50URTEC4dhzNwWaKuvmH0fwblGL4N+mgSRxdvgyKg7uQEZENLHbjnJ+\n+g/UW/sRADG/HiQt7GpBx+NfR+tAXw4pV+m9cAPYbEzt0YZvd56gQgkPWtSoxOWYBHp8uh6dg4YR\nLzdBq3kyd2qm/b0fp8b1KPvtApAkYid+ikfvLpiuXCd91yH0PuUx34h5+I5UYjy4F12d+rjPWQKS\nROpns3B8+TUsUdcwHT6AplwFrDej822T/s1yXN8bh+NLL2PLzCR1kfq7dFK2H8ClSV181s0DSSJq\n3AK8+r6KMfIGqTv+IfbTVVT4yn4RMmXLXrLOR2LNMlJ2rv0CnTkmnqiPFqrSNB2y2+o2ewkSEqmf\nz8Kx02tYorNtLXu3rZk/fYfLu2Nwn70Um8VM2sIZ99n7/cnYtQ/HhkGU/PJzQCJh2hxce3TFfO06\nmXsOorvHe+s5cgiSzgGvKWPt9kZeI3HmfW9muCf7tx6gXrO6LPj5U5Ak5n8wn84DX+VGxA0O/Xnv\n3PSWH7YybMa7LPxlAZIEn3+4SLW9qrDZnu7+HxPJVoADlGW5MzABSMd+hXOFoigrZFkuCSzE7oDB\nfpfIiOyUB7IstwMmYnfEbsBKRVGWZbeNB14DUrFH3bMVRdmUV/fPUq8Xyuj9f5peNarQpld98hd+\nC4KYXvXpMyBa//BOT4FtV7dIj7uPjDUTC+xznN78+LH11FKgd1RRlI3Y7wi5c/1N7DeD32+77cD2\n+7RNB4pGXkIgEPz/QDyaLhAIBEUEMVufQCAQFBH+5Tls4bAFAoHgNiLCFggEgiKCcNgCgUBQNLBZ\nRBFegUAgKBqICFsgEAiKCOK2vkfHIBXO4BXGQywOwQWeSuWJkp52z9vknzr6m4VTO8/ZTf0sc4+L\nKemZP18BFN4ND6nWwnko6olgFXeJCAQCQdFApEQEAoGgiCAuOgoEAkERQUTYAoFAUEQQOWyBQCAo\nIoi7RAQCgaCIICJsgUAgKBrYRA77KSBJVJ01ANcalbAaTYSP/C+ZEbmVOYq1roPPB93sFUtOXeLC\nuJUANDy+nIzLUQAkh4QTMUNddW+r1caMn3YTfj0OnYOWyd1bUbGEZ077138dY8uxcFwd9bzVph7N\na1TKaftu10niU9IZ3rHxYxh+b06dDWP+sq9YtVh9pZX7IkmU/XgIjtUrYzOauP7h5xgjo3KaXVsE\nUfK9N0CSyDhzgahJy9B6uFJ+wSg0rk5YElO4/tEiLPFJqnU9xwxH51cFjCYSZszDcu0GADq/KniO\nGJrTVV8zgLgxEzFfjqTYhNFIWi1IEokz52O+orLKkCTh9eF76KtVwWY0Ef/xp5ivZutWq4LXqNxi\nt4ZAf25+MJnM7Mrjbj06o/X24tailao13UaMwKFKFTCZSJ47F8v16wA4VK2K27vv5nTVBQRwa8IE\njIft9Q11tWvjMX48ca+9pk4zW7fYuOHo/apgMxlJ+PhTzNdybS32QR5bawYQO2oSpkuReE8aBQ5a\nQCJhxnzVdR0lSWLUzOFUDaiCMcvErNHzuB5x464+c1fPYN/2A2z6dnPO+opVKrDityV0rNMFY5ZJ\nvc0F5X/5LhFZllsCbyuK0j3PulnY65j9DszDXpBXi70azUhFUaKz+zUDJgE6wAX4WlGUpQXR9W4f\njMZRz4n/jMetnh++U3pz7i27s9K6OOI7qRcnO0/BnJBC+aGd0Hm7o3VzJvX0Jc72nv3I9u48fYks\nk4XVI7pyKiKa+b/sZ+GADgCcvxHPlmPhfDuiKwB9PttAsF85JCSmrtvBmcibtK1d5ZG178dXa9az\neesOnBxVVux+CO7tGiHpdVzqOgqnOjKlP+rPlcGfAKBxcaL0uH5c7vEhlsRkig/qgtbLnRKDu5Ie\ncpbYpetxaVKbUqN6c0NlSSfHFk2R9HpiBwxDX9Mfz+HvED96IgCm8xeJHTISAKfWLXCKjSPr0BGK\nTRpL6vri420VAAAgAElEQVRNZO7Zj6FhfTyGDCB+3GRVuk6tmiDp9US/9R76QH+KjXib2JGT7Lrh\nF4kZZC8F5ty2OZabcWQeOIJk0OM98QP0NWXS/96nSg/A0NRua+LQoegCAnB95x2SJkwAwHzhAonv\nv2/v16IFhtjYHGetKVEC527dwOHRPr5OLe22xvTLHuMRbxP3Qa6tNwfbbXVqk23rwSN4TRlLyo+/\nkLF7P46N6uM5dABxY6ao0m3+YlP0Bj2DOw2jRj1/hk16h3H9JubrM2hsP9w93fKtc3Z1ZtjkdzAZ\nn6Kjvs2/PCXyZAoA3o2EvULNRkVRWiqK0gz4CvhNlmWtLMu+wOdAT0VRWmKvEdk7uybkQ/Fo4E/i\nDnuV7JRj53HL4wjdg2XSQq9QZUpvam+ahik2CVN8Mm61fdGX9qLWhsnUXPMhTlXKqjbq+KUomvhX\nBKBWpdKcvZpbEPRSTAJBVcth0Dlg0DlQsbgn52/EYzSb6RhcnQHPB6nWKwgVypZh4YwJT3y/zvVr\nkLrnGAAZJxScAnMrkTvX8yczPILSH/Wn8rrZmOMSsSQkY/CrSMquowCkh4TiUj9Ata6hdk0yD9kj\nV+OZUPTV764KLjk64j6oD7fm2wtMJ322jMz9h+xtDlpsRvVPMzrWqUlGdsRsPB2KPqDaPXU93u5D\nwrwl9mW9ntTftpP0pbpfarfRBQaSle2ETefOoZPvUQHd0RHXvn1JWZxdTFuvx33kSFIWqqsfmRdD\nnUAyD+YZY/97j7Hn4LdIzLb11oJlZOyzjzHaRxvjWg1qcminXffssVCq18qv27JDc6xWW06f24yd\nM5Lls1aSmfEMnqB8vKrpT52n5bC9gSRFUX65vUJRlL+Ai9idcy9gdXbFYBRFyQBewF5t/aFo3Zww\np6TnLNssVtDaTdF5ueHZpCaXPl7D6R4zKDewA06+ZTDGJHJ10c+c6jKVK5/9TPUlw1QblZZlxNUx\nt16dVpIwW+xvnF8Zb45dvEFappFbaZmcjIgmw2jC3dmR56pXVK1VUJ5v1RSHR4y0HoTG1QlLSm4t\nQpvVkjPGWi93XBrVImb210T2nYx335fRVy5L5rlLuLVtCIBb24ZITuqjfo2LM7bUe+vexqVTezL+\n3o01KRnA/tdiwaFiBTyGvU3yytWqdSUXZ6x5dMlzTt3G9ZUXSf9rN9Zb2bopqWQeOqpa6zYaFxds\nqam5K6xW0Grz9XF66SUyd+3ClmRPLbkPH07aunVY4+IeQ/cOW+81xi+3t9t65xj7lKfY+4NJWqF+\njF1cnUnLc05ZrBa02bqV5Uq0e6UNK+d+nW+bfiP7cODvf7hw7pJqvUfCaiv4qxB4WjnsWOzO+U4u\nYU+RlAVO5G1QFKXAyU5LSgZaV6ecZUkj2T9ggCkxlZQTFzDF3gIg6VAoLjUrkfDnUWxme5/kw2Ho\nS3mpMgjAxaAnLSs3srDabDhkn3C+pb3o3iyQIcs3U8bTjUCfUni6ON1vV/96rKkZaPIcvyRpcsbY\nkphMxqnzmOPsY5x2+CyO/r7ELltPmcmDqLx2Fik7j2C6od6pWNPSkZzzjJsmV/c2zi+0Jf7DKfnW\nGYLq4Dl6OAlTZqrPXwO2tHQ0Ls55dKW7dF3atyF2zDTV+74f1rQ0JOe8mpq7cqhObdtya7I9vaPx\n9kYXGIi2XDn7spsbHpMmkTRN3TFZ09LR5B1j6e4xdmnfhrixU/OtMwTVwWvce8RPmqU6fw2QlpqO\nc57PrUajwZKt275rO4qXLs7nP35KmQqlMZlMRF2N5oXObbkZFct/urfHq4QXC76fy9Au76vWLjD/\n47f1ZQB3hlGuQKXs1534YY+iywIV8jbIslwb0CiKcvxhoslHwvBqV5+4Xw/iVs+PtLArOW2ppy/h\nUr0iDl5umJPScAvyI2rNX/h80A1TYgrXlvyKS4APWY/gTOr4lmH3mQheqOvHqYho/Mp457QlpGaQ\nlmnim+FdSMnI4p1lv1K1jPovhX8L6UfP4da6Acl/7MOpjkymEpHTlnH2Io7VKqIt5o4lORXnujKJ\n67bh3KAGCWu3kXEsDPcXnyP9aKhqXeOpMzg2bUzG37vR1/THfCF/ZCW5uIBeh+VmbjrKEFQHjxFD\niXt/HJbomEeyN+vEWZyaNyL9z93oA/0xXbicX9fVBUmvwxITe589qMd05gyG554ja9cudAEBmC/d\ny1Y91li7pjU+nvjevXPai2/cqNpZA2SdPINTs8ak/2Uf47tsdXFB0uW31RBUh2KjhnJz2Dgs0TdV\nawKcPnKGJs83Zsfm3dSo58/F0Fx7l07/Iuf/fiP7kBCbwD+7jvB6014563869D0jeox+JO0C8y/P\nYT+uww4F6sqyXEZRlChZlh2xpzy6AC/JstxRUZTNANn56arAbuyR9iZZltcpihIry7IrsByYBjzU\nYcf9cRjP5rWovfkTJElCeX8J5Qb/h4zL0SRsD+Hy9DUE/mDP68ZuPkB62FWuLtqEvOQ9vNoGYTNb\nUIYvUW1s60BfDilX6b1wA9hsTO3Rhm93nqBCCQ9a1KjE5ZgEeny6Hp2DhhEvN0GreVoZp6dP8raD\nuDati+/6uSBJXBuzEO/+r2CMuEHK34eJmbuaSqvsziLpj71khUdiyzJSfp79oqApJp7r4z5TrZux\nax+GBkGUWLEIJEj8eA6ub3TFfO0GmXsP4FCxPJao6HzbeIwYiqTT4TVprF37ylVuzVqgSjd95z4c\nG9Wj1NefIUkScVPm4vZmF8xXb5Cx5yC6iuUx33i0L4P7kbV3L/r69Sm2eDFIEsmzZ+PcrRuW69fJ\nOnAAbfnyWKKjH74jlWTs3IdjwyBKffk5SBLxU+fg9mZXzFev2231KY85Kr+txT4YgqRzwHtq9hhH\nXiNxhrox3r1lH8HNg/jvL4uQJJg+Yg6vD+rK9cs32PfngSdm3+NgM/+77xKRbI85B6Msy52BCUA6\noAdWKIqyQpblksBCoHJ216vACEVRrmdv1w6YCFgAN2CloijL8u57T+luhfJ1F/x1s2euWVjTqyoN\n3isUXc8S6Q/v9BSwmJ79l6ije+FMJZuVqn14p6fAG1GFE6Xuv77jseexTR39aoEP3nXuz8983tzH\nzmErirIR+x0hd66/CfR4wHbbgcKZjFkgEAjuxf94DlsgEAj+d/gfz2ELBALB/ww24bAFAoGgiPAv\nv+goHLZAIBDcRkTYAoFAUEQQDlsgEAiKBo97m/PTRjhsgUAguI2IsB+dJYZnMJ3iPZg55rdnrpme\nVji3pMuHPy8U3VV1JhWKbl1SH97pCWOLeebPVwAQikuh6KZY7jWNUBFBOGyBQCAoGtyeIO7finDY\nAoFAcJt/t78WDlsgEAhuIx6cEQgEgqKCcNgCgUBQRBApEYFAICgaiJSIQCAQFBFs5kd32LIsa4Cl\nQG0gCxigKMqF7LY62OsD3KYR8ApwGAgHzmSv/1lRlPtW/iiSDluSJPp/MhifgEqYsswsH7uYmMjo\nu/qMXTWBkO2H+WvNNlw8XBn22QicXJ1ISUzhi3FLSY4vcBnJ2zul+IRh6GVfbEYTsZMXYL56AwC9\n7Iv32Hdyuhpq+RMzfAoZ+0MAcO/5Kg7Fi5Gw8CvVmmU/HoJj9crYjCauf/g5xsionGbXFkGUfO8N\nkCQyzlwgatIytB6ulF8wyl5INzGF6x8twqLW1gJw6mwY85d9xarFc57cTiWJpjPewiugIhajmb2j\nV5IckVv9pPHUXpQKroYpLROA7f3m4+jpSosFg5EkiZTrcewd8yWWTJVVvSWJijMG4xxQCZvRTMTo\nxWRF5J5T7q3qUXbE6yBJpJ+6yJXxyyk9tDMeLesBoHV3QVfCk5P1+qrS9Jlp17RmmYgYvSSfpker\nepQd+RogkX76IpEffZGtWRcABw+75om6/dTZescxNJyZPd5ZZg6OXklKnvEu26oWtUd2BiDhdAT/\nfLTqMaQkxs8ajVyjKkajiSkjZ3I14tpdfZas+ZSdW/eyfvXPuLq5MHPJFFzcXNDpHJg7+XNOHT1z\nH4UnwOOlRF4BHBVFaSzLciPgU+BlAEVRTgAtAWRZ7gZcVxRlqyzLbYEfFEUpUFXwp1Z+Q5bllrIs\n22RZ7n7H+lOyLK/K/r+sLMvp2QYUmOAXGqIz6Jn46jh+mL2aXhPu/pC8PupNXN1dc5ZffbcrYUdC\nmdz1I7at+p03xvRUbZNz6+eQDHpu9HyfhIVf4j16UE6bUblEVL/RRPUbTfLaX0n7ax8Z+0OQDHpK\nzBqHR/dOqvUA3Ns1QtLruNR1FNFzVlH6o/45bRoXJ0qP60fkgGlc6vwBpms30Xq5U2LIa6SHnOXy\na2OJX72ZUqN6P0Dh0fhqzXomz/oMY5ZKx/gQKr0YhNag49eXp3Jk5loaTsxfA6N4YCW2vDmb37tN\n5/du0zGlZNBgwhuEfvc3m7t8TNTBUGoNaq9a1/PFhmgMesJeHse1maspPzH3nNK4OFJhfB8u9PmE\nsI5jMF67iYOXO9FLNqJ0m4DSbQLGqDguv6+uJFqxFxuiMegI7TSOazO/pcKk/JrlJ/ThfO/phHYc\nS9bVvJoTUbpNxBgVz6Xh6suw5aVi9nhv6TSVYzPXUn9S7ng7uDgSNOENdvSex5aOU0i9GovBy+2R\ntVq3b47BUU+v/wzis0+WMmrK3T5q2LjBuHvkavR++w3+2RtCv1eHMHH4J4yfOeqR9QuCzVrw1z1o\nCmwFUBTlEFD/zg6yLLsAU4Hh2auCgCBZlnfLsrxeluUyDzq+p10vKQzIcdiyLAdCvsev+gKfA0PV\n7FQO9ufk7mMAnD8eTpVaVfO1N3ypMTablRO7c8tDlverwIldR+0HFRKGXN9flSEAjvVqkr7PHjFn\nnQrDEFDtrj6SkyPFhvQmftZS+7JBT+ovf5K44nvVegDO9WuQusdua8YJBadAv9y2ev5khkdQ+qP+\nVF43G3NcIpaEZAx+FUnJtjU9JBSX+gGPpP0gKpQtw8IZE574fksHy1zddQqAm8cuUqJ25dxGScK9\ncmmazelPx58nUe315gAU8yvH1Z0nAYg5Ek6pBrJqXddgf5J22cc57Vg4LrVzzynX+tVJD4uk/KS+\nyBtmYIq9hTkhOafds30jLElpJO85oU6zgT9JO4/nataqkk8zIyySCpP7Un3jdExxSfk0i7VvhDkp\nleQ9J1XbmpeSDWRu7LSPd9yxi3jXyh3vkvX9uBV2jfqT3+SFjRPJiEsmKyHlkbXqNqjN/h2HADh1\n7CwBtfN/Bp//TyusViv7dx7KWfft8rWs/3YTAFoHLVlPOEC4C6uK1924A3l/ylpkWb4zi9EfWK8o\nyu0q4GHAJEVRWgCbgEUPOryn7bBPAj6yLHtkL/cE1gDIsiwBvbD/bNDLslyzoDt1dnUmPSW3JqDV\nYkWjtZtSoVpFmr7cnB8//SHfNhFnL1O/bQMA6j8fjMHpzmLvD0fj4ow1NS13hdUK2vxD6Pbqi6Rt\n34P1lv3DZU1OJePgUdVaOZquTlhScjVtVkuOptbLHZdGtYiZ/TWRfSfj3fdl9JXLknnuEm5tG9qP\np21DpEew9WE836opDg5PPqOmc3PCmOe9tVmsSNn26pwNnPt6OzuHLWNrzzkE9G6Ll38F4s9F4vN8\nEAA+7eqhewR7tW7OWO7QvT3ODl7uuD8XyLUZqznfaxqlBnTEULlsTt8yQ7twY8Fa9ZquTvk1rXdq\n1uTq9NWE9/yYUgP+g8E3j+a7nbkxf51qzTvRud4x3tbc8TZ4uVH6OX+OTl/L3z3nEDDgBdx8Sz+y\nlqubC6kpuVMDWC0WtFp73cmq1X1p/2o7lsxZkW+blORUsjKz8C7hxYzFU/hsRr6yr0+cx4ywk7HX\np72NRlGUOwt6vgmszLO8A9iZ/f/PQN0HHd+zqEi6Aeic7aAbALfLI7cBTiuKEgt8hYooOz01HUcX\np5xlSSNhtdhHsHmXVhQr5c3EHz6mRdfWdBjQidot6rJp6U+UKF+SKT9Op0T5ksRHxd1v9/fFmpaO\nJo8uGgks+d851w6tSd6wVfW+76uZmpFPU5I0OZqWxGQyTp3HHHcLa3omaYfP4ujvS+yy9ejLl6Ty\n2lnoy5fEdEO9rYWFKSUDfb4x1tidJ2DOyOLMl9uwZBoxpWVy48A5vAIqcmja9/i0q0uH9eOx2Wxk\nJqqPAi0p6WjvOKduj7M5MYW0kxcwx9rHOeWfszjXsEeijn7lsSSn5cs9F1gzNQONq2OBNFMPncO5\nRqUcTfMjat6JKTUDneu9xzsrMZW4k5fIjE3CnJ5FzCEFrxo+j6yVmpKGs2vuD2yNRoPFYi8Y0LFb\ne0qVKcHKDYvp9HoHeg3uTpNWjQDwq16FFT8tYtHMZRw9ePye+35S2MwFf92D/cBLANk57NN5G7MD\nV4OiKFfzrF4JdMn+vw3wwOjuWTjs77GnRZoDe/OsHwhUlmV5K/Ziva/licQfiBISRt1W9ojKr241\nriiROW1rZn7DhFfGMK37BHb/tIPfV/7Kyd3H8W9Yg7/XbmfKa+OJjoxGCQlTbUjW8bM4N7NH6YZa\n1TGej8jXLrk6I+l1WGJiVe/7fqQfPYdbS3sqzKmOTKaSq5lx9iKO1SqiLeYOWg3OdWWyLlzFuUEN\nEtZu43L3cRgjo0g/GvrEjudpEx0SToXWtQEoWa8KiWG557aHbxk6bpqEpJGQHLSUDq5G/OkIyjer\nyZFZ6/m923RsFivX9qi/KJUaEoZHa/s55VKvGhlhuedU+ulLOMkVcSjmBloNLvVkMs7bj8u9WW2S\ndh57JFtTj4TimUczPfTKAzSrkRl+LY/mk3FcN4+EUy57vIvXq8Kt0NzxTjgdgadcHkMxVySthuL1\nqpAUfv2RtU4cOUWzNo0BqFWvBufDcieJWvDxEt58aQD9Ow/l13W/8+3ytezfeQjfapWYt2I6496Z\nzL4dh+636yfGY0bYPwOZsiwfABYAI2RZHinL8u0LWNWAiDu2GQe8I8vyLuBtcnPb9+Sp3yWiKMql\n7ET7e8CHgC9QHAgEfBVFsQDIsrwC6IM9p/1Ajmw9RK2mtZm2cRaSBMtGLaLDgE5ER0Rx9K8j99zm\nxsXrDF3wPgCJ0fH8d8xi1bak/b0fp8b1KPvtApAkYid+ikfvLpiuXCd91yH0PuUx34h5+I5UkLzt\nIK5N6+K7fi5IEtfGLMS7/ysYI26Q8vdhYuauptKqaQAk/bGXrPBIbFlGys8bCYApJp7r4x7vwtSz\nJGJLCOWb1aTTpkkgSewe+QWBA9uTFBHDlT+PcWHDfl7+dSpWs5nzP+0jMfw6Di6OtFr0DhajmcTw\na+wf/41q3VtbDuHerDbVN80CCSJGLqLUwE5kRkSR9OcRrs36Fr81UwBI/G0fmYrduTr6liN576Pl\nkRO3/IN78zr4/zITJInLIxZRalAnsi5HcevPI1yb+R3Vvp8MQMLm/WTc1qxSTnW+/H5c2RJCmeY1\nefGXSUiSxP4RX+A/qD0pl2O49ucxjs/8kbbfjwUgYvM/3FKuPWSP9+fvP3bTqHkDVm/+AkmCie9P\np9fg7ly9fI1d2/fdc5vhH72D3lHP2E9GAJCanMrwt8Y+8jE8jMcpmq4oihW7081LWJ72I9jvJMm7\nzWWgVUE1pKc1Ybcsyy2BtxVF6S7L8jCgl6IoDWRZfhH4EVipKMrIPP0bAKsBf0VRbACv+7xSKHex\nz3RPf3inJ0x6mv6Za4KYXvVZYLP9/5pedV4hTa96KvrgYw90TMuWBfY5pXbteuZv7FOLsBVF2QXs\nyv5/EdlXPxVF2Yr9auqd/Q8D1Z/W8QgEAsHDeJwI+1lQJB+cEQgEgqeBzVo4v4YKinDYAoFAkI3V\nIhy2QCAQFAlESkQgEAiKCCIlIhAIBEWEp3TT3BNDOGyBQCDIRkTYAoFAUEQQFx0fg1LSk5+0qCB4\nVEp85pr6m/eenOBpU1gPsLx1Ylqh6K6o++ztLWkpnN/ZxWyWQtGtaihRKLpPAhFhCwQCQRGhsJ5K\nLSjCYQsEAkE24rY+gUAgKCJYRYQtEAgERQOREhEIBIIigrhLRCAQCIoI4i4RgUAgKCKIHPZTQJIk\nun3Sn3L+PpiNJn4Yu5y4yNxKL50n96FK/epkpmUAsGLgXJzcnOkx5x00DhokSWLth19w81KUWmFc\n3hmBtnJVMBlJXTQXa5S9ZJK2clVcBr6b09VBDiBl+gTM4aF4Lv8OS+RlAIwH95K5eYMqTc8xw9H5\nVQGjiYQZ87BcuwGAzq8KniNyS2HqawYQN2Yi5suRFJswGkmrBUkiceZ8zFeu3k/hvrpNZ7yFV0BF\nLEYze0evJDkid4wbT+1FqeBqmNIyAdjebz6Onq60WDAYSZJIuR7H3jFfYsl88lWuT50NY/6yr1i1\neM6T26kk0WL6W3hn27tzTH57m07pRek89m7pPx+DpyttFgwGSSL1Why7xn6JWY29kkTdWX3xDKiI\n1Wgi5IOVpOXRLN26Nv4jOyNJkHjqMsc/XIWDmxMNFg9B5+aERufAySnfkXD0gmpbA2f1w71GRaxG\nMydHfkF6Ht2SrWtT7YMuIEkknbrM6XFfoXU2UG/pu+g8XLCazJx4bxmZ0eqeV5AkiUGfvE2lgMqY\nskwsHbuY6Miou/qMXzWJw9v/YfuarTi7OTNy8WgcnR0xGU189v58bsXeUmevCv5nctiyLI8D2gI6\n7EXeRwHDgHpAQp6u3yqK8qUsy8WAeUDV7G2uAIMVRUnKri7zCfaakm7Aj4qifFrQYwlsF4zOoGNB\n54lUquvHqxN6sWLgvJz2CjV9Wdp7Bml5irF2mdKXPau3cnp7CNWb16bjmB58+XaBJQHQN2oKej3J\no4fgIAfg0m8IKdPHA2C5fIHkj+wlyPRNWqKPj8N07DC62kFk7d5B+hePVqbLsUVTJL2e2AHD0Nf0\nx3P4O8SPngiA6fxFYofYi/Y4tW6BU2wcWYeOUGzSWFLXbyJzz34MDevjMWQA8eMmq9Kt9GIQWoOO\nX1+eSsl6VWg4sQd/9l+Q0148sBJb3pxNVmJuBZfm8wYS+t3fXNx0EPmNltQa1J7jn//ySHbfj6/W\nrGfz1h04OT7Zh6p8XwhC66hj4ytTKVW3Ck0m9mBLHntLBFbit56zycxjb6u5Azn73d+c33QQ/+4t\nqT2oPUdV2Fu2vX2Md3acgle9qtSe/CYH+s4HwMHFkcCJb7C7yycYE1KpNuQ/6L3dqNq3HTf3neXC\niq24VilDw2VD+bvdBFW2lm5fH62jjv3/mYxnvarUmNKTI2/ZPwtaF0f8J73Jwc4fY0xIocrQjui9\n3SjXuSm3Tl3m/PyNlH+9OVWGduTsxNWqdBu80AidQc+Hr46hWl2Ztyb0Y9bA6fn69BjVE1d315zl\nVl3bEBkWybczV9G2ezteGdyZVZ98pUpXDf8Tc4nIshwAdAKaKIpik2W5DvANcBwYk11F5k5+AJYr\nivJz9j5GAMuxF+RdDPRWFCVMlmUdcECW5R2KohSosmiVYJnQ3fY6ehHHz1MhsEpOmyRJlKhUmu4z\nB+FW3IND63ZwaP0uNn3yLRkp9tJfGq0GU5b6yM8hoBamo4cBMCvncPCT7+5kcMSpR1+SP3wPAG3V\najhUrYb7zM+w3rpF2hefYUtMuHu7+2CoXZPMQ/Y6lcYzoeir360pOTriPqgPsYPtXxhJny3Dmppm\nb3PQYjOqt7V0sMzVXacAuHnsIiVqV84jKOFeuTTN5vTHqbgHytpdhK/bQzG/cuwdsxKAmCPhNJrS\nU7Xuw6hQtgwLZ0zgw2lzn+h+SzeQuZJtb8zxi5Sold9ej8qlaTnbbm/oul2EZdt7Zazd3uiQcJpM\nVmdv8QYy0Tvt53HCsQsUyzPG3sF+JIVepdbkN3H1KcnlNbswxqdw/ostWIwmwH4eWzNNqm31aiBz\nc4dd99axC3jU9s1tC65GSuhVAqb0xLliSa58vxNjfAqXV2wBjT36dCpXHFOy+jJ6/sH+HN9tL1gc\nflyhSq2q+dobv/QcVps1pw9ApBJJuSrlAXB2c8ZserpPBP/bUyIFrZqeBFQE+smyXE5RlBNAg/t1\nlmXZByh921ln8zkwOPv/GOBdWZaDsEfrTQrqrAEcXZ1znC+A1WJFo7Wbonc2sOebbax+fxHL+syg\naa92lK1ekbTEFKxmCyV9y/DK+F5s/eyngsrlIDk7Y0tPy1m2Wa2g0eY/tnYdMO7fhS05CQDLtStk\nrPmK5A+HYzy0F5fBDyyKfBcaF2dsqXk1LaDN/7a5dGpPxt+7sSYlA9j/Wiw4VKyAx7C3SV6pLhIC\n0Lk5YcwzxjaLFSlbV+ds4NzX29k5bBlbe84hoHdbvPwrEH8uEp/n7VXAfdrVQ+f05KcWeL5VUxwc\nnnwmT+/qhDH5/vaeXrWdv95bxm+95lCzd1u8q1cg7v/YO++4KI73j7/3jnoUFRvYFWUVK6ho7L2k\naGKLMTG22E1sMZqo2HvUJLbEaDTNxF4TWxJR7NixsCgKioIi0uu13x93Hgc2Fgvy++7b171kd2bn\nM+2em52dnedyBBXM5a3Q1hcbjbzy2jo7oktKy9I0ZGnaublQorE3wTP/JLDXfCoP7IBzJXe0iakY\n0rXYFy9E/aXDCJ6zXnZZbVwc0Vm1LfrsukUbe3NlxjpO9JpLpYEdcarkbopnMPLGpklUHNCe6L8f\n7+z6aWicNaQmZfVl6+9tOa9yNO3cnD8Xrst2TXJcInWa1eHbf5bSedB7/Lt+v2xdORgMQq4/+UGu\nDLYkSbcxj7CBY6IohgBvm4Pni6IYYPWpCZQCbuRIQy9JUoL58ENMRnsFcA9YKIpirnt7enIqDk4O\nWYVQCRj0pleUMtMyOLjmb7TpmWSkpBN69BKlq5UHoMob1flk5Th+Hb1U/vw1YExNRXDUZJ0QBDBk\n36/BrnkbMvbtshxrL5xBG2z6Lco8HohNpSqyNA0pqQgax6wTKhXos7+OpWnfhpTtf2c7Z1+3DkXn\nT+6mNnkAACAASURBVOfB1Dny568BbVIadk7ZdY1mXV1aBhdX70Wfnok2JZ07Ry/j5l2O49PXUb6d\nD29tnIjRaCTdakrqdSczOQ1b56zyCjnKe2H1XnTm8t4+cpmi3uU4OmMdFdr60HmDubwP5JVXm5yG\njVU/RsjSzIxL5sG562TEJKBPzeD+iRAKVzf1Y9eqZWm28Ssuzd3A/WMhj0v6qeiS0lA7W7etkE03\n/lyYRTf2+BVca1SwRD3WbSZHO0+j3urRsnVTk1NxtOpT1t/b5l1b4VbSjWl/zKRlt1Z0+qQzPs19\n6THqA7Z9v4WRbUYwvfcUxn3/pWxdORiMQq4/+UGuDLYoipWBREmS+kuSVA74CPgecMM0JdLC6hOM\nab66TI40bEVR/FAURQfAV5KkGZIk+QFVMI3eB+U209dPSXi39AGggk8V7kg3LWElKpZi1KbpCCoB\nlY0az/oity7eoMob1eni34cVfWZzK/h6bqWyobsSjG29BoDpoeLDB4kPETROCLa2GO7HWM45f/oF\ndo2aA2Bbqy66sFBZmpkXLuLQyKRpV6MaumvZ8y44OYGdLfp7WZr2detQaPRw7o+agDZEnt5Dok+F\nUrZVbQBK+HoSF5Jl9AtV8uCdbf4IKgHBRo17fS9ig8Mp07QGQXM38lf3WRj1BiIPXcyTdn4QHRRK\neXN5S/p4EmtV3sKVPOiy1d/SpzzqexFzMZyyTWtwYt5GtveYhdFg4FagvPLGBoXi3roOAG6+lUm0\n0oy/EE6hqmWwc3NGUKso6luZxNDbuHiVpuGPn3Fy2DKizdMacokLCqWkWbewb2WSrHQTgm/gWrUs\ndm4uCGoVRepWITk0ksqfdqZMtyYA6FLSLQZeDiGnruDbsh4AXj4iEVKEJezXOWuZ8O44/HtO5MCm\n/9ixajtnD54hOSGZFPPdQEJsPBrrH5qXgNEo5PqTH+T23rIWMEgUxU6SJGUCoUA88NjtwCRJui2K\n4n1RFDtLkvTwKcxITNMoG4HfRFFsJUlSqCRJD0RRjAAycpvpC3uDEJvWYvTm6SAI/D5uBS0HvEVM\nRDQX/zlN0NZAxmydhUGn4+SWQ0RfjaTPd59hY2fDRwuHAXDvehTrv/oxt5KAaYWHbZ16uM5fZloZ\n8O1cHDr3QB8VifbkUVSly2K4F53tmtSff8D5swk4vNkZY3o6yUvkrWxICziMvV9div+4BASImzEf\n5w+6oYu8Q3rgUWzKlUEflV2z0OjhCLa2uPmPB0B78xbxcxc/LvknEr77FGWa1qDTNn8QBA6OWUnN\ngR1JCL/Lzf1nuLb5CJ13TMOg03F102HiQm9j4+RAyyVD0WfqiAuN5MjEn2Vp5ifX95yibNMadNlq\nKu9/Y1dS21ze8P1nkDYfoeuOaRi0OqTNpvLaahxos2Qo+gwdD0IjCZwkr7y3/z5FiWY1abljCggC\np0b/QJXBHUm+cZeofWcInr2epn9MACByx3ESpUgarRmD2t6WOjN6A6BNTLM8qMwtUX8HUaxZTRrv\nnAYCnB/1A5UGv0nKjbvc3XeaK7P+pIFZ987O4ySFRJJxPwmf74ZStldLBJWKc6O+l6UJcGLPcWo3\nqcPsLfMQBIGln3/LO590Jjo8iqB/Tj72mj++/p1h80fQofeb2NiqWTFhmWxdObzuc9iCMZePRUVR\nnAj0AJIxjcznAe/y6CqRg5IkTRFFsRiwDNP0iB0QBgw1rxJpBMzHtHrECAQBoyVJyvZE4bMK7+fL\nM9spNaOfHekFk3ZP/exIL4Hdt0vli+7/1PaquvxZemCfTzsZrbFPeXakl8CWiB3PbW2Pl+qS68Zq\neGfLK7fuuX56I0nSLGBWjtPbnhL/PvD+E8KOAk1yq62goKDwKtAbcrsOI38okC/OKCgoKLwMXvPd\nVRWDraCgoPAQI6/3HLZisBUUFBTMGP4/vOmooKCg8L+AQRlhKygoKBQMlCkRBQUFhQKCXjHYCgoK\nCgUDZZXIc1BTZ5svuvdCnF65psblxe8bnRt8SH52pJdAfrzAAjDw7Kt/YedKPXkbfr0oDukL5Yvu\njqjT+aL7IlAMtoKCgkIBQZnDVlBQUCggvOYuHRWDraCgoPAQZVmfgoKCQgHhsduPvkYoBltBQUHB\njEFQRtgKCgoKBYLX/M10xWArKCgoPERZ1qegoKBQQHieVSKiKKqA5UBtTB60PpEk6ZpVeEdgCiAA\np4HhgAPwG1ACSAL6SJIUwxMomAZbEGgyuy9u3uXQZ+oIHLeKxPC7luA3pvWmZH0vtCnpAOzrvwiH\nws40XzwYQRBIun2fwC9Wo0+X+bKKIOA+bTj2VStizNQS9dW3aG9mOfN1alaP4p/2AkEg/eJVoqcu\nR1XImdJfj0PlrEEfn0jUxO/QP0h4isijmm5ffoadlyfGTC2xMxaiu3UHAFsvT9w+H2aJal+zGvfG\nTiH9qMmjtUuvLqiLuhG/ZJW8cpp1y80ejMa7AsZMHeHjlpIRnuWJx7WlL6VGvw+CQOqFMG5O/AH3\n4V0o1MIXALWrE7bFC3Pet59s3eaz+lLU3LYHvsjetk2m9sbdqm13D1iEfWFnWi8ebHLbFnmfgPGr\n0clt22dw4VIIi1b8xNql8ly8PRVBoPTMoThUM/WnyPFLyIzI6k8uLepSYmRPBEEgNTiMO5NXoHLR\nUG7JF6g1DhgytdwavRBdTLxs3Raz+lLMXMf/fbGKBKs6bjq1Nx5WdfzXgEXYuWho++0QEAQy4pPZ\nN2K57DoWBIGlS+ZQu5Y3GRkZDBoyjrCwcEv40CF9+PjjHmA0snDxD2zatNMSJoqeHD28i1Jl6pCR\nkWtvgrJ5zlfT3wUcJEl6QxTFhsBCoDOAKIouwAKghSRJ90VR/AIoBvQGgiVJmiqKYk9gEiZ3io/l\npRpsURRbABuAy5h+VeyBoeYMuUqS1MUqbrQkSe65SbdCh7qo7W3Z0XkaJXw9aTC5F/sHZPksLFaz\nArs/nEdGXNZbfM2+HsiV3/4lbNsxxA9aUGtQR85+t/1xyT8Rl7ZvINjbEtFjLA51REp++QmRQ2cA\noHJypMT4/tz8aAL6uETcBnZD7eZK0YHdST19idjvN6BpVIcSY/sSNfHbXGs6tmyMYGdHdN/PsKtZ\njSKjhxAzxvSWoDY0jLuDxgKgadMM/b37pB8NQrC3o+jksdjVEEn997CsMj6kcIcGqOztCOk8ASdf\nL8pM7kfYgDnmsjpQdmIfpO6T0MUl4T70PWzcXIletoXoZVsAqLx2IpGz5Pt0rNS+LmoHW7a8O42S\nPp40ntyL3VZtW7xmBXZ9NI90q7ZtuWAgl377l6vbjlGtZwtqD+rIaZlt+zR++n0jO/f8h6OD/QtL\nE8C1XUMEezvCuoxD4yPiMak/EQNNTp1UTo54fNmPsJ5foY9LpPjgLqjdXCncuTnpIeFEz12LW892\nFB/UhahZP8nSfVjHm6zq+O8cdbwjRx37jenC1Z0nuPjLPzT8ojvePZtzYe1+WbqdO3fAwcGeJs06\n0cDPlwXz/enStT8ARYsWYfDgj6lXvz0ODvYEnw+wGGwXF2cWzJtCRsbLfxv4OddhNwH2AEiSdFwU\nxXpWYY2AYGChKIqVgFWSJMWIotgEk7tEgN3A5KcJvAp/OP+Zvak3B/yBGebzTURR7J2XBN3ri9wK\nuADAvTNhFK9dMStQEHCt6E7T+QN4Z6s/Xu83A6BIldLcOmDyMn03KJSSfqJsXce61Uk5ZHrtNv2c\nhEONKllhPtXICA2nxIRPKL9uPvr7cegfJGJfuRzJh04BkHb6Mo51vWVpOtSpQZp5xJwZfAU7b69H\n4ggODhQa0ocHX5sclAp2diTv2kfC6nWyy/gQ5/rVSAg4A0DKmVCcalfOCqtXldSQCMr490PcPBtt\nTDy6B4mW8MIdG6JPSCHx0DnZuu5+IjfNbXv3bBjFa2Vv20IV3WkxbwDvbfGnqlXb3jS3bfSpUDzq\ny2/bp1G2lAffzJ70QtMEcKrvTdJBU39KPSuhqZnVnzR1q5IuheMxqT+eG+aiux+P/kEi6SERqM2e\nw1XOGow6+QvRSuWo4xI56rhwRXdazhtA1y3+VDPX8f1LN7EvpAHAztkRfR50mzTyY+++AwCcOHmG\nur61LGGxsXHUrdcOnU6Hu3sJ0tOzRtHfr5jPJP+5pKamydaUi0HG5zG4Ata3z3pRFB8OiosBLYHx\nQEdglCiKXjmuSQKeup/Aq54SKQLcw/RD8SUwTRTFA5IkRcpJxNbFkcykVMuxUW9AUKsw6g3Yauy5\nvGYfF1buRqVW8daGr7h/4QaxlyMo37YuVzcFUr6dL7aO8kdLamcNeitdDAZQq0BvQO3milODWlzv\n9CmG1DQq/LGAtLMhpF+5jkurhmRcvo5z64aoZOoKThoMyVZOTfVZmg9xfrcDqf8cxBBvMpqGpGTS\nj5/G6Z12sstoKatL9rIarXRt3FxxbVSTS+1HY0hJp+qW2SSflsi4YZqq8RjelesjFuZJ187ZkczE\nJ7dt8Np9nF+5G0GtovOGr4g5f4P7lyOo0LYu0qZAKrT1xUbzYkfCbVs24XbU3WdHlInaWYPhSXVc\nxBWnN2px9c3PMKSk47lxLilnQtDHJ+Lc1Aev/ctQF3YhrPt42bq2zo5kPKWOL6zdxzlzHb+34Svu\nnb9BctQD3pjwPl6dG6G2t+Xkoi2ydV1cnUlMSLIc6/UG1Go1er3efKxn2NC+TPH/nKXLVgPgP3kM\nf+/+lwsXLsvWywvPuUokEXCxOlZZORaPBYIkSYoGEEXxEFAnxzUuwFPnt17FCLuVKIoBoigeA9YA\nf5rP38Y0/F8tN0FtUhp2To5ZJ1SmzgagS8vg4uq96NMz0aakc+foZdy8y3F8+jrKt/PhrY0TMRqN\npMclPSH1J6NPTkWVQ/eh4dTHJZEWfBX9/TiMqemkBl3EvlolYn/YgG2ZkpRfNx+7MiXQRt2XpWlM\nSUXlpLHSFLIZawCnjq1J3rpbdnmehj4pFbVVWQUrXV1cEinnr6GLiceQmk7SiUtoqptGaQ5VyqBP\nTMk23y2HzOQ0bJ2tdbO37YXVe9GZ2/b2kcsU9S7H0RnrqNDWh84bzG37QH7b5geP9ierOo5PIu38\nVUsdp5y8hKN3JUqO/ICYH7YQ2nY4N3r7U37Fl7J1tclp2D2ljs9b1XHkkcsU8y5Ho4kf8O/YH/ij\nzQQCp/xKm2+GyNZNSkzG2cU5q7gqlcVYP2T5irWUKedD0yYNadG8Eb0+6EL/vj35d/9G3N2Ls+fv\nvN815gaDkPvPYzgCvAlgnsMOtgo7A9QQRbGYedTdENNUseUaTCPvwKfl71VOibwB+GAy2I4AkiT9\nDiSJojhUToLRp0Ip26o2ACV8PYkLuWUJK1TJg3e2+SOoBAQbNe71vYgNDqdM0xoEzd3IX91nYdQb\niDx0UXZB0s5cxrmFaVrKoY5IhhRuCUu/dA37KuVRF3EFtQrHOlXJvHYTTf0axK/fQ0SvL8iMiCLt\njLyRQsa5Szg29gPArmY1tNduZAsXnJ0Q7GzR333ig+U8kXwqhEKt6gLg5OtFWkiEJSw1+DqOYjls\niriAWoWTr0jaVVMbuDatTcKBM3nWjQ4Kpby5bUv6eBJr1baFK3nQZaupbVU2ajzqexFzMZyyTWtw\nYt5GtveYhdFg4Fag/LbND1JOXcGlpak/aXxE0qWsOk67GIaDmNWfND4i6Vdvok9IRp9kuuPSxcaj\ndtE8Nu2nEfWMOu76mDrOSEixjMpT7sZZpkfkcORYEB07tAKggZ8vFy9esYR5eXmyccOPAGi1WjIy\nMjAYDFT1bkLrtt1p3bY70dExdHizl2xdOTznlMhWIF0UxaPAYmC0KIpjRFHsJEnSPUyzCnuBE8AW\nSZIuAiuA6qIoHgYGAdOelr9XPSXyuPvKocBxst9KPJXw3aco07QGnbb5gyBwcMxKag7sSEL4XW7u\nP8O1zUfovGMaBp2Oq5sOExd6GxsnB1ouGYo+U0dcaCRHJsp/IJa07yhOjX0ov/5rEASiJizGrd97\nZEbcIfm/E8QsXEvZn0xT9Em7A8m4GoEhI5NSC0wPBnV3Y4n66htZmqkHDuPQ0JeSa75FEATuT12A\ny4dd0d26Q9qhY9iWK4Puzou/XY/ffRzXprWpum0uCBA+ZgklB3YiPTyKhP1BRM79lSq/TwUgbtdh\n0qWbADhUKk1i4Pk8617fc4qyTWvQZaupbf8bu5La5rYN338GafMRuu6YhkGrQ9psaltbjQNtlgxF\nn6HjQWgkgZPkt21+kLj3GC5N6+C5eT4IApHjvqXYgM5kRkSR+M9Joub/TMVfTN/fhL8OkxF6k+iF\nv1Nm3qcU/ehNBFsbIicsla0bZq7jrlv9EQSBf8aupM7AjsRb1XE3cx2HbD7Mg9DbHPL/meYz+iCo\nVabvXB7qeNu23bRp3YzAg9sRBIEBA0czauQgroXdYNeu/Vy4cJkjgTsxGo3s2fsfhwKPy9Z4XvTP\n8dBRkiQDkPPWI8Qq/E+yZhgenksFuudWQzAaX967PTlWiegxGeXlQAvgT0mS9pjjdQa2SZKUrbp+\nLPNRvrx41MTxwSvXzK/9sGPuOT870kvgpJA/usp+2C+fUXcP5IuuLvP2c79Xvrxs7m3OsFu/vfL3\n2F/qCFuSpABMC8JzsjZHvO3wmm+TpaCg8P8e5U1HBQUFhQKCspeIgoKCQgFBcWCgoKCgUEBQpkQU\nFBQUCgiKAwMFBQWFAoIyJaKgoKBQQFCmRJ6DKHX+PLMt6Z3y7EgvGG1C/vy0G+/mj24Jff60bX6s\nia52Kve7M75I0mp9ni+6qtfczdbTUFaJKCgoKBQQDK+5yVYMtoKCgoIZ5aGjgoKCQgFBmcNWUFBQ\nKCAoq0QUFBQUCgjKHLaCgoJCAeH1NteKwVZQUFCwoMxhKygoKBQQ9K/5GLtgGmxB4M2Z/XD3Locu\nQ8vO8auIi8jyutJ+Sm/K1hfJTDZ5WV4/cBGCSsWIgIXck0zukEL2nuLkmr2ydTVDRmNToTJGbSYp\nSxdgiL4NgLpiZTQDRlii2ojeJM+ehPbyBZyGjkFVwgPB1oaUld+ivxryJIXHarqMHo2NpydotSQu\nWID+tknTpnJlXEZkadp6exM/aRKZJ0+ajmvXptDEidzv0UNeOc265ecMRuNdAUOGlvBxy7L5aSzU\n0pdSY3oAAqnBYUR8tRL34V0o1MLHlLdCTtgWL8w5n/6ydX3m9qOwdzkMmVpOjV1FSnhW27q3qk21\nMV0QBIi7cIOzX67FxsURv6XDsHVxRGVrw/mpv/Hg9DXZuqVnDsWhWkWMmVoixy8hMyLKEuzSoi4l\nRvZEEEzlvTN5BSoXDeWWfIFa44AhU8ut0QvRxTzVh6psLlwKYdGKn1i7dP6LS1QQqDBnEBrvChgz\ntVz/fHmOtvWhzJj3QYCUC9cJ/2olHiPeo7C5bdXmtj1bZ4BMWYElS2ZTq6Y3GRmZDBk6jrCwcEv4\nkCF9+Lh3d4xGWLz4ezZt3gXAjeunuGZ2jXfixGkmTZ77nBXwZP6nR9g5PM4IgD0wVJKks6IoDgI+\nwlRHtsBEs8ODZ1K1fV1s7G356b2plPapTLtJH7J+4CJLuEfNivzeey5pccmWcxUbV+fijqPsmfJL\nnstj26AJgq0dieOHofbyRtN/GMmzJwKgv3GNpEmjTPEatcDuwX20Z0/i2LMv+ogbpHwzG3X5Sqgr\nVpZlsO2bNEGwsyNu+HBsvb1xHjqUhEmTANBdu0bcKJOmffPm2MfEWIy1qnhxNN27g03emrhIhwao\n7G250mkCTr5elPXvx7X+c0xpOzlQZlIfpG6T0MUl4T70XWzcXIletoXoZSZv2lV+nsitmfLdSJXq\nWBe1vS0H3pmKm29lak/5kKP9TG1r4+RAzckfcLDrTDIfJOM17G3sirpQuV877h2+xLUf9+Ds6UGD\nFcP5t90kWbqu7Roi2NsR1mUcGh8Rj0n9iRg4y1xeRzy+7EdYz6/QxyVSfHAX1G6uFO7cnPSQcKLn\nrsWtZzuKD+pC1KyfZJf5Sfz0+0Z27vkPR4cX6wW+SAc/VPa2XO70Jc6+XpSf0pfQfiYjqHJyoNzk\nPlzpNhndgyQ8hpnaNmrpVqKWbgXA6+evuDVT/veoc6cOONjb06x5Z/z8fJk/bzJdu5mMftGiRRg8\nqDf1/Trg4GDP+XMH2LR5F56eFTh3Lpj3uvR7cRXwFF73h46v0glvc8AfmCGKYk+gLdBakqQWmAz3\nr6IoFstNguXqi4QdNPkNvH32Gh61KmYFCgJuFdx5e+4n9Ns8hTo9mgMmI+5RsyJ91k+i2/LPcC5R\nWHZBbL1roT1rMoj60MvYVBYfjWTvgKZXP1J/XGK6xscPo06Ly9QFOL7fx3J9rjVr1iTDbIS1ly9j\nKz5G08EB5379SFpq9u9nZ4frmDEkfSPPf6Q1zn7VSDhwFoCUM6E41fLMCqtXlbSQCMpO6UfVLbPQ\n3k9A9yDREl6kY0N0CckkHpLv27GYn0j0AdN1D85co0jtrLYtWr8KCVduUWvKh7TYNpmMmAQyY5O4\nunI313/9FwCVWoUhXStb16m+N0kHTwOQelZCU7OKJUxTtyrpUjgek/rjuWEuuvvx6B8kkh4Sgdrs\nfVzlrMGoe7GvXZQt5cE3s+X98OQGF79qxAeY2jY5R9u61KtKakgE5fz7Um3rTLQx8TnatgH6hGQS\nDspv20aN67NvXwAAJ0+ewde3tiUsNjaOevXbo9PpcHcvQXpGBgC+PjUpVcqdfXs3sH37L3h5VcpL\nkXONUcYnP3gVBtuaIsA9YDAwW5IkLYAkSTeAOpIk3c9NInbOjmQkpVmOjXqDyTkoYKexJ+jnfWwd\nuZzfP55Hvd5tKFG1LPfDoghYuImf359JyL5TdJjWR37uNRqMKVn7jBgNBlCps0Wxb/sWmUcCMCYl\nACC4FkLl7ELS1HFkBh1F02+YLEmVkxPG5Kw7BQwGUGfXdHzzTdIDAjAmmDRdR44kZf16DPdzVZ2P\nRe3siD4p1XJsNBjAXMc2bq64NqrBrVm/EPrRDEp+8jb2lUpZ4nqM6MKdRevzpGvr7IjOum0NVm3r\n5kKJxt4Ez/yTwF7zqTywA86V3NEmpmJI12JfvBD1lw4jeI58bbWzBoN1efVW5S3iitMbtYieu5Yb\nfadSrH8n7CqWQh+fiHNTH7z2L6P44C48WL8vT2V+Em1bNsEmj3dIT0PtokGf+Ky2/RXpw5m4D3wb\nh0oelrilPu1K5KINedJ1dXEhITHJcqzX61Fb9WW9Xs/QoX0JPLSDP9aZ7tSiou8xf/5S2rXvwbx5\nS1i75rs8aeeW5/Sa/tJ5FQa7lSiKAaIoHgPWYPIaXAq4bh1JkqTY3CaYmZyGnZOD5VhQqUxfMECb\nlsGJn/agS88kMyWd8KOXKVmtPOFHLxF+7DIAIXtO4V69vPySpKYiOGqydAUBDNlHVfbN25Cxf5fl\n2JiUQObJI6a8nTzy+FH5UzCkpCBosjRRqUCfXdOxTRvS/vrLFFy0KLY1a+Lcty9FvvkGlYsLhfz9\nZWkC6JPTUDlb17EA5jrWxSWRcv4auph4DKnpJB+/jKZ6BQAcqpRBl5iSbU5UDtrkNGys2hYhq20z\n45J5cO46GTEJ6FMzuH8ihMLmdnStWpZmG7/i0twN3D8m4xmBpbypqJwcs05Ylzc+ibTzVy3lTTl5\nCUfvSpQc+QExP2whtO1wbvT2p/yKL/NU5leNPinVcmcAIAiqR9pWay5r0vHLaKqb7nIcq5RB/xxt\nm5iUhIuzk+VYpVKhz9GXV6xYS7nyvjRp2oDmzRtx+vR5duw0/RAePRqEh4d7nrRzix5jrj/5wauc\nEnkD8MFksG8CZa0jiaLYXhRFj8clkJObp0Kp3LIOAKV9KlseJAIUreRBv81TEFQCKhs1Zet7EX3x\nBu/MG0i1jn4AVGpcnajgG7ILor0SjG3dBgCovbzRRWRPQ9A4gY0thvsxWddcDsa2bkMAbKrXRn8z\nXJ7mxYvYNzRdb+vtje56tt85BCcnsLPDEGPSNMTGEvvxx8SNGkXcqFEYkpJImC7fU3hy0BUKt6oL\ngJOvF6lXblrCUoOv4yiWw6aIC6hVOPl6kR4aCYBr09qWqZS8EBsUintrU9u6+VYmMSSrbeMvhFOo\nahns3JwR1CqK+lYmMfQ2Ll6lafjjZ5wctozo/+TfqgOknLqCS8t6AGh8RNKlCEtY2sUwHMTyqIu4\nglplCr96E31CMvok0x2XLjYetYvmsWm/biQFhVC4lS8Azr5epIZklTXlYdu6mdrW2deLtIdt26wW\n8f+dybPusaOn6NChFQB+fr5cvJT1w+rlVYkN638EQKvVkpGRicFgYPKkMXz22ScA1KpZjcjIO3nW\nzw0GjLn+5AevepXIw8f9PwGTRVH8UJIknSiKXsAqoG5uEgnZc4pKTWrSb8sUBEFg++c/0PCTjjwI\nv0voP2e4sPUw/bdNw6DTc2HzYWKu3ubfeX/SacEg6vVugzYtg53jV8nOvPZ4ILZ16uEybxkCAsnf\nzcWhUw/00ZFoTx5FVaoshnvZRx/pm37DacQXuM5bjlGvI+Wb2bI0MwIDsatXjyJLl4IgkDhvHpru\n3dHfvk3G0aOoy5RBH523Ec/TiNt9Atdmdai2fQ4IAjdGL6HkoE5k3Igifn8QkXN+w2vdFAAe7DxC\nmmQy6A6epUk8dC7Purf/PkWJZjVpuWMKCAKnRv9AlcEdSb5xl6h9ZwievZ6mf0wAIHLHcRKlSBqt\nGYPa3pY6M3oDoE1MszyozC2Je4/h0rQOnpvngyAQOe5big3oTGZEFIn/nCRq/s9U/GUaAAl/HSYj\n9CbRC3+nzLxPKfrRmwi2NkROWJrncr9K4nafoFCz2njvmA0IXB+zFPdB75AeHk38viBuzfmNqutM\nd2WxO49a2tbRszQJeXgu8ZBt23fTuk1TDgZsQxAEBg4aw8iRAwkLC2fXrv1cuHCZwEM7MBqN3CMs\n9AAAIABJREFU7N17gMDA4wQHX2Ht2u/o2KE1Or2OTwaOfhFV8ERe70eOIBiNLy+LOVaJ6AEXYLkk\nSWtFURwNvA9kAmrgK0mSDlpfP738h/lSfyPqRL5yzfzaD/vm1SL5ohthyJ/RqJd94rMjvWDyaz/s\nM/m0H3bTWHkP1l8UmRmRz/0lGlyhe65tzg/hG1/5l/aljrDNy/RKPCFsMbD4ZeorKCgoyOF/eh22\ngoKCQkHC+JpPiigGW0FBQcGM8mq6goKCQgFBmRJRUFBQKCAYXuIijBeBYrAVFBQUzLze5lox2AoK\nCgoWXvfNn15rg50o5M+MUsqd/KmW/Lgbu4LTsyO9BIoY88c/9SF9oVev6eNPfX3qsyO+YHwvfP3K\nNQHq1Ho1O+u9DJRVIgq54jWfOlN4DvLDWCvkDZ1isBUUFBQKBsoIW0FBQaGAoCzrU1BQUCggvMy9\nlV4EisFWUFBQMKOsElFQUFAoICivpisoKCgUEJQRtoKCgkIBQZnDfgkIgkCXmf3xqFYOfaaODeNX\nEhtx1xLeecrHVKwnkpGSDsCagV9j62BPr2+Go7a1ITUhmT9GLbOEyxCmyPiR2FbxxKjNJG7mQnRm\nl0W2Xp4UHpPlYNe+hjf3x/mjvR6Bm//nJse5gkDc7EXoImQ4SBAEikwYiZ1Z88GM7JpFxmbXjPnc\npFnU/3OwUQMCD+RqPiYPDeb0xc27HPoMHcfGrSIpPKu+S7WsRe0xXQB4EBzOia/WPpdWzbn9ca1e\nDkOmjvNjVpJqpVWiVW28xnYFQSDhwg2CJ/yEWmOP7/IR2BZywqDVce6zFaRHx8nWbTGrL8W8TX3q\nvy9WkWCl23Rqbzzqe6E195m/BizCzkVD22+HgCCQEZ/MvhHL0aVnytKsMGcQGu8KGDO1XP98eTZ/\niYVa+lBmzPsgQMqF64R/tRKPEe9RuIUPAOpCTtgWL8zZOgPklTUXXLgUwqIVP7F26fwXlqYgCIyf\nM5oq3pXJzMxk1ucLiAy//Uicxb/O49Dew2z5dQcOjg7MWD4Z10IuaLVapo2cQ0x03p1LP4v/2VUi\nZm8z24EakmRyuiiK4lwgBJMz3nclSdpuPt8B6ClJUt/cpF29XT1s7G1Z2mUK5Xwq886kj1g7cKEl\nvEyNiqz8eC6pcVkemtuN7s6pzYc4vSWQdqO64tezJYGrd8sqk2OLxgj2dtwb8Cl2NapReNQQ7n9u\ncqWkDQ0jZshYU7zWzdDH3Cf9WBBuU8aTvGE7aQeP4NCwHoWGf0LsF1PladrZcbe/WXP0EO6PzdK8\nN9hK855Zc+p4kqw0Cw//hPsyNHNSrkNd1Pa27O40jWK+ntTz78WB/ibfEzZODtSd9AH7us0iIy6Z\n6kPfwt7NhYwHSc9I9fG4d6yH2sGWI29PobBvZapP/Yigvqa2VTs5UM3/Q451mUHmgyQ8h7+DXVEX\nSndpQvyFG1xdtIUy7zfDc/g7XJr8iyzdSu3ronawZdO70yjp40njyb34e0CWf43iNSuw46N5pMdl\nebD3G9OFqztPcPGXf2j4RXe8ezbnwtr9udYs0sEPlb0tlzt9ibOvF+Wn9CW031wAVE4OlJvchyvd\nJqN7kITHsHexcXMlaulWopZuBcDr56+4NVNeOXPDT79vZOee/3B0sH+h6Tbv0AQ7ezsGdBpGDV9v\nRk4Zxrh+E7PFGTL+E1wKOVuO3/3wbUIuhLJ68c+81aMDvYd9wCL/JS80X9Y8zzpsURRVwHKgNpAB\nfCJJ0rXHxPkL2C5J0veiKApAJHDVHOWYJElP9Ob8skfYGcAaURTbSpJkXROpwCJRFI9IkiT757Ji\nfRHpoMm33M2z1yhbs5IlTBAEilXwoPucT3AuVoiT6wMI2hjAjum/IAgCgiBQyKMocbfl/0rb165J\n2tEgADIvXsG22qMe0AUHBwoN6su9QaMAiP9mBYZkk6NW1GqMGTJGYIB9nZqkH8vStHuCZuHBfbk7\n0Ky5OIdmpjzNnJTwE7lz4AIA98+EUbRWxaywelWID4mk3pQPcS5Xgqt/BOTZWAO4+YncMzvSjT9z\njUK1s9rWrb4XSVdu4T31IzTlSnBz3QEyY5O48eNuk5dzwLF0MbSJ8t8sLOUncjPAVMa7Z8MoYVVG\nBIHCFd1pOW8AmmKFuLw+gCvrD3H/0k2cPEwu1uycHUm6EytL08WvGvEBJofFyWdCcarlmRVWryqp\nIRGU8++LffmSxKz7B92DLPdmRTo2QJ+QTMLBvPtYfBJlS3nwzexJfDl9wQtNt45fLY4FmNyHXTxz\nmWq1svflVm81x2gwcDwgy8XYn6s2oVKZfIW7ly5JUmIyL5PnnMN+F3CQJOkNURQbAguBzjnizASs\n/fJ5AmckSXonNwIv22D/h8kz+3DA2kNpEqbCrAC6y03UwdmR9KSsL6VBb0ClVmHQG7DT2HP4570c\nWvUXKrWKIX9MJjL4OlEhNxHUKsbunouNvS3/fLdFdmEEJw3GlJSsEwY9qFWgz7qRcurckdR/D2JI\nMH25Hv5vU74MhUcO5v44f1maKidNlvF9muY/j9csMmowMZ/L08yJrbMjmVb1bTQYENQqjHoD9m4u\nuDeqxs52E9GlpNNh62RiTl8l6XreHAPbuDiis9JCn6Vl5+ZC0cbeHGo9AV1KOo23TyXuVCgp16PB\nYOSNTZNwqVaW4z3kOTp+WMYMK0NvtNK11dhzYe0+zq3cjaBW8d6Gr7h3/gbJUQ94Y8L7eHVuhNre\nlpOL5PUptYsGfWL2en3YtjZurrg2qsHFtmPRp6TjvW0myacl0q9HAVDq065cGybP0XBuaduyCbej\n7j47okycXDQkJ2b1ZYPBgFqtRq/XU0msSPv32jBhoD+fjOmT7TqDwcDyDYvxrFaJET3HvvB8WaM3\nPtekSBNgD4AkScdFUaxnHSiKYjdMsy57rE7XBUqLongASANGS5IkPUlA9Ty5yyVDgdGiKFbOcX4F\nUEgUxV5yE0xPTsPeydFyLKgEDGYDlpmWweE1u9GmZ5KRks61o5fwqFYOAINOz4K249j05Sp6Lhr2\n2LSfhjElFUGTpYuQ3XACaDq0JmX739nO2detQ7EF04mdMlf2XLIhJRXVMzSdOrYm+TGaxb+eTqy/\nfM2caJPTsHW2yoPKZMgAMuKSuX/+OukxCehSM7h7XMKtevk8a+mS0lBn0xIsWplxycSfCyMjJgF9\nagaxx6/gWqOCJeqxbjM52nka9VbL96ytTU7Dztm6T2WVUZeWwfnVe9GlZ6JNSSfyyGWKeZej0cQP\n+HfsD/zRZgKBU36lzTdDZGnqk1KzlVWwaltdXBIp56+hjYnHkJpO0vHLaKqbRv2OVcqgT0zJNt9d\nEEhJSsXJOcv5siAI6PWmTcDe6t6e4u7FWL5xMW/16ECvQT1o2MLPEndYj9EMeu9T5v04/aXm0Sjj\n32NwBRKsjvWiKNoAiKJYA+gF5Bw9RQFzJElqCcwGfnta/l66wZYkKRYYBfxsrWeeIumP6RahlJw0\nw0+FUrVlHQDK+VQm2jRFDkDxih4M3zQNQSWgslFTsb7I7YvhdJnRH883vAHISEkzjWZkknH+Io6N\nGwBgV6Ma2rAb2cIFJycEO1v0d2Ms5+zr1qHw2OHEfDYB7ZXQPGk6WGtee4ym7aOaRT4fzr1PJ5CZ\nB82c3AsKpXSr2gAU8/Uk/kpWfT8IDqewWAb7Is4IahXFfD1JCL39pKSeSVxQKCVbm9q2sG9lkkKy\ntBKCb+BatSx2bi4IahVF6lYhOTSSyp92pky3JgDoUtIthlYOUUGhlDeXsaSPJ7FWuoUredB1q7+l\nT3nU9yLmYjgZCSmWUXnK3TjsC8nzBJ8UFELhVr4AOPt6kRoSYQlLCb6Oo1gOGzcXUKtw9vUiLdT0\nw+varBbx/52RXcb85nxQMI1amfpyDV9vwkKy+vKSmd/T/+2hDO02ir827GHdyg0cDzhJnxEf0rFr\nOwDSUtIsA7OXhcFozPXnMSQCLlbHKkmSdOa/PwZKY5p16AuMMT+7O4XpWR+SJB0GSpnntR/LK1kl\nIknSTlEU3zNn9Aur85GiKE4Fvgb+fvzVj3JxbxBeTWsyYvM0EGD9uB9oNuBN7kfc5fI/pzmzNZDP\nts5Ar9Nzeksgd69GErh2D91mDcD4WReMBiNbJv0kuxxpAYdxaFCXEqu/AwQeTJ+Pc69u6CJvk37o\nGLbly6C7k/1WsvCYYQi2NrhNHQ+ALiKSuDm5dxafdsCkWXL1dyAIxE6bj8uH3dDduk3aQ80ct69F\nxpo0i04zaWojIombnXcH9Td3n8KjWQ06bPdHEASOjF5JtUEdSbpxl8j9Zzg7ZwNt1pm0wneeIF7K\n+4g+6u8gijWrSeOdprY9P+oHKg1+k5Qbd7m77zRXZv1Jgz8mAHBn53GSQiLJuJ+Ez3dDKdurJYJK\nxblR38vWDdtzirJNa5gMsyDwz9iV1BnYkfjwu4TvP4O0+QjddkzDoNURsvkwD0Jvc8j/Z5rP6IOg\nVoEgcHDSz7I043afoFCz2njvmA0IXB+zFPdB75AeHk38viBuzfmNqutMA7LYnUdJk24C4OhZmoRD\nL37u+mUTsDuQBs3qsWrHMgQEpo+ZS69BPbgVHkngvqOPvWbnn38z5dsv6fTBm6jVaqaPmftS8/ic\ni/qOAO8AG8xz2MEPAyRJstg9s82LliRpjyiK84BYYL4oirWBWzme92VDeFnrDs2rRIZIktTTfOxq\nLsAUYK4kSe5WcbcCCTlXiXxe4YN8WRQ5svi9V66ZX8s/A6I88kW3iD5/9sOOsFW/cs382l41v/bD\nbpxP+2GfvHPwiSPT3NK4dKtcfxOP3P4vm57VKpFagAD0A94ErkmStMMq3lRMBvt7URSLYJoGcQZ0\nwHBJkkKepPnSRtiSJAUAAVbHicDDyc21OeK+97LyoaCgoJBbnmeViCRJBiDng4xHjK8kSVOt/o4D\n3sqtRoF8cUZBQUHhZfCcq0ReOorBVlBQUDCjODBQUFBQKCAoe4koKCgoFBCU3foUFBQUCgjKCFtB\nQUGhgKB/zffrUwy2goKCgpknvMH42vBaG+yl0UfyRTdYX/2VayYbMl65JkCSPixfdCvbF88X3R1R\np1+5pkp47vc58kSdfHqB5ciFNfmi+yJQVokoKCgoFBCUEbaCgoJCAUEZYSsoKCgUEJQRtoKCgkIB\nQXk1XUFBQaGAoEyJKCgoKBQQjMoIW0FBQaFg8Lq/mv4qfDq+cARBYMmS2QQEbGXfvvVUqpTdh+Dg\nwR9z+PBOAgN30LXr25bzYWEn2bdvPfv2rWfGjPF50v1s9ggWb1vE/A3zKFXh0c3/BUFg5i/Teeuj\nNwFQqVQMmTqYRVu+Zslf39Kgtd8j1zxLc9zcUfywYwlLNi6idIVHvakJgsDXv87h3d7ZHS+X8yzL\n3is7sLO3laX5MM1J877g110rWb1lGWUrlHlsnOXrFtH9Y9N25s4uTiz5ZQE/bV3Or7tWUqtujTzp\nDp41lDlb5zP9z1m4l398HU/6eQrtPuwAgMZFw6SfpzBz4xym/D6dwsUL50l32dK5HD60g3/3b8TT\ns0K28KFD+nDs6F8cO7KLbt2y17MoehIbcwV7e3vZmkuXzuHQwe3s3/eo5pAhfTh6ZBdHDu+im1U/\nvnH9FPv3bWT/vo3MnDFBluZD3Qlzx7B6x3JWbPqGMhVKPzbON7/Np0vvTgA4ODqwYM0sftjyHUvX\nL6S4ezHZurnhwqUQ+o744tkRXxJGozHXn/zglY+wzZ5oDgAfSJL0p9X5C5jcvfd9VhqdOrXHwcGe\nFi3ew8/Ph3nzJtO9+ycAFC1ahEGDetOgQUccHOw5d+5fNm/eRaVK5Tl37iJdu/bPc94btX8DWwc7\nRr87hqo+VRk0eSBTB2R3Ctr3i49xLpTl1q1111bY2NowpsvnFHUvSrO3msrSbNahCXb2dgzu9CnV\nfavxqf9QJvSfnC3OoPH9cS3sku2cxlnDp1OGos3UyiyliVYdm2HvYEfvtwdRy7c6n0/9lJF9s//I\nfTphMK5WZf14yAecCDzFbz+up4JnOeatmM777frK0vVr3xBbezu+fO8LvHxE+k7qz9yBs7LF6fX5\nRzi7OluOW3ZrTURIBL/OWUubnu14d3AX1s6U5wKuc+cOODjY06RZJxr4+bJgvj9dzH2laNEiDB78\nMfXqm/pd8PkANm3aCYCLizML5k0hIyNTlh5A504dcLC3p1nzzvj5+TJ/3mS6dhuQpTmoN/X9TPk6\nf+4AmzbvwtOzAufOBfNel7y/FNPc3KcGdBpGDV9vRk4Zxrh+E7PFGTL+E1wKZdXxux++TciFUFYv\n/pm3enSg97APWOS/JM95eBw//b6RnXv+w9FB3g/fi0QZYT+eEKDnwwNRFGsCTrm9uFGj+uzbFwDA\nyZNn8fWtZQmLjY3Dz68DOp2OkiWLk55ueoPQ17cWpUqVZO/eP9m2bS1VqlSSnenqftU5FWB6Uy7k\nbAhValXJFt7kzSYYDEZOBZyynKvbvC6x0bFMXzuNUfNGcvyfE7I0a/nV4PiBIAAunblC1VpitvAW\nbzXDYDBa4jxk/Pwx/DB3FelpeXuD0sevNkf+Ow7AhTOX8K5dLVt427dbYjAYOHLguOXcrz/8ycZf\ntwGgtlHnyYhVq1+NswdNDmZDz0p41qqcLfyNNxthMBoscQAipAgcnUzexzUuGnRaHXJp0siPvfsO\nAHDi5Bnq5uhTdeu1Q6fT4e5ewtKnAL5fMZ9J/nNJTU2TrdmosXU/PoOvb+1smvXqt8/SzDD3Y5+a\nlCrlzr69G9i+/Re8vOT34zp+tTgWcBKAi2cuUy1Hn2r1VnOMBgPHzXEA/ly1iTXf/gqAe+mSJCUm\ny9Z9FmVLefDN7EkvPF056A2GXH/yg/wy2OeB8qIoFjIffwT8ntuLXV2dSUhIshwbDHrU6ixffXq9\nniFD+nDo0Hb++GMrANHRd1mwYDnt2/dk/vxlrFnzrexMa5w1pCSmZOnqDajUpiosL5an5bst+OXr\nX7NdU6iIK6UqeODfdwobVmxk7MLRsjSdnDWkJGVp6g161GbNimIF2r3bmlULsr8K3H9MH47+e4Jr\nl6/L0rLG2cWJ5KSsL6VBn1XHlatWouN77Vg2/8ds1yQlJpORnkHR4m7MXjqVb2evkK2rcdaQmvT4\nOi7nVY6mnZvz58J12a5JjkukTrM6fPvPUjoPeo9/1++Xrevi6kyiVZ/S6w2P9KlhQ/tyJHAn6/7Y\nDID/5DH8vftfLly4LFsPwNXFhYREa81H+/HQoX0JPLSDP9ZtASAq+h7z5y+lXfsezJu3hLVrvpOt\n6+SiIdm6HxuyylpJrEj799rww4JH71AMBgPLNyymR/8uBOwOlK37LNq2bIKNTf4+VjPK+Jcf5Gft\nbAa6iKK4FvAD5gHlcnNhYmIyLi5Zt2uCoEKfw6nr99//zOrV69ix4xeaN3+DkyfPotOZ4hw9GoSH\nR0nZGU5NTkXj7Jilq1Jh0Jt+adt0bU0x96LMWz+XkmVKotNquRt5l8T4JE78YxqpBB8PpnTFR+cL\nn0ZKDk2VSoXerNmxWzuKuRfjuw0L8SjrjlarJepWNO27tOFeVAxv9+yIW3E3Fq9bwPCuo2TpJiel\noHHOuukx6Zrq753uHSnpUZxVm5dSqqwH2kwtd25FceTAcapU9WTeD9NZNG0Jp4+dlaUJpjp+OFo2\n6QqWOm7etRVuJd2Y9sdMSpQpgU6rIybyHm17tWfb91vYt24v5atWYNz3XzKmw2eydJMSk3G26lPW\n5X3I8hVr+XHV7/y18zdaNG9Erw+6cPt2FP379sTdvTh7/l5Hy9Zdc62ZmJSEyxPq+CErVqxl1arf\n2bnzV5o3b8TJk2dy9GN35JKSlIqTs8ZyLAiCRfet7u0p7l6M5RsX41HWHV2mjju3oi2j7WE9RlO+\ncjkW/zKXLo16ydZ+3VG2V30y64AVwHVA1s/1sWOneOutNmzevAs/Px8uXcryc1mlSiVmzBhPz56D\n0Wq1ZGRkYDAYmDhxNA8exLFo0ffUrFmNyMg7sjN8OegyDdo24NCuQKr6VCU85IYlbPXsrBHJR6M/\nJC4mjlMBpylVvhT1W9Xn8O4jVKpWkZg7MbI0g4Mu0rjtG/y38yDVfasRdiVr1Lx81krL3/3H9OFB\nzANOBATxfpPelvObjq9jdK9xsst6LugCzds1Yd+Of6nlW52rIVmbRC2esczy99DPB3D/3gOOHDhO\nJa8KfP3jLMYNnkTo5WuyNQFCTl2hXms/jv51BC8fkQgpwhL265y1lr/fH/UBcTFxnD14hjfeakxK\nkskzeUJsfLYfuNxy5FgQb7/Vlk2bdtLAz5eLF69Ywry8PJk1cwLdewzM1qeqejexxLkWepwOb8oz\nYMeOmvrxps278PPz5aJVP/byqsTMGV/S4/2HmpkYDAYmTxpD7IM4Fi5cQa089uPzQcE0bduIf3Ye\noIavN2FW/XjJzO8tfw8c25fYew84HnCSPiM+5F5UDLs37yMtJc3yI/r/jdd9DjvfDLYkSddFUXQC\nPgO+BHI9Gbd9+x5at27KgQNbEASBQYM+57PPPiEsLIK//tpPcPAVDh7chtFoZN++AAIDTxAcHMKa\nNd/QsWMrdDo9AweOlZ3nI3uO4tvUh8VbF4IgsGjsIroMfI874Xc4vv/xc9O7/9jDp7NH8M32xQgC\nfPelvAc1B3cfpn6zuny/fQmCALNGz+f9Qd24feMOh/cflV2G3PLv3wdp2MyPX3auRBBg8qhZ9B7c\nk1s3IgnYd/ix14z8aih2DnaMn2ma9klOTH7kQeWzOLHnOLWb1GH2lnmmVRSff8s7n3QmOjyKoH9O\nPvaaP77+nWHzR9Ch95vY2KpZMWHZY+M9jW3bdtOmdTMCD25HEAQGDBzNqJGDuBZ2g1279nPhwmWO\nBO7EaDSyZ+9/HAo8/uxEn6W5fTet2zTlYMA2BEFg4KAxjBw5kLCwcItm4KEdGI1G9u49QGDgcYKD\nr7B27Xd07NAanV7HJwPlTbEBBOwOpEGzeqzasQwBgelj5tJrUA9uhUcSuO/xfWrnn38z5dsv6fTB\nm6jVaqaPmfu8xX8ted1H2MKrzqB5lcgQSZJ6iqL4KdBbkiQ/URQ7AD2tV4k4OJTLl9prXvx/aXtV\n+Q/LXgTK9qovnzpFPfNFN7+2V7UtVum5K7qIc+Vc25y45GuvvGFf+QhbkqQAIMD89xJgifnvPcCe\nV50fBQUFhYcoUyIKCgoKBYTXfUpEMdgKCgoKZpTtVRUUFBQKCMpufQoKCgoFBGWEraCgoFBAMCjb\nqyooKCgUDJSHjgoKCgoFhNfdYL/yF2cUFBQUFPJGgXRgoKCgoPC/iGKwFRQUFAoIisFWUFBQKCD8\nv3joKIpidWA+oAGcgb+BZOAtoDBQCni4y3xrSZL0j0snR5otMG9SZXVuLiZvOX8BXwPlATVwCxgj\nSVK0OV5TwB+wxeRJZ40kScvNYROANuYwA/C5JEmP3ZHoZeVBLrlx6yaKYingGtBHkqSNMtN/pE6A\nTwFf4IFV1F8lSVotimIRTGWvbL7mJjBYkqQEURT9gJmYBiMuwAZJkhbmobwbMPUZAbAHhgIjAVdJ\nkrpYxY2WJEn+ptQydCVJOiuK4iBMjj4MmMo80bwvz4vQ3A7UkCTplvncwz62BnhXkqTt5vOPbND2\nIngRbgP/VyjwI2xRFAsDfwKjJElqCTQEagJJkiS1AEYB/0mS1ML8eaaxfgYCsAXYYk6vKfATsEsU\nRbUoipWA74CPzPrNgI9FUewgiqI30AloK0lSc2C0+dpXlofnKPez3Lr1M2sOl5PoM+rkC6t2ayFJ\n0mrz+T+AXZIkNZckqRFwAvjBHLYU+EySpDZAE6CnKIo+cvJk5mGfaY7ph2+G+XwTURR7P+W65+UR\nXVEUewJtMQ02WmAy3L+KoviiPOFmAGtEUcy5+1wqsOgF6jyN53Ib+L9CgTfYQGdMnfwqgNkgf0ze\nDGFuKAokPBx1mDX/AcIwGcbewC+SJN01h6UB7YH9QAImrzr9RVEsLUnSOUzedl5lHvLKE926mb/o\nvYGFgJ0oinLcpcuqE1EUywPukiRttTr9HTDY/PddYIQoinUxjUYbS5Ik3/VNdooA98x/fwlME0Xx\nUTfyL56HuoOB2ZIkaQEkSboB1JEk6f4L0vkP051Mzh/bJExtKt/Xm3yey23g/wr/Hwx2KUxeayxI\nkpQsSZJ8D7C5IwaTYczJdUzTE4/LT4IkSXpJkm5jGk02Bo6JohgCvP0q85AHLWseunUTMBnVh7vd\ntwaCJUmKwfRD+X/tnTtoFUEUhr9Cg4JBEIVEVAw+fixUYqHYBEmjIKI2FkaIiBHS+AgqIoiCnaAI\nUSJEBYMPbIIQFcsEBIuUFnIU31r4RKMoBGIsZi5sbu7GZG8S2Hg+2OLO7p4zMzv7z8yZvcyoR9n/\nqJMzkroTx0pC2V4W2Rgws+/xZwNBtNsIYndWUpZtuOujz0eE0EBhqv4eOAFcSb2zPEr5LfU8v4yz\n32bgkKSlReltwGxJk7EfWFr7ciJTQbBfAwuTCZJqJNWVafc3IYaYZBawOB7FLCPEUkvlZ7Wk2vgy\n9JnZHjNbRBhFXJI0Z7LykOJntNwkTFvrGLqtWxNQI+kBsBPYkRgpjUhanQBzGB4SeRzLt6DIxnRJ\nDZJmAGvM7LSZrSXUxyJgX4ayFkIT64FagnDOBDCzG8APSc0Z7Gbx+4bhz3OjpOrxcho7gIPANRK6\nYGaDwB7CusD88fKXQlr7ciJTQbDvApskLYHw8gLngLFMy0vxBKgtvBRRDOoIjapK0pbChTE2vBTo\nief3SpoXz80ixFergVXABUkV8danwDcgbeQ7EXnIjJm9IMQV9wPXY/JcwrrBOjPbZGb1hPh64yjN\njqlO4oj8s6StieQDhNDYH+C6pOXx2q+Ezqvc7Xw+lEhrJiyOVpZpezR+rwInJE0DiOWhDvtbAAAB\nJUlEQVS7THq7yYSZdQEG7C5KfwecAiZ0X7CU9uUkyP1XImbWJ6kRaJdU+DKgizLjbtFuC3BP0i+g\nAmg1s2dRKM9LOh4vfwtsjiGHV5KOAp2SBmJ+LpvZfQBJK4BeST8JHeaRxHR+UvJQJrcJ27o9jYub\nddF2UjzagQ5JrXGEloqZdZaqE2AbISRyLHF5j5mdJMTLL0o6TKiT50CTmfVL2gFcjR33INBLtvWM\nekndBFGsBFqADYl8f4rP5k4G22Pya2a3JFUBDyX1E74K2mVmH0ewk5WDhBDXEMysQ9L2CfBXTHH7\nchL4X9Mdx3FywlQIiTiO4/wXuGA7juPkBBdsx3GcnOCC7TiOkxNcsB3HcXKCC7bjOE5OcMF2HMfJ\nCS7YjuM4OeEvt0ee2f/zmaIAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f2007767c90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.heatmap(features.corr(), annot=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"How we can se thruth this graphs, that atributes BN and M are the best to separate the data in classes"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7f200806a490>"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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U1O6fIjpsxozMOS6/3F0O++EPMwd59113Ob773ZYn8Jtvu+/ubOZfqqraBrKs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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f2007bb3650>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"colormap = {0:'red',1:'blue'}\n",
"colors = [colormap[x] for x in labels]\n",
"plt.scatter(x='BN',y='M',data=features, color=colors)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# f, ax = plt.subplots(figsize=(6, 6))\n",
"# cmap = sns.cubehelix_palette(as_cmap=True, dark=0, light=1, reverse=True)\n",
"# for i in features.columns:\n",
"# for j in features.columns:\n",
"# sns.kdeplot(features[i], features[j], cmap=cmap, n_levels=60, shade=True)\n",
"# plt.show()\n",
"# plt.clf()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"df = df.values.tolist()\n",
"features = features.values.tolist()\n",
"labels = labels.tolist()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"90\n"
]
}
],
"source": [
"perc = int(input())/100"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"613 613 69 69\n"
]
},
{
"data": {
"text/plain": [
"1.7123893805309736"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"features_train = features[:int(perc*682)]\n",
"features_test = features[int(perc*682):]\n",
"\n",
"labels_train = [df[i][-1] for i in range(int(perc*682))]\n",
"labels_test = [df[i][-1] for i in range(int(perc*682),682)]\n",
"\n",
"print(len(features_train),\n",
" len(labels_train),\n",
" len(features_test),\n",
" len(labels_test))\n",
"\n",
"#checking the proportion of the classes on training labels\n",
"float(labels_train.count(1))/float(labels_train.count(0))\n",
"# print(labels_train)\n",
"# print(features_train,features_test)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"97.10144927536231"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"predictions = KNN.predict(features_test,features_train,labels_train,3)\n",
"KNN.score(predictions, labels_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So, now we see what happen when we use just 2 atributes (BN and M) to predict a object class"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"614 614 68 68\n"
]
},
{
"data": {
"text/plain": [
"1.7168141592920354"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"features_train = [[features[i][5], features[i][7]] for i in range(int(perc*682)+1)]\n",
"features_test = [[features[i][5], features[i][7]] for i in range(int(perc*682)+1,len(features))]\n",
"\n",
"labels_train = [labels[i] for i in range(int(perc*682)+1)]\n",
"labels_test = [labels[i] for i in range(int(perc*682)+1,len(features))]\n",
"\n",
"print(len(features_train),\n",
" len(labels_train),\n",
" len(features_test),\n",
" len(labels_test))\n",
"\n",
"#checking the proportion of the classes on training labels\n",
"labels_train.count(1)/labels_train.count(0)\n",
"\n",
"# print(len(features_train[0]), len(features_test[0]))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"23.52941176470588"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"predictions = KNN.predict(features_test,features_train,labels_train,1)\n",
"KNN.score(predictions, labels_test)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x163d236efd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import pylab as pl\n",
"\n",
"\n",
"x_min = 0.0; x_max = 10.0\n",
"y_min = 0.0; y_max = 10.0\n",
"h = .1 # step size in the mesh\n",
"xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
"Z = KNN.predict(np.c_[xx.ravel(), yy.ravel()], features_train, labels_train, 1)\n",
"\n",
"# Put the result into a color plot\n",
"Z = np.asarray(Z)\n",
"Z = Z.reshape(xx.shape)\n",
"plt.xlim(xx.min(), xx.max())\n",
"plt.ylim(yy.min(), yy.max())\n",
"\n",
"plt.pcolormesh(xx, yy, Z, cmap=pl.cm.seismic)\n",
"colormap = {0:'red',1:'blue'}\n",
"colors = [colormap[x] for x in labels]\n",
"# plt.scatter(x=[0],y=features[1],data=features, color=colors)\n",
"\n",
"# plt.pcolormesh(xx, yy, Z, cmap=pl.cm.seismic)\n",
"# colormap = {0:'red',1:'blue'}\n",
"# colors = [colormap[x] for x in labels]\n",
"# plt.scatter(x='BN',y='M',data=features, color=colors)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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